'.format(\n self.redis_key, self.interval, self.capacity, self.current_tokens,\n )\n\n\nclass RedisLimiter(object):\n\n def __init__(self,\n redis_cli=None,\n host='localhost', port=6379, db=0,\n key_prefix='limiter'):\n if not redis_cli:\n redis_cli = redis.StrictRedis(host=host, port=port, db=db)\n self.redis_cli = redis_cli\n self.key_prefix = key_prefix\n\n def get_redis_key(self, key, interval):\n return '{}:{}:{}'.format(self.key_prefix, key, interval)\n\n def get_token_count(self, key, interval):\n redis_key = self.get_redis_key(key, interval)\n return self.redis_cli.hget(redis_key, 'tokens')\n\n def consume(self, args):\n script_keys = []\n script_args = []\n the_now_ms = now_ms()\n for (key, interval, capacity, n) in args:\n redis_key = self.get_redis_key(key, interval)\n expire = interval * 2 + 15\n interval_ms = interval * 1000\n script_keys.append(redis_key)\n script_args.extend([interval_ms, capacity, n, the_now_ms, expire])\n\n for i in range(3):\n try:\n rv = self.redis_cli.evalsha(\n LUA_SCRIPT_SHA1, len(script_keys), *(script_keys + script_args)\n )\n if rv == ['', 0, 0, 0, 0]:\n return True, None\n else:\n return False, RedisConsumeDenied(rv)\n except RedisError:\n sha1 = self.redis_cli.script_load(LUA_SCRIPT)\n assert sha1 == LUA_SCRIPT_SHA1\n\n def consume_one(self, key, interval, capacity, n=1):\n return self.consume([(key, interval, capacity, n)])\n","repo_name":"TheWaWaR/bucket-limiter-py","sub_path":"bucket_limiter/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3762,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"9920454256","text":"\"\"\"\nOriginal file:\nhttps://github.com/nlplab/brat/blob/master/tools/conllXtostandoff.py\n\nLicence:\nhttps://github.com/nlplab/brat/blob/master/LICENSE.md\n\n\"\"\"\n\n\n# Script to convert a CoNLL X (2006) tabbed dependency tree format\n# file into BioNLP ST-flavored standoff and a reconstruction of the\n# original text.\n\n\nimport codecs\nimport os\nimport io\nimport re\nimport sys\n\n# maximum number of sentences to include in single output document\n# (if None, doesn't split into documents)\nMAX_DOC_SENTENCES = 10\n\n# whether to output an explicit root note\nOUTPUT_ROOT = True\n# the string to use to represent the root node\nROOT_STR = 'ROOT'\n\nINPUT_ENCODING = \"UTF-8\"\nOUTPUT_ENCODING = \"UTF-8\"\n\noutput_directory = None\n\n# rewrites for characters appearing in CoNLL-X types that cannot be\n# directly used in identifiers in brat-flavored standoff\ncharmap = {\n '<': '_lt_',\n '>': '_gt_',\n '+': '_plus_',\n '?': '_question_',\n '&': '_amp_',\n ':': '_colon_',\n '.': '_period_',\n '!': '_exclamation_',\n}\n\n\ndef maptype(s):\n return \"\".join([charmap.get(c, c) for c in s])\n\n\ndef tokstr(start, end, ttype, idnum, text):\n # sanity checks\n assert '\\n' not in text, \"ERROR: newline in entity '%s'\" % (text)\n assert text == text.strip(), \"ERROR: tagged span contains extra whitespace: '%s'\" % (text)\n return \"T%d\\t%s %d %d\\t%s\" % (idnum, maptype(ttype), start, end, text)\n\n\ndef depstr(depid, headid, rel, idnum):\n return \"R%d\\t%s Arg1:T%d Arg2:T%d\" % (idnum, maptype(rel), headid, depid)\n\n\ndef output(docnum, sentences):\n global output_directory\n offset, idnum, ridnum = 0, 1, 1\n doctext = \"\"\n final_entities = []\n final_deps = []\n for si, sentence in enumerate(sentences):\n tokens, deps = sentence\n\n # store mapping from per-sentence token sequence IDs to\n # document-unique token IDs\n idmap = {}\n\n # output tokens\n prev_form = None\n\n if OUTPUT_ROOT:\n # add an explicit root node with seq ID 0 (zero)\n tokens = [('0', ROOT_STR, ROOT_STR)] + tokens\n\n for ID, form, POS in tokens:\n if \"-\" in ID:\n continue\n if prev_form is not None:\n doctext = doctext + ' '\n offset += 1\n\n # output a token annotation\n ent = ['T{}'.format(idnum), maptype(\n POS), offset, offset + len(form), form]\n final_entities.append(ent)\n # print(tokstr(\n # offset, offset + len(form), POS, idnum, form))\n assert ID not in idmap, \"Error in data: dup ID\"\n idmap[ID] = idnum\n idnum += 1\n\n doctext = doctext + form\n offset += len(form)\n\n prev_form = form\n\n # output dependencies\n for dep, head, rel in deps:\n\n # if root is not added, skip deps to the root (idx 0)\n if not OUTPUT_ROOT and head == '0':\n continue\n dep_list = ['R{}'.format(ridnum), maptype(\n rel), [['Arg1', 'T{}'.format(idmap[head])], ['Arg2', 'T{}'.format(idmap[dep])]]]\n final_deps.append(dep_list)\n #print(depstr(idmap[dep], idmap[head], rel, ridnum))\n ridnum += 1\n\n if si + 1 != len(sentences):\n doctext = doctext + '\\n'\n offset += 1\n\n return [final_entities, final_deps]\n\n\ndef process(text):\n docnum = 1\n sentences = []\n tokens, deps = [], []\n\n lines = io.StringIO(text).readlines()\n for ln, l in enumerate(lines):\n l = l.strip()\n\n # igore lines starting with \"#\" as comments\n if len(l) > 0 and l[0] == \"#\":\n continue\n\n if re.match(r'^\\s*$', l):\n # blank lines separate sentences\n if len(tokens) > 0:\n sentences.append((tokens, deps))\n tokens, deps = [], []\n\n # limit sentences per output \"document\"\n if MAX_DOC_SENTENCES and len(sentences) >= MAX_DOC_SENTENCES:\n output(docnum, sentences)\n sentences = []\n docnum += 1\n continue\n\n # Assume it's a normal line. The format is tab-separated,\n # with ten fields, of which the following are used here\n # (from http://ilk.uvt.nl/conll/):\n # 1 ID Token counter, starting at 1 for each new sentence.\n # 2 FORM Word form or punctuation symbol.\n # 5 POSTAG Fine-grained part-of-speech tag\n # 7 HEAD Head of the current token\n # 8 DEPREL Dependency relation to the HEAD.\n fields = l.split('\\t')\n\n assert len(fields) == 10\n\n ID, form, POS = fields[0], fields[1], fields[4]\n head, rel = fields[6], fields[7]\n\n tokens.append((ID, form, POS))\n # allow value \"_\" for HEAD to indicate no dependency\n if head != \"_\":\n deps.append((ID, head, rel))\n\n # process leftovers, if any\n if len(tokens) > 0:\n sentences.append((tokens, deps))\n if len(sentences) > 0:\n return output(docnum, sentences)\n else:\n return [[], []]\n\n\n# dbg:\nif __name__ == '__main__':\n import sys\n fname = sys.argv[1]\n with open(fname, \"r\") as f:\n text = f.read()\n print(process(text))\n","repo_name":"BOUN-TABILab-TULAP/tabi-rop","sub_path":"backend/src/backend/backend_proxy/misc/conllXtostandoff.py","file_name":"conllXtostandoff.py","file_ext":"py","file_size_in_byte":5231,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"19186412195","text":"# -*- coding:utf-8 -*-\n\n# ---------------------------------------------\n# @file Requests.py\n# @description RequestsCookie\n# @author WcJun\n# @date 2020/07/05\n# ---------------------------------------------\n\n\nimport src.main.python.spider.SpiderUtils as SpiderUtils\nimport requests\n\n\ndef main():\n http_headers = SpiderUtils.populate_headers()\n\n hello_url = \"http://192.168.0.5:939/hello/greet\"\n cookie_url = \"http://192.168.0.5:939/hello/cookie\"\n\n session = requests.Session()\n hello_response = session.get(hello_url, verify=False, headers=http_headers)\n print(\"the hello response is:\", hello_response.text)\n \n cookie_response = session.get(cookie_url, verify=False, headers=http_headers)\n print(\"the user response is:\", cookie_response.text)\n","repo_name":"photowey/python-study","sub_path":"src/main/python/spider/RequestsCookie.py","file_name":"RequestsCookie.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71888403601","text":"\"\"\"# Workers\"\"\"\n\nfrom flask import current_app, render_template, request\nfrom sqlalchemy import Boolean, Column, String\nfrom sqlalchemy.inspection import inspect\nfrom sqlalchemy_modelid import ModelIdBase\nfrom sqlalchemy_mutable import MutableType\n\n\ndef enqueue(enqueue_method):\n # wraps the worker's enqueueing methods\n def enqueue_wrapper(worker, *args, **kwargs):\n if inspect(worker).identity is None:\n # ensure the worker has an id\n session = worker.manager.db.session\n session.add(worker)\n session.commit()\n if not worker.job_in_progress:\n # avoid repeat enqueuing\n job = enqueue_method(worker, *args, **kwargs)\n worker.job_finished, worker.job_in_progress = False, True\n worker.job_id = job.get_id()\n worker.manager.db.session.commit()\n # return the loading page HTML\n return render_template(worker.template, worker=worker)\n\n return enqueue_wrapper\n\n\nclass WorkerMixin(ModelIdBase):\n \"\"\"\n The worker executes a complex task using a Redis queue. When called, it \n enqueues a job and returns a loading page.\n\n When a Redis worker grabs the enqueued job, it executes the worker's \n function, `func`, passing in the worker's `args` and `kwargs`. After \n execution, the worker's script replaces the client's window location with \n a call to its `callback` view function.\n\n Parameters\n ----------\n callback : str or None, default=None\n Name of the view function to which the client will navigate once the \n worker has finished its job. If `None`, the current view function is \n re-called.\n\n template : str or None, default=None\n Name of the html template file for the worker's loading page. If \n `None`, the worker will use the manager's loading page template.\n\n loading_img_src : str or None, default=None\n Source of the loading image. If `None` the worker will use the \n manager's loading image.\n\n Attributes\n ----------\n callback : str\n Set from the `callback` parameter.\n\n template : str\n Set from the `template` parameter.\n\n loading_img_src : str\n Set from the `loading_img_src` parameter.\n\n manager : flask_worker.Manager\n The worker's manager.\n\n job_finished : bool, default=False\n Indicates that the worker has finished its job.\n\n job_in_progress : bool, default=False\n Indicates that the worker has a job in progress.\n\n job_id : str\n Identifier for the worker's job.\n \"\"\"\n _callback = Column(String)\n job_finished = Column(Boolean, default=False)\n job_in_progress = Column(Boolean, default=False)\n job_id = Column(String)\n template = Column(String)\n loading_img_src = Column(String)\n\n @property\n def callback(self):\n if self._callback:\n return self._callback\n try:\n # if operating in request context\n return request.url\n except:\n # operating outside request context\n return self._callback\n\n @callback.setter\n def callback(self, val):\n self._callback = val\n\n @property\n def manager(self):\n return current_app.extensions['manager']\n\n def __init__(self, callback=None, template=None, loading_img_src=None):\n self.callback = callback\n self.template = template or self.manager.template\n self.loading_img_src = loading_img_src or self.manager.loading_img_src\n self.reset()\n super().__init__()\n\n def reset(self):\n \"\"\"\n Resets the `job_finished`, `job_in_progress`, and `job_id` attributes.\n\n Returns\n -------\n self :\n \"\"\"\n self.job_finished, self.job_in_progress = False, False\n self.job_id = None\n return self\n\n @enqueue\n def enqueue_method(self, model, method_name, *args, **kwargs):\n \"\"\"\n Enqueue a database model's method for execution.\n\n Parameters\n ----------\n model : db.Model\n Model whose method will be enqueued.\n\n method_name : str\n Name of the model's method to enqueue.\n\n \\*args, \\*\\*kwargs :\n Arguments and keyword arguments passed to the method.\n\n Returns\n -------\n loading_page : str (html)\n The client's loading page.\n \"\"\"\n return current_app.task_queue.enqueue(\n 'flask_worker.tasks.execute_method',\n kwargs=dict(\n app_import=self.manager.app_import,\n worker_cls=self.__class__, \n worker_id=inspect(self).identity[0],\n model_cls=type(model),\n model_id=inspect(model).identity[0],\n method_name=method_name, args=args, kwargs=kwargs\n )\n )\n \n @enqueue\n def enqueue_function(self, func, *args, **kwargs):\n \"\"\"\n Enqueue the a function for execution.\n\n Parameters\n ----------\n func : callable\n Function which will be enqueued.\n\n \\*args, \\*\\*kwargs :\n Arguments and keyword arguments passed to the function.\n\n Returns\n -------\n loading_page : str (html)\n The client's loading page.\n \"\"\"\n return current_app.task_queue.enqueue(\n 'flask_worker.tasks.execute_func',\n kwargs=dict(\n app_import=self.manager.app_import,\n worker_cls=type(self), \n worker_id=inspect(self).identity[0],\n func=func, args=args, kwargs=kwargs\n )\n )","repo_name":"dsbowen/flask-worker","sub_path":"build/lib/flask_worker/worker_mixin.py","file_name":"worker_mixin.py","file_ext":"py","file_size_in_byte":5644,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71351496722","text":"\"\"\"setup.py file for package Pyrunc\"\"\"\n\nimport setuptools\n\nfrom utility import get_and_update_version\n\n_version = get_and_update_version()\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"Pyrunc\",\n version=\"0.0.\" + _version,\n author=\"Kartikei Mittal\",\n author_email=\"kartikeimittal@gmail.com\",\n description=\"Simple python package to write C code directly in python script\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/Kartikei-12/Pyrunc\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: GPLv3\",\n \"Operating System :: OS Independent\",\n ],\n)\n","repo_name":"Kartikei-12/Pyrunc","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"23593786129","text":"from abc import ABC\nimport exceptions\n\nclass Vehicle(ABC):\n def __init__(self, started, weight=0, fuel=0, fuel_consumption=0):\n self.weight = weight\n self.fuel = fuel\n self.fuel_consumption = fuel_consumption\n\n def start(self):\n if self.started is not True:\n if self.fuel > 0:\n self.started = True\n else:\n raise exceptions.LowFuelError\n\n def move(self, dist_km):\n fuel_to_move = dist_km * self.fuel_consumption\n if self.fuel >= fuel_to_move:\n self.fuel = self.fuel - fuel_to_move\n\n else:\n raise exceptions.NotEnoughFuel","repo_name":"OtusTeam/Machine-Learning","sub_path":"Homeworks/homework_02/Vehicle.py","file_name":"Vehicle.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74953851600","text":"INF =int(1e9)\r\n\r\nn,m =map(int, input().split())\r\ngraph = [[INF] * (n+1) for _ in range(n+1)]\r\n\r\n#자기 자신한테 가는건 비용 0\r\nfor a in range(1, n+1):\r\n for b in range(1, n+1):\r\n if a == b:\r\n graph[a][b] = 0\r\n\r\n#각 간선에 대한 정보를 입력받아 그 값으로 초기화\r\nfor _ in range(m):\r\n #A와 B가 서로에게 가는 비용은 1이라고 설정\r\n a,b = map(int,input().split())\r\n graph[a][b] = 1\r\n graph[b][a] = 1\r\n\r\n#거쳐 갈 노드 X와 최종 목적지 노드 K를 입력 받기\r\nx,k = map(int, input().split())\r\n\r\n#점화식에 따라 플로이드 워셜 알고리즘 수행\r\nfor k in range(1, n+1):\r\n for a in range(1,n+1):\r\n for b in range(1, n+1):\r\n graph[a][b] = min(graph[a][b], graph[a][k]+graph[k][b])\r\n\r\n#수행된 결과 출력\r\ndistance = graph[1][k] + graph[k][x]\r\n\r\n#도달할 수 없는 경유 ,-1을 출력\r\nif distance >= INF:\r\n print(\"-1\")\r\nelse:\r\n print(distance)\r\n \r\n\r\n","repo_name":"YoungJin00/Python_practice","sub_path":"미래도시.py","file_name":"미래도시.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5668543497","text":"import os\nfrom collections import namedtuple\nfrom pathlib import Path\n\nimport rx\nfrom dotenv import load_dotenv\nfrom slack_sdk.socket_mode import SocketModeClient\nfrom slack_sdk.web import WebClient\nfrom slack_sdk.socket_mode.response import SocketModeResponse\nfrom slack_sdk.socket_mode.request import SocketModeRequest\nfrom rx import operators as ops\nimport rx.subject\nfrom models.messages import RequestMessage, ResponseMessage, ResponseMessageType\n\n\nclass Slackbot:\n client: SocketModeClient\n # Subject to handle all messages\n __slack_response_subject = rx.subject.Subject\n\n def __init__(self):\n # load env\n env_path = Path('.') / '.env'\n load_dotenv(dotenv_path=env_path)\n\n # client setup\n self.client = SocketModeClient(\n app_token=os.environ['APP_TOKEN'],\n web_client=WebClient(os.environ['WEB_CLIENT_TOKEN'])\n )\n\n # Subject to handle all messages\n self.slack_response_subject = rx.subject.Subject()\n\n self.client.socket_mode_request_listeners.append(self.__process)\n self.client.connect()\n\n # Process events\n def __process(self, client: SocketModeClient, req: SocketModeRequest):\n message = namedtuple('message', ['client', 'request'])\n self.slack_response_subject.on_next(message(client, req))\n\n def get_stream(self):\n def acknowledge_message(msg):\n response = SocketModeResponse(envelope_id=msg.request.envelope_id)\n self.client.send_socket_mode_response(response)\n\n return self.slack_response_subject.pipe(\n ops.filter(lambda msg:\n msg.request.type == 'events_api'),\n ops.do_action(lambda msg: acknowledge_message(msg)),\n ops.filter(\n lambda msg: msg.request.payload[\"event\"][\"type\"] == \"message\" and msg.request.payload[\"event\"].get(\n \"subtype\") is None),\n ops.map(\n lambda msg: RequestMessage(msg.request.payload[\"event\"][\"user\"], msg.request.payload[\"event\"][\"text\"],\n msg.request.payload[\"event\"][\"channel\"], event=msg.request.payload[\"event\"]))\n )\n\n def send_message(self, response: ResponseMessage):\n if response.get_message_type() == ResponseMessageType.REACTION:\n self.client.web_client.reactions_add(\n name=\"eyes\",\n channel=response.get_channel(),\n timestamp=response.get_event()[\"ts\"]\n )\n elif response.get_message_type() == ResponseMessageType.MESSAGE:\n self.client.web_client.chat_postMessage(\n channel=response.get_channel(),\n text=response.get_text(),\n attachments=response.get_attachments()\n )\n","repo_name":"emmanuelrobles/slackbot","sub_path":"bot_init/slackbot.py","file_name":"slackbot.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"32459633551","text":"#!/usr/bin/env python\n\n###########################################################################\n# learn.py: Machine learning algorithms for TensorFlow skills test\n# Author: Chris Hodapp (hodapp87@gmail.com)\n# Date: 2017-11-28\n###########################################################################\n\nimport data_preprocessing\nimport graph_construction\n\nimport keras\nfrom keras.optimizers import SGD\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass BatchHistory(keras.callbacks.Callback):\n \"\"\"Keras callback for recording validation accuracy at regular\n intervals, i.e. every set number of batches. This should be\n passed to a 'fit' call via the list passed to its 'callbacks'\n keyword argument, and the model must be compiled to have\n 'accuracy' as one of its metrics.\n\n After this, the 'history' property will contain a list of\n dictionaries. Each dictionary will have the following keys and\n values in it:\n batch -- batch number for all loss & accuracy numbers\n val_loss -- validation loss\n val_acc -- validation accuracy\n loss -- training loss\n acc -- training accuracy\n \"\"\"\n def __init__(self, test_X, test_y, skip=100):\n \"\"\"Initialize a BatchHistory.\n\n Parameters:\n skip -- Interval (of batch number) at which to record accuracy\n test_X -- Input data for predicting accuracy\n test_y -- Correct labels corresponding to test_X\n \"\"\"\n self.skip = skip\n self.history = []\n # Below are required for evaluating:\n self.test_X = test_X\n self.test_y = test_y\n # 'i' tallies up the batch number:\n self.i = 0\n # These two lists store the past training accuracies and\n # losses for up to 'self.skip' batches, and they are\n # periodically used to provide an averaged training loss &\n # accuracy over several batches, and then cleared.\n self.losses = []\n self.accs = []\n def on_batch_end(self, batch, logs={}):\n self.i += 1\n self.accs.append(logs[\"acc\"])\n self.losses.append(logs[\"loss\"])\n if self.i % self.skip == 0:\n ev = self.model.evaluate(self.test_X, self.test_y, verbose=0)\n l = {}\n l[\"loss\"] = sum(self.losses) / len(self.losses)\n l[\"acc\"] = sum(self.accs) / len(self.accs)\n self.losses = []\n self.accs = []\n l[\"batch\"] = self.i\n l[\"val_loss\"] = ev[0]\n l[\"val_acc\"] = ev[1]\n self.history.append(l)\n\ndef train_model(num_epochs, batch_size, learning_rate):\n \"\"\"Trains a neural network for image classification from the SVHN\n dataset, and creates a plot giving training/testing accuracy as a\n function of batch number.\n\n Parameters:\n num_epochs -- Number of training epochs\n batch_size -- Number of examples in each training batch\n learning_rate -- Initial learning rate\n \"\"\"\n # Get data:\n train_X_orig, train_y_orig, _, _ = data_preprocessing.load_data()\n train_X_norm = data_preprocessing.normalize(train_X_orig)\n train_X, valid_X, train_y, valid_y = data_preprocessing.split(\n train_X_norm, train_y_orig)\n # One-hot encode so they can be used for input/validation:\n train_y_cat = keras.utils.to_categorical(train_y, num_classes=10)\n valid_y_cat = keras.utils.to_categorical(valid_y, num_classes=10)\n \n # Build & compile model:\n model = graph_construction.get_keras_model()\n sgd = SGD(lr=learning_rate, decay=1e-5, momentum=0.9, nesterov=True)\n model.compile(loss='categorical_crossentropy',\n optimizer=sgd,\n metrics=['accuracy'])\n\n # Train:\n history = BatchHistory(valid_X, valid_y_cat) \n model.fit(train_X,\n train_y_cat,\n epochs=num_epochs,\n batch_size=batch_size,\n callbacks=[history],\n validation_data=(valid_X, valid_y_cat))\n # The callback slows things down a bit, but I'm not sure of a good\n # way around it. If I were testing only on specific batches of\n # validation data, it might be less of an issue.\n\n fname_base = \"model_{0}_{1}_{2}_rgb\".format(learning_rate, num_epochs,\n batch_size)\n model.save_weights(\"{0}.h5\".format(fname_base))\n\n # Plot training & validation accuracy, and loss (not called for,\n # but useful):\n b = [i[\"batch\"] for i in history.history]\n plt.plot(b, [i[\"acc\"] for i in history.history])\n plt.plot(b, [i[\"val_acc\"] for i in history.history])\n plt.ylabel('Accuracy')\n plt.xlabel('Batch')\n plt.legend(['Training', 'Validation'], loc='lower right')\n plt.savefig(\"{0}_accuracy.png\".format(fname_base))\n plt.show()\n plt.close()\n \n plt.plot(b, [i[\"loss\"] for i in history.history])\n plt.plot(b, [i[\"val_loss\"] for i in history.history])\n plt.ylabel('Loss (categorical cross-entropy)')\n plt.xlabel('Batch')\n plt.legend(['Training', 'Validation'], loc='lower right')\n plt.savefig(\"{0}_loss.png\".format(fname_base))\n plt.close()\n\nif __name__ == '__main__':\n train_model(10, 64, 0.01)\n","repo_name":"Hodapp87/yazabi-tensorflow-image-test","sub_path":"learn.py","file_name":"learn.py","file_ext":"py","file_size_in_byte":5140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15075320254","text":"#!/usr/bin/env python\n\n\n# this module allows you to take user input\nimport subprocess\n# this module parses user input\nimport optparse\n#this module allows for filering using regex\nimport re\n#\n\n# \"input()\" is python 3 syntax\n# \"raw_input()\" is python 2 syntax\n\n\ndef get_arguments():\n # set method from the optparse object to parcer. allows for multiple arguments / values in cli.\n parser = optparse.OptionParser()\n parser.add_option(\"-i\", \"--interface\", dest=\"interface\", help=\"Interface to change its MAC address\")\n parser.add_option(\"-m\", \"--mac\", dest=\"new_mac\", help=\"New MAC address.\")\n #captures values / inputs for use\n ( options, arguments ) = parser.parse_args()\n\n # make sure user gives values or throw an error\n if not options.interface:\n parser.error(\"[-]Please specify an interface, use --help for more info \")\n elif not options.new_mac:\n parser.error(\"[-] Please specify a new mac, use --help for more info. \")\n # options holds the values of the user inputs\n return options\n\n\ndef change_mac(interface , new_mac):\n print(\"[*] Changing MAC address for \" + interface + \" to \" + new_mac)\n subprocess.call([\"sudo\", \"ifconfig\", interface, \"down\"])\n subprocess.call([\"sudo\", \"ifconfig\", interface, \"hw\", \"ether\", new_mac])\n subprocess.call([\"sudo\", \"ifconfig\", interface, \"up\"])\n # this line is not from the demom\n # print(\"All done! The new MAC address is: \" + new_mac)\n\n\ndef get_current_mac(interface):\n ifconfig_result = subprocess.check_output([\"ifconfig\", interface])\n # this is where we use the \"re\" module and regeex to fiter out the MAC address\n mac_address_search_result = re.search(rb\"\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w\", ifconfig_result)\n if mac_address_search_result:\n return mac_address_search_result.group(0)\n else:\n print(\"That's weird... where the hell is the MAC Address? Double check the interface.\")\n\n\n\n# the options variable holds the returned values from the get_arguments() function above\noptions= get_arguments()\ncurrent_mac = get_current_mac(options.interface)\n# in the below print statement, str() is used because \"current_mac\" is not a string\n# and cannot be read. str() converts the variable to a string and clears the error.\nprint(\"The current MAC ADDY is ===> \" + str(current_mac) + \" <===\")\nchange_mac(options.interface, options.new_mac)\n# the value of this variable should have changed to the new mac address\ncurrent_mac = get_current_mac(options.interface)\nprint(current_mac)\nif current_mac == current_mac:\n print(\"[+] MAC address was successfully changed to: ===> \" + str(current_mac) + \" <===\")\nelse:\n print(\"[+] MAC address did not change. :-(\")\n\n\n\n\n\n# this function does exactly what is says. it runs ifconfig and captures the state/value of the\n# selected hw component\n# subprocess.check_output([\"ifconfig\", options.interface])\n\n\n\n# print(ifconfig_result)\n# print(\"All done! The new MAC address is: \")\n\n# the return object will contain all of the stings that are found in groups. if there are\n# multiple matches, the firs one is .group(0)\n# print(mac_address_search_result.group(0))","repo_name":"tmosley03/mac_spfr","sub_path":"mac_spoof/mac_spoof.py","file_name":"mac_spoof.py","file_ext":"py","file_size_in_byte":3129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7181415556","text":"import math\n\n\nclass MergeSort:\n\n def sort(self, arrayToSort, low, high):\n if(low < high):\n mid = math.floor((low + high)/2)\n self.sort(arrayToSort, low, mid)\n self.sort(arrayToSort, mid+1, high)\n self.merge(arrayToSort, low, mid, high)\n return arrayToSort\n\n def merge(self, arrayToSort, low, mid, high):\n leftPart = arrayToSort[low: mid+1]\n rightPart = arrayToSort[mid+1:high+1]\n i = 0\n j = 0\n k = low\n m = len(leftPart)\n n = len(rightPart)\n while((i < m) and (j < n)):\n if(leftPart[i] < rightPart[j]):\n arrayToSort[k] = leftPart[i]\n k += 1\n i += 1\n else:\n arrayToSort[k] = rightPart[j]\n k += 1\n j += 1\n while(i < m):\n arrayToSort[k] = leftPart[i]\n k += 1\n i += 1\n while(j < n):\n arrayToSort[k] = rightPart[j]\n k += 1\n j += 1\n return arrayToSort\n\n\ninputArray = [8, 7, 6, 5, 4, 3, 2, 1]\nmergeSort = MergeSort()\nprint(\"::::::::: Merge Sort :::::::::\")\nprint(\"Before Sorting ::: \", inputArray)\nprint(\"After Sorting ::: \", mergeSort.sort(inputArray, 0, 7))\nprint(\"Time complexity ::: O(n log n)\")\nprint(\"Space complexity ::: O(n)\")\n","repo_name":"ka6thick54/learning","sub_path":"sorting/mergeSort.py","file_name":"mergeSort.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22479830304","text":"import json, os, sys\nsearch_id = 'malware--fde81448-ac55-4d27-87ee-1bc4501514e7'\n\nwith open(f'./just_rels.json') as input:\n\tsomething = json.load(input)\n#\tprint(something)\n#\tfor thing in something:\n#\t\tprint(thing)\n\tif search_id in something:\n\t\tprint(\"We found it!!!\")\n\telse:\n\t\tprint(\"Very sad, nonexistant\")\n","repo_name":"idaholab/cape2stix","sub_path":"clustering_scripts/find_sample.py","file_name":"find_sample.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"14184887052","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ntime_index_all = np.abs(np.load('time_index_all.npy'))\nspeed_right_all = np.load('speed_right_all.npy', )\nspeed_left_all = np.load('speed_left_all.npy')\n\nplt.plot(time_index_all, speed_right_all, 'r.-', label='right')\nplt.plot(time_index_all, speed_left_all, 'b.-', label='left')\nplt.xlabel('PWM')\nplt.ylabel('speed')\nplt.legend()\nplt.show()\n\n# 344, 90\n# 506, 161\n\nk = (161 - 90) / (506 - 344)\nki = 1/k\nprint(\"ki =\", ki) # ki = 2.2816901408450705 PWM/speed","repo_name":"vedenev/buggy","sub_path":"raw_codes/tt010_plot_frequency_vs_pwm.py","file_name":"tt010_plot_frequency_vs_pwm.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36561988464","text":"#Kristen Ching\nimport random\nclass User:\n def __init__(self, firstName, lastName, avat=\"\"):\n self.first = firstName\n self.last = lastName\n self.avatar = avat\n self.ID = random.randint(0, 100000)\n\n def setVals(self, firstName, lastName, avat):\n self.first = firstName\n self.last = lastName\n self.avatar = avat\n self.ID = random.randint(0, 100000)\n\n def __str__(self):\n return \"Customer Info... \\nFirst Name: \" + self.first + \\\n \"\\nLast Name: \" + self.last + \\\n \"\\navatar: \" + self.avatar + \\\n \"\\nUser ID: \" + str(self.ID)\n\ndef main():\n firstName = input(\"Please enter your first name. \")\n lastName = input(\"Please enter your last name. \")\n createAvat = input(\"Would you like to use a public avatar? Enter y/n \")\n if createAvat == \"y\":\n avat = input(\"Please enter a name for your public avatar. \")\n user1 = User(firstName, lastName, avat)\n else:\n user1 = User(firstName, lastName)\n\n print(user1.__str__())\n \nmain()\n","repo_name":"KristenChing/Ching_Kristen","sub_path":"Python/Lab_11/Lab_11.2/EX_05_The_User_Class.py","file_name":"EX_05_The_User_Class.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5224467458","text":"# imports\nimport sys\nsys.path.append('src')\n\nfrom config import DEFAULT_CHECKPOINT_PATH\nfrom config import DEFAULT_DATA_SET_PATH\nfrom data import DataSet\nfrom predicting import Predicter\n\nimport logging\n\nimport tensorflow as tf\n\n# setup\nlogging.basicConfig(level=logging.DEBUG)\n\n# variables\nflags = tf.app.flags\nflags.DEFINE_string('eval_text', None, 'Text to get output from.')\nflags.DEFINE_string('checkpoint_path', DEFAULT_CHECKPOINT_PATH,\n 'Path to checkpoint, if no file is given it tries to get '\n 'the latest checkpoint in that folder.')\nflags.DEFINE_string('data_set_path', DEFAULT_DATA_SET_PATH,\n 'Path to dataset.')\nFLAGS = flags.FLAGS\n\n\n# functions\ndef main():\n data_set = DataSet.load(FLAGS.data_set_path)\n print(data_set.output_vocabulary.token_to_id)\n\n logging.info('building predicter')\n predicter = Predicter(data_set.output_vocabulary, FLAGS.checkpoint_path)\n\n logging.info('getting output')\n text = ' '.join(predicter.get_output(FLAGS.eval_text))\n print(text)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"OliverEdholm/Tensorflow-Easy-Seq2Seq","sub_path":"evaluate_outputs.py","file_name":"evaluate_outputs.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"9494974342","text":"# %% [markdown]\n'''\n# Fitting geometric models with gradient descent\n'''\n# %% [markdown]\n'''\nThe process of fitting known geometric models to scattered data is well known and resolved since a long time. \nIndeed, Legendre was one of the first to use least-square to do such tasks legendre1805nouvelles ,\nwhen he wanted to fit an equation for the shape of the earth !\n\nThe idea is quite simple: knowing the model definition, it is possible to estimate its best parameters \nthat fits well to the input noisy data.\n'''\n# %% [markdown]\n'''\n## tl;dr\n1. Mathematical model definition for your data\n2. Cost function\n3. Optimisation (gradient descent with cost function derivative on all the parameters)\n4. Hyper-parameters optimisation with training set (learning rate)\n5. Model bias with testing set\n'''\n# %% [markdown]\n'''\n## 1. Optimisation\n'''\n# %% [markdown]\n\n# Given a mathematical model with parameters $\\Theta$, we can define its minimization function $\\xi(\\Theta)$ (the inverse likelihood).\n# This represents the fitness of the model given some data, so if $\\xi(\\Theta)$ is minimum, then the parameters are best suited to the data.\n\n# To optimize the parameters, one could check every possible parameters $\\Theta_i$ and compute directly $\\xi(\\Theta_i)$.\n# Unfortunately, with our current compute power, this is time consuming.\n# Especially if we have more than one parameter, then the manifold would be to large to explore.\n# Imagine the difference between exploring a one dimension line vs exploring a 3D surface!\n\n# (?P.*?)
',s)\n if not match:\n return '' + word + '
' + find_translation(from_lang, to_lang, word)\n return '' + word + '
' + '' + find_translation(from_lang, to_lang, re.sub(r'', '\\n', match.group('definition')))\n\n\ndef find_word(from_lang, to_lang, word):\n conn = connect_to_dictionary(word_url(from_lang, to_lang, word))\n s = ''.join((_.decode() for _ in conn.readlines()))\n match = re.search(r'(?m)(?P.*?) ', s)\n if not match:\n match = re.search(r'(?P.*?) ', s)\n if not match: \n return find_from_urban_dict(from_lang, to_lang, word)\n match = re.search(r'(?m)\\n(?P
.*?)$', s)\n return '' + word + '
' + match.group('definition')\n\n find_transcription = re.search(r'(?m)(?P(\\[|/).*?)$', s)\n if find_transcription:\n word = '' + match.group('definition').upper() + ' ' + find_transcription.group('definition') + '
'\n else: \n word = '' + match.group('definition').upper() + '
'\n\n l = [word]\n s = s[match.end():]\n index = 1\n\n while True:\n match = re.search(r'(?m)(?P|title=\"Part-of-speech\">)(?P(.|\\n)+?)(?P$| )', s)\n if not match: break\n if match.group('class') == 'title=\"Part-of-speech\">':\n if s[(match.end())%len(s)] != ':':\n l.append('' + match.group('definition') + '
')\n else:\n l.append('' + 'Synonymous ' + match.group('definition') + 's' + '
')\n index = 1\n else:\n if match.group('end'):\n tmp = re.search(r'dict_lk\">(?P.*)',match.group('definition'))\n if tmp: \n l.append('' + str(index) + '. ' + tmp.group('definition') + '
')\n else :\n l.append('' + str(index) + '. ' + match.group('definition') + '
')\n index += 1\n else:\n print(match.group('definition'))\n l.append('● ' + match.group('definition') + '
')\n s = s[match.end():]\n# l.append('proba :) ')\n return ''.join(l)\n \n\ndef find_translation(from_lang, to_lang, text):\n conn = connect_to_dictionary(text_url(from_lang, to_lang, text))\n s = ''.join((_.decode() for _ in conn.readlines()))\n \n res = '' + text + '
'\n\n match = re.search(r'(?P|)(?P.*?)', s)\n \n if not match:\n match = re.search(r'overflow:auto\">(?P.*?)<br>', s)\n if not match:\n return res + 'Could not find definition '\n return '' + re.sub('<br>', '
', match.group('definition')) + '
'\n index = 1\n\n while match:\n if match.group('class') == r'':\n res += ' ' + str(index) + '. ' + match.group('definition') + '
'\n index += 1\n else:\n res += '' + match.group('definition') + '
'\n index = 1\n \n s = s[match.end():]\n match = re.search(r'(?P|)(?P.*?)', s)\n\n return res \n \n","repo_name":"ivajloip/IMDict","sub_path":"search_engine.py","file_name":"search_engine.py","file_ext":"py","file_size_in_byte":4877,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"4023898937","text":"# fft the shit out of everything\n\nfrom physics import mass_streamfunction\nfrom data_handling import time_means\nimport matplotlib.pyplot as plt\nimport xarray as xr\nimport numpy as np\nfrom scipy.fftpack import fft\n\n\nL = 2.500e6\ncp = 287.04/(2./7.)\ng = 9.8\nstefan = 5.6734e-8\n\ndef fft_max(var, nmax=True, period_fac=1., plttitle=None):\n if nmax:\n var_max_loc = var.lat.values[np.argmax(var.mean('lon').values, axis=1)].tolist()\n else:\n var_max_loc = var.lat.values[np.argmin(var.mean('lon').values, axis=1)].tolist()\n \n # Number of samplepoints\n N = 72*period_fac\n yf = fft(var_max_loc)\n\n plt.plot( 2.0/N * np.abs(yf[:N/2]))\n plt.xlim(0,35)\n plt.xlabel('Wavenumber')\n plt.ylabel('Amplitude')\n plt.title(plttitle)\n plt.savefig('/scratch/rg419/plots/seasons_and_rotation/'+plttitle+'_fft.png')\n plt.close()\n \n plt.plot(var_max_loc)\n plt.xlabel('Pentad')\n plt.ylabel('Latitude')\n plt.title(plttitle)\n plt.savefig('/scratch/rg419/plots/seasons_and_rotation/'+plttitle+'_mean.png')\n plt.close()\n \n\ndata = time_means('sn_3.000',[121,481], filename='atmos_pentad', timeav='pentad', period_fac=3.)\nfft_max(data.omega[:,27,:,:], nmax=False, period_fac=3., plttitle='sn_3.000_omega')\nmse = (cp*data.temp + L*data.sphum + g*data.height)/1000.\nfft_max(mse[:,38,:,:], period_fac=3., plttitle='sn_3.000_mse')\n \ndata = time_means('aquaplanet_10m',[121,481], filename='atmos_pentad', timeav='pentad')\nfft_max(data.omega[:,27,:,:], nmax=False, plttitle='aquaplanet_10m_omega')\nmse = (cp*data.temp + L*data.sphum + g*data.height)/1000.\nfft_max(mse[:,38,:,:], plttitle='aquaplanet_10m_mse')\n\ndata = time_means('aquaplanet_2m',[121,481], filename='atmos_daily', timeav='pentad')\nfft_max(data.omega[:,27,:,:], nmax=False, plttitle='aquaplanet_2m_omega')\nmse = (cp*data.temp + L*data.sphum + g*data.height)/1000.\nfft_max(mse[:,38,:,:], plttitle='aquaplanet_2m_mse')\n","repo_name":"subond/python_scripts","sub_path":"seasons_and_rotation/fourier_max.py","file_name":"fourier_max.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71731561681","text":"import tkinter as tk\nfrom tkinter import ttk\n\ndef main_window():\n root = tk.Tk() # makes base window\n title_label = ttk.Label(root, text=\"New Patient\")\n title_label.grid(column=1, row=1, sticky='w')\n mrnlabel = ttk.Label(root, text=\"Enter patient MRN: \")\n mrnentry = tk.Entry(root)\n mrnentry.grid(column=3, row=3, columnspan=2)\n mrnlabel.grid(column=1, row=3, sticky='w')\n testlabel = ttk.Label(root, text=\"Choose test: \")\n testlabel.grid(column=1, row= 5, sticky='w')\n for i in [0,2,5]:\n root.columnconfigure(i, minsize=30)\n for i in [0,2,4,6]:\n root.rowconfigure(i, minsize=30)\n\n root.mainloop() # activate and show window\n\nif __name__ == \"__main__\":\n main_window()","repo_name":"slramirez02/First","sub_path":"patient_lab_gui.py","file_name":"patient_lab_gui.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13205140707","text":"import json\nfrom urllib.parse import urlparse\nimport sqlite3\n\nfrom bertopic import BERTopic\n\n# from sentence_transformers import SentenceTransformer\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n# from hdbscan import HDBSCAN\n# from umap import UMAP\n\n\n# def preprocess(docs):\n# cleaned_docs = [preprocess_string(d, CUSTOM_FILTERS) for d in docs]\n# cleaned_docs = [[lemmatizer.lemmatize(s) for s in t] for t in cleaned_docs]\n\n# return cleaned_docs\n\n\ndef bert_model(news=None):\n vectorizer_model = CountVectorizer(\n ngram_range=(1, 2), strip_accents=\"ascii\", stop_words=\"english\"\n )\n # embedding_model = SentenceTransformer(\"sentence-transformers/all-MiniLM-L6-v2\")\n # umap_model = UMAP(n_neighbors=5, n_components=15)\n # hdbscan_model = HDBSCAN(gen_min_span_tree=True, prediction_data=True)\n\n model = BERTopic(\n # hdbscan_model=hdbscan_model,\n # embedding_model=embedding_model,\n # umap_model=umap_model,\n vectorizer_model=vectorizer_model,\n top_n_words=10,\n language=\"english\",\n calculate_probabilities=True,\n verbose=True,\n )\n\n if news is None:\n try:\n # Connect to the database\n conn = sqlite3.connect('carbonation/resources/carbonaton.db')\n\n # Retrieve the latest key by date\n cursor = conn.execute(\"SELECT data FROM models ORDER BY id DESC LIMIT 1\")\n latest_data = cursor.fetchone()[0]\n\n # Close the connection\n conn.close()\n\n except sqlite3.Error as e:\n print(f\"An error occurred: {e}\")\n\n news = json.load(latest_data)\n\n # Enrich docs with bias\n news = enrich_docs(news)\n\n docs = []\n links = []\n for a in news[\"articles\"]:\n if a[\"excerpt\"] is not None:\n docs.append(\": \".join([a[\"title\"], a[\"excerpt\"]]))\n links.append(\n {\"domain\": a[\"clean_url\"], \"link\": a[\"link\"], \"bias\": a[\"bias\"]}\n )\n\n # Remove dupes\n # docs = list(set(docs))\n # docs = preprocess(docs)\n\n # Make model\n topics, probs = model.fit_transform(docs)\n\n # Make resultant json\n model, topic_docs = create_topic_docs(model, topics, probs, docs, links)\n\n return (model, topic_docs)\n\n\ndef create_topic_docs(model, topics, probs, docs, links):\n topic_docs = {\n topic: {\"topic\": model.get_topic(topic), \"docs\": []} for topic in set(topics)\n }\n\n for topic, doc, prob, link in zip(topics, docs, probs, links):\n if max(prob) > 0.25:\n topic_docs[topic][\"docs\"].append(\n {\"text\": doc, \"prob\": sorted(prob.tolist())[-5:][::-1], **link}\n )\n\n for topic, content in topic_docs.items():\n num_docs = len(content[\"docs\"])\n topic_docs[topic][\"num_docs\"] = num_docs\n topic_docs[topic][\"avg_bias\"] = 0\n\n if num_docs > 0:\n avg_bias = sum([doc[\"bias\"] for doc in content[\"docs\"]]) / num_docs\n topic_docs[topic][\"avg_bias\"] = avg_bias\n\n # topic_docs = sorted(topic_docs, key=lambda x: len(topic_docs[x]['docs']))\n\n # f\"resources/computed/bert_{'_'.join(news_json.split('_')[1:])}\", \"w\"\n with open(\"carbonation/resources/computed/bert_test.json\", \"w\") as f:\n f.write(json.dumps(topic_docs))\n\n return (model, topic_docs)\n\n\ndef enrich_docs(news):\n # news = json.load(open(f\"resources/articles/{news_json}\"))\n\n with open(\"carbonation/resources/bias/mbfc_bias.json\", \"r\") as f:\n mbfc_bias = json.load(f)\n\n mbfc_bias = {k: v[\"b\"] for (k, v) in mbfc_bias.items()}\n\n with open(\"carbonation/resources/bias/allsides_bias.json\", \"r\") as f:\n allsides_bias = json.load(f)[\"allsides_media_bias_ratings\"]\n\n # For AllSides compose dict\n allsides_bias = {\n \".\".join(urlparse(s[\"source_url\"]).netloc.split(\".\")[-2:]): s[\n \"media_bias_rating\"\n ]\n for s in allsides_bias\n if s[\"source_url\"] != \"\"\n }\n\n bias = mbfc_bias | allsides_bias\n\n bias_scale = {\n # \"CP\": \"Conspiracy-Pseudoscience\",\n # \"FN\": \"Questionable Sources\",\n # \"PS\": \"Pro-Science\",\n # \"S\": \"Satire\",\n \"L\": -1,\n \"Left\": -1,\n \"LC\": -0.5,\n \"Lean Left\": -0.5,\n \"C\": 0,\n \"Center\": 0,\n \"Mixed\": 0,\n \"RC\": 0.5,\n \"Lean Right\": 0.5,\n \"R\": 1,\n \"Right\": 1,\n }\n\n for a in news[\"articles\"]:\n a[\"bias\"] = 0\n\n domain = a.get(\"clean_url\", \"\")\n if domain in bias:\n a[\"bias\"] = bias_scale.get(bias[domain], 0)\n\n return news\n","repo_name":"jacksongoode/carbonation","sub_path":"carbonation/analysis/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39878925083","text":"\nfrom tkinter.tix import Tree\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nG = nx.DiGraph();\nV = {1, 2, 3, 4, 5, 6}\nE = [(1, 3), (4, 1), (4,2), (4, 5), (4, 6), (6, 1), (6, 2), (6, 4)]\nG.add_nodes_from(V)\nG.add_edges_from(E)\nprint(G.in_degree())\n\n# 1. in-degrees and out-degrees of node 2 and 6\nI2 = G.in_degree(2)\nI6 = G.in_degree(6)\nO2 = G.out_degree(2)\nO6 = G.out_degree(6)\nprint(\"\\n #1. in-degrees and out-degrees of node 2 and 6 are \", I2, \", \", I6,\", \", O2,\", \", O6, \" respectively\", \"\\n\")\n# 2. What is the shortest path from node 4 to 3?\nS = nx.shortest_path(G, 4, 3)\nprint(\"\\n #2. the shortest path from node 4 to 3 is \", S, \"\\n\")\n# 3. Is the graph a strongly connected graph?\nSC = nx.is_strongly_connected(G)\nprint(\"\\n #3. is this graph strongly connected? :\", SC, \"\\n\")\n# 4. What is the largest subgraph that is strongly connected?\nlargest = max(nx.strongly_connected_components(G), key=len)\nprint(\"\\n #4. largest connected subgraph is\", largest, \"\\n\")\n# 5. Is the graph a directed acyclic graph?\nDAG = nx.is_directed_acyclic_graph(G)\nprint(\"\\n #5. Is the graph a directed acyclic graph?: \", DAG, \"\\n\")\n# 6. What is the diameter of this graph?\n# Dm = nx.diameter(G)\nprint(\"\\n #6. diameter of this graph is - \",\"since this is not strongly connected directed network and diameter is infinite because we have nodes which has no path to other nodes ie. infinite diameter\" , \"\\n\")\n# 7. What is the edge list representation of 𝐺𝐺(𝐸𝐸, 𝑉𝑉)?\nprint( \"\\n #7. edge list of network is \", list(G.edges()))\n# 8. What is the adjacency list of 𝐺𝐺(𝐸𝐸, 𝑉𝑉)?\nprint( \"\\n #8. adjacency list of network is \", G.adj, \"\\n\")\n# 9. What is the adjacency matrix 𝐴 of 𝐺𝐺(𝐸𝐸, 𝑉𝑉)?\nA = nx.to_numpy_matrix(G) \nprint(\"\\n #9. the adjacency matrix of graph is\",\"\\n\", A, \"\\n\")\n\n# 11. What is the relationship between matrices 𝐴 , 𝐵 , and 𝐸 ?\nprint(\"\\n #11. product of out edge incidence matrix and transpose of in-edge incidence matrix gives Adjacent Matrix\", \"\\n\")\n\n# 12. Please draw the line graph of 𝐺𝐺(𝐸𝐸, 𝑉𝑉).\nplt.figure(1)\n# generating the line graph from the directed graph G\nH = nx.line_graph(G)\nnx.draw_networkx(H, arrows = True, arrowsize=10, node_size=100, with_labels = True)\nplt.show()\n\n# 13. What is the adjacency matrix 𝑀 of the line graph of 𝐺(𝐸, 𝑉)?\nAL = nx.to_numpy_matrix(H)\nprint(\"\\n #13. the adjacency matrix of line graph is\",\"\\n\", AL, \"\\n\")\n\n\n# 14. What is the relationship the matrices M , 𝐵 , and 𝐸?\nprint(\" \\n #14. product of transpose of in-edge incidence Matrix(E) and out-edge incidence matrix(E) is equal to M\")","repo_name":"pravinpoudel/ML-with-graph","sub_path":"Assignment 1/question2.py","file_name":"question2.py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33685089860","text":"# with the default parameters, the estimated run time is 15 hours on free colab\nimport msprime\nfrom pathlib import Path\n\n# set parameters\ntrial_number= 90000 # num of trials\nv_bin_number= 200 # num of bins\nsample_size=2 # num of individuals\nv_ploidy=1 # num of chromosomes of each type\ncombined_parameters= [300, 100, 30, 10, 1, 0] # sequence_length*recombination_rate*population_size\nsequence_lengths= [100] # sequence length\npopulation_sizes= [round(10*10**(idx/4)) for idx in range(9)] # population size\nparameter_packages= [[combined_parameter, v_sequence_length, v_population_size,\n combined_parameter/v_sequence_length/v_population_size]\n for combined_parameter in combined_parameters\n for v_sequence_length in sequence_lengths\n for v_population_size in population_sizes]\n# the third entry is recombination_rate, being the num of recombinations per base per gen\n# relevant_recombination_rate=2*sequence_length*recombination_rate\n\n# define the function that finds the least coalescent time\ndef mrca_time_api(tree_sequence):\n # ca_times= []\n ca_times= [0. for idx in range(tree_sequence.num_trees)]\n tree_counter= 0\n for tree in tree_sequence.trees():\n # ca_times.append(tree_sequence.tables.nodes[tree.root].time)\n ca_times[tree_counter]= tree_sequence.tables.nodes[tree.root].time\n tree_counter= tree_counter+ 1\n return(min(ca_times))\n\n# define the function that transforms lists to histograms\ndef list_2_histogram(f_list,bin_number):\n bin_size= max(f_list)/bin_number\n bin_locations= [(idx+0.5)*bin_size for idx in range(bin_number)]\n bin_heights= [0 for idx in range(bin_number)]\n for entry in f_list:\n bin_idx= int(entry/bin_size)\n if bin_idx== bin_number:\n bin_idx= bin_idx-1\n bin_heights[bin_idx]= bin_heights[bin_idx]+1/len(f_list)/bin_size\n return [bin_locations, bin_heights]\n\n# save the parameter packages\nsubdirectory= (\"{:.1e}\".format(trial_number)+ \"_\"+\n \"{:.1e}\".format(v_bin_number)+ \"_400/\")\nPath(subdirectory).mkdir()\nwith open(subdirectory+ \"parameter_packages.txt\", 'w') as txtfile:\n txtfile.write(str(parameter_packages))\nwith open(subdirectory+ \"combined_parameters.txt\", 'w') as txtfile:\n txtfile.write(str(combined_parameters))\nwith open(subdirectory+ \"sequence_lengths.txt\", 'w') as txtfile:\n txtfile.write(str(sequence_lengths))\nwith open(subdirectory+ \"population_sizes.txt\", 'w') as txtfile:\n txtfile.write(str(population_sizes))\n\n# run the simulation trial_number times for each parameter package\n# and save each histogram\nfor parameter_package in parameter_packages:\n # mrca_times= []\n mrca_times= [0. for idx in range(trial_number)]\n for idx in range(trial_number):\n tree_sequence_simulated= msprime.sim_ancestry(\n samples= sample_size,\n ploidy= v_ploidy,\n recombination_rate= parameter_package[3],\n sequence_length= parameter_package[1],\n population_size= parameter_package[2],\n record_full_arg= True)\n # mrca_times.append(mrca_time_api(tree_sequence_simulated)/parameter_package[2])\n mrca_times[idx]= mrca_time_api(tree_sequence_simulated)/parameter_package[2]\n histogram= list_2_histogram(mrca_times, v_bin_number)\n # with open(\"parameter_package\"+datetime.now().strftime(\"%H_%M_%S\")+\".txt\", 'w') as txtfile:\n filename= (\"{:.1e}\".format(parameter_package[0])+ \"_\"+\n \"{:.1e}\".format(parameter_package[1])+ \"_\"+\n \"{:.1e}\".format(parameter_package[2])+ \".txt\")\n with open(subdirectory+ filename, 'w') as txtfile:\n txtfile.write(str(histogram))\n","repo_name":"zhaoz0314/2-mrca","sub_path":"simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71045733521","text":"from sincnet import (\n SincNet,\n MLP\n)\nfrom models import (\n Cnn10,\n Cnn14,\n create_ResNet50_model,\n ModifiedEfficientNet,\n ASTModel\n)\nfrom datasets import (\n CachedDataset,\n WavDataset\n)\nfrom utils import (\n disaggregated_evaluation,\n evaluate_categorical,\n transfer_features,\n LabelEncoder,\n get_output_dim,\n get_df_from_dataset,\n GrayscaleToRGB\n)\nfrom transformers import (\n ASTFeatureExtractor\n)\n\n\n#from ml_utils import get_sharpness\nfrom torch.utils.tensorboard import SummaryWriter\nimport torchvision\nimport torchvision.transforms as transforms\nimport argparse\nimport audtorch\nimport numpy as np\nimport os\nimport pandas as pd\nimport random\nimport shutil\nimport torch\nimport copy\nimport tqdm\nimport yaml\nfrom KFACPytorch import KFACOptimizer, EKFACOptimizer\nfrom sam import SAM\n#from calculate_different_sharpness_values import calculate_sharpness\nfrom gradient_descent_the_ultimate_optimizer.gdtuo import ModuleWrapper, NoOpOptimizer\nfrom torchinfo import summary\n\nimport warnings\n# Ignore Using backward() UserWarning of gdtuo\nwarnings.filterwarnings(category=UserWarning, action=\"ignore\")\n\n\ndef fix_index(df, root):\n df.reset_index(inplace=True)\n df['filename'] = df['filename'].apply(\n lambda x: os.path.join(root, x))\n df.set_index('filename', inplace=True)\n return df\n\n\ndef replace_file_path(df: pd.DataFrame, col, new_path):\n def repl(st):\n return new_path + \"/\" + st.split(\"/\")[-1]\n df[col] = df[col].apply(repl)\n return df\n\n\nclass Model(torch.nn.Module):\n def __init__(self, cnn, mlp_1, mlp_2, wlen, wshift):\n super().__init__()\n self.cnn = cnn\n self.mlp_1 = mlp_1\n self.mlp_2 = mlp_2\n self.wlen = wlen\n self.wshift = wshift\n self.output_dim = self.mlp_2.fc_lay[-1]\n\n def forward(self, x):\n # x = x.transpose(1, 2)\n if not self.training:\n x = x.unfold(1, self.wlen, self.wshift).squeeze(0)\n out = self.mlp_2(self.mlp_1(self.cnn(x)))\n if not self.training:\n out = out.mean(0, keepdim=True)\n return out\n\n\ndef train_step_gdtuo(model,\n mw: ModuleWrapper,\n criterion,\n features,\n targets,\n device,\n clip_net=1.,\n clip_opt=1.,\n ):\n # * Train Step for GDTUO Optimizer using ModelWrapper\n # ? Reference: https://github.com/kach/gradient-descent-the-ultimate-optimizer\n mw.begin()\n output = mw.forward(transfer_features(features, device))\n targets = targets.to(device)\n loss = criterion(output, targets)\n mw.zero_grad()\n loss.backward(create_graph=True) # important! use create_graph=True\n # * GDTUO needs gradient clipping, a lot of stacked optimizers cause HUUUGE gradients!\n if clip_net is not None:\n for param in mw.all_params_with_gradients:\n _clip = torch.ones_like(param.grad) * clip_net\n param.grad = torch.minimum(param.grad, _clip)\n param.grad = torch.maximum(param.grad, -_clip)\n if clip_opt is not None:\n opt = mw.optimizer\n while not isinstance(opt, NoOpOptimizer):\n for param in opt.parameters.values():\n _clip = torch.ones_like(param.grad) * clip_opt\n param.grad = torch.minimum(param.grad, _clip)\n param.grad = torch.maximum(param.grad, -_clip)\n opt = opt.optimizer\n mw.step()\n _loss = loss.item()\n # * GDTUO leaks memory, so it needs to be dealt with manually!\n opt = mw\n while not isinstance(opt, NoOpOptimizer):\n if hasattr(opt, \"all_params_with_gradients\"):\n for param in opt.all_params_with_gradients:\n param.grad = None\n opt.all_params_with_gradients.clear()\n opt = opt.optimizer\n torch.cuda.empty_cache()\n return _loss\n\n\ndef train_step_kfac(model, optimizer, criterion, features, targets, device, _epoch, _batch):\n # * Train Step for (E)KFAC Optimizer\n # ? Reference: https://github.com/alecwangcq/KFAC-Pytorch \n optimizer.zero_grad()\n output = model(transfer_features(features, device))\n targets = targets.to(device)\n loss = criterion(output, targets)\n if optimizer.steps % optimizer.TCov == 0:\n # compute true fisher\n optimizer.acc_stats = True\n with torch.no_grad():\n sampled_y = torch.multinomial(torch.nn.functional.softmax(output.cpu().data, dim=1),\n 1).squeeze().cuda()\n loss_sample = criterion(output, sampled_y)\n loss_sample.backward(retain_graph=True)\n optimizer.acc_stats = False\n optimizer.zero_grad() # clear the gradient for computing true-fisher.\n loss.backward()\n optimizer.step()\n _loss = loss.item()\n return _loss\n\n\ndef train_step_normal(model, optimizer, criterion, features, targets, device):\n # * Train Step for Torch Base Optimizers\n # print(\"-\"*50)\n # TODO: Remove this part. It's only for testing.\n # sharp = get_sharpness(mode, train_dataset)\n # print(\"Sharpness: \", sharp)\n # print(\"Feature Shapes: \", features.shape)\n output = model(transfer_features(features, device))\n targets = targets.to(device)\n loss = criterion(output, targets)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n _loss = loss.item()\n return _loss\n\ndef train_step_SAM(model, optimizer, criterion, features, targets, device):\n # * Train Step for SAM optimizer\n output = model(transfer_features(features, device))\n targets = targets.to(device)\n # first forward-backward pass\n loss = criterion(output, targets) # use this loss for any training statistics\n loss.backward()\n optimizer.first_step(zero_grad=True)\n \n # second forward-backward pass\n output = model(transfer_features(features, device))\n targets = targets.to(device)\n loss = criterion(output, targets) # make sure to do a full forward pass\n loss.backward()\n optimizer.second_step(zero_grad=True)\n _loss = loss.item()\n return _loss\n\n\ndef run_training(args):\n def _get_device_multiprocessing(device):\n torch.cuda.set_device(torch.cuda.device(device))\n return \"cuda:\"+str(torch.cuda.current_device())\n args.device = args.device if isinstance(\n args.device, str) else _get_device_multiprocessing(args.device)\n torch.manual_seed(args.seed)\n gen_seed = torch.Generator().manual_seed(args.seed)\n np.random.seed(args.seed)\n random.seed(args.seed)\n device = args.device\n epochs = args.epochs\n experiment_folder = args.results_root\n os.makedirs(experiment_folder, exist_ok=True)\n ### DCASE\n if args.dataset == 'DCASE2020':\n def get_scene_category(x):\n if x in [\n 'airport',\n 'shopping_mall',\n 'metro_station'\n ]:\n return 'indoor'\n elif x in [\n 'park',\n 'public_square',\n 'street_pedestrian',\n 'street_traffic'\n ]:\n return 'outdoor'\n elif x in [\n 'bus',\n 'metro',\n 'tram'\n ]:\n return 'transportation'\n else:\n raise NotImplementedError(f'{x} not supported.')\n #print(\"-----------------hi---------------\")\n # old\n # df_train = pd.read_csv(\n # os.path.join(\n # args.data_root,\n # 'evaluation_setup',\n # 'fold1_train.csv'\n # ), sep='\\t').set_index('filename')\n \n # new\n df_train = pd.read_csv(\n os.path.join(\n args.data_root,\n 'evaluation_setup',\n 'fold1_train.csv'\n ), sep='\\t')\n df_train['filename'] = df_train['filename'].apply(lambda x: os.path.join(args.data_root, x))\n df_train = df_train.set_index('filename')\n\n\n df_train['scene_category'] = df_train['scene_label'].apply(\n get_scene_category)\n df_train['city'] = [\n os.path.basename(x).split('-')[1]\n for x in df_train.index.get_level_values('filename')\n ]\n df_train['device'] = [\n os.path.basename(x).split('-')[-1].split('.')[0]\n for x in df_train.index.get_level_values('filename')\n ]\n \n df_dev = pd.read_csv(\n os.path.join(\n args.data_root,\n 'evaluation_setup',\n 'fold1_evaluate.csv'\n ), sep='\\t')\n df_dev['filename'] = df_dev['filename'].apply(lambda x: os.path.join(args.data_root, x))\n df_dev = df_dev.set_index('filename')\n df_dev['scene_category'] = df_dev['scene_label'].apply(get_scene_category)\n df_dev['city'] = [\n os.path.basename(x).split('-')[1]\n for x in df_dev.index.get_level_values('filename')\n ]\n df_dev['device'] = [\n os.path.basename(x).split('-')[-1].split('.')[0]\n for x in df_dev.index.get_level_values('filename')\n ]\n \n df_test = pd.read_csv(\n os.path.join(\n args.data_root,\n 'evaluation_setup',\n 'fold1_evaluate.csv'\n ), sep='\\t')\n df_test['filename'] = df_test['filename'].apply(lambda x: os.path.join(args.data_root, x))\n df_test = df_test.set_index('filename')\n\n df_test['scene_category'] = df_test['scene_label'].apply(\n get_scene_category)\n df_test['city'] = [\n os.path.basename(x).split('-')[1]\n for x in df_test.index.get_level_values('filename')\n ]\n df_test['device'] = [\n os.path.basename(x).split('-')[-1].split('.')[0]\n for x in df_test.index.get_level_values('filename')\n ]\n\n if args.category is not None:\n df_train = df_train.loc[df_train['scene_category'] == args.category]\n df_dev = df_dev.loc[df_dev['scene_category'] == args.category]\n df_test = df_test.loc[df_test['scene_category'] == args.category]\n\n if args.exclude_cities != \"None\":\n df_train = df_train.loc[~df_train[\"city\"].isin(args.exclude_cities)]\n\n n_classes = len(df_train['scene_label'].unique())\n encoder = LabelEncoder(\n list(df_train['scene_label'].unique()))\n # old\n #features = pd.read_csv(args.features).set_index('filename')\n # new\n features = pd.read_csv(args.features)\n features['filename'] = features['filename'].apply(lambda x: os.path.join(args.data_root, x))\n features = features.set_index('filename')\n\n\n # * custom feature path support\n if args.custom_feature_path is not None:\n features = replace_file_path(\n features, \"features\", args.custom_feature_path)\n\n db_args = {\n 'features': features,\n 'target_column': 'scene_label',\n 'target_transform': encoder.encode,\n 'feature_dir': args.feature_dir\n }\n \n if args.approach == 'cnn14':\n model = Cnn14(\n output_dim=n_classes\n )\n print(\"Pretrained: \", args.pretrained)\n if args.pretrained:\n # model_old = copy.deepcopy(model)\n # sd = model_old.state_dict()\n # for params in sd:\n # print(params, sd[params]) \n checkpoint = torch.load(args.pretrained_dir + \"Cnn14_16k_mAP=0.438.pth\", map_location=torch.device(args.device))\n state_dict = checkpoint['model']\n model.load_state_dict(state_dict, strict=False)\n db_class = CachedDataset\n model.to_yaml(os.path.join(experiment_folder, 'model.yaml'))\n criterion = torch.nn.CrossEntropyLoss()\n \n elif args.approach == 'cnn10':\n model = Cnn10(\n output_dim=n_classes\n )\n print(\"Pretrained: \", args.pretrained)\n if args.pretrained:\n # model_old = copy.deepcopy(model)\n # sd = model_old.state_dict()\n # for params in sd:\n # print(params, sd[params]) \n checkpoint = torch.load(args.pretrained_dir + \"Cnn10_mAP=0.380.pth\", map_location=torch.device(args.device))\n state_dict = checkpoint['model']\n model.load_state_dict(state_dict, strict=False)\n # sd2 = model.state_dict()\n # for params in sd2:\n # print(params, sd2[params]) \n db_class = CachedDataset\n model.to_yaml(os.path.join(experiment_folder, 'model.yaml'))\n criterion = torch.nn.CrossEntropyLoss()\n elif args.approach == 'resnet50':\n model = create_ResNet50_model(n_classes, pretrained=args.pretrained)\n db_class = CachedDataset\n db_args['transform'] = transforms.Compose([GrayscaleToRGB()])\n criterion = torch.nn.CrossEntropyLoss()\n\n elif args.approach.startswith(\"efficientnet\"):\n model = ModifiedEfficientNet(n_classes, scaling_type=args.approach, pretrained=args.pretrained)\n db_class = CachedDataset\n # db_args['transform'] = transforms.Compose([GrayscaleToRGB(), transforms.ToTensor()])\n db_args['transform'] = transforms.Compose([GrayscaleToRGB()])\n # db_args['transform'] = torch.nn.Sequential(GrayscaleToRGB())\n #db_args['transform'] = torch.nn.Sequential(torchvision.transforms.v2.Grayscale(num_output_channels=3))\n\n # model.to_yaml(os.path.join(experiment_folder, 'model.yaml'))\n criterion = torch.nn.CrossEntropyLoss()\n elif args.approach == 'ast':\n model = ASTModel(\n num_hidden_layers=args.num_hidden_layers\n )\n db_class = WavDataset\n criterion = torch.nn.CrossEntropyLoss()\n feature_extractor = ASTFeatureExtractor()\n\n elif args.approach == 'sincnet':\n with open('sincnet.yaml', 'r') as fp:\n options = yaml.load(fp, Loader=yaml.Loader)\n\n feature_config = options['windowing']\n wlen = int(feature_config['fs'] * feature_config['cw_len'] / 1000.00)\n wshift = int(feature_config['fs'] *\n feature_config['cw_shift'] / 1000.00)\n\n cnn_config = options['cnn']\n cnn_config['input_dim'] = wlen\n cnn_config['fs'] = feature_config['fs']\n cnn = SincNet(cnn_config)\n\n mlp_1_config = options['dnn']\n mlp_1_config['input_dim'] = cnn.out_dim\n mlp_1 = MLP(mlp_1_config)\n\n mlp_2_config = options['class']\n mlp_2_config['input_dim'] = mlp_1_config['fc_lay'][-1]\n mlp_2 = MLP(mlp_2_config)\n model = Model(\n cnn,\n mlp_1,\n mlp_2,\n wlen,\n wshift\n )\n x = torch.rand(2, wlen)\n model.train()\n x = torch.rand(1, 44100)\n model.eval()\n # print(\"EVAL TEST:\")\n # # print(model(x).shape)\n # print()\n with open(os.path.join(experiment_folder, 'sincnet.yaml'), 'w') as fp:\n yaml.dump(options, fp)\n db_class = WavDataset\n df_train = fix_index(df_train, args.data_root)\n df_dev = fix_index(df_dev, args.data_root)\n df_test = fix_index(df_test, args.data_root)\n db_args['transform'] = audtorch.transforms.RandomCrop(wlen)\n criterion = torch.nn.NLLLoss()\n\n train_dataset = db_class(\n df_train,\n **db_args\n )\n dev_dataset = db_class(\n df_dev,\n **db_args\n )\n\n test_dataset = db_class(\n df_test,\n **db_args\n )\n elif args.dataset == \"CIFAR10\":\n # Parameters for development set\n devel_percentage = 0.2\n if args.approach in [\"cnn10\", \"cnn14\"]:\n transform = torchvision.transforms.Compose(\n [torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n torchvision.transforms.Resize((64,64))\n #torchvision.transforms.Resize((1001,64))\n ])\n else:\n transform = torchvision.transforms.Compose(\n [torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ])\n \n train_dev_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,\n download=True, transform=transform)\n \n generator1 = torch.Generator().manual_seed(42)\n train_dataset, dev_dataset = torch.utils.data.random_split(train_dev_dataset, [1 - devel_percentage, devel_percentage], generator=generator1)\n \n \n test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,\n download=True, transform=transform)\n\n df_dev = get_df_from_dataset(dev_dataset)\n df_test = get_df_from_dataset(test_dataset)\n \n encoder = LabelEncoder(\n list(test_dataset.class_to_idx.keys()))\n \n \n df_dev['label'] = df_dev['label'].apply(\n encoder.decode)\n df_test['label'] = df_test['label'].apply(\n encoder.decode)\n \n\n n_classes = len(test_dataset.class_to_idx.keys())\n input_channels = 3\n\n # So far only CNN14 and CNN10 are available\n if args.approach == 'cnn14':\n model = Cnn14(\n output_dim=n_classes,\n in_channels=input_channels\n )\n print(\"Pretrained: \", args.pretrained)\n if args.pretrained:\n # model_old = copy.deepcopy(model)\n # sd = model_old.state_dict()\n # for params in sd:\n # print(params, sd[params]) \n # TODO: not implemented\n print(\"Not implemented...\")\n # checkpoint = torch.load(args.pretrained_dir + \"Cnn14_16k_mAP=0.438.pth\", map_location=torch.device(args.device))\n # state_dict = checkpoint['model']\n # model.load_state_dict(state_dict, strict=False)\n db_class = CachedDataset\n model.to_yaml(os.path.join(experiment_folder, 'model.yaml'))\n criterion = torch.nn.CrossEntropyLoss()\n \n elif args.approach == 'cnn10':\n model = Cnn10(\n output_dim=n_classes,\n in_channels=input_channels\n )\n print(\"Pretrained: \", args.pretrained)\n if args.pretrained:\n # model_old = copy.deepcopy(model)\n # sd = model_old.state_dict()\n # for params in sd:\n # print(params, sd[params]) \n # TODO: not implemented\n print(\"Not implemented...\")\n # checkpoint = torch.load(args.pretrained_dir + \"Cnn10_mAP=0.380.pth\", map_location=torch.device(args.device))\n # state_dict = checkpoint['model']\n # model.load_state_dict(state_dict, strict=False)\n # sd2 = model.state_dict()\n # for params in sd2:\n # print(params, sd2[params]) \n db_class = CachedDataset\n model.to_yaml(os.path.join(experiment_folder, 'model.yaml'))\n criterion = torch.nn.CrossEntropyLoss()\n elif args.approach == 'resnet50':\n model = create_ResNet50_model(n_classes, pretrained=args.pretrained)\n elif args.approach.startswith(\"efficientnet\"):\n model = ModifiedEfficientNet(n_classes, scaling_type=args.approach, pretrained=args.pretrained)\n \n \n # Print a summary using torchinfo (uncomment for actual output)\n criterion = torch.nn.CrossEntropyLoss() \n print(args.approach)\n if args.state is not None:\n initial_state = torch.load(args.state)\n model.load_state_dict(\n initial_state,\n strict=False\n )\n \n x, y = train_dataset[0]\n x = np.expand_dims(x, axis=0)\n print(x.shape)\n summary(model=model, \n input_size=(x.shape), # make sure this is \"input_size\", not \"input_shape\"\n # col_names=[\"input_size\"], # uncomment for smaller output\n col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n col_width=20,\n row_settings=[\"var_names\"]\n )\n # print(\"-\" * 50)\n # personalized_plot_model(model)\n\n if args.approach == 'sincnet':\n db_args.pop('transform')\n \n # create DataLoaders\n train_loader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n batch_size=args.batch_size,\n num_workers=4,\n generator=gen_seed\n )\n\n dev_loader = torch.utils.data.DataLoader(\n dev_dataset,\n shuffle=False,\n batch_size=1 if args.approach == 'sincnet' else args.batch_size,\n num_workers=4,\n generator=gen_seed\n )\n \n\n # df_dev = pd.DataFrame(dev_dataset.dataset)\n\n test_loader = torch.utils.data.DataLoader(\n test_dataset,\n shuffle=False,\n batch_size=1 if args.approach == 'sincnet' else args.batch_size,\n num_workers=4,\n generator=gen_seed\n )\n\n accuracy_history = []\n uar_history = []\n f1_history = []\n train_loss_history = []\n valid_loss_history = []\n\n if not os.path.exists(os.path.join(experiment_folder, 'state.pth.tar')):\n\n encoder.to_yaml(os.path.join(experiment_folder, 'encoder.yaml'))\n with open(os.path.join(experiment_folder, 'hparams.yaml'), 'w') as fp:\n yaml.dump(vars(args), fp)\n\n writer = SummaryWriter(log_dir=os.path.join(experiment_folder, 'log'))\n\n torch.save(\n model.state_dict(),\n os.path.join(\n experiment_folder,\n 'initial.pth.tar')\n )\n if isinstance(args.optimizer, str):\n optim = args.optimizer\n if args.optimizer == 'SGD':\n optimizer = torch.optim.SGD(\n model.parameters(),\n momentum=0.9,\n lr=args.learning_rate\n )\n elif args.optimizer == 'Adam':\n optimizer = torch.optim.Adam(\n model.parameters(),\n lr=args.learning_rate\n )\n elif args.optimizer == 'RMSprop':\n optimizer = torch.optim.RMSprop(\n model.parameters(),\n lr=args.learning_rate,\n alpha=.95,\n eps=1e-7\n )\n else:\n optimizer = args.optimizer.create(\n model, lr=args.learning_rate, device=args.device)\n\n if not \"sheduler_wrapper\" in args or args.sheduler_wrapper == None:\n sheduler = None\n elif isinstance(args.sheduler_wrapper, list):\n sheduler = []\n for sh in args.sheduler_wrapper:\n sheduler.append(sh.create(optimizer))\n else:\n sheduler = args.sheduler_wrapper.create(optimizer)\n\n \n\n max_metric = -1\n best_epoch = 0\n best_state = None\n best_results = None\n\n # accuracy_history = []\n # uar_history = []\n # f1_history = []\n # train_loss_history = []\n # valid_loss_history = []\n\n for epoch in range(epochs):\n model.to(device)\n model.train()\n epoch_folder = os.path.join(\n experiment_folder,\n f'Epoch_{epoch+1}'\n )\n os.makedirs(epoch_folder, exist_ok=True)\n\n if \"train_timer\" in args:\n args.train_timer.start()\n _loss_history = []\n for index, (features, targets) in tqdm.tqdm(\n enumerate(train_loader),\n desc=f'Epoch {epoch}',\n total=len(train_loader),\n disable=args.disable_progress_bar\n ):\n\n if (features != features).sum():\n raise ValueError(features)\n\n if isinstance(optimizer, ModuleWrapper):\n loss = train_step_gdtuo(\n model, optimizer, criterion, features, targets, device)\n elif isinstance(optimizer, (KFACOptimizer, EKFACOptimizer)):\n loss = train_step_kfac(\n model, optimizer, criterion, features, targets, device, epoch+1, index+1)\n elif isinstance(optimizer, SAM):\n loss = train_step_SAM(\n model, optimizer, criterion, features, targets, device)\n else:\n loss = train_step_normal(\n model, optimizer, criterion, features, targets, device)\n if index % 50 == 0:\n writer.add_scalar(\n 'Loss',\n loss,\n global_step=epoch * len(train_loader) + index\n )\n _loss_history.append(loss)\n \n train_loss = sum(_loss_history)/len(_loss_history)\n # print(train_loss)\n if \"train_timer\" in args:\n args.train_timer.stop()\n\n if \"valid_timer\" in args:\n args.valid_timer.start()\n ## Sharpness\n\n # sharpness_values = calculate_sharpness(model, device, train_loader, transfer_features, args.disable_progress_bar, criterion)\n # print(sharpness_values)\n \n\n\n\n # dev set evaluation\n results, _, predictions, outputs, valid_loss = evaluate_categorical(\n model,\n device,\n dev_loader,\n transfer_features,\n args.disable_progress_bar,\n criterion\n )\n results_df = pd.DataFrame(\n index=df_dev.index,\n data=predictions,\n columns=['predictions']\n )\n results_df['predictions'] = results_df['predictions'].apply(\n encoder.decode)\n results_df.reset_index().to_csv(os.path.join(epoch_folder, 'dev.csv'), index=False)\n np.save(os.path.join(epoch_folder, 'outputs.npy'), outputs)\n \n # print(results_df)\n if args.dataset == \"DCASE2020\":\n task = 'scene_label'\n stratify = ['scene_category', 'city', 'device']\n else:\n task = \"label\"\n stratify = []\n logging_results = disaggregated_evaluation(\n results_df,\n df_dev,\n task,\n stratify,\n 'categorical'\n )\n\n with open(os.path.join(epoch_folder, 'dev.yaml'), 'w') as fp:\n yaml.dump(logging_results, fp)\n for metric in logging_results.keys():\n writer.add_scalars(\n f'dev/{metric}',\n logging_results[metric],\n (epoch + 1) * len(train_loader)\n )\n\n torch.save(model.cpu().state_dict(), os.path.join(\n epoch_folder, 'state.pth.tar'))\n results[\"train_loss\"] = train_loss\n results[\"val_loss\"] = valid_loss\n \n print(f'Dev results at epoch {epoch+1}:\\n{yaml.dump(results)}')\n # save accuracy metric\n accuracy_history.append(results[\"ACC\"])\n uar_history.append(results[\"UAR\"])\n f1_history.append(results[\"F1\"])\n train_loss_history.append(train_loss)\n valid_loss_history.append(valid_loss)\n if results['ACC'] > max_metric:\n max_metric = results['ACC']\n best_epoch = epoch\n best_state = model.cpu().state_dict()\n best_results = results.copy()\n\n # plateau_scheduler.step(results['ACC'])\n if \"valid_timer\" in args:\n args.valid_timer.stop()\n train_results, _, _, _, train_loss = evaluate_categorical(\n model,\n device,\n train_loader,\n transfer_features,\n args.disable_progress_bar,\n criterion\n )\n print(f'Final Train results:\\n {yaml.dump(train_results)}')\n print(f'Final Train loss:\\n {yaml.dump(train_loss)}')\n #results[\"sharpness_value\"] = sharpness_values\n print(\"Final Train results: \", train_results)\n print(\"Final Train loss: \", train_loss)\n\n # sharpness_values = calculate_sharpness(model, device, train_loader, transfer_features, args.disable_progress_bar, criterion)\n # print(f'Sharpness:\\n{yaml.dump(results)}')\n #results[\"sharpness_value\"] = sharpness_values\n # print(\"Sharpness Value: \", sharpness_values)\n print(\n f'Best dev results found at epoch {best_epoch+1}:\\n{yaml.dump(best_results)}')\n best_results['Epoch'] = best_epoch + 1\n with open(os.path.join(experiment_folder, 'dev.yaml'), 'w') as fp:\n yaml.dump(best_results, fp)\n writer.close()\n else:\n best_state = torch.load(os.path.join(\n experiment_folder, 'state.pth.tar'))\n print('Training already run')\n epoch_folder = os.path.join(\n experiment_folder,\n f'Epoch_{epochs}'\n )\n # if args.dataset == \"DCASE2020\":\n \n print(\"saving to: \", os.path.join(experiment_folder, 'test_holistic.yaml'))\n if not os.path.exists(os.path.join(experiment_folder, 'test_holistic.yaml')):\n # if True:\n model.load_state_dict(best_state)\n test_results, targets, predictions, outputs, valid_loss = evaluate_categorical(\n model, device, test_loader, transfer_features, args.disable_progress_bar, criterion)\n print(f'Best test results:\\n{yaml.dump(test_results)}')\n torch.save(best_state, os.path.join(\n experiment_folder, 'state.pth.tar'))\n np.save(os.path.join(experiment_folder, 'targets.npy'), targets)\n np.save(os.path.join(experiment_folder, 'outputs.npy'), outputs)\n np.save(os.path.join(experiment_folder, 'predictions.npy'), outputs)\n results_df = pd.DataFrame(\n index=df_test.index,\n data=predictions,\n columns=['predictions']\n )\n results_df['predictions'] = results_df['predictions'].apply(\n encoder.decode)\n results_df.reset_index().to_csv(os.path.join(epoch_folder, 'test.csv'), index=False)\n # print(results_df)\n # print(df_test)\n with open(os.path.join(experiment_folder, 'test.yaml'), 'w') as fp:\n yaml.dump(test_results, fp)\n if args.dataset == \"DCASE2020\":\n task = 'scene_label'\n stratify = ['scene_category', 'city', 'device']\n else:\n task = \"label\"\n stratify = []\n logging_results = disaggregated_evaluation(\n results_df,\n df_test,\n task,\n stratify,\n 'categorical'\n )\n with open(os.path.join(experiment_folder, 'test_holistic.yaml'), 'w') as fp:\n yaml.dump(logging_results, fp)\n else:\n print('Evaluation already run')\n # in case we don't have any training the benchrunner doesn't make a lot of sense.\n\n return accuracy_history, uar_history, f1_history, train_loss_history, valid_loss_history\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser('DCASE-T1 Training')\n parser.add_argument(\n '--data-root',\n help='Path data has been extracted',\n required=True\n )\n parser.add_argument(\n '--results-root',\n help='Path where results are to be stored',\n required=True\n )\n parser.add_argument(\n '--features',\n help='Path to features',\n required=True\n )\n \n parser.add_argument(\n '--device',\n help='CUDA-enabled device to use for training',\n required=True\n )\n parser.add_argument(\n '--state',\n help='Optional initial state'\n )\n\n # TODO: is here efficientnet not supported anymore????\n parser.add_argument(\n '--approach',\n default='cnn10',\n choices=[\n 'cnn14',\n 'cnn10',\n 'sincnet',\n 'ast'\n ]\n )\n parser.add_argument(\n '--category',\n default=None,\n choices=[\n 'indoor',\n 'outdoor',\n 'transportation',\n None\n ]\n )\n parser.add_argument(\n '--batch-size',\n type=int,\n default=32,\n help='Batch size'\n )\n parser.add_argument(\n '--epochs',\n type=int,\n default=60\n )\n parser.add_argument(\n '--learning-rate',\n type=float,\n default=0.001\n )\n parser.add_argument(\n '--seed',\n type=int,\n default=0\n )\n parser.add_argument(\n '--optimizer',\n default='SGD'\n )\n parser.add_argument(\n '--feature_dir',\n default=''\n )\n\n parser.add_argument(\n '--custom-feature-path',\n help='Custom .npy location of features',\n required=False\n )\n parser.add_argument(\n '--num-hidden-layers',\n type=int,\n default=12,\n help=\"Num hidden layers to use for AST (max 12)\"\n )\n\n parser.add_argument(\n '--disable-progress-bar',\n default=False,\n type=bool,\n help='Disable tqdm progress bar while training',\n choices=[\n 'True',\n 'False',\n ]\n )\n\n parser.add_argument(\n '--exclude-cities',\n default=\"None\",\n type=str,\n help='Exclude a City from training',\n choices=[\n \"barcelona\",\n \"helsinki\",\n \"lisbon\",\n \"london\",\n \"lyon\",\n \"milan\",\n \"paris\",\n \"prague\",\n \"stockholm\",\n \"vienna\",\n \"None\",\n ]\n )\n\n args = parser.parse_args()\n run_training(args)\n","repo_name":"EIHW/ASC_Sharpness","sub_path":"training/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":34749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29029450377","text":"import pytest\n\nfrom intranet.femida.src.candidates.helpers import close_candidate\nfrom intranet.femida.src.candidates.considerations.helpers import archive_consideration\nfrom intranet.femida.src.candidates.choices import (\n SUBMISSION_SOURCES,\n SUBMISSION_STATUSES,\n CONSIDERATION_RESOLUTIONS,\n CONSIDERATION_STATUSES,\n CANDIDATE_STATUSES,\n)\nfrom intranet.femida.src.candidates.models import CandidateSubmission\nfrom intranet.femida.src.candidates.submissions.helpers import filter_submissions_by_recruiter\nfrom intranet.femida.src.offers.choices import SOURCES\n\nfrom intranet.femida.tests import factories as f\n\n\npytestmark = pytest.mark.django_db\n\n\ndef _filter_submissions_by_recruiter(recruiter):\n return filter_submissions_by_recruiter(CandidateSubmission.unsafe.all(), recruiter)\n\n\ndef test_filter_submissions_by_recruiter_new_form():\n \"\"\"\n Новые отклики из КФ на вакансии данного рекрутера\n \"\"\"\n recruiter = f.create_recruiter()\n vacancy = f.VacancyFactory()\n vacancy.set_main_recruiter(recruiter)\n submission_form = f.SubmissionFormFactory()\n submission_form.vacancies.add(vacancy)\n f.SubmissionFactory.create_batch(3) # пустышки\n expected = f.SubmissionFactory(\n source=SUBMISSION_SOURCES.form,\n status=SUBMISSION_STATUSES.new,\n form=submission_form,\n )\n result = _filter_submissions_by_recruiter(recruiter)\n assert [expected] == list(result)\n\n\ndef test_filter_submissions_by_recruiter_new_reference():\n \"\"\"\n Новые отклики-рекомендации на вакансии данного рекрутера\n \"\"\"\n recruiter = f.create_recruiter()\n vacancy = f.VacancyFactory()\n vacancy.set_main_recruiter(recruiter)\n reference = f.ReferenceFactory()\n reference.vacancies.add(vacancy)\n f.SubmissionFactory.create_batch(3) # пустышки\n expected = f.SubmissionFactory(\n source=SUBMISSION_SOURCES.reference,\n status=SUBMISSION_STATUSES.new,\n reference=reference,\n )\n result = _filter_submissions_by_recruiter(recruiter)\n assert [expected] == list(result)\n\n\ndef test_filter_submissions_by_recruiter_closed():\n \"\"\"\n Отклики, обработанные данным рекрутером\n \"\"\"\n recruiter = f.create_recruiter()\n f.SubmissionFactory.create_batch(3) # пустышки\n expected = f.SubmissionFactory(\n status=SUBMISSION_STATUSES.closed,\n responsible=recruiter,\n )\n result = _filter_submissions_by_recruiter(recruiter)\n assert [expected] == list(result)\n\n\ndef test_filter_submissions_by_recruiter_processed_reference():\n \"\"\"\n Отклики, где рекрутер обработал рекомендацию\n \"\"\"\n recruiter = f.create_recruiter()\n reference = f.ReferenceFactory(processed_by=recruiter)\n f.SubmissionFactory.create_batch(3) # пустышки\n expected = f.SubmissionFactory(reference=reference)\n result = _filter_submissions_by_recruiter(recruiter)\n assert [expected] == list(result)\n\n\ndef test_archive_consideration():\n candidate = f.create_candidate_with_consideration()\n consideration = candidate.considerations.last()\n assert consideration.state == CONSIDERATION_STATUSES.in_progress\n assert consideration.extended_status == CONSIDERATION_STATUSES.in_progress\n assert consideration.resolution == ''\n assert list(consideration.responsibles.all()) == []\n\n archive_consideration(\n consideration=consideration,\n resolution=CONSIDERATION_RESOLUTIONS.rejected_by_resume,\n )\n assert consideration.state == CONSIDERATION_STATUSES.archived\n assert consideration.extended_status == CONSIDERATION_STATUSES.archived\n assert consideration.resolution == CONSIDERATION_RESOLUTIONS.rejected_by_resume\n assert set(consideration.responsibles.all()) == set(candidate.responsibles.all())\n candidate_responsibles = dict(candidate.candidate_responsibles.values_list('user', 'role'))\n consideration_responsibles = dict(\n consideration.consideration_responsibles.values_list('user', 'role'),\n )\n assert candidate_responsibles == consideration_responsibles\n\n\ndef test_close_candidate():\n candidate = f.create_candidate_with_consideration(source_description='test')\n assert candidate.status == CANDIDATE_STATUSES.in_progress\n assert candidate.source == SOURCES.other\n assert candidate.source_description == 'test'\n assert candidate.responsibles.count() == 2\n\n close_candidate(candidate)\n assert candidate.status == CANDIDATE_STATUSES.closed\n assert candidate.source == ''\n assert candidate.source_description == ''\n assert candidate.responsibles.count() == 0\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/unit/submissions/test_helpers.py","file_name":"test_helpers.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29181090978","text":"from __future__ import division, print_function\nimport os\nimport h5py\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom itertools import islice\nsns.set_style('ticks')\n\n\nclass RubinovAlgorithm:\n def __init__(self, hic_data, chr_number, n_iterations=1000):\n self.hic_file = hic_data\n self.chromosome_number = chr_number\n self.n_iterations = n_iterations\n # self.start = start\n # self.end = end\n self.get_hic_matrix()\n self.get_tad_orientation()\n self.get_first_level_tads()\n self.get_tads()\n\n\n def get_hic_matrix(self):\n print('Loading data...')\n hic_file = h5py.File(self.hic_file, 'r')\n chromosome = str(self.chromosome_number) + ' ' + str(self.chromosome_number)\n hic_matrix = hic_file[chromosome].value\n df = pd.DataFrame(hic_matrix)\n df = df.loc[:, (df != 0).any(axis=0)]\n df = df.loc[(df != 0).any(axis=1), :]\n hic_matrix = np.array(df)\n np.fill_diagonal(hic_matrix[1:, :], np.zeros(len(hic_matrix) - 1))\n np.fill_diagonal(hic_matrix[:, 1:], np.zeros(len(hic_matrix) - 1))\n self.hic_for_vizualization = hic_matrix\n self.hic_matrix = np.power(np.array(hic_matrix), 4)\n return self.hic_matrix\n\n def get_tad_orientation(self):\n left_summs = np.sum(np.triu(self.hic_matrix), axis=0)\n right_summs = np.sum(np.triu(self.hic_matrix), axis=1)\n self.pairs = list(zip(left_summs, right_summs))\n\n orientation = []\n for pair in self.pairs:\n if pair[0] < pair[1]:\n orientation.append(1)\n elif pair[0] > pair[1]:\n orientation.append(-1)\n self.orientation = orientation\n\n def get_first_level_tads(self):\n tads_all = []\n tads_left_pos = []\n tads_length = []\n borders = []\n new_pairs = []\n\n first_tads = []\n first_tads_left_pos = []\n first_tads_length = []\n brdr = []\n resupd = []\n numbers = iter(range(len(self.orientation)-1))\n for i in numbers:\n if self.orientation[i] == 1 and self.orientation[i + 1] == -1:\n brdr.append([i, i + 2])\n if self.pairs[i][0] + self.pairs[i + 1][0] > self.pairs[i][1] + self.pairs[i + 1][1]:\n first_tads.append(-1)\n elif self.pairs[i][0] + self.pairs[i + 1][0] < self.pairs[i][1] + self.pairs[i + 1][1]:\n first_tads.append(1)\n\n nres = [self.pairs[i][0] + self.pairs[i + 1][0], self.pairs[i][1] + self.pairs[i + 1][1]]\n resupd.append(nres)\n first_tads_left_pos.append(i)\n first_tads_length.append(2)\n next(islice(numbers, 1, 1), None)\n\n else:\n first_tads.append(self.orientation[i])\n first_tads_left_pos.append(i)\n first_tads_length.append(1)\n resupd.append(self.pairs[i])\n\n new_pairs.append(resupd)\n self.new_pairs = new_pairs\n\n tads_all.append(self.orientation)\n tads_all.append(first_tads)\n self.tads_all = tads_all\n\n borders.append(brdr)\n self.borders = borders\n\n tads_left_pos.append(first_tads_left_pos)\n self.tads_left_pos = tads_left_pos\n\n tads_length.append(first_tads_length)\n self.tads_length = tads_length\n\n def get_tads(self):\n print('Getting TADs...')\n for level in range(self.n_iterations):\n tads = []\n tads_left = []\n tads_len = []\n border = []\n updated_pairs = []\n numbers = iter(range(len(self.tads_all[level + 1])-1))\n\n if len(self.borders[level]) == 1 and self.borders[level][0][0] == 0 and\\\n self.borders[level][0][1] == len(self.hic_matrix):\n max_level = level\n print('Highest level', max_level)\n break\n\n for i in numbers:\n if self.tads_all[level + 1][i] == 1 and self.tads_all[level + 1][i + 1] == -1:\n border.append([self.tads_left_pos[level][i], self.tads_left_pos[level][i] +\n self.tads_length[level][i] + self.tads_length[level][i + 1]])\n\n if self.new_pairs[level][i][0] + self.new_pairs[level][i + 1][0] > \\\n self.new_pairs[level][i][1] + self.new_pairs[level][i + 1][1]:\n tads.append(-1)\n elif self.new_pairs[level][i][0] + self.new_pairs[level][i + 1][0] < \\\n self.new_pairs[level][i][1] + self.new_pairs[level][i + 1][1]:\n tads.append(1)\n\n n_pairs = [self.new_pairs[level][i][0] + self.new_pairs[level][i + 1][0],\n self.new_pairs[level][i][1] + self.new_pairs[level][i + 1][1]]\n\n updated_pairs.append(n_pairs)\n tads_left.append(self.tads_left_pos[level][i])\n tads_len.append(self.tads_length[level][i] + self.tads_length[level][i + 1])\n next(islice(numbers, 1, 1), None)\n\n else:\n tads.append(self.tads_all[level + 1][i])\n tads_left.append(self.tads_left_pos[level][i])\n tads_len.append(self.tads_length[level][i])\n updated_pairs.append(self.new_pairs[level][i])\n\n self.new_pairs.append(updated_pairs)\n self.tads_all.append(tads)\n self.borders.append(border)\n self.tads_left_pos.append(tads_left)\n self.tads_length.append(tads_len)\n print('Done')\n\n def mtxplot(self, letter, color):\n for j in range(len(letter)):\n bgn = letter[j][0] - self.startmtx\n end = letter[j][1] - self.startmtx\n plt.plot([bgn + 2, end + 2], [bgn, bgn], color=color)\n plt.plot([end + 2, end + 2], [bgn, end], color=color)\n\n def visualise_tree(self, start=None, end=None):\n print('Building hierarchical tree...')\n self.startmtx = start + 1\n self.endmtx = end\n plt.figure(figsize=(20, 20))\n sns.heatmap(self.hic_for_vizualization[start:end, start:end], cmap='Reds')\n for i in range(self.startmtx, self.endmtx):\n val = self.orientation[i]\n plt.text(i - self.startmtx, i + 0.8 - self.startmtx, val, {'color': 'red' if val > 0 else 'blue'})\n if len(self.borders) % 3 == 0:\n colors = ['red', 'blue', 'green'] * int(len(self.borders) / 3)\n else:\n colors = ['red', 'blue', 'green'] * int(len(self.borders) / 3)\n colors.append(['orange', 'blue', 'green'][: len(self.borders) % 3])\n for letter, color in zip(self.borders, colors):\n self.mtxplot(letter, color)\n plt.savefig(str(os.path.splitext(self.hic_file)[0]) + '_hierarhical_tree' + '.pdf', format='pdf')\n print('Done')\n\n","repo_name":"OPushkareva/Clustering-and-Comparison-of-Hierarchies-in-the-Spatial-Organization-of-Chromatin","sub_path":"RubinovAlgorithm.py","file_name":"RubinovAlgorithm.py","file_ext":"py","file_size_in_byte":7041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5509930336","text":"from time import sleep\nimport schedule\nfrom datetime import datetime, time\nfrom pytz import timezone\nfrom config import SCHEDULER_RUN_TIMES\nfrom dotenv import load_dotenv\nimport os\n\nload_dotenv()\nSCHEDULER_TIMEZONE: str = os.getenv(\"SCHEDULER_TIMEZONE\")\n\n\nclass Scheduler:\n\n def __init__(self, bot):\n self.bot = bot\n self.timezone = timezone(SCHEDULER_TIMEZONE)\n self._schedule_test_messaging()\n\n def _job(self, text: str) -> None:\n msg = f'{datetime.now(tz=self.timezone).strftime(\"%H:%M:%S\")}\\nScheduled message:\\n{text}'\n self.bot.send_admin_message(text=msg)\n # return schedule.CancelJob\n\n def _schedule_message_daily(self, text: str, run_time: str) -> None:\n time_obj = time.fromisoformat(run_time)\n schedule.every().day.at(time_obj.strftime(\"%H:%M:%S\"), tz=self.timezone).do(self._job, text=text)\n\n def _schedule_test_messaging(self) -> None:\n t1, t2, t3 = SCHEDULER_RUN_TIMES\n self._schedule_message_daily(text='Доброе утро! Бот на связи', run_time=t1)\n self._schedule_message_daily(text='Обед! Bon appetit!', run_time=t2)\n self._schedule_message_daily(text='Рабочий день закончен. Хорошего вечера!', run_time=t3)\n\n def start(self):\n while True:\n schedule.run_pending()\n sleep(1)\n","repo_name":"cOnT3ST/-BetBot","sub_path":"scheduler.py","file_name":"scheduler.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"1528563478","text":"import os\nimport json\nfrom controllers.helpers.dropbox import dbx\nfrom dropbox.exceptions import ApiError\n\n# Initialize elasticsearch\nfrom elasticsearch import Elasticsearch\nes = Elasticsearch(\n\tcloud_id=os.getenv('ELASTICSEARCH_CLOUD_ID'),\n basic_auth=(os.getenv('ELASTICSEARCH_USERNAME'), os.getenv('ELASTICSEARCH_PASSWORD')),\n)\n\n\n\nif not es.indices.exists(index=\"dropbox_files\"):\n\tes.indices.create(index=\"dropbox_files\")\n\n# Initialize tika for file parsing\nimport tika\ntika.initVM()\nfrom tika import parser\n\n# env vars\nfrom dotenv import load_dotenv\nload_dotenv()\n\n# Initialize Flask app\nfrom flask import Flask, jsonify, request\nfrom flask_cors import CORS\napp = Flask(__name__)\nCORS(app)\n\n\n# Retrieve already Stored Data from elastic search index\ndef get_old_data():\n\tdb = {}\n\tresponse = es.search(index=\"dropbox_files\", body={\"query\": {\"match_all\": {}}})\n\n\tfor x in response['hits']['hits']:\n\t\tdb[x['_id']] = x['_source']\n\treturn db\n\n# Get a list of metadata of all files in dropbox account\ndef get_new_metadata():\n\tnew_metadata = {}\n\tresult = dbx.files_list_folder(path=\"\", recursive=True)\n\tfor file in result.entries:\n\t\tif hasattr(file, 'content_hash'):\n\t\t\t# Key for elasticsearch index is the \n\t\t\tnew_metadata[file.id+\";\"+file.content_hash] = file.path_display\n\n\treturn new_metadata\n\n# Delete the files deleted from dropbox account, from the local db copy\ndef delete_old_data(db, to_delete):\n\tfor key in to_delete:\n\t\tdel db[key]\n\n# Download newly added/updated files from dropbox account & save it to local db copy\ndef save_new_files(db, to_download, new_metadata):\n\tfor key in to_download:\n\t\tmetadata, result = dbx.files_download(path=new_metadata[key])\n\t\tfile_contents = result.content\n\t\tparsed = parser.from_buffer(file_contents)\n\t\tdb[key] = [parsed['content'], metadata.path_display]\n \n# Update es index using local db copy\ndef update_search_index(db, to_download, to_delete):\n\tbulk_requests = []\n\tfor file_id in to_download:\n\t\tdoc = {\n\t\t\t'content': db[file_id][0],\n\t\t\t'path': db[file_id][1],\n\t\t}\n\t\taction= {\"index\": {\"_index\": \"dropbox_files\", \"_id\": file_id}}\n\t\tbulk_requests.append(action)\n\t\tbulk_requests.append(doc)\n\n\tfor file_id in to_delete:\n\t\tbulk_requests.append({\"delete\": {\"_index\": \"dropbox_files\", \"_id\": file_id}})\n\n\tif len(bulk_requests):\n\t\tes.bulk(operations=bulk_requests)\n\n# Fetch shareable links for all files & return the final result \ndef get_search_result(response):\n\tresult_paths = []\n\tfor x in response['hits']['hits']:\n\t\tresult_paths.append(x['_source']['path'])\n\n\tfinal_response = []\n\n\t# Fetch metadata containing links for each file from dropbox\n\tfor path in result_paths:\n\n\t\tresult = dbx.sharing_list_shared_links(path=path, direct_only=True)\n\t\tif len(result.links):\n\t\t\ttemp = result.links[0]\n\t\t\tfinal_response.append({\"link\": temp.url, \"name\": temp.name, \"path\": temp.path_lower})\n\t\n\treturn final_response\n\n\n@app.route(\"/search\", methods=['GET'])\ndef search():\n\n\t\ttry:\n\t\t\tq = request.args.get('q')\n\t\t\tif not q:\n\t\t\t\treturn jsonify({\"error\": \"no query\"}), 400\n\n\t\t\t# Get old data from elastic search index & store it in local copy\n\t\t\tdb = get_old_data();\n\n\t\t\t# Get new metadata of all files from dropbox account\n\t\t\tnew_metadata = get_new_metadata()\n\n\t\t\t# Get a list of keys in stored index and new_metadata from dropbox\n\t\t\told_keys = set(list(db.keys()))\n\t\t\tnew_keys = set(list(new_metadata.keys()))\n\n\t\t\tto_download = new_keys - old_keys\n\t\t\tto_delete = old_keys - new_keys\n\n\t\t\t# Delete keys for files that have been deleted from dropbox, from the local copy\n\t\t\tdelete_old_data(db, to_delete)\n\n\t\t\t# Download newly added files on dropbox & save it to local copy\n\t\t\tsave_new_files(db, to_download, new_metadata)\n\t\t\t\n\t\t\t# Update elastic search index using updated local copy\n\t\t\tupdate_search_index(db, to_download, to_delete)\n\n\n\t\t\t# Search files in elastic search index for given query q\n\t\t\tes.indices.refresh(index=\"dropbox_files\")\n\t\t\tresponse = es.search(index=\"dropbox_files\", body={\"query\": {\"match_phrase\": {\"content\": q}}})\n\n\t\t\t# Fetch shareable links with given file paths\n\t\t\tfinal_response = get_search_result(response)\n\n\t\texcept:\n\t\t\treturn jsonify({\"error\": \"internal server error\"}), 500\n\n # Output/send response to client\n\t\treturn jsonify(final_response), 200","repo_name":"neerizz/search-master-backend","sub_path":"controllers/dropbox_search_controller.py","file_name":"dropbox_search_controller.py","file_ext":"py","file_size_in_byte":4241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73749827282","text":"from py_stealth import *\nfrom datetime import datetime as dt\nimport re\n\nRULES = {\n \"Forensics\": 0x400FF777,\n \"Cooking\": 0x400FEF2B,\n \"Inscription\": 0x400FEE9F,\n \"Mining\": 0x400FEE8E,\n \"Blacksmithing\": 0x400FEEDF,\n \"Tactics\": 0x400FEF71,\n \"Veterinary\": 0x400FEF87,\n \"Alchemy\": 0x400FEEBE,\n \"MagicResistance\": 0x400FEF07,\n \"Anatomy\": 0x400FEF51,\n \"Taming\": 0x400FEEEE,\n \"Lumberjacking\": 0x400FEF75,\n \"DetectingHidden\": 0x400FEEF8,\n \"Archery\": 0x400FEF0D,\n \"Tinkering\": 0x400FEFA1,\n \"Carpentry\": 0x400FF0CC,\n \"Tailoring\": 0x400FEFB1,\n \"ArmsLore\": 0x400FF0DB,\n \"AnimalLore\": 0x400FF08A,\n \"Stealth\": 0x400FF0F2,\n \"Fishing\": 0x400FEF93,\n \"Fencing\": 0x400FF0A6,\n \"Anatomy\": 0x400FAFF7,\n}\n\nBOOKS_CONTAINER = 0x40212B61\nBAGS_CONTAINER = 0x400FF99A\nBOOK_TYPE = 0x1C27\n\ndef get_skillbook_bonus(serial: int) -> str:\n _started = dt.now()\n if IsObjectExists(serial):\n ClickOnObject(serial)\n Wait(500)\n _journal_line = InJournalBetweenTimes(\"%\", _started, dt.now())\n if _journal_line > 0: \n _match = re.search(r\"You\\ssee:\\s\\d\\.\\d\\%\\s(.+)\",Journal(_journal_line))\n if _match: \n return _match.group(1)\n return ''\n \ndef get_container_for_skillbook(name: str) -> int:\n if name in RULES:\n return RULES[name]\n return 0 \n\ndef process():\n if FindType(BOOK_TYPE, BOOKS_CONTAINER):\n for _book in GetFindedList():\n _name = get_skillbook_bonus(_book)\n _container = get_container_for_skillbook(_name)\n if _container > 0:\n print(f\"Moving {_name}...\")\n MoveItem(_book, 0, _container, 0, 0, 0)\n Wait(1000)\n \n\n###\nif __name__ == \"__main__\":\n for container in [BOOKS_CONTAINER, BAGS_CONTAINER] + list(RULES.values()):\n UseObject(container)\n Wait(200) \n process()\n","repo_name":"it-sova/olmer-stealth-public","sub_path":"sort_by_name.py","file_name":"sort_by_name.py","file_ext":"py","file_size_in_byte":1948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29060476837","text":"import pytest\n\nfrom hamcrest import assert_that, equal_to, match_equality, matches_regexp\n\nfrom billing.yandex_pay_admin.yandex_pay_admin.core.actions.document.create import CreateDocumentAction\nfrom billing.yandex_pay_admin.yandex_pay_admin.file_storage.documents import YandexPayAdminDocsFileStorage\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.entities.document import Document\n\nPATH_PREFIX = '/prefix'\nPATH_MATCHER = match_equality(matches_regexp(r'/prefix/[0-9a-f\\-]+'))\n\n\n@pytest.mark.asyncio\nasync def test_returned(partner, file_content_iter):\n document = await CreateDocumentAction(\n partner_id=partner.partner_id,\n path_prefix=PATH_PREFIX,\n original_name='file.png',\n content=file_content_iter(),\n ).run()\n\n assert_that(\n document,\n equal_to(\n Document(\n document_id=document.document_id,\n partner_id=partner.partner_id,\n path=PATH_MATCHER,\n digest='',\n name='file.png',\n created=document.created,\n updated=document.updated,\n )\n ),\n )\n\n\n@pytest.mark.asyncio\nasync def test_calls_file_storage(mocker, partner, file_content_iter, mock_storage):\n await CreateDocumentAction(\n partner_id=partner.partner_id,\n path_prefix=PATH_PREFIX,\n original_name='file.png',\n content=file_content_iter(),\n ).run()\n\n mock_storage.assert_exited_once()\n mock_storage.ctx_result.upload_stream.assert_called_once_with(PATH_MATCHER)\n mock_storage.ctx_result.upload_stream.assert_exited_once()\n mock_storage.ctx_result.upload_stream.ctx_result.write.assert_has_awaits(\n [\n mocker.call(b'chunk1'),\n mocker.call(b'chunk2'),\n ]\n )\n\n\n@pytest.fixture\ndef file_content_iter():\n async def _iter():\n yield b'chunk1'\n yield b'chunk2'\n\n return _iter\n\n\n@pytest.fixture(autouse=True)\ndef mock_storage(mocker, actx_mock):\n return mocker.patch.object(\n YandexPayAdminDocsFileStorage,\n 'acquire',\n actx_mock(\n return_value=mocker.Mock(upload_stream=actx_mock(return_value=mocker.Mock(write=mocker.AsyncMock())))\n ),\n )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/unit/core/actions/document/test_create.py","file_name":"test_create.py","file_ext":"py","file_size_in_byte":2237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4177807453","text":"#! /usr/bin/python\n#%% imports\n\nimport json\nimport os\nimport re\nimport struct\nimport pandas\nimport datetime\nimport statistics\nimport matplotlib.pyplot as plt\nimport argparse\nfrom si_prefix import si_format\nimport sys\n\n#%% args\nparser = argparse.ArgumentParser(description=\"Parse a SensorBoard can log\")\nparser.add_argument('inputFile', nargs='*', default=None, help=\"the can log to parse\")\nargs = parser.parse_args()\n\n\n#%% files\ndirname = os.path.dirname(__file__)\nconfigFolder = os.path.join(dirname, 'configs')\n# configFolder = r\"configs\"\nconfigpaths = []\n\nfor file in os.listdir(configFolder):\n if file.endswith(r\".json\"):\n configpaths.append(os.path.join(configFolder, file))\n\nprint(\"Found configs: \", configpaths)\n\n# print(json.dumps(canformat, indent=4, sort_keys=True))\n# for x in canformat:\n # print(\"{}\\n\".format(x[\"can_id\"]))\n\n#%% parse configs\nclass CanFrameInfo(object):\n def __init__(self,id,datatype,name,offset,checkBounds,minValue=None,maxValue=None,description=None):\n self.id = id\n self.description = description\n self.checkBounds = checkBounds\n self.dataTypeStr = datatype.rstrip()\n self.maxValue = maxValue\n self.minValue = minValue\n self.name = name\n self.offset = offset # Offset in bytes\n\n self.currentValue = None\n self.dataWidth = None\n self.parsestr = r''\n if (self.dataTypeStr == 'float32'):\n self.datatype = float\n self.dataWidth = 4\n self.parsestr = r'<' + r'x' * self.offset + r'f' + r'x' * (8 - self.offset - self.dataWidth)\n elif self.dataTypeStr == 'uint16':\n self.datatype = int\n self.dataWidth = 2\n self.parsestr = r'<' + r'x' * self.offset + r'H' + r'x' * (8 - self.offset - self.dataWidth)\n elif self.dataTypeStr == 'int16':\n self.datatype = int\n self.dataWidth = 2\n self.parsestr = r'<' + r'x' * self.offset + r'h' + r'x' * (8 - self.offset - self.dataWidth)\n elif self.dataTypeStr == 'uint8':\n self.datatype = int\n self.dataWidth = 1\n self.parsestr = r'<' + r'x' * self.offset + r'B' + r'x' * (8 - self.offset - self.dataWidth)\n elif self.dataTypeStr == 'uint32':\n self.datatype = int\n self.dataWidth = 4\n self.parsestr = r'<' + r'x' * self.offset + r'I' + r'x' * (8 - self.offset - self.dataWidth)\n elif self.dataTypeStr == 'bit':\n self.bitMask = 2**(7-(offset%8))\n self.offset = self.offset // 8\n self.datatype = bool\n self.dataWidth = 1\n self.parsestr = r'<' + r'x' * self.offset + r'B' + r'x' * (8 - self.offset - self.dataWidth)\n\n\n def parse(self, candata):\n candata = candata\n if len(candata) < 8:\n for _ in range(8-len(candata)):\n candata.append(0)\n if self.dataTypeStr == 'bit':\n self.currentValue = self.datatype(struct.unpack(self.parsestr, candata)[0] & self.bitMask)\n \n else:\n try:\n self.currentValue = self.datatype(struct.unpack(self.parsestr, candata)[0])\n except Exception as ex:\n print(\"Failed to parse:\", ex)\n print(f\"canid {self.id}, {self.datatype}\")\n print(\"\\t\", len(candata), candata)\n print(\"\\t\", self.parsestr)\n\n def __repr__(self):\n info = f\"\"\"id={self.id} offset={self.offset} name={self.name}\n description={self.description}\n checkBounds={self.checkBounds} minValue={self.minValue} maxValue={self.maxValue}\n parseStr={self.parsestr}\"\"\"\n\n return info\n\ndatabase = {}\n\nfor configPath in configpaths:\n with open(configPath) as f:\n canformat = json.load(f)\n\n for config in canformat:\n # print(type(config))\n if config['can_id'] not in database:\n database[config['can_id']] = []\n\n database[config['can_id']].append(CanFrameInfo(\n id=config.get('can_id', None),\n datatype=config.get('datatype', None),\n name=config.get('name', None),\n offset=config.get('offset', None),\n checkBounds=config.get('check_bounds', None),\n minValue=config.get('min_value', None),\n maxValue=config.get('max_value', None),\n description=config.get('description', None)\n ))\n\ndlcs = {}\nheaderlist = ['time']\nheaderstr = \"\"\nfor canid in sorted(database.items()):\n # print(canid, end='\\n\\n\\n')\n highestByte = 0\n for datum in canid[1]:\n offset = datum.offset\n if (datum.dataWidth + offset) > highestByte:\n highestByte = datum.dataWidth + datum.offset\n\n headerlist.append(datum.name)\n headerstr = headerstr + datum.name + \", \" \n headerstr = headerstr[:-2] # remove trailing ,\n if highestByte > 8:\n\n print(\"Too big\", canid)\n # print(canid[0], highestByte)\n # dlcs[canid[0]] = highestByte\n dlcs[canid[0]] = highestByte\n \n# print(headerstr)\nprint(\"Config parse done.\")\n\n#%%\nargs = {'inputFile': None}\nargs.inputFile = r\"C:\\Users\\benlt\\OneDrive\\School\\20211\\Solar\\incident\\sd card\\2020\\08\\19\\221358\"\nif not args.inputFile:\n driveRoot = r\"/media/ben/SENSORBOARD/\"\n\n logPath = driveRoot\n #find folder\n yearList = [x for x in os.listdir(logPath) if x.isdigit()]\n year = sorted(yearList)[-1]\n logPath = os.path.join(logPath, year)\n monthList = [x for x in os.listdir(logPath) if x.isdigit()]\n month = sorted(monthList)[-1]\n logPath = os.path.join(logPath, month)\n dayList = [x for x in os.listdir(logPath) if x.isdigit()]\n day = sorted(dayList)[-1]\n logPath = os.path.join(logPath, day)\n\n filesInFolder = [x for x in os.listdir(logPath) if not x.endswith(\".csv\")]\n\n fileName = sorted(filesInFolder)[-1]\n hour = int(fileName[0:2])\n mininute = int(fileName[2:4])\n seccond = int(fileName[4:6])\n\n (year, month, day, hour, mininute, seccond, logPath, fileName)\n\n fileLogPath = os.path.join(logPath, fileName)\nelse:\n fileLogPath = args.inputFile\n\n\n#%%\n# fileLogPath = r\"C:\\Users\\benlt\\OneDrive\\School\\20211\\Solar\\incident\\sd card\\2020\\08\\19\\221358_recon\"\nfileLogPath = r\"C:\\Users\\benlt\\OneDrive\\School\\20211\\Solar\\incident\\sd card\\recon\\221358_recon\"\n# ptrn = r\"\\s*(?P\\d*)\\%(?P0x[a-fA-F0-9]+)\\:(?P\\d*)\\:(?P[a-zA-Z0-9,]*)\"\nptrn = r\"^\\s*(?P\\d*.\\d*)\\%(?P0x[a-fA-F0-9]+)\\:(?P\\d*)\\:(?P[a-zA-Z0-9,]*)\"\nmtch = re.compile(pattern=ptrn)\nx = None\nfails = 0\nfirstTime = None\nlastTime = None\ntimes = []\noutputLines = []\nunknownIds = set()\ncountsPerIdnt = {}\nfakeLogRate = 10 # Create output log at this freq\nfakeLogPeriod = 1/fakeLogRate\ncurrentLogTime = 0\nlastLogTime = 0\nfakeLogCount = 0\nprint(\"Opening \", fileLogPath)\nwith open(fileLogPath) as inputdata:\n for linenum, line in enumerate(inputdata):\n if linenum < 1:\n #header on first row\n continue\n # print('\\n' + line.rstrip())\n\n try:\n result = mtch.fullmatch(line.rstrip()).groupdict()\n except Exception as ex:\n fails += 1\n print(\"failed to match: \" + line + str(ex))\n continue\n\n time = float(result[\"time\"])\n if not firstTime:\n firstTime = time\n if lastTime:\n times.append(time-lastTime)\n lastTime = time\n \n idnt = int(result[\"id\"], 16)\n countsPerIdnt[idnt] = countsPerIdnt.get(idnt, 0) + 1\n # if idnt == 0x708 or idnt ==0x0:\n # continue\n dlc = int(result[\"dlc\"], 10)\n fulldata = []\n if dlc > 0:\n if result['data'].endswith(','):\n result['data'] = result['data'][:-1]\n try:\n fulldata = [int(x, 16) for x in result[\"data\"].split(\",\")]\n except Exception as ex:\n print(\"failed while trying to get data array: \", ex)\n # if linenum < 100 and len(fulldata) != 8:\n # print(f\"line{linenum} id{idnt} had less than 8\")\n if dlc != len(fulldata):\n print(f\"DLC mismatch on line {linenum}!\")\n # if linenum < 100 and dlcs.get(idnt) and dlc != dlcs[idnt]:\n # print(f\"calc DLC mismatch on line {linenum}! Id {idnt} Expect {dlcs[idnt]} got {dlc}\")\n\n if idnt not in database:\n # print(\"idnt not found\", hex(idnt))\n # print(\"Frame not in database: id={}, data={}\".format(hex(idnt), fulldata))\n unknownIds.add(idnt)\n else:\n x = bytearray(fulldata)\n # parse each datum in the can data frame for this id\n for canObj in database[idnt]:\n # print(\"trying to parrse name = \", canObj.name)\n\n # print(\"type \", canObj.dataTypeStr, \" off \", canObj.offset)\n # try:\n canObj.parse(x)\n # except Exception as ex:\n # print(f\"fail to parse line {linenum}:\",ex, line)\n # pass\n # print(\"got value: \", canObj.currentValue)\n\n\n # print the fake log\n currentLogTime = time\n if (currentLogTime - lastLogTime) > fakeLogPeriod:\n lastLogTime = currentLogTime\n # lineToLog = \"\" \n numbersToLog = [currentLogTime] + [None] * (len(headerlist)-1)\n \n for i, canid in enumerate(sorted(database.items())):\n if canid[0] == idnt:\n for j, datum in enumerate(canid[1]):\n numbersToLog[i+i+j] = datum.currentValue\n # lineToLog += str(datum.currentValue) + \",\"\n # lineToLog = lineToLog + \"\\n\"\n outputLines.append(numbersToLog)\n fakeLogCount += 1\n break\n\n#%%\n numbersToLog = [currentLogTime]\n for canid in sorted(database.items()):\n for datum in canid[1]:\n numbersToLog.append(datum.currentValue)\n outputLines.append(numbersToLog)\n fakeLogCount += 1\n \n\n\n timeTaken = (lastTime-firstTime)\n\n print(f\"\\nLog parse complete with {fails} parse errors of {linenum} frames.\")\n print(f\"Capture length: {datetime.timedelta(seconds=round(timeTaken))} hh:mm:ss.\")\n print(f\"Estimated log rate: {linenum/timeTaken:.1f} frames/s.\")\n print(f\"Created {fakeLogCount} CSV log entires.\")\n\n mean = statistics.mean(times)\n stddev = statistics.stdev(times)\n median = statistics.median(times)\n print(f\"\\u0394t: mean = {si_format(mean)}s, median = {si_format(median)}s, stdev = {si_format(stddev)}s\")\n\n plt.plot(times)\n\n print(\"Unknown IDs:\", list(map(hex,sorted(unknownIds))))\n\n\n#%% output csv\n\ndf = pandas.DataFrame(outputLines, columns=headerlist)\n\n\noutputFilePath, _ = os.path.splitext(fileLogPath)\noutputFilePath += r'.csv'\ndf.to_csv(outputFilePath, index=False)\nprint(\"CSV saved to\", outputFilePath)\n# df\n# %%\n\n# %%\n","repo_name":"BLTsndwch/sb-log-parse","sub_path":"can-log-decode.py","file_name":"can-log-decode.py","file_ext":"py","file_size_in_byte":10955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10601296025","text":"from pyspark.context import SparkContext\nfrom pyspark.sql import SparkSession\n\nsc = SparkContext('local', 'test')\nsc.setLogLevel(\"WARN\")\nspark = (SparkSession.builder.getOrCreate())\n\nsample_list = [('Mona', 20), ('Jennifer', 34), ('John', 20), ('Jim', 26)]\n\n# Create an RDD from the list\nrdd = sc.parallelize(sample_list)\n\n# Create a PySpark DataFrame\nnames_df = spark.createDataFrame(rdd, schema=['Name', 'Age'])\n\n# Check the type of names_df\nprint(\"The type of names_df is\", type(names_df))","repo_name":"tgpmoraes/curso-python","sub_path":"datacamp/data_engineering/pyspark/bigdata_spark/rdd_to_dataframe.py","file_name":"rdd_to_dataframe.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30712540421","text":"while True:\n global ts\n c, addr = s.accept()\n data=c.recv(16)\n stringdata = data.decode('utf-8')\n if stringdata == \"Jump\" and (datetime.now()-ts).total_seconds() > 1:\n print(\"Jumped\")\n pgui.press(\"space\")\n ts=datetime.now()\n\n c.close()","repo_name":"Saswat1998/T-Rex-Dino-Multiplayer-","sub_path":"Game/autoJump.py","file_name":"autoJump.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42766556852","text":"from bokeh.layouts import column, row\nfrom bokeh.models import ColumnDataSource\nfrom bokeh.models import Select, LinearColorMapper, ColorBar, LassoSelectTool\nfrom bokeh.models.widgets import MultiSelect, DateRangeSlider, RangeSlider, RadioButtonGroup\nfrom bokeh.palettes import Category20\nfrom bokeh.plotting import figure, curdoc\nfrom bokeh.core.properties import value\nfrom bokeh.tile_providers import CARTODBPOSITRON\nfrom bokeh.transform import transform\n\nimport datetime\nimport json\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport pyproj\nfrom time import time\nimport os.path # getmtime, isfile\nimport urllib.request\n\n\nclass CityBikeAnalysis:\n def __init__(self, log_file, city='Oslo', coord_mapping_file='', altitude_file=''):\n\n self.city = city\n self.station_info_file = 'station_info.csv'\n self.df_s_info = pd.read_csv(self.station_info_file, index_col=0)\n self.df_s_info.index = self.df_s_info.index.astype(str)\n\n self.tz_offset = pd.Timedelta('0h')\n\n self.log_file = log_file\n self.df_log = pd.read_csv(log_file)\n self.re_index()\n self.stations = [str(s_id) for s_id in np.sort(self.df_log['station_id'].unique())]\n\n self.df_bikes_avail = pd.DataFrame()\n self.df_docs_avail = pd.DataFrame()\n self.pivot_df()\n\n self.df_bikes_source = pd.DataFrame()\n self.df_docks_source = pd.DataFrame()\n\n if os.path.isfile(altitude_file):\n self.df_altitude = pd.read_csv(altitude_file, index_col=0)\n\n self.id_coord_mapping_df = pd.DataFrame()\n\n if os.path.isfile(coord_mapping_file):\n s_mod = os.path.getmtime(coord_mapping_file)\n s_now = datetime.datetime.now().timestamp()\n if s_mod < (s_now - 3600):\n self.id_coord_mapping_df = self.make_station_coord_transformed_df(self.get_station_info())\n with open(coord_mapping_file, 'wb') as handle:\n pickle.dump(self.id_coord_mapping_df, handle, protocol=pickle.HIGHEST_PROTOCOL)\n # TODO Do not overwrite?\n else:\n with open('mapping_df.pickle', 'rb') as handle:\n self.id_coord_mapping_df = pickle.load(handle)\n\n if city == 'Oslo':\n self.mid_lat, self.mid_lon = pyproj.transform(pyproj.Proj(init='epsg:4326'),\n pyproj.Proj(init='epsg:3857'),\n 10.735, 59.928) # Center of map location\n self.coord_range_delta = 7500\n elif city == 'NewYorkCity':\n self.mid_lat, self.mid_lon = pyproj.transform(pyproj.Proj(init='epsg:4326'),\n pyproj.Proj(init='epsg:3857'),\n -73.973, 40.736)\n self.coord_range_delta = 12500\n\n # Colors\n self.empty_color = \"#7e7e7e\"\n self.avail_color = \"#87e03e\"\n self.full_color = \"#ff2332\"\n\n x_mul_sel_opt = [(s, s + ' | ' + name) for s, name in\n zip(self.stations, self.df_s_info.reindex(self.stations, fill_value='Unknown')['name'])]\n self.x_mul_select = MultiSelect(title=\"Stations\", value=[self.stations[0], self.stations[1]],\n options=x_mul_sel_opt, size=8)\n self.x_mul_select.on_change('value', self.x_mul_select_callback)\n\n s_date = datetime.date.fromtimestamp(self.df_bikes_avail.index[0].value/10**9)\n e_date = datetime.date.fromtimestamp(self.df_bikes_avail.index[-1].value/10**9)\n e_date += datetime.timedelta(days=1)\n self.dr_slider = DateRangeSlider(title='Date Range', start=s_date, end=e_date, step=24,\n value=(e_date - datetime.timedelta(days=5), e_date),\n callback_throttle=500)\n self.dr_slider.on_change('value', self.date_rage_slider_callback)\n\n self.range_slider = RangeSlider(title='Time of day', start=5, end=25, step=1, value=(5, 25))\n self.range_slider.on_change('value', self.hour_rage_slider_callback)\n\n self.histogram_policy = RadioButtonGroup(labels=[\"Aggregate\", \"Flatten\"], active=0)\n self.histogram_policy.on_change('active', self.histogram_policy_callback)\n\n self.x_select = Select(title=\"X-axis\", value=self.stations[0], options=self.stations)\n self.x_select.on_change('value', self.x_select_callback)\n self.y_select = Select(title=\"Y-axis\", value=self.stations[1], options=self.stations)\n self.y_select.on_change('value', self.y_select_callback)\n\n self.create_sliced_df()\n self.station_history_fig, self.station_history_fig_source = self.create_station_history_fig()\n self.station_avail_histogram, self.station_avail_histogram_source = self.create_station_avail_histogram()\n self.extremes_annulus, self.extremes_annulus_source = self.create_extremes_figure()\n self.station_map_fig, self.station_map_fig_source = self.create_station_map()\n self.station_map_fig_source.selected.indices = [self.id_coord_mapping_df.index.get_loc(s) for s in\n self.x_mul_select.value]\n\n self._layout = column(row(column(self.x_mul_select,\n self.dr_slider,\n self.range_slider,\n self.histogram_policy,\n self.extremes_annulus),\n self.station_avail_histogram,\n self.station_map_fig),\n row(self.station_history_fig))\n # row(column(self.x_select, self.y_select), self.two_station_heatmap()))\n\n @property\n def layout(self):\n return self._layout\n\n def re_index(self):\n self.df_log.index = pd.to_datetime(10**9*self.df_log['last_reported'])\n self.df_log.drop(['last_reported'], axis=1, inplace=True)\n self.df_log.index.tz_localize('UTC').tz_convert('Europe/Oslo')\n\n def pivot_df(self):\n\n def get_column_mapper(df):\n d = {}\n for col in df.columns.values:\n d[col] = str(col)\n return d\n\n self.df_bikes_avail = self.df_log.pivot_table(index='last_reported',\n columns='station_id',\n values='num_bikes_available')\n self.df_bikes_avail.rename(columns=get_column_mapper(self.df_bikes_avail), inplace=True)\n\n self.df_docs_avail = self.df_log.pivot_table(index='last_reported',\n columns='station_id',\n values='num_docks_available')\n self.df_docs_avail.rename(columns=get_column_mapper(self.df_docs_avail), inplace=True)\n\n def create_sliced_df(self):\n s_hour = self.range_slider.value[0]\n e_hour = self.range_slider.value[1]\n if e_hour == 25:\n hours_of_day = self.df_bikes_avail.index.hour.isin(np.append([0], range(s_hour, e_hour - 1)))\n else:\n hours_of_day = self.df_bikes_avail.index.hour.isin(range(s_hour, e_hour))\n\n df_bikes_sliced = self.df_bikes_avail.loc[hours_of_day][\n self.dr_slider.value_as_datetime[0]:self.dr_slider.value_as_datetime[1]]\n df_docks_sliced = self.df_docs_avail.loc[hours_of_day][\n self.dr_slider.value_as_datetime[0]:self.dr_slider.value_as_datetime[1]]\n\n self.df_bikes_source = df_bikes_sliced.loc[:, [val for val in self.x_mul_select.value]].fillna(0) # TODO Filling nan with 0 might not be best solution\n self.df_docks_source = df_docks_sliced.loc[:, [val for val in self.x_mul_select.value]].fillna(0) #\n\n def create_station_history_fig(self):\n\n source_dict = self.create_station_history_fig_source_dict()\n source = ColumnDataSource(source_dict)\n\n fig = figure(plot_width=800, plot_height=600,\n x_axis_type='datetime')\n\n colors = Category20[20][::2]\n for val, color in zip(self.x_mul_select.value, colors):\n fig.step('last_reported', val,\n color=color, legend=value(val), source=source)\n\n return fig, source\n\n def create_station_history_fig_source_dict(self):\n df_sliced = self.df_bikes_avail[self.dr_slider.value_as_datetime[0]:self.dr_slider.value_as_datetime[1]]\n df_source = df_sliced.loc[:, [val for val in self.x_mul_select.value]]\n df_source.index = df_source.index + self.tz_offset\n df_source = df_source.reset_index()\n return df_source.to_dict(orient='list')\n\n def create_station_avail_histogram(self):\n\n d = self.create_station_avail_histogram_source_dict()\n source = ColumnDataSource(d)\n\n fig = figure(plot_width=800, plot_height=600,\n title='Histogram of availability', background_fill_color=\"#fafafa\")\n\n fig.quad(top='top', bottom=0, left='left', right='right',\n color=\"colors\", line_color=\"white\", alpha=0.9,\n source=source)\n\n fig.y_range.start = 0\n # fig.legend.location = \"center_right\"\n # fig.legend.background_fill_color = \"#fefefe\"\n x_labl = 'x, Available bikes in station' if len(self.x_mul_select.value) == 1 else 'x, Available bikes in stations'\n fig.xaxis.axis_label = x_labl\n fig.yaxis.axis_label = 'P(x)'\n fig.grid.grid_line_color = \"white\"\n\n return fig, source\n\n def create_station_avail_histogram_source_dict(self):\n\n if self.histogram_policy.labels[self.histogram_policy.active] == 'Flatten':\n df_bikes_source = self.df_bikes_source\n df_docks_source = self.df_docks_source\n elif self.histogram_policy.labels[self.histogram_policy.active] == 'Aggregate':\n df_bikes_source = self.df_bikes_source.sum(axis=1)\n df_docks_source = self.df_docks_source.sum(axis=1)\n else:\n df_bikes_source = self.df_bikes_source\n df_docks_source = self.df_docks_source\n\n b = np.array((range(-1, int(df_bikes_source.max().max()) + 1))) + 0.5\n hist, edges = np.histogram(df_bikes_source, density=True, bins=b)\n\n colors = np.array([self.avail_color] * (len(hist)))\n\n if np.any(df_bikes_source.values == 0):\n colors[0] = self.empty_color\n\n if np.any(df_docks_source.values == 0):\n colors[-1] = self.full_color # TODO Add for all\n\n d = dict(top=hist, left=edges[:-1], right=edges[1:], colors=colors)\n\n return d\n\n def create_extremes_figure(self):\n\n fig = figure(title='Availability summary',\n width=300, height=300,\n x_range=(-100, 100), y_range=(-100, 100),\n tools='')\n fig.axis.visible = False\n fig.grid.visible = False\n fig.outline_line_alpha = 0.0\n\n source = ColumnDataSource(self.create_extremes_figure_source_dict())\n\n fig.annular_wedge(x=0, y=0,\n inner_radius=50, outer_radius=75,\n start_angle=0, end_angle='end_angle_empty',\n color=self.empty_color,\n source=source) # Totally empty\n fig.annular_wedge(x=0, y=0,\n inner_radius=50, outer_radius=75,\n start_angle='end_angle_empty', end_angle='end_angle_mid',\n color=self.avail_color,\n source=source) # Not empty, not full\n fig.annular_wedge(x=0, y=0,\n inner_radius=50, outer_radius=75,\n start_angle='end_angle_mid', end_angle=2*np.pi,\n color=self.full_color,\n source=source) # Totally full\n\n fig.circle(x=0, y=0, radius=50, color=\"#fafafa\")\n\n fig.text(x=0, y=14, text='empty_percent', text_color=self.empty_color, text_baseline='bottom',\n text_align='center', text_font_size='16pt', text_font_style='bold', source=source)\n fig.text(x=0, y=0, text='avail_percent', text_color=self.avail_color, text_baseline='middle',\n text_align='center', text_font_size='21pt', text_font_style='bold', source=source)\n fig.text(x=0, y=-14, text='full_percent', text_color=self.full_color, text_baseline='top',\n text_align='center', text_font_size='16pt', text_font_style='bold', source=source)\n\n return fig, source\n\n def create_extremes_figure_source_dict(self):\n\n df_bikes_source = self.df_bikes_source.sum(axis=1)\n df_docks_source = self.df_docks_source.sum(axis=1)\n\n d = dict(end_angle_empty=[2*np.pi*np.count_nonzero(df_bikes_source.values == 0)/len(df_bikes_source.values)],\n end_angle_mid=[2*np.pi*(1-(np.count_nonzero(df_docks_source.values == 0) / len(df_docks_source.values)))])\n\n d['empty_percent'] = [f\"{100 * (np.abs(d['end_angle_empty'][0]) / (2 * np.pi)):2.1f}% \"] # TODO deal with nan\n d['avail_percent'] = [f\"{100*(np.abs(d['end_angle_mid'][0] - d['end_angle_empty'][0])/(2 * np.pi)):.1f}%\"] # TODO deal with nan\n d['full_percent'] = [f\"{100 * (np.abs(2 * np.pi - d['end_angle_mid'][0]) / (2 * np.pi)):.1f}%\"] # TODO deal with nan\n\n return d\n\n def create_station_map(self):\n\n fig = figure(plot_width=780, plot_height=600, title='Stations map and selector',\n tools=['pan', 'box_zoom', 'wheel_zoom', 'lasso_select', 'reset'],\n x_range=(self.mid_lat - self.coord_range_delta, self.mid_lat + self.coord_range_delta),\n y_range=(self.mid_lon - self.coord_range_delta, self.mid_lon + self.coord_range_delta),\n x_axis_type=\"mercator\", y_axis_type=\"mercator\")\n fig.add_tile(CARTODBPOSITRON)\n\n lst = fig.select(dict(type=LassoSelectTool))[0]\n lst.select_every_mousemove = False\n\n # # Bikes available\n # fig.annular_wedge(x=status_df['x_proj'], y=status_df['y_proj'], color=self.avail_color,\n # inner_radius=np.zeros(len(status_df)), outer_radius=25 * np.sqrt(status_df['num_docs']),\n # start_angle=(np.pi / 2 + np.zeros(len(status_df))),\n # end_angle=(np.pi / 2 - status_df['docks_start_ang']), direction='clock')\n # # Docks available\n # fig.annular_wedge(x=status_df['x_proj'], y=status_df['y_proj'], color=\"#ea6d3f\",\n # inner_radius=np.zeros(len(status_df)), outer_radius=25 * np.sqrt(status_df['num_docs']),\n # start_angle=(np.pi / 2 - status_df['docks_start_ang']),\n # end_angle=(10 ** (-3) + np.pi / 2 * np.ones(len(status_df))), direction='clock')\n #\n # fig.text(x=status_df['x_proj'], y=status_df['y_proj'], text=status_df.index)\n\n source = ColumnDataSource(self.id_coord_mapping_df)\n\n c = fig.circle(x='x_proj', y='y_proj', size=10, color=\"navy\", source=source)\n fig.text(x='x_proj', y='y_proj', text='station_id', source=source)\n\n c.data_source.selected.on_change('indices', self.lasso_select_callback)\n\n return fig, source\n\n def two_station_heatmap(self):\n fig = figure(width=700,\n match_aspect=True,\n tools='')\n fig.xgrid.grid_line_color = None\n fig.ygrid.grid_line_color = None\n s_id1, s_id2 = self.x_select.value, self.y_select.value\n df_counts = self.df_bikes_avail.groupby([s_id1, s_id2]).size().reset_index(name='counts')\n df_counts.rename(columns={s_id1: s_id1, s_id2: s_id2}, inplace=True)\n source = ColumnDataSource(df_counts)\n\n pallette = ['#084594', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', '#c6dbef', '#deebf7', '#f7fbff'][::-1]\n mapper = LinearColorMapper(palette=pallette, low=0, high=df_counts.counts.max())\n color_bar = ColorBar(color_mapper=mapper)\n\n fig.rect(x=str(s_id1), y=str(s_id2),\n width=1.0, height=1.0,\n line_alpha = 0.0,\n fill_color=transform('counts', mapper), source=source)\n\n fig.add_layout(color_bar, 'right')\n\n return fig\n\n def lasso_select_callback(self, attr, old, new):\n # print(self.id_coord_mapping_df.iloc[new].index.values)\n if new:\n self.x_mul_select.value = list(self.id_coord_mapping_df.iloc[new].index.values)\n\n def get_station_info(self):\n\n if self.city == 'Oslo':\n json_url = \"https://gbfs.urbansharing.com/oslobysykkel.no/station_information.json\"\n elif self.city == 'NewYorkCity':\n json_url = \"https://gbfs.citibikenyc.com/gbfs/es/station_information.json\"\n\n with urllib.request.urlopen(json_url) as url:\n station_info = json.loads(url.read().decode())\n return station_info\n\n # @staticmethod\n def make_station_coord_transformed_df(self, station_info_in):\n\n def apply_transfomation(lon_in, lat_in):\n return pyproj.transform(pyproj.Proj(init='epsg:4326'), pyproj.Proj(init='epsg:3857'), lon_in, lat_in)\n\n lon = [s['lon'] for s in station_info_in['data']['stations']]\n lat = [s['lat'] for s in station_info_in['data']['stations']]\n x_proj, y_proj = zip(*map(apply_transfomation, lon, lat))\n index = [s['station_id'] for s in station_info_in['data']['stations']]\n df = pd.DataFrame(data={'x_proj': x_proj, 'y_proj': y_proj}, index=index)\n df.index.name = 'station_id'\n return df\n\n def update(self):\n t_start = time()\n self.station_history_fig, self.station_history_fig_source = self.create_station_history_fig()\n self.station_avail_histogram, self.station_avail_histogram_source = self.create_station_avail_histogram()\n print(f\"Used: {(time() - t_start) * 1000} ms to regenerate figures\")\n t_start = time()\n self._layout.children[0].children[1] = self.station_avail_histogram\n self._layout.children[1].children[0] = self.station_history_fig\n print(f\"Used: {(time() - t_start)*1000} ms to update layout\")\n # self._layout.children[1].children[1] = self.two_station_heatmap()\n\n def y_select_callback(self, attr, old, new):\n self.update()\n\n def x_select_callback(self, attr, old, new):\n self.update()\n\n def x_mul_select_callback(self, attr, old, new):\n self.create_sliced_df()\n self.station_history_fig, self.station_history_fig_source = self.create_station_history_fig()\n self.station_avail_histogram, self.station_avail_histogram_source = self.create_station_avail_histogram()\n self.extremes_annulus_source.data = self.create_extremes_figure_source_dict()\n self.station_map_fig_source.selected.indices = [self.id_coord_mapping_df.index.get_loc(s) for s in\n self.x_mul_select.value]\n self._layout.children[0].children[1] = self.station_avail_histogram\n self._layout.children[1].children[0] = self.station_history_fig\n\n def date_rage_slider_callback(self, attr, old, new):\n t_start = time()\n self.create_sliced_df()\n self.station_history_fig_source.data = self.create_station_history_fig_source_dict()\n self.station_avail_histogram_source.data = self.create_station_avail_histogram_source_dict()\n self.extremes_annulus_source.data = self.create_extremes_figure_source_dict()\n print(f\"Used: {(time() - t_start) * 1000} ms to calculate sources\")\n\n def hour_rage_slider_callback(self, attr, old, new):\n t_start = time()\n # self.station_history_fig_source.data = self.create_station_history_fig_source_dict()\n self.create_sliced_df()\n self.extremes_annulus_source.data = self.create_extremes_figure_source_dict()\n self.station_avail_histogram_source.data = self.create_station_avail_histogram_source_dict()\n print(f\"Used: {(time() - t_start) * 1000} ms to calculate sources\")\n\n def histogram_policy_callback(self, attr, old, new):\n self.station_avail_histogram_source.data = self.create_station_avail_histogram_source_dict()\n\n\ndef main():\n log_file = 'live_datalog/OCB.csv'\n altitude = 'altitude.csv'\n coord_mapping = 'mapping_df.pickle'\n cbd = CityBikeAnalysis(log_file, 'Oslo', coord_mapping, altitude)\n curdoc().add_root(cbd.layout)\n curdoc().title = \"OsloCityBike\"\n\n\nmain()\n","repo_name":"oaramsda/bikeshare-insights","sub_path":"city_bike_visualization.py","file_name":"city_bike_visualization.py","file_ext":"py","file_size_in_byte":20760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32852196016","text":"import pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import MinMaxScaler\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Dropout,LSTM\n\ndef main():\n # Reading the data file\n NYSE_Dataframe = pd.read_csv(\"^NYA-new.csv\")\n # Converting the date from a string to a datetime format\n NYSE_Dataframe[\"Date\"] = pd.to_datetime(NYSE_Dataframe.Date)\n NYSE_Dataframe.index = NYSE_Dataframe[\"Date\"]\n print(NYSE_Dataframe.head(100))\n # sorting the data oldest to newest\n Data = NYSE_Dataframe.sort_index()\n\n # making new series for new data\n Dates = pd.Series(Data.Date)\n Prices = pd.Series(Data.Open)\n # making a dictionary\n frame = {\"Date\":Dates, \"Price\":Prices}\n # making the dictionary into a new dataframe containing only dates/close values\n new_data = pd.DataFrame(frame)\n\n # Converting our value to float\n # for i in range(0, len(new_data.Price)):\n # new_data.Price[i] = float(new_data.Price[i].replace(',', ''))\n\n # removing the date field before we do any machine learning\n new_data = new_data.drop(\"Date\", 1)\n\n # making numpy array\n Prices_new = new_data.Price\n Prices_numpy = Prices_new.to_numpy()\n\n dataset = []\n for i in range(0, len(Prices_numpy) - 1):\n dataset.append([Prices_numpy[i]])\n\n # we will now normalise the data to allow the ML model to converge easily\n # we will use the minmax scaler to get all data in range (0,1)\n scaler = MinMaxScaler()\n scaled_data = scaler.fit_transform(dataset)\n # splitting the data into training and validation data\n training_data = new_data[:945]\n validation_data = new_data[945:]\n\n # LSTM's expect to receive data in a 3D array. Since we only have a single array\n # we will start by creating data in 60 time stamps and converting it to an array\n # using NumPy. Next we Convert the data into a 3 dimensional array with\n # x_train samples, 60 timestamps and one feature at each step\n x_train, y_train = [], []\n for i in range(60, len(training_data)):\n x_train.append(scaled_data[i - 60:i, 0])\n y_train.append(scaled_data[i, 0])\n x_train, y_train = np.array(x_train), np.array(y_train)\n\n x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n\n # creating a new model\n model = Sequential()\n model.add(LSTM(50))\n model.add(Dense(1))\n model.compile(\"rmsprop\", \"mse\")\n model.fit(x_train,y_train,1)\n #\n # In order to predict future stock prices we need to do a couple of things\n # after loading in the test set:\n #\n # 1. Merge the training set and the test set on the 0 axis.\n # 2. Set the time step as 60 (as seen previously)\n # 3. Use MinMaxScaler to transform the new dataset\n # 4. Reshape the dataset as done previously\n # 5. After making the predictions we use inverse_transform to get back the\n # stock prices in normal readable format.\n inputs = new_data[len(new_data) - len(validation_data) - 60:].values\n inputs = inputs.reshape(-1, 1)\n inputs = scaler.transform(inputs)\n\n X_test = []\n for i in range(60, inputs.shape[0]):\n X_test.append(inputs[i - 60:i, 0])\n X_test = np.array(X_test)\n\n X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n closing_price = model.predict(X_test)\n closing_price = scaler.inverse_transform(closing_price)\n\n train = new_data[:945]\n valid = new_data[945:]\n valid['Predictions'] = closing_price\n plt.plot(train['Price'])\n plt.plot(valid[['Price', 'Predictions']])\n plt.show()\n\nmain()","repo_name":"kvc9128/Kubera","sub_path":"Ujwal/NYSEcomposite sandbox.py","file_name":"NYSEcomposite sandbox.py","file_ext":"py","file_size_in_byte":3617,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"33193349718","text":"# -*- coding: utf-8 -*-\n# -----------------------------Импорт модулей-----------------------------------\n\nfrom PyQt5 import QtSql\n\nimport os\nimport os.path\nimport shutil\n\nfrom PyQt5.QtWidgets import QWidget, QLabel, QVBoxLayout, QFormLayout, QTableWidget, QComboBox, \\\n QSpinBox, QPushButton, QListWidgetItem, QLineEdit\n\n# -----------------------------------Форма--------------------------------------\n\nclass mechanicalProperties_form_class(QWidget):\n def __init__(self, parent=None):\n QWidget.__init__(self, parent)\n\n self.interface_lng_val = parent.interface_lng_val\n self.con = parent.con\n self.full_dir = parent.full_dir\n self.par = parent\n\n if self.con.open():\n\n table = QTableWidget(7, 2)\n table.setColumnWidth(0, 150)\n table.setColumnWidth(1, 230)\n table.setFixedSize(674, 480)\n table.setHorizontalHeaderLabels([\"Параметр\", \"Значение\"])\n # rho.type\n rho_type_lbl = QLabel('rho.type')\n self.rho_type = QComboBox()\n rho_type_list = [\"uniform\", \"demo\"]\n self.rho_type.addItems(rho_type_list)\n table.setCellWidget(0, 1, self.rho_type)\n table.setCellWidget(0, 0, rho_type_lbl)\n # rho.value\n rho_value_lbl = QLabel('rho.value')\n self.rho_value = QLineEdit()\n table.setCellWidget(1, 1, self.rho_value)\n table.setCellWidget(1, 0, rho_value_lbl)\n\n # nu.type\n nu_type_lbl = QLabel('nu.type')\n self.nu_type = QComboBox()\n nu_type_list = [\"uniform\", \"demo\"]\n self.nu_type.addItems(nu_type_list)\n table.setCellWidget(2, 1, self.nu_type)\n table.setCellWidget(2, 0, nu_type_lbl)\n # nu.value\n nu_value_lbl = QLabel('nu.value')\n self.nu_value = QLineEdit()\n table.setCellWidget(3, 1, self.nu_value)\n table.setCellWidget(3, 0, nu_value_lbl)\n\n # E.type\n E_type_lbl = QLabel('E.type')\n self.E_type = QComboBox()\n E_type_list = [\"uniform\", \"demo\"]\n self.E_type.addItems(E_type_list)\n table.setCellWidget(4, 1, self.E_type)\n table.setCellWidget(4, 0, E_type_lbl)\n # E.value\n E_value_lbl = QLabel('E.value')\n self.E_value = QLineEdit()\n table.setCellWidget(5, 1, self.E_value)\n table.setCellWidget(5, 0, E_value_lbl)\n\n # planeStress\n planeStress_type_lbl = QLabel('planeStress')\n self.planeStress_type = QComboBox()\n planeStress_type_list = [\"yes\", \"no\"]\n self.planeStress_type.addItems(planeStress_type_list)\n table.setCellWidget(6, 1, self.planeStress_type)\n table.setCellWidget(6, 0, planeStress_type_lbl)\n\n # вывод значений параметров\n if 'mechanicalProperties' in self.con.tables():\n #print('вах')\n\t\t\t\t\t\t\n query = QtSql.QSqlQuery()\n query.exec(\"SELECT * FROM mechanicalProperties\")\n if query.isActive():\n query.first()\n value_list = []\n while query.isValid():\n value_res = query.value('value')\n value_list.append(value_res)\n query.next()\n\t\t\t\t\t\t\n # rho_type\n rho_type_mas = self.rho_type.count() \n for i in range(rho_type_mas):\n if self.rho_type.itemText(i) == value_list[0]:\n self.rho_type.setCurrentIndex(i)\n\t\t\t\t\t\t\t\n # rho_value\n self.rho_value.setText(value_list[1])\n\t\t\t\t\t\n # nu_type\n nu_type_mas = self.nu_type.count() \n for i in range(nu_type_mas):\n if self.nu_type.itemText(i) == value_list[2]:\n self.nu_type.setCurrentIndex(i)\n\t\t\t\t\t\n # nu_value\n self.nu_value.setText(value_list[3])\n\t\t\t\t\t\n # E_type\n E_type_mas = self.E_type.count() \n for i in range(E_type_mas):\n if self.E_type.itemText(i) == value_list[4]:\n self.E_type.setCurrentIndex(i)\n\t\t\t\t\t\t\t\n # E_value\n self.E_value.setText(value_list[5])\n\t\t\t\t\t\n # planeStress_type\n planeStress_type_mas = self.planeStress_type.count() \n for i in range(planeStress_type_mas):\n if self.planeStress_type.itemText(i) == value_list[6]:\n self.planeStress_type.setCurrentIndex(i)\n\t\t\t\t\t\n btnSave = QPushButton()\n btnSave.setFixedSize(80, 25)\n btnSave.clicked.connect(self.on_btnSave_clicked)\n\n if self.interface_lng_val == 'Russian':\n btnSave.setText(\"Сохранить\")\n elif self.interface_lng_val == 'English':\n btnSave.setText(\"Save\")\n\n vbox = QVBoxLayout()\n vbox.addWidget(table)\n vbox.addWidget(btnSave)\n\n# ---------------------Размещение на форме всех компонентов-------------------------\n\n form = QFormLayout()\n form.addRow(vbox)\n self.setLayout(form)\n\n def on_btnSave_clicked(self):\n rho_type_txt = self.rho_type.currentText()\n rho_value_txt = self.rho_value.text()\n nu_type_txt = self.nu_type.currentText()\n nu_value_txt = self.nu_value.text()\n E_type_txt = self.E_type.currentText()\n E_value_txt = self.E_value.text()\n planeStress_type_txt = self.planeStress_type.currentText()\n\n msg_list = []\n if rho_value_txt == '':\n if self.interface_lng_val == 'Russian':\n msg_lbl = QLabel(\n 'Укажите параметр rho.value ')\n elif self.interface_lng_val == 'English':\n msg_lbl = QLabel(\n 'Set rho.value parameter ')\n msg_list.append(msg_lbl)\n if nu_value_txt == '':\n if self.interface_lng_val == 'Russian':\n msg_lbl = QLabel(\n 'Укажите параметр nu.value ')\n elif self.interface_lng_val == 'English':\n msg_lbl = QLabel(\n 'Set nu.value parameter ')\n msg_list.append(msg_lbl)\n\n if E_value_txt == '':\n if self.interface_lng_val == 'Russian':\n msg_lbl = QLabel(\n 'Укажите параметр E.value ')\n elif self.interface_lng_val == 'English':\n msg_lbl = QLabel(\n 'Set E.value parameter ')\n msg_list.append(msg_lbl)\n\n if msg_list == []:\n\t\t\t\n if 'mechanicalProperties' not in self.con.tables():\n query = QtSql.QSqlQuery()\n query.exec(\"CREATE TABLE mechanicalProperties(param, value)\")\n\n query.exec(\"INSERT INTO mechanicalProperties(param, value) VALUES ('%s','%s')\" % ('rho_type', ''))\n query.exec(\"INSERT INTO mechanicalProperties(param, value) VALUES ('%s','%s')\" % ('rho_value', ''))\n query.exec(\"INSERT INTO mechanicalProperties(param, value) VALUES ('%s','%s')\" % ('nu_type', ''))\n query.exec(\"INSERT INTO mechanicalProperties(param, value) VALUES ('%s','%s')\" % ('nu_value', ''))\n query.exec(\"INSERT INTO mechanicalProperties(param, value) VALUES ('%s','%s')\" % ('E_type', ''))\n query.exec(\"INSERT INTO mechanicalProperties(param, value) VALUES ('%s','%s')\" % ('E_value', ''))\n query.exec(\"INSERT INTO mechanicalProperties(param, value) VALUES ('%s','%s')\" % ('planeStress', ''))\n\n if 'mechanicalProperties' in self.con.tables():\n\n query = QtSql.QSqlQuery()\n\n query.prepare(\"UPDATE mechanicalProperties SET value=? WHERE param='rho_type'\")\n query.bindValue(0, rho_type_txt)\n query.exec_()\n\n query.prepare(\"UPDATE mechanicalProperties SET value=? WHERE param='rho_value'\")\n query.bindValue(0, rho_value_txt)\n query.exec_()\n\n query.prepare(\"UPDATE mechanicalProperties SET value=? WHERE param='nu_type'\")\n query.bindValue(0, nu_type_txt)\n query.exec_()\n\n query.prepare(\"UPDATE mechanicalProperties SET value=? WHERE param='nu_value'\")\n query.bindValue(0, nu_value_txt)\n query.exec_()\n\n query.prepare(\"UPDATE mechanicalProperties SET value=? WHERE param='E_type'\")\n query.bindValue(0, E_type_txt)\n query.exec_()\n\n query.prepare(\"UPDATE mechanicalProperties SET value=? WHERE param='E_value'\")\n query.bindValue(0, E_value_txt)\n query.exec_()\n\n query.prepare(\"UPDATE mechanicalProperties SET value=? WHERE param='planeStress'\")\n query.bindValue(0, planeStress_type_txt)\n query.exec_()\n\n # записываем файл mechanicalProperties\n if os.path.exists(self.full_dir + '/constant/mechanicalProperties'):\n os.remove(self.full_dir + '/constant/mechanicalProperties')\n\t\t\n shutil.copyfile(\"./matches/Shablon/constant/mechanicalProperties\", self.full_dir + '/constant/mechanicalProperties')\n\n mP = open(self.full_dir + '/constant/mechanicalProperties', 'a')\n\n ###rho###\n rho_bl = '\\n' + 'rho' + '\\n' + '{' + '\\n' \\\n + ' ' + 'type' + ' ' + rho_type_txt + ';' + '\\n' \\\n + ' ' + 'value' + ' ' + rho_value_txt + ';' + '\\n' \\\n + '}' + '\\n\\n'\n\n ###nu###\n nu_bl = 'nu' + '\\n' + '{' + '\\n' \\\n + ' ' + 'type' + ' ' + nu_type_txt + ';' + '\\n' \\\n + ' ' + 'value' + ' ' + nu_value_txt + ';' + '\\n' \\\n + '}' + '\\n\\n'\n\n ###E###\n E_bl = 'E' + '\\n' + '{' + '\\n' \\\n + ' ' + 'type' + ' ' + E_type_txt + ';' + '\\n' \\\n + ' ' + 'value' + ' ' + E_value_txt + ';' + '\\n' \\\n + '}' + '\\n\\n'\n\n ###planeStress###\n planeStress_bl = 'planeStress' + ' ' + planeStress_type_txt + ';' + '\\n\\n'\n\n mP.write(rho_bl + nu_bl + E_bl + planeStress_bl)\n close_str = '// ************************************************************************* //'\n mP.write(close_str)\n\n mP.close()\n\n self.par.cdw.setWidget(self.par.outf_scroll)\n outf = open(self.full_dir + '/constant/mechanicalProperties')\n\n if self.interface_lng_val == 'Russian':\n msg_lbl = QLabel(\n 'Файл mechanicalProperties сохранен ')\n elif self.interface_lng_val == 'English':\n msg_lbl = QLabel(\n 'The mechanicalProperties file was saved ')\n\n self.par.listWidget.clear()\n self.par.item = QListWidgetItem()\n self.par.listWidget.addItem(self.par.item)\n self.par.listWidget.setItemWidget(self.par.item, msg_lbl)\n\n data = outf.read()\n\n if self.interface_lng_val == 'Russian':\n self.par.outf_lbl.setText(\"Файл \" + \"\" + 'mechanicalProperties' + \" \")\n elif self.interface_lng_val == 'English':\n self.par.outf_lbl.setText(\"\" + 'mechanicalProperties' + \" \" + \" file\")\n self.par.outf_edit.setText(data)\n\n self.par.cdw.setTitleBarWidget(self.par.cdw_frame)\n outf.close()\n\n else:\n self.par.listWidget.clear()\n for msg_lbl in msg_list:\n self.par.item = QListWidgetItem()\n self.par.listWidget.addItem(self.par.item)\n self.par.listWidget.setItemWidget(self.par.item, msg_lbl)\n","repo_name":"DmitryChitalov/stress_analysis_app","sub_path":"forms/mechanicalProperties_form.py","file_name":"mechanicalProperties_form.py","file_ext":"py","file_size_in_byte":12581,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"27367102934","text":"import requests\nimport os\nimport time\nimport telegram\nfrom urllib.parse import urljoin\nfrom dotenv import load_dotenv\n\n\ndef main():\n load_dotenv()\n bot = telegram.Bot(token=os.getenv('TELEGRAM_TOKEN'))\n tg_chat_id = os.getenv('TELEGRAM_CHAT_ID')\n url_dvmn_query = 'https://dvmn.org/api/long_polling/'\n timestamp = time.time()\n while True:\n try:\n response_from_dvmn_api = requests.get(\n url_dvmn_query,\n headers={'Authorization': f'Token {os.getenv(\"DEVMAN_API_TOKEN\")}'},\n params={'timestamp': timestamp}\n )\n response_from_dvmn_api.raise_for_status()\n dvmn_api_response = response_from_dvmn_api.json()\n\n status = dvmn_api_response.get('status')\n\n if status == 'found':\n new_attempt = dvmn_api_response['new_attempts'][0]\n lesson = new_attempt['lesson_title']\n is_negative = new_attempt['is_negative']\n timestamp = dvmn_api_response['last_attempt_timestamp']\n lesson_url = dvmn_api_response[\"new_attempts\"][0][\"lesson_url\"]\n\n if is_negative:\n bot.send_message(\n chat_id=tg_chat_id,\n text=f'''\n Преподаватель проверил работу!\n «{lesson}»\n Ссылка на урок: {urljoin('https://dvmn.org/', lesson_url)}\n \n Нужно внести правки по ревью!\n ''')\n else:\n bot.send_message(\n chat_id=tg_chat_id,\n text=f'''\n Преподаватель проверил работу!\n «{lesson}»\n Ссылка на урок: {urljoin('https://dvmn.org/', lesson_url)}\n \n Код идеален, можно делать следующий урок!\n ''')\n\n timestamp = dvmn_api_response.get('last_attempt_timestamp')\n if status == 'timeout':\n timestamp = dvmn_api_response.get('timestamp_to_request')\n except requests.exceptions.ReadTimeout:\n pass\n except ConnectionError:\n time.sleep(5)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"uchakin/review_checker","sub_path":"dvmn_api.py","file_name":"dvmn_api.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3105890873","text":"import sys\nimport time\nimport uuid\nimport tnetstring\nimport zmq\n\nclient_id = b'getstream.py'\n\nctx = zmq.Context()\nout_sock = ctx.socket(zmq.PUSH)\nout_sock.connect('ipc://server-in')\nout_stream_sock = ctx.socket(zmq.ROUTER)\nout_stream_sock.connect('ipc://server-in-stream')\nin_sock = ctx.socket(zmq.SUB)\nin_sock.setsockopt(zmq.SUBSCRIBE, client_id)\nin_sock.connect('ipc://server-out')\n\ntime.sleep(0.5)\n\nrid = str(uuid.uuid4()).encode('utf-8')\ninseq = 0\noutseq = 0\nout_sock.send(b'T' + tnetstring.dumps({\n b'from': client_id,\n b'id': rid,\n b'seq': outseq,\n b'method': b'GET',\n b'uri': sys.argv[1].encode('utf-8'),\n b'stream': True,\n b'credits': 8192,\n}))\noutseq += 1\n\nwhile True:\n buf = in_sock.recv()\n at = buf.find(b' ')\n receiver = buf[:at]\n indata = tnetstring.loads(buf[at + 2:])\n if indata[b'id'] != rid:\n continue\n print('IN: {}'.format(indata))\n assert(indata[b'seq'] == inseq)\n inseq += 1\n if (b'type' in indata and (indata[b'type'] == b'error' or indata[b'type'] == b'cancel')) or (b'type' not in indata and b'more' not in indata):\n break\n raddr = indata[b'from']\n if b'body' in indata and len(indata[b'body']) > 0:\n outdata = {\n b'id': rid,\n b'from': client_id,\n b'seq': outseq,\n b'type': b'credit',\n b'credits': len(indata[b'body']),\n }\n print('OUT: {}'.format(outdata))\n out_stream_sock.send_multipart([raddr, b'', b'T' + tnetstring.dumps(outdata)])\n outseq += 1\n","repo_name":"fastly/pushpin","sub_path":"tools/zhttp/getstream.py","file_name":"getstream.py","file_ext":"py","file_size_in_byte":1532,"program_lang":"python","lang":"en","doc_type":"code","stars":3489,"dataset":"github-code","pt":"3"}
+{"seq_id":"25690011747","text":"from typing import List, Optional\nfrom exchanges_integration.bybit_local.data_consumers.market_info import (\n get_best_bids_and_asks_for_symbols,\n)\nfrom pybit import HTTP\n\n\nasync def label_exchange_bybit(session: HTTP, symbols: List[str]) -> Optional[dict]:\n \"\"\"\n function receive the symbols to retrieve data from bybit\n params:\n session: HTTP\n symbols: List[str]\n returns:\n bids_unordered: Optional(dict)\n \"\"\"\n\n bybit_prices = get_best_bids_and_asks_for_symbols(session, symbols=symbols)\n\n if bybit_prices:\n bybit_data = {\"exchange_name\": \"bybit\", \"data\": bybit_prices}\n else:\n bybit_data = None\n\n return bybit_data\n\n\nasync def get_bybit_unordered_bids_and_ask(\n exchange_name, exchange_data_item, bids_unordered, asks_unordered\n):\n\n \"\"\"\n Functions injects the bybit bids and asks into the unordered dicts\n Introspecting the data structure of the bybit data\n\n params:\n exchange_name: str\n exchange_data_item: dict\n bids_unordered: dict\n asks_unordered: dict\n returns:\n bids_unordered: dict\n asks_unordered: dict\n \"\"\"\n\n for item in exchange_data_item[\"data\"]:\n bid = item[\"bid_price\"]\n ask = item[\"ask_price\"]\n\n if item[\"symbol\"] not in bids_unordered.keys():\n bids_unordered[item[\"symbol\"]] = [(exchange_name, bid)]\n\n else:\n bids_unordered[item[\"symbol\"]].append((exchange_name, bid))\n\n if item[\"symbol\"] not in asks_unordered.keys():\n asks_unordered[item[\"symbol\"]] = [(exchange_name, ask)]\n else:\n asks_unordered[item[\"symbol\"]].append((exchange_name, ask))\n return bids_unordered, asks_unordered\n","repo_name":"johnnfujita/livre-arbitrio","sub_path":"src/data_processing/bybit_data_processing.py","file_name":"bybit_data_processing.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"39963654860","text":"import boto3\nimport base64\nimport io\nimport json\nimport csv\nfrom io import BytesIO, StringIO\n\n\nclass myBucket:\n def __init__(self, aws_access_key_id, aws_secret_access_key, region_name, bucket):\n self.aws_access_key_id = aws_access_key_id\n self.aws_secret_access_key = aws_secret_access_key\n self.region_name = region_name\n self.bucket = bucket\n\n def connectS3(self):\n s3 = boto3.client(\n service_name='s3',\n region_name=self.region_name,\n aws_access_key_id=self.aws_access_key_id,\n aws_secret_access_key=self.aws_secret_access_key\n )\n return s3\n\n def loadImage(self, imagebase64):\n imgdata = base64.b64decode(str(imagebase64))\n return io.BytesIO(imgdata)\n\n def putImage(self, imagebase64, key):\n if (imagebase64 is None):\n return \"\"\n\n res = self.connectS3().put_object(\n Body=self.loadImage(imagebase64),\n Bucket=self.bucket,\n ContentType='image/png',\n Key=key\n )\n\n url = '{}/{}/{}'.format(self.connectS3().meta.endpoint_url,\n self.bucket, key)\n\n return url\n\n def deleteImage(self, key):\n res = self.connectS3().delete_object(\n Bucket=self.bucket,\n Key=key,\n )\n return res\n\n def getImage(self, key):\n res = self.connectS3().get_object(\n Bucket=self.bucket,\n Key=key\n )\n imgContent = res['Body'].read()\n imgBase64 = base64.b64encode(imgContent)\n\n return imgBase64.decode('utf-8')\n\n def getDataFile(self, key):\n res = self.connectS3().get_object(\n Bucket=self.bucket,\n Key=key\n )\n\n body = res.get('Body', '').read()\n content = body.decode(\"windows-1252\")\n file = StringIO(content)\n csv_data = list(csv.reader(file))\n\n return csv_data[:10]\n\n def putObject(self, obj):\n return\n","repo_name":"nguyenlcsmile/myTemplate","sub_path":"myFunction/s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3642865981","text":"from sys import argv as CLIARGS\n\nonlyCount = False\n\nif '--only-count' in CLIARGS or '-oc' in CLIARGS:\n onlyCount = True\n\nprint()\n\nfor eachFile in CLIARGS[1:]:\n if eachFile.endswith('.spotdlTrackingFile'):\n dumps = eval(open(eachFile, 'rb').read().decode())\n\n songCount = 0\n\n for dump in dumps:\n songCount += 1\n\n songName = dump['rawTrackMeta']['name']\n\n contributingArtists = ''\n\n for artist in dump['rawTrackMeta']['artists']:\n if artist['name'].lower() not in songName.lower():\n contributingArtists += artist['name'] + ', '\n\n if not onlyCount:\n print('%3d %-60s %s' % (songCount, contributingArtists[:-2], songName))\n \n print(\"Found search results\")\n \n print('\\nThere are %d songs in \"%s\"' % (songCount, eachFile[2:-19]))\n","repo_name":"YouYou-droid/Spotdl","sub_path":"dev utils/spotdlTF.py","file_name":"spotdlTF.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"110777538","text":"import unittest\nimport grpc\nimport sys\nimport time\nfrom delayedassert import expect, expect_equal, assert_expectations\nimport logging\nimport os\n\nimport MexController as mex_controller\n\ncontroller_address = os.getenv('AUTOMATION_CONTROLLER_ADDRESS', '127.0.0.1:55001')\n\nnumber_of_flavors = 100\n\nflavor_name = 'flavor' + str(time.time())\nram = 1024\ndisk = 1\nvcpus = 1\n\nmex_root_cert = 'mex-ca.crt'\nmex_cert = 'mex-client.crt'\nmex_key = 'mex-client.key'\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\nclass tc(unittest.TestCase):\n @classmethod\n def setUpClass(self):\n self.controller = mex_controller.MexController(controller_address = controller_address,\n# root_cert = mex_root_cert,\n# key = mex_key,\n# client_cert = mex_cert\n )\n\n self.flavor_list = []\n for i in range(number_of_flavors):\n self.flavor_list.append(mex_controller.Flavor(flavor_name = flavor_name + str(i),\n ram = ram,\n disk = disk,\n vcpus = vcpus\n ))\n \n def test_createFlavor(self):\n # [Documentation] Flavor - User shall be able to create 100 flavors\n # ... Create 100 flavors\n # ... Verify all 100 are created\n\n # print flavors before add\n flavor_pre = self.controller.show_flavors()\n\n # create flavor\n for i in self.flavor_list:\n self.controller.create_flavor(i.flavor)\n\n # print flavorss after add\n flavor_post = self.controller.show_flavors()\n \n # find flavor in list\n for a in self.flavor_list:\n found_op = a.exists(flavor_post)\n expect_equal(found_op, True, 'find op' + a.flavor_name)\n\n # remove since causes problems with parallel execution. checking for all 100 above anyway\n #expect_equal(len(flavor_post), len(flavor_pre) + number_of_flavors, 'number of flavors')\n\n assert_expectations()\n\n @classmethod\n def tearDownClass(self):\n for a in self.flavor_list:\n self.controller.delete_flavor(a.flavor)\n\nif __name__ == '__main__':\n suite = unittest.TestLoader().loadTestsFromTestCase(tc)\n sys.exit(not unittest.TextTestRunner().run(suite).wasSuccessful())\n\n","repo_name":"mobiledgex/edge-cloud-qa","sub_path":"testcases/controller/flavor/test_flavorAdd_100.py","file_name":"test_flavorAdd_100.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7669255132","text":"#!/usr/bin/env python2\nDESCRIPTION = '''\nChecks the top 1M lines of a compressed/uncompressed read file for the presence of adapters.\n'''\nimport sys\nimport argparse\nimport logging\nimport os.path\nimport gzip\nfrom itertools import groupby\n\n## Pass arguments.\ndef main():\n\t# Pass command line arguments. \n\tparser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, description=DESCRIPTION)\n\tparser.add_argument('-f', '--fastq', metavar='reads.fastq.gz', type=lambda x: __pass_file_check_compression(parser, x), required=True, nargs='+', help='Input fastq file/s (can be gziped)')\n\tparser.add_argument('-a', '--adapters', type=argparse.FileType('r'), required=True, help='Adapter sequences to search for in fastq file/s')\n\tparser.add_argument('-o', '--adapters_out', default=None, type=argparse.FileType('w'), required=False, help='Adapter sequences found in fastq file/s')\n\tparser.add_argument('-n', '--number_reads', default=200000, type=int, required=False, help='Number of reads to check (default: %(default)s)')\n\tparser.add_argument('-r', '--report_read_count', default=1, type=int, required=False, help='Number of reads (across all files) with an adapter needed before that adapter is printed to output adapter file (default: %(default)s)')\n\tparser.add_argument('-q', '--quiet', action='store_true', required=False, help='Dont print any info to stdout (default: %(default)s)')\n\tparser.add_argument('--debug', action='store_true', required=False, help='Print DEBUG info (default: %(default)s)')\n\targs = parser.parse_args()\n\t\n\t# Set up basic debugger\n\tlogFormat = \"[%(levelname)s]: %(message)s\"\n\tlogging.basicConfig(format=logFormat, stream=sys.stderr, level=logging.INFO)\n\tif args.debug:\n\t\tlogging.getLogger().setLevel(logging.DEBUG)\n\telif args.quiet:\n\t\tlogging.getLogger().setLevel(logging.ERROR)\n\t\n\tlogging.debug('%s', args) ## DEBUG\n\t\n\t# Load adapter seqs into a list. This is just to preserve order.\n\tadapters = [[x, y] for x, y in fasta_iter(args.adapters)]\n\tlogging.debug('%s', adapters) ## DEBUG\n\t\n\tsearch_fastq_files_for_adapters(args.fastq, adapters, args.number_reads, args.report_read_count, args.adapters_out)\n\t\n\t\n\n\ndef search_fastq_files_for_adapters(fastq_fh_list, adapters, number_reads_to_process, report_read_count, adapters_out):\n\t'''\n\tSearch fo each adapter in the given fastq files.\n\tWrites the adapters found in the fastq files to a file.\n\t'''\n\t\n\ttotal_read_count = {x:0 for x, y in adapters}\n\t\n\tfor fastq_fh in fastq_fh_list:\n\t\tlogging.info('') ## INFO\n\t\tlogging.info('Processing file: %s', fastq_fh.name) ## INFO\n\t\t\n\t\treads_seen = 0\n\t\tread_count = {x:0 for x, y in adapters}\n\t\t\n\t\t# Take just the 'sequence' from each passed read\n\t\tfor read_name, read_seq, read_opt, read_qual in fastq_iter(fastq_fh):\n\t\t\t\n\t\t\t# Check for each adapter in read sequence\n\t\t\tfor adapter_name, adapter_seq in adapters:\n\t\t\t\tif adapter_seq in read_seq:\n\t\t\t\t\ttotal_read_count[adapter_name] += 1\n\t\t\t\t\tread_count[adapter_name] += 1\n\t\t\t\n\t\t\t# Break bottom loop if we have checked enough reads.\n\t\t\treads_seen += 1\n\t\t\tif reads_seen == number_reads_to_process:\n\t\t\t\tbreak\n\t\t\n\t\tfor adapter_name, adapter_seq in adapters:\n\t\t\tlogging.info('\\t%s\\t%s', read_count[adapter_name], adapter_name) ## INFO\n\t\n\tif adapters_out is not None:\n\t\tfor adapter_name, adapter_seq in adapters:\n\t\t\tif total_read_count[adapter_name] >= report_read_count:\n\t\t\t\tadapters_out.write('>' + adapter_name + '\\n')\n\t\t\t\tadapters_out.write(adapter_seq + '\\n')\n\t\n\n\ndef __pass_file_check_compression(parser, arg):\n\t'''\n\tCheck passed file name exists and opens using gzip when needed. \n\t'''\n\tif not os.path.exists(arg):\n\t\tparser.error(\"The file %s does not exist!\" % arg)\n\telse:\n\t\t## open with gzip if it has the *.gz extension\n\t\tif arg.endswith(\".gz\"):\n\t\t\treturn gzip.open(arg, 'rb')\n\t\telse:\n\t\t\treturn open(arg, 'r')\n\n\n\ndef fastq_iter(fh):\n\t'''\n\tGiven a fastq file handle, yield a list of lines for \n\teach read.\n\t['name', 'sequence', 'optional', 'quality']\n\t\n\tCode from: https://www.biostars.org/p/317524/\n\t'''\n\tn = 4\n\tlines = []\n\tfor line in fh:\n\t\tlines.append(line.rstrip())\n\t\tif len(lines) == n:\n\t\t\tyield lines\n\t\t\tlines = []\n\n\n\ndef fasta_iter(fh):\n '''\n Given a fasta file handle, yield tuples of header, sequence\n Clears description from seq name.\n \n Code from: https://www.biostars.org/p/710/\n Updated: 07/08/2019\n Version: 0.2\n '''\n # ditch the boolean (x[0]) and just keep the header or sequence since\n # we know they alternate.\n faiter = (x[1] for x in groupby(fh, lambda line: line[0] == \">\"))\n for header in faiter:\n # drop the \">\" and description\n header = header.next()[1:].strip().split(' ')[0]\n # join all sequence lines to one.\n seq = \"\".join(s.strip() for s in faiter.next())\n yield header, seq\n\n\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"TimothyStephens/Utils","sub_path":"Fastx/find_adapters_in_reads/find_adapters_in_reads.py","file_name":"find_adapters_in_reads.py","file_ext":"py","file_size_in_byte":4889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30533676850","text":"import streamlit as st\r\nimport joblib\r\nimport pandas as pd\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.impute import SimpleImputer\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.ensemble import IsolationForest\r\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\r\n\r\n# Load the dataset\r\ndata = pd.read_csv('new_fraud_dataset.csv') # Replace with the actual path to your dataset\r\n\r\n# Preprocessing code\r\n# Specify the categorical column you want to fill null values for\r\ncategorical_column = 'type'\r\n\r\n# Calculate the mode of the categorical column\r\nmode_value = data[categorical_column].mode()[0]\r\n\r\n# Fill null values in the categorical column with the mode\r\ndata[categorical_column].fillna(mode_value, inplace=True)\r\n\r\ndata = data.drop(columns=['isFlaggedFraud'])\r\n\r\n# Columns to standardize and replace\r\ncolumns_to_standardize = ['oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\r\n\r\n# Create a new DataFrame with only the columns to be standardized\r\ndata_to_standardize = data[columns_to_standardize]\r\n\r\n# Initialize the StandardScaler\r\nscaler = StandardScaler()\r\n\r\n# Fit and transform the selected columns\r\nstandardized_data = scaler.fit_transform(data_to_standardize)\r\n\r\n# Create a DataFrame from the standardized data\r\nstandardized_df = pd.DataFrame(standardized_data, columns=columns_to_standardize)\r\n\r\n# Replace the selected columns in the original DataFrame\r\ndata[columns_to_standardize] = standardized_df\r\n\r\nfraud = data[data['isFraud'] == 1]\r\nnormal = data[data['isFraud'] == 0]\r\n\r\ndata = pd.get_dummies(data, columns=['type'], prefix='type', drop_first=True)\r\ncolumns_to_drop = ['nameOrig', 'nameDest']\r\ndata.drop(columns=columns_to_drop, inplace=True)\r\n\r\n# Handle missing values by imputing with mean (you can adjust this strategy)\r\nimputer = SimpleImputer(strategy='mean')\r\ndata_imputed = imputer.fit_transform(data)\r\n\r\n# Convert imputed data back to a DataFrame\r\ndata_imputed = pd.DataFrame(data_imputed, columns=data.columns)\r\n\r\n# Specify the target variable and feature columns\r\ntarget_column = 'isFraud'\r\nfeature_columns = ['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest',\r\n 'type_CASH_OUT', 'type_DEBIT', 'type_PAYMENT', 'type_TRANSFER']\r\n\r\n# Split the data into features (X) and target variable (y)\r\nX = data_imputed[feature_columns]\r\ny = data_imputed[target_column]\r\n\r\n# Split the data into training and testing sets\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\r\n\r\n# Train the Isolation Forest model\r\ndef train_isolation_forest(X_train):\r\n isolation_forest = IsolationForest(contamination=0.05, random_state=42)\r\n isolation_forest.fit(X_train)\r\n return isolation_forest\r\n\r\n# Train the Isolation Forest model\r\nisolation_forest = train_isolation_forest(X_train)\r\n\r\n# Save the trained model\r\nmodel_filename = 'isolation_forest_model.joblib'\r\njoblib.dump(isolation_forest, model_filename)\r\n\r\n# Load the trained Isolation Forest model\r\nmodel_filename = 'isolation_forest_model.joblib'\r\nisolation_forest = joblib.load(model_filename)\r\n\r\n# Load the scaler info for scaling user input\r\nscaler_info = {\r\n 'amount': {'mean': 135960.73, 'std': 311907.25},\r\n 'oldbalanceOrg': {'mean': 839585.09, 'std': 2930765.27},\r\n 'newbalanceOrig': {'mean': 862786.81, 'std': 2992454.30},\r\n 'oldbalanceDest': {'mean': 1100700.49, 'std': 3399183.41},\r\n 'newbalanceDest': {'mean': 1224996.40, 'std': 3482463.80}\r\n}\r\n\r\n# Function to preprocess input data\r\ndef preprocess_input(amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest, type):\r\n scaled_data = {\r\n 'amount': (amount - scaler_info['amount']['mean']) / scaler_info['amount']['std'],\r\n 'oldbalanceOrg': (oldbalanceOrg - scaler_info['oldbalanceOrg']['mean']) / scaler_info['oldbalanceOrg']['std'],\r\n 'newbalanceOrig': (newbalanceOrig - scaler_info['newbalanceOrig']['mean']) / scaler_info['newbalanceOrig']['std'],\r\n 'oldbalanceDest': (oldbalanceDest - scaler_info['oldbalanceDest']['mean']) / scaler_info['oldbalanceDest']['std'],\r\n 'newbalanceDest': (newbalanceDest - scaler_info['newbalanceDest']['mean']) / scaler_info['newbalanceDest']['std'],\r\n 'type_CASH_OUT': type == 'CASH_OUT',\r\n 'type_DEBIT': type == 'DEBIT',\r\n 'type_PAYMENT': type == 'PAYMENT',\r\n 'type_TRANSFER': type == 'TRANSFER'\r\n }\r\n data = pd.DataFrame(scaled_data, index=[0])\r\n return data\r\n\r\n# Streamlit app\r\ndef main():\r\n st.title(\"Credit Card Fraud Detection\")\r\n\r\n amount = st.number_input(\"Amount\", value=1000.0)\r\n oldbalanceOrg = st.number_input(\"Old Balance Orig\", value=5000.0)\r\n newbalanceOrig = st.number_input(\"New Balance Orig\", value=4000.0)\r\n oldbalanceDest = st.number_input(\"Old Balance Dest\", value=0.0)\r\n newbalanceDest = st.number_input(\"New Balance Dest\", value=1000.0)\r\n type = st.selectbox(\"Type\", ['CASH_OUT', 'DEBIT', 'PAYMENT', 'TRANSFER'])\r\n\r\n if st.button(\"Predict\"):\r\n input_data = preprocess_input(amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest, type)\r\n prediction = isolation_forest.predict(input_data)\r\n result = \"Fraudulent\" if prediction[0] == 1 else \"Legitimate\"\r\n st.success(f\"The transaction is predicted to be: {result}\")\r\n\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"AravindKasyap/fraud_detection","sub_path":"new_file.py","file_name":"new_file.py","file_ext":"py","file_size_in_byte":5399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8476018410","text":"from random import randint\n\nfrom PIL import Image\nimport numpy as np\n\n\ndef rgb_shift(im: np.array, offset: int, source_channel: int = None,\n target_channel: int = None, rows: bool = True, columns: bool = False):\n \"\"\"\n :param im: numpy array representing an image\n :param offset: the offset to shift the band by\n :param source_channel: the color band to move from\n :param target_channel: the color band to move to\n :param rows: shift rows\n :param columns: shift columns\n :return:\n \"\"\"\n\n if source_channel is None:\n source_channel = randint(0, 2)\n if target_channel is None:\n target_channel = randint(0, 2)\n\n if columns:\n loffset = offset\n for column in range(im.shape[1]):\n if (loffset + column) >= im.shape[1]:\n loffset -= im.shape[1]\n\n source_band = im[:, column, source_channel]\n target_band = im[:, column + loffset, target_channel]\n im[:, column, source_channel] = target_band\n im[:, column + loffset, target_channel] = source_band\n\n if rows:\n loffset = offset\n for row in range(im.shape[0]):\n if (loffset + row) >= im.shape[0]:\n loffset -= im.shape[0]\n\n source_band = im[row, :, source_channel]\n target_band = im[row + loffset, :, target_channel]\n im[row, :, source_channel] = target_band\n im[row + loffset, :, target_channel] = source_band\n\n\n\ndef demo():\n im = np.array(Image.open('apt.jpg'))\n rgb_shift(im, 200, 0, 2, rows=False, columns=True)\n Image.fromarray(im).save('testnumpy1.jpg')\n im = np.array(Image.open('apt.jpg'))\n rgb_shift(im, 200, 0, 2, rows=True, columns=False)\n Image.fromarray(im).save('testnumpy2.jpg')\n im = np.array(Image.open('apt.jpg'))\n rgb_shift(im, 200, 0, 2, rows=True, columns=True)\n Image.fromarray(im).save('testnumpy3.jpg')\n\ndemo()","repo_name":"tetrismegistus/minutia","sub_path":"exercises/python_doodles/datamosher/test_numpy.py","file_name":"test_numpy.py","file_ext":"py","file_size_in_byte":1931,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"}
+{"seq_id":"19482739906","text":"'''Իրականացնել ծրագիր, որում պետք է ստեղծել ցուցակ(list),\r\nբաղկացած 100-ից 1000-ի միջև ընկած բոլոր պալինդրոմային թվերից՝\r\nօգատգործելով list-ի comprehension։'''\r\n\r\n#\r\n# num_list = [num for num in range(100, 1001) if (num == (num % 10 * 100) + (num // 10 % 10 * 10) + (num // 100 % 10))]\r\n# print(num_list)\r\n\r\n'''Իրականացնել ծրագիր, որը շրջում է մատրիցը 180 աստիճանով։ '''\r\n\r\n\r\ndef input_matrix(n, m):\r\n matrix = []\r\n for i in range(n):\r\n l = [int(v) for v in input('Enter matrix row. {} numbers only: '.format(m)).split(' ')]\r\n if len(l) != m:\r\n print('ERROR')\r\n return None\r\n matrix.append(l)\r\n return matrix\r\n\r\n\r\ndef print_matrix(matrix):\r\n n = len(matrix)\r\n m = len(matrix[0])\r\n for i in range(n):\r\n for j in range(m):\r\n print(matrix[i][j], end=' ')\r\n print()\r\n\r\n\r\n# columns = int(input(\"Number of columns: \"))\r\n# rows = int(input(\"Number of rows:\"))\r\n# mat = input_matrix(rows, columns)\r\n# print_matrix(mat)\r\n# print(\"Here's the result!\")\r\n# for ls in range(len(mat)):\r\n# mat[ls] = mat[ls][-1::-1]\r\n# mat = mat[-1::-1]\r\n# print_matrix(mat)\r\n\r\n'''Ունենք ամբողջ թվերից բաղկացած մատրից, որի վրա պետք է իրականացնել transpose։ \r\nTranspose կատարել նշանակում է մատրիցի տողերը փոխադրել սյուներով։'''\r\n\r\n# columns = int(input(\"Number of columns: \"))\r\n# rows = int(input(\"Number of rows: \"))\r\n# mat = input_matrix(rows, columns)\r\n# print_matrix(mat)\r\n# res_mat = []\r\n# mat_row = []\r\n# for j in range(len(mat[0])):\r\n# for i in range(len(mat)):\r\n# mat_row.append(mat[i][j])\r\n# res_mat.append(mat_row)\r\n# mat_row = []\r\n#\r\n# print(\"Here's the resulting matrix!\")\r\n# print_matrix(res_mat)\r\n\r\n'''Գրել ծրագիր, որը կստուգի մուտքային թիվը երկուսի աստիճան է, թե՝ ոչ։'''\r\n\r\n# num = int(input(\"Enter a number: \"))\r\n# n = num\r\n# while n > 1:\r\n# n /= 2\r\n# if n == 1:\r\n# print(\"{} is a power of 2\".format(num))\r\n# else:\r\n# print(\"{} is not a power of 2\".format(num))\r\n\r\n'''Գրել ծրագիր, որը որպես մուտքային արժեք կստանա ամբողջ թիվ և կարտածի նրանում պարունակվող ‘1’ բիթերի քանակը։'''\r\n\r\nnum = int(input(\"Enter a number: \"))\r\nn = num\r\nbin_ls = []\r\nwhile num > 0:\r\n n = num % 2\r\n num //= 2\r\n bin_ls.append(n)\r\ncounter = 0\r\nfor b in bin_ls:\r\n if b == 1:\r\n counter += 1\r\nbin_ls.reverse()\r\nprint(bin_ls)\r\nprint(counter)\r\n","repo_name":"arpi-gh/Python-Course","sub_path":"Homework_4.py","file_name":"Homework_4.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"hy","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28923079791","text":"from jsonpickle import json\nimport json\nimport Domain\nfrom Domain import tranzactie\nfrom Domain.tranzactie import Tranzactie\nfrom Repository.file_repository import FileRepository\nfrom ViewModels.client_view_models import ClientViewModel\nfrom ViewModels.masina_view_models import MasinaViewModel\nfrom ViewModels.tranzactie_view_models import TranzactieViewModel\n\n\nclass Functionalitati:\n\n def __init__(self, tranzactii_repository: FileRepository, masini_repository: FileRepository, clienti_repository: FileRepository):\n self.__tranzactii_repository = tranzactii_repository\n self.__masini_repository = masini_repository\n self.__clienti_repository = clienti_repository\n\n def get_all_tranzactii(self):\n view_models = []\n for tranzactie in self.__tranzactii_repository.get_all():\n masina = self.__masini_repository.get_by_id(tranzactie.id_masina)\n client = self.__clienti_repository.get_by_id(tranzactie.id_card_client)\n view_models.append(TranzactieViewModel(tranzactie.id_entitate, masina, client, tranzactie.suma_piese, tranzactie.suma_manopera, tranzactie.data, tranzactie.ora))\n\n return view_models\n\n def get_all_masini(self):\n view_models = []\n for masina in self.__masini_repository.get_all():\n masina = self.__masini_repository.get_by_id(masina.id_entitate)\n view_models.append(MasinaViewModel(masina.id_entitate, masina.model, masina.an_achizitie, masina.nr_km, masina.garantie))\n return view_models\n\n def get_all_clienti(self):\n view_models = []\n for client in self.__clienti_repository.get_all():\n client = self.__clienti_repository.get_by_id(client.id_entitate)\n view_models.append(ClientViewModel(client.id_entitate, client.nume, client.prenume, client.cnp, client.data_nasterii, client.data_inregistrarii))\n return view_models\n\n def cautare(self, cuvant_cheie):\n lista = []\n for o_masina in self.get_all_masini():\n if str(o_masina.id_masina) == cuvant_cheie or str(o_masina.model) == cuvant_cheie or str(o_masina.an_achizitie) == cuvant_cheie or str(o_masina.nr_km) == cuvant_cheie or str(o_masina.garantie) == cuvant_cheie:\n lista.append(o_masina)\n for un_client in self.get_all_clienti():\n if str(un_client.id_card_client) == cuvant_cheie or str(un_client.nume) == cuvant_cheie or str(un_client.prenume) == cuvant_cheie or str(un_client.cnp) == cuvant_cheie or str(un_client.data_nasterii) == cuvant_cheie or str(un_client.data_inregistrarii) == cuvant_cheie:\n lista.append(un_client)\n return lista\n\n def ordonare_suma_manopera(self):\n lista_masini = []\n tranzactii = self.get_all_tranzactii()\n sorted_obj= sorted(tranzactii, key=lambda x: x.suma_manopera, reverse=True)\n for tranzactii_sortate in sorted_obj:\n lista_masini.append(tranzactii_sortate.masina)\n return lista_masini\n\n #####################################\n def ordonare_suma_manopera_sortare(self):\n tranzactii_lista = self.get_all_tranzactii()\n lista = []\n for x in range (len(tranzactii_lista)-1, 0, -1):\n if tranzactii_lista[x].suma_manopera != None:\n lista.append(tranzactii_lista[x])\n for x in range (len(lista)-1, 0, -1):\n for i in range(x):\n if lista[i].suma_manopera < lista[i+1].suma_manopera:\n aux = lista[i]\n lista[i] = lista[i+1]\n lista[i+1] = aux\n for tranzactie in lista:\n return(tranzactie.masina)\n\n #########################################\n def ordonare_card_client_sortare(self):\n tranzactii_lista = self.get_all_tranzactii()\n lista = []\n for x in range (len(tranzactii_lista)-1, 0, -1):\n if (tranzactii_lista[x].suma_manopera//10 + tranzactii_lista[x].suma_piese) != None:\n lista.append(tranzactii_lista[x])\n for x in range (len(lista)-1, 0, -1):\n for i in range(x):\n if (lista[i].suma_manopera//10 + lista[i].suma_piese) < (lista[i+1].suma_manopera//10 + lista[i+1].suma_piese):\n aux = lista[i]\n lista[i] = lista[i+1]\n lista[i+1] = aux\n for tranzactie in lista:\n return(tranzactie.client)\n\n\n def ordonare_card_client(self):\n lista_clienti = []\n tranzactii = self.get_all_tranzactii()\n sorted_obj =sorted(tranzactii, key=lambda x: (x.suma_manopera), reverse=True)\n for tranzactii_sortate in sorted_obj:\n lista_clienti.append(tranzactii_sortate.client)\n return lista_clienti\n\n def suma_tranzactii_interval(self, st_interval, dr_interval):\n lista = []\n for o_tranzactie in self.get_all_tranzactii():\n suma = o_tranzactie.suma_piese + o_tranzactie.suma_manopera\n if st_interval < suma and dr_interval > suma:\n lista.append(o_tranzactie)\n return lista\n\n#########################\n def suma_tranzactii_interval_map(self, st_interval, dr_interval):\n lista = []\n for o_tranzactie in self.get_all_tranzactii():\n suma = map(lambda suma_piese, suma_manopera: suma_piese + suma_manopera, self.get_all_tranzactii())\n if st_interval < suma and dr_interval > suma:\n lista.append(o_tranzactie)\n return lista\n\n\n def stergere_interval_zile(self, st_interval, dr_interval):\n lista = []\n for o_tranzactie in self.get_all_tranzactii():\n data_str = str(o_tranzactie.data)\n zi = int(data_str.split('.')[2])\n if zi < st_interval or zi > dr_interval:\n lista.append(o_tranzactie)\n return lista\n\n ######################\n def stergere_interval_zile_filter(self, st_interval, dr_interval):\n lista = []\n for o_tranzactie in self.get_all_tranzactii():\n data_str = str(o_tranzactie.data)\n zi = int(data_str.split('.')[2])\n rezultat = filter(lambda zi: zidr_interval, self.get_all_tranzactii())\n if rezultat is not None:\n lista.append(o_tranzactie)\n return lista\n\n\n def actualizare_garantie(self):\n lista = []\n for o_masina in self.get_all_masini():\n if 2020 - int(o_masina.an_achizitie) <= 3 and int(o_masina.nr_km) <= 600000:\n o_masina.garantie = 'da'\n else:\n o_masina.garantie = 'nu'\n lista.append(o_masina)\n return lista\n\n\n","repo_name":"simonadaariana/lab-AlgorithmsAndProgramming","sub_path":"Service/functionalitati.py","file_name":"functionalitati.py","file_ext":"py","file_size_in_byte":6652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71325538002","text":"import pandas as pd\nimport os\nimport numpy as np\nimport datetime\nimport calendar\n\nimport json\nfrom calibtool.analyzers.Helpers import season_channel_age_density_csv_to_pandas, convert_to_counts, age_from_birth_cohort, age_from_birth_cohort, season_from_time, aggregate_on_index\n\n# dir = 'C://Users//pselvaraj//Desktop'\n# filename = 'ReportVectorStats.csv'\n# filepath = os.path.join(dir, filename)\n#\n# data = pd.read_csv(filepath, delimiter=',', encoding=\"utf-8-sig\").reset_index()\n# data = data[365:]\n# data['Day'] = data['Time'].apply(lambda x: (x+1)%365)\n# data = data[['Day', ' Population', ' VectorPopulation']]\n# data['Vector_per_Human'] = data[' VectorPopulation']/data[' Population']\n# data = data.groupby('Day')['Vector_per_Human'].apply(np.mean).reset_index()\n#\n# dateparser = lambda x: datetime.datetime.strptime(x, '%j').month\n# data['Month'] = data['Day'].apply(lambda x: calendar.month_abbr[dateparser(str(x+1))])\n# data = data.groupby('Month')['Vector_per_Human'].apply(np.mean).reset_index()\n\n\nmetadata = {\n 'parasitemia_bins': [0, 50, 200, 500, np.inf], # (, 0] (0, 50] ... (50000, ]\n 'age_bins': [0, 5, 15, np.inf], # (, 5] (5, 15] (15, ],\n 'seasons': ['DC2', 'DH2', 'W2'],\n 'seasons_by_month': {\n 'May': 'DH2',\n 'September': 'W2',\n 'January': 'DC2'\n },\n 'village': 'Matsari'\n }\n\nreference_csv = 'C://Github//PS_dtk_tools//dtk-tools-malaria//malaria//study_sites//inputs//GarkiDB_data//GarkiDBparasitology.csv'\nreference = season_channel_age_density_csv_to_pandas(reference_csv, metadata)\n\n# csvfilename = 'c:////github////ps_dtk_tools////dtk-tools-malaria////malaria////study_sites////inputs////Mozambique_ento_data////mosquito_count_by_house_day.csv'\n# df = pd.read_csv(csvfilename)\n#\n# metadata = {\n# 'village': 'Magude',\n# 'months': [calendar.month_abbr[i] for i in range(1, 13)],\n# 'species': ['gambiae', 'funestus']\n# }\n#\n# df = df[['date', 'gambiae_count', 'funestus_count', 'adult_house']]\n# df['gambiae'] = df['gambiae_count'] / df['adult_house']\n# df['funestus'] = df['funestus_count'] / df['adult_house']\n# df = df.dropna()\n#\n# df['date'] = pd.to_datetime(df['date'])\n# dateparser = lambda x: int(x.strftime('%m'))\n# df['Month'] = df['date'].apply(lambda x: calendar.month_abbr[int(dateparser(x))])\n# df2 = df.groupby('Month')['gambiae'].apply(np.mean).reset_index()\n# df2['funestus'] = list(df.groupby('Month')['funestus'].apply(np.mean))\n#\n# # Keep only species requested\n# for spec in metadata['species']:\n# df1 = df2[['Month', spec]]\n# df1 = df1.rename(columns={spec: 'Counts'})\n# df1['Channel'] = [spec] * len(df1)\n# if 'dftemp' in locals():\n# dftemp = pd.concat([dftemp, df1])\n# else:\n# dftemp = df1.copy()\n#\n# dftemp = dftemp.set_index(['Channel', 'Month'])\nfrom dtk.utils.parsers.malaria_summary import summary_channel_to_pandas\n\npopulation_channel = 'Average Population by Age Bin'\n\nref_ix = reference.index\nchannels_ix = ref_ix.names.index('Channel')\nchannels = ref_ix.levels[channels_ix].values\nseasons = {\n 'May': 'DH2',\n 'September': 'W2',\n 'January': 'DC2'\n }\n\nfile = 'C://Users//pselvaraj//Desktop//MalariaSummaryReport_Monthly_Report.json'\ndata = json.load(open(file))\n\n# Population by age and time series (to convert parasite prevalence to counts)\npopulation = summary_channel_to_pandas(data, population_channel)\n\n# Coerce channel data into format for comparison with reference\nchannel_data_dict = {}\nfor channel in channels:\n\n # Prevalence by density, age, and time series\n channel_data = summary_channel_to_pandas(data, channel)\n\n # Calculate counts from prevalence and population\n channel_counts = convert_to_counts(channel_data, population)\n\n # Reset multi-index and perform transformations on index columns\n df = channel_counts.reset_index()\n df = age_from_birth_cohort(df) # calculate age from time for birth cohort\n df = season_from_time(df, seasons=seasons) # calculate month from time\n\n # Re-bin according to reference and return single-channel Series\n rebinned = aggregate_on_index(df, reference.loc(axis=1)[channel].index, keep=[channel])\n channel_data_dict[channel] = rebinned[channel].rename('Counts')\n\nsim_data = pd.concat(channel_data_dict.values(), keys=channel_data_dict.keys(), names=['Channel'])\nsim_data = pd.DataFrame(sim_data) # single-column DataFrame for standardized combine/compare pattern\n","repo_name":"m-v-nikolov/dtk-tools-py3","sub_path":"examples/PS_scripts/CalibrationWeek/entomology_calibration/test_code.py","file_name":"test_code.py","file_ext":"py","file_size_in_byte":4485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71617849042","text":"from __future__ import (absolute_import, division, print_function)\n\nimport logging\nimport neovim\nfrom . import check_lldb\n\n__metaclass__ = type # pylint: disable=invalid-name\n\nif not check_lldb.probe():\n logging.getLogger(__name__).critical('LLDB could not be imported!')\n # ImportError will be raised in Controller import below.\n\n# pylint: disable=wrong-import-position\nfrom .controller import Controller, EventLoopError # NOQA\nfrom .vim_x import VimX # NOQA\n# pylint: enable=wrong-import-position\n\n\n@neovim.plugin # pylint: disable=too-few-public-methods\nclass Middleman:\n\n def __init__(self, vim):\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(logging.INFO)\n self.ctrl = Controller(VimX(vim))\n self.ctrl.start()\n if self.ctrl.vimx._vim_test: # pylint: disable=protected-access\n print(\"Note: `:LL-` commands are not bound with this test instance\")\n else:\n vim.command('call lldb#remote#init(%d)' % vim.channel_id)\n\n # The only interface that is predefined in the remote plugin manifest file.\n # The first execution of `:LLsession` initializes the remote part of the plugin.\n @neovim.command('LLsession', nargs='+', complete='customlist,lldb#session#complete')\n def _session(self, args):\n self.ctrl.safe_call(self.ctrl.session.handle, args)\n\n @neovim.rpc_export('mode')\n def _mode(self, mode):\n self.ctrl.safe_call(self.ctrl.session.mode_setup, [mode])\n\n @neovim.rpc_export('exec')\n def _exec(self, *args):\n if len(args) == 0:\n self.ctrl.vimx.log(\"Usage :LL [args...]\", level=2)\n elif args[0] in ['di', 'dis', 'disassemble']:\n self.ctrl.safe_call(self.ctrl.change_buffer_cmd, ['disassembly', ' '.join(args)])\n elif args[0] in ['bt', '_regexp-bt']:\n self.ctrl.safe_call(self.ctrl.change_buffer_cmd, ['backtrace', ' '.join(args)])\n else:\n self.ctrl.safe_execute(args)\n if args[0] == 'help':\n self.ctrl.vimx.command('drop [lldb]logs')\n\n @neovim.rpc_export('stdin')\n def _stdin(self, strin):\n self.ctrl.safe_call(self.ctrl.put_stdin, [strin])\n\n @neovim.rpc_export('exit')\n def _exit(self):\n self.ctrl.safe_exit()\n\n @neovim.rpc_export('complete', sync=True)\n def _complete(self, arg, line, pos):\n # FIXME user-customizable timeout?\n try:\n return self.ctrl.safe_call(self.ctrl.complete_command,\n [arg, line, pos], True, timeout=3)\n except EventLoopError as e:\n self.logger.warn(\"%s on %s | %s\", str(e), repr(line[:pos]), repr(line[pos:]))\n return []\n\n @neovim.rpc_export('get_modes', sync=True)\n def _get_modes(self):\n try:\n return self.ctrl.safe_call(self.ctrl.session.get_modes,\n [], True, timeout=1)\n except EventLoopError as e:\n self.logger.warn(str(e))\n return []\n\n @neovim.rpc_export('select_thread_and_frame')\n def _select_thread_and_frame(self, thread_and_frame_idx):\n if thread_and_frame_idx[0]:\n self.ctrl.safe_execute(['thread', 'select', thread_and_frame_idx[0]])\n if thread_and_frame_idx[1]:\n self.ctrl.safe_execute(['frame', 'select', thread_and_frame_idx[1]])\n\n @neovim.rpc_export('btswitch')\n def _btswitch(self):\n self.ctrl.safe_call(self.ctrl.do_btswitch)\n\n @neovim.rpc_export('breakswitch')\n def _breakswitch(self, bufnr, line):\n self.ctrl.safe_call(self.ctrl.do_breakswitch, [bufnr, line])\n\n @neovim.rpc_export('breakdelete')\n def _breakdelete(self, bp_id):\n self.ctrl.safe_call(self.ctrl.do_breakdelete, [bp_id])\n\n @neovim.rpc_export('refresh')\n def _refresh(self):\n self.ctrl.safe_call(self.ctrl.update_buffers)\n\n @neovim.rpc_export('watchswitch')\n def _watchpoint(self, var_name):\n pass # TODO create watchpoint from locals pane\n","repo_name":"dbgx/lldb.nvim","sub_path":"rplugin/python/lldb_nvim/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4018,"program_lang":"python","lang":"en","doc_type":"code","stars":262,"dataset":"github-code","pt":"3"}
+{"seq_id":"40823871048","text":"def check(a):\n\tlower=upper=0\n\tfor i in a:\n\t\tif i.islower():\n\t\t\tlower=lower+1\n\t\telif i.isupper():\n\t\t\tupper=upper+1\n\tprint('No. of Upper case characters:',upper)\n\tprint('No. of Lower case characters:',lower)\n\t\t\t\na=input('Enter the string : ')\ncheck(a)\n","repo_name":"komal-agarwal5/PCC-CS393","sub_path":"7)Function/pr73.py","file_name":"pr73.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39856329080","text":"import os\nimport json\nimport subprocess\n\nwith open('./package_list.json') as f:\n\tdata = json.load(f)\n\npackages=data[\"Dependencies\"]\n\nlist_failed=[]\n\nfor p in packages:\n\tcmd = 'pip install '+p\n\tretcode = subprocess.call(cmd,shell=True)\n\t#print(retcode)\n\tif retcode==1:\n\t\tlist_failed.append(p)\n\nif len(list_failed)==0:\n\tprint(\"Success\")\nelse:\n\tprint(\"Failed:\")\n\tfor x in list_failed:\n\t\tprint(x)\n\n\t\t\n","repo_name":"sahil2598/Coding-Test","sub_path":"LinuxProblem/install_packages.py","file_name":"install_packages.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33584672086","text":"#!/usr/bin/env python\n\n\nimport sys\nimport argparse as ap\nfrom Bio import Phylo\n\n\ndef read_params(args):\n p = ap.ArgumentParser(formatter_class=ap.ArgumentDefaultsHelpFormatter,\n description='Remove offending chars from tree node labels')\n\n p.add_argument('intree', nargs='?', default=None, type=str,\n help=\"the input tree, stdin if not present\")\n p.add_argument('-f', default='newick', type=str,\n choices=['newick', 'nexus', 'nexml', 'phyloxml', 'cdao'],\n help=\"inp/out -put format\")\n\n args = vars(p.parse_args())\n\n if not args['intree']:\n args['intree'] = sys.stdin\n\n return args\n\n\nif __name__ == \"__main__\":\n args = read_params(sys.argv)\n tree = Phylo.read(args['intree'], args['f'])\n Phylo.draw_ascii(tree)\n","repo_name":"fasnicar/utils","sub_path":"tree_draw_ascii.py","file_name":"tree_draw_ascii.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"5251052805","text":"import pprint\n\nwith open('input.txt') as f:\n data: list[tuple[int, int, int]] = [tuple(int(c) for c in droplet.split(',')) for droplet in f.read().splitlines()]\n\n\nnum_contacts = 0\n\nopen_sides = [[(x + 1, y, z), (x - 1, y, z), (x, y + 1, z), (x, y - 1, z), (x, y, z + 1), (x, y, z - 1)]\n for x, y, z in data]\n\nopen_sides = [s for d in open_sides for s in d]\n\nfor droplet in data:\n for droplet_2 in data:\n if droplet == droplet_2:\n continue\n if droplet[0] == droplet_2[0] and droplet[1] == droplet_2[1] and abs(droplet[2] - droplet_2[2]) == 1:\n if droplet in open_sides:\n open_sides.remove(droplet)\n if droplet_2 in open_sides:\n open_sides.remove(droplet_2)\n num_contacts += 1\n if droplet[0] == droplet_2[0] and droplet[2] == droplet_2[2] and abs(droplet[1] - droplet_2[1]) == 1:\n num_contacts += 1\n if droplet in open_sides:\n open_sides.remove(droplet)\n if droplet_2 in open_sides:\n open_sides.remove(droplet_2)\n if droplet[1] == droplet_2[1] and droplet[2] == droplet_2[2] and abs(droplet[0] - droplet_2[0]) == 1:\n num_contacts += 1\n if droplet in open_sides:\n open_sides.remove(droplet)\n if droplet_2 in open_sides:\n open_sides.remove(droplet_2)\n\nprint('part_1:', len(data) * 6 - num_contacts)\n\nmax_x = max(x for x, _, _ in data)\nmin_x = min(x for x, _, _ in data)\nmax_y = max(y for _, y, _ in data)\nmin_y = min(y for _, y, _ in data)\nmax_z = max(z for _, _, z in data)\nmin_z = min(z for _, _, z in data)\n\nprint(max_x, min_x, max_y, min_y, max_z, min_z)\n\nprint(len(open_sides))\n\n\ndef neighbours(coord: tuple[int, int, int]) -> list[tuple[int, int, int]]:\n x, y, z = coord\n neigb = [(x + 1, y, z), (x - 1, y, z), (x, y + 1, z), (x, y - 1, z), (x, y, z + 1), (x, y, z - 1)]\n return neigb\n\n\ndef find_way_out(coord: tuple[int, int, int]) -> bool:\n paths, path_idx, visited = [[coord]], 0, [coord]\n\n while path_idx < len(paths):\n droplet = paths[path_idx][-1]\n for neighbour in neighbours(droplet):\n # print(f'{neighbour=}')\n if (-1 in neighbour) or neighbour[0] > max_x or neighbour[1] > max_y or neighbour[2] > max_z:\n return True\n if (neighbour in data) or (neighbour in visited):\n continue\n paths.append(paths[path_idx][:] + [neighbour])\n visited.append(neighbour)\n path_idx += 1\n return False\n\n\nouter_opensites = 0\nfor i, os in enumerate(open_sides):\n if find_way_out(os):\n outer_opensites += 1\n if not i % 100:\n print(f'{i} sides checked.')\n\nprint('part 2:', outer_opensites)\n\n\n","repo_name":"TomBenRu/advent_of_code_2022","sub_path":"day_18/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15928128176","text":"class Solution:\n def maximumUnits(self, boxTypes: List[List[int]], truckSize: int) -> int:\n boxTypes.sort(key=lambda x:x[1] ,reverse=True)\n #print(boxTypes)\n ans=0\n for x in boxTypes:\n if x[0]\"\n prediction_key = \"\"\n iteration_id = \"\"\n\n predictor = prediction_endpoint.PredictionEndpoint(prediction_key)\n with open(filename, mode=\"rb\") as image_data:\n results = predictor.predict_image(project_id, image_data, iteration_id)\n tags = list()\n for prediction in results.predictions:\n if prediction.probability > 0.1:\n tags.append(prediction.tag_name)\n return tags","repo_name":"baileye/azure-machine-learning-pixstor-siggraph","sub_path":"azureclassification.py","file_name":"azureclassification.py","file_ext":"py","file_size_in_byte":2070,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"18328506636","text":"from flask import Flask, request, jsonify\nfrom flask_cors import CORS\nimport mysql\nimport mysql.connector\n\napp = Flask(__name__)\napp.config['JSON_SORT_KEYS'] = False\nCORS(app)\n\n@app.route('/')\ndef index():\n return \"collegecounsel api by Jiaqi Wang\"\n\n\n@app.route('/getData')\ndef getAllData():\n args = request.args\n con = mysql.connector.connect(user='XX', password='XX',\n host='XX',\n database='XX')\n cursor = con.cursor()\n alldata = dict()\n if args:\n if \"sort\" in args:\n if args[\"sort\"] == \"alpha\":\n print(\"here1\")\n sql = \"select * from data ORDER BY SchoolName\"\n elif args[\"sort\"] == \"admitasc\":\n print(\"here2\")\n sql = \"select * from data ORDER BY PercentAdmitted\"\n elif args[\"sort\"] == \"admitdes\":\n sql = \"select * from data ORDER BY PercentAdmitted DESC\"\n print(\"here3\")\n cursor.execute(sql)\n data = cursor.fetchall()\n con.close()\n elif \"schoolname\" in args:\n name = str(args[\"schoolname\"])\n sql = \"select * from data WHERE SchoolName = %s\"\n cursor.execute(sql,(name,))\n data = cursor.fetchall()\n con.close()\n print(data)\n #return jsonify(data)\n else:\n sql = \"SELECT * FROM data\"\n cursor.execute(sql)\n data = cursor.fetchall()\n con.close()\n\n for schoolData in data:\n alldata[schoolData[1]]= \\\n { 'name':schoolData[1],\n 'acceptance rate': schoolData[2],\n 'total admitted': schoolData[3],\n 'Interview': schoolData[4],\n \"gpa(uw) 75th percentile\": schoolData[5],\n \"gpa(uw) 25th percentile\": schoolData[6],\n 'SAT 75th percentile': schoolData[9],\n 'SAT 25th percenile': schoolData[10],\n 'ACT 75th percentile': schoolData[11],\n 'ACT 25th percentile': schoolData[12],\n 'estimated total enrollment': schoolData[13],\n 'estimated total undergrad enrollment': schoolData[14],\n 'admission yield': str(round(schoolData[15])) + \"%\",\n 'average cost of attendance per year': schoolData[16],\n 'out of state cost of attendance per year': schoolData[17],\n 'location': schoolData[18],\n 'graduation rate': str(round(schoolData[19])) + \"%\",\n 'strong majors': schoolData[20].split(\"breaker\"),\n 'admissions stats':schoolData[21],\n 'admissions req': schoolData[22],\n 'test optional':schoolData[23]\n }\n\n return jsonify(alldata)\n\n@app.route('/build')\ndef buildList():\n #https://collegedatasender.herokuapp.com/build?sat=1500&gpa=3.9&number=20&state=CA\n args = request.args\n gpa=float(args[\"gpa\"])\n if 'sat' in args:\n testscore=int(args[\"sat\"])\n ifsat = True\n else:\n testscore=int(args[\"act\"])\n ifsat=False\n\n numberOfSchools=int(args[\"number\"])\n if 'maxcost' in args:\n maxCostBeforeAid=int(args[\"maxcost\"])\n else:\n maxCostBeforeAid=99999999\n\n if('major' in args):\n desiredMajor=args['major']\n major = True\n else:\n major = False\n states=[]\n if('state' in args):\n states= args['state'].split('and')\n stateparm= True\n else:\n stateparm=False\n numberOfReach=round(0.3*numberOfSchools)\n numberOfMatch=round(0.5*numberOfSchools)\n numerOfSafety=round(0.3*numberOfSchools)\n\n con = mysql.connector.connect(user='xx', password='xx',\n host='xx',\n database='xx')\n cursor = con.cursor()\n cursor.execute(\"SELECT * FROM data\")\n data = cursor.fetchall()\n con.close()\n alldata = dict()\n for schoolData in data:\n loc = schoolData[18].split(\",\")[1]\n print(loc)\n add=False\n for state in states:\n print(state+\":\"+loc)\n if state in loc:\n add=True\n break\n if add or not stateparm:\n print('adding')\n alldata[schoolData[1]] = \\\n {'name': schoolData[1],\n \"gpa75\": schoolData[5],\n \"gpa25\": schoolData[6],\n 'sat75': schoolData[9],\n 'sat25': schoolData[10],\n 'act75': schoolData[11],\n 'act25': schoolData[12],\n 'cost': schoolData[16],\n 'strong majors': schoolData[20].split(\"breaker\")\n }\n\n\n reaches=[]\n safeties=[]\n macthes=[]\n\n for schoolname in alldata:\n gpa75=alldata[schoolname]['gpa75']\n gpa25=alldata[schoolname]['gpa25']\n sat75 = alldata[schoolname]['sat75']\n sat25 = alldata[schoolname]['sat25']\n meansat=(sat75+sat25)/2\n act75 = alldata[schoolname]['act75']\n act25 = alldata[schoolname]['act25']\n meanact=round((act75+act25)/2)\n cost = alldata[schoolname]['cost']\n #strongmajors=alldata[schoolname]['strong majors']\n if cost > maxCostBeforeAid:\n continue\n if ifsat:\n\n if testscore > 1500:\n testscore = 1500\n if gpa > gpa75:\n if testscore >= sat75:\n safeties.append(schoolname)\n elif testscore <= meansat:\n reaches.append(schoolname)\n else:\n macthes.append(schoolname)\n elif gpa < gpa25:\n if testscore >= sat75:\n macthes.append(schoolname)\n else:\n reaches.append(schoolname)\n else:\n if testscore<= meansat:\n reaches.append(schoolname)\n else:\n macthes.append(schoolname)\n else:\n if gpa > gpa75:\n if testscore > act75:\n safeties.append(schoolname)\n elif testscore < meanact:\n reaches.append(schoolname)\n else:\n macthes.append(schoolname)\n elif gpa < gpa25:\n if testscore > act75:\n macthes.append(schoolname)\n else:\n reaches.append(schoolname)\n else:\n if testscore <= meanact:\n reaches.append(schoolname)\n else:\n macthes.append(schoolname)\n\n totalReach = min(len(reaches),numberOfReach)\n totalMatch=min(len(macthes),numberOfMatch)\n totalSafty=min(len(macthes),numerOfSafety)\n print(reaches)\n print(macthes)\n print(safeties)\n majoredReach = []\n majoredMatch = []\n majoredSafety=[]\n if major:\n desiredMajor = desiredMajor.split(\" \")[0]\n counter = 0\n for school in reaches:\n if counter >= totalReach:\n break\n strongmajors = alldata[school]['strong majors']\n for major in strongmajors:\n if desiredMajor in major or major in desiredMajor:\n majoredReach.append(school)\n reaches.pop(reaches.index(school))\n counter+=1\n break\n\n counter = 0\n for school in macthes:\n if counter >= totalMatch:\n break\n strongmajors = alldata[school]['strong majors']\n for major in strongmajors:\n if desiredMajor in major or major in desiredMajor:\n majoredMatch.append(school)\n macthes.pop(macthes.index(school))\n counter += 1\n break\n\n counter = 0\n for school in safeties:\n if counter >= totalSafty:\n break\n strongmajors = alldata[school]['strong majors']\n for major in strongmajors:\n if desiredMajor in major or major in desiredMajor:\n majoredSafety.append(school)\n safeties.pop(safeties.index(school))\n counter += 1\n break\n\n print(majoredReach)\n print(majoredMatch)\n print(majoredSafety)\n\n if len(majoredReach)numberOfReach:\n majoredReach=majoredReach[0:numberOfReach]\n\n if len(majoredMatch)numberOfMatch:\n majoredMatch=majoredMatch[0:numberOfMatch]\n\n if len(majoredSafety)numerOfSafety:\n majoredSafety=majoredSafety[0:numerOfSafety]\n\n print(majoredReach)\n print(majoredMatch)\n print(majoredSafety)\n out = dict()\n\n out[\"Reach\"]=majoredReach\n out[\"Match\"]=majoredMatch\n out[\"Safety\"]=majoredSafety\n\n print(len(majoredSafety)+len(majoredMatch)+len(majoredReach))\n\n return jsonify(out)\n\n\n","repo_name":"JiaqiWang18/collegecounselAPI","sub_path":"server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16937588261","text":"import datetime\nimport multiprocessing\nimport subprocess\nimport tempfile\n\nfrom components.component import Component\nfrom components.coverage_processor import Coverage\nfrom models.verification_result import *\n\nDEFAULT_CIL_DIR = \"cil\"\nDEFAULT_MAIN_DIR = \"main\"\nDEFAULT_LAUNCHES_DIR = \"launches\"\nDEFAULT_SOURCE_PATCHES_DIR = \"patches/sources\"\nDEFAULT_PREPARATION_PATCHES_DIR = \"patches/preparation\"\nDEFAULT_ENTRYPOINTS_DIR = \"entrypoints\"\nDEFAULT_WORK_DIR = \"work_dir\"\nDEFAULT_RESULTS_DIR = \"results\"\nDEFAULT_BACKUP_PREFIX = \"backup_\"\n\nTAG_LIMIT_MEMORY = \"memory size\"\nTAG_LIMIT_CPU_TIME = \"CPU time\"\nTAG_LIMIT_CPU_CORES = \"number of cores\"\nTAG_CACHED = \"cached\"\nTAG_BRANCH = \"branch\"\nTAG_PATCH = \"patches\"\nTAG_BUILD_PATCH = \"build patch\"\nTAG_MAX_COVERAGE = \"max\"\nTAG_CALLERS = \"callers\"\nTAG_COMMITS = \"commits\"\nTAG_BACKUP_WRITE = \"backup write\"\nTAG_BACKUP_READ = \"backup read\"\nTAG_BENCHMARK_ARGS = \"benchmark args\"\nTAG_PARALLEL_LAUNCHES = \"parallel launches\"\nTAG_RESOURCE_LIMITATIONS = \"resource limits\"\nTAG_PROCESSES = \"processes\"\nTAG_SCHEDULER = \"scheduler\"\nTAG_CLOUD = \"cloud\"\nTAG_CLOUD_MASTER = \"master\"\nTAG_CLOUD_PRIORITY = \"priority\"\nTAG_UPLOADER_UPLOAD_RESULTS = \"upload results\"\nTAG_UPLOADER_IDENTIFIER = \"identifier\"\nTAG_UPLOADER_SERVER = \"server\"\nTAG_UPLOADER_USER = \"user\"\nTAG_UPLOADER_PASSWORD = \"password\"\nTAG_UPLOADER_PARENT_ID = \"parent id\"\nTAG_UPLOADER_REQUEST_SLEEP = \"request sleep\"\nTAG_SKIP = \"skip\"\nTAG_STATISTICS_TIME = \"statistics time\"\nTAG_BUILD_CONFIG = \"build config\"\nTAG_ID = \"id\"\nTAG_REPOSITORY = \"repository\"\nTAG_NAME = \"name\"\nTAG_VERIFIER_OPTIONS = \"verifier options\"\nTAG_EXPORT_HTML_ERROR_TRACES = \"standalone error traces\"\n\nTIMESTAMP_PATTERN = \"\"\nRUNDEFINITION_PATTERN = \"\"\nCOMMIT_PATTERN = \"\"\n\nSCHEDULER_CLOUD = \"cloud\"\nSCHEDULER_LOCAL = \"local\"\nSCHEDULERS = [SCHEDULER_CLOUD, SCHEDULER_LOCAL]\n\nCLOUD_PRIORITIES = [\"IDLE\", \"LOW\", \"HIGH\", \"URGENT\"]\nDEFAULT_CLOUD_PRIORITY = \"LOW\"\nCLOUD_BENCHMARK_LOG = \"benchmark_log.txt\"\n\nHARDCODED_RACES_OUTPUT_DIR = \"output\"\n\nROUND_DIGITS = 9 # nanoseconds.\n\nDEFAULT_TIME_FOR_STATISTICS = 0 # By default we do not allocate time for printing statistics.\n\nVERIFIER_OPTIONS_NOT_OPTIMIZED = [\n \"cpa.functionpointer.ignoreUnknownFunctionPointerCalls=false\",\n]\n\nVERIFIER_OPTIONS_COMMON = \"common\"\n\nSOURCE_QUEUE_BUILDER_RESOURCES = \"builder resources\"\nSOURCE_QUEUE_QUALIFIER_RESOURCES = \"qualifier resources\"\nSOURCE_QUEUE_FILES = \"files\"\nSOURCE_QUEUE_FUNCTIONS = \"functions\"\nSOURCE_QUEUE_RESULTS = \"results\"\n\nTAG_ENTRYPOINTS_DESC = \"entrypoints desc\"\nTAG_PREPARATION_CONFIG = \"preparation config\"\nDEFAULT_PREPARATION_CONFIG = \"conf.json\"\nDEFAULT_PROPERTY_MEMSAFETY = \"properties/memsafety.spc\"\nDEFAULT_PROPERTY_UNREACHABILITY = \"properties/unreachability.spc\"\n\nTAG_CONFIG_MEMORY_LIMIT = \"Memory limit\"\nTAG_CONFIG_CPU_TIME_LIMIT = \"CPU time limit\"\nTAG_CONFIG_CPU_CORES_LIMIT = \"CPU cores limit\"\nTAG_CONFIG_OPTIONS = \"Options\"\n\n\nclass Launcher(Component):\n \"\"\"\n Main component, which creates verification tasks for the given system,\n launches them and processes results.\n \"\"\"\n def __init__(self, name: str, config_file: str):\n self.config_file = os.path.basename(config_file).replace(JSON_EXTENSION, \"\")\n if os.path.exists(config_file):\n with open(config_file, errors='ignore', encoding='ascii') as data_file:\n config = json.load(data_file)\n else:\n config = {\n TAG_DIRS: {\n TAG_DIRS_RESULTS: DEFAULT_RESULTS_DIR,\n TAG_DIRS_WORK: DEFAULT_WORK_DIR\n }\n }\n\n super().__init__(name, config)\n\n # Since Launcher does not produce a lot of output and any of its failure is fatal,\n # we can put in on stdout.\n self.debug = self.config.get(TAG_DEBUG, False)\n if self.debug:\n self.output_desc = sys.stdout\n else:\n self.output_desc = subprocess.DEVNULL\n\n # Remember some useful directories.\n self.root_dir = os.getcwd() # By default, tool-set is run from this directory.\n self.work_dir = os.path.abspath(self.config.get(TAG_DIRS, {}).get(TAG_DIRS_WORK,\n DEFAULT_WORK_DIR))\n self.results_dir = os.path.abspath(self.config.get(TAG_DIRS, {}).get(TAG_DIRS_RESULTS,\n DEFAULT_RESULTS_DIR))\n if not os.path.exists(self.results_dir):\n os.makedirs(self.results_dir, exist_ok=True)\n\n if self.config.get(TAG_EXPORT_HTML_ERROR_TRACES, False):\n self.result_dir_et = os.path.abspath(os.path.join(\n self.config[TAG_DIRS][TAG_DIRS_RESULTS], self._get_result_file_prefix()))\n else:\n self.result_dir_et = None\n self.install_dir = os.path.join(self.root_dir, DEFAULT_INSTALL_DIR)\n\n self.cpu_cores = multiprocessing.cpu_count()\n\n self.backup = None # File, in which backup copy will be placed during verification.\n\n # Defines type of scheduler.\n self.scheduler = self.component_config.get(TAG_SCHEDULER)\n self.benchmark_args = self.component_config.get(TAG_BENCHMARK_ARGS, \"\")\n if self.scheduler == SCHEDULER_CLOUD:\n cloud_master = self.config.get(TAG_CLOUD, {}).get(TAG_CLOUD_MASTER)\n cloud_priority = self.config.get(TAG_CLOUD, {}).get(TAG_CLOUD_PRIORITY,\n DEFAULT_CLOUD_PRIORITY)\n self.benchmark_args = f\"{self.benchmark_args} --cloud --cloudMaster {cloud_master} \" \\\n f\"--cloudPriority {cloud_priority}\"\n self.job_name_suffix = \"\"\n self.export_safes = self.config.get(COMPONENT_EXPORTER, {}).get(TAG_ADD_VERIFIER_PROOFS,\n True)\n\n def __check_result_files(self, file: str, launch_dir: str):\n if file.endswith(\".log\"):\n dst = LOG_FILE\n else:\n dst = os.path.basename(file)\n if not self.export_safes and WITNESS_CORRECTNESS in file:\n return\n shutil.copy(file, os.path.join(launch_dir, dst))\n\n def _copy_result_files(self, files: list, group_directory: str) -> str:\n launch_dir = os.path.abspath(tempfile.mkdtemp(dir=group_directory))\n for file in files:\n if os.path.isfile(file):\n self.__check_result_files(file, launch_dir)\n for root, _, files_in in os.walk(file):\n for name in files_in:\n file = os.path.join(root, name)\n self.__check_result_files(file, launch_dir)\n return launch_dir\n\n def _process_coverage(self, result, launch_directory, source_dirs: list,\n default_source_file=None, work_dir=None):\n cov = Coverage(self, default_source_file=default_source_file)\n cov_queue = multiprocessing.Queue()\n cov_process = multiprocessing.Process(target=cov.compute_coverage,\n name=f\"coverage_{result.get_name()}\",\n args=(source_dirs, launch_directory, cov_queue, work_dir))\n cov_process.start()\n cov_process.join() # Wait since we are already in parallel threads for each launch.\n if not cov_process.exitcode:\n if cov_queue.qsize():\n data = cov_queue.get()\n result.cov_funcs = data.get(TAG_COVERAGE_FUNCS, 0.0)\n result.cov_lines = data.get(TAG_COVERAGE_LINES, 0.0)\n result.coverage_resources[TAG_CPU_TIME] = data.get(TAG_CPU_TIME, 0.0)\n result.coverage_resources[TAG_WALL_TIME] = data.get(TAG_WALL_TIME, 0.0)\n result.coverage_resources[TAG_MEMORY_USAGE] = data.get(TAG_MEMORY_USAGE, 0)\n else:\n warning_msg = f\"Coverage was not computed for {result.id} and entry-point \"\\\n f\"{result.entrypoint}\"\n self.logger.warning(warning_msg)\n\n def _get_from_queue_into_list(self, queue, result_list):\n while not queue.empty():\n launch = queue.get()\n result_list.append(launch)\n if self.backup:\n with open(self.backup, \"a\", encoding='ascii') as f_report:\n f_report.write(str(launch) + \"\\n\")\n return result_list\n\n def _get_result_file_prefix(self):\n return self.config_file + \"_\" + datetime.datetime.fromtimestamp(time.time()).\\\n strftime('%Y_%m_%d_%H_%M_%S')\n\n def _upload_results(self, uploader_config, result_file):\n server = uploader_config.get(TAG_UPLOADER_SERVER)\n identifier = uploader_config.get(TAG_UPLOADER_IDENTIFIER)\n user = uploader_config.get(TAG_UPLOADER_USER)\n password = uploader_config.get(TAG_UPLOADER_PASSWORD)\n request_sleep = uploader_config.get(TAG_UPLOADER_REQUEST_SLEEP)\n is_parent = uploader_config.get(TAG_UPLOADER_PARENT_ID, False)\n predefined_name = uploader_config.get(TAG_NAME, None)\n if not server:\n self.logger.error(\"Server was not provided for uploading results, skipping it.\")\n return\n if not identifier:\n self.logger.error(\"Job identifier was not provided for uploading results, skipping it.\")\n return\n if not user:\n self.logger.error(\"User name was not provided for uploading results, skipping it.\")\n return\n self.logger.info(\"Uploading results into server %s with identifier %s\", server, identifier)\n uploader = self.get_tool_path(self._get_tool_default_path(UPLOADER))\n uploader_python_path = os.path.abspath(os.path.join(os.path.dirname(uploader),\n os.path.pardir))\n commits = self.config.get(TAG_COMMITS)\n if commits:\n commit = commits[0]\n res = re.search(r'(\\w+)\\.\\.(\\w+)', commit)\n if res:\n commit = res.group(2)\n commits = commit[:7]\n timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y_%m_%d_%H_%M_%S')\n if predefined_name:\n job_name = predefined_name.replace(TIMESTAMP_PATTERN, timestamp)\n job_name = job_name.replace(RUNDEFINITION_PATTERN, self.job_name_suffix)\n job_name = job_name.replace(COMMIT_PATTERN, str(commits))\n elif commits:\n job_name = f\"{self.config_file}: {commits} ({timestamp})\"\n else:\n job_name = f\"{self.config_file} ({timestamp})\"\n self.logger.debug(\"Using name '%s' for uploaded report\", job_name)\n command = f\"PYTHONPATH={uploader_python_path} {uploader} '{identifier}' \" \\\n f\"--host='{server}' --username='{user}' --password='{password}' \" \\\n f\"--archive='{result_file}' --name='{job_name}'\"\n self.logger.debug(command)\n if request_sleep:\n command = f\"{command} --request-sleep {request_sleep}\"\n if is_parent:\n command = f\"{command} --copy\"\n try:\n subprocess.check_call(command, shell=True)\n self.logger.info(\"Results were successfully uploaded into the server: %s/jobs\", server)\n except Exception as any_exception:\n exception_msg = f\"Error on uploading of report archive '{result_file}' \"\\\n f\"via command '{command}': {any_exception}\\n\"\n self.logger.warning(exception_msg, exc_info=True)\n\n @staticmethod\n def _get_none_rule_key(verification_result: VerificationResults):\n return f\"{verification_result.id}_{verification_result.entrypoint}\"\n\n def _print_launches_report(self, file_name: str, report_resources: str, results: list,\n cov_lines: dict = None, cov_funcs: dict = None):\n self.logger.info(\"Preparing report on launches into file: '%s'\", file_name)\n with open(file_name, \"w\", encoding='ascii') as f_report, \\\n open(report_resources, \"w\", encoding='ascii') as f_resources:\n # Write header.\n f_report.write(\"Subsystem;Rule;Entrypoint;Verdict;Termination;CPU;Wall;Memory;\"\n \"Relevancy;Traces;Filtered traces;Work dir;Cov lines;Cov funcs;\"\n \"MEA time\\n\")\n f_resources.write(\"Counter;\" + \";\".join(ADDITIONAL_RESOURCES) + \"\\n\")\n counter = 1\n for result in results:\n # Add coverage information.\n if result.verdict == VERDICT_SAFE and not result.rule == PROPERTY_COVERAGE:\n key = self._get_none_rule_key(result)\n if not result.cov_lines and cov_lines:\n result.cov_lines = cov_lines.get(key, 0.0)\n if not result.cov_funcs and cov_funcs:\n result.cov_funcs = cov_funcs.get(key, 0.0)\n f_report.write(str(result) + \"\\n\")\n f_resources.write(f\"{counter};\" + result.print_resources() + \"\\n\")\n counter += 1\n\n def _get_results_names(self) -> tuple:\n reports_prefix = self._get_result_file_prefix()\n report_launches = os.path.join(self.results_dir, f\"report_launches_{reports_prefix}.csv\")\n result_archive = os.path.join(self.results_dir, f\"results_{reports_prefix}.zip\")\n report_components = os.path.join(self.results_dir,\n f\"report_components_{reports_prefix}.csv\")\n short_report = os.path.join(self.results_dir, f\"short_report_{reports_prefix}.csv\")\n report_resources = os.path.join(self.results_dir, f\"report_resources_{reports_prefix}.csv\")\n return report_launches, result_archive, report_components, short_report, report_resources\n","repo_name":"ispras/cv","sub_path":"scripts/components/launcher.py","file_name":"launcher.py","file_ext":"py","file_size_in_byte":13891,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"11668432477","text":"from typing import List\n\nclass Solution:\n def rotate(self, matrix: List[List[int]]) -> None:\n n = len(matrix)\n\n for y in range(n//2 + 1):\n for x in range(y, n-y-1):\n prev = matrix[y][x]\n curr = [y,x, prev]\n for _ in range(4):\n # 1) 다음 좌표를 계산한다.\n dy,dx = curr[1] - curr[0], n -1 - (curr[0] + curr[1])\n ny,nx = curr[0] + dy, curr[1] + dx\n\n # 2) 다음 좌표에 있는 값을 저장한다.\n prev = matrix[ny][nx]\n\n # 3) swap 한다.\n matrix[ny][nx] = curr[-1]\n\n # 4) 다음 좌표를 반영한다.\n curr = [ny,nx, prev]\n\n","repo_name":"mugglim/python-algo-study","sub_path":"categories/array/lc-array-48/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28947469931","text":"# -*- coding: utf-8 -*\nfrom __future__ import unicode_literals\nfrom threading import Timer\nimport re\nimport requests\nimport random\nimport schedule\nimport time\nimport datetime\nimport itchat\nfrom itchat.content import TEXT\nfrom itchat.content import *\nimport pymysql\nimport json\nimport threading\n#linux未更新\nimport cv2\nimport os\nfrom pyzbar.pyzbar import decode\n\n#网站正则\nurl_compile = re.compile(r\"(.*?http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*,]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)|([a-zA-Z]+.\\w+\\.+[a-zA-Z0-9\\/_]+.*)\", re.S)\n\n# 定向向标签为公众号的人发送信息\n\n\ndef thread_1():\n itchat.run()\ndef thread_2():\n while True:\n schedule.run_pending() # 确保schedule一直运行\n time.sleep(1)\n\n\ndef group(groupList):\n group = itchat.get_chatrooms(update=True)\n #print(group)\n from_group = ''\n groupUserName=[]\n for hufenGroup in groupList:\n for g in group:\n if g['NickName'] == hufenGroup: # 从群中找到指定的群聊\n from_group = g['UserName']\n data = {\"name\": hufenGroup, \"UserName\": from_group}\n groupUserName.append(data)\n return groupUserName\n\ndef getValues(groupUserName):\n # 打开数据库连接\n db = pymysql.connect(host='localhost', port=3306, user='root', passwd='admin', db='private', charset='utf8', #45.32.147.147 localhost\n cursorclass=pymysql.cursors.DictCursor) #cursorclass=pymysql.cursors.DictCursor代表返回json数据\n cursor = db.cursor()\n date = str(time.strftime('%Y-%m-%d', time.localtime(time.time())))\n for item in groupUserName:\n groupSql='%'+item['name' ]\n valueList=[]\n try:\n #sql = 'select @rowno:=@rowno+1 AS nm_order,f.* from( SELECT c.* FROM(SELECT d.*, DATE_FORMAT(d.DT_ENDDATE, \"%Y-%m-%d\") time FROM t_wechat_pinkread d WHERE nm_sid IN(SELECT t.id FROM(SELECT max(nm_sid) AS id, ST_WECHATID FROM t_wechat_pinkread GROUP BY ST_WECHATID ) t ) ) c WHERE c.time = \"'+date+'\" AND c.st_groupname LIKE \"'+groupSql+'\" ORDER BY c.ST_GZHNAME=\"求真帝\" desc,c.st_gzhtype ASC, c.nm_sid ASC)f,(select @rowno:=0) t '\n sql = 'select @rowno:=@rowno+1 AS nm_order,f.* from( SELECT c.* FROM(SELECT d.*, DATE_FORMAT(d.DT_ENDDATE, \"%Y-%m-%d\") time FROM t_wechat_pinkread d WHERE nm_sid IN(SELECT t.id FROM(SELECT max(nm_sid) AS id, ST_WECHATID FROM t_wechat_pinkread GROUP BY ST_WECHATID ) t ) ) c WHERE c.time = \"' + date + '\" AND c.st_groupname LIKE \"' + groupSql + '\" ORDER BY c.ST_GZHNAME=\"求真帝\" desc,c.nm_sid ASC)f,(select @rowno:=0) t '\n print(sql)\n cursor.execute(sql)\n valueList = cursor.fetchall()\n item['value']=valueList\n except:\n my_friend = itchat.search_friends(name='牧蓝天')[0]['UserName']\n itchat.send(item['name']+':数据库查询数据有误', my_friend)\n cursor.close()\n db.close()\n return groupUserName\n\n#每日规则和上报定时提醒\ndef job_tixing(groupUserName):\n date = str(time.strftime('%H:%M', time.localtime(time.time())))\n for item in groupUserName:\n try:\n itchat.send('定时提醒：'+date+'\\n\\n欢迎加入【壹周】，这是壹周的日常操作手册：\\nhttps://dwz.cn/xMRuLYcd\\n\\n常用链接:\\n1.文章上报▼\\nhttps://dwz.cn/Gp4tpKBP\\n2.加入打卡圈▼\\nhttp://t.cn/EVtfnnf', item['UserName']) #1.上周上报打卡统计结果▼\\nhttps://shimo.im/docs/2sBmrsZ73p4By3sx/\n time.sleep(1)\n itchat.send('~上周打卡统计已出炉，请红色标记人员尽快核实\\n\\n统计结果▼\\nhttps://shimo.im/docs/TqxeqFwrghkCfTMj/ ', item['UserName'])\n time.sleep(1)\n itchat.send('~欢迎大家拉人进群', item['UserName'])\n #picNamm = 'pic_qiandao' + item['name'][7:9] + '.jpg'\n #itchat.send_image(u'images/' + picNamm, item['UserName']) 签到小程序\n now_time = datetime.datetime.now()\n print('%s，已成功在%s发送提示消息' % (now_time,item['name']))\n except:\n my_friend= itchat.search_friends(name='牧蓝天')[0]['UserName']\n itchat.send('今天消息发送失败了',my_friend)\n\n# 趣信圈推广\ndef job_tixing2(groupUserName):\n date = str(time.strftime('%H:%M', time.localtime(time.time())))\n for item in groupUserName:\n try:\n itchat.send(\n '重要通知：\\n\\n现推出互阅方式升级版V2.0\\n\\n极速互阅，十篇换一篇\\n', #操作流程▼\\n1.扫码入圈、点击【公众号互粉互阅链接】、开始互阅\\n\\n额外提醒▼\\n1.目前测试阶段，有问题群内@丁丁猫儿\\n2.消息每半小时推送一次，若有打扰，还请见谅\\n3.大家周末愉快~~\\n\n item['UserName']) # 1.上周上报打卡统计结果▼\\nhttps://shimo.im/docs/2sBmrsZ73p4By3sx/\n time.sleep(2)\n itchat.send('1.点我开启极速互阅▼\\nhttps://cdn.xinmob.cn/flow/share/showSub?subId=5c766d5a76bd182bc45d358a&shareId=showSrc&from=groupmessage&isappinstalled=0', item['UserName'])\n time.sleep(2)\n itchat.send(\n '目前测试阶段，若有问题请@丁丁猫儿，欢迎大家参加~~\\n\\n每30分钟推送一次，若有打扰，请见谅~',item['UserName'])\n\n now_time = datetime.datetime.now()\n print('%s，已成功在%s发送提示消息' % (now_time, item['name']))\n except:\n my_friend = itchat.search_friends(name='牧蓝天')[0]['UserName']\n itchat.send('今天消息发送失败了', my_friend)\n\n#定时广告\ndef job_ad(groupUserName):\n for item in groupUserName:\n try:\n #itchat.send('~集中互阅、上报打卡、活跃互粉群~\\n\\n群详细介绍▼\\nhttps://dwz.cn/ukpsdtiL\\n\\n要加群的看我▼\\n人多了必需加微信拉：xigesi\\n\\n提示：加微信请备注一下：【互粉】两个字〜不然加微信的太多，很可能被忽略。', item['UserName'])\n itchat.send('~全网最高效的互粉群，限时加入！大家抓紧！', item['UserName'])\n time.sleep(1)\n itchat.send_image(u'images/ad1.png', item['UserName'])\n except:\n my_friend= itchat.search_friends(name='牧蓝天')[0]['UserName']\n itchat.send('今天在'+item['name']+'广告发送失败了',my_friend)\n\n# 定时广告\ndef job_ad2(groupUserName):\n for item in groupUserName:\n try:\n itchat.send(\n '重要通知：\\n\\n互阅方式大升级版\\n\\n极速互阅，攒卡发帖\\n',\n item['UserName']) # 1.上周上报打卡统计结果▼\\nhttps://shimo.im/docs/2sBmrsZ73p4By3sx/\n time.sleep(2)\n itchat.send(\n '1.点我开启极速互阅▼\\nhttps://cdn.xinmob.cn/flow/share/showSub?subId=5c766d5a76bd182bc45d358a&shareId=showSrc&from=groupmessage&isappinstalled=0',\n item['UserName'])\n time.sleep(2)\n itchat.send(\n '发布文章▼\\n点开上方链接后，打开第一篇文章查阅~~', item['UserName'])\n\n except:\n my_friend = itchat.search_friends(name='牧蓝天')[0]['UserName']\n itchat.send('今天在' + item['name'] + '广告发送失败了', my_friend)\n\n\n# 22：15填报提醒\ndef job_jihe(groupUserName):\n for item in groupUserName:\n try:\n #itchat.send('每日互阅内容传送词：爬楼梯（方便大家从聊天记录定位每日互阅链接）', item['UserName'])\n #time.sleep(3)\n itchat.send('21:00互阅即将开始，请大家做好准备\\n\\n注意事项：\\n\\n 1、白天有填报表单的朋友请准时参加，未填报表单的朋友不要求。\\n\\n 2、21:00会公布今日互阅内容，只互阅，互阅完后开始签到。\\n\\n3、签到结束后，自由发挥，该互粉的互粉。', item['UserName'])\n time.sleep(5)\n itchat.send('互阅签到规则-找寻宝藏：\\n\\n请在阅读文章过程中找到“宝藏图片A、B”，并截图。\\n\\nP.S：签到时候需要大家上传截图佐证，并在文本框输入截图对应阅读数。\\n\\n更多详情▼\\nhttps://dwz.cn/mHjLqbtB', item['UserName'])\n time.sleep(5)\n itchat.send('今日宝藏图片A▼', item['UserName'])\n time.sleep(5)\n itchat.send_image(u'images/baozangA.jpg', item['UserName'])\n time.sleep(5)\n itchat.send('今日宝藏图片B▼', item['UserName'])\n time.sleep(5)\n itchat.send_image(u'images/baozangB.jpg', item['UserName'])\n # itchat.send('请大家遵守规则，保持诚信！对于违规群员参考【壹周|规则说明】处理', item['UserName'])\n # itchat.send('【壹周|规则说明】:\\n https://dwz.cn/ukpsdtiL', item['UserName'])\n except:\n my_friend= itchat.search_friends(name='牧蓝天')[0]['UserName']\n itchat.send('20:55集合消息发送失败了',my_friend)\n\n#短网址生成\ndef get_short_url(longurl):\n querystring = {\"url\": longurl}\n url = \"http://suo.im/api.php\"\n\n response = requests.request(\"GET\", url, params=querystring)\n\n if response.status_code != 200:\n return longurl\n else:\n return response.text\n\n#推送今日互阅内容\ndef job_url(groupUserName):\n groupUserName = getValues(groupUserName)\n date = str(time.strftime('%Y-%m-%d', time.localtime(time.time())))\n #print(groupUserName)\n for item in groupUserName:\n #itchat.send('各位小伙伴们久等了，今晚的互粉互阅现在开始。\\n\\n截止到今晚10点，共收到互粉申请' + str(len(item['value']))+ '条，请依次打开链接阅读和关注。\\n\\n格式说明▼\\n编号-类型-群昵称-公众号名称：链接地址', item['UserName'])\n itchat.send('截止到 '+date+' 晚上8点，共收到互阅申请' + str(len(item['value'])) + '条，请依次打开链接阅读。\\n\\n今日宝藏图片▼\\n请往上翻阅聊天记录。\\n\\n快速定位关键词▼\\n「爬楼梯」', item['UserName'])\n time.sleep(1)\n content=''\n for val in item['value']:\n try:\n # val1=str(int(val['nm_order']))\n # val2=val['ST_GZHTYPE']\n # val3=val['ST_GROUPUSERNAME']\n # val4=val['ST_GZHNAME']\n # val5=val['ST_GZHURL']\n shortUrl=get_short_url(val['ST_GZHURL'])\n #content = '【' + str(int(val['nm_order'])) + '-' + val['ST_GZHTYPE'] + '-' + val['ST_GROUPUSERNAME'] + '-'+val['ST_GZHNAME'] + '】\\n' + val['ST_GZHURL']\n #content = '【' + str(int(val['nm_order'])) + '-' + val['ST_GZHTYPE'] + '-' + val[ 'ST_GROUPUSERNAME'] + '】\\n' + str(val['ST_GZHURL'])\n content += '【' + str(int(val['nm_order'])) + '-' + val['ST_GZHTYPE'] +'】\\n' + str(shortUrl)+'\\n\\n'\n if int(val['nm_order']) == 35:\n content += '\\n\\n(╯﹏╰)微信阅读冷却期1h，请后续内容间隔1小时后再进行阅读。为你带来的不便请见谅(╯﹏╰)\\n\\n快速定位关键词▼\\n「冷却期1」\\n\\n\\n\\n'\n elif int(val['nm_order'])== 70:\n content += '\\n\\n(╯﹏╰)微信阅读冷却期1h，请后续内容间隔1小时后再进行阅读。为你带来的不便请见谅(╯﹏╰)\\n\\n快速定位关键词▼\\n「冷却期2」\\n\\n\\n\\n'\n except:\n # my_friend= itchat.search_friends(name='牧蓝天')[0]['UserName']\n # itchat.send('推送互阅内容失败',my_friend)\n print('推送互阅内容失败')\n itchat.send(content, item['UserName'])\n #print(content)\n time.sleep(5)\n itchat.send('今日互阅内容到此结束，请浏览完所有链接的朋友进行签到（不用着急，当日24:00前签到即可）。感谢你的合作！\\n\\n签到说明：\\n①扫描二维码，点击打卡；\\n②文本框输入“A:XX B.XX”(XX代表A、B文章阅读量)，如果你有什么问题或意见也可以写出来\\n③上传A、B宝藏截图（完全COPY别人的图，发现一次就T）', item['UserName'])\n time.sleep(2)\n itchat.send('特别说明▼\\n非常重要！请保证【群昵称-上报填写的群昵称-小程序打卡昵称】三个一致！且不要包含特殊表情！', item['UserName'])\n time.sleep(2)\n itchat.send('打卡签到二维码▼', item['UserName'])\n picNamm = 'pic_qiandao'+item['name'][7:9]+'.jpg'\n itchat.send_image(u'images/'+picNamm,item['UserName'])\n time.sleep(2)\n itchat.send('打卡签到结果示例▼', item['UserName'])\n itchat.send_image(u'images/qiandao.png', item['UserName'])\n time.sleep(2)\n itchat.send('请群员们签到完成后自由发挥', item['UserName'])\n\n#自动回复\n@itchat.msg_register([TEXT, CARD,SHARING,PICTURE], isGroupChat=True)\ndef group_reply_media(msg):\n # 时间段\n d_time = datetime.datetime.strptime(str(datetime.datetime.now().date()) + '08:00', '%Y-%m-%d%H:%M')\n d_time1 = datetime.datetime.strptime(str(datetime.datetime.now().date()) + '21:00', '%Y-%m-%d%H:%M')\n # 当前时间\n n_time = datetime.datetime.now()\n # 消息来自于哪个群聊\n chatroom_id = msg['FromUserName']\n # 发送者的昵称\n # username = msg['ActualNickName'] #群聊昵称\n usernameList=[]\n for item in groupUserName:\n value=item['UserName']\n usernameList.append(value)\n # ①群判断：消息并不是来自于需要回复的群\n if not chatroom_id in usernameList:\n return\n # ②时间判断:判断当前时间是否在范围时间内\n if not(n_time > d_time and n_time < d_time1):\n return\n #③根据文本内容再判断\n replyText = '嘿，并不反对你互粉互阅，但只是希望能集中时间，晚上（20:55集合，21:00行动）大家集体行动，提高效率！\\n\\n对了，你可以将你需要互阅的内容上报一下，点这里▼\\nhttps://dwz.cn/Gp4tpKBP\\n\\n友情提示：还不听话会送飞机票哦~有什么问题可以看看群公告。'\n if msg['Type'] == TEXT:\n result = url_compile.search(msg['Content']) #判断文本内容是否有链接\n if result is not None:\n itchat.send(replyText, chatroom_id)\n else:\n itchat.send(replyText, chatroom_id)\n\n\n\nthreads = []\nt1 = threading.Thread(target=thread_1) #执行自动回复\nthreads.append(t1)\nt2 = threading.Thread(target=thread_2) #执行定时任务\nthreads.append(t2)\n\nif __name__ == \"__main__\":\n itchat.auto_login(hotReload=True)\n #itchat.auto_login(enableCmdQR=2, hotReload=True) #linux服务器\n print('已开启程序' )\n groupList=['【壹周|粉阅群02 20:55到场】','【壹周|粉阅群03 20:55到场】']\n #groupList = [ '【壹周|粉阅群03 20:55到场】']\n groupAd = [\"诚信者，行天下。互阅3群\",\"👏拒广告高效纯互阅群👏\",\"1🎉民众-【公众号】互粉群\",\"互阅互粉（禁砍价）\",\"😊互粉-需要开流量主私聊群主哦\",\"互粉互阅500群（关注阅读到几？）\",\"💪真诚互阅，回数字\",\"🚺🚹互粉1群（禁互阅、禁链接）\",\"精选高质量互粉\",\"互赞群 不要乱改群名\",'互粉互阅群不取关高质量1群','🐷 佛系互阅互粉互广②群','高质量公众号互粉群1',\"互关、互阅、广告！😊\",\"梦马●互粉\",\"最后的互阅群。\"]\n groupUserName=group(groupList) #获取需要管理的群UserName\n groupAdName=group(groupAd) #获取需要发送广告的的群UserName\n #job_ad(groupAdName)\n #job_tixing(groupUserName) #发送定时提醒和规则 #获取数据库里的数据】\n #job_tixing2(groupUserName) # 发送定时趣兴广告\n #job_jihe(groupUserName) #22:15集合提醒\n #job_url(groupUserName) #22:30发送互阅内容和签到图片\n\n schedule.every().day.at(\"08:00\").do(job_tixing,groupUserName)\n schedule.every().day.at(\"09:00\").do(job_tixing, groupUserName)\n schedule.every().day.at(\"10:00\").do(job_tixing,groupUserName)\n schedule.every().day.at(\"11:00\").do(job_tixing, groupUserName)\n schedule.every().day.at(\"12:00\").do(job_tixing,groupUserName)\n schedule.every().day.at(\"13:00\").do(job_tixing, groupUserName)\n schedule.every().day.at(\"14:00\").do(job_tixing,groupUserName)\n schedule.every().day.at(\"15:00\").do(job_tixing, groupUserName)\n schedule.every().day.at(\"16:00\").do(job_tixing,groupUserName)\n schedule.every().day.at(\"17:00\").do(job_tixing, groupUserName)\n schedule.every().day.at(\"18:00\").do(job_tixing,groupUserName)\n schedule.every().day.at(\"19:00\").do(job_tixing, groupUserName)\n schedule.every().day.at(\"20:00\").do(job_tixing, groupUserName)#规则和提醒每隔2H发送一次\n schedule.every().day.at(\"20:55\").do(job_jihe,groupUserName) # 规定每天提醒大家集合\n schedule .every().day.at(\"21:00\").do(job_url,groupUserName) # 规定每天执行公布内容\n schedule.every().day.at(\"23:59\").do(job_ad, groupAdName) # 每天定时发��告\n for t in threads:\n t.start()\n for t in threads:\n t.join()\n print (\"退出线程\")\n\n","repo_name":"qtianwai/Pythoon_pactice","sub_path":"practice/PR6_定时给微信发送信息/a壹周_指定微信群定时发送消息.py","file_name":"a壹周_指定微信群定时发送消息.py","file_ext":"py","file_size_in_byte":17308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36116580867","text":"\ndef get_weather_data(filename, devicename, days, skip_lines=0):\n import numpy as np\n #import time\n\n #timing1 = time.perf_counter()\n with open(filename, 'r', encoding=\"utf-8\") as fil:\n meas_time = []\n temp = []\n humidi = []\n pressure = []\n yLab = []\n lines=0\n nowTime = np.datetime64('now')\n timeDelta = np.timedelta64(days,'D')\n for count, line in enumerate(fil):\n splitter = line.split(',')\n if count==0:\n try:\n devInd = splitter.index(devicename)\n except ValueError:\n devInd = splitter.index(devicename+'\\n')\n elif count==1:\n xLab = \"Time\"\n #Manipulate labels to be more pleasing.\n brac_ind = splitter[3].find('[')\n yLab.append(splitter[3][:brac_ind] +' [°'+ splitter[3][brac_ind+1:])\n brac_ind = splitter[2].find('[')\n yLab.append(splitter[2][:brac_ind] +' '+ splitter[2][brac_ind:])\n brac_ind = splitter[4].find('[')\n yLab.append(splitter[4][:brac_ind] +' [k'+ splitter[4][brac_ind+1:]) \n pass\n else:\n if count10: #무한반복\n break\n for i in range(4):\n nrx, nry, rcnt = move(rx, ry, dx[i], dy[i]) #붉은 구술\n nbx, nby, bcnt = move(bx, by, dx[i], dy[i]) #파란 구슬\n\n if graph[nbx][nby] != \"O\": #파란 구슬이 구멍이 아니면\n if graph[nrx][nry] == \"O\" : #붉은 구슬이 구멍에 빠지면\n print(dis)\n return\n if nrx == nbx and nry == nby: #같은 위치에 구슬이 있다면\n if rcnt > bcnt: #붉은 구슬이 더 많이 움직였다면\n nrx -= dx[i] #붉은 구슬이 뒤에\n nry -= dy[i]\n else: #파란 구슬이 더 많이 움직였다면\n nbx -= dx[i] #파랑 구슬이 뒤에\n nby -= dy[i]\n if not visit[nrx][nry][nbx][nby]:\n visit[nrx][nry][nbx][nby] = True\n queue.append([nrx, nry, nbx, nby, dis+1])\n print(-1)\n\n#각 구슬 위치 알아내기\nfor i in range(n):\n for j in range(m):\n if graph[i][j] == 'R': #빨간색 구슬\n rx, ry = i, j\n if graph[i][j] == 'B': #파란색 구슬\n bx, by = i, j\n\nqueue = deque()\nqueue.append([rx,ry,bx,by,1]) #붉은구슬, 파란구슬 좌표, 방향을 바꾼 횟수\nvisit[rx][ry][bx][by] = True\nbfs()\n","repo_name":"cwaa079/BaekJoon-Algorithm","sub_path":"삼성 SW 역량 테스트 기출 문제/13460.py","file_name":"13460.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14663459644","text":"import logging\r\nimport os\r\nimport random\r\n\r\nimport moviepy.editor as mp\r\n\r\nfrom utilities.const import LOG_PATH\r\n\r\nlogging.basicConfig(level=logging.INFO, format='[%(levelname)s] %(message)s', filename=LOG_PATH)\r\n\r\n\r\nclass VideoProcessor:\r\n def __init__(self, _video_folder, _audio_file, _output_file):\r\n self.video_folder = _video_folder\r\n self.audio_file = _audio_file\r\n self.output_file = _output_file\r\n\r\n def process_video(self):\r\n if not self.video_folder or self.audio_file:\r\n logging.info(\"not found or audio_file\")\r\n logging.info(\"Step 1: Selecting a random video\")\r\n video_files = os.listdir(self.video_folder)\r\n selected_video = random.choice(video_files)\r\n video_path = os.path.join(self.video_folder, selected_video)\r\n\r\n logging.info(\"Step 2: Loading the audio file\")\r\n audio = mp.AudioFileClip(self.audio_file)\r\n audio_duration = audio.duration\r\n\r\n logging.info(\"Step 3: Loading the video clip\")\r\n video = mp.VideoFileClip(video_path)\r\n\r\n logging.info(\"Step 4: Resizing and cropping the video\")\r\n target_aspect_ratio = 9 / 16\r\n video_aspect_ratio = video.size[0] / video.size[1]\r\n\r\n if video_aspect_ratio > target_aspect_ratio:\r\n # The video is wider, crop the sides\r\n target_width = video.size[1] * target_aspect_ratio\r\n crop_left = (video.size[0] - target_width) / 2\r\n crop_right = crop_left + target_width\r\n video = video.crop(x1=crop_left, x2=crop_right)\r\n else:\r\n # The video is taller, crop the top and bottom\r\n target_height = video.size[0] / target_aspect_ratio\r\n crop_top = (video.size[1] - target_height) / 2\r\n crop_bottom = crop_top + target_height\r\n video = video.crop(y1=crop_top, y2=crop_bottom)\r\n\r\n video = video.resize(width=video.w, height=video.h).set_duration(audio_duration)\r\n\r\n logging.info(\"Step 5: Setting the audio for the final video\")\r\n final_video = video.set_audio(audio)\r\n\r\n logging.info(\"Step 6: Writing the final video\")\r\n final_video.write_videofile(self.output_file, codec='libx264', audio_codec='aac', fps=24)\r\n\r\n logging.info(\"Video processing completed successfully.\")\r\n return self.output_file\r\n\r\n\r\n# # Usage example:\r\n# video_folder = STOCK_VIDEO_FOLDER\r\n# audio_file = \"C:/YT/video/HowtoconnectaPS4controllertoaPC-16-05-2023.mp3\"\r\n# output_file = OUTPUT_TMP + \"output.mp4\"\r\n#\r\n# processor = VideoProcessor(video_folder, audio_file, output_file)\r\n# processed_file = processor.process_video()\r\n# print(\"Processed video file:\", processed_file)\r\n","repo_name":"Alfinjohnson/Auto-YouTube","sub_path":"video/create_vd.py","file_name":"create_vd.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"764119440","text":"import os\nimport glob\nimport hashlib\nimport json\nimport textwrap\nimport redis\nfrom transformers import AutoTokenizer, AutoModel\nfrom torch.nn.functional import normalize\nfrom torch import tensor\n\n\ndef read_text_files(directory):\n texts = []\n for file in glob.glob(os.path.join(directory, '*.txt')):\n with open(file, 'r', encoding='utf-8') as f:\n texts.append(f.read())\n\n print('Read', len(texts), 'documents')\n return texts\n\n\ndef create_bert_embeddings(model, texts):\n tokenizer = AutoTokenizer.from_pretrained(model)\n bert_model = AutoModel.from_pretrained(model)\n\n embeddings = []\n\n for text in texts:\n inputs = tokenizer(text, return_tensors='pt',\n truncation=True, padding=True)\n outputs = bert_model(**inputs)\n embeddings.append({\n \"values\": normalize(outputs.last_hidden_state.mean(\n dim=1), p=2).detach().numpy().tolist()[0]\n })\n\n return embeddings\n\n\ndef store_embeddings_in_redis(redis_connection, texts, embeddings):\n for i, (text, embedding) in enumerate(zip(texts, embeddings)):\n hash = hashlib.md5(text.encode('utf-8')).hexdigest()\n key = f'embedding:{hash}'\n embedding_json = json.dumps(embedding)\n redis_connection.hset(key, 'text', text)\n redis_connection.hset(key, 'embedding', embedding_json)\n print(f'Stored embedding for document {i} with key {key}')\n\n\ndef cosine_similarity(a, b):\n try:\n # Calculate the dot product of vectors a and b\n # Multiply each item in array 'a' with the corresponding item in array 'b' and then sum them\n dot_product = sum(ai * bi for ai, bi in zip(a, b))\n\n # Calculate the magnitude (Euclidean norm) of vector a\n # Square each item in array 'a', sum the squared values, and then take the square root of the sum\n magnitude_a = (sum(ai ** 2 for ai in a)) ** 0.5\n\n # Calculate the magnitude (Euclidean norm) of vector b\n # Square each item in array 'b', sum the squared values, and then take the square root of the sum\n magnitude_b = (sum(bi ** 2 for bi in b)) ** 0.5\n\n # Calculate the cosine similarity as the dot product divided by the product of magnitudes\n # Divide the dot product by the product of the magnitudes of vectors 'a' and 'b'\n return dot_product / (magnitude_a * magnitude_b)\n except Exception as e:\n print('Error computing cosine similarity:', e)\n return -1\n\n\n# Example usage:\na = [1, 2, 3]\nb = [4, 5, 6]\n\nsimilarity = cosine_similarity(a, b)\nprint('Cosine similarity:', similarity)\n\n\ndef retrieve_documents_and_compute_similarity(redis_connection, query_embedding):\n document_keys = redis_connection.keys('embedding:*')\n document_similarity_scores = []\n\n for key in document_keys:\n embedding_json = redis_connection.hget(key, 'embedding')\n document_embedding = json.loads(embedding_json)\n\n similarity = cosine_similarity(\n query_embedding['values'], document_embedding['values'])\n text = redis_connection.hget(key, 'text')\n document_similarity_scores.append((text, similarity))\n\n return document_similarity_scores\n\n\ndef choose_best_answer(scores: list[tuple[str, float]]):\n best_score = -1\n best_answer = None\n for text, score in scores:\n if score > best_score:\n best_score = score\n best_answer = text\n return best_answer\n\n\ndef main():\n directory = 'texts'\n texts = read_text_files(directory)\n\n model = 'sentence-transformers/all-MiniLM-L12-v2'\n try:\n embeddings = create_bert_embeddings(model, texts)\n except Exception as e:\n print('Error creating embeddings:', e)\n\n redis_url = 'redis://127.0.0.1/'\n r = redis.StrictRedis.from_url(redis_url)\n\n try:\n store_embeddings_in_redis(r, texts, embeddings)\n except Exception as e:\n print('Error storing embeddings in Redis:', e)\n\n query = 'TypeScript code'\n print(f'\\nSearching docs for {query}...')\n query_embeddings = create_bert_embeddings(model, [query])\n query_embedding = query_embeddings[0]\n\n scores = retrieve_documents_and_compute_similarity(r, query_embedding)\n\n best_answer = choose_best_answer(scores)\n decoded = best_answer.decode('utf-8')\n print(f'found:\\n{decoded}')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Sector-F-Labs/exp-005-berty-vector-search","sub_path":"snake/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15608324763","text":"import pypyodbc # doit être installé\nfrom tkinter import * # doit être installé\nimport datetime\nimport tkinter.ttk as ttk # doit être installé\nimport winreg\n\nlist_dsn = []\ni = 0\naccess_registry = winreg.ConnectRegistry(None, winreg.HKEY_CURRENT_USER)\nhkey = winreg.OpenKey(access_registry, 'SOFTWARE\\ODBC\\ODBC.ini')\n\ntry:\n while True:\n if winreg.EnumKey(hkey, i) == \"gdr\":\n pass\n else:\n list_dsn.append(winreg.EnumKey(hkey, i)) # Récupére les noms des dsn disponibles\n i += 1\nexcept OSError:\n pass\n\njourmodif = datetime.date.today()\njourmodif1 = str(jourmodif)\njourmodif1 = jourmodif1[:-6] # Récupération de l'année\njourmodif1 = int(jourmodif1)\njourdebut, moisdebut, anneedebut = list(range(1, 32)), list(range(1, 13)), list(range(2010, jourmodif1 + 1))\n\nprint(\"Connexion à la base gdr locale\")\nconnsource = pypyodbc.connect(\"DSN=GDR\")\ncursource = connsource.cursor()\nprint(\"Connexion ok\")\n\n\ndef echappement_carac(mot):\n if mot.find(\"'\"):\n mot = mot.replace(\"'\", \"\\\\'\")\n return mot\n\n\ndef creation_date(jour, mois, an):\n if int(jour) < 10:\n jour = jour.zfill(2)\n if int(mois) < 10:\n mois = mois.zfill(2)\n date_recherche = an + mois + jour\n return date_recherche\n\n\ndef analyseproduit(curdistant, date):\n print(\"analyse des produits\")\n print(\"détection des produits du serveur\")\n cursource.execute(\n f\"SELECT IDProduit FROM Produit WHERE idarrivage in (SELECT idarrivage from arrivage where date > {date})\")\n retour = cursource.fetchall()\n produitslocaux = []\n for prod in retour:\n produitslocaux.append(prod[0])\n print(\"Détection et injection des produits distants\")\n curdistant.execute(\n f\"SELECT IDproduit,IDEtat_produit,IDCatégorie,IDSous_catégorie,IDFlux,IDSortie,IDSTockage,IDArrivage,IDLot,Désignation,Poids,Commentaire,Volume,Nombre,Etiquette,Niveau_Valorisation,to_char(Date_sortie,'YYYYMMDD'),Prix_Etiquette,hauteur,largeur,Profondeur,DonnéesModèle,IDUnité,Promo,PourcPromo,PrixPromo,PrixUnitCollecte,NumTVA,LBE,PrixUnitaire,PoidsUnitaire,VolumeUnitaire,stocRestant,bGestionLot,to_char(DateLimiteValidité,'YYYMMDD'),to_char(Datelimitedistrib,'YYYYMMDD'),bGestionDates FROM Produit WHERE idarrivage in (SELECT idarrivage from arrivage where date > {date})\")\n produitsdistants = curdistant.fetchall()\n for pdist in produitsdistants:\n if pdist[0] not in produitslocaux:\n # for what are they for?\n \"\"\"des = echappement_carac(pdist[9])\n comm = echappement_carac(pdist[9])\"\"\"\n # remplacer par un join les multiples inserts avec {value[i]}\n cursource.execute(\n f\"INSERT INTO Produit(IDproduit,IDEtat_produit,IDCatégorie,IDSous_catégorie,IDFlux,IDSortie,IDSTockage,IDArrivage,IDLot,Poids,Volume,Nombre,Etiquette,Niveau_Valorisation,Date_sortie,Prix_Etiquette,hauteur,largeur,Profondeur,DonnéesModèle,IDUnité,Promo,PourcPromo,PrixPromo,PrixUnitCollecte,NumTVA,LBE,PrixUnitaire,PoidsUnitaire,VolumeUnitaire,stocRestant,bGestionLot,DateLimiteValidité,Datelimitedistrib,bGestionDates)values({pdist[0]},{pdist[1]},{pdist[2]},{pdist[3]},{pdist[4]},{pdist[5]},{pdist[6]},{pdist[7]},{pdist[8]},{pdist[10]},{pdist[12]},{pdist[13]},{pdist[14]},{pdist[15]},{pdist[16]},{pdist[17]},{pdist[18]},{pdist[19]},{pdist[20]},{pdist[21]},{pdist[22]},{pdist[23]},{pdist[24]},{pdist[25]},{pdist[26]},{pdist[27]},{pdist[28]},{pdist[29]},{pdist[30]},{pdist[31]},{pdist[32]},{pdist[33]},{pdist[34]},{pdist[35]},{pdist[36]})\")\n connsource.commit()\n print(\"terminé\")\n\n\ndef arrivage(curdistant, date):\n print(\"analyse des arrivages\")\n print(\"détection des arrivages du serveur\")\n cursource.execute(f\"SELECT IDarrivage FROM arrivage where date > {date}\")\n retour = cursource.fetchall()\n arrivlocaux = []\n for ar in retour:\n arrivlocaux.append(ar[0])\n print(\"Détection et injection des arrivages distants\")\n curdistant.execute(\n f\"SELECT IDarrivage ,to_char(Date,'YYYYMMDD'),origine,poids_total,cast(heure as character(5)),idcommune,Nombre,idtournée,volume_total,idclient,Nomposte,idsite FROM Arrivage where date > {date} \")\n arrivdistant = curdistant.fetchall()\n for ard in arrivdistant:\n if ard[0] not in arrivlocaux:\n cursource.execute(\n f\"insert into arrivage (IDarrivage ,Date,origine,poids_total,heure,idcommune,Nombre,idtournée,volume_total,idclient,Nomposte,idsite)values ({ard[0]},{ard[1]},'{ard[2]}',{ard[3]},{ard[4]},{ard[5]},{ard[6]},{ard[7]},{ard[8]},{ard[9]},'{ard[10]}',{ard[11]})\")\n cursource.commit()\n print(\"terminé\")\n\n\ndef clients(curdistant, date):\n print(\"analyse des clients\")\n print(\"détection des clients du serveur\")\n cursource.execute(\"SELECT IDClient FROM client\")\n retour = cursource.fetchall()\n clientlocaux = []\n for ct in retour:\n clientlocaux.append(ct[0])\n print(\"Détection et injection des clients distants\")\n curdistant.execute(\n \"SELECT Civilite,Idclient,Societe,Adresse,Pays,Nomclient,Telephone,EMail,Ville,Mobile,CodePostal,to_char(Saisile,'YYYYMMDD'),to_char(DateModif,'YYYYMMDD'),LivreMemeAdresse,FactureMemeadresse,Prénom,Ref_Magasin,TauxRemise,Mode_Réglement,Ref_collecte,Ref_sortiehorsmag,Habitation,Etage,longitude,latitude,Catégorie,IDGroupe_compte,IDcommune,CompteActif,IDSecteurCollecte,IDCedex,EtatAdhésion,Genre,indice FROM Client\")\n clientsdistant = curdistant.fetchall()\n for clt in clientsdistant:\n if clt[1] not in clientlocaux:\n \"\"\"society = echappement_carac(clt[2])\n name = echappement_carac(clt[5])\n name1 = echappement_carac(clt[15])\n city = echappement_carac(clt[8])\"\"\"\n cursource.execute(\n f\" insert into client (Civilite,Idclient,Societe,Adresse,Pays,Nomclient,Telephone,EMail,Ville,Mobile,CodePostal,Saisile,DateModif,LivreMemeAdresse,FactureMemeadresse,Prénom,Ref_Magasin,TauxRemise,Mode_Réglement,Ref_collecte,Ref_sortiehorsmag,Habitation,Etage,longitude,latitude,Catégorie,IDGroupe_compte,IDcommune,CompteActif,IDSecteurCollecte,IDCedex,EtatAdhésion,Genre,indice) values ({clt[0]},{clt[1]},'society','{clt[3]}','{clt[4]}','name','{clt[6]}','{clt[7]}','city','{clt[9]}','{clt[10]}',{clt[11]},{clt[12]},{clt[13]},{clt[14]},'name1','{clt[16]}',{clt[17]},'{clt[18]}','{clt[19]}',{clt[20]},{clt[21]},{clt[22]},{clt[23]},{clt[24]},{clt[25]},{clt[26]},{clt[27]},{clt[28]},{clt[29]},{clt[30]},{clt[31]},{clt[32]},'{clt[33]}')\")\n connsource.commit()\n print(\"terminé\")\n\n\ndef analysecaisse(curdistant, date):\n print(\"analyse des caisses\")\n print(\"détection des caisses du serveur\")\n cursource.execute(f\"SELECT IDcaisse FROM Caisse WHERE Date > {date}\")\n retour = cursource.fetchall()\n caisseslocales = []\n for caisse in retour:\n caisseslocales.append(caisse[0])\n print(\"Détection et injection des caisses distantes\")\n curdistant.execute(\n f\"SELECT idcaisse,to_char(date,'YYYYMMDD') , Montant,cast(heure as character(5)),nom_operateur,Prenom_operateur,Login_operateur,Operation,Ecart,cloture,caisse FROM caisse WHERE Date > {date} \")\n caissesdistantes = curdistant.fetchall()\n for caisse in caissesdistantes:\n if caisse[0] not in caisseslocales:\n cursource.execute(\n \"insert into caisse (idcaisse,Date,Montant,heure,Nom_Operateur,Prenom_Operateur,Login_Operateur,Operation,Ecart,Cloture,caisse) values (%s,%s,%s,'%s','%s','%s','%s','%s','%s','%s','%s')\" % \\\n (caisse[0], caisse[1], caisse[2], caisse[3], caisse[4], caisse[5], caisse[6], caisse[7], caisse[8],\n caisse[9], caisse[10]))\n connsource.commit()\n print(\"terminé\")\n\n\ndef avoir(curdistant, date):\n print(\"Analyse des avoirs\")\n print(\"Détection des avoirs du serveur\")\n cursource.execute(\"SELECT IDAvoir FROM avoir\")\n retour = cursource.fetchall()\n avoirlocaux = []\n for avr in retour:\n avoirlocaux.append(avr[0])\n print(\"Détection et injection des avoirs distants\")\n curdistant.execute(\n \"SELECT IDavoir,Indice,to_char(date,'YYYYMMDD'),IDclient,Montant,Utilise,Saisipar,to_char(dateUtilise,'YYYYMMDD'),Observations,Signature,cast(heure as character(5)),Caisse,cast(heureUtilise as character(5)),IDvente_magasin FROM avoir\")\n avoirsdistants = curdistant.fetchall()\n for avrdistant in avoirsdistants:\n if avrdistant[0] not in avoirlocaux:\n cursource.execute(\n f\"insert into avoir (IDavoir,Indice,date,IDclient,Montant,Utilise,Saisipar,dateUtilise,Observations,Signature,heure,Caisse,heureUtilise,IDvente_magasin) values ({avrdistant[0]},'{avrdistant[1]}',{avrdistant[2]},{avrdistant[3]},{avrdistant[4]},{avrdistant[5]},'{avrdistant[6]}','{avrdistant[7]}','{avrdistant[8]}','{avrdistant[9]}','{avrdistant[10]}','{avrdistant[11]}','{avrdistant[12]}',{avrdistant[13]}) \")\n connsource.commit()\n print(\"terminé\")\n\n\ndef regmultiple(curdistant, date):\n print(\"analyse des réglements multiples\")\n print(\"détection des réglements multiples du serveur\")\n cursource.execute(\"SELECT idrèglementmultiple FROM reglementmultiple\")\n retour = cursource.fetchall()\n reglocales = []\n for reg in retour:\n reglocales.append(reg[0])\n print(\"Détection et injection des règlements multiples distants\")\n curdistant.execute(\n \"SELECT idrèglementmultiple,Modereglement,Montant,IDVente_magasin,Signature,IDAvoir FROM reglementmultiple\")\n regdistants = curdistant.fetchall()\n for reg in regdistants:\n if reg[0] not in reglocales:\n cursource.execute(\n f\"insert into Reglementmultiple(idrèglementmultiple,Modereglement,Montant,IDVente_magasin,Signature,IDAvoir) values ({reg[0]},'{reg[1]}',{reg[2]},{reg[3]},'{reg[4]}',{reg[5]})\")\n connsource.commit()\n print(\"terminé\")\n\n\ndef analysemonnaie(curdistant, date):\n print(\"analyse des monnaies caisses\")\n print(\"détection des monnaies caisses du serveur\")\n cursource.execute(\"SELECT idmonnaiecaisse from MonnaieCaisse\")\n retour = cursource.fetchall()\n mcaisseslocales = []\n for moncaisse in retour:\n mcaisseslocales.append(moncaisse[0])\n print(\"Détection des monnaies caisses du serveur distant\")\n curdistant.execute(\n \"SELECT IDMonnaieCaisse,IDCaisse,P1c,P2c,P5c,P10c,P20c,P50c,P1e,P2e,B5e,B10e,B20e,B50e,B100e,B200e,B500e FROM MOnnaieCaisse\")\n monnaiesdistantes = curdistant.fetchall()\n for mo in monnaiesdistantes:\n if mo[0] not in mcaisseslocales:\n cursource.execute(\n f\"insert into monnaiecaisse(IDMonnaieCaisse,IDCaisse,P1c,P2c,P5c,P10c,P20c,P50c,P1e,P2e,B5e,B10e,B20e,B50e,B100e,B200e,B500e) values ({mo[0]},{mo[1]},{mo[2]},{mo[3]},{mo[4]},{mo[5]},{mo[6]},{mo[7]},{mo[8]},{mo[9]},{mo[10]},{mo[11]},{mo[12]},{mo[13]},{mo[14]},{mo[15]},{mo[16]})\")\n connsource.commit()\n print(\"terminé\")\n\n\ndef analysevente(curdistant, date):\n print(\"Analyse des ventes\")\n print(\"détection des ventes du serveur\")\n cursource.execute(f\"SELECT idvente_magasin from vente_magasin WHERE Date > {date} \")\n retour = cursource.fetchall()\n venteslocales = []\n for ventes in retour:\n venteslocales.append(ventes[0])\n print(\"Détection et injection des ventes du serveur distant\")\n curdistant.execute(\n f\"select idvente_magasin, to_char(date,'YYYYMMDD'), idclient,code_postal,ville,cast(heure as character(5)) ,montant_total,tauxremise,mode_reglement,poids_total,montant_especes,tauxtva,etat,Caisse,idavoir,idcaisse,signature,Montanttva,idacompte,idsite from vente_magasin WHERE Date > {date}\")\n ventesdistantes = curdistant.fetchall()\n for vt in ventesdistantes:\n if vt[0] not in venteslocales:\n cursource.execute(\n f\"insert into vente_magasin (idvente_magasin,date,idclient,code_postal,ville,heure,montant_total,tauxremise,mode_reglement,poids_total,montant_especes,idcaisse,caisse,tauxtva,etat,idavoir,signature,montanttva,idacompte,idsite) values({vt[0]},{vt[1]},'{vt[2]}','{vt[3]}','{vt[4]}','{vt[5]}',{vt[6]},'{vt[7]}','{vt[8]}','{vt[9]}','{vt[10]}','{vt[15]}','{vt[13]}',{vt[11]},{vt[12]},{vt[14]},'{vt[16]}',{vt[17]},{vt[18]},{vt[19]}) \")\n connsource.commit()\n print(\"terminé\")\n\n\ndef lignevente(curdistant, date):\n print(\"Analyse des lignes de vente\")\n print(\"détection des lignes ventes du serveur\")\n cursource.execute(\n f\"SELECT idlignes_vente from lignes_vente where idvente_magasin in (SELECT idvente_magasin from Vente_magasin WHERE date > {date})\")\n retour = cursource.fetchall()\n lignesventeslocales = []\n for lignes in retour:\n lignesventeslocales.append(lignes[0])\n print(\"Détection et injection des lignes ventes du serveur distant\")\n curdistant.execute(\n f\"SELECT IDLignes_Vente,IDproduit,IDcatégorie,IDSous_catégorie,IDVente_magasin,Montant,Nombre,Poids,Saisie_CatégorieProduit,Volume,Hauteur,Largeur,Profondeur,Tauxremise,Montant_Remise,IDunité,Promo,Signature,Etat,TauxTva,MontantTva from lignes_Vente where idvente_magasin in (SELECT idvente_magasin from Vente_magasin WHERE date > {date})\")\n lignesdistantes = curdistant.fetchall()\n for ld in lignesdistantes:\n if ld[0] not in lignesventeslocales:\n print(\n f\"{ld[0]},{ld[1]},{ld[2]},{ld[3]},{ld[4]},{ld[5]},{ld[6]},{ld[7]},{ld[8]},{ld[9]},{ld[10]},{ld[11]},{ld[12]},{ld[13]},{ld[14]},{ld[15]},{ld[16]},'{ld[17]}',{ld[18]},{ld[19]},{ld[20]}\")\n cursource.execute(\n f\"insert into Lignes_vente(IDLignes_Vente,IDproduit,IDcatégorie,IDSous_catégorie,IDVente_magasin,Montant,Nombre,Poids,Saisie_CatégorieProduit,Volume,Hauteur,Largeur,Profondeur,Tauxremise,Montant_Remise,IDunité,Promo,Signature,Etat,TauxTva,MontantTva) values ({ld[0]},{ld[1]},{ld[2]},{ld[3]},{ld[4]},{ld[5]},{ld[6]},{ld[7]},{ld[8]},{ld[9]},{ld[10]},{ld[11]},{ld[12]},{ld[13]},{ld[14]},{ld[15]},{ld[16]},'{ld[17]}',{ld[18]},{ld[19]},{ld[20]})\")\n connsource.commit()\n print(\"terminé\")\n\n\ndef connexion(fichier, day, month, year, nom_dsn):\n print(nom_dsn)\n dte = creation_date(day, month, year)\n print(\"Connexion à la base gdr distante\")\n conndistant = pypyodbc.connect(f\"DSN={nom_dsn}\")\n curdistant = conndistant.cursor()\n print(\"Connexion ok\")\n if fichier == \"caisse\":\n analysecaisse(curdistant, dte)\n if fichier == \"vente\":\n analysevente(curdistant, dte)\n if fichier == \"lignes\":\n lignevente(curdistant, dte)\n if fichier == \"reg\":\n regmultiple(curdistant, dte)\n if fichier == \"avoir\":\n avoir(curdistant, dte)\n if fichier == \"monnaie_caisse\":\n analysemonnaie(curdistant, dte)\n if fichier == \"clientele\":\n clients(curdistant, dte)\n if fichier == \"arrivee\":\n arrivage(curdistant, dte)\n if fichier == \"prods\":\n analyseproduit(curdistant, dte)\n\n\nfen = Tk()\nfor i in range(5):\n fen.columnconfigure(i, weight=1)\nfor i in range(4):\n fen.rowconfigure(i, weight=1)\n\nfen.geometry(\"800x600\")\nfen.title(\"replication\")\ndsn_label = Label(fen, text=\"choix du dsn distant\")\ndsn_label.grid(row=0, column=1)\nlist_dsn_combo = ttk.Combobox(fen, values=list_dsn)\nlist_dsn_combo.grid(row=0, column=2)\n# sai_dsn=Entry(fen)\n# sai_dsn.grid(row=1,column=1)\ndateanalyse = Label(fen, text=\"Date de début analyse\")\ndateanalyse.grid(row=1, column=1)\nsaisiejour = ttk.Combobox(fen, values=jourdebut, width=5)\nsaisiejour.grid(row=1, column=2)\nsaisiemois = ttk.Combobox(fen, values=moisdebut, width=5)\nsaisiemois.grid(row=1, column=3)\nsaisieannee = ttk.Combobox(fen, values=anneedebut, width=10)\nsaisieannee.grid(row=1, column=4)\ncontrole_caisse = Button(fen, text=\"Caisse\",\n command=lambda: connexion(\"caisse\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get()))\ncontrole_caisse.grid(row=3, column=1)\ncontrole_vente = Button(fen, text=\"ventes\",\n command=lambda: connexion(\"vente\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get()))\ncontrole_vente.grid(row=2, column=2)\ncontrole_ligne = Button(fen, text=\"Lignes ventes\",\n command=lambda: connexion(\"lignes\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get()))\ncontrole_ligne.grid(row=2, column=3)\ncontrole_reg = Button(fen, text=\"reg multiples\",\n command=lambda: connexion(\"reg\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get()))\ncontrole_reg.grid(row=2, column=4)\ncontrole_avoir = Button(fen, text=\"avoir\",\n command=lambda: connexion(\"avoir\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get()))\ncontrole_avoir.grid(row=3, column=2)\ncontrole_monnaie = Button(fen, text=\"Monnaie caisse\",\n command=lambda: connexion(\"monnaie_caisse\", saisiejour.get(), saisiemois.get(),\n saisieannee.get(), list_dsn_combo.get()))\ncontrole_monnaie.grid(row=3, column=3)\ncontrole_client = Button(fen, text=\"clients\",\n command=lambda: connexion(\"clientele\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get(), list_dsn_combo.get()))\ncontrole_client.grid(row=2, column=1)\ncontrole_arrivage = Button(fen, text=\"arrivages\",\n command=lambda: connexion(\"arrivee\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get()))\ncontrole_arrivage.grid(row=4, column=1)\ncontrole_produit = Button(fen, text=\"produits\",\n command=lambda: connexion(\"prods\", saisiejour.get(), saisiemois.get(), saisieannee.get(),\n list_dsn_combo.get()))\ncontrole_produit.grid(row=4, column=2)\nfen.mainloop()\n","repo_name":"Cap3c/home-made-replication","sub_path":"python_gdr_replic.py","file_name":"python_gdr_replic.py","file_ext":"py","file_size_in_byte":18209,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7848258657","text":"import pytest\n\nfrom math_a_dos.DTO.repository.game_repository_in_memory import GameRepositoryInMemory\nfrom math_a_dos.action.create_game.create_game_command import CreateGame\n\n\n@pytest.mark.parametrize(\"players, cases\", [(1, 10), (2, 30)])\ndef test_create_board(players, cases):\n board_repo = GameRepositoryInMemory()\n command = CreateGame(board_repo)\n\n command.execute(cases, players)\n\n game = board_repo.get_game_by_id(0)\n\n assert (len(game.dices) == 5 and game.result_dice is not None and game.moving_dice is not None and\n len(game.players) == players and len(game.cases) == cases)\n","repo_name":"aflori/math-a-dos","sub_path":"math_a_dos/action/create_game/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25830098804","text":"import os\nimport psutil\nimport json\nimport datetime\nfrom subprocess import call, check_output\nfrom .rest_utils import rest_get, rest_delete, check_rest_server_quick, check_response\nfrom .config_utils import Config, Experiments\nfrom .url_utils import trial_jobs_url, experiment_url, trial_job_id_url\nfrom .constants import NNICTL_HOME_DIR, EXPERIMENT_ID_INFO\nimport time\nfrom .common_utils import print_normal, print_error, detect_process\n\ndef get_experiment_port(args):\n '''get the port of an experiment'''\n experiment_config = Experiments()\n experiment_dict = experiment_config.get_all_experiments()\n #1.If there is an id specified, return the corresponding port\n #2.If there is no id specified, and there is an experiment running, return it as default port, or return Error\n #3.If the id matches an experiment, nnictl will return the id.\n #4.If the id ends with *, nnictl will match all ids matchs the regular\n #5.If the id does not exist but match the prefix of an experiment id, nnictl will return the matched id\n #6.If the id does not exist but match multiple prefix of the experiment ids, nnictl will give id information\n #7.Users could use 'nnictl stop all' to stop all experiments \n if not experiment_dict:\n print_normal('Experiment is not running...')\n return None\n if not args.id and len(experiment_dict.keys()) > 1:\n print_error('There are multiple experiments running, please set the experiment id...')\n experiment_information = \"\"\n for key in experiment_dict.keys():\n experiment_information += ('Id: ' + key + ' StartTime: ' + experiment_dict[key][1] + '\\n')\n print(EXPERIMENT_ID_INFO % experiment_information)\n return None\n if not args.id:\n return list(experiment_dict.values())[0][0]\n if experiment_dict.get(args.id):\n return experiment_dict[args.id][0]\n else:\n print_error('Id not correct!') \n return None\n\ndef convert_time_stamp_to_date(content):\n '''Convert time stamp to date time format'''\n start_time_stamp = content.get('startTime')\n end_time_stamp = content.get('endTime')\n if start_time_stamp:\n start_time = datetime.datetime.utcfromtimestamp(start_time_stamp // 1000).strftime(\"%Y/%m/%d %H:%M:%S\")\n content['startTime'] = str(start_time)\n if end_time_stamp:\n end_time = datetime.datetime.utcfromtimestamp(end_time_stamp // 1000).strftime(\"%Y/%m/%d %H:%M:%S\")\n content['endTime'] = str(end_time)\n return content\n\ndef check_rest(args):\n '''check if restful server is running'''\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n rest_port = nni_config.get_config('restServerPort')\n running, _ = check_rest_server_quick(rest_port)\n if not running:\n print_normal('Restful server is running...')\n else:\n print_normal('Restful server is not running...')\n\ndef parse_ids(args):\n '''Parse the arguments for nnictl stop'''\n experiment_config = Experiments()\n experiment_dict = experiment_config.get_all_experiments()\n if not experiment_dict:\n print_normal('Experiment is not running...')\n return None\n experiment_id_list = list(experiment_dict.keys())\n result_list = []\n if not args.id:\n if len(experiment_id_list) > 1:\n print_error('There are multiple experiments running, please set the experiment id...')\n experiment_information = \"\"\n for key in experiment_dict.keys():\n experiment_information += ('Id: ' + key + ' StartTime: ' + experiment_dict[key][1] + '\\n')\n print(EXPERIMENT_ID_INFO % experiment_information)\n return None\n result_list = experiment_id_list\n elif args.id == 'all':\n result_list = experiment_id_list\n elif args.id.endswith('*'):\n for id in experiment_id_list:\n if id.startswith(args.id[:-1]):\n result_list.append(id)\n elif args.id in experiment_id_list:\n result_list.append(args.id)\n else:\n for id in experiment_id_list:\n if id.startswith(args.id):\n result_list.append(id)\n if len(result_list) > 1:\n print_error(args.id + ' is ambiguous, please choose ' + ' '.join(result_list) )\n return None\n if not result_list:\n print_error('There are no experiments matched, please check experiment id...')\n return result_list\n\ndef stop_experiment(args):\n '''Stop the experiment which is running'''\n experiment_id_list = parse_ids(args)\n if experiment_id_list:\n experiment_config = Experiments()\n experiment_dict = experiment_config.get_all_experiments()\n for experiment_id in experiment_id_list:\n port = experiment_dict.get(experiment_id)[0]\n if port is None:\n return None\n print_normal('Stoping experiment %s' % experiment_id)\n nni_config = Config(port)\n rest_port = nni_config.get_config('restServerPort')\n rest_pid = nni_config.get_config('restServerPid')\n if not detect_process(rest_pid):\n print_normal('Experiment is not running...')\n return\n running, _ = check_rest_server_quick(rest_port)\n stop_rest_result = True\n if running:\n response = rest_delete(experiment_url(rest_port), 20)\n if not response or not check_response(response):\n print_error('Stop experiment failed!')\n stop_rest_result = False\n #sleep to wait rest handler done\n time.sleep(3)\n rest_pid = nni_config.get_config('restServerPid')\n if rest_pid:\n cmds = ['pkill', '-P', str(rest_pid)]\n call(cmds)\n if stop_rest_result:\n print_normal('Stop experiment success!')\n experiment_config = Experiments()\n experiment_config.remove_experiment(experiment_id)\n\ndef trial_ls(args):\n '''List trial'''\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n rest_port = nni_config.get_config('restServerPort')\n rest_pid = nni_config.get_config('restServerPid')\n if not detect_process(rest_pid):\n print_error('Experiment is not running...')\n return\n running, response = check_rest_server_quick(rest_port)\n if running:\n response = rest_get(trial_jobs_url(rest_port), 20)\n if response and check_response(response):\n content = json.loads(response.text)\n for index, value in enumerate(content): \n content[index] = convert_time_stamp_to_date(value)\n print(json.dumps(content, indent=4, sort_keys=True, separators=(',', ':')))\n else:\n print_error('List trial failed...')\n else:\n print_error('Restful server is not running...')\n\ndef trial_kill(args):\n '''List trial'''\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n rest_port = nni_config.get_config('restServerPort')\n rest_pid = nni_config.get_config('restServerPid')\n if not detect_process(rest_pid):\n print_error('Experiment is not running...')\n return\n running, _ = check_rest_server_quick(rest_port)\n if running:\n response = rest_delete(trial_job_id_url(rest_port, args.trialid), 20)\n if response and check_response(response):\n print(response.text)\n else:\n print_error('Kill trial job failed...')\n else:\n print_error('Restful server is not running...')\n\ndef list_experiment(args):\n '''Get experiment information'''\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n rest_port = nni_config.get_config('restServerPort')\n rest_pid = nni_config.get_config('restServerPid')\n if not detect_process(rest_pid):\n print_error('Experiment is not running...')\n return\n running, _ = check_rest_server_quick(rest_port)\n if running:\n response = rest_get(experiment_url(rest_port), 20)\n if response and check_response(response):\n content = convert_time_stamp_to_date(json.loads(response.text))\n print(json.dumps(content, indent=4, sort_keys=True, separators=(',', ':')))\n else:\n print_error('List experiment failed...')\n else:\n print_error('Restful server is not running...')\n\ndef experiment_status(args):\n '''Show the status of experiment'''\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n rest_port = nni_config.get_config('restServerPort')\n result, response = check_rest_server_quick(rest_port)\n if not result:\n print_normal('Restful server is not running...')\n else:\n print(json.dumps(json.loads(response.text), indent=4, sort_keys=True, separators=(',', ':')))\n\ndef get_log_content(file_name, cmds):\n '''use cmds to read config content'''\n if os.path.exists(file_name):\n rest = check_output(cmds)\n print(rest.decode('utf-8'))\n else:\n print_normal('NULL!')\n\ndef log_internal(args, filetype):\n '''internal function to call get_log_content'''\n port = get_experiment_port(args)\n if port is None:\n return None\n if filetype == 'stdout':\n file_full_path = os.path.join(NNICTL_HOME_DIR, str(port), 'stdout')\n else:\n file_full_path = os.path.join(NNICTL_HOME_DIR, str(port), 'stderr')\n if args.head:\n get_log_content(file_full_path, ['head', '-' + str(args.head), file_full_path])\n elif args.tail:\n get_log_content(file_full_path, ['tail', '-' + str(args.tail), file_full_path])\n elif args.path:\n print_normal('The path of stdout file is: ' + file_full_path)\n else:\n get_log_content(file_full_path, ['cat', file_full_path])\n\ndef log_stdout(args):\n '''get stdout log'''\n log_internal(args, 'stdout')\n\ndef log_stderr(args):\n '''get stderr log'''\n log_internal(args, 'stderr')\n\ndef log_trial(args):\n ''''get trial log path'''\n trial_id_path_dict = {}\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n rest_port = nni_config.get_config('restServerPort')\n rest_pid = nni_config.get_config('restServerPid')\n if not detect_process(rest_pid):\n print_error('Experiment is not running...')\n return\n running, response = check_rest_server_quick(rest_port)\n if running:\n response = rest_get(trial_jobs_url(rest_port), 20)\n if response and check_response(response):\n content = json.loads(response.text)\n for trial in content:\n trial_id_path_dict[trial['id']] = trial['logPath']\n else:\n print_error('Restful server is not running...')\n exit(1)\n if args.id:\n if trial_id_path_dict.get(args.id):\n print('id:' + args.id + ' path:' + trial_id_path_dict[args.id])\n else:\n print_error('trial id is not valid!')\n exit(1)\n else:\n for key in trial_id_path_dict.keys():\n print('id:' + key + ' path:' + trial_id_path_dict[key])\n\ndef get_config(args):\n '''get config info'''\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n print(nni_config.get_all_config())\n\ndef webui_url(args):\n '''show the url of web ui'''\n port = get_experiment_port(args)\n if port is None:\n return None\n nni_config = Config(port)\n print_normal('{0} {1}'.format('Web UI url:', ' '.join(nni_config.get_config('webuiUrl'))))\n\ndef experiment_id(args):\n '''get the id of all experiments'''\n experiment_config = Experiments()\n experiment_dict = experiment_config.get_all_experiments()\n if not experiment_dict:\n print('There is no experiment running...')\n else:\n experiment_information = \"\"\n for key in experiment_dict.keys():\n experiment_information += ('Id: ' + key + ' StartTime: ' + experiment_dict[key][1] + '\\n')\n print(EXPERIMENT_ID_INFO % experiment_information)","repo_name":"18600597055/nni","sub_path":"tools/nnicmd/nnictl_utils.py","file_name":"nnictl_utils.py","file_ext":"py","file_size_in_byte":12300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"12669779683","text":"import datetime\nfrom collections import namedtuple, defaultdict\nfrom functools import cached_property\nfrom typing import List, Dict, Optional, Tuple\nimport numpy as np\nfrom beancount.core import data, getters, prices, convert\nfrom beancount.core.number import ZERO\nfrom beancount.core.inventory import Inventory\nfrom beangrow import investments\nimport beangrow.config as configlib\nimport beangrow.returns as returnslib\nfrom beangrow.reports import (\n Table,\n compute_returns_table,\n get_calendar_intervals,\n get_cumulative_intervals,\n get_accounts_table,\n)\nfrom fava.ext import FavaExtensionBase\nfrom fava.helpers import FavaAPIError\nfrom fava.context import g\n\nConfig = namedtuple(\"Config\", [\"beangrow_config_path\", \"beangrow_debug_dir\"])\n\n\nclass FavaPortfolioReturns(FavaExtensionBase):\n report_title = \"Portfolio Returns\"\n has_js_module = True\n\n @cached_property\n def ext_config(self) -> Config:\n cfg = self.config if isinstance(self.config, dict) else {}\n beangrow_debug_dir = self.config.get(\"beangrow_debug_dir\")\n if beangrow_debug_dir:\n beangrow_debug_dir = self.ledger.join_path(beangrow_debug_dir)\n\n return Config(\n beangrow_config_path=self.ledger.join_path(\n cfg.get(\"beangrow_config\", \"beangrow.pbtxt\")\n ),\n beangrow_debug_dir=beangrow_debug_dir,\n )\n\n def extract(\n self, end_date\n ) -> Tuple[\n returnslib.Pricer, Dict, Dict[investments.Account, investments.AccountData]\n ]:\n entries = self.ledger.all_entries\n accounts = getters.get_accounts(entries)\n dcontext = self.ledger.options[\"dcontext\"]\n\n price_map = prices.build_price_map(entries)\n pricer = returnslib.Pricer(price_map)\n\n try:\n beangrow_config = configlib.read_config(\n self.ext_config.beangrow_config_path, None, accounts\n )\n except Exception as ex:\n raise FavaAPIError(\n f\"Cannot read beangrow configuration file {self.ext_config.beangrow_config_path}: {ex}\"\n )\n\n # Extract data from the ledger.\n account_data_map = investments.extract(\n entries,\n dcontext,\n beangrow_config,\n end_date,\n False,\n self.ext_config.beangrow_debug_dir,\n )\n\n return pricer, beangrow_config.groups.group, account_data_map\n\n @staticmethod\n def get_target_currency(adlist: List[investments.AccountData]) -> str:\n cost_currencies = set(ad.cost_currency for ad in adlist)\n if len(cost_currencies) != 1:\n raise FavaAPIError(\n \"Incompatible cost currencies {} for accounts {}\".format(\n \", \".join(cost_currencies), \", \".join([ad.account for ad in adlist])\n )\n )\n return cost_currencies.pop()\n\n @staticmethod\n def get_only_amount(inventory):\n pos = inventory.get_only_position()\n return pos.units if pos else None\n\n def calculate_group_performance(\n self,\n pricer: returnslib.Pricer,\n adlist: List[investments.AccountData],\n start_date: datetime.date,\n end_date: datetime.date,\n target_currency: Optional[str] = None,\n ):\n if not target_currency:\n target_currency = self.get_target_currency(adlist)\n\n units = Inventory() # commodities of this group\n cash_in_balance = Inventory()\n cash_out_balance = Inventory()\n\n for account_data in adlist:\n for flow in account_data.cash_flows:\n if flow.amount.number >= 0:\n cash_out_balance.add_amount(flow.amount)\n else:\n cash_in_balance.add_amount(-flow.amount)\n units.add_inventory(account_data.balance.reduce(convert.get_units))\n\n cash_in = self.get_only_amount(cash_in_balance)\n cash_out = self.get_only_amount(cash_out_balance)\n\n value_balance = units.reduce(convert.get_value, pricer.price_map, end_date)\n market_value_balance = value_balance.reduce(\n convert.convert_position, target_currency, pricer.price_map\n )\n market_value = self.get_only_amount(market_value_balance)\n\n returns_balance = market_value_balance + cash_out_balance + -cash_in_balance\n returns = self.get_only_amount(returns_balance)\n returns_pct = returns.number / cash_in.number\n\n truncated_cash_flows = returnslib.truncate_and_merge_cash_flows(\n pricer, adlist, start_date, end_date\n )\n irr = returnslib.compute_returns(\n truncated_cash_flows, pricer, target_currency, end_date\n )\n\n return {\n \"units\": units,\n \"cash_in\": cash_in,\n \"cash_out\": cash_out,\n \"market_value\": market_value,\n \"returns\": returns,\n \"returns_pct\": returns_pct,\n \"irr\": irr.total,\n }\n\n def overview(self):\n start_date = g.filtered._date_first\n end_date = g.filtered._date_last - datetime.timedelta(days=1)\n pricer, groups, account_data_map = self.extract(end_date)\n\n group_performances = []\n for group in groups:\n adlist = [\n account_data_map[name]\n for name in group.investment\n if name in account_data_map\n ]\n if not adlist:\n continue\n\n performance = self.calculate_group_performance(\n pricer, adlist, start_date, end_date, group.currency\n )\n group_performances.append(dict(name=group.name, **performance))\n\n return group_performances\n\n def create_plots(\n self,\n price_map: prices.PriceMap,\n target_currency: str,\n flows: List[investments.CashFlow],\n transactions: data.Entries,\n returns_rate: float,\n ) -> Dict[str, str]:\n # Group flows by date and accumulate div/exdiv flows.\n flowsByDate = defaultdict(list)\n flowsDivByDate = defaultdict(lambda: ZERO)\n flowsExDivByDate = defaultdict(lambda: ZERO)\n for flow in flows:\n flowsByDate[flow.date].append(flow)\n if flow.is_dividend:\n flowsDivByDate[flow.date] += flow.amount.number\n else:\n flowsExDivByDate[flow.date] += flow.amount.number\n\n # Render cash flows.\n cashflows_plot = {\n \"div\": list(flowsDivByDate.items()),\n \"exdiv\": list(flowsExDivByDate.items()),\n }\n\n # Render cumulative cash flows, with returns growth.\n cumvalue_plot = {}\n dates = [f.date for f in flows]\n if dates:\n date_min = dates[0] - datetime.timedelta(days=1)\n date_max = dates[-1]\n num_days = (date_max - date_min).days\n dates_all = [dates[0] + datetime.timedelta(days=x) for x in range(num_days)]\n gamounts = np.zeros(num_days)\n amounts = np.zeros(num_days)\n rate = (1 + returns_rate) ** (1.0 / 365)\n for flow in flows:\n remaining_days = (date_max - flow.date).days\n amt = -float(flow.amount.number)\n if remaining_days > 0:\n gflow = amt * (rate ** np.arange(0, remaining_days))\n gamounts[-remaining_days:] = np.add(\n gamounts[-remaining_days:],\n gflow,\n out=gamounts[-remaining_days:],\n casting=\"unsafe\",\n )\n amounts[-remaining_days:] += amt\n else:\n gamounts[-1] += amt\n amounts[-1] += amt\n\n cumvalue_plot[\"gamounts\"] = list(zip(dates_all, gamounts))\n cumvalue_plot[\"amounts\"] = list(zip(dates_all, amounts))\n\n # Overlay value of assets over time.\n value_dates, value_values = returnslib.compute_portfolio_values(\n price_map, target_currency, transactions\n )\n market_values = [\n (date, value)\n for date, value in zip(value_dates, value_values)\n if dates[0] <= date <= dates[-1]\n ]\n cumvalue_plot[\"value\"] = market_values\n\n # Render PnL plot\n pnl_plot = {\"value\": [], \"value_pct\": []}\n pnl_dates = dates + [date for date, _ in market_values]\n market_values_idx = 0\n cash_in = ZERO\n cash_out = ZERO\n\n for date in sorted(set(pnl_dates)):\n while (\n market_values_idx + 1 < len(market_values)\n and market_values[market_values_idx + 1][0] <= date\n ):\n market_values_idx += 1\n\n is_closed = False\n for flow in flowsByDate.get(date, []):\n if flow.amount.number >= 0:\n cash_out += flow.amount.number\n else:\n cash_in += -flow.amount.number\n\n if flow.source == \"close\":\n is_closed = True\n\n # beangrow truncates (virtually sells) all investments on the last day,\n # therefore we set the market value of the investment to 0\n market_value = market_values[market_values_idx][1] if not is_closed else 0\n returns = market_value + cash_out - cash_in\n pnl_plot[\"value\"].append([date, returns])\n pnl_plot[\"value_pct\"].append(\n [date, returns / cash_in if cash_in != ZERO else ZERO]\n )\n\n return {\n \"cashflows\": cashflows_plot,\n \"cumvalue\": cumvalue_plot,\n \"pnl\": pnl_plot,\n \"min_date\": dates[0] if dates else None,\n \"max_date\": dates[-1] if dates else None,\n }\n\n def generate_report(\n self,\n pricer: returnslib.Pricer,\n account_data: List[investments.AccountData],\n start_date: datetime.date,\n end_date: datetime.date,\n target_currency: Optional[str] = None,\n ):\n if not target_currency:\n target_currency = self.get_target_currency(account_data)\n\n # cash flows\n cash_flows = returnslib.truncate_and_merge_cash_flows(\n pricer, account_data, start_date, end_date\n )\n returns = returnslib.compute_returns(\n cash_flows, pricer, target_currency, end_date\n )\n transactions = data.sorted(\n [txn for ad in account_data for txn in ad.transactions]\n )\n\n # cumulative value plot\n plots = self.create_plots(\n pricer.price_map, target_currency, cash_flows, transactions, returns.total\n )\n\n # returns\n total_returns_tbl = Table(\n [\"Total\", \"Ex-Div\", \"Div\"], [[returns.total, returns.exdiv, returns.div]]\n )\n calendar_returns_tbl = compute_returns_table(\n pricer, target_currency, account_data, get_calendar_intervals(end_date)\n )\n cumulative_returns_tbl = compute_returns_table(\n pricer, target_currency, account_data, get_cumulative_intervals(end_date)\n )\n\n # accounts\n accounts_tbl = get_accounts_table(account_data)\n\n # cash flows\n cashflows_tbl = investments.cash_flows_to_table(cash_flows)\n\n return {\n \"target_currency\": target_currency,\n \"plots\": plots,\n \"total_returns\": total_returns_tbl,\n \"calendar_returns\": calendar_returns_tbl,\n \"cumulative_returns\": cumulative_returns_tbl,\n \"accounts\": accounts_tbl,\n \"cashflows\": cashflows_tbl,\n }\n\n def report(self, group_name):\n start_date = g.filtered._date_first\n end_date = g.filtered._date_last - datetime.timedelta(days=1)\n pricer, groups, account_data_map = self.extract(end_date)\n\n for group in groups:\n if group.name == group_name:\n break\n else:\n raise FavaAPIError(\"Group not found\")\n\n adlist = [\n account_data_map[name]\n for name in group.investment\n if name in account_data_map\n ]\n if not adlist:\n raise FavaAPIError(\"No transactions found in the specified time period\")\n\n return self.generate_report(\n pricer, adlist, start_date, end_date, group.currency\n )\n","repo_name":"andreasgerstmayr/fava-portfolio-returns","sub_path":"src/fava_portfolio_returns/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":12431,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"3"}
+{"seq_id":"3619199363","text":"from collections import deque\n\nHORIZONTAL = 0\nVERTICAL = 1\nMODE_XY = ((1, 0), (0, 1))\nDIRECTIONS = ((0, -1), (0, 1), (-1, 0), (1, 0))\nROTATIONS = (\n # 가로 (기준 변경, 체크 위치)\n (\n ((0, 0), (1, 1)),\n ((0, -1), (1, -1)),\n ((1, 0), (0, 1)),\n ((1, -1), (0, -1)) \n ),\n # 세로\n (\n ((0, 0), (1, 1)),\n ((0, 1), (1, 0)),\n ((-1, 0), (-1, 1)),\n ((-1, 1), (-1, 0))\n )\n)\n\nclass Step():\n\n def __init__(self, step, x, y, mode):\n self.step = step\n self.x = x\n self.y = y\n self.mode = mode\n\ndef solution(board):\n l = len(board)\n visited = [[[0, 0] for j in range(l)] for i in range(l)]\n q = deque()\n\n visited[0][0][HORIZONTAL] = 1\n q.append(Step(0, 0, 0, HORIZONTAL))\n\n # 모드에 해당하는 다른 블럭 체크\n def can_be_located(x, y, mode):\n \n nx, ny = x + MODE_XY[mode][0], y + MODE_XY[mode][1]\n\n return 0 <= nx < l and 0 <= ny < l and board[ny][nx] == 0\n\n def can_move(x, y, d, mode):\n nx, ny = x + d[0], y + d[1]\n\n if not(0 <= nx < l and 0 <= ny < l) or board[ny][nx] == 1 or can_be_located(nx, ny, mode) == False:\n return False\n \n return visited[ny][nx][mode] == 0\n\n\n def can_rotate(x, y, r, mode):\n nx, ny = x + r[0][0], y + r[0][1]\n bx, by = x + r[1][0], y + r[1][1]\n \n if all([0 <= pt < l for pt in (nx, ny, bx, by)]) and board[ny][nx] == 0 and board[by][bx] == 0 and can_be_located(nx, ny, mode):\n return visited[ny][nx][mode] == 0\n\n return False\n\n\n while q:\n f = q.popleft()\n\n # 디버깅\n # print(f.x, f.y, f.mode, f.step)\n\n if f.mode == HORIZONTAL and f.x == (l-2) and f.y == (l-1):\n return f.step\n elif f.mode == VERTICAL and f.x == (l-1) and f.y == (l-2):\n return f.step\n\n # 현재 모드에서 상하좌우 이동\n for d in DIRECTIONS:\n\n if can_move(f.x, f.y, d, f.mode):\n nx, ny = f.x + d[0], f.y + d[1]\n\n visited[ny][nx][f.mode] = 1\n q.append(Step(f.step + 1, nx, ny, f.mode))\n\n # 다른 모드로 변경하는 4가지 경우\n for r in ROTATIONS[f.mode]:\n\n if can_rotate(f.x, f.y, r, f.mode ^ 1):\n nx = f.x + r[0][0]\n ny = f.y + r[0][1]\n visited[ny][nx][f.mode ^ 1] = 1\n \n q.append(Step(f.step + 1, nx, ny, f.mode ^ 1))\n\n return -1\n\n\nprint(solution([[0, 0, 0, 1, 1],[0, 0, 0, 1, 0],[0, 1, 0, 1, 1],[1, 1, 0, 0, 1],[0, 0, 0, 0, 0]]))\nprint(solution([[0, 0, 1], [0, 0, 1], [0, 0, 0]]))\n\ntmp = [[0 for j in range(100)] for i in range(100)]\nprint(solution(tmp))\n\nprint(solution([\n [0, 0, 1, 1, 1], \n [0, 0, 0, 0, 1], \n [1, 1, 0, 0, 0], \n [1, 1, 1, 0, 0], \n [1, 1, 1, 1, 0]]))","repo_name":"lee-gyu/happy_algorithm","sub_path":"20_08_August/0807_60063_블록이동하기/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28032646664","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef normalized_columns_initializer(weights, std=1.0):\n out = torch.randn(weights.size())\n out *= std / torch.sqrt(out.pow(2).sum(1, keepdim=True))\n return out\n\n\ndef weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n weight_shape = list(m.weight.data.size())\n fan_in = np.prod(weight_shape[1:4])\n fan_out = np.prod(weight_shape[2:4]) * weight_shape[0]\n w_bound = np.sqrt(6. / (fan_in + fan_out))\n m.weight.data.uniform_(-w_bound, w_bound)\n m.bias.data.fill_(0)\n elif classname.find('Linear') != -1:\n weight_shape = list(m.weight.data.size())\n fan_in = weight_shape[1]\n fan_out = weight_shape[0]\n w_bound = np.sqrt(6. / (fan_in + fan_out))\n m.weight.data.uniform_(-w_bound, w_bound)\n m.bias.data.fill_(0)\n\n\nclass ActorCritic(nn.Module):\n def __init__(self, state_size, action_size, hidden_size):\n super(ActorCritic, self).__init__()\n self.state_size = state_size\n self.action_size = action_size\n self.hidden_size = hidden_size\n\n self.lstm = nn.LSTMCell(self.state_size, self.hidden_size)\n\n #self.critic_linear = nn.Linear(self.hidden_size, 1)\n self.critic_linear = nn.Sequential(\n nn.Linear(self.hidden_size, 64),\n nn.Tanh(),\n nn.Linear(64,1))\n\n self.actor_linear = nn.Sequential(\n nn.Linear(self.hidden_size, 64),\n nn.Tanh(),\n nn.Linear(64,self.action_size))\n #self.actor_linear = nn.Linear(self.hidden_size, self.action_size)\n\n self.apply(weights_init)\n self.lstm.bias_ih.data.fill_(0)\n self.lstm.bias_hh.data.fill_(0)\n self.train()\n\n def forward(self, x):\n # hx contains the next hidden state for each element in the bach\n # cx contains the next cell state for each element in the bach\n x , (hx, cx) = x\n hx, cx = self.lstm(x, (hx, cx))\n x = hx\n return self.critic_linear(x), self.actor_linear(x), (hx,cx)\n","repo_name":"willemvdmierop/Reinforcement-Learning","sub_path":"Lunar_Lander/A3C_Lunar/Lunar_A3C_model.py","file_name":"Lunar_A3C_model.py","file_ext":"py","file_size_in_byte":2137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18192782612","text":"import re\n\ndef arithmetic_arranger(problems, display_answer=False):\n \"\"\"\n This function receives a list of strings that are arithmetic problems and returns the problems arranged vertically and side-by-side. The function optionally takes a second argument. When the second argument is set to True, the answers will be displayed.\n \"\"\"\n\n if len(problems) > 5:\n return 'Error: Too many problems.'\n\n top = bottom = divider = answer = str()\n\n for problem in problems:\n try:\n operator = re.findall('\\ ([+-])\\ ', problem)[0]\n except:\n return \"Error: Operator must be '+' or '-'.\"\n\n values_str = list(i.strip() for i in problem.split(operator))\n\n try:\n values_int=[int(i) for i in values_str]\n except:\n return 'Error: Numbers must only contain digits.'\n\n if len(values_str[0]) > 4 or len(values_str[1]) > 4:\n return 'Error: Numbers cannot be more than four digits.'\n \n ops = {\"+\": (lambda x,y: x+y), \"-\": (lambda x,y: x-y)}\n \n result = ops[operator](values_int[0], values_int[1])\n\n\n n = 2 + max(len(values_str[0]), len(values_str[1]))\n top = top + \" \"*(n - len(values_str[0])) + values_str[0] + \" \"*4\n bottom = bottom + operator + \" \"*(n - 1 - len(values_str[1])) + values_str[1] + \" \"*4\n divider = divider + \"-\"*n + \" \"*4\n answer = answer + \" \"*(n - len(str(result))) + str(result) + \" \"*4\n \n\n arranged_problems = top.rstrip() + \"\\n\" + bottom.rstrip() + \"\\n\" + divider.rstrip()\n\n if display_answer:\n arranged_problems = arranged_problems + \"\\n\" + answer.rstrip()\n \n return arranged_problems","repo_name":"omininiab/arithmetic_arranger","sub_path":"arithmetic_arranger.py","file_name":"arithmetic_arranger.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23257568414","text":"import os\nimport csv\n\n# Directory containing your TXT files\ntxt_dir = 'data/instances3'\n# Output CSV file name\n# Output CSV file name\noutput_csv = 'instances3.csv'\n\n# List to store rows of data\ndata_rows = []\n\n# Function to extract vehicle number and capacity\ndef extract_vehicle_info(file_path):\n vehicle_info = {}\n vehicles = False\n with open(file_path, 'r') as file:\n lines = file.readlines()\n for line in lines:\n line = line.strip()\n parts = line.split()\n if vehicles:\n if len(parts) == 2:\n num_vehicles = int(parts[0])\n capacity = int(parts[1])\n vehicle_info[\"Number of Vehicles\"] = num_vehicles\n vehicle_info[\"Capacity\"] = capacity\n vehicles = False\n break\n if line.startswith(\"NUMBER\"):\n vehicles = True\n return vehicle_info\n\n\n# List to store rows of data\ndata_rows = []\n\n# Iterate through the TXT files in the directory\nfor filename in os.listdir(txt_dir):\n if filename.endswith('.txt'):\n file_path = os.path.join(txt_dir, filename)\n\n # Extract vehicle information\n vehicle_data = extract_vehicle_info(file_path)\n num_vehicles = vehicle_data.get(\"Number of Vehicles\", \"\")\n capacity = vehicle_data.get(\"Capacity\", \"\")\n\n # Read customer data\n with open(file_path, 'r') as txt_file:\n lines = txt_file.readlines()\n\n # Remove empty lines and header lines\n lines = [line for line in lines if\n line.strip() and not line.startswith(\"CUSTOMER\") and not line.startswith(\"CUST NO.\")]\n\n # Create a row for the CSV file\n\n row = [filename, len(lines)-5, num_vehicles, capacity]\n\n for i, line in enumerate(lines[1:], start=1): # Skip the first line\n customer = line.split()\n if len(customer) >= 3:\n row += customer[1:]\n #for value in customer:\n #row += ''.join(filter(str.isdigit, value))\n #print(value)\n print(customer[1:7])\n print(row)\n\n # Append the row to the data_rows list\n data_rows.append(row)\n\n# Create headers for the CSV file\nheaders = ['Filename', 'Customer Count', 'Number', 'Capacity']\nheaders += ['x_depot', 'y_depot', 'demand_depot', 'ready_depot', 'due_depot', 'service_depot']\nfor i in range(1,51):\n headers += [f'x_{i}']\n headers += [f'y_{i}']\n headers += [f'demand_{i}']\n headers += [f'ready_{i}']\n headers += [f'due_{i}']\n headers += [f'service_{i}']\n\n# Write data to CSV file\nwith open(output_csv, 'w', newline='') as csv_file:\n csv_writer = csv.writer(csv_file)\n csv_writer.writerow(headers)\n csv_writer.writerows(data_rows)\n\nprint(f'Data written to {output_csv}')","repo_name":"michaelebe/Masterseminar-py","sub_path":"modify_data/read_instances.py","file_name":"read_instances.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5139148893","text":"import pickle\nimport sys\nimport os\nimport pandas as pd\n\nOPTIMAL_THRESHOLD = 0.35553555355535554\nSUFFIX='.psv'\n\nfeatures = ['HR', 'O2Sat', 'Temp', 'SBP', 'MAP', 'DBP', 'Resp', 'EtCO2', 'BaseExcess', 'HCO3',\n 'FiO2', 'pH', 'PaCO2', 'SaO2', 'AST', 'BUN', 'Alkalinephos', 'Calcium',\n 'Chloride', 'Creatinine', 'Bilirubin_direct', 'Glucose', 'Lactate', 'Magnesium', 'Phosphate',\n 'Potassium', 'Bilirubin_total', 'TroponinI', 'Hct', 'Hgb', 'PTT', 'WBC', 'Fibrinogen', 'Platelets',\n 'Age', 'Gender', 'Unit1', 'Unit2', 'HospAdmTime', 'ICULOS', 'HR_missing', 'O2Sat_missing',\n 'Temp_missing', 'SBP_missing', 'MAP_missing', 'DBP_missing', 'Resp_missing', 'EtCO2_missing',\n 'BaseExcess_missing', 'HCO3_missing', 'FiO2_missing', 'pH_missing', 'PaCO2_missing',\n 'SaO2_missing', 'AST_missing', 'BUN_missing', 'Alkalinephos_missing', 'Calcium_missing',\n 'Chloride_missing', 'Creatinine_missing', 'Bilirubin_direct_missing', 'Glucose_missing',\n 'Lactate_missing', 'Magnesium_missing', 'Phosphate_missing', 'Potassium_missing', 'Bilirubin_total_missing',\n 'TroponinI_missing', 'Hct_missing', 'Hgb_missing', 'PTT_missing', 'WBC_missing', 'Fibrinogen_missing',\n 'Platelets_missing',\n 'Age_missing', 'Gender_missing', 'Unit1_missing', 'Unit2_missing', 'HospAdmTime_missing', 'ICULOS_missing']\n\n\ndef read_data(path):\n dfs = []\n for filename in os.listdir(path):\n if filename.endswith(\".psv\"):\n file_path = os.path.join(path, filename)\n df = pd.read_csv(file_path, sep='|')\n df['SepsisPatient'] = 0\n # Filter out rows after the row where SepsisLabel is 1\n if df['SepsisLabel'].eq(1).any():\n index = df.index[df['SepsisLabel'] == 1].min()\n df = df.iloc[:index + 1, :]\n df['SepsisPatient'] = 1\n # Add the filename/Patient name to the dataframe\n df['filename'] = filename[:-len(SUFFIX)]\n # df['filename'] = filename.removesuffix('.psv')\n dfs.append(df)\n df = pd.concat(dfs, axis=0)\n return df\n\n\ndef data_prep(data):\n # add a new column for each feature that indicates if the value is missing 0/1 values\n for col in data.columns:\n data[col + '_missing'] = data[col].isna().astype(int)\n return data\n\n\nif __name__ == '__main__':\n input_dir_path = sys.argv[1]\n # input_dir_path = '/Users/ofrihefetz/PycharmProjects/lab2_hw1/data/test'\n print('Reading dataframes')\n data = read_data(input_dir_path)\n\n print('Loading model')\n pickled_model = pickle.load(open('XGB_model.pkl', 'rb'))\n\n print('Preprocessing')\n df = data_prep(data)\n df_ = df.groupby(by=['filename']).mean().reset_index()\n features_new = df.columns.tolist()\n save_features = [i for i in features_new if i in features]\n\n final_df = df_[save_features]\n\n print('Predicting')\n y_predict = pickled_model.predict_proba(final_df)\n y_predicted_label = 1 * (y_predict[:, 1] > OPTIMAL_THRESHOLD)\n df_['predicted_label'] = y_predicted_label\n\n print('Saving to CSV')\n df_[['filename', 'predicted_label']].to_csv('prediction.csv')\n","repo_name":"OfriHefetz/Sepsis_Prediction","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40273265626","text":"# -*- coding: utf-8 -*-\n\n###############################################################################\n# web2py modelless scaffold app\n# Bruno Cezar Rocha \n#\n# This is the super class for all datamodels\n# fields and its properties are created here\n###############################################################################\n\nfrom gluon.dal import DAL\nfrom gluon.tools import Auth\n\n\nclass BaseModel(object):\n \"\"\"Base Model Class\n all define_ methods will be called, then\n all set_ methods (hooks) will be called.\"\"\"\n\n hooks = ['set_table',\n 'set_validators',\n 'set_visibility',\n 'set_representation',\n 'set_widgets',\n 'set_labels',\n 'set_comments',\n 'set_computations',\n 'set_updates',\n 'set_fixtures']\n\n def __init__(self, db=None, migrate=None, format=None):\n self.db = db\n assert isinstance(self.db, DAL)\n self.config = db.config\n if migrate != None:\n self.migrate = migrate\n elif not hasattr(self, 'migrate'):\n self.migrate = self.config.db.migrate\n if format != None or not hasattr(self, 'format'):\n self.format = format\n self.set_properties()\n self.check_properties()\n self.define_table()\n self.define_validators()\n self.define_visibility()\n self.define_representation()\n self.define_widgets()\n self.define_labels()\n self.define_comments()\n self.define_computations()\n self.define_updates()\n self.pre_load()\n\n def check_properties(self):\n pass\n\n def define_table(self):\n fakeauth = Auth(DAL(None))\n self.fields.extend([fakeauth.signature])\n self.entity = self.db.define_table(self.tablename,\n *self.fields,\n **dict(migrate=self.migrate,\n format=self.format))\n\n def define_validators(self):\n validators = self.validators if hasattr(self, 'validators') else {}\n for field, value in validators.items():\n self.entity[field].requires = value\n\n def define_visibility(self):\n try:\n self.entity.is_active.writable = self.entity.is_active.readable = False\n except:\n pass\n visibility = self.visibility if hasattr(self, 'visibility') else {}\n for field, value in visibility.items():\n self.entity[field].writable, self.entity[field].readable = value\n\n def define_representation(self):\n representation = self.representation if hasattr(self, 'representation') else {}\n for field, value in representation.items():\n self.entity[field].represent = value\n\n def define_widgets(self):\n widgets = self.widgets if hasattr(self, 'widgets') else {}\n for field, value in widgets.items():\n self.entity[field].widget = value\n\n def define_labels(self):\n labels = self.labels if hasattr(self, 'labels') else {}\n for field, value in labels.items():\n self.entity[field].label = value\n\n def define_comments(self):\n comments = self.comments if hasattr(self, 'comments') else {}\n for field, value in comments.items():\n self.entity[field].comment = value\n\n def define_computations(self):\n computations = self.computations if hasattr(self, 'computations') else {}\n for field, value in computations.items():\n self.entity[field].compute = value\n\n def define_updates(self):\n updates = self.updates if hasattr(self, 'updates') else {}\n for field, value in updates.items():\n self.entity[field].update = value\n\n def pre_load(self):\n for method in self.hooks:\n if hasattr(self, method):\n self.__getattribute__(method)()\n\n\nclass BaseAuth(BaseModel):\n def __init__(self, auth, migrate=None):\n self.auth = auth\n assert isinstance(self.auth, Auth)\n self.db = auth.db\n from gluon import current\n self.request = current.request\n self.config = self.db.config\n self.migrate = migrate or self.config.db.migrate\n self.set_properties()\n self.define_extra_fields()\n self.auth.define_tables(migrate=self.migrate)\n self.entity = self.auth.settings.table_user\n self.define_validators()\n self.hide_all()\n self.define_visibility()\n self.define_register_visibility()\n self.define_profile_visibility()\n self.define_representation()\n self.define_widgets()\n self.define_labels()\n self.define_comments()\n self.define_computations()\n self.define_updates()\n self.pre_load()\n\n def define_extra_fields(self):\n self.auth.settings.extra_fields['auth_user'] = self.fields\n\n def hide_all(self):\n alwaysvisible = ['first_name', 'last_name', 'password', 'email']\n for field in self.entity.fields:\n if not field in alwaysvisible:\n self.entity[field].writable = self.entity[field].readable = False\n\n def define_register_visibility(self):\n if 'register' in self.request.args:\n register_visibility = self.register_visibility if hasattr(self, 'register_visibility') else {}\n for field, value in register_visibility.items():\n self.entity[field].writable, self.entity[field].readable = value\n\n def define_profile_visibility(self):\n if 'profile' in self.request.args:\n profile_visibility = self.profile_visibility if hasattr(self, 'profile_visibility') else {}\n for field, value in profile_visibility.items():\n self.entity[field].writable, self.entity[field].readable = value\n","repo_name":"mdipierro/web2py-appliances","sub_path":"ModelLessApp/modules/basemodel.py","file_name":"basemodel.py","file_ext":"py","file_size_in_byte":5876,"program_lang":"python","lang":"en","doc_type":"code","stars":208,"dataset":"github-code","pt":"3"}
+{"seq_id":"15770599173","text":"from django.core.paginator import Paginator\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom .forms import DraftForm\nfrom .models import Draft\n\n\ndef home(request):\n draft_form = DraftForm()\n drafts_per_page = 8 # Set the number of drafts per page\n\n if request.method == 'POST':\n draft_form = DraftForm(request.POST)\n\n if draft_form.is_valid():\n title = draft_form.cleaned_data['title']\n content = draft_form.cleaned_data['content']\n button_clicked = request.POST.get('button_clicked', '')\n\n if button_clicked == 'downloadButton':\n response = HttpResponse(content_type = 'text/plain')\n response['Content-Disposition'] = f'attachment; filename=\"{title}.txt\"'\n response.write(content)\n\n # Save the draft as 'downloaded' in the database\n draft = Draft(title = title, content = content, status = 'downloaded')\n draft.save()\n\n return response\n\n elif button_clicked == 'backButton':\n if not title and not content:\n return redirect('writingapp:home')\n else:\n draft = Draft(title = title, content = content, status = 'drafted')\n draft.save()\n return redirect('writingapp:home')\n\n drafted_drafts = Draft.objects.filter(status = 'drafted').order_by('-created_at')\n # Paginate the drafts\n paginator = Paginator(drafted_drafts, drafts_per_page)\n page_number = request.GET.get('page')\n drafts_page = paginator.get_page(page_number)\n\n context = {\n 'draft_form': draft_form,\n 'drafts_page': drafts_page,\n }\n\n return render(request, 'writingapp/home.html', context)\n\n\ndef writing_area(request):\n draft_id = request.POST.get('draft_id')\n delete_draft_id = request.POST.get('delete_draft') # Get the delete_draft parameter\n\n if delete_draft_id: # Check if delete_draft parameter is present\n draft = get_object_or_404(Draft, id = delete_draft_id)\n draft.delete() # Delete the draft\n\n return redirect('writingapp:home')\n\n if draft_id:\n draft = get_object_or_404(Draft, id = draft_id)\n draft_form = DraftForm(instance = draft)\n\n else:\n draft_form = DraftForm()\n\n return render(request, 'writingapp/writing_area.html', {'draft_form': draft_form})\n","repo_name":"achris-alonzo30/Unblock","sub_path":"writingapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27887437777","text":"class Node:\n def __init__(self, data):\n self.data = data\n self.left = None\n self.right = None\n\n\n\ndef height(root):\n if not root:\n return 0\n\n return max(height(root.left), height(root.right)) + 1\n\n\n\ndef checkHeightBalanced(root):\n if not root:\n return True\n \n lh = height(root.left)\n rh = height(root.right)\n\n if (abs(lh - rh) > 1) and not checkHeightBalanced(root.left) and not checkHeightBalanced(root.right):\n return True\n\n return False\n\n\nif __name__ == \"__main__\":\n root = Node(1) \n root.left = Node(2) \n root.right = Node(3) \n root.left.left = Node(4) \n root.left.right = Node(5) \n root.left.left.left = Node(8) \n root.left.left.left.left = Node(8)\n\n\n if checkHeightBalanced(root): \n print(\"Tree is balanced\") \n else: \n print(\"Tree is not balanced\") ","repo_name":"Naveen7437/datastructures-algorithms","sub_path":"height_balanced.py","file_name":"height_balanced.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6347768492","text":"from collections import defaultdict\nimport re\n\nGENERATIONS = 20\n\ndef run(lines):\n #Just get all discreet sections of .'s and #'s\n #The first one is the initial state, and all of the rest of the rules\n initial, *pairs = re.findall(r'[.#]+', '\\n'.join(lines))\n\n #Setup defaultdict mapping pot position to value. Anything not specified will be a '.'\n pots = defaultdict(lambda: '.')\n pots.update( {i: v for i, v in enumerate(initial)} )\n\n #Clever use of extended ranges to zip even and odd indices from the pairs to combine them\n rules = {i: v for i,v in zip(pairs[::2], pairs[1::2])}\n\n pretty_print(pots)\n for time in range(GENERATIONS):\n evolve(pots, rules)\n pretty_print(pots)\n\n return score(pots)\n\ndef score(pots):\n score = 0\n for i in range(min(pots), max(pots) + 1):\n if(pots[i] == '#'):\n score += i\n\n return score\n\ndef pretty_print(pots):\n for i in range(min(pots), max(pots) + 1):\n print(pots[i], end='')\n print()\n\ndef evolve(pots, rules):\n new_pots = {}\n #Increases the size of search by 2 pots on each end\n for i in range(min(pots) - 2, max(pots) - 1):\n slice = ''.join([pots[j] for j in range(i, i+5)])\n new_pots[i + 2] = rules.get(slice, '.')\n\n pots.update(new_pots)\n","repo_name":"dbe/aoc2018py","sub_path":"12/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34439718177","text":"import os\nfrom time import sleep\nimport subprocess as sp\nimport colorama\nfrom colorama import Fore, Back, Style\na ='\\033[92m'\nb ='\\033[91m'\nc ='\\033[0m'\nPURPLE = '\\033[95m'\nCYAN = '\\033[96m'\nDARKCYAN = '\\033[36m'\nBLUE = '\\033[94m'\nGREEN = '\\033[92m'\nYELLOW = '\\033[93m'\nRED = '\\033[91m'\nBOLD = '\\033[1m'\nUNDERLINE = '\\033[4m'\nEND = '\\033[0m'\ncolorama.init(autoreset=True)\nhijau = Style.RESET_ALL+Style.BRIGHT+Fore.GREEN\nres = Style.RESET_ALL\nabu2 = Style.DIM+Fore.WHITE\nputih = Style.RESET_ALL+Style.BRIGHT+Fore.WHITE\nungu2 = Style.NORMAL+Fore.MAGENTA\nungu = Style.RESET_ALL+Style.BRIGHT+Fore.MAGENTA\nhijau2 = Style.NORMAL+Fore.GREEN\nyellow2 = Style.NORMAL+Fore.YELLOW\nyellow = Style.RESET_ALL+Style.BRIGHT+Fore.YELLOW\nred2 = Style.NORMAL+Fore.RED\nred = Style.RESET_ALL+Style.BRIGHT+Fore.RED\ncyan = Style.RESET_ALL+Style.BRIGHT+Fore.CYAN\ncyan2 = Style.NORMAL+Fore.CYAN\nkur1 = Style.BRIGHT+Fore.RED+\"[\"\nkur2 = Style.BRIGHT+Fore.RED+\"]\"\npink = Back.RED + Fore.WHITE\nblue = Back.BLUE + Fore.WHITE\nos.system('clear')\n#sp.call(['pip install colorama'], shell=True)\n\nprint(BOLD+pink+'\\n\\n\\t\\t Shorcut for help you - TERKEY (Termux Key) ')\n#print('\\t https://t.me/CRABS_ID')\nprint(blue+'\\t\\t'+'='*44)\nprint('\\nProses..')\nsleep(1)\nprint(b+'\\n[!] making termux properties directory..')\nsleep(1)\ntry:\n os.mkdir('/data/data/com.termux/files/home/.termux')\nexcept:\n pass\nprint(a+'[!]Success !')\nsleep(1)\nprint(b+'\\n[!] Making setup file..')\nsleep(1)\n\n#key = \"extra-keys = [['ESC','/','-','HOME','UP','END'],['TAB','CTRL','ALT','LEFT','DOWN','RIGHT']]\"\nkey = \"extra-keys = [['HOME','ESC','TAB','CTRL','ALT','-','LEFT','RIGHT','DOWN','UP']]\"\ntx = open('/data/data/com.termux/files/home/.termux/termux.properties','w')\ntx.write(key)\ntx.close()\nsleep(1)\nprint(a+'[!] Success !')\nsleep(1)\nprint(b+'\\n[!] Setting up..')\nsleep(2)\nos.system('termux-reload-settings')\nprint(a+'[!] Successfully !! ^^'+c+'\\n\\nThanks using this tools\\n\\n')\n\n\n# ini cuma shortcut buat bantu para nub\n# karjok pangesty\n","repo_name":"reedniv/delete","sub_path":"terkey.py","file_name":"terkey.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37011487304","text":"import re\nimport math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nfrom collections import OrderedDict\n\ndef conv_S(in_planes,out_planes,stride=1,padding=1):\n # as is descriped, conv S is 1x3x3\n return nn.Conv3d(in_planes,out_planes,kernel_size=(1,3,3),stride=1,\n padding=padding,bias=False)\n\ndef conv_T(in_planes,out_planes,stride=1,padding=1):\n # conv T is 3x1x1\n return nn.Conv3d(in_planes,out_planes,kernel_size=(3,1,1),stride=1,\n padding=padding,bias=False)\n\nclass _P3Dconv(nn.Module):\n def __init__(self, in_channels, out_channels, conv_mode = 'a'):\n super(_P3Dconv,self).__init__()\n self.conv_mode = conv_mode.lower()\n if self.conv_mode == '2d':\n self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,padding=1,bias=False)\n elif self.conv_mode == 'a':\n mid = (in_channels+out_channels)//2\n self.conS = conv_S(in_channels,mid,padding=(0,1,1))\n self.conT = conv_T(mid,out_channels,padding=(1,0,0))\n elif self.conv_mode == 'b':\n self.conS = conv_S(in_channels,out_channels,padding=(0,1,1))\n self.conT = conv_T(in_channels,out_channels,padding=(1,0,0))\n elif self.conv_mode == 'c':\n self.conS = conv_S(in_channels,out_channels,padding=(0,1,1))\n self.conT = conv_T(out_channels,out_channels,padding=(1,0,0))\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self,x):\n if self.conv_mode == '2d':\n temp = self.conv2d(x)\n elif self.conv_mode == 'a':\n temp = self.conS(x)\n temp = self.relu(temp)\n temp = self.conT(temp)\n elif self.conv_mode == 'b':\n temp_x = self.conS(x)\n temp_x = self.relu(temp_x)\n temp = self.conT(x)\n temp = temp + temp_x\n elif self.conv_mode == 'c':\n temp_x = self.conS(x)\n temp_x = self.relu(temp_x)\n temp = self.conT(temp_x)\n temp = temp + temp_x\n return temp\n\nclass _DenseLayer(nn.Module):\n def __init__(self, in_channels, growth_rate, bn_size = 4, conv_mode = 'a', batch_norm = False, drop_rate = 0.5):\n super(_DenseLayer,self).__init__()\n self.conv_mode = conv_mode.lower()\n self.batch_norm = batch_norm\n self.drop_rate = drop_rate\n self.relu = nn.ReLU(inplace=True)\n if conv_mode == '2d':\n self.conv1 = nn.Conv2d(in_channels, bn_size*growth_rate, 1, 1, bias=False)\n if batch_norm:\n self.bn1 = nn.BatchNorm2d(in_channels)\n self.bn2 = nn.BatchNorm2d(growth_rate*bn_size)\n self.dropout = nn.Dropout2d(drop_rate)\n else:\n self.conv1 = nn.Conv3d(in_channels, bn_size * growth_rate, 1, 1, bias=False)\n if batch_norm:\n self.bn1 = nn.BatchNorm3d(in_channels)\n self.bn2 = nn.BatchNorm3d(growth_rate*bn_size)\n self.dropout = nn.Dropout3d(drop_rate)\n self.conv2 = _P3Dconv(growth_rate * bn_size, growth_rate, conv_mode = conv_mode)\n\n def forward(self, x):\n if self.batch_norm:\n temp = self.bn1(x)\n else:\n temp = x\n temp = self.relu(temp)\n temp = self.conv1(temp)\n if self.batch_norm:\n temp = self.bn2(temp)\n temp = self.relu(temp)\n temp = self.conv2(temp)\n if self.drop_rate > 0:\n temp = self.dropout(temp)\n return torch.cat([x,temp],1)\n\nclass _DenseBlock(nn.Module):\n def __init__(self,in_channels,growth_rate,num_of_lauer, tf_conv3d, batch_norm = False, bn_size = 4, drop_rate = 0.5, ST_struc=['a','b','c']):\n super(_DenseBlock,self).__init__()\n if not tf_conv3d:\n ST_struc = ['2d']\n len_ST = len(ST_struc)\n curr_channels = in_channels\n self.features = nn.Sequential()\n for id in range(num_of_lauer):\n self.features.add_module(f'DenseLayer{id}', _DenseLayer(curr_channels, growth_rate, bn_size = bn_size, conv_mode=ST_struc[id % len_ST],batch_norm=batch_norm,drop_rate=drop_rate))\n curr_channels = curr_channels + growth_rate\n def forward(self,x):\n return self.features(x)\n\n\n \n\n\nclass _Transition(nn.Module):\n def __init__(self,in_channels,out_channels,batch_norm = False, tf_conv3d = True):\n super(_Transition,self).__init__()\n self.tf_conv3d = tf_conv3d\n self.batch_norm = batch_norm\n if tf_conv3d:\n if self.batch_norm:\n self.bn = nn.BatchNorm3d(in_channels)\n self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, bias=False)\n self.pool = nn.AvgPool3d(kernel_size=(2,2,2))\n else:\n if self.batch_norm:\n self.bn = nn.BatchNorm2d(in_channels)\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False)\n self.pool = nn.AvgPool2d(kernel_size = (2,2))\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self,x):\n if self.batch_norm:\n x = self.bn(x)\n x = self.relu(x)\n x = self.conv(x)\n x = self.pool(x)\n return x\n\nclass DenseNet(nn.Module):\n def __init__(self,in_channels,in_frames,init_channels,growth_rate,blocks,num_classes=2,drop_rate=0.5, bn_size = 4, batch_norm = False):\n super(DenseNet,self).__init__()\n self.batch_norm = batch_norm\n self.in_frames = in_frames\n self.preconv = nn.Sequential()\n self.preconv.add_module('conv0', nn.Conv3d(in_channels, init_channels, kernel_size = (1,7,7), stride = (1,2,2), padding = (0,3,3), bias = False))\n if batch_norm:\n self.preconv.add_module('batchnorm0',nn.BatchNorm3d(init_channels))\n self.preconv.add_module('relu0', nn.LeakyReLU(negative_slope=0.1,inplace=True))\n self.preconv.add_module('maxpool0', nn.MaxPool3d(kernel_size = (1,3,3), stride = (1,2,2), padding=(0,1,1)))\n curr_depth = in_frames\n curr_features = init_channels\n self.Conv_3d = nn.Sequential()\n self.Conv_2d = nn.Sequential()\n for i, num in enumerate(blocks):\n if curr_depth > 1:\n self.Conv_3d.add_module(f'DenseBlock{i+1}', _DenseBlock(curr_features, growth_rate, num, tf_conv3d = True, batch_norm=batch_norm, drop_rate=drop_rate))\n curr_features = curr_features + num * growth_rate\n self.Conv_3d.add_module(f'Transition{i+1}', _Transition(curr_features, curr_features//2, batch_norm=batch_norm, tf_conv3d=True))\n curr_features = curr_features//2\n curr_depth = curr_depth // 2\n else:\n self.Conv_2d.add_module(f'DenseBlock{i+1}', _DenseBlock(curr_features, growth_rate, num, tf_conv3d = False, batch_norm=batch_norm, drop_rate=drop_rate))\n curr_features = curr_features + num * growth_rate\n if i < len(blocks)-1:\n self.Conv_2d.add_module(f'Transition{i+1}', _Transition(curr_features, curr_features//2, batch_norm=batch_norm, tf_conv3d=False))\n curr_features = curr_features // 2\n self.avgpool_final = nn.AvgPool2d(kernel_size=(15,15)) #(15,15)\n self.fc = nn.Linear(curr_features, num_classes)\n \n def forward(self, x):\n a = torch.isnan(x).any()\n x = self.preconv(x)\n x = self.Conv_3d(x)\n sizes=x.size()\n x = x.view(-1,sizes[1],sizes[3],sizes[4])\n feature_map1 = x\n x = self.Conv_2d(x)\n feature_map = x\n x = self.avgpool_final(x)\n sizes = x.size()\n x = x.view(sizes[0], -1)\n x = self.fc(x)\n\n return x, feature_map\n\nif __name__ == '__main__':\n data = torch.autograd.Variable(torch.rand(10, 2, 8, 288, 288))\n model = DenseNet(in_channels = 2, in_frames = 8, init_channels = 64,growth_rate = 32,blocks = [3,4,6,3],num_classes=2,drop_rate=0.5, bn_size = 4, batch_norm = False)\n a = model(data)\n\n","repo_name":"liyuzhu1998/PFU_Detection_Codes","sub_path":"network_v3.py","file_name":"network_v3.py","file_ext":"py","file_size_in_byte":8113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20839363483","text":"import tensorflow as tf\nfrom .neural_network import NeuralNet\n\nclass PolicyNet(NeuralNet):\n \"\"\" Inherited class of NeuralNet for the policy optimization\n \"\"\"\n def __init__(self, shape, noise_cov, graph=None):\n NeuralNet.__init__(self, shape, graph)\n with self.graph.as_default():\n # initiliaze policy network variables\n variables = tf.trainable_variables()\n\n self.action_grads = tf.placeholder('float64', [1, 4])\n action_pred = self.model()\n\n # jacobian action wrt parameters\n J = tf.stack([tf.concat([tf.reshape(tf.gradients(action_pred[0, idx], param), [1, -1])\n for param in variables], 1) for idx in range(4)],\n axis=1, name='jac_Action_wrt_Param')[0]\n\n gk = tf.matmul(self.action_grads, J)\n\n # svd for hessian pseudo inverse\n s_, U_, V_ = tf.svd(tf.matmul(tf.transpose(tf.cholesky(tf.matrix_inverse(noise_cov))), J))\n h00_pinv = tf.matmul(tf.matmul(V_, tf.matrix_diag(tf.square(1/s_))), tf.transpose(V_))\n\n # multiply gk with hessian pseudo inverse\n nk = tf.matmul(h00_pinv, tf.transpose(gk))\n self.train_op = tf.transpose(nk)\n self.beta = tf.sqrt(tf.constant(14000., dtype=tf.float64) / tf.matmul(gk, nk))\n self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.)\n","repo_name":"tuunit/QuadRL","sub_path":"nn/policy_network.py","file_name":"policy_network.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2696938083","text":"def multiplesGet(i,max):\n \"get multiples of a number i till max\"\n k = i\n mset= set()\n j = 1\n while (j*k) < max:\n mset.add(j*k)\n j=j+1 \n return(mset)\n\nM=1000\nset3=multiplesGet(3,M)\nset5=multiplesGet(5,M)\nsetall=set()\nsetall.update(set3)\nsetall.update(set5)\nout=sum(setall)\nprint (out)","repo_name":"ddschmidt/Euler-Project","sub_path":"Problem1/multiples.py","file_name":"multiples.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27484553706","text":"from typing import List, Any, Union\n\nimport pandas as pd\nimport numpy as np\nimport re\n\nCOLUMNS = [\n \"date_full_with_time\",\n \"temp_dry_in_c\"\n]\n\n# import csv file\ndf = pd.read_csv('salalah.csv',\n usecols=COLUMNS)\n\n# print(df)\n\n\n# get higher and minimum value based on csv (dataframe) max & min\n# keep in mind this solution is not ok especially as\n# data here taken 24 time per day please keep reading down\nprint('maxmium temp: {}'.format(df['temp_dry_in_c'].max()))\nprint('minimum temp: {}'.format(df['temp_dry_in_c'].min()))\n\n# split (ready csv file) into seprate files every file for one year in out folder\n# 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12\n# YEAR_ = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']\n# another solution is by year as data appear to be started from 01/01/2011 to 01/01/2015\n# another important thing that every temp. taking 24 time per day\n# it is important here to take sum of 24 reading thin divided by 24 to get\n# the accurate reading in day except in 01/01/2015 it token once only\n# use simple loop here is taking much time please use function within pandas\nYEAR_ = [\n # year 1\n '01/01/2011',\n '31/12/2011',\n # year 2\n '01/01/2012',\n '31/12/2012',\n # year 3\n '01/01/2013',\n '31/12/2013',\n # year 4\n '01/01/2014',\n '31/12/2014',\n # year 5 will not be added in this example\n '01/01/2015']\n__YEAR_ = [\n # year 1\n '2011',\n # year 2\n '2012',\n # year 3\n '2013',\n # year 4\n '2014',\n # year 5 will not be added in this example\n '2015']\n\n# for col in df.date_full_with_time:\n\n# year one all date with 2011\nyear_one = df[df['date_full_with_time'].str.contains(__YEAR_[0])]\n\n# year one all date with 2012\nyear_two = df[df['date_full_with_time'].str.contains(__YEAR_[1])]\n\n# year one all date with 2013\nyear_three = df[df['date_full_with_time'].str.contains(__YEAR_[2])]\n\n# year one all date with 2014\nyear_four = df[df['date_full_with_time'].str.contains(__YEAR_[3])]\n\n\n# export data to new csv for each year\nyear_one.to_csv('out/year_one_.csv' , index=0)\nyear_two.to_csv('out/year_two_.csv')\nyear_three.to_csv('out/year_three_.csv')\nyear_four.to_csv('out/year_four_.csv')\n\n\n\n# year one high/low temprature\nprint('maxmium temp in year one: {}'.format(year_one['temp_dry_in_c'].max()))\nprint('minimum temp in year one: {}'.format(year_one['temp_dry_in_c'].min()))\n\n\n# year two high/low temprature\nprint('maxmium temp in year two: {}'.format(year_two['temp_dry_in_c'].max()))\nprint('minimum temp in year two: {}'.format(year_two['temp_dry_in_c'].min()))\n\n# year three high/low temprature\nprint('maxmium temp in year three: {}'.format(year_three['temp_dry_in_c'].max()))\nprint('minimum temp in year three: {}'.format(year_three['temp_dry_in_c'].min()))\n\n# year four high/low temprature\nprint('maxmium temp in year four: {}'.format(year_four['temp_dry_in_c'].max()))\nprint('minimum temp in year four: {}'.format(year_four['temp_dry_in_c'].min()))\n\n\n\ntotal_high_temp = ((year_one['temp_dry_in_c'].max() + year_two['temp_dry_in_c'].max() + year_three['temp_dry_in_c'].max() + year_four['temp_dry_in_c'].max()) / 4)\ntotal_low_temp = ((year_one['temp_dry_in_c'].min() + year_two['temp_dry_in_c'].min() + year_three['temp_dry_in_c'].min() + year_four['temp_dry_in_c'].min()) / 4)\n# year all year high/low temprature\n\nprint('\\n\\n\\n\\n Please check result above if you \\n '\n 'just grab max and min temp directly without \\n '\n 'divide data directly above first two result ..\\n')\n\nprint('maxmium temp in all four year: {}'.format(total_high_temp))\nprint('minimum temp in all four year: {}'.format(total_low_temp))\n\n\n\n# after split years now take reading of 24 time per day and then rewrite the sum result again\n\n# here you will jump every 24 row for learn only\n# splii = year_one_.iloc[::24, :]\n\n\n# STILL NOT FINISH \n\n\n\n","repo_name":"omancommunity/temprature","sub_path":"app_old.py","file_name":"app_old.py","file_ext":"py","file_size_in_byte":3861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19247695697","text":"__author__ = 'jonathan'\n\nimport datetime\nimport pytz\nfrom lib.rome.core.dataformat import get_decoder\nimport re\nimport uuid\nfrom sqlalchemy.sql.expression import BinaryExpression\n\nfrom lib.rome.core.rows.rows import get_attribute, has_attribute\nfrom lib.rome.core.utils import DATE_FORMAT, datetime_to_int\n\ndef uncapitalize(s):\n return s[:1].lower() + s[1:] if s else ''\n\ndef get_attribute_reccursively(obj, attr, otherwise=None):\n \"\"\" A reccursive getattr function.\n\n :param obj: the object that will be use to perform the search\n :param attr: the searched attribute\n :param otherwise: value returned in case attr was not found\n :return:\n \"\"\"\n try:\n if not \".\" in attr:\n return get_attribute(obj, attr.replace(\"\\\"\", \"\"))\n else:\n current_key = attr[:attr.index(\".\")]\n next_key = attr[attr.index(\".\") + 1:]\n if has_attribute(obj, current_key):\n current_object = get_attribute(obj, current_key)\n elif has_attribute(obj, current_key.capitalize()):\n current_object = get_attribute(obj, current_key.capitalize())\n elif has_attribute(obj, uncapitalize(current_key)):\n current_object = get_attribute(obj, uncapitalize(current_key))\n else:\n current_object = get_attribute(obj, current_key)\n\n if type(obj) is dict and next_key in obj:\n return obj[next_key]\n\n return get_attribute_reccursively(current_object, next_key, otherwise)\n except AttributeError:\n return otherwise\n\nclass LazyDictionnary:\n \"\"\"This temporary class is used to make a dict acting like an object. This code can be found at:\n http://stackoverflow.com/questions/1305532/convert-python-dict-to-object\n \"\"\"\n\n def __init__(self, **entries):\n self.entries = entries\n self._cache = {}\n self.deconverter = get_decoder()\n\n def keys(self):\n return self.entries.keys()\n\n def __getattr__(self, item):\n if item not in self._cache:\n raw_value = self.entries[item] if item in self.entries else None\n deconverted_value = self.deconverter.desimplify(raw_value)\n self._cache[item] = deconverted_value\n return self._cache[item]\n\nboolean_expression_str_memory = {}\n\nclass BooleanExpression(object):\n def __init__(self, operator, *exps):\n def transform_exp(exp):\n if type(exp) is not BooleanExpression and self.operator != \"NORMAL\":\n return BooleanExpression(\"NORMAL\", exp)\n else:\n return exp\n self.operator = operator\n self.exps = map(lambda x: transform_exp(x), exps)\n self.deconverter = get_decoder()\n self.compiled_expression = \"\"\n self.uuid = str(uuid.uuid1()).replace(\"-\", \"\")\n self.is_joining_expression = True\n self.tables_involved = []\n \"\"\" Prepare the expression. \"\"\"\n self.variable_substitution_dict = {}\n self.default_value_dict = {}\n self.prepare_expression()\n\n def is_boolean_expression(self):\n return True\n\n def extract_hint(self):\n from lib.rome.core.terms.terms import Hint\n result = []\n for expression in self.exps:\n try:\n if hasattr(expression, \"extract_hint\"):\n result += expression.extract_hint()\n elif hasattr(expression, \"right\") and hasattr(expression.right, \"value\"):\n table_name = str(expression.left.table)\n attribute_name = str(expression.left.key)\n # value = \"%s\" % (criterion.expression.right.value)\n value = expression.right.value\n if type(expression.left.type).__name__ == \"Integer\":\n value = int(value)\n if type(expression.left.type).__name__ == \"Float\":\n value = float(value)\n result += [Hint(table_name, attribute_name, value)]\n except:\n # TODO: this catch errors that occur when there are \"CASE WHEN\" expression (this is caused by _paginate_query in glance.db.api)\n pass\n return result\n\n def extract_joining_pairs(self):\n if self.operator == \"NORMAL\":\n word_pattern = \"[_a-zA-Z0-9]+\"\n joining_criterion_pattern = \"%s\\.%s == %s\\.%s\" % (word_pattern, word_pattern, word_pattern, word_pattern)\n m = re.search(joining_criterion_pattern, self.raw_expression)\n if m is not None:\n joining_pair = self.raw_expression[1:-1].split(\"==\")\n joining_pair = map(lambda x: x.strip(), joining_pair)\n joining_pair = sorted(joining_pair)\n return [joining_pair]\n else:\n return []\n result = []\n for exp in self.exps:\n if type(exp).__name__ == \"BooleanExpression\":\n result += exp.extract_joining_pairs()\n return result\n\n def extract_nonjoining_criterions(self):\n if self.operator == \"NORMAL\":\n word_pattern = \"[_a-zA-Z0-9]+\"\n joining_criterion_pattern = \"%s\\.%s == %s\\.%s\" % (word_pattern, word_pattern, word_pattern, word_pattern)\n m = re.search(joining_criterion_pattern, self.raw_expression)\n if m is None:\n return [self]\n else:\n return []\n return [self]\n\n def prepare_expression(self):\n\n def collect_expressions(exp):\n if type(exp) is BooleanExpression:\n return exp.compiled_expression\n if type(exp) is BinaryExpression:\n return self.prepare_criterion(exp)\n else:\n return exp\n\n compiled_expressions = map(lambda x: \"(%s)\" % (collect_expressions(x)), self.exps)\n\n joined_compiled_expressions = []\n if self.operator == \"and\":\n joined_compiled_expressions = \" and \".join(compiled_expressions)\n elif self.operator == \"or\":\n joined_compiled_expressions = \" or \".join(compiled_expressions)\n elif self.operator == \"NORMAL\":\n joined_compiled_expressions = \" or \".join(compiled_expressions)\n\n self.compiled_expression = joined_compiled_expressions\n\n for criterion_str in compiled_expressions:\n for expression in self.exps:\n if type(expression) is BinaryExpression:\n expression_parts = [expression.right, expression.left]\n other_part = expression.left\n for expression_part in expression_parts:\n # other_parts = filter(lambda x: x != expression_part,expression_parts)\n if hasattr(expression_part, \"default\") and expression_part.bind is None and expression_part.default is not None:\n expression_part.bind = expression_part.default.arg\n if \":\" in str(expression_part):\n \"\"\" Handle right part of the expression. \"\"\"\n if \" in \" in criterion_str:\n count = 1\n parts = getattr(expression_part, \"element\", [])\n like_operator_used = False\n if len(parts) == 0:\n \"\"\" This case happens when the LIKE operator is used. \"\"\"\n like_operator_used = True\n parts = [expression_part] if \"BindParameter\" in str(type(expression_part)) else []\n for i in parts:\n corrected_label = (\"%s_%s_%i\" % (i._orig_key, self.uuid, count))\n key = \":%s_%i\" % (i._orig_key, count)\n self.variable_substitution_dict[key] = corrected_label\n self.default_value_dict[corrected_label] = i.value\n if like_operator_used:\n \"\"\" Must remove the '%' used as the wildcard symbol in the LIKE synthax\"\"\"\n self.default_value_dict[corrected_label] = self.default_value_dict[corrected_label].replace(\"%\", \"\")\n count += 1\n elif not \".\" in str(expression_part):\n original_label = str(expression_part)\n corrected_label = (\"%s_%s\" % (original_label, self.uuid)).replace(\":\", \"\")\n self.variable_substitution_dict[original_label] = corrected_label\n value = expression_part.value\n # if len(other_parts) > 0:\n # other_part = other_parts[0]\n if type(other_part.expression.type).__name__ == \"Integer\":\n value = int(value)\n if type(other_part.expression.type).__name__ == \"Float\":\n value = float(value)\n if isinstance(value, datetime.datetime):\n value = datetime_to_int(value)\n self.default_value_dict[corrected_label] = value\n other_part = expression.right\n\n for sub in self.variable_substitution_dict:\n joined_compiled_expressions = joined_compiled_expressions.replace(sub, self.variable_substitution_dict[sub])\n joined_compiled_expressions = joined_compiled_expressions.replace(\":\", \"\")\n\n for exp in self.exps:\n if type(exp) is BooleanExpression:\n for default_value_key in exp.default_value_dict:\n self.default_value_dict[default_value_key] = exp.default_value_dict[default_value_key]\n\n self.compiled_expression = joined_compiled_expressions\n self.raw_expression = \"%s\" % (self.compiled_expression)\n keys = self.default_value_dict.keys()\n keys = sorted(keys, reverse=True, key=lambda x: len(x))\n for key in keys:\n value = self.default_value_dict[key]\n if type(value).__name__ in [\"int\", \"float\"]:\n self.raw_expression = self.raw_expression.replace(key, \"%s\" % (self.default_value_dict[key]))\n else:\n self.raw_expression = self.raw_expression.replace(key, \"\\\"%s\\\"\" % (self.default_value_dict[key]))\n\n return self.compiled_expression\n\n def prepare_criterion(self, criterion):\n criterion_str = criterion.__str__()\n\n if criterion_str in boolean_expression_str_memory:\n criterion_str = boolean_expression_str_memory[criterion_str]\n else:\n prev_criterion_str = criterion_str\n\n subs = {\n \" = \": \" == \",\n # \":\": \"\",\n \"\\\"\": \"\",\n \"IN\": \" in \",\n \"IS\": \" is \",\n \"NOT\": \" not \",\n \"NULL\": \"None\",\n \"(\": \"[\",\n \")\": \"]\"\n }\n compiled = re.compile('|'.join(map(re.escape, subs)))\n criterion_str = compiled.sub(lambda x: subs[x.group(0)], criterion_str)\n\n for sub in self.variable_substitution_dict:\n criterion_str = criterion_str.replace(sub, self.variable_substitution_dict[sub])\n\n # handle regex\n if \"REGEXP\" in criterion_str:\n tab = criterion_str.split(\"REGEXP\")\n a = tab[0]\n b = tab[1]\n criterion_str = (\"\"\"__import__('re').search(%s, %s) is not None\\n\"\"\" % (b, a))\n\n if \"LIKE\" in criterion_str:\n left = criterion_str.split(\"LIKE\")[0]\n right = criterion_str.split(\"LIKE\")[1]\n criterion_str = \"(%s in %s) or (%s in %s)\" % (left, right, right, left)\n\n boolean_expression_str_memory[prev_criterion_str] = criterion_str\n\n return criterion_str\n\n def evaluate(self, value, additional_parameters={}):\n\n orig_value = value\n # construct a dict with the values involved in the expression\n values_dict = {}\n if type(value) is not dict:\n for key in value.keys():\n try:\n s = LazyDictionnary(**value[value.keys().index(key)])\n values_dict[key] = s\n except Exception as e:\n print(\"[BUG] evaluation failed: %s -> %s\" % (key, value))\n # return False\n else:\n values_dict = value\n\n for key in self.default_value_dict:\n values_dict[key] = self.default_value_dict[key]\n final_values_dict = {}\n for key in values_dict.keys():\n value = values_dict[key]\n if key.startswith(\"id_\"):\n value = int(value)\n final_values_dict[key] = value\n for key in values_dict:\n if key in self.variable_substitution_dict:\n value = values_dict[key]\n if key.startswith(\"id_\"):\n value = int(value)\n final_values_dict[self.variable_substitution_dict[key]] = value\n for expression in self.exps:\n if type(expression) is BinaryExpression:\n expression_parts = [expression.right, expression.left]\n for expression_part in expression_parts:\n if hasattr(expression_part, \"default\") and expression_part.default is not None:\n key = str(expression_part).split(\".\")[0]\n attr = str(expression_part).split(\".\")[1]\n if getattr(final_values_dict[key], attr, None) is None:\n value = expression_part.default.arg\n setattr(final_values_dict[key], attr, value)\n second_final_values_dict = {}\n for key in additional_parameters:\n value = LazyDictionnary(**additional_parameters[key])\n second_final_values_dict[key] = value\n for key in final_values_dict:\n second_final_values_dict[key] = final_values_dict[key]\n try:\n result = eval(self.compiled_expression, second_final_values_dict)\n except:\n import traceback\n traceback.print_exc()\n if self.operator == \"NORMAL\":\n return False\n for exp in self.exps:\n if exp.evaluate(orig_value):\n if self.operator in [\"or\"]:\n return True\n else:\n if self.operator in [\"and\"]:\n return False\n if self.operator in [\"NORMAL\", \"or\"]:\n return False\n else:\n return True\n pass\n return result\n\n def __repr__(self):\n if self.operator == \"NORMAL\":\n return str(self.raw_expression)\n else:\n op = \" %s \".lower() % (self.operator)\n return \"(%s)\" % (op.join(map(lambda x: str(x), self.exps)))\n\nclass JoiningBooleanExpression(BooleanExpression):\n def __init__(self, operator, *exps):\n BooleanExpression.__init__(self, operator, *exps)\n self.is_joining_expression = True","repo_name":"BeyondTheClouds/rome","sub_path":"lib/rome/core/expression/expression.py","file_name":"expression.py","file_ext":"py","file_size_in_byte":15467,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"25006213745","text":"\"\"\"cluster_parameter_extractor\n\nExtracts basic parameters about the clusters found in a .clustering result file and\nwrites these parameters to a tab-delimited file.\n\nUsage:\n cluster_parameter_extractor.py --input= --output=\n [--synthetic_peptides]\n cluster_parameter_extractor.py (--help | --version)\n\nOptions:\n -i, --input= Path to the .clustering result file to process.\n -o, --output= Path to the output file that should be created.\n --synthetic_peptides If this option is specified, all spectra from the\n dataset on synthetic peptides (PXD004732) will be\n analysed separately.\n -h, --help Print this help message.\n -v, --version Print the current version.\n\"\"\"\n\nimport sys\nimport os\nfrom docopt import docopt\n\n# make the spectra_cluster packages available\nsys.path.insert(0, os.path.abspath('..') + os.path.sep + \"..\")\n\nimport spectra_cluster.clustering_parser as clustering_parser\n\n\ndef create_sequence_string(cluster):\n \"\"\"\n Creates a string containing the cluster's sequences with their\n respective spectrum counts.\n\n :param cluster: The cluster to process\n :return: A string containing the cluster's sequences with their counts.\n \"\"\"\n sequence_strings = list()\n\n if cluster.identified_spectra == 0:\n return \"[]\"\n\n for sequence in cluster.sequence_counts.keys():\n sequence_strings.append(sequence + \":\" + str(cluster.sequence_counts.get(sequence)))\n\n final_string = \"[\" + \",\".join(sequence_strings) + \"]\"\n\n return final_string\n\n\ndef process_cluster(cluster):\n \"\"\"\n Extracts the basic properties from the cluster object and\n returns a tab-delimited string that matches the output format.\n\n :param cluster: Cluster object to extract the parameters from\n :return: A tab-delimited string matching the output format\n \"\"\"\n # ignore empty clusters\n if len(cluster.consensus_mz) < 1:\n return \"\"\n\n result_fields = [cluster.id, str(cluster.precursor_mz), str(cluster.charge),\n str(cluster.n_spectra),\n str(cluster.identified_spectra), str(cluster.unidentified_spectra),\n str(cluster.max_ratio), str(cluster.max_il_ratio)]\n\n # precursor m/z range\n spectra = cluster.get_spectra()\n min_mz = sys.float_info.__getattribute__(\"max\")\n max_mz = 0\n\n for spectrum in spectra:\n if min_mz > spectrum.precursor_mz:\n min_mz = spectrum.precursor_mz\n if max_mz < spectrum.precursor_mz:\n max_mz = spectrum.precursor_mz\n\n mz_range = max_mz - min_mz\n\n result_fields.append(str(mz_range))\n\n # sequences\n sequence_string = create_sequence_string(cluster)\n result_fields.append(sequence_string)\n\n # max sequence\n if len(cluster.max_sequences) == 0:\n result_fields.append(\"NA\")\n result_fields.append(\"NA\")\n result_fields.append(\"NA\")\n if len(cluster.max_sequences) > 0:\n result_fields.append(cluster.max_sequences[0])\n result_fields.append(str(cluster.sequence_counts.get(cluster.max_sequences[0])))\n # TODO: add modifications to output\n result_fields.append(\"NA\")\n\n # second max sequence\n if cluster.identified_spectra == 0 or len(cluster.sequence_counts) == 1:\n # cluster only contains 1 sequence\n result_fields.append(\"NA\")\n result_fields.append(\"NA\")\n result_fields.append(\"NA\")\n elif len(cluster.max_sequences) > 1:\n # there is more than one max sequence\n result_fields.append(cluster.max_sequences[1])\n result_fields.append(str(cluster.sequence_counts.get(cluster.max_sequences[1])))\n # TODO: add modifications to output\n result_fields.append(\"NA\")\n else:\n # identify the second most common sequence\n counts = list(cluster.sequence_counts.values())\n counts.remove(cluster.sequence_counts.get(cluster.max_sequences[0]))\n second_max_count = max(counts)\n\n sequence_found = False\n\n for sequence in cluster.sequence_counts.keys():\n if cluster.sequence_counts.get(sequence) == second_max_count:\n result_fields.append(sequence)\n sequence_found = True\n break\n\n # this should never happen\n if not sequence_found:\n raise Exception(\"Failed to identify second most common sequence for cluster \" + cluster.id)\n\n result_fields.append(str(second_max_count))\n # TODO: add modifications\n result_fields.append(\"NA\")\n\n # n_input_files\n input_files = set()\n\n for spectrum in spectra:\n spectrum_filename = spectrum.get_filename()\n\n if spectrum_filename is not None:\n input_files.add(spectrum_filename)\n\n if len(input_files) < 1:\n result_fields.append(\"NA\")\n else:\n result_fields.append(str(len(input_files)))\n\n # max consensus peak tic\n try:\n if sum(cluster.consensus_intens) > 0:\n rel_max_intens = max(cluster.consensus_intens) / sum(cluster.consensus_intens)\n else:\n rel_max_intens = 0\n result_fields.append(str(rel_max_intens))\n # max consensus peak m/z\n max_peak_index = max(range(len(cluster.consensus_intens)), key=cluster.consensus_intens.__getitem__)\n result_fields.append(str(cluster.consensus_mz[max_peak_index]))\n except ValueError:\n print(\"Error: Failed to process consensus spectrum of cluster \" + cluster.id)\n sys.exit(1)\n\n return \"\\t\".join(result_fields)\n\n\ndef process_synthetic_peptides(cluster):\n \"\"\"\n Extracts information on identification from the dataset\n PXD004732 which contains reliable identifications of\n synthetic peptides.\n\n :param cluster: The cluster to process\n :return: A tab-delimited string containing the number of\n synthetic peptide spectra in the cluster, their\n max ratio and the max sequence.\n \"\"\"\n total_spectra = 0\n sequence_counts = dict()\n\n for spectrum in cluster.get_spectra():\n # ignore all none synthetic spectra\n if \"PXD004732\" not in spectrum.get_title():\n continue\n\n total_spectra += 1\n\n # spectra from this dataset only contain one psm\n if len(spectrum.psms) != 1:\n raise Exception(\"Unidentified spectrum from project PXD004732 encountered: \" + spectrum.get_title())\n\n psm = [p for p in spectrum.psms][0]\n\n if psm.sequence not in sequence_counts:\n sequence_counts[psm.sequence] = 1\n else:\n sequence_counts[psm.sequence] += 1\n\n # done if no synthetic peptides were found\n if total_spectra == 0:\n return \"0\\tNA\\tNA\"\n\n # get the max count\n max_count = max(sequence_counts.values())\n\n # get the matching sequences\n max_sequences = [s for s in sequence_counts if sequence_counts[s] == max_count]\n\n # calculate the ratio\n max_ratio = max_count / total_spectra\n\n return str(total_spectra) + \"\\t\" + str(max_ratio) + \"\\t\" + \";\".join(max_sequences)\n\n\ndef main():\n \"\"\"\n Primary entry function for the CLI.\n :return:\n \"\"\"\n arguments = docopt(__doc__, version='cluster_parameter_extractor 1.0 BETA')\n\n input_file = arguments['--input']\n output_file = arguments[\"--output\"]\n process_synthetic = arguments[\"--synthetic_peptides\"]\n\n # make sure the input file exists\n if not os.path.isfile(input_file):\n print(\"Error: Cannot find input file '\" + input_file + \"'\")\n sys.exit(1)\n\n # make sure the output file does not exist\n if os.path.isfile(output_file):\n print(\"Error: Output file exists '\" + output_file + \"'\")\n sys.exit(1)\n\n with open(output_file, \"w\") as OUT:\n # write the header\n OUT.write(\"id\\tprecursor_mz\\tav_charge\\tsize\\tidentified_spec_count\\tunidentified_spec_count\\t\"\n \"max_ratio\\tmax_il_ratio\\tprecursor_mz_range\\tsequences\\t\"\n \"max_sequence\\tmax_sequence_count\\tmax_sequence_mods\\t\"\n \"second_max_sequence\\tsecond_max_sequence_count\\tsecond_max_sequence_mods\\tn_input_files\\t\"\n \"max_consensus_peak_rel_tic\\tmax_consensus_peak_mz\")\n\n if process_synthetic:\n OUT.write(\"\\tsynth_count\\tsynth_ratio\\tsynth_max_sequence\")\n\n OUT.write(\"\\n\")\n\n # process the file\n parser = clustering_parser.ClusteringParser(input_file)\n\n for cluster in parser:\n cluster_line = process_cluster(cluster)\n OUT.write(cluster_line)\n\n # process synthetic peptides\n if process_synthetic:\n synth_line = process_synthetic_peptides(cluster)\n OUT.write(\"\\t\" + synth_line)\n\n OUT.write(\"\\n\")\n\n print(\"Results written to \" + output_file)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"spectra-cluster/spectra-cluster-py","sub_path":"spectra_cluster/ui/cluster_parameter_extractor.py","file_name":"cluster_parameter_extractor.py","file_ext":"py","file_size_in_byte":9009,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"9805263154","text":"from __future__ import print_function\n\nimport unittest as ut\nimport unittest_decorators as utx\nfrom tests_common import abspath\n\nimport espressomd\nimport espressomd.lb\n\nimport os\nimport numpy as np\n\ntry:\n import vtk\n from vtk.util.numpy_support import vtk_to_numpy\nexcept ImportError:\n pass\n\n\ndef calculate_vtk_max_pointwise_difference(file1, file2, tol=1e-6):\n arrays = [0] * 2\n\n reader = vtk.vtkStructuredPointsReader()\n for i, fname in enumerate([file1, file2]):\n reader.SetFileName(fname)\n reader.Update()\n data = reader.GetOutput().GetPointData()\n arrays[i] = np.array([vtk_to_numpy(data.GetArray(n))\n for n in range(data.GetNumberOfArrays())])\n\n try:\n return np.allclose(arrays[0], arrays[1], rtol=0, atol=tol), np.max(\n np.abs(arrays[0] - arrays[1]))\n except BaseException:\n return False, np.inf\n\n\n@utx.skipIfMissingFeatures([\"ENGINE\"])\n@utx.skipIfMissingModules(['vtk'])\nclass SwimmerTest(ut.TestCase):\n S = espressomd.System(box_l=[1.0, 1.0, 1.0])\n S.seed = S.cell_system.get_state()['n_nodes'] * [1234]\n\n def setUp(self):\n S = self.S\n S.box_l = 3 * [12]\n S.cell_system.skin = 0.1\n S.time_step = 0.01\n\n S.part.add(id=0, pos=[6.0, 3.0, 2.0], quat=np.sqrt([.5, .5, 0, 0]),\n swimming={\"mode\": \"pusher\", \"v_swim\": 0.10,\n \"dipole_length\": 1.0, \"rotational_friction\": 2.0})\n S.part.add(id=1, pos=[2.0, 3.0, 6.0], quat=np.sqrt([.5, 0, .5, 0]),\n swimming={\"mode\": \"pusher\", \"f_swim\": 0.03,\n \"dipole_length\": 2.0, \"rotational_friction\": 20.})\n S.part.add(id=2, pos=[3.0, 2.0, 6.0], quat=np.sqrt([.5, 0, 0, .5]),\n swimming={\"mode\": \"puller\", \"v_swim\": 0.15,\n \"dipole_length\": 0.5, \"rotational_friction\": 15.})\n S.part.add(id=3, pos=[3.0, 6.0, 2.0], quat=np.sqrt([0, 0, .5, .5]),\n swimming={\"mode\": \"puller\", \"f_swim\": 0.05,\n \"dipole_length\": 1.5, \"rotational_friction\": 6.0})\n S.part[:].rotation = [1, 1, 1]\n\n def tearDown(self):\n self.S.part.clear()\n\n def run_and_check(self, lbm, vtk_ref, vtk_out, tol):\n self.S.integrator.run(2000)\n\n lbm.print_vtk_velocity(vtk_out)\n identical, err_max = calculate_vtk_max_pointwise_difference(\n vtk_ref, vtk_out, tol=tol)\n os.remove(vtk_out)\n print(\n \"Maximum deviation to the reference point is: {}\".format(err_max))\n self.assertTrue(identical)\n\n\nclass SwimmerTestCPU(SwimmerTest):\n\n def test(self):\n lbm = espressomd.lb.LBFluid(\n agrid=1.0, tau=self.S.time_step, visc=1.0, dens=1.0)\n self.S.actors.add(lbm)\n self.S.thermostat.set_lb(LB_fluid=lbm, gamma=0.5)\n self.run_and_check(\n lbm, abspath(\"data/engine_lb.vtk\"),\n \"engine_test_cpu_tmp.vtk\", 1.5e-6)\n self.S.thermostat.turn_off()\n self.S.actors.remove(lbm)\n\n\n@utx.skipIfMissingGPU()\nclass SwimmerTestGPU(SwimmerTest):\n\n def test(self):\n lbm = espressomd.lb.LBFluidGPU(\n agrid=1.0,\n tau=self.S.time_step,\n visc=1.0,\n dens=1.0\n )\n self.S.actors.add(lbm)\n self.S.thermostat.set_lb(LB_fluid=lbm, gamma=0.5)\n self.run_and_check(\n lbm, abspath(\"data/engine_lbgpu_2pt.vtk\"),\n \"engine_test_gpu_tmp.vtk\", 2.0e-7)\n self.S.thermostat.turn_off()\n self.S.actors.remove(lbm)\n\n\nif __name__ == '__main__':\n ut.main()\n","repo_name":"opichals/espresso","sub_path":"testsuite/python/engine_lb.py","file_name":"engine_lb.py","file_ext":"py","file_size_in_byte":3629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"17369922532","text":"\"\"\"Initial migration.\n\nRevision ID: 0293ea1a0430\nRevises: 9d5f12822fd4\nCreate Date: 2023-02-14 17:25:18.465180\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '0293ea1a0430'\ndown_revision = '9d5f12822fd4'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('Show', sa.Column('artist_name', sa.String(), nullable=True))\n op.add_column('Show', sa.Column('artist_image_link', sa.String(length=500), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('Show', 'artist_image_link')\n op.drop_column('Show', 'artist_name')\n # ### end Alembic commands ###\n","repo_name":"ciaraxON/FyyurProject","sub_path":"migrations/versions/0293ea1a0430_initial_migration.py","file_name":"0293ea1a0430_initial_migration.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8125963404","text":"# -*- coding: utf-8 -*-\n\"\"\"\nAll in one verification workchain\n\"\"\"\n# pylint: disable=cyclic-import\nfrom aiida import orm\nfrom aiida.engine import ToContext, if_\nfrom aiida.engine.processes.exit_code import ExitCode\nfrom aiida.engine.processes.functions import calcfunction\nfrom aiida.plugins import DataFactory, WorkflowFactory\n\nfrom aiida_sssp_workflow.workflows import SelfCleanWorkChain\nfrom aiida_sssp_workflow.workflows.convergence import _BaseConvergenceWorkChain\nfrom aiida_sssp_workflow.workflows.convergence.caching import (\n _CachingConvergenceWorkChain,\n)\nfrom aiida_sssp_workflow.workflows.measure import _BaseMeasureWorkChain\n\nUpfData = DataFactory(\"pseudo.upf\")\n\n\n@calcfunction\ndef parse_pseudo_info(pseudo: UpfData):\n \"\"\"parse the pseudo info as a Dict\"\"\"\n from pseudo_parser.upf_parser import parse\n\n try:\n info = parse(pseudo.get_content())\n except ValueError:\n return ExitCode(100, \"cannot parse the info of pseudopotential.\")\n\n return orm.Dict(dict=info)\n\n\n_REMOTE_FOLDER_DEPENDENT_CONVERENCE_PROPERTIES_LIST = [\n \"convergence.bands\",\n \"convergence.phonon_frequencies\",\n]\n\n_REMOTE_FOLDER_INDEPENDENT_CONVERGENCE_PROPERTIES_LIST = [\n \"convergence.cohesive_energy\",\n \"convergence.pressure\",\n \"convergence.delta\",\n]\n\nDEFAULT_CONVERGENCE_PROPERTIES_LIST = (\n _REMOTE_FOLDER_DEPENDENT_CONVERENCE_PROPERTIES_LIST\n + _REMOTE_FOLDER_INDEPENDENT_CONVERGENCE_PROPERTIES_LIST\n)\n\nDEFAULT_MEASURE_PROPERTIES_LIST = [\n \"measure.precision\",\n \"measure.bands\",\n]\n\nDEFAULT_PROPERTIES_LIST = (\n DEFAULT_MEASURE_PROPERTIES_LIST + DEFAULT_CONVERGENCE_PROPERTIES_LIST\n)\n\n\nclass VerificationWorkChain(SelfCleanWorkChain):\n \"\"\"The verification workflow to run all test for the given pseudopotential\"\"\"\n\n # This two class attributes will control whether a WF flow is\n # run and results write to outputs ports.\n _VALID_CONGENCENCE_WF = [\n \"convergence.cohesive_energy\",\n \"convergence.phonon_frequencies\",\n \"convergence.pressure\",\n \"convergence.delta\",\n \"convergence.bands\",\n ]\n _VALID_MEASURE_WF = [\n \"measure.precision\",\n \"measure.bands\",\n ]\n\n @classmethod\n def define(cls, spec):\n super().define(spec)\n # yapf: disable\n spec.expose_inputs(_BaseMeasureWorkChain, namespace='measure',\n exclude=['code', 'pseudo', 'options', 'parallelization', 'clean_workdir'])\n spec.expose_inputs(_BaseConvergenceWorkChain, namespace='convergence',\n exclude=['code', 'pseudo', 'options', 'parallelization', 'clean_workdir'])\n spec.input('pw_code', valid_type=orm.AbstractCode,\n help='The `pw.x` code use for the `PwCalculation`.')\n spec.input('ph_code', valid_type=orm.AbstractCode, required=False,\n help='The `ph.x` code use for the `PhCalculation`.')\n spec.input('pw_code_large_memory', valid_type=orm.AbstractCode, required=False,\n help='The `pw.x` code use for the `PwCalculation` require large memory.')\n spec.input('pseudo', valid_type=UpfData, required=True,\n help='Pseudopotential to be verified')\n spec.input('wavefunction_cutoff', valid_type=orm.Float, required=False, default=lambda: orm.Float(100.0),\n help='The wavefunction cutoff for the Measure properties.')\n spec.input('charge_density_cutoff', valid_type=orm.Float, required=False, default=lambda: orm.Float(800.0),\n help='The charge density cutoff for the Measure properties.')\n spec.input('label', valid_type=orm.Str, required=False,\n help='label store for display as extra attributes.')\n spec.input('properties_list', valid_type=orm.List,\n default=lambda: orm.List(list=DEFAULT_PROPERTIES_LIST),\n help='The preperties will be calculated, passed as a list.')\n spec.input('options', valid_type=orm.Dict, required=False,\n help='Optional `options`')\n spec.input('parallelization', valid_type=orm.Dict, required=False,\n help='Parallelization options')\n\n spec.outline(\n cls.setup_code_resource_options,\n cls.parse_pseudo,\n cls.init_setup,\n if_(cls.is_verify_convergence)(\n if_(cls.is_caching)(\n cls.run_caching,\n cls.inspect_caching,\n ),\n cls.run_remote_folder_dependent_convergence,\n cls.run_remote_folder_independent_convergence,\n cls.inspect_convergence,\n ),\n if_(cls.is_verify_measure)(\n cls.run_measure,\n cls.inspect_measure,\n ),\n )\n spec.output('pseudo_info', valid_type=orm.Dict, required=True,\n help='pseudopotential info')\n for wfname in cls._VALID_MEASURE_WF:\n spec.output_namespace(wfname, dynamic=True,\n help=f'results of {wfname} calculation.')\n for wfname in cls._VALID_CONGENCENCE_WF:\n spec.output_namespace(wfname, dynamic=True,\n help=f'results of {wfname} calculation.')\n\n spec.exit_code(401, 'ERROR_CACHING_ON_BUT_FAILED',\n message='The caching is triggered but failed.')\n spec.exit_code(811, 'WARNING_NOT_ALL_SUB_WORKFLOW_OK',\n message='The sub-workflows {processes} is not finished ok.')\n # yapf: enable\n\n def setup_code_resource_options(self):\n \"\"\"\n setup resource options and parallelization for `PwCalculation` from inputs\n \"\"\"\n if \"options\" in self.inputs:\n self.ctx.options = self.inputs.options.get_dict()\n else:\n from aiida_sssp_workflow.utils import get_default_options\n\n self.ctx.options = get_default_options(\n with_mpi=True,\n )\n\n if \"parallelization\" in self.inputs:\n self.ctx.parallelization = self.inputs.parallelization.get_dict()\n else:\n self.ctx.parallelization = {}\n\n @staticmethod\n def _label_from_pseudo_info(pseudo_info) -> str:\n \"\"\"derive a label string from pseudo_info dict\"\"\"\n element = pseudo_info[\"element\"]\n pp_type = pseudo_info[\"pp_type\"]\n z_valence = pseudo_info[\"z_valence\"]\n\n return f\"{element}.{pp_type}.z_{z_valence}\"\n\n def parse_pseudo(self):\n \"\"\"parse pseudo\"\"\"\n pseudo_info = parse_pseudo_info(self.inputs.pseudo)\n self.ctx.pseudo_info = pseudo_info.get_dict()\n self.node.base.extras.set_many(\n self.ctx.pseudo_info\n ) # set the extra attributes for the node\n\n self.out(\"pseudo_info\", pseudo_info)\n\n def init_setup(self):\n \"\"\"prepare inputs for all verification process\"\"\"\n\n if \"label\" in self.inputs:\n label = self.inputs.label.value\n else:\n label = self._label_from_pseudo_info(self.ctx.pseudo_info)\n\n self.node.base.extras.set(\"label\", label)\n\n # Properties list\n valid_list = self._VALID_MEASURE_WF + self._VALID_CONGENCENCE_WF\n self.ctx.properties_list = [\n p for p in self.inputs.properties_list.get_list() if p in valid_list\n ]\n\n # Measure workflow: bands measure and precision measure workflows inputs setting\n measure_inputs = self.exposed_inputs(_BaseMeasureWorkChain, namespace=\"measure\")\n measure_inputs[\"pseudo\"] = self.inputs.pseudo\n measure_inputs[\"code\"] = self.inputs.pw_code\n measure_inputs[\"options\"] = self.inputs.options\n measure_inputs[\"parallelization\"] = self.inputs.parallelization\n\n measure_inputs[\"clean_workdir\"] = self.inputs.clean_workdir\n\n self.ctx.measure_inputs = {\n \"precision\": measure_inputs.copy(),\n \"bands\": measure_inputs.copy(),\n }\n\n # Convergence inputs setting, the properties of convergence test are:\n # 1. cohesive energy\n # 2. phonon frequencies\n # 3. pressue\n # 4. delta\n # 5. bands distance\n self.ctx.convergence_inputs = dict()\n\n convergence_inputs = self.exposed_inputs(\n _BaseConvergenceWorkChain, namespace=\"convergence\"\n )\n convergence_inputs[\"code\"] = self.inputs.pw_code\n convergence_inputs[\"pseudo\"] = self.inputs.pseudo\n convergence_inputs[\"options\"] = self.inputs.options\n convergence_inputs[\"parallelization\"] = self.inputs.parallelization\n\n convergence_inputs[\"clean_workdir\"] = self.inputs.clean_workdir\n\n for prop in [\"delta\", \"pressure\"]:\n self.ctx.convergence_inputs[prop] = convergence_inputs.copy()\n\n # The cohesive energy evaluation may hit the ran out of memory issue,\n # so use the pw_code_large_memory if provided.\n if \"convergence.cohesive_energy\" in self.ctx.properties_list:\n inputs_cohesive_energy = convergence_inputs.copy()\n if \"pw_code_large_memory\" in self.inputs:\n inputs_cohesive_energy[\n \"pw_code_large_memory\"\n ] = self.inputs.pw_code_large_memory\n\n self.ctx.convergence_inputs[\"cohesive_energy\"] = inputs_cohesive_energy\n\n # Here, the shallow copy can be used since the type of convergence_inputs\n # is AttributesDict.\n # The deepcopy can't be used, since it will create new data node.\n if \"convergence.phonon_frequencies\" in self.ctx.properties_list:\n inputs_phonon_frequencies = convergence_inputs.copy()\n inputs_phonon_frequencies.pop(\"code\", None)\n inputs_phonon_frequencies[\"pw_code\"] = self.inputs.pw_code\n inputs_phonon_frequencies[\"ph_code\"] = self.inputs.ph_code\n inputs_phonon_frequencies[\"clean_workdir\"] = orm.Bool(\n False\n ) # For phonon frequencies convergence workflow, the clean dir is taken care by the the finalize step of the verification workflow.\n\n self.ctx.convergence_inputs[\n \"phonon_frequencies\"\n ] = inputs_phonon_frequencies\n\n if \"convergence.bands\" in self.ctx.properties_list:\n inputs_bands = convergence_inputs.copy()\n inputs_bands[\"clean_workdir\"] = orm.Bool(\n False\n ) # For bands convergence workflow, the clean dir is taken care by the the finalize step of the verification workflow.\n\n self.ctx.convergence_inputs[\"bands\"] = inputs_bands\n\n # Caching inputs setting\n # The running strategy of caching is:\n # 1. run phonon_frequencies/bands convergence workflow\n # 2. run cleandir for workchains (which will be the finalize step of phonon_frequencies/bands convergence workflow)\n # 3. run cohesive_energy/pressure/delta convergence workflow which will use the cached data and clean on the fly\n # 4. get the recommended cutoffs\n # 5. run measure workflow using the recommended cutoffs\n self.ctx.caching_inputs = convergence_inputs.copy()\n self.ctx.caching_inputs[\"clean_workdir\"] = orm.Bool(\n False\n ) # shouldn't clean until last, default of _caching but do it here explicitly\n\n # to collect workchains in a dict\n self.ctx.workchains = dict()\n\n # For store the finished_ok workflow\n self.ctx.finished_ok_wf = dict()\n\n def inspect_measure(self):\n \"\"\"Inspect delta measure results\"\"\"\n return self._report_and_results(wname_list=self._VALID_MEASURE_WF)\n\n def is_verify_convergence(self):\n \"\"\"Whether to run convergence test workflows\"\"\"\n if \"_caching\" in self.ctx.properties_list:\n # for only run caching workflow\n return True\n\n for p in self.ctx.properties_list:\n if \"convergence\" in p:\n return True\n\n return False\n\n def is_caching(self):\n \"\"\"run caching when more than one convergence test\"\"\"\n # If the aiida config set pw caching off, then not caching any\n from aiida.manage.caching import get_use_cache\n\n identifier = \"aiida.calculations:quantumespresso.pw\"\n return get_use_cache(identifier=identifier)\n\n def run_caching(self):\n \"\"\"run pressure verification for caching\"\"\"\n running = self.submit(_CachingConvergenceWorkChain, **self.ctx.caching_inputs)\n self.report(\n f\"The caching is triggered, submit and run caching \"\n f\"workchain pk={running.pk} for following convergence test.\"\n \"\"\n )\n\n return ToContext(verify_caching=running)\n\n def inspect_caching(self):\n \"\"\"Simply check whether caching run finished okay.\"\"\"\n workchain = self.ctx.verify_caching\n\n if not workchain.is_finished_ok:\n return self.exit_codes.ERROR_CACHING_ON_BUT_FAILED\n\n def _run_convergence(self, plist):\n for property in self.ctx.properties_list:\n property_name = property.split(\".\")[1]\n if property in plist:\n ConvergenceWorkflow = WorkflowFactory(f\"sssp_workflow.{property}\")\n\n running = self.submit(\n ConvergenceWorkflow, **self.ctx[\"convergence_inputs\"][property_name]\n )\n self.report(\n f\"Submit {property_name} convergence workchain pk={running.pk}\"\n )\n\n self.to_context(_=running)\n self.ctx.workchains[f\"{property}\"] = running\n\n def run_remote_folder_dependent_convergence(self):\n \"\"\"\n running convergence workflow that requires remote_folder not cleaned, e.g. phonon_frequencies, bands\n \"\"\"\n self._run_convergence(_REMOTE_FOLDER_DEPENDENT_CONVERENCE_PROPERTIES_LIST)\n\n def run_remote_folder_independent_convergence(self):\n \"\"\"\n running convergence workflow that requires remote_folder not cleaned, e.g. phonon_frequencies, bands\n \"\"\"\n self._run_convergence(_REMOTE_FOLDER_INDEPENDENT_CONVERGENCE_PROPERTIES_LIST)\n\n def inspect_convergence(self):\n \"\"\"\n inspect the convergence result\n\n the list set the avaliable convergence workchain that will be inspected\n \"\"\"\n return self._report_and_results(wname_list=self._VALID_CONGENCENCE_WF)\n\n def is_verify_measure(self):\n \"\"\"\n Whether to run measure (delta measure, bands distance} workflow.\n \"\"\"\n for p in self.ctx.properties_list:\n if \"measure\" in p:\n return True\n\n return False\n\n def run_measure(self):\n \"\"\"Run delta measure sub-workflow\"\"\"\n for property in DEFAULT_MEASURE_PROPERTIES_LIST:\n property_name = property.split(\".\")[1]\n if property in self.ctx.properties_list:\n MeasureWorkflow = WorkflowFactory(f\"sssp_workflow.{property}\")\n\n running = self.submit(\n MeasureWorkflow, **self.ctx[\"measure_inputs\"][property_name]\n )\n self.report(f\"Submit {property_name} measure workchain pk={running.pk}\")\n\n self.to_context(_=running)\n self.ctx.workchains[f\"{property}\"] = running\n\n def _report_and_results(self, wname_list):\n \"\"\"result to respective output namespace\"\"\"\n\n not_finished_ok_wf = {}\n for wname, workchain in self.ctx.workchains.items():\n if wname in wname_list:\n # dump all output as it is to verification workflow output\n self.ctx.finished_ok_wf[wname] = workchain.pk\n for label in workchain.outputs:\n # output node and namespace -> verification workflow outputs\n self.out(f\"{wname}.{label}\", workchain.outputs[label])\n\n if not workchain.is_finished_ok:\n self.logger.warning(\n f\"The sub-workflow {wname} pk={workchain.pk} not finished ok.\"\n )\n not_finished_ok_wf[wname] = workchain.pk\n\n if not_finished_ok_wf:\n return self.exit_codes.WARNING_NOT_ALL_SUB_WORKFLOW_OK.format(\n processes=not_finished_ok_wf\n )\n\n def on_terminated(self):\n super().on_terminated()\n\n if not self.inputs.clean_workdir.value:\n self.report(f\"{type(self)}: remote folders will not be cleaned\")\n","repo_name":"aiidateam/aiida-sssp-workflow","sub_path":"aiida_sssp_workflow/workflows/verifications.py","file_name":"verifications.py","file_ext":"py","file_size_in_byte":16435,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74367012242","text":"# -*- coding: utf-8 -*-\r\n# @Time : 2022/7/1 23:10\r\n# @Author : yxq\r\n# @File : main.py\r\n\r\nfrom vocab import *\r\nfrom models import *\r\nfrom data import *\r\nfrom utils import *\r\nfrom config import Config\r\nfrom torch import optim\r\nimport os\r\n\r\ncfg = Config()\r\ntorch.backends.cudnn.enabled = True\r\ntorch.backends.cudnn.benchmark = True\r\n\r\n\r\ndef main():\r\n # 设备配置\r\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n\r\n # 数据准备\r\n corpus, vocab = load_reuters()\r\n # datas = NGramDataSet(corpus, vocab, cfg.window_size)\r\n # data_loader = get_loader(datas, cfg.batch_size, shuffle=True)\r\n datas = RNNDataSet(corpus, vocab)\r\n data_loader = get_loader(datas, 16, shuffle=True)\r\n\r\n # 模型配置\r\n # model = NGramLM(len(vocab), cfg.embedding_dim, cfg.hidden_size, cfg.window_size).to(device)\r\n model = RNNLM(len(vocab), 32, 64).to(device)\r\n # criterion = nn.NLLLoss().to(device)\r\n criterion = nn.NLLLoss(ignore_index=datas.pad).to(device)\r\n optimizer = optim.Adam(model.parameters(), lr=cfg.learning_rate)\r\n\r\n # 模型训练\r\n model.train()\r\n losses = []\r\n for epoch in range(cfg.num_epoch):\r\n total_loss = 0\r\n for batch in tqdm(data_loader, desc=f\"Training Epoch {epoch}\"):\r\n inputs, targets = [x.to(device) for x in batch]\r\n preds = model(inputs)\r\n # loss = criterion(preds, targets)\r\n loss = criterion(preds.view(-1, preds.shape[-1]), targets.view(-1)) # RNN时，需要将target变为1维， preds变为2维\r\n\r\n optimizer.zero_grad()\r\n loss.backward()\r\n optimizer.step()\r\n total_loss += loss.item()\r\n print(f\"Total loss: {total_loss: .2f}\")\r\n losses.append(total_loss)\r\n # save_pretrained(vocab, model.embedding.weight.data, os.path.join(cfg.pretrained_embedding_path, 'embeddings.txt'))\r\n save_pretrained(vocab, model.embedding.weight.data, os.path.join(cfg.pretrained_embedding_path, 'rnn_embeddings.txt'))\r\n plot_loss(losses)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"yxq9710/learning_pytorch","sub_path":"Language_Model/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9492510233","text":"#!/usr/bin/python2.7\n\"\"\"\n/***********************************************************************\n** File: Assignment 4: Query Processing\n** Name: Francisco Munoz\n** Date: 06/17/2020\n**************************\n** Change History\n**************************\n** PR Date Author Description \n** -- -------- ------- ------------------------------------\n** 1 06/17/2020 fmuno003 Initial Creation\n** \n*********************************************************************/\n\"\"\"\n\nimport psycopg2\nimport os\nimport sys\n\noutputRangeFile = 'RangeQueryOut.txt'\noutputPointFile = 'PointQueryOut.txt'\nnew_list = []\n\ndef RangeQuery(ratingsTableName, ratingMinValue, ratingMaxValue, openconnection):\n if(ratingMinValue < 0.0 or ratingMaxValue > 5.0):\n return\n\n new_list = []\n\n with openconnection.cursor() as cursor:\n sqlSelectCommand = \"SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'\"\n cursor.execute(sqlSelectCommand)\n tables = cursor.fetchall()\n\n for each_table in tables:\n if not (each_table[0] == 'rangeratingsmetadata' or each_table[0] == 'roundrobinratingsmetadata'):\n sqlSelectCommand = \"SELECT * FROM {} WHERE Rating >= {} AND Rating <= {}\".format(each_table[0] , ratingMinValue, ratingMaxValue)\n cursor.execute(sqlSelectCommand)\n values = cursor.fetchall()\n\n for each_value in values:\n newRow = \"{},{},{},{}\".format(each_table[0], each_value[0], each_value[1], each_value[2])\n new_list.append(newRow)\n\n writeToFile(outputRangeFile, new_list)\n cursor.close()\n\ndef PointQuery(ratingsTableName, ratingValue, openconnection):\n if(ratingValue > 5.0 or ratingValue < 0.0):\n return\n\n new_list = []\n\n with openconnection.cursor() as cursor:\n sqlSelectCommand = \"SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'\"\n cursor.execute(sqlSelectCommand)\n tables = cursor.fetchall()\n\n for eachTable in tables:\n if not (eachTable[0] == 'rangeratingsmetadata' or eachTable[0] == 'roundrobinratingsmetadata'):\n sqlSelectCommand = \"SELECT * FROM {} WHERE Rating = {}\".format(eachTable[0], ratingValue)\n cursor.execute(sqlSelectCommand)\n values = cursor.fetchall()\n\n for eachValue in values:\n newRow = \"{},{},{},{}\".format(eachTable[0], eachValue[0], eachValue[1], eachValue[2])\n new_list.append(newRow)\n\n writeToFile(outputPointFile, new_list)\n cursor.close()\n\ndef writeToFile(filename, rows):\n f = open(filename, 'w')\n for line in rows:\n f.write(line)\n f.write('\\n')\n f.close()\n","repo_name":"fmuno003/CSE511","sub_path":"Assignment_4/Interface.py","file_name":"Interface.py","file_ext":"py","file_size_in_byte":2786,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"1009697453","text":"from DataScienceExamples.Data.AdditionMultiplicationTables import subtract\nfrom DataScienceExamples.Data.NumbersProperties import compare\nfrom DataScienceExamples.Data.AdditionMultiplicationTables import dropzeroes\nfrom DataScienceExamples.Data.AdditionMultiplicationTables import multiplication\ndef inverseNumber(ar1,p=1):\n if not(isinstance(ar1,str) and isinstance(p,int)):\n raise TypeError('Only str attribute and int precision!')\n if not(len(ar1)>0 and p>0):\n raise AttributeError('No empty str is required and only positive precision which is greater than zero is permitted!')\n sign = ''\n if(ar1[0]=='-'):\n ar1 = ar1[1:]\n sign = '-'\n ini = '10'; an = '0.' #1/number\n l = len(ar1)\n i = 1; j = 0; q = 0\n while (i < l): #25 - 100 0.0\n ini += '0'\n an += '0'\n i += 1\n r = ini\n per = r # the head of period for periodic numbers\n i = 0\n while(ini!='00'):# has remainder\n while(compare(r,ar1)>=0):# can divide\n r = subtract(r,ar1)\n j+=1# 25 - 4.\n an+=str(j) # 0.04\n ini = r+'0'\n print(ar1 + ': ' + str(ini))\n if(ini=='00'): # end\n break\n i = 0\n while (compare(ini,ar1)<0):\n ini += '0'\n an += '0'\n i += 1\n r = ini\n j = 0\n if(r==per):\n q+=1\n if(q>=p):\n break\n return sign+dropzeroes(an)\n\ndef dividertoint(ar1,ar2):\n if not(isinstance(ar1,str) and isinstance(ar2,str)):\n raise TypeError('Only str attribute and int precision!')\n if not(len(ar1)>0 and len(ar2)>0):\n raise AttributeError('No empty str are required!')\n li1 = ar1.split('.')\n num = li1[0]; numr = ''\n if(len(li1)==2):\n numr = li1[1]\n li2 = ar2.split('.')\n if(len(li2)==1):\n return ar1,ar2\n\n ds = len(li2[1])\n integer = li2[0]\n real = li2[1]\n i = 0\n d = '' if integer=='0' else integer\n while(i0 or len(ar2)>0):\n raise AttributeError('At least one no empty str is required!')\n elif (not (len(ar1) > 0 and len(ar2) > 0) and (len(ar1) > 0 or len(ar2) > 0)): # XOR = AND(NOT(AND(P1,P2)),OR(P1,P2)) = NOT(P1 AND P2) AND (P1 OR P2))\n return ar1 if len(ar1)>0 else ar2 # Neitral means 1\n elif(ar1 == '0' and ar2 !='0'):\n return '0'\n elif(ar1 =='0' or ar2=='0'):\n raise ArithmeticError('Undefined!')\n ar1,ar2 = dividertoint(ar1,ar2)\n ar2 = inverseNumber(ar2)\n return multiplication(ar1,ar2)\n\ndef binary(ar1):\n if not (isinstance(ar1, str)):\n raise TypeError('Only str attributes!')\n if not(len(ar1)>0):\n raise AttributeError('No empty str is required!')\n elif(len(ar1)==1 and ar1[0]=='0'):\n return '0'\n\n li = ar1.split('.')\n num = li[0]\n r = num\n ans = ''\n while(r[0]!='0' or len(r)!=1):\n r = division(r,'2')\n l = r.split('.')\n re = l[1]\n if(re[0]=='0'):\n ans+='0'\n r = l[0] # 64\n else:\n ans+='1'\n r = l[0]\n if(r[0]=='1' and len(r)==1):\n ans+='1' #\n break\n return ans","repo_name":"Osipo/FuzzySystemsLabs","sub_path":"DataScienceExamples/Data/Division.py","file_name":"Division.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38720237240","text":"#!/usr/bin/env python3\nimport pandas as pd\nimport numpy as np\nimport sys \n\n# The data file needs to be tested\ntestFileName = sys.argv[1]\n# The source of truth \ncompareFileName = sys.argv[2]\n# Read in the test file\ntestFile = pd.read_excel(testFileName, sheetname = 0, header=0)\n# Fill NAN with zero\ntestFile.fillna(0, inplace=True)\n# Read in the scorecard file \nscorecardData = pd.read_excel(compareFileName, sheetname = 0, header=0)\n\npersonIds = testFile['PERSON_ID']\ncomparePersonIds = scorecardData['PERSON_ID']\n# Join two files by organisation description and year \nmergedData = pd.merge(personIds, comparePersonIds, indicator=True, how='outer')\n# Compare mersures \nfindMissings = mergedData[mergedData[\"_merge\"] != \"both\"]\nprint(compareResult)\n","repo_name":"rachelruifeng/PythonTraining","sub_path":"compareFiles.py","file_name":"compareFiles.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74554134162","text":"from django.apps import apps\nfrom django.core.exceptions import PermissionDenied\nfrom django.shortcuts import redirect\nfrom django.views.generic import View\nfrom django.urls import reverse\n\n\nclass InlineActionView(View):\n\n instance = None\n\n def get(self, request, **kwargs):\n app_label, parent_model_name = request.path.split('/')[2:4]\n\n model = apps.get_model(\n app_label=app_label,\n model_name=kwargs['model_name'],\n )\n obj = model.objects.get(pk=self.kwargs['pk'])\n\n parent_model = apps.get_model(\n app_label=app_label,\n model_name=parent_model_name,\n )\n parent_obj = parent_model.objects.get(pk=self.kwargs['parent_pk'])\n\n action_func = getattr(self.instance, kwargs['action'])\n\n if getattr(action_func, 'permissions', None) is not None:\n if not self.instance.check_inline_permissions(request, action_func.permissions, obj, parent_obj):\n raise PermissionDenied\n\n ret = action_func(request, obj, parent_obj)\n\n if ret is not None:\n return ret\n\n back_url = reverse(\n 'admin:%(app_label)s_%(parent_model_name)s_change' % {\n 'app_label': app_label,\n 'parent_model_name': parent_model_name,\n },\n kwargs={'object_id': kwargs['parent_pk']},\n )\n\n return redirect(back_url)\n","repo_name":"kvirt/django-inlines-actions","sub_path":"inlines_actions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"72257772880","text":"import numpy as np\r\nfrom math import sqrt\r\nfrom collections import Counter\r\n\r\ndef KNN_classifier(k,X_train,Y_train,x):\r\n #assert\r\n assert 1 <= k <= X_train.shape[0],\"k must legal\"\r\n assert X_train.shape[0] == Y_train.shape[0],\"the lines of X_train & Y_train must be equal\"\r\n assert X_train.shape[1] == x.shape[0],\"the columns of X_train & x must be equal\"\r\n #Algorithm\r\n distances = [sqrt(np.sum((x_train - x)**2)) for x_train in X_train]\r\n nearest = np.argsort(distances)\r\n\r\n nearest_label = [Y_train[i] for i in nearest[:k]]\r\n votes = Counter(nearest_label)\r\n\r\n return votes.most_common(1)[0][0]\r\n","repo_name":"Euraxluo/ProjectPractice","sub_path":"MachineLearning/MLstu/ML/KNN_function.py","file_name":"KNN_function.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10718308261","text":"import tensorflow as tf\nimport pandas as pd\nfrom keras.callbacks import CSVLogger\nimport numpy as np\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport seaborn as sn\nimport matplotlib as plt\nimport dataframe_image as dfi\nfrom yellowbrick.classifier import ClassificationReport\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\n\n#Plotting the Training and Testing accuracy \n\n#Original CNN model\nreconstructed_model = tf.keras.models.load_model(\"/home/pruthvi/Desktop/tensorflow_project/rps.h5\")\n#CNN model with Dropout layer removed\n#reconstructed_model = tf.keras.models.load_model(\"/home/pruthvi/Desktop/tensorflow_project/rps_nodropout.h5\")\n\n\n#Training log for original model\nlog_data = pd.read_csv(\"training.log\", sep=',' , engine='python')\n#Training log with for no dropout model\n#log_data = pd.read_csv(\"training2.log\", sep=',' , engine='python')\n\n#Plotting the Training and Testing accuracy\nimport matplotlib.pyplot as plt\nacc = log_data['accuracy']\nval_acc = log_data['val_accuracy']\nloss = log_data['loss']\nval_loss = log_data['val_loss']\n\nepochs = range(len(acc))\n\nplt.plot(epochs, acc, 'r', label='Training accuracy')\nplt.plot(epochs, val_acc, 'b', label='Validation accuracy')\nplt.plot(epochs, loss, 'g', label='Training loss')\nplt.plot(epochs, val_loss, 'm', label='Validation loss')\n\n\nplt.title('Training and validation accuracy and loss')\nplt.legend(loc=0)\n\n\n\n\n#Function to plot a confusion matrix\n\nimport keras_preprocessing\nfrom keras_preprocessing import image\nfrom keras_preprocessing.image import ImageDataGenerator\n\n\nTRAINING_DIR = \"/home/pruthvi/Desktop/tensorflow_project/test_data/rps/\"\ntraining_datagen = ImageDataGenerator(\n rescale = 1./255,\n rotation_range=40,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True,\n fill_mode='nearest')\n\n\nVALIDATION_DIR = \"/home/pruthvi/Desktop/tensorflow_project/test_data/rps-test-set/\"\nvalidation_datagen = ImageDataGenerator(rescale = 1./255)\n\ncfm = 0\n\ndef evaluate(reconstructed_model):\n global validation_generator\n global y_pred\n\n train_generator = training_datagen.flow_from_directory(\n TRAINING_DIR,\n target_size=(150,150),\n class_mode='categorical',\n batch_size=126\n )\n\n \n validation_generator = training_datagen.flow_from_directory(\n\tTRAINING_DIR,\n\ttarget_size=(150,150),\n class_mode='categorical',\n shuffle = False,\n batch_size=126)\n\n batch_size = 126\n num_test_samples = len(validation_generator.filenames)\n print (num_test_samples)\n Y_pred = reconstructed_model.predict_generator(validation_generator, num_test_samples // batch_size + 1)\n y_pred = np.argmax(Y_pred, axis=1)\n\n print(confusion_matrix(validation_generator.classes, y_pred))\n global cfm\n cfm = confusion_matrix(validation_generator.classes, y_pred)\n print(cfm)\n\n #Making Classification Matrix\n\n print('\\n\\nClassification Report\\n')\n target_names = ['Rock', 'Paper', 'Scissors']\n print(classification_report(validation_generator.classes, y_pred, target_names=target_names))\n global crp\n crp = classification_report(validation_generator.classes, y_pred, target_names=target_names, output_dict=True)\n\nevaluate(reconstructed_model)\nprint(cfm)\nprint(type(cfm))\nprint(cfm.shape)\n\n\n\n#Making Confusion matrix look pretty\nclasses = [\"Paper\", \"Rock\", \"Scissors\"]\n\ndf_cfm = pd.DataFrame(cfm, index = classes, columns = classes)\nplt.figure(figsize = (10,7))\ncfm_plot = sn.heatmap(df_cfm, annot=True, cmap = \"Purples\", fmt=\".3g\")\nplt.xlabel(\"Classifier Prediction\")\nplt.ylabel(\"Actual [Truth]\")\ncfm_plot.figure.savefig(\"confusion_matrix.png\")\n\n#Making Classification report look pretty\n\n#Definition of terms:\n\n#Precision - this is a measure of how exact the classifier is. (No.of true positives)/(Sum of true and false positives)\n#\"For all instances 'classified' positive, what percentage is correct?\"\n\n\n#Recall - this is a measure of how complete the classifier is : how able it is to correctly find all the true positive instances. (No. of true positives)/(Sum of true positives and false negatives)\n#For all instances that are actually positive, what percentage was classified correctly?\"\n\n\n#F1 score - Harmonic mean of precision and recall: 2/(recall^(-1) + precision^(-1))\n\ndf_crp = pd.DataFrame(crp)\nprint(df_crp.to_string())\ncrp_plot = df_crp.iloc[:3, :3].T.plot(kind='bar',figsize=(12,7))\nplt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\ncrp_plot.figure.savefig(\"classification_report.png\")\nplt.show()\n","repo_name":"phmehta95/tensorflow_project","sub_path":"plotting_script.py","file_name":"plotting_script.py","file_ext":"py","file_size_in_byte":4595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39829735736","text":"# for any vowels, insert into a PQ \n# loop twice\nclass Solution:\n def sortVowels(self, s: str) -> str:\n heap = []\n result = []\n for l in s: \n if l.lower() in (\"a\",\"e\",\"i\",\"o\",\"u\"): \n heapq.heappush(heap, l)\n for l in s: \n if l.lower() in (\"a\",\"e\",\"i\",\"o\",\"u\"): \n result.append(heapq.heappop(heap))\n else: \n result.append(l)\n return \"\".join(result)\n","repo_name":"MaTasty/Grind","sub_path":"sortVowels.py","file_name":"sortVowels.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39104006182","text":"import numpy as np\n\nfrom spafe.utils.converters import hz2mel, hz2bark, hz2erb\n\n\ndef tick_function(X, fb_type):\n \"\"\"\n Return correct converted ticks.\n\n Args:\n X (numpy.ndarray) : input ticks.\n fb_type (str) : filter banks type to choose the correct conversion.\n\n Returns:\n (numpy.ndarray) : converted ticks to the correct scale.\n \"\"\"\n return {\n \"mel\": [\"%.1f\" % z for z in [hz2mel(y) for y in X]],\n \"bark\": [\"%.1f\" % z for z in [hz2bark(y) for y in X]],\n \"gamma\": [\"%.1f\" % z for z in [hz2erb(y) for y in X]],\n }.get(fb_type, [\"%.1f\" % z for z in X])\n\n\ndef show_fbanks(\n fbanks,\n center_freqs,\n ref_freqs,\n title=\"Mel filter banks\",\n ylabel=\"Weight\",\n x1label=\"Frequency / Hz\",\n x2label=\"Frequency / mel\",\n figsize=(12, 5),\n fb_type=\"mel\",\n show_center_freqs=True,\n):\n \"\"\"\n visualize a matrix including the filter banks coordinates. Each row corresponds\n to a filter.\n\n Args:\n fbanks (numpy.ndarray) : 2d array including the the filter banks coordinates.\n ref_freqs (numpy.ndarray) : 1d array reference frequencies\n title (str) : plot title.\n (Default is \"Mel filter banks\").\n ylabel (str) : y-axis label.\n (Default is \"Weight\").\n x1label (str) : lower x-axis label.\n (Default is \"Frequency/ Hz\").\n x2label (str) : upper x-axis label.\n (Default is \"Frequency/ mel\").\n figsize (tuple) : size of figure.\n (Default is (12, 5)).\n fb_type (str) : type of filter banks.\n (Default is \"mel\").\n show_center_freqs (bool) : if true show center frequencies.\n (Default is True).\n \"\"\"\n import matplotlib.pyplot as plt\n\n # init plot\n fig = plt.figure(figsize=figsize)\n ax1 = fig.add_subplot(111)\n ax2 = ax1.twiny()\n\n # format and plot data\n xvals = ref_freqs\n data = fbanks.T\n ax1.plot(xvals, data, \"-\")\n\n if show_center_freqs:\n for f in center_freqs:\n plt.vlines(f, 0, 1, \"grey\", \":\")\n plt.text(f, 1, \"({:.1f}, {})\".format(f, 1))\n\n ax1Ticks = ax1.get_xticks()\n ax2Ticks = ax1Ticks\n\n ax2.set_xticks(ax2Ticks)\n ax2.set_xbound(ax1.get_xbound())\n ax2.set_xticklabels(tick_function(ax2Ticks, fb_type))\n\n # fix axes\n ax1.set_title(title)\n ax1.set_ylabel(ylabel)\n ax1.set_xlabel(x1label)\n ax2.set_xlabel(x2label)\n ax1.grid(True)\n plt.show()\n\n\ndef show_spectrogram(\n spectrogram,\n fs,\n xmin,\n xmax,\n ymin,\n ymax,\n dbf=80.0,\n xlabel=\"Time (s)\",\n ylabel=\"Frequency (Hz)\",\n title=\"Mel spectrogram (dB)\",\n figsize=(14, 4),\n cmap=\"jet\",\n colorbar=True,\n):\n \"\"\"\n Visualize the spectrogram.\n\n\n Args:\n feats (numpy.ndarray) : 2d array including the the features coefficients.\n fs (int) : sampling rate.\n xmin (float) : minimum x-axis value.\n xmax (float) : maximum x-axis value.\n ymin (float) : minimum y-axis value.\n ymax (float) : maximum y-axis value.\n dbf (float) : db reference value.\n (Default is 80.0).\n xlabel (str) : x-axis label.\n (Default is \"Time (s)\").\n ylabel (str) : y-axis label.\n (Default is \"Frequency (Hz)\").\n title (str) : plot title.\n (Default is \"Mel spectrogram (dB)\").\n figsize (tuple) : size of figure.\n (Default is (14, 4)).\n cmap (str) : matplotlib colormap to use.\n (Default is \"jet\").\n colorbar (bool) : if true add colorbar.\n (Default is True).\n \"\"\"\n import matplotlib.pyplot as plt\n\n # init vars\n amin = 1e-10\n magnitude = np.abs(spectrogram)\n ref_value = np.max(magnitude)\n\n # compute log spectrum (in dB)\n log_spec = 10.0 * np.log10(\n np.maximum(amin, magnitude) / np.maximum(amin, ref_value)\n )\n log_spec = np.maximum(log_spec, log_spec.max() - dbf)\n\n # Display the mel spectrogram in dB, seconds, and Hz\n plt.figure(figsize=figsize)\n mSpec_img = plt.imshow(\n log_spec,\n origin=\"lower\",\n extent=(xmin, xmax, ymin, ymax),\n aspect=\"auto\",\n cmap=cmap,\n )\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.title(title)\n plt.tight_layout()\n if colorbar:\n plt.colorbar(mSpec_img, format=\"%+2.0f dB\")\n plt.show()\n\n\ndef show_features(feats, title, ylabel, xlabel, figsize=(14, 4), cmap=\"jet\"):\n \"\"\"\n visualize a matrix including the features coefficients. Each row corresponds\n to a frame.\n\n Args:\n feats (numpy.ndarray) : 2d array including the the features coefficients.\n title (str) : plot title.\n ylabel (str) : y-axis label.\n xlabel (str) : x-axis label.\n figsize (tuple) : size of figure.\n (Default is (14, 4)).\n cmap (str) : matplotlib colormap to use.\n (Default is \"jet\").\n \"\"\"\n import matplotlib.pyplot as plt\n\n plt.figure(figsize=figsize)\n plt.imshow(\n feats.T, origin=\"lower\", aspect=\"auto\", cmap=cmap, interpolation=\"nearest\"\n )\n plt.title(title)\n plt.ylabel(ylabel)\n plt.xlabel(xlabel)\n plt.show(block=False)\n","repo_name":"SuperKogito/spafe","sub_path":"spafe/utils/vis.py","file_name":"vis.py","file_ext":"py","file_size_in_byte":5775,"program_lang":"python","lang":"en","doc_type":"code","stars":402,"dataset":"github-code","pt":"3"}
+{"seq_id":"36075117204","text":"\"\"\"\nThis module provides the basic interface to work with dynamically\nscoped variables. It provides the following functions for that:\n\nconstruct() returns a dynamic scope object and a function to introduce\nlocal dynamic bindings.\n\nfluid is the default dynamic scope set up ready to use.\n\nflet(**kw) is the function to introduce local bindings on fluid. It\nreturns a context manager that is used in combination with the with\nstatement and will handle unwinding of dynamic namespaces.\n\"\"\"\n__version__ = '0.1'\n\nfrom .fluids import DynamicScope\n\n__all__ = ['fluid', 'flet', 'construct']\n\nconstruct = DynamicScope._construct\nfluid, flet = construct()\n","repo_name":"bogiebro/dynscope","sub_path":"dynscope/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20141921715","text":"# print tree like: [[1],[2,3],[3,4,5]]\nimport tree\ndef print_tree(t):\n head = t.root \n result = [[head.data]]\n if (not head):\n return result \n que = [head] \n while (len(que) > 0):\n new_row = [] \n que_len = len(que)\n for i in range(0,que_len):\n current = que.pop(0) \n if current.left:\n que.append(current.left)\n new_row.append(current.left.data) \n if current.right:\n que.append(current.right)\n new_row.append(current.right.data)\n if len(new_row) > 0:\n result.append(new_row)\n return result\nt = tree.Tree()\nn1 = tree.Node(1)\nt.root = n1 \nn1.left = tree.Node(2)\nn1.right = tree.Node(3)\nn1.left.left = tree.Node(4)\nn1.right.left = tree.Node(5)\n \nprint(print_tree(t))","repo_name":"kathleenfwang/algorithms-and-data-structures","sub_path":"daily_byte_questions/week4to7/day36print_tree_level.py","file_name":"day36print_tree_level.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21694960936","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\na_ = 1\r\nq = 1\r\nl = 5\r\nk = 1\r\ns = 1\r\n\r\n\r\ndef func(x, t):\r\n return 0\r\n\r\n\r\ndef gamma1(t):\r\n return 0\r\n\r\n\r\ndef gamma0(t):\r\n if t < 1:\r\n return -1/k/s\r\n else:\r\n return 0\r\n\r\n\r\n\r\ndef u0n(x):\r\n return 0 #-x*q/(k*s)+l/(k*s)\r\n\r\n\r\nalpha = [1, 1]\r\nbeta = [0, 0]\r\n\r\n\r\ndef koef(old, time, tau, xrange, h):\r\n d = np.zeros(len(xrange))\r\n d[0] = gamma0(time*tau)\r\n d[-1] = gamma1(time*tau)\r\n\r\n b = np.zeros(len(xrange))\r\n b[0] = - alpha[0]/h + beta[0]\r\n b[-1] = alpha[1]/h + beta[1]\r\n\r\n c = np.zeros(len(xrange))\r\n c[0] = alpha[0]/h\r\n\r\n a = np.zeros(len(xrange))\r\n a[-1] = -alpha[1]/h\r\n\r\n for i in range(1, len(xrange)-1):\r\n a[i] = 1/2 * (tau * a_**2 / h**2)\r\n b[i] = -1 - 2 * 1/2 * (tau * a_**2 / h**2)\r\n c[i] = 1/2 * (tau * a_**2 / h**2)\r\n d[i] = (tau * a_**2 / h ** 2)*(k-1)*(old[i+1] - 2*old[i] + old[i-1]) - tau * func(xrange[i], (time - 0.5)*tau) \\\r\n - old[i]\r\n\r\n return solve(a, b, c, d, xrange)\r\n\r\n\r\ndef solve(a, b, c, d, x):\r\n A = np.zeros(len(x))\r\n A[0] = - c[0] / b[0]\r\n\r\n B = np.zeros(len(x))\r\n B[0] = d[0] / b[0]\r\n\r\n for i in range(1, len(x)):\r\n A[i] = -c[i] / (b[i] + a[i] * A[i - 1])\r\n B[i] = (d[i] - a[i] * B[i - 1]) / (b[i] + a[i] * A[i - 1])\r\n\r\n result = np.zeros(len(x))\r\n result[-1] = B[-1]\r\n\r\n for i in range(len(x) - 2, -1, -1):\r\n result[i] = B[i] + A[i] * result[i + 1]\r\n\r\n return result\r\n\r\n\r\ndef graph():\r\n left_end = 0\r\n right_end = l\r\n t0 = 0\r\n tn = 100\r\n n = 50\r\n h = (right_end - left_end) / n\r\n tau = h / 2\r\n time_range = np.linspace(t0, tn, int((tn - t0) / tau))\r\n xrange = np.arange(left_end, right_end, h)\r\n\r\n next = np.zeros(len(xrange))\r\n old_next = np.zeros(len(xrange))#u0n(xrange) #тот что был до некст\r\n\r\n for j in range(1, len(time_range) + 1):\r\n next = koef(old_next, j, tau, xrange, h)\r\n old_next = next\r\n\r\n plt.xlabel(\"x\")\r\n plt.ylabel(\"u\")\r\n plt.ylim((None, 0.5))\r\n plt.grid()\r\n plt.plot(xrange, next, label=\"t = \"+str(tn))\r\n plt.legend()\r\n\r\n plt.show()\r\n\r\n\r\ngraph()\r\n","repo_name":"respair/labs-on-numerical-methods-2","sub_path":"lab2_sem6.py","file_name":"lab2_sem6.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27708789582","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport dgl\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport math\nfrom torch.nn import init\nfrom dgl.nn import GraphConv\nfrom layer import GaussianSample\nfrom distribution import log_gaussian\nfrom distribution import log_standard_gaussian\nfrom distribution import log_standard_categorical\n\n\n# In[ ]:\n\n\nclass Encoder(nn.Module):\n def __init__(self, dims, dropout, sample_layer=GaussianSample):\n \"\"\"\n Inference network with GCN hidden layer \n and stochastic output layer\n Returns latent sample from q_φ(z|a,x) \n and the two parameters of the distribution (µ, log σ²).\n :param dims: dimensions of the networks\n given by the number of neurons on the form\n [input_dim, [hidden_dims], latent_dim].\n \"\"\"\n super().__init__()\n\n [x_dim, h_dim, z_dim] = dims\n neurons = [x_dim, *h_dim]\n graphconv_layers = [GraphConv(neurons[i-1], neurons[i], bias=False) for i in range(1, len(neurons))]\n \n self.dropout = dropout\n self.hidden = nn.ModuleList(graphconv_layers)\n self.sample = sample_layer(h_dim[-1], z_dim)\n \n def forward(self, g, x):\n for layer in self.hidden:\n x = F.relu(layer(g, x))\n x = F.dropout(x, self.dropout, training=self.training)\n \n return self.sample(g, x)\n\n \nclass Decoder_X(nn.Module):\n def __init__(self, dims, dropout):\n \n super().__init__()\n\n [z_dim, h_dim, x_dim] = dims\n\n neurons = [z_dim, *h_dim]\n linear_layers = [nn.Linear(neurons[i-1], neurons[i]) for i in range(1, len(neurons))]\n self.hidden = nn.ModuleList(linear_layers)\n self.dropout = dropout\n self.reconstruction = nn.Linear(h_dim[-1], x_dim)\n \n self.output_activation = nn.Sigmoid()\n\n def forward(self, x):\n for layer in self.hidden:\n x = F.relu(layer(x))\n x = F.dropout(x, self.dropout, training=self.training)\n \n return self.output_activation(self.reconstruction(x))\n\n\nclass Decoder(nn.Module):\n def __init__(self):\n \"\"\"\n Generative network given by inner product\n between latent variables with\n logistic sigmoid output layer\n Returns adjacency matrix A from p(A|Y,Z).\n \"\"\"\n super().__init__()\n self.output_activation = nn.Sigmoid()\n\n def forward(self, x):\n x = torch.mm(x, x.t())\n return self.output_activation(x)\n\n\nclass Classifier(nn.Module): \n def __init__(self, dims, dropout):\n \"\"\"\n Single hidden layer classifier\n with softmax output(one hot encoding)\n \"\"\"\n super().__init__()\n \n [x_dim, h_dim, y_dim] = dims\n \n self.hidden = GraphConv(x_dim, h_dim, bias=False)\n self.logits = GraphConv(h_dim, y_dim, bias=False)\n self.dropout = dropout\n \n def forward(self, g, x):\n \n x = F.relu(self.hidden(g, x))\n x = F.dropout(x, self.dropout, training=self.training)\n x = F.softmax(self.logits(g, x), dim=-1)\n \n return x \n\n\n# In[ ]:\n\n\nclass VariationalGraphAutoencoder(nn.Module):\n def __init__(self, dims):\n \"\"\"\n Variational Graph Autoencoder [Kipf & Welling 2016] model\n where an inference model parameterized by a two-layer GCN\n and an generative model is given by an inner product \n between latent variables.\n \"\"\"\n super().__init__()\n\n [x_dim, z_dim, h_dim] = dims\n self.z_dim = z_dim\n self.flow = None\n\n self.encoder = Encoder([x_dim, h_dim, z_dim], dropout=0.5)\n # self.decoder_X = Decoder_X([z_dim, list(reversed(h_dim)), x_dim], dropout=0.5)\n self.decoder = Decoder()\n self.kl_divergence = 0\n \n for m in self.modules():\n if isinstance(m, GraphConv):\n init.xavier_normal_(m.weight.data)\n if m.bias is not None:\n m.bias.data.zero_()\n \n for n in self.modules():\n if isinstance(n, nn.Linear):\n init.xavier_normal_(n.weight.data)\n if n.bias is not None:\n n.bias.data.zero_()\n\n def _kld(self, z, q_param, p_param=None):\n \"\"\"\n Computes the KL-divergence of\n some element z.\n KL(q||p) = -∫ q(z) log [ p(z) / q(z) ]\n = -E[log p(z) - log q(z)]\n :param z: sample from q-distribuion\n :param q_param: (mu, log_var) of the q-distribution\n :param p_param: (mu, log_var) of the p-distribution\n :return: KL(q||p)\n \"\"\"\n (mu, log_var) = q_param\n\n if self.flow is not None:\n f_z, log_det_z = self.flow(z)\n qz = log_gaussian(z, mu, log_var) - sum(log_det_z)\n z = f_z\n else:\n qz = log_gaussian(z, mu, log_var)\n\n if p_param is None:\n pz = log_standard_gaussian(z)\n else:\n (mu, log_var) = p_param\n pz = log_gaussian(z, mu, log_var)\n\n kl = qz - pz\n\n return kl\n\n def add_flow(self, flow):\n self.flow = flow\n\n def forward(self, g, x, y=None):\n \"\"\"\n Runs a data point through the model in order\n to provide its reconstruction and q distribution\n parameters.\n :param x: features\n :return: adjacency matrix\n \"\"\"\n z, z_mu, z_log_var = self.encoder(g, x)\n\n self.kl_divergence = self._kld(z, (z_mu, z_log_var))\n\n A = self.decoder(z)\n \n # X = self.decoder_X(z)\n\n return A #, X\n\n def sample(self, z):\n \"\"\"\n Given z ~ N(0, I) generates a sample from\n the learned distribution based on p_θ(x|z).\n :param z: Random normal variable\n :return: generated adjacency matrix A\n \"\"\"\n A = self.decoder(z)\n return A\n\n\n# In[ ]:\n\n\nclass DeepGraphGenerativeModel(VariationalGraphAutoencoder):\n def __init__(self, dims):\n \"\"\"\n \n Deep Graph Generative Model inspired from the paper\n 'Semi-Supervised Learning with Deep Generative Models'\n (Kingma 2014) in PyTorch.\n The DGGM is a probabilistic\n model that incorporates label information in both\n inference and generation.\n Initialise a new generative model\n :param dims: dimensions of x, y, z and hidden layers.\n \"\"\"\n [x_dim, self.y_dim, z_dim, h_dim] = dims\n super(DeepGraphGenerativeModel, self).__init__([x_dim, z_dim, h_dim])\n \n self.encoder = Encoder([x_dim + self.y_dim, h_dim, z_dim], dropout=0.5)\n # self.decoder_X = Decoder_X([z_dim + self.y_dim, list(reversed(h_dim)), x_dim], dropout=0.5) \n self.decoder = Decoder()\n self.classifier = Classifier([x_dim, h_dim[0], self.y_dim], dropout=0.5)\n \n for m in self.modules():\n if isinstance(m, GraphConv):\n init.xavier_normal_(m.weight.data)\n if m.bias is not None:\n m.bias.data.zero_()\n \n for n in self.modules():\n if isinstance(n, nn.Linear):\n init.xavier_normal_(n.weight.data)\n if n.bias is not None:\n n.bias.data.zero_()\n\n def forward(self, g, x, y):\n z, z_mu, z_log_var = self.encoder(g, torch.cat([x, y], dim=1))\n\n self.kl_divergence = self._kld(z, (z_mu, z_log_var))\n\n A = self.decoder(torch.cat([z, y], dim=1))\n \n # X = self.decoder_X(torch.cat([z, y], dim=1))\n\n return A #,X\n\n def classify(self, g, x):\n logits = self.classifier(g, x)\n return logits\n\n def sample(self, z, y):\n \"\"\"\n Samples from the Decoder to generate an A.\n :param z: latent normal variable\n :param y: label (one-hot encoded)\n :return: x\n \"\"\"\n \n A = self.decoder(torch.cat([z, y], dim=1))\n \n return A\n\n\n# In[ ]:\n\n\nclass AuxiliaryDeepGraphGenerativeModel(DeepGraphGenerativeModel):\n def __init__(self, dims):\n \"\"\"\n Auxiliary Deep Graph Generative Model inspired from the paper\n 'Auxiliary Deep Generative Models' (Maaløe 2016) in PyTorch.\n The ADGGM introduces an additional latent variable 'a', \n which enables the model to fit more complex variational distributions.\n :param dims: dimensions of x, y, z, a and hidden layers.\n \"\"\"\n [x_dim, y_dim, z_dim, a_dim, h_dim] = dims\n super(AuxiliaryDeepGraphGenerativeModel, self).__init__([x_dim, y_dim, z_dim, h_dim])\n\n self.aux_encoder = Encoder([x_dim, h_dim, a_dim], dropout=0.5)\n self.aux_decoder = Encoder([x_dim + z_dim + y_dim, list(reversed(h_dim)), a_dim], dropout=0.5) # adding x_dim\n self.classifier = Classifier([x_dim + a_dim, h_dim[0], y_dim], dropout=0.5)\n\n self.encoder = Encoder([a_dim + y_dim + x_dim, h_dim, z_dim], dropout=0.5)\n self.decoder = Decoder()\n self.decoder_X = Decoder_X([z_dim + y_dim + a_dim, list(reversed(h_dim)), x_dim], dropout=0.5)\n \n def _kld(self, z, q_param, p_param=None):\n\n (mu, log_var) = q_param\n\n if self.flow is not None:\n f_z, log_det_z = self.flow(z)\n qz = log_gaussian(z, mu, log_var) - sum(log_det_z)\n z = f_z\n else:\n qz = log_gaussian(z, mu, log_var)\n\n if p_param is None:\n pz = log_standard_gaussian(z)\n else:\n (mu, log_var) = p_param\n pz = log_gaussian(z, mu, log_var)\n\n kl = qz - pz\n\n return kl\n\n def forward(self, g, x, y):\n \"\"\"\n Forward through the model\n :param x: features\n :param y: labels\n :return: adjacency matrix\n \"\"\"\n q_a, q_a_mu, q_a_log_var = self.aux_encoder(g, x)\n \n z, z_mu, z_log_var = self.encoder(g, torch.cat([x, y, q_a], dim=1))\n \n p_a, p_a_mu, p_a_log_var = self.aux_decoder(g, torch.cat([x, z, y], dim=1)) # adding x\n \n A = self.decoder(torch.cat([y, z, p_a], dim=1))\n X = self.decoder_X(torch.cat([y, z, p_a], dim=1))\n \n a_kl = self._kld(q_a, (q_a_mu, q_a_log_var), (p_a_mu, p_a_log_var)) \n \n z_kl = self._kld(z, (z_mu, z_log_var))\n\n self.kl_divergence = a_kl + z_kl\n\n return A, X\n \n def classify(self, g, x): \n \n a, a_mu, a_log_var = self.aux_encoder(g, x)\n\n logits = self.classifier(g, torch.cat([x, a], dim=1))\n \n return logits\n \n def sample(self, g, z, y):\n \"\"\"\n Samples from the Decoder to generate an A.\n :param z: latent normal variable\n :param y: label (one-hot encoded)\n :return: x\n \"\"\"\n p_a, p_a_mu, p_a_log_var = self.aux_decoder(g, torch.cat([x, y, z], dim=1))\n \n # A = self.decoder(p_a)\n \n return p_a\n","repo_name":"IlgeeHong/conditionalVAE","sub_path":"fullGCN_model_with_feature.py","file_name":"fullGCN_model_with_feature.py","file_ext":"py","file_size_in_byte":10999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6011736312","text":"# Microsoft Word Mass Merge Script:\r\n\r\n# Library And Module Call List:\r\n\r\nfrom __future__ import print_function\r\nfrom mailmerge import MailMerge\r\nimport datetime\r\nimport time\r\nimport pandas\r\nimport numpy as np\r\nimport os\r\nimport json\r\n\r\n## ENVIRONMENT PROVIDED VALUES ##\r\n\r\n# Current Environment Variables:\r\n# This first set of actions capture information about the current environment for use at\r\n# later points in the script, these in memory variables can be called at anytime from this\r\n# point onward.\r\n\r\n# starttime - time stamp used for filename generation\r\nstarttime = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S%f\")\r\nprint(starttime)\r\n\r\n# usr - the username of person running the script\r\nusr = os.getlogin()\r\n\r\n## SCRIPT PULLED VALUES ##\r\n\r\n# Script Configuration Files:\r\n# The following set of commands read lines from a set of JSON files and interpret \r\n# them as variables to be used at later points within the script.\r\n\r\n# baseconfig - the file object in the line below is the primary configuration file\r\n# for all script actions, this is the only file where a recompile or line edit will\r\n# be required if it's location and/or name changes\r\nbaseconfig = json.load(open(\"###########.json\",\"r\"))\r\n\r\n# Storage Locations And Directory Variables:\r\n# The following set of variables are director locations the script will read and write \r\n# data to. Note that some paths use \"/\" instead of \"\\\", the \"\\\" symbol is read by python\r\n# as and escape character or a character that triggers the next character(s) to be\r\n# interpeted differently, in order to use the \"\\\" you need to escape it out or write\r\n# them in sets of 2 for every 1 instance.\r\n\r\n# sourcedir - the location of the source files for which new documents will obtain data\r\n# from written as a network path\r\nsourcedir = baseconfig[\"sourcedir\"].replace(\"+usr+\",usr)\r\n\r\n# sourcefiles - the file names of the data files for to be read by this script\r\nsourcefiles = baseconfig[\"sourcefiles\"]\r\n\r\n# delimiterline - the single line string that denotes the end of a given set of data for\r\n# a single document generation task\r\ndelimiterline = baseconfig[\"delimiterline\"]\r\n\r\n# templatepath - this directory is the location for which the Microsoft Word templates will\r\n# be stored\r\ntemplatepath = baseconfig[\"templatepath\"]\r\n\r\n# nontemplates - the line below checks the JSON configuration file for an array containing a\r\n# list of currently used document codes that don't have any real templates associated with them,\r\n# these codes may have other tasks associated them and thus are documented as safe to skip\r\n# exceptions\r\nnontemplates = baseconfig[\"nontemplates\"]\r\n\r\n# outputdir1 - in order for this script to produce actual documents it requires a location to\r\n# write the new files to, the lines below checks if the user already has a folder in the their\r\n# home directory for merged documents, if it does not it will create that folder labled \r\n# \"MERGE_REVIEW\", afterwards it will associate that location as the named varible \"outputdir1\"\r\nif not os.path.exists(baseconfig[\"outputdir1\"].replace(\"+usr+\",usr)):\r\n os.makedirs(baseconfig[\"outputdir1\"].replace(\"+usr+\",usr))\r\n\r\noutputdir1 = baseconfig[\"outputdir1\"].replace(\"+usr+\",usr)\r\n\r\n# File Types Produced:\r\n# A few of the files generated from this script will have similar prefix and name, the\r\n# thing the sets them appart is their suffix or file type.\r\n\r\n# outputtype1 - file type of all documents file objects produced\r\noutputtype1 = baseconfig[\"outputtype1\"]\r\n\r\n## MAIN LOOP ##\r\n\r\n# Convert Text List to Clean DataFrame:\r\n# This is the part of the script that actually reads the raw data that using\r\n# the pandas library the data is read and stored in memory as dataframe, or a pandas\r\n# in-memory table, this data can then be searched indexed and called using different pandas\r\n# commands as well as imported into other objects - which is what the next section does.\r\n# Before anything can be done with the raw data, the text needs to be cleaned up of any\r\n# unnecessary and/or incompatible hidden characters, hense the data is read through a series\r\n# of regular expression filters clearing our new line characters, tabs, and a few others.\r\n\r\nfor index in range(len(sourcefiles)):\r\n merge_data = ((pandas.DataFrame(((((open(sourcedir+sourcefiles[index], \"r\")).read()).replace('\\x12', '')).replace('\\x05', '')).split(delimiterline)))[0].apply(lambda x: pandas.Series(x.split('\\n')))).replace(np.nan, '', regex=True)[:-1]\r\n print(merge_data)\r\n colcount = str(object = len(merge_data.columns) )\r\n#\r\n# Document Generation Loop:\r\n# The follow section generates the documents from the data in a series of steps:\r\n# - The newly re-organized data in the pandas dataframe is read per column per row\r\n# at a time. The first column of each row is the actual document template code called,\r\n# if said code matches any of the codes listed the \"nontemplates\" variable the\r\n# job is skipped and the next row begins.\r\n# - Assuming the code is not listed in the \"nontemplates\", the rest of the data\r\n# will be added to a dictionary, \"mrg_dt\" where in each column's data in the current row is\r\n# associated with a number, these numbers are the field codes used in the templates\r\n# to indicate a specific data point, they are 1 to infinity so each line number is a different\r\n# mergeable data field\r\n#\r\n for index, row in merge_data.iterrows():\r\n if str(object = row[0]) in nontemplates:\r\n print(\"Document code \"+row[0]+\" is a non-template code and will automatically be skipped.\")\r\n else:\r\n print(\"Document code \"+row[0]+\" was called for data ID number \"+row[1]+\".\")\r\n if os.path.exists(templatepath+row[0]+'.docx'):\r\n print(\"A document template was detected for the document code called, proceeding to generate Microsoft Word document, please wait,...\")\r\n field_1 = str(object = row[0])\r\n print(row[0])\r\n templatefile = templatepath+row[0]+'.docx'\r\n mrg_dt = {}\r\n count = 0\r\n for i in row:\r\n count += 1\r\n mrg_dt.update({str(object = count):str(object = i)})\r\n#\r\n# File Output:\r\n# Using the previously saved variables each file is contructed with a unique file name using\r\n# a combination of the time of generation and the username of the user running the job, proceed\r\n# a set of fields defined in the configuration JSON file.\r\n#\r\n rt = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S%f\")\r\n filename = outputdir1+row[1]+'_'+row[0]+'_'+rt+'_'+usr+outputtype1\r\n document = MailMerge(templatefile)\r\n document.merge_pages([mrg_dt])\r\n document.write(filename)\r\n print(filename)\r\n if os.path.exists(filename):\r\n print(\"Document generation process sucessful, check the MERGE_REVIEW folder for the new file.\")\r\n#\r\n# Merge Error Handling:\r\n# In line with the opertunistic nature and narrow function set this script errors fall into 2 categories,\r\n# errors with the merge process or just not having a template. Errors with the merge process can occur,\r\n# if for one or more reasons, the process or actual command line running the script is interrupted or for\r\n# whatever in the rare occurance that their is incompatible formatting present in a template. Errors where\r\n# there is no template present are exactly that, the document code called does not have an associated template\r\n# in the template storage location defined above and is also not included in the file listing exceptions\r\n# that are \"nontemplate\" codes.\r\n#\r\n else:\r\n print(\"Document merge process unsucessful, please check the script or merge process for errors.\")\r\n else:\r\n print(\"Document code \"+row[0]+\" does not have an associated document template, the merge task for data ID number \"+row[1]+\" with said code was skipped.\")\r\n \r\n# Script End Time:\r\n# This last portion is included purely for testing and benchmarking purposes, in theory you can\r\n# measure the performance of a specific job on compare it to different compute environments.\r\nstoptime = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S%f\")\r\nprint(stoptime)","repo_name":"TechnoNinj4/merge_script","sub_path":"merge_script.py","file_name":"merge_script.py","file_ext":"py","file_size_in_byte":8298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33818301356","text":"# Aprimore o desafio 93 para que ele funcione com vários jogadores, incluindo um sistema de\n# visualização de detalhes do aproveitamento de cada jogador.\njogador = dict()\npartidas = list()\ntime = list()\nsoma = 0\nwhile True:\n jogador.clear()\n jogador['nome'] = str(input('Nome do Jogador: '))\n tot = int(input(f'Quantas partidas o {jogador[\"nome\"]} jogou? '))\n partidas.clear()\n for c in range(0, tot):\n partidas.append(int(input(f'Quantos gols na partida {c+1}: ')))\n jogador['gol'] = partidas[:]\n jogador['total'] = sum(partidas)\n time.append(jogador.copy())\n resp = str(input('Quer continuar?[S/N] ')).strip().upper()[0]\n while resp != 'S' and resp != 'N':\n resp = str(input('Responda somente [S/N] ')).strip().upper()[0]\n if resp == 'N':\n break\nprint('-='*30)\nprint('cod ', end='')\nfor i in jogador.keys():\n print(f'{i:<15}', end='')\nprint()\nprint('-'*40)\nfor k, v in enumerate(time):\n print(f'{k:>3} ', end='')\n for d in v.values():\n print(f'{str(d):<15}', end='')\n print()\nprint('-'*40)\nwhile True:\n busca = int(input('Quer mostrar dados de qual jogador? (999 para sair) '))\n print('-'*40)\n if busca == 999:\n break\n if busca >= len(time):\n print(f'ERRO! Não existe jogador com código {busca}!')\n else:\n print(f' -- LEVANTAMENTO DO JOGADOR {time[busca][\"nome\"]} -- ')\n for i, g in enumerate(time[busca][\"gol\"]):\n print(f' => No jogo {i+1}, fez {g} gols')\n print('-'*40)\nprint('<< Volte Sempre >>')\n","repo_name":"mroya/ProjectGit","sub_path":"Python/Curso em Video Python/Aula19-Dicionários/ex095_Aprimorando Dicionários.py","file_name":"ex095_Aprimorando Dicionários.py","file_ext":"py","file_size_in_byte":1540,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"4994410593","text":"__author__ = 'manishankargoswami'\n\nimport cherrypy as cp\nfrom cherrypy.process.plugins import Daemonizer,PIDFile\nimport argparse\nfrom cherrypy.wsgiserver import CherryPyWSGIServer\nfrom cherrypy.process.servers import ServerAdapter\n\n\ndef run_decoupled(app, host='0.0.0.0', port=8080, **config):\n server = CherryPyWSGIServer((host, port), app, **config)\n try:\n server.start()\n except KeyboardInterrupt:\n server.stop()\n\n\ndef run_in_cp_tree(app, host='0.0.0.0', port=8080, pidfile='/tmp/', **config):\n cp.tree.graft(app, '/')\n cp.config.update(config)\n cp.config.update({\n 'server.socket_port': port,\n 'server.socket_host': host\n })\n Daemonizer(cp.engine).subscribe()\n PIDFile(cp.engine, pidfile).subscribe()\n cp.engine.start()\n\n\ndef run_with_adapter(app, host='0.0.0.0', port=8080, config=None, **kwargs):\n cp.server.unsubscribe()\n bind_addr = (host, port)\n cp.server = ServerAdapter(cp.engine,\n CherryPyWSGIServer(bind_addr, app, **kwargs),\n bind_addr).subscribe()\n cp.config.update({\n 'global': {\n 'engine.autoreload.on': False\n }\n })\n if config:\n cp.config.update(config)\n cp.engine.signals.subscribe() # optional\n cp.engine.start()\n cp.engine.block()\n","repo_name":"manigoswami/lightningbolt","sub_path":"src/serverlib.py","file_name":"serverlib.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"41758038106","text":"# Imports\nimport sys\nimport pandas as pd\nfrom sklearn import linear_model\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import PolynomialFeatures\n\n# Reading CSV dataset with ANSI encoding as it includes German Tokens\ndf = pd.read_csv(r\"dataset/accident_by_date_insgesamt.csv\", encoding=\"utf8\")\ndf.rename(columns = {'Unnamed: 0':'Date'}, inplace = True)\n# df.tail()\n\n# Regression Setter for Alkoholunfälle: insgesamt\nX = df['Date']\nx = pd.DataFrame(X)\ny_alcohol_subtotal = df['alcohol_subtotal']\n\n# Model\nx_train, x_test, y_train, y_test = train_test_split(x, y_alcohol_subtotal, test_size = 0.1)\npoly_reg = PolynomialFeatures(degree = 2)\nx_train = poly_reg.fit_transform(x_train)\nx = poly_reg.fit_transform(x)\nreg = LinearRegression()\nreg.fit(x_train, y_train)\nreg.fit(x, y_alcohol_subtotal)\nscore = reg.score(poly_reg.fit_transform(x_test), y_test)\n# print('Score of model: ', score)\n\n# Prediction\nprint(reg.predict(poly_reg.fit_transform([[int(sys.argv[1])]]))[0])","repo_name":"SwagatSBhuyan/Accident-Forcasting-using-LSTM---DPS-AI-Track","sub_path":"forecasting_script.py","file_name":"forecasting_script.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"41506453166","text":"from rest_framework import permissions\n\nclass IsStaffEditorPermissions(permissions.DjangoModelPermissions):\n perms_map = {\n 'GET': ['%(app_label)s.add_%(model_name)s'],\n 'OPTIONS': [],\n 'HEAD': [],\n 'POST': ['%(app_label)s.add_%(model_name)s'],\n 'PUT': ['%(app_label)s.change_%(model_name)s'],\n 'PATCH': ['%(app_label)s.change_%(model_name)s'],\n 'DELETE': ['%(app_label)s.delete_%(model_name)s'],\n }\n # def has_permission(self, request, view):\n # # if not request.user.username == \"levi\":\n # # return False\n # if not request.user.is_staff: # checks if staff yser\n # return False\n # return super().has_permission(request, view)\n\n# class IsStaffEditorPermissions(permissions.DjangoModelPermissions):\n# def has_permission(self, request, view):\n# user = request.user\n# print(user.get_all_permissions())\n# if request.user.is_staff: # check if staff\n# # \"posts.view_post\" <-- syntax\n# if user.has_perm(\"products.add_product\"): # \"app_name.verb_model_name\"\n# return True\n# if user.has_perm(\"products.change_product\"):\n# return True\n# if user.has_perm(\"products.delete_product\"):\n# return True\n# if user.has_perm(\"products.view_product\"):\n# return True\n# return False\n# return False\n\n # def has_object_permission(self, request, view, obj):\n # return obj.owner == request.user # not worried about obj permission, ex in django docs","repo_name":"VioGarden/drp1","sub_path":"backend/api/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20984572207","text":"# coding: utf-8\n# 適当なサンプルデータを生成するスクリプトです。実行すると sample.csv が作成されますが、iamachへの読み込みは .xlsx に変換して読み込ませてください。希望順位が高い研究室には履修ポイントが高く出るようになります。\n# ID,開始時刻,完了時刻,メール,名前,学修番号,氏名,累積GPA,取得単位総計,第k希望 スタジオ,第k希望 履修ポイント, メールアドレス（緊急時に連絡の取れるアドレス）, 携帯電話（緊急時に連絡の取れる電話番号）\n# // Format\n# // 0: タイムスタンプ\n# // 1: メールアドレス\n# // 2: 学習番号\n# // 3: 氏名\n# // 4: GPA\n# // 5: 単位取得数\n# // 6+n*2: 研究室の希望数値（第何希望なのか？）\n# // 6+n*2+1: 研究室履修ポイント\nimport csv\nimport random\nimport uuid\n\nfilename_output = \"sample.csv\"\nnumber_of_row = 50 # 50人分\narray_studio = [\n \"Editorial\", \"EquipmentService\", \"Ergonomics\", \"Interactive\",\n \"Interface\", \"Interior\", \"Kinematograph\", \"Network\", \"Software\",\n \"Spatial\", \"Transportation\", \"VisualCommunication\"\n]\n# csvファイルの最初の列が指定する文字列を含めばその行を別ファイルに出力する例\nf_out = open(filename_output, 'w')\n\nprint('timestamp', 'email', 'number', 'name',\n 'GPA', 'credit', sep=',', end=',', file=f_out)\nfor studio in array_studio:\n print(studio, 'score', sep=',', end=',', file=f_out)\nprint('', end='\\n', file=f_out)\n\nfor id in range(0, number_of_row):\n print('timestamp', 'email', 'number',\n str(id),\n str(random.uniform(2.0, 4.0)),\n str(random.randint(94, 131)),\n sep=',', end=',', file=f_out)\n\n rank_choice = list(range(1, len(array_studio)+1))\n random.shuffle(rank_choice)\n\n count = 0\n for studio in array_studio:\n score = int(5 *\n ((len(array_studio)-rank_choice[count]))/float(len(array_studio)))\n print(rank_choice[count], score)\n print(rank_choice[count],\n str(random.randint(int(score), 5)),\n sep=',', end=',', file=f_out)\n count += 1\n\n print('', end='\\n', file=f_out)\nf_out.close()\n","repo_name":"TomohikoMukai/iamatch","sub_path":"sample_data/createSampleCsv.py","file_name":"createSampleCsv.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"23870985415","text":"\"\"\"Interface to Logind over D-Bus.\"\"\"\nimport logging\n\nfrom dbus_fast.aio.message_bus import MessageBus\n\nfrom ..exceptions import DBusError, DBusInterfaceError\nfrom .const import DBUS_NAME_LOGIND, DBUS_OBJECT_LOGIND\nfrom .interface import DBusInterface\nfrom .utils import dbus_connected\n\n_LOGGER: logging.Logger = logging.getLogger(__name__)\n\n\nclass Logind(DBusInterface):\n \"\"\"Logind function handler.\n\n https://www.freedesktop.org/software/systemd/man/org.freedesktop.login1.html\n \"\"\"\n\n name = DBUS_NAME_LOGIND\n bus_name: str = DBUS_NAME_LOGIND\n object_path: str = DBUS_OBJECT_LOGIND\n\n async def connect(self, bus: MessageBus):\n \"\"\"Connect to D-Bus.\"\"\"\n _LOGGER.info(\"Load dbus interface %s\", self.name)\n try:\n await super().connect(bus)\n except DBusError:\n _LOGGER.warning(\"Can't connect to systemd-logind\")\n except DBusInterfaceError:\n _LOGGER.info(\"No systemd-logind support on the host.\")\n\n @dbus_connected\n async def reboot(self) -> None:\n \"\"\"Reboot host computer.\"\"\"\n await self.dbus.Manager.call_reboot(False)\n\n @dbus_connected\n async def power_off(self) -> None:\n \"\"\"Power off host computer.\"\"\"\n await self.dbus.Manager.call_power_off(False)\n","repo_name":"home-assistant/supervisor","sub_path":"supervisor/dbus/logind.py","file_name":"logind.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":1510,"dataset":"github-code","pt":"3"}
+{"seq_id":"23327110454","text":"import os, sys, time, json\nimport ringdown\nimport numpy as np\nimport bilby.gw.utils as bgu\nimport scipy.linalg as sl\nimport matplotlib.pyplot as plt\nbase_name = os.path.expanduser('~')\n\nplt.style.use('~/Project/mine.mplstyle')\nb_file = base_name+'/Project/'\nd_file = base_name+'/Allthing/Data/'\n##########################################\nlabel_source = 'GW190521'## 'GW150914'## \n\nwith open(b_file+'Data/event_tc_ifos.json', 'r') as f:\n eti = json.load(f)\n\ntime_of_event = eti[label_source]['tc']\nprint('Event time of %s: '%label_source, time_of_event)\ndet_names = eti[label_source]['ifos']\n##########################################\n# Init CONFIG\nf_filter = 20.\nsampling_frequency = 2048\nbuffer_time = 0.5\nslice_duration = 4.\nduration_longer = 16.\ndata_len = {'H1': 2046., 'L1': 2046., 'V1': 512.} ## 512 for GW190521 V1, 2046 for others\nstrain_data = {name:{} for name in det_names}\nrandom_strain = {name:{} for name in det_names}\nacfs = {name:{} for name in det_names}\nPSDs = {name:{} for name in det_names}\nPSDs_4s = {name:{} for name in det_names}\nacfs_mix = {name:{} for name in det_names}\ncovariance_inverse = {name:{} for name in det_names}\ntime_se = {'start_time':time_of_event-slice_duration*2., 'end_time':time_of_event+slice_duration*2.}\n\nfrom pycbc.psd import interpolate as ppi\nfrom pycbc.psd import inverse_spectrum_truncation as ppist\n\n##########################################\nfor det in det_names:\n frame_file = d_file+'GW/%s-%s_GWOSC_4KHZ_R1-%s-4096.gwf'%(det[0], det, int(time_of_event-2047))\n strain = bgu.read_frame_file(frame_file, start_time=time_of_event-data_len[det], end_time=time_of_event+data_len[det], buffer_time=buffer_time, channel='%s:GWOSC-4KHZ_R1_STRAIN'%det).to_pycbc()\n ## nan_index = np.isnan(strain.data)## nan data of GW190521 in V1 is during [t_c-744, t_c-735]\n ## strain.data = np.where(nan_index, 0., strain.data)\n #######################################\n strain = strain.resample(1./sampling_frequency).highpass_fir(f_filter, order=512)\n strain_data[det] = strain.time_slice(time_se['start_time'], time_se['end_time'])\n data = ringdown.Data(strain.data, index=strain.sample_times, ifo=det)\n acf = data.get_acf()## for TTD1\n acfs[det] = acf.values[:int(slice_duration/strain.delta_t+10)]\n #######################################\n ## for TTD2\n psd0 = ppi(strain.psd(duration_longer), strain.delta_f)\n psd0 = ppist(psd0, int(duration_longer*strain.sample_rate), low_frequency_cutoff=f_filter)\n PSDs[det] = ppi(psd0, 1./duration_longer)\n ## plt.style.use('~/Project/mine.mplstyle')\n ## plt.plot(PSDs[det].sample_frequencies, PSDs[det].data, label=int(duration_longer))\n acf0 = PSDs[det].astype(complex).to_timeseries()/2\n acfs_mix[det] = acf0[:int(slice_duration/strain.delta_t+10)]\n #######################################\n psd1 = ppi(strain.psd(slice_duration), strain.delta_f)\n psd1 = ppist(psd1, int(slice_duration*strain.sample_rate), low_frequency_cutoff=f_filter)\n psd1 = ppi(psd1, 1./slice_duration)\n PSDs_4s[det] = psd1.copy()\n ## plt.plot(psd1.sample_frequencies, psd1.data, label=int(slice_duration))\n ## plt.loglog()\n ## plt.legend()\n ## plt.xlim((10., 2048.))\n ## plt.xlabel('f [Hz]')\n ## plt.ylabel('PSD')\n ## plt.subplots_adjust(left=0.15,right=0.97,bottom=0.13,top=0.95)\n ## plt.savefig('./Figures/{0}-{1}_PSDs_compare.pdf'.format(label_source, det))\n ## plt.show(), exit()\n cov_func = psd1.astype(complex).to_timeseries()/2.## *strain.delta_t**2\n ## for FTD\n l_cf = len(cov_func)\n try:\n print('Begin to calculate the inverse of the matrix!!!')\n cov_m_inv = sl.solve_circulant(cov_func, np.eye(l_cf))\n except sl.LinAlgError:\n print('Maybe check why LinAlgError occur!!!')\n fn = np.fft.fft(np.eye(l_cf))/l_cf\n cov_m_inv = (fn.conj()@np.diag(1./(fn@cov_func))@fn).real\n\n covariance_inverse[det] = np.array([np.mean(cov_m_inv.diagonal(-i)+cov_m_inv.diagonal(i))/2. for i in range(l_cf)])\n #######################################\n\nkwargs = dict(f_filter=f_filter, sampling_frequency=sampling_frequency, duration_longer=duration_longer, slice_duration=slice_duration)\nf_name = b_file+'TD_likelihood/TD_data/MSI_psd_acfs_{0}_{1}-{2}Hz_t{3}s.npy'.format(label_source, int(f_filter), int(sampling_frequency), int(slice_duration))\nwith open(f_name, 'wb') as g:\n np.save(g, {'kwargs':kwargs, 'time_se':time_se, 'PSDs':PSDs, 'PSDs_4s':PSDs_4s, 'acfs':acfs, 'acfs_mix':acfs_mix, 'covariance_inverse':covariance_inverse, 'strain_data':strain_data\n})\n","repo_name":"whaitian/FD_TD_IMR","sub_path":"save_psd_acf.py","file_name":"save_psd_acf.py","file_ext":"py","file_size_in_byte":4557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43256069516","text":"from tkinter import *\r\n#main area\r\nwindow = Tk()\r\ndef myClick():\r\n mylabel = Label(window, text=\"Fauxmos\", bg=\"black\", fg=\"white\")\r\n mylabel.pack()\r\nmyButton = Button(window, text=\"Graph\", padx=50, pady=25, command=myClick, bg=\"black\", fg=\"white\")\r\n\r\nmyButton.pack()\r\nwindow.mainloop()\r\n\r\n","repo_name":"Malenkey/Comp-sci-NEA","sub_path":"creating a button.py","file_name":"creating a button.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70331473362","text":"import pyaudiowpatch as pyaudio\nimport os\nimport time\nimport wave\nimport datetime\nimport keyboard\n\n\n# Variables\n\nduration = None\nfilename = f\"sample_{datetime.datetime.now().strftime('%H-%M-%S')}.wav\"\ndirectory = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'output')\nos.makedirs(directory, exist_ok=True) \nfilepath = os.path.join(directory, filename)\nstop_recording = False\n\n\n# Create PyAudio instance via context manager.\n\nif __name__ == \"__main__\":\n with pyaudio.PyAudio() as p: \n try:\n # Get default WASAPI info\n wasapi_info = p.get_host_api_info_by_type(pyaudio.paWASAPI)\n except OSError:\n print(\"WASAPI is not available on the system. Exiting...\")\n time.sleep(2)\n exit()\n\n \n # Get default WASAPI speakers\n\n default_speakers = p.get_device_info_by_index(wasapi_info[\"defaultOutputDevice\"])\n \n if not default_speakers[\"isLoopbackDevice\"]:\n for loopback in p.get_loopback_device_info_generator():\n\n if default_speakers[\"name\"] in loopback[\"name\"]:\n default_speakers = loopback\n break\n else:\n print(\"Default loopback output device not found.\\nRun this to check available devices.\\nExiting...\\n\")\n exit()\n \n print(f\"Recording from: {default_speakers['name']}...\")\n \n waveFile = wave.open(filepath, 'wb')\n waveFile.setnchannels(default_speakers[\"maxInputChannels\"])\n waveFile.setsampwidth(pyaudio.get_sample_size(pyaudio.paInt16))\n waveFile.setframerate(int(default_speakers[\"defaultSampleRate\"]))\n \n def callback(in_data, frame_count, time_info, status):\n waveFile.writeframes(in_data)\n return (in_data, pyaudio.paContinue)\n \n with p.open(format=pyaudio.paInt16,\n channels=default_speakers[\"maxInputChannels\"],\n rate=int(default_speakers[\"defaultSampleRate\"]),\n frames_per_buffer=pyaudio.get_sample_size(pyaudio.paInt16),\n input=True,\n input_device_index=default_speakers[\"index\"],\n stream_callback=callback\n ) as stream:\n print(f'Recording started. Press spacebar to stop and save the recording...')\n start_time = time.monotonic()\n while not stop_recording:\n if keyboard.is_pressed(' '):\n stop_recording = True\n elif duration and (time.monotonic() - start_time) >= duration:\n break\n time.sleep(0.1)\n \n waveFile.close()\n \n if stop_recording:\n print(f'Recording stopped. Sample saved as \"{filename}\"...')\n else:\n print(f'Recording finished. Sample saved as \"{filename}\"...')\n \n time.sleep(1)\n \n","repo_name":"Code-Moss/SamplerPy","sub_path":"data/sampler.py","file_name":"sampler.py","file_ext":"py","file_size_in_byte":2920,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"14307067010","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\n\nfrom config import FLAGS\n\nclass LinearSamplerTimestepLayer(nn.Module):\n def __init__(self) -> None:\n super(LinearSamplerTimestepLayer, self).__init__()\n self.a = nn.Parameter(torch.tensor(0.))\n self.b = nn.Parameter(torch.tensor(0.))\n self.c = nn.Parameter(torch.tensor(0.))\n\n def forward(self, x):\n # a * image + b * noise + c * gaussian\n out = self.a * x[:, :3] + self.b * x[:, 3:6] + self.c * x[:, 6:]\n return out\n\nclass LinearSampler(nn.Module):\n def __init__(self, time_steps=20):\n super(LinearSampler, self).__init__()\n self.time_steps = time_steps\n self.layers = nn.Sequential(*[LinearSamplerTimestepLayer() for _ in range(time_steps)])\n\n def step(self, model, x_t, time_step):\n ones = torch.ones((x_t.shape[0], ), dtype=torch.long).to(x_t.device)\n t = ones * int((FLAGS.T - 1) * (time_step) / (self.time_steps - 1))\n noise = torch.randn_like(x_t).to(x_t.device)\n eps = model(x_t, t).to(x_t.device)\n z = torch.cat([x_t, eps, noise], dim=1)\n return self.layers[time_step](z)\n\n def forward(self, model, x_T):\n x_t = x_T\n if self.training:\n x_t.requires_grad = True\n for time_step in reversed(range(self.time_steps)):\n # time steps\n if self.training:\n x_t = checkpoint.checkpoint(self.step, model, x_t, time_step)\n else:\n x_t = self.step(model, x_t, time_step)\n return x_t\n\nclass MomentumSampler(nn.Module):\n def __init__(self, time_steps=10):\n super(MomentumSampler, self).__init__()\n self.time_steps = time_steps\n self.layers = nn.Sequential(*[MomentumSamplerTimeStepLayer(i + 1) for i in reversed(range(time_steps))])\n\n def step(self, model, x_t, time_step, epsilons):\n ones = torch.ones((x_t.shape[0], ), dtype=torch.long).to(x_t.device)\n t = ones * int((FLAGS.T - 1) * (time_step) / (self.time_steps - 1))\n noise = torch.randn_like(x_t).to(x_t.device)\n eps = model(x_t, t).to(x_t.device)\n epsilons[time_step] = eps\n return self.layers[time_step](x_t, torch.cat(epsilons[time_step:], dim=1), noise)\n\n def forward(self, model, x_T):\n epsilons = [None] * self.time_steps\n\n x_t = x_T\n if self.training:\n x_t.requires_grad = True\n \n for time_step in reversed(range(self.time_steps)):\n # time steps\n if self.training:\n x_t = checkpoint.checkpoint(self.step, model, x_t, time_step, epsilons)\n else:\n x_t = self.step(model, x_t, time_step, epsilons)\n \n return x_t\n\nclass MomentumSamplerTimeStepLayer(nn.Module):\n def __init__(self, time_steps) -> None:\n super(MomentumSamplerTimeStepLayer, self).__init__()\n self.a = nn.Parameter(torch.tensor(1.))\n self.b = nn.Parameter(torch.zeros(time_steps, dtype=torch.float32))\n self.c = nn.Parameter(torch.tensor(0.))\n\n def forward(self, x, epsilons, noise):\n sum_r = torch.einsum('i,bijk->bjk', self.b, epsilons[:, ::3, :, :])\n sum_g = torch.einsum('i,bijk->bjk', self.b, epsilons[:, 1::3, :, :])\n sum_b = torch.einsum('i,bijk->bjk', self.b, epsilons[:, 2::3, :, :])\n epsilons_out = torch.stack([sum_r, sum_g, sum_b], dim=1)\n out = self.a * x + epsilons_out + self.c * noise\n return out\n","repo_name":"eitanporat/dl4cv-project","sub_path":"hyper_diffusion.py","file_name":"hyper_diffusion.py","file_ext":"py","file_size_in_byte":3541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13695705334","text":"import codecs\nimport json\n\nimport pandas\nfrom faker import Faker\nimport re\n\n\nclass Graph:\n def __init__(self, n_vertices, start_id):\n self.vexnum = n_vertices\n self.start_id = start_id\n self.arcnum = 0\n self.adj = [[] for _ in range(n_vertices)]\n self.nodes = [dict(Uname=None, id=None, group=[], index=None, recommend=[]) for _ in range(n_vertices)]\n self.links = [] # 方便起见，将节点对单独存储（无向边）\n self.groups = []\n\n def give_node_data(self, circle_path):\n # id，姓名\n for i in range(self.vexnum):\n self.nodes[i] = dict(id=i + self.start_id, Uname=Faker('zh_CN').name(), group=[], index=i)\n # 群组\n with open(circle_path, 'r') as file1:\n circles = file1.readlines()\n idx = 0\n for circle in circles:\n members = re.split('[ |\\t]', circle.split('\\n')[0])\n _group = []\n # print(members)\n for i in range(1, len(members)):\n # print(int(members[i]) - self.start_id)\n memberIdx = int(members[i]) - self.start_id\n self.nodes[memberIdx]['group'].append(idx)\n _group.append(memberIdx)\n idx += 1\n self.groups.append(_group)\n\n def give_relations(self, _path):\n # 关系\n # 节点对（用于生成json）\n relations = pandas.DataFrame(pandas.read_csv(_path, sep=' ', names=['A', 'B']))\n for i in range(len(relations)):\n self.links.append(\n dict(source=int(relations.loc[i, 'A']), target=int(relations.loc[i, 'B']), relation='friend'))\n # 邻接表\n # print(self.links[0:10])\n\n for link in self.links:\n # print(link)\n self.adj[link['source'] - self.start_id].append(link['target'] - self.start_id) # 索引到nodes的index\n self.adj[link['target'] - self.start_id].append(link['source'] - self.start_id) # 无向图\n\n def form_json_data(self, savepath):\n\n file = codecs.open(savepath, 'w', 'utf-8')\n json.dump(dict(links=self.links, nodes=self.nodes), file, ensure_ascii=False, indent=1)\n\n def recommend_by_common_friend(self, userId):\n # 按共同好友数量推荐感兴趣的用户,返回推荐列表\n userIndex = userId - self.start_id\n assist = [0.0 for _ in range(self.vexnum)] # 包含所有成员的一张列表，用于记录共同好友数量\n assist[userIndex] = -1\n for friend in self.adj[userIndex]:\n assist[friend] = -1 # 直接好友标记为-1，不参与推荐\n for friend in self.adj[userIndex]:\n # 这里的friend是node的下标，表示user的好友\n for ff in self.adj[friend]:\n # 遍历朋友的朋友->ff\n if assist[ff] >= 0:\n assist[ff] += 1 # 共同好友数加一\n return assist\n\n def recommend_by_common_group(self, userId):\n userIndex = userId - self.start_id\n assist = [0.0 for _ in range(self.vexnum)] # 包含所有成员的一张列表，用于记录共同好友数量\n assist[userIndex] = -1\n # 排除已有的好友\n for friend in self.adj[userIndex]:\n # 这里的friend是node的下标，表示user的好友\n assist[friend] = -1 # 直接好友标记为-1，不参与推荐\n for group in self.nodes[userIndex]['group']:\n for member in self.groups[group]:\n if assist[member] >= 0:\n assist[member] +=1\n return assist\n\n def recommendilist(self, userId, length, friend_weight=1.5, group_weight=0.4):\n assist = []\n friend_array = [0 for _ in range(self.vexnum)]\n group_array = [0 for _ in range(self.vexnum)]\n if friend_weight != 0:\n friend_array = self.recommend_by_common_friend(userId)\n # print(friend_array)\n if group_weight != 0:\n group_array = self.recommend_by_common_group(userId)\n # print(group_array)\n for i in range(self.vexnum):\n g=1 if group_array[i]>0 else 0\n r = friend_array[i] * friend_weight *(g * group_weight +1)\n assist.append((r, i))\n assist.sort(reverse=True, key=lambda d: d[0]) # 得到推荐指数列表，排序\n recommendlist = []\n count = 0\n for j in range(self.vexnum):\n r = assist[j][0]\n idx = assist[j][1]\n if r > 0 and count < length:\n recommendinfo = {}\n recommendinfo['id'] = self.nodes[idx]['id']\n recommendinfo['Uname'] = self.nodes[idx]['Uname']\n recommendinfo['recommended'] = r\n recommendlist.append(recommendinfo)\n count += 1\n else:\n break\n return recommendlist\n\n def form_json_data_with_recommend(self, savepath):\n for idx in range(self.vexnum):\n rlist = self.recommendilist(idx + self.start_id, length=10, friend_weight=1, group_weight=0.4)\n print(rlist)\n self.nodes[idx]['recommend'] = rlist\n file = codecs.open(savepath, 'w', 'utf-8')\n json.dump(dict(links=self.links, nodes=self.nodes), file, ensure_ascii=False, indent=1)\n\n def get_user_info(self,sign):\n if type (sign) == int or (type(sign)==str and sign.isdigit() ):\n idx=int(sign)-self.start_id\n if(idx>=0 and idx 0:\n yes_no = input(\"Did you mean %s instead? Enter Yes/No\" % get_close_matches(w,data.keys())[0])\n if yes_no.lower() in [\"y\",\"yes\"]:\n return data[get_close_matches(w,data.keys())[0]]\n elif yes_no.lower() in [\"n\",\"no\"]:\n return \"The word doesn't exist.Please cross-check the spelling.\"\n else:\n return \"We did not understand your chioce. It should either be yes or no\"\n else:\n return \"The word doesn't exist.Please cross-check the spelling.\"\n\nword = input(\"Enter the word: \")\noutput = translate(word)\n\nif type(output) == list: # if output is list\n for item in output:\n print(item)\nelse:\n print(output) # if output is string\n","repo_name":"saylikale/Python-Interactive-English-Dictionary","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10057143998","text":"# -*- coding: utf-8 -*-\n\nfrom ast import Str\nfrom asyncio import constants\nfrom . import GeneralMap as GM\nimport re\nimport string\nfrom . import PostProcess\nfrom . import ConvertFix as CF\nfrom aksharamukha.ScriptMap.EastIndic import PhagsPa, Burmese\nfrom aksharamukha.ScriptMap.MainIndic import Tamil, Malayalam, Limbu, Chakma\n### Use escape char in all functions\n\ndef BengaliSubojinedVa(Strng):\n Strng = re.sub('(? modifier letter comma\n consonants = '|'.join(GM.CrunchSymbols(GM.Consonants, 'IAST'))\n Strng = re.sub('(' + consonants + ')(тАШ)', r'\\1' + '╩╗', Strng)\n\n Strng = Strng.replace('oтАШ', 'o╩╗')\n\n # reverse o' to au\n Strng = Strng.replace('o╩╗', 'au')\n\n return Strng\n\ndef segmentBurmeseSyllables(Strng):\n # segment text into syllables\n\n # https://github.com/ye-kyaw-thu/myWord/blob/main/syl_segment.py\n myConsonant = r\"сАА-сАб\"\n otherChar = r\"сАгсАдсАесАжсАзсАйсАксА┐сБМсБНсБПсБА-сБЙсБКсБЛ!-/:-@[-`{-~\\s\"\n ssSymbol = r'сА╣'\n aThat = r'сА║'\n\n BreakPattern = re.compile(r\"((? ui\n Strng = Strng.replace('\\u102D\\u102F', '\\u102F\\u102D')\n\n # mark Independent au -> o'\n Strng = Strng.replace('сАк','сАй╩╗')\n\n ## sort subjoined consonants\n yrvh = Burmese.ConsonantMap[25:27] + Burmese.ConsonantMap[28:29] + Burmese.ConsonantMap[32:33]\n yrvhsub = ['\\u103B','\\u103C','\\u103D','\\u103E']\n vir = Burmese.ViramaMap[0]\n\n for x,y in zip(yrvh,yrvhsub):\n # Undo Replace subjoining forms: exp-virama + y/r/v/h <- subjoining y/r/v/h\n Strng = Strng.replace(y, vir + vir + x)\n\n # swap tone + virama to proper order\n Strng = Strng.replace(\"сА╖сА║\", \"сА║сА╖\")\n\n # mark pure viramas\n aThat = r'сА║'\n Strng = Strng.replace(aThat, aThat + '╩╗')\n\n # mark a as glottalstop\n Strng = Strng.replace('сАб','тАЩсАб')\n\n # replace vowel + diac with vowels\n vowDep = 'сАбсА▒сАмсА║ сАбсА▒сАм сАбсАн сАбсАо сАбсАп сАбсА░ сАбсА▒'.split(' ')\n vowIndep = 'сАк сАй сАг сАд сАе сАж сАз'.split(' ')\n\n for x, y in zip(vowDep, vowIndep):\n Strng = Strng.replace(x, y)\n\n # danda to comma and double danda to full stop\n Strng = Strng.replace('сБК', ',').replace('сБЛ', '.')\n\n #print(Strng)\n\n return Strng\n\ndef insertViramaSyriac(Strng):\n Strng += \"\\uF001\"\n return Strng\n\ndef BengaliSwitchYaYYa(Strng):\n Strng = re.sub('(? Aleph approximation\n AyinAlephInitial = [('╩╛a', '╩╛'),('╩╛─Б', '─Б╠В'), ('╩╛i', '├о'), ('╩╛─л', '─л╠В'), ('╩╛u', '├╗'), ('╩╛┼л', '┼л╠В'), ('╩╛e', '├к'), ('╩╛─У', '─У╠В'),\\\n ('╩╛o', '├┤'), ('╩╛┼Н', '┼Н╠В')]\n\n for x, y in AyinAlephInitial:\n Strng = Strng.replace(x, y)\n\n ## Also make changes in PostOptionsLatn\n if Source == 'Arab':\n Strng = PostProcess.arabizeLatn(Strng, target=\"indic\")\n #print('after2', Strng)\n elif Source == 'Arab-Ur' or Source == 'Arab-Pa' or Source == 'Arab-Fa':\n Strng = PostProcess.urduizeLatn(Strng, target=\"indic\")\n elif Source == 'Syrn':\n Strng = PostProcess.syricizeLatn(Strng, target=\"indic\")\n elif Source == 'Syrj' or Source == 'Hebr':\n Strng = PostProcess.hebraizeLatn(Strng, target=\"indic\")\n\n # remove sin/shin dot\n Strng = Strng.replace('\\u032A', '').replace('\\u032E', '')\n\n # duplicate consonants\n ## move do chashmee ha + gemination sign\n ## t╩░ъЮЙ -> tъЮЙ╩░\n Strng = re.sub('([╩░])(ъЮЙ)', r'\\2\\1', Strng)\n\n Strng = re.sub('([aiu─Б─л┼л])(ъЮЙ)', r'\\2\\1', Strng)\n Strng = re.sub('(.)(ъЮЙ)', r'\\1' + vir + r'\\1', Strng)\n\n # avoid fusion of Ayn\n Strng = Strng.replace(\"╩┐\" + vir, \"╩┐\" + vir + \"\\u200B\")\n\n cons_prev = '|'.join(GM.SemiticConsonants)\n\n if 'Syr' in Source: ## converting from Syriac through Latin\n consSyrc = \"|\".join([\"╩╛\", \"b\", \"v\", \"g\", \"─б\", \"d\", \"с╕П\", \"h\", \"w\", \"z\", \"с╕е\", \"с╣н\", \"y\", \"k\", \"с╕л\", \"l\", \"m\", \"n\", \"s\", \"╩┐\", \"p\", \"f\", \"с╣г\", \"q\", \"r\", \"┼б\", \"t\", \"с╣п\", \"─Н\", \"┼╛\", \"j\"])\n vowelSyrc = [\"a\", \"─Б\", \"e\", \"─У\", \"┼л\", \"┼Н\", \"─л\", \"├в\", \"a╠Д╠В\", \"├к\", \"e╠Д╠В\"]\n\n vowelsDepSyrc = \"|\".join([\"a\", \"─Б\", \"e\", \"─У\", \"u\", \"i\", \"o\"])\n vowelsInDepSyrc1 = [\"i\", \"u\", \"o\"]\n vowelsInDepSyrc2 = [\"─л╠В\", \"├╗\", \"├┤\"]\n\n if any([vow in Strng for vow in vowelSyrc]):\n #print(Strng)\n #print([(vow, vow in Strng) for vow in vowelSyrc])\n Strng = Strng.replace('─л', 'i').replace('┼л', 'u').replace('┼Н', 'o')\n\n for vow1, vow2 in zip(vowelsInDepSyrc1, vowelsInDepSyrc2):\n Strng = re.sub('(? i^\n\n if \"\\uF001\" in Strng:\n Strng = re.sub('(' + consSyrc + ')' + '(?!' + vowelsDepSyrc + ')', r'\\1' + vir, Strng)\n\n # print(Strng)\n Strng = re.sub('(?<=' + cons_prev + ')' + 'a(?!\\u0304)', '', Strng)\n\n Strng = Strng.replace(\"\\uF001\", \"\")\n\n ## Add scripts with vowels here\n if \"Arab\" in Source or Source == 'Latn' or Source == \"Hebr\" or Source == \"Thaa\" or Source == 'Type':\n basic_vowels = '(' + '|'.join(['a', '─Б', 'i', '─л', 'u', '┼л', '─У', '┼Н', 'e', 'o', '#', vir]) + ')'\n Strng = re.sub('(┼╡)(?=' + basic_vowels + ')', \"w\", Strng)\n Strng = re.sub('(┼╖)(?=' + basic_vowels + ')', \"y\", Strng)\n\n Strng = re.sub('(?<=' + cons_prev + ')' + 'a(?!(┼╡|┼╖|\\u0304|\\u032E))', '', Strng)\n Strng = re.sub('(?<=h╠И)' + 'a(?!(┼╡|┼╖|\\u0304|\\u032E))', '', Strng)\n\n ## Hamza with vowels\n if 'Arab' in Source:\n simp_vow = 'a ─Б i ─л u ┼л'.split(' ')\n init_vow = '├в ─Б╠В ├о ─л╠В ├╗ ┼л╠В'.split(' ')\n\n # Hamsa + vow -> indep vow\n for x, y in zip(simp_vow, init_vow):\n Strng = re.sub('╩Ф' + x, y, Strng)\n\n # remove leftover hamza if nativizing\n if \"\\u05CC\" in Strng:\n Strng = Strng.replace(\"╩Ф\", \"\")\n\n ## Semitic to Indic equivalents\n SemiticIndic=[('с╣г', 'sQ'), ('╩┐', '╩╛Q'), ('с╣н', 'tQ'), ('с╕е', 'hQ'), \\\n ('с╕Н', 'dQ'), ('p╠г', 'pQ'), ('┼╛', '┼бQ'), ('с║У', 'j╩░Q'), ('с╕П', 'd╩░Q'), ('с╣п', 't╩░Q'),\\\n ('w', 'vQ'), ('с╕╡', 'k'), ('\\u032A', ''), ('\\u032E', ''),('a╠о', \"─Б\"),('\\u0308', \"\"),\\\n ('─Х\\u0302', '├к'), ('─Г\\u0302', '├в'), ('┼П\\u0302','├┤'), ('─Х', 'e'), ('─Г', ''), ('┼П','o'), ('с╕╡', 'k'),\\\n ('╩╛Q─Б', '─Б╠ВQ'), ('╩╛Qi', '├оQ'), ('╩╛Q─л', '─л╠ВQ'), ('╩╛Qu', '├╗Q'), ('╩╛Q┼л', '┼л╠ВQ'), ('╩╛Qe', '├кQ'), ('╩╛Q─У', '─У╠ВQ'),\\\n ('╩╛Qo', '├┤Q'), ('╩╛Q┼Н', '┼Н╠ВQ'), ('тБ┐', 'n\\u033D'), ('╩╛─Б', '─Б╠В')]\n\n for s, i in SemiticIndic:\n Strng = Strng.replace(s, i)\n\n if 'Arab' in Source:\n Strng = re.sub('(\\u033D)([iu─Б])', r'\\2', Strng)\n Strng = re.sub('(\\u033D)([a])', '', Strng)\n\n ## Lone Aliph to Nukta\n Strng = Strng.replace('╩╛', '├в')\n\n return Strng\n\ndef perisanizeArab(Strng):\n arabKafYe = '┘Г ┘К'.split(' ')\n persKafYe = '┌й █М'.split(' ')\n\n for x, y in zip(arabKafYe, persKafYe):\n Strng = Strng.replace(x, y)\n\n return Strng\n\ndef ArabizePersian(Strng):\n ## how to deal with ga\n arabKafYe = '┘Г ┘К'.split(' ')\n persKafYe = '┌й █М'.split(' ')\n\n for x, y in zip(arabKafYe, persKafYe):\n Strng = Strng.replace(y, x)\n\n return Strng\n\ndef semiticizeUrdu(Strng):\n urduSpecific = '█Т ┌И ┘╣ █Б'.split(' ')\n semitic = '┘К ╪п ╪к ┘З'.split(' ')\n\n for x, y in zip(urduSpecific, semitic):\n Strng = Strng.replace(x, y)\n\n ## remove Do chashme he\n Strng = Strng.replace('┌╛', '')\n\n return Strng\n\ndef ShowChillus(Strng):\n\n return PostProcess.MalayalamChillu(Strng, True, True)\n\ndef ShowKhandaTa(Strng):\n print(Strng)\n Strng = Strng.replace('рзО', 'рждрзН╦Н')\n print(Strng)\n\n return Strng\n\ndef eiaudipthongs(Strng):\n\n return Strng\n\ndef wasvnukta(Strng):\n\n return Strng\n\ndef default(Strng):\n\n return Strng\n\ndef SogdReshAyin(Strng):\n Strng = Strng.replace('ЁР╜А', '[\\uEA01-\\uEA02]') # resh yin\n\n return Strng\n\ndef SogoReshAyinDaleth(Strng):\n Strng = Strng.replace('ЁР╝Ш', '[\\uEA01-\\uEA02-\\uEA06]') # resh yin daleth\n\n return Strng\n\ndef PhlpMemQoph(Strng):\n Strng = Strng.replace('ЁРоЛ', '[\\uEA03-\\uEA04]') # mem qoph\n\n return Strng\n\ndef PhlpWawAyinResh(Strng):\n Strng = Strng.replace('ЁРоЕ', '[\\uEA05-\\uEA02-\\uEA01]') # resh yin\n\n return Strng\n\ndef PhliWawAyinResh(Strng):\n Strng = Strng.replace('ЁРне', '[\\uEA05-\\uEA02-\\uEA01]') # resh yin\n\n return Strng\n\ndef HatrDalethResh(Strng):\n Strng = Strng.replace('ЁРгг', '[\\uEA06-\\uEA01]') # resh yin\n\n return Strng\n\ndef MalayalamHalfu(Strng):\n consu = '[р┤Хр┤Ър┤Яр┤др┤▒р┤ир┤гр┤░р┤▓р┤╡р┤┤р┤│р┤▒]'\n vir = GM.CrunchSymbols(GM.VowelSigns, 'Malayalam')[0]\n consAll = \"(\" + '|'.join(GM.CrunchSymbols(GM.Consonants, 'Malayalam')) + \")\"\n\n Strng = re.sub('(?<=' + consu + ')' + '(' + vir + ')' + '(?!' + consAll + ')', r'\\2' + 'р╡Бр╡Н', Strng)\n\n return Strng\n\ndef MalayalamTranscribe(Strng):\n Strng = MalayalamHalfu(Strng)\n\n script = \"Malayalam\"\n\n ListC = GM.CrunchList('ConsonantMap',script)\n ListSC = GM.CrunchList('SouthConsonantMap',script)\n vir = GM.CrunchSymbols(GM.VowelSigns, script)[0]\n\n ConUnVoiced = [ListC[x] for x in [0,5,10,15,20]]\n ConVoicedJ = [ListC[x] for x in [2,7,12,17,22]]\n ConVoicedS = [ListC[x] for x in [2,5,12,17,22]]\n\n ConNasalsAll = '|'.join([ListC[x] for x in [4, 9, 14, 19, 24]])\n conNasalCa = '|'.join([ListC[x] for x in [9]])\n ConNasalsGroup = [ConNasalsAll, conNasalCa, ConNasalsAll, ConNasalsAll, ConNasalsAll]\n\n ConMedials = '|'.join(ListC[25:28]+ListSC[0:2]+ListSC[3:4])\n Vowels = '|'.join(GM.CrunchSymbols(GM.Vowels+GM.VowelSignsNV, script))\n Aytham = GM.CrunchList('Aytham',script)[0]\n Consonants = '|'.join(GM.CrunchSymbols(GM.Consonants,script))\n\n NRA =ListSC[3] + vir + ListSC[2]\n NDRA = ListC[14] + vir + ListC[12] + vir + ListC[26]\n\n ### Check Siva Siva Mails\n ### Do something about Eyelash ra in Transliterated text\n\n for i in range(len(ConUnVoiced)):\n pass\n #Strng = re.sub('('+Vowels+Consonants+')'+ConUnVoiced[i]+'('+Vowels+Consonants+')',r'\\1'+ConVoicedS[i]+r'\\2',Strng)\n Strng = re.sub('('+Vowels+'|'+Consonants+'|'+Aytham+')'+ConUnVoiced[i]+'(?!'+vir+')',r'\\1'+ConVoicedS[i],Strng)\n Strng = re.sub('('+ConVoicedS[i]+')'+ConUnVoiced[i]+'(?!'+vir+')',r'\\1'+ConVoicedS[i],Strng)\n\n # Nasals + Unvoiced -> voiced\n # Rule applied even if spaced -> ulakaJ cuRRu -> ulakaJ juRRu; cenJu -> senju\n # Strng = re.sub('('+ConNasalsSpace+')'+'('+vir+')'+'( ?)'+ConUnVoiced[i],r'\\1\\2\\3'+ConVoicedJ[i],Strng)\n\n # Only without space : vampu -> vambu; varim panam -> varim panam\n Strng = re.sub('('+ConNasalsGroup[i]+')'+'('+vir+')'+ConUnVoiced[i],r'\\1\\2'+ConVoicedJ[i],Strng)\n\n # Medials + Unvoiced -> Voiced\n Strng = re.sub('('+ConMedials+')'+'('+vir+')'+ConUnVoiced[i]+'(?!'+vir+')',r'\\1\\2'+ConVoicedS[i],Strng)\n\n #RRA NRA\n\n Strng = Strng.replace('р┤▒р╡Нр┤▒', 'р┤Яр╡Нр┤Я').replace('р┤ир╡Нр┤▒', 'р┤гр╡Нр┤б')\n\n return Strng\n\ndef retainLatin(Strng, reverse=False):\n latn_basic_lower = 'a b c d e f g h i j k l m n o p q r s t u v w x y z с╕е с╣н с╣г ╩╛ ╩┐ ┼б ─Б ─л ┼л ─У ┼Н'\n latn_basic_upper = latn_basic_lower.upper()\n latn_all = latn_basic_lower + latn_basic_upper\n latn_all = latn_all.split(' ')\n\n if not reverse:\n for i, c in enumerate(latn_all):\n Strng = Strng.replace(c, chr(60929+i))\n\n # for disambiguating preprocessed consonants\n Strng = Strng.replace('\\uEA01', 'r').replace('\\uEA02', '╩┐').replace('\\uEA03', 'm').replace('\\uEA04', 'q').replace('\\uEA05', 'w').replace('\\uEA06', 'd')\n\n else:\n # print('I am trying to reverse Lating')\n for i, c in enumerate(latn_all):\n Strng = Strng.replace(chr(60929+i), c)\n\n return Strng\n\ndef JapanesePreProcess(src, txt, preoptions):\n import pykakasi\n from . import Convert\n\n if src == 'Hiragana' or src == 'Katakana':\n # preserve roman words\n kks = pykakasi.kakasi()\n\n txt = Convert.convertScript(txt.lower(), \"ISO\", \"Devanagari\")\n #print(txt)\n\n cv = kks.convert(txt)\n txt = ''\n\n for item in cv:\n txt = txt + ' ' + item['hepburn']\n\n if 'eiaudipthongs' in preoptions:\n txt = txt.replace('ou', 'o\\u02BDu').replace('ei', 'e\\u02BDi')\n\n txt = re.sub('(r)([aiueo])(\\u309A\\u309A)', 'l' + r'\\2\\2', txt)\n txt = re.sub('(r)([a─Бi─лu┼лe─Уo┼Н])(\\u309A)', 'l' + r'\\2', txt)\n\n txt = re.sub('(k)([aiueo])(\\u309A\\u309A)', 'с╣Е' + r'\\2\\2', txt)\n txt = re.sub('(k)([a─Бi─лu┼лe─Уo┼Н])(\\u309A)', 'с╣Е' + r'\\2', txt)\n\n txt = txt.replace('aa', '─Б').replace('ii', '─л').replace('ee', '─У').replace('oo', '┼Н').replace('uu','┼л')\n txt = txt.replace('a-', '─Б').replace('i-', '─л').replace('e-', '─У').replace('o-', '┼Н').replace('u-','┼л')\n txt = txt.replace(\"n'\", 'n_').replace('ch', 'c').replace('sh', 'с╣г').replace('sс╣г', 'с╣гс╣г').replace('ai', 'a_i').replace('au', 'a_u')\n txt = txt.replace('w', 'v')\n txt = txt.replace('ou', '┼Н').replace('ei', '─У')\n txt = txt.replace('уАБ', ',').replace('уАВ', '.')\n\n # Nasalization of n\n\n txt= txt.replace('ng', 'с╣Еg').replace('nk', 'с╣Еk').replace('nk', 'с╣Еk').replace('np', 'mp').replace('nb', 'mb').replace('nm', 'mm')\n\n if 'wasvnukta' in preoptions:\n txt = txt.replace('v', 'v╠И')\n\n #print(txt)\n\n txt = txt.replace('уАБ', ',').replace('уАВ', '.')\n\n return txt\n\ndef holamlong(Strng):\n Strng = Strng.replace('╓╣╓╝','╓╝╓╣')\n Strng = re.sub('(? bhArtIy\n Strng = re.sub(ISyl+Cons+Syl+SylAny+\"(?!\" + Char + \")\", r'\\1\\8' + vir + r'\\9\\15', Strng) # bhAratIya --> bhArtIy\n Strng = re.sub(ISyl+Cons+Syl+\"(?!\" + Char + \")\", r'\\1\\8' + vir + r'\\9', Strng) # namakIn -> namkIn\n\n Strng = re.sub(ISyl+Cons+\"(?!\" + Char + \")\", r'\\1\\8' + vir, Strng) # kama -> kam\n\n Cons_sss = '((' + Cons + vir + ')' +'([рд╢рд╖рд╕]))' # delete if double ends with s, sh, s\n Strng = re.sub(ISyl+Cons_sss+\"(?!\" + Char + \")\", r'\\1\\8' + vir, Strng)\n\n ## remove from geminated consonants\n Target = 'Devanagari'\n\n ConUnAsp = [GM.CrunchList('ConsonantMap', Target)[x] for x in [0,2,5,7,10,12,15,17,20,22,4,9,14,19,24,25,26,27,28,29,30,31,32]]\n ConUnAsp = ConUnAsp + GM.CrunchList('SouthConsonantMap',Target) + GM.CrunchList('NuktaConsonantMap',Target)\n ConAsp = [GM.CrunchList('ConsonantMap', Target)[x] for x in [1,3,6,8,11,13,16,18,21,23]]\n\n Strng = re.sub(ISyl + '('+'|'.join(ConUnAsp)+')'+'('+vir+')('+r'\\8'+\")(?!\" + Char + \")\", r'\\1\\8\\9\\10' + vir,Strng)\n\n for i in range(len(ConAsp)):\n Strng = re.sub(ISyl +'('+ConUnAsp[i]+')'+'('+vir+')'+'('+ConAsp[i]+')'+'(?!\" + Char + \")', r'\\1\\8\\9\\10' + vir,Strng)\n\n #Strng = re.sub(VowI + Nas + Cons2+\"(?!\" + Char + \")\", r'\\1\\2\\3' + vir, Strng)\n\n cons_pyramid = ['[рдпрд░рд▓рд╡]', '[рдирдордг]', '[рд╢рд╖рд╕]', '[рдХрдЦрдкрдлрдЧрдШрдмрдн]', '[рдЯрдарддрдердбрдврджрдз]', '[рдЪрдЫрдЬрдЭреЫ]']\n for c1, cons1 in enumerate(cons_pyramid):\n for c2, cons2 in enumerate(cons_pyramid):\n if c1 < c2: # delete if ascending order\n Cons_pyr = '((' + cons1 + vir + ')' +'('+cons2+'))'\n Strng = re.sub(ISyl+Cons_pyr+\"(?!\" + Char + \")\", r'\\1\\8' + vir, Strng)\n\n\n Strng = Strng.replace(vir, vir2)\n\n return Strng\n\n# consider adding an optional NUkta to the post consonantal position\ndef RemoveFinal(Strng, Target):\n if Target == 'Bengali':\n Strng = PostProcess.KhandaTa(Strng, Target, True)\n\n VowI = \"(\" + '|'.join(GM.CrunchSymbols(GM.Vowels,Target)) + \")\"\n VowS = \"(\" + '|'.join(GM.CrunchSymbols(GM.VowelSignsNV, Target)) + \")\"\n Cons = \"(\" + '|'.join(GM.CrunchSymbols(GM.Consonants, Target)) + \")\"\n Char = \"(\" + '|'.join(GM.CrunchSymbols(GM.Characters, Target)) + \")\"\n\n Nas = \"([\" + '|'.join(GM.CrunchList('AyogavahaMap',Target)) + \"]?)\"\n\n ISyl = \"((\" + VowI + \"|\" + \"(\" + Cons + VowS + \"?\" + \")\" + Nas + '))'\n Syl = \"((\" + Cons + VowS + ')' + Nas + \")\"\n SylAny = \"((\" + Cons + VowS + \"?\" + ')' + Nas + \")\"\n\n vir = GM.CrunchList(\"ViramaMap\", Target)[0]\n if Target != 'Bengali':\n Cons2 = '((' + Cons + vir + ')?' + Cons + ')'\n else:\n Cons2 = '(()?' + Cons + ')'\n\n Strng = re.sub(ISyl + Cons2+\"(?!\" + Char + \")\", r'\\1\\8' + vir, Strng) # kama -> kam\n Strng = re.sub(ISyl + Cons2+\"(?!\" + Char + \")\", r'\\1\\8' + vir, Strng) # kama -> kam\n\n #Strng = re.sub(VowI + Nas + Cons2+\"(?!\" + Char + \")\", r'\\1\\2\\3' + vir, Strng)\n\n return Strng\n\ndef SchwaFinalGurmukhi(Strng):\n\n Strng = RemoveFinal(Strng, 'Gurmukhi')\n\n return Strng\n\ndef SchwaFinalGujarati(Strng):\n\n Strng = RemoveFinal(Strng, 'Gujarati')\n\n return Strng\n\ndef SchwaFinalBengali(Strng):\n\n Strng = RemoveFinal(Strng, 'Bengali')\n\n return Strng\n\ndef SchwaFinalWarangCiti(Strng):\n Target = \"WarangCiti\"\n\n VowI = \"(\" + '|'.join(GM.CrunchSymbols(GM.Vowels,Target)) + \")\"\n VowS = \"(\" + '|'.join(GM.CrunchSymbols(GM.VowelSignsNV, Target)) + \")\"\n Cons = \"(\" + '|'.join(GM.CrunchSymbols(GM.Consonants, Target)) + \")\"\n Char = \"(\" + '|'.join(GM.CrunchSymbols(GM.Characters, Target)) + \")\"\n\n Nas = \"([\" + '|'.join(GM.CrunchList('AyogavahaMap',Target)) + \"]?)\"\n\n ISyl = \"((\" + VowI + \"|\" + \"(\" + Cons + VowS + \"?\" + \")\" + Nas + '))'\n Syl = \"((\" + Cons + VowS + ')' + Nas + \")\"\n SylAny = \"((\" + Cons + VowS + \"?\" + ')' + Nas + \")\"\n\n vir = '\\u02BB'\n Cons2 = '((' + Cons + vir + ')?' + Cons + ')'\n\n Strng = re.sub(ISyl+Cons2+\"(?!\" + Char + \")\", r'\\1\\8' + vir, Strng) # kama -> kam\n\n return Strng\n\ndef siddhamUnicode(Strng):\n return Strng\n\ndef ThaiOrthography(Strng):\n Strng += \"\\u02BB\\u02BB\"\n\n return Strng\n\ndef LaoTranscription(Strng):\n Strng += \"\\u02BB\\u02BB\"\n\n return Strng\n\ndef LimbuDevanagariConvention(Strng):\n Strng = Strng.replace('рдПрд╝', 'рдО')\n Strng = Strng.replace('рдУрд╝', 'рдТ')\n Strng = Strng.replace('реЗрд╝', 'реЖ')\n Strng = Strng.replace('реЛрд╝', 'реК')\n Strng = Strng.replace('рдГ', 'ъЮЙ')\n\n return Strng\n\ndef LimbuSpellingSaI(Strng): ## Fix this not for all\n vir = Limbu.ViramaMap[0]\n\n FCons = [x+vir for x in [Limbu.ConsonantMap[x] for x in[0,4,15,19,20,24,26,27]]]\n FinalCons = ['\\u1930','\\u1931','\\u1933','\\u1934','\\u1935','\\u1936','\\u1937','\\u1938']\n\n for x, y in zip(FCons, FinalCons):\n Strng = Strng.replace(x, '\\u193A' + y)\n\n return Strng\n\n\ndef removeChillus(Strng):\n Chillus=['\\u0D7A','\\u0D7B','\\u0D7C','\\u0D7D','\\u0D7E']\n\n vir = Malayalam.ViramaMap[0]\n ConVir =[\n Malayalam.ConsonantMap[14]+vir,\n Malayalam.ConsonantMap[19]+vir,\n Malayalam.ConsonantMap[26]+vir,\n Malayalam.ConsonantMap[27]+vir,\n Malayalam.SouthConsonantMap[0]+vir,\n ]\n\n for x,y in zip(Chillus, ConVir):\n Strng = Strng.replace(x, y)\n\n return Strng\n\ndef SinhalaPali(Strng):\n Strng = PostProcess.SinhalaPali(Strng, reverse = True)\n\n return Strng\n\ndef IASTPali(Strng):\n Strng = Strng.replace('с╕╖', 'l╠д')\n\n return Strng\n\ndef CyrillicPali(Strng):\n Strng = Strng.replace('╨╗╠г', '╨╗╠д',)\n\n return Strng\n\ndef MalayalamPrakrit(Strng):\n ## Reverse Gemination\n Strng = PostProcess.ReverseGeminationSign(Strng, 'Malayalam')\n Strng = Strng.replace(\"р┤А\", \"р┤В\")\n\n return Strng\n\ndef GranthaPrakrit(Strng):\n ## Reverse Gemination\n Strng = PostProcess.ReverseGeminationSign(Strng, 'Grantha')\n\n Strng = Strng.replace(\"ЁСМА\", \"ЁСМВ\")\n\n return Strng\n\ndef RomanPreFix(Strng,Source):\n DepV = '\\u1E7F'\n Asp = '\\u02B0'\n\n Vir = GM.CrunchList(\"ViramaMap\", Source)[0]\n Nuk = GM.CrunchList(\"NuktaMap\", Source)[0]\n VowelA = GM.CrunchSymbols(['VowelMap'],Source)[0]\n\n ListV = '|'.join(GM.CrunchSymbols(GM.VowelSigns,Source))\n ListC = '|'.join(GM.CrunchSymbols(GM.Consonants,Source))\n\n Strng = re.sub('('+ListC+')'+'(?!'+ListV+'|'+VowelA+')',r'\\1'+DepV+Vir,Strng)\n\n Strng = re.sub('('+ListC+'|'+Nuk+')'+'('+ListV+')',r'\\1'+DepV+r'\\2',Strng)\n #print Strng\n\n Strng = re.sub('(?<='+ListC+')'+'('+VowelA+')',r'',Strng)\n #print Strng\n\n #Fix Nukta рддреНрд╝ рддреНрд╝рдЕ рддреНрд╝рд╛ --> рддреНрд╝ рддрд╝ рддрд╝рд╛\n Strng = Strng.replace(DepV+Vir+Nuk+VowelA,Nuk)\n Strng = re.sub(DepV+Vir+Nuk+'(?=['+DepV+'])',Nuk,Strng)\n Strng = Strng.replace(DepV+Vir+Nuk,Nuk+DepV+Vir)\n\n #print Strng\n\n return Strng\n\ndef joinVowelCons(Strng, script):\n consonantsAll = '(' + '|'.join(sorted(GM.CrunchSymbols(GM.Consonants, script), key = len, reverse=True)) + ')'\n vowelsAll = '(' + '|'.join(sorted(GM.CrunchSymbols(GM.Vowels, script), key = len, reverse=True)) + ')'\n\n Strng = re.sub(consonantsAll + ' ' + vowelsAll, r'\\1\\2', Strng)\n Strng = re.sub(consonantsAll + ' ' + consonantsAll, r'\\1\\2', Strng)\n\n return Strng\n\ndef joinVowelConsIAST(Strng):\n return joinVowelCons(Strng, 'IAST')\n\ndef joinVowelConsISO(Strng):\n return joinVowelCons(Strng, 'ISO')\n\ndef PreProcess(Strng,Source,Target):\n if Source in GM.RomanDiacritic or Source == 'Latn':\n Strng = Strng.lower()\n\n\n pipeScripts = [\"HK\", \"IASTPali\", \"IAST\", \"ISO\"]\n\n if Source in pipeScripts:\n Strng = Strng.replace(\"|\", \".\").replace(\"||\", \"..\")\n\n if 'Arab' in Source:\n Strng = re.sub('([┘И┘К])(?=[\\u064E\\u0650\\u064F\\u0651\\u064B\\u064C\\u064D])', '\\u02DE' + r'\\1', Strng)\n #Strng = re.sub('((\\u064E|\\u0650|\\u0652|\\u064F))(\\u0651)', r'\\2', Strng)\n\n if Source in [\"Syrj\", \"Syrn\"]:\n Strng = Strng.replace('\\u0323', '\\u0742')\n\n\n if Source == 'Itrans':\n sOm = 'OM'\n tOm = 'oM'\n\n punc = '(' + '|'.join([\"\\u005C\"+x for x in list(string.punctuation)]+ ['\\s']\n + [x.replace('.', '\\.') for x in GM.CrunchSymbols(GM.Signs,Source)[1:3]]) + ')'\n\n Strng = re.sub(punc + sOm + punc, r'\\1' + tOm + r'\\2', Strng)\n Strng = re.sub('^' + sOm + punc, tOm + r'\\1', Strng)\n Strng = re.sub(punc + sOm + '$', r'\\1' + tOm, Strng)\n Strng = re.sub('^' + sOm + '$', tOm, Strng)\n\n punc = '(\\s)'\n\n Strng = re.sub(punc + sOm + punc, r'\\1' + tOm + r'\\2', Strng)\n Strng = re.sub('^' + sOm + punc, tOm + r'\\1', Strng)\n Strng = re.sub(punc + sOm + '$', r'\\1' + tOm, Strng)\n Strng = re.sub('^' + sOm + '$', tOm, Strng)\n\n\n AltForm = ['O', 'aa','ii','uu','RRi','RRI','LLi','LLI','N^','JN','chh','shh','x','GY','.n','.m','.h', 'AUM', 'E', 'J', 'c.o', 'c.e']\n NormForm = ['^o', 'A','I','U','R^i','R^I','L^i','L^I','~N','~n','Ch','Sh','kSh','j~n','M','M','','oM', '^e', 'z', 'A.c', 'e.c']\n\n for x,y in zip(AltForm,NormForm):\n Strng = Strng.replace(x,y)\n\n AltForms = [('ee', 'I'), ('dny', 'j~n'), ('oo', 'U'), ('kS', 'kSh'), ('w', 'v'), ('|', '.'), ('kShh', 'kSh')]\n\n for x,y in AltForms:\n Strng = Strng.replace(x,y)\n\n Strng = Strng.replace('OM', 'oM')\n\n if Source == 'BarahaNorth' or Source == 'BarahaSouth':\n Strng = Strng.replace('A', 'aa')\n Strng = Strng.replace('I', 'ee')\n Strng = Strng.replace('U', 'oo')\n\n Strng = Strng.replace('ou', 'au')\n Strng = Strng.replace('K', 'kh')\n Strng = Strng.replace('G','gh')\n Strng = Strng.replace('ch', 'c')\n Strng = Strng.replace('Ch', 'C')\n Strng = Strng.replace('J','jh')\n Strng = Strng.replace('w', 'v')\n Strng = Strng.replace('sh', 'S')\n Strng = Strng.replace('~h', '_h')\n Strng = Strng.replace('^', '()')\n Strng = Strng.replace('^^', '{}')\n\n Strng = Strng.replace('tx', 'rx')\n Strng = Strng.replace('zh', 'Lx')\n\n Strng = Strng.replace('q', '\\_')\n Strng = Strng.replace('#', \"\\\\'\")\n Strng = Strng.replace('$', '\\\\\"')\n\n\n if Source == 'IAST':\n Strng = Strng.replace(\"a├п\", \"a_i\")\n Strng = Strng.replace(\"a├╝\", \"a_u\")\n Strng = Strng.replace('\\u0303', 'с╣Г')\n\n if Source == \"ISO\":\n Strng = Strng.replace('a:i', 'a_i')\n Strng = Strng.replace('a:u', 'a_u')\n Strng = Strng.replace('\\u0303', 'с╣Б')\n\n if Source == \"Titus\":\n Strng = Strng\n\n if Source == \"ISO\" or Source == \"IAST\" or Source == \"Titus\" or \"RussianCyrillic\":\n Strng = CF.VedicSvarasNonDiacritic(Strng)\n\n if Source == \"Latn\" and 'Syr' in Target:\n Strng = Strng.replace('с╕З', 'v').replace('с╕б', 'с╕б').replace('с╕╡', 'с╕л').replace('p╠Д', 'f')\n\n if ('тЖУ' in Strng or 'тЖС' in Strng) and Target in GM.IndicScripts :\n Strng = Strng.replace('тЖУ', 'реТ')\n Strng = Strng.replace('тЖСтЖС', 'с│Ъ')\n Strng = Strng.replace('тЖС', 'реС')\n\n if ('тЖУ' in Strng or 'тЖС' in Strng) and Target in GM.LatinScripts :\n Strng = Strng.replace('тЖУ', '\\\\_')\n Strng = Strng.replace('тЖСтЖС', '\\\\\"')\n Strng = Strng.replace('тЖС', '\\\\\\'')\n\n if Source == \"WarangCiti\":\n Strng = Strng.replace('\\u200D', '\\u00D7')\n\n if Source == \"Hebr-Ar\":\n # accept variants with dotes\n dot_var = [('╫в╫Д','╫Т'), ('╫к╫Д','╫к╓Т'), ('╫к','╫к╠И'), ('╫з','╫з╠И')]\n\n for char, char_var in dot_var:\n Strng = Strng.replace(char_var, char)\n\n ## Normalize Input Strings\n\n Strng = normalize(Strng,Source)\n\n ## Remove unsupported letters and replace with supported ones\n\n return Strng\n\ndef ISO259Target(Strng):\n Strng = Strng.replace('╫Р', '╩╛').replace('╫│', 'тАЩ')\n\n return Strng\n\ndef ISO233Target(Strng):\n replacements = [('╪г', '╦И╩╛'), ('╪б', '┬ж'), ('╪е', '╦М╩╛')]\n\n for x, y in replacements:\n Strng = Strng.replace(x, y)\n\n return Strng\n\ndef PersianDMGTarget(Strng):\n replacements = [('╪з', '╩╛')]\n\n for x, y in replacements:\n Strng = Strng.replace(x, y)\n\n return Strng\n\ndef ISO233Source(Strng):\n replacements = [('╪г', '╦И╩╛'), ('╪б', '┬ж'), ('╪е', '╦М╩╛')]\n\n for x, y in replacements:\n Strng = Strng.replace(y, x)\n\n replacements = [('j', '╟з'), ('g', '╟з'), ('h╠И', 'с║Ч'), ('с╕л', 'с║Ц'), ('a╠о', 'с╗│'), \\\n ('aтБ┐', '├б'), ('iтБ┐', '├н'), ('uтБ┐', '├║'), ('─Б╠В', '╩╛├в'), ('╦А', '╦И')]\n\n for x, y in replacements:\n Strng = Strng.replace(y, x)\n\n return Strng\n\ndef HebrewSBLTarget(Strng):\n Strng = Strng.replace('╫Р', '╩╛').replace('╫│', 'тАЩ')\n\n return Strng\n\ndef HebrewSBLSource(Strng):\n Strng = Strng.replace('╩╛', '╫Р',).replace('тАЩ', '╫│')\n Strng = Strng.replace('\\u0307\\u00B0', '\\u00B0\\u0307')\n\n replacements = [('v', 'с╕З'), ('f', 'p╠Д'), ('dъЮЙ', 'd'), ('d', 'с╕П'), \\\n ('gъЮЙ', 'g'), ('g', 'с╕б'), \\\n ('tъЮЙ', 't'), ('t', 'с╣п'),\n ('┼б╠о', '┼б'), ('┼б╠к', '┼Ы'),\n ('o', '┼Н'), ('┼Н', '├┤'), ('┼л', '├╗'), ('\\u033D', '─Х')\n ]\n\n for x, y in replacements:\n Strng = Strng.replace(y, x)\n\n return Strng\n\ndef ISO259Source(Strng):\n Strng = Strng.replace('╩╛', '╫Р',).replace('тАЩ', '╫│')\n Strng = Strng.replace('\\u0307\\u00B0', '\\u00B0\\u0307')\n\n replacements = [('с╕╡', 'k'), ('v', 'b'), ('f', 'p'), ('b', 'с╕Г'), ('p', 'с╣Ч'), ('k', 'k╠З'), ('ъЮЙ', '\\u0307'),\\\n ('┼б╠о', '┼б'), ('┼б', 's╠А'), ('┼б╠к', '┼Ы'),\n ('─Б', '├е'), ('e', '╚й'), ('┼Н', '┼╡'), ('┼л', 'с║З'), ('\\u033D', '┬░'), ('─Х', 'с╕Э')\n ]\n\n for x, y in replacements:\n Strng = Strng.replace(y, x)\n\n import unicodedata\n Strng = unicodedata.normalize('NFD', Strng)\n Strng = Strng.replace('\\u0307', 'ъЮЙ')\n Strng = unicodedata.normalize('NFC', Strng)\n\n return Strng\n\ndef UnSupThaana(Strng):\n\n return Strng\n\ndef RemoveJoiners(Strng):\n Strng = Strng.replace(\"\\u200D\", \"\")\n Strng = Strng.replace(\"\\u200C\", \"\")\n\n return Strng\n\ndef ArabicGimelJa(Strng):\n Strng = Strng.replace(\"╪м\", \"┌и\")\n\n return Strng\n\n# Normalize Input\n# ka + -> qa etc\ndef normalize(Strng,Source):\n\n # Replace Decomposed Nukta Characters by pre-composed Nukta consonants\n\n #nuktaDecom = [u\"рдХрд╝\",u\"рдЦрд╝\",u\"рдЧрд╝\",u\"рдЬрд╝\",u\"рдбрд╝\",u\"рдврд╝\",u\"рдлрд╝\",u\"рдпрд╝\",u\"ри▓ри╝\",u\"ри╕ри╝\",u\"риЦри╝\",u\"риЧри╝\",u\"риЬри╝\",u\"рилри╝\",u\"ржбрж╝\",u\"ржврж╝\",u\"ржпрж╝\",u\"рмбрм╝\",u\"рмврм╝\"]\n #nuktaPrecom = [u\"реШ\",u\"реЩ\",u\"реЪ\",u\"реЫ\",u\"реЬ\",u\"реЭ\",u\"реЮ\",u\"реЯ\",u\"ри│\",u\"ри╢\",u\"рйЩ\",u\"рйЪ\",u\"рйЫ\",u\"рйЮ\",u\"рзЬ\",u\"рзЭ\",u\"рзЯ\",u\"рнЬ\",u\"рнЭ\"]\n\n nuktaDecom = [\"\\u0915\\u093C\",\"\\u0916\\u093C\",\"\\u0917\\u093C\",\"\\u091C\\u093C\",\"\\u0921\\u093C\",\"\\u0922\\u093C\",\"\\u092B\\u093C\",\"\\u092F\\u093C\",\"\\u0A32\\u0A3C\",\"\\u0A38\\u0A3C\",\"\\u0A16\\u0A3C\",\"\\u0A17\\u0A3C\",\"\\u0A1C\\u0A3C\",\"\\u0A2B\\u0A3C\",\"\\u09A1\\u09BC\",\"\\u09A2\\u09BC\",\"\\u09AF\\u09BC\",\"\\u0B21\\u0B3C\",\"\\u0B22\\u0B3C\"]\n nuktaPrecom = [\"\\u0958\",\"\\u0959\",\"\\u095A\",\"\\u095B\",\"\\u095C\",\"\\u095D\",\"\\u095E\",\"\\u095F\",\"\\u0A33\",\"\\u0A36\",\"\\u0A59\",\"\\u0A5A\",\"\\u0A5B\",\"\\u0A5E\",\"\\u09DC\",\"\\u09DD\",\"\\u09DF\",\"\\u0B5C\",\"\\u0B5D\"]\n\n if Source not in [\"Grantha\",\"TamilGrantha\"]:\n for x,y in zip(nuktaDecom,nuktaPrecom):\n Strng = Strng.replace(x,y)\n\n if Source in ['IAST', 'ISO', 'ISOPali', 'Titus']:\n Strng = Strng.replace(\"├╝\", \"u╩╝\").replace(\"╟Ц\", \"┼л╩╝\").replace( '├╢', 'a╩╝',).replace('╚л', '─Б╩╝')\n\n if Source == 'Arab-Ur' or Source == 'Arab-Pa':\n Strng = Strng.replace('┘Г', '┌й')\n Strng = Strng.replace('┘К', '█М')\n\n if Source == \"Hebr\":\n vowels = ['╓░','╓▒','╓▓','╓│','╓┤','╓╡','╓╢','╓╖','╓╕','╓╣','╓║','╓╗','╫З']\n vowelsR = '(' + '|'.join(vowels + ['╫Х╓╣', '╫Х╓╝']) + ')'\n\n # Swap the order of diacritics\n Strng = re.sub(vowelsR + '([╫Б╫В╓╝])', r'\\2\\1', Strng)\n Strng = Strng.replace('\\u05BC\\u05B0\\u05C1', '\\u05C1\\u05BC\\u05B0')\n\n # Sindhi C+Anudatta <- Sindhi Underscore characters\n # For easy Transliteration\n\n # Malayalam Chillu Characters\n\n chilluZwj=[\"р┤гр╡НтАН\",\"р┤ир╡НтАН\",\"р┤░р╡НтАН\",\"р┤▓р╡НтАН\",\"р┤│р╡НтАН\",\"р┤Хр╡НтАН\"]\n chilluAtom=[\"р╡║\",\"р╡╗\",\"р╡╝\",\"р╡╜\",\"р╡╛\",\"р╡┐\"]\n\n for x,y in zip(chilluZwj,chilluAtom):\n Strng = Strng.replace(x,y)\n\n Strng = Strng.replace('р╡М', 'р╡Ч')\n Strng = Strng.replace('р╡Я', 'р┤И')\n Strng = Strng.replace('р╡О', 'р┤░р╡Н')\n\n # Malayalam chill + virama to just letter + virama\n\n Strng = Strng.replace('р╡╗р╡Нр┤▒', 'р┤ир╡Нр┤▒')\n\n # Telugu/Kananda Nakaarapollu\n\n Strng = Strng.replace('р│Э', 'р▓ир│Н') # Kannada\n Strng = Strng.replace('р▒Э', 'р░ир▒Н') # Telugu\n\n # Bengali Khanda Ta\n\n # Strng = Strng.replace(\"рзО\",\"рждрзНтАН\")\n\n # Tamil\n\n tamAlt = [\"ро╕рпНро░рпА\",\"роХрпНтАМро╖\",\"ро░тАМро┐\",\"ро░тАМрпА\"]\n tamNorm = [\"ро╢рпНро░рпА\",\"роХрпНро╖\",\"ро░ро┐\",\"ро░рпА\"]\n\n # Burmese\n\n Strng = Strng.replace(\"сА┐\", \"сАЮсА╣сАЮ\")\n\n # Tamil\n\n Strng.replace(\"ро╕┬▓\", \"ро╢\")\n\n ## The following is a reversal of custom options check !!!!\n\n subNum = [\"┬╣\",\"тВБ\",\"тВВ\",\"тВГ\",\"тВД\"]\n supNum = [\"\",\"\",\"┬▓\",\"┬│\",\"тБ┤\"]\n\n for x,y in zip(tamAlt+subNum,tamNorm+supNum):\n Strng = Strng.replace(x,y)\n\n # Tibetan Vowels\n\n oldVow = [\"р╜╢\",\"р╜╖\",\"р╜╕\",\"р╜╣\",\"р╜│\",\"р╜╡\",\"р╜й\",\"р╛╣\"]\n newVow= [\"р╛▓р╛А\",\"р╛▓р╜▒р╛А\",\"р╛│р╛А\",\"р╛│р╜▒р╛А\",\"р╜▒р╜▓\",\"р╜▒р╜┤\",\"р╜Ар╛╡\",\"р╛Рр╛╡\"]\n\n for x,y in zip(oldVow,newVow):\n Strng = Strng.replace(x,y)\n\n ## Tibetan ca, ja\n\n Strng = Strng.replace('р╜Е', 'р╜Щ')\n Strng = Strng.replace('р╜Ж', 'р╜Ъ')\n\n # Decomposed to Precomposed Roman Characters\n\n latinDecom= [\"a╠Д\",\"i╠Д\",\"u╠Д\",\"e╠Д\",\"o╠Д\",\"m╠г\",\"m╠З\",\"h╠г\",\"s╠Б\",\"s╠г\",\"n╠г\",\"r╠г\",\"r╠г╠Д\",\"l╠г\",\"l╠г╠Д\",\"l╠▒\",\"n╠▒\",\"r╠▒\"]\n latinPrecom = [\"─Б\",\"─л\",\"┼л\",\"─У\",\"┼Н\",\"с╣Г\",\"с╣Б\",\"с╕е\",\"┼Ы\",\"с╣г\",\"с╣З\",\"с╣Ы\",\"с╣Э\",\"с╕╖\",\"с╕╣\",\"с╕╗\",\"с╣Й\",\"с╣Я\"]\n\n for x,y in zip(latinDecom,latinPrecom):\n Strng = Strng.replace(x,y)\n\n # Thai Decomposed AM to Precomposed AM\n\n Strng = Strng.replace(\"р╣Нр╕▓\",\"р╕│\")\n\n # For Lao\n\n Strng = Strng.replace(\"р╗Нр║▓\", 'р║│')\n\n ## Two Single Danda to Double Danda\n\n Strng = Strng.replace(\"редред\",\"рее\")\n\n ## Decomposed Limbu to precomposed\n\n Strng = Strng.replace(\"сдасд║сдг\", \"сдасдгсд║\")\n Strng = Strng.replace(\"сд║сдг\", \"сдгсд║\")\n Strng = Strng.replace(\"сдасдг\", \"сде\")\n\n ## Replace Thai р╕О\n\n Strng = Strng.replace(\"р╕О\", \"р╕П\")\n\n ## Replace Grantha old au with new au\n\n Strng = Strng.replace('ЁСНМ', 'ЁСНЧ')\n\n ## Replace Tibetan Chandra with Nada to the normal one\n\n Strng = Strng.replace('\\u0F82', '\\u0F83')\n\n ## Replace Candra A iwht regualr AE\n\n Strng = Strng.replace('ре▓', 'рдН')\n\n #Strng = Strng.replace('', \"р║г\\uE00A\")\n\n ## Normalization for Bengali, Tamil, Malayalam and Grantha\n\n # Bengali o/au\n\n Strng = Strng.replace('рзЗрж╛', 'рзЛ')\n Strng = Strng.replace('рж╛рзЗ', 'рзЛ')\n\n Strng = Strng.replace('рзЗрзЧ', 'рзМ')\n Strng = Strng.replace('рзЧрзЗ', 'рзМ')\n\n # Tamil o, O, au\n\n Strng = Strng.replace('рпЖро╛', 'рпК')\n Strng = Strng.replace('ро╛рпЖ', 'рпК')\n\n Strng = Strng.replace('рпЗро╛', 'рпЛ')\n Strng = Strng.replace('ро╛рпЗ', 'рпЛ')\n\n Strng = Strng.replace('рпЖрпЧ', 'рпМ')\n Strng = Strng.replace('рпЧрпЖ', 'рпМ')\n\n # Malayalam\n\n Strng = Strng.replace('р╡Жр┤╛', 'р╡К')\n Strng = Strng.replace('р┤╛р╡Ж', 'р╡К')\n\n Strng = Strng.replace('р╡Зр┤╛', 'р╡Л')\n Strng = Strng.replace('р┤╛р╡З', 'р╡Л')\n\n # Grantha\n\n Strng = Strng.replace('ЁСНЗЁСМ╛', 'ЁСНЛ')\n Strng = Strng.replace('ЁСМ╛ЁСНЗ', 'ЁСНЛ')\n\n return Strng\n\n# Remove ZWJ/ZWNJ characters\ndef removeZW(Strng):\n Strng = Strng.replace(\"\\u200C\").replace(\"\\u200D\")\n\n return Strng\n\ndef PhagsPaArrange(Strng,Source):\n if Source in GM.IndicScripts:\n ListC = \"|\".join(sorted(GM.CrunchSymbols(GM.Consonants, Source),key=len,reverse=True)) #Do this for all\n ListV = \"|\".join(sorted(GM.CrunchSymbols(GM.Vowels, Source),key=len,reverse=True))\n ListVS = \"|\".join(sorted(GM.CrunchSymbols(GM.VowelSignsNV, Source),key=len,reverse=True))\n ListCS = \"|\".join(sorted(GM.CrunchSymbols(GM.CombiningSigns, Source),key=len,reverse=True))\n\n vir = GM.CrunchSymbols(GM.VowelSigns,Source)[0]\n\n yrv = \"|\".join([GM.CrunchSymbols(GM.Consonants, Source)[i] for i in [25,26,28]])\n\n Strng = re.sub(\"(\"+ListC+\")\"+\"(\"+vir+\")\"+\"(\"+yrv+\")\"+\"(\"+\"(\"+ListVS+\")?\"+\"(\"+ListCS+\")?\"+\")\",r' \\1\\2\\3\\4',Strng)\n Strng = re.sub(\"(\"+ListC+ListV+\")\"+\"(\"+\"(\"+ListVS+\")?\"+\"(\"+ListCS+\")?\"+\")\"+\"(\"+ListC+\")\"+\"(\"+vir+\")\"+\"(?!\\s)\",r\"\\1\\2\\5\\6 \",Strng)\n Strng = re.sub(\"(\"+ListC+ListV+\")\"+\"(\"+\"(\"+ListVS+\")?\"+\"(\"+ListCS+\")?\"+\")\"+\"(\"+ListC+\")\"+'(?!'+vir+')',r\"\\1\\2 \\5\",Strng)\n Strng = re.sub(\"(\"+ListC+ListV+\")\"+\"(\"+\"(\"+ListVS+\")?\"+\"(\"+ListCS+\")?\"+\")\"+\"(\"+ListC+\")\"+'(?!'+vir+')',r\"\\1\\2 \\5\",Strng)\n\n elif Source in GM.LatinScripts:\n pass\n\n return Strng\n\ndef TamilTranscribeCommon(Strng, c = 31):\n script = \"Tamil\"\n\n ListC = GM.CrunchList('ConsonantMap',script)\n ListSC = GM.CrunchList('SouthConsonantMap',script)\n vir = GM.CrunchSymbols(GM.VowelSigns, script)[0]\n\n ConUnVoiced = [ListC[x] for x in [0,5,10,15,20]]\n ConVoicedJ = [ListC[x] for x in [2,7,12,17,22]]\n ConVoicedS = [ListC[x] for x in [2,31,12,17,22]]\n\n ConNasalsAll = '|'.join([ListC[x] for x in [4, 9, 14, 19, 24]])\n conNasalCa = '|'.join([ListC[x] for x in [9]])\n ConNasalsGroup = [ConNasalsAll, conNasalCa, ConNasalsAll, ConNasalsAll, ConNasalsAll]\n\n ConMedials = '|'.join(ListC[25:28]+ListSC[0:2]+ListSC[3:4])\n Vowels = '|'.join(GM.CrunchSymbols(GM.Vowels+GM.VowelSignsNV, script))\n Aytham = GM.CrunchList('Aytham',script)[0]\n Consonants = '|'.join(GM.CrunchSymbols(GM.Consonants,script))\n NRA =ListSC[3] + vir + ListSC[2]\n NDRA = ListC[14] + vir + ListC[12] + vir + ListC[26]\n\n ### Check Siva Siva Mails\n ### Do something about Eyelash ra in Transliterated text\n\n for i in range(len(ConUnVoiced)):\n pass\n #Strng = re.sub('('+Vowels+Consonants+')'+ConUnVoiced[i]+'('+Vowels+Consonants+')',r'\\1'+ConVoicedS[i]+r'\\2',Strng)\n Strng = re.sub('('+Vowels+'|'+Consonants+'|'+Aytham+')'+ConUnVoiced[i]+'(?!'+vir+')',r'\\1'+ConVoicedS[i],Strng)\n Strng = re.sub('([┬│])'+ConUnVoiced[i]+'(?!'+vir+')',r'\\1'+ConVoicedS[i],Strng)\n Strng = re.sub('┬│+', '┬│', Strng)\n\n # Nasals + Unvoiced -> voiced\n # Rule applied even if spaced -> ulakaJ cuRRu -> ulakaJ juRRu; cenJu -> senju\n # Strng = re.sub('('+ConNasalsSpace+')'+'('+vir+')'+'( ?)'+ConUnVoiced[i],r'\\1\\2\\3'+ConVoicedJ[i],Strng)\n\n # Only without space : vampu -> vambu; varim panam -> varim panam\n Strng = re.sub('('+ConNasalsGroup[i]+')'+'('+vir+')'+ConUnVoiced[i],r'\\1\\2'+ConVoicedJ[i],Strng)\n\n # Medials + Unvoiced -> Voiced\n Strng = re.sub('('+ConMedials+')'+'('+vir+')'+ConUnVoiced[i]+'(?!'+vir+')',r'\\1\\2'+ConVoicedS[i],Strng)\n\n Strng = Strng.replace(NRA,NDRA)\n\n # Intervocalic S\n Strng = re.sub('(? CCA\n Strng = re.sub('('+ListC[5]+vir+')'+'(('+punct+')+)'+'('+ListC[c]+')',r'\\1\\2'+ListC[5],Strng)\n\n # JA + Spac | Punct + SA -> JCA\n Strng = re.sub('('+ListC[9]+vir+')'+'(('+punct+')+)'+'('+ListC[c]+')',r'\\1\\2'+ListC[7],Strng)\n\n # GA + Spac | Punct + KA ->\n Strng = re.sub('('+ListC[4]+vir+')'+'(('+punct+')+)'+'('+ListC[0]+')',r'\\1\\2'+ListC[2],Strng)\n\n # NA + Spac | Punct + TTA ->\n Strng = re.sub('('+ListC[14]+vir+')'+'(('+punct+')+)'+'('+ListC[10]+')',r'\\1\\2'+ListC[12],Strng)\n\n # NA + Spac | Punct + TA ->\n Strng = re.sub('('+ListC[19]+vir+')'+'(('+punct+')+)'+'('+ListC[15]+')',r'\\1\\2'+ListC[17],Strng)\n\n # K + k -> hh\n Strng = Strng.replace(Tamil.Aytham[0]+ListC[0],ListC[32]+vir+ListC[32])\n\n # K -> H\n Strng = Strng.replace(Tamil.Aytham[0],ListC[32]+vir)\n\n # RR + RRA -> TT+RA\n Strng = re.sub(ListSC[2]+vir+ListSC[2],ListC[10]+vir+ListC[26],Strng)\n\n # RR | TT + /s + SA -> RR + /s + CA\n Strng = re.sub(\"(\"+'['+ListC[10]+ListSC[2]+']'+vir+')'+'(\\s)'+'('+ListC[c]+')',r'\\1\\2'+ListC[5],Strng)\n\n ## NNN to N, RR to R\n\n Strng = Strng.replace(ListSC[3],ListC[19])\n #Strng = Strng.replace(ListSC[2],ListC[26])\n\n return Strng\n\ndef TamilTranscribe(Strng):\n Strng = TamilTranscribeCommon(Strng)\n\n return Strng\n\ndef TamilTranscribeDialect(Strng):\n Strng = TamilTranscribeCommon(Strng, c=29)\n\n return Strng\n\ndef IPAIndic(Strng):\n Strng = Strng.replace(\"╩К\",\"u\")\n Strng = Strng.replace(\"╔Ы\",\"e\")\n\n return\n","repo_name":"virtualvinodh/aksharamukha-python","sub_path":"aksharamukha/PreProcess.py","file_name":"PreProcess.py","file_ext":"py","file_size_in_byte":47866,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"3"}
+{"seq_id":"18333060891","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\nEI = lambda m: [NLI() for _ in range(m)]\n\n\ndef main():\n H, W = NMI()\n\n def f(b):\n res = []\n for _ in range(H):\n res.append(b % 4)\n b //= 4\n return res\n\n # bの3進数表示\n bits = [f(b) for b in range(4**H)]\n ok_bs = set()\n for b, bit in enumerate(bits):\n ok = True\n\n for i in range(H-1):\n bp = bit[i]\n bn = bit[i+1]\n if bp * bn == 0:\n continue\n if bp != bn:\n ok = False\n\n if 1 not in bit:\n ok = False\n\n nz = [x for x in bit if x > 0]\n if ok and sorted(nz) != nz:\n ok = False\n\n if ok and (max(nz) + 1 != len(set(bit))):\n ok = False\n\n if ok:\n ok_bs.add(b)\n print(bit)\n\n print(len(bits))\n print(len(ok_bs))\n\n # dp[i][b]: 左からi列決めて、右端の状態がbの場合の数\n # dp[W][b]のうち、bits[b][-1]=1であるものの総和が答え\n dp = [[0]*(3**H) for _ in range(W+1)]\n\n for i in range(W):\n pass\n\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"TDPC/S.py","file_name":"S.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7786576862","text":"from csv import reader\nfrom os import listdir, walk\n\nimport pygame\n\n\ndef import_tilemap_csv(path):\n with open(path) as csvfile:\n return list(reader(csvfile, delimiter=\",\"))\n\n\ndef import_folder(path):\n all = []\n for _, _, data in walk(path):\n all += [\n pygame.image.load(path + \"/\" + item).convert_alpha()\n for item in data\n ]\n return all\n\n\ndef import_enemy_assets(name):\n enemy_path = \"../graphics/monsters/\"\n animations = {\n \"idle\": [],\n \"move\": [],\n \"attack\": [],\n }\n\n main = f\"{enemy_path}{name}/\"\n for animation in animations.keys():\n animations[animation] = import_folder(main + animation)\n\n return animations\n\n\ndef import_player_assets():\n character_path = \"../graphics/player/\"\n animations = {\n \"up\": [],\n \"down\": [],\n \"left\": [],\n \"right\": [],\n \"up_attack\": [],\n \"down_attack\": [],\n \"left_attack\": [],\n \"right_attack\": [],\n \"up_idle\": [],\n \"down_idle\": [],\n \"left_idle\": [],\n \"right_idle\": [],\n }\n\n for animation in animations.keys():\n files = listdir(character_path + animation)\n for filename in files:\n animations[animation].append(\n pygame.image.load(\n character_path + animation + \"/\" + filename\n ).convert_alpha()\n )\n return animations\n\n\n# print(import_tilemap_csv(\"../map/map_FloorBlocks.csv\"))\n","repo_name":"ddomin212/pygame-zelda","sub_path":"helpers/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36195809358","text":"from error_percentage import __version__ as version\nfrom logging import exception\nfrom setuptools import setup\n\ntry:\n with open('README.md', mode='r') as readme:\n long_description = readme.read()\nexcept Exception as error:\n exception(msg=error)\n\nsetup(name='error_percentage',\n version=version,\n packages=['error_percentage'],\n url='none yet',\n license='none yet',\n author='Mike',\n author_email='timberg.1@osu.edu',\n description='Vimeo Challenge',\n long_description=long_description,\n long_description_content_type='text/markdown')\n","repo_name":"miketimberg/vimeo-challenge","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30198303795","text":"from enum import Enum\n\n\nclass ResultStatus(str, Enum):\n MATCH = \"MATCH\"\n VARIANT = \"VARIANT\"\n REJECT = \"REJECT\"\n\n\nLANG_DICTIONARY = {\n \"EN\": {\n \"accept\": \"Accept all\",\n \"results\": \"Visual matches\"\n },\n \"PL\": {\n \"accept\": \"Akceptuj wszystko\",\n \"results\": \"Dopasowania wisualne\"\n }\n}\n","repo_name":"FunTribe/bargain-watch-data","sub_path":"src/bargain_watch_data/image_search/consts.py","file_name":"consts.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"27229224289","text":"import sys\nimport time\n\nfrom PySide2.QtWidgets import QApplication\nfrom viaems.model import Model\nfrom serialsource import TCPTarget\nfrom logfilesource import LogFileTarget\nfrom autocalibrate import AutoCalibrate\nfrom windows import MainWindow, GaugesDialog, LogViewDialog\n\napp = QApplication(sys.argv)\n\n\nclass TfiUI():\n def connUpdate(self):\n pass\n\n def update_cb(self, nodes):\n time_since = time.time() - self.last_update\n if time_since > (1/30):\n self.last_update = time.time()\n self.gauge_dialog.updateStats(self.model.nodes)\n self.gauge_dialog.update()\n self.main_window.status_updates(nodes)\n\n if self.autocal:\n self.autocal.update(nodes)\n curtime = self.model.get_node('status.current_time')\n map = self.model.get_node('status.sensors.map')\n rpm = self.model.get_node('status.decoder.rpm')\n ego = self.model.get_node('status.sensors.ego')\n\n if curtime and map:\n self.logview.add_data(int(curtime.val), {\"rpm\": int(rpm.val), \"map\":\n float(map.val), \"ego\": float(ego.val)})\n\n def enumerate_cb(self):\n self.main_window.enumeration_completed()\n\n def interrogate_cb(self):\n self.main_window.interrogation_completed()\n vetable = self.model.get_node('config.tables.ve')\n #self.autocal = AutoCalibrate(vetable, tickrate=4000000)\n\n def __init__(self):\n target = TCPTarget()\n# target = LogFileTarget(\"testlog\")\n target.start()\n self.last_update = time.time()\n\n self.logview = LogViewDialog()\n self.logview.show()\n self.logview.adjustSize()\n\n self.gauge_dialog = GaugesDialog()\n self.gauge_dialog.show()\n self.gauge_dialog.adjustSize()\n\n self.model = Model(target, update_cb=self.update_cb,\n enumerate_cb=self.enumerate_cb,\n interrogate_cb=self.interrogate_cb)\n self.main_window = MainWindow(self.model)\n self.main_window.closed.connect(app.quit)\n self.main_window.show()\n self.main_window.adjustSize()\n\n self.model.start_interrogation()\n\n self.autocal = None\n\n\ntfi = TfiUI()\nprint(app.topLevelWidgets())\nsys.exit(app.exec_())\n","repo_name":"via/tfi-computer-ui","sub_path":"tfi-computer-ui/tfiui.py","file_name":"tfiui.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34518763990","text":"import json\nfrom operator import itemgetter\nimport requests\n\nurl = 'https://hacker-news.firebaseio.com/v0/topstories.json'\nr = requests.get(url)\nprint(f\"Status code: {r.status_code}\")\n\nerror_url, error_comment = 0, 0\n\nsubmission_ids = r.json()\nsubmission_dicts = []\nfor submission_id in submission_ids:\n url = f'https://hacker-news.firebaseio.com/v0/item/{submission_id}.json'\n r = requests.get(url)\n progress = (submission_ids.index(submission_id) + 1) / len(\n submission_ids) * 100\n print(f\"id: {submission_id}\\tstatus: {r.status_code}\\t\\tprogress: \"\n f\"{'{:.3}'.format(progress)}%\")\n response_dict = r.json()\n\n try:\n comments = response_dict['descendants']\n except KeyError:\n comments = -1\n print(f'---------------------WARNING----------------------\\n'\n f'Error in extracting comment of article id {submission_id}'\n f'\\n--------------------------------------------------')\n error_comment += 1\n\n try:\n submission_url = response_dict['url']\n except KeyError:\n submission_url = 'Not Available'\n print(f'-------------------WARNING--------------------\\n'\n f'Error in extracting url of article id {submission_id}'\n f'\\n----------------------------------------------')\n error_url += 1\n\n submission_dict = {\n 'title': response_dict['title'],\n 'hn_link': submission_url,\n 'comments': comments\n }\n submission_dicts.append(submission_dict)\n\nsubmission_dicts = sorted(submission_dicts, key=itemgetter('comments'),\n reverse=True)\n\nhn_news = {'article': []}\nfor submission_dict in submission_dicts:\n print(f\"\\nTitle: {submission_dict['title']}\")\n print(f\"Discussion link: {submission_dict['hn_link']}\")\n print(f\"Comments: {submission_dict['comments']}\")\n\n hn_news['article'].append({\n 'title': submission_dict['title'],\n 'discussion link': submission_dict['hn_link'],\n 'comments': submission_dict['comments'],\n })\n\nwith open('data/sorted_hn_news.json', 'w') as outfile:\n json.dump(hn_news, outfile, indent=4)\n\nprint(f'\\nFailed to extract comment: {error_comment}')\nprint(f'Failed to extract url: {error_url}')\nprint(f'Failed to extract article:'\n f' {len(submission_ids) - len(submission_dicts)}')\n","repo_name":"Lazy-Beee/PythonLearning2021","sub_path":"01 Python-Crash-Course/PythonCrashCourse-Part_2/Data visualization/hn_submissions.py","file_name":"hn_submissions.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70269890323","text":"import cv2 as cv\r\nimport numpy as np\r\nimport sys\r\nimport random as rng\r\n\r\n\r\nclass ProcessImage:\r\n\r\n def DetectObject(self):\r\n\r\n vid = cv.VideoCapture(0)\r\n\r\n if (vid.isOpened() == False):\r\n print('Cannot open input video')\r\n return\r\n\r\n width = int(vid.get(3))\r\n height = int(vid.get(4))\r\n\r\n while (vid.isOpened()):\r\n rc, frame = vid.read()\r\n\r\n if (rc == True):\r\n\r\n # [pinkyX, pinkyY] = self.DetectBall(frame, 0, 154, 83, 19, 239, 115)\r\n self.DetectBall(frame, 0, 135, 196, 8, 255, 244)\r\n\r\n if (cv.waitKey(300) & 0xFF == ord('q')):\r\n break\r\n\r\n else:\r\n break\r\n\r\n vid.release()\r\n cv.destroyAllWindows()\r\n\r\n # Segment the green ball in a given frame\r\n def DetectBall(self, frame, loH, loS, loV, hiH, hiS, hiV):\r\n\r\n lower = np.array([loH, loS, loV], dtype=\"uint8\")\r\n upper = np.array([hiH, hiS, hiV], dtype=\"uint8\")\r\n frame_HSV = cv.cvtColor(frame, cv.COLOR_BGR2HSV)\r\n mask = cv.inRange(frame_HSV, lower, upper)\r\n kernel = np.ones((5, 5), np.uint8)\r\n mask = cv.dilate(mask, kernel)\r\n\r\n contour_image = np.copy(mask)\r\n contours, _ = cv.findContours(contour_image, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)\r\n\r\n # greenMask = cv.inRange(frame_HSV,(loH, loS, loV),(hiH, hiS, hiV)) #This is the line being tested\r\n # output = cv.bitwise_and(frame,frame, mask= mask)\r\n\r\n # contours, = cv.findContours(canny_output,cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE)\r\n\r\n contours_poly = []\r\n centers = []\r\n radius = []\r\n\r\n for i, c in enumerate(contours):\r\n # calculate moments for each contour\r\n # contours_poly[i] = cv.approxPolyDP(c, 3, True)\r\n if cv.contourArea(c) > 500:\r\n center, rad = cv.minEnclosingCircle(c)\r\n centers.append(center)\r\n radius.append(rad)\r\n\r\n drawing = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8)\r\n\r\n for i in range(len(centers)):\r\n # color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))\r\n # cv.drawContours(drawing, contours_poly, i, color)\r\n # cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \\\r\n # (int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)\r\n cv.circle(frame, (int(centers[i][0]), int(centers[i][1])), int(radius[i]), [0, 0, 255], -1)\r\n cv.putText(frame, \"x: {}, y: {}\".format(int(centers[i][0]), int(centers[i][1]), int(radius[i])),\r\n (int(centers[i][0] + radius[i] + 10), int(centers[i][1])), cv.FONT_HERSHEY_SIMPLEX, 0.5,\r\n [0, 0, 255])\r\n cv.putText(frame, \"{}\".format(int(radius[i])),\r\n (int(centers[i][0] + radius[i] + 10), int(centers[i][1] + 20)), cv.FONT_HERSHEY_SIMPLEX, 0.5,\r\n [0, 0, 255])\r\n\r\n cv.imshow('Contours', frame)\r\n\r\n\r\n# Main Function\r\ndef main():\r\n processImg = ProcessImage()\r\n processImg.DetectObject()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"zjyin-umich/eecs598_interactiongame15","sub_path":"python3test.py","file_name":"python3test.py","file_ext":"py","file_size_in_byte":3230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23156900699","text":"from math import log, trunc, pow, ceil\nimport argparse\n\nparser = argparse.ArgumentParser(description='Loan calculator')\nparser.add_argument('--type', type=str)\nparser.add_argument('--principal', type=int)\nparser.add_argument('--periods', type=int)\nparser.add_argument('--interest', type=float)\nparser.add_argument('--payment', type=int)\nargs = parser.parse_args()\n\nif args.type == 'diff':\n if args.interest is None:\n print('Incorrect parameters')\n elif args.interest is not None:\n P = args.principal\n n = args.periods\n i = args.interest / (12 * 100)\n sum = 0\n for m in range(1, n + 1):\n D = ceil(P / n + i * (P - (P * (m - 1)) / n))\n print(f'Month {m}: payment is {D}')\n sum += D\n overpayment = sum - P\n print(f'\\nOverpayment = {overpayment}')\n\nif args.type == 'annuity':\n if args.interest is None:\n print('Incorrect parameters')\n elif args.interest is not None:\n\n if not args.periods:\n P = args.principal\n i = args.interest / (12 * 100)\n a = args.payment\n n = ceil(log(a / (a - i * P), 1 + i))\n if n >= 12:\n years = trunc(n / 12)\n months = n % 12\n if years == 1 and months == 1:\n print(f'It will take {years} year and {months} month to repay this credit!')\n elif years == 1 and months > 1:\n print(f'It will take {years} year and {months} months to repay this credit!')\n elif years > 1 and months > 1:\n print(f'It will take {years} years and {months} months to repay this credit!')\n else:\n print(f'It will take {years} years to repay this credit!')\n else:\n if n == 1:\n print(f'It will take {n} month to repay this credit!')\n print(f'It will take {n} months to repay this credit!')\n\n overpayment = (n * a) - P\n print(f'Overpayment = {overpayment}')\n\n if not args.payment:\n P = args.principal\n n = args.periods\n i = args.interest / (12 * 100)\n\n a = P * ((i * pow(1 + i, n)) / (pow(1 + i, n) - 1))\n print(f'Your monthly payment = {ceil(a)}!')\n\n if not args.principal:\n n = args.periods\n i = args.interest / (12 * 100)\n a = args.payment\n\n P = trunc(a / ((i * pow(1 + i, n)) / (pow(1 + i, n) - 1)))\n print(f'Your credit principal = {P}!')\n overpayment = (n * a) - P\n print(f'Overpayment = {overpayment}')","repo_name":"Dozerzoc/Jetbrains_Loan_Calculator","sub_path":"Loan Calculator/task/creditcalc/creditcalc.py","file_name":"creditcalc.py","file_ext":"py","file_size_in_byte":2667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11374307873","text":"import pytest\nfrom moz_books.exception import InvalidResponseError\nfrom moz_books.google.google_book import GoogleBook\nfrom moz_books.google.google_book_factory import GoogleBookFactory\nfrom tests.ut.moz_books.mock_response import google_one_books_response\n\n\nclass TestGoogleBookFactory:\n @pytest.fixture()\n def factory(self):\n return GoogleBookFactory()\n\n def test_create(self, factory: GoogleBookFactory) -> None:\n item_dic = google_one_books_response().get(\"items\", \"\")[0]\n book: GoogleBook = factory.create(item_dic)\n assert book.author == \"author1\"\n assert book.isbn == \"9784532280208\"\n assert book.isbn10 == \"4532280206\"\n assert book.title == \"title1\"\n assert book.description == \"description1\"\n assert book.page_count == 284\n assert book.image_url == \"http://books.google.com/books/content?id=xxxxx\"\n assert book.info_url == \"http://books.google.co.jp/books?id=yyyyyy\"\n assert book.published_date == \"2013-10\"\n assert book.type == \"GoogleBook\"\n\n def test_create_list(self, factory: GoogleBookFactory) -> None:\n books = factory.create_list(google_one_books_response())\n assert books[0].title == \"title1\"\n\n @pytest.mark.parametrize(\n \"response\",\n [\n ({\"totalItems\": 0, \"items\": []}),\n ({\"totalItems\": 0}),\n ],\n )\n def test_create_list_empty(self, response, factory: GoogleBookFactory) -> None:\n books = factory.create_list(response)\n assert len(books) == 0\n\n @pytest.mark.parametrize(\n \"response\",\n [\n ({}),\n ],\n )\n def test_create_list_invalid_response(self, response, factory: GoogleBookFactory) -> None:\n with pytest.raises(ValueError):\n factory.create_list(response)\n\n def test_create_authorが取れない場合(self, factory: GoogleBookFactory) -> None:\n item_dic = google_one_books_response().get(\"items\", \"\")[0]\n item_dic.get(\"volumeInfo\").pop(\"authors\")\n book: GoogleBook = factory.create(item_dic)\n assert book.author == \"UNKNOWN\"\n\n def test_create_isbn13とisbn10がともに取れない場合(self, factory: GoogleBookFactory) -> None:\n item_dic = google_one_books_response().get(\"items\", \"\")[0]\n item_dic.get(\"volumeInfo\").pop(\"industryIdentifiers\")\n with pytest.raises(InvalidResponseError):\n factory.create(item_dic)\n","repo_name":"mozkzki/moz-books","sub_path":"tests/ut/moz_books/google/test_google_book_factory.py","file_name":"test_google_book_factory.py","file_ext":"py","file_size_in_byte":2445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17584046533","text":"from time import time\r\nfrom numpy import sqrt\r\nimport random\r\nimport matplotlib.pyplot as plt\r\n\r\ndef coherent_det2(x):\r\n y = 0\r\n if x>=2:\r\n y = 3\r\n elif x<2 and x>=0:\r\n y = 1\r\n elif x<0 and x>=-2:\r\n y = -1\r\n else:\r\n y = -3\r\n return y\r\n\r\npam_map = [-3, -1, 1, 3]\r\n\r\nt=time()\r\n\r\nN = 50000\r\nEbNodB_range = [0,5,10,15,25]\r\nitr = len(EbNodB_range)\r\nber = [None]*itr\r\n\r\nfor n in range (0, itr): \r\n \r\n EbNodB = EbNodB_range[n] \r\n EbNo=10.0**(EbNodB/10.0)\r\n noise_std = 1/sqrt(2*EbNo)\r\n noise_mean = 0\r\n \r\n no_errors = 0\r\n for m in range (0, N):\r\n tx_symbol1 = random.choice(pam_map)\r\n tx_symbol2 = random.choice(pam_map)\r\n \r\n noise1 = (random.gauss(noise_mean, noise_std)+\r\n 1j*random.gauss(noise_mean, noise_std))\r\n noise2 = (random.gauss(noise_mean, noise_std)+\r\n 1j*random.gauss(noise_mean, noise_std))\r\n \r\n \r\n ch_coeff1 = (random.gauss(0,1/sqrt(2))+\r\n 1j*random.gauss(0,1/sqrt(2)))\r\n ch_coeff2 = (random.gauss(0,1/sqrt(2))+\r\n 1j*random.gauss(0,1/sqrt(2)))\r\n \r\n rx_symbol1 = ((1/sqrt(2))*tx_symbol1*ch_coeff1+ \r\n (1/sqrt(2))*tx_symbol2*ch_coeff2 + noise1)\r\n rx_symbol2 = (-(1/sqrt(2))*tx_symbol2*ch_coeff1+ \r\n (1/sqrt(2))*tx_symbol1*ch_coeff2 + noise2)\r\n \r\n estimate1 = (ch_coeff1.conjugate()*rx_symbol1+\r\n ch_coeff2*rx_symbol2.conjugate())\r\n estimate2 = (ch_coeff2.conjugate()*rx_symbol1-\r\n ch_coeff1*rx_symbol2.conjugate())\r\n \r\n det_symbol1 = coherent_det2(estimate1.real)\r\n det_symbol2 = coherent_det2(estimate2.real)\r\n \r\n no_errors += 1*(tx_symbol1 != det_symbol1)+1*(tx_symbol2 != det_symbol2) \r\n \r\n ber[n] = 1.0*no_errors/(2*N)\r\n print(\"EbNodB:\", EbNodB)\r\n print(\"Numbder of errors:\", no_errors)\r\n print(\"Error probability:\", ber[n])\r\n \r\nplt.plot(EbNodB_range, ber, 'bo-')\r\nplt.axis([0, 10, 0.001, 0.125])\r\n#plt.xscale('linear')\r\nplt.yscale('log')\r\nplt.xlabel('EbNo(dB)')\r\nplt.ylabel('BER')\r\n#plt.grid(True)\r\nplt.title('BPSK Modulation - Alamouti Scheme')\r\nplt.show()\r\nprint(time() - t, \"seconds\")","repo_name":"jjpro100/MIMO","sub_path":"HW3/alamouti.py","file_name":"alamouti.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2637187874","text":"import random\nimport pygame\nimport time\nfrom Images import Tile, eTileType\n\n\nclass Board:\n def __init__(self, boardSize, tileSize, spacing, pooCount):\n self.Spacing = spacing\n self.BoardSize = boardSize\n self.pointsBoardHeight = 50\n self.Tiles = self.CreateBoard(self.BoardSize, tileSize)\n self.PooCount = pooCount\n self.generatePoos()\n self.setCats()\n self.gameStart = time.time()\n self.isGameOver = False\n self.isWon = False\n\n def Draw(self):\n for tiles in self.Tiles:\n display.blit(tiles.GetImage(), (tiles.X, tiles.Y))\n\n def DrawPoints(self):\n notMarkedPoos = self.PooCount - sum(1 for tile in self.Tiles if tile.isMarked)\n currTime = time.time() - self.gameStart\n text = pygame.font.Font(None, 36).render(\"poos: \" + str(notMarkedPoos), True, (255, 255, 255))\n timeText = pygame.font.Font(None, 36).render(\"time: \" + str(int(currTime)), True, (255, 255, 255))\n display.blit(text, [10,self.pointsBoardHeight/2 - 10])\n display.blit(timeText, [(20 + self.Spacing) * self.BoardSize, self.pointsBoardHeight/2 - 10])\n\n def CheckIfWon(self):\n countOfCorrectlyMarkedPoos = sum(1 for tile in self.Tiles if tile.Type == eTileType.poo and tile.isMarked == True)\n allMarks = sum(1 for tile in self.Tiles if tile.isMarked == True)\n self.isWon = countOfCorrectlyMarkedPoos == self.PooCount == allMarks\n\n def CreateBoard(self, boardSize, tileSize):\n tiles = []\n for col in range(0, boardSize):\n for row in range(0, boardSize):\n tile = Tile(row * tileSize + self.Spacing * row,\n col * tileSize + self.Spacing * col + self.pointsBoardHeight,\n tileSize,\n eTileType.uncovered)\n tiles.append(tile)\n return tiles\n\n def DisplayMessage(self):\n TextToDispay = \"Game Over\" if self.isGameOver else \"You Won!\"\n text = pygame.font.Font(None, 36).render(TextToDispay, True, (255, 255, 255))\n text_rect = text.get_rect()\n text_x = display.get_width() / 2 - text_rect.width / 2\n text_y = display.get_height() / 2 - text_rect.height / 2\n display.blit(text, [text_x, text_y])\n\n def showBlanks(self, tileId):\n neightbours = {k: v for k, v in self.getNeightbours(tileId).iteritems() if self.Tiles[v].isCovered}\n\n for x in neightbours:\n self.Tiles[neightbours[x]].isCovered = False\n if (self.Tiles[neightbours[x]].Type == eTileType.catNeutral):\n self.showBlanks(neightbours[x])\n\n def getClickedTile(self, mousePos):\n for tileId in range(0, self.Tiles.__len__(), 1):\n if (self.Tiles[tileId].isClicked(mousePos)):\n if (self.Tiles[tileId].isMarked or not self.Tiles[tileId].isCovered):\n continue\n\n self.neightbours = self.getNeightbours(tileId)\n self.Tiles[tileId].isCovered = False\n\n if (self.Tiles[tileId].Type == 0):\n self.showBlanks(tileId)\n if (self.Tiles[tileId].Type == eTileType.poo):\n self.isGameOver = True\n\n if(self.CheckIfWon()):\n self.DisplayMessage()\n\n def MarkTile(self, mousePos):\n for tileId in range(0, self.Tiles.__len__(), 1):\n if (self.Tiles[tileId].isClicked(mousePos) and self.Tiles[tileId].isCovered == True):\n self.Tiles[tileId].setMarked(not self.Tiles[tileId].isMarked)\n if(self.CheckIfWon()):\n self.DisplayMessage()\n\n def generatePoos(self):\n seq = random.sample(xrange(0, self.Tiles.__len__()), self.PooCount)\n for pooId in seq:\n self.Tiles[pooId].SetType(eTileType.poo)\n\n def setCats(self):\n for tileId in range(0, self.Tiles.__len__(), 1):\n catLevel = 0\n if (self.Tiles[tileId].Type == eTileType.poo):\n continue\n neightboursIds = self.getNeightbours(tileId)\n for id in neightboursIds.values():\n if (self.Tiles[id].Type == eTileType.poo):\n catLevel += 1\n\n self.Tiles[tileId].SetType(catLevel)\n\n def getNeightbours(self, tileId):\n allNeightbours = {\n 'upperLeft': tileId - self.BoardSize - 1,\n 'upperMiddle': tileId - self.BoardSize,\n 'upperRight': tileId - self.BoardSize + 1,\n 'MiddleLeft': tileId - 1,\n 'MiddleRight': tileId + 1,\n 'lowerLeft': tileId + self.BoardSize - 1,\n 'lowerMiddle': tileId + self.BoardSize,\n 'lowerRight': tileId + self.BoardSize + 1\n }\n\n if (tileId < self.BoardSize):\n allNeightbours['upperLeft'] = None\n allNeightbours['upperMiddle'] = None\n allNeightbours['upperRight'] = None\n\n if (tileId >= self.BoardSize ** 2 - self.BoardSize):\n allNeightbours['lowerLeft'] = None\n allNeightbours['lowerMiddle'] = None\n allNeightbours['lowerRight'] = None\n\n if (tileId % self.BoardSize == 0):\n allNeightbours['upperLeft'] = None\n allNeightbours['MiddleLeft'] = None\n allNeightbours['lowerLeft'] = None\n\n if (tileId % self.BoardSize == self.BoardSize - 1):\n allNeightbours['upperRight'] = None\n allNeightbours['MiddleRight'] = None\n allNeightbours['lowerRight'] = None\n\n return {k: v for k, v in allNeightbours.items() if v is not None}\n\n\ndef main():\n pygame.init()\n tileSize = 30\n boardSize = 10\n pooCount = 10\n spacing = 2\n clock = pygame.time.Clock()\n global display\n display = pygame.display.set_mode([boardSize * (tileSize + spacing), boardSize * (tileSize + spacing) + 50])\n board = Board(boardSize, tileSize, spacing, pooCount)\n running = True\n while running:\n for event in pygame.event.get(): # User did something\n if event.type == pygame.QUIT: # If user clicked close\n running = False\n elif event.type == pygame.MOUSEBUTTONDOWN and pygame.mouse.get_pressed() == (1, 0, 0): # LEFT BUTTON\n if board.isWon or board.isGameOver:\n board = Board(boardSize, tileSize, spacing, pooCount)\n else:\n board.getClickedTile(pygame.mouse.get_pos())\n elif event.type == pygame.MOUSEBUTTONDOWN and pygame.mouse.get_pressed() == (0, 0, 1): # RIGHT BUTTON\n board.MarkTile(pygame.mouse.get_pos())\n elif event.type == pygame.MOUSEBUTTONDOWN and pygame.mouse.get_pressed() == (1, 0, 1): # RIGHT AND LEFT\n pass\n\n display.fill((0, 0, 0))\n if (board.isGameOver or board.isWon):\n board.DisplayMessage()\n else:\n board.Draw()\n board.DrawPoints()\n pygame.display.flip()\n clock.tick(60)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"wniemkowski/Catsweeper","sub_path":"Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":7030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73873503122","text":"#!/usr/bin/env python\n\nimport rbac.acl\n\n\n# create access control list\nacl = rbac.acl.Registry()\n\n# add roles\nacl.add_role(\"member\")\nacl.add_role(\"student\", [\"member\"])\nacl.add_role(\"teacher\", [\"member\"])\nacl.add_role(\"junior-student\", [\"student\"])\n\n# add resources\nacl.add_resource(\"course\")\nacl.add_resource(\"senior-course\", [\"course\"])\n\n# set rules\nacl.allow(\"member\", \"view\", \"course\")\nacl.allow(\"student\", \"learn\", \"course\")\nacl.allow(\"teacher\", \"teach\", \"course\")\nacl.deny(\"junior-student\", \"learn\", \"senior-course\")\n\n# use acl to check permission\nif acl.is_allowed(\"student\", \"view\", \"course\"):\n print(\"Students chould view courses.\")\nelse:\n print(\"Students chould not view courses.\")\n\n# use acl to check permission again\nif acl.is_allowed(\"junior-student\", \"learn\", \"senior-course\"):\n print(\"Junior students chould learn senior courses.\")\nelse:\n print(\"Junior students chould not learn senior courses.\")\n","repo_name":"tonyseek/simple-rbac","sub_path":"examples/acl.py","file_name":"acl.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":269,"dataset":"github-code","pt":"3"}
+{"seq_id":"7177665860","text":"# -*- coding: utf-8 -*-\nimport os\nimport socket\nimport datetime\nfrom copy import deepcopy\nfrom bson.objectid import ObjectId\nfrom itertools import groupby\n\nimport numpy as np\n\nlog_metric_fn = None\nmongo = None\n\n\n\"\"\"some operators for mongodb.\"\"\"\n\n\ndef init_mongo(conf):\n from pymongo import MongoClient\n\n mongo_client = MongoClient(\n host=os.getenv(\"JOBMONITOR_METADATA_HOST\"),\n port=int(os.getenv(\"JOBMONITOR_METADATA_PORT\")),\n )\n global mongo\n\n mongo = getattr(mongo_client, os.getenv(\"JOBMONITOR_METADATA_DB\"))\n # init the job status at mongodb.\n job_content = {\n \"user\": conf.user,\n \"project\": conf.project,\n \"experiment\": conf.experiment,\n \"config\": get_clean_arguments(conf),\n \"job_id\": conf.timestamp,\n \"rank_id\": get_rank(conf),\n \"host\": socket.gethostname(),\n \"status\": \"SCHEDULED\",\n \"schedule_time\": datetime.datetime.utcnow(),\n \"output_dir\": get_checkpoint_dir(conf),\n \"is_cuda\": conf.on_cuda,\n }\n\n conf._mongo_job_id = mongo.job.insert_one(job_content)\n conf.mongo_job_id = {\"_id\": ObjectId(str(conf._mongo_job_id.inserted_id))}\n\n # set job to 'started' in MongoDB\n update_mongo_record(\n conf.mongo_job_id,\n {\"$set\": {\"status\": \"RUNNING\", \"start_time\": datetime.datetime.utcnow()}},\n )\n\n\ndef announce_job_termination_to_mongo(conf):\n global mongo\n end_time = datetime.datetime.utcnow()\n\n # update.\n mongo.job.update(\n conf.mongo_job_id,\n {\n \"$set\": {\n \"status\": \"FINISHED\",\n \"end_time\": end_time,\n \"lasted_time\": (\n end_time\n - find_record_from_mongo(conf.mongo_job_id)[0][\"start_time\"]\n ).seconds,\n }\n },\n )\n\n\ndef update_mongo_record(mongo_job_id, content):\n global mongo\n mongo.job.update_one(mongo_job_id, content)\n\n\ndef find_mongo_record(mongo_job_id):\n global mongo\n return mongo.job.find_one(mongo_job_id)\n\n\ndef delete_mongo_record(mongo_job_id):\n global mongo\n return mongo.job.delete_one(mongo_job_id)\n\n\ndef delete_mongo_collection():\n global mongo\n mongo.job.drop()\n\n\ndef find_record_from_mongo(condition, projection=None):\n # some examples.\n # db.find(projection={\"pmc_id\": True})\n # db.find({\"pmc_id\": {\"$ne\": \"\"}})\n global mongo\n return [s for s in mongo.job.find(condition, projection=projection)]\n\n\ndef _get_non_duplicated_time(records):\n def _get_used_gpu_ids(record):\n if \"conf\" in record:\n conf = record[\"conf\"]\n gpus = conf[\"world\"].split(\",\")[: conf[\"blocks\"]]\n return set(int(gpu) for gpu in gpus)\n else:\n return set([0])\n\n def _organize_results_per_host(_records):\n # preprocessing.\n used_gpus = set()\n list_of_start_time = []\n list_of_end_time = []\n\n for record in _records:\n # get used gpus.\n record[\"gpus\"] = _get_used_gpu_ids(record)\n used_gpus.update(record[\"gpus\"])\n\n # drop the microsecond\n record[\"start_time\"] = record[\"start_time\"].replace(microsecond=0)\n record[\"end_time\"] = record[\"end_time\"].replace(microsecond=0)\n list_of_start_time.append(record[\"start_time\"])\n list_of_end_time.append(record[\"end_time\"])\n\n # build time matrix.\n num_gpus = len(used_gpus)\n start_time = min(list_of_start_time)\n end_time = max(list_of_end_time)\n time_steps = int((end_time - start_time).total_seconds())\n time_matrix = np.zeros((num_gpus, time_steps))\n\n # fill in the time matrix.\n for record in _records:\n for gpu in record[\"gpus\"]:\n start_time_idx = int(\n (record[\"start_time\"] - start_time).total_seconds()\n )\n end_time_idx = int((record[\"end_time\"] - start_time).total_seconds())\n time_matrix[gpu, list(range(start_time_idx, end_time_idx))] = 1\n\n # merge results\n return time_matrix.sum()\n\n # sort records.\n new_records = []\n records = sorted(records, key=lambda x: x[\"host\"])\n\n for _, values in groupby(records, key=lambda x: x[\"host\"]):\n new_records += [_organize_results_per_host(list(values))]\n return sum(new_records)\n\n\ndef get_gpu_hours_from_mongo(year, month, day):\n # init client.\n from pymongo import MongoClient\n\n mongo_client = MongoClient(\n host=os.getenv(\"JOBMONITOR_METADATA_HOST\"),\n port=int(os.getenv(\"JOBMONITOR_METADATA_PORT\")),\n )\n\n mongo = getattr(mongo_client, os.getenv(\"JOBMONITOR_METADATA_DB\"))\n\n # define the time range.\n end_time = datetime.datetime(year, month, day, 23, 59, 59)\n start_time = end_time - datetime.timedelta(days=7)\n\n # get all GPU hours.\n matched_records = [\n s\n for s in mongo.job.find(\n {\n \"is_cuda\": True,\n \"status\": \"FINISHED\",\n \"start_time\": {\"$gt\": start_time, \"$lt\": end_time},\n }\n )\n ]\n return 1.0 * _get_non_duplicated_time(matched_records) / 60 / 60\n\n\ndef get_clean_arguments(conf):\n copy_conf = deepcopy(conf)\n\n if \"graph\" in conf:\n copy_conf._graph = conf.graph.__dict__\n copy_conf.graph = None\n return copy_conf.__dict__\n\n\ndef get_rank(args):\n return args.graph.rank if \"graph\" in args else \"root\"\n\n\ndef get_checkpoint_dir(args):\n return args.checkpoint_root if \"checkpoint_root\" in args else \"\"\n\n\n\"\"\"some operators for telegraf.\"\"\"\n\n\ndef init_telegraf(args):\n from telegraf.client import TelegrafClient\n\n telegraf_client = TelegrafClient(\n host=os.getenv(\"JOBMONITOR_TELEGRAF_HOST\"),\n port=int(os.getenv(\"JOBMONITOR_TELEGRAF_PORT\")),\n tags={\n \"host\": socket.gethostname(),\n \"user\": args.user,\n \"project\": args.project,\n \"experiment\": args.experiment,\n \"job_id\": args.timestamp,\n \"job_details\": args.job_details,\n \"job_info\": args.job_info,\n },\n )\n\n global log_metric_fn\n log_metric_fn = telegraf_client.metric\n\n\ndef log_metric(*args):\n return log_metric_fn(*args)\n\n\n\"\"\"some operators for influxdb.\"\"\"\n\n\ndef init_influxdb(db_name=\"jobmonitor\"):\n from influxdb import InfluxDBClient\n\n influx_client = InfluxDBClient(\n host=os.getenv(\"JOBMONITOR_TIMESERIES_HOST\"), database=db_name\n )\n return influx_client\n\n\ndef get_measurement(cli, measurement=None, tags={}):\n rs = cli.query(\"select * from {}\".format(measurement))\n return list(rs.get_points(measurement=measurement, tags=tags))\n","repo_name":"epfml/federated-learning-public-code","sub_path":"codes/FedDF-code/pcode/tools/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":6696,"program_lang":"python","lang":"en","doc_type":"code","stars":144,"dataset":"github-code","pt":"3"}
+{"seq_id":"35771270947","text":"import pymunk as pm\r\nfrom pymunk import Vec2d # To define gravity vertor in space\r\n\r\nclass Bird():\r\n def __init__(self,distance,angle,x,y,space):\r\n self.life = 20 # To define over all life of a Bird\r\n mass = 5 # Mass/ weight of the bird keep it low other wise it will accordingly follow the laws of physics\r\n radius = 12\r\n inertia = pm.moment_for_circle(mass, 0, radius,(0,0)) # We have taken inner radius as 0 to depict a solid body\r\n body = pm.Body(mass,inertia) # Creating a body for bird which will follow laws of physics\r\n \r\n body.position = x,y # To check position of body\r\n \r\n power = distance * 53 # Movement of the bird\r\n \r\n impulse = power * Vec2d(1,0)\r\n \r\n angle = -angle # To give oposite direction\r\n \r\n body.apply_impulse_at_local_point(impulse.rotated(angle)) # Add the local impulse to the body as if applied from body loacal point\r\n \r\n shape = pm.Circle(body, radius , (0,0))\r\n shape.elasticity = 0.95 # For bird to bounce\r\n self.friction = 1\r\n \r\n shape.collision_type = 0 # there will be three type of collision bird, pigs and wood\r\n \r\n space.add(body,shape)\r\n self.body = body\r\n self.shape = shape\r\n \r\n\r\nclass Pig():\r\n def __init__(self,x,y,space):\r\n self.life = 20 \r\n mass = 5 \r\n radius = 14\r\n \r\n inertia = pm.moment_for_circle(mass, 0, radius,(0,0))\r\n body = pm.Body(mass,inertia)\r\n body.position = x, y\r\n \r\n shape = pm.Circle(body, radius , (0,0))\r\n shape.elasticity = 0.95 \r\n self.friction = 1\r\n shape.collision_type = 1\r\n \r\n space.add(body,shape)\r\n self.body = body\r\n self.shape = shape","repo_name":"AmanSharma2896/Angry-Bird","sub_path":"character.py","file_name":"character.py","file_ext":"py","file_size_in_byte":2306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40620878356","text":"import json\nimport csv\nfrom colorama import Fore, init\n\ninit()\n\n\ndef load_data(file_path):\n \"\"\" Loads a JSON file \"\"\"\n with open(file_path, \"r\") as f:\n data = json.load(f)\n return data\n\n\ndef load_csv(file_path):\n \"\"\"loads a csv into a dict\"\"\"\n data = []\n with open(file_path, 'r') as file:\n reader = csv.reader(file)\n for row in reader:\n data.append(row)\n return data\n\n\ndef save_csv(filename, data_to_save):\n \"\"\"loads a csv into a dict\"\"\"\n with open(filename, 'w') as csv_file:\n writer = csv.writer(csv_file)\n writer.writerows(data_to_save)\n return filename\n\n\ndef load_document(file):\n \"\"\"loads the content of a file\"\"\"\n with open(file, \"r\") as file:\n file_data = file.read()\n return file_data\n\n\ndef save_file(filename, data_to_save):\n \"\"\"Creates a new json file with the given file name and writes the provided content to it.\"\"\"\n try:\n with open(filename, 'w') as f:\n json.dump(data_to_save, f)\n return filename\n\n except Exception as e:\n print(Fore.RED + 'An unexpected error occurred while saving movie. Please try again later.'\n + Fore.RESET)\n print(Fore.RED + f'Error message: {str(e)}' + Fore.RESET)\n\n\ndef save_html(filename, data_to_save):\n \"\"\"Creates a new HTML file with the given file name and writes the provided content to it.\"\"\"\n with open(filename, 'w') as f:\n f.write(data_to_save)\n return f\n","repo_name":"inesfolha/movie_app","sub_path":"file_handler.py","file_name":"file_handler.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1715142046","text":"#!/usr/bin/python3\n\"\"\" Defines a function that prints top ten hot posts for a given subreddit\"\"\"\nimport requests\n\n\ndef recurse(subreddit, hot_list=[], after=''):\n \"\"\" Queries the reddit.com API and returns a list containing the titles\n of all hot articles for a given subreddit\n\n Args:\n subreddit (str): name of subreddit\n hot_list (list): list of titles\n after (str): id of next set of results\n \"\"\"\n url = 'https://api.reddit.com/r/'\n headers = {'User-Agent': 'my bot 0.1'}\n response = requests.get(\n '{}{}/hot?after={}'.format(\n url,\n subreddit, after), headers=headers, allow_redirects=False)\n\n if response.status_code != 200:\n return None\n else:\n hot_articles = response.json()\n if len(hot_articles['data']['children']) == 0:\n return hot_list\n else:\n for d in hot_articles['data']['children']:\n hot_list.append(d['data']['title'])\n\n after = hot_articles['data']['after']\n if after is None:\n return hot_list\n return recurse(subreddit, hot_list, after=after)\n","repo_name":"Wa2hingt0n/alx-system_engineering-devops","sub_path":"0x16-api_advanced/2-recurse.py","file_name":"2-recurse.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15747822942","text":"N, M = map(int, input().split())\nmiro = []\nfor i in range(N):\n temp = list(map(int, input()))\n miro.append(temp)\n\nmove_X = [-1, 1, 0, 0]\nmove_Y = [0, 0, -1, 1]\n\ndef find_exit(miro, x,y):\n queue = []\n queue.append([x, y])\n\n while queue:\n temp = queue.pop(0)\n x = temp[0]\n y = temp[1]\n\n for i in range(4):\n mx = x + move_X[i]\n my = y + move_Y[i]\n\n if mx < 0 or my < 0 or mx >= N or my >= M:\n continue\n elif miro[mx][my] == 0 or miro[mx][my] != 1:\n continue\n elif miro[mx][my] == 1:\n miro[mx][my] = miro[x][y] + 1\n queue.append([mx,my])\n #x, y 좌표가 아닌 N,M 좌표로 마지막 queue가 M,N 이라는 보장이 없다.\n print(miro[N-1][M-1])\n\n\nfind_exit(miro, 0, 0)\n\n","repo_name":"agmon5959/TS_Study","sub_path":"BOJ_STUDY/DFS & BFS/오원준/2178_wj.py","file_name":"2178_wj.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37983559149","text":"from shopLocation import ShopLocation\nfrom shopProduct import ShopProduct\nfrom sortedcontainers import SortedListWithKey\n\nclass SearchProvider(object):\n \"\"\" A class which provides a higher level abstraction for the search functionality \n Use an instance of this class to execute search requests.\n \"\"\"\n\n def __init__(self, locator):\n self.Locator = locator\n\n def Search(self, lat, lng, radiusMeters, selectedTags, itemCount):\n if(selectedTags!=None):\n #Changing tags to lower case to enable a virtual case-insensitive comparison. Note that this does not work for all cultures\n selectedTags = set([tag.lower() for tag in selectedTags])\n else:\n selectedTags = set()\n\n #Get elements within the search radius\n filteredResults = self.Locator.Search(lat,lng,radiusMeters)\n\n #If any tags were selected, filter elements which do not share at least one tag with selectedTags\n if len(selectedTags)>0:\n filteredResults = [shopLocation for shopLocation in filteredResults if not shopLocation.Shop.tags.isdisjoint(selectedTags)]\n\n #Get top N most popular products of shops\n results = SortedListWithKey(key=lambda val: -1*float(val.Product.popularity))\n for item in filteredResults:\n #results = results[0:itemCount] #might be useful if concerns related to memory take precedence over processing time\n \n #Get most popular products of this shop\n topProducts = item.Shop.products[0:itemCount]\n\n for product in topProducts:\n #Check at which position this element would be if inserted into the aggregated popular products list\n index = results.bisect_right(ShopProduct(item.Shop,product))\n if index < itemCount:\n #The product is higher in popularity than the current threshold \n results.add(ShopProduct(item.Shop,product))\n else:\n #The aggregated list already contains enough number of products which are more popular than the one being considered\n break\n results = results[0:itemCount]\n\n #results is a SortedListWithKey consisting of ShopProduct objects\n\n return results","repo_name":"flaskmc/challenge","sub_path":"server/searchProvider.py","file_name":"searchProvider.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25276833051","text":"# Functions, Lambda, Arguments (required, keyword, default)\n# REQUIRED Arguments\ndef fn_required(arg1):\n \"\"\"Required argument as arg1\"\"\"\n print(arg1)\n return\n\n\nfn_required(\"This is required argument function\")\n# fn1() # error missing positional argument\n\n# KEYWORD Arguments\n\n\ndef fn_keyword(name, age):\n \"\"\"Keyword argument function.\"\"\"\n print(f\"Name: {name}, age: {age}\")\n return\n\n\nfn_keyword(age=31, name=\"john\") # Name: john, age: 31\nfn_keyword(age=20, name=\"\") # Name: , age: 20\n\n# DEFAULT Arguments\n\n\ndef fn_default(name, job=\"Programmer\"):\n \"\"\"Default argument function.\"\"\"\n print(f\"Employee: {name}, job: {job}\")\n return\n\n\nfn_default(\"Mark\") # Employee: Mark, job: Programmer\nfn_default(\"Dan\", \"Data Analyst\") # Employee: Dan, job: Data Analyst\n\n\n# VARIABLE-LENGTH Arguments\n\ndef fn_var_length(*args_tuple):\n \"\"\"Function with arbitrary number of arguments which forms a tuple.\"\"\"\n for args in args_tuple:\n print(args, end=\" \")\n return\n\n\nfn_var_length(100, 540, 'f', ['docs', 'test']) # 100 540 f ['docs', 'test']\n\n# LAMBDA (Anonymous) Functions\n\nsumTwo = lambda arg1, arg2: arg1 + arg2\n\nprint(f\"\\nSum two: {sumTwo(50, 130)}\")\n\n\n\n\n","repo_name":"RadosavPanic/Python-Misc","sub_path":"functions/functions_tryit.py","file_name":"functions_tryit.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37773666594","text":"import argparse\nimport logging\nimport os\nimport rasterio as rio\n#import opencv as cv\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nfrom PIL import Image\nfrom torchvision import transforms\nimport torchvision\nfrom osgeo import gdal, gdal_array\nimport matplotlib.pyplot as plt\nfrom skimage import exposure\nimport cv2\n\nfrom unet import UNet_VAE\nfrom unet import UNet_VAE_old, UNet_VAE_RQ_old, UNet_VAE_RQ_test, UNet_VAE_RQ_old_trainable, UNet_VAE_RQ_old_torch\nfrom unet import UNet_VAE_RQ_new_torch, UNet_VAE_RQ_scheme3\nfrom unet import UNet_VAE_RQ_scheme1\nfrom utils.utils import plot_img_and_mask, plot_img_and_mask_3, plot_img_and_mask_2, plot_img_and_mask_4\n\nimage_path = '/home/geoint/tri/sentinel/train/sat/2016105_10.tif'\nmask_true_path = '/home/geoint/tri/sentinel/train/map/nlcd_2016105_10.tif'\n\n#image_path = '/home/geoint/tri/va059/train/sat/number34823.TIF'\n#mask_true_path = '/home/geoint/tri/va059/train/map/number34823.TIF'\n\nuse_cuda = True\n#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nim_type = image_path[17:25]\nprint(im_type)\nsegment=True\nalpha = 0\nunet_option = 'unet_vae_RQ_scheme1' # options: 'unet_vae_old', 'unet_vae_RQ_old', 'unet_vae_RQ_allskip_trainable', 'unet_vae_RQ_torch', 'unet_vae_RQ_scheme3'\nimage_option = \"clean\" # \"clean\" or \"noisy\"\n\n\ndef rescale(image):\n map_img = np.zeros((256,256,3))\n for band in range(3):\n p2, p98 = np.percentile(image[:,:,band], (2, 98))\n map_img[:,:,band] = exposure.rescale_intensity(image[:,:,band], in_range=(p2, p98))\n return map_img\n\n#accept a file path to a jpg, return a torch tensor\ndef jpg_to_tensor(filepath=image_path):\n\n naip_fn = filepath\n driverTiff = gdal.GetDriverByName('GTiff')\n naip_ds = gdal.Open(naip_fn, 1)\n nbands = naip_ds.RasterCount\n # create an empty array, each column of the empty array will hold one band of data from the image\n # loop through each band in the image nad add to the data array\n data = np.empty((naip_ds.RasterXSize*naip_ds.RasterYSize, nbands))\n for i in range(1, nbands+1):\n band = naip_ds.GetRasterBand(i).ReadAsArray()\n data[:, i-1] = band.flatten()\n\n img_data = np.zeros((naip_ds.RasterYSize, naip_ds.RasterXSize, naip_ds.RasterCount),\n gdal_array.GDALTypeCodeToNumericTypeCode(naip_ds.GetRasterBand(1).DataType))\n for b in range(img_data.shape[2]):\n img_data[:, :, b] = naip_ds.GetRasterBand(b + 1).ReadAsArray()\n\n pil = np.array(img_data)\n if im_type != \"sentinel\":\n pil=pil/255\n else:\n pil = rescale(pil)\n\n ## add gaussian noise\n row,col,ch= pil.shape\n sigma = 0.05\n noisy = pil + sigma*np.random.randn(row,col,ch)\n\n #pil_to_tensor = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\n transform_tensor = transforms.ToTensor()\n if use_cuda:\n noisy_tensor = transform_tensor(noisy).cuda()\n tensor = transform_tensor(pil).cuda()\n\n return tensor.view([1]+list(tensor.shape)), noisy_tensor.view([1]+list(noisy_tensor.shape))\n\n#accept a torch tensor, convert it to a jpg at a certain path\ndef tensor_to_jpg(tensor):\n #tensor = tensor.view(tensor.shape[1:])\n tensor = tensor.squeeze(0)\n if use_cuda:\n tensor = tensor.cpu()\n #tensor_to_pil = torchvision.transforms.Compose([torchvision.transforms.ToPILImage()])\n #pil = tensor_to_pil(tensor)\n pil = tensor.permute(1, 2, 0).numpy()\n pil = np.array(pil)\n pil = rescale(pil)\n \n return pil\n\n#predict image\ndef predict_img(net,\n filepath,\n device,\n scale_factor=1,\n out_threshold=0.5):\n net.eval()\n\n if image_option=='clean':\n img = jpg_to_tensor(filepath)[0] ## clean image\n elif image_option=='noisy':\n img = jpg_to_tensor(filepath)[1] ## noisy image\n img = img.to(device=device, dtype=torch.float32)\n\n print(\"img shape: \", img.shape)\n\n with torch.no_grad():\n output = net(img)\n\n test_output = output\n\n #print(\"output shape: \", output.shape)\n\n if unet_option == 'unet' or unet_option == 'simple_unet' or unet_option == 'unet_jaxony':\n #output = output[0]\n output = output.squeeze()\n #output = output\n else:\n #output = output[0][0]\n output = output[0].squeeze()\n\n print(\"output squeeze shape: \", output.shape)\n\n #print(torch.unique(output))\n\n if net.num_classes > 1:\n #probs = F.softmax(output, dim=1)[0]\n probs = F.softmax(output, dim=1)\n #probs = F.log_softmax(output, dim=1)\n else:\n probs = torch.sigmoid(output[0])[0]\n\n tf = transforms.Compose([\n transforms.ToPILImage(),\n transforms.Resize((256, 256)),\n transforms.ToTensor()\n ])\n\n print(\"probs shape: \", probs.shape)\n\n #print(probs)\n\n #full_mask = tf(probs.cpu()).squeeze()\n #full_mask = probs.cpu()\n\n probs = probs.detach().cpu()\n full_mask = torch.argmax(probs, dim=0)\n\n #print(torch.unique(full_mask))\n full_mask = torch.squeeze(full_mask).cpu().numpy()\n\n #print(full_mask)\n #plt.imshow(full_mask)\n #plt.show()\n\n print(\"full mask shape: \",full_mask.shape)\n\n if net.num_classes == 1:\n return (full_mask > out_threshold).numpy()\n else:\n #img = F.one_hot(full_mask.argmax(dim=0), net.num_classes).permute(2, 0, 1).numpy()\n #img_2 = full_mask.argmax(dim=0).numpy()\n return full_mask\n\n\n\ndef get_args():\n parser = argparse.ArgumentParser(description='Predict masks from input images')\n parser.add_argument('--model', '-m', default='/home/geoint/tri/github_files/github_checkpoints/checkpoint_unet_vae_old_epoch10_0.0_3-23_batchnorm_segment.pth', metavar='FILE',\n help='Specify the file in which the model is stored')\n #parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', default='F:\\\\NAIP\\\\256\\\\pa101\\\\test\\\\sat\\\\number13985.TIF', help='Filenames of input images', required=True)\n #parser.add_argument('--output', '-o', metavar='OUTPUT', nargs='+', default='out/predict1.tif', help='Filenames of output images')\n parser.add_argument('--viz', '-v', action='store_true',\n help='Visualize the images as they are processed')\n parser.add_argument('--no-save', '-n', action='store_true', help='Do not save the output masks')\n parser.add_argument('--mask-threshold', '-t', type=float, default=0.5,\n help='Minimum probability value to consider a mask pixel white')\n parser.add_argument('--scale', '-s', type=float, default=0.5,\n help='Scale factor for the input images')\n\n return parser.parse_args()\n\n\ndef get_output_filenames(args):\n def _generate_name(fn):\n split = os.path.splitext(fn)\n return f'{split[0]}_OUT{split[1]}'\n\n return args.output or list(map(_generate_name, args.input))\n\ndef mask_to_image(mask: np.ndarray):\n if mask.ndim == 2:\n return Image.fromarray((mask * 255).astype(np.uint8))\n elif mask.ndim == 3:\n #return Image.fromarray((np.argmax(mask, axis=0) * 255 / mask.shape[0]).astype(np.uint8))\n return (np.argmax(mask, axis=0) * 255 / mask.shape[0]).astype(np.uint8)\n\n\nif __name__ == '__main__':\n args = get_args()\n #in_files = args.input\n #out_files = get_output_filenames(args)\n\n if unet_option == 'unet_vae_1':\n net = UNet_VAE(3)\n elif unet_option == 'unet_vae_old':\n net = UNet_VAE_old(3)\n \n elif unet_option == 'unet_vae_RQ_old':\n net = UNet_VAE_RQ_old(3, alpha)\n \n elif unet_option == 'unet_vae_RQ_allskip_trainable':\n net = UNet_VAE_RQ_old_trainable(3,alpha)\n\n elif unet_option == 'unet_vae_RQ_torch':\n #net = UNet_VAE_RQ_old_torch(3, alpha = alpha)\n net = UNet_VAE_RQ_new_torch(3, segment, alpha)\n\n elif unet_option == 'unet_vae_RQ_scheme3':\n net = UNet_VAE_RQ_scheme3(3, segment, alpha)\n elif unet_option == 'unet_vae_RQ_scheme1':\n net = UNet_VAE_RQ_scheme1(3, segment, alpha)\n\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n #logging.info(f'Loading model {args.model}')\n logging.info(f'Using device {device}')\n\n net.to(device=device)\n net.load_state_dict(torch.load(args.model, map_location=device))\n\n logging.info('Model loaded!')\n\n #for i, filename in enumerate(in_files):\n logging.info(f'\\nPredicting image {image_path} ...')\n\n mask = predict_img(net=net,\n filepath=image_path,\n scale_factor=1,\n out_threshold=0.5,\n device=device)\n\n\n #out_files = 'out/predict_va_softshrink_all_0.02.tif'\n out_files = 'out/predict_va_unet_epoch40_new.tif'\n \n #if not args.no_save:\n #if args.no_save:\n out_filename = out_files\n #result = mask_to_image(mask)\n #arr_to_tif(raster_f=image_path, segments=mask, out_tif=out_files)\n #result.show()\n #result.save(out_files)\n logging.info(f'Mask saved to {out_files}')\n \n ## get image\n if image_option=='clean':\n img = jpg_to_tensor(image_path)[0]\n else:\n img = jpg_to_tensor(image_path)[1]\n img = tensor_to_jpg(img)\n\n #img = read_sentinel2(image_path)\n\n #print(naip_ds)\n\n ## get ground truth label\n naip_fn = mask_true_path\n #print(naip_fn)\n driverTiff = gdal.GetDriverByName('GTiff')\n naip_ds = gdal.Open(naip_fn, 1)\n nbands = naip_ds.RasterCount\n # create an empty array, each column of the empty array will hold one band of data from the image\n # loop through each band in the image and add to the data array\n data = np.empty((naip_ds.RasterXSize*naip_ds.RasterYSize, nbands))\n for i in range(1, nbands+1):\n band = naip_ds.GetRasterBand(i).ReadAsArray()\n data[:, i-1] = band.flatten()\n\n img_data = np.zeros((naip_ds.RasterYSize, naip_ds.RasterXSize, naip_ds.RasterCount),\n gdal_array.GDALTypeCodeToNumericTypeCode(naip_ds.GetRasterBand(1).DataType))\n for b in range(img_data.shape[2]):\n img_data[:, :, b] = naip_ds.GetRasterBand(b + 1).ReadAsArray()\n\n label = np.array(img_data)\n print(label.shape)\n label = label.reshape((256,256))\n\n #mask = (np.argmax(mask, axis=2)).astype(np.uint8)\n #mask = mask.reshape((256,256))\n error = mask - img_data\n #print(\"errors: \", error)\n print(np.unique(mask))\n if im_type == 'sentinel':\n plot_img_and_mask_4(img, label, mask)\n else:\n plot_img_and_mask_3(img, label, mask)\n #plot_img_and_mask_2(img, mask)\n\n if not args.viz:\n logging.info(f'Visualizing results for image {image_path}, close to continue...')\n #plot_img_and_mask(img, mask)","repo_name":"trile83/RQUNetVAE","sub_path":"unetvae_segment_sentinel_predict.py","file_name":"unetvae_segment_sentinel_predict.py","file_ext":"py","file_size_in_byte":10844,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"10391691315","text":"from flask import g\nimport pymysql\n\ndef get_db():\n \n if 'db' not in g:\n g.db = pymysql.connect('localhost','root','test','todolist_borntodev')\n return g.db\n\n\ndef close_db(e=None):\n db = g.pop('db', None)\n\n if db is not None:\n db.close()","repo_name":"anpdgr/todolist_borntoDev","sub_path":"db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10398236085","text":"import os\nimport numpy\nimport networkx as nx\nfrom collections import namedtuple\nfrom glob import glob\nfrom agalma import config\nfrom agalma import database\nimport biolite.database\nfrom biolite import diagnostics\nfrom biolite import report\nfrom biolite import utils\nfrom biolite import wrappers\nfrom biolite import workflows\nfrom biolite.pipeline import Pipeline\n\npipe = Pipeline('homologize', __doc__)\n\n### ARGUMENTS ###\n\npipe.add_arg('load_ids', nargs='+', metavar='LOAD_ID', help=\"\"\"\n\tSpecifies which datasets to analyze; A space delimited list of run_id's\n\tcorresponding to the load runs that populated the database\"\"\")\n\npipe.add_arg('--seq_type', '-t', metavar='TYPE', default='protein', help=\"\"\"\n\tChoose whether comparisons are based on 'nucleotide', 'protein', or\n\t'masked_protein' sequences.\"\"\")\n\npipe.add_arg(\n\t'--genome_type', '-g', nargs=\"+\", metavar='TYPE',\n\tdefault=['nuclear'], help=\"\"\"\n\t\tSelect sequences with genome type: nuclear, mitochondrial, and/or\n\t\tplastid.\"\"\")\n\npipe.add_arg(\n\t'--molecule_type', '-m', nargs=\"+\", metavar='TYPE',\n\tdefault=['protein-coding'], help=\"\"\"\n\t\tSelect sequences with molecule type: protein-coding, ribosomal-small,\n\t\tribosomal-large, ribosomal, and/or unknown.\"\"\")\n\npipe.add_arg('--min_bitscore', type=float, default=0.0, help=\"\"\"\n\tFilter out BLAST hit edges with a SRV (Blast Score Ratio Values) below min_bitscore.\"\"\")\n\npipe.add_arg('--min_overlap', type=float, default=0.5, help=\"\"\"\n\tFilter out BLAST hit edges with a max HSP length less than this fraction\n\tof target length.\"\"\")\n\npipe.add_arg('--min_nodes', type=int, default=4, help=\"\"\"\n\tMinimum number of nodes to be retained as a homolgous gene cluster.\"\"\")\n\npipe.add_path_arg('--genesets', nargs='+', default=None, help=\"\"\"\n\tConcatenate the specified amino acid FASTAs and use as the reference for\n\ta blastx or blastp comparison, instead of performing an all-to-all\n\tcomparison. FASTA headers must start with a non-digit.\"\"\")\n\npipe.add_path_arg('--blast_hits', default=None, help=\"\"\"\n\tUse a pre-computed blast alignment when starting from the parse_edges\n\tstage.\"\"\")\n\npipe.add_arg('--mcl_inflation', type=float, default=2.1, help=\"\"\"\n \tSets the main inflation value for the mcl algorithm. This value is the\n\tmain handle for affecting cluster granularity. It is usually chosen\n\tsomewhere in the range [1.2-5.0].\"\"\")\n\nSpeciesData = namedtuple('SpeciesData', \"name ncbi_id itis_id catalog_id\")\n\n### STAGES ###\n\n@pipe.stage\ndef lookup_species(load_ids):\n\t\"\"\"Lookup the species data for each run\"\"\"\n\n\tspecies = {}\n\n\t# Given a run_id, returns a named tuple with the following elements:\n\t#\n\t# name\t\tThe species names\n\t# ncbi_id\t\tThe NCBI taxon id\n\t# itis_id\t\tThe ITIS taxon id\n\t# catalog_id\tThe agalma catalog id\n\n\tfor load_id in load_ids:\n\t\trow = biolite.database.execute(\"\"\"\n\t\t\tSELECT catalog.species, catalog.ncbi_id, catalog.itis_id, catalog.id\n\t\t\tFROM catalog, runs\n\t\t\tWHERE runs.run_id=? AND catalog.id=runs.id;\"\"\",\n\t\t\t(load_id,)).fetchone()\n\t\tif not row:\n\t\t\tutils.die(\"Couldn't find species data for run ID %s\" % load_id)\n\t\tif row[0] is None:\n\t\t\tutils.die(\"Species name is empty for catalog ID '%s'\" % row[3])\n\t\tspecies[load_id] = SpeciesData(*row)\n\t\tdiagnostics.log(str(load_id), row)\n\n\tdiagnostics.log(\"species\", species)\n\tingest('species')\n\n\n@pipe.stage\ndef write_fasta(\n\t\tid, _run_id, load_ids, species, seq_type,\n\t\tmolecule_type, genome_type, outdir):\n\t\"\"\"Write sequences from the Agalma database to a FASTA file\"\"\"\n\n\tblast_dir = utils.safe_mkdir('allvall_blast_%s_%s' % (id, _run_id))\n\tfasta = os.path.join(blast_dir, 'all.fa')\n\n\t# The nodes file contains a header, which describes the attributes, as well\n\t# as a line for every node (transcript) in the analysis.\n\tnodes = os.path.join(outdir, 'nodes.txt')\n\n\tnloads = 0\n\tnseqs = 0\n\tnbases = 0\n\n\twith open(nodes, 'w') as fnodes, open(fasta, 'w') as ffasta:\n\t\tprint >>fnodes, \"label\\tid\\tassembly\\tassembly_number\"\n\t\tfor load_id in load_ids:\n\t\t\tnloads += 1\n\t\t\ttaxon = species[load_id].name\n\t\t\tfor record in database.load_seqs(\n\t\t\t\t\t\t\t\t\tload_id, taxon, seq_type,\n\t\t\t\t\t\t\t\t\tmolecule_type, genome_type):\n\t\t\t\tnseqs += 1\n\t\t\t\tnbases += len(record.seq)\n\t\t\t\t# The id of the node (the second column) is a unique identifier\n\t\t\t\t# and because we already have one in the table, use that here.\n\t\t\t\tprint >>fnodes, \"%s\\t%d\\t%s\\t%d\" % (\n\t\t\t\t record.header, record.id, load_id, nloads)\n\t\t\t\tutils.write_fasta(ffasta, record.seq, record.id)\n\n\tif not nseqs:\n\t\tutils.die(\"no sequences were written to the FASTA file\")\n\n\tdiagnostics.log_path(nodes, 'nodes')\n\n\tdiagnostics.log('nloads', nloads)\n\tdiagnostics.log('nseqs', nseqs)\n\tdiagnostics.log('nbases', nbases)\n\n\tingest('blast_dir', 'fasta', 'nodes')\n\n\n@pipe.stage\ndef prepare_blast(blast_dir, fasta, seq_type, genesets):\n\t\"\"\"Prepare all-by-all BLAST database and command list\"\"\"\n\n\tdb = os.path.join(blast_dir, 'db')\n\n\tif genesets:\n\t\tif seq_type == 'nucleotide':\n\t\t\tprogram = 'blastx'\n\t\telse:\n\t\t\tprogram = 'blastp'\n\t\tfor geneset in genesets:\n\t\t\tutils.cat_to_file(geneset, db+'.fa')\n\t\t\t# Also add genesets to the query file, to find overlapping genes\n\t\t\t# between sets.\n\t\t\tutils.cat_to_file(geneset, fasta)\n\t\twrappers.MakeBlastDB(db+'.fa', db, 'prot')\n\telse:\n\t\tif (seq_type == 'masked_protein') or (seq_type == 'protein'):\n\t\t\tdbtype = 'prot'\n\t\t\tprogram = 'blastp'\n\t\telif seq_type == 'nucleotide':\n\t\t\tdbtype = 'nucl'\n\t\t\tprogram = 'blastn'\n\t\telse:\n\t\t\tutils.die(\"unrecognized sequence type '%s'\" % seq_type)\n\t\twrappers.MakeBlastDB(fasta, db, dbtype)\n\n\tcommand = program + \" -db db -evalue 1e-20\" \\\n\t + \" -outfmt \\\"6 qseqid sseqid bitscore qlen length\\\"\"\n\tcommands = workflows.blast.split_query(fasta, command, 100000, blast_dir)\n\n\tingest('commands')\n\n\n@pipe.stage\ndef run_blast(blast_dir, commands, outdir):\n\t\"\"\"Run all-by-all BLAST\"\"\"\n\n\tblast_hits = os.path.join(blast_dir, 'hits.tab')\n\twrappers.Parallel(\n\t\tcommands, \"--joblog blast.log --resume-failed --halt 1\",\n\t\tstdout=blast_hits, cwd=blast_dir)\n\tingest('blast_hits')\n\n\n@pipe.stage\ndef parse_edges(_run_id, seq_type, blast_hits, min_overlap, min_bitscore, min_nodes):\n\t\"\"\"Parse BLAST hits into edges weighted by bitscore\"\"\"\n\n\tgraph = nx.Graph()\n\tedge_file = 'allvall_edges_%s.abc' % _run_id\n\n\tnseqs = 0\n\tnedges = {\n\t\t'all': 0,\n\t\t'non-self': 0,\n\t\t'passed-overlap': 0,\n\t\t'passed-bitscore': 0,\n\t\t'passed-bitscore-unique': 0}\n\tmax_bitscore = None\n\tlast_query = \"\" # For identification of a new query in the table\n\n\n\tfor f in glob(blast_hits):\n\t\tfor line in open(f):\n\t\t\t# fields:\n\t\t\t# qseqid sseqid bitscore qlen length\n\t\t\tid_from, id_to, bitscore, qlen, length = line.rstrip().split()\n\t\t\t# Sometimes blast outputs a bad query id if the query is longer\n\t\t\t# than 10Kb\n\t\t\tif id_from.startswith('Query_'):\n\t\t\t\tutils.info(\"discarding bad hit with query id '%s'\" % id_from)\n\t\t\t\tcontinue\n\t\t\tbitscore = float(bitscore)\n\t\t\tlength = float(length) / float(qlen)\n\t\t\t# Correct for nucleotide vs. amino acid length ### WHY? lenght here is the overlap proportion\n\t\t\t#if seq_type == 'nucleotide'\n\t\t\t#\tlength *= 3.0\n\t\t\t# Filter out self hits, low scoring hits, and short hits\n\t\t\tnedges['all'] += 1\n\t\t\tif id_from != last_query:\n\t\t\t\tmax_bitscore = bitscore ## The self-hit is not always at the top as the file is sorted by e-values and ties happens.\n\t\t\t\tlast_query = id_from\t## however the self-hit always have the same bitscore as the first hit.\n\t\t\tif id_from != id_to:\n\t\t\t\tnedges['non-self'] += 1\n\t\t\t\tif length > min_overlap:\n\t\t\t\t\tnedges['passed-overlap'] += 1\n\t\t\t\t\tsrv = float(bitscore) / float(max_bitscore) #SRV: BLAST Score Ratio Values. Blom et al. BMC Bioinformatics 2009, 10:154 doi:10.1186/1471-2105-10-154\n\t\t\t\t\tif srv > min_bitscore:\n\t\t\t\t\t\tnedges['passed-bitscore'] += 1\n\t\t\t\t\t\t# If an edge already exists between the nodes, update\n\t\t\t\t\t\t# its score to be the max\n\t\t\t\t\t\tif graph.has_edge(id_from, id_to):\n\t\t\t\t\t\t\te = graph.edge[id_from][id_to]\n\t\t\t\t\t\t\te['score'] = max(e['score'], bitscore)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tnedges['passed-bitscore-unique'] += 1\n\t\t\t\t\t\t\tgraph.add_node(id_from)\n\t\t\t\t\t\t\tgraph.add_node(id_to)\n\t\t\t\t\t\t\tgraph.add_edge(id_from, id_to, score=bitscore)\n\n\tdiagnostics.prefix.append('nedges')\n\tdiagnostics.log_dict(nedges)\n\tdiagnostics.prefix.pop()\n\n\twith open(edge_file, 'w') as f:\n\t\tfor subgraph in nx.connected_component_subgraphs(graph):\n\t\t\tnnodes = subgraph.number_of_nodes()\n\t\t\tif nnodes >= min_nodes:\n\t\t\t\tnseqs += nnodes\n\t\t\t\tfor id_from, id_to in subgraph.edges_iter():\n\t\t\t\t\tprint >>f, \"%s\\t%s\\t%f\" % (id_from, id_to, subgraph.edge[id_from][id_to]['score'])\n\n\tif not nseqs:\n\t\tutils.die(\"no sequences were written to the FASTA file\")\n\n\tdiagnostics.log('nseqs', nseqs)\n\n\tingest('edge_file')\n\n\n@pipe.stage\ndef mcl_cluster(edge_file, mcl_inflation, outdir):\n\t\"\"\"Run mcl on all-by-all graph to form gene clusters\"\"\"\n\tcluster_file = os.path.join(outdir, 'mcl_gene_clusters.abc')\n\twrappers.Mcl(edge_file, \"--abc -I \", str(mcl_inflation), \" -o\", cluster_file)\n\tingest('cluster_file')\n\n\n@pipe.stage\ndef load_mcl_cluster(_run_id, cluster_file):\n\t\"\"\"Load cluster file from mcl into homology database\"\"\"\n\n\tnseqs = 0\n\thist = {}\n\n\tdatabase.execute(\"BEGIN\")\n\tdatabase.execute(\"DELETE FROM homology WHERE run_id=?;\", (_run_id,))\n\twith open(cluster_file, 'r') as f:\n\t\tcluster_id = 0\n\t\tfor line in f:\n\t\t\tcluster = filter(lambda s : s[0].isdigit(), line.rstrip().split())\n\t\t\tn = len(cluster)\n\t\t\thist[n] = hist.get(n, 0) + 1\n\t\t\tif n >= 4:\n\t\t\t\tnseqs += n\n\t\t\t\tfor seq_id in cluster:\n\t\t\t\t\tdatabase.execute(\"\"\"\n\t\t\t\t\t\tINSERT INTO homology (run_id, component_id, sequence_id)\n\t\t\t\t\t\tVALUES (?,?,?);\"\"\",\n\t\t\t\t\t\t(_run_id, cluster_id, seq_id))\n\t\t\t\tcluster_id += 1\n\tdatabase.execute(\"COMMIT\")\n\n\tutils.info(\n\t\t\"histogram of gene cluster sizes:\\n\",\n\t\t'\\n '.join(\"%d\\t:\\t%d\" % (k, hist[k]) for k in sorted(hist)))\n\n\tdiagnostics.log('nseqs', nseqs)\n\tdiagnostics.log('histogram', hist)\n\n\n### RUN ###\n\nif __name__ == \"__main__\":\n\t# Run the pipeline.\n\tpipe.run()\n\t# Push the local diagnostics to the global database.\n\tdiagnostics.merge()\n\n### REPORT ###\n\nclass Report(report.BaseReport):\n\tdef init(self):\n\t\tself.name = pipe.name\n\t\t# Lookups\n\t\tself.lookup('load_ids', diagnostics.INIT, 'load_ids')\n\t\tself.str2list('load_ids')\n\t\tself.lookup('species', 'homologize.lookup_species')\n\t\tself.lookup('nseqs', 'homologize.write_fasta', 'nseqs')\n\t\t# Generators\n\t\tself.generator(self.species_table)\n\t\tself.generator(self.component_histogram)\n\n\tdef species_table(self):\n\t\t\"\"\"\n\t\tSummary of all species processed.\n\t\t\"\"\"\n\t\thtml = []\n\t\tif self.check('nseqs'):\n\t\t\thtml.append(\"Total sequences: %s
\" % self.data.nseqs)\n\t\tif self.check('load_ids', 'species'):\n\t\t\theaders = (\"Load ID\", \"Catalog ID\", \"Species\", \"NCBI ID\", \"ITIS ID\")\n\t\t\trows = []\n\t\t\tfor load_id in self.data.load_ids:\n\t\t\t\trow = self.data.species.get(str(load_id), '()')\n\t\t\t\trow = diagnostics.str2list(row)\n\t\t\t\tif len(row) == 4:\n\t\t\t\t\trows.append((load_id, row[3], row[0], row[1], row[2]))\n\t\t\t\telse:\n\t\t\t\t\trows.append((load_id, '-', '-', '-', '-'))\n\t\t\thtml += self.table(rows, headers)\n\t\treturn html\n\n\tdef component_histogram(self):\n\t\t\"\"\"\n\t\tDistribution of the number of nodes in each cluster.\n\t\t\"\"\"\n\t\thist = {}\n\t\tsql = \"\"\"\n\t\t\tSELECT component_id, COUNT(*)\n\t\t\tFROM homology\n\t\t\tWHERE run_id=?\n\t\t\tGROUP BY component_id;\"\"\"\n\t\tfor _, count in database.execute(sql, (self.run_id,)):\n\t\t\thist[count] = hist.get(count, 0) + 1\n\t\tif hist:\n\t\t\thist = numpy.array([(k,hist[k]) for k in sorted(hist.iterkeys())])\n\t\t\timgname = \"%d.component.hist.png\" % self.run_id\n\t\t\tprops = {\n\t\t\t\t'title': \"Distribution of Cluster Sizes\",\n\t\t\t\t'xlabel': \"# Nodes in Cluster\",\n\t\t\t\t'ylabel': \"Frequency\"}\n\t\t\treturn [self.histogram_overlay(imgname, [hist], props=props)]\n\n# vim: noexpandtab ts=4 sw=4\n","repo_name":"anpefi/agalma-modules","sub_path":"agalma/homologizeSRV.py","file_name":"homologizeSRV.py","file_ext":"py","file_size_in_byte":11547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27514184061","text":"# Create a sarcastic program that asks a user for their honest opinion,\n# then prints the same sentence back to them in aLtErNaTiNg CaPs.\n\nopinion = input(\"What's your honest opinion? \")\n\nfor i in range(0, len(opinion)):\n if i%2==0:\n print(opinion[i].lower(),end=\"\")\n else:\n print(opinion[i].upper(), end=\"\")","repo_name":"jsiu1248/coding_nomads_101","sub_path":"12_user-input-string-formatting/12_10_sarcastic_prompt_done.py","file_name":"12_10_sarcastic_prompt_done.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22793078043","text":"def setRowZeroesCore(matrix, row):\n i = 0\n j = 0\n while i < len(matrix[0]):\n if i == row:\n j = 0\n while j < len(matrix):\n matrix[j][i] = 0\n j += 1\n i += 1\n\n\ndef setLineZeroesCore(matrix, line):\n i = 0\n j = 0\n while i < len(matrix):\n if i == line:\n j = 0\n while j < len(matrix):\n matrix[i][j] = 0\n j += 1\n i += 1\n\n\ndef setZeroes(matrix):\n ignore_rows = []\n ignore_lines = []\n i = 0\n while i < len(matrix):\n j = 0\n while j < len(matrix[i]):\n if matrix[i][j] == 0:\n if not i in ignore_lines:\n ignore_lines.append(i)\n if not j in ignore_rows:\n ignore_rows.append(j)\n j += 1\n i += 1\n for row in ignore_rows:\n setRowZeroesCore(matrix, row)\n\n for line in ignore_lines:\n setLineZeroesCore(matrix, line)\n\n\nzero = [\n [1, 1, 1], [1, 0, 1], [1, 1, 1]\n]\n\n\nsetZeroes(zero)\nfor value in zero:\n print(value)\n","repo_name":"qq1334713380/leetcode","sub_path":"算法/med/list/02.py","file_name":"02.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35829966744","text":"# You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a linked list.\r\n#\r\n# You may assume the two numbers do not contain any leading zero, except the number 0 itself.\r\n#\r\n# Example:\r\n#\r\n# Input: (2 -> 4 -> 3) + (5 -> 6 -> 4)\r\n# Output: 7 -> 0 -> 8\r\n# Explanation: 342 + 465 = 807.\r\n\r\n# Definition for singly-linked list.\r\n# class ListNode:\r\n# def __init__(self, x):\r\n# self.val = x\r\n# self.next = None\r\n\r\nclass Solution:\r\n def addTwoNumbers(self, l1, l2):\r\n \"\"\"\r\n :type l1: ListNode\r\n :type l2: ListNode\r\n :rtype: ListNode\r\n \"\"\"\r\n adder_1 = 0\r\n adder_2 = 0\r\n flag_1 = 0\r\n flag_2 = 0\r\n while (l1):\r\n adder_1 += l1.val * 10 ** flag_1\r\n flag_1 += 1\r\n l1 = l1.next\r\n\r\n while (l2):\r\n adder_2 += l2.val * 10 ** flag_2\r\n flag_2 += 1\r\n l2 = l2.next\r\n res = adder_1 + adder_2\r\n fin = []\r\n str_res = str(res)\r\n length = len(str_res)\r\n for i in range(length):\r\n temp = int(str_res[i])\r\n fin.append(temp)\r\n fin.reverse()\r\n return fin","repo_name":"KamyC/LeetCode","sub_path":"2_Add Two Numbers.py","file_name":"2_Add Two Numbers.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6585666718","text":"import argparse\nimport os\nimport sys\n\nimport h5py\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\n\n\ndef process_img(img, key):\n if 'distance' in key or 'depth' in key or 'seg' in key:\n img = img.astype(np.float)\n if not \"seg\" in key:\n min_val = np.min(img)\n img -= min_val\n max_val = np.max(img)\n if \"seg\" in key:\n max_val = 36.0\n print(\"Assuming, there are 36 total classes (NYU), if there are more this will cause a bug!\")\n if max_val != np.inf:\n img /= max_val\n else:\n img /= np.max(img)\n # clipping to avoid image errors\n img = np.clip(img, 0.0, 1.0)\n if len(img.shape) == 3:\n img = img[:, :, 0]\n elif 'flow' in key:\n try:\n # This import here is ugly, but else everytime someone uses this script it demands opencv and the progressbar\n sys.path.append(os.path.join(os.path.dirname(__file__)))\n from utils import flow_to_rgb\n except ImportError:\n raise ImportError(\"Using .hdf5 containers, which contain flow images needs opencv-python and progressbar \"\n \"to be installed!\")\n\n img = flow_to_rgb(img)\n elif \"normals\" in key:\n img = np.clip(img, 0.0, 1.0)\n return img\n\n\ndef save_array_as_image(array, key, file_path):\n if len(array.shape) == 2 or len(array.shape) == 3 and array.shape[2] == 3:\n val = process_img(array, key)\n if len(val.shape) == 2 or len(val.shape) == 3 and val.shape[2] == 1:\n plt.imsave(file_path, val, cmap='jet', vmin=0.0, vmax=1.0)\n else:\n plt.imsave(file_path, val)\n\n elif len(array.shape) == 3 and array.shape[2] == 4:\n val = process_img(array, key)\n plt.imsave(file_path, val)\n\n #error message if shape is incorrect\n else:\n print(\"Can't save numpy array with shape {} as an image\".format(array.shape))\n\n\n\ndef convert_hdf(base_file_path, output_folder=None):\n if os.path.exists(base_file_path):\n if os.path.isfile(base_file_path):\n base_name = str(os.path.basename(base_file_path)).split('.')[0]\n if output_folder is not None:\n base_name = os.path.join(output_folder, base_name)\n with h5py.File(base_file_path, 'r') as data:\n print(\"{}:\".format(base_file_path))\n keys = [key for key in data.keys()]\n for key in keys:\n val = np.array(data[key])\n if np.issubdtype(val.dtype, np.string_) or len(val.shape) == 1:\n pass # metadata\n else:\n print(\"key: {} {} {}\".format(key, val.shape, val.dtype.name))\n\n if val.shape[0] != 2:\n # mono image\n file_path = '{}_{}.png'.format(base_name, key)\n save_array_as_image(val, key, file_path)\n else:\n # stereo image\n for image_index, image_value in enumerate(val):\n file_path = '{}_{}_{}.png'.format(base_name, key, image_index)\n save_array_as_image(image_value, key, file_path)\n else:\n print(\"The path is not a file\")\n else:\n print(\"The file does not exist: {}\".format(args.hdf5))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\"Script to save images out of a hdf5 files\")\n\n parser.add_argument('hdf5', nargs='*', help='Path to hdf5 file/s')\n\n args = parser.parse_args()\n\n if args.hdf5 is None:\n print(parser.format_help())\n exit(0)\n\n if isinstance(args.hdf5, str):\n convert_hdf(args.hdf5)\n elif isinstance(args.hdf5, list):\n for file in args.hdf5:\n convert_hdf(file)\n else:\n print(\"Input must be a path\")\n","repo_name":"A-guridi/MistubaRenderer","sub_path":"scripts/saveAsImg.py","file_name":"saveAsImg.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"6309253161","text":"import multiprocessing\nimport traceback\n\n\nclass LogExceptions(object):\n def __init__(self, callable):\n self.__callable = callable\n return\n\n def __call__(self, *args, **kwargs):\n try:\n result = self.__callable(*args, **kwargs)\n\n except Exception:\n print(traceback.format_exc())\n raise\n\n return result\n\n\ndef doParallel(func, items, *args, **kwargs):\n pool = multiprocessing.Pool(processes=None)\n results = []\n\n try:\n for item in items:\n if isinstance(item, str):\n argument = [item] + list(args)\n else:\n argument = list(item) + list(args)\n results.append(pool.apply_async(LogExceptions(func), argument, kwargs))\n pool.close()\n pool.join()\n\n for result in results:\n result.get()\n\n except (KeyboardInterrupt, Exception):\n pool.terminate()\n pool.join()\n raise\n\n\ndef doPlatformParallel(func, platforms, *args, **kwargs):\n pool = multiprocessing.Pool(processes=None)\n results = dict()\n output = dict()\n\n try:\n for platform in platforms:\n argument = [platform] + list(args)\n results[platform] = pool.apply_async(LogExceptions(func), argument, kwargs)\n pool.close()\n pool.join()\n\n for item in results:\n output[item] = results[item].get()\n\n except (KeyboardInterrupt, Exception):\n pool.terminate()\n pool.join()\n raise\n\n return output\n\n\ndef parallelDict(dict):\n pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n results = []\n output = []\n\n try:\n for func in dict:\n for item in dict[func]:\n results.append(pool.apply_async(LogExceptions(func), list(item)))\n pool.close()\n pool.join()\n\n for result in results:\n ret = result.get()\n if ret:\n output.append(ret)\n except (KeyboardInterrupt, Exception):\n pool.terminate()\n pool.join()\n raise\n\n return output\n","repo_name":"SynologyOpenSource/pkgscripts-ng","sub_path":"include/python/parallel.py","file_name":"parallel.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","stars":148,"dataset":"github-code","pt":"3"}
+{"seq_id":"20702334716","text":"import time, math\nfrom machine import Pin\n\ntrig = Pin(10,Pin.OUT)\necho = Pin(4,Pin.IN,Pin.PULL_DOWN)\n\ndef ping():\n trig.value(1)\n time.sleep_us(10)\n trig.value(0)\n count=0\n timeout=False\n \n while not echo.value():\n time.sleep_us(10)\n count += 1\n if count > 10000:\n timeout=True\n Break\n \n if timeout:\n duration=0\n else:\n count=0\n start=time.ticks_us()\n while echo.value():\n time.sleep_us(10)\n count+=1\n \n if count>2320:\n break\n duration=time.ticks_diff(time.ticks_us(),start)\n return duration\n \n while True:\n distance=round(ping()/58)\n print(\"%scm\"%distance)","repo_name":"Rhine-Ho/Raspberry-pi-pico-","sub_path":"Tools/Sonar.py","file_name":"Sonar.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6529472544","text":"from joblib import Parallel, delayed\n\nimport pandas as pd\n\nfrom medium_crawler import MediumArchiveCrawler\nfrom medium_article_scraper import MediumArticleScraper\n# from medium_article_dataset import MediumArticleDataset\n\nif __name__ == '__main__':\n df = pd.DataFrame()\n loop = Parallel(n_jobs=16)\n for date_ in pd.date_range(start='2022-01-01', end='2022-01-01'):\n crawler = MediumArchiveCrawler(date_)\n crawler.get_article_urls()\n # articles = []\n # for article_url in crawler.article_urls:\n # article = MediumArticleScraper(article_url)\n # article.get_data()\n # articles.append(article.data)\n scrapers = [MediumArticleScraper(url) for url in crawler.article_urls]\n articles = loop(delayed(scraper.get_data)() for scraper in scrapers)\n df = df.append(pd.concat(articles, ignore_index=True), ignore_index=True)\n\n print(df)","repo_name":"nikola-rahman/medium_scraping","sub_path":"scrape_towardsdatascience.py","file_name":"scrape_towardsdatascience.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21730447504","text":"# Jokenpô, a pessoa dirá qual jogada decidirá fazer: PEDRA, PAPEL ou TESOURA e, por fim, saberá se ganhou ou\r\n# não comparando com a jogada da máquina\r\nfrom random import choice\r\nfrom time import sleep\r\nprint('---JOKENPÔ---')\r\nopcaoMaquina = ['PEDRA', 'PAPEL', 'TESOURA']\r\njogadaPlayer = str(input('Escolha se você vai querer jogar entre\\n'\r\n 'PEDRA, PAPEL OU TESOURA: ')).strip().upper()\r\njogadaMaquina = choice(opcaoMaquina)\r\nprint('JO...')\r\nsleep(0.5)\r\nprint('KEN...')\r\nsleep(1)\r\nprint('PO!!!')\r\nif jogadaPlayer == 'PEDRA' and jogadaMaquina == 'TESOURA' or jogadaPlayer == 'TESOURA' and jogadaMaquina == 'PAPEL' \\\r\n or jogadaPlayer == 'PAPEL' and jogadaMaquina == 'PEDRA':\r\n print(f'Jogada do computador: {jogadaMaquina}\\n'\r\n f'Você ganhou, PARABÉNS!!!')\r\nelif jogadaPlayer == 'PEDRA' and jogadaMaquina == 'PAPEL' or jogadaPlayer == 'TESOURA' and jogadaMaquina == 'PEDRA' \\\r\n or jogadaPlayer == 'PAPEL' and jogadaMaquina == 'TESOURA':\r\n print(f'Jogada do computador: {jogadaMaquina}\\n'\r\n f'Infelizmente, você perdeu.')\r\nelif jogadaPlayer == jogadaMaquina:\r\n print(f'Jogada do computador: {jogadaMaquina}\\n'\r\n f'O jogo deu empate!')\r\nelse:\r\n print('ERROR')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"martins8/exerciciosPython","sub_path":"ex045.py","file_name":"ex045.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70544665361","text":"import pytest\n\nfrom travesty.cantrips.dispatcher import Dispatcher\n\ndef test_default_inheritance():\n add = Dispatcher()\n @add.when(int)\n def add_int(d, v, x):\n return v + x\n assert add(2, x=1) == 3\n with pytest.raises(TypeError):\n add(2)\n add.default_value('x', 1)\n assert add(2) == 3\n\n mul = add.sub()\n @mul.when(int)\n def mul_int(d, v, x):\n return v * x\n assert mul(2, x=4) == 8\n assert mul(2) == 2\n mul.default_factory('x', lambda:2)\n assert mul(2) == 4\n","repo_name":"dplepage/travesty","sub_path":"tests/test_dispatcher.py","file_name":"test_dispatcher.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"1498110066","text":"from turtle import (shape, forward, right, left, speed, exitonclick)\r\n\r\nshape(\"turtle\")\r\nspeed(0)\r\n\r\ndef dragon(x, angle=1, n=0):\r\n if n > 0:\r\n left(45*angle)\r\n dragon(2 ** .5 / 2 * x, 1, n -1)\r\n right(90 * angle)\r\n dragon(2 ** .5 / 2 * x, -1, n -1)\r\n left(45*angle)\r\n else:\r\n forward(x)\r\ndragon(100,2,10)\r\n\r\nexitonclick()\r\n","repo_name":"DIDIx13/py","sub_path":"dragon.py","file_name":"dragon.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12842189856","text":"from io import SEEK_CUR\r\nfrom os import write\r\nfrom flask import *\r\nfrom sqlalchemy.orm import query\r\nfrom . import app, db\r\nfrom .model.school import school\r\nfrom .model.bus_stop import bus_stop\r\nfrom .model.medical_institution import medical_institution\r\nfrom .model.building import building\r\nfrom .model.coordinate import coordinate\r\nfrom sqlalchemy import *\r\nfrom .form.view_form import *\r\nfrom .distance import distance\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n@app.route('/')\r\ndef home():\r\n return render_template('home.html')\r\n\r\n\r\n@app.route('/price', methods=['GET', 'POST'])\r\ndef price():\r\n form = Form1()\r\n if form.validate_on_submit():\r\n return redirect(url_for('display_price', name=form.name.data, range=form.range.data))\r\n return render_template('price.html', form=form)\r\n\r\n\r\n@app.route('/price_result//')\r\ndef display_price(name, range):\r\n target = coordinate.query.filter_by(name=name).first()\r\n building_list = building.query.all()\r\n building_near_list = []\r\n for item in building_list:\r\n if(name != item.name):\r\n tmp = coordinate.query.filter_by(name=item.name).first()\r\n if(float(distance(target.x, target.y, tmp.x, tmp.y)) <= float(range)):\r\n building_near_list.append(item.id)\r\n building_query = building.query.filter(building.id.in_(building_near_list))\r\n return render_template('result_price.html', data=building_query)\r\n\r\n\r\n@app.route('/information', methods=['GET', 'POST'])\r\ndef information():\r\n form = Form7()\r\n if form.validate_on_submit():\r\n return redirect(url_for('display_single', name=form.name.data))\r\n return render_template('information.html', form=form)\r\n\r\n\r\n@app.route('/single_result/')\r\ndef display_single(name):\r\n query = building.query.filter_by(name=name).all()\r\n return render_template('result_single.html', data=query)\r\n\r\n\r\n@app.route('/compare', methods=['GET', 'POST'])\r\ndef compare():\r\n form = Form2()\r\n if form.validate_on_submit():\r\n return redirect(url_for('display_compare', name1=form.name1.data, name2=form.name2.data))\r\n return render_template('compare.html', form=form)\r\n\r\n\r\n@app.route('/compare_result//')\r\ndef display_compare(name1, name2):\r\n query1 = building.query.filter_by(name=name1).first()\r\n query2 = building.query.filter_by(name=name2).first()\r\n return render_template('result_compare.html', data1=query1, data2=query2)\r\n\r\n\r\n@app.route('/inrange', methods=['GET', 'POST'])\r\ndef inrange():\r\n form = Form1()\r\n if form.validate_on_submit():\r\n return redirect(url_for('display_mul', name=form.name.data, range=form.range.data))\r\n return render_template('inrange.html', form=form)\r\n\r\n\r\n@app.route('/range_result//')\r\ndef display_mul(name, range):\r\n target = coordinate.query.filter_by(name=name).first()\r\n school_list = school.query.all()\r\n bus_list = bus_stop.query.all()\r\n medical_list = medical_institution.query.all()\r\n sch_near_list = []\r\n bus_near_list = []\r\n medical_near_list = []\r\n for item in school_list:\r\n if(float(distance(target.x, target.y, item.x, item.y)) <= float(range)):\r\n sch_near_list.append(item.id)\r\n for item in bus_list:\r\n if(float(distance(target.x, target.y, item.x, item.y)) <= float(range)):\r\n bus_near_list.append(item.id)\r\n for item in medical_list:\r\n if(float(distance(target.x, target.y, item.x, item.y)) <= float(range)):\r\n medical_near_list.append(item.id)\r\n sch_query = school.query.filter(school.id.in_(sch_near_list))\r\n bus_query = bus_stop.query.filter(bus_stop.id.in_(bus_near_list))\r\n medical_query = medical_institution.query.filter(\r\n medical_institution.id.in_(medical_near_list))\r\n return render_template('result_multi.html', name=name, sch_data=sch_query, bus_data=bus_query, medical_data=medical_query)\r\n\r\n\r\n@app.route('/add', methods=['GET', 'POST'])\r\ndef add():\r\n form = Form4()\r\n if form.validate_on_submit():\r\n cur = db.session.query(func.max(building.id)).scalar()+1\r\n Building = building(id=cur, name=form.name.data, address=form.address.data, land_area=form.land_area.data, floor_no=form.floor_no.data, floor_total=form.floor_total.data,\r\n type=form.type.data, total_area=form.total_area.data, room=form.room.data, parlor=form.parlor.data, bath=form.bath.data, total_price=form.total_price.data, price_per=form.price_per.data,\r\n parking_space_area=form.parking_space_area.data, parking_space_price=form.parking_space_price.data, balcony_area=form.balcony_area.data, elevator=form.elevator.data)\r\n db.session.add(Building)\r\n db.session.commit()\r\n return '新增成功'\r\n return render_template('add.html', form=form)\r\n\r\n\r\n@app.route('/delete', methods=['GET', 'POST'])\r\ndef delete():\r\n form = Form5()\r\n if form.validate_on_submit():\r\n query = building.query.filter_by(\r\n name=form.name.data, floor_no=form.floor_no.data, address=form.address.data).first()\r\n if query == None:\r\n return '查無此建案，請重新輸入'\r\n db.session.delete(query)\r\n db.session.commit()\r\n return '刪除成功'\r\n return render_template('delete.html', form=form)\r\n\r\n\r\n@app.route('/rate', methods=['GET', 'POST'])\r\ndef rate():\r\n form = Form7()\r\n if form.validate_on_submit():\r\n target = coordinate.query.filter_by(name=form.name.data).first()\r\n avg = db.session.query(func.avg(building.price_per)).filter_by(\r\n name=form.name.data).one()\r\n school_list = school.query.all()\r\n bus_list = bus_stop.query.all()\r\n medical_list = medical_institution.query.all()\r\n sum = 0\r\n for item in school_list:\r\n if(float(distance(target.x, target.y, item.x, item.y)) <= 1):\r\n sum += 3\r\n for item in bus_list:\r\n if(float(distance(target.x, target.y, item.x, item.y)) <= 0.5):\r\n sum += 1\r\n for item in medical_list:\r\n if(float(distance(target.x, target.y, item.x, item.y)) <= 2):\r\n sum += 2\r\n return render_template('result_rate.html', name=form.name.data, sum=sum, price=avg[0], CP=round(sum/avg[0]*1000, 2))\r\n return render_template('rate.html', form=form)\r\n\r\n\r\nif __name__ == '__main__':\r\n app.debug = True\r\n app.run()\r\n","repo_name":"waterm80/DB-Final-Project","sub_path":"final project/DB/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"605476527","text":"import os\nimport sys\nimport os.path\nfrom flask import Flask, config\nfrom flask_mail import Message, Mail\nfrom model.credit import Credit\nfrom model.credit_evaluation import CreditEvaluation\nfrom provider.customer_provider import CustomerProvider\nfrom utility.sql_server import open_connection, close_cursor\nfrom utility.blob_storage import create_container, upload_file, download_file\nfrom werkzeug.datastructures import FileStorage\nfrom werkzeug.utils import secure_filename\n\n# initializations\nroot_folder = os.path.abspath(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nsys.path.append(root_folder)\napp = Flask(__name__)\napp.config.from_object('config')\nmail = Mail(app)\napp_secret_key = app.config[\"APP_SECRET_KEY\"]\ncustomer_provider = CustomerProvider()\n\n# Create a directory in a known location to save files to.\nuploads_dir = os.path.join(app.instance_path, 'generated')\nif not os.path.exists(uploads_dir):\n os.makedirs(uploads_dir)\n\nclass CreditProvider():\n\n # methods\n def save_property(self, property: FileStorage, container: str, file_dir: str):\n cnn = None\n property_full_path = os.path.join(file_dir, property.filename)\n property.save(property_full_path)\n create_container(container)\n url_file = upload_file(container, property.filename, property_full_path)\n try:\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" INSERT INTO credits (property_url_original, upload_url_path) VALUES (?, ?);\n SELECT SCOPE_IDENTITY() AS credit_id \"\"\"\n params = (url_file,\n container)\n cursor.execute(query, params)\n cursor.nextset()\n for credit_id in cursor:\n cnn.commit()\n return credit_id[0]\n finally:\n close_cursor(cnn, cursor)\n\n def insert_credit(self, credit: Credit):\n credit.customer.customer_id = int(customer_provider.insert_customer(customer=credit.customer))\n cnn = None \n try:\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" UPDATE credits\n SET amount_requested = ?,\n comments = ?,\n customer_id = ?\n WHERE credit_id = ?\"\"\"\n params = (credit.amount_requested,\n credit.comments,\n credit.customer.customer_id,\n credit.credit_id)\n cursor.execute(query, params)\n cnn.commit()\n finally:\n close_cursor(cnn, cursor)\n\n def evaluate_credit(self, credit_evaluation: CreditEvaluation):\n cnn = None \n try:\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" INSERT INTO creditEvaluations\n (\n credit_id, \n user_id, \n is_approved, \n comments\n ) VALUES\n (?, ?, ?, ?) \"\"\"\n params = (credit_evaluation.credit_id,\n credit_evaluation.user_id,\n credit_evaluation.is_approved,\n credit_evaluation.comments)\n cursor.execute(query, params)\n cnn.commit()\n finally:\n close_cursor(cnn, cursor)\n\n def send_email_evaluation(self, credit_evaluation: CreditEvaluation):\n cnn = None \n try:\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" SELECT cu.email\n FROM\tcredits c\n INNER JOIN customers cu\n ON\tc.customer_id = cu.customer_id\n WHERE\tc.credit_id = ? \"\"\"\n params = (credit_evaluation.credit_id,)\n cursor.execute(query, params)\n credit_db = cursor.fetchone()\n to = credit_db[0]\n msg = Message('Resultado Aprobacion Microcredito', sender=app.config['MAIL_DEFAULT_SENDER'], recipients = [to])\n approval_message = \"Su credito ha sido aprobado y uno de nuestros analistas se contactara con usted para indicarle el resto del proceso.\"\n reject_message = \"Su credito ha sido rechazado. De acuerdo a las siguientes observaciones: \" + credit_evaluation.comments\n msg.body = approval_message if credit_evaluation.is_approved else reject_message\n mail.send(msg)\n finally:\n close_cursor(cnn, cursor)\n\n def get_open_credits_ready_to_process(self):\n cnn = None \n try:\n result = []\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" SELECT c.credit_id,\n cu.fullname,\n cu.identity_number,\n c.amount_requested,\n c.date_requested \n FROM credits c\n INNER JOIN customers cu\n ON c.customer_id = cu.customer_id\n LEFT JOIN creditEvaluations ce\n ON c.credit_id = ce.credit_id\n WHERE ce.credit_evaluation_id IS NULL \n \t\tAND c.property_url_modified IS NOT NULL\n\t\t AND c.format_credit_url IS NOT NULL\n ORDER BY c.date_requested DESC \"\"\"\n cursor.execute(query)\n rows = cursor.fetchall()\n for row in rows:\n result.append({\n 'credit_id': row[0],\n 'fullname': row[1],\n 'identity_number': row[2],\n 'amount_requested': float(row[3]),\n 'date_requested': row[4],\n })\n return result\n finally:\n close_cursor(cnn, cursor)\n\n def get_credit(self, credit_id: int):\n cnn = None \n try:\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" SELECT amount_requested,\n comments,\n property_url_modified,\n format_credit_url\n FROM credits\n WHERE credit_id = ?\n \"\"\" \n params = (credit_id,)\n cursor.execute(query, params)\n credit_db = cursor.fetchone()\n if credit_db is None:\n return credit_db\n return ({\n 'amount_requested': float(credit_db[0]),\n 'comments': credit_db[1],\n 'property_url_modified': credit_db[2],\n 'format_credit_url': credit_db[3],\n })\n finally:\n close_cursor(cnn, cursor)\n\n def get_format_url_credit(self, credit_id: int):\n cnn = None \n try:\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" SELECT format_credit_url,\n upload_url_path\n FROM credits\n WHERE credit_id = ?\n \"\"\" \n params = (credit_id,)\n cursor.execute(query, params)\n credit_db = cursor.fetchone()\n if credit_db is None:\n return credit_db\n format_credit_url = credit_db[0]\n upload_url_path = credit_db[1]\n local_full_path = download_file(upload_url_path, app.config['UPLOAD_FILES_PATH'], format_credit_url)\n return local_full_path\n finally:\n close_cursor(cnn, cursor)\n\n def get_property_url_credit(self, credit_id: int):\n cnn = None \n try:\n cnn = open_connection()\n cursor = cnn.cursor()\n query = \"\"\" SELECT property_url_modified,\n upload_url_path\n FROM credits\n WHERE credit_id = ?\n \"\"\" \n params = (credit_id,)\n cursor.execute(query, params)\n credit_db = cursor.fetchone()\n if credit_db is None:\n return credit_db\n property_url_modified = credit_db[0]\n upload_url_path = credit_db[1]\n local_full_path = download_file(upload_url_path, app.config['UPLOAD_FILES_PATH'], property_url_modified)\n return local_full_path\n finally:\n close_cursor(cnn, cursor)","repo_name":"soadcam/azure_bizagi_project","sub_path":"backend/provider/credit_provider.py","file_name":"credit_provider.py","file_ext":"py","file_size_in_byte":8724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8633017732","text":"name = input(\"What is your name? \")\nage = int(input(\"How old are you? \"))\nprint(f\"Your name is {name}\")\nprint(f\"You are {age} years old\")\n\nage = int(input(\"what is your age? \"))\nif age < 18:\n print(\"you cannot vote at this age\")\nelse:\n print(\"You can vote at this age\")\n\ncost = float(input(\"What is the cost of an apple? $\"))\nquantity = int(input(\"How many apples do you wish to purchase? \"))\nTotal = cost * quantity\nprint(f\"Your total cost is ${Total}\")\n\n\n\nw = input(\"Enter Word \")\ni = 0\nwhile i < len(w):\n char = w[i]\n print(char)\n i += 1\n\ni = len(w)\nwhile i <= len(w):\n if w[i] == 'e':\n print(f\"found an e at index {i}\")\n","repo_name":"danielbuzan/coding-practice-09-22.md","sub_path":"https:/repl.it/@DanielBuzan/KeyJuniorCases#main.py","file_name":"KeyJuniorCases#main.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17831771238","text":"import os\nimport tempfile\nimport pytest\n\nimport keras\nfrom keras import layers\nimport numpy as np\n\nfrom combine_layer import Combine\n\nfashion_mnist = keras.datasets.fashion_mnist\n\n(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n\n#Normalise the data\ntrain_images = train_images / 255.0\ntest_images = test_images / 255.0\n\n# Layers:\n#[Tops, 'Trouser', Shoes, 'Bag']\n#['T-shirt/top', 'Pullover', 'Dress', 'Coat', 'Shirt']\n#['Sandal', 'Sneaker', 'Ankle boot']\n\noutput_spec = [\n [0, 0], # T-shirt/top\n [1], # Trouser\n [0, 1], # Pullover\n [0, 2], # Dress\n [0, 3], # Coat\n [2, -1, 0],# Sandal\n [0, 4], # Shirt\n [2, -1, 1],# Sneaker\n [3], # Bag\n [2, -1, 2] # Ankle boot\n ]\n\n# Test Normal Softmax vs. Combine Layer \ndef test_model_baseline():\n model_baseline = keras.Sequential([\n layers.Flatten(input_shape=(28, 28)),\n layers.Dense(10, activation='softmax')\n ])\n\n model_baseline.compile(optimizer='adam', \n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n print('Testing baseline softmax:\\n')\n model_baseline.fit(train_images, train_labels, epochs=5)\n\n test_loss, test_acc = model_baseline.evaluate(test_images, test_labels)\n print('Test accuracy:', test_acc)\n\ndef test_hrch_model():\n model = keras.Sequential([\n layers.Flatten(input_shape=(28, 28)),\n Combine([\n layers.Dense(4, activation='softmax', name='root'),\n layers.Dense(5, activation='softmax', name='tops'),\n layers.Dense(3, activation='softmax', name='shoes')],\n output_spec = output_spec)\n ])\n model.compile(optimizer='adam', \n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n print('\\nTesting combine layer:\\n')\n model.fit(train_images, train_labels, epochs=5)\n\n test_loss, test_acc = model.evaluate(test_images, test_labels)\n print('Test accuracy:', test_acc)\n\n#Test Reduction Options\ndef test_reduction_opts():\n reduction_opts = ['max', 'mean', 'min', 'prod', 'std', 'sum', 'var']\n\n for reduction in reduction_opts:\n print('\\nTesting reduction with %s:\\n' % reduction)\n model = keras.Sequential([\n layers.Flatten(input_shape=(28, 28)),\n layers.Dense(128, activation='relu'),\n Combine([\n layers.Dense(4, activation='softmax', name='root'),\n layers.Dense(5, activation='softmax', name='tops'),\n layers.Dense(3, activation='softmax', name='shoes')],\n output_spec = output_spec,\n reduction=reduction)\n ])\n\n model.compile(optimizer='adam', \n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n model.fit(train_images, train_labels, epochs=1)\n\n test_loss, test_acc = model.evaluate(test_images, test_labels)\n print('Test accuracy:', test_acc)\n\n\n# Test too long output_spec\ndef test_long_output_spec():\n print('\\nTesting too long output_spec elements:\\n')\n with pytest.raises(AssertionError):\n output_spec_invalid = [\n [0, 0, 0, 0], #T-shirt/top\n [1],\n [0, 1],\n [0, 2], #Dress\n [0, 3],\n [2, -1, 0], # Sandal\n [0, 4],\n [2, -1, 1],\n [3],\n [2, -1, 2] # Ankle boot\n ]\n\n model = keras.Sequential([\n layers.Flatten(input_shape=(28, 28)),\n Combine([\n layers.Dense(4, activation='softmax', name='root'),\n layers.Dense(5, activation='softmax', name='tops'),\n layers.Dense(3, activation='softmax', name='shoes')],\n output_spec = output_spec_invalid)\n ])\n\n model.compile(optimizer='adam', \n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n model.fit(train_images, train_labels, epochs=1)\n print('Completed model fit from too long output_spec (wrong)')\n\n# Test invalid reduction option\ndef test_invalid_reduction():\n print('\\nTesting invalid reduction option:\\n')\n with pytest.raises(AssertionError):\n Combine([\n layers.Dense(4, activation='softmax', name='root'),\n layers.Dense(5, activation='softmax', name='tops'),\n layers.Dense(3, activation='softmax', name='shoes')],\n output_spec = output_spec,\n reduction='xyz')\n print('Layer built with invalid reduction option (wrong)')\n\n# Test serialization and deserialization\ndef test_serialization_deserialization():\n print('\\nTesting serialization and deserialization:\\n')\n\n with tempfile.TemporaryDirectory() as save_dir:\n model = keras.Sequential([\n layers.Flatten(input_shape=(28, 28)),\n Combine([\n layers.Dense(4, activation='softmax', name='root'),\n layers.Dense(5, activation='softmax', name='tops'),\n layers.Dense(3, activation='softmax', name='shoes')],\n output_spec = output_spec)\n ])\n model.compile(optimizer='adam', \n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n model.fit(train_images, train_labels, epochs=1)\n\n test_loss, test_acc = model.evaluate(test_images, test_labels)\n print('Test accuracy before serialization:', test_acc)\n\n pred = model.predict(test_images)\n\n # saving the weights worked with tensorflow 1.15.2 but not 2.1\n model.save_weights(os.path.join(save_dir, 'model'))\n model.load_weights(os.path.join(save_dir, 'model'))\n\n print('\\nPredictions same after reloading weights:', (pred == model.predict(test_images)).all())\n assert (pred == model.predict(test_images)).all()\n\n model_path = os.path.join(save_dir, 'model.h5')\n model.save(model_path)\n model = keras.models.load_model(model_path, custom_objects={'Combine': Combine})\n\n print('Predictions same after reloading entire model:', (pred == model.predict(test_images)).all())\n assert (pred == model.predict(test_images)).all()\n","repo_name":"Lynton-Morgan/combine_layer","sub_path":"test_all.py","file_name":"test_all.py","file_ext":"py","file_size_in_byte":6420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6389245526","text":"import numpy as np\n\nwith open('scraped_courses_magistrale.txt', 'r') as f:\n lines = f.readlines()\n courses = []\n for line in lines:\n courses.append((line.split('/')[0], line.split('/')[1]))\narray = np.array(courses)\nunique = np.unique(array, axis=0)\nwith open('scraped_courses_magistrale_unique.txt', 'w') as o:\n for course in unique:\n if ',' in course[1]:\n o.write(str(course[0]) + ',\"' + str(course[1]) + '\"')\n else:\n o.write(str(course[0]) + ',' + str(course[1]))","repo_name":"enniorampello/group-generator","sub_path":"parsing.py","file_name":"parsing.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6352556787","text":"import sys\nsys.path.append(\"/usr/local/cython/\")\n\nimport numpy as np\nimport os\n\nimport logging\n\nimport gtsam\n\nimport open3d as o3d\nimport pandas as pd\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom inhandpy.utils import tf_utils\n\nimport matplotlib.pyplot as plt\n\nplt.rcParams.update({'font.size': 36})\nplt.ion()\n\nlog = logging.getLogger(__name__)\n\nBASE_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), \"../..\"))\n\nclass GraphOpt():\n def __init__(self):\n\n self.graph = gtsam.NonlinearFactorGraph()\n self.init_vals = gtsam.Values()\n\n self.optimizer = self.init_isam2()\n self.est_vals = gtsam.Values()\n\n def init_isam2(self):\n params_isam2 = gtsam.ISAM2Params()\n params_isam2.setRelinearizeThreshold(0.01)\n params_isam2.setRelinearizeSkip(10)\n\n return gtsam.ISAM2(params_isam2)\n\ndef reset_graph(graphopt): \n graphopt.graph.resize(0)\n graphopt.init_vals.clear()\n\n return graphopt\n\ndef optimizer_update(graphopt):\n graphopt.optimizer.update(graphopt.graph, graphopt.init_vals)\n graphopt.est_vals = graphopt.optimizer.calculateEstimate()\n\n return graphopt\n\ndef init_vars_step(graphopt, step):\n\n key_tm1 = gtsam.symbol(ord('o'), step-1)\n key_t = gtsam.symbol(ord('o'), step)\n graphopt.init_vals.insert(key_t, graphopt.est_vals.atPose3(key_tm1))\n\n key_tm1 = gtsam.symbol(ord('e'), step-1)\n key_t = gtsam.symbol(ord('e'), step)\n graphopt.init_vals.insert(key_t, graphopt.est_vals.atPose3(key_tm1))\n\n return graphopt\n\ndef log_step_curr_pose(logger, step, est_pose, gt_pose):\n\n rot_est = (est_pose.rotation().rpy()).tolist()\n trans_est = [est_pose.x(), est_pose.y(), est_pose.z()]\n\n rot_gt = (gt_pose.rotation().rpy()).tolist()\n trans_gt = [gt_pose.x(), gt_pose.y(), gt_pose.z()]\n\n logger.log_val(names=['graphopt/est/rot', 'graphopt/est/trans', 'graphopt/gt/rot', 'graphopt/gt/trans'],\n vals=[rot_est, trans_est, rot_gt, trans_gt], index_val=step, index_name='step')\n\n return logger\n\ndef log_step(logger, step, graph_poses, gt_poses):\n\n nposes = step + 1\n\n graph_obj_rot_list, graph_obj_trans_list = [], []\n graph_ee_rot_list, graph_ee_trans_list = [], []\n \n gt_obj_rot_list, gt_obj_trans_list = [], []\n gt_ee_rot_list, gt_ee_trans_list = [], []\n\n for i in range(1, nposes):\n obj_key = gtsam.symbol(ord('o'), i)\n ee_key = gtsam.symbol(ord('e'), i)\n\n # read graph obj poses\n obj_pose3d = graph_poses.atPose3(obj_key) \n graph_obj_rot_list.append((obj_pose3d.rotation().matrix()).tolist())\n graph_obj_trans_list.append([obj_pose3d.x(), obj_pose3d.y(), obj_pose3d.z()]) \n\n # read graph ee poses\n ee_pose3d = graph_poses.atPose3(ee_key)\n graph_ee_rot_list.append((ee_pose3d.rotation().matrix()).tolist())\n graph_ee_trans_list.append([ee_pose3d.x(), ee_pose3d.y(), ee_pose3d.z()]) \n\n # read gt obj poses\n obj_pose3d = gt_poses.atPose3(obj_key) \n gt_obj_rot_list.append((obj_pose3d.rotation().matrix()).tolist())\n gt_obj_trans_list.append([obj_pose3d.x(), obj_pose3d.y(), obj_pose3d.z()]) \n\n # read gt ee poses\n ee_pose3d = graph_poses.atPose3(ee_key)\n gt_ee_rot_list.append((ee_pose3d.rotation().matrix()).tolist())\n gt_ee_trans_list.append([ee_pose3d.x(), ee_pose3d.y(), ee_pose3d.z()])\n\n logger.log_val(names=['graph/obj/rot', 'graph/obj/trans', 'graph/ee/rot', 'graph/ee/trans'],\n vals=[graph_obj_rot_list, graph_obj_trans_list, graph_ee_rot_list, graph_ee_trans_list], \n index_val=step, index_name='step')\n\n logger.log_val(names=['gt/obj/rot', 'gt/obj/trans', 'gt/ee/rot', 'gt/ee/trans'],\n vals=[gt_obj_rot_list, gt_obj_trans_list, gt_ee_rot_list, gt_ee_trans_list], \n index_val=step, index_name='step')\n\n return logger\n\ndef print_step(est_pose, gt_pose, type='', decimals=3):\n\n rot_est = np.round(est_pose.rotation().rpy(), decimals=decimals)\n tran_est = np.round([est_pose.x(), est_pose.y(), est_pose.z()], decimals=decimals)\n\n rot_gt = np.round(gt_pose.rotation().rpy(), decimals=decimals)\n tran_gt = np.round([gt_pose.x(), gt_pose.y(), gt_pose.z()], decimals=decimals)\n\n err_pose = gt_pose.between(est_pose)\n rot_err = np.round(err_pose.rotation().rpy(), decimals=decimals)\n tran_err = np.round([err_pose.x(), err_pose.y(), err_pose.z()], decimals=decimals)\n\n print(f'ESTIMATED {type} pose\\n rot_est: {rot_est}, tran_est: {tran_est}')\n print(f'GROUNDTRUTH {type} pose\\n rot_gt: {rot_gt}, tran_gt: {tran_gt}')\n print(f'ERROR {type} pose\\n rot_err: {rot_err}, tran_err: {tran_err}')\n","repo_name":"psodhi/tactile-in-hand","sub_path":"inhandpy/scripts/graphopt/graphopt_helpers.py","file_name":"graphopt_helpers.py","file_ext":"py","file_size_in_byte":4721,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"27536258199","text":"import pandas as pd\nimport numpy as np\nimport math\nclass Activation_Function():\n ''' \n This class will produce activations of the input data\n \n Input:\n data (dataframe) -> A normalised dataframe containing all the input data\n params (iterable) -> Iterable containing the parameter values\n activation (int) -> Number determining the activation function to be employed:\n 0 - Linear Activation\n 1 - Binary Activation\n 2 - Sigmoid Activation\n 3 - Tanh Activation\n 4 - ReLU Activation\n 5 - Leaky ReLU Activation\n 6 - SiLU Activation\n 7 - Softmax Function\n \n Returns:\n f_x (iterable) -> Representing an numpy array of m activation values - one for each instance.\n \n \n '''\n \n def __init__(self, data, params, activation = 0, divide_point = 1):\n self.data = data\n self.params = params\n self.activation = activation\n self.divide_point = divide_point # I haven't taken this data as input from grad_loss function (so this can't be changed from calling function)\n if self.activation == 0:\n self.f_x = self.linear_activation()\n if self.activation == 1:\n self.f_x = self.binary_activation(self.divide_point)\n if self.activation == 2:\n self.f_x = self.sigmoid_activation()\n if self.activation == 3:\n self.f_x = self.tanh_activation()\n if self.activation == 4:\n self.f_x = self.relu_activation()\n if self.activation == 5:\n self.f_x = self.leaky_relu_activation()\n if self.activation == 6:\n self.f_x = self.silu_activation()\n if self.activation == 7:\n self.f_x = self.softmax_activation()\n \n \n def linear_activation(self):\n '''\n This function will find linear activations of the input data\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) - 1\n activated_value = []\n if len(self.params)!= no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n activated_value.append(np.sum(np.multiply(instance_array, self.params)))\n f_x = np.array(activated_value)\n return f_x\n \n def binary_activation(self):\n '''\n This function will return the binary activation values of the input data\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) - 1\n activated_value = []\n if len(self.params) != no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n if np.sum(np.multiply(instance_array, self.params)) < self.divide_point:\n activated_value.append(0)\n else:\n activated_value.append(1)\n f_x = np.array(activated_value)\n return f_x\n\n \n \n def sigmoid_activation(self):\n '''\n This function will return the sigmoid activation values of the input data\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) - 1\n activated_value = []\n if len(self.params) != no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n param_value = np.sum(np.multiply(instance_array, self.params))\n exp_param_value = math.exp(param_value)\n activated_value.append((1/(1+exp_param_value)))\n f_x = np.array(activated_value)\n return f_x\n \n def tanh_activation(self):\n '''\n This function will return the tanh activation values of the input data\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) -1 \n activated_value = []\n if len(self.params) != no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n param_value = np.sum(np.multiply(instance_array, self.params))\n exp_param_value = math.exp(param_value)\n neg_exp = math.exp(-1*param_value)\n activated_value.append((exp_param_value - neg_exp)/(exp_param_value + neg_exp))\n f_x = np.array(activated_value)\n return f_x\n \n \n def relu_activation(self):\n '''\n This function will return the relu activation values of the input data\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) -1 \n activated_value = []\n if len(self.params) != no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n param_value = np.sum(np.multiply(instance_array, self.params))\n if param_value>0:\n activated_value.append(param_value)\n else:\n activated_value.append(0)\n f_x = np.array(activated_value)\n return f_x\n \n \n def leaky_relu_activation(self):\n '''\n This function will return the leaky relu activation values of the input data\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) - 1\n activated_value = []\n if len(self.params) != no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n param_value = np.sum(np.multiply(instance_array, self.params))\n if param_value>0:\n activated_value.append(param_value)\n else:\n activated_value.append(0.1 * param_value)\n f_x = np.array(activated_value)\n return f_x\n \n \n def silu_activation(self):\n '''\n This function will return the silu activaton values of the input data. SiLU was proposed by researchers at Google,\n and has been tauted as better performer than Leaky ReLU in Computer Vision domain\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) - 1\n activated_value = []\n if len(self.params) != no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n param_value = np.sum(np.multiply(instance_array, self.params))\n exp_param_value = math.exp(param_value)\n activated_value.append(param_value*(1/(1+exp_param_value)))\n \n f_x = np.array(activated_value)\n return f_x\n \n def softmax_activation(self):\n '''\n This function will returnt the softmax activaion values of the input data. This is typically used in the last layer\n of a multiclass classification problem.\n \n Returns:\n f_x (iterable) -> A numpy array of m activation values\n \n '''\n no_of_instance = len(self.data)\n no_of_cols = len(self.data.columns) - 1\n activated_value = []\n exp_value = []\n sum_value = 0\n if len(self.params) != no_of_cols:\n raise ValueError(\"The number of parameters should be equal to the number of columns\")\n for i in range(no_of_instance):\n instance_array = np.array(self.data.iloc[i, :-1])\n param_value = np.sum(np.multiply(instance_array, self.params))\n exp_param_value = math.exp(param_value)\n exp_value.append(exp_param_value)\n sum_value = sum_value + exp_param_value\n exp_value_array = np.array(exp_param_value)\n f_x = exp_value_array/sum_value\n return f_x\n ","repo_name":"Shivanshu17/ML-From-Scratch","sub_path":"optim/activation_function.py","file_name":"activation_function.py","file_ext":"py","file_size_in_byte":9115,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72020158163","text":"import os\nfrom typing import Tuple, List\n# Ignore Tensor-RT warning from huggingface\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n\nimport torch\nimport time\nfrom transformers import AutoTokenizer, TextStreamer\nimport json\nimport pathlib\nimport numpy as np\n\nimport argparse\n\n\ndef boolean_string(string):\n low_string = string.lower()\n if low_string not in {\"false\", \"true\"}:\n raise ValueError(\"Not a valid boolean string\")\n return low_string == \"true\"\n\n\nDTYPE_LIST = [\"fp16\", \"bf16\", \"int8\", \"bf16_fp16\", \"bf16_int8\", \"bf16_int4\", \"int4\", \"bf16_nf4\", \"nf4\", \"w8a8\", \"bf16_w8a8\", \"w8a8_int8\", \"w8a8_int4\", \"w8a8_nf4\"]\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--token_path\", type=str, default=\"/data/chatglm-6b\", help=\"Path to token file\")\nparser.add_argument(\"--model_path\", type=str, default=\"/data/chatglm-6b/cpu\", help=\"Path to model file\")\nparser.add_argument(\"--model_name\", type=str, default=\"Model\", help=\"Model name\")\nparser.add_argument(\"--prompt_path\", type=str, default=\"./prompt_pool.json\", help=\"Path to model file\")\nparser.add_argument(\"--token_in\", type=str, default=32, help=\"Input Token Len\")\nparser.add_argument(\"--token_out\", type=int, default=32, help=\"Output Token Len, MaxLen=IN+OUT\")\nparser.add_argument(\"--beam_width\", type=int, default=1, help=\"Beam Search Width\")\nparser.add_argument(\"--input_prompt\", type=str, default=None, help=\"Input Prompt\")\nparser.add_argument(\"--batch_size\", type=int, default=1, help=\"Batch size\")\nparser.add_argument(\"--iteration\", type=int, default=10, help=\" Benchmakr Iterations\")\nparser.add_argument(\"--warmup\", type=int, default=2, help=\"Warm up Iterations\")\nparser.add_argument(\"--dtype\", type=str, choices=DTYPE_LIST, default=\"fp16\", help=\"Data type\")\nparser.add_argument(\"--padding\", help=\"Enable padding, Default to True.\", type=boolean_string, default=True)\nparser.add_argument(\"--chat\", help=\"Enable chat mode, Default to False.\", type=boolean_string, default=False)\n\ndef build_inputs_chatglm(tokenizer, query: List[str], padding, history: List[Tuple[str, str]] = []):\n prompts = []\n for item in query:\n prompt = \"\"\n for i, (old_query, response) in enumerate(history):\n prompt += \"[Round {}]\\n\\n问：{}\\n\\n答：{}\\n\\n\".format(i + 1, old_query, response)\n prompt += \"[Round {}]\\n\\n问：{}\\n\\n答：\".format(len(history) + 1, item)\n prompts.append(prompt)\n # print('### prompt={}'.format(prompt))\n inputs = tokenizer(prompts, return_tensors=\"pt\", padding=padding).input_ids\n return inputs\n\nimport importlib.util\n\nxft_spec = importlib.util.find_spec(\"xfastertransformer\")\n\nif xft_spec is None:\n import sys\n\n sys.path.append(\"../src\")\n print(\"[INFO] xfastertransformer is not installed in pip, using source code.\")\nelse:\n print(\"[INFO] xfastertransformer is installed, using pip installed package.\")\n\n\nimport xfastertransformer\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n with open(args.prompt_path, \"r\") as json_file:\n prompt_pool = json.load(json_file)\n \n if \"chatglm\" in args.model_name.lower() :\n model_prompt=prompt_pool[\"chatglm\"]\n if \"chatglm2\" in args.model_name.lower() :\n model_prompt=prompt_pool[\"chatglm2\"]\n if \"chatglm3\" in args.model_name.lower() :\n model_prompt=prompt_pool[\"chatglm3\"]\n if \"llama\" in args.model_name.lower() :\n model_prompt=prompt_pool[\"llama\"]\n if \"baichuan\" in args.model_name.lower() :\n model_prompt=prompt_pool[\"baichuan\"]\n if \"baichuan2\" in args.model_name.lower() :\n model_prompt=prompt_pool[\"baichuan2\"]\n if \"opt\" in args.model_name.lower() :\n model_prompt=prompt_pool[\"opt\"]\n\n tokenizer = AutoTokenizer.from_pretrained(\n args.token_path, use_fast=False, padding_side=\"left\", trust_remote_code=True\n )\n model = xfastertransformer.AutoModel.from_pretrained(args.model_path, dtype=args.dtype)\n input_prompts = []\n if model.rank == 0:\n # input prompt\n print(\"======start=======\")\n if args.input_prompt is not None:\n input_prompt = args.input_prompt\n elif args.token_in in model_prompt:\n input_prompt = model_prompt[args.token_in]\n print(input_prompt)\n else:\n raise SystemExit(\"[ERROR] Plese use --input_prompt if you want custom input.\")\n for _ in range(args.batch_size):\n input_prompts.append(input_prompt)\n # Master\n if args.chat and \"chatglm\" in args.model_name.lower() :\n if args.batch_size > 1:\n print(\"[INFO] chat mode only support batchsize=1\")\n input_ids = build_inputs_chatglm(tokenizer, input_prompts, args.padding)\n else :\n input_ids = tokenizer(input_prompts, return_tensors=\"pt\", padding=args.padding).input_ids\n input_token_nums = int(torch.numel(input_ids) / args.batch_size)\n if args.token_in is not None and int(args.token_in) != input_token_nums :\n print(f\"[WARN] input_token_size ({input_token_nums}) != required_input_size ({args.token_in})\")\n print(\"Input token Length is\", input_token_nums)\n print(\"Batch_size:\", args.batch_size)\n max_len = input_token_nums + args.token_out\n print(\"Max_len:\", max_len)\n print(\"=\" * 50)\n # Perform 100 runs and store execution times in a list\n execution_times = []\n first_token_times = []\n remained_token_times = []\n total_times = []\n # warm up\n for i in range(args.warmup):\n model.generate(input_ids, num_beams=args.beam_width, max_length=max_len, streamer=None)\n print(\"Start benchmark:\")\n for i in range(args.iteration):\n total_time = 0.0\n print(\"iteration\", i, \":\")\n model.config(max_length=max_len, num_beams=args.beam_width)\n model.input(input_ids)\n # first token\n start_time = time.perf_counter()\n next_tokens = model.forward()\n end_time = time.perf_counter()\n first_token_time = end_time - start_time\n total_time += first_token_time\n first_token_times.append(first_token_time)\n # remaining tokens\n cost_list = []\n while not model.is_done():\n start_time = time.perf_counter()\n next_tokens = model.forward()\n end_time = time.perf_counter()\n total_time += (end_time - start_time)\n cost_list.append(end_time - start_time)\n # print(next_tokens)\n generated_ids = model.finalize()\n total_times.append(total_time)\n remained_token_times.append(sum(cost_list))\n\n output_token_nums = int(torch.numel(generated_ids) / args.batch_size) - input_token_nums\n # Sort the execution times in ascending order\n remained_token_times.sort()\n # Get the max, min, avg and 90th element (index 89) from the sorted list\n next_token_latency_max = remained_token_times[-1] * 1000 / (output_token_nums - 1)\n next_token_latency_min = remained_token_times[0] * 1000 / (output_token_nums - 1)\n next_token_latency_avg = np.mean(remained_token_times) * 1000 / (output_token_nums - 1)\n next_token_latency_90 = remained_token_times[int(args.iteration * 0.9) - 1] * 1000 / (output_token_nums - 1)\n # Calculate total latency\n inference_latency = sum(total_times) / len(total_times)\n # Calculate the first token latency\n first_token_latency = sum(first_token_times) / len(first_token_times) * 1000\n Next_token_throughput = 1000 / next_token_latency_90 * args.batch_size\n print(\"\\n\")\n print(\"=\" * 50 + args.model_name + \" Final Performance\" + \"=\" * 50)\n print(f\"Inference Latency:\\t{inference_latency:.2f} s\")\n print(f\"First token Avg Latency:\\t{first_token_latency:.2f} ms\")\n print(f\"Next token Max Latency:\\t{next_token_latency_max:.2f} ms\")\n print(f\"Next token Min Latency:\\t{next_token_latency_min:.2f} ms\")\n print(f\"Next token P90 Latency:\\t{next_token_latency_90:.2f} ms\")\n print(f\"Next token Avg Latency:\\t{next_token_latency_avg:.2f} ms\")\n print(f\"Next token Latency:\\t{next_token_latency_90:.2f} ms\")\n print(f\"Throughput without 1st token:\\t{Next_token_throughput:.2f} tokens/s\")\n else:\n for i in range(args.warmup + args.iteration):\n model.generate()\n","repo_name":"intel/xFasterTransformer","sub_path":"benchmark/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":8450,"program_lang":"python","lang":"en","doc_type":"code","stars":91,"dataset":"github-code","pt":"3"}
+{"seq_id":"43799097123","text":"# 31256 KB / 40 ms\n\nN = int(input())\nlst = [int(x) for x in input().split()]\nres = [0] * N\n\nfor i in range(N):\n cnt = 0\n for j in range(N):\n if res[j] == 0 and cnt == lst[i]:\n res[j] = i + 1\n break\n elif res[j] == 0:\n cnt += 1\nprint(' '.join(map(str, res)))","repo_name":"KDT-02-Algorithm-Study/Algorithm-Study","sub_path":"week19_230525/1138_한_줄로_서기/1138_박현준.py","file_name":"1138_박현준.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"19225269172","text":"from django.urls import path, include\nfrom .views import UserModelViewSet, KeystrokeTokenObtainPairView, UserCreationView, index\nfrom rest_framework.routers import DefaultRouter\n\nrouter = DefaultRouter()\n# router.register(r'users', UserModelViewSet, basename='user')\n\nfrom rest_framework_simplejwt.views import (\n TokenObtainPairView,\n TokenRefreshView,\n)\n\n\n\nurlpatterns = [\n # path('login/', KeystrokeAuthView.as_view(), name='login'),\n path('register/', UserCreationView.as_view(), name='register'),\n path('token/', KeystrokeTokenObtainPairView.as_view(), name='token_obtain_pair'),\n path('token/refresh/', TokenRefreshView.as_view(), name='token_refresh'),\n\n path('', include(router.urls)),\n]\n","repo_name":"ahmadabdeen2/Django-Senior-Backend","sub_path":"senior/core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"108991378","text":"import json\nimport logging\n\nimport shared_variables\n\nfrom mex_master_controller.MexOperation import MexOperation\nfrom mex_master_controller.ClusterInstance import ClusterInstance\n\nlogger = logging.getLogger('mex_app rest')\n\n\nclass RequestAppInstLatency(MexOperation):\n def __init__(self, root_url, prov_stack=None, token=None, super_token=None, thread_queue=None):\n super().__init__(root_url=root_url, prov_stack=prov_stack, token=token, super_token=super_token, thread_queue=thread_queue)\n\n self.request_url = '/auth/ctrl/RequestAppInstLatency'\n\n def _build(self, app_name=None, app_version=None, cloudlet_name=None, operator_org_name=None, developer_org_name=None, cluster_instance_name=None, cluster_instance_developer_org_name=None, use_defaults=True, include_fields=False):\n if app_name == 'default':\n app_name = shared_variables.app_name_default\n if developer_org_name == 'default':\n developer_org_name = shared_variables.developer_name_default\n if app_version == 'default':\n app_version = shared_variables.app_version_default\n if operator_org_name == 'default':\n operator_org_name = shared_variables.operator_name_default\n if cloudlet_name == 'default' and operator_org_name != 'developer': # special case for platos where they use operator=developer and cloudlet=default\n cloudlet_name = shared_variables.cloudlet_name_default\n\n if use_defaults:\n if not app_name: app_name = shared_variables.app_name_default\n if not developer_org_name: developer_org_name = shared_variables.developer_name_default\n if not cluster_instance_name: cluster_instance_name = shared_variables.cluster_name_default\n if not cluster_instance_developer_org_name: \n if cluster_instance_name.startswith('autocluster'):\n cluster_instance_developer_org_name = 'MobiledgeX'\n else:\n cluster_instance_developer_org_name = developer_org_name\n if not app_version: app_version = shared_variables.app_version_default\n if not cloudlet_name: cloudlet_name = shared_variables.cloudlet_name_default\n if not operator_org_name: operator_org_name = shared_variables.operator_name_default\n\n if cluster_instance_name == 'default':\n cluster_instance_name = shared_variables.cluster_name_default\n\n if operator_org_name: shared_variables.operator_name_default = operator_org_name\n if cloudlet_name: shared_variables.cloudlet_name_default = cloudlet_name\n\n appinst_dict = {}\n appinst_key_dict = {}\n app_key_dict = {}\n cloudlet_key_dict = {}\n clusterinst_key_dict = {}\n cluster_key_dict = {}\n loc_dict = None\n configs_dict = {}\n\n if app_name:\n app_key_dict['name'] = app_name\n if app_version:\n app_key_dict['version'] = app_version\n if developer_org_name is not None:\n app_key_dict['organization'] = developer_org_name\n\n if cluster_instance_name is not None:\n cluster_key_dict['name'] = cluster_instance_name\n if cloudlet_name is not None:\n cloudlet_key_dict['name'] = cloudlet_name\n\n if operator_org_name is not None:\n cloudlet_key_dict['organization'] = operator_org_name\n if cloudlet_key_dict:\n clusterinst_key_dict['cloudlet_key'] = cloudlet_key_dict\n if cluster_key_dict:\n clusterinst_key_dict['cluster_key'] = cluster_key_dict\n if cluster_instance_developer_org_name is not None:\n clusterinst_key_dict['organization'] = cluster_instance_developer_org_name\n\n if app_key_dict:\n appinst_key_dict['app_key'] = app_key_dict\n if clusterinst_key_dict:\n appinst_key_dict['cluster_inst_key'] = clusterinst_key_dict\n\n if appinst_key_dict:\n appinst_dict['key'] = appinst_key_dict\n \n return appinst_dict\n\n def request_appinst_latency(self, token=None, region=None, app_name=None, app_version=None, cloudlet_name=None, operator_org_name=None, developer_org_name=None, cluster_instance_name=None, cluster_instance_developer_org_name=None, json_data=None, use_defaults=True, use_thread=False):\n msg = self._build(app_name=app_name, app_version=app_version, cloudlet_name=cloudlet_name, operator_org_name=operator_org_name, cluster_instance_name=cluster_instance_name, cluster_instance_developer_org_name=cluster_instance_developer_org_name, developer_org_name=developer_org_name, use_defaults=use_defaults)\n msg_dict = {'appinstlatency': msg}\n\n return self.show(token=token, url=self.request_url, region=region, json_data=json_data, use_defaults=use_defaults, use_thread=use_thread, message=msg_dict)[0]\n","repo_name":"mobiledgex/edge-cloud-qa","sub_path":"modules/mex_master_controller/RequestAppInstLatency.py","file_name":"RequestAppInstLatency.py","file_ext":"py","file_size_in_byte":4855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16515398217","text":"from typing import List\n\nfrom .message_chain import MessageChain\n\n\nclass MatcherUtil:\n @staticmethod\n def convert_to_half_width(string: str) -> str:\n \"\"\"\n > 说明\n 将文本中所有全角符号替换为半角符号.\n > 参数\n + string [str]: 待替换的文本\n \"\"\"\n full_width = '，。？！～：；＆％＃＊＄“”‘’（）【】｛｝《》'\n half_width = ',.?!~:;&%#*$\"\"\\'\\'()[]{}<>'\n for i in full_width:\n string = string.replace(i, half_width[full_width.index(i)])\n return string\n\n @staticmethod\n def filter_elements(chain: MessageChain, ignore: List['Element']) -> MessageChain:\n \"\"\"\n > 说明\n 过滤元素, 保留特定元素.\n > 参数\n + chain [MessageChain]: 消息链实例\n + ignore [list[Element]]: 要过滤的元素列表\n \"\"\"\n filtered_elements = []\n for element in chain.elements:\n if element.__class__ not in ignore:\n filtered_elements.append(element)\n return MessageChain.create(*filtered_elements)\n","repo_name":"WindLeaf233/JustBot","sub_path":"jbot/utils/matcher_util.py","file_name":"matcher_util.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"zh","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"6771839012","text":"import numpy as np\n\nfrom models.Base import TensorflowModel\nfrom models.Base import PytorchModel\n\nfrom utils.metrics import metric_dict\n\n\nfrom keras.layers import LSTM\nfrom keras.layers import Dense\nfrom keras.layers import Conv3D\nfrom keras.layers import Flatten\nfrom keras.layers import ConvLSTM2D\nfrom keras.layers import Bidirectional\nfrom keras.layers import BatchNormalization\nfrom keras.initializers import GlorotUniform\n\nimport torch.nn as nn\nimport torch\n\nclass VanillaLSTM__Tensorflow(TensorflowModel):\n def body(self):\n # Normalization\n if self.normalize_layer: self.model.add(self.normalize_layer)\n # LSTM Layer \n self.model.add(LSTM(name='LSTM_layer_0',\n units=self.units[0],\n return_sequences=True,\n kernel_initializer=GlorotUniform(seed=self.seed), \n activation=self.activations[0]))\n self.model.add(LSTM(name='LSTM_layer_1',\n units=self.units[1],\n return_sequences=True,\n kernel_initializer=GlorotUniform(seed=self.seed), \n activation=self.activations[1]))\n self.model.add(LSTM(name='LSTM_layer_2',\n units=self.units[2],\n return_sequences=True,\n kernel_initializer=GlorotUniform(seed=self.seed), \n activation=self.activations[2]))\n self.model.add(LSTM(name='LSTM_layer_3',\n units=self.units[3],\n return_sequences=True,\n kernel_initializer=GlorotUniform(seed=self.seed), \n activation=self.activations[3]))\n self.model.add(LSTM(name='LSTM_layer_4',\n units=self.units[4],\n return_sequences=True,\n kernel_initializer=GlorotUniform(seed=self.seed), \n activation=self.activations[4]))\n self.model.add(LSTM(name='LSTM_layer_5',\n units=self.units[5],\n return_sequences=False,\n kernel_initializer=GlorotUniform(seed=self.seed), \n activation=self.activations[5]))\n # FC Layer\n self.model.add(Dense(name='Fully_Connected_layer',\n units=self.units[6],\n kernel_initializer=GlorotUniform(seed=self.seed),\n activation=self.activations[6]))\n # Output Layer\n self.model.add(Dense(name='Output_layer',\n units=self.output_shape, \n kernel_initializer=GlorotUniform(seed=self.seed),\n activation=self.activations[7]))\n\nclass _VanillaLSTM__Pytorch(nn.Module):\n def __init__(self, input_shape, output_shape, units):\n super().__init__()\n self.lstm1 = nn.LSTM(input_size=input_shape, hidden_size=units[0], batch_first=True)\n self.lstm2 = nn.LSTM(input_size=units[0], hidden_size=units[1], batch_first=True)\n self.lstm3 = nn.LSTM(input_size=units[1], hidden_size=units[2], batch_first=True)\n self.dense = nn.Linear(in_features=units[2], out_features=units[3])\n self.activation = nn.ReLU()\n self.out = nn.Linear(in_features=units[3], out_features=output_shape)\n \n def forward(self, input):\n output, _ = self.lstm1(input)\n output, _ = self.lstm2(output)\n output, _ = self.lstm3(output)\n output = self.dense(output)\n output = self.activation(output)\n output = self.out(output)\n return output \n\nclass VanillaLSTM__Pytorch(PytorchModel):\n def build(self):\n self.model = _VanillaLSTM__Pytorch(input_shape=self.input_shape[-1],\n output_shape=self.output_shape,\n units=self.units)\n\nimport tensorflow as tf\nclass BiLSTM__Tensorflow(TensorflowModel):\n def build(self):\n self.model = tf.keras.Sequential([\n # Input layer \n tf.keras.Input(shape=self.input_shape, name='input_layer'), \n\n # normalize_layer,\n\n # BiLSTM Layer 1 \n tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128, \n return_sequences=True, \n kernel_initializer=tf.initializers.GlorotUniform(seed=self.seed)),\n name='bilstm_layer_1'), \n\n # BiLSTM Layer 2\n tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, \n return_sequences=True, \n kernel_initializer=tf.initializers.GlorotUniform(seed=self.seed)),\n name='bilstm_layer_2'), \n\n # BiLSTM Layer 3\n tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, \n return_sequences=False, \n kernel_initializer=tf.initializers.GlorotUniform(seed=self.seed)),\n name='bilstm_layer_3'), \n \n # FC Layer 1\n tf.keras.layers.Dense(32,\n activation='relu',\n kernel_initializer=tf.initializers.GlorotUniform(seed=self.seed),\n name='fc_layer_1'\n ),\n \n # Output Layer\n tf.keras.layers.Dense(self.output_shape, \n kernel_initializer=tf.initializers.GlorotUniform(seed=self.seed),\n name='output_layer') \n ],\n name='bilstm_model')\n\n# class BiLSTM__Tensorflow(TensorflowModel):\n# def body(self):\n# # Normalization\n# if self.normalize_layer: self.model.add(self.normalize_layer)\n# # BiLSTM Layer 1 \n# self.model.add(Bidirectional(name='BiLSTM_layer_1',\n# layer=LSTM(units=self.units[0],\n# return_sequences=True,\n# kernel_initializer=GlorotUniform(seed=self.seed),\n# activation=self.activations[0])))\n# # BiLSTM Layer 2 \n# self.model.add(Bidirectional(name='BiLSTM_layer_2',\n# layer=LSTM(units=self.units[1],\n# return_sequences=True,\n# kernel_initializer=GlorotUniform(seed=self.seed),\n# activation=self.activations[1])))\n# # BiLSTM Layer 3 \n# self.model.add(Bidirectional(name='BiLSTM_layer_3',\n# layer=LSTM(units=self.units[2],\n# return_sequences=False,\n# kernel_initializer=GlorotUniform(seed=self.seed),\n# activation=self.activations[2])))\n# # FC Layer\n# self.model.add(Dense(name='Fully_Connected_layer',\n# units=self.units[3],\n# kernel_initializer=GlorotUniform(seed=self.seed),\n# activation=self.activations[3]))\n\n# # Output Layer\n# self.model.add(Dense(name='Output_layer',\n# units=self.output_shape, \n# kernel_initializer=GlorotUniform(seed=self.seed),\n# activation=self.activations[4]))\n \n\n\n# class _BiLSTM__Pytorch(nn.Module):\n# def __init__(self, input_shape, output_shape, units, **kwargs):\n# super().__init__()\n# print(input_shape)\n# self.lstm1 = nn.LSTM(input_size=input_shape[-1], hidden_size=128, batch_first=True)\n# self.lstm2 = nn.LSTM(input_size=128, hidden_size=64, batch_first=True)\n# self.lstm3 = nn.LSTM(input_size=64, hidden_size=32, batch_first=True)\n# self.dense = nn.Linear(in_features=32, out_features=32)\n# self.activation = nn.ReLU()\n# self.out = nn.Linear(in_features=32, out_features=1)\n \n# def forward(self, input):\n# output, _ = self.lstm1(input)\n# output, _ = self.lstm2(output)\n# output, _ = self.lstm3(output)\n# output = self.dense(output)\n# output = self.activation(output)\n# output = self.out(output)\n# return output \n\n# import torch.nn as nn\n\n# class _BiLSTM__Pytorch(nn.Module):\n# def __init__(self, input_shape, output_shape, units):\n# super(_BiLSTM__Pytorch, self).__init__()\n\n# # Normalization\n# # if self.normalize_layer:\n# # self.normalize = nn.BatchNorm1d(input_dim)\n \n# # BiLSTM Layer 1 \n# self.bilstm1 = nn.LSTM(input_size=input_shape[0], \n# hidden_size=units[0], \n# bidirectional=True,\n# batch_first=True)\n\n# # BiLSTM Layer 2 \n# self.bilstm2 = nn.LSTM(input_size=units[0] * 2, \n# hidden_size=units[1], \n# bidirectional=True,\n# batch_first=True)\n\n# # BiLSTM Layer 3 \n# self.bilstm3 = nn.LSTM(input_size=units[1] * 2, \n# hidden_size=units[2], \n# bidirectional=True,\n# batch_first=True)\n\n# self.activation = nn.ReLU()\n\n# # FC Layer\n# self.fc = nn.Linear(units[2] * 2, units[3])\n\n# # Output Layer\n# self.output = nn.Linear(units[3], output_shape)\n \n# def forward(self, x):\n# # # Normalization\n# # if self.normalize_layer:\n# # x = self.normalize(x)\n\n# # BiLSTM Layer 1 \n# x, _ = self.bilstm1(x)\n\n# # BiLSTM Layer 2 \n# x, _ = self.bilstm2(x)\n\n# # BiLSTM Layer 3 \n# x, _ = self.bilstm3(x)\n\n# x = self.activation(x)\n\n# # FC Layer\n# x = self.fc(x[:, -1, :])\n\n# # Output Layer\n# x = self.output(x)\n# return x\n\n\n# class BiLSTM__Pytorch(PytorchModel):\n# def build(self):\n# self.model = _BiLSTM__Pytorch(input_shape=self.input_shape, \n# output_shape=self.output_shape,\n# units=self.units).double()\n\n\nclass ConvLSTM__Tensorflow(TensorflowModel):\n def body(self): \n self.model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3), padding='same', return_sequences=True))\n self.model.add(BatchNormalization())\n self.model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3), padding='same', return_sequences=True))\n self.model.add(BatchNormalization())\n self.model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3), padding='same', return_sequences=True))\n self.model.add(BatchNormalization())\n self.model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3), padding='same', return_sequences=True))\n self.model.add(BatchNormalization())\n self.model.add(Conv3D(filters=self.output_shape, kernel_size=(3, 3, 3), activation='sigmoid', padding='same', data_format='channels_last'))\n # self.model.add(Flatten())\n # self.model.add(Dense(name='Output_layer',\n # units=self.output_shape, \n # kernel_initializer=GlorotUniform(seed=self.seed)))\n \n def fit(self, X_train, y_train, X_val, y_val, patience, learning_rate, epochs, save_dir, batchsz, optimizer='Adam', loss='MSE', **kwargs):\n if self.model == None:\n X_train = np.array(X_train)[:,:, np.newaxis, np.newaxis]\n y_train = np.array(y_train)[:,:,np.newaxis, np.newaxis]\n X_val = np.array(X_val)[:,:,np.newaxis, np.newaxis]\n y_val = np.array(y_val)[:,:,np.newaxis, np.newaxis]\n self.input_shape = (None, *X_train.shape[-3:])\n self.build()\n\n self.model.compile(optimizer=self.function_dict[optimizer](learning_rate=learning_rate), loss=self.function_dict[loss]())\n self.model.fit(self.preprocessing(x=X_train, y=y_train, batchsz=batchsz), \n validation_data=self.preprocessing(x=X_val, y=y_val, batchsz=batchsz),\n epochs=epochs, \n callbacks=self.callbacks(patience=patience, save_dir=save_dir, min_delta=0.001, epochs=epochs))\n\n def predict(self, X):\n return self.model.predict(np.array(X)[:,:, np.newaxis, np.newaxis])\n\n def score(self, y, yhat, r):\n y = np.array(y)[:,:, np.newaxis, np.newaxis]\n if r != -1:\n results = [str(np.round(np.float64(metric_dict[key](y, yhat)), r)) for key in metric_dict.keys()]\n else:\n results = [str(metric_dict[key](y, yhat)) for key in metric_dict.keys()]\n return results\n return results","repo_name":"neko941/UTSF","sub_path":"models/LSTM.py","file_name":"LSTM.py","file_ext":"py","file_size_in_byte":13501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"75042418001","text":"\"\"\"q04_dice.py\"\"\"\n\nfrom random import choice\n\n\nclass Dice:\n \"\"\"\n 4. Create a class called \"Dice\". This class will be used to simulate\n throwing a die, with the side labels defined by the user when the dice is\n created __init__(side_labels) - where side_labels is a list of any length.\n The constructor should also have a variable which calculates the number of\n sides on the dice (you should be able to get this from the length of the\n list). The class will need one method, throw_dice(), which simulates\n throwing the dice, and returns the side it lands on.\n \"\"\"\n\n def __init__(self, side_labels):\n self.side_labels = side_labels\n self.sides = len(side_labels)\n\n def throw_dice(self):\n return choice(self.side_labels)\n\n\nif __name__ == \"__main__\":\n dice1 = Dice([1, 2, 3, 4, 5, 6])\n print(\"Die 1 has\", dice1.sides, \"sides\")\n for i in range(5):\n print(\"Roll\", str(i + 1) + \":\", dice1.throw_dice())\n\n print()\n\n dice2 = Dice([3, 3, 4, 4, 5, 6])\n print(\"Die 2 has\", dice2.sides, \"sides\")\n for i in range(5):\n print(\"Roll\", str(i + 1) + \":\", dice2.throw_dice())\n\n print()\n\n dice3 = Dice([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\"])\n print(\"Die 3 has\", dice3.sides, \"sides\")\n for i in range(5):\n print(\"Roll\", str(i + 1) + \":\", dice3.throw_dice())\n","repo_name":"Dagonite/python-exercises","sub_path":"Worksheets/Pract10/q04_dice.py","file_name":"q04_dice.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29183573927","text":"import json\n\nimport os\nimport mock\nimport subprocess\nfrom infra.rtc.nodeinfo.lib.modules import lshw_cpu\n\n\nTEST_OUTPUT = \\\n'''\n[\n {\n \"id\" : \"cpu:0\",\n \"class\" : \"processor\",\n \"claimed\" : true,\n \"handle\" : \"DMI:00AC\",\n \"description\" : \"CPU\",\n \"product\" : \"ARMv8 (Q80-30)\",\n \"vendor\" : \"Ampere(R)\",\n \"physid\" : \"ac\",\n \"businfo\" : \"cpu@0\",\n \"version\" : \"Ampere(R) Altra(R) Processor\",\n \"serial\" : \"000000000000000081940609033842C8\",\n \"slot\" : \"CPU 0\",\n \"units\" : \"Hz\",\n \"size\" : 3000000000,\n \"capacity\" : 3000000000,\n \"clock\" : 1650000000,\n \"configuration\" : {\n \"cores\" : \"80\",\n \"enabledcores\" : \"80\",\n \"threads\" : \"80\"\n },\n \"capabilities\" : {\n \"lm\" : \"64-bit capable\"\n }\n },\n {\n \"id\" : \"cpu:1\",\n \"class\" : \"processor\",\n \"claimed\" : true,\n \"handle\" : \"DMI:00B1\",\n \"description\" : \"CPU\",\n \"product\" : \"ARMv8 (Q80-30)\",\n \"vendor\" : \"Ampere(R)\",\n \"physid\" : \"b1\",\n \"businfo\" : \"cpu@1\",\n \"version\" : \"Ampere(R) Altra(R) Processor\",\n \"serial\" : \"000000000000000081900906033842C8\",\n \"slot\" : \"CPU 1\",\n \"units\" : \"Hz\",\n \"size\" : 3000000000,\n \"capacity\" : 3000000000,\n \"clock\" : 1650000000,\n \"configuration\" : {\n \"cores\" : \"80\",\n \"enabledcores\" : \"80\",\n \"threads\" : \"80\"\n },\n \"capabilities\" : {\n \"lm\" : \"64-bit capable\"\n }\n }\n]\n'''\n\n\ndef test_lshw_output_no_lshw(monkeypatch):\n exists = mock.Mock(return_value=False)\n monkeypatch.setattr(os.path, 'exists', exists)\n _, err = lshw_cpu.lshw_output()\n assert err == 'lshw is not installed at /usr/bin/lshw'\n exists.assert_called_once_with('/usr/bin/lshw')\n\n\ndef test_lshw_output_ok(monkeypatch):\n exists = mock.Mock(return_value=True)\n monkeypatch.setattr(os.path, 'exists', exists)\n output = mock.Mock(return_value='{\"mock\": \"mock\"}')\n monkeypatch.setattr(subprocess, 'check_output', output)\n\n j, err = lshw_cpu.lshw_output()\n assert err is None\n output.assert_called_once_with(['/usr/bin/lshw', '-json', '-notime', '-quiet', '-c', 'cpu'])\n assert j == {'mock': 'mock'}\n\n output.reset_mock()\n output.return_value=\"malformed{}\"\n j, err = lshw_cpu.lshw_output()\n assert j is None\n assert err is not None\n\n\ndef test_get_cpu_info():\n lshw = mock.Mock(return_value=(json.loads(TEST_OUTPUT), None,))\n ci, err = lshw_cpu.get_cpu_info(lshw)\n assert err is None\n assert ci == [\n lshw_cpu.LSHWCPUInfo(\"ARMv8 (Q80-30)\", \"Ampere(R)\", 80, 80),\n lshw_cpu.LSHWCPUInfo(\"ARMv8 (Q80-30)\", \"Ampere(R)\", 80, 80),\n ]\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/test_lshw_cpu.py","file_name":"test_lshw_cpu.py","file_ext":"py","file_size_in_byte":2579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31555486725","text":"from django.contrib import admin\nfrom django.urls import path\nfrom app1.views import question_detail,question_list, choices_detail, choices_list\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('question/',question_list),\n path('question/',question_detail),\n path('choices/',choices_list),\n path('choices/',choices_detail)\n]","repo_name":"tripathideepak1997/RestFramework","sub_path":"restframework/restframework/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24544179345","text":"# rightChars = \"The correct Chars in your exp\"\n# memeryChars = \"The Chars in the target machine memery dump\"\nflag = 1\nfor i in range(len(memeryChars)):\n\tif rightChars[i] != memeryChars[i]:\n\t\tprint(\"[-] Find bad char!\")\n\t\tprint(\"[-] Current char: \" + hex(ord(rightChars[i])))\n\t\tif i != 0:\n\t\t\tprint(\"[-] Previous char: \" + hex(ord(rightChars[i-1])))\n\t\tflag = 0\n\t\tbreak\nif flag == 1:\n\tprint(\"[+] Not Find bad char!\")","repo_name":"xiaopan233/OSCP-Script","sub_path":"bof/badchars_check.py","file_name":"badchars_check.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"10600185055","text":"class Employee:\n\n def __init__(self, first, last, pay):\n self.first = first\n self.last = last\n self.email = f'{first}.{last}@email.com'\n self.pay = pay\n\n def fullname(self):\n return f'{self.first} {self.last}'\n\n # alternative constructor\n @classmethod\n def from_string(cls, emp_str):\n first, last, pay = emp_str.split('-')\n cls(first, last, pay)\n return cls(first, last, pay)\n\n\nclass Developer(Employee):\n\n def __init__(self, *args, prog_lang=None):\n if len(args) > 1:\n super().__init__(*args)\n\n self.prog_lang = prog_lang\n\n def from_string(self, *args):\n prog_lang = self.prog_lang\n dev = super().from_string(args[0])\n dev.prog_lang = prog_lang\n return dev\n\n\nemp_1 = Developer('Tiago', 'Moraes', 50000, prog_lang='Python')\n\nemp_str_1 = 'John-Doe-70000'\n\n# new_emp_1 = Employee.from_string(emp_str_1)\nnew_emp_1 = Developer(prog_lang='Ruby')\nnew_emp_1 = new_emp_1.from_string(emp_str_1)\n\nprint(new_emp_1.fullname())\n","repo_name":"tgpmoraes/curso-python","sub_path":"cod3r/poo/desafio_pessoal/alternative_constructor_subclass.py","file_name":"alternative_constructor_subclass.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16636337926","text":"import os\nimport math\nimport cv2 as cv\nimport numpy as np\nimport random\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\n\nclass AdaMattingDataset(Dataset):\n\n def __init__(self, raw_data_path, mode):\n self.crop_size = 320\n self.mode = mode\n self.raw_data_path = raw_data_path\n\n self.fg_path = os.path.join(self.raw_data_path, \"train/fg/\")\n self.bg_path = os.path.join(self.raw_data_path, \"train/bg/\")\n self.a_path = os.path.join(self.raw_data_path, \"train/mask/\")\n\n data_transforms = {\n # values from ImageNet\n 'train': transforms.Compose([\n transforms.ColorJitter(brightness=0.125, contrast=0.125, saturation=0.125),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]),\n 'valid': transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ]),\n }\n self.transformer = data_transforms[self.mode]\n\n with open(os.path.join(self.raw_data_path, \"Combined_Dataset/Training_set/training_fg_names.txt\")) as f:\n self.fg_files = f.read().splitlines()\n with open(os.path.join(self.raw_data_path, \"Combined_Dataset/Training_set/training_bg_names.txt\")) as f:\n self.bg_files = f.read().splitlines()\n\n filename = \"dataset/{}_names.txt\".format(self.mode)\n with open(filename, 'r') as file:\n self.names = file.read().splitlines()\n \n\n def __len__(self):\n return len(self.names)\n \n\n def __getitem__(self, index):\n name = self.names[index]\n fcount = int(name.split('.')[0].split('_')[0])\n bcount = int(name.split('.')[0].split('_')[1])\n im_name = self.fg_files[fcount]\n bg_name = self.bg_files[bcount]\n fg = cv.imread(self.fg_path + im_name)\n alpha = cv.imread(self.a_path + im_name, 0)\n bg = cv.imread(self.bg_path + bg_name)\n\n # Resize and crop the bg to fit the fg\n h, w = fg.shape[:2]\n bh, bw = bg.shape[:2]\n wratio = w / bw\n hratio = h / bh\n ratio = wratio if wratio > hratio else hratio\n if ratio > 1:\n bg = cv.resize(src=bg, dsize=(math.ceil(bw * ratio), math.ceil(bh * ratio)), interpolation=cv.INTER_CUBIC)\n bh, bw = bg.shape[:2]\n x = random.randint(0, bw - w - 1) if self.mode == \"train\" and bw > w else 0\n y = random.randint(0, bh - h - 1) if self.mode == \"train\" and bh > h else 0\n bg = np.array(bg[y:y + h, x:x + w], np.float32)\n\n # Composite the merged image\n normalized_alpha = np.zeros((h, w, 1), np.float32)\n normalized_alpha[:, :, 0] = alpha / 255.\n merged = normalized_alpha * fg + (1 - normalized_alpha) * bg\n merged = merged.astype(np.uint8)\n\n # Resize randomly if in training mode\n resize_factor = 0.75 * random.random() + 0.75 if self.mode == \"train\" else 1.0\n input_img = cv.resize(merged, None, fx=resize_factor, fy=resize_factor)\n gt_alpha = cv.resize(alpha, None, fx=resize_factor, fy=resize_factor)\n\n # Resize if in validation mode\n if self.mode == \"valid\":\n h, w = input_img.shape[:2]\n if h > w:\n input_img = cv.resize(input_img, (int(w * self.crop_size / h), self.crop_size))\n gt_alpha = cv.resize(gt_alpha, (int(w * self.crop_size / h), self.crop_size))\n left_border = int((self.crop_size - input_img.shape[1]) / 2)\n right_border = self.crop_size - input_img.shape[1] - left_border\n input_img = cv.copyMakeBorder(input_img, 0, 0, left_border, right_border, cv.BORDER_CONSTANT, value=[0, 0, 0])\n gt_alpha = cv.copyMakeBorder(gt_alpha, 0, 0, left_border, right_border, cv.BORDER_CONSTANT, value=[0, 0, 0])\n else:\n input_img = cv.resize(input_img, (self.crop_size, int(h * self.crop_size / w)))\n gt_alpha = cv.resize(gt_alpha, (self.crop_size, int(h * self.crop_size / w)))\n top_border = int((self.crop_size - input_img.shape[0]) / 2)\n bottom_border = self.crop_size - input_img.shape[0] - top_border\n input_img = cv.copyMakeBorder(input_img, top_border, bottom_border, 0, 0, cv.BORDER_CONSTANT, value=[0, 0, 0])\n gt_alpha = cv.copyMakeBorder(gt_alpha, top_border, bottom_border, 0, 0, cv.BORDER_CONSTANT, value=[0, 0, 0])\n\n # Rotate randomly in training mode\n if self.mode == \"train\":\n angle = random.randint(-45, 45)\n input_img = self.rotate_cv_image(input_img, angle)\n gt_alpha = self.rotate_cv_image(gt_alpha, angle)\n\n # Generate gt_trimap\n gt_trimap = np.zeros(gt_alpha.shape, np.float32)\n gt_trimap.fill(128)\n gt_trimap[gt_alpha == 0] = 0\n gt_trimap[gt_alpha == 255] = 255\n\n # Crop randomly if in training mode\n if self.mode == \"train\":\n different_size = random.randint(self.crop_size, 800)\n crop_size = (different_size, different_size)\n x, y = self.random_crop_pos(gt_trimap, crop_size)\n input_img = self.do_crop(input_img, x, y, crop_size)\n gt_alpha = self.do_crop(gt_alpha, x, y, crop_size)\n gt_trimap = self.do_crop(gt_trimap, x, y, crop_size)\n\n # Flip randomly in training mode\n if self.mode == \"train\" and random.random() > 0.5:\n input_img = np.fliplr(input_img)\n gt_alpha = np.fliplr(gt_alpha)\n gt_trimap = np.fliplr(gt_trimap)\n\n # Generate input_trimap\n k_size = random.randint(5, 20) if self.mode == \"train\" else 15\n iterations = random.randint(1, 20) if self.mode == \"train\" else 20\n kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (k_size, k_size))\n eroded = cv.erode(gt_trimap, kernel, iterations)\n dilated = cv.dilate(gt_trimap, kernel, iterations)\n input_trimap = np.zeros(gt_trimap.shape, np.float32)\n input_trimap.fill(0.5)\n input_trimap[eroded == 255] = 1.0\n input_trimap[dilated == 0] = 0.0\n\n # Convert all from BGR to RGB and store as PIL images\n rgb_input_img = cv.cvtColor(input_img, cv.COLOR_BGR2RGB)\n pil_input_img = transforms.ToPILImage()(rgb_input_img)\n pil_input_trimap = transforms.ToPILImage()(input_trimap)\n pil_gt_alpha = transforms.ToPILImage()(gt_alpha)\n\n display_rgb = transforms.ToTensor()(pil_input_img)\n\n inputs = torch.zeros((4, self.crop_size, self.crop_size), dtype=torch.float)\n inputs[0:3, :, :] = self.transformer(pil_input_img)\n inputs[3, :, :] = transforms.ToTensor()(pil_input_trimap)\n\n gts = torch.zeros((2, self.crop_size, self.crop_size), dtype=torch.float)\n gts[0, :, :] = transforms.ToTensor()(pil_gt_alpha)\n out_gt_trimap = np.zeros(gt_trimap.shape, np.float32)\n out_gt_trimap.fill(1.0)\n out_gt_trimap[gt_trimap == 255] = 2.0\n out_gt_trimap[gt_trimap == 0] = 0.0\n gts[1, :, :] = torch.from_numpy(out_gt_trimap)\n\n return display_rgb, inputs, gts\n\n\n def random_crop_pos(self, gt_trimap, crop_size=(320, 320)):\n crop_height, crop_width = crop_size\n y_indices, x_indices = np.where(gt_trimap == 128)\n num_unknowns = len(y_indices)\n x, y = 0, 0\n if num_unknowns > 0:\n ix = random.choice(range(num_unknowns))\n center_x = x_indices[ix]\n center_y = y_indices[ix]\n x = max(0, center_x - int(crop_width / 2))\n y = max(0, center_y - int(crop_height / 2))\n return x, y\n\n\n def do_crop(self, mat, x, y, crop_size):\n crop_height, crop_width = crop_size\n if len(mat.shape) == 2:\n ret = np.zeros((crop_height, crop_width), np.uint8)\n else:\n ret = np.zeros((crop_height, crop_width, 3), np.uint8)\n crop = mat[y:y + crop_height, x:x + crop_width]\n h, w = crop.shape[:2]\n ret[0:h, 0:w] = crop\n if crop_size != (self.crop_size, self.crop_size):\n ret = cv.resize(ret, dsize=(self.crop_size, self.crop_size), interpolation=cv.INTER_NEAREST)\n return ret\n\n\n def rotate_cv_image(self, image, angle=None, center=None, scale=1.0):\n (h, w) = image.shape[:2]\n\n if center is None:\n center = (w // 2, h // 2)\n if angle is None:\n angle = random.randint(-45, 45)\n\n M = cv.getRotationMatrix2D(center, angle, scale)\n rotated = cv.warpAffine(image, M, (w, h))\n return rotated\n","repo_name":"pinglmlcv/AdaMatting","sub_path":"dataset/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":8712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"74251512400","text":"\"\"\"\r\nCriando um Relatório automático de Vagas do Indeed com o Selenium + Python\r\n\r\nParte II\r\n\r\n@2021 Created By Geovana Sousa - LearnData\r\n\"\"\"\r\n\r\n# $ python3 -m pip install reportlab\r\n\r\n# Módulos do ReportLab\r\nfrom reportlab.pdfgen.canvas import Canvas #\r\nfrom reportlab.lib.pagesizes import letter\r\nfrom reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer\r\nfrom reportlab.lib.styles import getSampleStyleSheet\r\n\r\n# Módulos para envio de E-mail\r\nimport smtplib\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.text import MIMEText\r\nfrom email.mime.base import MIMEBase\r\nfrom email import encoders\r\n\r\n# PDF -----------------------------------------------------------------------------------\r\n# Estrutura das Páginas\r\nPAGE_HEIGHT = letter[1] # Altura da Página\r\nPAGE_WIDTH = letter[0] # Largura da Página\r\nstyles = getSampleStyleSheet() # Estilo da Página\r\n\r\nimage = \"LearningData_dark.png\"\r\nmy_canvas = Canvas(\"Vagas.pdf\", pagesize=letter)\r\npageinfo = \"LearningData\"\r\ntitle = \"Vagas Indeed\"\r\n\r\n\r\ndef first_page(canvas, doc):\r\n canvas.saveState()\r\n canvas.setFont('Times-Bold', 20)\r\n canvas.drawCentredString(300, 720, title)\r\n canvas.setFont('Times-Roman', 9)\r\n canvas.drawString(x=500, y=15, text=\"Página 1 / %s\" % pageinfo)\r\n canvas.drawImage(image, x=20, y=750, width=110, height=22, mask='rgba(252, 92, 101, 0.5)')\r\n canvas.drawString(547, 28, date.today().strftime(\"%d/%m/%Y\"))\r\n canvas.restoreState()\r\n\r\n\r\ndef later_pages(canvas, doc):\r\n canvas.saveState()\r\n canvas.setFont('Times-Roman', 9)\r\n canvas.drawString(x=500, y=15, text=\"Página %d / %s\" % (doc.page, pageinfo))\r\n canvas.drawImage(image, x=20, y=750, width=110, height=22, mask='rgba(252, 92, 101, 0.5)')\r\n canvas.drawString(547, 28, date.today().strftime(\"%d/%m/%Y\"))\r\n canvas.restoreState()\r\n\r\n\r\ndef pdf_file_generator():\r\n doc = SimpleDocTemplate(\"Vagas.pdf\", pagesize=letter)\r\n story = [Spacer(1, 2)]\r\n style = styles[\"Normal\"]\r\n style_h = styles[\"Heading3\"]\r\n with open('vagas_ids.csv', 'r') as file:\r\n leitura_ids = csv.reader(file)\r\n titles = next(file)\r\n if titles is not None:\r\n for row in leitura_ids:\r\n vaga_title = (row[1])\r\n vaga_link = (row[2])\r\n p = Paragraph(vaga_title, style_h)\r\n p2 = Paragraph(vaga_link, style)\r\n story.append(p)\r\n story.append(Spacer(1, 2))\r\n story.append(p2)\r\n story.append(Spacer(1, 5))\r\n doc.build(story, onFirstPage=first_page, onLaterPages=later_pages)\r\n\r\n\r\n# Enviar por Email ---------------------------------------------------------------------\r\n\r\ndef enviar_email():\r\n corpo_email = \"\"\"\r\nBoa tarde! \r\n\r\nSegue em anexo o relatório de vagas dos últimos 3 dias.\r\n \"\"\"\r\n\r\n mensagem = MIMEMultipart()\r\n # mensagem = email.message.Message()\r\n mensagem['Subject'] = 'Relatório de Vagas Indeed - Últimos 3 dias'\r\n mensagem['From'] = 'seu_email@xx.com'\r\n mensagem['To'] = 'email_que_vai_enviar@xx.com'\r\n password = 'sua_senha'\r\n mensagem.add_header('Content-Type', 'text/html')\r\n mensagem.attach(MIMEText(corpo_email, 'plain'))\r\n\r\n filename = 'Vagas.pdf'\r\n anexo = open(filename, 'rb')\r\n part = MIMEBase('application', 'octet-stream')\r\n part.set_payload(anexo.read())\r\n encoders.encode_base64(part)\r\n part.add_header('Content-Disposition', \"anexo; filename=%s\" % filename)\r\n\r\n mensagem.attach(part)\r\n anexo.close()\r\n\r\n s = smtplib.SMTP('smtp.gmail.com: 587')\r\n s.starttls()\r\n # Credenciais de Login para realizar o envio\r\n s.login(mensagem['From'], password)\r\n s.sendmail(mensagem['From'], mensagem['To'], mensagem.as_string().encode('utf-8'))\r\n\r\n\r\n# Rodando ---------------------------------------------------------------------\r\n\r\n# Definindo as Palavras-chaves para pesquisa\r\ntitulos = ['assistente administrativo', 'assistente financeiro']\r\n\r\n# Extrair Vagas\r\nfor pesquisa in titulos:\r\n extrair_vagas(pesquisa)\r\n\r\n# Encerrar o webdriver\r\ndriver.close()\r\n\r\n# Gerar o PDF\r\npdf_file_generator()\r\n\r\n# Enviar o E-mail\r\nenviar_email()\r\n","repo_name":"GeovanaSLima/RelatorioVagas-web-scraping","sub_path":"Parte 2.py","file_name":"Parte 2.py","file_ext":"py","file_size_in_byte":4173,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"23322924815","text":"#! /usr/bin/env python\n\nimport os\nimport io\nfrom distutils.core import setup, Extension\n# from setuptools import setup, Extension\nimport numpy as np\n\n# NOTE: to compile, run in the current directory\n# python setup.py build_ext --inplace\n# python setup.py develop\n\ntry:\n from Cython.Build import cythonize\n # from Cython.Distutils import build_ext\nexcept ImportError:\n use_cython = False\nelse:\n use_cython = True\n\n\ndef read(*filenames, **kwargs):\n encoding = kwargs.get('encoding', 'utf-8')\n sep = kwargs.get('sep', '\\n')\n buf = []\n for filename in filenames:\n with io.open(filename, encoding=encoding) as f:\n buf.append(f.read())\n return sep.join(buf)\n\n\ncmdclass = {}\next_modules = []\n\nhere = os.path.abspath(os.path.dirname(__file__))\nextentions_info = [\n {\n \"cython_sourcefile\": \"knapsack_python/python/cython/mthm.pyx\"\n },\n {\n \"cython_sourcefile\": \"knapsack_python/python/cython/mthg.pyx\"\n },\n {\n \"cython_sourcefile\": \"knapsack_python/python/cython/assign_all.pyx\"\n }\n]\ninclude_path = [np.get_include(), 'knapsack_python/cpp/include']\n\nextensions = []\nfor extention_info in extentions_info:\n sourcefiles = extention_info.get(\"cpp_sourcefiles\", [])\n cython_sourcefile = extention_info.get(\"cython_sourcefile\")\n if use_cython:\n sourcefiles += [cython_sourcefile]\n else:\n sourcefiles += [cython_sourcefile.replace('.pyx', '.cpp')]\n # NOTE: extension name must match .pyx file name\n extension_name = cython_sourcefile.replace('.pyx', '').replace(os.sep, '.')\n extensions.append(Extension(extension_name,\n sources=sourcefiles,\n include_dirs=include_path,\n language='c++'))\n\n\nif use_cython:\n ext_modules += \\\n cythonize(extensions, compiler_directives={'embedsignature': True,\n 'profile': True})\nelse:\n ext_modules += extensions\n\nlong_description = read('README.rst', 'CHANGELOG.rst')\n\nsetup(\n name=\"knapsack_python\",\n version='0.1.3',\n url='https://github.com/jhetherly/python_knapsack',\n license='MIT',\n author='Jeff Hetherly',\n author_email='jeffrey.hetherly@gmail.com',\n platforms='any',\n description='Solves a variety of knapsack problems',\n long_description=long_description,\n install_requires=['numpy'],\n packages=['knapsack_python'],\n cmdclass=cmdclass,\n ext_modules=ext_modules,\n include_dirs=[np.get_include(), 'knapsack_python/cpp/include']\n)\n","repo_name":"jhetherly/python_knapsack","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"39451094107","text":"\"\"\"Marketsimcode.py\"\"\"\n#improved version of your marketsim code that accepts a \"trades\" data frame (instead of a file).\n\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport os\nimport matplotlib.pyplot as plt\nfrom util import get_data\n\ndef author():\n return 'sbaek47'\n\ndef compute_portvals(dataframe, start_val=100000, commission=0, impact=0):\n df = dataframe.sort_index()\n start_date = df.index[0]\n end_date = df.index[len(df)-1]\n\n#convert SELL shares to negative stocks\n df['Shares'][df['Order'].str.contains(\"SELL\")] = -df['Shares'][df['Order'].str.contains(\"SELL\")]\n stocks = df.Symbol.unique()\n\n#DF1\n dates = pd.date_range(start_date, end_date)\n prices = get_data(stocks, dates, False)\n prices = prices.assign(Cash = 1.0)\n prices = prices.dropna(axis=0)\n\n#DF2\n trades=prices.copy()\n trades[trades != 0] = 0\n\n for i in range(len(df)):\n trades[df['Symbol'][i]][df.index[i]] = df['Shares'][i] +trades[df['Symbol'][i]][df.index[i]]\n\n for i in range(len(df)):\n trades['Cash'][df.index[i]] += (prices[df['Symbol'][i]][df.index[i]] * - df['Shares'][i])\n\n#DF3\n holdings = trades.copy()\n for j in range(len(holdings.columns)):\n stock = 0\n for i in range(len(holdings)):\n holdings.iloc[i,j] = stock + holdings.iloc[i,j]\n stock = holdings.iloc[i,j]\n holdings['Cash'] = holdings['Cash']+start_val\n\n value = holdings * prices\n\n#DF4\n portvals = value.sum(axis=1)\n return portvals\n\n\nif __name__ == \"__main__\":\n test_code()\n","repo_name":"seungkwanbaek/ML4Crypto","sub_path":"manual_strategy/marketsimcode.py","file_name":"marketsimcode.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73077348537","text":"# This is mergesort\n# mergesort(p,q) sorts array a from position p to q\n\n\nfrom time import clock\n\n\nc = 0\n\n\ndef mergesort(p, q, a, b):\n if p < q:\n m = int((p + q) / 2)\n mergesort(p, m, a, b)\n mergesort(m + 1, q, a, b)\n merge(p, m + 1, q + 1, a, b)\n\n# merge(p,m,q) merges array a from position p to m\n# and position m+1 to q\n\n\ndef merge(p, r, q, a, b):\n global c # c is comparison counter\n i = p\n j = r\n k = p\n while i < r and j < q:\n c += 1\n if a[i] <= a[j]:\n b[k] = a[i]\n i += 1\n else:\n b[k] = a[j]\n j += 1\n k += 1\n while i < r:\n b[k] = a[i]\n i += 1\n k += 1\n \n while j < q:\n b[k] = a[j]\n j += 1\n k += 1\n for k in range(p, q):\n a[k] = b[k]\n\n\ndef main(n, a):\n d = c\n #{ main program }\n b = []\n for i in range(0, n + 1):\n b += [0]\n t = clock()\n mergesort(1, n, a, b)\n t = clock() - t\n assert a[1:] == sorted(a[1:]), \"List not sorted, error!!!!!!!!!!\"\n return t, c - d","repo_name":"Awarua-/Cosc262_Assignment_Part1","sub_path":"mergesort.py","file_name":"mergesort.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21042207981","text":"import unittest\nimport time\nfrom ComponentErrors import ComponentErrorsEx\nfrom maciErrType import CannotGetComponentEx\nfrom Acspy.Clients.SimpleClient import PySimpleClient\nfrom Antenna import ANT_NORTH\nfrom Management import MNG_TRACK\n\n\nclass GetSetPositionTest(unittest.TestCase):\n \"\"\"Test the DewarPositioner.getPosition() method\"\"\"\n def setUp(self):\n client = PySimpleClient()\n self.positioner = client.getComponent('RECEIVERS/DewarPositioner')\n self.derotator = client.getComponent('RECEIVERS/SRTKBandDerotator')\n self.positioner.setup('KKG')\n time.sleep(1)\n\n def test_notReady(self):\n \"\"\"The DewarPositioner must raise a NotAllowedEx when it's not ready\"\"\"\n self.positioner.park()\n time.sleep(1)\n self.assertRaises(ComponentErrorsEx, self.positioner.getPosition)\n\n def test_value(self):\n \"\"\"Verify it returns the right value\"\"\"\n self.derotator.setPosition(1.5)\n time.sleep(2)\n self.assertAlmostEqual(self.positioner.getPosition(), 1.5, places=1)\n self.positioner.park()\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"discos/discos","sub_path":"Common/Servers/PyDewarPositioner/test/interface/test_get-setPosition.py","file_name":"test_get-setPosition.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"37765332649","text":"from typing import List, Tuple\nimport json\nimport pytest\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom freak import control\nfrom fastapi.testclient import TestClient\n\n\nclass Head(BaseModel):\n activation: str = \"relu\"\n\n\nclass Model(BaseModel):\n hidden_dim: List[int] = [128, 256, 512]\n head: Head = Head()\n\n\nclass Checkpoints(BaseModel):\n every_epochs: int = 2\n save_dir: str = \"checkpoints\"\n\n\nclass State(BaseModel):\n lr: float = 1e-3\n checkpoints: Checkpoints = Checkpoints()\n model: Model = Model()\n\n\n@pytest.fixture(scope=\"function\")\ndef state() -> State:\n return State()\n\n\n@pytest.fixture(scope=\"function\")\ndef state_and_client(state: State) -> Tuple[State, TestClient]:\n app = FastAPI()\n client = TestClient(app)\n\n control(state, app, serve=False)\n\n return state, client\n\n\ndef test_full_state(state_and_client: Tuple[State, TestClient]):\n state, client = state_and_client\n init_state = state.copy(deep=True)\n assert json.loads(state.json()) == client.get(\"/get\").json()\n\n # let's test these references will still work\n # after we update the upper objects\n def get_lr():\n return state.lr\n\n def get_activation():\n return state.model.head.activation\n\n assert get_lr() == 0.001\n assert get_activation() == \"relu\"\n\n new_state = State(\n lr=2.0,\n model=Model(hidden_dim=[256, 512, 1024], head=Head(activation=\"sigmoid\")),\n )\n\n resp = client.patch(\"/set\", json=json.loads(new_state.json()))\n assert resp.status_code == 200\n assert json.loads(new_state.json()) == json.loads(state.json())\n\n assert get_lr() == 2.0\n assert get_activation() == \"sigmoid\"\n\n resp = client.patch(\"/set\", json=json.loads(init_state.json()))\n assert resp.status_code == 200\n assert json.loads(init_state.json()) == json.loads(state.json())\n\n\ndef test_simple_types(state_and_client: Tuple[State, TestClient]):\n state, client = state_and_client\n assert state.lr == float(client.get(\"/get/lr\").text)\n assert state.lr == client.get(\"/get_from_path?path=lr\").json()\n\n resp = client.put(\"/set/lr?value=2.0\")\n assert resp.status_code == 200\n assert state.lr == 2.0\n\n\ndef test_pydantic_types_full_update(state_and_client: Tuple[State, TestClient]):\n state, client = state_and_client\n\n assert json.loads(state.checkpoints.json()) == client.get(\"/get_from_path?path=checkpoints\").json()\n\n resp = client.patch(\"/set/checkpoints\", json={\"every_epochs\": 3, \"save_dir\": \"other_dir\"})\n assert resp.status_code == 200\n assert state.checkpoints.every_epochs == 3\n assert state.checkpoints.save_dir == \"other_dir\"\n\n\ndef test_pydantic_types_partial_update(state_and_client: Tuple[State, TestClient]):\n state, client = state_and_client\n\n assert json.loads(state.checkpoints.json()) == client.get(\"/get_from_path?path=checkpoints\").json()\n\n resp = client.patch(\"/set/checkpoints\", json={\"every_epochs\": 3})\n assert resp.status_code == 200\n assert state.checkpoints.every_epochs == 3\n assert state.checkpoints.save_dir == \"checkpoints\"\n\n # check the update doesn't revert other fields to default values\n resp = client.patch(\"/set/checkpoints\", json={\"save_dir\": \"other_dir\"})\n assert resp.status_code == 200\n assert state.checkpoints.every_epochs == 3\n assert state.checkpoints.save_dir == \"other_dir\"\n\n # check the inner field update doesn't affect outer fields\n resp = client.patch(\"/set/model\", json={\"hidden_dim\": [256, 512, 1024]})\n assert resp.status_code == 200\n assert state.model.hidden_dim == [256, 512, 1024]\n\n assert state.model.head.activation == \"relu\"\n resp = client.patch(\"/set/model/head\", json={\"activation\": \"sigmoid\"})\n assert resp.status_code == 200\n resp = client.put(\"/set/lr?value=10.\")\n assert resp.status_code == 200\n assert state.model.head.activation == \"sigmoid\"\n assert state.model.hidden_dim == [256, 512, 1024]\n assert state.lr == 10.0\n resp = client.put(\"/set/model/head/activation?value=silu\")\n assert resp.status_code == 200\n assert state.model.head.activation == \"silu\"\n assert state.model.hidden_dim == [256, 512, 1024]\n\n\ndef test_reset(state_and_client: Tuple[State, TestClient]):\n state, client = state_and_client\n init_state = state.copy(deep=True)\n new_state = State(\n lr=2.0,\n model=Model(hidden_dim=[256, 512, 1024], head=Head(activation=\"sigmoid\")),\n )\n\n client.patch(\"/set\", json=json.loads(new_state.json()))\n assert state != init_state\n resp = client.delete(\"/reset\")\n assert resp.status_code == 200\n assert init_state == state\n","repo_name":"danielgafni/freak","sub_path":"tests/test_freak.py","file_name":"test_freak.py","file_ext":"py","file_size_in_byte":4625,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"5373547093","text":"# -*- coding: utf-8 -*-\n\nfrom setuptools import setup, find_packages\nfrom pathlib import Path\n\nhere = Path(__file__).parent.resolve()\n\nwith open(here/'README.md') as fh:\n long_description = fh.read()\n\nsetup(\n name='cellular_automata',\n version='1.0.1',\n author='Greg Sotiropoulos',\n author_email='greg.sotiropoulos@gmail.com',\n description='Cellular Automata (Game of Life) simulator and GUI',\n long_description=long_description,\n long_description_content_type='text/markdown',\n keywords=[\n 'cellular',\n 'automaton',\n 'game of life',\n 'conway',\n 'algorithms',\n 'games'\n ],\n url='https://github.com/gregsotiropoulos/cellular_automata',\n packages=find_packages(),\n classifiers=[\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Programming Language :: Python :: 3.9',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n 'Topic :: Games/Entertainment :: Life Games',\n ],\n python_requires='>=3.6',\n install_requires=[\n 'numpy != 1.19.4',\n 'scipy',\n 'matplotlib',\n 'keyboard'\n ],\n package_data={\n '': ['LICENSE', 'README.md'],\n # package name : [filename1, filename2, ...]\n 'cellular_automata': ['glider.png']\n },\n project_urls={\n 'Source': 'https://github.com/gregsotiropoulos/cellular_automata'\n }\n)\n","repo_name":"GregSotiropoulos/cellular_automata","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74424880697","text":"# encoding:utf-8\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\n__all__ = ['BasicBlock', 'FuseBlock', 'ConvBNReLU', 'CenterBlock', 'FCNHead', 'IRB_Block', 'customized_module']\n\n\nclass BasicBlock(nn.Module):\n def __init__(self, inplanes, planes, upsample=False):\n super(BasicBlock, self).__init__()\n self.upsample = upsample\n self.CBR1 = nn.Sequential(nn.Conv2d(inplanes, planes, kernel_size=3, stride=1, padding=1, bias=False),\n nn.BatchNorm2d(planes),\n nn.ReLU(inplace=True))\n\n if not upsample:\n self.conv2 = nn.Sequential(nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False),\n nn.BatchNorm2d(planes))\n if planes!= inplanes:\n self.up = nn.Sequential(nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False),\n nn.BatchNorm2d(planes))\n else:\n self.up = None\n else:\n self.conv2=nn.Sequential(nn.ConvTranspose2d(planes, planes, kernel_size=2, stride=2, padding=0, bias=False),\n nn.BatchNorm2d(planes))\n self.up = nn.Sequential(nn.ConvTranspose2d(inplanes, planes, kernel_size=2, stride=2, padding=0, bias=False),\n nn.BatchNorm2d(planes))\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self, x):\n if self.up is not None:\n identity = self.up(x)\n else:\n identity = x\n\n out = self.CBR1(x)\n out = self.conv2(out)\n out += identity\n out = self.relu(out)\n\n return out\n\n\ndef customized_module(info, feats):\n module_dict = {\n 'rbb': BasicBlock,\n 'luu': LearnedUpUnit,\n 'irb': IRB_Block,\n }\n # Format1: 'xxx[a->b]', i.e. 'module[in_feats->out_feats]'\n # Format2: 'xxx(a)', i.e. 'module(feats)'\n module_name = info[:3]\n assert module_name in module_dict\n # print(info)\n if info.find('(') != -1:\n return module_dict[module_name]((int(info[4]) * feats // 2))\n elif info.find('[') != -1:\n return module_dict[module_name]((int(info[4]) * feats // 2), (int(info[7]) * feats // 2))\n else:\n raise ValueError('Invalid customized module format: %s' % info)\n\n\nclass IRB_Block(nn.Module):\n def __init__(self, in_feats, out_feats=None, act='idt', expand_ratio=6):\n super().__init__()\n mid_feats = round(in_feats * expand_ratio)\n out_feats = in_feats if out_feats is None else out_feats\n act_layer = nn.Identity() if act == 'idt' else nn.ReLU6(inplace=True)\n self.idt = (in_feats == out_feats)\n self.irb = nn.Sequential(\n # point-wise conv\n nn.Conv2d(in_feats, mid_feats, kernel_size=1, stride=1, padding=0, bias=False),\n nn.BatchNorm2d(mid_feats),\n nn.ReLU6(inplace=True),\n # depth-wise conv\n nn.Conv2d(mid_feats, mid_feats, kernel_size=3, stride=1, padding=1, groups=mid_feats, bias=False),\n nn.BatchNorm2d(mid_feats),\n nn.ReLU6(inplace=True),\n # point-wise conv\n nn.Conv2d(mid_feats, out_feats, kernel_size=1, stride=1, padding=0, bias=False),\n nn.BatchNorm2d(out_feats),\n act_layer\n )\n\n def forward(self, x):\n return (x + self.irb(x)) if self.idt else self.irb(x)\n\n\nclass LearnedUpUnit(nn.Module):\n def __init__(self, in_feats):\n super().__init__()\n self.up = nn.UpsamplingNearest2d(scale_factor=2)\n self.dep_conv = nn.Conv2d(in_feats, in_feats, kernel_size=3, stride=1, padding=1, groups=in_feats, bias=False)\n\n def forward(self, x):\n x = self.up(x)\n x = self.dep_conv(x)\n return x\n\n\nclass ConvBNReLU(nn.Module):\n\n def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):\n super(ConvBNReLU, self).__init__()\n self.conv = nn.Conv2d(in_chan, out_chan, kernel_size = ks, stride = stride, padding = padding, bias = False)\n self.bn = nn.BatchNorm2d(out_chan)\n self.relu = nn.ReLU(inplace=True)\n self.init_weight()\n\n def forward(self, x):\n x = self.conv(x)\n x = self.bn(x)\n x = self.relu(x)\n return x\n\n def init_weight(self):\n for ly in self.children():\n if isinstance(ly, nn.Conv2d):\n nn.init.kaiming_normal_(ly.weight, a=1)\n if not ly.bias is None: nn.init.constant_(ly.bias, 0)\n\n\nclass UpSample(nn.Module):\n\n def __init__(self, n_chan, factor=2):\n super(UpSample, self).__init__()\n out_chan = n_chan * factor * factor\n self.proj = nn.Conv2d(n_chan, out_chan, 1, 1, 0)\n self.up = nn.PixelShuffle(factor)\n self.init_weight()\n\n def forward(self, x):\n feat = self.proj(x)\n feat = self.up(feat)\n return feat\n\n def init_weight(self):\n nn.init.xavier_normal_(self.proj.weight, gain=1.)\n\n\nclass FuseBlock(nn.Module):\n def __init__(self, in_chs, out_ch):\n # in_chs=[g_ch, x_ch]: g_ch 为深层特征的channel数， x_ch is num of channels for features from encoder\n super().__init__()\n assert len(in_chs)==2, 'provide num of channels for both inputs'\n g_ch, x_ch = in_chs\n self.g_rcu0 = BasicBlock(g_ch, g_ch)\n self.x_rcu0 = BasicBlock(x_ch, x_ch)\n\n self.g_rcu1 = BasicBlock(g_ch, out_ch, upsample=True)\n self.x_rcu1 = BasicBlock(x_ch, out_ch, upsample=False)\n\n self.g_conv2 = nn.Conv2d(out_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=False)\n self.x_conv2 = nn.Conv2d(out_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=False)\n\n self.relu = nn.ReLU(inplace=True)\n\n def forward(self, g, x):\n g = self.g_rcu1(self.g_rcu0(g))\n x = self.x_rcu1(self.x_rcu0(x))\n g = self.g_conv2(g)\n x = self.x_conv2(x)\n out = self.relu(g+x)\n return out\n\n\nclass CenterBlock(nn.Module):\n def __init__(self, in_ch, out_ch):\n super().__init__()\n self.rcu = nn.Sequential(BasicBlock(in_ch, in_ch),\n BasicBlock(in_ch, out_ch))\n\n def forward(self, g):\n out = self.rcu(g)\n return out\n\n\n\n\nclass Identity(nn.Module):\n def __init__(self):\n super(Identity, self).__init__()\n\n def forward(self, x):\n return x\n\n\nclass GlobalPooling(nn.Module):\n def __init__(self, in_channels, out_channels, norm_layer, up_kwargs):\n super(GlobalPooling, self).__init__()\n self._up_kwargs = up_kwargs\n self.gap = nn.Sequential(nn.AdaptiveAvgPool2d(1),\n nn.Conv2d(in_channels, out_channels, 1, bias=False),\n norm_layer(out_channels),\n nn.ReLU(True))\n\n def forward(self, x):\n _, _, h, w = x.size()\n pool = self.gap(x)\n return F.interpolate(pool, (h, w), **self._up_kwargs)\n\n\nclass ConcurrentModule(nn.ModuleList):\n r\"\"\"Feed to a list of modules concurrently. The outputs of the layers are concatenated at channel dimension.\n\n Args: modules (iterable, optional): an iterable of modules to add\n \"\"\"\n def __init__(self, modules=None):\n super(ConcurrentModule, self).__init__(modules)\n\n def forward(self, x):\n outputs = []\n for layer in self:\n outputs.append(layer(x))\n return torch.cat(outputs, 1)\n\n\nclass FCNHead(nn.Module):\n def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d, up_kwargs={'mode': 'bilinear', 'align_corners': True}, with_global=False, d=2):\n super(FCNHead, self).__init__()\n inter_channels = in_channels // d\n self._up_kwargs = up_kwargs\n if with_global:\n self.conv5 = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),\n norm_layer(inter_channels),\n nn.ReLU(),\n ConcurrentModule([\n Identity(),\n GlobalPooling(inter_channels, inter_channels,\n norm_layer, self._up_kwargs),\n ]),\n nn.Dropout(0.1, False),\n nn.Conv2d(2 * inter_channels, out_channels, 1))\n else:\n self.conv5 = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),\n norm_layer(inter_channels),\n nn.ReLU(),\n nn.Dropout(0.1, False),\n nn.Conv2d(inter_channels, out_channels, 1))\n\n def forward(self, x):\n return self.conv5(x)\n\n","repo_name":"TeamOfProfGuo/Greene_Code","sub_path":"encoding/nn/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":9075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38191135525","text":"from datetime import datetime\nimport math\nimport time\n\nimport tensorflow.python.platform\nimport tensorflow as tf\nfrom tensorflow.python.ops import array_ops\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.app.flags.DEFINE_integer('batch_size', 80,\n \"\"\"Batch size.\"\"\")\ntf.app.flags.DEFINE_integer('num_batches', 100,\n \"\"\"Number of batches to run.\"\"\")\n\nparameters = []\n\nconv_counter = 1\npool_counter = 1\naffine_counter = 1\n\ndef _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType):\n global conv_counter\n global parameters\n name = 'conv' + str(conv_counter)\n conv_counter += 1\n with tf.name_scope(name) as scope:\n kernel = tf.Variable(tf.truncated_normal([kH, kW, nIn, nOut],\n dtype=tf.float32,\n stddev=1e-1), name='weights')\n conv = tf.nn.conv2d(inpOp, kernel, [1, dH, dW, 1], padding=padType)\n biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),\n trainable=True, name='biases')\n bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())\n conv1 = tf.nn.relu(bias, name=scope)\n parameters += [kernel, biases]\n return conv1\n\ndef _affine(inpOp, nIn, nOut):\n global affine_counter\n global parameters\n name = 'affine' + str(affine_counter)\n affine_counter += 1\n with tf.name_scope(name) as scope:\n kernel = tf.Variable(tf.truncated_normal([nIn, nOut],\n dtype=tf.float32,\n stddev=1e-1), name='weights')\n biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),\n trainable=True, name='biases')\n affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name)\n parameters += [kernel, biases]\n return affine1\n\ndef _mpool(inpOp, kH, kW, dH, dW, padding):\n global pool_counter\n global parameters\n name = 'pool' + str(pool_counter)\n pool_counter += 1\n return tf.nn.max_pool(inpOp,\n ksize=[1, kH, kW, 1],\n strides=[1, dH, dW, 1],\n padding=padding,\n name=name)\n\ndef _apool(inpOp, kH, kW, dH, dW, padding):\n global pool_counter\n global parameters\n name = 'pool' + str(pool_counter)\n pool_counter += 1\n return tf.nn.avg_pool(inpOp,\n ksize=[1, kH, kW, 1],\n strides=[1, dH, dW, 1],\n padding=padding,\n name=name)\n\ndef _inception(inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2):\n conv1 = _conv(inp, inSize, o1s, 1, 1, 1, 1, 'SAME')\n\n conv3_ = _conv(inp, inSize, o2s1, 1, 1, 1, 1, 'SAME')\n conv3 = _conv(conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME')\n\n conv5_ = _conv(inp, inSize, o3s1, 1, 1, 1, 1, 'SAME')\n conv5 = _conv(conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME')\n\n pool_ = _mpool(inp, o4s1, o4s1, 1, 1, 'SAME')\n pool = _conv(pool_, inSize, o4s2, 1, 1, 1, 1, 'SAME')\n\n incept = array_ops.concat(3, [conv1, conv3, conv5, pool])\n return incept\n\n\ndef loss(logits, labels):\n batch_size = tf.size(labels)\n labels = tf.expand_dims(labels, 1)\n indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)\n concated = tf.concat(1, [indices, labels])\n onehot_labels = tf.sparse_to_dense(\n concated, tf.pack([batch_size, 1000]), 1.0, 0.0)\n cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,\n onehot_labels,\n name='xentropy')\n loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')\n return loss\n\ndef inference(images):\n conv1 = _conv (images, 3, 64, 7, 7, 2, 2, 'SAME')\n pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME')\n conv2 = _conv (pool1, 64, 64, 1, 1, 1, 1, 'SAME')\n conv3 = _conv (conv2, 64, 192, 3, 3, 1, 1, 'SAME')\n pool3 = _mpool(conv3, 3, 3, 2, 2, 'SAME')\n\n incept3a = _inception(pool3, 192, 64, 96, 128, 16, 32, 3, 32)\n incept3b = _inception(incept3a, 256, 128, 128, 192, 32, 96, 3, 64)\n pool4 = _mpool(incept3b, 3, 3, 2, 2, 'SAME')\n incept4a = _inception(pool4, 480, 192, 96, 208, 16, 48, 3, 64)\n incept4b = _inception(incept4a, 512, 160, 112, 224, 24, 64, 3, 64)\n incept4c = _inception(incept4b, 512, 128, 128, 256, 24, 64, 3, 64)\n incept4d = _inception(incept4c, 512, 112, 144, 288, 32, 64, 3, 64)\n incept4e = _inception(incept4d, 528, 256, 160, 320, 32, 128, 3, 128)\n pool5 = _mpool(incept4e, 3, 3, 2, 2, 'SAME')\n incept5a = _inception(pool5, 832, 256, 160, 320, 32, 128, 3, 128)\n incept5b = _inception(incept5a, 832, 384, 192, 384, 48, 128, 3, 128)\n pool6 = _apool(incept5b, 7, 7, 1, 1, 'VALID')\n\n resh1 = tf.reshape(pool6, [-1, 1024])\n affn1 = _affine(resh1, 1024, 1000)\n\n return affn1\n\n","repo_name":"lilac/facenet","sub_path":"facenet/tmp/googlenet.py","file_name":"googlenet.py","file_ext":"py","file_size_in_byte":4989,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"36439595739","text":"import pygame, sys, random\nimport pygame.event as GAME_EVENT\nimport pygame.locals as GAME_GLOBALS\n\npygame.init()\n\nclock = pygame.time.Clock()\n\nwindowwidth = 400\nwindowheight = 400\nrectX = 10.0\nrectY = 10.0\nrectEndX = 200.0\nrectEndY = 100.0\n#middle = (windowwidth/2, windowheight/2)\nrectXmv = 1\nrectYmv = 1\n\ndef quitgame():\n\tpygame.quit()\n\tsys.exit()\n\nwindow = pygame.display.set_mode((windowwidth,windowheight)) #width & height . could also write (windowwidth,windowheight)\ndirection = 1\n#a while loop is done indefinately or until the condition is no longer true. For loops continue until list is empty.\n\nwhile True: #true is always true so it will run indefinately.\n\twindow.fill((0,0,0))\n\tpygame.draw.rect(window, (232,16,131), (rectX,rectY,rectEndX,rectEndY)) #rect(Surface, color, Rect, width=0). color tuple -(red, green, blue) (position, size)\n\tpygame.draw.rect(window, (16,232,16), (0,0,100,100))\n\tpygame.draw.rect(window, (232,124,16), (0,100,100,100))\n\tpygame.draw.rect(window, (232,228,16), (0,200,100,100))\n\tpygame.draw.rect(window, (19,214,214), (0,300,100,100))\n\tpygame.draw.rect(window, (16,232,16), (400,300,100,100))\n\tpygame.draw.lines(window, (232,124,16), False, [(300,100), (350,50), (400,100)], 3) #false = not a closed line, [(how far from left, how far from top)], thickness of line\n\tpygame.draw.lines(window, (232,124,16), False, [(325,75), (375,75)], 3) #these two lines are meant to make an A.\n\tpygame.draw.circle(window, (164,16,232), ((windowwidth/2), (windowheight/2)-40), 30)\n\tpygame.draw.circle(window, (164,16,232), ((windowwidth/2)+40, (windowheight/2)), 30)\n\tpygame.draw.circle(window, (164,16,232), ((windowwidth/2), (windowheight/2)+40), 30)\n\tpygame.draw.circle(window, (164,16,232), ((windowwidth/2)-40, windowheight/2), 30)\n\tpygame.draw.circle(window, (232,228,16), (windowwidth/2, windowheight/2), 30)\n\tif rectX > windowwidth:\n\t\tdirection = -1\n\tif rectX < 0:\n\t\tdirection = 1\n\trectX += direction * random.randint(0,10) #reassign\n\tif rectY > windowheight:\n\t\tdirection = -1\n\tif rectX < 0:\n\t\tdirection = 1\n\trectY += direction * random.randint(0,10)\n\n\t\n\tfor event in GAME_EVENT.get(): #to allopw it to quit properly\n\t\tif event.type == GAME_GLOBALS.QUIT:\n\t\t\tquitgame()\n\t\tif event.type == pygame.KEYDOWN:\n\t\t\tif event.key == pygame.K_ESCAPE:\n\t\t\t\tquitgame()\n\tclock.tick(60)\n\tpygame.display.update()\n\n#save in retrogaming folder (not the VE folder) - the one from git hub.\n#dont save as library names\n\n\n","repo_name":"abigailkubrick/retro2.0","sub_path":"drawing2.py","file_name":"drawing2.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5663015223","text":"class Solution:\n def maxScoreWords(self, words: List[str], letters: List[str], score: List[int]) -> int:\n c = Counter(letters)\n # technically there are only 26 letters, so this should be constant time\n res = 0\n maps = [Counter(x) for x in words]\n\n n = len(words)\n def dfs(i, cur):\n nonlocal res\n if i == n:\n res = max(res,cur )\n return\n # should make\n can = True\n for ch in maps[i]:\n\n if maps[i][ch] > c[ch]:\n can = False\n if can:\n s = 0\n for ch in maps[i]:\n s += maps[i][ch] * score[ord(ch) - ord('a')]\n c[ch] -= maps[i][ch]\n dfs(i + 1, cur + s)\n for ch in maps[i]:\n c[ch] += maps[i][ch]\n # should not make\n dfs(i + 1, cur)\n return\n\n dfs(0, 0)\n\n return res","repo_name":"KaiKaizxc/Data-stuctures-and-algo","sub_path":"Dynamic Programming/1255. Maximum Score Words Formed by Letters.py","file_name":"1255. Maximum Score Words Formed by Letters.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"74283337977","text":"import transformers\n\nimport textattack\n\nmodel_path = \"distilbert-base-uncased-finetuned-sst-2-english\"\n\ntokenizer = transformers.AutoTokenizer.from_pretrained(model_path)\nmodel = transformers.AutoModelForSequenceClassification.from_pretrained(model_path)\n\nmodel = textattack.models.wrappers.HuggingFaceModelWrapper(model, tokenizer)\n\ndataset = textattack.datasets.HuggingFaceDataset(\n \"glue\", subset=\"sst2\", split=\"train\", shuffle=False\n)\n","repo_name":"QData/TextAttack","sub_path":"tests/sample_inputs/sst_model_and_dataset.py","file_name":"sst_model_and_dataset.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":2559,"dataset":"github-code","pt":"22"}
+{"seq_id":"33556868168","text":"from contextlib import closing\n\nfrom rpcclient.client_factory import create_tcp_client\n\n\ndef test_new_process_per_client(client):\n with closing(create_tcp_client('127.0.0.1')) as client2:\n assert client.pid != client2.pid\n client_process = client.processes.get_by_pid(client.pid)\n client2_process = client2.processes.get_by_pid(client2.pid)\n assert client_process.ppid == client2_process.ppid\n","repo_name":"doronz88/rpc-project","sub_path":"src/rpcclient/tests/test_worker_processes.py","file_name":"test_worker_processes.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"22"}
+{"seq_id":"43312774390","text":"#import files\nimport matplotlib.pyplot as plt\nfrom shapely.geometry import Polygon,LineString\nimport random\n\n\n\n#global variables\ncoords1=[(0,4),(0.25,3.75),(0.25,0.25),(1,0.25),(1,3),(2,3),(2,2.25),(1.5,2.25),(1.5,0.25),(3.75,0.25),(4,0),(0,0)]\ncoords2=[(0,4),(0.25,3.75),(2.5,3.75),(2.5,2),(3,2),(3,3.75),(3.75,3.75),(3.75,0),(4,0),(4,4)]\npoly1=Polygon(coords1)\npoly2=Polygon(coords2)\n\nLI_NODES = []\nLI_CONNECT = []\nlimit=5000\n\nstart_coords=(0.5,0.5)\nend_coords=(3.5,3.5)\n\n\nisJoined = False\n\nfig,axs=plt.subplots()\n\n\naxs.fill([x[0] for x in coords1],[y[1] for y in coords1],alpha=1,fc='#000000',ec='none')\naxs.fill([x[0] for x in coords2],[y[1] for y in coords2],alpha=1,fc='#000000',ec='none')\n\n\n\nplt.xticks([(float)(i/2) for i in range(0,9)])\nplt.yticks([(float)(j/2) for j in range(0,9)])\n\n\n\n\n#class for node\nclass Node:\n def __init__(self,xcoord,ycoord,weight,connect,marker,markersize):\n self.xcoord=xcoord\n self.ycoord=ycoord\n self.weight=weight\n self.connect=connect\n self.marker=marker\n self.markersize=markersize\n plt.plot([self.xcoord],[self.ycoord],self.marker,markersize = self.markersize)\n\n def Los(self):\n global isJoined\n line=LineString([[self.xcoord,self.ycoord],[end_coords[0],end_coords[1]]])\n if not (line.intersects(poly1) or line.intersects(poly2)):\n isJoined=True\n plt.plot([self.xcoord,end_coords[0]],[self.ycoord,end_coords[1]],'go-')\n\nstart = Node(start_coords[0], start_coords[1], 0, 0, 'bo', 20)\nend = Node(end_coords[0], end_coords[1], 0, 0, 'bo', 20)\n\ndef dist(Node, x, y):\n return ((Node.xcoord-x)**2+(Node.ycoord-y)**2)**(0.5)\n\nLI_NODES.append(start)\n\ncv=0\nwhile (cv', '', '', ' ']\r\n # special_words = []\r\n int_to_vocab = {idx: word for idx, word in enumerate(special_words + words)}\r\n vocab_to_int = {word: idx for idx, word in int_to_vocab.items()}\r\n # print(int_to_vocab)\r\n return int_to_vocab, vocab_to_int\r\n\r\ndef mkdir_p(path):\r\n try:\r\n os.makedirs(path)\r\n except OSError as exc: # Python >2.5\r\n if exc.errno == errno.EEXIST and os.path.isdir(path):\r\n pass\r\n else:\r\n raise\r\n\r\n\r\ndef find_files(directory, pattern):\r\n for root, dirs, files in os.walk(directory):\r\n for basename in files:\r\n if fnmatch.fnmatch(basename, pattern):\r\n filename = os.path.join(root, basename)\r\n yield filename\r\n\r\n\r\ndef _int64_feature(value):\r\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\r\n\r\n\r\ndef _bytes_feature(value):\r\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\r\n\r\n\r\ndef _float_feature(value):\r\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\r\n\r\n\r\ndef _int64_feature_list(values):\r\n return tf.train.FeatureList(feature=[_int64_feature(v) for v in values])\r\n\r\n\r\ndef _bytes_feature_list(values):\r\n return tf.train.FeatureList(feature=[_bytes_feature(v) for v in values])\r\n\r\n\r\ndef _to_sequence_example(sub_mouth_frames, audio_frames, sub_labels):\r\n '''Build a SequenceExample proto for an video-label pair.\r\n Args:\r\n video_path: Path of video data.\r\n label_path: Path of label data.\r\n vocab: A Vocabulary object.\r\n Returns:\r\n A SequenceExample proto.\r\n '''\r\n\r\n labels = sub_labels\r\n label_len = len(labels)\r\n mouth_frames = sub_mouth_frames\r\n\r\n #print(\"mouth_frames.shape\",mouth_frames )\r\n mouth_frames_byte = [mouth_frame.tobytes() for mouth_frame in mouth_frames]\r\n audio_frames_byte = [audio_frame.tobytes() for audio_frame in audio_frames]\r\n\r\n example = tf.train.SequenceExample(\r\n # context 用来放置非序列化的部分\r\n context=tf.train.Features(feature={\r\n \"words_num\": _int64_feature(label_len)\r\n\r\n }),\r\n # feature_lists用来放置变长序列\r\n feature_lists=tf.train.FeatureLists(feature_list={\r\n \"mouth_frames\": _bytes_feature_list(mouth_frames_byte),\r\n \"audio_frames\": _bytes_feature_list(audio_frames_byte),\r\n \"labels\": _int64_feature_list(labels)\r\n })\r\n )\r\n return example\r\n\r\ndef snr_factor(signal, noise, snr_db):\r\n s = signal\r\n n = noise\r\n\r\n if s.size != n.size:\r\n raise Exception('signal and noise must have the same length')\r\n\r\n eq = np.sqrt(np.var(s) / np.var(n))\r\n factor = eq * (10 ** (-snr_db / 20.0))\r\n\r\n return factor\r\n\r\n\r\nSOURCE_PATH = '/data-private/nas/lrs3/trainval'\r\nFACE_PREDICTOR_PATH = '/home/xub/src/audio_video_process_to_tfrecord/shape_predictor_68_face_landmarks.dat'\r\n\r\nif __name__ == '__main__':\r\n #############################video process###################\r\n gg=1\r\n j = 0\r\n md = 0\r\n sess = tf.Session()\r\n\r\n noise_path = \"/data-private/nas/lrw/noisy_audio\"\r\n int_to_vocab, vocab_to_int = _extract_charater_vocab(dic_path)\r\n\r\n filename = (\"/data-private/nas/lrs3/trainval/data2.tfrecords\" )\r\n writer = tf.python_io.TFRecordWriter(filename)\r\n\r\n for filepath in find_files(SOURCE_PATH, '*.mp4'):\r\n\r\n try:\r\n filepath_wo_ext = os.path.splitext(filepath)[0].split('\\\\')[-1]\r\n\r\n # process video to frames\r\n print(\"process video to frames\")\r\n video = Video(vtype='face', face_predictor_path=FACE_PREDICTOR_PATH).from_video(filepath)\r\n print(\"jjjj: {}\".format(filepath_wo_ext))\r\n\r\n # process wav\r\n print(\"process wav\")\r\n filepath_no_ext = os.path.splitext(filepath)[0]\r\n wav_path = filepath_no_ext + \".wav\"\r\n\r\n y, sr = librosa.load(wav_path, sr=None)\r\n D = np.abs(librosa.core.stft(y, n_fft=640, hop_length=160, win_length=640, window='hann'))\r\n noise_txt_path = noise_path + \"/{}\".format(\"noise.txt\")\r\n n_D = np.loadtxt(noise_txt_path)\r\n b = int((D.shape[1] - n_D.shape[1]) / 2)\r\n a = D.shape[1] - n_D.shape[1] - b\r\n n_D = np.pad(n_D, ((0, 0), (a, b)), 'constant')\r\n\r\n D_T = (librosa.amplitude_to_db(D, ref=np.max)).T\r\n\r\n minMax = MinMaxScaler()\r\n audio_feature_std = minMax.fit_transform(D_T)\r\n print(\"original audio_feature shape\", audio_feature_std.shape)\r\n\r\n length = int((audio_feature_std.shape)[0])\r\n\r\n original_feature = audio_feature_std\r\n if length == 4 * int(length / 4):\r\n loop_num = int(length / 4)\r\n\r\n else:\r\n loop_num = 1 + int(length / 4)\r\n\r\n audio_feature = np.zeros((loop_num, 1284), dtype=np.float32)\r\n\r\n PAD = np.zeros((1, 321), dtype=np.float32)\r\n if length == 4 * int(length / 4):\r\n for i in range(loop_num):\r\n audio_feature[i, :] = np.hstack((original_feature[4 * i], original_feature[4 * i + 1],\r\n original_feature[4 * i + 2], original_feature[4 * i + 3]))\r\n else:\r\n for i in range(loop_num):\r\n if i != loop_num - 1:\r\n audio_feature[i, :] = np.hstack((original_feature[4 * i], original_feature[4 * i + 1],\r\n original_feature[4 * i + 2], original_feature[4 * i + 3]))\r\n else:\r\n if length - 4 * int(length / 4) == 1:\r\n audio_feature[i, :] = np.hstack((original_feature[4 * i], PAD[0], PAD[0], PAD[0]))\r\n\r\n elif length - 4 * int(length / 4) == 2:\r\n audio_feature[i, :] = np.hstack(\r\n (original_feature[4 * i], original_feature[4 * i + 1], PAD[0], PAD[0]))\r\n\r\n elif length - 4 * int(length / 4) == 3:\r\n audio_feature[i, :] = np.hstack(\r\n (original_feature[4 * i], original_feature[4 * i + 1], original_feature[4 * i + 2],\r\n PAD[0]))\r\n\r\n #print(\"original audio feature\", audio_feature.shape)\r\n audio_feature_frames = np.zeros((video.mouth.shape[0], 4*321), dtype=np.float32)\r\n if audio_feature.shape[0] > video.mouth.shape[0]:\r\n for fff in range(video.mouth.shape[0]):\r\n audio_feature_frames[fff] = audio_feature[fff]\r\n else:\r\n audio_feature_frames = audio_feature\r\n print(\"final audio_feature\", audio_feature_frames.shape)\r\n # process labels\r\n print(\"process labels\")\r\n\r\n txt_path = filepath_no_ext + \".txt\"\r\n # print \"txt path \",txt_path\r\n with open(txt_path, 'r') as f:\r\n txt = f.readlines()\r\n\r\n # whole words in one sample\r\n whole_info = txt[0]\r\n print(\"whole txt info\", whole_info)\r\n\r\n # the length of whole words in one sample\r\n role = whole_info.split(\":\")\r\n line_spoke = role[1].strip()\r\n\r\n length = len(line_spoke)\r\n print('word', line_spoke)\r\n\r\n txt_labels = [vocab_to_int[token] for token in line_spoke]\r\n print('text label', txt_labels)\r\n sequence_example = _to_sequence_example(video.mouth, audio_feature_frames, txt_labels)\r\n\r\n if video.missing_Detect == True:\r\n md += 1\r\n print(\"the %dth missing detection number\" % md)\r\n else:\r\n writer.write(sequence_example.SerializeToString())\r\n j += 1\r\n print(\"the %dth sample number\" % j)\r\n except:\r\n print(\"the %dth failed\" % gg, filepath)\r\n gg += 1\r\n continue\r\n\r\n #############################txt label process##################\r\n writer.close()\r\n\r\n","repo_name":"JackSyu/Discriminative-Multi-modality-Speech-Recognition","sub_path":"preprocessing/dataset_tfrecord_trainval.py","file_name":"dataset_tfrecord_trainval.py","file_ext":"py","file_size_in_byte":9189,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"22"}
+{"seq_id":"32308958257","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 17 17:24:18 2021\r\n\r\n@author: Brayan\r\n\"\"\"\r\n\r\n#Importanding bibliotecas\r\n\r\nimport numpy as np\r\nimport math\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\n# Importando datos de tracker\r\n\r\n#Datos servilleta arrugada del documento \"Servilleta Comprimida, Datos (t,y,v,a).xlsx\"\r\ndatos_sc = pd.read_excel(r\"C:\\Users\\braya\\Documents\\Brayan\\Introducción a la física\\Código - Fricción en fluidos\\Servilleta Comprimida, Datos (t,y,v,a).xlsx\", usecols=(\"F:H\"), skiprows = 3, nrows = 19)\r\n#Datos servilleta extendida del documento \"Aceleraciones servilleta extendida.xlsx\"\r\ndatos_se = pd.read_excel(r\"C:\\Users\\braya\\Documents\\Brayan\\Introducción a la física\\Código - Fricción en fluidos\\Aceleraciones servilleta extendida.xlsx\", usecols=(\"A:BB\"), skiprows = 8)\r\n \r\n#Definición de variables para la servilleta arrugada\r\n\r\nVi = 0\r\ng = 9.78\r\nh = 2\r\nm = 0.0007\r\nTmax = math.sqrt((2*h)/g) #Tiempo máximo de caída\r\n\r\n\r\n#Funciones para calcular la velocidad y la posición en función del tiempo\r\n\r\ndef Vs(t):\r\n V1 = g*t\r\n return V1\r\n\r\ndef Y(t):\r\n Y1 = h-(g * t**2)/2\r\n return Y1\r\n\r\ntime = np.arange(0,Tmax+0.01,0.01) # Tiempos\r\n\r\ncv = Vs(time)\r\ncy = Y(time)\r\n\r\n#fuentes bonitas para las gráficas\r\n\r\nfont = {\"family\" : \"serif\",\r\n \"color\" : \"darkred\",\r\n \"weight\" : \"normal\",\r\n \"size\" : 16,\r\n }\r\n\r\nfont2 = {\"family\" : \"serif\",\r\n \"color\" : \"black\",\r\n \"weight\" : \"bold\",\r\n \"size\" : 12,\r\n }\r\n\r\n#Gráficas teóricas de caída libre\r\n#Velocidad vs tiempo\r\nplt.figure()\r\nplt.plot(time,cv, \"k-\", linewidth = 2, markersize = 10)\r\nplt.ylabel(\"Velocidad (m/s)\", fontdict = font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict = font)\r\nplt.title(\"Servilleta Arrugada\", fontdict = font)\r\nplt.text(-0.02, 5.7,\"$V_{f} = 6.25 m/s$\", fontdict = font2)\r\nplt.text(0.495, 0, \"$T_{max} = 0.639s$\", fontdict = font2)\r\nplt.grid()\r\nplt.show()\r\n\r\n#Posición vs tiempo\r\nplt.figure()\r\nplt.plot(time,cy, \"k-\", linewidth= 2, markersize=10)\r\nplt.ylabel(\"Altura (m)\", fontdict = font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict = font)\r\nplt.title(\"Servilleta Arrugada\", fontdict = font)\r\nplt.grid()\r\nplt.show()\r\n\r\n#Datos obtenidos del experimento: servilleta arrugada\r\n\r\ndatos_t_sc = np.array(datos_sc[\"t\"])\r\ndatos_y_sc = np.array(datos_sc[\"y\"])\r\ndatos_v_sc = np.array(datos_sc[\"v\"])\r\n\r\n#Gráfica experimento - servilleta cerrada\r\nplt.figure()\r\nplt.plot(datos_t_sc,datos_v_sc,\"c-\", linewidth = 2, markersize = 10)\r\nplt.grid()\r\nplt.ylabel(\"Velocidad (m/s)\", fontdict = font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict = font)\r\nplt.title(\"Servilleta Arrugada - Experimento\", fontdict = font)\r\nplt.grid()\r\nplt.show()\r\n\r\nplt.figure()\r\nplt.plot(datos_t_sc, datos_y_sc, \"c-\", linewidth = 2, markersize = 10)\r\nplt.grid()\r\nplt.ylabel(\"Altura (m)\", fontdict = font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict = font)\r\nplt.title(\"Servilleta Arrugada\", fontdict = font)\r\nplt.grid()\r\nplt.show()\r\n\r\n#Comparación de Datos\r\n\r\n#Altura vs tiempo - Servilleta Arrugada\r\nplt.figure()\r\nplt.plot(datos_t_sc,datos_y_sc, \"c-\", linewidth = 2, markersize=10, label= \"Experimento\")\r\nplt.plot(time, cy, \"k-\", linewidth = 2, markersize=10, label = \"Simulación\")\r\nplt.legend(loc=\"upper right\")\r\nplt.grid()\r\nplt.ylabel(\"Altura (m)\", fontdict= font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict= font)\r\nplt.title(\"Servilleta Arrugada - Comparación\", fontdict= font)\r\nplt.show()\r\n\r\n#Velocidad vs Tiempo - Servilleta Arrugada\r\n\r\nplt.figure()\r\nplt.plot(datos_t_sc,datos_v_sc, \"c-\", linewidth = 2, markersize=10, label= \"Experimento\")\r\nplt.plot(time, cv, \"k-\", linewidth = 2, markersize=10, label = \"Simulación\")\r\nplt.legend(loc=\"upper left\")\r\nplt.grid()\r\nplt.ylabel(\"Velocidad (v)\", fontdict= font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict= font)\r\nplt.title(\"Servilleta Arrugada - Comparación\", fontdict= font)\r\nplt.show()\r\n\r\n#Cálculo de la varianza\r\n\r\ncv2 = Vs(datos_t_sc)\r\ncy2 = Y(datos_t_sc)\r\nvarianza_sc = 0\r\nvarianza_sc_y = 0\r\n\r\nfor i in range(0,len(datos_v_sc+1)):\r\n var = (datos_v_sc[i] - cv2[i])**2\r\n varianza_sc = varianza_sc + var \r\nvarianza_sc = varianza_sc/len(cv2)\r\nprint(\"Varianza en V para la servilleta cerrada:\", varianza_sc)\r\n\r\nfor i in range(0,len(datos_y_sc+1)):\r\n var = (datos_y_sc[i] - cy2[i])**2\r\n varianza_sc_y = varianza_sc_y + var \r\nvarianza_sc_y = varianza_sc_y/len(cv2)\r\nprint(\"Varianza en X para la servilleta cerrada:\", varianza_sc_y)\r\n\r\n\r\n\r\n#Datos del experimento necesarios para calcular K\r\n\r\nTiempo, Altura, Velocidad = [], [], []\r\nfor i in range (0,11):\r\n n = str(i+1)\r\n Tiempo.append(np.array(datos_se[\"t{}\".format(n)]))\r\n Tiempo[i] = [x for x in Tiempo[i] if np.isnan(x) == False]\r\n Altura.append(np.array(abs(2-datos_se[\"y{}\".format(n)])))\r\n Altura[i] = [x for x in Altura[i] if np.isnan(x) == False]\r\n Velocidad.append(np.array(datos_se[\"v{}\".format(n)]))\r\n Velocidad[i] = [x for x in Velocidad[i] if np.isnan(x) == False]\r\n \r\n#Hallando k\r\n\r\n\r\nK, T, V, X, a = [], [], [], [], []\r\nResist = []\r\n\r\nfor j in range(0,10):\r\n K.append(np.zeros(len(Tiempo[j])))\r\n time2= Tiempo[j][-1]\r\n taza_cambio = time2/len(Tiempo[j])\r\n T.append(np.arange(0, time2, taza_cambio))\r\n V.append(np.zeros(len(T[j])))\r\n X.append(np.zeros(len(T[j])))\r\n a.append([g for i in range(len(T[j]))])\r\n for i in range(2,len(Tiempo[j])):\r\n Delta_vel = Velocidad[j][i]-Velocidad[j][i-1]\r\n Delta_tiempo = Tiempo[j][i]-Tiempo[j][i-1]\r\n a[j][i] = (Delta_vel)/(Delta_tiempo)\r\n K[j][i] = m*(g-a[j][i])/Velocidad[j][i]\r\n k = sum(K[j]/len(K[j]))\r\n print(\"Modelo {} para una k de:\".format(j+1), k)\r\n \r\n Resist.append(k)\r\n for l in range(1,len(T[j])):\r\n a[j][l] = g - ((k)/m)*V[j][l-1]\r\n V[j][l] = V[j][l-1] + (a[j][l]*(T[j][l]-T[j][l-1]))\r\n X[j][l] = (X[j][l-1] + V[j][l] * (T[j][l]-T[j][l-1]) + a[j][l-1]*((T[j][l] - T[j][l-1])**2)/2)\r\n \r\n \r\n \r\n\r\n \r\nplt.figure()\r\nplt.plot(T[0], a[0], \"c.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 1\")\r\nplt.plot(T[1], a[1], \"b.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 2\")\r\nplt.plot(T[2], a[2], \"g.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 3\")\r\nplt.plot(T[3], a[3], \"m.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 4\")\r\nplt.plot(T[4], a[4], \"y.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 5\")\r\nplt.plot(T[5], a[5], \"b.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 6\")\r\nplt.plot(T[6], a[6], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 7\")\r\nplt.plot(T[7], a[7], \"c.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 8\")\r\nplt.plot(T[8], a[8], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 9\")\r\nplt.plot(T[9], a[9], \"m.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 10\")\r\nplt.legend(loc = \"best\", bbox_to_anchor=(0.8, 0.5, 0.5, 0.5))\r\nplt.ylabel(\"Aceleración ($m/s^{2}$)\", fontdict= font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict= font)\r\nplt.title(\"Servilleta Extendida\", fontdict= font)\r\nplt.grid()\r\nplt.show() \r\n\r\nplt.figure()\r\n\r\n# plt.plot(T[0], V[0], \"c.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 1\")\r\n# plt.plot(T[1], V[1], \"b.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 2\")\r\n# plt.plot(T[2], V[2], \"g.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 3\")\r\n# plt.plot(T[3], V[3], \"m.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 4\")\r\nplt.plot(T[4], V[4], \"y-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 5\")\r\nplt.plot(T[5], V[5], \"b-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 6\")\r\n# plt.plot(T[6], V[6], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 7\")\r\nplt.plot(T[7], V[7], \"c-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 8\")\r\nplt.plot(T[8], V[8], \"r-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 9\")\r\nplt.plot(T[9], V[9], \"m-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 10\")\r\nplt.legend(loc = \"best\")\r\nplt.ylabel(\"Velocidad (m/s)\", fontdict= font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict= font)\r\nplt.title(\"Servilleta Extendida\", fontdict= font)\r\nplt.grid()\r\nplt.show()\r\n\r\n\r\nplt.figure()\r\nplt.plot(T[0], 2-X[0], \"c.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 1\")\r\nplt.plot(T[1], 2-X[1], \"b.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 2\")\r\nplt.plot(T[2], 2-X[2], \"g.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 3\")\r\nplt.plot(T[3], 2-X[3], \"m.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 4\")\r\nplt.plot(T[4], 2-X[4], \"y.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 5\")\r\nplt.plot(T[5], 2-X[5], \"b.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 6\")\r\nplt.plot(T[6], 2-X[6], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 7\")\r\nplt.plot(T[7], 2-X[7], \"c.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 8\")\r\nplt.plot(T[8], 2-X[8], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 9\")\r\nplt.plot(T[9], 2-X[9], \"m.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 10\")\r\nplt.legend(loc = \"best\", bbox_to_anchor=(0.8, 0.5, 0.5, 0.5))\r\nplt.ylabel(\"Altura (m/s)\", fontdict= font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict= font)\r\nplt.title(\"Servilleta Extendida\", fontdict= font)\r\nplt.grid()\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\nplt.figure()\r\nplt.plot(Tiempo[0], Velocidad[0], \"c-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 1\")\r\nplt.plot(Tiempo[1], Velocidad[1], \"b-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 2\")\r\nplt.plot(Tiempo[2], Velocidad[2], \"g-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 3\")\r\nplt.plot(Tiempo[3], Velocidad[3], \"m-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 4\")\r\nplt.plot(Tiempo[4], Velocidad[4], \"y-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 5\")\r\nplt.plot(Tiempo[5], Velocidad[5], \"b-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 6\")\r\nplt.plot(Tiempo[6], Velocidad[6], \"r-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 7\")\r\nplt.plot(Tiempo[7], Velocidad[7], \"c-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 8\")\r\nplt.plot(Tiempo[8], Velocidad[8], \"r-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 9\")\r\nplt.plot(Tiempo[9], Velocidad[9], \"m-\", linewidth = 1, markersize=3, alpha=0.3, label = \"Exp. 10\")\r\nplt.plot(T[4], V[4], \"y-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 5\")\r\nplt.plot(T[5], V[5], \"b-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 6\")\r\n# plt.plot(T[6], V[6], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Modelo 7\")\r\nplt.plot(T[7], V[7], \"c-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 8\")\r\nplt.plot(T[8], V[8], \"r-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 9\")\r\nplt.plot(T[9], V[9], \"m-\", linewidth = 2, markersize=3, alpha=1, label = \"Modelo 10\")\r\nplt.plot(Tiempo[10], Velocidad[10], \"k-\", linewidth = 2, markersize = 3, label = \"Promedio\")\r\nplt.legend(loc = \"best\",bbox_to_anchor=(0.8, 0.5, 0.5, 0.5))\r\nplt.ylabel(\"Velocidad (m/s)\", fontdict= font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict= font)\r\nplt.title(\"Servilleta Extendida\", fontdict= font)\r\nplt.grid()\r\nplt.show()\r\n\r\nplt.figure()\r\nplt.plot(Tiempo[0], Altura[0], \"c.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 1\")\r\nplt.plot(Tiempo[1], Altura[1], \"b.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 2\")\r\nplt.plot(Tiempo[2], Altura[2], \"g.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 3\")\r\nplt.plot(Tiempo[3], Altura[3], \"m.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 4\")\r\nplt.plot(Tiempo[4], Altura[4], \"y.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 5\")\r\nplt.plot(Tiempo[5], Altura[5], \"b.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 6\")\r\nplt.plot(Tiempo[6], Altura[6], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 7\")\r\nplt.plot(Tiempo[7], Altura[7], \"c.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 8\")\r\nplt.plot(Tiempo[8], Altura[8], \"r.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 9\")\r\nplt.plot(Tiempo[9], Altura[9], \"m.-\", linewidth = 1, markersize=3, alpha=0.5, label = \"Exp. 10\")\r\nplt.plot(Tiempo[10], Altura[10], \"k-\", linewidth = 2, markersize = 3, label = \"Promedio\")\r\nplt.legend(loc = \"best\",bbox_to_anchor=(0.8, 0.5, 0.5, 0.5))\r\nplt.ylabel(\"Altura (m/s)\", fontdict= font)\r\nplt.xlabel(\"Tiempo (s)\", fontdict= font)\r\nplt.title(\"Servilleta Extendida\", fontdict= font)\r\nplt.grid()\r\nplt.show()\r\n\r\n\r\n\r\n\r\n#Graficando K\r\nModelos = [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\"]\r\nplt.bar(Modelos, Resist, width=0.8)\r\nplt.ylabel(\"Resistencia del aire\", fontdict= font)\r\nplt.xlabel(\"Experimento\", fontdict= font)\r\nplt.title(\"Estimación de k\", fontdict= font)\r\nplt.show()\r\n\r\n\r\n#Varianza de la servilleta extendida\r\n\r\nfor j in range(0,10): \r\n var = []\r\n varianza_se_v = 0 \r\n varianza_se_X = 0\r\n\r\n for i in range(0, len(Velocidad[j])):\r\n var.append(Velocidad[j][i]-V[j][i])\r\n varianza= (Velocidad[j][i]-V[j][i])**2\r\n varianza_se_v = varianza_se_v + varianza\r\n \r\n \r\n varianza_se_v = varianza_se_v\r\n print(\"La varianza para el experimento {} en V es de\".format(j+1), varianza_se_v)\r\n\r\n plt.scatter(T[j], var, alpha=1)\r\n plt.axhline(y = 0, linestyle = '--', color = 'black', lw=2)\r\n plt.title(\"Análisis de residuos del modelo {}\".format(j+1), fontweight = \"bold\", fontdict= font)\r\n plt.xlabel('Tiempo(s)', fontdict= font)\r\n plt.ylabel('Residuo', fontdict= font) \r\n plt.show()\r\n\r\n\r\n\r\nfor j in range(0, 10):\r\n var = []\r\n for i in range(0, len(Altura[j])):\r\n \r\n var.append(Altura[j][i]-X[j][i])\r\n varianza = (Altura[j][i]-X[j][i])**2\r\n varianza_se_X = varianza_se_X + varianza\r\n # plt.scatter(T[j], var)\r\n # plt.show()\r\n varianza_se_X = varianza_se_X/len(V)\r\n print(\"La varianza para el experimento {} en X es de\".format(j+1), varianza_se_X)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"BrayanAmor8/Friccion-en-fluidos","sub_path":"Fricción en fluidos - Código.py","file_name":"Fricción en fluidos - Código.py","file_ext":"py","file_size_in_byte":13971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74809885174","text":"import random as rd\r\n\r\n# Не совсем уверена, можно ли использовать прекрасную функцию sample из пакета random,\r\n# которая, собственно, и создает список уникальных чисел из диапазона\r\ndef get_unique_list_numbers() -> list[int]:\r\n return rd.sample(range(-10, 10), 15)\r\n\r\n\r\n# Поэтому написала функцию без использования sample. Но с ней, конечно, намного проще\r\ndef unique_list_numbers() -> list[int]:\r\n list2_ = []\r\n while len(list2_) != 15:\r\n num = rd.randint(-10, 10)\r\n if num not in list2_:\r\n list2_.append(num)\r\n\r\n return list2_\r\n\r\n\r\nlist_unique_numbers = get_unique_list_numbers()\r\nprint(list_unique_numbers)\r\nprint(len(list_unique_numbers) == len(set(list_unique_numbers)))\r\n\r\nunique_numbers = unique_list_numbers()\r\nprint(unique_numbers)\r\nprint(len(unique_numbers) == len(set(unique_numbers)))","repo_name":"MargaritaLebedeva11/DD-PY1","sub_path":"Лабораторная работа 5/task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15831099718","text":"import cv2 as cv\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n'''\nQUESTION 2\n'''\n'''\nRead the data.\n'''\ndata = np.loadtxt('data/hands_aligned_train.txt',skiprows=1)\n'''\nCaclulate mu\n'''\nmu=np.mean(data,axis=1)\n'''\nCalculate W = w-mu\n'''\nW = data - mu.reshape(-1,1)\n'''\nCaclulate covariance matrix W.Wt\n'''\ncov = np.cov(W)\n'''\nFind the eigen vectors and eigen value of covariance matrix\n'''\neigen_value,eigen_vectors=np.linalg.eig(cov)\n'''\nTake the first 5 eigen vectors and find phi and sigma square.\n'''\nK=5\nsigma_square = 1/(112-K)*(np.sum(eigen_value[K+1:]))\nL2 = np.diag(eigen_value)\nsecond = L2[:K,:K] - sigma_square*np.eye(K,K)\nphi = np.dot(eigen_vectors[:,:K],np.sqrt(second))\n'''\nCalculate wi with the weights given in the sheet.\n'''\nwi=(mu + np.dot(phi,np.array([-0.4,-0.2,0,0.2,0.4])).real)\n'''\nPlot the wi.\n'''\nplt.suptitle(\"Statistical Shape Model , K = 5 and W = [-0.4,-0.2,0,0.2,0.4]\")\nplt.plot(wi[:56],wi[56:])\nplt.show()\nplt.pause(2)\nplt.close()\n\n\n\n'''\nQUESTION 3\n'''\n\n'''\nRead the test shape\n'''\ntest_shape = np.loadtxt('data/hands_aligned_test.txt',skiprows=1)\nh = np.array([0,0,0,0,0]) #Assume random h\nh_org = np.copy(h)\ncount_iter=0\nwhile(True):\n w = mu + np.dot(phi,h) # w\n w=w.real\n '''\n Shape changes to calculate psuedo inverse equation components\n '''\n w_xy = np.array([np.split(w,2)[0],np.split(w,2)[1]]).T\n A= []\n for point in w_xy:\n A = A + [[point[0], point[1], 0, 0, 1, 0]]\n A = A + [[0, 0, point[0], point[1], 0, 1]]\n A = np.array(A)\n test_split = np.split(test_shape, 2)\n y = np.array([test_split[0], test_split[1]]).T.flatten()\n '''\n Calculate transformation vector psi \n '''\n psi = np.dot(np.dot(np.linalg.inv(np.dot(A.T, A)), A.T), y)\n psi_1 = psi[:4].reshape(2,2)\n psi_2 = psi[4:]\n '''\n Transform points according to psi\n '''\n w = np.dot(A,psi).real\n '''\n Caclulate parts of the h update equation\n '''\n second_term = y - (np.dot(A,psi))\n second_term=np.array(np.split(second_term, 56)).T.flatten()\n second_sum = np.zeros((5,1))\n for n in range(56):\n phi_n=np.array([phi[n, :], phi[n+ 56, :]])\n first_n = np.dot(psi_1,phi_n).T.real\n second_n = np.array([second_term[n],second_term[n+56]]).reshape(-1,1)\n second_sum = second_sum + np.dot(first_n,second_n)\n\n sum_first = np.zeros((5,5))\n for n in range(56):\n phi_n = np.array([phi[n, :], phi[n + 56, :]])\n sum_first += phi_n.T.dot(psi_1.T).dot(psi_1).dot(phi_n).real\n sum_first = sum_first+ sigma_square*np.eye(5,5)\n sum_first_inv = np.linalg.inv(sum_first)\n '''\n Evalulate h\n '''\n h = np.dot(sum_first_inv,second_sum).real.flatten()\n\n print(h)\n '''\n Check if h has converged\n '''\n if count_iter>15:\n plt.plot(np.array(np.split(w, 56))[:, 0], np.array(np.split(w, 56))[:, 1], c='r')\n plt.show()\n break\n else:\n h_org=h\n count_iter+=1\n '''\n Calculate RMS error\n '''\n RMS = np.sqrt(np.sum(np.square(w-y)))\n plt.suptitle(\"Inference Step - RMS\" + str(RMS))\n plt.plot(np.array(np.split(w,56))[:,0],np.array(np.split(w,56))[:,1],c='r')\n plt.plot(test_shape[:56],test_shape[56:])\n plt.pause(0.2)\n plt.cla()\n\n\n\n\n","repo_name":"sanket-pixel/vision-ary","sub_path":"statistical-shape-model/StatisticalShapeModel.py","file_name":"StatisticalShapeModel.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12796298128","text":"# Define a function to count local maxima in a list of temperatures\ndef count_local_maxima(temperatures):\n local_maxima = 0 #initialize a counter for local maxima\n n = len(temperatures) #check the number of temperature samples in the list\n\n if n < 3:\n return local_maxima # If they're fewer than 3 samples return 0 local maxima\n\n # Loop through the temperature samples. excluding the first and last elements\n for i in range(1, n - 1):\n #Check if the current sample is greater than its neighbors\n if temperatures[i] > temperatures[i - 1] and temperatures[i] > temperatures[i + 1]:\n local_maxima += 1 #increment the local maxima counter if a local maxima is found\n\n return local_maxima #Return the total count of local maxima\n\n#read temperature data from the text file\nwith open('hw1a.txt', 'r') as file:\n #rread each line from the file, convert it to a float, and store it in a list\n temp_samples = [float(line.strip()) for line in file]\n\n#count local maxima in the temperature data using function\ntotal_maxima = count_local_maxima(temp_samples)\n\n#print the number of local maxima found in the temperature data\nprint(f'The number of local maxima is: {total_maxima}')\n","repo_name":"Azeem-Raza/Count-Local-Maxima","sub_path":"TempRead.py","file_name":"TempRead.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2616211017","text":"from tinydb import TinyDB, Query, where\n\n\nclass BaseItem:\n\n def __init__(self, table):\n db = TinyDB('./data/ocr3_db.json')\n self.table = db.table(table)\n\n def get_next_id(self):\n list_id = self.get_list_id()\n if len(list_id) > 0:\n next_id = list_id[-1] + 1\n else:\n next_id = 0\n return next_id\n\n def get_list_id(self):\n liste_items = self.table.all()\n if len(liste_items) > 0:\n list_id = [item['id'] for item in liste_items]\n else:\n list_id = []\n return list_id\n\n def get_count(self):\n return len(self.get_list_id())\n\n def get_from_id(self, id):\n if isinstance(id, int):\n query = Query()\n item = self.table.search(query.id == id)\n if len(item) > 0:\n return item[0]\n else:\n return None\n if isinstance(id, list):\n query = Query()\n items = self.table.search(query.id.one_of(id))\n return items\n\n def delete_from_base(self, id):\n self.table.remove(where('id') == id)\n\n def add_to_base(self, item):\n self.table.insert(item)\n return item['id']\n\n def get_all(self):\n return self.table.all()\n","repo_name":"MatthieuEngles/ocr_projet_3","sub_path":"src/models/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33097117405","text":"\nimport pandas as pd\nimport numpy as np\nimport random\nimport seaborn as sns\nimport statsmodels.api as sm\nimport statsmodels.formula.api as smf\nimport matplotlib.pyplot as plt\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import r2_score\nfrom sklearn.preprocessing import scale\n\ndef calculate_vif(X, thresh=10.0):\n print(thresh)\n dropped = True\n while dropped:\n variables = X.columns\n dropped = False\n # print(X.columns)\n vif = [variance_inflation_factor(X[variables].values, X.columns.get_loc(var)) for var in X.columns]\n # print(vif)\n max_vif = max(vif)\n # print(max(vif))\n if max_vif > thresh:\n maxloc = vif.index(max_vif)\n # print(X.columns.tolist()[maxloc])\n X = X.drop([X.columns.tolist()[maxloc]], axis=1)\n dropped = True\n\n return X\n\nif __name__ == \"__main__\":\n print(\"Loading modeling data...\")\n covid19_papers_modeling_filename = \"../dataset/COVID19_papers_modeling_with_features.csv\"\n covid19_paper_df = pd.read_csv(covid19_papers_modeling_filename)\n\n print(\"Cleaning data...\")\n citation_count_replaced = covid19_paper_df['citation_count'].replace(-1,np.nan)\n covid19_paper_df = covid19_paper_df.assign(citation_count = citation_count_replaced)\n\n\n # replace average topic distr similarity -1 as nan\n # value of -1 means zero or one authors have abstracts available\n avg_tdsim_replaced = covid19_paper_df['avg_tdsim'].replace(-1, np.nan)\n covid19_paper_df = covid19_paper_df.assign(avg_tdsim = avg_tdsim_replaced)\n\n team_size_log_transformed = np.log(covid19_paper_df['team_size']+1)\n max_hindex_log_transformed = np.log(covid19_paper_df['max_hindex']+1)\n citation_count_log_transformed = np.log(covid19_paper_df['citation_count']+1)\n mention_log_transformed = np.log(covid19_paper_df['mention']+1)\n\n covid19_paper_df = covid19_paper_df.assign(team_size_log = team_size_log_transformed)\n covid19_paper_df = covid19_paper_df.assign(max_hindex_log = max_hindex_log_transformed)\n covid19_paper_df = covid19_paper_df.assign(citation_count_log = citation_count_log_transformed)\n covid19_paper_df = covid19_paper_df.assign(mention_log = mention_log_transformed)\n\n # define variables\n feature_var = [\"avg_tdsim\", \"new_tie_rate\", \"hindex_gini\",\"cultural_similarity\", \"topic_familiarity\", \"max_hindex_log\", \"time_since_pub\", \"team_size_log\", \"prac_affil_rate\"] + [\"topic_distr{}\".format(i) for i in range(1,20)]\n target_colname = \"citation_count_log\"\n # drop na rows\n covid19_paper_df = covid19_paper_df.dropna(subset=feature_var)\n print(\"Number of instances: {}\".format(covid19_paper_df.shape[0]))\n\n # Standardize\n X = np.array(covid19_paper_df[feature_var])\n X_scaled = scale(X)\n\n covid19_paper_scaled_df = covid19_paper_df.copy()\n covid19_paper_scaled_df[feature_var] = X_scaled\n\n X = covid19_paper_scaled_df[feature_var]\n aftervif = calculate_vif(X)\n\n ##### Model 2: Square of new_tie_rate added\n formula = \"{} ~ I(new_tie_rate**2) + {}\".format(target_colname, \"+\".join(aftervif.columns))\n\n square_model = smf.ols(formula=formula, data=covid19_paper_scaled_df).fit()\n\n # write_filename = \"covid19_linreg_curvilinear_new_tie_rate_citation\"\n # with open(\"../results/{}.csv\".format(write_filename), \"w\") as fh:\n # fh.write(model.summary().as_csv())\n\n ##### Model 3: Interaction of new_tie_rate and topic_familiarity added\n formula = \"{} ~ new_tie_rate:topic_familiarity + {}\".format(target_colname, \"+\".join(aftervif.columns))\n interaction_model = smf.ols(formula=formula, data=covid19_paper_scaled_df).fit()\n\n write_filename = \"covid19_linreg_interaction_new_tie_rate_topic_familiarity\"\n with open(\"../results/{}.csv\".format(write_filename), \"w\") as fh:\n fh.write(interaction_model.summary().as_csv())\n\n # plot the interaction term\n covid19_paper_scaled_df['new_tie_rate_med'] = covid19_paper_scaled_df.new_tie_rate > covid19_paper_scaled_df.new_tie_rate.median() \n covid19_paper_scaled_df['new_tie_rate_med'] = np.where(covid19_paper_scaled_df.new_tie_rate_med == False, \"Below Median\", \"Above Median\")\n sns.lmplot(x=\"topic_familiarity\", y=\"citation_count_log\", hue=\"new_tie_rate_med\", data=covid19_paper_scaled_df,\\\n ci=None, size=5, aspect=2.5, scatter=False)\n # plt.ylim(0, 8)\n plt.savefig(\"../plots/interaction_term_scatter_plot.png\")\n plt.close()\n\n\n","repo_name":"SulyunLee/COVID19_team_collaboration","sub_path":"src/covid19_interaction_model.py","file_name":"covid19_interaction_model.py","file_ext":"py","file_size_in_byte":4631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28532077114","text":"\"\"\"Testing for the VotingClassifier\"\"\"\n\nimport numpy as np\nfrom sklearn.utils.testing import assert_almost_equal, assert_array_equal\nfrom sklearn.utils.testing import assert_equal, assert_true, assert_false\nfrom sklearn.utils.testing import assert_raise_message\nfrom sklearn.utils.testing import assert_warns_message\nfrom sklearn.exceptions import NotFittedError\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn import datasets\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.svm import SVC\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.base import BaseEstimator, ClassifierMixin\n\n\n# Load the iris dataset and randomly permute it\niris = datasets.load_iris()\nX, y = iris.data[:, 1:3], iris.target\n\n\ndef test_estimator_init():\n eclf = VotingClassifier(estimators=[])\n msg = ('Invalid `estimators` attribute, `estimators` should be'\n ' a list of (string, estimator) tuples')\n assert_raise_message(AttributeError, msg, eclf.fit, X, y)\n\n clf = LogisticRegression(random_state=1)\n\n eclf = VotingClassifier(estimators=[('lr', clf)], voting='error')\n msg = ('Voting must be \\'soft\\' or \\'hard\\'; got (voting=\\'error\\')')\n assert_raise_message(ValueError, msg, eclf.fit, X, y)\n\n eclf = VotingClassifier(estimators=[('lr', clf)], weights=[1, 2])\n msg = ('Number of classifiers and weights must be equal'\n '; got 2 weights, 1 estimators')\n assert_raise_message(ValueError, msg, eclf.fit, X, y)\n\n eclf = VotingClassifier(estimators=[('lr', clf), ('lr', clf)],\n weights=[1, 2])\n msg = \"Names provided are not unique: ['lr', 'lr']\"\n assert_raise_message(ValueError, msg, eclf.fit, X, y)\n\n eclf = VotingClassifier(estimators=[('lr__', clf)])\n msg = \"Estimator names must not contain __: got ['lr__']\"\n assert_raise_message(ValueError, msg, eclf.fit, X, y)\n\n eclf = VotingClassifier(estimators=[('estimators', clf)])\n msg = \"Estimator names conflict with constructor arguments: ['estimators']\"\n assert_raise_message(ValueError, msg, eclf.fit, X, y)\n\n\ndef test_predictproba_hardvoting():\n eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),\n ('lr2', LogisticRegression())],\n voting='hard')\n msg = \"predict_proba is not available when voting='hard'\"\n assert_raise_message(AttributeError, msg, eclf.predict_proba, X)\n\n\ndef test_notfitted():\n eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),\n ('lr2', LogisticRegression())],\n voting='soft')\n msg = (\"This VotingClassifier instance is not fitted yet. Call \\'fit\\'\"\n \" with appropriate arguments before using this method.\")\n assert_raise_message(NotFittedError, msg, eclf.predict_proba, X)\n\n\ndef test_majority_label_iris():\n \"\"\"Check classification by majority label on dataset iris.\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = GaussianNB()\n eclf = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='hard')\n scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')\n assert_almost_equal(scores.mean(), 0.95, decimal=2)\n\n\ndef test_tie_situation():\n \"\"\"Check voting classifier selects smaller class label in tie situation.\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2)],\n voting='hard')\n assert_equal(clf1.fit(X, y).predict(X)[73], 2)\n assert_equal(clf2.fit(X, y).predict(X)[73], 1)\n assert_equal(eclf.fit(X, y).predict(X)[73], 1)\n\n\ndef test_weights_iris():\n \"\"\"Check classification by average probabilities on dataset iris.\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = GaussianNB()\n eclf = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft',\n weights=[1, 2, 10])\n scores = cross_val_score(eclf, X, y, cv=5, scoring='accuracy')\n assert_almost_equal(scores.mean(), 0.93, decimal=2)\n\n\ndef test_predict_on_toy_problem():\n \"\"\"Manually check predicted class labels for toy dataset.\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = GaussianNB()\n\n X = np.array([[-1.1, -1.5],\n [-1.2, -1.4],\n [-3.4, -2.2],\n [1.1, 1.2],\n [2.1, 1.4],\n [3.1, 2.3]])\n\n y = np.array([1, 1, 1, 2, 2, 2])\n\n assert_equal(all(clf1.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))\n assert_equal(all(clf2.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))\n assert_equal(all(clf3.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))\n\n eclf = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='hard',\n weights=[1, 1, 1])\n assert_equal(all(eclf.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))\n\n eclf = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft',\n weights=[1, 1, 1])\n assert_equal(all(eclf.fit(X, y).predict(X)), all([1, 1, 1, 2, 2, 2]))\n\n\ndef test_predict_proba_on_toy_problem():\n \"\"\"Calculate predicted probabilities on toy dataset.\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = GaussianNB()\n X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])\n y = np.array([1, 1, 2, 2])\n\n clf1_res = np.array([[0.59790391, 0.40209609],\n [0.57622162, 0.42377838],\n [0.50728456, 0.49271544],\n [0.40241774, 0.59758226]])\n\n clf2_res = np.array([[0.8, 0.2],\n [0.8, 0.2],\n [0.2, 0.8],\n [0.3, 0.7]])\n\n clf3_res = np.array([[0.9985082, 0.0014918],\n [0.99845843, 0.00154157],\n [0., 1.],\n [0., 1.]])\n\n t00 = (2*clf1_res[0][0] + clf2_res[0][0] + clf3_res[0][0]) / 4\n t11 = (2*clf1_res[1][1] + clf2_res[1][1] + clf3_res[1][1]) / 4\n t21 = (2*clf1_res[2][1] + clf2_res[2][1] + clf3_res[2][1]) / 4\n t31 = (2*clf1_res[3][1] + clf2_res[3][1] + clf3_res[3][1]) / 4\n\n eclf = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft',\n weights=[2, 1, 1])\n eclf_res = eclf.fit(X, y).predict_proba(X)\n\n assert_almost_equal(t00, eclf_res[0][0], decimal=1)\n assert_almost_equal(t11, eclf_res[1][1], decimal=1)\n assert_almost_equal(t21, eclf_res[2][1], decimal=1)\n assert_almost_equal(t31, eclf_res[3][1], decimal=1)\n\n try:\n eclf = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='hard')\n eclf.fit(X, y).predict_proba(X)\n\n except AttributeError:\n pass\n else:\n raise AssertionError('AttributeError for voting == \"hard\"'\n ' and with predict_proba not raised')\n\n\ndef test_multilabel():\n \"\"\"Check if error is raised for multilabel classification.\"\"\"\n X, y = make_multilabel_classification(n_classes=2, n_labels=1,\n allow_unlabeled=False,\n random_state=123)\n clf = OneVsRestClassifier(SVC(kernel='linear'))\n\n eclf = VotingClassifier(estimators=[('ovr', clf)], voting='hard')\n\n try:\n eclf.fit(X, y)\n except NotImplementedError:\n return\n\n\ndef test_gridsearch():\n \"\"\"Check GridSearch support.\"\"\"\n clf1 = LogisticRegression(random_state=1)\n clf2 = RandomForestClassifier(random_state=1)\n clf3 = GaussianNB()\n eclf = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft')\n\n params = {'lr__C': [1.0, 100.0],\n 'voting': ['soft', 'hard'],\n 'weights': [[0.5, 0.5, 0.5], [1.0, 0.5, 0.5]]}\n\n grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)\n grid.fit(iris.data, iris.target)\n\n\ndef test_parallel_fit():\n \"\"\"Check parallel backend of VotingClassifier on toy dataset.\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = GaussianNB()\n X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])\n y = np.array([1, 1, 2, 2])\n\n eclf1 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft',\n n_jobs=1).fit(X, y)\n eclf2 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft',\n n_jobs=2).fit(X, y)\n\n assert_array_equal(eclf1.predict(X), eclf2.predict(X))\n assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))\n\n\ndef test_sample_weight():\n \"\"\"Tests sample_weight parameter of VotingClassifier\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = SVC(probability=True, random_state=123)\n eclf1 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('svc', clf3)],\n voting='soft').fit(X, y, sample_weight=np.ones((len(y),)))\n eclf2 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('svc', clf3)],\n voting='soft').fit(X, y)\n assert_array_equal(eclf1.predict(X), eclf2.predict(X))\n assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))\n\n sample_weight = np.random.RandomState(123).uniform(size=(len(y),))\n eclf3 = VotingClassifier(estimators=[('lr', clf1)], voting='soft')\n eclf3.fit(X, y, sample_weight)\n clf1.fit(X, y, sample_weight)\n assert_array_equal(eclf3.predict(X), clf1.predict(X))\n assert_array_equal(eclf3.predict_proba(X), clf1.predict_proba(X))\n\n clf4 = KNeighborsClassifier()\n eclf3 = VotingClassifier(estimators=[\n ('lr', clf1), ('svc', clf3), ('knn', clf4)],\n voting='soft')\n msg = ('Underlying estimator \\'knn\\' does not support sample weights.')\n assert_raise_message(ValueError, msg, eclf3.fit, X, y, sample_weight)\n\n\ndef test_sample_weight_kwargs():\n \"\"\"Check that VotingClassifier passes sample_weight as kwargs\"\"\"\n class MockClassifier(BaseEstimator, ClassifierMixin):\n \"\"\"Mock Classifier to check that sample_weight is received as kwargs\"\"\"\n def fit(self, X, y, *args, **sample_weight):\n assert_true('sample_weight' in sample_weight)\n\n clf = MockClassifier()\n eclf = VotingClassifier(estimators=[('mock', clf)], voting='soft')\n\n # Should not raise an error.\n eclf.fit(X, y, sample_weight=np.ones((len(y),)))\n\n\ndef test_set_params():\n \"\"\"set_params should be able to set estimators\"\"\"\n clf1 = LogisticRegression(random_state=123, C=1.0)\n clf2 = RandomForestClassifier(random_state=123, max_depth=None)\n clf3 = GaussianNB()\n eclf1 = VotingClassifier([('lr', clf1), ('rf', clf2)], voting='soft',\n weights=[1, 2])\n eclf1.fit(X, y)\n eclf2 = VotingClassifier([('lr', clf1), ('nb', clf3)], voting='soft',\n weights=[1, 2])\n eclf2.set_params(nb=clf2).fit(X, y)\n assert_false(hasattr(eclf2, 'nb'))\n\n assert_array_equal(eclf1.predict(X), eclf2.predict(X))\n assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))\n assert_equal(eclf2.estimators[0][1].get_params(), clf1.get_params())\n assert_equal(eclf2.estimators[1][1].get_params(), clf2.get_params())\n\n eclf1.set_params(lr__C=10.0)\n eclf2.set_params(nb__max_depth=5)\n\n assert_true(eclf1.estimators[0][1].get_params()['C'] == 10.0)\n assert_true(eclf2.estimators[1][1].get_params()['max_depth'] == 5)\n assert_equal(eclf1.get_params()[\"lr__C\"],\n eclf1.get_params()[\"lr\"].get_params()['C'])\n\n\ndef test_set_estimator_none():\n \"\"\"VotingClassifier set_params should be able to set estimators as None\"\"\"\n # Test predict\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = GaussianNB()\n eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),\n ('nb', clf3)],\n voting='hard', weights=[1, 0, 0.5]).fit(X, y)\n\n eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2),\n ('nb', clf3)],\n voting='hard', weights=[1, 1, 0.5])\n eclf2.set_params(rf=None).fit(X, y)\n assert_array_equal(eclf1.predict(X), eclf2.predict(X))\n\n assert_true(dict(eclf2.estimators)[\"rf\"] is None)\n assert_true(len(eclf2.estimators_) == 2)\n assert_true(all([not isinstance(est, RandomForestClassifier) for est in\n eclf2.estimators_]))\n assert_true(eclf2.get_params()[\"rf\"] is None)\n\n eclf1.set_params(voting='soft').fit(X, y)\n eclf2.set_params(voting='soft').fit(X, y)\n assert_array_equal(eclf1.predict(X), eclf2.predict(X))\n assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))\n msg = ('All estimators are None. At least one is required'\n ' to be a classifier!')\n assert_raise_message(\n ValueError, msg, eclf2.set_params(lr=None, rf=None, nb=None).fit, X, y)\n\n # Test soft voting transform\n X1 = np.array([[1], [2]])\n y1 = np.array([1, 2])\n eclf1 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],\n voting='soft', weights=[0, 0.5]).fit(X1, y1)\n\n eclf2 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],\n voting='soft', weights=[1, 0.5])\n eclf2.set_params(rf=None).fit(X1, y1)\n assert_array_equal(eclf1.transform(X1), np.array([[[0.7, 0.3], [0.3, 0.7]],\n [[1., 0.], [0., 1.]]]))\n assert_array_equal(eclf2.transform(X1), np.array([[[1., 0.], [0., 1.]]]))\n eclf1.set_params(voting='hard')\n eclf2.set_params(voting='hard')\n assert_array_equal(eclf1.transform(X1), np.array([[0, 0], [1, 1]]))\n assert_array_equal(eclf2.transform(X1), np.array([[0], [1]]))\n\n\ndef test_estimator_weights_format():\n # Test estimator weights inputs as list and array\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n eclf1 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2)],\n weights=[1, 2],\n voting='soft')\n eclf2 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2)],\n weights=np.array((1, 2)),\n voting='soft')\n eclf1.fit(X, y)\n eclf2.fit(X, y)\n assert_array_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))\n\n\ndef test_transform():\n \"\"\"Check transform method of VotingClassifier on toy dataset.\"\"\"\n clf1 = LogisticRegression(random_state=123)\n clf2 = RandomForestClassifier(random_state=123)\n clf3 = GaussianNB()\n X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])\n y = np.array([1, 1, 2, 2])\n\n eclf1 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft').fit(X, y)\n eclf2 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft',\n flatten_transform=True).fit(X, y)\n eclf3 = VotingClassifier(estimators=[\n ('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n voting='soft',\n flatten_transform=False).fit(X, y)\n\n warn_msg = (\"'flatten_transform' default value will be \"\n \"changed to True in 0.21.\"\n \"To silence this warning you may\"\n \" explicitly set flatten_transform=False.\")\n res = assert_warns_message(DeprecationWarning, warn_msg,\n eclf1.transform, X)\n assert_array_equal(res.shape, (3, 4, 2))\n assert_array_equal(eclf2.transform(X).shape, (4, 6))\n assert_array_equal(eclf3.transform(X).shape, (3, 4, 2))\n assert_array_equal(res.swapaxes(0, 1).reshape((4, 6)),\n eclf2.transform(X))\n assert_array_equal(eclf3.transform(X).swapaxes(0, 1).reshape((4, 6)),\n eclf2.transform(X))\n","repo_name":"ryfeus/lambda-packs","sub_path":"LightGBM_sklearn_scipy_numpy/source/sklearn/ensemble/tests/test_voting_classifier.py","file_name":"test_voting_classifier.py","file_ext":"py","file_size_in_byte":17078,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"71802742455","text":"case = int(input())\n\nresult=[]\n\nfor index in range(case):\n get = list(map(int,input().split()))\n avg = sum(get[1:]) / get[0]\n new = list(filter(lambda x : x> avg , get[1:]))\n so = len(new) / len(get[1:]) * 100\n result.append(so)\nfor index in result:\n print(f'{index:.3f}%')\n ","repo_name":"DongHyunKIM-Hi/Algorithm","sub_path":"백준 알고리즘/4344.py","file_name":"4344.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34240878683","text":"from django.urls import path, include \nfrom .views import *\n\nurlpatterns = [\n path('', userHomePage, name='user_home'),\n path('delete_file/',deleteFile, name=\"delete_file\"),\n path('upload/',fileUploadView,name='upload'),\n path('keywords/', keywordView, name='keyword'),\n path('display_summary/',displaySummaryView, name=\"display_summary_view\"),\n path('grammar_check/',grammarCheckerView, name=\"grammar_checker_view\"),\n path('download/',downloadView, name='download'),\n path('logout/',logout_page, name='logout'),\n]\n","repo_name":"mahirjain25/ReRead","sub_path":"user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"71854011577","text":"import json\nimport mqtt as m\nimport pathplanner as pp\nimport robot as bot\nimport time\nimport threading\n\nfrom typing import List\n# Global variables\nstartSwarm = False\n\nrobots: List[bot.Robot] = []\nbot0 = bot.Robot(0)\nbot1 = bot.Robot(1)\nbot2 = bot.Robot(2)\nbot3 = bot.Robot(3)\nrobots.append(bot0)\nrobots.append(bot1)\nrobots.append(bot2)\nrobots.append(bot3)\n\npathPlanner = pp.PathPlanner(robots)\n# grid = pathPlanner.mapCells(10)\n\nnoOfSimulatedBots = 2\nnoOfHardwareBots = 2\n\nsubTopicHard = \"/bots/hardware/{}\"\nsubTopicSoft = \"/bots/software/{}\"\nsubTopicStart = \"/swarm/start\"\npubTopicHard = \"/bots/hardware/path/{}\"\npubTopicSoft = \"/bots/software/path/{}\"\npubTopicStart = \"/bots/hardware/start\"\n\n\ndef sendAll(message):\n for i in range(noOfSimulatedBots):\n client.publish(pubTopicSoft.format(i), message)\n print(pubTopicSoft.format(i))\n print(message)\n time.sleep(1)\n for i in range(noOfHardwareBots):\n client.publish(pubTopicHard.format(i), message)\n print(pubTopicHard.format(i))\n print(message)\n time.sleep(1)\n\n\ndef worker(event):\n while True:\n time.sleep(2)\n sendAll(\"pos\") # updates position x, y and dir (up, down, left, right)\n time.sleep(2)\n sendAll(\"check\") #checks walls left front right\n #send direction to bot\n #send forward if bladibla\n time.sleep(5)\n \n print(\"bot0 position: \", bot0.position)\n print(\"bot0 direction: \", bot0.facingDirection)\n print(\"bot0 walls: \", bot0.sensorWall)\n \n print(\"bot1 position: \", bot1.position)\n print(\"bot1 direction: \", bot1.facingDirection)\n print(\"bot1 walls: \", bot1.sensorWall)\n \n print(\"bot2 position: \", bot2.position)\n print(\"bot2 direction: \", bot2.facingDirection)\n print(\"bot2 walls: \", bot2.sensorWall)\n \n print(\"bot3 position: \", bot3.position)\n print(\"bot3 direction: \", bot3.facingDirection)\n print(\"bot3 walls: \", bot3.sensorWall)\n \n pathPlanner.finalLoc(robots)\n \n for i in range(noOfSimulatedBots):\n print(\"forward to \", robots[i].nextDirection, \" for bot \", i)\n if(robots[i].nextDirection != None):\n client.publish(pubTopicSoft.format(i), robots[i].nextDirection)\n time.sleep(2)\n client.publish(pubTopicSoft.format(i), \"forward\")\n robots[i].nextDirection = None\n time.sleep(1)\n for i in range(noOfHardwareBots):\n print(\"forward to \", robots[i+2].nextDirection, \" for bot \", i+2)\n if(robots[i+2].nextDirection != None):\n client.publish(pubTopicHard.format(i), robots[i+2].nextDirection)\n time.sleep(2)\n client.publish(pubTopicHard.format(i), \"forward\")\n robots[i+2].nextDirection = None\n time.sleep(1)\n \n \n time.sleep(5)\n #event.wait()\n\ndef on_connect(client, userdata, flags, rc):\n print(\"Connected with result code \"+str(rc))\n\n for i in range(noOfHardwareBots):\n client.subscribe(subTopicHard.format(i))\n for i in range(noOfSimulatedBots):\n client.subscribe(subTopicSoft.format(i))\n client.subscribe(subTopicStart)\n\n print(\"Subbed to topics\")\n\n# Callback function when receiving a message\ndef on_message(client, userdata, msg):\n topic = str(msg.topic)\n m_decode = str(msg.payload.decode(\"utf-8\", \"ignore\"))\n # pause.clear() # pause loop thread\n\n print(\"Message received in topic: \" + topic)\n print(\"Message: \", m_decode)\n\n if topic == subTopicStart:\n print(\"boo\")\n # time.sleep(0.5)\n # sendAll(\"pos\")\n # time.sleep(1)\n # sendAll(\"check\")\n updateThread.start()\n elif \"hardware\" in topic:\n if \"dir\" in m_decode:\n index = int(topic[-1])+noOfSimulatedBots\n pos = json.loads(m_decode)\n print(\"hardwaredir\")\n print(pos)\n print(pos[\"x\"])\n print(pos[\"y\"])\n print(type(pos))\n robots[index].updatePosition(pos)\n elif \"left\" in m_decode:\n index = int(topic[-1])+noOfSimulatedBots\n dist = json.loads(m_decode)\n left = True\n front = True\n right = True\n print(\"hardwarewall\")\n \n \n if(dist[\"left\"]<1000):\n left = False\n \n if(dist[\"front\"]<1000):\n front = False\n \n if(dist[\"right\"]<1000):\n right = False\n \n robots[index].updateWalls(left, front, right)\n elif \"software\" in topic:\n if \"dir\" in m_decode:\n index = int(topic[-1])\n pos = json.loads(m_decode)\n print(\"softwaredir\")\n print(pos)\n print(pos[\"x\"])\n print(pos[\"y\"])\n print(type(pos))\n robots[index].updatePosition(pos)\n elif \"left\" in m_decode:\n index = int(topic[-1])\n dist = json.loads(m_decode)\n left = True\n front = True\n right = True\n print(\"softwarewall\")\n \n if(dist[\"left\"]<1000):\n left = False\n \n if(dist[\"front\"]<1000):\n front = False\n \n if(dist[\"right\"]<1000):\n right = False\n \n robots[index].updateWalls(left, front, right)\n\n # pause.set() do this while sending messages back\n\n # pathPlanner.doUpdate()\n\n\nif __name__ == \"__main__\":\n client = m.Mqtt(\"server\", on_connect, on_message)\n client.connectMQTT()\n pause = threading.Event()\n updateThread = threading.Thread(target=worker, args=(pause,))\n\n while(1):\n pass #poo","repo_name":"JellevdHelm/MazeSolver","sub_path":"server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24465539562","text":"from loadingdata import loadingdata\nfrom rnn_classify import RNN,rnnconfig\nimport tensorflow as tf\nfrom numpy import *\n\n\ndef feed_data(x_batch, y_batch, keep_prob):\n feed_dict = {\n model.input_x: x_batch,\n model.input_y: y_batch,\n model.keep_prob: keep_prob\n }\n return feed_dict\ndef evaluate(sess,x,y):\n length=len(x)\n acc_sum=0.0\n loss_sum=0.0\n for x_batch,y_batch in readdata.batch_item(x,y,batch_size=128):\n batch_len=len(x_batch)\n feed_dict=feed_data(x_batch,y_batch,rnnconfig.dropout_keep_prob)\n loss,acc=sess.run([model.loss,model.acc],feed_dict=feed_dict)\n acc_sum+=acc*batch_len\n loss_sum+=loss*batch_len\n return acc_sum/length,loss_sum/length\n\n\nif __name__==\"__main__\":\n config=rnnconfig()\n model=RNN(config)\n readdata=loadingdata(path='/Users/zuobangbang/Desktop/tb.txt', seq_length=190, word_size=5000, batch_size=64,rate=0.8)\n train_data,train_label,test_data,test_label=readdata.read_data()\n print(shape(train_data))\n print(shape(train_label))\n batch_size=128\n total_batch=0\n stop_inter=1000\n session=tf.Session()\n session.run(tf.global_variables_initializer())\n for i in range(rnnconfig.num_epochs):\n print('Epoch is %d'%i)\n result=readdata.batch_item(train_data,train_label,batch_size)\n for train_x,train_y in result:\n feed_dict=feed_data(train_x,train_y,config.dropout_keep_prob)\n session.run(model.opti,feed_dict=feed_dict)\n if total_batch%rnnconfig.print_per_batch==0:\n loss,acc=session.run([model.loss,model.acc],feed_dict=feed_dict)\n test_acc,test_loss=evaluate(session,test_data,test_label)\n print(\"total batch is {4},train loss is {0},train acc is {1},test loss is {2},test acc is {3}\".format(loss,acc,test_loss,test_acc,total_batch))\n total_batch+=1\n\n # config=rnnconfig()\n # model=RNN.rnn(config)\n # print(model.loss)","repo_name":"zuobangbang/workspace","sub_path":"tensorflow---/rnn/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4215227805","text":"import os\nscript_dir = os.path.dirname(__file__)\nrel_path = \"params.config\"\nabs_file_path = os.path.join(script_dir, rel_path)\n\n\nHOSTNAME='HOSTNAME'\nPORT='PORT'\nUSER='USER'\nPASSWORD='PASSWORD'\nTENSOR_SCHEMA='TENSOR_SCHEMA'\nTENSOR_TRAINING_DATA_TABLE='TENSOR_TRAINING_DATA_TABLE'\nTENSOR_TEST_DATA_TABLE='TENSOR_TEST_DATA_TABLE'\nTENSOR_RESULT_TABLE='TENSOR_RESULT_TABLE'\n\n\nACCURACY='accuracy'\nACCURACY_BASELINE_LABEL_MEAN='accuracy/baseline_label_mean'\nACCURACY_THRESHOLD_MEAN='accuracy/threshold_0.500000_mean'\nAUC='auc'\nGLOBAL_STEP='global_step'\nLABEL_ACTUAL_MEAN='labels/actual_label_mean'\nLABEL_PREDICTION_MEAN='labels/prediction_mean'\nLOSS='loss'\nPRECISION='precision/positive_threshold_0.500000_mean'\nRECALL='recall/positive_threshold_0.500000_mean'\n\nMETRICS=[ACCURACY, ACCURACY_BASELINE_LABEL_MEAN, ACCURACY_THRESHOLD_MEAN, AUC, GLOBAL_STEP, LABEL_ACTUAL_MEAN, LABEL_PREDICTION_MEAN, LOSS, PRECISION, RECALL]\n\n\n\ndef getParamsFromFile():\n with open(abs_file_path) as f:\n lines = list(f)\n params = {}\n for l in lines:\n l = l.rstrip(\"\\r\\n\")\n entry = l.split('=')\n params[str(entry[0])] = str(entry[1])\n return params\n","repo_name":"hxeml/GoogleMLHANAExample","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2733252910","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import messagebox\nimport os.path\nimport pickle\n\nclass Disciplina:\n\n def __init__(self, codigo, nome, cargaHoraria, curso):\n self.__codigo = codigo\n self.__nome = nome\n self.__cargaHoraria = cargaHoraria\n self.__curso = curso\n\n def getCodigo(self):\n return self.__codigo\n def getNome(self):\n return self.__nome\n def getCargaHoraria(self):\n return self.__cargaHoraria\n def getCurso(self):\n return self.__curso\n\nclass LimiteInsereDisciplinas(tk.Toplevel):\n def __init__(self, controle, listaCodCursos):\n \n tk.Toplevel.__init__(self)\n self.geometry('250x200')\n self.title(\"Disciplina\")\n self.controle = controle\n\n self.frameCodigo = tk.Frame(self)\n self.frameNome = tk.Frame(self)\n self.frameCargaHoraria = tk.Frame(self)\n self.frameCurso = tk.Frame(self)\n self.frameButton = tk.Frame(self)\n\n self.frameCodigo.pack()\n self.frameNome.pack()\n self.frameCargaHoraria.pack()\n self.frameCurso.pack()\n self.frameButton.pack()\n\n self.labelCursos = tk.Label(self.frameCurso,text=\"Código do curso: \")\n self.labelCursos.pack(side=\"top\")\n self.escolhaCombo = tk.StringVar()\n self.combobox = ttk.Combobox(self.frameCurso, width = 15 , textvariable = self.escolhaCombo)\n self.combobox.pack(side=\"top\")\n self.combobox['values'] = listaCodCursos\n\n self.labelCodigo = tk.Label(self.frameCodigo,text=\"Código: \")\n self.labelNome = tk.Label(self.frameNome,text=\"Nome: \")\n self.labelCargaHoraria = tk.Label(self.frameCargaHoraria, text=\"Carga Horária: \")\n self.labelCodigo.pack(side=\"top\")\n self.labelNome.pack(side=\"top\")\n self.labelCargaHoraria.pack(side=\"top\") \n\n self.inputCodigo = tk.Entry(self.frameCodigo, width=20)\n self.inputCodigo.pack(side=\"top\")\n\n self.inputNome = tk.Entry(self.frameNome, width=20)\n self.inputNome.pack(side=\"top\")\n\n self.inputCargaHoraria = tk.Entry(self.frameCargaHoraria, width=20)\n self.inputCargaHoraria.pack(side=\"top\")\n\n self.buttonSubmit = tk.Button(self.frameButton ,text=\"Enter\") \n self.buttonSubmit.pack(side=\"left\")\n self.buttonSubmit.bind(\"\", controle.enterHandler)\n \n self.buttonClear = tk.Button(self.frameButton ,text=\"Clear\") \n self.buttonClear.pack(side=\"left\")\n self.buttonClear.bind(\"\", controle.clearHandler) \n\n self.buttonFecha = tk.Button(self.frameButton ,text=\"Concluído\") \n self.buttonFecha.pack(side=\"left\")\n self.buttonFecha.bind(\"\", controle.fechaHandler)\n\n def mostraJanela(self, titulo, msg):\n messagebox.showinfo(titulo, msg)\n\nclass LimiteMostraDisciplinas():\n def __init__(self, str):\n messagebox.showinfo('Lista de disciplinas', str)\n\n \nclass CtrlDisciplina(): \n def __init__(self, ctrlPrincipal):\n self.ctrlPrincipal = ctrlPrincipal\n \n if not os.path.isfile(\"Disciplina.pickle\"):\n disciplina1 = Disciplina('COM112', 'Alg. Est. Dados 2', 64, 'SIN')\n disciplina2 = Disciplina('MAT011', 'Álgebra Linear', 64, 'CCO')\n disciplina3 = Disciplina('ELE101', 'Introdução a Engenharia Elétrica', 48, 'ENG')\n \n self.ctrlPrincipal.ctrlCurso.addDisciplina('SIN', disciplina1)\n self.ctrlPrincipal.ctrlCurso.addDisciplina('CCO', disciplina2)\n self.ctrlPrincipal.ctrlCurso.addDisciplina('ENG', disciplina3)\n \n self.listaDisciplinas = [disciplina1, disciplina2, disciplina3]\n else:\n with open(\"Disciplina.pickle\", \"rb\") as f:\n self.listaDisciplinas = pickle.load(f)\n\n def salvaDisciplinas(self):\n if len(self.listaDisciplinas) != 0:\n with open(\"disciplina.pickle\",\"wb\") as f:\n pickle.dump(self.listaDisciplinas, f)\n\n def getListaDisciplinas(self):\n return self.listaDisciplinas\n\n def getNomeDisciplinaCod(self, codDisc):\n discRet = None\n for disc in self.listaDisciplinas:\n if disc.getCodigo() == codDisc:\n discRet = disc\n return discRet\n\n def getListaCodDisciplinas(self):\n listaCod = []\n for disc in self.listaDisciplinas:\n listaCod.append(disc.getCodigo())\n return listaCod\n\n def insereDisciplinas(self):\n listaCodCursos = self.ctrlPrincipal.ctrlCurso.getListaNomeCursos()\n self.limiteIns = LimiteInsereDisciplinas(self, listaCodCursos) \n\n def mostraDisciplinas(self):\n str = 'Código -- Nome\\n'\n for disc in self.listaDisciplinas:\n str += disc.getCodigo() + ' -- ' + disc.getNome() + '\\n'\n self.limiteLista = LimiteMostraDisciplinas(str)\n\n def enterHandler(self, event):\n cursoSel = self.limiteIns.escolhaCombo.get()\n codigo = self.limiteIns.inputCodigo.get()\n nome = self.limiteIns.inputNome.get()\n cargaHoraria = self.limiteIns.inputCargaHoraria.get()\n\n disciplina = Disciplina(codigo, nome, cargaHoraria, cursoSel)\n self.listaDisciplinas.append(disciplina)\n self.limiteIns.mostraJanela('Sucesso', 'Disciplina cadastrada com sucesso')\n \n self.ctrlPrincipal.ctrlCurso.addDisciplina(cursoSel, disciplina)\n self.clearHandler(event)\n\n def clearHandler(self, event):\n self.limiteIns.escolhaCombo.set(\" \")\n self.limiteIns.inputCodigo.delete(0, len(self.limiteIns.inputCodigo.get()))\n self.limiteIns.inputNome.delete(0, len(self.limiteIns.inputNome.get()))\n self.limiteIns.inputCargaHoraria.delete(0, len(self.limiteIns.inputCargaHoraria.get()))\n\n def fechaHandler(self, event):\n self.limiteIns.destroy()\n ","repo_name":"matheusmenezs/com220","sub_path":"Atividades/Trabalho12/disciplina.py","file_name":"disciplina.py","file_ext":"py","file_size_in_byte":5892,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3530544789","text":"from Bio.SeqIO import parse\nfrom easyFunctions import Coordinate\n# from xlrd import open_workbook as read_excel\n# from xlrd import empty_cell\nfrom pyexcel import get_array as read_excel\n\n\nclass ErpinOut:\n def __init__(self, outfile=\"tmp/rhoInd.out\", inputfile=\"tmp/possibleTracrs.fasta\"):\n self.terminators = []\n self.records={}\n for rec in parse(inputfile,\"fasta\"): self.records[rec.id]=str(rec.seq)\n self.numRecords = len(self.records)\n with open(outfile) as file:\n for i in range(10): file.readline()\n skip=False\n for line in file:\n if line.strip() == '':break\n if skip:\n skip = not skip\n continue\n if (\">\" in line): seqName = line.strip().replace(\">\",\"\")\n else: \n skip = not skip\n line = line.strip().replace(\" \",\" \").replace(\" \",\" \").replace(\" \",\" \")\n newRho = RhoIndTerminator(seqName,line.split(\" \"))\n if (newRho.strand and newRho.upstream) or (not newRho.strand and not newRho.upstream): continue\n try:\n lastRho = self.terminators[-1]\n if lastRho.name == newRho.name and lastRho.strand == newRho.strand: #same location and strand\n if not newRho.strand: self.terminators[-1]=newRho #Replace because this on is shorter\n else: self.terminators.append(newRho)\n except: self.terminators.append(newRho)\n # try:\n # lastRho = self.terminators[-1]\n # if lastRho.upstream == newRho.upstream and lastRho.name == newRho.name and lastRho.strand == newRho.strand:\n # lastShorter = lastRho.Rholocation.end < newRho.Rholocation.end\n # if newRho.upstream and lastShorter: self.terminators[-1] = newRho\n # elif not newRho.upstream and not lastShorter: self.terminators[-1] = newRho\n # else: self.terminators.append(newRho)\n # except: self.terminators.append(newRho)\n\nclass RhoTermPredictOut:\n def __init__(self,seqFile=\"tmp/possibleTracrs.fasta\",rhoPredictFile=\"tmp/RhoPredictions.xlsx\"):\n self.terminators = []\n self.records={}\n for rec in parse(seqFile,\"fasta\"): self.records[rec.id]=str(rec.seq)\n self.numRecords = len(self.records)\n predictions = read_excel(file_name=rhoPredictFile)\n for rec in predictions[1:]: #Start in 2nd element because first is header\n self.terminators.append(RhoIndTerminator(rec[0],rec,'rhoterm'))\n \nclass RhoIndTerminator:\n def __init__(self,name,info,rtype = \"erpin\"):\n self.name = name\n self.upstream = self.name[-1] == 'U'\n self.seq = None\n locRec = name.split(\"_\")\n self.genomeLocation = Coordinate(locRec[-2],locRec[-3]) #Location w/in the genome\n\n if rtype == \"erpin\":\n self.strand = (info[0]==\"FW\")\n start, end = info[2].split(\"..\")\n self.Rholocation = Coordinate(start,end) #Location within the subsequence\n else:\n self.strand = (info[3]==\"plus\")\n start, end = info[1], info[2]\n self.Rholocation = Coordinate(start,end)\n def __str__(self): return \"%s\\t%s\\t%s\\t%s\\t\" % (self.name,str(self.strand),str(self.Rholocation),str(self.upstream))","repo_name":"skDooley/TRACR_RNA","sub_path":"scripts/Rho.py","file_name":"Rho.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"}
+{"seq_id":"30414444973","text":"import time\nimport os\nfrom pathlib import Path\nimport logging\nfrom selenium import webdriver\nfrom behave.model import Status\n\nTAG_WEBBROWSER_REQUIRED = \"webbrowser_required\"\n\n\ndef webbrowser_required_in_scenario(scenario) -> bool:\n return TAG_WEBBROWSER_REQUIRED in [*scenario.tags, *scenario.feature.tags]\n\n\ndef create_browser() -> webdriver:\n opts = webdriver.FirefoxOptions()\n opts.add_argument(\"--headless\")\n return webdriver.Firefox(options=opts)\n\n\ndef setup_logger() -> None:\n BASE_DIR = Path(__file__).resolve().parent\n LOG_DIR = os.path.join(BASE_DIR, \"logs\")\n run_id = os.environ.get(\"RUN_ID\", \"manual\")\n\n if not os.path.exists(LOG_DIR):\n os.makedirs(LOG_DIR)\n filename = os.path.join(LOG_DIR, f\"{run_id}_behave.log\")\n\n logging.basicConfig(\n level=logging.INFO,\n format=\"%(asctime)s - %(levelname)s: %(message)s\",\n filename=filename,\n )\n\n logging.info(f\"=== Starting new test suite ===\")\n\n\ndef before_all(context):\n setup_logger()\n\n\ndef before_scenario(context, scenario):\n if webbrowser_required_in_scenario(scenario):\n context.browser = create_browser()\n\n\ndef after_scenario(context, scenario):\n if webbrowser_required_in_scenario(scenario):\n context.browser.quit()\n\n if scenario.status == Status.failed:\n logging.error(f\"Scenario {scenario.feature.name}-{scenario.name} failed\")\n","repo_name":"plpabla/fileslist","sub_path":"fileslist/features/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70963867256","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nimport sys, os\n\ndef eyes_detect():\n\n face_cascade = \"{base_path}/haarcascades/haarcascade_frontalface_alt.xml\".format(\n base_path=os.path.abspath(os.path.dirname(__file__)))\n\n eye_cascade = \"{base_path}/haarcascades/haarcascade_eye.xml\".format(\n base_path=os.path.abspath(os.path.dirname(__file__)))\n\n mouth_cascade = \"{base_path}/haarcascades/haarscascade_mcs_mouth\".format(\n base_path=os.path.abspath(os.path.dirname(__file__)))\n\n faceCascade = cv2.CascadeClassifier(face_cascade)\n mouthCascade = cv2.CascadeClassifier(mouth_cascade)\n eyeCascade = cv2.CascadeClassifier(eye_cascade)\n\n cap = cv2.VideoCapture(0)\n\n ds_factor = 0.5\n\n while True:\n ret, frame = cap.read()\n frame = cv2.resize(frame, None, fx=ds_factor, fy=ds_factor, interpolation=cv2.INTER_AREA)\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n cv2.imshow('gray scale', gray)\n\n faces = faceCascade.detectMultiScale(gray, 1.3, 5)\n\n mouth_rects = mouthCascade.detectMultiScale(gray, 1.7, 11)\n\n #mouth\n for (x,y,w,h) in mouth_rects:\n y = int(y - 0.15*h)\n cv2.rectangle(frame, (x,y), (x+w, y+h), (0,255,0), 3)\n break\n\n for (x,y,w,h) in faces:\n #eyes\n roi_gray = gray[y:y+h, x:x+w]\n roi_color = frame[y:y+h, x:x+w]\n\n eyes = eyeCascade.detectMultiScale(roi_gray)\n\n for (x_eye, y_eye, w_eye, h_eye) in eyes:\n center = (int(x_eye + 0.5 * w_eye), int(y_eye + 0.5 * h_eye))\n radius = int(0.3 * (w_eye + h_eye))\n color = (0, 255, 0)\n thickness = 3\n cv2.circle(roi_color, center, radius, color, thickness)\n\n cv2.imshow('Eye detector', frame)\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n cap.release()\n cv2.destroyAllWindows()\n\nif __name__ == \"__main__\":\n eyes_detect()\n","repo_name":"ceesietopc/negev_hackathon","sub_path":"turtle_sine/src/eyes_detect.py","file_name":"eyes_detect.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"9501376306","text":"#Data: 22/03/2023\n#Author: Gabrielli Danker Matéria: Estrutura de Dados I\n#Questão 2- Lista 1\n\nimport random\n\ndef criaVetorEmbaralhado(n):\n vetor = list(range(1, n+1)) # cria um vetor com valores de 1 a n (ou 0 a n-1)\n random.shuffle(vetor) # embaralha o vetor aleatoriamente\n return vetor\n \ndef main():\n n = 10\n vetor = criaVetorEmbaralhado(n)\n print(vetor)\n\nif __name__ == '__main__':\n main()\n\n#[3, 9, 6, 2, 5, 10, 8, 7, 1, 4] ","repo_name":"gabsdnker/ED-I","sub_path":"Lista 1/GD-EDI-01-Ex-02.py","file_name":"GD-EDI-01-Ex-02.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"14958452871","text":"additional_properties = \"additionalProperties\"\nall_of = \"allOf\"\nallow_empty_value = \"allowEmptyValue\"\nany_of = \"anyOf\"\napi_key = \"apiKey\"\narray = \"array\"\nbasic = \"basic\"\nbinary = \"binary\"\nbody = \"body\"\nboolean = \"boolean\"\nbyte = \"byte\"\ncollection_format = \"collectionFormat\"\ncomponents = \"components\"\nconsumes = \"consumes\"\ncontent = \"content\"\ncsv = \"csv\"\ndate = \"date\"\ndate_time = \"date-time\"\ndefault = \"default\"\ndefinitions = \"definitions\"\ndescription = \"description\"\ndouble = \"double\"\nenum = \"enum\"\nexternal_docs = \"externalDocs\"\nexclusive_maximum = \"exclusiveMaximum\"\nexclusive_minimum = \"exclusiveMinimum\"\nexplode = \"explode\"\nfloat_ = \"float\"\nform = \"form\"\nformat_ = \"format\"\nheader = \"header\"\nhost = \"host\"\nin_ = \"in\"\ninfo = \"info\"\ninteger = \"integer\"\nint32 = \"int32\"\nint64 = \"int64\"\nitems = \"items\"\nmax_items = \"maxItems\"\nmax_length = \"maxLength\"\nmax_properties = \"maxProperties\"\nmaximum = \"maximum\"\nmin_items = \"minItems\"\nmin_length = \"minLength\"\nmin_properties = \"minProperties\"\nminimum = \"minimum\"\nmulti = \"multi\"\nmultiple_of = \"multipleOf\"\nname = \"name\"\nnull = \"null\"\nnullable = \"x-nullable\"\nnumber = \"number\"\noauth2 = \"oauth2\"\nobject_ = \"object\"\none_of = \"oneOf\"\nopenapi = \"openapi\"\noperation_id = \"operationId\"\nparameters = \"parameters\"\npassword = \"password\"\npath = \"path\"\npaths = \"paths\"\npattern = \"pattern\"\nproduces = \"produces\"\nproperties = \"properties\"\nquery = \"query\"\nref = \"$ref\"\nrequest_body = \"requestBody\"\nrequired = \"required\"\nresponses = \"responses\"\nschema = \"schema\"\nschemas = \"schemas\"\nschemes = \"schemes\"\nsecurity = \"security\"\nsecurity_definitions = \"securityDefinitions\"\nsecurity_schemes = \"securitySchemes\"\nservers = \"servers\"\nsimple = \"simple\"\nstring = \"string\"\nstyle = \"style\"\nsummary = \"summary\"\nswagger = \"swagger\"\ntags = \"tags\"\ntitle = \"title\"\ntype_ = \"type\"\nunique_items = \"uniqueItems\"\nurl = \"url\"\nuuid = \"uuid\"\nvariables = \"variables\"\nversion = \"version\"\n","repo_name":"plangrid/flask-rebar","sub_path":"flask_rebar/swagger_generation/swagger_words.py","file_name":"swagger_words.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","stars":229,"dataset":"github-code","pt":"22"}
+{"seq_id":"27743202510","text":"from django.shortcuts import render, redirect\nfrom .models import Products, OrderItem, Order, BookImage\nfrom django.views.generic import ListView, DetailView, CreateView, DeleteView, UpdateView, View\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\nfrom products.forms import ProductForm, ProductUpdateForm, ImageForm\nfrom django.urls import reverse_lazy\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render, get_object_or_404\nfrom django.urls import reverse\nfrom django.contrib import messages\nfrom django.contrib.messages.views import SuccessMessageMixin\nfrom django.utils import timezone\nfrom django import template\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.forms import modelformset_factory\nfrom django.views.generic.edit import FormMixin\n\nfrom statsd import StatsClient\nstats = StatsClient()\nimport logging\nlogger = logging.getLogger(__name__)\n\n\nclass ProductList(ListView):\n model = Products\n template_name = \"books/books.html\"\n context_object_name = 'books'\n ordering = ['title', 'price', '-quantity']\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['obj'] = BookImage.objects.all()\n return context\n\n\nclass ProductDetailItem(FormMixin, DetailView):\n model = Products\n template_name = \"books/book.html\"\n form_class = ImageForm\n\n def get_context_data(self, **kwargs):\n stats.incr('book-viewed')\n context = super().get_context_data(**kwargs)\n context['obj'] = BookImage.objects.filter(book=self.object.id)\n return context\n\n\nclass MyBooksList(ListView):\n model = Products\n template_name = \"books/mybooks.html\"\n context_object_name = 'books'\n ordering = ['title', 'price', '-quantity']\n\n\nclass ProductCreate(LoginRequiredMixin, SuccessMessageMixin, CreateView, ):\n form_class = ProductForm\n model = Products\n template_name = 'books/new_book.html'\n success_message = \"Books Added\"\n\n def form_valid(self, form):\n timer = stats.timer('create-book')\n timer.start()\n form.instance.seller = self.request.user\n Products = form.save()\n logger.info(\"New Book added by user\")\n Products.save()\n timer.stop()\n return super().form_valid(form)\n\n\nclass ImageAdd(LoginRequiredMixin, SuccessMessageMixin, CreateView, ):\n form_class = ImageForm\n model = BookImage\n template_name = 'books/add_image.html'\n success_message = \"Image Added\"\n success_url = '/books/'\n\n def form_valid(self, form):\n timer = stats.timer('image-added')\n timer.start()\n form.instance.book = Products.objects.get(pk=self.kwargs['pk'])\n BookImage = form.save()\n BookImage.save()\n logger.info(\"New Image added by user\")\n timer.stop()\n return super().form_valid(form)\n\n\nclass ProductUpdate(LoginRequiredMixin, UserPassesTestMixin, SuccessMessageMixin, UpdateView):\n form_class = ProductUpdateForm\n model = Products\n template_name = 'books/update_book.html'\n success_message = \"Books Updated\"\n\n def test_func(self):\n timer = stats.timer('update-book')\n timer.start()\n book = self.get_object()\n logger.info(\"Book is Updated my User\")\n timer.stop()\n if self.request.user == book.seller:\n return True\n return False\n\n\nclass ProductDelete(LoginRequiredMixin, DeleteView):\n model = Products\n template_name = 'books/delete_book.html'\n success_url = '/books'\n \n def test_func(self):\n timer = stats.timer('product-deleted')\n timer.start()\n book = self.get_object()\n logger.info(\"Book Deleted by User\")\n timer.stop()\n if self.request.user == book.seller:\n return True\n return False\n\n\ndef BookAddToCart(request, pk):\n if not request.user.is_authenticated:\n return redirect(\"login\")\n item = get_object_or_404(Products, id=pk)\n cart_book, created = OrderItem.objects.get_or_create(\n item=item,\n user=request.user,\n ordered=False)\n cart = Order.objects.filter(user=request.user, ordered=False)\n if cart.exists():\n order = cart[0]\n # check if the order item is in the order\n if order.items.filter(item__id=item.pk).exists():\n if item.quantity == 0:\n messages.info(request, \"Maximum amount of book added to the cart.\")\n return redirect(\"cart\")\n timer = stats.timer('book-added-to-cart')\n timer.start()\n cart_book.quantity += 1\n cart_book.save()\n item.quantity -= 1\n item.save()\n messages.info(request, \"The Book is added to the cart.\")\n logger.info(\"The Book is added to the cart.\")\n timer.stop()\n return redirect(\"cart\")\n else:\n timer = stats.timer('book-added-to-cart')\n timer.start()\n order.items.add(cart_book)\n item.quantity -= 1\n item.save()\n messages.info(request, \"The Book is added to the cart.\")\n logger.info(\"The Book is added to the cart.\")\n timer.stop()\n return redirect(\"book_item\", pk=pk)\n else:\n timer = stats.timer('book-added-to-cart')\n timer.start()\n ordered_date = timezone.now()\n order = Order.objects.create(user=request.user, ordered_date=ordered_date)\n order.items.add(cart_book)\n item.quantity -= 1\n item.save()\n messages.info(request, \"This item was added to your cart.\")\n logger.info(\"The Book is added to the cart.\")\n timer.stop()\n return redirect(\"cart\")\n\n\ndef RemoveAllBooks(request, pk):\n book = get_object_or_404(Products, pk=pk)\n cart = Order.objects.filter(\n user=request.user,\n ordered=False)\n if cart.exists():\n order = cart[0]\n # check if the order item is in the order\n if order.items.filter(item__id=book.pk).exists():\n cart_book = OrderItem.objects.filter(\n item=book,\n user=request.user,\n ordered=False)[0]\n timer = stats.timer('books-removed-from-cart')\n timer.start()\n book.quantity += cart_book.quantity\n book.save()\n order.items.remove(cart_book)\n cart_book.delete()\n messages.info(request, \"This item was removed from your cart.\")\n logger.info( \"The book was removed from your cart.\")\n timer.stop()\n return redirect(\"cart\")\n else:\n messages.info(request, \"This item was not in your cart\")\n logger.info( \"The book was removed from your cart.\")\n return redirect(\"book_item\", pk=pk)\n else:\n messages.info(request, \"You do not have an active order\")\n return redirect(\"book_item\", pk=pk)\n\n\ndef BookRemoveFromCart(request, pk):\n book = get_object_or_404(Products, pk=pk)\n cart = Order.objects.filter(\n user=request.user,\n ordered=False)\n if cart.exists():\n order = cart[0]\n # check if the order item is in the order\n if order.items.filter(item__id=book.pk).exists():\n cart_book = OrderItem.objects.filter(\n item=book,\n user=request.user,\n ordered=False)[0]\n if cart_book.quantity > 1:\n timer = stats.timer('book-removed-from-cart')\n timer.start()\n cart_book.quantity -= 1\n cart_book.save()\n book.quantity += 1\n book.save()\n messages.info(request, \"The Book was removed from the cart.\")\n logger.info( \"The Book was removed from your cart.\")\n timer.stop()\n return redirect(\"cart\")\n else:\n timer = stats.timer('book-removed-from-cart')\n timer.start()\n order.items.remove(cart_book)\n messages.info(request, \"The Book was removed from the cart.\")\n logger.info(\"The Book was removed from your cart.\")\n book.quantity += 1\n book.save()\n timer.stop()\n return redirect(\"cart\")\n else:\n messages.info(request, \"This item was not in your cart\")\n return redirect(\"cart\")\n else:\n messages.info(request, \"You do not have an active order\")\n return redirect(\"cart\")\n\n\ndef Cart(request):\n if not request.user.is_authenticated:\n return redirect(\"login\")\n try:\n cart = Order.objects.get(user=request.user, ordered=False)\n context = {\n 'object': cart\n }\n return render(request, 'books/order_summary.html', context)\n except ObjectDoesNotExist:\n messages.warning(request, \"Cart is empty\")\n return redirect(\"/books/\")\n\n\nclass ImageDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\n model = BookImage\n template_name = 'books/delete_image.html'\n success_url = '/books/'\n\n def test_func(self):\n timer = stats.timer('image-removed')\n timer.start()\n bookimage = self.get_object()\n logger.info( \"This image was removed.\")\n timer.stop()\n return True\n","repo_name":"KMVanesa/Web-app-using-Django-and-CI-CD-Pipelines","sub_path":"DjangoApp1/products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33465437677","text":"import csv\nfrom collections import defaultdict\nfrom Sources.Model.Testing.Test import Test\nfrom Sources.Model.Configuration import config\n\ndef read_csv(columns, input_file):\n with open(input_file) as f:\n reader = csv.DictReader(f) # read rows into a dictionary format\n for row in reader: # read a row as {column1: value1, column2: value2,...}\n for (k,v) in row.items(): # go over each column name and value)\n columns[k].append(v)\n return columns\n\n\ndef test_file(filename):\n testing = Test(config)\n testing.load_model()\n\n # for filename in filenames:\n data = {}\n columns = defaultdict(list) # each value in each column is appended to a list\n columns = read_csv(columns, filename)\n libelle = columns['libelle']\n sku = columns['sku']\n code = columns['sd_code_sogge']\n result = columns['sd_code_check']\n key = '1sogge-'\n id = 0\n with open(filename,'r') as csvinput:\n with open('./output.csv', 'w') as csvoutput:\n writer = csv.writer(csvoutput, lineterminator='\\n')\n reader = csv.reader(csvinput)\n\n all = []\n row = next(reader)\n # row.append('sku')\n # row.append('libelle')\n row.insert(3, 'result')\n # row.append('sd_code_sogge')\n row.insert(4, 'sd_code_check')\n all.append(row)\n for i in range(0, len(libelle)):\n text = ' '.join(x.strip() for x in libelle[i].split(' ') if len(x.strip()) > 0)\n result = testing.test_line(text)\n\n if result not in data:\n id += 1\n data[result] = key + str(id)\n\n row = next(reader)\n row.insert(3, result)\n row.insert(4, data[result])\n # row.append(sku[i])\n # row.append(libelle[i])\n # row.append(result)\n # row.append(code[i])\n # row.append(data[result])\n all.append(row)\n writer.writerows(all)\n #print(text, '\\t ', result, data[result])\n\ntest_file(config.input_file)\n","repo_name":"MickaelCalatr/RNN-Text-Correction","sub_path":"File_tester.py","file_name":"File_tester.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34551866805","text":"from math import ceil\n\n\nclass PhotoAlbum:\n\n def __init__(self, pages: int):\n self.pages = pages\n self.photos: [list] = [] #matrtix\n\n @property\n def photos(self):\n if not self.__photos:\n return [list() for _ in range( self.pages)]\n return self.__photos\n\n @photos.setter\n def photos(self, value):\n try:\n self.__photos\n except:\n self.__photos = []\n if value and (not len(self.__photos) or len(self.__photos[-1]) == 4):\n self.__photos.append([value])\n elif value and len(self.__photos[-1]) < 4:\n self.__photos[-1].append(value)\n\n @staticmethod\n def from_photos_count(photos_count: int):\n album = PhotoAlbum(ceil(photos_count / 4))\n return album\n\n def add_photo(self, label:str):\n if len(self.photos) == self.pages and all(len(page)==4 for page in self.photos):\n return \"No more free slots\"\n self.photos = label\n return f\"{label} photo added successfully on page {len(self.photos)} slot {len(self.photos[-1])}\"\n\n def display(self):\n return '\\n'.join((\"-\"*11) + \"\\n\" + ' '.join(\"[]\" for _ in range(len(self.photos[page]) if page in range(len(self.photos)) else 0 )) for page in range(self.pages)) + f\"\\n{'-'*11}\"\n\n\nalbum = PhotoAlbum(2)\n\nprint(album.add_photo(\"baby\"))\nprint(album.add_photo(\"first grade\"))\nprint(album.add_photo(\"eight grade\"))\nprint(album.add_photo(\"party with friends\"))\nprint(album.photos)\nprint(album.add_photo(\"prom\"))\nprint(album.add_photo(\"wedding\"))\n\nprint(album.display())\n\n\n\n","repo_name":"b-angelov/SoftUni-exercises","sub_path":"Python_OOP/class_and_static_methods/photo_album.py","file_name":"photo_album.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8128155599","text":"import logging.config\n\nfrom async_fastapi_jwt_auth import AuthJWT\nfrom async_fastapi_jwt_auth.exceptions import AuthJWTException\nfrom fastapi import FastAPI, Request\nfrom fastapi.responses import ORJSONResponse\nfrom redis.asyncio import Redis\n\nfrom api.v1 import auth, registration, roles, sessions, user, users\nfrom core.logging import LOGGING\nfrom core.settings import settings\nfrom db import redis\n\nlogging.config.dictConfig(LOGGING)\n\napp = FastAPI(\n title='auth',\n docs_url='/api/openapi',\n openapi_url='/api/openapi.json',\n default_response_class=ORJSONResponse,\n)\n\n\n@app.on_event('startup')\nasync def startup():\n redis.redis = Redis.from_url(settings.redis_dsn)\n\n\n@app.on_event('shutdown')\nasync def shutdown():\n await redis.redis.close()\n\n\napp.include_router(registration.router, prefix='/api/v1', tags=['Регистрация'])\napp.include_router(auth.router, prefix='/api/v1', tags=['Авторизация'])\napp.include_router(user.router, prefix='/api/v1/user',\n tags=['Профиль пользователя'])\napp.include_router(sessions.router, prefix='/api/v1/user/sessions',\n tags=['История входов в аккаунт'])\napp.include_router(roles.router, prefix='/api/v1/roles',\n tags=['Управление ролями'])\napp.include_router(users.router, prefix='/api/v1/users',\n tags=['Управление доступами'])\n\n\n@AuthJWT.load_config\ndef get_config():\n return settings\n\n\n@app.exception_handler(AuthJWTException)\ndef authjwt_exception_handler(request: Request, exc: AuthJWTException):\n return ORJSONResponse(\n status_code=exc.status_code,\n content={'detail': exc.message}\n )\n\n\n@AuthJWT.token_in_denylist_loader\nasync def check_if_token_in_denylist(decrypted_token):\n return await redis.redis.get(decrypted_token['jti'])\n","repo_name":"latiennetendresse/Auth_sprint_1","sub_path":"auth-service/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36603115542","text":"from swimlane import Swimlane\n\n# Stores the Personal Access Token needed to use the driver\npat = sw_context.inputs['pat']\n\n# Establishes a Swimlane Drive Connection\nswimlane = Swimlane(sw_context.config['InternalSwimlaneUrl'], access_token=pat, verify_ssl=False)\n# Defines the Application ID where the GreyNoise Lookups are being done\nlookup_app = swimlane.apps.get(id=sw_context.config['ApplicationId'])\n# Defines the Application Name where the GreyNoise Tags are stored.\n# This value should be updated if the name is different\ntags_app = swimlane.apps.get(name='GreyNoise Tags')\n# Grabs the record ID of the current Record\nrecord = lookup_app.records.get(id=sw_context.config['RecordId'])\n# Gathers the list of Tags on the record.\n# This value should be updated if the name is different\ntag_list_cursor = record['GN Tags']\nif tag_list_cursor:\n for tag in tag_list_cursor:\n # Searches for the associated Tag Records in the Tag App.\n # This value should be updated if the name is different\n tag_record = tags_app.records.search(('GN Tag Name', 'equals', tag))\n # Attaches the first Tag record returned in the search to the current record.\n # This value should be updated if the name is different\n record['Tag Reference'].add(tag_record[0])\n # Patches the record ones all the references are added.\n record.patch()\n","repo_name":"GreyNoise-Intelligence/greynoise-swimlane","sub_path":"additional_content/add_greynoise_tag_refs_to_lookups.py","file_name":"add_greynoise_tag_refs_to_lookups.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"33233871631","text":"### LIBRARIES / HEADER STUFF {\nimport json # Initial dataset import\nfrom graphviz import Digraph # Primary renderer\n### }\n\n### IMPORT FROM DATASET {\n\ndef importData(loc): # File location should have all_prereqs and subjects.json in it\n # Get relational prereq data\n with open(loc + '/201740_all_prereqs.json','r') as f:\n data = json.load(f)\n\n # Define course object (for code readability reasons)\n class course:\n def __init__(self, courseName, preReq, courseID, subject):\n self.courseName = courseName\n self.preReq = preReq\n self.courseID = courseID\n self.subject = subject\n\n # Now get all of the {'Computer Science': 'CS', ...} information for abbreviation conversion\n with open(loc + '/subjects.json') as f:\n codes = json.load(f)\n lookup = {x['description']:x['code'] for x in codes}\n\n # This parser is a bit brittle - weaknesses described below.\n def parseAndOr(testString):\n '''Input example:'''\n \n '''TODO: Input/output example for docstring'''\n # TODO: Allow for an \"ignore\" input for specific classes if they're causing issues,\n # or just have better error reporting.\n # Presumption: AND statements are top level, then there are OR statements.\n # So there's nothing like (CS 111 AND CS 11) OR (MATH 031 AND MATH 10A)\n # This is likely, but not guaranteed.\n if testString == dict():\n return dict()\n if len(testString.split('\\n')) < 3:\n return dict()\n if 'Score for' in testString: # TODO: EXTREMELY HACKY SOLUTION (ignores all required testing scores)\n return dict()\n mod = testString.split('and\\n')\n mod = [x.split('or\\n') for x in mod]\n mod = [[y.split('\\n') for y in x] for x in mod]\n\n def classconvert(classname):\n '''TODO: Input/output example for docstring'''\n intermed = classname.split(' ')\n intermed = [' '.join(intermed[:-1]), intermed[-1]]\n # lookup is defined above\n if intermed[0] in lookup:\n return lookup[intermed[0]] + intermed[1]\n else:\n return classname\n\n try:\n # This is the most horrific list comprehension I've every written, but it works\n mod = [[[\n classconvert(y[1][y[1].index('Test:')+6:].strip()), # Class name\n y[2].split(' ')[-1], # Grade\n 'NC' if y[3].strip()[:7] == 'May not' else 'C' # Concurrent allowed?\n ] if y[0] == '(' else [\n classconvert(y[0][y[0].index('Test:')+6:].strip()), # Class name\n y[1].split(' ')[-1], # Grade\n 'NC' if y[2].strip()[:7] == 'May not' else 'C' # Concurrent allowed?\n ] for y in x] for x in mod]\n except:\n print(testString)\n print(mod)\n raise\n return mod\n\n courseList = []\n for currentCourse in data:\n try: # Dealing with situations where prereq didn't get written\n mycourse = course(currentCourse['subjectCourse'], parseAndOr(currentCourse['prereqs']), currentCourse['id'], currentCourse['subject'])\n except KeyError:\n mycourse = course(currentCourse['subjectCourse'], dict(), currentCourse['id'], currentCourse['subject'])\n courseList.append(mycourse)\n\n return courseList\n### }\n\n### RENDER GRAPH {\n\ndef constructGraph(courseList,REMOVEGRADCLASS,REQUIREMENTCOLOR,REQUIRED,REMOVENONMAJOR,MAJOR,SPECIALFLAG,DARKTHEME,DARKTHEMECOLORS,MANUALREMOVAL,REMOVALCLASSES):\n def undergradOnly(courseList):\n numbers = set('0123456789')\n return [x for x in courseList if [y for y in x.courseName if y in numbers][0] in {'0','1'}]\n\n def selectMajor(courseList,major,flag=None):\n # Use 4-code for major (eg 'PHYS')\n if flag == 'all':\n return [x for x in courseList if x.subject not in {'TFDP','PSYC'}] # These two have errors of some sort\n else:\n return [x for x in courseList if x.subject in major]\n\n majorList = selectMajor(courseList, MAJOR, flag=SPECIALFLAG)\n if REMOVEGRADCLASS:\n majorList = undergradOnly(majorList)\n\n if MANUALREMOVAL:\n print(len(majorList))\n majorList = [x for x in majorList if x.courseName not in REMOVALCLASSES]\n print(len(majorList))\n\n if DARKTHEME:\n dot = Digraph(comment='classflow test',graph_attr={'rankdir': 'LR', 'ranksep': '2', 'overlap': 'false', 'bgcolor':DARKTHEMECOLORS['background']})\n else:\n dot = Digraph(comment='classflow test',graph_attr={'rankdir': 'LR', 'ranksep': '2', 'overlap': 'false'})\n\n # Robustly add new classes (on a new line) to the labels inside the nodes\n def safeAddLabel(course_name, new_prereq):\n # Check to see if the node already has a label\n dotline = [x for x in dot.__dict__['body'] if x.split(' ')[0][1:] == course_name and 'label=' in x]\n if dotline: # If non-empty, then node already has a label\n dotline = dotline[-1] # We want the most recent label written\n def addPrereqHTML(label,new_prereq):\n posn = label.index('' + new_prereq + label[posn:]\n # I have literally no idea why the ']' bug happens; I think it's a dot issue\n newlabel = addPrereqHTML(dotline[dotline.index('label=')+6:] if dotline[-1] != ']' else dotline[dotline.index('label=')+6:-1],new_prereq)\n dot.node(course_name,label=newlabel)\n else: # No current label (or node doesn't exist), so initialize it\n dot.node(course_name,label='<' + course_name + '' + new_prereq +' >')\n\n # Define how to draw between courses (with all the relevant conditions)\n def requisiteLine(fromCourse,toCourse,graph,fromOrGroup=False,**args):\n \"\"\"fromCourse example: \"['ANTH001', 'D-', 'NC']\"\n toCourse example: \"course\" object with courseName, etc.\n Creates nodes on both ends, then draws the requisite line between the two of them.\n Also references the global REQUIRED list\"\"\"\n\n # Exits doing anything if it finds out that the prerequisite is in removal classes\n if MANUALREMOVAL:\n if fromCourse[0] in REMOVALCLASSES:\n return True\n\n shape = 'box'\n\n ### ALTERNATE ROUTE: If one of the edges isn't a part of the major, instead make a label under the\n ### class pointed to, then ignore the edge initialization\n if REMOVENONMAJOR:\n # Uses same code as not REMOVENONMAJOR, but intelligently changes the iterator\n numbers = set('0123456789')\n for i,x in enumerate(fromCourse[0]):\n if x in numbers:\n index = i\n break\n subj = fromCourse[0][:index]\n iterator = [fromCourse[0],toCourse.courseName] if subj in MAJOR else [toCourse.courseName]\n # Create nodes\n if REQUIREMENTCOLOR:\n for toNode in iterator:\n if toNode in REQUIRED:\n graph.node(toNode,style='filled',color='gold',shape=shape)\n else:\n graph.node(toNode,style='filled',color='lightblue2',shape=shape)\n else:\n for toNode in iterator:\n if DARKTHEME:\n graph.node(toNode,shape=shape,style='filled',color=DARKTHEMECOLORS['boxes'],fontcolor=DARKTHEMECOLORS['textcolor'])\n else:\n graph.node(toNode,shape=shape)\n\n # Since edge auto-creates nodes, now only run if len(iterator) == 2.\n if len(iterator) == 2:\n if DARKTHEME:\n graph.edge(fromCourse[0],toCourse.courseName,color=DARKTHEMECOLORS['lines'],**args)\n else:\n graph.edge(fromCourse[0],toCourse.courseName,color='black',**args)\n else: # If not, add a label for the missing class (unless already written as a part of the orGroup)\n if not fromOrGroup:\n safeAddLabel(toCourse.courseName,fromCourse[0])\n\n else:\n # Create nodes\n if REQUIREMENTCOLOR:\n for toNode in [fromCourse[0],toCourse.courseName]:\n if toNode in REQUIRED:\n graph.node(toNode,style='filled',color='gold',shape=shape)\n else:\n graph.node(toNode,style='filled',color='lightblue2',shape=shape)\n else:\n for toNode in [fromCourse[0],toCourse.courseName]:\n if DARKTHEME:\n graph.node(toNode,shape=shape,style='filled',color=DARKTHEMECOLORS['boxes'],fontcolor=DARKTHEMECOLORS['textcolor'])\n else:\n graph.node(toNode,shape=shape)\n # Draw line\n if DARKTHEME:\n graph.edge(fromCourse[0],toCourse.courseName,color=DARKTHEMECOLORS['lines'],**args)\n else:\n graph.edge(fromCourse[0],toCourse.courseName,color='black',**args)\n return False\n\n # Create lines between courses\n runID = 0\n for _class in majorList:\n if _class.preReq != dict():\n # _class.preReq is an \"and_group\"\n for orGroup in _class.preReq:\n if len(orGroup) > 1:\n for _prereq in orGroup:\n # specialexit was added as a passthrough for when a class should be ignored during prerequisite line drawing\n # It's a bit more elegant than wrapping the whole thing in a giant if statement\n specialexit = requisiteLine(_prereq,_class,dot,style='dashed', fromOrGroup=True)\n # Now add the or group as a prereq under the node\n # Be careful to not overwrite other labels; automatically do a \"\n if dx == -1 and dy == 0:\n tile_str = \"<\"\n if dx == 0 and dy == 1:\n tile_str = \"v\"\n if dx == 0 and dy == -1:\n tile_str = \"^\"\n elif \"path\" in style and node_id in style[\"path\"]:\n tile_str = \"@\"\n elif node_id in grid.walls:\n tile_str = \"#\" * tile_width\n return \"{0:>{width}}\".format(tile_str, width=tile_width)\n\n\ndef draw_grid(grid, tile_width=2, **style):\n grid_str = \"\\n\".join(\n \"\".join(draw_tile(grid, (x, y), tile_width, style) for x in range(grid.width))\n for y in range(grid.height)\n )\n return grid_str\n","repo_name":"FitzOReilly/pathfinder","sub_path":"src/pathfinder/views/ascii_drawer.py","file_name":"ascii_drawer.py","file_ext":"py","file_size_in_byte":1199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"27670128569","text":"class Crop(object):\n\n @staticmethod\n def querify(data):\n \"\"\" split data for queries format \"\"\"\n\n laptops = None\n learners = None\n activities = None\n instances = None\n launches = None\n counters = None\n extras = None\n\n # dijkstra... forgive me!\n laptops = [data[0]]\n learners = [[data[0][0]] + data[1]]\n\n for activity in data[2].keys():\n if activities is None:\n activities = []\n activities.append([activity])\n\n for instance in data[2][activity]:\n if instances is None:\n instances = []\n instances.append(instance[:-1] + [activity] + learners[0])\n\n for launch in instance[-1]:\n if launches is None:\n launches = []\n launches.append(launch + [instance[0]] + learners[0])\n\n for counter in data[3]:\n if counters is None:\n counters = []\n counters.append(counter + learners[0])\n\n if len(data) > 4:\n serial_number = data[0][0]\n if data[4] and extras is None:\n extras = []\n for activity in data[4].keys():\n metadata = data[4][activity][0]\n object_id = metadata['object_id']\n for key in metadata:\n if key != 'object_id':\n extras.append([serial_number] + [object_id] +\n [key] + [metadata[key]])\n\n return laptops, learners, activities, instances, launches, counters, \\\n extras\n","repo_name":"tchx84/harvest-server","sub_path":"harvest/crop.py","file_name":"crop.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"18427759324","text":"import requests\nfrom tqdm import tqdm\n\n\nclass PapersWithCodeCrawler:\n \"\"\"A simple PapersWithCode Crawler.\"\"\"\n\n def __init__(self) -> None:\n self.SEARCH_API_URL = f\"https://paperswithcode.com/api/v1/search\"\n self.PAPER_API_URL = f\"https://paperswithcode.com/api/v1/papers/{{paper}}/repositories\"\n\n def get_paper_from_paperswithcode(self, q: str, items_per_page=10, verbose=False):\n \"\"\"Get list of papers with a keyword q.\"\"\"\n response = requests.get(self.SEARCH_API_URL, {\n \"q\": q,\n \"items_per_page\": items_per_page\n })\n\n paper_list = []\n\n response_dict = response.json()\n\n results_count = response_dict.get(\"count\")\n\n if results_count > 0:\n result_tqdm = tqdm(response_dict.get(\"results\")\n ) if verbose else response_dict.get(\"results\")\n\n for result in result_tqdm:\n paper = result.get(\"paper\")\n\n paper_list.append({\n \"id\": paper.get(\"id\"),\n \"url_abs\": paper.get(\"url_abs\"),\n \"url_pdf\": paper.get(\"url_pdf\"),\n \"published\": paper.get(\"published\"),\n \"title\": paper.get(\"title\")\n })\n\n if verbose:\n print(f\"Fetched {results_count} papers.\")\n\n return paper_list\n\n def get_repositories_from_paper(self, paper: str, items_per_page=10, verbose=False):\n \"\"\"Get repositories list from a paper site.\"\"\"\n response = requests.get(self.PAPER_API_URL.format(paper=paper), {\n \"items_per_page\": items_per_page\n })\n\n repositories_list = []\n\n response_dict = response.json()\n\n results_count = response_dict.get(\"count\")\n\n if results_count > 0:\n result_tqdm = tqdm(response_dict.get(\"results\")\n ) if verbose else response_dict.get(\"results\")\n\n for result in result_tqdm:\n url = result.get(\"url\")\n is_official = result.get(\"is_official\")\n\n repositories_list.append(\n {\"url\": url, \"is_official\": is_official})\n\n if verbose:\n print(f\"Fetched {results_count} repositories.\")\n\n return repositories_list\n\n def get(self, q: str, paper_per_page=10, repo_per_page=10, verbose=False):\n \"\"\"A magic to get all needed informations.\"\"\"\n paper_list = self.get_paper_from_paperswithcode(\n q=q, items_per_page=paper_per_page, verbose=verbose)\n\n paper_and_repos = []\n\n for paper in paper_list:\n repos = self.get_repositories_from_paper(\n paper.get(\"id\"), items_per_page=repo_per_page, verbose=verbose)\n paper_and_repos.append({\n \"paper\": paper,\n \"repos\": repos\n })\n\n return paper_and_repos\n","repo_name":"trhgquan/MTH089","sub_path":"homework/paperwithcode-crawler/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":2894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18115436958","text":"class SklearnTranform(object):\n\n def __init__(self, Cls, *args, **kwds):\n super().__init__()\n self.estimator = Cls(*args, **kwds)\n self.tnfm_before, self.tnfm_after = [], []\n\n def add(self, tnfm_before=None, tnfm_after=None):\n self.tnfm_before.append(tnfm_before)\n self.tnfm_after.append(tnfm_after)\n return self\n\n def __call__(self, input_diagram):\n if len(self.tnfm_before) > 0:\n for tn in self.tnfm_before:\n input_diagram = tn(input_diagram)\n result = self.estimator.fit_transform([input_diagram])[0]\n if len(self.tnfm_after) > 0:\n for tn in reversed(self.tnfm_after):\n result = tn(result)\n return result\n","repo_name":"marek094/persian","sub_path":"src/persian/torchize.py","file_name":"torchize.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28843471606","text":"from django.views.generic import TemplateView\nfrom signetsim.views.HasWorkingProject import HasWorkingProject\nfrom signetsim.views.HasUserLoggedIn import HasUserLoggedIn\nfrom signetsim.views.HasErrorMessages import HasErrorMessages\n\nfrom django.core.files import File\nfrom django.conf import settings\nfrom django.shortcuts import redirect\n\nfrom signetsim.models import SbmlModel, new_model_filename, ModelsDependency\nfrom signetsim.forms import DocumentForm\nfrom signetsim.managers.models import getDetailedModelDependencies\nfrom libsignetsim import SbmlDocument, ModelException, MissingSubmodelException\n\nimport os\nfrom os.path import join\n\n\nclass ListOfModelsView(TemplateView, HasWorkingProject, HasUserLoggedIn, HasErrorMessages):\n\n\ttemplate_name = 'models/models.html'\n\n\tdef __init__(self, **kwargs):\n\n\t\tTemplateView.__init__(self, **kwargs)\n\t\tHasWorkingProject.__init__(self)\n\t\tHasUserLoggedIn.__init__(self)\n\t\tHasErrorMessages.__init__(self)\n\n\t\tself.listOfModels = None\n\t\tself.fileUploadForm = DocumentForm()\n\n\n\tdef get_context_data(self, **kwargs):\n\n\t\tkwargs = HasWorkingProject.get_context_data(self, **kwargs)\n\t\tkwargs = HasErrorMessages.get_context_data(self, **kwargs)\n\n\t\tkwargs['form'] = self.fileUploadForm\n\t\tkwargs['sbml_models'] = self.listOfModels\n\n\t\treturn kwargs\n\n\n\tdef get(self, request, *args, **kwargs):\n\n\t\tif len(args) > 0:\n\t\t\tHasWorkingProject.load(self, request, *args, **kwargs)\n\t\t\tself.setProject(request, args[0])\n\t\t\treturn redirect('models')\n\n\t\tself.load(request, *args, **kwargs)\n\t\treturn TemplateView.get(self, request, *args, **kwargs)\n\n\n\tdef post(self, request, *args, **kwargs):\n\n\t\tself.load(request, *args, **kwargs)\n\n\t\tif \"action\" in request.POST:\n\t\t\tif HasWorkingProject.isChooseProject(self, request):\n\t\t\t\tself.loadModels(request)\n\n\t\t\telif request.POST['action'] == \"delete_model\":\n\t\t\t\tself.deleteModel(request)\n\t\t\t\tself.load(request, *args, **kwargs)\n\n\n\t\t\telif request.POST['action'] == \"load_model\":\n\t\t\t\tself.loadModel(request)\n\t\t\t\tself.load(request, *args, **kwargs)\n\n\t\t\telif request.POST['action'] == \"duplicate_model\":\n\t\t\t\tself.duplicateModel(request)\n\t\t\t\tself.load(request, *args, **kwargs)\n\n\t\t\telif request.POST['action'] == \"new_model\":\n\t\t\t\tself.newModel(request)\n\t\t\t\tself.load(request, *args, **kwargs)\n\n\t\t\telif request.POST['action'] == \"load_biomodels\":\n\t\t\t\tself.loadBiomodels(request)\n\t\t\t\tself.load(request, *args, **kwargs)\n\n\n\t\treturn TemplateView.get(self, request, *args, **kwargs)\n\n\n\tdef load(self, request, *args, **kwargs):\n\n\t\tHasWorkingProject.load(self, request, *args, **kwargs)\n\n\t\tself.loadModels(request)\n\n\tdef newModel(self, request):\n\n\t\tmodel_name = str(request.POST['model_name'])\n\t\tmodel_filename = os.path.join(settings.MEDIA_ROOT, new_model_filename())\n\n\t\topen(model_filename, \"a\")\n\t\tnew_model = SbmlModel(project=self.project, name=model_name, sbml_file=File(open(model_filename, \"rb\")))\n\t\tos.remove(model_filename)\n\t\tnew_model.save()\n\n\t\tdoc = SbmlDocument()\n\t\tdoc.model.newModel(model_name)\n\t\tdoc.writeSbmlToFile(os.path.join(settings.MEDIA_ROOT, str(new_model.sbml_file)))\n\n\n\tdef duplicateModel(self, request):\n\n\t\tmodel = SbmlModel.objects.get(id=request.POST['id'])\n\t\tt_file = File(open(os.path.join(settings.MEDIA_ROOT, str(model.sbml_file)), 'rb'))\n\n\t\tnew_sbml_model = SbmlModel(project=self.project,\n\t\t\t\t\t\t\t\t\t\tname=(\"%s (copy)\" % str(model.name)),\n\t\t\t\t\t\t\t\t\t\tsbml_file=t_file)\n\n\t\tnew_sbml_model.save()\n\t\tt_filename = os.path.join(settings.MEDIA_ROOT, str(new_sbml_model.sbml_file))\n\n\t\tdoc = SbmlDocument()\n\t\tdoc.readSbmlFromFile(t_filename)\n\t\tdoc.model.setName(str(new_sbml_model.name))\n\t\tdoc.writeSbmlToFile(t_filename)\n\n\n\tdef loadModel(self, request):\n\n\t\tself.fileUploadForm = DocumentForm(request.POST, request.FILES)\n\t\tif self.fileUploadForm.is_valid():\n\n\t\t\tnew_sbml_model = SbmlModel(project=self.project, sbml_file=request.FILES['docfile'])\n\t\t\tnew_sbml_model.save()\n\n\t\t\ttry:\n\t\t\t\tdoc = SbmlDocument()\n\t\t\t\tdoc.readSbmlFromFile(os.path.join(settings.MEDIA_ROOT, str(new_sbml_model.sbml_file)))\n\n\t\t\t\tdependencies = getDetailedModelDependencies(doc)\n\t\t\t\tif len(dependencies) > 0:\n\n\t\t\t\t\tfor submodel_filename, submodel_ref in dependencies:\n\t\t\t\t\t\tsubmodel_filename = join(self.project.folder, \"models\", submodel_filename)\n\t\t\t\t\t\tsubmodel = SbmlModel.objects.get(sbml_file=submodel_filename)\n\t\t\t\t\t\tnew_dependency = ModelsDependency(\n\t\t\t\t\t\t\tproject=self.project,\n\t\t\t\t\t\t\tmodel=new_sbml_model,\n\t\t\t\t\t\t\tsubmodel=submodel,\n\t\t\t\t\t\t\tsubmodel_ref=submodel_ref\n\t\t\t\t\t\t)\n\t\t\t\t\t\tnew_dependency.save()\n\n\t\t\t\tnew_sbml_model.name = doc.model.getName()\n\t\t\t\tnew_sbml_model.save()\n\n\t\t\texcept MissingSubmodelException:\n\t\t\t\tnew_sbml_model.delete()\n\t\t\t\tself.addError(\n\t\t\t\t\t\"This model is importing some models which were not found in the project folder. Please import them first\")\n\n\t\t\t# Is triggered where name is None ??\n\t\t\texcept ModelException:\n\t\t\t\tname = os.path.splitext(str(new_sbml_model.sbml_file))[0]\n\t\t\t\tnew_sbml_model.name = name\n\t\t\t\tnew_sbml_model.save()\n\n\n\tdef deleteModel(self, request):\n\n\t\tmodel = SbmlModel.objects.get(project=self.project, id=request.POST['id'])\n\n\t\tfor model_dependency in ModelsDependency.objects.filter(project=self.project, submodel=model):\n\t\t\tif model_dependency.model != model:\n\t\t\t\tself.addError(\"This model is used as a submodel by %s and cannot be removed.\" % model_dependency.model.name)\n\n\t\tif not self.hasErrors():\n\t\t\tModelsDependency.objects.filter(project=self.project, model=model).delete()\n\t\t\tos.remove(os.path.join(settings.MEDIA_ROOT, str(model.sbml_file)))\n\t\t\tmodel.delete()\n\n\tdef loadBiomodels(self, request):\n\n\t\tmodel_id = str(request.POST['model_id'])\n\t\tmodel_filename = os.path.join(settings.MEDIA_ROOT, new_model_filename())\n\n\t\topen(model_filename, \"a\")\n\t\tnew_model = SbmlModel(project=self.project, sbml_file=File(open(model_filename, \"rb\")))\n\t\tos.remove(model_filename)\n\t\tnew_model.save()\n\n\n\t\tdoc = SbmlDocument()\n\t\ttry:\n\n\t\t\tdoc.readFromBiomodels(model_id)\n\t\t\tdoc.writeSbmlToFile(os.path.join(settings.MEDIA_ROOT, str(new_model.sbml_file)))\n\t\t\tnew_model.name = doc.model.getName()\n\t\t\tnew_model.save()\n\n\t\texcept:\n\t\t\tself.addError(\"Unable to load model from biomodels\")\n\t\t\tnew_model.delete()\n\n\tdef loadModels(self, request, *args, **kwargs):\n\n\t\t# self.projectId = str(args[0])\n\t\t# self.project = ModelFolder.objects.get(user=request.user, id=self.projectId)\n\t\tif self.project is not None:\n\t\t\tself.listOfModels = []\n\t\t\tfor model in SbmlModel.objects.filter(project=self.project):\n\t\t\t\tfilename = str(model.sbml_file)\n\t\t\t\tif filename.startswith(settings.MEDIA_ROOT):\n\t\t\t\t\tfilename = filename.replace(settings.MEDIA_ROOT, \"\")\n\t\t\t\tself.listOfModels.append((model.id, model.name, filename))","repo_name":"vincent-noel/SigNetSim","sub_path":"signetsim/views/ListOfModelsView.py","file_name":"ListOfModelsView.py","file_ext":"py","file_size_in_byte":6584,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"23468485854","text":"import cv2\r\nimport numpy as np\r\n\r\ncap = cv2.VideoCapture(0)\r\nback = cv2.imread('./image1.jpg')\r\n\r\nprint(\"Enter 1 if you want to see through the items which are in red and the remaining in black.\")\r\nprint(\"Enter 2 if you want to see through the items which are in red and the remaining as same.\")\r\nprint(\"Enter 3 if you want to see the items which are in red as white and the remaining in black.\")\r\nprint(\"Enter 4 if you want to see through the items which are not in red and the remaining in black.\")\r\nc = int(input(\"Enter your choice: \"))\r\nif c == 1 or c == 2 or c == 3 or c == 4:\r\n while cap.isOpened():\r\n ret, frame = cap.read()\r\n\r\n if ret:\r\n #hsv defines colors based on human eyes, so we use hsv instead of rgb\r\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) #convertung rgb to hsv\r\n #cv2.imshow(\"hsv\",hsv)\r\n red = np.uint8([[[0,0,255]]])\r\n hsv_red = cv2.cvtColor(red, cv2.COLOR_BGR2HSV)\r\n #print(hsv_red)\r\n\r\n l_red = np.array([0,100,100])\r\n u_red = np.array([10,255,255])\r\n #all the objects in red is shown highlighted(i.e. white)\r\n mask = cv2.inRange(hsv, l_red, u_red)\r\n if c== 3:\r\n cv2.imshow(\"mask\",mask)\r\n #shows the background when red color appeard on screen\r\n part1 = cv2.bitwise_and(back, back, mask=mask)\r\n if c == 2:\r\n cv2.imshow(\"part1\",part1)\r\n #shows things highlighted which are not red\r\n\r\n mask = cv2.bitwise_not(mask)\r\n\r\n\r\n part2 = cv2.bitwise_and(frame, frame, mask=mask)\r\n #shows everything in red and things other than red are shown in black.\r\n if c == 4:\r\n cv2.imshow(\"part2\",part2)\r\n if c==1:\r\n cv2.imshow(\"cloak\",part1 + part2)\r\n\r\n if cv2.waitKey(5) == ord('q'):\r\n break\r\n#else:\r\n #print(\"Invalid!!\")\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n","repo_name":"ana-pat/Cloak-of-Invisibility","sub_path":"invisible_cloak.py","file_name":"invisible_cloak.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22704837889","text":"r\"\"\"Cloud Functions implementation which takes a patient bundle from a FHIR\nStore whenever a questionnaire gets answered, runs prediction against a\npre-trained model and writes the results back to the same FHIR Store.\n\"\"\"\n\nimport base64\nimport datetime\nimport googleapiclient.discovery\nimport google.auth\nimport json\nimport logging\nimport os\n\nfrom google.auth.transport.urllib3 import AuthorizedHttp\nfrom utils import *\n\n# These should be passed in through deployment.\nMODEL = os.environ.get('MODEL')\nVERSION = os.environ.get('VERSION')\n\nFHIR_STORE_ENDPOINT_PREFIX = 'https://healthcare.googleapis.com/v1beta1'\nCREATE_RESOURCE_ACTION = 'CreateResource'\nUPDATE_RESOURCE_ACTION = 'UpdateResource'\n\nRISKS = ['negligible', 'low', 'moderate', 'high', 'certain']\n\nLOGGER = logging.getLogger('main')\n\ndef get_resource(http, resource_name):\n \"\"\"Fetches a resource from the FHIR Store.\n\n Args:\n resource_name (str): the name of the resource, e.g. 'projects/my-project\n /locations/us-central1/datasets/my-dataset/fhirStores/my-store\n /fhir/Patient/patient-id'\n Returns:\n Object: the resource loaded from the FHIR Store.\n \"\"\"\n response = http.request('GET', format_url(resource_name))\n if response.status > 299:\n LOGGER.critical(\"Failed to retrieve resource %s, response: %s\" % (\n resource_name, response.data))\n return None\n\n return json.loads(response.data)\n\n\ndef build_risk_assessment(pid, qid, disease, risk, rid=None):\n \"\"\"Builds a risk assessment JSON object.\n\n Returns:\n Str: JSON representation of a RiskAssessment resource.\n \"\"\"\n risk_assessment = {\n 'resourceType': RISKASSESSMENT_TYPE,\n 'basis': [{'reference': pid}, {'reference': qid}],\n 'status': 'final',\n 'subject': {'reference': pid},\n 'occurrenceDateTime':\n datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ'),\n 'prediction': [{\n 'outcome': {\n 'coding': [{'display': disease}],\n },\n 'qualitativeRisk': {\n 'coding': [{\n 'system': \"http://hl7.org/fhir/risk-probability\",\n 'code': risk\n }]\n }\n }]\n }\n\n if rid is not None:\n risk_assessment['id'] = rid\n\n return json.dumps(risk_assessment)\n\n\ndef get_action(data):\n \"\"\"Reads operation action (e.g. Create or Update) from pubsub message.\"\"\"\n if data['attributes'] is not None:\n return data['attributes']['action']\n return None\n\n\ndef format_url(path, query=None):\n \"\"\"Formats request URL with path and query string.\"\"\"\n if query is None:\n return \"%s/%s\" % (FHIR_STORE_ENDPOINT_PREFIX, path)\n else:\n return \"%s/%s?%s\" % (FHIR_STORE_ENDPOINT_PREFIX, path, query)\n\n\ndef create_or_update_resource(http, path, payload):\n \"\"\"Writes a resource to the FHIR Store.\n\n Args:\n path (str): path to the endpoint, e.g. 'projects/my-project\n /locations/us-central1/datasets/my-dataset/fhirStores/my-store\n /fhir/Patient' for create requests and 'projects/my-project\n /locations/us-central1/datasets/my-dataset/fhirStores/my-store\n /fhir/Patient/patient-id' for update requests.\n payload (str): resource to be written to the FHIR Store.\n Returns:\n Object: the resource from the server, usually this is an\n OperationOutcome resource if there is anything wrong.\n \"\"\"\n # Determine which HTTP method we need to use: POST for create, and PUT for\n # update. The path of update requests have one more component than create\n # requests.\n method = 'POST' if path.count('/') == 9 else 'PUT'\n response = http.request(method, format_url(path), body=payload,\n headers={'Content-Type': 'application/fhir+json;charset=utf-8'})\n if response.status > 299:\n LOGGER.error(\"Failed to create or update resource %s, response: %s\" % (\n payload, response.data))\n return None\n\n return json.loads(response.data)\n\n\ndef search_resource(http, path, query):\n \"\"\"Searches a resource in the FHIR Store.\n\n Args:\n path (str): path to the search endpoint, e.g. 'projects/my-project\n /locations/us-central1/datasets/my-dataset/fhirStores/my-store\n /fhir/Patient'\n query (str): query parameter, e.g. 'age=gt30'\n Returns:\n List[dict]: a list of resources matching the search criteria.\n \"\"\"\n response = http.request('GET', format_url(path, query=query))\n if response.status > 299:\n LOGGER.error(\"Failed to search resource %s, response: %s\" % (query,\n response.data))\n return None\n\n bundle = json.loads(response.data)\n return list(map(lambda r: r['resource'], bundle['entry']))\n\n\ndef filter_resource(resources, qid, disease):\n \"\"\"Finds a RiskAssessment.\n\n The target references a certain QuestionnaireResponse and is about the\n specified disease\n \"\"\"\n def match(res):\n return extract_qid(res) == qid and extract_disease(res) == disease\n\n return next(filter(match, resources), None)\n\n\ndef build_examples(patient, questionnaire_response):\n \"\"\"Builds examples to be sent for prediction.\n\n Two examples are created for the two diseases we are targeting at.\n \"\"\"\n\n def map_example(disease):\n return {\n 'age': calculate_age(patient['birthDate']),\n 'gender': 1 if patient['gender'] == 'male' else 0,\n 'country': COUNTRY_MAP[extract_country(questionnaire_response)],\n 'duration': calculate_duration(\n *extract_start_end_date(questionnaire_response)),\n 'disease': disease\n }\n\n return list(map(map_example, range(len(DISEASE_MAP))))\n\n\ndef predict(examples):\n \"\"\"Sends features to Cloud ML Engine for online prediction.\n\n Args:\n examples (list): features to be fed into the model for prediction.\n Returns:\n Mapping[str: any]: dictionary of prediction results defined by the model.\n \"\"\"\n service = googleapiclient.discovery.build('ml', 'v1', cache_discovery=False)\n name = \"projects/%s/models/%s/versions/%s\" % (\n os.environ.get('GCP_PROJECT'), MODEL, VERSION)\n\n response = service.projects().predict(name=name,\n body={'instances': examples}).execute()\n\n if 'error' in response:\n LOGGER.error(\"Prediction failed: %s\" % response['error'])\n return None\n\n return response['predictions']\n\n\ndef main(data, context):\n \"\"\"Extracts features from a patient bundle for online prediction.\n\n This process is broken down into a few steps:\n\n 1. Fetch the QuestionnaireResponse we get triggered on (note that we\n only react to this resource type), and extract the patient that\n answered it.\n 2. Fetch everything for the patient from step 1, and extract the\n features we are interested in.\n 3. Send the features to Cloud ML for online prediction, and write the\n results back to the FHIR store.\n\n Args:\n data (dict): Cloud PubSub payload. The `data` field is what we are\n looking for.\n context (google.cloud.functions.Context): Metadata for the event.\n \"\"\"\n\n if 'data' not in data:\n LOGGER.info('`data` field is not present, skipping...')\n return\n\n resource_name = base64.b64decode(data['data']).decode('utf-8')\n if QUESTIONNAIRERESPONSE_TYPE not in resource_name:\n LOGGER.info(\"Skipping resource %s which is irrelevant for prediction.\" %\n resource_name)\n return\n\n credentials, _ = google.auth.default()\n http = AuthorizedHttp(credentials)\n questionnaire_response = get_resource(http, resource_name)\n if questionnaire_response is None:\n return\n\n patient_id = questionnaire_response['subject']['reference']\n project_id, location, dataset_id, fhir_store_id, _ = _parse_resource_name(\n resource_name)\n patient = get_resource(http, _construct_resource_name(project_id, location,\n dataset_id, fhir_store_id, patient_id))\n if patient is None:\n return\n\n predictions = predict(build_examples(patient, questionnaire_response))\n if predictions is None:\n return\n\n pid = \"%s/%s\" % (PATIENT_TYPE, patient['id'])\n qid = \"%s/%s\" % (QUESTIONNAIRERESPONSE_TYPE, questionnaire_response['id'])\n\n action = get_action(data)\n for disease, idx in DISEASE_MAP.items():\n scores = predictions[idx]['probabilities']\n LOGGER.info(\"Prediction results: %s\", scores)\n # Last element represents risk.\n score = scores[1]\n risk = RISKS[-1] if score == 1 else RISKS[int(score / 0.2)]\n\n path = _construct_resource_name(project_id, location, dataset_id,\n fhir_store_id, RISKASSESSMENT_TYPE)\n if action == UPDATE_RESOURCE_ACTION:\n resources = search_resource(http, path, \"subject=%s\" % pid)\n res = filter_resource(resources, qid, disease)\n if res is None:\n LOGGER.info(\"No existing RiskAssessment, createing a new one...\")\n create_or_update_resource(http, path, build_risk_assessment(pid,\n qid, disease, risk))\n continue\n rid = res['id']\n\n path = _construct_resource_name(project_id, location, dataset_id,\n fhir_store_id, \"%s/%s\" % (RISKASSESSMENT_TYPE, rid))\n create_or_update_resource(http, path, build_risk_assessment(pid,\n qid, disease, risk, rid=rid))\n elif action == CREATE_RESOURCE_ACTION:\n create_or_update_resource(http, path, build_risk_assessment(pid,\n qid, disease, risk))\n\n\ndef _parse_resource_name(name):\n \"\"\"Extracts project id, location, dataset id etc from the resource name.\"\"\"\n parts = name.split('/')\n return parts[1], parts[3], parts[5], parts[7], \\\n \"%s/%s\" % (parts[9], parts[10])\n\n\ndef _construct_resource_name(project_id, location, dataset_id, fhir_store_id,\n resource_id):\n \"\"\"Constructs a resource name.\"\"\"\n return '/'.join([\n 'projects', project_id, 'locations', location, 'datasets', dataset_id,\n 'fhirStores', fhir_store_id, 'fhir', resource_id\n ])\n","repo_name":"GoogleCloudPlatform/healthcare","sub_path":"fhir/immunizations_demo/inference/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9558,"program_lang":"python","lang":"en","doc_type":"code","stars":356,"dataset":"github-code","pt":"22"}
+{"seq_id":"5667709439","text":"from app import app, db\nimport jwt\nfrom flask import Blueprint, request, Response, jsonify\nimport datetime\n\nutilty = Blueprint(\"utilty\", __name__)\n\n\n@utilty.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef auth_encoding():\n try:\n if request.method == \"POST\":\n person_id = request.args.get(\"person_id\")\n if person_id:\n payload = {\n \"exp\": datetime.datetime.utcnow() + datetime.timedelta(days=0, seconds=86400),\n \"iat\": datetime.datetime.utcnow(),\n \"sub\": person_id\n }\n\n token = jwt.encode(payload, app.config[\"SECRET_KEY\"])\n print(token)\n\n return Response(token.decode(\"UTF-8\"))\n\n return Response({\"Message\": \"Error on authenticate\"})\n\n return Response({\"Message\": \"Method not Allowed \"}, status=405)\n\n except Exception as e:\n print(e)\n\n return Response(status=200)","repo_name":"waploaj/FieldForceApi","sub_path":"Utily/person.py","file_name":"person.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28726927439","text":"if __name__ == '__main__':\n N = int(input())\n l=[]\n arr=[]\n for i in range(0,N):\n s=input().split();\n st=s[0]\n arr=list(map(int,s[1:]))\n #print(s)\n if(st ==\"insert\"):\n #arr=list(map(int,l))\n l.insert(arr[0],arr[1])\n elif(st==\"print\"):\n print(l)\n elif(st==\"remove\"):\n l.remove(arr[0])\n elif(st==\"append\"):\n l.append(arr[0])\n elif(st==\"sort\"):\n l.sort()\n elif(st==\"pop\"):\n l.pop()\n elif(st==\"reverse\"):\n l.reverse()","repo_name":"JayantGoel001/HackerRank","sub_path":"Lists.py","file_name":"Lists.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"22"}
+{"seq_id":"74010081975","text":"\n\nimport sys\nimport re\nimport datetime\nimport xlsxwriter\n\nif len(sys.argv) == 3:\n print('Only Three Arguments')\n # Path_excel='C:/Users/m.ali/Desktop/GAMAL_RF1_Status_22_3_2022_2.xlsx'\n Path_excel='C:/Users/m.ali/Desktop/Hybrid_2271_4.xlsx'\n Path_excel_Retry='C:/Users/m.ali/Desktop/Hybrid_2271_5.xlsx'\n path_txt='C:/Users/m.ali/Desktop/Serial_Hybrid.txt' #File Contain Serial meter\n #path_txt='C:/Users/m.ali/Desktop/Gamal_RF1_Serial.txt'\n Kpi_File_Path='C:/Users/m.ali/All_Sites/Shaheen_Elsour/kpi_plc'+ datetime.date.today().strftime('%d-%m-%Y')+'.txt'\n # Kpi_File_Path = 'C:/Users/m.ali/All_Sites/GAMAL/kpi_rf1.txt'\n \nelse:\n print(\"okay\")\n path_txt=sys.argv[4]\n Path_excel=sys.argv[3]\n\n\n\n#print(\"number of arguments=%d\"%len(sys.argv))\n\nworkbook = xlsxwriter.Workbook(Path_excel)\n\nworksheet = workbook.add_worksheet('Sheet2')\n#worksheet_1= workbook.get_worksheet_by_name('Sheet2')\ndate_format = \"%Y-%m-%d\"\noffset_try = 0\n#worksheet_1.write(0,0,1)\n#worksheet_1.write(100,1,1000)\na = datetime.datetime.strptime(sys.argv[1], date_format)\nb = datetime.datetime.strptime(sys.argv[2], date_format)\ndelta = b - a\nprint (delta.days) # that's it\n\ndate_generated = [a + datetime.timedelta(days=x) for x in range(0, (b-a).days)]\n\n#print(date_generated)\n\n\n\nfor i,date in enumerate(date_generated):\n print (date.strftime(\"%Y-%m-%d\"))\n worksheet.write(i+1+offset_try, 0,date.strftime(\"%Y-%m-%d\")) #######33\n #worksheet.write(i+1, 1,'FAIL')\n\nCount_Match=0\n\nf = open(path_txt, \"r\") # Serial Meters\nfor index_1,x in enumerate(f):\n print(\"Look for Serial Meter:\")\n print(x)\n Count_Match=0 # init count for new Serial\n#Write Serial meter\n worksheet.write(0+offset_try, index_1+1, x)\n #worksheet.write_column(1,index_1+1,'FAIL')\n #for ind in range(delta.days):\n #print(ind)\n #worksheet.write(ind+1,index_1+1,'FAIL')\n for index_reow,date_1_1 in enumerate(date_generated):\n worksheet.write(index_reow+1 +offset_try,index_1+1,'FAIL')\n pattern = re.compile(date_1_1.strftime(\"%Y-%m-%d\")+\" \\S* \\S*\\s*\\S* \\SDONE\\S \\S* \\S\\d* \\S*\\s\"+x.rstrip('\\n'))\n print(\"Pattern:\")\n print(pattern)\n#Count_All=0;\n \n for i, line in enumerate(open(Kpi_File_Path)):\n #Count_All +=1\n for match in re.finditer(pattern, line):\n print ('Found on line %s: %s' % (i+1, match.group()))\n Count_Match+=1\n #worksheet.write(,1,'DONE')\n worksheet.write(index_reow+1+offset_try,index_1+1,'DONE')\n\n print('LINE:%s'%(match.group()[:10]))\n \n \n \n print(x)\n print(\"match count=%d\"%Count_Match)\n print(\"All_count=%d\"%delta.days)\n print((Count_Match/delta.days)*100)\n worksheet.write(delta.days+1+offset_try, index_1+1,(Count_Match/delta.days)*100)\n\n worksheet.write(delta.days+2+offset_try, index_1+1,Count_Match)\n\n worksheet.write(delta.days+3+offset_try, index_1+1,delta.days)\n \n\nworksheet.write(delta.days+1 +offset_try, 0,'Percentage')\nworksheet.write(delta.days+2+offset_try, 0,'Done_Count')\nworksheet.write(delta.days+3+offset_try, 0,'Total_Count')\nworkbook.close()\nf.close()\n\n\n\n\n","repo_name":"ahmedsaid12345/Tools_Python","sub_path":"All_Tools/Tool_To_Look_Meter_enchanced.py","file_name":"Tool_To_Look_Meter_enchanced.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32335764194","text":"import tritonclient.grpc as t_client\nfrom tritonclient.utils import InferenceServerException\nimport argparse\n\ndef assert_error(func, *args, contains=None):\n try:\n func(*args)\n msg = \"Should raise error: \" + ', '.join([str(arg) for arg in args])\n assert False, msg\n except InferenceServerException as err:\n err_msg = str(err)\n if contains is not None:\n assert contains in err_msg, f'Error message:\\n {err_msg}\\nshould contain:\\n{contains}'\n\ndef test_loading(url):\n client = t_client.InferenceServerClient(url=url)\n\n client.load_model('model0_gpu_valid')\n print('model0_gpu_valid OK')\n\n assert_error(client.load_model, 'model1_gpu_invalid_i_type',\n contains='Invalid argument: Mismatch of data_type config for \"DALI_INPUT_1\".\\n'\n 'Data type defined in config: TYPE_FP32\\n'\n 'Data type defined in pipeline: TYPE_FP16')\n print('model1_gpu_invalid_i_type OK')\n\n assert_error(client.load_model, 'model2_gpu_invalid_o_ndim',\n contains='Invalid argument: Mismatch in number of dimensions for \"DALI_OUTPUT_1\"\\n'\n 'Number of dimensions defined in config: 2\\n'\n 'Number of dimensions defined in pipeline: 1')\n print('model2_gpu_invalid_o_ndim OK')\n\n client.load_model('model3_cpu_valid')\n print('model3_cpu_valid OK')\n\n assert_error(client.load_model, 'model4_cpu_invalid_missing_output',\n contains='The number of outputs specified in the DALI pipeline '\n 'and the configuration file do not match.\\n'\n 'Model config outputs: 1\\n'\n 'Pipeline outputs: 2')\n print('model4_cpu_invalid_missing_output OK')\n\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-u', '--url', type=str, required=False, default='localhost:8001',\n help='Inference server GRPC URL. Default is localhost:8001.')\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = parse_args()\n test_loading(args.url)\n","repo_name":"triton-inference-server/dali_backend","sub_path":"qa/L0_config_validation/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2067,"program_lang":"python","lang":"en","doc_type":"code","stars":106,"dataset":"github-code","pt":"22"}
+{"seq_id":"4362072123","text":"from collections import defaultdict\n\nimport mock\n\nfrom chroma_core.lib.cache import ObjectCache\nfrom chroma_core.services.job_scheduler.job_scheduler import JobScheduler\nfrom django.db import connection\nfrom tests.unit.chroma_api.tastypie_test import ResourceTestCase\nfrom tests.unit.chroma_core.helpers import synthetic_volume_full\nfrom chroma_core.models import ManagedTarget\n\n\nclass ChromaApiTestCase(ResourceTestCase):\n \"\"\"\n Unit tests which drive the *Resource classes in chroma_api/\n \"\"\"\n\n def __init__(self, methodName=None, username=\"admin\", password=\"lustre\"):\n super(ChromaApiTestCase, self).__init__(methodName=methodName)\n self.username = username\n self.password = password\n\n def setUp(self):\n super(ChromaApiTestCase, self).setUp()\n from chroma_api.authentication import CsrfAuthentication\n\n self.old_is_authenticated = CsrfAuthentication.is_authenticated\n CsrfAuthentication.is_authenticated = mock.Mock(return_value=True)\n\n self.assertTrue(self.api_client.client.login(username=self.username, password=self.password))\n\n from tests.unit.chroma_core.helpers import load_default_profile\n\n load_default_profile()\n\n # If the test that just ran imported storage_plugin_manager, it will\n # have instantiated its singleton, and created some DB records.\n # Django TestCase rolls back the database, so make sure that we\n # also roll back (reset) this singleton.\n import chroma_core.lib.storage_plugin.manager\n\n chroma_core.lib.storage_plugin.manager.storage_plugin_manager = (\n chroma_core.lib.storage_plugin.manager.StoragePluginManager()\n )\n\n # Fetching available transitions from the JobSchedule would require\n # Avoiding moving this to integration scope by mocking the rpc\n # fetched method.\n\n @classmethod\n def fake_available_transitions(cls, object_list):\n transitions = defaultdict(list)\n for obj_ct, obj_id in object_list:\n obj = JobScheduler._retrieve_stateful_object(obj_ct, obj_id)\n # fake receiving ['fake_trans', 'make_fake_trans'] from JS\n # and converting to this form with verbs\n transitions[obj.id] = [\n {\"state\": \"fake_trans\", \"verb\": \"make_fake_trans\"},\n {\"state\": \"fake_other\", \"verb\": \"make_fake_other\"},\n ]\n return transitions\n\n from chroma_core.services.job_scheduler import job_scheduler_client\n\n self.old_available_transitions = job_scheduler_client.JobSchedulerClient.available_transitions\n job_scheduler_client.JobSchedulerClient.available_transitions = fake_available_transitions\n\n @classmethod\n def fake_available_jobs(cls, object_list):\n jobs = defaultdict(list)\n for obj_ct, obj_id in object_list:\n obj = JobScheduler._retrieve_stateful_object(obj_ct, obj_id)\n jobs[obj.id] = []\n return jobs\n\n self.old_available_jobs = job_scheduler_client.JobSchedulerClient.available_jobs\n job_scheduler_client.JobSchedulerClient.available_jobs = fake_available_jobs\n\n @classmethod\n def fake_get_locks(cls, obj_key, obj_id):\n return {\"read\": [1, 2], \"write\": [3, 4]}\n\n self.old_get_locks = job_scheduler_client.JobSchedulerClient.get_locks\n job_scheduler_client.JobSchedulerClient.get_locks = fake_get_locks\n\n def get_targets_fn():\n from chroma_core.models import ManagedHost\n\n ids = [x.id for x in ManagedHost.objects.all()]\n host_id = ids[0]\n\n return [\n {\"name\": \"MGS\", \"active_host_id\": host_id, \"host_ids\": ids, \"uuid\": \"uuid_mgt\"},\n {\"name\": \"testfs-MDT0000\", \"active_host_id\": host_id, \"host_ids\": ids, \"uuid\": \"uuid_mdt\"},\n {\"name\": \"testfs-OST0000\", \"active_host_id\": host_id, \"host_ids\": ids, \"uuid\": \"uuid_ost0\"},\n {\"name\": \"testfs-OST0001\", \"active_host_id\": host_id, \"host_ids\": ids, \"uuid\": \"uuid_ost1\"},\n ]\n\n self.get_targets_mock = mock.MagicMock(side_effect=get_targets_fn)\n mock.patch(\"chroma_core.lib.graphql.get_targets\", new=self.get_targets_mock).start()\n\n self.addCleanup(mock.patch.stopall)\n\n def tearDown(self):\n from chroma_api.authentication import CsrfAuthentication\n\n CsrfAuthentication.is_authenticated = self.old_is_authenticated\n\n # Restore\n from chroma_core.services.job_scheduler import job_scheduler_client\n\n job_scheduler_client.JobSchedulerClient.available_transitions = self.old_available_transitions\n job_scheduler_client.JobSchedulerClient.available_jobs = self.old_available_jobs\n\n ObjectCache.clear()\n\n def api_set_state_full(self, uri, state):\n original_object = self.api_get(uri)\n original_object[\"state\"] = state\n response = self.api_client.put(uri, data=original_object)\n try:\n self.assertHttpAccepted(response)\n except AssertionError:\n raise AssertionError(\"response = %s:%s\" % (response.status_code, self.deserialize(response)))\n self.assertHttpAccepted(response)\n\n def api_set_state_partial(self, uri, state):\n response = self.api_client.put(uri, data={\"state\": state})\n try:\n self.assertHttpAccepted(response)\n except AssertionError:\n raise AssertionError(\"response = %s:%s\" % (response.status_code, self.deserialize(response)))\n\n def api_get(self, uri):\n response = self.api_client.get(uri)\n try:\n self.assertHttpOK(response)\n except AssertionError:\n raise AssertionError(\"response = %s:%s\" % (response.status_code, self.deserialize(response)))\n return self.deserialize(response)\n\n def api_post(self, uri, data=None, assertion_test=lambda self, response: self.assertHttpAccepted(response)):\n response = self.api_client.post(uri, data=data)\n try:\n assertion_test(self, response)\n except AssertionError:\n raise AssertionError(\"response = %s:%s\" % (response.status_code, self.deserialize(response)))\n return self.deserialize(response)\n\n def api_get_list(self, uri, **kwargs):\n response = self.api_client.get(uri, **kwargs)\n try:\n self.assertHttpOK(response)\n except AssertionError:\n raise AssertionError(\"response = %s:%s\" % (response.status_code, self.deserialize(response)))\n return self.deserialize(response)[\"objects\"]\n\n def api_patch_attributes(self, uri, attributes):\n response = self.api_client.patch(uri, data=attributes)\n try:\n self.assertHttpAccepted(response)\n except AssertionError:\n raise AssertionError(\"response = %s:%s\" % (response.status_code, self.deserialize(response)))\n\n def spider_api(self):\n ObjectCache.clear()\n\n from chroma_api.urls import api\n\n xs = filter(lambda x: x[0] != \"action\", api._registry.items())\n xs = filter(lambda x: \"get\" in x[1]._meta.list_allowed_methods, xs)\n\n for _, resource in xs:\n list_uri = resource.get_resource_uri()\n response = self.api_client.get(list_uri, data={\"limit\": 0})\n self.assertEqual(\n response.status_code, 200, \"%s: %s %s\" % (list_uri, response.status_code, self.deserialize(response))\n )\n if \"get\" in resource._meta.detail_allowed_methods:\n objects = self.deserialize(response)[\"objects\"]\n\n for object in objects:\n # Deal with the newer bulk data format, if it is in that format.\n if (\"resource_uri\" not in object) and (\"traceback\" in object) and (\"error\" in object):\n del object[\"traceback\"]\n del object[\"error\"]\n self.assertEqual(len(object), 1)\n object = object.values()[0]\n\n response = self.api_client.get(object[\"resource_uri\"])\n self.assertEqual(\n response.status_code,\n 200,\n \"resource_url: %s, %s %s %s\"\n % (object[\"resource_uri\"], response.status_code, self.deserialize(response), object),\n )\n","repo_name":"whamcloud/integrated-manager-for-lustre","sub_path":"tests/unit/chroma_api/chroma_api_test_case.py","file_name":"chroma_api_test_case.py","file_ext":"py","file_size_in_byte":8450,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"22"}
+{"seq_id":"4627244250","text":"'''\nAuthor : Tianzw\nDate : 2021-03-26 15:38:52\nLastEditors : Please set LastEditors\nLastEditTime : 2021-04-08 15:21:20\nFilePath : /activity-recognition/src/py/analysis_mdsp.py\n'''\nfrom datetime import datetime\nfrom multiprocessing import Process\nfrom pathlib import Path\n\nimport click\nimport matplotlib as mpl\nfrom matplotlib import ticker\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom activity_recognizer_demo import (analysis_result, process_df,\n process_log_file)\n\nSPORTS = ['UNKNOWN', 'WALK', 'RUN', 'ELLIPTICAL', 'BIKE']\n\nACCEL_LOG_COLUMNS = [\n 'CurrentTimeMillis',\n 'EventTimestamp(ns)',\n 'AccelX',\n 'AccelY',\n 'AccelZ',\n]\n\nACCEL_RAW_COLUMNS = [\n 'CurrentTimeMillis',\n 'AccelX',\n 'AccelY',\n 'AccelZ',\n]\n\nSPORT_COLUMNS = ['CurrentTimeMillis', 'sport']\n\n\ndef get_timestamp_acc(line: str) -> tuple:\n fields = line.strip().split('|')\n time_timestamp = fields[0].strip().split(' ')\n timestamp = time_timestamp[-1]\n timestamp = int(timestamp)\n feed_data = fields[1].strip().split(',')\n accel_x, accel_y, accel_z = feed_data[2:]\n timestamp_acc = (timestamp, accel_x, accel_y, accel_z)\n return timestamp_acc\n\n\ndef get_timestamp_sport(line: str) -> tuple:\n fields = line.strip().split('|')\n timestamp = int(fields[0].strip().split(' ')[-1])\n sport = fields[-1].strip().split(' ')[0].split('_')[-1]\n timestamp_sport = (timestamp, sport)\n\n return timestamp_sport\n\n\ndef plot_sport(df: pd.DataFrame):\n\n timestamp = df['CurrentTimeMillis'].values.astype(np.int)\n sport = df['sport'].apply(lambda x: SPORTS.index(x)\n if x in SPORTS else '-1').values\n print(f'len = {len(sport)}')\n\n print(f'sport = {sport}')\n\n mpl.use('Qt5Agg')\n\n _, ax = plt.subplots(1, 1, figsize=(10, 5))\n\n ax.plot(timestamp, sport, marker='o')\n\n ax.set_yticks([i for i in range(len(SPORTS))])\n ax.set_yticklabels(SPORTS)\n\n @ticker.FuncFormatter\n def func(x, pos):\n return datetime.fromtimestamp(int(x)).time()\n\n ax.xaxis.set_major_formatter(func)\n\n plt.grid()\n\n plt.show()\n\n\ndef clip_mdsp_with_timerange(mdsp_log_path: Path, timerange: tuple) -> tuple:\n low, high = timerange[0], timerange[1]\n\n timestamp_accs = []\n timestamp_sports = []\n with open(mdsp_log_path, 'r') as f:\n lines = f.readlines()\n for line in lines:\n fields = line.strip().split('|')\n timestamp = fields[0].strip().split(' ')[-1] # not 1\n timestamp = int(timestamp)\n if timestamp < low:\n continue\n if timestamp > high:\n break\n if line.find('feed_data') != -1:\n timestamp_acc = get_timestamp_acc(line)\n timestamp_accs.append(timestamp_acc)\n if len(fields) == 3 and line.find('MI_HAR') != -1:\n timestamp_sport = get_timestamp_sport(line)\n timestamp_sports.append(timestamp_sport)\n timestamp_acc_df = pd.DataFrame(timestamp_accs, columns=ACCEL_RAW_COLUMNS)\n timestamp_sport_df = pd.DataFrame(timestamp_sports, columns=SPORT_COLUMNS)\n\n return (timestamp_acc_df, timestamp_sport_df)\n\n\ndef plot_acc_and_sport(acc_df: pd.DataFrame, sport_df: pd.DataFrame) -> None:\n pass\n\n\ndef read_raw_feed_data(log_path: Path) -> pd.DataFrame:\n log_path = Path(log_path)\n\n records = []\n with open(log_path, 'r') as f:\n line = f.readlines()\n for i, line in enumerate(line):\n fields = line.strip().split('|')\n time_timestamp = fields[0].strip().split(' ')\n timestamp = time_timestamp[-1]\n timestamp = int(timestamp)\n feed_data = fields[1].strip().split(',')\n accel_x, accel_y, accel_z = feed_data[2:]\n record = (timestamp, accel_x, accel_y, accel_z)\n records.append(record)\n raw_feed_data_df = pd.DataFrame(records, columns=ACCEL_RAW_COLUMNS)\n return raw_feed_data_df\n\n\ndef get_timerange(libai_record: Path) -> tuple:\n libai_record = Path(libai_record)\n libai_name = libai_record.name\n timestamp_key = libai_name.split('-')[0]\n accel_name = timestamp_key + '-accel-52HZ.csv'\n accel_path = libai_record / accel_name\n\n print(f'accel_path = {accel_path}')\n\n accel_df = pd.read_csv(accel_path, header=1)\n timestamp_begin = accel_df.iloc[0, 0].astype(np.int) // 1000 - 60\n timestamp_end = accel_df.iloc[-1, 0].astype(np.int) // 1000\n\n return (timestamp_begin, timestamp_end)\n\n\ndef clip_raw_feed_data(raw_feed_data_df: pd.DataFrame,\n timerange: tuple) -> pd.DataFrame:\n timestamp_begin = timerange[0]\n timestamp_end = timerange[1]\n\n flag = (raw_feed_data_df.iloc[:, 0] >=\n timestamp_begin) & (raw_feed_data_df.iloc[:, 0] <= timestamp_end)\n feed_data_cliped = raw_feed_data_df[flag]\n\n return feed_data_cliped\n\n\ndef plot_timestamp(timestamp_values: np.ndarray, timerange: tuple):\n begin, end = timerange[0], timerange[1]\n\n timestamp_cnt = np.zeros(end - begin + 1).astype(np.int)\n ele, cnt = np.unique(timestamp_values, return_counts=True)\n\n timestamp_cnt[ele - begin] = cnt\n\n x = np.arange(begin, end + 1, 1)\n y = timestamp_cnt\n\n _, axes = plt.subplots(2, 1, figsize=(12, 6))\n\n axes[0].plot(x, y, marker='o', label='timestamp_cnt')\n axes[1].plot(x, y, marker='o', label='timestamp_cnt')\n\n @ticker.FuncFormatter\n def func(x, pos):\n return datetime.fromtimestamp(int(x)).time().strftime('%H:%M:%S')\n\n @ticker.FuncFormatter\n def func_str(x, pos):\n return str(int(x))\n\n axes[0].xaxis.set_major_formatter(func)\n axes[1].xaxis.set_major_formatter(func_str)\n\n for ax in axes:\n ax.grid()\n ax.legend()\n plt.show()\n\n\ndef post_process(feed_data_cliped: pd.DataFrame,\n activity_type=-1) -> pd.DataFrame:\n ms_delta = (1 / 26) * 1e3\n ns_delta = (1 / 26) * 1e9\n\n size = len(feed_data_cliped)\n\n timestamp_base = feed_data_cliped.iloc[0, 0] * 1000\n timestamps = [int(ms_delta * i) for i in range(size)] + timestamp_base\n ns = [int(ns_delta * i) for i in range(size)]\n\n feed_data_cliped.loc[:, 'CurrentTimeMillis'] = timestamps\n feed_data_cliped.loc[:, 'EventTimestamp(ns)'] = ns\n\n return feed_data_cliped[ACCEL_LOG_COLUMNS]\n\n\ndef save_df_nearly(df: pd.DataFrame, log_path: Path) -> Path:\n log_path = Path(log_path)\n\n feed_data_path = log_path.parent / (log_path.stem + '_cliped' + '.log')\n\n df.to_csv(feed_data_path, index=False)\n\n return feed_data_path\n\n\ndef run(log_path: Path, libai_record: Path):\n timerange = get_timerange(libai_record)\n\n acc_df, sport_df = clip_mdsp_with_timerange(log_path, timerange)\n\n plot_sport(sport_df)\n plot_timestamp(acc_df['CurrentTimeMillis'].values, timerange)\n\n standard_acc_df = post_process(acc_df)\n feed_data_path = save_df_nearly(standard_acc_df, log_path)\n\n df, result = process_log_file(feed_data_path)\n\n analysis_result(df, result)\n\n\n@click.command()\n@click.argument('log-path')\n@click.option('-l', '--libai-record', required=True)\ndef main(log_path, libai_record):\n run(log_path, libai_record)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"zhongwei8/MYGIT","sub_path":"xiaomi/analysis_mdsp.py","file_name":"analysis_mdsp.py","file_ext":"py","file_size_in_byte":7251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73631125177","text":"import math\n\nimport matplotlib.colors as mpl_colors\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nimport numpy as np\n\nfrom . import constants as const\nfrom . import plot_utils as plt_u\nfrom .constants import RGBA, Colour\n\n\ndef plot_point_cloud_2D(config, point_cloud, subtitle, name):\n # Create Figure\n fig, ax = plt_u.init_plot_2D(f\"Point Cloud {subtitle}\", \"X\", \"Y\")\n plot = ax.scatter(\n point_cloud[\"x\"],\n point_cloud[\"y\"],\n c=point_cloud[\"intensity\"] / 255,\n cmap=plt.cm.gist_rainbow,\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n vmin=0.0,\n vmax=1.0,\n )\n\n plt_u.calibrate_axis(ax)\n plt_u.add_colourbar(fig, plot, \"Point Intensity\", RGBA.MS_ORANGE.value, RGBA.WHITE.value)\n\n ax.text(\n 0.01,\n 0.99,\n f\"Points: {point_cloud.shape[0]}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef plot_segments_2D(config, point_cloud, point_norms, segments, name):\n unique_segments = np.unique(segments)\n fig, ax = plt_u.init_plot_2D(\"Point Cloud Discretised into Segments\", \"X\", \"Y\")\n\n for unique_segment in unique_segments:\n max_norm_in_seg = np.max(point_norms[segments == unique_segment])\n\n center_adjust = 0.5\n if unique_segment < 0:\n center_adjust = -0.5\n\n end_x = max_norm_in_seg * math.cos((unique_segment + center_adjust) * const.DELTA_ALPHA)\n end_y = max_norm_in_seg * math.sin((unique_segment + center_adjust) * const.DELTA_ALPHA)\n\n ax.plot(\n [0, end_x],\n [0, end_y],\n c=Colour.MS_BLUE.value,\n linewidth=(72.0 / fig.dpi) ** 2,\n zorder=1,\n )\n\n colour_set = [Colour.MS_ORANGE.value, Colour.WHITE.value]\n ax.scatter(\n point_cloud[\"x\"],\n point_cloud[\"y\"],\n c=(segments % len(colour_set)),\n cmap=mpl_colors.ListedColormap(colour_set),\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n zorder=2,\n )\n\n ax.plot(\n 0,\n 0,\n c=Colour.DARK_GREY.value,\n marker=\"o\",\n markersize=3,\n zorder=3,\n )\n\n plt_u.calibrate_axis(ax)\n\n ax.text(\n 0.01,\n 0.99,\n f\"Segments: {unique_segments.shape[0]}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef plot_bins_2D(config, point_cloud, segments, bins, name):\n fig, ax = plt_u.init_plot_2D(\"Segments Discretised into Bins\", \"X\", \"Y\")\n\n theta = np.linspace((np.min(segments) - 1) * const.DELTA_ALPHA, np.max(segments) * const.DELTA_ALPHA, 201)\n line_colour = RGBA.MS_BLUE.value\n for curr_bin in range(bins.min(), bins.max() + 1):\n x = curr_bin * const.BIN_SIZE * np.cos(theta)\n y = curr_bin * const.BIN_SIZE * np.sin(theta)\n\n if curr_bin == bins.max():\n line_colour = RGBA.MS_ORANGE.value\n\n ax.plot(x, y, c=line_colour, linewidth=(72.0 / fig.dpi) ** 2, zorder=1)\n\n colour_set = [Colour.GREEN.value, Colour.LIGHT_BLUE.value, Colour.RED.value]\n ax.scatter(\n point_cloud[\"x\"],\n point_cloud[\"y\"],\n c=(bins % len(colour_set)),\n cmap=mpl_colors.ListedColormap(colour_set),\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n zorder=2,\n )\n\n plt_u.calibrate_axis(ax)\n\n ax.text(\n 0.01,\n 0.99,\n f\"Max Bin Count: {bins.max()}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.MS_ORANGE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n ax.text(\n 0.01,\n 0.95,\n f\"Bin Size: {const.BIN_SIZE}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef plot_prototype_points_2D(config, point_norms, segments, proto_segs_arr, proto_segs, name):\n unique_segments = np.unique(segments)\n fig, ax = plt_u.init_plot_2D(\"Prototype Points\", \"X\", \"Y\")\n\n for unique_segment in unique_segments:\n max_norm_in_seg = np.max(point_norms[segments == unique_segment])\n\n center_adjust = 0.5\n if unique_segment < 0:\n center_adjust = -0.5\n\n end_x = max_norm_in_seg * math.cos((unique_segment + center_adjust) * const.DELTA_ALPHA)\n end_y = max_norm_in_seg * math.sin((unique_segment + center_adjust) * const.DELTA_ALPHA)\n\n ax.plot(\n [0, end_x],\n [0, end_y],\n c=Colour.MS_BLUE.value,\n linewidth=(72.0 / fig.dpi) ** 2,\n zorder=1,\n )\n\n colour_set = [Colour.MS_ORANGE.value, Colour.WHITE.value]\n for idx, segment in enumerate(proto_segs_arr):\n segment_num = proto_segs[idx]\n\n center_adjust = 0.5\n if segment_num < 0:\n center_adjust = -0.5\n\n x = segment[:, 0] * math.cos((segment_num + center_adjust) * const.DELTA_ALPHA)\n y = segment[:, 0] * math.sin((segment_num + center_adjust) * const.DELTA_ALPHA)\n\n ax.scatter(\n x,\n y,\n c=colour_set[proto_segs[idx] % len(colour_set)],\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n zorder=2,\n )\n\n ax.plot(\n 0,\n 0,\n c=Colour.DARK_GREY.value,\n marker=\"o\",\n markersize=3,\n zorder=3,\n )\n\n plt_u.calibrate_axis(ax)\n\n ax.text(\n 0.01,\n 0.99,\n f\"Prototype Points: {sum(len(proto_points) for proto_points in proto_segs_arr)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef plot_ground_plane_2D(config, segments, ground_plane, proto_segs_arr, proto_segs, name):\n fig, ax = plt_u.init_plot_2D(\"Ground Plane Mapped\", \"X\", \"Y\")\n\n # Plot Ground Plane\n ground_line_count = 0\n colour_set = [Colour.LIGHT_BLUE.value, Colour.DIM_BLUE.value]\n for segment in np.unique(segments):\n ground_set = ground_plane[segment]\n if ground_set != 0:\n for jdx, ground_line in enumerate(ground_set):\n ground_line_count += 1\n p1 = ground_line[2]\n p2 = ground_line[3]\n\n x = np.array([p1[0], p2[0]]) * math.cos(const.DELTA_ALPHA * segment)\n y = np.array([p1[0], p2[0]]) * math.sin(const.DELTA_ALPHA * segment)\n\n ax.plot(\n x,\n y,\n color=colour_set[jdx % len(colour_set)],\n linewidth=(72.0 / fig.dpi) ** 2,\n zorder=1,\n )\n\n # Plot Prototype Points\n for idx, segment in enumerate(proto_segs_arr):\n segment_num = proto_segs[idx]\n x = segment[:, 0] * math.cos(const.DELTA_ALPHA * segment_num)\n y = segment[:, 0] * math.sin(const.DELTA_ALPHA * segment_num)\n\n ax.scatter(\n x,\n y,\n c=Colour.WHITE.value,\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n zorder=2,\n )\n\n plt_u.calibrate_axis(ax)\n\n ax.text(\n 0.01,\n 0.99,\n f\"Ground Lines: {ground_line_count}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.LIGHT_BLUE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef point_cloud_3D(config, point_cloud, subtitle, name):\n # Create Figure\n fig, ax = plt_u.init_plot_3D(subtitle, \"X\", \"Y\", \"Z\")\n\n # Plot Point Cloud\n ax.scatter(\n point_cloud[:, 0],\n point_cloud[:, 1],\n point_cloud[:, 2],\n c=Colour.MS_BLUE.value,\n marker=\".\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n zorder=1,\n )\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef plot_cones_3D(config, point_cloud, segments, ground_plane, point_labels, reconstructed_centers, cone_centers, name):\n # Create Figure\n fig, ax = plt_u.init_plot_3D(\"Cone Locations Identified\", \"X\", \"Y\", \"Z\")\n\n # Plot Ground Plane\n ground_line_count = 0\n non_empty_segs = np.unique(segments)\n for idx, ground_set in enumerate(ground_plane):\n if ground_set != 0:\n for jdx, ground_line in enumerate(ground_set):\n ground_line_count += 1\n p1 = ground_line[2]\n p2 = ground_line[3]\n\n x = np.array([p1[0], p2[0]]) * math.cos(const.DELTA_ALPHA * non_empty_segs[idx])\n y = np.array([p1[0], p2[0]]) * math.sin(const.DELTA_ALPHA * non_empty_segs[idx])\n z = np.array([p1[1], p2[1]])\n\n ax.plot(\n x,\n y,\n z,\n color=[Colour.LIGHT_BLUE.value, Colour.DIM_BLUE.value][jdx % 2],\n linewidth=(72.0 / fig.dpi) ** 2,\n zorder=1,\n )\n\n # Plot rings around cones\n theta = np.linspace(0, 2 * np.pi, 201)\n x = const.CONE_DIAM * np.cos(theta)\n y = const.CONE_DIAM * np.sin(theta)\n for center in cone_centers:\n ax.plot(\n x + center[0],\n y + center[1],\n -const.LIDAR_HEIGHT_ABOVE_GROUND,\n c=RGBA.WHITE.value,\n linewidth=(72.0 / fig.dpi) ** 2 * 8,\n zorder=2,\n )\n\n # Plot rings around cones\n for center in reconstructed_centers:\n ax.scatter(\n x + center[0],\n y + center[1],\n -const.LIDAR_HEIGHT_ABOVE_GROUND,\n c=RGBA.MS_ORANGE.value,\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2 * 4,\n linewidths=0,\n zorder=4,\n )\n\n ax.scatter(\n point_cloud[\"x\"],\n point_cloud[\"y\"],\n point_cloud[\"z\"],\n c=point_labels,\n cmap=mpl_colors.ListedColormap([RGBA.GREEN.value, RGBA.RED.value]),\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n zorder=5,\n )\n\n # Plot car model\n if config.plot_car:\n ax.add_collection(create_car_model(config))\n\n dist = const.LIDAR_RANGE / 5\n min_height = np.min(point_cloud[\"z\"])\n plt_u.calibrate_axis(ax, [-dist, dist], [-dist, dist], [-const.LIDAR_HEIGHT_ABOVE_GROUND, dist])\n ax.axes.set_zlim(min_height, dist * 2 + min_height)\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=False)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef plot_cones_2D(config, point_cloud, point_labels, cone_centers, cone_points, name):\n fig, ax = plt_u.init_plot_2D(\"Cone Locations Identified\", \"X\", \"Y\")\n ax.scatter(\n point_cloud[\"x\"],\n point_cloud[\"y\"],\n c=point_labels,\n cmap=mpl_colors.ListedColormap([RGBA.GREEN.value, RGBA.RED.value]),\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n vmin=0.0,\n vmax=1.0,\n )\n\n ax.scatter(cone_centers[:, 0], cone_centers[:, 1], c=RGBA.WHITE.value, marker=\".\")\n\n for i, cone_center in enumerate(cone_centers):\n ax.text(\n cone_center[0] - 0.5,\n cone_center[1] - 1,\n round(np.mean(cone_points[i][\"intensity\"]), 2),\n c=\"white\",\n fontsize=4,\n )\n\n plt_u.calibrate_axis(ax)\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225)\n\n\ndef plot_detailed_2D(\n config,\n point_cloud,\n segments,\n bins,\n ground_plane,\n point_labels,\n reconstructed_objects,\n reconstructed_centers,\n cone_intensities,\n cone_centers,\n cone_points,\n duration,\n name,\n):\n fig, ax = plt_u.init_plot_2D(\"LiDAR Perception Pipeline\", \"X\", \"Y\")\n\n # Plot Ground Plane\n ground_line_count = 0\n non_empty_segs = np.unique(segments)\n for idx, ground_set in enumerate(ground_plane):\n if ground_set != 0:\n for jdx, ground_line in enumerate(ground_set):\n ground_line_count += 1\n p1 = ground_line[2]\n p2 = ground_line[3]\n\n x = np.array([p1[0], p2[0]]) * math.cos(const.DELTA_ALPHA * non_empty_segs[idx])\n y = np.array([p1[0], p2[0]]) * math.sin(const.DELTA_ALPHA * non_empty_segs[idx])\n\n ax.plot(\n x,\n y,\n color=[Colour.LIGHT_BLUE.value, Colour.DIM_BLUE.value][jdx % 2],\n linewidth=(72.0 / fig.dpi) ** 2,\n zorder=1,\n )\n\n # Plot Point Cloud with Labels\n ax.scatter(\n point_cloud[\"x\"][~point_labels],\n point_cloud[\"y\"][~point_labels],\n c=RGBA.GREEN.value,\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n )\n ax.scatter(\n reconstructed_centers[:, 0],\n reconstructed_centers[:, 1],\n c=RGBA.MS_ORANGE.value,\n marker=\"x\",\n s=12,\n linewidths=2 * (72.0 / fig.dpi) ** 2,\n )\n ax.scatter(\n point_cloud[\"x\"][point_labels],\n point_cloud[\"y\"][point_labels],\n c=RGBA.RED.value,\n marker=\"s\",\n s=(72.0 / fig.dpi) ** 2,\n linewidths=0,\n label=\"test\",\n )\n\n max_intensity = max([np.max(i[\"intensity\"]) for i in cone_points])\n for i, cone_center in enumerate(cone_centers):\n # ax.scatter(cone_center[0], cone_center[1], c=np.mean(cone_points[i]['intensity']) / 255, cmap=plt.cm.gist_rainbow, marker=\".\")\n ax.scatter(\n cone_center[0],\n cone_center[1],\n c=np.mean(cone_points[i][\"intensity\"] / max_intensity),\n cmap=plt.cm.gray_r,\n marker=\".\",\n s=24,\n linewidths=(72.0 / fig.dpi) ** 2,\n )\n # ax.text(cone_center[0] - 0.5, cone_center[1] - 1, round(np.mean(cone_points[i]['intensity']), 2), c='white', fontsize=4)\n # ax.text(cone_center[0] - 1.15, cone_center[1] - 0.75, f'({round(cone_center[0], 1):.1f}, {round(cone_center[1], 1):.1f})', c='white', fontsize=3)\n ax.text(\n cone_center[0] - 1.15, cone_center[1] - 0.75, f\"({round(cone_intensities[i], 1):.1f}\", c=\"white\", fontsize=3\n )\n\n for i, rec_center in enumerate(reconstructed_centers):\n ax.text(\n rec_center[0] - 0.7,\n rec_center[1] + 0.45,\n f\"{len(reconstructed_objects[i]):<5}\",\n c=Colour.MS_ORANGE.value,\n fontsize=4,\n )\n\n ax.text(\n 0.01,\n 0.99,\n f\"Non Ground Points: {np.count_nonzero(point_labels)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.RED.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n ax.text(\n 0.01,\n 0.95,\n f\"Ground Points: {np.count_nonzero(point_labels == 0)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.GREEN.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n ax.text(\n 0.01,\n 0.91,\n f\"Ground Lines: {ground_line_count}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.LIGHT_BLUE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n ax.text(\n 0.01,\n 0.87,\n f\"Objects: {reconstructed_centers.shape[0]}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.MS_ORANGE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n ax.text(\n 0.01,\n 0.83,\n f\"Cones: {cone_centers.shape[0]}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n\n # ax.text(.01, .12, f'Non Empty Segments: {non_empty_segs.shape[0]}', ha='left', va='top', c=Colour.WHITE.value, fontsize=8, transform=ax.transAxes)\n # ax.text(.01, .08, f'Max Bin Assigned: {np.max(bins)}', ha='left', va='top', c=Colour.WHITE.value, fontsize=8, transform=ax.transAxes)\n ax.text(\n 0.01,\n 0.04,\n f\"Current Hz: {round(1 / duration, 1)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=8,\n transform=ax.transAxes,\n )\n\n ax.text(1.01, 0.99, \"Parameters\", ha=\"left\", va=\"top\", c=Colour.MS_ORANGE.value, fontsize=6, transform=ax.transAxes)\n ax.text(\n 1.01,\n 0.96,\n f\"LIDAR_RANGE = {round(const.LIDAR_RANGE, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.93,\n f\"DELTA_ALPHA = {round(const.DELTA_ALPHA, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.90,\n f\"BIN_SIZE = {round(const.BIN_SIZE, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.87,\n f\"T_M = {round(const.T_M, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.84,\n f\"T_M_SMALL = {round(const.T_M_SMALL, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.81,\n f\"T_B = {round(const.T_B, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.78,\n f\"T_RMSE = {round(const.T_RMSE, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.75,\n f\"REGRESS_BTWN_BINS = {round(const.REGRESS_BETWEEN_BINS, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.72,\n f\"T_D_GROUND = {round(const.T_D_GROUND, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.69,\n f\"CONE_DIAM = {round(const.CONE_DIAM, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.66,\n f\"CONE_HEIGHT = {round(const.CONE_HEIGHT, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.63,\n f\"LIDAR_VERT_RES = {round(const.LIDAR_VERTICAL_RES, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n ax.text(\n 1.01,\n 0.60,\n f\"LIDAR_HORIZ_RES = {round(const.LIDAR_HORIZONTAL_RES, 4)}\",\n ha=\"left\",\n va=\"top\",\n c=Colour.WHITE.value,\n fontsize=6,\n transform=ax.transAxes,\n )\n\n plt_u.calibrate_axis(ax)\n\n # Save Figure\n plt_u.add_logo(fig, dpi=225 * 2, small=True)\n plt.savefig(f\"{config.image_dir}/{name}.png\", dpi=225 * 2)\n\n\nimport os\n\n\ndef create_car_model(config):\n model_path = const.MODELS_DIR\n\n material_file = None\n object_file = None\n for item in os.listdir(model_path):\n item_split = item.split(\".\")\n if item_split[-1] == \"mtl\":\n material_file = item\n elif item_split[-1] == \"obj\":\n object_file = item\n\n mat_name = None\n materials = dict()\n with open(model_path + \"/\" + material_file) as file:\n for line in file.readlines():\n values = line.split()\n if not values:\n continue\n if values[0] == \"newmtl\":\n mat_name = values[1]\n if values[0] == \"Kd\":\n materials[mat_name] = tuple([float(values[1]), float(values[2]), float(values[3])])\n\n colours = []\n vertices = []\n triangles = []\n with open(model_path + \"/\" + object_file) as file:\n for line in file.readlines():\n values = line.split()\n if not values:\n continue\n if values[0] == \"usemtl\":\n mat_name = values[1]\n elif values[0] == \"v\":\n vertices.append(values[1:4])\n elif values[0] == \"f\":\n triangles.append(values[1:4])\n colours.append(materials[mat_name])\n\n np_vertices = np.array(vertices, dtype=np.float32)\n np_triangles = np.array(triangles, dtype=np.int32) - 1\n\n # Convert model from inches to metres\n INCH_TO_M = 0.0254\n x = np_vertices[:, 0] * INCH_TO_M\n y = np_vertices[:, 2] * INCH_TO_M\n z = np_vertices[:, 1] * INCH_TO_M\n\n # Align bottom of model's wheels with the lowest point in point cloud\n min_z = np.amin(z)\n point_min_z = -const.LIDAR_HEIGHT_ABOVE_GROUND\n z_diff = min_z - point_min_z\n z = z - z_diff\n\n # Align the front of the model (LiDAR camera location) with origin of point cloud (x=0)\n x_max = np.amax(x)\n x = x - x_max\n\n # Model vertices and faces\n triangle_vertices = np.array(\n [\n np.array([[x[T[0]], y[T[0]], z[T[0]]], [x[T[1]], y[T[1]], z[T[1]]], [x[T[2]], y[T[2]], z[T[2]]]])\n for T in np_triangles\n ]\n )\n\n collection = Poly3DCollection(triangle_vertices, facecolors=colours, edgecolors=None)\n return collection\n","repo_name":"QUT-Motorsport/QUTMS_Driverless","sub_path":"src/perception/lidar_pipeline/lidar_pipeline/library/visualiser_2.py","file_name":"visualiser_2.py","file_ext":"py","file_size_in_byte":22158,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"22"}
+{"seq_id":"40162375512","text":"import numpy as np\nimport shutil\nimport csv\nimport os\n#Looping through Categories\nanimal_category = []\nwith open('./animal_categories.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n animal_category.append(row)\n\nlandmark_path = os.path.join('valid_annotations')\n\nfor animal in animal_category:\n filename = './valid_annotations/val/id/animal_{}_test.csv'.format(animal[1])\n \n all_list = []\n unseen_image_list = []\n unseen_id_list = []\n seen_image_list = []\n valid_list = []\n\n with open(filename) as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n all_list.append(row)\n\n for i in all_list:\n if i[2] not in unseen_id_list: \n unseen_id_list.append(i[2])\n unseen_image_list.append(i)\n else:\n seen_image_list.append(i[2])\n \n for k in unseen_image_list:\n if k[2] not in seen_image_list:\n valid_list.append([k[0],k[1],k[2], k[3], 1])\n\n filename_valid = './valid_annotations/val/valid/animal_{}_id.csv'.format(animal[1])\n \n with open(str(filename_valid), 'w', newline='') as myfile:\n wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)\n for i in valid_list:\n wr.writerow(i)\n","repo_name":"malikilamalik/TA","sub_path":"filtering_animal_single_pose.py","file_name":"filtering_animal_single_pose.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74198506617","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 15 19:18:18 2016\n\n@author: mark\n\"\"\"\n\nimport backend\nimport api\n#from os import \nimport yaml\nimport sys,getopt\n\ndef get_raw_python_from_yaml(yaml_string):\n return yaml.load(yaml_string)\n \ndef process_header_from_raw_data(raw_python):\n \"\"\" \n input whole raw yaml data and returns a dictionary of the \n information for the header\n \"\"\"\n header = raw_python['header']\n header['zipcode']=str(header['zipcode'])\n return header\n \ndef process_body_from_raw_data(raw_python):\n \"\"\" \n input whole raw yaml data and returns a dictionary of the \n information for the header\n \"\"\"\n body = raw_python['body']\n return body\n \ndef create_point_objects_from_point_list(raw_python):\n \"\"\"\n input a list of dictionaries of point attributes\n ensure proper keys or throw error\n return list of point objects\n \"\"\"\n if not isinstance(raw_python, list):\n print(raw_python)\n raise TypeError('input to create_point_objects_from_point_list needs to be a list. currently is ' + str(type(raw_python)))\n points = []\n for point in raw_python:\n if not isinstance(point, dict):\n print(point)\n raise TypeError('input to create_point_objects_from_point_list needs to be a list of dictionaries. currently is a list of ' + str(type(point)))\n if 'text' not in point.keys():\n raise Exception('point objects must have a \"text\" attribute. current point contains ' + str(point))\n points.append(api.Point(point['text']))\n if 'year' in point.keys():\n points[-1].addYear(point['year'])\n if 'emphasis' in point.keys():\n points[-1].addEmphasis(point['emphasis'])\n return points\n \ndef create_task_objects_from_task_list(raw_python):\n \"\"\"\n input a list of dictionaries of task attributes\n ensure proper keys or throw error\n return list of task objects\n \"\"\"\n \n if not isinstance(raw_python, list):\n raise TypeError('input to create_task_objects_from_task_list needs to be a list. currently is ' + str(type(raw_python)))\n tasks=[]\n for task in raw_python:\n if not isinstance(task, dict):\n raise TypeError('input to create_point_objects_from_point_list needs to be a list of dictionaries. currently is a list of ' + str(type(task)))\n if 'name' not in task.keys():\n raise Exception('task objects must have a \"name\" attribute. current task contains ' + str(task))\n tasks.append(api.Task(task['name']))\n #add items to tasks\n if 'year' in task.keys():\n tasks[-1].year = task['year']\n if 'emphasis' in task.keys():\n tasks[-1].emphasis = task['emphasis']\n if 'time' in task.keys():\n tasks[-1].dates = task['time']\n if 'org' in task.keys():\n tasks[-1].entity = task['org']\n if 'points' in task.keys():\n tasks[-1].points = create_point_objects_from_point_list(task['points'])\n return tasks\n \ndef process_list_category_dicts_to_python_objects(raw_python):\n \"\"\"\n input a list of dictionaries of sections.\n ensure proper keys or throw error\n return list of category objects\n \"\"\"\n if not isinstance(raw_python, list):\n raise TypeError('input to process_body_to_python_objects needs to be a list. currently is ' + str(type(raw_python)))\n sections = []\n for section in raw_python:\n if not isinstance(section, dict):\n raise TypeError('input to create_section_objects_from_section_list needs to be a list of dictionaries. currently is a list of ' + str(type(section)))\n if 'topic' not in section.keys():\n raise Exception('section objects must have a \"topic\" attribute. current section contains ' + str(section))\n sections.append(api.Category(section['topic']))\n if 'year' in section.keys():\n sections[-1].year = section['year']\n if 'emphasis' in section.keys():\n sections[-1].emphasis = section['emphasis']\n if 'tasks' in section.keys():\n sections[-1].tasks = create_task_objects_from_task_list(section['tasks'])\n if 'points' in section.keys():\n sections[-1].points = create_point_objects_from_point_list(section['points'])\n return sections\n \n\ndef prepare_resume_data(resume_file):\n \n reader = open(resume_file,'r')\n resume_text = reader.read()\n resume_raw_python = yaml.load(resume_text)\n header = process_header_from_raw_data(resume_raw_python)\n list_of_category_dicts = process_body_from_raw_data(resume_raw_python)\n category_list = process_list_category_dicts_to_python_objects(list_of_category_dicts)\n reader.close()\n return header, category_list\n \ndef prepare_resume_preferences(preferences_file):\n if preferences_file =='':\n reader = open('default_preferences.yml','r')\n else:\n reader = open(preferences_file,'r')\n preferences_text = reader.read()\n preference_dict = yaml.load(preferences_text)\n pref = api.DocumentPreferences(preference_dict['desired categories'],\n preference_dict['desired year'],\n preference_dict['desired emphasis'])\n pref.all_emph(preference_dict['acceptable emphasis'])\n reader.close()\n return pref\n \ndef set_writing_file(file_name):\n if file_name == '':\n backend.file_writer = open('temp_tex_resume.tex','w')\n else:\n backend.file_writer = open(file_name,'w')\n #return file_writer\n \ndef parse_arguments(arguments):\n usage = 'generatory.py -d test.yml -p preferences.py -o output.tex'\n input_file = ''\n output_file = ''\n preferences_file = ''\n \n try:\n opts,args = getopt.getopt(arguments,\"hd:p:o:\",[\"data\",'preferences','tex-output'])\n except getopt.GetoptError:\n raise getopt.GetoptError(usage)\n for option, argument in opts:\n if option == '-h':\n print(usage)\n sys.exit()\n elif option in ('-d','--data'):\n input_file = argument\n elif option in ('-o','--output'):\n output_file = argument\n elif option in ('-p','--preferences'):\n preferences_file=argument\n return input_file, preferences_file, output_file\n \nif __name__ =='__main__':\n resume_file, resume_preferences, output_file_name = parse_arguments(sys.argv[1:])\n set_writing_file(output_file_name)\n hd, category_list = prepare_resume_data(resume_file)\n pref = prepare_resume_preferences(resume_preferences)\n # check that input of resume preferences match\n category_list_names = [cat.name for cat in category_list]\n category_dict = dict(zip(category_list_names,category_list))\n for cat in pref.categories:\n if cat not in category_list_names:\n raise AttributeError('`{0}` not in list of categories'.format(cat))\n backend.write(backend.makeheader(),process_text = False)\n backend.write(backend.begindocument())\n # makes name logo of the document\n backend.write(api.header(hd['name'], hd['address'],hd['city'],hd['state'],hd['zipcode'], hd['phone'],hd['email']))\n for category_name in pref.categories:\n if category_name in category_dict.keys():\n category = category_dict[category_name]\n category_string = backend.writeobj(category,pref)\n backend.write(category_string)\n \n \n \n backend.write(backend.enddocument())\n \n","repo_name":"goldmanm/job_specific_resume_generator","sub_path":"generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":7496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12237988627","text":"class TimeMap:\n\n def __init__(self):\n \n self.cache = {}\n \n \n self.returnValues = {}\n \n\n def set(self, key: str, value: str, timestamp: int) -> None:\n \n if key in self.cache:\n time_dict = self.cache[key]\n time_dict[timestamp] = value\n \n self.cache[key].update(time_dict)\n else:\n new_dict = {}\n new_dict[timestamp] = value\n \n self.cache[key] = new_dict\n \n\n def get(self, key: str, timestamp: int) -> str:\n \n if key in self.cache:\n temp_result = self.cache[key]\n else: \n return \"\"\n \n \n \n \n for i in range(timestamp, -1, -1):\n if i in temp_result:\n return temp_result[i]\n \n return \"\"\n\n","repo_name":"Soumadeep-Basu/LeetCodeSolutions","sub_path":"981(Time Based Key-Value Store).py","file_name":"981(Time Based Key-Value Store).py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20811871445","text":"if __name__ == '__main__': \n import os\n import glob\n from picture_nnunet_package.doInference import do_segmentation\n from nnunet.evaluation.evaluator import aggregate_scores\n import sys\n print(sys.platform)\n\n sesDirs = glob.glob(f'./test_data/LUMIERE/Patient-025/*')\n for sesDir in sesDirs:\n sesID = os.path.split(sesDir)[1]\n print(sesID)\n imgs = {}\n for mod in ['CT1','T1','T2','FLAIR']:\n imgs[mod] = glob.glob(sesDir+'/'+mod+'*.nii.gz')[0]\n if 'week-000' in sesID:\n sessionType = 'preop'\n else:\n sessionType = 'postop_beta'\n\n do_segmentation(imgs['CT1'], t1=imgs['T1'], t2=imgs['T2'], flair=imgs['FLAIR'], sessionType =sessionType, remove_intermediate_files = False, mni=False, wdir_postfix='random',skip_skullstrip=True)\n \n my_seg = glob.glob(f'{sesDir}/segmentation_native*.nii.gz')[0]\n ref_seg = glob.glob(f'{sesDir}/reference_segmentation_native*.nii.gz')[0]\n scores = aggregate_scores([(my_seg,ref_seg)],json_output_file=\"./summary.json\",labels=(0,1,2,3))\n print(scores['mean'])\n for label in scores['mean'].keys():\n if scores['mean'][label]['Total Positives Reference']>5:\n assert scores['mean'][label]['Dice'] >0.8\n","repo_name":"roelant001/picture-nnunet-package-test","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"19343671955","text":"import io\r\nimport logging\r\nimport os\r\nimport base64\r\n\r\nimport torch\r\nfrom PIL import Image\r\nfrom torchvision import transforms\r\n\r\n# Create model object\r\nmodel = None\r\n\r\ndef model_handler(data, context):\r\n \"\"\"\r\n Works on data and context to create model object or process inference request.\r\n Following sample demonstrates how model object can be initialized for jit mode.\r\n Similarly you can do it for eager mode models.\r\n\r\n Example archiver:\r\n torch-model-archiver --model-name transformer_model --version 1.0 \\\r\n --model-file src/models/visual_transformer_model.py \\\r\n --serialized-file=model_store/deployable_model.pt \\\r\n --handler=src/models/model_handler:model_handler \\\r\n --export-path=model_store -f\r\n\r\n Example torchserve:\r\n torchserve --start --ncs --model-store model_store \\\r\n --models transformer_model=transformer_model.mar\r\n\r\n Create request file:\r\ncat > instances.json < None:\n self.net = arch.VanillaNet(50, 3, DTYPE, name='sphere-geodesic')\n self.save_folder = save_folder\n if model_path is not None:\n self.net.load_weights(model_path).expect_partial()\n \n def error(self):\n e = L2_error(self.net)\n o = length(self.net)\n ce = constraint_error(self.net)\n return e.numpy(), o.numpy(), (o/objective-1.).numpy(), ce.numpy()\n\n @tf.function\n def train_step(self, b, g):\n with tf.GradientTape() as tape:\n loss_a = length(self.net) * g\n loss_b = tf.reduce_mean(boundary(self.net)**2)\n L = loss_a + 0.5*b*loss_b\n grads = tape.gradient(L, self.net.trainable_weights)\n self.optimizer.apply_gradients(zip(grads, self.net.trainable_weights))\n return loss_a, loss_b, L\n\n def learn(self, epochs=10000, n_sample=1000):\n lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(1e-3, decay_steps=epochs, decay_rate=1e-3, staircase=False)\n self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)\n print(\"{:>6}{:>12}{:>12}{:>12}{:>18}\".format('iteration', 'loss_a', 'loss_b', 'loss', 'runtime(s)'))\n start = time.time()\n log = {'iteration': [], 'beta': [], 'loss_a': [], 'loss_b': [], 'loss': [], 'L2-error': [],\\\n 'objective': [], 'objective-error': [], 'constraint-error': [], 'runtime': []}\n b = tf.constant(1., dtype=DTYPE)\n g = b/b\n epoch, tau = 0, 10\n # t = domain_sampler(n_sample)\n while epoch < epochs+1:\n loss_a, loss_b, L = self.train_step(b, g)\n if epoch % 10 == 0:\n step_details = [epoch, loss_a.numpy(), loss_b.numpy(), L.numpy(), time.time()-start]\n print('{:6d}{:15.6f}{:15.6f}{:12.6f}{:12.4f}'.format(*step_details))\n if epoch % 100 == 0:\n error, objective, objective_error, ce = self.error()\n log['iteration'].append(step_details[0])\n log['loss_a'].append(step_details[1])\n log['loss_b'].append(step_details[2])\n log['loss'].append(step_details[3])\n log['L2-error'].append(error)\n log['objective'].append(objective)\n log['objective-error'].append(objective_error)\n log['constraint-error'].append(ce)\n log['beta'].append(b.numpy())\n log['runtime'].append(step_details[4])\n self.net.save_weights('{}/{}_{}'.format(self.save_folder, self.net.name, epoch))\n \n epoch += 1\n\n if epoch % 2*tau == 0:\n if b < 1:\n b += 10.\n # t = domain_sampler(n_sample)\n\n if False:#epoch % 1000==0:\n a_, b_ = loss_a.numpy(), 0.5*b.numpy()*loss_b.numpy()\n c_ = np.ceil(np.log10(b_/a_))\n if a_> 1. and b_ < a_ and c_ < 0.:\n g = np.float32(10**c_) * tf.ones_like(b)\n pd.DataFrame(log).to_csv('{}/train_log.csv'.format(self.save_folder), index=None)\n self.net.save_weights('{}/{}'.format(self.save_folder, self.net.name))\n\n \nsave_folder = '../data/sphere-geodesic-inf'\nSolver(save_folder=save_folder).learn(epochs=10000, n_sample=int(1e3))","repo_name":"pinakm9/var-al","sub_path":"scripts/sphere-geo-inf.py","file_name":"sphere-geo-inf.py","file_ext":"py","file_size_in_byte":5030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38390192833","text":"# Write a python program to create a School class and 3 instance variables and 1 class variable.\n\nclass school:\n school=\"KV\"\n\n def __init__(self,roll,name,clss) :\n self.roll=roll\n self.name=name\n self.clss=clss\n\n def show(self):\n print(self.school,self.roll,self.name,self.clss)\n\nstudent=school(45 ,\"akash\" ,9)\n\nstudent.show()\n\n\n\n","repo_name":"sauravsingh0604/Python-","sub_path":"Assignment/Classes and Objects 24/qn9.py","file_name":"qn9.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32947577703","text":"#%%\r\n\r\nimport pybullet as p\r\nimport numpy as np\r\nimport cv2\r\nfrom random import uniform\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom utils import get_physics, image_size\r\n\r\n\r\n\r\nclass Arena():\r\n def __init__(self, GUI = False):\r\n self.arena_map = cv2.imread(\"map.png\")\r\n self.w, self.h, _ = self.arena_map.shape\r\n self.physicsClient = get_physics(GUI, self.w, self.h)\r\n self.already_constructed = False\r\n\r\n def start(self):\r\n if(not self.already_constructed):\r\n p.loadURDF(\"plane.urdf\", [0,0,0], globalScaling = 2,\r\n useFixedBase = True, physicsClientId = self.physicsClient) \r\n for loc in ((x,y) for x in range(self.w) for y in range(self.h)):\r\n pos = [loc[0],loc[1],.5]\r\n ors = p.getQuaternionFromEuler([0,0,0])\r\n color = self.arena_map[loc][::-1] / 255\r\n color = np.append(color, 1)\r\n cube = p.loadURDF(\"cube.urdf\", (pos[0], pos[1], pos[2]), \r\n ors, globalScaling = 1, useFixedBase = True, physicsClientId = self.physicsClient)\r\n p.changeVisualShape(cube, -1, rgbaColor = color, physicsClientId = self.physicsClient)\r\n self.already_constructed = True\r\n\r\n def take_photo(self, show = False):\r\n x, y, z = uniform(0, self.w), uniform(0, self.h), uniform(1, 10)\r\n tx, ty, tz = uniform(-1, 1), uniform(-1, 1), uniform(-1, 1)\r\n \r\n view_matrix = p.computeViewMatrix(\r\n cameraEyePosition = [x, y, z], \r\n cameraTargetPosition = [x - tx, y - ty, 0], \r\n cameraUpVector = [0, 0, 1], physicsClientId = self.physicsClient)\r\n proj_matrix = p.computeProjectionMatrixFOV(\r\n fov = 90, aspect = 1, nearVal = .01, \r\n farVal = 100, physicsClientId = self.physicsClient)\r\n _, _, rgba, depth, _ = p.getCameraImage(\r\n width=image_size, height=image_size,\r\n projectionMatrix=proj_matrix, viewMatrix=view_matrix, shadow = 0,\r\n physicsClientId = self.physicsClient)\r\n rgba = np.divide(rgba, 255)\r\n rgb = rgba[:, :, :-1]\r\n \r\n if(show):\r\n plt.imshow(rgb)\r\n plt.axis('off')\r\n plt.show()\r\n plt.close()\r\n \r\n return(rgb)\r\n \r\n \r\n\r\nif __name__ == \"__main__\":\r\n arena = Arena(GUI = True)\r\n arena.start()\r\n for i in range(100):\r\n arena.take_photo(show = True)\r\n# %%\r\n","repo_name":"TedTinker/sample","sub_path":"arena.py","file_name":"arena.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70846760377","text":"\nclass Map():\n def __init__(self, map, turn, count):\n self.map = map\n self.play = 1\n self.turn = turn\n self.remain_play = 9-count\n def draw_map(self):\n # print each case map\n for row in range(0, 3): # 행 탐색 iterator\n for col in range(0, 3): # 열 탐색 iterator\n print(self.map[row][col], end=\" \")\n print(\"\") # print(\"\\n\") 이면 한줄이 더 추가로 띄어지던데\n\n def inc_play(self):\n self.play = self.play + 1\n self.remain_play = self.remain_play - 1\n\n def update_map(self, where): # map 에 새로운 수를 넣자 마자 play 횟수, turn 자동 갱신\n self.map[where[0]][where[1]] = self.turn + str(self.play)\n self.draw_map()\n self.inc_play()\n self.next_turn()\n\n def enemy(self):\n if (self.turn == \"X\"):\n return \"O\"\n else:\n return \"X\"\n def next_turn(self):\n if (self.turn == \"X\"):\n self.turn = \"O\"\n else:\n self.turn = \"X\"\n\n\ndef main():\n # set read/write file variables\n rfile = open(\"input.txt\", mode=\"r\")\n wfile = open(\"output.txt\", mode=\"wt\", encoding=\"utf-8\")\n\n # read each lines\n rlines = rfile.readlines()\n\n # the first line has (total) the number of case\n totalCase = int(rlines[0])\n\n # iterative each Case\n # make map each case.\n for case in range(1, totalCase +1):\n result = \"\"\n\n # initialize\n map_cur = [ ['.','.','.'], ['.', '.', '.'], ['.', '.', '.']]\n\n # push elements\n for row in range(0,3): # 행 탐색 iterator\n line = rlines[(case-1)*3+(row+1)].split(\" \")\n line[-1] = line[-1].replace(\"\\n\", \"\") # 마지막 줄바꿈 없애기\n for col in range(0,3): # 열 탐색 iterator\n map_cur[row][col] = line[col] + \"s\" # setting turn 기록\n\n # who's turn?\n turn_count = who_turn(map_cur)\n\n # print each case map\n map = Map(map_cur, \"\", turn_count) # turn 멤버값 은 나중에 추가\n print(\"----------\")\n print(\"Case #%d\"%case)\n map.draw_map()\n print(\"remain Play is\", map.remain_play)\n\n # 예외상황 처리\n if(turn_count == 0 or turn_count == 1 ):\n result = \"TIE\" # 볼 것도 없이 무승부 (확실함)\n elif(turn_count == 9):\n result = \"The End\"\n # 결과 판독하고 나서\n\n else:\n if(turn_count % 2 == 1) : # 놓아진 돌이 홀수 개이면, 'O' 차례\n map.turn = \"O\"\n result = play_game(map)\n\n # ★☆★☆★☆★☆ 여기 recursive ★☆★☆★☆★☆\n if(result == \"continue\"):\n play_game(map)\n\n else :\n map.turn = \"X\"\n result = play_game(map)\n\n # ★☆★☆★☆★☆ 여기 recursive ★☆★☆★☆★☆\n if(result == \"continue\"):\n play_game(map)\n\n print(\"result :\", result)\n\ndef who_turn(map_cur):\n # 항상 'x' 먼저\n # x_count = 0\n # o_count = 0\n #\n # for row in range(0,3): # 행 탐색 iterator\n # for col in range(0,3): # 열 탐색 iterator\n # if(map_cur[row][col][0] == 'X'):\n # x_count = x_count + 1\n # elif(map_cur[row][col][0] == 'O'):\n # o_count = o_count + 1\n # if(x_count <= o_count):\n # return 'X' # I want to change this to number, 0\n # else:\n # return 'O'\n\n count = 0\n for row in range(0, 3): # 행 탐색 iterator\n for col in range(0, 3): # 열 탐색 iterator\n if(map_cur[row][col][0] == \".\"):\n continue\n else:\n count = count + 1\n return count\n\ndef play_game(map):\n\n print(\"play now\")\n\n # 1. 바로 이기는 공격 유무 확인\n # 현재 turn 의 돌이 연속(혹은 띄어서) 2개인지를 확인한다.\n my2series = check2series(map.map, map.turn)\n if(my2series[0]):\n print(\"BINGO!!\", my2series[1])\n return map.turn\n\n # 2. 방어할 곳 확인1\n enemy = map.enemy()\n enemy2series = check2series(map.map, enemy)\n where = []\n if(enemy2series[0]):\n if(enemy2series[1][0] == \"r\"): # 막을 곳에 한 수 놓기\n where = [ int(enemy2series[1][1]), map.map[int(enemy2series[1][1])].index(\".s\") ]\n elif(enemy2series[1][0] == \"c\"):\n # print(\"col danger\")\n col_pos = int(enemy2series[1][1])\n where = [col_pos] # row_pos 찾아서 넣을 것임.\n for row in range(0,3):\n if(map.map[row][col_pos] == \".s\"):\n where.insert(0, row)\n break\n elif(enemy2series[1][0] == \"s\"): # cross check\n # print(\"cross danger\")\n cross_num = int(enemy2series[1][1])\n # if crossList[0] = for L->R cross : s1 인 상황\n crossList = [[[0, 0], [1, 1], [2, 2]], [[0, 2], [1, 1], [2, 0]]]\n\n for idx in crossList[cross_num - 1]:\n if (map.map[idx[0]][idx[1]] == \".s\"):\n where = [idx[0], idx[1]]\n break\n print(\"--> Dangerous!! Depend\", where)\n map.update_map(where)\n return \"continue\" # 아직 승부가 안났음\n\n # 3. min-max algorithm\n\n\n\n\n # 3. 한수를 두려고 했는데, 굳이 할 필요 없을 듯?\n # 아니야 그 한 수가 필요해\n return \"I don't know\"\n\ndef check2series(map_cur, check):\n # map_cur : 클래스가 아니라 오직 map list\n # check : check 하고 싶은 멤버\n\n # result is list 형태 [존재함, 어느 줄 빙고?] 예로, [1, \"r1\"] r1에서 빙고해서 이길 것임\n enemy = \"\"\n if (check == \"X\"):\n enemy = \"O\"\n else:\n enemy = \"X\"\n # row check\n for row in range(0, 3): # 행 탐색 iterator ※ 중간까지만 확인해도 됨.\n for col in range(0, 2): # 열 탐색 iterator\n if(map_cur[row][col][0] == check):\n if(map_cur[row][(col+1)%3][0] == check and map_cur[row][(col+2)%3][0] != enemy):\n # 이미 Game Over 인 상태 (already 3 빙고) 끝 은 없는 걸로\n return [1, \"r\" + str(row)]\n elif(map_cur[row][(col+2)%3][0] == check and map_cur[row][(col+1)%3][0] != enemy):\n return [1, \"r\" + str(row)]\n\n # col check\n for row in range(0, 2): # 행 탐색 iterator\n for col in range(0, 3): # 열 탐색 iterator\n if(map_cur[row][col][0] == check):\n if(map_cur[(row+1)%3][col][0] == check and map_cur[(row+2)%3][col][0] != enemy):\n # 이미 Game over 인 상태(already 3 빙고) 끝 은 없는 걸로\n return [1, \"c\" + str(col)]\n elif(map_cur[(row+2)%3][col][0] == check and map_cur[(row+1)%3][col][0] != enemy):\n return [1, \"c\" + str(col)]\n\n\n # cross check\n cross1 = [[0,0], [1,1]] # check only 2, ignore [2,2]]\n cross2 = [[0,2], [1,1]] # check only 2, ignore [2,0]]\n\n for idx in cross1:\n if (map_cur[idx[0]][idx[1]][0] == check) :\n if(map_cur[(idx[0]+1)%3][(idx[1]+1)%3][0] == check and map_cur[(idx[0]+2)%3][(idx[1]+2)%3][0] != enemy):\n return [1, \"s1\"]\n elif(map_cur[(idx[0]+2)%3][(idx[1]+2)%3][0] == check and map_cur[(idx[0]+1)%3][(idx[1]+1)%3][0] != enemy):\n return [1, \"s1\"]\n for idx in cross2:\n if (map_cur[idx[0]][idx[1]][0] == check) :\n if(map_cur[(idx[0]+1)%3][(idx[1]-1)%3][0] == check and map_cur[(idx[0]+2)%3][(idx[1]-2)%3][0] != enemy):\n return [1, \"s2\"]\n elif(map_cur[(idx[0]+2)%3][(idx[1]-2)%3][0] == check and map_cur[(idx[0]+1)%3][(idx[1]-1)%3][0] != enemy):\n return [1, \"s2\"]\n\n return [0, \"\"] # 2 Series 없으면\n\n\n# main 함수 호출\nmain()\n","repo_name":"ddubbu/AI-Study-with-python","sub_path":"0. Algorithm/[Jan_week3] Tic_Tac_Toe/practice/backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":7979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31931085973","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport cv2\r\nfrom tqdm import tqdm\r\n\r\nIMAGE_SIZE = 50\r\n\r\nDATADIR = \"C:/Users/Amon/Desktop/study/MV/dotaset/counting/train/images\"\r\n\r\nCATEGORIES = [\"0apple\", \"1apple\", \"2apple\", \"3apple\", \"4apple\", \"5apple\", \"6apple\"]\r\n\r\n# for category in CATEGORIES:\r\n# path = os.path.join(DATADIR,category)\r\n# for img in os.listdir(path):\r\n# img_array = cv2.imread(os.path.join(path,img))\r\n# plt.imshow(img_array)\r\n# plt.show()\r\n# break\r\n# break\r\n# print(img_array)\r\n\r\ntraining_data = []\r\ndef create_training_data():\r\n for category in CATEGORIES:\r\n path = os.path.join(DATADIR, category)\r\n class_num = CATEGORIES.index(category)\r\n for img in tqdm(os.listdir(path)):\r\n try:\r\n img_array = cv2.imread(os.path.join(path,img))\r\n new_array = cv2.resize(img_array,(IMAGE_SIZE, IMAGE_SIZE))\r\n training_data.append([new_array,class_num])\r\n except Exception:\r\n pass\r\ncreate_training_data()\r\n\r\nprint(len(training_data))\r\n\r\nimport random\r\n\r\nrandom.shuffle(training_data)\r\n\r\nfor sample in training_data[:10]:\r\n print(sample[1])\r\nX = []\r\ny = []\r\n\r\nfor feature,label in training_data:\r\n X.append(feature)\r\n y.append(label)\r\nprint(X[0].reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1))\r\nX = np.array(X)\r\ny = np.array(y)\r\nprint(len(X))\r\nprint('-------------------------------------------------------------------------------------------------------------------------------')\r\nprint(len(y))\r\nprint(y)\r\nimport pickle\r\n\r\npickle_out = open(\"X.pickle\", \"wb\")\r\npickle.dump(X, pickle_out)\r\npickle_out.close()\r\n\r\npickle_out = open(\"y.pickle\", \"wb\")\r\npickle.dump(y, pickle_out)\r\npickle_out.close()\r\n\r\npickle_in = open(\"X.pickle\", \"rb\")\r\nX = pickle.load(pickle_in)\r\n\r\npickle_in = open(\"y.pickle\", \"rb\")\r\ny = pickle.load(pickle_in)\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow.keras import datasets,layers,models\r\nfrom tensorflow.keras.datasets import cifar10\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\r\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D\r\n\r\n\r\nmodel = Sequential()\r\n\r\nmodel.add(Conv2D(32, (3, 3), input_shape=X.shape[1:]))\r\nmodel.add(Activation('relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n\r\nmodel.add(Conv2D(32, (3, 3)))\r\nmodel.add(Activation('relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n\r\nmodel.add(Conv2D(32, (3,3)))\r\nmodel.add(Activation('relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\n\r\nmodel.add(Flatten())\r\n\r\nmodel.add(Dense(64))\r\n\r\nmodel.add(Dense(1))\r\nmodel.add(Activation('sigmoid'))\r\n\r\nmodel.compile(loss='binary_crossentropy',\r\n optimizer='adam',\r\n metrics=['accuracy'])\r\n\r\nmodel.fit(X, y, batch_size=32, epochs=3, validation_split=0.3)\r\n\r\n","repo_name":"ZhouShaoyang16420/UWE_MV_2020_CW","sub_path":"Method2: CNN/CNN4AppleCounting.py","file_name":"CNN4AppleCounting.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32171170987","text":"from trytond.model import ModelView, ModelSQL, fields, Unique\nfrom trytond.pool import PoolMeta, Pool\nfrom trytond.pyson import Eval\nfrom trytond.transaction import Transaction\n\n\nclass Project(ModelSQL, ModelView):\n 'Project'\n __name__ = 'lims.project'\n _rec_name = 'description'\n\n code = fields.Char('Code', states={'required': True})\n description = fields.Char('Description', required=True)\n type = fields.Selection([], 'Type', sort=False)\n start_date = fields.Date('Start date')\n end_date = fields.Date('End date')\n client = fields.Many2One('party.party', 'Client')\n storage_time = fields.Integer('Storage time (in months)', required=True)\n comments = fields.Text('Comments')\n external_quality_control = fields.Boolean('External quality control')\n\n @classmethod\n def __setup__(cls):\n super().__setup__()\n t = cls.__table__()\n cls._sql_constraints += [\n ('code_uniq', Unique(t, t.code),\n 'lims_project.msg_code_unique_id'),\n ]\n\n @staticmethod\n def default_storage_time():\n return 3\n\n @staticmethod\n def default_external_quality_control():\n return False\n\n def get_rec_name(self, name):\n if self.code:\n return self.code + ' - ' + self.description\n else:\n return self.description\n\n @classmethod\n def search_rec_name(cls, name, clause):\n field = None\n for field in ('code', 'description'):\n records = cls.search([(field,) + tuple(clause[1:])], limit=1)\n if records:\n break\n if records:\n return [(field,) + tuple(clause[1:])]\n return [(cls._rec_name,) + tuple(clause[1:])]\n\n\nclass Entry(metaclass=PoolMeta):\n __name__ = 'lims.entry'\n\n project = fields.Many2One('lims.project', 'Project',\n domain=[('client', '=', Eval('party'))], depends=['party'])\n project_type = fields.Function(fields.Selection([],\n 'Type', sort=False),\n 'on_change_with_project_type')\n\n @classmethod\n def __setup__(cls):\n super().__setup__()\n cls.samples.context.update({\n 'project': Eval('project', None),\n })\n if 'project' not in cls.samples.depends:\n cls.samples.depends.append('project')\n\n @fields.depends('project', '_parent_project.type')\n def on_change_with_project_type(self, name=None):\n res = None\n if self.project:\n res = self.project.type\n return res\n\n\nclass Sample(metaclass=PoolMeta):\n __name__ = 'lims.sample'\n\n project_type = fields.Function(fields.Selection([], 'Type'),\n 'on_change_with_project_type')\n\n @staticmethod\n def default_project_type():\n Project = Pool().get('lims.project')\n if Transaction().context.get('project'):\n return Project(Transaction().context.get('project')).type\n return ''\n\n @fields.depends('entry', '_parent_entry.project')\n def on_change_with_project_type(self, name=None):\n res = ''\n if self.entry and self.entry.project:\n res = self.entry.project.type\n return res\n\n\nclass CreateSampleStart(metaclass=PoolMeta):\n __name__ = 'lims.create_sample.start'\n\n project_type = fields.Char('Type')\n\n\nclass CreateSample(metaclass=PoolMeta):\n __name__ = 'lims.create_sample'\n\n def default_start(self, fields):\n Entry = Pool().get('lims.entry')\n\n defaults = super().default_start(fields)\n defaults['project_type'] = ''\n\n entry = Entry(Transaction().context['active_id'])\n if entry.project:\n defaults['project_type'] = entry.project.type\n return defaults\n","repo_name":"Kalenis/kalenislims","sub_path":"lims_project/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":3683,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"22"}
+{"seq_id":"9675981433","text":"import time\n\nfrom app import Base, session, createAll\nfrom app import Column, Integer, String\n\nfrom app.users import getUser, User\n\nclass Message(Base):\n __tablename__ = 'messages'\n\n id = Column('id', Integer, primary_key=True)\n content = Column('content', String)\n time = Column('time', Integer)\n votes = Column('votes', Integer)\n\n def __init__(self, content):\n self.content = content\n self.time = int(round(time.time() * 1000))\n self.votes = 0\n session.add(self)\n session.commit()\n\nclass Join(Base):\n __tablename__ = 'join'\n\n user = Column('user', Integer, primary_key=True)\n message = Column('message', Integer, primary_key=True)\n\n def __init__(self, user, message):\n self.user = user.id\n self.message = message.id\n session.add(self)\n session.commit()\n\ndef addVote(messageID):\n theMessage = session.query(Message).get(messageID)\n theMessage.votes = theMessage.votes + 1\n session.commit()\n return theMessage.votes\n\ndef addMessage(user, content):\n theUser = getUser(user)\n if theUser is not None:\n return Join(theUser, Message(content)) \n return None;\n\ndef getMessages(user=None):\n userFilter = (True)\n if user is not None:\n userFilter = (Join.user == getUser(user).id)\n\n joins = session.query(Join) \\\n .filter(userFilter).all()\n\n return list(map(lambda x: { \\\n \"user\": session.query(User).get(x.user).name, \\\n \"id\": session.query(Message).get(x.message).id, \\\n \"votes\": session.query(Message).get(x.message).votes, \\\n \"content\": session.query(Message).get(x.message).content \\\n }, joins))\n\ncreateAll()\n","repo_name":"jrkolsby/SocketChat","sub_path":"app/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36000439873","text":"# 백준 11049번: 행렬 곱셈 순서\n\nimport sys\n\ninput = sys.stdin.readline\n\n# N <= 5 * 10^2\nN = int(input())\n\n# (5 * 3 * 2) + (5 * 2 * 6) or\n# (3 * 2 * 6) + (5 * 3 * 6)\n\n# 5 3 2 6\n# 5\n\nr, c = map(int, input().rstrip().split())\narr = [r, c]\n\nfor _ in range(N - 1):\n _, c = map(int, input().rstrip().split())\n arr.append(c)\n\n# dp[i][j] = i번째부터 j번째 행렬까지의 최소 곱셈 횟수\ndp = [[0 for _ in range(N)] for _ in range(N)]\n# for row in dp:\n# print(row)\n# print()\n\n# N = 3\nfor d in range(N): # 0 1 2\n for i in range(N - d): # (0 1 2) (0 1) (0)\n j = i + d # (0 1 2) (1 2) (2)\n\n if i == j:\n continue\n\n dp[i][j] = int(1e9)\n for k in range(i, j):\n dp[i][j] = min(dp[i][j],\\\n (dp[i][k] + dp[k + 1][j]) + (arr[i] * arr[k + 1] * arr[j + 1]))\n\n # for row in dp:\n # print(row)\n # print()\n\nprint(dp[0][-1])\n","repo_name":"DeveloperAcademy-POSTECH/Algorithm","sub_path":"TEAM A (Season 1, 2)/Week 11/Poodle/[백준 - 11049] 행렬 곱셈 순서/11049.py","file_name":"11049.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"}
+{"seq_id":"1109767977","text":"import serial\nimport socket, struct\nimport time\n\nHOST = 'localhost'\nPORT = 6581\nBAUD = 2000000\nCOMPORT = 4\n\n# Request commands:\ncommand_list = [ 'FLUSH',\n 'TRY_SET_SID_COUNT',\n 'MUTE',\n 'TRY_RESET',\n 'TRY_DELAY',\n 'TRY_WRITE',\n 'TRY_READ',\n 'GET_VERSION',\n 'TRY_SET_SAMPLING',\n 'TRY_SET_CLOCKING',\n 'GET_CONFIG_COUNT',\n 'GET_CONFIG_INFO',\n 'SET_SID_POSITION',\n 'SET_SID_LEVEL',\n 'TRY_SET_SID_MODEL' ]\n\n# Response:\nOK, BUSY, ERR, READ, VERSION, COUNT, INFO = range( 7 )\n\nPHI2 = 985248\n\ndef send_OK():\n conn.send( struct.pack( '!B', OK ) )\n\ndef send_BUSY():\n conn.send( struct.pack( '!B', BUSY ) )\n\ndef send_VERSION():\n conn.send( struct.pack( '!BB', VERSION, 2 ) )\n\ndef send_COUNT():\n conn.send( struct.pack( '!BB', COUNT, 2 ) )\n\ndef send_INFO( sid_number ):\n MOS_6581 = 0\n MOS_8580 = 1\n sid_list = [ '6581', '8580' ]\n name_template = 'HybridSID %s\\x00'\n name = name_template % sid_list[ sid_number ]\n conn.send( struct.pack( '!BB%ss' % len( name ), INFO, MOS_8580, name.encode('utf-8') ) )\n\nclass ACID64Closed:\n pass\n\nser = None\nwhile True:\n while ser is None:\n try:\n ser = serial.Serial( 'COM%s' % COMPORT, timeout=0, baudrate=BAUD)\n print( 'using COM%s' % COMPORT)\n break\n except serial.serialutil.SerialException:\n pass\n else:\n print( 'no serial device found')\n time.sleep( 0.5 )\n\n s = socket.socket( socket.AF_INET, socket.SOCK_STREAM )\n s.bind( ( HOST, PORT ) )\n print( 'listening on port %s' % PORT)\n s.listen( 1 )\n conn, address = s.accept()\n print( 'connected')\n\n try:\n status = ''\n paused = 0\n cycles = 0\n byte_count = 0\n previous_time = time.time()\n start = time.time()\n while True:\n ##print( time.time() - start, cycles / float( PHI2 ))\n\n if paused == 1:\n status = ser.read()\n if len(status) > 0 :\n if status == b'S':\n paused = 0\n else:\n data = b''\n while len( data ) < 4:\n d = conn.recv( 4 - len( data ) )\n if len( d ) != 0:\n data += d\n else:\n raise ACID64Closed\n\n command, sid_number, data_length = struct.unpack( '!BBH', data )\n\n data = b''\n while len( data ) < data_length:\n d = conn.recv( data_length - len( data ) )\n if len( d ) != 0:\n data += d\n else:\n raise ACID64Closed\n\n assert command < len( command_list ), 'Unknown command: command = %s, sid_number = %s, data_length = %s.' % ( command, sid_number, data_length )\n ##print( '%s: #%s, [%s]' % ( command_list[ command ], sid_number, data_length ))\n\n if command == 0: # FLUSH\n cycles = 0\n start = time.time()\n send_OK()\n\n elif command == 1: # TRY_SET_SID_COUNT\n print( ' sid count: %s' % sid_number)\n \n send_OK()\n\n elif command == 2: # MUTE\n voice, enable = struct.unpack( '!BB', data )\n print( '[%s] voice: %s, enable: %s' % (sid_number, voice, enable) )\n send_OK()\n\n elif command == 3: # TRY_RESET\n volume_register_value, = struct.unpack( '!B', data )\n print( '[%s] volume register value: %s' % (sid_number, volume_register_value) )\n send_OK()\n\n elif command == 4: # TRY_DELAY\n ##if time.time() - start < cycles / float( PHI2 ):\n ## print( 'busy')\n ## send_BUSY()\n ##else:\n ## delay, = struct.unpack( '!H', data )\n ## cycles += delay\n ## print( ' delay: %s' % delay)\n ser.write( struct.pack( '!sBB', data, 30, 0 ) )\n send_OK()\n\n elif command == 5: # TRY_WRITE\n ##if time.time() - start < cycles / float( PHI2 ):\n ## print( 'busy')\n ## send_BUSY()\n ##else:\n ## for i in range( 0, data_length, 4 ):\n ## delay, register, value = struct.unpack( '!HBB', data[ i : i + 4 ] )\n ## cycles += delay\n ## ##print( ' delay: %s, register: %s, value: %s' % ( delay, register, value ))\n ## ##ser.write( data[ i : i + 4 ] )\n ser.write( data )\n status = ser.read()\n if len(status) > 0 :\n #if status == b'S':\n # paused = 0\n # #print( 'START')\n if status == b'E':\n paused = 1\n print( ' FPGA requested pause, buffer full...')\n #print( '>>>>>>BUSY')\n send_OK()\n #print( '>>>>>>OK')\n byte_count += data_length\n current_time = time.time()\n if previous_time + 0.1 < current_time:\n print( '[%s] %s Bytes/s, block size: %s' % (sid_number, int( byte_count / ( current_time - previous_time ) ), data_length ) )\n byte_count = 0\n previous_time = current_time\n\n elif command == 6: # TRY_READ\n assert False, 'not implemented'\n\n elif command == 7: # GET_VERSION\n assert data_length == 0\n send_VERSION()\n\n elif command == 8: # TRY_SET_SAMPLING\n resampling_method, = struct.unpack( '!B', data )\n print( '[%s] resampling method: %s quality' % ( sid_number, 'low' if resampling_method == 0 else 'high' ))\n send_OK()\n\n elif command == 9: # TRY_SET_CLOCKING\n clock_source_speed, = struct.unpack( '!B', data )\n print( '[%s] clock source speed: %s' % ( sid_number, 'PAL' if clock_source_speed == 0 else 'NTSC' ))\n send_OK()\n\n elif command == 10: # GET_CONFIG_COUNT\n send_COUNT()\n\n elif command == 11: # GET_CONFIG_INFO\n send_INFO( sid_number )\n\n elif command == 12: # SET_SID_POSITION\n position, = struct.unpack( '!B', data )\n print( '[%s] position: %s' % (sid_number, position) )\n send_OK()\n\n elif command == 13: # SET_SID_LEVEL\n assert False, 'not implemented'\n\n elif command == 14: # TRY_SET_SID_MODEL\n model, = struct.unpack( '!B', data )\n print( '[%s] model: %s' % (sid_number, model) )\n send_OK()\n\n except ACID64Closed:\n print( 'ACID 64 closed')\n\n except socket.error as text:\n print( 'socket.error:', text)\n\n except serial.serialutil.SerialTimeoutException:\n conn.close()\n s.close()\n ser = None\n print( 'serial device removed')\n time.sleep( 1 ) # Wait a bit before enumerating the COM ports again.\n","repo_name":"benbaker76/NetSID","sub_path":"server_v2_HybridSID - handshake.py","file_name":"server_v2_HybridSID - handshake.py","file_ext":"py","file_size_in_byte":7883,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"16767370277","text":"#!/usr/bin/env python3.8\nfrom math import gcd\n\n\nA,X,M=map(int, input().split())\n\n# def mod_pow(x: int, n: int):\n# bi = str(format(n, \"b\"))\n# res = 1\n# a = x\n# print('bi', bi)\n# for i in range(len(bi)):\n# if n >> i & 1:\n# res = (res * a) % M\n# a = (a * a) % M\n# return res\nif A == 1:\n print(X % M)\n exit()\n\nu = (pow(A, X, M * (A - 1)) - 1)\nd = (A- 1)\n\nprint((u // d) % M)","repo_name":"harukaeru/CompetitiveProgramming","sub_path":"abc293/E/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"43203326357","text":"#!/usr/bin/env python\nimport torch\nimport time\nfrom state import State\nfrom train import Net\nimport chess\n\nMODEL_PATH = f\"models\"\nMODEL_FILE = \"MODEL-e20-s2m.pth\"\n\nMAX_VALUE = 10000\nSEARCH_DEPTH = 3\n\nclass Evaluator():\n\n\tvalues = {\n\t\tchess.PAWN: 1,\n\t\tchess.KNIGHT: 2,\n\t\tchess.BISHOP: 3,\n\t\tchess.ROOK: 5,\n\t\tchess.QUEEN: 9,\n\t\tchess.KING: 0\n\t}\n\n\tdef __init__(self):\n\t\tself.reset()\n\t\tself.memo = {}\n\t\n\tdef reset(self):\n\t\tself.count = 0\n\n\tdef __call__(self, state):\n\t\tself.count += 1\n\t\tkey = state.key()\n\t\tif key not in self.memo:\n\t\t\tself.memo[key] = self.value(state)\n\t\treturn self.memo[key]\n\t\n\tdef value(self, state):\n\t\tboard = state.board\n\n\t\tif board.is_game_over():\n\t\t\tif board.result() == \"1-0\":\n\t\t\t\treturn MAX_VALUE\n\t\t\telif board.result() == \"0-1\":\n\t\t\t\treturn -MAX_VALUE\n\t\t\telse:\n\t\t\t\treturn 0\n\n\t\t# board piece values\n\t\tval = 0.0\n\t\tpm = state.board.piece_map()\n\t\tfor x in pm:\n\t\t\ttval = self.values[pm[x].piece_type] # integer piece value\n\t\t\tif pm[x].color == chess.WHITE:\n\t\t\t\tval += tval\n\t\t\telse:\n\t\t\t\tval -= tval\n\t\t\n\t\t# factor in # of legal moves\n\t\tcurrent_turn = board.turn\n\t\tboard.turn = chess.WHITE\n\t\tval += (0.1 * board.legal_moves.count())\n\t\tboard.turn = chess.BLACK\n\t\tval -= (0.1 * board.legal_moves.count())\n\t\tboard.turn = current_turn\n\n\t\treturn val\n\n\n\tdef computer_minimax(self, state, depth, a, b, big=False):\n\t\tif depth >= SEARCH_DEPTH or state.board.is_game_over():\n\t\t\treturn self(state)\n\n\t\tturn = state.board.turn\n\t\tif turn == chess.WHITE:\n\t\t\tret = -MAX_VALUE\n\t\telse:\n\t\t\tret = MAX_VALUE\n\n\t\tif big:\n\t\t\tbret = []\n\n\t\tmoves = []\n\t\tfor move in state.board.legal_moves:\n\t\t\tstate.board.push(move)\n\t\t\tmoves.append((self(state), move))\n\t\t\tstate.board.pop()\n\t\tmoves = sorted(moves, key=lambda x: x[0], reverse=state.board.turn)\n\n\t\t# beam\n\t\tif depth >= 3:\n\t\t\tmoves = moves[:10]\n\n\t\tfor move in [x[1] for x in moves]:\n\t\t\tstate.board.push(move)\n\t\t\ttval = self.computer_minimax(state, depth+1, a, b)\n\t\t\tstate.board.pop()\n\t\t\t\n\t\t\tif big:\n\t\t\t\tbret.append((tval, move))\n\n\t\t\tif turn == chess.WHITE:\n\t\t\t\t\n\t\t\t\tret = max(ret, tval)\n\t\t\t\ta = max(a, ret)\n\t\t\t\tif a >= b:\n\t\t\t\t\t# \n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\t# Black turn\n\t\t\t\tret = min(ret, tval)\n\t\t\t\tb = min(b, ret)\n\t\t\t\tif a >= b:\n\t\t\t\t\tbreak\n\n\t\tif big:\n\t\t\treturn ret, bret\n\t\telse:\n\t\t\treturn ret\n\n\tdef search_moves(self, state):\n\t\tret = []\n\t\tstart = time.time()\n\t\tself.reset()\n\t\tbval = self(state)\n\t\tcval, ret = self.computer_minimax(state, depth=0, a=-MAX_VALUE, b=MAX_VALUE, big=True)\n\t\teta = time.time() - start\n\t\tbest_moves = sorted(ret, key=lambda x: x[0], reverse=state.board.turn)\n\t\tbest_move = best_moves[0][1]\n\t\tprint(f\"'{best_move}' ({bval:.2f} -> {cval:.2f}): explored {self.count} nodes in {eta:.3f} seconds {int(self.count/eta)}/sec\")\n\t\treturn best_moves\n\n# ASSUME PLAYER IS WHITE -> add logic to check value based on what color ai is later\n\nif __name__ == \"__main__\":\n\tpass\n\t# play through an entire game\n# state = State()\n\n# evaluator = Evaluator()\n\t\n# evaluator.value(state)\n# best_moves = evaluator.search_moves(state)\n# best_move = best_moves[0][0]\n\t# value = evaluator(state)\n\n# while not state.board.is_checkmate(): \n# # legal moves\n# best_move = None\n# best_value = None\n# for move in state.edges():\n# state.board.push(move)\n# value = evaluator(state)\n# # assume ai is black, best move is smallest (-1 score)\n# if best_move is None or value < best_value:\n# best_value = value\n# best_move = move\n#\n# state.board.pop()\n#\n# print(best_move)\n# print(best_value)\n# # now that we have the best move - play it.\n# state.board.push(best_move)\n# print(state.board)\n","repo_name":"e-Dylan/chessai","sub_path":"evaluator.py","file_name":"evaluator.py","file_ext":"py","file_size_in_byte":3642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"27329246310","text":"\nimport json\nimport requests\nfrom django.conf import settings\n\n\nclass cleint:\n\n def __init__(self):\n self.webClient = requests.session()\n\n def push(self, widget_uuid, payload):\n requests.post(\n \"https://push.geckoboard.com/v1/send/{}\".format(widget_uuid), data=payload)\n\n def leaderboard_format(self, data):\n formated_board = list()\n widget_data = dict()\n for info in data:\n colour = 'green'\n if info['percent_diff'] > 0:\n colour = 'red'\n\n formated_board.append({'title': {'text': ''}, 'label': {'name': info['name'], 'color': colour},\n \"description\": \"Forecast: {} Change: {}%\".format(info['forecast'], info['percent_diff'])})\n\n widget_data = {'api_key': settings.GECKO_TOKEN, 'data': formated_board}\n return json.dumps(widget_data)\n","repo_name":"uktrade/cost_monitor","sub_path":"report/helper/gecko.py","file_name":"gecko.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12884008179","text":"import logging\nimport hashlib\n\n# Import classes and helpers from the Laika framework\nfrom laikaboss.objectmodel import ExternalVars, ModuleObject, ScanError\nfrom laikaboss.util import get_option\nfrom laikaboss.si_module import SI_MODULE\n\nclass EXPLODE_HELLOWORLD(SI_MODULE):\n ''' \n A Hello World Laika module to use as a template and guide for your development. \n\n Classes of Laika modules follow these rules:\n * Name MUST be in all capitals.\n * Name SHOULD use one of the predefined prefixes followed by an expressive name of what the module is or interacts with/on.\n * Prefixes: SCAN, META, EXPLODE, LOG, DECODE.\n * Class MUST inherit from SI_MODULE.\n * Saved in a file with name that is lowercase of the class name.\n * Define the _run(...) method, as seen below.\n * Should define an __init__(...) method that defines the self.module_name instance variable set to the class name, as seen below.\n '''\n\n def __init__(self):\n '''\n Typical class constructor. Should define instance variable self.module_name set to the class name, to be used for adding metadata and logging. Any other non-runtime specific initialization can happen here, such as making connections to external systems or compiling regular expressions for improved runtime performance.\n\n Note: no additional parameters (besides self) can be defined here, as the framework does not support parameterized initialization at module loading. Instead, use lazy-loading techniques during the first time _run(...) is called, using that methods 'args' parameter or the laikaboss.config for customization.\n '''\n self.module_name = \"EXPLODE_HELLOWORLD\"\n\n def _run(self, scanObject, result, depth, args):\n '''\n Here is where the actual magic happens. This method is called on the object being scanned given that the dispatcher has a matching rule triggering this module.\n\n There are four types of actions that modules do on scan objects:\n * Add flags.\n * Add metadata.\n * Explode children.\n * Interact with external systems.\n\n Any module can perform as many of these actions as necessary, and many do more than one, such as adding flags and metadata.\n\n The parameters provided to this method by the framework are:\n * scanObject: This is the object currently being scanned (duh) of type ScanObject. It contains all of the flags, metadata and other assigned fields regarding this spefic instance of the object. This parameter is used by modules to access the buffer, add flags and add metadata.\n * result: This is the umbrella object that contains all of the ScanObjects created during the root object's scan. It is primarily used by modules to access the parent object of the scanObject when needed.\n * depth: This is a leftover parameter from the tracking of the depth of recursion of objects. It is recommended to get this value from the scanObject itself.\n * args: This is a dictionary of the runtime arguments provided to the module by the dispatcher. This parameter provides customization of module runs from the dispatcher, so that the module can operate differently based on the type/context of the scanObject.\n\n This method MUST return a list of ModuleObjects. ModuleObjects represent the children exploded (or the less violent extracted) from the scanObject by this module. If the module does not explode any children (as most do not), simply return an empty list. Not returning a list causes the framework to log an error for this module each time it is run, but will not prevent it from running next time, nor will it remove any flags/metadata added by the module run.\n '''\n # This variable is recommended to be used by all modules as the returned list of ModuleObjects, populated as the children objects are found.\n moduleResult = [] \n\n # A typical first step is define the configuration options of the module.\n # A best practice for configuration options to a module is to honor them in this precedence:\n # 3. value set as default in this code\n # 2. value specified in config file\n # 1. value specified in arguments of the invocation of this module (via the dispatcher)\n # To help with this, the get_option(...) method provided in the laikaboss.util module provides a single method call to set the option according to this precedence.\n helloworld_param = int(get_option(args, 'param', 'helloworldparam', 10))\n\n # To add flags to the object, use the addFlag method of the scanObject.\n # Flags should have the following three parts, separated by ':'s:\n # * Shortened name of the module.\n # * 'nfo' if the flag is informational, 'err' if the flag is for a policy/logic error (versus a programatic error), or leave blank if a typical flag.\n # * Expressive name representing the atomic concept of the flag.\n scanObject.addFlag('e_helloworld:nfo:justsayinghello')\n\n # To add metadata to the object, use the addMetadata method of the scanObject.\n scanObject.addMetadata(self.module_name, \"minsize\", helloworld_param)\n\n # If you want to call a separate function, pass the data and let the \n # function set flags on the data. The function will also modify the moduleResult variable\n # to add subobjects \n flags = self._helloworld(scanObject.buffer, moduleResult, helloworld_param)\n\n for flag in flags:\n scanObject.addFlag(flag)\n\n # Whenever you need to do a try-except, you must make sure to catch and raise the framework ScanError exceptions.\n try:\n nonexistant_var.bad_method_call()\n except NameError:\n pass\n except ScanError:\n raise\n\n # Always return a list of ModuleObjects (or empty list if no children)\n return moduleResult\n\n def _close(self):\n '''\n Laika module destructor. This method is available for any actions that need to be done prior to the closing of the module, such as shutting down cleanly any client connections or closing files. It does not need to be defined for every module, such as this one, since there is nothing to do here. It is here to remind you that it is available.\n '''\n pass\n\n @staticmethod\n def _helloworld(buffer, moduleResult, helloworld_param):\n ''' \n An example of a worker function you may include in your module.\n Note the @staticmethod \"decorator\" on the top of the function.\n These private methods are set to static to ensure immutability since\n they may be called more than once in the lifetime of the class\n '''\n flags = []\n\n # Using the logging module is a great way to create debugging output during testing without generating anything during production.\n # The Laika framework does not use the logging module for its logging (it uses syslog underneath several helpers found it laikaboss.util),\n # so none of thses messages will clutter up Laika logs.\n logging.debug('Hello world!')\n logging.debug('HELLOWORLD invoked with helloworld_param value %i', helloworld_param)\n\n if helloworld_param < 10: \n flags.append('e_helloworld:nfo:helloworldsmall')\n else:\n logging.debug('HELLOWORLD(%i >= 10) setting flag', helloworld_param)\n flags.append('e_helloworld:nfo:helloworld')\n\n\n if helloworld_param > 20:\n logging.debug('HELLOWORLD(%i > 20) adding new object', helloworld_param)\n flags.append('e_helloworld:nfo:helloworldx2')\n \n if len(buffer) > helloworld_param:\n\n # Take the module buffer and trim the first helloworld_param size bytes.\n buff = buffer[helloworld_param:]\n\n object_name = 'e_helloworld_%s_%s' % (len(buff), hashlib.md5(buff).hexdigest())\n\n logging.debug('HELLOWORLD - New object: %s', object_name)\n \n # And we can create new objects that go back to the dispatcher and subsequent laika modules\n # Any modifications we make to the \"moduleResult\" variable here will go back to the main function \n \n # laikaboss/objectmodel.py defines the variables you can set for externalVars. Two most common to set are\n # contentType\n # filename\n moduleResult.append(ModuleObject(buffer=buff, externalVars=ExternalVars(filename=object_name)))\n\n else:\n logging.debug('HELLOWORLD - object is too small to carve (%i < %i)', len(buffer), helloworld_param)\n\n return set(flags)\n\n","repo_name":"lmco/laikaboss","sub_path":"laikaboss/modules/explode_helloworld.py","file_name":"explode_helloworld.py","file_ext":"py","file_size_in_byte":8755,"program_lang":"python","lang":"en","doc_type":"code","stars":713,"dataset":"github-code","pt":"22"}
+{"seq_id":"42299131277","text":"from typing import (\n Any,\n Dict,\n List,\n Tuple,\n TYPE_CHECKING,\n)\n\nfrom beacon_chain.beacon_typing.custom import (\n Hash32,\n ShardId,\n)\n\nfrom ssz import (\n serialize,\n)\n\nfrom beacon_chain.state.config import (\n DEFAULT_CONFIG,\n)\nfrom beacon_chain.state.shard_and_committee import (\n ShardAndCommittee,\n)\nfrom beacon_chain.utils.blake import (\n blake,\n)\n\n\nif TYPE_CHECKING:\n from .active_state import ActiveState # noqa: F401\n from .attestation import AttestationRecord # noqa: F401\n from .block import Block # noqa: F401\n from .crystallized_state import CrystallizedState # noqa: F401\n from .validator_record import ValidatorRecord # noqa: F401\n\n\ndef is_power_of_two(num: int) -> bool:\n return ((num & (num - 1)) == 0) and num != 0\n\n\ndef int_sqrt(n: int) -> int:\n x = n\n y = (x + 1) // 2\n while y < x:\n x = y\n y = (x + n // x) // 2\n return x\n\n\n# Given the head block to attest to, collect the list of hashes to be\n# signed in the attestation\ndef get_hashes_to_sign(active_state: 'ActiveState',\n block: 'Block',\n config: Dict[str, Any]=DEFAULT_CONFIG) -> List[Hash32]:\n cycle_length = config['cycle_length']\n\n hashes = get_hashes_from_active_state(\n active_state,\n block,\n from_slot=block.slot_number - cycle_length + 1,\n to_slot=block.slot_number - 1,\n config=config,\n ) + [blake(serialize(block))]\n\n return hashes\n\n\n# Given an attestation and the block they were included in,\n# the list of hashes that were included in the signature\ndef get_signed_parent_hashes(active_state: 'ActiveState',\n block: 'Block',\n attestation: 'AttestationRecord',\n config: Dict[str, Any]=DEFAULT_CONFIG) -> List[Hash32]:\n cycle_length = config['cycle_length']\n parent_hashes = get_hashes_from_active_state(\n active_state,\n block,\n from_slot=attestation.slot - cycle_length + 1,\n to_slot=attestation.slot - len(attestation.oblique_parent_hashes),\n config=config,\n ) + attestation.oblique_parent_hashes\n\n return parent_hashes\n\n\ndef get_hashes_from_active_state(active_state: 'ActiveState',\n block: 'Block',\n from_slot: int,\n to_slot: int,\n config: Dict[str, Any]=DEFAULT_CONFIG) -> List[Hash32]:\n hashes = [\n get_block_hash(\n active_state,\n block,\n slot,\n config,\n )\n for slot\n in range(from_slot, to_slot + 1)\n ]\n return hashes\n\n\ndef get_attestation_indices(crystallized_state: 'CrystallizedState',\n attestation: 'AttestationRecord',\n config: Dict[str, Any]=DEFAULT_CONFIG) -> List[int]:\n shard_id = attestation.shard_id\n\n filtered_shards_and_committees_for_slot = list(\n filter(\n lambda x: x.shard_id == shard_id,\n get_shards_and_committees_for_slot(\n crystallized_state,\n attestation.slot,\n config=config,\n )\n )\n )\n\n attestation_indices = [] # type: List[int]\n if filtered_shards_and_committees_for_slot:\n attestation_indices = filtered_shards_and_committees_for_slot[0].committee\n\n return attestation_indices\n\n\ndef get_new_recent_block_hashes(old_block_hashes: List[Hash32],\n parent_slot: int,\n current_slot: int,\n parent_hash: Hash32) -> List[Hash32]:\n d = current_slot - parent_slot\n return old_block_hashes[d:] + [parent_hash] * min(d, len(old_block_hashes))\n\n\ndef get_active_validator_indices(dynasty: int,\n validators: List['ValidatorRecord']) -> List[int]:\n o = []\n for index, validator in enumerate(validators):\n if (validator.start_dynasty <= dynasty and dynasty < validator.end_dynasty):\n o.append(index)\n return o\n\n\ndef shuffle(lst: List[Any],\n seed: Hash32,\n config: Dict[str, Any]=DEFAULT_CONFIG) -> List[Any]:\n lst_count = len(lst)\n assert lst_count <= 16777216\n o = [x for x in lst]\n source = seed\n i = 0\n while i < lst_count:\n source = blake(source)\n for pos in range(0, 30, 3):\n m = int.from_bytes(source[pos:pos+3], 'big')\n remaining = lst_count - i\n if remaining == 0:\n break\n rand_max = 16777216 - 16777216 % remaining\n if m < rand_max:\n replacement_pos = (m % remaining) + i\n o[i], o[replacement_pos] = o[replacement_pos], o[i]\n i += 1\n return o\n\n\ndef split(lst: List[Any], N: int) -> List[Any]:\n list_length = len(lst)\n return [\n lst[(list_length * i // N): (list_length * (i+1) // N)] for i in range(N)\n ]\n\n\ndef get_new_shuffling(seed: Hash32,\n validators: List['ValidatorRecord'],\n dynasty: int,\n crosslinking_start_shard: ShardId,\n config: Dict[str, Any]=DEFAULT_CONFIG) -> List[List[ShardAndCommittee]]:\n cycle_length = config['cycle_length']\n min_committee_size = config['min_committee_size']\n shard_count = config['shard_count']\n avs = get_active_validator_indices(dynasty, validators)\n if len(avs) >= cycle_length * min_committee_size:\n committees_per_slot = len(avs) // cycle_length // (min_committee_size * 2) + 1\n slots_per_committee = 1\n else:\n committees_per_slot = 1\n slots_per_committee = 1\n while (len(avs) * slots_per_committee < cycle_length * min_committee_size and\n slots_per_committee < cycle_length):\n slots_per_committee *= 2\n o = []\n\n shuffled_active_validator_indices = shuffle(avs, seed, config)\n validators_per_slot = split(shuffled_active_validator_indices, cycle_length)\n for slot, slot_indices in enumerate(validators_per_slot):\n shard_indices = split(slot_indices, committees_per_slot)\n shard_id_start = crosslinking_start_shard + (\n slot * committees_per_slot // slots_per_committee\n )\n o.append([ShardAndCommittee(\n shard_id=(shard_id_start + j) % shard_count,\n committee=indices\n ) for j, indices in enumerate(shard_indices)])\n return o\n\n\ndef get_shards_and_committees_for_slot(\n crystallized_state: 'CrystallizedState',\n slot: int,\n config: Dict[str, Any]=DEFAULT_CONFIG) -> List[ShardAndCommittee]:\n cycle_length = config['cycle_length']\n\n start = crystallized_state.last_state_recalc - cycle_length\n assert start <= slot < start + cycle_length * 2\n return crystallized_state.shard_and_committee_for_slots[slot - start]\n\n\ndef get_block_hash(\n active_state: 'ActiveState',\n current_block: 'Block',\n slot: int,\n config: Dict[str, Any]=DEFAULT_CONFIG) -> Hash32:\n cycle_length = config['cycle_length']\n\n sback = current_block.slot_number - cycle_length * 2\n assert sback <= slot < sback + cycle_length * 2\n return active_state.recent_block_hashes[slot - sback]\n\n\ndef get_proposer_position(parent_block: 'Block',\n crystallized_state: 'CrystallizedState',\n config: Dict[str, Any]=DEFAULT_CONFIG) -> Tuple[int, int]:\n shard_and_committee = get_shards_and_committees_for_slot(\n crystallized_state,\n parent_block.slot_number,\n config=config,\n )[0]\n\n # `proposer_index_in_committee` th attester in `shard_and_committee`\n # is the proposer of the parent block.\n assert len(shard_and_committee.committee) > 0\n proposer_index_in_committee = (\n parent_block.slot_number %\n len(shard_and_committee.committee)\n )\n\n return proposer_index_in_committee, shard_and_committee.shard_id\n","repo_name":"ethereum/beacon_chain","sub_path":"beacon_chain/state/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":8062,"program_lang":"python","lang":"en","doc_type":"code","stars":206,"dataset":"github-code","pt":"22"}
+{"seq_id":"32863778736","text":"#! /usr/bin/env python3\n\nimport os\n\ndef main():\n\n files = os.listdir('./sjis')\n for file in files:\n print(file)\n # with open('.\\\\sjis\\\\' + file, encoding='shift_jis') as fi, open('.\\\\utf8\\\\' + file, 'w',encoding='utf-8') as fo:\n with open('./sjis/' + file, encoding='shift_jis') as fi, open('./utf8/' + file, 'w',encoding='utf-8') as fo:\n fo.write(fi.read())\n\nif __name__ == '__main__':\n main()\n","repo_name":"nakazt/toutf8","sub_path":"toutf8.py","file_name":"toutf8.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72818345975","text":"import unittest\n\nfrom test.helpers import new_test_service_context, RequestParamsMock\nfrom xcube_server.context import ServiceContext\nfrom xcube_server.controllers.tiles import get_dataset_tile, get_ne2_tile, get_dataset_tile_grid, get_ne2_tile_grid, \\\n get_legend\nfrom xcube_server.defaults import API_PREFIX\nfrom xcube_server.errors import ServiceBadRequestError, ServiceResourceNotFoundError\n\n\nclass TilesControllerTest(unittest.TestCase):\n\n def test_get_dataset_tile(self):\n ctx = new_test_service_context()\n tile = get_dataset_tile(ctx, 'demo', 'conc_tsm', '0', '0', '0', RequestParamsMock())\n self.assertIsInstance(tile, bytes)\n\n tile = get_dataset_tile(ctx, 'demo', 'conc_tsm', '-20', '0', '0', RequestParamsMock())\n self.assertIsInstance(tile, bytes)\n\n def test_get_dataset_tile_with_all_params(self):\n ctx = new_test_service_context()\n tile = get_dataset_tile(ctx, 'demo', 'conc_tsm', '0', '0', '0', RequestParamsMock(time='current', cbar='plasma',\n vmin='0.1', vmax='0.3'))\n self.assertIsInstance(tile, bytes)\n\n def test_get_dataset_tile_with_time_dim(self):\n ctx = new_test_service_context()\n tile = get_dataset_tile(ctx, 'demo', 'conc_tsm', '0', '0', '0', RequestParamsMock(time='2017-01-26'))\n self.assertIsInstance(tile, bytes)\n\n ctx = new_test_service_context()\n tile = get_dataset_tile(ctx, 'demo', 'conc_tsm', '0', '0', '0', RequestParamsMock(time='current'))\n self.assertIsInstance(tile, bytes)\n\n with self.assertRaises(ServiceBadRequestError) as cm:\n get_dataset_tile(ctx, 'demo', 'conc_tsm', '0', '0', '0', RequestParamsMock(time='Gnaaark!'))\n self.assertEqual(400, cm.exception.status_code)\n self.assertEqual(\"'Gnaaark!' is not a valid value for \"\n \"dimension 'time' of variable 'conc_tsm' of dataset 'demo'\",\n cm.exception.reason)\n\n def test_get_ne2_tile(self):\n ctx = new_test_service_context()\n tile = get_ne2_tile(ctx, '0', '0', '0', RequestParamsMock())\n self.assertIsInstance(tile, bytes)\n\n def test_get_dataset_tile_grid(self):\n self.maxDiff = None\n\n ctx = new_test_service_context()\n tile_grid = get_dataset_tile_grid(ctx, 'demo', 'conc_chl', 'ol4', 'http://bibo')\n self.assertEqual({\n 'url': self.base_url + '/datasets/demo/vars/conc_chl/tiles/{z}/{x}/{y}.png',\n 'projection': 'EPSG:4326',\n 'minZoom': 0,\n 'maxZoom': 2,\n 'tileGrid': {'extent': [0.0, 50.0, 5.0, 52.5],\n 'origin': [0.0, 52.5],\n 'resolutions': [0.01, 0.005, 0.0025],\n 'tileSize': [250, 250]},\n }, tile_grid)\n\n tile_grid = get_dataset_tile_grid(ctx, 'demo', 'conc_chl', 'cesium', 'http://bibo')\n self.assertEqual({\n 'url': self.base_url + '/datasets/demo/vars/conc_chl/tiles/{z}/{x}/{y}.png',\n 'rectangle': dict(west=0.0, south=50.0, east=5.0, north=52.5),\n 'minimumLevel': 0,\n 'maximumLevel': 2,\n 'tileWidth': 250,\n 'tileHeight': 250,\n 'tilingScheme': {'rectangle': dict(west=0.0, south=50.0, east=5.0, north=52.5),\n 'numberOfLevelZeroTilesX': 2,\n 'numberOfLevelZeroTilesY': 1},\n }, tile_grid)\n\n with self.assertRaises(ServiceBadRequestError) as cm:\n get_dataset_tile_grid(ctx, 'demo', 'conc_chl', 'ol2.json', 'http://bibo')\n self.assertEqual(400, cm.exception.status_code)\n self.assertEqual('Unknown tile client \"ol2.json\"', cm.exception.reason)\n\n def test_get_legend(self):\n ctx = new_test_service_context()\n image = get_legend(ctx, 'demo', 'conc_chl', RequestParamsMock())\n self.assertEqual(\"\", str(type(image)))\n\n with self.assertRaises(ServiceResourceNotFoundError) as cm:\n get_legend(ctx, 'demo', 'conc_chl', RequestParamsMock(cbar='sun-shine'))\n self.assertEqual('color bar sun-shine not found', cm.exception.reason)\n\n with self.assertRaises(ServiceBadRequestError) as cm:\n get_legend(ctx, 'demo', 'conc_chl', RequestParamsMock(vmin='sun-shine'))\n self.assertEqual(\"\"\"Parameter \"vmin\" must be a number, but was 'sun-shine'\"\"\", cm.exception.reason)\n\n with self.assertRaises(ServiceBadRequestError) as cm:\n get_legend(ctx, 'demo', 'conc_chl', RequestParamsMock(width='sun-shine'))\n self.assertEqual(\"\"\"Parameter \"width\" must be an integer, but was 'sun-shine'\"\"\", cm.exception.reason)\n\n def test_get_ne2_tile_grid(self):\n ctx = ServiceContext()\n tile_grid = get_ne2_tile_grid(ctx, 'ol4', 'http://bibo')\n self.assertEqual({\n 'url': self.base_url + '/ne2/tiles/{z}/{x}/{y}.jpg',\n 'projection': 'EPSG:4326',\n 'minZoom': 0,\n 'maxZoom': 2,\n 'tileGrid': {'extent': [-180.0, -90.0, 180.0, 90.0],\n 'origin': [-180.0, 90.0],\n 'resolutions': [0.703125, 0.3515625, 0.17578125],\n 'tileSize': [256, 256]},\n }, tile_grid)\n\n with self.assertRaises(ServiceBadRequestError) as cm:\n get_ne2_tile_grid(ctx, 'cesium', 'http://bibo')\n self.assertEqual(400, cm.exception.status_code)\n self.assertEqual(\"Unknown tile client 'cesium'\", cm.exception.reason)\n\n @property\n def base_url(self):\n return f'http://bibo/xcube{API_PREFIX}'\n","repo_name":"dcs4cop/xcube-server","sub_path":"test/controllers/test_tiles.py","file_name":"test_tiles.py","file_ext":"py","file_size_in_byte":5677,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"73143412216","text":"#!/usr/bin/env python3\nfrom flask import Blueprint\n\nfrom calculations.flow_equations import manning_equation, velocity_equation\nfrom calculations.headloss_equations import darcy_friction_coefficient, darcy_weisbach_equation, reynolds_equation\nfrom calculations.hydraulic_surfaces import (\n angle_in_partial_filled_pipe,\n circular_pipe,\n circular_water_cross_sectional_area,\n circular_wetted_perimeter,\n get_internal_diameters,\n hydraulic_radius,\n rectangular_dict,\n rectangular_wetted_perimeter,\n)\nfrom calculations.unit_convertion import unit_convertion\nfrom response_tools.response_tools import api_response, error_response\nfrom validations.decorators import check_pipe_parameters, get_fluid_parameters, json_validate, power_to_flow\nfrom validations.json_validation_schemas import headloss_all_pipes, headloss_selected_pipe, manning_schema\n\n\napi = Blueprint('api', __name__)\n\n\n@api.route('/health', methods=['GET'])\ndef health():\n \"\"\"Check api health.\"\"\"\n return api_response({'status': 'everything is ok :)'})\n\n\n@api.route('/calculate/headloss', methods=['POST'])\n@json_validate(headloss_selected_pipe)\n@check_pipe_parameters\n@get_fluid_parameters\n@power_to_flow\ndef headloss(req, roughness, internal_dimension):\n \"\"\"Calculate velocity and headloss for selected dimension.\n\n :param req: request.get_json() flask's method to get json from user\n :param roughness: roughness of pipe in [mm]\n :param internal_dimension: internal dimension of pipe depends on nominal diameter\n \"\"\"\n density = req['density']\n viscosity = req['viscosity']\n # pipe\n area = circular_pipe(internal_dimension, 'mm')\n length = req['length']\n llc = req.get('local_loss_coefficient', 0)\n # flow\n velocity = velocity_equation(req['flow'], req['flow_unit'], area)\n reynolds = reynolds_equation(velocity, internal_dimension, viscosity)\n # headloss\n headloss_unit = req.get('headloss_unit', 'Pa')\n dfc = darcy_friction_coefficient(reynolds, internal_dimension, roughness)\n loss = darcy_weisbach_equation(\n dfc, llc, length, unit_convertion(internal_dimension, 'mm', 'm', 'lenght'), density, velocity\n )\n return api_response(\n {\n 'velocity': velocity,\n 'velocity_unit': 'm/s',\n 'headloss': unit_convertion(loss, 'Pa', headloss_unit, 'pressure'),\n 'headloss_unit': headloss_unit,\n }\n )\n\n\n@api.route('/calculate/pipes', methods=['POST'])\n@json_validate(headloss_all_pipes)\n@get_fluid_parameters\n@power_to_flow\ndef selecting_optimum_pipe_size(req):\n \"\"\"Calculate velocity and headloss for every dimension.\n\n :param req: request.get_json() flask's method to get json from user\n \"\"\"\n density = req['density']\n viscosity = req['viscosity']\n roughness = req.get('roughness', 1.5)\n results = []\n for nominal_diameter, internal_dimension in get_internal_diameters(req['material']):\n area = circular_pipe(internal_dimension, 'mm')\n velocity = velocity_equation(req['flow'], req['flow_unit'], area)\n reynolds = reynolds_equation(velocity, internal_dimension, viscosity)\n dfc = darcy_friction_coefficient(reynolds, internal_dimension, roughness)\n loss = darcy_weisbach_equation(\n dfc, 0, 1, unit_convertion(internal_dimension, 'mm', 'm', 'lenght'), density, velocity\n )\n results.append({'nominal_diameter': nominal_diameter, 'headloss': loss, 'velocity': velocity})\n return api_response({'headloss_unit': 'Pa/m', 'velocity_unit': 'm/s', 'results': results})\n\n\n@api.route('/calculate/gravity_flow', methods=['POST'])\n@json_validate(manning_schema)\ndef gravity_flow(req):\n \"\"\"Calculate gravity flow and velocity.\n\n :param req: request.get_json() flask's method to get json from user\n \"\"\"\n height = req['height']\n if 'diameter' in req:\n diameter = req['diameter']\n if height > diameter:\n return error_response(400, 'Height couldn\\'t be higher than diameter.')\n angle = angle_in_partial_filled_pipe(diameter, height)\n area = circular_water_cross_sectional_area(angle, diameter, height)\n perimeter = circular_wetted_perimeter(angle, diameter)\n else:\n width = req['width']\n area = rectangular_dict(width, height, 'm')\n perimeter = rectangular_wetted_perimeter(width, height)\n hydraulic_radius_value = hydraulic_radius(area, perimeter)\n velocity = manning_equation(hydraulic_radius_value, req['manning_coefficient'], req['slope'])\n return api_response(\n {\n 'velocity': round(velocity, 2),\n 'velocity_unit': 'm/s',\n 'flow': round(velocity * area * 3600, 2),\n 'flow_unit': 'm3/h',\n }\n )\n\n\n@api.app_errorhandler(404)\ndef not_found(e):\n return error_response(404, 'not found')\n\n\n@api.app_errorhandler(405)\ndef wrong_method(e):\n return error_response(405, 'wrong method')\n","repo_name":"Zurek92/fluid_mechanics_api","sub_path":"api/endpoints.py","file_name":"endpoints.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"74629814774","text":"#Lista 1, exercício 8\np = float(input('Peso em kg: '))\nh = float(input('Altura em m: '))\nimc = p/(h**2)\nc = ''\n\nif imc < 18.5:\n c = 'Abaixo do peso'\nelif imc >= 18.5 and imc < 25:\n c = 'Peso normal'\nelif imc >= 25 and imc < 30:\n c = 'Sobrepeso'\nelif imc >= 30 and imc < 35:\n c = 'Obesidade grau I'\nelif imc >= 35 and imc < 40:\n c = 'Obesidade grau II'\nelif imc >= 40:\n c = 'Obesidade grau III ou mórbida'\n \nprint(c)","repo_name":"bmgroppo/Python","sub_path":"BigDataPython/Exercícios1/Lista1/L1E8.py","file_name":"L1E8.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14187821674","text":"import pygame\nimport random\nimport shapes\n\nfrom grid import Grid\nfrom piece import Piece\nfrom window import Window\n\npygame.font.init()\n\ns_width = 800\ns_height = 700\nplay_width = 300 # meaning 300 // 10 = 30 width per block\nplay_height = 600 # meaning 600 // 20 = 20 height per block\n\ndef draw_text_middle(text, size, color, surface):\n font = pygame.font.SysFont('comicsans', size, bold=True)\n label = font.render(text, 1, color)\n\n surface.blit(label, (surface.get_width()/2 - label.get_width()/2, surface.get_height()/2 - label.get_height()/2))\n\ndef get_piece():\n shape = random.choice(shapes.list)\n color = shapes.colors[shapes.list.index(shape)]\n\n return Piece(5, 0, shape, color)\n\ndef main(surface: pygame.Surface):\n\n run = True\n\n change_piece = False\n current_piece = get_piece()\n next_piece = get_piece()\n clock = pygame.time.Clock()\n fall_time = 0\n fall_speed = 0.50\n\n score = 0\n lines = 0\n level = 1\n\n grid = Grid(10, 20)\n window = Window(surface, play_width, play_height)\n\n while run:\n fall_time += clock.get_rawtime()\n clock.tick()\n\n if fall_time/1000 > fall_speed:\n fall_time = 0\n\n grid.update(current_piece.get_positions(), (0,0,0))\n\n new_positions = current_piece.moveDown()\n\n if not(grid.check_position(new_positions)) and current_piece.y > 0:\n current_piece.moveUp()\n change_piece = True\n \n\n grid.update(current_piece.get_positions(), current_piece.color)\n\n if change_piece == True:\n if grid.check_game_over() == True:\n run = False\n\n completed_lines = grid.check_completed_lines()\n lines += completed_lines\n score += level * completed_lines * completed_lines * 20\n if score > level * 100 and fall_speed > 0.05:\n level += 1\n fall_time -= 0.05\n\n current_piece = next_piece\n next_piece = get_piece()\n change_piece = False\n\n for event in pygame.event.get():\n\n if event.type == pygame.QUIT:\n run = False\n pygame.display.quit()\n quit()\n\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n grid.update(current_piece.get_positions(), (0,0,0))\n new_positions = current_piece.moveLeft()\n if not(grid.check_position(new_positions)):\n current_piece.moveRight()\n\n grid.update(current_piece.get_positions(), current_piece.color)\n\n if event.key == pygame.K_RIGHT:\n grid.update(current_piece.get_positions(), (0,0,0))\n new_positions = current_piece.moveRight()\n if not(grid.check_position(new_positions)):\n current_piece.moveLeft()\n\n grid.update(current_piece.get_positions(), current_piece.color)\n\n if event.key == pygame.K_DOWN:\n grid.update(current_piece.get_positions(), (0,0,0))\n new_positions = current_piece.moveDown()\n if not(grid.check_position(new_positions)):\n current_piece.moveUp()\n \n grid.update(current_piece.get_positions(), current_piece.color)\n\n if event.key == pygame.K_UP:\n grid.update(current_piece.get_positions(), (0,0,0))\n new_positions = current_piece.rotateRight()\n if not(grid.check_position(new_positions)):\n current_piece.rotateLeft()\n\n grid.update(current_piece.get_positions(), current_piece.color)\n\n window.draw()\n window.draw_grid(grid)\n window.draw_score(score, lines, level)\n window.draw_next_piece(next_piece)\n pygame.display.update()\n\n draw_text_middle(\"Game Over\", 40, (255,255,255), surface)\n pygame.display.update()\n pygame.time.delay(2000)\n\n \n\ndef main_menu():\n surface = pygame.display.set_mode((s_width, s_height))\n pygame.display.set_caption('Tetris')\n\n run = True\n while run:\n surface.fill((0,0,0))\n draw_text_middle('Press any key to begin.', 60, (255, 255, 255), surface)\n\n pygame.display.update()\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n if event.type == pygame.KEYDOWN:\n main(surface)\n pygame.quit()\n \n\nmain_menu() # start game","repo_name":"mallsbill/pytetris","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11450999620","text":"import numpy as np\nimport plotly.plotly as py\nimport plotly.graph_objs as go\nimport pandas as pd\n\ndata_911 = pd.read_csv('/Users/KatieHanss/Documents/424_seattle/seattle/seattle_data/911_w_parks.csv')\n\ngroups = ['ASSAULTS', 'BURGLARY', 'MENTAL HEALTH',\n'MOTOR VEHICLE COLLISION INVESTIGATION', 'NARCOTICS COMPLAINTS',\n'SHOPLIFTING', 'THREATS, HARASSMENT', 'TRESPASS', 'AUTO THEFTS']\n\nmonths = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n\ntotal = []\nmonth_counts = []\n\nfor group in groups:\n group_subset = data_911[data_911['Event Clearance Group'] == group]\n total.append(len(group_subset))\n\n y_vals = []\n for month in months:\n y_vals.append(len(group_subset[group_subset['month'] == month]))\n\n month_counts.append(y_vals)\n\ndata = []\nfor i in range(len(groups)):\n data.append(\n go.Scatter(\n x = months,\n y = month_counts[i],\n mode = 'lines+markers',\n name = groups[i]\n )\n )\n\nurl = py.plot(data, filename = 'Number of Crimes per Month')\n\nmonth_prop = []\nfor i in range(len(month_counts)):\n month_prop.append(\n [float(x) / total[i] for x in month_counts[i]]\n )\n\ndata = []\nfor i in range(len(groups)):\n data.append(\n go.Scatter(\n x = months,\n y = month_prop[i],\n mode = 'lines+markers',\n name = groups[i]\n )\n )\n\nurl = py.plot(data, filename = 'Proportion of Crimes per Month')","repo_name":"katieh/seattle","sub_path":"seattle_get_feature_graphs/get_month_graph.py","file_name":"get_month_graph.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72837729975","text":"from __main__ import app\nfrom flask import request\nimport json\nfrom utils.api_utils.api_utils import endpoint_handler\nfrom src.settings import settings as ST\nfrom src.core.olympics import olympics\nfrom src.core.co2 import co2\nfrom src.core.sheep import sheep\nfrom src.core.virus import virus\nfrom src.core.piles import piles\n\n\n@app.route(\"/health\")\n@endpoint_handler\ndef health():\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: {ST.MSG: 'Aloha', ST.SUCCESS: True}}\n\n\n@app.route(\"/olympics\")\n@endpoint_handler\ndef olympics_get():\n res = olympics.get_df()\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: res}\n\n\n@app.route(\"/co2\")\n@endpoint_handler\ndef co2_get():\n res = co2.get_df()\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: res}\n\n\n@app.route(\"/co2-spec\")\n@endpoint_handler\ndef co2_get_spec():\n res = co2.get_df_spec(request.args.get('spec'))\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: res}\n\n\n@app.route(\"/sheep\")\n@endpoint_handler\ndef sheep_get():\n res = sheep.get_df()\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: res}\n\n\n@app.route(\"/virus\")\n@endpoint_handler\ndef virus_get():\n res = virus.get_df()\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: res}\n\n\n@app.route(\"/piles\")\n@endpoint_handler\ndef piles_get():\n res = piles.get_df(request.args)\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: res}\n\n\n@app.route(\"/piles-area\")\n@endpoint_handler\ndef piles_work_get():\n res = piles.get_work_df(request.args)\n return {ST.HTTP_STATUS: 200, ST.PAYLOAD: res}","repo_name":"leoclo/dse241","sub_path":"flask/endpoints/open_endpoints.py","file_name":"open_endpoints.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"21809110842","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\"\"\"875. 爱吃香蕉的珂珂\n\n珂珂喜欢吃香蕉。这里有 N 堆香蕉，第 i 堆中有 piles[i] 根香蕉。警卫已经离开了，将在 H 小时后回来。\n\n珂珂可以决定她吃香蕉的速度 K （单位：根/小时）。每个小时，她将会选择一堆香蕉，从中吃掉 K 根。如果这堆香蕉少于 K 根，她将吃掉这堆的所有香蕉，然后这一小时内不会再吃更多的香蕉。 \n\n珂珂喜欢慢慢吃，但仍然想在警卫回来前吃掉所有的香蕉。\n\n返回她可以在 H 小时内吃掉所有香蕉的最小速度 K（K 为整数）。\n\n示例 1：\n\n输入: piles = [3,6,7,11], H = 8\n输出: 4\n示例 2：\n\n输入: piles = [30,11,23,4,20], H = 5\n输出: 30\n示例 3：\n\n输入: piles = [30,11,23,4,20], H = 6\n输出: 23\n\n提示：\n\n1 <= piles.length <= 10^4\npiles.length <= H <= 10^9\n1 <= piles[i] <= 10^9\n\n来源：力扣（LeetCode）\n��接：https://leetcode-cn.com/problems/koko-eating-bananas\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\n\nfrom typing import List\n\n\nclass Solution:\n\n def each_pile_eat_time(self, banana_number, eat_speed):\n return banana_number // eat_speed + (1 if banana_number % eat_speed else 0)\n\n def can_finish(self, piles, eat_speed, total_hour):\n total_eat_time = 0\n for pile in piles:\n total_eat_time += self.each_pile_eat_time(pile, eat_speed)\n\n return total_eat_time <= total_hour\n\n def split_search(self, start: int, end: int, piles: List[int], total_hour: int) -> int:\n if end <= start:\n return start\n\n middle_speed = start + (end - start) // 2\n\n if self.can_finish(piles, middle_speed, total_hour):\n end = middle_speed\n else:\n start = middle_speed + 1\n\n return self.split_search(start, end, piles, total_hour)\n\n def minEatingSpeed(self, piles: List[int], H: int) -> int:\n max_number = max(piles)\n min_number = 1\n\n return self.split_search(min_number, max_number + 1, piles, H)\n\n\nif __name__ == \"__main__\":\n piles = [3, 6, 7, 11]\n H = 8\n solution = Solution()\n print(solution.minEatingSpeed(piles, H))\n\n","repo_name":"SugarP1g/LearningPython","sub_path":"src/LeetCode/koko-eating-bananas.py","file_name":"koko-eating-bananas.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"30031835506","text":"class Solution:\n def rotate(self, nums: list[int], k: int) -> None:\n \"\"\"\n Do not return anything, modify nums in-place instead.\n \"\"\"\n k = k % len(nums)\n reverse(nums, 0, len(nums) - k - 1)\n reverse(nums, len(nums) - k, len(nums) - 1)\n reverse(nums, 0, len(nums) - 1)\n return nums\n\ndef reverse(nums, start, end):\n while start < end:\n nums[start], nums[end] = nums[end], nums[start]\n start += 1\n end -= 1\n\nprint(Solution.rotate(\"Testcase 1\", [1,2,3,4,5,6,7], 3))\nprint(Solution.rotate(\"Testcase 2\", [-1,-100,3,99], 2))","repo_name":"aswinmguptha/leetcode","sub_path":"Algorithm/Algorithm I/Rotate_Array/Rotate_Array.py","file_name":"Rotate_Array.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13154175808","text":"import tabulate\nfrom .ELF_Phdr_values import *\n\nclass Elf_Phdr:\n\n\tdef __init__(self):\n\t\tself.p_type\t\t= None\n\t\tself.p_flags\t= None\n\t\tself.p_offset\t= None\n\t\tself.p_vaddr\t= None\n\t\tself.p_paddr\t= None\n\t\tself.p_filesz\t= None\n\t\tself.p_memsz\t= None\n\t\tself.p_align\t= None\n\n\n\tdef set_values(self):\n\t\tvalues = list()\n\t\tvalues.append((\"p_type \" , self.p_type ))\n\t\tvalues.append((\"p_flags \" , self.p_flags ))\n\t\tvalues.append((\"p_offset\" , self.p_offset + \" ( %d bytes from the beginning of the file )\" % int(self.p_offset, 16)))\n\t\tvalues.append((\"p_vaddr \" , self.p_vaddr + \" ( Address at which the segment will be loaded into memory )\"))\n\t\tvalues.append((\"p_paddr \" , self.p_paddr + \" ( Address at which the sehment resides on the disk )\"))\n\t\tvalues.append((\"p_filesz\" , self.p_filesz + \" ( The size of the segment is %d bytes )\" % int(self.p_filesz, 16)))\n\t\tvalues.append((\"p_memsz \" , self.p_memsz + \" ( The size of the segment in memory is %d bytes)\" % int(self.p_memsz, 16)))\n\t\tvalues.append((\"p_align \" , self.p_align + \" ( Alignment for loading into memory)\"))\n\t\treturn values\n\n\tdef print_phdr(self):\n\t\tvalues = self.set_values()\n\t\tprint(tabulate.tabulate(values, headers=[\"Program Header\"], tablefmt=\"rst\"))\n\t\tprint()\n\t\tprint()\n\n\tdef check_type(self):\n\t\tif self.p_type in dict_type:\n\t\t\tself.p_type = dict_type[self.p_type]\n\n\tdef check_flags(self):\n\t\tif self.p_flags in dict_flags:\n\t\t\tself.p_flags = dict_flags[self.p_flags]\n\n\tdef load_attributes(self):\n\t\tself.check_type()\n\t\tself.check_flags()\n","repo_name":"evilbyt3/interelf","sub_path":"ELF_lib/ELF_Phdr.py","file_name":"ELF_Phdr.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33741447726","text":"#%%\nimport numpy as np\nimg = np.array([[2, 4, 2, 2, 4],\n [0, 6, 7, 0, 8],\n [0, 4, 3, 6, 3],\n [5, 8, 7, 9, 7],\n [4, 4, 4, 8, 1]])\n\nconv01 = np.array([[-1, 0, -2],\n [-1, 2, 3],\n [1, -1, 1]])\n\n# conv02 = np.array([[0, -1, 0],\n# [-1, 4, -1],\n# [0, -1, 0]])\n\n#%%\n# Conv01\nx = img\nconv_filter = conv01\nfilter_size = 3\noutput_size = 5 - filter_size + 1\nout = np.zeros([output_size, output_size])\nfor row in range(x.shape[0] - filter_size + 1):\n for col in range(x.shape[1] - filter_size + 1):\n block = x[row:row + filter_size, col:col + filter_size]\n conv = np.sum(np.multiply(block, conv_filter))\n out[row, col] = conv\n\nout01 = out \n\n#%%\n# ReLU01\nx = out01\nout02 = x*(x > 0)\n\n#%%\n# Conv02\nx = out02\nconv_filter = conv02\nfilter_size = 3\noutput_size = 6 - filter_size + 1\nout = np.zeros([output_size, output_size])\nfor row in range(x.shape[0] - filter_size + 1):\n for col in range(x.shape[1] - filter_size + 1):\n block = x[row:row + filter_size, col:col + filter_size]\n conv = np.sum(np.multiply(block, conv_filter))\n out[row, col] = conv\n\nout03 = out \n\n#%%\n# ReLU02\nx = out03\nout04 = x*(x > 0)\nout04\n#%%\n# Pooling\nx = out04\ninput_size = 4\nfilter_size = 2\nstride = 2\noutput_size = ((input_size - filter_size) // stride) + 1\nout = np.zeros([output_size, output_size])\nfor row in range(0, x.shape[0] - filter_size + 1, stride):\n for col in range(0, x.shape[1] - filter_size + 1, stride):\n block = x[row:row + filter_size, col:col + filter_size]\n pool = np.max(block)\n out[row//2, col//2] = pool\n \nout05 = out\nout05\n#%%\n# FC01\nx = out05\nx = x.reshape(1, -1)\nW01 = np.array([[0.5, -0.5, 0.5, -0.5],\n [0.5, -1.0, 1.0, -0.5]]).T\nout = np.dot(x, W01)\nout06 = out\nout06\n#%%\n# Sigmoid\nx = out06\nout = 1 / (1 + np.exp(-x))\nout07 = out\n#%%\n# FC2\nx = out07\nx = x.reshape(1, -1)\nW02 = np.array([[-0.5, -1],\n [0.5, 1]]).T\nout = np.dot(x, W02)\nout08 = out\n#%%\n# Sigmoid\nx = out08\nout = 1 / (1 + np.exp(-x))\nout09 = out\n#%%\n# Softmax\nx = out09\nnp.exp(x[:, 0]) / np.sum(np.exp(x))\nnp.exp(x[:, 1]) / np.sum(np.exp(x))\n\n# %%\n(1 - 0.6537)*(0.6537 - 0.6537**2)*0.8176(1 - 0.8176)","repo_name":"hcygeorge/build-an-image-filter-tool-from-scratch","sub_path":"bp_conv.py","file_name":"bp_conv.py","file_ext":"py","file_size_in_byte":2309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5425950982","text":"from django.urls import path\nfrom . import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\n\nurlpatterns = [\n path('home/', views.home, name='home'),\n path('profile/', views.profile, name='profile'),\n\n path('chat/', views.group_chat, name='chat'),\n path('group_chat//', views.group_chat, name='group_chat'),\n path('friend_chat//', views.friend_chat, name='friend_chat'),\n path('group_chat_delete//', views.group_chat_delete, name='group_chat_delete'),\n path('friend_chat_delete//', views.friend_chat_delete, name='friend_chat_delete'),\n\n path('friend_delete//', views.friend_delete, name='friend_delete'),\n path('friend_promise//', views.friend_promise, name='friend_promise'),\n path('friend_deny//', views.friend_deny, name='friend_deny'),\n\n path('friend_search/', views.friend_search, name='friend_search'),\n path('friend_request//', views.friend_request, name='friend_request'),\n\n path('group_create/', views.group_create, name='group_create'),\n path('group_delete//', views.group_delete, name='group_delete'),\n path('group_invite/', views.group_invite, name='group_invite'),\n path('group_invite_request//', views.group_invite_request, name='group_invite_request'),\n path('group/', views.group, name='group'),\n\n]\n","repo_name":"karasawa/django-sns","sub_path":"sns/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35507223085","text":"import datetime\nimport json\nimport os\nimport time\n\nimport auth_spotipy\n\n\ndef main(event=False, context=False):\n # record time for filename\n dt_now = datetime.datetime.now()\n jst_list = [dt_now.year, dt_now.month, dt_now.day, dt_now.hour]\n time_list = [\n \"0\" + str(time) if len(str(time)) == 1 else str(time) for time in jst_list\n ]\n idir_path = \"data\"\n if not os.path.isdir(idir_path):\n try:\n os.makedirs(idir_path)\n except Exception as e:\n idir_path = \"/tmp/data\"\n os.makedirs(idir_path)\n print(f\"Error is {e}\")\n json_path = f\"{idir_path}/{time_list[0]}{time_list[1]}{time_list[2]}{time_list[3]}_recently.json\"\n\n # setup\n auth = auth_spotipy.Auth()\n spotify = auth.authWithScope()\n\n recently_played = spotify.current_user_recently_played(limit=50)\n with open(json_path, \"w\", encoding=\"utf-8\") as f:\n f.write(json.dumps(recently_played))\n print(\"path is\", json_path)\n\n\nif __name__ == \"__main__\":\n while True:\n main()\n time.sleep(10000)\n","repo_name":"gh-tyl/spotify","sub_path":"main_spotipy.py","file_name":"main_spotipy.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21954349323","text":"__author__ = \"Adam Sindelar \"\n\nfrom efilter import errors\n\nfrom efilter.parsers.common import grammar\n\n\nclass TokenStream(object):\n \"\"\"Manages and enforces grammar over tokenizer output.\n\n Most recursive descent parsers need a mechanism to accept, reject, expect\n or peek at the next token based on matching loging supplied by grammar\n functions. This class manages the tokenizer for the parser, and enforces\n the expectations set by grammar.\n\n Arguments:\n tokenizer: Must support the tokenizer interface (skip and peek).\n \"\"\"\n\n tokenizer = None\n matched = None\n\n def __init__(self, tokenizer=None):\n self.tokenizer = tokenizer\n\n def match(self, f, *args):\n \"\"\"Match grammar function 'f' against next token and set 'self.matched'.\n\n Arguments:\n f: A grammar function - see efilter.parsers.common.grammar. Must\n return TokenMatch or None.\n args: Passed to 'f', if any.\n\n Returns:\n Instance of efilter.parsers.common.grammar.TokenMatch or None.\n\n Comment:\n If a match is returned, it will also be stored in self.matched.\n \"\"\"\n try:\n match = f(self.tokenizer, *args)\n except StopIteration:\n # The grammar function might have tried to access more tokens than\n # are available. That's not really an error, it just means it didn't\n # match.\n return\n\n if match is None:\n return\n\n if not isinstance(match, grammar.TokenMatch):\n raise TypeError(\"Invalid grammar function %r returned %r.\"\n % (f, match))\n\n self.matched = match\n return match\n\n def accept(self, f, *args):\n \"\"\"Like 'match', but consume the token (tokenizer advances.)\"\"\"\n match = self.match(f, *args)\n if match is None:\n return\n\n self.tokenizer.skip(len(match.tokens))\n return match\n\n def reject(self, f, *args):\n \"\"\"Like 'match', but throw a parse error if 'f' matches.\n\n This is useful when a parser wants to be strict about specific things\n being prohibited. For example, DottySQL bans the use of SQL keywords as\n variable names.\n \"\"\"\n match = self.match(f, *args)\n if match:\n token = self.peek(0)\n raise errors.EfilterParseError(\n query=self.tokenizer.source, token=token,\n message=\"Was not expecting a %s here.\" % token.name)\n\n def expect(self, f, *args):\n \"\"\"Like 'accept' but throws a parse error if 'f' doesn't match.\"\"\"\n match = self.accept(f, *args)\n if match:\n return match\n\n try:\n func_name = f.func_name\n except AttributeError:\n func_name = \"\"\n\n start, end = self.current_position()\n raise errors.EfilterParseError(\n query=self.tokenizer.source, start=start, end=end,\n message=\"Was expecting %s here.\" % (func_name))\n\n def current_position(self):\n \"\"\"Return a tuple of (start, end).\"\"\"\n token = self.tokenizer.peek(0)\n if token:\n return token.start, token.end\n\n return self.tokenizer.position, self.tokenizer.position + 1\n\n def peek(self, n):\n \"\"\"Same as self.tokenizer.peek.\"\"\"\n return self.tokenizer.peek(n)\n\n def skip(self, n):\n \"\"\"Same as self.tokenizer.skip.\"\"\"\n return self.tokenizer.skip(n)\n\n def __iter__(self):\n \"\"\"Self as iter(self.tokenizer).\"\"\"\n return iter(self.tokenizer)\n","repo_name":"google/dotty","sub_path":"efilter/parsers/common/token_stream.py","file_name":"token_stream.py","file_ext":"py","file_size_in_byte":3639,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"22"}
+{"seq_id":"4642865173","text":"\n\nlst = [1, 2, 3, 4, 5, 6]\nbook = {\n 'title': 'The Langoliers',\n 'author': 'Stephen King',\n 'year_published': 1990\n}\nstring = \"Hello, World!\"\n\nfor i in lst:\n print(i)\n\nit = iter(lst)\nwhile True:\n try:\n i = next(it)\n print(i)\n except StopIteration:\n break\n\n\n\n\n","repo_name":"vk0/java","sub_path":"stepic_py_basic_use/w2/module2/iteration.py","file_name":"iteration.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"24290140882","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport re\nimport inspect\nfrom six import PY2\n\nfrom .recording_tuple import RecordingTuple, ROITuple\n\n\n__all__ = [\n 'match_recordings',\n 'make_filter',\n 'make_filters',\n 'make_matcher',\n 'make_white_black_matcher',\n 'filter_recordings',\n 'split_recordings']\n\n\ndef match_recordings(root, recording_tuple_regex, experiment_on_unmatched=None):\n recordings = list()\n expected_type = None\n for current_directory, sub_directories, files in os.walk(root):\n for current_file in files:\n full_path = os.path.join(current_directory, current_file)\n match_object = recording_tuple_regex.match(full_path)\n if match_object is not None:\n group_dict = match_object.groupdict()\n if 'experiment' not in group_dict and experiment_on_unmatched is not None:\n group_dict['experiment'] = experiment_on_unmatched\n if 'roi' in group_dict:\n recordings.append(ROITuple(full_path=full_path, **group_dict))\n else:\n recordings.append(RecordingTuple(full_path=full_path, **group_dict))\n if expected_type is None:\n expected_type = type(recordings[-1])\n elif not isinstance(recordings[-1], expected_type):\n raise ValueError('Mixed tuple types in recordings')\n\n return recordings\n\n\ndef _is_match(regex, part):\n match_obj = regex.match(part)\n return match_obj is not None and match_obj.end() - match_obj.start() == len(part)\n\n\ndef get_tuple_type_properties(tuple_type):\n property_names = [n for n, v in inspect.getmembers(tuple_type, lambda m: isinstance(m, property))]\n if PY2:\n # noinspection PyDeprecation\n init_kwargs = inspect.getargspec(tuple_type.__init__).args\n else:\n init_kwargs = inspect.getfullargspec(tuple_type.__init__).args\n property_names = [n for n in init_kwargs if n != 'full_path' and n in property_names]\n return property_names\n\n\ndef make_filter(property_names, filter_parts):\n\n if len(filter_parts) != len(property_names):\n raise ValueError('Expected {}'.format('$'.join(['<{}>'.format(n) for n in property_names])))\n\n name_regex_pairs = [(property_name, re.compile(part, re.IGNORECASE))\n for property_name, part in zip(property_names, filter_parts)]\n\n def _filter(to_check):\n return all([_is_match(regex, getattr(to_check, name)) for name, regex in name_regex_pairs])\n\n return _filter\n\n\ndef make_filters(property_names, string_filter_list):\n return None if string_filter_list is None \\\n else list(map(lambda s: make_filter(property_names, s), string_filter_list))\n\n\ndef make_matcher(property_names, string_filter_list, result_on_empty_filters):\n filters = make_filters(property_names, string_filter_list)\n\n def matches_any(recording_tuple):\n return result_on_empty_filters if filters is None else any(map(lambda f: f(recording_tuple), filters))\n\n return matches_any\n\n\ndef make_white_black_matcher(property_names, white_list=None, black_list=None):\n white_list_matcher = make_matcher(property_names, white_list, True)\n black_list_matcher = make_matcher(property_names, black_list, False)\n\n def is_match(recording_tuple):\n return white_list_matcher(recording_tuple) and not black_list_matcher(recording_tuple)\n\n return is_match\n\n\ndef filter_recordings(recording_tuples, white_list=None, black_list=None):\n property_names = get_tuple_type_properties(type(recording_tuples[0]))\n is_match = make_white_black_matcher(property_names, white_list, black_list)\n return list(filter(lambda r: is_match(r), recording_tuples))\n\n\ndef split_recordings(recording_tuples, second_set_white_list=None, second_set_black_list=None):\n\n if second_set_white_list is None and second_set_black_list is None:\n return recording_tuples, list()\n\n property_names = get_tuple_type_properties(type(recording_tuples[0]))\n is_match = make_white_black_matcher(property_names, second_set_white_list, second_set_black_list)\n first_set = list()\n second_set = list()\n for r in recording_tuples:\n if is_match(r):\n second_set.append(r)\n else:\n first_set.append(r)\n return first_set, second_set\n","repo_name":"danrsc/paradigms","sub_path":"paradigms/data_match.py","file_name":"data_match.py","file_ext":"py","file_size_in_byte":4425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70966273336","text":"import numpy as np\nfrom PIL import Image, ImageDraw, ImageFont\n\ndef create_entry(text, width, height, font_size, text_color, shadow_color, center):\n # file_name = \"{0}_w{1}_h{2}_{3}\".format(text, width, height, \"center\" if center else \"left\")\n file = open(\"data/\" + \"lvl 1 lvl 2\" + \".txt\", \"w\")\n print(\"Making entry for '{0}'\".format(text))\n\n img = Image.new('RGBA', (width, height), \"Black\")\n font_path = r\"C:\\Users\\bened\\Downloads\\pixel_lcd7\\pixel_lcd_7.ttf\"\n font = ImageFont.truetype(font_path, font_size)\n draw = ImageDraw.Draw(img)\n\n textlen = draw.textlength(text, font)\n if not center:\n draw.line([2, 10, textlen, 10])\n\n # Offset for the main text\n main_offset_x = 48 - textlen // 2 if center else 2\n main_offset_y = 1\n\n # Offset for the shadow or outline\n shadow_offset_x = main_offset_x + 1 # Adjust this value for the desired shadow offset\n shadow_offset_y = main_offset_y + 1 # Adjust this value for the desired shadow offset\n\n # Draw the shadow or outline text\n draw.text((shadow_offset_x, shadow_offset_y), text, font=font, fill=shadow_color)\n # Draw the main text on top of the shadow or outline\n draw.text((main_offset_x, main_offset_y), text, font=font, fill=text_color)\n\n img.save(\"images/\" + \"lvl 1 lvl 2\" + \".png\")\n data = np.array(img)\n print(np.shape(data))\n\n count = 0\n pixel_count = len(data) * len(data[0]) - 1\n file.write(\"wire [15:0] data [{0}:0];\\n\".format(pixel_count))\n\n for i in range(len(data)):\n for j in range(len(data[i])):\n r, g, b, a = data[i][j]\n file.write(\"assign data[4608+\" + str(count) + \"] = \")\n file.write(\"16'b\" + '{0:05b}'.format(r // 8) + \"_\" + '{0:06b}'.format(g // 4) + \"_\" + '{0:05b}'.format(b // 8) + \";\")\n file.write('\\n')\n count += 1\n file.close()\n\nwidth = 96\nwelcome_color = (255, 255, 0, 255) # Yellow color (RGBA format)\nstart_color = (255, 0, 0, 255) # Red Color (RGBA format)\nshadow_welcome_color = (255, 0, 0, 255) # Shadow color (RGBA format)\nshadow_start_color = (0, 0, 255, 255) # Shadow color (RGBA format)\n\n# # Welcome screen with a 3D effect\n# create_entry(\"Welcome to\", width, 16, 9, welcome_color, shadow_welcome_color, center=True)\n# create_entry(\"Brick Breaker!\", width, 16, 9, welcome_color, shadow_welcome_color, center=True)\n# create_entry(\"Press btnC to start\", width, 32, 6, start_color, shadow_start_color, center=True)\n# create_entry(\"> LVL 1 LVL 2\", width, 32, 7, start_color, shadow_start_color, center=True)\n# create_entry(\"A:HS B:Ben\", width, 16, 9, start_color, shadow_start_color, center=True)\ncreate_entry(\"C:JX D:JY\", width, 16, 9, start_color, shadow_start_color, center=True)","repo_name":"BenedictChannn/EE2026-Project","sub_path":"textDrawing.py","file_name":"textDrawing.py","file_ext":"py","file_size_in_byte":2723,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"34773256578","text":"### wrf_roms_cross_section.py ###################\n# Author: Ueslei Adriano Sutil #\n# Created: 25 aug 2017 #\n# Last modified: 29 aug 2017 #\n# #\n# About: Create a cross-section from #\n# WRF and ROMS outputs. #\n#################################################\n\nimport numpy as np\nimport matplotlib.pyplot as plt1\nfrom matplotlib.cm import get_cmap\nimport cartopy.crs as crs\nfrom cartopy.feature import NaturalEarthFeature\nfrom cmocean import cm\nfrom wrf import to_np, getvar, CoordPair, vertcross\nimport matplotlib.gridspec as gridspec\nfrom netCDF4 import Dataset,num2date\nfrom pyroms import vgrid\n# from octant import tools\nimport numpy as np\nimport matplotlib.pyplot as pltexitr\n\n####### WRF options #######\n# WRF input\nfilename_wrf = \"/media/ueslei/Ueslei/INPE/2014/Outputs/WRF/wrf_I_t01.nc\"\nfilename_wr = \"/media/ueslei/Ueslei/INPE/2014/Outputs/WR/wr_I_t01.nc\"\nfilename_wrs = \"/media/ueslei/Ueslei/INPE/2014/Outputs/WRS/wrs_I_t01.nc\"\n\n# Create the start point and end point for the cross section\nstart_point = CoordPair(lat=-41, lon=-60)\nend_point = CoordPair(lat=-41, lon=-30)\n# WRF max height (meters)\nlevels = np.arange(0,601.,1.)\n# WRF grid height spacing\nwrf_spacing = 100\n# WRF timestep\ntimeidx = 80\n\n####### ROMS options #######\n# ROMS input\npath = '/media/ueslei/Ueslei/INPE/2014/Outputs/WRS/'\nname = 'cbm12_ocean_his.nc'\n\n\n# ROMS time\nntime = 40\n# ROMS grid index\n#ind_lat = 125:420\nind_lon = 250\n# ROMS max depth\ndep_max = -600\n\n###### Plot options ######\ncmap1 = cm.balance\ncol_label = ' '\nclevs = np.arange(-3,3,0.005) # (min,max,spacing)\ncxlatlon = 'Longitude'\nfig_dir = './'\nfig_name = 'temp2.png'\ndpi_opt = 300\n\n################### Don't change above ###################\n\n# WRF module\n# Open the NetCDF file\nncfile_wrs = Dataset(filename_wrs)\nncfile_wr = Dataset(filename_wr)\nncfile_wrf = Dataset(filename_wrf)\n\n# Extract the model height and wind speed\nz_wrs = getvar(ncfile_wrs, \"z\")\ntemp_wrs = getvar(ncfile_wrs, \"tc\", timeidx=timeidx)\n\n\nz_wr = getvar(ncfile_wr, \"z\")\ntemp_wr = getvar(ncfile_wr, \"tc\", timeidx=timeidx)\n\n\nz_wrf = getvar(ncfile_wrf, \"z\")\ntemp_wrf = getvar(ncfile_wrf, \"tc\", timeidx=timeidx)\n\n\n# Compute the vertical cross-section interpolation. Also, include the lat/lon\n# points along the cross-section.\ntemp_cross_wrs = vertcross(temp_wrs, z_wrs, wrfin=ncfile_wrs, start_point=start_point, end_point=end_point,\n latlon=True, meta=True,levels=levels)\n\ntemp_cross_wr = vertcross(temp_wr, z_wr, wrfin=ncfile_wr, start_point=start_point, end_point=end_point,\n latlon=True, meta=True,levels=levels)\n\ntemp_cross_wrf = vertcross(temp_wrf, z_wrf, wrfin=ncfile_wrf, start_point=start_point, end_point=end_point,\n latlon=True, meta=True,levels=levels)\n\n# Differences\n#temp_cross_wrf_wrs = temp_cross_wrf-temp_cross_wrs\n#temp_cross_wrf_wr = temp_cross_wrf-temp_cross_wr\ntemp_cross_wrs_wr = temp_cross_wrf-temp_cross_wrs\n\n\n# Create the figure\nfig = plt1.figure(1,figsize=(12,6))\nax = fig.add_subplot(2,1,1)\nax = plt1.axes()\ngs = gridspec.GridSpec(2, 1, hspace=0)\nax.set_subplotspec(gs[0:1])\nfig.tight_layout()\n\n# Make the contour plot\ntemp_contours = ax.contourf(to_np(temp_cross_wrs_wr), cmap=cmap1,levels=clevs)\n\n# Set the x-ticks to use latitude and longitude labels.\ncoord_pairs = to_np(temp_cross_wrs_wr.coords[\"xy_loc\"])\nx_ticks = np.arange(coord_pairs.shape[0])\nx_labels = [pair.latlon_str(fmt=\"{:.2f}, {:.2f}\") for pair in to_np(coord_pairs)]\n\n# Disable xticks\nplt1.setp( ax.get_xticklabels(), visible=False)\nax.xaxis.grid(False, which='minor')\n\n# Set the y-ticks to be height.\nvert_vals = to_np(temp_cross_wrs_wr.coords[\"vertical\"])\nv_ticks = np.arange(vert_vals.shape[0])\n\nax.set_yticks(v_ticks[::wrf_spacing])\nax.set_yticklabels(vert_vals[::wrf_spacing])\n\n# Set the x-axis and y-axis labels\nax.set_ylabel(\"Altitude (m)\", fontsize=12)\n\n# ROMS module\n# Set variables\ndata = Dataset(path+name)\ncs_r = data.variables[u'Cs_r'][:]\nhc = data.variables[u'hc'][:]\ns_rho = data.variables[u's_rho'][:]\nN = len(s_rho)\nrho_pt = data.variables[u'lon_rho'][260,120:485]\nVtrans = data.variables[u'Vtransform'][:]\nzeta = data.variables[u'zeta'][ntime,:,:]\ndepth = data.variables[u'h'][:,:]\nlat = rho_pt.repeat(N)\nlats = lat.reshape(rho_pt.shape[0],N)\nvar0 = data.variables[u'temp'][ntime,:,260,120:485]\nvar = var0.transpose()\ntime = data.variables['ocean_time']\n\n\n# Calculate the depth (in meters)\n[z]=vgrid.z_r(depth,hc,N,s_rho,cs_r,zeta,Vtrans)\nzz=z[:,260,120:485]\nzt=zz.transpose()\n\n# Setting the initial figure\nfig1 = plt.figure(1,figsize=(12,6))\n\nax1 = fig1.add_subplot(gs[1])\nfig1.tight_layout()\nax1.set_ylabel('Depth (m)',fontsize=12)\nax1.set_xlabel(cxlatlon, fontsize=12)\nax1.set_ylim(dep_max,zt.max())\n\n# Plotting values\nvare = ax1.contourf(lats,zt,var,clevs,shading='flat',cmap=cmap1)\ncb = plt.colorbar(vare,ax=[ax,ax1])\n\n#cb = plt.colorbar(vare,shrink=1.0,orientation='horizontal',fraction=0.046, pad=0.04)\ncb.set_label(col_label,fontsize=12)\nplt.savefig(fig_dir+fig_name,dpi=dpi_opt,bbox_inches='tight')\n","repo_name":"uesleisutil/cousteau","sub_path":"diff_wrf_roms_cross_section.py","file_name":"diff_wrf_roms_cross_section.py","file_ext":"py","file_size_in_byte":5411,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"40177232549","text":"import numpy as np\n\n#V X V\ndef naive_vector_dot(x, y):\n assert len(x.shape) == 1\n assert len(y.shape) == 1\n assert x.shape[0] == y.shape[0]\n\n z = 0\n for i in range[x.shape[0]]:\n z += x[i] * y[i]\n return z\n\n\n#M X V\ndef naive_matrix_vector_dot(x, y):\n assert len(x.shape) == 2\n assert len(y.shape) == 1\n assert x.shape[1] == y.shape[0]\n\n z = np.zeros(x.shape[0])\n for i in range(x.shape[0]):\n for j in range(x.shape[1]):\n z[i] += x[i, j] * y[j]\n return z\n\n\n#M X V\ndef naive_matrix_vector_dot2(x, y):\n z = np.zeros(x.shape[0])\n for i in range(x.shape[0]):\n z[i] = naive_vector_dot(x[i, :], y)\n return z\n\n\n#M X M\ndef naive_matrix_dot(x, y):\n assert len(x.shape) == 2\n assert len(x.shape) == 2\n assert x.shape[0] == y.shape[1]\n\n z = np.zeros(len(x.shape[0]), len(y.shape[1]))\n for i in range(x.shape[0]):\n for j in range(y.shape[1]):\n row_x = x[i, :]\n column_y = y[:, j]\n z[i, j] = naive_vector_dot(row_x, column_y)\n return z\n","repo_name":"Maxpa1n/AIhomework","sub_path":"chongchongchong/keras_2.3.3.py","file_name":"keras_2.3.3.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23335567487","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.urls import reverse\n\nfrom .models import Images, Money\nfrom .forms import ImageForm, DiscountForm\n\n# View to render main page\ndef home_view(request):\n queryset = Images.objects.all()\n money = Money.objects.get(id=1)\n form = DiscountForm(request.POST or None, instance=money)\n\n # Calculate the total with discount applied\n discount_total = float(money.total) * (1 - (float(money.discount) * 0.01))\n\n # Save the form discount value if valid\n if form.is_valid():\n form.save()\n return redirect(reverse('home'))\n\n context = {\n \"object_list\": queryset,\n \"money\": money,\n \"discount_total\": discount_total,\n \"form\": form\n }\n return render(request, \"home.html\", context)\n\n# View to create new image to sell\ndef create_view(request):\n form = ImageForm(request.POST, request.FILES)\n\n if form.is_valid():\n form.save()\n return redirect(reverse('home'))\n \n context = {\n 'form': form\n }\n\n return render(request, \"create.html\", context)\n\n# View to delete an image from the database\ndef delete_view(request, id):\n obj = get_object_or_404(Images, id=id)\n\n if request.method==\"POST\":\n obj.delete()\n return redirect(reverse('home'))\n\n context = {\n 'object': obj\n }\n return render(request, \"delete.html\", context)\n\n# View to sell an image and update the money earned\ndef sell_view(request, id):\n obj = get_object_or_404(Images, id=id)\n money = Money.objects.get(id=1)\n \n # When selling an image, its quantity decreases and earnings increases by the image price\n if obj.quantity > 0:\n message = \"Sell successful!\"\n obj.quantity -= 1\n obj.save()\n\n money.total += obj.price\n money.save()\n else:\n message = \"Sell failed, insufficient quantity.\"\n \n context = {\n 'object': obj,\n 'message': message\n }\n\n return render(request, \"sell.html\", context)\n\n# View to reset the earnings and discount values\ndef reset_view(request):\n money = Money.objects.get(id=1)\n\n money.total = 0\n money.discount = 0\n money.save()\n\n context = {\n 'money': money\n }\n\n return render(request, \"reset.html\", context)","repo_name":"cathkyu/image-repo","sub_path":"src/items/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21943842184","text":"import os, sys\nimport shutil\nfrom subprocess import call\nimport git\nimport argparse\nfrom distutils.dir_util import copy_tree\n\ndef remove(path):\n \"\"\" param could either be relative or absolute. \"\"\"\n if os.path.isfile(path) or os.path.islink(path):\n os.remove(path) # remove the file\n elif os.path.isdir(path):\n shutil.rmtree(path) # remove dir and all contains\n else:\n raise ValueError(\"file {} is not a file or dir.\".format(path))\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--push', action='store_true')\nargs = parser.parse_args()\n\nscript_dir = os.path.dirname(os.path.realpath(__file__))\nroot_dir = os.path.join(script_dir,'..')\nthumbnail_dir = os.path.join(root_dir, 'images/photography/thumbnails')\nsite_dir = os.path.join(root_dir,'_site')\nsetup_photos_script = os.path.join(root_dir, 'scripts/setup-photos.py')\n\n\nos.chdir(root_dir)\nshutil.rmtree(thumbnail_dir, ignore_errors=True)\nwith open(setup_photos_script, 'r') as f:\n exec(f.read())\n\nif args.push:\n print(\"\\nBuild Jekyll site\")\n if sys.platform != \"win32\":\n call(['bundle','exec','jekyll', 'build', '--destination','_site'])\n else:\n os.system('bundle exec jekyll build --destination _site')\n repo = git.Repo(search_parent_directories=True)\n sha = repo.head.object.hexsha\n os.chdir(site_dir)\n\n print(\"\\nAdd to git repo and commit\")\n git_commit_ret = call(['git','commit', '-am', 'deploying from '+sha])\n if git_commit_ret == 0:\n print(\"Commited to blog repo, ready to push\")\n web_dir = os.path.join(root_dir,'../website')\n os.chdir(web_dir)\n old_content = os.listdir(web_dir)\n for clean_up in old_content:\n if not clean_up.endswith('.git'): \n remove(clean_up)\n copy_tree(site_dir, web_dir)\n website_commit = call(['git','commit', '-am', 'deploying from '+sha])\n print(\"Commited to website repo, ready to push\")\n else:\n print ('Deployment failed, could not commit _site')\nelse:\n print ('Deployment not requested')\n\n","repo_name":"chuckleplant/blog","sub_path":"scripts/deploy-site.py","file_name":"deploy-site.py","file_ext":"py","file_size_in_byte":2072,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"22"}
+{"seq_id":"29057288860","text":"from loader import bot, db\nfrom states.states import EditPair, CreatePair\nfrom bot_locale.translate import _\nfrom keyboards.inline.menu import AdminKeyboard, MenuKeyboard\nfrom telebot.formatting import escape_markdown\n\nimport json\n\ncallback_data_admin_params = 'admin.params'\ncallback_data_admin_pairs = 'admin.params_pairs'\ncallback_data_admin_view_pair = 'admin.params_pair_view_'\ncallback_data_admin_edit_pair = 'admin.params_pair_edit_'\ncallback_data_admin_create_pair = 'admin.params_pair_create'\ncallback_data_admin_create_pair_accept = 'admin.params_pair_accept_create'\n\ncallback_data_admin_delete_pair = 'admin.params_pair_delete_'\ncallback_data_admin_pair_accept_delete = 'admin.params_pair_accept_delete_'\n\nparams = {\n 'from_name': 'inline_admin_pair_edit_from_name',\n 'from_country_code': 'inline_admin_pair_edit_from_country_code',\n 'from_type': 'inline_admin_pair_edit_from_type',\n 'min_from_amount': 'inline_admin_pair_edit_min_from_amount',\n 'max_from_amount': 'inline_admin_pair_edit_max_from_amount',\n 'to_name': 'inline_admin_pair_edit_to_name',\n 'to_country_code': 'inline_admin_pair_edit_to_country_code',\n 'to_type': 'inline_admin_pair_edit_from_type',\n 'to_requisites_comment': 'inline_admin_pair_edit_to_requisites_comment',\n 'spread': 'inline_admin_pair_edit_spread',\n 'auto_requisites': 'inline_admin_pair_edit_price_handler',\n 'handler_inverted': 'inline_admin_pair_edit_handler_inverted',\n 'from_handler_name': 'inline_admin_pair_edit_from_handler_name',\n 'to_handler_name': 'inline_admin_pair_edit_to_handler_name',\n 'handler_inverted': 'inline_admin_pair_edit_handler_inverted',\n}\n\n@bot.callback_query_handler(is_chat=False, func=lambda call: call.data == callback_data_admin_pairs, role=['admin'])\ndef admin_banks_list(call):\n \"\"\" Отображает список валютных пар\n \"\"\"\n user = db.get_user(call.from_user.id)\n pairs = db.get_pairs(data=[0, 1])\n\n bot.delete_state(call.from_user.id)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=_(user['language_code'], 'admin_page_pairs'),\n reply_markup=AdminKeyboard.pairs(\n user,\n pairs,\n callback_data_admin_view_pair,\n back_menu_data=callback_data_admin_params,\n key_string_back_to='inline_back_to'\n )\n )\n\n\n@bot.callback_query_handler(is_chat=False, func=lambda call: call.data.startswith(callback_data_admin_delete_pair), role=['admin'])\ndef admin_delete_pair(call):\n \"\"\" Предупреждение об удалении пары\n \"\"\"\n user = db.get_user(call.from_user.id)\n pair_id = call.data.replace(callback_data_admin_delete_pair ,\"\")\n pair = db.get_pair(pair_id, name_id=\"id\")\n\n edit_pair_text = _(\n user['language_code'],\n 'admin_data_pair_delete'\n ).format(**pair)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=edit_pair_text,\n reply_markup=MenuKeyboard.accept_or_decline(\n user,\n cl_accept=callback_data_admin_pair_accept_delete+str(pair['id']),\n key_string_accept='inline_accept_delete',\n cl_decline=callback_data_admin_pairs,\n key_string_decline='inline_back_to',\n )\n )\n\n\n@bot.callback_query_handler(is_chat=False, func=lambda call: call.data.startswith(callback_data_admin_pair_accept_delete), role=['admin'])\ndef admin_delete_pair_accept(call):\n \"\"\" Удаление пары\n \"\"\"\n\n user = db.get_user(call.from_user.id)\n pair_id = call.data.replace(callback_data_admin_pair_accept_delete ,\"\")\n pair = db.get_pair(pair_id, name_id=\"id\")\n\n db.delete_object(\n table=\"pairs\",\n name_id=\"id\",\n value=pair['id']\n )\n\n edit_pair_text = _(\n user['language_code'],\n 'admin_data_pair_delete_success'\n ).format(**pair)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=edit_pair_text,\n reply_markup=MenuKeyboard.back_to(\n user,\n data=callback_data_admin_pairs,\n key_string='inline_back_to',\n )\n )\n\n\n@bot.callback_query_handler(is_chat=False, func=lambda call: call.data.startswith(callback_data_admin_view_pair), role=['admin'])\ndef admin_preview_pair(call):\n \"\"\" Отображает содержимое и кнопки управления валютной парой\n \"\"\"\n user = db.get_user(call.from_user.id)\n pair_id = call.data.replace(callback_data_admin_view_pair ,\"\")\n pair = db.get_pair(pair_id, name_id=\"id\")\n pair['is_active'] = bool(pair['is_active'])\n\n edit_pair_text = _(\n user['language_code'],\n 'admin_data_pair_view'\n ).format(**pair)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=edit_pair_text,\n reply_markup=AdminKeyboard.edit_pair(user, pair, params)\n )\n\n # Для кнопки \"Назад\" из редактирования страны\n bot.delete_state(call.from_user.id)\n\n@bot.callback_query_handler(is_chat=False, func=lambda call: call.data.startswith(callback_data_admin_edit_pair), role=['admin'])\ndef admin_edit_pair(call):\n \"\"\" Редактирование пары\n \"\"\"\n user = db.get_user(call.from_user.id)\n pair_param = call.data.replace(callback_data_admin_edit_pair, \"\").split(\"|\")\n parameter = pair_param[0]\n pair_id = pair_param[1]\n pair = db.get_pair(pair_id, name_id=\"id\")\n\n if parameter == 'status':\n bot.answer_callback_query(\n call.id,\n 'Success!'\n )\n\n status = 1 if pair['is_active'] == 0 else 0\n pair['is_active'] = status\n\n db.update_pair(pair_id=pair['id'], args={'is_active': status})\n\n edit_pair_text = _(\n user['language_code'],\n 'admin_data_pair_view'\n ).format(**pair)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=edit_pair_text,\n reply_markup=AdminKeyboard.edit_pair(user, pair, params)\n )\n return\n\n if parameter == 'verification_account':\n bot.answer_callback_query(\n call.id,\n 'Success!'\n )\n\n verification_account = 1 if pair['verification_account'] == 0 else 0\n pair['verification_account'] = verification_account\n\n db.update_pair(pair_id=pair['id'], args={'verification_account': verification_account})\n\n edit_pair_text = _(\n user['language_code'],\n 'admin_data_pair_view'\n ).format(**pair)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=edit_pair_text,\n reply_markup=AdminKeyboard.edit_pair(user, pair, params)\n )\n return\n\n if parameter == 'auto_requisites':\n bot.answer_callback_query(\n call.id,\n 'Success!'\n )\n\n auto_requisites = 1 if pair['auto_requisites'] == 0 else 0\n pair['auto_requisites'] = auto_requisites\n\n db.update_pair(pair_id=pair['id'], args={'auto_requisites': auto_requisites})\n\n edit_pair_text = _(\n user['language_code'],\n 'admin_data_pair_view'\n ).format(**pair)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=edit_pair_text,\n reply_markup=AdminKeyboard.edit_pair(user, pair, params)\n )\n return\n\n\n bot.set_state(call.from_user.id, EditPair.A1)\n\n with bot.retrieve_data(call.from_user.id) as data:\n data['message_id'] = call.message.message_id\n data['pair_id'] = pair['id']\n data['parameter'] = parameter\n\n edit_country_text = _(\n user['language_code'],\n 'edit_pair_parameter'\n ).format(\n _(user['language_code'], params[parameter])\n )\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=edit_country_text,\n reply_markup=MenuKeyboard.back_to(user, key_string='inline_back_to', data=callback_data_admin_view_pair+str(pair['id']))\n )\n\n if parameter == 'price_handler':\n bot.send_message(\n chat_id=call.message.chat.id,\n text='`cryptoexchage` или `binance`'\n )\n\n if parameter in ['from_country_code', 'to_country_code']:\n countries = db.get_countries()\n country_text = \"*Отправьте код страны в ответ:*\\n\"\n\n for country in countries:\n banks = db.get_banks(args={'country_code': country['slug']})\n banks_text = '' if len(banks) > 0 else 'Нет банков\\n'\n for bank in banks:\n banks_text += f\"— {bank['name']}\\n\"\n\n\n country_text += f\"\\n{country['name']} — `{country['slug']}`\\n\"\n country_text += banks_text\n\n bot.send_message(\n chat_id=call.message.chat.id,\n text=country_text\n )\n\n@bot.message_handler(is_chat=False, state=EditPair.A1, role=['admin'])\ndef edit_pair_param(message):\n user = db.get_user(message.from_user.id)\n\n with bot.retrieve_data(message.from_user.id) as data:\n # Обрезаем\n param_value = message.text[0:4000]\n\n try:\n if data['parameter'] in ['spread', 'min_from_amount', 'max_from_amount']:\n param_value = float(param_value)\n except Exception as e:\n param_value = 0\n\n if (\n data['parameter'] == 'type' and\n param_value not in ['crypto', 'fiat']\n ):\n param_value = 'fiat'\n\n try:\n # Обновляем\n db.update_pair(pair_id=data['pair_id'], args={data['parameter']: param_value})\n\n except Exception as e:\n print(e)\n\n # Получаем актуальные данные и возвращаем в админку\n pair = db.get_pair(data['pair_id'], name_id=\"id\")\n edit_pair_text = _(\n user['language_code'],\n 'admin_data_pair_view'\n ).format(**pair)\n\n try:\n bot.delete_message(\n chat_id=message.chat.id,\n message_id=data['message_id']\n )\n except Exception as e:\n pass\n\n bot.send_message(\n chat_id=message.chat.id,\n text=_(user['language_code'], 'admin_data_success_edit')\n )\n bot.send_message(\n chat_id=message.chat.id,\n text=edit_pair_text,\n reply_markup=AdminKeyboard.edit_pair(user, pair, params)\n )\n\n bot.delete_state(message.from_user.id)\n\n\n### ### ### ### ###\n############### ############### ###\n### ############### ###############\n### ### ### ### ###\n\n@bot.callback_query_handler(is_chat=False, func=lambda call: call.data == callback_data_admin_create_pair, role=['admin'])\ndef admin_create_pair(call):\n \"\"\" Создание пары\n \"\"\"\n user_id = call.from_user.id\n user = db.get_user(user_id)\n\n bot.edit_message_text(\n chat_id=call.message.chat.id,\n message_id=call.message.message_id,\n text=_(user['language_code'], 'create_pair_input_from_name'),\n reply_markup=MenuKeyboard.back_to(user, key_string='inline_back_to', data=callback_data_admin_pairs)\n )\n\n bot.set_state(user_id, CreatePair.A1)\n\n with bot.retrieve_data(user_id) as data:\n data['user'] = json.dumps(\n user,\n indent=4,\n sort_keys=True,\n default=str\n )\n\n\n@bot.message_handler(is_chat=False, state=CreatePair.A1, role=['admin'])\ndef create_pair_a1(message):\n \"\"\" Сохранение отдаваемого актива\n и ввод получаемого\n \"\"\"\n user_id = message.from_user.id\n with bot.retrieve_data(message.from_user.id) as data:\n user = json.loads(data['user'])\n data['from_name'] = message.text\n bot.send_message(\n user_id,\n text=_(user['language_code'], 'create_pair_input_to_name')\n )\n\n bot.set_state(user_id, CreatePair.A2)\n\n@bot.message_handler(is_chat=False, state=CreatePair.A2, role=['admin'])\ndef create_pair_a2(message):\n \"\"\" Сохранение получаемого актива\n и ввод мин/макс суммы отдаваемого актива\n \"\"\"\n user_id = message.from_user.id\n with bot.retrieve_data(message.from_user.id) as data:\n user = json.loads(data['user'])\n data['to_name'] = message.text\n bot.send_message(\n user_id,\n text=_(user['language_code'], 'create_pair_input_from_amount').format(data['from_name'] )\n )\n\n bot.set_state(user_id, CreatePair.A3)\n\n@bot.message_handler(is_chat=False, state=CreatePair.A3, role=['admin'])\ndef create_pair_a3(message):\n \"\"\" Сохранение мин/макс суммы\n и ввод кода страны для отдаваемой пары\n \"\"\"\n user_id = message.from_user.id\n with bot.retrieve_data(message.from_user.id) as data:\n min_max = message.text.split(\"-\")\n min = 0\n max = 0\n\n try:\n min = int(min_max[0])\n max = int(min_max[1])\n except Exception as e:\n print(e)\n\n user = json.loads(data['user'])\n data['from_min_amount'] = min\n data['from_max_amount'] = max\n bot.send_message(\n user_id,\n text=_(user['language_code'], 'create_pair_input_from_country_code').format(data['from_name'])\n )\n\n bot.set_state(user_id, CreatePair.A4)\n\n@bot.message_handler(is_chat=False, state=CreatePair.A4, role=['admin'])\ndef create_pair_a4(message):\n \"\"\" Сохранение кода страны для отдаваемой пары\n и ввод кода страны для получаемой пары\n \"\"\"\n user_id = message.from_user.id\n with bot.retrieve_data(message.from_user.id) as data:\n user = json.loads(data['user'])\n data['from_country_code'] = message.text\n bot.send_message(\n user_id,\n text=_(user['language_code'], 'create_pair_input_from_country_code').format(data['to_name'])\n )\n\n bot.set_state(user_id, CreatePair.A5)\n\n@bot.message_handler(is_chat=False, state=CreatePair.A5, role=['admin'])\ndef create_pair_a5(message):\n \"\"\" Сохранение кода страны для получаемой пары\n и ввод спреда\n \"\"\"\n user_id = message.from_user.id\n with bot.retrieve_data(message.from_user.id) as data:\n user = json.loads(data['user'])\n data['to_country_code'] = message.text\n bot.send_message(\n user_id,\n text=_(user['language_code'], 'create_pair_input_spread')\n )\n\n bot.set_state(user_id, CreatePair.A6)\n\n@bot.message_handler(is_chat=False, state=CreatePair.A6, role=['admin'])\ndef create_pair_a5(message):\n \"\"\" Сохранение спреда\n и создание пары\n \"\"\"\n user_id = message.from_user.id\n with bot.retrieve_data(message.from_user.id) as data:\n user = json.loads(data['user'])\n\n try:\n data['spread'] = float(message.text)\n except Exception as e:\n data['spread'] = 0\n\n try:\n text = _(\n user['language_code'],\n 'create_pair_view_info'\n ).format(**{\n \"from_name\": data['from_name'],\n \"from_min_amount\": data['from_min_amount'],\n \"from_max_amount\": data['from_max_amount'],\n \"from_country_code\": data['from_country_code'],\n \"to_name\": data['to_name'],\n \"to_country_code\": data['to_country_code'],\n \"spread\": data['spread'],\n })\n\n bot.send_message(\n user_id,\n text=text,\n reply_markup=MenuKeyboard.accept_or_decline(\n user,\n cl_accept=callback_data_admin_create_pair_accept,\n key_string_accept='inline_pair_create',\n cl_decline=callback_data_admin_pairs,\n key_string_decline='inline_back_to',\n )\n )\n except Exception as e:\n print(e)\n\n bot.set_state(user_id, CreatePair.A7)\n\n@bot.callback_query_handler(is_chat=False, state=CreatePair.A7, func=lambda call: call.data.startswith(callback_data_admin_create_pair_accept), role=['admin'])\ndef create_pair_a6(call):\n \"\"\" Сохранение пары\n \"\"\"\n user_id = call.from_user.id\n with bot.retrieve_data(user_id) as data:\n user = json.loads(data['user'])\n try:\n pair_id = db.create_pair(\n user['id'],\n data['from_name'],\n data['from_min_amount'],\n data['from_max_amount'],\n data['from_country_code'],\n data['to_name'],\n data['to_country_code'],\n data['spread']\n )\n except Exception as e:\n print(e)\n return\n\n msg = _(\n user['language_code'],\n 'create_pair_success'\n ).format(**{\n \"from_name\": data['from_name'],\n \"to_name\": data['to_name'],\n })\n\n bot.delete_message(\n user_id,\n call.message.message_id,\n )\n bot.send_message(\n user_id,\n text=msg,\n reply_markup=MenuKeyboard.back_to(user, key_string='inline_to_pair', data=callback_data_admin_view_pair+str(pair_id))\n\n )\n\n bot.delete_state(user_id)\n","repo_name":"ILoveAlgorythms/exchange-telegram-bot","sub_path":"handlers/users/admin/pairs.py","file_name":"pairs.py","file_ext":"py","file_size_in_byte":18204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41020453401","text":"\n# coding: utf-8\n\n# **Computational Intelligence Homework #2** # single sport\n $> ./manage.py seasonfppg ... # multiple sports\n $> ./manage.py seasonfppg all # all sports\n\n \"\"\"\n\n # help is a Command inner variable\n help = 'usage: ./manage.py updatecontent '\n\n def add_arguments(self, parser):\n # Positional arguments\n parser.add_argument('sport', nargs='+', type=str)\n\n def handle(self, *args, **options):\n \"\"\"\n generate a salary pool with a default config\n\n :param args:\n :param options:\n :return:\n \"\"\"\n msg = '* Player.season_fppg updater'\n self.stdout.write( msg )\n\n site_sport = None\n for sport in options['sport']:\n if sport == 'all':\n player_fppg_generator = PlayerFppgGenerator()\n player_fppg_generator.update()\n else:\n player_fppg_generator = PlayerFppgGenerator()\n player_fppg_generator.update_sport(sport)\n","repo_name":"nakamotohideyoshi/draftboard-web","sub_path":"salary/management/commands/seasonfppg.py","file_name":"seasonfppg.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74951008696","text":"import seaborn as sns\nsns.set(rc={'figure.figsize':(30, 30)})\ncorr = X.corr()\nplt.figure() \nax = sns.heatmap(corr, linewidths=.5, annot=True, cmap=\"YlGnBu\", fmt='.1g')\nplt.savefig('corr_heatmap.png')\nplt.show()\n\n# Drop highly correlated features (37->30)\ncolumns = np.full((corr.shape[0],), True, dtype=bool)\nfor i in range(corr.shape[0]):\n for j in range(i+1, corr.shape[0]):\n if corr.iloc[i,j] >= 0.99:\n if columns[j]:\n columns[j] = False\n\nfeature_columns = X.columns[columns].values\ndrop_columns = X.columns[columns == False].values\nprint(feature_columns)\nprint('-'*73)\nprint(drop_columns)\n","repo_name":"alex-deineha/my-library","sub_path":"correlation.py","file_name":"correlation.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"74304378615","text":"from PyQt5.QtWidgets import QMainWindow, QFileDialog\nfrom PyQt5 import uic\nfrom PyQt5.QtCore import Qt\n\nimport sqlite3\nfrom datetime import datetime\n\n\nclass AddKonsFormWidget(QMainWindow): # Форма добавление и изменения консультации в БД\n def __init__(self, parent=None, child_id=0):\n super().__init__(parent)\n uic.loadUi('forms/KonsForm.ui', self)\n self.agreement_blob_data = ''\n self.child_id = child_id\n self.initUI()\n\n def initUI(self):\n self.all_poles_are_true = False\n self.setWindowTitle('Добавление Ребёнка')\n self.addAgreeing.clicked.connect(self.adding_agreeing)\n self.back_button.clicked.connect(self.back)\n if self.child_id: # Проверка на изменение консультации\n self.changing_ui()\n self.addButton.clicked.connect(self.changing)\n else:\n self.addButton.clicked.connect(self.adding)\n\n def get_values(self): # Получение данных с полей\n con = sqlite3.connect('database.sql')\n cur = con.cursor()\n if self.fioRod.text() and self.fioReb.text() and self.reck.text():\n\n self.perantfio = self.fioRod.text() # ФИО родителя\n self.childfio = self.fioReb.text() # ФИО ребёнка\n self.ch_both_day = self.rebBothDay.text() # Дата рождения ребёнка\n self.dou_visiting = self.douButons.checkedButton().text() # Посищает ли ребёнок ДОУ\n self.dou_visiting = 1 if self.dou_visiting == 'Да' else 0 # Превращение duo_visiting в bool\n self.ask = self.reck.text() # Запрос со слов родителя\n self.theam = self.comboBox.currentText() # Тема запроса\n self.theam = cur.execute(\"\"\"SELECT id from KonsTheams WHERE name = ?\"\"\", (self.theam,)).fetchall()\n self.konsul = self.KonsButtons.checkedButton().text() # Первичность консультации\n self.konsul = 1 if self.konsul == 'Первичная' else 0 # Превращение konsul в bool\n self.konsulDate = self.dateKons.text() # Дата консультации\n\n self.ch_both_day = datetime.strptime(self.ch_both_day, '%d.%m.%Y').date()\n self.konsulDate = datetime.strptime(self.konsulDate, '%d.%m.%Y').date()\n self.all_poles_are_true = True\n\n else:\n self.form_error.setText('Форма заполнена не полностью!')\n\n def adding(self): # Добавление консультации в БД\n con = sqlite3.connect('database.sql')\n cur = con.cursor()\n self.get_values()\n if self.all_poles_are_true:\n\n # Добаление Данных Ребёнка в БД\n res = cur.execute(\"\"\"INSERT INTO form(Perant_Name, Child_Name, Child_Both_Day, DOU_Visiting, Ask,\n Theam, First_Kons, Kons_Date, Agreement) VALUES(?, ?, ?, ?, ?, ?, ?, ?, ?)\"\"\", (self.perantfio, self.childfio,\n self.ch_both_day,\n self.dou_visiting, self.ask,\n self.theam[0][0], self.konsul,\n self.konsulDate,\n self.agreement_blob_data))\n con.commit()\n self.hide()\n\n def adding_agreeing(self): # Прикрепление согласия\n fname = QFileDialog.getOpenFileName(self, 'Выбрать картинку', '')[0]\n\n with open(fname, 'rb') as f:\n self.agreement_blob_data = f.read()\n\n def changing_ui(self): # Замена UI при изменении данных\n con = sqlite3.connect('database.sql')\n cur = con.cursor()\n res = cur.execute(\"\"\"SELECT * from form WHERE id = ?\"\"\", (self.child_id,)).fetchall()\n\n self.fioRod.setText(res[0][1])\n self.rebBothDay.setDate(datetime.strptime(res[0][3], '%Y-%m-%d'))\n self.reck.setText(res[0][5])\n self.comboBox.setCurrentIndex(res[0][6] - 1)\n self.dateKons.setDate(datetime.strptime(res[0][8], '%Y-%m-%d'))\n self.fioReb.setText(res[0][2])\n\n self.radioButton.setChecked(True) if res[0][4] == 1 else self.radioButton_2.setChecked(True)\n self.radioButton_3.setChecked(True) if res[0][7] == 1 else self.radioButton_4.setChecked(True)\n\n self.addButton.setText('Изменить')\n self.agreement_blob_data = res[0][9]\n\n def changing(self): # Замена данных\n from ShowInfo import ShowInfoWidget\n\n con = sqlite3.connect('database.sql')\n cur = con.cursor()\n self.get_values()\n if self.all_poles_are_true:\n\n res = cur.execute(\"\"\"UPDATE form SET Perant_Name = ?, Child_Name = ?, Child_Both_Day = ?, DOU_Visiting = ?,\n Ask = ?, Theam = ?, First_Kons = ?, Kons_Date = ?, Agreement = ? WHERE id = ?\"\"\", (self.perantfio,\n self.childfio,\n self.ch_both_day,\n self.dou_visiting, self.ask,\n self.theam[0][0],\n self.konsul,\n self.konsulDate,\n self.agreement_blob_data,\n self.child_id))\n con.commit()\n self.form = ShowInfoWidget(self, self.child_id)\n self.form.show()\n self.hide()\n\n def back(self):\n self.hide()\n\n def keyPressEvent(self, event):\n if event.key() == Qt.Key_Escape: # Выход при нажатии Esc\n self.back()","repo_name":"MrNomid/pyqt_Project_LMS","sub_path":"AddKonsForm.py","file_name":"AddKonsForm.py","file_ext":"py","file_size_in_byte":6613,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"1444968872","text":"import re\n\nwith open(\"input.txt\") as f:\n PUZZLE_INPUT = f.read()\n\n\n# Part 1\n# Extract coords\ncoords = []\nmin_x = 100000000\nmin_y = 100000000\nmax_x = 0\nmax_y = 0\n\nfor d in PUZZLE_INPUT.splitlines():\n m = re.match(r\"(\\d+),\\s(\\d+)\", d)\n if not m:\n print(\"Ooops\", d)\n c = (int(m.groups()[0]), int(m.groups()[1]))\n coords.append(c)\n\n min_x = min(c[0], min_x)\n max_x = max(c[0], max_x)\n min_y = min(c[1], min_y)\n max_y = max(c[1], max_y)\n\n# Draw it\n# alpha = [chr(i) for i in range(65, 91)]\n# letter = 0\n#\n# for j in range(min_y, max_y + 1):\n# line = \"\"\n# for i in range(min_x, max_x + 1):\n# if (i,j) in coords:\n# line += alpha[letter]\n# letter += 1\n# if letter == len(alpha):\n# letter = 0\n# else:\n# line += \".\"\n# print(line)\n\n\ndef calc_dist(a, b):\n return abs(a[0] - b[0]) + abs(a[1] - b[1])\n\n\ndef get_closest(a, coords):\n closest = []\n dist = calc_dist(a, coords[0])\n\n for b in coords:\n d = calc_dist(a, b)\n if d < dist:\n dist = d\n closest = [b]\n elif d == dist:\n # Double match\n closest.append(b)\n return closest\n\n\nresults = {}\n\nfor j in range(min_y, max_y + 1):\n for i in range(min_x, max_x + 1):\n # Find closest\n c = get_closest((i, j), coords)\n if len(c) == 1:\n if c[0] in results:\n results[c[0]] += 1\n else:\n results[c[0]] = 1\n\nmost = 0\nfor k, v in results.items():\n most = max(most, v)\n\n# 6047\nprint(f\"Part 1: {most}\")\n\n\n# Part 2\ndef get_total_distance(a, coords):\n total = 0\n\n for b in coords:\n total += calc_dist(a, b)\n return total\n\n\nlimit = 10000 # 32 for test case\ncount = 0\n\nfor j in range(min_y, max_y + 1):\n for i in range(min_x, max_x + 1):\n t = get_total_distance((i, j), coords)\n if t < limit:\n # print(\"{},{} = {}\".format(i,j,t))\n count += 1\n\n# 46320\nprint(f\"Part 2: {count}\")\n","repo_name":"mattclarke/advent_of_code_18","sub_path":"day_6/day_6.py","file_name":"day_6.py","file_ext":"py","file_size_in_byte":2032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19047233962","text":"# 1\n# 新建变量 单独接收 不建议使用\n# with open(\"pi_digits.txt\",\"r\",encoding=\"UTF-8\"):\n# file_test = open(\"pi_digits.txt\") # 繁琐\n# contents = file_test.read()\n# print(contents)\n# # 2\n# 起别名 最好使用这个\n# with open(\"pi_digits.txt\",\"r\",encoding=\"UTF\") as test_file:\n# contents = test_file.read()\n# print(contents)\n\n# 3\n# 需要单独关闭文件 麻烦\n# f = open(\"pi_digits.txt\",\"r\",encoding=\"UTF\")\n# contents = f.read()\n# print(contents)\n# f.close()\n\n# 4\n#\n# f = open(\"pi_digits.txt\",\"r\",encoding=\"UTF\")\n# contents = f.read()\n# print(contents.strip()) # 删除字符串末尾空白\n\n\n# 逐行读取\n# 提前定义所需要读取文件的名字 将文件名称赋值给filename\n# filename = 'pi_digits.txt'\n#\n# with open(filename) as file_obiection:\n# for line in file_obiection:\n# print(line)\n\n\n# filename = 'pi_digits.txt'\n#\n# with open(filename) as file_obiection:\n# for line in file_obiection:\n# line = line.strip() # 将去除空格后的文件重新赋值给line\n# print(line)\n\nfilename = 'pi_digits.txt'\n\nwith open(filename) as file_obiection:\n lines = file_obiection.readlines()\nfor line in lines:\n line = line.strip() # 将去除空格后的文件重新赋值给line\n print(line)\nprint(type(line))","repo_name":"penghang1223/PythonLerning","sub_path":"Python_从入门到实践/第10章/10.1.1.py","file_name":"10.1.1.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31903790453","text":"# Create a function that can return the squares of even elements for the range\n# 0 to 1000000000 inclusive. The solution should work and not freeze your computer.\n\ndef even_num_filter_func():\n custom_generator = (x for x in range(0, 1000000001))\n for data in custom_generator:\n if data % 2 == 0:\n yield data ** 2\n\n\nif __name__ == '__main__':\n for result in even_num_filter_func():\n print(result)\n","repo_name":"serhiihero/Homeworks","sub_path":"HW8/gen_dec_compreh_task_sort_func.py","file_name":"gen_dec_compreh_task_sort_func.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28875582218","text":"# Run the build process by running the command\n# 'python setup.py build'\n\nimport sys\nfrom cx_Freeze import setup, Executable\n\noptions = {\n 'build_exe': {\n 'include_files': [\n 'assets/image1.png',\n 'assets/image2.png',\n 'assets/image3.png',\n 'assets/image6.gif',\n 'assets/sound1.wav'\n ],\n 'path': sys.path + ['modules']\n }\n}\n\nexecutables = [\n Executable('BikeRider.py')\n]\n\nsetup(name='Bike Rider',\n version='0.1',\n description='Python Bike Rider',\n options=options,\n executables=executables\n )\n","repo_name":"brainmentorspvtltd/cucumber","sub_path":"PythonNov/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"2993685177","text":"import os\nimport math\nimport torch\nimport torch.distributed as dist\n\nfrom logging import getLogger\n\nlogger = getLogger()\n\ndef gpu_timer(closure, log_timings=True):\n \"\"\" Helper to time gpu-time to execute closure() \"\"\"\n elapsed_time = -1.\n if log_timings:\n start = torch.cuda.Event(enable_timing=True)\n end = torch.cuda.Event(enable_timing=True)\n start.record()\n\n result = closure()\n\n if log_timings:\n end.record()\n torch.cuda.synchronize()\n elapsed_time = start.elapsed_time(end)\n\n return result, elapsed_time\n\n\ndef init_distributed(port=40101, rank_and_world_size=(None, None)):\n\n if dist.is_available() and dist.is_initialized():\n return dist.get_world_size(), dist.get_rank()\n\n rank, world_size = rank_and_world_size\n os.environ['MASTER_ADDR'] = 'localhost'\n\n if (rank is None) or (world_size is None):\n try:\n world_size = int(os.environ['SLURM_NTASKS'])\n rank = int(os.environ['SLURM_PROCID'])\n os.environ['MASTER_ADDR'] = os.environ['HOSTNAME']\n except Exception:\n logger.info('distributed training not available')\n world_size, rank = 1, 0\n return world_size, rank\n\n try:\n os.environ['MASTER_PORT'] = str(port)\n torch.distributed.init_process_group(\n backend='nccl',\n world_size=world_size,\n rank=rank)\n except Exception:\n world_size, rank = 1, 0\n logger.info('distributed training not available')\n\n return world_size, rank\n\n\nclass WarmupCosineSchedule(torch.optim.lr_scheduler.LambdaLR):\n\n def __init__(\n self,\n optimizer,\n warmup_steps,\n start_lr,\n ref_lr,\n T_max,\n last_epoch=-1,\n final_lr=0.\n ):\n self.start_lr = start_lr\n self.ref_lr = ref_lr\n self.final_lr = final_lr\n self.warmup_steps = warmup_steps\n self.T_max = T_max - warmup_steps\n super(WarmupCosineSchedule, self).__init__(\n optimizer,\n self.lr_lambda,\n last_epoch=last_epoch)\n\n def lr_lambda(self, step):\n if step < self.warmup_steps:\n progress = float(step) / float(max(1, self.warmup_steps))\n new_lr = self.start_lr + progress * (self.ref_lr - self.start_lr)\n return new_lr / self.ref_lr\n\n # -- progress after warmup\n progress = float(step - self.warmup_steps) / float(max(1, self.T_max))\n new_lr = max(self.final_lr,\n self.final_lr + (self.ref_lr - self.final_lr) * 0.5 * (1. + math.cos(math.pi * progress)))\n return new_lr / self.ref_lr\n\n\nclass CSVLogger(object):\n\n def __init__(self, fname, *argv):\n self.fname = fname\n self.types = []\n # -- print headers\n with open(self.fname, '+a') as f:\n for i, v in enumerate(argv, 1):\n self.types.append(v[0])\n if i < len(argv):\n print(v[1], end=',', file=f)\n else:\n print(v[1], end='\\n', file=f)\n\n def log(self, *argv):\n with open(self.fname, '+a') as f:\n for i, tv in enumerate(zip(self.types, argv), 1):\n end = ',' if i < len(argv) else '\\n'\n print(tv[0] % tv[1], end=end, file=f)\n\n\nclass AverageMeter(object):\n \"\"\"computes and stores the average and current value\"\"\"\n\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.max = float('-inf')\n self.min = float('inf')\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.max = max(val, self.max)\n self.min = min(val, self.min)\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n\nclass AllGather(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, x):\n if (\n dist.is_available()\n and dist.is_initialized()\n and (dist.get_world_size() > 1)\n ):\n outputs = [torch.zeros_like(x) for _ in range(dist.get_world_size())]\n dist.all_gather(outputs, x)\n return torch.cat(outputs, 0)\n return x\n\n @staticmethod\n def backward(ctx, grads):\n if (\n dist.is_available()\n and dist.is_initialized()\n and (dist.get_world_size() > 1)\n ):\n s = (grads.shape[0] // dist.get_world_size()) * dist.get_rank()\n e = (grads.shape[0] // dist.get_world_size()) * (dist.get_rank() + 1)\n grads = grads.contiguous()\n dist.all_reduce(grads)\n return grads[s:e]\n return grads\n\n\nclass AllReduce(torch.autograd.Function):\n\n @staticmethod\n def forward(ctx, x):\n if (\n dist.is_available()\n and dist.is_initialized()\n and (dist.get_world_size() > 1)\n ):\n x = x.contiguous() / dist.get_world_size()\n dist.all_reduce(x)\n return x\n\n @staticmethod\n def backward(ctx, grads):\n return grads\n\nlogger = getLogger()\n\n\ndef init_partion_loss(\n multicrop=6,\n tau=0.1,\n T=0.25,\n me_max=True\n):\n softmax = torch.nn.Softmax(dim=1)\n\n def sharpen(p):\n sharp_p = p**(1./T)\n sharp_p /= torch.sum(sharp_p, dim=1, keepdim=True)\n return sharp_p\n\n def snn(query, supports, labels):\n query = torch.nn.functional.normalize(query)\n supports = torch.nn.functional.normalize(supports)\n supports = AllGather.apply(supports)\n return softmax(query @ supports.T / tau) @ (labels.float())\n\n def loss(\n anchor_views,\n anchor_supports,\n anchor_support_labels,\n target_views,\n target_supports,\n target_support_labels,\n sharpen=sharpen,\n snn=snn\n ):\n\n batch_size = len(anchor_views) // (2+multicrop)\n probs = snn(anchor_views, anchor_supports, anchor_support_labels)\n targets = snn(target_views, target_supports, target_support_labels)\n targets = sharpen(targets)\n targets[targets < 1e-4] *= 0\n loss = torch.mean(torch.sum(torch.log(probs**(-targets)), dim=1))\n rloss = 0.\n if me_max:\n avg_probs = AllReduce.apply(torch.mean(sharpen(probs), dim=0))\n rloss -= torch.sum(torch.log(avg_probs**(-avg_probs)))\n return loss, rloss\n return loss\n\n\ndef make_labels_matrix(\n num_classes,\n s_batch_size,\n world_size,\n device,\n unique_classes=False,\n smoothing=0.0\n):\n \"\"\"\n Make one-hot labels matrix for labeled samples\n \"\"\"\n\n local_images = s_batch_size*num_classes\n total_images = local_images*world_size\n\n off_value = smoothing/(num_classes*world_size) if unique_classes else smoothing/num_classes\n\n if unique_classes:\n labels = torch.zeros(total_images, num_classes*world_size).to(device) + off_value\n for r in range(world_size):\n s1 = r * local_images\n e1 = s1 + local_images\n offset = r * num_classes\n for i in range(num_classes):\n labels[s1:e1][i::num_classes][:, offset+i] = 1. - smoothing + off_value\n else:\n labels = torch.zeros(total_images, num_classes*world_size).to(device) + off_value\n for i in range(num_classes):\n labels[i::num_classes][:, i] = 1. - smoothing + off_value\n\n return labels\n\n\ndef gather_from_all(tensor):\n gathered_tensors = gather_tensors_from_all(tensor)\n gathered_tensor = torch.cat(gathered_tensors, 0)\n return gathered_tensor\n\n\ndef gather_tensors_from_all(tensor):\n \"\"\"\n Wrapper over torch.distributed.all_gather for performing\n 'gather' of 'tensor' over all processes in both distributed /\n non-distributed scenarios.\n \"\"\"\n if tensor.ndim == 0:\n # 0 dim tensors cannot be gathered. so unsqueeze\n tensor = tensor.unsqueeze(0)\n\n if (\n torch.distributed.is_available()\n and torch.distributed.is_initialized()\n and (torch.distributed.get_world_size() > 1)\n ):\n tensor, orig_device = convert_to_distributed_tensor(tensor)\n gathered_tensors = [\n torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())\n ]\n torch.distributed.all_gather(gathered_tensors, tensor)\n gathered_tensors = [\n convert_to_normal_tensor(_tensor, orig_device)\n for _tensor in gathered_tensors\n ]\n else:\n gathered_tensors = [tensor]\n\n return gathered_tensors\n\n\ndef convert_to_distributed_tensor(tensor):\n \"\"\"\n For some backends, such as NCCL, communication only works if the\n tensor is on the GPU. This helper function converts to the correct\n device and returns the tensor + original device.\n \"\"\"\n orig_device = 'cpu' if not tensor.is_cuda else 'gpu'\n if (\n torch.distributed.is_available()\n and torch.distributed.get_backend() == torch.distributed.Backend.NCCL\n and not tensor.is_cuda\n ):\n tensor = tensor.cuda()\n return (tensor, orig_device)\n\n\ndef convert_to_normal_tensor(tensor, orig_device):\n \"\"\"\n For some backends, such as NCCL, communication only works if the\n tensor is on the GPU. This converts the tensor back to original device.\n \"\"\"\n if tensor.is_cuda and orig_device == 'cpu':\n tensor = tensor.cpu()\n return tensor","repo_name":"WillDreamer/DIOR","sub_path":"losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":9456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12672423538","text":"# -*- coding:UTF-8 -*-\n''' 用DCGAN的生成器模型和生成得到的生成器参数文件来生成图片'''\n\nimport numpy as np\nfrom PIL import Image\nimport tensorflow as tf\n\nfrom DCGAN.network import *\n\n\ndef generator():\n # 构造生成器\n g = generator_model()\n # 配置生成器\n g.compile(loss=\"binary_crossentropy\", optimizer=tf.keras.optimizers.Adam(lr=learning_rate, beta_1=beta_1))\n # 加载训练好的生成器参数\n g.load_weights(\"../DCGAN/checkpoints/generator_weight\")\n # 连续型均匀分布的随记数据（噪声）\n random_data = np.random.uniform(-1, 1, size=(batch_size, 100))\n # 用随机数据作为输入，生成器 生成图片数据\n images = g.predict(random_data, verbose=1)\n # 用生成的图片数据生成PNG图片\n for i in range(batch_size):\n image = images[i] * 127.5 + 127.5\n Image.fromarray(image.astype(np.uint8)).save(\"../DCGAN/generate/image-%s.png\" % i)\n\n\nif __name__ == '__main__':\n generator()\n","repo_name":"biaofendou/TensorFlowLearning","sub_path":"DCGAN/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14439640730","text":"from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\nfrom keras.models import Model\nfrom keras import backend as K\nfrom keras.callbacks import TensorBoard\nfrom keras.optimizers import Adam\nimport tensorflow as tf\nadam = Adam(lr=1, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.01, amsgrad=False)\n\nimport numpy as np\nimport pickle as pkl\nimport keras.backend as K\n\n\ndef get_model():\n input_img = Input(shape=(80, 80, 3)) # adapt this if using `channels_first` image data format\n\n x = Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)\n x = MaxPooling2D((2, 2), padding='same')(x)\n x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n x = MaxPooling2D((2, 2), padding='same')(x)\n x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)\n encoded = MaxPooling2D((2, 2), padding='same')(x)\n\n # at this point the representation is (4, 4, 8) i.e. 128-dimensional\n\n x = Conv2D(16, (4, 4), activation='relu', padding='same')(encoded)\n x = UpSampling2D((2, 2))(x)\n x = Conv2D(32, (4, 4), activation='relu', padding='same')(x)\n x = UpSampling2D((2,2))(x)\n x = Conv2D(64, (1,1), activation='relu')(x)\n x = UpSampling2D((2, 2))(x)\n decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)\n\n autoencoder = Model(input_img, decoded)\n autoencoder.compile(optimizer='rmsprop', loss='binary_crossentropy')\n return autoencoder\n\ndef trainModel(model,mx_train,mx_test,name):\n model.fit(mx_train, mx_train,\n epochs=1,\n batch_size=128,\n shuffle=True,\n validation_data=(mx_test, mx_test),\n callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])\n model.save(name+\".h5\")\nmitoticModel = get_model()\nnon_mitoticModel = get_model()\nfolder = '../../processed_dataset/'\nmx_train,mx_test = pkl.load(open(folder+'mitotic.pkl','rb'))\nnx_train,nx_test = pkl.load(open(folder+'non_mitotic.pkl','rb'))\n\ntrainModel(mitoticModel,mx_train,mx_test,\"mitoticModel\")\ntrainModel(non_mitoticModel,nx_train,nx_test,\"nonMitoticModel\")\n","repo_name":"Surya165/Models-Comparison-Cls-Imb","sub_path":"autoencoder/autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72246599736","text":"import os\n\nfrom django.core.exceptions import ImproperlyConfigured\nimport dj_database_url\n\nfrom cityyouthmatrix.settings.base import * # noqa\n\n\nDEBUG = False\nTEMPLATES[0]['OPTIONS'].update({'debug': DEBUG})\n\nALLOWED_HOSTS = ['cityyouthmatrix.herokuapp.com']\n\nMIDDLEWARE = ['whitenoise.middleware.WhiteNoiseMiddleware'] + MIDDLEWARE\n\nSECRET_KEY = os.getenv('SECRET_KEY')\nif not SECRET_KEY:\n raise ImproperlyConfigured('No SECRET_KEY is set!')\n\ndb_url = os.getenv('DATABASE_URL')\nif not db_url:\n raise ImproperlyConfigured('DATABASE_URL is not set!')\nDATABASES['default'] = dj_database_url.parse(\n db_url, 'django.contrib.gis.db.backends.postgis')\n","repo_name":"johnathaningle/CityYouthMatrix","sub_path":"cityyouthmatrix/settings/heroku.py","file_name":"heroku.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"28311438114","text":"'''\nConfigure functions for simple drone emulator that,\n1) searches the graph for shacl classes\n2) creates a self configured input form using teh shacl parameters\n3) uses the POSTed result to add an instance of the target class to the graph\n\nOriginal code at https://github.com/CSIRO-enviro-informatics/shacl-form\n\nLaura Guillory\nLead Developer\nGriffith University Industrial Placement Student at CSIRO Land & Water\nlaura.guillory@griffithuni.edu.au\n\nNicholas Car\nProduct Owner\nSenior Experimental Scientist\nCSIRO Land & Water\nnicholas.car@csiro.au\n\nModifications and re-factoring for LANDRS\n\nChris Sweet 07/02/2020\nUniversity of Notre Dame, IN\nLANDRS project https://www.landrs.org\n\n'''\n# Imports\nfrom jinja2 import FileSystemLoader, Environment\nimport sys\nfrom config.templates.render_template import render_template\nimport os\nimport re\n\n########################################\n# Generate the form from the shacl data\n########################################\n\n\ndef generate_form(shape, form_destination=None):\n \"\"\"\n :param shape: An RDF Graph extracted to dict.\n :return: updated shape and pre-rendered template\n \"\"\"\n # Get a name for the form by cutting off part of the target class URI to find a more human readable name\n # Example: http://schema.org/Person -> Person\n form_name = shape['target_class'].rsplit(\n '/', 1)[1] if 'target_class' in shape else 'Entry'\n\n # Add ignored properties\n if 'ignoredProperties' in shape:\n for ignored_property_path in shape['ignoredProperties']:\n ignored_property = {\n 'path': ignored_property_path,\n 'name': re.split('[#/]', ignored_property_path)[-1],\n 'order': None,\n 'nodeKind': 'http://www.w3.org/ns/shacl#IRIOrLiteral'\n }\n shape['properties'].append(ignored_property)\n\n # Sort the groups\n shape['groups'] = sort_by_order(shape['groups'])\n # Sort properties in groups\n for g in shape['groups']:\n g['properties'] = sort_by_order(g['properties'])\n for prop in g['properties']:\n sort_composite_property(prop)\n # Sort ungrouped properties\n shape['properties'] = sort_by_order(shape['properties'])\n for prop in shape['properties']:\n sort_composite_property(prop)\n\n # Assign every property a unique ID\n next_id = 0\n for g in shape['groups']:\n for prop in g['properties']:\n assign_id(prop, next_id)\n next_id += 1\n for prop in shape['properties']:\n assign_id(prop, next_id)\n next_id += 1\n\n # Link pair property constraints by ID\n for g in shape[\"groups\"]:\n for prop in g[\"properties\"]:\n for constraint in prop:\n find_paired_properties(shape, prop, constraint)\n for prop in shape[\"properties\"]:\n for constraint in prop:\n find_paired_properties(shape, prop, constraint)\n\n # Put things into template\n if form_destination:\n os.makedirs(os.path.dirname(\n os.path.abspath(form_destination)), exist_ok=True)\n with open(form_destination, 'w') as file:\n file.write(render_template(form_name, shape=shape))\n \n # Create map for converting submitted data into RDF\n return shape, render_template(form_name, shape=shape)\n #rdf_handler.create_rdf_map(shape, map_destination)\n\n# utilies for generate_form\n\n\ndef sort_by_order(properties):\n \"\"\"\n This lambda expression uses a tuple to sort items with an order before unordered items. Tuples are compared by their\n first element first, then the second, etc. False sorts before True, so all None values will be sorted to the end\n \"\"\"\n properties.sort(key=lambda x: (x['order'] is None, x['order']))\n return properties\n\n# utilies for generate_form\n\n\ndef sort_composite_property(prop):\n if 'property' in prop:\n prop['property'] = sort_by_order(prop['property'])\n for p in prop['property']:\n sort_composite_property(p)\n\n# utilies for generate_form\n\n\ndef assign_id(prop, next_id, parent_id=None):\n # Assigns the property an ID\n # Additionally, assigns an ID to any property within this property\n if parent_id is not None:\n prop[\"id\"] = str(parent_id) + \":\" + str(next_id)\n else:\n prop[\"id\"] = next_id\n if \"property\" in prop:\n next_internal_id = 0\n for p in prop[\"property\"]:\n assign_id(p, next_internal_id, parent_id=prop[\"id\"])\n next_internal_id += 1\n\n# utilies for generate_form\n\n\ndef find_paired_properties(shape, prop, constraint):\n # If the constraint is a pair property constraint, iterates through all the properties looking for the one that\n # matches\n # Additionally, looks for pair property constraints in the properties contained in this property using recursion\n if constraint == \"property\":\n for p in prop[constraint]:\n for c in p:\n find_paired_properties(shape, p, c)\n if constraint in [\"equals\", \"disjoint\", \"lessThan\", \"lessThanOrEquals\"]:\n for g in shape[\"groups\"]:\n for p in g[\"properties\"]:\n result = check_property(p, prop[constraint])\n if result is not None:\n prop[constraint] = result\n return\n for p in shape[\"properties\"]:\n result = check_property(p, prop[constraint])\n if result is not None:\n prop[constraint] = result\n return\n\n# utilies for generate_form\n\n\ndef check_property(prop, path):\n # If the property path matches the path being searched for, return the property id\n # Also searches the properties inside this property using recursion\n if prop[\"path\"] == path:\n return prop[\"id\"]\n if \"property\" in prop:\n for p in prop[\"property\"]:\n result = check_property(p, path)\n if result is not None:\n return result\n\n\nif __name__ == \"__main__\":\n # File name passed as command-line argument\n if len(sys.argv) < 2:\n raise Exception('Usage - python main.py '\n '= 4:\n with open(file_path) as f:\n generate_form(f, sys.argv[2], sys.argv[3])\n elif len(sys.argv) >= 2:\n with open(file_path) as f:\n generate_form(f)\n","repo_name":"landrs-toolkit/landrs-drone-TOAST","sub_path":"config/config_generate_form.py","file_name":"config_generate_form.py","file_ext":"py","file_size_in_byte":6509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30036865813","text":"import csv\r\n#reads a CSV and finds any rows that are in one CSV but not the other\r\n\r\n#csv with fewer lines - the \"old\" file\r\ncsv_one = input('Please enter path to CSV1: ')\r\n#csv with more lines - the \"new\" file\r\ncsv_two = input('Please enter path to CSV2: ')\r\n\r\nwith open(csv_one, 'r', encoding='utf-8') as c1, open(csv_two, 'r', encoding='utf-8') as c2:\r\n file_one = c1.readlines()\r\n file_two = c2.readlines()\r\n\r\nwith open('matches.csv', 'w', encoding='utf-8') as outfile1, open('diffs.csv', 'w', encoding='utf-8') as outfile2:\r\n for line in file_two:\r\n# print(line)\r\n if line in file_one:\r\n outfile1.write(line)\r\n elif line not in file_one:\r\n outfile2.write(line)\r\n\r\nprint('All Done! Check outfiles for results.')\r\n \r\n ","repo_name":"ucancallmealicia/aspace_data_audit_demo","sub_path":"scripts/find_diffs_csv.py","file_name":"find_diffs_csv.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39426752141","text":"import numpy as np\nfrom skmultiflow.trees.hoeffding_tree import HoeffdingTree, MAJORITY_CLASS, NAIVE_BAYES\nfrom skmultiflow.utils import check_random_state\nfrom skmultiflow.trees.nominal_attribute_class_observer import NominalAttributeClassObserver\nfrom skmultiflow.trees.numeric_attribute_class_observer_gaussian import NumericAttributeClassObserverGaussian\nfrom skmultiflow.utils.utils import get_dimensions\nfrom skmultiflow.bayes.naive_bayes import do_naive_bayes_prediction\n\n\nclass ARFHoeffdingTree(HoeffdingTree):\n \"\"\" Adaptive Random Forest Hoeffding Tree.\n\n Parameters\n __________\n max_byte_size: int, optional (default=33554432)\n Maximum memory consumed by the tree.\n\n memory_estimate_period: int, optional (default=2000000)\n Number of instances between memory consumption checks.\n\n grace_period: int, optional (default=50)\n Number of instances a leaf should observe between split attempts.\n\n split_criterion: string, optional (default='info_gain')\n Split criterion to use.\n\n - 'gini' - Gini\n - 'info_gain' - Information Gain\n\n split_confidence: float, optional (default=0.01)\n Allowed error in split decision, a value closer to 0 takes longer to decide.\n\n tie_threshold: float, optional (default=0.05)\n Threshold below which a split will be forced to break ties.\n\n binary_split: bool, optional (default=False)\n If True, only allow binary splits.\n\n stop_mem_management: bool, optional (default=False)\n If True, stop growing as soon as memory limit is hit.\n\n remove_poor_atts: bool, optional (default=False)\n If True, disable poor attributes.\n\n no_preprune: bool, optional (default=False)\n If True, disable pre-pruning.\n\n leaf_prediction: string, optional (default='nba')\n Prediction mechanism used at leafs.\n\n - 'mc' - Majority Class\n - 'nb' - Naive Bayes\n - 'nba' - Naive Bayes Adaptive\n\n nb_threshold: int, optional (default=0)\n Number of instances a leaf should observe before allowing Naive Bayes.\n\n nominal_attributes: list, optional\n List of Nominal attributes. If emtpy, then assume that all attributes are numerical.\n\n random_state: int, RandomState instance or None, optional (default=None)\n If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used by `np.random`.\n\n Notes\n _____\n This is the base model for the Adaptive Random Forest ensemble learner\n (See skmultiflow.classification.meta.adaptive_random_forests).\n This Hoeffding Tree includes a max_features parameter, which defines the number of randomly selected features to\n be considered at each split.\n\n \"\"\"\n\n class RandomLearningNode(HoeffdingTree.ActiveLearningNode):\n \"\"\"Random learning node class.\n\n Parameters\n ----------\n initial_class_observations: dict (class_value, weight) or None\n Initial class observations.\n\n max_features: int\n Number of attributes per subset for each node split.\n\n random_state: int, RandomState instance or None, optional (default=None)\n If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used by `np.random`.\n\n \"\"\"\n\n def __init__(self, initial_class_observations, max_features, random_state=None):\n \"\"\" RandomLearningNode class constructor. \"\"\"\n super().__init__(initial_class_observations)\n\n self.max_features = max_features\n self._attribute_observers = {}\n self.list_attributes = np.array([])\n self.random_state = random_state\n\n def learn_from_instance(self, X, y, weight, ht):\n \"\"\"Update the node with the provided instance.\n\n Parameters\n ----------\n X: numpy.ndarray of length equal to the number of features.\n Instance attributes for updating the node.\n y: int\n Instance class.\n weight: float\n Instance weight.\n ht: HoeffdingTree\n Hoeffding Tree to update.\n\n \"\"\"\n try:\n self._observed_class_distribution[y] += weight\n except KeyError:\n self._observed_class_distribution[y] = weight\n self._observed_class_distribution = dict(sorted(self._observed_class_distribution.items()))\n\n if self.list_attributes.size == 0:\n self.list_attributes = self._sample_features(get_dimensions(X)[1])\n\n for i in self.list_attributes:\n try:\n obs = self._attribute_observers[i]\n except KeyError:\n if i in ht.nominal_attributes:\n obs = NominalAttributeClassObserver()\n else:\n obs = NumericAttributeClassObserverGaussian()\n self._attribute_observers[i] = obs\n obs.observe_attribute_class(X[i], int(y), weight)\n\n def _sample_features(self, n_features):\n return self.random_state.choice(n_features, size=self.max_features, replace=False)\n\n class LearningNodeNB(RandomLearningNode):\n \"\"\"Naive Bayes learning node class.\n\n Parameters\n ----------\n initial_class_observations: dict (class_value, weight) or None\n Initial class observations.\n\n max_features: int\n Number of attributes per subset for each node split.\n\n random_state: int, RandomState instance or None, optional (default=None)\n If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used by `np.random`.\n\n \"\"\"\n\n def __init__(self, initial_class_observations, max_features, random_state):\n \"\"\" LearningNodeNB class constructor. \"\"\"\n super().__init__(initial_class_observations, max_features, random_state)\n\n def get_class_votes(self, X, ht):\n \"\"\"Get the votes per class for a given instance.\n\n Parameters\n ----------\n X: numpy.ndarray of length equal to the number of features.\n Instance attributes.\n ht: HoeffdingTree\n Hoeffding Tree.\n\n Returns\n -------\n dict (class_value, weight)\n Class votes for the given instance.\n\n \"\"\"\n if self.get_weight_seen() >= ht.nb_threshold:\n return do_naive_bayes_prediction(X, self._observed_class_distribution, self._attribute_observers)\n else:\n return super().get_class_votes(X, ht)\n\n class LearningNodeNBAdaptive(LearningNodeNB):\n \"\"\"Naive Bayes Adaptive learning node class.\n\n Parameters\n ----------\n initial_class_observations: dict (class_value, weight) or None\n Initial class observations.\n\n max_features: int\n Number of attributes per subset for each node split.\n\n random_state: int, RandomState instance or None, optional (default=None)\n If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used by `np.random`.\n\n \"\"\"\n\n def __init__(self, initial_class_observations, max_features, random_state):\n \"\"\"LearningNodeNBAdaptive class constructor. \"\"\"\n super().__init__(initial_class_observations, max_features, random_state)\n self._mc_correct_weight = 0.0\n self._nb_correct_weight = 0.0\n\n def learn_from_instance(self, X, y, weight, ht):\n \"\"\"Update the node with the provided instance.\n\n Parameters\n ----------\n X: numpy.ndarray of length equal to the number of features.\n Instance attributes for updating the node.\n y: int\n Instance class.\n weight: float\n The instance's weight.\n ht: HoeffdingTree\n The Hoeffding Tree to update.\n\n \"\"\"\n if self._observed_class_distribution == {}:\n # All classes equal, default to class 0\n if 0 == y:\n self._mc_correct_weight += weight\n elif max(self._observed_class_distribution, key=self._observed_class_distribution.get) == y:\n self._mc_correct_weight += weight\n nb_prediction = do_naive_bayes_prediction(X, self._observed_class_distribution, self._attribute_observers)\n if max(nb_prediction, key=nb_prediction.get) == y:\n self._nb_correct_weight += weight\n super().learn_from_instance(X, y, weight, ht)\n\n def get_class_votes(self, X, ht):\n \"\"\"Get the votes per class for a given instance.\n\n Parameters\n ----------\n X: numpy.ndarray of length equal to the number of features.\n Instance attributes.\n ht: HoeffdingTree\n Hoeffding Tree.\n\n Returns\n -------\n dict (class_value, weight)\n Class votes for the given instance.\n\n \"\"\"\n if self._mc_correct_weight > self._nb_correct_weight:\n return self._observed_class_distribution\n return do_naive_bayes_prediction(X, self._observed_class_distribution, self._attribute_observers)\n\n def __init__(self,\n max_byte_size=33554432,\n memory_estimate_period=2000000,\n grace_period=50,\n split_criterion='info_gain',\n split_confidence=0.01,\n tie_threshold=0.05,\n binary_split=False,\n stop_mem_management=False,\n remove_poor_atts=False,\n no_preprune=False,\n leaf_prediction='nba',\n nb_threshold=0,\n nominal_attributes=None,\n max_features=2,\n random_state=None):\n \"\"\"ARFHoeffdingTree class constructor.\"\"\"\n super().__init__(max_byte_size,\n memory_estimate_period,\n grace_period,\n split_criterion,\n split_confidence,\n tie_threshold,\n binary_split,\n stop_mem_management,\n remove_poor_atts,\n no_preprune,\n leaf_prediction,\n nb_threshold,\n nominal_attributes)\n self.max_features = max_features\n self.remove_poor_attributes = False\n self.random_state = check_random_state(random_state)\n\n def _new_learning_node(self, initial_class_observations=None):\n \"\"\"Create a new learning node. The type of learning node depends on the tree configuration.\"\"\"\n if initial_class_observations is None:\n initial_class_observations = {}\n # MAJORITY CLASS\n if self._leaf_prediction == MAJORITY_CLASS:\n return self.RandomLearningNode(initial_class_observations, self.max_features,\n random_state=self.random_state)\n # NAIVE BAYES\n elif self._leaf_prediction == NAIVE_BAYES:\n return self.LearningNodeNB(initial_class_observations, self.max_features,\n random_state=self.random_state)\n # NAIVE BAYES ADAPTIVE\n else:\n return self.LearningNodeNBAdaptive(initial_class_observations, self.max_features,\n random_state=self.random_state)\n\n @staticmethod\n def is_randomizable():\n return True\n\n def new_instance(self):\n return ARFHoeffdingTree(max_byte_size=self.max_byte_size,\n memory_estimate_period=self.memory_estimate_period,\n grace_period=self.grace_period,\n split_criterion=self.split_criterion,\n split_confidence=self.split_confidence,\n tie_threshold=self.tie_threshold,\n binary_split=self.binary_split,\n stop_mem_management=self.stop_mem_management,\n remove_poor_atts=self.remove_poor_atts,\n no_preprune=self.no_preprune,\n leaf_prediction=self.leaf_prediction,\n nb_threshold=self.nb_threshold,\n nominal_attributes=self.nominal_attributes,\n max_features=self.max_features,\n random_state=self.random_state)\n","repo_name":"marouabahri/UMAP-kNN","sub_path":"src/skmultiflow/trees/arf_hoeffding_tree.py","file_name":"arf_hoeffding_tree.py","file_ext":"py","file_size_in_byte":13421,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"7039326823","text":"\r\n\"\"\"\r\ndeclare global variables \r\n\"\"\"\r\nruntimes = 0\r\n\r\nboard = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\r\n\r\nplayer = \"X\"\r\n\r\nwin_loss_stats = {\"X\": 0, \"O\": 0, \"tie\": 0}\r\n\r\ndef board_visual():\r\n print(board[0] + \" | \" + board[1] + \" | \" + board[2])\r\n print(\" -- -- -- \")\r\n print(board[3] + \" | \" + board[4] + \" | \" + board[5])\r\n print(\" -- -- -- \")\r\n print(board[6] + \" | \" + board[7] + \" | \" + board[8])\r\n\r\n\r\ndef is_valid_slot(userinput):\r\n if (board[int(userinput) - 1] == \"X\" or board[int(userinput) - 1] == \"O\"):\r\n return False\r\n else:\r\n return True\r\n\r\n\"\"\"\r\n try:\r\n slot = input(\"Which slot would you like to move to?\")\r\n except IndexError or ValueError:\r\n print(\"Not a valid number. Please select another value.\")\r\n\"\"\"\r\n\r\ndef userinput_number():\r\n\r\n\r\n slot = input(\"Which slot would you like to move to?\")\r\n\r\n while not (is_valid_slot(slot)):\r\n\r\n slot = input(\"That slot has already been taken. Which slot would you \"\r\n \"like to move to?\")\r\n\r\n return slot\r\n\r\n\r\ndef create_updated_board(slotnumber, whichplayer):\r\n\r\n global board\r\n board[int(slotnumber) - 1] = whichplayer\r\n\r\n return board\r\n\r\n\"\"\"\r\n\r\n\"\"\"\r\n\r\n\r\ndef switchTurns():\r\n\r\n global player\r\n\r\n if player == \"X\":\r\n player = \"O\"\r\n else:\r\n player = \"X\"\r\n\r\ndef check_who_won(player):\r\n #top row\r\n if (player == board[0]) and (board[0] == board[1] == board[2]):\r\n return player\r\n #second row\r\n if (player == board[3]) and (board[3] == board[4] == board[5]):\r\n return player\r\n #third row\r\n if (player == board[6]) and (board[6] == board[7] == board[8]):\r\n return player\r\n #left column\r\n if (player == board[0]) and (board[0] == board[3] == board[6]):\r\n return player\r\n # middle column\r\n if (player == board[1]) and (board[1] == board[4] == board[7]):\r\n return player\r\n # right column\r\n if (player == board[2]) and (board[2] == board[5] == board[8]):\r\n return player\r\n #diagonal, starting from top left\r\n if (player == board[0]) and (board[0] == board[4] == board[8]):\r\n return player\r\n #diagonal, starting from top right\r\n if (player == board[2]) and (board[2] == board[4] == board[6]):\r\n return player\r\n #check for tie at the end of the game\r\n if runtimes == 8:\r\n return \"tie\"\r\n else:\r\n return \"continue\"\r\n\r\n\r\n\r\ndef runGame():\r\n\r\n global win_loss_stats, runtimes, board\r\n\r\n # show board for the first time (for subsequent times code will be\r\n # written at bottom)\r\n print(\"Hey there! Welcome to tic-tac-toe for two.\")\r\n\r\n board = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\r\n\r\n runtimes = 0\r\n\r\n board_visual()\r\n\r\n who_won = \"continue\"\r\n\r\n while who_won == \"continue\":\r\n #indicate whose turn it is\r\n\r\n print(\"It is \" + player + \"'s turn.\")\r\n\r\n #take player's selection, update board\r\n\r\n create_updated_board(userinput_number(), player)\r\n\r\n board_visual()\r\n\r\n #switch turns\r\n\r\n who_won = check_who_won(player)\r\n\r\n switchTurns()\r\n\r\n runtimes += 1\r\n\r\n if who_won == \"X\":\r\n win_loss_stats[\"X\"] += 1\r\n return \"X won!\"\r\n if who_won == \"O\":\r\n win_loss_stats[\"O\"] += 1\r\n return \"O won!\"\r\n else:\r\n win_loss_stats[\"tie\"] += 1\r\n return \"It's a tie!\"\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n onemoregame = 'yes'\r\n\r\n while onemoregame == 'yes':\r\n game_result = runGame()\r\n print(game_result)\r\n onemoregame = input(\"Do you want to play again? yes or no>\")\r\n\r\n print(\"Thanks for playing tic-tac-toe!\" + game_result)\r\n print(\"X won \" + str(win_loss_stats[\"X\"]) + \" games; O won \" + str(\r\n win_loss_stats[\"0\"]) + \" games; There were \" + str(win_loss_stats[\r\n \"tie\"]) + \" ties.\")","repo_name":"jhjrhee/TicTacToe","sub_path":"TicTacToe.py","file_name":"TicTacToe.py","file_ext":"py","file_size_in_byte":3974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34290568191","text":"import mysql.connector\nimport authentication as auth\n\nclass sql_manipulation():\n def __init__(self) -> None:\n try:\n self._mydb = mysql.connector.connect(\n host = \"172.20.233.63\",\n user = auth._user,\n password = auth._password,\n database = \"perfil\"\n #database = \"teste\"\n )\n except Exception as e:\n print(f\"database connection failure. {e}\")\n \n self._mycursor = self._mydb.cursor()\n\n def post_data_wellding(self, vals, count = 0):\n \"\"\"\n Realizes a data commit to the wellding database.\n \"\"\"\n sql = \"INSERT INTO wellding (line,electrode_num, name_robot, n_points_applied, n_millins,tool, tool_name, time) VALUES (%s,%s,%s,%s,%s,%s,%s,%s)\"\n self._mycursor.executemany(sql,vals)\n\n self._mydb.commit()\n print(count, \"record inserted in wellding db.\") \n\n def post_data_psq(self,line_name: str, robot_name: str, status_psq: int, number_points: int, num_points_psq_off: int, last_update, count = 0):\n \"\"\"\n Realizes a data commit to the psq database.\n \"\"\"\n \n self.del_data_psq(line_name,robot_name)\n\n sql = \"INSERT INTO psq (line,tool_name, status_psq, number_points, num_points_psq_off, last_update) VALUES (%s,%s,%s,%s,%s,%s)\"\n val = (line_name,robot_name, status_psq, number_points, num_points_psq_off, last_update) \n self._mycursor.execute(sql,val)\n\n self._mydb.commit()\n print(count, \"record inserted in psq db. \") \n\n def post_data_dadosbosch(self, sql, vals, count = 0):\n \"\"\"\n Realizes a data commit to the dadosbosch database.\n \"\"\"\n self._mycursor.executemany(sql,vals)\n self._mydb.commit()\n print(count, \"record inserted in dadosbosch db.\")\n \n def post_data_alarms(self,val):\n \"\"\"\n Realizes a data commit to the alarms database.\n \"\"\"\n sql = \"INSERT INTO alarms (prot_record_id,date_time,timer_name,error_code_1,error_code_1_txt,code_2_interpret,error_code_2,error_code_2_txt,is_error,is_error_txt) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n self._mycursor.executemany(sql,val)\n self._mydb.commit()\n\n def del_data_psq(self, line_name: str, robot_name: str):\n \"\"\"\n Deletes a data from the psq database.\n \"\"\"\n sql = \"SELECT `line`,`tool_name`,`id` FROM `psq` WHERE `line` = %s AND `tool_name` = %s\"\n val = (line_name, robot_name)\n self._mycursor.execute(sql,val)\n \n result = self._mycursor.fetchall()\n\n if len(result) != 0:\n sql = f\"DELETE FROM `psq` WHERE `psq`.`id` = {result[0][2]}\"\n self._mycursor.execute(sql)\n\n self._mydb.commit()\n \n \n def get_last_time_wellding(self,line_name: str,tool_name: str):\n \"\"\"\n Get the last delta time recorded in the database.\n \"\"\"\n sql = \"SELECT `tool_name`,`line`,`time` FROM `wellding` WHERE `line` = %s AND `tool_name` = %s ORDER BY `time` DESC LIMIT 1\"\n val = (line_name, tool_name)\n\n self._mycursor.execute(sql,val)\n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return '2000-01-01 00:00:00'#default time\n else:\n return str(result[0][2])\n\n def get_last_electrode_num(self,line_name: str,robot_name: str, tool: int):\n \"\"\"\n Get the last registered electrode number in the database.\n \"\"\"\n sql = \"SELECT `name_robot`,`line`,`time`,`electrode_num`,`tool` FROM `wellding` WHERE `line` = %s AND `name_robot` = %s AND `tool` = %s ORDER BY `time` DESC LIMIT 1\"\n val = (line_name, robot_name, tool)\n\n self._mycursor.execute(sql,val)\n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return 0 #electrode default\n else:\n return result[0][3]\n\n def get_last_milling(self,line_name: str,tool_name: str):\n \"\"\"\n Get the last milling number resgistered in the database.\n \"\"\"\n sql = \"SELECT `tool_name`,`line`,`time`,`n_millins` FROM `wellding` WHERE `line` = %s AND `tool_name` = %s ORDER BY `time` DESC LIMIT 1\"\n val = (line_name, tool_name)\n self._mycursor.execute(sql,val)\n\n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return 0 #milling default\n else:\n return result[0][3]\n \n def get_num_points_psq(self, line_name: str, robot_name: str):\n \"\"\"\n gets the value of the number of points applied, if the robot already exists in the database.\n \"\"\"\n sql = \"SELECT `line`,`tool_name`,`number_points` FROM `psq` WHERE `line` = %s AND `tool_name` = %s\"\n val = (line_name, robot_name)\n self._mycursor.execute(sql,val)\n \n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return 0 \n else:\n return result[0][2]\n \n def get_num_points_psq_off(self, line_name: str, robot_name: str):\n \"\"\"\n gets the value of the number of points applied with psq disabled, if the robot already exists in the database.\n \"\"\"\n sql = \"SELECT `line`,`tool_name`,`num_points_psq_off` FROM `psq` WHERE `line` = %s AND `tool_name` = %s\"\n val = (line_name, robot_name)\n self._mycursor.execute(sql,val)\n \n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return 0 \n else:\n return result[0][2]\n\n def get_last_update_psq(self, line_name: str, robot_name: str):\n \"\"\"\n get the last time of the last applied weld spot counted (last_update)\n \"\"\"\n sql = \"SELECT `line`,`tool_name`,`last_update` FROM `psq` WHERE `line` = %s AND `tool_name` = %s\"\n val = (line_name, robot_name)\n self._mycursor.execute(sql,val)\n \n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return '2000-01-01 00:00:00'#default time\n else:\n return str(result[0][2])\n\n def get_last_time_dadosbosch(self,line_name: str,tool_name: str):\n \"\"\"\n Get the last delta time recorded in the database.\n \"\"\"\n sql = \"SELECT `RobotName`,`Line`,`dateTime` FROM `dadosbosch` WHERE `Line` = %s AND `RobotName` = %s ORDER BY `dateTime` DESC LIMIT 1\"\n val = (line_name, tool_name)\n\n self._mycursor.execute(sql,val)\n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return '2000-01-01 00:00:00'#default time\n else:\n return str(result[0][2])\n \n def get_last_time_alarms(self):\n \"\"\"\n Get the last delta time recorded in the database.\n \"\"\"\n sql = \"SELECT `date_time` FROM `alarms` ORDER BY `date_time` DESC LIMIT 1\"\n\n self._mycursor.execute(sql)\n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return '2000-01-01 00:00:00'#default time\n else:\n return str(result[0][0])\n\n def get_last_record_id_alarms(self):\n \"\"\"\n Get the last ProtrecordId recorded in the database.\n \"\"\"\n sql = \"SELECT `date_time`,`prot_record_id` FROM `alarms` ORDER BY `date_time` DESC LIMIT 1\"\n\n self._mycursor.execute(sql)\n result = self._mycursor.fetchall()\n\n if len(result) == 0:\n return 0 #default id\n else:\n return result[0][1]\n\n ","repo_name":"PedroHSAlves/bot_welding","sub_path":"SQL_manipulation.py","file_name":"SQL_manipulation.py","file_ext":"py","file_size_in_byte":7506,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20534820188","text":"#!/usr/bin/env python\nfrom numpy.random import choice\nfrom datetime import datetime\nfrom writeSong import writeFile\n\nrest = '|'\n\nMarkovChain = []\nPitchMarkovChain = []\nBassline = []\n\n\n# Everything from C1 to B4\ndef getPitchIndex(s):\n split = s.split('_')\n index = 0\n if (split[0] == \"C\"):\n index = 0;\n elif (split[0] == \"Db\"):\n index = 1\n elif (split[0] == \"D\"):\n index = 2\n elif (split[0] == \"Eb\"):\n index = 3\n elif (split[0] == \"E\"):\n index = 4\n elif (split[0] == \"F\"):\n index = 5\n elif (split[0] == \"Gb\"):\n index = 6\n elif (split[0] == \"G\"):\n index = 7\n elif (split[0] == \"Ab\"):\n index = 8\n elif (split[0] == \"A\"):\n index = 9\n elif (split[0] == \"Bb\"):\n index = 10\n elif (split[0] == \"B\"):\n index = 11\n else:\n print(\"eRROR!\")\n if (index < 9):\n octave = (int(split[1]) - 1)\n else:\n octave = (int(split[1]) - 2)\n index = index + (12 * octave) + 1\n return index\n \ndef getPitchName(p):\n if p == 0:\n return \"error\"\n elif p == 1:\n return \"C_1\"\n elif p == 2:\n return \"Db_1\"\n elif p == 3:\n return \"D_1\"\n elif p == 4:\n return \"Eb_1\"\n elif p == 5:\n return \"E_1\"\n elif p == 6:\n return \"F_1\"\n elif p == 7:\n return \"Gb_1\"\n elif p == 8:\n return \"G_1\"\n elif p == 9:\n return \"Ab_1\"\n elif p == 10:\n return \"A_2\"\n elif p == 11:\n return \"Bb_2\"\n elif p == 12:\n return \"B_2\"\n elif p == 13:\n return \"C_2\"\n elif p == 14:\n return \"Db_2\"\n elif p == 15:\n return \"D_2\"\n elif p == 16:\n return \"Eb_2\"\n elif p == 17:\n return \"E_2\"\n elif p == 18:\n return \"F_2\"\n elif p == 19:\n return \"Gb_2\"\n elif p == 20:\n return \"G_2\"\n elif p == 21:\n return \"Ab_2\"\n elif p == 22:\n return \"A_3\"\n elif p == 23:\n return \"Bb_3\"\n elif p == 24:\n return \"B_3\"\n elif p == 25:\n return \"C_3\"\n elif p == 26:\n return \"Db_3\"\n elif p == 27:\n return \"D_3\"\n elif p == 28:\n return \"Eb_3\"\n elif p == 29:\n return \"E_3\"\n elif p == 30:\n return \"F_3\"\n elif p == 31:\n return \"Gb_3\"\n elif p == 32:\n return \"G_3\"\n elif p == 33:\n return \"Ab_3\"\n elif p == 34:\n return \"A_4\"\n elif p == 35:\n return \"Bb_4\"\n elif p == 36:\n return \"B_4\"\n else:\n return \"ERROR\"\n \n \n \n \ndef initMarkovChains():\n # Initialize rhythm markov chain\n for i in range(0,18):\n mylist = []\n for j in range(0,18):\n # index 0 is start\n # index 1-16 is measure\n # index 17 is stop\n mylist.append(0.0)\n MarkovChain.append(mylist)\n # Initialize pitch markov chain. Range: C1 - B4 = 12 * 3 = 36\n # Index 0 is the null pitch (before the first note in the sequence)\n for i in range(0,37):\n transitionList = []\n for j in range(0,37):\n transitionList.append(0.0)\n PitchMarkovChain.append(transitionList)\n\ndef extractData():\n # single level Markov chain\n with open('basslines.txt') as fp:\n for line in fp:\n array = line.split()\n if array and array[0] != '%':\n if len(array) != 32: # Check to see that the input bassline is the right length\n print('ERROR! wrong size')\n print(array)\n else:\n # First do all the rhythm stuff\n oldIndex = 0\n for i in range(1,17):\n if (array[i-1] != rest):\n MarkovChain[oldIndex][i] += 1\n oldIndex = i\n MarkovChain[oldIndex][17] += 1\n \n # Then get pitch information\n oldPitch = 0;\n for i in range(0,16):\n if (array[i] != rest):\n pitch = getPitchIndex(array[i])\n PitchMarkovChain[oldPitch][pitch] += 1\n oldPitch = pitch\n \ndef computeProbabilities():\n # Compute rhythm probabilities\n for i in range(0,18):\n total = 0.0\n for j in range(0,18):\n total += MarkovChain[i][j]\n if (total != 0):\n for j in range(0,18):\n MarkovChain[i][j] /= total\n \n # Compute pitches probabilities\n for i in range(0,37):\n total = 0.0\n for j in range(0,37):\n total += PitchMarkovChain[i][j]\n if (total != 0):\n for j in range(0,37):\n PitchMarkovChain[i][j] /= total\n\ndef createBassline():\n beat = 0\n newBassline = []\n for i in range(0,16):\n newBassline.append('|')\n i = 0;\n l = range(18)\n #target = open('beats.txt', 'w')\n beatList = []\n while (i < 17):\n weights = MarkovChain[i]\n nextBeat = choice(l, p=weights)\n beatList.append(nextBeat)\n i = nextBeat\n\n note = []\n note.append(int(nextBeat)*4)\n Bassline.append(note)\n print(beatList)\n i=0\n for beat in beatList:\n l = range(37)\n weights = PitchMarkovChain[0]\n pitch = choice(l, p=weights)\n Bassline[i].append(int(pitch)+12)\n i = i + 1\n if (beat != 17):\n newBassline[beat-1] = getPitchName(pitch)\n weights = PitchMarkovChain[pitch]\n pitch = choice(l, p=weights)\n str = \"\"\n for note in newBassline:\n note += \" \"\n str += note\n f = open('newBassline.txt', 'w')\n f.write(str)\n f.close()\n\n print(Bassline)\n\n\n \ninitMarkovChains();\nextractData();\ncomputeProbabilities();\ncreateBassline();\nwriteFile(Bassline,Bassline,\"bassline\" + \".mod\")\n","repo_name":"krs013/amigabyte","sub_path":"basslines.py","file_name":"basslines.py","file_ext":"py","file_size_in_byte":5924,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"73272234937","text":"import numpy as np\nimport pandas as pd\nfrom time import time, sleep\nimport datetime\nfrom collections.abc import Iterable\nimport math\nfrom typing import Dict, Tuple, List, Optional, Any\nfrom tqdm import trange\n\nimport Population as p\nimport Constants as c\n\n\nclass AcorContinuousDomain(object):\n \"\"\"\n Ant Colony Optimization for Continuous Domains (ACOR) in Python.\n\n This code is used to implement the meta-heuristic algorithm know as Ant Colony Optimization \n to solve the optimization (minimization/minimization) of bounded continous-domain functions\n\n This code is a python port of the Matlab implementation provided Kalami Heris and further details can be found in this reference:\n\n Mostapha Kalami Heris, ACO for Continuous Domains in MATLAB (URL: https://yarpiz.com/67/ypea104-acor), Yarpiz, 2015.\n\n Also further reference was made to the Indra Chandra Joseph Riadi thesis that can be found here:\n\n http://usir.salford.ac.uk/30721/1/Riadi_Thesis_2014_jan.pdf\n\n \"\"\"\n\n def __init__(self, \n n_pop: int, \n n_vars: int,\n cost_func: object, \n domain_bounds: Dict):\n \"\"\"\n Constructor\n :param: n_pop: population size\n :param: n_vars: number of variables\n :param: cost_func_ cost function\n :param: domain_bounds: Continuous domain lower/upper bounds\n \"\"\"\n self.__domain_bounds = domain_bounds\n self.__l_bound = domain_bounds[0]\n self.__u_bound = domain_bounds[1]\n self.__cost_func = cost_func\n self.__n_pop = n_pop\n self.__pops_sorted = None\n self.__n_vars = n_vars\n self.__n_ants = c.AcoConstants.N_ANTS\n self.__max_terations = c.AcoConstants.MAX_ITERATIONS\n self.__q = c.AcoConstants.Q\n self.__zeta = c.AcoConstants.ZETA\n self.__random = np.random.RandomState(c.HelperConstants.RANDOM_SEED)\n self.__final_best_solution = None \n self.__best_solutions = [None]*self.__max_terations\n self.__probs = None\n self.__new_pops = None \n\n def __initialization(self):\n \"\"\"\n Initialization of the ACO algorithm\n \"\"\"\n pops = p.Populations(self.__n_pop, self.__n_vars, self.__cost_func, self.__domain_bounds)\n self.__pops_sorted = pops.ant_populations\n self.__final_best_solution = pops.best_population\n self.__w = self.__computePdf()\n self.__probs = self.__w/np.sum(self.__w)\n self.__means = np.zeros((self.__n_pop, self.__n_vars))\n self.__sigmas = np.zeros((self.__n_pop, self.__n_vars))\n\n def initialization(self):\n \"\"\"\n Wrapper method (for unit testing) of Initialization of the ACO algorithm\n \"\"\"\n self.__initialization()\n\n def __computePdf(self) -> object:\n \"\"\"\n Computes the PDF values\n \"\"\"\n points = np.array(range(self.__n_pop), dtype=np.float)\n # Solution Weights\n w = 1/(np.sqrt(2*np.pi)*self.__q*float(self.__n_pop))*np.square(np.exp(-0.5*((points-1)/(self.__q*float(self.__n_pop)))))\n return w\n\n def computePdf(self) -> object:\n \"\"\"\n Wrapper method (for unit testing) of computation of the PDF values\n \"\"\"\n self.__computePdf()\n \n\n def __createMeans(self):\n \"\"\"\n Creates the ACO algorithm means \n \"\"\"\n for i in range(self.__n_pop):\n self.__means[i, :] = np.reshape(self.__pops_sorted[i].position, (1, -1)) \n\n def createMeans(self):\n \"\"\"\n Wrapper method (for unit testing) of the creation of the ACO algorithm means \n \"\"\" \n self.__createMeans() \n\n def __createStandardDeviation(self):\n \"\"\"\n Creates the ACO algorithm Satndard Deviation (sigmas)\n \"\"\"\n for l_i in range(self.__n_pop):\n d = 0.0\n for r_i in range(self.__n_pop):\n d += np.abs(self.__means[l_i, :] - self.__means[r_i, :])\n self.__sigmas[l_i, :] = (self.__zeta * d) / (self.__n_pop - 1) \n\n def createStandardDeviation(self):\n \"\"\"\n Wrapper method (for unit testing) of the creation of the ACO algorithm Satndard Deviation (sigmas)\n \"\"\"\n self.__createStandardDeviation()\n\n def __rouletteWheelSelection(self):\n \"\"\"\n Roulette wheel selection strategy for selecting the optimal Guassain Kernel\n \"\"\"\n r = self.__random.rand()\n c = np.cumsum(np.reshape(self.__probs, (-1)))\n j = np.argwhere(r <= c)[0,0]\n return j\n\n def rouletteWheelSelection(self):\n \"\"\"\n Wrapper method (for unit testing) of the creation of Roulette wheel selection strategy for selecting the optimal Guassain Kernel\n \"\"\"\n return self.__rouletteWheelSelection()\n\n def __constructNewPopulationSolution(self):\n \"\"\"\n Constructs the new ACO Population solution\n \"\"\" \n self.__createMeans()\n self.__createStandardDeviation()\n self.__new_pops = p.Populations.createEmptyNewPopulations(self.__n_ants, self.__n_vars)\n for i in range(self.__n_ants):\n for j in range(self.__n_vars):\n # Select Gaussian Kernel\n k = self.__rouletteWheelSelection()\n # Generate Gaussian Random Variable\n self.__new_pops[i].position[j] = self.__means[k, j] + self.__sigmas[k, j] * self.__random.randn()\n # Apply Variable Bounds\n self.__new_pops[i].position = np.maximum(self.__new_pops[i].position, self.__l_bound)\n self.__new_pops[i].position = np.minimum(self.__new_pops[i].position, self.__u_bound)\n # Evaluation\n self.__new_pops[i].cost_function = self.__cost_func(self.__new_pops[i].position)\n\n def constructNewPopulationSolution(self):\n \"\"\"\n Wrapper method (for unit testing) of the creation of the new ACO Population solution\n \"\"\"\n self.__constructNewPopulationSolution() \n \n def runMainLoop(self):\n \"\"\"\n Executes ACO algorithm main loop\n \"\"\"\n self.__initialization()\n for i in trange(self.__max_terations):\n self.__constructNewPopulationSolution()\n # Merge Main Population (Archive) and New Population (Samples)\n self.__pops_sorted = self.__pops_sorted + self.__new_pops\n # Sort Population\n self.__pops_sorted = sorted(self.__pops_sorted, key=lambda x: x.cost_function, reverse=False)\n # Delete Extra Members\n self.__pops_sorted = self.__pops_sorted[:self.__n_pop]\n # Update Best Solution Ever Found\n self.__final_best_solution = self.__pops_sorted[0]\n # Store Best Cost\n self.__best_solutions[i] = self.__final_best_solution\n \n @property\n def pops(self):\n \"\"\"\n Getter property of the 'self.__pops' attribute\n \"\"\"\n return self.__pops_sorted\n\n @property\n def new_pops(self):\n \"\"\"\n Getter property of the 'self.__new_pops' attribute\n \"\"\"\n return self.__new_pops\n\n @property\n def final_best_solution(self):\n \"\"\"\n Getter property of the 'self.__best_solution' attribute\n \"\"\"\n return self.__final_best_solution\n\n @property\n def best_solutions(self):\n \"\"\"\n Getter property of the 'self.__best_solutions' attribute\n \"\"\"\n return self.__best_solutions\n\n @property\n def probs(self):\n \"\"\"\n Getter property of the 'self.__probs' attribute\n \"\"\"\n return self.__probs\n\n @property\n def means(self):\n \"\"\"\n Getter property of the 'self.__meanss' attribute\n \"\"\"\n return self.__means\n\n @property\n def sigmas(self):\n \"\"\"\n Getter property of the 'self.__sigmas' attribute\n \"\"\"\n return self.__sigmas\n\n","repo_name":"aidowu1/Py-Optimization-Algorithms","sub_path":"AntColonyOptimization/Acor.py","file_name":"Acor.py","file_ext":"py","file_size_in_byte":7892,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"27669745","text":"from django.shortcuts import render, redirect\nfrom .models import Plant, PlantImage\nfrom .forms import PlantForm, PlantImageForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import JsonResponse\nfrom django.db.models import Count\nfrom django.core.paginator import Paginator\nfrom django.http import HttpResponseForbidden\nfrom sales.models import Product\n\n\n# Create your views here.\ndef index(request):\n plants = Plant.objects.order_by('-id')\n paginator = Paginator(plants, 12)\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n\n unique_tags = []\n all_tags = []\n for plant in page_obj:\n for tag in plant.tags.all():\n if tag.name not in all_tags:\n all_tags.append(tag.name)\n unique_tags.append(tag.name)\n\n context = {\n 'page_obj': page_obj,\n 'unique_tags': unique_tags[:10],\n 'room_name': \"broadcast\",\n }\n return render(request, 'plants/index.html', context)\n\n\n\n@login_required(login_url='admin:login')\ndef create(request):\n\n if not request.user.is_superuser:\n return redirect('home')\n \n if request.method == 'POST':\n tags = request.POST.get('tags').split(',')\n form = PlantForm(request.POST, request.FILES)\n if form.is_valid():\n plant = form.save(commit=False)\n plant.user = request.user\n plant.save()\n\n images = request.FILES.getlist('images')\n for image in images:\n PlantImage.objects.create(plant=plant, image=image)\n \n for tag in tags:\n plant.tags.add(tag.strip())\n return redirect('plants:detail', plant.pk)\n else:\n form = PlantForm()\n imageForm = PlantImageForm()\n context = {\n 'form':form,\n 'imageForm':imageForm,\n 'room_name': \"broadcast\"\n }\n return render(request, 'plants/create.html', context)\n\n\n\ndef update(request, plant_pk):\n \n if not request.user.is_superuser:\n return redirect('home')\n \n plant = Plant.objects.get(pk=plant_pk)\n if request.method == 'POST':\n form = PlantForm(request.POST, request.FILES, instance=plant)\n if form.is_valid():\n form.save()\n plant.tags.clear()\n tags = request.POST.get('tags').split(',')\n for tag in tags:\n plant.tags.add(tag.strip())\n return redirect('plants:detail', plant_pk)\n else:\n form = PlantForm(instance=plant)\n context = {\n 'form':form,\n 'plant_pk':plant_pk,\n 'plant':plant,\n 'room_name': \"broadcast\"\n }\n return render(request, 'plants/update.html', context)\n\n\n\ndef delete(request, plant_pk):\n \n if not request.user.is_superuser:\n return redirect('home')\n \n plant = Plant.objects.get(pk=plant_pk)\n if request.user == plant.user:\n plant.delete()\n return redirect('plants:index')\n\n\ndef detail(request, plant_pk):\n plant = Plant.objects.get(pk=plant_pk)\n\n def extract_list(field):\n if plant is not None and \"[\" in field:\n word = \"\"\n for item in field:\n if item in [\"[\", \"'\", \",\", \"]\"]:\n continue\n else:\n word += item\n return word.split()\n else:\n return []\n \n category_list = extract_list(plant.category)\n watering_list = extract_list(plant.watering)\n sunlight_list = extract_list(plant.sunlight)\n humidity_list = extract_list(plant.humidity)\n temperature_list = extract_list(plant.temperature)\n allergy = plant.allergy\n\n\n # filtered_products = []\n # products = Product.objects.all()\n \n # print(products)\n # product_title = []\n # for product in products:\n # tags = product.tags.all()\n # for tag in tags:\n # if '따뜻한' in tag.name:\n # product_title.append(product.title)\n # print(product_title)\n\n # 위에서는 나오는데 아래서 안나오네요\n # product_titles = []\n # if watering_list == '주1~2회':\n # products = Product.objects.all()\n # products = Product.objects.filter(tags__name='따뜻한')\n # for product in products:\n # print(product.title)\n\n\n # watering_plants = Plant.objects.filter(watering='주1~2회')\n # watering_products = Product.objects.filter(tags_name='따뜻한', plant__in=watering_plants)\n\n # product_titles = []\n # filtered_products = Product.objects.filter(tags__name__in='따뜻한')\n \n # if plant.watering == '주1~2회':\n # for product in products:\n \n # tags = product.tags.all()\n # for tag in tags:\n # if '따뜻한' in tag.name: # 이건 product\n # product_titles.append(product.title)\n # # elif watering_list == '월1~2회':\n # # if '월1~2회 태그' in product.tags:\n # # filtered_products.append(product)\n # # elif watering_list == '월1회이하':\n # # if '월1회이하 태그' in product.tags:\n # # filtered_products.append(product)\n # # elif watering_list == '주2~3회':\n # # if '주2~3회 태그' in product.tags:\n # # filtered_products.append(product)\n\n products = Product.objects.all()\n \n\n context = {\n 'plant': plant,\n 'category_list': category_list,\n\n 'watering_list': watering_list,\n 'sunlight_list': sunlight_list,\n 'humidity_list': humidity_list,\n 'temperature_list': temperature_list,\n 'allergy': allergy,\n 'products':products,\n }\n return render(request, 'plants/detail.html', context)\n\n\n@login_required\ndef likes(request, plant_pk):\n plant = Plant.objects.get(pk=plant_pk)\n\n if request.user in plant.like_users.all():\n plant.like_users.remove(request.user)\n is_liked=False\n else:\n plant.like_users.add(request.user)\n is_liked=True\n\n context = {\n 'is_liked' : is_liked,\n 'like_count': plant.like_users.count(),\n }\n return JsonResponse(context)\n\n\ndef recommendation(request):\n recommended_plants = Plant.objects.annotate(like_count=Count('like_users')).order_by('-like_count') # 좋아요 순으로 정렬\n context = {\n 'recommended_plants': recommended_plants,\n 'room_name': \"broadcast\"\n }\n return render(request, 'plants/recommendation.html', context)\n\n\ndef search(request):\n query = request.GET.get('searched', '')\n plants = Plant.objects.filter(title__contains=query)\n\n context = {\n 'plants':plants,\n 'query':query,\n 'room_name': \"broadcast\"\n }\n return render(request, 'plants/search.html', context)\n\n\ndef filter_plants(request, tag):\n filtered_plants = Plant.objects.filter(tags__name__in=[tag])\n unique_tags = Plant.objects.values_list('tags__name', flat=True).distinct()\n\n context = {\n 'filtered_plants': filtered_plants,\n 'unique_tags': unique_tags,\n 'room_name': \"broadcast\"\n }\n\n return render(request, 'plants/filter.html', context)\n\n\ndef category_plants(request):\n category = request.GET.getlist('category')\n\n if '전체' in category:\n filtered_plants = Plant.objects.all()\n elif '실내식물' in category:\n filtered_plants = Plant.objects.filter(category__contains='실내식물')\n elif '실외식물' in category:\n filtered_plants = Plant.objects.filter(category__contains='실외식물')\n else:\n filtered_plants = Plant.objects.none()\n\n context = {\n 'filtered_plants': filtered_plants,\n 'category': category,\n }\n return render(request, 'plants/category.html', context)\n\n\n\n\n\n","repo_name":"dnjsxo0616/mureokmureok","sub_path":"plants/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7824,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"35496237119","text":"import mock\nimport pickle\nimport unittest\n\nfrom pinball.master.thrift_lib.ttypes import Token\nfrom pinball.parser.repository_config_parser import \\\n RepositoryConfigParser\nfrom pinball.repository.config import JobConfig\nfrom pinball.repository.config import WorkflowScheduleConfig\nfrom pinball.scheduler.overrun_policy import OverrunPolicy\nfrom pinball.workflow.name import Name\n\n\n__author__ = 'Pawel Garbacki'\n__copyright__ = 'Copyright 2015, Pinterest, Inc.'\n__credits__ = [__author__]\n__license__ = 'Apache'\n__version__ = '2.0'\n\n\nclass SomeJobTemplate(object):\n def __init__(self, name, max_attempts=None, emails=None, priority=None):\n assert name == 'some_job'\n assert max_attempts == 10\n assert emails == ['some_email@pinterest.com',\n 'some_other_email@pinterest.com']\n assert priority == 100\n\n def get_pinball_job(self, inputs, outputs, params=None):\n assert inputs == ['__WORKFLOW_START__']\n assert outputs == []\n assert params == {'some_param': 'some_value'}\n\n return Token(name='/workflow/some_workflow/job/waiting/some_job')\n\n\nclass RepositoryConfigParserTestCase(unittest.TestCase):\n def setUp(self):\n self._call_count = 0\n\n @mock.patch('time.time')\n @mock.patch('pinball.parser.repository_config_parser.'\n 'GithubRepository')\n def test_get_schedule_token(self, repository_mock, time_mock):\n repository = mock.Mock()\n repository_mock.return_value = repository\n\n schedule_config = WorkflowScheduleConfig()\n schedule_config.workflow = 'some_workflow'\n schedule_config.start_date = '2012-01-01'\n schedule_config.time = '00.00.01.000'\n schedule_config.recurrence = '1d'\n schedule_config.overrun_policy = 'START_NEW'\n schedule_config.emails = ['some_email@pinterest.com',\n 'some_other_email@pinterest.com']\n repository.get_schedule.return_value = schedule_config\n\n time_mock.return_value = 100. # the value must be a float\n\n parser = RepositoryConfigParser()\n\n schedule_token = parser.get_schedule_token('some_workflow')\n\n self.assertEqual('/schedule/workflow/some_workflow',\n schedule_token.name)\n # 1325376000 = 01 Jan 2012 00:00:00 UTC\n self.assertEqual(1325376000, schedule_token.expirationTime)\n schedule = pickle.loads(schedule_token.data)\n self.assertEqual(1325376000, schedule.next_run_time)\n self.assertEqual(24 * 60 * 60, schedule.recurrence_seconds)\n self.assertEqual(OverrunPolicy.START_NEW, schedule.overrun_policy)\n self.assertEqual('some_workflow', schedule.workflow)\n self.assertEqual(['some_email@pinterest.com',\n 'some_other_email@pinterest.com'], schedule.emails)\n\n repository.get_schedule.assert_called_once_with('some_workflow')\n\n @mock.patch('pinball.parser.repository_config_parser.'\n 'GithubRepository')\n def test_get_workflow_tokens(self, repository_mock):\n repository = mock.Mock()\n repository_mock.return_value = repository\n\n repository.get_job_names.return_value = ['some_job']\n\n job_config = JobConfig()\n job_config.workflow = 'some_workflow'\n job_config.job = 'some_job'\n job_config.is_condition = False\n job_config.template = ('tests.pinball.parser.'\n 'repository_config_parser_test.SomeJobTemplate')\n job_config.template_params = {'some_param': 'some_value'}\n job_config.parents = []\n job_config.emails = ['some_email@pinterest.com',\n 'some_other_email@pinterest.com']\n job_config.max_attempts = 10\n job_config.retry_delay_sec = 20\n job_config.priority = 100\n repository.get_job.return_value = job_config\n\n parser = RepositoryConfigParser()\n\n workflow_tokens = parser.get_workflow_tokens('some_workflow')\n\n self.assertEqual(2, len(workflow_tokens))\n\n # Verify the triggering event token.\n if Name.from_event_token_name(workflow_tokens[0].name).workflow:\n event_token = workflow_tokens[0]\n else:\n event_token = workflow_tokens[1]\n\n event_name = Name.from_event_token_name(event_token.name)\n self.assertEqual('some_workflow', event_name.workflow)\n self.assertEqual('some_job', event_name.job)\n self.assertEqual('__WORKFLOW_START__', event_name.input)\n\n event = pickle.loads(event_token.data)\n self.assertEqual('repository_config_parser', event.creator)\n\n repository.get_job_names.assert_called_once_with('some_workflow')\n repository.get_job.assert_called_once_with('some_workflow', 'some_job')\n","repo_name":"pinterest/pinball","sub_path":"tests/pinball/parser/repository_config_parser_test.py","file_name":"repository_config_parser_test.py","file_ext":"py","file_size_in_byte":4802,"program_lang":"python","lang":"en","doc_type":"code","stars":1047,"dataset":"github-code","pt":"22"}
+{"seq_id":"40160266719","text":"import torch\nimport torch.nn as nn\nimport argparse\nfrom torch.optim import SGD\nfrom torch.utils.data import DataLoader\nimport time\nfrom torch.utils.data.dataset import random_split\nfrom torchtext.datasets import text_classification\nimport torch.utils.data.distributed\nimport os\n\nfrom tqdm import tqdm\n\n\ndef metric_average(args, val, name):\n tensor = torch.tensor(val)\n avg_tensor = args.hvd.allreduce(tensor, name=name)\n return avg_tensor.item()\n\n\nclass TextSentiment(nn.Module):\n def __init__(self, vocab_size, embed_dim, num_class):\n super().__init__()\n self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)\n self.fc = nn.Linear(embed_dim, num_class)\n self.init_weights()\n\n def init_weights(self):\n initrange = 0.5\n self.embedding.weight.data.uniform_(-initrange, initrange)\n self.fc.weight.data.uniform_(-initrange, initrange)\n self.fc.bias.data.zero_()\n\n def forward(self, text, offsets):\n embedded = self.embedding(text, offsets)\n return self.fc(embedded)\n\n\ndef generate_batch(batch):\n label = torch.tensor([entry[0] for entry in batch])\n text = [entry[1] for entry in batch]\n offsets = [0] + [len(entry) for entry in text]\n # torch.Tensor.cumsum returns the cumulative sum\n # of elements in the dimension dim.\n # torch.Tensor([1.0, 2.0, 3.0]).cumsum(dim=0)\n\n offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n text = torch.cat(text)\n return text, offsets, label\n\n\ndef train_func(args, sub_train_, model, optimizer, criterion, scheduler):\n # Train the model\n train_loss = 0\n train_acc = 0\n if args.horovod:\n args.hvd.broadcast_parameters(model.state_dict(), root_rank=0)\n args.hvd.broadcast_optimizer_state(optimizer, root_rank=0)\n compression = args.hvd.Compression.fp16 if args.fp16_allreduce else args.hvd.Compression.none\n optimizer = args.hvd.DistributedOptimizer(optimizer,\n named_parameters=model.named_parameters(),\n compression=compression)\n\n if args.horovod:\n train_sampler = torch.utils.data.distributed.DistributedSampler(\n sub_train_, num_replicas=args.hvd_size, rank=args.rank)\n data = torch.utils.data.DataLoader(\n sub_train_, args.batch_size, sampler=train_sampler, collate_fn=generate_batch, **args.kwargs)\n else:\n data = DataLoader(sub_train_, batch_size=args.batch_size, shuffle=True,\n collate_fn=generate_batch)\n\n for i, (text, offsets, cls) in enumerate(tqdm(data, desc='data train')):\n optimizer.zero_grad()\n if args.cuda:\n text, offsets, cls = text.cuda(), offsets.cuda(), cls.cuda()\n output = model(text, offsets)\n loss = criterion(output, cls)\n train_loss += loss.item()\n loss.backward()\n optimizer.step()\n train_acc += (output.argmax(1) == cls).sum().item()\n\n # Adjust the learning rate\n scheduler.step()\n\n return train_loss / len(sub_train_), train_acc / len(sub_train_)\n\n\ndef test(args, data_, model, criterion):\n loss = 0\n acc = 0\n if args.horovod:\n train_sampler = torch.utils.data.distributed.DistributedSampler(\n data_, num_replicas=args.hvd_size, rank=args.rank)\n data = torch.utils.data.DataLoader(\n data_, args.batch_size, sampler=train_sampler, collate_fn=generate_batch, **args.kwargs)\n else:\n data = DataLoader(data_, batch_size=args.batch_size, shuffle=True,\n collate_fn=generate_batch)\n for text, offsets, cls in data:\n if args.cuda:\n text, offsets, cls = text.cuda(), offsets.cuda(), cls.cuda()\n with torch.no_grad():\n output = model(text, offsets)\n loss = criterion(output, cls)\n loss += loss.item()\n acc += (output.argmax(1) == cls).sum().item()\n\n return loss / len(data_), acc / len(data_)\n\n\ndef main():\n parser = argparse.ArgumentParser(description='PyTorch NMIST example')\n parser.add_argument('--batch_size', type=int, default=32, metavar='N',\n help='batch size (default: 32)')\n parser.add_argument('--num_epochs', type=int, default=5, metavar='N',\n help='number of epochs (default: 5)')\n parser.add_argument('--embedding_dim', type=int, default=128, metavar='N',\n help='dimension of embedding (default: 128)')\n parser.add_argument('--ngrams', type=int, default=2, metavar='N',\n help='size of ngrams (default: 2)')\n parser.add_argument('--horovod', action='store_true', default=False,\n help='use horovod')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='enables CUDA training')\n parser.add_argument('--fp16-allreduce', action='store_true', default=False,\n help='use fp16 compression during allreduce')\n parser.add_argument('--seed', type=int, default=42,\n help=\"random seed for initialization\")\n args = parser.parse_args()\n args.cuda = not args.no_cuda and torch.cuda.is_available()\n\n if not os.path.isdir('data'):\n os.mkdir('data')\n train_dataset, test_dataset = text_classification.DATASETS['AG_NEWS'](\n root='data', ngrams=args.ngrams, vocab=None)\n\n args.vocab_size = len(train_dataset.get_vocab())\n args.num_class = len(train_dataset.get_labels())\n model = TextSentiment(args.vocab_size, args.embedding_dim, args.num_class)\n\n # horovod training\n if args.horovod:\n try:\n import horovod.torch as hvd\n except ImportError:\n raise ImportError(\"Please install horovod from https://github.com/horovod to use horovod training.\")\n hvd.init()\n args.kwargs = {'num_workers': 1, 'pin_memory': True}\n args.hvd_size = hvd.size()\n args.rank = hvd.rank()\n args.hvd = hvd\n\n if args.cuda:\n # Horovod: pin GPU to local rank.\n torch.cuda.set_device(hvd.local_rank())\n torch.cuda.manual_seed(args.seed)\n\n torch.set_num_threads(1)\n args.kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n min_valid_loss = float('inf')\n\n criterion = torch.nn.CrossEntropyLoss()\n optimizer = SGD(model.parameters(), lr=0.001)\n scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.9)\n if args.cuda:\n criterion.cuda()\n model.cuda()\n\n train_len = int(len(train_dataset) * 0.95)\n sub_train_, sub_valid_ = \\\n random_split(train_dataset, [train_len, len(train_dataset) - train_len])\n\n for epoch in range(args.num_epochs):\n start_time = time.time()\n train_loss, train_acc = train_func(args, sub_train_, model, optimizer, criterion, scheduler)\n valid_loss, valid_acc = test(args, sub_valid_, model, criterion)\n\n secs = int(time.time() - start_time)\n mins = secs / 60\n secs = secs % 60\n\n if args.horovod:\n valid_loss = metric_average(valid_loss, 'avg_loss')\n valid_acc = metric_average(valid_acc, 'avg_accuracy')\n if hvd.rank() == 0:\n print('Epoch: %d' % (epoch + 1), \" | time in %d minutes, %d seconds\" % (mins, secs))\n print(f'\\tLoss: {train_loss:.4f}(train)\\t|\\tAcc: {train_acc * 100:.1f}%(train)')\n print(f'\\tLoss: {valid_loss:.4f}(valid)\\t|\\tAcc: {valid_acc * 100:.1f}%(valid)')\n else:\n print('Epoch: %d' % (epoch + 1), \" | time in %d minutes, %d seconds\" % (mins, secs))\n print(f'\\tLoss: {train_loss:.4f}(train)\\t|\\tAcc: {train_acc * 100:.1f}%(train)')\n print(f'\\tLoss: {valid_loss:.4f}(valid)\\t|\\tAcc: {valid_acc * 100:.1f}%(valid)')\n\n\nif __name__ == '__main__':\n main()","repo_name":"Maxpa1n/modules_pytorch","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":7889,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"39671162765","text":"from flask import Blueprint, redirect, Flask, request\nfrom ..models import db,Project\nimport json\n\n\nbp = Blueprint('project', __name__, url_prefix='/api/projects')\n\n# Get all project data\n@bp.route('')\ndef all_projects():\n return [project.to_dict() for project in Project.query.all()]\n\n# Get project data by Id\n@bp.route('/')\ndef single_project(id):\n oneProject = Project.query.get(id).to_dict()\n return oneProject\n\n# Create a project\n@bp.route('', methods=['POST'])\ndef add_project():\n data=json.loads(request.data)\n newproject=Project(**data)\n db.session.add(newproject)\n db.session.commit()\n return newproject.to_dict()\n\n# Edit a project\n@bp.route('/', methods=['PUT'])\ndef edit_project(id):\n \n data=json.loads(request.data)\n Project.query.filter(Project.id==id).update(data)\n db.session.commit()\n return Project.query.get(id).to_dict()\n\n# Delete a project\n@bp.route('/', methods=['DELETE'])\ndef delete_project(id):\n oneProject = Project.query.get(id)\n db.session.delete(oneProject)\n db.session.commit()\n return \"delete succeful\"\n\n\n\n#post /\n# @bp.route('', methods=['POST'])\n# def main_page_add_pokemon():\n # form = PokemonForm()\n # form['csrf_token'].data = request.cookies['csrf_token']\n # if form.validate_on_submit():\n # data = Pokemon()\n # form.populate_obj(data)\n # db.session.add(data)\n # db.session.commit()\n \n # json post\n # data=json.loads(request.data)\n # temp=data[\"name\"]\n \n # form post\n \n # try:\n # form = SimpleForm(name=request.form.get(\"name\"))\n # result=\"good\"\n # except:\n # result=\"bad\"\n # return result\n","repo_name":"no8cai/pregroupREF","sub_path":"flaskapp/app/routes/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34477638939","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Feb 1 11:46:20 2021\r\n\r\n@author: T30518\r\n\"\"\"\r\n\r\n\r\nimport numpy as np \r\nimport matplotlib.pyplot as plt\r\nX = [[6], [8], [10], [14], [18]] #pizza大小\r\ny = [[7], [9], [13], [17.5], [18]] #pizza价格\r\n\r\nX2 = [[1], [10], [14], [25]] \r\ny2 = reg.predict(X2) \r\nprint(y2) #绘制线性回归图形 \r\nplt.figure() \r\nplt.title(u'PizzaPrice with diameter.') #标题 \r\nplt.xlabel(u'diameter') #x轴座标 \r\nplt.ylabel(u'price') #y轴座标 \r\nplt.axis([0, 25, 0, 25]) #区间 \r\nplt.grid(True) #显示网格 \r\nplt.plot(X, y, 'k.') #绘制训练资料集散点图 plt.plot(X2, y2, '-g.') #绘制预测资料集直线\r\nplt.plot(X2, y2, '-g.') # #绘制预测资料集直线\r\n\r\n\r\n#plt.figure() \r\n#plt.title('Pizza Price with diameter.') \r\n#plt.xlabel('diameter(inch)') \r\n#plt.ylabel('price($)') \r\n#plt.axis([0, 25, 0, 25]) #区间\r\n#plt.grid(True) #显示网格or not\r\n#plt.plot(X, y, 'b.') \r\nplt.show()\r\n","repo_name":"albastienex/TEST_FEB","sub_path":"ex2.py","file_name":"ex2.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36147268055","text":"##From PySpellChecker\r\nfrom spellchecker import SpellChecker\r\n\r\n# simple text spelling error correction\r\nspell = SpellChecker()\r\ndef correct_sentence(line):\r\n lines = line.strip().split(' ')\r\n new_line = \"\"\r\n similar_word = {}\r\n for l in lines:\r\n new_line += spell.correction(l) + \" \"\r\n return new_line\r\n","repo_name":"saimj7/Handwritten-Text-Recognition-in-Real-Time","sub_path":"mylib/SpellChecker.py","file_name":"SpellChecker.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"22"}
+{"seq_id":"8992640732","text":"__author__ = 'PC-LiNing'\n\nimport numpy as np\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n\ndef get_sample_target(phi, target, sample):\n sum = 0.0\n for id_, v in enumerate(phi[target]):\n sum += v\n if sum > sample:\n return id_\n\n\ndef transfer_label(target, num_class=10):\n vec = np.zeros(shape=(num_class,), dtype=np.float32)\n vec[target] = 1.0\n return vec\n\n\n# train_labels = [60000,10]\ndef add_uniform_noise(train_labels, q=0.2, num_class=10):\n e = np.identity(num_class, dtype=np.float32)\n ones = np.ones(shape=(num_class, num_class), dtype=np.float32)\n phi = (1-q)*e + (q/num_class)*ones\n all_size = train_labels.shape[0]\n noise_labels = np.zeros(shape=(all_size, num_class), dtype=np.float32)\n correct = 0\n for id_ in range(all_size):\n sample = np.random.random_sample()\n target = np.argmax(train_labels[id_])\n sample_target = get_sample_target(phi, target=target, sample=sample)\n if target == sample_target:\n correct += 1\n noise_labels[id_] = transfer_label(sample_target, num_class)\n\n print(\"generate noise label, \" + str(correct/all_size*100) + \"% is correct.\")\n return noise_labels, phi\n\n\ndef batch_iter(data, batch_size, num_epochs, shuffle=True):\n \"\"\"\n Generates a batch iterator for a dataset.\n len(data) is not divisible by batch_size .\n \"\"\"\n data = np.array(data)\n data_size = len(data)\n if len(data) % batch_size == 0:\n num_batches_per_epoch = int(len(data)/batch_size)\n else:\n num_batches_per_epoch = int(len(data)/batch_size) + 1\n\n for epoch in range(num_epochs):\n # Shuffle the data at each epoch\n if shuffle:\n # shuffle_indices = train_shuffle[epoch]\n shuffle_indices = np.random.permutation(np.arange(data_size))\n shuffled_data = data[shuffle_indices]\n else:\n shuffled_data = data\n for batch_num in range(num_batches_per_epoch):\n start_index = batch_num * batch_size\n end_index = min((batch_num + 1) * batch_size, data_size)\n yield shuffled_data[start_index:end_index]\n","repo_name":"QqqingYuan/Noise_label","sub_path":"data_uitl.py","file_name":"data_uitl.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6102709253","text":"from django.urls import path\nfrom . import views\n\napp_name = 'BackEnd'\n\nurlpatterns = [\n path('CheckName', views.CheckName, name='CheckName'),\n path('CheckEmail', views.CheckEmail, name='CheckEmail'),\n\n path('LogOut', views.LogOut, name='LogOut'),\n\n path('GetPosts', views.GetPosts, name='GetPosts'),\n\n path('MakeComment', views.MakeComment, name='MakeComment'),\n path('LikeDisLikePost', views.Like_DisLikePost, name='Like_DisLikePost'),\n\n path('DeletePost', views.DeletePost, name='DeletePost'),\n\n path('GetNotifications', views.GetMoreNotifications, name='GetNotifications')\n]\n","repo_name":"Hady-Eslam/Articles_Analyzing","sub_path":"BackEnd/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26099746843","text":"from airflow.models import DAG\nfrom airflow.operators.python_operator import PythonOperator\nfrom datetime import datetime, timedelta\n# from . import extract,transform,load\nimport pandas as pd\nimport psycopg2\nimport psycopg2.extras\nimport numpy as np\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': datetime(2023, 2, 2),\n 'email_on_failure': False,\n 'email_on_retry': False,\n 'retries': 1,\n 'retry_delay': timedelta(minutes=5),\n}\n\ndag = DAG(\n 'etl_pipeline_3',\n default_args=default_args,\n description='A simple ETL pipeline in Airflow',\n schedule_interval=timedelta(hours=1),\n)\n\ndef extract():\n \"\"\"\n function for extracting \n data from a csv file\n \"\"\"\n avito_df=pd.read_csv('avito.csv')\n \n return avito_df\n\n\ndef fill_missing_values(data):\n #replacing Nan(string) with np.nan \n data=data.replace({'Nan':np.nan})\n\n ### filling missing values with mode for categorical variables and with mean for continuous variables\n data=data.fillna({\"Etat\":data[\"Etat\"].mode()[0],\n \"price\":data['price'].mean(),\n \"Number_door\":data['Number_door'].mode()[0],\n \"first_hand\":data['first_hand'].mode()[0],\n \"origin\":data['first_hand'].mode()[0] })\n\n return data \n\n\n\n\ndef drop_columns(data):\n ##\" removing unimportant columns\n data=data.drop([\"name\",\"Secteur\",\"type\"],axis=1)\n return data\n\n\ndef kilometrage(data):\n\n data[\"minimum_distance\"]=data[\"kilometrage\"].str.split(\"-\").apply(lambda x:x[0].replace(\" \",\"\"))\n data[\"maximum_distance\"]=data[\"kilometrage\"].str.split(\"-\").apply(lambda x:x[1].replace(\" \",\"\"))\n data[\"minimum_distance\"]=data[\"minimum_distance\"].astype(int)\n data[\"maximum_distance\"]=data[\"maximum_distance\"].astype(int)\n \n\n\n return data\n\ndef transform(**kwargs):\n data = kwargs['ti'].xcom_pull(task_ids='extract')\n data=fill_missing_values(data)\n data=drop_columns(data)\n data=kilometrage(data)\n\n return data\n\n\n\ndef load(**kwargs):\n df = kwargs['ti'].xcom_pull(task_ids='transform')\n\n ### deployement in postgres container\n hostname = \"localhost\"\n database = \"exampledb\"\n username = \"docker\"\n pwd = \"docker\"\n port_id = 5431\n conn = None\n\n try:\n with psycopg2.connect(\n host = hostname,\n dbname = database,\n user = username,\n password = pwd,\n port = port_id) as conn:\n\n with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:\n\n cur.execute(\"DROP TABLE IF EXISTS cars_feature\")\n\n create_script = \"CREATE TABLE if not exists \\\n cars_feature (cars_id SERIAL,name text, price float,type text,\\\n number_door text,kilometrage text,first_hand text,\\\n secteur text,marque text,etat text,year_model integer,origin text)\"\n cur.execute(create_script)\n\n count=0\n for car in df.iterrows():\n tuples=tuple(car[1])\n\n\n cur.execute(\n f\"\"\" INSERT INTO cars_feature(name, price, type, number_door, kilometrage, first_hand, secteur, marque, etat, year_model, origin) \n VALUES('{tuples[0]}','{tuples[1]}','{tuples[2]}','{tuples[3]}','{tuples[4]}','{tuples[5]}','{tuples[6]}','{tuples[7]}','{tuples[8]}','{tuples[9]}','{tuples[10]}')\"\"\")\n\n\n count +=1\n except Exception as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()\n\n\n\n\n\n\n\n\n\n\n\n\n### defining pipeline tasks\n\nextract_task=PythonOperator(\n task_id='extract',\n python_callable=extract,\n dag=dag,\n)\n\ntransform_task=PythonOperator(\n task_id='transform',\n python_callable=transform,\n dag=dag,\n provide_context=True,\n)\n\nload_task=PythonOperator(\n task_id='load',\n python_callable=load,\n dag=dag,\n provide_context=True,\n)\n\n#defining pipeline\n\nextract_task >> transform_task >> load_task\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"taha-nl/Scraping-avito-ETL-airflow-docker","sub_path":"airflow/dags/etl_dag.py","file_name":"etl_dag.py","file_ext":"py","file_size_in_byte":4376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42105601920","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# ROS ver. Kineic Kame / ubuntu 16.04LTS\n# Creme execution node preparator\n\nimport rospy\nfrom std_msgs.msg import String\n\ndef creme_recipe():\n\tpub = rospy.Publisher('creme_recipe', String, queue_size=3)\n\trospy.init_node('creme_recipe_sender')\n\trate = rospy.Rate(1)\n\n\t# this is abstract for now\n\t# connectivity with debug functions is here gotta add HW spesc funcs\n\t# array is for debug and will be replaced\t\n\n\trecipeArray=[\"Marshmallow-Cheese Creme: \", \"7 - Put 120g Marshmallows in a bowl, add 160g Cream Cheese and put it without mixing into the microwave for 1 Minute by 600 Watts.\", \"8 - Beat the mixture up with your whisk until it doesn't look like it originally did anymore (A creme).\", \"9 - Add 120g Milk and continue mixing.\", \"10 - Add a teaspoon of lemon juice and continue mixing for a couple of seconds.\", \"11 - Now pour the liquified remains into the previously made cake ground so it fills the hole in its heart.\", \"12 - Put everything in the fridge for another hour.\", \"13 - Eat it.\", \"14 - Clean your mess up so you can make another one.\" ]\t\n\t\n\n\twhile not rospy.is_shutdown():\n\t\ti = 0\n\t\tstep = 0\n\t\twhile step <= 8:\n\t\t\trecipe_str = recipeArray[i]\n\t\t\ti += 1\n\t\t\tstep += 1\n\t\t\t# this is for debug and will be muted \n\t\t\trospy.loginfo(recipe_str)\n\t\t\tpub.publish(recipe_str)\n\t\t\n\t\trate.sleep()\n\nif __name__ == '__main__':\n\ttry:\n\t\tcreme_recipe()\n\texcept rospy.ROSInterruptException:\n\t\tpass\n","repo_name":"Parou/Cocoa-Biscuit-Cake-with-Marshmallow-Cheese-Creme","sub_path":"ros_pckg/scripts/cremeRecipeSend.py","file_name":"cremeRecipeSend.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"22"}
+{"seq_id":"40507233513","text":"import argparse\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nfrom typing import cast\n\nfrom magpie.core import Rule, check_threshold\n\n\ndef html(content: str):\n return b\"\"\"\n\n\n %s \n\n\"\"\" % content.encode(\n \"utf-8\"\n )\n\n\nclass MagpieHandler(BaseHTTPRequestHandler):\n def get_content(self) -> str:\n server = cast(MagpieServer, self.server) # type: ignore\n return check_threshold(server.rules, send_msg_funcs=[])\n\n def do_GET(self): # noqa: N802\n self.send_response(200)\n self.send_header(\"Content-type\", \"text/html\")\n self.end_headers()\n\n self.wfile.write(html(self.get_content()))\n\n\nclass MagpieServer(HTTPServer):\n def __init__(self, *args, rules: dict[str, Rule], **kwargs):\n self.rules = rules\n super().__init__(*args, **kwargs)\n\n\ndef add_server_arguments(parser: argparse.ArgumentParser):\n parser.add_argument(\n \"-b\",\n \"--bind\",\n default=\"0.0.0.0\",\n metavar=\"ADDRESS\",\n help=\"bind to this address \" \"(default: all interfaces)\",\n )\n parser.add_argument(\n \"-p\",\n \"--port\",\n default=8000,\n type=int,\n help=\"bind to this port \" \"(default: %(default)s)\",\n )\n\n\ndef run_server(rules: dict[str, Rule]):\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-b\",\n \"--bind\",\n default=\"0.0.0.0\",\n metavar=\"ADDRESS\",\n help=\"bind to this address \" \"(default: all interfaces)\",\n )\n parser.add_argument(\n \"-p\",\n \"--port\",\n default=8000,\n type=int,\n help=\"bind to this port \" \"(default: %(default)s)\",\n )\n args, _ = parser.parse_known_args()\n\n server = MagpieServer((args.bind, args.port), MagpieHandler, rules=rules)\n print(f\"Started http server: {args.bind}:{args.port}\")\n server.serve_forever()\n\n\nif __name__ == \"__main__\":\n run_server({})\n","repo_name":"vicalloy/magpie","sub_path":"magpie/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"22"}
+{"seq_id":"72055708535","text":"from unittest.mock import MagicMock\n\nimport numpy as np\nimport pytest\n\nfrom datumaro.components.annotation import AnnotationType, Bbox, LabelCategories\nfrom datumaro.components.dataset import Dataset, eager_mode\nfrom datumaro.components.dataset_base import DatasetItem\nfrom datumaro.components.media import Image\n\nfrom tests.requirements import Requirements, mark_requirement\n\n\nclass MissingAnnotationDetectionTest:\n @pytest.fixture\n def fxt_dataset(self) -> Dataset:\n return Dataset.from_iterable(\n [\n DatasetItem(\n id=\"item\",\n media=Image.from_numpy(np.zeros([2, 2, 3])),\n annotations=[\n Bbox(0, 0, 1, 1, label=0),\n Bbox(1, 0, 1, 1, label=1),\n Bbox(0, 1, 1, 1, label=2),\n ],\n )\n ],\n categories={\n AnnotationType.label: LabelCategories.from_iterable(\n [f\"label_{label_id}\" for label_id in range(3)]\n ),\n },\n )\n\n @pytest.fixture\n def fxt_launcher(self) -> MagicMock:\n gt_overlapped = Bbox(0, 0, 1, 1, label=1, attributes={\"score\": 1.0})\n missing_label = Bbox(1, 1, 1, 1, label=1, attributes={\"score\": 0.5})\n\n launcher_mock = MagicMock()\n launcher_mock.categories.return_value = None\n launcher_mock.launch.return_value = [[gt_overlapped, missing_label]]\n return launcher_mock\n\n @mark_requirement(Requirements.DATUM_GENERAL_REQ)\n @pytest.mark.parametrize(\"label_agnostic_matching, n_anns_expected\", [(True, 1), (False, 2)])\n def test_label_matching_flags(\n self,\n fxt_dataset: Dataset,\n fxt_launcher: MagicMock,\n label_agnostic_matching: bool,\n n_anns_expected: int,\n ):\n with eager_mode():\n dataset = fxt_dataset.transform(\n \"missing_annotation_detection\",\n launcher=fxt_launcher,\n label_agnostic_matching=label_agnostic_matching,\n )\n\n item = dataset.get(id=\"item\")\n\n assert len(item.annotations) == n_anns_expected\n\n @mark_requirement(Requirements.DATUM_GENERAL_REQ)\n @pytest.mark.parametrize(\"score_threshold, n_anns_expected\", [(0.6, 0), (0.4, 1)])\n def test_score_threshold(\n self,\n fxt_dataset: Dataset,\n fxt_launcher: MagicMock,\n score_threshold: float,\n n_anns_expected: int,\n ):\n with eager_mode():\n dataset = fxt_dataset.transform(\n \"missing_annotation_detection\",\n launcher=fxt_launcher,\n score_threshold=score_threshold,\n label_agnostic_matching=True,\n )\n\n item = dataset.get(id=\"item\")\n\n assert len(item.annotations) == n_anns_expected\n","repo_name":"openvinotoolkit/datumaro","sub_path":"tests/unit/test_missing_annotation_detection.py","file_name":"test_missing_annotation_detection.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","stars":435,"dataset":"github-code","pt":"22"}
+{"seq_id":"11462811235","text":"from math import sqrt\nfrom CLSColor import Color\nfrom CLSVector import Vector\nfrom CLSWireframe import Wireframe\n\n\nclass Line(Wireframe):\n def __init__(self, point1, point2, name=\"Line\"):\n super().__init__(name)\n\n self.point1 = point1\n self.point2 = point2\n self.Name = name\n self.Color = Color(124, 124, 124)\n self.Show = True\n self.Length = sqrt(pow((self.point2.X - self.point1.X), 2) +\n pow((self.point2.Y - self.point1.Y), 2) +\n pow((self.point2.Z - self.point1.Z), 2))\n self.Vector = Vector(self.point1, self.point2)","repo_name":"dankoman30/marc-20221005","sub_path":"CLSLine.py","file_name":"CLSLine.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43478459645","text":"from Lane import Lane\nimport globalV # global variables\nimport numpy as np\n\n# Initialize Road Network\n# we have a lane segment with 1km length\nln = Lane([], 1)\n\n# Run Simulation\nfor t in np.arange(0, globalV.sim_time, globalV.dt):\n # each time step, generate cars at the beginning of the lane\n ln.veh_arrival(ln.q, globalV.dt)\n\n # update all vehicles' speed and location on each lane\n for tempVeh in ln.cars:\n tempVeh.updateV()\n tempVeh.updateX()\n\n # each time step, remove vehicles reach the end of the lane\n ln.veh_transfer()\n\n # print('number of vehs', len(ln.cars), 'at time', t)\n if len(ln.cars) > 0:\n print('position of first car', ln.cars[0].x)\n print('velocity of first car', ln.cars[0].v)\n #print(t)\n\n","repo_name":"jiayi-ye/CSE-6730-ModelSim-Project2","sub_path":"Checkpoint Submission/Queuing Networking Model - Activity Scanning Checkpoint Model/Simulation.py","file_name":"Simulation.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72418647417","text":"import socket, random, threading, sys, time\nimport os\nimport binascii\n\ntry:\n target = str(sys.argv[1])\n threads = int(sys.argv[2])\n timer = float(sys.argv[3])\nexcept IndexError:\n print('\\n[+] command used: python ' + sys.argv[0] + ' ')\n\ntimeout = time.time() + 1 * timer\n\n\ndef attack():\n byte = binascii.hexlify(os.urandom(1024))\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n while time.time() < timeout:\n port = 80\n sock.sendto(byte * random.randint(5, 15), (target, port))\n return\n sys.exit()\n\n\nprint('\\n[+] Starting Attack...')\n\nfor x in range(0, threads):\n threading.Thread(target=attack()).start()\n\nprint('\\n[+] attack done')\n","repo_name":"duong-dx/botnet","sub_path":"Bot.py","file_name":"Bot.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34067704380","text":"import cv2\nimport torch\nimport numpy as np\nimport random\nfrom scipy.stats import truncnorm\nfrom src.config import cfg\n\nclass GANCollator():\n def __init__(self, netG, conditions, weights, channels, sobel, n_to_generate, gan_sampling, generator_batch_size,\n Y=\"concentration\", conc_intervals=cfg.data.concentration_intervals,\n input_type=\"noise\", truncated=False, only_fake=True, transforms=None, device=cfg.device):\n netG.to(device)\n netG.eval()\n self.Y = Y\n self.conc_intervals = conc_intervals\n self.netG = netG\n self.conditions = conditions\n self.weights = weights\n self.generator_batch_size = generator_batch_size\n self.channels = channels\n self.sobel = sobel\n self.n_to_generate = n_to_generate\n self.gan_sampling = gan_sampling\n self.truncated = truncated\n self.only_fake = only_fake\n self.input_type = input_type\n self.transforms = transforms\n self.device = device\n\n def __call__(self, data):\n real_images, real_labels = zip(*data)\n real_images = torch.tensor(np.array(real_images))\n real_labels = torch.tensor(np.array(real_labels))\n\n if real_labels.shape[1] > 0:\n n_to_generate = self.n_to_generate\n else:\n n_to_generate = len(real_labels)\n\n batches = [self.generator_batch_size] * (n_to_generate // self.generator_batch_size)\n batches.append(n_to_generate % self.generator_batch_size) if (n_to_generate % self.generator_batch_size) != 0 else None\n\n gen_imgs = torch.tensor([]).to(self.device)\n gen_labels = torch.tensor([]).to(self.device)\n if self.netG.Y == \"concentration\":\n if self.gan_sampling == \"random\":\n min_conc = self.conditions.min().item()\n max_conc = self.conditions.max().item()\n for batch in batches:\n z = get_input_noise(real_images, batch, self.netG.nz, self.truncated, self.input_type).to(self.device)\n cond = torch.randint(min_conc, max_conc, (batch, 1), dtype=torch.float32, device=self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n \n elif self.gan_sampling == \"skewed_random\":\n min_conc = self.conditions.min().item()\n max_conc = self.conditions.max().item()\n concentration_values = np.arange(min_conc,max_conc,1)\n for batch in batches:\n z = get_input_noise(real_images, batch, self.netG.nz, self.truncated, self.input_type).to(self.device)\n cond = random.choices(concentration_values, weights=1/(concentration_values+max_conc/10), k=batch)\n cond = torch.tensor(cond).unsqueeze(1).float().to(self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n\n elif self.gan_sampling == \"weighted\":\n for batch in batches:\n z = get_input_noise(real_images, batch, self.netG.nz, self.truncated, self.input_type).to(self.device)\n idx = torch.multinomial(self.weights, batch, replacement=True)\n cond = self.conditions[idx].unsqueeze(1).float().to(self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n \n elif self.gan_sampling == \"weighted_interval\":\n for batch in batches:\n z = get_input_noise(real_images, batch, self.netG.nz, self.truncated, self.input_type).to(self.device)\n idx = torch.multinomial(self.weights, batch, replacement=True)\n cond = self.conditions[idx].float()\n vary_cond = (0.75 - 1.25) * torch.rand_like(cond) + 1.25\n cond = (cond*vary_cond).unsqueeze(1).int().float().to(self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n \n elif self.gan_sampling == \"weighted_interval_2\":\n for batch in batches:\n z = get_input_noise(real_images, batch, self.netG.nz, self.truncated, self.input_type).to(self.device)\n #conditions = torch.tensor([0, 5, 9, 20, 100, 400, 800, 3, 7, 15, 50, 200, 600], dtype=torch.float32)\n conditions = torch.tensor([0,5,10,15,20,30,35,80,113,218,580,1000,12,60,105,160,450], dtype=torch.float32)\n idx = torch.randint(0, len(conditions), (batch, ))\n cond = conditions[idx].unsqueeze(1).float().to(self.device)\n #vary_cond = (0.75 - 1.25) * torch.rand_like(cond) + 1.25\n #cond = (cond*vary_cond).unsqueeze(1).int().float().to(self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n \n elif self.gan_sampling.startswith(\"restricted\"):\n low = int(self.gan_sampling.split(\"_\")[1])\n high = int(self.gan_sampling.split(\"_\")[2])\n for batch in batches:\n z = get_input_noise(real_images, batch, self.netG.nz, self.truncated, self.input_type).to(self.device)\n cond = torch.randint(low, high, (batch, 1), dtype=torch.float32, device=self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n\n elif self.netG.Y == \"label\":\n unique_labels, counts = real_labels.unique(return_counts=True)\n\n if self.gan_sampling == \"balanced\":\n samples_per_label = int((len(real_labels) + self.n_to_generate ) / len(unique_labels))\n excess_samples = (len(real_labels) + self.n_to_generate) - (samples_per_label * len(unique_labels))\n extra_sample = random.sample(unique_labels.tolist(), excess_samples)\n all_conds = torch.tensor([])\n for label, count in zip(unique_labels, counts):\n n_to_generate = samples_per_label - count\n if label in extra_sample:\n n_to_generate += 1\n cond = torch.ones(n_to_generate, dtype=torch.float32, device=self.device)*label\n all_conds = torch.concat([all_conds, cond])\n\n prev_batch = 0\n for batch in batches:\n z = get_input_noise(batch, self.netG.nz, self.truncated).to(self.device)\n cond = all_conds[prev_batch:batch]\n prev_batch = batch\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n\n elif self.gan_sampling == \"real_frequencies\":\n for batch in batches:\n z = get_input_noise(batch, self.netG.nz, self.truncated).to(self.device)\n cond = torch.multinomial(self.weights, batch, replacement=True).to(self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n\n elif self.gan_sampling == \"random\":\n for batch in batches:\n z = get_input_noise(batch, self.netG.nz, self.truncated).to(self.device)\n cond = torch.randint(0, self.netG.n_classes, batch, replacement=True).to(self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n\n elif self.gan_sampling == \"weighted\":\n for batch in batches:\n z = get_input_noise(batch, self.netG.nz, self.truncated).to(self.device)\n cond = torch.multinomial(self.weights, batch, replacement=True).to(self.device)\n gen_imgs = torch.concat([gen_imgs, self.netG(z, cond)])\n gen_labels = torch.concat([gen_labels, cond])\n\n if self.sobel:\n gen_imgs = (gen_imgs - gen_imgs.min()) / (gen_imgs.max() - gen_imgs.min())\n gen_imgs = (gen_imgs * 255).cpu().detach().numpy()[:,0,:,:]\n sobel_gen_imgs = np.array([])\n for image in gen_imgs:\n sobel_image = np.expand_dims(cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=5), (0,1))\n sobel_gen_imgs = np.concatenate([sobel_gen_imgs, sobel_image]) if len(sobel_gen_imgs) > 0 else sobel_image\n gen_imgs = sobel_gen_imgs - np.min(sobel_gen_imgs)\n gen_imgs = gen_imgs / (np.max(gen_imgs)- np.min(sobel_gen_imgs))\n else:\n gen_imgs = (gen_imgs - torch.amin(gen_imgs, dim=(1,2,3), keepdim=True))/(torch.amax(gen_imgs, dim=(1,2,3), keepdim=True) - torch.amin(gen_imgs, dim=(1,2,3), keepdim=True))\n gen_imgs = gen_imgs.cpu().detach().numpy()\n\n gen_imgs = (gen_imgs-0.5) / 0.5\n\n if self.transforms:\n transformed_gen_imgs = np.array([])\n for image in gen_imgs:\n data = self.transforms(image=image)\n image = data['image']\n transformed_gen_imgs = np.concatenate([transformed_gen_imgs, image]) if len(transformed_gen_imgs) > 0 else image\n gen_imgs = transformed_gen_imgs\n\n gen_imgs = np.repeat(gen_imgs, self.channels, axis=1)\n\n gen_imgs = torch.tensor(gen_imgs)\n gen_labels = gen_labels.cpu()\n\n if self.Y == \"label\":\n y=0\n for conc in self.conc_intervals:\n lower_conc, higher_conc = conc.split(\"-\")\n if higher_conc == \"inf\":\n higher_conc = np.inf\n else:\n higher_conc = int(higher_conc)\n gen_labels[(gen_labels >= int(lower_conc)) & (gen_labels <= higher_conc)] = y\n y+=1\n gen_labels = gen_labels.type(torch.LongTensor)\n gen_labels = gen_labels.squeeze(1)\n\n if self.only_fake:\n images = gen_imgs\n labels = gen_labels\n real_fake_labels = torch.zeros_like(gen_labels, dtype=torch.float32)\n else:\n idx = torch.randperm(real_labels.shape[0] + gen_labels.shape[0])\n images = torch.concat([real_images, gen_imgs])[idx]\n labels = torch.concat([real_labels, gen_labels])[idx]\n real_fake_labels = torch.concat([torch.ones_like(real_labels, dtype=torch.float32), torch.zeros_like(gen_labels, dtype=torch.float32)])[idx]\n\n return images, labels, real_fake_labels\n \ndef get_input_noise(real_images, batch, z_dim, truncated=False, input_type=\"noise\"):\n if input_type == \"noise\":\n if truncated:\n z = torch.tensor(truncnorm.rvs(-1.5, 1.5, size=(batch, z_dim)))\n else:\n z = torch.randn(batch, z_dim)\n elif input_type == \"img\":\n if len(real_images) >= batch:\n idx = torch.randperm(len(real_images))[:batch]\n else:\n idx = torch.randint(len(real_images), (batch,))\n z = real_images[idx]\n return z\n\n","repo_name":"ALROAL/gan-data-augmentation","sub_path":"src/data/gan_collator.py","file_name":"gan_collator.py","file_ext":"py","file_size_in_byte":11452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34439077706","text":"\"\"\"\nWritten by Matteo Dunnhofer - 2017\n\nHelper functions and procedures\n\"\"\"\nimport os\nimport random\nimport tensorflow as tf\nimport numpy as np\nfrom scipy.io import loadmat\nimport cv2\n\n\n# ############### TensorFlow standard operations wrappers #####################\ndef weight(shape, name):\n initial = tf.truncated_normal(shape, stddev=0.01)\n w = tf.Variable(initial, name=name)\n tf.add_to_collection('weights', w)\n return w\n\n\ndef bias(value, shape, name):\n initial = tf.constant(value, shape=shape)\n return tf.Variable(initial, name=name)\n\n\ndef conv2d(x, W, stride, padding):\n return tf.nn.conv2d(x, W, strides=[1, stride[0], stride[1], 1], padding=padding)\n\n\ndef max_pool2d(x, kernel, stride, padding):\n return tf.nn.max_pool(x, ksize=kernel, strides=stride, padding=padding)\n\n\ndef lrn(x, depth_radius, bias, alpha, beta):\n return tf.nn.local_response_normalization(x, depth_radius, bias, alpha, beta)\n\n\ndef relu(x):\n return tf.nn.relu(x)\n\n\ndef batch_norm(x):\n epsilon = 1e-3\n batch_mean, batch_var = tf.nn.moments(x, [0])\n return tf.nn.batch_normalization(x, batch_mean, batch_var, None, None, epsilon)\n\n\n# ############### batch creation functions #####################\n\ndef onehot(index):\n \"\"\" It creates a one-hot vector with a 1.0 in\n position represented by index\n \"\"\"\n onehot = np.zeros(1000)\n onehot[index] = 1.0\n return onehot\n\n\ndef read_batch(batch_size, images_source, wnid_labels):\n \"\"\" It returns a batch of single images (no data-augmentation)\n\n ILSVRC 2012 training set folder should be srtuctured like this:\n ILSVRC2012_img_train\n |_n01440764\n |_n01443537\n |_n01484850\n |_n01491361\n |_ ...\n\n Args:\n batch_size: need explanation? :)\n images_sources: path to ILSVRC 2012 training set folder\n wnid_labels: list of ImageNet wnid lexicographically ordered\n\n Returns:\n batch_images: a tensor (numpy array of images) of shape [batch_size, width, height, channels]\n batch_labels: a tensor (numpy array of onehot vectors) of shape [batch_size, 1000]\n \"\"\"\n batch_images = []\n batch_labels = []\n\n for i in range(batch_size):\n # random class choice\n # (randomly choose a folder of image of the same class from a list of previously sorted wnids)\n class_index = random.randint(0, 999)\n\n folder = wnid_labels[class_index]\n batch_images.append(read_image(os.path.join(images_source, folder)))\n batch_labels.append(onehot(class_index))\n\n np.vstack(batch_images)\n np.vstack(batch_labels)\n return batch_images, batch_labels\n\n\ndef read_image(images_folder):\n \"\"\" It reads a single image file into a numpy array and preprocess it\n\n Args:\n images_folder: path where to random choose an image\n\n Returns:\n im_array: the numpy array of the image [width, height, channels]\n \"\"\"\n # random image choice inside the folder\n # (randomly choose an image inside the folder)\n image_path = os.path.join(images_folder, random.choice(os.listdir(images_folder)))\n\n # load and normalize image\n im_array = preprocess_image(image_path)\n\n return im_array\n\n\ndef preprocess_image(image_path):\n \"\"\" It reads an image, it resize it to have the lowest dimesnion of 256px,\n it randomly choose a 224x224 crop inside the resized image and normilize the numpy\n array subtracting the ImageNet training set mean\n\n Args:\n images_path: path of the image\n\n Returns:\n cropped_im_array: the numpy array of the image normalized [width, height, channels]\n \"\"\"\n IMAGENET_MEAN = np.array([104., 117., 124.])\n\n img = cv2.imread(image_path)\n height, width, _ = img.shape\n # if np.random.random()<0.5:\n # img = cv2.flip(img,1)\n # resize of the image (setting lowest dimension to 256px)\n if width < height:\n h = int(float(256 * height) / width)\n img = cv2.resize(img, (256, h), interpolation=cv2.INTER_AREA)\n else:\n w = int(float(256 * width) / height)\n img = cv2.resize(img, (w, 256), interpolation=cv2.INTER_AREA)\n img = img.astype(np.float32)\n # random 244x224 patch\n height, width, _ = img.shape\n x = random.randint(0, width - 224)\n y = random.randint(0, height - 224)\n img_cropped = img[y:y + 224, x:x + 224]\n img_cropped -= IMAGENET_MEAN\n\n return img_cropped\n\n\ndef read_k_patches(image_path, k):\n \"\"\" It reads k random crops from an image\n\n Args:\n images_path: path of the image\n k: number of random crops to take\n\n Returns:\n patches: a tensor (numpy array of images) of shape [k, 224, 224, 3]\n\n \"\"\"\n IMAGENET_MEAN = np.array([104., 117., 124.])\n img = cv2.imread(image_path)\n height, width, _ = img.shape\n #\tif self.horizontal and np.random.random()<0.5:\n # \timg = cv2.flip(img,1)\n # resize of the image (setting lowest dimension to 256px)\n if width < height:\n h = int(float(256 * height) / width)\n img = cv2.resize(img, (256, h), interpolation=cv2.INTER_AREA)\n else:\n w = int(float(256 * width) / height)\n img = cv2.resize(img, (w, 256), interpolation=cv2.INTER_AREA)\n img = img.astype(np.float32)\n # random 244x224 patch\n\n\n\n\n\n patches = []\n for i in range(k):\n # random 244x224 patch\n height, width, _ = img.shape\n x = random.randint(0, width - 224)\n y = random.randint(0, height - 224)\n img_cropped = img[y:y + 224, x:x + 224]\n img_cropped -= IMAGENET_MEAN\n patches.append(cropped_im_array)\n\n np.vstack(patches)\n return patches\n\n\n\"\"\" reading a batch of validation images from the validation set, \n groundthruths label are inside an annotations file \"\"\"\n\n\ndef read_validation_batch(batch_size, validation_source, annotations):\n batch_images_val = []\n batch_labels_val = []\n\n images_val = sorted(os.listdir(validation_source))\n\n # reading groundthruths labels\n with open(annotations) as f:\n gt_idxs = f.readlines()\n gt_idxs = [(int(x.strip()) - 1) for x in gt_idxs]\n\n for i in range(batch_size):\n # random image choice\n idx = random.randint(0, len(images_val) - 1)\n\n image = images_val[idx]\n batch_images_val.append(preprocess_image(os.path.join(validation_source, image)))\n batch_labels_val.append(onehot(gt_idxs[idx]))\n\n np.vstack(batch_images_val)\n np.vstack(batch_labels_val)\n return batch_images_val, batch_labels_val\n\n\n# ############### Other helper procedures #####################\n\ndef load_imagenet_meta(meta_path):\n \"\"\" It reads ImageNet metadata from ILSVRC 2012 dev tool file\n\n Args:\n meta_path: path to ImageNet metadata file\n\n Returns:\n wnids: list of ImageNet wnids labels (as strings)\n words: list of words (as strings) referring to wnids labels and describing the classes\n\n \"\"\"\n metadata = loadmat(meta_path, struct_as_record=False)\n\n # ['ILSVRC2012_ID', 'WNID', 'words', 'gloss', 'num_children', 'children', 'wordnet_height', 'num_train_images']\n synsets = np.squeeze(metadata['synsets'])\n ids = np.squeeze(np.array([s.ILSVRC2012_ID for s in synsets]))\n wnids = np.squeeze(np.array([s.WNID for s in synsets]))\n words = np.squeeze(np.array([s.words for s in synsets]))\n return wnids, words\n\n\ndef read_test_labels(annotations_path):\n \"\"\" It reads groundthruth labels from ILSRVC 2012 annotations file\n\n Args:\n annotations_path: path to the annotations file\n\n Returns:\n gt_labels: a numpy vector of onehot labels\n \"\"\"\n gt_labels = []\n\n # reading groundthruths labels from ilsvrc12 annotations file\n with open(annotations_path) as f:\n gt_idxs = f.readlines()\n gt_idxs = [(int(x.strip()) - 1) for x in gt_idxs]\n\n for gt in gt_idxs:\n gt_labels.append(onehot(gt))\n\n np.vstack(gt_labels)\n\n return gt_labels\n\n\ndef format_time(time):\n \"\"\" It formats a datetime to print it\n\n Args:\n time: datetime\n\n Returns:\n a formatted string representing time\n \"\"\"\n m, s = divmod(time, 60)\n h, m = divmod(m, 60)\n d, h = divmod(h, 24)\n return ('{:02d}d {:02d}h {:02d}m {:02d}s').format(int(d), int(h), int(m), int(s))\n\n\ndef imagenet_size(im_source):\n \"\"\" It calculates the number of examples in ImageNet training-set\n\n Args:\n im_source: path to ILSVRC 2012 training set folder\n\n Returns:\n n: the number of training examples\n\n \"\"\"\n n = 0\n for d in os.listdir(im_source):\n for f in os.listdir(os.path.join(im_source, d)):\n n += 1\n return n\n","repo_name":"JenifferWuUCLA/pulmonary-nodules-MaskRCNN","sub_path":"Github-Codes/TensorFlow-CNN-Dataset/alexnet/codes/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":8778,"program_lang":"python","lang":"en","doc_type":"code","stars":203,"dataset":"github-code","pt":"22"}
+{"seq_id":"40142743976","text":"import gym\nimport os\nimport sys\nimport datetime\nimport rospkg\nimport rospy\n\nfrom baselines.ppo2 import ppo2\nfrom baselines.bench import Monitor\nfrom baselines import logger\nfrom baselines.common.cmd_util import make_vec_env\n\ntimestamp = datetime.datetime.now().strftime('%Y-%m-%d_%Hh%Mmin')\nrospack = rospkg.RosPack()\nEnv_path = rospack.get_path('pickbot_training') + \"/src/2_Environment\"\nsys.path.insert(0, Env_path)\nimport pickbot_env_npstate\nimport gazebo_connection\n\n\"\"\"\nmake_vec_env(env_id, env_type, num_env, seed,\n wrapper_kwargs=None,\n start_index=0,\n reward_scale=1.0,\n flatten_dict_observations=True,\n gamestate=None):\n\"\"\"\n\nnum_env = 1\nenv_id = \"Pickbot-v0\"\nenv_type = \"classic_control\"\nseed = None\n\n# Create needed folders\nlogdir = './log/' + env_id + '/ppo2/' + timestamp\n\n# Generate tensorboard file\nformat_strs = ['stdout', 'log', 'csv', 'tensorboard']\nlogger.configure(os.path.abspath(logdir), format_strs)\n\n\ndef main():\n # unpause Simulation so that robot receives data on all topics\n gazebo_connection.GazeboConnection().unpauseSim()\n # create node\n rospy.init_node('pickbot_gym', anonymous=True, log_level=rospy.FATAL)\n\n env = make_vec_env(env_id, env_type, num_env, seed,\n wrapper_kwargs=Monitor,\n start_index=0,\n reward_scale=1.0,\n flatten_dict_observations=True,\n gamestate=None)\n\n act = ppo2.learn(\n env=env,\n network='mlp',\n total_timesteps=10000\n )\n print(\"Saving model to pickbot_model_\" + str(timestamp))\n act.save(\"./pickbot_model_\" + str(timestamp))\n # Environment Object: \n\n\nif __name__ == '__main__':\n main()\n","repo_name":"auliyafitri/AI-robot-control","sub_path":"experiments/reach/PPO2/train_pickbot-v0_PPO2_discrete.py","file_name":"train_pickbot-v0_PPO2_discrete.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"16609494260","text":"import requests\nimport sys as s\nfrom bs4 import BeautifulSoup\n\n\ndef GetTheWeather():\n if len(s.argv) == 2:\n by = s.argv[1]\n else:\n by = 'ams'\n\n URL = ''\n if(by == 'ams' or by == 'amsterdam'):\n URL = 'https://www.yr.no/sted/Nederland/Nord-Holland/Amsterdam/time_for_time.html'\n by = 'AMSTERDAM'\n elif(by == 'be' or by == 'bergen'):\n URL = 'https://www.yr.no/sted/Norge/Vestland/Bergen/Bergen/time_for_time.html'\n by = 'BERGEN'\n elif(by == 'os' or by == 'oslo'):\n URL = 'https://www.yr.no/place/Norway/Oslo/Oslo/Oslo/hour_by_hour.html'\n by = 'OSLO'\n else:\n assert('No city was selected')\n\n # Get Info\n page = requests.get(URL)\n\n # Pretty\n soup = BeautifulSoup(page.content, 'html.parser')\n\n # Print\n kls = soup.find_all('td', scope=\"row\")\n mms = soup.find_all('td', class_='precipitation')\n cels = soup.find_all('td', class_=\"temperature plus\")\n print('This is the weather for the next days:')\n print('--------------------------------------')\n print(by)\n print('--------------------------------------')\n for cel, kl, mm in zip(cels, kls, mms):\n print(kl.text + \"\\t \\t\" + cel.text + \"\\t\" + mm.text)\n\n\nGetTheWeather()\n","repo_name":"TorsteinTh/WebScraping","sub_path":"fwebscraper.py","file_name":"fwebscraper.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17657077098","text":"import struct\r\nimport zlib\r\nfrom math import sqrt\r\n\r\nfrom .EmbConstant import *\r\nfrom .EmbThread import EmbThread\r\n\r\nSEQUIN_CONTINGENCY = CONTINGENCY_SEQUIN_STITCH\r\nFULL_JUMP = True\r\n\r\n# Static characters for writing to image.\r\ncharacters = {\r\n \"0\": [\r\n [9, 9, 4, 1, 0, 1, 5, 9, 9],\r\n [9, 3, 0, 0, 0, 0, 0, 5, 9],\r\n [7, 0, 1, 7, 9, 6, 0, 0, 9],\r\n [4, 0, 5, 9, 9, 9, 4, 0, 6],\r\n [3, 0, 8, 9, 9, 9, 6, 0, 4],\r\n [2, 0, 9, 5, 0, 6, 8, 0, 3],\r\n [2, 0, 9, 4, 0, 6, 8, 0, 3],\r\n [2, 0, 9, 9, 9, 9, 8, 0, 3],\r\n [3, 0, 8, 9, 9, 9, 6, 0, 4],\r\n [4, 0, 5, 9, 9, 9, 4, 0, 6],\r\n [7, 0, 1, 7, 9, 6, 0, 0, 8],\r\n [9, 3, 0, 0, 0, 0, 0, 5, 9],\r\n [9, 9, 4, 1, 0, 1, 5, 9, 9],\r\n ],\r\n \"1\": [\r\n [9, 8, 5, 2, 0, 1, 9, 9, 9],\r\n [9, 1, 0, 0, 0, 1, 9, 9, 9],\r\n [9, 2, 4, 7, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 9, 9, 9, 1, 1, 9, 9, 9],\r\n [9, 3, 0, 0, 0, 0, 0, 0, 3],\r\n [9, 3, 0, 0, 0, 0, 0, 0, 3],\r\n ],\r\n \"2\": [\r\n [8, 5, 2, 1, 1, 2, 6, 9, 9],\r\n [4, 0, 0, 0, 0, 0, 0, 4, 9],\r\n [5, 4, 7, 8, 8, 5, 0, 0, 8],\r\n [9, 9, 9, 9, 9, 9, 3, 0, 7],\r\n [9, 9, 9, 9, 9, 9, 3, 0, 9],\r\n [9, 9, 9, 9, 9, 8, 0, 4, 9],\r\n [9, 9, 9, 9, 8, 1, 2, 9, 9],\r\n [9, 9, 9, 8, 2, 2, 9, 9, 9],\r\n [9, 9, 8, 1, 2, 9, 9, 9, 9],\r\n [9, 8, 1, 2, 9, 9, 9, 9, 9],\r\n [7, 1, 2, 9, 9, 9, 9, 9, 9],\r\n [3, 0, 0, 0, 0, 0, 0, 0, 6],\r\n [3, 0, 0, 0, 0, 0, 0, 0, 6],\r\n ],\r\n \"3\": [\r\n [9, 6, 2, 1, 1, 2, 6, 9, 9],\r\n [5, 0, 0, 0, 0, 0, 0, 4, 9],\r\n [6, 3, 7, 8, 9, 6, 1, 0, 8],\r\n [9, 9, 9, 9, 9, 9, 3, 0, 8],\r\n [9, 9, 9, 9, 8, 6, 1, 1, 9],\r\n [9, 9, 4, 0, 0, 0, 2, 8, 9],\r\n [9, 9, 4, 0, 0, 0, 2, 8, 9],\r\n [9, 9, 9, 9, 8, 6, 1, 1, 8],\r\n [9, 9, 9, 9, 9, 9, 5, 0, 6],\r\n [9, 9, 9, 9, 9, 9, 5, 0, 5],\r\n [3, 5, 7, 9, 8, 6, 1, 0, 7],\r\n [2, 0, 0, 0, 0, 0, 0, 3, 9],\r\n [8, 4, 2, 0, 1, 2, 5, 9, 9],\r\n ],\r\n \"4\": [\r\n [9, 9, 9, 9, 7, 0, 0, 7, 9],\r\n [9, 9, 9, 9, 2, 1, 0, 7, 9],\r\n [9, 9, 9, 5, 2, 4, 0, 7, 9],\r\n [9, 9, 8, 1, 7, 4, 0, 7, 9],\r\n [9, 9, 3, 4, 9, 4, 0, 7, 9],\r\n [9, 6, 1, 9, 9, 4, 0, 7, 9],\r\n [8, 1, 6, 9, 9, 4, 0, 7, 9],\r\n [4, 3, 9, 9, 9, 4, 0, 7, 9],\r\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\r\n [0, 0, 0, 0, 0, 0, 0, 0, 0],\r\n [9, 9, 9, 9, 9, 4, 0, 7, 9],\r\n [9, 9, 9, 9, 9, 4, 0, 7, 9],\r\n [9, 9, 9, 9, 9, 4, 0, 7, 9],\r\n ],\r\n \"5\": [\r\n [7, 0, 0, 0, 0, 0, 0, 5, 9],\r\n [7, 0, 0, 0, 0, 0, 0, 5, 9],\r\n [7, 0, 5, 9, 9, 9, 9, 9, 9],\r\n [7, 0, 5, 9, 9, 9, 9, 9, 9],\r\n [7, 0, 1, 1, 0, 2, 6, 9, 9],\r\n [7, 0, 0, 0, 0, 0, 0, 5, 9],\r\n [7, 4, 7, 9, 8, 4, 0, 0, 9],\r\n [9, 9, 9, 9, 9, 9, 3, 0, 6],\r\n [9, 9, 9, 9, 9, 9, 5, 0, 6],\r\n [9, 9, 9, 9, 9, 9, 3, 0, 6],\r\n [3, 5, 8, 9, 8, 5, 0, 0, 9],\r\n [2, 0, 0, 0, 0, 0, 0, 6, 9],\r\n [8, 4, 1, 0, 1, 2, 7, 9, 9],\r\n ],\r\n \"6\": [\r\n [9, 9, 7, 2, 1, 0, 3, 8, 9],\r\n [9, 6, 0, 0, 0, 0, 0, 3, 9],\r\n [8, 0, 0, 4, 8, 9, 6, 4, 9],\r\n [5, 0, 4, 9, 9, 9, 9, 9, 9],\r\n [3, 0, 8, 9, 9, 9, 9, 9, 9],\r\n [2, 1, 8, 3, 0, 1, 3, 8, 9],\r\n [2, 2, 2, 0, 0, 0, 0, 1, 8],\r\n [2, 0, 2, 7, 9, 8, 2, 0, 5],\r\n [3, 0, 7, 9, 9, 9, 7, 0, 3],\r\n [4, 0, 7, 9, 9, 9, 7, 0, 3],\r\n [7, 0, 2, 7, 9, 8, 2, 0, 5],\r\n [9, 3, 0, 0, 0, 0, 0, 1, 8],\r\n [9, 9, 4, 1, 0, 1, 3, 8, 9],\r\n ],\r\n \"7\": [\r\n [2, 0, 0, 0, 0, 0, 0, 0, 5],\r\n [2, 0, 0, 0, 0, 0, 0, 0, 7],\r\n [9, 9, 9, 9, 9, 9, 3, 2, 9],\r\n [9, 9, 9, 9, 9, 8, 0, 5, 9],\r\n [9, 9, 9, 9, 9, 4, 0, 8, 9],\r\n [9, 9, 9, 9, 9, 1, 3, 9, 9],\r\n [9, 9, 9, 9, 6, 0, 6, 9, 9],\r\n [9, 9, 9, 9, 2, 1, 9, 9, 9],\r\n [9, 9, 9, 7, 0, 4, 9, 9, 9],\r\n [9, 9, 9, 3, 0, 8, 9, 9, 9],\r\n [9, 9, 8, 0, 2, 9, 9, 9, 9],\r\n [9, 9, 5, 0, 6, 9, 9, 9, 9],\r\n [9, 9, 1, 0, 9, 9, 9, 9, 9],\r\n ],\r\n \"8\": [\r\n [9, 8, 3, 1, 0, 1, 4, 8, 9],\r\n [8, 1, 0, 0, 0, 0, 0, 1, 9],\r\n [5, 0, 3, 8, 9, 7, 2, 0, 6],\r\n [5, 0, 7, 9, 9, 9, 5, 0, 6],\r\n [7, 0, 3, 8, 9, 7, 2, 1, 8],\r\n [9, 7, 2, 0, 0, 0, 2, 7, 9],\r\n [9, 5, 1, 0, 0, 0, 1, 6, 9],\r\n [6, 0, 3, 8, 9, 7, 2, 0, 7],\r\n [2, 0, 9, 9, 9, 9, 7, 0, 4],\r\n [2, 0, 9, 9, 9, 9, 7, 0, 3],\r\n [3, 0, 3, 8, 9, 7, 2, 0, 4],\r\n [7, 0, 0, 0, 0, 0, 0, 1, 8],\r\n [9, 8, 3, 1, 0, 1, 4, 8, 9],\r\n ],\r\n \"9\": [\r\n [9, 7, 3, 0, 0, 2, 6, 9, 9],\r\n [7, 0, 0, 0, 0, 0, 0, 5, 9],\r\n [3, 0, 4, 8, 9, 7, 1, 0, 9],\r\n [1, 0, 9, 9, 9, 9, 5, 0, 6],\r\n [1, 1, 9, 9, 9, 9, 5, 0, 5],\r\n [3, 0, 4, 8, 9, 7, 1, 0, 4],\r\n [7, 0, 0, 0, 0, 0, 3, 0, 4],\r\n [9, 7, 2, 0, 1, 4, 7, 0, 4],\r\n [9, 9, 9, 9, 9, 9, 5, 0, 5],\r\n [9, 9, 9, 9, 9, 9, 2, 0, 7],\r\n [9, 3, 7, 9, 8, 3, 0, 2, 9],\r\n [9, 1, 0, 0, 0, 0, 0, 7, 9],\r\n [9, 7, 2, 0, 1, 3, 8, 9, 9],\r\n ],\r\n \"-\": [\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 1, 0, 0, 0, 3, 9, 9],\r\n [9, 9, 1, 0, 0, 0, 3, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n ],\r\n \"m\": [\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [9, 9, 9, 9, 9, 9, 9, 9, 9],\r\n [0, 5, 2, 1, 7, 6, 1, 2, 8],\r\n [0, 1, 0, 0, 1, 1, 0, 0, 3],\r\n [0, 2, 9, 4, 0, 4, 8, 2, 1],\r\n [0, 5, 9, 6, 0, 7, 9, 4, 1],\r\n [0, 5, 9, 6, 0, 7, 9, 4, 0],\r\n [0, 5, 9, 6, 0, 7, 9, 4, 0],\r\n [0, 5, 9, 6, 0, 7, 9, 4, 0],\r\n [0, 5, 9, 6, 0, 7, 9, 4, 0],\r\n [0, 5, 9, 6, 0, 7, 9, 4, 0],\r\n [0, 5, 9, 6, 0, 7, 9, 4, 0],\r\n ],\r\n}\r\n\r\n\r\ndef write_png(buf, width, height):\r\n \"\"\"\r\n Writes PNG file to disk. Buffer must be RGBA * width * height\r\n \"\"\"\r\n width_byte_4 = width * 4\r\n raw_data = b\"\".join(\r\n b\"\\x00\" + buf[span : span + width_byte_4]\r\n for span in range(0, height * width * 4, width_byte_4)\r\n )\r\n\r\n def png_pack(png_tag, data):\r\n chunk_head = png_tag + data\r\n return (\r\n struct.pack(\"!I\", len(data))\r\n + chunk_head\r\n + struct.pack(\"!I\", 0xFFFFFFFF & zlib.crc32(chunk_head))\r\n )\r\n\r\n return b\"\".join(\r\n [\r\n b\"\\x89PNG\\r\\n\\x1a\\n\",\r\n png_pack(b\"IHDR\", struct.pack(\"!2I5B\", width, height, 8, 6, 0, 0, 0)),\r\n png_pack(b\"IDAT\", zlib.compress(raw_data, 9)),\r\n png_pack(b\"IEND\", b\"\"),\r\n ]\r\n )\r\n\r\n\r\nclass PngBuffer:\r\n def __init__(self, width, height):\r\n self.width = int(width + 3)\r\n self.height = int(height + 3)\r\n self.buf = bytearray(4 * self.width * self.height)\r\n self.line_width = 3\r\n self.fancy = True\r\n self._red = 0\r\n self._green = 0\r\n self._blue = 0\r\n self._alpha = 0\r\n self._distance_from_black = 0\r\n self._gradient_shade_ends = 0.65\r\n self._gradient_shade_edge = 1.1\r\n self._gradient_shade_center = 1.55\r\n self._gradient_color_position1 = 0.40\r\n self._gradient_color_position2 = 0.50\r\n self._gradient_color_position3 = 0.70\r\n\r\n def modify_gradient(\r\n self,\r\n gradient_shade_ends=0.65,\r\n gradient_shade_edge=1.1,\r\n gradient_shade_center=1.55,\r\n gradient_color_position1=0.40,\r\n gradient_color_position2=0.50,\r\n gradient_color_position3=0.70,\r\n ):\r\n self._gradient_shade_ends = gradient_shade_ends\r\n self._gradient_shade_edge = gradient_shade_edge\r\n self._gradient_shade_center = gradient_shade_center\r\n self._gradient_color_position1 = gradient_color_position1\r\n self._gradient_color_position2 = gradient_color_position2\r\n self._gradient_color_position3 = gradient_color_position3\r\n\r\n def set_color(self, r, g, b, a=255):\r\n self._red = r\r\n self._green = g\r\n self._blue = b\r\n self._alpha = a\r\n rmean = int(r / 2)\r\n self._distance_from_black = sqrt(\r\n (((512 + rmean) * r * r) >> 8) + 4 * g * g + (((767 - rmean) * b * b) >> 8)\r\n )\r\n\r\n def gradient(self, position_in_line):\r\n \"\"\"\r\n This function gives different value scales between 0 and 1 which are used to multiply\r\n all the components of the color to create a darker value. This generally works save for\r\n black which is already (0,0,0) and can't increase value.\r\n \"\"\"\r\n if (\r\n position_in_line <= self._gradient_color_position1\r\n ): # start_of_grdient -> Position 1\r\n from_shade = self._gradient_shade_ends\r\n to_shade = self._gradient_shade_edge\r\n range = self._gradient_color_position1 - 0 # Range of transition\r\n amount = (position_in_line - 0) * (1 / range)\r\n\r\n # light to extra_light\r\n elif (\r\n position_in_line <= self._gradient_color_position2\r\n ): # Position 1 -> Position 2.\r\n from_shade = self._gradient_shade_edge\r\n to_shade = self._gradient_shade_center\r\n range = self._gradient_color_position2 - self._gradient_color_position1\r\n amount = (position_in_line - self._gradient_color_position1) * (1 / range)\r\n\r\n # center of gradient\r\n\r\n # extra_light to light\r\n elif (\r\n position_in_line <= self._gradient_color_position3\r\n ): # Position 2 -> Position 3\r\n from_shade = self._gradient_shade_center\r\n to_shade = self._gradient_shade_edge\r\n range = self._gradient_color_position3 - self._gradient_color_position2\r\n amount = (position_in_line - self._gradient_color_position2) * (1 / range)\r\n\r\n # light to dark\r\n elif position_in_line <= 1: # Position 3 -> end_of_gradient\r\n from_shade = self._gradient_shade_edge\r\n to_shade = self._gradient_shade_ends\r\n range = 1 - self._gradient_color_position3\r\n amount = (position_in_line - self._gradient_color_position3) * (1 / range)\r\n else:\r\n raise ValueError(\"Did not occur within line.\")\r\n v = amount * (to_shade - from_shade) + from_shade\r\n return max(min(v, 1.0), 0.0)\r\n\r\n def background(self, red, green, blue, alpha):\r\n for i in range(0, len(self.buf), 4):\r\n self.buf[i] = red\r\n self.buf[i + 1] = green\r\n self.buf[i + 2] = blue\r\n self.buf[i + 3] = alpha\r\n\r\n def plot(self, x, y, v=None, a=None):\r\n \"\"\"\r\n Plot a particular point in the canvas.\r\n :param x: x position to plot\r\n :param y: y position to plot\r\n :param v: value scale of particular color (0-1)\r\n :param a: alpha to set for this particular plot if not self.alpha\r\n :return:\r\n \"\"\"\r\n a = int(a) if a is not None else self._alpha\r\n if v is None:\r\n v = 1.0\r\n try:\r\n x += 1\r\n y += 1\r\n pos = (self.width * y) + x\r\n idx = pos * 4\r\n background_a = self.buf[idx + 3]\r\n\r\n # get rgb and check if close to black and make off dark gray\r\n # this makes black have highlights\r\n if self._distance_from_black < 15:\r\n r = 35\r\n g = 35\r\n b = 35\r\n r = r * v\r\n g = g * v\r\n b = b * v\r\n # end of check and make gray\r\n else:\r\n r = self._red * v\r\n g = self._green * v\r\n b = self._blue * v\r\n if 0 > r:\r\n r = 0\r\n if 0 > g:\r\n g = 0\r\n if 0 > b:\r\n b = 0\r\n if r > 255:\r\n r = 255\r\n if g > 255:\r\n g = 255\r\n if b > 255:\r\n b = 255\r\n if background_a != 0 and a != 255:\r\n s_alpha = a / 255.0\r\n s_background_a = background_a / 255.0\r\n one_minus_salpha = 1 - s_alpha\r\n\r\n background_r = self.buf[idx]\r\n background_g = self.buf[idx + 1]\r\n background_b = self.buf[idx + 2]\r\n r = r * s_alpha + one_minus_salpha * background_r\r\n g = g * s_alpha + one_minus_salpha * background_g\r\n b = b * s_alpha + one_minus_salpha * background_b\r\n a = (s_alpha + one_minus_salpha * s_background_a) * 255\r\n self.buf[idx] = int(r)\r\n self.buf[idx + 1] = int(g)\r\n self.buf[idx + 2] = int(b)\r\n self.buf[idx + 3] = (\r\n int(a) - 4\r\n ) # remove just a little opacity for background color tint show thru\r\n except IndexError:\r\n pass\r\n\r\n def draw_line(self, x0, y0, x1, y1):\r\n dy = y1 - y0 # BRESENHAM LINE DRAW ALGORITHM\r\n dx = x1 - x0\r\n if dy < 0:\r\n dy = -dy\r\n step_y = -1\r\n else:\r\n step_y = 1\r\n odx = abs(dx)\r\n ody = abs(dy)\r\n if dx < 0:\r\n dx = -dx\r\n step_x = -1\r\n else:\r\n step_x = 1\r\n i = 0\r\n if dx > dy:\r\n dy <<= 1 # dy is now 2*dy\r\n dx <<= 1\r\n fraction = dy - (dx >> 1) # same as 2*dy - dx\r\n self.line_for_point(x0, y0, False, odx, i)\r\n i += 1\r\n\r\n while x0 != x1:\r\n if fraction >= 0:\r\n y0 += step_y\r\n fraction -= dx # same as fraction -= 2*dx\r\n x0 += step_x\r\n fraction += dy # same as fraction += 2*dy\r\n self.line_for_point(x0, y0, False, odx, i)\r\n i += 1\r\n else:\r\n dy <<= 1 # dy is now 2*dy\r\n dx <<= 1 # dx is now 2*dx\r\n fraction = dx - (dy >> 1)\r\n self.line_for_point(x0, y0, True, ody, i)\r\n i += 1\r\n while y0 != y1:\r\n if fraction >= 0:\r\n x0 += step_x\r\n fraction -= dy\r\n y0 += step_y\r\n fraction += dx\r\n self.line_for_point(x0, y0, True, ody, i)\r\n\r\n def line_for_point(self, x, y, dy, max_pos, index):\r\n w = self.line_width\r\n left = w >> 1\r\n right = w - left\r\n if self.fancy and max_pos > 0:\r\n v = self.gradient(index / max_pos)\r\n else:\r\n v = 1.0\r\n if dy:\r\n for pos in range(-left, right):\r\n self.plot(x + pos, y, v)\r\n else:\r\n for pos in range(-left, right):\r\n self.plot(x, y + pos, v)\r\n\r\n def draw_text(self, x, y, string, rotate=False):\r\n for c in string:\r\n m = characters[c]\r\n for cx in range(len(m[0])):\r\n for cy in range(len(m)):\r\n v = m[cy][cx]\r\n if v == 9:\r\n continue\r\n if rotate:\r\n gx = x + (len(m) - cy) + 1\r\n gy = y + cx + 2\r\n else:\r\n gx = x + cx + 2\r\n gy = y + cy + 2\r\n pos = (self.width * gy) + gx\r\n idx = pos * 4\r\n a2 = (9.0 - v) / 9.0\r\n r = (1.0 - a2) * self.buf[idx]\r\n g = (1.0 - a2) * self.buf[idx + 1]\r\n b = (1.0 - a2) * self.buf[idx + 2]\r\n a1 = self.buf[idx + 3] / 255.0\r\n a = a2 + a1 * (1.0 - a2)\r\n try:\r\n self.buf[idx] = int(r)\r\n self.buf[idx + 1] = int(g)\r\n self.buf[idx + 2] = int(b)\r\n self.buf[idx + 3] = int(a * 255)\r\n except IndexError:\r\n pass\r\n if rotate:\r\n y += 11\r\n else:\r\n x += 11\r\n\r\n\r\ndef draw_guides(draw_buff, extends):\r\n width = int(extends[2] - extends[0])\r\n height = int(extends[3] - extends[1])\r\n draw_buff.set_color(0, 0, 0, 255)\r\n draw_buff.line_width = 1\r\n min_x = int(extends[0])\r\n min_y = int(extends[1])\r\n points = 50\r\n draw_buff.draw_text(0, 0, \"mm\")\r\n for x in range(points - (min_x % points), width - 30, points):\r\n if x < 30:\r\n continue\r\n draw_buff.draw_text(x, 0, str(int((x + min_x) / 10)), rotate=True)\r\n draw_buff.draw_line(x, 0, x, 30)\r\n for y in range(points - (min_y % points), height - 30, points):\r\n if y < 30:\r\n continue\r\n draw_buff.draw_text(0, y, str(int((y + min_y) / 10)))\r\n draw_buff.draw_line(0, y, 30, y)\r\n\r\n\r\ndef write(pattern, f, settings=None):\r\n guides = settings.get(\"guides\", False)\r\n extends = pattern.bounds()\r\n pattern.translate(-extends[0], -extends[1])\r\n width = int(extends[2] - extends[0])\r\n height = int(extends[3] - extends[1])\r\n draw_buff = PngBuffer(width, height)\r\n draw_buff.fancy = settings.get(\"fancy\", False)\r\n if settings is not None:\r\n background = settings.get(\"background\")\r\n if background is not None:\r\n b = EmbThread()\r\n b.set(background)\r\n draw_buff.background(b.get_red(), b.get_green(), b.get_blue(), 0xFF)\r\n linewidth = settings.get(\"linewidth\")\r\n if linewidth is not None and isinstance(linewidth, int):\r\n draw_buff.line_width = linewidth\r\n\r\n for stitchblock in pattern.get_as_stitchblock():\r\n block = stitchblock[0]\r\n thread = stitchblock[1]\r\n draw_buff.set_color(\r\n thread.get_red(), thread.get_green(), thread.get_blue(), 255\r\n )\r\n last_x = None\r\n last_y = None\r\n for stitch in block:\r\n x = int(stitch[0])\r\n y = int(stitch[1])\r\n if last_x is not None:\r\n draw_buff.draw_line(last_x, last_y, x, y)\r\n last_x = x\r\n last_y = y\r\n\r\n if guides:\r\n draw_guides(draw_buff, extends)\r\n\r\n f.write(write_png(draw_buff.buf, draw_buff.width, draw_buff.height))\r\n","repo_name":"EmbroidePy/pyembroidery","sub_path":"pyembroidery/PngWriter.py","file_name":"PngWriter.py","file_ext":"py","file_size_in_byte":18877,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"22"}
+{"seq_id":"28537314634","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 16 14:00:22 2016\n\n@author: Raphael Proux\n\"\"\"\n\nimport visa\nimport time\n\nclass LockIn7265:\n def __init__(self, eqAddress):\n rm = visa.ResourceManager()\n self.dev = rm.open_resource(eqAddress)\n# eqName = self.dev.query('*IDN?')\n# print eqName\n \n def __enter__(self):\n return self\n \n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close() \n \n def sendPulse(self):\n \"\"\"\n Sends a pulse through the low bit pin of the digital output.\n \"\"\"\n self.dev.write(\"BYTE 1\")\n self.dev.write(\"BYTE 0\")\n \n def readADCdigital(self, inputId=1):\n \"\"\"\n Reads the ADC input specified by its number inputId and returns a boolean\n according to TTL convention (True > 2 V)\n \"\"\"\n \n assert(1 <= inputId <= 3)\n res = int(self.dev.query(\"ADC {}\".format(inputId)))\n #print res\n if res > 2000:\n return True\n else:\n return False\n\n def writeDAC(self, value, inputId=2):\n \"\"\"\n Writes a voltage in volts to DAC specified by its inputID\n \"\"\"\n self.dev.write(\"DAC. {} {}\".format(inputId, float(value))) \n \n def close(self):\n pass\n \nif __name__ == \"__main__\":\n \n with LockIn7265() as test:\n time.sleep(1)\n for i in xrange(10):\n while True:\n res = test.readADCdigital()\n print(res)\n if not res:\n break\n time.sleep(0.1)\n test.sendPulse()\n time.sleep(0.5) # important! due to lag in the controller response\n \n\n ","repo_name":"diamond2nv/measurements","sub_path":"instruments/LockIn7265_aux.py","file_name":"LockIn7265_aux.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35144422695","text":"from ..permutations import InterestingPermutations, transposePermutation\nimport sympy\nfrom sympy.combinatorics import Permutation\n\n#class PlainChanges(InterestingPermutations):\nclass PlainChanges:\n \"\"\"Present plain changes as swaps and act in domains to make peals\n\n http://www.gutenberg.org/files/18567/18567-h/18567-h.htm\"\"\"\n def __init__(self,nelements=4,nhunts=None,hunts=None):\n # InterestingPermutations.__init__(self,nelements)\n hunts = self.initializeHunts(nelements, nhunts)\n self.neutral_perm=sympy.combinatorics.Permutation([0],size=nelements)\n self.neighbor_swaps=[\n sympy.combinatorics.Permutation(\n i,i+1,size=nelements)\n for i in range(nelements-1)]\n self.domains=[]\n hunts_=self.performPeal(nelements,dict(hunts)) # with the hunts, etc.\n self.hunts=hunts\n self.nelements = nelements\n #self.hunts_=hunts_\n def initializeHunts(self,nelements=4,nhunts=None):\n if not nhunts:\n if nelements>4:\n nhunts=2\n else:\n nhunts=1\n assert nelements>0\n if nhunts>nelements:\n raise ValueError(\"There cannot be more hunts than elements\")\n elif nhunts>nelements-3:\n print(\"peals are the same if there are\", nhunts-(nelements-3), \"hunts less\")\n hunts_dict={}\n for hunt in range(nhunts):\n # implement different starting settings here\n if hunt==nhunts-1:\n next_=None\n else:\n next_=\"hunt\"+str(hunt+1)\n hunts_dict[\"hunt\"+str(hunt)]=dict(level=hunt,position=hunt,status=\"started\",direction=\"up\",next_=next_)\n return hunts_dict\n def performPeal(self,nelements,hunts=None):\n if hunts==None:\n hunts=self.initializeHunts(nelements)\n permutation,hunts=self.performChange(nelements,hunts)\n #peal_sequence=[permutation]\n total_perm=permutation\n peal_direct=[self.neutral_perm]\n peal_sequence=[permutation]\n while total_perm!=self.neutral_perm:\n peal_direct+=[total_perm]\n permutation,hunts=self.performChange(nelements,hunts)\n #total_perm*=permutation\n total_perm=permutation*total_perm\n peal_sequence+=[permutation]\n self.peal_direct=peal_direct\n self.peal_sequence=peal_sequence\n return hunts\n def performChange(self,nelements,hunts,hunt=None):\n \"\"\"Perform change procedure from 'hunt' on to subsequent hunts.\n\n Return permutation of the change and the hunts dictionary.\n Peals should be classified by restrictions satisfied by permutations\n between changes:\n 1) canonical peal: only adjacent swaps allowed. E.g. plain changes, twenty all over.\n 2) semi-canonical peal: only adjacent chunks are displaced, at least one permutation needs more than one swap. E.g.: rotations, mirrors.\n 3) free peal: at least one permutation displaces non-adjacent indexes. E.g. paradox peal, phoenix peal, any nondihedral? \"\"\"\n if hunt==None:\n hunt=\"hunt0\"\n hunt_=hunts[hunt]\n direction=hunt_[\"direction\"]\n assert direction in {\"up\",\"down\"}\n position=hunt_[\"position\"]\n position_=position\n swap_with=(position-1,position+1)[direction==\"up\"]\n # find domain by iterating upper hunts\n cut_bellow=sum([hunts[\"hunt\"+str(i)][\"direction\"]==\"up\" for i in range(hunt_[\"level\"])])\n cut_above=nelements-(hunt_[\"level\"]-cut_bellow)\n #cut_above=nelements-cut_bellow\n domain=list(range(nelements))[cut_bellow:cut_above]\n self.domains+=[(domain,cut_bellow,cut_above,hunt_[\"level\"],hunt,position,swap_with)]\n if swap_with in domain: # move\n swap=self.neighbor_swaps[(position-1,position)[direction==\"up\"]]\n for ahunt in hunts:\n if hunts[ahunt][\"position\"]==swap_with:\n hunts[ahunt][\"position\"]=position\n hunts[hunt][\"position\"]=swap_with\n else: # invert direction and move to next hunt and one less element\n new_direction=(\"up\",\"down\")[direction==\"up\"]\n hunts[hunt][\"direction\"]=new_direction\n self.domains+=[\"invert\",new_direction, hunt]\n if hunt_[\"next_\"]==None:\n #swap=self.neighbor_swaps[(domain[0],domain[-2+cut_bellow])[new_direction==\"up\"]]\n swap=self.neighbor_swaps[(domain[0],domain[-2])[new_direction==\"up\"]]\n else:\n subsequent_hunt=hunt_[\"next_\"]\n swap,hunts=self.performChange(nelements,hunts,subsequent_hunt)\n #swap=transposePermutation(swap,(0,1)[new_direction==\"up\"])\n self.domains+=[swap]\n return swap, hunts\n def act(self,domain=None,peal=None):\n if domain==None:\n domain=list(range(self.nelements))\n if peal==None:\n peal=self.peal_direct\n return [i(domain) for i in peal]\n def actAll(self,domain=None):\n if domain==None:\n domain=list(range(self.nelements))\n acted_peals={}\n for peal in self.peals:\n acted_peals[peal+\"_acted\"]=[i(domain) for i in self.peals[peal]]\n self.domain=domain\n self.acted_peals=acted_peals\n","repo_name":"ttm/music","sub_path":"music/structures/peals/plainChanges.py","file_name":"plainChanges.py","file_ext":"py","file_size_in_byte":5329,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"22"}
+{"seq_id":"36646015755","text":"from fastapi import APIRouter, File, UploadFile, Response\n\nfrom openpyxl import load_workbook, Workbook\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\n\nfrom reportlab.pdfgen import canvas\nfrom reportlab.pdfbase import pdfmetrics\nfrom reportlab.pdfbase.ttfonts import TTFont\nfrom reportlab.lib.pagesizes import letter\n\nfrom io import BytesIO\nfrom bs4 import BeautifulSoup\nfrom zipfile import ZipFile\n\nimport re\nimport math\nimport pdfplumber\n\nfrom typing import List\n\n# spark\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import StructType, StructField, StringType\nfrom pyspark.sql import Row\n\nrouter = APIRouter(prefix=\"/api/file\", tags=[\"file\"])\n\n# add Font\npdfmetrics.registerFont(TTFont(\"D2Coding\", \"D2Coding.ttf\"))\n\n# 스파크 세션 생성\nspark = SparkSession.builder.config('spark.driver.host', '127.0.0.1').getOrCreate()\n\n# spark로 json 파일을 읽은 뒤 SQL View(이름은 'SKU')로 생성\nspark.read.json('./config/SKU.json', multiLine=True).createOrReplaceTempView(\"SKU\")\n\n@router.get(\"/\")\nasync def img_root():\n return \"file root\"\n\n@router.post(\"/sparktopdf\")\nasync def sparktopdf(files: List[UploadFile] = File(...)):\n \n xlsx_files = list(filter(lambda file: True if file.filename.find('.xlsx') >= 0 else False, files))\n html_files = list(filter(lambda file: True if file.filename.find('.html') >= 0 else False, files))\n pdf_files = list(filter(lambda file: True if file.filename.find('.pdf') >= 0 else False, files))\n \n zio = BytesIO()\n zips = ZipFile(zio, 'w')\n \n # 송장감지 텍스트\n regex = re.compile(r'LZDID|First Mile Warehouse')\n \n # 생성할 spark View Schema 정의\n Schema = StructType([\n StructField(\"orderNumber\", StringType(), True),\n StructField(\"orderSku\", StringType(), True)\n ])\n \n # spark 에 구성될 행 데이터\n rows = []\n \n # xlsx 파일 갯수만큼 반복\n for xlsx_file in xlsx_files:\n idx = 2\n \n wb = load_workbook(filename=BytesIO(xlsx_file.file.read()))\n ws = wb.active\n \n # M열과 F열의 데이터가 둘 중에 하나라도 없으면 break\n while ws[\"M{}\".format(idx)].value is not None and ws[\"F{}\".format(idx)].value is not None:\n # 주문번호\n order_number = str(ws[\"M{}\".format(idx)].value)\n # text 정렬작업\n text = str(ws[\"F{}\".format(idx)].value)\n text = re.sub(r'\\)\\s1', ')', text)\n text = re.sub(r'\\(\\d{4}\\)|[+]', ',', text)\n text = text.strip()\n texts = text.split(',')\n texts = list(filter(None, texts))\n texts = list(map(lambda s: s.strip(), texts))\n\n # spark View에 행 추가\n for text in texts:\n rows.append(\n Row(\n order_number,\n text\n )\n )\n\n idx = idx + 1\n \n spark.createDataFrame(rows, Schema).createOrReplaceTempView(\"ORDERSKU\")\n \n for pdf_file in pdf_files:\n baseName = re.sub(r\"\\.pdf\", \"\", pdf_file.filename)\n pdf_file = list(filter(lambda file: True if file.filename.find('{}.pdf'.format(baseName)) >= 0 else False, pdf_files))\n pdf_file = pdf_file[0]\n html_file = list(filter(lambda file: True if file.filename.find('{}.html'.format(baseName)) >= 0 else False, html_files))\n html_file = html_file[0]\n html = html_file.file.read()\n dom = BeautifulSoup(html, 'html.parser')\n \n # html 파일에�� order_numbers 담는 리스트\n order_numbers = []\n\n # order_number 이 있는 셀렉터\n contents = dom.select('table tbody tr td p')\n # 셀렉터 반복하여 order_number 추출\n for content in contents:\n text = re.findall(r'Order Number[:] \\d+', content.text)\n if len(text) > 0:\n order_number = re.sub(r'Order Number[:]', '', text[0])\n order_numbers.append(order_number.strip())\n\n \n pdf_data = BytesIO(pdf_file.file.read())\n \n pdf = PdfFileReader(pdf_data)\n pages = pdfplumber.open(pdf_data).pages\n # pdf 작성\n output = PdfFileWriter()\n \n idx = 0\n \n for pageNum, page in enumerate(pages):\n # 텍스트를 추출하여 송장 감지\n texts = page.extract_text()\n check_invoice = regex.search(texts)\n\n if check_invoice is not None:\n query_order = \"SELECT orderSku FROM ORDERSKU WHERE orderNumber like '{}'\".format(order_numbers[idx])\n df = spark.sql(query_order)\n products = {}\n for row in df.rdd.collect():\n query_sku = \"SELECT * FROM SKU WHERE NAME like '%{}%'\".format(row.orderSku)\n df2 = spark.sql(query_sku)\n try:\n print(df2.rdd.take(1))\n row_data = df2.rdd.take(1)[0]\n data_text = \"{} ({})\".format(row_data.NAME, row_data.CODE)\n if data_text in products:\n products[data_text] = products[data_text] + 1\n else:\n products[data_text] = 1\n except:\n continue\n\n packet = BytesIO()\n can = canvas.Canvas(packet, pagesize=letter) # pdf 출력 letter 사이즈\n can.setFillColorRGB(255, 255, 255) # 캔버스 색상 흰 색\n can.setLineWidth(0.75) # 라인 너비\n can.rect(6.45, 525, 272.3, 70, fill=1) # 사각형 생성\n can.setFillColorRGB(0, 0, 0) # 캔버스 색상 검정색\n\n i = 0 # 딕셔너리 index\n for key, value in products.items():\n pos_y = 587.5 - (11 * math.floor(i / 3)) # text y축\n pos_x = 8 # text x축\n if i % 3 == 0:\n pos_x = 8\n elif i % 3 == 1:\n pos_x = 100\n else:\n pos_x = 192\n\n write_text = '{} {}'.format(key, value)\n if len(write_text) < 15:\n can.setFont(\"D2Coding\", 8) # 폰트종류: D2Coding, 폰트크기: 8\n elif len(write_text) >= 15 & len(write_text) < 17:\n can.setFont(\"D2Coding\", 7) # 폰트종류: D2Coding, 폰트크기: 7\n elif len(write_text) >= 17 & len(write_text) < 19:\n can.setFont(\"D2Coding\", 6) # 폰트종류: D2Coding, 폰트크기: 6\n else:\n can.setFont(\"D2Coding\", 5) # 폰트종류: D2Coding, 폰트크기: 5\n\n can.drawString(pos_x, pos_y, write_text)\n i = i + 1\n\n can.save()\n packet.seek(0)\n new_pdf = PdfFileReader(packet)\n invoice_page = pdf.getPage(pageNum)\n invoice_page.mergePage(new_pdf.getPage(0))\n output.addPage(invoice_page)\n\n idx = idx + 1\n \n bio = BytesIO()\n output.write(bio)\n \n zips.writestr('{}.pdf'.format(baseName), bio.getbuffer())\n \n zips.close()\n headers = { 'Content-Disposition': 'attachment; filename=\"union.zip\"' }\n \n return Response(zio.getvalue(), headers=headers)\n\n@router.post(\"/xlsxtopdf\")\nasync def xlsxtopdf(files: List[UploadFile] = File(...)):\n \n # filtering text\n regex = re.compile(r'\\d*[가-힣].*')\n regex_count = re.compile(r'數量: \\d+')\n \n xlsx_files = list(filter(lambda file: True if file.filename.find('.xlsx') >= 0 else False, files))\n pdf_files = list(filter(lambda file: True if file.filename.find('.pdf') >= 0 else False, files))\n \n zio = BytesIO()\n zips = ZipFile(zio, 'w')\n \n for xlsx_file in xlsx_files:\n # Write pdf\n output = PdfFileWriter()\n \n file_name = re.sub(\".xlsx\", \"\", xlsx_file.filename)\n pdf_file = list(filter(lambda file: True if file.filename.find('{}.pdf'.format(file_name)) >= 0 else False, pdf_files))\n pdf_file = pdf_file[0]\n \n wb = load_workbook(filename=BytesIO(xlsx_file.file.read()))\n ws = wb.active\n pdf = PdfFileReader(BytesIO(pdf_file.file.read()))\n \n for num in range(pdf.getNumPages()):\n # col\n xlsx_num_data = \"C{}\".format(num + 2)\n str_list = str(ws[xlsx_num_data].value).split('\\n')\n count_list = list(map(regex_count.findall, str_list))\n str_list = list(map(regex.findall, str_list))\n str_list = list(map(lambda s: s[0], str_list))\n # make tuple to lists\n str_list = list(zip(count_list, str_list))\n \n posY = 75\n posY_chk = posY\n\n # create pdf Text\n packet = BytesIO()\n can = canvas.Canvas(packet, pagesize=letter)\n can.setFont(\"D2Coding\", 7) # set Font: D2Coding, Size: 7\n \n # posY 미리 계산기능 추가\n for idx, (count, str_data) in enumerate(str_list): # drawing Text, idx, str_data, exception case: G01305644544\n if posY_chk < 0:\n posY_chk = posY_chk + 10\n posY = posY + 10\n texts = re.sub(\".*:|;.*\", \"\", str_data).strip().split(',')\n text_len = math.ceil(len(texts) / 3)\n if text_len > 1:\n for i in range(text_len):\n posY_chk -= 10\n else:\n posY_chk -= 10\n\n for idx, (count, str_data) in enumerate(str_list): # drawing Text, idx, str_data, exception case: G01305644544\n texts = re.sub(\".*:|;.*\", \"\", str_data).strip().split(',')\n counts = re.sub('數量: ', \" \", count[0])\n text_len = math.ceil(len(texts) / 3)\n if text_len > 1:\n for i in range(text_len):\n startPoint = i * 3\n endPoint = (i + 1) * 3\n write_text = \",\".join(texts[startPoint:endPoint])\n if i is text_len - 1:\n write_text = write_text + counts\n can.drawString(65, posY, write_text)\n posY -= 10\n else:\n can.drawString(65, posY, \",\".join(texts) + counts)\n posY -= 10\n can.save()\n packet.seek(0)\n new_pdf = PdfFileReader(packet)\n \n # get Page\n page = pdf.getPage(num)\n # page Merging\n page.mergePage(new_pdf.getPage(0))\n output.addPage(page)\n \n \n bio = BytesIO()\n output.write(bio)\n \n zips.writestr('{}.pdf'.format(file_name), bio.getbuffer())\n \n zips.close()\n headers = { 'Content-Disposition': 'attachment; filename=\"union.zip\"' }\n \n return Response(zio.getvalue(), headers=headers)\n \n \n\n# file 작업 multifile 파일 수신\n@router.post(\"/merge_xlsx\")\nasync def file_work(files: List[UploadFile] = File(...)):\n \n write_wb = Workbook()\n write_ws = write_wb.active\n \n xlsx_files = list(filter(lambda file: True if file.filename.find('.xlsx') >= 0 else False, files))\n \n for xlsx_file in xlsx_files:\n wb = load_workbook(filename=BytesIO(xlsx_file.file.read()))\n ws = wb.active\n \n idx = 1\n \n for row in ws.iter_rows(values_only=True):\n if idx != 1:\n write_ws.append(row)\n idx = idx + 1\n \n bio = BytesIO()\n write_wb.save(bio)\n \n headers = { 'Content-Disposition': 'attachment; filename=\"union.xlsx\"' }\n \n return Response(bio.getvalue(), headers=headers)","repo_name":"Melting-Face/fastApiProject","sub_path":"routes/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":12052,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"37660220895","text":"import imp\nfrom stl import mesh\nimport math\nimport numpy\nimport trimesh\nfrom mpl_toolkits import mplot3d\nfrom matplotlib import pyplot\n\n# Create 3 faces of a cube\ndata = numpy.zeros(6, dtype=mesh.Mesh.dtype)\n# Top of the cube\ndata['vectors'][0] = numpy.array([[0, 1, 1],\n [1, 0, 1],\n [0, 0, 1]])\ndata['vectors'][1] = numpy.array([[1, 0, 1],\n [0, 1, 1],\n [1, 1, 1]])\n# Front face\ndata['vectors'][2] = numpy.array([[1, 0, 0],\n [1, 0, 1],\n [1, 1, 0]])\ndata['vectors'][3] = numpy.array([[1, 1, 1],\n [1, 0, 1],\n [1, 1, 0]])\n# Left face\ndata['vectors'][4] = numpy.array([[0, 0, 0],\n [1, 0, 0],\n [1, 0, 1]])\ndata['vectors'][5] = numpy.array([[0, 0, 0],\n [0, 0, 1],\n [1, 0, 1]])\n# Since the cube faces are from 0 to 1 we can move it to the middle by\n# substracting .5\n# data['vectors'] -= .5\n\nprint(data)\n\n########################################\n# Create a new mesh\n\nyour_mesh = mesh.Mesh(data, remove_empty_areas=False)\n\nm = trimesh.Trimesh(**trimesh.triangles.to_kwargs(your_mesh.vectors))\nm.show()\n\n\n########################################\n# pyplot.show() method (does not work)\n\n# # Create a new plot\n# figure = pyplot.figure()\n# axes = mplot3d.Axes3D(figure)\n# # Render the cube\n# axes.add_collection3d(mplot3d.art3d.Poly3DCollection(your_mesh.vectors))\n# # Auto scale to the mesh size\n# scale = your_mesh.points.flatten()\n# axes.auto_scale_xyz(scale, scale, scale)\n# # Show the plot to the screen\n# pyplot.show()\n","repo_name":"Ishaan-Bansal/Crater-Analysis","sub_path":"ArchivedFiles/creatingCube.py","file_name":"creatingCube.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20045318109","text":"import requests\nfrom bs4 import BeautifulSoup\nimport string\nimport re\nimport os\n\ncol_page = int(input())\narticle = input()\ntot = 0\ntot_art = []\n\nfor i in range(1, col_page + 1): # list 183\n name_dir = os.path.join('Page_' + str(i))\n if not os.access(name_dir, os.F_OK):\n os.mkdir(name_dir)\n url = ('https://www.nature.com/nature/articles?sort=PubDate&year=2020&page=' + str(i))\n response = requests.get(url, headers={'Accept-Language': 'en-US,en;q=0.5'})\n soup = BeautifulSoup(response.content, 'html.parser')\n type_article = soup.find_all('span', attrs = {'class': 'c-meta__type'}, string=article)\n\n for col in type_article:\n name_article = col.find_previous('div', class_=\"c-card__body u-display-flex u-flex-direction-column\").find('a')\n name_file2 = ''\n for letters in name_article:\n if letters in string.punctuation:\n letters = \"_\"\n elif letters == r':':\n letters = '_'\n name_file2 += letters\n print(name_file2)\n name_file = name_file2.strip()\n new_name_file = os.path.join(name_file.replace(' ', \"_\") + r'.txt')\n print(new_name_file)\n link_article = 'https://www.nature.com' + name_article.get('href')\n re_article = requests.get(link_article, headers={'Accept-Language': 'en-US,en;q=0.5'})\n art_soup = BeautifulSoup(re_article.content, 'html.parser')\n path = os.path.join(name_dir, new_name_file)\n file_article = open(path, 'w', encoding='utf-8')\n text_article = art_soup.find('div', class_='c-article-body u-clearfix')\n file_article.write(text_article.text)\n tot_art.append(new_name_file)\n file_article.close()\n tot += 1\n # print('Total = ', tot)\nprint(tot_art)\n","repo_name":"svd-it/Web-Scraper-nature.com","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
![\"TwitterIcon\"](\"https://www.creativefreedom.co.uk/wp-content/uploads/2017/06/Twitter-featured.png\")
![\"manifold\"](\"imgs/sphere_fitting/manifold.svg\")
\n\n# This is why it is important to have a dynamic strategy to find the local minimum, and this can be done using gradients.\n# We can calculate the gradient of the energy function among our parameters:\n# \\begin{equation}\n# \\frac{\\delta \\xi(\\Theta)}{\\delta \\Theta} = \\nabla\\xi(\\Theta)\n# \\end{equation}\n\n# The gradient of the energy function can then be used to update the parameter every step, and converge to a local minima:\n# \\begin{equation}\n# \\Theta(t+1) = \\Theta(t) - \\mu\\nabla\\xi(\\Theta(t)) \n# \\end{equation}\n\n# One could compute directly the optimal solution by equalizing the gradient of energy function with 0:\n# \\begin{equation}\n# \\frac{\\delta \\xi(\\Theta)}{\\delta \\Theta} = 0\n# \\end{equation}\n\n# When this method works well with little data and few parameters, it can be computationnaly impossible to compute the best solution [see this thread for more information](https://stats.stackexchange.com/questions/278755/why-use-gradient-descent-for-linear-regression-when-a-closed-form-math-solution). \n# Morevover, it is not always easy to mathematically extract the optimal parameters.\n\n# %% [markdown]\n'''\n## 2. Example with sphere fitting\n'''\n# %% [markdown]\n'''\n### 2.1 Cost function\n'''\n# %% [markdown]\n\n# To extract the cost function, we first need to define what is a sphere.\n# The equation of a 3D sphere with radius $r$ and center $(a, b, c)$ is known as:\n# \\begin{equation}\n# r^2 = (x-a)^2 + (y-b)^2 + (z-c)^2\n# \\end{equation}\n\n# Every point $(x,y,z)$ must satisfy this equation to be on the sphere, if not, the point is outside the sphere.\n# Using this condition, we can deduce the energy function:\n# \\begin{equation}\n# \\xi(\\Theta) = \\sum_i^n(L_i - r)^2\n# \\end{equation}\n# with,\n# \\begin{equation}\n# L_i = \\sqrt{(x_i-a)^2 + (y_i-b)^2 + (z_i-c)^2}\n# \\end{equation}\n# This function will verify if every known point $i$ fits well with the parameters $a, b, c$ and $r$.\n# Let's implement it in python !\n# %%\n## imports\nimport numpy as np\nimport math\nimport plotly.graph_objects as go\n\n# fixing numpy random state for reproducibility\nnp.random.seed(0)\n# %%\ndef sph_loss(T, x):\n L = np.sqrt((x[:,0] - T[0])**2 + (x[:,1] - T[1])**2 + (x[:,2] - T[2])**2)\n return L\n# %%\ndef cost_function(T, x):\n L = sph_loss(T, x)\n return np.sum( (L - T[3])**2 )\n# %% [markdown]\n'''\n### 2.2 Gradient of the cost function\n'''\n# %% [markdown]\n\n# Now that we have the cost funtion, we can compute its gradient for every parameters (a, b, c, r). \n# \\begin{equation}\n# \\nabla\\xi(\\Theta) =\n# \\begin{bmatrix}\n# \\frac{\\delta \\xi(\\Theta)}{\\delta r} \\\\\n# \\frac{\\delta \\xi(\\Theta)}{\\delta a} \\\\\n# \\frac{\\delta \\xi(\\Theta)}{\\delta b} \\\\\n# \\frac{\\delta \\xi(\\Theta)}{\\delta c} \n# \\end{bmatrix}\n# \\end{equation}\n\n# Then, with $m$ as the number of 3D points:\n\n# \\begin{equation}\n# \\frac{\\delta \\xi(\\Theta)}{\\delta r} = -2\\sum_{i=1}^m (L_i -r)\n# \\end{equation}\n\n# \\begin{equation}\n# \\frac{\\delta \\xi(\\Theta)}{\\delta a} = 2\\sum_{i=1}^{m}((x_i-a) + r\\frac{\\delta L_i}{\\delta a}); \\qquad \\frac{\\delta L_i}{\\delta a} = \\frac{a-x_i}{L_i}\n# \\end{equation}\n\n# \\begin{equation}\n# \\frac{\\delta \\xi(\\Theta)}{\\delta b} = 2\\sum_{i=1}^{m}((y_i-b) + r\\frac{\\delta L_i}{\\delta b}); \\qquad \\frac{\\delta L_i}{\\delta b} = \\frac{b-y_i}{L_i}\n# \\end{equation}\n\n# \\begin{equation}\n# \\frac{\\delta \\xi(\\Theta)}{\\delta c} = 2\\sum_{i=1}^{m}((z_i-c) + r\\frac{\\delta L_i}{\\delta c}); \\qquad \\frac{\\delta L_i}{\\delta c} = \\frac{c-z_i}{L_i}\n# \\end{equation}\n\n# %% [markdown]\n# In python,\n\n# %%\n## cost function derivative\ndef derivative_cost_function(T, x):\n L = sph_loss(T, x)\n \n dr = (-1)*(-2)*np.sum( (L - T[3]) )\n \n dLa = (T[0] - x[:,0])/L\n da = 2*np.sum( (x[:,0] - T[0]) + T[3]*dLa )\n \n dLb = (T[1] - x[:,1])/L\n db = 2*np.sum( (x[:,1] - T[1]) + T[3]*dLb )\n \n dLc = (T[2] - x[:,2])/L\n dc = 2*np.sum( (x[:,2] - T[2]) + T[3]*dLc )\n \n return np.array([da, db, dc, dr])\n# %% [markdown]\n'''\n### 2.3 Gradient descent\n'''\n# %% [markdown]\n'''\nUsing the gradient of the cost function, it is now possible to optimize the best parameters with gradient descent.\n'''\n# %%\ndef grad_descent(data, param_init):\n T = param_init #initial guess\n lr = 5e-3\n it_max = 10000\n grad = [1e99, 1e99, 1e99]\n\n for it in range(it_max): \n if( abs(np.sum(grad))<1e-6 ):\n continue\n # gradient descent\n grad = derivative_cost_function(T, data)\n T = T + lr*grad\n it_max = it\n print(\"Done in %d epochs with grad: [%1.4e, %1.4e, %1.4e, %1.4e]\" %(it_max, grad[0], grad[1], grad[2], grad[3]))\n \n return T, grad\n\n# %% [markdown]\n'''\n### 2.4 Training phase\n'''\n# %% [markdown]\n'''\nWe will first generated data for a standard 3D sphere, we can use spherical coordinates to sample random points given the sphere parameters. To reduce CPU time, we will not use a high number of points.\n'''\n# %%\ndef gen_points_from_sph(model, n):\n res = int(np.ceil(math.sqrt(n)))\n theta = np.linspace(0,2*np.pi,res)\n phi = np.linspace(0,np.pi,res)\n \n x = model[3]*np.outer(np.cos(theta),np.sin(phi))\n y = model[3]*np.outer(np.sin(theta),np.sin(phi))\n z = model[3]*np.outer(np.ones(res),np.cos(phi))\n \n X = x + model[0]\n Y = y + model[1]\n Z = z + model[2]\n \n points = np.zeros((n, 3))\n points[:,0] = X.ravel()[0:n]\n points[:,1] = Y.ravel()[0:n]\n points[:,2] = Z.ravel()[0:n]\n\n return points, X, Y, Z\n# %% [markdown]\n'''\nLet's generate 250 points with gaussian noise, from an unknown sphere centered at (1,2,4) and 10 radius.\nThen, we will use 2/3 of the data and try to find these parameters. Remember that the hyper-parameters for the gradient descent are supposed to be optimized during training set.\n'''\n# %%\n#Training phase\nsph_model = [1, 2, 4, 10]\nn = 150\nsph_points = gen_points_from_sph(sph_model, n)[0] + np.random.randn(n,3)\nsph_points_train = sph_points[0:int(n/3),:]\n\nparam_init = np.array([0, 0, 0, 10]) #initial guess\n\nparam = grad_descent(sph_points_train, param_init)[0]\n\n# We use the formula for the radius\nprint(param)\n# %% [markdown]\n'''\nThe optimization returned a sphere centered at (1.21, 2.09, 3.94) with 9.94 radius.\n'''\n# %% [markdown]\n'''\n### 2.5 Testing phase\n'''\n# %% [markdown]\n\n# Now that we have an estimation on the model, we can test it and see how weel this model fits to the data. We use the 1/3 remaining points to estimate the error of the model.\n\n# The error estimation can be done using the fitting function $\\xi(\\Theta)$.\n\n# %%\n#Testing phase\nsph_points_test = sph_points[int(n/3)::,:]\nmodel_error = cost_function(sph_model, sph_points_test)/sph_points_test.shape[0]\n\nprint(\"Model has an error of %.2f per point\"%model_error)\n# %% [markdown]\n'''\nThe error per point is quite high. But considering the noise that was introduced, we see that the algorithm performs quite well. \n'''\n# %%\nmodel_error = cost_function(param, sph_points_test)/sph_points_test.shape[0]\n\nprint(\"Noise of the data %.2f per point\"%model_error)\n\n# %% [markdown]\n'''\nThis is using these error can we can optimized the hyper-parameters (optimization parameters), to find which one are best suited for the data.\n'''\n# %% [markdown]\n'''\n### 2.6 Qualitative result\n'''\n# %% [markdown]\n'''\nWe will use [plotly](https://plot.ly/python/) to render the result.\n'''\n# %%\n## Qualitative results with plotly\n_,X, Y, Z = gen_points_from_sph(param, 5*n)\n \ntrace = go.Surface(\n x=X,\n y=Y,\n z=Z,\n showscale=False,\n opacity = 0.5,\n colorscale=[[0, 'rgb(50,50,125)'], [1, 'rgb(50,50,125)']])\n\ntrace2=go.Scatter3d(\n x=sph_points[:,0].ravel(),\n y=sph_points[:,1].ravel(),\n z=sph_points[:,2].ravel(),\n mode='markers',\n name='points',\n marker=go.scatter3d.Marker(symbol='circle',\n size=3,\n color='rgb(0,0,255)',\n opacity=1),)\n\nlayout = go.Layout(\n title=\"Sphere fitting\",\n scene=go.layout.Scene(\n aspectmode = \"data\",\n xaxis_title=\"x(mm)\",\n yaxis_title=\"y(mm)\",\n zaxis_title=\"z(mm)\", \n camera=dict(center=dict(x=0.1, y=0.1, z=0))))\n\nfig = go.Figure(data=[trace, trace2], layout=layout)\nfig.show(renderer=\"iframe_connected\", config={'showLink': False})\n# %% [markdown]\n'''\n## To go further\n'''\n# %% [markdown]\n'''\nYou can look at other examples for standard mathematical models [here](https://www.geometrictools.com/Documentation/LeastSquaresFitting.pdf).\n'''\n# %% [markdown]\n'''\n## Tags\n'''\n# %% [markdown]\n'''\nData-Science; Geometry; Optimization\n'''","repo_name":"ltetrel/ltetrel.github.io","sub_path":"notebooks/sphere_fitting.py","file_name":"sphere_fitting.py","file_ext":"py","file_size_in_byte":10257,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"25797781300","text":"import os\n\nfrom movie_search.settings import POSTER_ROOT\n\n\nclass FileManage(object):\n def __init__(self, filename):\n self.filename = filename\n\n def upload(self, input_file):\n filename = os.path.basename(self.filename)\n filename = filename.replace(' ', '_')\n file_path = os.path.join(POSTER_ROOT, filename)\n if os.path.exists(file_path):\n filename = self.get_unique_filename(filename)\n file_path = os.path.join(POSTER_ROOT, filename)\n with open(file_path, 'wb') as output_file:\n for chunk in input_file:\n output_file.write(chunk)\n return filename\n\n def remove(self):\n file_path = os.path.join(POSTER_ROOT, self.filename)\n if os.path.exists(file_path):\n os.remove(file_path)\n return True\n return False\n\n def get_unique_filename(self, filename):\n new_name = ''\n counter = 0\n while True:\n new_name = filename.split('.')[0] + str(counter) + '.' + filename.split('.')[-1]\n file_path = os.path.join(POSTER_ROOT, new_name)\n counter += 1\n if not os.path.exists(file_path):\n break\n return new_name\n\n def get_length(self):\n file_path = os.path.join(POSTER_ROOT, self.filename)\n if os.path.exists(file_path):\n return os.path.getsize(file_path)\n return\n","repo_name":"olegkovalov/movie_search","sub_path":"movie_search/utils/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26736400147","text":"from urllib.request import FancyURLopener\nimport urllib.request\nimport urllib.parse \nimport re\n\nclass MyOpener(FancyURLopener):\n version = 'Mozilla/5.0 (compatible; Konqueror/3.5; Linux) KHTML/3.5.5 (like Gecko) (Kubuntu)'\n\ndef connect_to_dictionary(url):\n if not url:\n return None\n myopener = MyOpener()\n try:\n page = myopener.open(url)\n except:\n return None\n return page\n\ndef text_url(from_lang, to_lang, text):\n url = 'http://translate.google.bg/translate_t?prev=hp&hl=bg&js=y&text=' + urllib.parse.quote_plus(text) + '%0D%0A&file=&sl=' + from_lang + '&tl=' + to_lang + '&history_state0=#' \n return url\n\ndef word_url(from_lang, to_lang, word):\n address = 'http://www.google.bg/dictionary?source=translation&hl=' + to_lang + '&q=' + urllib.parse.quote_plus(word) + '&langpair=' + from_lang + '|' + to_lang\n return address\n\ndef urban_dict_url(from_lang, to_lang, word):\n address = 'http://www.urbandictionary.com/define.php?term=' + urllib.parse.quote_plus(word)\n return address\n\ndef find_from_urban_dict(from_lang, to_lang, word):\n print(\"using urban dict\")\n conn = connect_to_dictionary(urban_dict_url(from_lang, to_lang, word))\n s = ''.join((_.decode() for _ in conn.readlines()))\n match = re.search(r'(?m)(?s)