diff --git "a/091.jsonl" "b/091.jsonl" new file mode 100644--- /dev/null +++ "b/091.jsonl" @@ -0,0 +1,414 @@ +{"seq_id": "132399924", "text": "import glob, os.path, re, json\nfrom sklearn import svm, metrics\nimport matplotlib.pyplot\nimport pandas \n\ntrain_data = list()\ntrain_label = list()\n\nfiles = glob.glob('./download/lang/train/*.txt') # Collect all file in directory\n\ncode_a = ord('a')\ncode_z = ord('z')\n\nfor file_name in files:\n base_name = os.path.basename(file_name) # Extract file name w/o directory path\n lang = base_name.split('-')[0]\n\n with open(file_name, 'r', encoding='utf-8') as file:\n text = file.read()\n text = text.lower() # If there are mixed up Uppercase and Lowercase, could be mad to encode to ASCII\n\n # Alphabet frequency \n\n count = [0 for _ in range(0, 26)] \n\n for character in text:\n code_current = ord(character)\n\n if code_a <= code_current <= code_z:\n count[code_current - code_a] += 1\n\n # Normalization\n \n total = sum(count)\n count = list(map(lambda n : n/total, count))\n\n train_data.append(count)\n train_label.append(lang)\n\n# Plotting \n\ngraph_dict = dict()\n\nfor index in range(0, len(train_label)):\n\n label = train_label[index]\n\n data = train_data[index]\n\n if not (label in graph_dict):\n graph_dict[label] = data\n\nasclist = [[chr(n) for n in range(97, 97+26)]]\n\ndf = pandas.DataFrame(graph_dict, index = asclist)\n\nmatplotlib.pyplot.style.use('ggplot')\n\ndf.plot(kind = 'bar', subplots = True, ylim = (0, 0.15))\n\nmatplotlib.pyplot.savefig('./download/lang/lang-plot.png')", "sub_path": "scikit-learn/lang_graph.py", "file_name": "lang_graph.py", "file_ext": "py", "file_size_in_byte": 1494, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "glob.glob", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.path.basename", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot.style.use", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.savefig", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 61, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "404653127", "text": "import logging\nfrom colorlog import ColoredFormatter\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\nch = logging.StreamHandler()\nch.setLevel(logging.DEBUG)\n\ncolor_formatter = ColoredFormatter(\n datefmt='%y-%m-%d %H;%M:%S',\n log_colors={\n 'DEBUG': 'blue',\n 'INFO': 'cyan',\n 'WARNING': 'yellow',\n 'ERROR': 'red',\n 'CRITICAL': 'bold_red',\n }\n)\nch.setFormatter(color_formatter)\n\nlogger.addHandler(ch)\n", "sub_path": "normalize/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 464, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 5, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 8, "usage_type": "attribute"}, {"api_name": "colorlog.ColoredFormatter", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "325259042", "text": "#!/usr/bin/python3\nfrom sklearn import tree\n#features about APPLE=0 and ORANGE=1\n\ndata=[[100,0],[130,0],[135,1],[150,1]]\n\noutput=[\"APPLE\",\"APPLE\",\"ORANGE\",\"ORANGE\"]\n\n#decision tree algorithm call\ntrained_algo=tree.DecisionTreeClassifier()\n\n#train the data\ntrained_data=trained_algo.fit(data,output)\n\n#now testing phase\npredict_1=trained_algo.predict([[100,1]])\npredict_2=trained_algo.predict([[145,1]])\n\n#printing the output\nprint(predict_1)\nprint(predict_2)\n", "sub_path": "SML_eg.py", "file_name": "SML_eg.py", "file_ext": "py", "file_size_in_byte": 459, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "599645998", "text": "\nimport requests\n\nfrom ..base_utils import *\n\nclass ApiParams(Params):\n#{\n def __init__( self, i_params = None ):\n #{\n defaults = {\n 'api_key': \"xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx\",\n 'endpoint': \"https://chapi.cloudhealthtech.com/olap_reports/cost/history\",\n 'options': {\"interval\" : \"hourly\"}\n }\n\n Params.__init__( self, defaults, i_params )\n #} def __init__\n#}\n\nclass ApiEngine(Engine):\n#{\n def __init__( self, i_params = None ):\n #{\n self.params_class = \"ApiParams\"\n if i_params is None:\n i_params = ApiParams()\n Engine.__init__( self, i_params )\n #} def __init__\n\n def run_( self ):\n #{\n request_params = dict( [(\"api_key\", self.params[\"api_key\"])] +\n list(self.params[\"options\"].items()) )\n self.response = requests.get( self.params[\"endpoint\"], request_params )\n return self.response\n #} def run_\n#}\n", "sub_path": "chatk/data/api_utils.py", "file_name": "api_utils.py", "file_ext": "py", "file_size_in_byte": 972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "requests.get", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "475449291", "text": "from django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom django.shortcuts import HttpResponse\nfrom .models import users\n# Create your views here.\nimport json\n\n\nimport os,django\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"test_platform.settings\")\ndjango.setup()\n\n# 文件列表路径\n\nfile_path = os.path.join(os.path.dirname(os.path.dirname(__file__)),'media')\n\n\n#_______________________首页_______________________________________________________________________________\n\ndef index(request):\n return render(request,'index.html')\n\n#_______________________登陆_______________________________________________________________________________\n\ndef login(request):\n if request.method == 'POST':\n user = request.POST.get('user')\n pws = request.POST.get('pws')\n u = users.objects.filter(username=user)\n print(u,user,pws)\n if u and user and pws:\n print(user,pws,u[0].username)\n rsp = {'status': 1, 'data': {'username':u[0].username},'message':''}\n return HttpResponse(json.dumps(rsp))\n else:\n rsp = {'status':0,'data':{},'message':'用户名错误'}\n return HttpResponse(json.dumps(rsp))\n else:\n return render(request,'login.html')\n\n#_______________________wiki_______________________________________________________________________________\n\ndef wiki(request):\n files = os.listdir(file_path)\n return render(request,'wiki.html',{'files':files})\n\n\ndef upload_file(request):\n if request.method == 'POST':\n file_obj = request.FILES.get('file')\n f = open(os.path.join(file_path, file_obj.name), 'wb')\n for chunk in file_obj.chunks():\n f.write(chunk)\n f.close()\n return HttpResponse('OK')\n\n#_______________________接口测试_______________________________________________________________________________\n\npro_info = ['pro1','pro2','pro3','pro4']\n\nver_info = {\n 'pro1':['v1.11','v1.1.12','v1.3.1'],\n 'pro2':['v2.11','v2.1.12','v2.3.1'],\n 'pro3':['v3.11','v3.1.12','v3.3.1'],\n 'pro4':['v4.11','v4.1.12','v4.3.1'],\n }\n\n\npro = [{'p_name':'pro1','p_version':'v1.0', 'IF_count':10, 'IF_add':'http://192.168.50.244/index.html', 'ps':'',},\n {'p_name':'pro2','p_version':'v1.2', 'IF_count':30, 'IF_add':'https://xiaochongtestcube.hc.top/product', 'ps':'',}\n ]\n\nIF = [{'name':'IF1','type':'get','url':'url1','parameter':['para1','para2','para3'],'example':'example1','script':1,'id':0,'ps':'',},\n {'name':'IF2','type':'post','url':'url2','parameter':['para4','para5','para6'],'example':'example2','script':1,'id':1,'ps':'',},\n {'name':'IF3','type':'get','url':'url3','parameter':['para7','para8','para9'],'example':'example3','script':0,'id':2,'ps':'',}]\n\ndef interface(request):\n\n return render(request, 'interface.html', {'product':pro})\n\ndef add_IF(request):\n import random\n if request.method == \"POST\":\n\n newData = {'p_name':request.POST.get('proName'),\n 'p_version':request.POST.get('version'),\n 'IF_count':request.POST.get('IF_count'),\n 'IF_add':request.POST.get('IF_add'),\n 'ps':request.POST.get('ps'),\n 'id': random.randint(3,100)\n }\n print(newData)\n pro.append(newData)\n return HttpResponse('OK')\n\n\ndef IF_list(request,pro_id,ver_id):\n\n return render(request, 'IF_list.html', {'IF_info':IF})\n\ndef addsrp(request,IF_id):\n\n\n rsp = IF[int(IF_id)]\n\n print(rsp)\n return render(request, 'srp.html',{'i':rsp})\n\n\n\n\n\n\n\n#_______________________产品_______________________________________________________________________________\n\ndef product(request):\n\n return render(request,'product.html')", "sub_path": "home/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "os.environ.setdefault", "line_number": 10, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "models.users.objects.filter", "line_number": 29, "usage_type": "call"}, {"api_name": "models.users.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.users", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 37, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 94, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 99, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 107, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "364678203", "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 ('api', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='TradeshowSettings',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('created', models.DateTimeField(auto_now_add=True, null=True)),\n ('updated', models.DateTimeField(auto_now=True, null=True)),\n ('settingValue', models.TextField()),\n ('defaultSettingValue', models.CharField(max_length=100)),\n ],\n options={\n 'db_table': 'TradeshowSettings',\n },\n ),\n migrations.RemoveField(\n model_name='exhibitorsettings',\n name='exhibitor',\n ),\n migrations.RemoveField(\n model_name='exhibitorsettings',\n name='setting',\n ),\n migrations.RemoveField(\n model_name='exhibitor',\n name='settings',\n ),\n migrations.AddField(\n model_name='settings',\n name='created',\n field=models.DateTimeField(auto_now_add=True, null=True),\n ),\n migrations.AddField(\n model_name='settings',\n name='updated',\n field=models.DateTimeField(auto_now=True, null=True),\n ),\n migrations.DeleteModel(\n name='ExhibitorSettings',\n ),\n migrations.AddField(\n model_name='tradeshowsettings',\n name='setting',\n field=models.ForeignKey(to='api.Settings'),\n ),\n migrations.AddField(\n model_name='tradeshowsettings',\n name='tradeshow',\n field=models.ForeignKey(to='api.Tradeshow'),\n ),\n migrations.AddField(\n model_name='tradeshow',\n name='settings',\n field=models.ManyToManyField(to='api.Settings', through='api.TradeshowSettings'),\n ),\n ]\n", "sub_path": "tradeshow/api/migrations/0002_auto_20170808_1205.py", "file_name": "0002_auto_20170808_1205.py", "file_ext": "py", "file_size_in_byte": 2140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "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.DateTimeField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.migrations.DeleteModel", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 57, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}]} +{"seq_id": "613956510", "text": "#!/usr/bin/env python3\n\nfrom pathlib import Path\n\nfrom mahiru.definitions.workflows import Job, Workflow, WorkflowStep\nfrom mahiru.rest.internal_client import InternalSiteRestClient\n\n\nCERTS_DIR = Path.home() / 'mahiru' / 'certs'\nPRIVATE_DIR = Path.home() / 'mahiru' / 'private'\n\n\nif __name__ == '__main__':\n # create single-step workflow\n workflow = Workflow(\n ['input'], {'result': 'compute.output0'}, [\n WorkflowStep(\n name='compute',\n inputs={'input0': 'input'},\n outputs={'output0':\n 'asset:party1.mahiru.example.org:da.data.output_base'\n ':party1.mahiru.example.org:site1'},\n compute_asset_id=(\n 'asset:party1.mahiru.example.org:da.software.script1'\n ':party1.mahiru.example.org:site1'))\n ]\n )\n\n inputs = {\n 'input':\n 'asset:party2.mahiru.example.org:da.data.input'\n ':party2.mahiru.example.org:site2'}\n\n # run workflow\n client = InternalSiteRestClient(\n 'party:party1.mahiru.example.org:party1',\n 'site:party1.mahiru.example.org:site1',\n 'https://site1.mahiru.example.org:1443',\n CERTS_DIR / 'trust_store.pem',\n (\n CERTS_DIR / 'site1_https_cert.pem',\n PRIVATE_DIR / 'site1_https_key.pem'))\n print('Submitting job...')\n job_id = client.submit_job(Job(\n 'party:party1.mahiru.example.org:party1', workflow, inputs))\n print(f'Submitted, waiting for result at {job_id}')\n result = client.get_job_result(job_id)\n\n print(f'Job complete:')\n print(f'Job: {result.job}')\n print(f'Plan: {result.plan}')\n print(f'Outputs: {result.outputs}')\n", "sub_path": "scenarios/data_access/client1/submit_job.py", "file_name": "submit_job.py", "file_ext": "py", "file_size_in_byte": 1815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "pathlib.Path.home", "line_number": 9, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 9, "usage_type": "name"}, {"api_name": "pathlib.Path.home", "line_number": 10, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "name"}, {"api_name": "mahiru.definitions.workflows.Workflow", "line_number": 15, "usage_type": "call"}, {"api_name": "mahiru.definitions.workflows.WorkflowStep", "line_number": 17, "usage_type": "call"}, {"api_name": "mahiru.rest.internal_client.InternalSiteRestClient", "line_number": 35, "usage_type": "call"}, {"api_name": "mahiru.definitions.workflows.Job", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "340309858", "text": "import multiprocessing\nimport os\nfrom click import echo, style\n\nif os.path.exists(\".env\"):\n echo(style(text=\"Importing environment variables\", fg=\"green\", bold=True))\n for line in open(\".env\"):\n var = line.strip().split(\"=\")\n if len(var) == 2:\n os.environ[var[0]] = var[1]\n\nbind = f\"0.0.0.0:{os.environ.get('PORT', 7070)}\"\nworkers = multiprocessing.cpu_count() * 2 + 1\naccesslog = \"-\" # STDOUT\naccess_log_format = '%(h)s %(l)s %(u)s %(t)s \"%(r)s\" %(s)s %(b)s \"%(f)s\" \"%(a)s\"'\nloglevel = \"debug\" if os.environ.get(\"FLASK_ENV\") == \"development\" else \"info\"\ncapture_output = True\nenable_stdio_inheritance = True\n", "sub_path": "gunicorn_conf.py", "file_name": "gunicorn_conf.py", "file_ext": "py", "file_size_in_byte": 641, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "os.path.exists", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "click.echo", "line_number": 6, "usage_type": "call"}, {"api_name": "click.style", "line_number": 6, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "multiprocessing.cpu_count", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}]} +{"seq_id": "267637835", "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 ('carrito', '0018_sale_with_shipping'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='detailsend',\n name='send',\n field=models.ForeignKey(related_name='detail_sends', verbose_name=b'envio', to='carrito.Send'),\n ),\n ]\n", "sub_path": "carrito/migrations/0019_auto_20160428_0515.py", "file_name": "0019_auto_20160428_0515.py", "file_ext": "py", "file_size_in_byte": 461, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "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.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "318377217", "text": "import collections\nimport logging\nimport threading\nimport time\n\n\nclass PykkaTestLogHandler(logging.Handler):\n def __init__(self, *args, **kwargs):\n self.lock = threading.RLock()\n with self.lock:\n self.events = collections.defaultdict(threading.Event)\n self.messages = {}\n self.reset()\n logging.Handler.__init__(self, *args, **kwargs)\n\n def emit(self, record):\n with self.lock:\n level = record.levelname.lower()\n self.messages[level].append(record)\n self.events[level].set()\n\n def reset(self):\n with self.lock:\n for level in (\"debug\", \"info\", \"warning\", \"error\", \"critical\"):\n self.events[level].clear()\n self.messages[level] = []\n\n def wait_for_message(self, level, num_messages=1, timeout=5):\n \"\"\"Wait until at least ``num_messages`` log messages have been emitted\n to the given log level.\"\"\"\n deadline = time.time() + timeout\n while time.time() < deadline:\n with self.lock:\n if len(self.messages[level]) >= num_messages:\n return\n self.events[level].clear()\n self.events[level].wait(1)\n raise Exception(f\"Timeout: Waited {timeout:d}s for log message\")\n", "sub_path": "tests/log_handler.py", "file_name": "log_handler.py", "file_ext": "py", "file_size_in_byte": 1313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "logging.Handler", "line_number": 7, "usage_type": "attribute"}, {"api_name": "threading.RLock", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.Handler.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.Handler", "line_number": 14, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "293979151", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nfrom django.apps import AppConfig\n\n\nclass ProjectsConfig(AppConfig):\n name = 'projects'\n verbose_name = 'Projects'\n\n def ready(self):\n from projects.signals import (\n new_experiment_group,\n experiment_group_deleted,\n new_project,\n project_deleted,\n )\n", "sub_path": "polyaxon/projects/apps.py", "file_name": "apps.py", "file_ext": "py", "file_size_in_byte": 408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "django.apps.AppConfig", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "530804880", "text": "# -*- coding: utf-8 -*-\n# Copyright 2017 Green IT Globe NV\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# @@license_version:1.1@@\n\nfrom auth import get_current_user_id, get_current_session, get_browser_language\nfrom bizz.channel import send_update\nfrom bizz.i18n import set_user_language\nfrom bizz.old_channel import create_channel_token\nfrom mcfw.restapi import rest\nfrom mcfw.rpc import arguments, returns\nfrom plugin_loader import get_auth_plugin\nfrom to.authentication import IdentityTO, SetLanguageTO\n\n\n@rest('/identity', 'get')\n@returns(IdentityTO)\n@arguments()\ndef api_get_identity():\n session = get_current_session()\n auth_plugin = get_auth_plugin()\n modules = auth_plugin.get_visible_modules()\n language = auth_plugin.get_user_language() or get_browser_language()\n return IdentityTO(session.user_id, session.scopes, modules, language)\n\n\n@rest('/language', 'put')\n@returns(SetLanguageTO)\n@arguments(data=SetLanguageTO)\ndef api_set_language(data):\n return set_user_language(data)\n\n\n@rest('/channel/old', 'get', silent=True, silent_result=True)\n@returns(dict)\n@arguments()\ndef api_get_channel_token():\n return {\n 'token': create_channel_token()\n }\n\n\n@rest('/channel/firebase', 'get', silent=True, silent_result=True)\n@returns(dict)\n@arguments()\ndef api_get_firebase_token():\n return {\n 'username': get_current_user_id(),\n 'token': create_channel_token()\n }\n\n\n@rest('/channel/test/', 'get')\n@returns()\n@arguments(test=unicode)\ndef channel_test(test):\n send_update('channel.test', {'test': test})\n", "sub_path": "framework/server/api/authenticated.py", "file_name": "authenticated.py", "file_ext": "py", "file_size_in_byte": 2070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "auth.get_current_session", "line_number": 32, "usage_type": "call"}, {"api_name": "plugin_loader.get_auth_plugin", "line_number": 33, "usage_type": "call"}, {"api_name": "auth.get_browser_language", "line_number": 35, "usage_type": "call"}, {"api_name": "to.authentication.IdentityTO", "line_number": 36, "usage_type": "call"}, {"api_name": "mcfw.restapi.rest", "line_number": 28, "usage_type": "call"}, {"api_name": "mcfw.rpc.returns", "line_number": 29, "usage_type": "call"}, {"api_name": "to.authentication.IdentityTO", "line_number": 29, "usage_type": "argument"}, {"api_name": "mcfw.rpc.arguments", "line_number": 30, "usage_type": "call"}, {"api_name": "bizz.i18n.set_user_language", "line_number": 43, "usage_type": "call"}, {"api_name": "mcfw.restapi.rest", "line_number": 39, "usage_type": "call"}, {"api_name": "mcfw.rpc.returns", "line_number": 40, "usage_type": "call"}, {"api_name": "to.authentication.SetLanguageTO", "line_number": 40, "usage_type": "argument"}, {"api_name": "mcfw.rpc.arguments", "line_number": 41, "usage_type": "call"}, {"api_name": "to.authentication.SetLanguageTO", "line_number": 41, "usage_type": "name"}, {"api_name": "bizz.old_channel.create_channel_token", "line_number": 51, "usage_type": "call"}, {"api_name": "mcfw.restapi.rest", "line_number": 46, "usage_type": "call"}, {"api_name": "mcfw.rpc.returns", "line_number": 47, "usage_type": "call"}, {"api_name": "mcfw.rpc.arguments", "line_number": 48, "usage_type": "call"}, {"api_name": "auth.get_current_user_id", "line_number": 60, "usage_type": "call"}, {"api_name": "bizz.old_channel.create_channel_token", "line_number": 61, "usage_type": "call"}, {"api_name": "mcfw.restapi.rest", "line_number": 55, "usage_type": "call"}, {"api_name": "mcfw.rpc.returns", "line_number": 56, "usage_type": "call"}, {"api_name": "mcfw.rpc.arguments", "line_number": 57, "usage_type": "call"}, {"api_name": "bizz.channel.send_update", "line_number": 69, "usage_type": "call"}, {"api_name": "mcfw.restapi.rest", "line_number": 65, "usage_type": "call"}, {"api_name": "mcfw.rpc.returns", "line_number": 66, "usage_type": "call"}, {"api_name": "mcfw.rpc.arguments", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "236885940", "text": "import logging\nfrom urllib.parse import parse_qsl\nfrom channels.auth import AuthMiddlewareStack\nfrom django.contrib.auth.models import AnonymousUser\nfrom django.db import close_old_connections\nfrom bdn.auth.models import User\nfrom bdn.auth.utils import recover_to_addr\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass SignatureAuthMiddleware:\n\n def __init__(self, inner):\n self.inner = inner\n\n def __call__(self, scope):\n querystring = scope['query_string'].decode()\n query_params = dict(parse_qsl(querystring))\n eth_address = query_params.get('auth_eth_address')\n auth_signature = query_params.get('auth_signature')\n user = AnonymousUser()\n\n if auth_signature and eth_address:\n try:\n recovered_eth_address = recover_to_addr(\n eth_address, auth_signature)\n if eth_address.lower() == recovered_eth_address[2:].lower():\n user, _ = User.objects.get_or_create(\n username=recovered_eth_address.lower())\n logger.info('Authenticating {}'.format(user.username))\n except ValueError:\n logger.warning('Could not authenticate {}'.format(eth_address))\n finally:\n close_old_connections()\n else:\n logger.warning('No auth parameters provided to WebSocket')\n\n scope['user'] = user\n return self.inner(scope)\n\n\ndef SignatureAuthMiddlewareStack(inner):\n return SignatureAuthMiddleware(AuthMiddlewareStack(inner))\n", "sub_path": "bdn/auth/signature_auth_middleware.py", "file_name": "signature_auth_middleware.py", "file_ext": "py", "file_size_in_byte": 1554, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qsl", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 23, "usage_type": "call"}, {"api_name": "bdn.auth.utils.recover_to_addr", "line_number": 27, "usage_type": "call"}, {"api_name": "bdn.auth.models.User.objects.get_or_create", "line_number": 30, "usage_type": "call"}, {"api_name": "bdn.auth.models.User.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "bdn.auth.models.User", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.close_old_connections", "line_number": 36, "usage_type": "call"}, {"api_name": "channels.auth.AuthMiddlewareStack", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "59240366", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom random import shuffle\nimport json\n\nimport pickle\nimport csv\n\n\ndef detail(request, question_id):\n return HttpResponse(\"You're looking at question %s.\" % question_id)\n\ndef results(request, question_id):\n response = \"You're looking at the results of question %s.\"\n return HttpResponse(response % question_id)\n\ndef vote(request, question_id):\n return HttpResponse(\"You're voting on question %s.\" % question_id)\n\ndef save_obj(obj, name):\n with codecs.open('trendsData/'+ name + '.pkl', 'wb') as f:\n pickle.dump(obj, f)\n\ndef load_obj(name):\n with open('./trendsData/' + name + '.pkl', 'rb') as f:\n return pickle.load(f)\n\ndef getItemList(itemDictList):\n keywordsList = itemDictList[\"keywordsList\"]\n itemList = []\n n = 0\n while len(itemList) < 20:\n tlen = len(itemList)\n for keyword in keywordsList:\n if (len(itemDictList[keyword]) > n):\n itemList.append(itemDictList[keyword][n])\n if len(itemList) == 20: break\n if (tlen == len(itemList)): break\n n = n + 1\n return itemList\n\n\ndef loadeBay(name):\n eBayItemList = []\n with open('./trendsData/' + name + '.csv', mode='r') as infile:\n reader = csv.reader(infile)\n keywordsList = next(reader)\n for row in reader:\n tdict = {}\n for i in range(len(keywordsList)):\n tdict[keywordsList[i]] = row[i]\n eBayItemList.append(tdict)\n return eBayItemList\n\n\ndef index(request):\n itemDictList = load_obj('parsedData')\n itemList = getItemList(itemDictList)\n\n eBayItemList = loadeBay('ebayHomePage')\n\n # itemList = itemList[0:min(10, len(itemList))] + eBayItemList\n\n # shuffle(itemList)\n\n print('length of lists of items: ', len(itemList))\n\n nitemList = []\n for i in range(4):\n nitemList.append(itemList[i*5:(i+1)*5])\n itemList = nitemList\n print('length of lists of items: ', len(itemList))\n \n url_list = []\n for lists in itemList:\n url_list.extend([item['viewItemURL'] for item in lists])\n print (url_list)\n # print (itemList[0][0]['viewItemURL'])\n\n context = {'itemList': itemList}\n return render(request, 'store/index.html', context)\n # return HttpResponse(\"Hello, world. You're at the polls index.\")\n", "sub_path": "store/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2367, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "django.http.HttpResponse", "line_number": 11, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 15, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 18, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 22, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 26, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "337929960", "text": "import sys\r\nimport torch\r\nfrom sklearn.cluster import MiniBatchKMeans\r\nfrom sklearn.metrics import f1_score, pairwise_distances, roc_auc_score\r\nfrom scipy.cluster.vq import vq, kmeans\r\nfrom sklearn.decomposition import PCA\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,\r\n TensorDataset)\r\nimport numpy as np\r\nfrom model import fcn_autoencoder\r\nfrom cnn2 import conv2_autoencoder\r\n\r\n\r\ndef knn(y, n): # y is encoder output, n is num of clusters\r\n y = y.reshape(len(y), -1)\r\n scores = list()\r\n kmeans_y = MiniBatchKMeans(n_clusters=n, batch_size=100).fit(y)\r\n y_cluster = kmeans_y.predict(y)\r\n y_dist = np.sum(np.square(kmeans_y.cluster_centers_[y_cluster] - y), axis=1)\r\n y_pred = y_dist\r\n return y_pred\r\n\r\ndef pca(y, n): # y is encoder output, n is num of clusters\r\n y = y.reshape(len(y), -1)\r\n pca = PCA(n_components=n).fit(y)\r\n y_projected = pca.transform(y)\r\n y_reconstructed = pca.inverse_transform(y_projected) \r\n dist = np.sqrt(np.sum(np.square(y_reconstructed - y).reshape(len(y), -1), axis=1))\r\n y_pred = dist\r\n return y_pred\r\n\r\nif __name__ == \"__main__\":\r\n \r\n batch_size = 128\r\n\r\n data_pth = sys.argv[1]\r\n model_pth = sys.argv[2]\r\n output_pth = sys.argv[3]\r\n\r\n if \"baseline\" in model_pth:\r\n model_type = \"fcn\"\r\n elif \"best\" in model_pth:\r\n model_type = \"cnn\"\r\n else:\r\n print(\"unknown model type\")\r\n\r\n test = np.load(data_pth, allow_pickle=True)\r\n \r\n if model_type == 'fcn':\r\n y = test.reshape(len(test), -1)\r\n else:\r\n y = test\r\n \r\n data = torch.tensor(y, dtype=torch.float)\r\n test_dataset = TensorDataset(data)\r\n test_sampler = SequentialSampler(test_dataset)\r\n test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=batch_size)\r\n\r\n \r\n model = torch.load(model_pth, map_location='cuda')\r\n\r\n model.eval()\r\n reconstructed = list()\r\n for i, data in enumerate(test_dataloader): \r\n \r\n if model_type == 'cnn':\r\n img = data[0].transpose(3, 1).cuda()\r\n else:\r\n img = data[0].cuda()\r\n \r\n output = model(img)\r\n \r\n if model_type == 'cnn':\r\n output = output.transpose(3, 1)\r\n\r\n reconstructed.append(output.cpu().detach().numpy())\r\n\r\n reconstructed = np.concatenate(reconstructed, axis=0)\r\n\r\n \r\n anomality = np.sqrt(np.sum(np.square(reconstructed - y).reshape(len(y), -1), axis=1))\r\n y_pred = anomality\r\n \r\n with open(output_pth, 'w') as f:\r\n f.write('id,anomaly\\n')\r\n for i in range(len(y_pred)):\r\n f.write('{},{}\\n'.format(i+1, y_pred[i]))\r\n", "sub_path": "hw10/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2704, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "sklearn.cluster.MiniBatchKMeans", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.utils.data.SequentialSampler", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 61, "usage_type": "call"}, {"api_name": "model.eval", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "558598992", "text": "# Лабораторная работа 2\n\n\"\"\"\nИспользуя обучающий набор данных о пассажирах Титаника,\nнаходящийся в проекте (оригинал: https://www.kaggle.com/c/titanic/data), визуализируйте данные:\n- стоимости билетов пассажиров с помощью диаграммы рассеяния (scatterplot):\nпо оси X - пассажиры в порядке увеличения PassengerId, по оси Y - стоимость билетов\n- проанализировать как наилучшим образом визуализировать данные о ценовом распределении билетов (предложить собственный вариант реализации после создании визуализации ниже).\n\n Отобразить два графика (subplot) на одном изображении (figure):\n 1. График типа boxplot, на котором отобразить распределение цен билетов по классам (1, 2, 3).\n 2. Столбчатую диаграмму (countplot) с распределением средних цен на билеты сгруппированным по трем портам (S, C, Q).\n\nСохранить получившиеся графики в файлах: result1.png, result2.png.\nНастроить название графиков, подписи осей, отобразить риски с числовыми значениями на графике, сделать сетку на графике\n(если необходимо для улучшения изучения данных на графике).\n\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas\nimport seaborn as sns\n\nfrom collections import OrderedDict\n\n# считаем данных из файла, в качестве столбца индексов используем PassengerId\ndata = pandas.read_csv('train.csv', index_col=\"PassengerId\")\nprices = data['Fare']\n\n'''\nГрафик типа boxplot, на котором отобразить распределение цен билетов по классам (1, 2, 3)\n'''\n\n# Вариант 1\nfirst_class_list = data['Fare'].loc[data['Pclass'] == 1].to_list()\nsecond_class_list = data['Fare'].loc[data['Pclass'] == 2].to_list()\nthird_class_list = data['Fare'].loc[data['Pclass'] == 3].to_list()\n\nprices_classes = [first_class_list, second_class_list, third_class_list]\n\nfig = plt.figure(figsize=(10,10))\nax = fig.add_subplot(111)\nax.boxplot(prices_classes)\nfig.savefig(\"boxplot_1.png\")\n\n# Вариант 2\nplot = data.boxplot(by='Pclass', column=['Fare'], grid=False)\nfig2 = plot.get_figure()\nfig2.savefig(\"boxplot_2.png\")\nplt.close()\n\n'''\n 2. Столбчатую диаграмму (countplot) с распределением средних цен на билеты сгруппированным по трем портам (S, C, Q).\n'''\n\nembarked_list = data[['Embarked', 'Fare']].groupby('Embarked')['Fare'].mean().to_frame().reset_index()['Embarked'].to_list()\nfare_list = data[['Embarked', 'Fare']].groupby('Embarked')['Fare'].mean().to_frame().reset_index()['Fare'].to_list()\n\nlists = OrderedDict([('Embarked', embarked_list), ('Fare', fare_list)])\ndf = pandas.DataFrame.from_dict(lists)\n\nfig3 = sns.countplot(x=\"Embarked\", data=df).get_figure()\nfig3.savefig(\"countplot.png\")\n", "sub_path": "LR/LR2.py", "file_name": "LR2.py", "file_ext": "py", "file_size_in_byte": 3491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "attribute"}, {"api_name": "seaborn.countplot", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "356515590", "text": "import logging\n\nimport numpy as np\nfrom carla import image_converter\nfrom carla.client import VehicleControl\nfrom carla.client import make_carla_client\n\nfrom cadendrive.driver import AbstractDriverBase, StaticCruiseDriver\nfrom cadendrive.enum import CTL_AUTOPILOT, RecordMode, OriginType\nfrom cadendrive.gtav.handler import GtavAutoDriver\nfrom cadendrive.utils import hwc_rgb_to_hwc_bgr, TripStitch\n\nlogger = logging.getLogger(__name__)\n\n\nclass CarlaAutoDriver(AbstractDriverBase):\n def __init__(self):\n super(CarlaAutoDriver, self).__init__(CTL_AUTOPILOT)\n\n def next_recorder(self, mode):\n return RecordMode.DRIVING if mode == RecordMode.NONE else RecordMode.NONE\n\n def get_action(self, blob):\n blob.desired_speed = blob.velocity_y\n blob.steering = 0\n blob.throttle = 0\n blob.steering_driver = OriginType.CARLA_AUTOPILOT\n blob.speed_driver = OriginType.CARLA_AUTOPILOT\n if blob.extra is not None:\n _auto = blob.extra\n blob.steering = _auto.steer\n blob.throttle = -1. * _auto.brake if _auto.brake > 0 else _auto.throttle\n blob.save_event = True\n blob.publish = True\n\n\nclass CarlaClientHandler(object):\n def __init__(self, carla_settings, carla_host, carla_port):\n self._carla_host = carla_host\n self._carla_port = carla_port\n self._carla_settings = carla_settings\n self._is_on_reverse = False\n self._carla_client = None\n self._trip_stitch = TripStitch(left_homography=np.array([[2.22938557e-01, -6.06368890e-02, 3.16666845e+02],\n [-2.88657988e-01, 6.91657624e-01, 8.89683267e+01],\n [-9.46204320e-04, -4.88964242e-05, 1.00000000e+00]]),\n left_matches=60,\n right_homography=np.array([[2.96284445e-01, 5.59870490e-04, 2.89328115e+02],\n [-2.68830036e-01, 7.38580441e-01, 7.91286917e+01],\n [-8.84125478e-04, -2.42977087e-06, 1.00000000e+00]]),\n right_matches=58)\n\n # def _run(self):\n # try:\n # self._loop()\n # except TCPConnectionError:\n # logger.info(\"Carla server connection lost. Retrying ...\")\n # time.sleep(1)\n # self._run()\n # except KeyboardInterrupt:\n # return\n # except Exception as e:\n # logger.error(\"{} {}\".format(e, traceback.format_exc(e)))\n\n @staticmethod\n def create_autopilot_driver():\n return GtavAutoDriver()\n\n @staticmethod\n def create_cruise_driver():\n return StaticCruiseDriver()\n\n def fill(self, blob):\n measurements, sensor_data = self._carla_client.read_data()\n # The image standard within caden is hwc bgr.\n images = map(lambda img: None if img is None else hwc_rgb_to_hwc_bgr(image_converter.to_rgb_array(img)),\n (sensor_data.get('CameraFrontLeft', None),\n sensor_data.get('CameraFrontCenter', None),\n sensor_data.get('CameraFrontRight', None)))\n if all(img is not None for img in images):\n # Record - store a concatenation of the camera images\n # (record the facts - not an interpretation e.g. the stitched view).\n blob.image = np.hstack(images)\n blob.display_image = self._trip_stitch.stitch(images, recalculate=False)\n blob.x_coordinate = measurements.player_measurements.transform.location.x\n blob.y_coordinate = measurements.player_measurements.transform.location.y\n blob.heading = measurements.player_measurements.transform.rotation.yaw\n blob.velocity_y = measurements.player_measurements.forward_speed\n blob.extra = measurements.player_measurements.autopilot_control\n\n def publish(self, blob):\n # steer, throttle, brake, hand_brake, reverse\n control = VehicleControl()\n control.steer = blob.steering\n if blob.throttle > 0:\n control.throttle = blob.throttle\n else:\n control.brake = abs(blob.throttle)\n self._carla_client.send_control(control)\n\n def quit(self):\n if self._carla_client is not None:\n self._carla_client.close()\n\n def reset(self):\n if self._carla_client is not None:\n self._carla_settings.randomize_seeds()\n self._carla_settings.randomize_weather()\n scene = self._carla_client.load_settings(self._carla_settings)\n number_of_player_starts = len(scene.player_start_spots)\n player_start = np.random.randint(number_of_player_starts)\n logger.info('Starting new episode...')\n self._carla_client.start_episode(player_start)\n self._is_on_reverse = False\n\n def start(self):\n self._carla_client = make_carla_client(self._carla_host, self._carla_port)\n self.reset()\n", "sub_path": "cadendrive/carla/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 5131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "cadendrive.driver.AbstractDriverBase", "line_number": 16, "usage_type": "name"}, {"api_name": "cadendrive.enum.CTL_AUTOPILOT", "line_number": 18, "usage_type": "argument"}, {"api_name": "cadendrive.enum.RecordMode.NONE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cadendrive.enum.RecordMode", "line_number": 21, "usage_type": "name"}, {"api_name": "cadendrive.enum.RecordMode.DRIVING", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cadendrive.enum.OriginType.CARLA_AUTOPILOT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cadendrive.enum.OriginType", "line_number": 27, "usage_type": "name"}, {"api_name": "cadendrive.enum.OriginType.CARLA_AUTOPILOT", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cadendrive.enum.OriginType", "line_number": 28, "usage_type": "name"}, {"api_name": "cadendrive.utils.TripStitch", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "cadendrive.gtav.handler.GtavAutoDriver", "line_number": 67, "usage_type": "call"}, {"api_name": "cadendrive.driver.StaticCruiseDriver", "line_number": 71, "usage_type": "call"}, {"api_name": "cadendrive.utils.hwc_rgb_to_hwc_bgr", "line_number": 76, "usage_type": "call"}, {"api_name": "carla.image_converter.to_rgb_array", "line_number": 76, "usage_type": "call"}, {"api_name": "carla.image_converter", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.hstack", "line_number": 83, "usage_type": "call"}, {"api_name": "carla.client.VehicleControl", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 111, "usage_type": "attribute"}, {"api_name": "carla.client.make_carla_client", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "488555491", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\n\nTARGET_URL = 'https://archive.ics.uci.edu/ml/machine-learning-'\\\n 'databases/undocumented/connectionist-bench/sonar/sonar.all-data'\n\n\ndef main():\n dataset = pd.read_csv(TARGET_URL, header=None, prefix='V')\n\n for i in range(208):\n if dataset.iat[i, 60] == 'M':\n pcolor = 'red'\n\n else:\n pcolor = 'blue'\n\n datarow = dataset.iloc[i, 0:60]\n datarow.plot(color=pcolor)\n\n plt.xlabel('Attribute Index')\n plt.ylabel('Attribute Values')\n plt.show()\n\nif __name__ == '__main__':\n main()\n", "sub_path": "rocks_vs_mines/parallel_coordinates.py", "file_name": "parallel_coordinates.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "644814957", "text": "# Voor het starten van server het commando:\n# set FLASK_APP=server.py vanaf normale command prompt\n# set FLASK_ENV=development vanaf normale command prompt\n# met alleen set commando kan je checken of deze gezet is\n# server vanuit (venv) opstarten met flask run\n\nfrom flask import Flask, render_template, url_for, request\nimport csv\napp = Flask(__name__)\n\n@app.route('/')\ndef my_home():\n return render_template('index.html')\n\n# Algemene route\n@app.route('/')\ndef htmlpage(page_name=None):\n return render_template(page_name)\n\n@app.route('/submit_form', methods=['POST', 'GET'])\ndef submit_form():\n #return 'form submitted!'\n if request.method == \"POST\":\n try:\n data = request.form.to_dict()\n #print(data)\n write_to_csv(data)\n return render_template('thankyou.html')\n except:\n return 'Sorry, not saved to the database.'\n else:\n return \"oops....thats not good!\"\n\ndef write_to_file(data):\n with open('database.txt', mode='a') as database:\n email = data[\"email\"]\n subject = data[\"subject\"]\n message = data[\"message\"]\n file = database.write(f\"\\n{email},{subject},{message}\")\n # schrijf values naar file\n\ndef write_to_csv(data):\n with open('database.csv', newline='', mode='a') as database2:\n email = data[\"email\"]\n subject = data[\"subject\"]\n message = data[\"message\"]\n csv_writer = csv.writer(database2, delimiter=\",\", quotechar='\"', quoting =csv.QUOTE_MINIMAL)\n csv_writer.writerow([email, subject, message])\n # schrijf values naar file\n\n\n\n# @app.route('/index.html')\n# def my_index():\n# return render_template('index.html')\n\n# @app.route('/works.html')\n# def works():\n# return render_template('works.html')\n\n# @app.route('/work.html')\n# def work():\n# return render_template('work.html')\n\n# # @app.route('/about.html')\n# # def about():\n# # return render_template('about.html')\n\n# @app.route('/contact.html')\n# def contact():\n# return render_template('contact.html')\n\n# @app.route('/components.html')\n# def components():\n# return render_template('components.html')\n\n\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2180, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 47, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 47, "usage_type": "attribute"}]} +{"seq_id": "325006726", "text": "# test suite for Pade_multi.py\n\nfrom Pade_multi import Pade_fit, Pade_eval\nimport numpy as np\n# let's see how well it does with a polynomial\nX1 = np.linspace(-1.0,1.0, 20)\nX2 = np.copy(X1)\nX1v,X2v = np.meshgrid(X1,X2)\nYv = X1v * X1v + X2v * X2v\nX1 = np.ravel(X1v)\nX2 = np.ravel(X2v)\nY = np.ravel(Yv)\n\nXV = np.vstack((X1,X2,Y))\nXV = XV.T\nans, ae, be = Pade_fit(XV,2,2)\n\n\nYA = []\nfor i in range(XV.shape[0]) :\n YA.append(Pade_eval( XV[i,:-1], ans, ae, be ))\nYA = np.array(YA)\n#YA = YA.reshape(X1v.shape)\n\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfig = plt.figure()\nax = fig.add_subplot(111, projection='3d')\nax.scatter(X1,X2,Y,c=u'r')\nax.scatter(X1,X2,YA)\nplt.show()\n", "sub_path": "pade_example.py", "file_name": "pade_example.py", "file_ext": "py", "file_size_in_byte": 701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "numpy.linspace", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 14, "usage_type": "call"}, {"api_name": "Pade_multi.Pade_fit", "line_number": 16, "usage_type": "call"}, {"api_name": "Pade_multi.Pade_eval", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "159490160", "text": "from datetime import datetime\nimport json\n\nimport boto.sqs\nimport boto.sqs.message\n\nfrom .base import Connector\n\nimport logging\nlogger = logging.getLogger('sqjobs.sqs')\n\n\nclass SQS(Connector):\n \"\"\"\n Manages a single connection to SQS\n \"\"\"\n\n def __init__(self, access_key, secret_key, region='us-east-1', is_secure=True, port=443):\n \"\"\"\n Creates a new SQS object\n\n :param access_key: access key with access to SQS\n :param secret_key: secret key with access to SQS\n :param region: a valid region of AWS, like 'us-east-1'\n :param port: connection port, default to 443\n\n \"\"\"\n self.access_key = access_key\n self.secret_key = secret_key\n self.region = region\n self.is_secure = is_secure\n self.port = port\n\n self._cached_connection = None\n\n def __repr__(self):\n return 'SQS(\"{ak}\", \"{sk}\", region=\"{region}\", port=\"{port}\")'.format(\n ak=self.access_key,\n sk=\"%s******%s\" % (self.secret_key[0:6], self.secret_key[-4:]),\n region=self.region,\n port=self.port\n )\n\n @property\n def connection(self):\n \"\"\"\n Creates (and saves in a cache) a SQS connection\n \"\"\"\n if self._cached_connection is None:\n self._cached_connection = boto.sqs.connect_to_region(\n self.region,\n aws_access_key_id=self.access_key,\n aws_secret_access_key=self.secret_key,\n is_secure=self.is_secure,\n port=self.port\n )\n\n logger.debug('Created new SQS connection')\n\n return self._cached_connection\n\n def get_queue(self, name):\n \"\"\"\n Gets a queue given it name\n\n :param name: the name of the queue\n \"\"\"\n queue = self.connection.get_queue(name)\n return queue\n\n def get_queues(self):\n \"\"\"\n Gets all the available queues\n \"\"\"\n queues = self.connection.get_all_queues()\n return [q.name for q in queues]\n\n def get_dead_letter_queues(self):\n \"\"\"\n Gets all the available dead letter queues\n \"\"\"\n dead_letter_queues = set()\n for queue in self.connection.get_all_queues():\n # This returns the source queue of a dead letter queue.\n # So, if it returns something, it means that the current `queue` is\n # a dead letter queue\n dead_letter_queue = self.connection.get_dead_letter_source_queues(queue)\n if dead_letter_queue:\n dead_letter_queues.add(queue.name)\n\n return list(dead_letter_queues)\n\n def enqueue(self, queue_name, payload):\n \"\"\"\n Sends a new message to a queue\n\n :param queue_name: the name of the queue\n :param payload: the payload to send inside the message\n \"\"\"\n message = self._encode_message(payload)\n queue = self.get_queue(queue_name)\n\n if not queue:\n raise ValueError('The queue does not exist: %s' % queue_name)\n\n response = queue.write(message)\n logger.info('Sent new message to %s', queue_name)\n return response\n\n def dequeue(self, queue_name, wait_time=20):\n \"\"\"\n Receive new messages from a queue\n\n :param queue_name: the queue name\n :param wait_time: how much time to wait until a new message is\n retrieved (long polling). If set to zero, connection will return\n inmediately if no messages exist.\n \"\"\"\n messages = None\n queue = self.get_queue(queue_name)\n\n if not queue:\n raise ValueError('The queue does not exist: %s' % queue_name)\n\n while not messages:\n messages = queue.get_messages(\n wait_time_seconds=wait_time,\n attributes='All',\n )\n\n if not messages and wait_time == 0:\n return None # Non-blocking mode\n\n if not messages:\n logger.debug('No messages retrieved from %s', queue_name)\n\n logger.info('New message retrieved from %s', queue_name)\n payload = self._decode_message(messages[0])\n\n return payload\n\n def delete(self, queue_name, message_id):\n \"\"\"\n Deletes a message from a queue\n\n :param queue_name: the name of the queue\n :param message_id: the message id\n \"\"\"\n queue = self.get_queue(queue_name)\n\n if not queue:\n raise ValueError('The queue does not exist: %s' % queue_name)\n\n self.connection.delete_message_from_handle(queue, message_id)\n logger.info('Deleted message from queue %s', queue_name)\n\n def retry(self, queue_name, message_id, delay=None):\n \"\"\"\n Retries a job\n\n :param queue_name: the name of the queue\n :param message_id: the message id\n :param delay: delay (in seconds) of the next retry\n \"\"\"\n if delay is None:\n # SQS will requeue the message automatically if no ACK was received\n return\n queue = self.get_queue(queue_name)\n\n if not queue:\n raise ValueError('The queue does not exist: %s' % queue_name)\n\n self.connection.change_message_visibility(queue, message_id, delay)\n logger.info('Changed retry time of a message from queue %s', queue_name)\n\n def _encode_message(self, payload):\n payload_str = json.dumps(payload)\n\n message = boto.sqs.message.Message()\n message.set_body(payload_str)\n\n return message\n\n def _decode_message(self, message):\n payload = json.loads(message.get_body())\n\n retries = int(message.attributes['ApproximateReceiveCount'])\n created_on = int(message.attributes['SentTimestamp'])\n first_execution_on = int(message.attributes['ApproximateFirstReceiveTimestamp'])\n\n payload['_metadata'] = {\n 'id': message.receipt_handle,\n 'retries': retries,\n 'created_on': datetime.fromtimestamp(created_on / 1000),\n 'first_execution_on': datetime.fromtimestamp(first_execution_on / 1000)\n }\n\n logging.debug('Message payload: %s', str(payload))\n\n return payload\n", "sub_path": "sqjobs/connectors/sqs.py", "file_name": "sqs.py", "file_ext": "py", "file_size_in_byte": 6207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "base.Connector", "line_number": 13, "usage_type": "name"}, {"api_name": "boto.sqs.sqs.connect_to_region", "line_number": 50, "usage_type": "call"}, {"api_name": "boto.sqs.sqs", "line_number": 50, "usage_type": "attribute"}, {"api_name": "boto.sqs", "line_number": 50, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 177, "usage_type": "call"}, {"api_name": "boto.sqs.sqs.message.Message", "line_number": 179, "usage_type": "call"}, {"api_name": "boto.sqs.sqs", "line_number": 179, "usage_type": "attribute"}, {"api_name": "boto.sqs", "line_number": 179, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 185, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 194, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 195, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 195, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "649751802", "text": "from django.test import override_settings\nfrom django.urls import reverse_lazy\n\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase\n\nfrom vng_api_common.tests import JWTAuthMixin\n\nfrom ..constants import SCOPE_NOTIFICATIES_PUBLICEREN_LABEL\n\n\n@override_settings(ROOT_URLCONF=\"vng_api_common.notifications.tests.urls\")\nclass WebhookTests(JWTAuthMixin, APITestCase):\n\n scopes = [SCOPE_NOTIFICATIES_PUBLICEREN_LABEL]\n url = reverse_lazy(\"notificaties-webhook\")\n\n def test_auth_required(self):\n self.client.credentials() # clear any credentials\n\n response = self.client.post(self.url)\n\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_receive_nofication(self):\n data = {\n \"kanaal\": \"zaken\",\n \"hoofdObject\": \"https://zaken-api.vng.cloud/api/v1/zaken/d7a22\",\n \"resource\": \"status\",\n \"resourceUrl\": \"https://zaken-api.vng.cloud/api/v1/statussen/d7a22/721c9\",\n \"actie\": \"create\",\n \"aanmaakdatum\": \"2018-01-01T17:00:00Z\",\n \"kenmerken\": {\n \"bron\": \"082096752011\",\n \"zaaktype\": \"https://example.com/api/v1/zaaktypen/5aa5c\",\n \"vertrouwelijkeidaanduiding\": \"openbaar\",\n },\n }\n\n response = self.client.post(self.url, data)\n\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n", "sub_path": "vng_api_common/notifications/tests/test_webhook_endpoint.py", "file_name": "test_webhook_endpoint.py", "file_ext": "py", "file_size_in_byte": 1427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "vng_api_common.tests.JWTAuthMixin", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.test.APITestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "constants.SCOPE_NOTIFICATIES_PUBLICEREN_LABEL", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 42, "usage_type": "name"}, {"api_name": "django.test.override_settings", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "419895098", "text": "from ast import literal_eval\n\nimport colander\n\nfrom validators import BytesLimit\n\n\nclass Hex(colander.Number):\n @staticmethod\n def num(x):\n return hex(x) if isinstance(x, int) else literal_eval(x)\n\n\nclass FieldSchema(colander.TupleSchema):\n \"\"\"\n Схема для полей вида ключ/значение\n \"\"\"\n key = colander.SchemaNode(colander.String(), validator=BytesLimit(8))\n value = colander.SchemaNode(colander.Int(), validator=BytesLimit(4))\n\n\nclass FieldsSchema(colander.SequenceSchema):\n field = FieldSchema()\n\n\nclass MessageSchema(colander.TupleSchema):\n \"\"\"\n Схема для входящего сообщения\n \"\"\"\n def validator(self, node, cstruct):\n \"\"\"\n Дополнительная валидация входящего сообщения\n \"\"\"\n if cstruct[4] != len(cstruct[5]):\n err = '\"numfields\" incorrect number of elements (expected {}, was {})'\n self.raise_invalid(err.format(cstruct[4], len(cstruct[5])))\n\n message_xor = (cstruct[1] >> 8 if cstruct[1] >> 8 else cstruct[1]) ^ cstruct[3]\n if message_xor != cstruct[6]:\n err = '\"message_xor\" not valid (expected {}, was {})'\n self.raise_invalid(err.format(cstruct[6], message_xor))\n\n header = colander.SchemaNode(Hex(), validator=colander.OneOf([0x01]))\n number = colander.SchemaNode(colander.Int(), validator=BytesLimit(2))\n id = colander.SchemaNode(colander.String(), validator=BytesLimit(8))\n state = colander.SchemaNode(Hex(), validator=colander.OneOf([0x01, 0x02, 0x03]))\n numfields = colander.SchemaNode(colander.Int(), validator=BytesLimit(1))\n fields = FieldsSchema()\n message_xor = colander.SchemaNode(colander.Int(), validator=BytesLimit(1))\n\n\nclass AnswerSchema(colander.TupleSchema):\n \"\"\"\n Схема ответа для успешно обработанного сообщения\n \"\"\"\n header = colander.SchemaNode(Hex(), validator=colander.OneOf([0x11, 0x12]))\n number = colander.SchemaNode(colander.Int(), validator=BytesLimit(2))\n message_xor = colander.SchemaNode(colander.Int(), validator=BytesLimit(1))\n\n\nclass WrongAnswerSchema(colander.TupleSchema):\n \"\"\"\n Схема ответа для не обработанного сообщения\n \"\"\"\n header = colander.SchemaNode(Hex(), validator=colander.OneOf([0x11, 0x12]))\n number = colander.SchemaNode(Hex(), validator=BytesLimit(2))\n message_xor = colander.SchemaNode(colander.Int(), validator=BytesLimit(1))\n", "sub_path": "schemes.py", "file_name": "schemes.py", "file_ext": "py", "file_size_in_byte": 2549, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "colander.Number", "line_number": 8, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 11, "usage_type": "call"}, {"api_name": "colander.TupleSchema", "line_number": 14, "usage_type": "attribute"}, {"api_name": "colander.SchemaNode", "line_number": 18, "usage_type": "call"}, {"api_name": "colander.String", "line_number": 18, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 18, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 19, "usage_type": "call"}, {"api_name": "colander.Int", "line_number": 19, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 19, "usage_type": "call"}, {"api_name": "colander.SequenceSchema", "line_number": 22, "usage_type": "attribute"}, {"api_name": "colander.TupleSchema", "line_number": 26, "usage_type": "attribute"}, {"api_name": "colander.SchemaNode", "line_number": 43, "usage_type": "call"}, {"api_name": "colander.OneOf", "line_number": 43, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 44, "usage_type": "call"}, {"api_name": "colander.Int", "line_number": 44, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 44, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 45, "usage_type": "call"}, {"api_name": "colander.String", "line_number": 45, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 45, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 46, "usage_type": "call"}, {"api_name": "colander.OneOf", "line_number": 46, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 47, "usage_type": "call"}, {"api_name": "colander.Int", "line_number": 47, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 47, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 49, "usage_type": "call"}, {"api_name": "colander.Int", "line_number": 49, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 49, "usage_type": "call"}, {"api_name": "colander.TupleSchema", "line_number": 52, "usage_type": "attribute"}, {"api_name": "colander.SchemaNode", "line_number": 56, "usage_type": "call"}, {"api_name": "colander.OneOf", "line_number": 56, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 57, "usage_type": "call"}, {"api_name": "colander.Int", "line_number": 57, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 57, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 58, "usage_type": "call"}, {"api_name": "colander.Int", "line_number": 58, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 58, "usage_type": "call"}, {"api_name": "colander.TupleSchema", "line_number": 61, "usage_type": "attribute"}, {"api_name": "colander.SchemaNode", "line_number": 65, "usage_type": "call"}, {"api_name": "colander.OneOf", "line_number": 65, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 66, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 66, "usage_type": "call"}, {"api_name": "colander.SchemaNode", "line_number": 67, "usage_type": "call"}, {"api_name": "colander.Int", "line_number": 67, "usage_type": "call"}, {"api_name": "validators.BytesLimit", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "517928694", "text": "from custom.math import digits\nfrom collections import Counter\n\ncubes = set([x ** 3 for x in range(10000)])\n\ncount = Counter()\n\nfor cube in cubes:\n count[str(sorted(digits(cube)))] += 1\n\ncurrent_min = 1e20\n\nfor test in count.most_common(2):\n for cube in cubes:\n if str(sorted(digits(cube))) == test[0]:\n if cube < current_min:\n current_min = cube\n\nprint(current_min)\n", "sub_path": "Problems 051 - 100/Problem 062.py", "file_name": "Problem 062.py", "file_ext": "py", "file_size_in_byte": 406, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "collections.Counter", "line_number": 6, "usage_type": "call"}, {"api_name": "custom.math.digits", "line_number": 9, "usage_type": "call"}, {"api_name": "custom.math.digits", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "652890218", "text": "import os\nimport sys\nimport random\nimport cv2\nimport numpy as np\n\nimport tensorflow as tf\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.app.flags.DEFINE_string(\n 'dataset_dir', './datasets/small_target/',\n 'Directory where the original dataset is stored.')\ntf.app.flags.DEFINE_string(\n 'split_name', 'train',\n 'split directory for the dataset')\ntf.app.flags.DEFINE_string(\n 'label_file', './datasets/small_target/train.txt',\n 'The txt file where label and image name are stored'\n)\ntf.app.flags.DEFINE_string(\n 'dataset_name', 'std',\n 'The name of dataset'\n)\n\ndef int64_feature(value):\n \"\"\"Wrapper for inserting int64 features into Example proto.\n \"\"\"\n if not isinstance(value, list):\n value = [value]\n return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n\n\ndef float_feature(value):\n \"\"\"Wrapper for inserting float features into Example proto.\n \"\"\"\n if not isinstance(value, list):\n value = [value]\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n\n\ndef bytes_feature(value):\n \"\"\"Wrapper for inserting bytes features into Example proto.\n \"\"\"\n if not isinstance(value, list):\n value = [value]\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))\n\n\ndef convert_to_example(image, label):\n \"\"\"\n Build an Example proto for an image example\n :param image_data:\n :param label:\n :return: example proto\n \"\"\"\n # shape = list(image.shape)\n image_data = image.tobytes()\n image_format = b'raw'\n example = tf.train.Example(features=tf.train.Features(feature={\n 'image/encoded': bytes_feature(image_data),\n 'label': int64_feature(label),\n # 'image/shape': int64_feature(shape),\n 'image/format': bytes_feature(image_format)\n\n }))\n\n return example\n\n\ndef main(_):\n writer = tf.python_io.TFRecordWriter(FLAGS.dataset_dir + FLAGS.dataset_name\n + '_' + FLAGS.split_name + '.tfrecords')\n with open(FLAGS.label_file,'r') as f:\n lines = f.readlines()\n random.shuffle(lines)\n image_num = 0\n for l in lines:\n try:\n l = l.split()\n except ValueError:\n continue\n image = cv2.imread(FLAGS.dataset_dir + FLAGS.split_name+\"/\" + l[0], 0)\n image = cv2.resize(image, (32, 32))\n label = int(l[1])\n example = convert_to_example(image, label)\n writer.write(example.SerializeToString())\n image_num += 1\n sys.stdout.write('\\r>> Converting image %d/%d' % (image_num, len(lines)))\n sys.stdout.flush()\n\n\nif __name__ == \"__main__\":\n tf.app.run()\n\n\n\n", "sub_path": "convert_to_tfrecord.py", "file_name": "convert_to_tfrecord.py", "file_ext": "py", "file_size_in_byte": 2704, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "tensorflow.app", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Feature", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Int64List", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.train.FloatList", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.train.BytesList", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.train.Example", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.python_io.TFRecordWriter", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.python_io", "line_number": 72, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 84, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 89, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 90, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.app.run", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 94, "usage_type": "attribute"}]} +{"seq_id": "131801437", "text": "from django.db import migrations\n\n\nclass ObsoleteAppNameInMigrationsTableException(Exception):\n pass\n\n\ndef _assert_and_rename_app_in_migrations(old_app_name, new_app_name):\n def inner(apps, schema_editor):\n with schema_editor.connection.cursor() as cursor:\n cursor.execute(\n \"SELECT COUNT(*) FROM django_migrations WHERE app=%s;\",\n [old_app_name]\n )\n obsolete_migration_record_count = cursor.fetchone()[0]\n if obsolete_migration_record_count:\n cursor.execute(\n \"UPDATE django_migrations SET app=%s WHERE app=%s;\",\n [new_app_name, old_app_name]\n )\n\n # Hack to forcibly and immediately commit the above UPDATE\n # command to avoid it being rolled back when this migration\n # fails by raising the exception, or by failing on the next\n # step in the overall migration which is likely with obsolete\n # migration data.\n # See http://stackoverflow.com/a/31253540/4970\n if schema_editor.connection.in_atomic_block:\n schema_editor.atomic.__exit__(None, None, None)\n schema_editor.connection.commit()\n\n raise ObsoleteAppNameInMigrationsTableException(\n \"%d migrations existed for obsolete app name '%s' in the\"\n \" 'django_migrations` database table. These migrations\"\n \" have been renamed to use app name '%s'. Re-run the\"\n \" migrate command to apply migrations for the renamed app\"\n % (obsolete_migration_record_count,\n old_app_name, new_app_name)\n )\n return inner\n\n\ndef RenameAppInMigrationsTable(old_app_name, new_app_name):\n \"\"\"\n Check whether an obsolete application name `old_app_name` is present in\n Django's `django_migrations` DB table and handle the situation as cleanly\n as possible.\n\n If there are migrations for the old app name, perform an UPDATE command to\n rename the app in this table so future migration runs will succeed, then\n exit with a `ObsoleteAppNameInMigrationsTableException` to indicate that\n migrations need to be re-run.\n\n If there are no migrations for the old app name -- e.g. the app has already\n been renamed in the table, or the old pre-rename migrations were never run\n on the DB -- then no action is performed.\n \"\"\"\n return migrations.RunPython(\n _assert_and_rename_app_in_migrations(old_app_name, new_app_name)\n )\n", "sub_path": "icekit/utils/migrations.py", "file_name": "migrations.py", "file_ext": "py", "file_size_in_byte": 2634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "django.db.migrations.RunPython", "line_number": 58, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "29824542", "text": "\"\"\"web URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom demo import views as demo_views\nurlpatterns = [\n path('admin/', admin.site.urls),\n path(\"\",demo_views.home, name=\"home\"),\n path(\"about.html\",demo_views.about, name=\"about\"),\n path(\"index.html\",demo_views.home, name=\"home\"),\n path(\"result.html\",demo_views.show, name=\"show\"),\n path(\"blastp.html\",demo_views.blastp, name=\"blastp\"),\n path(\"blast.html\",demo_views.blast, name=\"blast\"),\n path(\"Download\",demo_views.Download, name=\"Download\"),\n]\n", "sub_path": "web/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1158, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "demo.views.home", "line_number": 21, "usage_type": "attribute"}, {"api_name": "demo.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "demo.views.about", "line_number": 22, "usage_type": "attribute"}, {"api_name": "demo.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "demo.views.home", "line_number": 23, "usage_type": "attribute"}, {"api_name": "demo.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "demo.views.show", "line_number": 24, "usage_type": "attribute"}, {"api_name": "demo.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "demo.views.blastp", "line_number": 25, "usage_type": "attribute"}, {"api_name": "demo.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "demo.views.blast", "line_number": 26, "usage_type": "attribute"}, {"api_name": "demo.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "demo.views.Download", "line_number": 27, "usage_type": "attribute"}, {"api_name": "demo.views", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "443904650", "text": "from django.urls import path\n\napp_name = 'ebook'\n\nfrom . import views\n\nurlpatterns = [\n path('', views.IndexView.as_view(), name='index'),\n path('publisher/', views.PublisherView.as_view(), name='publisher'),\n path('book//', views.BookView.as_view(), name='book'),\n]\n", "sub_path": "src/ebook/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 377, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"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"}]} +{"seq_id": "367289007", "text": "from setuptools import setup\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, \"README.md\"), encoding=\"utf-8\") as f:\n long_description = f.read()\n\n\nsetup(\n name=\"pharynx-redox\",\n version=\"0.1.0\",\n description=\"Software tools for measuring cytosolic redox state in the pharynx of the nematode C. elegans\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/half-adder/pharynx_redox\",\n author=\"Sean Johnsen\",\n author_email=\"sean.b.johnsen@gmail.com\",\n maintainer=\"Sean Johnsen\",\n maintainer_email=\"sean.b.johnsen@gmail.com\",\n classifiers=[\n \"Development Status :: 3 - Alpha\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: MIT License\",\n \"Topic :: Scientific/Engineering :: Bio-Informatics\",\n \"Programming Language :: Python :: 3.7\",\n ],\n keywords=\"celegans nematode redox image-analysis biology microscopy\",\n packages=[\"pharynx_redox\"],\n python_requires=\"~=3.7\",\n install_requires=[\n \"cached-property\",\n \"jupyter\",\n \"matplotlib\",\n \"numpy\",\n \"pandas\",\n \"scikit-image\",\n \"scikit-learn\",\n \"scipy\",\n \"seaborn\",\n \"statsmodels\",\n \"tabulate\",\n \"tqdm\",\n \"xarray\",\n \"xlrd\",\n \"netcdf4\",\n ],\n test_suite=\"tests\",\n extras_require={\n \"test\": [\"pytest\", \"pytest-cov\"],\n \"docs\": [\"sphinx\", \"sphinx-rtd-theme\", \"sphinx-autodoc-typehints\"],\n },\n project_urls={\"Bug Reports\": \"https://github.com/half-adder/pharynx_redox/issues\"},\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1658, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "389454406", "text": "\r\n##############################\r\n# Import Libraries:\r\nimport os,sys\r\nimport numpy as np\r\nimport matplotlib.pylab as plt\r\nimport seaborn as sns\r\n\r\n\r\n##############################\r\n\r\n#plotting\r\n\r\nr = 1\r\n\r\nx_vec = np.arange(-r,r,r/500.)\r\nupper = np.exp(-np.absolute(x_vec))\r\nlower = -upper\r\n\r\nax = plt.figure(figsize=(10,3))\r\n\r\nplt.plot(x_vec,upper,'k')\r\nplt.plot(x_vec,lower,'k')\r\n\r\nplt.axvline(x=-r,color='#CCCCCC',linewidth=3)\r\nplt.axvline(x=0,color='#CCCCCC',linewidth=3)\r\nplt.axvline(x=r,color='#CCCCCC',linewidth=3)\r\nplt.xlim((-r,r))\r\nplt.xticks([-r,0.,r],('March','September','March'),fontsize=14)\r\nplt.yticks([])\r\nplt.ylabel('Ice Extent Anomaly',fontsize=14)\r\n\r\nax.text(0.2, 0.5, r'Ice albedo feedback', fontsize=14, color='#B84D4D')\r\nax.text(0.6, 0.5, r'Longwave stabilization', fontsize=14, color='#5C9D5C')\r\n\r\nplt.tight_layout()\r\n\r\nplt.show()\r\n\r\n", "sub_path": "std_schematic.py", "file_name": "std_schematic.py", "file_ext": "py", "file_size_in_byte": 856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pylab.figure", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pylab.axvline", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pylab.axvline", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pylab.axvline", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pylab.xlim", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pylab.xticks", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pylab.yticks", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pylab.ylabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pylab.tight_layout", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "9613952", "text": "# -*- coding: utf-8 -*-\n# @COPYRIGHT_begin\n#\n# Copyright [2015] Michał Szczygieł, M4GiK Software\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# @COPYRIGHT_end\n\nimport unittest\n\nfrom teamcity import is_running_under_teamcity\nfrom teamcity.unittestpy import TeamcityTestRunner\n\nfrom dev_cloud.database.models.applications import Applications\nfrom dev_cloud.database.models.installed_applications import InstalledApplications\nfrom dev_cloud.database.models.template_instances import TemplateInstances\nfrom dev_cloud.database.models.virtual_machines import VirtualMachines\n\n\nclass ApplicationTestCase(unittest.TestCase):\n def test_model_application(self):\n instance = Applications(application_name=\"application_test\")\n self.assertTrue(instance)\n\n def test_model_template_instance(self):\n instance = TemplateInstances(template_name=\"template_test\")\n self.assertTrue(instance)\n\n def test_model_virtual_machine(self):\n example_instance = TemplateInstances(template_name=\"template_test\")\n instance = VirtualMachines(template_instance=example_instance)\n self.assertTrue(instance)\n\n def test_model_installed_aplication(self):\n example_app = Applications(application_name=\"application_test\")\n example_instance = TemplateInstances(template_name=\"template_test\")\n example_virtual_machine = VirtualMachines(template_instance=example_instance)\n instance = InstalledApplications(workspace=\"test_workspace\", application=example_app,\n virtual_machine=example_virtual_machine)\n self.assertTrue(instance)\n\n\nif __name__ == '__main__':\n if is_running_under_teamcity():\n runner = TeamcityTestRunner()\n else:\n runner = unittest.TextTestRunner()\n unittest.main(testRunner=runner)", "sub_path": "dev_cloud/database/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 2314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "unittest.TestCase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "dev_cloud.database.models.applications.Applications", "line_number": 33, "usage_type": "call"}, {"api_name": "dev_cloud.database.models.template_instances.TemplateInstances", "line_number": 37, "usage_type": "call"}, {"api_name": "dev_cloud.database.models.template_instances.TemplateInstances", "line_number": 41, "usage_type": "call"}, {"api_name": "dev_cloud.database.models.virtual_machines.VirtualMachines", "line_number": 42, "usage_type": "call"}, {"api_name": "dev_cloud.database.models.applications.Applications", "line_number": 46, "usage_type": "call"}, {"api_name": "dev_cloud.database.models.template_instances.TemplateInstances", "line_number": 47, "usage_type": "call"}, {"api_name": "dev_cloud.database.models.virtual_machines.VirtualMachines", "line_number": 48, "usage_type": "call"}, {"api_name": "dev_cloud.database.models.installed_applications.InstalledApplications", "line_number": 49, "usage_type": "call"}, {"api_name": "teamcity.is_running_under_teamcity", "line_number": 55, "usage_type": "call"}, {"api_name": "teamcity.unittestpy.TeamcityTestRunner", "line_number": 56, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "321437418", "text": "from playwright.sync_api import sync_playwright\n\n\"\"\" \nRun script with load playwright inspector\n$env:PWDEBUG=1\n \nRun with playwright dev tools\n$env:PWDEBUG=\"console\"\n\"\"\"\n\n\ndef hidden_info():\n # Start playwright in context manager\n with sync_playwright() as p:\n # Set browser and other options\n # Play with slowmow and headless to see how that works\n browser = p.chromium.launch(slow_mo=2000, channel=\"chrome\", headless=False)\n # Create page object\n page = browser.new_page()\n\n # Pause and start playwright inspector\n # page.pause()\n\n # Go to Lebron's Basketball Ref page\n page.goto(\"https://www.basketball-reference.com/players/j/jamesle01.html\")\n # Click playoff per game button\n page.click(\"a[data-show='.assoc_playoffs_per_game']\")\n # Click share/export button\n page.click(\"div.assoc_playoffs_per_game .hasmore\")\n # Click get table as csv(excel)\n page.click(\n \"div#all_per_game-playoffs_per_game div.assoc_playoffs_per_game button[tip='Get a link directly to this table on this page']\"\n )\n # Get text from transformed table\n csv_text = page.inner_text(\"pre#csv_playoffs_per_game\")\n print(csv_text)\n page.wait_for_timeout(100000)\n browser.close()\n\n\nif __name__ == \"__main__\":\n hidden_info()\n", "sub_path": "playwright/5_debugging.py", "file_name": "5_debugging.py", "file_ext": "py", "file_size_in_byte": 1361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "playwright.sync_api.sync_playwright", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "99542442", "text": "import json\nimport socket\nimport logging\nimport urllib2\nimport ssl\n\n\nfrom collections import namedtuple\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass Messenger:\n \"\"\"A class which takes care of the socket communication with oxd Server.\n The object is initialized with the port number\n \"\"\"\n def __init__(self, port=8099):\n \"\"\"Constructor for Messenger\n\n Args:\n port (integer) - the port number to bind to the localhost, default\n is 8099\n \"\"\"\n self.host = 'localhost'\n self.port = port\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n LOGGER.debug(\"Creating a AF_INET, SOCK_STREAM socket.\")\n self.firstDone = False\n\n\n\n def __connect(self):\n \"\"\"A helper function to make connection.\"\"\"\n try:\n LOGGER.debug(\"Socket connecting to %s:%s\", self.host, self.port)\n self.sock.connect((self.host, self.port))\n except socket.error as e:\n LOGGER.exception(\"socket error %s\", e)\n LOGGER.error(\"Closing socket and recreating a new one.\")\n self.sock.close()\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.sock.connect((self.host, self.port))\n\n def __json_object_hook(self, d):\n \"\"\"Function to customize the json.loads to return named tuple instead\n of a dict\"\"\"\n\n #if a response key contains '-' then replace it with 'hyphen'\n d = self.encodeHyphen(d)\n\n return namedtuple('response', d.keys())(*d.values())\n\n def __json2obj(self, data):\n \"\"\"Helper function which converts the json string into a namedtuple\n so the reponse values can be accessed like objects instead of dicts\"\"\"\n return json.loads(data, object_hook=self.__json_object_hook)\n\n def encodeHyphen(self, d):\n \"\"\"TO-DO: This is just for work around purpose. Original fix needs to be worked on.\"\"\"\n for i in d.keys():\n if '-' in i:\n newkey = i.replace('-', 'hyphen')\n val = d[i]\n d.pop(i)\n d[newkey] = val\n return d\n\n def send(self, command):\n \"\"\"Send function sends the command to the oxd server and recieves the\n response.\n\n Args:\n command (dict) - Dict representation of the JSON command string\n\n Returns:\n response (dict) - The JSON response from the oxd Server as a dict\n \"\"\"\n cmd = json.dumps(command)\n cmd = \"{:04d}\".format(len(cmd)) + cmd\n msg_length = len(cmd)\n\n # Makes the first time connection\n if not self.firstDone:\n LOGGER.info('Initiating first time socket connection.')\n self.__connect()\n self.firstDone = True\n\n # Send the message to the server\n totalsent = 0\n while totalsent < msg_length:\n try:\n LOGGER.debug(\"Sending: %s\", cmd[totalsent:])\n sent = self.sock.send(cmd[totalsent:])\n totalsent = totalsent + sent\n except socket.error as e:\n LOGGER.exception(\"Reconneting due to socket error. %s\", e)\n self.__connect()\n LOGGER.info(\"Reconnected to socket.\")\n\n # Check and recieve the response if available\n parts = []\n resp_length = 0\n recieved = 0\n done = False\n while not done:\n part = self.sock.recv(1024)\n if part == \"\":\n LOGGER.error(\"Socket connection broken, read empty.\")\n self.__connect()\n LOGGER.info(\"Reconnected to socket.\")\n\n # Find out the length of the response\n if len(part) > 0 and resp_length == 0:\n resp_length = int(part[0:4])\n part = part[4:]\n\n # Set Done flag\n recieved = recieved + len(part)\n if recieved >= resp_length:\n done = True\n\n parts.append(part)\n\n response = \"\".join(parts)\n # return the JSON as a namedtuple object\n return self.__json2obj(response)\n\n\n def sendtohttp(self, params, rest_url):\n \"\"\"send function sends the command to the oxd server and recieves the\n response for web connection type.\n\n Args:\n params (dict) - Dict representation of the JSON param string\n rest_url - Url of the rest API\n\n Returns:\n response (dict) - The JSON response from the oxd Server as a dict\n \"\"\"\n\n\n param = json.dumps(params)\n\n if 'protection_access_token' in param:\n accessToken = 'Bearer ' + params.get('protection_access_token', 'None')\n else:\n accessToken = ''\n\n headers = {'Content-Type': 'application/json', 'Authorization': accessToken}\n\n request = urllib2.Request(rest_url, param, headers)\n gcontext = ssl.SSLContext(ssl.PROTOCOL_TLSv1)\n response = urllib2.urlopen(request, context=gcontext)\n\n resp_page = response.read()\n\n response = \"\".join(resp_page)\n # return the JSON as a namedtuple object\n return self.__json2obj(response)\n", "sub_path": "oxdpython/messenger.py", "file_name": "messenger.py", "file_ext": "py", "file_size_in_byte": 5191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "68", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 26, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 26, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 26, "usage_type": "attribute"}, {"api_name": "socket.error", "line_number": 37, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 41, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 41, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 41, "usage_type": "attribute"}, {"api_name": "collections.namedtuple", "line_number": 51, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 78, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 95, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 142, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 151, "usage_type": "call"}, {"api_name": "ssl.SSLContext", "line_number": 152, "usage_type": "call"}, {"api_name": "ssl.PROTOCOL_TLSv1", "line_number": 152, "usage_type": "attribute"}, {"api_name": "urllib2.urlopen", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "632618801", "text": "# This file is part of Indico.\n# Copyright (C) 2002 - 2019 CERN\n#\n# Indico is free software; you can redistribute it and/or\n# modify it under the terms of the MIT License; see the\n# LICENSE file for more details.\n\nfrom __future__ import absolute_import, unicode_literals\n\nfrom datetime import datetime\n\nfrom flask import g, has_request_context, jsonify, render_template, request, session\nfrom markupsafe import Markup\nfrom werkzeug.exceptions import ImATeapot\n\nfrom indico.util.i18n import _\nfrom indico.web.flask.templating import get_template_module\n\n\ndef inject_js(js):\n \"\"\"Injects JavaScript into the current page.\n\n :param js: Code wrapped in a ``