diff --git "a/3126.jsonl" "b/3126.jsonl" new file mode 100644--- /dev/null +++ "b/3126.jsonl" @@ -0,0 +1,678 @@ +{"seq_id":"129428584","text":"#Bij Ziggy\nimport pygame\npygame.init()\n\n# RIJEN MOETEN NOG KLEUREN KRIJGEN\n\n(width, height) = (800,600)\n\nblack = ( 0, 0, 0)\nwhite = ( 255, 255, 255)\nred = (255, 0, 0)\ngreen = (0, 200, 0)\ncolor3 = (0, 200, 200)\ncolor4 = (255, 0, 200)\n\ncolors = (red, green, color3, color4)\n\nscreen = pygame.display.set_mode((width, height))\n\npygame.display.flip()\npygame.display.set_caption(\"De Euromast\")\n\n#rijen zie Setup and Turn Layout.docx\nrowsx = 8\nrowsy = 16\n\nblocksizex = (width/2)/rowsx\nblocksizey = height/rowsy\n\nrowstower = 5 # hoeveel blokken krijgt de top van de toren?\n\nbeginwidth = blocksizey\n\nprint(\"blocksize is: width =\",blocksizex, \"height =\",blocksizey)\n\ndef game_board():\n board = True\n\n while board:\n\n for event in pygame.event.get():\n #print(event)\n if event.type == pygame.QUIT:\n pygame.quit()\n quit()\n\n screen.fill(white)\n color = 2 # NOT DONE YET\n\n for i in range(rowsx):\n for j in range(rowsy):\n if(j > (rowstower-1)): #toren (dikker) 2 rij blokken\n pygame.draw.rect(screen, red, (i*blocksizex, j*blocksizey, blocksizex, blocksizey), 1)\n else:\n if i % 2 == 0: #toren 1 rij blokken\n pygame.draw.rect(screen, colors[color], ((i * blocksizex)+(blocksizex/2), j * blocksizey, blocksizex, blocksizey), 1)\n\n pygame.display.update()\n\ngame_board()","sub_path":"blokkentest.py","file_name":"blokkentest.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"43820981","text":"menulist = []\n\ndef Bill():\n total = 0\n print(\"Menu bill\".center(21,'-'))\n for number in range(len(menulist)):\n print(menulist[number][0],menulist[number][1])\n total += menulist[number][1]\n print(\"Total price :\", total)\n print('-'*21)\n\nwhile True:\n menuName = input(\"Please enter menu : \")\n if(menuName.lower() == \"exit\"):\n break\n else:\n menuPrice = int(input(\"Price : \"))\n menulist.append([menuName,menuPrice])\n\nBill()","sub_path":"Assignments/Lecture72_Suthinan_P.py","file_name":"Lecture72_Suthinan_P.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"377813362","text":"import pandas as pd\nimport os\nimport numpy as np\n\ndata_path = './data/stage_2_train_images_conv'\n\ntrain_path = './data/train'\nvalidation_path = './data/validation'\n\npos_split = 0.9\nneg_split = 0.8\n\ndef write_data(df, path):\n for index, row in df.iterrows():\n f = open('{}/{}.txt'.format(path, row['patientId']),'a')\n if row['Target'] == 1:\n f.write('0 {} {} {} {}\\n'.format((row['x'] + row['width'] / 2) / 1024, (row['y'] + row['height'] / 2) / 1024, row['width'] / 1024, row['height'] / 1024))\n else:\n f.write('')\n f.close()\n\ndef main():\n if not os.path.exists(os.path.join(train_path)):\n os.makedirs(os.path.join(train_path))\n if not os.path.exists(os.path.join(validation_path)):\n os.makedirs(os.path.join(validation_path))\n\n print('Gathering data')\n\n df = pd.read_csv('./data/stage_2_train_labels.csv')\n pos = df.loc[df['Target'] == 1]\n neg = df.loc[df['Target'] == 0]\n\n drop_indices = np.random.choice(neg.index, len(neg) - len(pos), replace=False)\n neg = neg.drop(drop_indices)\n\n n_split = np.random.rand(len(pos)) < neg_split\n p_split = np.random.rand(len(pos)) < pos_split\n\n print('Writing data')\n\n #write_data(neg[n_split], train_path)\n write_data(pos[p_split], train_path)\n write_data(pos[~p_split], validation_path)\n #write_data(neg[~n_split], validation_path)\n\n print('Done!')\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"scripts/prepare_data.py","file_name":"prepare_data.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"453979880","text":"#!/usr/bin/env python3\nimport sys\n\n\ndef parametric_fibonacci(factor=1):\n a, b = 1, 1\n while True:\n yield a\n a, b = b, factor*a + b\n\n\nif __name__ == '__main__':\n try:\n n, k = int(sys.argv[1]), int(sys.argv[2])\n except IndexError:\n print('Missing arguments (expects two integers)', file=sys.stderr)\n sys.exit(1)\n except ValueError:\n print('Bad arguments (expects two integers)', file=sys.stderr)\n sys.exit(1)\n\n fibonacci_gen = parametric_fibonacci(k)\n for _ in range(n):\n result = next(fibonacci_gen)\n\n print(result)\n","sub_path":"fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"551679866","text":"\"\"\"Unit tests for hyperparameter module.\"\"\"\n\nimport numpy as np\nimport sklearn\n\nfrom metalearn.components import hyperparameter\n\n\ndef test_float_hyperparameter():\n num_hp = hyperparameter.UniformFloatHyperparameter(\n \"num_hyperparam\", 2, 3, default=4, log=False, n=5)\n expected = np.array([2, 2.25, 2.5, 2.75, 3, 4])\n assert (num_hp.get_state_space() == expected).all()\n\n\ndef test_float_log_hyperparameter():\n num_hp = hyperparameter.UniformFloatHyperparameter(\n \"num_hyperparam\", 1e-3, 1e-6, default=1e-7, log=True, n=4)\n expected = np.array([1e-6, 1e-5, 1e-4, 1e-3, 1e-7])\n assert (num_hp.get_state_space() == expected).all()\n\n\ndef test_int_hyperparameter():\n num_hp = hyperparameter.UniformIntHyperparameter(\n \"num_hyperparam\", 0, 100, default=5, log=False, n=5)\n expected = np.array([0, 25, 50, 75, 100, 5])\n assert (num_hp.get_state_space() == expected).all()\n\n\ndef test_int_log_hyperparameter():\n num_hp = hyperparameter.UniformIntHyperparameter(\n \"num_hyperparam\", 1, 1000, default=1000, log=True, n=4)\n expected = np.array([1, 10, 100, 1000])\n assert (num_hp.get_state_space() == expected).all()\n\n\ndef test_tuple_pair_hyperparameter():\n tup_hp = hyperparameter.TuplePairHyperparameter(\n \"tuple_hyperparam\", [\n hyperparameter.UniformIntHyperparameter(\n \"hyperparam1\", 1, 5, default=1, n=5),\n hyperparameter.UniformIntHyperparameter(\n \"hyperparam2\", 2, 6, default=2, n=5),\n ], default=(1, 2))\n expected = np.array([\n (i, j) for i in range(1, 6) for j in range(2, 7)])\n assert (tup_hp.get_state_space() == expected).all()\n\n\ndef test_tuple_repeating_hyperparameter():\n tup_hp = hyperparameter.TupleRepeatingHyperparameter(\n \"tuple_hyperparam\",\n hyperparameter.UniformIntHyperparameter(\n \"hyperparam1\", 1, 5, default=1, n=5),\n max_nrepeats=1, default=(1, ))\n expected = np.array([\n [(1, ), (2, ), (3, ), (4, ), (5, )]])\n assert (tup_hp.get_state_space() == expected).all()\n\n # max_nrepeats > 1\n mult_tup_hp = hyperparameter.TupleRepeatingHyperparameter(\n \"tuple_hyperparam\",\n hyperparameter.UniformIntHyperparameter(\n \"hyperparam1\", 1, 2, default=1, n=2),\n max_nrepeats=2, default=(1, ))\n mult_expected = np.array([\n [(1, ), (2, ), (1, 1), (1, 2), (2, 1), (2, 2)]])\n assert (mult_tup_hp.get_state_space() == mult_expected).all()\n\n\ndef test_base_estimator_hyperparameter():\n base_est_hp = hyperparameter.BaseEstimatorHyperparameter(\n \"base_estimator_hyperparam\",\n sklearn.tree.DecisionTreeClassifier,\n hyperparameters=[\n hyperparameter.UniformIntHyperparameter(\n \"max_depth\", 1, 10, default=1, n=10, log=False)],\n default=sklearn.tree.DecisionTreeClassifier(max_depth=1))\n\n expected = [\n sklearn.tree.DecisionTreeClassifier(max_depth=i + 1)\n for i in range(10)]\n\n for i, base_est in enumerate(base_est_hp.get_state_space()):\n assert base_est.get_params() == expected[i].get_params()\n\n\ndef test_embedded_estimator_hyperparameter():\n embedded_est_hp = hyperparameter.EmbeddedEstimatorHyperparameter(\n \"embedded_estimator_hyperparam\",\n hyperparameter.CategoricalHyperparameter(\n \"hyperparam\", [\"a\", \"b\", \"c\"], default=None))\n\n assert embedded_est_hp.get_state_space() == [\"a\", \"b\", \"c\"]\n assert embedded_est_hp.hname == \"embedded_estimator_hyperparam__hyperparam\"\n","sub_path":"metalearn/tests/unit_tests/test_hyperparameter.py","file_name":"test_hyperparameter.py","file_ext":"py","file_size_in_byte":3526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"71937692","text":"def pyth_triples():\n for i in range(1, 501):\n for j in range(1, 1001 - i):\n res = (i * i + j * j) ** 0.5\n if res == int(res) and i + j + res == 1000:\n return int(i * j * res)\n\n\nprint(pyth_triples())\n# 31875000\n","sub_path":"1-10/9.py","file_name":"9.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"35730000","text":"from __future__ import annotations\n\nfrom Output import log_indent, log_unindent, log, log_decorator\n\n#MARKDOWN_NAIVE\n@log_decorator\ndef factor_naive(num: int) -> set[int]:\n log(f'Factoring {num}...')\n log_indent()\n\n factors: set[int] = set()\n for factor1 in range(1, num+1):\n for factor2 in range(1, num+1):\n log(f'Testing if {factor1} and {factor2} are factors...')\n if factor1 * factor2 == num:\n factors.add(factor1)\n factors.add(factor2)\n log(f'Yes')\n else:\n log(f'No')\n\n log_unindent()\n log(f'{factors}')\n\n return factors\n#MARKDOWN_NAIVE\n\n\n#MARKDOWN_FAST\n@log_decorator\ndef factor_fast(num: int) -> set[int]:\n log(f'Factoring {num}...')\n log_indent()\n\n factors: set[int] = set()\n for factor1 in range(1, num+1):\n log(f'Test if {factor1} is a factor...')\n factor2 = num // factor1\n remainder = num - (factor1 * factor2)\n if remainder == 0:\n factors.add(factor1)\n factors.add(factor2)\n log(f'Yes: ({factor1} and {factor2} are factors)')\n else:\n log(f'No')\n\n log_unindent()\n log(f'{factors}')\n\n return factors\n#MARKDOWN_FAST\n\n\n#MARKDOWN_FASTEST\n@log_decorator\ndef factor_fastest(num: int) -> set[int]:\n log(f'Factoring {num}...')\n log_indent()\n\n factors: set[int] = set()\n for factor1 in range(1, num+1):\n log(f'Test if {factor1} is a factor...')\n factor2 = num // factor1\n remainder = num - (factor1 * factor2)\n if remainder == 0:\n factors.add(factor1)\n factors.add(factor2)\n log(f'Yes: ({factor1} and {factor2} are factors)')\n else:\n log(f'No')\n\n if factor2 <= factor1:\n break\n\n log_unindent()\n log(f'{factors}')\n\n return factors\n#MARKDOWN_FASTEST\n\n\n#MARKDOWN_PRIMETEST\n@log_decorator\ndef is_prime(num: int) -> bool:\n log(f'Test if {num} is prime...')\n log_indent()\n\n num_factors = factor_fastest(num)\n\n # At a minimum, all counting numbers have the factors 1 and the number itself (2 factors). If\n # there are more factore than that, it's a composite. Otherwise, it's a primse.\n\n log_unindent()\n if len(num_factors) == 2:\n log(f'{num}\\'s factors are {num_factors} -- it is a prime')\n return True\n else:\n log(f'{num}\\'s factors are {num_factors} -- it is a composite')\n return False\n#MARKDOWN_PRIMETEST\n\n\n#MARKDOWN_FACTORTREE\n@log_decorator\ndef factor_tree(num: int) -> FactorTreeNode:\n log(f'Creating factor tree for {num}...')\n\n factors = factor_fastest(num)\n\n # remove factor pairs that can't used in factor true: (1, num) or (num, 1)\n factors = set([f for f in factors if f != 1 and f != num])\n\n ret = FactorTreeNode()\n if len(factors) == 0:\n ret.value = num\n log(f'Cannot factor {num} is prime -- resulting tree: {ret}')\n else:\n factor1 = next(iter(factors))\n factor2 = num // factor1\n ret.value = num\n ret.left = factor_tree(factor1)\n ret.right = factor_tree(factor2)\n log(f'Factored {num} to {factor1} and {factor2} -- resulting tree: {ret}')\n return ret\n#MARKDOWN_FACTORTREE\n\n\nclass FactorTreeNode:\n value: int\n left: FactorTreeNode | None\n right: FactorTreeNode | None\n\n def __init__(self):\n self.left = None\n self.right = None\n\n def get_prime_factors(self, output_list: list[int] = None) -> list[int]:\n if output_list is None:\n output_list = []\n\n if self.left is None and self.right is None:\n if self.value != 1: # REMEMBER: 1 is not a prime number\n output_list.append(self.value)\n\n if self.left is not None:\n self.left.get_prime_factors(output_list)\n if self.right is not None:\n self.right.get_prime_factors(output_list)\n\n return output_list\n\n def __str__(self):\n ret = str(self.value)\n if self.left is not None and self.right is not None:\n ret += '('\n if self.left is not None:\n ret += str(self.left)\n ret += ','\n if self.right is not None:\n ret += str(self.right)\n ret += ')'\n return ret\n\n\n#MARKDOWN_LADDER\n@log_decorator\ndef ladder(num: int) -> list[int]:\n prime_factors: list[int] = []\n\n log(f'Testing primes (using ladder method) to see which is factor of {num}...')\n\n log_indent()\n while not is_prime(num):\n prime_to_test = 2\n\n while True:\n log(f'Testing if {prime_to_test} is divisible by {num}...')\n new_num = num // prime_to_test\n remainder = num - (new_num * prime_to_test)\n if remainder == 0:\n break\n prime_to_test = calculate_next_prime(prime_to_test)\n\n log(f'Found! {prime_to_test} is a prime factor -- {new_num} * {prime_to_test} = {num}')\n prime_factors.append(prime_to_test)\n num = new_num\n\n log(f'Testing primes to see which is factor of {num}...')\n\n log(f'{num} itself is a prime!')\n prime_factors.append(num)\n\n log_unindent()\n log(f'Prime factors: {prime_factors}')\n\n return prime_factors\n#MARKDOWN_LADDER\n\n\ndef calculate_next_prime(last_prime: int) -> int:\n next_possible_prime = last_prime + 1\n while True:\n if is_prime(next_possible_prime):\n return next_possible_prime\n else:\n next_possible_prime += 1\n\n\n\n\nif __name__ == '__main__':\n # factors = factor_naive(int(24))\n # factors = factor_fast(int(24))\n # factors = factor_fastest(int(24))\n # print(f'{factors}')\n # print(f'{prime_test(int(49))}')\n tree = factor_tree(24)\n print(f'{tree}')\n # print(f'{ladder(int(24))}')","sub_path":"docs/data/learn/Algebra/input/arithmetic_code/Factor.py","file_name":"Factor.py","file_ext":"py","file_size_in_byte":5814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"542501789","text":"### * Description\n\n# Script to trim reads from a FASTQ file. Reads are trimmed from the 3' end\n# until the Phred score is >= scoreThreshold. Only reads of length >=\n# lengthThreshold are kept.\n#\n# Usage\n# python script.py inputFile scoreThrehsold lengthThreshold\n\n### * Set up\n\n### ** Import\n\nimport sys\n\n### ** Parameters\n\nN_ARGS = len(sys.argv) - 1\nINPUT_FILES = sys.argv[1 : (N_ARGS - 1)]\nSCORE_THRESHOLD = int(sys.argv[(N_ARGS - 1)])\nLENGTH_THRESHOLD = int(sys.argv[N_ARGS])\nPHRED_CONSTANT = 64\n\n### * Run\n\nfor INPUT_FILE in INPUT_FILES :\n with open(INPUT_FILE, \"r\") as fi :\n with open(INPUT_FILE + \".trimmed\", \"w\") as fo :\n for l1 in fi :\n l2 = fi.next()\n l3 = fi.next()\n l4 = fi.next()\n phred_score = [ord(x) - PHRED_CONSTANT for x in l4.strip()]\n i = len(phred_score) - 1\n while (i > 0 and phred_score[i] < SCORE_THRESHOLD) :\n i -= 1\n if (i + 1) >= LENGTH_THRESHOLD :\n fo.write(l1)\n fo.write(l2.strip()[0:(i+1)] + \"\\n\")\n fo.write(l3)\n fo.write(\"\".join([chr(x + PHRED_CONSTANT) for x in phred_score[0:(i+1)]]) + \"\\n\")\n","sub_path":"pelican_website/content/resources/trim_reads.py","file_name":"trim_reads.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"515084822","text":"from __future__ import print_function\nfrom six.moves import cPickle as pickle\nimport numpy as np\nimport os\nimport platform\nimport matplotlib.pyplot as plt\n\n\ndef load_pickle(f):\n version = platform.python_version_tuple()\n if version[0] == '2':\n return pickle.load(f)\n elif version[0] == '3':\n return pickle.load(f, encoding='latin1')\n raise ValueError(\"invalid python version: {}\".format(version))\n\n\ndef load_CIFAR_batch(filename, n_class):\n with open(filename, 'rb') as f:\n datadict = load_pickle(f)\n X = datadict['data']\n Y = datadict['labels']\n XX = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype(\"float\") / 255.0\n Y = np.array(Y)\n YY = np.zeros([Y.shape[0], n_class])\n YY[np.arange(Y.shape[0]), Y] = 1.0\n return XX, YY\n\n\ndef Load_CIFAR10(path):\n data_train = dict()\n data_test = dict()\n xs = []\n ys = []\n for b in range(1, 6):\n f = os.path.join(path, 'data_batch_%d' % (b,))\n X, Y = load_CIFAR_batch(f, 10)\n xs.append(X)\n ys.append(Y)\n Xtr = np.concatenate(xs)\n Ytr = np.concatenate(ys)\n del X, Y\n Xte, Yte = load_CIFAR_batch(os.path.join(path, 'test_batch'), 10)\n data_train['input'] = Xtr\n data_train['output'] = Ytr\n data_test['input'] = Xte\n data_test['output'] = Yte\n return data_train, data_test\n","sub_path":"dataset/cifar10.py","file_name":"cifar10.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"183861823","text":"from discord.ext import commands\nimport discord\nimport logging\nimport traceback\nimport auth\nimport time\nimport os\n\nfrom utils import util\n\nlogging.basicConfig(level=logging.INFO,\n format=\"[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s\",\n datefmt=\"%y.%m.%d %H:%M:%S\",\n filename=\"discord.log\",\n filemode=\"w+\")\n\nconsole = logging.StreamHandler()\nconsole.setLevel(logging.INFO)\nconsole.setFormatter(logging.Formatter(\"[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s\", datefmt=\"%H:%M:%S\"))\nlogging.getLogger(\"\").addHandler(console)\nbot_logger = logging.getLogger(\"bot.client\")\n\n\ndef get_prefix(client, message):\n prefix = \"%\"\n if message.guild is not None:\n prefix = client.config.get(message.guild.id, \"prefix\")\n return commands.when_mentioned_or(prefix)(client, message)\n\n\nclass PhoneBot(commands.Bot):\n\n def __init__(self, logger, self_bot=False, interactive=False):\n super().__init__(command_prefix=get_prefix,\n description=f\"A \\\"simple\\\" bot made by @5space#9502\",\n help_attrs=dict(hidden=True))\n\n if self_bot:\n if interactive:\n self._skip_check = lambda x, y: False\n else:\n self._skip_check = lambda x, y: x != y\n else:\n self._skip_check = lambda x, y: x == y\n\n self.config = util.Config(self, util.DEFAULT_CONFIG)\n self.config.load()\n\n self.blacklisted = auth.BLACKLISTED\n\n self.logger = logger\n self.time = time.time()\n self.admin_ids = auth.ADMINS\n\n @staticmethod\n def log(msg):\n bot_logger.info(msg)\n\n async def update_presence(self):\n name = f\"{len(list(self.get_all_members()))} users in {len(self.guilds)} guilds\"\n activity = discord.Activity(type=discord.ActivityType.watching,\n name=name)\n await self.change_presence(status=discord.Status.online,\n activity=activity)\n\n # Bot events\n async def on_ready(self):\n self.log(f\"Logged in as {self.user.name} (ID: {self.user.id})\")\n await self.update_presence()\n\n async def on_message(self, message):\n if message.guild is None:\n return\n await self.process_commands(message)\n\n async def invoke(self, ctx):\n if ctx.command is not None:\n if ctx.author.id in auth.BLACKLISTED:\n await ctx.send(f\"Sorry, {ctx.author.name}, you've been blacklisted.\")\n return\n await super().invoke(ctx)\n\n async def on_command_error(self, ctx, error):\n if isinstance(error, commands.CommandNotFound):\n return\n elif isinstance(error, commands.CommandOnCooldown):\n await ctx.send(error)\n elif isinstance(error, util.ModCheckFailure):\n await ctx.send(\"You must have Manage Guild permissions to execute this command.\")\n elif isinstance(error, commands.NotOwner):\n await ctx.send(\"You do not own this bot.\")\n elif isinstance(error, commands.MissingRequiredArgument):\n await ctx.send(f\"\\\"{error.param}\\\" is a required argument and is missing.\")\n else:\n await ctx.send(error)\n await super().on_command_error(ctx, error)\n\n async def on_guild_join(self, guild):\n if guild.system_channel is not None:\n await guild.system_channel.send(util.JOIN_STRING)\n self.log(f\"Joined guild {guild.name}. (ID: {guild.id})\")\n await self.update_presence()\n\n async def on_guild_remove(self, guild):\n self.log(f\"Left guild {guild.name}. (ID: {guild.id})\")\n await self.update_presence()\n\n # Override to allow multiple ownership\n\n async def is_owner(self, user):\n return user.id in self.admin_ids\n\n def shut_down(self):\n self.log(\"Shutting down...\")\n\n\nbot = PhoneBot(bot_logger)\n\n\ndef main():\n extensions = [\"modules.\" + module + \".cog\" for module in os.listdir(\"modules\") if module[0] not in \"._\"]\n for ext in extensions:\n try:\n bot.load_extension(ext)\n except Exception:\n bot.log(f\"Failed to load extension {ext}:\")\n bot.log(traceback.format_exc(limit=-1))\n try:\n bot.run(auth.TOKEN)\n finally:\n bot.shut_down()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":4412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"320028380","text":"import socket\nimport os\n\ntarget_host = \"localhost\"\ntarget_port = 5000\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclient.connect((target_host, target_port))\n\nmensagem = \"/home/anderson/dados.txt\"\nb = bytes(mensagem, 'utf-8')\nclient.send(b)\narquivo = open('/home/anderson/Área de Trabalho/file.txt', 'w')\n\nwhile 1: \n\tresposta = client.recv(1024)\n\tif not resposta:\n\t\tbreak\n\tdados = resposta.decode('utf-8')\n\tprint(resposta)\n\tarquivo.write(dados)\n\narquivo.close()\nclient.close()\n","sub_path":"TRABcliente.py","file_name":"TRABcliente.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"604043909","text":"import socket\r\nfrom threading import Thread\r\nimport os\r\nimport time\r\n\r\nTCP_IP = 'localhost'\r\nTCP_PORT = 9001\r\nBUFFER_SIZE = 1024\r\n\r\n\r\nclass ClientThread(Thread):\r\n\r\n def __init__(self, ip, port, sock):\r\n Thread.__init__(self)\r\n self.ip = ip\r\n self.port = port\r\n self.sock = sock\r\n print(\" New thread started for \"+ip+\":\"+str(port))\r\n\r\n def run(self):\r\n pc_name = self.sock.recv(1024).decode('utf-8')\r\n self.sock.send(b'ok')\r\n if not os.path.exists(os.path.join(os.curdir, pc_name)):\r\n os.makedirs(f'./{pc_name}')\r\n name = f\"{pc_name}/{time.time()}.zip\"\r\n with open(name, 'wb') as f:\r\n print('file opened')\r\n while True:\r\n #print('receiving data...')\r\n data = self.sock.recv(BUFFER_SIZE)\r\n print('data=%s', (data))\r\n if not data:\r\n f.close()\r\n print('file close()')\r\n break\r\n # write data to a file\r\n f.write(data)\r\n\r\n\r\n\r\ntcpsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\ntcpsock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\r\ntcpsock.bind((TCP_IP, TCP_PORT))\r\nthreads = []\r\n\r\nwhile True:\r\n tcpsock.listen(5)\r\n print(\"Waiting for incoming connections...\")\r\n (conn, (ip, port)) = tcpsock.accept()\r\n print('Got connection from ', (ip, port))\r\n newthread = ClientThread(ip, port, conn)\r\n newthread.start()\r\n threads.append(newthread)\r\n\r\nfor t in threads:\r\n t.join()","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"534133729","text":"'''\r\nCreated on Sep 22, 2015\r\nThis file implements approach of using PCA in sklearn to find patterns among users though weekdays\r\n\r\n@author: HCH\r\n'''\r\nimport pandas as pd\r\nfrom sklearn import decomposition, preprocessing\r\nimport datetime\r\nimport numpy as np\r\nfrom scipy.stats.stats import pearsonr\r\nimport matplotlib.pylab as plt\r\nimport re\r\nif __name__ == \"__main__\":\r\n data = pd.read_csv('student.csv', encoding = 'utf8') \r\n# students in strategi course who viewed content/link \r\n data_strategi = data[(data.space_1 == 'Entrepreneurship ELE3702') & \r\n (data.event_1 == 'Viewed folder') ]\r\n print(data_strategi.shape)\r\n # data from 2015-08-24 to 2015-09-21\r\n data_strategi['date'] = data_strategi.time_1.map(lambda x : datetime.datetime.strptime(x,'%Y-%m-%d %H:%M:%S').date())\r\n data_sl = data_strategi[(data_strategi.date >datetime.date(2015,8,30))\r\n &\r\n (data_strategi.date < datetime.date(2015,9,21))]\r\n print(data_sl.shape) \r\n data_sl['weekday'] = data_sl.time_1.map(lambda x : datetime.datetime.strptime(x,'%Y-%m-%d %H:%M:%S').strftime('%a'))\r\n df = pd.crosstab(data_sl.distinct_id, data_sl.name)\r\n# df = df.loc[:, ['Mon', 'Tue', 'Wed', 'Thu','Fri','Sat','Sun']] \r\n user_id = df.index.values \r\n ##### pca transform\r\n # scale array\r\n usr_day = df.values\r\n usr_day = usr_day / np.sum(usr_day, axis = 1).reshape(-1,1) \r\n usr_day = usr_day.T\r\n scl = preprocessing.StandardScaler()\r\n usr_day_scl = scl.fit_transform(usr_day)\r\n # fit pca\r\n n_components = 0.95\r\n pca = decomposition.PCA(n_components = n_components, copy = True, whiten = True)\r\n pca.fit(usr_day_scl) \r\n print(pca.explained_variance_ratio_)\r\n# print(pca.mean_)\r\n print(pca.n_components_)\r\n \r\n ### find most correlated variables/students\r\n usr_cmp = pca.transform(usr_day_scl) \r\n usr_view_col = []\r\n for i in range(pca.n_components_):\r\n cor_lst = []\r\n for j in range(usr_day.shape[1]):\r\n cor = np.abs(pearsonr(usr_day[:,j], usr_cmp[:,i])[0])\r\n cor_lst.append((cor, user_id[j], usr_day[:,j]))\r\n \r\n cor_lst = sorted(cor_lst, reverse= True) \r\n print(cor_lst[0])\r\n usr_view_col.append(cor_lst[0][2])\r\n# df = pd.DataFrame(usr_cmp, columns = ['Type 1','Type 2', 'Type 3'])\r\n# df.to_csv('pca_analysis/pca_3.csv', index=False)\r\n \r\n ### plot the system \r\n color_vec = ['red','green', 'blue', 'yellow', 'black', 'cyan', 'magenta']\r\n# x_lev = ('Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sar', 'Sun')\r\n print(df.columns)\r\n x_lev = tuple(map(lambda x: re.split(' ', x)[0], df.columns))\r\n print(x_lev)\r\n legend, legend_name = [], []\r\n for idx in range(len(usr_view_col)):\r\n p = plt.plot(usr_view_col[idx], c = color_vec[idx], linestyle = '-')\r\n legend.append(p[0])\r\n legend_name.append(str(int(pca.explained_variance_ratio_[idx] * 100 )) + '%')\r\n plt.xticks(np.arange(len(x_lev)), x_lev)\r\n plt.xlabel('folder name'); plt.ylabel('folder view ratio')\r\n plt.title('User types on folder view ratio - Entrepreneurship')\r\n plt.legend(tuple(legend), tuple(legend_name))\r\n plt.show()\r\n \r\n \r\n \r\n \r\n \r\n ","sub_path":"EdTech/user_pca.py","file_name":"user_pca.py","file_ext":"py","file_size_in_byte":3310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"369288737","text":"from numpy import * \n\ndef read(filename): \n f = open(filename,'r')\n data = [x.strip().split() for x in f.readlines()]\n for i in range(0,len(data)): \n for j in range(0,len(data[0])): \n data[i][j] = float(data[i][j])\n\n data = array(data)\n return(data)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"505104028","text":"# @Author : t1an5t\n\nfrom pwn import *\n#context.log_level = \"debug\"\n\nREMOTE = 1\n\nif REMOTE:\n sh = remote(\"150.109.44.250\", 20002)\n sh.recvuntil(\"token:\")\n sh.sendline(\"OtHPZK42sCF3Ri1eAPuSYTCGEUNfhKew\")\nelse:\n sh = process(\"./the_end\", env={'LD_PRELOAD':'./libc64.so'})\n\nelf = ELF(\"./the_end\")\nlibc = ELF(\"./libc64.so\")\none_offset = 0xf1147\n\nsh.recvuntil(\"gift \")\nsleep_addr = int(sh.recvuntil(\",\")[:-1].strip(), 16)\nlibc_base = sleep_addr - libc.symbols[\"sleep\"]\nsh.recvline()\n\nlog.success(\"sleep's address: %s\" %hex(sleep_addr))\nlog.success(\"libc's base: %s\" %hex(libc_base))\n\none_addr = one_offset + libc_base\nlog.success(\"onegadget's address: %s\" %hex(one_addr))\n\nio_list_all = libc_base + libc.symbols[\"_IO_list_all\"]\nlog.success(\"IO_list_all: %s\" %hex(io_list_all))\n\nfake_vtable_addr = io_list_all - 0x140\nlog.success(\"fake vtable data's address: %s\" %hex(fake_vtable_addr))\nfake_vtable_overflow = fake_vtable_addr + 0x18\nlog.success(\"fake vatble->_overflow's address: %s\" %hex(fake_vtable_overflow))\nvtable_ptr_addr = io_list_all + 0x100 + 0xd8\nlog.success(\"IO_stdout -> vtable's address: %s\" %hex(vtable_ptr_addr))\nio_write_ptr = io_list_all + 0x100 + 0x28\nlog.success(\"IO_stdout -> _IO_write_ptr's address: %s\" %hex(io_write_ptr))\n\n\ndef change_one_byte(address, byte):\n sh.send(address)\n sh.send(byte)\n\n\nchange_one_byte(p64(fake_vtable_overflow), p64(one_addr)[0])\nchange_one_byte(p64(fake_vtable_overflow+1), p64(one_addr)[1])\nchange_one_byte(p64(fake_vtable_overflow+2), p64(one_addr)[2])\nchange_one_byte(p64(vtable_ptr_addr+1), p64(fake_vtable_addr)[1])\nchange_one_byte(p64(io_write_ptr), chr(0xff))\nsh.interactive()\n","sub_path":"hctf2018/pwn/the_end/exp.py","file_name":"exp.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"56622564","text":"import math\n\n# preprocesses tree and handles lca queries\n# preprocessing takes O(nlgn) and query answering - O(1)\nclass LCA:\n def __init__(self):\n self.rmq_array = []\n self.sparse_table = []\n self.first_index_of = {}\n self.depth = {}\n self.internal_indexing = {}\n self.internal_indexing_reversed = {}\n self.internal_index = 0\n\n def create_rmq_array(self, node):\n self.rmq_array.append(self.internal_indexing[node.index])\n for child in node.children.values():\n self.create_rmq_array(child)\n self.rmq_array.append(self.internal_indexing[node.index])\n\n def create_sparse_table(self):\n n = len(self.rmq_array)\n lgn = int(math.log2(n)) + 1\n self.sparse_table = [[-1 for i in range(n)] for i in range(lgn)]\n for i in range(1, n):\n if self.rmq_array[i] > self.rmq_array[i - 1]:\n self.sparse_table[0][i - 1] = i - 1\n else:\n self.sparse_table[0][i - 1] = i\n for j in range(1, lgn):\n for i in range(n - 2 ** (j - 1)):\n s = self.sparse_table[j - 1][i]\n t = self.sparse_table[j - 1][i + 2 ** (j - 1)]\n if self.rmq_array[s] > self.rmq_array[t]:\n self.sparse_table[j][i] = t\n else:\n self.sparse_table[j][i] = s\n\n def create_first_index_of(self):\n for i in range(len(self.rmq_array)):\n a = self.rmq_array[i]\n if a not in self.first_index_of:\n self.first_index_of[a] = i\n\n def create_depth(self, node, depth):\n self.depth[node.index] = depth\n for child in node.children.values():\n self.create_depth(child, depth + len(child.label))\n\n def create_internal_indexing(self, node):\n self.internal_index += 1\n self.internal_indexing[node.index] = self.internal_index\n self.internal_indexing_reversed[self.internal_index] = node.index\n for child in node.children.values():\n self.create_internal_indexing(child)\n\n def preprocess(self, root):\n self.create_internal_indexing(root)\n self.create_rmq_array(root)\n self.create_sparse_table()\n self.create_first_index_of()\n self.create_depth(root, 0)\n\n def query_depth(self, a, b):\n return self.depth[self.query(a, b)]\n\n def query(self, a, b):\n a = self.first_index_of[self.internal_indexing[a]]\n b = self.first_index_of[self.internal_indexing[b]]\n a, b = min(a, b), max(a, b)\n if a == b:\n return self.internal_indexing_reversed[self.rmq_array[a]]\n d = int(math.log2(b - a))\n return self.internal_indexing_reversed[\n min(self.rmq_array[self.sparse_table[d][a]],\n self.rmq_array[self.sparse_table[d][b - 2 ** d]])]\n","sub_path":"common/lca.py","file_name":"lca.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"462896267","text":"import sublime_plugin\nimport sublime\nimport os\n\nclass MyEvents( sublime_plugin.EventListener ):\n def on_activated( self, view ):\n s = view.file_name()\n\n if s:\n if not os.path.exists( s ):\n if not \"sublime-\" in s:\n view.set_scratch( True )\n sublime.set_timeout(lambda: view.window().run_command(\"close_file\"), 0)\n\n","sub_path":"modules/sublime/user/auto-close.py","file_name":"auto-close.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"237622231","text":"from flask import Flask, request\nfrom data import GetcourseData\nimport requests\nimport datetime\n\ntoken = TOKEN\napp = Flask(__name__)\n\n\n@app.route('/')\ndef getcourse():\n\n data = GetcourseData(request.args.get('payment'), request.args.get('order_id'))\n payment = data.order_payment\n order = 'Заказ с Геткурс # '+ data.order_id\n\n p = ''.join(x for x in payment if x.isdigit())\n d = datetime.date.today()\n\n url = 'https://api.planfact.io/api/v1/operations/income'\n payload = {'operationDate': d, 'accountId': '93386', 'value': float(p), 'comment': order}\n headers = {'X-ApiKey': token}\n if p > str(0):\n r = requests.post(url, headers=headers, data=payload)\n return 'OK'\n\nif __name__ == '__main__':\n app.run(host='localhost')\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"19363010","text":"import requests\nfrom lxml import etree\nimport json as js\n\n\ndef get_one_page(start):\n start = i * 20\n print(\"----------------------第%s页--------------------\" % (i+1))\n url = 'https://movie.douban.com/subject/24852545/comments?start='+str(start)+'&limit=20&sort=new_score&status=P'\n response = requests.get(url)\n return response.text\n\n\ndef parse_page(page):\n html = etree.HTML(page)\n comments = html.xpath(\"//div[@class='comment']\")\n # score = html.xpath(\"//div[@class='comment']//h3//span[@class='comment-info']//span[contains(@class,'rating')]/@title\")\n for comment in comments:\n text = comment.find(path=\"p\").find(path=\"span[@class='short']\").text\n score = comment.find(path=\"h3\").find(path=\"span[@class='comment-info']\").findall(path=\"span\")[1].get('title')\n print('%s %s' % (text,score))\n\n\nif __name__ == '__main__':\n for i in range(0, 11):\n page = get_one_page(i)\n parse_page(page)\n\n\n\n","sub_path":"base/aqgy_new.py","file_name":"aqgy_new.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"574010846","text":"from flask import Flask\nfrom pymongo import MongoClient\nfrom bson.json_util import dumps\nfrom datetime import datetime\nfrom kafka import KafkaConsumer\nfrom kafka import KafkaProducer\nimport json\n\nproducer = KafkaProducer(bootstrap_servers=['host.docker.internal:9092'],value_serializer=lambda x:dumps(x).encode('utf-8'))\n\nconsumer = KafkaConsumer(bootstrap_servers=['host.docker.internal:9092'],auto_offset_reset='latest')\n\n\nmongo = MongoClient('mongodb://mongo/test').db\n# mongo = MongoClient('mongodb://localhost:27017').db\n\n\nconsumer.subscribe(['node_3'])\nfor message in consumer:\n\tdata = json.loads(message.value.decode(\"utf-8\"))\n\tif data[\"operation\"] == \"insert\":\n\t\tinsert_response = mongo.record_table.insert_one({\n\t\t\t\"key\": data[\"key\"],\n\t\t\t\"value\": data[\"value\"],\n\t\t})\n\t\tproducer.send(\"result\",str(insert_response.inserted_id))\n\telif data[\"operation\"] == \"query\" and 'key' in data:\n\t\tresponse = mongo.record_table.find_one({\n\t\t\t\"key\": data[\"key\"]\n\t\t})\n\t\tif response:\n\t\t\tproducer.send(\"result\", str(response[\"value\"]))\n\t\telse:\n\t\t\tproducer.send(\"result\", \"Invalid Request\")\n\telse:\n\t\tproducer.send(\"result\", \"Invalid Request\")\n","sub_path":"nodes/node_3/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"582199101","text":"from collections import namedtuple\nimport glob\nimport os.path\n\nimport pytoml\nimport pytest\n\n\ntestcase_fields = 'data,method,path,version,headers,body,error'\n\nHttpTestCase = namedtuple('HTTPTestCase', testcase_fields)\n\n\ndef parametrize_cases(suite, *args):\n suite = suites[suite]\n cases_list = [\n [suite[c] for c in sel.split('+')] for sel in args]\n return pytest.mark.parametrize('cases', cases_list, ids=args)\n\n\ndef load_casefile(path):\n result = {}\n\n with open(path) as casefile:\n cases = pytoml.load(casefile)\n\n for case_name, case_data in cases.items():\n case_data['data'] = case_data['data'].encode('utf-8')\n case_data['body'] = case_data['body'].encode('utf-8') \\\n if 'body' in case_data else None\n case = HttpTestCase._make(\n case_data.get(f) for f in testcase_fields.split(','))\n result[case_name] = case\n\n return result\n\n\ndef load_cases():\n cases = {}\n\n for filename in glob.glob('cases/*.toml'):\n suite_name, _ = os.path.splitext(os.path.basename(filename))\n cases[suite_name] = load_casefile(filename)\n\n return cases\n\n\nsuites = load_cases()\nglobals().update(suites)\n","sub_path":"evolution/evolution_009/cases.py","file_name":"cases.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"392431548","text":"# Train the model\n\nimport pickle\nimport numpy as np\nfrom sklearn.utils import shuffle\n\nfrom keras.models import Sequential\nfrom keras.layers import Flatten, Dense, Lambda, Cropping2D, Dropout\nfrom keras.layers.convolutional import Convolution2D\nfrom keras.layers.pooling import MaxPooling2D\nfrom keras.optimizers import Adam\n\n\n# Reload the data\n\ndef reload_data(pickle_file):\n print('reload: ', pickle_file)\n with open(pickle_file, 'rb') as f:\n pickle_data = pickle.load(f)\n X_train = pickle_data['X_train']\n y_train = pickle_data['y_train']\n del pickle_data # Free up memory\n return X_train, y_train\n\nX_train, y_train = reload_data('./pre-data.pickle')\nX_train, y_train = shuffle(X_train, y_train)\nprint('X_train shape: ', X_train.shape, 'y_train shape: ',y_train.shape)\n\n\n# Model architecture\n\nnvidia = Sequential()\nnvidia.add(Lambda(lambda x: x/255. - 0.5, input_shape=(80, 80, 3)))\nnvidia.add(Cropping2D(cropping=((35, 13), (0, 0))))\nnvidia.add(Convolution2D(24, 3, 3, subsample=(2, 2), activation='relu'))\nnvidia.add(Convolution2D(36, 3, 3, subsample=(2, 2), activation='relu'))\nnvidia.add(Convolution2D(48, 3, 3, activation='relu'))\nnvidia.add(Convolution2D(64, 3, 3, activation='relu'))\nnvidia.add(Convolution2D(64, 3, 3, activation='relu'))\nnvidia.add(Dropout(0.5))\nnvidia.add(Flatten())\nnvidia.add(Dense(100))\nnvidia.add(Dense(50))\nnvidia.add(Dense(10))\nnvidia.add(Dense(1))\n\n\n# Training method\n\n# Hyperparameters\nLEARNING_RATE = 1e-4\nEPOCHS = 5\n\n# Training\nnvidia.compile(loss='mse', optimizer=Adam(LEARNING_RATE))\nnvidia.fit(X_train, y_train, validation_split=0.2, shuffle=True, nb_epoch=EPOCHS)\nnvidia.save('model.h5')\n\n","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":1670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"634907703","text":"from pokeradio.history.models import ArchiveTrack, Artist, Play\nfrom pokeradio.models import Track\n\n\ndef get_or_create_artist(track):\n \"\"\" Used by get_or_create_track, finds existing Artist object if\n available, otherwise creates one.\n \"\"\"\n\n try:\n artist = Artist.objects.get(spotify_artist_href=track.artist_href)\n except Artist.DoesNotExist:\n artist = Artist.objects.create(spotify_artist_href=track.artist_href,\n name=track.artist)\n return artist\n\n\ndef get_or_create_track(track):\n \"\"\" Accepts a Playlist Track and looks for a record of it in the archive\n using the Spotify URI as a unique reference.\n If an ArchiveTrack cannot be found, one is created\n \"\"\"\n\n try:\n track = ArchiveTrack.objects.get(spotify_href=track.href)\n except ArchiveTrack.DoesNotExist:\n artist = get_or_create_artist(track)\n track = ArchiveTrack.objects.create(\n spotify_href=track.href,\n name=track.name,\n spotify_album_href=track.album_href,\n length=track.length,\n artist=artist)\n return track\n\n\ndef record_track_play(track):\n \"\"\" Takes a Playlist Track object, and finds or creates an ArchiveTrack\n and adds a play to it against the user linked to the playlist track\n \"\"\"\n archive_track = get_or_create_track(track)\n play = Play.objects.create(track=archive_track, user=track.user)\n","sub_path":"web/pokeradio/history/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"180300080","text":"from PIL import Image, ImageDraw\nimport cv2\nfrom operator import add\n\nimport numpy as np\nfrom timeit import default_timer as timer\nclass AttentionModel(object):\n \"\"\"\n Calculation of which crop should be active. Stands in the middle of two evaluations - attention evaluation\n should produce a rough estimation where we should look, this class determines which crops are active from it.\n \"\"\"\n\n def __init__(self, settings, cropscoordinates, evaluation, history):\n self.settings = settings\n self.cropscoordinates = cropscoordinates\n self.evaluation = evaluation\n self.history = history\n\n def get_all_crops(self, evaluation_coordinates):\n \"\"\"\n Even faster, just looking at intersections\n \"\"\"\n start = timer()\n\n active_coordinates = []\n already_loaded_ids = []\n\n for b_coordinate in evaluation_coordinates:\n box = list(b_coordinate[1])\n id = b_coordinate[0]\n\n if id not in already_loaded_ids:\n active_coordinates.append([id, box])\n already_loaded_ids.append(id)\n\n end = timer()\n time_active_coords = end - start\n if self.settings.verbosity > 2:\n print(\"Attention model found\",len(active_coordinates),\"active crops in the image (out of\",len(evaluation_coordinates),\") in \",time_active_coords)\n\n self.history.report_time_getting_active_crops(time_active_coords, self.settings.frame_number)\n self.history.report_attention(len(active_coordinates), len(evaluation_coordinates), self.settings.frame_number)\n\n return active_coordinates\n\n\n def get_active_crops_intersections(self, projected_evaluation, evaluation_coordinates, frame):\n \"\"\"\n Even faster, just looking at intersections\n \"\"\"\n start = timer()\n\n if self.settings.turn_off_attention_baseline:\n already_loaded_ids = []\n active_coordinates = []\n\n for b_coordinate in evaluation_coordinates:\n box = list(b_coordinate[1])\n id = b_coordinate[0]\n intersects = True\n if intersects:\n if id not in already_loaded_ids:\n active_coordinates.append([id, box])\n already_loaded_ids.append(id)\n\n end = timer()\n time_active_coords = end - start\n if self.settings.verbosity > 2:\n print(\"Attention model found\",len(active_coordinates),\"active crops in the image (out of\",len(evaluation_coordinates),\") in \",time_active_coords)\n\n self.history.report_time_getting_active_crops(time_active_coords, self.settings.frame_number)\n self.history.report_attention(len(active_coordinates), len(evaluation_coordinates), self.settings.frame_number)\n return active_coordinates\n\n # careful, this has to be calculated exactly once! (its changing the values)\n projected_evaluation = self.cropscoordinates.project_evaluation_to(projected_evaluation, 'original_image_to_evaluation_space')\n\n scaled_extend = self.settings.extend_mask_by * self.cropscoordinates.scale_ratio_of_evaluation_crop\n\n evaluations_bboxes = self.cropscoordinates.evaluations_to_bboxes(projected_evaluation)\n\n active_coordinates = []\n already_loaded_ids = []\n\n scale = self.cropscoordinates.scale_ratio_of_evaluation_crop\n nw = int(self.settings.w * scale)\n nh = int(self.settings.h * scale)\n image_size = (nw, nh)\n\n # look at % sized corner\n crop_size = 608 # self.crop_size_in_evaluation\n mask_over = 0.1\n\n for a in evaluations_bboxes:\n\n ## edit the a into a_extended\n top = a[1]#bbox[\"topleft\"][\"y\"]\n left = a[0]#bbox[\"topleft\"][\"x\"]\n bottom = a[3]#bbox[\"bottomright\"][\"y\"]\n right = a[2]#bbox[\"bottomright\"][\"x\"]\n top = max(0, np.floor(top + 0.5).astype('int32'))\n left = max(0, np.floor(left + 0.5).astype('int32'))\n bottom = min(image_size[1], np.floor(bottom + 0.5).astype('int32'))\n right = min(image_size[0], np.floor(right + 0.5).astype('int32'))\n\n a_extended = [left - scaled_extend, top - scaled_extend, right + scaled_extend, bottom + scaled_extend]\n\n for b_coordinate in evaluation_coordinates:\n box = list(b_coordinate[1])\n id = b_coordinate[0]\n\n\n # intersections in corners\n\n a_l = [box[0]+0, box[1]+0, box[0]+crop_size * (1 - mask_over), box[1]+crop_size * (1 - mask_over)]\n b_l = [box[0]+0, box[1]+crop_size * (mask_over), box[0]+crop_size * (1 - mask_over), box[1]+crop_size * (1 - mask_over) + crop_size * (mask_over)]\n c_l = [box[0]+crop_size * mask_over, box[1]+0, box[0]+crop_size * (1 - mask_over) + crop_size * mask_over, box[1]+crop_size * (1 - mask_over)]\n d_l = [box[0]+crop_size * mask_over, box[1]+crop_size * mask_over, box[0]+crop_size * (1 - mask_over) + crop_size * mask_over, box[1]+crop_size * (1 - mask_over) + crop_size * mask_over]\n\n corner_empty = False\n for p in [a_l, b_l, c_l, d_l]:\n intersects = self.bounding_boxes_intersect(a_extended, p)\n\n if not intersects:\n corner_empty = True\n break\n if corner_empty:\n continue\n\n # intersections in the main square\n\n intersects = self.bounding_boxes_intersect(a_extended, box)\n\n # print(intersects)\n if intersects:\n if id not in already_loaded_ids:\n #print(id, \"<=>\", a_extended, box)\n\n active_coordinates.append([id, box])\n already_loaded_ids.append(id)\n\n end = timer()\n time_active_coords = end - start\n if self.settings.verbosity > 2:\n print(\"Attention model found\",len(active_coordinates),\"active crops in the image (out of\",len(evaluation_coordinates),\") in \",time_active_coords)\n\n self.history.report_time_getting_active_crops(time_active_coords, self.settings.frame_number)\n self.history.report_attention(len(active_coordinates), len(evaluation_coordinates), self.settings.frame_number)\n\n return active_coordinates\n\n def get_active_crops_older(self, projected_evaluation, evaluation_coordinates, frame):\n \"\"\"\n Faster, because we don't scale the temp mask image\n possibly can be further rewritten...\n \"\"\"\n start = timer()\n projected_evaluation = self.cropscoordinates.project_evaluation_to(projected_evaluation, 'original_image_to_evaluation_space')\n scaled_extend = self.settings.extend_mask_by * self.cropscoordinates.scale_ratio_of_evaluation_crop\n\n mask_image = self.mask_image_eval_space_size(projected_evaluation, scaled_extend)\n # building both the mask and in the evaluation space\n\n #self.settings.debugger.debug_attention_mask(mask_image, custom_name=\"__\"+str(self.settings.frame_number)+\"checkIfItsTheSame\")\n \n # evaluation_coordinates are also in the evaluation space\n active_coordinates = self.active_coordinates_in_mask(mask_image, evaluation_coordinates)\n \n end = timer()\n time_active_coords = end - start\n if self.settings.verbosity > 2:\n print(\"Attention model found\",len(active_coordinates),\"active crops in the image (out of\",len(evaluation_coordinates),\") in \",time_active_coords)\n\n self.history.report_time_getting_active_crops(time_active_coords, self.settings.frame_number)\n self.history.report_attention(len(active_coordinates), len(evaluation_coordinates), self.settings.frame_number)\n\n return active_coordinates\n\n def in_between(self,p,a,b):\n # p in between of a and b\n if p >= a and p <= b:\n return True\n if p >= b and p <= a:\n return True\n return False\n\n def intervals_intersect(self, start1, end1, start2, end2):\n if start1 > end1:\n t = end1\n end1 = start1\n start1 = t\n if start2 > end2:\n t = end2\n end2 = start2\n start2 = t\n \"\"\"Does the range (start1, end1) overlap with (start2, end2)?\"\"\"\n return end1 >= start2 and end2 >= start1\n\n def bounding_boxes_intersect(self, bbox1, bbox2):\n left1, top1, right1, bottom1 = bbox1\n left2, top2, right2, bottom2 = bbox2\n\n if (self.intervals_intersect(left1, right1, left2, right2) and\n self.intervals_intersect(bottom1, top1, bottom2, top2)):\n return True\n\n return False\n\n def active_coordinates_in_mask(self, mask_image, coordinates):\n #print(\"coordinates\", coordinates)\n\n crop_size = self.cropscoordinates.crop_size_in_evaluation\n mask_over = 0.1\n #print(\"crop_size\", crop_size)\n\n active_coordinates = []\n for crop_coordinates in coordinates:\n id = crop_coordinates[0]\n area = crop_coordinates[1]\n\n cropped_mask = mask_image.crop(box=area)\n cropped_mask = cropped_mask.resize((crop_size, crop_size), resample=Image.BILINEAR)\n cropped_mask.load()\n\n\n # four corners\n a = cropped_mask.crop(box=(0, 0, crop_size * (1 - mask_over), crop_size * (1 - mask_over)))\n b = cropped_mask.crop(\n box=(0, crop_size * (mask_over), crop_size * (1 - mask_over), crop_size * (1 - mask_over) + crop_size * (mask_over)))\n c = cropped_mask.crop(\n box=(crop_size * mask_over, 0, crop_size * (1 - mask_over) + crop_size * mask_over, crop_size * (1 - mask_over)))\n d = cropped_mask.crop(box=(\n crop_size * mask_over, crop_size * mask_over, crop_size * (1 - mask_over) + crop_size * mask_over,\n crop_size * (1 - mask_over) + crop_size * mask_over))\n\n a_l = [0, 0, crop_size * (1 - mask_over), crop_size * (1 - mask_over)]\n b_l = [0, crop_size * (mask_over), crop_size * (1 - mask_over), crop_size * (1 - mask_over) + crop_size * (mask_over)]\n c_l = [crop_size * mask_over, 0, crop_size * (1 - mask_over) + crop_size * mask_over, crop_size * (1 - mask_over)]\n d_l = [crop_size * mask_over, crop_size * mask_over, crop_size * (1 - mask_over) + crop_size * mask_over,crop_size * (1 - mask_over) + crop_size * mask_over]\n\n corner_empty = False\n for p in [a, b, c, d]:\n p.load()\n lum = np.sum(np.sum(p.getextrema(), 0))\n # print(p.size, lum)\n if lum == 0:\n corner_empty = True\n break\n\n if corner_empty:\n continue\n\n extrema = cropped_mask.getextrema()\n extrema_sum = np.sum(extrema, 0)\n #print(\"summed extrema\", extrema_sum)\n\n if extrema_sum == 0: # and extrema_sum[1] == 0:\n continue\n\n active_coordinates.append([id, area])\n\n return active_coordinates\n\n def mask_image(self, bboxes, EXTEND_BY):\n\n image_size = (self.settings.w, self.settings.h)\n image = Image.new(\"L\", image_size, \"black\")\n\n draw = ImageDraw.Draw(image)\n\n for bbox in bboxes:\n predicted_class = bbox[\"label\"]\n if predicted_class is 'crop':\n continue\n\n top = bbox[\"topleft\"][\"y\"]\n left = bbox[\"topleft\"][\"x\"]\n bottom = bbox[\"bottomright\"][\"y\"]\n right = bbox[\"bottomright\"][\"x\"]\n top = max(0, np.floor(top + 0.5).astype('int32'))\n left = max(0, np.floor(left + 0.5).astype('int32'))\n bottom = min(image_size[1], np.floor(bottom + 0.5).astype('int32'))\n right = min(image_size[0], np.floor(right + 0.5).astype('int32'))\n\n draw.rectangle([left - EXTEND_BY, top - EXTEND_BY, right + EXTEND_BY, bottom + EXTEND_BY], outline=\"white\", fill=\"white\")\n\n del draw\n return image\n\n def mask_image_eval_space_size(self, bboxes_in_eval_space, EXTEND_BY):\n\n scale = self.cropscoordinates.scale_ratio_of_evaluation_crop\n nw = int(self.settings.w * scale)\n nh = int(self.settings.h * scale)\n\n image_size = (nw, nh)\n image = Image.new(\"L\", image_size, \"black\")\n\n draw = ImageDraw.Draw(image)\n\n for bbox in bboxes_in_eval_space:\n predicted_class = bbox[\"label\"]\n if predicted_class is 'crop':\n continue\n\n top = bbox[\"topleft\"][\"y\"]\n left = bbox[\"topleft\"][\"x\"]\n bottom = bbox[\"bottomright\"][\"y\"]\n right = bbox[\"bottomright\"][\"x\"]\n top = max(0, np.floor(top + 0.5).astype('int32'))\n left = max(0, np.floor(left + 0.5).astype('int32'))\n bottom = min(image_size[1], np.floor(bottom + 0.5).astype('int32'))\n right = min(image_size[0], np.floor(right + 0.5).astype('int32'))\n\n draw.rectangle([left - EXTEND_BY, top - EXTEND_BY, right + EXTEND_BY, bottom + EXTEND_BY], outline=\"white\", fill=\"white\")\n\n del draw\n return image","sub_path":"video_parser_v2/AttentionModel.py","file_name":"AttentionModel.py","file_ext":"py","file_size_in_byte":13511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"452638288","text":"\nfrom multiprocessing import Pool\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.keys import Keys\nimport pyperclip\n\n\noptions = Options()\noptions.headless = True\ndriver = webdriver.Chrome(executable_path='./chromedriver_win32/chromedriver.exe',options=options)\ndriver.implicitly_wait(2)\n\ndef yonginmiddle(urlnumber):\n\n\n alltextlist=[]\n checklist = []\n textchecklist = []\n numm=1\n\n driver.get('https://www.yongin.ms.kr/b2520.brd/0'+str(urlnumber)+'..196c4ed9?shell=/index.shell:11')\n\n print(urlnumber,'페이지')\n\n a = driver.find_elements_by_class_name('cell4pline')\n\n for i in a:\n checklist.append(i.text)\n\n\n for i in checklist:\n if i == '':\n del checklist[checklist.index(i)]\n\n\n for i in checklist:\n if len(i) > 10:\n textchecklist.append(i)\n else:\n continue\n\n for i in textchecklist:\n maybealltext = str(urlnumber)+'페이지'+str(numm)+'번째 게시물'+i\n print(maybealltext)\n alltextlist.append(maybealltext)\n numm += 1\n urlnumber += 1\n if not textchecklist:\n print('empty')\n else:\n print('not empty')\n print(alltextlist)\n\n driver.close()\n return alltextlist\n\nif __name__ == '__main__':\n globallist = []\n\n urlnumberlist = []\n start = time.time()\n\n driver.get('https://www.yongin.ms.kr/b2520.brd/01..196c4ed9?shell=/index.shell:11')\n\n add = driver.find_elements_by_class_name('pg_menu')\n\n print(len(add))\n for i in range(1, len(add) + 1):\n urlnumberlist.append(i)\n\n print(urlnumberlist)\n pool = Pool(processes=len(add))\n globallist.append(pool.map(yonginmiddle,urlnumberlist))\n pool.close()\n pool.join()\n print('end')\n print(\"time :\", time.time() - start)\n for i in globallist:\n for l in i:\n print('||||||\\n\\n\\n')\n for ll in l:\n print(ll)","sub_path":"Python/crawling/YMSinforChrome.py","file_name":"YMSinforChrome.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"626338423","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport pybaseball as pb\nimport plotly.express as px\nimport pandas as pd\n\nfrom datetime import datetime as dt\nfrom dash.dependencies import Input, Output, State\nfrom dash.exceptions import PreventUpdate\n\nexternal_stylesheets = [\n \"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\"\n]\n\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\napp.title = \"MLB Pitcher Scouting Report\"\nserver = app.server\n\napp.layout = html.Div(\n children=[\n html.Nav(\n className=\"navbar\",\n children=[\n html.H1(\n children=[\n html.A(\n className=\"navbar-brand\",\n children=\"MLB Pitcher Scouting Report\",\n href=\"/\",\n )\n ]\n )\n ],\n ),\n html.Div(\n className=\"mr-5 ml-5 mt-0 pt-2 jumbotron\",\n children=[\n dcc.Markdown(\"#### First Name\"),\n dcc.Input(\n className=\"mb-3\", id=\"first-name\", value=\"Gerrit\", type=\"text\"\n ),\n dcc.Markdown(\"#### Last Name\"),\n dcc.Input(className=\"mb-3\", id=\"last-name\", value=\"Cole\", type=\"text\"),\n dcc.Markdown(\"#### Date Range\"),\n dcc.DatePickerRange(\n className=\"mb-3\",\n id=\"date-picker\",\n min_date_allowed=dt(2019, 3, 20),\n max_date_allowed=dt(2019, 9, 29),\n initial_visible_month=dt(2019, 3, 20),\n ),\n dcc.Markdown(\"#### Infield Fielding Alignment\"),\n dcc.Dropdown(\n className=\"mb-3 w-25\",\n id=\"infield-dropdown\",\n options=[\n {\"label\": \"Standard\", \"value\": \"Standard\"},\n {\"label\": \"Shift\", \"value\": \"Infield shift\"},\n ],\n value=\"Standard\",\n ),\n dcc.Markdown(\"#### Outfield Fielding Alignment\"),\n dcc.Dropdown(\n className=\"w-25\",\n id=\"outfield-dropdown\",\n options=[\n {\"label\": \"Standard\", \"value\": \"Standard\"},\n {\"label\": \"Strategic\", \"value\": \"Strategic\"},\n ],\n value=\"Standard\",\n ),\n html.Button(\n \"Create Data Visualizations!\",\n id=\"button\",\n className=\"btn btn-primary mt-3 mb-3\",\n ),\n ],\n ),\n html.Div(id=\"graph-div\"),\n ],\n)\n\n\n@app.callback(\n Output(component_id=\"graph-div\", component_property=\"children\"),\n [\n Input(\"button\", \"n_clicks\"),\n Input(\"date-picker\", \"start_date\"),\n Input(\"date-picker\", \"end_date\"),\n Input(\"infield-dropdown\", \"value\"),\n Input(\"outfield-dropdown\", \"value\"),\n ],\n [\n State(component_id=\"first-name\", component_property=\"value\"),\n State(component_id=\"last-name\", component_property=\"value\"),\n ],\n)\ndef update_output_div(\n n_clicks, start_date, end_date, infield, outfield, first_name, last_name,\n):\n # only update on increment\n prev_clicks = 0\n if (\n start_date is None\n or n_clicks is None\n or end_date is None\n or first_name is None\n or last_name is None\n or n_clicks == prev_clicks\n or infield is None\n or outfield is None\n ):\n raise PreventUpdate\n else:\n data = get_data(first_name, last_name, start_date, end_date)\n\n data = data[data[\"if_fielding_alignment\"] == infield]\n data = data[data[\"of_fielding_alignment\"] == outfield]\n\n strikes = [\n i\n for i in data[\"Result of Pitch\"]\n if i == \"called_strike\" or i == \"swinging_strike\"\n ]\n balls = [i for i in data[\"Result of Pitch\"] if i == \"ball\"]\n foul = [i for i in data[\"Result of Pitch\"] if i == \"foul\"]\n\n FF = len(data[data[\"Pitch Type\"] == \"4-Seam Fastball\"])\n KC = len(data[data[\"Pitch Type\"] == \"Knuckle Curve\"])\n CU = len(data[data[\"Pitch Type\"] == \"Curveball\"])\n CH = len(data[data[\"Pitch Type\"] == \"Changeup\"])\n SL = len(data[data[\"Pitch Type\"] == \"Slider\"])\n FT = len(data[data[\"Pitch Type\"] == \"2-Seam Fastball\"])\n\n pitch_type_pie = (\n px.pie(\n values=[FF, KC, CU, CH, SL, FT],\n names=[\n \"4-Seam Fastball\",\n \"Knuckle Curve\",\n \"Curveball\",\n \"Changeup\",\n \"Slider\",\n \"2-Seam Fastball\",\n ],\n title=f\"Pie Chart of {first_name} {last_name}'s Pitch Selection Between {start_date} and {end_date}\",\n template=\"plotly_dark\"\n )\n .update_traces(textinfo=\"percent+label\")\n )\n\n pitch_type_scatter = px.scatter(\n data,\n x=\"Pitch Number\",\n y=\"Pitch Speed\",\n color=\"Pitch Type\",\n trendline=\"ols\",\n hover_data=[\"Result of Pitch\", \"Play by Play\"],\n title=f\"3D Scatter Plot of {first_name} {last_name}'s Pitch Speed Between {start_date} and {end_date}\",\n template=\"plotly_dark\"\n )\n\n pitch_type_box = px.box(\n data,\n x=\"Pitch Type\",\n y=\"Pitch Speed\",\n color=\"Pitch Type\",\n points=\"all\",\n hover_data=[\"Result of Pitch\", \"Play by Play\"],\n title=f\"Box Plot of {first_name} {last_name}'s Pitch Speed Between {start_date} and {end_date}\",\n template=\"plotly_dark\"\n )\n\n prev_clicks = prev_clicks + 1\n\n return [\n dcc.Graph(figure=pitch_type_pie),\n dcc.Graph(figure=pitch_type_scatter),\n dcc.Graph(figure=pitch_type_box),\n ]\n\ndef get_data(first_name, last_name, start_date, end_date):\n try:\n key = pb.playerid_lookup(last_name, first_name)[\"key_mlbam\"].values[\n 0\n ] # get unique pitcher identifier\n except:\n pass\n\n data = pb.statcast_pitcher(\n start_date, end_date, key\n ) # get dataset of pitches thrown by pitcher\n data = data.sort_values(\n [\"pitch_number\"]\n ) # sort pitches by order thrown, earliest first\n data = data.dropna(\n subset=[\"pitch_type\", \"des\", \"description\", \"release_spin_rate\"]\n ) # make sure dataset does not contain nulls\n\n data[\"order\"] = data.reset_index().index # create new column with pitch order\n\n df = pd.DataFrame(data)\n\n df = df.rename(\n {\n \"des\": \"Play by Play\",\n \"description\": \"Result of Pitch\",\n \"order\": \"Pitch Number\",\n \"pitch_name\": \"Pitch Type\",\n \"release_speed\": \"Pitch Speed\",\n },\n axis=1,\n )\n\n return df\n\n\nif __name__ == \"__main__\":\n app.run_server(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"354229944","text":"from PyQt5 import QtSql\nfrom models import Recordset, AllocatException\n\n\nclass Employee(object):\n\n def __init__(self, src=None):\n self.query = QtSql.QSqlQuery()\n if src:\n if type(src) is list:\n self.__init_from_list__(src)\n else:\n self.__init_from_record__(src)\n self.assignments = None\n\n def __init_from_record__(self, record):\n self.id = record.value('id')\n self.duz = record.value('duz')\n self.name = record.value('name')\n self.grade = record.value('grade')\n self.step = record.value('step')\n self.fte = record.value('fte')\n self.cp = record.value('cp')\n self.notes = record.value('notes')\n self.investigator = record.value('investigator')\n self.assignments = None\n\n def __init_from_list__(self, lst):\n self.id = lst[0]\n self.duz = lst[1]\n self.name = lst[2]\n self.grade = lst[3]\n self.step = lst[4]\n self.fte = lst[5]\n self.cp = lst[6]\n self.investigator = lst[7]\n self.notes = lst[8]\n\n @staticmethod\n def get_all():\n sql = \"SELECT * FROM employees\"\n qry = QtSql.QSqlQuery()\n if not qry.exec_(sql):\n raise AllocatException(qry.lastError().text())\n return Recordset.to_dict(qry, Employee, 'name')\n\n def save(self):\n self._update() if self.id else self._insert()\n\n def _insert(self):\n flds = \"(duz, name, grade, step, fte, cp, notes)\"\n vals = \"('%s', '%s', %d, %d, %d, '%s', '%s', 1)\" % (\n self.duz,\n self.name,\n self.grade,\n self.step,\n self.fte,\n self.cp,\n self.notes\n )\n sql = \"INSERT INTO employees %s VALUES %s\" % (flds, vals)\n if not self.query.exec_(sql):\n raise AllocatException(self.query.lastError().text())\n self.id = self.query.lastInsertId()\n\n def _update(self):\n sets = \"duz='%s', name='%s', grade=%d, step=%d, fte=%d, cp='%s', notes=%s\" % (\n self.duz,\n self.name,\n self.grade,\n self.step,\n self.fte,\n self.cp,\n self.notes\n )\n sql = \"UPDATE employees SET %s WHERE id=%d\" % (sets, self.id)\n if not self.query.exec_(sql):\n raise AllocatException(self.query.lastError().text())\n\n def remove(self):\n sql = \"DELETE FROM employees WHERE id=%d\" % (self.id,)\n if not self.query.exec_(sql):\n raise AllocatException(self.query.lastError().text())\n\n def set_assignments(self):\n if self.assignments:\n return\n from models import Assignment\n sql = (\"SELECT asn.*, prj.nickname as project_name, emp.name as employee_name \"\n \"FROM assignments asn \"\n \"JOIN projects prj on (asn.project_id=prj.id) \"\n \"JOIN employees emp on (asn.employee_id=emp.id) \"\n \"WHERE asn.employee_id=\" + str(self.id))\n if not self.query.exec_(sql):\n raise AllocatException(self.query.lastError().text())\n self.assignments = Recordset.to_list(self.query, Assignment)\n\n def get_projects(self):\n from models import Project\n self.set_assignments()\n if not self.assignments:\n return None\n prj_ids = set([asn.project_id for asn in self.assignments])\n sql = \"SELECT nickname FROM projects WHERE id IN (\" +\\\n ','.join(str(prj_id) for prj_id in prj_ids) + \")\"\n if not self.query.exec_(sql):\n raise AllocatException(self.query.lastError().text())\n return Recordset.to_list(self.query, Project)\n\n @staticmethod\n def is_valid_name(name):\n if not name:\n return False\n import re\n pattern = \"^[A-Z'-\\.]+,[A-Z\\s'-\\.]+$\"\n return re.match(pattern, name)\n\n @staticmethod\n def is_valid_grade_step(val):\n if not val.isdigit():\n return False\n val = int(val)\n return val in range(1, 15)\n\n @staticmethod\n def is_valid_fte(fte):\n if not fte.isdigit():\n return False\n fte = int(fte)\n return fte in range(1, 100)\n","sub_path":"models/employee.py","file_name":"employee.py","file_ext":"py","file_size_in_byte":4237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"114038279","text":"from django.shortcuts import render\nfrom django.views.generic import View\nfrom django.http import JsonResponse\nfrom goods.models import GoodsSKU\nfrom django_redis import get_redis_connection\nimport json\n\n# Create your views here.\n#加入购物车\nclass AddCartView(View):\n # 1.post请求方式\n def post(self,request):\n # 2.获取商品id,数量\n sku_id = request.POST.get('sku_id')\n count = request.POST.get(\"count\")\n\n # 3.校验参数,判断商品是否存在\n if not all([sku_id,count]):\n return JsonResponse({'code':2,'message':'参数不完整'})\n\n try:\n sku = GoodsSKU.objects.get(id=sku_id)\n except GoodsSKU.DoesNotExist:\n return JsonResponse({'code':3,'messagge':'商品不存在'})\n\n # 4.判断商品数量类型, 库存多少\n try:\n count = int(count)\n except Exception:\n return JsonResponse({'code':4,'message':'参数错误'})\n if count > sku.stock:\n return JsonResponse({'code':5,'message':'库存不足'})\n\n if request.user.is_authenticated():\n # 5.如果用户已登录,数据保存到redis中\n\n # 获取商品id\n user_id = request.user.id\n\n # 购物车 不存在该商品,添加购物记录\n # 购物车存在该商品,累加数量\n redis_conn = get_redis_connection('default')\n origin_count = redis_conn.hget('cart_%s'%user_id,sku_id)\n\n if origin_count is not None:\n count += int(origin_count)\n\n redis_conn.hset('cart_%s'%user_id,sku_id)\n\n cart_num = 0\n cart = redis_conn.hgetall('cart_%s'%user_id)\n for val in cart.values():\n cart_num += int(val)\n\n return JsonResponse({'code':0,'message':'添加购物车成功','cart_num':cart_num})\n\n else:\n # 6.如果用户未登录,数据保存到cookie中\n # 获取cookie商品记录\n cart_json = request.COOKIES.get('cart')\n\n if cart_json is not None:\n cart = json.loads(cart_json)\n else:\n cart = {}\n\n # 判断商品记录信息\n if sku_id in cart:\n origin_count = cart[sku_id]\n count += origin_count\n\n cart[sku_id] = count\n\n # 将购物车字典数据转换为json字符串\n new_cart_json = json.dumps(cart)\n\n # 计算数量,保存cookie, 返回处理信息\n cart_num = 0\n for val in cart.values():\n cart_num += val\n\n response = JsonResponse({'code':0,'message':'添加购物车成功','cart_num':cart_num})\n\n response.set_cookie('cart',new_cart_json)\n\n return response\n\n#购物车信息:\nclass CartInfoView(View):\n # 1.get请求方式,提供购物车页面\n def get(self,request):\n # 2.查询购物车数据\n if not request.user.is_authenticated():\n # 用户未登录,从cookie中获取数据\n cart_json = request.COOKIES.get('cart')\n if cart_json is not None:\n cart = json.loads(cart_json)\n else:\n cart = {}\n else:\n # 用户已登录,从redis中获取数据\n redis_conn = get_redis_connection('default')\n user_id = request.user.id\n cart = redis_conn.hgetall('cart_%s'%user_id)\n\n\n # 3.遍历购物车,形成模板所需要的数据\n skus = []\n total_amount = 0\n total_count = 0\n for sku_id,count in cart.items():\n try:\n sku = GoodsSKU.objects.get(id=sku_id)\n except GoodsSKU.DoesNotExist:\n continue\n\n\n # 4.获取商品金额,数量,返回结果\n count = int(count)\n amount = sku.price * count\n sku.amount = amount\n\n sku.count = count\n skus.append(sku)\n total_amount += amount\n total_count += count\n\n context = {\n 'skus':skus,\n 'total_amount':total_amount,\n 'total_count':total_count\n }\n\n return render(request,'cart.html',context)\n\n\n#更新购物车数据\nclass UpdateCartView(View):\n # 1.post方式请求\n def post(self,request):\n # 2.获取商品id与数量\n sku_id = request.POST.get('sku_id')\n count = request.POST.get('count')\n\n # 3.校验参数,商品存在,整数库存\n if not all([sku_id,count]):\n return JsonResponse({'code': 2,'message':'参数不完整'})\n\n try:\n sku = GoodsSKU.objects.get(id = sku_id)\n except GoodsSKU.DoesNotExist:\n return JsonResponse({'code':3,'message':'商品不存在'})\n\n try:\n count = int(count)\n except Exception:\n return JsonResponse({'code':4,'message':'数量异常'})\n\n if count > sku.stock:\n return JsonResponse({'code':5,'message':'库存不足'})\n\n # 4.保存购物车数据\n if not request.user.is_authenticated():\n\n # 用户未登录,保存在cookie中\n cart_json = request.COOKIES.get('cart')\n if cart_json is not None:\n cart = json.loads(cart_json)\n else:\n cart = {}\n\n cart[sku_id] = count\n\n response = JsonResponse({'code':0,'message':'修改成功'})\n response.set_cookie('cart',json.dumps(cart))\n return response\n else:\n # 用户已登录,保存在redis中\n user_id = request.user.id\n redis_conn = get_redis_connection('default')\n redis_conn.hset('cart_%s'&user_id,sku_id,count)\n\n return JsonResponse({'code':0,'message':'修改成功'})\n\n\n#删除购物车信息\nclass DeleteCartView(View):\n # 1.post方式\n def post(self,request):\n sku_id = request.POST.get('sku_id')\n if not sku_id:\n return JsonResponse({'code':1,'message':'参数缺少'})\n\n # 2.从购物车中删除数据\n if not request.user.is_authenticated():\n\n # 用户未登录,从cookie中删除\n cart_json = request.COOKIES.get('cart')\n if cart_json is not None:\n cart = json.loads(cart_json)\n\n if sku_id in cart:\n del cart[sku_id]\n\n response = JsonResponse({'code':0,'message':'删除成功'})\n response.set_cookie('cart',json.dumps(cart))\n return response\n else:\n\n # 用户已登录,从redis中删除\n redis_conn = get_redis_connection('default')\n user_id = request.user.id\n\n redis_conn.hdel('cart_%s'%user_id,sku_id)\n\n return JsonResponse({'code':0,'message':'删除成功'})\n\n\n\n\n\n\n\n\n","sub_path":"apps/cart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"92635105","text":"import math\r\nfrom PIL import Image, ImageDraw,ImageFont\r\nimport numpy as np\r\nfile1 = open(\"crypt.txt\",\"w\")\r\ntext = textract.process('C:/Users/ramak/Downloads/1.txt')\r\ntxt=str(text)\r\ntxt=txt[2:len(txt)-1]\r\nn=math.ceil(len(txt)/40)\r\nfnt = ImageFont.truetype(\"C:/Users/ramak/PycharmProjects/pythonProject/venv/Lib/site-packages/tesseract/fonts/times.ttf\", 100)\r\nimg = Image.new('RGB', (1920, 1080), color=(0, 0, 0))\r\nd = ImageDraw.Draw(img)\r\nfor i in range(n):\r\n ll=i*40\r\n smalltxt=txt[ll:ll+40]\r\n d.text((10,(i*100)+10), smalltxt, font=fnt, fill=(255,255,255,255))\r\npixels = list(img.getdata())\r\nwidth, height = img.size\r\npixels = [pixels[i * width:(i + 1) * width] for i in range(height)]\r\na='i'\r\nfor row in pixels:\r\n c=(0,0,0)\r\n c1=0\r\n for i in row:\r\n if(i==c):\r\n c1+=1\r\n elif(i!=c):\r\n if(c==(0,0,0)):\r\n a+=' '\r\n c=(255,255,255)\r\n a+='a'+str(c1)\r\n c1=1\r\n elif(c==(255,255,255)):\r\n a+=' '\r\n c = (0, 0, 0)\r\n a+='b'+str(c1)\r\n c1=1\r\n if (c == (0, 0, 0)):\r\n a +=' '+'a' + str(c1)\r\n elif (c == (255, 255, 255)):\r\n a += \" \"+'b' + str(c1)\r\n a+=\"\\n\"\r\nfile1.write(a)\r\nfile1.close()\r\nimg.save(\"1.png\")\r\n","sub_path":"encryption.py","file_name":"encryption.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"224405424","text":"import numpy\nfrom sklearn.linear_model import LinearRegression\nimport pandas\nfrom matplotlib import pyplot\n\n\n\n# 载入数据\ndata=pandas.read_csv(\"data.csv\",delimiter=\",\")\ndata.columns=['a','b']\nx_data=data[\"a\"].values\ny_data=data[\"b\"].values\n# pyplot.scatter(x_data,y_data)\n# pyplot.show()\nprint(x_data.shape)\n\n# 处理数据类型\nx_data=pandas.DataFrame(x_data.reshape(x_data.shape[0],1))\ny_data=pandas.DataFrame(y_data.reshape(y_data.shape[0],1))\nprint(x_data.shape,y_data.shape)\n# x_data=data[:,0,numpy.newaxis]\n# y_data=data[:,1,numpy.newaxis]\n# 创建并拟合模型\nmodel = LinearRegression()\nmodel.fit(x_data,y_data)\n\n\n# 画图\npyplot.plot(x_data,y_data,'b.')\npyplot.plot(x_data,model.predict(x_data),'r')\npyplot.show()\n","sub_path":"suanfa/huigui/yiyuan_sklearn.py","file_name":"yiyuan_sklearn.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"166785777","text":"#open file with data\ntext_file = open(\"tall.txt\", \"r\")\nlines = text_file.read()\ntext_file.close()\n#split with comma\ntext=lines\ntext=text.split(',')\n#makes the list from str to int\ntext = list(map(int, text))\n#for loop for all numbers in text , finds highest number\nx=0\nfor num in text:\n if num>x:\n x=num\nprint(\"This is the highest number : \",x)\n","sub_path":"readfile_findlargest.py","file_name":"readfile_findlargest.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"304407638","text":"import numpy as np\nimport matplotlib\nimport os\nfrom matplotlib import pyplot as plt\nmatplotlib.use('Agg')\n\n\nclass OutputGra:\n def __init__(self, x, y, file_name):\n a = 0\n self.x = x\n self.y = y\n self.file_name = file_name\n\n# 棒グラフの作成\n def make_bar(self):\n plt.bar(self.x, self.y)\n plt.savefig(self.file_name)\n\n# 折れ線グラフの作成\n def make_plot(self):\n plt.plot(self.y, self.x)\n plt.savefig(self.file_name)\n\n# ファイルの存在を確認し、削除\n def file_remove(self):\n flag = os.path.exists(self.file_name)\n\n if flag:\n os.remove(self.file_name)\n else:\n pass\n\n\n\n\n\n\n\n\n","sub_path":"proces/glaphApp/learn/pygra.py","file_name":"pygra.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"281565128","text":"# coding: utf-8\nfrom __future__ import unicode_literals\nfrom synonyms import get_synonym\nfrom pymorphy2 import MorphAnalyzer\nimport re\n\n\n# Функция для непосредственной обработки диалога.\ndef handle_dialog(request, response, user_storage):\n if request.is_new_session:\n response.set_text('Привет! Назови слово, к которому нужно подобрать синоним.')\n return response, user_storage\n word_to_search = request.command.lower()\n text = analyze(word_to_search)\n if text is None:\n response.set_text('Я тебя не поняла. Назови слово, синоним к которому ты хочешь подобрать.')\n else:\n response.set_text(get_synonym(text))\n return response, user_storage\n\n\ndef analyze(response):\n whitelist = ['найти', 'придумать', 'сказать', 'подсказать']\n text = re.findall('([а-яА-Я\\-]+)', response)\n\n if len(text) == 1:\n return text\n else:\n parser = MorphAnalyzer()\n a = []\n for word in text:\n a.append((word, parser.parse(word)[0]))\n if {'VERB', 'INFN'} & a[0][1].tag.grammemes:\n verb = a.pop(0)\n if verb[1].normal_form not in whitelist:\n return None\n\n # print(a[0][0])\n if a[0][0] == 'синоним':\n if a[1][0] in ('к', 'для'):\n if a[2][1].normal_form == 'слово':\n return a[3][0]\n if a[1][1].normal_form == 'слово':\n return a[2][0]\n\n return None\n\n\nprint(analyze('синоним слову красивый'))\n","sub_path":"synonym.py","file_name":"synonym.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"128431233","text":"# ---------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# ---------------------------------------------------------\n\nfrom marshmallow import fields, pre_load, post_load, ValidationError\nfrom azure.ai.ml._schema import PatchedSchemaMeta\nfrom azure.ai.ml._restclient.v2021_10_01.models import UriReference\n\n\nclass PathSchema(metaclass=PatchedSchemaMeta):\n folder = fields.Str(\n metadata={\"description\": \"URI pointing to folder.\"},\n )\n file = fields.Str(\n metadata={\"description\": \"URI pointing to file.\"},\n )\n\n @post_load\n def make(self, data, **kwargs):\n return UriReference(**data)\n\n @pre_load\n def validate(self, data, **kwargs):\n # AnonymousCodeAssetSchema does not support None or arm string(fall back to ArmVersionedStr)\n folder = data.get(\"folder\", None)\n file = data.get(\"file\", None)\n if not folder and not file:\n raise ValidationError(\"Please provide one folder path or one file path\")\n if folder and file:\n raise ValidationError(\"Please provide only one folder or one file\")\n if folder and not folder.strip():\n raise ValidationError(\"Please provide valid path for one folder, whitespace is not allowed\")\n if file and not file.strip():\n raise ValidationError(\"Please provide valid path for one file, whitespace is not allowed\")\n return data\n","sub_path":"sdk/ml/azure-ai-ml/azure/ai/ml/_schema/assets/dataset_paths.py","file_name":"dataset_paths.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"305307972","text":"import sys\n\nf = open(\"{}\".format(sys.argv[1]),\"r\")\nft = f.readlines()\nf.close()\n\ng = open(\"damaging_predictions.txt\",\"w\")\ng1 = open(\"damaging_predictions_right.txt\",\"w\")\ng2 = open(\"damaging_predictions_wrong.txt\",\"w\")\n\n# FORMING DICTIONARIES\nk = 1\nd1, d2 = dict(), dict()\nwhile k < len(ft):\n\tft1 = ft[k].split(\",\")\n\n\tt1 = ft1[6].strip(\"\\n\")\n\tpre = ft1[5]\n\ttvalue = ft1[4]\n\tif t1 != \"NA\" and len(t1) > 0:\n\t\tt2 = t1.split(\"|\")\n\t\tk1 = 0\n\t\twhile k1 < len(t2):\n\t\t\ttemp = t2[0].split()\n\t\t\tk2 = 0\n\t\t\tt3 = \"\"\n\t\t\twhile k2 < len(temp):\n\t\t\t\tif len(t3) == 0:\n\t\t\t\t\tt3 = t3 + \"{}\".format(temp[k2])\n\t\t\t\telse:\n\t\t\t\t\tt3 = t3 + \"_{}\".format(temp[k2])\n\t\t\t\tk2 = k2 + 1\n\t\t\tif pre == tvalue:\n\t\t\t\tif (\"T\",t3) not in d1.keys():\n\t\t\t\t\td1[(\"T\",t3)] = 0\n\t\t\t\telse:\n\t\t\t\t\td1[(\"T\",t3)] = d1[(\"T\",t3)] + 1\n\t\t\telse:\n\t\t\t\tif (\"F\",t3) not in d2.keys():\n\t\t\t\t\td2[(\"F\",t3)] = 0\n\t\t\t\telse:\n\t\t\t\t\td2[(\"F\",t3)] = d2[(\"F\",t3)] + 1\n\t\t\tk1 = k1 + 1\n\tif t1 != \"NA\" and len(t1) == 0:\n\t\tt3 = \"BURIED_CHARGE_INTRODUCED\"\n\t\tif pre == tvalue:\n\t\t\tif (\"T\",t3) not in d1.keys():\n\t\t\t\td1[(\"T\",t3)] = 0\n\t\t\telse:\n\t\t\t\td1[(\"T\",t3)] = d1[(\"T\",t3)] + 1\n\t\telse:\n\t\t\tif (\"F\",t3) not in d2.keys():\n\t\t\t\td2[(\"F\",t3)] = 0\n\t\t\telse:\n\t\t\t\td2[(\"F\",t3)] = d2[(\"F\",t3)] + 1\n\tk = k + 1\n\nfor x in d1.keys():\t\n\tvalue1 = d1[x]\t# RIGHT PREDICTIONS\n\tif (\"F\",x[1]) in d2.keys():\n\t\tvalue2 = d2[(\"F\",x[1])]\t# WRONG PREDICTIONS\n\telse:\n\t\tvalue2 = 0\n\n\tg.write(\"{}\t{}\t{}\\n\".format(x[1],value1,value2))\n\tg1.write(\"{}\t{}\\n\".format(x[1],value1))\n\tg2.write(\"{}\t{}\\n\".format(x[1],value2))\n\ng.close()\ng1.close()\ng2.close()\n\n\n\n\n\n\n","sub_path":"missese3d_performancs/damaging_reasons.py","file_name":"damaging_reasons.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"21812525","text":"\"\"\"\nhttps://leetcode.com/problems/clone-graph/\n\nGiven a reference of a node in a connected undirected graph.\n\nReturn a deep copy (clone) of the graph.\n\nEach node in the graph contains a val (int) and a list (List[Node]) of its neighbors.\n\nclass Node {\n public int val;\n public List neighbors;\n}\n\n\nTest case format:\n\nFor simplicity sake, each node's value is the same as the node's index (1-indexed). For example, the first node with val = 1, the second node with val = 2, and so on. The graph is represented in the test case using an adjacency list.\n\nAdjacency list is a collection of unordered lists used to represent a finite graph. Each list describes the set of neighbors of a node in the graph.\n\nThe given node will always be the first node with val = 1. You must return the copy of the given node as a reference to the cloned graph.\n\n\n\nExample 1:\n\n\nInput: adjList = [[2,4],[1,3],[2,4],[1,3]]\nOutput: [[2,4],[1,3],[2,4],[1,3]]\nExplanation: There are 4 nodes in the graph.\n1st node (val = 1)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).\n2nd node (val = 2)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).\n3rd node (val = 3)'s neighbors are 2nd node (val = 2) and 4th node (val = 4).\n4th node (val = 4)'s neighbors are 1st node (val = 1) and 3rd node (val = 3).\nExample 2:\n\n\nInput: adjList = [[]]\nOutput: [[]]\nExplanation: Note that the input contains one empty list. The graph consists of only one node with val = 1 and it does not have any neighbors.\nExample 3:\n\nInput: adjList = []\nOutput: []\nExplanation: This an empty graph, it does not have any nodes.\nExample 4:\n\n\nInput: adjList = [[2],[1]]\nOutput: [[2],[1]]\n\n\nConstraints:\n\n1 <= Node.val <= 100\nNode.val is unique for each node.\nNumber of Nodes will not exceed 100.\nThere is no repeated edges and no self-loops in the graph.\nThe Graph is connected and all nodes can be visited starting from the given node.\n\n\"\"\"\n\n\n# Definition for a Node.\nclass Node:\n def __init__(self, val=0, neighbors=None):\n self.val = val\n self.neighbors = neighbors if neighbors is not None else []\n\n\nclass Solution:\n def cloneGraph(self, node: 'Node') -> 'Node':\n memo = set()\n\n def dfs(node):\n if node:\n memo.add(node.val)\n for o in node.neighbors:\n if o.val in memo:\n continue\n dfs(o)\n\n dfs(node)\n nodes = {v: Node(v) for v in memo}\n visited = set()\n\n def f(node):\n if not node:\n return\n if node in visited:\n return\n visited.add(node)\n new_node = nodes[node.val]\n for o in node.neighbors:\n new_node.neighbors.append(nodes[o.val])\n for o in node.neighbors:\n f(o)\n\n f(node)\n return nodes[node.val] if node else None\n\n\n\"\"\"\n# Definition for a Node.\nclass Node:\n def __init__(self, val = 0, neighbors = None):\n self.val = val\n self.neighbors = neighbors if neighbors is not None else []\n\"\"\"\n\nclass Solution:\n def cloneGraph(self, node: 'Node') -> 'Node':\n if node is None:\n return None\n\n nodes = {}\n\n def f(node):\n if node is None or node in nodes:\n return\n nodes[node] = Node(node.val)\n if node.neighbors:\n for neig in node.neighbors:\n f(neig)\n\n def g(node):\n if node is None or node.neighbors is None or nodes[node].neighbors:\n return\n nodes[node].neighbors = [nodes[node] for node in node.neighbors]\n for neig in node.neighbors:\n g(neig)\n\n f(node)\n g(node)\n\n return nodes[node]\n\n\n\n\n\nif __name__ == '__main__':\n adjList = [[2, 4], [1, 3], [2, 4], [1, 3]]\n","sub_path":"python/133.py","file_name":"133.py","file_ext":"py","file_size_in_byte":3857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"225194630","text":"import os\nfrom multiprocessing import Pipe, Process\nfrom time import sleep\n\ndef sender(conn, msgs):\n for item in msgs:\n print('Message sending : {}'.format(item))\n conn.send(item)\n sleep(1)\n conn.close()\n\ndef receiver(conn):\n while True:\n msg = conn.recv()\n print('Message received : {}'.format(msg))\n if (msg == 'END'):\n break\n conn.close()\n\nmsgs = 'ASIF IS MY NAME END'.split(' ')\n\ncon1,con2 = Pipe()\np1 = Process(target=sender, args=(con1,msgs))\np2 = Process(target=receiver, args=(con2,))\np1.start()\np2.start()\np1.join()\np2.join()\nprint('FINISHED')","sub_path":"p6.py","file_name":"p6.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"547325887","text":"import tensorflow as tf\nimport polyaxon as plx\n\nfrom polyaxon.examples.mnist_data import load_mnist\n\n\ndef create_experiment_json_fn(output_dir):\n X_train, Y_train, X_test, Y_test = load_mnist()\n\n config = {\n 'name': 'real_mnsit',\n 'output_dir': output_dir,\n 'eval_every_n_steps': 5,\n 'run_config': {'save_checkpoints_steps': 100},\n 'train_input_data_config': {\n 'input_type': plx.configs.InputDataConfig.NUMPY,\n 'pipeline_config': {'name': 'train', 'batch_size': 64, 'num_epochs': None,\n 'shuffle': True},\n 'x': X_train,\n 'y': Y_train\n },\n 'eval_input_data_config': {\n 'input_type': plx.configs.InputDataConfig.NUMPY,\n 'pipeline_config': {'name': 'eval', 'batch_size': 32, 'num_epochs': None,\n 'shuffle': False},\n 'x': X_test,\n 'y': Y_test\n },\n 'estimator_config': {'output_dir': output_dir},\n 'model_config': {\n 'model_type': 'classifier',\n 'loss_config': {'name': 'softmax_cross_entropy'},\n 'eval_metrics_config': [{'name': 'streaming_accuracy'},\n {'name': 'streaming_precision'}],\n 'optimizer_config': {'name': 'Adam', 'learning_rate': 0.008},\n 'graph_config': {\n 'name': 'mnist',\n 'definition': [\n (plx.layers.Conv2d, {'num_filter': 32, 'filter_size': 5, 'strides': 1}),\n (plx.layers.MaxPool2d, {'kernel_size': 2}),\n (plx.layers.Conv2d, {'num_filter': 64, 'filter_size': 5}),\n (plx.layers.MaxPool2d, {'kernel_size': 2}),\n (plx.layers.FullyConnected, {'n_units': 1024, 'activation': 'tanh'}),\n (plx.layers.FullyConnected, {'n_units': 10}),\n ]\n }\n }\n }\n experiement_config = plx.configs.ExperimentConfig.read_configs(config)\n return plx.experiments.create_experiment(experiement_config)\n\n\ndef main(*args):\n plx.experiments.run_experiment(experiment_fn=create_experiment_json_fn,\n output_dir=\"/tmp/polyaxon_logs/lenet\",\n schedule='continuous_train_and_eval')\n\n\nif __name__ == \"__main__\":\n tf.logging.set_verbosity(tf.logging.INFO)\n tf.app.run()\n","sub_path":"polyaxon/examples/lenet.py","file_name":"lenet.py","file_ext":"py","file_size_in_byte":2425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"254053853","text":"# Copyright (c) 2013, Web Notes Technologies Pvt. Ltd. and Contributors\n# MIT License. See license.txt\n\n# For license information, please see license.txt\n\nfrom __future__ import unicode_literals\n\nimport webnotes\nfrom webnotes.utils import get_fullname\nfrom webnotes.utils.email_lib.bulk import send\nfrom webnotes.utils.email_lib import sendmail\nfrom webnotes.utils.file_manager import save_file\n\nfrom webnotes.webutils import get_access\nfrom webnotes.templates.generators.website_group import clear_cache\nfrom webnotes.templates.website_group.post import clear_post_cache\n\nclass DocType:\n\tdef __init__(self, d, dl):\n\t\tself.doc, self.doclist = d, dl\n\t\t\n\tdef validate(self):\n\t\tif not self.doc.parent_post and not self.doc.title:\n\t\t\twebnotes.throw(\"Please enter title!\")\n\t\t\n\t\tself.assigned_to = webnotes.conn.get_value(self.doc.doctype, self.doc.name, \"assigned_to\")\n\t\tif self.doc.is_task:\n\t\t\tif not self.doc.status:\n\t\t\t\tself.doc.status = \"Open\"\n\t\t\tif self.doc.assigned_to:\n\t\t\t\tif not self.doc.assigned_to_fullname:\n\t\t\t\t\tself.doc.assigned_to_fullname = get_fullname(self.doc.assigned_to)\n\t\t\telse:\n\t\t\t\tself.doc.assigned_to_fullname = None\n\t\telse:\n\t\t\tself.doc.assigned_to = self.doc.assigned_to_fullname = self.doc.status = None\n\t\t\t\n\t\tif self.doc.is_event:\n\t\t\tif not self.doc.event_datetime:\n\t\t\t\twebnotes.throw(\"Please specify Event's Date and Time\")\n\t\telse:\n\t\t\tself.doc.event_datetime = None\n\t\t\t\n\tdef on_update(self):\n\t\tclear_cache(website_group=self.doc.website_group)\n\t\tclear_post_cache(self.doc.parent_post or self.doc.name)\n\n\t\tif self.doc.assigned_to and self.doc.assigned_to != self.assigned_to \\\n\t\t\tand webnotes.session.user != self.doc.assigned_to:\n\t\t\t\n\t\t\t# send assignment email\n\t\t\tsendmail(recipients=[self.doc.assigned_to], \n\t\t\t\tsubject=\"You have been assigned this Task by {}\".format(get_fullname(self.doc.modified_by)),\n\t\t\t\tmsg=self.get_reply_email_message(self.doc.name, get_fullname(self.doc.owner)))\n\t\t\n\tdef send_email_on_reply(self):\n\t\towner_fullname = get_fullname(self.doc.owner)\n\t\t\n\t\tparent_post = webnotes.bean(\"Post\", self.doc.parent_post).doc\n\t\t\n\t\tmessage = self.get_reply_email_message(self.doc.name, owner_fullname)\n\t\t\n\t\t# send email to the owner of the post, if he/she is different\n\t\tif parent_post.owner != self.doc.owner:\n\t\t\tsend(recipients=[parent_post.owner], \n\t\t\t\tsubject=\"{someone} replied to your post\".format(someone=owner_fullname), \n\t\t\t\tmessage=message,\n\t\t\t\n\t\t\t\t# to allow unsubscribe\n\t\t\t\tdoctype='Post', \n\t\t\t\temail_field='owner', \n\t\t\t\t\n\t\t\t\t# for tracking sent status\n\t\t\t\tref_doctype=self.doc.doctype, ref_docname=self.doc.name)\n\t\t\n\t\t# send email to members who part of the conversation\n\t\tparticipants = webnotes.conn.sql(\"\"\"select owner, name from `tabPost`\n\t\t\twhere parent_post=%s and owner not in (%s, %s) order by creation asc\"\"\", \n\t\t\t(self.doc.parent_post, parent_post.owner, self.doc.owner), as_dict=True)\n\t\t\n\t\tsend(recipients=[p.owner for p in participants], \n\t\t\tsubject=\"{someone} replied to a post by {other}\".format(someone=owner_fullname, \n\t\t\t\tother=get_fullname(parent_post.owner)), \n\t\t\tmessage=message,\n\t\t\n\t\t\t# to allow unsubscribe\n\t\t\tdoctype='Post',\n\t\t\temail_field='owner', \n\t\t\t\n\t\t\t# for tracking sent status\n\t\t\tref_doctype=self.doc.doctype, ref_docname=self.doc.name)\n\t\t\n\tdef get_reply_email_message(self, post_name, owner_fullname=None):\n\t\tmessage = self.doc.content\n\t\tif self.doc.picture_url:\n\t\t\tmessage += \"\"\"
\"\"\"\\\n\t\t\t\t.format(url=self.doc.picture_url)\n\t\tmessage += \"

By {fullname}

\".format(fullname=owner_fullname)\n\t\tmessage += \"

Click here to view the post

\".format(fullname=owner_fullname,\n\t\t\tpost_name=post_name)\n\t\treturn message\n\t\n@webnotes.whitelist(allow_guest=True)\ndef add_post(group, content, picture, picture_name, title=None, parent_post=None, \n\tassigned_to=None, status=None, event_datetime=None):\n\t\n\taccess = get_access(group)\n\tif not access.get(\"write\"):\n\t\traise webnotes.PermissionError\n\n\tif parent_post:\n\t\tif webnotes.conn.get_value(\"Post\", parent_post, \"parent_post\"):\n\t\t\twebnotes.throw(\"Cannot reply to a reply\")\n\t\t\n\tgroup = webnotes.doc(\"Website Group\", group)\t\n\tpost = webnotes.bean({\n\t\t\"doctype\":\"Post\",\n\t\t\"title\": (title or \"\").title(),\n\t\t\"content\": content,\n\t\t\"website_group\": group.name,\n\t\t\"parent_post\": parent_post or None\n\t})\n\t\n\tif not parent_post:\n\t\tif group.group_type == \"Tasks\":\n\t\t\tpost.doc.is_task = 1\n\t\t\tpost.doc.assigned_to = assigned_to\n\t\telif group.group_type == \"Events\":\n\t\t\tpost.doc.is_event = 1\n\t\t\tpost.doc.event_datetime = event_datetime\n\t\n\tpost.ignore_permissions = True\n\tpost.insert()\n\n\tif picture_name and picture:\n\t\tprocess_picture(post, picture_name, picture)\n\t\n\t# send email\n\tif parent_post:\n\t\tpost.run_method(\"send_email_on_reply\")\n\t\t\n\treturn post.doc.parent_post or post.doc.name\n\t\t\n@webnotes.whitelist(allow_guest=True)\ndef save_post(post, content, picture=None, picture_name=None, title=None,\n\tassigned_to=None, status=None, event_datetime=None):\n\t\n\tpost = webnotes.bean(\"Post\", post)\n\n\taccess = get_access(post.doc.website_group)\n\tif not access.get(\"write\"):\n\t\traise webnotes.PermissionError\n\t\n\t# TODO improve error message\n\tif webnotes.session.user != post.doc.owner:\n\t\tfor fieldname in (\"title\", \"content\"):\n\t\t\tif post.doc.fields.get(fieldname) != locals().get(fieldname):\n\t\t\t\twebnotes.throw(\"You cannot change: {}\".format(fieldname.title()))\n\t\t\t\t\n\t\tif picture and picture_name:\n\t\t\twebnotes.throw(\"You cannot change: Picture\")\n\t\t\t\n\tpost.doc.fields.update({\n\t\t\"title\": (title or \"\").title(),\n\t\t\"content\": content,\n\t\t\"assigned_to\": assigned_to,\n\t\t\"status\": status,\n\t\t\"event_datetime\": event_datetime\n\t})\n\tpost.ignore_permissions = True\n\tpost.save()\n\t\n\tif picture_name and picture:\n\t\tprocess_picture(post, picture_name, picture)\n\t\t\n\treturn post.doc.parent_post or post.doc.name\n\t\t\ndef process_picture(post, picture_name, picture):\n\tfile_data = save_file(picture_name, picture, \"Post\", post.doc.name, decode=True)\n\tpost.doc.picture_url = file_data.file_name or file_data.file_url\n\twebnotes.conn.set_value(\"Post\", post.doc.name, \"picture_url\", post.doc.picture_url)\n\tclear_cache(website_group=post.doc.website_group)\n\t\n@webnotes.whitelist()\ndef suggest_user(group, term):\n\t\"\"\"suggest a user that has read permission in this group tree\"\"\"\n\tprofiles = webnotes.conn.sql(\"\"\"select \n\t\tpr.name, pr.first_name, pr.last_name, \n\t\tpr.user_image, pr.fb_location, pr.fb_hometown\n\t\tfrom `tabProfile` pr\n\t\twhere (pr.first_name like %(term)s or pr.last_name like %(term)s)\n\t\tand pr.name not in (\"Guest\", \"Administrator\") is not null and pr.enabled=1\"\"\", \n\t\t{\"term\": \"%{}%\".format(term), \"group\": group}, as_dict=True)\n\t\n\ttemplate = webnotes.get_template(\"templates/includes/profile_display.html\")\n\treturn [{\n\t\t\"value\": \"{} {}\".format(pr.first_name or \"\", pr.last_name or \"\").strip(), \n\t\t\"profile_html\": template.render({\"profile\": pr}),\n\t\t\"profile\": pr.name\n\t} for pr in profiles]\n","sub_path":"webnotes/website/doctype/post/post.py","file_name":"post.py","file_ext":"py","file_size_in_byte":6858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"600515554","text":"#-------------------------------------------------------------------------------\r\n#Program: Homework Assignment #3B\r\n#Programmer: Jonathan Faulk\r\n#Date: 9/11/2014\r\n# \r\n#Abstract: This program ccomputes the amount telephone solicitors will be paid\r\n# each week. It will take as input the employee's name, their total\r\n# sales amount for the week, and their hours worked. Global constansts\r\n# will be used for hourly rate, commision rate, and witholding amount.\r\n# The program displays the hourly\r\n# pay amount, the commission amount, the gross pay amount, the witholding\r\n# amount, and the net pay.\r\n#-------------------------------------------------------------------------------\r\n\r\n#Define global constants\r\nHOURLY_PAY = 7.50\r\nCOMMISION_RATE = .05\r\nWITHHOLDING_AMOUNT_RATE = 0.25\r\n\r\n#Define main function\r\ndef main():\r\n #call display message\r\n display_message()\r\n \r\n #Inputs for employee name, total sales, and hours worked\r\n employee_name = input('Please enter employee name: ') \r\n sales_amount = float(input('Please enter total sales amount: '))\r\n hours_worked = float(input('Please enter total hours worked: '))\r\n \r\n #Calculates hourly pay, commission amount, gross pay, withholding amount, and net pay for employee\r\n hourly_pay_amount = hours_worked * HOURLY_PAY\r\n commission_amount = sales_amount * COMMISION_RATE\r\n gross_pay = hourly_pay_amount + commission_amount\r\n withholding_amount = gross_pay * WITHHOLDING_AMOUNT_RATE\r\n net_pay = gross_pay - withholding_amount\r\n \r\n #Call the display results function passing 5 values for printing\r\n display_results(hourly_pay_amount, commission_amount, gross_pay, withholding_amount, net_pay)\r\n \r\n #Define the display message function \r\ndef display_message():\r\n print('This program calculates pay amounts.')\r\n print('Five values are displayed: '\r\n 'hourly pay amount, commission amount, gross pay, witholding amount, and net pay')\r\n\r\n#Define the display results function\r\ndef display_results(hourly_pay_amount, commission_amount, gross_pay, withholding_amount, net_pay):\r\n \r\n #Print the 5 values formatted as currency\r\n print('The hourly pay amount is $', format(hourly_pay_amount, ',.2f'), sep='')\r\n print('The commission amount is $', format(commission_amount, ',.2f'), sep='')\r\n print('The gross_pay is $', format(gross_pay, ',.2f'), sep='')\r\n print('The witholding amount is $', format(withholding_amount, ',.2f'), sep='')\r\n print('The net pay is $', format(net_pay, ',.2f'), sep='')\r\n \r\n#Call the main function\r\nmain()\r\n \r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n ","sub_path":"FaulkJHW3.py","file_name":"FaulkJHW3.py","file_ext":"py","file_size_in_byte":2751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"4781656","text":"import os\nfrom os import listdir\nfrom os.path import isfile, join\n\nfrom main.core.tools.python_console_tools import warning_print\nfrom main.core.config.config import * # DO NOT REMOVE\n\n\ndef is_compiled():\n \"\"\"Checks whether the code is being ran from the exe or not\"\"\"\n\n if (r\"C:\\Users\\Reece\\floobits\\share\\Reece\\The_Ensuing_Panic\"\n r\"\\The_Ensuing_Panic_\" in os.getcwd()): # TODO: Always return True in\n # final verion?\n return False\n return True\n\n\ndef get_path():\n \"\"\"Returns the path to the root of the game folder depending on\n is_compiled()\n \"\"\"\n\n if is_compiled():\n return \"..\\\\\"\n else:\n return \"C:\\\\Users\\\\Reece\\\\Desktop\\\\The Ensuing Panic\\\\\"\n\n\ndef is_folder(folder, has_root=False):\n \"\"\"Checks whether given path is a folder off the root of the game folder.\"\"\"\n\n if os.path.isdir(get_path() + folder if not has_root else folder):\n return True\n return False\n\n\ndef is_file(path, has_root=False):\n \"\"\"Checks whether given path is a file off the root of the game folder.\"\"\"\n\n if os.path.exists(get_path() + path if not has_root else path):\n return True\n return False\n\n\ndef get_files_in_path(path, has_root=False):\n \"\"\"Credit: http://stackoverflow.com/questions/\n 3207219/how-to-list-all-files-of-a-directory\n \"\"\"\n\n if has_root:\n n_path = path\n else:\n n_path = get_path() + path\n return [f for f in listdir(n_path) if isfile(join(n_path, f))]\n\n\ndef restore_default(default_path):\n \"\"\"Restores specified files to defaults. Returns new file.\"\"\"\n\n path_splice = get_path()\n default_path_parts = default_path.split(\"\\\\\")\n new_file = eval(default_path_parts[-1][:-4])\n for path_part in default_path_parts:\n path_splice += path_part\n if not path_splice.endswith(\".txt\") and not is_folder(path_splice,\n True):\n warning_print(\"Creating folder: \" + path_splice)\n os.mkdir(path_splice)\n elif path_splice.endswith(\".txt\"):\n warning_print(\"Creating file: \" + path_splice)\n if is_file(path_splice, True):\n os.remove(path_splice)\n file = open(path_splice, \"w\")\n file.write(new_file)\n file.close()\n path_splice += \"\\\\\"\n default = open(get_path() + default_path, \"r\")\n default_string = default.read()\n default.close()\n return eval(default_string)\n\n\ndef get_file_name(path):\n \"\"\"Converts file path to file name\"\"\"\n\n file_name = path.split(\"\\\\\")\n file_name = file_name[len(file_name) - 1]\n file_name = file_name[:-len(\".txt\")]\n file_name = file_name.title()\n file_name += \" file\"\n return file_name\n\n\ndef get_object_from_txt_file(path, object_type, redo=False):\n \"\"\"Gets and object from a txt file.\n Handles any corrupt and missing files.\n \"\"\"\n\n file_name = get_file_name(path)\n\n # Checking if config exists\n if is_file(path):\n txt_file = open(get_path() + path, \"r\")\n found_object = txt_file.read()\n txt_file.close()\n corrupt = False\n try:\n found_object = eval(found_object)\n except SyntaxError:\n corrupt = True\n if type(found_object) is not object_type or corrupt:\n warning_print(file_name + \" is corrupted. Restoring to default.\")\n os.remove(get_path() + path)\n\n # Trying Defaults\n return get_object_from_txt_file(path, object_type, True)\n # Success\n return found_object\n\n # Does not exist. Creating new config\n else:\n if not redo:\n warning_print(file_name + \" is missing. Restoring to default.\")\n found_object = restore_default(path)\n return found_object\n\n\ndef var_from_dict(var_name, _dict, path):\n \"\"\"Takes the name of a variable, its config object, and the config's path.\n If the var_name is in the config, it is returned with the original object.\n Otherwise, the file is restored and the new var is returned with a new\n config object.\n \"\"\"\n\n try:\n return _dict[var_name], _dict\n except KeyError:\n warning_print(\n get_file_name(\n path) + \" is missing \" + var_name + \". Restoring to default.\")\n restore_default(path)\n _dict = get_object_from_txt_file(path, dict)\n return _dict[var_name], _dict\n","sub_path":"main/core/files/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":4404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"74473486","text":"from datasets.coco import TCTDataset\nfrom transforms.transforms import *\nfrom mxnet.gluon.data import DataLoader\nfrom mxnet import nd, autograd, gluon, gpu, init\nfrom mxnet.gluon import utils as gutils\nfrom visualize.bbox import draw\nfrom PIL import Image\nfrom utils.data_pipline import collate_fn\nfrom network import FasterRCNNDetector\nimport os\nimport sys\nfrom engine import inference\nfrom pycocotools.cocoeval import COCOeval\n\n\nif __name__ == '__main__':\n # os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1,3\"\n os.environ[\"MXNET_ENABLE_GPU_P2P\"] = \"0\"\n gpus = [0, 1, 2, 3]\n is_multi_gpus = len(gpus) > 1\n images_per_gpu = 1\n batch_size = images_per_gpu * len(gpus)\n num_workers = images_per_gpu * len(gpus)\n\n root = \"/root/userfolder/datasets/TCT\"\n # root = \"/run/media/hezhujun/DATA1/Document/dataset/TCT\"\n train_ann_file = os.path.join(root, \"annotations/train.json\")\n val_ann_file = os.path.join(root, \"annotations/val.json\")\n test_ann_file = os.path.join(root, \"annotations/test.json\")\n train_transforms = Compose([\n Resize((1333, 800), True),\n RandomHorizontalFlip(0.5),\n RandomVerticalFlip(0.5),\n Normalize(mean=(127, 127, 127), std=(255, 255, 255)),\n ToTensor()\n ])\n val_transforms = Compose([\n Resize((1333, 800), True),\n Normalize(mean=(127, 127, 127), std=(255, 255, 255)),\n ToTensor()\n ])\n train_dataset = TCTDataset(root, \"tct_train\", train_transforms)\n train_data_loader = DataLoader(train_dataset, batch_size, True, last_batch=\"rollover\", batchify_fn=collate_fn,\n num_workers=num_workers)\n val_dataset = TCTDataset(root, \"tct_val\", val_transforms)\n val_data_loader = DataLoader(val_dataset, batch_size, False, last_batch=\"discard\", batchify_fn=collate_fn, num_workers=num_workers)\n\n # anchor_scales = {\n # \"c4\": (64, 128, 256),\n # }\n # anchor_ratios = {\n # \"c4\": ((1, 2, 0.5),) * 3,\n # }\n\n anchor_scales = {\n \"p2\": (32,),\n \"p3\": (64,),\n \"p4\": (128,),\n \"p5\": (256,),\n \"p6\": (512,),\n }\n anchor_ratios = {\n \"p2\": ((1, 2, 0.5),),\n \"p3\": ((1, 2, 0.5),),\n \"p4\": ((1, 2, 0.5),),\n \"p5\": ((1, 2, 0.5),),\n \"p6\": ((1, 2, 0.5),),\n }\n\n if is_multi_gpus:\n device = [gpu(i) for i in gpus]\n else:\n device = gpu(gpus[0])\n\n detector = FasterRCNNDetector(val_dataset.num_classes, anchor_scales, anchor_ratios, use_fpn=True, ctx=device)\n if detector.use_fpn:\n detector.fpn.initialize(init=init.Xavier(), ctx=device)\n detector.rpn.initialize(init=init.Xavier(), ctx=device)\n detector.roi_extractor.initialize(init=init.Xavier(), ctx=device)\n detector.head.initialize(init=init.Xavier(), ctx=device)\n detector.cls.initialize(init=init.Xavier(), ctx=device)\n detector.reg.initialize(init=init.Xavier(), ctx=device)\n detector.cls_loss.initialize(init=init.Xavier(), ctx=device)\n detector.reg_loss.initialize(init=init.Xavier(), ctx=device)\n\n detector.hybridize()\n\n trainer = gluon.Trainer(detector.collect_params(), 'sgd', {'learning_rate': 0.001, \"wd\": 1e-5})\n\n num_epochs = 5\n for epoch in range(num_epochs):\n iteration = 0\n for images, labels, bboxes, scale_factors, image_ids in train_data_loader:\n if is_multi_gpus:\n images = gutils.split_and_load(images, device)\n labels = gutils.split_and_load(labels, device)\n bboxes = gutils.split_and_load(bboxes, device)\n scale_factors = gutils.split_and_load(scale_factors, device)\n\n rpn_cls_losses = []\n rpn_reg_losses = []\n cls_lossse = []\n reg_lossse = []\n total_losses = []\n with autograd.record():\n for images_, labels_, bboxes_, scale_factors_ in zip(images, labels, bboxes, scale_factors):\n rpn_cls_loss, rpn_reg_loss, cls_loss, reg_loss = detector(images_, scale_factors_, labels_, bboxes_)\n rpn_cls_losses.append(rpn_cls_loss)\n rpn_reg_losses.append(rpn_reg_loss)\n cls_lossse.append(cls_loss)\n reg_lossse.append(reg_loss)\n total_loss = sum([rpn_cls_loss, rpn_reg_loss, cls_loss, reg_loss])\n total_losses.append(total_loss)\n\n for total_loss in total_losses:\n total_loss.backward()\n rpn_cls_loss = np.mean([loss.asscalar() for loss in rpn_cls_losses])\n rpn_reg_loss = np.mean([loss.asscalar() for loss in rpn_reg_losses])\n cls_loss = np.mean([loss.asscalar() for loss in cls_lossse])\n reg_loss = np.mean([loss.asscalar() for loss in reg_lossse])\n total_loss = np.mean([loss.asscalar() for loss in total_losses])\n else:\n images = images.as_in_context(device)\n labels = labels.as_in_context(device)\n bboxes = bboxes.as_in_context(device)\n scale_factors = scale_factors.as_in_context(device)\n\n with autograd.record():\n rpn_cls_loss, rpn_reg_loss, cls_loss, reg_loss = detector(images, scale_factors, labels, bboxes)\n total_loss = sum([rpn_cls_loss, rpn_reg_loss, cls_loss, reg_loss])\n\n total_loss.backward()\n rpn_cls_loss = rpn_cls_loss.asscalar()\n rpn_reg_loss = rpn_reg_loss.asscalar()\n cls_loss = cls_loss.asscalar()\n reg_loss = reg_loss.asscalar()\n total_loss = total_loss.asscalar()\n\n trainer.step(1)\n nd.waitall()\n\n print(\"epoch {}, iter {}/{}, rpn_cls_loss {}, rpn_reg_loss {}, cls_loss {}, reg_loss {} total_loss {}\".format(\n epoch, iteration, len(train_data_loader), rpn_cls_loss, rpn_reg_loss, cls_loss, reg_loss, total_loss))\n\n iteration += 1\n\n print(\"Evaluate...\")\n results = inference(detector, val_data_loader, device)\n if len(results) > 0:\n cocoGT = val_data_loader._dataset.coco\n imgIds = val_data_loader._dataset.ids\n cocoDT = cocoGT.loadRes(results)\n cocoEval = COCOeval(cocoGT, cocoDT, \"bbox\")\n cocoEval.params.imgIds = imgIds\n cocoEval.evaluate()\n cocoEval.accumulate()\n cocoEval.summarize()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"222701557","text":"import tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\ntf.set_random_seed(1)\nmnist=input_data.read_data_sets('MNIST_data',one_hot=True)\n# parameters\nlearning_rate=0.001\ntrain_iter=100000\nbatch_size=128\nn_in=28\nn_step=28\nn_HU=128\nn_classes=10\nx=tf.placeholder(tf.float32,[None,n_step,n_in])\ny=tf.placeholder(tf.float32,[None,n_classes])\nweight={\n 'in': tf.Variable(tf.random_normal([n_in,n_HU])),\n 'out':tf.Variable(tf.random_normal([n_HU,n_classes]))\n}\nbias={\n 'in': tf.Variable(tf.constant(0.1,shape=[n_HU,])),\n 'out':tf.Variable(tf.constant(0.1,shape=[n_classes,]))\n}\ndef RNN(X,weight,bias):\n X=tf.reshape(X,[-1,n_in])\n X_in=tf.matmul(X,weight['in'])+bias['in']\n print(type(X_in))\n X_in=tf.reshape(X_in,[-1,n_step,n_HU])\n if int((tf.__version__).split('.')[1])<12 and int((tf.__version__).split('.')[0]<1):\n lstm_cell=tf.nn.rnn_cell.BasicLSTMCell(n_HU,forget_bias=0.0,state_is_tupple=True)\n else:\n lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_HU, forget_bias=0.0, state_is_tuple=True)\n init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32)\n\n #cell=tf.contrib.rnn();\n #cell=tf.nn.dynamic_rnn(n_HU,forget_bias=1.0,state_is_tupple=True)\n outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=init_state, time_major=False)\n print (\"OUT:\\t\" + str(outputs) + \"\\n\")\n if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:\n outputs = tf.unpack(tf.transpose(outputs, [1, 0, 2])) # states is the last outputs\n else:\n outputs = tf.unstack(tf.transpose(outputs, [1, 0, 2]))\n\n results = tf.matmul(outputs[-1], weight['out']) + bias['out']\n return results\n\npred = RNN(x, weight, bias)\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\ntrain_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n\ncorrect_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n'''\nwith tf.Session() as sess:\n # tf.initialize_all_variables() no long valid from\n # 2017-03-02 if using tensorflow >= 0.12\n if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:\n init = tf.initialize_all_variables()\n else:\n init = tf.global_variables_initializer()\n sess.run(init)\n step = 0\n while step * batch_size < train_iter:\n batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n batch_xs = batch_xs.reshape([batch_size, n_step,n_in])\n sess.run([train_op], feed_dict={\n x: batch_xs,\n y: batch_ys,\n })\n if step % 20 == 0:\n print(sess.run(accuracy, feed_dict={x: batch_xs,y: batch_ys,}))\n step += 1\nprint 'FINISH\\n'\n'''","sub_path":"practice/RNN.py","file_name":"RNN.py","file_ext":"py","file_size_in_byte":2800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"567097764","text":"from mamba import description, before, context, it, after\nfrom doublex import Spy\nfrom doublex_expects import have_been_called_with\nfrom expects import expect, have_keys, be_a, have_len, be_above_or_equal\n\nfrom os import getpid\n\nfrom infcommon import logger\nfrom infrabbitmq import factory\nfrom infrabbitmq.rabbitmq import (\n RabbitMQQueueEventProcessor,\n TOPIC_EXCHANGE_TYPE,\n)\n\n# --------------------------------------------------\n# Avoid pika logging\nfactory.configure_pika_logger_to_error()\n# --------------------------------------------------\n\n\nA_TOPIC_EXCHANGE_NAME = 'a_topic_exchange_name'\nA_QUEUE_NAME = 'a_queue_name_{}'.format(getpid())\nA_LIST_OF_TOPICS = '#'\nA_NETWORK = 'a_network'\nAN_EVENT_NAME = 'an_event_name'\nAN_EVENT_DATA = 'an_event_data'\n\n\nwith description('RabbitMQEventPublisher integration test: Feature publish') as self:\n with before.each:\n self.sut_event_publisher = factory.rabbitmq_event_publisher(exchange=A_TOPIC_EXCHANGE_NAME)\n self.rabbitmq_client = factory.no_singleton_rabbitmq_client()\n self.event_processor = Spy()\n self.event_builder = factory.raw_event_builder()\n self.logger = logger\n self.sut_event_processor = RabbitMQQueueEventProcessor(queue_name=A_QUEUE_NAME,\n event_processor=self.event_processor,\n rabbitmq_client=self.rabbitmq_client,\n exchange=A_TOPIC_EXCHANGE_NAME,\n list_of_topics=A_LIST_OF_TOPICS,\n event_builder=self.event_builder,\n logger=self.logger,\n exchange_type=TOPIC_EXCHANGE_TYPE,\n queue_options={},\n exchange_options={}\n )\n\n with after.each:\n self.rabbitmq_client.queue_unbind(queue_name=A_QUEUE_NAME,\n exchange=A_TOPIC_EXCHANGE_NAME,\n routing_key=A_LIST_OF_TOPICS[0])\n self.rabbitmq_client.queue_delete(queue_name=A_QUEUE_NAME)\n self.rabbitmq_client.exchange_delete(exchange=A_TOPIC_EXCHANGE_NAME)\n\n with context('publish and processing an event'):\n with it('calls the processor with event object data'):\n self.sut_event_publisher.publish(AN_EVENT_NAME, A_NETWORK, data=AN_EVENT_DATA)\n self.sut_event_publisher.publish(AN_EVENT_NAME, A_NETWORK, data=AN_EVENT_DATA)\n self.sut_event_processor.process_body(max_iterations=1)\n\n expect(self.event_processor.process).to(have_been_called_with(have_keys(name=AN_EVENT_NAME,\n network=A_NETWORK,\n data=AN_EVENT_DATA,\n timestamp=be_a(float),\n timestamp_str=have_len(be_above_or_equal(1))\n )\n ).once\n )\n","sub_path":"integration_specs/rabbitmq_event_publisher_specs/rabbitmq_event_publisher__publish_spec.py","file_name":"rabbitmq_event_publisher__publish_spec.py","file_ext":"py","file_size_in_byte":3674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"448569442","text":"import tensorflow as tf\nimport numpy as np\n\n# data = hihello\n#1. DATA \nidx2char = ['e', 'h', 'i', 'l', 'o']\n\n_data = np.array([['h', 'i', 'h', 'e', 'l', 'l', 'o']]).reshape(7,1)\nprint(_data.shape) # (1, 7)\nprint(_data) # [['h' 'i' 'h' 'e' 'l' 'l' 'o']]\nprint(type(_data)) # \n\n# ENC = tf.one_hot(_data, depth=5, axis=1, dtype=tf.float64)\n# print(ENC)\n\nfrom sklearn.preprocessing import OneHotEncoder\nenc = OneHotEncoder()\nenc.fit(_data)\n_data = enc.transform(_data).toarray()\n\nprint(_data)\nprint(type(_data))\nprint(_data.dtype)\n\nx_data = _data[:6, ]\ny_data = _data[1:, ]\n\n# print('=========================')\n# print(X_DATA)\n# print('=========================')\n# print(Y_DATA)\n\ny_data = np.argmax(y_data, axis=1)\n# print('=========================')\n# print(Y_DATA)\n\nx_data = x_data.reshape(1, 6, 5)\ny_data = y_data.reshape(1, 6)\n# print(Y_DATA.shape)\n\nX = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 6, 5])\nY = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 6])\n\nSEQUENCE_LENGTH = 6\nINPUT_DIM = 5\nOUTPUT = 5\nBATCH_SIZE = 1 # All row\n\nX = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, SEQUENCE_LENGTH, INPUT_DIM])\nY = tf.compat.v1.placeholder(dtype=tf.int32, shape=[None, SEQUENCE_LENGTH])\n\nprint(X)\nprint(Y)\nprint(Y.dtype)\n\n#. MODEL\n\n# cell = tf.nn.rnn_cell.BasicLSTMCell(OUTPUT)\ncell = tf.keras.layers.LSTMCell(OUTPUT)\nhypothesis, _state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)\nprint(hypothesis) # (?, 6, 100)\n\n\n# COMPILE\nweights = tf.ones([1, SEQUENCE_LENGTH])\nsequence_loss = tf.contrib.seq2seq.sequence_loss(\n logits=hypothesis, targets=Y, weights=weights)\n\ncost = tf.compat.v1.reduce_mean(sequence_loss)\n\ntrain = tf.compat.v1.train.AdamOptimizer(learning_rate=0.1).minimize(cost)\n\nprediction = tf.compat.v1.argmax(hypothesis, axis=2)\n\n# TRAINING\nwith tf.compat.v1.Session() as sess:\n sess.run(tf.compat.v1.global_variables_initializer())\n for i in range(401):\n loss, _ = sess.run([cost, train], feed_dict={X:x_data, Y:y_data})\n result = sess.run(prediction, feed_dict={X:x_data})\n print(i, \"LOSS : \", loss, \"PREDICTION : \", result, \"TRUE Y : \", y_data)\n\n result_str = [idx2char[c] for c in np.squeeze(result)]\n print(\"\\nPREDICTION STR : \", ''.join(result_str))","sub_path":"tensorflow1.0/tf20_rnn1.py","file_name":"tf20_rnn1.py","file_ext":"py","file_size_in_byte":2291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"235470719","text":"'''\nexist1 is better than exist:\nusing index rather than change word -- word[1:]\n\n\n'''\n\nclass Solution(object):\n def exist1(self, board, word):\n \"\"\"\n :type board: List[List[str]]\n :type word: str\n :rtype: bool\n \"\"\"\n def preCheck():\n # Build Check Dic\n preDic = {}\n for i in word:\n if i in preDic:\n preDic[i] += 1\n else:\n preDic[i] = 1\n\n # Check is there over alpha in word\n for i in board:\n for j in i:\n if j in preDic and preDic[j] > 0:\n preDic[j] -= 1\n\n # for i in preDic.values():\n # if i > 0: return False\n return True\n\n\n def helper(word_index, x, y):\n if board[x][y] != word[word_index]:\n return False\n elif word_index == ls - 1: return True\n else:\n word_index += 1\n tempChar = board[x][y]\n board[x][y] = None\n for d in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n x_next = x + d[0]\n y_next = y + d[1]\n if -1 < x_next < m and -1 < y_next < n and board[x_next][y_next]:\n if helper(word_index, x_next, y_next):\n return True\n board[x][y] = tempChar\n return False\n\n if not board: return False\n if not word: return True\n if not preCheck(): return False\n\n m = len(board)\n n = len(board[0])\n ls = len(word)\n\n for i in range(m):\n for j in range(n):\n if helper(0, i, j):\n return True\n return False\n # 23%\n def exist(self, board, word):\n if not board: return False\n for i in range(len(board)):\n for j in range(len(board[0])):\n if self.dfs(board, i, j, word):\n return True\n return False\n\n def dfs(self, board, i, j, word):\n if len(word) == 0: return True\n if i < 0 or i >= len(board) or j < 0 or j >= len(board[0]) or word[0] != board[i][j]:\n return False\n # Avoid visited again\n temp = board[i][j]\n board[i][j] = '#'\n direction = ((0, 1), (1, 0), (-1, 0), (0, -1))\n for dx, dy in direction:\n x = i + dx\n y = j + dy\n res = self.dfs(board, x, y, word[1:])\n if res == True: return True\n else: continue\n board[i][j] = temp\n return False\n\n\nif __name__ == '__main__':\n S = Solution()\n test = S.exist([[\"A\",\"B\",\"C\",\"E\"],[\"S\",\"F\",\"C\",\"S\"],[\"A\",\"D\",\"E\",\"E\"]], \"ABCCED\")\n test = S.exist([[\"a\"]], \"ab\")\n print(test)","sub_path":"Project/Leetcode/Backtracking/79. Word Search.py","file_name":"79. Word Search.py","file_ext":"py","file_size_in_byte":2823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"285875121","text":"import os,sys\nimport math\nimport numpy as np\nimport json\nfrom smartapi import SmartConnect\nimport matplotlib.pyplot as plt\ndef getApiData():\n #or from smartapi.smartConnect import SmartConnect\n #import smartapi.smartExceptions(for smartExceptions)\n\n #create object of call\n #obj=SmartConnect(api_key=\"\",\n #optional\n #access_token = \"your access token\",\n #refresh_token = \"your refresh_token\")\n obj = SmartConnect(api_key=\"Test\")\n #login api call\n #T88892\", \"Sairam@21\",\"j36iHU6d\"\n data = obj.generateSession(\"T$est\",\"Test@21\")\n refreshToken = data['data']['refreshToken']\n\n #fetch the feedtoken\n feedToken = obj.getfeedToken()\n\n #fetch User Profile\n #userProfile = obj.getProfile(refreshToken)\n #print(userProfile)\n\n try:\n historicParam={\n \"exchange\": \"NSE\",\n \"symboltoken\": \"3045\",\n \"interval\": \"ONE_MINUTE\",\n \"fromdate\": \"2021-02-08 09:15\", \n \"todate\": \"2021-02-08 10:20\"\n }\n hisotricdata = obj.getCandleData(historicParam)\n #print(hisotricdata)\n #{timestamp, open, high, low, close, volume}\n json_object = json.dumps(hisotricdata['data'], indent = 4)\n plt.rcParams.update({'font.size:10'})\n fig, axis = plt.subplots(figsize=(14,7))\n\n \n except Exception as e:\n print(\"Historic Api failed: {}\".format(e))\n\n return json_object","sub_path":"getData.py","file_name":"getData.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"318396473","text":"from typing import Optional\n\nfrom talon import Module, actions, app, imgui, speech_system, ui\n\n# We keep command_history_size lines of history, but by default display only\n# command_history_display of them.\nmod = Module()\nsetting_command_history_auto = mod.setting(\"command_history_auto\", bool, default=0)\nsetting_command_history_auto_more = mod.setting(\n \"command_history_auto_more\", bool, default=0\n)\nsetting_command_history_size = mod.setting(\"command_history_size\", int, default=50)\nsetting_command_history_display = mod.setting(\n \"command_history_display\", int, default=10\n)\nsetting_command_history_sticky = mod.setting(\"command_history_sticky\", int, default=0)\n\nhist_more = False\nhistory = []\ngui = None\n\n\ndef on_phrase(j):\n global history\n\n words = j.get(\"text\")\n\n text = actions.user.history_transform_phrase_text(words)\n\n if text is not None:\n history.append(text)\n history = history[-setting_command_history_size.get() :]\n\n\n# todo: dynamic rect?\ndef update_gui(gui: imgui.GUI):\n global history\n global hist_more\n gui.text(\"Command History\")\n gui.line()\n text = (\n history[:] if hist_more else history[-setting_command_history_display.get() :]\n )\n for line in text:\n gui.text(line)\n\n gui.spacer()\n if gui.button(\"Command history close\"):\n actions.user.history_disable()\n\n\ndef on_ready():\n global hist_more\n global gui\n if setting_command_history_auto_more.get():\n hist_more = True\n\n y = 0\n # XXX - This should be used once we have sticky support\n main_screen = ui.main_screen()\n x = main_screen.x + main_screen.width / 2.6\n\n img = imgui.open(y=y)\n gui = img(update_gui)\n if setting_command_history_sticky.get():\n gui.fixed(0, 100)\n\n if setting_command_history_auto.get():\n if not gui.showing:\n gui.show()\n\n\napp.register(\"ready\", on_ready)\n\n\nspeech_system.register(\"phrase\", on_phrase)\n\n\n@mod.action_class\nclass Actions:\n def history_toggle():\n \"\"\"Toggles viewing the history\"\"\"\n if gui.showing:\n gui.hide()\n else:\n gui.show()\n\n def history_enable():\n \"\"\"Enables the history\"\"\"\n gui.show()\n\n def history_disable():\n \"\"\"Disables the history\"\"\"\n gui.hide()\n\n def history_clear():\n \"\"\"Clear the history\"\"\"\n global history\n history = []\n\n def history_more():\n \"\"\"Show more history\"\"\"\n global hist_more\n hist_more = True\n\n def history_less():\n \"\"\"Show less history\"\"\"\n global hist_more\n hist_more = False\n\n def history_get(number: int) -> str:\n \"\"\"returns the history entry at the specified index\"\"\"\n num = (0 - number) - 1\n return history[num]\n\n def history_transform_phrase_text(words: list[str]) -> Optional[str]:\n \"\"\"Transforms phrase text for presentation in history. Return `None` to omit from history\"\"\"\n\n if not actions.speech.enabled():\n return None\n\n return \" \".join(words) if words else None\n","sub_path":"plugin/command_history/command_history.py","file_name":"command_history.py","file_ext":"py","file_size_in_byte":3062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"574470544","text":"import json\nimport config_default\n\ndef merge(default_data,override_data):\n\tnew_data = {}\n\t#遍历默认配置\n\tfor k,v in default_data.items():\n\t\t#如果这个配置在override里面，修改之\n\t\tif k in override_data:\n\t\t\t#如果又是个dict，递归\n\t\t\tif isinstance(v,dict):\n\t\t\t\tnew_data[k] = merge(v,override_data[k])\n\t\t\telse:\n\t\t\t\tnew_data[k] = override_data[k]\n\t\t#如果不在，直接添加\n\t\telse:\n\t\t\tnew_data[k] =v\n\treturn new_data\nif __name__ == '__main__':\n\tconfig_data = config_default.configs\n\ttry:\n\t\timport config_override\n\t\t#合并default和override里面的数据\n\t\tconfig_data = merge(config_data,config_override.configs)\n\texcept ImportError:\n\t\tprint('没有这个模块')","sub_path":"www/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"499824572","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 25 17:43:00 2016\n多任务：（一个任务就是一个进程，一个进程内至少含一个线程，或并发多个线程）\n1.并发：单核CPU，同一时刻只能处理一个进程，多进程交替执行\n2.并行：多核CPU，同一时刻可处理CPU核数的进程\n\npython 多进程：multiprocessing\n@author: thinkpad\n\"\"\"\n\nfrom multiprocessing import Process,Pool\nimport os\nimport time\nimport random\n'''\n#单个进程\ndef run_process(process_name):\n time.sleep(10)#延迟10秒，以便在任务管理器观察\n print('Run child process %s(PID=%s)'%(process_name,os.getpid()))\n\nif __name__=='__main__':\n print('Parent process PID=%s'%os.getpid())\n p=Process(target=run_process,args=('test',))#创建进程\n p.start()\n p.join()#join()阻塞当前进程（主进程），直到p进程结束后，再执行当前进程。如果不用join，进程的并发执行会使p未运行完，就运行print语句\n print('Child process end.')\n'''\n'''\ncmd:python+脚本运行，运行结果：\nParent process PID=1240\nRun child process test(PID=12500)\nChild process end.\n'''\n\n#进程池\ndef run_task(task_name):\n print('Run task%s'%(task_name,os.getpid()))\n start=time.time()\n time.sleep(random.random()*3)\n end=time.time()\n print('Task %s runs %0.2f seconds'%(task_name,(end-start)))\nif __name__=='__main__':\n print('Parent process is %s'%os.getpid())\n p=Pool(4)\n for i in range(5):\n p.apply_async(run_task,args=(i,))\n print('Waiting for all subprocesses done...')\n p.close()\n p.join()\n print('All subprocesses done.')\n \n\n\n\n\n\n\n\n\n","sub_path":"多进程.py","file_name":"多进程.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"233511193","text":"class Solution:\n def movesToMakeZigzag(self, nums) -> int:\n def helper(a):\n n = len(a)\n if n <= 2:\n return 0\n res = 0\n for i in range(1, n):\n if i % 2:\n res += max(0, a[i] - (a[i - 1] - 1))\n a[i] = min(a[i], a[i - 1] - 1)\n else:\n res += max(0, a[i - 1] - (a[i] - 1))\n a[i - 1] = min(a[i - 1], a[i] - 1)\n return res\n\n res1 = helper(nums[::])\n res2 = helper([nums[1]] + nums[::])\n return min(res1, res2)\n\n\ns = Solution()\nprint(s.movesToMakeZigzag([7, 4, 8, 9, 7, 7, 5]))\nprint(s.movesToMakeZigzag([1, 2, 3]))\nprint(s.movesToMakeZigzag([9, 6, 1, 6, 2]))\n","sub_path":"leetcode/2021/decrease-elements-to-make-array-zigzag.py","file_name":"decrease-elements-to-make-array-zigzag.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"201044646","text":"#!/usr/bin/env python\n\nfrom radical.entk import Pipeline, Stage, Task, AppManager\nimport os\nimport traceback\nimport sys\nimport time\n\n# ------------------------------------------------------------------------------\n# Set default verbosity\n\nif os.environ.get('RADICAL_ENTK_VERBOSE') == None:\n os.environ['RADICAL_ENTK_VERBOSE'] = 'True'\n\n# Description of how the RabbitMQ process is accessible\n# No need to change/set any variables if you installed RabbitMQ has a system\n# process. If you are running RabbitMQ under a docker container or another\n# VM, set \"RMQ_HOSTNAME\" and \"RMQ_PORT\" in the session where you are running\n# this script.\n#hostname = os.environ.get('RMQ_HOSTNAME', 'two.radical-project.org')\n#port = int(os.environ.get('RMQ_PORT', 33235))\nhostname = os.environ.get('RMQ_HOSTNAME', 'localhost')\nport = int(os.environ.get('RMQ_PORT', 5672))\n\nif __name__ == '__main__':\n\n start_time = time.time()\n\n pipelines = set()\n p = Pipeline()\n s = Stage()\n \n for cnt in range(8):\n \n t = Task()\n t.name = 't%s' % (cnt + 1)\n t.pre_exec = ['export PATH=/home/karahbit/stress-ng-0.10.16:$PATH']\n t.executable = ['stress-ng'] \n t.arguments = ['-c', '1', '-t', '100']\n t.cpu_reqs = {'processes': 1, 'thread_type': None, 'threads_per_process': 1, 'process_type': None}\n\n s.add_tasks(t)\n\n p.add_stages(s)\n pipelines.add(p)\n\n # Resource and AppManager\n amgr = AppManager(hostname = hostname, port = port)\n amgr.workflow = pipelines\n amgr.shared_data = []\n \n amgr.resource_desc = {\n 'resource': 'local.localhost',\n\t 'walltime': 10,\n\t 'cpus': 8\n }\n \n amgr.run()\n\n print(\"--- %s seconds ---\" % (time.time() - start_time))","sub_path":"Containers/First experiments/not-used/src/stress_entk.py","file_name":"stress_entk.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"380086584","text":"import data\nimport discord\nimport time\n\nfrom cogs import update_cog\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom .dependencies import owner_or_admin\nfrom .models import BotNameMapping\nfrom constants import *\n\nimport config\nconfig.parse_args()\n\nrouter = APIRouter(\n prefix=\"/mappings/bot_name\",\n tags=[\"bot_name\"],\n dependencies=[Depends(owner_or_admin)],\n responses={404: {\"description\": \"Not found\"}},\n)\n\n\n@router.get(\"/{guildId}\", response_model=BotNameMapping)\nasync def read_mapping(guildId: str):\n bot_instance = data.get_bot_instance(guildId)\n if not bot_instance:\n return {\"guildId\": guildId, \"bot_name\": \"rallybot\"}\n\n return {\"guildId\": guildId, \"bot_name\": bot_instance[BOT_NAME_KEY], 'name_timeout': int(bool(bot_instance[NAME_TIMEOUT_KEY]))}\n\n\n@router.post(\"/\", response_model=BotNameMapping)\nasync def add_mapping(mapping: BotNameMapping, guildId: str):\n bot_instance = data.get_bot_instance(guildId)\n if not bot_instance:\n raise HTTPException(status_code=404, detail=\"Bot config not found\")\n\n # name timout\n if bot_instance[NAME_TIMEOUT_KEY] and int(bot_instance[NAME_TIMEOUT_KEY]) <= time.time():\n data.set_name_timeout(bot_instance[GUILD_ID_KEY], 0)\n bot_instance[NAME_TIMEOUT_KEY] = 0\n\n if mapping.bot_name and not bot_instance[NAME_TIMEOUT_KEY]:\n task = {\n 'kwargs': {\n 'guild_id': int(guildId),\n 'bot_id': int(bot_instance[BOT_ID_KEY]),\n 'new_name': mapping.bot_name,\n },\n 'function': 'update_name'\n }\n data.add_task(task)\n\n return {\"guildId\": guildId, \"bot_name\": mapping.bot_name, 'name_timeout': int(bool(bot_instance[NAME_TIMEOUT_KEY]))}\n","sub_path":"rallyrolebot/api/bot_name_mappings.py","file_name":"bot_name_mappings.py","file_ext":"py","file_size_in_byte":1740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"204821278","text":"from src.Base.Evaluation.K_Fold_Evaluator import K_Fold_Evaluator_MAP\nfrom src.Utils.ICM_preprocessing import *\nfrom src.Utils.load_ICM import load_ICM\nfrom src.Utils.load_URM import load_URM\n\nURM_all = load_URM(\"../../../in/data_train.csv\")\nICM_all = load_ICM(\"../../../in/data_ICM_title_abstract.csv\")\nfrom src.Data_manager.split_functions.split_train_validation_random_holdout import \\\n split_train_in_two_percentage_global_sample\n\nURMs_train = []\nURMs_validation = []\nignore_users_list = []\n\nimport numpy as np\n\nfor k in range(5):\n URM_train, URM_validation = split_train_in_two_percentage_global_sample(URM_all, train_percentage=0.80)\n URMs_train.append(URM_train)\n URMs_validation.append(URM_validation)\n\n profile_length = np.ediff1d(URM_train.indptr)\n block_size = int(len(profile_length) * 0.25)\n\n start_pos = 3 * block_size\n end_pos = len(profile_length)\n sorted_users = np.argsort(profile_length)\n\n users_in_group = sorted_users[start_pos:end_pos]\n\n users_in_group_p_len = profile_length[users_in_group]\n sorted_users = np.argsort(profile_length)\n\n users_not_in_group_flag = np.isin(sorted_users, users_in_group, invert=True)\n ignore_users_list.append(sorted_users[users_not_in_group_flag])\n\nevaluator_validation = K_Fold_Evaluator_MAP(URMs_validation, cutoff_list=[10], verbose=False,\n ignore_users_list=ignore_users_list)\n\nICMs_combined = []\nfor URM in URMs_train:\n ICMs_combined.append(combine(ICM=ICM_all, URM=URM))\n\nfrom src.GraphBased.UserRP3betaRecommender import UserRP3betaRecommender\n\nfrom bayes_opt import BayesianOptimization\n\nuserRp3beta_recommenders = []\n\nfor index in range(len(URMs_train)):\n userRp3beta_recommenders.append(\n UserRP3betaRecommender(\n URM_train=ICMs_combined[index].T,\n verbose=False\n )\n )\n\ntuning_params = {\n \"alpha\": (0, 1),\n \"beta\": (0, 1),\n \"topK\": (100, 700)\n}\n\nresults = []\n\n\ndef BO_func(\n alpha,\n beta,\n topK\n):\n for recommender in userRp3beta_recommenders:\n recommender.fit(alpha=alpha, beta=beta, topK=int(topK), implicit=False)\n\n result = evaluator_validation.evaluateRecommender(userRp3beta_recommenders)\n return sum(result) / len(result)\n\n\noptimizer = BayesianOptimization(\n f=BO_func,\n pbounds=tuning_params,\n verbose=5,\n random_state=5,\n)\n\noptimizer.maximize(\n init_points=100,\n n_iter=50,\n)\n\n\nimport json\n\nwith open(\"logs/FeatureCombined\" + userRp3beta_recommenders[0].RECOMMENDER_NAME + \"_best25_logs.json\", 'w') as json_file:\n json.dump(optimizer.max, json_file)\n","sub_path":"parameter_tuning/Hybrids/FeatureCombinedUserRP3beta/FeatureCombinedUserRP3beta_tuningBest25.py","file_name":"FeatureCombinedUserRP3beta_tuningBest25.py","file_ext":"py","file_size_in_byte":2619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"226799712","text":"import spacy\nfrom collections import defaultdict\nfrom flask import Flask, request\nfrom flask_cors import CORS\nimport json\n\nmodel = spacy.load(\"en_core_web_sm\")\n\napp = Flask(__name__)\ncors = CORS(app)\napp.config[\"CORS_HEADERS\"] = \"Content-Type\"\n\ndef generate_response(content):\n result = json.dumps(content, indent = 2).encode(\"utf-8\")\n response = app.response_class(\n response = result,\n status = 200,\n mimetype = \"application/json\"\n )\n return response \n\n@app.route(\"/\")\ndef hello_world():\n return \"Hello World!\"\n\n@app.route(\"/ner\", methods=[\"POST\"])\ndef get_ner():\n text = str(request.data)\n recoginized = model(text)\n result = {\"PERSON\": [], \"GPE\": [], \"LOC\": [], \"ORG\": []}\n targetEntity = [\"PERSON\", \"GPE\", \"LOC\", \"ORG\"]\n\n for word in sorted(recoginized.ents):\n if word.label_ in targetEntity and word.text not in result[word.label_]:\n result[word.label_].append(word.text)\n for key in result.keys():\n result[key] = \", \".join(result[key])\n if len(result[key]) > 2 and result[key][:2] == \"b'\":\n result[key] = result[key][2:]\n \n return generate_response(result)\n ","sub_path":"flask-backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"444842004","text":"import sys\nimport os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\n\nfrom Utils.utils import *\n\n\nclass MolDataset(torch.utils.data.Dataset):\n def __init__(self, smiles_list, seq_len):\n super(MolDataset, self).__init__()\n self.smiles_list = smiles_list\n self.seq_len = seq_len\n\n def __len__(self):\n return len(self.smiles_list)\n\n def __getitem__(self, item):\n x = self.smiles_list[item]\n x_len = len(x)\n x = torch.tensor(x, dtype=torch.long)\n buf = torch.zeros(self.seq_len, dtype=torch.long)\n buf[:len(x)] = x\n x = buf\n # x = torch.nn.functional.one_hot(x.to(torch.int64), num_classes=len(self.vocab))\n\n return x, x_len\n","sub_path":"Model/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"137011574","text":"\"\"\"Main script\"\"\"\nimport os\nfrom pathlib import Path\nfrom src.pipeline import Pipeline\nfrom src import utils\n\n\ndef main():\n \"\"\"Main function\"\"\"\n utils.initialize_coloredlog()\n utils.initialize_rich()\n utils.initialize_logging()\n # Project path\n project_dir = str(Path(__file__).resolve().parents[1])\n # Load enviromental variables\n env_var = utils.load_env_variables(project_dir)\n # Load paramenters\n params = utils.load_json(os.path.join(project_dir, \"parameters\", \"parameters.json\"))\n params[\"global\"][\"root_path\"] = env_var[\"root_path\"]\n # Load switchers\n switchers = utils.load_json(\n os.path.join(project_dir, \"parameters\", \"switchers.json\")\n )\n # Creates and run the census processing pipeline\n pipeline_locations = Pipeline(\"census\", params, switchers[\"census\"])\n pipeline_locations.run()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"285934262","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\n# 使用笑話及新聞測試，未知文本是笑話。\n\nfrom collections import Counter\nfrom ArticutAPI import Articut\nimport json\nimport math\n\ndef wordExtractor(inputLIST, unify=True):\n '''\n 配合 Articut() 的 .getNounStemLIST() 和 .getVerbStemLIST() …等功能，拋棄位置資訊，只抽出詞彙。\n '''\n resultLIST = []\n for i in inputLIST:\n if i == []:\n pass\n else:\n for e in i:\n resultLIST.append(e[-1])\n if unify == True:\n return sorted(list(set(resultLIST)))\n else:\n return sorted(resultLIST)\n \n \ndef counterCosineSimilarity(counter01, counter02):\n '''\n 計算 counter01 和 counter02 兩者的餘弦相似度\n '''\n terms = set(counter01).union(counter02)\n dotprod = sum(counter01.get(k, 0) * counter02.get(k, 0) for k in terms)\n magA = math.sqrt(sum(counter01.get(k, 0)**2 for k in terms))\n magB = math.sqrt(sum(counter02.get(k, 0)**2 for k in terms))\n return dotprod / (magA * magB)\n\n\ndef lengthSimilarity(counter01, counter02):\n '''\n 計算 counter01 和 counter02 兩者在長度上的相似度\n '''\n\n lenc1 = sum(iter(counter01.values()))\n lenc2 = sum(iter(counter02.values()))\n return min(lenc1, lenc2) / float(max(lenc1, lenc2))\n\n\nif __name__ == \"__main__\":\n with open(\"account.info\", encoding=\"utf-8\") as f:\n userinfoDICT = json.loads(f.read())\n \n #articut = Articut(username, apikey, level=\"lv1\")\n articut = Articut(username=userinfoDICT[\"username\"], apikey=userinfoDICT[\"apikey\"])\n \n# 已知類別文本：笑話文本 & 新聞文本\n jokeSTR = \"\"\"去醫院做核酸檢測(PCR)，一位醫生用嚴肅的語氣問我：「你有理由死嗎？還是沒有理由死？」\n 聽到這個問題，我沉默了也想了很多，想了家人，想了朋友，想了還沒實現的夢想.....\n 最後我堅定的回答：「沒有理由死！」\n 醫生聽到我的回答，提筆就寫了「沒有旅遊史」\"\"\".replace(\" \", \"\").replace(\"\\n\", \"\")\n \n newsSTR = \"\"\"松山文創園區宣布7/13起開放，松菸小賣所、風格店家也同步開店營業，在相關管制措施下，\n 民眾進入室內場館需配合量體溫、手部消毒、實聯制、全程戴口罩、維持1.5公尺社交距離，同時室內場館禁止飲食。\n 而展覽場依中央藝文場館防疫管理措施辦理，餐廳與商店等附屬場域也遵從主管機關及地方政府指示作業。\n 另展覽場設置「防疫小組」，進行前置防疫措施與工作人員健康管理。\"\"\".replace(\" \", \"\").replace(\"\\n\", \"\") \n \n HW_JokeDICT = {}\n with open(\"HW_JokeDICT.json\", mode=\"w\", encoding=\"utf-8\") as f:\n json.dump(HW_JokeDICT, f, ensure_ascii=False)\n \n jokeResultDICT = articut.parse(jokeSTR, userDefinedDictFILE=\"./HW_JokeDICT.json\")\n newsResultDICT = articut.parse(newsSTR, userDefinedDictFILE=\"./HW_JokeDICT.json\")\n \n \n# 未知類別文本(笑話)\n unknownSTR = \"\"\"吳國將軍甘興因為一次割東西時，不小心割壞了吳國之主孫權心愛的桌子，孫權很生氣的決定將他斬首，\n 這時吳國軍師周瑜就決定想個方法救甘興，於是他讓全國的人在割東西之前先在桌子上墊一張紙，孫權也就因為這個方法原\n 諒了甘興，於是這就是全國電子就甘心的由來\"\"\".replace(\" \", \"\").replace(\"\\n\", \"\") \n \n \n \n \n# 取得「動詞」做為特徵列表\n jokeVerbLIST = articut.getVerbStemLIST(jokeResultDICT)\n print(\"笑話文本動詞：\")\n print(wordExtractor(jokeVerbLIST, unify=False))\n print(\"\\n\")\n newsVerbLIST = articut.getVerbStemLIST(newsResultDICT)\n print(\"新聞文本動詞：\")\n print(wordExtractor(newsVerbLIST, unify=False))\n print(\"\\n\")\n unknownResultDICT = articut.parse(unknownSTR, userDefinedDictFILE=\"./HW_JokeDICT.json\")\n unknownVerbLIST = articut.getVerbStemLIST(unknownResultDICT)\n print(\"未知文本動詞：\")\n print(wordExtractor(unknownVerbLIST, unify=False))\n print(\"\\n\")\n\n\n # 利用 Counter() 模組計算每個動詞出現的次數\n jokeCOUNT = Counter(wordExtractor(jokeVerbLIST, unify=False))\n newsCOUNT = Counter(wordExtractor(newsVerbLIST, unify=False))\n unknownCOUNT = Counter(wordExtractor(unknownVerbLIST, unify=False))\n\n # 計算 [笑話文本 vs. 未知文本] 的餘弦相似度；計算 [新聞文本 vs. 未知文本] 的餘弦相似度；\n joke2unknownSIM = counterCosineSimilarity(jokeCOUNT, unknownCOUNT)\n news2unknownSIM = counterCosineSimilarity(newsCOUNT, unknownCOUNT)\n\n print(\"[笑話文本 vs. 未知文本] 的動詞餘弦相似度:{}\".format(joke2unknownSIM))\n print(\"[新聞文本 vs. 未知文本] 的動詞餘弦相似度:{}\".format(news2unknownSIM))\n print(\"\\n\")\n \n\n\n\n\n# 取得「名詞」做為特徵列表\n jokeNounLIST = articut.getNounStemLIST(jokeResultDICT)\n print(\"笑話文本名詞：\")\n print(wordExtractor(jokeNounLIST, unify=False))\n print(\"\\n\")\n print(\"新聞文本名詞：\")\n newsNounLIST = articut.getNounStemLIST(newsResultDICT)\n print(wordExtractor(newsNounLIST, unify=False))\n print(\"\\n\")\n unknownResultDICT = articut.parse(unknownSTR, userDefinedDictFILE=\"./HW_JokeDICT.json\")\n unknownNounLIST = articut.getNounStemLIST(unknownResultDICT)\n print(\"未知文本名詞：\")\n print(wordExtractor(unknownNounLIST, unify=False))\n print(\"\\n\")\n\n\n # 利用 Counter() 模組計算每個名詞出現的次數\n jokeCOUNT = Counter(wordExtractor(jokeNounLIST, unify=False))\n newsCOUNT = Counter(wordExtractor(newsNounLIST, unify=False))\n unknownCOUNT = Counter(wordExtractor(unknownNounLIST, unify=False))\n\n # 計算 [笑話文本 vs. 未知文本] 的餘弦相似度；計算 [新聞文本 vs. 未知文本] 的餘弦相似度；\n joke2unknownSIM = counterCosineSimilarity(jokeCOUNT, unknownCOUNT)\n news2unknownSIM = counterCosineSimilarity(newsCOUNT, unknownCOUNT)\n\n print(\"[笑話文本 vs. 未知文本] 的名詞餘弦相似度:{}\".format(joke2unknownSIM))\n print(\"[新聞文本 vs. 未知文本] 的名詞餘弦相似度:{}\".format(news2unknownSIM))\n print(\"\\n\")\n \n \n \n \n \n# 取得「TF-IDF」做為特徵列表\n jokeTFIDFLIST = articut.analyse.extract_tags(jokeResultDICT)\n print(\"笑話文本 TF-IDF：\")\n print(jokeTFIDFLIST)\n print(\"\\n\")\n print(\"新聞文本 TF-IDF：\")\n newsTFIDFLIST = articut.analyse.extract_tags(newsResultDICT)\n print(newsTFIDFLIST)\n print(\"\\n\")\n unknownResultDICT = articut.parse(unknownSTR, userDefinedDictFILE=\"./HW_JokeDICT.json\")\n unknownTFIDFLIST = articut.analyse.extract_tags(unknownResultDICT)\n print(\"未知文本 TF-IDF：\")\n print(unknownTFIDFLIST)\n print(\"\\n\")\n\n\n # 利用 Counter() 模組計算每個 TF-IDF 特徵詞出現的次數\n jokeCOUNT = Counter(jokeTFIDFLIST)\n newsCOUNT = Counter(newsTFIDFLIST)\n unknownCOUNT = Counter(unknownTFIDFLIST)\n\n # 計算 [笑話文本 vs. 未知文本] 的 TF-IDF 餘弦相似度；計算 [新聞文本 vs. 未知文本] 的 TF-IDF 餘弦相似度；\n joke2unknownSIM = counterCosineSimilarity(jokeCOUNT, unknownCOUNT)\n news2unknownSIM = counterCosineSimilarity(newsCOUNT, unknownCOUNT)\n\n print(\"[笑話文本 vs. 未知文本] 的 TF-IDF 特徵詞餘弦相似度:{}\".format(joke2unknownSIM))\n print(\"[新聞文本 vs. 未知文本] 的 TF-IDF 特徵詞餘弦相似度:{}\".format(news2unknownSIM))\n \n \n # 取出「人」\n jokePeopleLIST = articut.getPersonLIST(jokeResultDICT)\n print('笑話文本中的「人」:', wordExtractor(jokePeopleLIST))\n\n newsPeopleLIST = articut.getPersonLIST(newsResultDICT)\n print('新聞文本中的「人」:', wordExtractor(newsPeopleLIST))\n print(\"\\n\")\n\n # 取出「事」\n #jokeEventLIST = articut.parse(jokeSTR, userDefinedDictFILE=\"./mixedDICT.json\", level=\"lv3\")[\"event\"]\n #print('笑話文本中的「事」:', jokeEventLIST)\n\n #newsEventLIST = articut.parse(newsSTR, userDefinedDictFILE=\"./mixedDICT.json\", level=\"lv3\")[\"event\"]\n #print('新聞文本中的「事」:', newsEventLIST)\n #print(\"\\n\")\n\n\n # 取出「時」\n #jokeTimeLIST = articut.parse(jokeSTR, userDefinedDictFILE=\"./mixedDICT.json\", level=\"lv3\")[\"time\"]\n #print('笑話文本中的「時」:', jokeTimeLIST)\n\n #newsTimeLIST = articut.parse(newsSTR, userDefinedDictFILE=\"./mixedDICT.json\", level=\"lv3\")[\"time\"]\n #print('新聞文本中的「時」:', newsTimeLIST)\n #print(\"\\n\")\n\n\n # 取出「地」\n jokeLocLIST = articut.getLocationStemLIST(jokeResultDICT)\n print('笑話文本中的「地」:', wordExtractor(jokeLocLIST))\n\n newsLocLIST = articut.getLocationStemLIST(newsResultDICT)\n print('新聞文本中的「地」:', wordExtractor(newsLocLIST))\n print(\"\\n\")\n\n # 取出「物」\n jokeNounLIST = articut.getNounStemLIST(jokeResultDICT)\n print('笑話文本中的「物」:', wordExtractor(jokeNounLIST))\n\n newsNounLIST = articut.getNounStemLIST(newsResultDICT)\n print('新聞文本中的「物」:', wordExtractor(newsNounLIST))","sub_path":"Unit04/HW/HW_Joke.py","file_name":"HW_Joke.py","file_ext":"py","file_size_in_byte":9180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"399306935","text":"import sys\r\nimport socket\r\nimport _thread\r\nfrom _thread import start_new_thread\r\n\r\nserverName = '192.168.1.152'\r\nserverPort = 12002\r\n\r\ndef connect(serverName,serverPort):\r\n\tclientSocket= socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\tclientSocket.connect((serverName,serverPort))\r\n\treturn clientSocket\r\n\r\ndef client(clientSocket):\r\n while 1:\r\n text=clientSocket.recv(1024)\r\n if not text: break\r\n print(text.decode())\r\n\r\ndef connect_client(clientSocket):\r\n\tmessage=\"\"\r\n\tstart_new_thread(client, (clientSocket,))\r\n\twhile message !=\"exit\":\r\n\t\tmessage=input(\"enter message:\\n\")\r\n\t\tclientSocket.send(str.encode(message))\r\n\tclientSocket.close()\r\n\tprint (\"connection closed\")\r\n\r\n\r\nclientSocket=connect(serverName, serverPort)\r\nconnect_client(clientSocket)\r\nclientSocket.close()\r\n","sub_path":"2d_client.py","file_name":"2d_client.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"489649716","text":"from Utilities import InvasiveUtility\nimport itertools\nimport copy\n\nclass VSF:\n \n def __init__(self, state, agent, agents, factors, nbrReaches, allJointActions):\n self.agent = agent\n self.agents = set()\n for f in factors:\n self.agents |= set(f.agents)\n self.agents.remove(self.agent)\n self.dict = dict()\n \n for jAct in allJointActions:\n payoffs = set()\n for f in factors:\n payoffs = self.crossSum(payoffs, f.getPayoffs(jAct))\n for p in payoffs:\n p.tag(self.agent.number, jAct[self.agent.number])\n self.dict[self.convertJointToDepend(jAct)] = payoffs\n self.prune()\n \n \n def dependsOn(self,agent):\n return agent in self.agents\n \n def getPayoffs(self,jointAction):\n return self.dict[self.convertJointToDepend(jointAction)]\n \n def convertJointToDepend(self,jointAction):\n if not self.agents:\n return jointAction[self.agent.number]\n action = [jointAction[self.agent.number]]\n for agent in self.agents:\n action.append(jointAction[agent.number])\n action = tuple(action)\n return action\n \n def prune(self):\n actions = self.dict.keys()\n for a in actions:\n payoffs = self.dict[a]\n optimalPayoff = Pruner.paretoOptimal(payoffs)\n # keep only payoffs that are pareto optimal\n self.dict[a] = optimalPayoff\n \n def crossSum(self, p1, p2):\n total = set()\n if not p1:\n return p2\n if not p2:\n return p1\n for i in p1:\n for j in p2:\n newValues = (i.values[0]+j.values[0], i.values[1]+j.values[1])\n pNew = MultiObjectivePayoff(newValues) \n tagDict = dict(i.tags)\n tagDict.update(j.tags)\n pNew.tags = tagDict\n total.add(pNew)\n return total\n\n \n \nclass Factor:\n \n def __init__(self, agents, nbrReaches, state, allJointActions):\n self.agents = agents\n self.dict = dict()\n for jAct in allJointActions:\n payoffSet = set()\n sumQ = 0\n sumCost = 0\n for agent in self.agents:\n sumQ += agent.getQ(state,jAct)\n sumCost += agent.getCost(state,jAct)\n newPayoff = MultiObjectivePayoff((sumQ,sumCost)) \n payoffSet.add(newPayoff)\n self.dict[self.convertJointToDepend(jAct)] = payoffSet\n self.prune()\n \n def dependsOn(self,agent):\n return agent in self.agents\n \n def convertJointToDepend(self,jointAction):\n action = []\n for agent in self.agents:\n action.append(jointAction[agent.number])\n return tuple(action)\n \n def getPayoffs(self,jointAction):\n return self.dict[self.convertJointToDepend(jointAction)]\n \n def prune(self):\n actions = self.dict.keys()\n for a in actions:\n payoffs = self.dict[a]\n optimalPayoff = Pruner.paretoOptimal(payoffs)\n # keep only payoffs that are pareto optimal\n self.dict[a] = optimalPayoff\n \nclass MultiObjectivePayoff:\n \n def __init__(self,values):\n self.values = values\n self.tags = dict()\n \n def tag(self, agent, action):\n self.tags[agent] = action\n\nclass ReachAgent:\n \n def __init__(self, number, edge, habitatSize, actionParameterObj):\n self.number = number\n self.name = edge\n self.habitatSize = habitatSize\n self.neighbors = []\n self.actionParameterObj = actionParameterObj\n self.Q = dict()\n \n def actions(self,state):\n ownState = self.convertState(state)[:self.habitatSize]\n actions = [InvasiveUtility.Not, InvasiveUtility.Erad, InvasiveUtility.Res, InvasiveUtility.EradRes]\n if sum([1 for x in xrange(len(ownState)) if ownState[x] == InvasiveUtility.Emp]) == 0:\n actions.remove(InvasiveUtility.Res)\n if sum([1 for x in xrange(len(ownState)) if ownState[x] == InvasiveUtility.Tam]) == 0:\n actions.remove(InvasiveUtility.Erad)\n actions.remove(InvasiveUtility.EradRes)\n return actions\n \n \n def convertState(self,state):\n state = list(state)\n newState = state[self.number * self.habitatSize : (self.number + 1) * self.habitatSize]\n for agent in self.neighbors:\n n = agent.number\n newState.extend(state[n * self.habitatSize : (n + 1) * self.habitatSize])\n return tuple(newState)\n \n def getCost(self,state,jointAction):\n ownState = self.convertState(state)[:self.habitatSize]\n ownAction = [jointAction[self.number]]\n return -1 * InvasiveUtility.get_budget_cost_actions(ownAction, ownState, self.actionParameterObj)\n \n def getQ(self,state,jointAction):\n jointAction = self.getRelevantJointActionList(jointAction)\n convertedState = self.convertState(state)\n \n # reduced state space\n s = []\n for i in xrange(0,len(convertedState),self.habitatSize):\n s.append(sum([1 for x in convertedState[i:i+self.habitatSize] if x == 2]))\n convertedState = tuple(s)\n\n if not self.neighbors:\n if not convertedState in self.Q:\n self.Q[convertedState] = dict()\n for action in self.actions(state):\n self.Q[convertedState][action] = 0\n \n if not convertedState in self.Q:\n self.Q[convertedState] = dict()\n for joint in self.createAllJointActions(state):\n self.Q[convertedState][tuple(joint)] = 0\n if not jointAction in self.Q[convertedState]:\n return 0\n returnValue = self.Q[convertedState][jointAction]\n return returnValue\n\n def createAllJointActions(self,state):\n allActions = [self.actions(state)]\n if not self.neighbors:\n return self.actions(state)\n for agent in self.neighbors:\n actions = agent.actions(state)\n allActions.append(actions)\n return list(itertools.product(*allActions))\n \n def getRelevantJointActionList(self, allJointActions):\n # prunes the joint action list to contain its own action + \n # the joint actions of the neighbours self depends on \n ownAction = allJointActions[self.number]\n if not self.neighbors:\n return ownAction\n \n relevantActions = [ownAction]\n for agent in self.neighbors:\n relevantActions.append(allJointActions[agent.number])\n return tuple(relevantActions)\n\n\n def updateQ(self, state, action, reward, newState, bestJointAction, discount, alpha):\n oldQ = self.getQ(state, action)\n Q_optimal = self.getQ(newState, bestJointAction)\n \n Q = oldQ + alpha*(reward + discount*Q_optimal - oldQ)\n \n state = self.convertState(state)\n \n# # reduced state space\n s = []\n for i in xrange(0,len(state),self.habitatSize):\n s.append(sum([1 for x in state[i:i+self.habitatSize] if x == 2]))\n state = tuple(s)\n\n self.Q[state][self.getRelevantJointActionList(action)] = Q \n return Q\n\nclass Pruner:\n \n @staticmethod \n def paretoOptimal(payoffSet):\n payoffs = list(payoffSet)\n optimalPayoffs = set()\n while payoffs:\n u = payoffs[0]\n for v in payoffs[1:-1]:\n if Pruner.paretoDominates(v,u):\n u = v\n \n payoffs2 = []\n for v in payoffs: \n if u == v:\n continue\n if not Pruner.paretoDominates(u,v):\n payoffs2.append(v)\n \n optimalPayoffs.add(u)\n payoffs = payoffs2\n return optimalPayoffs\n \n @staticmethod \n def paretoDominates(payoff1, payoff2):\n # x pareto dominates y\n # Pareto dominance: greater or equal in all objectives\n # and strictly greater in at least one objective\n \n x = payoff1.values\n y = payoff2.values\n \n if(len(x) == len(y)):\n # check if all x_i >= y_i\n for i in range(len(x)):\n if not x[i] >= y[i]:\n return False\n \n # check if there exists at least one x_i s.t. x_i > y_i\n for i in range(len(x)):\n if x[i] > y[i]:\n return True\n \n return False","sub_path":"code/helperClasses.py","file_name":"helperClasses.py","file_ext":"py","file_size_in_byte":8729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"4974423","text":"from typing import List, Optional, Set\nimport unittest\nfrom unittest import TestCase\nimport sys \n\nfrom tap import Tap\n\n\nclass EdgeCaseTests(TestCase):\n def test_empty(self) -> None:\n class EmptyTap(Tap):\n pass\n\n EmptyTap().parse_args()\n\n def test_empty_add_argument(self) -> None:\n class EmptyAddArgument(Tap):\n def add_arguments(self) -> None:\n self.add_argument('--hi')\n\n hi = 'yo'\n args = EmptyAddArgument().parse_args(['--hi', hi])\n self.assertEqual(args.hi, hi)\n\n def test_no_typed_args(self) -> None:\n class NoTypedTap(Tap):\n hi = 3\n\n args = NoTypedTap().parse_args()\n self.assertEqual(args.hi, 3)\n\n hi = 'yo'\n args = NoTypedTap().parse_args(['--hi', hi])\n self.assertEqual(args.hi, hi)\n\n def test_only_typed_args(self) -> None:\n class OnlyTypedTap(Tap):\n hi: str = 'sup'\n\n args = OnlyTypedTap().parse_args()\n self.assertEqual(args.hi, 'sup')\n\n hi = 'yo'\n args = OnlyTypedTap().parse_args(['--hi', hi])\n self.assertEqual(args.hi, hi)\n\n\nclass RequiredClassVariableTests(TestCase):\n\n def setUp(self) -> None:\n class RequiredArgumentsParser(Tap):\n arg_str_required: str\n arg_list_str_required: List[str]\n\n self.tap = RequiredArgumentsParser()\n\n # Suppress prints from SystemExit\n class DevNull:\n def write(self, msg):\n pass\n self.dev_null = DevNull()\n\n def test_arg_str_required(self):\n with self.assertRaises(SystemExit):\n sys.stderr = self.dev_null\n self.tap.parse_args([\n '--arg_str_required', 'tappy',\n ])\n\n def test_arg_list_str_required(self):\n with self.assertRaises(SystemExit):\n sys.stderr = self.dev_null\n self.tap.parse_args([\n '--arg_list_str_required', 'hi', 'there',\n ])\n \n def test_both_assigned_okay(self):\n args = self.tap.parse_args([\n '--arg_str_required', 'tappy',\n '--arg_list_str_required', 'hi', 'there',\n ])\n self.assertEqual(args.arg_str_required, 'tappy')\n self.assertEqual(args.arg_list_str_required, ['hi', 'there'])\n\n\nclass Person:\n def __init__(self, name: str):\n self.name = name\n\n def __eq__(self, other) -> bool:\n if not isinstance(other, Person):\n return False\n\n return self.name == other.name\n\n\nclass IntegrationDefaultTap(Tap):\n \"\"\"Documentation is boring\"\"\"\n arg_untyped = 42\n arg_str: str = 'hello there'\n arg_int: int = -100\n arg_float: float = 77.3\n # TODO: how to handle untyped arguments? users might accidentally think they should behave according to the inferred type\n # arg_bool_untyped_true = True\n # arg_bool_untyped_false = False\n arg_bool_true: bool = True\n arg_bool_false: bool = False\n arg_optional_str: Optional[str] = None\n arg_optional_int: Optional[int] = None\n arg_optional_float: Optional[float] = None\n arg_list_str: List[str] = ['hello', 'how are you']\n arg_list_int: List[int] = [10, -11]\n arg_list_float: List[float] = [3.14, 6.28]\n arg_list_str_empty: List[str] = []\n arg_set_str: Set[str] = {'hello', 'how are you'}\n arg_set_int: Set[int] = {10, -11}\n arg_set_float: Set[float] = {3.14, 6.28}\n # TODO: move these elsewhere since we don't support them as defaults\n # arg_other_type_required: Person\n # arg_other_type_default: Person = Person('tap')\n\n\nclass SubclassTests(TestCase):\n def test_subclass(self):\n class IntegrationSubclassTap(IntegrationDefaultTap):\n arg_subclass_untyped = 33\n arg_subclass_str: str = 'hello'\n arg_subclass_str_required: str\n arg_subclass_str_set_me: str = 'goodbye'\n arg_float: float = -2.7\n\n arg_subclass_str_required = 'subclassing is fun'\n arg_subclass_str_set_me = 'all set!'\n arg_int = '77'\n self.args = IntegrationSubclassTap().parse_args([\n '--arg_subclass_str_required', arg_subclass_str_required,\n '--arg_subclass_str_set_me', arg_subclass_str_set_me,\n '--arg_int', arg_int\n ])\n\n arg_int = int(arg_int)\n\n self.assertEqual(self.args.arg_str, IntegrationDefaultTap.arg_str)\n self.assertEqual(self.args.arg_int, arg_int)\n self.assertEqual(self.args.arg_float, -2.7)\n self.assertEqual(self.args.arg_subclass_str_required, arg_subclass_str_required)\n self.assertEqual(self.args.arg_subclass_str_set_me, arg_subclass_str_set_me)\n\n\nclass DefaultClassVariableTests(TestCase):\n\n def test_get_default_args(self) -> None:\n args = IntegrationDefaultTap().parse_args()\n\n self.assertEqual(args.arg_untyped, 42)\n self.assertEqual(args.arg_str, 'hello there')\n self.assertEqual(args.arg_int, -100)\n self.assertEqual(args.arg_float, 77.3)\n self.assertEqual(args.arg_bool_true, True)\n self.assertEqual(args.arg_bool_false, False)\n self.assertTrue(args.arg_optional_str is None)\n self.assertTrue(args.arg_optional_int is None)\n self.assertTrue(args.arg_optional_float is None)\n self.assertEqual(args.arg_list_str, ['hello', 'how are you'])\n self.assertEqual(args.arg_list_int, [10, -11])\n self.assertEqual(args.arg_list_float, [3.14, 6.28])\n self.assertEqual(args.arg_list_str_empty, [])\n self.assertEqual(args.arg_set_str, {'hello', 'how are you'})\n self.assertEqual(args.arg_set_int, {10, -11})\n self.assertEqual(args.arg_set_float, {3.14, 6.28})\n\n def test_set_default_args(self) -> None:\n arg_untyped = 'yes'\n arg_str = 'goodbye'\n arg_int = '2'\n arg_float = '1e-2'\n arg_optional_str = 'hello'\n arg_optional_int = '77'\n arg_optional_float = '7.7'\n arg_list_str = ['hi', 'there', 'how', 'are', 'you']\n arg_list_int = ['1', '2', '3', '10', '-11']\n arg_list_float = ['2.2', '-3.3', '2e20']\n arg_list_str_empty = []\n arg_set_str = ['hi', 'hi', 'hi', 'how']\n arg_set_int = ['1', '2', '2', '2', '3']\n arg_set_float = ['1.23', '4.4', '1.23']\n\n\n args = IntegrationDefaultTap().parse_args([\n '--arg_untyped', arg_untyped,\n '--arg_str', arg_str,\n '--arg_int', arg_int,\n '--arg_float', arg_float,\n '--arg_bool_true',\n '--arg_bool_false',\n '--arg_optional_str', arg_optional_str,\n '--arg_optional_int', arg_optional_int,\n '--arg_optional_float', arg_optional_float,\n '--arg_list_str', *arg_list_str,\n '--arg_list_int', *arg_list_int,\n '--arg_list_float', *arg_list_float,\n '--arg_list_str_empty', *arg_list_str_empty,\n '--arg_set_str', *arg_set_str,\n '--arg_set_int', *arg_set_int,\n '--arg_set_float', *arg_set_float\n ])\n\n arg_int = int(arg_int)\n arg_float = float(arg_float)\n arg_optional_int = float(arg_optional_int)\n arg_optional_float = float(arg_optional_float)\n arg_list_int = [int(arg) for arg in arg_list_int]\n arg_list_float = [float(arg) for arg in arg_list_float]\n arg_set_str = set(arg_set_str)\n arg_set_int = {int(arg) for arg in arg_set_int}\n arg_set_float = {float(arg) for arg in arg_set_float}\n\n\n self.assertEqual(args.arg_untyped, arg_untyped)\n self.assertEqual(args.arg_str, arg_str)\n self.assertEqual(args.arg_int, arg_int)\n self.assertEqual(args.arg_float, arg_float)\n # Note: setting the bools as flags results in the opposite of their default\n self.assertEqual(args.arg_bool_true, False)\n self.assertEqual(args.arg_bool_false, True)\n self.assertEqual(args.arg_optional_str, arg_optional_str)\n self.assertEqual(args.arg_optional_int, arg_optional_int)\n self.assertEqual(args.arg_optional_float, arg_optional_float)\n self.assertEqual(args.arg_list_str, arg_list_str)\n self.assertEqual(args.arg_list_int, arg_list_int)\n self.assertEqual(args.arg_list_float, arg_list_float)\n self.assertEqual(args.arg_list_str_empty, arg_list_str_empty)\n self.assertEqual(args.arg_set_str, arg_set_str)\n self.assertEqual(args.arg_set_int, arg_set_int)\n self.assertEqual(args.arg_set_float, arg_set_float)\n\n\nclass AddArgumentTests(TestCase):\n def test_positional(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('arg_str')\n\n arg_str = 'positional'\n self.args = IntegrationAddArgumentTap().parse_args([arg_str])\n\n self.assertEqual(self.args.arg_str, arg_str)\n\n def test_positional_ordering(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('arg_str')\n self.add_argument('arg_int')\n self.add_argument('arg_float')\n\n arg_str = 'positional'\n arg_int = '5'\n arg_float = '1.1'\n self.args = IntegrationAddArgumentTap().parse_args([arg_str, arg_int, arg_float])\n\n arg_int = int(arg_int)\n arg_float = float(arg_float)\n\n self.assertEqual(self.args.arg_str, arg_str)\n self.assertEqual(self.args.arg_int, arg_int)\n self.assertEqual(self.args.arg_float, arg_float)\n\n def test_one_dash(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('-arg_str')\n\n arg_str = 'one_dash'\n self.args = IntegrationAddArgumentTap().parse_args(['-arg_str', arg_str])\n\n self.assertEqual(self.args.arg_str, arg_str)\n\n def test_two_dashes(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_str')\n\n arg_str = 'two_dashes'\n self.args = IntegrationAddArgumentTap().parse_args(['--arg_str', arg_str])\n\n self.assertEqual(self.args.arg_str, arg_str)\n\n def test_not_class_variable(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--non_class_arg')\n\n arg_str = 'non_class_arg'\n self.tap = IntegrationAddArgumentTap()\n self.assertFalse('non_class_arg' in self.tap._get_argument_names()) # ensure it's actually not a class variable\n self.args = self.tap.parse_args(['--non_class_arg', arg_str])\n\n self.assertEqual(self.args.non_class_arg, arg_str)\n\n def test_complex_type(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n arg_person: Person = Person('tap')\n # arg_person_required: Person # TODO\n arg_person_untyped = Person('tap untyped')\n\n # TODO: assert a crash if any complex types are not explicitly added in add_argument\n def add_arguments(self) -> None:\n self.add_argument('--arg_person', type=Person)\n # self.add_argument('--arg_person_required', type=Person) # TODO\n self.add_argument('--arg_person_untyped', type=Person)\n\n args = IntegrationAddArgumentTap().parse_args()\n self.assertEqual(args.arg_person, Person('tap'))\n self.assertEqual(args.arg_person_untyped, Person('tap untyped'))\n\n arg_person = Person('hi there')\n arg_person_untyped = Person('heyyyy')\n args = IntegrationAddArgumentTap().parse_args([\n '--arg_person', arg_person.name,\n '--arg_person_untyped', arg_person_untyped.name\n ])\n self.assertEqual(args.arg_person, arg_person)\n self.assertEqual(args.arg_person_untyped, arg_person_untyped)\n\n def test_repeat_default(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_str', default=IntegrationDefaultTap.arg_str)\n\n args = IntegrationAddArgumentTap().parse_args()\n self.assertEqual(args.arg_str, IntegrationDefaultTap.arg_str)\n\n def test_conflicting_default(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_str', default='yo dude')\n\n args = IntegrationAddArgumentTap().parse_args()\n self.assertEqual(args.arg_str, 'yo dude')\n\n # TODO: this\n def test_repeat_required(self) -> None:\n pass\n\n # TODO: this\n def test_conflicting_required(self) -> None:\n pass\n\n def test_repeat_type(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_int', type=int)\n\n args = IntegrationAddArgumentTap().parse_args()\n self.assertEqual(type(args.arg_int), int)\n self.assertEqual(args.arg_int, IntegrationDefaultTap.arg_int)\n\n arg_int = '99'\n args = IntegrationAddArgumentTap().parse_args(['--arg_int', arg_int])\n arg_int = int(arg_int)\n self.assertEqual(type(args.arg_int), int)\n self.assertEqual(args.arg_int, arg_int)\n\n def test_conflicting_type(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_int', type=str)\n\n arg_int = 'yo dude'\n args = IntegrationAddArgumentTap().parse_args(['--arg_int', arg_int])\n self.assertEqual(type(args.arg_int), str)\n self.assertEqual(args.arg_int, arg_int)\n\n # TODO\n def test_repeat_help(self) -> None:\n pass\n\n # TODO\n def test_conflicting_help(self) -> None:\n pass\n\n def test_repeat_nargs(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_list_str', nargs='*')\n\n arg_list_str = ['hi', 'there', 'person', '123']\n args = IntegrationAddArgumentTap().parse_args(['--arg_list_str', *arg_list_str])\n self.assertEqual(args.arg_list_str, arg_list_str)\n\n # TODO: figure out how to check for system exit\n # def test_conflicting_nargs(self) -> None:\n # class IntegrationAddArgumentTap(IntegrationDefaultTap):\n # def add_arguments(self) -> None:\n # self.add_argument('--arg_list_str', nargs=3)\n #\n # arg_list_str = ['hi', 'there', 'person', '123']\n # self.assertRaises(SystemExit, IntegrationAddArgumentTap().parse_args(['--arg_list_str', *arg_list_str]))\n\n def test_repeat_action(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_bool_false', action='store_true', default=False)\n\n args = IntegrationAddArgumentTap().parse_args()\n self.assertEqual(args.arg_bool_false, False)\n\n args = IntegrationAddArgumentTap().parse_args(['--arg_bool_false'])\n self.assertEqual(args.arg_bool_false, True)\n\n def test_conflicting_action(self) -> None:\n class IntegrationAddArgumentTap(IntegrationDefaultTap):\n def add_arguments(self) -> None:\n self.add_argument('--arg_bool_false', action='store_false', default=True)\n\n args = IntegrationAddArgumentTap().parse_args()\n self.assertEqual(args.arg_bool_false, True)\n\n args = IntegrationAddArgumentTap().parse_args(['--arg_bool_false'])\n self.assertEqual(args.arg_bool_false, False) \n\n\nclass KnownTap(Tap):\n arg_int: int = 2\n\n\nclass ParseKnownArgsTests(TestCase):\n arg_int = 3\n arg_float = 3.3\n\n def test_all_known(self):\n args = KnownTap().parse_args([\n '--arg_int', str(self.arg_int)\n ], known_only=True)\n self.assertEqual(args.arg_int, self.arg_int)\n self.assertEqual(args.extra_args, [])\n\n def test_some_known(self):\n args = KnownTap().parse_args([\n '--arg_int', str(self.arg_int),\n '--arg_float', str(self.arg_float)\n ], known_only=True)\n self.assertEqual(args.arg_int, self.arg_int)\n self.assertEqual(args.extra_args, ['--arg_float', '3.3'])\n\n def test_none_known(self):\n args = KnownTap().parse_args([\n '--arg_float', str(self.arg_float)\n ], known_only=True)\n self.assertEqual(args.extra_args, ['--arg_float', '3.3'])\n\n\n\"\"\"\n- crash if default type not supported\n- user specifying process_args\n- test save args\n- test get reproducibility info\n- test as_dict\n- test str?\n- test comments\n\"\"\"\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/test_integration.py","file_name":"test_integration.py","file_ext":"py","file_size_in_byte":17017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"576391399","text":"# -*- coding: utf-8 -*-\n# Uncomment the import only for coding support\n#import numpy\nimport torch\n#import torchvision\n#import tensorflow\n#import tensorboard\nfrom openeo_udf.api.base import SpatialExtent, RasterCollectionTile, FeatureCollectionTile, UdfData, MachineLearnModel\n\n__license__ = \"Apache License, Version 2.0\"\n__author__ = \"Soeren Gebbert\"\n__copyright__ = \"Copyright 2018, Soeren Gebbert\"\n__maintainer__ = \"Soeren Gebbert\"\n__email__ = \"soerengebbert@googlemail.com\"\n\n\ndef rct_pytorch_ml(udf_data: UdfData):\n \"\"\"Apply a pre-trained pytorch machine learn model on the first tile\n\n The model must be a pytorch model that has expects the input data in the constructor\n The prediction method must accept a torch.autograd.Variable as input.\n\n Args:\n udf_data (UdfData): The UDF data object that contains raster and vector tiles\n\n Returns:\n This function will not return anything, the UdfData object \"udf_data\" must be used to store the resulting\n data.\n\n \"\"\"\n tile = udf_data.raster_collection_tiles[0]\n\n # This is the input data of the model.\n input = torch.autograd.Variable(torch.Tensor(tile.data))\n\n # Get the first model\n mlm = udf_data.get_ml_model_list()[0]\n m = mlm.get_model()\n # Predict the data\n pred = m(input)\n # Create the new raster collection tile\n rct = RasterCollectionTile(id=mlm.name, extent=tile.extent, data=numpy.array(pred.tolist()),\n start_times=tile.start_times, end_times=tile.end_times)\n # Insert the new tiles as list of raster collection tiles in the input object. The new tiles will\n # replace the original input tiles.\n udf_data.set_raster_collection_tiles([rct,])\n\n\n# This function call is the entry point for the UDF.\n# The caller will provide all required data in the **data** object.\nrct_pytorch_ml(data)\n","sub_path":"src/openeo_udf/functions/raster_collections_pytorch_ml.py","file_name":"raster_collections_pytorch_ml.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"626904044","text":"import sys\nimport requests\nimport json\nimport time\nfrom cred import app_id, app_key\n\n\ndef get_ipa(word, test=False):\n try:\n langs = ['en-us'] # 'en-gb' does not work\n\n spellings = []\n for lang in langs:\n url = \"https://od-api.oxforddictionaries.com:443/api/v2/entries/\" + lang + \"/\" + word.lower()\n reply_json = requests.get(url, headers={\"app_id\": app_id, \"app_key\": app_key}).json()\n\n reply_str = json.dumps(reply_json, indent=2)\n reply_dict = json.loads(reply_str)\n spellings.append(\n reply_dict['results'][0]['lexicalEntries'][0]['entries'][0]['pronunciations'][1]['phoneticSpelling']\n )\n\n # ret = f'uk[{spellings[0]}] us[{spellings[1]}]'\n ret = f'us[{spellings[0]}]'\n if not test:\n time.sleep(2.1) # limit utilization of Hits per minute: 30/60\n except:\n ret = ''\n\n return ret\n\nif __name__ == \"__main__\":\n query = sys.argv[1]\n print(get_ipa(query, test=True))\n","sub_path":"ipa.py","file_name":"ipa.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"432807118","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass AnalyticaSpider(scrapy.Spider):\n name = 'analytica'\n allowed_domains = ['http://https://www.analytica-media.de/onlinecatalog/2018/exhibitorlist/']\n start_urls = ['http://http://https://www.analytica-media.de/onlinecatalog/2018/exhibitorlist//']\n\n def parse(self, response):\n for href in response.css('div.jl_lexname a::attr(href)'):\n yield response.follow(href, callback=self.parse)\n \n next_page_url = response.css('li.next > a::attr(href)').extract_first()\n if next_page_url:\n next_page_url = response.urljoin(next_page_url)\n yield scrapy.Request(url=next_page_url, callback=self.parse)\n\n def parse_details(self, response):\n yield {\n 'company': response.css('div.jl_dt_hfont a::text').extract(),\n 'strasse': response.css('div.jl_adr_strasse::text').extract(),\n 'ort': response.css('div.jl_adr_ort::text').extract(),\n 'land': response.css('div.jl_adr_land::text').extract(),\n 'phone': response.css('div.adr_phone::text').extract(), #needs tweaking\n 'email': response.css('div.view_kontakt_mail a::attr(href)').extract() #also needs tweaking\n\n\n }\n \n \n \"\"\"\n\n def parse(self, response):\n urls = response.css('div.quote > span > a::attr(href)').extract()\n for url in urls:\n url = response.urljoin(url)\n yield scrapy.Request(url=url, callback=self.parse_details)\n\n # follow pagination link\n next_page_url = response.css('li.next > a::attr(href)').extract_first()\n if next_page_url:\n next_page_url = response.urljoin(next_page_url)\n yield scrapy.Request(url=next_page_url, callback=self.parse)\n\n def parse_details(self, response):\n yield {\n 'name': response.css('h3.author-title::text').extract_first(),\n 'birth_date': response.css('span.author-born-date::text').extract_first(),\n }\n \n \"\"\"\n","sub_path":"spiderweb/spiders/analytica.py","file_name":"analytica.py","file_ext":"py","file_size_in_byte":2022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"174429358","text":"\"\"\"quizbot URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom rest_framework_simplejwt.views import (\n TokenObtainPairView,\n TokenRefreshView,\n)\nfrom question.views import RandomQuestion\nfrom score.views import UpdateScores\nfrom score.views import Leaderboard\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('api/token/', TokenObtainPairView.as_view(), name='token_obtain_pair'),\n path('api/token/refresh/', TokenRefreshView.as_view(), name='token_refresh'),\n path('api/user/', include('users.urls', namespace='users')),\n path('api-auth/', include('rest_framework.urls', namespace='rest_framework')), # login to admin area\n path('api/quiz/', include('quiz.urls', namespace='quiz')),\n \n path('api/v0/random/', RandomQuestion.as_view(), name='random'),\n path('api2/v0/score/update/', UpdateScores.as_view(), name='score_update'),\n path('api2/v0/score/leaderboard/', Leaderboard.as_view(), name='leaderboard'),\n]\n","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"589169824","text":"import os\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.web\nimport requests\n\n\n\nGITHUB_OAUTH_ENDPOINT = 'https://github.com/login/oauth/access_token'\n\nAPP_CLIENT_ID = os.environ.get('APP_CLIENT_ID')\nAPP_CLIENT_SECRET = os.environ.get('APP_CLIENT_SECRET')\n \n\nclass AuthHandler(tornado.web.RequestHandler):\n def get(self):\n code = self.get_argument(\"code\", None, True)\n\n oauth_payload = {\n 'client_id': APP_CLIENT_ID,\n 'client_secret': APP_CLIENT_SECRET,\n 'code': code,\n #'redirect_uri': None,\n #'state': None\n }\n\n headers = {\n 'Accept': 'application/json'\n }\n\n response = requests.post(\n GITHUB_OAUTH_ENDPOINT,\n data=oauth_payload,\n headers=headers)\n\n self.write(response.text)\n\n \napp = tornado.web.Application([\n (r\"/\", AuthHandler)\n])\n\n \nif __name__ == \"__main__\":\n http_server = tornado.httpserver.HTTPServer(app)\n port = int(os.environ.get(\"PORT\", 5000))\n http_server.listen(port)\n tornado.ioloop.IOLoop.instance().start()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"478261881","text":"'''\r\nWrite a program that takes in two words as input and returns a list containing three elements, in the following order:\r\n\r\nShared letters between two words. Letters unique to word 1. Letters unique to word 2. \r\nEach element of the output should have unique letters, and should be alphabetically sorted. Use set operations. \r\nThe strings will always be in lowercase and will not contain any punctuation.\r\n\r\nExample:\r\n\r\nInput1>>> \"sharp\"\r\n\r\nInput2>>> \"soap\"\r\n\r\nOutput>>> ['aps', 'hr', 'o']\r\n'''\r\nimport string\r\nfirst_word = set(input(\"Please write your 1. word: \").lower())\r\nsecond_word = set(input(\"Please write your 2. word: \").lower())\r\nexclude = set(string.punctuation)\r\n\r\nintersection = sorted(first_word & second_word)\r\nintersection = ''.join(intersection)\r\nunique1 = sorted(first_word - second_word)\r\nunique1 = ''.join(unique1)\r\nunique2 = sorted(second_word - first_word)\r\nunique2 = ''.join(unique2)\r\nfirst_word = ''.join(ch for ch in first_word if ch not in exclude)\r\nsecond_word = ''.join(ch for ch in first_word if ch not in exclude)\r\n'''yukarıdaki iki satır yerine bu şekilde de yazabilirdik:'''\r\n# intersection.translate(str.maketrans('', '', string.punctuation))\r\n# unique1.translate(str.maketrans('', '', string.punctuation))\r\n# unique2.translate(str.maketrans('', '', string.punctuation))\r\nsecond_word = ''.join(ch for ch in second_word if ch not in exclude)\r\nmylist = [intersection,unique1,unique2]\r\nprint(mylist)\r\n","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"640021801","text":"import pyexcel as p\nimport pprint\nimport os\nfrom database.db_conn import connect\nfrom database.db_models import (BusinessModelStage,\n CaseStudyQuestion,\n CaseStudy,\n QuestionChoice,\n Company)\nfrom sqlalchemy.orm import sessionmaker\n\n\ndb_conn = connect()\nSession = sessionmaker(db_conn)\nsession = Session()\n\n\ndef all_empty(items):\n return all(x == items[0] for x in items)\n\n\ndef insert_records(_records):\n res = []\n data = {}\n order_question = 1\n order_choice = 1\n for row in _records:\n if all_empty(row):\n if data:\n res.append(data)\n data = {}\n order_choice = 1\n continue\n\n if row[0] == 'END OF SHEET':\n break\n\n if row[0]:\n data[f'question_{order_question}'] = row[0]\n order_question += 1\n\n else:\n data[f'choice_{order_choice}'] = {\n 'choice_text': row[1],\n 'choice_is_correct': str(row[2]).lower(),\n 'explanation': row[3]\n }\n order_choice += 1\n\n res.append(data)\n return res\n\n\ndef insert_case_study(id_case_study, id_stage, _records):\n order_question = 1\n order_choice = 1\n question = None\n for row in _records:\n if all_empty(row):\n order_choice = 1\n continue\n\n elif row[0] == 'END OF SHEET':\n break\n\n elif row[0]:\n question = CaseStudyQuestion(id_case_study=id_case_study,\n id_stage=id_stage,\n order=order_question,\n question=row[0])\n session.add(question)\n session.commit()\n order_question += 1\n\n else:\n explanation = row[3] if 3 < len(row) else ''\n choice = QuestionChoice(id_question=question.id,\n choice_text=row[1],\n explanation=explanation,\n is_correct=row[2]\n )\n session.add(choice)\n session.commit()\n order_choice += 1\n\n\nfor file in os.listdir(\"content\"):\n header = p.get_sheet(file_name=f'content/{file}', sheet_name='Header')\n case_study = None\n\n for item in header:\n print(item[0])\n if item[0] == 'NAME':\n company = Company(id_specialization=1, name=item[1])\n session.add(company)\n session.commit()\n case_study = CaseStudy(id_company=company.id, name=company.name)\n elif item[0] == 'EMPLOYEES_NUM' and case_study:\n case_study.employees_num = int(item[1])\n elif item[0] == 'REVENUES':\n case_study.revenue = int(item[1])\n elif item[0] == 'DESCRIPTION':\n case_study.description = item[1]\n elif item[0] == 'MOTIVATION':\n case_study.motivation = item[1]\n elif item[0] == 'UNIQUE_VALUE':\n case_study.unique_value = item[1]\n elif item[0] == 'TYPE':\n case_study.type = item[1]\n else:\n break\n\n session.add(case_study)\n session.commit()\n\n stages = session.query(BusinessModelStage)\n\n for stage in stages:\n records = p.get_sheet(file_name=f'content/{file}', sheet_name=stage.name)\n insert_case_study(case_study.id, stage.id, records)\n","sub_path":"source/flask_backend/database/get_excel_content.py","file_name":"get_excel_content.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"195790185","text":"#coding=utf-8\n'''\n1) Exercício - Contagem de Ocorrências\nA partir de um texto fornecido pelo usuário, conte o número de caracteres e quantos deles são vogais.\n'''\n\ntext = (input('Digite um texto:\\t')).lower()\nn = len(text)\ncont = 0\nvogais = ['a','e','i','o','u']\nfor i in text:\n if i in vogais: \n cont+=1\nprint('A quantidade de caracteres é:\\t',n)\nprint('A quantidade de vogais é:\\t',cont)\n","sub_path":"03 - string/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"166935293","text":"# -*- coding: utf-8 -*-\nclass Solution(object):\n def backspaceCompare(self, S, T):\n \"\"\"\n Solution: Stack\n Time Complexity: O(n)\n Space Complexity: O(n)\n Perf: Runtime: 24 ms, faster than 54.36% / Memory Usage: 11.9 MB, less than 5.06%\n :type S: str\n :type T: str\n :rtype: bool\n \"\"\"\n\n def textEditor(S):\n stack = []\n for s in S:\n if not stack and s != '#':\n stack.append(s)\n elif s != '#':\n stack.append(s)\n elif stack:\n stack.pop()\n return stack\n\n return textEditor(S) == textEditor(T)","sub_path":"高頻面經/Google/844. Backspace String Compare/GoogleScan_Stack.py","file_name":"GoogleScan_Stack.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"383879927","text":"\"\"\"\r\nProgram to scale an Image \r\ndepending on the values read through IR Distance Sensor.\r\n\r\nIR Distance Sensor is connected at IO pin 1 \r\n\r\nscaling width & Height is adjusted by mapping \r\nHighest Width(700), Highest Height (640) \r\nto Heighest Analog Reading by sensor(4095)\r\n\r\nIO Pin 1 is configured to Analog Input, to read analog values from sensor\r\n\r\nWe used floor operator to get integer values of Width and Height\r\n\"\"\"\r\nimport phygital_v2 as phy\r\nfrom time import sleep as s\r\nimport pygame\r\n\r\npygame.init()\r\n\r\n\r\n\"\"\" Set the Dimension of the Screen\"\"\"\r\nWidth = 700\r\nHeight = 640\r\n\r\n\"\"\" Set the Screen\"\"\"\r\nscreen = pygame.display.set_mode((Width,Height))\r\n\r\n\r\n\"\"\" Set The Title of Screen\"\"\"\r\npygame.display.set_caption(\"Sensor Based Display\")\r\n\r\n\"\"\" Load the Image\"\"\"\r\nscreenImg = pygame.image.load(\"Images/background.jpg\")\r\n\r\n\"\"\" Display Image at Specific Co-Ordinate\"\"\"\r\n\r\nscreen.blit(screenImg,(0,0))\r\n\r\nimg=pygame.image.load(\"Images/apple.jpg\")\r\nscreen.blit(img,(60,60))\r\n\r\nphy.pinMode(1,\"ainput\")\r\nphy.init(\"COM13\")\r\n\r\nwhile True:\r\n try:\r\n pygame.display.update()\r\n \r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n phy.close()\r\n EventStatus=\"Quit\"\r\n break\r\n \r\n \r\n data=phy.aRead(1)\r\n print(data)\r\n \r\n newWidth=data//8\r\n newHeight= data//8\r\n \r\n # print(bright)\r\n screenImg = pygame.image.load(\"Images/background.jpg\")\r\n screen.blit(screenImg,(0,0)) \r\n \r\n img= pygame.image.load(\"Images/apple.jpg\")\r\n \r\n # #Function to adjust brightness of the image\r\n # img.fill((bright,bright,bright),special_flags=pygame.BLEND_RGB_ADD)\r\n newimg=pygame.transform.scale(img,(newWidth,newHeight))\r\n screen.blit(newimg,(10,10)) \r\n \r\n # time.sleep(1)\r\n \r\n except:\r\n if KeyboardInterrupt:\r\n pygame.quit()\r\n phy.close()\r\n break\r\n \r\n \r\nprint(\"Closing\")\r\n","sub_path":"Gesture Controlled Image Zoom/AnalogSensorBasedImageScaling.py","file_name":"AnalogSensorBasedImageScaling.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"145720839","text":"from recognizer.utils import detect_and_save_face, align, logger\nfrom recognizer.settings import recognizer, labels_dict\nfrom recognizer.train import train\nimport cv2\n\n\ndef recognize(npimg):\n face_store = []\n # images = cv2.imread('test_data/brody.jpg')\n images = cv2.imdecode(npimg, cv2.IMREAD_COLOR)\n copy_image = images\n # train()\n # images_new = cv2.cvtColor(images, cv2.COLOR_BGR2GRAY)\n # logger.info(images)\n face_st, rect = detect_and_save_face(images, 'output.jpg')\n output = align(face_st)\n\n if output[0] == 'y':\n for ix in range(0, len(output[1])):\n x, y, w, h = rect[ix][0], rect[ix][1], rect[ix][2], rect[ix][3]\n cv2.rectangle(copy_image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n out = output[1][ix]\n lab = recognizer.predict(out)\n logger.debug(lab)\n for k, v in labels_dict.items():\n if v == lab[0]:\n logger.info('NAME: {}\\nProbability: {} '.format(k, lab[1]))\n result = k\n # if lab[0] == 1:\n cv2.putText(copy_image, result, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)\n # else:\n # cv2.putText(copy_image, 'Unknown', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)\n cv2.imwrite('static/people/out.jpg', copy_image)\n return copy_image\n","sub_path":"recognizer/recognize.py","file_name":"recognize.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"623296779","text":"# %%\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport time\nimport pandas as pd\n\n\ndf = pd.read_excel(\"info.xlsx\")\nnumofinfants = 0\nnumofchilds = 0\nnumofpepole18 = 0\nfor age in df[\"Age\"]:\n if age >= 18:\n numofpepole18 += 1\n elif age > 2:\n numofchilds += 1\n else:\n numofinfants += 1\npepole = [numofpepole18-1, numofchilds, numofinfants]\ndriver = webdriver.Chrome()\ndriver.get(\"https://www.easyjet.com/en\")\n\n\n# Send Origin info\norigin = driver.find_element_by_name(\"origin\")\norigin.clear()\norigin.send_keys(\"Tel Aviv (TLV)\")\n\n# Send Destination info\ndest = driver.find_element_by_name(\"destination\")\ndest.clear()\ndest.send_keys(\"Geneva\")\ndest.send_keys(Keys.ENTER)\n\n\n# Adding number of passengers\naddPassButton = driver.find_elements_by_class_name(\"quantity-button-add\")\nj = 0\nfor element in addPassButton:\n for i in range(pepole[j]):\n element.click()\n j += 1\n\npepole[0]+=2\nx = input()\n# %%\nsubmitButton = submitBT = driver.find_element_by_xpath(\n '//div[@class=\"search-pod-section ej-text\"]//button[@class=\"ej-button rounded-corners arrow-button search-submit\"]')\nsubmitButton.click()\ntime.sleep(0.5)\nif pepole[2] != 0:\n button = driver.find_element_by_xpath(\n \"//div[@class='drawer-button']/button\")\n button.click()\n\n\ndriver.switch_to.window(driver.window_handles[1])\nfirstFlight = driver.find_element_by_xpath(\n '//button[@class=\"flight-grid-flight-fare ej-text standard selectable not-selected\"]')\nfirstFlight.click()\ntime.sleep(1)\nsecFlight = driver.find_elements_by_xpath(\n '//button[@class=\"flight-grid-flight-fare ej-text standard selectable not-selected\"][1]')\nsecFlight[1].send_keys(Keys.ENTER)\nx = input()\n# %%\ntry:\n showBasket = driver.find_element_by_xpath(\n '//a[@class=\"basket-tablet-trigger\"]')\n showBasket.click()\nexcept:\n pass\ntime.sleep(1)\nsubmitForm = driver.find_element_by_xpath(\n '//button[@class=\"ej-button rounded-corners continue-button\"]')\nsubmitForm.click()\ntime.sleep(2)\n# skipSeats = driver.find_element_by_xpath(\n# '//button[@class=\"button-link arrow-button\"]')\n# skipSeats.click()\n# time.sleep(2)\n# skipSeats = driver.find_element_by_xpath(\n# '//button[@class=\"button-link arrow-button\"]')\n# skipSeats.click()\n# time.sleep(3)\nskipbag = driver.find_element_by_xpath(\n '//button[@class=\"button-link arrow-button\"]')\nskipbag.click()\ntime.sleep(2)\nacceptandsubmit = driver.find_element_by_xpath('//input[@type=\"submit\"]')\nacceptandsubmit.click()\ntime.sleep(2)\nuser = driver.find_element_by_xpath(\"//input[@id='signin-username']\")\nuser.send_keys(\"\")\npassword = driver.find_element_by_xpath(\"//input[@id='signin-password']\")\npassword.send_keys(\"\")\ntime.sleep(1)\nnext_ = driver.find_element_by_xpath(\"//input[@id='signin-login']\")\nnext_.click()\n\ntime.sleep(2)\n\n\n\n#%%\ndef getDetailsByIndex(df, index):\n _dict = {}\n entry = df.iloc[index]\n _dict[\"First Name\"] = entry[\"First Name\"]\n _dict[\"Last Name\"] = entry[\"Last Name\"]\n _dict[\"Title\"] = entry[\"Title\"]\n if entry[\"Age\"] >= 18:\n _dict[\"age\"] = 'adult'\n elif entry[\"Age\"] >= 2:\n _dict[\"age\"] = 'child'\n else:\n _dict[\"age\"] = 'infant'\n return _dict\n\n\ndef submitData(dataDict, driver, index, category):\n\n firstNameString = \"//input[@id='firstname-textbox-{}-{}']\".format(\n category, index)\n firstNameInput = driver.find_element_by_xpath(firstNameString)\n firstNameInput.send_keys(dataDict[\"First Name\"])\n\n lastNameString = \"//input[@id='lastname-textbox-{}-{}']\".format(\n category, index)\n lastNameInput = driver.find_element_by_xpath(lastNameString)\n lastNameInput.send_keys(dataDict[\"Last Name\"])\n if category != \"infant\":\n titleClassNameXpath = \"//select[@id='title-dropdown-{}-{}']/option[text()='{}']\".format(category,\n index, dataDict[\"Title\"])\n driver.find_element_by_xpath(titleClassNameXpath).click()\n ageString = \"//select[@id='age-dropdown-{}-{}']/option[text()='18+']\".format(\n category, index)\n ageInput = driver.find_element_by_xpath(ageString)\n ageInput.click()\n\n\nreason = driver.find_element_by_xpath(\n \"//input[@name='reasonForTravel-2']\").click()\ndf = pd.read_excel(\"info.xlsx\")\nnames = [\"adult\", \"child\", \"infant\"]\ncounting = {\"adult\": 1, \"child\": 1, \"infant\": 1}\nfor category in pepole:\n for i in range(category):\n tempDict = getDetailsByIndex(df, i)\n submitData(tempDict, driver,\n counting[tempDict[\"age\"]], tempDict[\"age\"])\n print(\"Working on {} which is {} and he is the {}\".format(tempDict[\"First Name\"] +\" \" +tempDict[\"Last Name\"], tempDict[\"age\"], counting[tempDict[\"age\"]]))\n counting[tempDict[\"age\"]] += 1\n\n\n\n\n#%%\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"486146677","text":"# encoding: utf-8\nimport math, re\nfrom copy import deepcopy\n\nfrom statscraper import BaseScraper, Dataset, Dimension, Result, DimensionValue\nimport numpy as np\nimport requests\n\nclass KoladaScraper(BaseScraper):\n \"\"\"Sample scraper for statscraper boilerplate.\"\"\"\n\n @BaseScraper.on('init')\n def _init(self):\n \"\"\"Inits variables for class\"\"\"\n self.base_url = 'http://api.kolada.se/v2/'\n self._municipalities = None\n self._municipality_groups = None\n\n def _fetch_itemslist(self, item):\n \"\"\"Yield a collection or dataset at\n the current cursor position.\"\"\"\n r = requests.get(self.base_url + '/kpi')\n data = r.json()\n for d in data['values']:\n yield Dataset(d['id'], label=d['title'], blob=d) # blob (data)\n\n def _fetch_dimensions(self, dataset):\n \"\"\"Yield the available dimensions in .\"\"\"\n yield Dimension('municipality_groups', label='municipality groups')\n yield Dimension('municipality', label='municipality')\n yield Dimension('kpi', label='indicator')\n yield Dimension('kpi_label', label='indicator name')\n yield Dimension('gender', label='gender')\n yield Dimension('period', label='period')\n yield Dimension('status', label='status')\n \n def _get_allowed_municipalities(self, type):\n \"\"\"Caches municipalities and returns them by type (`K`|`L`)\"\"\"\n if self._municipalities == None:\n self._municipalities = {\n 'K': [],\n 'L': []\n }\n data = requests.get(self.base_url + '/municipality').json()\n for row in data['values']:\n r = (row['id'], row['title'])\n if row['type'] in ('K','L'):\n self._municipalities[row['type']].append(r)\n if type in self._municipalities:\n return self._municipalities[type]\n return []\n \n def _get_allowed_municipality_groups(self, type):\n \"\"\"Caches municipality groups and returns them by type (`K`|`L`)\"\"\"\n if self._municipality_groups == None:\n self._municipality_groups = {\n 'K': [],\n 'L': []\n }\n\n municipalityReg = re.compile(r'[Kk]ommuner')\n regionalReg = re.compile(r'[Ll]andsting')\n\n data = requests.get(self.base_url + '/municipality_groups').json()\n for row in data['values']:\n r = (row['id'], row['title'])\n if municipalityReg.search(row['title']):\n self._municipality_groups['K'].append(r)\n elif regionalReg.search(row['title']):\n self._municipality_groups['L'].append(r)\n if type in self._municipality_groups:\n return self._municipality_groups[type]\n return []\n\n\n def _fetch_allowed_values(self, dimension):\n \"\"\"Yield the allowed values for .\"\"\"\n if dimension.id == 'municipality':\n municipalities = self._get_allowed_municipalities(\n dimension.dataset.blob['municipality_type']\n )\n\n for m_id, m_name in municipalities:\n yield DimensionValue(m_id,\n dimension,\n label=m_name)\n elif dimension.id == 'municipality_groups':\n municipality_groups = self._get_allowed_municipality_groups(\n dimension.dataset.blob['municipality_type']\n )\n\n for m_id, m_name in municipality_groups:\n yield DimensionValue(m_id,\n dimension,\n label=m_name)\n\n\n\n\n def _fetch_data(self, dataset, query):\n \"\"\"Make query for actual data.\n Get all regions and years by default.\n `period` (year), `municipality` and `municipality_groups` are the only \n implemented queryable dimensions.\n\n :param query: a dict with dimensions and values to query by.\n Examples:\n {\"municipality\": [\"0180\"]}\n {\"period\": 2016 }\n \"\"\"\n \n # Make query a dict if it already isn't\n if isinstance(query, dict) == False:\n query = {}\n\n # If nothing is set, default to all allowed municipalities\n queryable_dims = ['municipality', 'period', 'municipality_groups']\n if all([x not in query for x in queryable_dims]):\n query['municipality'] = []\n for x in dataset.dimensions['municipality'].allowed_values:\n query['municipality'].append(x.value)\n\n # Listify queried values (to allow single values in query, like {\"year\": 2016})\n for key, values in query.items():\n if not isinstance(values, list):\n query[key] = [values]\n # Format all values as strings for url creation\n query[key] = [str(x) for x in query[key]]\n\n # Validate query\n for dim in query.keys():\n if dim not in queryable_dims:\n raise Exception(\"You cannot query on dimension '{}'\".format(dim))\n # Check if the values are allowed\n if dim in ('municipality', 'municipality_groups'):\n allowed = [x.value for x in dataset.dimensions[dim].allowed_values]\n for dimVal in query[dim]:\n if dimVal not in allowed:\n raise Exception(\"You cannot query on dimension '{}' with '{}'\".format(dim, dimVal))\n\n # base url for query\n next_url = '{}data/kpi/{}'.format(self.base_url, dataset.id)\n\n # Merge `municipality` and `municipality_groups`\n municipalities = []\n if 'municipality' in query:\n municipalities = municipalities + query['municipality']\n if 'municipality_groups' in query:\n municipalities = municipalities + query['municipality_groups']\n\n if len(municipalities) > 0:\n next_url += '/municipality/{}'.format(','.join(municipalities))\n if 'period' in query:\n next_url += '/year/{}'.format(','.join(query['period']))\n\n while next_url:\n print('/GET {}'.format(next_url))\n r = requests.get(next_url)\n r.raise_for_status()\n json_data = r.json()\n for row in json_data['values']:\n for d in row['values']:\n yield Result(d['value'], {\n 'kpi': dataset.id,\n 'kpi_label': dataset.label,\n 'municipality': row['municipality'],\n 'period': row['period'],\n 'gender': d['gender'],\n 'status': d['status'],\n }\n )\n\n #\n if 'next_page' in json_data:\n next_url = json_data['next_page']\n else:\n next_url = False\n","sub_path":"kolada/KoladaScraper.py","file_name":"KoladaScraper.py","file_ext":"py","file_size_in_byte":6977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"305987099","text":"import scipy\nimport math\nimport matplotlib.pyplot as plt\nimport cPickle as pickle\nimport sys\nimport odor_tracking_sim.utility as utility\n\nf = sys.argv[1]\nlast_spot = sys.argv[2]\n\ninput_file = f+'.pkl'\n\nwith open(input_file,'r') as f:\n (swarm,wind_field) = pickle.load(f)\n\n\nnum_bins = 40\n\ntrap_num_list = swarm.get_trap_nums()\n\nplt.figure(1)\nt = swarm.get_time_trapped()\nplt.hist(t,num_bins)\n\nplt.xlabel('(s)')\nplt.ylabel('count')\nplt.title('time trapped (all traps)')\n\nplt.figure(2)\nax1 = plt.subplot2grid((3,4),(1,3))\nax2 = plt.subplot2grid((3,4),(0,2))\nax3 = plt.subplot2grid((3,4),(0,1))\nax4 = plt.subplot2grid((3,4),(1,0))\nax5 = plt.subplot2grid((3,4),(2,1))\nax6 = plt.subplot2grid((3,4),(2,2))\ntrap_axes = [ax1,ax2,ax3,ax4,ax5,ax6]\n\n\npeak_counts = scipy.zeros(len(trap_axes))\nrasters = []\nfor i in trap_num_list:\n t = swarm.get_time_trapped(i)\n ax = trap_axes[i]\n\n (n, bins, patches) = ax.hist(t,num_bins,range=(0,7800))\n peak_counts[i]=max(n)\n t1 = swarm.get_time_trapped(i,straight_shots=True)\n ax.hist(t1,bins,color='red')\n utility.customaxis(ax)\n ax.set_xlabel('(s)',horizontalalignment='left',x=1.0)\n ax.set_ylabel('trap:{0}'.format(i))\n\n #This is the raster plot option:\n # r = ax.eventplot(t,colors=['green'])[0]\n # rasters.append(r)\n #Add on a plot for the flies that went straight into the trap\nprint(len(rasters))\ntop = max(peak_counts)\ntrap_counts = swarm.get_trap_counts()\nfor i,num in enumerate(trap_num_list):\n ax = trap_axes[num]\n ax.set_ylim(0,top)\n # rasters[i].set_lineoffset(top-1 )\n # rasters[i].set_linelength(3)\n xmin,xmax = ax.get_xlim()\n ax.text(xmin+(xmax-xmin)/2,top/2,str(trap_counts[i]),color='maroon',size =20)\n #plt.title('time trapped, trap_num = {0}'.format(num))\n\n\nax7 = plt.subplot2grid((3,4),(1,1),polar=True)\nheadings = swarm.param['initial_heading']\nheading_dist = swarm.param['initial_heading_dist']\nheading_mean = heading_dist.mean()\nprint(heading_mean)\n(n,bins,patches) = ax7.hist(headings,bins=100)\nax7.set_yticks([])\nr = max(n)\nax7.set_ylim((0,r))\nax7.set_xlabel('Initial heading distr')\nif not(heading_dist.dist.name=='uniform'):\n ax7.annotate(\"\", xy=(heading_mean,r*0.75), xytext=(0, 0), arrowprops=dict(arrowstyle=\"->,head_width=0.5,head_length=1.\",edgecolor = 'red',\n facecolor = 'red',linewidth=5.))\n#ax7.arrow(0,0,r*scipy.cos(heading_mean),r*scipy.sin(heading_mean),color='red',lw=4)\n\n\nax8 = plt.subplot2grid((3,4),(1,2))\n\nif last_spot == 'pdf':\n #The below will plot the pdf of the heading distribution\n angles = scipy.linspace(heading_mean-scipy.pi,heading_mean+scipy.pi,400)\n ax8.plot(angles,heading_dist.pdf(angles))\n try:\n last_bit = '(Kappa = '+str(round(heading_dist.kwds['kappa'],4))+') '\n except KeyError:\n last_bit = ''\n ax8.set_xlabel('Initial heading distr. ''(Variance = '+str(round(heading_dist.var(),3))+\n last_bit+')')\n ax8.set_ylim(0,0.6)\n ax8.set_xlim(heading_mean-scipy.pi,heading_mean+scipy.pi)\nelif last_spot == 'dep':\n #The below will plot the time course of the departures from the release point\n release_times = swarm.param['release_time'] - swarm.param['release_delay']\n ax8.hist(release_times,bins=100)\n ax8.set_xlabel('Release Time Course (Time Constant= '+str(round(swarm.param['release_time_constant'],3))+') (s)')\n ax8.set_xlim((0,1000))\n #ax8.set_xlabel('(s)',horizontalalignment='left',x=1.0)\nutility.customaxis(ax8)\n\n#Plot the wind direction\nif wind_field.evolving:\n wind_angle_0 = wind_field.angle[0]\nelse:\n wind_angle_0 = wind_field.angle\n\n\nax9 = plt.subplot2grid((3,4),(0,3),polar=True)\nax9.set_yticks([])\nax9.set_ylim((0,1))\n# ax9.arrow(0,0,wind_angle_0,0.5, edgecolor = 'teal',\n# facecolor = 'blue' , alpha = 0.5, width = 0.015,\n# lw = 2, zorder = 5)\nax9.annotate(\"\", xy=(wind_angle_0,0.75), xytext=(0, 0), arrowprops=dict(arrowstyle=\"->,head_width=0.5,head_length=1.\",edgecolor = 'teal',\n facecolor = 'blue',linewidth=5.))\nax9.set_xlabel('(Initial) Wind Direction')\n\nplt.show()\n","sub_path":"examples/plot_swarm_data.py","file_name":"plot_swarm_data.py","file_ext":"py","file_size_in_byte":4028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"328603540","text":"import sys\n\ndef perm(k,n,used):\n if k == n:\n return\n for i in range(n):\n if used & (1< 100:\n print(\"Digite um número válido \\n\")\n continue\n\n if numero_secreto == palpite:\n print(f\"\\nVocê acertou e fez {pontos} pontos\")\n break\n else:\n pontos -= abs(palpite - numero_secreto)\n if palpite > numero_secreto:\n print(\"Seu palpite foi maior que o número secreto\")\n else:\n print(\"Seu palpite foi menor que o número secreto\")\n\n print(f\"\\nO número secreto era {numero_secreto}\")\n print(\"\\nGAME OVER!\")\n\n\nif __name__ == '__main__':\n jogar()\n","sub_path":"exercicio1/adivinhacao.py","file_name":"adivinhacao.py","file_ext":"py","file_size_in_byte":1450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"318728642","text":"# -*- coding: utf-8 -*-\nfrom django.core.paginator import EmptyPage, Paginator\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponse, HttpResponseRedirect, HttpResponseNotFound, Http404\nfrom django.shortcuts import get_object_or_404, render, render_to_response\n\nfrom .models import Answer, Question\nfrom .forms import AnswerForm, AskForm\n\n\n# Create your views here.\ndef test(request, *args, **kwargs):\n return HttpResponse('OK')\n\n\ndef question(request, pk):\n \"\"\"\n Страница с конкретным вопросом и ответами на этот вопрос\n \"\"\"\n question = get_object_or_404(Question, pk=pk)\n answers = Answer.objects.filter(question=question).order_by('-added_at')\n \n form = AnswerForm(initial={'question': pk})\n return render(request, 'question_details.html', {\n 'question': question,\n 'answers': answers,\n 'form': form,\n })\n\n\ndef paginate(request, qs):\n \"\"\"\n Постраничная выборка из qs (query set)\n \"\"\"\n try:\n limit = int(request.GET.get('limit', 10))\n except ValueError:\n limit = 10\n \n if limit > 100:\n limit = 10\n \n try:\n page = int(request.GET.get('page', 1))\n except ValueError:\n raise Http404\n \n paginator = Paginator(qs, limit)\n \n try:\n page = paginator.page(page)\n except EmptyPage:\n page = paginator.page(paginator.num_pages)\n \n return page, paginator\n\n\ndef popular(request):\n \"\"\"\n Список вопросов с убыванием популярности\n \"\"\"\n questions_query_set = Question.objects.order_by('-rating')\n questions, paginator = paginate(request, questions_query_set)\n paginator.url = reverse('pop_questions') + '?page='\n \n return render(request, 'popular_questions.html', {\n 'questions': questions,\n 'paginator': paginator\n })\n\n \ndef questions_list(request):\n \"\"\"\n Список вопросов по убыванию даты добавления\n \"\"\"\n questions_query_set = Question.objects.order_by('-added_at') # test\n questions, paginator = paginate(request, questions_query_set)\n paginator.url = reverse('questions_lst') + '?page='\n \n return render(request, 'questions.html', {\n 'questions': questions,\n 'paginator': paginator\n })\n\n\ndef ask(request):\n \"\"\"\n Задать вопрос\n \"\"\"\n if request.method == 'POST':\n form = AskForm(request.POST)\n if form.is_valid():\n question = form.save()\n return HttpResponseRedirect(question.get_url())\n else:\n form = AskForm()\n\n return render(request, 'ask.html', {\n 'form': form\n })\n\n\ndef answer(request):\n \"\"\"\n Ответить на вопрос\n \"\"\"\n if request.method == 'POST':\n form = AnswerForm(request.POST)\n if form.is_valid():\n answer = form.save()\n return HttpResponseRedirect(answer.question.get_url())\n return HttpResponseRedirect('/')\n","sub_path":"ask/qa/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"83053485","text":"from collections import defaultdict\r\n\r\ngraph = defaultdict(list)\r\n\r\nN = int(input(\"Number of nodes: \")) # Nodes will be 1..N\r\nE = int(input(\"Number of edges: \")) # Number of edges\r\n\r\ndistance = [[99999999] * (N + 1) for n in range(N + 1)]\r\n\r\nfor _ in range(E):\r\n x, y, d = map(int, input().split())\r\n graph[x].append(y)\r\n distance[x][y] = min(d, distance[x][y])\r\n graph[y].append(x) # Comment this out if edge is unidirectional\r\n distance[y][x] = min(d, distance[y][x]) # This also, if edge is unidirectional\r\n\r\ndef dijkstra(graph, source, N):\r\n dist = [99999999] * (N + 1)\r\n visited = [False] * (N + 1)\r\n queue = {source}\r\n\r\n dist[source] = 0\r\n visited[source] = True\r\n\r\n while queue:\r\n min_node = -1\r\n for node in queue:\r\n if min_node == -1 or dist[node] < dist[min_node]:\r\n min_node = node\r\n\r\n queue.remove(min_node)\r\n\r\n for adj in graph[min_node]:\r\n new_dist = dist[min_node] + distance[min_node][adj]\r\n if new_dist < dist[adj]:\r\n dist[adj] = new_dist\r\n if not visited[adj]:\r\n queue.add(adj)\r\n visited[adj] = True\r\n\r\n return dist\r\n\r\ns = int(input(\"Source Node: \"))\r\n\r\ndist = dijkstra(graph, s, N)\r\n\r\nfor node in range(1, N + 1):\r\n print(\"It takes %d to get from %d to %d\" % (dist[node], s, node))\r\n","sub_path":"Algorithms and Data Structures/Single Source Shortest Path/Dijkstra's Algorithm/Dijkstra's Algorithm.py","file_name":"Dijkstra's Algorithm.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"310422183","text":"from django.urls import path,include\nfrom .views import *\n\nurlpatterns = [\n path('products/',product,name=\"Products \"),\n path('product//',productDetail,name=\"Product detail\"),\n path('sales/',sale,name=\"Sales \"),\n path('customers/',customer,name=\"Customers\"),\n path('counties/',county,name=\"Counties\"),\n path('dashboard/',dashboard,name=\"Dashboard\"),\n path('auth/',include('djoser.urls')),\n path('auth/',include('djoser.urls.jwt'))\n]","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"482536113","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\"\"\"\nfix_me.py by Brant Faircloth was alterered by me in this program. The alterations\nI made were small and allowed the program to retain both its function and recursive\nnature while eliminating all errors.\n\nEdited by Alicia Reigel. 4 February 2016.\nCopyright Alicia Reigel. Louisiana State University. 4 February 2016. All rights\nreserved.\n\n\"\"\"\n\ndef sequence_eater(sequence, position):\n \"\"\"prints each letter of the sequence in order, each on a new line\"\"\"\n if position inp_list[num2]:\n LIS[num1] = max(LIS[num1], LIS[num2]+1)\n\n maximum = 0\n for idx, num in enumerate(LIS):\n if num > maximum:\n print(inp_list[idx], end=\"\")\n print(\" \", end=\"\")\n maximum = num\n\n print(\"\")\n return maximum\n\n\n\ndef get_lis_recursive(inp_list, prev, curpos):\n if curpos == len(inp_list): return 0\n taken = 0\n if inp_list[curpos] > prev:\n taken = 1 + get_lis_recursive(inp_list, inp_list[curpos], curpos+1)\n not_taken = get_lis_recursive(inp_list, prev, curpos+1)\n return max(taken, not_taken)\n\n\ndef get_lis_recursive_memo(inp_list, prev, curpos, memo):\n if curpos == len(inp_list): return 0\n taken = 0\n\n if inp_list[curpos] > prev:\n if memo.get(curpos):\n taken = memo.get(curpos)\n else:\n taken = 1 + get_lis_recursive(inp_list, inp_list[curpos], curpos+1)\n\n if memo.get(curpos):\n not_taken = memo.get(curpos)\n else:\n not_taken = get_lis_recursive(inp_list, prev, curpos+1)\n\n max_value = max(taken, not_taken)\n memo[curpos] = max_value\n\n return max_value\n\n\n\nmemo = dict()\nprint(get_lis_recursive(my_list2, 0, 0))\nprint(get_lis_recursive_memo(my_list2, 0, 0, memo))\nprint(get_lis_iterative(my_list2))\n\n\n","sub_path":"Python/dynamic_programming/longest_increasing_subsequence.py","file_name":"longest_increasing_subsequence.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"427885529","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 18 14:57:04 2018\n\n@author: Dyass\n\"\"\"\n\ndef f(s):\n return 'a' in s\n\ndef satisfiesF(L):\n \"\"\"\n Assumes L is a list of strings\n Assume function f is already defined for you and it maps a string to a Boolean\n Mutates L such that it contains all of the strings, s, originally in L such\n that f(s) returns True, and no other elements. Remaining elements in L\n should be in the same order.\n Returns the length of L after mutation\n \"\"\"\n # Your function implementation here\n a=list()\n for i in L:\n if(f(i)==True):\n a.append(i)\n L[:] = a\n return len(L)\n \nL = ['a', 'b', 'a']\nprint(satisfiesF(L))\nprint(L)","sub_path":"Week3Part2/Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"576576078","text":"f = open(\"name and ages.txt\", \"w+\")\nx = open(\"name.txt\", \"r\")\ny = open(\"age.txt\", \"r\")\nnames = x.read()\nages = y.read()\nprint(names)\nprint(ages)\n\n\ndesc = names+\" \"+ages\nf.write(desc)\n\nf.close()\nx.close()\ny.close()\n","sub_path":"descriptive.py","file_name":"descriptive.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"497155583","text":"\nimport numpy as np\nimport netCDF4\nimport geojson\nfrom icepack.grid import arcinfo, GridData\n\ndef main():\n with netCDF4.Dataset(\"BedMachineGreenland-2017-09-20.nc\", 'r') as dem:\n nx, ny = len(dem['x']), len(dem['y'])\n xmin, ymax = dem.xmin, dem.ymax\n dx = dem.spacing\n\n mask = dem['mask'][::-1,:]\n mask = np.logical_or(mask == 0, mask == 4)\n\n bed = GridData((xmin, ymax - ny * dx), dx, dem['bed'][::-1,:],\n missing_data_value=-9999)\n surface = GridData((xmin, ymax - ny * dx), dx, dem['surface'][::-1,:],\n mask=mask)\n\n with open(\"../regions/greenland.geojson\", 'r') as geojson_file:\n regions = geojson.loads(geojson_file.read())\n\n for region in regions['features']:\n region_name = region['properties']['name'].lower()\n box = region['geometry']['coordinates']\n\n with open(region_name + \"-bed.txt\", 'w') as region_bed:\n arcinfo.write(region_bed, bed.subset(box[0], box[1]), -2e9)\n\n with open(region_name + \"-surface.txt\", 'w') as region_surface:\n arcinfo.write(region_surface, surface.subset(box[0], box[1]), -2e9)\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"bedmachine_greenland/partition.py","file_name":"partition.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"483463153","text":"\"\"\"\nThis file is a python prototype of Algorithm 1 from the paper:\nMolloy, E.K., Warnow, T. (2020). FastMulRFS: Fast and accurate species tree\n estimation under generic gene duplication and loss models.\nhttps://doi.org/10.1101/835553\n\nCopyright (c) 2020 Erin K. Molloy\nAll rights reserved.\n\nLicense: 3-Clause BSD,\nsee https://opensource.org/licenses/BSD-3-Clause\n\"\"\"\nimport argparse\nimport dendropy\nimport sys\n\n\ndef count_leaves(tree):\n \"\"\"\n Count number of leaves in tree\n\n Parameters\n ----------\n tree : treeswift tree object\n\n Returns number of leaves in tree\n \"\"\"\n return len([l for l in tree.leaf_nodes()])\n\n\ndef build_down_profiles(tree, g2s_map):\n \"\"\"\n Annotates edge above each node with an 'down profile', i.e., the set of\n species below the edge\n\n Parameters\n ----------\n tree : dendropy tree object\n \"\"\"\n for node in tree.postorder_node_iter():\n if node.is_leaf():\n gene = node.taxon.label\n species = g2s_map[gene]\n node.down = set([species])\n else:\n node.down = set([])\n for child in node.child_nodes():\n node.down = node.down.union(child.down)\n\n\ndef build_up_profiles(tree):\n \"\"\"\n Annotates edge above each node with an 'up profile', i.e., the set of\n species above the edge\n\n NOTE: Must be called after build_down_profiles()\n\n Parameters\n ----------\n tree : dendropy tree object\n \"\"\"\n root = tree.seed_node\n children_of_root = root.child_nodes()\n\n for node in children_of_root:\n node.up = set([])\n for sibl in children_of_root:\n if node != sibl:\n node.up = node.up.union(sibl.down)\n\n children_of_root = set(children_of_root)\n\n for node in tree.preorder_node_iter():\n if (node == root) or (node in children_of_root):\n pass\n elif node.is_leaf():\n pass\n else:\n parent = node.parent_node\n node.up = parent.up\n for sibl in parent.child_nodes():\n if node != sibl:\n node.up = node.up.union(sibl.down)\n\n\ndef contract_edges_w_invalid_bipartitions(tree):\n \"\"\"\n Contracts edges that do not induce valid bipartitions\n\n NOTE: Must be called after build_down_profiles() and build_up_profiles()\n\n Parameters\n ----------\n tree : dendropy tree object\n \"\"\"\n nLM = 0\n nX = 0\n nR = 0\n nO = 0\n\n for node in tree.postorder_node_iter():\n if node == tree.seed_node:\n pass\n elif node.is_leaf():\n node.edge.length = 1.0\n nLM += 1\n else:\n test = node.down.intersection(node.up)\n if len(test) != 0:\n nX += 1\n node.edge.length = 0.0\n else:\n if (len(node.down) == 1) or (len(node.up) == 1):\n nR += 1\n else:\n nO += 1\n node.edge.length = 1.0\n\n tree.collapse_unweighted_edges()\n\n for edge in tree.edges():\n edge.length = None\n\n nEM = nLM + nX + nR + nO\n\n return [nLM, nEM, nR, nO]\n\n\ndef prune_multiple_copies_of_species(tree, g2s_map, s2g_map):\n \"\"\"\n Removes all but one leaf with the same species label\n\n Parameters\n ----------\n tree : dendropy tree object\n g2s_map : dictionary\n maps gene copy labels to species labels\n s2g_map : dictionary\n maps species label to gene copy labels\n \"\"\"\n found_duplicate = set([])\n nLMX = 0\n c = 0\n\n for leaf in tree.leaf_nodes():\n gene = leaf.taxon.label\n species = g2s_map[gene]\n all_genes = s2g_map[species]\n\n if gene == all_genes[0]:\n leaf.taxon.label = species\n nLMX += 1\n else:\n leaf.taxon = None\n if not (species in found_duplicate):\n found_duplicate.add(species)\n c += 1\n\n tree.prune_leaves_without_taxa()\n\n return [nLMX, c]\n\n\ndef preprocess_multree(tree, g2s_map, s2g_map):\n \"\"\"\n Preprocesses MUL-tree as described in the FastMulRFS paper\n\n Parameters\n ----------\n tree : dendropy tree object\n g2s_map : dictionary\n maps gene copy labels to species labels\n s2g_map : dictionary\n maps species label to gene copy labels\n \"\"\"\n tree.is_rooted = False\n tree.collapse_basal_bifurcation(set_as_unrooted_tree=True)\n\n build_down_profiles(tree, g2s_map)\n build_up_profiles(tree)\n\n [nLM, nEM, nR, nO] = contract_edges_w_invalid_bipartitions(tree)\n [nLMX, c] = prune_multiple_copies_of_species(tree, g2s_map, s2g_map)\n\n nEMX = nO + nLMX\n\n return [nEM, nLM, nR, c, nEMX, nLMX]\n\n\ndef compute_score_shift(nEM, nLM, nR, c, nEMX, nLMX):\n \"\"\"\n Compute constant shift for RF score as described in FastMulRFS paper\n\n Parameters\n ----------\n nEM : int\n Number of edges in MUL-tree\n nLM : int\n Number of leaves in MUL-tree\n nR : int\n Number of edges in MUL-tree that induce invalid bipartitions\n (i.e., edges split the label set into two non-disjoint sets)\n c : int\n Number of species with multiple copies in the MUL-tree\n nEMX : int\n Number of edges in preprocessed MUL-tree\n nLMX : int\n Number of leaves in preprocessed MUL-tree,\n which is the same as the number of species\n\n Returns\n -------\n Constant shift for RF score as described in FastMulRFS paper\n \"\"\"\n return nLMX + c + nEM - nEMX - (2 * nR) - nLM\n\n\ndef read_label_map(ifile):\n \"\"\"\n Reads file containing map from gene copy to species labels into dictionary\n\n Parameters\n ----------\n ifile : string\n name of file containing map between gene copy and species labels\n each row has form:\n species_name:gene_name_1,gene_name_2,...\n\n Returns\n -------\n g2s_map : dictionary\n maps gene copy labels to species labels\n s2g_map : dictionary\n maps species label to gene copy labels\n \"\"\"\n g2s_map = {}\n s2g_map = {}\n\n with open(ifile, 'r') as f:\n for line in f.readlines():\n [species, genes] = line.split(':')\n genes = genes.split(',')\n genes[-1] = genes[-1].replace('\\n', '')\n\n s2g_map[species] = genes\n\n for gene in genes:\n if gene == species:\n sys.exit(\"Error: Gene copy label cannot be the species \"\n \"label!\")\n g2s_map[gene] = species\n\n return [g2s_map, s2g_map]\n\n\ndef read_preprocess_and_write_multrees(ifile, mfile, ofile, verbose):\n \"\"\"\n Creates file with preprocessed MUL-trees for FastRFS\n\n Parameters\n ----------\n ifile : string\n name of file containing gene family trees\n (one newick string per line)\n mfile : string\n name of file containing label map file; each row has form:\n species_name:gene_name_1,gene_name_2,...\n ofile : string\n name of output file (one newick string per line)\n \"\"\"\n [g2s_map, s2g_map] = read_label_map(mfile)\n\n with open(ifile, 'r') as fi, open(ofile, 'w') as fo:\n g = 1\n for line in fi.readlines():\n if verbose:\n sys.stdout.write(\"Preprocessing gene tree on line %d...\\n\" % g)\n sys.stdout.flush()\n\n temp = \"\".join(line.split())\n\n donot = 0\n if not temp:\n donot = 1\n else:\n tree = dendropy.Tree.get(data=temp,\n schema=\"newick\",\n rooting=\"force-unrooted\",\n preserve_underscores=True)\n\n if count_leaves(tree) < 4:\n dotnot = 2\n else:\n [nEM, nLM, nR, c, nEMX, nLMX] = preprocess_multree(tree,\n g2s_map,\n s2g_map)\n score_shift = compute_score_shift(nEM, nLM, nR, c, nEMX,\n nLMX)\n\n if nLMX < 4:\n donot = 3\n else:\n fo.write(tree.as_string(schema=\"newick\")[5:])\n\n if donot and verbose:\n sys.stdout.write(\"...did not write tree as \")\n if donot == 1:\n sys.stdout.write(\"as line is empty!\")\n elif donot == 2:\n sys.stdout.write(\"as tree has <4 leaves before \"\n \"preprocessing!\")\n elif donot == 3:\n sys.stdout.write(\"as tree has <4 leaves after \"\n \"preprocessing!\")\n sys.stdout.write('\\n')\n sys.stdout.flush()\n\n g += 1\n\n\ndef main(args):\n read_preprocess_and_write_multrees(args.input,\n args.map,\n args.output,\n args.verbose)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"-i\", \"--input\", type=str,\n help=\"Input file containing gene family trees \"\n \"(one newick string per line)\",\n required=True)\n parser.add_argument(\"-a\", \"--map\", type=str,\n help=\"Input file containing label map; \"\n \"each row has form: \"\n \"'species_name:gene_name_1,gene_name_2,...')\",\n required=True)\n parser.add_argument(\"-o\", \"--output\", type=str,\n help=\"Output file name\",\n required=True)\n parser.add_argument(\"--verbose\", action=\"store_true\")\n\n main(parser.parse_args())\n","sub_path":"python-tools/preprocess_multrees_v1.py","file_name":"preprocess_multrees_v1.py","file_ext":"py","file_size_in_byte":10082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"567446933","text":"\"\"\"\nProblem 1\nAsk the user to enter a number.\nThe number is considered \"frue\" if it is\ndivisible by 6, but not divisible by 8.\nState whether the number is \"frue\" \n(2 marks)\n\nInputs:\na number\n\nOutputs:\nxx is frue\nxx is not frue\n\nexample:\nEnter a number: 48\n48 is frue.\n\"\"\"\n\n#! python3\n\nx = input(\"Enter a number:\")\nx = float(x)\nx1 = x/6\nx2 = int(x1)\n\ny1 = x/8\ny2 = int(y1)\n\nif (x1 - x2) == 0 and (y1 - y2) != 0:\n print(x ,end=\"\")\n print(\" is frue\")\nelse:\n print(x ,end=\"\")\n print(\" is not frue\")\n","sub_path":"problem1.py","file_name":"problem1.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"272514750","text":"# encoding:utf-8\n__author__ = 'l_t'\n\n#mongo dataBase\ndb_type = 'QueueMongoDB'\ndb_host = '127.0.0.1'\n# db_host= '182.150.37.55'\ndb_port = 50070\n\n# db_table\ndb_table_news_zhengwen = 'news_zw'\ndb_table_user_info = 'usr_info'\ndb_table_usr_comment = 'usr_comment'\ndb_table_usr_follow = 'usr_follow'\ndb_table_user_CommNet = 'usr_CommNet'\n\n#db—queue\nqueue_usr_id = 'usr_id'\nqueue_usr_geted = 'usr_id_geted'\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"51"} +{"seq_id":"446650205","text":"import pygame\nimport random\nfrom datetime import datetime\nfrom datetime import timedelta\n\nfrom init import *\n\ndir_key = {\n pygame.K_UP: 'up',\n pygame.K_DOWN: 'down',\n pygame.K_LEFT: 'left',\n pygame.K_RIGHT: 'right'\n}\n\ns_x = scr_w // block // 2\ns_y = scr_h // block // 2\ns_pos = [(s_x, s_y), (s_x, s_y + 1), (s_x, s_y + 2), (s_x, s_y + 3)]\nspeed = 0.2\n\n\nclass ItemException(Exception):\n pass\n\n\nclass EarthWorm:\n\n def __init__(self):\n self.pos = s_pos\n self.dir = 'up'\n self.img = pygame.image.load('./images/dinosaur.png').convert_alpha()\n self.img = pygame.transform.scale(self.img, (scr_w // block // 2, scr_h // block // 2))\n\n def draw(self, s):\n for p in self.pos:\n draw_image(s, self.img, p)\n\n def go(self):\n h_pos = self.pos[0]\n x, y = h_pos\n\n if self.dir == 'up':\n self.pos = [(x, y - 1)] + self.pos[:-1]\n elif self.dir == 'down':\n self.pos = [(x, y + 1)] + self.pos[:-1]\n elif self.dir == 'left':\n self.pos = [(x - 1, y)] + self.pos[:-1]\n elif self.dir == 'right':\n self.pos = [(x + 1, y)] + self.pos[:-1]\n\n def turn(self, d):\n self.dir = d\n\n def next(self):\n t_pos = self.pos[-1]\n x, y = t_pos\n\n if self.dir == 'up':\n self.pos.append((x, y - 1))\n elif self.dir == 'down':\n self.pos.append((x, y + 1))\n elif self.dir == 'left':\n self.pos.append((x - 1, y))\n elif self.dir == 'right':\n self.pos.append((x + 1, y))\n\n\nclass GameItem:\n\n def __init__(self, p=(5,5)):\n self.pos = p\n self.c = blue\n self.img = pygame.image.load('./images/redball.png').convert_alpha()\n self.img = pygame.transform.scale(self.img, (scr_w // block, scr_h // block))\n\n def draw(self, s):\n draw_image(s, self.img, self.pos)\n\n\nclass Game:\n\n def __init__(self):\n self.worm = EarthWorm()\n self.item = GameItem()\n self.w = scr_w // block\n self.h = scr_h // block\n self.score = 0\n\n def draw(self, s):\n self.worm.draw(s)\n self.item.draw(s)\n\n def go(self):\n self.worm.go()\n\n if self.worm.pos[0] in self.worm.pos[1:]:\n raise ItemException()\n\n x, y = self.worm.pos[0]\n\n if x < 0 or x >= self.w or y < 0 or y >= self.h:\n raise ItemException()\n\n if self.worm.pos[0] == self.item.pos:\n self.score += 100\n self.worm.next()\n self.new_item()\n\n def new_item(self):\n nx = random.randint(0, self.w - 1)\n ny = random.randint(0, self.h - 1)\n self.item = GameItem((nx, ny))\n\n for p in self.worm.pos:\n if self.item.pos == p:\n self.new_item()\n break\n\n\ndef draw_bg(s):\n bg = pygame.Rect((0, 0), (scr_w, scr_h))\n pygame.draw.rect(s, white, bg)\n\n\ndef draw_image(s, img, p):\n s.blit(img, (p[0] * block, p[1] * block))\n\n\npygame.init()\nscr = pygame.display.set_mode((scr_w, scr_h))\n\ndraw_bg(scr)\n\npygame.display.update()\n\ngame = Game()\n\nv = timedelta(seconds=speed)\nt = datetime.now()\n\nwhile True:\n events = pygame.event.get()\n\n for e in events:\n if e.type == pygame.QUIT:\n exit()\n if e.type == pygame.KEYDOWN:\n if e.key in dir_key:\n game.worm.turn(dir_key[e.key])\n\n if v < datetime.now() - t:\n try:\n game.go()\n except ItemException:\n quit()\n\n t = datetime.now()\n\n draw_bg(scr)\n game.draw(scr)\n pygame.display.update()\n","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"449864998","text":"from collections import Counter\n\n# “Counter is a great tool to quickly count a list.”\n# class collections.Counter([iterable-or-mapping])\n\n# Tally occurrences of words in a list\ncnt = Counter()\n# for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:\nfor word in ('red', 'blue', 'red', 'green', 'blue', 'blue'):\n cnt[word]+=1\n# It is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values.\nprint(cnt)\n\n# The same result\ncnt=Counter(word for word in ['red', 'blue', 'red', 'green', 'blue', 'blue'])\nprint(cnt)\n\n\n\n","sub_path":"src/basicKB/dev/automation/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"387038638","text":"from ResearchGenerator.StatusSuggestion import *\n\n###################Status check part###################\nclass Statuscheck():\n def __init__(self, value,allanswer,pos):\n self.ID = value\n self.allanswer = allanswer\n self.pos = pos\n def Status(self):\n A1,A2,A3,A4,A5,A6,A7,A8,A9,A10 = self.allanswer\n problemlist=[]\n TopicName=0\n q1=0\n q2=0\n q3=0\n q4=0\n q5=0\n q6=0\n q7=0\n q8=0\n q9=0\n q10=0\n position=0\n # print(\"Topic:Input output.\")\n # print(\"question 1: What is the output of this code? Printf('\\\\\\\\'); \\n\")\n ans1=A1\n\n if(ans1== 1 ): ##//\n # print('Right, You have got 1 marks')\n q1=1\n else:\n TopicName=1\n # print('Wrong, Correct Answer is: \\\\ \\n')\n\n # print(\"\\nTopic: Simple calculation \\n\")\n # print(\"Question 2: What is the output of this line? printf(\"'\"%d%d\"'\",10+10,10-3);\")\n ans1 = A2\n if (ans1 == 2): ##207\n # print('Right, You have got 2 marks')\n q2 = 2\n else:\n # print('Wrong, Correct Answer is: 207\\n')\n problemlist.append(\"simple calculation\")\n #\n # print(\"\\nTopic: String: \\n\")\n # print('Question 3:If char V[100] ='\"Hello world\"' then choose option for printing Hello World. ')\n # print('A. printf(\"%ld\",V )')\n # print('B. printf(\"%c\",V)')\n # print('C. printf(\"%s\",V)')\n # print('D. printf(\"%f\",V)')\n ans1 = A3\n # ans1 = ans1.lower()\n if (ans1 == 3):\n # print('Right, You have got 3 marks')\n q3 = 3\n else:\n problemlist.append('String')\n # print('Wrong, Correct Answer is: Option C\\n')\n\n # print(\"\\nTopic: Input Output\\n\")\n # s = '\\\\n'\n # print(\"Question 4: How will you print \" + s + \" on the screen?\")\n # print('A.\tprintf(\"\\\\n\");')\n # print('B.\techo \"\\\\\\\\n\";')\n # print(\"C.\tprintf('\\\\n');\")\n # print('D.\tprintf(\"\\\\\\\\n\");')\n ans1 = A4\n # ans1 = ans1.lower()\n if (ans1 == 4):\n # print('Right, You have got 4 marks')\n q4 = 4\n else:\n TopicName=1\n # print('Wrong, Correct Answer is: Option D\\n')\n\n if (TopicName==1):\n problemlist.append(\"Input output\")\n\n # print( \"\\nTopic: Expression\\n\")\n # print(\"Question 5: Write C expression of 2x^2+3xy \")\n # print(\"A.\t2*xx+3*xy\")\n # print(\"B.\t2x^2+3*x*y\")\n # print(\"C.\t2*x*x+3*x*y\")\n # print(\"D. 2*x*2+3*x*y\")\n ans1=A5\n# # ans1=ans1.lower()\n if(ans1== 5):\n # print('Right, You have got 5 marks')\n q5=5\n else:\n problemlist.append(\"Expression\")\n # print('Wrong, Correct Answer is: Option C\\n')\n\n # print(\"Question 6:\"+\n # \"int A =10;\\n \" +\"if( A < 10) \\n\"+ \" {A += 10;}\\n\"+\"else\\n\"\n # \" {A -= 10; }\\n\"+'printf(''\"%d\"'',A); ')\n ans1=A6\n# # ans1=ans1.lower()\n if(ans1== 6):\n # print('Right, You have got 6 marks')\n q6=6\n else:\n TopicName=2\n # print('Wrong Correct Answer is: 0\\n')\n\n\n # print( \"\\nTopic: Conditional statement\\n\")\n #\n # print('Question 7: \\nint i = 2; \\nprintf(\"output is: \");\\nswitch (i)\\n { \\n case 1: \\n printf(\"case 1\");\\n } ')\n ans1= A7\n # ans1=ans1.lower()\n if(ans1==7):\n # print('Right, You have got 7 marks')\n q7=7\n else:\n pass\n # print('Wrong Correct Answer is: output is: \\n')\n if (TopicName == 2):\n problemlist.append(\"Conditional statement\")\n\n # print( \"\\nTopic: Control Instructions\\n\")\n #\n # print(\"Question 8: \\n\"+\n # \" int main() { \\n\"+\n # \" char a = 10, b = 20, c = 10, d=0; \\n\"\n # \" d = (a * b) / c;\\n\"\n # \" d++;\\n\" + 'printf (''\"Output is: %d \"'', d);\\n'+\n # \" return 0;\\n} \")\n ans1=A8\n # ans1=ans1.lower()\n if(ans1==8):\n # print('Right, You have got 8 Marks')\n q8=8\n else:\n problemlist.append(\"Arithmetic operation\")\n # print('Wrong Correct Answer is: Output is: 21 \\n')\n\n # print( \"\\nTopic: loop\\n\")\n #\n # print(\"Question 9:\\n\"+\n # \"int i, a=5, b=5, sum=0;\\n\"+\n # \"sum=a+b;\\n\"+\n # \"for (i = 0; i <=2; i++) {\\n\"+\n # \" sum++;\\n\"+\n # 'printf(\"%d, %d \\\\n\", i , sum);\\n}' )\n # print('A. 1, 11\\n 1, 12\\n 2, 13')\n # print('B. 0, 11\\n 1, 12\\n 2, 13')\n # print(\"C. 0, 10\\n 1, 11\\n 2, 13\")\n # print('D. 1, 11\\n 1, 12\\n 2, 13')\n\n ans1=A9\n # ans1=ans1.lower()\n if(ans1== 9):\n # print('Right, You have got 9 marks')\n q9=9\n else:\n problemlist.append(\"loop\")\n # print('Wrong, Correct Answer is: Option B\\n')\n # print(\"\\nTopic: Problem solving\\n\")\n #\n # print(\" Question 10: \\nWhich expression is right for 25% of $320 \")\n # print(\"A. (25/100)*320\")\n # print(\"B. (100/25)*320\")\n # print(\"C. (100/325)*25\")\n # print(\"D. 25/(100*320)\")\n\n ans1 = A10\n # ans1 = ans1.lower()\n if (ans1 == 10):\n # print('Right, You have got 10 point')\n q10 = 10\n else:\n problemlist.append(\"Problem solving\")\n # print('Wrong, Correct Answer is: Option A\\n')\n\n credit=20\n totalpoint=q1*2+q2*2+q3*2+q4+q5*2+q6*2+q7*2+q8*2+q9*2+q10*2\n totalmarks=q1+q2+q3+q4+q5+q6+q7+q8+q9+q10\n point=totalpoint/credit\n # print(\"your total marks \",totalmarks)\n # print('Your total points are ', point)\n f = open('C:\\\\Users\\Mehedi\\Desktop\\suggestion\\{0}.doc'.format(self.ID), 'a')\n f.write(\"Your total point is: \")\n f.write(str(point)+\"\\n\\n\")\n f.write(\"Your total mark is:\")\n f.write(str(totalmarks)+\"\\n\\n\")\n\n # huge scope for analysis\n\n # print(\"\\n\")\n quality=None\n find=5.5/3\n if(point>=0 and point<=find):\n quality='beginner'\n\n elif(point>find and point<=find*2):\n\n quality='intermediate'\n\n elif(point>find*2 and point <=find*3):\n quality='expert'\n\n f.close()\n\n # print(\"You are a \",quality)\n f = open('C:\\\\Users\\Mehedi\\Desktop\\suggestion\\{0}.doc'.format(self.ID), 'a')\n f.write(\"You are a \"+quality+\"\\n\\n\")\n\n if (len(problemlist)>0):\n # print(\"your problem are:\\n\")\n cou=1\n for i in problemlist:\n\n # print(cou,\". \",i)\n cou=cou+1\n else:\n pass\n # print(\"Welcome, you have no problem.\")\n\n f.close()\n #file:\n co2 = 1\n f = open('C:\\\\Users\\Mehedi\\Desktop\\suggestion\\{0}.doc'.format(self.ID), 'a')\n\n if (len(problemlist) > 0):\n f.write(\"your problem list is:\\n\")\n for i in problemlist:\n f.write(str(co2))\n f.write(\". \" + i + \"\\n\")\n co2 = co2 + 1\n else:\n f.write(\"welcome, You have no problem.\")\n f.close()\n\n if(quality=='expert'):\n ex=expert(self.ID)\n ex.books()\n for p in problemlist:\n if(p==\"simple calculation\"):\n ex.calculation()\n elif(p==\"Expression\"):\n ex.Expressions()\n elif(p=='Problem solving'):\n ex.problem()\n elif(p==\"loop\"):\n ex.loop()\n elif(p=='Input output'):\n ex.inputOutput()\n elif(p==\"String\"):\n ex.string()\n elif(p==\"Conditional statement\"):\n ex.conditional()\n elif(p=='Arithmetic operation'):\n ex.arithmetic()\n\n if(quality=='beginner'):\n beg=beginner(self.ID)\n beg.books()\n for p in problemlist:\n if (p == \"simple calculation\"):\n beg.calculation()\n elif (p == \"Expression\"):\n beg.Expressions()\n elif (p == 'Problem solving'):\n beg.problem()\n elif (p == \"loop\"):\n beg.loop()\n elif (p == 'Input output'):\n beg.inputOutput()\n elif (p == \"String\"):\n beg.string()\n elif (p == \"Conditional statement\"):\n beg.conditional()\n elif (p == 'Arithmetic operation'):\n beg.arithmetic()\n\n if (quality == 'intermediate'):\n inme = intermediate(self.ID)\n inme.books()\n for p in problemlist:\n if (p == \"simple calculation\"):\n inme.calculation()\n elif (p == \"Expression\"):\n inme.Expressions()\n elif (p == 'Problem solving'):\n inme.problem()\n elif (p == \"loop\"):\n inme.loop()\n elif (p == 'Input output'):\n inme.inputOutput()\n elif (p == \"String\"):\n inme.string()\n elif (p == \"Conditional statement\"):\n inme.conditional()\n elif (p == 'Arithmetic operation'):\n inme.arithmetic()\n\n\n\n with open(\"./SecondaryDatabases/PreStatusdata.csv\", 'a', encoding='utf-8') as data:\n data.write(str(self.ID) + \" ,\" + str(q1) + \",\" + str(q2) + \",\" + str(q3) + \",\" + str(q4) + \",\" + str(\n q5) + \",\" + str(q6) + \",\" + str(q7) + \",\" + str(q8) + \",\" + str(q9) + \",\" + str(q10) + \",\" + str(\n totalmarks) + \"\\n\")\n data.close()\n\n\n","sub_path":"ResearchGenerator/PreStatuscCheck.py","file_name":"PreStatuscCheck.py","file_ext":"py","file_size_in_byte":10300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"52"} +{"seq_id":"561333190","text":"from __future__ import division\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\nimport sys\nimport time\ndef walk(dimen,iterat):\n \"\"\"\n Performs the random walk. Creates the Matrix with the coefficients of the velocity profile created when\n one of the spheres moves through the fluid.\n \"\"\"\n if dimen >1:\n for j in xrange(dimen):\n for i in xrange(iterat -1):\n #print np.sum(np.square(displacement2[j,i,:]) - np.square(displacement1[j,i,:]))\n #S is the stokeslet tensor\n distance_sq = np.sum(np.square(displacement2[j,i,:] - displacement1[j,i,:]))\n Sxy = (3/(4*np.sqrt(distance_sq)))*(\n (displacement2[j,i,0]-displacement1[j,i,0])*(displacement2[j,i,1] - displacement1[j,i,1])/\n distance_sq)\n Sxx = (3/(4*np.sqrt(distance_sq)))*(1+\n (displacement2[j,i,0]-displacement1[j,i,0])**2/distance_sq)\n Syy = (3/(4*np.sqrt(distance_sq)))*(1+\n (displacement2[j,i,1] - displacement1[j,i,1])**2/distance_sq)\n #print (\"{} run {} step {}\".format(Sxy,j,i))\n #print (3/(4))*(\n #(displacement2[j,i,0]-displacement1[j,i,0])*(displacement2[j,i,1] - displacement1[j,i,1])/\n # distance_sq)\n Matrix = np.array([[1,0,-Sxx,-Sxy],[0,1,-Sxy,-Syy],[-Sxx,-Sxy,1,0],[-Sxy,-Syy,0,1]])\n Minv = np.linalg.inv(Matrix)\n random_array = np.random.randn(4)\n displacement1[j,i+1,0] = displacement1[j,i,0] + np.dot(Minv,random_array)[0]*np.sqrt(constant * time_step)\n displacement1[j, i + 1, 1] = displacement1[j, i, 1] + np.dot(Minv, random_array)[1] * np.sqrt(\n constant * time_step)\n displacement2[j, i + 1, 0] = displacement2[j, i, 0] + np.dot(Minv, random_array)[2] * np.sqrt(\n constant * time_step)\n displacement2[j, i + 1, 1] = displacement2[j, i, 1] + np.dot(Minv, random_array)[3] * np.sqrt(\n constant * time_step)\n #array[j,i + 1] = array[j,i] + np.sqrt(constant * time_step) * random.gauss(0, 1)\n update_progress(j / (runs))\n # else:\n # for i in xrange(iterat - 1):\n # np.array[i + 1] = array[i] + np.sqrt(constant * time_step) * random.gauss(0, 1)\n # # update_progress(i / (dimen*iterat))\n # if i%runs ==0:\n # update_progress(i / (2*iterat) +1/2)\n\n# update_progress() : Displays or updates a console progress bar\n## Accepts a float between 0 and 1. Any int will be converted to a float.\n## A value under 0 represents a 'halt'.\n## A value at 1 or bigger represents 100%\ndef update_progress(progress):\n barLength = 10 # Modify this to change the length of the progress bar\n status = \"\"\n if isinstance(progress, int):\n progress = float(progress)\n if not isinstance(progress, float):\n progress = 0\n status = \"error: progress var must be float\\r\\n\"\n if progress < 0:\n progress = 0\n status = \"Halt...\\r\\n\"\n if progress >= 1:\n progress = 1\n status = \"Done...\\r\\n\"\n block = int(round(barLength*progress))\n text = \"\\rPercent: [{0}] {1}% {2}\".format( \"#\"*block + \"-\"*(barLength-block), progress*100, status)\n sys.stdout.write(text)\n sys.stdout.flush()\n\nif __name__ == '__main__':\n\n if len(sys.argv) != 7:\n print (\"Usage: main.py