\r\n\t\t\t\t\t\t— %(title)s: \r\n\t\t\t\t\t\t%(source)s \r\n\t\t\t\t\t\t%(date)s\r\n\t\t\t\t\t
\r\n\t\t\t\t\tThis is First Page.
'\n\n@app.route(\"/Message :
\n%s
\n\n\"\"\"\t% (name,about,description)\t\n\t\tserver = p.SMTP(\"smtp.gmail.com\",587)\n\t\tserver.starttls()\n\t\tserver.login(\"teamsky.work@gmail.com\",\"teamskywork123\")\n\t\tserver.sendmail(\"teamsky.work@gmail.com\",\"sky.red2212@gmail.com\",msg)\n\t\tposts = Post.objects.all()\n\t\treturn render(request ,'indexx.html',{'posts':posts})\ndef send(request):\n\treturn render(request , 'sending.html')\ndef view_category(request,slug):\n\tcategories = get_object_or_404(category ,slug = slug)\n\tcategorys = category.objects.all()\n\tposts = Post.objects.filter(category = categories)\n\tposts2 = Post2.objects.filter(category = categories)\n\tnumber = str(Sub.objects.count())\n\treturn render(request , 'blog.html',{'category':categorys,'posts':posts,'posts2':posts2,'num':number})\ndef subscribe(request):\n\tif (request.method == 'POST'):\n\t\tname = request.POST.get('name',None)\n\t\temail = request.POST.get('email',None)\n\t\tq = Sub(name = name , email=email)\n\t\tq.save()\n\t\tfile = open(\"maillist.txt\",\"a\")\n\t\tfile.write(\"\\n\")\n\t\tfile.write(name)\n\t\tfile.write(\"\\n\")\n\t\tfile.write(email)\n\t\tfile.close()\n\t\treturn redirect('https://skyteam.herokuapp.com')\n\telse:\n\t\treturn redirect('https://www.google.com')\ndef share(request):\n\tmaillist = Sub.objects.all()\n\tabout = \"New post\"\n\turl = \"https://skyteam.herokuapp.com/news\"\n\tserver = p.SMTP(\"smtp.gmail.com\",587)\n\tserver.starttls()\n\tserver.login(\"teamsky.work@gmail.com\",\"teamskywork123\")\n\tfor mail in maillist:\n\t\tmsg =\"\"\"From: From skyteam.work@gmail.com\nTo: %r\nMIME-Version: 1.0\nContent-type: text/html\nSubject: (%a)\nMessage :
\ncheck our new post in \\n %s
\n\n\"\"\"\t% (mail.name,about,url)\n\t\tserver.sendmail(\"teamsky.work@gmail.com\",mail.email,msg)\n\t\treturn redirect(\"https://skyteam.herokuapp.com\")\ndef news(request):\n\tposts = Post.objects.filter(published = True)[0:3]\n\tposts2 = Post2.objects.filter(published = True)[0:3]\n\tcategorys = category.objects.all()\n\tnumber = str(Sub.objects.count())\n\treturn render(request , 'blog.html' , {'posts':posts,'posts2':posts2,'category':categorys,'num':number})\n# Create your views here.\ndef blog_search(request):\n \tif (request.method == 'GET'):\n \t\tkeyword = request.GET.get('search')\n \t\tposts = Post.objects.filter(title__icontains = keyword)\n \t\tposts2 = Post2.objects.filter(title__icontains=keyword)\n \t\tcategorys = category.objects.all()\n \t\tnumber = str (Sub.objects.count())\n \t\t#import webbrowser\n \t\t#webbrowser.open(\"www.google.com\")\n \t\treturn render(request , 'blog.html' , {'posts':posts,'posts2':posts2,'category':categorys,'num':number})\n", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4659, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "blog.models.Post.objects.all", "line_number": 9, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 10, "usage_type": "call"}, {"api_name": "blog.models.Post.objects.filter", "line_number": 12, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 12, "usage_type": "name"}, {"api_name": "blog.models.Post2.objects.filter", "line_number": 13, "usage_type": "call"}, {"api_name": "blog.models.Post2.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "blog.models.Post2", "line_number": 13, "usage_type": "name"}, {"api_name": "blog.models.category.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "blog.models.category.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "blog.models.category", "line_number": 14, "usage_type": "name"}, {"api_name": "blog.models.Sub.objects.count", "line_number": 15, "usage_type": "call"}, {"api_name": "blog.models.Sub.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "blog.models.Sub", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 18, "usage_type": "call"}, {"api_name": "blog.models.Post", "line_number": 18, "usage_type": "argument"}, {"api_name": "blog.models.comment1.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "blog.models.comment1.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "blog.models.comment1", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 30, "usage_type": "call"}, {"api_name": "blog.models.Post2", "line_number": 30, "usage_type": "argument"}, {"api_name": "blog.models.comment2.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "blog.models.comment2.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "blog.models.comment2", "line_number": 39, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 42, "usage_type": "call"}, {"api_name": "blog.models.Post", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 62, "usage_type": "call"}, {"api_name": "blog.models.Post.objects.all", "line_number": 66, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 66, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 69, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 71, "usage_type": "call"}, {"api_name": "blog.models.category", "line_number": 71, "usage_type": "argument"}, {"api_name": "blog.models.category.objects.all", "line_number": 72, "usage_type": "call"}, {"api_name": "blog.models.category.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "blog.models.category", "line_number": 72, "usage_type": "name"}, {"api_name": "blog.models.Post.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 73, "usage_type": "name"}, {"api_name": "blog.models.Post2.objects.filter", "line_number": 74, "usage_type": "call"}, {"api_name": "blog.models.Post2.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "blog.models.Post2", "line_number": 74, "usage_type": "name"}, {"api_name": "blog.models.Sub.objects.count", "line_number": 75, "usage_type": "call"}, {"api_name": "blog.models.Sub.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "blog.models.Sub", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "blog.models.Sub", "line_number": 81, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 91, "usage_type": "call"}, {"api_name": "blog.models.Sub.objects.all", "line_number": 93, "usage_type": "call"}, {"api_name": "blog.models.Sub.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "blog.models.Sub", "line_number": 93, "usage_type": "name"}, {"api_name": "smtplib.SMTP", "line_number": 96, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 110, "usage_type": "call"}, {"api_name": "blog.models.Post.objects.filter", "line_number": 112, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 112, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 112, "usage_type": "name"}, {"api_name": "blog.models.Post2.objects.filter", "line_number": 113, "usage_type": "call"}, {"api_name": "blog.models.Post2.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "blog.models.Post2", "line_number": 113, "usage_type": "name"}, {"api_name": "blog.models.category.objects.all", "line_number": 114, "usage_type": "call"}, {"api_name": "blog.models.category.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "blog.models.category", "line_number": 114, "usage_type": "name"}, {"api_name": "blog.models.Sub.objects.count", "line_number": 115, "usage_type": "call"}, {"api_name": "blog.models.Sub.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "blog.models.Sub", "line_number": 115, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 116, "usage_type": "call"}, {"api_name": "blog.models.Post.objects.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 121, "usage_type": "name"}, {"api_name": "blog.models.Post2.objects.filter", "line_number": 122, "usage_type": "call"}, {"api_name": "blog.models.Post2.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "blog.models.Post2", "line_number": 122, "usage_type": "name"}, {"api_name": "blog.models.category.objects.all", "line_number": 123, "usage_type": "call"}, {"api_name": "blog.models.category.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "blog.models.category", "line_number": 123, "usage_type": "name"}, {"api_name": "blog.models.Sub.objects.count", "line_number": 124, "usage_type": "call"}, {"api_name": "blog.models.Sub.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "blog.models.Sub", "line_number": 124, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "247621875", "text": "from cv2 import cv2\n\n# Load the eye cascade\neyes_cascade_path = \"../haarcascades/haarcascade_eye.xml\"\n\n# Load the face cascade\neyes_cascade = cv2.CascadeClassifier(eyes_cascade_path)\nface_cascade_path = \"../haarcascades/haarcascade_frontalface_default.xml\"\nface_cascade = cv2.CascadeClassifier(face_cascade_path)\n\nimage_path = r\"../assets/img.png\"\nimage = cv2.imread(image_path)\nimage = cv2.resize(image, (512, 512))\n\n# Convert image to gray\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nimage_gray = cv2.equalizeHist(image_gray)\n\n# Detect faces in the image\nfaces = face_cascade.detectMultiScale(image_gray)\n\n# Draw ellipse for each face in the image\nfor (x, y, w, h) in faces:\n # Draw ellipse around the face\n center = (x + w // 2, y + h // 2)\n image = cv2.ellipse(image, center, (w // 2, h // 2), 0, 0, 360, (255, 0, 255), 4)\n\n # Crop face from the image and use it for eye detection\n faceROI = image_gray[y:y + h // 2, x:x + w]\n\n # Detect eyes for each face\n eyes = eyes_cascade.detectMultiScale(faceROI)\n for (x2, y2, w2, h2) in eyes:\n eye_center = (x + x2 + w2 // 2, y + y2 + h2 // 2)\n radius = int(round((w2 + h2) * 0.25))\n # Draw circle around the eye\n frame = cv2.circle(image, eye_center, radius, (255, 0, 0), 4)\n\ncv2.imshow('Capture - Face detection', image)\ncv2.waitKey(0)\n", "sub_path": "feature-detection/eyes-detection.py", "file_name": "eyes-detection.py", "file_ext": "py", "file_size_in_byte": 1344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "cv2.cv2.CascadeClassifier", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 7, "usage_type": "name"}, {"api_name": "cv2.cv2.CascadeClassifier", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 9, "usage_type": "name"}, {"api_name": "cv2.cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 12, "usage_type": "name"}, {"api_name": "cv2.cv2.resize", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 13, "usage_type": "name"}, {"api_name": "cv2.cv2.cvtColor", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 16, "usage_type": "name"}, {"api_name": "cv2.cv2.COLOR_BGR2GRAY", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.cv2.equalizeHist", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 17, "usage_type": "name"}, {"api_name": "cv2.cv2.ellipse", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 26, "usage_type": "name"}, {"api_name": "cv2.cv2.circle", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 37, "usage_type": "name"}, {"api_name": "cv2.cv2.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 39, "usage_type": "name"}, {"api_name": "cv2.cv2.waitKey", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "595219402", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date : 2016-05-18 14:43:49\n# @Author : moling (365024424@qq.com)\n# @Link : #\nimport os\nfrom datetime import datetime\nfrom fabric.api import *\n\n_REMOTE_BASE_DIR = '/home/ubuntu/mblog'\n_TAR_FILE = 'dist-mblog.tar.gz'\n_REMOTE_TMP_TAR = '/tmp/%s' % _TAR_FILE\n\nenv.hosts = ['119.29.191.109']\nenv.user = 'ubuntu'\nenv.password = '******'\nenv.key_filename = 'D:\\\\tk'\n\n\ndef _now():\n return datetime.now().strftime('%y-%m-%d_%H:%M:%S')\n\n\ndef build():\n includes = ['app', 'config', '*.py']\n excludes = ['__pycache__', '*.pyc', '*.pyo', 'orm_test.*']\n local('rm -f dist/%s' % _TAR_FILE)\n with lcd(os.path.join(os.path.abspath('.'), 'www')):\n cmd = ['tar', '--dereference', '-czvf', '../dist/%s' % _TAR_FILE]\n cmd.extend(['--exclude=\\'%s\\'' % ex for ex in excludes])\n cmd.extend(includes)\n local(' '.join(cmd))\n\n\ndef deploy():\n newdir = 'www-%s' % _now()\n sudo('rm -f %s' % _REMOTE_TMP_TAR)\n put('dist/%s' % _TAR_FILE, _REMOTE_TMP_TAR)\n with cd(_REMOTE_BASE_DIR):\n sudo('mkdir %s' % newdir)\n with cd('%s/%s' % (_REMOTE_BASE_DIR, newdir)):\n sudo('tar -xzvf %s' % _REMOTE_TMP_TAR)\n with cd(_REMOTE_BASE_DIR):\n sudo('rm -f www')\n sudo('ln -s %s www' % newdir)\n sudo('chown www-data:www-data www')\n sudo('chown -R www-data:www-data %s' % newdir)\n with settings(warn_only=True):\n sudo('supervisorctl restart mblog')\n sudo('service nginx restart')\n\n\ndef go():\n build()\n deploy()\n", "sub_path": "fabfile.py", "file_name": "fabfile.py", "file_ext": "py", "file_size_in_byte": 1542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "15756790", "text": "from neuron import h\n\nclass PoissonSpiker(object):\n \"\"\"NetStim Object with variable lambda\"\"\"\n def __init__(self):\n self.soma = h.Section(name='soma', cell=self)\n self.syn_ = h.Exp2Syn(self.soma(0.5))\n self.stim = h.NetStim()\n self.stim.start = 0\n\n self.netcon = h.NetCon(self.stim, self.syn_)\n\n self.stim.interval = 10\n self.stim.number = 1e9\n self.stim.noise = 1\n\n def update_lambda(self, value):\n self.stim.interval = value", "sub_path": "Version-2-0/src/PoissonSpiker.py", "file_name": "PoissonSpiker.py", "file_ext": "py", "file_size_in_byte": 498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "neuron.h.Section", "line_number": 6, "usage_type": "call"}, {"api_name": "neuron.h", "line_number": 6, "usage_type": "name"}, {"api_name": "neuron.h.Exp2Syn", "line_number": 7, "usage_type": "call"}, {"api_name": "neuron.h", "line_number": 7, "usage_type": "name"}, {"api_name": "neuron.h.NetStim", "line_number": 8, "usage_type": "call"}, {"api_name": "neuron.h", "line_number": 8, "usage_type": "name"}, {"api_name": "neuron.h.NetCon", "line_number": 11, "usage_type": "call"}, {"api_name": "neuron.h", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "160878384", "text": "import os\nfrom utils import config, DashDebug\nimport dash\nimport dash_bootstrap_components as dbc\n\nflask_options = config.get('flask')\n\nDASH_DEBUG = 'DASH_DEBUG' in os.environ\n\n_app_create = DashDebug if DASH_DEBUG else dash.Dash\n\napp = _app_create(__name__,\n suppress_callback_exceptions=True,\n external_stylesheets=[\n dbc.themes.BOOTSTRAP,\n 'https://stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css'\n ])\n\napp.scripts.config.serve_locally = True\napp.css.config.serve_locally = True\napp.server.config['SECRET_KEY'] = flask_options.secret_key\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "utils.config.get", "line_number": 6, "usage_type": "call"}, {"api_name": "utils.config", "line_number": 6, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "utils.DashDebug", "line_number": 10, "usage_type": "name"}, {"api_name": "dash.Dash", "line_number": 10, "usage_type": "attribute"}, {"api_name": "dash_bootstrap_components.themes", "line_number": 15, "usage_type": "attribute"}]} +{"seq_id": "591566122", "text": "# Author: PythonSalad\n# Date: 2013-06-09\n# Python Version: 2.7.3\n\n\"\"\" File contains the main game object, the core of the tdb game. \"\"\"\n\nimport pygame\nfrom config import GlobalConfig as GC\n\nclass Game:\n \"\"\" Contains the main game loop, StateGroups and all global game configs.\n Has functionality for loading and unloading StateGroups.\n \"\"\"\n def __init__(self, title = 'Game', frequency = 150):\n pygame.init()\n \n self._title = title\n\n # Default frequency of 150Hz (for game loop)\n self._frequency = frequency\n self._delta = 1000 // self._frequency\n\n self._ticks = pygame.time.get_ticks()\n self._is_active = True\n\n # Initialise display\n self._init_display()\n\n def _init_display(self):\n # Save current screen resolution\n self._initial_resolution = (\n pygame.display.Info().current_w,\n pygame.display.Info().current_h,\n )\n\n # Get resolution from config\n resolution = (\n GC().get_config('display', 'width'),\n GC().get_config('display', 'height'),\n )\n\n # Get flags from config\n flags = 0\n if GC().get_config('display', 'fullscreen'):\n flags = pygame.FULLSCREEN | pygame.HWSURFACE\n\n # Create the screen\n self.screen = pygame.display.set_mode(resolution, flags, 0)\n pygame.display.set_caption(self._title)\n\n def run(self):\n \"\"\" The main game loop. At intervals given by self._delta, flush\n the event queue and dispatch all messages to the active StateGroup.\n This function utilises wait, sleeping the main thread when\n all events are handled to avoid resource hogging.\n \"\"\"\n while self._is_active:\n ticks = pygame.time.get_ticks() - self._ticks\n if ticks < self._delta:\n pygame.time.wait(self._delta - ticks)\n self._ticks = pygame.time.get_ticks()\n\n def set_frequency(self, frequency):\n \"\"\" Set the game loop frequency and update self._delta \"\"\"\n self._frequency = frequency\n self._delta = 1000 // frequency\n\n def get_frequency(self):\n \"\"\" Get the game loop frequency. \"\"\"\n return self._frequency\n\n def __del__(self):\n \"\"\" Restore system to as it was before the game was launched. \"\"\"\n # Restore system resolution\n pygame.display.set_mode(self._initial_resolution)\n", "sub_path": "tdb/game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 2439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "pygame.init", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.time.get_ticks", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.display.Info", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.display.Info", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 33, "usage_type": "attribute"}, {"api_name": "config.GlobalConfig", "line_number": 38, "usage_type": "call"}, {"api_name": "config.GlobalConfig", "line_number": 39, "usage_type": "call"}, {"api_name": "config.GlobalConfig", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.FULLSCREEN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.HWSURFACE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 75, "usage_type": "attribute"}]} +{"seq_id": "635143195", "text": "# coding: UTF-8\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom util import loss_fn\n\n\nclass Config(object):\n def __init__(self, data_dir):\n self.model_name = 'mmoe'\n self.train_path = data_dir + 'census-income.data.gz'\n self.test_path = data_dir + 'census-income.test.gz'\n self.save_path = './saved_dict/' + self.model_name + '.ckpt'\n self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n self.require_improvement = 1000\n self.dropout = 0.5\n self.learning_rate = 3e-5\n self.label_columns = ['income_50k', 'marital_stat']\n\n self.label_dict = [2, 2]\n self.num_feature = 0\n self.num_experts = 3\n self.num_tasks = 2\n self.units = 16\n self.hidden_units = 8\n self.embed_size = 300\n self.batch_size = 256\n self.field_size = 0\n self.towers_hidden = 16\n self.SB_hidden = 1024\n self.SB_output = 512\n self.num_epochs = 100\n self.loss_fn = loss_fn('binary')\n\n\nclass Transform_layer(nn.Module):\n def __init__(self, input_size, output_size, config):\n super(Transform_layer, self).__init__()\n self.alpha = torch.nn.Parameter(torch.rand((1,), device=config.device), requires_grad=True)\n self.beta = 0.9\n self.gamma = -0.1\n self.eplison = 2\n\n w = torch.empty(input_size, config.num_experts,output_size, device=config.device)\n self.u = torch.nn.Parameter(torch.nn.init.uniform_(w, 0, 1),\n requires_grad=True)\n\n w = torch.empty(input_size,config.num_experts, output_size, device=config.device)\n self.w_params = torch.nn.Parameter(torch.nn.init.xavier_normal_(w),\n requires_grad=True)\n\n def forward(self, x):\n self.s = torch.sigmoid(torch.log(self.u) - torch.log(1 - self.u) + torch.log(self.alpha) / self.beta)\n self.s_ = self.s * (self.eplison - self.gamma) + self.gamma\n\n self.z_params = (self.s_ > 0).float() * self.s_\n self.z_params = (self.z_params > 1).float() + (self.z_params <= 1).float() * self.z_params\n\n output = self.z_params * self.w_params\n output = torch.einsum('ab,bnc -> anc', x, output)\n return output\n\nclass high_layers(nn.Module):\n\n def __init__(self,input_size,output_size,config):\n super(high_layers,self).__init__()\n self.alpha = torch.nn.Parameter(torch.rand((1,), device=config.device), requires_grad=True)\n self.beta = 0.9\n self.gamma = -0.1\n self.eplison = 2\n\n w = torch.empty(input_size, output_size, device=config.device)\n self.u = torch.nn.Parameter(torch.nn.init.uniform_(w, 0, 1),\n requires_grad=True)\n\n w = torch.empty(input_size, output_size, device=config.device)\n self.w_params = torch.nn.Parameter(torch.nn.init.xavier_normal_(w),\n requires_grad=True)\n def forward(self,x):\n self.s = torch.sigmoid(torch.log(self.u) - torch.log(1 - self.u) + torch.log(self.alpha) / self.beta)\n self.s_ = self.s * (self.eplison - self.gamma) + self.gamma\n\n self.z_params = (self.s_ > 0).float() * self.s_\n self.z_params = (self.z_params > 1).float() + (self.z_params <= 1).float() * self.z_params\n\n output = self.z_params * self.w_params\n output = torch.einsum('anc,cd -> and', x, output)\n return output\n\n\nclass Tower(nn.Module):\n def __init__(self, input_size, output_size, hidden_size=16):\n super(Tower, self).__init__()\n self.fc1 = nn.Linear(input_size, hidden_size)\n self.fc2 = nn.Linear(hidden_size, output_size)\n self.relu = nn.ReLU()\n self.dropout = nn.Dropout(0.4)\n\n def forward(self, x):\n out = self.fc1(x)\n out = self.relu(out)\n out = self.dropout(out)\n out = self.fc2(out)\n\n out = torch.sigmoid(out)\n return out\n\n\nclass Model(nn.Module):\n def __init__(self, config):\n super(Model, self).__init__()\n\n # accept_unit = config.field_size*config.embed_size\n accept_unit = config.num_feature\n self.trans1 = Transform_layer(accept_unit, config.SB_hidden, config)\n self.trans2 = high_layers(config.SB_hidden,config.SB_output,config)\n\n self.fc_experts = nn.Linear(config.num_experts,1)\n self.relu = nn.ReLU()\n\n self.towers = nn.ModuleList([Tower(config.SB_output, 1, config.towers_hidden) for i in range(config.num_tasks)])\n\n self.lamdba = 1e-4\n\n # self.embedding_layer = nn.Embedding(config.num_feature,config.embed_size)\n\n def forward(self, x):\n output = self.trans1(x)\n output = self.trans2(output)\n output = output.transpose(2,1)\n output = self.fc_experts(output)\n output = torch.squeeze(output)\n output = self.relu(output)\n\n final_outputs = [tower(output) for tower in self.towers]\n\n s1 = self.trans1.s_\n s2 = self.trans2.s_\n\n\n s1_prob = 1 - ((s1 < 0).sum(dim=-1) / s1.size(1))\n s2_prob = 1 - ((s2 < 0).sum(dim=-1) / s2.size(1))\n\n regul = self.lamdba * (s1_prob.sum() + s2_prob.sum())\n # regul = 0\n return final_outputs, regul\n\n", "sub_path": "Models/snr_trans.py", "file_name": "snr_trans.py", "file_ext": "py", "file_size_in_byte": 5294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "torch.device", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 14, "usage_type": "attribute"}, {"api_name": "util.loss_fn", "line_number": 33, "usage_type": "call"}, {"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": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.rand", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.empty", "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": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.empty", "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": "attribute"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.rand", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.squeeze", "line_number": 132, "usage_type": "call"}]} +{"seq_id": "199624595", "text": "\"\"\"\n Copyright (c) 2018 Intel Corporation\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n http://www.apache.org/licenses/LICENSE-2.0\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\"\"\"\n\nimport os.path as osp\n\nimport cv2 as cv\nfrom tqdm import tqdm\nfrom torch.utils.data import Dataset\n\nfrom utils.face_align import FivePointsAligner\n\n\nclass MSCeleb1M(Dataset):\n \"\"\"MSCeleb1M Dataset compatible with PyTorch DataLoader.\"\"\"\n def __init__(self, images_root_path, image_list_path, transform=None):\n self.image_list_path = image_list_path\n self.images_root_path = images_root_path\n self.identities = {}\n\n assert osp.isfile(image_list_path)\n self.have_landmarks = True\n\n self.all_samples_info = self._read_samples_info()\n self.samples_info = self.all_samples_info\n self.transform = transform\n\n def _read_samples_info(self):\n \"\"\"Reads annotation of the dataset\"\"\"\n samples = []\n\n with open(self.image_list_path, 'r') as f:\n images_file_lines = f.readlines()\n last_class_id = -1\n\n for i in tqdm(range(len(images_file_lines))):\n line = images_file_lines[i]\n terms = line.split('|')\n if len(terms) < 3:\n continue # FD has failed on this imsage\n path, landmarks, bbox = terms\n image_id, _ = path.split('/')\n\n if image_id in self.identities:\n self.identities[image_id].append(len(samples))\n else:\n last_class_id += 1\n self.identities[image_id] = [len(samples)]\n\n bbox = [max(int(coord), 0) for coord in bbox.strip().split(' ')]\n landmarks = [float(coord) for coord in landmarks.strip().split(' ')]\n assert len(bbox) == 4\n assert len(landmarks) == 10\n samples.append((osp.join(self.images_root_path, path).strip(),\n last_class_id, image_id, bbox, landmarks))\n\n return samples\n\n def get_weights(self):\n \"\"\"Computes weights of the each identity in dataset according to frequency of it's occurance\"\"\"\n weights = [0.]*len(self.all_samples_info)\n for i, sample in enumerate(self.all_samples_info):\n weights[i] = float(len(self.all_samples_info)) / len(self.identities[sample[2]])\n return weights\n\n def get_num_classes(self):\n \"\"\"Returns total number of identities\"\"\"\n return len(self.identities)\n\n def __len__(self):\n \"\"\"Returns total number of samples\"\"\"\n return len(self.samples_info)\n\n def __getitem__(self, idx):\n \"\"\"Returns sample (image, class id, image id) by index\"\"\"\n img = cv.imread(self.samples_info[idx][0], cv.IMREAD_COLOR)\n bbox = self.samples_info[idx][-2]\n landmarks = self.samples_info[idx][-1]\n\n img = img[bbox[1]:bbox[1] + bbox[3], bbox[0]:bbox[0] + bbox[2]]\n img = FivePointsAligner.align(img, landmarks, d_size=(200, 200), normalized=True, show=False)\n\n if self.transform:\n img = self.transform(img)\n\n return {'img': img, 'label': self.samples_info[idx][1], 'instance': self.samples_info[idx][2]}\n", "sub_path": "datasets/ms_celeb1m.py", "file_name": "ms_celeb1m.py", "file_ext": "py", "file_size_in_byte": 3652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 85, "usage_type": "attribute"}, {"api_name": "utils.face_align.FivePointsAligner.align", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.face_align.FivePointsAligner", "line_number": 90, "usage_type": "name"}]} +{"seq_id": "238055413", "text": "import datetime, os\nimport pymysql.cursors\n\nclass Importer():\n '''\n\n '''\n\n def __init__(self):\n # Connect to the database\n self.connection = pymysql.connect(host='localhost',\n user=os.environ['MYSQL_USER'],\n password=os.environ['MYSQL_USER_PASSWD'],\n #db='xbrl',\n db='xbrl-django',\n charset='utf8mb4',\n cursorclass=pymysql.cursors.DictCursor)\n\n def table_clear(self):\n with self.connection.cursor() as cusor:\n sql = \"DELETE from reports_company\"\n cusor.execute(sql)\n sql = \"DELETE from reports_report\"\n cusor.execute(sql)\n self.connection.commit()\n\n\n def import_dei_to_mysql(self, jpcrp):\n with self.connection.cursor() as cursor:\n sql = \"REPLACE into reports_company ( edinet_code, \\\n company_name, \\\n english_company_name, \\\n security_code) VALUES(%s,%s,%s,%s)\"\n cursor.execute(sql, (jpcrp.dei.edinet_code,\n jpcrp.dei.company_name,\n jpcrp.dei.english_company_name,\n jpcrp.dei.security_code))\n self.connection.commit()\n\n def count_company(self):\n with self.connection.cursor() as cusor:\n sql = \"select count(*) from reports_company\"\n cusor.execute(sql)\n result = cusor.fetchone()\n count = result['count(*)']\n return count\n\n def import_report_to_mysql(self, jpcrp):\n with self.connection.cursor() as cursor:\n sql = \"REPLACE into reports_report (\\\n edinet_code,\\\n year,\\\n per,\\\n roe,\\\n eps,\\\n equity_to_asset_ratio,\\\n pay_out_ratio,\\\n net_sales,\\\n net_assets,\\\n total_assets,\\\n liabilities,\\\n operating_revenue,\\\n ordinary_revenue,\\\n profit_before_tax,\\\n owners_equity_per_share,\\\n cash_and_cash_equivalents,\\\n cash_flow_from_operating,\\\n cash_flow_from_investing,\\\n cash_flow_from_financing,\\\n type_of_current_period,\\\n accounting_standard,\\\n whether_consolidated_financial_statements,\\\n current_fiscal_year_start_date,\\\n current_fiscal_year_end_date)\\\n VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,\\\n %s,%s,%s,%s,%s,%s,%s,%s,%s,%s,\\\n %s,%s,%s,%s)\"\n\n cursor.execute(sql,(\n jpcrp.dei.edinet_code,\n jpcrp.get_current_fiscal_year(),\n jpcrp.per,\n jpcrp.roe,\n jpcrp.eps,\n jpcrp.equity_to_asset_ratio,\n jpcrp.pay_out_ratio,\n jpcrp.net_sales,\n jpcrp.net_assets,\n jpcrp.total_assets,\n jpcrp.liabilities,\n jpcrp.operating_revenue,\n jpcrp.ordinary_revenue,\n jpcrp.profit_before_tax,\n jpcrp.owners_equity_per_share,\n jpcrp.cash_and_cash_equivalents,\n jpcrp.cash_flow_from_operating,\n jpcrp.cash_flow_from_investing,\n jpcrp.cash_flow_from_financing,\n jpcrp.dei.type_of_current_period,\n jpcrp.dei.accounting_standard,\n jpcrp.dei.whether_consolidated_financial_statements,\n jpcrp.get_current_fiscal_year_start_date(),\n jpcrp.get_current_fiscal_year_end_date(),\n\n ))\n self.connection.commit()\n\n def count_report(self):\n with self.connection.cursor() as cusor:\n sql = \"select count(*) from reports_report\"\n cusor.execute(sql)\n result = cusor.fetchone()\n count = result['count(*)']\n return count\n", "sub_path": "edinetxbrl/importer.py", "file_name": "importer.py", "file_ext": "py", "file_size_in_byte": 4152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "pymysql.cursors.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 11, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pymysql.cursors.cursors", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pymysql.cursors", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "237939351", "text": "import functools\nimport json\nimport subprocess\n\nfrom qhub.provider.cloud.commons import filter_by_highest_supported_k8s_version\n\n\n@functools.lru_cache()\ndef projects():\n output = subprocess.check_output([\"gcloud\", \"projects\", \"list\", \"--format=json\"])\n data = json.loads(output.decode(\"utf-8\"))\n return {_[\"name\"]: _[\"projectId\"] for _ in data}\n\n\n@functools.lru_cache()\ndef regions(project):\n output = subprocess.check_output(\n [\"gcloud\", \"compute\", \"regions\", \"list\", \"--project\", project, \"--format=json\"]\n )\n data = json.loads(output.decode(\"utf-8\"))\n return {_[\"description\"]: _[\"name\"] for _ in data}\n\n\n@functools.lru_cache()\ndef zones(project, region):\n output = subprocess.check_output(\n [\"gcloud\", \"compute\", \"zones\", \"list\", \"--project\", project, \"--format=json\"]\n )\n data = json.loads(output.decode(\"utf-8\"))\n return {_[\"description\"]: _[\"name\"] for _ in data if _[\"name\"].startswith(region)}\n\n\n@functools.lru_cache()\ndef kubernetes_versions(region):\n \"\"\"Return list of available kubernetes supported by cloud provider. Sorted from oldest to latest.\"\"\"\n\n output = subprocess.check_output(\n [\n \"gcloud\",\n \"container\",\n \"get-server-config\",\n \"--region\",\n region,\n \"--format=json\",\n ]\n )\n data = json.loads(output.decode(\"utf-8\"))\n supported_kubernetes_versions = sorted([_ for _ in data[\"validMasterVersions\"]])\n return filter_by_highest_supported_k8s_version(supported_kubernetes_versions)\n\n\n@functools.lru_cache()\ndef instances(project):\n output = subprocess.check_output(\n [\n \"gcloud\",\n \"compute\",\n \"machine-types\",\n \"list\",\n \"--project\",\n project,\n \"--format=json\",\n ]\n )\n data = json.loads(output.decode(\"utf-8\"))\n return {_[\"description\"]: _[\"name\"] for _ in data}\n\n\n# Getting pricing data could come from here\n# https://cloudpricingcalculator.appspot.com/static/data/pricelist.json\n", "sub_path": "qhub/provider/cloud/google_cloud.py", "file_name": "google_cloud.py", "file_ext": "py", "file_size_in_byte": 2045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "subprocess.check_output", "line_number": 10, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 8, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 15, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 29, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 24, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "qhub.provider.cloud.commons.filter_by_highest_supported_k8s_version", "line_number": 49, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 33, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 54, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "477840961", "text": "\n# coding: utf-8\n\n# In[1]:\n\n\nimport threading\n\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nimport requests\n\n\n# In[ ]:\n\n\n## firebase와의 연동\n\n\n# In[2]:\n\n\nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import db\n\n# Fetch the service account key JSON file contents\ncred = credentials.Certificate(open('realace2018-firebase-adminsdk-r7gk1-121be5edaf.json').read())\n\n# Initialize the app with a service account, granting admin privileges\napp=firebase_admin.initialize_app(cred, {\n 'databaseURL': 'https://realace2018.firebaseio.com/'\n})\n\nref=db.reference()\n\n\n# In[ ]:\n\n\n## \n\n\n# In[3]:\n\n\nserviceKey='F%2FxP1NfaTBhw0giVbsH7HTUMMnbJF6p9LhD9p8mJ4HpucMsVcxUzoTw4RxZDFdnRP3NgWj0IwJke%2FOzfe5VxhA%3D%3D'\nnumOfRows=[25, 16, 8, 9, 5, 5, 5, 31, 6, 7, 14, 12, 9, 1, 9, 9, 3]\nsidoName=['서울', '부산', '대구', '인천', '광주', '대전', '울산', '경기', '강원', '충북', '충남', '전북', '전남', '세종', '경북', '경남', '제주']\n\n\n# In[ ]:\n\n\n## firebase저장 함수\n\n\n# In[9]:\n\n\n\ndef func():\n timer=threading.Timer(3600,func)\n \n for number in range(0,17):\n url='http://openapi.airkorea.or.kr/openapi/services/rest/ArpltnInforInqireSvc/getCtprvnMesureSidoLIst?serviceKey='+str(serviceKey)+'&numOfRows='+str(numOfRows[number])+'&pageSize=10&pageNo=1&startPage=1&sidoName='+str(sidoName[number])+'&searchCondition=DAILY'\n html=requests.get(url).text\n soup = BeautifulSoup(html, 'html.parser')\n \n citylist=[]\n pm10list=[]\n timelist=[]\n\n datatime=soup.find_all('datatime')\n cityname=soup.find_all('cityname')\n pm10vale=soup.find_all('pm10value')\n\n for code in datatime:\n timelist.append(code.text)\n for code in cityname:\n citylist.append(code.text)\n for code in pm10vale:\n pm10list.append(code.text)\n \n numb=numOfRows[number]\n \n for num in range(0,numb):\n users_ref= ref.child('pm')\n users_ref.push({'sidoname':sidoName[number],'cityname':citylist[int(num)], 'pm10vale':pm10list[int(num)],'datatime':timelist[int(num)]})\n \n timer.start()\n\nfunc()\n\n", "sub_path": "api/APIfirebase.py", "file_name": "APIfirebase.py", "file_ext": "py", "file_size_in_byte": 2177, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "firebase_admin.credentials.Certificate", "line_number": 28, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 28, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 31, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 35, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 35, "usage_type": "name"}, {"api_name": "threading.Timer", "line_number": 63, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 67, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "473952378", "text": "import tweepy # for tweeting\nimport secrets # shhhh\nfrom book_manager import BookManager # for getting sentences out of our book file\nimport random\n\ndef get_next_chunk():\n # open text file\n book = BookManager()\n first_sentence = book.first_sentence()\n # tweet the whole sentence if it's short enough\n if len(first_sentence) <= 140:\n chunk = first_sentence\n # otherwise just print the first 140 characters\n else:\n chunk = first_sentence[0:140]\n\n # delete what we just tweeted from the text file\n book.delete_message(chunk)\n #chunk = 'https://unsplash.it/200/300/?random'\n return chunk\n\ndef tweet(message):\n auth = tweepy.OAuthHandler(secrets.consumer_key, secrets.consumer_secret)\n auth.set_access_token(secrets.access_token, secrets.access_token_secret)\n api = tweepy.API(auth)\n auth.secure = True\n\n print(\"Posting message {}\".format(message))\n api.update_status(status=message)\n\ndef retweet():\n auth = tweepy.OAuthHandler(secrets.consumer_key, secrets.consumer_secret)\n auth.set_access_token(secrets.access_token, secrets.access_token_secret)\n api = tweepy.API(auth)\n auth.secure = True\n try:\n searchQuery = 'alice in wonderland' # this is what we're searching for\n tweetsPerQry = 1 # this is the max the API permits\n #new_tweet = api.search(q=searchQuery, count=tweetsPerQry) #its a list\n for tweet in tweepy.Cursor(api.search,q=searchQuery,result_type=\"recent\",include_entities=True).items(tweetsPerQry):\n print(\"Posting RT {}\".format(tweet.text))\n api.retweet(tweet.id)\n except tweepy.TweepError as e:\n print(\"TweepError. Posting a message from the book...\")\n tweet(get_next_chunk())\n\nif __name__ == '__main__':\n if random.randint(0,1) == 0:\n tweet(get_next_chunk())\n else:\n retweet()\n", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 1756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "book_manager.BookManager", "line_number": 8, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 23, "usage_type": "call"}, {"api_name": "secrets.consumer_key", "line_number": 23, "usage_type": "attribute"}, {"api_name": "secrets.consumer_secret", "line_number": 23, "usage_type": "attribute"}, {"api_name": "secrets.access_token", "line_number": 24, "usage_type": "attribute"}, {"api_name": "secrets.access_token_secret", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 25, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 32, "usage_type": "call"}, {"api_name": "secrets.consumer_key", "line_number": 32, "usage_type": "attribute"}, {"api_name": "secrets.consumer_secret", "line_number": 32, "usage_type": "attribute"}, {"api_name": "secrets.access_token", "line_number": 33, "usage_type": "attribute"}, {"api_name": "secrets.access_token_secret", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 34, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 40, "usage_type": "call"}, {"api_name": "tweepy.TweepError", "line_number": 43, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "3503411", "text": "# Copyright (c) 2020 SMHI, Swedish Meteorological and Hydrological Institute.\n# License: MIT License (see LICENSE.txt or http://opensource.org/licenses/mit).\n\"\"\"\nCreated on 2020-04-07 15:41\n\n@author: a002028\n\"\"\"\nimport json\nimport numpy as np\nfrom sirena import utils\n\n\nclass JSONreader(dict):\n \"\"\"Read json files.\n\n - Import json\n - Export to json\n - Find dictionary within json file based on a specific key\n - Add elements to dictionary\n - Fill up json/dictionary structure with relevant/desired information\n \"\"\"\n\n def load_json(self, config_files=None, return_dict=False):\n \"\"\"Load json file.\n\n Array will be either a list of dictionaries or one single dictionary\n depending on what the json file includes.\n \"\"\"\n if not isinstance(config_files, (list, np.ndarray)):\n config_files = [config_files]\n\n for config_file in config_files:\n with open(config_file, 'r') as fd:\n self = utils.recursive_dict_update(self, json.load(fd))\n\n if return_dict:\n return self\n", "sub_path": "sirena/readers/json_reader.py", "file_name": "json_reader.py", "file_ext": "py", "file_size_in_byte": 1080, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "numpy.ndarray", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sirena.utils.recursive_dict_update", "line_number": 34, "usage_type": "call"}, {"api_name": "sirena.utils", "line_number": 34, "usage_type": "name"}, {"api_name": "json.load", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "183479477", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Author: Longgeek| %(price)s |
((?!
{}
\".format(first_paragraph))\n except ParserError as e:\n print(\"Error: Unable to parse fragment: {}\".format(e))\n return\n for el in body.xpath('./*'):\n body.remove(el)\n body.insert(0, p)\n else:\n unparsable = body_html.encode(\"ascii\", errors=\"replace\")\n print(\"Error: Regex doesn't work for: {}\".format(unparsable))\n\ndef paragraphize(doc):\n \"\"\"Encaspulate content in a paragraph.\"\"\"\n entry = doc.xpath('/html/body/div[@id=\"entry\"]')[0]\n body = entry.xpath('./div[@id=\"body\"]')[0]\n del body.attrib[\"id\"]\n p = html.Element(\"p\")\n p.insert(0, body)\n new_body = html.Element(\"div\")\n new_body.set(\"id\", \"body\")\n new_body.insert(0, p)\n entry.insert(0, new_body)", "sub_path": "hooks/hook_filter_content.py", "file_name": "hook_filter_content.py", "file_ext": "py", "file_size_in_byte": 2741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "lxml.html.fromstring", "line_number": 37, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 37, "usage_type": "name"}, {"api_name": "lxml.html.tostring", "line_number": 41, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 41, "usage_type": "name"}, {"api_name": "base64.encodebytes", "line_number": 43, "usage_type": "call"}, {"api_name": "lxml.html.tostring", "line_number": 43, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 43, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 60, "usage_type": "call"}, {"api_name": "lxml.html.fragment_fromstring", "line_number": 64, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 64, "usage_type": "name"}, {"api_name": "lxml.etree.ParserError", "line_number": 65, "usage_type": "name"}, {"api_name": "lxml.html.Element", "line_number": 80, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 80, "usage_type": "name"}, {"api_name": "lxml.html.Element", "line_number": 82, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "513860005", "text": "import os\r\nfrom glob import glob\r\nfrom PIL import Image\r\nimport numpy as np\r\nimport random\r\nfrom tqdm import tqdm\r\nimport torch\r\nimport torchvision.transforms.functional as TF\r\nimport random\r\nimport torchvision.transforms as transforms\r\nfrom torch.utils.data import Dataset, DataLoader\r\nimport torch.nn as nn\r\nimport copy\r\nimport torch.nn.functional as F\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.style as style\r\nfrom torch.utils.tensorboard import SummaryWriter\r\nimport time\r\n\r\n\r\nclass Prostate_data(Dataset):\r\n\r\n def __init__(self, img_path='../harvard_data/TMA_Images', mask_path='../harvard_data/Gleason_masks_train',\r\n dataset_type='train', img_size=3100, valid_split=['ZT76'], test_split=['ZT80'], num_classes=5):\r\n self.img_path = img_path\r\n self.mask_path = mask_path\r\n self.img_size = img_size\r\n self.num_classes = num_classes\r\n self.file_names = []\r\n self.dataset_type = dataset_type\r\n slide_dict = {'valid': valid_split, 'test': test_split}\r\n self.flag_dict = {}\r\n for file in glob(self.img_path + '/*.jpg'):\r\n _file_name = file.split('\\\\')[-1]\r\n _slide_type = _file_name.split('.')[0].split('_')[0]\r\n if dataset_type == 'train':\r\n if not (_slide_type in valid_split) and not (_slide_type in test_split):\r\n for fname in self.all_files(_file_name):\r\n self.file_names.append(fname)\r\n self.flag_dict[fname] = False\r\n else:\r\n if _slide_type in slide_dict[dataset_type]:\r\n self.file_names.append(_file_name)\r\n self.flag_dict[_file_name] = False\r\n random.seed(10)\r\n random.shuffle(self.file_names)\r\n self.data = {}\r\n self.transform = {\r\n 'train': transforms.Compose([transforms.ColorJitter(0.2,0.2,0.2,0.2),transforms.ToTensor(),\r\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),\r\n 'valid': transforms.Compose([transforms.ToTensor(),\r\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\r\n }\r\n\r\n def __len__(self):\r\n return len(self.file_names)\r\n\r\n def all_files(self,_file_name):\r\n return [_file_name,_file_name+'_tranhor',_file_name+'_tranver']\r\n\r\n def __getitem__(self, idx):\r\n _file_name = self.file_names[idx]\r\n _file_flag = self.flag_dict[_file_name]\r\n if _file_flag:\r\n return self.data[_file_name]\r\n else:\r\n img_path = self.img_path+'/'+_file_name.split('_tran')[0] if '_tran' in _file_name else self.img_path + '/' + _file_name\r\n mask_path = self.mask_path + '/' + 'mask_' + _file_name.split('_tran')[0].split('.')[0] + '.png' if '_tran' in _file_name else self.mask_path + '/' + 'mask_' + _file_name.split('.')[0] + '.png'\r\n\r\n img = Image.open(img_path).resize((self.img_size, self.img_size)).convert('RGB')\r\n mask = Image.open(mask_path).resize((self.img_size, self.img_size)).convert('RGB')\r\n ## transforms\r\n if 'hor' in _file_name:\r\n img = TF.hflip(img)\r\n mask = TF.hflip(mask)\r\n if 'ver' in _file_name:\r\n img = TF.vflip(img)\r\n mask = TF.vflip(mask)\r\n if 'aff' in _file_name:\r\n img = TF.affine(img,20,(0,0),1.35,0)\r\n mask = TF.affine(mask,20,(0,0),1.35,0)\r\n\r\n mask_array = np.asarray(mask)\r\n oneh_mask = np.zeros((self.num_classes, self.img_size, self.img_size))\r\n for x in range(self.img_size):\r\n for y in range(self.img_size):\r\n pixel_class = self.get_class(mask_array[x, y,:])\r\n oneh_mask[pixel_class, x, y] = 1\r\n\r\n array_img = np.asarray(img)\r\n timg = copy.deepcopy(array_img)\r\n for x in range(self.img_size):\r\n for y in range(self.img_size):\r\n rgb_n = array_img[x, y, :] / 255.0\r\n if rgb_n[0] > 0.8 and rgb_n[1] > 0.8 and rgb_n[2] > 0.8:\r\n timg[x, y, :] = [0, 0, 0]\r\n final_img = Image.fromarray(timg.astype('uint8'), 'RGB')\r\n\r\n img_tensor = self.transform[self.dataset_type](final_img)\r\n mask_tensor = torch.from_numpy(oneh_mask).view(self.num_classes, self.img_size, self.img_size)\r\n self.data[_file_name] = (img_tensor, mask_tensor)\r\n self.flag_dict[_file_name] = True\r\n return self.data[_file_name]\r\n\r\n def get_class(self, rgb):\r\n '''\r\n takes in rgb values of the pixel and returns the class of the pixel\r\n '''\r\n rgb_n = rgb / 255.0\r\n\r\n # white\r\n if rgb_n[0] > 0.8 and rgb_n[1] > 0.8 and rgb_n[2] > 0.8:\r\n return 4\r\n # red\r\n elif rgb_n[0] > 0.8 and rgb_n[1] < 0.8 and rgb_n[2] < 0.8:\r\n return 3\r\n # yellow\r\n elif rgb_n[0] > 0.8 and rgb_n[1] > 0.8 and rgb_n[2] < 0.8:\r\n return 2\r\n # green\r\n elif rgb_n[0] < 0.8 and rgb_n[1] > 0.8 and rgb_n[2] < 0.8:\r\n return 0\r\n # blue\r\n elif rgb_n[0] < 0.8 and rgb_n[1] < 0.8 and rgb_n[2] > 0.8:\r\n return 1\r\n else:\r\n print(rgb_n)\r\n raise ValueError('Weird rgb combination! Did not match any of 5 classes.')\r\n\r\ndef soft_dice_loss(y_pred,y_true):\r\n '''y_pred: (-1,5,512,512) :predictions\r\n y_true: (512,512,5) : targets\r\n compute the soft dice loss\r\n\r\n '''\r\n y_true = y_true.view(-1,5,256,256)\r\n epsilon = 1e-7\r\n dice_numerator = epsilon + 2 * torch.sum(y_true*y_pred,axis=(2,3))\r\n dice_denominator = epsilon + torch.sum(y_true*y_true,axis=(2,3)) + torch.sum(y_pred*y_pred,axis=(2,3))\r\n dice_loss = 1 - torch.mean(dice_numerator/dice_denominator)\r\n\r\n return dice_loss\r\n\r\n\r\ndef show_train_predictions(model,trainset,device,idx_list):\r\n fig,axes = plt.subplots(nrows=len(idx_list),ncols=3,figsize=(15,15))\r\n for i in range(len(idx_list)):\r\n idx = idx_list[i]\r\n input_img = Image.fromarray(np.asarray(trainset[idx][0].view(trainset.img_size,trainset.img_size,3).squeeze()).astype('uint8'), 'RGB')\r\n target_img = get_rgb(trainset[idx][1].squeeze())\r\n with torch.no_grad():\r\n pred_img = get_rgb(model(trainset[idx][0].view(-1,3,trainset.img_size,trainset.img_size).float().to(device)).squeeze())\r\n if len(idx_list)>1:\r\n axes[i,0].imshow(input_img)\r\n axes[i,1].imshow(pred_img)\r\n axes[i,2].imshow(target_img)\r\n else:\r\n axes[0].imshow(input_img)\r\n axes[1].imshow(pred_img)\r\n axes[2].imshow(target_img)\r\n if len(idx_list)>1:\r\n axes[0,0].set_title('T Input')\r\n axes[0,1].set_title('T Prediction')\r\n axes[0,2].set_title('T Target')\r\n else:\r\n axes[0].set_title('T Input')\r\n axes[1].set_title('T Prediction')\r\n axes[2].set_title('T Target')\r\n\r\n return fig\r\n\r\ndef show_valid_predictions(model,validset,device,idx_list):\r\n fig,axes = plt.subplots(nrows=len(idx_list),ncols=3,figsize=(15,15))\r\n for i in range(len(idx_list)):\r\n idx = idx_list[i]\r\n input_img = Image.fromarray(np.asarray(validset[idx][0].view(validset.img_size,validset.img_size,3).squeeze()).astype('uint8'), 'RGB')\r\n target_img = get_rgb(validset[idx][1].squeeze())\r\n with torch.no_grad():\r\n pred_img = get_rgb(model(validset[idx][0].view(-1,3,validset.img_size,validset.img_size).float().to(device)).squeeze())\r\n if len(idx_list)>1:\r\n axes[i,0].imshow(input_img)\r\n axes[i,1].imshow(pred_img)\r\n axes[i,2].imshow(target_img)\r\n else:\r\n axes[0].imshow(input_img)\r\n axes[1].imshow(pred_img)\r\n axes[2].imshow(target_img)\r\n if len(idx_list)>1:\r\n axes[0,0].set_title('V Input')\r\n axes[0,1].set_title('V Prediction')\r\n axes[0,2].set_title('V Target')\r\n else:\r\n axes[0].set_title('V Input')\r\n axes[1].set_title('V Prediction')\r\n axes[2].set_title('V Target')\r\n\r\n return fig\r\n\r\ndef get_rgb(tensor_img):\r\n pallete_dict = {\r\n 0 : [0,255,0],\r\n 1 : [0,0,255],\r\n 2 : [255,255,255],\r\n 3 : [255,0,0],\r\n 4 : [255,255,0]\r\n }\r\n img_h = tensor_img.size()[2]\r\n out_img = np.zeros((img_h,img_h,3))\r\n for h in range(img_h):\r\n for w in range(img_h):\r\n pixel_class = torch.argmax(tensor_img[:,h,w]).item()\r\n out_img[h,w,:] = pallete_dict[pixel_class]\r\n final_img = Image.fromarray(out_img.astype('uint8'), 'RGB')\r\n return final_img\r\n\r\nclass Focalloss(nn.Module):\r\n\r\n def __init__(self,gamma=0):\r\n super(Focalloss,self).__init__()\r\n self.gamma = gamma\r\n\r\n def forward(self,outputs,targets_oneh,targets):\r\n soft_outs = F.softmax(outputs,dim=1)\r\n log_soft = F.log_softmax(outputs,dim=1)\r\n weight_loss = torch.pow((1 - soft_outs),self.gamma) * log_soft\r\n loss = 0.4*F.nll_loss(weight_loss,targets) + 0.6*soft_dice_loss(outputs,targets_oneh)\r\n return loss\r\n\r\ndef main():\r\n from learner import Learner\r\n from res_unet_dropout import ResUnet\r\n # lr=3e-5\r\n dprob=0.2\r\n epochs = 8\r\n\r\n trainset = Prostate_data(img_size=256, num_classes=3)\r\n validset = Prostate_data(dataset_type='valid', img_size=256, num_classes=3)\r\n datasets = {'train': trainset, 'valid': validset}\r\n\r\n for lr in [1e-4,5e-4,1e-3,5e-3,1e-2]:\r\n for gamma in [0] :\r\n # fig,axes = plt.subplots(nrows=1,ncols=6,figsize=(24,4))\r\n # imgs = []\r\n # for i in tqdm(range(6)):\r\n # imgs.append(get_rgb(trainset[i][1]))\r\n # for j in range(6):\r\n # axes[j].imshow(imgs[j])\r\n # plt.show()\r\n\r\n model = ResUnet(num_classes=5,dprob=dprob)\r\n criterion = Focalloss(gamma=gamma)\r\n optimizer = torch.optim.SGD(model.parameters(),lr=lr,momentum=0.9)\r\n scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,patience=10)\r\n dtime = '0057_1806'\r\n tb_logs = {'path':'logdirs/onevall_trials_aug/respre_SGD_plateau','comment':f'lr={lr}_gamma={gamma}_dprob={dprob}_{dtime}'}\r\n trainer = Learner(datasets,model,criterion,optimizer,scheduler,bs=8,num_workers=4)\r\n try :\r\n trainer.fit(tb_logs=tb_logs,epochs=epochs)\r\n # torch.save(trainer.model,f'logdirs/onevall_trials_aug/respre_SGD_plateau/lr={lr}_gamma={gamma}_dprob={dprob}_{dtime}/{dtime}')\r\n except KeyboardInterrupt:\r\n pass\r\n # torch.save(trainer.model,f'logdirs/onevall_trials_aug/respre_SGD_plateau/lr={lr}_gamma={gamma}_dprob={dprob}_{dtime}/{dtime}')\r\n\r\n\r\nif __name__=='__main__':\r\n main()\r\n", "sub_path": "focal_loss.py", "file_name": "focal_loss.py", "file_ext": "py", "file_size_in_byte": 11017, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 21, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 33, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 45, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.ColorJitter", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 51, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 52, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 70, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 70, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 71, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 71, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.hflip", "line_number": 74, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 74, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.hflip", "line_number": 75, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 75, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.vflip", "line_number": 77, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 77, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.vflip", "line_number": 78, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 78, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.affine", "line_number": 80, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 80, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.affine", "line_number": 81, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 90, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 97, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 149, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 176, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 176, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 211, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 213, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 213, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 216, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 216, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 223, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 224, "usage_type": "name"}, {"api_name": "torch.pow", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 226, "usage_type": "name"}, {"api_name": "res_unet_dropout.ResUnet", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 252, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.ReduceLROnPlateau", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 253, "usage_type": "attribute"}, {"api_name": "learner.Learner", "line_number": 256, "usage_type": "call"}]} +{"seq_id": "634302455", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Nov 3 10:21:19 2018\r\n\r\n@author: Srinivas\r\n\"\"\"\r\n\r\n# Word Negation Tracking\r\nimport nltk\r\n\r\nsentence = \"I was not happy with the team's performance\"\r\nwords = nltk.word_tokenize(sentence)\r\nnew_words = []\r\ntemp_word = \"\"\r\n\r\nfor word in words:\r\n if word == 'not':\r\n temp_word = \"not_\"\r\n elif temp_word == \"not_\":\r\n word = temp_word + word # It will be not_happy\r\n temp_word = \"\"\r\n if word != \"not\":\r\n new_words.append(word)\r\nsentence = ' '.join(new_words) \r\n\r\n ", "sub_path": "Word_Negation1.py", "file_name": "Word_Negation1.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "nltk.word_tokenize", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "404925934", "text": "#!/usr/bin/env python\n'''\nCopyright (c) 2015 Jared E. Stroud\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\n'''\n\ntry:\n import socket\n import os\n import sys\n import argparse\n import time\nexcept ImportError as err:\n print(\"[Error] I don't have \" + str(err))\n\nclass sendData():\n \n '''\n Name: __init__\n Parameters: self\n Purpose: Initializes socket for the rest of the methods.\n '''\n def __init__(self):\n self.sock = socket.socket()\n\n '''\n Name: sendFile\n Parameters: fileName\n '''\n def sendFile(self, fileName, address, port):\n self.sock.connect((str(address), int(port)))\n data = open(fileName, \"rb\")\n\n while True:\n chunk = file.read(data)\n if not chunk:\n break # Entire file has been read in.\n self.sock.sendall(chunk)\n\n '''\n Name: copyFile\n Parameters: srcFile , destination\n srcFile: File desired to be transfered.\n destination: End point for file being transfered.\n Return: Nothing.\n '''\n def copyFile(self, srcFile, destination):\n try:\n os.path.join(str(fileName), str(destination))\n except Error:\n print(\"[Error] I could not copy the file\")\n sys.exit()\n\n\n\nif __name__ == \"__main__\":\n\n print(\"Starting Data send\")\n sendObj = sendData()\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--fileName\", required=True, type=str, help=\"Specify file to be sent.\")\n parser.add_argument(\"--address\", required=True, type=str, help=\"Specify the address to send the file to.\")\n parser.add_argument(\"--port\", required=True, type=int, help=\"Specify the port of th destination server.\")\n args = parser.parse_args()\n\n if args.fileName and args.address and args.port: # Ensuring all arguments are fulfilled.\n address = args.address# User specified file size.\n fileName = args.fileName # User specified file name.\n port = args.port # User specified port.\n\n sendObj.sendFile(fileName, address, port)\n", "sub_path": "lib/pipeData.py", "file_name": "pipeData.py", "file_ext": "py", "file_size_in_byte": 3073, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "socket.socket", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 69, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "96372097", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nnum = 100\n\nx = np.linspace(0, 100,num)\n\nx0 = 1.0\nx1 = 2.0\nx2 = 3.5\nx3 = -0.00\nscalar = 1000.0\n\n\ny = x0*pow(x,0.0) + x1*pow(x,1.0) + x2*pow(x,2.0) + x3*pow(x,3.0)\nz = y + scalar*np.random.randn(num)\nplt.plot(x,y)\nplt.plot(x,z,'ro')\n\nplt.show()\n\n\n\n", "sub_path": "regressionGui.py", "file_name": "regressionGui.py", "file_ext": "py", "file_size_in_byte": 298, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "numpy.linspace", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "158584806", "text": "\r\nimport os\r\nimport sys\r\nimport json\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torchsummary import summary\r\nwith open(os.path.join(sys.path[0], 'config.json')) as json_file:\r\n\tconfig = json.load(json_file)\r\n\r\nclass AudioNet(nn.Module):\r\n\r\n\tdef __init__(self):\r\n\t\tsuper(AudioNet, self).__init__() # (128, 1292)\r\n\t\tself.num_output = 5\r\n\r\n\t\tif(config[\"SAL0\"] == \"Wi\"):\r\n\t\t\tself.W_i = nn.Parameter(torch.randn(40,1)) # with channel num 16\r\n\t\t\tself.bi = nn.Parameter(torch.randn(1))\r\n\t\t\tself.SmSA0 = nn.Softmax(dim = 1)\r\n\t\tself.conv0 = nn.Sequential(nn.Conv1d(128, 32, kernel_size=8), nn.MaxPool1d(4, stride=4), nn.ReLU(), nn.BatchNorm1d(32),)\r\n\t\tif(config[\"CAL1\"] == \"FC\"):\r\n\t\t\tself.CA1_avg_pool = nn.AdaptiveAvgPool1d(1)\r\n\t\t\tself.CA1 = nn.Sequential(nn.Linear(in_features=32, out_features=32), nn.Softmax(dim=1),)\r\n\t\tif(config[\"CAL1\"] == \"Conv\"):\r\n\t\t\tself.CA1_avg_pool = nn.AdaptiveAvgPool1d(1)\r\n\t\t\tself.conv_du1 = nn.Sequential(nn.Conv1d(32, 32//2, 1, bias=True), nn.ReLU(inplace=True), nn.Conv1d(32//2, 32, 1, bias=True), nn.Sigmoid())\r\n\t\tself.conv1 = nn.Sequential(nn.Conv1d(32, 16, kernel_size=8), nn.MaxPool1d(4, stride=4), nn.ReLU(), nn.BatchNorm1d(16),)\r\n\t\tif(config[\"CAL2\"] == \"FC\"):\r\n\t\t\tself.CA2_avg_pool = nn.AdaptiveAvgPool1d(1)\r\n\t\t\tself.CA2 = nn.Sequential(nn.Linear(in_features=16, out_features=16), nn.Softmax(dim=1),)\r\n\t\tif(config[\"CAL2\"] == \"Conv\"):\r\n\t\t\tself.CA2_avg_pool = nn.AdaptiveAvgPool1d(1)\r\n\t\t\tself.conv_du2 = nn.Sequential(nn.Conv1d(16, 16//2, 1, bias=True), nn.ReLU(inplace=True), nn.Conv1d(16//2, 16, 1, bias=True),nn.Sigmoid())\r\n\t\tself.fc0 = nn.Sequential(nn.Linear(in_features=1248, out_features=64), nn.Tanh(), nn.Dropout(), nn.Linear(in_features=64, out_features=self.num_output),)\r\n\t\tself.logsoftmax = nn.LogSoftmax(dim=1)\r\n\t\tself.apply(self._init_weights)\r\n\r\n\tdef forward(self, x):\r\n\t\tif(config[\"SAL0\"] == \"Wi\"):\r\n\t\t\tz0 = x.permute(0, 2, 1) #(N, L, C)\r\n\t\t\talpha0 = (self.SmSA0((torch.matmul(z0, self.W_i) + self.bi).squeeze(-1))).unsqueeze(-1) #(N, L, 1)-->(N, L)\r\n\t\t\tx = (z0*alpha0).permute(0, 2, 1)\r\n\t\tx = self.conv0(x) #(N, 32, 321)\r\n\t\tif(config[\"CAL1\"] == \"FC\"):\r\n\t\t\tbeta1 = self.CA1_avg_pool(x).squeeze(-1) #(N, 32, 1) --> (N, 32)\r\n\t\t\tbeta1 = self.CA1(beta1).unsqueeze(-1)\r\n\t\t\tx = x*beta1\r\n\t\tif(config[\"CAL1\"] == \"Conv\"):\r\n\t\t\tbeta1 = self.CA1_avg_pool(x) #(N, 32, 1) --> (N, 32)\r\n\t\t\tbeta1 = self.conv_du1(beta1)\r\n\t\t\tx = x*beta1\r\n\t\tx = self.conv1(x) #(N, 16, 78)\r\n\t\tif(config[\"CAL2\"] == \"FC\"):\r\n\t\t\tbeta2 = self.CA2_avg_pool(x).squeeze(-1) #(N, 16, 1) --> (N, 16)\r\n\t\t\tbeta2 = self.CA2(beta2).unsqueeze(-1) #(N, 16, 1)\r\n\t\tx = x*beta2 #(N, 16, 78)\r\n\t\tif(config[\"CAL2\"] == \"Conv\"):\r\n\t\t\tbeta2 = self.CA2_avg_pool(x) #(N, 16, 1) \r\n\t\t\tbeta2 = self.conv_du2(beta2)\r\n\t\t\tx = x*beta2\r\n\t\tflatten = x.view(x.size(0), -1)\r\n\t\t# print(flatten.shape)\r\n\t\tout = self.logsoftmax(self.fc0(flatten))\r\n\t\t#x = F.log_softmax(x, dim=1) # output (N, 5)\r\n\t\treturn out\r\n\r\n\tdef _init_weights(self, layer) -> None:\r\n\t\tif(isinstance(layer, nn.Conv1d)):\r\n\t\t\tnn.init.kaiming_uniform_(layer.weight)\r\n\t\telif(isinstance(layer, nn.Linear)):\r\n\t\t\tnn.init.xavier_uniform_(layer.weight)\r\n\r\n\r\nif __name__ == '__main__':\r\n\tnet = AudioNet()\r\n\tprint(net)\r\n\tnet = net.cuda()\r\n\tsummary(net, (128, 1292))", "sub_path": "project_CNNFC_Atten_Cls/models/CNNFC.py", "file_name": "CNNFC.py", "file_ext": "py", "file_size_in_byte": 3456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool1d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveAvgPool1d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveAvgPool1d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.MaxPool1d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveAvgPool1d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveAvgPool1d", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.Tanh", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.Dropout", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.Conv1d", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_uniform_", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torchsummary.summary", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "118373364", "text": "import os\nfrom setuptools import setup\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\n\nsetup(\n name='django-active-menu',\n version='0.1',\n packages=['active_menu'],\n description='Simple, fast and easy django template tags to get active url in your html menu.',\n long_description=README,\n author='Slawomir Kabik',\n author_email='slawek@redsoftware.pl',\n url='https://github.com/yourname/django-myapp/',\n license='MIT',\n install_requires=[\n 'Django>=1.6',\n 'BeautifulSoup4==4.4.1'\n ]\n)\n\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 591, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "2944149", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('trueguide', '0003_place'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Photos',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('url', models.CharField(max_length=500)),\n ],\n ),\n migrations.AddField(\n model_name='place',\n name='photoid',\n field=models.ForeignKey(default=0, to='trueguide.Photos'),\n ),\n ]\n", "sub_path": "Server/tg/trueguide/migrations/0004_auto_20150517_1209.py", "file_name": "0004_auto_20150517_1209.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "83", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "371879336", "text": "from django.conf.urls import url, include\nfrom . import views\n \nurlpatterns = [\n url(r'^$', views.index ), #LOG/REG\n url(r'^login$', views.login), #LOGIN\n url(r'^create$', views.create ), #this is the REG\n url(r'^ideas$', views.ideas ), #ideas\n url(r'^view/(?P