\\r\\n')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\ndef render_right_content(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n def right_content():\n return render_right_content(context)\n __M_writer = context.writer()\n __M_writer('\\r\\n')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\n\"\"\"\n__M_BEGIN_METADATA\n{\"filename\": \"C:/Users/Isaac/intexsite/portal/templates/app_base.htm\", \"uri\": \"app_base.htm\", \"source_encoding\": \"utf-8\", \"line_map\": {\"18\": 2, \"31\": 0, \"49\": 1, \"50\": 2, \"55\": 5, \"60\": 23, \"65\": 39, \"70\": 43, \"75\": 59, \"80\": 62, \"86\": 5, \"92\": 5, \"98\": 8, \"105\": 8, \"106\": 10, \"107\": 11, \"108\": 12, \"109\": 13, \"110\": 14, \"111\": 15, \"112\": 16, \"113\": 17, \"114\": 18, \"115\": 19, \"121\": 25, \"128\": 25, \"129\": 26, \"130\": 27, \"131\": 28, \"132\": 29, \"133\": 30, \"134\": 31, \"135\": 32, \"136\": 33, \"137\": 34, \"138\": 35, \"144\": 42, \"150\": 42, \"156\": 45, \"163\": 45, \"164\": 46, \"165\": 47, \"166\": 48, \"167\": 49, \"168\": 50, \"169\": 51, \"170\": 52, \"171\": 53, \"172\": 54, \"173\": 55, \"179\": 61, \"185\": 61, \"191\": 185}}\n__M_END_METADATA\n\"\"\"\n","sub_path":"portal/templates/__dmpcache__/app_base.htm.py","file_name":"app_base.htm.py","file_ext":"py","file_size_in_byte":7583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"608076464","text":"import urllib.request, urllib.parse\nimport json\n\nfhand = urllib.request.urlopen('http://py4e-data.dr-chuck.net/comments_171352.json')\ndata = fhand.read()\n\ndata = json.loads(data)\n\ncommentList = data['comments']\n\nsum = 0\nfor item in commentList:\n sum = sum + int(item['count'])\n\nprint(sum)","sub_path":"python/ch13_json_ex2.py","file_name":"ch13_json_ex2.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"185261335","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import logout, decorators\nfrom django.contrib import messages\nfrom django.contrib.messages.views import SuccessMessageMixin\nfrom django.contrib.auth.views import LoginView\nfrom django.views.generic import DetailView, ListView\nfrom django.urls import reverse\nfrom django.db.models import Q\n\nfrom accounts.models import UserProfile\nfrom .forms import RegistrationForm, LoginAuthForm, UserProfileUpdateForm, UserUpdateForm\n\n# Create your views here.\ndef follow(request):\n user_id = request.POST.get('userid')\n user_profile = get_object_or_404(UserProfile, user=user_id)\n curr_user = request.user\n\n if user_profile not in curr_user.userprofile.get_all_followings():\n curr_user.userprofile.following.add(user_profile)\n\n page_redirect_url = request.POST.get('pageredirect')\n if page_redirect_url:\n return redirect(page_redirect_url)\n\n blog_id = request.POST.get('blogid')\n if blog_id:\n curr_page = request.POST.get('page', 1)\n return redirect(reverse('home') + '?page={curr_page}#blog-{blog_id}'.format(curr_page=curr_page, blog_id=blog_id))\n\n return redirect(reverse('home'))\n\ndef unfollow(request):\n user_id = request.POST.get('userid')\n user_profile = get_object_or_404(UserProfile, user=user_id)\n curr_user = request.user\n\n if user_profile in curr_user.userprofile.get_all_followings():\n curr_user.userprofile.following.remove(user_profile)\n\n page_redirect_url = request.POST.get('pageredirect')\n if page_redirect_url:\n return redirect(page_redirect_url)\n\n blog_id = request.POST.get('blogid')\n if blog_id:\n curr_page = request.POST.get('page', 1)\n return redirect(reverse('home') + '?page={curr_page}#blog-{blog_id}'.format(curr_page=curr_page, blog_id=blog_id))\n\n return redirect(reverse('home'))\n\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nclass UserDetails(DetailView):\n template_name = \"accounts/profile.html\"\n model = User\n slug_field = \"username\"\n slug_url_kwarg = \"username\"\n\n \"\"\" Get context data. \"\"\"\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n object = context['object']\n blogs = object.blogpost_set.all().order_by('-timestamp')\n paginator = Paginator(blogs, 3)\n page = self.request.GET.get('page', None)\n paged_blogs = paginator.get_page(page)\n context['blogs'] = paged_blogs\n return context\n\n\n \"\"\" Get slug field.\n def get_slug_field(self, **kwargs):\n slug = super().get_slug_field(**kwargs)\n return slug\n \"\"\"\n\n\ndef register_user(request):\n\n if request.user.is_authenticated:\n return redirect('/')\n\n form = RegistrationForm(request.POST or None)\n\n if request.method == 'POST':\n if form.is_valid():\n form.save()\n messages.success(request, \"You are registered successfully. Welcome to our community. Please Login.\")\n form = RegistrationForm(None)\n return redirect(reverse('user-login'))\n\n context = {\n \"form\" : form\n }\n return render(request, 'accounts/user_registration.html', context)\n\nclass AccountsLoginView(SuccessMessageMixin, LoginView):\n template_name = 'accounts/user_login.html'\n form_class = LoginAuthForm\n redirect_authenticated_user = True\n # success_message = \"You're welcome.\"\n\n def get_success_message(self, cleaned_data):\n username = cleaned_data['username']\n user = User.objects.get(username=username)\n return f\"{user.get_full_name()}, Welcome to InstaGo.\"\n\n def form_invalid(self, form):\n messages.error(self.request, 'Please insert a valid username or password.')\n return self.render_to_response(self.get_context_data(form=form))\n\n # def get_context_data(self, *args, **kwargs):\n # context = super(AccountsLoginView, self).get_context_data(*args, **kwargs)\n # return context\n\n\ndef logout_user(request):\n logout(request)\n messages.success(request, 'You are logged out successfully.')\n return redirect(reverse('user-login'))\n\n\n\n\"\"\"\nProfile updation goes here.\n=========================================\n\"\"\"\n@decorators.login_required(login_url='/accounts/login/')\ndef user_profile_update(request):\n user = request.user\n\n user_form = UserUpdateForm(\n request.POST or None,\n request.FILES or None,\n initial={\n 'first_name' : user.first_name,\n 'last_name' : user.last_name,\n }\n )\n profile_form = UserProfileUpdateForm(\n request.POST or None,\n request.FILES or None,\n initial={\n 'bio' : user.userprofile.bio,\n 'profile_image' : user.userprofile.profile_image,\n }\n )\n\n if request.method == 'POST':\n if user_form.is_valid() and profile_form.is_valid():\n user.first_name = user_form.cleaned_data['first_name']\n user.last_name = user_form.cleaned_data['last_name']\n user.save()\n\n profile = UserProfile.objects.get(user=user)\n profile.bio = profile_form.cleaned_data['bio']\n profile.profile_image = profile_form.cleaned_data['profile_image']\n profile.save()\n\n messages.success(request, 'Profile saved successfully.')\n return redirect(profile.get_absolute_url())\n\n else:\n messages.error(request, \"Invalid input.\")\n\n\n context = {\n \"profile_form\" : profile_form,\n \"user_form\" : user_form\n }\n return render(request, 'accounts/user_update.html' , context)\n\n\n\n\"\"\"\nProfile search goes here.\n=========================================\n\"\"\"\nclass UsersListView(ListView):\n model = User\n template_name = 'accounts/list.html'\n # paginate_by = 50\n\n def get_queryset(self, *args, **kwargs):\n obj_list = super().get_queryset(*args, **kwargs)\n user = self.request.user\n if user.is_authenticated:\n obj_list = obj_list.exclude(username=user.username).order_by('first_name')\n\n q = self.request.GET.get('q', None)\n if q is not None:\n lookup = (Q(username__icontains=q) |\n Q(first_name__startswith=q) | \n Q(last_name__startswith=q) |\n Q(email__icontains=q)\n )\n obj_list = obj_list.filter(lookup).distinct()\n return obj_list\n\n\n@decorators.login_required(login_url='/accounts/login/')\ndef edit_bio(request):\n user = request.user\n if request.method == 'POST':\n bio = request.POST.get('bio', None)\n if bio is not None:\n user_profile = user.userprofile\n user_profile.bio = bio\n user_profile.save()\n messages.success(request, 'Bio updated.')\n return redirect(reverse('user-profile', kwargs={'username': user.username}))\n\n","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"367452355","text":"from connect import db\n\n\nfor item in db.find():\n container = []\n for column in ['name', 'province', 'country']:\n value = item.get(column)\n if value:\n container.append(value)\n item['location'] = ', '.join(container)\n db.save(item)\n","sub_path":"parse_location.py","file_name":"parse_location.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"204149428","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom album.models import Category, Photo\nfrom django.views.generic import ListView, DetailView\nfrom django.views.generic.edit import CreateView, UpdateView, DeleteView\nfrom django.urls import reverse_lazy\n\ndef base(request):\n return render(request, 'base.html')\n\ndef first_view( request ):\n return HttpResponse( 'Esta es mi primera vista!' )\n\ndef category(request):\n category_list = Category.objects.all()\n context = { 'object_list':category_list }\n return render( request, 'album/category_list.html', context )\n\ndef category_detail(request, category_id):\n category = Category.objects.get( id=category_id )\n context = { 'object':category }\n return render( request, 'album/category_detail.html', context )\n\nclass CategoryListView(ListView):\n model = Category\n\nclass CategoryDetailView(DetailView):\n model = Category\n\nclass PhotoListView(ListView):\n model = Photo\n\nclass PhotoDetailView(DetailView):\n model = Photo\n\nclass PhotoCreate(CreateView):\n model = Photo\n\nclass PhotoUpdate(UpdateView):\n model = Photo\n fields = ['category', 'title', 'photo', 'favorite', 'comment']\n\nclass PhotoDelete(DeleteView):\n model = Photo\n success_url = reverse_lazy('photo-list')\n","sub_path":"album/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"408668649","text":"from aws_cdk import core\nfrom aws_cdk import aws_lambda as _lambda\nfrom aws_cdk import aws_events as events\nfrom aws_cdk import aws_events_targets as targets\nfrom aws_cdk import aws_dynamodb as dynamodb\nfrom aws_cdk import aws_iam as iam\n\nTABLE_NAME = 'Scheduler'\nTAG_KEY = \"Schedule\"\nREGION = \"us-west-2\"\nLAMBDA_FUNC_PATH = 'aws_automated_scheduler/lambda'\n\n\nclass AutomatedSchedulerStack(core.Stack):\n \"\"\"\n Setup stack for automated scheduler\n \"\"\"\n\n def __init__(self, scope: core.Construct, id: str, **kwargs) -> None:\n super().__init__(scope, id, **kwargs)\n\n # Create a DynamoDB table with our requirements\n schedule_table = dynamodb.Table(\n self,\n \"AutomatedSchedulerTable\",\n table_name=TABLE_NAME,\n partition_key=dynamodb.Attribute(\n name=\"pk\",\n type=dynamodb.AttributeType.STRING\n ),\n sort_key=dynamodb.Attribute(\n name=\"sk\",\n type=dynamodb.AttributeType.STRING\n ),\n removal_policy=core.RemovalPolicy.DESTROY\n )\n\n # Create lambda resource using code from local disk\n lambda_handler = _lambda.Function(\n self, \"AutomatedScheduler\",\n code=_lambda.Code.from_asset(LAMBDA_FUNC_PATH),\n runtime=_lambda.Runtime.PYTHON_3_7,\n handler=\"automated_scheduler.event_handler\",\n memory_size=256,\n timeout=core.Duration.seconds(5)\n )\n\n schedule_table.grant_read_write_data(lambda_handler)\n\n lambda_handler.add_environment(\n key=\"scheduler_table\",\n value=TABLE_NAME\n )\n\n lambda_handler.add_environment(\n key=\"scheduler_tag\",\n value=TAG_KEY\n )\n\n lambda_handler.add_environment(\n key=\"scheduler_region\",\n value=REGION\n )\n\n ec2_read_only = iam.PolicyStatement(\n actions=[\n \"ec2:DescribeInstances\",\n \"ec2:DescribeTags\"\n ],\n effect=iam.Effect.ALLOW,\n resources=[\n \"*\"\n ]\n )\n\n s3_read_only = iam.PolicyStatement(\n actions=[\n \"s3:GetObject\",\n ],\n effect=iam.Effect.ALLOW,\n resources=[\n \"*\"\n ]\n )\n\n lambda_handler.add_to_role_policy(ec2_read_only)\n lambda_handler.add_to_role_policy(s3_read_only)\n\n # rule = events.Rule(\n # self,\n # \"AutomatedSchedulerRule\",\n # schedule=events.Schedule.expression(\"cron(0/1 * * * ? *)\")\n # )\n\n # Add lambda function as target of event rule\n # rule.add_target(targets.LambdaFunction(lambda_handler))\n","sub_path":"aws_automated_scheduler/automated_scheduler_stack.py","file_name":"automated_scheduler_stack.py","file_ext":"py","file_size_in_byte":2772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"606114321","text":"#\n# Copyright (c) 2020-2021 Arm Limited and Contributors. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n#\n\"\"\"Internal helper to retrieve target information from the online database.\"\"\"\n\nimport pathlib\nfrom http import HTTPStatus\nimport json\nfrom json.decoder import JSONDecodeError\nimport logging\nfrom typing import List, Optional, Dict, Any\n\nimport requests\n\nfrom mbed_tools.targets._internal.exceptions import ResponseJSONError, BoardAPIError\n\nfrom mbed_tools.targets.env import env\n\n\nINTERNAL_PACKAGE_DIR = pathlib.Path(__file__).parent\nSNAPSHOT_FILENAME = \"board_database_snapshot.json\"\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_board_database_path() -> pathlib.Path:\n \"\"\"Return the path to the offline board database.\"\"\"\n return pathlib.Path(INTERNAL_PACKAGE_DIR, \"data\", SNAPSHOT_FILENAME)\n\n\n_BOARD_API = \"https://os.mbed.com/api/v4/targets\"\n\n\ndef get_offline_board_data() -> Any:\n \"\"\"Loads board data from JSON stored in offline snapshot.\n\n Returns:\n The board database as retrieved from the local database snapshot.\n\n Raises:\n ResponseJSONError: error decoding the local database JSON.\n \"\"\"\n boards_snapshot_path = get_board_database_path()\n try:\n return json.loads(boards_snapshot_path.read_text())\n except JSONDecodeError as json_err:\n raise ResponseJSONError(f\"Invalid JSON received from '{boards_snapshot_path}'.\") from json_err\n\n\ndef get_online_board_data() -> List[dict]:\n \"\"\"Retrieves board data from the online API.\n\n Returns:\n The board database as retrieved from the boards API\n\n Raises:\n ResponseJSONError: error decoding the response JSON.\n BoardAPIError: error retrieving data from the board API.\n \"\"\"\n board_data: List[dict] = [{}]\n response = _get_request()\n if response.status_code != HTTPStatus.OK:\n warning_msg = _response_error_code_to_str(response)\n logger.warning(warning_msg)\n logger.debug(f\"Response received from API:\\n{response.text}\")\n raise BoardAPIError(warning_msg)\n\n try:\n json_data = response.json()\n except JSONDecodeError as json_err:\n warning_msg = f\"Invalid JSON received from '{_BOARD_API}'.\"\n logger.warning(warning_msg)\n logger.debug(f\"Response received from API:\\n{response.text}\")\n raise ResponseJSONError(warning_msg) from json_err\n\n try:\n board_data = json_data[\"data\"]\n except KeyError as key_err:\n warning_msg = f\"JSON received from '{_BOARD_API}' is missing the 'data' field.\"\n logger.warning(warning_msg)\n keys_found = \", \".join(json_data.keys())\n logger.debug(f\"Fields found in JSON Response: {keys_found}\")\n raise ResponseJSONError(warning_msg) from key_err\n\n return board_data\n\n\ndef _response_error_code_to_str(response: requests.Response) -> str:\n if response.status_code == HTTPStatus.UNAUTHORIZED:\n return (\n f\"Authentication failed for '{_BOARD_API}'. Please check that the environment variable \"\n f\"'MBED_API_AUTH_TOKEN' is correctly configured with a private access token.\"\n )\n else:\n return f\"An HTTP {response.status_code} was received from '{_BOARD_API}'.\"\n\n\ndef _get_request() -> requests.Response:\n \"\"\"Make a GET request to the API, ensuring the correct headers are set.\"\"\"\n header: Optional[Dict[str, str]] = None\n mbed_api_auth_token = env.MBED_API_AUTH_TOKEN\n if mbed_api_auth_token:\n header = {\"Authorization\": f\"Bearer {mbed_api_auth_token}\"}\n\n try:\n return requests.get(_BOARD_API, headers=header)\n except requests.exceptions.ConnectionError as connection_error:\n if isinstance(connection_error, requests.exceptions.SSLError):\n logger.warning(\"Unable to verify an SSL certificate with requests.\")\n elif isinstance(connection_error, requests.exceptions.ProxyError):\n logger.warning(\"Failed to connect to proxy. Please check your proxy configuration.\")\n\n logger.warning(\"Unable to connect to the online database. Please check your internet connection.\")\n raise BoardAPIError(\"Failed to connect to the online database.\") from connection_error\n","sub_path":"src/mbed_tools/targets/_internal/board_database.py","file_name":"board_database.py","file_ext":"py","file_size_in_byte":4181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"597316433","text":"#-*- coding: utf-8 -*-\n'''\n聚类离散化，最后的result的格式为：\n 1 2 3 4\nA 0 0.178698 0.257724 0.351843\nAn 240 356.000000 281.000000 53.000000\n即(0, 0.178698]有240个，(0.178698, 0.257724]有356个，依此类推。\n'''\nfrom __future__ import print_function\nimport pandas as pd\nfrom sklearn.cluster import KMeans #导入K均值聚类算法\n\ndatafile = '../data/data.xls' #待聚类的数据文件\nprocessedfile = '../tmp/data_processed.xls' #数据处理后文件\n\"\"\"\noutfile = '../tmp/data_discret.xls' #离散化后的数据\n\ndata = pd.read_excel(datafile)\nresult = pd.read_excel(processedfile)\n\ndf = data[['肝气郁结证型系数', '热毒蕴结证型系数', '冲任失调证型系数', '气血两虚证型系数', '脾胃虚弱证型系数', '肝肾阴虚证型系数']].copy()\ndf.columns = ['A','B','C','D','E','F']\n\ndf.loc[(df.A>result.iloc[0,0])&(df.A
result.iloc[0,1])&(df.Aresult.iloc[0,2])&(df.Aresult.iloc[0,3]),'Ax']= 'A4'\n\ndf.loc[(df.B>result.iloc[2,0])&(df.Bresult.iloc[2,1])&(df.Bresult.iloc[2,2])&(df.Bresult.iloc[2,3]),'Bx']= 'B4'\n\ndf.loc[(df.C>result.iloc[4,0])&(df.Cresult.iloc[4,1])&(df.Cresult.iloc[4,2])&(df.Cresult.iloc[4,3]),'Cx']= 'C4'\n\ndf.loc[(df.D>result.iloc[6,0])&(df.Dresult.iloc[6,1])&(df.Dresult.iloc[6,2])&(df.Dresult.iloc[6,3]),'Dx']= 'D4'\n\ndf.loc[(df.E>result.iloc[8,0])&(df.Eresult.iloc[8,1])&(df.Eresult.iloc[8,2])&(df.Eresult.iloc[8,3]),'Ex']= 'E4'\n\ndf.loc[(df.F>result.iloc[10,0])&(df.Fresult.iloc[10,1])&(df.Fresult.iloc[10,2])&(df.Fresult.iloc[10,3]),'Fx']= 'F4'\n\n\n\ndf.to_excel(outfile)\n\n\"\"\"\ntypelabel ={u'肝气郁结证型系数':'A', u'热毒蕴结证型系数':'B', u'冲任失调证型系数':'C', u'气血两虚证型系数':'D', u'脾胃虚弱证型系数':'E', u'肝肾阴虚证型系数':'F'}\nk = 4 #需要进行的聚类类别数\n\n#读取数据并进行聚类分析\ndata = pd.read_excel(datafile) #读取数据\nkeys = list(typelabel.keys())\nresult = pd.DataFrame()\n\nif __name__ == '__main__': #判断是否主窗口运行，这句代码的作用比较神奇，有兴趣了解的读取请自行搜索相关材料。\n for i in range(len(keys)):\n #调用k-means算法，进行聚类离散化\n print(u'正在进行“%s”的聚类...' % keys[i])\n kmodel = KMeans(n_clusters = k, n_jobs = 1) #n_jobs是并行数，一般等于CPU数较好\n kmodel.fit(data[[keys[i]]].as_matrix()) #训练模型\n \n r1 = pd.DataFrame(kmodel.cluster_centers_, columns = [typelabel[keys[i]]]) #聚类中心\n #print(r1.columns,r1.values)\n r2 = pd.Series(kmodel.labels_).value_counts() #分类统计\n r2 = pd.DataFrame(r2, columns = [typelabel[keys[i]]+'n']) #转为DataFrame，记录各个类别的数目\n #print(r2.columns,r2.values)\n #r = pd.concat([r1, r2], axis = 1).sort(typelabel[keys[i]]) #匹配聚类中心和类别数目\n r = pd.concat([r1, r2], axis = 1).sort_values(typelabel[keys[i]])\n #print(r.columns,r.values)\n r.index = [1, 2, 3, 4]\n \n #r[typelabel[keys[i]]] = pd.rolling_mean(r[typelabel[keys[i]]], 2) #rolling_mean()用来计算相邻2列的均值，以此作为边界点。\n r[typelabel[keys[i]]] = r[typelabel[keys[i]]].rolling(2).mean()\n r[typelabel[keys[i]]][1] = 0.0 #这两句代码将原来的聚类中心改为边界点。 \n result = result.append(r.T)\n\n #result = result.sort() #以Index排序，即以A,B,C,D,E,F顺序排\n result = result.sort_values(by = list(result.index),axis=1) #以Index排序，即以A,B,C,D,E,F顺序排\n result.to_excel(processedfile)\n\n\n ","sub_path":"中医证型关联规则挖掘/code/discretization.py","file_name":"discretization.py","file_ext":"py","file_size_in_byte":4356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"467342812","text":"# Written by Laurenz Mädje\r\n\r\nimport random\r\nimport overlay\r\nimport var\r\n\r\n# Alle Karten mit Preisen und Bildquellen\r\nCARDS = dict(Keller=[2, 'res/karten/keller.png'], Kapelle=[2, 'res/karten/kapelle.png'],\r\n Burggraben=[2, 'res/karten/burggraben.png'], Werkstatt=[3, 'res/karten/werkstatt.png'],\r\n Holzfäller=[3, 'res/karten/holzfaeller.png'], Kanzler=[3, 'res/karten/kanzler.png'],\r\n Dorf=[3, 'res/karten/dorf.png'], Festmahl=[4, 'res/karten/festmahl.png'],\r\n Spion=[4, 'res/karten/spion.png'], Bürokrat=[4, 'res/karten/buerokrat.png'],\r\n Dieb=[4, 'res/karten/dieb.png'], Miliz=[4, 'res/karten/miliz.png'],\r\n Schmiede=[4, 'res/karten/schmiede.png'], Umbau=[4, 'res/karten/umbau.png'],\r\n Geldverleiher=[4, 'res/karten/geldverleiher.png'], Thronsaal=[4, 'res/karten/thronsaal.png'],\r\n Markt=[5, 'res/karten/markt.png'], Mine=[5, 'res/karten/mine.png'],\r\n Jahrmarkt=[5, 'res/karten/jahrmarkt.png'], Laboratorium=[5, 'res/karten/laboratorium.png'],\r\n Bibliothek=[5, 'res/karten/bibliothek.png'], Ratsversammlung=[5, 'res/karten/ratsversammlung.png'],\r\n Hexe=[5, 'res/karten/hexe.png'], Abenteurer=[6, 'res/karten/abenteurer.png'],\r\n Anwesen=[2, 'res/karten/anwesen.png'], Herzogtum=[5, 'res/karten/herzogtum.png'],\r\n Provinz=[8, 'res/karten/provinz.png'], Fluch=[0, 'res/karten/fluch.png'],\r\n Kupfer=[0, 'res/karten/kupfer.png'], Silber=[0, 'res/karten/silber.png'], Gold=[0, 'res/karten/gold.png'],\r\n Garten=[4, 'res/karten/garten.png'])\r\n\r\n# Alle Karten in 4 Sets aufgeteilt\r\nACTION_CARDS = {'Keller', 'Kapelle', 'Burggraben', 'Werkstatt', 'Holzfäller', 'Kanzler', 'Dorf',\r\n 'Festmahl', 'Spion', 'Bürokrat', 'Dieb', 'Miliz', 'Schmiede', 'Umbau', 'Geldverleiher', 'Thronsaal',\r\n 'Markt', 'Mine', 'Jahrmarkt', 'Laboratorium', 'Bibliothek', 'Ratsversammlung', 'Hexe', 'Abenteurer'}\r\nPOINT_CARDS = {'Anwesen', 'Herzogtum', 'Provinz', 'Fluch'}\r\nMONEY_CARDS = {'Kupfer', 'Silber', 'Gold'}\r\nOTHER_CARDS = {'Garten'}\r\n\r\nstacks = None\r\nstacklist = None\r\n\r\ndef createStacklist():\r\n global stacklist\r\n stacklist = sorted(list(random.sample(ACTION_CARDS | OTHER_CARDS, 10)), key=lambda card: CARDS[card]) + list(MONEY_CARDS) + list(POINT_CARDS)\r\n\r\ndef createStacks():\r\n global stacks\r\n\r\n stacks = dict.fromkeys(stacklist, 10)\r\n stacks['Anwesen'], stacks['Herzogtum'], stacks['Provinz'], stacks['Fluch'] = 8, 8, 8, 8\r\n stacks['Kupfer'], stacks['Silber'], stacks['Gold'] = 30, 30, 30\r\n if 'Garten' in stacklist: stacks['Garten'] = 8\r\n\r\ndef gameOver():\r\n if stacks['Provinz'] == 0:\r\n return True\r\n counter = 0\r\n for key in stacks:\r\n if stacks[key] == 0:\r\n counter += 1\r\n if counter >= 3:\r\n return True\r\n return False\r\n\r\n\r\nclass Card:\r\n\r\n def __init__(self, name):\r\n\r\n # Member variables\r\n self.name = name\r\n self.path = CARDS[name][1]\r\n self.cost = CARDS[name][0]\r\n\r\n def play(self):\r\n\r\n if var.myplayer.playCard(self):\r\n var.state = 'playing'\r\n self.doEffect()\r\n var.connect.send('I_PLAY:' + self.name)\r\n\r\n def doEffect(self):\r\n\r\n if self.name in ['Abenteurer', 'Burggraben', 'Holzfäller','Dorf', 'Bürokrat', 'Geldverleiher', 'Miliz', 'Schmiede', 'Hexe', 'Miliz', 'Jahrmarkt', 'Laboratorium', 'Markt', 'Ratsversammlung']:\r\n self.do(None)\r\n\r\n elif self.name == 'Kapelle':\r\n var.overlay.infobox.setText('Wähle die Karten, die du entsorgen möchtest!')\r\n var.interface = overlay.HandSelector('multiple', 'Entsorgen', self.do)\r\n\r\n elif self.name == 'Keller':\r\n var.overlay.infobox.setText('Wähle die Karten, die du austauschen möchtest!')\r\n var.interface = overlay.HandSelector('multiple', 'Ablegen', self.do)\r\n\r\n elif self.name == 'Kanzler':\r\n var.interface = overlay.TwoOptionsSelector('Nachziehstapel ablegen?', ('Ja', 'Nein'), self.do)\r\n\r\n elif self.name == 'Werkstatt':\r\n var.overlay.infobox.setText('Wähle die Karte, die du nehmen möchtest!')\r\n var.interface = overlay.BuySelector('Nehmen', self.do)\r\n\r\n elif self.name == 'Festmahl':\r\n var.overlay.infobox.setText('Wähle die Karte, die du nehmen möchtest!')\r\n var.interface = overlay.BuySelector('Nehmen', self.do)\r\n\r\n elif self.name == 'Umbau':\r\n args = []\r\n\r\n def step2(arg):\r\n args.append(arg)\r\n self.do(args)\r\n\r\n def step1(arg):\r\n var.overlay.infobox.setText('Wähle die Karte, die du nehmen möchtest!')\r\n var.interface = overlay.BuySelector('Nehmen', step2)\r\n args.append(arg)\r\n\r\n var.overlay.infobox.setText('Wähle die Karte, die du entsorgen möchtest!')\r\n var.interface = overlay.HandSelector('single', 'Entsorgen', step1)\r\n\r\n elif self.name == 'Bibliothek':\r\n card = None\r\n\r\n def trueconfirm():\r\n confirm(True)\r\n\r\n def confirm(arg):\r\n\r\n if arg:\r\n var.myplayer.hand.append(card)\r\n else:\r\n var.myplayer.ablage.append(card)\r\n\r\n if len(var.myplayer.hand) < 7:\r\n var.overlay.cardShower.hide()\r\n nextCard()\r\n else:\r\n self.do(None)\r\n var.overlay.cardShower.hide()\r\n\r\n def nextCard():\r\n nonlocal card\r\n if len(var.myplayer.nachzieh) <= 0:\r\n var.myplayer.reshuffle()\r\n\r\n card = var.myplayer.nachzieh.pop(0)\r\n\r\n var.overlay.cardShower.setShow(card, 'Aufgedeckt:')\r\n var.overlay.cardShower.showUntimed()\r\n\r\n if card.name in ACTION_CARDS:\r\n var.interface = overlay.TwoOptionsSelector('Behalten?', ('Ja', 'Nein'), confirm, pos=(1070, 320))\r\n else:\r\n overlay.Waitor(1, trueconfirm)\r\n\r\n nextCard()\r\n\r\n elif self.name == 'Mine':\r\n args = []\r\n\r\n def step2(arg):\r\n args.append(arg)\r\n self.do(args)\r\n\r\n def step1(arg):\r\n var.overlay.infobox.setText('Wähle die Karte, die du nehmen möchtest!')\r\n var.interface = overlay.BuySelector('Nehmen', step2)\r\n args.append(arg)\r\n\r\n var.overlay.infobox.setText('Wähle die Karte, die du entsorgen möchtest!')\r\n var.interface = overlay.HandSelector('single', 'Entsorgen', step1)\r\n\r\n\r\n\r\n\r\n def do(self, args):\r\n if var.myplayer.doEffect(self, args):\r\n var.overlay.handpos = []\r\n if var.myplayer.actions > 0:\r\n var.interface = overlay.HandSelector('single', 'Spielen', var.overlay.playPressed)\r\n var.overlay.infobox.setText('Wähle eine Karte. newline Drücke Spielen, um sie auszuspielen! newline Drücke Weiter, um in die Kaufphase zu gelangen!')\r\n else:\r\n var.interface = None\r\n var.overlay.infobox.setText('Drücke Weiter, um in die Kaufphase zu gelangen!')\r\n var.state = 'action'\r\n else:\r\n var.overlay.handpos = []\r\n self.doEffect()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"cards.py","file_name":"cards.py","file_ext":"py","file_size_in_byte":7474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"404003098","text":"import requests\ntopic = \"eu referendum\"\nkey = \"4d547cbd-99e2-4b00-a40e-987c67c252b8\"\nfrom_date = \"2016-02-19\"\nto_date =\"2016-02-21\"\nurl = \"http://content.guardianapis.com/search?q=\" + topic + \"&\" + \"from-date=\" + from_date + \"&\" + \\\n \"to-date=\" + \\\n to_date \\\n + \"&use-date=published&api-key=\" + key\nimport json\nimport pprint\na = requests.get(url)\nb = a.json()\n\npprint.pprint([i[\"webTitle\"] for i in b[\"response\"][\"results\"]])\n\n\n\n\n\n","sub_path":"server/src/Scripts/GuardianAPI/Guardian.py","file_name":"Guardian.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"584085037","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 22 12:17:54 2018\n\n@author: craig\n\"\"\"\n\nimport unittest\nfrom plane import Plane\nfrom vector import Vector\n\nclass PlaneTestCase(unittest.TestCase):\n def test_is_parallel_to(self):\n ## Two parallel planes\n p1 = Plane(Vector((1, 1, 1)), 0)\n p2 = Plane(Vector((1, 1, 1)), 1)\n self.assertTrue(p1.is_parallel_to(p2))\n self.assertTrue(p2.is_parallel_to(p1))\n \n ## Two intersecting planes\n p3 = Plane(Vector((1, -1, -1)), 0)\n self.assertFalse(p1.is_parallel_to(p3))\n self.assertFalse(p3.is_parallel_to(p1))\n \n ## One factor = 0\n p4 = Plane(Vector((0, 1, 1)), 0)\n p5 = Plane(Vector((0, 1, 1)), 1)\n self.assertTrue(p4.is_parallel_to(p5))\n self.assertFalse(p4.is_parallel_to(p1))\n \n ## Two factors = 0\n p6 = Plane(Vector((0, 0, 1)), 0)\n p7 = Plane(Vector((0, 0, 1)), 1)\n self.assertTrue(p6.is_parallel_to(p7))\n self.assertFalse(p6.is_parallel_to(p1))\n \n ## Two planes with same definition\n p8 = Plane(Vector((1, 1, 1)), 0)\n self.assertTrue(p1.is_parallel_to(p8))\n \n ## Same plane with two definitions\n p9 = Plane(Vector((2, 2, 2)), 2)\n self.assertTrue(p2.is_parallel_to(p9))\n \n def test__eq__(self):\n ## Two planes with same definition\n p1 = Plane(Vector((1, 1, 1)), 1)\n p2 = Plane(Vector((1, 1, 1)), 1)\n self.assertTrue(p1 == p2)\n \n ## Same plane with two defnitions\n p3 = Plane(Vector((2, 2, 2)), 2)\n self.assertTrue(p1 == p3)\n \n ## Two parallel planes\n p4 = Plane(Vector((1, 1, 1)), 0)\n self.assertFalse(p1 == p4)\n \n ## Two intersecting planes\n p5 = Plane(Vector((1, -1, 1)), 0)\n self.assertFalse(p1 == p5)\n \n \nif __name__ == '__main__':\n unittest.main()\n","sub_path":"plane_test.py","file_name":"plane_test.py","file_ext":"py","file_size_in_byte":1967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"90304293","text":"from data_classes import Rocket, State\nimport numpy as np\n\nclass Simulator:\n def __init__(self, rocket = None, state = None, dt = 1, g = -9.81):\n self.dt = dt\n self.rocket = rocket if rocket else Rocket()\n self.s = state if state else State(fuel_level=self.rocket.start_fuel_mass)\n self.g = g\n\n # rewards\n self.v_penalty = -1\n self.x_penalty = -1\n self.angle_penalty = -10 # its in radians...\n\n def get_state(self):\n return self.s\n\n def get_state_arr(self):\n return [self.s.px, self.s.py, self.s.vx, self.s.vyself.s.v_angular, self.s.orientation_angle, self.s.fuel_level]\n\n def final_state_reached(self):\n return self.s.py <=0\n\n def take_action(self, gimble_angle, thrust_proportion, fin_angle):\n CoM, MoI = self.get_mass_vars()\n fin_forward, fin_ang_acc = self.get_fin_forces(fin_angle, CoM, MoI)\n\n # Check if we have enough fuel to burn the requested about, and calculate the amount of resulting thrust\n if self.s.fuel_level > 0:\n thrust_forward, thrust_ang_acc = self.get_thrust_forces(thrust_proportion, gimble_angle, CoM, MoI)\n fuel_burn = thrust_proportion * self.rocket.burn_rate * self.dt\n if fuel_burn > self.s.fuel_level:\n prop_burn = self.s.fuel_level / fuel_burn\n thrust_forward *= prop_burn\n thrust_ang_acc *= prop_burn\n self.s.fuel_level = 0\n else:\n self.s.fuel_level -= fuel_burn\n else:\n thrust_forward, thrust_ang_acc = 0, 0\n\n total_thrust_forward = thrust_forward + fin_forward\n total_ang_acc = thrust_ang_acc + fin_ang_acc\n self.update_p_and_v(total_thrust_forward, total_ang_acc)\n\n def get_mass_vars(self):\n # Calculate mass, center of mass, and moment of inertia\n mass = self.s.fuel_level + self.rocket.dry_mass\n fuel_height = (self.s.fuel_level / self.rocket.start_fuel_mass) * self.rocket.fuel_height_prop * self.rocket.height\n fuel_CoM = fuel_height * .5 * self.s.fuel_level\n total_CoM = (fuel_CoM + self.rocket.rocket_CoM) / mass\n fuel_MoI = self.get_MoI(self.s.fuel_level, total_CoM, fuel_height)\n rocket_MoI = self.get_MoI(self.rocket.dry_mass, total_CoM, self.rocket.height)\n total_MoI = fuel_MoI + rocket_MoI\n\n return total_CoM, total_MoI\n\n def get_MoI(self, mass, c, h):\n if h==0: return 0\n return mass / (3 * h) * (h ** 3 + 3 * h * c ** 2 - 3 * h ** 2 * c)\n\n def get_fin_forces(self, fin_angle, CoM, MoI):\n # TODO: This will depend on angular velocity also\n v_angle = np.arctan2(self.s.vx, self.s.vy)\n fin_v_angle = self.s.orientation_angle + fin_angle - v_angle\n\n f_fin = -1*(np.cos(fin_v_angle) * self.total_v()) ** 2 * self.rocket.fin_drag\n fin_forward = np.abs(np.cos(fin_angle)) * f_fin\n fin_lever_arm = self.rocket.height - self.rocket.fin_offset - CoM\n fin_torque = np.sin(fin_angle) * f_fin * fin_lever_arm\n fin_ang_acc = fin_torque / MoI\n\n return fin_forward, fin_ang_acc\n\n def total_v(self):\n return np.sqrt(self.s.vx ** 2 + self.s.vy ** 2)\n\n def get_thrust_forces(self, thrust_proportion, gimble_angle, CoM, MoI):\n thrust = self.rocket.full_thrust * thrust_proportion\n thrust_forward = np.cos(gimble_angle) * thrust\n thrust_torque = np.sin(gimble_angle) * thrust * CoM\n thrust_ang_acc = thrust_torque / MoI\n\n return thrust_forward, thrust_ang_acc\n\n def update_p_and_v(self, thrust_forward, ang_acc):\n # TODO: handle end case, where the ground is hit. Only some of the acceleration will happen before impact.\n # TODO: angular velocity will also affect this, as over dt the rotation will cause a curve in thrust\n mass = self.s.fuel_level + self.rocket.dry_mass\n total_thrust_x = np.cos(self.s.orientation_angle) * thrust_forward\n total_thrust_y = np.sin(self.s.orientation_angle) * thrust_forward\n\n d_v_angular = ang_acc * self.dt\n d_vx = total_thrust_x / mass * self.dt\n d_vy = (self.g + total_thrust_y / mass) * self.dt\n\n # Assuming that changes in vx, vy, and v_angular happen smoothly, the average vs over dt will be halfway between the old v and the new v\n self.s.orientation_angle += (self.s.v_angular + d_v_angular / 2) * self.dt\n self.s.px += (self.s.vx + d_vx / 2) * self.dt\n self.s.py += (self.s.vy + d_vy / 2) * self.dt\n\n self.s.v_angular += d_v_angular\n self.s.vx += d_vx\n self.s.vy += d_vy\n\n def get_reward(self):\n return self.v_penalty*self.total_v() \\\n + self.x_penalty*self.s.px \\\n + self.angle_penalty*np.abs(self.s.orientation_angle)","sub_path":"rocket_sim.py","file_name":"rocket_sim.py","file_ext":"py","file_size_in_byte":4815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"453804125","text":"\n\"\"\"\n@author: Milena Bajic (DTU Compute)\n\"\"\"\nimport sys,os, glob, pickle, time\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tsfel\nfrom mlxtend.feature_selection import SequentialFeatureSelector\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import make_scorer, confusion_matrix\nfrom sklearn.metrics import classification_report, plot_confusion_matrix\nfrom mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\nimport scipy.stats\nfrom scipy import stats, interpolate\nfrom numpy.random import choice\nfrom sklearn.model_selection import TimeSeriesSplit\nfrom matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\nfrom sklearn.model_selection import TimeSeriesSplit, GridSearchCV\nimport seaborn as sns\nfrom sklearn.svm import SVR, SVC\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\nfrom sklearn.linear_model import LinearRegression, LogisticRegression\nfrom sklearn.neural_network import *\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.tree import export_graphviz\nfrom sklearn import tree\nfrom sklearn import linear_model\nfrom sklearn.dummy import DummyRegressor, DummyClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.pipeline import Pipeline\nfrom scipy.signal import find_peaks, argrelmin, argrelextrema, find_peaks_cwt\nfrom mlxtend.evaluate import PredefinedHoldoutSplit\n\ndef sort2(f):\n try:\n num = int(f.split('/')[-1].split('_')[4] )\n except:\n if '_fulltrip.png' in f:\n num = -10\n elif '_clusters.png' in f:\n num = -9\n elif '_removed_outliers.png' in f:\n num = -8\n elif '_fulltrip_minima.png' in f:\n num = -7\n else:\n num = -1\n return num\n\ndef sort3(f):\n if 'mapmatched_map_printout.png' in f:\n num = -10\n elif'interpolated_300th_map_printout.png' in f:\n num = -9\n else:\n num = -1\n return num\n\ndef polynomial_model(degree=10):\n polynomial_features = PolynomialFeatures(degree=degree,\n include_bias=False)\n linear_regression = LinearRegression(normalize=True) #normalize=True normalize data\n pipeline = Pipeline([(\"polynomial_features\", polynomial_features),#Add polynomial features\n (\"linear_regression\", linear_regression)])\n return pipeline\n \n \ndef plot_fs(nf, res, var_label = 'MSE',title='', size=2,\n out_dir = '.', save_plot=True, filename='plot-fs'): \n if size==2:\n plt.rcParams.update({'font.size': 6})\n figsize=[2.5,2]\n dpi= 1000\n ms = 3 \n ls = 7\n if size==3:\n plt.rcParams.update({'font.size': 7})\n figsize=[4,3]\n dpi= 1000\n ms = 6\n ls = 9\n\n #var_min = true.min() - 0.3*true.min()\n #var_max = true.max() + 0.35*true.max()\n\n #var_min = 0.3\n #var_max = 3.5\n \n plt.figure(figsize=figsize, dpi=dpi)\n #plt.plot(true, m*true + b, c='blue', label='Best fit') \n plt.scatter(nf, res, marker='o',s=ms, facecolors='b', edgecolors='b', label='MSE')\n plt.plot(nf, res, linewidth=1)\n plt.ylabel('{0}'.format(var_label), fontsize=ls)\n plt.xlabel('Number of features', fontsize=ls)\n #plt.xlim([var_min, var_max])\n #plt.ylim([var_min, var_max])\n #plt.title(title)\n ax = plt.gca()\n ax.yaxis.set_major_formatter('{x:.2e}')\n ax.xaxis.set_major_formatter('{x:.0f}')\n # For the minor ticks, use no labels; default NullFormatter.\n ax.xaxis.set_minor_locator(AutoMinorLocator())\n #ax.yaxis.set_minor_locator(AutoMinorLocator())\n plt.tight_layout()\n \n if save_plot:\n out_file_path = filename\n plt.savefig(out_file_path, dpi=dpi, bbox_inches = \"tight\")\n plt.savefig(out_file_path.replace('.png','.eps'),format='eps',dpi=dpi, bbox_inches = \"tight\")\n plt.savefig(out_file_path.replace('.png','.pdf'),dpi=dpi, bbox_inches = \"tight\")\n print('file saved as: ',out_file_path)\n \n return\ndef format_col(x):\n if x=='R2':\n return r'$\\textbf{R^2}$'\n else:\n return r'\\textbf{' + x + '}'\n \ndef get_flattened(row, vars):\n row_data = []\n for var in vars:\n row_data.append(pd.Series(row[var]))\n df = pd.concat(row_data, axis = 1) \n return df\n \n \ndef set_class(y_cont, bins = [0,2,5,50]):\n labels = list(range(len(bins)-1))\n y_cat = pd.cut(y_cont, bins, labels=labels).astype(np.int8)\n print(y_cat.value_counts())\n return y_cat\n\n\ndef get_nan_cols(df, nan_percent=0.01, exclude_cols = ['IRI_mean_end']):\n # return cols to remove\n threshold = len(df.index) * nan_percent \n res = [c for c in df.drop(exclude_cols,axis=1).columns if sum(df[c].isnull()) >= threshold] \n return res\n\ndef clean_nans(df, col_nan_percent = 0.01, exclude_cols = ['IRI_mean_end']):\n cols_to_remove = get_nan_cols(df, nan_percent=col_nan_percent, exclude_cols = exclude_cols) \n \n # Replace infinities with nans \n df.replace([np.inf, -np.inf], np.nan, inplace=True)\n \n # Drop\n df.drop(columns=cols_to_remove,axis=1, inplace=True) # remove features with more than 1% nans\n df.dropna(axis=0, inplace=True) # drop rows with at least 1 nan\n \n # Reset index\n df.reset_index(inplace=True, drop = True)\n return \n \ndef compute_features_per_series(seq, cfg):\n try:\n t = tsfel.time_series_features_extractor(cfg, seq, window_size=len(seq),overlap=0, fs=None)\n except:\n t = None\n return t\n\n\ndef extract_col(tsfel_obj, col):\n if col in list(tsfel_obj.columns):\n t = tsfel_obj[col] \n else:\n t = pd.Series() # not None!!\n return t\n\n \ndef feature_extraction(df, target_name, out_dir, keep_cols = [], feats = ['GM.obd.spd_veh.value','GM.acc.xyz.x', 'GM.acc.xyz.y', 'GM.acc.xyz.z'], \n file_suff='', recreate=False, write_out_file = True, feats_list_to_extract = None, predict_mode = False, sel_features = None):\n\n # Output filename\n out_filename = '{0}/{1}'.format(out_dir, file_suff)\n\n # Load if it exists\n if not recreate and os.path.exists(out_filename):\n with open(out_filename, 'rb') as handle:\n df = pickle.load(handle)\n print('File succesfully loaded.')\n \n # Compute features \n else:\n \n # Remove those\n stat_to_rem = ['Histogram', 'ECDF', 'ECDF Percentile Count','ECDF Percentile']\n \n # Set cfg\n cfg = tsfel.get_features_by_domain() \n \n # Delete spectral\n del cfg['spectral']\n \n # Set stat\n for key in stat_to_rem:\n del cfg['statistical'][key]\n \n \n # Compute features\n if not predict_mode and target_name not in keep_cols:\n keep_cols.append(target_name)\n \n for var in feats:\n print('===== Computing features for: {0} ======'.format(var))\n \n # Additional features: maxmin\n df[var+'-0_Maxmin diff'] = df[var].apply(lambda seq: seq.max()-seq.min())\n keep_cols.append(var+'-0_Maxmin diff')\n \n # ECDF percentils\n for i, p in enumerate([0.05, 0.10, 0.20, 0.80]):\n perc_col_name = var+'-0_ECDF Percentile '+str(p)\n df[perc_col_name] = df[var].apply(lambda seq: tsfel.ecdf_percentile(seq, percentile=[p]) if seq.shape[0]>20 else None)\n keep_cols.append(perc_col_name)\n \n # Compute default tsfel features\n colname_tsfel = var+'_fe'\n df[colname_tsfel] = df[var].apply(lambda seq: compute_features_per_series(seq, cfg) )\n keep_cols.append(colname_tsfel)\n \n # Drop rows where tsfel is not computed \n df = df[keep_cols] #here\n #df.dropna(axis=0, inplace=True)\n df.reset_index(inplace=True, drop = True)\n\n # Save \n if write_out_file:\n df.to_pickle(out_filename) \n print('Wrote to ',out_filename)\n \n return df, out_filename\n\n\n\ndef extract_inner_df(df, feats = ['GM.obd.spd_veh.value','GM.acc.xyz.z'], remove_vars = ['GM.acc.xyz.x', 'GM.acc.xyz.y'], do_clean_nans = False):\n \n # Extract to nice structure\n for var in feats: \n \n var_tsfel = var + '_fe'\n # Check if present\n if var_tsfel not in df.columns:\n print('Feature {0} not present in df'.format(var))\n continue\n \n # Get feature names for this var\n #try:\n col_names = df[var_tsfel].iloc[0].columns\n print('Will extract: {0}\\n'.format(col_names.to_list()))\n time.sleep(2)\n #except:\n # print('Extraction failed')\n # time.sleep(3)\n # return None, None\n\n # Extract\n for col in col_names:\n print('Extracting: ',col)\n df[var+'-'+col] = df[var_tsfel].apply(lambda tsfel: extract_col(tsfel, col))\n df[var+'-'+col].astype(np.float16)\n \n # Remove tsfel and additional variables\n cols_to_rem = [col for col in df.columns if col.endswith('_fe')] # all tsfel\n for var in remove_vars:\n add_cols_rem = [col for col in df.columns if var in col and var not in cols_to_rem]\n cols_to_rem.extend(add_cols_rem)\n df.drop(cols_to_rem,axis=1,inplace=True)\n\n # Clean the dataframe\n if do_clean_nans:\n exclude = [col for col in df.columns if not col.startswith('GM')]\n clean_nans(df, exclude_cols = exclude)\n \n # Rename \n #for col in df.columns:\n # if '_resampled' in col:\n # new_col = col.replace('_resampled','')\n # df.rename(columns={col:new_col}, inplace=True)\n \n return\n \ndef find_optimal_subset(X, y, valid_indices = None, n_trees=50, fmax = None, reg_model = True, bins = None, target_name = 'target', sel_features_names = None,\n out_dir = '.', outfile_suff = 'feature_selection', recreate = False, save_output = True):\n \n \n # Iinput filenames\n if reg_model:\n x_filename = '{0}/{1}_regression.pickle'.format(out_dir, outfile_suff)\n else:\n x_filename = '{0}/{1}_bins-{2}_classification.pickle'.format(out_dir, GM_trip_id, '-'.join([str(b) for b in bins]))\n \n # Load files if they exists\n if not recreate and os.path.exists(x_filename):\n with open(x_filename, 'rb') as handle:\n Xy_filt = pickle.load(handle)\n \n X_filt = Xy_filt.drop([target_name],axis=1) \n y = Xy_filt[target_name]\n \n sel_features_names = list(X_filt.columns)\n \n print('Files loaded.')\n \n # Create files if they do not exist\n elif not sel_features_names:\n \n print('Starting SFS')\n \n # Remove features with zero variance\n features = list(X.columns)\n for col in features:\n if X[col].var()==0:\n print('=== Removing: {0} (0 variance)'.format(col)) # Zero crossing rate\n X.drop(col,axis=1,inplace=True)\n #test.drop(col,axis=1,inplace=True)\n \n # Feature search\n tscv = TimeSeriesSplit(n_splits=5)\n if not fmax:\n fmax = X.shape[1]-1\n \n if reg_model:\n f=(1,fmax)\n if valid_indices is not None:\n valid_subset = PredefinedHoldoutSplit(valid_indices)\n feature_selector = SequentialFeatureSelector(RandomForestRegressor(n_trees, bootstrap = True, min_impurity_decrease=1e-6), \n n_jobs=-1,\n k_features=f,\n forward=True,\n verbose=2,\n scoring='neg_mean_squared_error',\n cv = tscv)\n #cv=valid_subset)\n else:\n feature_selector = SequentialFeatureSelector(RandomForestRegressor(n_trees, bootstrap = True, min_impurity_decrease=1e-6), \n n_jobs=-1,\n k_features=f,\n forward=True,\n verbose=2,\n scoring='neg_mean_squared_error',\n cv=tscv)\n \n else:\n f=(1,fmax)\n if valid_indices is not None:\n valid_subset = PredefinedHoldoutSplit(valid_indices)\n feature_selector = SequentialFeatureSelector(RandomForestClassifier(n_trees,class_weight = 'balanced_subsample', max_depth=5, min_impurity_decrease=1e-4),\n n_jobs=-1,\n k_features=f,\n forward=True,\n verbose=2,\n scoring=make_scorer(f1_score, average='macro'),\n cv=valid_subset)\n else:\n feature_selector = SequentialFeatureSelector(RandomForestClassifier(n_trees,class_weight = 'balanced_subsample', max_depth=5, min_impurity_decrease=1e-4),\n n_jobs=-1,\n k_features=f,\n forward=True,\n verbose=2,\n scoring=make_scorer(f1_score, average='macro'),\n cv=tscv)\n \n \n features = feature_selector.fit(X,y)\n sel_features_names = list(feature_selector.k_feature_names_)\n print('Selected features ', sel_features_names)\n \n # Plot\n fig1 = plot_sfs(feature_selector.get_metric_dict(), kind='std_dev', figsize=(25,20))\n plot_name = x_filename.replace('.pickle','.png')\n if save_output:\n plt.savefig(plot_name)\n #print(feature_selector_backward.asubsets_)\n \n # Get metrics per feature \n feats = pd.DataFrame.from_dict(feature_selector.get_metric_dict()).T\n feats['Added Feature'] = None\n for i in range(1,feats.shape[0]+1):\n if i==1:\n prev = set()\n else:\n prev = set(feats.at[i-1,'feature_names'])\n curr = set(feats.at[i,'feature_names'])\n diff = curr.difference(prev)\n diff = list(diff)[0]\n feats.at[i, 'Added Feature'] = diff\n print(i,diff)\n feats['MSE (subset)'] = feats['avg_score'].apply(lambda row:abs(row)) \n feats['Added Feature'] = feats['Added Feature'].apply(lambda row: get_var_name(row))\n feats['Added Feature'] = feats['Added Feature'].apply(lambda row: row.replace('GM.obd.spd_veh.value-0_','Vehicle speed '))\n \n if save_output:\n feats.to_pickle(x_filename.replace('.pickle','_feats_info.pickle'))\n print('Saved: ',x_filename.replace('.pickle','_feats_info.pickle'))\n \n # Save latex\n #feats = feats[['Added Feature','MSE (subset)']]\n feats = feats[['Added Feature', 'MSE (subset)']]\n feats.to_latex('reg_fs_table.tex', columns = feats.columns, index = True, \n float_format = lambda\n x: '%.2e' % x, label = 'table:reg_fs', \n header=[ format_col(col) for col in feats.columns] ,escape=False)\n\n latex_file = x_filename.replace('.pickle','_table.tex')\n print('Wrote latex to: ',latex_file)\n \n # Plot\n plot_filename = x_filename.replace('.pickle','_sfs.pdf')\n plot_fs(feats.index, res = feats['MSE (subset)'], var_label='MSE',filename=plot_filename)\n print('Saved: ',plot_filename)\n \n # Select best features\n X_filt = X[sel_features_names]\n \n # Merge with the target\n X_filt[target_name] = y\n \n # Dump them\n if save_output:\n X_filt.to_pickle(x_filename)\n print('Wrote to ',x_filename)\n \n \n # If test, only select features and save files \n else:\n print('Selecting given features.')\n \n X_filt = X[sel_features_names]\n \n # Merge with the target\n X_filt[target_name] = y\n \n # Dump them\n if save_output:\n X_filt.to_pickle(x_filename)\n print('Wrote to ',x_filename)\n \n \n return X_filt, sel_features_names\n\n\ndef compute_di_aran(data):\n print('Computing DI')\n \n # DI\n data['DI'] = (data[\"AlligCracksSmall\"]*3+data[\"AlligCracksMed\"]*4+data[\"AlligCracksLarge\"]*5)**0.3 + (data[\"CracksLongitudinalSmall\"]**2+data[\"CracksLongitudinalMed\"]**3+data[\"CracksLongitudinalLarge\"]**4+data[\"CracksLongitudinalSealed\"]**2+data[\"CracksTransverseSmall\"]*3+data[\"CracksTransverseMed\"]*4+data[\"CracksTransverseLarge\"]*5+data[\"CracksTransverseSealed\"]*2)**0.1 + (data[\"PotholeAreaAffectedLow\"]*5+data[\"PotholeAreaAffectedMed\"]*7+data[\"PotholeAreaAffectedHigh\"]*10+data[\"PotholeAreaAffectedDelam\"]*5)**0.1\n\n # DI reduced\n data['DI_red'] = (data[\"AlligCracksMed\"]*4+data[\"AlligCracksLarge\"]*5)**0.3 + (data[\"CracksTransverseMed\"]*4+data[\"CracksTransverseLarge\"]*5)**0.1 + (data[\"PotholeAreaAffectedMed\"]*7+data[\"PotholeAreaAffectedHigh\"]*10)**0.1\n \n return \n\n \n#custom function for ecdf\ndef empirical_cdf(data):\n percentiles = []\n n = len(data)\n sort_data = np.sort(data)\n \n for i in np.arange(1,n+1):\n p = i/n\n percentiles.append(p)\n return sort_data,percentiles\n\n\ndef ent(data):\n \"\"\"Calculates entropy of the passed `pd.Series`\n \"\"\"\n p_data = data.value_counts() # counts occurrence of each value\n entropy = scipy.stats.entropy(p_data) # get entropy from counts\n return entropy\n\ndef resample(seq, to_length, window_size):\n '''\n Resample a sequence/\n\n Parameters\n ----------\n seq : np.array\n Sequence to be resampled.\n to_length : int\n Resample to this number of points.\n\n Returns\n -------\n d_resampled : np.array\n resampled distance (0,10)\n y_resampled : np.array\n resampled input sequence.\n ''' \n # Downsample if needed\n seq_len = seq.shape[0] \n if seq_len>to_length:\n seq = choice(seq, to_length)\n seq_len = seq.shape[0] #\n \n # Current\n d = np.linspace(0, window_size, seq_len)\n f = interpolate.interp1d(d, seq)\n \n # Generate new points \n d_new = np.random.uniform(low=0, high=d[-1], size=(to_length - seq_len))\n \n # Append new to the initial\n d_resampled = sorted(np.concatenate((d, d_new)))\n \n # Estimate y at points\n y_resampled = f(d_resampled) \n \n return d_resampled, y_resampled\n\n\ndef resample_df(df, feats_to_resample, to_lengths_dict = {}, window_size = None):\n input_feats_resampled = []\n \n # Filter rows with less than 2 points (can't resample those)\n for feat in feats_to_resample:\n df[feat+'_len'] = df[feat].apply(lambda seq: 1 if isinstance(seq, float) else seq.shape[0]) \n df.mask(df[feat+'_len']<2, inplace = True)\n \n # Drop nans (rows with NaN/len<2) and reset index\n df.dropna(subset = feats_to_resample, inplace = True)\n df.reset_index(drop = True, inplace = True)\n \n # Resample to the maximum\n for feat in feats_to_resample:\n print('Resampling feature: ',feat)\n #max_len = max(df[feat].apply(lambda seq: seq.shape[0]))\n to_length = to_lengths_dict[feat]\n new_feats_resampled = ['{0}_d_resampled'.format(feat), '{0}_resampled'.format(feat)]\n df[new_feats_resampled ] = df.apply(lambda seq: resample(seq[feat], to_length = to_length, window_size = window_size), \n axis=1, result_type=\"expand\")\n input_feats_resampled.append('{0}_resampled'.format(feat))\n \n return df, input_feats_resampled \n\n\n\ndef mean_absolute_percentage_error(y_true, y_pred): \n\n return np.mean(np.abs((y_true - y_pred) / y_true))\n\ndef get_var_name(row, short = False):\n new_row = row.replace(' diff', ' difference')\n if short:\n if 'GM.acc.xyz.z-0' in new_row: \n return '{0} (Acc-z)'.format(new_row.split('-0_')[1])\n else:\n return '{0} (Speed)'.format(new_row.split('-0_')[1]) \n else:\n if 'GM.acc.xyz.z-0' in new_row: \n return '{0} (Acceleration-z)'.format(new_row.split('-0_')[1])\n else:\n return '{0} (Vehicle Speed)'.format(new_row.split('-0_')[1])\n \ndef get_regression_model(model, f_maxsel, random_state = None, use_default = False, is_pca = False):\n model_title = model.replace('_',' ').title()\n nt = 500\n # Define models\n \n if model=='dummy':\n rf = DummyRegressor(strategy=\"mean\")\n parameters = {}\n if model=='linear':\n model_title='Multiple Linear'\n rf = linear_model.LinearRegression()\n parameters = {}\n elif model=='lasso':\n rf = linear_model.Lasso(random_state=random_state)\n lasso_alpha = np.logspace(-5,5,11)\n lasso_alpha = np.array([0.00000001, 0.0000001, 0.000001, 0.00001, 0.0001, 0.0005, 0.001,0.002, 0.005, 0.01,1]) #CPH1\n #lasso_alpha = np.linspace(0,1,21) #M3\n parameters={'alpha':lasso_alpha}\n elif model=='kNN':\n rf = KNeighborsRegressor()\n k = np.arange(5,41,step=5) \n parameters={'n_neighbors':k}\n elif model=='ridge':\n rf = linear_model.Ridge(random_state=random_state)\n ridge_alpha = np.linspace(0,1000,21)\n parameters={'alpha': ridge_alpha}\n elif model=='elastic_net':\n rf = linear_model.ElasticNet(random_state=random_state)\n alpha = np.array([0.001, 0.01,10, 20, 50, 100, 500, 700, 1000])\n parameters={'alpha':alpha, 'l1_ratio':np.linspace(0,1,21)} \n elif model=='random_forest':\n rf = RandomForestRegressor(random_state=random_state)\n depths = np.arange(5,15,5)\n n_estimators = [100]\n #np.arange(300,500,100)\n #fs = np.arange(6,16,2) motorway\n fs = np.arange(10,f_maxsel,2)\n parameters = {'n_estimators': n_estimators, 'max_depth':depths,'min_impurity_decrease':[1e-4, 1e-5], 'max_features':fs}\n elif model=='SVR_poly':\n rf = SVR(kernel='poly')\n C = [0.01,0.8,1,1.5,2,3,5,10,15,20,50]\n epsilon = [0.01,0.01,0.05,0.1,0.2,0.3,0.4,0.5]\n gamma = [0.001,0.01,0.02, 0.05,0.1 ]\n parameters = {'C':C,\n 'epsilon': epsilon,\n 'gamma':gamma}\n elif model=='SVR_rbf':\n model_title = 'SVR'\n rf = SVR(kernel='rbf', epsilon = 0.1)\n #C = np.linspace(0,20,5)\n #C = np.append(C,[1])\n #C.sort()\n #gamma = np.logspace(-4,-2,3) \n C = np.array([0.1,1,5,10,15,20,50,100,200,500,1000])\n gamma = np.array([0.001, 0.01, 0.1, 1, 5])\n n = C.shape[0]*gamma.shape[0]\n print(n)\n parameters = {'C':C, 'gamma':gamma}\n elif model=='ANN':\n model_title = 'ANN'\n \n # L2 regularization parameter\n ann_alpha = np.array([10,15,20])\n \n # Learning rate init\n #learning_rate_init = np.logspace(-5,0,6)\n learning_rate_init = np.array([0.001,0.01, 0.1, 1])\n \n # Architecture\n hs1 = [(2),(4),(8)]\n hs2 = [(16, 8),(12,6),(12,4),(12,2),(10,4)]\n hs3 = [(2, 4, 6), (2,6,8), (2,6,10),(4,8,10), (4,8,12), (4,8,16), (12,8,6), (10,8,6),(10,8,4),(8, 6, 4)]\n hs4 = [(2,4,8,12),(12,8,6,2)]\n hs5 = [(2,4,6,8,12),(12,8,6,4,2)]\n hs = hs2+hs3+hs4\n #hs = [(4,6,8)]\n \n #if use_default and is_pca:\n # parameters = {'hidden_layer_sizes':[(8,6,4)], 'alpha':[1], 'learning_rate_init':[0.1], 'random_state':rs}\n #elif use_default and not is_pca:\n # parameters = {'hidden_layer_sizes':[(2,4,6)], 'alpha':[1], 'learning_rate_init':[0.01], 'random_state':rs}\n #else:\n # parameters = {'hidden_layer_sizes':hs, 'alpha':ann_alpha, 'learning_rate_init':learning_rate_init, 'random_state':rs}\n \n parameters = {'hidden_layer_sizes':hs, 'alpha':ann_alpha, 'learning_rate_init':learning_rate_init}\n \n # Model\n rf = MLPRegressor(learning_rate='adaptive', max_iter=1000)\n \n \n\n return rf, parameters, model_title\n \n \ndef grid_search(rf, parameters, X, y, score, n_splits = 10):\n tscv = TimeSeriesSplit(n_splits=n_splits)\n clf = GridSearchCV(rf, parameters, cv=tscv, scoring=score, verbose=1)\n clf.fit(X,y)\n best_parameters = clf.best_params_\n print(best_parameters) \n rf = clf.best_estimator_\n return clf, rf\n\n\n\ndef get_classification_predictions(X_trainvalid, y_trainvalid, X_test, y_test, rf, \n test_results = None, model_title='', row = 0, labels = None,\n save_plots = True, out_dir = '.', is_pca = False):\n \n labels_train = [l for l in labels if int(l) in y_trainvalid.unique()] \n labels_test = [l for l in labels if int(l) in y_test.unique()]\n labels_plot = ['Low', 'Medium', 'High']\n \n # Train results \n y_trainvalid_pred = rf.predict(X_trainvalid) \n train_report = classification_report(y_trainvalid, y_trainvalid_pred, labels=labels_train)\n train_cm = confusion_matrix(y_trainvalid,y_trainvalid_pred, labels=labels_train)\n #plot_confusion_matrix(rf, X_trainvalid, y_true = y_trainvalid, labels=labels)\n #plt.title('Train')\n\n # Test results\n y_test_pred = rf.predict(X_test)\n test_report = classification_report(y_test, y_test_pred, labels=labels_test)\n test_cm = confusion_matrix(y_test,y_test_pred, labels=labels_test)\n \n plt.rcParams.update({'font.size': 19})\n plot_confusion_matrix(rf, X_test, y_true = y_test,labels=labels_test,display_labels=labels_plot, colorbar = False) \n #ax=plt.gca()\n #ax.set_xticklabels(labels)\n #ax.set_yticklabels(labels)\n \n #if is_pca:\n # plt.title(model_title + ' (PCA)')\n #else:\n # plt.title(model_title \n \n # Compute average metrics over all classes\n test_report_dict = classification_report(y_test, y_test_pred, labels=labels_test, output_dict=True)\n test_report_dict = { str(label): test_report_dict[str(label)] for label in labels_test}\n test_report_df = pd.DataFrame(test_report_dict)\n\n # Compute average metrics over all classes and update results with all models\n test_results.at[row, 'Model'] = model_title\n test_results.at[row, 'Precision'] = test_report_df.loc['precision',:].mean()\n test_results.at[row, 'Recall'] = test_report_df.loc['recall',:].mean()\n test_results.at[row, 'F1-Score'] = test_report_df.loc['f1-score',:].mean()\n \n # Save\n if save_plots:\n dpi=1000\n out_file_path = '{0}/{1}_test.png'.format(out_dir, model_title.replace(' ','_'))\n if is_pca:\n out_file_path = out_file_path.replace('_test.png','_pca_test.png')\n \n plt.savefig(out_file_path, dpi=dpi, bbox_inches = \"tight\")\n plt.savefig(out_file_path.replace('.png','.eps'),format='eps',dpi=dpi, bbox_inches = \"tight\")\n plt.savefig(out_file_path.replace('.png','.pdf'),dpi=dpi, bbox_inches = \"tight\")\n print('file saved as: ',out_file_path)\n \n # Print\n print('=== Train ====')\n print(train_report)\n print('=== Test ====')\n print(test_report)\n \n return train_report, test_report\n \ndef get_regression_predictions(X_trainvalid, y_trainvalid, X_test, y_test, rf, train_results = None, test_results = None, model_title='', row = 0, labels = None):\n\n # Predict\n y_trainvalid_pred = rf.predict(X_trainvalid)\n y_test_pred = rf.predict(X_test)\n \n # MSE: train\n rmse_train = np.sqrt(mean_squared_error(y_true = y_trainvalid, y_pred = y_trainvalid_pred))\n mae_train = mean_absolute_error(y_true = y_trainvalid, y_pred = y_trainvalid_pred)\n mape_train = mean_absolute_percentage_error(y_true = y_trainvalid, y_pred = y_trainvalid_pred)\n r2_train = r2_score(y_true = y_trainvalid, y_pred = y_trainvalid_pred)\n print('\\nMODEL: \\n',model_title)\n print('==== Train error: ==== ')\n print('MRSE: ', rmse_train)\n print('MAE: ', mae_train)\n print('R2: ', r2_train)\n print('MRE: ',mape_train)\n print('====================== \\n')\n\n # MSE: test\n rmse_test = np.sqrt(mean_squared_error(y_true = y_test, y_pred = y_test_pred))\n mae_test = mean_absolute_error(y_true = y_test, y_pred = y_test_pred)\n r2_test = r2_score(y_true = y_test, y_pred = y_test_pred)\n mape_test = mean_absolute_percentage_error(y_true = y_test, y_pred = y_test_pred)\n print('==== Test error: ==== ')\n print('RMSE: ', rmse_test)\n print('MAE: ', mae_test)\n print('R2: ', r2_test)\n print('MRE: ',mape_test)\n print('====================== \\n') \n\n # Update results\n train_results.at[row, 'Model'] = model_title\n train_results.at[row, 'R2'] = r2_train\n train_results.at[row, 'MAE'] = mae_train\n train_results.at[row, 'RMSE'] = rmse_train\n train_results.at[row, 'MRE'] = mape_train\n \n # Update results\n test_results.at[row, 'Model'] = model_title\n test_results.at[row, 'R2'] = r2_test\n test_results.at[row, 'MAE'] = mae_test\n test_results.at[row, 'RMSE'] = rmse_test\n test_results.at[row, 'MRE'] = mape_test\n \n \n \n return y_trainvalid_pred, y_test_pred\n \ndef get_classification_model(model, f_maxsel, random_state = None, is_pca= False):\n \n model_title = model.replace('_',' ').title()\n nt = 500\n \n # Define models\n if model=='dummy':\n rf = DummyClassifier(strategy='most_frequent')\n parameters = {}\n if model=='logistic_regresion':\n #rf = linear_model.LogisticRegression(class_weight = 'balanced')\n rf = linear_model.LogisticRegression(random_state=random_state)\n C = np.linspace(0,20,5)\n #C = np.arange(0,10,2)\n parameters = {'C':C}\n if model=='naive_bayes':\n rf = GaussianNB()\n parameters = {} \n if model=='kNN':\n rf = KNeighborsClassifier()\n k = np.arange(1,41,step=1) \n #n = np.arange(0,10,2)\n parameters = {'n_neighbors':k}\n if model=='random_forest':\n #rf = RandomForestClassifier(nt, class_weight = 'balanced_subsample')\n rf = RandomForestClassifier(nt, random_state=random_state)\n depths = np.arange(5,10,1)\n n_estimators = np.arange(300,500,100)\n fs = np.arange(6,16,2)\n parameters = {'n_estimators': n_estimators, 'max_depth':depths, 'max_features':fs}\n elif model=='SVC_rbf':\n model_title = 'SVC'\n rf = SVC(class_weight='balanced', kernel ='rbf', random_state=random_state)\n #C = np.linspace(0,20,5)\n #C = np.append(C,[1])\n #C.sort()\n #gamma = np.logspace(-4,-2,3)\n C = np.array([1,5])\n gamma = np.array([0.001])\n n = C.shape[0]*gamma.shape[0]\n print(n)\n parameters = {'C':C, 'gamma':gamma}\n elif model=='ANN':\n model_title = 'ANN'\n \n ann_alpha = np.array([1,5,10,12])\n \n rs = [0]\n \n hs1 = [(4),(8),(12),(16)]\n hs2 = [(8,4),(12,4),(4,8)]\n hs3 = [(4,6,8),(4,8,16)]\n hs4 = [(2,4,8,12),(12,8,6,2)]\n hs5 = [(2,4,6,8,12),(12,8,6,4,2)]\n #hs = hs1+hs2+hs3+hs4+hs5\n hs = hs1\n #hs = [(4,6,8)]\n #learning_rate_init = np.logspace(-5,0,6)\n learning_rate_init = np.array([0.01,0.1])\n acct = ['identity', 'relu']\n rf = MLPClassifier(max_iter=1000)\n parameters = {'hidden_layer_sizes':hs, 'alpha':ann_alpha, 'learning_rate_init':learning_rate_init, 'random_state':rs, 'activation':acct}\n #parameters = {'hidden_layer_sizes':hs} \n #print(parameters)\n \n return rf, parameters, model_title","sub_path":"utils/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":32688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"293341545","text":"#!/bin/usr/python3\nfrom Hidden.secret import verify_integrity\nfrom Crypto.Util.number import *\n\ndef save_key(n,e,d,p,q):\n string = \"n = {}\\ne = {}\\nd = {}\\np = {}\\nq = {}\\n\".format(n,e,d,p,q)\n print(string)\n open('Baby-RSA-challenge/Hidden/key.txt','w').write(string)\n\ndef generate_key(bits):\n p = getPrime(bits//2)\n q = getPrime(bits//2)\n n = p * q\n e = 13\n phin = (p-1) * (q-1)\n d = inverse(e,phin)\n assert verify_integrity(n,e,d) == True , ' Invalid Key :( '\n save_key(n,e,d,p,q)\n return n,e,d,p,q\n\nn,e,d,p,q = generate_key(64)\n\nparams = \"n = {}\\ne = {}\\nd = {}\\np = {}\\nq = {}\".format(n,e,d,p,q)\nopen('Baby-RSA-challenge/params.txt','w').write(params)\n","sub_path":"SHELL-CTF/Crypto/Baby-RSA-Challenge/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"214962568","text":"from django.db import models\nfrom django.db import router\nfrom django.db.models import signals\n\nfrom . import managers\nfrom .utils import cascade_archive, cascade_unarchive\nfrom .signals import post_undelete, post_softdelete, pre_softdelete\n\n\nclass ArchiveMixin(models.Model):\n \"\"\"\n A model that is only marked as deleted when the .delete() method is called,\n instead of actually deleted. Calling .delete() on this object will only\n mark it as deleted, and it will not show up in the default queryset. If you\n want to see all objects, including the ones marked as deleted, use:\n\n ArchiveModel.all_objects.all()\n\n If you want to just see the ones marked as deleted, use:\n\n ArchiveModel.all_objects.deleted.all()\n \"\"\"\n deleted = models.DateTimeField(null=True, blank=True)\n\n objects = managers.ArchiveManager()\n all_objects = models.Manager()\n\n class Meta:\n abstract = True\n\n def get_candidate_relations_to_delete(self):\n \"\"\"\n Returns\n \"\"\"\n return models.deletion.get_candidate_relations_to_delete(self._meta)\n\n def related_objects(self, relation_field):\n \"\"\"\n Given a relation field return the QuerySet of objects that are\n related to the current object (self).\n\n Arguments:\n relation_field (django.db.models.fields.related): related\n field instance.\n \"\"\"\n return relation_field.related_model.objects.filter(\n **{'{}__in'.format(relation_field.field.name): [self]})\n\n def delete(self, using=None, keep_parents=False):\n using = using or router.db_for_write(self.__class__, instance=self)\n\n assert self._get_pk_val() is not None, \\\n \"%s object can't be deleted because its %s attribute \" \\\n \"is set to None.\" % (self._meta.object_name, self._meta.pk.attname)\n\n if self.deleted:\n # short-circuit here to prevent lots of nesting\n return\n\n # Start delete, send the pre-delete signal.\n signals.pre_delete.send(\n sender=self.__class__, instance=self, using=using)\n pre_softdelete.send(\n sender=self.__class__, instance=self, using=using)\n\n collector = cascade_archive(self, using, keep_parents)\n resp = collector.delete()\n # End delete, send the post-delete signal\n signals.post_delete.send(\n sender=self.__class__, instance=self, using=using)\n post_softdelete.send(\n sender=self.__class__, instance=self, using=using)\n\n return resp\n\n delete.alters_data = True\n\n def really_delete(self, using=None):\n \"\"\"\n Actually deletes the instance.\n \"\"\"\n super(ArchiveMixin, self).delete(using=using)\n\n def undelete(self, using=None, keep_parents=False):\n using = using or router.db_for_write(self.__class__, instance=self)\n\n assert self._get_pk_val() is not None, \\\n \"%s object can't be undeleted because its %s attribute \" \\\n \"is set to None.\" % (self._meta.object_name, self._meta.pk.attname)\n\n assert self.deleted is not None, \\\n \"%s object can't be undeleted because it is not deleted.\" % (self._meta.object_name)\n\n collector = cascade_unarchive(self, using, self.deleted, keep_parents)\n resp = collector.delete()\n # End undelete, send the post-undelete signal\n post_undelete.send(\n sender=self.__class__, instance=self, using=using)\n\n return resp\n","sub_path":"django_archive_mixin/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":3512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"467208047","text":"# Copyright [2020] [KTH Royal Institute of Technology] Licensed under the\n# Educational Community License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may\n# obtain a copy of the License at http://www.osedu.org/licenses/ECL-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an \"AS IS\"\n# BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\n# or implied. See the License for the specific language governing\n# permissions and limitations under the License.\n#\n# Course: EL2805 - Reinforcement Learning - Lab 2 Problem 1\n# Code author: [Alessio Russo - alessior@kth.se]\n# Last update: 6th October 2020, by alessior@kth.se\n#\n\n# NOTE: MODIFIED TO WORK WITH DQN CLASS FROM UTILS.PY\n\n# Load packages\nimport numpy as np\nimport gym\nimport torch\nfrom tqdm import trange\n\nimport matplotlib.pyplot as plt\n\nimport utils as ut\n\n\ndef running_average(x, N):\n ''' Function used to compute the running average\n of the last N elements of a vector x\n '''\n if len(x) >= N:\n y = np.copy(x)\n y[N - 1:] = np.convolve(x, np.ones((N,)) / N, mode='valid')\n else:\n y = np.zeros_like(x)\n return y\n\n\ndef check_solution(path, device):\n # Import and initialize Mountain Car Environment\n env = gym.make('LunarLander-v2')\n env.reset()\n\n # Load model\n NN = torch.load('neural-network-1.pth', map_location=torch.device(device))\n\n # Create DQN class\n n_actions = env.action_space.n # Number of available actions\n dim_state = len(env.observation_space.high) # State dimensionality\n hidden_dimension = 64\n DQN = ut.DQN(ut.net_builder, dim_state, n_actions, hidden_dimension, device)\n\n # Assign the loaded model to the DQN class\n DQN.network = NN\n\n # Parameters\n N_EPISODES = 50 # Number of episodes to run for trainings\n CONFIDENCE_PASS = 50\n\n # Reward\n episode_reward_list = [] # Used to store episodes reward\n\n # ---- FOr plotting episodic reward -----\n # list of episodes\n I = []\n\n # Reward\n episode_reward_list_random_agent = [] # Used to store episodes reward (random agent)\n\n # Simulate episodes\n print('Checking solution...')\n EPISODES = trange(N_EPISODES, desc='Episode: ', leave=True)\n for i in EPISODES:\n I.append(i)\n EPISODES.set_description(\"Episode {}\".format(i))\n # Reset enviroment data\n done = False\n state = env.reset()\n total_episode_reward = 0.\n while not done:\n # env.render()\n # Get next state and reward. The done variable\n # will be True if you reached the goal position,\n # False otherwise\n q_values = DQN.forward(torch.tensor([state]).to(device))\n action = q_values.max(1)[1].item()\n next_state, reward, done, _ = env.step(action)\n\n # Update episode reward\n total_episode_reward += reward\n\n # Update state for next iteration\n state = next_state\n\n # Append episode reward\n episode_reward_list.append(total_episode_reward)\n\n # Close environment\n env.close()\n\n avg_reward = np.mean(episode_reward_list)\n confidence = np.std(episode_reward_list) * 1.96 / np.sqrt(N_EPISODES)\n\n print('Policy achieves an average total reward of {:.1f} +/- {:.1f} with confidence 95%.'.format(\n avg_reward,\n confidence))\n\n if avg_reward - confidence >= CONFIDENCE_PASS:\n print('Your policy passed the test!')\n else:\n print(\n \"Your policy did not pass the test! The average reward of your policy needs to be greater than {} with 95% \"\n \"confidence\".format(\n CONFIDENCE_PASS))\n\n EPISODES = trange(N_EPISODES, desc='Episode: ', leave=True)\n for i in EPISODES:\n EPISODES.set_description(\"Episode {}\".format(i))\n # Reset enviroment data\n done = False\n state = env.reset()\n total_episode_reward = 0.\n while not done:\n # Run random agent\n stupid_action = np.random.randint(0, n_actions)\n\n next_state, reward, done, _ = env.step(stupid_action)\n\n # Update episode reward\n total_episode_reward += reward\n\n # Update state for next iteration\n state = next_state\n\n # Append episode reward\n episode_reward_list_random_agent.append(total_episode_reward)\n\n # Close environment\n env.close()\n\n plt.plot(I, episode_reward_list, label=\"Our model\")\n plt.plot(I, episode_reward_list_random_agent, label=\"Random model\")\n plt.xlabel('Episodes')\n plt.ylabel('Reward for episode')\n plt.legend(loc=\"lower left\")\n plt.show()\n\n # ---- Plot the max Q value ----\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n xs = np.arange(0, 1.5, 0.1).tolist()\n ys = np.arange(-np.pi, np.pi, 0.2).tolist()\n Y = []\n W = []\n Z = []\n\n def fun(x, y, net):\n state = np.array([0, x, 0, 0, y, 0, 0, 0])\n Q = net.forward(torch.tensor([state], dtype=torch.float32).to(device))\n val = Q.max(1)[0].item()\n return val\n\n # Create the triplets for plotting\n for i in range(len(xs)):\n for j in range(len(ys)):\n Y.append(xs[i])\n W.append(ys[j])\n Z.append(fun(xs[i], ys[j], DQN))\n ax.scatter(Y, W, Z)\n ax.set_xlabel('y')\n ax.set_ylabel('w')\n ax.set_zlabel('max Q value')\n plt.show()\n\n # ---- Plot the best action ----\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n xs = np.arange(0, 1.5, 0.1).tolist()\n ys = np.arange(-np.pi, np.pi, 0.2).tolist()\n Y = []\n W = []\n Z = []\n\n def fun(x, y, net):\n state = np.array([0, x, 0, 0, y, 0, 0, 0])\n Q = net.forward(torch.tensor([state], dtype=torch.float32).to(device))\n # print(Q)\n val = Q.max(1)[1].item()\n # print(val)\n return val\n\n # Create the triplets for plotting\n for i in range(len(xs)):\n for j in range(len(ys)):\n Y.append(xs[i])\n W.append(ys[j])\n Z.append(fun(xs[i], ys[j], DQN))\n ax.scatter(Y, W, Z)\n ax.set_xlabel('y')\n ax.set_ylabel('w')\n ax.set_zlabel('Best action')\n plt.show()\n","sub_path":"lab2/problem1/DQN_check_solution_full.py","file_name":"DQN_check_solution_full.py","file_ext":"py","file_size_in_byte":6335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"188169638","text":"import itertools\n\nfrom fireant import utils\nfrom fireant.reference_helpers import (\n reference_alias,\n reference_label,\n)\nfrom .base import TransformableWidget\nfrom .chart_base import ChartWidget\n\nMAP_SERIES_TO_PLOT_FUNC = {\n ChartWidget.LineSeries: 'line',\n ChartWidget.AreaSeries: 'area',\n ChartWidget.AreaStackedSeries: 'area',\n ChartWidget.AreaPercentageSeries: 'area',\n ChartWidget.PieSeries: 'pie',\n ChartWidget.BarSeries: 'bar',\n ChartWidget.StackedBarSeries: 'bar',\n ChartWidget.ColumnSeries: 'bar',\n ChartWidget.StackedColumnSeries: 'bar',\n}\n\n\nclass Matplotlib(ChartWidget, TransformableWidget):\n def __init__(self, title=None):\n super(Matplotlib, self).__init__()\n self.title = title\n\n def transform(self, data_frame, dataset, dimensions, references, annotation_frame=None):\n import matplotlib.pyplot as plt\n data_frame = data_frame.copy()\n\n n_axes = len(self.items)\n figsize = (14, 5 * n_axes)\n fig, plt_axes = plt.subplots(n_axes,\n sharex='row',\n figsize=figsize)\n fig.suptitle(self.title)\n\n if not hasattr(plt_axes, '__iter__'):\n plt_axes = (plt_axes,)\n\n colors = itertools.cycle('bgrcmyk')\n for axis, plt_axis in zip(self.items, plt_axes):\n for series in axis:\n series_color = next(colors)\n\n linestyles = itertools.cycle(['-', '--', '-.', ':'])\n for reference in [None] + references:\n metric = series.metric\n f_metric_key = utils.alias_selector(reference_alias(metric, reference))\n f_metric_label = reference_label(metric, reference)\n\n plot = self.get_plot_func_for_series_type(data_frame[f_metric_key], f_metric_label, series)\n plot(ax=plt_axis,\n label=axis.label,\n color=series_color,\n stacked=series.stacking is not None,\n linestyle=next(linestyles)) \\\n .legend(loc='center left',\n bbox_to_anchor=(1, 0.5))\n\n return plt_axes\n\n @staticmethod\n def get_plot_func_for_series_type(pd_series, label, chart_series):\n pd_series.name = label\n plot = pd_series.plot\n plot_func_name = MAP_SERIES_TO_PLOT_FUNC[type(chart_series)]\n plot_func = getattr(plot, plot_func_name)\n return plot_func\n","sub_path":"fireant/widgets/matplotlib.py","file_name":"matplotlib.py","file_ext":"py","file_size_in_byte":2544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"50846465","text":"import twitter\nimport threading\nimport requests\nimport os\n\ntwit_api = twitter.Api(consumer_key=os.environ.get('consumer_key'),\n\tconsumer_secret=os.environ.get('consumer_secret'),\n\taccess_token_key=os.environ.get('access_token_key'),\n\taccess_token_secret=os.environ.get('access_token_secret'))\n\ncurrent_block_hash = 0\n\ndef is_new_block(block_hash):\n\tglobal current_block_hash\n\n\tif block_hash != current_block_hash:\n\t\tcurrent_block_hash = block_hash\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef block_update():\n\tthreading.Timer(5.0, block_update).start()\n\n\tnew_block = requests.get(\"https://blockchain.info/latestblock\").json()\n\tblock_hash = new_block[\"hash\"]\n\t\n\tif (is_new_block(block_hash)):\n\t\tcurrent_block = requests.get(\"https://blockchain.info/rawblock/\" + str(block_hash)).json()\n\t\n\t\tintro = \"Latest Block (bitcoin):\" + \"\\n\\n\"\n\t\ttwit_height = \"Block Height: \" + str(current_block[\"height\"]) + \"\\n\"\n\t\ttwit_num_transactions = \"Transactions: \" + str(current_block[\"n_tx\"]) + \"\\n\"\n\t\tblock_size = current_block[\"size\"] / 1000.0\n\t\ttwit_size = \"Size: \" + str(block_size) + \" kB\" + \"\\n\"\n\t\tversion = \"Version: \" + str(current_block[\"ver\"])\n\n\t\ttweet = (intro + twit_height + twit_num_transactions + twit_size + version)\n\t\tprint(tweet)\n\t\ttwit_api.PostUpdate(tweet)\n\nblock_update()\n","sub_path":"twitter_block_report.py","file_name":"twitter_block_report.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"74888922","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2005 onwards University of Deusto\n# All rights reserved.\n#\n# This software is licensed as described in the file COPYING, which\n# you should have received as part of this distribution.\n#\n# This software consists of contributions made by many individuals,\n# listed below:\n#\n# Author: Jaime Irurzun \n#\n\nfrom test.unit.weblab.proxy import adds_triple_translator, fake_time\nfrom voodoo.sessions import exc as SessionErrors\nfrom voodoo.gen.coordinator import CoordAddress\nfrom voodoo.gen.exceptions.locator import LocatorErrors\nfrom voodoo.gen.locator import EasyLocator\nfrom voodoo.sessions import session_id as SessionId\nfrom weblab.data import server_type as ServerType\nfrom weblab.data.command import Command\nimport weblab.lab.exc as LaboratoryErrors\nimport weblab.proxy.exc as ProxyErrors\nfrom weblab.proxy import server as ProxyServer\nfrom weblab.translator.translators import StoresNothingTranslator, StoresEverythingTranslator\nimport mocker\nimport test.unit.configuration as configuration_module\nimport unittest\nimport voodoo.configuration as ConfigurationManager\nimport weblab.experiment.util as ExperimentUtil\nimport weblab.methods as weblab_methods\n\n\nclass CreatingProxyServerTestCase(mocker.MockerTestCase):\n\n def setUp(self):\n self._cfg_manager = ConfigurationManager.ConfigurationManager()\n self._cfg_manager.append_module(configuration_module)\n\n def test_invalid_session_type_name(self):\n self._cfg_manager._set_value(ProxyServer.WEBLAB_PROXY_SERVER_SESSION_TYPE, \"this_will_never_be_a_valid_session_type\")\n self.assertRaises(\n ProxyErrors.NotASessionTypeError,\n ProxyServer.ProxyServer, None, None, self._cfg_manager\n )\n\n def test_invalid_default_translator_klazz_name(self):\n self._cfg_manager._set_value(ProxyServer.WEBLAB_PROXY_SERVER_DEFAULT_TRANSLATOR_NAME, \"ThisWillNeverBeAValidDefaultTranslatorKlazzName\")\n self.assertRaises(\n ProxyErrors.InvalidDefaultTranslatorNameError,\n ProxyServer.ProxyServer, None, None, self._cfg_manager\n )\n\n\nclass UsingProxyServerTestCase(mocker.MockerTestCase):\n\n def setUp(self):\n self._cfg_manager = ConfigurationManager.ConfigurationManager()\n self._cfg_manager.append_module(configuration_module)\n\n self.RESERVATION_ID = \"my_reservation_id\"\n self.RESERVATION_SESS_ID = SessionId.SessionId(self.RESERVATION_ID)\n self.LAB_SESS_ID = \"my_lab_sess_id\"\n self.ANY_COORD_ADDR = CoordAddress.CoordAddress.translate_address('myserver:myprocess@mymachine')\n self.LAB_COORD_ADDR = self.ANY_COORD_ADDR\n\n def _create_proxy(self, laboratories=(), translators=(), time_mock=None):\n locator = FakeLocator({'laboratories': laboratories, 'translators': translators})\n easylocator = EasyLocator.EasyLocator(self.ANY_COORD_ADDR, locator)\n proxy = ProxyServer.ProxyServer(None, easylocator, self._cfg_manager)\n if time_mock is not None:\n proxy._time = time_mock\n return proxy\n\n def _create_custom_translator(self, translator_klazz):\n locator = FakeLocator()\n easylocator = EasyLocator.EasyLocator(self.ANY_COORD_ADDR, locator)\n return translator_klazz(self.ANY_COORD_ADDR, easylocator, self._cfg_manager)\n\n #===========================================================================\n # _find_translator()\n #===========================================================================\n\n def test_find_translator_being_a_suitable_translator_available(self):\n translator = self._create_custom_translator(StoresNothingTranslator)\n proxy = self._create_proxy(translators=(translator,))\n\n found_translator, is_default = proxy._find_translator(\"whichever experiment_id, because FakeLocator will find it ;-)\")\n self.assertEquals(translator, found_translator)\n self.assertFalse(is_default)\n\n def test_find_translator_not_being_any_suitable_translator_available_so_using_an_explicit_default_one(self):\n self._cfg_manager._set_value(ProxyServer.WEBLAB_PROXY_SERVER_DEFAULT_TRANSLATOR_NAME, \"StoresNothingTranslator\")\n proxy = self._create_proxy()\n\n found_translator, is_default = proxy._find_translator(\"whichever experiment_id, because FakeLocator won't find it...\")\n self.assertEquals(StoresNothingTranslator, found_translator.__class__)\n self.assertTrue(is_default)\n\n def test_find_translator_not_being_any_suitable_translator_available_so_using_the_implicit_default_one(self):\n proxy = self._create_proxy()\n\n found_translator, is_default = proxy._find_translator(\"whichever experiment_id, because FakeLocator won't find it...\")\n self.assertEquals(StoresEverythingTranslator, found_translator.__class__)\n self.assertTrue(is_default)\n\n #===========================================================================\n # Using the API: enable_access(), send_command(), send_file(), are_expired(), disable_access(), retrieve_results()\n #===========================================================================\n\n def _test_command_sent(self, command_sent, expected_command, expected_response):\n self.assertEquals(expected_command, command_sent.command.commandstring)\n self.assertEquals(expected_response, command_sent.response.commandstring)\n self.assertTrue(isinstance(command_sent.timestamp_before, float))\n self.assertTrue(isinstance(command_sent.timestamp_after, float))\n self.assertTrue(command_sent.timestamp_after >= command_sent.timestamp_before)\n\n def _test_file_sent(self, file_sent, expected_file_info, expected_response):\n self.assertEquals(expected_file_info, file_sent.file_info)\n self.assertEquals(expected_response, file_sent.response.commandstring)\n self.assertTrue(isinstance(file_sent.timestamp_before, float))\n self.assertTrue(isinstance(file_sent.timestamp_after, float))\n self.assertTrue(file_sent.timestamp_after >= file_sent.timestamp_before)\n\n def _test_happy_path(self, translator_name):\n FILE_CONTENT = ExperimentUtil.serialize('Huuuuuuuuge file!')\n FILE_INFO = \"My file's description\"\n\n self._cfg_manager._set_value(ProxyServer.WEBLAB_PROXY_SERVER_DEFAULT_TRANSLATOR_NAME, translator_name)\n fake_time.TIME_TO_RETURN = 1289548551.2617509 # 2010_11_12___07_55_51\n\n laboratory = self.mocker.mock()\n laboratory.send_command(self.LAB_SESS_ID, Command('Do this!'))\n self.mocker.result(Command('Done!'))\n laboratory.send_file(self.LAB_SESS_ID, Command(FILE_CONTENT), FILE_INFO)\n self.mocker.result(Command('File received!'))\n\n self.mocker.replay()\n proxy = self._create_proxy(laboratories=(laboratory,), time_mock=fake_time)\n\n proxy.do_enable_access(self.RESERVATION_ID, \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n\n command_response = proxy.send_command(self.RESERVATION_SESS_ID, Command('Do this!'))\n self.assertEquals(Command('Done!'), command_response)\n\n file_response = proxy.send_file(self.RESERVATION_SESS_ID, Command(FILE_CONTENT), FILE_INFO)\n self.assertEquals(Command('File received!'), file_response)\n\n proxy.do_disable_access(self.RESERVATION_ID)\n\n commands, files = proxy.do_retrieve_results(self.RESERVATION_ID)\n return commands, files\n\n def test_happy_path_using_a_translator_that_stores(self):\n # Since this is not a really default Translator, we have to make it available for the test\n ProxyServer.DEFAULT_TRANSLATORS['AddsATrippleAAtTheBeginingTranslator'] = adds_triple_translator.AddsATrippleAAtTheBeginingTranslator\n\n commands, files = self._test_happy_path(\"AddsATrippleAAtTheBeginingTranslator\")\n\n self.assertEquals(2, len(commands))\n self._test_command_sent(\n commands[0],\n 'AAADo this!', 'AAADone!'\n )\n self._test_command_sent(\n commands[1],\n 'on_finish', 'on_start before_send_command after_send_command before_send_file after_send_file do_on_finish '\n )\n\n self.assertEquals(1, len(files))\n self._test_file_sent(\n files[0],\n \"My file's description\",\n 'AAAFile received!'\n )\n\n def test_happy_path_using_a_translator_that_does_not_store(self):\n commands, files = self._test_happy_path(\"StoresNothingTranslator\")\n self.assertEquals(0, len(commands))\n self.assertEquals(0, len(files))\n\n def test_doing_anything_before_enabling(self):\n proxy = self._create_proxy()\n\n # Can't disable access, of course\n self.assertRaises(\n ProxyErrors.InvalidReservationIdError,\n proxy.do_disable_access,\n self.RESERVATION_ID\n )\n\n # Can't check if the user is online\n expirations = proxy.do_are_expired([self.RESERVATION_ID])\n self.assertEquals(\"Y \", expirations[0])\n\n # Can't retrieve results\n self.assertRaises(\n SessionErrors.SessionNotFoundError,\n proxy.do_retrieve_results,\n self.RESERVATION_ID\n )\n\n # Can't poll\n self.assertRaises(\n ProxyErrors.InvalidReservationIdError,\n proxy.poll,\n self.RESERVATION_SESS_ID\n )\n\n # Can't work with the experiment\n self.assertRaises(\n ProxyErrors.InvalidReservationIdError,\n proxy.send_command,\n self.RESERVATION_SESS_ID, 'command'\n )\n self.assertRaises(\n ProxyErrors.InvalidReservationIdError,\n proxy.send_file,\n self.RESERVATION_SESS_ID, 'file'\n )\n\n def test_doing_anything_after_disabling(self):\n proxy = self._create_proxy()\n proxy.do_enable_access(self.RESERVATION_ID, \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n proxy.do_disable_access(self.RESERVATION_ID)\n\n # Can't poll\n self.assertRaises(\n ProxyErrors.AccessDisabledError,\n proxy.poll,\n self.RESERVATION_SESS_ID\n )\n\n # Can't work with the experiment\n self.assertRaises(\n ProxyErrors.AccessDisabledError,\n proxy.send_command,\n self.RESERVATION_SESS_ID, 'command'\n )\n self.assertRaises(\n ProxyErrors.AccessDisabledError,\n proxy.send_file,\n self.RESERVATION_SESS_ID, 'file'\n )\n\n # Can't disable access again, of course\n self.assertRaises(\n ProxyErrors.AccessDisabledError,\n proxy.do_disable_access,\n self.RESERVATION_ID\n )\n\n # CAN retrieve results!\n proxy.do_retrieve_results(self.RESERVATION_ID)\n\n def test_failed_to_send_command(self):\n laboratory = self.mocker.mock()\n laboratory.send_command(self.LAB_SESS_ID, 'command')\n self.mocker.throw(LaboratoryErrors.FailedToSendCommandError)\n\n self.mocker.replay()\n proxy = self._create_proxy(laboratories=(laboratory,))\n\n proxy.do_enable_access(self.RESERVATION_ID, \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n\n self.assertRaises(\n ProxyErrors.FailedToSendCommandError,\n proxy.send_command,\n self.RESERVATION_SESS_ID, 'command'\n )\n\n # Access becomes disabled\n self.assertRaises(\n ProxyErrors.AccessDisabledError,\n proxy.send_command,\n self.RESERVATION_SESS_ID, 'command'\n )\n\n def test_failed_to_send_file(self):\n laboratory = self.mocker.mock()\n laboratory.send_file(self.LAB_SESS_ID, 'file', 'info')\n self.mocker.throw(LaboratoryErrors.FailedToSendFileError)\n\n self.mocker.replay()\n proxy = self._create_proxy(laboratories=(laboratory,))\n\n proxy.do_enable_access(self.RESERVATION_ID, \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n\n self.assertRaises(\n ProxyErrors.FailedToSendFileError,\n proxy.send_file,\n self.RESERVATION_SESS_ID, 'file', 'info'\n )\n\n # Access becomes disabled\n self.assertRaises(\n ProxyErrors.AccessDisabledError,\n proxy.send_file,\n self.RESERVATION_SESS_ID, 'file', 'info'\n )\n\n def test_invalid_laboratory_session_id_when_sending_a_command(self):\n laboratory = self.mocker.mock()\n laboratory.send_command(self.LAB_SESS_ID, 'command')\n self.mocker.throw(LaboratoryErrors.SessionNotFoundInLaboratoryServerError)\n\n self.mocker.replay()\n proxy = self._create_proxy(laboratories=(laboratory,))\n\n proxy.do_enable_access(self.RESERVATION_ID, \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n\n self.assertRaises(\n ProxyErrors.NoCurrentReservationError,\n proxy.send_command,\n self.RESERVATION_SESS_ID, 'command'\n )\n\n def test_invalid_laboratory_session_id_when_sending_a_file(self):\n laboratory = self.mocker.mock()\n laboratory.send_file(self.LAB_SESS_ID, 'file', 'info')\n self.mocker.throw(LaboratoryErrors.SessionNotFoundInLaboratoryServerError)\n\n self.mocker.replay()\n proxy = self._create_proxy(laboratories=(laboratory,))\n\n proxy.do_enable_access(self.RESERVATION_ID, \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n\n self.assertRaises(\n ProxyErrors.NoCurrentReservationError,\n proxy.send_file,\n self.RESERVATION_SESS_ID, 'file', 'info'\n )\n\n def test_are_expired(self):\n proxy = self._create_proxy()\n session_ids = [\"reservation_id1\", \"reservation_id2\", \"reservation_id3\"]\n proxy.do_enable_access(session_ids[0], \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n proxy.do_enable_access(session_ids[1], \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n proxy.do_enable_access(\"invalid-reservation-id\", \"ud-fpga@FPGA experiments\", \"student1\", self.LAB_COORD_ADDR, self.LAB_SESS_ID)\n\n expirations = proxy.do_are_expired(session_ids)\n self.assertEquals(3, len(expirations))\n self.assertEquals(\"N\", expirations[0])\n self.assertEquals(\"N\", expirations[1])\n self.assertEquals(\"Y \", expirations[2])\n\n\nclass FakeLocator(object):\n\n def __init__(self, clients={}):\n self.clients = clients\n\n def retrieve_methods(self, server_type):\n if server_type == ServerType.Laboratory:\n return weblab_methods.Laboratory\n else:\n return weblab_methods.Translator\n\n def get_server_from_coord_address(self, coord_address, client_coord_address, server_type, restrictions):\n if server_type == ServerType.Translator:\n return self.clients['translators']\n else:\n return self.clients['laboratories']\n\n def get_server(self, coord_addr, server_type, restrictions=()):\n if server_type == ServerType.Translator:\n if len(self.clients['translators']) > 0:\n return self.clients['translators'][0]\n else:\n raise LocatorErrors.NoServerFoundError()\n else:\n return self.clients['laboratories'][0]\n\n def inform_server_not_working(self, server_not_working, server_type, restrictions_of_server):\n pass\n\n\ndef suite():\n return unittest.TestSuite(\n (\n unittest.makeSuite(CreatingProxyServerTestCase),\n unittest.makeSuite(UsingProxyServerTestCase)\n )\n )\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"server/src/test/unit/weblab/proxy/test_server.py","file_name":"test_server.py","file_ext":"py","file_size_in_byte":15917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"62729551","text":"def updateUpsSupply(gridpriceState, curWorkload, workloadOVR, renState, upsStorage, upsCapability, upsSupplyFlu):\n upsSupply = 0\n updatedUpsStorage = upsStorage\n if renState == 'outage':\n if curWorkload >= workloadOVR and gridpriceState == 'high':\n upsSupply = upsStorage\n updatedUpsStorage = 0\n elif curWorkload < workloadOVR and gridpriceState == 'low':\n upsSupply = - (upsCapability - upsStorage)\n updatedUpsStorage = upsCapability\n\n elif renState == 'fluctuate' and gridpriceState == 'low':\n if curWorkload < workloadOVR:\n upsSupply = - (upsCapability - upsStorage)\n updatedUpsStorage = upsCapability\n\n elif renState == 'stable':\n if gridpriceState == 'low' or (curWorkload < workloadOVR and gridpriceState == 'high'):\n upsSupply = - (upsCapability - upsStorage)\n updatedUpsStorage = upsCapability\n\n upsSupply += upsSupplyFlu\n\n return upsSupply, updatedUpsStorage\n\n\ngridpriceStateList = ['high', 'low']\ncurWorkload = 1200\nworkloadOVR = 1000\nrenStateList = ['stable', 'fluctuate', 'outage']\nupsStorage = 80\nupsCapability = 100\nupsSupplyFlu = -25\n\nfor gridpriceState in gridpriceStateList:\n for renState in renStateList:\n upsSupply, updatedUpsStorage = updateUpsSupply(gridpriceState, curWorkload, workloadOVR, renState, upsStorage, upsCapability, upsSupplyFlu)\n print('upsSupply = ', upsSupply, \"updatedUpsStorage = \", updatedUpsStorage)\n","sub_path":"sandbox/test_updateUpsSupply.py","file_name":"test_updateUpsSupply.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"397477979","text":"from tpot import TPOTClassifier\nfrom sklearn.model_selection import train_test_split\nfrom DataExtraction import DataExtraction as DE\nimport os\n\n\ndef select_pipeline_tpot(data_name, train_size, max_opt_time, n_gen, pop_size):\n \"\"\" Selects the best pipeline with tpot and exports its file\n\n :param data_name: Name of the data\n :param train_size: The sizes of the training and test set, in a fraction of the complete set\n :param max_opt_time: The maximal optimization time for the tpot classifier\n :param n_gen: The number of generations used in the tpot classifier\n :param pop_size: The population size used in the tpot classifier\n :return: an exported python file containing the best pipeline\n \"\"\"\n\n # Extract data\n print('Extracting data...')\n X, y, gene_ids, sample_ids = DE.extract_data(data_name)\n\n # Splitting into test and training\n print('Splitting into test and training...')\n X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=train_size, test_size=1-train_size)\n\n # Use tpot to find the best pipeline\n print('Starting PipelineFinder optimization...')\n tpot = TPOTClassifier(verbosity=2, max_time_mins=max_opt_time, population_size=pop_size, generations=n_gen)\n tpot.fit(X_train, y_train)\n\n # Calculate accuracy\n print('The accuracy of the best pipeline is: %f' % (tpot.score(X_test, y_test)))\n\n # Export pipeline\n print('Exporting as TPOT_' + data_name + '_pipeline.py')\n cwd = os.getcwd()\n os.chdir('../Pipelines')\n tpot.export('TPOT_' + data_name + '_pipeline.py')\n os.chdir(cwd)","sub_path":"Data Mining Seminar/SkinDiseaseTPOT/PipelineFinder/PipelineSelection.py","file_name":"PipelineSelection.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"599395371","text":"# coding=utf8\nfrom django.shortcuts import render,get_object_or_404\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\n\n@login_required\ndef index(request):\n #首页公共部分参数：项目数，发布次数，用户总数等 \n from release.models import projectinfo\n from release.models import release\n from django.contrib.auth.models import User\n\n release_all_counts = release.objects.all().count()\n user_counts = User.objects.all().count()\n project_counts = projectinfo.objects.filter(isinit=1).count() \n\n request.session['release_all_counts'] = release_all_counts\n request.session['user_counts'] = user_counts\n request.session['project_counts'] = project_counts\n\n #\n request.session['domain'] = request.META['HTTP_HOST']\n \n import django\n request.session['django_version'] = django.get_version()\n\n from django.db import connection\n cursor = connection.cursor()\n cursor.execute(\"SELECT VERSION()\")\n for i in cursor.fetchone():\n request.session['mysql_version'] = i\n\n request.session['server_ip'] = getserverip()\n\n #disk\n import statvfs\n import os\n vfs = os.statvfs(\"/\")\n available=vfs[statvfs.F_BAVAIL]*vfs[statvfs.F_BSIZE]/(1024*1024*1024)\n capacity=vfs[statvfs.F_BLOCKS]*vfs[statvfs.F_BSIZE]/(1024*1024*1024)\n\n request.session['disk_available'] = available \n request.session['disk_capacity'] = capacity \n\n return render(request,'index/index.html')\n\ndef getserverip():\n import socket\n try:\n csock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n csock.connect(('192.168.6.3', 80))\n (addr, port) = csock.getsockname()\n csock.close()\n return addr\n except socket.error:\n return \"127.0.0.1\"\n\n","sub_path":"index/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"559191363","text":"from sejits_caffe.operations.relu import relu\nfrom cstructures.array import Array\nimport unittest\nimport numpy as np\n\n\nclass TestRelu(unittest.TestCase):\n def _check(self, actual, expected):\n np.testing.assert_allclose(actual, expected, rtol=1e-5)\n\n def test_simple(self):\n bottom = Array.rand(256, 256).astype(np.float32) * 255\n actual = Array.zeros(bottom.shape, np.float32)\n relu(bottom, bottom, actual, 0.0)\n expected = np.clip(bottom, 0.0, float('inf'))\n self._check(actual, expected)\n\n def test_nonzero_slope(self):\n bottom = Array.rand(256, 256).astype(np.float32) * 255\n actual = Array.zeros(bottom.shape, np.float32)\n relu(bottom, bottom, actual, 2.4)\n expected = np.clip(bottom, 0.0, float('inf')) + \\\n 2.4 * np.clip(bottom, float('-inf'), 0.0)\n self._check(actual, expected)\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/operations/test_relu.py","file_name":"test_relu.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"92772422","text":"# -*- coding: utf-8 -*-\n# Copyright 2004 Tech-Receptives\n# Copyright 2016 LasLabs Inc.\n# License GPL-3.0 or later (http://www.gnu.org/licenses/gpl.html).\n\nfrom openerp import _, api, fields, models\nfrom openerp.exceptions import ValidationError\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nclass MedicalPatient(models.Model):\n _name = 'medical.patient'\n _description = 'Medical Patient'\n _inherits = {'res.partner': 'partner_id'}\n\n age = fields.Char(\n compute='_compute_age',\n )\n identification_code = fields.Char(\n string='Internal Identification',\n help='Patient identifier provided by the health center'\n ' (Different from the social security number)',\n )\n general_info = fields.Text(\n string='General Information',\n )\n dob = fields.Date(\n string='Date of Birth',\n )\n dod = fields.Datetime(\n string='Deceased Date',\n )\n active = fields.Boolean(\n default=True,\n )\n deceased = fields.Boolean(\n compute='_compute_deceased',\n store=True,\n help='Automatically True if deceased date is set',\n )\n partner_id = fields.Many2one(\n string='Related Partner',\n comodel_name='res.partner',\n required=True,\n ondelete='cascade',\n index=True,\n )\n gender = fields.Selection(\n [\n ('m', 'Male'),\n ('f', 'Female'),\n ],\n )\n medical_center_id = fields.Many2one(\n string='Medical Center',\n comodel_name='res.partner',\n domain=\"[('is_institution', '=', True)]\",\n )\n marital_status = fields.Selection(\n [\n ('s', 'Single'),\n ('m', 'Married'),\n ('w', 'Widowed'),\n ('d', 'Divorced'),\n ('x', 'Separated'),\n ('z', 'law marriage'),\n ],\n )\n is_pregnant = fields.Boolean(\n help='Check if the patient is pregnant',\n )\n\n @api.multi\n def _compute_age(self):\n now = datetime.now()\n for rec_id in self:\n if rec_id.dob:\n dob = fields.Datetime.from_string(rec_id.dob)\n\n if rec_id.deceased:\n dod = fields.Datetime.from_string(rec_id.dod)\n delta = relativedelta(dod, dob)\n deceased = _(' (deceased)')\n else:\n delta = relativedelta(now, dob)\n deceased = ''\n years_months_days = '%s%s %s%s %s%s%s' % (\n delta.years, _('y'),\n delta.months, _('m'),\n delta.days, _('d'), deceased\n )\n else:\n years_months_days = _('No DoB!')\n rec_id.age = years_months_days\n\n @api.multi\n @api.constrains('is_pregnant', 'gender')\n def _check_is_pregnant(self):\n for rec_id in self:\n if rec_id.is_pregnant and rec_id.gender != 'f':\n raise ValidationError(_(\n 'Invalid selection - males cannot be pregnant.',\n ))\n\n @api.multi\n def action_invalidate(self):\n for rec_id in self:\n rec_id.active = False\n rec_id.partner_id.active = False\n\n @api.multi\n @api.depends('dod')\n def _compute_deceased(self):\n for rec_id in self:\n rec_id.deceased = bool(rec_id.dod)\n\n @api.model\n @api.returns('self', lambda value: value.id)\n def create(self, vals):\n vals['is_patient'] = True\n if not vals.get('identification_code'):\n sequence = self.env['ir.sequence'].next_by_code('medical.patient')\n vals['identification_code'] = sequence\n return super(MedicalPatient, self).create(vals)\n","sub_path":"medical_patient.py","file_name":"medical_patient.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"438711465","text":"# %% module imports\nimport argparse\nimport logging\nimport os\nimport warnings\n\nimport jinja2\n# Import the package.\nimport lts_array\nimport matplotlib.dates as mdates\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom obspy import UTCDateTime\nfrom obspy.clients.fdsn import Client, header\nfrom obspy.clients.earthworm import Client as WClient\nfrom obspy.geodetics.base import gps2dist_azimuth\nfrom obspy.core import Stream\n\n# And the config file\nimport config\n\n# set up logging\nlogging.basicConfig(filename = \"/var/log/array_processing.log\")\nlogging.getLogger().setLevel(logging.INFO)\n\n\ndef get_volcano_backazimuth(latlist, lonlist, volcanoes):\n lon0 = np.mean(lonlist)\n lat0 = np.mean(latlist)\n for volc in volcanoes:\n if 'back_azimuth' not in volc:\n tmp = gps2dist_azimuth(lat0, lon0, volc['v_lat'], volc['v_lon'])\n volc['back_azimuth'] = tmp[1]\n return volcanoes\n\n\nif __name__ == \"__main__\":\n # Get command line arguments (if any)\n parser = argparse.ArgumentParser()\n # Use strftime so we always get a string out of here.\n # Default if no arguments given is current time\n parser.add_argument('T0', nargs='*',\n default=[(UTCDateTime.utcnow()).strftime('%Y-%m-%dT%H:%M:00Z'), ])\n\n args = parser.parse_args()\n if len(args.T0) > 2:\n warnings.warn('Too many input arguments')\n parser.print_usage()\n exit(1)\n\n ENDTIME = ''.join(args.T0) # Time given, or current time\n ENDTIME = UTCDateTime(ENDTIME) # Convert to a UTC date/time object\n\n # round down to the nearest 10-minute\n ENDTIME = ENDTIME.replace(minute=ENDTIME.minute - (ENDTIME.minute % 10),\n second=0,\n microsecond=0)\n\n STARTTIME = ENDTIME - config.duration\n\n # %% Read in and filter data\n # Array Parameters\n LOC = '*'\n\n # Filter limits\n FMIN = config.f1\n FMAX = config.f2\n\n # Processing parameters\n WINLEN = config.window_length\n WINOVER = config.overlap\n\n all_nets = []\n all_stations = {}\n for net in config.arrays:\n NET = net['network']\n NETDISP = net['display name']\n all_nets.append(NETDISP)\n all_stations[NETDISP] = []\n for array in net['arrays']:\n STA = array['id']\n STANAME = array['Name']\n all_stations[NETDISP].append(STANAME)\n\n CHAN = array['channel']\n\n # LTS alpha parameter - subset size\n ALPHA = array['Alpha']\n\n logging.info(f'Reading in data from Winston for station {STA}')\n wclient = WClient(config.winston_address, config.winston_port)\n # Get Availability\n try:\n avail = wclient.get_availability(NET, STA, channel = CHAN)\n except Exception:\n logging.error(f\"Unable to get location info for station {STA}\")\n continue\n\n locs = [x[2] for x in avail]\n st = Stream()\n for loc in locs:\n try:\n # Not sure why we can't use a wildcard for loc, but it\n # doesn't seem to work (at least, not for DLL), so we loop.\n tr = wclient.get_waveforms(NET, STA, loc, CHAN,\n STARTTIME - 2 * config.taper_val,\n ENDTIME + 2 * config.taper_val,\n cleanup=True)\n st += tr\n except Exception:\n continue\n\n if not st:\n logging.error(f\"No data retrieved for {STA}\")\n continue\n\n st.merge(fill_value='latest')\n st.trim(STARTTIME - 2 * config.taper_val, ENDTIME + 2 * config.taper_val, pad='true', fill_value=0)\n st.sort()\n logging.info(st)\n\n # print('Removing sensitivity...')\n # st.remove_sensitivity()\n\n stf = st.copy()\n stf.detrend('demean')\n stf.taper(max_percentage=None, max_length=config.taper_val)\n stf.filter(\"bandpass\", freqmin=FMIN, freqmax=FMAX, corners=2, zerophase=True)\n st.trim(STARTTIME, ENDTIME, pad='true', fill_value=0)\n\n # %% Get inventory and lat/lon info\n client = Client(\"IRIS\")\n try:\n inv = client.get_stations(network=NET, station=STA, channel=CHAN,\n location=LOC, starttime=STARTTIME,\n endtime=ENDTIME, level='channel')\n except header.FDSNNoDataException:\n logging.error(f\"No lat/lon info retrieved for {STA}\")\n continue\n\n latlist = []\n lonlist = []\n staname = []\n for network in inv:\n for station in network:\n for channel in station:\n latlist.append(channel.latitude)\n lonlist.append(channel.longitude)\n staname.append(channel.code)\n\n # Get element rijs\n rij = lts_array.getrij(latlist, lonlist)\n\n # %% Run LTS array processing\n try:\n lts_vel, lts_baz, t, mdccm, stdict, sigma_tau = lts_array.ltsva(stf, rij, WINLEN, WINOVER, ALPHA)\n except:\n logging.error(\"Error processing data. Moving on.\")\n continue\n\n # %% Plotting\n try:\n fig, axs = lts_array.lts_array_plot(stf, lts_vel, lts_baz, t, mdccm, stdict)\n except UnboundLocalError:\n logging.error(f\"Unable to generate plots for {STA}\")\n continue\n\n # NOTE: these are implementation dependant, and could easily change.\n backazimuth_axis = axs[2]\n velocity_axis = axs[1]\n\n ################ Velocity Graph ##########################\n # Tweak the y axis tick marks for the velocity plot\n v_ystart, v_yend = velocity_axis.get_ylim()\n velocity_axis.yaxis.set_ticks(np.arange(v_ystart, 0.55, 0.05))\n velocity_axis.set_ylim(top = 0.5)\n\n # Shade the background for the velocity area of interest\n max_vel = 0.45\n min_vel = 0.3\n velocity_axis.axhspan(min_vel, max_vel, color = \"gray\", zorder = -1,\n alpha=0.25)\n\n ##################### Pressure Graph ##################\n # Use thinner lines on the pressure graph\n for line in axs[0].lines:\n line.set_linewidth(0.6)\n\n ####################### X Axis Formatting #####################\n # Format the date/time stuff\n axs[-1].set_xlabel(f'UTC Time ({ENDTIME.strftime(\"%d %B %Y\")})')\n axs[-1].xaxis.set_major_formatter(mdates.DateFormatter(\"%H:%M\"))\n\n ######################### Backazimuth Plot #####################\n # Add volcano azimuth lines to plots\n volcanoes = get_volcano_backazimuth(latlist, lonlist,\n array['volcano'])\n\n # decide where to put the volcano labels (horizontal position)\n # Probably overkill, I suspect we could just use start+fixed offset\n # But since I don't actually know how \"long\" these graphs are, I'll\n # calculate for now\n limits = backazimuth_axis.get_xlim()\n # 25 is completly arbitrary, but it seems to work nicely enough.\n label_left = limits[0] + (((limits[1] - limits[0]) / 25))\n\n volc_azimuth_markers = []\n for volc in volcanoes:\n # Add the line\n volc_azimuth_markers.append(backazimuth_axis.axhline(volc['back_azimuth'],\n ls = '--',\n color = \"gray\",\n zorder = -1))\n\n # And the name\n volc_azimuth_markers.append(backazimuth_axis.text(label_left,\n volc['back_azimuth'] - 6,\n volc['name'],\n bbox={'facecolor': 'white',\n 'edgecolor': 'white',\n 'pad': 0},\n fontsize=8,\n style='italic',\n zorder=10))\n\n #################### Plot Layout ##################################\n # Replace the graph title\n for txt in axs[0].texts:\n txt.remove()\n\n title = fig.text(.5, 0.99, f\"{STANAME} Infrasound Array\",\n horizontalalignment = 'center',\n verticalalignment = 'top')\n\n # Tighten up the layout\n plt.tight_layout()\n plt.subplots_adjust(top = 0.97, right = .90, bottom = 0.11)\n\n # Adjust the colorbar positions to not cut off\n # FIXME: This is ugly, but works because the two axes are always\n # added in the same order.\n # The first one is the vertical bar, the second the horizontal.\n # Would be better if we had some positive indication of which was which.\n vertical_colorbar = None\n horizontal_bar = None\n\n for axis in fig.axes:\n if axis not in axs:\n # This is a colorbar\n pos = axis.get_position().get_points().flatten()\n if vertical_colorbar is None:\n # Vertical color bar. Move to the left.\n vertical_colorbar = axis\n pos[0] -= .03\n pos[1] += .01\n pos[2] = .02\n pos[3] -= 0.05\n else:\n # This is the horizontal color bar. Nudge it up (and to the left).\n horizontal_bar = axis\n pos[0] -= .02\n pos[1] += .015\n pos[2] -= 0.1\n pos[3] = .02\n\n # Make sure this one only has integer tick marks\n _, x_max = axis.get_xlim()\n axis.xaxis.set_ticks(np.arange(1, x_max))\n\n axis.set_position(pos)\n else:\n # Move the x axis ticks inside the plot\n axis.tick_params(axis = 'x', direction = \"in\")\n\n ###################################################################\n\n # Generate the save path\n d2 = os.path.join(config.out_web_dir, NETDISP, STANAME,\n str(ENDTIME.year),\n '{:03d}'.format(ENDTIME.julday))\n\n # Just for good measure, make sure it is the \"real\" path.\n # Probably completly paranoid and unnecessary.\n d2 = os.path.realpath(d2)\n\n # Make sure directory exists\n os.makedirs(d2, exist_ok = True)\n\n filename = os.path.join(d2, f\"{STANAME}_{ENDTIME.strftime('%Y%m%d-%H%M')}.png\")\n thumbnail_name = os.path.join(d2, f\"{STANAME}_{ENDTIME.strftime('%Y%m%d-%H%M')}_thumb.png\")\n\n # Finally, save the full size image\n fig.savefig(filename, dpi = 72, format = 'png')\n\n # Reconfigure plots for thumbnails\n # Remove the volcano back-azimuth stuff\n for volc in volc_azimuth_markers:\n volc.remove()\n\n # and the plot title\n title.remove()\n\n # Resize down to thumbnail size and spacing\n fig.set_size_inches(4.0, 5.5)\n plt.subplots_adjust(left=0, right=0.99, bottom= 0.01, top=1.0,\n wspace=0, hspace=0)\n\n for axis in fig.axes:\n if axis not in axs:\n axis.remove() # remove colorbars\n else:\n # Remove tick marks and labels\n axis.tick_params(axis = 'both', which = 'both',\n bottom = False, top = False,\n labelbottom = False, left = False,\n right = False, labelleft = False)\n\n # Remove text labels\n for txt in axis.texts:\n txt.remove()\n\n colorbar_axes = [x for x in fig.axes if x not in axs]\n for axis in colorbar_axes:\n axis.remove()\n\n # Lower DPI, but larger image size = smaller dots\n fig.savefig(thumbnail_name, dpi = 36, format = 'png')\n\n # Write out the new HTML file\n script_path = os.path.dirname(__file__)\n\n with open(os.path.join(script_path, 'index.template'), 'r') as f:\n template = jinja2.Template(f.read())\n\n html = template.render(networks = all_nets, stations = all_stations)\n with open(os.path.join(config.out_web_dir, 'index.html'), 'w') as f:\n f.write(html)\n","sub_path":"new_array_processing.py","file_name":"new_array_processing.py","file_ext":"py","file_size_in_byte":13554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"267803190","text":"from core.celery.tasks import add\r\n\r\nresult = add.delay(10, 12)\r\nprint(result.result)\r\n\r\n\r\ndef on_raw_message(body):\r\n print(body['result'])\r\n\r\nr = add.apply_async((4,4), retry=False)\r\nr.get(on_message=on_raw_message, propagate=False)\r\n","sub_path":"celeryapp/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"521930518","text":"from django.core.management.base import BaseCommand, CommandError\n\nfrom backend.helpers import aggregate_label\nfrom backend.models import Article\nfrom backend.xml_parsing.postgre_to_xml import database_to_xml\n\n\nclass Command(BaseCommand):\n help = 'Extracts all labeled articles from the database as XML files'\n\n def add_arguments(self, parser):\n parser.add_argument('path', help=\"Path to an empty directory where the articles should be stored.\")\n\n def handle(self, *args, **options):\n path = options['path']\n try:\n articles = Article.objects.all()\n for a in articles:\n if a.labeled['fully_labeled'] == 1:\n labels = []\n authors = []\n for s_id in range(len(a.sentences['sentences'])):\n sent_label, sent_authors, consensus = aggregate_label(a, s_id)\n labels.append(sent_label)\n authors.append(sent_authors)\n output_xml = database_to_xml(a, labels, authors)\n with open(f'{path}/article_{a.id}.xml', 'w') as f:\n f.write(output_xml)\n\n except IOError:\n raise CommandError('Articles could not be extracted. IO Error.')\n","sub_path":"activelearning/backend/management/commands/extractall.py","file_name":"extractall.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"559394415","text":"'''\ncustomer spec: gather yearly rainfall data; output high/low/average\n monthly rainfall; reject negative data entries\nadded feature: display multiple months in case months tie for highest\n or lowest rainfall\n'''\n\n#main prog as func\ndef assignment07():\n #assign strings to list for user-friendly prompt\n months=['Jan','Feb','March','April','May','June','July',\n 'Aug','Sept','Oct','Nov','Dec']\n records=[]\n print('Enter inches of rainfall for each month as prompted.\\n')\n #loop for 12 entries; validate input\n for m in months:\n data=-1\n while data<0:\n try:\n data=float(input(m+': '))\n #cause deliberate exception to streamline exception handling\n if data<0: raise ValueError \n records.append(data)\n #handle negative entries & alpha entries\n except ValueError:\n print('Invalid input for ',end='')\n #display results\n print('\\nAverage monthly rainfall:',format(sum(records)/12,'.2f'),'in')\n print('\\tYearly rainfall:',format(sum(records),'.2f'),'in')\n print('Month(s) with most rain: ',end='')\n #iterate across records/months in parallel & display max rainfall month(s)\n for value in range(len(records)):\n if records[value]==max(records):\n print(months[value],end=' ')\n print('\\nMonth(s) with least rain: ',end='')\n #iterate across records/months in parallel & display min rainfall month(s)\n for value in range(len(records)):\n if records[value]==min(records):\n print(months[value],end=' ')\n\n#run prog\nassignment07()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Completed Assignments/Wurster_A7.py","file_name":"Wurster_A7.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"435610205","text":"# coding=utf-8\nimport numpy as np\nfrom layers import *\n\n\nclass FullyConnectNet(object):\n\t\"\"\"docstring for ClassName\"\"\"\n\tdef __init__(self, layers=[], input_dim=10, reg=0.5, mode='train', weight_scale=1):\n\n\t\t# 此处接受layers(list or tuple)由用户自己定义多少层，每层多少个神经元\n\n\t\tself.layers = layers\n\t\tself.input_dim = input_dim\n\t\tself.reg = reg\n\t\tself.mode = mode\n\t\tself.weight_scale = weight_scale\n\n\t\t# 初始化各层参数\n\n\t\tself.params = {}\n\n\t\tfor idx, neurons in enumerate(layers):\n\n\t\t\tif idx == 0:\n\n\t\t\t\tself.params['W1'] = self.weight_scale * np.random.randn(input_dim, neurons)\n\n\t\t\telse:\n\n\t\t\t\tself.params['W%d'%(idx + 1)] = self.weight_scale * np.random.randn(layers[idx - 1], neurons)\n\n\t\t\tself.params['b%d'%(idx + 1)] = np.zeros(neurons)\n\n\n\t# 这里最后一层用softmax计算loss, 在参数为self.params时， current mini-batch计算出的损失和梯度\n\n\tdef loss(self, mini_batch_x, mini_batch_y=None):\n\n\t\t# 下面逐层计算前向传播及反向传播\n\t\tlayer_counts = len(self.layers)\n\n\t\tlayer_out = None\n\t\tlayer_cache = None\n\t\tcache = []\n\n\t\tregulation = 0\n\n\t\tfor layer in np.arange(layer_counts):\n\n\t\t\t# 如果是第一层\n\t\t\tif layer == 0:\n\n\t\t\t\tlayer_out, layer_cache = affine_relu_forward(mini_batch_x, self.params['W1'], self.params['b1'])\n\n\t\t\t# 如果是最后一层\n\t\t\telif layer == layer_counts - 1:\n\n\t\t\t\tlayer_out, layer_cache = affine_forward(layer_out, self.params['W%d'%(layer + 1)], self.params['b%d'%(layer + 1)])\n\t\t\telse:\n\n\t\t\t\tlayer_out, layer_cache = affine_relu_forward(layer_out, self.params['W%d'%(layer + 1)], self.params['b%d'%(layer + 1)])\n\n\t\t\tcache.append(layer_cache)\n\n\t\t# 如果是test mode，\n\t\t# if self.mode == 'test':\n\t\tif mini_batch_y is None:\n\t\t\treturn layer_out\n\n\t\t# print(layer_out)\n\n\t\t# 计算loss并且开���反向传播\n\n\t\tdW = None\n\n\t\tdb = None\n\n\t\tgrads = {}\n\n\t\tloss, dlayer_out = softmax_loss(layer_out, mini_batch_y)\n\n\t\tfor layer in range(layer_counts):\n\n\t\t\tregulation += np.sum(self.params['W%d'%(layer + 1)] ** 2)\n\n\t\tloss += 0.5 * self.reg * regulation\n\n\t\tfor layer in reversed(np.arange(layer_counts)):\n\n\t\t\tif layer == layer_counts - 1:\n\n\t\t\t\tdlayer_out, dW, db = affine_backward(dlayer_out, cache[layer])\n\n\t\t\telse:\n\n\t\t\t\tdlayer_out, dW, db = affine_relu_backward(dlayer_out, cache[layer])\n\n\t\t\tgrads['W%d'%(layer + 1)] = dW + self.reg * self.params['W%d'%(layer + 1)]\n\n\t\t\tgrads['b%d'%(layer + 1)] = db + self.reg * self.params['b%d'%(layer + 1)]\n\n\t\treturn loss, grads\n\n\tdef predict(self, X):\n\n\t\tlayer_counts = len(self.layers)\n\n\t\tlayer_out = None\n\n\t\tfor layer in np.arange(layer_counts):\n\n\t\t\t# 如果是第一层\n\t\t\tif layer == 0:\n\n\t\t\t\tlayer_out, _ = affine_relu_forward(X, self.params['W1'], self.params['b1'])\n\n\t\t\t# 如果是最后一层\n\t\t\telif layer == layer_counts - 1:\n\n\t\t\t\tlayer_out, _ = affine_forward(layer_out, self.params['W%d'%(layer + 1)], self.params['b%d'%(layer + 1)])\n\t\t\telse:\n\n\t\t\t\tlayer_out, _ = affine_relu_forward(layer_out, self.params['W%d'%(layer + 1)], self.params['b%d'%(layer + 1)])\n\n\t\treturn np.argmax(layer_out, axis=1)\n\t\t","sub_path":"fcnet.py","file_name":"fcnet.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"400960446","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nimport datetime\nimport json\nimport os\n\nfrom django.core.cache import cache\nfrom django.test import TestCase, RequestFactory\nfrom django.test.utils import override_settings\nfrom django.template import Template, Context\nfrom django.utils import translation\nfrom django.utils import timezone\nfrom django.contrib.auth.models import AnonymousUser\nfrom django.http import HttpResponseRedirect\nfrom django.http import HttpResponse\nfrom django.conf import settings\nfrom django.core import mail\nfrom django.core.exceptions import PermissionDenied\nfrom django.contrib import messages\nfrom django.utils.translation import ugettext as _\nfrom django.utils.timezone import utc\nfrom django.utils.http import urlunquote\nfrom django.contrib.auth import get_user_model\n\nfrom ...category.models import Category\nfrom .. import utils\nfrom ..utils.forms import NestedModelChoiceField\nfrom ..utils.timezone import TIMEZONE_CHOICES\nfrom ..utils.decorators import moderator_required, administrator_required\nfrom ...user.utils.tokens import UserActivationTokenGenerator, UserEmailChangeTokenGenerator\nfrom ...user.utils.email import send_activation_email, send_email_change_email, sender\nfrom ...user.utils import email\nfrom ..tags import time as ttags_utils\nfrom ..tests import utils as test_utils\nfrom ..tags.messages import render_messages\n\nUser = get_user_model()\n\n\nclass UtilsTests(TestCase):\n\n def setUp(self):\n cache.clear()\n\n def test_render_form_errors(self):\n \"\"\"\n return form errors string\n \"\"\"\n class MockForm:\n non_field_errors = [\"error1\", ]\n hidden_fields = [{'errors': \"error2\", }, ]\n visible_fields = [{'errors': \"error3\", }, ]\n\n res = utils.render_form_errors(MockForm())\n lines = [line.strip() for line in res.splitlines()]\n self.assertEqual(\"\".join(lines), '')\n\n def test_json_response(self):\n \"\"\"\n return json_response\n \"\"\"\n res = utils.json_response()\n self.assertIsInstance(res, HttpResponse)\n self.assertEqual(res.status_code, 200)\n self.assertEqual(res['Content-Type'], 'application/json')\n self.assertDictEqual(json.loads(res.content.decode('utf-8')), {})\n\n res = utils.json_response({\"foo\": \"bar\", })\n self.assertDictEqual(json.loads(res.content.decode('utf-8')), {\"foo\": \"bar\", })\n\n res = utils.json_response(status=404)\n self.assertEqual(res.status_code, 404)\n\n def test_mkdir_p(self):\n \"\"\"\n mkdir -p\n \"\"\"\n # Empty path should raise an exception\n self.assertRaises(OSError, utils.mkdir_p, \"\")\n\n # Try to create an existing dir should do nothing\n self.assertTrue(os.path.isdir(settings.BASE_DIR))\n utils.mkdir_p(settings.BASE_DIR)\n\n # Create path tree\n # setup\n path = os.path.join(settings.BASE_DIR, \"test_foo\")\n sub_path = os.path.join(path, \"bar\")\n self.assertFalse(os.path.isdir(sub_path))\n self.assertFalse(os.path.isdir(path))\n # test\n utils.mkdir_p(sub_path)\n self.assertTrue(os.path.isdir(sub_path))\n # clean up\n os.rmdir(sub_path)\n os.rmdir(path)\n\n def test_pushd(self):\n \"\"\"\n pushd bash like\n \"\"\"\n current_dir = {'dir': '.'}\n\n class MockOS:\n @classmethod\n def chdir(cls, new_dir):\n current_dir['dir'] = new_dir\n\n @classmethod\n def getcwd(cls):\n return current_dir['dir']\n\n org_os, utils.os = utils.os, MockOS\n try:\n with utils.pushd('./my_dir'):\n self.assertEqual(current_dir['dir'], './my_dir')\n\n with utils.pushd('./my_dir/my_other_dir'):\n self.assertEqual(current_dir['dir'], './my_dir/my_other_dir')\n\n self.assertEqual(current_dir['dir'], './my_dir')\n\n self.assertEqual(current_dir['dir'], '.')\n finally:\n utils.os = org_os\n\n\n# Mock out datetime in some tests so they don't fail occasionally when they\n# run too slow. Use a fixed datetime for datetime.now(). DST change in\n# America/Chicago (the default time zone) happened on March 11th in 2012.\n# Note: copy from django.contrib.humanize.tests.py\n\nnow = datetime.datetime(2012, 3, 9, 22, 30)\n\n\nclass MockDateTime(datetime.datetime):\n @classmethod\n def now(cls, tz=None):\n if tz is None or tz.utcoffset(now) is None:\n return now\n else:\n # equals now.replace(tzinfo=utc)\n return now.replace(tzinfo=tz) + tz.utcoffset(now)\n\n\nclass UtilsTemplateTagTests(TestCase):\n\n def test_shortnaturaltime(self):\n \"\"\"\"\"\"\n class naive(datetime.tzinfo):\n def utcoffset(self, dt):\n return None\n\n def render(date):\n t = Template('{% load spirit_tags %}'\n '{{ date|shortnaturaltime }}')\n return t.render(Context({'date': date, }))\n\n orig_humanize_datetime, ttags_utils.datetime = ttags_utils.datetime, MockDateTime\n try:\n with translation.override('en'):\n with override_settings(USE_TZ=True):\n self.assertEqual(render(now), \"now\")\n self.assertEqual(render(now.replace(tzinfo=naive())), \"now\")\n self.assertEqual(render(now.replace(tzinfo=utc)), \"now\")\n self.assertEqual(render(now - datetime.timedelta(seconds=1)), \"1s\")\n self.assertEqual(render(now - datetime.timedelta(minutes=1)), \"1m\")\n self.assertEqual(render(now - datetime.timedelta(hours=1)), \"1h\")\n self.assertEqual(render(now - datetime.timedelta(days=1)), \"8 Mar\")\n self.assertEqual(render(now - datetime.timedelta(days=69)), \"31 Dec '11\")\n\n # Tests it uses localtime\n # This is 2012-03-08HT19:30:00-06:00 in America/Chicago\n dt = datetime.datetime(2011, 3, 9, 1, 30, tzinfo=utc)\n\n # Overriding TIME_ZONE won't work when timezone.activate\n # was called in some point before (middleware)\n timezone.deactivate()\n\n with override_settings(TIME_ZONE=\"America/Chicago\"):\n self.assertEqual(render(dt), \"8 Mar '11\")\n finally:\n ttags_utils.datetime = orig_humanize_datetime\n\n def test_render_messages(self):\n \"\"\"\n Test messages grouped by level\n \"\"\"\n # TODO: test template rendering\n class MockMessage:\n def __init__(self, level, message):\n self.level = level\n self.tags = messages.DEFAULT_TAGS[level]\n self.message = message\n\n m1 = MockMessage(messages.constants.ERROR, 'error 1')\n m2 = MockMessage(messages.constants.ERROR, 'error 2')\n m3 = MockMessage(messages.constants.INFO, 'info 3')\n res = render_messages([m1, m2, m3])\n self.assertDictEqual(dict(res['messages_grouped']), {'error': [m1, m2],\n 'info': [m3, ]})\n\n def test_social_share(self):\n \"\"\"\n Test social share tags with unicode input\n \"\"\"\n t = Template('{% load spirit_tags %}'\n '{% get_facebook_share_url url=\"/á/foo bar/\" title=\"á\" %}'\n '{% get_twitter_share_url url=\"/á/foo bar/\" title=\"á\" %}'\n '{% get_gplus_share_url url=\"/á/foo bar/\" %}'\n '{% get_email_share_url url=\"/á/foo bar/\" title=\"á\" %}'\n '{% get_share_url url=\"/á/foo bar/\" %}')\n res = t.render(Context({'request': RequestFactory().get('/'), }))\n self.assertEqual(res.strip(), \"http://www.facebook.com/sharer.php?u=100&p%5Burl%5D=http%3A%2F%2Ftestserver\"\n \"%2F%25C3%25A1%2Ffoo%2520bar%2F&p%5Btitle%5D=%C3%A1\"\n \"https://twitter.com/share?url=http%3A%2F%2Ftestserver%2F%25C3%25A1%2F\"\n \"foo%2520bar%2F&text=%C3%A1\"\n \"https://plus.google.com/share?url=http%3A%2F%2Ftestserver%2F%25C3%25A1%2F\"\n \"foo%2520bar%2F\"\n \"mailto:?body=http%3A%2F%2Ftestserver%2F%25C3%25A1%2Ffoo%2520bar%2F\"\n \"&subject=%C3%A1&to=\"\n \"http://testserver/%C3%A1/foo%20bar/\")\n\n def test_social_share_twitter_length(self):\n \"\"\"\n Twitter allows up to 140 chars, takes 23 for urls (https)\n \"\"\"\n # so this unicode title when is *url-quoted* becomes really large, like 1000 chars large,\n # browsers support up to 2000 chars for an address, we should be fine.\n long_title = \"á\" * 150\n t = Template('{% load spirit_tags %}'\n '{% get_twitter_share_url url=\"/foo/\" title=long_title %}')\n res = t.render(Context({'request': RequestFactory().get('/'), 'long_title': long_title}))\n url = urlunquote(res.strip())\n self.assertEqual(len(url.split(\"text=\")[-1]) + 23, 139) # 140 for https\n\n\nclass UtilsFormsTests(TestCase):\n\n def test_nested_model_choise_form(self):\n \"\"\"\n NestedModelChoiceField\n \"\"\"\n Category.objects.all().delete()\n\n category = test_utils.create_category()\n category2 = test_utils.create_category()\n subcategory = test_utils.create_subcategory(category)\n field = NestedModelChoiceField(queryset=Category.objects.all(),\n related_name='category_set',\n parent_field='parent_id',\n label_field='title')\n self.assertSequenceEqual(list(field.choices), [('', '---------'),\n (3, '%s' % category.title),\n (5, '--- %s' % subcategory.title),\n (4, '%s' % category2.title)])\n\n\nclass UtilsTimezoneTests(TestCase):\n\n def test_timezone(self):\n \"\"\"\n Timezones, requires pytz\n \"\"\"\n for tz, text in TIMEZONE_CHOICES:\n timezone.activate(tz)\n\n self.assertRaises(Exception, timezone.activate, \"badtimezone\")\n\n\nclass UtilsDecoratorsTests(TestCase):\n\n def setUp(self):\n cache.clear()\n self.user = test_utils.create_user()\n\n def test_moderator_required(self):\n \"\"\"\n Tests the user is logged in and is also a moderator\n \"\"\"\n @moderator_required\n def view(req):\n pass\n\n req = RequestFactory().get('/')\n\n req.user = AnonymousUser()\n self.assertIsInstance(view(req), HttpResponseRedirect)\n\n req.user = self.user\n req.user.st.is_moderator = False\n self.assertRaises(PermissionDenied, view, req)\n\n req.user.st.is_moderator = True\n self.assertIsNone(view(req))\n\n def test_administrator_required(self):\n \"\"\"\n Tests the user is logged in and is also an admin\n \"\"\"\n @administrator_required\n def view(req):\n pass\n\n req = RequestFactory().get('/')\n\n req.user = AnonymousUser()\n self.assertIsInstance(view(req), HttpResponseRedirect)\n\n req.user = self.user\n req.user.st.is_administrator = False\n self.assertRaises(PermissionDenied, view, req)\n\n req.user.st.is_administrator = True\n self.assertIsNone(view(req))\n\n\nclass UtilsUserTests(TestCase):\n\n def setUp(self):\n cache.clear()\n self.user = test_utils.create_user()\n\n def test_user_activation_token_generator(self):\n \"\"\"\n Validate if user can be activated\n \"\"\"\n self.user.st.is_verified = False\n\n activation_token = UserActivationTokenGenerator()\n token = activation_token.generate(self.user)\n self.assertTrue(activation_token.is_valid(self.user, token))\n self.assertFalse(activation_token.is_valid(self.user, \"bad token\"))\n\n # Invalid after verification\n self.user.st.is_verified = True\n self.assertFalse(activation_token.is_valid(self.user, token))\n\n # Invalid for different user\n user2 = test_utils.create_user()\n self.assertFalse(activation_token.is_valid(user2, token))\n\n def test_user_email_change_token_generator(self):\n \"\"\"\n Email change\n \"\"\"\n new_email = \"footoken@bar.com\"\n email_change_token = UserEmailChangeTokenGenerator()\n token = email_change_token.generate(self.user, new_email)\n self.assertTrue(email_change_token.is_valid(self.user, token))\n self.assertFalse(email_change_token.is_valid(self.user, \"bad token\"))\n\n # get new email\n self.assertTrue(email_change_token.is_valid(self.user, token))\n self.assertEqual(email_change_token.get_email(), new_email)\n\n # Invalid for different user\n user2 = test_utils.create_user()\n self.assertFalse(email_change_token.is_valid(user2, token))\n\n # Invalid after email change\n self.user.email = \"email_changed@bar.com\"\n self.assertFalse(email_change_token.is_valid(self.user, token))\n\n def test_user_activation_email(self):\n \"\"\"\n Send activation email\n \"\"\"\n self._monkey_sender_called = False\n\n def monkey_sender(request, subject, template_name, context, email):\n self.assertEqual(request, req)\n self.assertEqual(email, [self.user.email, ])\n\n activation_token = UserActivationTokenGenerator()\n token = activation_token.generate(self.user)\n self.assertDictEqual(context, {'token': token, 'user_id': self.user.pk})\n\n self.assertEqual(subject, _(\"User activation\"))\n self.assertEqual(template_name, 'spirit/user/activation_email.html')\n\n self._monkey_sender_called = True\n\n req = RequestFactory().get('/')\n\n org_sender, email.sender = email.sender, monkey_sender\n try:\n send_activation_email(req, self.user)\n self.assertTrue(self._monkey_sender_called)\n finally:\n email.sender = org_sender\n\n def test_user_activation_email_complete(self):\n \"\"\"\n Integration test\n \"\"\"\n req = RequestFactory().get('/')\n send_activation_email(req, self.user)\n self.assertEquals(len(mail.outbox), 1)\n\n def test_email_change_email(self):\n \"\"\"\n Send change email\n \"\"\"\n self._monkey_sender_called = False\n\n def monkey_sender(request, subject, template_name, context, email):\n self.assertEqual(request, req)\n self.assertEqual(email, [self.user.email, ])\n\n change_token = UserEmailChangeTokenGenerator()\n token = change_token.generate(self.user, new_email)\n self.assertDictEqual(context, {'token': token, })\n\n self.assertEqual(subject, _(\"Email change\"))\n self.assertEqual(template_name, 'spirit/user/email_change_email.html')\n\n self._monkey_sender_called = True\n\n req = RequestFactory().get('/')\n new_email = \"newfoobar@bar.com\"\n\n org_sender, email.sender = email.sender, monkey_sender\n try:\n send_email_change_email(req, self.user, new_email)\n self.assertTrue(self._monkey_sender_called)\n finally:\n email.sender = org_sender\n\n def test_email_change_email_complete(self):\n \"\"\"\n Integration test\n \"\"\"\n req = RequestFactory().get('/')\n send_email_change_email(req, self.user, \"foo@bar.com\")\n self.assertEquals(len(mail.outbox), 1)\n\n def test_sender(self):\n \"\"\"\n Base email sender\n \"\"\"\n class SiteMock:\n name = \"foo\"\n domain = \"bar.com\"\n\n def monkey_get_current_site(request):\n return SiteMock\n\n def monkey_render_to_string(template, data):\n self.assertEquals(template, template_name)\n self.assertDictEqual(data, {'user_id': self.user.pk,\n 'token': token,\n 'site_name': SiteMock.name,\n 'domain': SiteMock.domain,\n 'protocol': 'https' if req.is_secure() else 'http'})\n return \"email body\"\n\n req = RequestFactory().get('/')\n token = \"token\"\n subject = SiteMock.name\n template_name = \"template.html\"\n context = {'user_id': self.user.pk, 'token': token}\n\n org_site, email.get_current_site = email.get_current_site, monkey_get_current_site\n org_render_to_string, email.render_to_string = email.render_to_string, monkey_render_to_string\n try:\n sender(req, subject, template_name, context, [self.user.email, ])\n finally:\n email.get_current_site = org_site\n email.render_to_string = org_render_to_string\n\n self.assertEquals(len(mail.outbox), 1)\n self.assertEquals(mail.outbox[0].subject, SiteMock.name)\n self.assertEquals(mail.outbox[0].body, \"email body\")\n self.assertEquals(mail.outbox[0].from_email, \"foo \")\n self.assertEquals(mail.outbox[0].to, [self.user.email, ])\n","sub_path":"spirit/core/tests/tests_utils.py","file_name":"tests_utils.py","file_ext":"py","file_size_in_byte":17681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"133180592","text":"#!/usr/bin/env python3\nfrom glob import glob\n\ndirec = glob('./*.txt')\nBLAST_dict = {}\n\nfor file in direc:\n files = open(file, \"r\")\n for line in files:\n line_strip = line.strip()\n if line_strip.startswith('#'):\n continue\n else:\n BLAST_dict[file] = line_strip.split('\\t')\n\nfor key,value in BLAST_dict.items():\n print(key,\" - \", \"\\n Percent ID:\", value[2], \"\\n Alignment Length:\", value[3], \"\\n E-Value:\", value[10], \"\\n Query Length:\", int(value[7])-int(value[6]))\n","sub_path":"ProblemSet13/BLAST_Parser.py","file_name":"BLAST_Parser.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"414429249","text":"import logging\nimport numpy as np\nfrom typing import Callable, Dict, Optional, Union, Tuple\nfrom alibi_detect.utils.frameworks import has_pytorch, has_tensorflow\n\nif has_pytorch:\n from alibi_detect.cd.pytorch.mmd import MMDDriftTorch\n\nif has_tensorflow:\n from alibi_detect.cd.tensorflow.mmd import MMDDriftTF\n\nlogger = logging.getLogger(__name__)\n\n\nclass MMDDrift:\n def __init__(\n self,\n x_ref: Union[np.ndarray, list],\n backend: str = 'tensorflow',\n p_val: float = .05,\n preprocess_x_ref: bool = True,\n update_x_ref: Optional[Dict[str, int]] = None,\n preprocess_fn: Optional[Callable] = None,\n kernel: Callable = None,\n sigma: Optional[np.ndarray] = None,\n configure_kernel_from_x_ref: bool = True,\n n_permutations: int = 100,\n device: Optional[str] = None,\n input_shape: Optional[tuple] = None,\n data_type: Optional[str] = None\n ) -> None:\n \"\"\"\n Maximum Mean Discrepancy (MMD) data drift detector using a permutation test.\n\n Parameters\n ----------\n x_ref\n Data used as reference distribution.\n backend\n Backend used for the MMD implementation.\n p_val\n p-value used for the significance of the permutation test.\n preprocess_x_ref\n Whether to already preprocess and store the reference data.\n update_x_ref\n Reference data can optionally be updated to the last n instances seen by the detector\n or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while\n for reservoir sampling {'reservoir_sampling': n} is passed.\n preprocess_fn\n Function to preprocess the data before computing the data drift metrics.\n kernel\n Kernel used for the MMD computation, defaults to Gaussian RBF kernel.\n sigma\n Optionally set the GaussianRBF kernel bandwidth. Can also pass multiple bandwidth values as an array.\n The kernel evaluation is then averaged over those bandwidths.\n configure_kernel_from_x_ref\n Whether to already configure the kernel bandwidth from the reference data.\n n_permutations\n Number of permutations used in the permutation test.\n device\n Device type used. The default None tries to use the GPU and falls back on CPU if needed.\n Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.\n input_shape\n Shape of input data.\n data_type\n Optionally specify the data type (tabular, image or time-series). Added to metadata.\n \"\"\"\n super().__init__()\n\n backend = backend.lower()\n if backend == 'tensorflow' and not has_tensorflow or backend == 'pytorch' and not has_pytorch:\n raise ImportError(f'{backend} not installed. Cannot initialize and run the '\n f'MMDDrift detector with {backend} backend.')\n elif backend not in ['tensorflow', 'pytorch']:\n raise NotImplementedError(f'{backend} not implemented. Use tensorflow or pytorch instead.')\n\n kwargs = locals()\n args = [kwargs['x_ref']]\n pop_kwargs = ['self', 'x_ref', 'backend', '__class__']\n [kwargs.pop(k, None) for k in pop_kwargs]\n\n if kernel is None:\n if backend == 'tensorflow':\n from alibi_detect.utils.tensorflow.kernels import GaussianRBF\n else:\n from alibi_detect.utils.pytorch.kernels import GaussianRBF # type: ignore\n kwargs.update({'kernel': GaussianRBF})\n\n if backend == 'tensorflow' and has_tensorflow:\n kwargs.pop('device', None)\n self._detector = MMDDriftTF(*args, **kwargs) # type: ignore\n else:\n self._detector = MMDDriftTorch(*args, **kwargs) # type: ignore\n self.meta = self._detector.meta\n\n def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True, return_distance: bool = True) \\\n -> Dict[Dict[str, str], Dict[str, Union[int, float]]]:\n \"\"\"\n Predict whether a batch of data has drifted from the reference data.\n\n Parameters\n ----------\n x\n Batch of instances.\n return_p_val\n Whether to return the p-value of the permutation test.\n return_distance\n Whether to return the MMD metric between the new batch and reference data.\n\n Returns\n -------\n Dictionary containing 'meta' and 'data' dictionaries.\n 'meta' has the model's metadata.\n 'data' contains the drift prediction and optionally the p-value, threshold and MMD metric.\n \"\"\"\n return self._detector.predict(x, return_p_val, return_distance)\n\n def score(self, x: Union[np.ndarray, list]) -> Tuple[float, float, np.ndarray]:\n \"\"\"\n Compute the p-value resulting from a permutation test using the maximum mean discrepancy\n as a distance measure between the reference data and the data to be tested.\n\n Parameters\n ----------\n x\n Batch of instances.\n\n Returns\n -------\n p-value obtained from the permutation test, the MMD^2 between the reference and test set\n and the MMD^2 values from the permutation test.\n \"\"\"\n return self._detector.score(x)\n","sub_path":"alibi_detect/cd/mmd.py","file_name":"mmd.py","file_ext":"py","file_size_in_byte":5503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"35103233","text":"class Node: \n\tdef __init__(self,value):\n\t\tself.left=None\n\t\tself.right=None\n\t\tself.key=value\n#fight with worst case first in recursion terminology\ndef insert(r,n):\n\tif(r!=None):\n\t\tif(r.key pd.Timestamp(end_date):\n print(\"Starting date must be greater than ending date\")\n raise Exception(\"Invalid dates\")\n\n def _check_zones(self, zones):\n \"\"\"Test zones.\n\n :param list zones: geographical zones.\n :raise Exception: if zone(s) are invalid.\n \"\"\"\n possible = list(self.grid.id2zone.values())\n if \"Western\" in self.interconnect:\n possible += [\"California\", \"Western\"]\n if \"Texas\" in self.interconnect:\n possible += [\"Texas\"]\n if \"Eastern\" in self.interconnect:\n possible += [\"Eastern\"]\n if self.interconnect == [\"Eastern\", \"Western\", \"Texas\"]:\n possible += [\"USA\"]\n for z in zones:\n if z not in possible:\n print(\"%s is incorrect. Possible zones are: %s\" % (z, possible))\n raise Exception(\"Invalid zone(s)\")\n\n def _check_resources(self, resources):\n \"\"\"Test resources.\n\n :param list resources: type of generators.\n :raise Exception: if resource(s) are invalid.\n \"\"\"\n for r in resources:\n if r not in type2label.keys():\n print(\n \"%s is incorrect. Possible resources are: %s\"\n % (r, type2label.keys())\n )\n raise Exception(\"Invalid resource(s)\")\n\n @staticmethod\n def _check_tz(tz):\n \"\"\"Test time zone.\n\n :param str tz: time zone.\n :raise Exception: if time zone is invalid.\n \"\"\"\n possible = [\"utc\", \"US/Pacific\", \"local\"]\n if tz not in possible:\n print(\"%s is incorrect. Possible time zones are: %s\" % (tz, possible))\n raise Exception(\"Invalid time zone\")\n\n @staticmethod\n def _check_freq(freq):\n \"\"\"Test freq.\n\n :param str freq: frequency for re-sampling.\n :raise Exception: if frequency is invalid.\n \"\"\"\n possible = [\"H\", \"D\", \"W\", \"auto\"]\n if freq not in possible:\n print(\"%s is incorrect. Possible frequency are: %s\" % (freq, possible))\n raise Exception(\"Invalid frequency\")\n\n @staticmethod\n def _check_kind(kind):\n \"\"\"Test kind.\n\n :param str kind: type of analysis.\n :raise Exception: if analysis is invalid.\n \"\"\"\n possible = [\n \"chart\",\n \"stacked\",\n \"comp\",\n \"curtailment\",\n \"correlation\",\n \"variability\",\n \"yield\",\n ]\n if kind not in possible:\n print(\"%s is incorrect. Possible analysis are: %s\" % (kind, possible))\n raise Exception(\"Invalid Analysis\")\n\n def _convert_tz(self, df_utc):\n \"\"\"Convert data frame from UTC time zone to desired time zone.\n\n :param pandas.DataFrame df_utc: data frame with UTC timestamp as\n indices.\n :return: (*pandas.DataFrame*) -- data frame converted to desired\n time zone.\n \"\"\"\n df_new = df_utc.tz_convert(self.tz)\n df_new.index.name = self.tz\n\n return df_new\n\n def _set_frequency(self, start_date, end_date):\n \"\"\"Sets frequency for resampling.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n \"\"\"\n delta = pd.Timestamp(start_date) - pd.Timestamp(end_date)\n\n if delta.days < 7:\n self.freq = \"H\"\n elif 31 < delta.days < 180:\n self.freq = \"D\"\n else:\n self.freq = \"W\"\n\n def _set_date_range(self, start_date, end_date):\n \"\"\"Calculates the appropriate date range after resampling in order to\n get an equal number of entries per sample.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n \"\"\"\n first_available = self.pg.index[0].tz_convert(self.tz)\n last_available = self.pg.index[-1].tz_convert(self.tz)\n\n timestep = (\n pd.DataFrame(\n index=pd.date_range(start_date, end_date, freq=\"H\", tz=self.tz)\n )\n .resample(self.freq, label=\"left\")\n .size()\n .rename(\"Number of Hours\")\n )\n\n if self.freq == \"H\":\n if first_available > pd.Timestamp(start_date, tz=self.tz):\n self.from_index = first_available\n else:\n self.from_index = pd.Timestamp(start_date, tz=self.tz)\n if last_available < pd.Timestamp(end_date, tz=self.tz):\n self.to_index = last_available\n else:\n self.to_index = pd.Timestamp(end_date, tz=self.tz)\n\n elif self.freq == \"D\":\n if timestep[0] == timestep[1]:\n first_full = pd.Timestamp(timestep.index.values[0], tz=self.tz)\n else:\n first_full = pd.Timestamp(timestep.index.values[1], tz=self.tz)\n if timestep[-1] == timestep[-2]:\n last_full = pd.Timestamp(timestep.index.values[-1], tz=self.tz)\n else:\n last_full = pd.Timestamp(timestep.index.values[-2], tz=self.tz)\n\n if first_available > first_full:\n self.from_index = first_available.ceil(\"D\")\n else:\n self.from_index = first_full\n if last_available < pd.Timestamp(end_date, tz=self.tz):\n self.to_index = last_available.floor(\"D\") - pd.Timedelta(\"1 days\")\n else:\n self.to_index = last_full\n\n elif self.freq == \"W\":\n if timestep[0] == timestep[1]:\n first_full = pd.Timestamp(timestep.index.values[0], tz=self.tz)\n else:\n first_full = pd.Timestamp(timestep.index.values[1], tz=self.tz)\n if timestep[-1] == timestep[-2]:\n last_full = pd.Timestamp(timestep.index.values[-1], tz=self.tz)\n else:\n last_full = pd.Timestamp(timestep.index.values[-2], tz=self.tz)\n\n if first_available > first_full:\n self.from_index = min(timestep[first_available:].index)\n else:\n self.from_index = first_full\n if last_available < last_full:\n self.to_index = max(timestep[:last_available].index)\n else:\n self.to_index = last_full\n\n self.timestep = timestep[self.from_index : self.to_index]\n\n def _do_chart(self, start_date, end_date):\n \"\"\"Performs chart analysis.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n \"\"\"\n print(\"Set UTC for all zones\")\n self.tz = \"utc\"\n\n self._set_date_range(start_date, end_date)\n self.data = []\n self.filename = []\n for z in self.zones:\n self.data.append(self._get_chart(z))\n\n def _get_chart(self, zone):\n \"\"\"Calculates proportion of resources and generation in one zone.\n\n :param str zone: zone to consider.\n :return: (*tuple*) -- First element is a time series of PG with type of\n generators as columns. Second element is a data frame with type of\n generators as indices and corresponding capacity as column.\n \"\"\"\n pg, _ = self._get_pg(zone, self.resources)\n if pg is not None:\n fig, ax = plt.subplots(1, 2, figsize=(20, 10), sharey=\"row\")\n plt.subplots_adjust(wspace=1)\n plt.suptitle(\"%s\" % zone, fontsize=30)\n ax[0].set_title(\"Generation (MWh)\", fontsize=25)\n ax[1].set_title(\"Resources (MW)\", fontsize=25)\n\n pg_groups = pg.T.groupby(self.grid.plant[\"type\"]).agg(sum).T\n pg_groups.name = \"%s (Generation)\" % zone\n\n capacity = self.grid.plant.loc[pg.columns].groupby(\"type\").agg(sum).Pmax\n capacity.name = \"%s (Capacity)\" % zone\n\n if self.storage_pg is not None:\n pg_storage, capacity_storage = self._get_storage_pg(zone)\n if capacity_storage is not None:\n capacity = capacity.append(\n pd.Series([capacity_storage], index=[\"storage\"])\n )\n pg_groups = pd.merge(\n pg_groups,\n pg_storage.clip(lower=0).sum(axis=1).rename(\"storage\"),\n left_index=True,\n right_index=True,\n )\n\n t2l = type2label.copy()\n for t in type2label.keys():\n if t not in pg_groups.columns:\n del t2l[t]\n\n ax[0] = (\n pg_groups[list(t2l.keys())]\n .rename(index=t2l)\n .sum()\n .plot(\n ax=ax[0],\n kind=\"barh\",\n alpha=0.7,\n color=[type2color[r] for r in t2l.keys()],\n )\n )\n\n ax[1] = (\n capacity[list(t2l.keys())]\n .rename(index=t2l)\n .plot(\n ax=ax[1],\n kind=\"barh\",\n alpha=0.7,\n color=[type2color[r] for r in t2l.keys()],\n )\n )\n\n y_offset = 0.3\n for i in [0, 1]:\n ax[i].tick_params(axis=\"y\", which=\"both\", labelsize=20)\n ax[i].set_xticklabels(\"\")\n ax[i].set_ylabel(\"\")\n ax[i].spines[\"right\"].set_visible(False)\n ax[i].spines[\"top\"].set_visible(False)\n ax[i].spines[\"bottom\"].set_visible(False)\n ax[i].set_xticks([])\n for p in ax[i].patches:\n b = p.get_bbox()\n val = format(int(b.x1), \",\")\n ax[i].annotate(val, (b.x1, b.y1 - y_offset), fontsize=20)\n\n self.filename.append(\n \"%s_%s_%s-%s.png\"\n % (\n self.kind,\n zone,\n self.from_index.strftime(\"%Y%m%d%H\"),\n self.to_index.strftime(\"%Y%m%d%H\"),\n )\n )\n\n return pg_groups, capacity\n else:\n return None\n\n def _do_stacked(self, start_date, end_date, tz):\n \"\"\"Performs stack analysis.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n :param str tz: timezone.\n \"\"\"\n self.data = []\n self.filename = []\n for z in self.zones:\n self.tz = self.zone2time[z] if tz == \"local\" else tz\n self._set_date_range(start_date, end_date)\n self.data.append(self._get_stacked(z))\n\n def _get_stacked(self, zone):\n \"\"\"Calculates time series of PG and demand in one zone.\n\n :param str zone: zone to consider.\n :return: (*pandas.DataFrame*) -- data frame of PG and load for selected\n zone.\n \"\"\"\n pg, capacity = self._get_pg(zone, self.resources)\n if pg is not None:\n\n pg_groups = pg.T.groupby(self.grid.plant[\"type\"])\n pg_stack = pg_groups.agg(sum).T\n\n if self.storage_pg is not None:\n pg_storage, capacity_storage = self._get_storage_pg(zone)\n if capacity_storage is not None:\n capacity += capacity_storage\n pg_stack = pd.merge(\n pg_stack,\n pg_storage.clip(lower=0).sum(axis=1).rename(\"storage\"),\n left_index=True,\n right_index=True,\n )\n fig, (ax, ax_storage) = plt.subplots(\n 2,\n 1,\n figsize=(20, 15),\n sharex=\"row\",\n gridspec_kw={\"height_ratios\": [3, 1], \"hspace\": 0},\n )\n plt.subplots_adjust(wspace=0)\n ax_storage = (\n pg_storage.tz_localize(None)\n .sum(axis=1)\n .rename(\"batteries\")\n .plot(color=type2color[\"storage\"], lw=4, ax=ax_storage)\n )\n ax_storage.fill_between(\n pg_storage.tz_localize(None).index.values,\n 0,\n pg_storage.tz_localize(None).sum(axis=1).values,\n color=type2color[\"storage\"],\n alpha=0.5,\n )\n\n ax_storage.tick_params(axis=\"both\", which=\"both\", labelsize=20)\n ax_storage.set_xlabel(\"\")\n ax_storage.set_ylabel(\"Energy Storage (MW)\", fontsize=22)\n for a in fig.get_axes():\n a.label_outer()\n else:\n fig = plt.figure(figsize=(20, 10))\n ax = fig.gca()\n else:\n fig = plt.figure(figsize=(20, 10))\n ax = fig.gca()\n\n t2l = type2label.copy()\n for t in type2label.keys():\n if t not in pg_stack.columns:\n del t2l[t]\n\n demand = self._get_demand(zone)\n net_demand = pd.DataFrame(\n {\"net_demand\": demand[\"demand\"]}, index=demand.index\n )\n\n for (t, key) in [\n (\"solar\", \"sc\"),\n (\"wind\", \"wonc\"),\n (\"wind_offshore\", \"woffc\"),\n ]:\n if t in t2l.keys():\n pg_t = self._get_pg(zone, [t])[0].sum(axis=1)\n net_demand[\"net_demand\"] = net_demand[\"net_demand\"] - pg_t\n curtailment_t = (\n self._get_profile(zone, t).sum(axis=1).tolist() - pg_t\n )\n pg_stack[key] = np.clip(curtailment_t, 0, None)\n\n if self.normalize:\n pg_stack = pg_stack.divide(capacity * self.timestep, axis=\"index\")\n demand = demand.divide(capacity * self.timestep, axis=\"index\")\n net_demand = net_demand.divide(capacity * self.timestep, axis=\"index\")\n ax.set_ylabel(\"Normalized Generation\", fontsize=22)\n else:\n pg_stack = pg_stack.divide(1000, axis=\"index\")\n demand = demand.divide(1000, axis=\"index\")\n net_demand = net_demand.divide(1000, axis=\"index\")\n ax.set_ylabel(\"Generation (GW)\", fontsize=22)\n\n t2c = [type2color[r] for r in t2l.keys()]\n if \"solar\" in t2l.keys():\n t2l[\"sc\"] = \"Solar Curtailment\"\n t2c.append(\"#e8eb34\")\n if \"wind\" in t2l.keys():\n t2l[\"wonc\"] = \"Wind Onshore Curtailment\"\n t2c.append(\"#b6fc03\")\n if \"wind_offshore\" in t2l.keys():\n t2l[\"woffc\"] = \"Wind Offhore Curtailment\"\n t2c.append(\"turquoise\")\n\n ax = (\n pg_stack[list(t2l.keys())]\n .tz_localize(None)\n .rename(columns=t2l)\n .plot.area(color=t2c, linewidth=0, alpha=0.7, ax=ax)\n )\n\n demand.tz_localize(None).plot(color=\"red\", lw=4, ax=ax)\n net_demand.tz_localize(None).plot(color=\"red\", ls=\"--\", lw=2, ax=ax)\n ax.set_ylim(\n [\n min(0, 1.1 * net_demand.min().values[0]),\n max(ax.get_ylim()[1], 1.1 * demand.max().values[0]),\n ]\n )\n\n ax.set_title(\"%s\" % zone, fontsize=25)\n ax.grid(color=\"black\", axis=\"y\")\n ax.tick_params(which=\"both\", labelsize=20)\n ax.set_xlabel(\"\")\n handles, labels = ax.get_legend_handles_labels()\n ax.legend(\n handles[::-1],\n labels[::-1],\n frameon=2,\n prop={\"size\": 18},\n loc=\"lower right\",\n )\n\n pg_stack[\"demand\"] = demand\n pg_stack.name = zone\n\n self.filename.append(\n \"%s_%s_%s-%s.png\"\n % (\n self.kind,\n zone,\n self.from_index.strftime(\"%Y%m%d%H\"),\n self.to_index.strftime(\"%Y%m%d%H\"),\n )\n )\n\n return pg_stack\n else:\n return None\n\n def _do_comp(self, start_date, end_date, tz):\n \"\"\"Performs comparison analysis.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n :param str tz: timezone.\n \"\"\"\n if tz == \"local\":\n print(\"Set US/Pacific for all zones\")\n self.tz = \"US/Pacific\"\n else:\n self.tz = tz\n self._set_date_range(start_date, end_date)\n self.data = []\n self.filename = []\n for r in self.resources:\n self.data.append(self._get_comp(r))\n\n def _get_comp(self, resource):\n \"\"\"Calculates time series of PG for one resource.\n\n :param str resource: resource to consider.\n :return: (*pandas.DataFrame*) -- data frame of PG for selected resource.\n \"\"\"\n fig = plt.figure(figsize=(20, 10))\n plt.title(\"%s\" % resource.capitalize(), fontsize=25)\n\n first = True\n total = pd.DataFrame()\n for z in self.zones:\n pg, capacity = self._get_pg(z, [resource])\n if pg is None:\n pass\n else:\n ax = fig.gca()\n col_name = \"%s: %d plants (%d MW)\" % (z, pg.shape[1], capacity)\n total_tmp = pd.DataFrame(pg.T.sum().rename(col_name))\n\n if self.normalize:\n total_tmp = total_tmp.divide(capacity * self.timestep, axis=\"index\")\n if first:\n total = total_tmp\n first = False\n else:\n total = pd.merge(\n total, total_tmp, left_index=True, right_index=True\n )\n\n total[col_name].tz_localize(None).plot(lw=4, alpha=0.8, ax=ax)\n\n ax.grid(color=\"black\", axis=\"y\")\n ax.tick_params(which=\"both\", labelsize=20)\n ax.set_xlabel(\"\")\n handles, labels = ax.get_legend_handles_labels()\n ax.legend(handles[::-1], labels[::-1], frameon=2, prop={\"size\": 18})\n if self.normalize:\n ax.set_ylabel(\"Normalized Generation\", fontsize=22)\n else:\n ax.set_ylabel(\"Generation (MWh)\", fontsize=22)\n if total.empty:\n plt.close()\n return None\n else:\n self.filename.append(\n \"%s_%s_%s_%s-%s.png\"\n % (\n self.kind,\n resource,\n \"-\".join(self.zones),\n self.from_index.strftime(\"%Y%m%d%H\"),\n self.to_index.strftime(\"%Y%m%d%H\"),\n )\n )\n total.name = resource\n return total\n\n def _do_curtailment(self, start_date, end_date, tz):\n \"\"\"Performs curtailment analysis.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n :param str tz: timezone.\n \"\"\"\n for r in self.resources:\n if r not in [\"solar\", \"wind\"]:\n print(\"Curtailment analysis is only for renewable energies\")\n raise Exception(\"Invalid resource\")\n\n self.data = []\n self.filename = []\n for z in self.zones:\n self.tz = self.zone2time[z] if tz == \"local\" else tz\n self._set_date_range(start_date, end_date)\n for r in self.resources:\n self.data.append(self._get_curtailment(z, r))\n\n def _get_curtailment(self, zone, resource):\n \"\"\"Calculates time series of curtailment for one resource in one zone.\n\n :param str zone: zone to consider.\n :param str resource: resource to consider.\n :return: (*pandas.DataFrame*) -- data frame of curtailment for selected\n zone and resource. Columns are energy available (in MWh) from\n generators using resource in zone, energy generated (in MWh) from\n generators using resource in zone, demand in selected zone (in MWh)\n and curtailment (in %).\n \"\"\"\n pg, capacity = self._get_pg(zone, [resource])\n if pg is None:\n return None\n else:\n fig = plt.figure(figsize=(20, 10))\n plt.title(\"%s (%s)\" % (zone, resource.capitalize()), fontsize=25)\n ax = fig.gca()\n ax_twin = ax.twinx()\n\n demand = self._get_demand(zone)\n available = self._get_profile(zone, resource)\n\n data = pd.DataFrame(available.T.sum().rename(\"available\"))\n data[\"generated\"] = pg.T.sum().values\n data[\"demand\"] = demand.values\n data[\"curtailment\"] = 1 - data[\"generated\"] / data[\"available\"]\n data[\"curtailment\"] *= 100\n\n # Numerical precision\n data.loc[abs(data[\"curtailment\"]) < 1, \"curtailment\"] = 0\n\n data[\"curtailment\"].tz_localize(None).plot(\n ax=ax, style=\"b\", lw=4, alpha=0.7\n )\n\n data[\"curtailment mean\"] = data[\"curtailment\"].mean()\n data[\"curtailment mean\"].tz_localize(None).plot(\n ax=ax, style=\"b\", ls=\"--\", lw=4, alpha=0.7\n )\n\n data[\"available\"].tz_localize(None).rename(\n \"%s energy available\" % resource\n ).plot(\n ax=ax_twin,\n lw=4,\n alpha=0.7,\n style={\"%s energy available\" % resource: type2color[resource]},\n )\n\n data[\"demand\"].tz_localize(None).plot(\n ax=ax_twin, lw=4, alpha=0.7, style={\"demand\": \"r\"}\n )\n\n ax.xaxis.set_major_locator(mdates.MonthLocator())\n ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%b\"))\n ax.tick_params(which=\"both\", labelsize=20)\n ax.grid(color=\"black\", axis=\"y\")\n ax.set_xlabel(\"\")\n ax.set_ylabel(\"Curtailment [%]\", fontsize=22)\n ax.legend(loc=\"upper left\", prop={\"size\": 18})\n ax_twin.tick_params(which=\"both\", labelsize=20)\n ax_twin.set_ylabel(\"MWh\", fontsize=22)\n ax_twin.legend(loc=\"upper right\", prop={\"size\": 18})\n\n data.name = \"%s - %s\" % (zone, resource)\n\n self.filename.append(\n \"%s_%s_%s_%s-%s.png\"\n % (\n self.kind,\n resource,\n zone,\n self.from_index.strftime(\"%Y%m%d%H\"),\n self.to_index.strftime(\"%Y%m%d%H\"),\n )\n )\n\n return data\n\n def _do_variability(self, start_date, end_date, tz):\n \"\"\"Performs variability analysis.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n :param str tz: timezone.\n \"\"\"\n for r in self.resources:\n if r not in [\"solar\", \"wind\"]:\n print(\"Curtailment analysis is only for renewable energies\")\n raise Exception(\"Invalid resource\")\n\n self.data = []\n self.filename = []\n for z in self.zones:\n self.tz = self.zone2time[z] if tz == \"local\" else tz\n self._set_date_range(start_date, end_date)\n for r in self.resources:\n self.data.append(self._get_variability(z, r))\n\n def _get_variability(self, zone, resource):\n \"\"\"Calculates time series of PG in one zone for one resource. Also,\n calculates the time series of the PG of 2, 8 and 15 randomly\n chosen plants in the same zone and using the same resource.\n\n :param str resource: resource to consider.\n :return: (*pandas.DataFrame*) -- data frame of PG for selected zone and\n plants.\n \"\"\"\n pg, capacity = self._get_pg(zone, [resource])\n if pg is None:\n return None\n else:\n n_plants = len(pg.columns)\n fig = plt.figure(figsize=(20, 10))\n plt.title(\"%s (%s)\" % (zone, resource.capitalize()), fontsize=25)\n ax = fig.gca()\n\n total = pd.DataFrame(\n pg.T.sum().rename(\"Total: %d plants (%d MW)\" % (n_plants, capacity))\n )\n total.name = \"%s - %s\" % (zone, resource)\n\n np.random.seed(self.seed)\n if n_plants < 20:\n print(\n \"Not enough %s plants in %s for variability analysis\"\n % (resource, zone)\n )\n plt.close()\n return None\n else:\n selected = np.random.choice(pg.columns, 15, replace=False).tolist()\n norm = [capacity]\n for i in [15, 8, 2]:\n norm += [sum(self.grid.plant.loc[selected[:i]].Pmax.values)]\n total[\"15 plants (%d MW)\" % norm[1]] = pg[selected].T.sum()\n total[\"8 plants (%d MW)\" % norm[2]] = pg[selected[:8]].T.sum()\n total[\"2 plants (%d MW)\" % norm[3]] = pg[selected[:2]].T.sum()\n\n if self.normalize:\n for i, col in enumerate(total.columns):\n total[col] = total[col].divide(\n norm[i] * self.timestep, axis=\"index\"\n )\n\n lws = [5, 3, 3, 3]\n lss = [\"-\", \"--\", \"--\", \"--\"]\n colors = [type2color[resource]]\n if resource == \"solar\":\n colors += [\"red\", \"orangered\", \"darkorange\"]\n elif resource == \"wind\":\n colors += [\"dodgerblue\", \"teal\", \"turquoise\"]\n\n for col, c, lw, ls in zip(total.columns, colors, lws, lss):\n total[col].tz_localize(None).plot(\n alpha=0.7, lw=lw, ls=ls, color=c, ax=ax\n )\n\n ax.grid(color=\"black\", axis=\"y\")\n ax.tick_params(which=\"both\", labelsize=20)\n ax.set_xlabel(\"\")\n handles, labels = ax.get_legend_handles_labels()\n ax.legend(\n handles[::-1],\n labels[::-1],\n frameon=2,\n prop={\"size\": 18},\n loc=\"best\",\n )\n if self.normalize:\n ax.set_ylabel(\"Normalized Generation\", fontsize=22)\n else:\n ax.set_ylabel(\"Generation (MWh)\", fontsize=22)\n\n self.filename.append(\n \"%s_%s_%s_%s-%s.png\"\n % (\n self.kind,\n resource,\n zone,\n self.from_index.strftime(\"%Y%m%d%H\"),\n self.to_index.strftime(\"%Y%m%d%H\"),\n )\n )\n\n return total\n\n def _do_correlation(self, start_date, end_date, tz):\n \"\"\"Performs correlation analysis.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n :param str tz: timezone.\n \"\"\"\n\n for r in self.resources:\n if r not in [\"solar\", \"wind\"]:\n print(\"Correlation analysis is only for renewable energies\")\n raise Exception(\"Invalid resource\")\n\n if tz == \"local\":\n print(\"Set US/Pacific for all zones\")\n self.tz = \"US/Pacific\"\n else:\n self.tz = tz\n self._set_date_range(start_date, end_date)\n self.data = []\n self.filename = []\n for r in self.resources:\n self.data.append(self._get_correlation(r))\n\n def _get_correlation(self, resource):\n \"\"\"Calculates correlation coefficients of power generated between\n multiple zones for one resource.\n\n :param str resource: resource to consider.\n :return: (*pandas.DataFrame*) -- data frame of PG for selected resource.\n Columns are zones for selected resource.\n \"\"\"\n\n fig = plt.figure(figsize=(12, 12))\n plt.title(\"%s\" % resource.capitalize(), fontsize=25)\n\n first = True\n pg = pd.DataFrame()\n for z in self.zones:\n pg_tmp, _ = self._get_pg(z, [resource])\n if pg_tmp is None:\n pass\n else:\n if first:\n pg = pd.DataFrame(\n {z: pg_tmp.sum(axis=1).values}, index=pg_tmp.index\n )\n first = False\n else:\n pg[z] = pg_tmp.sum(axis=1).values\n\n if pg.empty:\n plt.close()\n return None\n else:\n pg.name = resource\n corr = pg.corr()\n if resource == \"solar\":\n palette = \"OrRd\"\n color = \"red\"\n else:\n palette = \"Greens\"\n color = \"green\"\n\n ax_matrix = fig.gca()\n ax_matrix = sns.heatmap(\n corr,\n annot=True,\n fmt=\".2f\",\n cmap=palette,\n ax=ax_matrix,\n square=True,\n cbar=False,\n annot_kws={\"size\": 18},\n lw=4,\n )\n ax_matrix.set_yticklabels(pg.columns, rotation=40, ha=\"right\")\n ax_matrix.tick_params(which=\"both\", labelsize=20)\n\n scatter = scatter_matrix(\n pg,\n alpha=0.2,\n diagonal=\"kde\",\n figsize=(12, 12),\n color=color,\n density_kwds={\"color\": color, \"lw\": 4},\n )\n for ax_scatter in scatter.ravel():\n ax_scatter.tick_params(labelsize=20)\n ax_scatter.set_xlabel(ax_scatter.get_xlabel(), fontsize=22, rotation=0)\n ax_scatter.set_ylabel(ax_scatter.get_ylabel(), fontsize=22, rotation=90)\n\n for t in [\"matrix\", \"scatter\"]:\n self.filename.append(\n \"%s-%s_%s_%s_%s-%s.png\"\n % (\n self.kind,\n t,\n resource,\n \"-\".join(self.zones),\n self.from_index.strftime(\"%Y%m%d%H\"),\n self.to_index.strftime(\"%Y%m%dH\"),\n )\n )\n\n return pg\n\n def _do_yield(self, start_date, end_date):\n \"\"\"Performs yield analysis.\n\n :param str start_date: starting timestamp.\n :param str end_date: ending timestamp.\n \"\"\"\n\n for r in self.resources:\n if r not in [\"solar\", \"wind\"]:\n print(\"Correlation analysis is only for renewable energies\")\n raise Exception(\"Invalid resource\")\n\n self.tz = \"utc\"\n self.data = []\n self.filename = []\n for z in self.zones:\n self._set_date_range(start_date, end_date)\n for r in self.resources:\n self.data.append(self._get_yield(z, r))\n\n def _get_yield(self, zone, resource):\n \"\"\"Calculates capacity factor of one resource in one zone.\n\n :param str zone: zone to consider.\n :param str resource: resource to consider.\n :return: (*tuple*) -- first element is the average ideal capacity\n factor for the selected zone and resource. Second element is the\n average curtailed capacity factor for the selected zone and\n resource.\n \"\"\"\n\n pg, _ = self._get_pg(zone, [resource])\n if pg is None:\n return None\n else:\n available = self._get_profile(zone, resource)\n\n capacity = self.grid.plant.loc[pg.columns].Pmax.values\n\n uncurtailed = available.sum().divide(len(pg) * capacity, axis=\"index\")\n mean_uncurtailed = np.mean(uncurtailed)\n curtailed = pg.sum().divide(len(pg) * capacity, axis=\"index\")\n mean_curtailed = np.mean(curtailed)\n\n if len(pg.columns) > 10:\n fig = plt.figure(figsize=(12, 12))\n plt.title(\"%s (%s)\" % (zone, resource.capitalize()), fontsize=25)\n ax = fig.gca()\n cf = pd.DataFrame(\n {\"uncurtailed\": 100 * uncurtailed, \"curtailed\": 100 * curtailed},\n index=pg.columns,\n )\n cf.boxplot(ax=ax)\n plt.text(\n 0.5,\n 0.9,\n \"%d plants\" % len(capacity),\n ha=\"center\",\n va=\"center\",\n transform=ax.transAxes,\n fontsize=22,\n )\n ax.tick_params(labelsize=20)\n ax.set_ylabel(\"Capacity Factor [%]\", fontsize=22)\n\n self.filename.append(\n \"%s_%s_%s_%s-%s.png\"\n % (\n self.kind,\n resource,\n zone,\n self.from_index.strftime(\"%Y%m%d%H\"),\n self.to_index.strftime(\"%Y%m%d%H\"),\n )\n )\n\n return mean_uncurtailed, mean_curtailed\n\n def _get_zone_id(self, zone):\n \"\"\"Returns the load zone identification numbers for specified zone.\n\n :param zone: zone to consider. A specific load zone, *Eastern*,\n *Western*, *California* or *Texas*.\n :return (*list*): Corresponding load zones identification number.\n \"\"\"\n if zone == \"Western\":\n load_zone_id = list(range(201, 217))\n elif zone == \"Texas\":\n load_zone_id = list(range(301, 309))\n elif zone == \"California\":\n load_zone_id = list(range(203, 208))\n elif zone == \"Eastern\":\n load_zone_id = list(range(1, 53))\n elif zone == \"USA\":\n load_zone_id = (\n list(range(1, 53)) + list(range(201, 217)) + list(range(301, 309))\n )\n else:\n load_zone_id = [self.grid.zone2id[zone]]\n\n return load_zone_id\n\n def _get_plant_id(self, zone, resource):\n \"\"\"Extracts the plant identification number of all the generators\n located in one zone and using one specific resource.\n\n :param str zone: zone to consider.\n :param str resource: type of generator to consider.\n :return: (*list*) -- plant id of all the generators located in zone and\n using resource.\n \"\"\"\n plant_id = []\n for z in self._get_zone_id(zone):\n try:\n plant_id += (\n self.grid.plant.groupby([\"zone_id\", \"type\"])\n .get_group((z, resource))\n .index.values.tolist()\n )\n except KeyError:\n pass\n\n return plant_id\n\n def _get_pg(self, zone, resources):\n \"\"\"Returns PG of all the generators located in one zone and powered by\n resources.\n\n :param str zone: one of the zones.\n :param list resources: type of generators to consider.\n :return: (*tuple*) -- data frames of PG and associated capacity for all\n generators located in zone and using the specified resources.\n \"\"\"\n plant_id = []\n for r in resources:\n plant_id += self._get_plant_id(zone, r)\n\n if len(plant_id) == 0:\n print(\"No %s plants in %s\" % (\"/\".join(resources), zone))\n return [None] * 2\n else:\n capacity = sum(self.grid.plant.loc[plant_id].Pmax.values)\n pg = (\n self._convert_tz(self.pg[plant_id])\n .resample(self.freq, label=\"left\")\n .sum()[self.from_index : self.to_index]\n )\n\n return pg, capacity\n\n def _get_storage_pg(self, zone):\n \"\"\"Returns PG of all storage units located in zone\n\n :param str zone: one of the zones\n :return: (*tuple*) -- data frame of PG and associated capacity for all\n storage units located in zone.\n \"\"\"\n storage_id = []\n\n for c, bus in enumerate(self.grid.storage[\"gen\"].bus_id.values):\n if self.grid.bus.loc[bus].zone_id in self._get_zone_id(zone):\n storage_id.append(c)\n\n if len(storage_id) == 0:\n print(\"No storage units in %s\" % zone)\n return [None] * 2\n else:\n capacity = sum(self.grid.storage[\"gen\"].loc[storage_id].Pmax.values)\n pg = (\n self._convert_tz(self.storage_pg[storage_id])\n .resample(self.freq, label=\"left\")\n .sum()[self.from_index : self.to_index]\n )\n\n return pg, capacity\n\n def _get_demand(self, zone):\n \"\"\"Returns demand profile for a specific load zone, *Eastern*,\n *Western*, *California* or *Texas*.\n\n :param str zone: one of the zones.\n :return: (*pandas.DataFrame*) -- data frame of demand in zone (in MWh).\n \"\"\"\n demand = self.demand.tz_localize(\"utc\")\n demand = demand[self._get_zone_id(zone)].sum(axis=1).rename(\"demand\").to_frame()\n demand = (\n self._convert_tz(demand)\n .resample(self.freq, label=\"left\")\n .sum()[self.from_index : self.to_index]\n )\n\n return demand\n\n def _get_profile(self, zone, resource):\n \"\"\"Returns profile for resource.\n\n :param str zone: zone to consider.\n :param str resource: type of generators to consider.\n :return: (*pandas.DataFrame*) -- data frame of the generated energy (in\n MWh) in zone by generators using resource.\n \"\"\"\n plant_id = self._get_plant_id(zone, resource)\n\n if len(plant_id) == 0:\n print(\"No %s plants in %s\" % (resource, zone))\n return None\n\n if resource == \"wind_offshore\":\n profile = self.wind.tz_localize(\"utc\")\n else:\n profile = eval(\"self.\" + resource).tz_localize(\"utc\")\n\n return (\n self._convert_tz(profile[plant_id])\n .resample(self.freq, label=\"left\")\n .sum()[self.from_index : self.to_index]\n )\n\n def get_plot(self, save=False):\n \"\"\"Plots analysis.\n\n :param bool save: should plot be saved.\n \"\"\"\n if save:\n for i in plt.get_fignums():\n plt.figure(i)\n plt.savefig(self.filename[i - 1], bbox_inches=\"tight\", pad_inches=0)\n plt.show()\n\n def get_data(self):\n \"\"\"Get data.\n\n :return: (*dict*) -- the formatting of the data depends on the selected\n analysis.\n\n .. note::\n * *'stacked'*:\n 1D dictionary. Keys are zones and associated value is a data\n frame.\n * *'chart'*:\n 2D dictionary. First key is zone and associated value is a\n dictionary, which has *'Generation'* and *'Capacity'* as keys\n and a data frame for value.\n * *'comp'* and *'correlation'*:\n 1D dictionary. Keys are resources and associated value is a\n data frame.\n * *'variability'*, *'curtailment'* and *'yield'*:\n 2D dictionary. First key is zone and associated value is a\n dictionary, which has resources as keys and a data frame for\n value.\n\n \"\"\"\n data = None\n if self.kind == \"stacked\":\n data = {}\n for i, z in enumerate(self.zones):\n data[z] = self.data[i]\n elif self.kind == \"chart\":\n data = {}\n for i, z in enumerate(self.zones):\n data[z] = {}\n data[z][\"Generation\"] = self.data[i][0]\n data[z][\"Capacity\"] = self.data[i][1]\n elif self.kind == \"comp\" or self.kind == \"correlation\":\n data = {}\n for i, r in enumerate(self.resources):\n data[r] = self.data[i]\n elif (\n self.kind == \"variability\"\n or self.kind == \"curtailment\"\n or self.kind == \"yield\"\n ):\n data = {}\n index = 0\n for z in self.zones:\n data[z] = {}\n for r in self.resources:\n data[z][r] = self.data[index]\n index += 1\n\n return data\n","sub_path":"postreise/plot/analyze_pg.py","file_name":"analyze_pg.py","file_ext":"py","file_size_in_byte":46601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"221144669","text":"from reactopya import Component\nfrom mountaintools import client as mt\n\n\nclass SpikeForestAnalysis(Component):\n def __init__(self):\n super().__init__()\n\n def javascript_state_changed(self, prev_state, state):\n path = state.get('path', None)\n download_from = state.get('download_from', [])\n mt.configDownloadFrom(download_from)\n\n if not path:\n self.set_python_state(dict(\n status='error',\n status_message='No path provided.'\n ))\n return\n\n self.set_python_state(dict(status='running', status_message='Loading: {}'.format(path)))\n \n obj = mt.loadObject(path=path)\n if not obj:\n self.set_python_state(dict(\n status='error',\n status_message='Unable to realize object: {}'.format(path)\n ))\n return\n \n obj['StudyAnalysisResults'] = None\n\n self.set_python_state(dict(\n status='finished',\n object=obj\n ))","sub_path":"widgets/SpikeForestAnalysis/SpikeForestAnalysis.py","file_name":"SpikeForestAnalysis.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"211561207","text":"import numpy as np\nimport random\n\n'''\nCreates a 2D array of characters to represent the puzzle extracted from fileName\n'''\ndef parseArray(fileName):\n path = './' + fileName\n with open(path) as file:\n array = [[c for c in line.strip()] for line in file]\n return array\n\n\n'''\nWrites a 2D array of characters representing the puzzle to fileName\n'''\ndef writeArrayToFile(array, fileName):\n np.savetxt(fileName, np.array(array), fmt = '%s', delimiter = '')\n\n\n'''\nGets the domain of possible colors in the puzzle\n'''\ndef getDomain(array):\n domain = []\n for i in range(len(array)):\n for j in range(len(array[0])):\n if array[i][j] != \"_\" and array[i][j] not in domain:\n domain.append(array[i][j])\n return domain\n\n'''\nGets the source coordinates\n'''\ndef getSources(array):\n sources = []\n for i in range(len(array)):\n for j in range(len(array[0])):\n if array[i][j] != \"_\":\n sources.append((i,j))\n return sources\n\n'''\nGets a random unassigned value found in the puzzle\n'''\ndef getRandomUnassignedIndex(array):\n unassignedIndices = []\n for i in range(len(array)):\n for j in range(len(array[0])):\n if array[i][j] == \"_\":\n unassignedIndices.append((i, j))\n if unassignedIndices == []:\n return None\n else:\n return random.choice(unassignedIndices)\n\n'''\nGets all unassigned grids neighboring an assigned grid\n'''\ndef getAllUnassignedIndexNearColored(array):\n unassignedIndices = []\n for i in range(len(array)):\n for j in range(len(array[0])):\n if array[i][j] != \"_\":\n neighbors = getValidNeighbors(i, j, array)\n for neighbor in neighbors:\n if array[neighbor[0]][neighbor[1]] == \"_\":\n unassignedIndices.append(neighbor)\n return unassignedIndices\n\n'''\nGets most constrained unassigned grid\n'''\ndef getMostConstrainedVariable(indexes,domain,assignment,sources):\n if len(indexes) == 0:\n return None\n curr_mcv = None\n least_count = 100\n for idx in indexes:\n curr_count = 0\n for value in domain:\n if isValueConsistent(idx,value,assignment,sources):\n curr_count += 1\n if curr_count < least_count:\n least_count = curr_count\n curr_mcv = idx\n return curr_mcv\n\n'''\nGets a random unassigned grid that neighbors an assigned grid\n'''\ndef getUnassignedIndexNearColored(array):\n unassignedIndices = []\n for i in range(len(array)):\n for j in range(len(array[0])):\n if array[i][j] != \"_\":\n neighbors = getValidNeighbors(i,j, array)\n for neighbor in neighbors:\n if array[neighbor[0]][neighbor[1]] == \"_\":\n unassignedIndices.append(neighbor)\n if unassignedIndices == []:\n return None\n else:\n return random.choice(list(set(unassignedIndices)))\n\n'''\nChecks if a value assignment violates any constraints\n'''\ndef isValueConsistent(index, value, assignment, sources):\n x = index[0]\n y = index[1]\n\n #print(\"Trying assignment: \" + value + \" to \" + \"(\" + str(index[0]) + \", \" + str(index[1]) + \")\\n\")\n assignment[x][y] = value\n neighbors = getValidNeighbors(x, y, assignment)\n b = True\n for neighbor in neighbors:\n if(not isVariableConsistent(neighbor[0], neighbor[1], assignment, sources)):\n b = False\n break\n assignment[x][y] = \"_\"\n return b\n\n'''\nChecks if a variable assignment violates any constraints\n'''\ndef isVariableConsistent(x, y, assignment, sources):\n countSameColorNeighs = countSameColorNeighbors(x, y, assignment)\n countUnassignedNeighs = countNeighborsWithValue(x, y, assignment, )\n if(assignment[x][y] != \"_\"):\n if (x,y) in sources:\n if((countSameColorNeighs > 1) or (countUnassignedNeighs < ( 1 - countSameColorNeighs))):\n return False\n else:\n if((countSameColorNeighs > 2) or (countUnassignedNeighs < ( 2 - countSameColorNeighs))):\n return False\n return True\n\n'''\nChecks if puzzle is complete and no constraints violated\n'''\ndef isAssignmentValid(assignment, sources):\n for x in range(len(assignment)):\n for y in range(len(assignment[0])):\n countSameColorNeighs = countSameColorNeighbors(x, y, assignment)\n if (x,y) in sources:\n if(countSameColorNeighs != 1):\n return False\n else:\n if(countSameColorNeighs != 2):\n return False\n return True\n\n'''\nnumber of neighbors with a specific value\n'''\ndef countNeighborsWithValue(x, y, assignment, value = \"_\"):\n count = 0\n neighbors = getValidNeighbors(x, y, assignment)\n for neighbor in neighbors:\n neighborX = neighbor[0]\n neighborY = neighbor[1]\n if (assignment[neighborX][neighborY] == value):\n count += 1\n return count\n\n'''\nCounts the neighbors that have the same color as the cell specified\n'''\ndef countSameColorNeighbors(x, y, assignment):\n count = 0\n neighbors = getValidNeighbors(x, y, assignment)\n for neighbor in neighbors:\n neighborX = neighbor[0]\n neighborY = neighbor[1]\n if(assignment[neighborX][neighborY] == assignment[x][y]):\n count += 1\n return count\n'''\nTakes a list of potential neighbors and an array and finds valid neighbors\n'''\ndef getValidNeighbors(x,y, assignment):\n array = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)]\n validNeighbors = []\n cols = len(assignment)\n rows = len(assignment[0])\n for neighbor in array:\n x = neighbor[0]\n y = neighbor[1]\n if x >= 0 and y >= 0 and x < cols and y < rows:\n validNeighbors.append(neighbor)\n return validNeighbors\n","sub_path":"AI-MP2/Utilities.py","file_name":"Utilities.py","file_ext":"py","file_size_in_byte":5862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"198486383","text":"from rest_framework import permissions\n\nclass CanModifyOrReadOnly(permissions.BasePermission):\n \"\"\"\n Custom permission to only allow staff to edit it.\n \"\"\"\n\n def has_object_permission(self, request, view, obj):\n # Read permissions are allowed to any request,\n # so we'll always allow GET, HEAD or OPTIONS requests.\n if request.method in permissions.SAFE_METHODS:\n return True\n # Write permissions are only allowed to the staff.\n return request.user and request.user.is_staff;\n \n def has_permission(self, request, view):\n return (\n request.method in permissions.SAFE_METHODS or\n request.user and\n request.user.is_staff\n )","sub_path":"monolith_alt/rest_export/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"621399066","text":"import types\nimport collections\ndef flatten(x):\n def iselement(e):\n return not(isinstance(e, collections.Iterable) and not isinstance(e, str))\n for el in x:\n if iselement(el):\n yield el\n else:\n yield from flatten(el)\n#def flatten(x):\n# result = []\n# for el in x:\n# if isinstance(x, collections.Iterable) and not isinstance(el, str):\n# result.extend(flatten(el))\n# else:\n# result.append(el)\n# return result\n#from compiler.ast import flatten\nimport numpy as np\nimport matplotlib.pyplot as plt\ncaffe_root='../../'\nimport sys\nsys.path.insert(0,caffe_root+'python')\nimport caffe\n#model_def=caffe_root+'test_alexnet/bvlc_alexnet/deploy.prototxt'\n#model_caffe=caffe_root+'test_alexnet/bvlc_alexnet/alexnet_train_iter_4000.caffemodel'\n\n#model_def=caffe_root+'examples/mnist/deploy.prototxt'\n#model_caffe=caffe_root+'examples/mnist/lenet_iter_10000.caffemodel'\n\nmodel_def=caffe_root+'models/bvlc_alexnet/deploy.prototxt'\nmodel_caffe=caffe_root+'models/bvlc_alexnet/bvlc_alexnet.caffemodel'\nnet=caffe.Net(model_def,model_caffe,caffe.TEST)\n\ndef getVpt(v,k):\n v=abs(v)\n vList=v.tolist()\n vList=flatten(vList)\n #vList.sort()\n vList = sorted(vList)\n k=(int)(k*len(vList))\n return vList[k]\n\n\n\nfor k,v in net.params.items():\n idx=v[0].data.shape\n print (k)\n print (type(v[0].data))\n cnt=0\n count=0\n vpt=getVpt(v[0].data,0.8)\n if len(idx)==3:\n for n_idx in range(0,idx[0]):\n for c_idx in range(0,idx[1]):\n for h_idx in range(0,idx[2]):\n for w_idx in range(0,idx[3]):\n count=count+1\n if v[0].data[n_idx][c_idx][h_idx][w_idx]-1*vpt:\n cnt=cnt+1\n v[0].data[n_idx][c_idx][h_idx][w_idx]=0.0\n v[0].mask[n_idx][c_idx][h_idx][w_idx]=0.0\n print (cnt)\n print (count)\n elif len(idx)==2:\n for h_idx in range(0,idx[0]):\n for w_idx in range(0,idx[1]):\n count=count+1\n if v[0].data[h_idx][w_idx]< vpt and v[0].data[h_idx][w_idx]>-1*vpt:\n cnt=cnt+1\n v[0].data[h_idx][w_idx]=0.0\n v[0].mask[h_idx][w_idx]=0.0\n # print (cnt)\n # print (count)\n # print (v[0].data)\nnet.save('fixed.caffemodel')\n\n\n","sub_path":"examples/alexnet/fixModel.py","file_name":"fixModel.py","file_ext":"py","file_size_in_byte":2456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"447088573","text":"from osgeo import gdal\r\nimport os\r\nimport numpy as np\r\nclass GRID:\r\n\r\n #读图像文件\r\n def read_img(self,filename):\r\n dataset=gdal.Open(filename) #打开文件\r\n\r\n im_width = dataset.RasterXSize #栅格矩阵的列数\r\n im_height = dataset.RasterYSize #栅格矩阵的行数\r\n\r\n im_geotrans = dataset.GetGeoTransform() #仿射矩阵\r\n im_proj = dataset.GetProjection() #地图投影信息\r\n im_data = dataset.ReadAsArray(0,0,im_width,im_height) #将数据写成数组，对应栅格矩阵\r\n\r\n del dataset\r\n return im_data,im_width,im_height\r\n\r\n #写文件，以写成tif为例\r\n def write_img(self,filename,im_data,im_width,im_height,im_bands,datatype=np.uint16):\r\n #gdal数据类型包括\r\n #gdal.GDT_Byte,\r\n #gdal .GDT_UInt16, gdal.GDT_Int16, gdal.GDT_UInt32, gdal.GDT_Int32,\r\n #gdal.GDT_Float32, gdal.GDT_Float64\r\n options=[\"TILED=YES\", \"COMPRESS=LZW\"]\r\n #判断栅格数据的数据类型\r\n if 'int8' in im_data.dtype.name:\r\n datatype = gdal.GDT_Byte\r\n elif 'int16' in im_data.dtype.name:\r\n datatype = gdal.GDT_UInt16\r\n else:\r\n datatype = gdal.GDT_Float32\r\n\r\n #判读数组维数\r\n\r\n\r\n #创建文件\r\n driver = gdal.GetDriverByName(\"GTiff\") #数据类型必须有，因为要计算需要多大内存空间\r\n dataset = driver.Create(filename, im_width, im_height, im_bands, datatype,options)\r\n\r\n if im_bands == 1:\r\n dataset.GetRasterBand(1).WriteArray(im_data) #写入数组数据\r\n else:\r\n for i in range(im_bands):\r\n dataset.GetRasterBand(i+1).WriteArray(im_data[i])\r\n\r\n del dataset\r\n\r\nif __name__ == \"__main__\": #切换路径到待处理图像所在文件夹\r\n\r\n savefile=r'C:\\Users\\FLYVR\\Desktop\\cubert_0826_mirror_rgb_copy'\r\n run = GRID()\r\n file=r'C:\\Users\\FLYVR\\Desktop\\cubert_0826_mirror_rgb'\r\n filesName=os.listdir(file)\r\n\r\n for _,name in enumerate(filesName):\r\n\r\n filename=os.path.join(file,name)\r\n data,width,height = run.read_img(filename=filename) #读数据\r\n\r\n #声明保存影像文件路径\r\n savename=os.path.join(savefile,'flip'+name.split('.')[0]+'.tif')\r\n print(data.shape)\r\n #声明一个与原始影像大小相同的二维矩阵\r\n flip_img=np.zeros((data.shape[0],width,height))\r\n\r\n for i in range(width):\r\n for j in range(height):\r\n flip_img[:,i, height - 1 - j] = data[:,i, j]\r\n\r\n\r\n flip_img=np.array(flip_img,dtype=np.uint16)\r\n flip_img=flip_img[:125,:,:]\r\n print(flip_img.shape)\r\n\r\n # run.write_img(filename=savename,im_data=flip_img,\r\n # im_width=width,im_height=height,im_bands=flip_img.shape[0]) #写数据\r\n # print(name,'is successful')\r\n print()","sub_path":"hyspetracal_ interpolation/GDAL_write.py","file_name":"GDAL_write.py","file_ext":"py","file_size_in_byte":2940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"123146594","text":"from .loader import read_sparse_matrix \nfrom .util import load_phrase_word2vec\nfrom .util import load_gensim_word2vec\n\nimport torch\nimport numpy as np\nimport scipy.sparse as sparse\n\nclass Dataset(object):\n \"\"\"docstring for Dataset\"\"\"\n def __init__(self, config, svd=False, train=True):\n # generate ppmi matrix for co-occurence\n pattern_filename = config.get(\"data\", \"pattern_filename\")\n\n k = int(config.getfloat(\"hyperparameters\", \"svd_dimension\"))\n self.batch_size = int(config.getfloat(\"hyperparameters\", \"batch_size\"))\n self.negative_num = int(config.getfloat(\"hyperparameters\", \"negative_num\"))\n\n csr_m, self.id2word, self.vocab, _ = read_sparse_matrix(\n pattern_filename, same_vocab=True)\n\n self.word2id = {}\n for i in range(len(self.id2word)):\n self.word2id[self.id2word[i]] = i \n\n self.matrix = csr_m.todok()\n self.p_w = csr_m.sum(axis=1).A[:,0]\n self.p_c = csr_m.sum(axis=0).A[0,:]\n self.N = self.p_w.sum() \n\n # for w2v\n if train:\n #self.wordvecs = load_phrase_word2vec(\"/home/shared/acl-data/embedding/ukwac.model\", self.vocab)\n self.wordvecs = load_gensim_word2vec(\"/home/shared/acl-data/embedding/ukwac.model\", self.vocab)\n #print(self.wordvecs[\"united_states\"])\n\n self.wordvec_weights = self.build_emb()\n\n tr_matrix = sparse.dok_matrix(self.matrix.shape)\n #print(self.matrix.shape)\n\n self.left_has = {}\n self.right_has = {}\n for (l,r) in self.matrix.keys():\n pmi_lr = (np.log(self.N) + np.log(self.matrix[(l,r)]) \n - np.log(self.p_w[l]) - np.log(self.p_c[r]))\n\n ppmi_lr = np.clip(pmi_lr, 0.0, 1e12)\n tr_matrix[(l,r)] = ppmi_lr\n\n if l not in self.left_has:\n self.left_has[l] = []\n self.left_has[l].append(r)\n if r not in self.right_has:\n self.right_has[r] = []\n self.right_has[r].append(l)\n\n self.ppmi_matrix = tr_matrix\n\n U, S, V = sparse.linalg.svds(self.ppmi_matrix.tocsr(), k=k)\n self.U = U.dot(np.diag(S))\n self.V = V.T\n\n if train:\n # self.positive_data, self.positive_label = self.generate_positive()\n self.get_avail_vocab()\n\n def get_avail_vocab(self):\n avail_vocab = []\n for idx in range(len(self.vocab)):\n if self.id2word[idx] in self.wordvecs:\n avail_vocab.append(idx)\n self.avail_vocab = np.asarray(avail_vocab)\n shuffle_indices_left = np.random.permutation(len(self.avail_vocab))[:20000]\n shuffle_indices_right = np.random.permutation(len(self.avail_vocab))[:20000]\n dev_data = []\n dev_label = []\n self.dev_dict = {}\n for id_case in range(20000):\n id_left = self.avail_vocab[shuffle_indices_left[id_case]]\n id_right = self.avail_vocab[shuffle_indices_right[id_case]]\n dev_data.append([self.w2embid[id_left],self.w2embid[id_right]])\n dev_label.append(self.U[id_left].dot(self.V[id_right]))\n self.dev_dict[(id_left, id_right)] = 1\n self.dev_data = np.asarray(dev_data)\n self.dev_label = np.asarray(dev_label)\n\n def build_emb(self): \n\n tensors = []\n ivocab = []\n self.w2embid = {}\n self.embid2w = {}\n\n for word in self.wordvecs:\n vec = torch.from_numpy(self.wordvecs[word])\n self.w2embid[self.word2id[word]] = len(ivocab)\n self.embid2w[len(ivocab)] = self.word2id[word]\n\n ivocab.append(word)\n tensors.append(vec)\n\n assert len(tensors) == len(ivocab)\n print(len(tensors))\n tensors = torch.cat(tensors).view(len(ivocab), 300)\n\n return tensors\n\n def load_vocab(self, w2v_dir, data_dir):\n i2w_path = os.path.join(data_dir, 'ukwac_id2word.pkl')\n w2i_path = os.path.join(data_dir, 'ukwac_word2id.pkl')\n with open(i2w_path, 'rb') as fr:\n self.context_i2w = pickle.load(fr)\n with open(w2i_path, 'rb') as fr:\n self.context_w2i = pickle.load(fr)\n\n self.PAD = 0\n self.UNK = 1\n\n # w2v_model = Word2Vec.load(w2v_path)\n # emb = w2v_model.wv\n # oi2ni = {}\n # new_embedding = []\n # new_embedding.append(np.zeros(300))\n # new_embedding.append(np.zeros(300))\n # cnt_ni = 2\n # for _id, word in i2w.items():\n # if word in emb:\n # oi2ni[_id] = cnt_ni\n # cnt_ni += 1 \n # new_embedding.append(emb[word])\n # else:\n # oi2ni[_id] = self.UNK\n\n oi2ni_path = os.path.join(w2v_dir, 'context_word_oi2ni.pkl')\n w2v_path = os.path.join(w2v_dir, 'context_word_w2v.model.npy')\n with open(oi2ni_path, 'rb') as fr:\n self.context_i2embid = pickle.load(fr)\n self.context_word_emb = np.load(w2v_path)\n\n\n def generate_positive(self):\n\n positive = []\n label = []\n key_list = list(self.ppmi_matrix.keys())\n shuffle_indices = np.random.permutation(len(key_list))\n\n for shuffle_id in shuffle_indices:\n (l, r) = key_list[shuffle_id]\n if self.id2word[l] in self.wordvecs and self.id2word[r] in self.wordvecs:\n positive.append([self.w2embid[l],self.w2embid[r]])\n # if l in self.context_dict and r in self.context_dict:\n # positive.append([l, r])\n score = self.U[l].dot(self.V[r])\n label.append(score)\n # label.append(self.ppmi_matrix[(l,r)])\n # 119448 positive score \n positive_train = np.asarray(positive)[:-2000]\n\n self.dev_data = np.asarray(positive)[-2000:]\n \n label_train = np.asarray(label)[:-2000]\n self.dev_label = np.asarray(label)[-2000:]\n\n return positive_train, label_train\n\n def generate_negative(self, batch_data, negative_num):\n \n negative = []\n label = []\n\n batch_size = batch_data.shape[0]\n \n for i in range(batch_size):\n # random_idx = np.random.choice(len(self.vocab), 150 , replace=False)\n l = batch_data[i][0]\n l_w = self.embid2w[l]\n r = batch_data[i][1]\n r_w = self.embid2w[r]\n\n l_neg = l_w\n r_neg = r_w\n\n num = 0\n for j in range(negative_num):\n left_prob = np.random.binomial(1, 0.5)\n # while True:\n if left_prob:\n l_neg = np.random.choice(self.avail_vocab, 1)[0]\n else:\n r_neg = np.random.choice(self.avail_vocab, 1)[0]\n # if (l_neg, r_neg) not in self.matrix.keys() and self.id2word[l_neg] in self.wordvecs and self.id2word[r_neg] in self.wordvecs:\n # if (l_neg, r_neg) not in self.matrix.keys() and self.l_neg in self.context_dict and self.r_neg in self.context_dict:\n # break\n\n negative.append([self.w2embid[l_neg], self.w2embid[r_neg]])\n # negative.append([self.context_dict[l_neg], self.context_dict[r_neg]])\n score = self.U[l_neg].dot(self.V[r_neg])\n # score = 0\n label.append(score)\n\n negative = np.asarray(negative)\n label = np.asarray(label)\n return negative, label\n\n\n def get_batch(self):\n\n\n num_positive = len(self.positive_data)\n\n batch_size = self.batch_size\n\n if num_positive% batch_size == 0:\n batch_num = num_positive // batch_size\n else:\n batch_num = num_positive // batch_size + 1\n\n shuffle_indices = np.random.permutation(num_positive)\n\n for batch in range(batch_num):\n\n start_index = batch * batch_size\n end_index = min((batch+1) * batch_size, num_positive)\n\n batch_idx = shuffle_indices[start_index:end_index]\n \n batch_positive_data = self.positive_data[batch_idx]\n batch_positive_label = self.positive_label[batch_idx]\n\n batch_negative_data, batch_negative_label = self.generate_negative(batch_positive_data, self.negative_num)\n \n # batch_positive_data = []\n # for [l, r] in batch_positive_data:\n # batch_positive_data.append(self.context_dict[l], self.context_dict[r])\n\n # [batch, 2, doc, 2, seq]\n batch_input = np.concatenate((batch_positive_data, batch_negative_data), axis=0)\n batch_label = np.concatenate((batch_positive_label,batch_negative_label), axis=0)\n\n yield batch_input, batch_label \n\n def sample_batch(self):\n num_data = len(self.avail_vocab)\n\n batch_size = self.batch_size\n\n if num_data % batch_size == 0:\n batch_num = num_data // batch_size\n else:\n batch_num = num_data // batch_size + 1\n\n shuffle_indices = np.random.permutation(num_data)\n\n for batch in range(batch_num):\n\n start_index = batch * batch_size\n end_index = min((batch+1) * batch_size, num_data)\n\n batch_idx = shuffle_indices[start_index:end_index]\n batch_data_pair = []\n batch_data_score = []\n batch_data = self.avail_vocab[batch_idx]\n \n for idx_i in batch_data:\n for j in range(self.negative_num):\n left_prob = np.random.binomial(1, 0.5)\n if left_prob:\n while True:\n idx_j = np.random.choice(self.avail_vocab, 1)[0]\n if (idx_i, idx_j) not in self.dev_dict:\n break \n batch_data_pair.append([self.w2embid[idx_i], self.w2embid[idx_j]])\n score = self.U[idx_i].dot(self.V[idx_j])\n else:\n while True:\n idx_j = np.random.choice(self.avail_vocab, 1)[0]\n if (idx_j, idx_i) not in self.dev_dict:\n break \n batch_data_pair.append([self.w2embid[idx_j], self.w2embid[idx_i]])\n score = self.U[idx_j].dot(self.V[idx_i])\n batch_data_score.append(score)\n yield np.asarray(batch_data_pair), np.asarray(batch_data_score)\n\n def sample_pos_neg_batch(self):\n num_data = len(self.avail_vocab)\n\n batch_size = self.batch_size\n\n if num_data % batch_size == 0:\n batch_num = num_data // batch_size\n else:\n batch_num = num_data // batch_size + 1\n\n shuffle_indices = np.random.permutation(num_data)\n\n for batch in range(batch_num):\n\n start_index = batch * batch_size\n end_index = min((batch+1) * batch_size, num_data)\n\n batch_idx = shuffle_indices[start_index:end_index]\n batch_data_pair = []\n batch_data_score = []\n batch_data = self.avail_vocab[batch_idx]\n \n for idx_i in batch_data:\n if idx_i in self.left_has:\n idx_j_list = np.random.permutation(self.left_has[idx_i])\n for idx_j in idx_j_list:\n if idx_j in self.avail_vocab:\n batch_data_pair.append([self.w2embid[idx_i], self.w2embid[idx_j]])\n score = self.U[idx_i].dot(self.V[idx_j])\n batch_data_score.append(score)\n break\n\n if idx_i in self.right_has:\n idx_j_list = np.random.permutation(self.right_has[idx_i])\n for idx_j in idx_j_list:\n if idx_j in self.avail_vocab:\n batch_data_pair.append([self.w2embid[idx_j], self.w2embid[idx_i]])\n score = self.U[idx_j].dot(self.V[idx_i])\n batch_data_score.append(score)\n break\n \n for j in range(self.negative_num):\n # left_prob = np.random.binomial(1, 0.5)\n # if left_prob:\n while True:\n idx_j = np.random.choice(self.avail_vocab, 1)[0]\n if (idx_i, idx_j) not in self.dev_dict:\n break \n batch_data_pair.append([self.w2embid[idx_i], self.w2embid[idx_j]])\n score = self.U[idx_i].dot(self.V[idx_j])\n batch_data_score.append(score)\n # else:\n while True:\n idx_j = np.random.choice(self.avail_vocab, 1)[0]\n if (idx_j, idx_i) not in self.dev_dict:\n break \n batch_data_pair.append([self.w2embid[idx_j], self.w2embid[idx_i]])\n score = self.U[idx_j].dot(self.V[idx_i])\n batch_data_score.append(score)\n yield np.asarray(batch_data_pair), np.asarray(batch_data_score)\n\n","sub_path":"utils/data_helper.py","file_name":"data_helper.py","file_ext":"py","file_size_in_byte":13235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"76504173","text":"# Algoritmo 4\n\n\ndef main():\n a = int(input(\"a: \"))\n b = int(input(\"b: \"))\n\n if a % b == 0 or b % a == 0:\n print(\"Multiples\")\n else:\n print(\"Not multiples\")\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"EXERCÍCIOS RESOLVIDOS/python/alternatives/alg4.py","file_name":"alg4.py","file_ext":"py","file_size_in_byte":222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"199891290","text":"# -*- coding: utf-8 -*-\n\"\"\"Utility module for all network implementations\n\nIn this module you can find all utilities for network implementation,\neven for specific protocols.\n\"\"\"\n\n\n__author__ = 'douglasvinter@gmail.com'\n\n\n\nimport collections\nfrom StringIO import StringIO\nfrom httplib import HTTPResponse\nfrom core.util import JSONSerializable\n\n\n# udpPackage used to return datagram status\nUdpPackage = collections.namedtuple('UdpPackage',\n ('data', 'host', 'port', 'status')\n )\n\n\ndef mSearch(searchTarget, maxWaitTime):\n \"\"\"Simple method to return SSDP M-SEARCH payload.\n \"\"\"\n msearch = \"\\r\\n\".join(['M-SEARCH * HTTP/1.1',\n 'HOST: 239.255.255.250:1900',\n 'MAN: \"ssdp:discover\"',\n 'ST: {st}', 'MX: {mx}', '', ''])\n return msearch.format(st=searchTarget,\n mx=maxWaitTime)\n\n\nclass socketTTL(object):\n \"\"\"TTL Definition for sockets\n \n Definition table in short:\n 0 - hops on host only.\n 1 - hops over a subnet.\n 4 - UPnP definition - reserved.\n 32 - hops until a site.\n 64 - hops over a region. \n 128 - hops over a continent.\n 255 - hops unrestrictely until reaches maximum.\n \n Note:\n This does NOT mean that setting a HUGE TTL you may reach everything,\n TTL depends on swtich/router.\n \"\"\"\n hostOnlyTTL = 0\n subnetTTL = 1\n UPnPTTL = 4\n siteTTL = 32\n unrestrictedTTL = 255\n\n\nclass NetworkStatus(object):\n \"\"\"Simple network response enum\n \"\"\"\n ok = (0x01, 'Ok')\n error = (0x10, 'Error')\n timeout = (0x20, 'Timeout')\n\n\nclass SSDPResponseParser(JSONSerializable):\n \"\"\"Class to parse SSDP response Object\"\"\"\n\n class _fakeSocket(StringIO):\n \"\"\"Creates a file like object that can be parsed HTTPResponse\"\"\"\n\n def makefile(self, *args, **kw):\n return self\n\n def __init__(self, payload):\n r = HTTPResponse(self._fakeSocket(payload))\n r.begin()\n self.st = r.getheader('st') or None\n self.usn = r.getheader('usn') or None\n self.server = r.getheader('server') or None\n self.location = r.getheader('location') or None\n \n def _usn(self, usn):\n if not usn:\n return None\n \n\n def __repr__(self):\n return \"\".format(**self.__dict__)\n\n\nclass ResponseHandler(JSONSerializable):\n \"\"\"Class to handle and serialize network responses\"\"\"\n \n def __init__(self):\n self.responses = []\n\n def add(self, response):\n try:\n if isinstance(response, UdpPackage):\n self.responses.append(response.data)\n else:\n self.responses.append(response)\n except AttributeError:\n pass\n","sub_path":"protocols/networkutil.py","file_name":"networkutil.py","file_ext":"py","file_size_in_byte":2899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"244879360","text":"import urllib.request,json\nfrom flaskblog.models import Quotes\n\n\n# quotes_url = app.config['QUOTES_URL']\nquotes_url = 'http://quotes.stormconsultancy.co.uk/random.json'\n\n\ndef get_quotes():\n with urllib.request.urlopen(quotes_url) as url:\n get_quotes_data = url.read()\n get_quotes_response = json.loads(get_quotes_data)\n\n quotes_results = None\n\n if get_quotes_response:\n quotes_results = get_quotes_response\n author = quotes_results.get('author')\n id = quotes_results.get('id')\n quote = quotes_results.get('quote')\n\n quote_object = Quotes(author,id,quote)\n\n return quotes_results\n # if get_quotes_response:\n # quotes_results = process_results(get_quotes_response)\n","sub_path":"flaskblog/requests.py","file_name":"requests.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"542766961","text":"from django.contrib.auth.models import Group\nfrom rest_framework import serializers\nfrom rest_framework.exceptions import ValidationError\nfrom .models import Task, Comment\nfrom django.contrib.auth import get_user_model\nUser = get_user_model()\n\n\nclass TaskSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = Task\n fields = ('id', 'name', 'description', 'creator', 'user', 'completed', 'created', 'edited', 'group')\n read_only_fields = ('id', 'creator', 'created', 'edited')\n\n def validate(self, attrs):\n creator = self.context['request'].user\n user = attrs.get('user')\n group = attrs.get('group')\n group_users = group.user_set.all()\n\n if creator not in group_users:\n raise ValidationError(f'You cannot add user to group {group.name}.')\n\n if user not in group_users:\n raise ValidationError(f'You cannot assign task for user {user.username} to group {group.name}.')\n\n return attrs\n\n\nclass UserSerializer(serializers.ModelSerializer):\n password = serializers.CharField(write_only=True)\n\n def create(self, validated_data):\n user = User.objects.create(\n username=validated_data['username']\n )\n user.set_password(validated_data['password'])\n user.groups.set(validated_data.get('groups', user.groups))\n user.save()\n return user\n\n class Meta:\n model = User\n fields = ('username', 'password', 'groups')\n\n\nclass GroupSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = Group\n fields = ('id', 'name')\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n class Meta:\n model = Comment\n fields = ('id', 'creator', 'description', 'task', 'created')\n read_only_fields = ('id', 'creator', 'created')\n\n def validate(self, attrs):\n user_group = self.context['request'].user.groups.all()\n\n task = attrs.get('task')\n task_group = attrs.get('task').group\n creator = self.context['request'].user\n\n last_id = Comment.objects.values_list('pk', flat=True).filter(task=task).order_by('id').last()\n if last_id:\n creator_of_previous_post = Comment.objects.get(id=last_id).creator\n validate_creator(creator, creator_of_previous_post)\n\n if task_group not in user_group:\n raise ValidationError(f'You can not comment on this task because '\n f'your not the member of the group {task_group.name}.')\n\n return attrs\n\n def validate_description(self, description):\n\n content = [description]\n\n if content[0].islower():\n raise ValidationError('Your comment has to start with upper letter')\n\n return description\n\n\ndef validate_creator(current_creator, previous_creator):\n\n if current_creator == previous_creator:\n raise ValidationError('You can not comment on this post twice in a row')\n\n return current_creator, previous_creator\n","sub_path":"TaskApp/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"388209016","text":"from __future__ import unicode_literals\nfrom __future__ import absolute_import\nfrom io import BytesIO\nfrom shutil import rmtree\nfrom tempfile import mkdtemp\n\nfrom django.db import connections\nfrom django.test import TestCase\n\nimport corehq.blobs.fsdb as fsdb\nfrom corehq.blobs import CODES\nfrom corehq.blobs.models import BlobMeta\nfrom corehq.blobs.tests.util import get_meta, new_meta\nfrom corehq.form_processor.tests.utils import only_run_with_partitioned_database\nfrom corehq.sql_db.util import get_db_alias_for_partitioned_doc\n\n\nclass TestMetaDB(TestCase):\n\n @classmethod\n def setUpClass(cls):\n super(TestMetaDB, cls).setUpClass()\n cls.rootdir = mkdtemp(prefix=\"blobdb\")\n cls.db = fsdb.FilesystemBlobDB(cls.rootdir)\n\n @classmethod\n def tearDownClass(cls):\n cls.db = None\n rmtree(cls.rootdir)\n cls.rootdir = None\n super(TestMetaDB, cls).tearDownClass()\n\n def test_new(self):\n metadb = self.db.metadb\n with self.assertRaisesMessage(TypeError, \"domain is required\"):\n metadb.new()\n with self.assertRaisesMessage(TypeError, \"parent_id is required\"):\n metadb.new(domain=\"test\")\n with self.assertRaisesMessage(TypeError, \"type_code is required\"):\n metadb.new(domain=\"test\", parent_id=\"test\")\n meta = metadb.new(\n domain=\"test\",\n parent_id=\"test\",\n type_code=CODES.multimedia,\n )\n self.assertEqual(meta.id, None)\n self.assertTrue(meta.key)\n\n def test_save_on_put(self):\n meta = new_meta()\n self.assertEqual(meta.id, None)\n self.db.put(BytesIO(b\"content\"), meta=meta)\n self.assertTrue(meta.id)\n saved = get_meta(meta)\n self.assertTrue(saved is not meta)\n self.assertEqual(saved.key, meta.key)\n\n def test_save_properties(self):\n meta = new_meta(properties={\"mood\": \"Vangelis\"})\n self.db.put(BytesIO(b\"content\"), meta=meta)\n self.assertEqual(get_meta(meta).properties, {\"mood\": \"Vangelis\"})\n\n def test_save_empty_properties(self):\n meta = new_meta()\n self.assertEqual(meta.properties, {})\n self.db.put(BytesIO(b\"content\"), meta=meta)\n self.assertEqual(get_meta(meta).properties, {})\n dbname = get_db_alias_for_partitioned_doc(meta.parent_id)\n with connections[dbname].cursor() as cursor:\n cursor.execute(\n \"SELECT id, properties FROM blobs_blobmeta WHERE id = %s\",\n [meta.id],\n )\n self.assertEqual(cursor.fetchall(), [(meta.id, None)])\n\n def test_delete(self):\n meta = new_meta()\n self.db.put(BytesIO(b\"content\"), meta=meta)\n self.db.delete(key=meta.key)\n with self.assertRaises(BlobMeta.DoesNotExist):\n get_meta(meta)\n\n def test_delete_missing_meta(self):\n meta = new_meta()\n self.assertFalse(self.db.exists(key=meta.key))\n # delete should not raise\n self.db.metadb.delete(meta.key, 0)\n\n def test_bulk_delete(self):\n metas = []\n for name in \"abc\":\n meta = new_meta(parent_id=\"parent\", name=name)\n meta.content_length = 0\n metas.append(meta)\n self.db.metadb.put(meta)\n a, b, c = metas\n self.db.metadb.bulk_delete([a, b])\n for meta in [a, b]:\n with self.assertRaises(BlobMeta.DoesNotExist):\n get_meta(meta)\n get_meta(c) # should not have been deleted\n\n def test_bulk_delete_unsaved_meta_raises(self):\n meta = new_meta()\n with self.assertRaises(ValueError):\n self.db.metadb.bulk_delete([meta])\n\n\n@only_run_with_partitioned_database\nclass TestPartitionedMetaDB(TestMetaDB):\n pass\n","sub_path":"corehq/blobs/tests/test_metadata.py","file_name":"test_metadata.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"331982321","text":"from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.permissions import IsAuthenticated\nimport boto3\nfrom botocore.exceptions import ClientError\nimport re\n\nfrom study.utils.file_tree_generator import FileTreeGenerator\n\n\ns3_client = boto3.client('s3')\nBUCKET_NAME = 'letsstudy-test'\n\n\nclass CloudStorageFileDetail(APIView):\n permission_classes = (IsAuthenticated,)\n\n def post(self, request, format=None):\n groupId = request.data['groupId']\n file_path_in_group = request.data['filepath']\n file_path = '{}/{}'.format(groupId, file_path_in_group)\n try:\n url = s3_client.generate_presigned_url(\n 'get_object',\n Params={\n 'Bucket': BUCKET_NAME,\n 'Key': file_path,\n },\n ExpiresIn=3600)\n return Response(url, status=status.HTTP_200_OK)\n except ClientError as e:\n return Response({'error': e}, status.HTTP_204_NO_CONTENT)\n\n\nclass CloudStorageFileDelete(APIView):\n permission_classes = (IsAuthenticated,)\n\n # Doesn't check whether the file exists in the storage\n def post(self, request, format=None):\n groupId = request.data['groupId']\n file_path_in_group = request.data['filepath']\n file_path = '{}/{}'.format(groupId, file_path_in_group)\n try:\n response = s3_client.delete_object(\n Bucket=BUCKET_NAME,\n Key=file_path\n )\n return Response('', status=status.HTTP_200_OK)\n except ClientError as e:\n return Response({'error': e}, status.HTTP_204_NO_CONTENT)\n\n\nclass CloudStorageFileCreate(APIView):\n permission_classes = (IsAuthenticated,)\n\n def post(self, request, format=None):\n groupId = request.data['groupId']\n file_path_in_group = request.data['filepath']\n file_path = '{}/{}'.format(groupId, file_path_in_group)\n try:\n url = s3_client.generate_presigned_url(\n 'put_object',\n Params={\n 'Bucket': BUCKET_NAME,\n 'Key': file_path,\n },\n ExpiresIn=3600)\n return Response(url, status=status.HTTP_200_OK)\n except ClientError as e:\n return Response({'error': e})\n\n\nclass CloudStorageFileTree(APIView):\n permission_classes = (IsAuthenticated,)\n\n def get(self, request, format=None):\n groupId = self.request.query_params.get('groupId')\n try:\n response = s3_client.list_objects_v2(Bucket=BUCKET_NAME)\n global_file_paths = map(lambda content: content['Key'], response['Contents'])\n file_paths = self.filter_group_file_paths(global_file_paths, groupId)\n file_tree = FileTreeGenerator().put_all(file_paths).tree\n return Response(file_tree)\n except ClientError as e:\n return Response({'error': e})\n\n def filter_group_file_paths(self, global_file_paths, groupId):\n group_file_paths = filter(\n lambda global_file_path: re.match(r'^{}/'.format(groupId), global_file_path),\n global_file_paths)\n file_paths = map(\n lambda file_path: re.sub(r'^{}/'.format(groupId), '', file_path),\n group_file_paths)\n return file_paths\n","sub_path":"study/cloud_storage/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"134543812","text":"import numpy as np\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import GRU\nfrom keras.layers import RNN\nfrom keras.utils import np_utils\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.utils.np_utils import to_categorical\nimport keras.callbacks\nimport re\nimport pickle\n\nfrom keras.models import load_model\n\n\nclass History(keras.callbacks.Callback):\n #will contain all loss values for every epoch run\n def on_train_begin(self, logs={}):\n self.losses=[]\n def on_batch_end(self, batch, logs={}):\n self.losses.append(logs.get('loss'))\n with open('128gru40_losses.pickle', 'wb') as handle:\n pickle.dump(self.losses, handle)\n\n with open('128gru40.pickle', 'wb') as handle:\n pickle.dump(self.losses, handle)\n\n\n #save five models to use later to show text gen\n def on_epoch_end(self, epoch, logs={}):\n if epoch % 20 == 0:\n self.model.save(\"128gru40_at_epoch{}.hd5\".format(epoch))\n \nf = open('corpus.txt', 'r')\ntxt = ''\nfor line in f:\n txt+=line\n\ntxt = re.sub(' +', ' ', txt)\ntxt = txt.lower()\n\ncharacters = sorted(list(set(txt)))\n\nn_to_char = {n:char for n, char in enumerate(characters)}\nchar_to_n = {char:n for n, char in enumerate(characters)}\n\nvocab_size = len(characters)\nprint('Number of unique characters: ', vocab_size)\nprint(characters)\n\n\nX = []\nY = []\ncorp_len = len(txt)\nseq_len = 40\n\nfor i in range(0, corp_len - seq_len, 1):\n seq = txt[i:i+seq_len]\n label = txt[i + seq_len]\n X.append([char_to_n[char] for char in seq])\n Y.append(char_to_n[label])\n \nprint('num of extracted seqs: ', len(X))\n\nx_mod = np.reshape(X, (len(X), seq_len, 1))\nx_mod = x_mod / float(len(characters))\nY_mod = to_categorical(Y)\n\nmodel = load_model('128gru40_at_epoch20.hd5')\nmodel.compile(loss='categorical_crossentropy', optimizer='adam')\n\n# define how model checkpoints are saved\n# filepath = \"model_weights-{epoch:02d}-{loss:.4f}.hdf5\"\n# checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\nhistory = History()\nmodel.fit(x_mod, Y_mod, epochs=20, batch_size=128, callbacks = [history])\n\n# loss = history\n# val_loss = history.history['val_loss']\n\n\nlosses = history.losses\nepochs = range(len(losses))\n\nplt.plot(epochs, losses, 'b', label='Loss')\n# plt.plot(epochs, val_loss, 'r', label='Validation loss')\nplt.title('GRU Loss')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend()\n\n# plt.show()\nplt.savefig('lossplot_128gru40.png')\n\nwith open('128gru40.pickle', 'wb') as handle:\n pickle.dump(history, handle)\n\n","sub_path":"128gru40.py","file_name":"128gru40.py","file_ext":"py","file_size_in_byte":2648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"115581252","text":"#===============================================================================\n#\n# Secure Camera Library\n#\n# GENERAL DESCRIPTION\n# build script\n#\n# Copyright (c) 2016-2017 by Qualcomm Technologies, Inc. All Rights Reserved.\n# Qualcomm Technologies Proprietary and Confidential.\n#\n#-------------------------------------------------------------------------------\n#\n# $Header: //components/rel/apps.tz/2.0.2/securemsm/trustzone/qsapps/seccamlib/src/SConscript#1 $\n# $DateTime: 2018/02/06 03:27:17 $\n# $Author: pwbldsvc $\n# $Change: 15400261 $\n# EDIT HISTORY FOR FILE\n#\n# This section contains schedulerents describing changes made to the module.\n# Notice that changes are listed in reverse chronological order.\n#\n# when who what, where, why\n# -------- --- ---------------------------------------------------------\n# 08/14/17 dr Port to sdm845\n# 01/01/17 dr Created\n#===============================================================================\nImport('env')\nenv = env.Clone()\n\n\nlibname = 'seccam_lib'\n\nincludes = [\n \"${BUILD_ROOT}/core/api/services\",\n \"${BUILD_ROOT}/ssg/api/securemsm/trustzone/qsee\",\n \"${BUILD_ROOT}/core/api/kernel/libstd/stringl\",\n \"${BUILD_ROOT}/ssg/securemsm/accesscontrol/api\",\n \"${BUILD_ROOT}/core/kernel/smmu/ACv3.0/common/inc\",\n \"${BUILD_ROOT}/ssg/securemsm/trustzone/qsee/include/\",\n \"${BUILD_ROOT}/ssg/securemsm/trustzone/qsee/mink/include/\",\n \"../inc\",\n]\n\nsources = ['seccamlib.c',]\n\nlib = env.SecureAppLibBuilder(\n includes = includes,\n sources = sources,\n libname = libname,\n deploy_sources = ['SConscript',\n env.Glob('../inc/*.h'),\n ],\n deploy_lib = True,\n deploy_variants = env.GetDefaultPublicVariants()\n )\nReturn('lib')\n","sub_path":"trustzone_images/apps/securemsm/trustzone/qsapps/seccamlib/src/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"100169197","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport io\nimport itertools\n\nimport mock\nimport pytest\n\nfrom octane.util import db\nfrom octane.util import ssh\n\n\ndef test_mysqldump_from_env(mocker, mock_open, mock_subprocess, mock_ssh_popen,\n node):\n test_contents = b'test_contents\\nhere'\n buf = io.BytesIO()\n\n mock_open.return_value.write.side_effect = buf.write\n\n get_one_node_of = mocker.patch('octane.util.env.get_one_node_of')\n get_one_node_of.return_value = node\n\n proc = mock_ssh_popen.return_value.__enter__.return_value\n proc.stdout = io.BytesIO(test_contents)\n\n db.mysqldump_from_env('env', 'controller', ['db1'], 'filename')\n\n assert not mock_subprocess.called\n mock_ssh_popen.assert_called_once_with(\n ['bash', '-c', mock.ANY], stdout=ssh.PIPE, node=node)\n mock_open.assert_called_once_with('filename', 'wb')\n assert buf.getvalue() == test_contents\n\n\ndef test_mysqldump_restore_to_env(mocker, mock_open, mock_subprocess,\n mock_ssh_popen, node):\n test_contents = b'test_contents\\nhere'\n buf = io.BytesIO()\n\n mock_open.return_value = io.BytesIO(test_contents)\n\n get_one_node_of = mocker.patch('octane.util.env.get_one_node_of')\n get_one_node_of.return_value = node\n\n proc = mock_ssh_popen.return_value.__enter__.return_value\n proc.stdin.write.side_effect = buf.write\n\n db.mysqldump_restore_to_env('env', 'controller', 'filename')\n\n assert not mock_subprocess.called\n mock_ssh_popen.assert_called_once_with(\n ['sh', '-c', mock.ANY], stdin=ssh.PIPE, node=node)\n mock_open.assert_called_once_with('filename', 'rb')\n assert buf.getvalue() == test_contents\n\n\ndef test_db_sync(mocker, node, mock_subprocess, mock_ssh_call):\n get_one_controller = mocker.patch('octane.util.env.get_one_controller')\n get_one_controller.return_value = node\n\n fix_migration_mock = mocker.patch(\"octane.util.db.fix_neutron_migrations\")\n\n db.db_sync('env')\n\n fix_migration_mock.assert_called_once_with(node)\n\n assert not mock_subprocess.called\n assert all(call[1]['parse_levels']\n for call in mock_ssh_call.call_args_list)\n assert all(call[1]['node'] == node\n for call in mock_ssh_call.call_args_list)\n\n\n@pytest.mark.parametrize((\"version\", \"result\"), [\n (\"6.1\", False),\n (\"7.0\", True),\n (\"8.0\", False),\n])\ndef test_does_perform_flavor_data_migration(version, result):\n env = mock.Mock(data={\"fuel_version\": version})\n assert db.does_perform_flavor_data_migration(env) == result\n\n\n@pytest.mark.parametrize((\"statuses\", \"is_error\", \"is_timeout\"), [\n ([(0, 0)], True, False),\n ([(0, 0)], False, False),\n ([(10, 0), (10, 5), (5, 5)], False, False),\n ([(10, 0)], False, True),\n])\ndef test_nova_migrate_flavor_data(mocker, statuses, is_error, is_timeout):\n env = mock.Mock()\n mocker.patch(\"time.sleep\")\n mocker.patch(\"octane.util.env.get_one_controller\")\n mock_output = mocker.patch(\"octane.util.ssh.call_output\")\n attempts = len(statuses)\n mock_output.side_effect = itertools.starmap(FLAVOR_STATUS.format, statuses)\n if is_error:\n mock_output.side_effect = None\n mock_output.return_value = \"UNRECOGNIZABLE\"\n with pytest.raises(Exception) as excinfo:\n db.nova_migrate_flavor_data(env, attempts=attempts)\n assert excinfo.exconly().startswith(\n \"Exception: The format of the migrate_flavor_data command\")\n elif is_timeout:\n with pytest.raises(Exception) as excinfo:\n db.nova_migrate_flavor_data(env, attempts=attempts)\n assert excinfo.exconly().startswith(\n \"Exception: After {0} attempts flavors data migration\"\n .format(attempts))\n else:\n db.nova_migrate_flavor_data(env, attempts=attempts)\n\nFLAVOR_STATUS = \"{0} instances matched query, {1} completed\"\n\n\n@pytest.mark.parametrize((\"version\", \"result\"), [\n (\"6.1\", False),\n (\"7.0\", True),\n (\"8.0\", False),\n])\ndef test_does_perform_cinder_volume_update_host(version, result):\n env = mock.Mock(data={\"fuel_version\": version})\n assert db.does_perform_cinder_volume_update_host(env) == result\n\n\ndef test_cinder_volume_update_host(mocker):\n mock_orig_env = mock.Mock()\n mock_new_env = mock.Mock()\n\n mock_orig_cont = mock.Mock()\n mock_new_cont = mock.Mock()\n\n mock_get = mocker.patch(\"octane.util.env.get_one_controller\")\n mock_get.side_effect = [mock_orig_cont, mock_new_cont]\n\n mock_get_current = mocker.patch(\"octane.util.db.get_current_host\")\n mock_get_new = mocker.patch(\"octane.util.db.get_new_host\")\n\n mock_ssh = mocker.patch(\"octane.util.ssh.call\")\n db.cinder_volume_update_host(mock_orig_env, mock_new_env)\n mock_ssh.assert_called_once_with(\n [\"cinder-manage\", \"volume\", \"update_host\",\n \"--currenthost\", mock_get_current.return_value,\n \"--newhost\", mock_get_new.return_value],\n node=mock_new_cont, parse_levels=True)\n assert mock_get.call_args_list == [\n mock.call(mock_orig_env),\n mock.call(mock_new_env),\n ]\n mock_get_current.assert_called_once_with(mock_orig_cont)\n mock_get_new.assert_called_once_with(mock_new_cont)\n\n\n@pytest.mark.parametrize((\"func\", \"content\", \"expected\"), [\n (db.get_current_host, [\n (None, \"DEFAULT\", None, None),\n (None, \"DEFAULT\", \"host\", \"fakehost\"),\n (None, \"DEFAULT\", \"volume_backend_name\", \"fakebackend\"),\n ], \"fakehost#fakebackend\"),\n (db.get_new_host, [\n (None, \"DEFAULT\", None, None),\n (None, \"DEFAULT\", \"host\", \"fakehost_default\"),\n (None, \"RBD-backend\", None, None),\n (None, \"RBD-backend\", \"volume_backend_name\", \"fakebackend\"),\n ], \"fakehost_default@fakebackend#RBD-backend\"),\n (db.get_new_host, [\n (None, \"DEFAULT\", None, None),\n (None, \"DEFAULT\", \"host\", \"fakehost_default\"),\n (None, \"RBD-backend\", None, None),\n (None, \"RBD-backend\", \"backend_host\", \"fakehost_specific\"),\n (None, \"RBD-backend\", \"volume_backend_name\", \"fakebackend\"),\n ], \"fakehost_specific@fakebackend#RBD-backend\"),\n])\ndef test_get_hosts_functional(mocker, func, content, expected):\n mock_node = mock.Mock()\n mocker.patch(\"octane.util.ssh.sftp\")\n mock_iter = mocker.patch(\"octane.util.helpers.iterate_parameters\")\n mock_iter.return_value = content\n result = func(mock_node)\n assert expected == result\n","sub_path":"octane/tests/test_db.py","file_name":"test_db.py","file_ext":"py","file_size_in_byte":6915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"636495855","text":"\"\"\"\r\nSet Patient Hospital form\r\n\r\nDjango form for transferring patients.\r\n\r\n=== Fields ===\r\n\r\npatient_id -- (CharField) email ID of the patient being transferred\r\nhospital ---- (ChoiceField) hospital that the patient is being transferred to\r\n\r\n=== Methods ===\r\n\r\n__init__ --------- Initializes the form.\r\nbuild_form_dict -- Creates a dictionary of all the set patient hospital forms for each patient.\r\nhandle_post ------ Transfers patient given a completed form.\r\n\r\n\"\"\"\r\n\r\nfrom django import forms\r\nfrom HealthApp import staticHelpers\r\nfrom HealthApp.models import Hospital, Patient, LogEntry, Message\r\n\r\n\r\nclass SetPatientHospital(forms.ModelForm):\r\n def __init__(self, patient):\r\n super().__init__()\r\n staticHelpers.set_form_id(self, \"SetPatientHospital\")\r\n\r\n self.fields['patient_id'] = forms.CharField(widget=forms.HiddenInput(), initial=patient.id)\r\n\r\n # Generate hospital ChoiceField\r\n hospital_tuple = tuple(Hospital.objects.all().values_list(\"id\", \"name\").order_by(\"name\"))\r\n\r\n self.fields['hospital'] = forms.ChoiceField(\r\n widget=forms.Select(attrs={'class': 'form-control', 'placeholder': 'Hospital'}),\r\n choices=hospital_tuple,\r\n label='Hospital',\r\n initial=patient.hospital)\r\n\r\n class Meta:\r\n model = Patient\r\n fields = ('hospital',)\r\n\r\n @classmethod\r\n def build_form_dict(cls, all_patients):\r\n forms_dict = dict()\r\n\r\n for patient in all_patients:\r\n forms_dict[patient.username] = SetPatientHospital(patient)\r\n\r\n return forms_dict\r\n\r\n @classmethod\r\n def handle_post(cls, user_type, doctor, post_data):\r\n if user_type == staticHelpers.UserTypes.doctor:\r\n patient_id = post_data['patient_id']\r\n patient = Patient.objects.all().filter(id=patient_id)[0]\r\n\r\n hospital_id = post_data['hospital']\r\n patient.hospital = Hospital.objects.all().filter(id=hospital_id)[0]\r\n\r\n patient.save()\r\n\r\n LogEntry.log_action(doctor.username, \"Transferred patient \" + patient.username + \" to \" +\r\n patient.hospital.name)\r\n Message.sendNotifMessage(patient.username, \"You have been transferred to a new hospital\",\r\n \"Your new hospital is \" + patient.hospital.full_string())","sub_path":"HealthApp/forms/set_patient_hospital.py","file_name":"set_patient_hospital.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"618349250","text":"# coding=utf-8\nfrom __future__ import print_function, absolute_import, unicode_literals\n\nimport sys\nimport json\nimport numpy as np\nimport pandas as pd\nimport talib as ta\nfrom gm.api import *\n\ndef init(context):\n\n #context.avglen = 3 # 布林均线周期参数\n context.disp = 20 # 布林平移参数\n #ontext.sdlen = 13 # 布林标准差参数\n context.sdev = 1.03 # 布林通道倍数参数\n\n context.frequency = \"900s\"\n # context.goods交易的品种\n context.symbol = 'SHFE.AU'\n context.fields = \"high,low,close\"\n context.period = context.disp + 1 # 订阅数据滑窗长度\n #TimeFilter优化 \n #context.window_now = ['14:00:00', '09:45:00']\n\n # 订阅context.goods里面的品种, bar频率为frequency\n subscribe(symbols=context.symbol,frequency=context.frequency,count=context.period,wait_group=True)\n\ndef on_bar(context, bars):\n \n # 获取数据\n close_prices = context.data(symbol=context.symbol,frequency=context.frequency,\n count=context.period,fields='close')\n trade_prices = context.data(symbol=context.symbol,frequency=context.frequency,\n count=context.period,fields='high,low')\n last_price = close_prices['close'][context.disp-1]\n\n avgval = ta.MA(np.array(close_prices['close']), context.disp-13) \n sdmult = ta.STDDEV(np.array(close_prices['close']), context.disp-3)*context.sdev\n #布林带上线\n disptop = avgval[context.disp-1] + sdmult[-1]\n #布林带下线\n dispup = avgval[context.disp-1] - sdmult[-1]\n\n #print(\"last_price: \",last_price, \"avgval: \",avgval[context.disp-1],\"disptop: \",disptop,\"dispup\",dispup)\n HigherBand = disptop\n LowerBand = dispup\n\n #获取账户持仓字典\n Account_positions = context.account().positions()\n #当前持仓方向\n if len(Account_positions)>0:\n position_side = Account_positions[0]['side']\n else:\n position_side = 0\n \n #上穿布林带上带且未持仓\n if trade_prices['high'][context.disp-1] > disptop and position_side==0:\n print(str(context.now),\"做多\")\n # 开多仓\n order_target_percent(symbol=context.symbol, percent=0.1, position_side=1,\n order_type=OrderType_Limit, price=HigherBand)\n #下穿布林带均线且持多头仓\n if trade_prices['low'][context.disp-1] < avgval[context.disp-1] and position_side == 1:\n print(str(context.now),\"平多\")\n # 平多仓\n order_target_percent(symbol=context.symbol, percent=0, position_side=1, order_type=2)\n\n #下穿布林带下带且未持仓\n if trade_prices['low'][context.disp-1] < dispup and position_side==0:\n print(str(context.now),\"做空\")\n # 开空仓\n order_target_percent(symbol=context.symbol, percent=0.1, position_side=2,\n order_type=OrderType_Limit, price=LowerBand)\n #上穿布林带均线且持空头仓\n if trade_prices['high'][context.disp-1] > avgval[context.disp-1] and position_side == 2:\n print(str(context.now),\"平空\")\n # 平空仓\n order_target_percent(symbol=context.symbol, percent=0, position_side=2, order_type=2)\n \n\"\"\" \ndef on_backtest_finished(context, indicator):\n \n #以下用于在回测结束后保存回测指标\n indicator_data = {}\n indicator_data = indicator\n file_name = '20_0_95'\n \n if indicator_data[\"sharp_ratio\"]>0:\n with open('E:/Program Files/other/交易系统作业/代码/作业五/indicator/'+file_name+'.json','w',encoding=\"utf-8\") as json_file:\n json.dump(indicator_data, json_file)\n \n print(\"WINDOW %s done!\"%(file_name))\n\"\"\"\n\nif __name__ == '__main__':\n run(strategy_id='cc314c5e-0598-11e9-abd7-3c970e853b38',\n filename='project5_3.py',\n mode=MODE_BACKTEST,\n token='8401315ba754693611d3bb99131e9cbc527c605f',\n backtest_start_time='2016-10-20 09:15:00',\n backtest_end_time='2018-11-24 15:00:00',\n backtest_adjust=ADJUST_PREV,\n backtest_initial_cash=1000000,\n backtest_commission_ratio=0.0002,\n backtest_slippage_ratio=0)#.0001)\n","sub_path":"trading_system/project5/project5_3.py","file_name":"project5_3.py","file_ext":"py","file_size_in_byte":4274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"45556164","text":"# -*- coding:utf-8 -*- \nfrom ctypes import *\nimport math\nimport random\nfrom sqlalchemy import create_engine\nfrom sqlalchemy import text\nimport json\nimport sys\n# import lstm\nsys.path.append(\"..\")\nimport util\n\nclass BOX(Structure):\n _fields_ = [(\"x\", c_float),\n (\"y\", c_float),\n (\"w\", c_float),\n (\"h\", c_float)]\n\nclass IMAGE(Structure):\n _fields_ = [(\"w\", c_int),\n (\"h\", c_int),\n (\"c\", c_int),\n (\"data\", POINTER(c_float))]\n\nclass METADATA(Structure):\n _fields_ = [(\"classes\", c_int),\n (\"names\", POINTER(c_char_p))]\n\n\nwith open('/home/nvidia/Horus/config.cnf') as f:\n cnf = json.load(f)\nlib = CDLL(str(cnf['darknet_path'])+'libdarknet.so', RTLD_GLOBAL)\n\nlib.network_width.argtypes = [c_void_p]\nlib.network_width.restype = c_int\nlib.network_height.argtypes = [c_void_p]\nlib.network_height.restype = c_int\n\npredict = lib.network_predict\npredict.argtypes = [c_void_p, POINTER(c_float)]\npredict.restype = POINTER(c_float)\n\nset_gpu = lib.cuda_set_device\nset_gpu.argtypes = [c_int]\n\nmake_image = lib.make_image\nmake_image.argtypes = [c_int, c_int, c_int]\nmake_image.restype = IMAGE\n\nmake_boxes = lib.make_boxes\nmake_boxes.argtypes = [c_void_p]\nmake_boxes.restype = POINTER(BOX)\n\nfree_ptrs = lib.free_ptrs\nfree_ptrs.argtypes = [POINTER(c_void_p), c_int]\n\nnum_boxes = lib.num_boxes\nnum_boxes.argtypes = [c_void_p]\nnum_boxes.restype = c_int\n\nmake_probs = lib.make_probs\nmake_probs.argtypes = [c_void_p]\nmake_probs.restype = POINTER(POINTER(c_float))\n\ndetect = lib.network_predict\ndetect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]\n\nreset_rnn = lib.reset_rnn\nreset_rnn.argtypes = [c_void_p]\n\nload_net = lib.load_network\nload_net.argtypes = [c_char_p, c_char_p, c_int]\nload_net.restype = c_void_p\n\nfree_image = lib.free_image\nfree_image.argtypes = [IMAGE]\n\ndraw_box = lib.draw_box\ndraw_label = lib.draw_label\nget_label = lib.get_label\nsave_image = lib.save_image\ndraw_box_width=lib.draw_box_width\n\n\nletterbox_image = lib.letterbox_image\nletterbox_image.argtypes = [IMAGE, c_int, c_int]\nletterbox_image.restype = IMAGE\n\nload_meta = lib.get_metadata\nlib.get_metadata.argtypes = [c_char_p]\nlib.get_metadata.restype = METADATA\n\nload_image = lib.load_image_color\nload_image.argtypes = [c_char_p, c_int, c_int]\nload_image.restype = IMAGE\n\nrgbgr_image = lib.rgbgr_image\nrgbgr_image.argtypes = [IMAGE]\n\npredict_image = lib.network_predict_image\npredict_image.argtypes = [c_void_p, IMAGE]\npredict_image.restype = POINTER(c_float)\n\nnetwork_detect = lib.network_detect\nnetwork_detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]\n\nwith open('/home/nvidia/Horus/config.cnf') as f:\n cnf = json.load(f)\nnet = load_net(str(cnf['darknet_path'])+'cfg/'+str(cnf['cfg'])+'.cfg', str(cnf['darknet_path'])+'weight/'+str(cnf['weight'])+'.weights', 0)\n# net = load_net(str(cnf['darknet_path'])+'cfg/yolo9000.cfg', str(cnf['darknet_path'])+'weight/yolo.weights', 0)\n\n#初始化LSTM\n# lstm_bl=lstm.BasicLSTM()\n\n# c=config.TIME_FREQUENCY\n# if c == 0:\n# c = 200\n# elif c == None:\n# c = 200\n# timeF = c/1000.0\n\n#是否是所要的分类数据\ndef isPass(name):\n if name=='car' or name=='motorcycle' or name=='bus' or name=='truck' or name=='stop sign' or name=='traffic light' or name=='person' or name=='bicycle' or name=='clock':\n return True\n else:\n return False\n\n#处理tb_object数据\ndef dealData(name,probability,img_id,p_x,p_y,p_w,p_h):\n\n with open('/home/nvidia/Horus/config.cnf') as json_data:\n cnf = json.load(json_data) \n db = create_engine(cnf['db'])\n \n if not name.strip():\n pass\n else: \n resultProxy=db.execute(text('select * from tb_classify where classify = :classify'), {'classify':name})\n result = resultProxy.fetchall()\n if not result:\n db.execute(text('insert into tb_classify(classify) values( :classify)'), {'classify':name})\n\n resultProxy_id=db.execute(text('select id from tb_classify where classify = :classify'), {'classify':name})\n id_result = resultProxy_id.fetchall()\n\n type_id=id_result[0][0]\n insert_obj=\"insert into tb_object(type_id,probability,img_id,p_x,p_y,p_w,p_h) values (%s,%s,%s,%s,%s,%s,%s)\"%(type_id,probability,img_id,p_x,p_y,p_w,p_h)\n db.execute(insert_obj)\n\n # draw_box_width(im,int(p_x-p_w/2.),int(p_y-p_h/2.),int(p_x+p_w/2.),int(p_y+p_h/2.),3,255,0,0)\n # save_image(im,cnf['detect_path']+'/'+img_id)\n\n#处理统计数据\ndef dealDtatistics(img_id,car,motorcycle,bus,truck,stop_sign,traffic_light,person,bicycle,clock):\n with open('/home/nvidia/Horus/config.cnf') as json_data:\n cnf = json.load(json_data) \n db = create_engine(cnf['db'])\n\n insert=\"insert into tb_object_statistics(img_id,car,motorcycle,bus,truck,stop_sign,traffic_light,person,bicycle,clock) values (%s,%d,%d,%d,%d,%d,%d,%d,%d,%d)\"%(img_id,car,motorcycle,bus,truck,stop_sign,traffic_light,person,bicycle,clock)\n db.execute(insert)\n # #LSTM判断危险级别\n # pred_list=[[bicycle,bus,car,clock,person,stop_sign,traffic_light,truck,0]]\n # result=lstm_bl.prediction(pred_list)\n # print(\"lstm----：%d\",result)\n # sqlInsert='insert into tb_camera(timestamp,img_id,frequency,result) values(%s,%s,%s,%d)'%(img_id,img_id,timeF,result)\n # db.execute(sqlInsert)\n\n\n\n\n\n\ndef detect(image, hier_thresh=.5, nms=.45):\n with open('/home/nvidia/Horus/config.cnf') as f:\n cnf = json.load(f)\n meta = load_meta(str(cnf['darknet_path'])+'cfg/'+str(cnf['model'])+'.data')\n boxes = make_boxes(net)\n probs = make_probs(net)\n num = num_boxes(net)\n im = load_image(image, 0, 0)\n network_detect(net, im, float(cnf['probability']), hier_thresh, nms, boxes, probs)\n res = []\n img_id=util.OnlyNumber(image)\n \n for j in range(num):\n for i in range(meta.classes):\n if probs[j][i] > 0:\n res.append((meta.names[i], probs[j][i], (boxes[j].x-boxes[j].w/2., boxes[j].y-boxes[j].h/2., boxes[j].w, boxes[j].h)))\n if isPass(meta.names[i]):\n dealData(meta.names[i],probs[j][i],img_id,boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)\n\n res = sorted(res, key=lambda x: -x[1])\n if len(res)>0:\n car=0\n motorcycle=0\n bus=0\n truck=0\n stop_sign=0\n traffic_light=0\n person=0\n bicycle=0\n clock=0\n for m in range(len(res)):\n name=res[m][0]\n if name=='car':\n car += 1\n elif name=='motorcycle':\n motorcycle += 1\n elif name=='bus':\n bus += 1\n elif name=='truck':\n truck += 1\n elif name=='stop sign':\n stop_sign += 1\n elif name=='traffic light':\n traffic_light += 1\n elif name=='person':\n person += 1\n elif name=='bicycle':\n bicycle += 1\n elif name=='clock':\n clock +=1\n dealDtatistics(img_id,car,motorcycle,bus,truck,stop_sign,traffic_light,person,bicycle,clock)\n\n\n free_image(im)\n free_ptrs(cast(probs, POINTER(c_void_p)), num)\n return res\n\n\n \n\n","sub_path":"darknet/darknet.py","file_name":"darknet.py","file_ext":"py","file_size_in_byte":7477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"334624523","text":"import random\n\nprint(\"Welcome to Camel!\")\nprint(\"You have stolen a camel to make your way across the great Mobi desert.\")\nprint(\"The natives want their camel back and are chasing you down!\")\nprint(\"Survive your desert trek and outrun the natives.\")\n\nmiles_traveled = 0\nthirst = 0\ncamel_tiredness = 0\nnatives_distance = -20\nDrinks_in_canteen = 3\n\ndone = False\nwhile not done:\n print(\"\")\n print(\"A. Drink from your canteen.\")\n print(\"B. Ahead moderate speed.\")\n print(\"C. Ahead full speed.\")\n print(\"D. Stop for the night.\")\n print(\"E. Status check.\")\n print(\"Q. Quit.\\n\")\n\n user_choice = input(\"Your choice?\\n\").upper()\n\n if user_choice == \"Q\":\n done = True\n\n elif user_choice == \"E\":\n print(\"Miles traveled: %d\" % miles_traveled)\n print(\"Drinks in canteen: %d\" % Drinks_in_canteen)\n print(\"The natives are %d miles behind you.\" % (miles_traveled - natives_distance))\n\n elif user_choice == \"D\":\n camel_tiredness = 0\n print(\"The camel is happy!\")\n natives_distance += random.randrange(7, 15)\n\n elif user_choice == \"C\":\n x = random.randrange(10, 21)\n miles_traveled += x\n print(\"You have traveled \" + str(x) + \" miles\")\n thirst += 1\n camel_tiredness += random.randrange(1, 4)\n natives_distance += random.randrange(7, 15)\n\n elif user_choice == \"B\":\n y = random.randrange(5, 13)\n miles_traveled += y\n print(\"You have traveled \" + str(y) + \" miles\")\n thirst += 1\n camel_tiredness += random.randrange(0, 2)\n natives_distance += random.randrange(7, 15)\n\n elif user_choice == \"A\":\n if Drinks_in_canteen > 0:\n Drinks_in_canteen += -1\n thirst = 0\n else:\n print(\"You have no more drinks!\")\n\n if not done and thirst > 4:\n if thirst > 6:\n print(\"You died of thirst\")\n done = True\n else:\n print(\"You are thirsty\")\n\n if not done and camel_tiredness > 5:\n if camel_tiredness > 8:\n print(\"Your camel is dead\")\n done = True\n else:\n print(\"Your camel is getting tired\")\n\n if not done and (miles_traveled - natives_distance) < 15:\n if (miles_traveled - natives_distance) == 0:\n print(\"You have been caught by the natives!\")\n done = True\n else:\n print(\"The natives are getting close!\")\n\n if not done and miles_traveled >= 200:\n print(\"You made it across the desert! You win.\")\n done = True\n\n if not done and (user_choice == \"A\" or user_choice == \"B\"):\n z = random.randrange(1, 21)\n if z == 1:\n print(\"You found and oasis!\")\n Drinks_in_canteen = 3\n thirst = 0\n camel_tiredness = 0","sub_path":"CamelGame.py","file_name":"CamelGame.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"447812162","text":"\n\ndef numbers1(n):\n nums = []\n for i in range(n):\n nums.append(i)\n return nums\n\ndef numbers2(n):\n for i in range(n):\n yield i\n\nprint('A')\nfor n in numbers2(200000000):\n print(n)\n\nprint('B')\nfor n in numbers2(20000000):\n print(n)\nprint('C')\n","sub_path":"example_yield.py","file_name":"example_yield.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"15366970","text":"import socket\nimport sys\n\n# Creatae a TCP/IP socket\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# Connect the socket to the port where the server is listening\nserver_address = ('192.168.56.108', 7001)\n#server_address = ('127.0.0.1', 7000)\nprint ('connecting to %s port %s' % server_address)\nsock.connect(server_address)\n\ntry:\n\n # Send data\n message = 'close_server_socket'\n print( 'sending \"%s\"' % message)\n sock.sendall(message)\n\n # Look for the response\n amount_received = 0\n amount_expected = len(message)\n data_received = \"\"\n while amount_received < amount_expected:\n data_received = sock.recv(1024)\n amount_received += len(data_received)\n print ('received \"%s\"' % data_received)\n\nfinally:\n print ('closing socket')\n sock.close()","sub_path":"project/server_socket_puerta/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"185823733","text":"import argparse\r\nimport itertools\r\nimport numpy as np\r\nimport pandas as pd\r\nimport random\r\nimport matplotlib.pyplot as plt\r\nplt.rc('text', usetex=True)\r\n\r\n\r\n\"\"\"\r\nINFO8003-1 : Optimal decision making for complex problems\r\n2021 - Assignement 1\r\nPierre NAVEZ & Antoine DEBOR\r\n\r\nSECTION 5 - Q-Learning in a batch setting\r\n\"\"\"\r\n\r\nclass Domain:\r\n def __init__(self, domain_matrix, domain_type, discount_factor, stoch_thresh):\r\n self.domain_matrix = domain_matrix\r\n self.domain_type = domain_type\r\n self.discount_factor = discount_factor\r\n self.stoch_thresh = stoch_thresh\r\n return\r\n\r\n def state_space(self):\r\n \"\"\"\r\n Define dimensions of the considered domain\r\n ---\r\n parameters :\r\n\r\n None\r\n ---\r\n return :\r\n\r\n - height, width : dimensions of the considered domain\r\n \"\"\"\r\n\r\n height = np.shape(self.domain_matrix)[0]\r\n width = np.shape(self.domain_matrix)[1]\r\n return height, width\r\n\r\n def get_state_space_indices(self):\r\n \"\"\"\r\n Build a matrix whose elements are indices of the considered domain's cells\r\n ---\r\n parameters :\r\n\r\n None\r\n ---\r\n return :\r\n\r\n - indices : matrix whose elements are indices of the considered domain's cells\r\n \"\"\"\r\n\r\n n, m = self.state_space()\r\n indices = np.zeros([n, m], dtype=object)\r\n for i in range(n):\r\n for j in range(m):\r\n indices[i, j] = (i, j)\r\n return indices\r\n\r\n def action_space(self):\r\n \"\"\"\r\n Define the action space of the considered domain\r\n ---\r\n parameters :\r\n\r\n None\r\n ---\r\n return :\r\n\r\n - tuple of possible actions\r\n \"\"\"\r\n\r\n return ((1, 0), (0, 1), (-1, 0), (0, -1))\r\n\r\n def reward(self, state, action):\r\n \"\"\"\r\n Compute the reward corresponding to a given action from a given state\r\n ---\r\n parameters :\r\n\r\n - state : current state\r\n - action : action performed from state state\r\n ---\r\n return :\r\n\r\n - reward corresponding to action action, from state state\r\n \"\"\"\r\n\r\n state_prime = self.dynamics(state, action)\r\n return self.get_reward(state_prime)\r\n\r\n def get_reward(self, state):\r\n \"\"\"\r\n Extract the reward corresponding to a given cell from the considered domain\r\n ---\r\n parameters :\r\n\r\n - state : considered cell\r\n ---\r\n return :\r\n\r\n - reward corresponding to cell state\r\n \"\"\"\r\n\r\n x, y = state\r\n return self.domain_matrix[x,y]\r\n\r\n def dynamics(self, state, action):\r\n \"\"\"\r\n Define the dynamics of the considered domain\r\n ---\r\n parameters :\r\n\r\n - state : current state\r\n - action : action performed from state state\r\n ---\r\n return :\r\n\r\n - reward corresponding to action action, from state state, according to the considered domain's dynamics\r\n \"\"\"\r\n\r\n x, y = state\r\n i, j = action\r\n n, m = self.state_space()\r\n\r\n if self.domain_type==\"Deterministic\":\r\n return min(max(x+i, 0), n-1), min(max(y+j, 0), m-1)\r\n\r\n elif self.domain_type==\"Stochastic\":\r\n if random.random() <= self.stoch_thresh:\r\n return min(max(x+i, 0), n-1), min(max(y+j, 0), m-1)\r\n else:\r\n return 0,0\r\n\r\n def MDP_proba(self, state, state_prime, action):\r\n \"\"\"\r\n Compute probability p(x'|x, u) defining the structure of the equivalent MDP\r\n ---\r\n parameters :\r\n\r\n - state : current state x\r\n - state_prime : candidate state x'\r\n - action : performed action u\r\n ---\r\n return :\r\n\r\n - p(x'|x, u)\r\n \"\"\"\r\n\r\n x, y = state\r\n i, j = action\r\n n, m = self.state_space()\r\n if self.domain_type==\"Deterministic\":\r\n return (1 if state_prime==self.dynamics(state, action) else 0)\r\n else:\r\n prob = 0\r\n if state_prime==(min(max(x+i, 0), n-1), min(max(y+j, 0), m-1)):\r\n prob += self.stoch_thresh\r\n if state_prime==(0, 0):\r\n prob += 1-self.stoch_thresh\r\n return prob\r\n\r\n def MDP_reward(self, state, action):\r\n \"\"\"\r\n Compute reward r(x, u) defining the structure of the equivalent MDP\r\n ---\r\n parameters :\r\n\r\n - state : current state x\r\n - action : performed action u\r\n ---\r\n return :\r\n\r\n - r(x, u)\r\n \"\"\"\r\n\r\n if self.domain_type==\"Deterministic\":\r\n return self.reward(state, action)\r\n else:\r\n x, y = state\r\n i, j = action\r\n n, m = self.state_space()\r\n state_prime = (min(max(x+i, 0), n-1), min(max(y+j, 0), m-1))\r\n return self.stoch_thresh * self.get_reward(state_prime) + (1 - self.stoch_thresh) * self.get_reward((0, 0))\r\n\r\ndef state_action_value_function(domain, N):\r\n \"\"\"\r\n Compute the Q-function\r\n ---\r\n parameters :\r\n\r\n - domain : Domain instance corresponding to the considered domain\r\n - N : Maximum iterate of the recursive equation defining the state-action value functions\r\n ---\r\n return :\r\n\r\n - Q_mat : Q(x, u) matrix, for every initial state x and every possible action u\r\n \"\"\"\r\n\r\n n, m = domain.state_space()\r\n actions = domain.action_space()\r\n state_space = domain.get_state_space_indices()\r\n state_space = state_space.reshape(np.size(state_space))\r\n Q_mat = np.zeros([n, m, len(actions)])\r\n r = np.zeros([n, m, len(actions)])\r\n p = np.zeros([n, m, n, m, len(actions)])\r\n for i in range(N):\r\n Q_mat_prime = np.zeros([n, m, len(domain.action_space())])\r\n for x in range(n):\r\n for y in range(m):\r\n state = x, y\r\n for k, action in enumerate(actions):\r\n r[x, y, k] = domain.MDP_reward(state, action)\r\n sum = 0\r\n for state_prime in state_space:\r\n x_prime, y_prime = state_prime\r\n p[x, y, x_prime, y_prime, k] = domain.MDP_proba(state, state_prime, action)\r\n sum += p[x, y, x_prime, y_prime, k] * max(Q_mat[state_prime])\r\n Q_mat_prime[x, y, k] = r[x, y, k] + domain.discount_factor * sum\r\n Q_mat = Q_mat_prime\r\n return Q_mat, r, p\r\n\r\ndef derive_best_policy(domain, Q):\r\n \"\"\"\r\n Derives optimal policy from Q-function\r\n ---\r\n parameters :\r\n\r\n - domain : Domain instance corresponding to the considered domain\r\n - Q : Q-function from which to derive the expected return\r\n ---\r\n return :\r\n\r\n - best_policy : optimal policy derived from Q\r\n \"\"\"\r\n\r\n n, m = domain.state_space()\r\n actions = domain.action_space()\r\n best_policy = np.zeros([n, m], dtype=object)\r\n for x in range(n):\r\n for y in range(m):\r\n best_action = actions[np.argmax(Q[x, y])]\r\n best_policy[x, y] = best_action\r\n return best_policy\r\n\r\ndef derive_best_expected_return(domain, Q):\r\n \"\"\"\r\n Derives optimal expected return from Q-function\r\n ---\r\n parameters :\r\n\r\n - domain : Domain instance corresponding to the considered domain\r\n - Q : Q-function from which to derive the expected return\r\n ---\r\n return :\r\n\r\n - best_return : optimal expected return derived from Q\r\n \"\"\"\r\n\r\n n, m = domain.state_space()\r\n best_return = np.zeros([n, m])\r\n for x in range(n):\r\n for y in range(m):\r\n best_return[x, y] = max(Q[x, y])\r\n return best_return\r\n\r\ndef gen_trajectory(domain, traj_len):\r\n \"\"\"\r\n Generates a random trajectory of a certain size in a certain domain\r\n ---\r\n parameters :\r\n\r\n - domain : Domain instance corresponding to the considered domain\r\n - traj_len : size of the trajectory to generate\r\n ---\r\n return :\r\n\r\n - list corresponding to the generated trajectory\r\n \"\"\"\r\n\r\n traj = list()\r\n n, m = domain.state_space()\r\n x_start = random.randint(0, n-1)\r\n y_start = random.randint(0, m-1)\r\n state = x_start, y_start\r\n for j in range(traj_len):\r\n actions = domain.action_space()\r\n action = actions[random.randint(0, 3)]\r\n state_prime = domain.dynamics(state, action)\r\n r = domain.get_reward(state_prime)\r\n traj.append((state, action, r))\r\n state = state_prime\r\n return traj\r\n\r\ndef pairwise(iterable):\r\n \"\"\"\r\n Re-arrange an iterable pairwise\r\n ---\r\n parameters :\r\n\r\n - iterable : iterable to re-arrange\r\n ---\r\n return :\r\n\r\n - zip corresponding to the pairwise re-arrangement\r\n \"\"\"\r\n\r\n a, b = itertools.tee(iterable)\r\n next(b, None)\r\n return zip(a, b)\r\n\r\ndef arguments_parsing():\r\n \"\"\"\r\n Argument parser function\r\n ---\r\n parameters :\r\n\r\n None\r\n ---\r\n return :\r\n\r\n - args : Keyboard passed arguments\r\n \"\"\"\r\n\r\n parser = argparse.ArgumentParser(description=\"ODMCP - A1 - Section 1\")\r\n\r\n parser.add_argument(\"-stocha\", \"--stochastic\", action='store_true',\r\n help=\"Stochastic character of the domain, option string to be added for stochastic behaviour\")\r\n\r\n parser.add_argument(\"-s_th\", \"--stochastic_threshold\", type=float, default=0.5,\r\n help=\"Stochastic threshold involved in the stochastic dynamics\")\r\n\r\n parser.add_argument(\"-df\", \"--discount_factor\", type=float, default=0.99,\r\n help=\"Discount factor, 0.99 by default\")\r\n\r\n parser.add_argument(\"-f\", \"--domain_instance_file\", type=str, default='instance.csv',\r\n help=\"Filename of the domain instance\")\r\n\r\n parser.add_argument(\"-n_i\", \"--nb_iterations\", type=int, default=1000,\r\n help=\"Number of iterations for the computation of the expected return's approximation\")\r\n\r\n parser.add_argument(\"-lr\", \"--learning_rate\", type=float, default=0.05,\r\n help=\"Constant learning rate used in the Q-learning algorithm, 0.05 by default\")\r\n\r\n args = parser.parse_args()\r\n\r\n if args.stochastic:\r\n print(\"\\nStochastic domain chosen\")\r\n else:\r\n print(\"\\nDeterministic domain chosen (default)\")\r\n\r\n return args\r\n\r\ndef offline_Q_learning(domain, trajectory, alpha):\r\n \"\"\"\r\n Offline Q-Learning implementation\r\n ---\r\n parameters :\r\n\r\n - domain : Domain instance corresponding to the considered domain\r\n - trajectory : trajectory from which to perform the algorithm\r\n - alpha : learning rate\r\n ---\r\n return :\r\n\r\n - Q_hat : estimated Q-function computed with offline Q-Learning\r\n \"\"\"\r\n\r\n n, m = domain.state_space()\r\n actions = domain.action_space()\r\n Q_hat = np.zeros([n, m, len(actions)])\r\n for k, (state, action, r) in enumerate(trajectory):\r\n if k == len(trajectory)-1:\r\n return Q_hat\r\n u = actions.index(action)\r\n x, y = state\r\n x_prime, y_prime = trajectory[k+1][0]\r\n Q_hat[x, y, u] = (1 - alpha) * Q_hat[x, y, u] + alpha * (r + domain.discount_factor * max(Q_hat[x_prime, y_prime]))\r\n\r\nclass Intelligent_agent:\r\n\r\n def __init__(self, domain, state_0):\r\n self.domain = domain\r\n self.state_0 = state_0\r\n self.policy = None\r\n\r\n def select_action(self, Q, state, epsilon):\r\n \"\"\"\r\n Selects an action from a certain state, following an epsilon-greedy policy acc. to Q\r\n ---\r\n parameters :\r\n\r\n - Q : Q-function from which to derive the optimal policy\r\n - state : current state\r\n - epsilon : exploration rate\r\n ---\r\n return :\r\n\r\n - action to take\r\n \"\"\"\r\n\r\n if random.random() < epsilon:\r\n actions = self.domain.action_space()\r\n action = actions[random.randint(0,3)]\r\n return action\r\n else:\r\n self.policy = self.derive_best_policy(Q)\r\n x, y = state\r\n return self.policy[x, y]\r\n\r\n def derive_best_policy(self, Q):\r\n \"\"\"\r\n Derives optimal policy from Q-function\r\n ---\r\n parameters :\r\n\r\n - domain : Domain instance corresponding to the considered domain\r\n - Q : Q-function from which to derive the expected return\r\n ---\r\n return :\r\n\r\n - best_policy : optimal policy derived from Q\r\n \"\"\"\r\n\r\n n, m = self.domain.state_space()\r\n actions = self.domain.action_space()\r\n best_policy = np.zeros([n, m], dtype=object)\r\n for x in range(n):\r\n for y in range(m):\r\n best_action = actions[np.argmax(Q[x, y])]\r\n best_policy[x, y] = best_action\r\n return best_policy\r\n\r\n def online_Q_learning_first(self, alpha, epsilon_0, n_episodes, n_transitions, decay):\r\n \"\"\"\r\n Implements the first protocol\r\n ---\r\n parameters :\r\n\r\n - alpha : learning rate\r\n - epsilon_0 : initial exploration rate\r\n - n_episodes : number of episodes\r\n - n_transitions : number of transitions per episode\r\n - decay : decay factor to apply on epsilon\r\n ---\r\n return :\r\n\r\n - Q_vec : list of derived optimal expected returns (one for each episode)\r\n \"\"\"\r\n\r\n # Online epsilon-greedy Q-learning algorithm - Protocol 1\r\n n, m = self.domain.state_space()\r\n actions = self.domain.action_space()\r\n Q_vec = []\r\n # Initialisation\r\n Q_hat = np.zeros([n, m, len(self.domain.action_space())])\r\n # For each transition in each episode, update of Q_hat\r\n for e in range(n_episodes):\r\n # Reset initial state\r\n state = self.state_0\r\n epsilon = epsilon_0\r\n for t in range(n_transitions):\r\n # Choose action using epsilon-greedy policy derived from Q_hat\r\n action = self.select_action(Q_hat, state, epsilon)\r\n state_prime = self.domain.dynamics(state, action)\r\n r = self.domain.get_reward(state_prime)\r\n # Take action, observe reward and next state\r\n u = actions.index(action)\r\n x, y = state\r\n x_prime, y_prime = state_prime\r\n Q_hat[x, y, u] = (1 - alpha) * Q_hat[x, y, u] + alpha * (r + self.domain.discount_factor * max(Q_hat[x_prime, y_prime]))\r\n state = state_prime\r\n epsilon = decay * epsilon\r\n J_hat = derive_best_expected_return(self.domain, Q_hat)\r\n Q_vec.append(J_hat.copy())\r\n return Q_vec\r\n\r\n def online_Q_learning_second(self, alpha_0, epsilon, n_episodes, n_transitions):\r\n \"\"\"\r\n Implements the second protocol\r\n ---\r\n parameters :\r\n\r\n - alpha_0 : initial learning rate\r\n - epsilon : exploration rate\r\n - n_episodes : number of episodes\r\n - n_transitions : number of transitions per episode\r\n ---\r\n return :\r\n\r\n - Q_vec : list of derived optimal expected returns (one for each episode)\r\n \"\"\"\r\n\r\n # Online epsilon-greedy Q-learning algorithm - Protocol 2\r\n n, m = self.domain.state_space()\r\n actions = self.domain.action_space()\r\n Q_vec = []\r\n # Initialisation\r\n Q_hat = np.zeros([n, m, len(self.domain.action_space())])\r\n # For each transition in each episode, update of Q_hat\r\n for e in range(n_episodes):\r\n # Reset initial state\r\n state = self.state_0\r\n # Reset learning rate ??????\r\n alpha = alpha_0\r\n for t in range(n_transitions):\r\n # Choose action using epsilon-greedy policy derived from Q_hat\r\n action = self.select_action(Q_hat, state, epsilon)\r\n state_prime = self.domain.dynamics(state, action)\r\n r = self.domain.get_reward(state_prime)\r\n # Take action, observe reward and next state\r\n u = actions.index(action)\r\n x, y = state\r\n x_prime, y_prime = state_prime\r\n Q_hat[x, y, u] = (1 - alpha) * Q_hat[x, y, u] + alpha * (r + self.domain.discount_factor * max(Q_hat[x_prime, y_prime]))\r\n state = state_prime\r\n alpha = 0.8 * alpha\r\n J_hat = derive_best_expected_return(self.domain, Q_hat)\r\n Q_vec.append(J_hat.copy())\r\n return Q_vec\r\n\r\n def online_Q_learning_third(self, alpha, epsilon, n_episodes, n_transitions, n_replay):\r\n \"\"\"\r\n Implements the third protocol\r\n ---\r\n parameters :\r\n\r\n - alpha : learning rate\r\n - epsilon : exploration rate\r\n - n_episodes : number of episodes\r\n - n_transitions : number of transitions per episode\r\n - n_replay : number of experience replays per transition\r\n ---\r\n return :\r\n\r\n - Q_vec : list of derived optimal expected returns (one for each episode)\r\n \"\"\"\r\n\r\n # Online epsilon-greedy Q-learning algorithm - Protocol 3\r\n n, m = self.domain.state_space()\r\n actions = self.domain.action_space()\r\n Q_vec = []\r\n # Initialisation\r\n Q_hat = np.zeros([n, m, len(self.domain.action_space())])\r\n buffer = list()\r\n # For each transition in each episode, update of Q_hat\r\n for e in range(n_episodes):\r\n # Reset initial state\r\n state = self.state_0\r\n for t in range(n_transitions):\r\n # Choose action using epsilon-greedy policy derived from Q_hat\r\n action = self.select_action(Q_hat, state, epsilon)\r\n state_prime = self.domain.dynamics(state, action)\r\n r = self.domain.get_reward(state_prime)\r\n # Update buffer\r\n buffer.append((state, action, r, state_prime))\r\n state = state_prime\r\n # Experience replay\r\n for i in range(n_replay):\r\n # Draw randomly an action from the buffer\r\n state_replay, action_replay, reward_replay, state_prime_replay = random.choice(buffer)\r\n u = actions.index(action_replay)\r\n x, y = state_replay\r\n x_prime, y_prime = state_prime_replay\r\n # Update Q_hat\r\n Q_hat[x, y, u] = (1 - alpha) * Q_hat[x, y, u] + alpha * (r + self.domain.discount_factor * max(Q_hat[x_prime, y_prime]))\r\n J_hat = derive_best_expected_return(self.domain, Q_hat)\r\n Q_vec.append(J_hat.copy())\r\n return Q_vec\r\n\r\n\r\nif __name__ == \"__main__\":\r\n random.seed(1)\r\n\r\n args = arguments_parsing()\r\n\r\n domain_matrix = pd.read_csv(args.domain_instance_file, delimiter=',', header=None).values\r\n\r\n print(\"\\nInstance of the domain:\\n{}\".format(domain_matrix))\r\n domain = Domain(domain_matrix,\r\n \"Stochastic\" if args.stochastic else \"Deterministic\",\r\n args.discount_factor, args.stochastic_threshold)\r\n\r\n # --OFFLINE Q-LEARNING--\r\n print(\"\\nOFFLINE Q-LEARNING...\")\r\n T = [pow(10, i) for i in [2, 3, 4, 5, 6]]\r\n T.append(5*10**6)\r\n J_diff = np.zeros([len(T)])\r\n alpha = args.learning_rate\r\n print(\"\\nDeriving the optimal expected return...\")\r\n\r\n Q, _, _ = state_action_value_function(domain, args.nb_iterations)\r\n mu_opt = derive_best_policy(domain, Q)\r\n J_mu_opt = derive_best_expected_return(domain, Q)\r\n print(\"\\nOptimal expected return : \\n{}\".format(J_mu_opt))\r\n print(\"\\nQ-learning algorithm running...\")\r\n for i, t in enumerate(T):\r\n trajectory = gen_trajectory(domain, t)\r\n Q_hat = offline_Q_learning(domain, trajectory, alpha)\r\n mu_hat_opt = derive_best_policy(domain, Q_hat)\r\n J_mu_hat_opt = derive_best_expected_return(domain, Q_hat)\r\n diff = J_mu_hat_opt-J_mu_opt\r\n J_diff[i] = np.linalg.norm(diff.reshape(25),ord=np.inf)\r\n print(J_diff)\r\n plt.plot(T, J_diff)\r\n plt.xscale('log')\r\n plt.xlabel(\"$T$\", fontsize=18)\r\n plt.ylabel(\"$\\\\|J^N_{\\\\hat{\\\\mu}^*}-J^N_{\\\\mu^*}\\\\|_{\\\\infty}$\", fontsize=18)\r\n plt.show()\r\n\r\n print(\"\\nOptimal policy, for length = {} : \\n{}\".format(t, mu_hat_opt))\r\n print(\"\\nOptimal return, for length = {} : \\n{}\".format(t, J_mu_hat_opt))\r\n\r\n # --ONLINE Q-LEARNING--\r\n print(\"\\nONLINE Q-LEARNING...\")\r\n alpha = 0.05\r\n epsilon = 0.25\r\n n_episodes = 100\r\n n_transitions = 1000\r\n n_replay = 10\r\n state_0 = (3,0)\r\n agent = Intelligent_agent(domain, state_0)\r\n\r\n # First protocol\r\n print(\"\\n-- First protocol -- \\n\")\r\n Q = agent.online_Q_learning_first(alpha, epsilon, n_episodes, n_transitions, 1)\r\n Q_diff_1 = np.zeros([n_episodes])\r\n for i, q in enumerate(Q):\r\n Q_diff_1[i] = np.linalg.norm(np.ravel(q-J_mu_opt),ord=np.inf)\r\n\r\n # Second protocol\r\n print(\"\\n-- Second protocol -- \\n\")\r\n Q = agent.online_Q_learning_second(alpha, epsilon, n_episodes, n_transitions)\r\n Q_diff_2 = np.zeros([n_episodes])\r\n for i, q in enumerate(Q):\r\n Q_diff_2[i] = np.linalg.norm(np.ravel(q-J_mu_opt),ord=np.inf)\r\n\r\n # Third protocol\r\n print(\"\\n-- Third protocol -- \\n\")\r\n Q = agent.online_Q_learning_third(alpha, epsilon, n_episodes, n_transitions, n_replay)\r\n Q_diff_3 = np.zeros([n_episodes])\r\n for i, q in enumerate(Q):\r\n Q_diff_3[i] = np.linalg.norm(np.ravel(q-J_mu_opt),ord=np.inf)\r\n\r\n # Comparison\r\n plt.plot(range(1, n_episodes+1), Q_diff_1, label=\"First protocol\")\r\n plt.plot(range(1, n_episodes+1), Q_diff_2, label=\"Second protocol\")\r\n plt.plot(range(1, n_episodes+1), Q_diff_3, label=\"Third protocol\")\r\n plt.legend()\r\n plt.xlabel(\"Number of episodes\", fontsize=18)\r\n plt.ylabel(\"$\\\\|\\\\hat{Q}-J^N_{\\\\mu^*}\\\\|_{\\\\infty}$\", fontsize=18)\r\n plt.show()\r\n\r\n \"\"\"\r\n # \"Decay greedy\"\r\n for decay in [0.95, 0.96, 0.97, 0.99]:\r\n Q = agent.online_Q_learning_first(alpha, epsilon, n_episodes, n_transitions, decay)\r\n Q_diff_1 = np.zeros([n_episodes])\r\n for i, q in enumerate(Q):\r\n Q_diff_1[i] = np.linalg.norm(np.ravel(q-J_mu_opt),ord=np.inf)\r\n plt.plot(range(1, n_episodes+1), Q_diff_1, label=\"$k$ = {}\".format(decay))\r\n plt.legend()\r\n plt.xlabel(\"Number of episodes\", fontsize=18)\r\n plt.ylabel(\"$\\\\|\\\\hat{Q}-J^N_{\\\\mu^*}\\\\|_{\\\\infty}$\", fontsize=18)\r\n plt.savefig(\"comparison_decay.pdf\")\r\n plt.show()\r\n \"\"\"\r\n","sub_path":"Assignment 1/5/section5.py","file_name":"section5.py","file_ext":"py","file_size_in_byte":22500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"270857747","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom skimage.morphology import remove_small_objects\nimport time\nimport math\nimport os\n\nSRC = np.float32(\n [[127 * 2, 315 * 2],\n [239 * 2, 441 * 2],\n [585 * 2, 375 * 2],\n [328 * 2, 305 * 2]])\n\nCAM_MATRIX = np.matrix([[923.6709132611408, 0, 660.3716073305085], [0, 925.6373437421516, 495.2039455113797],\n [0, 0, 1]])\nDIST_COEF = np.array([-0.2947018330961229, 0.09105224521150024, 0.0001143430530863253, 0.0003862123859247846, 0])\n\n\ndef bird_view(image):\n img_size = (image.shape[1], image.shape[0])\n M = cv2.getPerspectiveTransform(SRC, DST)\n return cv2.warpPerspective(image, M, img_size, flags=cv2.INTER_LINEAR)\n\n\nif __name__ == '__main__':\n cap = cv2.VideoCapture('output.avi')\n # count = 0\n # total = 269\n while True:\n ret, frame = cap.read()\n start = time.time()\n height, width, depth = frame.shape\n kernel = np.ones((20, 100), np.uint8)\n\n DST = np.float32(\n [[(width - 716) / 2, 0],\n [(width - 716) / 2, height],\n [width - 282, height],\n [width - 282, 0]]\n )\n\n if not ret:\n break\n\n undistorted_image = cv2.undistort(frame, CAM_MATRIX, DIST_COEF)\n\n viewed = bird_view(undistorted_image)\n\n viewed = cv2.resize(viewed, dsize=(int(height / 2), int(width / 2)))\n\n # if count % 5 == 0:\n # total += 1\n # cv2.imwrite(os.path.join('E:/datasets/parking/birdview', str(total) + '.jpg'), viewed)\n #\n # count += 1\n\n region_of_interest_vertices = [\n (128, height - 128),\n (128, 128),\n (width - 288, 128),\n (width - 288, height - 128),\n ]\n region_of_interest_vertices = np.array([region_of_interest_vertices], dtype=np.int)\n mask = np.zeros_like(viewed)\n\n channels = viewed.shape[2]\n\n match_mask = (255,) * channels\n cv2.fillPoly(mask, region_of_interest_vertices, match_mask)\n\n masked = cv2.bitwise_and(viewed, mask)\n\n blur = cv2.GaussianBlur(masked, (5, 5), 0)\n hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)\n ranged = cv2.inRange(hsv, (5, 5, 150), (255, 255, 255))\n\n dilated_view = cv2.dilate(ranged, kernel, iterations=1)\n eroded_view = cv2.erode(dilated_view, kernel, iterations=1)\n\n ret, thresh = cv2.threshold(eroded_view, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n contours, hierarchy = cv2.findContours(thresh, 1, 2)\n contours_sorted_by_area = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)\n\n contours_coords = []\n\n for j, contour_sort in enumerate(contours_sorted_by_area):\n if 0 in contour_sort or cv2.contourArea(contour_sort) < 100:\n continue\n x, y, w, h = cv2.boundingRect(contour_sort)\n contours_coords.append([x, y, w, h])\n\n contours_sorted_by_x = sorted(contours_coords, key=lambda x: x[1])\n\n if len(contours_sorted_by_x) < 2:\n continue\n\n x1, y1, w1, h1 = contours_sorted_by_x[-1]\n x2, y2, w2, h2 = contours_sorted_by_x[-2]\n\n angle = int(math.atan((y2 - y1 + 1) / (x2 - x1 + 1)) * 180 / math.pi)\n\n if abs(angle) > 80 and abs(angle) < 100 and (y1 - y2) > 175 and (y1 - y2) < 400:\n length = y1 - y2\n cv2.line(viewed, (x1, y1), (x2, y2 + h2), (0, 0, 255), 2)\n cv2.circle(viewed, (x1, y1), 5, (0, 0, 255), 3)\n cv2.circle(viewed, (x2, y2 + h2), 5, (0, 0, 255), 3)\n cv2.circle(viewed, (x2, int((y2 + y1 + h2) / 2)), 5, (0, 255, 255), 3)\n # cv2.putText(viewed, str(length) + 'px', (100, 100), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 255, 255))\n\n end = time.time()\n ms = end - start\n cv2.putText(viewed, f'time spent {ms}', (50, 200), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 255, 255))\n cv2.imshow('viewed', viewed)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n cap.release()\n cv2.destroyAllWindows()\n","sub_path":"final.py","file_name":"final.py","file_ext":"py","file_size_in_byte":4108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"61830794","text":"import socket\n\nTCP_IP = '127.0.0.1'\nTCP_PORT = 5005\nSERVER_ADDRESS = (TCP_IP, TCP_PORT)\nBUFFER_SIZE = 1024\nMESSAGE = 'HELLO WORLD'\n\ndef main():\n\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ts.connect(SERVER_ADDRESS)\n\ts.send(MESSAGE)\n\tdata = s.recv(BUFFER_SIZE)\n\ts.close()\n\tprint(\"received data: %s\" % data)\n\nif __name__ == '__main__':\n\tmain()\n\n# https://github.com/bjornedstrom/toytls/blob/master/scripts/toytls-handshake.py\n# http://blog.bjrn.se/2012/07/fun-with-tls-handshake.html\n","sub_path":"handshake.py","file_name":"handshake.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"50964460","text":"# main.py\nimport lvgl as lv\nimport ujson as json\nfrom machine import Pin\n#import cdp_helper as helper\n#import cdp_gui as gui\nfrom cdp_classes import Usuario, StateMachine, Sensor_US, ControlUART\nfrom ili9XXX import ili9341\n\n# ==================== VARIABLES GLOBALES==================== #\n\n# Estados para la FSM\nSTARTING, IDLE, CALIBRATING, SENSOR_READING, USER_SCREEN = range(5)\n\n# Variables de pines\npin_encoder = 35\n\n# Sensor ultrasonido\nsensor_us = Sensor_US(16, 36)\n\n# Comunicacion UART con ATMega328P\nuart = ControlUART(9600, 17, 34)\n\n# Pines de los motores\nmotor_pines = {\n \"Adelante\": {\n 'cabezal' : [13],\n 'apbrazo' : [4, 15],\n 'assdepth' : [14],\n 'assheight' : [26],\n 'lumbar' : [33]\n },\n \"Atras\" : {\n 'cabezal' : [12],\n 'apbrazo' : [2, 0],\n 'assdepth' : [27],\n 'assheight' : [25],\n 'lumbar' : [32]\n }\n}\n\n# Aca se guardan los datos obtenidos del archivo \"cdp_config.json\"\n_global_config = {}\n\n# Aca se guardan las instancias de clase Usuario obtenidas desde motor_data.json\n_users_list = []\n\n# Inicializar pantalla LCD\nlv.init()\ndisp = ili9341(mosi=23, miso=19, clk=18, dc=21, cs=5, rst=22, power=-1, backlight=-1)\n\n# ==================== FUNCIONES ==================== #\n\ndef load_config_from_file_global():\n try:\n with open(\"settings/cdp_config.json\", \"r\") as file:\n _global_config = json.load(file)\n return _global_config\n except OSError:\n print(\"cdp_config.json is missing. Creating a new default one...\")\n with open(\"settings/cdp_config.json\", \"w\") as file:\n c = {\n \"first_time_open\" : True,\n 'calibration_data' : {\n \"assheight\" : ['TBD', ['piezo', [\"as1\", 0, 1023], [\"as2\", 0, 1023]], '000', 1024],\n \"assdepth\" : ['TBD', ['piezo', [\"lu1\", 0, 1023], [\"lu2\", 0, 1023]], '001', 1024],\n \"lumbar\" : ['TBD', ['piezo', [\"lu1\", 0, 1023], [\"lu2\", 0, 1023]], '010', 1024],\n \"cabezal\" : ['TBD', ['ultra'], '011', 1024],\n \"apbrazo\" : ['TBD', ['pin', [\"apb\", 0, 1023], [\"as2\", 0, 1023]], '100', 1024]\n }\n }\n json.dump(c, file)\n return load_config_from_file_global()\n\ndef load_users_from_file_global():\n new_list = []\n\n try:\n with open(\"settings/motor_data.json\", \"r\") as file:\n data = json.load(file)\n for user, config in data.items():\n if user == \"Actuales\":\n continue\n new_list.append(Usuario(user, config))\n return new_list\n except OSError:\n print(\"motor_data.json is missing. Creating a new default one...\")\n with open(\"settings/motor_data.json\", \"w\") as file:\n c = {\n \"Actuales\" : {\n \"cabezal\" : 0,\n \"apbrazo\" : 0,\n \"lumbar\" : 0,\n \"assprof\" : 0,\n \"assheight\" : 0\n }\n }\n json.dump(c, file)\n return load_users_from_file_global()\n\n# O(n^3) <-- Debe haber una manera de hacerlo mas optimo\ndef set_motorpin_output():\n for pin_list in motor_pines.values():\n for value in pin_list.values():\n for index, pin in enumerate(value):\n value[index] = Pin(pin, Pin.OUT, Pin.PULL_DOWN, 0)\n\ndef wait_for_action():\n # TODO: interacción con la GUI\n print(\".\")\n fsm.next_state()\n\ndef main():\n # Establecer entradas y salidas\n set_motorpin_output()\n\n if _global_config[\"first_time_open\"]:\n # TODO: Bienvenida por la GUI + primera calibracion\n print(\"Bienvenido a Silla CDP\")\n _global_config[\"first_time_open\"] = False\n # TODO: Carga de pantalla inicial por la GUI\n print(\"Pantalla de usuarios\")\n\n # Cambiar de estado a espera\n fsm.State = IDLE\n\n# Instancia de la FSM\nfsm = StateMachine((STARTING, main))\n\nif __name__ == \"__main__\":\n # Cargar datos desde archivos (se hace desde aca para modificar la variable global)\n _global_config = load_config_from_file_global()\n _users_list = load_users_from_file_global()\n\n # Agregar estados a la FSM\n fsm.add_states([\n (IDLE, wait_for_action)\n ])\n\n # Arrancar la FSM\n fsm.start()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"93828210","text":"from api.models.investments.user_investments import UserBalance, UserStatement\nfrom api.database.database import db, DBO\nfrom sqlalchemy import func\nfrom datetime import datetime, date\n\n\nclass InvestmentRepository():\n\n @classmethod\n def select_user_balance(cls, user_id):\n actual_user_balance = UserBalance.query.\\\n filter(UserBalance.user_id == user_id).\\\n first()\n if actual_user_balance:\n balance = actual_user_balance.balance\n user_statements = UserStatement.query.\\\n filter(UserStatement.user_id == user_id).\\\n filter(UserStatement.open_operation == 1).\\\n all()\n balance += sum(\n item.investment_value for item in user_statements)\n return balance\n return None\n\n @classmethod\n def save_user_balance(cls, user_id, balance):\n user_balance = UserBalance(user_id=user_id, balance=balance)\n user_balance.save()\n","sub_path":"Allgoo/api-andbank/api/repositories/investment_repository.py","file_name":"investment_repository.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"168687123","text":"from nltk.stem import WordNetLemmatizer\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nimport string\nimport re\n\n#Tokenize\ndef Tokenize(string_txt):\n mystring_tokens=word_tokenize(string_txt)\n return mystring_tokens\n\n#Remove Punctuations\ndef rem_punct(string_txt):\n mystring_tokens=Tokenize(string_txt)\n str_tokens=[char for char in mystring_tokens if char not in string.punctuation]\n return \" \".join(str_tokens)\n\n# Remove digits\ndef rem_digits(string_txt):\n proc_txt=re.sub(r'\\d[a-zA-Z0-9\\.\\/\\-\\@]+|[a-zA-Z0-9\\.\\/\\-\\@]+\\d|\\d',' ',string_txt) # Digits replaced with space\n return proc_txt\n \n# Remove web address and mail address\ndef rem_weblnk_mail(string_txt):\n proc_txt=re.sub(r'(https?:\\/\\/)?(www\\.)?[a-zA-Z0-9\\.\\/\\-\\@]\\[a-z]+|[a-zA-Z0-9\\.\\/\\-\\@]+(.com)\\s+|(.com)$',' ',string_txt) # Web_links and mails replaced with space\n return proc_txt \n\n#Remove all the special characters\ndef sp_char(string_txt):\n proc_txt = re.sub(r'\\W',' ',string_txt)\n return proc_txt\n\n#Removing all single characters appearing at the start\ndef sngle_char_strt(string_txt):\n proc_txt = re.sub(r'^\\s+[a-z]\\s+',' ',string_txt)\n return proc_txt\n \n# Remove all single characters \ndef sngle_char(string_txt):\n proc_txt = re.sub(r'\\s+[a-z]\\s+|\\s+[a-z]{2,2}\\s+',' ',string_txt) #|\\s+[a-z]{2,}\\s+\n return proc_txt\n\n#Substitute multiple spaces with a single space\ndef rem_mul_spc(string_txt):\n proc_txt = re.sub(r'\\s+',' ',string_txt, flags=re.I)\n return proc_txt\n\n#Remove stopwords\ndef RemoveStopWords(string_txt):\n mystring_tokens=Tokenize(string_txt) \n stopwords_english = list(set(stopwords.words('english')))\n add_stp_wrds=['http','html','attn','tel']\n stopwords_english.extend(add_stp_wrds)\n post_stopwords=[word for word in mystring_tokens if word.lower() not in stopwords_english]\n return \" \".join(post_stopwords)\n\n# Lemmatize String\ndef Lemmatize(string_txt):\n word_lem=WordNetLemmatizer()\n post_stopword_string = RemoveStopWords(string_txt)\n post_stopword_tokens=Tokenize(post_stopword_string)\n lemm_lst=[ word_lem.lemmatize(word) for word in post_stopword_tokens]\n return \" \".join(lemm_lst)\n\n# Convert string to lower case\ndef to_lower(string_txt):\n str_tokens=Tokenize(string_txt)\n str_tokens=[char.lower() for char in str_tokens]\n return \" \".join(str_tokens)\n\n#Substitute blank single quotes with no space\ndef rem_other(string_txt):\n lst=['``','` `',\"''\",\"' '\"]\n word_sent=Tokenize(string_txt)\n final_word=[word for word in word_sent if word not in lst]\n return \" \".join(final_word)\n\ndef preprocess_text(string_txt):\n \n\n proc_text=string_txt\n\n #Convert the string to lower case\n proc_text=to_lower(proc_text)\n\n # Remove weblinks & mail_ids\n proc_text=rem_weblnk_mail(proc_text)\n\n # Remove digits\n proc_text=rem_digits(proc_text)\n\n # Remove Stopwords\n proc_text=RemoveStopWords(proc_text)\n\n #Lemmatize\n proc_text=Lemmatize(proc_text)\n\n #Remove Special Character\n proc_text=sp_char(proc_text)\n\n #Remove Single Character\n proc_text=sngle_char(proc_text)\n\n # Remove all punctiations\n proc_text=rem_punct(proc_text)\n\n #Remove Single Character at Start\n proc_text=sngle_char_strt(proc_text)\n\n #Removing single quotes\n proc_text=rem_other(proc_text)\n\n #Remove Multiple Space\n proc_text=rem_mul_spc(proc_text)\n\n #Convert the string to lower case\n proc_text=to_lower(proc_text)\n \n return proc_text","sub_path":"PreProcess.py","file_name":"PreProcess.py","file_ext":"py","file_size_in_byte":3518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"535885036","text":"\"\"\" Visualize Points Segmentation: Main Results \"\"\"\n\"\"\" Original Author: Haoqiang Fan \"\"\"\nimport numpy as np\nimport show3d_balls\nimport sys\nimport os\nimport pdb\nimport argparse\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(BASE_DIR)\nimport color_settings as cg_\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--clsname', default='')\nparser.add_argument('--atrous_block_num', type=int, default=2)\nFLAGS = parser.parse_args()\n\n\ndef read_file(filename):\n ptc_list = []\n with open(filename, 'r') as f:\n for line in f:\n p_list = line.split()[1:] # point and color\n ptc = [float(i) for i in p_list]\n ptc_list.append(ptc)\n return np.asarray(ptc_list)\n\n\ndef pc_normalize(pc):\n centroid = np.mean(pc, axis=0)\n pc = pc - centroid\n m = np.max(np.sqrt(np.sum(pc**2, axis=1)))\n pc = pc / m\n return pc, centroid, m\n\n\ndef pts_aligned(pts):\n # rotate pts to fit octree data set rotation angles:\n rotation_angle = np.pi / 2\n cosval = np.cos(rotation_angle)\n sinval = np.sin(rotation_angle)\n rotation_matrix = np.array([[cosval, 0, sinval],\n [0, 1, 0],\n [-sinval, 0, cosval]]).T # counter-clock wise rotation\n norm_pts, pts_center, max_dis = pc_normalize(pts)\n rot_pts = np.dot(norm_pts.reshape((-1,3)), rotation_matrix)*max_dis + pts_center\n # rot_pts = np.dot(norm_pts.reshape((-1,3)), rotation_matrix)\n return rot_pts\n\n\ncolor_tab = cg_.color_table\nlabel_color = cg_.color_origin\n\n\ndef color_mapped(colors, clsname):\n # point label id start from 0;\n source_colors = np.asarray(label_color)\n target_colors = np.asarray(color_tab[clsname])\n label_num = target_colors.shape[0]\n colors_new = colors[:]+0.0\n # pdb.set_trace()\n for l in range(label_num):\n cur_src_clr = source_colors[l,...]\n cur_tar_clr = target_colors[l,...]\n idx = np.sum(np.abs(colors - cur_src_clr), axis=1) == 0\n colors_new[idx,...] = cur_tar_clr\n return colors_new\n\n\nshow_dict = {\n 'Airplane': 13, 'Bag': 2, 'Cap': 1,\n 'Car': 18, 'Chair': 50, 'Earphone': 0, 'Guitar': 13, 'Knife': 4,\n 'Lamp': 40, 'Laptop': 10, 'Motorbike': 11, 'Mug': 1, 'Pistol': 0,\n 'Rocket': 1, 'Skateboard': 2, 'Table': 18,\n }\n\n\nif __name__ == '__main__':\n \"\"\" input: point and color\n output: rendered color points figure\n pred-outs, pred ptnet, gt oc, gt ptnet\n \"\"\"\n ballsize = 7\n classes = sorted(show_dict.keys())\n for cname in classes:\n # CLSNAME = cname\n CLSNAME = 'Laptop'\n shape_idx = show_dict[CLSNAME] # Motor 3,4, 14, 17\n Atrous_dir = os.path.join(BASE_DIR, '../../result-data', 'test_results_PA_3DCNN_Atrous',\n CLSNAME+'-withBG-ABlock3-Res')\n PtNet_dir = os.path.join(BASE_DIR, '../../result-data', 'test_results_pointnet2',\n CLSNAME)\n fname_atrous = os.path.join(Atrous_dir, str(shape_idx)+'_pred'+'.obj')\n fname_ptnet = os.path.join(PtNet_dir, str(shape_idx)+'_pred'+'.obj')\n fname_atrous_gt = os.path.join(Atrous_dir, str(shape_idx)+'_gt'+'.obj')\n fname_ptnet_gt = os.path.join(PtNet_dir, str(shape_idx)+'_gt'+'.obj')\n\n data_atrous = read_file(fname_atrous)\n data_ptnet = read_file(fname_ptnet)\n data_atrous_gt = read_file(fname_atrous_gt)\n data_ptnet_gt = read_file(fname_ptnet_gt)\n\n pts_a = data_atrous[:,:3]\n\n pts_p_ = data_ptnet[:,:3]\n pts_p = pts_aligned(pts_p_)\n\n # if CLSNAME == 'Cap':\n # color_ptnet = data_ptnet[:,3:6] + 0.0\n # idx_1 = np.sum(np.abs(color_ptnet - np.array([0.65, 0.05, 0.05])), axis=1) == 0\n # idx_2 = np.sum(np.abs(color_ptnet - np.array([0.65, 0.35, 0.95])), axis=1) == 0\n # color_ptnet[idx_1,...] = np.array([0.7, 0.0, 0.0])\n # color_ptnet[idx_2,...] = np.array([0.0, 1.0, 0.0])\n # data_ptnet[:,3:6] = color_ptnet\n\n # color_ptnet = data_ptnet_gt[:,3:6] + 0.0\n # idx_1 = np.sum(np.abs(color_ptnet - np.array([0.65, 0.05, 0.05])), axis=1) == 0\n # idx_2 = np.sum(np.abs(color_ptnet - np.array([0.65, 0.35, 0.95])), axis=1) == 0\n # color_ptnet[idx_1,...] = np.array([0.7, 0.0, 0.0])\n # color_ptnet[idx_2,...] = np.array([0.0, 1.0, 0.0])\n # data_ptnet_gt[:,3:6] = color_ptnet\n\n clr_a = color_mapped(data_atrous[:,3:6], CLSNAME)\n clr_p = color_mapped(data_ptnet[:,3:6], CLSNAME)\n clr_a_gt = color_mapped(data_atrous_gt[:,3:6], CLSNAME)\n clr_p_gt = color_mapped(data_ptnet_gt[:,3:6], CLSNAME)\n # pdb.set_trace()\n\n clr_atrous = clr_a * 255\n clr_ptnet = clr_p * 255\n clr_a_gt = clr_a_gt * 255\n clr_p_gt = clr_p_gt * 255\n\n # savedir = os.path.join(BASE_DIR, '../../result-data', 'Rendered_imgs', CLSNAME+'_shape_'+str(shape_idx)+'.png')\n # show3d_balls.showpoints(xyz=pts_a, c_pred=clr_atrous[:,[1,0,2]], background=(255,255,255), # background=(b,g,r)\n # showrot=False,magnifyBlue=0,freezerot=False,\n # normalizecolor=False, ballradius=ballsize, savedir=savedir)\n\n savedir = os.path.join(BASE_DIR, '../../result-data', 'Rendered_imgs', CLSNAME+'_shape_'+str(shape_idx)+'_pred_atrous.png')\n show3d_balls.showpoints(xyz=pts_a, c_pred=clr_atrous[:,[1,0,2]], background=(255,255,255),\n showrot=False,magnifyBlue=0,freezerot=False,\n normalizecolor=False, ballradius=ballsize, savedir=savedir)\n\n savedir = os.path.join(BASE_DIR, '../../result-data', 'Rendered_imgs', CLSNAME+'_shape_'+str(shape_idx)+'_pred_ptnet2.png')\n show3d_balls.showpoints(xyz=pts_p, c_pred=clr_ptnet[:,[1,0,2]], background=(255,255,255),\n showrot=False,magnifyBlue=0,freezerot=False,\n normalizecolor=False, ballradius=ballsize, savedir=savedir)\n\n savedir = os.path.join(BASE_DIR, '../../result-data', 'Rendered_imgs', CLSNAME+'_shape_'+str(shape_idx)+'_gt_atrous.png')\n show3d_balls.showpoints(xyz=pts_a, c_pred=clr_a_gt[:,[1,0,2]], background=(255,255,255),\n showrot=False,magnifyBlue=0,freezerot=False,\n normalizecolor=False, ballradius=ballsize, savedir=savedir)\n\n savedir = os.path.join(BASE_DIR, '../../result-data', 'Rendered_imgs', CLSNAME+'_shape_'+str(shape_idx)+'_gt_ptnet2.png')\n show3d_balls.showpoints(xyz=pts_p, c_pred=clr_p_gt[:,[1,0,2]], background=(255,255,255),\n showrot=False,magnifyBlue=0,freezerot=False,\n normalizecolor=False, ballradius=ballsize, savedir=savedir)\n\n # CLSNAME = 'Airplane'\n # test_dir = os.path.join(BASE_DIR, '../../result-data', 'test_results_PA_3DCNN_Atrous',\n # CLSNAME+'-withBG-ABlock3-Res')\n # show_list = show_dict[CLSNAME] # [stage,channel]\n\n # for i in show_list:\n # shape_idx = i\n # fname = os.path.join(test_dir, str(shape_idx)+'_pred'+'.obj')\n # ptsclr = read_file(fname)\n\n # clr = ptsclr[:,3:6] * 255\n # # pdb.set_trace()\n # savedir = os.path.join(test_dir, 'dump', 'shape_'+str(shape_idx)+'.png')\n # show3d_balls.showpoints(xyz=ptsclr[:,:3], c_pred=clr[:,[1,0,2]], background=(255,255,255),\n # showrot=False,magnifyBlue=0,freezerot=False,\n # normalizecolor=False, ballradius=7, savedir=savedir)\n","sub_path":"application-code/visualize_pts_seg-comparePointnet2.py","file_name":"visualize_pts_seg-comparePointnet2.py","file_ext":"py","file_size_in_byte":7399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"40889116","text":"\n###############################################################################\n# Copyright 2013-2014 The University of Texas at Austin #\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\nimport json\nfrom xml.dom.minidom import getDOMImplementation\n\nfrom ipf.data import Data, Representation\n\nfrom .entity import *\n\n#######################################################################################################################\n\nclass Share(Entity):\n def __init__(self):\n Entity.__init__(self)\n\n self.Description = None # string\n self.EndpointID = [] # list of string (uri)\n self.ResourceID = [] # list of string (uri)\n self.ServiceID = \"urn:glue2:Service:unknown\" # string (uri)\n self.ActivityID = [] # list of string (uri)\n self.MappingPolicyID = [] # list of string (uri)\n\n#######################################################################################################################\n\nclass ShareTeraGridXml(EntityTeraGridXml):\n data_cls = Share\n\n def __init__(self, data):\n EntityTeraGridXml.__init__(self,data)\n\n def get(self):\n return self.toDom().toxml()\n\n def toDom(self):\n doc = getDOMImplementation().createDocument(\"http://info.teragrid.org/glue/2009/02/spec_2.0_r02\",\n \"Entities\",None)\n\n root = doc.createElement(\"Share\")\n doc.documentElement.appendChild(root)\n self.addToDomElement(doc,root)\n\n return doc\n\n def addToDomElement(self, doc, element):\n EntityTeraGridXml.addToDomElement(self,doc,element)\n\n if self.data.Description is not None:\n e = doc.createElement(\"Description\")\n e.appendChild(doc.createTextNode(self.data.Description))\n element.appendChild(e)\n for endpoint in self.data.EndpointID:\n e = doc.createElement(\"Endpoint\")\n e.appendChild(doc.createTextNode(endpoint))\n element.appendChild(e)\n for resource in self.data.ResourceID:\n e = doc.createElement(\"Resource\")\n e.appendChild(doc.createTextNode(resource))\n element.appendChild(e)\n if self.data.ServiceID is not None:\n e = doc.createElement(\"Service\")\n e.appendChild(doc.createTextNode(self.data.ServiceID))\n element.appendChild(e)\n for activity in self.data.ActivityID:\n e = doc.createElement(\"Activity\")\n e.appendChild(doc.createTextNode(activity))\n element.appendChild(e)\n for policy in self.data.MappingPolicyID:\n e = doc.createElement(\"MappingPolicy\")\n e.appendChild(doc.createTextNode(policy))\n element.appendChild(e)\n \n#######################################################################################################################\n\nclass ShareOgfJson(EntityOgfJson):\n data_cls = Share\n\n def __init__(self, data):\n EntityOgfJson.__init__(self,data)\n\n def get(self):\n return json.dumps(self.toJson(),sort_keys=True,indent=4)\n\n def toJson(self):\n doc = EntityOgfJson.toJson(self)\n\n if self.data.Description is not None:\n doc[\"Description\"] = self.data.Description\n\n associations = {}\n if len(self.data.EndpointID) > 0:\n associations[\"EndpointID\"] = self.data.EndpointID\n associations[\"ResourceID\"] = self.data.ResourceID\n associations[\"ServiceID\"] = self.data.ServiceID\n if len(self.data.ActivityID) > 0:\n associations[\"ActivityID\"] = self.data.ActivityID\n if len(self.data.MappingPolicyID) > 0:\n associations[\"MappingPolicyID\"] = self.data.MappingPolicyID\n doc[\"Associations\"] = associations\n\n return doc\n\n#######################################################################################################################\n","sub_path":"ipf/glue2/share.py","file_name":"share.py","file_ext":"py","file_size_in_byte":5024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"456385024","text":"from discord.ext import commands\nfrom collections import namedtuple\nfrom lxml import etree\nfrom urllib.parse import urlparse\n\nimport itertools\nimport traceback\nimport datetime\nimport discord\nimport asyncio\nimport asyncpg\nimport enum\nimport re\n\nfrom .utils import db, config, time\n\nclass Players(db.Table):\n id = db.PrimaryKeyColumn()\n discord_id = db.Column(db.Integer(big=True), unique=True, index=True)\n challonge = db.Column(db.String)\n switch = db.Column(db.String, unique=True)\n\nclass Teams(db.Table):\n id = db.PrimaryKeyColumn()\n active = db.Column(db.Boolean, default=True)\n challonge = db.Column(db.String, index=True)\n logo = db.Column(db.String)\n\nclass TeamMembers(db.Table, table_name='team_members'):\n id = db.PrimaryKeyColumn()\n team_id = db.Column(db.ForeignKey('teams', 'id'), index=True)\n player_id = db.Column(db.ForeignKey('players', 'id'), index=True)\n captain = db.Column(db.Boolean, default=False)\n\nclass ChallongeError(commands.CommandError):\n pass\n\nclass TournamentState(enum.IntEnum):\n invalid = 0\n pending = 1\n checking_in = 2\n checked_in = 3\n underway = 4\n complete = 5\n\nclass PromptResult(enum.Enum):\n timeout = 0\n error = 1\n cannot_dm = 2\n cancel = 3\n\n# Temporary results that can be rolled back\n\nclass PromptTransaction:\n def __init__(self, team_page):\n self.team_page = team_page\n self.captain_fc = None\n self.captain_id = None\n self.captain_discord = None\n self.team_logo = None\n self.members = []\n self.existing_members = []\n\n def add_captain(self, discord_id, fc):\n self.captain_fc = fc\n self.captain_discord = discord_id\n\n def add_pre_existing_captain(self, player_id):\n self.captain_id = player_id\n\n def add_existing_member(self, player_id):\n self.existing_members.append(player_id)\n\n def add_member(self, discord_id, fc):\n self.members.append({ 'discord_id': discord_id, 'fc': fc })\n\n async def execute_sql(self, con):\n # this will be in a transaction that can be rolled back\n if self.captain_id is not None:\n query = \"\"\"WITH team_insert AS (\n INSERT INTO teams(challonge, logo)\n VALUES ($1, $2)\n RETURNING id\n )\n INSERT INTO team_members(team_id, player_id, captain)\n SELECT id, $3, TRUE\n FROM team_insert\n RETURNING team_id;\n \"\"\"\n record = await con.fetchrow(query, self.team_page, self.team_logo, self.captain_id)\n team_id = record[0]\n else:\n query = \"\"\"WITH player_insert AS (\n INSERT INTO players(discord_id, switch)\n VALUES ($1, $2)\n RETURNING id\n ),\n team_insert AS (\n INSERT INTO teams(challonge, logo)\n VALUES ($3, $4)\n RETURNING id\n )\n INSERT INTO team_members(team_id, player_id, captain)\n SELECT x.team_id, y.player_id, TRUE\n FROM team_insert AS x(team_id),\n player_insert AS y(player_id)\n RETURNING team_id;\n \"\"\"\n record = await con.fetchrow(query, self.captain_discord, self.captain_fc, self.team_page, self.team_logo)\n team_id = record[0]\n\n # insert pre-existing members:\n if self.existing_members:\n query = \"\"\"INSERT INTO team_members(player_id, team_id)\n SELECT x.player_id, $1\n FROM UNNEST($2::int[]) AS x(player_id);\n \"\"\"\n await con.execute(query, team_id, self.existing_members)\n\n # insert new members\n if self.members:\n query = \"\"\"WITH player_insert AS (\n INSERT INTO players(discord_id, switch)\n SELECT x.discord_id, x.fc\n FROM jsonb_to_recordset($2::jsonb) AS x(discord_id bigint, fc text)\n RETURNING id\n )\n INSERT INTO team_members(player_id, team_id)\n SELECT x.id, $1\n FROM player_insert x;\n \"\"\"\n await con.execute(query, team_id, self.members)\n\n return team_id\n\n# Some validators for _prompt\n\n_friend_code = re.compile(r'^(?:(?:SW)[- _]?)?(?P[0-9]{4})[- _]?(?P[0-9]{4})[- _]?(?P[0-9]{4})$')\n\ndef fc_converter(arg, *, _fc=_friend_code):\n fc = arg.upper().strip('\"')\n m = _fc.match(fc)\n if m is None:\n raise commands.BadArgument('Invalid Switch Friend Code given.')\n return '{one}-{two}-{three}'.format(**m.groupdict())\n\ndef valid_fc(message):\n try:\n return fc_converter(message.content)\n except:\n return False\n\ndef yes_no(message):\n l = message.content.lower()\n if l in ('y', 'yes'):\n return 1\n elif l in ('n', 'no'):\n return -1\n return False\n\ndef validate_url(url):\n o = urlparse(url, scheme='http')\n if o.scheme not in ('http', 'https'):\n return False\n url = o.netloc + o.path\n if not url:\n return False\n if not url.lower().endswith(('.png', '.jpeg', '.jpg', '.gif')):\n return False\n return o.geturl()\n\ndef valid_logo(message):\n if message.content.lower() == 'none':\n return None\n\n url = message.content\n if message.attachments:\n url = message.attachments[0].url\n return validate_url(url)\n\nBOOYAH_GUILD_ID = 333799317385117699\nTOURNEY_ORG_ROLE = 333812806887538688\nPARTICIPANT_ROLE = 343137581564952587\nNOT_CHECKED_IN_ROLE = 343137740889522177\nANNOUNCEMENT_CHANNEL = 342925685729263616\nBOT_SPAM_CHANNEL = 343191203686252548\nTOP_PARTICIPANT_ROLE = 353646321028169729\n\ndef is_to():\n def predicate(ctx):\n return ctx.guild and any(r.id == TOURNEY_ORG_ROLE for r in ctx.author.roles)\n return commands.check(predicate)\n\ndef in_booyah_guild():\n def predicate(ctx):\n return ctx.guild and ctx.guild.id == BOOYAH_GUILD_ID\n return commands.check(predicate)\n\nChallongeTeamInfo = namedtuple('ChallongeTeamInfo', 'name members')\n\n# Members is an array of (Member, Switch-Code)\nParticipantInfo = namedtuple('ParticipantInfo', 'name logo members')\n\nclass Challonge:\n _validate = re.compile(\"\"\"(?:https?\\:\\/\\/)?(?:(?P[A-Za-z]+)\\.)?challonge\\.com\\/ # Main URL\n (?:(?:de|en|es|fr|hu|it|ja|ko|no|pl|pt|pt_BR|ru|sk|sv|tr|zh_CN)\\/)? # Language selection\n (?:teams\\/)?(?P[^\\/]+) # Slug\n \"\"\", re.VERBOSE)\n\n BASE_API = 'https://api.challonge.com/v1/tournaments'\n\n def __init__(self, bot, url, slug):\n self.session = bot.session\n self.api_key = bot.challonge_api_key\n self.url = url\n self.slug = slug\n\n @classmethod\n def from_url(cls, bot, url):\n if not url:\n return cls(bot, None, None)\n\n m = cls._validate.match(url)\n if m is None:\n raise ValueError('Invalid URL?')\n\n sub = m.group('subdomain')\n slug = m.group('slug')\n if sub:\n slug = f'{sub}-{slug}'\n\n return cls(bot, url, slug)\n\n @classmethod\n async def convert(cls, ctx, argument):\n try:\n return cls.from_url(ctx.bot, argument)\n except ValueError:\n raise ChallongeError('Not a valid challonge URL!') from None\n\n async def get_team_info(self, team_slug):\n url = f'http://challonge.com/teams/{team_slug}'\n\n # Challonge does not expose team info endpoint so we're gonna have\n # to end up using regular ol' web scraping.\n # So this code may break in the future if Challonge changes their DOM.\n # Luckily, it's straight forward currently.\n async with self.session.get(url) as resp:\n if resp.status != 200:\n raise ChallongeError(f'Challonge team page for {team_slug} responded with {resp.status}')\n\n root = etree.fromstring(await resp.text(), etree.HTMLParser())\n\n # get team name\n\n team_name = root.find(\".//div[@id='title']\")\n if team_name is None:\n raise ChallongeError(f'Could not find team name. Contact Danny. URL: <{url}>')\n\n team_name = ''.join(team_name.itertext()).strip()\n\n # get team members\n members = root.findall(\".//div[@class='team-member']/a\")\n if members is None or len(members) == 0:\n raise ChallongeError(f'Could not find team members. Contact Danny. URL: <{url}>')\n\n members = [\n member.get('href').replace('/users/', '')\n for member in members\n ]\n\n return ChallongeTeamInfo(team_name, members)\n\n async def show(self, *, include_matches=False, include_participants=False):\n params = {\n 'api_key': self.api_key,\n 'include_matches': int(include_matches),\n 'include_participants': int(include_participants),\n }\n url = f'{self.BASE_API}/{self.slug}.json'\n async with self.session.get(url, params=params) as resp:\n if resp.status == 200:\n js = await resp.json()\n return js.get('tournament', {})\n if resp.status == 422:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n else:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n\n async def start(self, *, include_matches=True, include_participants=False):\n params = {\n 'api_key': self.api_key,\n 'include_matches': int(include_matches),\n 'include_participants': int(include_participants)\n }\n\n url = f'{self.BASE_API}/{self.slug}/start.json'\n async with self.session.post(url, params=params) as resp:\n if resp.status == 200:\n js = await resp.json()\n return js.get('tournament', {})\n if resp.status == 422:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n else:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n\n async def finalize(self, *, include_matches=False, include_participants=True):\n params = {\n 'api_key': self.api_key,\n 'include_matches': int(include_matches),\n 'include_participants': int(include_participants)\n }\n\n url = f'{self.BASE_API}/{self.slug}/finalize.json'\n async with self.session.post(url, params=params) as resp:\n if resp.status == 200:\n js = await resp.json()\n return js.get('tournament', {})\n if resp.status == 422:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n else:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n\n async def matches(self, *, state=None, participant_id=None):\n params = {\n 'api_key': self.api_key\n }\n if state:\n params['state'] = state\n if participant_id is not None:\n params['participant_id'] = participant_id\n\n url = f'{self.BASE_API}/{self.slug}/matches.json'\n async with self.session.get(url, params=params) as resp:\n if resp.status != 200:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n return await resp.json()\n\n async def score_match(self, match_id, winner_id, player1_score, player2_score):\n params = {\n 'api_key': self.api_key,\n 'match[winner_id]': winner_id,\n 'match[scores_csv]': f'{player1_score}-{player2_score}'\n }\n\n url = f'{self.BASE_API}/{self.slug}/matches/{match_id}.json'\n async with self.session.put(url, params=params) as resp:\n if resp.status == 200:\n js = await resp.json()\n return js.get('match', {})\n if resp.status == 422:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n else:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n\n async def add_participant(self, username, *, misc=None):\n params = {\n 'api_key': self.api_key,\n 'participant[challonge_username]': username\n }\n\n if misc is not None:\n params['participant[misc]'] = str(misc)\n\n url = f'{self.BASE_API}/{self.slug}/participants.json'\n async with self.session.post(url, params=params) as resp:\n if resp.status == 200:\n js = await resp.json()\n return js.get('participant', {})\n if resp.status == 422:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n else:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n\n async def get_participant(self, participant_id, *, include_matches=False):\n params = {\n 'api_key': self.api_key,\n 'include_matches': int(include_matches)\n }\n url = f'{self.BASE_API}/{self.slug}/participants/{participant_id}.json'\n async with self.session.get(url, params=params) as resp:\n if resp.status == 200:\n js = await resp.json()\n return js.get('participant', {})\n if resp.status == 422:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n else:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n\n async def remove_participant(self, participant_id):\n url = f'{self.BASE_API}/{self.slug}/participants/{participant_id}.json'\n params = { 'api_key': self.api_key }\n async with self.session.delete(url, params=params) as resp:\n if resp.status != 200:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n\n async def participants(self):\n url = f'{self.BASE_API}/{self.slug}/participants.json'\n params = {\n 'api_key': self.api_key\n }\n\n async with self.session.get(url, params=params) as resp:\n if resp.status == 200:\n js = await resp.json()\n return [x['participant'] for x in js if 'participant' in x]\n elif resp.status == 422:\n js = await resp.json()\n raise ChallongeError('\\n'.join(x for x in js.get('errors', [])))\n else:\n raise ChallongeError(f'Challonge responded with {resp.status} for {url}.')\n\nclass Tournament(commands.Cog):\n \"\"\"Tournament specific tools.\"\"\"\n\n def __init__(self, bot):\n self.bot = bot\n self.config = config.Config('tournament.json')\n self._already_running_registration = set()\n\n async def cog_command_error(self, ctx, error):\n if isinstance(error, (ChallongeError, commands.BadArgument)):\n traceback.print_exc()\n await ctx.send(error)\n\n async def log(self, message, ctx=None, *, ping=False, error=False, **fields):\n if error is False:\n e = discord.Embed(colour=0x59b642, title=message)\n else:\n if error:\n e = discord.Embed(colour=0xb64259, title='Error')\n else:\n e = discord.Embed(colour=0xb69f42, title='Warning')\n\n exc = traceback.format_exc(chain=False, limit=10)\n if exc != 'NoneType: None\\n':\n e.description = f'```py\\n{exc}\\n```'\n e.add_field(name='Reason', value=message, inline=False)\n\n if ctx is not None:\n e.add_field(name='Author', value=f'{ctx.author} (ID: {ctx.author.id})', inline=False)\n e.add_field(name='Command', value=ctx.message.content, inline=False)\n\n for name, value in fields.items():\n e.add_field(name=name, value=value)\n\n e.timestamp = datetime.datetime.utcnow()\n\n wh_id, wh_token = self.bot.config.tourney_webhook\n hook = discord.Webhook.partial(id=wh_id, token=wh_token, adapter=discord.AsyncWebhookAdapter(self.bot.session))\n await hook.send(embed=e, content='@here' if ping else None)\n\n @property\n def tournament_state(self):\n return TournamentState(self.config.get('state', 0))\n\n @property\n def challonge(self):\n return Challonge.from_url(self.bot, self.config.get('url', ''))\n\n @commands.group(invoke_without_command=True, aliases=['tournament'])\n @in_booyah_guild()\n async def tourney(self, ctx):\n \"\"\"Shows you information about the currently tournament.\"\"\"\n data = await self.challonge.show()\n e = discord.Embed(title=data['name'], url=self.challonge.url, colour=0xa83e4b)\n description = data['description']\n if description:\n e.description = description\n\n participants = None\n not_checked_in = None\n for role in ctx.guild.roles:\n if role.id == PARTICIPANT_ROLE:\n participants = role\n elif role.id == NOT_CHECKED_IN_ROLE:\n not_checked_in = role\n\n attendance = f'{data.get(\"participants_count\", 0)} total attending'\n if participants:\n attendance = f'{attendance}\\n{len(participants.members)} active'\n if not_checked_in:\n not_checked_in = len(not_checked_in.members)\n if not_checked_in:\n attendance = f'{attendance}\\n{not_checked_in} not checked in'\n\n e.add_field(name='Attendance', value=attendance)\n\n current_round = self.config.get('round')\n if current_round is None:\n value = 'None'\n e.add_field(name='Current Round', value=None)\n else:\n round_ends = datetime.datetime.fromtimestamp(self.config.get('round_ends', 0.0))\n value = f'Round {current_round}\\nEnds in {time.human_timedelta(round_ends)}'\n\n e.add_field(name='Current Round', value=value)\n\n state = self.tournament_state.name.replace('_', ' ').title()\n e.add_field(name='State', value=state)\n await ctx.send(embed=e)\n\n @tourney.command(name='open')\n @is_to()\n async def tourney_open(self, ctx, *, url: Challonge):\n \"\"\"Opens a tournament for sign ups\"\"\"\n\n if self.tournament_state is not TournamentState.invalid:\n return await ctx.send(f'A tournament is already in progress. Try {ctx.prefix}tourney close')\n\n tourney = await url.show()\n if tourney.get('state') != 'pending':\n return await ctx.send('This tournament is not pending.')\n\n c = self.config.all()\n c['url'] = url.url\n c['state'] = TournamentState.pending.value\n await self.config.save()\n await ctx.send(f'Now accepting registrations until \"{ctx.prefix}tourney checkin\" is run.')\n return True\n\n @tourney.command(name='strict')\n @is_to()\n async def tourney_strict(self, ctx, *, url: Challonge):\n \"\"\"Opens a tournament for strict sign-ups.\n\n When done with strict sign-ups, only those with the\n Top Participant role can register.\n \"\"\"\n result = await ctx.invoke(self.tourney_open, url=url)\n if result is True:\n await self.config.put('strict', True)\n\n @tourney.command(name='checkin')\n @is_to()\n async def tourney_checkin(self, ctx):\n \"\"\"Opens the tournament for checking in.\n\n Check-ins last 2 hours. Users will be reminded to check-in\n at 1 hour remaining, 30 minutes, 15 minutes, and 5 minutes remaining.\n\n Tournament must be in the pending state.\n \"\"\"\n\n if self.tournament_state is not TournamentState.pending:\n return await ctx.send('This tournament is not pending.')\n\n # Create the check-in timers:\n reminder = self.bot.get_cog('Reminder')\n if reminder is None:\n return await ctx.send('Tell Danny the Reminder cog is off.')\n\n announcement = ctx.guild.get_channel(ANNOUNCEMENT_CHANNEL)\n if announcement is None:\n return await ctx.send('Missing the announcement channel to notify on.')\n\n not_checked_in = discord.utils.find(lambda r: r.id == NOT_CHECKED_IN_ROLE, ctx.guild.roles)\n if not_checked_in is None:\n return await ctx.send('Could not find the Not Checked In role.')\n\n base = datetime.datetime.utcnow() + datetime.timedelta(hours=1)\n durations = (\n (base, 0),\n # (base - datetime.timedelta(hours=1), 60),\n (base - datetime.timedelta(minutes=30), 30),\n (base - datetime.timedelta(minutes=15), 15),\n (base - datetime.timedelta(minutes=5), 5),\n )\n\n for when, remaining in durations:\n await reminder.create_timer(when, 'tournament_checkin', remaining, connection=ctx.db)\n\n await self.config.put('state', TournamentState.checking_in)\n await ctx.send(f'Check-ins are now being processed. When complete and ready, use {ctx.prefix}tourney start')\n\n await not_checked_in.edit(mentionable=True)\n msg = f\"<@&{NOT_CHECKED_IN_ROLE}> check-ins are now being processed. \" \\\n f\"To check-in please go to <#{BOT_SPAM_CHANNEL}> and use the `?checkin` command.\"\n await announcement.send(msg)\n await not_checked_in.edit(mentionable=False)\n\n @commands.Cog.listener()\n async def on_tournament_checkin_timer_complete(self, timer):\n minutes_remaining, = timer.args\n\n guild = self.bot.get_guild(BOOYAH_GUILD_ID)\n if guild is None:\n # wtf\n return\n\n announcement = guild.get_channel(ANNOUNCEMENT_CHANNEL)\n if announcement is None:\n return\n\n role = discord.utils.find(lambda r: r.id == NOT_CHECKED_IN_ROLE, guild.roles)\n if role is None:\n return\n\n if len(role.members) == 0:\n # everyone surprisingly checked in?\n if self.tournament_state is TournamentState.checking_in:\n await self.config.put('state', TournamentState.checked_in)\n await self.log(\"No Check-Ins To Process\", **{'Remaining Minutes': minutes_remaining})\n return\n\n if minutes_remaining != 0:\n # A reminder that they need to check-in\n msg = f'<@&{role.id}> Reminder: You have **{minutes_remaining} minutes** left to check-in.\\n\\n' \\\n f'To check-in please go to <#{BOT_SPAM_CHANNEL}> and use the `?checkin` command.'\n\n await role.edit(mentionable=True)\n await announcement.send(msg)\n await role.edit(mentionable=False)\n return\n\n # Check-in period is complete, so just terminate everyone who hasn't checked-in.\n has_not_checked_in = [m.id for m in role.members]\n\n # Remove the role from everyone who did not check in and notify them.\n\n msg = \"Hello. You've been disqualified due to failure to check-in. \" \\\n \"If you believe this is an error, please contact a TO.\"\n\n for member in role.members:\n try:\n await member.remove_roles(role)\n await member.send(msg)\n except:\n pass\n\n query = \"\"\"SELECT DISTINCT team_members.team_id\n FROM team_members\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE players.discord_id = ANY($1::bigint[]);\n \"\"\"\n\n disqualified_teams = await self.bot.pool.fetch(query, has_not_checked_in)\n disqualified_teams = { str(r[0]) for r in disqualified_teams }\n\n challonge = self.challonge\n tournament = await challonge.show(include_participants=True)\n participants = tournament['participants']\n\n removed_participants = []\n not_removed = 0\n\n for participant in participants:\n participant_id = participant['id']\n team_id = participant['misc']\n\n if team_id not in disqualified_teams:\n not_removed += 1\n continue\n\n try:\n await challonge.remove_participant(participant_id)\n except:\n pass\n else:\n remove_participants.append(participant_id)\n\n await self.config.put('state', TournamentState.checked_in)\n\n fields = {\n 'Totals': f'{len(removed_participants)} removed from check ins\\n{not_removed} checked in',\n 'Removed Team IDs': '\\n'.join(disqualified_teams),\n 'Removed Participant IDs': '\\n'.join(str(x) for x in removed_participants),\n }\n\n await self.log(\"Check-In Over\", **fields)\n\n msg = \"Check-ins are over! Please wait for a TO to start the tournament. \" \\\n \"If you failed to check-in you have received a direct message saying so.\"\n\n await announcement.send(msg)\n\n async def prepare_participant_cache(self, *, connection=None):\n # participant_id: [ParticipantInfo]\n cache = {}\n # member_id: participant_id\n member_cache = {}\n\n participants = await self.challonge.participants()\n\n con = connection or self.bot.pool\n\n mapping = {}\n for participant in participants:\n if participant['final_rank'] is not None:\n continue\n\n misc = participant['misc']\n if misc is None or not misc.isdigit():\n continue\n\n mapping[int(misc)] = (participant['id'], participant['display_name'])\n\n query = \"\"\"WITH team_info AS (\n SELECT team_id,\n array_agg(players.discord_id) AS \"discord\",\n array_agg(players.switch) AS \"switch\"\n FROM team_members\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE team_id = ANY($1::bigint[])\n GROUP BY team_id\n )\n SELECT t.team_id, teams.logo, t.discord, t.switch\n FROM team_info t\n INNER JOIN teams\n ON teams.id = t.team_id;\n \"\"\"\n\n records = await con.fetch(query, list(mapping))\n\n guild = self.bot.get_guild(BOOYAH_GUILD_ID)\n for team_id, team_logo, members, switch in records:\n participant_id, name = mapping[team_id]\n\n actual = []\n for index, member_id in enumerate(members):\n member_cache[member_id] = participant_id\n member = guild.get_member(member_id)\n if member is not None:\n code = switch[index]\n actual.append((member, code))\n\n cache[participant_id] = ParticipantInfo(name=name, logo=team_logo, members=actual)\n\n self._participants = cache\n self._member_participants = member_cache\n\n async def make_rooms_for_matches(self, ctx, matches, round_num, best_of):\n # channel_id mapped to:\n # match_id: match_id\n # player1_id: score\n # player2_id: score\n # confirmed: bool\n # identifier: str\n round_info = {}\n\n guild = self.bot.get_guild(BOOYAH_GUILD_ID)\n\n def embed_for_player(p, *, first=False):\n colour = 0xF02D7D if first else 0x19D719\n e = discord.Embed(title=p.name, colour=colour)\n if p.logo:\n e.set_thumbnail(url=p.logo)\n for member, switch in p.members:\n e.add_field(name=member, value=switch, inline=False)\n return e\n\n for match in matches:\n identifier = match['identifier'].lower()\n player1_id = match['player1_id']\n player2_id = match['player2_id']\n player_one = self._participants.get(player1_id)\n player_two = self._participants.get(player2_id)\n\n fields = {\n 'Match ID': match['id'],\n 'Player 1 ID': player1_id,\n 'Player 2 ID': player2_id\n }\n\n if not (player_one and player_two):\n await self.log(\"Unable to find player information\", error=True, **fields)\n continue\n\n overwrites = {\n guild.default_role: discord.PermissionOverwrite(read_messages=False),\n guild.me: discord.PermissionOverwrite(read_messages=True)\n }\n\n for member, _ in itertools.chain(player_one.members, player_two.members):\n overwrites[member] = discord.PermissionOverwrite(read_messages=True)\n\n try:\n channel = await guild.create_text_channel(f'group-{identifier}', overwrites=overwrites)\n round_info[str(channel.id)] = {\n 'match_id': match['id'],\n 'player1_id': player1_id,\n 'player2_id': player2_id,\n str(player1_id): None,\n str(player2_id): None,\n 'confirmed': False,\n 'identifier': match['identifier']\n }\n\n await channel.send(embed=embed_for_player(player_one, first=True))\n await channel.send(embed=embed_for_player(player_two))\n await asyncio.sleep(0.5)\n\n to_beat = (best_of // 2) + 1\n msg = \"@here Please use this channel to communicate!\\n\" \\\n \"When your match is complete, **both teams must report their own scores**.\\n\" \\\n f\"Reporting your score is done via the `?score` command. For example: `?score {to_beat}`\\n\" \\\n \"**The ?score command can only be done in this channel.**\"\n\n await channel.send(msg)\n except:\n await self.log(\"Failure when creating channel\", error=True, **fields)\n\n\n base = datetime.datetime.utcnow() + datetime.timedelta(minutes=30)\n\n conf = self.config.all()\n conf['round'] = round_num\n conf['best_of'] = best_of\n conf['state'] = TournamentState.underway\n conf['round_complete'] = False\n conf['total_matches'] = len(matches)\n conf['round_info'] = round_info\n conf['round_ends'] = base.timestamp()\n await self.config.save()\n\n times = (\n (base, 0),\n (base - datetime.timedelta(minutes=15), 15)\n )\n\n reminder = self.bot.get_cog('Reminder')\n for when, remaining in times:\n await reminder.create_timer(when, 'tournament_round', round_num, remaining, connection=ctx.db)\n\n fields = {\n 'Total Matches': len(matches)\n }\n\n await self.log(f\"Round {round_num} Started\", **fields)\n\n async def clean_tournament_participants(self):\n guild = self.bot.get_guild(BOOYAH_GUILD_ID)\n role = discord.utils.find(lambda r: r.id == PARTICIPANT_ROLE, guild.roles)\n\n participants = await self.challonge.participants()\n to_remove = {p['id'] for p in participants if p['final_rank'] is not None}\n\n cleaned = 0\n failed = 0\n total = 0\n for member in role.members:\n total += 1\n try:\n p_id = self._member_participants[member.id]\n except KeyError:\n continue\n\n if p_id not in to_remove:\n continue\n\n try:\n await member.remove_roles(role)\n except:\n failed += 1\n else:\n cleaned += 1\n\n fields = {\n 'Cleaned': cleaned,\n 'Failed': failed,\n 'Total Members': total\n }\n\n del self._participants\n await self.log(\"Participant Clean-up\", **fields)\n await self.prepare_participant_cache()\n\n @commands.Cog.listener()\n async def on_tournament_round_timer_complete(self, timer):\n round_num, remaining = timer.args\n\n guild = self.bot.get_guild(BOOYAH_GUILD_ID)\n\n if round_num != self.config.get('round'):\n fields = {\n 'Expected': round_num,\n 'Actual': self.config.get('round')\n }\n return await self.log(f'Round {round_num} Timer Outdated', **fields)\n\n total_matches = self.config.get('total_matches')\n matches_completed = sum(\n info.get('confirmed', False)\n for key, info in self.config.get('round_info', {}).items()\n )\n\n if total_matches == matches_completed:\n fields = {\n 'Total Matches': total_matches\n }\n await self.clean_tournament_participants()\n return await self.log(f'Round {round_num} Already Complete', **fields)\n\n if self.config.get('round_complete', False):\n fields = {\n 'Total Matches': total_matches,\n 'Matches Completed': matches_completed,\n }\n await self.clean_tournament_participants()\n return await self.log(f'Round {round_num} Marked Complete Already', **fields)\n\n announcement = guild.get_channel(ANNOUNCEMENT_CHANNEL)\n role = discord.utils.find(lambda r: r.id == PARTICIPANT_ROLE, guild.roles)\n\n if not (announcement and role):\n fields = {\n 'Round': round_num,\n 'Channel': 'Found' if announcement else 'Not Found',\n 'Role': 'Found' if role else 'Not Found',\n }\n return await self.log(\"Could not get role or channel for round announcement\", **fields)\n\n if remaining != 0:\n # A reminder that the round is almost over\n await role.edit(mentionable=True)\n msg = f'<@&{role.id}>, round {round_num} will conclude in {remaining} minutes. ' \\\n 'Please contact a TO if you have any issues.'\n await announcement.send(msg)\n await role.edit(mentionable=False)\n return\n\n # Round has concluded\n msg = f'<@&{role.id}>, round {round_num} has concluded! Please contact a TO if you require more time.'\n await role.edit(mentionable=True)\n await announcement.send(msg)\n await role.edit(mentionable=False)\n\n await self.config.put('round_complete', True)\n\n fields = {\n 'Round': round_num,\n 'Total Matches': total_matches,\n 'Matches Completed': matches_completed\n }\n await self.log('Round Complete', **fields)\n\n async def start_tournament(self, ctx, best_of):\n tourney = await self.challonge.start()\n\n matches = [\n o['match']\n for o in tourney.get('matches')\n if o['match']['round'] == 1\n ]\n\n await self.make_rooms_for_matches(ctx, matches, round_num=1, best_of=best_of)\n\n async def continue_tournament(self, ctx, best_of, round_num):\n if not self.config.get('round_complete'):\n confirm = await ctx.prompt('The round is not complete yet, are you sure you want to continue?')\n if not confirm:\n return await ctx.send('Aborting.')\n\n matches = await self.challonge.matches()\n matches = [\n o['match']\n for o in matches\n if o['match']['round'] == round_num\n ]\n\n await self.clean_tournament_participants()\n await self.make_rooms_for_matches(ctx, matches, round_num=round_num, best_of=best_of)\n\n @tourney.command(name='start')\n @is_to()\n async def tourney_start(self, ctx, best_of=5):\n \"\"\"Starts the tournament officially.\"\"\"\n\n if self.tournament_state is TournamentState.checking_in:\n role = discord.utils.find(lambda r: r.id == NOT_CHECKED_IN_ROLE, ctx.guild.roles)\n if role is None:\n return await ctx.send('Uh could not find the Not Checked In role.')\n\n if len(role.members) == 0:\n # everyone checked in somehow\n await self.config.put('state', TournamentState.checked_in)\n\n if self.tournament_state not in (TournamentState.checked_in, TournamentState.underway):\n return await ctx.send('Tournament is not started or finished checking in.')\n\n if self.bot.get_cog('Reminder') is None:\n return await ctx.send('Reminder cog is disabled, tell Danny.')\n\n if not hasattr(self, '_participants'):\n await self.prepare_participant_cache()\n\n current_round = self.config.get('round', None)\n if current_round is None:\n # fresh tournament\n await self.start_tournament(ctx, best_of)\n else:\n await self.continue_tournament(ctx, best_of, current_round + 1)\n\n @tourney.command(name='confirm')\n @is_to()\n async def tourney_confirm(self, ctx, *, channel: discord.TextChannel):\n \"\"\"Confirms a score and closes the channel.\"\"\"\n\n if self.tournament_state is not TournamentState.underway:\n return await ctx.send('The tournament has not started yet.')\n\n if not hasattr(self, '_participants'):\n await self.prepare_participant_cache()\n\n round_info = self.config.get('round_info', {})\n info = round_info.get(str(channel.id))\n if info is None:\n return await ctx.send('Could not get round info for this channel.')\n\n player1_id = info['player1_id']\n player2_id = info['player2_id']\n first_team = self._participants.get(player1_id).name\n second_team = self._participants.get(player2_id).name\n\n first_score, second_score = info[str(player1_id)], info[str(player2_id)]\n\n round_num = self.config.get('round')\n msg = f'Are you sure you want to confirm the results of round {round_num} match {info[\"identifier\"]}?\\n' \\\n f'{first_team} **{first_score}** - **{second_score}** {second_team}'\n\n confirm = await ctx.prompt(msg, delete_after=False, reacquire=False)\n if not confirm:\n return await ctx.send('Aborting')\n\n # actually confirm the score via challonge\n\n if first_score == second_score:\n winner_id = 'tie'\n elif first_score > second_score:\n winner_id = player1_id\n else:\n winner_id = player2_id\n\n await self.challonge.score_match(info['match_id'], winner_id, first_score, second_score)\n\n info['confirmed'] = True\n await self.config.put('round_info', round_info)\n fields = {\n 'Team A': first_team,\n 'Team B': second_team,\n 'Match': f\"{round_num}-{info['identifier']}: {info['match_id']}\",\n 'Team A Score': first_score,\n 'Team B Score': second_score\n }\n await self.log(\"Score Confirmation\", ctx, **fields)\n await channel.delete(reason='Score confirmation by TO.')\n await ctx.send('Confirmed.')\n\n @tourney.command(name='room')\n @is_to()\n async def tourney_room(self, ctx, *, channel: discord.TextChannel):\n \"\"\"Opens a room for the TO.\"\"\"\n\n if self.tournament_state is not TournamentState.underway:\n return await ctx.send('The tournament has not started yet.')\n\n round_info = self.config.get('round_info', {})\n if str(channel.id) not in round_info:\n return await ctx.send('This channel is not a group discussion channel.')\n\n await channel.set_permissions(ctx.author, read_messages=True)\n await ctx.send('Done.')\n\n @tourney.command(name='dq', aliases=['DQ', 'disqualify'])\n @is_to()\n async def tourney_dq(self, ctx, *, team: Challonge):\n \"\"\"Disqualifies a team from the tournament.\n\n This removes their roles and stuff for you.\n \"\"\"\n\n # get every member in the team\n\n query = \"\"\"SELECT id FROM teams WHERE challonge=$1;\"\"\"\n team_id = await ctx.db.fetchrow(query, team.slug)\n if team_id is None:\n return await ctx.send('This team is not in the database.')\n\n # remove from challonge\n challonge = self.challonge\n team_id = team_id[0]\n participants = await challonge.participants()\n\n participant_id = next((p['id'] for p in participants if p['misc'] == str(team_id)), None)\n if participant_id is not None:\n await challonge.remove_participant(participant_id)\n\n members = await self.get_discord_users_from_team(ctx.db, team_id=team_id[0])\n\n for member in members:\n try:\n await member.remove_roles(discord.Object(id=PARTICIPANT_ROLE), discord.Object(id=NOT_CHECKED_IN_ROLE))\n except:\n pass\n\n fields = {\n 'Members': '\\n'.join(member.mention for member in members),\n 'Participant ID': participant_id,\n 'Team': f'Team ID: {team_id}\\nURL: {team.url}',\n }\n\n await self.log('Disqualified', error=None, **fields)\n await ctx.send(f'Successfully disqualified <{team.url}>.')\n\n @tourney.command(name='top')\n @is_to()\n async def tourney_top(self, ctx, cut_off: int, *, url: Challonge):\n \"\"\"Adds Top Participant roles based on the cut off for a URL.\"\"\"\n\n tournament = await url.show(include_participants=True)\n if tournament.get('state') != 'complete':\n return await ctx.send('This tournament is incomplete.')\n\n team_ids = []\n for p in tournament['participants']:\n participant = p['participant']\n if participant['final_rank'] <= cut_off:\n team_ids.append(int(participant['misc']))\n\n query = \"\"\"SELECT players.discord_id, team_members.team_id\n FROM team_members\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE team_members.team_id = ANY($1::int[]);\n \"\"\"\n\n members = await ctx.db.fetch(query, team_ids)\n role = discord.Object(id=TOP_PARTICIPANT_ROLE)\n\n async with ctx.typing():\n good = 0\n for discord_id, team_id in members:\n member = ctx.guild.get_member(discord_id)\n try:\n await member.add_roles(role, reason=f'Top {cut_off} Team ID: {team_id}')\n except:\n pass\n else:\n good += 1\n\n await ctx.send(f'Successfully applied {good} roles out of {len(members)} for top {cut_off} in <{url.url}>.')\n\n @tourney.command(name='close')\n @is_to()\n async def tourney_close(self, ctx):\n \"\"\"Closes the currently running tournament.\"\"\"\n\n if self.tournament_state is not TournamentState.underway:\n return await ctx.send('The tournament has not started yet.')\n\n # Finalize tournament\n tourney = await self.challonge.finalize()\n\n # Remove participant roles\n async with ctx.typing():\n await self.clean_tournament_participants()\n\n # Delete lingering match channels\n for channel_id in self.config.get('round_info', {}):\n channel = ctx.guild.get_channel(channel_id)\n if channel is not None:\n await channel.delete(reason='Closing tournament.')\n\n # Clear state\n self.config._db = {}\n await self.config.save()\n await ctx.send('Tournament closed!')\n\n async def get_discord_users_from_team(self, connection, *, team_id):\n query = \"\"\"SELECT players.discord_id\n FROM team_members\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE team_members.team_id = $1;\n \"\"\"\n\n players = await connection.fetch(query, team_id)\n result = []\n guild = self.bot.get_guild(BOOYAH_GUILD_ID)\n for player in players:\n member = guild.get_member(player['discord_id'])\n if member is not None:\n result.append(member)\n return result\n\n async def register_pre_existing(self, ctx, team_id, team):\n # the captain has already registered their team before\n # so just fast track it and add it\n members = await self.get_discord_users_from_team(ctx.db, team_id=team_id)\n if not any(member.id == ctx.author.id for member in members):\n return await ctx.send(f'{ctx.author.mention}, You do not belong to this team so you cannot register with it.')\n\n challonge = self.challonge\n participant = await challonge.add_participant(team.name, misc=team_id)\n participant_id = participant['id']\n\n fields = {\n 'Team ID': team_id,\n 'Team Name': team.name,\n 'Participant ID': participant_id,\n 'Members': '\\n'.join(member.mention for member in members)\n }\n\n msg = f\"{ctx.author.mention}, you have been successfully invited to the tournament.\\n\" \\\n \"**Please follow these steps in order**\\n\" \\\n \"1. Go to \\n\" \\\n \"2. Click on the newest \\\"You have been challonged\\\" invitation.\\n\" \\\n \"3. Follow the steps in the invitation.\\n\" \\\n f\"4. Reply to this message with: {participant_id}\"\n\n await ctx.send(msg)\n await ctx.release()\n\n def check(m):\n return m.author.id == ctx.author.id and m.channel.id == ctx.channel.id and str(participant_id) == m.content\n\n try:\n await self.bot.wait_for('message', check=check, timeout=300.0)\n except asyncio.TimeoutError:\n await ctx.send(f'{ctx.author.mention}, you did not verify your invite! Cancelling your registration.')\n await challonge.remove_participant(participant_id)\n await self.log(\"Cancelled Pre-Existing Registration\", ctx, error=True, **fields)\n return\n\n participant = await challonge.get_participant(participant_id)\n\n if participant.get('invitation_pending', True):\n await ctx.send(f'{ctx.author.mention}, you did not accept your invite! Cancelling your registration.')\n await challonge.remove_participant(participant_id)\n await self.log(\"Failed Pre-Existing Registration\", ctx, error=True, **fields)\n return\n\n await ctx.send(f'{ctx.author.mention}, alright you are good to go!')\n await self.log(\"Successful Pre-Existing Registration\", ctx, **fields)\n for member in members:\n try:\n await member.add_roles(discord.Object(id=NOT_CHECKED_IN_ROLE), discord.Object(id=PARTICIPANT_ROLE))\n except discord.HTTPException:\n continue\n\n async def _prompt(self, dm, content, *, validator=None, max_tries=3, timeout=300.0, exit=True):\n validator = validator or (lambda x: True)\n\n def check(m):\n return m.channel.id == dm.id and m.author.id == dm.recipient.id\n\n try:\n await dm.send(content)\n except discord.HTTPException:\n return PromptResult.cannot_dm\n\n for i in range(max_tries):\n try:\n msg = await self.bot.wait_for('message', check=check, timeout=timeout)\n except asyncio.TimeoutError:\n if exit:\n await dm.send('Took too long. Exiting.')\n return PromptResult.timeout\n\n if msg.content == '?cancel':\n # special sentinel telling us to stop\n if exit:\n await dm.send('Aborting.')\n return PromptResult.cancel\n\n is_valid = validator(msg)\n if is_valid is True:\n return msg\n elif is_valid is not False:\n # returning a different value\n return is_valid\n\n await dm.send(f\"That doesn't seem right... {max_tries - i - 1} tries remaining.\")\n\n if exit:\n await dm.send('Too many tries. Exiting.')\n return PromptResult.error\n\n async def new_registration(self, ctx, url, team):\n self._already_running_registration.add(ctx.author.id)\n dm = await ctx.author.create_dm()\n result = PromptTransaction(url.slug)\n\n msg = \"Hello! I'm here to interactively set you up for the first-time registration of Booyah Battle.\\n\" \\\n f\"**If you want to cancel, you can cancel at any time by doing ?cancel**\\n\\n\" \\\n \"Let's get us started with a question, **are you the captain of this team?** (say yes or no)\"\n\n await ctx.release()\n reply = await self._prompt(dm, msg, validator=yes_no)\n if reply is PromptResult.cannot_dm:\n return await ctx.send(f'Hey {ctx.author.mention}, your DMs are disabled. Try again after you enable them.')\n\n if isinstance(reply, PromptResult):\n return\n\n if reply != 1:\n return await dm.send('Alright. Tell your captain to do this registration instead. Sorry!')\n\n # check if they're in the player database already\n await ctx.acquire()\n query = \"SELECT id FROM players WHERE discord_id=$1;\"\n record = await ctx.db.fetchrow(query, ctx.author.id)\n if record is None:\n await ctx.release()\n fc = await self._prompt(dm, \"What is your switch friend code?\", validator=valid_fc)\n if not isinstance(fc, str):\n return\n result.add_captain(ctx.author.id, fc)\n else:\n result.add_pre_existing_captain(record[0])\n\n logo_msg = \"What is your team's logo? You can either do the following:\\n\" \\\n \"- A URL pointing to the image, which must be a png/jpeg/jpg file.\\n\" \\\n \"- An image uploaded directly to this channel.\\n\" \\\n \"- Sending the message `None` to denote no logo.\"\n\n logo = await self._prompt(dm, logo_msg, validator=valid_logo)\n if logo is not None and not isinstance(logo, str):\n return\n\n result.team_logo = logo\n\n def valid_member(m, *, _find=discord.utils.find):\n name, _, discriminator = m.content.rpartition('#')\n value = _find(lambda u: u.name == name and u.discriminator == discriminator, self.bot.users)\n if value is not None:\n return value.id\n return False\n\n member_msg = \"What is the member's name? You must use name#tag. e.g. Danny#0007\"\n ask_member_msg = \"Do you have any other team members in the server? (say yes or no)\"\n members = {}\n for i in range(7):\n has_member = await self._prompt(dm, ask_member_msg, validator=yes_no, exit=False)\n if has_member is PromptResult.cancel:\n return\n\n if has_member != 1:\n break\n\n member = await self._prompt(dm, member_msg, validator=valid_member, timeout=120.0, exit=False)\n if member is PromptResult.cancel:\n return\n\n if member is PromptResult.timeout:\n await dm.send(\"Took too long... Let's move on.\")\n break\n\n if member is PromptResult.error:\n break\n\n has_fc = await self._prompt(dm, \"What is their switch friend code?\", exit=False, validator=valid_fc, max_tries=2)\n if has_fc is PromptResult.cancel:\n return\n\n if isinstance(has_fc, PromptResult):\n continue\n\n members[member] = has_fc\n await dm.send('Successfully added member.')\n ask_member_msg = \"Do you have any **additional** team members in the server? (say yes or no)\"\n\n await ctx.acquire()\n\n # remove members that are in a team already\n query = \"\"\"SELECT players.discord_id\n FROM team_members\n INNER JOIN players\n ON players.id = team_members.player_id\n INNER JOIN teams\n ON teams.id = team_members.team_id\n WHERE teams.active\n AND players.discord_id = ANY($1::bigint[]);\n \"\"\"\n\n records = await ctx.db.fetch(query, list(members))\n to_remove = {x[0] for x in records}\n\n members = [(a, b) for a, b in members.items() if a not in to_remove]\n\n # get pre-existing members\n query = \"\"\"SELECT players.id\n FROM players\n WHERE players.discord_id = ANY($1::bigint[])\n \"\"\"\n\n pre_existing = await ctx.db.fetch(query, [i for i, j in members])\n pre_existing = {x[0] for x in records}\n members = [(a, b) for a, b in members if a not in pre_existing]\n\n for player_id in pre_existing:\n result.add_existing_member(player_id)\n\n for discord_id, fc in members:\n result.add_member(discord_id, fc)\n\n transaction = ctx.db.transaction()\n await transaction.start()\n\n try:\n team_id = await result.execute_sql(ctx.db)\n except:\n await transaction.rollback()\n await self.log('Registration SQL Failure', ctx, error=True)\n return\n\n try:\n # send invite\n challonge = self.challonge\n participant = await challonge.add_participant(team.name, misc=team_id)\n\n # wait for accepting\n msg = f\"You have been successfully invited to the tournament.\\n\" \\\n \"**Please follow these steps in order**\\n\" \\\n \"1. Go to \\n\" \\\n \"2. Click on the newest \\\"You have been challonged\\\" invitation.\\n\" \\\n \"3. Follow the steps in the invitation.\\n\" \\\n f\"4. Reply to this message with: {team_id}\"\n\n def verify(m):\n return m.content == str(team_id)\n verified = await self._prompt(dm, msg, validator=verify, timeout=120.0)\n except:\n await transaction.rollback()\n await self.log(\"Registration failure\", ctx, error=True)\n await dm.send('An error happened while trying to register.')\n return\n\n participant_id = participant.get('id')\n fields = {\n 'Team ID': team_id,\n 'Team Name': team.name,\n 'Participant ID': participant_id,\n }\n\n if isinstance(verified, PromptResult):\n await transaction.rollback()\n await challonge.remove_participant(participant_id)\n await self.log('Took too long to accept invite', ctx, error=None, **fields)\n return\n\n try:\n participant = await challonge.get_participant(participant_id)\n except ChallongeError as e:\n await transaction.rollback()\n await self.log(f\"Challonge error while registering: {e}\", ctx, error=True, **fields)\n await dm.send(e)\n except:\n await transaction.rollback()\n await self.log(\"Unknown error while registering\", ctx, error=True, **fields)\n else:\n if participant.get('invitation_pending', True):\n await transaction.rollback()\n await challonge.remove_participant(participant_id)\n await self.log(\"Did not accept invite\", ctx, error=None, **fields)\n await dm.send('Invite not accepted! Aborting.')\n else:\n await transaction.commit()\n members = await self.get_discord_users_from_team(ctx.db, team_id=team_id)\n fields['Members'] = '\\n'.join(member.mention for member in members)\n\n await self.log(\"Successful Registration\", ctx, **fields)\n msg = \"You've successfully registered! Next time, to be easier you can skip this entire process.\"\n await dm.send(msg)\n\n for member in members:\n try:\n await member.add_roles(discord.Object(id=NOT_CHECKED_IN_ROLE), discord.Object(id=PARTICIPANT_ROLE))\n except discord.HTTPException:\n continue\n\n @commands.command()\n @in_booyah_guild()\n async def register(self, ctx, *, url: Challonge):\n \"\"\"Signs up for a running tournament.\"\"\"\n\n if self.tournament_state is not TournamentState.pending:\n return await ctx.send('No tournament is up for sign ups right now.')\n\n if self.config.get('strict', False):\n if not any(role.id == TOP_PARTICIPANT_ROLE for role in ctx.author.roles):\n return await ctx.send('You do not have the Top Participant role.')\n\n try:\n team = await self.challonge.get_team_info(url.slug)\n except ChallongeError:\n return await ctx.send('This is not a valid challonge team page!')\n\n query = \"\"\"SELECT id FROM teams WHERE challonge=$1;\"\"\"\n pre_existing = await ctx.db.fetchrow(query, url.slug)\n if pre_existing is not None:\n return await self.register_pre_existing(ctx, pre_existing['id'], team)\n\n if ctx.author.id in self._already_running_registration:\n return await ctx.send('You are already running a registration right now...')\n\n query = \"\"\"SELECT team_id\n FROM team_members\n INNER JOIN teams\n ON teams.id = team_members.team_id\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE teams.active\n AND players.discord_id=$1;\n \"\"\"\n\n team_id = await ctx.db.fetchrow(query, ctx.author.id)\n if team_id is not None:\n return await ctx.send('You are already part of another team.')\n\n try:\n async with ctx.acquire():\n await self.new_registration(ctx, url, team)\n finally:\n self._already_running_registration.discard(ctx.author.id)\n\n @commands.command()\n @in_booyah_guild()\n async def checkin(self, ctx):\n \"\"\"Checks you in to the current running tournament.\"\"\"\n\n if self.tournament_state is not TournamentState.checking_in:\n return await ctx.send('No tournament is up for check-ins right now.')\n\n query = \"\"\"SELECT team_members.team_id\n FROM team_members\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE players.discord_id = $1;\n \"\"\"\n\n record = await ctx.db.fetchrow(query, ctx.author.id)\n if record is None:\n return await ctx.send('You do not have a team signed up.')\n\n team_id = record[0]\n members = await self.get_discord_users_from_team(ctx.db, team_id=team_id)\n\n did_not_remove = []\n for member in members:\n try:\n await member.remove_roles(discord.Object(id=NOT_CHECKED_IN_ROLE))\n except:\n did_not_remove.append(member)\n\n fields = {\n 'Checked-in Members': '\\n'.join(member.mention for member in members),\n }\n\n if did_not_remove:\n fields['Failed Removals'] = '\\n'.join(member.mention for member in members)\n else:\n fields['Failed Removals'] = 'None'\n\n await self.log(\"Check-In Processed\", ctx, **fields)\n await ctx.message.add_reaction(ctx.tick(True).strip('<:>'))\n\n @commands.command()\n @in_booyah_guild()\n async def score(self, ctx, wins: int):\n \"\"\"Submits your score to the tournament.\"\"\"\n\n if self.tournament_state is not TournamentState.underway:\n return await ctx.send('A tournament is not currently running.')\n\n if not hasattr(self, '_participants'):\n await self.prepare_participant_cache()\n\n info = self.config.get('round_info', {})\n ours = info.get(str(ctx.channel.id))\n if ours is None:\n return await ctx.send('This channel is not a currently running group channel.')\n\n best_of = self.config.get('best_of')\n\n if wins > ((best_of // 2) + 1):\n return await ctx.send('That sort of score is impossible friend.')\n\n our_participant_id = self._member_participants.get(ctx.author.id)\n if our_participant_id is None:\n return await ctx.send('Apparently, you are not participating in this tournament.')\n\n if ours['confirmed']:\n return await ctx.send('This score has been confirmed by a TO and cannot be changed. Contact a TO.')\n\n their_participant_id = ours['player2_id'] if ours['player1_id'] == our_participant_id else ours['player1_id']\n our_score = ours[str(our_participant_id)]\n their_score = ours[str(their_participant_id)]\n round_num = self.config.get('round')\n\n fields = {\n 'Reporting Team': self._participants[our_participant_id].name,\n 'Room': f'{round_num}-{ours[\"identifier\"]}: {ctx.channel.mention}',\n 'Match ID': ours['match_id'],\n 'Reporter Score': None,\n 'Enemy Score': their_score,\n 'Round': f'Round {round_num}: Best of {best_of}'\n }\n\n changed_score = False\n ping = False\n round_complete = self.config.get('round_complete', False)\n if our_score is None:\n fields['Reporter Score'] = wins\n else:\n if our_score == wins:\n return await ctx.send('You already submitted this exact score before bud.')\n\n fields['Reporter Score'] = f'{our_score} -> {wins}'\n changed_score = True\n\n if their_score is None:\n ours[str(our_participant_id)] = wins\n title = 'Changed score submission' if changed_score else 'Score Submission'\n else:\n if their_score + wins > best_of:\n await ctx.send('Your score conflicts with the enemy score.')\n reason = 'Score conflict'\n if round_complete:\n reason = f'{reason} + done after round completion'\n await self.log(reason, ctx, error=True, **fields)\n return\n\n ours[str(our_participant_id)] = wins\n title = 'Changed complete score submission' if changed_score else 'Complete Score Submission'\n ping = True\n\n await ctx.send('Score reported.')\n if round_complete:\n fields['Info'] = title\n await self.log('Submission After Round Complete', ctx, ping=ping, error=None, **fields)\n else:\n await self.log(title, ctx, ping=ping, **fields)\n\n await self.config.put('round_info', info)\n\n @commands.group()\n @in_booyah_guild()\n async def team(self, ctx):\n \"\"\"Manages your team.\"\"\"\n pass\n\n @team.command(name='create')\n async def team_create(self, ctx, *, url: Challonge):\n \"\"\"Creates a team.\"\"\"\n\n # Check if they're an active player\n\n query = \"\"\"SELECT id FROM players WHERE discord_id = $1;\"\"\"\n record = await ctx.db.fetchrow(query, ctx.author.id)\n if record is None:\n return await ctx.send(f'You have not registered as a player. Try {ctx.prefix}player ' \\\n 'switch SW-1234-5678-9012 to register yourself as a player.')\n\n player_id = record['id']\n\n # Check if they're in an active team\n\n query = \"\"\"SELECT team_id, captain\n FROM team_members\n INNER JOIN teams\n ON teams.id = team_members.team_id\n WHERE teams.active\n AND team_members.player_id = $1\n \"\"\"\n\n record = await ctx.db.fetchrow(query, player_id)\n if record is not None:\n return await ctx.send('You are already an active member of a team.')\n\n team_info = await url.get_team_info(url.slug)\n\n # Check if this team already exists\n query = \"\"\"SELECT id FROM teams WHERE challonge=$1;\"\"\"\n exists = await ctx.db.fetchrow(query, url.slug)\n if exists:\n return await ctx.send('This team already exists.')\n\n # Actually insert\n query = \"\"\"WITH to_insert AS (\n INSERT INTO teams (challonge)\n VALUES ($1)\n RETURNING id\n )\n INSERT INTO team_members (team_id, player_id, captain)\n SELECT to_insert.id, $2, TRUE\n FROM to_insert;\n \"\"\"\n\n await ctx.db.execute(query, url.slug, player_id)\n await ctx.send(f'Successfully created team {team_info.name}. See \"{ctx.prefix}help team\" for more commands.')\n\n async def get_owned_team_info(self, ctx):\n query = \"\"\"SELECT team_id AS \"id\",\n players.id AS \"owner_id\",\n 'https://challonge.com/teams/' || teams.challonge AS \"challonge\"\n FROM team_members\n INNER JOIN teams\n ON teams.id = team_members.team_id\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE team_members.captain\n AND players.discord_id = $1\n AND teams.active;\n \"\"\"\n\n record = await ctx.db.fetchrow(query, ctx.author.id)\n return record\n\n @team.command(name='delete')\n async def team_delete(self, ctx):\n \"\"\"Marks your current team as inactive.\"\"\"\n team = await self.get_owned_team_info(ctx)\n\n # Get the owned team\n if team is None:\n return await ctx.send('You do not own any team.')\n\n query = \"\"\"UPDATE teams SET active = FALSE WHERE id = $1;\"\"\"\n await ctx.db.execute(query, team['id'])\n await ctx.send('Team successfully marked as inactive.')\n\n @team.command(name='add')\n async def team_add(self, ctx, *, member: discord.Member):\n \"\"\"Adds a member to your team.\"\"\"\n\n team = await self.get_owned_team_info(ctx)\n if team is None:\n return await ctx.send('You do not own any team.')\n\n query = \"\"\"SELECT id FROM players WHERE discord_id=$1;\"\"\"\n record = await ctx.db.fetchrow(query, member.id)\n\n if record is None:\n await ctx.send('It appears this member has not registered before. ' \\\n f'Ask them to do so by inputting their switch code via \"{ctx.prefix}player switch\" command.')\n return\n\n player_id = record['id']\n\n query = \"\"\"SELECT teams.challonge\n FROM team_members\n INNER JOIN teams\n ON teams.id = team_members.team_id\n WHERE team_members.player_id = $1\n AND teams.active\n AND teams.id <> $2;\n \"\"\"\n\n record = await ctx.db.fetchrow(query, player_id, team['id'])\n if record is not None:\n return await ctx.send(f'This member is already part of the team.')\n\n # Verify the member wants to be added to the team\n msg = f'Hello {member.mention}, {ctx.author.mention} would like to add you to <{team[\"challonge\"]}>. Do you agree?'\n verify = await ctx.prompt(msg, delete_after=False, author_id=member.id)\n if not verify:\n return await ctx.send('Aborting.')\n\n query = \"\"\"INSERT INTO team_members (player_id, team_id) VALUES ($1, $2)\"\"\"\n await ctx.db.execute(query, player_id, team['id'])\n await ctx.send('Successfully added member.')\n\n # transparently try to add roles depending on the tournament state\n participants = await self.challonge.participants()\n team_id = str(team['id'])\n participant_id = next((p['id'] for p in participants if p['misc'] == team_id and p['final_rank'] is None), None)\n if participant_id is None:\n return\n\n await member.add_roles(discord.Object(id=PARTICIPANT_ROLE))\n if self.tournament_state is TournamentState.pending:\n await member.add_roles(discord.Object(id=NOT_CHECKED_IN_ROLE))\n\n if self.tournament_state is TournamentState.underway:\n # see if they have a room active and add them there\n if not hasattr(self, '_participants'):\n await self.prepare_participant_cache()\n\n try:\n info = self._participants[participant_id]\n except KeyError:\n pass\n else:\n # add to the cache\n self._member_participants[member.id] = participant_id\n\n # add to the channel\n for channel_id, obj in self.config.get('round_info', {}).items():\n if obj['player1_id'] == participant_id or obj['player2_id'] == participant_id:\n channel = ctx.guild.get_channel(int(channel_id))\n if channel:\n await channel.set_permissions(member, read_messages=True)\n\n @team.command(name='remove')\n async def team_remove(self, ctx, *, member: discord.Member):\n \"\"\"Removes a member from your team.\"\"\"\n\n team = await self.get_owned_team_info(ctx)\n if team is None:\n return await ctx.send('You do not own any team.')\n\n query = \"\"\"DELETE FROM team_members\n USING players\n WHERE team_id = $1\n AND players.id = team_members.player_id\n AND players.discord_id = $2\n RETURNING players.id\n \"\"\"\n\n deleted = await ctx.db.fetchrow(query, team['id'], member.id)\n if not deleted:\n return await ctx.send('This member could not be removed. They might not be in your team.')\n\n await ctx.send('Removed member successfully.')\n\n # transparently try to remove roles depending on the tournament state\n participants = await self.challonge.participants()\n team_id = str(team['id'])\n participant_id = next((p['id'] for p in participants if p['misc'] == team_id and p['final_rank'] is None), None)\n if participant_id is None:\n return\n\n await member.remove_roles(discord.Object(id=PARTICIPANT_ROLE))\n if self.tournament_state is TournamentState.pending:\n await member.remove_roles(discord.Object(id=NOT_CHECKED_IN_ROLE))\n\n # we'll leave them in the room for now\n return\n\n @team.command(name='logo')\n async def team_logo(self, ctx, *, url=None):\n \"\"\"Sets the logo for your team.\n\n You can upload an image to Discord directly if you want to.\n \"\"\"\n\n team = await self.get_owned_team_info(ctx)\n if team is None:\n return await ctx.send('You do not own any team.')\n\n if url is None:\n if not ctx.message.attachments:\n return await ctx.send('No logo provided.')\n url = message.attachments[0].url\n\n actual_url = validate_url(url)\n if not actual_url:\n return await ctx.send('Invalid URL provided.')\n\n query = \"UPDATE teams SET logo = $1 WHERE id = $2;\"\n await ctx.db.execute(query, actual_url, team['id'])\n await ctx.send('Successfully updated team logo.')\n\n @team.command(name='captain')\n async def team_captain(self, ctx, *, member: discord.Member):\n \"\"\"Transfers ownership of a team to another member.\n\n They must belong on the team for ownership to transfer.\n \"\"\"\n\n team = await self.get_owned_team_info(ctx)\n if team is None:\n return await ctx.send('You do not own any team.')\n\n query = \"\"\"SELECT player_id\n FROM team_members\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE players.discord_id = $1\n AND team_members.team_id = $2;\n \"\"\"\n\n record = await ctx.db.fetchrow(query, member.id, team['id'])\n if record is None:\n return await ctx.send('Member does not belong to team.')\n\n query = \"\"\"UPDATE team_members\n SET captain = NOT captain\n WHERE team_id = $1\n AND player_id IN ($2, $3);\n \"\"\"\n\n await ctx.db.execute(query, team['id'], record[0], team['owner_id'])\n await ctx.send('Successfully transferred ownership.')\n\n @team.command(name='show')\n async def team_show(self, ctx, *, url: Challonge):\n \"\"\"Shows a team's info.\"\"\"\n\n query = \"SELECT * FROM teams WHERE challonge=$1;\"\n record = await ctx.db.fetchrow(query, url.slug)\n\n if record is None:\n return await ctx.send('No info for this team!')\n\n team_info = await self.challonge.get_team_info(url.slug)\n e = discord.Embed(title=team_info.name, url=url.url, colour=0x19D719)\n\n if record['logo']:\n e.set_thumbnail(url=record['logo'])\n\n query = \"\"\"SELECT players.discord_id, players.switch\n FROM team_members\n INNER JOIN players ON players.id = team_members.player_id\n WHERE team_id=$1;\n \"\"\"\n\n players = await ctx.db.fetch(query, record['id'])\n e.add_field(name='Active?', value='Yes' if record['active'] else 'No')\n\n for member_id, switch in players:\n member = ctx.guild.get_member(member_id)\n if member:\n e.add_field(name=str(member), value=switch, inline=False)\n\n await ctx.send(embed=e)\n\n @commands.group(invoke_without_command=True)\n @in_booyah_guild()\n async def player(self, ctx, *, member: discord.Member):\n \"\"\"Manages your player profile.\"\"\"\n\n query = \"\"\"SELECT * FROM players WHERE discord_id=$1;\"\"\"\n record = await ctx.db.fetchrow(query, member.id)\n\n if record is None:\n return await ctx.send('No info for this player.')\n\n query = \"\"\"SELECT teams.challonge, team_members.captain\n FROM team_members\n INNER JOIN teams\n ON teams.id = team_members.team_id\n INNER JOIN players\n ON players.id = team_members.player_id\n WHERE teams.active\n AND players.discord_id=$1\n \"\"\"\n\n info = await ctx.db.fetchrow(query, member.id)\n e = discord.Embed()\n e.set_author(name=str(member), icon_url=member.avatar_url)\n\n if record['challonge']:\n e.url = f'https://challonge.com/users/{record[\"challonge\"]}'\n\n challonge_url = f'https://challonge.com/teams/{info[\"challonge\"]}' if info else 'None'\n e.add_field(name='Switch', value=record['switch'])\n e.add_field(name='Active Team', value=challonge_url)\n e.add_field(name='Captain?', value='Yes' if info and info['captain'] else 'No')\n await ctx.send(embed=e)\n\n @player.command(name='switch')\n @in_booyah_guild()\n async def player_switch(self, ctx, *, fc: fc_converter):\n \"\"\"Sets your Nintendo Switch code for your player profile.\"\"\"\n\n query = \"\"\"INSERT INTO players (discord_id, switch)\n VALUES ($1, $2)\n ON CONFLICT (discord_id) DO\n UPDATE SET switch = $2;\n \"\"\"\n\n try:\n await ctx.db.execute(query, ctx.author.id, fc)\n except asyncpg.UniqueViolationError:\n await ctx.send('Someone already has this set as their switch code.')\n else:\n await ctx.send('Updated switch code.')\n\n\ndef setup(bot):\n bot.add_cog(Tournament(bot))\n","sub_path":"cogs/tournament.py","file_name":"tournament.py","file_ext":"py","file_size_in_byte":76537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"3821816","text":"from django.conf import settings\nfrom django.conf.urls.defaults import *\nfrom django.views.generic.simple import direct_to_template\nfrom django.views.generic.list_detail import object_list, object_detail\nfrom lists.models import Recipe, Menu\nfrom haystack.forms import ModelSearchForm\nfrom haystack.query import SearchQuerySet\nfrom haystack.views import SearchView, search_view_factory\nfrom django.contrib import admin\nadmin.autodiscover()\nfrom pinax.apps.account.openid_consumer import PinaxConsumer\n\n\nhandler500 = \"pinax.views.server_error\"\n\nrecipe_queryset = Recipe.objects.all()\nmenu_queryset = Menu.objects.all()\nsqs = SearchQuerySet()\n\nurlpatterns = patterns(\"\",\n url(r\"^$\", 'lists.views.homepage' , {}, name=\"home\"),\n url(r\"^recipe/(?P\\d+)/$\", object_detail, {'queryset' : recipe_queryset, 'template_name' : 'recipes/recipe.html', 'template_object_name' : 'recipe'}, name=\"recipe\"),\n url(r\"^menu/(?P\\d+)/$\", object_detail, {'queryset' : menu_queryset, 'template_name' : 'menus/menu.html', 'template_object_name' : 'menu'}, name=\"menu\"),\n url(r\"^admin/invite_user/$\", \"pinax.apps.signup_codes.views.admin_invite_user\", name=\"admin_invite_user\"),\n url(r\"^admin/\", include(admin.site.urls)),\n url(r\"^about/\", include(\"about.urls\")),\n url(r\"^account/\", include(\"pinax.apps.account.urls\")),\n url(r\"^openid/(.*)\", PinaxConsumer()),\n url(r\"^profiles/\", include(\"idios.urls\")),\n url(r\"^notices/\", include(\"notification.urls\")),\n url(r\"^announcements/\", include(\"announcements.urls\")),\n)\n\nurlpatterns += patterns('haystack.views',\n url(r'^search$', search_view_factory(\n view_class=SearchView,\n searchqueryset=sqs,\n form_class=ModelSearchForm\n ), name='haystack_search'),\n)\n\n\n\nif settings.SERVE_MEDIA:\n urlpatterns += patterns(\"\",\n url(r\"\", include(\"staticfiles.urls\")),\n )\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"88040831","text":"import IfxPyDbi\nConStr = \"SERVER=infor99;DATABASE=db0;HOST=127.0.0.1;SERVICE=27988;UID=informix;PWD=980120;\"\n\ntry:\n # netstat -a | findstr 9088\n conn = IfxPyDbi.connect(ConStr, \"\", \"\")\nexcept Exception as e:\n print('ERROR: Connect failed')\n print(e)\n quit()","sub_path":"test_obj/informix_dir/infoemix_con.py","file_name":"infoemix_con.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"486512568","text":"import os, h5py\nimport numpy as np\nfrom scipy import sparse\nfrom scipy.spatial import cKDTree\n\n\ndef fractional_difference(array, start_idx, d=0.75, lag=10):\n if start_idx - lag < 0: lag = start_idx\n\n output = array[start_idx]\n dm = d\n sign = -1\n for i in xrange(1,lag):\n output += (sign) * dm * array[start_idx - i]\n sign *= -1\n dm *= ( (d-i) / (i+1) )\n return output\n\n# N x N matrix\ndef threshold_matrix(matrix, threshold,double_sided=False):\n def check_threshold(a):\n if a <= threshold and a >= -threshold:\n return True\n return False\n\n shape = matrix.shape\n matrix_reshaped = matrix.reshape(-1,)\n if double_sided:\n check_vec = np.vectorize(check_threshold)\n matrix_reshaped[check_vec(matrix_reshaped)] = 0.0\n else:\n matrix_reshaped[matrix_reshaped <= threshold] = 0.0\n return matrix_reshaped.reshape(shape)\n\ndef binarize_matrix(matrix, to_sparse=True):\n shape = matrix.shape\n matrix_reshaped = matrix.reshape(-1,)\n matrix_reshaped[matrix_reshaped > 0] = 1\n \n binarized_matrix = matrix_reshaped.reshape(shape).astype('int8')\n if to_sparse:\n return matrix_to_sparse(binarized_matrix)\n return binarized_matrix\n\ndef matrix_to_sparse(matrix):\n if len(matrix.shape) <= 2:\n return sparse.csr_matrix(matrix)\n else: \n sparse_lst = []\n for i in xrange(matrix.shape[2]):\n this_matrix = matrix[:,:,i]\n sparse_lst.append(sparse.csr_matrix(this_matrix))\n return sparse_lst\n\n# cloud is N x D\ndef nearest_n_neurons(x,cloud,n=16,tree=None):\n if tree is None:\n tree = cKDTree(cloud)\n distances, idxs = tree.query(x,n+1)\n return distances, idxs\n\ndef nearest_in_radius(x,cloud,r=0.050,tree=None): # r in mm\n if tree is None:\n tree = cKDTree(cloud)\n idxs = tree.query_ball_point(x,r)\n return idxs\n\n\ndef nearest_in_width(x,cloud,r_inner=0.010,r_outer=0.050,tree=None):\n if tree is None:\n tree = cKDTree(cloud)\n inner_idxs = set(tree.query_ball_point(x,r_inner))\n outer_idxs = set(tree.query_ball_point(x,r_outer))\n # In width will be |outer_idxs| - |inner_idxs|\n #return [0] + sorted(list(inner_idxs.difference(outer_idxs)))\n return sorted(list(outer_idxs.difference(inner_idxs)))\n\ndef list_find(f, lst):\n i = 0\n for x in lst:\n if f(x): return i\n else: i+=1\n return None\n\ndef extract_continuous_cluster_times(cluster_assignment, clusters):\n cluster_times_dict = {cluster: [] for cluster in clusters}\n current_cluster = None\n for i in xrange(len(cluster_assignment)):\n if current_cluster is None:\n current_cluster = cluster_assignment[i]\n time_lst = []\n elif current_cluster != cluster_assignment[i]:\n cluster_times_dict[current_cluster].append(time_lst)\n time_lst = []\n current_cluster = cluster_assignment[i] \n elif current_cluster == cluster_assignment[i]:\n time_lst.append(i)\n return cluster_times_dict\n\ndef extract_spatial_information(h5_filepath, spatial_namespace):\n if not os.path.isfile(h5_filepath): raise Exception('Error, could not find h5 file')\n f = h5py.File(h5_filepath)\n if spatial_namespace not in f: raise Exception('Error, namespace not found')\n\n spatial_data_group = f[spatial_namespace]['data']\n keys = sorted([int(k) for k in spatial_data_group.keys()])\n coordinates_lst = []\n for key in keys:\n coordinates_lst.append(spatial_data_group[str(key)])\n return np.asarray(coordinates_lst, dtype='float32')\n \n \n\ndef extract_trace_information(h5_filepath, traces_namespace):\n if not os.path.isfile(h5_filepath):\n raise Exception('Error, could not find h5 file')\n f = h5py.File(h5_filepath)\n \n if traces_namespace not in f:\n raise Exception('Error, namespace not found')\n\n traces_group = f[traces_namespace]\n\n TR,N,T = traces_group['TR'][0], traces_group['n_neurons'][0], traces_group['tpoints'][0]\n\n trace_data_group = traces_group['data']\n trace_keys = sorted([int(k) for k in trace_data_group.keys()])\n traces = np.zeros((N,T))\n\n for key in trace_keys:\n traces[key,:] = trace_data_group[str(key)]\n f.close()\n\n return traces, TR, N, T\n\ndef extract_ticc_cluster_assignment(h5_filepath, ticc_namespace):\n if not os.path.isfile(h5_filepath):\n raise Exception('Error, could not find h5 file')\n\n f = h5py.File(h5_filepath, 'r')\n if ticc_namespace not in f:\n raise Exception('ticc namespace is incorrect or does not exist')\n ticc_group = f[ticc_namespace]\n\n nclusters = ticc_group['num_clusters'][0]\n ticc_cluster_assignment_group = ticc_group['cluster assignment']['data']\n cluster_assignment = list(ticc_cluster_assignment_group['output'])\n f.close()\n\n return np.asarray(cluster_assignment), nclusters \n\ndef extract_first_order_statistics(h5_filepath, stats_namespace):\n if not os.path.isfile(h5_filepath):\n raise Exception('Error, could not find h5 file')\n \n f = h5py.File(h5_filepath, 'r')\n if stats_namespace not in f:\n raise Exception('statistics namespace not in h5 file')\n\n stats_group = f[stats_namespace]\n mean_info, var_info = {}, {}\n\n mean_info['anova assumptions'] = stats_group['mean anova assumptions met'][0]\n var_info['anova assumptions'] = stats_group['var anova assumptions met'][0]\n \n mean_info['num comparisons'] = stats_group['num comparisons'][0]\n mean_info['num comparison'] = stats_group['num comparisons'][0]\n\n mean_group = stats_group['mean']['data']\n mean_keys = mean_group.keys()\n for mkey in mean_keys:\n mean_info[mkey] = np.asarray(mean_group[mkey], dtype='float32')\n var_group = stats_group['var']['data']\n var_keys = var_group.keys()\n for vkey in var_keys:\n var_info[vkey] = np.asarray(var_group[vkey], dtype='float32')\n\n return mean_info, var_info\n\ndef extract_periodogram_information(h5_filepath, periodogram_namespace):\n if not os.path.isfile(h5_filepath):\n raise Exception('Error, could not find h5 file')\n\n f = h5py.File(h5_filepath, 'r')\n if periodogram_namespace not in f:\n raise Exception('periodogram namespace is incorrect or does not exist')\n periodogram_group = f[periodogram_namespace]\n freq_info, P_info = {}, {}\n\n freqs_group = periodogram_group['f']['data']\n freqs_keys = freqs_group.keys()\n for fkey in freqs_keys:\n freq_info[fkey] = np.asarray(freqs_group[fkey], dtype='float32')\n \n Pf_group = periodogram_group['Pf']['data']\n pf_keys = Pf_group.keys()\n for pkey in pf_keys:\n P_info[pkey] = np.asarray(Pf_group[pkey], dtype='float32')\n\n return freq_info, P_info\n \ndef extract_ticc_mrfs(h5_filepath, ticc_namespace):\n if not os.path.isfile(h5_filepath):\n raise Exception('Error, could not find h5 file')\n f = h5py.File(h5_filepath, 'r')\n if ticc_namespace not in f:\n raise Exception('ticc namespace is incorrect or does not exist')\n\n mrf_info = {}\n ticc_group = f[ticc_namespace]\n n_neurons = ticc_group['n_neurons'][0]\n mrf_info['n_neurons'] = n_neurons\n mrf_info['num_clusters'] = ticc_group['num_clusters'][0]\n\n mrf_info['clusters'] = {}\n mrf_data_group = ticc_group['mrf']['data']\n mrf_keys = mrf_data_group.keys()\n for mkey in mrf_keys:\n mrf_info['clusters'][mkey] = np.asarray(mrf_data_group[mkey]).reshape(n_neurons, n_neurons)\n return mrf_info\n\ndef extract_mrf_network_info(h5_filepath, mrf_namespace, cluster_names):\n if not os.path.isfile(h5_filepath): raise Exception('h5 file does not exist')\n f = h5py.File(h5_filepath, 'r')\n if mrf_namespace not in f: raise Exception('namespace not in h5 file')\n\n network_group = f[mrf_namespace]\n edge_data = network_group['edge data']['data']\n node_data = network_group['node data']['data']\n network_info = {name: {} for name in cluster_names}\n \n for name in cluster_names:\n network_info[name]['nedges'] = network_group['%s nedges' % name][0]\n network_info[name]['nnodes'] = network_group['%s nnodes' % name][0]\n\n network_info[name]['nodes'] = np.asarray(node_data[name], dtype='int32')\n network_info[name]['edges'] = np.asarray(edge_data[name]).reshape((network_info[name]['nedges'], 4))\n\n return network_info\n\ndef extract_synch_sliding(h5_filepath, metric_namespace):\n return _extract_metric_sliding(h5_filepath, metric_namespace)\n\ndef extract_TE_sliding(h5_filepath, metric_namespace):\n return _extract_metric_sliding(h5_filepath, metric_namespace)\n\ndef _extract_metric_sliding(h5_filepath, metric_namespace):\n if not os.path.isfile(h5_filepath): raise Exception('h5 file does not exist')\n f = h5py.File(h5_filepath, 'r')\n if metric_namespace not in f: raise Exception('could not find transfer entropy namespace')\n\n metric_group = f[metric_namespace]\n N = metric_group['n_neurons'][0]\n \n metric_data_group = metric_group['sliding window']['data']\n keys = sorted([int(k) for k in metric_data_group.keys()])\n \n return_matrix = np.zeros((N,N,len(keys)))\n for (idx,key) in enumerate(keys):\n return_matrix[:,:, idx] = np.asarray(metric_data_group[str(key)]).reshape((N,N))\n return return_matrix\n\ndef extract_TE_cluster(h5_filepath, metric_namespace, cname):\n if not os.path.isfile(h5_filepath): raise Exception('h5 file does not exist')\n f = h5py.File(h5_filepath, 'r')\n if metric_namespace not in f: raise Exception('could not find transfer entropy namespace')\n \n metric_group = f[metric_namespace]\n N = metric_group['n_neurons'][0]\n \n metric_data_group = metric_group['cluster']['data']\n return np.asarray(metric_data_group[cname]).reshape(N,N)\n\n\ndef extract_TE_permutation_cluster(h5_filepath, metric_namespace, cname):\n if not os.path.isfile(h5_filepath): raise Exception('h5 file does not exist')\n f = h5py.File(h5_filepath, 'r')\n if metric_namespace not in f: raise Exception('could not find transfer entropy namespace')\n \n metric_group = f[metric_namespace]\n N = metric_group['n_neurons'][0]\n \n permutation_data_group = metric_group['cluster permutation']['data']\n mean_matrix = np.asarray(permutation_data_group['%s mean' % cname]).reshape(N,N)\n var_matrix = np.asarray(permutation_data_group['%s var' % cname]).reshape(N,N)\n return (mean_matrix, var_matrix)\n \n \n\n","sub_path":"fish_utils.py","file_name":"fish_utils.py","file_ext":"py","file_size_in_byte":10453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"309291872","text":"def island(i,j):\n if(n == 1 and lis[i][j] == 1):\n return True\n else:\n ies = [i-1,i,i,i+1]\n jes = [j,j-1,j+1,j]\n valid = 0\n water = 0\n for k in range(4):\n if(ies[k]>=0 and ies[k]=0 and jes[k] 0:\n return urls[0]\n else:\n return None\n\n @classmethod\n def parse_dump_file(cls, json_obj):\n configurations = defaultdict(list)\n\n for topic in json_obj:\n for config in json_obj[topic]:\n name = config[0]\n remotes = config[1]\n user_name = config[2]\n user_email = config[3]\n\n config = cls(topic, name, remotes, user_name, user_email)\n configurations[topic].append(config)\n\n return configurations\n\n def __str__(self):\n name = join(self.topic, self.name)\n return name\n\n def __eq__(self, other):\n if type(self) is not type(other):\n return NotImplemented\n\n same_topic = self.topic == other.topic\n same_repo = self.name == other.name\n\n return same_topic and same_repo\n\n def __gt__(self, other):\n if type(self) is not type(other):\n return NotImplemented\n\n gte_topic = self.topic >= other.topic\n gt_repo = self.name > other.name\n\n return gte_topic and gt_repo\n","sub_path":"gitool/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"404417786","text":"import asyncio\n\nfrom aiohttp import web\n\nfrom modules.context import Context\nfrom modules.data import *\nfrom modules.server.handlers import *\n\nModelItems = Dict[Base, ModelItem]\n\nHOST = 'localhost'\nPORT = 8888\n\n\ndef init_handlers(context) -> Tuple[ModelItems, List[DataHandler]]:\n tickets = Tickets()\n ticketsHandler = GroupHandler(model=tickets, item=Ticket,\n suffix='ticket', context=context)\n groups = Groups()\n groupsHandler = GroupHandler(model=groups, item=Group,\n suffix='group', context=context)\n\n users = Users()\n usersHandler = GroupHandler(model=users, item=User,\n suffix='user', context=context)\n\n comments = Comments()\n commentsHandler = GroupHandler(model=comments, item=Comment,\n suffix='comment', context=context)\n\n queues = Queues()\n queuesHandler = GroupHandler(model=queues, item=Queue,\n suffix='queue', context=context)\n\n history = History_model()\n historyHandler = HistoryHandler(model=history, item=History,\n suffix='history', context=context)\n\n model_items = {\n groups: Group,\n users: User,\n queues: Queue,\n comments: Comment,\n tickets: Ticket,\n Images(): Image,\n history: History,\n }\n handlers = [\n ticketsHandler,\n groupsHandler,\n usersHandler,\n commentsHandler,\n queuesHandler,\n historyHandler,\n ]\n\n return model_items, handlers\n\n\ndef main():\n context = Context()\n model_items, handlers = init_handlers(context)\n db = Database(model_items)\n context.set_db(db)\n context.db = db\n\n loop = asyncio.get_event_loop()\n loop.run_until_complete(db.init_db())\n\n app = web.Application()\n for handler in handlers:\n app.add_routes(handler.generate_routes())\n\n app.add_routes([])\n\n web.run_app(app, host=HOST, port=PORT)\n\n\nif __name__ == '__main__':\n # loop = asyncio.get_event_loop()\n # loop.run_until_complete(main())\n main()\n","sub_path":"dtracker/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"166802013","text":"# Chapter 10: Creating Components and Extending Functionality\r\n# Recipe 1: Customizing the ArtProvider\r\n#\r\nimport os\r\nimport wx\r\n\r\n#---- Recipe Code ----#\r\n\r\nclass TangoArtProvider(wx.ArtProvider):\r\n def __init__(self):\r\n super(TangoArtProvider, self).__init__()\r\n\r\n # Attributes\r\n self.bmps = [bmp.replace('.png', '')\r\n for bmp in os.listdir('tango')\r\n if bmp.endswith('.png')]\r\n\r\n def CreateBitmap(self, id,\r\n client=wx.ART_OTHER,\r\n size=wx.DefaultSize):\r\n\r\n # Return NullBitmap on GTK to allow\r\n # the default artprovider to get the\r\n # system theme bitmap.\r\n if wx.Platform == '__WXGTK__':\r\n return wx.NullBitmap\r\n\r\n # Non GTK Platform get custom resource\r\n # when one is available.\r\n bmp = wx.NullBitmap\r\n if client == wx.ART_MENU or size == (16,16):\r\n if id in self.bmps:\r\n path = os.path.join('tango', id+'.png')\r\n bmp = wx.Bitmap(path)\r\n else:\r\n # TODO add support for other bitmap sizes\r\n pass\r\n\r\n return bmp\r\n\r\n#---- End Recipe Code ----#\r\n\r\nclass ArtProviderApp(wx.App):\r\n def OnInit(self):\r\n # Push our custom ArtProvider on to\r\n # the provider stack.\r\n wx.ArtProvider.PushProvider(TangoArtProvider())\r\n self.frame = ArtProviderFrame(None,\r\n title=\"Tango ArtProvider\")\r\n self.frame.Show()\r\n return True\r\n\r\nclass ArtProviderFrame(wx.Frame):\r\n \"\"\"Main application window\"\"\"\r\n def __init__(self, *args, **kwargs):\r\n super(ArtProviderFrame, self).__init__(*args, **kwargs)\r\n\r\n # Attributes\r\n self.panel = ArtProviderPanel(self)\r\n\r\n # Layout\r\n sizer = wx.BoxSizer(wx.VERTICAL)\r\n sizer.Add(self.panel, 1, wx.EXPAND)\r\n self.SetSizer(sizer)\r\n self.SetInitialSize((300, 300))\r\n\r\nclass ArtProviderPanel(wx.Panel):\r\n def __init__(self, parent):\r\n super(ArtProviderPanel, self).__init__(parent)\r\n\r\n # Attributes\r\n # Lookup all the art provider ids\r\n art = [ getattr(wx, x) for x in dir(wx)\r\n if x.startswith('ART_') and \r\n not getattr(wx, x).endswith('_C')]\r\n art.sort()\r\n self.artch = wx.Choice(self, choices=art)\r\n self.bmp = wx.StaticBitmap(self)\r\n\r\n # Setup\r\n self.__DoLayout()\r\n\r\n # Event Handlers\r\n self.Bind(wx.EVT_CHOICE, self.OnChoice)\r\n\r\n def __DoLayout(self):\r\n vsizer = wx.BoxSizer(wx.VERTICAL)\r\n hsizer = wx.BoxSizer(wx.HORIZONTAL)\r\n\r\n vsizer.AddStretchSpacer()\r\n hsizer.AddStretchSpacer()\r\n hsizer.Add(self.bmp, 0, wx.ALL|wx.ALIGN_CENTER_VERTICAL, 10)\r\n hsizer.Add(self.artch, 0, wx.ALL|wx.ALIGN_CENTER_VERTICAL, 10)\r\n hsizer.AddStretchSpacer()\r\n vsizer.Add(hsizer, 0, wx.EXPAND)\r\n vsizer.AddStretchSpacer()\r\n self.SetSizer(vsizer)\r\n\r\n def OnChoice(self, event):\r\n sel = self.artch.GetStringSelection()\r\n bmp = wx.ArtProvider.GetBitmap(sel, wx.ART_MENU)\r\n self.bmp.SetBitmap(bmp)\r\n self.bmp.Refresh()\r\n self.Layout()\r\n\r\nif __name__ == '__main__':\r\n app = ArtProviderApp(False)\r\n app.MainLoop()\r\n","sub_path":"wxPython Test/wxPython 2.8 Application Development Cookbook Source Code/1780_10_Code/01/tangoprovider.py","file_name":"tangoprovider.py","file_ext":"py","file_size_in_byte":3331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"537891658","text":"import json\nimport os\nfrom settings import languages_folder\nimport settings\n\n\ndef get_language():\n with open(os.path.join(languages_folder,\"saved_language\")+\".json\") as file_object:\n data=json.load(file_object)\n LANGUAGE=data['saved']\n return LANGUAGE\n\ndef get_font_from_language():\n # try:\n # # with open(os.path.join(languages_folder,\"saved_language\")+\".json\") as file_object:\n # data=json.load(file_object)\n # LANGUAGE=data['saved']\n # if LANGUAGE==\"eng\":\n # FONT=\n # elif LANGUAGE=='rus':\n # FONT=settings.FONT_FILE_RUS\n ## elif LANGUAGE=='ukr':\n # FONT=settings.FONT_FILE_ENG\n #elif LANGUAGE=='pol':\n # FONT=settings.FONT_FILE_ENG\n return settings.FONT_FILE_ENG\n #xcept:\n # pass\n \ndef choosing_language():\n question=input(\"What language do you prefer?\\n 1)ENGLISH\\n 2)RUSSIAN\\n 3)UKRAINIAN\\n 4)POISH\\n ENTER : \")\n if question.startswith('1') or question.lower()=='eng' or question.lower()=='engish'or question.lower().startswith('eng') :\n LANGUAGE=\"eng\"\n elif question.startswith('2') or question.lower()=='rus' or question.lower()=='russian'or question.lower().startswith('rus'):\n LANGUAGE=\"rus\"\n elif question.startswith('3') or question.lower()=='ukr' or question.lower()=='ukrainian' or question.lower()=='як реве дніпро' or question.lower()=='мова солов'+\"'\"+\"їна\" or question.lower()=='українська'or question.lower().startswith('uk') or question.lower().startswith('ук'):\n LANGUAGE=\"ukr\"\n elif question.startswith('4') or question.lower()=='pol' or question.lower()=='polish' or question.lower().startswith('pol'):\n LANGUAGE=\"pol\"\n\n dict={\"saved\":LANGUAGE}\n\n with open(os.path.join(languages_folder,'saved_language.json'),'w') as file_obj:\n json.dump(dict,file_obj)\n\ndef load_controller():\n while True:\n try:\n\n with open(os.path.join(languages_folder,\"saved_language\")+\".json\") as file_object:\n data=json.load(file_object)\n LANGUAGE=data['saved']\n break\n except:\n choosing_language()\n\n with open(os.path.join(languages_folder,\"controller\")+\".json\") as file_object:\n controller_data=json.load(file_object)\n return controller_data\n\n\ndef language_text(search):\n controller_data=load_controller()\n file=controller_data[search]\n with open(languages_folder+\"\\\\\"+get_language()+\"\\\\\"+file+\".txt\") as file:\n text=file.read()\n return text\n\n\n","sub_path":"Project/language_manager.py","file_name":"language_manager.py","file_ext":"py","file_size_in_byte":2699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"589605020","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/nslocalizer/Helpers/Logger.py\n# Compiled at: 2019-02-23 14:43:18\n# Size of source mod 2**32: 4891 bytes\nimport logging\n\nclass Singleton(type):\n _instances = {}\n\n def __call__(cls, *args, **kwargs):\n if cls not in cls._instances.keys():\n cls._instances[cls] = (super(Singleton, cls).__call__)(*args, **kwargs)\n return cls._instances[cls]\n\n\nRESET_SEQ = '\\x1b[0m'\nBOLD_SEQ = '\\x1b[1m'\nCOLORS = {'BLACK':'\\x1b[1;30m', \n 'RED':'\\x1b[1;31m', \n 'GREEN':'\\x1b[1;32m', \n 'YELLOW':'\\x1b[1;33m', \n 'BLUE':'\\x1b[1;34m', \n 'MAGENTA':'\\x1b[1;35m', \n 'CYAN':'\\x1b[1;36m', \n 'WHITE':'\\x1b[1;37m'}\nLEVELS = {'WARNING':COLORS['YELLOW'], \n 'INFO':COLORS['BLACK'], \n 'DEBUG':COLORS['MAGENTA'], \n 'CRITICAL':COLORS['BLUE'], \n 'ERROR':COLORS['RED']}\n\nclass ColoredFormatter(logging.Formatter):\n\n def __init__(self, msg, use_color=True):\n logging.Formatter.__init__(self, msg)\n self.use_color = use_color\n\n def format(self, record):\n levelname = record.levelname\n if self.use_color:\n if levelname in LEVELS:\n levelname_color = LEVELS[levelname] + levelname + RESET_SEQ\n record.levelname = levelname_color\n return logging.Formatter.format(self, record)\n\n\nclass Logger(object):\n __metaclass__ = Singleton\n _internal_logger = None\n _debug_logging = False\n _use_ansi_codes = False\n\n def __init__(self, *args, **kwargs):\n pass\n\n @staticmethod\n def enableDebugLogger(is_debug_logger=False):\n Logger._debug_logging = is_debug_logger\n\n @staticmethod\n def disableANSI(disable_ansi=False):\n Logger._use_ansi_codes = not disable_ansi\n\n @staticmethod\n def setupLogger():\n Logger._internal_logger = logging.getLogger('com.pewpewthespells.py.logging_helper')\n level = logging.DEBUG if Logger._debug_logging else logging.INFO\n Logger._internal_logger.setLevel(level)\n handler = logging.StreamHandler()\n handler.setLevel(level)\n formatter = None\n if Logger._debug_logging is True:\n formatter = ColoredFormatter('[%(levelname)s][%(filename)s:%(lineno)s]: %(message)s', Logger._use_ansi_codes)\n else:\n formatter = ColoredFormatter('[%(levelname)s]: %(message)s', Logger._use_ansi_codes)\n handler.setFormatter(formatter)\n Logger._internal_logger.addHandler(handler)\n\n @staticmethod\n def isVerbose(verbose_logging=False):\n if Logger._internal_logger is None:\n Logger.setupLogger()\n if not verbose_logging:\n Logger._internal_logger.setLevel(logging.WARNING)\n\n @staticmethod\n def isSilent(should_quiet=False):\n if Logger._internal_logger is None:\n Logger.setupLogger()\n if should_quiet:\n logging_filter = logging.Filter(name='com.pewpewthespells.py.logging_helper.shut_up')\n Logger._internal_logger.addFilter(logging_filter)\n\n @staticmethod\n def write():\n if Logger._internal_logger is None:\n Logger.setupLogger()\n return Logger._internal_logger","sub_path":"pycfiles/nslocalizer-1.0.2-py3.7/Logger.cpython-37.py","file_name":"Logger.cpython-37.py","file_ext":"py","file_size_in_byte":3272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"632369219","text":"class Solution:\n def isValid(self, s):\n \"\"\"\n :type s: str\n :rtype: bool\n \"\"\"\n stack = []\n for c in s:\n stack.append(c)\n if len(stack)== 1 and (c == ')' or c == ']' or c == '}'):\n return False\n if c == ')' and stack[-2] == '(':\n stack.pop()\n stack.pop()\n elif c == ']' and stack[-2] == '[':\n stack.pop()\n stack.pop()\n elif c == '}' and stack[-2] == '{':\n stack.pop()\n stack.pop()\n if len(stack)>0:\n return False\n return True\n\n\nif __name__ == '__main__':\n sol = Solution()\n\n print(sol.isValid(\"()[]{}\"))\n print(sol.isValid(\"([)]\"))","sub_path":"lc_1-100/lc_20.py","file_name":"lc_20.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"654247456","text":"# https://leetcode.com/problems/combination-sum-ii/discuss/16944/Beating-98-Python-solution-using-recursion-with-comments\n\nclass Solution:\n \"\"\"\n @param num: Given the candidate numbers\n @param target: Given the target number\n @return: All the combinations that sum to target\n \"\"\"\n def combinationSum2(self, num, target):\n # write your code here\n if not num:\n return []\n \n if not target:\n return [[]]\n \n num.sort()\n res = []\n self.dfs(num, target, [], 0, res)\n return res\n \n def dfs(self, num, target, combination, start, res):\n if target == 0:\n res.append(combination)\n return\n \n for i in range(start, len(num)):\n if i > start and num[i] == num[i - 1]:\n continue\n \n if num[i] > target:\n break\n \n self.dfs(num, target - num[i], combination + [num[i]], i + 1, res)","sub_path":"LintCode/ladder 06 DFS/旧/153. Combination Sum II/.ipynb_checkpoints/solution-checkpoint.py","file_name":"solution-checkpoint.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"308451414","text":"#! /usr/bin/python3\n# -*- encoding: utf-8 -*-\n\nimport time\n\nfrom game import event\nfrom game.event import DrawCard\nfrom game.card import Minion\nfrom game.handler import createHandler\nfrom game.utils.consoleColor import colorMessage\n\nfrom game.engine import Engine\nfrom game.utils.dataUtils import absPath, getCards\n\n\n__author__ = 'fyabc'\n\n\ndef testCard():\n pass\n\n\ndef testEvent():\n pass\n\n\ndef testHandler():\n def process(self, event_):\n print('I am an echo handler %s' % self.IDinEngine)\n print(event_)\n\n EH = createHandler('EchoHandler', DrawCard, process)\n eh = EH()\n\n print('Listen event type: %d' % eh.eventType)\n eh.process('This is a event')\n\n\ndef testEngine():\n e = Engine(echoLevel=Engine.LDebug, playerData=None)\n\n # handler.TurnBeginHandler(e)\n # handler.TurnBeginHandler(e)\n # handler.AddCardHandler(e)\n\n cards = getCards(absPath(__file__, 'data', 'card'), 'basic')\n\n playerData = [\n {\n 'health': 30,\n 'clazz': 'mage',\n 'deck': [\n Minion(cards[0], e, 0),\n Minion(cards[1], e, 0),\n Minion(cards[2], e, 0),\n ],\n },\n {\n 'health': 30,\n 'clazz': 'paladin',\n 'deck': [\n Minion(cards[3], e, 1),\n Minion(cards[4], e, 1),\n ],\n }\n ]\n\n e.createNewGame(playerData)\n\n eventList = [\n event.GameBegin(e),\n event.TurnEnd(e),\n event.TurnEnd(e),\n event.DrawCard(e),\n event.TurnEnd(e),\n event.DrawCard(e),\n event.DrawCard(e),\n event.GameEnd(e),\n ]\n\n timeBefore = time.time()\n for event_ in eventList:\n colorMessage('\\n### === New Operation %s === ###' % event_, foreground=Engine.CInfo)\n e.runOneStep(event_)\n timeAfter = time.time()\n\n colorMessage('\\nTime passed: %.4fs' % (timeAfter - timeBefore), foreground=Engine.CError)\n\n\ndef test():\n testHandler()\n # testEngine()\n\n\nif __name__ == '__main__':\n test()\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"479039452","text":"import requests\nimport pytest\nimport unittest\nfrom client.client import Client\nfrom constants.environment import EnvConfig\nimport numpy as np\nimport time\n\nDEFAULT_CONFIG = EnvConfig(\n quote_asset='BTC',\n commission=0.075,\n feature_num=3,\n asset_num=50,\n window_size=90,\n selection_period=90,\n selection_method='s2vol',\n init_balance=1,\n env_type='sandbox',\n step_rate=3\n)\n\nclient = Client(\"http://localhost:5000\")\n\ndef test_performance():\n start = time.time()\n for x in range(1000):\n state = client.get_state(\n asset_number=DEFAULT_CONFIG.asset_num,\n window_size=DEFAULT_CONFIG.window_size,\n feature_number=DEFAULT_CONFIG.feature_num,\n selection_period=DEFAULT_CONFIG.selection_period,\n selection_method=DEFAULT_CONFIG.selection_method\n )\n print(x)\n elapsed_time_lc=(time.time()-start)\n print(elapsed_time_lc)\n assert elapsed_time_lc","sub_path":"environment/test/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"196516772","text":"\"\"\"PyNE nuclear data tests\"\"\"\nimport os\nimport math\n\nimport nose \nfrom nose.tools import assert_equal, assert_not_equal, assert_raises, raises, assert_in\n\nimport pyne\nfrom pyne import data\nimport numpy as np\n\n# These tests require nuc_data\nif not os.path.isfile(pyne.nuc_data):\n raise RuntimeError(\"Tests require nuc_data.h5. Please run nuc_data_make.\")\n\ndef test_atomic_mass():\n o16 = [15.99491461957, 16.0]\n u235 = [235.043931368, 235.0]\n am242m = [242.059550625, 242.0]\n\n # zzaam form\n assert_in(data.atomic_mass(80160), o16)\n assert_in(data.atomic_mass(922350), u235)\n assert_in(data.atomic_mass(952421), am242m)\n\n\ndef test_b_coherent():\n assert_equal(data.b_coherent('H1'), -3.7406E-13 + 0j)\n assert_equal(data.b_coherent(491150), 4.01E-13 - 5.62E-15j)\n\n\ndef test_b_incoherent():\n assert_equal(data.b_incoherent('PD105'), -2.6E-13 + 0j)\n assert_equal(data.b_incoherent(621490), 3.14E-12 - 1.03E-12j)\n\n\ndef test_b():\n bc = data.b_coherent(621490)\n bi = data.b_incoherent('SM149')\n assert_equal(data.b('SM149'), math.sqrt(abs(bc)**2 + abs(bi)**2))\n\n\ndef test_half_life():\n assert_equal(data.half_life('H1'), np.inf)\n assert_equal(data.half_life(922351), 1560.0) \n\n\ndef test_decay_const():\n assert_equal(data.decay_const('H1'), 0.0)\n assert_equal(data.decay_const(922351), np.log(2.0)/1560.0) \n\n\nif __name__ == \"__main__\":\n nose.main()\n\n","sub_path":"pyne/tests/test_data.py","file_name":"test_data.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"177629862","text":"from django.conf.urls import url\nfrom .views import *\n\napp_name = 'jobs'\n\nurlpatterns = [\n url(r'^addjobs/$', addjobs_view, name='addjobs'),\n url(r'^ilandetay/(?P[0-9]+)/$', isİlaniDetay, name='ilandetay'),\n url(r'^ilanguncelle/(?P[0-9]+)/$', isİlaniGuncelle, name='ilanguncelle'),\n url(r'^ilansil/(?P[0-9]+)/$', isİlaniSil, name='ilansil'),\n url(r'^ilanlarim/$', ilanlarim, name='ilanlarim'),\n url(r'^ilanlar/$', isİlaniGoruntule, name='ilanlar'),\n url(r'^stajekle/$', stajIlaniEkle, name='stajekle'),\n url(r'^stajilanlari/$', stajİlaniGoruntule, name='stajilanlari'),\n url(r'^stajilanidetay/(?P[0-9]+)/$', stajİlaniDetay, name='stajilanidetay'),\n url(r'^stajilaniguncelle/(?P[0-9]+)/$', stajİlaniGuncelle, name='stajilaniguncelle'),\n url(r'^stajilanisil/(?P[0-9]+)/$', stajİlaniSil, name='stajilanisil'),\n]","sub_path":"jobs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"559685295","text":"import unittest\r\nimport requests\r\nimport json\r\nimport datetime\r\nimport time\r\n#导入公用参数readConfig.py\r\nfrom Common.readConfig import *\r\n\r\nclass TestMethod(unittest.TestCase): # 定义一个类，继承自unittest.TestCase\r\n '''安装正常流程测试'''\r\n def test_a(self):\r\n '''录单'''\r\n #录单接口\r\n url1 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order/FhbOrder/saveOrder\"\r\n\r\n i = datetime.datetime.now()\r\n print(\"收件人姓名：yi装测试\" + str(i.month) + str(i.day))\r\n\r\n data1 = {\r\n \"businessNo\": \"BSTE02\",\r\n \"serviceNo\": \"FHB03\",\r\n \"orderWay\": 1,\r\n \"wokerUserName\": \"\",\r\n \"wokerPhone\": \"\",\r\n \"wokerPrice\": \"\",\r\n \"checked\": \"\",\r\n \"verfiyType\": \"\",\r\n \"goods\": [\r\n {\r\n \"num\":1,\r\n \"picture\":\"5b961f335b1fac00019758e4\",\r\n \"memo\":\"zzz\",\r\n \"bigClassNo\":\"FHB01\",\r\n \"middleClassNo\":\"FHB01002\",\r\n \"pictureType\":\"2\",\r\n \"pictureName\":\"黑色小角几\"\r\n }\r\n ],\r\n \"isElevator\": \"0\",\r\n \"predictServiceDate\": \"\",\r\n \"predictDevliveryDate\": \"\",\r\n \"memo\": \"\",\r\n \"isArriva\": 1,\r\n \"boolCollection\": \"0\",\r\n \"collectionMoney\": \"\",\r\n \"collectionMemo\": \"\",\r\n \"allVolume\": \"2\",\r\n \"allWeight\": \"12\",\r\n \"allPackages\": \"3\",\r\n \"allCargoPrice\": \"1212\",\r\n \"consigneeName\": \"yi装测试\" + str(i.month) + str(i.day),\r\n \"consigneePhone\": \"15023621702\",\r\n \"consigneeAddress\": \"武侯大道\",\r\n \"floor\": \"2\",\r\n \"deliveryName\": \"提货联系:\",\r\n \"deliveryPhone\": \"15023621702\",\r\n \"provinceNo\": \"510000\",\r\n \"province\": \"四川省\",\r\n \"cityNo\": \"510100\",\r\n \"city\": \"成都市\",\r\n \"districtNo\": \"510107\",\r\n \"district\": \"武侯区\",\r\n \"deliveryProvinceNo\": \"\",\r\n \"deliveryProvince\": \"\",\r\n \"deliveryCityNo\": \"\",\r\n \"deliveryCity\": \"\",\r\n \"deliveryDistrictNo\": \"\",\r\n \"deliveryDistrict\": \"\",\r\n \"verifyOrderNo\": \"\"\r\n }\r\n request1 = requests.post( url1, data = json.dumps(data1) ,headers = global_var.headers1)\r\n print(\"录单：\" + request1.text)\r\n time.sleep(3)\r\n self.assertIn(global_var.arg1, request1.text, msg='测试fail')\r\n\r\n def test_b(self):\r\n '''连接数据库查询订单'''\r\n global i\r\n i = datetime.datetime.now()\r\n consignee_name1 = \"yi装测试\" + str(i.month) + str(i.day)\r\n\r\n # 使用cursor()方法获取操作游标\r\n cursor = global_var.db.cursor()\r\n # 通过订单的收件人姓名查询出订单id\r\n sql1 = \"select id,order_no from fhb_order where id in (select fhb_order_id from fhb_order_consignee_info where consigne_name = '\" + consignee_name1 + \"') ORDER BY foundtime DESC\"\r\n # 执行SQL语句\r\n cursor.execute(sql1)\r\n # 获取所有记录列表\r\n results = cursor.fetchall()\r\n # print(results[0])\r\n # 有多个的情况，取第一个订单的id\r\n global orderid, orderno\r\n orderid = results[0]['id']\r\n orderno = results[0]['order_no']\r\n print(\"订单id:\" + orderid)\r\n print(\"订单编号:\" + orderno)\r\n\r\n def test_c(self):\r\n '''师傅报价'''\r\n url2 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order/bidding/quoted-price\"\r\n data2 = {\r\n \"memo\": \"\",\r\n \"money\": \"40\",\r\n \"orderId\": orderid\r\n }\r\n request2 = requests.request(\"POST\", url=url2, data=json.dumps(data2), headers=global_var.headers2)\r\n print(\"师傅报价：\" + request2.text)\r\n time.sleep(3)\r\n self.assertIn(global_var.arg1, request2.text, msg='测试fail')\r\n\r\n def test_d(self):\r\n '''web端报价中选择师傅'''\r\n global_var.db.connect()\r\n sql2 = \"select id from fhb_order_bidding_log where fhb_order_id = '\" + orderid + \"'\"\r\n # 使用cursor()方法获取操作游标\r\n cursor2 = global_var.db.cursor()\r\n # 执行SQL语句\r\n cursor2.execute(sql2)\r\n # 获取所有记录列表\r\n results2 = cursor2.fetchall()\r\n # 有多个的情况，取第一个订单的id\r\n biddinglogid = results2[0]['id']\r\n print(\"竞价记录id:\" + biddinglogid)\r\n url3 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order/FhbOrder/choice-pay?orderId=\" + orderid + \"&biddingLogId=\" + biddinglogid + \"\"\r\n request3 = requests.get(url3, headers=global_var.headers1)\r\n print(\"选择师傅get请求的url:\" + url3)\r\n print(\"选择师傅：\" + request3.text)\r\n self.assertIn(global_var.arg1, request3.text, msg='测试fail')\r\n\r\n def test_e(self):\r\n '''数据库更新竞价金额为0.01'''\r\n # 数据库更新竞价金额为0.01\r\n sql3 = \"UPDATE fhb_order_bidding_log set money = '0.01' where fhb_order_id = '\" + orderid + \"'\"\r\n print(sql3)\r\n cursor3 = global_var.db.cursor()\r\n # 执行SQL语句\r\n cursor3.execute(sql3)\r\n # MySQL的默认存储引擎就是InnoDB， 所以对数据库数据的操作会在事先分配的缓存中进行， 只有在commit之后， 数据库的数据才会改变\r\n global_var.db.commit()\r\n\r\n def test_f(self):\r\n '''钱包余额支付中标费用'''\r\n url4 = \"http://\" + global_var.api_host + \"/ms-fahuobao-user/wallet/balance-pay\"\r\n data4 = {\r\n \"objectList\": [orderid],\r\n \"money\": 0.01,\r\n \"password\": \"123456\"\r\n }\r\n request4 = requests.request(\"POST\", url=url4, data=json.dumps(data4), headers=global_var.headers1)\r\n print(\"钱包余额支付中标费用：\" + request4.text)\r\n time.sleep(6)\r\n self.assertIn(global_var.arg1, request4.text, msg='测试fail')\r\n\r\n def test_g(self):\r\n '''居家小二操作预约'''\r\n global_var.db1.connect()\r\n sql5 = \"select id from order_data where order_no = '\" + orderno + \"'\"\r\n print(sql5)\r\n # 使用cursor()方法获取操作游标\r\n cursor5 = global_var.db1.cursor()\r\n # 执行SQL语句\r\n cursor5.execute(sql5)\r\n global_var.db1.commit()\r\n # 获取所有记录列表\r\n results5 = cursor5.fetchall()\r\n # 有多个的情况，取第一个订单的id\r\n global xrid\r\n xrid = results5[0]['id']\r\n print(\"通过fhb订单号查询居家小二订单id:\" + xrid)\r\n url5 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order-data/appOrder/appointappoint-distributionOne-choose\"\r\n data5 = {\r\n \"branchUserId\": \"\",\r\n \"cause\": \"\",\r\n \"codeYT\": \"night\",\r\n \"ids\": [xrid],\r\n \"timeYT\": str(i.year) + \"-\" + str(i.month) + \"-\" + str(i.day)\r\n }\r\n request5 = requests.request(\"POST\", url=url5, data=json.dumps(data5), headers=global_var.headers2)\r\n print(\"预约：\" + request5.text)\r\n time.sleep(3)\r\n self.assertIn(global_var.arg1, request5.text, msg='测试fail')\r\n\r\n def test_h(self):\r\n '''居家小二操作上门'''\r\n global_var.db1.connect()\r\n sql6 = \"select id from assign_worker where order_id = '\" + xrid + \"'\"\r\n print(sql6)\r\n cursor6 = global_var.db1.cursor()\r\n cursor6.execute(sql6)\r\n # 获取所有记录列表\r\n results6 = cursor6.fetchall()\r\n # print(results6[0])\r\n global assignid\r\n assignid = results6[0][\"id\"]\r\n print(\"assigned:\" + assignid)\r\n url7 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order-data/appOrder/houseCall?assignId=\" + assignid + \"&orderId=\" + assignid + \"\"\r\n request7 = requests.request(\"POST\", url=url7, headers=global_var.headers2)\r\n print(\"上门：\" + request7.text)\r\n time.sleep(3)\r\n self.assertIn(global_var.arg1, request7.text, msg='测试fail')\r\n\r\n def test_i(self):\r\n '''居家小二操作签收'''\r\n global_var.db.connect()\r\n sql8 = \"select service_code from fhb_order where order_no = '\" + orderno + \"'\"\r\n cursor8 = global_var.db.cursor()\r\n cursor8.execute(sql8)\r\n # 获取所有记录列表\r\n results8 = cursor8.fetchall()\r\n # print(results8[0])\r\n serviceCode = results8[0][\"service_code\"]\r\n print(\"serviceCode:\" + serviceCode)\r\n\r\n url8 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order-data/appOrder/appOrderSign\"\r\n data8 = {\r\n \"assignId\": assignid,\r\n \"imgId\": [\"5b581a07d423d400017bf0d2\"],\r\n \"jdVerificationCode\": \"\",\r\n \"qmImg\": \"5b581a00d423d400017bf0d0\",\r\n \"serviceCode\": serviceCode,\r\n \"serviceTypeCode\": \"CZSETE01\"\r\n }\r\n request8 = requests.request(\"POST\", url=url8, data=json.dumps(data8), headers=global_var.headers2)\r\n print(\"签收：\" + request8.text)\r\n time.sleep(3)\r\n self.assertIn(global_var.arg1, request8.text, msg='测试fail')\r\n\r\n def test_j(self):\r\n '''发货宝确认评价'''\r\n url9 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order/FhbOrder/evaluation\"\r\n data9 = {\r\n \"fhbOrderId\": orderid,\r\n \"stars\": 5,\r\n \"pictures\": \"5b581cfbd423d400017bf0d4\",\r\n \"memo\": \"评价说明\",\r\n \"tips\": \"做事认真负责,技术超好,服务守时\"\r\n }\r\n request9 = requests.request(\"POST\", url=url9, data=json.dumps(data9), headers=global_var.headers1)\r\n print(\"确认评价：\" + request9.text)\r\n time.sleep(4)\r\n self.assertIn(global_var.arg1, request9.text, msg='测试fail')\r\n\r\n def test_k(self):\r\n '''运营管理进行订单结算'''\r\n url10 = \"http://\" + global_var.api_host + \"/ms-fahuobao-order-data/order-wallet/clearing-confirm\"\r\n data10 = xrid\r\n request10 = requests.request(\"POST\", url=url10, data=data10, headers=global_var.headers3)\r\n print(\"订单结算：\" + request10.text)\r\n self.assertIn(global_var.arg1, request10.text, msg='测试fail')\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()","sub_path":"SaaSFlowTest/NewFlowTestCase/testZhuangSaasNormalFlow.py","file_name":"testZhuangSaasNormalFlow.py","file_ext":"py","file_size_in_byte":10702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"425873104","text":"#!/usr/bin/env python\n\nimport json\nimport logging\nimport os\n\nfrom fuzzywuzzy import fuzz as fw_fuzz\nfrom textblob import TextBlob\n\nfrom lib.utils.systemtools import run_command\nfrom lib.utils.moduletools import ModuleIndexer\n\n\nclass FileIndexer(ModuleIndexer):\n\n files = []\n\n def __init__(self, checkoutdir=None, cmap=None):\n\n if not os.path.isfile(cmap):\n import lib.triagers.ansible as at\n basedir = os.path.dirname(at.__file__)\n basedir = os.path.dirname(basedir)\n basedir = os.path.dirname(basedir)\n cmap = os.path.join(basedir, cmap)\n\n self.checkoutdir = checkoutdir\n self.CMAP = {}\n if cmap:\n with open(cmap, 'rb') as f:\n self.CMAP = json.load(f)\n\n self.get_files()\n self.match_cache = {}\n\n def get_files(self):\n # manage the checkout\n if not os.path.isdir(self.checkoutdir):\n self.create_checkout()\n else:\n self.update_checkout()\n\n cmd = 'find %s' % self.checkoutdir\n (rc, so, se) = run_command(cmd)\n files = so.split('\\n')\n files = [x.strip() for x in files if x.strip()]\n files = [x.replace(self.checkoutdir + '/', '') for x in files]\n files = [x for x in files if not x.startswith('.git')]\n self.files = files\n\n def get_component_labels(self, valid_labels, files):\n '''Matches a filepath to the relevant c: labels'''\n labels = [x for x in valid_labels if x.startswith('c:')]\n\n clabels = []\n for cl in labels:\n l = cl.replace('c:', '', 1)\n al = os.path.join('lib/ansible', l)\n if al.endswith('/'):\n al = al.rstrip('/')\n for f in files:\n if not f:\n continue\n if f.startswith(l) or f.startswith(al):\n clabels.append(cl)\n\n # use the more specific labels\n clabels = sorted(set(clabels))\n tmp_clabels = [x for x in clabels]\n for cl in clabels:\n for x in tmp_clabels:\n if cl != x and x.startswith(cl):\n if cl in tmp_clabels:\n tmp_clabels.remove(cl)\n if tmp_clabels != clabels:\n clabels = [x for x in tmp_clabels]\n clabels = sorted(set(clabels))\n\n return clabels\n\n def _string_to_cmap_key(self, text):\n text = text.lower()\n matches = []\n if text.endswith('.'):\n text = text.rstrip('.')\n if text in self.CMAP:\n matches += self.CMAP[text]\n return matches\n elif (text + 's') in self.CMAP:\n matches += self.CMAP[text + 's']\n return matches\n elif text.rstrip('s') in self.CMAP:\n matches += self.CMAP[text.rstrip('s')]\n return matches\n return matches\n\n def find_component_match(self, title, body, template_data):\n '''Make a list of matching files for arbitrary text in an issue'''\n\n # DistributionNotFound: The 'jinja2<2.9' distribution was not found and\n # is required by ansible\n # File\n # \"/usr/lib/python2.7/site-packages/ansible/plugins/callback/foreman.py\",\n # line 30, in \n\n STOPWORDS = ['ansible', 'core', 'plugin']\n STOPCHARS = ['\"', \"'\", '(', ')', '?', '*', '`', ',']\n matches = []\n\n if 'Traceback (most recent call last)' in body:\n lines = body.split('\\n')\n for line in lines:\n line = line.strip()\n if line.startswith('DistributionNotFound'):\n matches = ['setup.py']\n break\n elif line.startswith('File'):\n fn = line.split()[1]\n for SC in STOPCHARS:\n fn = fn.replace(SC, '')\n if 'ansible_module_' in fn:\n fn = os.path.basename(fn)\n fn = fn.replace('ansible_module_', '')\n matches = [fn]\n elif 'cli/playbook.py' in fn:\n fn = 'lib/ansible/cli/playbook.py'\n elif 'module_utils' in fn:\n idx = fn.find('module_utils/')\n fn = 'lib/ansible/' + fn[idx:]\n elif 'ansible/' in fn:\n idx = fn.find('ansible/')\n fn1 = fn[idx:]\n\n if 'bin/' in fn1:\n if not fn1.startswith('bin'):\n\n idx = fn1.find('bin/')\n fn1 = fn1[idx:]\n\n if fn1.endswith('.py'):\n fn1 = fn1.rstrip('.py')\n\n elif 'cli/' in fn1:\n idx = fn1.find('cli/')\n fn1 = fn1[idx:]\n fn1 = 'lib/ansible/' + fn1\n\n elif 'lib' not in fn1:\n fn1 = 'lib/' + fn1\n\n if fn1 not in self.files:\n #import epdb; epdb.st()\n pass\n if matches:\n return matches\n\n craws = template_data.get('component_raw')\n if craws is None:\n return matches\n\n # compare to component mapping\n matches = self._string_to_cmap_key(craws)\n if matches:\n return matches\n\n # do not re-process the same strings over and over again\n if craws.lower() in self.match_cache:\n return self.match_cache[craws.lower()]\n\n # make ngrams from largest to smallest and recheck\n blob = TextBlob(craws.lower())\n wordcount = len(blob.tokens) + 1\n\n for ng_size in reversed(xrange(2,wordcount)):\n ngrams = [' '.join(x) for x in blob.ngrams(ng_size)]\n for ng in ngrams:\n\n matches = self._string_to_cmap_key(ng)\n if matches:\n self.match_cache[craws.lower()] = matches\n return matches\n\n # https://pypi.python.org/pypi/fuzzywuzzy\n matches = []\n for cr in craws.lower().split('\\n'):\n ratios = []\n for k in self.CMAP.keys():\n ratio = fw_fuzz.ratio(cr, k)\n ratios.append((ratio, k))\n ratios = sorted(ratios, key=lambda tup: tup[0])\n if ratios[-1][0] >= 90:\n cnames = self.CMAP[ratios[-1][1]]\n matches += cnames\n if matches:\n self.match_cache[craws.lower()] = matches\n return matches\n\n # try to match to repo files\n if craws:\n clines = craws.split('\\n')\n for craw in clines:\n cparts = craw.replace('-', ' ')\n cparts = cparts.split()\n\n for idx,x in enumerate(cparts):\n for SC in STOPCHARS:\n if SC in x:\n x = x.replace(SC, '')\n for SW in STOPWORDS:\n if x == SW:\n x = ''\n if x and '/' not in x:\n x = '/' + x\n cparts[idx] = x\n\n cparts = [x.strip() for x in cparts if x.strip()]\n\n for x in cparts:\n for f in self.files:\n if '/modules/' in f:\n continue\n if 'test/' in f and 'test' not in craw:\n continue\n if 'galaxy' in f and 'galaxy' not in body:\n continue\n if 'dynamic inv' in body.lower() and 'contrib' not in f:\n continue\n if 'inventory' in f and 'inventory' not in body.lower():\n continue\n if 'contrib' in f and 'inventory' not in body.lower():\n continue\n\n try:\n f.endswith(x)\n except UnicodeDecodeError:\n continue\n\n fname = os.path.basename(f).split('.')[0]\n\n if f.endswith(x):\n if fname.lower() in body.lower():\n matches.append(f)\n break\n if f.endswith(x + '.py'):\n if fname.lower() in body.lower():\n matches.append(f)\n break\n if f.endswith(x + '.ps1'):\n if fname.lower() in body.lower():\n matches.append(f)\n break\n if os.path.dirname(f).endswith(x):\n if fname.lower() in body.lower():\n matches.append(f)\n break\n\n logging.info('%s --> %s' % (craws, sorted(set(matches))))\n self.match_cache[craws.lower()] = matches\n return matches\n","sub_path":"lib/utils/file_tools.py","file_name":"file_tools.py","file_ext":"py","file_size_in_byte":9269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"289249136","text":"# script para geração da métrica JWCOSR - Jointly Developers Weighted Commit to Share Repositories\n\n# será gerado um arquivo contendo os valores da métrica por par de usuários\n# adicionalmente, será gerado um segundo arquivo com todas as opções de cálculo disponíveis\n# o arquivo gerado para a métrica SR é utilizado no cálculo da NL\n\n# cabeçalho do arquivo gerado (NL.csv):\n# 0 - programming_language_id\n# 1 - developer_id_1\n# 2 - developer_id_2\n# 3 - NL (NL_mod)\n\n# cabeçalho do arquivo gerado (NL_all_options.csv):\n# 0 - programming_language_id\n# 1 - developer_id_1\n# 2 - developer_id_2\n# 3 - NL_add\n# 4 - NL_sum\n# 5 - NL_dif\n# 6 - NL_mods\n\nimport sys, csv, operator, collections\n\n# cria dict com os valores de SR para cada par de usuários da rede\nSR_metric = {}\n\nprint(\"Reading SR.csv...\")\nwith open('../Files/SR.csv', 'r') as r:\n\tSR_file = csv.reader(r, delimiter=',')\n\n\tnext(SR_file) # cabeçalho\n\t# programming_language_id, developer_id_1, developer_id_2, SR\n\n\tfor row in SR_file:\n\t\tprog_lang_id = int(row[0])\n\t\tdev1 = int(row[1])\n\t\tdev2 = int(row[2])\n\t\tSR = int(row[3])\n\n\t\tSR_metric[prog_lang_id, dev1, dev2] = SR\nr.close()\nprint(\"Finished read SR.csv\")\n\n# cria dict com os repositórios da rede com sua linguagem e suas quantidades de linhas adicionadas e deletadas\nrep_dict_language = {}\n\nprint(\"Reading repository.csv...\")\nwith open('../DataSet/repository.csv', 'r') as r:\n\trepositories = csv.reader(r, delimiter=',')\n\n\tnext(repositories) # cabeçalho\n\t# repository_id,name,description,programming_language_id,url,create_date,end_date,duration_days,number_add_lines,number_del_lines,number_commits,number_commiters\n\n\tfor row in repositories:\n\t\trep_id = int(row[0])\n\t\tprog_lang_id = int(row[3])\n\t\tadd_lines_rep = int(row[8])\n\t\tdel_lines_rep = int(row[9])\n\n\t\t# inclui no dict cada repositório, o id da sua linguagem e as quantidades de linhas para a métrica\n\t\tsum_lines_rep = add_lines_rep + del_lines_rep\n\t\tdif_lines_rep = max(add_lines_rep - del_lines_rep, 0)\n\t\tmod_lines_rep = abs(add_lines_rep - del_lines_rep)\n\t\t\n\t\trep_dict_language[rep_id] = [prog_lang_id, add_lines_rep, sum_lines_rep, dif_lines_rep, mod_lines_rep]\nr.close()\nprint(\"Finished read repository.csv\")\n\n# lê informações da rede realizando os cálculos para a métrica\nNL_metric = {}\n\nprint(\"Reading developers_social_network.csv...\")\nwith open('../DataSet/developers_social_network.csv', 'r') as r:\n\tdata = csv.reader(r, delimiter=',')\n\n\tnext(data) # cabeçalho\n\t# repository_id,developer_id_1,developer_id_2,begin_contribution_date,end_contribution_date,contribution_days,number_add_lines,number_del_lines,number_commits\n\n\tfor row in data:\n\t\trep_id = int(row[0])\n\n\t\tdev_1_id = int(row[1])\n\t\tdev_2_id = int(row[2])\n\t\tadd_lines = int(row[6])\n\t\tdel_lines = int(row[7])\n\t\t\n\t\tsum_lines = add_lines + del_lines\n\t\tdif_lines = max(add_lines - del_lines, 0)\n\t\tmod_lines = abs(add_lines - del_lines)\n\n\t\tprog_lang_id = rep_dict_language[rep_id][0]\n\n\t\t# recalcula utilizando a divisão pelas quantidades do repositório\n\t\tadd_lines = 0 if rep_dict_language[rep_id][1] == 0 else (add_lines / rep_dict_language[rep_id][1])\n\t\tsum_lines = 0 if rep_dict_language[rep_id][2] == 0 else (sum_lines / rep_dict_language[rep_id][2])\n\t\tdif_lines = 0 if rep_dict_language[rep_id][3] == 0 else (dif_lines / rep_dict_language[rep_id][3])\n\t\tmod_lines = 0 if rep_dict_language[rep_id][4] == 0 else (mod_lines / rep_dict_language[rep_id][4])\n\n\t\t# normaliza valores menores que zero\n\t\tadd_lines = 0 if add_lines < 0 else add_lines\n\t\tsum_lines = 0 if sum_lines < 0 else sum_lines\n\t\tdif_lines = 0 if dif_lines < 0 else dif_lines\n\t\tmod_lines = 0 if mod_lines < 0 else mod_lines\n\t\t\t\n\t\t# cria um dict com cada par de desenvolvedores e o valor dos cálculos para eles\n\t\t# key = (programming_language_id, dev1, dev2) values = add_lines, sum_lines, dif_lines, mod_lines\n\t\tif (prog_lang_id, dev_1_id, dev_2_id) in NL_metric:\n\t\t\ttotal_add_lines = NL_metric[prog_lang_id, dev_1_id, dev_2_id][0] + add_lines\n\t\t\ttotal_sum_lines = NL_metric[prog_lang_id, dev_1_id, dev_2_id][1] + sum_lines\n\t\t\ttotal_dif_lines = NL_metric[prog_lang_id, dev_1_id, dev_2_id][2] + dif_lines\n\t\t\ttotal_mod_lines = NL_metric[prog_lang_id, dev_1_id, dev_2_id][3] + mod_lines\n\n\t\t\tNL_metric[prog_lang_id, dev_1_id, dev_2_id] = [total_add_lines, total_sum_lines, total_dif_lines, total_mod_lines]\n\t\telse:\n\t\t\tNL_metric[prog_lang_id, dev_1_id, dev_2_id] = [add_lines, sum_lines, dif_lines, mod_lines]\nr.close()\n\n# ordena dict da métrica\nprint(\"Sorting NL metric dict...\")\nNL = collections.OrderedDict(sorted(NL_metric.items()))\n\n# escreve todos os dados da métrica no arquivo final com métrica única\nprint(\"Writing NL.csv...\")\nwith open('../Files/NL.csv', 'w') as w:\n\tmetric_file = csv.writer(w, delimiter=',')\n\n\t# escreve cabeçalho\n\tmetric_file.writerow([\"programming_language_id\", \"developer_id_1\", \"developer_id_2\", \"NL\"])\n\n\t# a soma dos valores da métrica são divididos pelo número de repositórios compartilhados entre os desenvolvedores (SR)\n\tfor row in NL:\n\t\tmetric_file.writerow([row[0], row[1], row[2], NL[row][3]/SR_metric[row]])\nw.close()\n\n# escreve todos os dados da métrica no arquivo final com todas as opções\nprint(\"Writing NL_all_options.csv...\")\nwith open('../Files/NL_all_options.csv', 'w') as w:\n\tmetric_file = csv.writer(w, delimiter=',')\n\n\t# escreve cabeçalho\n\tmetric_file.writerow([\"programming_language_id\", \"developer_id_1\", \"developer_id_2\", \"NL_add\", \"NL_sum\", \"NL_dif\", \"NL_mod\"])\n\n\t# a soma dos valores da métrica são divididos pelo número de repositórios compartilhados entre os desenvolvedores (SR)\n\tfor row in NL:\n\t\tmetric_file.writerow([row[0], row[1], row[2], NL[row][0]/SR_metric[row], NL[row][1]/SR_metric[row], NL[row][2]/SR_metric[row], NL[row][3]/SR_metric[row]])\nw.close()","sub_path":"Metrics/04_generate_NL_metric.py","file_name":"04_generate_NL_metric.py","file_ext":"py","file_size_in_byte":5753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"619950021","text":"import sys\nsys.stdin = open('input.txt', 'r')\n\ndef dfs(y):\n if y == N:\n global count\n count += 1\n return\n\n for x in range(N):\n if row[x] or diag1[x + y] or diag2[x - y]:\n continue\n\n row[x] = diag1[x + y] = diag2[x - y]= 1\n dfs(y+1)\n row[x] = diag1[x + y] = diag2[x - y]= 0\n\n\nT = int(input())\n\nfor tc in range(T):\n N = int(input())\n count = 0\n row, diag1, diag2 = [0 for _ in range(N)], [0 for _ in range(2 * N - 1)], [0 for _ in range(2 * N - 1)]\n\n dfs(0)\n\n print('#{} {}'.format(tc+1, count))","sub_path":"2020/1030/swea_2806_N_Queen_2.py","file_name":"swea_2806_N_Queen_2.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"409487883","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# CHANGELOG:\n#\t0.2.1:\n#\t\tadded paused status\n#\t0.2:\n#\t\tretrieving title & artist rewritten with dbus\n#\t0.1:\n#\t\tfirst version; pid, title & artist collected by popen(), e.g. popen('banshee --query-artist').readline()\n\n# imports\nimport xchat\nimport dbus\nfrom os import popen\n\n# module info\n__module_name__ = \"banshee_np\"\n__module_version__ = \"0.2.1\"\n__module_description__ = \"Information about current Banshee track\"\n__module_autor__ = \"andrzej3393\"\n\n# code\ndef np_cb(word, word_eof, userdata):\n\tif popen('pidof banshee').readline():\n\t\tbus = dbus.SessionBus()\n\t\tbanshee = bus.get_object('org.bansheeproject.Banshee', '/org/bansheeproject/Banshee/PlayerEngine')\n\t\tstate = banshee.GetCurrentState()\n\t\tif state.encode('utf-8') == \"playing\":\n\t\t\tcurrenttrack = banshee.GetCurrentTrack()\n\t\t\txchat.command('ME np: %s - %s' % (currenttrack['artist'].encode('utf-8'), currenttrack['name'].encode('utf-8')))\n\t\telif state.encode('utf-8') == \"paused\":\n\t\t\tcurrenttrack = banshee.GetCurrentTrack()\n\t\t\txchat.command('ME np: %s - %s (paused)' % (currenttrack['artist'].encode('utf-8'), currenttrack['name'].encode('utf-8')))\n\t\telse:\n\t\t\txchat.prnt('There is nothing played.')\n\t\t\treturn xchat.EAT_ALL\n\telse:\n\t\txchat.prnt(\"Banshee is not already running.\")\n\treturn xchat.EAT_ALL\n\ndef unload_cb(userdata):\n\txchat.unhook(HOOKMUSIC)\n\nHOOKMUSIC = xchat.hook_command(\"np\", np_cb)\nHOOKUNLOAD = xchat.hook_unload(unload_cb)\n","sub_path":"banshee_np/np.py","file_name":"np.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"42320954","text":"from django.shortcuts import render, get_object_or_404\nfrom django.db.models import Q, Prefetch\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\n\n# 외부함수\nfrom .match_funcs import re_geocode, re_dongcode\n\n# 모델\nfrom users.models import User, BeforeMatch, AfterMatch\nfrom match.models import Sports, Match, MatchUser, Locations\n\n# 시리얼라이저\nfrom .serializers import BMSerializer\n\n# 임시로 사용하는 데코레이터입니다.\n# from .models import \n\n# 매칭 등록 시 \n@api_view(['POST'])\ndef before_match(request):\n data = request.data\n\n import datetime\n\n # 최대 매치 개수를 생각해서 제한할 것\n # 시간 겹치지 않게 처리\n\n all_user_bm = BeforeMatch.objects.filter(user=request.user, date = data['date']).exclude(Q(status= '4') | Q(status='5'))\n \n stime_str = data['start_time']\n stime = datetime.datetime.strptime(stime_str, '%H:%M').time()\n\n etime_str = data['end_time']\n etime = datetime.datetime.strptime(etime_str, '%H:%M').time()\n\n if all_user_bm:\n for user_bm in all_user_bm:\n if etime < user_bm.start_time or stime > user_bm.end_time:\n continue\n else:\n msg = {\"status_code\": 403, \"detail\": \"이미 해당 시간에 매칭 중인 게임이 있습니다.\"}\n return Response(msg)\n\n \n bm_match = BeforeMatch(\n user = request.user, \n status = 1, \n sports_name = data['sports_name'],\n date = data['date'],\n start_time = data['start_time'],\n end_time = data['end_time'],\n lat = data['lat'],\n lng = data['lng'],\n gu = re_geocode(data['lat'], data['lng']),\n device_token = data['device_token']\n )\n bm_match.save()\n serializer = BMSerializer(bm_match)\n\n\n # 매칭 전 / 종목 이름 / 날짜 / 구 이름이 같은 조건 탐색\n crnt_bm_matches = BeforeMatch.objects.filter(status = 1, sports_name = bm_match.sports_name, date = bm_match.date, gu = bm_match.gu)\n sports_count = {'tennis': 2, 'pool': 2, 'bowling': 2, 'basket_ball': 6, 'futsal': 12}\n \n # 해당 스포츠의 같은 동네에서 인원 수가 충족되고\n if crnt_bm_matches.count() >= sports_count[bm_match.sports_name]:\n # 시간도 겹치는지 확인해봅시다.\n match_users = [[] for _ in range(24)]\n matched = []\n # 다른 사람들의 정보를 집어넣는다.\n\n for user_bm in crnt_bm_matches.order_by('pk'):\n stime_idx = int(user_bm.start_time.strftime(\"%H:%M\")[:2])\n etime_idx = int(user_bm.end_time.strftime(\"%H:%M\")[:2])\n \n for i in range(stime_idx, etime_idx + 1):\n if len(match_users[i]) >= sports_count[bm_match.sports_name] - 1: \n import copy\n matched_users = copy.deepcopy(match_users[i])\n # 매칭된 유저의 내용 빼기\n match_stime = datetime.time(hour=00, minute=00)\n match_etime = datetime.time(hour=23, minute=59)\n for user_pk in matched_users:\n crnt_bm = get_object_or_404(BeforeMatch, pk=user_pk)\n s_idx = int(crnt_bm.start_time.strftime(\"%H:%M\")[:2])\n e_idx = int(crnt_bm.end_time.strftime(\"%H:%M\")[:2])\n\n if match_stime < crnt_bm.start_time:\n match_stime = crnt_bm.start_time\n if match_etime > crnt_bm.end_time:\n match_etime = crnt_bm.end_time\n\n for j in range(s_idx, e_idx + 1):\n match_users[j].remove(user_pk)\n\n if match_stime < user_bm.start_time:\n match_stime = user_bm.start_time\n if match_etime > user_bm.end_time:\n match_etime = user_bm.end_time\n\n # 현재 유저까지 추가해서 매칭된 유저에 넣어서 매칭된 게임에 전달해준다.\n # 매칭된 게임 정보는 matched에 넣어준다.\n matched_users.append(user_bm.pk)\n matched_users.append(match_stime)\n matched_users.append(match_etime)\n matched.append(matched_users)\n # 현재 넣고 있는 유저의 넣었던 내용 빼기\n for k in range(stime_idx, i):\n match_users[k].remove(user_bm.pk) \n break\n else:\n match_users[i].append(user_bm.pk)\n \n if not matched:\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n # 잡혀진 매치가 있다면 해당 매치를 게임으로 바꿔줘야겠죠.\n tokens = {}\n while matched:\n # BeforeMatch PK가 들어가 있는 리스트\n bm_pks = matched.pop()\n bm_etime = bm_pks.pop()\n bm_stime = bm_pks.pop()\n # 매치를 잡아줍니다.\n sports_name = get_object_or_404(Sports, sports_name=bm_match.sports_name)\n match = Match(sports = sports_name)\n match.date = bm_match.date\n match.start_time = bm_stime\n match.end_time = bm_etime\n match.save()\n\n lat_sum = 0\n lng_sum = 0\n # 유저 정보를 꺼내서 AfterMatch와 MatchUser를 만들어 줍니다.\n for idx, bm_pk in enumerate(bm_pks):\n bm = get_object_or_404(BeforeMatch, pk=bm_pk)\n bm.status = 2\n bm.save()\n \n # 중간 위치를 위한 위도 경도 계산\n lat_sum += bm.lat\n lng_sum += bm.lng\n\n tokens[bm.user.pk] = bm.device_token\n\n # MatchUser를 저장해줍니다.\n mmatch_user = MatchUser(\n match = match,\n user_pk = bm.user.pk\n )\n if idx % 2:\n mmatch_user.team = 1\n else:\n mmatch_user.team = 0\n mmatch_user.save()\n\n # AfterMatch를 만들어줍니다.\n am = AfterMatch(\n before_match = bm,\n matching_pk = match.pk\n )\n am.save()\n match.lat = lat_sum / len(bm_pks)\n match.lng = lng_sum / len(bm_pks)\n match.save()\n context = {\n 'result': 'true',\n 'device_tokens': tokens,\n }\n return Response(context, status=status.HTTP_200_OK)\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\ndef match_room(request):\n data = request.data\n match = get_object_or_404(Match, pk = data['match_pk'])\n match_users = MatchUser.objects.filter(match = match.pk)\n # 장소 정보 넣어서 보내기\n locations = Locations.objects.filter(sports = match.sports)\n\n users = {}\n for user in match_users:\n am = get_object_or_404(AfterMatch, matching_pk=match.pk, before_match__user=user.user_pk)\n bm = am.before_match\n users[user.user_pk] = {\n 'username': get_object_or_404(User, pk=user.user_pk).username, \n 'team': user.team,\n 'lat': bm.lat,\n 'lng': bm.lng\n }\n\n res = [\n {\n 'sports': match.sports.sports_name,\n 'match_pk': match.pk,\n 'date': match.date, \n 'match_lat': match.lat,\n 'match_lng': match.lng,\n 'start_time': match.start_time, \n 'end_time': match.end_time,\n 'users': users,\n 'locations': {}\n }\n ]\n\n context = {\n 'result' : 'true',\n 'data': res\n }\n return Response(context, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\ndef after_match(request):\n # 팀 / 시간 / 장소를 확정하고 각 플레이어들의 데이터를 저장해준다.\n data = request.data\n match = get_object_or_404(Match, pk=data['match_pk'])\n\n match.fixed_time = data['fixed_time']\n match.save()\n for user in data['users']:\n match_user = get_object_or_404(MatchUser, match=match, user_pk=user)\n match_user.team = data['users'][user]['team']\n match_user.save()\n\n am = get_object_or_404(AfterMatch, before_match__user=user, matching_pk=match.pk)\n bm = am.before_match\n bm.status = '3'\n bm.save()\n \n am.fixed_time = data['fixed_time']\n am.team_pk = data['users'][user]['team']\n \"\"\"\n 공간정보는 업데이트 필요\n \"\"\"\n am.fixed_lat = data['fixed_lat']\n am.fixed_lng = data['fixed_lng']\n am.save()\n return Response(status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\ndef result(request):\n user = request.user\n data = request.data\n if data['result'] == 'true':\n result = True\n elif data['result'] == 'false':\n result = False\n \n match = get_object_or_404(Match, pk=data['match_pk'])\n result_writer = get_object_or_404(MatchUser, match=match.pk, user_pk=user.pk)\n team = result_writer.team\n flag = 0\n if match.won_team == None:\n if team == True:\n match.won_team = result\n match.zero_resulted = True\n match.save()\n elif team == False:\n if result == True:\n match.won_team = False\n match.save()\n elif result == False:\n match.won_team = True\n match.save()\n match.one_resulted = True\n match.save()\n context = {\n 'result': 'ready',\n 'detail': '다른 팀의 결과 입력을 기다리고 있습니다.'\n }\n return Response(context, status=status.HTTP_200_OK)\n \n if team == True: # True 팀일 때\n if match.zero_resulted == True:\n context = {\n 'result': 'error',\n 'detail': '이미 결과를 투표하셨습니다.'\n }\n return Response(context, status=status.HTTP_200_OK)\n if result != match.won_team:\n flag = 1\n match.zero_resulted = True\n match.save()\n elif team == False: # False 팀일 때\n if match.one_resulted == True:\n context = {\n 'result': 'error',\n 'detail': '이미 결과를 투표하셨습니다.'\n }\n return Response(context, status=status.HTTP_200_OK)\n if result == match.won_team:\n flag = 1\n match.one_resulted = True\n match.save()\n \n if flag:\n match.won_team = None\n match.one_resulted = False\n match.zero_resulted = False\n match.save()\n context = {\n 'result': 'false',\n 'detail': '양팀의 게임 결과가 일치하지 않습니다. 결과를 다시 입력해주세요.'\n }\n return Response(context, status=status.HTTP_200_OK)\n\n match_users = MatchUser.objects.filter(match=match)\n for match_user in match_users:\n am = get_object_or_404(AfterMatch, before_match__user=match_user.user_pk, matching_pk=match.pk)\n bm = am.before_match\n bm.status = '5'\n bm.save()\n\n if match.won_team == match_user.team:\n am.result = True\n am.save()\n else:\n am.result = False\n am.save()\n context = {\n 'result': 'true',\n 'datail': f'팀 번호 {int(match.won_team)}의 승리결과에 대한 처리가 완료되었습니다.'\n }\n return Response(context, status=status.HTTP_200_OK)\n\n@api_view(['GET'])\ndef report(request):\n user = get_object_or_404(User, username=request.user)\n context = {}\n sports = ['pae_ssaum', 'futsal', 'basket_ball', 'bowling', 'tennis', 'pool']\n for i in range(1, 6):\n match_count = AfterMatch.objects.filter(before_match__user=user, before_match__status='5', before_match__sports_name=sports[i]).count()\n win_match_count = AfterMatch.objects.filter(before_match__user=user, before_match__status='5', before_match__sports_name=sports[i], result=True).count()\n lose_match_count = match_count - win_match_count\n temp = {}\n temp['win'] = win_match_count\n temp['lose'] = lose_match_count\n temp['total'] = match_count\n if match_count != 0:\n temp['rate'] = round((win_match_count / match_count) * 100, 2)\n else:\n temp['rate'] = round(0, 2)\n temp['sports_id'] = i\n temp['sports_name'] = sports[i]\n context[sports[i]] = temp\n return Response(context, status=status.HTTP_200_OK)\n\n\n@api_view(['GET'])\ndef report_detail(request, sports_pk):\n user = get_object_or_404(User, username=request.user)\n print(user)\n sports = ['pae_ssaum', 'futsal', 'basket_ball', 'bowling', 'tennis', 'pool']\n sports_kr = ['pae_ssaum', '풋살', '농구', '볼링', '테니스', '당구']\n result_kr = ['패배', '승리']\n matches = AfterMatch.objects.filter(before_match__user=user, before_match__sports_name=sports[int(sports_pk)], before_match__status='5')\n context = []\n for match in matches:\n temp = {}\n temp['id'] = match.id\n temp['date'] = match.before_match.date\n temp['sports_name'] = sports_kr[int(sports_pk)]\n temp['fixed_time'] = match.fixed_time\n temp['gu'] = re_dongcode(match.fixed_lat, match.fixed_lng)\n temp['result'] = result_kr[int(match.result)]\n context.append(temp)\n return Response(context, status=status.HTTP_200_OK)","sub_path":"backend/match/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"302896358","text":"import json\n\nfrom bottle import request, post, response\nfrom bson.objectid import ObjectId\n\nfrom src.dao import set_dao, test_dao\n\n__author__ = 'seggcsont'\n\n\n@post(\"/reporting/test/start\")\ndef start_test():\n try:\n data = json.loads(request.body.readline()) or {}\n test_name = data.get('testName')\n param_name = data.get('paramName')\n set_id = request.query.setId\n\n set_dao.test_reported_for_set(set_id)\n obj = test_dao.start_test(ObjectId(set_id), test_name, param_name)\n response.content_type = \"application/json\"\n return {'status': \"OK\", 'test_id': str(obj)}\n except Exception as e:\n return {'status': \"ERROR\", 'error': e.message}\n\n\n@post(\"/reporting/test/add_params\")\ndef add_test_params():\n try:\n data = json.loads(request.body.readline()) or {}\n test_id = request.query.testId\n for key in data.keys():\n test_dao.add_test_param(test_id, key, data.get(key))\n response.content_type = \"application/json\"\n return {'status': \"OK\", 'test_id': str(test_id)}\n except Exception as e:\n return {'status': \"ERROR\", 'error': e.message}\n\n\n@post(\"/reporting/test/add_labels\")\ndef add_test_labels():\n try:\n data = json.loads(request.body.readline()) or {}\n test_id = request.query.testId\n if isinstance(data, unicode) or isinstance(data, str):\n data = [data]\n for label in data:\n test_dao.add_test_label(test_id, label)\n response.content_type = \"application/json\"\n return {'status': \"OK\", 'test_id': str(test_id)}\n except Exception as e:\n return {'status': \"ERROR\", 'error': e.message}\n\n\n@post(\"/reporting/test/add_checkpoint\")\ndef add_test_checkpoint():\n try:\n data = json.loads(request.body.readline()) or {}\n test_id = request.query.testId\n test_dao.add_test_checkpoint(test_id, data)\n response.content_type = \"application/json\"\n return {'status': \"OK\", 'test_id': str(test_id)}\n except Exception as e:\n return {'status': \"ERROR\", 'error': e.message}\n\n\n@post(\"/reporting/test/stop\")\ndef stop_test():\n try:\n data = json.loads(request.body.readline()) or {}\n test_id = request.query.testId\n result = data.get(\"result\")\n error_msg = data.get(\"errorMessage\")\n test_dao.stop_test(test_id, result, error_msg)\n response.content_type = \"application/json\"\n return {'status': \"OK\", 'test_id': str(test_id)}\n except Exception as e:\n return {'status': \"ERROR\", 'error': e.message}","sub_path":"src/controllers/reporting_test.py","file_name":"reporting_test.py","file_ext":"py","file_size_in_byte":2574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"575789632","text":"from tealight.robot import (move, \n turn, \n look, \n touch, \n smell, \n left_side, \n right_side)\n\ndef _move(number):\n for i in range(0, number):\n move()\n \nglobal x\nx = 0 \n\ndef left():\n turn(-1)\n _move(4)\n turn(-1)\n\ndef right():\n turn(1)\n _move(4)\n turn(1)\n\ndef _turn():\n global x\n if x == 1:\n x = 0\n left()\n else:\n x = x + 1\n right()\n \nfor i in range(0,8):\n _move(32)\n _turn()\n\n_move(32)\nx = 1\nturn(-1)\nfor i in range(0,8):\n _move(31)\n _turn()\n_move(31)\n ","sub_path":"robot/chess.py","file_name":"chess.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"111864241","text":"import pandas as pd\nimport pandas.io.data as web\nimport datetime\n\nimport matplotlib.pyplot as plt \nfrom matplotlib import style\nfrom statistics import mean\n\nstyle.use('fivethirtyeight')\n\nstart = datetime.datetime(2007,1,1)\nend = datetime.datetime(2016,12,1)\n\natt = web.DataReader(\"T\", 'yahoo', start, end)\n\ndescribe = att.describe()\n\natt['50_close_movingAvg'] = pd.rolling_mean(att['Close'], 50)\natt['10_close_movingAvg'] = pd.rolling_mean(att['Close'], 10)\n\natt['50_close_std'] = pd.rolling_std(att['Close'], 50)\natt['movingAvg_withApply'] = pd.rolling_apply(att['Close'], 50, mean)\n\nprint(att.tail())\nprint(50*\"#\")\nprint(att.head())\nprint(50*\"#\")\n\natt = att.dropna(inplace=True)\nprint(att.head())","sub_path":"DataAnalysis/pdDropNA.py","file_name":"pdDropNA.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"52041822","text":"\"\"\"Main entry point\n\"\"\"\nimport couchdb\n\nfrom pyramid.config import Configurator\nfrom pyramid.events import NewRequest\nfrom couchdb.design import ViewDefinition\n\nfrom views import __design_docs__\n\n\ndef add_couchdb_to_request(event):\n request = event.request\n settings = request.registry.settings\n db = settings['db_server'][settings['db_name']]\n event.request.db = db\n\n\ndef sync_couchdb_views(db):\n ViewDefinition.sync_many(db, __design_docs__)\n\n\ndef main(global_config, **settings):\n config = Configurator(settings=settings)\n config.include(\"cornice\")\n config.scan(\"daybed.views\")\n # CouchDB initialization\n db_server = couchdb.client.Server(settings['couchdb_uri'])\n config.registry.settings['db_server'] = db_server\n sync_couchdb_views(db_server[settings['db_name']])\n config.add_subscriber(add_couchdb_to_request, NewRequest)\n return config.make_wsgi_app()\n","sub_path":"daybed/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"240377404","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 22 09:49:23 2018\n\n@author: Aminoo\n\"\"\"\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 20 17:41:49 2018\n\n@author: Aminoo\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\n#import matplotlib.pyplot as plt\n#import tensorlayer as tl\n#from tensorlayer.layers import *\nfrom ops import *\nfrom data import MRIDataHandler\nfrom config import *\nfrom STN import *\nimport os\n\ndef MRI_model(learning_rate, hparam,do,use_fc):#use_two_conv, use_two_fc, hparam):\n \n config = Config(is_train= True)\n mkdir(config.tmp_dir)\n mkdir(config.result_warped_path)\n dh = MRIDataHandler( is_train=True,select_ROI=False)\n \n tf.reset_default_graph()\n sess = tf.Session()\n [h,w]= dh.get_shape()\n \n im_shape = [1]+ [h,w]# config.batch_size->nothing\n im_mov= tf.placeholder(tf.float32, im_shape)\n im_fixed = tf.placeholder(tf.float32, im_shape)\n choice= tf.placeholder(tf.int32, 1)#config.batch_size->1\n keep_prob = tf.placeholder(tf.float32)\n \n input_vector = np.random.randn(config.sequence_size,config.input_size_H,config.input_size_W,1)\n input_vector = input_vector.astype('float32')\n with tf.variable_scope('spatial_transformer'):\n \n \n # input placeholder\n x = tf.Variable(input_vector, tf.float32, name='inputvector') \n tf.summary.histogram(\"input\", x)\n \n if use_fc:\n flattened = tf.reshape(tf.gather(x, choice), [-1, config.input_size_H * config.input_size_W ])\n net, w1 =fc_layer(x= flattened, name = 'fc1',str1='1', size_in= config.input_size_H * config.input_size_W, size_out=770,af='elu', do = do,keep_prob =keep_prob)\n \n# print(net.shape)\n net, w2 =fc_layer(net, name = 'fc2',str1='2', size_in= 770, size_out=1540,\n af='elu', do = do, keep_prob =keep_prob)\n# print(net.shape) \n \n net, w3 =fc_layer(net, name = 'fc3',str1='3', size_in= 1540, size_out=3080,\n af='elu', do = do, keep_prob =keep_prob)\n \n net2, w4 =fc_layer(net, name = 'fc4',str1='4', size_in= 3080, size_out=6160,\n af='elu', do = do, keep_prob =keep_prob)\n \n net3 = tf.reshape(net2, [1,h,w,2], name= \"out\")\n print(net3.shape)\n else:\n y =tf.gather(x, choice)\n print(y.shape)\n net,w1 = trans_conv_layer(tf.gather(x, choice), name = 'convt1',str1 = '1', dim = 32, k_h= 5,k_w = 5,s_h=5, s_w = 5, p='SAME',\n af='elu',maxp=False, is_train = True,do = do, keep_prob = keep_prob)\n print(net.shape)\n net,w2 = trans_conv_layer(net, name = 'convt2',str1 = '2', dim = 64, k_h= 5,k_w = 5,s_h= 2, s_w = 2, p='SAME',\n af='elu',maxp=False, is_train = True,do = do, keep_prob = keep_prob)\n print(net.shape) \n# net = trans_conv_layer(net, name = 'convt3', dim = 16, k_h= 5,k_w = 5,s_h= 2, s_w = 2, p='SAME',\n# bn=False,af='relu',maxp=False, is_train = True)\n# #print(net.shape)\n net3,w3 = trans_conv_layer(net, name = 'convt4',str1 = '3', dim = 2, k_h= 7,k_w=6,s_h= 1, s_w = 1, p='VALID',\n af='',maxp=False, is_train = True,do = do, keep_prob = keep_prob)\n print(net3.shape)\n \n \n \n print(im_mov.shape)\n im_warp = STN(im_mov,net3)\n #Outputs a Summary protocol buffer with images.\n im_warp2 = tf.expand_dims(im_warp,-1)#I remove channel value\n # print(im_warp2.shape)\n tf.summary.image('warped', im_warp2, 1)\n \n \n \n with tf.name_scope(\"loss\"):\n reg = tf.nn.l2_loss(w1) + tf.nn.l2_loss(w2) + tf.nn.l2_loss(w3) \n # loss = np.mean(np.absolute(WarpedJ_tf[0,:,:]-I))#mse(im_fixed, im_warp) # \n loss_ = mse(im_fixed,im_warp) \n #save that single number\n loss = tf.reduce_mean(loss_ + reg * config.beta)\n #save that single number\n tf.summary.scalar(\"loss_wol2\", loss_)\n tf.summary.scalar(\"losswl2\", loss)\n \n with tf.name_scope(\"train\"):\n train_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope ='spatial_transformer')\n # train_params = tl.layers.get_variables_with_name('spatial_transformer_0', train_only=True, printable=True)\n d_vars = [var for var in train_params if 'spatial_transformer' in var.name]\n train = tf.train.AdamOptimizer(learning_rate).minimize(loss, var_list=train_params)\n \n \n #merge them all so one write to disk, more comp efficient\n summ = tf.summary.merge_all()\n # Add ops to save and restore all the variables.\n saver = tf.train.Saver()\n sess.run(tf.global_variables_initializer())\n #filewriter is how we write the summary protocol buffers to disk\n writer = tf.summary.FileWriter(config.tmp_dir + os.path.join(\"/\",hparam))\n writer.add_graph(sess.graph)\n\n #training step\n for iter2 in range(config.iteration):\n for imagenumber in range (0,30):\n #mov_images_batch , fixed_images_batches, choice_batch = dh.sample_movingdata()\n mov_image , fixed_image = dh.select_MovingandFixeddata(imagenumber)\n imagenum = np.array([imagenumber])\n \n \n for iter in range(config.localiteration):\n \n if iter % 5 == 0:\n [train_loss, s] = sess.run([loss, summ], feed_dict={im_mov: mov_image,im_fixed:fixed_image, choice:imagenum,keep_prob : 0.5 })\n writer.add_summary(s, iter)\n if iter % 1000 == 0:\n #sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})\n #save checkpoints\n output_=sess.run(net3,feed_dict={im_mov: mov_image,im_fixed:fixed_image, choice:imagenum,keep_prob : 0.5 })\n dh.update_output(output_,imagenumber,iter, iter2, hparam, learning_rate,config.tmp_dir)\n saver.save(sess, config.tmp_dir+os.path.join(\"/\",hparam,str(iter)+\"im_model.ckpt\"))\n \n sess.run(train,feed_dict={im_mov: mov_image,im_fixed:fixed_image, choice:imagenum,keep_prob : 0.5 })\n #print(nx_error)\n\n\n \ndef make_hparam_string(learning_rate,do,use_fc):#, use_two_fc, use_two_conv):\n \n do_param = \"do=true\" if do else \"do=false\"\n fc_param = \"usefc\" if use_fc else \"usetransconv\"\n #return \"lr_%.0E,%s,%s\" % (learning_rate, conv_param, fc_param)\n return \"lr_%.0E,%s,%s\" % (learning_rate, do_param,fc_param)\n\ndef main():\n \n for learning_rate in [ 1E-4,1E-5]:\n for use_fc in [False,True]:\n #for bn in [True, False]:\n for do in [False, True]:\n \n hparam = make_hparam_string(learning_rate,do,use_fc)#, use_two_fc, use_two_conv)\n print('Starting run for %s' % hparam)\n MRI_model(learning_rate,hparam,do,use_fc)#, use_two_fc, use_two_conv, hparam)\n\nif __name__ == '__main__':\n main()\n","sub_path":"Train_imagesequence.py","file_name":"Train_imagesequence.py","file_ext":"py","file_size_in_byte":7129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"116577511","text":"import datetime\nimport json\nfrom tornado.web import RequestHandler\nimport motor.motor_tornado\n\nfrom pending import store_pending_visitor\nfrom ranking import calculate_atm_ranking\n\nclient = motor.motor_tornado.MotorClient()\ndb = client['otpmap']\n\nclass ATMLocatorService(RequestHandler):\n collection = db['atms']\n \n def set_default_headers(self):\n # For prototyping\n self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n self.set_header(\"Access-Control-Allow-Headers\", \"x-requested-with\")\n self.set_header('Access-Control-Allow-Methods', 'POST, GET, OPTIONS')\n \n async def get(self, uuid):\n longitude = float(self.get_argument(\"longitude\", 19.089563, True))\n latitude = float(self.get_argument(\"latitude\", 47.451316, True))\n deposit = bool(self.get_argument(\"deposit\", False, True))\n point = [longitude, latitude]\n print('Current location is: ', point)\n\n atms = await self.get_atms_in_radius(point, 3000, deposit)\n data = await calculate_atm_ranking(point, atms)\n\n self.write(json.dumps({'type': 'atmlist', 'data': data}))\n\n if data:\n await store_pending_visitor(uuid, data[0]['_id'], datetime.datetime.now(), data[0]['time_approx'])\n\n async def get_atms_in_radius(self, client_location, radius, deposit=False):\n query = {\n 'location': {\n '$near': {\n '$geometry': {\n 'type': \"Point\" ,\n 'coordinates': client_location\n },\n '$maxDistance': radius\n }\n }\n }\n if deposit:\n query['deposit'] = True\n\n cursor = self.collection.find(query)\n \n atms = []\n async for document in cursor:\n document['_id'] = str(document['_id'])\n atms.append(document)\n \n return atms\n","sub_path":"locator.py","file_name":"locator.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"407957212","text":"import json\n\nfrom sqlalchemy import Column, VARCHAR, Binary\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\nclass Sensor(Base):\n __tablename__ = \"sensor\"\n\n user_token = Column(VARCHAR(length=16), primary_key=True)\n device_id = Column(VARCHAR(length=8))\n sensor_id = Column(VARCHAR(length=8))\n sensor_name = Column(VARCHAR(length=30))\n sensor_type = Column(VARCHAR(length=30))\n is_alive = Column(Binary(length=1))\n\n def __init__(self, user_token, device_id, sensor_id, sensor_name, sensor_type, is_alive):\n self.user_token = user_token\n self.device_id = device_id\n self.sensor_id = sensor_id\n self.sensor_name = sensor_name\n self.sensor_type = sensor_type\n self.is_alive = is_alive\n\n def __repr__(self):\n result = {\n \"user_token\": self.user_token,\n \"device_id\": self.device_id,\n \"sensor_id\": self.sensor_id,\n \"sensor_name\": self.sensor_name,\n \"sensor_type\": self.sensor_type,\n \"is_alive\": str(self.is_alive.decode())}\n\n return json.dumps(result)\n","sub_path":"iplat_server/iplat_data_collector_server/src/database/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"160334333","text":"import os\nfrom typing import List, Optional\n\nimport spacy\nfrom nltk.stem import LancasterStemmer\nfrom spacy.language import Language\nfrom spacy.tokens import Doc, Token\n\nfrom errant import alignment, categorizer\nfrom errant.edit import Edit, ErrorType\nfrom errant.toolbox import load_dictionary, load_tag_map\n\n\nclass Errant:\n def __init__(self, spacy_model: Optional[Language] = None):\n # Spacy model\n self.nlp = spacy_model or spacy.load(\"en\", disable=[\"ner\"])\n # Lancaster Stemmer\n self.stemmer = LancasterStemmer()\n # GB English word list (inc -ise and -ize)\n self.gb_spell = load_dictionary()\n # Part of speech map file\n self.tag_map = load_tag_map()\n\n def parse(self, text: str, tokenize: bool = True) -> List[Token]:\n if tokenize:\n doc = self.nlp(text)\n elif text:\n doc = self.nlp.tokenizer.tokens_from_list(text.split(\" \"))\n self.nlp.tagger(doc)\n self.nlp.parser(doc)\n else:\n doc = []\n return [tok for tok in doc]\n\n def get_typed_edits(\n self,\n original_tokens: List[Token],\n corrected_tokens: List[Token],\n levenshtien_costs: bool = False,\n merge_type: str = alignment.RULES_MERGE,\n ) -> List[Edit]:\n edits = alignment.get_auto_aligned_edits(\n original_tokens, corrected_tokens, levenshtien_costs, merge_type\n )\n for edit in edits:\n edit.error_type = self.find_error_type(\n edit, original_tokens, corrected_tokens\n )\n return edits\n\n def find_error_type(\n self, edit: Edit, original_tokens: List[Token], corrected_tokens: List[Token]\n ) -> ErrorType:\n\n return categorizer.categorize(\n edit,\n original_tokens,\n corrected_tokens,\n self.gb_spell,\n self.tag_map,\n self.nlp,\n self.stemmer,\n )\n","sub_path":"errant/errant.py","file_name":"errant.py","file_ext":"py","file_size_in_byte":1961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"278968007","text":"import os\nfrom colorama import Fore, Style\n\n\ninterface = input(Fore.CYAN + '[*]Enter interface to use: ' + Style.RESET_ALL)\nos.system('airmon-ng start ' + interface)\n\ndef wait():\n wait = input('PRESS ENTER TO CONTINUE')\n\ndef main():\n print(' _ __ __ ______ ______ __')\n print(' / | / /__ / /_ / ____/____/ ____ \\_____/ /__')\n print(' / |/ / _ \\/ __/ / / / ___/ / __ `/ ___/ //_/')\n print(' / /| / __/ /_ / /___/ / / / /_/ / /__/ ,<')\n print(' /_/ |_/\\___/\\__/____\\____/_/ \\ \\__,_/\\___/_/|_|')\n print(' /_____/ \\____/')\n print('==========================================================')\n print('*[1] Scan Local Networks (Airodump-ng) *')\n print('*[2] Scan Local Networks (Wash) *')\n print('*[3] Crack WEP Network *')\n print('*[4] Crack WPA/WPA2 Network Using PMKID Method *')\n print('*[5] Crack WPA/WPA2 Network Using PIN (Pixie-Dust) Method*')\n print('*[6] MITM WPA/WPA2 Method *')\n print('*[7] Exit *')\n print('==========================================================')\n in_put = input(': ')\n if in_put == '1':\n print(Fore.CYAN + '[*]Make sure to note down network bssid and channel number...')\n print('[*]Enter ^C or ^Z to exit scanner mode...' + Style.RESET_ALL)\n os.system('airodump-ng ' + interface + 'mon')\n wait()\n main()\n if in_put == '2':\n print(Fore.CYAN + '[*]Make sure to note down network bssid and channel number...')\n print('[*]Enter ^C or ^Z to exit scanner mode...' + Style.RESET_ALL)\n os.system('wash -i ' + interface + 'mon')\n wait()\n main()\n if in_put == '3':\n bssid = input(Fore.CYAN + '[*]Enter WEP Network BSSID: ' + Style.RESET_ALL)\n channel = input(Fore.CYAN + '[*]Enter WEP Network Channel: ' + Style.RESET_ALL)\n print(Fore.CYAN + '[*]Gathering Packets From Network: ' + bssid + '... (Wait Until You Have About 1000 IVs)' + Style.RESET_ALL)\n os.system('besside-ng -b ' + bssid + ' -c ' + channel + ' ' + interface + 'mon')\n os.system('aircrack-ng wep.pcap')\n wait()\n main()\n if in_put == '4':\n adapt = input(Fore.CYAN + '[*]Do you have a wifi adapter with packet injection?[y/N]: ' + Style.RESET_ALL)\n if adapt == 'y' or adapt == 'Y':\n bssid = input(Fore.CYAN + '[*]Enter WPA/WPA2 Network BSSID: ' + Style.RESET_ALL)\n channel = input(Fore.CYAN + '[*]Enter WPA/WPA2 Network Channel: ' + Style.RESET_ALL)\n print(Fore.CYAN + '[*]Starting PMKID Attack...')\n print('[*]Wait about 10 minutes to gather enough packets, use ^C or ^Z to end hcxdumptool...' + Style.RESET_ALL)\n os.system('hcxdumptool -i ' + interface + 'mon -o output.pcapng --enable_status=1')\n print(Fore.CYAN + '[*]Converting output.pcapng to ouputHC.16800 for hashcat bruteforcing...' + Style.RESET_ALL)\n os.system('hcxpcaptool -E essidlist -I identitylist -U usernamelist -z outputHC.18600 output.pcapng')\n print(Fore.GREEN + '[+]File Converted! Use hashcat in these two methods to crack: ' + Style.RESET_ALL)\n print(Fore.CYAN + ' [*] Wordlist: hashcat -m 16800 outputHC.16800 -a 0 --force wordlist.lst -O')\n print(Fore.CYAN + ' [*]Bruteforce: hashcat -m 16800 outputHC.16800 -a 3 --force ?a?a?a?a?a?a -O')\n wait()\n main()\n else:\n print(Fore.RED + \"[*]You can't attack a WPA/WPA2 encrypted network without packet injection...\" + Style.RESET_ALL)\n wait()\n main()\n if in_put == '5':\n bssid = input(Fore.CYAN + '[*]Enter Network BSSID: ' + Style.RESET_ALL)\n channel = input(Fore.CYAN + '[*]Enter Network Channel: ' + Style.RESET_ALL)\n print(Fore.CYAN + '[*]Running Reaver to attack WPS PIN exploit...' + Style.RESET_ALL)\n os.system('reaver -i ' + interface + 'mon -b ' + bssid + ' -c ' + channel + ' -vv -Z')\n wait()\n main()\n if in_put == '6':\n warn = input(Fore.RED + '[*]Fluxion can only run in a graphical interface (no ssh). Are you running in a gui?[y/N]: ' + Style.RESET_ALL)\n if warn == 'y' or warn == 'Y':\n os.chdir('fluxion')\n os.system('sudo ./fluxion.sh')\n os.chdir('..')\n wait()\n main()\n else:\n print(Fore.RED + '[*]Run Fluxion in a gui...' + Style.RESET_ALL)\n wait()\n main()\n if in_put == '7':\n wait()\n exit()\n\nmain()\n","sub_path":"network_crack.py","file_name":"network_crack.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"97226487","text":"import gspread\r\nimport json\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom oauth2client.service_account import ServiceAccountCredentials\r\n\r\nclass google_sheets(object):\r\n\r\n def read_sheet(self, file_name, tab_name):\r\n\r\n scope = ['https://spreadsheets.google.com/feeds']\r\n json_creds = 'Forecast-aec31199e46b.json'\r\n\r\n credentials = ServiceAccountCredentials.from_json_keyfile_name(json_creds, scope)\r\n\r\n gc = gspread.authorize(credentials)\r\n\r\n # Open a worksheet from spreadsheet with one shot\r\n workbook = gc.open(file_name)\r\n\r\n # Open specific tab\r\n worksheet = workbook.worksheet(tab_name)\r\n\r\n # Snag all data in list form\r\n data = worksheet.get_all_values()\r\n\r\n # Format into dataframe\r\n df = pd.DataFrame.from_records(data[1:],columns=data[0])\r\n\r\n return df\r\n\r\n# import gdrive\r\ngs = gdrive.google_sheets()\r\nua_input = gs.read_sheet(file_name='VC Rev Forecast - Inputs', tab_name='UA Spend Assumptions')\r\nprint (ua_input)\r\n","sub_path":"Quick SQL/better.py","file_name":"better.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"504942406","text":"\nimport argparse\n\nfrom Workers.TaskWorker import TaskWorker\n\nparser = argparse.ArgumentParser(description=\"0MQ worker\")\nparser.add_argument('nodeId', metavar='N', type=int, help=\"nodeId\")\n\nargs = parser.parse_args()\n\nprint(\"Starting workernode {}\".format(args.nodeId))\n\ntw = TaskWorker(args.nodeId)\ntw.run(\"tcp://localhost:5557\",\"tcp://localhost:5558\",\"tcp://localhost:5559\")\n\nprint(\"workernode {} closed\".format(args.nodeId))","sub_path":"src/Worker.py","file_name":"Worker.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"174953418","text":"import json\nimport constants\nfrom trends import TrendingTopic\nfrom trends import ProcessedTrend\nfrom tweets import Tweet\n\n\nclass TrendsProcessor:\n\n def __init__(self):\n self.processed_trends = []\n self.open_file()\n\n def open_file(self):\n with open(constants.TRENDS_JSON) as json_data:\n data = json.load(json_data)\n\n self.analyze(data)\n\n def analyze(self, data):\n for t in data:\n trend = self.create_trending_topic(t)\n for tw in t.get(\"tweets\", []):\n tweet = self.create_tweet(tw)\n trend.tweets.append(tweet)\n\n processed_trend = ProcessedTrend(trend)\n processed_trend.process()\n self.processed_trends.append(processed_trend)\n processed_trend.clean()\n\n def create_trending_topic(self, t):\n trend = TrendingTopic(t.get(\"title\", \"\"), t.get(\"desc\", \"\"), t.get(\"url\", \"\"))\n trend.tweets_num = t.get(\"tweets_num\", \"\")\n return trend\n\n def create_tweet(self, tw):\n tweet = Tweet(tw.get(\"user\", \"\"), tw.get(\"text\", \"\"), tw.get(\"images\", []), tw.get(\"links\", []),\n tw.get(\"hashtags\", []), tw.get(\"mentions\", []), tw.get(\"chashtags\", []))\n return tweet\n","sub_path":"src/trends/trends_processor.py","file_name":"trends_processor.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"156823991","text":"# class prakt44:\n# var1 = 3 \n# def func(self, var2, var3 = var1):\n# self.var4 = var2*var3\n# print(self.var4)\n# obj1 = prakt44()\n# obj1.func(2)\n\n# print(\"#\"*30)\n\n# class prakt45:\n# def __init__(self, var):\n# self.var1 = var\n# obj2 = prakt45(4)\n# print(obj2.var1)\n\n# print(\"#\"*30)\n\n# class student:\n# def __init__(self, kurs):\n# self.kurs = kurs\n# student1=student(2)\n# student2=student(3)\n# print(F\"Student 1 studies on {student1.kurs} course\")\n\n\nclass student:\n def __init__(self, kurs, sex):\n self.kurs=kurs#обычныйаргумент\n self._sex=sex#защищаемыйаргумент\nsex=input('Введитепол1-гостудента:')\nstudent1=student(2,sex)#созданиеэкземпляра\nprint(student1._sex)#печатьатрибутаэкземпляра\n","sub_path":"CodingLessons/python/EasyWayPython3/6week211019/ex40-1.py","file_name":"ex40-1.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"174093145","text":"import csv\nimport os\nimport time\n\n'''\n#\nThis module contains the information for the global arrays\ncontaing all the data related to a certain Stock.\nAs well as the Stock class itself, containg all the individual data\n#\ninformation in this order\n#\n[0 [1 [2, 3, 4, 5, 6], [2a[3a, ..],[]..][...]]] ]\n#\n0 - a certain stock instance\n----- INTER-DAY DATA ---------------\n1 - on a certain day\n2 - avd OPEN value of that day\n3 - The HIGH value of that moment\n4 - The LOW value of that moment\n5 - The CLOSE value of that moment\n6 - the VOlUME of that moment\n----- INTRA-DAY DATA --------------\n2a - array of different time slices \n3a - avd OPEN value of a certain moment\n4a - The HIGH value of certain moment\n5a - The LOW value of certain moment\n6a - The CLOSE value of certain moment\n7a - the VOlUME of certain moment\n\n#\n'''\n\nh = {'OPEN': 0, 'HIGH': 1, 'LOW': 2, 'CLOSE': 3} # holds the values of the discreet stock values such as\ndata = [] # contains the total stock data as per above\nkeys = {} # dictionary containing the keys for each stock\n# # ie {'BP':0, 'AWD':1, 'APL':2, ...}\n# # eg stock.master_stock_list[stock.keys['BP']].in\n\n\ndef load_data(file_path_in):\n s_t_tot = int(time.time()*1000)\n array_loc = 0\n for filename in os.listdir(os.getcwd() + \"\\\\\" + file_path_in): # runs through the files in the stock data file path\n s_t = int(time.time() * 1000)\n print(filename + \"--LOADING\")\n stock_name = filename[6:-4] # sets the ticker name org = \"table_fu.csv\" new = \"fu\"\n keys[stock_name] = array_loc # sets the array location value to its corespoding ticker symbol\n data.append(Stock(stock_name)) # creates and adds a new stock class to the master list\n data[array_loc].init_data(file_path_in + '\\\\' + filename) # initialises the the daily values of the stock\n array_loc += 1\n f_t = int(time.time() * 1000) - s_t\n print(filename + \"--READY(\",f_t,\"ms)\")\n f_t_tot = int(time.time() * 1000) - s_t_tot\n print(f_t_tot)\n print(keys)\n\n\n#\n# STOCK CLASS --- Contains teh historical information for each individual company\n# interacts through a incrementing and specific data feeds\n#\n# XXXXX ---- Will in future know if indicator has already been calculated for the stocks.\n# ----\n#\n\n\nclass Stock:\n\n def __init__(self, ticker_in):\n #self.name = name_in\n self.ticker = ticker_in\n self.header_tot = 4\n self.days = {}\n self.info = []\n# Days dict contains date 'name' and array location ie. days['20100501'] = 136\n# Data is stored like so [ [0 [1],[2]], ...] [[],[]]\n# Each day is stored in its' own slot containing the info for Daily Avg [1] & the data for each time step [2]\n# so a call to the high of day one at minute 3 is self.data[0][1][2][1]\n# Stock data is stored as [(0)OPEN, (1)HIGH, (2)LOW, (3)CLOSE, (4)VOLUME]\n\n ##\n self.current_day = 0\n self.current_hour = 0\n self.current_minute = 0\n self.total_days = 0\n\n def init_data(self, file_name_in):\n file_name = file_name_in\n with open(file_name) as csv_file:\n dialect = csv.Sniffer().sniff(csv_file.read(1024)) # checks the dialect\n csv_file.seek(0) # rewinds the csv\n reader = csv.reader(csv_file, dialect) # initialise the file\n\n\n for lines in reader:\n t_lines = []\n if lines == []:\n break\n else:\n c_loc = reader.line_num - 1 # The current array location\n self.info.append([]) # initialise the over all day container\n self.total_days += 1 # done got another day in that array\n # print(self.total_days)\n # print(lines)\n for i in range(2, 7):\n t_lines.append(float(lines[i])) # convert stock data to floats and add to temp\n\n self.info[c_loc].append(t_lines) # add the day avg array to the day container\n self.days[lines[0]] = c_loc # set its key(ticker symbol) in the dictionary\n # print(reader.line_num ,': ' , self.info[c_loc][0]) # to its array position\n\n # print(self.info)\n\n# inc_data\n# if called repeatedly will loop over and return each day's data\n\n def inc_get_data(self):\n inc_type = 'DAY' # the kind of icrementation (day, hour, minute)\n\n if inc_type == 'DAY': # if we're looking at a daily resolution\n self.current_day +=1 # increment by a day\n if self.current_day < self.total_days:\n # print('incrementor : ', self.info[self.current_day][0])\n return self.info[self.current_day][0] # return the days avg\n if self.current_day >= self.total_days: # if we've reached the end of our data'\n return False # tell the reciever we've stopped\n self.current_day = 0 # and reset the counter\n\n def reset_inc(self):\n self.current_day = 0\n\n def add_ind_header(self, indicator_in):\n h[indicator_in] = self.header_tot\n self.header_tot +=1\n\n def add_data(self, data_in):\n # print(data_in)\n self.info[self.current_day].append(data_in)\n\n def data_dump(self):\n for i in self.info:\n print(i[0])\n\n\n #def get_ind_val(self):\n\n\n\n\n\n","sub_path":"stock.py","file_name":"stock.py","file_ext":"py","file_size_in_byte":5620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"307795870","text":"\"\"\" WIP\n For the authentication system I will use Auth0.\n\"\"\"\n\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy.orm import Session\nfrom core.models.database import get_db\nfrom core.models.schema import Auth, AuthCreate, AuthCreateRequest\nfrom core.authentication.authentication import required_auth\nfrom core.routers.logic.auth_logic import (_get_auth,\n _create_auth,\n _get_user_auth0_id, _remove_auth)\n\n\nrouter = APIRouter(prefix=\"/auth\")\n\n\n@router.post(\"/\",\n name=\"Create a new user\",\n description=\"\"\"Binds the user with auth0 registration\n in the system\"\"\",\n response_model=Auth)\nasync def create_auth(auth_request: AuthCreateRequest,\n db: Session = Depends(get_db),\n token: dict = Depends(required_auth)):\n auth0_unique_id = _get_user_auth0_id(token)\n db_auth = _get_auth(db=db, auth0_unique_id=auth0_unique_id)\n if db_auth:\n raise HTTPException(\n status_code=400, detail=\"auth_unique_id already registered\")\n auth = AuthCreate(github_nickname=auth_request.github_nickname,\n auth0_unique_id=auth0_unique_id)\n return _create_auth(db=db, auth=auth)\n\n\n@ router.delete(\"/\",\n name=\"Removes a user from database\",\n description=\"\"\"Removes a user from database\"\"\",\n response_model=Auth)\nasync def remove_user(token: dict = Depends(required_auth),\n db: Session = Depends(get_db)):\n\n auth0_unique_id = _get_user_auth0_id(token)\n db_auth = _remove_auth(auth0_unique_id=auth0_unique_id, db=db)\n return db_auth\n\n\n@ router.get(\"/\",\n name=\"Get user\",\n description=\"\"\"Finds user in db if exist\"\"\",\n responses={\n 200: {\n }\n })\nasync def get_user(db: Session = Depends(get_db),\n token: dict = Depends(required_auth)):\n auth0_unique_id = _get_user_auth0_id(token=token)\n user_db = _get_auth(db=db, auth0_unique_id=auth0_unique_id)\n if not user_db:\n raise HTTPException(\n status_code=404, detail=\"User not found\")\n return user_db\n","sub_path":"app/core/routers/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"22033605","text":"from data_analysis_python.FileDataUtil import readOriginalData\r\nfrom data_analysis_python.FrequencyAnalysisUtil import convertToFrequency, selectRangeOfFrequency\r\nimport matplotlib.pyplot as plt\r\n\r\nfileName = \"四号机司乐平202011261004.xls\"\r\nfilePath = r\"C:\\Users\\Bo Wang\\Documents\\太赫兹数据分析 Python\\data_analysis_python\\test_data\"\r\nt, x = readOriginalData(fileName, filePath)\r\nsampX = x[0]\r\n\r\nf, xf = convertToFrequency(t, sampX, 0.1, 3, denoise = False)\r\nf1, xf1 = convertToFrequency(t, sampX, 0.1, 3, denoise = True)\r\n\r\nfig = plt.figure(figsize=(10, 20))\r\nax1, ax2 = fig.subplots(nrows= 2)\r\nax1.plot(f, xf)\r\nax1.set_title('Original')\r\nax2.plot(f1, xf1)\r\nax2.set_title('Remove reflection peaks')\r\nplt.show()\r\n","sub_path":"terahertz/data_analysis_python/test/RemoveReflectionPeaksTest.py","file_name":"RemoveReflectionPeaksTest.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"582948078","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nwith open('./output_csv/loss_acc_sharpness.csv', 'r') as f:\n datas = f.readlines()\n\n batch_size = np.zeros(len(datas))\n loss_train = np.zeros(len(datas))\n loss = np.zeros(len(datas))\n acc_train = np.zeros(len(datas))\n acc = np.zeros(len(datas))\n sharp = np.zeros(len(datas))\n\nfor i, data in enumerate(datas):\n batch_size[i], loss_train[i], loss[i], acc_train[i], acc[i], sharp[i] = list(map(float,data.rstrip('\\n').split(',')))\n\nfor i in range(len(sharp)):\n sharp[i] = sharp[i] * 1e+5\n\nfig = plt.figure(1)\nax = fig.add_subplot(211)\n\nax2 = ax.twinx()\n\nax.plot(batch_size, loss_train, 'orange', label='train loss')\nax.plot(batch_size, loss, 'orange', linestyle='--', label='test loss')\n\n\nax2.plot(batch_size, sharp, 'g-', label='sharpness')\nfig.legend(loc=2, bbox_to_anchor=(0,1), bbox_transform=ax.transAxes)\n\nax.set_xlabel('batch_size')\nax.set_ylabel('cross entropy')\n\nax2.set_ylabel('sharpness')\nplt.draw()\n###\n\nax3 = fig.add_subplot(212)\nax4 = ax3.twinx()\n\nax3.plot(batch_size, acc_train, 'orange', label='train acc')\nax3.plot(batch_size, acc, 'orange', linestyle='--', label='test acc')\n\nax4.plot(batch_size, sharp, 'g-', label='sharpness')\n\nh1, l1 = ax3.get_legend_handles_labels()\nh2, l2 = ax4.get_legend_handles_labels()\n\nleg = ax4.legend(handles=h1+h2, labels=l1+l2, loc=3, bbox_to_anchor=(0,0), bbox_transform=ax3.transAxes)\nleg.get_frame().set_alpha(0.8)\n\nax3.set_xlabel('batch_size')\nax3.set_ylabel('accuracy')\n\nax4.set_ylabel('sharpness')\nplt.show()\n\n","sub_path":"hw1/hw1-3/plot_sharp.py","file_name":"plot_sharp.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"147155150","text":"from random import randint\n\ndef RPS():\n\n user_choice = int(input('Rock, Paper,Scissors? '))\n\n rock = 1\n paper = 2\n scissors = 3\n\n\n def computer_choice ():\n computer_choice = randint(1,3)\n \n if computer_choice == 1:\n print (\"computer chose rock\")\n elif computer_choice == 2:\n print (\"computer chose paper\")\n else:\n print(\"computer chose scissors\")\n\n return computer_choice\n \n \n def rock_option ():\n if user_choice == 1:\n print ('it is a tie')\n elif user_choice == 2:\n print ('you lose')\n else:\n print ('you win')\n\n \n\n for option in range (1,3):\n if user_choice < 1 or user_choice > 3:\n print ('Select\\n 1 for rock \\n 2 for paper \\n 3 for scissors')\n\n break\n \n\n if RPS == 0:\n return False\n else :\n return True \n\nwhile RPS ():\n print ('')\n \n\n","sub_path":"20160917/Florah_Rock_Paper_Scissors.py","file_name":"Florah_Rock_Paper_Scissors.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"444276989","text":"import os, sys\n\ncolorMapping = {}\ncolorMapping['black'] = 'shen'\ncolorMapping['red'] = 'red'\ncolorMapping['yellow'] = 'thy'\ncolorMapping['white'] = 'click'\ncolorMapping['green'] = 'qml'\ncolorMapping['blue'] = 'hb'\n\ndef genFlag(name, colors):\n directory = 'flags/' + name + '/'\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n order = ord('a')\n for color in colors:\n lang = colorMapping[color['name']]\n filename = directory + chr(order) + '.' + lang\n f = open(filename, 'w')\n f.write('A' * color['percentage'])\n order += 1\n\n print('Flag written to ' + directory)\n\nflag = [\n {'name': sys.argv[2], 'percentage': 3334},\n {'name': sys.argv[3], 'percentage': 3333},\n {'name': sys.argv[4], 'percentage': 3332},\n]\ngenFlag(sys.argv[1], flag)\n","sub_path":"gen_flag.py","file_name":"gen_flag.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"207377635","text":"import numpy as np\nfrom scipy import ndimage\nimport nrrd\n\n\nfrom ...tools.Logger import Logger\nfrom ..VolumeAnalysis import VolumeAnalysis\nfrom .PointGenerator import PointGenerator\n\n\nclass PointSelector_Pregenerated:\n\n @staticmethod\n def getPoints(volume:np.ndarray, config:dict):\n \n bestPoints = [{\"coordinates\":(-1,-1,-1), \"value\": -1}] * (config[\"number_of_selected_points\"]-2) \n radius = int((config[\"selection_environment\"]) / 2)\n\n volumeAnalysis = VolumeAnalysis(volume, config)\n getPointValue = PointSelector_Pregenerated.mapSelectorToFunction(config[\"point_selector\"])\n\n pointGenerator = PointGenerator()\n amount_of_points = config[\"number_of_selected_points\"] * config[\"multiplier_for_generated_points\"]\n options = {}\n options[\"steering_radius\"] = config[\"steering_radius\"]\n options[\"steering_function\"] = lambda position: PointSelector_Pregenerated.callPointValue(getPointValue, volume, position, config, volumeAnalysis)\n points = pointGenerator.getPoints(config[\"point_generator\"], volume.shape, options, amount_of_points)\n\n for point in points:\n x = point[0]\n y = point[1]\n z = point[2]\n\n if volumeAnalysis.isCubeInsideVolume(volume, config[\"selection_environment\"], (x,y,z)):\n cube = volume[(x - radius):(x + radius +1), (y - radius):(y + radius+1), (z - radius):(z + radius+1)]\n value = getPointValue(cube, (x/volume.shape[0], y/volume.shape[1], z/volume.shape[2]))\n\n if value > bestPoints[len(bestPoints)-1][\"value\"]:\n bestPoints[len(bestPoints)-1] = {\"coordinates\":(x,y,z), \"value\": value}\n bestPoints = sorted(bestPoints, key=lambda k: k[\"value\"], reverse=True)\n\n\n return bestPoints\n\n @staticmethod\n def callPointValue(getPointValue, volume, position, config, volumeAnalysis=None):\n x,y,z = position\n if volumeAnalysis.isCubeInsideVolume(volume, config[\"selection_environment\"], (x,y,z)):\n radius = int((config[\"selection_environment\"]) / 2)\n cube = volume[(x - radius):(x + radius +1), (y - radius):(y + radius+1), (z - radius):(z + radius+1)]\n normalizedPosition = (x/volume.shape[0], y/volume.shape[1], z/volume.shape[2])\n return getPointValue(cube, normalizedPosition)\n return 0\n\n @staticmethod\n def getPointValue_Sum(cube, normalizedPosition):\n value = cube.sum()\n return value\n\n def getPointValue_SumGaussian(cube, normalizedPosition):\n gaussian_kernel = np.zeros_like(cube)\n gaussian_kernel[int(cube.shape[0]/2), int(cube.shape[1]/2), int(cube.shape[2]/2)] = 1.0\n \n sigma = int(cube.shape[0]/2 /3) # dynamic gaussian based on the seleciton environment\n gaussian_kernel = ndimage.gaussian_filter1d(gaussian_kernel, 1)\n gaussian_kernel = ndimage.gaussian_filter1d(np.moveaxis(gaussian_kernel, 2, 0), 1)\n gaussian_kernel = ndimage.gaussian_filter1d(np.moveaxis(gaussian_kernel, 2, 0), 1)\n gaussian_kernel = np.moveaxis(gaussian_kernel, 2, 0)\n\n gauss_cube = np.multiply(cube, gaussian_kernel)\n value = cube.sum()\n return value\n\n def getPointValue_SumVariance(cube, normalizedPosition):\n sum_value = cube.sum()\n var_value = cube.var()\n return sum_value * var_value\n\n @staticmethod\n def getPointValue_Mean(cube, normalizedPosition):\n value = cube.mean()\n return value\n\n @staticmethod\n def getPointValue_Variance(cube, normalizedPosition):\n value = cube.var()\n return value\n\n @staticmethod\n def getPointValue_VariancePosition(cube, normalizedPosition):\n variance = cube.var()\n distance = np.sqrt((normalizedPosition[0] - 0.5) ** 2 + (normalizedPosition[1] - 0.5) ** 2 + (normalizedPosition[2] - 0.5) ** 2)\n position = -distance**6 + 1\n\n return variance * position\n\n\n @staticmethod\n def mapSelectorToFunction(selector_type):\n switcher = {\n \"sum\": PointSelector_Pregenerated.getPointValue_Sum,\n \"sumgaussian\": PointSelector_Pregenerated.getPointValue_SumGaussian,\n \"sum_variance\": PointSelector_Pregenerated.getPointValue_SumVariance,\n \"mean\": PointSelector_Pregenerated.getPointValue_Mean,\n \"variance\": PointSelector_Pregenerated.getPointValue_Variance,\n \"variance_position\": PointSelector_Pregenerated.getPointValue_VariancePosition,\n }\n # Get the function from switcher dictionary\n func = switcher.get(selector_type, lambda cube, normalizedPosition: 1)\n return func\n\n\n","sub_path":"tornado_server/business/PointSelector/PointSelector_Pregenerated.py","file_name":"PointSelector_Pregenerated.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"274618638","text":"\"\"\"Example library for warmup\"\"\"\n\nimport numpy as np\n\ndef prime_finder(np_array):\n prime_numbers = set() #set\n# iterations = 0 #for checking efficiency\n for n in np_array:\n not_prime = 0\n \n #this if block checks if <3. If so, adds to not_prime\n if n < 3:\n not_prime += 1\n# print(n, \"is less than 3\") #testing checkpoint\n\n else:\n test_range = np.arange(2,n)\n for test in test_range:\n if not n % test: #not prime\n not_prime += 1\n break #for efficiency\n# print(iterations, \" -- \", n, \" compared with \", test, \" not_prime is \", not_prime) #testing checkpoint\n# iterations += 1\n if not_prime == 0:\n prime_numbers.add(n)\n \n return sorted(list(prime_numbers)) #set\n\ndef fibonacci_finder(np_array):\n fibonacci_numbers = set() #set\n fibonacci_list = [1,1,2]\n fib_go = 1\n# iterations = 0 #for checking efficiency\n for n in np_array:\n ''' create Fib numbers list'''\n if fibonacci_list[-1] <= n:\n fib_go = 1\n while fib_go:\n if fibonacci_list[-1] > n:\n fib_go = 0\n fibonacci_list.append(fibonacci_list[-1] + fibonacci_list[-2])\n# print (fibonacci_list[-1]) #testing checkpoint\n\n \n '''If Fobonacci, add to the the list of Fibonacci Numbers'''\n if n in fibonacci_list:\n fibonacci_numbers.add(n)\n return sorted(list(fibonacci_numbers)) #set\n\n","sub_path":"Warm_Ups/number_finder.py","file_name":"number_finder.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"289386984","text":"from multiprocessing import Pool,cpu_count\nfrom datetime import datetime\nimport math\ncalc =[]\ndef i(x):\n c = math.sqrt((x**2)+(x**2))\ndef f(x):\n while x > 1:\n x *= 0.9999\ndef createList(n):\n for i in range(n):\n calc.append(i)\ndef startInt(n):\n threads = cpu_count()\n print(\"Creating list.\")\n createList(n)\n print(\"List created\")\n print(\"Starting {} integer calculations with 1 thread\".format(len(calc)))\n startTime = datetime.now()\n p = Pool(1)\n p.map(i, calc)\n print(\"Took {} with 1 thread(s)\".format((datetime.now() - startTime).total_seconds()))\n #multi core test#\n if threads > 1:\n print(\"Starting {} calculations with {} threads\".format(len(calc), threads))\n startTime = datetime.now()\n p = Pool(threads)\n p.map(i, calc)\n print(\"Took {} with {} thread(s)\".format((datetime.now() - startTime).total_seconds(), threads))\n else:\n print('Single core CPU skipping test')\n calc[:] = []\ndef startFloat(n):\n threads = cpu_count()\n print('Warning! float calculations can take a long time')\n #multi core test#\n if threads >= 4:\n print(\"Creating list.\")\n createList(n)\n print(\"List created\")\n print(\"Starting {} calculations with {} threads\".format(len(calc), threads))\n startTime = datetime.now()\n p = Pool(threads)\n p.map(f, calc)\n print(\"Took {} with {} thread(s)\".format((datetime.now() - startTime).total_seconds(), threads))\n input(\"Press enter to close program\")\n else:\n print('Float test takes too long with < 4 threads')\n calc[:] = []\nif __name__ == '__main__':\n startInt(10**7)\n startFloat(10**4)","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"100082518","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport pandas as pd\nimport numpy as np\nfrom pprint import pprint\nfrom collections import namedtuple\n\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--bril\", help=\"Bril file\", type=str)\nparser.add_argument(\"-t\",\"--time-interval\", help=\"Time interval in seconds\", type=int)\nparser.add_argument(\"-o\",\"--outputfile\", help=\"Output file\", type=str)\nargs = parser.parse_args()\n\n# ## Read brilcalc metadata\nbril = pd.read_csv(args.bril, sep=\",\", comment=\"#\")\nbril[\"run\"] = bril.apply(lambda row: int(row[\"run:fill\"].split(\":\")[0]), axis=1)\nbril[\"fill\"] = bril.apply(lambda row: int(row[\"run:fill\"].split(\":\")[1]), axis=1)\n\n\nbril.head()\n\nbril[\"lumi_in_fill\"] = bril.groupby(\"fill\")[\"delivered(/ub)\"].cumsum()\nbril[\"lumi_int\"] = bril[\"delivered(/ub)\"].cumsum()\nbril[\"time_in_fill\"] = bril.groupby(\"fill\")[\"time\"].transform(lambda t: t - t.min())\nbril[\"time_in_fill_stable\"] = bril[bril.beamstatus==\"STABLE BEAMS\"].groupby(\"fill\")[\"time\"].transform(lambda t: t - t.min())\nbril[\"time_in_fill_stable\"] = bril[\"time_in_fill_stable\"].fillna(0)\n\nbril.head()\n\n\n# Useful Namestupls with fill information\nfillinfo = namedtuple(\"fillinfo\", [\"fill\", \"beamstatus\", \"lumi_inst\", \"lumi_int\", \"lumi_in_fill\", \"time_in_fill\", \"time_in_fill_stable\"])\n\n\ndef is_in_fill(timestamp):\n a = bril[abs(bril.time - timestamp)<23]\n if len(a):\n a = a.iloc[0]\n return fillinfo(a.fill, a.beamstatus, a[\"delivered(/ub)\"], a.lumi_int, a.lumi_in_fill, a.time_in_fill, a.time_in_fill_stable)\n else:\n return fillinfo(0, \"NOBEAM\", 0, 0, 0,0,0)\n \n#is_in_fill(starting_time)\n\n\ndef get_lumi_interval(timestart, timestop):\n return bril[ (bril.time >= timestart) & (bril.time <= timestop)][\"delivered(/ub)\"].sum()\n\n\ndef get_last_fill_end(timestamp, fill=0):\n df = bril[(bril.time <=timestamp) & (bril.fill != fill)]\n if not df.empty:\n return df.iloc[-1]\n else: \n return pd.DataFrame()\n\n\ndef get_fill_timeinterval(fill):\n t = bril[bril.fill== fill].time\n return t.iloc[0], t.iloc[-1]\n\n\n#get_fill_timeinterval(6417)\n\n\n# ## Lumi/fill metadata output\n# Let's read bril data to create metadata points every N minutes\n\noutputs = {\n \"in_fill\" : [],\n \"time\": [],\n \"lumi_inst\": [],\n \"lumi_int\" : [],\n \"lumi_in_fill\": [],\n \"lumi_since_last_point\": [],\n \"lumi_last_fill\": [],\n \"fill_num\": [],\n \"time_in_fill\": [],\n \"time_in_fill_stable\": [],\n \"time_from_last_fill\" : [],\n \"last_dump_duration\" : [],\n \"last_fill_duration\": [],\n}\n\n\ndef add_output(out):\n for k, v in out.items():\n outputs[k].append(v)\n\n\n# Interpolation time\ntime_interval = args.time_interval\n\n\nprevious_time = bril.time.iloc[0]\nlast_int_lumi = 0.\ntot = (bril.time.iloc[-1]-bril.time.iloc[0])//time_interval\n\nfor iev, curr_time in enumerate(range(bril.time.iloc[0], bril.time.iloc[-1], time_interval)):\n if iev % 100 == 0:\n print(f\"{iev}/{tot}\")\n \n fill_info = is_in_fill(curr_time)\n if fill_info.fill != 0.:\n last_int_lumi = fill_info.lumi_int\n \n last_fill_info = get_last_fill_end(curr_time, fill_info.fill)\n \n if last_fill_info.empty:\n last_fill_end = curr_time\n last_fill_duration = 0\n last_dump_duration = 0\n lumi_last_fill = 0\n else:\n last_fill_end = last_fill_info.time\n last_fill_duration = last_fill_info.time_in_fill\n lumi_last_fill = last_fill_info.lumi_in_fill\n \n if fill_info.fill != 0:\n last_dump_duration = (curr_time - fill_info.time_in_fill) - last_fill_info.time \n else:\n last_dump_duration = curr_time - last_fill_info.time \n \n time_from_last_fill = curr_time - last_fill_end\n lumi_since_last_point = get_lumi_interval(previous_time,curr_time)\n \n out = {\n \"in_fill\": int(fill_info.fill != 0),\n \"time\": curr_time,\n \"fill_num\": fill_info.fill,\n \"lumi_inst\": fill_info.lumi_inst,\n \"lumi_int\": last_int_lumi,\n \"lumi_in_fill\": fill_info.lumi_in_fill,\n \"lumi_since_last_point\": lumi_since_last_point,\n \"lumi_last_fill\": lumi_last_fill,\n \n \"time_in_fill\": fill_info.time_in_fill,\n \"time_in_fill_stable\": fill_info.time_in_fill_stable,\n \"time_from_last_fill\": time_from_last_fill, \n \"last_dump_duration\": last_dump_duration, \n \"last_fill_duration\": last_fill_duration\n }\n \n previous_time = curr_time\n add_output(out)\n\n\noutput_df = pd.DataFrame(outputs)\noutput_df.to_csv(args.outputfile, index=False, sep=\",\")\n\nprint(\"Done\")","sub_path":"DataPreparation/TimestepsDataPreparation.py","file_name":"TimestepsDataPreparation.py","file_ext":"py","file_size_in_byte":4575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"623407833","text":"import torch\nimport torch.nn as nn\nfrom packaging import version\nfrom mmcv.cnn import kaiming_init, normal_init\n\nfrom .registry import INPUT_MODULES\nfrom .utils import build_norm_layer\n\n\n\n\ndef conv1x1(in_planes, out_planes, stride=1):\n \"\"\"1x1 convolution\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)\n\n\n@INPUT_MODULES.register_module\nclass Conv1x1Block(nn.Module):\n \"\"\"\n Conv1x1 => Batch Norm => RELU input module\n \"\"\"\n def __init__(self, in_channels, out_channels):\n super(Conv1x1Block, self).__init__()\n self.net = nn.Sequential(\n conv1x1(in_channels, out_channels), \n nn.BatchNorm2d(out_channels), \n nn.ReLU(inplace=True)\n )\n\n def init_weights(self, init_linear='normal'):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif isinstance(m, (nn.BatchNorm, nn.SyncBatchNorm, nn.GroupNorm)):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0) \n \n def forward(self, x):\n return self.net(x)\n","sub_path":"openselfsup/models/input_modules.py","file_name":"input_modules.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"502892353","text":"\"\"\"Derivé du premier exemple avec Tkinter.\n\nOn crée une fenêtre simple.\n\n\"\"\"\n\n# On importe Tkinter\nfrom tkinter import *\n\nclass Interface(Frame):\n\n\tdef __init__(self,fenetre, **kwargs):\n\t\tFrame.__init__(self, fenetre, width=600, height=400, borderwidth=1, **kwargs)\n\n\t\tself.champ_label = Label(self, text=\"Pour quel nombre d'étages ?\")\n\t\tself.master.title(\"titre.png\")\n\t\tself.master.geometry(\"600x400+10+10\")\n\t\tself.pack(fill=BOTH)\n\t\tself.champ_result = Label(self, text=\"résultat\")\n\t\tself.entr1 = Entry(self)\n\t\tself.bouton_quitter = Button(self, text=\"partir pour\\n une durée indefinie\", command=self.quit)\n\t\tself.bouton_lancer = Button(self, text=\"proceder\", command=self.calcule)\n\t\tself.can = Canvas(self,width=200, height=200, bg='white')\n\t\tself.champ_label.grid(row = 1, column = 1, columnspan = 2)\n\t\tself.champ_result.grid(row = 2, column = 4, columnspan = 2)\n\t\tself.bouton_quitter.grid(row = 3,column = 1)\n\t\tself.bouton_lancer.grid(row = 3,column = 2)\n\t\tself.entr1.grid(row = 2, column = 1)\n\t\tself.can.grid(row = 3, column = 4, columnspan = 1, rowspan = 3, padx = 5,pady = 5)\n\n\n\tdef calcule(self):\n\t\ta=2\n\t\tn=0\n\t\tlim=int(self.entr1.get())-1\n\t\twhile(n json obejct\r\n\r\ndef getUtcTimestamp(t):\r\n \"\"\" Converting the given time to UTC timzone time\"\"\"\r\n format = '%d-%m-%Y-%H:%M:%S'\r\n timezone_place = pytz.timezone(timezone)\r\n date_time = datetime.datetime.strptime(t, format)\r\n utc_time = timezone_place.localize(date_time) #get the time on that timezone place\r\n utc_time = utc_time.astimezone(pytz.utc)\r\n utc_timestamp = round(utc_time.timestamp() * 1000) #microsec --> millisec\r\n return utc_timestamp \r\n\r\ndef get_values_omnyk(parameter,tenantId, userId, now, one_day):\r\n \"\"\"\r\n retrieves omnytraq data from the database\r\n parameter: str, a feature of omnytraq ie heartRate/spo2 value\r\n tenantId: str\r\n ringId: str\r\n now: time in milliseconds\r\n one_day: time in milliseconds\r\n\r\n \"\"\"\r\n url = \"http://34.208.125.165:8004/api/v1/datapoints/query\"\r\n # payload = {\"metrics\":[{\"tags\":{\"ringId\":[ringId]},\r\n # \"name\": parameter + \"-\" + tenantId ,#\"heartRate-OMNYK_US\",\r\n # \"aggregators\":[{\"name\":\"avg\",\r\n # \"sampling\":{\"value\":\"1\",\"unit\":\"seconds\"}\r\n # }]\r\n # }],\r\n # \"plugins\":[],\r\n # \"cache_time\":0,\r\n # \"start_absolute\": one_day ,#1606513500000,\r\n # \"end_absolute\": now #1606602883962\r\n # }\r\n payload = {\"metrics\": [{\"tags\": {\"userId\": [userId]},\r\n \"name\": parameter + \"-\" + tenantId, # \"heartRate-OMNYK_US\",\r\n \"aggregators\": [{\"name\": \"avg\",\r\n \"sampling\": {\"value\": \"1\", \"unit\": \"seconds\"}\r\n }]\r\n }],\r\n \"plugins\": [],\r\n \"cache_time\": 0,\r\n \"start_absolute\": one_day, # 1606513500000,\r\n \"end_absolute\": now # 1606602883962\r\n }\r\n payload = json.dumps(payload) #json object --> string\r\n http = urllib3.PoolManager()\r\n response = http.request('POST', url, body=payload)\r\n if response.status != 200:# or response.status != 404:\r\n return\r\n json_obj = json.loads(response.data.decode('utf-8'))\r\n\r\n #converting json to dataframe\r\n values = json_obj[\"queries\"][0][\"results\"][0][\"values\"]\r\n columns = [\"time\",parameter]\r\n dataframe = pd.DataFrame(values,columns=columns)\r\n return dataframe\r\n\r\ndef get_values_comp(parameter,tenantId, userId, now, one_day):\r\n \"\"\"\r\n retrieves comparative data from the database\r\n parameter: str, a feature of omnytraq ie heartRate/spo2 value\r\n tenantId: str\r\n ringId: str\r\n now: time in milliseconds\r\n one_day: time in milliseconds\r\n\r\n \"\"\"\r\n url = \"http://34.208.125.165:8004/api/v1/datapoints/query\"\r\n # payload = {\"metrics\": [{\"tags\": {\"ringId\": [ringId]},\r\n # \"name\": parameter + \"Comp-\" + tenantId, # \"heartRate-OMNYK_US\",\r\n # \"aggregators\": [{\"name\": \"avg\",\r\n # \"sampling\": {\"value\": \"1\", \"unit\": \"seconds\"}\r\n # }]\r\n # }],\r\n # \"plugins\": [],\r\n # \"cache_time\": 0,\r\n # \"start_absolute\": one_day, # 1606513500000,\r\n # \"end_absolute\": now # 1606602883962\r\n # }\r\n\r\n payload = {\"metrics\": [{\"tags\": {\"userId\": [userId]},\r\n \"name\": parameter + \"Comp-\" + tenantId, # \"heartRate-OMNYK_US\",\r\n \"aggregators\": [{\"name\": \"avg\",\r\n \"sampling\": {\"value\": \"1\", \"unit\": \"seconds\"}\r\n }]\r\n }],\r\n \"plugins\": [],\r\n \"cache_time\": 0,\r\n \"start_absolute\": one_day, # 1606513500000,\r\n \"end_absolute\": now # 1606602883962\r\n }\r\n payload = json.dumps(payload)\r\n http = urllib3.PoolManager()\r\n response = http.request('POST', url, body=payload)\r\n\r\n if response.status != 200: # or response.status != 404:\r\n return\r\n json_obj = json.loads(response.data.decode('utf-8'))\r\n\r\n #converting json to dataframe\r\n values = json_obj[\"queries\"][0][\"results\"][0][\"values\"]\r\n columns = [\"time\",parameter]\r\n dataframe = pd.DataFrame(values,columns=columns)\r\n return dataframe\r\n\r\ndef main():\r\n strat_day , now = getUtcTimestamp(from_date) , getUtcTimestamp(to_date)\r\n users = get_all_users()\r\n from_email_id , password = 'analytics@omnyk.com' , 'Omnyk4you'\r\n \r\n for user in users:\r\n userId,firstName, emailId, tenantId = user[\"id\"],user['firstName'], user['emailId'], user['tenantId']\r\n if tenantId is None:\r\n tenantId = 'OMNYK_US'\r\n if tenantId != 'JAYADEVA-001':\r\n continue\r\n ringIds = user['ringId']\r\n ringIdList = ringIds.split(\",\") #why\r\n\r\n if ringIdList[0] == '':\r\n continue #skiping the person who has no ring id\r\n \r\n hr_data_omnyk = get_values_omnyk(\"heartRate\", tenantId, userId, now, strat_day)\r\n spo2_data_omnyk = get_values_omnyk(\"spo2\", tenantId, userId, now, strat_day)\r\n hr_data_comp = get_values_comp(\"hearRate\", tenantId, userId, now, strat_day)\r\n spo2_data_comp = get_values_comp(\"spo2\", tenantId, userId, now, strat_day)\r\n print(hr_data_omnyk , spo2_data_omnyk,hr_data_comp ,spo2_data_comp)\r\n continue\r\n if len(hr_data_comp) <=0 or len(spo2_data_comp) <=0:\r\n print('skipping user')\r\n continue #no data in 24 hours\r\n print(hr_data_omnyk , spo2_data_omnyk,hr_data_comp ,spo2_data_comp)\r\n break\r\n\r\n\r\n\r\n \r\n\r\n\r\n \r\n\r\nif __name__ =='__main__':\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"-fd\" , dest=\"from_date\" , required=False , help=\"Enter the From date\" , action=\"store\",default=None)\r\n parser.add_argument(\"-td\", dest=\"to_date\", required=False, help=\"Enter To Date\", action=\"store\", default=None)\r\n parser.add_argument(\"-tz\", dest=\"time_zone\", required=True, help=\"Enter Time Zone\", default=None)\r\n parser.add_argument(\"-r\", dest=\"quick_range\", required=False, help=\"Enter Range\")\r\n parser.add_argument(\"-s\" , dest=\"sd_5_min\" , required=True , help=\"Enter Stabdard deviation 5min Sample size\",default=5)\r\n parser.add_argument(\"-p\" , dest=\"sd_15_min\" , help=\"Enter Stabdard deviation 15min Sample size\" , default=15)\r\n args = parser.parse_args()\r\n \r\n sds_min, sdp_min , timezone= args.sd_5_min, args.sd_15_min , args.time_zone\r\n quick_range = args.quick_range \r\n if quick_range == None:\r\n from_date , to_date = args.from_date , args.to_date\r\n else:\r\n format = '%d-%m-%Y-%H:%M:%S'\r\n if quick_range == 'today':\r\n date = datetime.datetime.now(pytz.timezone(timezone))\r\n from_date = date.date().strftime(format)\r\n to_date = date.strftime(format)\r\n \r\n elif quick_range == 'yesterday':\r\n date = datetime.datetime.now(pytz.timezone('UTC'))\r\n from_date = (date.date() - datetime.timedelta(days=1)).strftime(format)\r\n to_date = date.strftime(format)\r\n to_date = datetime.datetime.strptime(to_date, format)\r\n to_date = (to_date - datetime.timedelta(minutes=1)).strftime(format)\r\n \r\n else:\r\n print(\"Range can not be recognized :( :(\")\r\n sys.exit()\r\n main()\r\n ","sub_path":"pavi/daily_control_compare_report.py","file_name":"daily_control_compare_report.py","file_ext":"py","file_size_in_byte":8072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"312770654","text":"import json\r\nimport urllib.parse\r\nfrom collections import OrderedDict\r\n\r\nclass UrlParamsBuilder(object):\r\n\r\n def __init__(self):\r\n self.param_map = OrderedDict()\r\n self.post_map = OrderedDict()\r\n\r\n def put_url(self, name, value):\r\n if value is not None:\r\n if isinstance(value, list):\r\n self.param_map[name] = json.dumps(value)\r\n # elif isinstance(value, float):\r\n # self.param_map[name] = ('%.20f' % (value))[slice(0, 16)].rstrip('0').rstrip('.')\r\n else:\r\n self.param_map[name] = str(value)\r\n def put_post(self, name, value):\r\n if value is not None:\r\n if isinstance(value, list):\r\n self.post_map[name] = value\r\n else:\r\n self.post_map[name] = str(value)\r\n\r\n def build_url(self):\r\n if len(self.param_map) == 0:\r\n return \"\"\r\n\r\n encoded_param = urllib.parse.urlencode(self.param_map)\r\n\r\n # Error -1022: Signature for this request is not valid.\r\n # => Ensure 'signature' parameter is at the end of the URL, as per doc.\r\n # signature = ''\r\n # if 'signature' in self.param_map:\r\n # # Remove it from the (unsorted) dic and append manually\r\n # signature = self.param_map['signature']\r\n # del self.param_map['signature']\r\n # signature = \"&signature=%s\" % signature\r\n # encoded_param = urllib.parse.urlencode(self.param_map)\r\n # # Append the signature manually. URL encoding is not necessary here.\r\n # encoded_param += signature\r\n\r\n return encoded_param\r\n\r\n def build_url_to_json(self):\r\n return json.dumps(self.param_map)\r\n","sub_path":"binance_f/impl/utils/urlparamsbuilder.py","file_name":"urlparamsbuilder.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"98473614","text":"#!/bin/python3\n\nimport os\nimport sys\n\n#\n# Complete the timeConversion function below.\n#\ndef timeConversion(s):\n time_of_day = s[-2:]\n hour = s[:2]\n time = s[2:-2]\n if time_of_day == 'AM':\n if hour == '12':\n return '00' + time\n else:\n if hour != '12':\n hour = str(int(hour) + 12)\n return hour + time\n return hour + time\n\n\nif __name__ == '__main__':\n f = open(os.environ['OUTPUT_PATH'], 'w')\n\n s = input()\n\n result = timeConversion(s)\n\n f.write(result + '\\n')\n\n f.close()\n\n","sub_path":"python/hr/algorithms/time_conversion_alt01_easy.py","file_name":"time_conversion_alt01_easy.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"329922753","text":"from tkinter import *\nimport requests\nimport mysql.connector\nimport random\n\nclass Color:\n def __init__(self):\n\n self.conn=mysql.connector.connect(host=\"remotemysql.com\", user=\" HCtIRqQfLj\",password=\"Ybw3JBxfky\",database=\" HCtIRqQfLj\")\n self.mycursor=self.conn.cursor()\n self.root=Tk()\n self.root.title(\"LOGIN SYSTEM\")\n\n self.root.minsize(400,600)\n self.root.maxsize(400,600)\n self.root.configure(background=\"#FFFFFF\")\n\n self.lable1=Label(self.root,text=\"LOGIN OR REGISTRATION\",bg=\"#FFFFFF\",fg=\"#7D9F4C\")\n self.lable1.configure(font=(\"Cooper black\",20))\n self.lable1.pack(pady=(30,10))\n\n self.click=Button(self.root,text=\"REGISTER\",bg=\"#fff\",fg=\"#992439\",width=25,height=2,command=lambda:self.register())\n self.click.configure(font=(\"Constantia\",10,\"bold\"))\n self.click.pack(pady=(10,20))\n\n self.click=Button(self.root,text=\"LOGIN\",bg=\"#fff\",fg=\"#992439\",width=25,height=2,command=lambda:self.login())\n self.click.configure(font=(\"Constantia\",10,\"bold\"))\n self.click.pack(pady=(10,20))\n\n\n self.result=Label(self.root,text=\"\",bg=\"#FFFFFF\",fg=\"#C61B6B\")\n self.result.configure(font=(\"Constantia\",14,\"bold\"))\n self.result.pack(pady=(5,10))\n \n self.root.mainloop()\n\n def register(self):\n \n self.root.destroy()\n self.root2=Tk()\n self.root2.title(\"REGISTRATION\")\n\n self.root2.minsize(400,600)\n self.root2.maxsize(400,600)\n self.root2.configure(background=\"#AF708D\")\n\n self.lable1=Label(self.root2,text=\"REGISTRATION FORM\",bg=\"#AF708D\",fg=\"#fff\")\n self.lable1.configure(font=(\"Algerian\",22,\"bold\"))\n self.lable1.pack(pady=(30,10))\n\n \n\n self.lable2=Label(self.root2,text=\"NAME\",bg=\"#AF708D\",fg=\"#fff\")\n self.lable2.configure(font=(\"Constantia\",22,\"bold\"))\n self.lable2.pack(pady=(30,10))\n\n self.user_name=Entry(self.root2)\n self.user_name.pack(ipadx=40,ipady=5)\n\n \n self.lable3=Label(self.root2,text=\"EMAIL\",bg=\"#AF708D\",fg=\"#fff\")\n self.lable3.configure(font=(\"Constantia\",22,\"bold\"))\n self.lable3.pack(pady=(30,10))\n\n self.user_email=Entry(self.root2)\n self.user_email.pack(ipadx=40,ipady=5)\n\n self.lable4=Label(self.root2,text=\"PASSWORD\",bg=\"#AF708D\",fg=\"#fff\")\n self.lable4.configure(font=(\"Constantia\",22,\"bold\"))\n self.lable4.pack(pady=(30,10))\n\n self.user_password=Entry(self.root2)\n self.user_password.pack(ipadx=40,ipady=5)\n\n\n self.click1=Button(self.root2,text=\"OK\",bg=\"#FFFFFF\",fg=\"#AF708D\",width=25,height=2,command=lambda:self.registered())\n self.click1.configure(font=(\"Constantia\",10,\"bold\"))\n self.click1.pack(pady=(10,20))\n\n self.root2.mainloop()\n\n\n def login(self):\n self.root.destroy()\n \n \n self.root3=Tk() \n \n\n\n \n self.root3.title(\"LOGIN FORM\")\n self.root3.minsize(600,600)\n self.root3.configure(background=\"#B6511A\")\n\n\n\n \n self.Label1=Label(self.root3,text=\"LOGIN FORM\",bg=\"#B6511A\",fg=\"#FFF\")\n self.Label1.configure(font=(\"Algerian\",22,\"bold\"))\n self.Label1.pack(pady=(30,10))\n\n\n \n \n self.Label2=Label(self.root3,text=\"Enter your E-mail:\",bg=\"#B6511A\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",15,\"bold\"))\n self.Label2.pack(pady=(10,5))\n self.user_email=Entry(self.root3)\n self.user_email.pack(ipadx=6,ipady=6)\n\n \n self.Label2=Label(self.root3,text=\"Enter your password:\",bg=\"#B6511A\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",15,\"bold\"))\n self.Label2.pack(pady=(10,5))\n self.user_password=Entry(self.root3)\n self.user_password.pack(ipadx=6,ipady=6)\n\n self.click2=Button(self.root3,text=\"OK\",bg=\"#FFFFFF\",fg=\"#B6511A\",width=5,height=1,command=lambda:self.logined())\n self.click2.configure(font=(\"Garamond\",10,\"bold\"))\n self.click2.pack(pady=(5,5))\n\n self.root3.mainloop()\n\n\n\n\n def registered(self):\n \n name=self.user_name.get()\n \n x=len(name)\n \n if (x==0):\n self.root=Tk()\n\n self.root.title(\"ERROR Form\")\n self.root.minsize(100,100)\n \n self.root3.configure(background=\"#EA347C\")\n \n self.Label3=Label(self.root,text=\"INVALID NAME\",bg=\"#EA347C\",fg=\"#FFF\")\n self.Label3.configure(font=(\"Garamond\",15,\"bold\"))\n self.Label3.pack(pady=(10,5))\n \n self.click2=Button(self.root,text=\"RESET\",bg=\"#FFF\",fg=\"#EA347C\",width=5,height=1,command=lambda:self.register1())\n self.click2.configure(font=(\"Garamond\",10,\"bold\"))\n self.click2.pack(pady=(5,5))\n self.root.mainloop()\n \n \n \n name1=name.upper()\n \n email=self.user_email.get()\n f=0\n \n for i in email:\n \n if(i==\"@\" or i==\".\"):\n f+=1\n \n if f!=2 :\n self.root=Tk()\n \n self.root.title(\"ERROR Form\")\n self.root.minsize(100,100)\n \n self.root.configure(background=\"#EA347C\")\n \n self.Label3=Label(self.root,text=\"INVALID EMAIL\",bg=\"#EA347C\",fg=\"#FFF\")\n self.Label3.configure(font=(\"Garamond\",15,\"bold\"))\n self.Label3.pack(pady=(10,5))\n \n self.click2=Button(self.root,text=\"RESET\",bg=\"#FFF\",fg=\"#EA347C\",width=5,height=1,command=lambda:self.register1())\n self.click2.configure(font=(\"Garamond\",10,\"bold\"))\n self.click2.pack(pady=(5,5))\n self.root.mainloop()\n \n password=self.user_password.get()\n \n try:\n self.mycursor.execute(\"INSERT INTO users (user_id,name,email,password) VALUES (NULL,'{}','{}','{}')\".format(name1,email,password))\n\n self.conn.commit()\n\n except Exception as e:\n self.root=Tk()\n \n self.root.title(\"ERROR Form\")\n self.root.minsize(100,100)\n \n self.root.configure(background=\"#EA347C\")\n \n self.Label3=Label(self.root,text=\"INVALID EMAIL\",bg=\"#EA347C\",fg=\"#FFF\")\n self.Label3.configure(font=(\"Garamond\",15))\n self.Label3.pack(pady=(10,5))\n \n self.click2=Button(self.root,text=\"RESET\",bg=\"#FFF\",fg=\"#EA347C\",width=5,height=1,command=lambda:self.register1())\n self.click2.configure(font=(\"Garamond\",10,\"bold\"))\n self.click2.pack(pady=(5,5))\n self.root.mainloop()\n \n\n\n\n \n self.root2.destroy() \n\n self.mycursor.execute(\"SELECT * FROM users WHERE email LIKE '{}' and password LIKE '{}'\".format(email,password))\n self.a=self.mycursor.fetchall()\n self.b=self.a[0][0]\n\n \n self.mycursor.execute(\"INSERT INTO leader VALUES({},0)\".format(self.b))\n self.conn.commit()\n \n self.root=Tk() \n\n\n \n self.root.title(\"REGISTRATION\")\n self.root.minsize(200,100)\n self.root.configure(background=\"#00a65a\")\n\n\n\n \n self.Label2=Label(self.root,text=\"Registered succesfully!!!!\",bg=\"#00a65a\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",20,\"bold\"))\n self.Label2.pack(pady=(10,5))\n\n self.click1=Button(self.root,text=\"OK\",bg=\"#FFF\",fg=\"#00a65a\",width=5,height=1,command=lambda:self.destroy())\n self.click1.configure(font=(\"Garamond\",10,\"bold\"))\n self.click1.pack(pady=(5,5))\n\n self.root.mainloop()\n\n\n \n #destroy the GUI\n def destroy(self):\n self.root.destroy()\n\n\n def register1(self):\n #print(\"x\")\n \n self.user_name.delete(first=0,last=100)\n self.user_email.delete(first=0,last=100)\n self.user_password.delete(first=0,last=100)\n \n self.root.destroy()\n\n\n\n\n def logined(self):\n \n email=self.user_email.get()\n password=self.user_password.get()\n self.email_save=email\n\n self.mycursor.execute(\"SELECT * FROM users WHERE email LIKE '{}' and password LIKE '{}'\".format(email,password))\n \n self.x=self.mycursor.fetchall()\n\n if(len(self.x)==0):\n \n self.root=Tk()\n\n self.root.title(\"ERROR Form\")\n self.root.minsize(100,100)\n #self.root.maxsize(600,600)\n self.root.configure(background=\"red\")\n \n self.Label3=Label(self.root,text=\"INVALID DATA\",bg=\"red\",fg=\"#FFF\")\n self.Label3.configure(font=(\"Garamond\",15))\n self.Label3.pack(pady=(10,5))\n #print(\"x\")\n self.click2=Button(self.root,text=\"RESET\",bg=\"#FFF\",fg=\"#FFFFFF\",width=5,height=1,command=lambda:self.login2())\n self.click2.configure(font=(\"Garamond\",20,\"bold\"))\n self.click2.pack(pady=(5,5))\n self.root.mainloop()\n\n self.root3.destroy() \n\n self.root=Tk() \n\n \n #frame of GUI\n self.root.title(\"Login Form\")\n self.root.minsize(200,100)\n self.root.configure(background=\"#00a65a\")\n\n\n\n \n self.Label2=Label(self.root,text=\"LOGIN Successfully!!!\",bg=\"#00a65a\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",20,\"bold\"))\n self.Label2.pack(pady=(10,5))\n\n \n \n self.Label2=Label(self.root,text=\"WELCOME\",bg=\"#00a65a\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",25))\n self.Label2.pack(pady=(10,5))\n \n y=self.x[0][1]\n\n self.Label2=Label(self.root,text=y,bg=\"#00a65a\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",20,\"bold\"))\n self.Label2.pack(pady=(10,5))\n\n self.z=self.x[0][0]\n\n self.Label2=Label(self.root,text=\"Your ID NO.:\",bg=\"#00a65a\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",20,\"bold\"))\n self.Label2.pack(pady=(10,5))\n\n \n self.Label2=Label(self.root,text=self.z,bg=\"#00a65a\",fg=\"#FFF\")\n self.Label2.configure(font=(\"Garamond\",20,\"bold\"))\n self.Label2.pack(pady=(10,5))\n\n\n self.click1=Button(self.root,text=\"OK\",bg=\"#FFF\",fg=\"#000\",width=5,height=1,command=lambda:self.user())\n self.click1.configure(font=(\"Garamond\",20,\"bold\"))\n self.click1.pack(pady=(5,5))\n\n\n\n def login2(self):\n \n \n self.user_email.delete(first=0,last=100)\n self.user_password.delete(first=0,last=100)\n \n self.root.destroy()\n\n\n\n def user(self):\n self.destroy()\n\n\n\n self.root1=Tk()\n \n self.root1.title(\"PORTAL\")\n\n self.root1.minsize(400,600)\n self.root1.maxsize(400,600)\n\n\n\n\n self.root1.configure(background=\"#FFFFFF\")\n\n\n \n\n self.lable1=Label(self.root1,text=\"WELCOME TO BRAINGAME\",bg=\"#FFFFFF\",fg=\"#7D9F4C\")\n self.lable1.configure(font=(\"Cooper black\",20))\n self.lable1.pack(pady=(30,10))\n\n self.click=Button(self.root1,text=\"PLAY GAME\",bg=\"#fff\",fg=\"#992439\",width=25,height=2,command=lambda:self.fetch())\n self.click.configure(font=(\"Constantia\",10,\"bold\"))\n self.click.pack(pady=(10,20))\n\n self.click=Button(self.root1,text=\"VIEW LEADERBOARD\",bg=\"#fff\",fg=\"#992439\",width=25,height=2,command=lambda:self.view())\n self.click.configure(font=(\"Constantia\",10,\"bold\"))\n self.click.pack(pady=(10,20))\n\n\n self.click=Button(self.root1,text=\"EXIT\",bg=\"#fff\",fg=\"#992439\",width=25,height=2,command=lambda:self.destroy1())\n self.click.configure(font=(\"Constantia\",10,\"bold\"))\n self.click.pack(pady=(10,20))\n\n\n\n self.root.mainloop()\n\n\n def destroy1(self):\n self.root1.destroy()\n \n\n\n colours = ['Red','Blue','Green','Pink','Black', \n\t\t'Yellow','Orange','White','Purple','Brown'] \n score = 0\n\n timeleft = 30\n\n def startGame(self,event):\n if Color.timeleft == 30:\n self.countdown()\n self.nextColour()\n\n def nextColour(self):\n #Color.score\n #Color.timeleft\n\n if Color.timeleft>0:\n self.e.focus_set()\n if self.e.get().lower() == Color.colours[1].lower():\n Color.score += 1\n self.e.delete(first=0,last=100)\n random.shuffle(Color.colours)\n self.label.config(fg = str(Color.colours[1]), text = str(Color.colours[0]))\n self.scoreLabel.config(text = \"Score: \" + str(Color.score)) \n\n\n def countdown(self):\n #Color.timeleft\n if Color.timeleft > 0:\n Color.timeleft -= 1\n self.timeLabel.config(text = \"Time left: \"+ str(Color.timeleft))\n self.timeLabel.after(1000, self.countdown) \n\n def fetch(self):\n #self.mycursor.execute(\"\"\"INSERT INTO leader VALUES({},0)\"\"\".format(self.z))\n \n self.root = Tk() \n self.root.title(\"COLORGAME\") \n\n \n self.root.geometry(\"375x300\") \n\n \n self.instructions =Label(self.root, text = \"Type in the colour\"\n \"of the words, and not the word text!\", \n font = ('Helvetica', 12)) \n self.instructions.pack() \n\n # add a score label \n self.scoreLabel = Label(self.root, text = \"Press enter to start\", \n font = ('Helvetica', 12)) \n self.scoreLabel.pack() \n\n # add a time left label \n self.timeLabel = Label(self.root, text = \"Time left: \" +\n str(Color.timeleft), font = ('Helvetica', 12)) \n \n self.timeLabel.pack() \n\n # add a label for displaying the colours \n self.label =Label(self.root, font = ('Helvetica', 60)) \n self.label.pack() \n\n \n #add entry box for typing in colours \n self.e =Entry(self.root) \n\n \n # run the game when the enter key is pressed \n self.root.bind('', self.startGame) \n self.e.pack() \n\n # set focus on the entry box \n self.e.focus_set()\n\n #self.root.destroy()\n\n self.Label3=Label(self.root,text=\"TYPE OK WHEN YOU ARE DONE\",bg=\"red\",fg=\"#FFF\")\n self.Label3.configure(font=(\"Garamond\",15))\n self.Label3.pack(pady=(10,5))\n \n\n self.click1=Button(self.root,text=\"OK\",bg=\"#FFF\",fg=\"#000\",width=5,height=1,command=lambda:self.scores())\n self.click1.pack(pady=(5,5))\n\n # start the GUI \n self.root.mainloop()\n\n\n def scores(self):\n #print(Color.score)\n new_score=Color.score\n print(Color.score)\n\n\n self.mycursor.execute(\"\"\"UPDATE leader SET score={}\n WHERE users_id={}\n AND score<{}\"\"\".format(Color.score,self.z,Color.score))\n\n self.conn.commit()\n\n self.destroy()\n\n\n\n def view(self):\n self.destroy1()\n self.mycursor.execute(\"\"\"SELECT name,score\n FROM users\n JOIN leader\n ON leader.users_id=users.user_id\n ORDER BY score DESC\"\"\")\n\n x=self.mycursor.fetchall()\n #print(self.x)\n\n temp=\"\"\n for i in x:\n for j in i:\n j=str(j)\n temp=temp+j+\" \"\n temp=temp+\"\\n\"\n \n\n\n\n self.root=Tk()\n \n self.root.title(\"LEADER BOARD\")\n\n self.root.minsize(400,600)\n self.root.maxsize(400,600)\n self.root.configure(background=\"#FFFFFF\")\n \n\n self.lable1=Label(self.root,text=\"LAEDER BOARD\",bg=\"#FFFFFF\",fg=\"#C00A50\")\n self.lable1.configure(font=(\"Garamond\",20,\"bold\"))\n self.lable1.pack(pady=(30,10))\n\n self.lable1=Label(self.root,text=\"\",bg=\"#FFFFFF\",fg=\"#2A266B\")\n self.lable1.configure(font=(\"Calibri\",18))\n self.lable1.pack(pady=(30,10))\n self.lable1.configure(text=temp)\n\n self.click1=Button(self.root,text=\"OK\",bg=\"#FFF\",fg=\"#C00A50\",width=5,height=1,command=lambda:self.destroy())\n self.click1.pack(pady=(5,5))\n\n\n\n self.root.mainloop()\n \n \n\nobj=Color()\n\n\n","sub_path":"color_game.py","file_name":"color_game.py","file_ext":"py","file_size_in_byte":16985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"499463946","text":"# coding:iso-8859-9 Türkçe\r\n\r\nfrom random import randint\r\nfrom math import *\r\n\r\nsayaç1=sayaç2=sayaç3=0\r\nkere = randint (1,1000)\r\nfor i in range (1, kere):\r\n if i**2 % 10 == 1: sayaç1 +=1\r\n elif i**2 % 10 == 4: sayaç2 +=1\r\n elif i**2 % 10 == 9: sayaç3 +=1\r\nprint (\"1->\", kere, \"karesi sayılardan sonu 1'le bitenler:\", sayaç1, \", 4'le bitenler:\", sayaç2, \"ve 9'la bitenlerin toplamı:\", sayaç3)\r\nprint()\r\nsayaç=0\r\nfor i in range (1, kere): sayaç = sayaç + (i**(-1))\r\nprint (sayaç, \"- ln(\", kere, \") =\", sayaç - log (kere))\r\nprint()\r\ntoplam=0\r\nfor i in range (1, 2001):\r\n if i % 2 == 0: toplam+=-i\r\n else: toplam+=i\r\n# Gerçekte 1000 kere -1'i toplar...\r\nprint (\"[1->2000] +tekler ve -çiftler toplamı:\", toplam)\r\nprint()\r\n\"\"\"\r\nfor i in range (3, 10000):\r\n toplam=1\r\n for j in range (2, i):\r\n if (i//j)*j == i: toplam+=j\r\n if toplam == i: print (i, \"bir ideal sayıdır\")\r\nÇıktı:\r\n6 bir ideal sayıdır\r\n28 bir ideal sayıdır\r\n496 bir ideal sayıdır\r\n8128 bir ideal sayıdır\r\n\"\"\"\r\nprint()\r\nbayrak=0\r\nfor j in range (2, kere):\r\n if (kere//j)*j==kere and trunc (sqrt (j))**2 == j: bayrak=1; break\r\nif not bayrak: print (kere, \"sayısının bölenleri kare-muaf'tır\")\r\nelse: print (kere, \"sayısının\", j, \"böleni ideal-kare'dir\")\r\nprint()\r\n","sub_path":"Brian Heinold (243) ile Python/p10509a_sınav.py","file_name":"p10509a_sınav.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"47571414","text":"# -*- coding: utf-8 -*-\nfrom collective.behavior.relatedmedia import messageFactory as _\nfrom z3c.form.interfaces import ITextWidget\nfrom zope import schema\nfrom zope.interface import Interface\n\n\nclass IRelatedMediaWidget(ITextWidget):\n \"\"\" marker for widget \"\"\"\n\n\nclass IRelatedMediaSettings(Interface):\n \"\"\" various settings \"\"\"\n\n media_container_path = schema.TextLine(\n title=_(u'Media Container'),\n description=_(u'Traversable path to media container. '\n u'We respect IPloneSiteRoot, INavigationRoot '\n u'and IChildSite (lineage) as \"/\"'),\n required=True,\n )\n\n media_container_in_assets_folder = schema.Bool(\n title=_('uCreate Media Container in Assets Folder '\n '(language independent)?'),\n description=_(u'If True, the Media Container path defined above is '\n u'generated in the language independend Assets folder. '\n u'This requires plone.app.multilingual.'),\n default=False,\n required=False,\n )\n\n image_gallery_cssclass = schema.List(\n title=_(u'Gallery CSS classes'),\n value_type=schema.TextLine(title=u'CSS Class'),\n required=False,\n )\n\n image_gallery_default_class = schema.TextLine(\n title=_(u'Default gallery class for new articles'),\n required=True,\n )\n\n image_gallery_default_preview_scale_direction = schema.Bool(\n title=_(u'Default setting for cropping gallery images'),\n default=False,\n required=False,\n )\n\n open_attachment_in_new_window = schema.Bool(\n title=_(u'Open Attachment links in new window'),\n default=True,\n required=False,\n )\n","sub_path":"collective/behavior/relatedmedia/interfaces.py","file_name":"interfaces.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"455022460","text":"import numpy as np\nimport os\nimport matplotlib.pyplot as plt\n\n\n\nclass GraphicDesigner:\n\n def __init__(self, backup, parameters, folder):\n \n self.exchanges_list = backup[\"exchanges\"]\n self.mean_utility_list = backup[\"consumption\"]\n self.n_exchanges_list = backup[\"n_exchanges\"]\n self.good_accepted_as_medium = backup[\"good_accepted_as_medium\"]\n self.proportions = backup[\"proportions\"]\n \n self.parameters = parameters\n\n self.n_goods = len(self.good_accepted_as_medium[0])\n \n self.main_figure_name = self.get_fig_name(name=\"main\", folder=folder)\n self.proportions_figure_name = self.get_fig_name(name=\"proportions\", folder=folder)\n \n @staticmethod\n def get_fig_name(name, folder):\n\n os.makedirs(folder, exist_ok=True)\n\n fig_name = os.path.expanduser(\"{}/{}.pdf\".format(folder, name))\n\n init_fig_name = fig_name.split(\".\")[0]\n i = 2\n while os.path.exists(fig_name):\n fig_name = \"{}{}.pdf\".format(init_fig_name, i)\n i += 1\n \n return fig_name\n\n def plot_main_fig(self):\n\n # What is common to all subplots\n fig = plt.figure(figsize=(25, 12))\n fig.patch.set_facecolor('white')\n \n n_lines = 2\n n_columns = 3\n \n x = np.arange(self.parameters[\"t_max\"])\n\n # First subplot\n ax = plt.subplot(n_lines, n_columns, 1)\n ax.set_title(\"Proportion of each type of exchange according to time \\n\")\n \n type_of_exchanges = sorted([i for i in self.exchanges_list[0].keys()])\n y = []\n for i in range(len(type_of_exchanges)):\n y.append([])\n for t in range(self.parameters[\"t_max\"]):\n for exchange_idx in range(len(type_of_exchanges)):\n y[exchange_idx].append(self.exchanges_list[t][type_of_exchanges[exchange_idx]])\n\n ax.set_ylim([-0.02, 1.02])\n\n for exchange_idx in range(len(type_of_exchanges)):\n \n ax.plot(x, y[exchange_idx], label=\"Exchange {}\".format(type_of_exchanges[exchange_idx]), linewidth=2)\n\n ax.legend()\n\n # Second subplot\n\n ax = plt.subplot(n_lines, n_columns, 2)\n ax.set_title(\"Consumption average according to time \\n\")\n ax.plot(x, self.mean_utility_list, linewidth=2)\n\n # Third subplot\n ax = plt.subplot(n_lines, n_columns, 3)\n ax.set_title(\"Total number of exchanges according to time \\n\")\n ax.plot(x, self.n_exchanges_list, linewidth=2)\n\n # Fourth subplot\n ax = plt.subplot(n_lines, n_columns, 4)\n ax.set_title(\"Frequency at which a good is accepted as a mean of exchange \\n\")\n\n ax.set_ylim([-0.02, 1.02])\n\n for i in range(self.n_goods):\n\n ax.plot(x, [j[i] for j in self.good_accepted_as_medium],\n label=\"Good {}\".format(i), linewidth=2)\n\n ax.legend()\n\n # Fifth subplot\n ind = np.arange(self.n_goods)\n width = 0.5\n ax = plt.subplot(n_lines, n_columns, 5)\n ax.set_title(\"Storing costs \\n\")\n\n ax.bar(ind, self.parameters[\"storing_costs\"], width)\n\n # Hide the right and top spines\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n\n # Only show ticks on the left and bottom spines\n ax.yaxis.set_ticks_position('left')\n ax.xaxis.set_ticks_position('none')\n ax.set_xticks(ind + width / 2)\n\n x_labels = []\n for i in range(self.n_goods):\n x_labels.append(\n '$c_{}$'.format(i)\n )\n ax.set_xticklabels(x_labels, fontsize=16)\n\n # Sixth subplot\n ax = plt.subplot(n_lines, n_columns, 6)\n ax.set_title(\"Parameters\")\n ax.axis('off')\n\n msg = \\\n \"Agent model: {}; \\n \\n\" \\\n \"Cognitive parameters: {}; \\n \\n\" \\\n \"Repartition of roles: {}; \\n \\n \" \\\n \"Trials: {}. \\n \\n\".format(\n self.parameters[\"agent_model\"].name,\n self.parameters[\"cognitive_parameters\"],\n self.parameters[\"repartition_of_roles\"],\n self.parameters[\"t_max\"]\n )\n\n ax.text(0.5, 0.5, msg, ha='center', va='center', size=12)\n\n plt.savefig(self.main_figure_name)\n\n plt.close()\n\n def plot_proportions(self):\n\n # Container for proportions of agents having this or that in hand according to their type\n # - rows: type of agent\n # - columns: type of good\n\n fig = plt.figure(figsize=(25, 12))\n fig.patch.set_facecolor('white')\n\n n_lines = self.n_goods\n n_columns = 1\n\n x = np.arange(len(self.proportions))\n\n for agent_type in range(self.n_goods):\n\n # First subplot\n ax = plt.subplot(n_lines, n_columns, agent_type + 1)\n ax.set_title(\"Proportion of agents of type {} having good i in hand\\n\".format(agent_type))\n\n y = []\n for i in range(self.n_goods):\n y.append([])\n\n for proportions_at_t in self.proportions:\n for good in range(self.n_goods):\n y[good].append(proportions_at_t[agent_type, good])\n\n ax.set_ylim([-0.02, 1.02])\n\n for good in range(self.n_goods):\n\n ax.plot(x, y[good], label=\"Good {}\".format(good), linewidth=2)\n\n ax.legend()\n\n plt.tight_layout()\n\n plt.savefig(self.proportions_figure_name)\n \n\ndef represent_results(backup, parameters, folder=\"~/Desktop/\"):\n\n g = GraphicDesigner(backup=backup, parameters=parameters, folder=folder)\n g.plot_main_fig()\n g.plot_proportions()\n","sub_path":"analysis/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":5718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"445173529","text":"# Copyright 2019 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom io import StringIO\nfrom typing import Any, Dict, Iterable, Optional\n\nfrom pants.engine.console import Console\nfrom pants.engine.fs import Workspace\nfrom pants.engine.goal import Goal\nfrom pants.engine.selectors import Params\nfrom pants.init.options_initializer import BuildConfigInitializer\nfrom pants.init.specs_calculator import SpecsCalculator\nfrom pants.option.global_options import GlobalOptions\nfrom pants.testutil.option.util import create_options_bootstrapper\nfrom pants.testutil.test_base import TestBase\nfrom pants.util.meta import classproperty\n\n\nclass GoalRuleTestBase(TestBase):\n \"\"\"A baseclass useful for testing a Goal defined as a @goal_rule.\n\n :API: public\n \"\"\"\n\n @classproperty\n def goal_cls(cls):\n \"\"\"Subclasses must return the Goal type to test.\n\n :API: public\n \"\"\"\n raise NotImplementedError()\n\n def setUp(self):\n super().setUp()\n\n if not issubclass(self.goal_cls, Goal):\n raise AssertionError(f\"goal_cls() must return a Goal subclass, got {self.goal_cls}\")\n\n def execute_rule(\n self,\n args: Optional[Iterable[str]] = None,\n global_args: Optional[Iterable[str]] = None,\n env: Optional[Dict[str, str]] = None,\n exit_code: int = 0,\n additional_params: Optional[Iterable[Any]] = None,\n ) -> str:\n \"\"\"Executes the @goal_rule for this test class.\n\n :API: public\n\n Returns the text output of the task.\n \"\"\"\n # Create an OptionsBootstrapper for these args/env, and a captured Console instance.\n options_bootstrapper = create_options_bootstrapper(\n args=(*(global_args or []), self.goal_cls.name, *(args or [])), env=env,\n )\n BuildConfigInitializer.get(options_bootstrapper)\n full_options = options_bootstrapper.get_full_options(\n [*GlobalOptions.known_scope_infos(), *self.goal_cls.subsystem_cls.known_scope_infos()]\n )\n stdout, stderr = StringIO(), StringIO()\n console = Console(stdout=stdout, stderr=stderr)\n scheduler = self.scheduler\n workspace = Workspace(scheduler)\n\n # Run for the target specs parsed from the args.\n specs = SpecsCalculator.parse_specs(full_options.specs, self.build_root)\n params = Params(\n specs.provided_specs,\n console,\n options_bootstrapper,\n workspace,\n *(additional_params or []),\n )\n actual_exit_code = self.scheduler.run_goal_rule(self.goal_cls, params)\n\n # Flush and capture console output.\n console.flush()\n stdout_val = stdout.getvalue()\n stderr_val = stderr.getvalue()\n\n assert (\n exit_code == actual_exit_code\n ), f\"Exited with {actual_exit_code} (expected {exit_code}):\\nstdout:\\n{stdout_val}\\nstderr:\\n{stderr_val}\"\n\n return stdout_val\n\n def assert_entries(self, sep, *output, **kwargs):\n \"\"\"Verifies the expected output text is flushed by the console task under test.\n\n NB: order of entries is not tested, just presence.\n\n :API: public\n\n sep: the expected output separator.\n *output: the output entries expected between the separators\n **kwargs: additional kwargs passed to execute_rule.\n \"\"\"\n # We expect each output line to be suffixed with the separator, so for , and [1,2,3] we expect:\n # '1,2,3,' - splitting this by the separator we should get ['1', '2', '3', ''] - always an extra\n # empty string if the separator is properly always a suffix and not applied just between\n # entries.\n self.assertEqual(\n sorted(list(output) + [\"\"]), sorted((self.execute_rule(**kwargs)).split(sep))\n )\n\n def assert_console_output(self, *output, **kwargs):\n \"\"\"Verifies the expected output entries are emitted by the console task under test.\n\n NB: order of entries is not tested, just presence.\n\n :API: public\n\n *output: the expected output entries\n **kwargs: additional kwargs passed to execute_rule.\n \"\"\"\n self.assertEqual(sorted(output), sorted(self.execute_rule(**kwargs).splitlines()))\n\n def assert_console_output_contains(self, output, **kwargs):\n \"\"\"Verifies the expected output string is emitted by the console task under test.\n\n :API: public\n\n output: the expected output entry(ies)\n **kwargs: additional kwargs passed to execute_rule.\n \"\"\"\n self.assertIn(output, self.execute_rule(**kwargs))\n\n def assert_console_output_ordered(self, *output, **kwargs):\n \"\"\"Verifies the expected output entries are emitted by the console task under test.\n\n NB: order of entries is tested.\n\n :API: public\n\n *output: the expected output entries in expected order\n **kwargs: additional kwargs passed to execute_rule.\n \"\"\"\n self.assertEqual(list(output), self.execute_rule(**kwargs).splitlines())\n\n def assert_console_raises(self, exception, **kwargs):\n \"\"\"Verifies the expected exception is raised by the console task under test.\n\n :API: public\n\n **kwargs: additional kwargs are passed to execute_rule.\n \"\"\"\n with self.assertRaises(exception):\n self.execute_rule(**kwargs)\n","sub_path":"src/python/pants/testutil/goal_rule_test_base.py","file_name":"goal_rule_test_base.py","file_ext":"py","file_size_in_byte":5445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"542961028","text":"# coding: utf-8\n#########################################################################\n# 网站: 疯狂Java联盟 #\n# author yeeku.H.lee kongyeeku@163.com #\n# #\n# version 1.0 #\n# #\n# Copyright (C), 2001-2018, yeeku.H.Lee #\n# #\n# This program is protected by copyright laws. #\n# #\n# Program Name: #\n# #\n# limit:\n # split addrlist by limit-sized chunks\n chunks = [addrlist[i:i + limit] for i in range(0, num, limit)]\n\n data = []\n for chunk in chunks:\n cnum = len(chunk)\n msg = b''.join((b'\\x20', pack(' 3:\n raise ValueError(\"grp_no should be 0...3\")\n\n if mem_no != 0 and mem_no != 1:\n raise ValueError(\"mem_no is 0 or 1\")\n\n reg_addr = SIS3316_DATA_TRANSFER_GRP_CTRL_REG + 0x4 * grp_no\n\n if self.read(reg_addr) & BITBUSY:\n raise self._TransferLogicBusyExcept(group=grp_no)\n\n # Fire \"Start Read Transfer\" command (FIFO programming)\n cmd = 0b10 << 30 # Read cmd\n cmd += woffset # Start address\n\n if mem_no == 1:\n cmd += 1 << 28 # Space select bit\n\n self.write(reg_addr, cmd) # Prepare Data transfer logic\n\n def _fifo_transfer_write(self, grp_no, mem_no, datalist, offset=0): # Why would we do this?\n pass\n\n def _fifo_transfer_reset(self, grp_no):\n \"\"\" Reset memory transfer logic. \"\"\"\n reg = SIS3316_DATA_TRANSFER_GRP_CTRL_REG + 0x4 * grp_no\n self.write(reg, 0)\n\n # ---------------------------\n\n def read_fifo(self, dest, grp_no, mem_no, nwords, woffset=0): # TODO: I am here (12/24)\n \"\"\"\n Get data from ADC unit's DDR memory.\n Readout is robust (retransmit on failure) and congestion-aware (adjusts an amount of data per request).\n Attrs:\n dest: an object which has a `push(smth)' method and an `index' property.\n grp_no: ADC group number.\n mem_no: memory unit number.\n nwords: number of words to read to dest.\n woffset: index of the first word.\n Returns:\n Number of words.\n \"\"\"\n # TODO: make finished an argument by ref, so we can get the value even after Except\n\n fifo_addr = SIS3316_FPGA_ADC_GRP_MEM_BASE + grp_no * SIS3316_FPGA_ADC_GRP_MEM_OFFSET\n\n # Network congestion window:\n wcwnd_limit = FIFO_READ_LIMIT\n wcwnd = wcwnd_limit / 2\n wcwnd_max = wcwnd_limit / 2\n\n wmtu = 1440 / 4 # TODO: use mtu.py to determine MTU automatically (can be jumbo frames).\n # This converts to 16-bit words\n\n wfinished = 0\n binitial_index = dest.index\n\n while wfinished < nwords:\n try: # Configure FIFO\n self._fifo_transfer_reset(grp_no) # cleanup\n self._fifo_transfer_read(grp_no, mem_no, int(woffset + wfinished))\n\n except self._WrongResponseExcept: # some trash in socket\n self.cleanup_socket()\n sleep(self.default_timeout)\n continue\n\n except self._TimeoutExcept:\n sleep(self.default_timeout)\n continue # FIXME: When to stop trying?\n\n # Data transmission\n while wfinished < nwords:\n\n try:\n wnum = int(min(nwords - wfinished, FIFO_READ_LIMIT, wcwnd))\n\n msg = b''.join((b'\\x30', pack(' wcwnd: # recovery after congestion\n wcwnd += (wcwnd_max - wcwnd) / 2\n\n else: # probe new maximum\n wcwnd = int(min(wcwnd_limit, wcwnd + wmtu + (wcwnd - wcwnd_max)))\n\n except self._UnorderedPacketExcept:\n # soft fail: some packets dropped\n break\n\n except self._TimeoutExcept:\n # hard fail (network congestion)\n wcwnd_max = wcwnd\n wcwnd = wcwnd / 2 # Reduce window by 50%\n break\n\n finally: # Note: executes before `break'\n bfinished = (dest.index - binitial_index)\n assert bfinished % 4 == 0, \"Should read a four-byte words. %d, init %d\" % (\n bfinished, binitial_index)\n wfinished = bfinished / 4\n\n # end while\n if wcwnd is 0:\n raise self._TimeoutExcept(\"many\")\n\n # end while\n self._fifo_transfer_reset(grp_no) # cleanup\n return wfinished\n\n def write_fifo(self, source, grp_no, mem_no, nwords, woffset=0): # For the future, but why would we do this?\n pass\n\n # ----------- Exceptions ----------------------\n\n class _GarbageInSocketExcept(Sis3316Except):\n \"\"\" Socket is not empty. \"\"\"\n\n class _MalformedResponseExcept(Sis3316Except):\n \"\"\" Response does not match the protocol. \"\"\"\n\n class _WrongResponseExcept(Sis3316Except):\n \"\"\" Response does not match the request. {0}\"\"\"\n\n class _UnexpectedResponseLengthExcept(Sis3316Except):\n \"\"\" Was waiting for {0} bytes, but received {1}. \"\"\"\n\n class _UnorderedPacketExcept(Sis3316Except):\n \"\"\" Ack packet not in right order. Probably some packets have been lost. \"\"\"\n\n class _PacketsLossExcept(Sis3316Except):\n \"\"\" It looks like some packets have been lost. \"\"\"\n\n class _WrongAddressExcept(Sis3316Except):\n \"\"\" Address {0} does not seem to make sense. \"\"\"\n\n class _SisNoGrantExcept(Sis3316Except):\n \"\"\" sis3316 Link interface has no grant anymore. Use open() to request it.\"\"\"\n\n class _SisFifoTimeoutExcept(Sis3316Except):\n \"\"\" sis3316 Access timeout during request (Fifo Empty). \"\"\"\n\n class _SisProtocolErrorExcept(Sis3316Except):\n \"\"\" sis3316 Request command packet Except. \"\"\"\n\n class _TimeoutExcept(Sis3316Except):\n \"\"\" Response timeout. Retried {0} times. \"\"\"\n\n class _TransferLogicBusyExcept(Sis3316Except):\n \"\"\" Data transfer logic for unit #{group} is busy, or you forgot to do _fifo_transfer_reset. \"\"\"\n\n class _PacketIDMismatchExcept(Sis3316Except):\n \"\"\" Expected Request ID: {0}. Received ID: {1} \"\"\"\n\n\n# You can run this file as a script for debug purposes.\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('host', help='hostname or IP address')\n parser.add_argument('port', type=int, nargs=\"?\", default=1234, help='UDP port number')\n args = parser.parse_args()\n # print(args.host, ' is string: ', isinstance(args.host, str))\n # print('Port: ', args.port)\n\n dev = Sis3316(args.host, args.port)\n print(\"mod ID:\", hex(dev._read_link(0x4)))\n print(\"Hardware Version: \", hex(dev.hardwareVersion))\n dev.open()\n print(\"Temperature (Celsius): \", dev.temp)\n print(\"Serial Number: \", dev.serno)\n print(\"Start Up Frequency: \", dev.freq)\n # dev.configure()\n dev.freq = [250, 0, None]\n print(\"Set Frequency: \", dev.freq)\n\n\nif __name__ == \"__main__\":\n import argparse\n main()\n","sub_path":"sis3316_eth_new.py","file_name":"sis3316_eth_new.py","file_ext":"py","file_size_in_byte":21229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"162234156","text":"import pandas as pd\ndf = pd.read_csv(\"../../input/question-1/main.csv\")\nl = []\nfor i in df:\n if i!='Total':\n l.append(i)\nd={}\nfor i in l:\n d[i]=0\nimport csv\nf = open('main.csv', 'w')\nwriter = csv.writer(f)\nwriter.writerow(['Year', 'Population', 'Violent', 'Property', 'Murder', 'Forcible_Rape', 'Robbery', 'Aggravated_assault', 'Burglary', 'Larceny_Theft', 'Vehicle_Theft'])\n\ncnt = 1\nfor i in range(0, len(df['Population'])):\n if cnt == 1:\n d['Year'] = df['Year'][i]\n for j in l[1:]:\n d[j] += df[j][i]\n\n if cnt==10:\n data = []\n for i in l:\n data.append(d[i])\n print(data)\n writer.writerow(data)\n for j in l:\n d[j] = 0\n cnt = 0\n cnt += 1","sub_path":"output/answer-1/question1.py","file_name":"question1.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"368265741","text":"import lightgbm as lgb\nimport numpy as np\nimport pandas as pd\nimport math\nimport re\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport scipy.stats as stats\nimport statsmodels.api as sm\nimport statsmodels.formula.api as smf\nfrom ggplot import *\nfrom sklearn.model_selection import StratifiedShuffleSplit, KFold, StratifiedKFold\nfrom sklearn.metrics import accuracy_score, auc, roc_auc_score, roc_curve, classification_report\nfrom sklearn.grid_search import GridSearchCV\nfrom sklearn.model_selection import train_test_split\nfrom model_helper.visualize import *\nimport random\nimport pickle\nfrom tqdm import tqdm\nimport operator\n\n\ndef split_into_folds(df, target, random_state=42):\n skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_state)\n res = []\n for big_ind, small_ind in skf.split(np.zeros(len(df)), df[target]):\n res.append((df.iloc[big_ind], df.iloc[small_ind]))\n\n\nclass RunLGBM:\n def __init__(self, params, df,\n df_train, target_train,\n df_test, target_test, id_test,\n df_oot, target_oot, id_oot,\n target, pk,\n num_of_test_splits=5, categorical_names=[], experiment_title='ExperimentTitle'):\n\n self.params = params\n self.target = target\n self.pk = pk\n self.df = df\n self.num_of_test_splits = num_of_test_splits\n self.df_train = df_train\n self.df_test = df_test\n self.target_train = target_train\n self.target_test = target_test\n self.target_oot = target_oot\n self.dtrain = lgb.Dataset(df_train, label=target_train, categorical_feature=categorical_names)\n self.dtest = lgb.Dataset(df_test, label=target_test, categorical_feature=categorical_names)\n self.d_oot = lgb.Dataset(df_oot, label=target_oot, categorical_feature=categorical_names)\n self.df_oot = df_oot\n self.target_oot = target_oot\n self.id_oot = id_oot\n self.categorical_names = categorical_names\n self.id_test = id_test\n self.experiment_title = experiment_title\n\n def get_features_importance(self, clf, limit_top=25, debug=False, show_fi=True):\n if debug:\n print(\"Features importance...\")\n gain = clf.feature_importance('gain')\n ft = pd.DataFrame({'feature': clf.feature_name(),\n 'split': clf.feature_importance('split'),\n 'gain': 100 * gain / gain.sum()}).sort_values('gain', ascending=False)\n # plt.figure()\n if show_fi:\n ft[['feature', 'gain']].head(limit_top).plot(kind='barh', x='feature', y='gain',\n legend=False, figsize=(10, 20))\n plt.gcf().savefig('imgs/%s_features_importance.png' % self.experiment_title)\n return ft\n\n def go2(self, num_boost_round=850, show_graph=True, threshold_useless=3, files_prefix='',\n debug=True, show_auc=True,\n count_extra_run=None, save_features_info=True, save_averaged=False,\n step_without_bagging=3, seed_shift=0):\n\n # TODO save classifier to the local properties\n params = dict(self.params, silent=1, seed=0 + seed_shift)\n clf = lgb.train(params, train_set=self.dtrain, num_boost_round=num_boost_round,\n valid_sets=[self.dtrain, self.dtest],\n early_stopping_rounds=30, verbose_eval=30)\n\n features_scores = self.get_features_importance(clf, limit_top=25, debug=debug, show_fi=show_graph)\n if debug:\n print(\"Using %d features\" % len(features_scores[features_scores > 0]))\n print(features_scores)\n\n list_useless = features_scores[features_scores['split'] == 0]['feature']\n almost_useless = features_scores[(features_scores['split'] > 0)\n & (features_scores['split'] < threshold_useless)]['feature']\n\n if debug:\n print(\"List useless:\", list_useless, '\\n', \"List almost useless:\", almost_useless)\n\n y_pred = clf.predict(self.df_oot)\n _roc = roc_auc_score(self.target_oot, y_pred)\n\n predictions_extra_run = []\n df_preds = pd.DataFrame({'pred_first': y_pred})\n\n rocs = [_roc]\n\n if count_extra_run:\n for i in tqdm(range(count_extra_run)):\n if (i + 1) % step_without_bagging == 0:\n dtrain = lgb.Dataset(self.df_train, label=self.target_train,\n categorical_feature=self.categorical_names)\n else:\n df_train_tmp = self.df_train.sample(frac=1, replace=True, random_state=i + 1 + seed_shift)\n target_train_tmp = self.target_train.ix[df_train_tmp.index]\n dtrain = lgb.Dataset(df_train_tmp, label=target_train_tmp,\n categorical_feature=self.categorical_names)\n\n extra_model = lgb.train(dict(params, seed=i + 1 + seed_shift), train_set=dtrain,\n num_boost_round=num_boost_round,\n valid_sets=[self.dtrain, self.dtest],\n early_stopping_rounds=30, verbose_eval=50)\n extra_y_pred = extra_model.predict(self.df_oot)\n rocs.append(roc_auc_score(self.target_oot, extra_y_pred))\n predictions_extra_run.append(extra_y_pred)\n df_preds['pred_' + str(i) + '_is_full_%s' % str(int(i % step_without_bagging))] = extra_y_pred\n\n if save_averaged:\n if debug:\n print('Results are averaged')\n for prediction in predictions_extra_run:\n y_pred += prediction\n\n y_pred /= count_extra_run + 1\n\n if debug:\n print('Avg auc: %.2f +- %.2f; [%.2f .. %.2f]; range: %.2f; median: %.2f' %\n (np.mean(rocs) * 100, np.std(rocs) * 100, np.min(rocs) * 100, np.max(rocs) * 100,\n (np.max(rocs) - np.min(rocs)) * 100, np.median(rocs) * 100))\n print('Averaged model auc: %.2f' % (roc_auc_score(self.target_oot, y_pred) * 100))\n\n if show_auc:\n draw_roc_curve(y_pred, self.target_oot, files_prefix + '_auc.png', 'imgs', _roc)\n\n if debug:\n if count_extra_run is None:\n print(\"Roc on test: %.2f (one run)\" % (_roc * 100))\n\n print('Features info: ' + 'imgs/' + files_prefix + 'features.png')\n print('Distribution: imgs/' + files_prefix + 'distributions.png')\n print('Scores: data/' + files_prefix + 'result.csv')\n\n result = pd.DataFrame({'id': self.id_oot, self.target: y_pred, 'realVal': self.target_oot})\n\n if show_graph:\n draw_distributions(result, self.target, files_prefix + 'distributions.png', 'imgs')\n\n result.to_csv('data/' + files_prefix + 'result.csv', index=False)\n\n return result, df_preds, {'used': features_scores, 'useless': list_useless,\n 'almost_useless': almost_useless}\n\n # TODO\n def get_max_boost(self, debug=False, num_folds=6, max_boost_rounds=1500, verbose_eval=50,\n count_extra_run=1, metric='auc'):\n\n raise NotImplementedError()\n\n # Data structure in which to save out-of-folds preds\n early_stopping_rounds = 30\n\n metric_keys = ['test-%s-mean' % metric, 'test-%s-std' % metric, 'train-%s-mean' % metric,\n 'train-%s-std' % metric]\n\n results = {'num_rounds': []}\n for k in metric_keys:\n results[k] = []\n\n iter_cv_result = []\n for i in tqdm(range(count_extra_run)):\n verb = verbose_eval if i < 2 else None\n iter_cv_result.append(xgb.cv(dict(self.xgb_params, silent=1, seed=i + 1), self.dtrain,\n num_boost_round=max_boost_rounds,\n early_stopping_rounds=early_stopping_rounds,\n verbose_eval=verb, show_stdv=False,\n metrics={metric}, stratified=False, nfold=num_folds))\n\n results['num_rounds'].append(len(iter_cv_result[i]))\n t = iter_cv_result[i].ix[results['num_rounds'][i] - 1, :]\n\n for c in metric_keys:\n results[c].append(t[c])\n\n num_boost_rounds = np.mean(results['num_rounds'])\n\n # Show results\n res = []\n for c in metric_keys + ['num_rounds']:\n factor = 100 if c != 'num_rounds' else 1\n\n res.append({'type': c,\n 'val': round(np.mean(results[c]) * factor, 2),\n 'std': round(np.std(results[c]) * factor, 2),\n 'min': round(np.min(results[c]) * factor, 2),\n 'max': round(np.max(results[c]) * factor, 2),\n 'median': round(np.median(results[c]) * factor, 2)\n })\n\n # print('Avg %s: %.2f +- %.2f; [%.2f .. %.2f]; range: %.2f; median: %.2f' %\n # (c, np.mean(results[c]) * factor, np.std(results[c]) * factor,\n # np.min(results[c]) * factor,\n # np.max(results[c]) * factor, (np.max(results[c]) - np.min(results[c])) * factor,\n # np.median(results[c]) * factor))\n\n if debug:\n # print('Results of the first round', iter_cv_result[0])\n print('Best iteration:', num_boost_rounds)\n\n return int(num_boost_rounds), pd.DataFrame(res)[['type', 'val', 'std', 'min', 'max', 'median']]\n\n # TODO\n def get_max_boost_by_last_periods(self, debug=False, count_last_periods=3):\n # kf = KFold(n_splits=self.num_of_test_splits)\n # kf.get_n_splits(self.df_train)\n\n raise NotImplementedError()\n\n kf = KFold(n_splits=self.num_of_test_splits)\n l = kf.get_n_splits(self.df_train)\n g = kf.split(self.df_train)\n f = []\n for i in range(0, self.num_of_test_splits - count_last_periods):\n next(g)\n\n for i in range(0, count_last_periods):\n f.append(next(g))\n\n # kf.split(self.df_train)\n # for train_index, test_index in kf.split(self.df_train):\n\n cv_result = xgb.cv(dict(self.xgb_params, silent=1), self.dtrain, num_boost_round=1500,\n early_stopping_rounds=50, verbose_eval=50, show_stdv=False,\n metrics={'auc'}, stratified=False, nfold=self.num_of_test_splits,\n folds=f)\n\n # cv_result[['train-logloss-mean', 'test-logloss-mean']].plot()\n # plt.show()\n num_boost_rounds = len(cv_result)\n if debug:\n print(cv_result)\n print(num_boost_rounds)\n\n return num_boost_rounds\n\n def load_model(self, model_full_path='data/model.dat'):\n return pickle.load(open(model_full_path, \"rb\"))\n\n # TODO\n def go_and_save_model(self, num_boost_round, p_o_s, target, show_graph=True, threshold_useless=3,\n files_prefix='', debug=True, show_auc=True,\n model_full_path='data/model.dat', count_extra_run=1):\n\n raise NotImplementedError()\n\n params = dict(self.xgb_params, silent=1)\n\n df = self.df.copy()\n df = df.iloc[:math.ceil(len(df) * p_o_s), :]\n\n df_train = df.ix[:, df.columns != target]\n target_train = df.ix[:, target]\n df_train.drop([self.pk], axis=1, inplace=True)\n dtrain = lgbm.Dataset(df_train, target_train)\n\n if debug:\n print(df_train.shape)\n\n model = xgb.train(params, dtrain, num_boost_round=num_boost_round)\n\n if count_extra_run:\n for i in tqdm(range(count_extra_run)):\n extra_model = xgb.train(dict(params, seed=i + 1), dtrain, num_boost_round=num_boost_round)\n pickle.dump(extra_model, open(model_full_path + '_seed' + str(i), \"wb\"))\n\n # save model to file\n pickle.dump(model, open(model_full_path, \"wb\"))\n\n files_prefix = '_' + files_prefix\n\n if debug:\n print(\"Using %d features\" % len(model.feature_names))\n print(model.get_fscore(), len(model.get_fscore()))\n\n scores = model.get_fscore()\n list_useless = []\n almost_useless = []\n for fn in model.feature_names:\n if fn not in scores:\n list_useless.append(fn)\n elif scores[fn] < threshold_useless:\n almost_useless.append(fn)\n\n if debug:\n print(\"List useless:\", list_useless)\n print(\"List almost useless:\", almost_useless)\n\n if show_graph:\n fig, ax = plt.subplots(1, 1, figsize=(8, 16))\n xgb.plot_importance(model, height=0.5, ax=ax)\n plt.show()\n plt.savefig('imgs/all_data' + files_prefix + 'features.png')\n\n # TODO compare!\n def go(self, num_boost_round, show_graph=True, threshold_useless=3, files_prefix='', debug=True, show_auc=True,\n count_extra_run=None, save_features_info=True, save_averaged=False,\n step_without_bagging=3, seed_shift=0):\n params = dict(self.xgb_params, silent=1, seed=0 + seed_shift)\n model = xgb.train(params, self.dtrain, num_boost_round=num_boost_round)\n files_prefix = '_' + files_prefix\n features_interaction_file = 'data/' + files_prefix + '_features_info.xlsx'\n\n # print(model.feature_names)\n features_scores = model.get_fscore()\n if debug:\n print(\"Using %d features\" % len(model.feature_names))\n print(features_scores, len(features_scores))\n\n scores = model.get_fscore()\n list_useless, almost_useless = [], []\n for fn in model.feature_names:\n if fn not in scores:\n list_useless.append(fn)\n elif scores[fn] < threshold_useless:\n almost_useless.append(fn)\n\n if debug:\n print(\"List useless:\", list_useless)\n print(\"List almost useless:\", almost_useless)\n\n if show_graph:\n fig, ax = plt.subplots(1, 1, figsize=(8, 16))\n xgb.plot_importance(model, height=0.5, ax=ax)\n plt.show()\n plt.tight_layout()\n plt.savefig('imgs/' + files_prefix + 'features.png')\n\n y_pred = model.predict(self.dtest)\n\n if save_features_info:\n xgbfir.saveXgbFI(model, OutputXlsxFile=features_interaction_file,\n MaxTrees=200, MaxHistograms=30)\n\n # _acc = accuracy_score(self.target_test, np.round(y_pred))\n _roc = roc_auc_score(self.target_test, y_pred)\n\n predictions_extra_run = []\n df_preds = pd.DataFrame({'pred_first': y_pred})\n\n if count_extra_run:\n rocs = [_roc]\n for i in tqdm(range(count_extra_run)):\n\n # self.df_train, self.target_train - split\n if (i + 1) % step_without_bagging == 0:\n dtrain = self.dtrain\n else:\n df_train_tmp = self.df_train.sample(frac=1, replace=True, random_state=i + 1 + seed_shift)\n target_train_tmp = self.target_train.ix[df_train_tmp.index]\n dtrain = xgb.DMatrix(df_train_tmp, target_train_tmp)\n\n extra_model = xgb.train(dict(params, seed=i + 1 + seed_shift), dtrain, num_boost_round=num_boost_round)\n extra_y_pred = extra_model.predict(self.dtest)\n rocs.append(roc_auc_score(self.target_test, extra_y_pred))\n predictions_extra_run.append(extra_y_pred)\n df_preds['pred_' + str(i) + '_is_full_%s' % str(int(i % step_without_bagging))] = extra_y_pred\n\n if save_averaged:\n if debug:\n print('Results are averaged')\n for prediction in predictions_extra_run:\n y_pred += prediction\n\n y_pred /= count_extra_run + 1\n\n if debug:\n print('Avg auc: %.2f +- %.2f; [%.2f .. %.2f]; range: %.2f; median: %.2f' %\n (np.mean(rocs) * 100, np.std(rocs) * 100, np.min(rocs) * 100, np.max(rocs) * 100,\n (np.max(rocs) - np.min(rocs)) * 100, np.median(rocs) * 100))\n print('Averaged model auc: %.2f' % (roc_auc_score(self.target_test, y_pred) * 100))\n\n if show_auc:\n draw_roc_curve(y_pred, self.target_test, files_prefix + '_auc.png', 'imgs', _roc)\n\n # print(\"Accuracy on hold-out: %.2f\" % _acc)\n if count_extra_run is None and debug:\n print(\"Roc on test: %.2f (one run)\" % (_roc * 100))\n\n if debug:\n print('Features info: ' + 'imgs/' + files_prefix + 'features.png')\n print('Distribution: imgs/' + files_prefix + 'distributions.png')\n print('Scores: data/' + files_prefix + 'result.csv')\n print('Features interactions: ' + features_interaction_file)\n\n result = pd.DataFrame({'id': self.id_test, self.target: y_pred, 'realVal': self.target_test})\n\n if show_graph:\n draw_distributions(result, self.target, files_prefix + 'distributions.png', 'imgs')\n\n result.to_csv('data/' + files_prefix + 'result.csv', index=False)\n return result, df_preds, {'used': features_scores, 'useless': list_useless,\n 'almost_useless': almost_useless}\n\n # def go(self, num_boost_round, show_graph=True, threshold_useless=3, files_prefix='',\n # debug=True, show_auc=True):\n # params = dict(self.xgb_params, silent=1)\n # model = xgb.train(params, self.dtrain, num_boost_round=num_boost_round)\n #\n # if debug:\n # print(\"Using %d features\" % len(model.feature_names))\n # # print(model.feature_names)\n # print(model.get_fscore())\n # print(len(model.get_fscore()))\n #\n # scores = model.get_fscore()\n # list_useless, almost_useless = [], []\n # for fn in model.feature_names:\n # if fn not in scores:\n # list_useless.append(fn)\n # elif scores[fn] < threshold_useless:\n # almost_useless.append(fn)\n #\n # if debug:\n # print(\"Useless:\", list_useless)\n # print(\"Almost useless:\", almost_useless)\n #\n # if show_graph:\n # fig, ax = plt.subplots(1, 1, figsize=(8, 16))\n # xgb.plot_importance(model, height=0.5, ax=ax)\n # plt.show()\n # plt.savefig('imgs/' + files_prefix + '_features.png')\n #\n # y_pred = model.predict(self.dtest)\n # _acc = accuracy_score(self.target_test, np.round(y_pred))\n # _roc = roc_auc_score(self.target_test, y_pred)\n #\n # # false_positive_rate, true_positive_rate, thresholds = roc_curve(target_test, y_pred)\n # # may differ\n # # print auc(false_positive_rate, true_positive_rate)\n #\n # if show_auc:\n # draw_roc_curve(y_pred, self.target_test, files_prefix + '_auc.png', 'imgs', _roc)\n #\n # print(\"Auc on hold-out: %.3f\" % _roc, \"Acc on hold-out: %.2f\" % _acc)\n #\n # result = pd.DataFrame({'id': self.id_test, self.target: y_pred, 'realVal': self.target_test})\n #\n # if show_graph:\n # draw_distributions(result, self.target, files_prefix + 'distributions.png', 'imgs')\n #\n # result.to_csv('data/' + files_prefix + 'result.csv', index=False)\n # return _roc\n\n def go_multi(self, num_boost_round, columns_sets, show_graph=True, threshold_useless=3,\n files_prefix='', debug=True, show_auc=True, count_rounds=10, min_auc=0.660):\n\n y_preds = []\n files_prefix = '_' + files_prefix\n\n med_result = pd.DataFrame({'id': self.id_test, 'realVal': self.target_test})\n\n for j in range(0, count_rounds):\n # For random sampling\n # sub_cols = random.sample(set(self.df_train.columns.values), 70)\n # sub_cols = columns_sets[j]\n\n df_train_with_reap = self.df_train.sample(frac=1, replace=True, random_state=j, axis=0)\n target_train_with_reap = self.target_train.ix[df_train_with_reap.index]\n\n sub_cols = [x for x in columns_sets if (x not in [self.target, self.pk])]\n dtrain = xgb.DMatrix(df_train_with_reap[sub_cols], target_train_with_reap)\n dtest = xgb.DMatrix(self.df_test[sub_cols])\n\n params = dict(self.xgb_params, silent=1,\n scale_pos_weight=len(target_train_with_reap) / target_train_with_reap.sum())\n\n cv_result = xgb.cv(params, dtrain, num_boost_round=120, early_stopping_rounds=50,\n verbose_eval=50, show_stdv=False, metrics={'auc'}, stratified=False, nfold=5)\n\n test_cv = cv_result['test-auc-mean'].iloc[-1]\n print(\"Test cv: %.3f\" % test_cv)\n\n if test_cv > min_auc:\n model = xgb.train(params, dtrain, num_boost_round=round(num_boost_round * 1.2))\n\n # print(model.feature_names)\n if debug:\n print(\"Using %d features\" % len(model.feature_names))\n print(model.get_fscore())\n print(len(model.get_fscore()))\n\n scores = model.get_fscore()\n list_useless = []\n almost_useless = []\n for fn in model.feature_names:\n if fn not in scores:\n list_useless.append(fn)\n elif scores[fn] < threshold_useless:\n almost_useless.append(fn)\n\n if debug:\n print(\"List useless:\")\n print(list_useless)\n print(\"List almost useless:\")\n print(almost_useless)\n\n if show_graph:\n fig, ax = plt.subplots(1, 1, figsize=(8, 16))\n xgb.plot_importance(model, height=0.5, ax=ax)\n plt.show()\n plt.savefig('imgs/' + files_prefix + 'features.png')\n\n y_pred = model.predict(dtest)\n y_preds.append(y_pred)\n med_result['target_' + str(j)] = y_pred\n\n for i in range(0, len(y_preds) - 1):\n print(i)\n y_pred += y_preds[i]\n\n y_pred /= len(y_preds)\n\n _roc = roc_auc_score(self.target_test, y_pred)\n\n corr_matrix = med_result.corr()\n print(corr_matrix)\n plt.figure()\n sns.heatmap(corr_matrix, cmap='bwr')\n plt.savefig(\"imgs/\" + files_prefix + \"_corr_matrix_results.png\")\n plt.clf()\n\n # false_positive_rate, true_positive_rate, thresholds = roc_curve(target_test, y_pred)\n # may differ\n # print auc(false_positive_rate, true_positive_rate)\n\n if show_auc:\n draw_roc_curve(y_pred, self.target_test, files_prefix + '_auc.png', 'imgs', _roc)\n\n # print(\"Accuracy on hold-out: %.2f\" % _acc)\n print(\"Roc on hold-out: %.3f\" % _roc)\n\n result = pd.DataFrame({'id': self.id_test, self.target: y_pred, 'realVal': self.target_test})\n\n # if show_graph:\n draw_distributions(result, self.target, files_prefix + 'distributions.png', 'imgs')\n\n result.to_csv('data/' + files_prefix + 'result.csv', index=False)\n return _roc\n\n def cv_xgb(self, xgb_params, train, y_train, num_boost_rounds=474,\n split_useless_bottom=2, debug=True, monitor_last_periods=3,\n random_state=42, runs_for_fold=1, plot_features_score=False):\n # stratified_split = StratifiedShuffleSplit(n_splits=self.num_of_test_splits, random_state=0)\n X = train.values\n y = y_train.values\n kf = KFold(n_splits=self.num_of_test_splits, random_state=random_state)\n kf.get_n_splits(X)\n\n i = 0\n list_useless, list_alm, l_f = {}, {}, {}\n # for train_index, test_index in stratified_split.split(X, y):\n # Cont.df_res = df_train[[pk]].copy()\n # Cont.df_res['scores'] = np.NAN\n # Cont.df_res['real_y'] = np.NAN\n\n res = []\n for train_index, test_index in kf.split(X):\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n\n if debug:\n print(\" Set %d:\" % i, end=' ')\n i += 1\n\n dtrain = xgb.DMatrix(X_train, y_train, feature_names=train.columns.values)\n dtest = xgb.DMatrix(X_test, feature_names=train.columns.values)\n\n _roc = []\n for j in range(0, max(1, runs_for_fold)):\n model = xgb.train(dict(xgb_params, silent=1, seed=i * 1000 + j),\n dtrain, num_boost_round=num_boost_rounds, verbose_eval=0)\n train_predictions = model.predict(dtest)\n _roc.append(roc_auc_score(y_test, train_predictions))\n\n if plot_features_score and j == 0:\n try:\n fig, ax = plt.subplots(1, 1, figsize=(8, 16))\n xgb.plot_importance(model, height=0.5, ax=ax)\n print(model.get_fscore())\n model.dump_model('reports/dump.raw_fold_%d.txt' % i)\n xgbfir.saveXgbFI(model,\n OutputXlsxFile='reports/features_importance_fold%d.xlsx' % i,\n MaxTrees=200, MaxHistograms=30)\n except ValueError:\n print('Scores are not available for this booster')\n\n plt.show()\n\n # draw_distributions(result, self.target, 'cv_distributions.png', 'imgs', inline=True)\n\n # Cont.df_res.ix[test_index, 'scores'] = train_predictions\n # Cont.df_res.ix[test_index, 'real_y'] = y_test\n\n # _acc = accuracy_score(y_test, np.round(train_predictions))\n _roc = np.mean(_roc)\n res.append(_roc)\n\n # false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, clf.predict(dtest))\n # may differ\n # print(auc(false_positive_rate, true_positive_rate))\n\n # print(_acc)\n if debug:\n print(round(_roc * 100, 2))\n list_useless[i], list_alm[i] = report_useless_features(model, split_useless_bottom)\n l_f[i] = np.unique(list_useless[i] + list_alm[i]).tolist()\n # list(set(list_useless[i] + list_alm[i]))\n\n # acc_overall += _acc\n # acc_overall /= self.num_of_test_splits\n # print(predictions, original)\n\n # print(\"Avg accuracy %.2f \" % acc_overall)\n # list_useless[1], list_useless[2], list_useless[3],\n useless = list_useless[self.num_of_test_splits]\n almost_u = l_f[self.num_of_test_splits]\n\n for h in range(1, monitor_last_periods + 1):\n useless = find_lists_intersection(useless, list_useless[self.num_of_test_splits - h])\n almost_u = find_lists_intersection(useless, l_f[self.num_of_test_splits - h])\n\n if debug:\n print(\"Avg auc: %.2f std %.2f\" % (round(np.mean(res) * 100, 2), (round(np.std(res) * 100, 2))),\n end=\"\\n\\n\")\n print(\"Useless in all periods:\", useless, end=\"\\n\\n\")\n print(\"Almost useless(< 2 splits) in all periods:\", almost_u)\n\n return [useless, almost_u]\n\n # With wait_useless_eliminate=True run until all useless are empty\n def cv_chain(self, max_rounds, max_cv_rounds=2, min_split_prun=0, debug=True, random_state=42,\n monitor_last_periods=3, runs_for_fold=1):\n useless, tmp = self.cv_xgb(self.xgb_params, self.df_train, self.target_train,\n max_rounds, min_split_prun + 1,\n random_state=random_state,\n monitor_last_periods=monitor_last_periods,\n runs_for_fold=runs_for_fold)\n\n i = 0\n usefull_features = []\n while len(useless) and i < max_cv_rounds - 1:\n i += 1\n try:\n df_train_copy\n except NameError:\n df_train_copy = self.df_train.copy()\n if min_split_prun > 0:\n useless = tmp\n\n df_train_copy.drop(useless, axis=1, inplace=True)\n usefull_features = df_train_copy.columns.values\n if debug:\n print(\"Full CV round: %d; Features left: %d\" % (i, len(df_train_copy.columns.values)))\n\n if min_split_prun > 0:\n tmp, useless = self.cv_xgb(self.xgb_params, df_train_copy, self.target_train,\n max_rounds, min_split_prun + 1,\n random_state=random_state,\n monitor_last_periods=monitor_last_periods,\n runs_for_fold=runs_for_fold)\n else:\n useless, almos_u = self.cv_xgb(self.xgb_params, df_train_copy, self.target_train,\n max_rounds,\n random_state=random_state,\n monitor_last_periods=monitor_last_periods,\n runs_for_fold=runs_for_fold)\n\n return useless, usefull_features\n\n # TODO finish\n def do_score(self, num_boost_round, show_graph=True, threshold_useless=3, files_prefix='',\n debug=True, show_auc=True):\n y_preds = []\n files_prefix = '_' + files_prefix\n\n # false_positive_rate, true_positive_rate, thresholds = roc_curve(target_test, y_pred)\n\n columns_sets = [core_columns, only_columns_v1, only_columns_adjusted, only_columns_last]\n\n for j in range(0, len(columns_sets)):\n sub_cols = columns_sets[j]\n sub_cols = [x for x in sub_cols if (x not in [target, pk])]\n dtrain = xgb.DMatrix(self.df_train[sub_cols], self.target_train)\n dtest = xgb.DMatrix(self.df_test[sub_cols])\n\n params = dict(self.xgb_params, silent=1)\n model = xgb.train(params, dtrain, num_boost_round=num_boost_round)\n\n # print(model.feature_names)\n if debug:\n print(\"Using %d features\" % len(model.feature_names))\n print(model.get_fscore())\n print(len(model.get_fscore()))\n\n scores = model.get_fscore()\n list_useless = []\n almost_useless = []\n for fn in model.feature_names:\n if fn not in scores:\n list_useless.append(fn)\n elif scores[fn] < threshold_useless:\n almost_useless.append(fn)\n\n if debug:\n print(\"List useless:\")\n print(list_useless)\n print(\"List almost useless:\")\n print(almost_useless)\n\n y_pred = model.predict(dtest)\n y_preds.append(y_pred)\n\n for i in range(0, len(y_preds) - 1):\n print(i)\n y_pred += y_preds[i]\n\n y_pred /= len(y_preds)\n\n result = pd.DataFrame({'id': self.id_test, self.target: y_pred, 'realVal': self.target_test})\n\n if show_graph:\n plt.hist(result.loc[self.target], 100, alpha=0.5, label='All')\n plt.legend(loc='upper right')\n plt.savefig('imgs/' + files_prefix + '_final_distributions.png')\n\n result.to_csv('imgs/' + files_prefix + 'result.csv', index=False)\n return _roc\n\n @staticmethod\n def wrap_cv(min_child_weight, colsample_bytree, max_depth, subsample, gamma, alpha, reg_lambda,\n debug=False, num_folds=6, max_boost_rounds=300, verbose_eval=50, count_extra_run=3,\n train_test_penalize=0.05):\n # Data structure in which to save out-of-folds preds\n early_stopping_rounds = 30\n\n params['min_child_weight'] = int(min_child_weight)\n params['cosample_bytree'] = max(min(colsample_bytree, 1), 0)\n params['max_depth'] = int(max_depth)\n params['subsample'] = max(min(subsample, 1), 0)\n params['gamma'] = max(gamma, 0)\n params['alpha'] = max(alpha, 0)\n params['reg_lambda'] = max(reg_lambda, 0)\n\n # , target_train = tr.ix[:, tr.columns != target], tr.ix[:, target]\n\n results = {'test-auc-mean': [], 'test-auc-std': [], 'train-auc-mean': [],\n 'train-auc-std': [], 'num_rounds': []}\n iter_cv_result = []\n for i in range(count_extra_run):\n verb = verbose_eval if i < 2 else None\n iter_cv_result.append(xgb.cv(dict(params, silent=1, seed=i + 1), dtrain,\n num_boost_round=max_boost_rounds,\n early_stopping_rounds=early_stopping_rounds,\n verbose_eval=verb, show_stdv=False,\n metrics={'auc'}, stratified=False, nfold=num_folds))\n\n results['num_rounds'].append(len(iter_cv_result[i]))\n t = iter_cv_result[i].iloc[results['num_rounds'][i] - 1, :]\n\n for c in ['test-auc-mean', 'test-auc-std', 'train-auc-mean', 'train-auc-std']:\n results[c].append(t[c])\n\n num_boost_rounds = np.mean(results['num_rounds'])\n\n # Show results\n res = []\n for c in ['train-auc-mean', 'test-auc-mean', 'train-auc-std', 'test-auc-std', 'num_rounds']:\n factor = 100 if c != 'num_rounds' else 1\n\n res.append({'type': c,\n 'val': round(np.mean(results[c]) * factor, 2),\n 'std': round(np.std(results[c]) * factor, 2),\n 'min': round(np.min(results[c]) * factor, 2),\n 'max': round(np.max(results[c]) * factor, 2),\n 'median': round(np.median(results[c]) * factor, 2)\n })\n\n if debug:\n print('Best iteration:', num_boost_rounds)\n\n r = pd.DataFrame(res)[['type', 'val', 'std', 'min', 'max', 'median']]\n\n train_val = float(r.loc[r['type'] == 'train-auc-mean']['val'])\n test_val = float(r.loc[r['type'] == 'test-auc-mean']['val'])\n return test_val - (test_val - train_val) * train_test_penalize\n\n @staticmethod\n def sort_features(f_list, print_score=True):\n sorted_features = sorted(f_list.items(), key=operator.itemgetter(1), reverse=True)\n for m in sorted_features:\n print(\"'\" + m[0] + \"',\", end='')\n if print_score:\n print(str(m[1]) + \",\")\n else:\n print()\n\n\nclass RunLGBMv2(RunLGBM):\n def __init__(self, xgb_params, df, columns, target, pk, oos_split=0.7, num_of_test_splits=5):\n\n df_train = df.ix[:math.ceil(len(df) * oos_split), ]\n df_test = df.ix[math.ceil(len(df) * oos_split):, ]\n\n target_train = df_train[target]\n target_test = df_test[target]\n\n df_train = df_train[columns]\n df_test = df_test[columns + [pk]]\n\n id_test = df_test[pk]\n\n df_test.drop([pk], axis=1, inplace=True)\n\n super(RunLGBMv2, self).__init__(xgb_params, df, df_train, target_train, df_test, target_test,\n id_test, target, pk, num_of_test_splits)\n\n def cv_xgb2(self, xgb_params, num_boost_rounds=474,\n split_useless_bottom=2, debug=True, monitor_last_periods=3,\n random_state=42, runs_for_fold=1, plot_features_score=False,\n shuffle=False):\n # stratified_split = StratifiedShuffleSplit(n_splits=self.num_of_test_splits, random_state=0)\n X = self.df_train.values\n y = self.target_train.values\n kf = KFold(n_splits=self.num_of_test_splits, random_state=random_state, shuffle=shuffle)\n kf.get_n_splits(X)\n\n i = 0\n list_useless, list_alm, l_f = {}, {}, {}\n # for train_index, test_index in stratified_split.split(X, y):\n # Cont.df_res = df_train[[pk]].copy()\n # Cont.df_res['scores'] = np.NAN\n # Cont.df_res['real_y'] = np.NAN\n\n res = []\n for train_index, test_index in kf.split(X):\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]\n\n if debug:\n print(\" Set %d:\" % i, end=' ')\n i += 1\n\n dtrain = xgb.DMatrix(X_train, y_train, feature_names=self.df_train.columns.values)\n dtest = xgb.DMatrix(X_test, feature_names=self.df_train.columns.values)\n\n _roc = []\n for j in range(0, max(1, runs_for_fold)):\n model = xgb.train(dict(xgb_params, silent=1, seed=i * 1000 + j),\n dtrain, num_boost_round=num_boost_rounds, verbose_eval=0)\n train_predictions = model.predict(dtest)\n _roc.append(roc_auc_score(y_test, train_predictions))\n\n if plot_features_score and j == 0:\n try:\n fig, ax = plt.subplots(1, 1, figsize=(8, 16))\n xgb.plot_importance(model, height=0.5, ax=ax)\n print(model.get_fscore())\n model.dump_model('reports/dump.raw_fold_%d.txt' % i)\n xgbfir.saveXgbFI(model,\n OutputXlsxFile='reports/features_importance_fold%d.xlsx' % i,\n MaxTrees=200, MaxHistograms=30)\n except ValueError:\n print('Scores are not available for this booster')\n\n plt.show()\n\n # draw_distributions(result, self.target, 'cv_distributions.png', 'imgs', inline=True)\n\n # Cont.df_res.ix[test_index, 'scores'] = train_predictions\n # Cont.df_res.ix[test_index, 'real_y'] = y_test\n\n # _acc = accuracy_score(y_test, np.round(train_predictions))\n _roc = np.mean(_roc)\n res.append(_roc)\n\n # false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, clf.predict(dtest))\n # may differ\n # print(auc(false_positive_rate, true_positive_rate))\n\n # print(_acc)\n if debug:\n print(round(_roc * 100, 2))\n list_useless[i], list_alm[i] = report_useless_features(model, split_useless_bottom)\n l_f[i] = np.unique(list_useless[i] + list_alm[i]).tolist()\n # list(set(list_useless[i] + list_alm[i]))\n\n # acc_overall += _acc\n # acc_overall /= self.num_of_test_splits\n # print(predictions, original)\n\n # print(\"Avg accuracy %.2f \" % acc_overall)\n # list_useless[1], list_useless[2], list_useless[3],\n useless = list_useless[self.num_of_test_splits]\n almost_u = l_f[self.num_of_test_splits]\n\n for h in range(1, monitor_last_periods + 1):\n useless = find_lists_intersection(useless, list_useless[self.num_of_test_splits - h])\n almost_u = find_lists_intersection(useless, l_f[self.num_of_test_splits - h])\n\n if debug:\n print(\"Avg auc: %.2f std %.2f\" % (round(np.mean(res) * 100, 2), (round(np.std(res) * 100, 2))),\n end=\"\\n\\n\")\n print(\"Useless in all periods:\", useless, end=\"\\n\\n\")\n print(\"Almost useless(< 2 splits) in all periods:\", almost_u)\n\n return [useless, almost_u]\n\n\nclass XgbCvScores:\n \"\"\"\n Save cross validation scores for all folds\n \"\"\"\n def __init__(self, xgb_params, df, target, pk, num_boost_rounds, num_of_test_splits=5, train_test_split=0.8):\n self.xgb_params = xgb_params\n self.target = target\n self.pk = pk\n self.df = df\n self.num_of_test_splits = num_of_test_splits\n self.num_boost_rounds = num_boost_rounds\n self.df.sort_values(pk, ascending=True, inplace=True)\n self.df = self.df.iloc[:math.ceil(len(self.df) * train_test_split), :]\n self.is_scored = False\n\n def split_into_folds(self, columns, random_state=42):\n skf = KFold(n_splits=self.num_of_test_splits, shuffle=False, random_state=random_state)\n res = []\n\n for big_ind, small_ind in skf.split(np.zeros(len(self.df)), self.df[self.target]):\n res.append((self.df[columns].iloc[big_ind], self.df[columns].iloc[small_ind]))\n\n return res\n\n def go(self, count_extra_run=3, save_folder='reports', save_file='oos_result'):\n self.df['scores'] = np.NaN\n\n acc_overall, roc_overall = 0, 0\n i = 0\n columns = [x for x in self.df.columns.values if x not in [self.pk]]\n columns2 = [x for x in columns if x not in [self.target]]\n splits = self.split_into_folds(columns)\n\n for train, test in splits:\n df_train, target_train = train.loc[:, columns2], train.loc[:, self.target]\n df_test, target_test = test.loc[:, columns2], test.loc[:, self.target]\n\n X_train, y_train = df_train.values, target_train.values\n X_test, y_test = df_test.values, target_test.values\n\n print(\"Set: %d\" % i, ':')\n i += 1\n\n dtrain = xgb.DMatrix(X_train, y_train, feature_names=df_train.columns.values)\n dtest = xgb.DMatrix(X_test, feature_names=df_train.columns.values)\n\n _rocs = []\n for j in tqdm(range(0, count_extra_run)):\n model = xgb.train(dict(self.xgb_params, silent=1, seed=42 + j), dtrain,\n num_boost_round=self.num_boost_rounds, verbose_eval=0)\n train_predictions = model.predict(dtest)\n\n if j == 0:\n self.df.loc[test.index, 'scores'] = train_predictions\n\n _rocs.append(roc_auc_score(y_test, train_predictions))\n\n print(np.round(np.mean(_rocs) * 100, 2),\n np.round(np.std(_rocs) * 100, 2),\n np.round(_rocs, 4) * 100)\n roc_overall += np.mean(_rocs)\n\n roc_overall /= self.num_of_test_splits\n\n print(\"Avg roc %.2f \" % (roc_overall * 100))\n\n result = pd.DataFrame({'id': self.df[self.pk], self.target: self.df['scores'], 'realVal': self.df[self.target]})\n result.to_csv(save_folder + '/' + save_file + '.csv', index=False)\n\n return result\n\n def show_stats(self, inline=True, imgs_folder='imgs', auc_img_file='oos_cv_auc',\n distr_img_file='oos_distributions'):\n _roc = roc_auc_score(self.df[self.target], self.df['scores'])\n draw_roc_curve(self.df['scores'], self.df[self.target], auc_img_file + '.png',\n imgs_folder, _roc, inline=inline)\n result = pd.DataFrame({'id': self.df[self.pk], self.target: self.df['scores'], 'realVal': self.df[self.target]})\n draw_distributions(result, self.target, distr_img_file + '.png', imgs_folder, inline=inline)\n\n\n\n'''\n def final_scores(self, num_boost_round, show_graph=1, threshold_useless=3, files_prefix=''):\n df_train = self.df.ix[:, df.columns != self.target]\n target_train = df.ix[:, self.target]\n target_test = target_train\n id_test = df_train[self.pk]\n df_train.drop([self.pk], axis=1, inplace=True)\n df_test = df_train \n files_prefix = '_' + files_prefix\n\n dtrain = xgb.DMatrix(df_train, target_train)\n dtest = xgb.DMatrix(df_test)\n\n params = dict(xgb_params, silent=1)\n model = xgb.train(params, dtrain, num_boost_round=num_boost_round)\n\n scores = model.get_fscore()\n list_useless = []\n almost_useless = []\n for fn in model.feature_names:\n if fn not in scores:\n list_useless.append(fn)\n elif scores[fn] < threshold_useless:\n almost_useless.append(fn)\n\n print(\"List useless:\")\n print(list_useless)\n print(\"List almost useless:\")\n print(almost_useless)\n\n y_pred = model.predict(dtest)\n _acc = accuracy_score(target_test, np.round(y_pred))\n _roc = roc_auc_score(target_test, y_pred)\n\n show_auc = True\n if show_auc:\n fpr, tpr, _ = roc_curve(target_test, y_pred)\n\n # Plot of a ROC curve for a specific class\n plt.figure()\n plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % _roc)\n plt.plot([0, 1], [0, 1], 'k--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title('ROC Curve')\n plt.legend(loc=\"lower right\")\n plt.savefig('imgs/' + provider + '_aucc_full.png')\n plt.clf()\n\n print(\"Accuracy on hold-out: %.2f\" % _acc)\n print(\"Roc on hold-out: %.3f\" % _roc)\n result = pd.DataFrame({'id': id_test, target: y_pred, 'realVal': target_test})\n\n # TODO\n show_graph = True\n if show_graph:\n plt.hist(result.loc[result.realVal == 1, target], 100, alpha=0.8, label='Bad', color=\"red\")\n plt.hist(result.loc[result.realVal == 0, target], 100, alpha=0.5, label='Good')\n plt.legend(loc='upper right')\n # pyplot.show()\n plt.savefig('imgs/' + provider + '_distributions_full.png')\n\n # TODO change dir\n result.to_csv('imgs/' + provider + '_full_result.csv', index=False)\n'''","sub_path":"model_helper/run_lgbm_helper.py","file_name":"run_lgbm_helper.py","file_ext":"py","file_size_in_byte":45861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"353978319","text":"class Solution(object):\n def change(self, amount, coins):\n \"\"\"\n :type amount: int\n :type coins: List[int]\n :rtype: int\n \"\"\"\n if not coins:\n return 0 if amount else 1\n coins.sort()\n dp = [0] * (amount + 1)\n dp[0] = 1\n for coin in coins:\n for curAmount in range(coin, amount + 1):\n dp[curAmount] += dp[curAmount - coin]\n \n return dp[amount]","sub_path":"coinChange2.py","file_name":"coinChange2.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"85424400","text":"from django import forms\nfrom django.forms import ModelForm\n\nfrom sbs.models import Competition\n\n\nclass CompetitionSearchForm(ModelForm):\n\n\n\n class Meta:\n model = Competition\n\n fields = (\n 'name', 'startDate', 'finishDate','compType','compGeneralType')\n\n labels = {'name': 'İsim', 'startDate': 'Başlangıç Yılı', 'finishDate': 'Yılı ', 'compType': 'Türü',\n 'compGeneralType': 'Genel Türü'}\n\n widgets = {\n\n 'startDate': forms.DateInput(\n attrs={'class': 'form-control pull-right', 'id': 'datepicker5', 'autocomplete': 'on',\n 'onkeydown': 'return true'}),\n\n 'name': forms.TextInput(attrs={'class': 'form-control'}),\n\n 'compType': forms.Select(attrs={'class': 'form-control select2 select2-hidden-accessible',\n 'style': 'width: 100%; '}),\n 'compGeneralType': forms.Select(attrs={'class': 'form-control select2 select2-hidden-accessible',\n 'style': 'width: 100%; '}),\n\n\n\n }\n","sub_path":"sbs/Forms/CompetitionSearchForm.py","file_name":"CompetitionSearchForm.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"553283952","text":"from create_db import load_json_data_from_file\nimport json\n\n\n\ndef make_shorten_file(raw_file, shortened_filename):\n raw_file_data = load_json_data_from_file(raw_file)\n new_file_data = []\n for vacancy in raw_file_data:\n new_vacancy = {\n 'payment_from': vacancy['payment_from'],\n 'payment_to': vacancy['payment_to'],\n 'age_from': vacancy['age_from'],\n 'age_to': vacancy['age_to'],\n 'experience': vacancy['experience'],\n 'education': vacancy['education'],\n 'profession': vacancy['profession'],\n 'candidat': vacancy['candidat']\n }\n new_file_data.append(new_vacancy)\n with open(shorten_filename, 'w') as file:\n json.dump(new_file_data, file, indent=4, sort_keys=True, separators=(',', ':'))\n\n\n\nif __name__ == '__main__':\n raw_file = input('Введите название файла для считывания: ')\n shorten_filename = input('Введите название нового файла: ')\n make_shorten_file(raw_file, shorten_filename)","sub_path":"shorten_file.py","file_name":"shorten_file.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"258216085","text":"import discord\nfrom discord.ext import commands, tasks\nfrom itertools import cycle\n\nclient = commands.Bot(command_prefix = 'met!')\ntoken = 'NTk1MjI4MDkxOTAzNjM5NTY0.XRoHaw.QgCHNjBI9CDJWX-mQvRHArInZzY'\nstatus = cycle(['met!score for Results', 'met!winner for Winner of Every Rounds', 'MET Asia Series', 'PUBG Classic'])\n\n@client.event\nasync def on_ready():\n change_status.start()\n print('บอทพร้อมแล้ว!')\n\n@tasks.loop(seconds=5)\nasync def change_status():\n await client.change_presence(activity=discord.Game(next(status)))\n\ndef itme3k(ctx):\n return ctx.author.id == 562877788911042570\n\n# Recent WINNERS\n@client.command()\n@commands.check(itme3k)\nasync def rewin(ctx):\n channel = ctx.message.channel\n embed = discord.Embed(\n title = 'MET Asia Series: PUBG Classic',\n description=\"Winner of Round 1\",\n color=0xff8000\n )\n\n embed.set_author(name='MET Events',\n url=\"https://liquipedia.net/pubg/MET/Asia_Series/2019\",\n icon_url='https://liquipedia.net/commons/images/thumb/a/a0/MET_Asia_Classic_logo.png/600px-MET_Asia_Classic_logo.png')\n embed.set_thumbnail(url='https://liquipedia.net/commons/images/thumb/a/a0/MET_Asia_Classic_logo.png/600px-MET_Asia_Classic_logo.png')\n embed.set_image(url='https://liquipedia.net/commons/images/thumb/a/a0/MET_Asia_Classic_logo.png/600px-MET_Asia_Classic_logo.png')\n embed.add_field(name='TBD', value='TBD', inline=False)\n\n await ctx.send(embed=embed)\n\n@client.command()\n@commands.check(itme3k)\nasync def schedule(ctx):\n await ctx.send('https://www.img.in.th/images/a4e6ddae000963efed90bb69624f3e3b.jpg')\n \n# Armory Gaming LINEUP\n@client.command(aliases=['armory', 'armorygaming'])\nasync def ag(ctx):\n await ctx.send('''Armory Gaming LINEUP\n1.DUCKMANZ\n2.ThanawatTH\n3.DMG\n4.GeneralGaming''')\n\n# Score\n@client.command(aliases=['คะแนน'])\nasync def score(ctx):\n await ctx.send('''MET Asia Series: PUBG Classic\nTop 5 Leaderboard (Played 0 of ?? Rounds)\n1. TBA\n2. TBA\n3. TBA\n4. TBA\n5. TBA''')\n\n# Winner of every rounds\n@client.command()\nasync def winner(ctx):\n await ctx.send('''MET Asia Series: PUBG Classic\nWINNERS\nROUND 1 -\nROUND 2 -\n...''')\n\n# MET Teams\n@client.command(aliases=['ทีม', 'teams'])\nasync def team(ctx):\n await ctx.send('''MET Asia Series: PUBG Classic\nTeams\nSEA\n1. Armory Gaming [TH]\n2. Rex Regum Qeon [ID]\n3. Tokio Striker [TH]\n\nChina\n1. VC Gaming\n2. Black Ananas\n3. 17 Gaming\n4. ViCi Gaming\n5. Team Weibo\n\nKorea\n1. Gen.G\n2. DPG danawa\n3. DeToNator.KOREA\n4. DPG EVGA\n\nJapan\n1. TBD\n2. TBD\n\nTaiwan\n1. TBD\n2. TBD''')\n\nclient.run(token)\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":2597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"15644286","text":"import os\nimport datetime\nimport csv\n\nfrom flask import request, jsonify\nfrom typing import Dict, Optional\n\n\ndef get_nps_data():\n\n start_date = request.args.get('start_date')\n if start_date:\n start_date = convert_date(date=start_date)\n\n end_date = request.args.get('end_date')\n if end_date:\n end_date = convert_date(date=end_date)\n\n print(f'start date is {start_date}')\n print(f'end date is {end_date}')\n\n base_dir = os.path.dirname(__file__)\n filepath = os.path.join(\n base_dir,\n 'data',\n 'nps.csv'\n )\n nps_data = _get_nps_csv_data(\n filepath=filepath,\n start_date=start_date,\n end_date=end_date,\n )\n return jsonify({'nps': nps_data})\n\n\ndef _get_nps_csv_data(filepath: str,\n start_date: Optional[datetime.date],\n end_date: Optional[datetime.date]) -> Dict[str, int]:\n \"\"\"\n Return the NPS data stored in the csv file at \"filepath\"\n Returns a dict of the form \"date -> score\", where date is an IOS 8601\n formatted date string.\n \"\"\"\n\n nps_records = []\n\n with open(filepath) as csv_file:\n\n reader = csv.DictReader(csv_file)\n\n for row in reader:\n\n # Parse the time from the CSV.\n response_date = convert_date(date=row['Date Published'])\n\n # Don't return records outside of the specified time range\n # (there may be no time range specified).\n if start_date and response_date < start_date:\n continue\n elif end_date and response_date > end_date:\n continue\n\n try:\n nps = int(row['NPS'])\n except:\n # Error parsing score - just skip and continue.\n continue\n\n nps_records.append(\n dict(\n year=response_date.year,\n month=response_date.month,\n day=response_date.day,\n nps=int(nps),\n )\n )\n\n return nps_records\n\n\ndef convert_date(date: str) -> datetime.date:\n return datetime.datetime.strptime(date, '%d/%m/%Y')\n","sub_path":"kapiche_api/nps/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"423054664","text":"import mock\nimport unittest\n\nfrom app.models.user import User, Role\n\n\nclass UserEntityTest(unittest.TestCase):\n\n def test_role_str_method_should_return_role_name(self):\n role_name = \"exRoleName\"\n\n mock_role = mock.Mock(spec=Role)\n mock_role.name = role_name\n\n self.assertEqual(Role.__str__(mock_role), role_name)\n\n def test_user_str_method_should_return_username(self):\n role_name = \"exRoleName\"\n\n username = \"example_username\"\n name = \"ExName\"\n surname = \"ExLAstName\"\n email = \"example@mail.ru\"\n phone = \"+9960000000000000\"\n\n mock_role = mock.Mock(spec=Role)\n mock_role.name = role_name\n\n mock_instance = mock.Mock(spec=User)\n conf = {\n 'username': username,\n 'name': name,\n 'surname': surname,\n 'email': email,\n 'phone': phone\n }\n\n mock_instance.configure_mock(**conf)\n\n self.assertEqual(User.__str__(mock_instance), username)\n","sub_path":"app/tests/unit/test_user.py","file_name":"test_user.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"158776979","text":"from rest_framework import permissions\nfrom rest_framework.exceptions import PermissionDenied\n\n\n# Simply does not let them delete anything if they are not a super user\nclass IsOwnerOrSuperUser(permissions.BasePermission):\n\n def has_permission(self, request, view):\n # Checks to see if they are a super user, if not then they cannot\n # delete\n if_super_user = request.user.is_superuser\n # If they are not a super user\n if if_super_user:\n return True\n # ... they cannot delete the user entry\n else:\n if request.method == \"DELETE\":\n raise PermissionDenied(\n 'Sorry, you must be an admin to otherusers.')\n # ... but they can edit their own entry\n else:\n return True\n\n\nclass IsSuperUser(permissions.BasePermission):\n\n def has_permission(self, request, view):\n if request.user.is_superuser:\n return True\n else:\n raise PermissionDenied(\n 'Sorry, you must be an admin to create new users.')\n","sub_path":"ShelfieUser/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"157405360","text":"import pygame, random, math\n\n\nclass App:\n def __init__(self):\n self._display_surf = None\n self.size = self.width, self.height = 1000, 700\n self._grid = Grid(self.width, self.height, 6)\n self._running = True\n self._game_start = False\n self._colour = False\n self._paused = False\n self._stochastic = False\n self.clock = pygame.time.Clock()\n self._fps = 5\n\n def on_init(self):\n pygame.init()\n self._display_surf = pygame.display.set_mode(self.size, pygame.HWSURFACE | pygame.DOUBLEBUF)\n return self._display_surf\n\n def on_event(self, event):\n if event.type == pygame.QUIT:\n self._running = False\n elif event.type == pygame.KEYDOWN:\n if event.key == pygame.K_ESCAPE:\n self._running = False\n elif event.key == pygame.K_SPACE:\n if not self._game_start:\n self._game_start = not self._game_start\n else:\n self._paused = True\n self._display_surf.blit(self._grid.draw_grid(), (0, 0))\n pygame.display.update()\n self._grid.cells = self._grid.populate()\n self.clock.tick(self._fps)\n elif event.key == pygame.K_UP:\n self._fps += 1\n elif event.key == pygame.K_DOWN:\n self._fps -= 1\n elif event.key == pygame.K_c:\n self._colour= not self._colour\n elif event.key == pygame.K_p:\n self._paused = not self._paused\n elif event.key == pygame.K_r:\n self._stochastic = not self._stochastic\n elif event.type == pygame.MOUSEBUTTONDOWN:\n self._grid.birth_cell(event.pos)\n\n def on_loop(self):\n self._display_surf.blit(self._grid.draw_cells(), (0, 0))\n pygame.display.update()\n\n def on_render(self):\n if not self._paused:\n if self._game_start:\n self._grid.update()\n if self._stochastic:\n self._grid.stochastic(2)\n if self._colour:\n self._grid.change_colour()\n\n self.clock.tick(self._fps)\n\n def on_cleanup(self):\n pygame.quit()\n\n def on_execute(self):\n if self.on_init() is None:\n self._running = False\n\n while self._running:\n for event in pygame.event.get():\n self.on_event(event)\n self.on_loop()\n self.on_render()\n self.on_cleanup()\n\n\nclass Grid:\n def __init__(self, width, height, unit):\n self.height = height\n self.width = width\n self.grain = unit\n self.x_cells = int(self.width / self.grain)\n self.y_cells = int(self.height / self.grain)\n self.colours = self.colour_cell, self.colour_grid = (0, 255, 65), (0, 143, 17)\n self.total = self.x_cells * self.y_cells\n self.cells = self.populate()\n\n def birth_cell(self, pos):\n x = math.floor(pos[0] / self.grain)\n y = math.floor(pos[1] / self.grain)\n cell = self.cells[x + (y * self.x_cells)]\n cell.alive = not cell.alive\n\n def change_colour(self):\n self.colour_cell = (self.colour_cell[2], self.colour_cell[0], self.colour_cell[1])\n self.colour_grid = (self.colour_grid[2], self.colour_grid[0], self.colour_grid[1])\n\n def draw_cells(self):\n grid = self.draw_grid()\n for cell in self.cells:\n if cell.alive:\n rect = pygame.Rect(cell.x * self.grain, cell.y * self.grain, self.grain, self.grain)\n pygame.draw.rect(grid, self.colour_cell, rect)\n return grid\n\n def draw_grid(self):\n grid = pygame.Surface((self.width, self.height))\n grid.fill((0, 0, 0))\n for y in range(self.y_cells):\n for x in range(self.x_cells):\n rect = pygame.Rect(x * self.grain, y * self.grain, self.grain, self.grain)\n pygame.draw.rect(grid, self.colour_grid, rect, 1)\n return grid\n\n def populate(self):\n cell_array = []\n for y in range(self.y_cells):\n for x in range(self.x_cells):\n cell_array.append(Cell(x, y, self.x_cells, self.y_cells))\n\n return cell_array\n\n def stochastic(self, n):\n for i in range(n):\n rando = random.randint(0, self.total - 1)\n if not self.cells[rando].alive:\n self.cells[rando].alive = True\n\n def update(self):\n [cell.implement_rules(self) for cell in self.cells]\n [cell.check_status() for cell in self.cells]\n\n\nclass Cell:\n def __init__(self, x, y, x_cells, y_cells):\n self.x = x\n self.y = y\n self.alive = False\n self.change = False\n self.neighbours = self.get_neighbours(x_cells, y_cells)\n\n def check_status(self):\n if self.change:\n if self.alive:\n self.alive = False\n self.change = False\n elif not self.alive:\n self.alive = True\n self.change = False\n\n def get_neighbours(self, width, height):\n if self.x == 0 and self.y == 0:\n return [[1, self.y], [self.x, 1], [1, 1]]\n elif self.x == width - 1 and self.y == 0:\n return [[width - 2, self.y], [width - 2, 1], [self.x, 1]]\n elif self.x == 0 and self.y == height - 1:\n return [[self.x, height - 2], [1, height - 2], [1, self.y]]\n elif self.x == width - 1 and self.y == height - 1:\n return [[width - 2, height - 2], [self.x, height - 2], [width - 2, self.y]]\n elif self.y == 0:\n return [\n [self.x - 1, self.y], [self.x + 1, self.y],\n [self.x - 1, self.y + 1], [self.x, self.y + 1], [self.x + 1, self.y + 1]\n ]\n elif self.y == height - 1:\n return [\n [self.x - 1, self.y - 1], [self.x, self.y - 1], [self.x + 1, self.y - 1],\n [self.x - 1, self.y], [self.x + 1, self.y]\n ]\n elif self.x == 0:\n return [\n [self.x, self.y - 1], [self.x, self.y + 1],\n [self.x + 1, self.y - 1], [self.x + 1, self.y], [self.x + 1, self.y + 1]\n ]\n elif self.x == width - 1:\n return [\n [self.x - 1, self.y - 1], [self.x - 1, self.y], [self.x - 1, self.y + 1],\n [self.x, self.y - 1], [self.x, self.y + 1],\n ]\n else:\n return [\n [self.x - 1, self.y - 1], [self.x, self.y - 1], [self.x + 1, self.y - 1],\n [self.x - 1, self.y], [self.x + 1, self.y],\n [self.x - 1, self.y + 1], [self.x, self.y + 1], [self.x + 1, self.y + 1]\n ]\n\n def implement_rules(self, grid):\n live_cells = 0\n\n for cell in self.neighbours:\n if grid.cells[(cell[0] + (cell[1] * grid.x_cells))].alive:\n live_cells += 1\n\n if live_cells < 2 and self.alive:\n self.change = True\n elif live_cells > 3 and self.alive:\n self.change = True\n elif live_cells == 3 and not self.alive:\n self.change = True\n\n\nif __name__ == \"__main__\":\n theApp = App()\n theApp.on_execute()\n","sub_path":"GOLmouse.py","file_name":"GOLmouse.py","file_ext":"py","file_size_in_byte":7452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"222005169","text":"#!/usr/bin/env python\n\n# ===============================================================================\n# Copyright (c) 2014 Geoscience Australia\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither Geoscience Australia nor the names of its contributors may be\n# used to endorse or promote products derived from this software\n# without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\n# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY\n# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n# ===============================================================================\n\n\n__author__ = \"Simon Oldfield\"\n\n\nimport argparse\nimport gdal\nimport logging\nimport luigi\nimport os\nimport sys\nfrom datacube.api import parse_date_min, parse_date_max, Month, PqaMask, writeable_dir\nfrom datacube.api import satellite_arg, pqa_mask_arg, dataset_type_arg, season_arg, output_format_arg, OutputFormat\nfrom datacube.api.query import Season, list_cells_as_generator, build_season_date_criteria, list_tiles_as_list\nfrom datacube.api.model import Satellite, DatasetType, Ls57Arg25Bands\nfrom datacube.api.utils import get_dataset_type_ndv, get_dataset_metadata\nfrom datacube.api.utils import get_dataset_type_datatype, get_mask_pqa, get_dataset_data_masked, format_date\nfrom datacube.api.workflow import Task\n\n\n_log = logging.getLogger()\n\n\nSEASONS = {\n Season.SUMMER: ((Month.NOVEMBER, 17), (Month.APRIL, 25)),\n Season.AUTUMN: ((Month.FEBRUARY, 16), (Month.JULY, 25)),\n Season.WINTER: ((Month.MAY, 17), (Month.OCTOBER, 25)),\n Season.SPRING: ((Month.AUGUST, 17), (Month.JANUARY, 25))\n}\n\n\ndef ls57_arg_band_arg(s):\n if s in [t.name for t in Ls57Arg25Bands]:\n return Ls57Arg25Bands[s]\n raise argparse.ArgumentTypeError(\"{0} is not a supported LS57 ARG25 band\".format(s))\n\n\nclass Arg25BandStackWorkflow(object):\n\n def __init__(self, name=\"Arg25 Band Stack Workflow\"):\n\n # Workflow.__init__(self, name=\"Arg25 Band Stack Workflow\")\n\n self.name = name\n\n self.parser = argparse.ArgumentParser(prog=sys.argv[0], description=self.name)\n\n self.x_min = None\n self.x_max = None\n\n self.y_min = None\n self.y_max = None\n\n self.acq_min = None\n self.acq_max = None\n\n self.epoch = None\n\n self.seasons = None\n\n self.satellites = None\n\n self.output_directory = None\n\n self.mask_pqa_apply = None\n self.mask_pqa_mask = None\n\n self.local_scheduler = None\n self.workers = None\n\n self.dataset_type = None\n self.bands = None\n\n self.output_format = None\n\n def setup_arguments(self):\n\n # # Call method on super class\n # # super(self.__class__, self).setup_arguments()\n # workflow.Workflow.setup_arguments(self)\n\n # TODO get the combinations of mutually exclusive arguments right\n\n # TODO months and time slices are sort of mutually exclusive\n\n self.parser.add_argument(\"--x-min\", help=\"X index of tiles\", action=\"store\", dest=\"x_min\", type=int,\n choices=range(110, 155 + 1), required=True, metavar=\"110 ... 155\")\n\n self.parser.add_argument(\"--x-max\", help=\"X index of tiles\", action=\"store\", dest=\"x_max\", type=int,\n choices=range(110, 155 + 1), required=True, metavar=\"110 ... 155\")\n\n self.parser.add_argument(\"--y-min\", help=\"Y index of tiles\", action=\"store\", dest=\"y_min\", type=int,\n choices=range(-45, -10 + 1), required=True, metavar=\"-45 ... -10\")\n\n self.parser.add_argument(\"--y-max\", help=\"Y index of tiles\", action=\"store\", dest=\"y_max\", type=int,\n choices=range(-45, -10 + 1), required=True, metavar=\"-45 ... -10\")\n\n self.parser.add_argument(\"--output-directory\", help=\"output directory\", action=\"store\", dest=\"output_directory\",\n type=writeable_dir, required=True)\n\n self.parser.add_argument(\"--acq-min\", help=\"Acquisition Date\", action=\"store\", dest=\"acq_min\", type=str,\n default=\"1985\")\n\n self.parser.add_argument(\"--acq-max\", help=\"Acquisition Date\", action=\"store\", dest=\"acq_max\", type=str,\n default=\"2014\")\n\n self.parser.add_argument(\"--epoch\", help=\"Size of year chunks (e.g. 5 means do in 5 chunks of 5 years)\",\n action=\"store\", dest=\"epoch\", type=int, default=5)\n\n self.parser.add_argument(\"--satellite\", help=\"The satellite(s) to include\", action=\"store\", dest=\"satellites\",\n type=satellite_arg, nargs=\"+\", choices=Satellite,\n default=[Satellite.LS5, Satellite.LS7],\n metavar=\" \".join([ts.name for ts in Satellite]))\n\n self.parser.add_argument(\"--mask-pqa-apply\", help=\"Apply PQA mask\", action=\"store_true\", dest=\"mask_pqa_apply\",\n default=True)\n\n self.parser.add_argument(\"--mask-pqa-mask\", help=\"The PQA mask to apply\", action=\"store\", dest=\"mask_pqa_mask\",\n type=pqa_mask_arg, nargs=\"+\", choices=PqaMask,\n default=[PqaMask.PQ_MASK_SATURATION, PqaMask.PQ_MASK_CONTIGUITY, PqaMask.PQ_MASK_CLOUD],\n metavar=\" \".join([ts.name for ts in PqaMask]))\n\n self.parser.add_argument(\"--local-scheduler\", help=\"Use local luigi scheduler rather than MPI\",\n action=\"store_true\",\n dest=\"local_scheduler\", default=True)\n\n self.parser.add_argument(\"--workers\", help=\"Number of worker tasks\", action=\"store\", dest=\"workers\", type=int,\n default=16)\n\n group = self.parser.add_mutually_exclusive_group()\n\n group.add_argument(\"--quiet\", help=\"Less output\", action=\"store_const\", dest=\"log_level\", const=logging.WARN)\n group.add_argument(\"--verbose\", help=\"More output\", action=\"store_const\", dest=\"log_level\", const=logging.DEBUG)\n\n self.parser.set_defaults(log_level=logging.INFO)\n\n self.parser.add_argument(\"--dataset-type\", help=\"The type of dataset to process\", action=\"store\",\n dest=\"dataset_type\",\n type=dataset_type_arg,\n choices=[DatasetType.ARG25], # required=True,\n default=DatasetType.ARG25,\n metavar=\" \".join([dt.name for dt in [DatasetType.ARG25]]))\n\n self.parser.add_argument(\"--band\", help=\"The band(s) to process\", action=\"store\",\n default=Ls57Arg25Bands, # required=True,\n dest=\"bands\", type=ls57_arg_band_arg, nargs=\"+\", metavar=\" \".join([b.name for b in Ls57Arg25Bands]))\n\n self.parser.add_argument(\"--season\", help=\"The seasons for which to produce statistics\", action=\"store\",\n default=Season, # required=True,\n dest=\"season\", type=season_arg, nargs=\"+\",\n metavar=\" \".join([s.name for s in Season]))\n\n self.parser.add_argument(\"--output-format\", help=\"The format of the output dataset\",\n action=\"store\",\n dest=\"output_format\",\n type=output_format_arg,\n choices=OutputFormat, default=OutputFormat.GEOTIFF,\n metavar=\" \".join([f.name for f in OutputFormat]))\n\n def process_arguments(self, args):\n\n # # Call method on super class\n # # super(self.__class__, self).process_arguments(args)\n # workflow.Workflow.process_arguments(self, args)\n\n self.x_min = args.x_min\n self.x_max = args.x_max\n\n self.y_min = args.y_min\n self.y_max = args.y_max\n\n self.output_directory = args.output_directory\n\n self.acq_min = parse_date_min(args.acq_min)\n self.acq_max = parse_date_max(args.acq_max)\n\n self.satellites = args.satellites\n\n self.epoch = args.epoch\n\n self.seasons = args.season\n\n self.mask_pqa_apply = args.mask_pqa_apply\n self.mask_pqa_mask = args.mask_pqa_mask\n\n self.local_scheduler = args.local_scheduler\n self.workers = args.workers\n\n _log.setLevel(args.log_level)\n\n self.dataset_type = args.dataset_type\n self.bands = args.bands\n\n # # Verify that all the requested satellites have the requested bands\n #\n # for satellite in self.satellites:\n # if not all(item in [b.name for b in get_bands(self.dataset_type, satellite)] for item in self.bands):\n # _log.error(\"Requested bands [%s] not ALL present for satellite [%s]\", self.bands, satellite)\n # raise Exception(\"Not all bands present for all satellites\")\n\n self.output_format = args.output_format\n\n def log_arguments(self):\n\n # # Call method on super class\n # # super(self.__class__, self).log_arguments()\n # workflow.Workflow.log_arguments(self)\n\n _log.info(\"\"\"\n x = {x_min:03d} to {x_max:03d}\n y = {y_min:04d} to {y_max:04d}\n \"\"\".format(x_min=self.x_min, x_max=self.x_max, y_min=self.y_min, y_max=self.y_max))\n\n _log.info(\"\"\"\n acq = {acq_min} to {acq_max}\n epoch = {epoch}\n satellites = {satellites}\n output directory = {output_directory}\n PQA mask = {pqa_mask}\n local scheduler = {local_scheduler}\n workers = {workers}\n \"\"\".format(acq_min=self.acq_min, acq_max=self.acq_max,\n epoch=self.epoch and self.epoch or \"\",\n satellites=\" \".join([ts.name for ts in self.satellites]),\n output_directory=self.output_directory,\n pqa_mask=self.mask_pqa_apply and \" \".join([mask.name for mask in self.mask_pqa_mask]) or \"\",\n local_scheduler=self.local_scheduler, workers=self.workers))\n\n _log.info(\"\"\"\n dataset to retrieve = {dataset_type}\n bands = {bands}\n seasons = {seasons}\n \"\"\".format(dataset_type=self.dataset_type.name, bands=\" \".join([b.name for b in self.bands]),\n seasons=\" \".join([s.name for s in self.seasons])))\n\n _log.info(\"\"\"\n output format = {output_format}\n \"\"\".format(output_format=self.output_format.name))\n\n def get_epochs(self):\n\n from dateutil.rrule import rrule, YEARLY\n from dateutil.relativedelta import relativedelta\n\n for dt in rrule(YEARLY, interval=self.epoch, dtstart=self.acq_min, until=self.acq_max):\n acq_min = dt.date()\n acq_max = acq_min + relativedelta(years=self.epoch, days=-1)\n\n acq_min = max(self.acq_min, acq_min)\n acq_max = min(self.acq_max, acq_max)\n\n yield acq_min, acq_max\n\n def get_seasons(self):\n\n for season in self.seasons:\n yield season\n\n def create_tasks(self):\n\n x_list = range(self.x_min, self.x_max + 1)\n y_list = range(self.y_min, self.y_max + 1)\n\n dataset_types = [self.dataset_type]\n\n if self.mask_pqa_apply:\n dataset_types.append(DatasetType.PQ25)\n\n from itertools import product\n\n for (acq_min, acq_max), season in product(self.get_epochs(), self.get_seasons()):\n _log.info(\"%s %s %s\", acq_min, acq_max, season)\n\n acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(acq_min, acq_max, season,\n seasons=SEASONS,\n extend=True)\n\n _log.info(\"\\tcriteria is %s\", criteria)\n\n for cell in list_cells_as_generator(x=x_list, y=y_list, satellites=self.satellites,\n acq_min=acq_min_extended, acq_max=acq_max_extended,\n dataset_types=dataset_types, include=criteria):\n _log.info(\"\\t%3d %4d\", cell.x, cell.y)\n yield self.create_task(x=cell.x, y=cell.y, acq_min=acq_min, acq_max=acq_max, season=season)\n\n def create_task(self, x, y, acq_min, acq_max, season):\n _log.info(\"Creating task for %s %s %s %s %s\", x, y, acq_min, acq_max, season)\n return Arg25BandStackTask(x=x, y=y,\n acq_min=acq_min, acq_max=acq_max, season=season,\n satellites=self.satellites,\n dataset_type=self.dataset_type, bands=self.bands,\n mask_pqa_apply=self.mask_pqa_apply, mask_pqa_mask=self.mask_pqa_mask, output_format=self.output_format)\n\n def run(self):\n\n self.setup_arguments()\n self.process_arguments(self.parser.parse_args())\n self.log_arguments()\n\n luigi.build(self.create_tasks(), local_scheduler=self.local_scheduler, workers=self.workers)\n\n\nclass Arg25BandStackTask(Task):\n\n x = luigi.IntParameter()\n y = luigi.IntParameter()\n\n acq_min = luigi.DateParameter()\n acq_max = luigi.DateParameter()\n\n season = luigi.Parameter()\n\n satellites = luigi.Parameter(is_list=True)\n\n dataset_type = luigi.Parameter()\n bands = luigi.Parameter(is_list=True)\n\n mask_pqa_apply = luigi.BooleanParameter()\n mask_pqa_mask = luigi.Parameter(is_list=True)\n\n output_format = luigi.Parameter()\n\n def requires(self):\n\n _log.info(\"bands = %s\", self.bands)\n\n for band in self.bands:\n yield self.create_band_task(band)\n\n def create_band_task(self, band):\n\n yield Arg25BandStackBandTask(x=self.x, y=self.y,\n acq_min=self.acq_min, acq_max=self.acq_max, season=self.season,\n satellites=self.satellites, dataset_type=self.dataset_type, band=band,\n mask_pqa_apply=self.mask_pqa_apply, mask_pqa_mask=self.mask_pqa_mask, output_format=self.output_format)\n\n\nclass Arg25BandStackBandTask(Task):\n\n x = luigi.IntParameter()\n y = luigi.IntParameter()\n\n acq_min = luigi.DateParameter()\n acq_max = luigi.DateParameter()\n\n season = luigi.Parameter()\n\n satellites = luigi.Parameter(is_list=True)\n\n dataset_type = luigi.Parameter()\n band = luigi.Parameter()\n\n mask_pqa_apply = luigi.BooleanParameter()\n mask_pqa_mask = luigi.Parameter(is_list=True)\n\n output_format = luigi.Parameter()\n\n def output(self):\n acq_min = self.acq_min.strftime(\"%Y%m%d\")\n acq_max = self.acq_max.strftime(\"%Y%m%d\")\n\n season = SEASONS[self.season]\n season_start = \"{month}{day:02d}\".format(month=season[0][0].name[:3], day=season[0][1])\n season_end = \"{month}{day:02d}\".format(month=season[1][0].name[:3], day=season[1][1])\n\n return luigi.LocalTarget(\n \"ARG25_{x:03d}_{y:04d}_{acq_min}_{acq_max}_{season_start}_{season_end}_{band}_STACK.tif\".format(x=self.x,\n y=self.y,\n acq_min=acq_min,\n acq_max=acq_max,\n season_start=season_start,\n season_end=season_end,\n band=self.band.name\n ))\n\n def run(self):\n\n _log.info(\"Creating stack for band [%s]\", self.band.name)\n\n data_type = get_dataset_type_datatype(self.dataset_type)\n ndv = get_dataset_type_ndv(self.dataset_type)\n metadata = None\n driver = None\n raster = None\n\n acq_min, acq_max, criteria = build_season_date_criteria(self.acq_min, self.acq_max, self.season,\n seasons=SEASONS, extend=True)\n\n _log.info(\"\\tacq %s to %s criteria is %s\", acq_min, acq_max, criteria)\n\n dataset_types = [self.dataset_type]\n\n if self.mask_pqa_apply:\n dataset_types.append(DatasetType.PQ25)\n\n tiles = list_tiles_as_list(x=[self.x], y=[self.y], satellites=self.satellites,\n acq_min=acq_min, acq_max=acq_max,\n dataset_types=dataset_types, include=criteria)\n\n for index, tile in enumerate(tiles, start=1):\n\n dataset = tile.datasets[self.dataset_type]\n assert dataset\n\n # band = dataset.bands[self.band]\n # assert band\n band = self.band\n\n pqa = (self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets) and tile.datasets[DatasetType.PQ25] or None\n\n if self.dataset_type not in tile.datasets:\n _log.debug(\"No [%s] dataset present for [%s] - skipping\", self.dataset_type.name, tile.end_datetime)\n continue\n\n filename = self.output().path\n\n if not metadata:\n metadata = get_dataset_metadata(dataset)\n assert metadata\n\n if not driver:\n\n if self.output_format == OutputFormat.GEOTIFF:\n driver = gdal.GetDriverByName(\"GTiff\")\n\n elif self.output_format == OutputFormat.ENVI:\n driver = gdal.GetDriverByName(\"ENVI\")\n\n assert driver\n\n if not raster:\n\n if self.output_format == OutputFormat.GEOTIFF:\n raster = driver.Create(filename, metadata.shape[0], metadata.shape[1], len(tiles), data_type, options=[\"BIGTIFF=YES\", \"INTERLEAVE=BAND\"])\n\n elif self.output_format == OutputFormat.ENVI:\n raster = driver.Create(filename, metadata.shape[0], metadata.shape[1], len(tiles), data_type, options=[\"INTERLEAVE=BSQ\"])\n\n assert raster\n\n # NOTE: could do this without the metadata!!\n raster.SetGeoTransform(metadata.transform)\n raster.SetProjection(metadata.projection)\n\n raster.SetMetadata(self.generate_raster_metadata())\n\n mask = None\n\n if pqa:\n mask = get_mask_pqa(pqa, self.mask_pqa_mask, mask=mask)\n\n _log.info(\"Stacking [%s] band data from [%s] with PQA [%s] and PQA mask [%s] to [%s]\",\n band.name, dataset.path,\n pqa and pqa.path or \"\", pqa and self.mask_pqa_mask or \"\",\n filename)\n\n data = get_dataset_data_masked(dataset, mask=mask, ndv=ndv)\n\n _log.debug(\"data is [%s]\", data)\n\n stack_band = raster.GetRasterBand(index)\n\n stack_band.SetDescription(os.path.basename(dataset.path))\n stack_band.SetNoDataValue(ndv)\n stack_band.WriteArray(data[band])\n stack_band.ComputeStatistics(True)\n stack_band.SetMetadata({\"ACQ_DATE\": format_date(tile.end_datetime), \"SATELLITE\": dataset.satellite.name})\n\n stack_band.FlushCache()\n del stack_band\n\n if raster:\n raster.FlushCache()\n del raster\n raster = None\n\n def generate_raster_metadata(self):\n return {\n \"X_INDEX\": \"{x:03d}\".format(x=self.x),\n \"Y_INDEX\": \"{y:04d}\".format(y=self.y),\n \"DATASET_TYPE\": \"{dataset_type} STACK\".format(dataset_type=self.dataset_type.name),\n \"ACQUISITION_DATE\": \"{acq_min} to {acq_max}\".format(acq_min=format_date(self.acq_min), acq_max=format_date(self.acq_max)),\n \"SEASON\": self.season.name,\n \"SATELLITES\": \" \".join([s.name for s in self.satellites]),\n \"PIXEL_QUALITY_FILTER\": self.mask_pqa_apply and \" \".join([mask.name for mask in self.mask_pqa_mask]) or \"\"\n }\n\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')\n\n Arg25BandStackWorkflow().run()\n","sub_path":"workflow/band_stack_arg25.py","file_name":"band_stack_arg25.py","file_ext":"py","file_size_in_byte":21739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"104837294","text":"import requests\r\nimport pandas as pd\r\nimport datetime\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\ndef real_time_predict(crypto_code):\r\n url = ('https://in.finance.yahoo.com/quote/') + \\\r\n crypto_code + ('-INR?p=') + crypto_code + ('-INR')\r\n r = requests.get(url)\r\n web_content = BeautifulSoup(r.text, 'html.parser')\r\n web_content = web_content.find('div',\r\n {'class': \"D(ib) smartphone_Mb(10px) W(70%) W(100%)--mobp smartphone_Mt(6px)\"})\r\n web_content = web_content.find('span')\r\n if web_content == []:\r\n web_content = '999999'\r\n return (web_content.text + \" \" + 'RS')\r\ndef get_codes():\r\n return HSI\r\n\r\nif __name__ == '__main__':\r\n HSI = ['ETH', 'BTC', 'XRP', 'DOGE', 'ADA', 'USDT', 'DOT2', 'D0T1', 'XRP', 'LTC', 'LINK', 'BCH', 'XLM', 'USDC',\r\n 'XEM', 'SOL2', 'ATOM2', 'ATOM1', 'SOL1', 'EOS', 'XMR', 'BSV', 'TRX', 'MIOTA', 'THETA']\r\n print('PLEASE SEE THE FOLLOWING CODES AND ENTER THE ONE WHOS REAL TIME DATA YOU WANT TO KNOW')\r\n for i in HSI:\r\n print(i)\r\n code = str(input('ENTER CODE HERE:'))\r\n print(real_time_predict(code))\r\n","sub_path":"project/crypto.py","file_name":"crypto.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"340521554","text":"import glob\nimport numpy as np\nimport math\nfrom process import *\n\nfiles = sorted(glob.glob(\"../../data/raw/trunk/60gs/3rd_eval/*.txt\"))\npath = files[3]\n\ndata = np.loadtxt(path, skiprows=6)\ntime = data[:, 0]\ntorque = data[:, 1]\nvelocity = data[:, 4]\nangle = abs(data[:, 3])\nangle = abs(angle - 60)\n\n# Gravity torque vars\nmass = 48.90154\ng = 9.81\nupper_body_length = 0.73\npercent = 0.5\ndistance = upper_body_length * percent\nangle_c = angle + 10\nangle_c = 90 + angle_c\nangle_c = angle_c * (math.pi / 180)\n\na = np.zeros(torque.shape)\n\nTg = mass * g * distance * np.sin(angle_c)\n\n\nTc = np.empty(shape=torque.shape, dtype='object')\n# for i in range(0, len(torque)):\n# if velocity[i] > 0:\n# Tc[i] = torque[i] Tg[i]\n# elif velocity[i] < 0:\n# Tc[i] = torque[i] - Tg[i]\n# elif velocity[i] == 0:\n# Tc[i] = torque[i]\nTc = torque - Tg\n\n# Plot\nfig1 = plt.figure(figsize=(18, 9))\nax1 = fig1.add_subplot(2, 1, 1)\nax2 = fig1.add_subplot(2, 1, 2)\n# Add a multicursor to all subplotsg\nmulti = MultiCursor(fig1.canvas, (ax1, ax2), color=\"k\", linewidth=1)\nmulti\n\nax1.plot(time, torque, color=\"tab:blue\", label=\"Torque (Nm)\")\nax1.plot(time, velocity, color=\"tab:orange\", label=\"Velocity (°/s)\")\nax1.plot(time, Tg, color=\"tab:red\", label=\"Torque corrected\")\nax2.plot(time, angle, color=\"tab:green\", label=\"Anatomical position (°)\")\n\nax1.legend(loc=\"upper right\")\nax2.legend(loc=\"upper right\")\n\n# Add a horizontal line at torque and velocity = 0\nax1.axhline(y=0, color=\"tab:blue\", linestyle=\"dotted\")\n# Add horizontal lines at the selected isokinetic velocity\nax1.axhline(y=60, color=\"tab:orange\", linestyle=\"dotted\")\nax1.axhline(y=-60, color=\"tab:orange\", linestyle=\"dotted\")\n\ntitle = set_plot_title(path)\n\nax1.set_title(title)\nplt.tight_layout()\nplt.show()\n","sub_path":"code/Python/gravity_correction_trunk.py","file_name":"gravity_correction_trunk.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"332914883","text":"import wikipedia\n\ndef get_wiki():\n \"Search for a wikipedia page.\"\n user_input = 'blank'\n while input != '':\n try:\n user_input = input('Search for a wikipedia page: ')\n page = wikipedia.page(user_input)\n except wikipedia.exceptions.DisambiguationError as e:\n print('The pages that \"{0}\" could refer to are: \\nPlease be more specific with your search'.\n format(user_input))\n print(e.options)\n except wikipedia.exceptions.PageError:\n print('{0} does not match any pages, try another query!'.format(user_input))\n else:\n print(page.url)\n print(page.title)\n print(page.summary)\n\nget_wiki()\n\n\n\n","sub_path":"prac_10/wiki.py","file_name":"wiki.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"213327047","text":"#!/usr/bin/env python\n\nfrom unittest import TestCase\nimport pytest\nimport numpy as np\nfrom astropy.table import Table\n\nfrom ...composite_models import *\nfrom ...factories import HodModelFactory, SubhaloModelFactory\n\nfrom ....utils.table_utils import compute_conditional_percentiles\nfrom ....sim_manager import HaloCatalog\nfrom ....custom_exceptions import *\n\n### Determine whether the machine is mine\n# This will be used to select tests whose \n# returned values depend on the configuration \n# of my personal cache directory files\nfrom astropy.config.paths import _find_home \naph_home = u'/Users/aphearin'\ndetected_home = _find_home()\nif aph_home == detected_home:\n APH_MACHINE = True\nelse:\n APH_MACHINE = False\n\nclass TestHearin15(TestCase):\n\n\t@pytest.mark.slow\n\t@pytest.mark.skipif('not APH_MACHINE')\n\tdef setup_class(self):\n\n\t\tNpts = 1e4\n\t\tmass = np.zeros(Npts) + 1e12\n\t\tconc = np.random.random(Npts)\n\t\td = {'halo_mvir': mass, 'halo_nfw_conc': conc}\n\t\tself.toy_halo_table = Table(d)\n\t\tself.toy_halo_table['halo_nfw_conc_percentile'] = compute_conditional_percentiles(\n\t\t\thalo_table = self.toy_halo_table, \n\t\t\tprim_haloprop_key = 'halo_mvir', \n\t\t\tsec_haloprop_key = 'halo_nfw_conc', \n\t\t\tdlog10_prim_haloprop = 0.05)\n\n\t\thighz_mask = self.toy_halo_table['halo_nfw_conc_percentile'] >= 0.5\n\t\tself.highz_toy_halos = self.toy_halo_table[highz_mask]\n\t\tself.lowz_toy_halos = self.toy_halo_table[np.invert(highz_mask)]\n\n\t\tself.snapshot = HaloCatalog(preload_halo_table = True)\n\n\t\tself.snapshot2 = HaloCatalog(preload_halo_table = True, redshift = 2.)\n\n\t@pytest.mark.slow\n\t@pytest.mark.skipif('not APH_MACHINE')\n\tdef test_Hearin15(self):\n\n\t\tmodel = HodModelFactory('hearin15', concentration_binning = (1, 35, 5))\n\t\tmodel.populate_mock(snapshot = self.snapshot)\n\n\t@pytest.mark.slow\n\t@pytest.mark.skipif('not APH_MACHINE')\n\tdef test_Leauthaud11(self):\n\n\t\tmodel = HodModelFactory('leauthaud11', concentration_binning = (1, 35, 5))\n\t\tmodel.populate_mock(snapshot = self.snapshot)\n\n\t\tmodel2 = HodModelFactory('leauthaud11', concentration_binning = (1, 35, 5), \n\t\t\tcentral_velocity_bias = True, satellite_velocity_bias = True)\n\t\tmodel2.param_dict['velbias_centrals'] = 10\n\t\tmodel2.populate_mock(snapshot = self.snapshot)\n\n\t\t# Test that the velocity bias is actually operative\n\t\tcentral_mask = ( \n\t\t\t(model.mock.galaxy_table['gal_type'] == 'centrals') & \n\t\t\t(model.mock.galaxy_table['halo_mvir'] > 5e12) & \n\t\t\t(model.mock.galaxy_table['halo_mvir'] > 1e13)\n\t\t\t)\n\t\tcens1 = model.mock.galaxy_table[central_mask]\n\n\t\tcentral_mask = ( \n\t\t\t(model2.mock.galaxy_table['gal_type'] == 'centrals') & \n\t\t\t(model2.mock.galaxy_table['halo_mvir'] > 5e12) & \n\t\t\t(model2.mock.galaxy_table['halo_mvir'] > 1e13)\n\t\t\t)\n\t\tcens2 = model2.mock.galaxy_table[central_mask]\n\n\t\tassert np.std(cens1['vx']) < np.std(cens2['vx'])\n\t\tassert np.std(cens1['vy']) < np.std(cens2['vy'])\n\t\tassert np.std(cens1['vz']) < np.std(cens2['vz'])\n\n\t\t# Test that an attempt to repopulate with a different snapshot raises an exception\n\t\twith pytest.raises(HalotoolsError) as exc:\n\t\t\tmodel2.populate_mock(redshift=2)\n\t\twith pytest.raises(HalotoolsError) as exc:\n\t\t\tmodel2.populate_mock(simname='consuelo')\n\t\twith pytest.raises(HalotoolsError) as exc:\n\t\t\tmodel2.populate_mock(halo_finder='bdm')\n\n\t\tmodel_highz = HodModelFactory('leauthaud11', redshift = 2., \n\t\t\tconcentration_binning = (1, 35, 5))\n\t\tmodel_highz.populate_mock(snapshot = self.snapshot2)\n\t\twith pytest.raises(HalotoolsError) as exc:\n\t\t\tmodel_highz.populate_mock()\n\t\twith pytest.raises(HalotoolsError) as exc:\n\t\t\tmodel_highz.populate_mock(snapshot = self.snapshot)\n\t\tmodel_highz.populate_mock(redshift = 2.)\n\n\n\n\n\n\n\n\n\n","sub_path":"halotools/empirical_models/composite_models/tests/test_preloaded_models.py","file_name":"test_preloaded_models.py","file_ext":"py","file_size_in_byte":3621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"307024062","text":"# -*- coding = utf-8 -*-\nimport os,sys\nimport re\nimport scrapy\nfrom bs4 import BeautifulSoup\n\nbase = './why/'\nfilename_withouts = ['?', '/', '\\\\', '\"', '<', '>', ':', '*', '|']\nfw = open('urls.txt','wb')\n\nclass SwgwsmSpider(scrapy.Spider):\n\tname = \"swgwsm\"\n\t#allowed_domains = [\"dmoz.org\"]\n\tstart_urls = [\n\t\t\"http://www.tom61.com/shiwangeweishime/\"\n\t]\n\tdef parse(self, response):\n\t\tsoup = BeautifulSoup(response.body_as_unicode(),\"lxml\")\n\t\tfounds = soup.find(id='Mhead1_5')\n\t\tfounds = founds.find_all('a')\n\t\tfor found in founds:\n\t\t\tlinks = found.get('href')\n\t\t\t#print 'found = ', found, 'links = ', links\n\t\t\tyield scrapy.Request(links,callback=self.parse_url)\n\n\tdef parse_url(self,response):\n\t\t#print 'res = ', response.body\n\t\tsoup = BeautifulSoup(response.body_as_unicode(),\"lxml\")\n\t\tlink = soup.find(id=\"Mhead2_0\")\n\t\t_urls = link.find_all('a')\n\t\t#print 'title = ',soup.title.string, 'len = ', len(_urls)\n\t\tfor _url in _urls:\n\t\t\tstory = _url.get('href')\n\t\t\tif 'http' not in story:\n\t\t\t\tstory = 'http://www.tom61.com' + story\n\t\t\tfw.write(story + '\\n')\n\t\t\t#print 'story = ', story\n\t\t\tyield scrapy.Request(story,callback=self.parse_story)\n\n\t\tnextPage = soup.find_all('div',class_='t_fy')\n\t\tfor np in nextPage:\n\t\t\tpages = np.find_all('a',class_='c_page')\n\t\t\tfor page in pages:\n\t\t\t\t#if re.match('http',page) == None:\n\t\t\t\t_url = 'http://www.tom61.com' + page.get('href')\n\t\t\t\t#fw.write(story + '\\n')\n\t\t\t\t#print '_url = ', _url\n\t\t\t\tyield scrapy.Request(_url,callback=self.parse_url_next)\n\n\tdef parse_url_next(self,response):\n\t\tsoup = BeautifulSoup(response.body_as_unicode(),\"lxml\")\n\t\tlink = soup.find(id=\"Mhead2_0\")\n\t\t_urls = link.find_all('a')\n\t\tfor _url in _urls:\n\t\t\tstory = _url.get('href')\n\t\t\tif 'http' not in story:\n\t\t\t\tstory = 'http://www.tom61.com' + story\n\t\t\t\t#print \"story = \", story\n\t\t\tfw.write(story + '\\n')\n\t\t\tyield scrapy.Request(story,callback=self.parse_story)\n\n\tdef parse_story(self,response):\n\t\tsoup = BeautifulSoup(response.body_as_unicode(),\"lxml\")\n\t\ttop = soup.title.string\n\t\ttitle = top.split('_')\n\t\tname = title[0]\n\t\tcontent = soup.find(class_='t_news_txt')\n\t\tstory = ''\n\t\t_cnts = content.find_all('p')\n\t\tfor _cnt in _cnts:\n\t\t\ttemp = _cnt.get_text().encode('utf-8')\n\t\t\tstory = story + temp.replace(' ','')\n\t\tstory = story.strip().replace('\\xc2\\xa0','')\n\t\tfor filename_con in filename_withouts:\n\t\t\tif filename_con in name:\n\t\t\t\tname = name.replace(filename_con, '')\n\t\t#print \"name = \", name\n\t\tfirstpath = base + title[2]\n\t\tsecondpath = firstpath + '/' + title[1]\n\t\tfilepath = base + name\n\t\tfw = open(filepath, 'w')\n\t\tfw.write(story)\n","sub_path":"spiders/spiders/spiders/swgwsm_spider.py","file_name":"swgwsm_spider.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"45311741","text":"#!/usr/bin/env python\n\n# A palindromic number reads the same both ways.\n# The largest palindrome made from the product of two 2-digit\n# numbers is 9009 = 91 × 99.\n#\n# Find the largest palindrome made from the product of two 3-digit numbers.\n\nfrom timeit import default_timer as timer\n\nprimes = [ 2 ]\n\n# Procedure to factorize the given number to powers of prime numbers\n# it will keep all prime numbers it encounter in global list\n# it will return list of the factors of the number\ndef factorize(num):\n\n factor_value = []\n factor_power = []\n\n for i in primes:\n\n if num % i == 0:\n\n factor_value.append(i)\n factor_power.append(1)\n\n num /= factor_value[-1]\n\n while num % factor_value[-1] == 0 and num != 1:\n factor_power[-1] += 1\n num /= factor_value[-1]\n\n if num == 1:\n return [ factor_value, factor_power ]\n else:\n i = primes[-1]\n\n while i < int(num) + 1:\n\n i += 1\n\n prime_flag = True\n\n for k in primes:\n if i%k == 0:\n prime_flag = False\n break\n\n if prime_flag:\n primes.append(i)\n\n if num % primes[-1] == 0:\n\n factor_value.append(primes[-1])\n factor_power.append(1)\n\n num /= factor_value[-1]\n\n while num % factor_value[-1] == 0 and num != 1:\n factor_power[-1] += 1\n num /= factor_value[-1]\n\n if num == 1:\n return [ factor_value, factor_power ]\n\n# Procedure to check if a given number is palindrome or not\n# returns True if it is\n# returns False if it is not\n# It achieves the goal by reversing num and checking if they are equal\ndef palindrome_check(num):\n\n n = num\n rev = 0\n\n while n > 0:\n\n dig = int(n % 10)\n rev = rev*10 + dig\n n = int(n / 10)\n\n if(rev == num):\n return True\n else:\n return False\n\n# We provide how many digits each of the numbers, whos product should be\n# a palindrome should have\n# We calculate the largest possible product and start reducing it by 1\n# We check the number if it is a palindrome\n# if it is, we factorize it by keeping track of the prime numbers we encounter\n# when we have factorized the number we create half of all possible combinations\n# we can create from the factor list and calculate one of the products\n# if the product is equal to the desired amount of digits we check if\n# it is a factor of the palindrome and if the result is also equal to the\n# desired amount of digits - if so this is our result\ndef problem_4(num_digits):\n\n \"\"\"\n >>> problem_4(2)\n [9009, 99, 91]\n \"\"\"\n\n from math import pow\n from itertools import combinations\n\n max_number = pow(( pow(10,num_digits)-1 ), 2)\n\n for single_number in range(int(max_number), 10, -1):\n\n # check if single_number is palindrome\n if palindrome_check(single_number):\n\n # factorize the number to prime factors in res[0]\n # and their powers containing in res[1]\n res = factorize(single_number)\n\n # list containing the factors of the single_number\n factors = [ m**n for m,n in zip(res[0], res[1]) ]\n\n # start creating half of all possible combinations of the factors\n for combination_class in \\\n range( \\\n len(factors) - 1, \\\n int(len(factors)/2)-1 \\\n if (len(factors)/2)%2 == 0 else int(len(factors)/2), \\\n -1 \\\n ):\n for combination_single in combinations(factors,combination_class):\n\n # of this particular combination create the product\n product_1= 1\n for _i in combination_single:\n product_1 *= _i\n\n # if the product is with the desired number of digits\n # calculate the other product and its reminder\n if len(str(abs(product_1))) == num_digits:\n product_2_reminder = single_number % product_1\n product_2 = int(single_number / product_1)\n\n #print(\\\n #single_number, \\\n #res, \\\n #combination_single, \\\n #rest, \\\n #product_1, \\\n #product_2 \\\n #)\n\n # if the second product has divides with no reminder\n # and has the desired number of digits\n # we have our answer\n if product_2_reminder == 0 and len(str(abs(product_2))) == num_digits:\n return [ single_number, product_1, product_2]\n return 0\n\nif __name__ == \"__main__\":\n\n import doctest\n doctest.testmod()\n\n num = 3\n t0 = timer()\n\n print(\"result = {}\".format(problem_4(num)))\n print(\"time = {} ms\".format((timer()-t0)*1e3))\n","sub_path":"problem_4/problem_4.py","file_name":"problem_4.py","file_ext":"py","file_size_in_byte":5155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"643052978","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nimport argparse\nimport os\nimport tempfile\nfrom subprocess import Popen, PIPE\n\nfrom buildfarm import jenkins_support, release_jobs\n\nfrom buildfarm.ros_distro import debianize_package_name\n\nimport rospkg.distro\n\ntry:\n from urllib.parse import urlsplit\nexcept ImportError:\n from urlparse import urlsplit\n\n\ndef parse_options():\n parser = argparse.ArgumentParser(description='Create a set of jenkins jobs for source packages and binary packages for a catkin package.')\n parser.add_argument('--fqdn', dest='fqdn',\n help='The source repo to push to, fully qualified something. Default: taken from distro-build.yaml, for Fuerte: repos.ros.org')\n parser.add_argument(dest='rosdistro',\n help='The ros distro. fuerte, groovy, hydro, ...')\n parser.add_argument('--distros', nargs='+', default=[],\n help='A list of platform distros. Default: %(default)s')\n parser.add_argument('--arches', nargs='+',\n help='A list of platform architectures. Default: taken from distro-build.yaml, for Fuerte: [amd64, i386]')\n parser.add_argument('--commit', action='store_true', default=False,\n help='Really?')\n parser.add_argument('--delete', action='store_true', default=False,\n help='Delete extra jobs')\n parser.add_argument('--no-update', dest='skip_update', action='store_true', default=False,\n help='Assume packages have already been downloaded')\n parser.add_argument('--wet-only', action='store_true', default=False,\n help='Only setup wet jobs')\n parser.add_argument('--repo-workspace', action='store',\n help='A directory into which all the repositories will be checked out into.')\n parser.add_argument('--repos', nargs='+',\n help='A list of repository (or stack) names to create. Default: creates all')\n parser.add_argument('--ssh-key-id',\n help=\"Jenkins SSH key ID for accessing the package server\")\n parser.add_argument('--platform', default='ubuntu',\n help='Linux platform (ubuntu, fedora)')\n args = parser.parse_args()\n if args.repos and args.delete:\n parser.error('A set of repos to create can not be combined with the --delete option.')\n\n if args.rosdistro == 'fuerte':\n if args.fqdn is None:\n args.fqdn = 'repos.ros.org'\n if args.arches is None:\n args.arches = ['amd64', 'i386']\n\n return args\n\n\ndef verify_heads(repo_uri, expected_head):\n expected_head = 'refs/heads/' + expected_head\n process = Popen(['git', 'ls-remote', '--heads', repo_uri, expected_head], stdout=PIPE, stderr=PIPE)\n heads = process.communicate()[0]\n if not process.poll() == 0:\n heads = \"\"\n\n head_list = []\n for head in heads.split('\\n'):\n if head != '':\n head_list += [head.split()[-1]]\n\n #if expected_head in head_list:\n # return expected_head\n #else:\n # print(\"No matching head found. Are you sure you pointed to the right repository or the version is right?, expected %s:\\nHeads:\\n%s\" % (expected_head, heads))\n return head_list or None\n\n\ndef verify_tags(repo_uri, expected_tag):\n expected_tag = 'refs/tags/' + expected_tag\n process = Popen(['git', 'ls-remote', '--tags', repo_uri, expected_tag], stdout=PIPE, stderr=PIPE)\n tags = process.communicate()[0]\n if not process.poll() == 0:\n tags = \"\"\n\n tag_list = []\n for tag in tags.split('\\n'):\n if tag != '':\n tag_list += [tags.split()[-1]]\n\n #if expected_tag in tag_list:\n # return expected_tag\n #else:\n # print(\"No matching tag found. Are you sure you pointed to the right repository or the version is right?, expected %s:\\nTags:\\n%s\" % (expected_tag, tags))\n return tag_list or None\n\n\ndef doit(rd, distros, arches, target_repository, fqdn, jobs_graph, rosdistro, packages, dry_maintainers, commit=False, delete_extra_jobs=False, whitelist_repos=None, sourcepkg_timeout=None, binarypkg_timeout=None, ssh_key_id=None, platform='ubuntu'):\n jenkins_instance = None\n if args.commit or delete_extra_jobs:\n jenkins_instance = jenkins_support.JenkinsConfig_to_handle(jenkins_support.load_server_config_file(jenkins_support.get_default_catkin_debs_config()))\n\n # Figure out default distros. Command-line arg takes precedence; if\n # it's not specified, then read targets.yaml.\n if distros:\n default_distros = distros\n else:\n default_distros = rd.get_target_distros()[platform]\n\n # TODO: pull arches from rosdistro\n target_arches = arches\n\n # We take the intersection of repo-specific targets with default\n # targets.\n results = {}\n\n for repo_name in sorted(rd.get_repo_list()):\n if whitelist_repos and repo_name not in whitelist_repos:\n continue\n\n r = rd.get_repo(repo_name)\n #todo add support for specific targets, needed in rosdistro.py too\n #if 'target' not in r or r['target'] == 'all':\n target_distros = default_distros\n #else:\n # target_distros = list(set(r['target']) & set(default_distros))\n\n print('Configuring WET repo \"%s\" at \"%s\" for \"%s\"' % (r.name, r.url, target_distros))\n\n # TODO: Workaround until repos have rpm branches\n manual_workarounds = []\n if rosdistro == 'jade':\n manual_workarounds += ['bfl'] # https://github.com/ros-gbp/bfl-release/pull/9\n manual_workarounds += ['robot_upstart'] # missing daemontools\n # manual_workarounds += ['ueye_cam'] # https://github.com/anqixu/ueye_cam/pull/23\n elif rosdistro == 'indigo':\n pass\n # manual_workarounds += ['ardrone_autonomy'] # https://github.com/AutonomyLab/ardronelib/pull/1\n # manual_workarounds += ['bride'] # Missing build ids\n manual_workarounds += ['care_o_bot'] # https://github.com/ipa320/care-o-bot/issues/5\n # manual_workarounds += ['euslisp'] # https://github.com/tork-a/euslisp-release/pull/4\n # manual_workarounds += ['graft'] # https://github.com/ros-perception/graft/issues/23\n # manual_workarounds += ['hrpsys'] # https://bugzilla.redhat.com/1207045\n # manual_workarounds += ['joystick_drivers'] # https://github.com/ros-drivers/joystick_drivers/pull/66\n manual_workarounds += ['libnabo'] # -DSHARED_LIBS:BOOL=ON (no official rpm branch yet)\n # manual_workarounds += ['libpointmatcher'] # TODO: Not sure how to phrase this one yet\n # manual_workarounds += ['librms'] # https://github.com/ros/rosdistro/pull/6619\n # manual_workarounds += ['neo_driver'] # https://github.com/neobotix/neo_driver/pull/3\n # manual_workarounds += ['ocl'] # https://github.com/ros/rosdistro/pull/6959\n manual_workarounds += ['openni_camera'] # https://github.com/ros-drivers/openni_camera/pull/32\n manual_workarounds += ['openni2_camera'] # valid branch has wrong rosdep entry for openni2-devel\n manual_workarounds += ['razer_hydra'] # udev rules...\n manual_workarounds += ['robot_upstart'] # missing daemontools\n # manual_workarounds += ['srv_tools'] # https://github.com/srv/srv_tools/pull/3\n # manual_workarounds += ['stage'] # https://github.com/ros-simulation/stage_ros/issues/14\n # manual_workarounds += ['stage_ros'] # https://github.com/ros-simulation/stage_ros/issues/14\n # manual_workarounds += ['uwsim_bullet'] # https://github.com/uji-ros-pkg/uwsim_bullet/pull/1\n # manual_workarounds += ['warehouse_ros'] # https://github.com/ros-planning/warehouse_ros/pull/17\n\n import re\n expected_tags = ['rpm/%s-%s_%s' % (rd.debianize_package_name(r.packages.keys()[0]), r.full_version, target_distro) for target_distro in target_distros]\n if r.name in manual_workarounds or None in [verify_tags(r.url, expected_tag) for expected_tag in expected_tags]:\n re_url = re.match('(http|https|git|ssh)://(git@)?github\\.com[:/]([^/]*)/(.*)', r.url)\n if not re_url:\n print('- failed to parse URL: %s' % r.url)\n continue\n temporary_url = '://github.com/smd-ros-rpm-release/%s' % re_url.group(4)\n expected_branch = 'rpm/' + rosdistro + '/*'\n if verify_heads('git' + temporary_url, expected_branch):\n r.url = 'https' + temporary_url\n print('- using workaround URL since no RPM branch exists: %s' % r.url)\n else:\n print('- skipping all of \"%s\" since no RPM branch or workaround repo exist' % r.name)\n continue\n # End workaround\n\n for p in sorted(r.packages.iterkeys()):\n if not r.version:\n print('- skipping \"%s\" since version is null' % p)\n continue\n pkg_name = rd.debianize_package_name(p)\n results[pkg_name] = release_jobs.doit(r.url,\n pkg_name,\n packages[p],\n target_distros,\n target_arches,\n target_repository,\n fqdn,\n jobs_graph,\n rosdistro=rosdistro,\n short_package_name=p,\n commit=commit,\n jenkins_instance=jenkins_instance,\n sourcepkg_timeout=sourcepkg_timeout,\n binarypkg_timeout=binarypkg_timeout,\n ssh_key_id=ssh_key_id,\n platform=platform)\n #time.sleep(1)\n #print ('individual results', results[pkg_name])\n\n if args.wet_only:\n print(\"wet only selected, skipping dry and delete\")\n return results\n\n if rosdistro == 'backports' or platform == 'fedora':\n print(\"No dry backports support\")\n return results\n\n if rosdistro == 'fuerte':\n packages_for_sync = 300\n elif rosdistro == 'groovy':\n packages_for_sync = 740\n elif rosdistro == 'hydro':\n packages_for_sync = 865\n elif rosdistro == 'indigo':\n packages_for_sync = 1\n else:\n packages_for_sync = 10000\n\n if rosdistro == 'groovy':\n #dry stacks\n # dry dependencies\n d = rospkg.distro.load_distro(rospkg.distro.distro_uri(rosdistro))\n\n for s in sorted(d.stacks.iterkeys()):\n if whitelist_repos and s not in whitelist_repos:\n continue\n print(\"Configuring DRY job [%s]\" % s)\n if not d.stacks[s].version:\n print('- skipping \"%s\" since version is null' % s)\n continue\n results[rd.debianize_package_name(s)] = release_jobs.dry_doit(s, dry_maintainers[s], default_distros, target_arches, fqdn, rosdistro, jobgraph=jobs_graph, commit=commit, jenkins_instance=jenkins_instance, packages_for_sync=packages_for_sync, ssh_key_id=ssh_key_id)\n #time.sleep(1)\n\n # special metapackages job\n if not whitelist_repos or 'metapackages' in whitelist_repos:\n results[rd.debianize_package_name('metapackages')] = release_jobs.dry_doit('metapackages', [], default_distros, target_arches, fqdn, rosdistro, jobgraph=jobs_graph, commit=commit, jenkins_instance=jenkins_instance, packages_for_sync=packages_for_sync, ssh_key_id=ssh_key_id)\n\n if not whitelist_repos or 'sync' in whitelist_repos:\n results[rd.debianize_package_name('sync')] = release_jobs.dry_doit('sync', [], default_distros, target_arches, fqdn, rosdistro, jobgraph=jobs_graph, commit=commit, jenkins_instance=jenkins_instance, packages_for_sync=packages_for_sync, ssh_key_id=ssh_key_id)\n\n if delete_extra_jobs:\n assert(not whitelist_repos)\n # clean up extra jobs\n configured_jobs = set()\n\n for jobs in results.values():\n release_jobs.summarize_results(*jobs)\n for e in jobs:\n configured_jobs.update(set(e))\n\n existing_jobs = set([j['name'] for j in jenkins_instance.get_jobs()])\n relevant_jobs = existing_jobs - configured_jobs\n relevant_jobs = [j for j in relevant_jobs if rosdistro in j and ('_sourcedeb' in j or '_binarydeb' in j)]\n\n for j in relevant_jobs:\n print('Job \"%s\" detected as extra' % j)\n if commit:\n jenkins_instance.delete_job(j)\n print('Deleted job \"%s\"' % j)\n\n return results\n\n\nif __name__ == '__main__':\n args = parse_options()\n\n print('Loading rosdistro %s' % args.rosdistro)\n\n workspace = args.repo_workspace\n if not workspace:\n workspace = os.path.join(tempfile.gettempdir(), 'repo-workspace-%s' % args.rosdistro)\n\n if args.rosdistro != 'fuerte':\n from buildfarm.ros_distro import Rosdistro\n rd = Rosdistro(args.rosdistro)\n from buildfarm import dependency_walker\n packages = dependency_walker.get_packages(workspace, rd, skip_update=args.skip_update)\n dependencies = dependency_walker.get_jenkins_dependencies(args.rosdistro, packages)\n\n # TODO does only work with one build file\n build_config = rd._build_files[0].get_target_configuration()\n target_repository = build_config['apt_target_repository']\n # TODO Building URL Workaround\n if not args.platform == 'ubuntu':\n target_repository = os.path.join(target_repository, args.platform)\n # End Workaround\n if args.fqdn is None:\n fqdn_parts = urlsplit(target_repository)\n args.fqdn = fqdn_parts.netloc\n if args.arches is None:\n args.arches = rd.get_arches()\n\n # TODO Fedora Arch Workaround\n if 'amd64' in args.arches and args.platform == 'fedora':\n args.arches.remove('amd64')\n args.arches.append('x86_64',)\n # End Workaround\n\n # TODO Another Fedora Arch Workaround\n if args.platform == 'fedora' and args.rosdistro in ['indigo', 'jade']:\n args.arches.append('armhfp')\n # End Workaround\n\n # TODO does only work with one build file\n sourcepkg_timeout = rd._build_files[0].jenkins_sourcedeb_job_timeout\n binarypkg_timeout = rd._build_files[0].jenkins_binarydeb_job_timeout\n else:\n target_repository = 'http://' + args.fqdn + '/repos/building'\n from buildfarm.ros_distro_fuerte import Rosdistro\n rd = Rosdistro(args.rosdistro)\n from buildfarm import dependency_walker_fuerte\n stacks = dependency_walker_fuerte.get_stacks(workspace, rd._repoinfo, args.rosdistro, skip_update=args.skip_update)\n dependencies = dependency_walker_fuerte.get_dependencies(args.rosdistro, stacks)\n packages = stacks\n sourcepkg_timeout = None\n binarypkg_timeout = None\n\n release_jobs.check_for_circular_dependencies(dependencies)\n\n if args.rosdistro == 'groovy':\n # even for wet_only the dry packages need to be consider, else they are not added as downstream dependencies for the wet jobs\n stack_depends, dry_maintainers = release_jobs.dry_get_stack_dependencies(args.rosdistro)\n dry_jobgraph = release_jobs.dry_generate_jobgraph(args.rosdistro, dependencies, stack_depends)\n else:\n stack_depends, dry_maintainers = {}, {}\n dry_jobgraph = {}\n\n combined_jobgraph = {}\n for k, v in dependencies.iteritems():\n combined_jobgraph[k] = v\n for k, v in dry_jobgraph.iteritems():\n combined_jobgraph[k] = v\n\n # setup a job triggered by all other debjobs\n combined_jobgraph[debianize_package_name(args.rosdistro, 'metapackages')] = combined_jobgraph.keys()\n combined_jobgraph[debianize_package_name(args.rosdistro, 'sync')] = [debianize_package_name(args.rosdistro, 'metapackages')]\n\n results_map = doit(\n rd,\n args.distros,\n args.arches,\n target_repository,\n args.fqdn,\n combined_jobgraph,\n rosdistro=args.rosdistro,\n packages=packages,\n dry_maintainers=dry_maintainers,\n commit=args.commit,\n delete_extra_jobs=args.delete,\n whitelist_repos=args.repos,\n sourcepkg_timeout=sourcepkg_timeout,\n binarypkg_timeout=binarypkg_timeout,\n ssh_key_id=args.ssh_key_id,\n platform=args.platform)\n\n if not args.commit:\n print('This was not pushed to the server. If you want to do so use \"--commit\" to do it for real.')\n","sub_path":"scripts/create_release_jobs.py","file_name":"create_release_jobs.py","file_ext":"py","file_size_in_byte":17001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"420356035","text":"\nfrom datetime import datetime\nimport redis\nimport re\n\nrecommendDB=redis.StrictRedis(host='localhost', port=6379, db=0)\n\ndef main():\n try:\n rec=open('recommendations.txt','r')\n for line in iter(rec):\n arr=re.findall('\\d+', line)\n key=arr[0]\n arr=arr[1:]\n #print(key, arr[1:])\n recommendDB.set(key, arr)\n rec.close()\n except IOError:\n err=open('reports/Upload-Recommendations.txt', 'w')\n err.write(str(datetime.now())+'\\nFailed To Open Recommendations File\\n')\n err.close()\n\nmain()\n","sub_path":"controller/indexRecommendations.py","file_name":"indexRecommendations.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"81964185","text":"from selenium import webdriver\nimport time\nfrom selenium.webdriver.common.keys import Keys\n\n\ndriver = webdriver.Chrome(executable_path=r\"C:\\_luke\\_test\\chromedriver_win32_75\\chromedriver.exe\")\nurl = 'https://demobank.jaktestowac.pl/logowanie_etap_1.html'\ndriver.get(url)\ntitle = driver.title\nprint(f'Actual title: {title}')\n\n\nlogin_form_header_element = driver.find_element_by_xpath('//*[@id=\"login_form\"]/h1')\nlogin_form_header_text = login_form_header_element.text\nprint(f'Login form header text: {login_form_header_text}')\n\ninput_login_element = driver.find_element_by_xpath('//*[@id=\"login_id\"]')\ninput_login_element.send_keys('kocur13', Keys.BACKSPACE)\ninput_login_element.send_keys(Keys.BACKSPACE)\n\ntime.sleep(2)\ninput_login_element.clear()\ntime.sleep(1)\n\n\ndriver.quit()","sub_path":"scratches/001_scratch_xpath.py","file_name":"001_scratch_xpath.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"441190037","text":"import slam.plot as splt\nimport slam.io as sio\nimport slam.remeshing as srem\n\n\nif __name__ == '__main__':\n # source object files\n source_mesh_file = 'data/example_mesh.gii'\n source_texture_file = 'data/example_texture.gii'\n source_spherical_mesh_file = 'data/example_mesh_spherical.gii'\n # target object files\n target_mesh_file = 'data/example_mesh_2.gii'\n target_spherical_mesh_file = 'data/example_mesh_2_spherical.gii'\n\n source_mesh = sio.load_mesh(source_mesh_file)\n source_tex = sio.load_texture(source_texture_file)\n source_spherical_mesh = sio.load_mesh(source_spherical_mesh_file)\n splt.pyglet_plot(source_mesh, source_tex.darray[0])\n splt.pyglet_plot(source_spherical_mesh, source_tex.darray[0])\n\n target_mesh = sio.load_mesh(target_mesh_file)\n target_spherical_mesh = sio.load_mesh(target_spherical_mesh_file)\n\n interpolated_tex_values = \\\n srem.spherical_interpolation_nearest_neigbhor(source_spherical_mesh,\n target_spherical_mesh,\n source_tex.darray[0])\n splt.pyglet_plot(target_mesh, interpolated_tex_values, plot_colormap=True)\n","sub_path":"examples/example_remeshing.py","file_name":"example_remeshing.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"334640804","text":"from __future__ import absolute_import\n###########################################\n# Plotting\n# \n#\n#\n#\n###########################################\nimport matplotlib.pyplot as plt\nimport numpy as np\nplt.style.use('ggplot')\n###########################################\n\n\n\n\n#def plot_spec(cc,w,x=int(),y=int(), name='fig.pdf'):\ndef plot_spec(cc,w,show=None,name='fig.pdf'):\n\t'''\n\n\n\t'''\n\tfrom datetime import datetime\n\t# current date and time\n\tnow = datetime.now()\n\n\tn = str(now)\n\n\tnamedate = '%s%s'%( n.strip(), name)\n\n\tif show == True:\n\n\t\tplt.ylabel('a.u.')\n\n\t\tplt.xlabel('WVN (1/cm)')\n\n\t\tplt.plot(w.T,cc.T)\n\t\t\n\t\tax = plt.gca()\n\t\t\n\t\tax.invert_xaxis()\n\t\t\n\t\tplt.show()\n\telse:\n\t\tplt.ylabel('a.u.')\n\t\t\n\t\tplt.xlabel('WVN (1/cm)')\n\t\t\n\t\tplt.plot(w.T,cc.T)\n\t\t\n\t\tax = plt.gca()\n\t\t\n\t\tax.invert_xaxis()\n\t\t\n\t\tif name != 'fig.pdf':\n\n\t\t\tplt.savefig(name)\n\t\t\n\t\telse:\n\n\t\t\tplt.savefig(namedate)\n\t\t\n\t\tplt.clf()\n\n\treturn\n\ndef plot_class_spec(cc,w,classnames, cl=1,name='fig.pdf'):\n\t\n\tplt.style.use('grayscale')\n\t\n\tplt.ylabel('a.u.')\n\t\n\tplt.xlabel('WVN (1/cm)')\n\t\n\tax = plt.gca()\n\t\n\tax.invert_xaxis()\n\n\t#y = classnames[np.where(classnames == cl)]\n\t\n\t#d = cc[y]\n\td = cc[np.where(classnames == cl)]\n\tprint(d.shape)\n\t\n\tplt.plot(w.T,d.T)#,label='Spectra of Class' +'%cl' );\n\t\n\t#ax = plt.gca()\n\t#ax.invert_xaxis()\n\tcm = np.mean(d,axis=0)\n\n\tplt.style.use('ggplot')\n\n\tplt.plot(w.T,cm.T, color='orange', label='Mean Spec');\n\t#ax = plt.gca()\n\t#ax.invert_xaxis()\n\tplt.legend()\n\t\n\tplt.savefig(name)\n\t\n\tplt.clf()\n\treturn\n\n\n\n#\t\t\t\tdef plot_class_spec2(cc,w,classnames, cl=1,name='fig.pdf'):\n#\t\t\t\t\tplt.style.use('grayscale')\n#\t\t\t\t\tplt.ylabel('a.u.')\n#\t\t\t\t\tplt.xlabel('WVN (1/cm)')\n#\t\t\t\t\tax = plt.gca()\n#\t\t\t\t\tax.invert_xaxis()\n#\t\t\t\t\ty = classnames[np.where(classnames == cl)]\n#\t\t\t\t\td = cc[y]\n#\t\t\t\t\tplt.style.use('ggplot')\n#\t\t\t\t\tcm = np.mean(d,axis=0)\n#\t\t\t\t\t#plt.plot(w.T,cm.T, color='orange', label='Mean Spec');\n#\t\t\t\t\n#\t\t\t\t\n#\t\t\t\t\n#\t\t\t\t\n#\t\t\t\t\n#\t\t\t\t\n#\t\t\t\t\n#\t\t\t\t\tprint(d.shape)\n#\t\t\t\t\tplt.plot(w.T,cc[y].T)#,label='Spectra of Class' +'%cl' );\n#\t\t\t\t\t#cm = np.mean(d,axis=0)\n#\t\t\t\t\t#plt.style.use('ggplot')\n#\t\t\t\t\t#ax = plt.gca()\n#\t\t\t\t\t#ax.invert_xaxis()\n#\t\t\t\t\t#plt.plot(w.T,cm.T, color='orange', label='Mean Spec');\n#\t\t\t\t\tplt.legend()\n#\t\t\t\t\tplt.savefig(name)\n#\t\t\t\t\t#plt.show()\n#\t\t\t\t\tplt.clf()\n#\t\t\t\t\treturn\n#\t\t\t\t","sub_path":"scr/openvibspec/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"613401879","text":"import csv\n\nfrom airflow.plugins_manager import AirflowPlugin\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.models import BaseOperator\nfrom airflow.utils.log.logging_mixin import LoggingMixin\n\n\nclass CSVValidatorOperator(BaseOperator, LoggingMixin):\n \"\"\"\n Validate a csv file against the schema provided.\n The return value of the execute function has to be used with xCom.\n\n :param file_path: absolute path of the csv file to check\n :type file_path: string\n :param file_schema: valid column header yo use in the validation\n :type file_schema: list\n :param header: true if file_path has header\n :type header: boolean\n :param delimiter: csv delimiter\n :type delimiter: string\n :param quotechar: csv quotechar\n :type quotechar: string\n \"\"\"\n\n @apply_defaults\n def __init__(self, file_path, file_schema, header=True, delimiter=\",\",\n quotechar=\"|\", *args, **kwargs):\n super(CSVValidatorOperator, self).__init__(*args, **kwargs)\n self.file_path = file_path\n self.file_schema = file_schema\n self.delimiter = delimiter\n self.header = header\n self.quotechar = quotechar\n\n def execute(self, context):\n self.log.debug(\"Validating file against file schema provided\")\n\n with open(self.file_path, \"r\") as f_in:\n csv_reader = csv.reader(f_in, delimiter=self.delimiter,\n quotechar=self.quotechar)\n header_checked = True\n for row in csv_reader:\n\n if self.header and header_checked:\n self.log.debug(\"Checking header before checking file content\")\n file_header = row\n if file_header == self.file_schema:\n self.log.debug(\"Header definition matches\")\n header_checked = False\n else:\n self.log.warning(\"Header definition does not match\")\n return False # this needs to be picked up by xCom\n\n self.log.debug(\"Checking file content\")\n if len(row) != len(self.file_schema):\n return False\n\n return True\n\n\nclass CSVValidatorOperatorPlugin(AirflowPlugin):\n name = \"csv_validator_plugin\"\n operators = [CSVValidatorOperator]\n","sub_path":"plugins/csv_validator_plugin.py","file_name":"csv_validator_plugin.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"632406167","text":"from exts import db\n\n\nclass Author(db.Model):\n __tablename__ = 'author'\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n username = db.Column(db.String(100), nullable=False)\n\n\nclass Article(db.Model):\n __tablename__ = 'article'\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n title = db.Column(db.String(100), nullable=False)\n content = db.Column(db.Text, nullable=False)\n\n # 设置外键 db.ForeignKey('author.id')，author 为表名\n author_id = db.Column(db.Integer, db.ForeignKey('author.id'), nullable=False)\n\n # 设置外键的关系，relationship 设置article关联用户author，backref 是反向关联\n author = db.relationship('Author', backref=db.backref('articles'))\n\n # 设置外键关系，多对多用secondary 来设置关联的中间表\n tags = db.relationship('Tag', secondary='article_tag', backref=db.backref('articles'))\n\n\nclass Tag(db.Model):\n __tablename__ = 'tag'\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n name = db.Column(db.String(100), nullable=False)\n\n\n# 多对多关系，设置中间表，用db.Table来实现\narticle_tag = db.Table('article_tag',\n db.Column('article_id', db.Integer, db.ForeignKey('article.id'), primary_key=True),\n db.Column('tag_id', db.Integer, db.ForeignKey('tag.id'), primary_key=True))\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"607895149","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 31 16:01:12 2015\n\n@author: jrc88\n\"\"\"\n#document creation\n# read in line from spreadsheet\n #get identifier, title, year\n #get text from address in spreadsheet\n #split text into thousand word chunks\n #output one line per chuck, stamped with identifier, title, year into csv\n#throw out the first and last 1000 word chunk of each document\n\n#author finder\n# open an original text (pre-chunking)\n # regex to find 'by A'\n # print filename, A to new line in csv file\n\n#year finder\n # for each file name\n #regex search for four digits in a row\n #print filename, year to to a row in a csv file\n\n#collections of multiple capital letters -> authors, titles?\n\n\n\n\nimport csv\nimport codecs\nimport numpy as np\nfrom scipy import dot\nfrom scipy import linalg\nimport matplotlib\nimport os\nimport glob\nfrom nltk.tokenize import TreebankWordTokenizer\n\ntokenizer = TreebankWordTokenizer()\n\ndef split_text(filename,n_words):\n input = open(filename, 'r')\n words = input.read()\n words = tokenizer.tokenize(words.lower())\n input.close()\n chunks= []\n current_chunk_words = []\n current_chunk_word_count = 0\n for word in words:\n current_chunk_words.append(word)\n current_chunk_word_count += 1\n if current_chunk_word_count == n_words:\n chunks.append(' '.join(current_chunk_words))\n current_chunk_words = []\n current_chunk_word_count = 0\n chunks.append(' '.join(current_chunk_words))\n return chunks\n \npulp_files = glob.glob(\"*/*story.txt\")\nchunk_length = 1000\nchunks = []\nwriter = csv.writer(codecs.open('stories.txt', 'wb'))\nfor filename in pulp_files:\n chunk_counter = 0\n texts = split_text(filename,chunk_length)\n for text in texts:\n text = str.join(\" \", text.splitlines())\n chunk = {'number': chunk_counter, 'filename':filename, 'text': text}\n chunks.append(chunk)\n chunk_counter += 1\n filename = filename.replace(\"/\",\"_\")\n filename = filename.replace(\".\",\"_\")\n filename = filename.replace(\" \",\"_\")\n #if chunk_counter > 2:\n writer.writerow([filename+\"_\"+str(chunk_counter)+'\\t'+filename+'\\t'+text]) \n\n #this looks right in python's editor and in sublime text, can't get excel to read it at all\n ","sub_path":"corrected.py","file_name":"corrected.py","file_ext":"py","file_size_in_byte":2319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"547689679","text":"import unittest\nfrom unittest.mock import MagicMock\nfrom utils.event_aggregator import EventAggregator\n\n\nclass TestEventAggregator(unittest.TestCase):\n def test_constructor(self):\n ea = EventAggregator()\n\n self.assertIsInstance(ea.event_lookup, dict)\n\n def test_subscribe(self):\n ea1 = EventAggregator()\n ea2 = EventAggregator()\n mock_handler = MagicMock()\n\n ea1.subscribe('test_subscribe', mock_handler)\n self.assertGreater(len(ea1.event_lookup), 0)\n self.assertIs(mock_handler, ea1.event_lookup['test_subscribe'][0])\n self.assertGreater(len(ea2.event_lookup), 0)\n self.assertIs(mock_handler, ea2.event_lookup['test_subscribe'][0])\n\n self.assertRaises(ValueError, ea1.subscribe, '', mock_handler)\n self.assertRaises(ValueError, ea1.subscribe, 'test_subscribe', None)\n\n def test_publish(self):\n ea1 = EventAggregator()\n ea2 = EventAggregator()\n mock_handler = MagicMock()\n mock_data = MagicMock()\n\n ea1.subscribe('test_publish', mock_handler)\n ea2.publish('test_publish', mock_data)\n mock_handler.assert_called_with(mock_data)\n self.assertFalse(ea2.publish('dont_exist', mock_data))\n\n def test_subscribe_publish(self):\n mock_handler = MagicMock()\n mock_data = MagicMock()\n ea1 = EventAggregator()\n ea1.subscribe('test_publish_subscribe', mock_handler)\n ea2 = EventAggregator()\n ea2.publish('test_publish_subscribe', mock_data)\n\n mock_handler.assert_called_with(mock_data)\n self.assertGreater(len(ea2.event_lookup), 0)\n","sub_path":"src/utils/test/test_event_aggregator.py","file_name":"test_event_aggregator.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"366757694","text":"import requests\nfrom lxml import html as parser\nfrom mongo import offers\nimport itertools\nfrom decouple import config\n\n\ndef construct_offer(node):\n offer = dict()\n try:\n offer['title'] = node.cssselect('a[data-cy=\"listing-ad-title\"]>strong')[0].text_content().strip()\n offer['link'] = node.cssselect('a[data-cy=\"listing-ad-title\"]')[0].get('href')\n offer['location'] = node.cssselect('i[data-icon=\"location-filled\"]')[0].getparent().text_content().strip()\n offer['date'] = node.cssselect('i[data-icon=\"clock\"]')[0].getparent().text_content().strip()\n except Exception as e:\n print(e)\n offer['title'] = \"Для этой квартиры что-то упало, чекай логи.\"\n return offer\n\n\ndef parse_offers():\n data = {'search[city_id]': (None, 141), 'search[region_id]': (None, 4), 'search[category_id]': (None, 1147),\n 'search[filter_float_number_of_rooms:from]': (None, 1),\n 'search[filter_float_number_of_rooms:to]': (None, 1)}\n proxy = {\n \"https\": config('proxy')\n }\n response = requests.post('https://www.olx.ua/ajax/donetsk/search/list/', files=data, proxies=proxy, verify=False)\n offer_nodes = parser.fromstring(response.content.decode('utf-8')).cssselect('table#offers_table>tbody>tr.wrap')\n return [construct_offer(node) for node in offer_nodes]\n\n\ndef find_new_offers():\n parsed_offers = parse_offers()\n old_offers = offers.find_all()\n new_offers = list(itertools.filterfalse(lambda x: x in old_offers, parsed_offers))\n if new_offers:\n offers.insert_many(new_offers)\n return new_offers\n","sub_path":"parser_functions/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"92034644","text":"from collections import defaultdict\n\n\ndef main():\n f = open('./stores.txt')\n d = defaultdict(int)\n for l in f:\n x = ' '.join(l.splitlines())\n d[x] += 1\n\n s = sorted(d, key=d.get, reverse=True)\n print(s)\n #s = [(k, d[k]) for k in sorted(d, key=d.get, reverse=True)]\n\n # for k, v in s:\n # print(k, v)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"unique/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"459401161","text":"import datetime\nimport sys\n\nsys.path.append(\"..\")\nfrom lib.MysqlUtil import MysqlUtil\nfrom urllib import request\nfrom pyquery import PyQuery as pq\n\n\nclass PixivRankingCrawler(object):\n pixiv_base = \"https://www.pixiv.net\"\n pixiv_ranking_url = pixiv_base + '/ranking.php'\n pixiv_r18_ranking_url = 'https://www.pixiv.net/ranking.php?mode=daily_r18'\n headers = {\n \"accept-language\": \"zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7\",\n \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36\"\n }\n mysql_util = None\n\n def __init__(self, config_path):\n self.config = config_path\n self.mysql_util = MysqlUtil(host=\"localhost\", port=3306, user=\"root\", pass_word=\"Sdz123456\")\n\n def get_config(self, key):\n return\n\n def process(self):\n proxy = request.ProxyHandler({'https': 'http://localhost:8118', 'http': 'http://localhost:8118'})\n opener = request.build_opener(proxy, request.HTTPHandler)\n request.install_opener(opener)\n req = request.Request(self.pixiv_ranking_url)\n if len(self.headers) > 0:\n for key, value in self.headers.items():\n req.add_header(key=key,\n val=value)\n data = request.urlopen(req).read().decode('utf-8')\n ranks = self.process_html_code_2_rank(html_code=data)\n self.save_ranks(ranks)\n\n def save_ranks(self, ranks):\n for rank in ranks:\n rank['id'] = self.mysql_util.new_id()\n rank['detail-link'] = self.pixiv_base + rank['detail-link']\n rank['rank_date'] = datetime.date.today().strftime('%Y-%m-%d')\n rank['img_url'] = self.get_image_url(detail_link=rank['detail-link'])\n self.mysql_util.batch_insert(\n datasource=\"pixiv\",\n table=\"pixiv_rank\",\n data_dicts=ranks,\n columns=[\n \"id\",\n \"rank\",\n [\"detail_link\", \"detail-link\"],\n [\"thumbnail_url\", \"thumbnail-img-url\"],\n \"rank_date\",\n \"img_url\",\n [\"img_url_referer\", \"detail-link\"]\n ]\n\n )\n self.mysql_util.commit(datasource=\"pixiv\")\n return\n\n def get_image_url(self, detail_link):\n \"\"\"根据详情页面的地址抓取原图地址\"\"\"\n # req = request.Request(url=detail_link, headers=self.headers)\n # data = request.urlopen(req).read().decode('utf-8')\n return None\n\n def process_html_code_2_rank(self, html_code):\n ranks = []\n html_source = pq(html_code)\n ranking_items_container = html_source(\".ranking-items-container\").html()\n divs = pq(ranking_items_container)\n all_section = divs('section')\n for one in all_section:\n selection = html_source(one)\n detail_link_div_html = selection(\".ranking-image-item\").html()\n detail_link_div = pq(detail_link_div_html)\n detail_link = detail_link_div(\"a\").attr(\"href\")\n id = selection.attr(\"id\")\n rank = int(selection.attr(\"data-rank\"))\n thumbnail_img_url = detail_link_div(\"a\")(\"img\").attr(\"data-src\")\n ranks.append(\n {'id': id,\n \"rank\": rank,\n \"thumbnail-img-url\": thumbnail_img_url,\n \"detail-link\": detail_link\n # \"master-img-url\": self.get_image_url(detail_link=self.pixiv_base + detail_link)\n })\n return ranks\n\n\nif __name__ == '__main__':\n pp = PixivRankingCrawler(\"../config/pixiv.config\")\n pp.process()\n","sub_path":"processor/PixivRankingCrawler.py","file_name":"PixivRankingCrawler.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"572808797","text":"\"\"\"\nwriten by stephen\n\"\"\"\n\nimport os\nimport numpy as np\nimport tensorflow as tf\nfrom alexnet import AlexNet\nfrom datagenerator import ImageDataGenerator\nfrom datetime import datetime\nimport glob\nfrom tensorflow.contrib.data import Iterator\n\nlearning_rate = 1e-4\nnum_epochs = 100 # 代的个数\nbatch_size = 1024\ndropout_rate = 0.5\nnum_classes = 2 # 类别标签\ntrain_layers = ['fc8', 'fc7', 'fc6']\ndisplay_step = 20\n\nfilewriter_path = \"./tmp/tensorboard\" # 存储tensorboard文件\ncheckpoint_path = \"./tmp/checkpoints\" # 训练好的模型和参数存放目录\n\nif not os.path.isdir(checkpoint_path):\n os.mkdir(checkpoint_path)\n\ntrain_image_path = 'train/' # 指定训练集数据路径（根据实际情况指定训练数据集的路径）\ntest_image_cat_path = 'test/cat/' # 指定测试集数据路径（根据实际情况指定测试数据集的路径）\ntest_image_dog_path = 'test/dog/' # 指定测试集数据路径（根据实际情况指定测试数据集的路径）\n\nlabel_path = []\ntest_label = []\n\n# 打开训练数据集目录，读取全部图片，生成图片路径列表\nimage_path = np.array(glob.glob(train_image_path + 'cat.*.jpg')).tolist()\nimage_path_dog = np.array(glob.glob(train_image_path + 'dog.*.jpg')).tolist()\nimage_path[len(image_path):len(image_path)] = image_path_dog\nfor i in range(len(image_path)):\n if 'dog' in image_path[i]:\n label_path.append(1)\n else:\n label_path.append(0)\n\n# 打开测试数据集目录，读取全部图片，生成图片路径列表\ntest_image = np.array(glob.glob(test_image_cat_path + '*.jpg')).tolist()\ntest_image_path_dog = np.array(glob.glob(test_image_dog_path + '*.jpg')).tolist()\ntest_image[len(test_image):len(test_image)] = test_image_path_dog\nfor i in range(len(test_image)):\n if i < 1500:\n test_label.append(0)\n else:\n test_label.append(1)\n\n# 调用图片生成器，把训练集图片转换成三维数组\ntr_data = ImageDataGenerator(\n images=image_path,\n labels=label_path,\n batch_size=batch_size,\n num_classes=num_classes)\n\n# 调用图片生成器，把测试集图片转换成三维数组\ntest_data = ImageDataGenerator(\n images=test_image,\n labels=test_label,\n batch_size=batch_size,\n num_classes=num_classes,\n shuffle=False)\nwith tf.name_scope('input'):\n # 定义迭代器\n iterator = Iterator.from_structure(tr_data.data.output_types,\n tr_data.data.output_shapes)\n\n training_initalize = iterator.make_initializer(tr_data.data)\n testing_initalize = iterator.make_initializer(test_data.data)\n\n # 定义每次迭代的数据\n next_batch = iterator.get_next()\n\nx = tf.placeholder(tf.float32, [batch_size, 227, 227, 3])\ny = tf.placeholder(tf.float32, [batch_size, num_classes])\nkeep_prob = tf.placeholder(tf.float32)\n\n# 图片数据通过AlexNet网络处理\nmodel = AlexNet(x, keep_prob, num_classes, train_layers)\n\n# List of trainable variables of the layers we want to train\nvar_list = [v for v in tf.trainable_variables() if v.name.split('/')[0] in train_layers]\n\n# 执行整个网络图\nscore = model.fc8\n\nwith tf.name_scope('loss'):\n # 损失函数\n loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=score,\n labels=y))\n\ngradients = tf.gradients(loss, var_list)\n\ngradients = list(zip(gradients, var_list))\n\nwith tf.name_scope('optimizer'):\n # 优化器，采用梯度下降算法进行优化\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n train_op = optimizer.apply_gradients(grads_and_vars=gradients)\n\n# 定义网络精确度\nwith tf.name_scope(\"accuracy\"):\n correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1))\n accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\n# 把精确度加入到Tensorboard\ntf.summary.scalar('loss', loss)\ntf.summary.scalar('accuracy', accuracy)\nmerged_summary = tf.summary.merge_all()\nwriter = tf.summary.FileWriter(filewriter_path)\nsaver = tf.train.Saver()\n\n# 定义一代的迭代次数\ntrain_batches_per_epoch = int(np.floor(tr_data.data_size / batch_size))\ntest_batches_per_epoch = int(np.floor(test_data.data_size / batch_size))\n\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n\n # 把模型图加入Tensorboard\n writer.add_graph(sess.graph)\n\n # 把训练好的权重加入未训练的网络中\n model.load_initial_weights(sess)\n\n print(\"{} Start training...\".format(datetime.now()))\n print(\"{} Open Tensorboard at --logdir {}\".format(datetime.now(),\n filewriter_path))\n\n # 总共训练10代\n for epoch in range(num_epochs):\n sess.run(training_initalize)\n print(\"{} Epoch number: {} start\".format(datetime.now(), epoch + 1))\n\n # 开始训练每一代\n for step in range(train_batches_per_epoch):\n img_batch, label_batch = sess.run(next_batch)\n sess.run(train_op, feed_dict={x: img_batch,\n y: label_batch,\n keep_prob: dropout_rate})\n if step % display_step == 0:\n s = sess.run(merged_summary, feed_dict={x: img_batch,\n y: label_batch,\n keep_prob: 1.})\n\n writer.add_summary(s, epoch * train_batches_per_epoch + step)\n\n # 测试模型精确度\n print(\"{} Start validation\".format(datetime.now()))\n sess.run(testing_initalize)\n test_acc = 0.\n test_count = 0\n\n for _ in range(test_batches_per_epoch):\n img_batch, label_batch = sess.run(next_batch)\n acc = sess.run(accuracy, feed_dict={x: img_batch,\n y: label_batch,\n keep_prob: 1.0})\n test_acc += acc\n test_count += 1\n\n test_acc /= test_count\n\n print(\"{} Validation Accuracy = {:.4f}\".format(datetime.now(), test_acc))\n\n # 把训练好的模型存储起来\n print(\"{} Saving checkpoint of model...\".format(datetime.now()))\n\n checkpoint_name = os.path.join(checkpoint_path, 'model_epoch' + str(epoch + 1) + '.ckpt')\n save_path = saver.save(sess, checkpoint_name)\n\n print(\"{} Epoch number: {} end\".format(datetime.now(), epoch + 1))\n","sub_path":"Github-Codes/TensorFlow-CNN-Dataset/tensorflow_alexnet_classify/finetune.py","file_name":"finetune.py","file_ext":"py","file_size_in_byte":6478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"183082102","text":"import time\nimport signal\nimport sys\nimport functools\nimport threading\nimport thread\n\nfrom zope.interface import implements, Interface\nfrom pyramid.threadlocal import get_current_registry\n\nfrom powerhose.jobrunner import JobRunner\nfrom powerhose.job import Job\nfrom powerhose.client.workers import Workers\nfrom powerhose import logger\n\nfrom google.protobuf.message import DecodeError\nfrom tokenserver.crypto.messages import (\n CheckSignature,\n CheckSignatureWithCert,\n Response\n)\n\n# association between the function names and the appropriate protobuf classes\nPROTOBUF_CLASSES = {\n 'check_signature': CheckSignature,\n 'check_signature_with_cert': CheckSignatureWithCert\n}\n\n\n# interface to be able to register the powerhose worker and retrieve it in the\n# registry\nclass IPowerhoseRunner(Interface):\n pass\n\n\ndef get_runner():\n \"\"\"Utility function returning the powerhose runner actually in the\n registry.\n \"\"\"\n return get_current_registry().getUtility(IPowerhoseRunner)\n\n\n# some signal handling to exit when on SIGINT or SIGTERM\ndef bye(*args, **kw):\n stop_runners()\n sys.exit(1)\n\nsignal.signal(signal.SIGTERM, bye)\nsignal.signal(signal.SIGINT, bye)\n\n# to keep track of the runners and workers already instanciated\n_runners = {}\n_workers = {}\n\n\ndef stop_runners():\n logger.debug(\"stop_runner starts\")\n\n for workers in _workers.values():\n workers.stop()\n\n logger.debug(\"workers killed\")\n\n for runner in _runners.values():\n logger.debug('Stopping powerhose master')\n runner.stop()\n\n logger.debug(\"stop_runner ends\")\n\n\nclass CryptoWorkers(threading.Thread):\n \"\"\"Class to spawn powerhose worker in a separate thread\"\"\"\n def __init__(self, workers_cmd, num_workers, working_dir, env, controller,\n pubsub_endpoint, **kw):\n threading.Thread.__init__(self)\n pid = str(thread.get_ident())\n # XXX will want to set up a tcp port for the circus controller\n\n self.workers = Workers(workers_cmd, num_workers=num_workers,\n working_dir=working_dir, env=env, **kw)\n\n def run(self):\n logger.debug('Starting powerhose workers')\n self.workers.run()\n logger.debug('Powerhose workers ended')\n\n def stop(self):\n logger.debug('Stopping powerhose workers')\n self.workers.stop()\n self.join()\n\n\nclass PowerHoseRunner(object):\n \"\"\"Implements a simple powerhose runner.\n\n This class is the one spawning the powerhose master and the workers, if\n any need to be created.\n\n You need to instanciate this class with the following parameters::\n\n >>> runner = PowerHoseRunner(endpoint, workers_cmd)\n\n :param endpoint: the zmq endpoint used to communicate between the powerhose\n master process and the workers.\n :param workers_cmd: the command to run in the workers.\n :param num_workers: the number of workers to spawn\n :param working_dir: the working directory\n :param env: additional environment variables. Can either be a dict or a\n string with the following syntax:\n \"ENV_VAR=value;ENV_VAR2=value\". This is to be able to load this\n class with settings coming from an ini file.\n\n\n This class also provides methods to ease the communication with the\n workers. You can directly send information to the workers by using the\n methods defined in \"methods\".\n\n This allows to make calls directly to this object. IOW, it is possible\n to call the methods listed in \"methods\" on the object::\n\n >>> runner.check_signature(**args)\n\n However, all the arguments need to be passed as keyword arguments.\n \"\"\"\n # We implement an interface to be able to retrieve the object with the\n # pyramid registry system. This means that this class will only be\n # instanciated once, and this instance will be returned each time.\n implements(IPowerhoseRunner)\n\n methods = ['derivate_key', 'check_signature', 'check_signature_with_cert']\n\n def __init__(self, endpoint, workers_cmd, num_workers=5, working_dir=None,\n circus_controller='tcp://127.0.0.1:555',\n circus_pubsub_endpoint='tcp://127.0.0.1:5556', env=None):\n\n # initialisation\n pid = str(thread.get_ident())\n self.endpoint = endpoint.replace('$PID', pid)\n self.workers_cmd = workers_cmd.replace('$PID', pid)\n circus_controller = circus_controller.replace('$PID', pid)\n circus_pubsub_endpoint = circus_pubsub_endpoint.replace('$PID', pid)\n envdict = {}\n\n if env is not None:\n if isinstance(env, dict):\n envdict = env\n else:\n for pair in env.split(';'):\n key, value = pair.split('=', 1)\n envdict[key] = value\n\n # register the runner and the workers in the global vars.\n if self.endpoint not in _runners:\n _runners[self.endpoint] = JobRunner(self.endpoint)\n _workers[self.endpoint] = CryptoWorkers(self.workers_cmd,\n num_workers=num_workers,\n working_dir=working_dir,\n controller=circus_controller,\n pubsub_endpoint=circus_pubsub_endpoint,\n env=envdict)\n self.runner = _runners[self.endpoint]\n logger.debug('Starting powerhose master')\n\n # start the runner ...\n self.runner.start()\n time.sleep(.5)\n self.workers = _workers[self.endpoint]\n\n # ... and the workers\n self.workers.start()\n\n # wait a bit\n time.sleep(1.)\n\n def __getattr__(self, attr):\n \"\"\"magic method getter to be able to do direct function calls on this\n object.\n \"\"\"\n if attr in self.methods:\n return functools.partial(self._execute, attr)\n raise KeyError(\"'%s' is not supported by the powerhose runner\" % attr)\n\n def _execute(self, function_id, **data):\n \"\"\"Send a message to the underlying runner.\n\n This is the low level function, and shouldn't be used directly as-is.\n You should use the high level messages to send crypto works to the\n workers.\n\n This function takes care of the serialisation / deserialization,\n depending the function given.\n\n In the eventuality that the invoked function returns an error, an\n exception will be raised.\n\n :param function_id: the name of the function to be invoked.\n :param data: the parameters to send to the function.\n \"\"\"\n obj = PROTOBUF_CLASSES[function_id]()\n for key, value in data.items():\n setattr(obj, key, value)\n\n # XXX use headers here\n data = \"::\".join((function_id, obj.SerializeToString()))\n job = Job(data)\n serialized_resp = self.runner.execute(job)\n resp = Response()\n try:\n resp.ParseFromString(serialized_resp)\n except DecodeError:\n raise Exception(serialized_resp)\n\n if resp.error:\n raise Exception(resp.error)\n else:\n return resp.value\n","sub_path":"tokenserver/crypto/master.py","file_name":"master.py","file_ext":"py","file_size_in_byte":7295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"329283736","text":"import time\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import confusion_matrix, accuracy_score, auc\nfrom sklearn.model_selection import KFold, cross_val_predict, train_test_split\n\ndef do_classification(clf):\n file_name = \"../datasets/Malicious_Software/dataset_cleaned_minimal.csv\"\n dataset = pd.read_csv(file_name, low_memory=False)\n\n X_train, X_test, y_train, y_test = train_test_split(dataset.drop(\"class\", axis=1),\n dataset[\"class\"], test_size=0.3, shuffle=True)\n\n y_pred = cross_val_predict(clf, dataset.drop(\"class\", axis=1), dataset[\"class\"], cv=10)\n\n print(\"FITTING\")\n start = time.time()\n clf.fit(X_train, y_train)\n end = time.time()\n print(\"finished in\", end - start)\n\n print(\"PREDICTING\")\n start = time.time()\n predictions = clf.predict(X_test)\n end = time.time()\n print(\"finished in\", end - start)\n\n conf_mat = confusion_matrix(dataset[\"class\"], y_pred)\n acc_score = accuracy_score(dataset[\"class\"], y_pred)\n print(conf_mat)\n print(\"ACC: \",acc_score)\n # print(auc)\n\n","sub_path":"AntiVirus/analysis_utils.py","file_name":"analysis_utils.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"238252055","text":"import factory\nfrom factory.declarations import (LazyAttribute, SubFactory, RelatedFactory, SelfAttribute)\nfrom factory.django import DjangoModelFactory\nfrom faker.factory import Factory\n\nfrom lily.accounts.factories import AccountFactory\nfrom lily.tenant.factories import TenantFactory\nfrom lily.utils.models.factories import EmailAddressFactory\n\nfrom .models import Contact, Function\n\n\nfaker = Factory.create()\n\n\nclass ContactFactory(DjangoModelFactory):\n tenant = SubFactory(TenantFactory)\n first_name = LazyAttribute(lambda o: faker.first_name())\n last_name = LazyAttribute(lambda o: faker.last_name())\n\n class Meta:\n model = Contact\n\n\nclass ContactWithEmailFactory(ContactFactory):\n @factory.post_generation\n def email_addresses(self, create, extracted, **kwargs):\n if create:\n email_str = '%s.%s@%s' % (\n self.first_name.lower(),\n self.last_name.lower(),\n faker.free_email_domain()\n )\n\n email_address = EmailAddressFactory(tenant=self.tenant, email_address=email_str)\n self.email_addresses.add(email_address)\n\n\ndef function_factory(tenant):\n # This factory is method wrapped, because Function itself does not accept tenant.\n # (Otherwise we could just pass the factory a tenant kwarg).\n class FunctionFactory(DjangoModelFactory):\n contact = SubFactory(ContactFactory, tenant=tenant)\n account = SubFactory(AccountFactory, tenant=tenant)\n\n class Meta:\n model = Function\n\n return FunctionFactory\n\n\nclass ContactWithAccountFactory(ContactWithEmailFactory):\n function = RelatedFactory(function_factory(SelfAttribute('..contact.tenant')), 'contact')\n","sub_path":"lily/contacts/factories.py","file_name":"factories.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"642797067","text":"\nimport argparse\nimport os\nimport sys\n\n##############################################\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--epochs', type=int, default=50)\nparser.add_argument('--batch_size', type=int, default=50)\nparser.add_argument('--gpu', type=int, default=0)\nparser.add_argument('--lr', type=float, default=1e-3)\nparser.add_argument('--eps', type=float, default=1e-6)\nargs = parser.parse_args()\n\nif args.gpu >= 0:\n os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"]=str(args.gpu)\n\nimport numpy as np\nimport tensorflow as tf\nimport keras\nfrom collections import deque\n\n(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n\nassert(np.shape(x_train) == (50000, 32, 32, 3))\nx_train = x_train - np.mean(x_train, axis=0, keepdims=True)\nx_train = x_train / np.std(x_train, axis=0, keepdims=True)\ny_train = keras.utils.to_categorical(y_train, 10)\n\nassert(np.shape(x_test) == (10000, 32, 32, 3))\nx_test = x_test - np.mean(x_test, axis=0, keepdims=True)\nx_test = x_test / np.std(x_test, axis=0, keepdims=True)\ny_test = keras.utils.to_categorical(y_test, 10)\n\n####################################\n\nx = tf.placeholder(tf.float32, [None, 32 , 32 , 3])\ny = tf.placeholder(tf.float32, [None, 10])\nlr = tf.placeholder(tf.float32, ())\n\n####################################\n\nw = np.load('cifar10_weights.npy', allow_pickle=True).item()\nref_w1_init = w['conv1_weights'][:, :, :, 0:8]\nref_w2_init = w['conv2_weights'][:, :, 0:8, 0:16]\n\nref_w1 = tf.Variable(ref_w1_init, dtype=tf.float32)\nref_w2 = tf.Variable(ref_w2_init, dtype=tf.float32)\n\n####################################\n\nctrl_w1_init = np.random.normal(loc=np.average(ref_w1_init), scale=np.std(ref_w1_init), size=np.shape(ref_w1_init))\nctrl_w2_init = np.random.normal(loc=np.average(ref_w2_init), scale=np.std(ref_w2_init), size=np.shape(ref_w2_init))\n\nctrl_w1 = tf.Variable(ctrl_w1_init, dtype=tf.float32)\nctrl_w2 = tf.Variable(ctrl_w2_init, dtype=tf.float32)\n\n####################################\n\nw1_init = np.random.normal(loc=np.average(ref_w1_init), scale=np.std(ref_w1_init), size=[32, 32, 3*3*3, 8])\nw2_init = np.random.normal(loc=np.average(ref_w2_init), scale=np.std(ref_w2_init), size=[16, 16, 3*3*8, 16])\n\nw1 = tf.Variable(w1_init, dtype=tf.float32)\nw2 = tf.Variable(w2_init, dtype=tf.float32)\n\n####################################\n\ndef conv_op(x, w):\n conv = tf.nn.conv2d(x, w, [1,1,1,1], 'SAME')\n conv = tf.nn.relu(conv)\n return conv\n \ndef local_op(x, w):\n patches = tf.image.extract_image_patches(images=x, ksizes=[1,3,3,1], strides=[1,1,1,1], padding='SAME', rates=[1,1,1,1])\n patches = tf.transpose(patches, [1, 2, 0, 3])\n \n local = tf.keras.backend.batch_dot(patches, w)\n local = tf.transpose(local, [2, 0, 1, 3])\n \n local = tf.nn.relu(local)\n return local\n\n####################################\n\nref_conv1 = conv_op(x, ref_w1)\nref_pool1 = tf.nn.avg_pool(ref_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n\nref_conv2 = conv_op(ref_pool1, ref_w2)\nref_pool2 = tf.nn.avg_pool(ref_conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n\n####################################\n\nconv1 = local_op(x, w1)\npool1 = tf.nn.avg_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n\nconv2 = local_op(pool1, w2)\npool2 = tf.nn.avg_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n\nloss = tf.losses.mean_squared_error(labels=ref_pool2, predictions=pool2)\nparams = [w1, w2]\ngrads = tf.gradients(loss, params)\ngrads_and_vars = zip(grads, params)\ntrain = tf.train.AdamOptimizer(learning_rate=lr, epsilon=args.eps).apply_gradients(grads_and_vars)\n\n####################################\n\nctrl_conv1 = conv_op(x, ctrl_w1)\nctrl_pool1 = tf.nn.avg_pool(ctrl_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n\nctrl_conv2 = conv_op(ctrl_pool1, ctrl_w2)\nctrl_pool2 = tf.nn.avg_pool(ctrl_conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n\nctrl_loss = tf.losses.mean_squared_error(labels=ref_pool2, predictions=ctrl_pool2)\nctrl_params = [ctrl_w1, ctrl_w2]\nctrl_grads = tf.gradients(ctrl_loss, ctrl_params)\nctrl_grads_and_vars = zip(ctrl_grads, ctrl_params)\nctrl_train = tf.train.AdamOptimizer(learning_rate=lr, epsilon=args.eps).apply_gradients(ctrl_grads_and_vars)\n\n####################################\n\nsess = tf.InteractiveSession()\ntf.global_variables_initializer().run()\n\n####################################\n\nrandom_losses = []\nctrl_random_losses = []\n\nfor jj in range(0, 50000, args.batch_size):\n s = jj\n e = jj + args.batch_size\n xs = x_train[s:e]\n ys = y_train[s:e]\n \n [l, cl] = sess.run([loss, ctrl_loss], feed_dict={x: xs, y: ys, lr: 0.0})\n \n random_losses.append(l)\n ctrl_random_losses.append(cl)\n\n####################################\n\nfor ii in range(args.epochs):\n \n losses = []\n ctrl_losses = []\n \n for jj in range(0, 50000, args.batch_size):\n s = jj\n e = jj + args.batch_size\n xs = x_train[s:e]\n ys = y_train[s:e]\n \n [l, cl, _, _] = sess.run([loss, ctrl_loss, train, ctrl_train], feed_dict={x: xs, y: ys, lr: args.lr})\n \n losses.append(l)\n ctrl_losses.append(cl)\n \n print ('loss %f/%f | ctrl loss %f/%f' % (np.average(losses), np.average(random_losses), np.average(ctrl_losses), np.average(ctrl_random_losses)))\n \n####################################\n \n","sub_path":"neg_equiv/v3/t7.py","file_name":"t7.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"304418073","text":"import sys\r\nfi=open(sys.argv[1])\r\n\r\n#fi=open('GSE89567_IDH_A_processed_data.txt')\r\n\r\nheader=fi.readline().upper().replace('-','_').rstrip().split('\\t')\r\nP=set()\r\ntmp={}\r\ni=1\r\nwhile i 4\n#E => 3\n#G => 6\n#I => 1\n#O => 0\n#S => 5\n#T => 7\n#Example: Leet => l337\n\nstring = \" What is leat spEak. How should we do this. What more needs adfadfadfasdfadsfasdfasdfasdfasdfasdfasdfadfadsfadfasdf\"\nstring = string.upper()\nfinalString = []\nfor i in range(len(string)):\n if string[i] == \"A\":\n finalString.append(\"4\")\n elif string[i] == \"E\":\n finalString.append(\"3\")\n elif string[i] == \"G\":\n finalString.append(\"6\")\n elif string[i] == \"I\":\n finalString.append(\"1\")\n elif string[i] == \"O\":\n finalString.append(\"0\")\n elif string[i] == \"S\":\n finalString.append(\"5\")\n elif string[i] == \"T\":\n finalString.append(\"7\")\n else:\n finalString.append(string[i])\nprint (\"Here is Leekspeak: \", ''.join(finalString))\n\n#leatspeek = {'A':4, 'E':3, 'G':6, 'I':1, 'O':}\n\n\n#5. Long-long Vowels\n\n#Given a word as a string, print the result of extending any long vowels to the length of 5. Examples:\n\n#Good => Goooood\n#Cheese => Cheeeeese\n#Man => Man\n#Spoon => Spooooon\n\nword = \"chyyysee\"\nreplaceWord = []\n\nfor i in range(len(word)):\n if len(word) != i+1:\n if word[i] == \"a\" and word[i+1] == \"a\":\n replaceWord.append(\"aaaa\")\n elif word[i] == \"e\" and word[i+1] == \"e\":\n replaceWord.append(\"eeee\")\n elif word[i] == \"i\" and word[i+1] == \"i\":\n replaceWord.append(\"iiii\")\n elif word[i] == \"o\" and word[i+1] == \"o\":\n replaceWord.append(\"oooo\")\n elif word[i] == \"u\" and word[i+1] == \"u\":\n replaceWord.append(\"uuuu\")\n else:\n replaceWord.append(word[i])\n else:\n replaceWord.append(word[i])\n\nprint (\"Here is the Long Vovel \", ''.join(replaceWord))\n","sub_path":"Python2Ex/strings.py","file_name":"strings.py","file_ext":"py","file_size_in_byte":2465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"147568986","text":"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import Float64, Float32MultiArray\nfrom geometry_msgs.msg import Vector3Stamped\nfrom custom_msgs.msg import ThrusterSpeeds\nimport numpy as np\nfrom thruster_manager import ThrusterManager\nfrom std_srvs.srv import SetBool\nfrom tf import TransformListener\nimport controls_utils as utils\nimport resource_retriever as rr\n\n\nclass ThrusterController:\n\n SIM_PUB_TOPIC = '/sim/move'\n ROBOT_PUB_TOPIC = '/offboard/thruster_speeds'\n\n enabled = False\n\n def __init__(self):\n rospy.init_node('thruster_controls')\n\n self.sim = rospy.get_param('~/thruster_controls/sim')\n if not self.sim:\n self.pub = rospy.Publisher(self.ROBOT_PUB_TOPIC, ThrusterSpeeds, queue_size=3)\n else:\n self.pub = rospy.Publisher(self.SIM_PUB_TOPIC, Float32MultiArray, queue_size=3)\n\n self.enable_service = rospy.Service('enable_controls', SetBool, self.handle_enable_controls)\n\n self.tm = ThrusterManager(rr.get_filename('package://controls/config/cthulhu.config', use_protocol=False))\n\n self.listener = TransformListener()\n\n for d in utils.get_axes():\n rospy.Subscriber(utils.get_controls_move_topic(d), Float64, self._on_pid_received, d)\n rospy.Subscriber(utils.get_power_topic(d), Float64, self._on_power_received, d)\n\n self.pid_outputs = np.zeros(6)\n self.pid_outputs_local = np.zeros(6)\n self.powers = np.zeros(6)\n self.t_allocs = np.zeros(8)\n\n def handle_enable_controls(self, req):\n self.enabled = req.data\n return {'success': True, 'message': 'Successfully set enabled to ' + str(req.data)}\n\n def transform_twist(self, base_frame, target_frame, twist):\n lin = Vector3Stamped()\n ang = Vector3Stamped()\n\n lin.header.frame_id = base_frame\n ang.header.frame_id = base_frame\n\n lin.vector.x = twist[0]\n lin.vector.y = twist[1]\n lin.vector.z = twist[2]\n ang.vector.x = twist[3]\n ang.vector.y = twist[4]\n ang.vector.z = twist[5]\n\n lin_local = self.listener.transformVector3(target_frame, lin)\n ang_local = self.listener.transformVector3(target_frame, ang)\n\n return np.array([lin_local.vector.x,\n lin_local.vector.y,\n lin_local.vector.z,\n ang_local.vector.x,\n ang_local.vector.y,\n ang_local.vector.z])\n\n def update_thruster_allocs(self):\n if self.enabled:\n self.pid_outputs_local = self.transform_twist('odom', 'base_link', self.pid_outputs)\n\n for i in range(len(self.powers)):\n if self.powers[i] != 0:\n self.pid_outputs_local[i] = self.powers[i]\n\n self.t_allocs = self.tm.calc_t_allocs(self.pid_outputs_local)\n\n def _on_pid_received(self, val, direction):\n self.pid_outputs[utils.get_axes().index(direction)] = val.data\n self.update_thruster_allocs()\n\n def _on_power_received(self, val, direction):\n self.powers[utils.get_axes().index(direction)] = val.data\n self.update_thruster_allocs()\n\n def run(self):\n rate = rospy.Rate(10) # 10 Hz\n\n while not rospy.is_shutdown():\n if not self.enabled:\n # If not enabled, publish all 0s.\n if not self.sim:\n i8_t_allocs = ThrusterSpeeds()\n i8_t_allocs.speeds = np.zeros(8)\n self.pub.publish(i8_t_allocs)\n else:\n f32_t_allocs = Float32MultiArray()\n f32_t_allocs.data = np.zeros(8)\n self.pub.publish(f32_t_allocs)\n\n if self.enabled:\n # Scale thruster alloc max to PID max\n t_alloc_max = float(np.max(np.absolute(self.t_allocs)))\n pid_max = float(np.max(np.absolute(self.pid_outputs_local)))\n\n if t_alloc_max != 0:\n # Multiply each thruster allocation by scaling ratio\n self.t_allocs *= pid_max / t_alloc_max\n # Clamp values of t_allocs to between -1 to 1\n self.t_allocs = np.clip(self.t_allocs, -1, 1)\n\n if not self.sim:\n i8_t_allocs = ThrusterSpeeds()\n i8_t_allocs.speeds = (self.t_allocs * 127).astype(int)\n self.pub.publish(i8_t_allocs)\n else:\n f32_t_allocs = Float32MultiArray()\n f32_t_allocs.data = self.t_allocs\n self.pub.publish(f32_t_allocs)\n\n rate.sleep()\n\n\ndef main():\n try:\n ThrusterController().run()\n except rospy.ROSInterruptException:\n pass\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"onboard/catkin_ws/src/controls/scripts/thruster_controls.py","file_name":"thruster_controls.py","file_ext":"py","file_size_in_byte":4811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"493668241","text":"import pathlib\nimport unittest\n\nfrom skua.preprocessors import Config\nfrom skua.preprocessors.markdown import MarkdownPreprocessor\n\n\nclass TestMarkdownPreprocessor(unittest.TestCase):\n def testFile1(self):\n config = Config({\n 'site_name': \"HELLO WORLD!\",\n \"author\": \"Person 1\"\n })\n markdown_preprocessor = MarkdownPreprocessor(config)\n output = markdown_preprocessor('tests/src/index.md')\n\n self.assertTrue(output['site_name'] == config.config['site_name'])\n self.assertTrue(output['author'] == config.config['author'])\n self.assertTrue(output['content'] is not None)\n\n\nclass TestConfig(unittest.TestCase):\n def test_load_from_file(self):\n config = Config.from_file(pathlib.Path('tests/src/config.json'))\n self.assertTrue(config.config == {\n \"site_name\": \"Hello World!\",\n \"site_author\": \"Me!\"\n })\n\n def test_overwrite(self):\n config = Config.from_file(pathlib.Path('tests/src/config.json'))\n input_dict = {\"site_name\": \"New name!\"}\n output = config(input_dict)\n self.assertTrue(output['site_name'] == input_dict['site_name'])\n","sub_path":"tests/preprocessors/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"10702110","text":"\"\"\"\nContinuously poll the specified rotctld instance for angles,\nand print timestamp and angle if angles change.\n\nUsage:\n\npython3 rotctld_angle_printer.py ROTCTLD_HOST[:ROTCTLD_PORT] > angle_file\n\nExample: `python3 rotctld_angle_printer.py localhost` will connect to the\nrotctld instance on localhost:4533 (default rotctld port) and print the rotor\nangles to stdout.\n\"\"\"\n\nimport socket\nimport time\nimport datetime\nimport sys\n\nif len(sys.argv) < 2:\n print(__doc__)\n sys.exit()\n\n#parse command line options\nhost_port_args = sys.argv[1].split(':') #assume argument 1 on form host:port\nrotctld_host = host_port_args[0]\nrotctld_port = 4533\nif len(host_port_args) > 1:\n rotctld_port = int(host_port_args[1])\n\n#polling time\npoll_time_ms = 10\n\n#connect to rotctld\nrotctl = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nrotctl.connect((rotctld_host, rotctld_port))\n\nprev_azimuth = float('NaN')\nprev_elevation = float('NaN')\n\nprint(\"timestamp\\tazimuth\\televation\")\n\nwhile True:\n time.sleep(poll_time_ms*1.0/(1000.0))\n\n #get current angle\n rotctl.send(b'p\\n')\n azimuth, elevation = rotctl.recv(1024).decode('ascii').splitlines()\n\n #print if it differs from previous angle\n if (azimuth != prev_azimuth) or (elevation != prev_elevation):\n print(datetime.datetime.now().isoformat() + \"\\t\" + str(azimuth) + '\\t' + str(elevation), flush=True)\n\n prev_azimuth = azimuth\n prev_elevation = elevation\n\n\n","sub_path":"rotctld_angle_printer.py","file_name":"rotctld_angle_printer.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"565783359","text":"import numpy as np\nimport sys\nnp.random.seed(1337) # for reproducibility\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics.classification import accuracy_score, precision_score, recall_score\n\nfrom sklearn import linear_model\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neural_network import MLPClassifier\n\n#parameters: sys.argv[1] = input dataset as matrix of k-mers\nfrom pathlib import Path\nfrom datetime import datetime\nfrom yaml import dump\n\nfrom tqdm import tqdm\n\n# Loading dataset\nnome_train = Path(sys.argv[1]).stem\nprint(nome_train)\n\ndef load_data(file):\n lista = []\n records = list(open(file, \"r\"))\n records = records[1:]\n for seq in tqdm(records):\n elements = seq.split(\",\")\n level = elements[-1].split(\"\\n\")\n classe = level[0]\n lista.append(classe)\n\n lista = set(lista)\n classes = list(lista)\n X = []\n Y = []\n for seq in tqdm(records):\n elements = seq.split(\",\")\n X.append(elements[1:-1])\n level = elements[-1].split(\"\\n\")\n classe = level[0]\n Y.append(classes.index(classe))\n X = np.array(X, dtype=float)\n Y = np.array(Y, dtype=int)\n data_max = np.amax(X)\n X = X / data_max\n return X, Y, len(classes), len(X[0])\n\n\n# Training\ndef create_model():\n classifier = linear_model.LogisticRegression(class_weight=\"balanced\") # 88.3% with k=7\n return classifier\n\ndef train_and_evaluate_model(model, X_train, Y_train, X_test, Y_test):\n train_start = datetime.now()\n model.fit(X_train, Y_train)\n train_duration_sec = (datetime.now() - train_start).seconds\n\n test_start = datetime.now()\n Y_pred = model.predict(X_test)\n test_duration_sec = (datetime.now() - test_start).seconds\n\n accuracy = accuracy_score(Y_test, Y_pred)\n precision = precision_score(Y_test, Y_pred, average=\"weighted\")\n recall = recall_score(Y_test, Y_pred, average=\"weighted\")\n return dict(accuracy=float(accuracy),\n precision=float(precision),\n recall=float(recall),\n train_duration_sec=train_duration_sec,\n test_duration_sec=test_duration_sec)\n\n\nif __name__ == \"__main__\":\n X_train, Y_train, nb_classes, input_length = load_data(sys.argv[1])\n\n n_splits = 10\n\n results = []\n kfold = StratifiedKFold(n_splits=n_splits, shuffle=True)\n for train, test in tqdm(kfold.split(X_train, Y_train), total=n_splits):\n model = create_model()\n results.append(train_and_evaluate_model(model, X_train[train], Y_train[train], X_train[test], Y_train[test]))\n\n dump(results, open(f\"results/{nome_train}_results.yml\", \"w+\"), default_flow_style=False)\n","sub_path":"models/SK.py","file_name":"SK.py","file_ext":"py","file_size_in_byte":2675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"8160062","text":"\"\"\"\nProduces a LaTeX table summarising the datasets\n\"\"\"\nfrom __future__ import print_function, division\nfrom os.path import expanduser, join\nimport pandas as pd\nfrom nilmtk.dataset import DataSet\nfrom collections import OrderedDict\n\n\nLOAD_DATASETS = True\n\nDATASET_PATH = expanduser('~/Dropbox/nilmtk_datasets/')\n\n# Maps from human-readable name to path\nDATASETS = OrderedDict()\nDATASETS['REDD'] = 'redd/low_freq'\nDATASETS['Pecan Street'] = 'pecan_1min'\nDATASETS['AMDds'] = 'ampds'\nDATASETS['iAWE'] = 'iawe'\n\n#for dataset_name, dataset in DATASETS:\n # Choose first home from each\n\n\n# Maps from short col name to human-readable name\nCOLUMNS = OrderedDict()\nCOLUMNS['n_appliances'] = \"\"\"number of\\\\\\\\appliances\"\"\"\nCOLUMNS['energy_submetered'] = \"\"\"% energy\\\\\\\\submetered\"\"\"\nCOLUMNS['dropout_rate'] = 'dropout rate'\nCOLUMNS['dropout_rate_ignoring_gaps'] = \"\"\"dropout rate\\\\\\\\(ignoring gaps)\"\"\"\nCOLUMNS['uptime'] = \"\"\"mains uptime\\\\\\\\per building\\\\\\\\(days)\"\"\"\nCOLUMNS[\n 'prop_timeslices'] = \"\"\"% timeslices\\\\\\\\where energy\\\\\\\\submetered > 70%\"\"\"\n\nfor key, value in COLUMNS.iteritems():\n COLUMNS[key] = \"\"\"\\textbf{\\specialcell[h]{\"\"\" + value + \"\"\"}}\"\"\"\n\ndf = pd.DataFrame(index=DATASETS.keys(), columns=COLUMNS.values())\n\nfor ds_name in DATASETS.iterkeys():\n print('Calculating stats for', ds_name)\n dataset = dataset_objs[ds_name]\n ds_stats = dataset.descriptive_stats()\n for col_short, col_long in COLUMNS.iteritems():\n s = \"\"\"\\specialcell{\"\"\"\n s += summary_stats_string(ds_stats[col_short], sep=\"\"\"\\\\\\\\\"\"\",\n stat_strings=['min', 'mean', 'max']).replace(' ', '')\n s += \"\"\"}\"\"\"\n df[col_long][ds_name] = s\n\nprint(\"------------LATEX BEGINS-----------------\")\nlatex = df.to_latex()\nfor str_to_replace in ['midrule', 'toprule', 'bottomrule']:\n latex = latex.replace(str_to_replace, 'hline')\nprint(latex)\nprint(\"------------LATEX ENDS-------------------\")\n","sub_path":"scripts/plots_for_e_energy_2014/fridge_comparison_table.py","file_name":"fridge_comparison_table.py","file_ext":"py","file_size_in_byte":1931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"510193303","text":"import random\nimport sys\n\ndef select_rand_words(num_words = 1):\n '''\n Selects num_words random words from the words.txt corpus\n Args:\n num_words: number of random words to select\n Returns:\n num_words random words in a String\n '''\n with open('corpus/words.txt', 'r') as corpus:\n words = corpus.read().split('\\n')\n\n rand_words = []\n\n for _ in range(num_words):\n rand_words.append(random.choice(words))\n\n return(' '.join(rand_words))\n\nif __name__ == '__main__':\n if len(sys.argv) == 2:\n print(select_rand_words(int(sys.argv[1])))\n else:\n print(select_rand_words())\n","sub_path":"main_challenges/select_rand_words.py","file_name":"select_rand_words.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"559180424","text":"import numpy as np\nimport random\n\nimport json\nimport sys\n\nfrom two_player.pong import PongGame\nfrom prey_predator.env import PreyPredatorEnv\nimport time\nimport argparse\nfrom PIL import Image\n# from vae.vae import ConvVAE\n# from rnn.rnn import hps_model, MDNRNN, rnn_init_state, rnn_next_state, rnn_output, rnn_output_size\n\n\n\ndef pong_simulate(mode, arglist, seed = -1, max_len = -1):\n\n reward_list = []\n t_list = []\n\n max_episode_length = 1000\n\n penalize_turning = False\n\n if train_mode and max_len > 0:\n max_episode_length = max_len\n\n if (seed >= 0):\n random.seed(seed)\n np.random.seed(seed)\n model.env.seed(seed)\n\n for episode in range(num_episode):\n\n model.reset()\n\n obs = model.env.reset()\n # obs = Image.fromarray(obs)\n \n total_reward = 0.0\n\n random_generated_int = np.random.randint(2**31-1)\n \n # filename = arglist.data_dir +\"/\"+str(random_generated_int)+\".npz\"\n filename = arglist.data_dir+str(random_generated_int)+\".npz\"\n\n recording_mu = []\n recording_logvar = []\n recording_action = []\n recording_reward = [0]\n\n for t in range(max_episode_length):\n\n if render_mode:\n model.env.render(\"human\")\n else:\n model.env.render('rgb_array')\n obs = Image.fromarray(obs)\n obs = obs.resize((64,64),Image.ANTIALIAS)\n obs = np.array(obs)\n z, mu, logvar = model.encode_obs(obs)\n action = model.get_action(z)\n\n recording_mu.append(mu)\n recording_logvar.append(logvar)\n recording_action.append(action)\n recording_reward = []\n if arglist.competitive:\n obs, rewards, [act1, act2], goals, win = model.env.step([action[0], 'script'])\n else: \n obs, rewards, [act1, act2], goals, win = model.env.step(action)\n\n extra_reward = 0.0 # penalize for turning too frequently\n reward = 0.\n if arglist.competitive:\n if train_mode and penalize_turning:\n extra_reward -= np.abs(action[0])/10.0\n rewards[0] += extra_reward\n reward = rewards[0]\n else:\n if train_mode and penalize_turning:\n reward = np.sum(rewards)\n extra_reward -= np.abs(action[0])/10.0\n reward += extra_reward\n\n recording_reward.append(reward)\n total_reward += reward \n if win:\n break\n\n #for recording:\n # obs = Image.fromarray(obs)\n obs = Image.fromarray(obs)\n obs = obs.resize((64,64),Image.ANTIALIAS)\n z, mu, logvar = model.encode_obs(obs)\n action = model.get_action(z)\n recording_mu.append(mu)\n recording_logvar.append(logvar)\n recording_action.append(action)\n\n recording_mu = np.array(recording_mu, dtype=np.float16)\n recording_logvar = np.array(recording_logvar, dtype=np.float16)\n recording_action = np.array(recording_action, dtype=np.float16)\n recording_reward = np.array(recording_reward, dtype=np.float16)\n\n if not render_mode:\n if arglist.recording_mode:\n \tnp.savez_compressed(filename, mu=recording_mu, logvar=recording_logvar, action=recording_action, reward=recording_reward)\n\n if render_mode:\n print(\"total reward\", total_reward, \"timesteps\", t)\n reward_list.append(total_reward)\n t_list.append(t)\n\n return reward_list, t_list\n","sub_path":"util/pong_simulate.py","file_name":"pong_simulate.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"172223034","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 18 13:19:58 2014\n\n@author: hxu\n\nit used for getting drifter data from erddap based on different conditions()\nAfter running this program, you could get a file of drifter data\nThe saving file will be in same folder as this program\ninput values: time period,gbox(maxlon, minlon,maxlat,minlat),ids\nfunction uses:getobs_drift_byid, getobs_drift_byrange\noutput : a data file which includes ids, time, lat,lon\n\"\"\"\nimport datetime as dt\nimport sys\nimport os\nimport pytz\nfrom drifter_functions import getobs_drift_byrange,getobs_drift_byid\nops=os.defpath\npydir='../'\nsys.path.append(pydir)\n#################Input values#############################################\ninput_time=[dt.datetime(2013,1,1,0,0,0,0,pytz.UTC),dt.datetime(2013,7,1,0,0,0,0,pytz.UTC)] # start time and end time\ngbox=[-70.0,-72.0,42.0,40.0] # maxlon, minlon,maxlat,minlat\nid=[135410701] # id list, if you are not clear dedicated id, let id=[]\n'125450842''125450841'\n#↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑Input values↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑#\n\nf = open(dt.datetime.now().strftime('%Y-%m-%d %H:%M')+'.dat', 'w') # create file and name it\nf.writelines('id'+' '+'lat '+' lon '+' time'+' \\n')\nif id==[]:\n time,ids,lat,lon=getobs_drift_byrange(gbox,input_time) #get and organize data\n for k in range(len(ids)): # write them\n f.writelines(str(ids[k])+' '+'%10.2f' % lat[k]+' '+'%10.2f' % lon[k]+' '+' '\\\n +str(time[k].strftime('%Y-%m-%d %H:%M:%S'))+'\\n')\n \nelse:\n for q in range(len(id)):\n time,ids,lat,lon=getobs_drift_byid(id[q],input_time) #get and organize data\n for k in range(len(ids)): #write them\n f.writelines(str(ids[k])+' '+'%10.2f' % lat[k]+' '+'%10.2f' % lon[k]+' '+' '\\\n +str(time[k].strftime('%Y-%m-%d %H:%M:%S'))+'\\n')\n\nf.close()\n\n\n''' \nfig = plt.figure()\nax = fig.add_subplot(111) \nplt.title(str(time[0].strftime(\"%d-%b-%Y %H\"))+'h')\nlat_wanted=lat[-1]\nlon_wanted=lon[-1]\nplt.plot(lon_wanted,lat_wanted,'.',markersize=30,color='r',label='end')\n \n #plt.plot(np.reshape(lon,np.size(lon)),np.reshape(lat,np.size(lat)))\nplt.plot(np.reshape(lon,np.size(lon)),np.reshape(lat,np.size(lat)),color='black')\n \n #basemap_usgs([minlat-1,maxlat+1],[minlon-1,maxlon+1],'True')\nplt.plot(lon[0],lat[0],'.',markersize=20,color='g',label='start') # start time\npylab.ylim([min(lat)-0.1,max(lat)+0.1])\npylab.xlim([min(lon)-0.1,max(lon)+0.1])\nax.patch.set_facecolor('lightblue') #set background color\n\nplt.legend( numpoints=1,loc=2) \nplt.savefig('./'+str(time[0].strftime(\"%d-%b-%Y %H\"))+'h' + '.png')\n \n#datetime_wanted=date2num(num2date(datetime_wanted)+datetime.timedelta( 0,step_size*60*60 ))\nplt.show()\n'''","sub_path":"getdrifter_erddap.py","file_name":"getdrifter_erddap.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"6836764","text":"from django.urls import path\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom . import views\nurlpatterns = [\n path('', views.home, name='home'),\n path('search/', views.search.as_view(), name='search'),\n path('search//', views.bookDetail, name = 'detail'),\n path('books/ingest/', views.IngestBook.as_view()),\n path('books/process', views.ProcessBook.as_view()),\n]","sub_path":"testBookstore/store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"370636619","text":"import time\nimport base64\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nimport numpy as np\nimport cv2\nfrom PIL import Image\nimport pytesseract\nimport re\nimport random\n\ncv2.namedWindow('buttons')\ncv2.namedWindow('equation')\n\nDRIVER = 'chromedriver'\ndriver = webdriver.Chrome(DRIVER)\ndriver.get('https://tbot.xyz/math/#eyJ1IjoxMjEwNTg2OTMsIm4iOiJSYXVmIFlhZ2Zhcm92IiwiZyI6Ik1hdGhCYXR0bGUiLCJjaSI6IjYxNjEzMjA5NTc4NTUzMzY2NzkiLCJpIjoiQWdBQUFPLUhBd0NGTlRjSFBfdDAxOUx6eWFrIn00ZTQyMjJlMzg4MjNkMTg0Mzc3NDEyMmMxZDk5ZDU0MA==&tgShareScoreUrl=tg%3A%2F%2Fshare_game_score%3Fhash%3DZQB37YM-E8jyXD59EH07RtSxfOOP-SnN9eurLE97hKI')\ntask_x = driver.find_element_by_id('task_x')\ntask_y = driver.find_element_by_id('task_y')\ntask_op = driver.find_element_by_id('task_op')\ntask_res = driver.find_element_by_id('task_res')\nbutton_wrong = driver.find_element_by_id('button_wrong')\nbutton_correct = driver.find_element_by_id('button_correct')\nbutton_correct.click()\nwhile True:\n print(str(task_x.text+task_op.text+task_y.text)+\" = \"+task_res.text)\n if len(task_x.text) != 0:\n a = int(task_x.text)\n b = int(task_y.text)\n op = str(task_op.text)\n\n if op == '/':\n answer = a/b\n elif op == '–':\n answer = a-b\n elif op == '+':\n answer = a+b\n else:\n answer = a*b\n\n if answer==int(task_res.text):\n button_correct.click()\n else:\n button_wrong.click()\n time.sleep(0.001)\n pass\n\ndriver.quit()","sub_path":"mathBattle.py","file_name":"mathBattle.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"128578014","text":"import os\nimport sys\n\nimport configparser\nfrom jinja2 import Environment, FileSystemLoader, select_autoescape, StrictUndefined\nfrom jinja2.exceptions import UndefinedError\n\n\ndef read_config(\n defaults={\n \"delete_if_exists\": \"no\",\n \"remember_password\": \"yes\",\n \"userdata\": \"userdata-default.yaml.jinja\", # This can be overridden in the INI file, globally or per-instance\n \"security_groups\": \"default\",\n \"config_drive\": \"no\",\n },\n configfile=\"create-instances.ini\",\n):\n\n config = configparser.ConfigParser(defaults=defaults, interpolation=None)\n\n readfile = config.read(configfile)\n\n if len(readfile) < 1:\n print(\"Failed to read config file. Bailing...\", file=sys.stderr)\n sys.exit(1)\n\n return config\n\n\ndef apply_userdata_template(userdatafile, userdata_vars, server_name):\n jinja_env = Environment(\n loader=FileSystemLoader(os.getcwd()),\n autoescape=select_autoescape(),\n undefined=StrictUndefined,\n )\n jinja_template = jinja_env.get_template(userdatafile)\n\n userdata_vars[\"server_name\"] = server_name\n\n return jinja_template.render(userdata_vars)\n\n\ndef main():\n print(\"Generating userdata\")\n config = read_config()\n for section in config.sections():\n if not section.endswith(\"-userdata-vars\"):\n server_name = section\n userdata_vars = {}\n if (section + \"-userdata-vars\") in config:\n userdata_vars = config[section + \"-userdata-vars\"]\n else:\n userdata_vars = config[config.default_section]\n userdatafile = config[section][\"userdata\"]\n\n print(\n \" Userdata for server {server_name}:\".format(server_name=server_name)\n )\n try:\n userdata = apply_userdata_template(\n userdatafile, userdata_vars, server_name\n )\n\n print(userdata)\n except UndefinedError as ue:\n print(\" Error: {msg}\".format(msg=ue.message))\n continue\n else:\n continue\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"experiments/Set-004/X-001/generate-userdata.py","file_name":"generate-userdata.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"507305829","text":"from django.conf.urls import include, url\nfrom django.contrib import admin\nfrom . import views\nfrom django.contrib.auth.decorators import login_required\nfrom dashboard.views import *\n\nurlpatterns = [\n\n url(r'^$', views.index, name='index'),\n url(r'^dashboard/$', login_required(views.dashboard), name='dashboard'),\n #url(r'^companies/$', views.companies, name='companies'),\n url(r'^companies/$', login_required(CompaniesListView.as_view()), name='companies'),\n url(r'^newsroom/$', login_required(views.company_news), name='newsroom'),\n url(r'^funds/$', login_required(views.funds), name='funds'),\n #url(r'^company_detail/(?P[0-9]+)/$', views.company_detail, name='company_detail'),\n url(r'^company_detail/(?P[0-9]+)/$', login_required(CompanyView.as_view()), name='company_detail'),\n url(r'^fund_detail/(?P[0-9]+)/$', login_required(views.fund_detail), name='fund_detail'),\n url(r'^company/new/$', login_required(views.company_post), name='company_add'),\n url(r'^company_detail/(?P[0-9]+)/edit/$', login_required(views.company_edit), name='company_edit'),\n url(r'^fund/new/$', login_required(views.fund_post), name='fund_add'),\n url(r'^fund_detail/(?P[0-9]+)/edit/$', login_required(views.fund_edit), name='fund_edit'),\n\n]","sub_path":"dashboard/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"293910385","text":"import setup_path \nimport airsim\n\nimport sys\nimport time\nimport socket\n\nHEADERSIZE = 10\n\nclient = airsim.MultirotorClient()\nclient.confirmConnection()\nclient.enableApiControl(True)\nclient.armDisarm(True)\n\n\n# response = client.simGetImages([airsim.ImageRequest(\"0\", airsim.ImageType.Scene, False, False)])\n# img1d = np.frombuffer(response[0].image_data_uint8, dtype=np.uint8) # get numpy array\n# img_rgb = img1d.reshape(response[0].height, response[0].width, 4) # reshape array to 4 channel image array H X W X 3\n\n\n##### socket #####\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.bind(('10.4.203.98', 9999))\ns.listen(5)\n\n\n\n##################\n\nprint(\"Flying a small square box using moveByVelocityZ\")\nprint(\"Try pressing 't' in the AirSim view to see a pink trace of the flight\")\n\n# AirSim uses NED coordinates so negative axis is up.\n# z of -7 is 7 meters above the original launch point.\nz = -7\n\n# Fly given velocity vector for 5 seconds\nduration = 5\nspeed = 1\ndelay = duration * speed\n\t\t\n\nwhile True:\n\tclientsocket, address = s.accept()\n\t\n\tprint(f\"Connection from {address} has been established\")\n\t\n\twhile True:\n\t\n\t# d = {1: \"hi\", 2: \"Huseyn\"}\n\t\n\t# msg = pickle.dumps(d)\n\t\n\t# msg = bytes(f'{len(msg):<{HEADERSIZE}}', 'utf-8')+msg\n\t# print(msg)\n\t# b,g,r = cv2.split(png_image)\n\t# res = b-r\n\t# ret = res[res<26]\n\t# mask = cv2.dilate(ret, kernel, iterations=1)\n\t\n\t\t# response = client.simGetImages([airsim.ImageRequest(\"0\", airsim.ImageType.Scene, False, False)])\n\t\t# img1d = np.frombuffer(response[0].image_data_uint8, dtype=np.uint8) # get numpy array\n\t\t# img_rgb = img1d.reshape(response[0].height, response[0].width, 4) # reshape array to 4 channel image array H X W X 3\n\t\t\n\t\t# msg = pickle.dumps(img_rgb)\n\t\tmsg = client.simGetImage(\"0\", airsim.ImageType.Scene)\n\t\tmsg = bytes(f'{len(msg):<{HEADERSIZE}}', 'utf-8')+msg\n\t\tclientsocket.sendall(msg)\n\t\ttime.sleep(0.005)\n\t\n# using airsim.DrivetrainType.MaxDegreeOfFreedom means we can control the drone yaw independently\n# from the direction the drone is flying. I've set values here that make the drone always point inwards\n# towards the inside of the box (which would be handy if you are building a 3d scan of an object in the real world).\n\n# airsim.YawMode(False, yaw)\n\n# while True:\n # pitch = float(input(\"pitch: \"))\n # roll = float(input(\"roll: \"))\n # z = float(input(\"z: \"))\n # yaw = float(input(\"yaw: \"))\n # duration = float(input(\"duration: \"))\n # print(\"pitch: \" + str(pitch) + \", roll: \" + str(roll) + \", yaw: \" + str(yaw) + \", z: \" + str(z))\n # client.moveByAngleZAsync(pitch, roll, z, yaw, duration).join()","sub_path":"Simulator Server/respond.py","file_name":"respond.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"353285460","text":"import pygame\r\nimport time\r\nimport random\r\n\r\npygame.init()\r\nclock = pygame.time.Clock()\r\n\r\nwidth = 1280\r\nheight = 720\r\nFPS = 5\r\n\r\n\r\nscreen = pygame.display.set_mode((width, height))\r\n\r\nwhite = (255,255,255)\r\nblack = (0,0,0)\r\nred = (255,0,0)\r\ngreen = (0,255,0)\r\nblue = (0,0,255)\r\nyellow = (255, 255, 0)\r\n\r\n\r\nbuttonSizeX = 130\r\nbuttonSizeY = 10\r\n\r\npygame.display.update()\r\n\r\ndef button1():\r\n\r\n gameExit = 0\r\n colourNum = 1\r\n colourNum2 = 1\r\n colourNum3 = 1\r\n\r\n while not gameExit:\r\n\r\n colourNum += 1\r\n\r\n randX1 = random.randint(0, width)\r\n randY1 = random.randint(0, height)\r\n randX2 = random.randint(0, width)\r\n randY2 = random.randint(0, height)\r\n\r\n sizeX1 = random.randint(5, 500)\r\n sizeY1 = random.randint(5, 500)\r\n sizeX2 = random.randint(5, 500)\r\n sizeY2 = random.randint(5, 500)\r\n\r\n colourNum = random.randint(0,255)\r\n colourNum2 = random.randint(0,255)\r\n colourNum3 = random.randint(0,255)\r\n\r\n c1 = random.randint(0,255)\r\n c2 = random.randint(0,255)\r\n c3 = random.randint(0,255)\r\n C1 = (c1, c2, c3)\r\n\r\n d1 = random.randint(0,255)\r\n d2 = random.randint(0,255)\r\n d3 = random.randint(0,255)\r\n D1 = (d1, d2, d3)\r\n\r\n colour = (colourNum, colourNum2, colourNum3)\r\n\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n gameExit = 1\r\n\r\n screen.fill(colour)\r\n pygame.draw.rect(screen, C1, (randX1, randY1, sizeX1, sizeY1))\r\n pygame.draw.rect(screen, D1, (randX2, randY2, sizeX2, sizeY2))\r\n\r\n pygame.display.update()\r\n clock.tick(FPS)\r\n \r\n\r\n \r\n\r\ndef runloop():\r\n\r\n button1X = width/4 - buttonSizeX/2\r\n button1Y = height/(5/4) - buttonSizeY\r\n button2X = width/(4/3) - buttonSizeX/2\r\n button2Y = height/(5/4) - buttonSizeY\r\n\r\n gameExit = 0\r\n\r\n while not gameExit:\r\n \r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n gameExit = 1\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n x, y = pygame.mouse.get_pos()\r\n if x > button1X and x < button1X + buttonSizeX and y > button1Y and y < button1Y + buttonSizeY:\r\n button1()\r\n\r\n clock.tick(FPS)\r\n\r\n screen.fill(white)\r\n pygame.draw.rect(screen, black, (button1X, button2Y, buttonSizeX, buttonSizeY))\r\n pygame.draw.rect(screen, black, (button2X, button2Y, buttonSizeX, buttonSizeY))\r\n pygame.display.update()\r\n\r\nrunloop()\r\n\r\n\r\nquit()\r\n","sub_path":"pygameTest.py","file_name":"pygameTest.py","file_ext":"py","file_size_in_byte":2600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"264284391","text":"from tkinter import *\nfrom quiz_brain import QuizBrain\n\nTHEME_COLOR = \"#375362\"\n\nclass QuizInterface:\n def __init__(self, quiz_brain: QuizBrain):\n self.quiz = quiz_brain\n\n self.window = Tk()\n self.window.title('Quizzler')\n self.window.config(bg=THEME_COLOR, pady=20, padx=10)\n\n self.score_label = Label(text=\"Score: 0\",\n font=\"Arial 20 italic\", bg=THEME_COLOR, fg='white')\n self.score_label.grid(row=0, column=1)\n\n self.q_canva = Canvas(width=300, height=250, bg='white')\n self.question_text = self.q_canva.create_text(\n 150,\n 125,\n width=280,\n fill=\"black\",\n font=\"Times 20 italic\",\n text=\"There will be question\")\n self.q_canva.grid(row=1,column=0, columnspan=2, pady=20)\n\n self.true_image = PhotoImage(file=r'./images/true.png')\n self.true_button = Button(\n image=self.true_image,\n highlightthickness=0,\n command=self.true_pressed\n )\n self.true_button.grid(row=2, column=0, padx=30)\n\n self.false_image = PhotoImage(file=r'./images/false.png')\n self.false_button = Button(\n image=self.false_image,\n highlightthickness=0,\n command=self.false_pressed\n )\n self.false_button.grid(row=2, column=1, padx=30)\n\n self.get_next_question()\n\n self.window.mainloop()\n\n\n def get_next_question(self):\n self.q_canva.config(bg='white')\n\n if self.quiz.still_has_questions():\n self.score_label.config(text=f\"Score: {self.quiz.score}\")\n q_text = self.quiz.next_question()\n self.q_canva.itemconfig(self.question_text, text=q_text)\n\n else:\n self.q_canva.itemconfig(self.question_text, text=\"You've reached the end of the quizz.\")\n self.true_button.config(state='disabled')\n self.false_button.config(state='disabled')\n\n def true_pressed(self):\n is_right = self.quiz.check_answer('True')\n self.give_feedback(is_right)\n\n def false_pressed(self):\n is_right = self.quiz.check_answer('False')\n self.give_feedback(is_right)\n\n\n def give_feedback(self, is_right):\n if is_right:\n self.q_canva.config(bg='green')\n else:\n self.q_canva.config(bg='red')\n\n self.window.after(1000, self.get_next_question)","sub_path":"Trivia_API/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"275214582","text":"import smbus2 as smbus\nimport board\nimport busio\nimport adafruit_sgp30\nimport VL53L1X\nimport Adafruit_DHT\nimport threading\nfrom datetime import datetime\nimport time\nimport pyaudio\nimport wave\nimport numpy as np\nimport wave\nimport os\nimport sys\nimport logging\nimport subprocess\nimport json\n\n# logging.basicConfig(filename = '/home/pi/sensors_logfile.log', level = logging.INFO,\n# format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',\n# datefmt='%d-%m-%Y:%H:%M:%S',)\n\nclass HPD_APDS9301():\n def __init__(self):\n # Module variables\n self.i2c_ch = 1\n self.bus = smbus.SMBus(self.i2c_ch)\n\n # APDS-9301 address on the I2C bus\n self.apds9301_addr = 0x39\n\n # Register addresses\n self.apds9301_control_reg = 0x80\n self.apds9301_timing_reg = 0x81\n self.apds9301_data0low_reg = 0x8C\n self.apds9301_data1low_reg = 0x8E\n\n # Read the CONTROL register (1 byte)\n val = self.bus.read_i2c_block_data(self.apds9301_addr, self.apds9301_control_reg, 1)\n \n # Set POWER to on in the CONTROL register\n val[0] = val[0] & 0b11111100\n val[0] = val[0] | 0b11\n\n # Enable the APDS-9301 by writing back to CONTROL register\n self.bus.write_i2c_block_data(self.apds9301_addr, self.apds9301_control_reg, val)\n\n # Read light data from sensor and calculate lux\n def read(self):\n\n # Read channel 0 light value and combine 2 bytes into 1 number\n val = self.bus.read_i2c_block_data(self.apds9301_addr, self.apds9301_data0low_reg, 2)\n ch0 = (val[1] << 8) | val[0]\n\n # Read channel 1 light value and combine 2 bytes into 1 number\n val = self.bus.read_i2c_block_data(self.apds9301_addr, self.apds9301_data1low_reg, 2)\n ch1 = (val[1] << 8) | val[0]\n\n # Make sure we don't divide by 0\n if ch0 == 0.0:\n return 0.0\n\n # Calculate ratio of ch1 and ch0\n ratio = ch1 / ch0\n\n # Assume we are using the default 13.7 ms integration time on the sensor\n # So, scale raw light values by 1/0.034 as per the datasheet\n ch0 *= 1 / 0.034\n ch1 *= 1 / 0.034\n\n # Assume we are using the default low gain setting\n # So, scale raw light values by 16 as per the datasheet\n ch0 *= 16;\n ch1 *= 16;\n\n # Calculate lux based on the ratio as per the datasheet\n if ratio <= 0.5:\n return int((0.0304 * ch0) - ((0.062 * ch0) * ((ch1/ch0) ** 1.4)))\n elif ratio <= 0.61:\n return int((0.0224 * ch0) - (0.031 * ch1))\n elif ratio <= 0.8:\n return int((0.0128 * ch0) - (0.0153 * ch1))\n elif ratio <= 1.3:\n return int((0.00146 * ch0) - (0.00112*ch1))\n else:\n return int(0.0)\n\n\nclass HPD_SGP30():\n def __init__(self):\n self.i2c = busio.I2C(board.SCL, board.SDA, frequency=100000)\n self.sensor = adafruit_sgp30.Adafruit_SGP30(self.i2c)\n self.sensor.iaq_init()\n self.sensor.set_iaq_baseline(0x8973, 0x8aae)\n\n def read(self):\n try:\n return((self.sensor.eCO2, self.sensor.TVOC))\n except:\n return((self.sensor.co2eq, self.sensor.tvoc)) # returns co2eq in ppm and TVOC in ppb\n\n def read_baseline(self):\n try:\n return((self.sensor.baseline_eCO2, self.sensor.baseline_TVOC))\n except:\n return((self.sensor.baseline_co2eq, self.sensor.baseline_tvoc))\n \n\nclass HPD_VL53L1X():\n def __init__(self):\n self.sensor = VL53L1X.VL53L1X(i2c_bus=1, i2c_address=0x29)\n self.sensor.open()\n\n def read(self):\n self.sensor.start_ranging(3) # 1 = Short range, 2 = Medium Range, 3 = Long Range\n distance = self.sensor.get_distance() # Default returns the distance in mm\n self.sensor.stop_ranging()\n return distance\n\n\nclass HPD_DHT22():\n def __init__(self):\n self.sensor = Adafruit_DHT.DHT22\n self.pin = 17\n\n def to_f(self, t):\n return t * 9/5.0 + 32\n \n def read(self):\n # h, t = Adafruit_DHT.read_retry(self.sensor, self.pin) # returns humidity in % and temp in celsius\n h, t = Adafruit_DHT.read(self.sensor, self.pin) # returns humidity in % and temp in celsius\n return((h, t))\n\n\nclass Sensors(threading.Thread):\n def __init__(self, read_interval, debug, env_params_root):\n threading.Thread.__init__(self)\n self.debug = debug\n self.gas = HPD_SGP30()\n self.light = HPD_APDS9301()\n self.temp_humid = HPD_DHT22()\n self.dist = HPD_VL53L1X()\n self.read_interval = read_interval\n self.readings = []\n self.env_params_root = env_params_root\n self.env_params_root_date = os.path.join(self.env_params_root, datetime.now().strftime('%Y-%m-%d'))\n self.create_root_env_params_dir()\n self.start()\n\n def create_root_env_params_dir(self):\n if not os.path.isdir(self.env_params_root):\n os.makedirs(self.env_params_root)\n logging.info('{} created'.format(self.env_params_root)) \n\n def env_params_dir_update(self):\n start = True\n while 1:\n date_dir = os.path.join(self.env_params_root, datetime.now().strftime('%Y-%m-%d'))\n if not os.path.isdir(date_dir):\n os.makedirs(date_dir) \n \n self.env_params_root_date = date_dir\n \n if start:\n min_dir = os.path.join(self.env_params_root_date, datetime.now().strftime('%H%M'))\n if not os.path.isdir(min_dir):\n os.makedirs(min_dir)\n logging.info('{} created'.format(min_dir))\n \n self.env_params_dir = min_dir\n start = False\n\n if datetime.now().minute % 5 == 0:\n min_dir = os.path.join(self.env_params_root_date, datetime.now().strftime('%H%M'))\n if not os.path.isdir(min_dir):\n os.makedirs(min_dir)\n logging.info('{} created'.format(min_dir))\n \n self.env_params_dir = min_dir\n time.sleep(60)\n\n def write_to_file(self, f_path, to_write):\n logging.info('in write_to_file. f_path: {}'.format(f_path))\n # logging.info('data to write: {}'.format(to_write))\n with open(f_path, 'w+') as f:\n json.dump(to_write, f)\n\n def run(self):\n dir_create = threading.Thread(target=self.env_params_dir_update, daemon=True)\n dir_create.start()\n logging.info('Sensors run')\n first = True\n while True:\n if first:\n while datetime.now().second != 0:\n pass\n first = False\n\n if datetime.now().second == 3:\n f_name = datetime.now().strftime('%Y-%m-%d %H%M_env_params.json')\n f_path = os.path.join(self.env_params_dir, f_name)\n time.sleep(1)\n\n # if datetime.now().second > 0:\n # written = False\n if datetime.now().second % self.read_interval == 0:\n (h, t) = self.temp_humid.read()\n (co2, tvoc) = self.gas.read()\n (co2_base, tvoc_base) = self.gas.read_baseline()\n self.readings.append({\"time\": datetime.now().strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n \"light_lux\": self.light.read(),\n \"temp_c\": t,\n \"rh_percent\": h,\n \"dist_mm\": self.dist.read(),\n \"co2eq_ppm\": co2,\n \"tvoc_ppb\": tvoc,\n \"co2eq_base\": co2_base,\n \"tvoc_base\": tvoc_base})\n\n # logging.info('Length of self.readings: {}'.format(len(self.readings)))\n time.sleep(1)\n \n # if datetime.now().second == 59 and not written:\n if datetime.now().second == 59:\n logging.info('Second == 59')\n new_list = self.readings.copy()\n writer = threading.Thread(target=self.write_to_file, args = (f_path, new_list))\n writer.start()\n writer.join()\n self.readings.clear()\n time.sleep(1)\n # written = True\n\nclass MyAudio(threading.Thread):\n def __init__(self, audio_root, debug, tape_length):\n threading.Thread.__init__(self)\n self.chunk = 4000\n self.rate = 8000\n self.tape_length = tape_length\n self.format = pyaudio.paInt32\n self.channels = 1\n self.audio_root = audio_root\n self.debug = debug\n self.audio_root_date = os.path.join(self.audio_root, datetime.now().strftime('%Y-%m-%d'))\n self.create_root_audio_dir()\n self.p = pyaudio.PyAudio()\n self.frames = []\n self.stream = False\n self.start()\n\n def start_stream(self):\n while not type(self.p) == pyaudio.PyAudio:\n self.p = pyaudio.PyAudio()\n time.sleep(1)\n if self.debug:\n print('type(self.p) != pyaudio.PyAudio')\n logging.info('type(self.p) != pyaudio.PyAudio')\n \n while datetime.now().second % self.tape_length != 0:\n pass\n \n logging.info('Starting audio stream. Time is: ' + datetime.now().strftime('%Y-%m-%d %H:%M'))\n if self.debug:\n print('Starting audio stream. Time is: ' + datetime.now().strftime('%Y-%m-%d %H:%M'))\n \n # try: \n if not self.stream:\n if self.debug:\n print('not self.stream')\n self.stream = self.p.open(format = self.format,\n channels = self.channels,\n rate = self.rate,\n input = True,\n frames_per_buffer = self.chunk)\n # except:\n if self.debug:\n print('pyaudio.PyAudio() could not be opened.')\n if not self.stream:\n logging.info('pyaudio.PyAudio() could not be opened.')\n if self.debug:\n print('pyaudio.PyAudio() could not be opened.')\n self.start_stream()\n \n def create_root_audio_dir(self):\n if not os.path.isdir(self.audio_root):\n os.makedirs(self.audio_root)\n \n def audio_dir_update(self):\n while 1:\n date_dir = os.path.join(self.audio_root, datetime.now().strftime('%Y-%m-%d'))\n if not os.path.isdir(date_dir):\n os.makedirs(date_dir)\n\n self.audio_root_date = date_dir\n \n min_dir = os.path.join(self.audio_root_date, datetime.now().strftime('%H%M'))\n if not os.path.isdir(min_dir):\n os.makedirs(min_dir)\n \n self.audio_dir = min_dir\n \n def write_to_file(self, f_path, to_write):\n wf = wave.open(f_path, 'wb')\n wf.setnchannels(self.channels)\n wf.setsampwidth(self.p.get_sample_size(self.format))\n wf.setframerate(self.rate)\n wf.writeframes(b''.join(to_write))\n wf.close()\n if self.debug:\n print('Attempted to write: {}'.format(f_path))\n \n def run(self):\n dir_create = threading.Thread(target=self.audio_dir_update, daemon=True)\n # dir_create = threading.Thread(target=self.audio_dir_update)\n dir_create.start()\n\n # Wait for self.audio_dir to exist\n time.sleep(1)\n\n stream_start = threading.Thread(target=self.start_stream, daemon=True)\n # stream_start = threading.Thread(target=self.start_stream)\n stream_start.start()\n while not self.stream:\n pass\n log_this = True\n while True:\n if datetime.now().minute % 10 == 0 and log_this:\n logging.info('MyAudio thread staying alive')\n log_this = False\n if datetime.now().minute in [1, 11, 21, 31, 41, 51]:\n log_this = True\n while datetime.now().second % self.tape_length != 0:\n pass\n f_name = datetime.now().strftime('%Y-%m-%d %H%M%S_audio.wav')\n f_path = os.path.join(self.audio_dir, f_name) \n self.frames.clear()\n \n for i in range(0, int(self.rate / self.chunk * self.tape_length)):\n self.frames.append(self.stream.read(self.chunk))\n\n writer = threading.Thread(target=self.write_to_file, args = (f_path, self.frames))\n writer.start()\n writer.join()\n","sub_path":"server/hpd_sensors.py","file_name":"hpd_sensors.py","file_ext":"py","file_size_in_byte":12840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"118918278","text":"import datetime\nimport simplejson as json\n\nfrom django.db import models\nfrom django.core.urlresolvers import reverse\n\nfrom baseconv import base62\n\nimport jsonfield\n\nclass RecentEntry(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(max_length=50)\n date = models.DateField()\n num_speakers = models.IntegerField()\n page_id = models.CharField(max_length=10)\n\n class Meta:\n ordering = ['-date', '-num_speakers', ]\n unique_together = (('slug', 'date', 'page_id', ), )\n\n def __unicode__(self):\n return self.title\n\n @models.permalink\n def get_absolute_url(self):\n return ('cwod_entry_detail', [self.date.strftime('%Y'),\n self.date.strftime('%m'),\n self.date.strftime('%d'),\n self.page_id,\n self.slug, ])\n\n @models.permalink\n def date_url(self):\n return ('cwod_date_detail', [self.date.strftime('%Y'),\n self.date.strftime('%m'),\n self.date.strftime('%d'), ])\n\n\nclass Embed(models.Model):\n\n CHART_COLOR_CHOICES = (\n (1, 'Light'),\n (2, 'Dark'),\n )\n\n CHART_TYPE_CHOICES = (\n (1, 'Overall'),\n (2, 'By Party'),\n (3, 'Double'),\n )\n\n img_src = models.TextField(blank=True, default='')\n overall_img_src = models.TextField(blank=True, default='')\n by_party_img_src = models.TextField(blank=True, default='')\n url = models.TextField()\n title = models.CharField(max_length=255)\n start_date = models.DateField(default='1996-01-01')\n end_date = models.DateField(default=datetime.date.today())\n chart_color = models.SmallIntegerField(max_length=255, choices=CHART_COLOR_CHOICES)\n chart_type = models.SmallIntegerField(max_length=255, choices=CHART_TYPE_CHOICES)\n extra = jsonfield.JSONField(blank=True, default='{}')\n\n def from_decimal(self):\n return base62.from_decimal(self.pk)\n\n def js_url(self):\n return '%s?c=%s' % (reverse('cwod_embed_js'), self.from_decimal())\n\n","sub_path":"cwod_site/cwod/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"424838600","text":"import git\nimport os\nimport json\nimport jsonlines\nfrom sklearn.isotonic import IsotonicRegression\nfrom srtml.modellib.constants import THROUGHPUT_KEY, LATENCY_KEY\nimport srtml\nimport ray\nimport pandas as pd\nfrom srtml_exp.sysinfo import get_sysinfo\nimport numpy as np\nfrom srtml.planner.optim import (\n SimulatedAnnealing,\n VanillaInferline,\n ImprovedInferline,\n DummyPlanner,\n)\n\nROOT_DIR = git.Repo(\".\", search_parent_directories=True).working_tree_dir\nIMAGE_CLASSIFICATION_DIR = os.path.join(ROOT_DIR, \"image_preprocessing\")\nIMAGE_CLASSIFICATION_DIR_TWO_VERTEX = os.path.join(\n IMAGE_CLASSIFICATION_DIR, \"two_vertex\"\n)\n\nIMAGE_CLASSIFICATION_FEATURE = \"ImageClassification\"\nIMAGE_DATASET_INFORMATION = {\"dataset\": \"Imagenet\", \"dataset_category\": \"IMAGE\"}\nPLANNER_CLS = {\n \"SimulatedAnnealing\": SimulatedAnnealing,\n \"VanillaInferline\": VanillaInferline,\n \"ImprovedInferline\": ImprovedInferline,\n \"DummyPlanner\": DummyPlanner,\n}\nimport numpy as np\n\n\ndef gamma(mean, cv, size):\n if cv == 0.0:\n return np.ones(size) * mean\n else:\n return np.random.gamma(1.0 / cv, cv * mean, size=size)\n\n\ndef generate_fixed_arrival_process(mean_qps, cv, num_requests):\n \"\"\"\n mean_qps : float\n Mean qps\n cv : float\n duration: float\n Duration of the trace in seconds\n \"\"\"\n # deltas_path = os.path.join(arrival_process_dir,\n # \"fixed_{mean_qps}_{cv}_{dur}_{ts:%y%m%d_%H%M%S}.deltas\".format(\n # mean_qps=mean_qps, cv=cv, dur=duration, ts=datetime.now()))\n inter_request_delay_ms = 1.0 / float(mean_qps) * 1000.0\n num_deltas = num_requests - 1\n if cv == 0:\n deltas = np.ones(num_deltas) * inter_request_delay_ms\n else:\n deltas = gamma(inter_request_delay_ms, cv, size=num_deltas)\n deltas = np.clip(deltas, a_min=2.5, a_max=None)\n return deltas\n\n\nclass BytesEncoder(json.JSONEncoder):\n \"\"\"Allow bytes to be part of the JSON document.\n BytesEncoder will walk the JSON tree and decode bytes with utf-8 codec.\n (Adopted from serve 0.8.2)\n Example:\n >>> json.dumps({b'a': b'c'}, cls=BytesEncoder)\n '{\"a\":\"c\"}'\n \"\"\"\n\n def default(self, o): # pylint: disable=E0202\n if isinstance(o, bytes):\n return o.decode(\"utf-8\")\n return super().default(o)\n\n\ndef get_latency(filename):\n latency = list()\n with jsonlines.open(filename) as reader:\n for obj in reader:\n latency.append((obj[\"end\"] - obj[\"start\"]))\n return latency\n\n\ndef convert_profiles_to_regression_models(profile_dict):\n learned_profile = dict()\n for ppu in profile_dict:\n learned_profile[ppu] = dict()\n for hardware in profile_dict[ppu]:\n learned_profile[ppu][hardware] = dict()\n learned_profile[ppu][hardware][\n THROUGHPUT_KEY\n ] = IsotonicRegression().fit(\n *list(zip(*profile_dict[ppu][hardware][THROUGHPUT_KEY]))\n )\n learned_profile[ppu][hardware][\n LATENCY_KEY\n ] = IsotonicRegression().fit(\n *list(zip(*profile_dict[ppu][hardware][LATENCY_KEY]))\n )\n return learned_profile\n\n\ndef get_dataframe_from_profile(pgraph_identifier, profile_dict):\n columns = [\n \"pgraph\",\n \"ppu\",\n \"hardware\",\n \"batch size\",\n \"latency (ms)\",\n \"throughput (qps)\",\n ]\n df_dict = dict()\n row_index = 0\n for ppu in profile_dict:\n for hardware in profile_dict[ppu]:\n for latency_item, throughput_item in zip(\n profile_dict[ppu][hardware][LATENCY_KEY],\n profile_dict[ppu][hardware][THROUGHPUT_KEY],\n ):\n assert latency_item[0] == throughput_item[0], \"Wrong Profile\"\n batch_size = latency_item[0]\n latency_ms = latency_item[1]\n throughput_qps = throughput_item[1]\n df_row = [\n pgraph_identifier,\n ppu,\n hardware,\n batch_size,\n latency_ms,\n throughput_qps,\n ]\n df_dict[row_index] = df_row\n row_index += 1\n\n idx = pd.MultiIndex.from_product(\n [[get_sysinfo()], columns],\n names=[\"system information\", \"model repository\"],\n )\n return pd.DataFrame.from_dict(df_dict, orient=\"index\", columns=idx)\n\n\ndef _get_ingest_observed_throughput(start_time_list):\n start_time_list.sort()\n avg_time_diff = 0\n cnt = 0\n for i in range(len(start_time_list) - 1):\n avg_time_diff += start_time_list[i + 1] - start_time_list[i]\n cnt += 1\n avg_time_diff = avg_time_diff / cnt\n return 1.0 / avg_time_diff\n\n\ndef get_latency_stats(collected_latency):\n\n latency_list_ms = [\n (d[\"end\"] - d[\"start\"]) * 1000 for d in collected_latency\n ]\n p95_ms, p99_ms = np.percentile(latency_list_ms, [95, 99])\n\n ingest_throughput = _get_ingest_observed_throughput(\n [d[\"start\"] for d in collected_latency]\n )\n return ingest_throughput, latency_list_ms, p95_ms, p99_ms\n\n\ndef shutdown():\n ray.shutdown()\n srtml.serve.api.global_state = None\n srtml.modellib.api.global_state = None","sub_path":"srtml_exp/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"55422259","text":"graph=[[]]\n #사전 graph 작업\ndef pre(n,fares): \n global graph\n graph=[[100000001 for col in range(n+1)]for i in range(n+1)]\n for i in range(n+1):\n for k in range(n+1):\n if i==k:\n graph[i][k]=0\n elif i==0 or k==0:\n graph[i][k]=0\n for i in fares:\n graph[i[0]][i[1]]=i[2]\n graph[i[1]][i[0]]=i[2]\n#가장 비용이 적은 노드 찾는 함수\ndef minnode(v,f):#방문하지 않은 노드중 최단거리 노드를 구함\n ans=0\n min=100000001\n for i in range(1,len(v)):\n if f[i]')[1]\n if '%)' in recipient_genome:\n recipient_genome = recipient_genome.split('(')[0]\n\n if recipient_genome not in bin_to_HGT_num_dict:\n bin_to_HGT_num_dict[recipient_genome] = 1\n else:\n bin_to_HGT_num_dict[recipient_genome] += 1\n\nbin_to_HGT_num_normalized_dict = {}\nfor genome_bin in bin_to_HGT_num_dict:\n bin_HGT_num = bin_to_HGT_num_dict[genome_bin]\n bin_size = bin_size_dict[genome_bin]\n bin_HGT_num_normalized = float(\"{0:.2f}\".format(bin_HGT_num/bin_size))\n bin_to_HGT_num_normalized_dict[genome_bin] = bin_HGT_num_normalized\n\nprint(bin_to_HGT_num_normalized_dict)\nprint(len(bin_to_HGT_num_normalized_dict))\n\n\nkelp_mag_HGT_num_list = []\ntara_mag_HGT_num_list = []\nfor hgt_num in bin_to_HGT_num_normalized_dict:\n if '_Refined_' in hgt_num:\n kelp_mag_HGT_num_list.append(bin_to_HGT_num_normalized_dict[hgt_num])\n else:\n tara_mag_HGT_num_list.append(bin_to_HGT_num_normalized_dict[hgt_num])\n\n\nprint(len(kelp_mag_HGT_num_list))\nprint(len(tara_mag_HGT_num_list))\nprint(sum(kelp_mag_HGT_num_list)/len(kelp_mag_HGT_num_list))\nprint(sum(tara_mag_HGT_num_list)/len(tara_mag_HGT_num_list))\n\n","sub_path":"tmp_2.py","file_name":"tmp_2.py","file_ext":"py","file_size_in_byte":2943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"216486446","text":"import jsonpickle\nimport board\n\nfrom apistar.test import TestClient\n\n\ndef test_board_transmission():\n \"\"\"\n Testing if we can construct a board\n \"\"\"\n client = TestClient()\n\n # Reset the board first so we have a consistent state\n response = client.post(\"http://localhost/board/reset\")\n assert response.status_code == 200\n\n response = client.get(\"http://localhost/board/\")\n assert response.status_code == 200\n b = jsonpickle.decode(response.json())\n assert b.width == 200\n assert b.height == 200\n for line in b.board:\n for color in line:\n assert color == board.colors[\"default\"]\n\n\ndef test_color_setting():\n \"\"\"\n Testing a view, using the test client.\n \"\"\"\n client = TestClient()\n\n # Reset the board first so we have a consistent state\n response = client.post(\"http://localhost/board/reset\")\n assert response.status_code == 200\n\n # Set a pixel to green\n response = client.post('http://localhost/board/set/2/2/green')\n assert response.status_code == 200\n\n # Receive the board\n response = client.get(\"http://localhost/board/\")\n assert response.status_code == 200\n b = jsonpickle.decode(response.json())\n\n # Check if the pixel was set\n assert b.board[2][2] == board.colors[\"green\"]\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"292913285","text":"'''\n实验名称：流水灯\n版本：v2.0\n日期：2019.4\n作者：01Studio\n'''\n\nfrom pyb import LED,delay #从pyb导入LED模块\n\n# 相当于for i in [2, 3, 4]，LED(i).off()执行3次，分别是LED 2，3，4\nfor i in range(2,5):\n LED(i).off()\n\nwhile True:\n #使用for循环\n for i in range(2,5):\n LED(i).on()\n delay(1000) #延时1000毫秒，即1秒\n LED(i).off()\n\n","sub_path":"1.pyBoard v1.1(STM32)/1.基础实验/2.流水灯/v2.0/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"409068356","text":"import random\r\nimport time\r\ndef BackGround():\r\n print(\"===================================\")\r\n print(\" 타자 연습 게임 Ver 0.9c\")\r\n print(\" Enter를 누르면 게임을 시작합니다.\")\r\n print(\"===================================\")\r\n input()\r\n start=time.time()\r\n GoQuiz(getQ(),start)\r\ndef timeㅋㅋ(start,end):\r\n print(\"%.2f 초 걸렸습니다.\" %(end-start))\r\ndef getQ():\r\n Questions = [\"문제1\", \"문제2\", \"문제3\", \"문제4\"]\r\n random.shuffle(Questions)\r\n return Questions\r\ndef GoQuiz(Questions,start):\r\n while True:\r\n print(Questions[0])\r\n answer = input()\r\n if Questions[0] == answer:\r\n print(\"정답!\")\r\n Questions.pop(0)\r\n else:\r\n print(\"오타! 다시도전!\")\r\n if Questions == []:\r\n end=time.time()\r\n print(\"===================================\")\r\n print(\" 모든 문제를 정확하게 맞히셨습니다.\")\r\n timeㅋㅋ(start, end)\r\n print(\"===================================\")\r\n break\r\ndef main():\r\n BackGround()\r\nmain()\r\n","sub_path":"Keyboard_Practice.py","file_name":"Keyboard_Practice.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"465593990","text":"import peewee\nimport peewee_mssql\n# import sqlite3\nimport pdb\nimport time\nimport threading\nimport sys\n\nfrom common import common\nfrom common.orm import fields\nfrom datetime import datetime\n\nparser = common.createParser()\nnamespace = parser.parse_args(sys.argv[1:])\n\nsql_host = namespace.sql_host\nsql_base = namespace.sql_base\nsql_user = namespace.sql_user\nsql_pass = namespace.sql_pass\n\n\nclass LegacyDatabase(peewee_mssql.MssqlDatabase):\n def execute_sql(self, sql, params=None, require_commit=True):\n try:\n return super().execute_sql(sql, params, require_commit)\n except peewee.ProgrammingError as e:\n common.print_query((sql,params), True)\n if len(e.args) > 1:\n pass\n else:\n raise e\n\nclass LocalDatabase(peewee.SqliteDatabase):\n pass\n\nimport os\ntry:\n os.remove('/tmp/test.db')\nexcept Exception:\n pass\n\nclass ModelAlias(peewee.ModelAlias):\n\n def select(self, *selection, **kwargs):\n local = kwargs.pop('local', None)\n\n if not selection:\n selection = self.get_proxy_fields()\n query = SelectQuery(self, *selection, **kwargs)\n if self._meta.order_by:\n query = query.order_by(*self._meta.order_by)\n return query\n\n\nclass SelectQuery(peewee.SelectQuery):\n def __init__(self, model_class, *selection, **kwargs):\n super().__init__(model_class, *selection)\n self.local = kwargs.get('local', True)\n self.slice_aliases = {}\n\n def clone(self):\n inst = super().clone()\n inst.local = self.local\n inst.slice_aliases = self.slice_aliases\n return inst\n\n def select(self, *selection, **kwargs):\n if self._select:\n self._select += self._model_shorthand(selection)\n return self\n\n query = SelectQuery(self, *selection, **kwargs)\n if self._meta.order_by:\n query = query.order_by(*self._meta.order_by)\n return query\n\n def swap_slices(self, fieldset):\n if fieldset is None: return\n\n def swap_field(field):\n if issubclass(type(field), peewee.Field):\n for alias, val in self.slice_aliases.items():\n cls, c = val\n if cls == field.model_class:\n new_field = getattr(c, field.db_column)\n new_field._alias = field._alias\n return new_field\n return field\n\n # SELECT\n if isinstance(fieldset, list):\n for k, field in enumerate(fieldset):\n fieldset[k] = swap_field(field)\n return fieldset\n\n # WHERE\n elif isinstance(fieldset, peewee.Expression):\n def branch(field):\n if isinstance(field, peewee.Expression):\n return self.swap_slices(field)\n return swap_field(field)\n\n fieldset.rhs = branch(fieldset.rhs)\n fieldset.lhs = branch(fieldset.lhs)\n\n return fieldset\n\n # JOIN\n elif isinstance(fieldset, dict):\n for k, exs in fieldset.items():\n for m, ex in enumerate(exs):\n self.swap_slices(ex.on)\n # DEBUG\n else:\n pdb.set_trace()\n\n # @common.debug_result\n def sql(self):\n self.swap_slices(self._select)\n self.swap_slices(self._where)\n self.swap_slices(self._joins)\n self.swap_slices(self._group_by)\n\n sql, params = super().sql()\n\n # common.print_query((sql, params)); print(\"\\n\")\n return sql, params\n\n @peewee.returns_clone\n def group_all(self):\n group = []\n for s in self._select:\n if isinstance(s, peewee.Func): continue\n s = s.clone()\n s._alias = ''\n group.append(s)\n\n self._group_by = self._model_shorthand(group)\n\n def slice(self, *args, **kwargs):\n cls = kwargs.get('cls')\n period = kwargs.get('period')\n on = kwargs.get('on')\n alias = kwargs.get('alias')\n date = kwargs.get('date')\n join_type = kwargs.get('join_type', peewee.JOIN.LEFT_OUTER)\n\n if cls is None:\n for arg in args:\n cls = arg.model_class\n break\n\n if period is None:\n period = cls.period\n\n if alias is None:\n alias = cls.__name__\n\n # ищем максимальный по полю period\n sub = (cls.select(*args, peewee.fn.MAX(period).alias(cls.__name__ + '_MAXPERIOD'), local = False)\n .group_by(*args)\n .alias(cls.__name__ + '_sub')\n )\n if (date):\n sub = sub.where(period > date)\n\n exs = peewee.Expression(period, peewee.OP.EQ, getattr(sub.c, cls.__name__ + '_MAXPERIOD'))\n for arg in args:\n if arg:\n ex = peewee.Expression(\n getattr(cls, arg.name), peewee.OP.EQ,\n getattr(sub.c, arg.db_column)\n )\n exs &= ex\n\n slice_ = cls.select(cls, local = False).join(sub, on=(exs)).alias(cls.__name__)\n\n if alias:\n self.slice_aliases[alias] = (cls, slice_.c)\n\n def through_ex(ex):\n def branch(field):\n if isinstance(field, peewee.Expression):\n field = through_ex(field)\n\n elif issubclass(type(field), peewee.Field) and cls == field.model_class:\n return getattr(slice_.c, field.db_column)\n return field\n\n ex.rhs = branch(ex.rhs)\n ex.lhs = branch(ex.lhs)\n\n return ex\n \n\n on = through_ex(on)\n\n res = self.join(slice_, join_type=join_type, on=(on))\n\n return res\n\n\n # def _execute(self):\n # # debug.log_info(hex(id(self)), self.local and \"LOCAL\" or \"Legacy\", self.model_class)\n # if self.local:\n # self.database = self.model_class._meta.database \n # self.require_commit = False\n # try:\n # cursor = super()._execute()\n # return cursor\n # except (peewee.OperationalError, sqlite3.OperationalError) as e:\n # estr = e.args[0]\n # param = None\n # if estr.find(':'):\n # estr, param = estr.split(':')\n\n # if estr == 'no such table':\n # for table in self._joins:\n # if table.table_exists():\n # continue\n\n # if issubclass(table, OnesEnum):\n # db_column = table.order.db_column\n # table.order.db_column = 'Представление'\n # table.order.null = True\n\n # debug.log_warning(\"Создаем таблицу в кеше\", table._meta.db_table)\n # try:\n # table.create_table(fail_silently = False)\n # except (peewee.OperationalError, sqlite3.OperationalError) as e:\n # # pdb.set_trace()\n # debug.log_warning(\"Ошибка при создании таблицы\", table._meta.db_table, e)\n\n # if issubclass(table, OnesEnum):\n # table.order.db_column = db_column\n\n # self.local = False\n # return self._execute()\n # else:\n # self.database = self.model_class._meta.legacy_db\n # return super()._execute()\n\ndef save_queue():\n global unsaved \n unsaved = set()\n while True:\n if unsaved:\n local = set(unsaved)\n print(\"Unsaved queue len\", len(unsaved))\n print(\"Processing \", end='', flush=True)\n unsaved.clear()\n for el in local:\n print(\".\", end='', flush=True)\n # if issubclass(type(el), OnesEnum):\n el.cached = True\n el.save(recursive = True)\n print(\" OK\")\n else:\n time.sleep(2)\n # print(\"Empty queue\")\n\n# threading.Thread(name = \"Save Queue\", target = save_queue).start()\n\nclass OnesCore(peewee.Model):\n # table = peewee.CharField(db_column = '_ИмяТаблицы', max_length = 100, null = True)\n\n class Meta:\n # legacy_db = LegacyDatabase(sql_base, \n database = LegacyDatabase(sql_base, \n host = sql_host,\n user = sql_user,\n password = sql_pass, \n appname = \"CRM Python Module\"\n )\n primary_key = False\n # database = LocalDatabase('/tmp/test.db')\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.cached = True\n\n def prepare_local_db(self):\n for k, field in self._meta.fields.items():\n if isinstance(field, fields.ForeignKey):\n el = getattr(self, k)\n if el is not None:\n # print(\"unsaved.add(%s)\" % el)\n unsaved.add(el)\n # el.save()\n # print(el, type(el))\n\n def save(self, force_insert = False, recursive = True):\n return # important if legacy\n cls = type(self)\n try:\n # local only\n exist = cls.select().where(cls.ref == self.ref).get()\n except peewee.DoesNotExist as e:\n self.prepare_local_db()\n # if issubclass(type(self), OnesEnum):\n super().save(force_insert = True)\n\n def __str__(self):\n return self.table or self.__class__.__name__ + \": has NO name\"\n\n @classmethod\n def warm_up(cls, query=None):\n if not cls.table_exists():\n cls.create_table(fail_silently = False)\n\n if query is None:\n res = cls.select(local = False) # for begin check local cache?\n else:\n res = query\n\n for row in list(res):\n if res.local:\n print(\"local\", row)\n continue\n # break\n if row:\n # print(\"unsaved.add(%s)\" % row)\n unsaved.add(row)\n\n @classmethod\n def select(cls, *selection, **kwargs):\n local = kwargs.pop('local', None)\n if local is None:\n query = peewee.SelectQuery(cls, *selection, **kwargs)\n else:\n query = SelectQuery(cls, *selection, **kwargs)\n query.local = local\n\n if cls._meta.order_by:\n query = query.order_by(*cls._meta.order_by)\n\n return query\n\n @classmethod\n def get(cls, *args, **kwargs):\n\n local = kwargs.pop('local', None) # if true => using local cache\n sq = cls.select(local = local).naive()\n\n try:\n if args:\n sq = sq.where(*args)\n if kwargs:\n sq = sq.filter(**kwargs)\n\n result = sq.get()\n\n if local is False:\n result.cached = False\n # print('В очереди на сохранение', result)\n unsaved.add(result)\n \n return result\n\n except peewee.DoesNotExist as e:\n if local is True:\n return cls.get(local = False)\n else:\n return cls()\n\n @classmethod\n def alias(cls):\n return ModelAlias(cls)\n\n\n\nclass OnesBase(OnesCore):\n ref = fields.LinkField()\n ref_db = fields.LinkFieldDB()\n ref_ones = fields.LinkFieldOneS()\n\n def __bool__(self):\n return self.ref and True or False\n\n @classmethod\n def warm_up(cls, query=None):\n super(OnesBase, cls).warm_up(query = cls.select().order_by(cls.ref).limit(1000))\n\nclass OnesEnum(OnesBase):\n order = peewee.IntegerField(db_column = \"Порядок\")\n values = []\n\n def __str__(self):\n if isinstance(self.name, str):\n return self.name \n\n return self.order or self.__class__.__name__ + \": has NO name\"\n\n\nclass OnesRef(OnesBase):\n mark = fields.BoolField(db_column = 'ПометкаУдаления', inverted = False)\n code = peewee.CharField(db_column = 'Код', max_length = 10)\n name = peewee.CharField(db_column = 'Наименование', max_length = 100)\n\n def __str__(self):\n return self.name or self.ref or self.__class__.__name__ + \": has NO name\"\n\nclass OnesBP(OnesBase):\n date = fields.DateField()\n mark = fields.BoolField(db_column = 'ПометкаУдаления', inverted = False)\n number = peewee.CharField(db_column = 'Номер', max_length = 10)\n completed = fields.BoolField(db_column='Завершен')\n started = fields.BoolField(db_column='Стартован')\n\n def __str__(self):\n return self.name or self.ref or self.__class__.__name__ + \": has NO name\"\n\nclass OnesPointRouteBP(OnesBase):\n order = peewee.IntegerField(db_column = \"Порядок\")\n\n def __str__(self):\n return self.name or self.ref or self.__class__.__name__ + \": has NO name\"\n\n\nclass OnesTask(OnesBase):\n mark = fields.BoolField(db_column = 'ПометкаУдаления', inverted = False)\n name = peewee.CharField(db_column = 'Наименование', max_length = 100)\n date = fields.DateField()\n completed = fields.BoolField(db_column = 'Выполнена')\n\n def __str__(self):\n return self.name or self.ref or self.__class__.__name__ + \": has NO name\"\n\n\nclass OnesDoc(OnesBase):\n mark = fields.BoolField(db_column = 'ПометкаУдаления', inverted = False)\n number = peewee.CharField(db_column = 'Номер', max_length = 10)\n # prefix = peewee.CharField(db_column=\"_NumberPrefix\", max_length = 3)\n # name = peewee.CharField(db_column = '_ИмяТаблицы', max_length = 100)\n date = fields.DateField()\n posted = fields.BoolField(db_column = 'Проведен')\n\n def __str__(self):\n return self.table + ' №' + self.number + ' от ' + self.date.strftime(\"%d.%m.%Y\")\n\n\nclass OnesTable(OnesBase):\n key = peewee.BlobField(db_column = '_KeyField')\n order = peewee.IntegerField(db_column = \"НомерСтроки\")\n # name = peewee.CharField(db_column = '_ИмяТаблицы', max_length = 100)\n\n class Meta:\n primary_key = peewee.CompositeKey('ref', 'key')\n","sub_path":"etc/old2/common/orm/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":14372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"158980704","text":"# -*- coding: utf-8 -*-\n# @UpdateTime : 2021/3/16 21:34\n# @Author : 27\n# @File : rotate_metrix2.py\n\"\"\"\n59. 螺旋矩阵 II\n给你一个正整数 n ，生成一个包含 1 到 n2 所有元素，且元素按顺时针顺序螺旋排列的 n x n 正方形矩阵 matrix 。\n\n示例 1：\n输入：n = 3\n输出：[[1,2,3],[8,9,4],[7,6,5]]\n示例 2：\n\n输入：n = 1\n输出：[[1]]\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def generateMatrix(self, n: int) -> List[List[int]]:\n dirs = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n matrix = [[0] * n for _ in range(n)]\n row, col, dirIdx = 0, 0, 0\n for i in range(n * n):\n matrix[row][col] = i + 1\n print(\"row:{}, col:{}\".format(row, col), matrix[row][col])\n dx, dy = dirs[dirIdx]\n r, c = row + dx, col + dy\n if r < 0 or r >= n or c < 0 or c >= n or matrix[r][c] > 0:\n dirIdx = (dirIdx + 1) % 4 # 顺时针旋转至下一个方向\n dx, dy = dirs[dirIdx]\n row, col = row + dx, col + dy\n return matrix\n\n def generateMatrix2(self, n: int) -> List[List[int]]:\n matrix = [[0] * n for _ in range(n)]\n num = 1\n left, right, top, bottom = 0, n - 1, 0, n - 1\n while left <= right and top <= bottom:\n for col in range(left, right + 1):\n matrix[top][col] = num\n num += 1\n for row in range(top + 1, bottom + 1):\n matrix[row][right] = num\n num += 1\n if left < right and top < bottom:\n for col in range(right - 1, left, -1):\n matrix[bottom][col] = num\n num += 1\n print(bottom, col, num)\n for row in range(bottom, top, -1):\n matrix[row][left] = num\n num += 1\n print(row, left, num)\n\n left += 1\n right -= 1\n top += 1\n bottom -= 1\n\n return matrix\n\n def tttt(self, n):\n matrix = [[0] * n for _ in range(n)]\n num = 1\n top, right, bottom, left = 0, n - 1, n - 1, 0\n\n while left <= right and top <= bottom:\n for col in range(left, right + 1):\n matrix[top][col] = num\n num += 1\n for row in range(top + 1, bottom + 1):\n matrix[row][right] = num\n num += 1\n if left < right and top < bottom:\n for col in range(right - 1, left, -1):\n matrix[bottom][col] = num\n num += 1\n for row in range(bottom, top, -1):\n matrix[row][left] = num\n num += 1\n left += 1\n top += 1\n right -= 1\n bottom -= 1\n return matrix\n\n def generateMatrix3(self, n: int) -> List[List[int]]:\n target_number = n * n\n dirs = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n row, col, dir_index = 0, 0, 0\n metrix = [[0] * n for _ in range(n)]\n for i in range(target_number):\n metrix[row][col] = i + 1\n # print(\"row:{}, col:{}\".format(row, col), metrix[row][col])\n\n dx, dy = dirs[dir_index]\n # 验证下一个方向正确性\n r, c = row + dx, col + dy\n if r < 0 or r >= n or c < 0 or c >= n or metrix[r][c] > 0:\n # 如果不正确则修正方向\n dir_index = (dir_index + 1) % 4\n dx, dy = dirs[dir_index]\n # 给定正确的行列\n row, col = row + dx, col + dy\n # print(row, col)\n return metrix\n\n\nif __name__ == '__main__':\n s = Solution()\n print(s.generateMatrix2(3))\n pass\n\n","sub_path":"content/algorithms_leetcode/rotate_metrix2.py","file_name":"rotate_metrix2.py","file_ext":"py","file_size_in_byte":3777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"420317913","text":"\nfrom datetime import timedelta\n\nfrom django.db import connection, transaction\nfrom django.utils.timezone import now as timezone_now\nfrom zerver.models import (Message, UserMessage, ArchivedMessage, ArchivedUserMessage, Realm,\n Attachment, ArchivedAttachment, Reaction, ArchivedReaction,\n SubMessage, ArchivedSubMessage)\n\nfrom typing import Any, Dict, List\n\nmodels_with_message_key = [\n {\n 'class': Reaction,\n 'archive_class': ArchivedReaction,\n 'table_name': 'zerver_reaction',\n 'archive_table_name': 'zerver_archivedreaction'\n },\n {\n 'class': SubMessage,\n 'archive_class': ArchivedSubMessage,\n 'table_name': 'zerver_submessage',\n 'archive_table_name': 'zerver_archivedsubmessage'\n },\n] # type: List[Dict[str, Any]]\n\n@transaction.atomic\ndef move_expired_rows(src_model: Any, raw_query: str, returning_id: bool=False,\n **kwargs: Any) -> List[int]:\n src_db_table = src_model._meta.db_table\n src_fields = [\"{}.{}\".format(src_db_table, field.column) for field in src_model._meta.fields]\n dst_fields = [field.column for field in src_model._meta.fields]\n sql_args = {\n 'src_fields': ','.join(src_fields),\n 'dst_fields': ','.join(dst_fields),\n 'archive_timestamp': timezone_now()\n }\n sql_args.update(kwargs)\n with connection.cursor() as cursor:\n cursor.execute(\n raw_query.format(**sql_args)\n )\n if returning_id:\n return [row[0] for row in cursor.fetchall()] # return list of row ids\n else:\n return []\n\ndef ids_list_to_sql_query_format(ids: List[int]) -> str:\n assert len(ids) > 0\n\n ids_tuple = tuple(ids)\n if len(ids_tuple) > 1:\n ids_string = str(ids_tuple)\n elif len(ids_tuple) == 1:\n ids_string = '({})'.format(ids_tuple[0])\n\n return ids_string\n\ndef move_expired_messages_to_archive(realm: Realm) -> List[int]:\n query = \"\"\"\n INSERT INTO zerver_archivedmessage ({dst_fields}, archive_timestamp)\n SELECT {src_fields}, '{archive_timestamp}'\n FROM zerver_message\n INNER JOIN zerver_userprofile ON zerver_message.sender_id = zerver_userprofile.id\n LEFT JOIN zerver_archivedmessage ON zerver_archivedmessage.id = zerver_message.id\n WHERE zerver_userprofile.realm_id = {realm_id}\n AND zerver_message.pub_date < '{check_date}'\n AND zerver_archivedmessage.id is NULL\n RETURNING id\n \"\"\"\n assert realm.message_retention_days is not None\n check_date = timezone_now() - timedelta(days=realm.message_retention_days)\n\n return move_expired_rows(Message, query, returning_id=True,\n realm_id=realm.id, check_date=check_date.isoformat())\n\ndef move_expired_user_messages_to_archive(msg_ids: List[int]) -> List[int]:\n if not msg_ids:\n return []\n\n query = \"\"\"\n INSERT INTO zerver_archivedusermessage ({dst_fields}, archive_timestamp)\n SELECT {src_fields}, '{archive_timestamp}'\n FROM zerver_usermessage\n LEFT JOIN zerver_archivedusermessage ON zerver_archivedusermessage.id = zerver_usermessage.id\n WHERE zerver_usermessage.message_id IN {message_ids}\n AND zerver_archivedusermessage.id is NULL\n RETURNING id\n \"\"\"\n\n return move_expired_rows(UserMessage, query, returning_id=True,\n message_ids=ids_list_to_sql_query_format(msg_ids))\n\ndef move_expired_models_with_message_key_to_archive(msg_ids: List[int]) -> None:\n if not msg_ids:\n return\n\n for model in models_with_message_key:\n query = \"\"\"\n INSERT INTO {archive_table_name} ({dst_fields}, archive_timestamp)\n SELECT {src_fields}, '{archive_timestamp}'\n FROM {table_name}\n LEFT JOIN {archive_table_name} ON {archive_table_name}.id = {table_name}.id\n WHERE {table_name}.message_id IN {message_ids}\n AND {archive_table_name}.id IS NULL\n \"\"\"\n move_expired_rows(model['class'], query, table_name=model['table_name'],\n archive_table_name=model['archive_table_name'],\n message_ids=ids_list_to_sql_query_format(msg_ids))\n\ndef move_expired_attachments_to_archive(realm: Realm, msg_ids: List[int]) -> None:\n if not msg_ids:\n return\n\n query = \"\"\"\n INSERT INTO zerver_archivedattachment ({dst_fields}, archive_timestamp)\n SELECT {src_fields}, '{archive_timestamp}'\n FROM zerver_attachment\n INNER JOIN zerver_attachment_messages\n ON zerver_attachment_messages.attachment_id = zerver_attachment.id\n LEFT JOIN zerver_archivedattachment ON zerver_archivedattachment.id = zerver_attachment.id\n WHERE zerver_attachment_messages.message_id IN {message_ids}\n AND zerver_attachment.realm_id = {realm_id}\n AND zerver_archivedattachment.id IS NULL\n GROUP BY zerver_attachment.id\n \"\"\"\n assert realm.message_retention_days is not None\n move_expired_rows(Attachment, query, realm_id=realm.id,\n message_ids=ids_list_to_sql_query_format(msg_ids))\n\n\ndef move_expired_attachments_message_rows_to_archive(realm: Realm, msg_ids: List[int]) -> None:\n if not msg_ids:\n return\n\n query = \"\"\"\n INSERT INTO zerver_archivedattachment_messages (id, archivedattachment_id, archivedmessage_id)\n SELECT zerver_attachment_messages.id, zerver_attachment_messages.attachment_id,\n zerver_attachment_messages.message_id\n FROM zerver_attachment_messages\n INNER JOIN zerver_attachment\n ON zerver_attachment_messages.attachment_id = zerver_attachment.id\n LEFT JOIN zerver_archivedattachment_messages\n ON zerver_archivedattachment_messages.id = zerver_attachment_messages.id\n WHERE zerver_attachment_messages.message_id IN {message_ids}\n AND zerver_attachment.realm_id = {realm_id}\n AND zerver_archivedattachment_messages.id IS NULL\n \"\"\"\n assert realm.message_retention_days is not None\n with connection.cursor() as cursor:\n cursor.execute(query.format(realm_id=realm.id,\n message_ids=ids_list_to_sql_query_format(msg_ids)))\n\n\ndef delete_expired_messages(realm: Realm, msg_ids: List[int]) -> None:\n removing_messages = Message.objects.filter(\n usermessage__isnull=True, id__in=msg_ids,\n sender__realm_id=realm.id\n )\n removing_messages.delete()\n\n\ndef delete_expired_user_messages(realm: Realm, usermsg_ids: List[int]) -> None:\n assert realm.message_retention_days is not None\n removing_user_messages = UserMessage.objects.filter(\n id__in=usermsg_ids,\n user_profile__realm_id=realm.id,\n )\n removing_user_messages.delete()\n\n\ndef delete_expired_attachments(realm: Realm) -> None:\n attachments_to_remove = Attachment.objects.filter(\n messages__isnull=True, id__in=ArchivedAttachment.objects.all(),\n realm_id=realm.id\n )\n attachments_to_remove.delete()\n\ndef move_expired_to_archive() -> Dict[int, Dict[str, List[int]]]:\n archived_data_info = {} # type: Dict[int, Dict[str, List[int]]]\n for realm in Realm.objects.filter(message_retention_days__isnull=False).order_by(\"id\"):\n msg_ids = move_expired_messages_to_archive(realm)\n usermsg_ids = move_expired_user_messages_to_archive(msg_ids)\n move_expired_models_with_message_key_to_archive(msg_ids)\n move_expired_attachments_to_archive(realm, msg_ids)\n move_expired_attachments_message_rows_to_archive(realm, msg_ids)\n\n archived_data_info[realm.id] = {\n 'message_ids': msg_ids,\n 'usermessage_ids': usermsg_ids,\n }\n\n return archived_data_info\n\ndef clean_expired(archived_data_info: Dict[int, Dict[str, List[int]]]) -> None:\n for realm in Realm.objects.filter(message_retention_days__isnull=False).order_by(\"id\"):\n delete_expired_user_messages(realm, archived_data_info[realm.id]['usermessage_ids'])\n delete_expired_messages(realm, archived_data_info[realm.id]['message_ids'])\n delete_expired_attachments(realm)\n\ndef archive_messages() -> None:\n archived_data_info = move_expired_to_archive()\n clean_expired(archived_data_info)\n\ndef move_attachment_messages_to_archive_by_message(message_ids: List[int]) -> None:\n # Move attachments messages relation table data to archive.\n id_list = ', '.join(str(message_id) for message_id in message_ids)\n\n query = \"\"\"\n INSERT INTO zerver_archivedattachment_messages (id, archivedattachment_id,\n archivedmessage_id)\n SELECT zerver_attachment_messages.id, zerver_attachment_messages.attachment_id,\n zerver_attachment_messages.message_id\n FROM zerver_attachment_messages\n LEFT JOIN zerver_archivedattachment_messages\n ON zerver_archivedattachment_messages.id = zerver_attachment_messages.id\n WHERE zerver_attachment_messages.message_id in ({message_ids})\n AND zerver_archivedattachment_messages.id IS NULL\n \"\"\"\n with connection.cursor() as cursor:\n cursor.execute(query.format(message_ids=id_list))\n\n\n@transaction.atomic\ndef move_messages_to_archive(message_ids: List[int]) -> None:\n messages = list(Message.objects.filter(id__in=message_ids).values())\n if not messages:\n raise Message.DoesNotExist\n\n ArchivedMessage.objects.bulk_create([ArchivedMessage(**message) for message in messages])\n\n # Move user_messages to the archive.\n user_messages = UserMessage.objects.filter(\n message_id__in=message_ids).exclude(id__in=ArchivedUserMessage.objects.all()).values()\n user_messages_ids = [user_message['id'] for user_message in user_messages]\n ArchivedUserMessage.objects.bulk_create(\n [ArchivedUserMessage(**user_message) for user_message in user_messages]\n )\n\n for model in models_with_message_key:\n elements = model['class'].objects.filter(message_id__in=message_ids).exclude(\n id__in=model['archive_class'].objects.all()\n ).values()\n\n model['archive_class'].objects.bulk_create(\n [model['archive_class'](**element) for element in elements]\n )\n\n # Move attachments to archive\n attachments = Attachment.objects.filter(messages__id__in=message_ids).exclude(\n id__in=ArchivedAttachment.objects.all()).distinct().values()\n ArchivedAttachment.objects.bulk_create(\n [ArchivedAttachment(**attachment) for attachment in attachments]\n )\n\n move_attachment_messages_to_archive_by_message(message_ids)\n\n # Remove data from main tables\n Message.objects.filter(id__in=message_ids).delete()\n UserMessage.objects.filter(id__in=user_messages_ids, message_id__isnull=True).delete()\n\n archived_attachments = ArchivedAttachment.objects.filter(messages__id__in=message_ids).distinct()\n Attachment.objects.filter(messages__isnull=True, id__in=archived_attachments).delete()\n","sub_path":"zerver/lib/retention.py","file_name":"retention.py","file_ext":"py","file_size_in_byte":10957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"272447822","text":"import tensorflow as tf\nfrom attention import attention,Self_Attention\nimport opennmt as onmt\nimport numpy as np\n#from attention_all import self_align_attention\nfrom tcn import TemporalConvNet\nclass LSTM_CNN(object):\n def __init__(self, sequence_length, num_classes, embedding_size, filter_sizes, num_filters, num_hidden):\n l2_reg_lambda=0.0\n # PLACEHOLDERS\n self.input_x = tf.placeholder(tf.float32, [None, sequence_length, embedding_size], name=\"input_x\") # X - The Data\n self.input_y = tf.placeholder(tf.float32, [None, num_classes], name=\"input_y\") # Y - The Lables\n self.dropout_keep_prob = tf.placeholder(tf.float32, name=\"dropout_keep_prob\") # Dropout\n self.training = tf.placeholder(tf.bool)\n with tf.name_scope(\"dropout1\"):\n self.input_x_ = tf.nn.dropout(self.input_x, 0.8)\n \n l2_loss = tf.constant(0.0) # Keeping track of l2 regularization loss\n\n #1. EMBEDDING LAYER ################################################################\n# with tf.device('/cpu:0'), tf.name_scope(\"embedding\"):\n# self.W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),name=\"W\")\n# self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)\n #self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)\n def length(sequence):\n used = tf.sign(tf.reduce_max(tf.abs(sequence), 2))\n length = tf.reduce_sum(used, 1)\n length = tf.cast(length, tf.int32)\n return length\n #2. LSTM LAYER ###################################################################### \n self.cell_fw = tf.nn.rnn_cell.BasicLSTMCell(num_hidden,forget_bias=1.0)\n self.cell_bw = tf.nn.rnn_cell.BasicLSTMCell(num_hidden,forget_bias=1.0) \n self.lstm_out,_ = tf.nn.bidirectional_dynamic_rnn(self.cell_fw,self.cell_bw,self.input_x_,sequence_length=length(self.input_x),dtype=tf.float32)\n #self.lstm_out,_ = tf.nn.bidirectional_dynamic_rnn(self.cell_fw,self.cell_bw,self.input_x_,dtype=tf.float32)\n self.lstm_out=tf.concat(self.lstm_out, 2)\n# with tf.variable_scope(\"self_attention\"):\n# val = multihead_attention(y,None,None,num_hidden,num_hidden,num_hidden,4,0.9)\n# print(val) \n print(self.lstm_out)\n# num_chans = [100] * (3 - 1) + [num_hidden]\n# self.tcn = TemporalConvNet(num_chans, stride=1, kernel_size=2, dropout=0.2)\n# self.semantic = self.tcn(self.lstm_out)\n# print(self.semantic)\n self.lstm_out = tf.nn.dropout(self.lstm_out, self.dropout_keep_prob) \n pooled_outputs = []\n for i, filter_size in enumerate(filter_sizes):\n with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\n # CONVOLUTION LAYER\n filter_shape = [filter_size, embedding_size, num_filters]\n W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W\")\n print(W)\n b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name=\"b\")\n conv = tf.nn.conv1d(self.lstm_out, W,stride = 1,padding=\"SAME\",name=\"conv\")\n print(conv)\n h = tf.nn.relu(tf.nn.bias_add(conv, b), name=\"relu\")\n #h = tf.layers.batch_normalization(h,training=True)\n # MAXPOOLING\n# pooled = tf.nn.max_pool(h, ksize=[1, sequence_length - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='SAME', name=\"pool\")\n# print(pooled)\n pooled_outputs.append(h)\n # COMBINING POOLED FEATURES\n h = tf.concat(pooled_outputs, 2)\n h = tf.nn.dropout(h, self.dropout_keep_prob)\n filter_shape = [3, 120, 150]\n W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W2\")\n b = tf.Variable(tf.constant(0.1, shape=[150]), name=\"b2\")\n conv2 = tf.nn.conv1d(h, W, stride=1, padding=\"SAME\", name=\"conv2\")\n h = tf.nn.relu(tf.nn.bias_add(conv2, b), name=\"relu\")\n# with tf.name_scope('self_attention'):\n# encoder = onmt.encoders.self_attention_encoder.SelfAttentionEncoder(num_layers=1,num_units=120) \n# self.semantic, state, encoded_length = encoder.encode(self.semantic, sequence_length=length(self.input_x))\n print(h) \n \n self.semantic = tf.nn.dropout(h, self.dropout_keep_prob)\n \n \n # #3. DROPOUT LAYER ###################################################################\n with tf.name_scope('Attention_layer'):\n self.val, alphas = attention(self.semantic, 150, return_alphas=True)\n tf.summary.histogram('alphas', alphas)\n \n #self.val2, _ = attention(self.lstm_out, 100, return_alphas=True) \n #self.val = tf.concat([self.val1,self.val2],axis=1)\n #print(self.val)\n drop = tf.nn.dropout(self.val, self.dropout_keep_prob)\n denses = tf.layers.dense(inputs=tf.reshape(drop, shape=[-1,150]), units=75,activation=tf.nn.relu, trainable=True)\n out_weight = tf.Variable(tf.random_normal([75, num_classes]))\n out_bias = tf.Variable(tf.random_normal([num_classes])) \n \n with tf.name_scope(\"output\"):\n self.scores = tf.nn.xw_plus_b(denses, out_weight,out_bias, name=\"scores\")\n self.predictions = tf.nn.softmax(self.scores, name=\"predictions\")\n #self.predictions = tf.argmax(self.scores, 1, name=\"predictions\")\n # CalculateMean cross-entropy loss\n with tf.name_scope(\"loss\"):\n losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)\n self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss\n\n # Accuracy\n with tf.name_scope(\"accuracy\"):\n #correct_predictions = tf.equal(tf.argmax(self.predictions,1), tf.argmax(self.input_y, 1))\n correct_predictions = tf.equal(tf.argmax(self.scores, 1, name=\"predictions\"), tf.argmax(self.input_y, 1))\n self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), name=\"accuracy\")\n\n\n print (\"(!!) LOADED LSTM-CNN! :)\")\n #embed()\n\n total_parameters = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])\n print(\"Total number of trainable parameters: %d\" % total_parameters)\n\n# 1. Embed --> LSTM\n# 2. LSTM --> CNN\n# 3. CNN --> Pooling/Output\n","sub_path":"PSL_WCLA/lstm_cnn_new.py","file_name":"lstm_cnn_new.py","file_ext":"py","file_size_in_byte":6478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"138442079","text":"from math import inf\n\nfrom galaxy.util import asbool\nfrom galaxy.util.bytesize import parse_bytesize\n\n\ndef _assert_number(count, n, delta, min, max, negate, n_text, min_max_text):\n \"\"\"\n helper function for assering that count is in\n - [n-delta:n+delta]\n - [min:max]\n\n raising an assertion error using n_text and min_max_text (resp)\n substituting {n}, {delta}, {min}, and {max}\n (and keeping potentially present {text} and {output})\n\n n, delta, min, max can be suffixed by (K|M|G|T|P|E)i?\n \"\"\"\n negate = asbool(negate)\n expected = \"Expected\" if not negate else \"Did not expect\"\n if n is not None:\n n_bytes = parse_bytesize(n)\n delta_bytes = parse_bytesize(delta)\n assert (not negate) == (abs(count - n_bytes) <= delta_bytes), (\n n_text.format(expected=expected, n=n, delta=delta, text=\"{text}\", output=\"{output}\") + f\" found {count}\"\n )\n if min is not None or max is not None:\n if min is None:\n min = -inf # also replacing min/max for output\n min_bytes = -inf\n else:\n min_bytes = parse_bytesize(min)\n if max is None:\n max = inf\n max_bytes = inf\n else:\n max_bytes = parse_bytesize(max)\n assert (not negate) == (min_bytes <= count <= max_bytes), (\n min_max_text.format(expected=expected, min=min, max=max, text=\"{text}\", output=\"{output}\")\n + f\" found {count}\"\n )\n\n\ndef _assert_presence_number(\n output, text, n, delta, min, max, negate, check_presence_foo, count_foo, presence_text, n_text, min_max_text\n):\n \"\"\"\n helper function to assert that\n - text is present in output using check_presence_foo\n this is done only if n, min, and max are None\n - text appears a certain number of times, where the count is determined with count foo\n\n raising an assertion error using presence_text, n_text or min_max_text (resp)\n substituting {n}, {delta}, {min}, {max}, {text}, and {output}\n\n n, delta, min, max can be suffixed by (K|M|G|T|P|E)i?\n \"\"\"\n negate = asbool(negate)\n expected = \"Expected\" if not negate else \"Did not expect\"\n if n is None and min is None and max is None:\n assert (not negate) == check_presence_foo(output, text), presence_text.format(\n expected=expected, output=output, text=text\n )\n try:\n _assert_number(count_foo(output, text), n, delta, min, max, negate, n_text, min_max_text)\n except AssertionError as e:\n raise AssertionError(str(e).format(output=output, text=text))\n","sub_path":"lib/galaxy/tool_util/verify/asserts/_util.py","file_name":"_util.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"619355252","text":"#-\n# Copyright (c) 2014, Red Hat, Inc.\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# 2. Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the\n# distribution.\n# 3. Neither the name of the Red Hat nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n#\n# Authors: Stanislav Ochotnicky \n# Michal Srb \n\nfrom __future__ import print_function\n\nimport gzip\nimport logging\nimport os.path\nimport xml\n\nfrom javapackages.metadata.artifact import MetadataArtifact\nfrom javapackages.metadata.dependency import MetadataDependency\nfrom javapackages.metadata.skippedartifact import MetadataSkippedArtifact\nimport javapackages.metadata.pyxbmetadata as m\n\nimport pyxb\n\n\nclass MetadataLoadingException(Exception):\n pass\n\n\nclass MetadataInvalidException(Exception):\n pass\n\n\nclass Metadata(object):\n\n def __init__(self, path):\n self._path = path\n self.artifacts = []\n self.skipped_artifacts = []\n self.properties = {}\n\n try:\n metadata = self._load_metadata(self._path)\n except (pyxb.UnrecognizedContentError,\n pyxb.UnrecognizedDOMRootNodeError,\n xml.sax.SAXParseException) as e:\n logging.warning(\"Failed to parse metadata {path}: {e}\"\n .format(path=path, e=e))\n raise MetadataLoadingException()\n\n self.artifacts = self._read_artifacts(metadata)\n self.skipped_artifacts = self._read_skipped_artifacts(metadata)\n self.properties = self._read_properties(metadata)\n\n def _load_metadata(self, metadata_path):\n with open(metadata_path, 'rb') as f:\n try:\n gzf = gzip.GzipFile(os.path.basename(metadata_path),\n 'rb', fileobj=f)\n data = gzf.read()\n except IOError:\n # not a compressed metadata, just rewind and read the data\n f.seek(0)\n data = f.read()\n return m.CreateFromDocument(data)\n\n def _read_artifacts(self, metadata):\n artifacts = []\n if metadata.artifacts and metadata.artifacts.artifact:\n for a in metadata.artifacts.artifact:\n artifact = MetadataArtifact.from_metadata(a)\n if not artifact.version:\n raise MetadataInvalidException(\"Artifact {a} does not have version in maven provides\".format(a=artifact))\n artifacts.append(artifact)\n return artifacts\n\n def _read_skipped_artifacts(self, metadata):\n artifacts = []\n if metadata.skippedArtifacts and metadata.skippedArtifacts.skippedArtifact:\n for a in metadata.skippedArtifacts.skippedArtifact:\n artifact = MetadataSkippedArtifact.from_metadata(a)\n artifacts.append(artifact)\n return list(artifacts)\n\n def _read_properties(self, metadata):\n properties = {}\n if hasattr(metadata, 'properties') and metadata.properties:\n properties = dict((prop.tagName, prop.firstChild.value)\n for prop in metadata.properties.wildcardElements())\n return properties\n\n def get_provided_artifacts(self):\n \"\"\"Returns list of MetadataArtifact provided by given metadata.\"\"\"\n return self.artifacts\n\n def get_skipped_artifacts(self):\n \"\"\"Returns list of MetadataSkippedArtifact provided by given metadata.\"\"\"\n return self.skipped_artifacts\n\n def get_required_artifacts(self):\n \"\"\"Returns list of Dependency required by given metadata.\"\"\"\n dependencies = set()\n for artifact in self.artifacts:\n for dependency in artifact.dependencies:\n dependencies.add(dependency)\n return list(dependencies)\n\n def get_java_requires(self):\n \"\"\"Returns JVM version required by metadata or None\"\"\"\n try:\n return self.properties[u'requiresJava']\n except KeyError:\n pass\n return None\n\n def get_java_devel_requires(self):\n \"\"\"Returns JVM development version required by metadata or None\"\"\"\n try:\n return self.properties[u'requiresJavaDevel']\n except KeyError:\n pass\n return None\n\n def get_osgi_provides(self):\n bundles = []\n for artifact in self.artifacts:\n bundle = artifact.get_osgi_bundle()\n if bundle:\n bundles.append(bundle)\n return bundles\n\n def get_osgi_requires(self):\n reqs = []\n bundles = self.get_osgi_provides()\n for bundle in bundles:\n reqs.extend(bundle.requires)\n return reqs\n\n def contains_only_poms(self):\n \"\"\"Check if metadata file contains only POM file(s)\"\"\"\n for artifact in self.artifacts:\n if artifact.extension != \"pom\":\n return False\n return True\n\n def get_artifact_for_path(self, path, can_be_dir=False):\n path = os.path.abspath(path)\n for artifact in self.artifacts:\n artifact_path = artifact.get_buildroot_path()\n if can_be_dir:\n # artifact_path can be a directory\n if path.startswith(artifact_path):\n return artifact\n else:\n if path == artifact_path:\n return artifact\n return None\n","sub_path":"jre8/opt/rh/rh-java-common/root/usr/lib/python2.7/site-packages/javapackages/metadata/metadata.py","file_name":"metadata.py","file_ext":"py","file_size_in_byte":6672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"241720535","text":"import pandas as pd\nfrom matplotlib.pyplot import show, savefig\nimport sys\nfrom mlplot.load import load_randomized_algos\nfrom itertools import product\n\n\ndef calc_avg_times(func):\n df = load_randomized_algos()\n\n values = df[(df[\"maximumIterations\"] == 50000) & (df[\"evaluationFunction\"] == func)\n & (df[\"bitstringSize\"] == 60)]\n\n if not values[\"maximumTheoreticalValue\"].max() == values[\"maximumTheoreticalValue\"].min():\n raise ValueError(\"something stinks\")\n\n maxValue = values[\"maximumTheoreticalValue\"].max()\n\n maximized_values = values[values[\"actualValue\"] == maxValue]\n nonmaximized_values = values[values[\"actualValue\"] < maxValue]\n return maximized_values, nonmaximized_values\n\n\nif __name__ == \"__main__\":\n out = sys.stdout\n LATEX_HEADER = r\"\\begin{longtable}{l c c c c}\" + \"\\n\"\n LATEX_FOOTER = r\"\\end{longtable}\" + \"\\n\"\n\n for func in [\"FourPeaksEvaluationFunction\", \"SixPeaksEvaluationFunction\", \"FlipFlopEvaluationFunction\"]:\n out.write(LATEX_HEADER)\n maxim, nonmaxim = calc_avg_times(func)\n funcname = func.rsplit(\"EvaluationFunction\")[0]\n out.write(f\"\\\\caption{{{funcname} timings (ms avg/std)}}\\\\\\\\\\n\")\n for algo in (\"RHC\", \"SA\", \"GA\", \"MIMIC\"):\n out.write(f\"& {algo} \")\n out.write(\"\\\\\\\\\\n\")\n\n out.write(f\"Optimum \")\n for algo in (\"RHC\", \"SA\", \"GA\", \"MIMIC\"):\n max_avg = maxim[maxim[\"algorithm\"] == algo][\"executionTimeMillis\"].mean()\n max_std = maxim[maxim[\"algorithm\"] == algo][\"executionTimeMillis\"].std()\n out.write(f\"& {max_avg:.2f} / {max_std:.2f} \")\n out.write(\"\\\\\\\\\\n\")\n\n out.write(\"Non-optimum \")\n for algo in (\"RHC\", \"SA\", \"GA\", \"MIMIC\"):\n nonmax_avg = nonmaxim[nonmaxim[\"algorithm\"] == algo][\"executionTimeMillis\"].mean()\n nonmax_std = nonmaxim[nonmaxim[\"algorithm\"] == algo][\"executionTimeMillis\"].std()\n\n out.write(f\"& {nonmax_avg:.2f} / {nonmax_std:.2f} \")\n out.write(\"\\\\\\\\\\n\")\n\n out.write(\"\\n\")\n\n out.write(LATEX_FOOTER)\n\n\n\n\n\n","sub_path":"mlplot/mlplot/randomized_speed.py","file_name":"randomized_speed.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"152096093","text":"import asyncio\nimport websockets\n\n\n# https://www.adictosaltrabajo.com/2012/08/03/web-sockets-java-tomcat/\n# https://pypi.org/project/websocket_client/\n# https://websockets.readthedocs.io/en/stable/cheatsheet.html#client\n# https://websockets.readthedocs.io/en/stable/\n# https://techtutorialsx.com/2018/02/11/python-websocket-client/\nasync def servidorEscucha(websocket, path):\n async for message in websocket:\n print(message)\n await websocket.send(\"\")\n\nstart_server = websockets.serve(servidorEscucha, \"0.0.0.0\", 8080)\n\nasyncio.get_event_loop().run_until_complete(start_server)\nasyncio.get_event_loop().run_forever()\n","sub_path":"Python/ScriptsAntiguos/websocketserver.py","file_name":"websocketserver.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"527695203","text":"from metric import iou_accu\nimport numpy as np\n\n# These are utility functions, we need not to initialize any dataloader or model here (ENet or Cityscapes)\n# Just pass those in these functions wherever these are called (test.py or train.py) ~mradul\n\ndef final_metrics(config, model, train_loader, valid_loader, device):\n model.eval()\n \n val_results = []\n\n train_accuracy = np.zeros((config.num_classes,), dtype=float)\n train_iou = np.zeros((config.num_classes,), dtype=float)\n \n val_accuracy = np.zeros((config.num_classes,), dtype=float)\n val_iou = np.zeros((config.num_classes,), dtype=float)\n \n for batch in train_loader:\n \n inputs = batch[0].float().to(device)\n labels = batch[1].float().to(device).long()\n \n outputs = model(inputs)\n\n np_outputs, iou, accu = iou_accu(config, outputs, labels)\n \n train_accuracy += accu\n train_iou += iou\n \n for batch in valid_loader:\n \n inputs = batch[0].float().to(device)\n labels = batch[1].float().to(device).long()\n\n outputs = model(inputs)\n \n np_outputs, iou, accu = iou_accu(config, outputs, labels)\n val_result.append(np_outputs)\n \n val_accuracy += accu\n val_iou += iou\n \n train_accuracy /= len(train_loader)\n val_accuracy /= len(val_loader)\n \n train_iou /= len(train_loader)\n val_iou /= len(val_loader)\n\n val_results = np.array(val_results)\n\n return train_accuracy, val_accuracy, train_iou, val_iou, val_results ","sub_path":"utils/tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"426228392","text":"# 백준 1654 랜선자르기\n\nimport sys # 인터프리터가 제공하는 변수와 함수를 제어하는 모듈\ninput = sys.stdin.readline\nK, N = map(int, input().split())\narr = [int(input()) for _ in range(K)]\nleft, right = 1, max(arr) # 최소값을 1 최대값을 배열의 가장 큰 값으로 지정한다.\n\nwhile(left <= right): #값이 같아지거나 더 커질때 까지 탐색한다.\n mid = (left+right) // 2 # 먼저 중간값을 구한다.\n sum = 0\n for i in arr: # 랜선 길이를 중간값으로 자른다\n sum += i // mid # 자르고 나오는 랜선의 개수 저장\n\n if sum >= N: # 랜선의 개수가 지정한 개수 보다 많으면\n left = mid+1 # 시작 값을 현재 자른값+1 즉 값을 올리면서 탐색한다.\n else:\n right = mid-1 # 아니라면 자른값-1로 값을 내리면서 탐색한다.\n\nprint(right)","sub_path":"2020_07_30_Algorithm/python/LancableCut_1654.py","file_name":"LancableCut_1654.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"346483226","text":"# -*- coding: utf-8 -*-\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.test.utils import override_settings\n\nfrom olympia.lib.crypto.tasks import sign_addons\n\n\nclass Command(BaseCommand):\n help = 'Sign a list of addons.'\n\n def add_arguments(self, parser):\n \"\"\"Handle command arguments.\"\"\"\n parser.add_argument('addon_id', nargs='*')\n parser.add_argument(\n '--signing-server', action='store', type=str,\n dest='signing_server',\n help='The signing server to use for full reviews.')\n\n parser.add_argument(\n '--force', action='store_true', dest='force',\n help='Sign the addon if it is already signed.')\n\n parser.add_argument(\n '--reason', action='store', type=str, dest='reason',\n help='The reason for the resign that we will send '\n 'the developer.')\n\n parser.add_argument(\n '--use-autograph',\n action='store_true',\n dest='use_autograph',\n help='Use our new autograph signing.')\n\n def handle(self, *args, **options):\n if len(options['addon_id']) == 0: # Sign all the addons?\n raise CommandError(\n 'Please provide at least one addon id to sign. If you want to '\n 'sign them all, use the \"process_addons --task sign_addons\" '\n 'management command.')\n\n full_server = options.get('signing_server') or settings.SIGNING_SERVER\n\n addon_ids = [int(addon_id) for addon_id in options['addon_id']]\n with override_settings(\n SIGNING_SERVER=full_server):\n sign_addons(\n addon_ids, force=options['force'], reason=options['reason'],\n use_autograph=options['use_autograph'])\n","sub_path":"src/olympia/addons/management/commands/sign_addons.py","file_name":"sign_addons.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"266028060","text":"# -*- coding: utf-8 -*-\n\n# Copyright (C) 2014 AT&T Labs All Rights Reserved.\n# Copyright (C) 2014 University of Pennsylvania All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport logging\n\nfrom oslo.config import cfg\n\nfrom ryu.lib.dpid import str_to_dpid\nfrom ryu.lib.packet import dhcp\n\nLOGGER = logging.getLogger(__name__)\n\nCONF = cfg.CONF\nCONF.import_opt('zookeeper_storage', 'ryu.app.inception_conf')\n\nSERVER_PORT = 67\nCLIENT_PORT = 68\n\n\nclass InceptionDhcp(object):\n \"\"\"Inception Cloud DHCP module for handling DHCP packets.\"\"\"\n\n def __init__(self, inception):\n self.switch_dpid = None\n self.switch_port = None\n\n # name shortcuts\n self.inception = inception\n self.dpset = inception.dpset\n self.dcenter_id = inception.dcenter_id\n self.arp_manager = inception.arp_manager\n self.vm_manager = inception.vm_manager\n self.zk_manager = inception.zk_manager\n self.rpc_manager = inception.rpc_manager\n\n def update_server(self, dpid, port):\n if self.switch_dpid is not None and self.switch_port is not None:\n LOGGER.warning(\"DHCP-server-connected switch registered before!\")\n\n self.switch_dpid = dpid\n self.switch_port = port\n LOGGER.info(\"DHCP server: (dpid=%s), (port=%s)\", dpid, port)\n\n def handle(self, dhcp_header, raw_data, txn):\n # Process DHCP packet\n LOGGER.info(\"Handle DHCP packet\")\n\n if self.switch_dpid is None or self.switch_port is None:\n LOGGER.warning(\"No DHCP server has been found!\")\n return\n\n # Do ARP learning on a DHCP ACK message\n for option in dhcp_header.options.option_list:\n if option.tag == dhcp.DHCP_MESSAGE_TYPE_OPT:\n option_value = ord(option.value)\n if option_value == dhcp.DHCP_ACK:\n ip_addr = dhcp_header.yiaddr\n mac_addr = dhcp_header.chaddr\n if not self.arp_manager.mapping_exist(ip_addr):\n self.arp_manager.learn_arp_mapping(ip_addr, mac_addr)\n icp_rpc = self.inception.inception_rpc\n rpc_func_name = icp_rpc.update_arp_mapping.__name__\n rpc_args = (ip_addr, mac_addr)\n self.rpc_manager.do_rpc(rpc_func_name, rpc_args)\n self.zk_manager.log_arp_mapping(ip_addr, mac_addr, txn)\n break\n\n # A packet received from client. Find out the switch connected\n # to dhcp server and forward the packet\n if dhcp_header.op == dhcp.DHCP_BOOT_REQUEST:\n LOGGER.info(\"Forward DHCP message to server at (switch=%s) \"\n \"(port=%s)\", self.switch_dpid, self.switch_port)\n datapath = self.dpset.get(str_to_dpid(self.switch_dpid))\n action_out = [\n datapath.ofproto_parser.OFPActionOutput(\n int(self.switch_port))]\n datapath.send_msg(\n datapath.ofproto_parser.OFPPacketOut(\n datapath=datapath,\n buffer_id=0xffffffff,\n in_port=datapath.ofproto.OFPP_LOCAL,\n data=raw_data,\n actions=action_out))\n\n # A packet received from server. Find out the mac address of\n # the client and forward the packet to it.\n elif dhcp_header.op == dhcp.DHCP_BOOT_REPLY:\n _, dpid, port = self.vm_manager.get_position(dhcp_header.chaddr)\n LOGGER.info(\"Forward DHCP message to client (mac=%s) at \"\n \"(switch=%s, port=%s)\",\n dhcp_header.chaddr, dpid, port)\n datapath = self.dpset.get(str_to_dpid(dpid))\n action_out = [datapath.ofproto_parser.OFPActionOutput(int(port))]\n datapath.send_msg(\n datapath.ofproto_parser.OFPPacketOut(\n datapath=datapath,\n buffer_id=0xffffffff,\n in_port=datapath.ofproto.OFPP_LOCAL,\n data=raw_data,\n actions=action_out))\n","sub_path":"ryu/app/inception_dhcp.py","file_name":"inception_dhcp.py","file_ext":"py","file_size_in_byte":4672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"201719707","text":"#As listas podem ser mutaveis\r\nlista = ['bolo', 'chocolate', 'marmita']\r\nlista.append('carne') #Adiciona carne para a lista no ultimo Indice\r\nlista.insert(0, 'fotos') #Adiciona fotos a qualquer indice. Esse e exemplo adiciona no indice 0\r\ndel lista[2] # Apaga qualquer valor da lista em qualquer indice Obs: indicando o INDICE\r\nlista.pop() #Apaga o ultimo Indice\r\nlista.remove('chocolate') #Apaga um valor obs: Indicando o Valor\r\n\r\nvalores = list(range(4, 11))#vai criar uma lista com numeros de 4 ate 10\r\nvalores.sort() # organiza os valores da lista parar ordem crescente\r\nnum.sort(reverse=True)# aruma inversamente","sub_path":"Listas1.py","file_name":"Listas1.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"144271556","text":"# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\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.\nimport copy\nfrom typing import Dict\n\nimport pytest\nimport pytorch_lightning as pl\nimport torch\nfrom omegaconf import DictConfig, OmegaConf, open_dict\n\nimport nemo.collections.asr as nemo_asr\nfrom nemo.collections.asr.data import audio_to_text\nfrom nemo.collections.asr.metrics.wer import CTCDecoding, CTCDecodingConfig\nfrom nemo.collections.asr.models import EncDecCTCModel, configs\nfrom nemo.core.classes.mixins import AccessMixin\nfrom nemo.utils.config_utils import assert_dataclass_signature_match, update_model_config\n\n\ndef jasper_encoder_config(num_layers=1) -> Dict:\n return {\n '_target_': 'nemo.collections.asr.modules.ConvASREncoder',\n 'feat_in': 64,\n 'activation': 'relu',\n 'conv_mask': True,\n 'jasper': [\n {\n 'filters': 1024,\n 'repeat': 1,\n 'kernel': [1],\n 'stride': [1],\n 'dilation': [1],\n 'dropout': 0.0,\n 'residual': False,\n 'separable': True,\n 'se': True,\n 'se_context_size': -1,\n }\n ]\n * num_layers,\n }\n\n\ndef conformer_encoder_config() -> Dict:\n return {\n '_target_': 'nemo.collections.asr.modules.ConformerEncoder',\n 'feat_in': 64,\n 'n_layers': 8,\n 'd_model': 4,\n }\n\n\ndef squeezeformer_encoder_config() -> Dict:\n return {\n '_target_': 'nemo.collections.asr.modules.SqueezeformerEncoder',\n 'feat_in': 64,\n 'n_layers': 8,\n 'd_model': 4,\n }\n\n\n@pytest.fixture()\ndef asr_model():\n preprocessor = {'_target_': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor'}\n encoder = jasper_encoder_config()\n\n decoder = {\n '_target_': 'nemo.collections.asr.modules.ConvASRDecoder',\n 'feat_in': 1024,\n 'num_classes': 28,\n 'vocabulary': [\n ' ',\n 'a',\n 'b',\n 'c',\n 'd',\n 'e',\n 'f',\n 'g',\n 'h',\n 'i',\n 'j',\n 'k',\n 'l',\n 'm',\n 'n',\n 'o',\n 'p',\n 'q',\n 'r',\n 's',\n 't',\n 'u',\n 'v',\n 'w',\n 'x',\n 'y',\n 'z',\n \"'\",\n ],\n }\n modelConfig = DictConfig(\n {'preprocessor': DictConfig(preprocessor), 'encoder': DictConfig(encoder), 'decoder': DictConfig(decoder)}\n )\n\n model_instance = EncDecCTCModel(cfg=modelConfig)\n return model_instance\n\n\nclass TestInterCTCLoss:\n @pytest.mark.unit\n @pytest.mark.parametrize(\n \"encoder_config\",\n [jasper_encoder_config(num_layers=8), conformer_encoder_config(), squeezeformer_encoder_config()],\n )\n @pytest.mark.parametrize(\n \"apply_at_layers,loss_weights\",\n [\n ([2, 4], [0.1, 0.3]),\n ([4], [0.3]),\n ([], []),\n # errors\n ([2, 4], [0.1]),\n ([2], [0.1, 0.3]),\n ([], [0.3]),\n ],\n )\n def test_forward(self, encoder_config, apply_at_layers, loss_weights):\n preprocessor_config = {'_target_': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor'}\n\n decoder_config = {\n '_target_': 'nemo.collections.asr.modules.ConvASRDecoder',\n 'feat_in': None,\n 'num_classes': 28,\n 'vocabulary': [\n ' ',\n 'a',\n 'b',\n 'c',\n 'd',\n 'e',\n 'f',\n 'g',\n 'h',\n 'i',\n 'j',\n 'k',\n 'l',\n 'm',\n 'n',\n 'o',\n 'p',\n 'q',\n 'r',\n 's',\n 't',\n 'u',\n 'v',\n 'w',\n 'x',\n 'y',\n 'z',\n \"'\",\n ],\n }\n\n model_config = DictConfig(\n {\n 'preprocessor': DictConfig(preprocessor_config),\n 'encoder': DictConfig(encoder_config),\n 'decoder': DictConfig(decoder_config),\n 'interctc': {'loss_weights': loss_weights, 'apply_at_layers': apply_at_layers},\n 'optim': {'name': 'adamw'},\n }\n )\n\n class DummyDataset(torch.utils.data.Dataset):\n \"\"\"Simply returns a single set of values.\"\"\"\n\n def __init__(self, values):\n self.values = values\n\n def __len__(self):\n return 1\n\n def __getitem__(self, idx):\n return self.values\n\n input_signal = torch.randn(size=(1, 512))\n input_length = torch.randint(low=161, high=500, size=[1])\n target = torch.randint(size=(1, input_length[0]), low=0, high=28)\n target_length = torch.tensor([input_length[0]])\n\n if len(apply_at_layers) != len(loss_weights):\n # has to throw an error here\n with pytest.raises(\n ValueError, match=\"Length of interctc.apply_at_layers has to match interctc.loss_weights\"\n ):\n asr_model = EncDecCTCModel(cfg=model_config)\n asr_model.train()\n logprobs, _, _ = asr_model.forward(input_signal=input_signal, input_signal_length=input_length)\n else:\n asr_model = EncDecCTCModel(cfg=model_config)\n asr_model.train()\n AccessMixin.set_access_enabled(access_enabled=True)\n logprobs, _, _ = asr_model.forward(input_signal=input_signal, input_signal_length=input_length)\n captured_tensors = asr_model.get_captured_interctc_tensors()\n AccessMixin.reset_registry(asr_model)\n assert len(captured_tensors) == len(apply_at_layers)\n for output in captured_tensors:\n # checking that values are not the same, but shape is the same\n assert not torch.allclose(output[0], logprobs)\n assert output[0].shape == logprobs.shape\n\n trainer = pl.Trainer(max_epochs=1)\n trainer.fit(\n asr_model,\n train_dataloaders=torch.utils.data.DataLoader(\n DummyDataset([input_signal, input_length, target, target_length]), collate_fn=lambda x: x[0],\n ),\n val_dataloaders=torch.utils.data.DataLoader(\n DummyDataset([input_signal, input_length, target, target_length]), collate_fn=lambda x: x[0],\n ),\n )\n required_metrics = ['final_ctc_loss'] if len(loss_weights) > 0 else []\n required_metrics += [f'inter_ctc_loss_l{idx}' for idx in apply_at_layers]\n prefix = \"val_\"\n required_metrics += [f'{prefix}{metric}' for metric in required_metrics]\n required_metrics += [f'{prefix}wer'] + [f'{prefix}inter_wer_l{idx}' for idx in apply_at_layers]\n for metric in required_metrics:\n assert metric in trainer.logged_metrics\n\n trainer.test(\n asr_model,\n dataloaders=torch.utils.data.DataLoader(\n DummyDataset([input_signal, input_length, target, target_length]), collate_fn=lambda x: x[0],\n ),\n )\n required_metrics = [f'inter_ctc_loss_l{idx}' for idx in apply_at_layers]\n prefix = \"test_\"\n # note that \"=\" is on purpose here, not \"+=\", since we only log test metrics\n required_metrics = [f'{prefix}{metric}' for metric in required_metrics]\n required_metrics += [f'{prefix}wer'] + [f'{prefix}inter_wer_l{idx}' for idx in apply_at_layers]\n for metric in required_metrics:\n assert metric in trainer.logged_metrics\n\n\nclass TestEncDecCTCModel:\n @pytest.mark.unit\n def test_constructor(self, asr_model):\n asr_model.train()\n # TODO: make proper config and assert correct number of weights\n # Check to/from config_dict:\n confdict = asr_model.to_config_dict()\n instance2 = EncDecCTCModel.from_config_dict(confdict)\n assert isinstance(instance2, EncDecCTCModel)\n\n @pytest.mark.unit\n def test_forward(self, asr_model):\n asr_model = asr_model.eval()\n\n asr_model.preprocessor.featurizer.dither = 0.0\n asr_model.preprocessor.featurizer.pad_to = 0\n\n input_signal = torch.randn(size=(4, 512))\n length = torch.randint(low=161, high=500, size=[4])\n\n with torch.no_grad():\n # batch size 1\n logprobs_instance = []\n for i in range(input_signal.size(0)):\n logprobs_ins, _, _ = asr_model.forward(\n input_signal=input_signal[i : i + 1], input_signal_length=length[i : i + 1]\n )\n logprobs_instance.append(logprobs_ins)\n print(len(logprobs_ins))\n logprobs_instance = torch.cat(logprobs_instance, 0)\n\n # batch size 4\n logprobs_batch, _, _ = asr_model.forward(input_signal=input_signal, input_signal_length=length)\n\n assert logprobs_instance.shape == logprobs_batch.shape\n diff = torch.mean(torch.abs(logprobs_instance - logprobs_batch))\n assert diff <= 1e-6\n diff = torch.max(torch.abs(logprobs_instance - logprobs_batch))\n assert diff <= 1e-6\n\n @pytest.mark.unit\n def test_vocab_change(self, asr_model):\n old_vocab = copy.deepcopy(asr_model.decoder.vocabulary)\n nw1 = asr_model.num_weights\n asr_model.change_vocabulary(new_vocabulary=old_vocab)\n # No change\n assert nw1 == asr_model.num_weights\n new_vocab = copy.deepcopy(old_vocab)\n new_vocab.append('!')\n new_vocab.append('$')\n new_vocab.append('@')\n asr_model.change_vocabulary(new_vocabulary=new_vocab)\n # fully connected + bias\n assert asr_model.num_weights == nw1 + 3 * (asr_model.decoder._feat_in + 1)\n\n @pytest.mark.unit\n def test_decoding_change(self, asr_model):\n assert asr_model.decoding is not None\n assert isinstance(asr_model.decoding, CTCDecoding)\n assert asr_model.decoding.cfg.strategy == \"greedy\"\n assert asr_model.decoding.preserve_alignments is False\n assert asr_model.decoding.compute_timestamps is False\n\n cfg = CTCDecodingConfig(preserve_alignments=True, compute_timestamps=True)\n asr_model.change_decoding_strategy(cfg)\n\n assert asr_model.decoding.preserve_alignments is True\n assert asr_model.decoding.compute_timestamps is True\n\n @pytest.mark.unit\n def test_change_conv_asr_se_context_window(self, asr_model):\n old_cfg = copy.deepcopy(asr_model.cfg)\n asr_model.change_conv_asr_se_context_window(context_window=32) # 32 * 0.01s context\n new_config = asr_model.cfg\n\n assert old_cfg.encoder.jasper[0].se_context_size == -1\n assert new_config.encoder.jasper[0].se_context_size == 32\n\n for name, m in asr_model.encoder.named_modules():\n if type(m).__class__.__name__ == 'SqueezeExcite':\n assert m.context_window == 32\n\n @pytest.mark.unit\n def test_change_conv_asr_se_context_window_no_config_update(self, asr_model):\n old_cfg = copy.deepcopy(asr_model.cfg)\n asr_model.change_conv_asr_se_context_window(context_window=32, update_config=False) # 32 * 0.01s context\n new_config = asr_model.cfg\n\n assert old_cfg.encoder.jasper[0].se_context_size == -1\n assert new_config.encoder.jasper[0].se_context_size == -1 # no change\n\n for name, m in asr_model.encoder.named_modules():\n if type(m).__class__.__name__ == 'SqueezeExcite':\n assert m.context_window == 32\n\n @pytest.mark.unit\n def test_dataclass_instantiation(self, asr_model):\n model_cfg = configs.EncDecCTCModelConfig()\n\n # Update mandatory values\n vocabulary = asr_model.decoder.vocabulary\n model_cfg.model.labels = vocabulary\n\n # Update encoder\n model_cfg.model.encoder.activation = 'relu'\n model_cfg.model.encoder.feat_in = 64\n model_cfg.model.encoder.jasper = [\n nemo_asr.modules.conv_asr.JasperEncoderConfig(\n filters=1024,\n repeat=1,\n kernel=[1],\n stride=[1],\n dilation=[1],\n dropout=0.0,\n residual=False,\n se=True,\n se_context_size=-1,\n )\n ]\n\n # Update decoder\n model_cfg.model.decoder.feat_in = 1024\n model_cfg.model.decoder.num_classes = 28\n model_cfg.model.decoder.vocabulary = vocabulary\n\n # Construct the model\n asr_cfg = OmegaConf.create({'model': asr_model.cfg})\n model_cfg_v1 = update_model_config(model_cfg, asr_cfg)\n new_model = EncDecCTCModel(cfg=model_cfg_v1.model)\n\n assert new_model.num_weights == asr_model.num_weights\n # trainer and exp manager should be there\n # assert 'trainer' in model_cfg_v1\n # assert 'exp_manager' in model_cfg_v1\n # datasets and optim/sched should not be there after ModelPT.update_model_dataclass()\n assert 'train_ds' not in model_cfg_v1.model\n assert 'validation_ds' not in model_cfg_v1.model\n assert 'test_ds' not in model_cfg_v1.model\n assert 'optim' not in model_cfg_v1.model\n\n # Construct the model, without dropping additional keys\n asr_cfg = OmegaConf.create({'model': asr_model.cfg})\n model_cfg_v2 = update_model_config(model_cfg, asr_cfg, drop_missing_subconfigs=False)\n\n # Assert all components are in config\n # assert 'trainer' in model_cfg_v2\n # assert 'exp_manager' in model_cfg_v2\n assert 'train_ds' in model_cfg_v2.model\n assert 'validation_ds' in model_cfg_v2.model\n assert 'test_ds' in model_cfg_v2.model\n assert 'optim' in model_cfg_v2.model\n\n # Remove extra components (optim and sched can be kept without issue)\n with open_dict(model_cfg_v2.model):\n model_cfg_v2.model.pop('train_ds')\n model_cfg_v2.model.pop('validation_ds')\n model_cfg_v2.model.pop('test_ds')\n\n new_model = EncDecCTCModel(cfg=model_cfg_v2.model)\n\n assert new_model.num_weights == asr_model.num_weights\n # trainer and exp manager should be there\n\n @pytest.mark.unit\n def test_ASRDatasetConfig_for_AudioToCharDataset(self):\n # ignore some additional arguments as dataclass is generic\n IGNORE_ARGS = [\n 'is_tarred',\n 'num_workers',\n 'batch_size',\n 'tarred_audio_filepaths',\n 'shuffle',\n 'pin_memory',\n 'drop_last',\n 'tarred_shard_strategy',\n 'shuffle_n',\n 'use_start_end_token',\n 'use_start_end_token',\n 'bucketing_batch_size',\n 'bucketing_strategy',\n 'bucketing_weights',\n 'channel_selector',\n ]\n\n REMAP_ARGS = {'trim_silence': 'trim'}\n\n result = assert_dataclass_signature_match(\n audio_to_text.AudioToCharDataset, configs.ASRDatasetConfig, ignore_args=IGNORE_ARGS, remap_args=REMAP_ARGS,\n )\n signatures_match, cls_subset, dataclass_subset = result\n\n assert signatures_match\n assert cls_subset is None\n assert dataclass_subset is None\n\n @pytest.mark.unit\n def test_ASRDatasetConfig_for_TarredAudioToCharDataset(self):\n # ignore some additional arguments as dataclass is generic\n IGNORE_ARGS = [\n 'is_tarred',\n 'num_workers',\n 'batch_size',\n 'shuffle',\n 'pin_memory',\n 'drop_last',\n 'global_rank',\n 'world_size',\n 'use_start_end_token',\n 'bucketing_batch_size',\n 'bucketing_strategy',\n 'bucketing_weights',\n 'max_utts',\n ]\n\n REMAP_ARGS = {\n 'trim_silence': 'trim',\n 'tarred_audio_filepaths': 'audio_tar_filepaths',\n 'tarred_shard_strategy': 'shard_strategy',\n 'shuffle_n': 'shuffle',\n }\n\n result = assert_dataclass_signature_match(\n audio_to_text.TarredAudioToCharDataset,\n configs.ASRDatasetConfig,\n ignore_args=IGNORE_ARGS,\n remap_args=REMAP_ARGS,\n )\n signatures_match, cls_subset, dataclass_subset = result\n\n assert signatures_match\n assert cls_subset is None\n assert dataclass_subset is None\n","sub_path":"tests/collections/asr/test_asr_ctcencdec_model.py","file_name":"test_asr_ctcencdec_model.py","file_ext":"py","file_size_in_byte":17417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"151446465","text":"from django.urls import path\nfrom . import views\nfrom django.conf import settings\n\n\nurlpatterns = [\n path('main.html', views.home, name=\"home\" ),\n path('about.html', views.about, name=\"about\" ),\n path('add_stock.html', views.add_stock, name=\"add_stock\" ),\n path('delete/', views.delete, name=\"delete\"),\n path('delete_stock.html', views.delete_stock, name=\"delete_stock\"),\n path('chart.html', views.chart, name=\"chart\"),\n path('plots.html', views.tickplot, name=\"plots\"),\n \n] ","sub_path":"Quotes/url.py","file_name":"url.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"97344123","text":"# -*- coding: utf-8 -*-\n\n\nfrom odoo import api, fields, models, _\nimport logging\nfrom odoo.exceptions import AccessError, UserError, RedirectWarning, ValidationError, Warning\n\n\nclass AccountAccount(models.Model):\n _inherit = 'account.account'\n\n code_number = fields.Integer(string=\"Código Entero\", store=True, compute=\"_compute_code_number\")\n\n @api.depends('code')\n @api.multi\n def _compute_code_number(self):\n for account in self:\n try:\n code = account.code\n code = code.replace('-','')\n account.code_number = int(code)\n except:\n pass\n\n @api.multi\n def _check_code_format(self, code=False):\n for account in self:\n if not code:\n code = account.code\n # Si no tiene la cantidad de caracteres necesarios\n if len(code) > 9:\n raise UserError(_(\"El código no contiene los 8 digitos y el guión\"))\n else:\n if len(code) <= 9:\n long_fault = 9 - len(code)\n if long_fault !=0: \n for i in range(0, long_fault):\n code += '0'\n \n if len(code) == 9:\n new_code = code[0:4] + '-' + code[4] + code[5] + code[6] + code[7]\n code = new_code \n else:\n new_code = code[0:4] + '-' + code[5] + code[6] + code[7] + code[8]\n code = new_code \n\n # Primeros 4 digitos\n first_numbers = code[0:4]\n\n try:\n first_numbers_int = int(first_numbers)\n except Exception as e:\n raise UserError(_(\"Has puesto algo mal en los primeros 4 digitos\"))\n\n # Signo del guión\n guion = code[4:5]\n if not guion == '-':\n raise UserError(_(\"No has puesto un guión entre medias de los 4 digitos\"))\n\n # Últimos 4 digitos\n last_numbers = code[5:len(code)]\n try:\n last_numbers_int = int(last_numbers)\n except Exception as e:\n raise UserError(_(\"Has puesto algo mal en los ultimos 4 digitos\"))\n\n if first_numbers_int >= 0 and first_numbers_int <= 9999 and guion == '-' and last_numbers_int >= 0 and last_numbers_int <= 9999:\n return code\n else:\n raise UserError(_(\"El código de la cuenta contable no cumple con los requisitos revisa los digitos y si tiene guión\")) \n \n @api.model\n def create(self, vals):\n account = super(AccountAccount, self).create(vals)\n account.code = account._check_code_format()\n return account\n\n @api.multi\n def write(self, vals):\n if 'code' in vals:\n vals['code'] = self._check_code_format(vals['code'])\n account = super(AccountAccount, self).write(vals)\n return account\n \n @api.model\n def duplicate_data(self, primary_company_id, secondary_company_id):\n obj_model = self.env[self._name]\n if primary_company_id and secondary_company_id:\n accounts = obj_model.sudo().search([('company_id', '=', primary_company_id.id)])\n if accounts:\n for account in accounts:\n code = account._check_code_format(code=account.code)\n search_account = obj_model.sudo().search([('code', '=', code), ('company_id', '=', secondary_company_id.id)])\n if search_account: \n continue\n \n dicc = {\n 'company_id': secondary_company_id.id,\n 'code': code\n }\n new_account = account.copy(default=dicc)\n","sub_path":"chariots_core/models/core/account_account.py","file_name":"account_account.py","file_ext":"py","file_size_in_byte":3892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"621818640","text":"import pygame\n\nclock = pygame.time.Clock()\n# Define Constants\nBOARD_SIZE = 20 # Size of the board, in block\nBLOCK_SIZE = 20 # Size of 1 block, in pixel\nGAME_SPEED = 8 # Game speed (Normal = 10), The bigger, the faster\nscreen = pygame.display.set_mode((BOARD_SIZE * BLOCK_SIZE * 2, BOARD_SIZE * BLOCK_SIZE * 2))\nwindow = pygame.Surface((BOARD_SIZE * BLOCK_SIZE, BOARD_SIZE * BLOCK_SIZE))\npygame.display.set_caption(\"window\")\nscore = 0\nglobal music\nmusic = 0\n# SURFACES\n\n# head = pygame.Surface((20, 20))\n# head.fill((255, 255, 0))\nhead = pygame.image.load(\"imgs/head20.png\").convert()\nbody = pygame.image.load(\"imgs/skin20.png\").convert()\nblacktail = pygame.image.load(\"imgs/tail.png\").convert()\n\n\n# fruit = pygame.Surface((20, 20))\n# fruit.fill((255, 0, 0))\nfruit = pygame.image.load(\"imgs/apple2.png\").convert()\nfruit.set_colorkey((255, 255, 255))\nbscore2 = pygame.Surface((80, 15))\nbscore2.fill((0, 0, 0))\n\ndef save_image(screen: pygame.Surface, name: str=\"screenshot.png\"):\n \"Saves an image of the screen;\\\n arg 1 screen surface, arg 2 name to save\"\n pygame.image.save(screen, name)","sub_path":"snake188/dist/functions/costants.py","file_name":"costants.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"244696144","text":"import json\nimport logging\nimport collections\nfrom logging.config import dictConfig\n\n## 공통로그 생성 by yhs\ndef logger_initialize(file=None):\n if file is None:\n file = 'logger_setting.json'\n global logger\n logger = common_logger(file)\n \ndef common_logger(file):\n ## 환경 세팅 - JSON 파일로 부터 처리\n with open(file) as fp:\n logging_config = json.load(fp)\n ## 버전 항목, RotatingFileHandler의 backupCount\n dictConfig(logging_config)\n return logging.getLogger()\n## 로그 남기기, 얻어온 공통로그와 로그레벨을 이용하여 로그 처리\ndef log(messages,level=None):\n if type(messages) != str and isinstance(messages,collections.Iterable):\n messages = ' :: '.join([str(m) for m in messages])\n if level is None:\n level = logging.DEBUG\n if level == logging.DEBUG:\n logger.debug(messages)\n elif level == logging.INFO:\n logger.info(messages)\n elif level == logging.WARNING:\n logger.warning(messages)\n elif level == logging.ERROR:\n logger.error(messages)\n else:\n print(\"$$ Check :\",messages)","sub_path":"common/log_util.py","file_name":"log_util.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"142315106","text":"from flask import Flask, url_for, jsonify\napp = Flask(__name__)\n\nfrom flask_sqlalchemy import SQLAlchemy\n\nimport api\napi.register(app)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///test.db'\ndb = SQLAlchemy(app)\nimport model\n\n\n@app.route(\"/hello\")\ndef hello():\n return \"Hello There! \"\n\n@app.route(\"/site-map\")\ndef site_map():\n links = {}\n for rule in app.url_map.iter_rules():\n links[rule.rule] = rule.endpoint\n\n return jsonify(links)\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', debug=True)\n","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"330299909","text":"#! /usr/bin/env python\n'''\nCreated on May 12, 2020\n\n@author: kyberszittya\n'''\n\n\nimport rospy\n\nfrom rei_monitoring_msgs.msg import ReiStateMachineTransitionSignal\n\nimport json\nimport rei_monitoring_msgs\n\nSIGNALS = {\n \"AllStateMessagesReceived\": 1,\n \"Replanning\": 32\n}\n\nclass TestCaseFactorySignal(object):\n \n def __init__(self):\n pass\n \n def loadTestCase(self, path):\n self.testcase = json.load(open(path))\n \n def generateTestCase(self):\n sigs = self.testcase[\"expected_signals\"]\n signals = []\n for p in sigs:\n seq = []\n for s in p[\"sequence\"]:\n seq.append(SIGNALS[s])\n signals.append([p[\"name\"], seq])\n return signals\n\nclass MonitorSeq(object):\n \n def __init__(self, parent, topic_name, sig_seq):\n self.parent = parent\n self.topic_name = topic_name\n self.sig_seq = sig_seq\n self.sub = rospy.Subscriber(topic_name, ReiStateMachineTransitionSignal, self.cbSig)\n \n def cbSig(self, data):\n print(self.sig_seq) \n if (len(self.sig_seq) > 0):\n s = self.sig_seq.pop(0)\n if (not data.sig_id == s):\n rospy.logerr(\"Unexpected signal received on {0}! SIG: {1}\".format(self.topic_name, data.sig_id))\n self.parent.setInvalidTest()\n else:\n rospy.logerr(\"Unexpected signal received on {0}! SIG: {1}\".format(self.topic_name, data.sig_id))\n self.parent.setInvalidTest()\n\nclass HybridSyncMonitor(object):\n \n def __init__(self):\n self.test_case = True\n self.subs = []\n \n def setInvalidTest(self):\n self.test_case = False\n \n def isTestValid(self):\n valid = True\n for sub in self.subs:\n valid = self.test_case and len(sub.sig_seq) == 0\n return valid\n \n def initEvaluation(self, signals):\n self.signals = signals\n for sig in self.signals:\n self.subs.append(MonitorSeq(self, sig[0], sig[1]))\n \n \ndef main():\n rospy.init_node(\"eval_sync_signal\")\n testcase_factory = TestCaseFactorySignal()\n testcase_factory.loadTestCase(rospy.get_param(\"~test_case_path\"))\n signals = testcase_factory.generateTestCase()\n hysyncmonitor = HybridSyncMonitor()\n hysyncmonitor.initEvaluation(signals)\n rospy.spin()\n if (hysyncmonitor.isTestValid()):\n rospy.loginfo(\"Test case successful, all signals received in order\")\n else:\n rospy.logerr(\"Test case invalid\")\n \nif __name__==\"__main__\":\n main()","sub_path":"rei/rei_sync_monitor/scripts/sync_monitor/syncsignalmonitor.py","file_name":"syncsignalmonitor.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"551393355","text":"import os\nimport logging\nimport json\n\nimport yaml\n\n\nclass ResolutionError(Exception):\n pass\n\n\nclass InternalResolutionError(ResolutionError):\n pass\n\n\nclass FileResolutionError(ResolutionError):\n pass\n\n\nclass DecodeError(ResolutionError):\n pass\n\n\nlog = logging.getLogger('dollar-ref.lib')\n\n\ndef resolve(data, root=None, cwd=None, *, external_only=False):\n if not isinstance(data, dict):\n if isinstance(data, list):\n for i, item in enumerate(data):\n data[i] = resolve(item, root, cwd,\n external_only=external_only)\n\n return data\n\n if root is None:\n root = data\n\n if '$ref' not in data:\n for subkey in data:\n data[subkey] = resolve(data[subkey], root, cwd,\n external_only=external_only)\n\n return data\n\n ref = data['$ref']\n\n if ref.startswith('#'):\n if not external_only:\n return resolve_internal(ref, root)\n\n return data\n elif ref.startswith(('http://', 'https://')):\n raise ResolutionError(\"Web resolution is not implemented yet\")\n else:\n return resolve_file(ref, cwd, external_only=external_only)\n\n\ndef _follow_path(ref: str, data: dict):\n if ref in ('', '#', '#/'):\n return data\n\n ref_path = ref[2:].split('/')\n\n ref_data = data\n for path_item in ref_path:\n try:\n ref_data = ref_data[path_item]\n except KeyError:\n raise InternalResolutionError(\n f\"Error resolving '{ref}', \"\n f\"'{path_item}' not found in '{ref_data}'.\"\n )\n\n return ref_data\n\n\ndef resolve_internal(ref: str, root: dict):\n ref_data = _follow_path(ref, root)\n\n return resolve(ref_data, root=root)\n\n\ndef resolve_file(ref: str, cwd: str, *, external_only=False):\n ref_split = ref.split('#')\n\n if len(ref_split) == 1:\n path, in_ref = ref_split[0], ''\n elif len(ref_split) == 2:\n path, in_ref = ref_split\n\n in_ref = f\"#{in_ref}\"\n\n if not os.path.isabs(path):\n path = os.path.join(cwd, path)\n\n try:\n file_data = read_file(path)\n except FileNotFoundError:\n raise FileResolutionError(\n f\"Could not resolve '{ref}', \"\n f\"'{path}' file not found.\"\n )\n\n data = _follow_path(in_ref, file_data)\n\n new_cwd = os.path.dirname(path)\n\n return resolve(data, root=file_data, cwd=new_cwd,\n external_only=external_only)\n\n\ndef read_file(path):\n with open(path, 'r') as file:\n raw = file.read()\n\n try:\n if raw.startswith('---'):\n data = yaml.load(raw)\n else:\n data = json.loads(raw)\n except json.decoder.JSONDecodeError as exc:\n raise DecodeError(\n f\"Error decoding '{path}' file.\"\n ) from exc\n\n return data\n\n\ndef pluck(root, *path):\n data = root\n for path_item in path:\n data = data[path_item]\n\n return resolve(data, root)\n","sub_path":"dollar_ref/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"16790467","text":"from flask import Flask,render_template,request,session,redirect,url_for\nimport config,json\nfrom exts import db\nfrom flask_uploads import configure_uploads,UploadSet\nimport time\nfrom model import User,Article,Comment\nimport re\nimport threading\napp = Flask(__name__)\napp.config.from_object(config)\ndb.init_app(app)\nimage=UploadSet('IMAGE')\nconfigure_uploads(app,image)\n\n\n@app.route('/')\ndef index():\n #article=Article.query.order_by(Article.id.desc()).all()\n page=request.args.get('page',1,type=int)\n pagination=Article.query.order_by(Article.id.desc()).paginate(page,per_page=4,error_out=False)\n posts=pagination.items\n return render_template('index.html',posts=posts,pagination=pagination)\n\n\n@app.route('/login/',methods=['post','get'])\ndef login():\n if request.method=='GET':\n return render_template('login.html')\n else:\n email=request.form.get('email')\n password=request.form.get('password')\n user=User.query.filter(User.email==email,User.password==password).first()\n if user:\n session['user_id']=user.id\n session.permanent=True\n last_time=time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))\n user=User.query.filter(User.id==session['user_id']).first()\n user.last_time=last_time\n db.session.commit()\n if session.get('next_url_after_login'):\n return redirect(session.get('next_url_after_login'))\n else:\n return redirect(url_for('index'))\n else:\n return '用户名或密码错误'\n\n\n@app.route('/register/',methods=['post','get'])\ndef register():\n if request.method=='GET':\n return render_template('register.html')\n else:\n username=request.form.get('username')\n password=request.form.get('password')\n email=request.form.get('email')\n register_time=time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))\n last_time=register_time\n user=User(username=username,email=email,password=password,register_time=register_time,last_time=last_time)\n db.session.add(user)\n db.session.commit()\n return redirect(url_for('login'))\n\n@app.route('/logout/')\ndef logout():\n session.clear()\n return redirect(url_for('index'))\n\n\n@app.route('/add_content/',methods=['get','post'])\ndef add_content():\n if request.method=='GET':\n if session.get('user_id'):\n return render_template('add_content.html')\n else:\n session['next_url_after_login'] = request.url\n return redirect(url_for('login'))\n else:\n data=request.data.decode('utf-8')\n json_data=json.loads(data)\n title=json_data['title']\n content=json_data['content']\n content_text=json_data['content_text']\n if '',content).group()\n else:\n first_content=re.search(r'.{15,100}',content_text)\n if first_content==None:\n first_content=' '\n else:\n first_content=first_content.group()\n first_image=''\n user_id=session.get('user_id')\n date=time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))\n author_name=User.query.filter(User.id==user_id).first().username\n author_email=User.query.filter(User.id==user_id).first().email\n article=Article(title=title,content=content,user_id=user_id,time=date,author_name=author_name,first_content=first_content,first_image=first_image)\n db.session.add(article)\n db.session.commit()\n\n return redirect(url_for('add_content'))\n\n@app.route('/add_comment/',methods=['post'])\ndef add_comment():\n if session.get('user_id'):\n article_id=request.form.get('article_id')\n\n comment_content=request.form.get('comment')\n date=time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))\n username=User.query.filter(User.id==session.get('user_id')).first().username\n email=User.query.filter(User.id==session.get('user_id')).first().email\n comment=Comment(article_id=article_id,content=comment_content,time=date,comment_user_id=session.get('user_id'),comment_user_name=username)\n article_username=Article.query.filter(Article.id==article_id).first().author_name\n article_email=User.query.filter(User.username==article_username).first().email\n db.session.add(comment)\n db.session.commit()\n\n return redirect(url_for('show',article_id=article_id))\n else:\n return redirect(url_for('login'))\n\n\n\n\n@app.route('/people/')\ndef people():\n return render_template('people.html')\n\n\n@app.route('/show/')\ndef show(article_id):\n article=Article.query.filter(Article.id==article_id).first()\n return render_template('show.html',article=article)\n\n@app.route('/save/',methods=['post'])\ndef save():\n data=request.data.decode('utf-8')\n json_data=json.loads(data)\n print(json_data)\n return 'ok'\n\n@app.route('/upload_images',methods=['post','get'])\ndef upload_images():\n if request.method=='POST':\n filename=image.save(request.files['image_name'])\n response={\n 'url':image.url(filename)\n }\n print(response)\n return json.dumps(response)\n\n\n\n@app.context_processor\ndef my_context_processor():\n id=session.get('user_id')\n if id:\n user=User.query.filter(User.id==id).first()\n return {'user':user}\n return {}\n\nif __name__ == '__main__':\n app.run(debug=True,threaded=True)\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"118193867","text":"import unittest\nimport re\n\n\ndef user_kw(keywords, keyword_prefix, sublime_prefix):\n head = keyword_prefix.lower()\n head = head.lstrip()\n head = head if head.rstrip() else head.rstrip()\n prefix = sublime_prefix if head and head[-1] != ' ' else ''\n user_kw = []\n for kw in filter(lambda kw: kw.lower().startswith(head), keywords):\n tail = re.sub(head, '', kw.lower())\n value = prefix + tail if head and ' ' in tail and ' ' in head else kw\n user_kw.append((kw.title(), value.title()))\n return user_kw\n\n\nclass TestGetCompletionValue(unittest.TestCase):\n def setUp(self):\n self.keywords = [\n 'Go', 'Go To', 'Go Tom', 'Go To Home', 'Go To Tom To', 'Go To Zoom Zoom', 'Home Go To'\n ]\n\n def test_(self):\n self.assertEquals(\n zip(self.keywords, self.keywords),\n user_kw(self.keywords, '', '')\n )\n\n def test_ssss(self):\n self.assertEquals(\n zip(self.keywords, self.keywords),\n user_kw(self.keywords, ' ', '')\n )\n\n def test_ssssG(self):\n self.assertEquals(\n zip(self.keywords[:-1], self.keywords[:-1]),\n user_kw(self.keywords, ' G', 'G')\n )\n\n def test_ssssg(self):\n self.assertEquals(\n zip(self.keywords[:-1], self.keywords[:-1]),\n user_kw(self.keywords, ' g', 'g')\n )\n\n def test_gos(self):\n self.assertEquals(\n [('Go To', 'Go To'), ('Go Tom', 'Go Tom'), ('Go To Home', 'To Home'), ('Go To Tom To', 'To Tom To'), ('Go To Zoom Zoom', 'To Zoom Zoom')],\n user_kw(self.keywords, 'go ', '')\n )\n\n def test_Gos(self):\n self.assertEquals(\n [('Go To', 'Go To'), ('Go Tom', 'Go Tom'), ('Go To Home', 'To Home'), ('Go To Tom To', 'To Tom To'), ('Go To Zoom Zoom', 'To Zoom Zoom')],\n user_kw(self.keywords, 'Go ', '')\n )\n\n def test_gosto(self):\n self.assertEquals(\n [('Go To', 'Go To'), ('Go Tom', 'Go Tom'), ('Go To Home', 'To Home'), ('Go To Tom To', 'To Tom To'), ('Go To Zoom Zoom', 'To Zoom Zoom')],\n user_kw(self.keywords, 'go to', 'to')\n )\n\n def test_gostos(self):\n self.assertEquals(\n [('Go To Home', 'Go To Home'), ('Go To Tom To', 'Tom To'), ('Go To Zoom Zoom', 'Zoom Zoom')],\n user_kw(self.keywords, 'go to ', '')\n )\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"lib/user_kw.py","file_name":"user_kw.py","file_ext":"py","file_size_in_byte":2445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"433713018","text":"import os\nos.system(\"clear\")\n#\n# r = rlst\n# c = clst\n#\n#\n#\n# clst = input(\"Ok give me a list of numbers to change\")\n#\n# frstnum = int(str(max_num)[0])\n# clst2 = []\n# for i in clst:\n# if str(frstnum) in str(i):\n# new_list.append(max_num)\n# else:\n# new_list.append(i)\n\n\nrlst = input(\"Ok give me a list of items and ill reverse it \\n:\")\nrlst2 = rlst.split(\",\")\n# print(rlst.reverse())\nprint(type(rlst2))\nprint(rlst2)\n# for i in reversed(rlst):\n# print(rlst[::-1]())\n\n# def max_chng(clst)\n","sub_path":"ListP1.py","file_name":"ListP1.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"596421985","text":"#!/usr/bin/env python\nimport sys\nsys.path.append('xypath')\nimport xypath\nimport tcore\n\n# Inheriting from tcore.TCore means that self.table contains an\n# example table automatically, without us having to open a file\nclass TestTable(tcore.TCore):\n\n def test_get_at_gets_the_correct_cell(self):\n cells = self.table.get_at(4, 17)\n\n assert isinstance(cells, xypath.xypath.Bag)\n cells.assert_one() # will raise exception if more than one cell in bag\n assert cells.value == 900\n\n def test_get_at_returns_empty_bag_for_invalid_coordinates(self):\n index_size_before = len(self.table.xy_index)\n cells = self.table.get_at(9999,9999)\n index_size_after = len(self.table.xy_index)\n\n assert isinstance(cells, xypath.xypath.Bag)\n assert len(cells) == 0\n assert index_size_before == index_size_after\n","sub_path":"test/test_table.py","file_name":"test_table.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"629861484","text":"import os\nimport re\nimport shutil\nimport sys\nimport datetime\n\nLETTERS = {'а': 'a', 'б': 'b', 'в': 'v', 'г': 'g', 'д': 'd', 'е': 'e', 'ё': 'yo', 'ж': 'zh', 'з': 'z', 'и': 'i',\n 'й': 'y', 'к': 'k', 'л': 'l', 'м': 'm', 'н': 'n', 'о': 'o', 'п': 'p', 'р': 'r', 'с': 's', 'т': 't',\n 'у': 'u', 'ф': 'f', 'х': 'h', 'ц': 'ts', 'ч': 'ch', 'ш': 'sh', 'щ': 'shch', 'ъ': 'y', 'ы': 'y', 'ь': \"'\",\n 'э': 'e', 'ю': 'yu', 'я': 'ya', 'А': 'A', 'Б': 'B', 'В': 'V', 'Г': 'G', 'Д': 'D', 'Е': 'E', 'Ё': 'Yo',\n 'Ж': 'Zh', 'З': 'Z', 'И': 'I', 'Й': 'Y', 'К': 'K', 'Л': 'L', 'М': 'M', 'Н': 'N', 'О': 'O', 'П': 'P',\n 'Р': 'R', 'С': 'S', 'Т': 'T', 'У': 'U', 'Ф': 'F', 'Х': 'H', 'Ц': 'Ts', 'Ч': 'Ch', 'Ш': 'Sh', 'Щ': 'Shch',\n 'Ъ': 'Y', 'Ы': 'Y', 'Ь': \"'\", 'Э': 'E', 'Ю': 'Yu', 'Я': 'Ya', }\n\nCATEGORIES = {'images': ('JPEG', 'PNG', 'JPG', 'SVG'), 'documents': ('DOC', 'DOCX', 'TXT', 'PDF', 'XLSX', 'PPTX'),\n 'audio': ('MP3', 'OGG', 'WAV', 'AMR'), 'video': ('AVI', 'MP4', 'MOV', 'MKV'), 'archives': ('ZIP', 'GZ', 'TAR')}\n\nfile_log = []\nknown_extension_list = []\nunown_extension_list = []\n\n\ndef folder_path():\n if len(sys.argv) != 2:\n print('Принимает только один аргумент!')\n else:\n if os.path.exists(sys.argv[1]):\n global base_path\n base_path = sys.argv[1]\n return sort_files(base_path)\n else:\n print('Неправильный путь!')\n\n\ndef rename_exists_files(name):\n return name + '_edit_' + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S.%f')\n\n\ndef normalize(name):\n for key in LETTERS:\n name = name.replace(key, LETTERS[key])\n return re.sub(r'\\W', '_', name)\n\n\ndef ignore_folder_list():\n ignore = []\n for k in CATEGORIES.keys():\n ignore.append(k)\n return ignore\n\n\ndef remove_empty_folders(path):\n folders = list(os.walk(path))\n for path, _, _ in folders[::-1]:\n if len(os.listdir(path)) == 0:\n os.rmdir(path)\n\n\ndef log():\n final_dict = {}\n for i in file_log:\n for k, v in i.items():\n final_dict.setdefault(k, []).append(v)\n for k, v in final_dict.items():\n print(f'-{k}' + '-' * 100)\n print(', '.join(v))\n print(f'Известные расширения: {known_extension_list}')\n print(f'Неизвестные расширения: {list(set(unown_extension_list) - set(known_extension_list))}')\n\n\ndef move_to_category_folders(file_path):\n dirname, fname = os.path.split(file_path)\n extension = os.path.splitext(fname)[1].upper().replace('.', '')\n for k, v in CATEGORIES.items():\n if extension in v and k == 'archives':\n if extension not in known_extension_list:\n known_extension_list.append(extension)\n os.makedirs(base_path + '/' + k, exist_ok=True)\n shutil.unpack_archive(os.path.join(file_path), base_path + '/' + k + '/' + os.path.splitext(fname)[0])\n files = os.listdir(base_path + '/' + k + '/' + os.path.splitext(fname)[0])\n file_log.append({k: ', '.join(files)})\n os.remove(os.path.join(file_path))\n elif extension in v:\n if extension not in known_extension_list:\n known_extension_list.append(extension)\n os.makedirs(base_path + '/' + k, exist_ok=True)\n if os.path.exists(os.path.join(base_path + '/' + k, fname)):\n new_f_renamed = rename_exists_files(os.path.splitext(fname)[0]) + os.path.splitext(fname)[1]\n shutil.move(os.path.join(file_path), os.path.join(base_path + '/' + k, new_f_renamed))\n file_log.append({k: new_f_renamed})\n else:\n shutil.move(os.path.join(file_path), os.path.join(base_path + '/' + k, fname))\n file_log.append({k: fname})\n else:\n if extension not in unown_extension_list:\n unown_extension_list.append(extension)\n\n\ndef sort_files(path):\n subfolders = []\n files = []\n ignore = ignore_folder_list()\n for i in os.scandir(path):\n if i.is_dir():\n if i.name not in ignore:\n old_path = os.path.dirname(i.path)\n new_name = normalize(i.name)\n os.rename(os.path.join(old_path, i.name), os.path.join(old_path, new_name))\n subfolders.append(os.path.join(old_path, new_name))\n if i.is_file():\n name = os.path.splitext(i.name)[0]\n extension = os.path.splitext(i.name)[1]\n new_name = normalize(name) + extension\n old_path = os.path.dirname(i.path)\n os.rename(os.path.join(old_path, i.name), os.path.join(old_path, new_name))\n files.append(os.path.join(old_path, new_name))\n move_to_category_folders(os.path.join(old_path, new_name))\n for dir in list(subfolders):\n sf, i = sort_files(dir)\n subfolders.extend(sf)\n files.extend(i)\n return subfolders, files\n\n\ndef main():\n folder_path()\n remove_empty_folders(base_path)\n log()","sub_path":"clean_folder/clean_folder/clean.py","file_name":"clean.py","file_ext":"py","file_size_in_byte":5149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"612206220","text":"from PIL import Image\nimport os\nimport numpy as np\nimport tqdm\nimport cv2\n\n\ndef main():\n # file_path = r'/SSD64/Smooth/train/GEN'\n save_path = '/media/server/80SSD/LihuaJian/train/TrainData490'\n\n gt_path = '/media/server/80SSD/LihuaJian/train/490/GT'\n in_path = '/media/server/80SSD/LihuaJian/train/490/Input'\n\n gt_save = save_path + '/' + 'GT'\n input_save = save_path + '/' + 'In'\n\n if not os.path.isdir(gt_save):\n os.makedirs(gt_save)\n if not os.path.isdir(input_save):\n os.makedirs(input_save)\n\n patch_size = 128\n stride = 32\n resize_set = [1.0, 0.5]\n flip_set = [0, 2]\n rotate_set = [0, 1]\n\n count = 0\n for name in tqdm.tqdm(os.listdir(gt_path)):\n for resize in resize_set:\n for flip in flip_set:\n for rotate in rotate_set:\n gt = Image.open(gt_path + '/' + name)\n gt = np.asarray(gt)\n Input = Image.open(in_path + '/' + name)\n Input = np.asarray(Input)\n\n\n row, col = gt.shape[0], gt.shape[1]\n row = int(row * resize)\n col = int(col*resize)\n\n gt = cv2.resize(gt,(col, row))\n if not flip == 2:\n gt = np.flip(gt, flip)\n M = cv2.getRotationMatrix2D(((col - 1) / 2.0, (row - 1) / 2.0), 90 * rotate, 1)\n gt = cv2.warpAffine(gt, M, (col, row))\n Input = cv2.resize(Input, (col, row))\n if not flip == 2:\n Input = np.flip(Input, flip)\n Input = cv2.warpAffine(Input, M, (col, row))\n\n for i in range(0, row - patch_size, stride):\n for j in range(0, col - patch_size, stride):\n patch_name = '{}.png'.format(count)\n count = count + 1\n\n gt_patch = Image.fromarray(gt[i:i + patch_size, j:j + patch_size, :])\n gt_patch.save(gt_save + '/' + patch_name)\n input_patch = Image.fromarray(Input[i:i + patch_size, j:j + patch_size, :])\n input_patch.save(input_save + '/' + patch_name)\n # patch_name = '{}.png'.format(count)\n # count = count + 1\n # gt_patch = Image.fromarray(gt)\n # gt_patch.save(gt_save + '/' + patch_name)\n # input_patch = Image.fromarray(Input)\n # input_patch.save(input_save + '/' + patch_name)\n a = 0\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"data/gen_patch_enhance.py","file_name":"gen_patch_enhance.py","file_ext":"py","file_size_in_byte":2667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"139740200","text":"from sys import stdin\n\ndi = [-1, -1, -1, 0, 1, 1, 1, 0]\ndj = [-1, 0, 1, 1, 1, 0, -1, -1]\n\n\ndef inside(i,j):\n global n\n return i >= 0 and i < n and j >= 0 and j < n\n\ndef floodArea(i,j):\n global mapa,vis\n vis.add((i,j))\n stack = [(i,j)]\n\n while stack:\n i,j = stack.pop()\n for z in range(8):\n a = i + di[z]\n b = j + dj[z]\n\n if inside(a,b) and mapa[a][b] == '1' and (a,b) not in vis:\n vis.add((a,b))\n stack.append((a,b))\n \ndef main():\n global mapa,vis,n\n t = 1\n line = stdin.readline().strip()\n while line != '':\n n = int(line)\n mapa = []\n\n for i in range(n): mapa.append(stdin.readline().strip())\n\n vis = set()\n \n eagles = 0\n for i in range(n):\n for j in range(n):\n if mapa[i][j] == '1' and (i,j) not in vis:\n eagles += 1\n floodArea(i,j)\n\n print(\"Image number {0} contains {1} war eagles.\".format(t,eagles))\n t += 1\n line = stdin.readline().strip()\nmain()\n \n","sub_path":"352 The Seasonal War.py","file_name":"352 The Seasonal War.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"501036281","text":"#!/usr/bin/python\nimport sys, os\nfrom socket import *\ntestfile = sys.argv[1] + \"_tmp_\"\ntry:\n socket(AF_UNIX, SOCK_DGRAM).bind(testfile)\nexcept:\n sys.exit(1)\nfinally:\n os.remove(testfile) if os.path.exists(testfile) else ''\nsys.exit(0)\n\n","sub_path":"scripts/testSockets.py","file_name":"testSockets.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"461408396","text":"# -*- coding: utf-8 -*-\n\"\"\"\nthebestcolor.net app.\n\nProvides insights gathered from my study of @everycolorbot on Twitter.\n\n:authored: 2018 by Dylan Harper.\n:license: MIT, see LICENSE for more details.\n\"\"\"\n\nimport pickle\nfrom string import hexdigits\n\nfrom flask import Flask, render_template\n\nimport app_charts as charts\nimport color_tools as ct\n\n\napp = Flask(__name__)\n\nmodel = pickle.load(open('../data/knn_60_lab.pkl', 'rb'))\nbest_colors = pickle.load(open('../data/best_colors.pkl', 'rb'))\n\n\n@app.route('/', methods=['GET'])\ndef index():\n \"\"\"Homepage with explanatory text and list of top colors.\"\"\"\n return render_template('home.html', best_colors=best_colors)\n\n\n@app.route('//')\ndef catch_all(color):\n \"\"\"Page with quality score, and some other info,for specified color.\"\"\"\n # check if 6 digit hex (rgb value)\n if all(c in hexdigits for c in color) and (6 == len(color)):\n score = charts.score(model, color)\n jumbotron_font_color = charts.font_choice(color)\n return render_template('color.html',\n page_color=color,\n score=score,\n jumbotron_font_color=jumbotron_font_color)\n else:\n background_color = ct.random_color()\n font_color = charts.font_choice(background_color)\n return render_template('404.html', background_color=background_color, font_color=font_color)\n\n\n@app.route('//.png')\ndef chart(color, chart_type):\n \"\"\"Partial dependency plot for hue in HSV space of our model.\"\"\"\n if chart_type == \"hsv_h_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'HSV', 'h')\n elif chart_type == \"hsv_s_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'HSV', 's')\n elif chart_type == \"hsv_v_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'HSV', 'v')\n elif chart_type == \"lab_l_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'LAB', 'l')\n elif chart_type == \"lab_a_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'LAB', 'a')\n elif chart_type == \"lab_b_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'LAB', 'b')\n elif chart_type == \"rgb_r_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'RGB', 'r')\n elif chart_type == \"rgb_g_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'RGB', 'g')\n elif chart_type == \"rgb_b_pd_chart\":\n image_value = charts.partial_dependancy_chart(model, color, 'RGB', 'b')\n return image_value, 200, {'Content-Type': 'image/png'}\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', threaded=True)\n","sub_path":"src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"346663279","text":"import bmesh\nfrom .floor_types import create_floors\n\nfrom ...utils import select, get_edit_mesh\n\n\nclass Floor:\n\n has_mat_groups = False\n\n @classmethod\n def build(cls, context, prop):\n me = get_edit_mesh()\n bm = bmesh.from_edit_mesh(me)\n\n if cls.validate(bm):\n if any([f for f in bm.faces if f.select]):\n create_floors(bm, None, prop)\n select(bm.faces, False)\n else:\n edges = [e for e in bm.edges if e.is_boundary]\n create_floors(bm, edges, prop)\n bmesh.update_edit_mesh(me, True)\n return {\"FINISHED\"}\n return {\"CANCELLED\"}\n\n @classmethod\n def validate(cls, bm):\n if any([f for f in bm.faces if f.select]):\n selection = [f for f in bm.faces if f.select]\n if len({round(v.co.z, 4) for f in selection for v in f.verts}) == 1:\n return True\n elif len({round(v.co.z, 4) for v in bm.verts}) == 1:\n return True\n return False\n","sub_path":"core/floor/floor.py","file_name":"floor.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"369654835","text":"#encoding: utf-8\n\nimport tornado\nimport json\nfrom tornado import gen\nfrom .base_handler import BaseHandler\nfrom .config import *\nfrom .helper import *\nfrom boto.dynamodb2.table import Table\n\nclass PasswordHandler(BaseHandler):\n @property\n def user_table(self):\n return Table('User_Table',connection=self.dynamo)\n\n @property\n def user_activate_table(self):\n return Table('User_Activate_Table',connection=self.dynamo)\n\n @gen.coroutine\n def post():\n \"\"\"\n send an activate code to user's email and make an record in User_Activate_Table\n PAYLOAD:\n {\n \"userid\":\"a serious user id\"\n }\n \"\"\"\n client_data = self.data\n userid = client_data['userid']\n try:\n # fetch user data from dynamodb\n user = yield gen.maybe_future(self.user_table.get_item(UserID=userid))\n except:\n self.write_json_with_status(400,{\n 'result' : 'fail',\n 'reason' : 'invalid userid'\n })\n try:\n # generate activate code and send email\n activate_code = yield gen.maybe_future(\n send_email(\n self.ses,\n user[\"Email\"],\n user[\"FirstName\"],\n user[\"LastName\"]\n )\n )\n except:\n # process exception\n self.write_json_with_status(400,{\n 'result' : 'fail',\n 'reason' : 'failed to send email'\n })\n \n # save activator code to dynamodb\n yield gen.maybe_future(\n self.user_activate_table.put_item(data={\n \"UserID\" : userid,\n \"Timestamp\" : str(time.time()).split(\".\")[0],\n \"Code\" : activate_code,\n \"Attempt\" : 1\n })\n )\n\n self.write_json({\n 'result': 'ok',\n })\n\n @gen.coroutine\n def get(userid, code):\n \"\"\"\n verify code from client\n \"\"\"\n try:\n activator = yield gen.maybe_future(self.user_activate_table.get_item(UserID=userid))\n except:\n self.write_json_with_status(400,{\n 'result' : 'fail',\n 'reason' : 'invalid userid'\n })\n if code == activator[\"Code\"]:\n self.write_json({\n 'result': 'ok',\n })\n else:\n # wrong code\n self.write_json_with_status(403,{\n 'result' : 'fail',\n 'reason' : 'authantication failed'\n })\n\n @gen.coroutine\n def put():\n \"\"\"\n resend email.\n PAYLOAD:\n {\n \"userid\": \"a serious user id\"\n }\n \"\"\"\n try:\n activator = yield gen.maybe_future(self.user_activate_table.get_item(UserID=userid))\n user = yield gen.maybe_future(self.user_table.get_item(UserID=userid))\n except:\n self.write_json_with_status(400,{\n 'result' : 'fail',\n 'reason' : 'invalid userid'\n })\n\n # increment on counter\n activator['Attempt'] = activator['Attempt'] + 1\n if activator['Attempt'] > 3:\n # no more than 3 emails per day per user\n self.write_json({\n 'result' : 'fail',\n 'reason' : 'too many attempts recorded'\n })\n\n try:\n # generate a new activate code and send email\n activate_code = yield gen.maybe_future(\n send_email(\n self.ses,\n user[\"Email\"],\n user[\"FirstName\"],\n user[\"LastName\"]\n )\n )\n except:\n # process exception\n self.write_json_with_status(400,{\n 'result' : 'fail',\n 'reason' : 'failed to send email'\n })\n\n activator['Code'] = activate_code\n\n yield gen.maybe_future(activator.partial_save())\n\n self.write_json({\n 'result':'ok'\n })","sub_path":"doodle/password_handler.py","file_name":"password_handler.py","file_ext":"py","file_size_in_byte":4171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"490000575","text":"\"\"\"\nSession, a convenient wrapper around the low-level _NiFpga class.\n\nCopyright (c) 2017 National Instruments\n\"\"\"\n\nfrom .nifpga import (_SessionType, _IrqContextType, _NiFpga, DataType,\n OPEN_ATTRIBUTE_NO_RUN, RUN_ATTRIBUTE_WAIT_UNTIL_DONE,\n CLOSE_ATTRIBUTE_NO_RESET_IF_LAST_SESSION)\nfrom .bitfile import Bitfile\nfrom .status import InvalidSessionError\nfrom collections import namedtuple\nimport ctypes\nfrom builtins import bytes\nfrom future.utils import iteritems\nfrom .nifpga import BoolArrayMappedDatatype\nimport numpy as np\n\n\nclass Session(object):\n \"\"\"\n Session, a convenient wrapper around the low-level _NiFpga class.\n\n The Session class uses regular python types, provides convenient default\n arguments to C API functions, and makes controls, indicators, and FIFOs\n available by name. If any NiFpga function return status is non-zero, the\n appropriate exception derived from either WarningStatus or ErrorStatus is\n raised.\n Example usage of FPGA configuration functions::\n\n with Session(bitfile=\"myBitfilePath.lvbitx\", resource=\"RIO0\") as session:\n session.run()\n session.download()\n session.abort()\n session.reset()\n\n Note:\n It is always recommended that you use a Session with a context manager\n (with). Opening a Session without a context manager could cause you to\n leak the session if :meth:`Session.close` is not called.\n\n Controls and indicators are accessed directly via a _Register object\n obtained from the session::\n\n my_control = session.registers[\"MyControl\"]\n my_control.write(data=4)\n data = my_control.read()\n\n FIFOs are accessed directly via a _FIFO object obtained from the session::\n\n myHostToFpgaFifo = session.fifos[\"MyHostToFpgaFifo\"]\n myHostToFpgaFifo.stop()\n actual_depth = myHostToFpgaFifo.configure(requested_depth=4096)\n myHostToFpgaFifo.start()\n empty_elements_remaining = myHostToFpgaFifo.write(data=[1, 2, 3, 4],\n timeout_ms=2)\n\n myFpgaToHostFifo = session.fifos[\"MyHostToFpgaFifo\"]\n read_values = myFpgaToHostFifo.read(number_of_elements=4,\n timeout_ms=0)\n print(read_values.data)\n \"\"\"\n\n def __init__(self,\n bitfile,\n resource,\n no_run=False,\n reset_if_last_session_on_exit=False,\n **kwargs):\n \"\"\"Creates a session to the specified resource with the specified\n bitfile.\n\n Args:\n bitfile (str)(Bitfile): A bitfile.Bitfile() instance or a string\n filepath to a bitfile.\n resource (str): e.g. \"RIO0\", \"PXI1Slot2\", or \"rio://hostname/RIO0\"\n no_run (bool): If true, don't run the bitfile, just open the\n session.\n reset_if_last_session_on_exit (bool): Passed into Close on\n exit. Unused if not using this session as a context guard.\n **kwargs: Additional arguments that edit the session.\n \"\"\"\n if not isinstance(bitfile, Bitfile):\n \"\"\" The bitfile we were passed is a path to an lvbitx.\"\"\"\n bitfile = Bitfile(bitfile)\n self._nifpga = _NiFpga()\n self._session = _SessionType()\n\n open_attribute = 0\n for key, value in kwargs.items():\n if key == '_open_attribute':\n open_attribute = value\n\n if no_run:\n open_attribute = open_attribute | OPEN_ATTRIBUTE_NO_RUN\n\n bitfile_path = bytes(bitfile.filepath, 'ascii')\n bitfile_signature = bytes(bitfile.signature, 'ascii')\n resource = bytes(resource, 'ascii')\n self._nifpga.Open(bitfile_path,\n bitfile_signature,\n resource,\n open_attribute,\n self._session)\n\n self._reset_if_last_session_on_exit = reset_if_last_session_on_exit\n self._registers = {}\n self._internal_registers_dict = {}\n base_address_on_device = bitfile.base_address_on_device()\n for name, bitfile_register in iteritems(bitfile.registers):\n assert name not in self._registers, \\\n \"One or more registers have the same name '%s', this is not supported\" % name\n if bitfile_register.is_array():\n array_register = _ArrayRegister(self._session, self._nifpga,\n bitfile_register,\n base_address_on_device)\n if bitfile_register.is_internal():\n self._internal_registers_dict[name] = array_register\n else:\n self._registers[name] = array_register\n\n else:\n if isinstance(bitfile_register.datatype, DataType):\n register = _Register(self._session, self._nifpga,\n bitfile_register, base_address_on_device)\n else:\n register = _BoolArrayMappedRegister(self._session, self._nifpga,\n bitfile_register, base_address_on_device)\n if bitfile_register.is_internal():\n self._internal_registers_dict[name] = register\n else:\n self._registers[name] = register\n\n self._fifos = {}\n for name, bitfile_fifo in iteritems(bitfile.fifos):\n assert name not in self._fifos, \\\n \"One or more FIFOs have the same name '%s', this is not supported\" % name\n self._fifos[name] = _FIFO(self._session, self._nifpga, bitfile_fifo)\n\n def __enter__(self):\n return self\n\n def __exit__(self, exception_type, exception_val, trace):\n try:\n self.close(reset_if_last_session=self._reset_if_last_session_on_exit)\n except InvalidSessionError:\n pass\n\n def close(self, reset_if_last_session=False):\n \"\"\" Closes the FPGA Session.\n\n Args:\n reset_if_last_session (bool): If True, resets the FPGA on the\n last close. If true, does not reset the FPGA on the last\n session close.\n \"\"\"\n close_attr = CLOSE_ATTRIBUTE_NO_RESET_IF_LAST_SESSION if reset_if_last_session is False else 0\n self._nifpga.Close(self._session, close_attr)\n\n def run(self, wait_until_done=False):\n \"\"\" Runs the FPGA VI on the target.\n\n Args:\n wait_until_done (bool): If true, this functions blocks until the\n FPGA VI stops running\n \"\"\"\n run_attr = RUN_ATTRIBUTE_WAIT_UNTIL_DONE if wait_until_done else 0\n self._nifpga.Run(self._session, run_attr)\n\n def abort(self):\n \"\"\" Aborts the FPGA VI. \"\"\"\n self._nifpga.Abort(self._session)\n\n def download(self):\n \"\"\" Re-downloads the FPGA bitstream to the target. \"\"\"\n self._nifpga.Download(self._session)\n\n def reset(self):\n \"\"\" Resets the FPGA VI. \"\"\"\n self._nifpga.Reset(self._session)\n\n def _irq_ordinals_to_bitmask(self, ordinals):\n bitmask = 0\n for ordinal in ordinals:\n assert 0 <= ordinal and ordinal <= 31, \"Valid IRQs are 0-31: %d is invalid\" % ordinal\n bitmask |= (1 << ordinal)\n return bitmask\n\n def wait_on_irqs(self, irqs, timeout_ms):\n \"\"\" Stops the calling thread until the FPGA asserts any IRQ in the irqs\n parameter or until the function call times out.\n\n Args:\n irqs: A list of irq ordinals 0-31, e.g. [0, 6, 31].\n timeout_ms: The timeout to wait in milliseconds.\n\n Returns:\n session_wait_on_irqs (namedtuple)::\n\n session_wait_on_irqs.irqs_asserted (list): is a list of the\n asserted IRQs.\n session_wait_on_irqs.timed_out (bool): Outputs whether or not\n the time out expired before all irqs were asserted.\n\n \"\"\"\n if type(irqs) != list:\n irqs = [irqs]\n irqs_bitmask = self._irq_ordinals_to_bitmask(irqs)\n\n context = _IrqContextType()\n self._nifpga.ReserveIrqContext(self._session, context)\n\n irqs_asserted_bitmask = ctypes.c_uint32(0)\n timed_out = DataType.Bool._return_ctype()()\n try:\n self._nifpga.WaitOnIrqs(self._session,\n context,\n irqs_bitmask,\n timeout_ms,\n irqs_asserted_bitmask,\n timed_out)\n finally:\n self._nifpga.UnreserveIrqContext(self._session, context)\n irqs_asserted = [i for i in range(32) if irqs_asserted_bitmask.value & (1 << i)]\n WaitOnIrqsReturnValues = namedtuple('WaitOnIrqsReturnValues',\n [\"irqs_asserted\", \"timed_out\"])\n return WaitOnIrqsReturnValues(irqs_asserted=irqs_asserted,\n timed_out=bool(timed_out.value))\n\n def acknowledge_irqs(self, irqs):\n \"\"\" Acknowledges an IRQ or set of IRQs.\n\n Args:\n irqs (list): A list of irq ordinals 0-31, e.g. [0, 6, 31].\n \"\"\"\n self._nifpga.AcknowledgeIrqs(self._session,\n self._irq_ordinals_to_bitmask(irqs))\n\n def _get_unique_register_or_fifo(self, name):\n assert not (name in self._registers and name in self._fifos), \\\n \"Ambiguous: '%s' is both a register and a FIFO\" % name\n assert name in self._registers or name in self._fifos, \\\n \"Unknown register or FIFO '%s'\" % name\n try:\n return self._registers[name]\n except KeyError:\n return self._fifos[name]\n\n @property\n def registers(self):\n \"\"\" This property returns a dictionary containing all registers that\n are associated with the bitfile opened with the session. A register can\n be accessed by its unique name.\n \"\"\"\n return self._registers\n\n @property\n def _internal_registers(self):\n \"\"\" This property contains interal regis\"\"\"\n return self._internal_registers_dict\n\n @property\n def fifos(self):\n \"\"\" This property returns a dictionary containing all FIFOs that are\n associated with the bitfile opened with the session. A FIFO can be\n accessed by its unique name.\n \"\"\"\n return self._fifos\n\n\nclass _Register(object):\n \"\"\" _Register is a private class that is a wrapper of logic that is\n associated with controls and indicators.\n\n All Registers will exists in a sessions session.registers property. This\n means that all possible registers for a given session are created during\n session initialization; a user should never need to create a new instance\n of this class.\n\n \"\"\"\n def __init__(self, session, nifpga, bitfile_register, base_address_on_device):\n self._datatype = bitfile_register.datatype\n self._name = bitfile_register.name\n self._session = session\n self._nifpga = nifpga\n if isinstance(self._datatype, DataType):\n if bitfile_register.is_array():\n self._write_func = nifpga[\"WriteArray%s\" % self._datatype]\n self._read_func = nifpga[\"ReadArray%s\" % self._datatype]\n else:\n self._write_func = nifpga[\"Write%s\" % self._datatype]\n self._read_func = nifpga[\"Read%s\" % self._datatype]\n else:\n self._write_func = None\n self._read_func = None\n\n self._ctype_type = self._datatype._return_ctype()\n self._resource = bitfile_register.offset + base_address_on_device\n if bitfile_register.access_may_timeout():\n self._resource = self._resource | 0x80000000\n\n def __len__(self):\n \"\"\" A single register will always have one and only one element.\n\n Returns:\n (int): Always a constant 1.\n \"\"\"\n return 1\n\n def write(self, data):\n \"\"\" Writes the specified data to the control or indicator\n\n Args:\n data (DataType.value): The data to be written into the register\n \"\"\"\n if self._write_func is None:\n import ipdb; ipdb.set_trace()\n self._write_func(self._session, self._resource, data)\n\n def read(self):\n \"\"\" Reads a single element from the control or indicator\n\n Returns:\n data (DataType.value): The data inside the register.\n \"\"\"\n data = self._ctype_type()\n self._read_func(self._session, self._resource, data)\n if self._datatype is DataType.Bool:\n return bool(data.value)\n return data.value\n\n @property\n def name(self):\n \"\"\" Property of a register that returns the name of the control or\n indicator. \"\"\"\n return self._name\n\n @property\n def datatype(self):\n \"\"\" Property of a register that returns the datatype of the control or\n indicator. \"\"\"\n return self._datatype\n\nclass _BoolArrayMappedRegister(_Register):\n def __init__(self, session, nifpga, bitfile_register, base_address_on_device):\n super(_BoolArrayMappedRegister, self).__init__(session, nifpga, bitfile_register, base_address_on_device)\n assert isinstance(self._datatype, BoolArrayMappedDatatype)\n\n def getEmptyValue(self):\n return self._datatype.getEmptyValue()\n\n def write(self, data):\n boolarray = self._datatype.toBoolArray(data)\n buf = self._ctype_type(*boolarray)\n self._nifpga['WriteArrayBool'](self._session, self._resource, buf, len(boolarray))\n\n def read(self):\n buf = self._ctype_type()\n self._nifpga['ReadArrayBool'](self._session, self._resource, buf, len(buf))\n boolarray = np.array(buf, dtype=np.uint8)\n return self._datatype.fromBoolArray(boolarray)\n\n\nclass _ArrayRegister(_Register):\n \"\"\"\n _ArryRegister is a private class that inherits from _Register with\n additional interfaces unique to the logic of array controls and indicators.\n \"\"\"\n def __init__(self,\n session,\n nifpga,\n bitfile_register,\n base_address_on_device):\n super(_ArrayRegister, self).__init__(session,\n nifpga,\n bitfile_register,\n base_address_on_device)\n self._num_elements = len(bitfile_register)\n self._ctype_type = self._ctype_type * self._num_elements\n self._write_func = nifpga[\"WriteArray%s\" % self._datatype]\n self._read_func = nifpga[\"ReadArray%s\" % self._datatype]\n\n def __len__(self):\n \"\"\" Returns the length of the array.\n\n Returns:\n (int): The number of elements in the array.\n \"\"\"\n return self._num_elements\n\n def write(self, data):\n \"\"\" Writes the specified array of data to the control or indicator\n\n Args:\n data (list): The data \"array\" to be written into the registers\n wrapped into a python list.\n \"\"\"\n # if data is not iterable make it iterable\n try:\n iter(data)\n except TypeError:\n data = [data]\n assert len(data) == len(self), \\\n \"Bad data length %d for register '%s', expected %s\" \\\n % (len(data), self._name, len(self))\n buf = self._ctype_type(*data)\n self._write_func(self._session, self._resource, buf, len(self))\n\n def read(self):\n \"\"\" Reads the entire array from the control or indicator.\n\n Returns:\n (list): The data in the register in a python list.\n \"\"\"\n buf = self._ctype_type()\n self._read_func(self._session, self._resource, buf, len(self))\n return [bool(elem) if self._datatype is DataType.Bool else elem for elem in buf]\n\n\nReadValues = namedtuple(\"ReadValues\", [\"data\", \"elements_remaining\"])\nclass _FIFO(object):\n \"\"\" _FIFO is a private class that is a wrapper for the logic that\n associated with a FIFO.\n\n All FIFOs will exists in a sessions session.fifos property. This means that\n all possible FIFOs for a given session are created during session\n initialization; a user should never need to create a new instance of this\n class.\n \"\"\"\n def __init__(self, session, nifpga, bitfile_fifo):\n self._datatype = bitfile_fifo.datatype\n self._number = bitfile_fifo.number\n self._session = session\n self._write_func = nifpga[\"WriteFifo%s\" % self._datatype]\n self._read_func = nifpga[\"ReadFifo%s\" % self._datatype]\n self._acquire_read_func = nifpga[\"AcquireFifoReadElements%s\" % self._datatype]\n self._acquire_write_func = nifpga[\"AcquireFifoWriteElements%s\" % self._datatype]\n self._release_elements_func = nifpga[\"ReleaseFifoElements\"]\n self._nifpga = nifpga\n self._ctype_type = self._datatype._return_ctype()\n self._name = bitfile_fifo.name\n\n def configure(self, requested_depth):\n \"\"\" Specifies the depth of the host memory part of the DMA FIFO.\n\n Args:\n requested_depth (int): The depth of the host memory part of the DMA\n FIFO in number of elements.\n\n Returns:\n actual_depth (int): The actual number of elements in the host\n memory part of the DMA FIFO, which may be more than the\n requested number.\n \"\"\"\n actual_depth = ctypes.c_size_t()\n self._nifpga.ConfigureFifo2(self._session, self._number,\n requested_depth, actual_depth)\n return actual_depth.value\n\n def start(self):\n \"\"\" Starts the FIFO. \"\"\"\n self._nifpga.StartFifo(self._session, self._number)\n\n def stop(self):\n \"\"\" Stops the FIFO. \"\"\"\n self._nifpga.StopFifo(self._session, self._number)\n\n def write(self, data, timeout_ms=0):\n \"\"\" Writes the specified data to the FIFO.\n\n NOTE:\n If the FIFO has not been started before calling\n :meth:`_FIFO.write()`, then it will automatically start and\n continue to work as expected.\n\n Args:\n data (list): Data to be written to the FIFO.\n timeout_ms (int): The timeout to wait in milliseconds.\n\n Returns:\n elements_remaining (int): The number of elements remaining in the\n host memory part of the DMA FIFO.\n \"\"\"\n # if data is not iterable make it iterable\n try:\n iter(data)\n except TypeError:\n data = [data]\n buf_type = self._ctype_type * len(data)\n buf = buf_type(*data)\n empty_elements_remaining = ctypes.c_size_t()\n self._write_func(self._session,\n self._number,\n buf,\n len(data),\n timeout_ms,\n empty_elements_remaining)\n return empty_elements_remaining.value\n\n def read(self, number_of_elements, timeout_ms=0):\n \"\"\" Read the specified number of elements from the FIFO.\n\n NOTE:\n If the FIFO has not been started before calling\n :meth:`_FIFO.read()`, then it will automatically start and continue\n to work as expected.\n\n Args:\n number_of_elements (int): The number of elements to read from the\n FIFO.\n timeout_ms (int): The timeout to wait in milliseconds.\n\n Returns:\n ReadValues (namedtuple)::\n\n ReadValues.data (list): containing the data from\n the FIFO.\n ReadValues.elements_remaining (int): The amount of elements\n remaining in the FIFO.\n \"\"\"\n buf_type = self._ctype_type * number_of_elements\n buf = buf_type()\n elements_remaining = ctypes.c_size_t()\n self._read_func(self._session,\n self._number,\n buf,\n number_of_elements,\n timeout_ms,\n elements_remaining)\n data = [bool(elem) if self._datatype is DataType.Bool else elem for elem in buf]\n return ReadValues(data=data,\n elements_remaining=elements_remaining.value)\n\n def _acquire_write(self, number_of_elements, timeout_ms=0):\n \"\"\" Write the specified number of elements from the FIFO.\n\n Args:\n number_of_elements (int): The number of elements to read from the\n FIFO.\n timeout_ms (int): The timeout to wait in milliseconds.\n\n Returns:\n AcquireWriteValues(namedtuple)::\n\n AcquireWriteValues.data (ctypes.pointer): Contains the data\n from the FIFO.\n AcquireWriteValues.elements_acquired (int): The number of\n elements that were actually acquired.\n AcquireWriteValues.elements_remaining (int): The amount of\n elements remaining in the FIFO.\n \"\"\"\n block_out = ctypes.POINTER(self._ctype_type)()\n elements_acquired = ctypes.c_size_t()\n elements_remaining = ctypes.c_size_t()\n self._acquire_write_func(self._session,\n self._number,\n block_out,\n number_of_elements,\n timeout_ms,\n elements_acquired,\n elements_remaining)\n\n AcquireWriteValues = namedtuple(\"AcquireWriteValues\",\n [\"data\", \"elements_acquired\",\n \"elements_remaining\"])\n return AcquireWriteValues(data=block_out,\n elements_acquired=elements_acquired.value,\n elements_remaining=elements_remaining.value)\n\n def _acquire_read(self, number_of_elements, timeout_ms=0):\n \"\"\" Read the specified number of elements from the FIFO.\n\n Args:\n number_of_elements (int): The number of elements to read from the\n FIFO.\n timeout_ms (int): The timeout to wait in milliseconds.\n\n Returns:\n AcquireWriteValues(namedtuple): has the following members::\n\n AcquireWriteValues.data (ctypes.pointer): Contains the data\n from the FIFO.\n AcquireWriteValues.elements_acquired (int): The number of\n elements that were actually acquired.\n AcquireWriteValues.elements_remaining (int): The amount of\n elements remaining in the FIFO.\n \"\"\"\n buf = self._ctype_type()\n buf_ptr = ctypes.pointer(buf)\n elements_acquired = ctypes.c_size_t()\n elements_remaining = ctypes.c_size_t()\n self._acquire_read_func(self._session,\n self._number,\n buf_ptr,\n number_of_elements,\n timeout_ms,\n elements_acquired,\n elements_remaining)\n AcquireReadValues = namedtuple(\"AcquireReadValues\",\n [\"data\", \"elements_acquired\",\n \"elements_remaining\"])\n return AcquireReadValues(data=buf_ptr,\n elements_acquired=elements_acquired.value,\n elements_remaining=elements_remaining.value)\n\n def _release_elements(self, number_of_elements):\n \"\"\" Releases the FIFOs elements. \"\"\"\n self._release_elements_func(self._session, self._number, number_of_elements)\n\n def get_peer_to_peer_endpoint(self):\n \"\"\" Gets an endpoint reference to a peer-to-peer FIFO. \"\"\"\n endpoint = ctypes.c_uint32(0)\n self._nifpga.GetPeerToPeerFifoEndpoint(self._session, self._number, endpoint)\n return endpoint.value\n\n @property\n def name(self):\n \"\"\" Property of a Fifo that contains its name. \"\"\"\n return self._name\n\n @property\n def datatype(self):\n \"\"\" Property of a Fifo that contains its datatype. \"\"\"\n return self._datatype\n","sub_path":"nifpga/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":24783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"196187273","text":"def __init__(self):\n self.argument_spec = netapp_utils.na_ontap_host_argument_spec()\n self.argument_spec.update(dict(state=dict(required=False, choices=['present', 'absent'], default='present')))\n self.module = AnsibleModule(argument_spec=self.argument_spec, supports_check_mode=True)\n parameters = self.module.params\n self.state = parameters['state']\n if (HAS_NETAPP_LIB is False):\n self.module.fail_json(msg='the python NetApp-Lib module is required')\n else:\n self.server = netapp_utils.setup_na_ontap_zapi(module=self.module)","sub_path":"Data Set/bug-fixing-5/9dac00fabebeb7da0f4469f853eadb2e69ba4971-<__init__>-bug.py","file_name":"9dac00fabebeb7da0f4469f853eadb2e69ba4971-<__init__>-bug.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"84306069","text":"#!/usr/bin/env python3\n\"\"\"Run pylint against a provided set of python files.\"\"\"\nimport argparse\nimport glob\nimport logging\nfrom typing import List\nimport sys\n\nfrom pylint import lint\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef lint_py(src_dirs: List[str], min_score: int) -> int:\n errors = []\n for src_dir in src_dirs:\n LOGGER.info(\"=\" * 78)\n LOGGER.info(\"Running pylint against %s with minimum score %d\", src_dir, min_score)\n LOGGER.info(\"=\" * 78)\n files = glob.glob(\"{}/**/*.py\".format(src_dir), recursive=True)\n errors += lint_files(files, min_score)\n if errors:\n LOGGER.error(\"=\" * 78)\n LOGGER.error(\"pylint with minimum score %s failed:\", min_score)\n for error in errors:\n LOGGER.error(error)\n LOGGER.error(\"=\" * 78)\n return 1\n LOGGER.info(\"=\" * 78)\n LOGGER.info(\"pylint against %s with minimum score %d completed successfully\", src_dirs, min_score)\n LOGGER.info(\"=\" * 78)\n return 0\n\n\ndef lint_files(files: List[str], min_score: int) -> List[str]:\n \"\"\"Run pylint against a provided set of python files. Return a list of strings indicating\n filepaths and scores for any which do not meet min_score.\"\"\"\n errors = []\n for py_file in files:\n score = lint_file(py_file)\n if score < min_score:\n error = \"pylint {} yielded score {}, < {}\".format(py_file, score, min_score)\n LOGGER.error(error)\n errors.append(error)\n return errors\n\n\ndef lint_file(py_file: str) -> float:\n \"\"\"Run pylint against a file returning a score\"\"\"\n run = lint.Run([py_file], do_exit=False)\n score = run.linter.stats.get(\"global_note\", 10)\n return float(score)\n\n\ndef check_range(n: int) -> int:\n try:\n value = int(n)\n if 0 <= value <= 10:\n return value\n except ValueError:\n pass\n raise argparse.ArgumentTypeError(f\"{n} should be an integer between 0 and 10\")\n\n\ndef main(argv=None):\n if argv is None:\n argv = sys.argv[1:]\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"src_dirs\", type=str, nargs=\"+\", help=\"Source dirs to recursively lint\")\n parser.add_argument(\n \"--min_score\", type=check_range, help=\"Minimum integer score, between 0 and 10.\"\n )\n\n args_ns = parser.parse_args(argv)\n return lint_py(args_ns.src_dirs, args_ns.min_score)\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","sub_path":"ci/lint.py","file_name":"lint.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"437325944","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n\nclass VisuallieClassification(object):\n def __init__(self):\n\n return\n def plt_bar_2(self):\n N = 5\n menMeans = (0.8, 0.1, 0.05,0.025, 0.025)\n menStd = (2, 3, 4, 1, 2)\n\n ind = np.arange(N) # the x locations for the groups\n width = 0.35 # the width of the bars\n \n fig, ax = plt.subplots()\n rects1 = ax.bar(ind, menMeans, width, color='g')\n \n \n\n \n # add some text for labels, title and axes ticks\n ax.set_ylabel('Scores')\n ax.set_title('Scores by group and gender')\n ax.set_xticks(ind)\n ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))\n \n\n\n return\n def plt_bar(self, class_score, class_name, ax):\n # Example data\n \n y_pos = np.arange(len(class_score), 0, -1)\n for i in range(len(class_name)):\n class_name[i] = class_name[i] + '(' + str(class_score[i]) + ')'\n class_name = class_name[::-1]\n #just to make sure socre with very little value can be displayed \n class_score = np.array(class_score) + 0.02\n\n ax.barh(y_pos, class_score, align='center', alpha=0.4)\n for y in y_pos:\n ax.text(0, y, class_name[y-1], fontsize=15)\n ax.set_xlabel('probability')\n ax.get_yaxis().set_ticks([])\n ax.get_xaxis().set_ticks([])\n ax.set_title('Top 5 prediction')\n return\n def plt_classification(self, img_title, img, class_score, class_name):\n fig, axarr = plt.subplots(nrows=1, ncols=2,squeeze=False, figsize=(12, 4))\n fig.set\n axarr[0,0].imshow(img)\n axarr[0,0].set_title(img_title)\n self.plt_bar(class_score, class_name, axarr[0,1])\n plt.show()\n \n return\n \n \n \n def run(self):\n class_score = [0.8, 0.1, 0.05,0.025, 0.025]\n class_name= ['A', 'B','C','D','E']\n img = mpimg.imread('../data/01.jpg')\n img_title = 'Image #12\\nTrue Lable: Stop'\n \n self.plt_classification(img_title, img, class_score, class_name)\n plt.show()\n\n return\n \n\n\nif __name__ == \"__main__\": \n obj= VisuallieClassification()\n obj.run()","sub_path":"explore/visualizeclassification.py","file_name":"visualizeclassification.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"490715695","text":"from pylearn2.devtools.record import RecordMode\nfrom pylearn2.devtools.record import Record\nfrom collections import OrderedDict\nfrom pylearn2.devtools import disturb_mem\nimport numpy as np\nimport theano\nfrom pylearn2.utils import sharedX\nfrom theano.printing import var_descriptor\n\ndef run():\n disturb_mem.disturb_mem()\n\n\n b = sharedX(np.zeros((2,)))\n channels = OrderedDict()\n\n disturb_mem.disturb_mem()\n\n v_max = b.max(axis=0)\n v_min = b.min(axis=0)\n v_range = v_max - v_min\n\n updates = []\n for i, val in enumerate([\n v_max.max(),\n v_max.min(),\n v_range.max(),\n ]):\n disturb_mem.disturb_mem()\n s = sharedX(0., name='s_'+str(i))\n updates.append((s, val))\n\n for var in theano.gof.graph.ancestors(update for var, update in updates):\n if var.name is not None:\n if var.name[0] != 's' or len(var.name) != 2:\n var.name = None\n\n for key in channels:\n updates.append((s, channels[key]))\n file_path='nondeterminism_5.txt'\n mode = RecordMode(file_path=file_path,\n replay=0)\n f = theano.function([], mode=mode, updates=updates, on_unused_input='ignore', name='f')\n\n for i in xrange(100):\n disturb_mem.disturb_mem()\n f()\n\n mode.record.f.flush()\n mode.record.f.close()\n\n mode.set_record(Record(file_path=file_path, replay=1))\n\n for i in xrange(100):\n disturb_mem.disturb_mem()\n f()\n\n\n# Do several trials, since failure doesn't always occur\n# (Sometimes you sample the same outcome twice in a row)\nfor i in xrange(10):\n run()\n","sub_path":"dbm/inpaint/nondeterminism_5.py","file_name":"nondeterminism_5.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"97180199","text":"import sys\nif not (sys.path[0] + '/../') in sys.path:\n sys.path.append(sys.path[0] + '/../')\n\nfrom models import logger as log\nfrom models.adminuser import AdminUser\nfrom models.msg import Msg\nfrom models.process import Process\nfrom models.user import User\nimport logic.filter\n\n\ndef savemsg(msg):\n m = Msg(msg)\n m.content = logic.filter.filter(m.content)\n if len(m.content) > 10:\n res = m.select({'id':1},{'id':msg['id']});\n if res[0] and len(res[1]) == 0:\n m.insert()\n p = Process({'id':msg['id']})\n p.insert()\n else:\n log.e(m.content)\n\ndef saveuser(user):\n u = User(user)\n res = u.select({'id':1},{'id': user['id']});\n if res[0] and len(res[1]) == 0:\n u.insert()\n","sub_path":"src/spider/logic/saver.py","file_name":"saver.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"280292954","text":"#wiki4d2v\n\nimport os\nimport sys\nfrom numpy import array\nimport numpy as np\nfrom gensim.models import doc2vec,word2vec\nimport collections\nimport re\nimport MeCab\n\nimport time\nstart = time.time()\ntagger = MeCab.Tagger('-F\\s%f[6] -U\\s%m -E\\\\n -u wikidic.dic -d /usr/lib/mecab/dic/mecab-ipadic-neologd')\ndef mkdatasets():\n global tempdoc\n wakati = open(\"wiki.txt\",\"r\")\n line = wakati.readline()\n line = re.sub(r\"(https?|ftp)(:\\/\\/[-_\\.!~*\\'()a-zA-Z0-9;\\/?:\\@&=\\+\\$,%#]+)\", \"\" ,line)\n flags=[0,0]\n conts=\"\"\n cnt=0\n while line:\n if line.startswith(\"[[\") and line.endswith(\"]]\\n\") and not \"|\" in line:\n #print(line)\n if flags[0]==1:\n cont=re.split(\"\\[\\[|\\]\\]\",conts)\n #print(cont)\n\n flags[1]=0\n conts=\"\"\n for x in cont:\n if flags[1]==0:\n x=tagger.parse(x)\n flags[1]=1\n conts += x+\" \"\n continue\n if flags[1]==1:\n x=x.replace(\" \",\"\\'_\\'\")\n flags[1]=0\n conts += x+\" \"\n\n #print(conts)\n temp=re.split(\"\\s|\\t|\\n|\\|\",conts)\n cont=temp\n for x in range(temp.count(\"\")):\n cont.remove(\"\")\n #print(cont)\n for x in cont:\n if \"\\'_\\'\" in x:\n x = re.sub(\"\\'_\\'\",\" \",x)\n #print(cont)\n if \"|\" in title:\n titles=title.split(\"|\")\n else:\n titles=[title]\n yield(doc2vec.TaggedDocument(cont, titles))\n #yield(title)\n title=line[2:-3]\n #print(title)\n flags[0]=1\n conts=title\n line = wakati.readline()\n continue\n if flags[0]==1:\n #print(\"a\")\n conts+=line\n cnt+=1\n #print(cnt)\n line = wakati.readline()\n print(cnt)\n cont=re.split(\"\\[\\[|\\]\\]\",conts)\n #print(cont)\n\n flags[1]=0\n conts=\"\"\n for x in cont:\n if flags[1]==0:\n x=tagger.parse(x)\n flags[1]=1\n conts += x+\" \"\n continue\n if flags[1]==1:\n x=x.replace(\" \",\"\\'_\\'\")\n flags[1]=0\n conts += x+\" \"\n\n #print(conts)\n temp=re.split(\"\\s|\\t|\\n|\\|\",conts)\n cont=temp\n for x in range(temp.count(\"\")):\n cont.remove(\"\")\n #print(cont)\n for x in cont:\n if \"\\'_\\'\" in x:\n x = re.sub(\"\\'_\\'\",\" \",x)\n #print(cont)\n if \"|\" in title:\n titles=title.split(\"|\")\n else:\n titles=[title]\n yield(doc2vec.TaggedDocument(temp, titles))\n #yield(title)\n\ndef training(sentences):\n model = doc2vec.Doc2Vec(sentences, size=300, alpha=0.0015, sample=1e-6, min_count=10, workers=8)\n for x in range(100):\n print(x)\n model.train(sentences,total_examples=model.corpus_count,epochs=model.iter)\n ranks = []\n for doc_id in range(100):\n inferred_vector = model.infer_vector(sentences[doc_id].words)\n sims = model.docvecs.most_similar([inferred_vector], topn=len(model.docvecs))\n rank = [docid for docid, sim in sims].index(sentences[doc_id].tags[0])\n ranks.append(rank)\n print(collections.Counter(ranks))\n if collections.Counter(ranks)[0] >= 60:\n break\n return model\n\nsentences=list(mkdatasets())\nmodel=training(sentences)\nelapsed_time = time.time() - start\nprint(elapsed_time)\n\"\"\"\nf=open(\"wiki.csv\",\"w\")\nfor x in sentences:\n #print(str(x))\n f.write(str(x)+\"\\n\")\nf.close()\n\"\"\"\n\n\"\"\"\nfor epoch in range(100):\n print('Epoch: {}'.format(epoch + 1))\n model.train(sentences,total_examples=model.corpus_count,epochs=model.iter)\n model.alpha -= (0.0015 - 0.0001) / 99\n model.min_alpha = model.alpha\n\"\"\"\nmodel.save(\"wiki.d2v\")\nelapsed_time = time.time() - start\nprint(elapsed_time)\n","sub_path":"wikipediad2v.py","file_name":"wikipediad2v.py","file_ext":"py","file_size_in_byte":4105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"533617159","text":"#!/usr/bin/env python\n\nimport sys\nimport copy\nimport rospy\nimport moveit_commander\nimport moveit_msgs.msg\nimport geometry_msgs.msg\nfrom math import pi\nfrom std_msgs.msg import String\nfrom moveit_commander.conversions import pose_to_list\nimport tf\n\n\ndef all_close(goal, actual, tolerance):\n\n \"\"\"\n Convenience method for testing if a list of values are within a tolerance of their counterparts in another list\n @param: goal A list of floats, a Pose or a PoseStamped\n @param: actual A list of floats, a Pose or a PoseStamped\n @param: tolerance A float\n @returns: bool\n \"\"\"\n all_equal = True\n if type(goal) is list:\n for index in range(len(goal)):\n if abs(actual[index] - goal[index]) > tolerance:\n return False\n\n elif type(goal) is geometry_msgs.msg.PoseStamped:\n return all_close(goal.pose, actual.pose, tolerance)\n\n elif type(goal) is geometry_msgs.msg.Pose:\n return all_close(pose_to_list(goal), pose_to_list(actual), tolerance)\n\n return True\n\ndef callback(data,args):\n print(\"hello there\")\n ur_robot = args[0]\n ur_scene = args[1]\n ur_move_group = args[2]\n ur_planning_frame = args[3]\n ur_eef_link = args[4]\n ur_group_names = args[5]\n\n move_group = ur_move_group\n \n print(\"elo grab\") \n print(data)\n data.position.x = data.position.x - 0.05\n data.position.y = data.position.y - 0.03\n data.position.z = 0.15\n data.orientation.x = -0.0\n data.orientation.y = 1.0\n data.orientation.z = 0.0\n data.orientation.w = -0.0\n\n\n move_group.set_pose_target(data)\n\n plan = move_group.go(wait=True)\n move_group.stop()\n move_group.clear_pose_targets()\n current_pose = move_group.get_current_pose().pose\n return all_close(data, current_pose, 0.01)\n\n\ndef main():\n try:\n print(\"Grab\")\n moveit_commander.roscpp_initialize(sys.argv)\n robot = moveit_commander.RobotCommander()\n scene = moveit_commander.PlanningSceneInterface()\n group_name = \"manipulator\"\n move_group = moveit_commander.MoveGroupCommander(group_name)\n\n planning_frame = move_group.get_planning_frame()\n \n eef_link = move_group.get_end_effector_link()\n\n group_names = robot.get_group_names()\n robot.get_current_state()\n\n # Misc variables \n ur_robot = robot\n ur_scene = scene\n ur_move_group = move_group\n ur_planning_frame = planning_frame\n ur_eef_link = eef_link\n ur_group_names = group_names\n\n rospy.init_node('move_ur_python_interface', anonymous=True)\n rospy.Subscriber(\"/aruco_pose\",geometry_msgs.msg.Pose,callback,(ur_robot,\n ur_scene,\n ur_move_group,\n ur_planning_frame,\n ur_eef_link,\n ur_group_names))\n rospy.spin()\n except rospy.ROSInterruptException:\n return\n except KeyboardInterrupt:\n return\n\n\nif __name__ == '__main__':\n main()","sub_path":"catkin_ws/src/aruco_intercept/src/grab_object.py","file_name":"grab_object.py","file_ext":"py","file_size_in_byte":3103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"339734074","text":"__author__ = 'noe'\n\nimport mdtraj\nimport numpy as np\n\nfrom mdtraj.core.trajectory import Trajectory\nfrom pyemma.coordinates.io.reader import ChunkedReader\nfrom pyemma.util.log import getLogger\nfrom featurizer import MDFeaturizer\n\nlog = getLogger('FeatureReader')\n\n__all__ = ['FeatureReader']\n\n\nclass FeatureReader(ChunkedReader):\n\n \"\"\"\n Reads features from MD data.\n\n To select a feature, access the :attr:`featurizer` and call a feature\n selecting method (e.g) distances.\n\n Parameters\n ----------\n trajectories: list of strings\n paths to trajectory files\n\n topologyfile: string\n path to topology file (e.g. pdb)\n\n Examples\n --------\n Iterator access:\n\n >>> reader = FeatureReader('mytraj.xtc', 'my_structure.pdb')\n >>> chunks = []\n >>> for itraj, X in reader:\n >>> chunks.append(X)\n\n \"\"\"\n\n def __init__(self, trajectories, topologyfile):\n # init with chunksize 100\n ChunkedReader.__init__(self, 100)\n\n # files\n if isinstance(trajectories, str):\n trajectories = [trajectories]\n self.trajfiles = trajectories\n self.topfile = topologyfile\n\n # featurizer\n self.featurizer = MDFeaturizer(topologyfile)\n\n # _lengths\n self._lengths = []\n self._totlength = 0\n\n # iteration\n self.mditer = None\n # current lag time\n self.curr_lag = 0\n # time lagged iterator\n self.mditer2 = None\n\n # cache size\n self.in_memory = False\n self.Y = None\n # basic statistics\n for traj in trajectories:\n sum_frames = sum(t.n_frames for t in self._create_iter(traj))\n self._lengths.append(sum_frames)\n\n self._totlength = np.sum(self._lengths)\n\n self.t = 0\n\n def describe(self):\n \"\"\"\n Returns a description of this transformer\n\n :return:\n \"\"\"\n return \"Feature reader, features = \", self.featurizer.describe()\n\n def operate_in_memory(self):\n \"\"\"\n If called, the output will be fully stored in memory\n\n :return:\n \"\"\"\n self.in_memory = True\n # output data\n self.Y = [np.empty((self.trajectory_length(itraj), self.dimension()))\n for itraj in xrange(self.number_of_trajectories())]\n\n def parametrize(self):\n \"\"\"\n Parametrizes this transformer\n\n :return:\n \"\"\"\n if self.in_memory:\n self.map_to_memory()\n\n def number_of_trajectories(self):\n \"\"\"\n Returns the number of trajectories\n\n :return:\n number of trajectories\n \"\"\"\n return len(self.trajfiles)\n\n def trajectory_length(self, itraj):\n \"\"\"\n Returns the length of trajectory\n\n Parameters\n ----------\n itraj : int\n\n :return:\n length of trajectory\n \"\"\"\n return self._lengths[itraj]\n\n def trajectory_lengths(self):\n \"\"\"\n Returns the trajectory _lengths in a list\n :return:\n \"\"\"\n return self._lengths\n\n def n_frames_total(self):\n \"\"\"\n Returns the total number of frames, summed over all trajectories\n :return:\n \"\"\"\n return self._totlength\n\n def dimension(self):\n \"\"\"\n Returns the number of output dimensions\n\n :return:\n \"\"\"\n return self.featurizer.dimension()\n\n def get_memory_per_frame(self):\n \"\"\"\n Returns the memory requirements per frame, in bytes\n\n :return:\n \"\"\"\n return 4 * self.dimension()\n\n def get_constant_memory(self):\n \"\"\"\n Returns the constant memory requirements, in bytes\n\n :return:\n \"\"\"\n return 0\n\n def map_to_memory(self):\n self.reset()\n # iterate over trajectories\n last_chunk = False\n itraj = 0\n while not last_chunk:\n last_chunk_in_traj = False\n t = 0\n while not last_chunk_in_traj:\n y = self.next_chunk()\n assert y is not None\n L = np.shape(y)[0]\n # last chunk in traj?\n last_chunk_in_traj = (t + L >= self.trajectory_length(itraj))\n # last chunk?\n last_chunk = (\n last_chunk_in_traj and itraj >= self.number_of_trajectories() - 1)\n # write\n self.Y[itraj][t:t + L] = y\n # increment time\n t += L\n # increment trajectory\n itraj += 1\n\n def _create_iter(self, filename):\n return mdtraj.iterload(filename, chunk=self.chunksize, top=self.topfile)\n\n def _open_time_lagged(self):\n log.debug(\"open time lagged iterator for traj %i\" % self.curr_itraj)\n if self.mditer2 is not None:\n self.mditer2.close()\n self.mditer2 = self._create_iter(self.trajfiles[self.curr_itraj])\n self.skip_n = int(np.floor(1.0 * self.curr_lag / self.chunksize))\n log.debug(\"trying to skip %i frames in advanced iterator\" %\n self.skip_n)\n i = 0\n for _ in xrange(self.skip_n):\n try:\n self.mditer2.next()\n i += 1\n except StopIteration:\n log.debug(\"was able to increment %i times\" % i)\n break\n\n def reset(self):\n \"\"\"\n resets the chunk reader\n \"\"\"\n self.curr_itraj = 0\n self.curr_lag = 0\n if len(self.trajfiles) >= 1:\n self.t = 0\n self.mditer = self._create_iter(self.trajfiles[0])\n\n def next_chunk(self, lag=0):\n \"\"\"\n gets the next chunk. If lag > 0, we open another iterator with same chunk\n size and advance it by one, as soon as this method is called with a lag > 0.\n\n :return: a feature mapped vector X, or (X, Y) if lag > 0\n \"\"\"\n chunk = self.mditer.next()\n\n if lag > 0:\n if self.curr_lag == 0:\n # lag time changed, so open lagged iterator\n self.curr_lag = lag\n self._open_time_lagged()\n try:\n self.last_advanced_chunk = self.mditer2.next()\n except StopIteration:\n log.debug(\n \"No more data in mditer2 during last_adv_chunk assignment. Padding with zeros\")\n lagged_xyz = np.zeros_like(chunk.xyz)\n self.last_advanced_chunk = Trajectory(\n lagged_xyz, chunk.topology)\n try:\n adv_chunk = self.mditer2.next()\n except StopIteration:\n # no more data available in mditer2, so we have to take data from\n # current chunk and padd it with zeros!\n log.debug(\"No more data in mditer2. Padding with zeros.\"\n \" Data avail: %i\" % chunk.xyz.shape[0])\n lagged_xyz = np.zeros_like(chunk.xyz)\n adv_chunk = Trajectory(lagged_xyz, chunk.topology)\n\n # build time lagged Trajectory by concatenating\n # last adv chunk and advance chunk\n i = lag - (self.chunksize * self.skip_n)\n padding_length = max(0, chunk.xyz.shape[0]\n - (self.last_advanced_chunk.xyz.shape[0] - i)\n - adv_chunk.xyz.shape[0])\n padding = np.zeros(\n (padding_length, chunk.xyz.shape[1], chunk.xyz.shape[2]))\n merged = Trajectory(np.concatenate(\n (self.last_advanced_chunk.xyz,\n adv_chunk.xyz, padding)), chunk.topology)\n # assert merged.xyz.shape[0] >= chunk.xyz.shape[0]\n # skip \"lag\" number of frames and truncate to chunksize\n chunk_lagged = merged[i:][:chunk.xyz.shape[0]]\n\n # remember last advanced chunk\n self.last_advanced_chunk = adv_chunk\n\n self.t += chunk.xyz.shape[0]\n\n if (self.t + lag >= self.trajectory_length(self.curr_itraj) and\n self.curr_itraj < len(self.trajfiles) - 1):\n log.debug('closing current trajectory \"%s\"'\n % self.trajfiles[self.curr_itraj])\n self.mditer.close()\n self.t = 0\n self.curr_itraj += 1\n self.mditer = self._create_iter(self.trajfiles[self.curr_itraj])\n # we open self.mditer2 only if requested due lag parameter!\n self.curr_lag = 0\n\n # map data\n if lag == 0:\n return self.featurizer.map(chunk)\n else:\n X = self.featurizer.map(chunk)\n Y = self.featurizer.map(chunk_lagged)\n return X, Y\n\n def __iter__(self):\n self.reset()\n return self\n\n def next(self):\n \"\"\" enable iteration over transformed data.\n\n Returns\n -------\n (itraj, X) : (int, ndarray(n, m)\n itraj corresponds to input sequence number (eg. trajectory index)\n and X is the transformed data, n = chunksize or n < chunksize at end\n of input.\n\n \"\"\"\n # iterate over trajectories\n if self.curr_itraj >= self.number_of_trajectories():\n raise StopIteration\n\n # next chunk already maps output\n if self.lag == 0:\n X = self.next_chunk()\n else:\n X, Y = self.next_chunk(self.lag)\n\n last_itraj = self.curr_itraj\n # note: t is incremented in next_chunk\n if self.t >= self.trajectory_length(self.curr_itraj):\n self.curr_itraj += 1\n self.t = 0\n\n if self.lag == 0:\n return (last_itraj, X)\n\n return (last_itraj, X, Y)\n","sub_path":"pyemma/coordinates/io/feature_reader.py","file_name":"feature_reader.py","file_ext":"py","file_size_in_byte":9761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"644861138","text":"def fixed_num(seq, num):\n avg = len(seq) / float(num)\n out = []\n last = 0.0\n while last < len(seq):\n out.append(seq[int(last):int(last + avg)])\n last += avg\n return out\n\ndef fixed_size(seq, size):\n for i in xrange(0, len(seq), size):\n yield seq[i:i+size]\n\n","sub_path":"core/scripts/chunkIt.py","file_name":"chunkIt.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"653693258","text":"from io import open\nfrom setuptools import find_packages, setup\nimport re\nimport sys\n\n\ndef fetch_requirements(path):\n with open(path, 'r') as fd:\n return [r.strip() for r in fd.readlines()]\n\n\ntry:\n filepath = './neural_compressor/version.py'\n with open(filepath) as version_file:\n __version__, = re.findall('__version__ = \"(.*)\"', version_file.read())\nexcept Exception as error:\n assert False, \"Error: Could not open '%s' due %s\\n\" % (filepath, error)\n\nneural_insights = False\nif \"neural_insights\" in sys.argv:\n neural_insights = True\n sys.argv.remove(\"neural_insights\")\n\n\n# define include packages\ninclude_packages = find_packages(include=['neural_compressor', 'neural_compressor.*',\n 'neural_coder', 'neural_coder.*'])\nneural_insights_packages = find_packages(include=['neural_insights', 'neural_insights.*'])\n\n# define package data\npackage_data = {'': ['*.yaml']}\nneural_insights_data = {'': ['*.yaml', 'web/app/*.*']}\n\n# define install requirements\ninstall_requires_list = fetch_requirements('requirements.txt')\nneural_insights_requires = fetch_requirements('neural_insights/requirements.txt')\n\n# define scripts\nscripts_list = []\nneural_insights_scripts_list = ['neural_insights/bin/neural_insights']\n\nif neural_insights:\n project_name = \"neural_insights\"\n package_data = neural_insights_data\n install_requires_list = neural_insights_requires\n scripts_list = neural_insights_scripts_list\n include_packages = neural_insights_packages\nelse:\n project_name = \"neural_compressor\"\n\nif __name__ == '__main__':\n\n setup(\n name=project_name,\n version=__version__,\n author=\"Intel AIA Team\",\n author_email=\"feng.tian@intel.com, haihao.shen@intel.com, suyue.chen@intel.com\",\n description=\"Repository of Intel® Neural Compressor\",\n long_description=open(\"README.md\", \"r\", encoding='utf-8').read(),\n long_description_content_type=\"text/markdown\",\n keywords='quantization, auto-tuning, post-training static quantization, post-training dynamic quantization, quantization-aware training',\n license='Apache 2.0',\n url=\"https://github.com/intel/neural-compressor\",\n packages=include_packages,\n include_package_data=True,\n package_data=package_data,\n install_requires=install_requires_list,\n scripts=scripts_list,\n python_requires='>=3.6.0',\n classifiers=[\n 'Intended Audience :: Science/Research',\n 'Programming Language :: Python :: 3',\n 'Topic :: Scientific/Engineering :: Artificial Intelligence',\n 'License :: OSI Approved :: Apache Software License',\n ],\n )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"292438792","text":"from flask import current_app, request\nfrom flask_jwt_extended import jwt_required\nfrom flask_restful import fields, marshal, reqparse\n\nfrom api.models import Chamber, Rack, Tray\nfrom api.models.database import BaseModel\nfrom api.models.freezer import Freezer\nfrom api.resources.base_resource import BaseResource\nfrom api.resources.decorators.user_role_decorators import is_theme_admin\nfrom api.resources.lab_resource import LaboratoryResource\nfrom api.utils import format_and_lower_str, non_empty_int, log_create, has_required_request_params, \\\n log_update, log_delete, log_duplicate, standard_non_empty_string, log_304, get_query_params, fake, get_chambers\n\n\nclass FreezerResource(BaseResource):\n fields = {\n 'number': fields.String,\n 'lab.building': fields.String,\n 'lab.room': fields.String,\n 'code': fields.String\n }\n\n def get(self):\n query_strings = get_query_params()\n if query_strings is not None:\n for query_string in query_strings:\n query, total = Freezer.search(query_string, 1, 15)\n\n # query freezer to check for chambers\n freezer = FreezerResource.get_freezer(query_string)\n\n if freezer is not None:\n data = get_chambers(freezer.id)\n return BaseResource.send_json_message(data, 200)\n else:\n freezers = query.all()\n\n data = marshal(freezers, self.fields)\n return BaseResource.send_json_message(data, 200)\n\n elif request.headers.get('code') is not None:\n code = format_and_lower_str(request.headers['code'])\n freezer = FreezerResource.get_freezer(code)\n if freezer is None:\n return BaseResource.send_json_message(\"Freezer not found\", 404)\n else:\n data = marshal(freezer, self.fields)\n return BaseResource.send_json_message(data, 200)\n else:\n freezer = Freezer.query.all()\n if freezer is None:\n return BaseResource.send_json_message(\"Freezers not found\", 404)\n else:\n data = marshal(freezer, self.fields)\n return BaseResource.send_json_message(data, 200)\n\n @jwt_required\n @is_theme_admin\n def post(self):\n args = FreezerResource.freezer_args()\n if type(args['laboratory']) is str:\n lab = LaboratoryResource.get_laboratory(args['laboratory']).id\n else:\n lab = args['laboratory']\n\n number = args['number']\n code = fake.ean(length=8)\n chambers = int(args['chambers'])\n racks = int(args['racks'])\n trays = int(args['trays'])\n\n if not Freezer.freezer_exists(code):\n try:\n freezer = Freezer(lab=lab, num=number, code=code)\n BaseModel.db.session.add(freezer) # add freezer to session\n BaseModel.db.session.flush() # flush session to make it available for reference even b4 commit\n\n # create other resources\n for _ in range(chambers):\n number = _ + 1\n chamber = Chamber(freezer=freezer.id, _type=number, code=fake.ean(length=8))\n BaseModel.db.session.add(chamber)\n BaseModel.db.session.flush()\n for _ in range(racks):\n num = _ + 1\n rack = Rack(chamber=chamber.id, num=num, code=fake.ean(length=8))\n BaseModel.db.session.add(rack)\n BaseModel.db.session.flush()\n for _ in range(trays):\n nums = _ + 1\n tray = Tray(rack=rack.id, num=nums, code=fake.ean(length=8))\n BaseModel.db.session.add(tray)\n BaseModel.db.session.flush()\n\n BaseModel.db.session.commit()\n log_create(freezer)\n return BaseResource.send_json_message(\"Freezer Successfully Created\", 201)\n except Exception as e:\n current_app.logger.error(e)\n BaseModel.db.session.rollback()\n return BaseResource.send_json_message(\"Error while adding freezer\", 500)\n log_duplicate(Freezer.query.filter(Freezer.code == code).first())\n return BaseResource.send_json_message(\"Freezer already exists\", 409)\n\n @jwt_required\n @is_theme_admin\n @has_required_request_params\n def put(self):\n code = format_and_lower_str(request.headers['code'])\n freezer = FreezerResource.get_freezer(code)\n\n if freezer is None:\n return BaseResource.send_json_message(\"Freezer does not exist\", 404)\n\n else:\n args = FreezerResource.freezer_args()\n if type(args['laboratory']) is str:\n lab = LaboratoryResource.get_laboratory(args['laboratory'])\n laboratory = lab.id\n else:\n laboratory = args['laboratory']\n\n number = args['number']\n code = args['code']\n\n if laboratory != freezer.laboratory_id or number != freezer.number or \\\n code != freezer.code:\n old_info = str(freezer)\n try:\n freezer.laboratory_id = laboratory\n freezer.number = number\n freezer.code = code\n BaseModel.db.session.commit()\n log_update(old_info, freezer)\n return BaseResource.send_json_message(\"Successfully updated freezer\", 202)\n\n except Exception as e:\n current_app.logger.error(e)\n BaseModel.db.session.rollback()\n return BaseResource.send_json_message(\"Error while updating freezer\", 500)\n log_304(freezer)\n return BaseResource.send_json_message(\"No changes made\", 304)\n\n @jwt_required\n @is_theme_admin\n @has_required_request_params\n def delete(self):\n code = format_and_lower_str(request.headers['code'])\n freezer = FreezerResource.get_freezer(code)\n\n if freezer is None:\n return BaseResource.send_json_message(\"Freezer does not exist\", 404)\n\n BaseModel.db.session.delete(freezer)\n BaseModel.db.session.commit()\n log_delete(freezer)\n return BaseResource.send_json_message(\"Freezer deleted\", 200)\n\n @staticmethod\n def freezer_args():\n parser = reqparse.RequestParser()\n parser.add_argument('laboratory', required=True, type=non_empty_int)\n parser.add_argument('number', required=True, type=non_empty_int)\n parser.add_argument('code', required=True, type=standard_non_empty_string)\n parser.add_argument('chambers', required=False, type=non_empty_int)\n parser.add_argument('racks', required=False, type=non_empty_int)\n parser.add_argument('trays', required=False, type=non_empty_int)\n\n args = parser.parse_args()\n return args\n\n @staticmethod\n def get_freezer(code):\n return BaseModel.db.session.query(Freezer).filter_by(code=code).first()\n","sub_path":"api/resources/freezer_resource.py","file_name":"freezer_resource.py","file_ext":"py","file_size_in_byte":7228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"330644557","text":"import json\nfrom typing import Dict\n\nfrom csp.decorators import csp_exempt\nfrom auth.normal_auth import jwt_valid\nfrom django.middleware.csrf import get_token\nfrom django.urls import reverse\nfrom django.http import Http404\nfrom django.http import HttpResponse, HttpRequest\nfrom django.views.generic import TemplateView\n\nfrom mixins.view import ElmLoadJsView\nfrom text.models import Text\nfrom question.models import Answer\n\nfrom user.views.instructor import InstructorView\n\nfrom ereadingtool.menu import MenuItems, instructor_create_a_text_menu_item\n\n\nclass AdminView(InstructorView, TemplateView):\n pass\n\n\nclass TextAdminView(AdminView):\n model = Text\n template_name = 'instructor_admin/admin.html'\n\n\nclass TextAdminElmLoadView(ElmLoadJsView):\n def get_instructor_menu_items(self) -> MenuItems:\n instructor_menu_items = super(TextAdminElmLoadView, self).get_instructor_menu_items()\n\n instructor_menu_items.append(instructor_create_a_text_menu_item())\n\n instructor_menu_items.select('admin-text-search')\n\n return instructor_menu_items\n\n def get_context_data(self, **kwargs) -> Dict:\n context_data = super(TextAdminElmLoadView, self).get_context_data()\n\n context_data['elm']['text_api_endpoint_url'] = {\n 'quote': True,\n 'safe': True,\n 'value': reverse('text-api')\n }\n\n return context_data\n\n\nclass TextDefinitionElmLoadView(ElmLoadJsView):\n def get_context_data(self, **kwargs) -> Dict:\n context = super(TextDefinitionElmLoadView, self).get_context_data(**kwargs)\n\n if 'pk' in context:\n try:\n text = Text.objects.get(pk=context['pk'])\n words, word_freqs = text.definitions\n\n context['elm']['words'] = {\n 'quote': False,\n 'safe': True,\n 'value': json.dumps(list(words.items()))\n }\n\n context['elm']['word_frequencies'] = {\n 'quote': False,\n 'safe': True,\n 'value': json.dumps(list(word_freqs.items()))\n }\n except Text.DoesNotExist:\n pass\n\n return context\n\n\nclass TextDefinitionView(AdminView):\n model = Text\n template_name = 'instructor_admin/text_definitions.html'\n\n @jwt_valid()\n def get(self, request: HttpRequest, *args, **kwargs) -> HttpResponse:\n if not self.model.objects.filter(pk=kwargs['pk']):\n raise Http404('text does not exist')\n\n return super(TextDefinitionView, self).get(request, *args, **kwargs)\n\n\nclass AdminCreateEditTextView(AdminView):\n model = Text\n\n fields = ('source', 'difficulty', 'body',)\n template_name = 'instructor_admin/create_edit_text.html'\n\n @jwt_valid()\n def get(self, request, *args, **kwargs) -> HttpResponse:\n if 'pk' in kwargs and not self.model.objects.filter(pk=kwargs['pk']):\n raise Http404('text does not exist')\n\n return super(AdminCreateEditTextView, self).get(request, *args, **kwargs)\n\n # GA's nonce CSP policy conflicts with previously used unsafe-inline.\n # Attempts at using nonce with CkEditor failed so making this csp_exempt for now\n @csp_exempt\n def dispatch(self, request, *args, **kwargs):\n return super(AdminCreateEditTextView, self).dispatch(request, *args, **kwargs)\n\n\nclass AdminCreateEditElmLoadView(ElmLoadJsView):\n template_name = 'instructor_admin/load_elm.html'\n\n def get_context_data(self, **kwargs):\n context = super(AdminCreateEditElmLoadView, self).get_context_data(**kwargs)\n text = None\n\n if 'pk' in context:\n try:\n text = Text.objects.get(pk=context['pk'])\n except Text.DoesNotExist:\n pass\n\n context['elm']['text'] = {\n 'quote': False,\n 'safe': True,\n 'value': json.dumps(text.to_dict() if text else None)\n }\n\n context['elm']['tags'] = {\n 'quote': False,\n 'safe': True,\n 'value': json.dumps([tag.name for tag in Text.tag_choices()])\n }\n\n context['elm']['translation_flags'] = {\n 'quote': False,\n 'safe': True,\n 'value': json.dumps({\n 'csrftoken': get_token(self.request),\n 'add_as_text_word_endpoint_url': reverse('text-word-api'),\n 'merge_textword_endpoint_url': reverse('text-word-group-api'),\n 'text_translation_match_endpoint': reverse('text-translation-match-method')\n })\n }\n\n context['elm']['text_endpoint_url'] = {\n 'quote': False,\n 'safe': True,\n 'value': json.dumps(reverse('text-api'))\n }\n\n context['elm']['answer_feedback_limit'] = {\n 'quote': False,\n 'safe': True,\n 'value': Answer._meta.get_field('feedback').max_length\n }\n\n return context\n","sub_path":"instructor_admin/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"5437829","text":"import sys\nfrom stack import YowsupServerStack\n\nif __name__ == \"__main__\":\n credentials = tuple(sys.argv[1].split(\":\"))\n if not credentials:\n print(\"Error: You must specify a configuration method\")\n sys.exit(1)\n try:\n stack = YowsupServerStack(credentials, True)\n stack.start()\n except KeyboardInterrupt:\n print(\"\\nYowsdown\")\n\n","sub_path":"whatsapp/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"630594528","text":"from django.db.models import Q\nfrom django.shortcuts import render, redirect\n\nfrom projects.models import Position, Project\n\n\ndef home(request):\n \"\"\"Redirects User to respective Profile page on signed in\"\"\"\n user = request.user.id\n if user:\n return redirect('accounts:profile', pk=user)\n else:\n return redirect('accounts:sign_in')\n\n\ndef all_projects(request):\n \"\"\"Redirects User clean search & filter page\"\"\"\n positions = Position.objects.values('title').distinct().order_by('title')\n projects = Project.objects.all()\n return render(request, 'index.html', {\n 'projects': projects,\n 'positions': positions\n })\n\n\ndef search(request):\n \"\"\"Searches database by Project title or description\"\"\"\n term = request.GET.get('q')\n positions = Position.objects.values('title').distinct().order_by('title')\n projects = Project.objects.filter(\n Q(name__icontains=term) |\n Q(description__icontains=term))\n return render(request, 'index.html', {\n 'projects': projects,\n 'positions': positions\n })\n\n\ndef filter(request, filter):\n \"\"\"Filters database and returns all Projects for selected Positions\"\"\"\n # Query all Positions distinct title values\n positions = Position.objects.values('title').distinct().order_by('title')\n\n # Filter all queried Positions by selected Position title\n filtered_positions = Position.objects.filter(\n title__icontains=filter).distinct().order_by('title')\n\n # Filter for all distinct Project id's associated with filtered Positions\n project_ids = Position.objects.filter(\n title__icontains=filter).values('project_id').distinct()\n\n # Create list of all filtered Project id's\n project_ids_list = [ids['project_id'] for ids in project_ids]\n\n # Query all Projects related to Positions\n # with a Project id in the above list\n projects = Project.objects.filter(id__in=project_ids_list)\n\n return render(request, 'index.html', {\n 'positions': positions,\n 'filtered_positions': filtered_positions,\n 'projects': projects\n })\n","sub_path":"team_builder/team_builder/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"583986347","text":"\n\n#calss header\nclass _REMOVER():\n\tdef __init__(self,): \n\t\tself.name = \"REMOVER\"\n\t\tself.definitions = [u'a substance that removes something: ', u'a person or company who helps people to move their furniture and other possessions when they move to a new home']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_remover.py","file_name":"_remover.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"278669023","text":"import logging\nimport logging.handlers\nimport queue\n\nque = queue.Queue()\nqueue_handler = logging.handlers.QueueHandler(que)\nhandler = logging.StreamHandler()\n\nlistener = logging.handlers.QueueListener(que, handler)\n\nroot = logging.getLogger()\nroot.addHandler(queue_handler)\nformatter = logging.Formatter('%(asctime)s <%(threadName)s>: %(message)s')\nhandler.setFormatter(formatter)\nlistener.start()\n\nroot.warning('Look out!')\nlistener.stop()\n\n","sub_path":"libs/StandardLibrary/14_application/logging/queue_handler.py","file_name":"queue_handler.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"389793236","text":"\ndef mergesort(A,B):\n a= len(A) \n b= len(B) \n \n res = [] \n i, j = 0, 0\n \n while i < a and j < b: \n if A[i] < B[j]: \n res.append(A[i]) \n i += 1\n \n else: \n res.append(B[j]) \n j += 1\n \n res = res + A[i:] + B[j:] \n \n print (\"sorted array \" + str(res)) \n\nA = [17,26,54,77,93]\nB = [20,25,31,44,55]\n\nmergesort(A,B)\n","sub_path":"merge_sort/mergesort.py","file_name":"mergesort.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"429772115","text":"from model.sklearn_like_model.BaseClassifierModel import BaseClassifierModel\nfrom util.Stacker import Stacker\nfrom util.tensor_ops import *\nimport tensorflow as tf\n\n\nclass MLPClassifier(BaseClassifierModel):\n VERSION = 1.0\n\n @property\n def hyper_param_key(self):\n return [\n 'batch_size',\n 'learning_rate',\n 'beta1',\n 'dropout_rate',\n 'K_average_top_k_loss',\n 'net_shapes',\n 'activation',\n 'l1_norm_lambda',\n 'l2_norm_lambda'\n ]\n\n @property\n def _Xs(self):\n return self.Xs\n\n @property\n def _Ys(self):\n return self.Ys\n\n @property\n def _predict_ops(self):\n return self.predict_index\n\n @property\n def _score_ops(self):\n return self.acc_mean\n\n @property\n def _proba_ops(self):\n return self.h\n\n @property\n def _metric_ops(self):\n return self.loss\n\n @property\n def _train_ops(self):\n return [self.train_op, self.op_inc_global_step]\n\n def hyper_parameter(self):\n self.batch_size = 100\n self.learning_rate = 0.01\n self.beta1 = 0.5\n self.K_average_top_k_loss = 20\n self.net_shapes = [128, 128]\n self.activation = 'relu'\n self.l1_norm_lambda = 0.0001\n self.l2_norm_lambda = 0.001\n\n def build_input_shapes(self, input_shapes):\n self.X_batch_key = 'Xs'\n self.X_shape = input_shapes[self.X_batch_key]\n self.Xs_shape = [None] + self.X_shape\n\n self.Y_batch_key = 'Ys'\n self.Y_shape = input_shapes[self.Y_batch_key]\n self.Ys_shape = [None] + self.Y_shape\n self.Y_size = self.Y_shape[0]\n\n def classifier(self, Xs, net_shapes, name='classifier'):\n with tf.variable_scope(name):\n layer = Stacker(flatten(Xs))\n\n for net_shape in net_shapes:\n layer.linear_block(net_shape, relu)\n\n layer.linear(self.Y_size)\n logit = layer.last_layer\n h = softmax(logit)\n return logit, h\n\n def build_main_graph(self):\n self.Xs = tf.placeholder(tf.float32, self.Xs_shape, name='Xs')\n self.Ys = tf.placeholder(tf.float32, self.Ys_shape, name='Ys')\n\n self.logit, self.h = self.classifier(self.Xs, self.net_shapes)\n\n self.vars = collect_vars(join_scope(get_scope(), 'classifier'))\n\n self.predict_index = tf.cast(tf.argmax(self.h, 1, name=\"predicted_label\"), tf.float32)\n self.label_index = onehot_to_index(self.Ys)\n self.acc = tf.cast(tf.equal(self.predict_index, self.label_index), tf.float64, name=\"acc\")\n self.acc_mean = tf.reduce_mean(self.acc, name=\"acc_mean\")\n\n def build_loss_function(self):\n self.loss = tf.nn.softmax_cross_entropy_with_logits_v2(labels=self.Ys, logits=self.logit)\n\n self.l1_norm_penalty = L1_norm(self.vars, lambda_=self.l1_norm_lambda)\n self.l1_norm_penalty_mean = tf.reduce_mean(self.l1_norm_penalty, name='l1_norm_penalty_mean')\n # self.l1_norm_penalty *= wall_decay(0.999, self.global_step, 100)\n self.l2_norm_penalty = L2_norm(self.vars, lambda_=self.l2_norm_lambda)\n self.l2_norm_penalty_mean = tf.reduce_mean(self.l2_norm_penalty, name='l2_norm_penalty_mean')\n\n self.loss = self.loss + self.l1_norm_penalty\n # average top k loss\n # self.loss = average_top_k_loss(self.loss, self.K_average_top_k_loss)\n self.loss_mean = tf.reduce_mean(self.loss, name='loss_mean')\n\n def build_train_ops(self):\n self.train_op = tf.train.AdamOptimizer(\n learning_rate=self.learning_rate).minimize(self.loss, var_list=self.vars)\n","sub_path":"model/sklearn_like_model/MLPClassifier.py","file_name":"MLPClassifier.py","file_ext":"py","file_size_in_byte":3661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"41038318","text":"import random\r\nimport string\r\nfrom datetime import datetime\r\nfrom datetime import date\r\nimport os\r\nfrom nltk.corpus import names\r\nfrom prettytable import PrettyTable\r\n\r\n\r\nclass Claim:\r\n\t#An event (such as a doctor's appointment) claimed from our policyholders\r\n\tdef __init__(self, lossDate, claimBilled,claimCovered, uID, age):\r\n\t\tself.lossDate = datetime.strptime(lossDate, \"%m/%d/%Y\")\r\n\t\tself.lossYear = self.lossDate.year\r\n\t\tself.claimBilled = claimBilled\r\n\t\tself.claimantAge = age\r\n\t\tself.claimantName = uID\r\n\t\tself.claimCovered = claimCovered\r\n\r\nclass PolicyHolder:\r\n\t#An individual's insurance information \r\n\tdef __init__(self, firstName,lastName, DOB, SSN, gender, smokingStatus, medicalConditions):\r\n\t\tself.firstName = firstName\r\n\t\tself.lastName = lastName\r\n\t\tself.fullName = firstName + ' ' + lastName\r\n\t\tself.DOB = datetime.strptime(DOB, \"%m/%d/%Y\")\r\n\t\tself.birthYear = self.DOB.year\r\n\t\tself.SSN = \"###-##-\"+SSN[-5:-1]\r\n\t\tself.gender = gender\r\n\t\tself.age = int(date.today().year) - int(self.birthYear)\r\n\t\tself.smokingStatus = smokingStatus\r\n\t\tself.medicalConditions = medicalConditions\r\n\t\tself.claimHistory = []\r\n\t\tself.uniqueIdentifier = ''\r\n\t\tself.assignUniqueIdentifier()\r\n\tdef reportLoss(self,lossDate,claimBilled,claimCovered):\r\n\t\tself.claimHistory.append(Claim(lossDate,claimBilled,claimCovered, self.uniqueIdentifier, self.age))\r\n\tdef assignUniqueIdentifier(self):\r\n\t\tself.uniqueIdentifier = ''.join(random.choice(string.ascii_uppercase + string.digits) for x in range(4))\r\n\t\t\r\nclass Dashboard:\r\n\t#A basic dashboard object used to pull aggregations from our policyholders info\r\n\tdef __init__(self,claims):\r\n\t\tself.totalPaid = sum([claim.claimCovered for claim in claims])\r\n\t\tself.averageAge = sum([claim.claimantAge for claim in claims])/len(claims)\r\n\t\tself.yearsWithclaims = {claim.lossYear for claim in claims}\r\n\t\tself.yearClaimCounts = {}\r\n\t\tself.getClaimCounts(claims)\r\n\t\tself.claims = claims\r\n\tdef getClaimCounts(self,claims):\r\n\t\tfor year in self.yearsWithclaims:\r\n\t\t\tself.yearClaimCounts[year] = sum(claim.lossYear == year for claim in claims)\r\n\tdef printDashboard(self):\r\n\t\taggTable = PrettyTable()\r\n\t\taggTable.field_names = [\"Description\",\"Value\"]\r\n\t\taggTable.add_row([\"Total Paid\",convertToMoney(self.totalPaid)])\r\n\t\taggTable.add_row([\"Average Age\",round(self.averageAge,3)])\r\n\t\tyearTable = PrettyTable()\r\n\t\tyearTable.field_names= [\"Year\",\"Claim Count\"]\r\n\t\tfor year in self.yearClaimCounts:\r\n\t\t\tyearTable.add_row([year,self.yearClaimCounts[year]])\r\n\t\tprint(yearTable)\r\n\t\tprint(aggTable)\r\n\t\t\t\r\n\t\t\t\r\n\r\ndef convertToMoney(value):\r\n\treturn '${:,.2f}'.format(value)\r\n\r\ndef randomDate(beginYear,endYear):\r\n\treturn str(random.randint(1,12))+\"/\"+str(random.randint(1,28))+\"/\"+str(random.randint(beginYear,endYear))\r\n\r\ndef simulateData(insuredCount):\r\n\tinsuredList = []\r\n\tfirstNames = names.words('male.txt')+names.words('female.txt')\r\n\tfor insured in range(insuredCount):\r\n\t\tinsuredList.append(PolicyHolder(random.choice(firstNames),\"Smith\",randomDate(1970,2000),str(random.randint(100000000,999999999)),random.choice([\"Male\",\"Female\"]),random.choice([\"Y\",\"N\"]),[]))\r\n\tfor insured in insuredList:\r\n\t\tclaimCount = random.randint(0,10)\r\n\t\tfor claim in range(claimCount):\r\n\t\t\tbill = random.randint(10,99999)\r\n\t\t\tinsured.reportLoss(randomDate(1990,2019), bill,bill*random.randint(50,100)/100)\r\n\treturn insuredList\r\n\t\t\r\n\r\ndef addInsured(data):\r\n\tfirstName, lastName = str(input(\"Please enter the insured's full name:\\n\")).split(\" \")\r\n\t# DOB, SSN, gender, smokingStatus, medicalConditions\r\n\tDOB = str(input(\"Please enter the DOB of the insured: \\n\"))\r\n\tSSN = str(input(\"Please enter the SSN of the insured: \\n\"))\r\n\tGender = str(input(\"What is the insured's gender? \\n\"))\r\n\tsmokingStatus = str(input(\"do they smoke? (Y/N)\"))\r\n\tmedicalConditions = str(input(\"Please input any medical conditions, separated by commas below: \\n\")).split(\",\")\r\n\tnewInsured = PolicyHolder(firstName,lastName,DOB,SSN,Gender,smokingStatus,medicalConditions)\r\n\tdata.append(newInsured)\r\n\tos.system('cls')\r\n\tprint(\"The new Insured's unique ID is: %s\" % newInsured.uniqueIdentifier)\r\n\treturn data\r\n\r\n\r\ndef reportClaim(data):\r\n\t#lossDate,claimBilled,claimCovered\r\n\tfindID = str(input(\"Please enter the ID of the insured you are looking up: \\n\"))\r\n\tfor insured in data:\r\n\t\tif insured.uniqueIdentifier == findID:\r\n\t\t\tlossDate = str(input(\"please enter the date of loss in MM/DD/YYYY format below: \\n\"))\r\n\t\t\tclaimBilled = int(input(\"Please enter the amount billed (numbers only): \\n\"))\r\n\t\t\tclaimCovered = int(input(\"Please enter the amount that the bill was covered (numbers only): \\n\"))\r\n\t\t\tinsured.reportLoss(lossDate,claimBilled,claimCovered)\r\n\treturn data\r\n\r\ndef listInsured(data):\r\n\tinsuredTable = PrettyTable()\r\n\tinsuredTable.field_names = [\"ID\",\"Insured Name\",\"DOB\",\"Gender\",\"Smoking Status\",\"SSN\",\"Claim Count\"]\r\n\tfor insured in data:\r\n\t\tinsuredTable.add_row([insured.uniqueIdentifier,insured.fullName,insured.DOB,insured.gender,insured.smokingStatus,insured.SSN,len(insured.claimHistory)])\r\n\treturn insuredTable\r\n\t\t\r\ndef listClaims(data):\r\n\twhile True:\r\n\t\tfindID = str(input(\"Please enter the ID of the insured you are looking up: \\n\"))\r\n\t\tfor insured in data:\r\n\t\t\tif insured.uniqueIdentifier == findID:\r\n\t\t\t\tclaimTable = PrettyTable()\r\n\t\t\t\tclaimTable.field_names = [\"Event Date\",\"Billed Amount\",\"Covered Amount\"]\r\n\t\t\t\tfor claim in insured.claimHistory:\r\n\t\t\t\t\tclaimTable.add_row([claim.lossDate,claim.claimBilled,claim.claimCovered])\t\t\r\n\t\treturn claimTable\r\n\t\t\t\r\n\r\n\r\ndef mainMenu(data):\r\n\twhile True:\r\n\t\tprint(\"Welcome to Health-Connect, please choose from the following options:\")\r\n\t\tprint(\"1 - Add an insured name\")\r\n\t\tprint(\"2 - Report a Claim\")\r\n\t\tprint(\"3 - List all insured individuals\")\r\n\t\tprint(\"4 - Display all claims by uniqueIdentifier\")\r\n\t\tprint(\"5 - Basic Aggregate Data\")\r\n\t\tOption = str(input()).lower()\r\n\t\tif \"add\" in Option or Option == \"1\":\r\n\t\t\tos.system('cls')\r\n\t\t\tdata = addInsured(data)\r\n\t\t\tcontinue\r\n\t\telif (\"report\" or \"claim\") in Option or Option == \"2\":\r\n\t\t\tos.system('cls')\r\n\t\t\tdata = reportClaim(data)\r\n\t\t\tcontinue\r\n\t\telif \"list\" in Option or Option == \"3\":\r\n\t\t\tos.system('cls')\r\n\t\t\tprint(listInsured(data))\r\n\t\t\tcontinue\r\n\t\telif \"display\" in Option or Option == \"4\":\r\n\t\t\tos.system('cls')\r\n\t\t\tprint(listClaims(data))\r\n\t\t\tcontinue\r\n\t\telif (\"aggregate\" or \"data\") in Option or Option == \"5\":\r\n\t\t\tos.system('cls')\r\n\t\t\tclaims = []\r\n\t\t\tfor insured in data:\r\n\t\t\t\tclaims += insured.claimHistory\r\n\t\t\tDashboard(claims).printDashboard()\r\n\t\telse:\r\n\t\t\tos.system('cls')\r\n\t\t\tprint(\"It seems we did not recognize your input, please try again\")\r\n\t\t\tcontinue\r\n\r\ndef startMenu():\r\n\tos.system('cls')\r\n\twhile True:\r\n\t\tsimulate = str(input(\" Would you like to simulate some random data to sample the program? (Y/N)\\n\"))\r\n\t\tif simulate.lower().startswith(\"y\"):\r\n\t\t\tmainMenu(simulateData(20))\r\n\t\telif simulate.lower().startswith(\"n\"):\r\n\t\t\tmainMenu([])\r\n\t\telse:\r\n\t\t\tprint(\"Sorry, please enter 'Y' or 'N'\")\r\n\t\t\tcontinue\r\n\r\nstartMenu()","sub_path":"TakeHome.py","file_name":"TakeHome.py","file_ext":"py","file_size_in_byte":6926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"54765759","text":"'''\nAuthor : Mohammad Altaaf Hussein Hamod (AllStar) \nDetails & All about info : This module is for displaying the game menu.\n'''\n\ndef gamesfunct(time, os, mes, username, adminset, guestfile):\n \n choosen = \"0\"\n \n if username==\"guest\" and adminset[0]==1 and guestfile[1]==5:\n \n print(\"-----------------------------------------------------\")\n print(\"Sorry, Guest gameplay time has reached its MAX Limit!\")\n print(\"-----------------------------------------------------\")\n print()\n print(\"--------------------------------------------------\")\n print(\"What can be done\")\n print(\" - Ask admin to reset the limit ammount again to 0\")\n print(\" - Ask admin to remove the login limit\")\n print(\" - Just create an account! :)\")\n print(\"--------------------------------------------------\")\n print()\n mes.press()\n \n else:\n while choosen!=\"4\":\n os.system(\"cls\")\n print(\"=================================\")\n print(\" User Menu \")\n if username==\"guest\":\n if adminset[0]==0:\n print(\"Guest play time :- Unlimited\")\n else:\n print(\"Guest play time :- \", guestfile[1])\n print(\"=================================\")\n print(\"1. The Lottery\")\n print(\"2. Tic-Tac-Toe (vs PC)\")\n print(\"3. Tic-Tac-Toe (2 Player)\")\n print(\"4. Back to Menu\")\n print(\"=================================\")\n \n choosen = input(\"Choose a menu item (1 or 2 or ... etc) : \")\n os.system(\"cls\")\n \n if username==\"guest\" and adminset[0]==1 and guestfile[1]==5 and choosen!=\"4\":\n \n print(\"-----------------------------------------------------\")\n print(\"Sorry, Guest gameplay time has reached its MAX Limit!\")\n print(\"-----------------------------------------------------\")\n print()\n print(\"--------------------------------------------------\")\n print(\"What can be done\")\n print(\" - Ask admin to reset the limit ammount again to 0\")\n print(\" - Ask admin to remove the login limit\")\n print(\" - Just create an account! :)\")\n print(\"--------------------------------------------------\")\n print()\n mes.press()\n \n elif choosen==\"4\":\n \n mes.back()\n mes.press()\n \n else: \n if choosen==\"1\":\n \n if adminset[0]==1 and username==\"guest\":\n guestfile[1] += 1 \n file = open(GUESTFILE, \"w\")\n file.write(guestfile[0])\n file.write(guestfile[1])\n file.close()\n from module.game import lottery\n lottery.lotteryfunct(os, mes, time)\n \n elif choosen==\"2\":\n \n if adminset[0]==1 and username==\"guest\":\n guestfile[1] += 1\n file = open(GUESTFILE, \"w\")\n file.write(guestfile[0])\n file.write(guestfile[1])\n file.close() \n from module.game import tictactoepc\n tictactoepc.main()\n \n elif choosen==\"3\":\n \n if adminset[0]==1 and username==\"guest\":\n guestfile[1] += 1\n file = open(GUESTFILE, \"w\")\n file.write(guestfile[0])\n file.write(guestfile[1])\n file.close() \n from module.game import tictactoemulti\n tictactoemulti.main()\n \n else:\n \n mes.err()\n mes.press()","sub_path":"module/games.py","file_name":"games.py","file_ext":"py","file_size_in_byte":4219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"270748201","text":"import networkx as nx\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef draw_graph(G):\n # G = nx.Graph()\n # G.add_edge('A', 'B', weight=4)\n # G.add_edge('B', 'D', weight=2)\n # G.add_edge('A', 'C', weight=3)\n # G.add_edge('C', 'D', weight=5)\n # G.add_edge('A', 'D', weight=10)\n # for u, v, d in G.edges(data=True):\n # print(u, v, d['weight'])\n\n # edge_labels = dict([((u, v,), d['weight']) for u, v, d in G.edges(data=True)])\n # fixed_position = {'A': [0.55072989, 0.00426975], 'B': [1., 0.], 'D': [0.38252302, 0.10520343],\n # 'C': [0., 0.09481996]} # 每个点在坐标轴中的位置\n # pos = nx.spring_layout(G, pos=fixed_position) # 获取结点的位置,每次点的位置都是随机的\n # nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels) # 绘制图中边的权重\n # # print(edge_labels)\n nx.draw_networkx(G)\n plt.show()\n\n\ngraph = nx.DiGraph()\nedges = [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'A'), ('B', 'D'), ('C', 'A'), ('D', 'B'), ('D', 'C')]\n\ngraph.add_edges_from(edges)\npage_rank = nx.pagerank(graph, alpha=0.85)\nprint(page_rank)\ndraw_graph(graph)\n","sub_path":"33丨PageRank/page_rank.py","file_name":"page_rank.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"596775336","text":"#coding=utf-8\n\nfrom selenium import webdriver\nimport time\nimport unittest\n\n\nclass DemoUnit(unittest.TestCase):\n\n\turl = 'https://mall.imeihao.shop/my'\n\n\tdef setUp(self):\n\t\tself.driver = webdriver.Chrome()\n\t\tself.driver.get(self.url)\n\t\tself.driver.implicitly_wait(30)\n\n\tdef tearDown(self):\n\t\tself.driver.close()\n\t\tself.driver.quit()\n\n\tdef test_Login(self):\n\t\t# 查找登入的按钮并点击登入\n\t\tbtn_prelogin = self.driver.find_element_by_xpath('//*[@id=\"mh-scroll-load\"]/div/div/div[1]/h2')\n\t\tprint(btn_prelogin.text)\n\t\tbtn_prelogin.click()\n\t\tself.driver.implicitly_wait(10)\n\t\ttime.sleep(2)\n\t\tlogin_frame = self.driver.find_element_by_xpath('/html/body/div[10]/div/div[2]/div/div/div[1]/h1')\n\t\tprint(login_frame.is_displayed())\n\t\t#检查界面是否显示\n\t\tif login_frame.is_displayed():\n\t\t\tprint('登入框能正常显示，其标题是：%s'%login_frame.text)\n\t\t\t# 开始定位用户名密码跟登入按钮\n\t\t\ttxt_Mobile = self.driver.find_element_by_xpath('/html/body/div[10]/div/div[2]/div/div/div[2]/div[1]/input')\n\t\t\ttxt_Mobile.send_keys('13713572468')\n\t\t\ttxt_Pwd = self.driver.find_element_by_xpath('/html/body/div[10]/div/div[2]/div/div/div[2]/div[2]/input')\n\t\t\ttxt_Pwd.send_keys('123456789')\n\t\t\tbtn_login = self.driver.find_element_by_xpath('/html/body/div[10]/div/div[2]/div/div/button')\n\t\t\tbtn_login.click()\n\t\t\tself.driver.implicitly_wait(30)\n\t\telse:\n\t\t\tprint('登入框没有正确激活。。。。。')\n\nif __name__ == '__main__':\n\tunittest.main(verbosity=2)\n\t# suite = unittest.TestSuite()\n\t# suite.addTest(DemoUnit('test_Login'))\n\t# runner = unittest.TextTestRunner()\n\t# runner.run(suite)","sub_path":"demo_login.py","file_name":"demo_login.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"486952759","text":"from dao.db import *\nimport time\nimport collections\nfrom model import Push\nfrom bson.objectid import ObjectId\n\ndef insert_push(push):\n\tcoll = db.push\n\ts = collections.OrderedDict()\n\tnow = int(time.time())\n\ttimeArray = time.localtime(now)\n\tdatetime = time.strftime(\"%Y-%m-%d %H:%M:%S\",timeArray)\n\ts[\"box\"] = push.box\n\ts[\"user\"] = push.user\n\ts[\"title\"] = push.title\n\ts[\"desc\"] = push.desc\n\ts[\"datetime\"] = datetime\n\tprint(s)\n\tpush_id = coll.insert_one(s).inserted_id\n\tprint(push_id)\n\treturn push_id\n\t\ndef select_all_push(skip, limit):\n\tcoll = db.push\n\tpushs = []\n\tc = coll.find().sort(\"_id\",pymongo.DESCENDING).skip(skip).limit(limit)\n\tif c == None:\n\t\treturn None\n\telse:\n\t\tfor push in c :\n\t\t\tpushs.append(Push(push['box'],push['user'],push['title'],push['desc'],push['_id'],push['datetime']))\n\t\t\n\treturn pushs\n\t\ndef select_box_push(box_id):\n\tcoll = db.push\n\tpushs = []\n\tc = coll.find({\"box\": ObjectId(box_id)}).sort(\"_id\",pymongo.DESCENDING)\n\tif(c == None):\n\t\treturn None\n\telse:\n\t\tfor push in c :\n\t\t\tpushs.append(Push(push['box'],push['user'],push['title'],push['desc'],push['_id'],push['datetime']))\n\t\t\n\treturn pushs\n\t\ndef select_user_push(user_id, skip, limit):\n\tcoll = db.push\n\tpushs= []\n\tc = coll.find({\"user\":ObjectId(user_id)}).sort(\"_id\",pymongo.DESCENDING).skip(skip).limit(limit)\n\tif c==None:\n\t\treturn None\n\telse:\n\t\tfor push in c :\n\t\t\tpushs.append(Push(push['box'],push['user'],push['title'],push['desc'],push['_id'],push['datetime']))\n\treturn pushs\n\t\ndef delete_push(push_id):\n\tcoll = db.push\n\tr = coll.remove({\"_id\": ObjectId(push_id)})\n\treturn r['n']\n\t\ndef delete_push_by_box(box_id):\n\tcoll = db.push\n\tr = coll.remove({\"box\": ObjectId(box_id)})\n\treturn r['n']","sub_path":"dao/push_dao.py","file_name":"push_dao.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"599474793","text":"import tensorflow as tf\nfrom tensorflow.contrib.rnn import BasicLSTMCell\nimport numpy as np\nimport tensorflow_hub as hub\nfrom models.encoder_cell import GatedSkimLSTMCell, GatedSkimLSTMStateTuple\n\nELMOSIZE = 1024\n\nclass ModelGumbel:\n\tdef __init__(self, args, textData, initializer=None, eager=False):\n\t\tprint('Creating single lstm Model')\n\t\tself.args = args\n\t\tself.textData = textData\n\n\t\tself.dropOutRate = None\n\t\tself.initial_state = None\n\t\tself.learning_rate = None\n\t\tself.loss = None\n\t\tself.optOp = None\n\t\tself.labels = None\n\t\tself.input = None\n\t\tself.target = None\n\t\tself.length = None\n\t\tself.embedded = None\n\t\tself.predictions = None\n\t\tself.batch_size = None\n\t\tself.corrects = None\n\t\tself.initializer = initializer\n\t\tself.eager = eager\n\n\t\tif self.args.elmo:\n\t\t\tself.embedding_size = ELMOSIZE\n\t\telse:\n\t\t\tself.embedding_size = self.args.embeddingSize\n\n\t\tself.mask = None\n\t\tself.v0 = None\n\t\tself.v1 = None\n\t\tself.v2 = None\n\t\tself.v3 = None\n\t\tself.v4 = None\n\t\tself.v5 = None\n\t\tself.v6 = None\n\t\tself.v7 = None\n\n\t\tif not eager:\n\t\t\tself.buildNetwork()\n\n\tdef buildInputs(self):\n\t\twith tf.name_scope('placeholders'):\n\t\t\t# [batch_size, max_steps]\n\t\t\tinput_shape = [None, self.args.maxSteps]\n\n\t\t\tif self.args.elmo:\n\t\t\t\tself.data = tf.placeholder(tf.string, shape=input_shape, name='data')\n\t\t\telse:\n\t\t\t\tself.data = tf.placeholder(tf.int32, shape=input_shape, name='data')\n\t\t\t# [batch_size]\n\t\t\tself.length = tf.placeholder(tf.int32, shape=[None], name='length')\n\n\t\t\t# [batch_size]\n\t\t\tself.labels = tf.placeholder(tf.int32, shape=[None,], name='labels')\n\n\t\t\t# scalar\n\t\t\tself.batch_size = tf.placeholder(tf.int32, shape=(), name='batch_size')\n\t\t\tself.dropOutRate = tf.placeholder(tf.float32, shape=(), name='dropOut')\n\t\t\tself.is_training = tf.placeholder(tf.bool, shape=(), name='is_training')\n\n\tdef buildEmbeddings(self):\n\t\twith tf.name_scope('embedding_layer'):\n\t\t\tif not self.args.preEmbedding:\n\t\t\t\tprint('Using randomly initialized embeddings!')\n\t\t\t\tembeddings = tf.get_variable(\n\t\t\t\t\tshape=[self.textData.getVocabularySize(), self.args.embeddingSize],\n\t\t\t\t\tinitializer=tf.contrib.layers.xavier_initializer(),\n\t\t\t\t\tname='embeddings')\n\t\t\t\t# [batch_size, n_turn, max_steps, embedding_size]\n\t\t\t\tself.embedded = tf.nn.embedding_lookup(embeddings, self.data)\n\t\t\telif not self.args.elmo:\n\t\t\t\tprint('Using pretrained glove word embeddings!')\n\t\t\t\tembeddings = tf.Variable(self.textData.preTrainedEmbedding, name='embedding', dtype=tf.float32, trainable=False)\n\t\t\t\t# [batch_size, n_turn, max_steps, embedding_size]\n\t\t\t\tself.embedded = tf.nn.embedding_lookup(embeddings, self.data)\n\t\t\telse:\n\t\t\t\t# elmo not supported for eager execution\n\n\t\t\t\telmo = hub.Module(\"https://tfhub.dev/google/elmo/2\", trainable=self.args.trainElmo)\n\t\t\t\t# [batch_size*n_turn, max_steps]\n\t\t\t\tdata_elmo = tf.reshape(self.data, shape=[-1, self.args.maxSteps], name='data_elmo')\n\t\t\t\t# [batch_size*n_turn]\n\t\t\t\tlength_elmo = tf.reshape(self.length, shape=[-1], name='length_elmo')\n\t\t\t\t# [batch_size*n_turn, elmo_size]\n\t\t\t\tself.embedded = elmo(\n\t\t\t\t\tinputs={\n\t\t\t\t\t\t\"tokens\": data_elmo,\n\t\t\t\t\t\t\"sequence_len\": length_elmo\n\t\t\t\t\t},\n\t\t\t\t\tsignature=\"tokens\",\n\t\t\t\t\tas_dict=True)['elmo']\n\t\t\t\tself.embedded = tf.reshape(self.embedded, shape=[self.batch_size, self.args.maxSteps, ELMOSIZE], name='elmo_embedded')\n\t\t\t# [batch_size, n_turn, max_steps, embedding_size]\n\t\t\tself.embedded = tf.nn.dropout(self.embedded, self.dropOutRate, name='embedding_dropout')\n\n\tdef buildNetwork(self):\n\t\twith tf.name_scope('inputs'):\n\t\t\tif not self.eager:\n\t\t\t\tself.buildInputs()\n\t\t\tself.buildEmbeddings()\n\n\t\twith tf.name_scope('generator'):\n\t\t\t# [batch_size, max_steps]\n\t\t\tmask = self.generator()\n\t\t\tself.mask = mask\n\n\t\twith tf.name_scope('encoder'):\n\t\t\toutputs = self.encoder(mask)\n\n\t\twith tf.variable_scope('output'):\n\t\t\tweights = tf.get_variable(name='weights', shape=[self.args.hiddenSize, self.args.nClasses],\n\t\t\t\t\t\t\t\t\t initializer=self.initializer)\n\n\t\t\tbiases = tf.get_variable(name='biases', shape=[self.args.nClasses],\n\t\t\t initializer=self.initializer)\n\t\t\t# [batchSize, nClasses]\n\t\t\tlogits = tf.nn.xw_plus_b(x=outputs, weights=weights, biases=biases)\n\t\twith tf.name_scope('predictions'):\n\t\t\t# [batchSize]\n\t\t\tself.predictions = tf.argmax(logits, axis=-1, name='predictions', output_type=tf.int32)\n\t\t\t# single number\n\t\t\tself.corrects = tf.reduce_sum(tf.cast(tf.equal(self.predictions, self.labels), tf.int32), name='corrects')\n\n\t\twith tf.name_scope('loss'):\n\t\t\t# [batch_size]\n\t\t\tloss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=self.labels, name='loss')\n\n\t\t\t# punish selections, [batch_size]\n\t\t\tvalid_masks = tf.sequence_mask(self.length, maxlen=self.args.maxSteps, dtype=tf.float32)\n\t\t\tmask = mask * valid_masks\n\t\t\tself.true_mask = mask\n\t\t\t# [batch_size]\n\t\t\tmask_per_sample = tf.reduce_sum(mask, axis=-1) / tf.cast(self.length, tf.float32)\n\t\t\tself.mask_per_sample = mask_per_sample\n\t\t\t# discourages transitions\n\n\t\t\t# [batch_size, max_steps-1]\n\t\t\tmask_shift_right = tf.slice(mask, begin=[0, 0], size = [-1, self.args.maxSteps-1])\n\t\t\tpad = tf.expand_dims(mask[:, 0], -1)\n\t\t\tmask_shift_right = tf.concat([pad, mask_shift_right], axis=-1, name='mask_shift_right')\n\n\t\t\ttransitions = tf.abs(mask - mask_shift_right)\n\t\t\ttransitions = transitions * valid_masks\n\n\t\t\ttransitions_per_sample = tf.reduce_sum(transitions, axis=-1) / tf.cast(self.length, tf.float32)\n\n\t\t\tadditional_loss = self.args.theta * mask_per_sample + self.args.gamma * transitions_per_sample\n\n\t\t\tself.loss = tf.reduce_mean(loss+additional_loss)\n\n\t\tif self.args.eager:\n\t\t\treturn\n\n\t\twith tf.name_scope('backpropagation'):\n\t\t\ttrainable_params = tf.trainable_variables()\n\n\t\t\tm = tf.reduce_mean(self.args.theta * mask_per_sample)\n\t\t\tt = tf.reduce_mean(self.args.gamma * transitions_per_sample)\n\t\t\tgradients_m = tf.gradients(m, trainable_params)\n\t\t\tgradients_t = tf.gradients(t, trainable_params)\n\t\t\t#\n\t\t\t# opt = tf.train.AdamOptimizer(learning_rate=self.args.learningRate, beta1=0.9, beta2=0.999,\n\t\t\t# epsilon=1e-08)\n\t\t\t# self.optOp = opt.apply_gradients(zip(gradients, trainable_params))\n\n\t\t\topt = tf.train.AdamOptimizer(learning_rate=self.args.learningRate, beta1=0.9, beta2=0.999,\n\t\t\t\t\t\t\t\t\t\t\t epsilon=1e-08)\n\t\t\tself.optOp = opt.minimize(self.loss)\n\n\tdef encoder(self, mask):\n\t\t\"\"\"\n\n\t\t:param mask: [batch_size, mask], the prob of the word being read\n\t\t:return:\n\t\t\"\"\"\n\t\twith tf.variable_scope('cell', reuse=False):\n\n\t\t\tdef get_cell(hiddenSize, dropOutRate):\n\t\t\t\tprint('building skim cell!')\n\t\t\t\tcell = GatedSkimLSTMCell(num_units=hiddenSize, state_is_tuple=True,\n\t\t\t\t initializer=self.initializer, threshold=self.args.threshold)\n\t\t\t\tcell = tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=dropOutRate,\n\t\t\t\t output_keep_prob=dropOutRate)\n\t\t\t\treturn cell\n\n\t\t\t# https://stackoverflow.com/questions/47371608/cannot-stack-lstm-with-multirnncell-and-dynamic-rnn\n\n\t\t\tcell = get_cell(self.args.hiddenSize, self.dropOutRate)\n\n\n\t\tc = tf.zeros([self.batch_size, self.args.hiddenSize], dtype=tf.float32)\n\t\th = tf.zeros([self.batch_size, self.args.hiddenSize], dtype=tf.float32)\n\t\t# [batchSize, maxSteps, hiddenSize]\n\t\tstate = (GatedSkimLSTMStateTuple(c, h, mask[:, 0]))\n\n\t\toutputs = []\n\t\t# TODO: remember the length of each sentence\n\t\twith tf.variable_scope(\"loop\", reuse=tf.AUTO_REUSE):\n\t\t\tfor time_step in range(self.args.maxSteps):\n\t\t\t\t# [batch_size, hidden_size]\n\t\t\t\t# [batch_size, 1]\n\t\t\t\t(cell_output, state) = cell(self.embedded[:, time_step, :], state)\n\t\t\t\tc, h, read_prob = state\n\t\t\t\toutputs.append(cell_output)\n\t\t\t\tif time_step == self.args.maxSteps - 1:\n\t\t\t\t\tbreak\n\t\t\t\tstate = GatedSkimLSTMStateTuple(c, h, mask[:, time_step+1])\n\n\t\t# [maxSteps, batchSize, hiddenSize]\n\t\toutputs = tf.stack(outputs)\n\t\t# [batchSize, maxSteps, hiddenSize]\n\t\toutputs = tf.transpose(outputs, [1, 0, 2], name='outputs')\n\n\t\t# [batchSize, maxSteps]\n\t\tlast_relevant_mask = tf.one_hot(indices=self.length - 1, depth=self.args.maxSteps, name='last_relevant',\n\t\t dtype=tf.int32)\n\t\t# [batchSize, hiddenSize]\n\t\tlast_relevant_outputs = tf.boolean_mask(outputs, last_relevant_mask, name='last_relevant_outputs')\n\n\t\treturn last_relevant_outputs\n\n\t@staticmethod\n\tdef gumbel(logits, temperature):\n\t\t\"\"\"\n\n\t\t:param logits:\n\t\t:param temperature:\n\t\t:return:\n\t\t\"\"\"\n\t\t# g = -log(-log(u)), u ~ U(0, 1)\n\t\tnoise = tf.random.uniform(shape=tf.shape(logits), name='noise')\n\n\t\tnoise = tf.add(noise, 1e-9)\n\t\tnoise = -tf.log(noise)\n\n\t\tnoise = tf.add(noise, 1e-9)\n\t\tnoise = -tf.log(noise)\n\n\t\tlogits_new = (logits + noise) / temperature\n\n\t\tprobs = tf.nn.softmax(logits=logits_new, axis=-1, name='probs')\n\n\t\treturn probs\n\n\tdef sample(self, probs):\n\t\thard_mask = tf.cast(tf.greater(probs, self.args.threshold), tf.float32, name='hard_mask')\n\n\t\t# x if true\n\t\tmask_final = tf.where(condition=self.is_training, x=probs, y=hard_mask)\n\n\t\treturn mask_final\n\n\tdef generator(self):\n\t\twith tf.name_scope('cell'):\n\t\t\tdef get_cell(hiddenSize, dropOutRate):\n\t\t\t\tprint('building ordinary cell!')\n\t\t\t\tcell = BasicLSTMCell(num_units=hiddenSize, state_is_tuple=True)\n\t\t\t\tcell = tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=dropOutRate,\n\t\t\t\t output_keep_prob=dropOutRate)\n\t\t\t\treturn cell\n\n\t\t\t# https://stackoverflow.com/questions/47371608/cannot-stack-lstm-with-multirnncell-and-dynamic-rnn\n\t\t\tmultiCell = []\n\t\t\tfor i in range(self.args.rnnLayers):\n\t\t\t\tmultiCell.append(get_cell(self.args.hiddenSize, self.dropOutRate))\n\t\t\tmultiCell = tf.contrib.rnn.MultiRNNCell(multiCell, state_is_tuple=True)\n\n\t\twith tf.name_scope('get_rnn_outputs'):\n\t\t\t# outputs: [batch_size, max_steps, hidden_size]\n\t\t\toutputs, states = tf.nn.dynamic_rnn(cell=multiCell,\n\t\t inputs=self.embedded, sequence_length=self.length,\n\t\t dtype=tf.float32)\n\t\twith tf.name_scope('hidden'):\n\t\t\tweights = tf.get_variable(name='weights', shape=[self.args.hiddenSize, 2], dtype=tf.float32)\n\t\t\tbiases = tf.get_variable(name='biases', shape=[self.args.nClasses], dtype=tf.float32)\n\n\t\t\t# logits: [batch_size*max_steps, 2]\n\t\t\tlogits = tf.nn.xw_plus_b(x=tf.reshape(outputs, [-1, self.args.hiddenSize]),\n\t\t\t weights = weights, biases = biases, name='logits')\n\t\t\t# probs: [batch_size*max_steps, 2]\n\t\t\tprobs = self.gumbel(logits=logits, temperature=self.args.temperature)\n\n\t\t\t# probs_selected: [batch_size*max_steps, 1]\n\t\t\tprobs_selected = tf.slice(probs, begin=[0, 1], size=[-1, 1], name='probs_selected')\n\t\t\t# probs_selected: [batch_size, max_steps]\n\t\t\tprobs_selected = tf.reshape(probs_selected, [self.batch_size, self.args.maxSteps])\n\n\t\t\tmask = self.sample(probs_selected)\n\n\t\t\treturn mask\n\n\n\tdef step_eager(self, data, length, labels, test):\n\t\t\"\"\"\n\t\tnot supported for training, currently only debugging\n\t\t:param data:\n\t\t:param length:\n\t\t:param labels:\n\t\t:param test:\n\t\t:return:\n\t\t\"\"\"\n\n\t\tself.labels = labels\n\t\tself.data = data\n\t\tself.length = length\n\t\tself.batch_size = len(labels)\n\t\tself.is_training = not test\n\n\t\tif not test:\n\t\t\tself.dropOutRate = self.args.dropOut\n\t\telse:\n\t\t\tself.dropOutRate = 1.0\n\n\tdef step_graph(self, data, length, labels, test):\n\t\t\"\"\"\n\t\t:param data:\n\t\t:param length:\n\t\t:param labels:\n\t\t:param test:\n\t\t:return:\n\t\t\"\"\"\n\t\tfeed_dict = dict()\n\n\t\tfeed_dict[self.labels] = labels\n\t\tfeed_dict[self.data] = np.asarray(data)\n\t\tfeed_dict[self.length] = np.asarray(length)\n\t\tfeed_dict[self.batch_size] = len(labels)\n\t\tfeed_dict[self.is_training] = not test\n\n\t\tif not test:\n\t\t\tfeed_dict[self.dropOutRate] = self.args.dropOut\n\t\t\tops = (self.optOp, self.loss, self.predictions, self.corrects, self.mask_per_sample, self.true_mask)\n\t\telse:\n\t\t\t# during test, do not use drop out!!!!\n\t\t\tfeed_dict[self.dropOutRate] = 1.0\n\t\t\tops = (self.loss, self.predictions, self.corrects, self.mask_per_sample, self.true_mask)\n\n\t\treturn ops, feed_dict, labels\n\n\tdef step(self, batch, test=False, eager=False):\n\n\t\t# [batch_size, max_steps]\n\t\tdata = []\n\t\t# [batch_size]\n\t\tlength = []\n\t\t# [batch_size]\n\t\tlabels = []\n\n\t\tfor sample in batch.samples:\n\t\t\tlabels.append(sample.label)\n\t\t\tif self.args.elmo:\n\t\t\t\tdata.append(sample.words)\n\t\t\telse:\n\t\t\t\tdata.append(sample.word_ids)\n\t\t\tlength.append(sample.length)\n\n\t\tdata = np.asarray(data)\n\t\tlength = np.asarray(length)\n\t\tlabels = np.asarray(labels)\n\n\t\tif eager:\n\t\t\treturn self.step_eager(data=data, length=length, labels=labels, test=test)\n\t\telse:\n\t\t\treturn self.step_graph(data=data, length=length, labels=labels, test=test)\n","sub_path":"models/model_gumbel.py","file_name":"model_gumbel.py","file_ext":"py","file_size_in_byte":12435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"486584766","text":"from ftw.builder import Builder\nfrom ftw.builder import create\nfrom opengever.base.model import create_session\nfrom opengever.contact.models import MailAddress\nfrom opengever.contact.models import Organization\nfrom opengever.contact.models import OrgRole\nfrom opengever.contact.models import Person\nfrom opengever.contact.syncer.object_syncer import AddressSyncer\nfrom opengever.contact.syncer.object_syncer import MailSyncer\nfrom opengever.contact.syncer.object_syncer import OrganizationSyncer\nfrom opengever.contact.syncer.object_syncer import OrgRoleSyncer\nfrom opengever.contact.syncer.object_syncer import PersonSyncer\nfrom opengever.contact.syncer.object_syncer import PhoneNumberSyncer\nfrom opengever.contact.syncer.object_syncer import UrlSyncer\nfrom opengever.testing import FunctionalTestCase\nfrom path import Path\nfrom pkg_resources import resource_string\nfrom sqlalchemy import Boolean\nfrom sqlalchemy import Column\nfrom sqlalchemy import create_engine\nfrom sqlalchemy import Integer\nfrom sqlalchemy import MetaData\nfrom sqlalchemy import String\nfrom sqlalchemy import Table\nfrom sqlalchemy.orm import sessionmaker\nimport os\nimport transaction\n\n\nassets = Path('{}/assets/object_syncer/'.format(os.path.dirname(__file__)))\n\n\ndef load_asset(name):\n return resource_string('opengever.contact.tests',\n 'assets/object_syncer/{}'.format(name))\n\n\nclass SyncerBaseTest(FunctionalTestCase):\n\n SAMPLE_DATA = []\n\n def setUp(self):\n super(SyncerBaseTest, self).setUp()\n self.session = create_session()\n\n engine = create_engine('sqlite:///:memory:')\n self.source_db = sessionmaker(bind=engine)()\n self.source_metadata = MetaData(engine)\n self.create_source_db()\n self.insert_sample_data()\n\n def create_source_db(self):\n raise NotImplementedError\n\n def insert_sample_data(self):\n for item in self.SAMPLE_DATA:\n self.source_db.execute(\n self.source_table.insert().values(**item))\n\n def tearDown(self):\n super(SyncerBaseTest, self).tearDown()\n self.session.close()\n\n\nclass TestPersonSyncer(SyncerBaseTest):\n\n SAMPLE_DATA = [{u'is_active': True, u'former_contact_id': 1,\n u'firstname': u'Frank G.', u'lastname': u'Dippel',\n u'salutation': u'Herr', u'title': u'Dr.'},\n {u'is_active': False, u'former_contact_id': 2,\n u'firstname': u'Christiane',\n u'lastname': u'W\\xfcrsd\\xf6rfer',\n u'salutation': u'Frau', u'title': u'B.Sc.'},\n {u'is_active': True, u'former_contact_id': 3,\n u'firstname': u'Dolorene', u'lastname': u'Dolores',\n u'salutation': u'Frau', u'title': u''}]\n\n def create_source_db(self):\n self.source_table = Table(\"persons\",\n self.source_metadata,\n Column(\"former_contact_id\", Integer),\n Column(\"salutation\", String),\n Column(\"title\", String),\n Column(\"firstname\", String),\n Column(\"lastname\", String),\n Column(\"is_active\", Boolean))\n self.source_table.create()\n\n def test_add_objects_properly_while_syncing(self):\n syncer = PersonSyncer(self.source_db, 'SELECT * from persons')\n syncer()\n\n self.assertEqual(3, len(Person.query.all()))\n\n person = Person.query.get_by_former_contact_id(2)\n self.assertItemsEqual(\n [u'Frau', u'B.Sc.', u'Christiane', u'W\\xfcrsd\\xf6rfer', False, 2],\n [person.salutation, person.academic_title, person.firstname,\n person.lastname, person.is_active, person.former_contact_id])\n\n person = Person.query.get_by_former_contact_id(1)\n self.assertItemsEqual(\n [u'Herr', u'Dr.', u'Frank G.', u'Dippel', True],\n [person.salutation, person.academic_title, person.firstname,\n person.lastname, person.is_active])\n\n def test_update_object_properly_while_syncing(self):\n syncer = PersonSyncer(self.source_db, 'SELECT * from persons')\n syncer()\n\n self.assertEqual(3, len(Person.query.all()))\n\n self.source_db.execute(\n self.source_table.update().where(\n self.source_table.c.former_contact_id == 3).values(\n lastname=u'Meier', is_active=False))\n\n PersonSyncer(self.source_db, 'SELECT * from persons')()\n\n self.assertEqual(3, len(Person.query.all()))\n person = Person.query.get_by_former_contact_id(3)\n self.assertEquals('Meier', person.lastname)\n self.assertFalse(person.is_active)\n\n\nclass TestOrganizationSyncer(SyncerBaseTest):\n\n SAMPLE_DATA = [{u'is_active': True,\n u'former_contact_id': 2344,\n u'name': u'Soziale Dienste'},\n {u'is_active': False,\n u'former_contact_id': 5637,\n u'name': u'Poliz\\xe4iwache'}]\n\n def create_source_db(self):\n self.source_table = Table(\"organizations\",\n self.source_metadata,\n Column(\"former_contact_id\", Integer),\n Column(\"name\", String),\n Column(\"is_active\", Boolean))\n self.source_table.create()\n\n def test_add_objects_properly_while_syncing(self):\n OrganizationSyncer(self.source_db, 'SELECT * from organizations')()\n\n self.assertEqual(2, len(Organization.query.all()))\n\n organizations = Organization.query.all()\n self.assertEqual(\n [u'Soziale Dienste', u'Poliz\\xe4iwache'],\n [org.name for org in organizations])\n self.assertEqual(\n [2344, 5637],\n [org.former_contact_id for org in organizations])\n\n def test_update_existing_objects_properly_while_syncing(self):\n OrganizationSyncer(self.source_db, 'SELECT * from organizations')()\n transaction.commit()\n self.assertEqual(2, len(Organization.query.all()))\n\n self.source_db.execute(\n self.source_table.update().where(\n self.source_table.c.former_contact_id == 5637).values(\n name=u'CIA', is_active=True))\n\n OrganizationSyncer(self.source_db, 'SELECT * from organizations')()\n transaction.commit()\n\n self.assertEqual(2, len(Organization.query.all()))\n\n organization = Organization.query.get_by_former_contact_id(5637)\n self.assertEquals('CIA', organization.name)\n self.assertTrue(organization.is_active)\n\n\nclass TestMailSyncer(SyncerBaseTest):\n\n SAMPLE_DATA = [{u'former_contact_id': 2344,\n u'address': u'foo@example.com',\n u'label': u'E-Mail (gesch\\xe4ftlich)'},\n {u'former_contact_id': 2344,\n u'address': u'bar@example.com',\n u'label': u'E-Mail (privat)'}]\n\n def create_source_db(self):\n self.source_table = Table(\"mails\",\n self.source_metadata,\n Column(\"former_contact_id\", Integer),\n Column(\"address\", String),\n Column(\"label\", String))\n self.source_table.create()\n\n def test_add_mails_properly_while_syncing(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2344))\n\n syncer = MailSyncer(self.source_db, 'SELECT * from mails')\n syncer()\n\n organization = Organization.query.first()\n self.assertEquals(2, len(organization.mail_addresses))\n self.assertEquals(\n [u'E-Mail (gesch\\xe4ftlich)', u'E-Mail (privat)'],\n [mail.label for mail in organization.mail_addresses])\n self.assertEquals(\n [u'foo@example.com', u'bar@example.com'],\n [mail.address for mail in organization.mail_addresses])\n\n self.assertEqual(2, syncer.stats.get('added'))\n self.assertEqual(0, syncer.stats.get('updated'))\n\n def test_updates_existing_mailaddresses_properly_by_label(self):\n org_1 = create(Builder('organization')\n .having(name=u'Meier AG', former_contact_id=2344))\n org_2 = create(Builder('organization')\n .having(name=u'James AG', former_contact_id=5555))\n\n create(Builder('mailaddress')\n .for_contact(org_1)\n .having(label=u'E-Mail (privat)',\n address=u'private@example.com'))\n create(Builder('mailaddress')\n .for_contact(org_2)\n .having(label=u'E-Mail (gesch\\xe4ftlich)',\n address=u'james@example.com'))\n\n syncer = MailSyncer(self.source_db, 'SELECT * from mails')\n syncer()\n\n self.assertEquals(3, MailAddress.query.count())\n\n organization = Organization.query.first()\n self.assertEquals(2, len(organization.mail_addresses))\n self.assertEquals(\n [u'E-Mail (privat)', u'E-Mail (gesch\\xe4ftlich)'],\n [mail.label for mail in organization.mail_addresses])\n self.assertEquals(\n [u'bar@example.com', u'foo@example.com'],\n [mail.address for mail in organization.mail_addresses])\n\n self.assertEqual(1, syncer.stats.get('added'))\n self.assertEqual(1, syncer.stats.get('updated'))\n\n\nclass TestURLSyncer(SyncerBaseTest):\n\n SAMPLE_DATA = [{u'former_contact_id': 2344,\n u'url': u'www.shop.example.com',\n u'label': u'Shop'},\n {u'former_contact_id': 2344,\n u'url': u'http://www.website.example.com',\n u'label': u'Website'},\n {u'former_contact_id': 2344,\n u'url': u'https://intern.example.com',\n u'label': u'Intranet'}]\n\n def create_source_db(self):\n self.source_table = Table(\"urls\",\n self.source_metadata,\n Column(\"former_contact_id\", Integer),\n Column(\"url\", String),\n Column(\"label\", String))\n self.source_table.create()\n\n def test_add_urls_properly_while_syncing(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2344))\n\n UrlSyncer(self.source_db, 'SELECT * from urls')()\n\n organization = Organization.query.first()\n self.assertEquals(3, len(organization.urls))\n self.assertEquals(\n [u'Shop', u'Website', u'Intranet'],\n [url.label for url in organization.urls])\n self.assertEquals(\n [u'http://www.shop.example.com',\n u'http://www.website.example.com',\n u'https://intern.example.com'],\n [url.url for url in organization.urls])\n\n def test_updates_existing_urls_properly_by_label(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2344))\n create(Builder('url')\n .for_contact(organization)\n .having(label=u'Shop',\n url=u'http://www.old_shop.example.com'))\n\n UrlSyncer(self.source_db, 'SELECT * from urls')()\n\n organization = Organization.query.first()\n self.assertEquals(3, len(organization.urls))\n self.assertEquals(\n [u'Shop', u'Website', u'Intranet'],\n [url.label for url in organization.urls])\n self.assertEquals(\n [u'http://www.shop.example.com',\n u'http://www.website.example.com',\n u'https://intern.example.com'],\n [url.url for url in organization.urls])\n\n\nclass TestPhoneNumberSyncer(SyncerBaseTest):\n\n SAMPLE_DATA = [{u'former_contact_id': 2344,\n u'phone_number': u'099 11 22 33',\n u'label': u'Gesch\\xe4ftlich'},\n {u'former_contact_id': 2344,\n u'phone_number': u'099 22 33 44',\n u'label': u'Privat'}]\n\n def create_source_db(self):\n self.source_table = Table(\"phonenumbers\",\n self.source_metadata,\n Column(\"former_contact_id\", Integer),\n Column(\"phone_number\", String),\n Column(\"label\", String))\n self.source_table.create()\n\n def test_add_phonenumbers_properly_while_syncing(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2344))\n\n PhoneNumberSyncer(self.source_db, 'SELECT * from phonenumbers')()\n\n organization = Organization.query.first()\n self.assertEquals(2, len(organization.phonenumbers))\n self.assertEquals(\n [u'Gesch\\xe4ftlich', u'Privat'],\n [phone.label for phone in organization.phonenumbers])\n self.assertEquals(\n [u'099 11 22 33', u'099 22 33 44'],\n [phone.phone_number for phone in organization.phonenumbers])\n\n def test_updates_existing_phonenumbers_properly_by_label(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2344))\n create(Builder('phonenumber')\n .for_contact(organization)\n .having(label=u'Privat',\n phone_number=u'099 33 44 55'))\n\n PhoneNumberSyncer(self.source_db, 'SELECT * from phonenumbers')()\n\n organization = Organization.query.first()\n self.assertEquals(2, len(organization.phonenumbers))\n self.assertEquals(\n [u'Privat', u'Gesch\\xe4ftlich'],\n [phone.label for phone in organization.phonenumbers])\n self.assertEquals(\n [u'099 22 33 44', u'099 11 22 33'],\n [phone.phone_number for phone in organization.phonenumbers])\n\n\nclass TestAddressSyncer(SyncerBaseTest):\n\n SAMPLE_DATA = [{u'former_contact_id': 2344,\n u'label': u'Hauptsitz',\n u'street': u'Teststrasse 1',\n u'zip_code': u'1111',\n u'city': u'Bern',\n u'country': u'Schweiz'},\n {u'former_contact_id': 2344,\n u'label': u'Standort Romandie',\n u'street': u'Rue de Lausanne',\n u'zip_code': u'2222',\n u'city': u'Fribourg',\n u'country': u'Schweiz'}]\n\n def create_source_db(self):\n self.source_table = Table(\"addresses\",\n self.source_metadata,\n Column(\"former_contact_id\", Integer),\n Column(\"label\", String),\n Column(\"street\", String),\n Column(\"zip_code\", String),\n Column(\"city\", String),\n Column(\"country\", String))\n self.source_table.create()\n\n def test_add_addresses_properly_while_syncing(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2344))\n\n AddressSyncer(self.source_db, 'SELECT * from addresses')()\n\n organization = Organization.query.first()\n self.assertEquals(2, len(organization.addresses))\n\n address_1, address_2 = organization.addresses\n\n self.assertEquals(u'Hauptsitz', address_1.label)\n self.assertEquals(u'Teststrasse 1', address_1.street)\n self.assertEquals(u'1111', address_1.zip_code)\n self.assertEquals(u'Bern', address_1.city)\n self.assertEquals(u'Schweiz', address_1.country)\n\n def test_updates_addresses_while_syncing(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2344))\n\n create(Builder('address')\n .for_contact(organization)\n .labeled(u'Hauptsitz')\n .having(street=u'Dammweg 9', zip_code=u'3013', city=u'Bern'))\n\n syncer = AddressSyncer(self.source_db, 'SELECT * from addresses')\n syncer()\n\n organization = Organization.query.first()\n self.assertEquals(2, len(organization.addresses))\n\n self.assertEquals(\n [u'Hauptsitz', u'Standort Romandie'],\n [address.label for address in organization.addresses])\n self.assertEquals(\n [u'Teststrasse 1', u'Rue de Lausanne'],\n [address.street for address in organization.addresses])\n\n self.assertEquals(1, syncer.stats.get('updated'))\n self.assertEquals(1, syncer.stats.get('added'))\n\n\nclass TestOrganizationRoleSyncer(SyncerBaseTest):\n\n SAMPLE_DATA = [{u'person_id': 1111,\n u'organisation_id': 2222,\n u'function': u'Leitung'},\n {u'person_id': 3333,\n u'organisation_id': 2222,\n u'function': u'Vorsteher'}]\n\n def create_source_db(self):\n self.source_table = Table(\"orgroles\",\n self.source_metadata,\n Column(\"person_id\", Integer),\n Column(\"organisation_id\", String),\n Column(\"function\", String))\n self.source_table.create()\n\n def test_add_addresses_properly_while_syncing(self):\n create(Builder('organization')\n .having(name=u'Meier AG', former_contact_id=2222))\n\n create(Builder('person')\n .having(lastname=u'Meier', firstname=u'Peter',\n former_contact_id=1111))\n create(Builder('person')\n .having(lastname=u'Bond', firstname=u'James',\n former_contact_id=3333))\n\n OrgRoleSyncer(self.source_db, 'SELECT * from orgroles')()\n\n org_roles = Organization.query.first().persons\n self.assertEquals([u'Leitung', u'Vorsteher'],\n [role.function for role in org_roles])\n self.assertEquals(['Peter', 'James'],\n [role.person.firstname for role in org_roles])\n\n def test_does_not_remove_existing_org_roles(self):\n organization = create(Builder('organization')\n .having(name=u'Meier AG',\n former_contact_id=2222))\n\n create(Builder('person')\n .having(lastname=u'Meier', firstname=u'Peter',\n former_contact_id=1111))\n create(Builder('person')\n .having(lastname=u'Bond', firstname=u'James',\n former_contact_id=3333))\n\n # existing OrgRole\n hans = create(Builder('person')\n .having(lastname=u'Hans', firstname=u'Muster'))\n create(Builder('org_role')\n .having(person=hans, organization=organization))\n\n OrgRoleSyncer(self.source_db, 'SELECT * from orgroles')()\n self.assertEquals(3, OrgRole.query.count())\n","sub_path":"opengever/contact/tests/test_object_syncer.py","file_name":"test_object_syncer.py","file_ext":"py","file_size_in_byte":19730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"364149013","text":"def get_segments(paths):\n x1 = 0\n y1 = 0\n x2 = 0\n y2 = 0\n p_length = 0\n points = []\n for p in paths:\n instr = p[0]\n val = int(p[1:])\n if instr == 'U':\n y2 += val\n elif instr == 'D':\n y2 -= val\n elif instr == 'R':\n x2 += val\n elif instr == 'L':\n x2 -= val\n else:\n raise RuntimeError('unknown direction {}'.format(instr))\n points.append((x1, y1, x2, y2, p_length))\n x1 = x2\n y1 = y2\n p_length += val\n return points\n\n\ndef horizontal(segment):\n if segment[0] == segment[2]:\n return False\n return True\n\n\ndef between(val, lim1, lim2):\n if lim1 <= val <= lim2:\n return True\n elif lim2 <= val <= lim1:\n return True\n return False\n\n\ndef get_intersections(circ1, circ2):\n intersections = []\n for seg1 in circ1:\n for seg2 in circ2:\n if horizontal(seg1) and not horizontal(seg2):\n if between(seg1[1], seg2[1], seg2[3]) and between(seg2[0], seg1[0], seg1[2]):\n so_far_1 = seg1[4]\n so_far_2 = seg2[4]\n last1 = abs(seg1[0] - seg2[0])\n last2 = abs(seg2[1] - seg1[1])\n intersections.append((seg1[1], seg2[0], so_far_1 + so_far_2 + last1 + last2))\n elif not horizontal(seg1) and horizontal(seg2):\n if between(seg2[1], seg1[1], seg1[3]) and between(seg1[0], seg2[0], seg2[2]):\n so_far_1 = seg1[4]\n so_far_2 = seg2[4]\n last1 = abs(seg2[0] - seg1[0])\n last2 = abs(seg1[1] - seg2[1])\n intersections.append((seg2[1], seg1[0], so_far_1 + so_far_2 + last1 + last2))\n return intersections\n\n\ndef main():\n with open(\"input\") as file:\n circuit1 = file.readline().split(',')\n circuit2 = file.readline().split(',')\n\n print(circuit1)\n print(circuit2)\n segments1 = get_segments(circuit1)\n segments2 = get_segments(circuit2)\n print(segments1)\n print(segments2)\n\n intersections = get_intersections(segments1, segments2)\n print(intersections)\n\n distances = list(map(lambda x: x[2], intersections))\n print(distances)\n\n minvalue = min(filter(lambda x: x > 0, distances))\n\n print(minvalue)\n\n\nmain()\n","sub_path":"day3/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":2362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"324916296","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 23 16:34:31 2020\n\n@author: Klinik_Muh\n\"\"\"\nzırhlar = {\"demir\": 10, \"çelik\" : 20}\n\nkarakterler = { \n \"karakter1\": {\"silah\":\"kılıç\",\n \"güç\": 30,\n \"sağlık\" : 100,\n \"zırh\" : zırhlar[\"demir\"]}, \n \n \"karakter2\": {\"silah\":\"kılıç\",\n \"güç\": 30,\n \"sağlık\" : 100,\n \"zırh\" : zırhlar[\"çelik\"]},\n }\n \ndef vur(saldiran,saldirilan):\n guc = saldiran[\"güç\"]\n saglik = saldirilan[\"sağlık\"]\n zırh = saldirilan[\"zırh\"]\n damage = guc - zırh\n saglik -= damage\n saldirilan[\"sağlık\"] = saglik\n \nprint(karakterler[\"karakter2\"][\"sağlık\"])\n\nvur(karakterler[\"karakter1\"],karakterler[\"karakter2\"])\n\nprint(karakterler[\"karakter2\"][\"sağlık\"])\n","sub_path":"oyun.py","file_name":"oyun.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"312230009","text":"# import argparse\r\n# import codecs\r\n# import glob\r\nimport os\r\n# import re\r\n# import shutil\r\n# import subprocess\r\n# import sys\r\n# from collections import namedtuple\r\n# from contextlib import contextmanager\r\n# from datetime import datetime, timedelta\r\n# from functools import reduce\r\n# from itertools import chain\r\n# from time import sleep\r\nimport traceback\r\n\r\nimport numpy as np\r\nimport cv2\r\nimport vapoursynth as vs\r\n\r\nimport myutils as ut\r\nimport videotools as vt\r\n# import tile\r\n\r\nPLUGINS_DIR = 'C:/Program Files (x86)/VapourSynth/plugins64'\r\nPLUGINS = (('avsr', f'{PLUGINS_DIR}/vsavsreader.dll'),\r\n ('lsmas', f'{PLUGINS_DIR}/vslsmashsource.dll'))\r\n\r\nSTORE_ = {}\r\n\r\n\r\n################################################################################\r\n\r\nclass Video(object):\r\n\r\n def __init__(self, path, *args, **kwargs):\r\n abspath = os.path.abspath(path)\r\n\r\n if not os.path.isfile(abspath):\r\n raise FileNotFoundError(abspath)\r\n\r\n self.path = abspath\r\n self.cap_ = None\r\n self.ar_ = None\r\n\r\n def __len__(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\r\n\r\n def __getitem__(self, key):\r\n if self.cap is None:\r\n raise Exception('VideoCapture is closed')\r\n\r\n else:\r\n self.cap.set(cv2.CAP_PROP_POS_FRAMES, key)\r\n ret, frame = self.cap.read()\r\n if not ret:\r\n raise IndexError\r\n return frame\r\n\r\n def __iter__(self):\r\n while True:\r\n ret, frame = self.cap.read()\r\n if not ret:\r\n raise IndexError\r\n yield frame\r\n\r\n def __next__(self):\r\n if self.cap is None:\r\n raise Exception('VideoCapture is closed')\r\n\r\n else:\r\n ret, frame = self.cap.read()\r\n if not ret:\r\n raise StopIteration\r\n return frame\r\n\r\n def __enter__(self):\r\n cap = cv2.VideoCapture(self.path)\r\n if not cap.isOpened():\r\n raise Exception('VideoCapture cannot be opened')\r\n self.cap_ = cap\r\n return self\r\n\r\n def __exit__(self, exc_type, exc_value, traceback):\r\n if self.cap:\r\n self.cap.release()\r\n self.cap_ = None\r\n if exc_type:\r\n print('VSVideo exit:', exc_type)\r\n\r\n def set(self, sec):\r\n if self.cap is None:\r\n raise Exception('VideoCapture is closed')\r\n self.cap.set(cv2.CAP_PROP_POS_MSEC, 1000 * sec)\r\n return self\r\n\r\n def get(self, sec):\r\n if self.cap is None:\r\n raise Exception('VideoCapture is closed')\r\n self.cap.set(cv2.CAP_PROP_POS_MSEC, 1000 * sec)\r\n return next(self)\r\n\r\n @property\r\n def cap(self):\r\n if self.cap_ is None:\r\n raise Exception('VideoCapture is closed')\r\n return self.cap_\r\n\r\n @property\r\n def height(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\n\r\n @property\r\n def width(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\r\n\r\n @property\r\n def ar(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n if not self.ar_:\r\n ar = vt.get_ar(self.path)\r\n if ar:\r\n self.ar_ = ar[0] / ar[1]\r\n else:\r\n self.ar_ = self.width / self.height\r\n return self.ar_\r\n\r\n @property\r\n def fps(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return self.cap.get(cv2.CAP_PROP_FPS)\r\n\r\n @property\r\n def length(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return len(self) / self.fps if self.fps > 1 else 0\r\n\r\n @property\r\n def pos(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return int(self.cap.get(cv2.CAP_PROP_POS_FRAMES))\r\n\r\n @property\r\n def time(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return self.cap.get(cv2.CAP_PROP_POS_MSEC) / 1000\r\n\r\n @property\r\n def sec(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return round(self.time)\r\n\r\n @property\r\n def ms(self):\r\n if self.cap is None:\r\n return 0\r\n\r\n else:\r\n return int(self.cap.get(cv2.CAP_PROP_POS_MSEC)) % 1000\r\n\r\n\r\n################################################################################\r\n\r\nclass VSVideo(Video):\r\n\r\n core = None\r\n\r\n def __init__(self, *args, **kwargs):\r\n super().__init__(*args, **kwargs)\r\n size = kwargs.get('size', 'half')\r\n if not (size in ('half', 'full') or type(size) in (int, tuple)):\r\n raise TypeError('Invalid type of \"size\"', size)\r\n self.size = size\r\n self.current_idx = -1\r\n self.gray_mode = kwargs.get('gray_mode', False)\r\n\r\n def __len__(self):\r\n return self.video.num_frames\r\n\r\n def __getitem__(self, key):\r\n n = len(self) + key if key < 0 else key\r\n if not 0 <= n < len(self):\r\n raise IndexError\r\n self.current_idx = key\r\n return self.get_image(self.get_frame(n))\r\n\r\n def __iter__(self):\r\n for i, frame in enumerate(self.video.frames()):\r\n if i <= self.current_idx:\r\n continue\r\n self.current_idx = i\r\n yield self.get_image(frame)\r\n\r\n def __next__(self):\r\n self.current_idx += 1\r\n if self.current_idx >= len(self):\r\n raise StopIteration\r\n return self[self.current_idx]\r\n\r\n def __enter__(self):\r\n # print(os.path.isfile(self.path))\r\n try:\r\n video = self.get_videonode(self.path)\r\n self.cap_ = video\r\n\r\n # エラー回避\r\n STORE_[None] = video\r\n return self\r\n\r\n except:\r\n traceback.print_exc()\r\n print('???')\r\n raise\r\n\r\n def __exit__(self, exc_type, exc_value, traceback):\r\n # global STORE_\r\n if self.cap_ is not None:\r\n # STORE_ = self.cap_\r\n self.cap_ = None\r\n if exc_type:\r\n print('VSVideo exit:', exc_type)\r\n\r\n def get_image(self, frame):\r\n if type(self.size) is str:\r\n size = self.size\r\n else:\r\n size = self.W, self.H\r\n img = get_vsimage(frame, size=size, fmt=self.fmt,\r\n gray_mode=self.gray_mode)\r\n if img is None:\r\n raise Exception('Unknown error in get_image')\r\n return img\r\n\r\n def set(self, sec):\r\n self.current_idx = int(self.fps * sec) - 1\r\n return self\r\n\r\n def get(self, sec):\r\n return self[int(self.fps * sec)]\r\n\r\n @property\r\n def height(self):\r\n return self.video.height\r\n\r\n @property\r\n def width(self):\r\n return self.video.width\r\n\r\n @property\r\n def H(self):\r\n if type(self.size) is int:\r\n return self.size\r\n elif type(self.size) is tuple:\r\n return self.size[1]\r\n elif self.size == 'full' or 'YUV422' in self.fmt:\r\n return self.video.height\r\n elif self.size == 'half':\r\n return self.video.height // 2\r\n\r\n @property\r\n def W(self):\r\n if type(self.size) is int:\r\n for n in (10, 8, 4, 2):\r\n if self.size % n == 0:\r\n return n * round(self.size * self.ar / n)\r\n raise ValueError('Invalid size:', self.size)\r\n elif type(self.size) is tuple:\r\n return self.size[0]\r\n elif self.size == 'full' or 'YUV422' in self.fmt:\r\n return self.video.width\r\n elif self.size == 'half':\r\n return self.video.width // 2\r\n\r\n @property\r\n def fps(self):\r\n return self.video.fps\r\n\r\n @property\r\n def pos(self):\r\n return self.current_idx\r\n\r\n @property\r\n def time(self):\r\n return self.pos / self.fps\r\n\r\n @property\r\n def ms(self):\r\n int(self.time * 1000 / 1000)\r\n\r\n def get_frame(self, n):\r\n return self.video.get_frame(n)\r\n\r\n @property\r\n def video(self):\r\n if self.cap_ is None:\r\n raise Exception('VideoNode is None')\r\n return self.cap_\r\n\r\n @property\r\n def fmt(self):\r\n return self.video.format.name\r\n\r\n @classmethod\r\n def get_core(cls):\r\n if cls.core is not None:\r\n return cls.core\r\n print('get_core')\r\n core = vs.get_core()\r\n for name, dllpath in PLUGINS:\r\n if not hasattr(core, name):\r\n # print(dir(core), name, hasattr(core, name))\r\n core.std.LoadPlugin(dllpath)\r\n cls.core = core\r\n return core\r\n\r\n\r\n @classmethod\r\n def get_videonode(cls, file):\r\n if not os.path.isfile(file):\r\n raise FileNotFoundError(file)\r\n\r\n ext = ut.extname(file)\r\n core = cls.get_core()\r\n\r\n if ext == '.mp4':\r\n return core.lsmas.LibavSMASHSource(file)\r\n\r\n elif ext == '.avs':\r\n return core.avsr.Import(file)\r\n\r\n else:\r\n raise Exception('Invalid ext:', ext)\r\n\r\n\r\ndef get_vsimage(frame, size='half', fmt='YUV420P8', gray_mode=False):\r\n '''\r\n size: 'half' or 'full' or (W, H)\r\n '''\r\n if gray_mode:\r\n return np.asarray(frame.get_read_array(0))\r\n\r\n if not fmt in ('YUV420P8', 'YUV420P10', 'YUV422P8', 'YUV422P10'):\r\n raise ValueError('Unknown format:', fmt)\r\n\r\n y, u, v = map(np.asarray, map(frame.get_read_array, range(3)))\r\n\r\n if fmt.endswith('P10'):\r\n y, u, v = (np.right_shift(x, 2).astype('u1') for x in (y, u, v))\r\n\r\n if fmt.startswith('YUV422'):\r\n u, v = (x.repeat(2, axis=1) for x in (u, v))\r\n\r\n if size == 'half':\r\n # y = y[::2, ::2]\r\n y = cv2.resize(y, u.shape[::-1], interpolation=cv2.INTER_AREA)\r\n\r\n elif size == 'full':\r\n u, v = (cv2.resize(x, y.shape[::-1]) for x in (u, v))\r\n\r\n elif type(size) is tuple:\r\n ips = (cv2.INTER_AREA if x.shape[0] > size[1] else cv2.INTER_LINEAR\r\n for x in (y, u, v))\r\n y, u, v = (cv2.resize(x, size, interpolation=ip)\r\n for ip, x in zip(ips, (y, u, v)))\r\n\r\n else:\r\n raise TypeError('Invalid type of \"size\"', size)\r\n\r\n img = np.stack([y, u, v], axis=2)\r\n img = cv2.cvtColor(img, cv2.COLOR_YUV2BGR)\r\n return img\r\n\r\n\r\n################################################################################\r\n\r\ndef openvideo(path, *args, **kwargs):\r\n if ut.extname(path) in ('.mp4', '.avs'):\r\n return VSVideo(path, *args, **kwargs)\r\n else:\r\n return Video(path, *args, **kwargs)\r\n\r\n\r\nvideohandle = openvideo\r\n\r\n\r\n################################################################################\r\n\r\ndef resize(img, size):\r\n if type(size) in (int, float):\r\n size = tuple(int(size*s) for s in img.shape[1::-1])\r\n\r\n if img.shape[0] == size[1] and img.shape[1] == size[0]:\r\n return img\r\n\r\n if size[1] > img.shape[0]:\r\n # 拡大\r\n interpolation = cv2.INTER_LINEAR\r\n else:\r\n # 縮小\r\n interpolation = cv2.INTER_AREA\r\n return cv2.resize(img, size, interpolation=interpolation)\r\n\r\n\r\ndef imread(filename, flags=cv2.IMREAD_COLOR, dtype=np.uint8):\r\n try:\r\n n = np.fromfile(filename, dtype)\r\n img = cv2.imdecode(n, flags)\r\n return img\r\n except Exception as e:\r\n print(e)\r\n return None\r\n\r\n\r\ndef imwrite(filename, img, params=None):\r\n try:\r\n ext = os.path.splitext(filename)[1]\r\n result, n = cv2.imencode(ext, img, params)\r\n\r\n if result:\r\n with open(filename, mode='w+b') as f:\r\n n.tofile(f)\r\n return True\r\n else:\r\n return False\r\n except Exception as e:\r\n print(e)\r\n return False\r\n\r\n\r\ndef imshow(img, wait=0, title='image', scale=None):\r\n if scale:\r\n if type(scale) in (int, float):\r\n img = resize(img, tuple(int(scale*s) for s in img.shape[1::-1]))\r\n elif type(scale) is tuple:\r\n img = resize(img, scale)\r\n cv2.imshow(title, np.asarray(img))\r\n cv2.waitKey(wait)\r\n\r\n\r\n################################################################################\r\n\r\ndef yuv2bgr(y, u, v, t='yuv'):\r\n Y, Cb, Cr = (np.asarray(x).astype('f4') for x in (y, u, v))\r\n Cb, Cr = Cb - 128, Cr - 128\r\n if t == 'yuv':\r\n U, V = 0.872 * Cb, 1.23 * Cr\r\n R = Y + 1.13983 * V\r\n G = Y - 0.39465 * U - 0.58060 * V\r\n B = Y + 2.03211 * U\r\n elif t == 'bt601': # 'yuv'と同一\r\n R = Y + 1.402 * Cr\r\n G = Y - 0.344136 * Cb - 0.714136 * Cr\r\n B = Y + 1.772 * Cb\r\n elif t == 'bt709':\r\n R = Y + 1.5748 * Cr\r\n G = Y - 0.187324 * Cb - 0.468124 * Cr\r\n B = Y + 1.8556 * Cb\r\n r, g, b = (x.clip(0, 255).astype('u1') for x in (R, G, B))\r\n return b, g, r\r\n\r\n\r\ndef grayscale(image):\r\n assert image.ndim == 3\r\n return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n","sub_path":"videoutils.py","file_name":"videoutils.py","file_ext":"py","file_size_in_byte":13232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"243769961","text":"#!/usr/bin/env python3\n# coding: utf-8\n\n#*****************************************************************************\n# Copyright (C) 2019 Nicolas Borie \n#\n# Distributed under the terms of the GNU General Public License (GPL)\n#\n# This code is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n# General Public License for more details.\n#\n# The full text of the GPL is available at:\n#\n# http://www.gnu.org/licenses/\n#*****************************************************************************\n\nimport sys\nimport json\nimport random\nfrom utils import subset_index, knuth_mixing\n\ndef ParseQuestion(opened_file):\n \"\"\"\n Parse a Python open file of formated questions in AMC style and return a \n list of parsed questions together with theirs answers.\n \"\"\"\n text = None\n goods = []\n bads = []\n current = None\n MCQ_lst = []\n # We manually add a last \"*\" in the parsing to register the last question \n for line in opened_file.readlines()+[\"*\"]:\n if line[0] in \"*+-\":\n # We did read a new item\n # First, we register the last item\n if current is not None:\n if current[0] == \"*\":\n text = current[1:]\n elif current[0] == \"+\":\n goods.append(current[1:])\n elif current[0] == \"-\":\n bads.append(current[1:])\n else:\n raise ValueError(\"Error during parsing the questions file.\")\n \n if line[0] == \"*\":\n # In this case, the new item is a new question\n # It is time to insert a potential question\n if text is not None:\n MCQ_lst.append([text, goods, bads])\n text = None\n goods = []\n bads = []\n \n # the new item overwrite the current one\n current = line \n else:\n # If this is not a new item, we concat to the previous item.\n current = current.replace(\"\\n\", \" \")\n current += line\n return MCQ_lst\n\nif __name__ == \"__main__\":\n \"\"\"\n This programm generate a nice Multiple Choice formulary from the context \n exercice. The MCQ is mainly build from a file specified by key \n `data_from_file` inside the exercise.\n \"\"\"\n with open(sys.argv[1],'r') as f:\n context = json.load(f)\n \n # Here is the name of the file containing all available questions\n file_question_name = context['data_from_file']\n \n if file_question_name == \"None\":\n context['text'] = (\"Cet exercise n'utilise pas le template qcm \"\n \"correctement. L'entrée 'data_from_file' de l'exercice doit spécifier \"\n \"le chemin d'un fichier accessible contenant les questions.\")\n sys.exit(1)\n \n # The parsing is done here\n with open(file_question_name, \"r\") as file_question :\n question_lst = ParseQuestion(file_question)\n context['mcq'] = question_lst\n \n # Set the number of questions in this MCQ\n if 'number_question' in context:\n number_of_mcq = int(context['number_question'])\n # By security, one can not have more questions than availlable.\n number_of_mcq = min(number_of_mcq, len(context['mcq']))\n else:\n # If the number of question is not given, take half of them \n # but no more than 20.\n number_of_mcq = min(20, len(context['mcq']) // 2)\n \n # Set the text at begining of MCQ\n if number_of_mcq > 1:\n context['text'] = \"Cliquez sur **Valider** pour entammer une série de \" + str(number_of_mcq) + \" questions !\"\n context['text'] += \"')\ndef get_user(userid):\n person = next((user for user in users if str(user['ID']) == userid), {})\n return jsonify(person)\n\n@app.route('/users')\ndef get_userlist():\n user_list = []\n for user in users:\n user_name = ', '.join([user['LastName'], user['FirstName']])\n user_list.append([user['ID'], user_name])\n return jsonify(user_list)\n\nif __name__ == \"__main__\":\n app.run(debug=True, port=8000)\n\n","sub_path":"ch19/webserver.py","file_name":"webserver.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"285042035","text":"import re\nimport pickle\n\nwith open(\"../AMiner-Paper/AMiner-Paper.txt\", encoding=\"utf-8\") as inputfile:\n\tcontent = inputfile.read()\n\tpapers = re.findall(r'#index.+?(?=\\n\\n)', content, re.S)\n\tinputfile.close()\n\tprint(\"number of papers: \", len(papers))\n\tprint(papers[-1])\n\n\tauthor2paper = dict()\n\t## count = 0\n\twith open(\"../AMiner-Author2Paper/AMiner-Author2Paper.txt\", encoding=\"utf-8\") as mapfile:\n\t\tfor line in mapfile:\n\t\t\t## count += 1\n\t\t\tline_spilted = line.split()\n\t\t\tpaper_id = int(line_spilted[2])\n\t\t\tauthor_id = int(line_spilted[1])\n\t\t\tif paper_id not in author2paper:\n\t\t\t\tauthor2paper[paper_id] = [author_id]\n\t\t\telse:\n\t\t\t\tassert author_id not in author2paper[paper_id]\n\t\t\t\tauthor2paper[paper_id].append(author_id)\n\t\tmapfile.close()\n\n\t\t## paperids = list(paper_author.keys())\n\t\t## print(len(paperids))\n\t\t## print(max(paperids), min(paperids))\n\t\t## print(\"Count: \", count)\n\n\t## paper_property = list()\n\t## candidates = set()\n\twith open(\"../Filtered_PaperInfo.txt\", \"w\", encoding=\"utf-8\") as outputfile:\n\t\t\n\t\ttime_span = dict()\n\t\tfor each in author2paper:\n\t\t\t## print(each)\n\t\t\tparagraph = papers[each - 1]\n\t\t\t## print(paragraph)\n\t\t\tif_time = re.search(r'(?<=#t )(\\d+?)[\\n$]', paragraph)\n\t\t\tif_citation = re.search(r'(?<=#% )(\\d+?)[\\n$]', paragraph)\n\t\t\tif_abstract = re.search(r'(?<=#! ).+', paragraph)\n\t\t\t\n\t\t\tif if_time and if_citation and if_abstract:\n\n\t\t\t\t_title = re.search(r'(?<=#\\* ).+?(?=\\n)', paragraph).group()\n\t\t\t\t_author_text = re.search(r'(?<=#@ ).+?(?=\\n)', paragraph).group()\n\t\t\t\t_author_text_splited = _author_text.split(';')\n\t\t\t\tassert len(author2paper[each]) <= len(_author_text_splited)\n\t\t\t\t_time = if_time.group(1)\n\n\t\t\t\tif int(_time) in time_span:\n\t\t\t\t\ttime_span[int(_time)] += 1\n\t\t\t\telse:\n\t\t\t\t\ttime_span[int(_time)] = 1\n\n\t\t\t\t_venue = None\n\t\t\t\tif_venue = re.search(r'(?<=#c ).+', paragraph)\n\t\t\t\tif if_venue:\n\t\t\t\t\t_venue = if_venue.group()\n\n\t\t\t\t_ref = re.findall(r'(?<=#% )\\d+', paragraph)\n\n\t\t\t\t_abstract = if_abstract.group()\n\n\t\t\t\toutputfile.write('#index ' + str(each) + '\\n')\n\t\t\t\toutputfile.write('#* ' + _title + '\\n')\n\t\t\t\toutputfile.write('#@ ' + ';'.join([str(item) for item in author2paper[each]]) + '\\n')\n\t\t\t\toutputfile.write('#t ' + _time + '\\n')\n\t\t\t\toutputfile.write('#c ')\n\t\t\t\tif _venue:\n\t\t\t\t\toutputfile.write(_venue + '\\n')\n\t\t\t\telse:\n\t\t\t\t\toutputfile.write('\\n')\n\n\t\t\t\toutputfile.write('#% ' + ';'.join(_ref) + '\\n')\n\t\t\t\toutputfile.write('#! ' + _abstract + '\\n')\n\n\t\toutputfile.close()\n\n\t\twith open(\"../time_span.pkl\", \"wb\") as time_span_fp:\n\t\t\tpickle.dump(time_span, time_span_fp)\n\t\t\ttime_span_fp.close()\n\n\t\t\t\t## paper_property.append(each)\n\t\t\t\t## for a in paper_author[each]:\n\t\t\t\t\t## candidates.add(a)\n\n\t\t\t\t## print(if_time.group(1))\n\t\t\t\t## print(int(if_time.group(1)))\n\t\t\t\t## time_span.add(int(if_time.group(1)))\n\n\n\t\t## print(len(paper_property))\n\t\t## print(len(candidates))\n\t\t## print(len(time_span), min(time_span), max(time_span))\n\n\t\t# print(paragraph)\n\t\t# if_time = re.search(r'(?<=#t).*?(?=\\n)', paragraph)\n\t\t# if if_time:\n\t\t\t# print(if_time.group())\n\t\t\t# paper_time.append(each)\n\t# print(len(paper_author))\n\t# print(len(paper_time))\n\n\n\n\t","sub_path":"scripts/see_data.py","file_name":"see_data.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"86462539","text":"# Copyright 2020 Espressif Systems (Shanghai) PTE LTD\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\nimport uuid\nimport time\nimport sys\n\nTRANSPORT_MODE_SOFTAP = 'softap'\nMAX_HTTP_CONNECTION_RETRIES = 5\nPROVISION_FAILURE_MSG = ('Provisioning Failed. Reset your board to factory '\n 'defaults and retry.')\n\ntry:\n from rmaker_tools.rmaker_prov.esp_rainmaker_prov import provision_device\n from rmaker_lib.logger import log\n from rmaker_lib import session, configmanager, node\n from rmaker_lib.exceptions import NetworkError, SSLError,\\\n RequestTimeoutError\nexcept ImportError as err:\n print(\"Failed to import ESP Rainmaker library.\\n\" + str(err))\n raise err\n\n\ndef provision(vars=None):\n \"\"\"\n Provisioning of the node.\n\n :raises NetworkError: If there is a network connection issue\n during provisioning\n :raises Exception: If there is an HTTP issue during provisioning\n\n :param vars: `pop` - Proof of Possession of the node, defaults to `None`\n :type vars: dict\n\n :return: None on Success and Failure\n :rtype: None\n \"\"\"\n log.info('Provisioning the node.')\n secret_key = str(uuid.uuid4())\n pop = vars['pop']\n try:\n config = configmanager.Config()\n userid = config.get_user_id()\n log.debug('User session is initialized for the user ' + userid)\n except Exception as get_user_id_err:\n log.error(get_user_id_err)\n sys.exit(1)\n try:\n input('Please connect to the wifi PROV_XXXXXX and '\n 'Press Enter to continue...')\n except Exception:\n print(\"Exiting...\")\n sys.exit(0)\n\n node_id = provision_device(TRANSPORT_MODE_SOFTAP, pop, userid, secret_key)\n if node_id is None:\n log.error(PROVISION_FAILURE_MSG)\n return\n log.debug('Node ' + node_id + ' provisioned successfully.')\n\n print('------------------------------------------')\n input('Please ensure host machine is connected to internet and '\n 'Press Enter to continue...')\n\n retries = MAX_HTTP_CONNECTION_RETRIES\n node_object = None\n while retries > 0:\n try:\n # If session is expired then to initialise the new session\n # internet connection is required.\n node_object = node.Node(node_id, session.Session())\n except SSLError:\n log.error(SSLError())\n break\n except (NetworkError, RequestTimeoutError) as conn_err:\n print(conn_err)\n log.warn(conn_err)\n except Exception as node_init_err:\n log.error(node_init_err)\n break\n else:\n break\n time.sleep(5)\n retries -= 1\n if retries:\n print(\"Retries left:\", retries)\n log.info(\"Retries left: \" + str(retries))\n\n if node_object is None:\n log.error('Initialising new session...Failed\\n' +\n '\\n' + PROVISION_FAILURE_MSG)\n return\n\n print('\\nAdding User-Node association')\n log.info(\"Adding User-Node association\")\n\n retries = MAX_HTTP_CONNECTION_RETRIES\n request_id = None\n log.info('Sending User-Node Association Request...')\n while retries > 0:\n print('Sending User-Node Association Request...')\n try:\n request_id = node_object.add_user_node_mapping(secret_key)\n except SSLError:\n log.error(SSLError())\n break\n except (NetworkError, RequestTimeoutError) as conn_err:\n print(conn_err)\n log.warn(conn_err)\n except Exception as mapping_err:\n print(mapping_err)\n log.warn(mapping_err)\n break\n else:\n if request_id is not None:\n log.debug('User-Node mapping added successfully '\n 'with request_id '\n + request_id)\n break\n\n retries -= 1\n if retries:\n print(\"Retries left:\", retries)\n log.info(\"Retries left: \" + str(retries))\n time.sleep(5)\n\n if request_id is None:\n log.error('User-Node Association Request...Failed\\n' +\n '\\n' + PROVISION_FAILURE_MSG)\n return\n\n print('User-Node Association Request...Success')\n log.info('User-Node Association Request...Success')\n\n retries = MAX_HTTP_CONNECTION_RETRIES\n status = None\n log.info('Checking User-Node Association Status...')\n while retries > 0:\n print('Checking User-Node Association Status...')\n try:\n status = node_object.get_mapping_status(request_id)\n except SSLError:\n log.error(SSLError())\n break\n except (NetworkError, RequestTimeoutError) as conn_err:\n print(conn_err)\n log.warn(conn_err)\n status = None\n except Exception as mapping_status_err:\n print(mapping_status_err)\n log.warn(mapping_status_err)\n break\n else:\n if status == 'requested':\n print('User-Node Association Status - Requested'\n '\\n')\n log.debug('User-Node Association Status - Requested'\n '\\n')\n elif status == 'confirmed':\n print('User-Node Association Status - Confirmed'\n '\\nProvisioning was Successful.')\n log.debug('User-Node Association Status - Confirmed'\n '\\nProvisioning was Successful.')\n break\n elif status == 'timedout':\n print('User-Node Association Status - Timedout')\n log.debug('User-Node Association Status - Timedout')\n break\n elif status == 'discarded':\n print('User-Node Association Status - Discarded')\n log.debug('User-Node Association Status - Discarded')\n break\n else:\n log.debug('User-Node Association Status - ' + status)\n break\n\n if status not in [\"requested\"]:\n retries -= 1\n if retries:\n print(\"Retries left:\", retries)\n log.info(\"Retries left: \" + str(retries))\n time.sleep(5)\n\n if status not in [\"confirmed\"]:\n log.error('Checking User-Node Association Status...Failed.\\n'\n '\\nCould not confirm User-Node Association Status. '\n '\\nPlease use cli command '\n '`python3 rainmaker.py getnodes` to confirm.')\n return\n return\n","sub_path":"cli/rmaker_cmd/provision.py","file_name":"provision.py","file_ext":"py","file_size_in_byte":7062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"497962998","text":"from functools import wraps\nfrom pymongo.errors import AutoReconnect\n\nQUERY_RETRIES_COUNT = 2\n\n\ndef mongo_query(func, retry_count=QUERY_RETRIES_COUNT):\n \"\"\"\n A decorator that retries the query (function) execution when AutoReconnect exception is raised.\n :param func: The function to wrap\n :param retry_count: How many times to retry on AutoReconnectException.\n :return: The function, wrapped with auto retries mechanism.\n\n >>> @mongo_query\n >>> def insert_to_collection(self, obj)\n >>> # trying to execute the pymongo insert_one() function\n >>> self.db['example_collection'].insert_one(obj.__dict__)\n \"\"\"\n\n @wraps(func)\n def execute_with_retries(*args, **kwargs):\n failures = 0\n while retry_count > failures:\n try:\n return func(*args, **kwargs)\n except AutoReconnect:\n failures += 1\n\n return execute_with_retries\n","sub_path":"pymongo_decorators.py","file_name":"pymongo_decorators.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"316488431","text":"#파일명 : selenium06.py\n# 이미지 크롤링\n\nfrom selenium import webdriver\nimport urllib.request\nimport time\nimport os\nfrom selenium.webdriver.common.keys import Keys\n\noptions = webdriver.ChromeOptions()\n#options.add_argument('headless') #화면표시 안됨.\n\ndriver = webdriver.Chrome('./Crawling/chromedriver.exe', chrome_options=options)\n\n# options.add_argument('headless') #화면 표시 X\noptions.add_argument(\"disable-gpu\") \noptions.add_argument(\"lang=ko_KR\") \noptions.add_argument('user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36') # user-agent \n\n\ndriver=webdriver.Chrome(('./Crawling/chromedriver.exe'), options=options)\ndriver.get('http://naver.com')\ntime.sleep(2)\ndriver.find_element_by_xpath('//*[@id=\"query\"]').send_keys('삼겹살')\ndriver.find_element_by_xpath('//*[@id=\"query\"]').send_keys(Keys.ENTER)\ntime.sleep(1)\ndriver.find_element_by_xpath('//*[@id=\"lnb\"]/div/div[1]/ul/li[2]/a').click()\n# driver.find_element_by_xpath('//*[@id=\"_sau_imageTab\"]/div[2]/div[2]/div[1]/a[1]/span[1]')\n# driver.find_element_by_xpath('//*[@id=\"_sau_imageTab\"]/div[2]/div[2]/div[2]/a[1]/span[1]')\n# driver.find_element_by_xpath('//*[@id=\"_sau_imageTab\"]/div[2]/div[2]/div[3]/a[1]/span[1]')\n\nlink=[]\nfor i in range(1, 5, 1): \n try:\n img = driver.find_element_by_xpath('//*[@id=\"_sau_imageTab\"]/div[1]/div[2]/div['+ str(i) + ']/a[1]/img') \n except:\n img = driver.find_element_by_xpath('//*[@id=\"_sau_imageTab\"]/div[2]/div[2]/div['+ str(i) + ']/a[1]/img') \n print(img) \n link.append(img.get_attribute(\"src\"))\n\n#파일명 n0.jpg, n1.jpg, n2.jpg, n3.jpg, n4.jpg\nfor idx, tmp in enumerate(link):\n urllib.request.urlretrieve(tmp,'./Download/imge'+str(idx)+\".jpg\")","sub_path":"Crawling/selenium07.py","file_name":"selenium07.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"237580664","text":"from django.db import models\nfrom django.utils import timezone\n\n# Create your models here.\nfrom apps.clients.models import Client\nfrom apps.exercises.models import Exercise\n\n\nclass Task(models.Model):\n\n client = models.ForeignKey(Client, related_name='tasks')\n date = models.DateField(default=timezone.now)\n count = models.PositiveSmallIntegerField(default=0)\n comment = models.CharField(max_length=256)\n exercise = models.ForeignKey(Exercise, related_name='tasks')\n index = models.PositiveSmallIntegerField(default=1)\n\n def save(self, *args, **kwargs):\n super(Task, self).save(*args, **kwargs)\n task_list = self.client.tasks.filter(date=self.date)\n if len(task_list) > 1:\n self.index = len(task_list)\n else:\n self.index = 1\n super(Task, self).save(*args, **kwargs)\n\n def __str__(self):\n string = [\n str(self.index),\n self.client.name,\n self.exercise.name,\n str(self.date)\n ]\n return '-'.join(string)\n","sub_path":"django-boilerplate/apps/tasks/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"366187777","text":"import copy\nimport math\nimport random\nimport CasesGraphiques as CG\nimport pygame\n\n\n# random.seed(234789)\n\ndef _find_getch():\n \"\"\"Single char input, only works only on mac/linux/windows OS terminals\"\"\"\n try:\n import termios\n except ImportError:\n # Non-POSIX. Return msvcrt (Windows') getch.\n import msvcrt\n return lambda: msvcrt.getch().decode('utf-8')\n # POSIX system. Create and return a getch that manipulates the tty.\n import sys\n import tty\n\n def _getch():\n fd = sys.stdin.fileno()\n old_settings = termios.tcgetattr(fd)\n try:\n tty.setraw(fd)\n ch = sys.stdin.read(1)\n finally:\n termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)\n return ch\n\n return _getch\n\n\ndef sign(x: int) -> int:\n if x > 0:\n return 1\n return -1\n\n\ndef opp(x: int) -> int:\n return -x + 1\n\n\ndef clear_list(list_to_clear: list) -> None:\n for i in range(len(list_to_clear)):\n for j in range(len(list_to_clear)):\n list_to_clear[i][j] = None\n\n\nclass Coord(object):\n \"\"\"Implementation of a map coordinate\"\"\"\n\n def __init__(self, x: int, y: int) -> None:\n \"\"\"\n Is a coordinate of the map\n :param x: x coordinate\n :param y: y coordinate\n \"\"\"\n self.x = x\n self.y = y\n\n def __eq__(self, other: \"Coord\") -> bool:\n return self.x == other.x and self.y == other.y\n\n def __repr__(self) -> str:\n return f\"<{str(self.x)},{str(self.y)}>\"\n\n def __add__(self, other: \"Coord\") -> \"Coord\":\n return Coord(self.x + other.x, self.y + other.y)\n\n def __sub__(self, other: \"Coord\") -> \"Coord\":\n return Coord(self.x - other.x, self.y - other.y)\n\n def distance(self, other: \"Coord\") -> float:\n \"\"\"Returns the distance between two coordinates.\"\"\"\n d = self - other\n return math.sqrt(d.x * d.x + d.y * d.y)\n\n def empty_around(self, actual_map: \"Map\") -> bool:\n # Return True if the coordinates around are corresponding\n res = True\n for y in range(-1, 2):\n for x in range(-1, 2):\n res = res and (actual_map.get(self + Coord(x, y)) == Map.ground or actual_map.get(\n self + Coord(x, y)) == actual_map.hero)\n return res\n\n def get_empty_coord_around(self, actual_map: \"Map\") -> \"Coord\":\n available_coord_list = []\n o = actual_map.get(self)\n for i in range(-1, 2):\n for j in range(-1, 2):\n way = Coord(i, j)\n if actual_map.check_move(o, way) == Map.ground:\n available_coord_list.append(self + way)\n return random.choice(available_coord_list)\n\n def get_tuple(self) -> tuple:\n return self.x, self.y\n\n\nclass Element(object):\n \"\"\"Base class for game elements. Have a name.\n Abstract class.\"\"\"\n\n def __init__(self, name: str, abbreviation: str = \"\") -> None:\n self.name = name\n if abbreviation == \"\":\n abbreviation = name[0]\n self.abbreviation = abbreviation\n\n self.graphicOutput = None\n\n def __repr__(self) -> str:\n return self.abbreviation\n\n def description(self) -> str:\n \"\"\"Description of the element\"\"\"\n return f\"<{self.name}>\"\n\n def meet(self, hero: \"Hero\") -> None:\n \"\"\"Makes the hero meet an element. Not implemented. \"\"\"\n raise NotImplementedError('Abstract Element')\n\n\nclass RoomObject(Element):\n\n def __init__(self, name: str = \"\", abbreviation: str = \"\", usage: \"function\" = None) -> None:\n Element.__init__(self, name, abbreviation)\n\n self.usage = usage\n\n self.graphicOutput = []\n\n for i in range(2):\n try:\n self.graphicOutput.append(CG.get_room_object_image(self.name + '-' + str(i)))\n except FileNotFoundError:\n print(\"Not image for:\", self.name + '-' + str(i))\n pass\n\n def meet(self, hero: \"Hero\") -> bool:\n \"\"\"The roomObject is encountered by hero.\n The hero uses the roomObject.\n Return True if used.\"\"\"\n if not isinstance(hero, Hero):\n return False\n return self.usage()\n\n @staticmethod\n def go_upstair() -> bool:\n g = the_game()\n if g.actual_floor + 1 < len(g.floor_list):\n g.floor.rm(g.floor.pos(g.hero))\n\n g.actual_floor += 1\n g.floor = g.gv.floor = g.floor_list[g.actual_floor]\n g.add_message('You are now in stage ' + str(g.actual_floor + 1) + '/' + str(len(g.floor_list)))\n\n stair_coord = g.floor.pos(g._room_objects['downstair'])\n new_coord = stair_coord.get_empty_coord_around(g.floor)\n\n g.floor.put(new_coord, g.hero)\n g.hero.x = new_coord.x\n g.hero.y = new_coord.y\n return True\n return False\n\n @staticmethod\n def go_downstair() -> bool:\n g = the_game()\n if g.actual_floor - 1 >= 0:\n g.floor.rm(g.floor.pos(g.hero))\n\n g.actual_floor -= 1\n g.floor = g.gv.floor = g.floor_list[g.actual_floor]\n g.add_message('You are now in stage ' + str(g.actual_floor + 1) + '/' + str(len(g.floor_list)))\n\n stair_coord = g.floor.pos(g._room_objects['upstair'])\n new_coord = stair_coord.get_empty_coord_around(g.floor)\n\n g.floor.put(new_coord, g.hero)\n g.hero.x = new_coord.x\n g.hero.y = new_coord.y\n return True\n return False\n\n @staticmethod\n def meet_trader() -> None:\n list_of_items_sold = []\n for i in range(2):\n list_of_items_sold.append(\n the_game().rand_element(Game.equipments, the_game().floor_list[the_game().actual_floor].floor_number))\n list_of_items_sold.append(\n the_game().rand_element(Game.weapons, the_game().floor_list[the_game().actual_floor].floor_number))\n\n the_game().gv.draw_trader(list_of_items_sold)\n\n\nclass Creature(Element):\n \"\"\"A creature that occupies the dungeon.\n Is an Element. Has hit points and strength.\"\"\"\n\n default_inventory_size = 10\n\n def __init__(self, name: str, hp: int, abbreviation: str = \"\", strength: int = 1, xp: int = 0,\n weapon_slot: list = None, powers_list: list = None, cooldown: int = 0) -> None:\n super().__init__(name, abbreviation)\n self.hp = hp\n self.default_hp = hp\n self.strength = strength\n self.xp = xp\n\n if weapon_slot is not None:\n self.weapon_slot = weapon_slot\n else:\n self.weapon_slot = []\n\n if powers_list is not None:\n self.powers_list = powers_list\n else:\n self.powers_list = []\n\n self.cooldown = 0\n self._default_cooldown = cooldown\n\n self._inventory = []\n\n # Graphics\n self.graphicOutput = CG.get_monster_image(self.name)\n\n def description(self) -> str:\n \"\"\"Description of the creature\"\"\"\n if self.hp > 0:\n return Element.description(self) + \"(\" + str(self.hp) + \")\"\n return Element.description(self) + \"(0)\"\n\n def gain_xp(self, xp_point):\n raise NotImplementedError\n\n def gain_level(self, nb_of_level):\n raise NotImplementedError\n\n def meet(self, other: \"Creature\") -> bool:\n\n \"\"\"The creature is encountered by an other creature.\n The other one hits the creature. Return True if the creature is dead.\"\"\"\n\n self.hit(other)\n\n the_game().add_message(\"The \" + other.name + \" hits the \" + self.description())\n if self.hp > 0:\n return False\n if isinstance(self, Creature) and not isinstance(self, Hero):\n other.gain_xp(self.xp)\n return True\n\n def hit(self, other: \"Creature\") -> None:\n\n if len(other.powers_list) != 0:\n for effect_infos_list in other.powers_list:\n if other.cooldown == 0: # The cooldown ended\n effect_infos_list[0].add_effect(\n effect_infos_list[0](self, effect_infos_list[1], effect_infos_list[2]))\n other.cooldown = other._default_cooldown\n else:\n other.cooldown -= 1\n\n if other.has_weapon():\n self.hp -= other.current_weapon().damage\n else:\n self.hp -= other.strength\n\n def equip_weapon(self, weapon: \"Weapon\") -> None:\n if len(self.weapon_slot) != 0:\n self._inventory.append(self.weapon_slot[0])\n self.weapon_slot.clear()\n\n self.weapon_slot.append(weapon)\n self._inventory.remove(weapon)\n the_game().add_message(f\"You equipped {weapon.name}\")\n\n def remove_current_weapon(self) -> None:\n if self.current_weapon():\n if len(self._inventory) < self.default_inventory_size:\n self._inventory.append(self.current_weapon())\n self.weapon_slot.clear()\n the_game().add_message(\"You removed your weapon from it's slot\")\n else:\n the_game().add_message(\"You don't have any space in your inventory to place your weapon\")\n else:\n the_game().add_message(\"You currently don't have a weapon to remove from it's slot\")\n\n def has_weapon(self) -> bool:\n if len(self.weapon_slot) >= 1:\n return True\n\n def current_weapon(self) -> bool or \"Weapon\":\n if self.has_weapon():\n return self.weapon_slot[0]\n else:\n return False\n\n\nclass Hero(Creature):\n \"\"\"The hero of the game.\n Is a creature. Has an inventory of elements. \"\"\"\n\n default_inventory_size = 10\n default_stomach_size = 10\n default_level_size = 25\n\n default_hp = 10\n\n def __init__(self, name=\"Hero\", hp=default_hp, abbreviation=\"@\", strength=2, level=1, xp=24, gold=0,\n stomach=default_stomach_size,\n weapon_slot=None):\n Creature.__init__(self, name, hp, abbreviation, strength, xp, weapon_slot)\n\n self.xp = xp\n self.level_step = Hero.default_level_size\n\n self.level = level\n self.gold = gold\n self.stomach = stomach\n self.default_stomach_size = stomach\n\n # GRAPHICS\n images = CG.get_hero_image(\"Template\")\n\n self.graphicOutput = images[0]\n self.animationUDLR = {(0, -1): images[12:16], # cannot put Coord since it's not hashable\n\n (0, 1): images[:4],\n (-1, 1): images[:4],\n (1, 1): images[:4],\n\n (-1, 0): images[4:8],\n (-1, -1): images[4:8],\n\n (1, 0): images[8:12],\n (1, -1): images[8:12],\n\n }\n\n self.state = 0\n self.moving_UDLR = [False, False, False, False]\n\n self.x = 0\n self.y = 0\n\n def description(self):\n \"\"\"Description of the hero\"\"\"\n if len(self.weapon_slot) != 0:\n return Creature.description(self) + \" |\" + str(self.current_weapon()) + \"|\"\n else:\n return Creature.description(self)\n\n def full_description(self):\n \"\"\"Complete description of the hero\"\"\"\n res = ''\n for e in self.__dict__:\n\n if e[0] != ' ' and \"default\" not in e:\n if e == \"xp\":\n res += '> ' + e + ' : ' + str(self.__dict__[e]) + \"/\" + str(\n self.default_level_size * self.level) + '\\n'\n else:\n res += '> ' + e + ' : ' + str(self.__dict__[e]) + '\\n'\n res += '> INVENTORY : ' + str([x.name for x in self._inventory]) + '\\n'\n res += '> Effects : ' + str(\n [f\"{x.name}<{x.level}>({x.duration})\" for x in the_game().active_effects if x.creature is self])\n\n if self.has_weapon():\n res += '> Weapon : ' + str(self.current_weapon().name)\n return res\n\n @staticmethod\n def check_equipment(o):\n \"\"\"Check if o is an Equipment.\"\"\"\n if not isinstance(o, Equipment):\n raise TypeError('Not a Equipment')\n\n def take(self, elem):\n \"\"\"The hero takes adds the equipment to its inventory\"\"\"\n self.check_equipment(elem)\n if elem.name == \"gold\":\n self.gold += 1\n else:\n if len(self._inventory) + 1 <= self.default_inventory_size:\n self._inventory.append(elem)\n\n elif len(self._inventory) > Hero.default_inventory_size:\n the_game().add_message(\"You don't have enough space in your inventory\")\n\n def check_inventory_size(self):\n if len(self._inventory) > Hero.default_inventory_size:\n the_game().add_message(\"Inventory full. Delete an item to gain space\")\n return False\n return True\n\n def use(self, elem):\n \"\"\"Use a piece of equipment\"\"\"\n if elem is None:\n return\n self.check_equipment(elem)\n if elem not in self._inventory:\n raise ValueError('Equipment ' + elem.name + 'not in inventory')\n if elem.use(self):\n self._inventory.remove(elem)\n\n def delete_item(self, elem, throwing=False):\n \"\"\"Delete an element from the inventory\"\"\"\n if len(self._inventory) >= 0:\n if elem in self._inventory:\n self._inventory.remove(elem)\n if throwing:\n the_game().add_message(f\"You have successfully thrown the item : {elem.name}\")\n else:\n the_game().add_message(f\"You have successfully deleted the item : {elem.name}\")\n elif elem in self.weapon_slot:\n self.weapon_slot.remove(elem)\n if throwing:\n the_game().add_message(f\"You have successfully thrown the item : {elem.name}\")\n else:\n the_game().add_message(f\"You have successfully deleted the item : {elem.name}\")\n else:\n the_game().add_message(\"Could not find the item to delete. Maybe try with another value\")\n\n def gain_xp(self, creature_xp):\n\n self.xp += creature_xp\n\n the_game().add_message(\"You gained {0} XP points\".format(creature_xp))\n\n xp_to_use = self.xp\n self.level_step = self.default_level_size * self.level\n level_won = 0\n\n if xp_to_use > self.level_step:\n while xp_to_use > self.level_step:\n xp_to_use -= self.level_step\n\n self.gain_level(1)\n\n self.level_step = self.default_level_size * self.level\n level_won += 1\n\n self.xp = xp_to_use\n the_game().add_message(\"You won {0} level(s) and are now level {1}\".format(level_won, self.level))\n\n def gain_level(self, nb_of_level):\n self.level += 1\n self.strength += nb_of_level\n self.gold += nb_of_level + self.level\n\n the_game().add_message(\n \"You now have a strength of {0} and won {1} gold coins\".format(self.strength, self.level))\n\n def check_stomach(self):\n cool_down_value = 5\n if self.stomach == 0:\n if not hasattr(Hero.check_stomach, \"cool_down\"):\n setattr(Hero.check_stomach, \"cool_down\", cool_down_value)\n else:\n if Hero.check_stomach.cool_down == 0:\n self.hp -= 1\n Hero.check_stomach.cool_down = cool_down_value - 1\n the_game().add_message(\"WARNING : No more food !\")\n else:\n Hero.check_stomach.cool_down -= 1\n\n def buy(self, o):\n if isinstance(o, Equipment):\n if self.check_inventory_size():\n if self.gold >= o.price:\n self.gold -= o.price\n self.take(o)\n the_game().add_message(f'You bought {o.name} for {o.price} gold')\n else:\n the_game().add_message(f'Not enough gold. {o.price - self.gold} more gold needed')\n\n @staticmethod\n def choose_direction():\n the_game().add_message(\"Choose a direction to orientate yourself using the keys to move\")\n the_game().gv.draw_message(200)\n pygame.display.update()\n\n choice = None\n while choice is None:\n for event in pygame.event.get():\n\n if event.type == pygame.KEYDOWN:\n if the_game().gv.qwerty:\n if event.key == pygame.K_w:\n choice = \"z\"\n elif event.key == pygame.K_a:\n choice = \"q\"\n elif event.key == pygame.K_s or event.key == pygame.K_x:\n choice = \"x\"\n elif event.key == pygame.K_d:\n choice = \"d\"\n elif event.key == pygame.K_q:\n choice = \"a\"\n elif event.key == pygame.K_e:\n choice = \"e\"\n elif event.key == pygame.K_z:\n choice = \"w\"\n elif event.key == pygame.K_c:\n choice = \"c\"\n else:\n if event.key == pygame.K_z:\n choice = \"z\"\n elif event.key == pygame.K_q:\n choice = \"q\"\n elif event.key == pygame.K_s or event.key == pygame.K_x:\n choice = \"x\"\n elif event.key == pygame.K_d:\n choice = \"d\"\n elif event.key == pygame.K_a:\n choice = \"a\"\n elif event.key == pygame.K_e:\n choice = \"e\"\n elif event.key == pygame.K_w:\n choice = \"w\"\n elif event.key == pygame.K_c:\n choice = \"c\"\n\n if choice is not None:\n the_game().gv.inventory_on = False\n return Map.dir[choice]\n\n def throw_item(self, item, distance):\n\n if not isinstance(item, Equipment):\n return False\n\n hero_coord = the_game().floor.pos(self)\n direction = Hero.choose_direction()\n\n if not direction:\n return False\n\n item_coord = hero_coord + direction\n\n for i in range(distance):\n if i == 0:\n things_on_next_cell = the_game().floor.get(the_game().floor.pos(self) + direction)\n else:\n things_on_next_cell = the_game().floor.get(the_game().floor.pos(item) + direction)\n\n if isinstance(things_on_next_cell, Creature):\n hit = False\n # Verify that the item is a weapon\n if isinstance(item, Weapon):\n things_on_next_cell.hp -= item.launching_damage\n hit = True\n if things_on_next_cell.hp <= 0:\n self.gain_xp(creature_xp=things_on_next_cell.xp)\n the_game().floor.rm(the_game().floor.pos(things_on_next_cell))\n the_game().add_message(f\"[{things_on_next_cell.name}] has been killed using {item.name}\")\n # If this item is not a weapon, use the item on the creature encountered\n else:\n item.use(things_on_next_cell, monster=True)\n # If it is not the first throw, the item has been placed on the map and needs to be removed\n if i != 0:\n the_game().floor.rm(the_game().floor.pos(item))\n # The item is removed from the inventory only if he doesn't come back\n if not item.come_back:\n self.delete_item(item, True)\n # If a creature has been hit it appears in the game chat\n if hit:\n the_game().add_message(f\"[{things_on_next_cell.name}] lost {item.launching_damage} hp\")\n break\n\n # Verify that the item encounter an equipment\n elif isinstance(things_on_next_cell, Equipment) or isinstance(things_on_next_cell, RoomObject):\n # If it is the first movement of the throw it can't be placed\n if i == 0:\n the_game().add_message(\"You can't throw the item in this direction\")\n else:\n # The item is removed from the inventory only if he doesn't come back\n if not item.come_back:\n self.delete_item(item, True)\n # The item is removed from the map if he comes back\n else:\n the_game().floor.rm(the_game().floor.pos(item))\n the_game().add_message(f\"The {item.name} came back to it's owner\")\n break\n\n # Verify that the item encounter something different from the floor\n elif things_on_next_cell != the_game().floor.ground:\n if i == 0:\n the_game().add_message(\"You can't throw the item in this direction\")\n else:\n # The item is removed from the inventory only if he doesn't come back\n if not item.come_back:\n self.delete_item(item, True)\n # The item is removed from the map is he comes back\n else:\n the_game().floor.rm(the_game().floor.pos(item))\n the_game().add_message(f\"The {item.name} came back to it's owner\")\n break\n\n # Verify that the item encounters a floor cell\n elif things_on_next_cell == the_game().floor.ground:\n # If it is the first movement, just place the item\n if i == 0:\n the_game().floor.put(item_coord, item)\n # If the item reach the max distance...\n elif i == distance - 1:\n # ... and can come back, delete it from the map, add text in game chat\n if item.come_back:\n the_game().floor.rm(the_game().floor.pos(item))\n the_game().add_message(f\"The {item.name} came back to it's owner\")\n # ... and can't come back, delete it from the inventory\n else:\n self.delete_item(item, True)\n\n # If the item can continue, move it in it's direction\n else:\n the_game().floor.move(item, direction)\n\n else:\n raise NotImplementedError(\"Error might be due to an object not being managed.\")\n\n\nclass Effect(object):\n\n def __init__(self, creature):\n self.game = the_game()\n self.creature = creature\n self.value = 0\n self.info = \"\"\n self.duration = None\n self.level = 0\n\n image = CG.get_image('Effects/' + self.name + '.png') # change\n self.graphicOutput = pygame.transform.scale(image, (32, 32))\n\n def delete(self):\n try:\n self.game.active_effects.remove(self)\n del self\n except ValueError:\n pass\n\n def update(self):\n if isinstance(self, EphemeralEffect):\n self.action()\n\n if self.duration is not None:\n self.duration -= 1\n\n if self.duration <= 0:\n self.deactivate()\n return True\n\n def action(self):\n self.game.add_message(self.info)\n\n def add_effect(self):\n self.game.active_effects.append(self)\n\n def activate(self) -> None:\n self.action()\n if self not in self.game.active_effects:\n self.add_effect()\n\n def deactivate(self):\n self.game.add_message(self.info)\n self.delete()\n\n @staticmethod\n def clear(unique=True):\n effects_to_delete = []\n for effect in the_game().active_effects:\n if effect.creature is the_game().hero:\n effects_to_delete.append(effect)\n\n for effect in effects_to_delete:\n Effect.delete(effect)\n\n return unique\n\n\nclass EphemeralEffect(Effect):\n\n def __init__(self, creature, duration, level):\n super().__init__(creature)\n self.duration = duration\n self.level = level\n\n def activate(self, unique: bool = True) -> bool:\n super().activate()\n\n if self.duration is not None:\n self.duration -= 1\n\n if self.duration <= 0:\n self.deactivate()\n\n return unique\n\n def deactivate(self, kill: bool = False) -> None:\n if kill:\n self.info = f\"[{self.creature.name}] has been killed by {self.name}<{self.level}>\"\n else:\n self.info = f\"[{self.creature.name}] {self.name} effect disappeared\"\n super().deactivate()\n\n\nclass HealEffect(EphemeralEffect):\n LEVEL_FACTOR = 1\n DESCRIPTION = \"Recovering hp : +\"\n\n def __init__(self, creature, duration, level):\n self.name = \"Heal\"\n super().__init__(creature, duration, level)\n self.value = self.level * HealEffect.LEVEL_FACTOR\n\n def action(self):\n if self.creature.default_hp > self.creature.hp + self.value:\n self.creature.hp += self.value\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {HealEffect.DESCRIPTION}{self.value}\"\n\n else:\n self.creature.hp = self.creature.default_hp\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | Full Health : {self.creature.hp}/{self.creature.hp}\"\n\n super().action()\n\n\nclass PoisonEffect(EphemeralEffect):\n LEVEL_FACTOR = 1\n DESCRIPTION = \"Losing hp : -\"\n\n def __init__(self, creature, duration, level):\n self.name = \"Poison\"\n super().__init__(creature, duration, level)\n self.value = self.level * PoisonEffect.LEVEL_FACTOR\n\n def action(self):\n self.creature.hp -= self.value\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {PoisonEffect.DESCRIPTION}{self.value}\"\n\n if self.creature.hp == 0:\n the_game().floor.rm(the_game().floor.pos(self.creature))\n super().deactivate(True)\n else:\n super().action()\n\n\nclass FeedEffect(EphemeralEffect):\n \"\"\"\n Effect used to feed the hero. Creatures don't have a stomach so they can't be applied this effect.\n \"\"\"\n LEVEL_FACTOR = 1\n DESCRIPTION = \"I'm eating : +\"\n\n def __init__(self, creature, duration, level):\n\n if isinstance(creature, Hero):\n self.name = \"Feed\"\n super().__init__(creature, duration, level)\n self.value = self.level * FeedEffect.LEVEL_FACTOR\n\n def action(self) -> None:\n if self.creature.default_stomach_size > self.creature.stomach + self.value:\n self.creature.stomach += self.value\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {FeedEffect.DESCRIPTION}{self.value}\"\n\n else:\n self.creature.stomach = self.creature.default_stomach_size\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | Max food : {self.creature.stomach}/{self.creature.stomach}\"\n\n super().action()\n\n\nclass HungerEffect(EphemeralEffect):\n LEVEL_FACTOR = 1\n DESCRIPTION = \"I'm hungry : -\"\n\n def __init__(self, creature, duration, level):\n if isinstance(creature, Hero):\n self.name = \"Hunger\"\n super().__init__(creature, duration, level)\n self.value = self.level * HungerEffect.LEVEL_FACTOR\n\n def action(self):\n if isinstance(self.creature, Hero):\n self.creature.stomach -= self.value\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {HungerEffect.DESCRIPTION}{self.value}\"\n super().action()\n\n\nclass TeleportEffect(EphemeralEffect): # IS AN INSTANT EFFECT\n\n DESCRIPTION = \"You have been teleported\"\n\n def __init__(self, creature, duration=1):\n self.name = \"Teleportation\"\n super().__init__(creature, duration, 0)\n\n def action(self):\n \"\"\"Teleport the creature\"\"\"\n r = the_game().floor.rand_room()\n c = r.rand_coord()\n\n while not the_game().floor.get(c) == Map.ground:\n c = r.rand_coord()\n the_game().floor.rm(the_game().floor.pos(self.creature))\n the_game().floor.put(c, self.creature)\n\n self.info = f\"The creature <{self.creature.name}> has been teleported\"\n\n\n# CONSTANT EFFECTS\n\nclass ConstantEffect(Effect):\n\n def __init__(self, creature):\n super().__init__(creature)\n self.has_been_activated = False\n\n def activate(self, unique=True):\n if not self.has_been_activated:\n super().activate()\n self.has_been_activated = True\n\n return unique\n\n def deactivate(self):\n self.info += f\" [{self.creature.name}] {self.name} effect disappeared\"\n super().deactivate()\n\n\nclass StrengthEffect(ConstantEffect):\n LEVEL_FACTOR = 1\n DESCRIPTION_ACTIVATE = \"I feel stronger : +\"\n DESCRIPTION_DEACTIVATE = \" I feel weaker : -\"\n\n def __init__(self, creature, duration=None, level=1):\n self.name = \"Strength\"\n super().__init__(creature)\n self.duration = duration\n self.level = level\n self.value = self.level * StrengthEffect.LEVEL_FACTOR\n\n def action(self):\n self.creature.strength += self.value\n\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {StrengthEffect.DESCRIPTION_ACTIVATE}{self.value}\"\n super().action()\n\n def activate(self, unique=True):\n if not self.has_been_activated:\n super().activate(unique)\n super().update()\n return unique\n\n def deactivate(self):\n self.creature.strength -= self.value\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {StrengthEffect.DESCRIPTION_DEACTIVATE}{self.value}\"\n super().deactivate()\n\n\nclass WeaknessEffect(ConstantEffect):\n LEVEL_FACTOR = 1\n DESCRIPTION_ACTIVATE = \"I feel weaker : -\"\n DESCRIPTION_DEACTIVATE = \" I feel stronger : +\"\n\n def __init__(self, creature, duration=None, level=1):\n self.name = \"Weakness\"\n super().__init__(creature)\n self.duration = duration\n self.level = level\n self.value = self.level * WeaknessEffect.LEVEL_FACTOR\n\n def action(self):\n self.creature.strength -= self.value\n\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {WeaknessEffect.DESCRIPTION_ACTIVATE}{self.value}\"\n super().action()\n\n def activate(self, unique=True):\n if not self.has_been_activated:\n super().activate(unique)\n super().update()\n return unique\n\n def deactivate(self):\n self.creature.strength += self.value\n self.info = f\"[{self.creature.name}] | {self.name}<{self.level}> | {WeaknessEffect.DESCRIPTION_DEACTIVATE}{self.value}\"\n super().deactivate()\n\n\n# EQUIPMENT\nclass Equipment(Element):\n \"\"\"A piece of equipment\"\"\"\n\n def __init__(self, name, abbreviation=\"\", usage=None, durability=None, price=1):\n Element.__init__(self, name, abbreviation)\n self.usage = usage\n self.durability = durability\n self.price = price\n\n self.come_back = False\n\n # GRAPHICS\n image = CG.get_item_image(self.name)\n self.graphicOutput = [pygame.transform.scale(image, (16, 16)), pygame.transform.scale(image, (32, 32))]\n\n def meet(self, hero):\n \"\"\"Makes the hero meet an element. The hero takes the element.\"\"\"\n hero.take(self)\n the_game().add_message(\"You pick up a \" + self.name)\n return True\n\n def use(self, creature, monster=False):\n \"\"\"Uses the piece of equipment. Has effect on the hero according usage.\n Return True if the object is consumed.\"\"\"\n if self.usage is None:\n if not monster:\n the_game().add_message(f\"The {creature.name} can't use the item {self.name}\")\n return False\n\n else:\n the_game().add_message(f\"The {creature.name} uses the item {self.name}\")\n try:\n return self.usage(self, creature)\n except AttributeError:\n pass\n\n\nclass Weapon(Equipment):\n \"\"\"A weapon which can be used by the Hero or the monsters\"\"\"\n\n def __init__(self, name, abbreviation=\"\", price=1, damage=1, launching_damage=1, come_back=False):\n Equipment.__init__(self, name, abbreviation, price)\n self.damage = damage\n self.launching_damage = launching_damage\n self.come_back = come_back # The weapon can come back to the hero when used (like a boomerang)\n\n\nclass Room(object):\n \"\"\"A rectangular room in the map\"\"\"\n\n def __init__(self, c1, c2, special_objects=None):\n self.c1 = c1\n self.c2 = c2\n if special_objects is None:\n special_objects = []\n self.specialObjects = special_objects\n\n def __repr__(self):\n return \"[\" + str(self.c1) + \", \" + str(self.c2) + \"]\"\n\n def __contains__(self, coord):\n return self.c1.x <= coord.x <= self.c2.x and self.c1.y <= coord.y <= self.c2.y\n\n def intersect(self, other):\n \"\"\"Test if the room has an intersection with another room\"\"\"\n sc3 = Coord(self.c2.x, self.c1.y)\n sc4 = Coord(self.c1.x, self.c2.y)\n return self.c1 in other or self.c2 in other or sc3 in other or sc4 in other or other.c1 in self\n\n def center(self):\n \"\"\"Returns the coordinates of the room center\"\"\"\n return Coord((self.c1.x + self.c2.x) // 2, (self.c1.y + self.c2.y) // 2)\n\n def rand_coord(self):\n \"\"\"A random coordinate inside the room\"\"\"\n return Coord(random.randint(self.c1.x, self.c2.x), random.randint(self.c1.y, self.c2.y))\n\n def rand_empty_coord(self, map):\n \"\"\"A random coordinate inside the room which is free on the map.\"\"\"\n c = self.rand_coord()\n while map.get(c) != Map.ground or c == self.center():\n c = self.rand_coord()\n return c\n\n def rand_empty_middle_coord(self, map):\n \"\"\"Same as rand_empty_coord but surrounded by ground\"\"\"\n list_of_coord_available = []\n for y in range(self.c1.y + 1, self.c2.y):\n for x in range(self.c1.x + 1, self.c2.x):\n c = Coord(x, y)\n if c.empty_around(map) and c != self.center():\n list_of_coord_available.append(c)\n if len(list_of_coord_available) == 0:\n return self.rand_coord()\n else:\n return random.choice(list_of_coord_available)\n\n def decorate(self, map):\n \"\"\"Decorates the room by adding a random equipment and monster.\"\"\"\n for elem in self.specialObjects:\n map.put(self.rand_empty_middle_coord(map), elem)\n map.put(self.rand_empty_coord(map), the_game().rand_equipment(map.floor_number))\n map.put(self.rand_empty_coord(map), the_game().rand_monster(map.floor_number))\n\n\nclass Map(object):\n \"\"\"A map of a game floor.\n Contains game elements.\"\"\"\n\n ground = '.' # A walkable ground cell\n\n dir = {'z': Coord(0, -1),\n 'x': Coord(0, 1),\n 'd': Coord(1, 0),\n 'q': Coord(-1, 0),\n 'a': Coord(-1, -1),\n 'e': Coord(1, -1),\n 'w': Coord(-1, 1),\n 'c': Coord(1, 1),\n }\n\n empty = e = ' ' # A non walkable cell\n sizeFactor = round(16 * 1.25)\n\n def __init__(self, size=20, hero=None, put_hero=True, floor_number=None, special_room=None):\n self._mat = []\n self._elem = {}\n self._rooms = []\n self._rooms_to_reach = []\n self.floor_number = floor_number\n self.special_room = special_room\n\n for i in range(size):\n self._mat.append([Map.empty] * size)\n if hero is None:\n hero = Hero()\n self.hero = hero\n self.generate_rooms(7)\n self.reach_all_rooms()\n\n # Graphics\n\n self.graphic_map = []\n CG.generate_graphic_map(self)\n self.graphic_elements = []\n\n for i in range(len(self.graphic_map)):\n self.graphic_elements.append([None] * len(self.graphic_map))\n\n self.put_room_objects()\n if put_hero:\n self.put(self._rooms[0].center(), hero)\n self.hero.x = self._rooms[0].center().x\n self.hero.y = self._rooms[0].center().y\n for r in self._rooms:\n r.decorate(self)\n\n self.update_elements(0)\n\n def add_room(self, room):\n \"\"\"Adds a room in the map.\"\"\"\n self._rooms_to_reach.append(room)\n for y in range(room.c1.y, room.c2.y + 1):\n for x in range(room.c1.x, room.c2.x + 1):\n self._mat[y][x] = Map.ground\n\n def find_room(self, coord):\n \"\"\"If the coord belongs to a room, returns the room elsewhere returns None\"\"\"\n for r in self._rooms_to_reach:\n if coord in r:\n return r\n return None\n\n def intersect_none(self, room):\n \"\"\"Tests if the room shall intersect any room already in the map.\"\"\"\n for r in self._rooms_to_reach:\n if room.intersect(r):\n return False\n return True\n\n def dig(self, coord):\n \"\"\"Puts a ground cell at the given coord.\n If the coord corresponds to a room, considers the room reached.\"\"\"\n self._mat[coord.y][coord.x] = Map.ground\n r = self.find_room(coord)\n if r:\n self._rooms_to_reach.remove(r)\n self._rooms.append(r)\n\n def corridor(self, cursor, end):\n \"\"\"Digs a corridors from the coordinates cursor to the end, first vertically, then horizontally.\"\"\"\n d = end - cursor\n self.dig(cursor)\n while cursor.y != end.y:\n cursor = cursor + Coord(0, sign(d.y))\n self.dig(cursor)\n while cursor.x != end.x:\n cursor = cursor + Coord(sign(d.x), 0)\n self.dig(cursor)\n\n def reach(self):\n \"\"\"Makes more rooms reachable.\n Start from one random reached room, and dig a corridor to an unreached room.\"\"\"\n room_a = random.choice(self._rooms)\n room_b = random.choice(self._rooms_to_reach)\n\n self.corridor(room_a.center(), room_b.center())\n\n def reach_all_rooms(self):\n \"\"\"Makes all rooms reachable.\n Start from the first room, repeats @reach until all rooms are reached.\"\"\"\n self._rooms.append(self._rooms_to_reach.pop(0))\n while len(self._rooms_to_reach) > 0:\n self.reach()\n\n def put_room_objects(self):\n for key in Game._room_objects:\n if key == \"downstair\" and self.floor_number > 0:\n r = random.choice(self._rooms)\n r.specialObjects.append(Game._room_objects[key])\n if key == \"upstair\" and self.floor_number + 1 < the_game().nb_floors:\n r = random.choice(self._rooms)\n r.specialObjects.append(Game._room_objects[key])\n\n def rand_room(self):\n \"\"\"A random room to be put on the map.\"\"\"\n c1 = Coord(random.randint(0, len(self) - 3), random.randint(0, len(self) - 3))\n c2 = Coord(min(c1.x + random.randint(3, 8), len(self) - 1), min(c1.y + random.randint(3, 8), len(self) - 1))\n return Room(c1, c2)\n\n def generate_rooms(self, n):\n \"\"\"Generates n random rooms and adds them if non-intersecting.\"\"\"\n if self.special_room is not None:\n self.add_room(Game._special_rooms_list[self.special_room])\n for i in range(n):\n r = self.rand_room()\n if self.intersect_none(r):\n self.add_room(r)\n\n def __len__(self):\n return len(self._mat)\n\n def __contains__(self, item):\n if isinstance(item, Coord):\n return 0 <= item.x < len(self) and 0 <= item.y < len(self)\n return item in self._elem\n\n def in_graphic_map(self, c):\n return 0 <= c.x < len(self.graphic_map) and 0 <= c.y < len(self.graphic_map)\n\n def __repr__(self):\n s = \"\"\n for i in self._mat:\n for j in i:\n s += str(j)\n s += '\\n'\n return s\n\n def check_coord(self, c):\n \"\"\"Check if the coordinates c is valid in the map.\"\"\"\n if not isinstance(c, Coord):\n raise TypeError('Not a Coord')\n if not c in self:\n raise IndexError('Out of map coord')\n\n @staticmethod\n def check_element(o):\n \"\"\"Check if o is an Element.\"\"\"\n if not isinstance(o, Element):\n raise TypeError('Not a Element')\n\n def put(self, c, o):\n \"\"\"Puts an element o on the cell c\"\"\"\n self.check_coord(c)\n self.check_element(o)\n if self._mat[c.y][c.x] != Map.ground:\n raise ValueError('Incorrect cell')\n if o in self._elem:\n raise KeyError('Already placed')\n self._mat[c.y][c.x] = o\n self._elem[o] = c\n\n def get(self, c):\n \"\"\"Returns the object present on the cell c\"\"\"\n try:\n self.check_coord(c)\n except IndexError:\n return Map.empty\n return self._mat[c.y][c.x]\n\n def get_without_coord(self, x, y):\n self.check_coord(Coord(x, y))\n return self._mat[y][x]\n\n def pos(self, o):\n \"\"\"Returns the coordinates of an element in the map \"\"\"\n if o in self._elem:\n self.check_element(o)\n return self._elem[o]\n else:\n return None\n\n def rm(self, c):\n \"\"\"Removes the element at the coordinates c\"\"\"\n self.check_coord(c)\n del self._elem[self._mat[c.y][c.x]]\n self._mat[c.y][c.x] = Map.ground\n\n def move(self, e, way):\n \"\"\"Moves the element e in the direction way.\"\"\"\n orig = self.pos(e)\n dest = orig + way\n if dest in self:\n if self.get(dest) == Map.ground:\n self._mat[orig.y][orig.x] = Map.ground\n self._mat[dest.y][dest.x] = e\n self._elem[e] = dest\n if isinstance(e, Hero):\n self.hero.x, self.hero.y = dest.x, dest.y\n elif isinstance(self.get(dest), RoomObject) and self.get(dest).meet(e):\n pass\n elif self.get(dest) != Map.empty and self.get(dest).meet(e) and self.get(dest) != self.hero:\n self.rm(dest)\n\n def check_move(self, e, way):\n \"\"\"Returns element in way\"\"\"\n orig = self.pos(e)\n dest = orig + way\n if dest in self:\n return self.get(dest)\n return self.empty\n\n def direction(self, c1, c2):\n \"\"\"Returns the direction between two coordinates.\"\"\"\n final_way = Coord(0, 0)\n for i in range(-1, 2):\n for j in range(-1, 2):\n way = Coord(i, j)\n in_map = 0 <= c1.x + way.x < len(self._mat) and 0 <= c1.y + way.y < len(self._mat)\n if in_map and (c1 + way).distance(c2) < (c1 + final_way).distance(c2) and (self.get(\n c1 + way) == self.ground or self.get(c1 + way) == self.hero):\n final_way = way\n return final_way\n\n def move_all_monsters(self):\n \"\"\"Moves all monsters in the map.\n If a monster is at distance lower than 6 from the hero, the monster advances.\"\"\"\n\n h = self.pos(self.hero)\n for e in self._elem:\n c = self.pos(e)\n if isinstance(e, Creature) and e != self.hero and c.distance(h) < 6:\n d = self.direction(c, h)\n if self.get(c + d) in [Map.ground, self.hero]:\n self.move(e, d)\n\n def update_elements(self, state):\n clear_list(self.graphic_elements)\n for y in range(len(self._mat)):\n for x in range(len(self._mat)):\n\n elem = self.get(Coord(x, y))\n if elem != self.ground and elem != self.empty:\n if isinstance(elem, Hero):\n elem.x = x\n elem.y = y\n elif isinstance(elem, Creature):\n self.graphic_elements[y][x] = elem.graphicOutput[state]\n elif isinstance(elem, Equipment):\n self.graphic_elements[y][x] = elem.graphicOutput[0]\n elif isinstance(elem, RoomObject):\n if len(elem.graphicOutput) == 2:\n self.graphic_elements[y][x] = elem.graphicOutput[state]\n else:\n self.graphic_elements[y][x] = elem.graphicOutput[0]\n the_game().gv.update_fog(self)\n\n\nclass GraphicVariables(object):\n def __init__(self, hero):\n\n self.height = None\n self.width = None\n self.orig_x = None\n self.orig_y = None\n\n self.hero = hero\n self.floor = None\n self.screen = None\n\n # Frames\n self.running = True\n self.frame_count = 0\n self.monster_state = 0\n\n self.stop = False\n self.choice = 0\n self.choice_inv = 0\n self.newRound = False\n\n # Messages\n self.game_font = None\n self.menu_font = None\n self._msg = []\n\n self.qwerty = False\n\n self.options_menu_start = [(\"Menu\", False), (\"\", False), (\"New Game\", True), (\"Preferences\", True),\n (\"Exit Game\", True)]\n self.options_menu = [(\"Menu\", False), (\"\", False), (\"Resume Game\", True), (\"Preferences\", True),\n (\"Exit Game\", True)]\n self.options_hero = [(\"Characters\", False), (\"\", False), (\"Template\", True), (\"Rogue\", True),\n (\"Engineer\", True), (\"Warrior\", True), (\"Mage\", True), (\"Paladin\", True)]\n self.options_controls = [\n (\"i : open/close inventory\", False),\n (\"k : suicide\", False),\n (\"t : delete item\", False),\n (\"b : select weapon\", False),\n (\"n : remove current weapon\", False),\n (\"l : launch from weapon slot\", False),\n (\"u / Enter : use item selected\", False),\n (\"Return\", True)]\n self.options_preferences = [('Preferences', False),\n ('', False),\n ('Show Controls', True),\n ('Set Qwerty', True),\n ('Set Azerty', True),\n ('Choose Character', True),\n ('', False),\n ('Return', True)]\n self.options_game_over = [(\"-- Game Over --\", False), (\"\", False), (\"Exit Game\", True)]\n\n # Menu\n self.menu_on = True\n self.inventory_on = False\n self.list_menu = self.options_menu_start\n self.colour_menu = (140, 140, 150)\n\n # Game Surfaces\n self.explosion = []\n for i in range(6):\n image = CG.get_image(\"Animations/explosion-\" + str(i) + \".png\")\n self.explosion.append(pygame.transform.scale(image, (40, 40)))\n\n self.hearts = []\n for i in range(5):\n image1 = CG.get_image(\"GUI/heart\" + str(i) + \"-0.png\")\n image2 = CG.get_image(\"GUI/heart\" + str(i) + \"-1.png\")\n self.hearts.append([image1, image2])\n\n self.black_hearts = []\n for i in range(5):\n image1 = CG.get_image(\"GUI/blackheart\" + str(i) + \"-0.png\")\n image2 = CG.get_image(\"GUI/blackheart\" + str(i) + \"-1.png\")\n self.black_hearts.append([image1, image2])\n\n self.xp_bord = []\n for i in range(3):\n self.xp_bord.append(CG.get_image(\"GUI/bordexp\" + str(i) + \".png\"))\n\n self.blockSpace = pygame.transform.scale(CG.get_image(\"GUI/blockSpace.png\"), (32, 32))\n self.fog = CG.get_image(\"Background/Void.png\")\n\n self.food = [CG.get_image(\"GUI/food0.png\"), CG.get_image(\"GUI/food1.png\")]\n self.dollar = CG.get_image(\"GUI/dollar.png\")\n self.arrow = CG.get_image(\"GUI/arrow.png\")\n\n # Music effects\n self._songs = ['song-1.mp3', 'song-2.mp3', 'song-3.mp3']\n\n def draw_gui(self, state):\n self.screen.fill((72, 62, 87), (self.width / 2, 0, self.width / 2, self.height))\n\n # Draw Character\n scale = round(self.height / 5)\n hero_image = pygame.transform.scale(self.hero.graphicOutput, (scale, scale))\n hero_drawing_x = (self.width / 2) * (1 + 1 / 10)\n self.screen.blit(hero_image, (hero_drawing_x, self.height / 10))\n h_d_width = 200\n\n # Draw hearts\n for i in range(Hero.default_hp):\n if self.hero.hp - i > 0:\n image = self.hearts[0][state]\n elif self.hero.hp - i == -0.25:\n image = self.hearts[1][state]\n elif self.hero.hp - i == -0.5:\n image = self.hearts[2][state]\n elif self.hero.hp - i == -0.75:\n image = self.hearts[3][state]\n else:\n image = self.hearts[4][state]\n\n self.screen.blit(image, (hero_drawing_x + h_d_width + 18 * i, self.height * 3 / 20))\n\n # Draw food\n for i in range(Hero.default_stomach_size):\n if self.hero.stomach - i > 0:\n self.screen.blit(self.food[0], (hero_drawing_x + h_d_width + 18 * i, self.height * 3 / 20 + 25))\n else:\n self.screen.blit(self.food[1], (hero_drawing_x + h_d_width + 18 * i, self.height * 3 / 20 + 25))\n\n # Draw xp\n xp_percentage = self.hero.xp * 100 / self.hero.level_step\n\n self.screen.fill((0, 255, 0), (hero_drawing_x + h_d_width, self.height * 3 / 20 + 56, xp_percentage * 2, 6))\n\n for i in range(11):\n if i == 0:\n self.screen.blit(self.xp_bord[0],\n (hero_drawing_x + h_d_width + 18 * i, self.height * 3 / 20 + 50))\n elif i == 10:\n self.screen.blit(self.xp_bord[2],\n (hero_drawing_x + h_d_width + 18 * i, self.height * 3 / 20 + 50))\n else:\n self.screen.blit(self.xp_bord[1],\n (hero_drawing_x + h_d_width + 18 * i, self.height * 3 / 20 + 50))\n\n # Draw Hero Level\n text = self.game_font.render('Level: ' + str(self.hero.level), True, (0, 0, 0))\n self.screen.blit(text, (hero_drawing_x + h_d_width, self.height * 3 / 20 - 58))\n\n # Draw Hero Strength\n text = self.game_font.render('Strength: ' + str(self.hero.strength), True, (0, 0, 0))\n self.screen.blit(text, (hero_drawing_x + h_d_width, self.height * 3 / 20 - 30))\n\n # Draw gold\n self.screen.blit(self.dollar, (hero_drawing_x + h_d_width, self.height * 3 / 20 + 75))\n text = self.game_font.render(str(self.hero.gold), True, (0, 0, 0))\n self.screen.blit(text, (hero_drawing_x + h_d_width + 30, self.height * 3 / 20 + 76))\n\n # Effects\n for i, x in enumerate(the_game().active_effects):\n if x.creature == self.hero:\n self.screen.blit(x.graphicOutput, (hero_drawing_x + h_d_width + 32 * i, self.height * 3 / 20 + 100))\n\n # Inventory\n sf = 2\n case = 16\n\n self.screen.blit(self.blockSpace, (self.width / 2 * (1 + 1 / 10) + 65, self.height * 7 / 20))\n if len(self.hero.weapon_slot) != 0:\n self.screen.blit(self.hero.weapon_slot[0].graphicOutput[1],\n (self.width / 2 * (1 + 1 / 10) + 65, self.height * 7 / 20))\n\n for i in range(Hero.default_inventory_size):\n self.screen.blit(self.blockSpace, (self.width / 2 * (1 + 3 / 10) + case * i * sf, self.height * 7 / 20))\n if i < len(self.hero._inventory):\n if sf != 1:\n image = pygame.transform.scale(self.hero._inventory[i].graphicOutput[1], (16 * sf, 16 * sf))\n else:\n image = self.hero._inventory[i].graphicOutput[1]\n self.screen.blit(image, (self.width / 2 * (1 + 3 / 10) + case * i * sf, self.height * 7 / 20))\n\n # Arrow Inventory\n if self.inventory_on:\n if len(self.hero._inventory) != 0:\n self.choice_inv %= len(self.hero._inventory)\n else:\n self.choice_inv = 0\n if sf != 2:\n image = pygame.transform.scale(self.arrow, (16 * sf, 16 * sf))\n else:\n image = self.arrow\n self.screen.blit(image, (\n self.width / 2 * (1 + 3 / 10) + 7 + case * self.choice_inv * sf, self.height * 7 / 20 - 21))\n\n def draw_map(self):\n self.screen.fill((80, 74, 85), (0, 0, self.width / 2, self.height))\n for y in range(self.height // Map.sizeFactor):\n for x in range(self.width // (2 * Map.sizeFactor)):\n self.screen.blit(self.fog, (x * Map.sizeFactor, y * Map.sizeFactor))\n\n for y in range(len(self.floor.graphic_map)):\n for x in range(len(self.floor.graphic_map[y])):\n pos = (Map.sizeFactor * x + self.orig_x, Map.sizeFactor * y + self.orig_y)\n if self.floor.graphic_map[y][x][1]:\n self.screen.blit(self.floor.graphic_map[y][x][0], pos)\n else:\n self.screen.blit(self.fog, pos)\n\n # Draw Map level\n string = f\"Floor number: {the_game().floor_list[the_game().actual_floor].floor_number + 1} / {the_game().nb_floors}\"\n text = self.game_font.render(string, True, (0, 0, 0))\n\n text_width, text_height = self.game_font.size(string)\n self.screen.fill((72, 62, 87), (45, self.orig_y / 3 - 5, text_width + 10, text_height + 10))\n self.screen.blit(text, (50, self.orig_y / 3))\n\n def draw_elements(self, monster_state):\n self.floor.update_elements(monster_state)\n for y in range(len(self.floor.graphic_elements)):\n for x in range(len(self.floor.graphic_elements)):\n case = self.floor.graphic_elements[y][x]\n if case is not None and self.floor.graphic_map[y][x][1]:\n if isinstance(self.floor._mat[y][x], RoomObject):\n if self.floor._mat[y][x].name == \"upstair\":\n relief = 20\n else:\n relief = 0\n else:\n relief = Map.sizeFactor / 4\n self.screen.blit(case,\n (Map.sizeFactor * x + self.orig_x, Map.sizeFactor * y + self.orig_y - relief))\n\n def draw_message(self, time):\n # Draw Message self.screen\n self.screen.fill((20, 12, 28), (\n (self.width / 2) * (1 + 1 / 8), self.height * 3 / 4, (self.width / 2) * 6 / 8, self.height / 5))\n b = 5\n self.screen.fill((140, 140, 150), (\n (self.width / 2) * (1 + 1 / 8) + b, self.height * 3 / 4 + b, (self.width / 2) * 6 / 8 - 2 * b,\n self.height / 5 - 2 * b))\n\n new_msg = the_game().read_messages()\n nb_lines = len(self._msg)\n msg_nuls = []\n for k in new_msg:\n self._msg.append([k, time])\n if len(self._msg) > 5:\n self._msg.pop(0)\n for i in range(nb_lines):\n self.screen.blit(self._msg[i][0],\n ((self.width / 2) * (1 + 1 / 8) + 15, self.height * 3 / 4 + 5 + 20 * i + 10))\n self._msg[i][1] -= 1\n if self._msg[i][1] <= 0:\n msg_nuls.append(i)\n\n for j in msg_nuls:\n if j < len(self._msg):\n self._msg.pop(j)\n\n def draw_menu(self, list_menu, colour=(140, 140, 150)):\n menu_y = self.height / 6\n self.screen.fill((255, 255, 51), (self.width / 4, menu_y, self.width / 2, self.height * 4 / 6))\n b = 5\n self.screen.fill(colour,\n (self.width / 4 + b, self.height / 6 + b, self.width / 2 - 2 * b, self.height * 4 / 6 - 2 * b))\n\n self.choice %= len(list_menu)\n\n for i in range(len(list_menu)):\n if i == self.choice:\n if list_menu[i][1]:\n f = \">> \"\n else:\n f = \" \"\n self.choice = (self.choice + 1) % len(list_menu)\n else:\n f = \" \"\n current_item = list_menu[i][0]\n if isinstance(current_item, Weapon):\n if current_item.come_back:\n to_add = \"(comes back)\"\n else:\n to_add = \"\"\n objet = f\"{current_item.name} [ {str(current_item.price)} $ ] < dmg = {current_item.damage}, launch dmg = {current_item.launching_damage} >\" + to_add\n elif isinstance(current_item, Equipment):\n objet = f\"{current_item.name} [ {str(current_item.price)} $ ]\"\n else:\n objet = current_item\n\n o_height = self.menu_font.size(objet)[1]\n text = self.menu_font.render(f + objet, True, (0, 0, 0))\n text_rect = text.get_rect(center=(self.width / 2, menu_y + (i + 1) * (o_height - 15)))\n self.screen.blit(text, text_rect)\n\n def draw_trader(self, list_objects):\n lm = [('Do you want something ?', False), ('', False)]\n\n for o in list_objects:\n lm.append((o, True))\n lm += [('', False), ('Maybe Later', True)]\n\n self.list_menu = lm\n self.colour_menu = (30, 212, 157)\n self.menu_on = True\n\n def draw_hero_move(self):\n h = self.hero\n sf = Map.sizeFactor\n persp = -sf / 4\n\n has_moved = False\n\n way = Coord(0, 0)\n if h.moving_UDLR[0]:\n # UP\n way += Coord(0, -1)\n elif h.moving_UDLR[1]:\n # DOWN\n way += Coord(0, 1)\n elif h.moving_UDLR[2]:\n # LEFT\n way += Coord(-1, 0)\n elif h.moving_UDLR[3]:\n # RIGHT\n way += Coord(1, 0)\n elif h.moving_UDLR[4]:\n # UP LEFT\n way += Coord(-1, -1)\n elif h.moving_UDLR[5]:\n # UP RIGHT\n way += Coord(1, -1)\n elif h.moving_UDLR[6]:\n # DOWN RIGHT\n way += Coord(1, 1)\n elif h.moving_UDLR[7]:\n # DOWN LEFT\n way += Coord(-1, 1)\n\n if way != Coord(0, 0):\n\n elem_in_way = self.floor.check_move(h, way)\n if elem_in_way == self.floor.ground:\n pos = (sf * h.x + way.x * h.state * sf / 4 + self.orig_x,\n sf * h.y + way.y * h.state * sf / 4 + self.orig_y + persp)\n\n self.screen.blit(h.animationUDLR[way.get_tuple()][h.state], pos)\n h.state += 1\n has_moved = True\n\n if h.state >= 4:\n h.state = 0\n self.floor.move(h, way)\n self.newRound = True\n\n if self.stop:\n h.moving_UDLR = [False] * 8\n self.stop = False\n\n elif isinstance(elem_in_way, Creature):\n self.floor.move(h, way)\n self.newRound = True\n\n for i in range(6):\n self.screen.blit(self.explosion[i], (\n sf * (h.x + way.x) + self.orig_x - 12, sf * (h.y + way.y) + self.orig_y + persp - 12))\n self.screen.blit(h.graphicOutput, (sf * h.x + self.orig_x, sf * h.y + self.orig_y + persp))\n pygame.display.update()\n pygame.time.delay(50)\n\n elif isinstance(elem_in_way, Element):\n self.floor.move(h, way)\n\n if not has_moved:\n self.screen.blit(h.graphicOutput, (sf * h.x + self.orig_x, sf * h.y + self.orig_y + persp))\n\n def player_plays(self, event):\n do = False\n keydown_bool = False\n\n if event.type == pygame.KEYDOWN:\n keydown_bool = True\n do = True\n elif event.type == pygame.KEYUP:\n keydown_bool = False\n do = True\n\n # Movement\n if do:\n\n if keydown_bool or self.hero.state == 0:\n self.hero.moving_UDLR = [False] * 8\n\n if self.qwerty: # change\n if event.key == pygame.K_w: # UP\n self.hero.moving_UDLR[0] = keydown_bool\n if event.key == pygame.K_x: # DOWN\n self.hero.moving_UDLR[1] = keydown_bool\n elif event.key == pygame.K_a: # LEFT\n self.hero.moving_UDLR[2] = keydown_bool\n elif event.key == pygame.K_d: # RIGHT\n self.hero.moving_UDLR[3] = keydown_bool\n\n # Diagonales\n elif event.key == pygame.K_q: # UP LEFT\n self.hero.moving_UDLR[4] = keydown_bool\n elif event.key == pygame.K_e: # UP RIGHT\n self.hero.moving_UDLR[5] = keydown_bool\n elif event.key == pygame.K_c: # DOWN RIGHT\n self.hero.moving_UDLR[6] = keydown_bool\n elif event.key == pygame.K_z: # DOWN LEFT\n self.hero.moving_UDLR[7] = keydown_bool\n else:\n if event.key == pygame.K_z: # UP\n self.hero.moving_UDLR[0] = keydown_bool\n if event.key == pygame.K_x: # DOWN\n self.hero.moving_UDLR[1] = keydown_bool\n elif event.key == pygame.K_q: # LEFT\n self.hero.moving_UDLR[2] = keydown_bool\n elif event.key == pygame.K_d: # RIGHT\n self.hero.moving_UDLR[3] = keydown_bool\n\n # Diagonals\n elif event.key == pygame.K_a: # UP LEFT\n self.hero.moving_UDLR[4] = keydown_bool\n elif event.key == pygame.K_e: # UP RIGHT\n self.hero.moving_UDLR[5] = keydown_bool\n elif event.key == pygame.K_c: # DOWN RIGHT\n self.hero.moving_UDLR[6] = keydown_bool\n elif event.key == pygame.K_w: # DOWN LEFT\n self.hero.moving_UDLR[7] = keydown_bool\n\n if event.key == pygame.K_UP: # UP\n self.hero.moving_UDLR[0] = keydown_bool\n elif event.key == pygame.K_DOWN or event.key == pygame.K_s: # DOWN\n self.hero.moving_UDLR[1] = keydown_bool\n elif event.key == pygame.K_LEFT: # LEFT\n self.hero.moving_UDLR[2] = keydown_bool\n elif event.key == pygame.K_RIGHT: # RIGHT\n self.hero.moving_UDLR[3] = keydown_bool\n\n # Actions\n if keydown_bool:\n self.choose_action(event)\n\n else:\n self.stop = True\n\n def choose_action(self, event):\n if event.key == pygame.K_k:\n Game._actions['k'](self.hero)\n elif event.key == pygame.K_b:\n Game._actions['b'](self.hero)\n elif event.key == pygame.K_n:\n Game._actions['n'](self.hero)\n elif event.key == pygame.K_l:\n Game._actions['l'](self.hero)\n\n def choose_in_menu(self, event):\n move_choice = 0\n if self.qwerty:\n if event.key == pygame.K_w:\n move_choice = - 1\n elif event.key == pygame.K_s:\n move_choice = 1\n else:\n if event.key == pygame.K_z:\n move_choice = - 1\n elif event.key == pygame.K_s:\n move_choice = 1\n\n if event.key == pygame.K_UP:\n move_choice = - 1\n elif event.key == pygame.K_DOWN:\n move_choice = 1\n\n for i in range(len(self.list_menu)):\n self.choice += move_choice\n self.choice %= len(self.list_menu)\n if self.list_menu[self.choice][1]:\n break\n\n if event.key == pygame.K_RETURN:\n\n this_choice = self.list_menu[self.choice][0]\n\n if this_choice == \"New Game\":\n self.menu_on = not self.menu_on\n self.list_menu = self.options_menu\n\n elif this_choice == \"Resume Game\" or this_choice == 'Maybe Later':\n self.menu_on = not self.menu_on\n\n elif this_choice == \"Exit Game\":\n self.running = False\n\n elif this_choice == \"Choose Character\":\n self.list_menu = self.options_hero\n self.choice = 0\n\n elif self.list_menu[self.choice] in self.options_hero:\n self.change_hero_appearance(this_choice)\n self.choice = 0\n self.list_menu = self.options_menu\n\n\n elif this_choice == \"Show Controls\":\n self.list_menu = self.options_controls\n\n elif this_choice == \"Return\":\n self.list_menu = self.options_menu\n\n elif this_choice == \"Preferences\":\n self.list_menu = self.options_preferences\n\n\n elif this_choice == \"Set Qwerty\":\n self.qwerty = True\n self.choice = 0\n self.list_menu = self.options_menu\n\n elif this_choice == \"Set Azerty\":\n self.qwerty = False\n self.choice = 0\n self.list_menu = self.options_menu\n\n # Marchand\n\n elif isinstance(this_choice, Equipment):\n self.hero.buy(this_choice)\n self.menu_on = False\n\n def choose_in_inventory(self, event):\n\n if self.qwerty:\n if event.key == pygame.K_a:\n self.choice_inv -= 1\n elif event.key == pygame.K_d:\n self.choice_inv += 1\n\n else:\n if event.key == pygame.K_q:\n self.choice_inv -= 1\n elif event.key == pygame.K_d:\n self.choice_inv += 1\n\n if event.key == pygame.K_LEFT:\n self.choice_inv -= 1\n elif event.key == pygame.K_RIGHT:\n self.choice_inv += 1\n\n # Use equipment\n if event.key == pygame.K_RETURN or event.key == pygame.K_u:\n Game._actions['u'](self.hero)\n self.inventory_on = False\n\n elif event.key == pygame.K_t:\n Game._actions[\"t\"](self.hero)\n self.inventory_on = False\n\n elif event.key == pygame.K_b:\n Game._actions[\"b\"](self.hero)\n self.inventory_on = False\n\n elif event.key == pygame.K_n:\n Game._actions[\"n\"](self.hero)\n self.inventory_on = False\n\n elif event.key == pygame.K_l:\n the_game().hero.throw_item(the_game().gv.select_from_inventory(Equipment), 5)\n\n self.floor.update_elements(self.monster_state)\n\n def change_hero_appearance(self, costume):\n images = CG.get_hero_image(costume)\n self.hero.graphicOutput = images[0]\n\n images = CG.get_hero_image(costume)\n\n self.hero.graphicOutput = images[0]\n self.hero.animationUDLR = {(0, -1): images[12:16], # cannot put Coord since it's not hashable\n\n (0, 1): images[:4],\n (-1, 1): images[:4],\n (1, 1): images[:4],\n\n (-1, 0): images[4:8],\n (-1, -1): images[4:8],\n\n (1, 0): images[8:12],\n (1, -1): images[8:12],\n\n }\n\n def select_from_inventory(self, item_chosen_class):\n if self.choice_inv < len(self.hero._inventory) and isinstance(self.hero._inventory[self.choice_inv],\n item_chosen_class):\n return self.hero._inventory[self.choice_inv]\n\n return None\n\n def draw_game_screen(self):\n self.draw_map()\n self.draw_elements(self.monster_state)\n self.draw_hero_move()\n\n def update_fog(self, actual_map):\n for o in [actual_map.hero, Game.monsters[20][0]]:\n if isinstance(o, Hero):\n x = self.hero.x\n y = self.hero.y\n else:\n c = actual_map.pos(o)\n if c is None:\n break\n x = c.x\n y = c.y\n\n radius = 5\n\n for i in range(-radius, radius + 1):\n for j in range(-radius, radius + 1):\n c = Coord(x + i, y + j)\n if self.floor is not None and self.floor.in_graphic_map(c):\n if Coord(x, y).distance(c) <= radius:\n self.floor.graphic_map[y + j][x + i][1] = True\n\n def play_next_song(self):\n cle = 'Images/musiques/'\n self._songs = self._songs[1:] + [self._songs[0]] # move current song to the back of the list\n pygame.mixer.music.load(cle + self._songs[0])\n pygame.mixer.music.play()\n\n\nclass Game(object):\n \"\"\" Class representing game state \"\"\"\n\n \"\"\" available equipments \"\"\"\n equipments = {0: [Equipment(\"gold\", \"o\"),\n Equipment(\"basic bread\", \"§\", usage=lambda self, hero: FeedEffect.activate(\n FeedEffect(hero, 1, hero.default_stomach_size))),\n Equipment(\"hunger mushroom\", \"£\",\n usage=lambda self, hero: HungerEffect.activate(HungerEffect(hero, 3, 1))),\n Equipment(\"poisonous mushroom\", \"%\", price=2,\n usage=lambda self, hero: PoisonEffect.activate(PoisonEffect(hero, 3, 1))),\n ],\n 1: [Equipment(\"strength potion\", \"!\", price=3,\n usage=lambda self, hero: StrengthEffect.activate(\n StrengthEffect(hero, 10, 3))),\n Equipment(\"weakness potion\", \"!\", price=3,\n usage=lambda self, hero: WeaknessEffect.activate(WeaknessEffect(hero, 10))),\n Equipment(\"teleport potion\", \"!\", price=3,\n usage=lambda self, hero: TeleportEffect.activate(TeleportEffect(hero))),\n Equipment(\"healing potion\", \"!\", price=3,\n usage=lambda self, hero: HealEffect.activate(HealEffect(hero, 1, 3))),\n ],\n 2: [Equipment(\"milk\", \"m\", price=4, usage=lambda self, hero: Effect.clear()),\n ],\n 3: [Equipment(\"portoloin\", \"w\", price=15,\n usage=lambda self, hero: TeleportEffect.activate(TeleportEffect(hero),\n False)),\n Equipment(\"healing potion\", \"!\", price=5,\n usage=lambda self, hero: HealEffect.activate(HealEffect(hero, 1, 6))),\n Equipment(\"strength potion\", \"!\", price=5,\n usage=lambda self, hero: StrengthEffect.activate(\n StrengthEffect(hero, 10, 10))),\n ],\n }\n\n \"\"\" available weapons \"\"\"\n weapons = {\n 0: [Weapon(\"Basic Sword\", \"†\", price=2, damage=random.randint(2, 6), launching_damage=random.randint(1, 3))],\n 1: [Weapon(\"Shuriken\", \"*\", damage=random.randint(1, 2), launching_damage=random.randint(3, 5))],\n 2: [Weapon(\"Boomerang\", \"¬\", price=3, damage=random.randint(1, 2), launching_damage=random.randint(2, 3),\n come_back=True)],\n }\n\n \"\"\" available monsters \"\"\"\n monsters = {0: [Creature(\"Goblin\", hp=4, xp=4),\n Creature(\"Bat\", hp=2, abbreviation=\"W\", xp=2),\n Creature(\"BabyDemon\", hp=2, strength=2, xp=4)],\n 1: [Creature(\"Ork\", hp=4, strength=2, xp=10),\n Creature(\"Blob\", hp=10, xp=8),\n Creature(\"Angel\", hp=10, xp=4)],\n 2: [Creature(\"Poisonous spider\", hp=5, xp=10, strength=0, abbreviation=\"&\",\n powers_list=[[PoisonEffect, 3, 1]], cooldown=5),\n Creature(\"BabyDemon\", hp=2, strength=2, xp=4),\n ],\n 5: [Creature(\"Dragon\", hp=20, strength=3, xp=50)],\n 20: [Creature(\"Death\", hp=50, strength=3, xp=100, abbreviation='ñ')]\n }\n\n \"\"\" available actions \"\"\"\n _actions = {'k': lambda h: h.__setattr__('hp', 0),\n 'u': lambda h: h.use(the_game().gv.select_from_inventory(Equipment)),\n ' ': lambda h: None,\n 't': lambda hero: hero.delete_item(\n the_game().gv.select_from_inventory(Equipment) if len(hero._inventory) > 0 else False),\n 'b': lambda hero: hero.equip_weapon([x for x in hero._inventory if isinstance(x, Weapon)][0]) if any(\n isinstance(elem, Weapon) for elem in hero._inventory) else the_game().add_message(\n \"You don't have any weapon in your inventory\"),\n 'n': lambda hero: hero.remove_current_weapon(),\n 'l': lambda hero: hero.throw_item(hero.weapon_slot[0], 5) if len(hero.weapon_slot) != 0 else False,\n\n }\n\n _room_objects = {'upstair': RoomObject('upstair', \"^\", usage=lambda: RoomObject.go_upstair()),\n 'downstair': RoomObject('downstair', \"v\", usage=lambda: RoomObject.go_downstair()),\n 'marchand': RoomObject('marchand', \"\", usage=lambda: RoomObject.meet_trader()),\n }\n\n _special_rooms_list = {\"finalBoss\": Room(Coord(1, 1), Coord(19, 10), [monsters[20][0]]),\n 'marchand': Room(Coord(15, 15), Coord(19, 19), [_room_objects['marchand']]),\n }\n\n sizeFactor = Map.sizeFactor\n\n def __init__(self, level=1, hero=None, nb_floors=4):\n\n self.level = level\n self.active_effects = []\n self._message = []\n\n if hero is None:\n hero = Hero()\n self.hero = hero\n\n self.nb_floors = nb_floors\n self.floor_list = []\n self.actual_floor = 0\n self.floor = None\n\n self.number_of_round = 0\n self.apply_effects_bool = False\n\n # GRAPHICS\n self.gv = GraphicVariables(self.hero)\n\n self.paused = False\n\n def build_floor(self):\n \"\"\"Creates a map for the current floor.\"\"\"\n\n place_hero = True\n rand = random.randint(0, self.nb_floors - 2)\n\n for i in range(self.nb_floors):\n print('Building Floor ' + str(i + 1) + '/' + str(self.nb_floors))\n\n if i == rand:\n self.floor_list.append(\n Map(hero=self.hero, put_hero=place_hero, floor_number=i, special_room='marchand'))\n elif i == self.nb_floors - 1:\n self.floor_list.append(\n Map(hero=self.hero, put_hero=place_hero, floor_number=i, special_room='finalBoss'))\n else:\n self.floor_list.append(Map(hero=self.hero, put_hero=place_hero, floor_number=i))\n place_hero = False\n\n self.gv.floor = self.floor = self.floor_list[self.actual_floor]\n\n @staticmethod\n def rearrange_sentences(text_message, length_max=50):\n text_message_list = text_message.split(\" \")\n\n res = []\n word_to_add = \"\"\n\n while len(text_message_list) != 0:\n\n if len(text_message_list[0]) > length_max:\n res.append(text_message_list[0][:length_max])\n word_to_recount = text_message_list[0][length_max + 1:]\n text_message_list.pop(0)\n word_to_add = word_to_recount + \" \" + word_to_add\n\n if len(word_to_add + text_message_list[0]) <= length_max:\n word_to_add += text_message_list.pop(0) + \" \"\n if len(text_message_list) == 0:\n res.append(word_to_add)\n else:\n res.append(word_to_add)\n word_to_add = \"\"\n\n return res\n\n def add_message(self, msg):\n \"\"\"Adds a message in the message list.\"\"\"\n\n line_list = Game.rearrange_sentences(msg)\n for line_text in line_list:\n self._message.append(line_text)\n\n def read_messages(self):\n \"\"\"Returns the message list and clears it.\"\"\"\n renders = []\n for m in self._message:\n renders.append(self.gv.game_font.render(m, True, (0, 0, 0)))\n\n self._message.clear()\n return renders.copy()\n\n @staticmethod\n def rand_element(collect, floor_level):\n \"\"\"Returns a clone of random element from a collection using exponential random law.\"\"\"\n x = random.expovariate(1 / (floor_level + 1))\n for k in collect.keys():\n if k <= x:\n element_list = collect[k]\n return copy.copy(random.choice(element_list))\n\n def rand_equipment(self, floor_level):\n \"\"\"Returns a random equipment.\"\"\"\n return self.rand_element(Game.equipments, floor_level)\n\n def rand_monster(self, floor_level):\n \"\"\"Returns a random monster.\"\"\"\n return self.rand_element(Game.monsters, floor_level)\n\n def play_with_graphics(self):\n\n print(\"\\n--- Initialising Graphics ---\")\n print(\"Loading ...\")\n\n self.build_floor()\n\n pygame.init()\n\n # Create the screen\n info_object = pygame.display.Info()\n height = self.gv.height = info_object.current_h - 60\n width = self.gv.width = info_object.current_w\n\n self.gv.orig_x = width / 4 - 10 * Map.sizeFactor\n self.gv.orig_y = height / 6\n\n self.gv.screen = pygame.display.set_mode((width, height))\n\n # Title and Icon\n pygame.display.set_caption(\"Rogue: Jaime et Raphael\")\n icon = pygame.image.load(\"images/magicsword.png\")\n pygame.display.set_icon(icon)\n\n # Font\n self.gv.game_font = pygame.font.SysFont('Agencyfc', 30)\n self.gv.menu_font = pygame.font.SysFont('papyrus', 40)\n\n # Music\n song_end = pygame.USEREVENT + 1\n pygame.mixer.music.set_endevent(song_end)\n self.gv.play_next_song()\n\n # Initialize Brouillard\n self.gv.update_fog(self.floor)\n\n while self.gv.running:\n\n pygame.time.delay(50)\n\n if self.gv.frame_count > 10:\n self.gv.monster_state = opp(self.gv.monster_state)\n self.gv.frame_count = 0\n self.gv.frame_count += 1\n\n # Events\n for event in pygame.event.get():\n\n if event.type == pygame.QUIT:\n self.gv.running = False\n\n elif event.type == song_end:\n self.gv.play_next_song()\n\n elif event.type == pygame.KEYDOWN:\n\n if event.key == pygame.K_ESCAPE:\n self.gv.menu_on = not self.gv.menu_on\n self.gv.list_menu = self.gv.options_menu\n self.gv.colour_menu = (140, 140, 150)\n\n self.gv.inventory_on = False\n\n if self.gv.menu_on:\n self.gv.choose_in_menu(event)\n\n if event.key == pygame.K_i:\n self.gv.inventory_on = not self.gv.inventory_on\n\n if self.gv.inventory_on:\n self.gv.choose_in_inventory(event)\n\n if not self.gv.inventory_on:\n self.gv.player_plays(event)\n\n # Menu\n if self.gv.menu_on:\n self.gv.draw_menu(self.gv.list_menu, self.gv.colour_menu)\n\n else:\n # Background\n self.gv.draw_gui(self.gv.monster_state)\n\n if self.hero.hp <= 0:\n # self.hero.hp = 1\n self.gv.list_menu = self.gv.options_game_over\n self.gv.menu_on = True\n\n self.hero.check_inventory_size()\n\n if self.gv.newRound:\n self.gv.newRound = False\n self.number_of_round += 1\n self.apply_effects_bool = True\n self.floor.move_all_monsters()\n\n if self.number_of_round % 5 == 0 and self.hero.stomach == Hero.default_stomach_size:\n self.hero.hp += 1\n if self.hero.hp > self.hero.default_hp:\n self.hero.hp -= 1\n\n if self.number_of_round % 20 == 0 and self.hero.__dict__[\"stomach\"] > 0:\n self.hero.__dict__[\"stomach\"] -= 1\n self.hero.check_stomach()\n\n if self.apply_effects_bool:\n if len(self.active_effects) != 0:\n i = 0\n while i < len(self.active_effects):\n if not self.active_effects[i].update():\n i += 1\n self.apply_effects_bool = False\n\n # Messages\n self.gv.draw_message(200)\n\n self.gv.draw_game_screen()\n\n pygame.display.update()\n\n pygame.quit()\n\n\ndef the_game(game=Game()):\n return game\n\n\nthe_game().play_with_graphics()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":81892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"468855497","text":"from tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout\n\nnp.random.seed(33)\n\ntrain_datagen = ImageDataGenerator(rescale=1./255, \n horizontal_flip=True, #50% 확률로 수평으로 뒤집음\n vertical_flip=True, #50% 확률로 수직으로 뒤집음\n width_shift_range=0.1, #왼쪽, 오른쪽 움직임 (평행 이동)\n height_shift_range=0.1, #위, 아래 움직임 (평행 이동)\n rotation_range=5, #n도 안에서 랜덤으로 이미지 회전\n zoom_range=1.2, #range 안에서 랜덤하게 zoom\n shear_range=0.7, #0.7 라디안 내외로 시계반대방향으로 변형\n fill_mode='nearest', #이미지를 회전, 이동하거나 축소할 때 생기는 공간을 채우는 방식\n validation_split=0.2\n )\n\ntest_datagen = ImageDataGenerator(rescale=1./255)\n\nxy_train = train_datagen.flow_from_directory(\n './data/data2', # target directory\n target_size=(200,200), \n batch_size=8,\n class_mode='binary', # 이진분류\n subset='training'\n)\n\nxy_valid = train_datagen.flow_from_directory(\n './data/data2', # target directory\n target_size=(200,200),\n batch_size=8,\n class_mode='binary', # 이진분류 \n subset='validation'\n)\n# test valid 나누기 전\n# print(xy_train[0][0].shape) #(1736, 300, 300, 3)\n\n# test valid 나누기 후\n# print(xy_train[0][0].shape) #(1389, 300, 300, 3)\n# print(xy_valid[0][0].shape) #(347, 300, 300, 3)\n\n# numpy 저장\n# np.save('./data/keras64_x.npy', arr=xy_train[0][0])\n# np.save('./data/keras64_y.npy', arr=xy_train[0][1])\n\nmodel = Sequential()\nmodel.add(Conv2D(64, (3,3), padding=\"same\", input_shape=(200,200,3)))\nmodel.add(MaxPooling2D(pool_size=2))\nmodel.add(Dropout(0.3))\nmodel.add(Conv2D(16, (3,3), padding=\"same\"))\nmodel.add(MaxPooling2D(pool_size=2))\nmodel.add(Dropout(0.3))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"acc\"])\n\n# 학습\nhist = model.fit_generator(\n xy_train,\n steps_per_epoch=100, #augmentation한거에서 100개만 뽑음 /= 제너레이터로부터 얼마나 많은 샘플을 뽑을 것인지\n #보통은 데이터셋의 샘플 수를 배치 크기로 나눈 값\n epochs=50,\n validation_data=xy_valid,\n validation_steps=50, #한 epoch 종료 시 마다 검증할 때 사용되는 검증 스텝 수를 지정\n #보통은 검증 데이터셋의 샘플 수를 배치 크기로 나눈 값\n verbose=2\n)\n\n# scores = model.evaluate_generator(test_generator, steps=5)\n# output = model.predict_generator(test_generator, steps=5)\n\nacc = hist.history['acc']\nval_acc = hist.history['val_acc']\ny_vloss = hist.history['val_loss']\ny_loss = hist.history['loss']\n\n# print(acc[-1])\n# print(val_acc[-1])\n\n\n#시각화\nimport matplotlib.pyplot as plt\nplt.figure(figsize=(10,6)) #인치 단위\n#1번째 그림\nplt.subplot(2, 1, 1) #2행 1열 중 첫번째\nplt.plot(hist.history['loss'], marker='.', c='red', label='loss')\nplt.plot(hist.history['val_loss'], marker='.', c='blue', label='val_loss')\nplt.grid()\n\nplt.title('loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(loc='upper right') #plt.plot에서 명시한 label이 박스형태로 상단 오른쪽에 나옴\n\n#2번째 그림\nplt.subplot(2, 1, 2) #2행 1열 중 두번째\nplt.plot(hist.history['acc'], marker='.', c='red')\nplt.plot(hist.history['val_acc'], marker='.', c='blue')\nplt.grid()\n\nplt.title('accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.legend(['acc','val_acc']) #location 명시 안하면 알아서 빈자리에 박스 그림\n\nplt.show()","sub_path":"keras/keras64_ImageDataGene1.py","file_name":"keras64_ImageDataGene1.py","file_ext":"py","file_size_in_byte":4080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"379396894","text":"# -*- encoding: utf-8 -*-\n#\n# Copyright © 2012 New Dream Network, LLC (DreamHost)\n#\n# Author: Doug Hellmann \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\"\"\"Tests for ceilometer/agent/manager.py\n\"\"\"\n\nimport datetime\n\nfrom ceilometer import meter\nfrom ceilometer.collector import manager\nfrom ceilometer.storage import base\nfrom ceilometer.tests import base as tests_base\n\n\nclass TestCollectorManager(tests_base.TestCase):\n\n def setUp(self):\n super(TestCollectorManager, self).setUp()\n self.mgr = manager.CollectorManager()\n self.ctx = None\n\n def test_valid_message(self):\n msg = {'counter_name': 'test',\n 'resource_id': self.id(),\n 'counter_volume': 1,\n }\n msg['message_signature'] = meter.compute_signature(msg)\n\n self.mgr.storage_conn = self.mox.CreateMock(base.Connection)\n self.mgr.storage_conn.record_metering_data(msg)\n self.mox.ReplayAll()\n\n self.mgr.record_metering_data(self.ctx, msg)\n self.mox.VerifyAll()\n\n def test_invalid_message(self):\n msg = {'counter_name': 'test',\n 'resource_id': self.id(),\n 'counter_volume': 1,\n }\n msg['message_signature'] = 'invalid-signature'\n\n class ErrorConnection:\n\n called = False\n\n def record_metering_data(self, data):\n self.called = True\n\n self.mgr.storage_conn = ErrorConnection()\n\n self.mgr.record_metering_data(self.ctx, msg)\n\n assert not self.mgr.storage_conn.called, \\\n 'Should not have called the storage connection'\n\n def test_timestamp_conversion(self):\n msg = {'counter_name': 'test',\n 'resource_id': self.id(),\n 'counter_volume': 1,\n 'timestamp': '2012-07-02T13:53:40Z',\n }\n msg['message_signature'] = meter.compute_signature(msg)\n\n expected = {}\n expected.update(msg)\n expected['timestamp'] = datetime.datetime(2012, 7, 2, 13, 53, 40)\n\n self.mgr.storage_conn = self.mox.CreateMock(base.Connection)\n self.mgr.storage_conn.record_metering_data(expected)\n self.mox.ReplayAll()\n\n self.mgr.record_metering_data(self.ctx, msg)\n self.mox.VerifyAll()\n","sub_path":"tests/collector/test_manager.py","file_name":"test_manager.py","file_ext":"py","file_size_in_byte":2814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"67981340","text":"# Copyright 2014 Microsoft Corporation\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# Requires Python 2.4+ and Openssl 1.0+\n#\n\nimport unittest\nimport azurelinuxagent.common.utils.restutil as restutil\nfrom azurelinuxagent.common.future import httpclient\nfrom tests.tools import AgentTestCase, patch, Mock, MagicMock\n\n\nclass TestHttpOperations(AgentTestCase):\n def test_parse_url(self):\n test_uri = \"http://abc.def/ghi#hash?jkl=mn\"\n host, port, secure, rel_uri = restutil._parse_url(test_uri)\n self.assertEquals(\"abc.def\", host)\n self.assertEquals(\"/ghi#hash?jkl=mn\", rel_uri)\n\n test_uri = \"http://abc.def/\"\n host, port, secure, rel_uri = restutil._parse_url(test_uri)\n self.assertEquals(\"abc.def\", host)\n self.assertEquals(\"/\", rel_uri)\n self.assertEquals(False, secure)\n\n test_uri = \"https://abc.def/ghi?jkl=mn\"\n host, port, secure, rel_uri = restutil._parse_url(test_uri)\n self.assertEquals(True, secure)\n\n test_uri = \"http://abc.def:80/\"\n host, port, secure, rel_uri = restutil._parse_url(test_uri)\n self.assertEquals(\"abc.def\", host)\n\n host, port, secure, rel_uri = restutil._parse_url(\"\")\n self.assertEquals(None, host)\n self.assertEquals(rel_uri, \"\")\n\n host, port, secure, rel_uri = restutil._parse_url(\"None\")\n self.assertEquals(None, host)\n self.assertEquals(rel_uri, \"None\")\n\n @patch(\"azurelinuxagent.common.future.httpclient.HTTPSConnection\")\n @patch(\"azurelinuxagent.common.future.httpclient.HTTPConnection\")\n def test_http_request(self, HTTPConnection, HTTPSConnection):\n mock_http_conn = MagicMock()\n mock_http_resp = MagicMock()\n mock_http_conn.getresponse = Mock(return_value=mock_http_resp)\n HTTPConnection.return_value = mock_http_conn\n HTTPSConnection.return_value = mock_http_conn\n\n mock_http_resp.read = Mock(return_value=\"_(:3| <)_\")\n\n # Test http get\n resp = restutil._http_request(\"GET\", \"foo\", \"bar\")\n self.assertNotEquals(None, resp)\n self.assertEquals(\"_(:3| <)_\", resp.read())\n\n # Test https get\n resp = restutil._http_request(\"GET\", \"foo\", \"bar\", secure=True)\n self.assertNotEquals(None, resp)\n self.assertEquals(\"_(:3| <)_\", resp.read())\n\n # Test http get with proxy\n mock_http_resp.read = Mock(return_value=\"_(:3| <)_\")\n resp = restutil._http_request(\"GET\", \"foo\", \"bar\", proxy_host=\"foo.bar\",\n proxy_port=23333)\n self.assertNotEquals(None, resp)\n self.assertEquals(\"_(:3| <)_\", resp.read())\n\n # Test https get\n resp = restutil._http_request(\"GET\", \"foo\", \"bar\", secure=True)\n self.assertNotEquals(None, resp)\n self.assertEquals(\"_(:3| <)_\", resp.read())\n\n # Test https get with proxy\n mock_http_resp.read = Mock(return_value=\"_(:3| <)_\")\n resp = restutil._http_request(\"GET\", \"foo\", \"bar\", proxy_host=\"foo.bar\",\n proxy_port=23333, secure=True)\n self.assertNotEquals(None, resp)\n self.assertEquals(\"_(:3| <)_\", resp.read())\n\n @patch(\"time.sleep\")\n @patch(\"azurelinuxagent.common.utils.restutil._http_request\")\n def test_http_request_with_retry(self, _http_request, sleep):\n mock_http_resp = MagicMock()\n mock_http_resp.read = Mock(return_value=\"hehe\")\n _http_request.return_value = mock_http_resp\n\n # Test http get\n resp = restutil.http_get(\"http://foo.bar\")\n self.assertEquals(\"hehe\", resp.read())\n\n # Test https get\n resp = restutil.http_get(\"https://foo.bar\")\n self.assertEquals(\"hehe\", resp.read())\n\n # Test http failure\n _http_request.side_effect = httpclient.HTTPException(\"Http failure\")\n self.assertRaises(restutil.HttpError, restutil.http_get,\n \"http://foo.bar\")\n\n # Test http failure\n _http_request.side_effect = IOError(\"IO failure\")\n self.assertRaises(restutil.HttpError, restutil.http_get,\n \"http://foo.bar\")\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/utils/test_rest_util.py","file_name":"test_rest_util.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"505810667","text":"import os, datetime, time, markdown\nimport logging\nfrom datetime import *\nfrom threading import Thread\nfrom flask import Flask, render_template, session, redirect, url_for, flash, Markup, request\nfrom flask.ext.script import Manager, Shell\nfrom flask.ext.bootstrap import Bootstrap\nfrom flask.ext.moment import Moment\nfrom flask.ext.wtf import Form\nfrom wtforms import StringField, SubmitField, TextAreaField, PasswordField\nfrom wtforms.validators import Required, Email, Length\nfrom flask.ext.mail import Mail, Message\nfrom flask.ext.pagedown import PageDown\nfrom flask.ext.pagedown.fields import PageDownField\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = os.environ.get('SECRET_KEY')\napp.config['MAIL_SERVER'] = 'smtp.googlemail.com'\napp.config['MAIL_PORT'] = 587\napp.config['MAIL_USE_TLS'] = True\napp.config['MAIL_USERNAME'] = os.environ.get('MAIL_USERNAME')\napp.config['MAIL_PASSWORD'] = os.environ.get('MAIL_PASSWORD')\napp.config['BYU_MAIL_SUBJECT_PREFIX'] = '[byu.io]'\napp.config['BYU_MAIL_SENDER'] = 'Byu '\napp.config['BYU_ADMIN'] = os.environ.get('BYU_ADMIN')\nmanager = Manager(app)\nbootstrap = Bootstrap(app)\nmoment = Moment(app)\nmail = Mail(app)\npagedown = PageDown(app)\n\nclass ContactForm(Form):\n name = StringField('What is your name?', validators=[Required()])\n email = StringField('What is your email?', validators=[Required(), Email(), Length(min=6)])\n text = TextAreaField('What would you like to say?', validators=[Required()])\n submit = SubmitField('Submit')\n\ndef send_async_email(app, msg):\n with app.app_context():\n mail.send(msg)\n\n\ndef send_email(to, subject, template, **kwargs):\n msg = Message(app.config['BYU_MAIL_SUBJECT_PREFIX'] + ' ' + subject,\n sender=app.config['BYU_MAIL_SENDER'], recipients=[to])\n msg.body = render_template(template + '.txt', **kwargs)\n msg.html = render_template(template + '.html', **kwargs)\n thr = Thread(target=send_async_email, args=[app, msg])\n thr.start()\n return thr\n\nclass PageDownForm(Form):\n title = StringField(\"Title\", validators=[Required()])\n pagedown = PageDownField('Markdown', validators=[Required()])\n submit = SubmitField('Submit')\n\n@app.errorhandler(404)\ndef page_not_found(e):\n return render_template('404.html'), 404\n\n\n@app.errorhandler(500)\ndef internal_server_error(e):\n return render_template('500.html'), 500\n\n\n@app.route('/')\ndef index():\n return render_template('index.html',\n current_time=datetime.utcnow(), current='index')\n\n@app.route('/about')\ndef about():\n return render_template('about.html',\n current_time=datetime.utcnow(), current='about')\n\n@app.route('/contact', methods=['GET', 'POST'])\ndef contact():\n from time import time\n form = ContactForm()\n success = False\n if form.validate_on_submit():\n\n if 'last' not in session or time() - session['last'] > 900:\n session['last'] = time()\n name = form.name.data\n email = form.email.data\n text = form.text.data\n if app.config['BYU_ADMIN']:\n send_email(app.config['BYU_ADMIN'], 'New Message',\n 'mail/new_msg', name=name, email=email, text=text)\n success = True\n flash('Message sent!')\n else:\n flash('Please wait 15 minutes to send another email.')\n session['name'] = form.name.data\n return redirect(url_for('contact'))\n return render_template('contact.html',\n current_time=datetime.utcnow(), form=form, current='contact', success = success)\n\n@app.route('/blog')\ndef blog():\n \n return render_template('blog.html', current_time=datetime.utcnow(), current='blog')\n\n@app.route('/submit')\ndef submitBlog():\n form = PageDownForm()\n success = False\n if form.validate_on_submit():\n #TODO - use a database to store title, markdown, date created\n #\n pass #do stuff\n return render_template('submit.html', current_time=datetime.utcnow(), form=form, success = success)\n\n@app.route('/countdown')\ndef countdown():\n return render_template('countdown.html',\n current_time=datetime.utcnow())\n\nlabDesc = {0: \"Basic bio.\",\n 1: \"HTML tag quiz.\",\n 2: \"DOM Tree navigation exercise.\",\n 3: \"Forms and AJAX practice.\",\n 4: \"PHP AJAX and Regex lab.\",\n 5: \"PHP and SQLite lab.\",\n 6: \"Cat Facts. Built using Twilio and Catfacts API.\"}\n\n@app.route('/lab')\ndef lab():\n return render_template('lab.html',\n current_time=datetime.utcnow(), labList = (labDesc)) \n\n@app.route('/lab/')\ndef show_lab(id):\n if id not in range(len(labDesc)):\n return render_template('404.html'), 404\n else:\n return render_template(\"lab{}.html\".format(id),\n current_time=datetime.utcnow())\n\nif __name__ == '__main__':\n manager.run()\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"200610534","text":"# install hypothesis, pytest, and pytest-cov\n# into python using pip command\n\n# Execute using\n# py.test -p no:django -v test_stack.py\n\nfrom hypothesis import given, assume, example\nimport hypothesis.strategies as st \nimport pytest\nfrom impl import Stack\n#from correct_stack import Stack\n#from incorrect_stack_p1 import Stack\n\n\ndef populate_stack(values):\n stack=Stack()\n for v in values:\n stack.push(v)\n return stack\n\nnon_none_value_generator = st.one_of(st.integers(), \n st.floats(allow_nan=False, allow_infinity=False),\n st.booleans(), st.text())\n\n\n# # p1: length increases by 1 after a successful push \n# @given(st.lists(st.integers() | st.text()), st.integers())\n# def test_p1_successful_push_increases_len_by_one(values, v):\n# stack = populate_stack(values)\n# tmp1 = stack.len()\n# stack.push(v)\n# assert stack.len() == (tmp1 + 1)\n# \n# \n# # p2: length does not change after an unsuccessful push\n# @given(st.lists(st.integers() | st.none()))\n# def test_p2_unsuccessful_push_does_not_affect_length(values):\n# assume(None in values)\n# stack = Stack()\n# tmp1 = 0\n# try:\n# for v in values:\n# tmp1 = stack.len()\n# stack.push(v)\n# assert False\n# except ValueError:\n# assert stack.len() == tmp1\n# \n# \n# # # p3: length decreases by 1 after a non-None returning pop\n# # @given(st.lists(st.integers() | st.text()))\n# # def test_p3_non_none_returning_pop_decreases_len_by_one(values):\n# # stack = populate_stack(values)\n# # tmp1 = stack.len()\n# # if stack.pop() != None:\n# # assert stack.len() == (tmp1 - 1)\n# # \n# # \n# # # p4: length does not change after a None returning pop\n# # @given(st.lists(st.integers() | st.text()), st.integers(1, 100))\n# # def test_p4_none_returning_pop_does_not_affect_length(values, n):\n# # stack = populate_stack(values)\n# # for i in range(0, len(values) + n):\n# # tmp1 = stack.len()\n# # if stack.pop() == None:\n# # assert stack.len() == tmp1\n# # break\n# # else:\n# # assert False\n# # \n# # \n# # p5: popping an empty stack returns None\n# # @given(st.lists(st.integers() | st.text()))\n# # @example([])\n# # def test_p5_popping_empty_stack_returns_none(values):\n# # stack = populate_stack(values)\n# # for i in range(0, len(values)):\n# # stack.pop()\n# # assert stack.pop() == None\n# # \n# # \n# # p6: pop returns the tos\n# @given(st.lists(st.integers() | st.text()), st.integers())\n# def test_p6_pop_returns_tos(values, v):\n# stack = populate_stack(values)\n# stack.push(v)\n# assert stack.pop() == v\n# \n# \n# # p7: pop affects only the tos\n# @given(st.lists(st.integers() | st.text()))\n# def test_p7_pop_only_changes_tos(values):\n# stack = populate_stack(values)\n# stack.pop()\n# for v in list(reversed(values))[1:]:\n# assert stack.pop() == v\n# assert stack.pop() == None\n# \n# \n# # # p8: push affects only the tos\n# # @given(st.lists(st.integers() | st.text()), st.integers())\n# # def test_p8_push_only_changes_tos(values, v):\n# # stack = populate_stack(values)\n# # stack.push(v)\n# # values.append(v)\n# # for t in reversed(values):\n# # assert stack.pop() == t\n# # assert stack.pop() == None\n# # \n# # \n# # # p9: push raises ValueError with None argument\n# # @given(st.lists(st.integers() | st.text()))\n# # def test_p9_push_does_not_support_None(values):\n# # stack = populate_stack(values)\n# # with pytest.raises(ValueError):\n# # stack.push(None)\n# # \n# # \n# # # p10: len is idempotent\n# # @given(st.lists(st.integers() | st.text()))\n# # def test_p10_len_is_idempotent(values):\n# # stack = Stack()\n# # for v in values:\n# # stack.push(v)\n# # stack.len()\n# # for v in reversed(values):\n# # assert stack.pop() == v\n# # assert stack.pop() == None\n# # \n# # \n# # # p11: pop returns None implies length is zero\n# # @given(st.lists(st.integers()), st.integers(1, 100))\n# # def test_p11_pop_returns_none_implies_len_is_zero(values, n):\n# # stack = populate_stack(values)\n# # for i in range(0, len(values) + n):\n# # tmp1 = stack.pop()\n# # if tmp1 == None:\n# # assert stack.len() == 0\n# # break\n# # else:\n# # assert False\n# # \n# # \n# # p12: length is zero implies pop will return None \n# @given(st.lists(st.integers()), st.integers(1, 100))\n# def test_p12_len_is_zero_implies_pop_returns_none(values, n):\n# stack = populate_stack(values)\n# for i in range(0, len(values) + n):\n# stack.pop()\n# if stack.len() == 0:\n# assert stack.pop() == None\n# break\n# else:\n# assert False\n# \n# \n# # # p13: length returns 0 or positive number\n# # @given(st.lists(st.integers()), st.integers(0, 100))\n# # def test_p13_len_returns_zero_or_pos_num(values, n):\n# # stack = populate_stack(values)\n# # for i in range(0, n):\n# # stack.pop()\n# # assert stack.len() >= 0\n# # \n\n\n# p1: after pushing v1, v2, v3 .. vn, popping will return vn, .. v3, v2, v1\n@given(st.lists(non_none_value_generator))\ndef test_p1_lifo(values):\n stack = Stack()\n for v in values:\n stack.push(v)\n for v in reversed(values):\n assert stack.pop() == v\n \n# p2: successful push will increment length by 1\n@given(st.lists(non_none_value_generator), non_none_value_generator)\ndef test_p2_successful_push_increments_len_by_one(values, v):\n stack = populate_stack(values)\n tmp1 = stack.len()\n stack.push(v)\n assert stack.len() == tmp1 + 1\n \n# p3: unsuccessful push will not affect length \n@given(st.lists(non_none_value_generator))\ndef test_p3_unsuccessful_push_will_not_affect_length(values):\n stack = populate_stack(values)\n tmp1 = stack.len()\n with pytest.raises(ValueError):\n stack.push(None)\n with pytest.raises(ValueError):\n stack.push(float('Nan'))\n with pytest.raises(ValueError):\n stack.push(float('Inf'))\n assert stack.len() == tmp1\n\n\n# p4: unsuccessful push raises ValueError\n@given(non_none_value_generator)\ndef test_p4_pushing_None_raises_ValueError(v):\n stack = Stack()\n if v == None:\n with pytest.raises(ValueError):\n s.push(v)\n\n\n# p5: successful pop will decrement length by 1\n@given(st.lists(non_none_value_generator))\ndef test_p5_successful_pop_decrements_len_by_one(values):\n stack = populate_stack(values)\n tmp1 = stack.len()\n if stack.pop() != None:\n assert stack.len() == tmp1 - 1\n\n# p6: unsuccessful pop will not affect length\n@given(st.lists(non_none_value_generator))\ndef test_p6_unsuccessful_pop_will_not_affect_length(values):\n stack = populate_stack(values)\n tmp1 = stack.len()\n if stack.pop() == None:\n assert stack.len() == tmp1\n\n# p7: length is zero for empty stack\n@given(st.lists(non_none_value_generator))\n@example([])\ndef test_p7_length_is_zero_for_empty_stack(values):\n stack = populate_stack(values)\n for i in range(0, len(values)):\n stack.pop()\n assert stack.len() == 0\n \n# p8: pop returns None for empty stack\n@given(st.lists(non_none_value_generator))\n@example([])\ndef test_p8_pop_returns_none_for_empty_stack(values):\n stack = populate_stack(values)\n for i in range(0, len(values)):\n stack.pop()\n assert stack.pop() == None\n \n# p9: len is side effect free\n@given(st.lists(non_none_value_generator))\ndef test_p9_len_is_side_effect_free(values):\n stack = Stack()\n for v in values:\n stack.push(v)\n stack.len()\n for v in reversed(values):\n assert stack.pop() == v\n assert stack.pop() == None\n\n\n# p10: push does not return value\n@given(st.lists(non_none_value_generator))\ndef test_p10_push_does_not_return_value(values):\n stack = Stack()\n for v in values:\n assert stack.push(v) == None\n","sub_path":"homework/Testing with Properties (Stack)/test_stack.py","file_name":"test_stack.py","file_ext":"py","file_size_in_byte":7851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"573001185","text":"#!/usr/bin/python3.5\n#-*-coding: utf-8 -*-\n\nimport pymongo\nimport config\n\n\nuri = config.uri\n\nDATABASE = config.DATABASE\n\n\nclass newsDBHelper:\n\n def __init__(self):\n client = pymongo.MongoClient(uri)\n self.db = client[DATABASE]\n\n \n def read_news(self):\n news = []\n for art in self.db.news.find():\n article = {}\n article['link'] = art['link']\n article['title'] = art['title']\n article['summary'] = art['summary']\n article['date'] = art['date']\n article['topic'] = art['topic']\n article['id'] = art['ml_id']\n news.append(article)\n return news\n\n def push_feed_back(self, fed_back):\n self.db.feedback.insert({'Feedback': fed_back}) \n \n def feed_back_by_account(self, email):\n feed = []\n for fed in self.db.feedback.find():\n if fed['Feedback']['email'] == email:\n feed.append(fed)\n \n return feed \n\n","sub_path":"newsdbhelper.py","file_name":"newsdbhelper.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"90933010","text":"from file_class import File\r\n\r\n\r\nclass Folder(File):\r\n '''Represents a folder in a directory.\r\n Fields:\r\n children: a list of Folder ojects and or File ojects that the Folder is\r\n holding'''\r\n\r\n def __init__(self, path):\r\n '''(File, str) -> NoneType'''\r\n\r\n File.__init__(self, path)\r\n self.children = []\r\n\r\n def __repr__(self, ind=''):\r\n '''(Folder, str) -> str\r\n String representation of Folder'''\r\n\r\n if self.path is None:\r\n return ''\r\n s = ind + '%s (size: %s size_perc: %.2f):\\n' % (self.path, self.size,\r\n self.size_perc)\r\n ind += ' '\r\n for child in self.children:\r\n if type(child) == Folder and child.children:\r\n s += child.__repr__(ind)\r\n else:\r\n s += ind + '%s (size: %s size_perc: %.2f)\\n'\\\r\n % (child.path, child.size, child.size_perc)\r\n return str(s)\r\n\r\n def set_sizes(self):\r\n '''(Folder) -> NoneType\r\n Set the size of the Folder and all of its children'''\r\n\r\n self.size = self._set_sizes()\r\n\r\n def _set_sizes(self):\r\n '''(Folder) -> int\r\n Return the total size of the Folder and also set the sizes of its\r\n children (and their children and so forth) recursively'''\r\n\r\n subtotal = 0.0\r\n for child in self.children:\r\n if type(child) == Folder:\r\n child.size = child._set_sizes()\r\n subtotal += child.size\r\n return subtotal\r\n\r\n def set_perc(self):\r\n '''(Folder) -> NoneType\r\n Set the percentage (of sizes) of all the children in Folder as\r\n well as their children recursively'''\r\n\r\n for i, child in enumerate(self.children):\r\n if self.size == 0: # No files in the Folder.\r\n # The children don't have any size, so are all equal size_perc\r\n self.children[i].size_perc = 100 / len(self.children)\r\n else:\r\n self.children[i].size_perc = (child.size / self.size) * 100.0\r\n if type(child) == Folder and child.children:\r\n child.set_perc()","sub_path":"src/folder_class.py","file_name":"folder_class.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"208074737","text":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\n\"\"\"\nThis script provides a more program-friendly representation of HPO search space.\nThe format is considered internal helper and is not visible to end users.\n\nYou will find this useful when you want to support nested search space.\n\"\"\"\n\n__all__ = [\n 'ParameterSpec',\n 'deformat_parameters',\n 'format_search_space',\n]\n\nimport math\nfrom typing import Any, List, NamedTuple, Optional, Tuple\n\nclass ParameterSpec(NamedTuple):\n \"\"\"\n Specification (aka space / range) of one single parameter.\n \"\"\"\n\n name: str # The object key in JSON\n type: str # \"_type\" in JSON\n values: List[Any] # \"_value\" in JSON\n\n key: Tuple[str] # The \"path\" of this parameter\n parent_index: Optional[int] # If the parameter is in a nested choice, this is its parent's index;\n # if the parameter is at top level, this is `None`.\n\n categorical: bool # Whether this paramter is categorical (unordered) or numerical (ordered)\n size: int = None # If it's categorical, how many canidiates it has\n\n # uniform distributed\n low: float = None # Lower bound of uniform parameter\n high: float = None # Upper bound of uniform parameter\n\n normal_distributed: bool = None # Whether this parameter is uniform or normal distrubuted\n mu: float = None # Mean of normal parameter\n sigma: float = None # Scale of normal parameter\n\n q: Optional[float] = None # If not `None`, the value should be an integer multiple of this\n log_distributed: bool = None # Whether this parameter is log distributed\n\n def is_activated(self, partial_parameters):\n \"\"\"\n For nested search space, check whether this parameter should be skipped for current set of paremters.\n This function works because the return value of `format_search_space()` is sorted in a way that\n parents always appear before children.\n \"\"\"\n return self.parent_index is None or partial_parameters.get(self.key[:-1]) == self.parent_index\n\ndef format_search_space(search_space, ordered_randint=False):\n formatted = _format_search_space(tuple(), None, search_space)\n if ordered_randint:\n for i, spec in enumerate(formatted):\n if spec.type == 'randint':\n formatted[i] = _format_ordered_randint(spec.key, spec.parent_index, spec.values)\n return {spec.key: spec for spec in formatted}\n\ndef deformat_parameters(parameters, formatted_search_space):\n \"\"\"\n `paramters` is a dict whose key is `ParamterSpec.key`, and value is integer index if the parameter is categorical.\n Convert it to the format expected by end users.\n \"\"\"\n ret = {}\n for key, x in parameters.items():\n spec = formatted_search_space[key]\n if not spec.categorical:\n _assign(ret, key, x)\n elif spec.type == 'randint':\n lower = min(math.ceil(float(x)) for x in spec.values)\n _assign(ret, key, lower + x)\n elif _is_nested_choices(spec.values):\n _assign(ret, tuple([*key, '_name']), spec.values[x]['_name'])\n else:\n _assign(ret, key, spec.values[x])\n return ret\n\ndef _format_search_space(parent_key, parent_index, space):\n formatted = []\n for name, spec in space.items():\n if name == '_name':\n continue\n key = tuple([*parent_key, name])\n formatted.append(_format_parameter(key, parent_index, spec['_type'], spec['_value']))\n if spec['_type'] == 'choice' and _is_nested_choices(spec['_value']):\n for index, sub_space in enumerate(spec['_value']):\n formatted += _format_search_space(key, index, sub_space)\n return formatted\n\ndef _format_parameter(key, parent_index, type_, values):\n spec = {}\n spec['name'] = key[-1]\n spec['type'] = type_\n spec['values'] = values\n\n spec['key'] = key\n spec['parent_index'] = parent_index\n\n if type_ in ['choice', 'randint']:\n spec['categorical'] = True\n if type_ == 'choice':\n spec['size'] = len(values)\n else:\n lower, upper = sorted(math.ceil(float(x)) for x in values)\n spec['size'] = upper - lower\n\n else:\n spec['categorical'] = False\n if type_.startswith('q'):\n spec['q'] = float(values[2])\n spec['log_distributed'] = ('log' in type_)\n\n if 'normal' in type_:\n spec['normal_distributed'] = True\n spec['mu'] = float(values[0])\n spec['sigma'] = float(values[1])\n\n else:\n spec['normal_distributed'] = False\n spec['low'], spec['high'] = sorted(float(x) for x in values[:2])\n if 'q' in spec:\n spec['low'] = math.ceil(spec['low'] / spec['q']) * spec['q']\n spec['high'] = math.floor(spec['high'] / spec['q']) * spec['q']\n\n return ParameterSpec(**spec)\n\ndef _format_ordered_randint(key, parent_index, values):\n lower, upper = sorted(math.ceil(float(x)) for x in values)\n return ParameterSpec(\n name = key[-1],\n type = 'randint',\n values = values,\n key = key,\n parent_index = parent_index,\n categorical = False,\n low = float(lower),\n high = float(upper - 1),\n normal_distributed = False,\n q = 1.0,\n log_distributed = False,\n )\n\ndef _is_nested_choices(values):\n if not values:\n return False\n for value in values:\n if not isinstance(value, dict):\n return False\n if '_name' not in value:\n return False\n return True\n\ndef _assign(params, key, x):\n if len(key) == 1:\n params[key[0]] = x\n else:\n if key[0] not in params:\n params[key[0]] = {}\n _assign(params[key[0]], key[1:], x)\n","sub_path":"nni/common/hpo_utils/formatting.py","file_name":"formatting.py","file_ext":"py","file_size_in_byte":5964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"546188845","text":"# !/usr/env python\nfrom __future__ import print_function\n\n\"\"\"\nBFB steady state with collocation over finite elements\nAbstract\nModified equations\nThis no longer contains the exact same equations as Mingzhao's paper\nnow with dp_dx again\nroll back to previous discretization\nnow with Legendre o-coll\nchanged the name to bfb_leg_oc_v1.py\ntrying something with hi\nvg as dof works?\nnow with the two first eqns for valvs\ndv for Je and Jc a11, discontinuities found w?\nchecked lgrdot\nGb_l, Tgb_l and yb_l reformulation\nimplementation of remaining valvs equations\nnow with customizable parameters for collocation; at least Radau and Legendre\nreformulation of dJe and dJc, minimize alp positive?\n\"\"\"\n\nfrom __future__ import division\nfrom pyomo.environ import *\nfrom cpoinsc import collptsgen\nimport math\n\n# assuming redgta_py is okay\n__author__ = 'David M T'\n\n_maxiter = 100\n\nkord_x = 3\n# finite-elements\nnfe_x = 5\n# lenght of the bed\n_L = 5.\n# time horizon\n_t = 1.\n# alpha and beta of gauss orthogonal polynomials\n_alp_gauB_x = 0\n_bet_gauB_x = 0\n\n_zi0 = -3.\n_maxh = 0.5\n# tolerance\n_epsi = 1e-04\n_taunc = 0.5\n_hs = 0.\n\n\n# differential variables\n# cbin\n# cein\n# ebin\n# ecwin\n# eein\n# hxh\n# Jc\n# P\n# Phx\n# ccwin\n# Je\n\n\n# tauk = [0]\n# still undecided if collocation file should return the 0 point\n\n\ndef lgr_x(j, tau, kord, alp, bet):\n tauk = collptsgen(kord, alp, bet)\n tauk.reverse()\n tauk.append(0.)\n tauk.reverse()\n # tauk = [\n # 0.,\n # 0.155051,\n # 0.644949,\n # 1.000000]\n out = 1\n for k in range(0, kord + 1):\n if j != k:\n out *= (tau - tauk[k]) / (tauk[j] - tauk[k])\n return out\n\n\ndef lgry(j, tau, kord, alp, bet):\n tauk = collptsgen(kord, alp, bet)\n tauk.reverse()\n tauk.append(0.)\n tauk.reverse()\n out = 1\n # for legendre [0, K-1]\n if j == 0:\n return 0\n else:\n for k in range(1, kord + 1):\n if j != k:\n out *= (tau - tauk[k]) / (tauk[j] - tauk[k])\n return out\n\n\ndef lgrdot_x(j, tau, kord, alp, bet):\n tauk = collptsgen(kord, alp, bet)\n tauk.reverse()\n tauk.append(0.)\n tauk.reverse()\n out1 = 1\n for k in range(0, kord + 1):\n if k != j:\n out1 *= 1 / (tauk[j] - tauk[k])\n out2 = 1\n out3 = 0\n for m in range(0, kord + 1):\n if m != j:\n out2 = 1 # initialize multiplication\n for n in range(0, kord + 1):\n if n != m and n != j:\n out2 *= tau - tauk[n]\n # elif n == j:\n # print (\"we've got a problem here\")\n out3 += out2\n out = out3 * out1\n\n return out\n\n\ndef lgrydot(j, tau, kord, alp, bet):\n tauk = collptsgen(kord, alp, bet)\n tauk.reverse()\n tauk.append(0.)\n tauk.reverse()\n out1 = 1\n for k in range(1, kord + 1):\n if k != j:\n out1 *= 1 / (tauk[j] - tauk[k])\n out2 = 1\n out3 = 0\n for m in range(1, kord + 1):\n if m != j:\n out2 = 1 # initialize multiplication\n for n in range(1, kord + 1):\n if n != m and n != j:\n out2 *= tau - tauk[n]\n # elif n == j:\n # print (\"we've got a problem here\")\n out3 += out2\n out = out3 * out1\n\n return out\n\n\nmod = AbstractModel()\n\nmod.nfe_x = Param(initialize=nfe_x)\nmod.ncp_x = Param(initialize=kord_x)\n\n# collocation points alp = 1 bet = 0\n# taui = {0: 0.}\n\ntau_x = collptsgen(kord_x, _alp_gauB_x, _bet_gauB_x)\n\n# start at zero\ntau_i_x = {0: 0.}\n# create a list\nfor ii in range(1, kord_x + 1):\n tau_i_x[ii] = tau_x[ii - 1]\n\nprint('taui', tau_i_x)\n\n# ======= SETS ======= #\n# For finite element = 1 .. K\n# This has to be > 0\n# ohh python..\nfe_x_list = [ii for ii in range(1, nfe_x + 1)]\ncp_x_list = [ii for ii in range(0, kord_x + 1)]\n\nmod.fe_x = Set(initialize=fe_x_list)\nmod.fe_x_i = Set(initialize=fe_x_list)\nmod.cp_x_i = Set(initialize=cp_x_list)\n\n# collocation points\n# right nofirsw we only take ord = 3\n# mod.cp = Set(initialize=cp_list)\n\n# collocation points for diferential variables\nmod.cp_x = Set(initialize=cp_x_list)\n\n# components\nmod.sp = Set(initialize=['c', 'h', 'n'])\n\n# create collocation param\nmod.taucp_x = Param(mod.cp_x, initialize=tau_i_x)\n\n\n# ldot def\ndef __ldoti_x(m, j, k):\n return lgrdot_x(j, m.taucp_x[k], kord_x, _alp_gauB_x, _bet_gauB_x)\n\n\ndef __ldotyi(m, j, k):\n if j > 0:\n return lgrydot(j, m.taucp_x[k], kord_x, _alp_gauB_x, _bet_gauB_x)\n else:\n return 0.0\n\n\ndef __lj1_x(m, j):\n return lgr_x(j, 1, kord_x, _alp_gauB_x, _bet_gauB_x)\n\n\ndef __ljy1(m, j):\n if j > 0:\n return lgry(j, 1, kord_x, _alp_gauB_x, _bet_gauB_x)\n else:\n return 0.0\n\n\nmod.ldot_x = Param(mod.cp_x, mod.cp_x, initialize=__ldoti_x)\nmod.lydot = Param(mod.cp_x, mod.cp_x, initialize=__ldotyi)\nmod.l1_x = Param(mod.cp_x, initialize=__lj1_x)\nmod.l1y = Param(mod.cp_x, initialize=__ljy1)\n#\nmod.A1 = Param()\nmod.A2 = Param()\nmod.A3 = Param()\nmod.ah = Param()\nmod.Ao = Param()\nmod.ap = Param()\nmod.Ax = Param()\nmod.cpg_mol = Param()\nmod.cpgcsb = Param(mod.sp)\nmod.cpgcst = Param(mod.sp)\nmod.cpgcgc = Param(mod.sp)\nmod.cpgcge = Param(mod.sp)\nmod.cpgcsc = Param(mod.sp)\nmod.cpgcse = Param(mod.sp)\nmod.cps = Param()\nmod.Cr = Param()\n\nmod.dH1 = Param()\nmod.dH2 = Param()\nmod.dH3 = Param()\nmod.dp = Param()\nmod.dPhx = Param()\nmod.dS1 = Param()\nmod.dS2 = Param()\nmod.dS3 = Param()\nmod.Dt = Param()\nmod.Dte = Param()\nmod.dx = Param()\nmod.E1 = Param()\nmod.E2 = Param()\nmod.E3 = Param()\nmod.emf = Param()\nmod.fw = Param()\nmod.hw = Param()\nmod.K_d = Param()\nmod.kg = Param()\nmod.kp = Param()\nmod.Lb = Param()\nmod.m1 = Param()\nmod.mug = Param()\nmod.nv = Param()\nmod.Nx = Param()\nmod.phis = Param()\nmod.Pr = Param()\nmod.rhohx = Param()\nmod.rhos = Param()\n\nmod.pi = Param()\nmod.R = Param()\nmod.gc = Param()\n\n# heat exchanger input condition\nmod.HXIn_P = Param()\nmod.HXIn_T = Param()\nmod.HXIn_F = Param()\n\n# Gas input condition\nmod.GasIn_T = Param()\nmod.GasIn_z = Param(mod.sp)\nmod.flue_gas_P = Param()\n\n# Solid input condition\nmod.nin = Param(mod.sp)\n# __nin_x = {'h':0.7, 'c':0.01, 'n':0.7}\n# mod.nin = Var(mod.sp, within=NonNegativeReals, initialize=__nin_x)\n# mol/kg\nmod.SolidIn_T = Param()\nmod.sorbent_P = Param()\n\n# atmosphere pressure\nmod.Out2_P = Param()\n\n# input gas valve\nmod.CV_1 = Param()\nmod.per_opening1 = Param()\n\n# output gas valve\nmod.CV_2 = Param()\nmod.per_opening2 = Param()\n\n# input solid valve\nmod.CV_3 = Param()\nmod.per_opening3 = Param()\nmod.eavg = Param()\n\n# output solid valve\nmod.CV_4 = Param()\nmod.per_opening4 = Param()\n\n# ######################################################################################################################\n\nmod.HXIn_h_ix = Param()\nmod.GasIn_P_ix = Param()\nmod.GasIn_F_ix = Param()\nmod.GasOut_P_ix = Param()\nmod.GasOut_F_ix = Param()\nmod.GasOut_T_ix = Param()\nmod.GasOut_z_ix = Param(mod.sp)\nmod.SolidIn_Fm_ix = Param()\nmod.SolidOut_Fm_ix = Param()\nmod.SolidIn_P_ix = Param()\nmod.SolidOut_P_ix = Param()\nmod.SolidOut_T_ix = Param()\nmod.SorbOut_F_ix = Param()\nmod.rhog_in_ix = Param()\nmod.rhog_out_ix = Param()\nmod.DownOut_P_ix = Param()\nmod.h_downcomer_ix = Param()\n\nmod.cb_ix = Param(mod.fe_x, mod.sp)\nmod.cbin_ix = Param(mod.fe_x, mod.sp)\nmod.cc_ix = Param(mod.fe_x, mod.sp)\nmod.ccwin_ix = Param(mod.fe_x, mod.sp)\nmod.ce_ix = Param(mod.fe_x, mod.sp)\nmod.cein_ix = Param(mod.fe_x, mod.sp)\nmod.D_ix = Param(mod.fe_x, mod.sp)\nmod.dl_ix = Param()\nmod.g1_ix = Param()\nmod.Kbc_ix = Param(mod.fe_x, mod.sp)\nmod.Kce_ix = Param(mod.fe_x, mod.sp)\nmod.Kgbulk_ix = Param(mod.fe_x, mod.sp)\nmod.Ksbulk_ix = Param(mod.fe_x, mod.sp)\nmod.nc_ix = Param(mod.fe_x, mod.sp)\nmod.ne_ix = Param(mod.fe_x, mod.sp)\nmod.rgc_ix = Param(mod.fe_x, mod.sp)\nmod.rge_ix = Param(mod.fe_x, mod.sp)\nmod.rsc_ix = Param(mod.fe_x, mod.sp)\nmod.rse_ix = Param(mod.fe_x, mod.sp)\nmod.Sit_ix = Param()\nmod.Sot_ix = Param()\nmod.yb_ix = Param(mod.fe_x, mod.sp)\nmod.yc_ix = Param(mod.fe_x, mod.sp)\nmod.ye_ix = Param(mod.fe_x, mod.sp)\n\nmod.hsinb_ix = Param()\nmod.hsint_ix = Param()\nmod.vmf_ix = Param()\nmod.db0_ix = Param()\n#\n# /////////////\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ #\nlst2 = [ii for ii in range(1, 102)]\nmod.fe2 = Set(initialize=lst2)\nmod.ar_ix = Param(mod.fe2)\nmod.cbt_ix = Param(mod.fe2)\nmod.cct_ix = Param(mod.fe2)\nmod.cet_ix = Param(mod.fe2)\nmod.db_ix = Param(mod.fe2)\nmod.dbe_ix = Param(mod.fe2)\nmod.dbm_ix = Param(mod.fe2)\nmod.dbu_ix = Param(mod.fe2)\nmod.delta_ix = Param(mod.fe2)\nmod.dthx_ix = Param(mod.fe2)\nmod.e_ix = Param(mod.fe2)\nmod.ebin_ix = Param(mod.fe2)\nmod.ecwin_ix = Param(mod.fe2)\nmod.ed_ix = Param(mod.fe2)\nmod.eein_ix = Param(mod.fe2)\nmod.fb_ix = Param(mod.fe2)\nmod.fc_ix = Param(mod.fe2)\nmod.fcw_ix = Param(mod.fe2)\nmod.fn_ix = Param(mod.fe2)\nmod.g2_ix = Param(mod.fe2)\nmod.g3_ix = Param(mod.fe2)\nmod.gb_ix = Param(mod.fe2)\nmod.hbc_ix = Param(mod.fe2)\nmod.hce_ix = Param(mod.fe2)\nmod.hd_ix = Param(mod.fe2)\nmod.hgbulk_ix = Param(mod.fe2)\nmod.hl_ix = Param(mod.fe2)\nmod.hp_ix = Param(mod.fe2)\nmod.hsbulk_ix = Param(mod.fe2)\nmod.hsc_ix = Param(mod.fe2)\nmod.hse_ix = Param(mod.fe2)\nmod.ht_ix = Param(mod.fe2)\nmod.hxh_ix = Param(mod.fe2)\nmod.jc_ix = Param(mod.fe2)\nmod.je_ix = Param(mod.fe2)\nmod.k1c_ix = Param(mod.fe2)\nmod.k1e_ix = Param(mod.fe2)\nmod.k2c_ix = Param(mod.fe2)\nmod.k2e_ix = Param(mod.fe2)\nmod.k3c_ix = Param(mod.fe2)\nmod.k3e_ix = Param(mod.fe2)\nmod.kcebs_ix = Param(mod.fe2)\nmod.ke1c_ix = Param(mod.fe2)\nmod.ke1e_ix = Param(mod.fe2)\nmod.ke2c_ix = Param(mod.fe2)\nmod.ke2e_ix = Param(mod.fe2)\nmod.ke3c_ix = Param(mod.fe2)\nmod.ke3e_ix = Param(mod.fe2)\nmod.kpa_ix = Param(mod.fe2)\nmod.nup_ix = Param(mod.fe2)\nmod.p_ix = Param(mod.fe2)\nmod.phx_ix = Param(mod.fe2)\nmod.r1c_ix = Param(mod.fe2)\nmod.r1e_ix = Param(mod.fe2)\nmod.r2c_ix = Param(mod.fe2)\nmod.r2e_ix = Param(mod.fe2)\nmod.r3c_ix = Param(mod.fe2)\nmod.r3e_ix = Param(mod.fe2)\nmod.red_ix = Param(mod.fe2)\nmod.rhog_ix = Param(mod.fe2)\nmod.tau_ix = Param(mod.fe2)\nmod.tgb_ix = Param(mod.fe2)\nmod.tgc_ix = Param(mod.fe2)\nmod.tge_ix = Param(mod.fe2)\nmod.thx_ix = Param(mod.fe2)\nmod.tsc_ix = Param(mod.fe2)\nmod.tse_ix = Param(mod.fe2)\nmod.ttube_ix = Param(mod.fe2)\nmod.vb_ix = Param(mod.fe2)\nmod.vbr_ix = Param(mod.fe2)\nmod.ve_ix = Param(mod.fe2)\nmod.vg_ix = Param(mod.fe2)\n\n# /////////////\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ #\nmod.cb_ix_c = Param(mod.fe2)\nmod.cb_ix_h = Param(mod.fe2)\nmod.cb_ix_n = Param(mod.fe2)\nmod.cbin_ix_c = Param(mod.fe2)\nmod.cbin_ix_h = Param(mod.fe2)\nmod.cbin_ix_n = Param(mod.fe2)\nmod.cc_ix_c = Param(mod.fe2)\nmod.cc_ix_h = Param(mod.fe2)\nmod.cc_ix_n = Param(mod.fe2)\nmod.ccwin_ix_c = Param(mod.fe2)\nmod.ccwin_ix_h = Param(mod.fe2)\nmod.ccwin_ix_n = Param(mod.fe2)\nmod.ce_ix_c = Param(mod.fe2)\nmod.ce_ix_h = Param(mod.fe2)\nmod.ce_ix_n = Param(mod.fe2)\nmod.cein_ix_c = Param(mod.fe2)\nmod.cein_ix_h = Param(mod.fe2)\nmod.cein_ix_n = Param(mod.fe2)\nmod.d_ix_c = Param(mod.fe2)\nmod.d_ix_h = Param(mod.fe2)\nmod.d_ix_n = Param(mod.fe2)\nmod.kbc_ix_c = Param(mod.fe2)\nmod.kbc_ix_h = Param(mod.fe2)\nmod.kbc_ix_n = Param(mod.fe2)\nmod.kce_ix_c = Param(mod.fe2)\nmod.kce_ix_h = Param(mod.fe2)\nmod.kce_ix_n = Param(mod.fe2)\nmod.kgbulk_ix_c = Param(mod.fe2)\nmod.kgbulk_ix_h = Param(mod.fe2)\nmod.kgbulk_ix_n = Param(mod.fe2)\nmod.ksbulk_ix_c = Param(mod.fe2)\nmod.ksbulk_ix_h = Param(mod.fe2)\nmod.ksbulk_ix_n = Param(mod.fe2)\nmod.nc_ix_c = Param(mod.fe2)\nmod.nc_ix_h = Param(mod.fe2)\nmod.nc_ix_n = Param(mod.fe2)\nmod.ne_ix_c = Param(mod.fe2)\nmod.ne_ix_h = Param(mod.fe2)\nmod.ne_ix_n = Param(mod.fe2)\nmod.rsc_ix_c = Param(mod.fe2)\nmod.rsc_ix_h = Param(mod.fe2)\nmod.rsc_ix_n = Param(mod.fe2)\nmod.rse_ix_c = Param(mod.fe2)\nmod.rse_ix_h = Param(mod.fe2)\nmod.rse_ix_n = Param(mod.fe2)\nmod.rgc_ix_c = Param(mod.fe2)\nmod.rgc_ix_h = Param(mod.fe2)\nmod.rgc_ix_n = Param(mod.fe2)\nmod.rge_ix_c = Param(mod.fe2)\nmod.rge_ix_h = Param(mod.fe2)\nmod.rge_ix_n = Param(mod.fe2)\nmod.yb_ix_c = Param(mod.fe2)\nmod.yb_ix_h = Param(mod.fe2)\nmod.yb_ix_n = Param(mod.fe2)\nmod.yc_ix_c = Param(mod.fe2)\nmod.yc_ix_h = Param(mod.fe2)\nmod.yc_ix_n = Param(mod.fe2)\nmod.ye_ix_c = Param(mod.fe2)\nmod.ye_ix_h = Param(mod.fe2)\nmod.ye_ix_n = Param(mod.fe2)\nmod.llast = Param(initialize=5.)\n#\n# variable finite element lenght\n# mod.hi = Var(within=NonNegativeReals0, _L), initialize=(_L / 100))\nmod.lenleft = Param()\n\n\ndef __ir_hi(m, i):\n reg = m.lenleft / m.nfe_x\n return reg\n # nreg = reg*0.001\n # if i <= 15:\n # hi = nreg\n # return hi\n # else:\n # hi = (m.lenleft - nreg*15)/15\n # return hi\n\n\nmod.hi_x = Param(mod.fe_x, initialize=__ir_hi)\n\n\n# mod.lenleft.display()\n\n\ndef __l_irule(m, j, k):\n # type: (model, fe_x, cp) -> current_x\n h0 = sum(m.hi_x[i] for i in range(1, j))\n # return float(m.hi[j] * taui[k] + m.hi[j] * (float(j) - 1.))\n return float(m.hi_x[j] * tau_i_x[k] + h0)\n\n\n# print taui\n\nmod.l = Param(mod.fe_x, mod.cp_x, initialize=__l_irule)\n\n\ndef __ini_cp(i, y, k, taucp):\n dy = y[i + 1] - y[i]\n if i == 1 and k == 1:\n yx = y[i]\n # yx = dy * taucp[k] + y[i]\n else:\n yx = dy * taucp[k] + y[i]\n return yx\n\n\ndef __ini_cp_dv(i, y, k, taucp):\n dy = y[i + 1] - y[i]\n yx = dy * taucp[k] + y[i]\n return yx\n\n\ndef __fdp_x(x, y, l):\n n = len(y)\n dx = l / (n)\n i1 = math.floor(x / dx + 1)\n i1 = int(i1)\n i2 = i1 + 1\n # print(n, dx, x, i1, i2)\n if i2 > n:\n return y[n]\n else:\n dy = y[i2] - y[i1]\n out = (dy) * x + y[i1]\n return out\n\n\n# /////////////\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ #\n\n\ndef __ir_ar(m, ix, k):\n h = __l_irule(m, ix, k)\n # print(h)\n return __fdp_x(h, m.ar_ix, m.lenleft)\n # return __ini_cp(ix, m.ar_ix, k, taui)\n\n\ndef __ir_cbt(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.cbt_ix, m.lenleft)\n # return __ini_cp(i, m.cbt_ix, k, taui)\n\n\ndef __ir_cct(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.cct_ix, m.lenleft)\n # return __ini_cp(i, m.cct_ix, k, taui)\n\n\ndef __ir_cet(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.cet_ix, m.lenleft)\n # return __ini_cp(i, m.cet_ix, k, taui)\n\n\ndef __ir_db(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.db_ix, m.lenleft)\n # return __ini_cp(i, m.db_ix, k, taui)\n\n\ndef __ir_dbe(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.dbe_ix, m.lenleft)\n # return __ini_cp(i, m.dbe_ix, k, taui)\n\n\ndef __ir_dbm(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.dbm_ix, m.lenleft)\n # return __ini_cp(i, m.dbm_ix, k, taui)\n\n\ndef __ir_dbu(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.dbu_ix, m.lenleft)\n # return __ini_cp(i, m.dbu_ix, k, taui)\n\n\ndef __ir_delta(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.delta_ix, m.lenleft)\n # return __ini_cp(i, m.delta_ix, k, taui)\n\n\ndef __ir_dthx(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.dthx_ix, m.lenleft)\n # return __ini_cp(i, m.dthx_ix, k, taui)\n\n\ndef __ir_e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.e_ix, m.lenleft)\n # return __ini_cp(i, m.e_ix, k, taui)\n\n\ndef __ir_ebin(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ebin_ix, m.lenleft)\n # return __ini_cp_dv(i, m.ebin_ix, k, taui)\n\n\ndef __ir_ecwin(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ecwin_ix, m.lenleft)\n # return __ini_cp_dv(i, m.ecwin_ix, k, taui)\n\n\ndef __ir_ed(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ed_ix, m.lenleft)\n # return __ini_cp(i, m.ed_ix, k, taui)\n\n\ndef __ir_eein(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.eein_ix, m.lenleft)\n # return __ini_cp_dv(i, m.eein_ix, k, taui)\n\n\ndef __ir_fb(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.fb_ix, m.lenleft)\n # return __ini_cp(i, m.fb_ix, k, taui)\n\n\ndef __ir_fc(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.fc_ix, m.lenleft)\n # return __ini_cp(i, m.fc_ix, k, taui)\n\n\ndef __ir_fcw(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.fcw_ix, m.lenleft)\n # return __ini_cp(i, m.fcw_ix, k, taui)\n\n\ndef __ir_fn(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.fn_ix, m.lenleft)\n # return __ini_cp(i, m.fn_ix, k, taui)\n\n\ndef __ir_g2(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.g2_ix, m.lenleft)\n # return __ini_cp(i, m.g2_ix, k, taui)\n\n\ndef __ir_g3(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.g3_ix, m.lenleft)\n # return __ini_cp(i, m.g3_ix, k, taui)\n\n\ndef __ir_gb(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.gb_ix, m.lenleft)\n # return __ini_cp(i, m.gb_ix, k, taui)\n\n\ndef __ir_hbc(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hbc_ix, m.lenleft)\n # return __ini_cp(i, m.hbc_ix, k, taui)\n\n\ndef __ir_hce(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hce_ix, m.lenleft)\n # return __ini_cp(i, m.hce_ix, k, taui)\n\n\ndef __ir_hd(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hd_ix, m.lenleft)\n # return __ini_cp(i, m.hd_ix, k, taui)\n\n\ndef __ir_hgbulk(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hgbulk_ix, m.lenleft)\n # return __ini_cp(i, m.hgbulk_ix, k, taui)\n\n\ndef __ir_hl(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hl_ix, m.lenleft)\n # return __ini_cp(i, m.hl_ix, k, taui)\n\n\ndef __ir_hp(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hp_ix, m.lenleft)\n # return __ini_cp(i, m.hp_ix, k, taui)\n\n\ndef __ir_hsbulk(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hsbulk_ix, m.lenleft)\n # return __ini_cp(i, m.hsbulk_ix, k, taui)\n\n\ndef __ir_hsc(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hsc_ix, m.lenleft)\n # return __ini_cp(i, m.hsc_ix, k, taui)\n\n\ndef __ir_hse(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hse_ix, m.lenleft)\n # return __ini_cp(i, m.hse_ix, k, taui)\n\n\ndef __ir_ht(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ht_ix, m.lenleft)\n # return __ini_cp(i, m.ht_ix, k, taui)\n\n\ndef __ir_hxh(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.hxh_ix, m.lenleft)\n # return __ini_cp_dv(i, m.hxh_ix, k, taui)\n\n\ndef __ir_jc(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.jc_ix, m.lenleft)\n # return __ini_cp_dv(i, m.jc_ix, k, taui)\n\n\ndef __ir_je(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.je_ix, m.lenleft)\n # return __ini_cp_dv(i, m.je_ix, k, taui)\n\n\ndef __ir_k1c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.k1c_ix, m.lenleft)\n # return __ini_cp(i, m.k1c_ix, k, taui)\n\n\ndef __ir_k1e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.k1e_ix, m.lenleft)\n # return __ini_cp(i, m.k1e_ix, k, taui)\n\n\ndef __ir_k2c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.k2c_ix, m.lenleft)\n # return __ini_cp(i, m.k2c_ix, k, taui)\n\n\ndef __ir_k2e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.k2e_ix, m.lenleft)\n # return __ini_cp(i, m.k2e_ix, k, taui)\n\n\ndef __ir_k3c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.k3c_ix, m.lenleft)\n # return __ini_cp(i, m.k3c_ix, k, taui)\n\n\ndef __ir_k3e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.k3e_ix, m.lenleft)\n # return __ini_cp(i, m.k3e_ix, k, taui)\n\n\ndef __ir_kcebs(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.kcebs_ix, m.lenleft)\n # return __ini_cp(i, m.kcebs_ix, k, taui)\n\n\ndef __ir_ke1c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ke1c_ix, m.lenleft)\n # return __ini_cp(i, m.ke1c_ix, k, taui)\n\n\ndef __ir_ke1e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ke1e_ix, m.lenleft)\n # return __ini_cp(i, m.ke1e_ix, k, taui)\n\n\ndef __ir_ke2c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ke2c_ix, m.lenleft)\n # return __ini_cp(i, m.ke2c_ix, k, taui)\n\n\ndef __ir_ke2e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ke2e_ix, m.lenleft)\n # return __ini_cp(i, m.ke2e_ix, k, taui)\n\n\ndef __ir_ke3e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ke3c_ix, m.lenleft)\n # return __ini_cp(i, m.ke3c_ix, k, taui)\n\n\ndef __ir_ke3c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ke3e_ix, m.lenleft)\n # return __ini_cp(i, m.ke3e_ix, k, taui)\n\n\ndef __ir_kpa(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.kpa_ix, m.lenleft)\n # return __ini_cp(i, m.kpa_ix, k, taui)\n\n\ndef __ir_nup(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.nup_ix, m.lenleft)\n # return __ini_cp(i, m.nup_ix, k, taui)\n\n\ndef __ir_p(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.p_ix, m.lenleft)\n # return __ini_cp_dv(i, m.p_ix, k, taui)\n\n\ndef __ir_phx(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.phx_ix, m.lenleft)\n # return __ini_cp_dv(i, m.phx_ix, k, taui)\n\n\ndef __ir_r1c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.r1c_ix, m.lenleft)\n # return __ini_cp(i, m.r1c_ix, k, taui)\n\n\ndef __ir_r1e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.r1e_ix, m.lenleft)\n # return __ini_cp(i, m.r1e_ix, k, taui)\n\n\ndef __ir_r2c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.r2c_ix, m.lenleft)\n # return __ini_cp(i, m.r2c_ix, k, taui)\n\n\ndef __ir_r2e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.r2e_ix, m.lenleft)\n # return __ini_cp(i, m.r2e_ix, k, taui)\n\n\ndef __ir_r3c(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.r3c_ix, m.lenleft)\n # return __ini_cp(i, m.r3c_ix, k, taui)\n\n\ndef __ir_r3e(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.r3e_ix, m.lenleft)\n # return __ini_cp(i, m.r3e_ix, k, taui)\n\n\ndef __ir_red(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.red_ix, m.lenleft)\n # return __ini_cp(i, m.red_ix, k, taui)\n\n\ndef __ir_rhog(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.rhog_ix, m.lenleft)\n # return __ini_cp(i, m.rhog_ix, k, taui)\n\n\ndef __ir_tau(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.tau_ix, m.lenleft)\n # return __ini_cp(i, m.tau_ix, k, taui)\n\n\ndef __ir_tgb(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.tgb_ix, m.lenleft)\n # return __ini_cp(i, m.tgb_ix, k, taui)\n\n\ndef __ir_tgc(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.tgc_ix, m.lenleft)\n # return __ini_cp(i, m.tgc_ix, k, taui)\n\n\ndef __ir_tge(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.tge_ix, m.lenleft)\n # return __ini_cp(i, m.tge_ix, k, taui)\n\n\ndef __ir_thx(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.thx_ix, m.lenleft)\n # return __ini_cp(i, m.thx_ix, k, taui)\n\n\ndef __ir_tsc(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.tsc_ix, m.lenleft)\n # return __ini_cp(i, m.tsc_ix, k, taui)\n\n\ndef __ir_tse(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.tse_ix, m.lenleft)\n # return __ini_cp(i, m.tse_ix, k, taui)\n\n\ndef __ir_ttube(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ttube_ix, m.lenleft)\n # return __ini_cp(i, m.ttube_ix, k, taui)\n\n\ndef __ir_vb(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.vb_ix, m.lenleft)\n # return __ini_cp(i, m.vb_ix, k, taui)\n\n\ndef __ir_vbr(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.vbr_ix, m.lenleft)\n # return __ini_cp(i, m.vbr_ix, k, taui)\n\n\ndef __ir_ve(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.ve_ix, m.lenleft)\n # return __ini_cp(i, m.ve_ix, k, taui)\n\n\ndef __ir_vg(m, ix, k):\n h = __l_irule(m, ix, k)\n return __fdp_x(h, m.vg_ix, m.lenleft)\n # return __ini_cp(i, m.vg_ix, k, taui)\n\n\n# /////////////\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ #\n# double index variables\n\n\ndef __ir_cb(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.cb_ix_c, m.lenleft)\n # return __ini_cp(i, m.cb_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.cb_ix_h, m.lenleft)\n # return __ini_cp(i, m.cb_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.cb_ix_n, m.lenleft)\n # return __ini_cp(i, m.cb_ix_n, k, taui)\n\n\ndef __ir_cbin(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.cbin_ix_c, m.lenleft)\n # return __ini_cp_dv(ix, m.cbin_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.cbin_ix_h, m.lenleft)\n # return __ini_cp_dv(ix, m.cbin_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.cbin_ix_n, m.lenleft)\n # return __ini_cp_dv(ix, m.cbin_ix_n, k, taui)\n\n\ndef __ir_cc(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.cc_ix_c, m.lenleft)\n # return __ini_cp(ix, m.cc_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.cc_ix_h, m.lenleft)\n # return __ini_cp(ix, m.cc_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.cc_ix_n, m.lenleft)\n # return __ini_cp(ix, m.cc_ix_n, k, taui)\n\n\n# def __if_cc_h(m, ix, k):\n# def __if_cc_n(m, ix, k):\n\n\ndef __ir_ccwin(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.ccwin_ix_c, m.lenleft)\n # return __ini_cp_dv(ix, m.ccwin_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.ccwin_ix_h, m.lenleft)\n # return __ini_cp_dv(ix, m.ccwin_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.ccwin_ix_n, m.lenleft)\n # return __ini_cp_dv(ix, m.ccwin_ix_n, k, taui)\n\n\n# def __if_ccwin_h(m, ix, k):\n# def __if_ccwin_n(m, ix, k):\n\n\ndef __ir_ce(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.ce_ix_c, m.lenleft)\n # return __ini_cp(ix, m.ce_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.ce_ix_h, m.lenleft)\n # return __ini_cp(ix, m.ce_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.ce_ix_n, m.lenleft)\n # return __ini_cp(ix, m.ce_ix_n, k, taui)\n\n\n# def __if_ce_h(m, ix, k):\n# def __if_ce_n(m, ix, k):\n\n\ndef __ir_cein(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.cein_ix_c, m.lenleft)\n # return __ini_cp_dv(ix, m.cein_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.cein_ix_h, m.lenleft)\n # return __ini_cp_dv(ix, m.cein_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.cein_ix_n, m.lenleft)\n # return __ini_cp_dv(ix, m.cein_ix_n, k, taui)\n\n\n# def __if_cein_h(m, ix, k):\n# def __if_cein_n(m, ix, k):\n\n\ndef __ir_d(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.d_ix_c, m.lenleft)\n # return __ini_cp(ix, m.d_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.d_ix_h, m.lenleft)\n # return __ini_cp(ix, m.d_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.d_ix_n, m.lenleft)\n # return __ini_cp(ix, m.d_ix_n, k, taui)\n\n\n# def __if_d_h(m, ix, k):\n# def __if_d_n(m, ix, k):\n\n\ndef __ir_kbc(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.kbc_ix_c, m.lenleft)\n # return __ini_cp(ix, m.kbc_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.kbc_ix_h, m.lenleft)\n # return __ini_cp(ix, m.kbc_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.kbc_ix_n, m.lenleft)\n # return __ini_cp(ix, m.kbc_ix_n, k, taui)\n\n\n# def __if_kbc_h(m, ix, k):\n# def __if_kbc_n(m, ix, k):\n\n\ndef __ir_kce(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.kce_ix_c, m.lenleft)\n # return __ini_cp(ix, m.kce_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.kce_ix_h, m.lenleft)\n # return __ini_cp(ix, m.kce_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.kce_ix_n, m.lenleft)\n # return __ini_cp(ix, m.kce_ix_n, k, taui)\n\n\ndef __ir_kgbulk(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.kgbulk_ix_c, m.lenleft)\n # return __ini_cp(ix, m.kgbulk_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.kgbulk_ix_h, m.lenleft)\n # return __ini_cp(ix, m.kgbulk_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.kgbulk_ix_n, m.lenleft)\n # return __ini_cp(ix, m.kgbulk_ix_n, k, taui)\n\n\ndef __ir_ksbulk(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.ksbulk_ix_c, m.lenleft)\n # return __ini_cp(ix, m.ksbulk_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.ksbulk_ix_h, m.lenleft)\n # return __ini_cp(ix, m.ksbulk_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.ksbulk_ix_n, m.lenleft)\n # return __ini_cp(ix, m.ksbulk_ix_n, k, taui)\n\n\ndef __ir_nc_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.nc_ix_c, m.lenleft)\n # return __ini_cp(ix, m.nc_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.nc_ix_h, m.lenleft)\n # return __ini_cp(ix, m.nc_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.nc_ix_n, m.lenleft)\n # return __ini_cp(ix, m.nc_ix_n, k, taui)\n\n\ndef __ir_ne_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.ne_ix_c, m.lenleft)\n # return __ini_cp(ix, m.ne_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.ne_ix_h, m.lenleft)\n # return __ini_cp(ix, m.ne_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.ne_ix_n, m.lenleft)\n # return __ini_cp(ix, m.ne_ix_n, k, taui)\n\n\ndef __ir_rsc_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.rsc_ix_c, m.lenleft)\n # return __ini_cp(ix, m.rsc_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.rsc_ix_h, m.lenleft)\n # return __ini_cp(ix, m.rsc_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.rsc_ix_n, m.lenleft)\n # return __ini_cp(ix, m.rsc_ix_n, k, taui)\n\n\ndef __ir_rse_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.rse_ix_c, m.lenleft)\n # return __ini_cp(ix, m.rse_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.rse_ix_h, m.lenleft)\n # return __ini_cp(ix, m.rse_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.rse_ix_n, m.lenleft)\n # return __ini_cp(ix, m.rse_ix_n, k, taui)\n\n\ndef __ir_rgc_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n if j == 'c':\n return __fdp_x(h, m.rgc_ix_c, m.lenleft)\n # return __ini_cp(ix, m.rgc_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.rgc_ix_h, m.lenleft)\n # return __ini_cp(ix, m.rgc_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.rgc_ix_n, m.lenleft)\n # return __ini_cp(ix, m.rgc_ix_n, k, taui)\n\n\ndef __ir_rge_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.rge_ix_c, m.lenleft)\n # return __ini_cp(ix, m.rge_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.rge_ix_h, m.lenleft)\n # return __ini_cp(ix, m.rge_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.rge_ix_n, m.lenleft)\n # return __ini_cp(ix, m.rge_ix_n, k, taui)\n\n\ndef __ir_yb_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.yb_ix_c, m.lenleft)\n # return __ini_cp(ix, m.yb_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.yb_ix_h, m.lenleft)\n # return __ini_cp(ix, m.yb_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.yb_ix_n, m.lenleft)\n # return __ini_cp(ix, m.yb_ix_n, k, taui)\n\n\ndef __ir_yc_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.yc_ix_c, m.lenleft)\n # return __ini_cp(ix, m.yc_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.yb_ix_h, m.lenleft)\n # return __ini_cp(ix, m.yb_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.yb_ix_n, m.lenleft)\n # return __ini_cp(ix, m.yb_ix_n, k, taui)\n\n\ndef __ir_ye_c(m, ix, k, j):\n h = __l_irule(m, ix, k)\n\n if j == 'c':\n return __fdp_x(h, m.ye_ix_c, m.lenleft)\n # return __ini_cp(ix, m.ye_ix_c, k, taui)\n elif j == 'h':\n return __fdp_x(h, m.ye_ix_h, m.lenleft)\n # return __ini_cp(ix, m.ye_ix_h, k, taui)\n elif j == 'n':\n return __fdp_x(h, m.ye_ix_n, m.lenleft)\n # return __ini_cp(ix, m.ye_ix_n, k, taui)\n\n\n# /////////////\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ #\n\n\ndef gasout_zi_rule(m, i):\n return m.GasOut_z_ix[i]\n\n\nmod.HXIn_h = Var(within=Reals, initialize=mod.HXIn_h_ix)\nmod.GasIn_P = Var(within=NonNegativeReals, initialize=mod.GasIn_P_ix)\nmod.GasIn_F = Var(within=NonNegativeReals, initialize=mod.GasIn_F_ix)\nmod.GasOut_P = Var(within=NonNegativeReals, initialize=mod.GasOut_P_ix)\nmod.GasOut_F = Var(within=NonNegativeReals, initialize=mod.GasOut_F_ix)\nmod.GasOut_T = Var(within=NonNegativeReals, initialize=mod.GasOut_T_ix)\nmod.GasOut_z = Var(mod.sp, within=NonNegativeReals, initialize=gasout_zi_rule)\nmod.SolidIn_Fm = Var(within=NonNegativeReals, initialize=mod.SolidIn_Fm_ix)\n# mod.SolidOut_Fm = Var(within=NonNegativeReals0, 10000000), initialize=mod.SolidOut_Fm_ix)\nmod.SolidIn_P = Var(within=NonNegativeReals, initialize=mod.SolidIn_P_ix)\nmod.SolidOut_P = Var(within=NonNegativeReals, initialize=mod.SolidOut_P_ix)\n# mod.SolidOut_T = Var(within=NonNegativeReals, initialize=mod.SolidOut_T_ix)\n# mod.SorbOut_F = Var(within=NonNegativeReals, initialize=mod.SorbOut_F_ix)\nmod.rhog_in = Var(within=NonNegativeReals, initialize=mod.rhog_in_ix)\nmod.rhog_out = Var(within=NonNegativeReals, initialize=mod.rhog_out_ix)\nmod.DownOut_P = Var(within=NonNegativeReals, initialize=mod.DownOut_P_ix)\nmod.h_downcomer = Var(within=NonNegativeReals, initialize=mod.h_downcomer_ix)\n# mod.hsinb = Var(within=Reals-100, initialize=mod.hsinb_ix)\nmod.hsint = Var(within=Reals, initialize=mod.hsint_ix)\nmod.vmf = Var(within=NonNegativeReals, initialize=mod.vmf_ix)\nmod.db0 = Var(within=NonNegativeReals, initialize=mod.db0_ix)\nmod.Sit = Var(within=NonNegativeReals, initialize=mod.Sit_ix)\nmod.Sot = Var(within=NonNegativeReals, initialize=mod.Sot_ix)\nmod.g1 = Var(within=NonNegativeReals, initialize=mod.g1_ix)\n\n# dvs\nmod.Ar = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ar)\nmod.cbt = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_cbt)\nmod.cct = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_cct)\nmod.cet = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_cet)\nmod.cb = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_cb)\n\n# der var mod.cbin\nmod.cbin = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_cbin)\n# ////derivative\\\\\\\\ #\nmod.dcbin_dx = Var(mod.fe_x, mod.cp_x, mod.sp, within=Reals, initialize=0)\n\n# three indices\nmod.cc = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_cc)\nmod.ccwin = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_ccwin)\nmod.ce = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_ce)\n\nmod.cein = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_cein)\n# ////derivative\\\\\\\\ #\nmod.dcein_dx = Var(mod.fe_x, mod.cp_x, mod.sp, initialize=0)\n\n# der var mod.cein\n\nmod.D = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_d)\nmod.db = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_db)\n\nmod.dbe = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_dbe)\nmod.dbm = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_dbm)\nmod.dbu = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_dbu)\nmod.delta = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_delta)\n\n# mod.dl = Var(within=NonNegativeReals0, 10), initialize=mod.dl_ix)\nmod.dThx = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_dthx)\nmod.e = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_e)\n\nmod.ebin = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ebin)\n# der var mod.ebin\n# ////derivative\\\\\\\\ #\nmod.debin_dx = Var(mod.fe_x, mod.cp_x, initialize=0)\n\nmod.ecwin = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_ecwin)\n# der var ecwin\n# ////derivative\\\\\\\\ #\nmod.decwin_dx = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=0)\n\nmod.ed = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ed)\n\nmod.eein = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_eein)\n# der var mod.eein\n# ////derivative\\\\\\\\ #\nmod.deein_dx = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=0)\n\nmod.fb = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_fb)\nmod.fc = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_fc)\nmod.fcw = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_fcw)\nmod.fn = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_fn)\n\nmod.g2 = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_g2)\nmod.g3 = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_g3)\nmod.Gb = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_gb)\nmod.Hbc = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_hbc)\nmod.Hce = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_hce)\nmod.hd = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_hd)\nmod.Hgbulk = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_hgbulk)\nmod.hl = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_hl)\nmod.hp = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_hp)\nmod.Hsbulk = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_hsbulk)\nmod.hsc = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_hsc)\nmod.hse = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_hse)\nmod.ht = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ht)\n\nmod.hxh = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_hxh)\n# der var mod.hxh\n# ////derivative\\\\\\\\ #\nmod.dhxh_dx = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=0)\n\nmod.Jc = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_jc)\n# der var mod.Jc\n# ////derivative\\\\\\\\ #\n# mod.dJc_dx = Var(mod.fe_x, mod.cp_x, within=Reals-10000, 10000), initialize=0)\n\n# ////derivative\\\\\\\\ #\nmod.z = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=0)\nmod.dz_dx = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=0)\nmod.z_l = Var(within=Reals, initialize=0)\n\nmod.Je = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_je)\nmod.k1c = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_k1c)\nmod.k1e = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_k1e)\nmod.k2c = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_k2c)\nmod.k2e = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_k2e)\nmod.k3c = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_k3c)\nmod.k3e = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_k3e)\nmod.Kbc = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_kbc)\nmod.Kce = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_kce)\nmod.Kcebs = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_kcebs)\nmod.Ke1c = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ke1c)\nmod.Ke1e = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ke1e)\nmod.Ke2c = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ke2c)\nmod.Ke2e = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ke2e)\nmod.Ke3c = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ke3c)\nmod.Ke3e = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ke3e)\nmod.Kgbulk = Var(mod.fe_x, mod.cp_x, mod.sp, within=Reals, initialize=__ir_kgbulk)\nmod.kpa = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_kpa)\nmod.Ksbulk = Var(mod.fe_x, mod.cp_x, mod.sp, within=Reals, initialize=__ir_ksbulk)\nmod.nc = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_nc_c)\nmod.ne = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_ne_c)\nmod.Nup = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_nup)\n\nmod.P = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_p)\n# der var mod.P\n# ////derivative\\\\\\\\ #\nmod.dP_dx = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=0)\n\nmod.Phx = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_phx)\n# der var mod.Phx\n# ////derivative\\\\\\\\ #\nmod.dPhx_dx = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=0)\n# why? why not?\n\n\nmod.r1c = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_r1c)\nmod.r1e = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_r1e)\nmod.r2c = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_r2c)\nmod.r2e = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_r2e)\nmod.r3c = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_r3c)\nmod.r3e = Var(mod.fe_x, mod.cp_x, within=Reals, initialize=__ir_r3e)\nmod.Red = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_red)\nmod.rgc = Var(mod.fe_x, mod.cp_x, mod.sp, within=Reals, initialize=__ir_rgc_c)\nmod.rge = Var(mod.fe_x, mod.cp_x, mod.sp, within=Reals, initialize=__ir_rge_c)\nmod.rhog = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_rhog)\nmod.rsc = Var(mod.fe_x, mod.cp_x, mod.sp, within=Reals, initialize=__ir_rsc_c)\nmod.rse = Var(mod.fe_x, mod.cp_x, mod.sp, within=Reals, initialize=__ir_rse_c)\nmod.tau = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_tau)\nmod.Tgb = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_tgb)\nmod.Tgc = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_tgc)\nmod.Tge = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_tge)\nmod.Thx = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_thx)\nmod.Tsc = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_tsc)\nmod.Tse = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_tse)\nmod.Ttube = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ttube)\nmod.vb = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_vb)\nmod.vbr = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_vbr)\nmod.ve = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_ve)\nmod.vg = Var(mod.fe_x, mod.cp_x, within=NonNegativeReals, initialize=__ir_vg)\nmod.yb = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_yb_c)\nmod.yc = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_yc_c)\nmod.ye = Var(mod.fe_x, mod.cp_x, mod.sp, within=NonNegativeReals, initialize=__ir_ye_c)\n# dvs end\nmod.dccwin_dx = Var(mod.fe_x, mod.cp_x, mod.sp, initialize=0)\n\n\ndef __icbl(m, j):\n if j == 'c':\n return m.cbin_ix_c[100]\n elif j == 'h':\n return m.cbin_ix_h[100]\n elif j == 'n':\n return m.cbin_ix_n[100]\n\n\ndef __iceinl(m, j):\n if j == 'c':\n return m.cein_ix_c[100]\n elif j == 'h':\n return m.cein_ix_h[100]\n elif j == 'n':\n return m.cein_ix_n[100]\n\n\ndef __ebinl(m):\n return m.ebin_ix[100]\n\n\ndef __ecwinl(m):\n return m.ecwin_ix[100]\n\n\ndef __eeinl(m):\n return m.eein_ix[100]\n\n\ndef __hxhl(m):\n return m.hxh_ix[100]\n\n\ndef __pl(m):\n return m.p_ix[100]\n\n\ndef __phxl(m):\n return m.phx_ix[100]\n\n\ndef __ccwinl(m, j):\n if j == 'c':\n return m.ccwin_ix_c[100]\n elif j == 'h':\n return m.ccwin_ix_h[100]\n elif j == 'n':\n return m.ccwin_ix_n[100]\n\n\n# BCS\nmod.cbin_l = Var(mod.sp, within=NonNegativeReals, initialize=__icbl)\nmod.cein_l = Var(mod.sp, within=NonNegativeReals, initialize=__iceinl)\nmod.ebin_l = Var(within=NonNegativeReals, initialize=__ebinl)\nmod.ecwin_l = Var(within=Reals, initialize=__ecwinl)\nmod.eein_l = Var(within=Reals, initialize=__eeinl)\nmod.hxh_l = Var(within=Reals, initialize=__hxhl)\n\nmod.P_l = Var(within=NonNegativeReals, initialize=__pl)\nmod.Phx_l = Var(within=NonNegativeReals, initialize=__phxl)\n\nmod.ccwin_l = Var(mod.sp, within=NonNegativeReals, initialize=__ccwinl)\n\n\ndef __ir_hse_l(m):\n return m.hse_ix[100]\n\n\ndef __ir_ne_c_l(m, j):\n if j == 'c':\n return m.ne_ix_c[100]\n elif j == 'h':\n return m.ne_ix_h[100]\n elif j == 'n':\n return m.ne_ix_n[100]\n\n\ndef __gbl(m):\n return m.gb_ix[100]\n\n\ndef __tgbl(m):\n return m.tgb_ix[100]\n\n\ndef __ybl(m, j):\n if j == 'c':\n return m.yb_ix_c[100]\n elif j == 'h':\n return m.yb_ix_h[100]\n elif j == 'n':\n return m.yb_ix_n[100]\n\n\n# mod.Jc_l = Var(within=NonNegativeReals0, 10000), initialize=__jcl)\n# mod.Je_l = Var(within=NonNegativeReals0, 10000), initialize=__jel)\n\nmod.hse_l = Var(within=Reals, initialize=__ir_hse_l)\nmod.ne_l = Var(mod.sp, within=NonNegativeReals, initialize=__ir_ne_c_l)\nmod.Gb_l = Var(within=NonNegativeReals, initialize=__gbl)\nmod.Tgb_l = Var(within=NonNegativeReals, initialize=__tgbl)\nmod.yb_l = Var(mod.sp, within=NonNegativeReals, initialize=__ybl)\n\n\ndef __jcl(m):\n return m.jc_ix[100]\n\n\ndef __jel(m):\n return m.je_ix[100]\n\n\n# mod.Jc_l = Var(within=NonNegativeReals0, 10000), initialize=__jcl)\n# mod.Je_l = Var(within=NonNegativeReals0, 10000), initialize=__jel)\n\n# def __hub_p(m):\n# return min([_maxh, m.lenleft])\n#\n#\n# mod.hub = Param(initialize=__hub_p)\n#\n\n# constraint on the upper bound of the lengt\n# def __hup(m):\n# return m.hi <= m.hub\n#\n#\n# mod.chup = Constraint(rule=__hup)\n# ???//////////////////////////////////////////////???\n\n# ???//////////////////////////////////////////////???\n#\n#\n#\ndef a1_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.vg[i, j] * m.Ax * m.cbt[i, j] * 3600 == m.Gb[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a1 = Constraint(mod.fe_x, mod.cp_x, rule=a1_rule)\n\n\n# def a2_rule(m):\n# actual lenght\n# return ` == m.hi + m.llast - m.lenleft\n#\n#\n# mod.a2 = Constraint(rule=a2_rule)\n\n# bc_ebin\ndef a3_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.ebin[i, j] == (m.Gb[i, j] / 3600) * m.cpg_mol * m.Tgb[i, j]\n elif j == 0 and i == 1:\n return m.ebin[i, j] == (m.Gb[i, j] / 3600) * m.cpg_mol * m.Tgb[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a3 = Constraint(mod.fe_x, mod.cp_x, rule=a3_rule)\n\n\n# ic\ndef a4_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.ecwin[i, j] == m.Jc[i, j] * m.hsc[i, j]\n # elif j == 0 and i == 1:\n # return m.ecwin[i, j] == m.Jc[i, j] * m.hsc[i, j]\n else:\n\n return Constraint.Skip\n\n\nmod.a4 = Constraint(mod.fe_x, mod.cp_x, rule=a4_rule)\n\n\ndef a5_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.eein[i, j] == m.Je[i, j] * m.hse[i, j]\n # elif j == 0 and i == 1:\n # return m.eein[i, j] == m.Je[i, j] * m.hse[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a5 = Constraint(mod.fe_x, mod.cp_x, rule=a5_rule)\n\n\n# bc_cbin\ndef a7_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.cbin[i, j, k] == m.yb[i, j, k] * m.Gb[i, j] / 3600\n elif j == 0 and i == 1:\n return m.cbin[i, j, k] == m.yb[i, j, k] * m.Gb[i, j] / 3600\n else:\n return Constraint.Skip\n\n\nmod.a7 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a7_rule)\n\n\n# ic_ccwin\ndef a8_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.ccwin[i, j, k] == m.Jc[i, j] * m.nc[i, j, k]\n # elif j == 0 and i == 1:\n # return m.ccwin[i, j, k] == m.Jc[i, j] * m.nc[i, j, k]\n else:\n return Constraint.Skip\n\n\nmod.a8 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a8_rule)\n\n\ndef a9_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.cein[i, j, k] == m.Je[i, j] * m.ne[i, j, k]\n # elif j == 0 and i == 1:\n # return m.cein[i, j, k] == m.Je[i, j] * m.ne[i, j, k]\n else:\n return Constraint.Skip\n\n\nmod.a9 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a9_rule)\n\n\n# mod.dummyJ = Var(mod.fe_x, mod.cp_x,bounds=(-1000,1000), initialize=1.)\n\n\ndef a11_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.dz_dx[i, j] == 0.0\n # return m.dJc_dx[i, j] == m.dummyJ[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a11 = Constraint(mod.fe_x, mod.cp_x, rule=a11_rule)\n\n\ndef a11_rule_2(m, i, j):\n if 0 < j <= kord_x:\n return m.z[i, j] == m.Je[i, j] - m.Jc[i, j]\n # return m.dJc_dx[i, j] == m.dummyJ[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a11_2 = Constraint(mod.fe_x, mod.cp_x, rule=a11_rule_2)\n\n\ndef a12_rule(m, i, j):\n if 0 < j <= kord_x:\n return (m.dP_dx[i, j]) * 100000 == -m.hi_x[i] * (1 - m.e[i, j]) * m.rhos * m.gc\n else:\n return Constraint.Skip\n\n\nmod.a12 = Constraint(mod.fe_x, mod.cp_x, rule=a12_rule)\n\n\ndef a13_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Gb[i, j] / 3600 == m.vb[i, j] * m.Ax * m.delta[i, j] * m.cbt[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a13 = Constraint(mod.fe_x, mod.cp_x, rule=a13_rule)\n\n\ndef a14_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Jc[i, j] == m.fw * m.delta[i, j] * m.rhos * (1 - m.ed[i, j]) * m.vb[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a14 = Constraint(mod.fe_x, mod.cp_x, rule=a14_rule)\n\n\ndef a15_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.cb[i, j, k] == m.yb[i, j, k] * m.cbt[i, j]\n # elif j == 0 and i == 1:\n # bc_cb\n # return m.cb[i, j, k] == m.yb[i, j, k] * m.cbt[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a15 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a15_rule)\n\n\ndef a16_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.cc[i, j, k] == m.yc[i, j, k] * m.cct[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a16 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a16_rule)\n\n\ndef a17_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.ce[i, j, k] == m.ye[i, j, k] * m.cet[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a17 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a17_rule)\n\n\ndef a18_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.cet[i, j] == sum(m.ce[i, j, k] for k in m.sp)\n else:\n return Constraint.Skip\n\n\nmod.a18 = Constraint(mod.fe_x, mod.cp_x, rule=a18_rule)\n\n\ndef a19_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.cct[i, j] == sum(m.cc[i, j, k] for k in m.sp)\n else:\n return Constraint.Skip\n\n\nmod.a19 = Constraint(mod.fe_x, mod.cp_x, rule=a19_rule)\n\n\ndef a20_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.cbt[i, j] == sum(m.cb[i, j, k] for k in m.sp)\n # elif j == 0 and i == 1:\n # return m.cbt[i, j] == sum(m.cb[i, j, k] for k in m.sp)\n else:\n return Constraint.Skip\n\n\nmod.a20 = Constraint(mod.fe_x, mod.cp_x, rule=a20_rule)\n\n\n# bc_P\ndef a21_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.cbt[i, j] == m.P[i, j] * 100 / (8.314 * (m.Tgb[i, j] + 273.16))\n # elif j == 0 and i == 1:\n # maybe not\n # return m.cbt[i, j] == m.P[i, j] * 100 / (8.314 * (m.Tgb[i, j] + 273.16))\n else:\n return Constraint.Skip\n\n\nmod.a21 = Constraint(mod.fe_x, mod.cp_x, rule=a21_rule)\n\n\ndef a22_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.D[i, j, 'c'] == (0.1593 - 0.1282 * (m.P[i, j] - 1.4) + 0.001 * (m.Tge[i, j] - 60) + 0.0964 * (\n (m.P[i, j] - 1.4) ** 2) - 0.0006921 * ((m.P[i, j] - 1.4) * (m.Tge[i, j] - 60)) -\n 3.3532e-06 * (m.Tge[i, j] - 60) ** 2) * m.ye[i, j, 'h'] / (\n m.ye[i, j, 'h'] + m.ye[i, j, 'n']) + \\\n (0.1495 - 0.1204 * (m.P[i, j] - 1.4) + 0.0008896 * (m.Tge[i, j] - 60) + 0.0906 * (\n (m.P[i, j] - 1.4) ** 2) -\n 0.0005857 * (m.P[i, j] - 1.4) * (m.Tge[i, j] - 60) -\n 3.559e-06 * (m.Tge[i, j] - 60) ** 2) * m.ye[i, j, 'n'] / (\n m.ye[i, j, 'h'] + m.ye[i, j, 'n'])\n else:\n return Constraint.Skip\n\n\nmod.a22 = Constraint(mod.fe_x, mod.cp_x, rule=a22_rule)\n\n\ndef a23_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.D[i, j, 'h'] == (0.1593 - 0.1282 * (m.P[i, j] - 1.4) + 0.001 * (m.Tge[i, j] - 60) +\n 0.0964 * ((m.P[i, j] - 1.4) ** 2) - 0.0006921 * (\n (m.P[i, j] - 1.4) * (m.Tge[i, j] - 60)) -\n 3.3532e-06 * (m.Tge[i, j] - 60) ** 2) * m.ye[i, j, 'c'] / (\n m.ye[i, j, 'c'] + m.ye[i, j, 'n']) + \\\n (0.2165 - 0.1743 * (m.P[i, j] - 1.4) + 0.001377 * (m.Tge[i, j] - 60) + 0.13109 * (\n (m.P[i, j] - 1.4) ** 2) -\n 0.0009115 * (m.P[i, j] - 1.4) * (m.Tge[i, j] - 60) -\n 4.8394e-06 * (m.Tge[i, j] - 60) ** 2) * m.ye[i, j, 'n'] / (\n m.ye[i, j, 'c'] + m.ye[i, j, 'n'])\n else:\n return Constraint.Skip\n\n\nmod.a23 = Constraint(mod.fe_x, mod.cp_x, rule=a23_rule)\n\n\ndef a24_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.D[i, j, 'n'] == (0.1495 - 0.1204 * (m.P[i, j] - 1.4) + 0.0008896 * (m.Tge[i, j] - 60) + 0.0906 * (\n (m.P[i, j] - 1.4) ** 2) -\n 0.0005857 * (m.P[i, j] - 1.4) * (m.Tge[i, j] - 60) -\n 3.559e-06 * (m.Tge[i, j] - 60) ** 2) * m.ye[i, j, 'c'] / (\n m.ye[i, j, 'h'] + m.ye[i, j, 'c']) + \\\n (0.2165 - 0.1743 * (m.P[i, j] - 1.4) + 0.001377 * (m.Tge[i, j] - 60) + 0.13109 * (\n (m.P[i, j] - 1.4) ** 2) -\n 0.0009115 * (m.P[i, j] - 1.4) * (m.Tge[i, j] - 60) -\n 4.8394e-06 * (m.Tge[i, j] - 60) ** 2) * m.ye[i, j, 'h'] / (\n m.ye[i, j, 'h'] + m.ye[i, j, 'c'])\n else:\n return Constraint.Skip\n\n\nmod.a24 = Constraint(mod.fe_x, mod.cp_x, rule=a24_rule)\n\n\n# density\ndef a25_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rhog[i, j] == m.P[i, j] * 100 * (\n m.ye[i, j, 'c'] * 44.01 + m.ye[i, j, 'n'] * 28.01 + m.ye[i, j, 'h'] * 18.02) \\\n / (8.314 * (m.Tge[i, j] + 273.16))\n else:\n return Constraint.Skip\n\n\nmod.a25 = Constraint(mod.fe_x, mod.cp_x, rule=a25_rule)\n\n\ndef a26_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Ar[i, j] == (m.dp ** 3) * m.rhog[i, j] * (m.rhos - m.rhog[i, j]) * m.gc / (m.mug ** 2)\n else:\n return Constraint.Skip\n\n\nmod.a26 = Constraint(mod.fe_x, mod.cp_x, rule=a26_rule)\n\n\ndef a27_rule(m, i, j):\n if 0 < j <= kord_x:\n return (1 - m.e[i, j]) == (1 - m.ed[i, j]) * (1 - m.delta[i, j])\n else:\n return Constraint.Skip\n\n\nmod.a27 = Constraint(mod.fe_x, mod.cp_x, rule=a27_rule)\n\n\ndef a28_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.vbr[i, j] == 0.711 * sqrt(m.gc * m.db[i, j])\n else:\n return Constraint.Skip\n\n\nmod.a28 = Constraint(mod.fe_x, mod.cp_x, rule=a28_rule)\n\n\ndef a29_rule(m):\n return m.db0 == 1.38 * (m.gc ** (-0.2)) * ((m.vg[1, 1] - m.ve[1, 1]) * m.Ao) ** 0.4\n\n\nmod.a29 = Constraint(rule=a29_rule)\n\n\ndef a30_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.dbe[i, j] == (m.Dt / 4) * (-m.g1 + m.g3[i, j]) ** 2\n else:\n return Constraint.Skip\n\n\nmod.a30 = Constraint(mod.fe_x, mod.cp_x, rule=a30_rule)\n\n\ndef a31_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.dbm[i, j] == 2.59 * (m.gc ** (-0.2)) * ((m.vg[i, j] - m.ve[i, j]) * m.Ax) ** 0.4\n else:\n return Constraint.Skip\n\n\nmod.a31 = Constraint(mod.fe_x, mod.cp_x, rule=a31_rule)\n\n\ndef a32_rule(m):\n return m.g1 == 2.56E-2 * sqrt(m.Dt / m.gc) / m.vmf\n\n\nmod.a32 = Constraint(rule=a32_rule)\n\n\ndef a33_rule(m, i, j):\n if 0 < j <= kord_x:\n return 4 * m.g2[i, j] == m.Dt * (m.g1 + m.g3[i, j]) ** 2\n else:\n return Constraint.Skip\n\n\nmod.a33 = Constraint(mod.fe_x, mod.cp_x, rule=a33_rule)\n\n\ndef a34_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.g3[i, j] == sqrt(m.g1 ** 2 + 4 * m.dbm[i, j] / m.Dt)\n else:\n return Constraint.Skip\n\n\nmod.a34 = Constraint(mod.fe_x, mod.cp_x, rule=a34_rule)\n\n\n# x included?\ndef a35_rule(m, i, j):\n if 0 < j <= kord_x:\n return (((sqrt(m.dbu[i, j]) - sqrt(m.dbe[i, j])) / (sqrt(m.db0) - sqrt(m.dbe[i, j]))) ** (\n 1 - m.g1 / m.g3[i, j])) * \\\n (((sqrt(m.dbu[i, j]) - sqrt(m.g2[i, j])) / (sqrt(m.db0) - sqrt(m.g2[i, j]))) ** (\n 1 + m.g1 / m.g3[i, j])) == \\\n exp(-0.3 * (m.l[i, j]) / m.Dt)\n else:\n return Constraint.Skip\n\n\nmod.a35 = Constraint(mod.fe_x, mod.cp_x, rule=a35_rule)\n\n\ndef a36_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.fc[i, j] == 3. * (m.vmf / m.emf) / (m.vbr[i, j] - (m.vmf / m.emf))\n else:\n return Constraint.Skip\n\n\nmod.a36 = Constraint(mod.fe_x, mod.cp_x, rule=a36_rule)\n\n\ndef a37_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.fcw[i, j] == m.fc[i, j] + m.fw\n else:\n return Constraint.Skip\n\n\nmod.a37 = Constraint(mod.fe_x, mod.cp_x, rule=a37_rule)\n\n\ndef a38_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.Kbc[i, j, k] == 1.32 * 4.5 * (m.vmf / m.db[i, j]) + 5.85 * (\n ((m.D[i, j, k] * 1E-4) ** 0.5) * (m.gc ** 0.25) / (m.db[i, j] ** (5 / 4)))\n else:\n return Constraint.Skip\n\n\nmod.a38 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a38_rule)\n\n\ndef a39_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.Kce[i, j, k] == 6.77 * sqrt(m.ed[i, j] * (m.D[i, j, k] * 1E-4) * m.vbr[i, j] / (m.db[i, j] ** 3))\n else:\n return Constraint.Skip\n\n\nmod.a39 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=a39_rule)\n\n\ndef a40_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Kcebs[i, j] == 3 * (1 - m.ed[i, j]) / ((1 - m.delta[i, j]) * m.ed[i, j]) * (m.ve[i, j] / m.db[i, j])\n else:\n return Constraint.Skip\n\n\nmod.a40 = Constraint(mod.fe_x, mod.cp_x, rule=a40_rule)\n\n\ndef a41_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Hbc[i, j] == 1.32 * 4.5 * m.vmf * m.cbt[i, j] * m.cpg_mol / m.db[i, j] + \\\n 5.85 * sqrt((m.kg / 1000) * m.cbt[i, j] * m.cpg_mol) * (m.gc ** 0.25) / (\n m.db[i, j] ** (5 / 4))\n else:\n return Constraint.Skip\n\n\nmod.a41 = Constraint(mod.fe_x, mod.cp_x, rule=a41_rule)\n\n\ndef a42_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Hce[i, j] == 6.78 * sqrt(\n m.ed[i, j] * m.vb[i, j] * (m.kg / 1000) * m.cct[i, j] * m.cpg_mol / (m.db[i, j] ** 3))\n else:\n return Constraint.Skip\n\n\nmod.a42 = Constraint(mod.fe_x, mod.cp_x, rule=a42_rule)\n\n\ndef a43_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Nup[i, j] == 1000 * m.hp[i, j] * m.dp / m.kg\n else:\n return Constraint.Skip\n\n\nmod.a43 = Constraint(mod.fe_x, mod.cp_x, rule=a43_rule)\n\n\ndef a44_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Red[i, j] == m.ve[i, j] * m.dp * m.rhog[i, j] / m.mug\n else:\n return Constraint.Skip\n\n\nmod.a44 = Constraint(mod.fe_x, mod.cp_x, rule=a44_rule)\n\n\ndef a45_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Nup[i, j] == 0.03 * (m.Red[i, j] ** 1.3)\n else:\n return Constraint.Skip\n\n\nmod.a45 = Constraint(mod.fe_x, mod.cp_x, rule=a45_rule)\n\n\ndef a46_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.kpa[i, j] == (3.58 - 2.5 * m.ed[i, j]) * m.kg * ((m.kp / m.kg) ** (0.46 - 0.46 * m.ed[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.a46 = Constraint(mod.fe_x, mod.cp_x, rule=a46_rule)\n\n\ndef a47_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.fn[i, j] == m.vg[i, j] / m.vmf\n else:\n return Constraint.Skip\n\n\nmod.a47 = Constraint(mod.fe_x, mod.cp_x, rule=a47_rule)\n\n\ndef a48_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.tau[i, j] == 0.44 * ((m.dp * m.gc / ((m.vmf ** 2) * ((m.fn[i, j] - m.ah) ** 2))) ** 0.14) * (\n (m.dp / m.dx) ** 0.225)\n else:\n return Constraint.Skip\n\n\nmod.a48 = Constraint(mod.fe_x, mod.cp_x, rule=a48_rule)\n\n\ndef a49_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.fb[i, j] == 0.33 * (((m.vmf ** 2) * ((m.fn[i, j] - m.ah) ** 2) / (m.dp * m.gc)) ** 0.14)\n else:\n return Constraint.Skip\n\n\nmod.a49 = Constraint(mod.fe_x, mod.cp_x, rule=a49_rule)\n\n\ndef a50_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.hd[i, j] == 2 * sqrt((m.kpa[i, j] / 1000) * m.rhos * m.cps * (1 - m.ed[i, j]) / (m.pi * m.tau[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.a50 = Constraint(mod.fe_x, mod.cp_x, rule=a50_rule)\n\n\ndef a51_rule(m, i, j):\n if 0 < j <= kord_x:\n return 1000 * m.hl[i, j] * m.dp / m.kg == 0.009 * (m.Ar[i, j] ** 0.5) * (m.Pr ** 0.33)\n else:\n return Constraint.Skip\n\n\nmod.a51 = Constraint(mod.fe_x, mod.cp_x, rule=a51_rule)\n\n\ndef a52_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.ht[i, j] == m.fb[i, j] * m.hd[i, j] + (1 - m.fb[i, j]) * m.hl[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a52 = Constraint(mod.fe_x, mod.cp_x, rule=a52_rule)\n\n\n# pde\ndef a53_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.dPhx_dx[i, j] == m.hi_x[i] * m.dPhx + m.hi_x[i] * m.rhohx * 1E-5\n else:\n return Constraint.Skip\n\n\nmod.a53 = Constraint(mod.fe_x, mod.cp_x, rule=a53_rule)\n\n\ndef a54_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.dThx[i, j] == m.Ttube[i, j] - m.Tse[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a54 = Constraint(mod.fe_x, mod.cp_x, rule=a54_rule)\n\n\ndef a55_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.ht[i, j] * m.dThx[i, j] * m.Cr == m.hw * (m.Thx[i, j] - m.Ttube[i, j])\n else:\n return Constraint.Skip\n\n\nmod.a55 = Constraint(mod.fe_x, mod.cp_x, rule=a55_rule)\n\n\ndef a56_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Thx[i, j] == 33.2104 + 14170.15 * (m.hxh[i, j] + 0.285)\n else:\n return Constraint.Skip\n\n\nmod.a56 = Constraint(mod.fe_x, mod.cp_x, rule=a56_rule)\n\n\ndef a57_rule(m):\n return 10 * 1.75 / (m.phis * m.emf ** 3) * (m.dp * m.vmf * m.rhog[1, 1] / m.mug) ** 2 + \\\n 10 * 150 * (1 - m.emf) / ((m.phis ** 2) * (m.emf ** 3)) * (m.dp * m.vmf * m.rhog[1, 1] / m.mug) == \\\n 10 * m.dp ** 3 * m.rhog[1, 1] * (m.rhos - m.rhog[1, 1]) * m.gc / m.mug ** 2\n\n\nmod.a57 = Constraint(rule=a57_rule)\n\n\ndef a58_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.k1c[i, j] == m.A1 * (m.Tsc[i, j] + 273.15) * exp(-m.E1 / (m.R * (m.Tsc[i, j] + 273.15)))\n else:\n return Constraint.Skip\n\n\nmod.a58 = Constraint(mod.fe_x, mod.cp_x, rule=a58_rule)\n\n\ndef a59_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.k2c[i, j] == m.A2 * (m.Tsc[i, j] + 273.15) * exp(-m.E2 / (m.R * (m.Tsc[i, j] + 273.15)))\n else:\n return Constraint.Skip\n\n\nmod.a59 = Constraint(mod.fe_x, mod.cp_x, rule=a59_rule)\n\n\ndef a60_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.k3c[i, j] == m.A3 * (m.Tsc[i, j] + 273.15) * exp(-m.E3 / (m.R * (m.Tsc[i, j] + 273.15)))\n else:\n return Constraint.Skip\n\n\nmod.a60 = Constraint(mod.fe_x, mod.cp_x, rule=a60_rule)\n\n\ndef a61_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.k1e[i, j] == m.A1 * (m.Tse[i, j] + 273.15) * exp(-m.E1 / (m.R * (m.Tse[i, j] + 273.15)))\n else:\n return Constraint.Skip\n\n\nmod.a61 = Constraint(mod.fe_x, mod.cp_x, rule=a61_rule)\n\n\ndef a62_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.k2e[i, j] == m.A2 * (m.Tse[i, j] + 273.15) * exp(-m.E2 / (m.R * (m.Tse[i, j] + 273.15)))\n else:\n return Constraint.Skip\n\n\nmod.a62 = Constraint(mod.fe_x, mod.cp_x, rule=a62_rule)\n\n\ndef a63_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.k3e[i, j] == m.A3 * (m.Tse[i, j] + 273.15) * exp(-m.E3 / (m.R * (m.Tse[i, j] + 273.15)))\n else:\n return Constraint.Skip\n\n\nmod.a63 = Constraint(mod.fe_x, mod.cp_x, rule=a63_rule)\n\n\ndef a64_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Ke1c[i, j] * m.P[i, j] * 1E5 == exp(-m.dH1 / (m.R * (m.Tsc[i, j] + 273.15)) + m.dS1 / m.R)\n else:\n return Constraint.Skip\n\n\nmod.a64 = Constraint(mod.fe_x, mod.cp_x, rule=a64_rule)\n\n\ndef a65_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Ke2c[i, j] * m.P[i, j] * 1E5 == exp(-m.dH2 / (m.R * (m.Tsc[i, j] + 273.15)) + m.dS2 / m.R)\n else:\n return Constraint.Skip\n\n\nmod.a65 = Constraint(mod.fe_x, mod.cp_x, rule=a65_rule)\n\n\ndef a66_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Ke3c[i, j] * m.P[i, j] * 1E5 == exp(-m.dH3 / (m.R * (m.Tsc[i, j] + 273.15)) + m.dS3 / m.R)\n else:\n return Constraint.Skip\n\n\nmod.a66 = Constraint(mod.fe_x, mod.cp_x, rule=a66_rule)\n\n\ndef a67_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Ke1e[i, j] * m.P[i, j] * 1E5 == exp(-m.dH1 / (m.R * (m.Tse[i, j] + 273.15)) + m.dS1 / m.R)\n else:\n return Constraint.Skip\n\n\nmod.a67 = Constraint(mod.fe_x, mod.cp_x, rule=a67_rule)\n\n\ndef a68_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Ke2e[i, j] * m.P[i, j] * 1E5 == exp(-m.dH2 / (m.R * (m.Tse[i, j] + 273.15)) + m.dS2 / m.R)\n else:\n return Constraint.Skip\n\n\nmod.a68 = Constraint(mod.fe_x, mod.cp_x, rule=a68_rule)\n\n\ndef a69_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Ke3e[i, j] * m.P[i, j] * 1E5 == exp(-m.dH3 / (m.R * (m.Tse[i, j] + 273.15)) + m.dS3 / m.R)\n else:\n return Constraint.Skip\n\n\nmod.a69 = Constraint(mod.fe_x, mod.cp_x, rule=a69_rule)\n\n\ndef a70_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.r1c[i, j] == m.k1c[i, j] * (\n (m.P[i, j] * m.yc[i, j, 'h'] * 1E5) - (m.nc[i, j, 'h'] * m.rhos / m.Ke1c[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.a70 = Constraint(mod.fe_x, mod.cp_x, rule=a70_rule)\n\n\ndef a71_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.r2c[i, j] == m.k2c[i, j] * ((1 - 2 * (m.nc[i, j, 'n'] * m.rhos / m.nv) -\n (m.nc[i, j, 'c'] * m.rhos / m.nv)) * m.nc[i, j, 'h'] * m.rhos * m.P[\n i, j] *\n m.yc[i, j, 'c'] * 1E5 -\n (\n ((m.nc[i, j, 'n'] * m.rhos / m.nv) + (\n m.nc[i, j, 'c'] * m.rhos / m.nv)) *\n m.nc[\n i, j, 'c'] * m.rhos /\n m.Ke2c[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.a71 = Constraint(mod.fe_x, mod.cp_x, rule=a71_rule)\n\n\ndef a72_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.r3c[i, j] == m.k3c[i, j] * (((1 - 2 * (m.nc[i, j, 'n'] * m.rhos / m.nv) -\n (m.nc[i, j, 'c'] * m.rhos / m.nv)) ** 2) * (\n (m.P[i, j] * m.yc[i, j, 'c'] * 1E5) ** m.m1) -\n ((m.nc[i, j, 'n'] * m.rhos / m.nv) * (\n (m.nc[i, j, 'n'] * m.rhos / m.nv) + (\n m.nc[i, j, 'c'] * m.rhos / m.nv)) /\n m.Ke3c[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.a72 = Constraint(mod.fe_x, mod.cp_x, rule=a72_rule)\n\n\ndef a73_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.r1e[i, j] == m.k1e[i, j] * (\n (m.P[i, j] * m.ye[i, j, 'h'] * 1E5) - (m.ne[i, j, 'h'] * m.rhos / m.Ke1e[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.a73 = Constraint(mod.fe_x, mod.cp_x, rule=a73_rule)\n\n\ndef a74_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.r2e[i, j] == m.k2e[i, j] * ((1. - 2. * (m.ne[i, j, 'n'] * m.rhos / m.nv) -\n (m.ne[i, j, 'c'] * m.rhos / m.nv)) * m.ne[i, j, 'h'] * m.rhos * (\n m.P[i, j] * m.ye[i, j, 'c'] * 1E5) -\n (((m.ne[i, j, 'n'] * m.rhos / m.nv) +\n (m.ne[i, j, 'c'] * m.rhos / m.nv)) * m.ne[i, j, 'c'] * m.rhos / m.Ke2e[\n i, j])\n )\n else:\n return Constraint.Skip\n\n\nmod.a74 = Constraint(mod.fe_x, mod.cp_x, rule=a74_rule)\n\n\ndef a75_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.r3e[i, j] == \\\n m.k3e[i, j] * (\n ((1. - 2. * (m.ne[i, j, 'n'] * m.rhos / m.nv) -\n (m.ne[i, j, 'c'] * m.rhos / m.nv)) ** 2) * ((m.P[i, j] * m.ye[i, j, 'c'] * 1E5) ** m.m1) -\n ((m.ne[i, j, 'n'] * m.rhos / m.nv) * (\n (m.ne[i, j, 'n'] * m.rhos / m.nv) + (m.ne[i, j, 'c'] * m.rhos / m.nv)) / m.Ke3e[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.a75 = Constraint(mod.fe_x, mod.cp_x, rule=a75_rule)\n\n\ndef a76_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rgc[i, j, 'c'] == (m.nv * m.r3c[i, j] + m.r2c[i, j]) / 1000.\n else:\n return Constraint.Skip\n\n\nmod.a76 = Constraint(mod.fe_x, mod.cp_x, rule=a76_rule)\n\n\ndef a77_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rge[i, j, 'c'] == (m.nv * m.r3e[i, j] + m.r2e[i, j]) / 1000.\n else:\n return Constraint.Skip\n\n\nmod.a77 = Constraint(mod.fe_x, mod.cp_x, rule=a77_rule)\n\n\ndef a78_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rsc[i, j, 'c'] == m.r2c[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a78 = Constraint(mod.fe_x, mod.cp_x, rule=a78_rule)\n\n\ndef a79_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rse[i, j, 'c'] == m.r2e[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a79 = Constraint(mod.fe_x, mod.cp_x, rule=a79_rule)\n\n\ndef a80_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rgc[i, j, 'h'] == m.r1c[i, j] / 1000\n else:\n return Constraint.Skip\n\n\nmod.a80 = Constraint(mod.fe_x, mod.cp_x, rule=a80_rule)\n\n\ndef a81_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rge[i, j, 'h'] == m.r1e[i, j] / 1000\n else:\n return Constraint.Skip\n\n\nmod.a81 = Constraint(mod.fe_x, mod.cp_x, rule=a81_rule)\n\n\ndef a82_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rsc[i, j, 'h'] == m.r1c[i, j] - m.r2c[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a82 = Constraint(mod.fe_x, mod.cp_x, rule=a82_rule)\n\n\ndef a83_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rse[i, j, 'h'] == m.r1e[i, j] - m.r2e[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a83 = Constraint(mod.fe_x, mod.cp_x, rule=a83_rule)\n\n\ndef a84_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rgc[i, j, 'n'] == 0\n else:\n return Constraint.Skip\n\n\nmod.a84 = Constraint(mod.fe_x, mod.cp_x, rule=a84_rule)\n\n\ndef a85_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rge[i, j, 'n'] == 0\n else:\n return Constraint.Skip\n\n\nmod.a85 = Constraint(mod.fe_x, mod.cp_x, rule=a85_rule)\n\n\ndef a86_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rsc[i, j, 'n'] == m.nv * m.r3c[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a86 = Constraint(mod.fe_x, mod.cp_x, rule=a86_rule)\n\n\ndef a87_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.rse[i, j, 'n'] == m.nv * m.r3e[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a87 = Constraint(mod.fe_x, mod.cp_x, rule=a87_rule)\n\n\ndef a88_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.hsc[i, j] == ((m.nc[i, j, 'h'] + m.nc[i, j, 'c']) * (m.cpgcsc['h'] * m.Tsc[i, j] + m.dH1) +\n m.nc[i, j, 'c'] * (m.cpgcsc['c'] * m.Tsc[i, j] + m.dH2) +\n m.nc[i, j, 'n'] * (m.cpgcsc['c'] * m.Tsc[i, j] + m.dH3)) * 1E-3 + m.cps * m.Tsc[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a88 = Constraint(mod.fe_x, mod.cp_x, rule=a88_rule)\n\n\ndef a89_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.hse[i, j] == ((m.ne[i, j, 'h'] + m.ne[i, j, 'c']) * (m.cpgcse['h'] * m.Tse[i, j] + m.dH1) +\n m.ne[i, j, 'c'] * (m.cpgcse['c'] * m.Tse[i, j] + m.dH2) +\n m.ne[i, j, 'n'] * (m.cpgcse['c'] * m.Tse[i, j] + m.dH3)) * 1E-3 + m.cps * m.Tse[i, j]\n else:\n return Constraint.Skip\n\n\nmod.a89 = Constraint(mod.fe_x, mod.cp_x, rule=a89_rule)\n\n\n# ic_nc\n# def a90_rule(m, k):\n# return m.nc[1, 0, k] == m.ne[1, 0, k]\n\n\n# mod.a90 = Constraint(mod.sp, rule=a90_rule)\n\n# ic_hsc\n# def a95_rule(m):\n# return m.hsc[1, 0] == m.hse[1, 0]\n\n\n# mod.a95 = Constraint(rule=a95_rule)\n\n\n# d_rules diff?\n# put derivative space here\n# equation A.1 Gas phase component balance\n# IC\ndef d1_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return 0 == -m.dcbin_dx[i, j, k] + m.hi_x[i] * (\n -m.Ax * m.delta[i, j] * m.Kbc[i, j, k] * (m.cb[i, j, k] - m.cc[i, j, k])\n ) + m.Kgbulk[i, j, k]\n else:\n return Constraint.Skip\n\n\nmod.d1 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=d1_rule)\n\n\n# put derivative space here\n# equation A.2 Gas phase energy balance\n# IC\ndef d2_rule(m, i, j):\n if 0 < j <= kord_x:\n return 0 == -m.debin_dx[i, j] + \\\n m.hi_x[i] * (-m.Ax * m.delta[i, j] * m.Hbc[i, j] * (m.Tgb[i, j] - m.Tgc[i, j])) + m.Hgbulk[i, j]\n else:\n return Constraint.Skip\n\n\nmod.d2 = Constraint(mod.fe_x, mod.cp_x, rule=d2_rule)\n\n\n# equation A.3 Gas phase component balance\ndef d3_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return 0 == m.Kbc[i, j, k] * (m.cb[i, j, k] - m.cc[i, j, k]) - m.Kce[i, j, k] * (m.cc[i, j, k] - m.ce[i, j, k]) \\\n - m.fcw[i, j] * (1. - m.ed[i, j]) * m.rgc[i, j, k]\n else:\n return Constraint.Skip\n\n\nmod.d3 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=d3_rule)\n\n\n# equation A.7 Gas phase component balance\ndef d4_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return 0 == m.hi_x[i] * m.Ax * m.delta[i, j] * m.Kce[i, j, k] * (m.cc[i, j, k] - m.ce[i, j, k]) - \\\n m.hi_x[i] * m.Ax * (1. - m.fcw[i, j] * m.delta[i, j] - m.delta[i, j]) * (1. - m.ed[i, j]) * m.rge[\n i, j, k] - \\\n m.Kgbulk[i, j, k]\n else:\n return Constraint.Skip\n\n\nmod.d4 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=d4_rule)\n\n\n# equation A.4 Gas phase energy balance\ndef d5_rule(m, i, j):\n if 0 < j <= kord_x:\n return 0 == m.Hbc[i, j] * (m.Tgb[i, j] - m.Tgc[i, j]) - m.Hce[i, j] * (m.Tgc[i, j] - m.Tge[i, j]) - \\\n m.fcw[i, j] * (1 - m.ed[i, j]) * m.rhos * m.ap * m.hp[i, j] * (m.Tgc[i, j] - m.Tsc[i, j]) - \\\n m.fcw[i, j] * (1 - m.ed[i, j]) * sum(m.rgc[i, j, k] * m.cpgcgc[k] for k in m.sp) * m.Tgc[i, j]\n else:\n return Constraint.Skip\n\n\nmod.d5 = Constraint(mod.fe_x, mod.cp_x, rule=d5_rule)\n\n\n# equation A.8 Gas phase energy balance\ndef d6_rule(m, i, j):\n if 0 < j <= kord_x:\n return 0 == m.hi_x[i] * m.Ax * m.delta[i, j] * m.Hce[i, j] * (m.Tgc[i, j] - m.Tge[i, j]) - \\\n m.hi_x[i] * m.Ax * (1 - m.fcw[i, j] * m.delta[i, j] - m.delta[i, j]) * (\n 1. - m.ed[i, j]) * m.rhos * m.ap * m.hp[i, j] * (m.Tge[i, j] - m.Tse[i, j]) - \\\n m.Hgbulk[i, j] - \\\n m.hi_x[i] * m.Ax * (1. - m.fcw[i, j] * m.delta[i, j] - m.delta[i, j]) * (1. - m.ed[i, j]) * \\\n sum(m.rge[i, j, k] * m.cpgcge[k] for k in m.sp) * m.Tge[i, j]\n else:\n return Constraint.Skip\n\n\nmod.d6 = Constraint(mod.fe_x, mod.cp_x, rule=d6_rule)\n\n\n# equation A.5 Solid phase adsorbed species balance\n# IC\ndef d7_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return 0 == -m.dccwin_dx[i, j, k] * m.Ax - m.Ksbulk[i, j, k] + \\\n m.hi_x[i] * (-\n m.Ax * m.delta[i, j] * m.rhos * m.Kcebs[i, j] * (m.nc[i, j, k] - m.ne[i, j, k]) +\n m.Ax * m.fcw[i, j] * m.delta[i, j] * (1 - m.ed[i, j]) * m.rsc[i, j, k])\n else:\n return Constraint.Skip\n\n\nmod.d7 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=d7_rule)\n\n\n# put derivative space here\n# equation A.9 Solid phase adsorbed species balance\ndef d8_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return 0 == m.dcein_dx[i, j, k] * m.Ax + m.Ksbulk[i, j, k] + \\\n m.hi_x[i] * (\n m.Ax * m.delta[i, j] * m.rhos * m.Kcebs[i, j] * (m.nc[i, j, k] - m.ne[i, j, k]) +\n m.Ax * (1 - m.fcw[i, j] * m.delta[i, j] -\n m.delta[i, j]) * (1 - m.ed[i, j]) * m.rse[i, j, k])\n else:\n return Constraint.Skip\n\n\nmod.d8 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=d8_rule)\n\n\n# put derivative space here\n# equation A.6 Solid phase energy balance\ndef d9_rule(m, i, j):\n if 0 < j <= kord_x:\n return 0 == -m.decwin_dx[i, j] * m.Ax - m.Hsbulk[i, j] + \\\n m.hi_x[i] * (\n - m.Ax * m.delta[i, j] * m.rhos * m.Kcebs[i, j] * (m.hsc[i, j] - m.hse[i, j]) +\n m.Ax * m.fcw[i, j] * m.delta[i, j] * (1 - m.ed[i, j]) * sum(\n (m.rgc[i, j, k] * m.cpgcgc[k]) for k in m.sp) * (m.Tgc[i, j]) +\n m.Ax * m.fcw[i, j] * m.delta[i, j] * (1 - m.ed[i, j]) * m.rhos * m.ap * m.hp[i, j] * (\n m.Tgc[i, j] - m.Tsc[i, j]))\n else:\n return Constraint.Skip\n\n\nmod.d9 = Constraint(mod.fe_x, mod.cp_x, rule=d9_rule)\n\n\n# put derivative space here\n# equation A.10 Solid phase energy balance\ndef d10_rule(m, i, j):\n if 0 < j <= kord_x:\n return 0 == m.deein_dx[i, j] * m.Ax + m.Hsbulk[i, j] + \\\n m.hi_x[i] * (\n m.Ax * m.delta[i, j] * m.rhos * m.Kcebs[i, j] * (m.hsc[i, j] - m.hse[i, j]) +\n m.Ax * (1 - m.fcw[i, j] * m.delta[i, j] - m.delta[i, j]) * (1 - m.ed[i, j]) *\n sum((m.rge[i, j, k] * m.cpgcge[k]) for k in m.sp) * m.Tge[i, j] +\n m.Ax * (\n 1. - m.fcw[i, j] * m.delta[i, j] - m.delta[i, j]\n ) * (1. - m.ed[i, j]) * m.rhos * m.ap * m.hp[i, j] * (m.Tge[i, j] - m.Tse[i, j])\n + m.pi * m.dx * m.ht[i, j] * m.dThx[i, j] * m.Nx * m.Cr)\n else:\n return Constraint.Skip\n\n\n# shift the AV?\n\nmod.d10 = Constraint(mod.fe_x, mod.cp_x, rule=d10_rule)\n\n\n# d11 --> d7 bc\n# d12 --> d8 ic\n# d13 --> d9 bc\n# d14 --> d10 ic\n\n\ndef i1_rule(m, i, j, k):\n if 0 < j <= kord_x:\n return m.Kgbulk[i, j, k] == m.K_d * (m.cet[i, j] - m.cbt[i, j]) * m.yb[i, j, k]\n else:\n return Constraint.Skip\n\n\nmod.i1 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=i1_rule)\n\n\ndef i2_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.Hgbulk[i, j] == m.K_d * (m.cet[i, j] - m.cbt[i, j]) * m.cpg_mol * m.Tgb[i, j]\n else:\n return Constraint.Skip\n\n\nmod.i2 = Constraint(mod.fe_x, mod.cp_x, rule=i2_rule)\n\n\n# oddly derivative looking term here and in the next one\n# definetly derivatives e19 and e20 from bfb ss paper\ndef i3_rule(m, i, k, c):\n if 0 < k <= kord_x:\n return m.Ksbulk[i, k, c] == \\\n -m.Ax * sum(m.lydot[j, k] * m.Jc[i, j] for j in m.cp_x if 0 < j <= kord_x) * m.ne[i, k, c]\n else:\n return Constraint.Skip\n\n\n# else:\n# return Constraint.Skip\nmod.i3 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=i3_rule)\n\n\n# sum(m.ldot[j, k] * m.cbin[i, j, c] for j in m.cp_x if j <= kord_x)\n# m.Jc[i, j]-m.Jc[i-1]\ndef i4_rule(m, i, k):\n if 0 < k <= kord_x:\n return m.Hsbulk[i, k] == \\\n -m.Ax * sum(m.lydot[j, k] * m.Jc[i, j] for j in m.cp_x if 0 < j <= kord_x) * m.hse[i, k]\n # elif j == kord_x:\n # return m.Hsbulk[i, j] == -m.Ax * (m.Jc[i, j] - m.Jc[i, j - 1]) * m.hse[i, j]\n else:\n return Constraint.Skip\n\n\n# else:\n# return Constraint.Skip\nmod.i4 = Constraint(mod.fe_x, mod.cp_x, rule=i4_rule)\n\n\ndef i5_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.db[i, j] == m.dbu[i, j]\n else:\n return Constraint.Skip\n\n\nmod.i5 = Constraint(mod.fe_x, mod.cp_x, rule=i5_rule)\n\n\ndef i6_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.vb[i, j] == 1.55 * ((m.vg[i, j] - m.vmf) + 14.1 * (m.db[i, j] + 0.005)) * (m.Dte ** 0.32) + m.vbr[i, j]\n else:\n return Constraint.Skip\n\n\nmod.i6 = Constraint(mod.fe_x, mod.cp_x, rule=i6_rule)\n\n\ndef i7_rule(m, i, j):\n if 0 < j <= kord_x:\n return (1 - m.emf) * (\n (m.dp ** 0.1) * (m.gc ** 0.118) * 2.05 * ((m.l[i, j]) ** 0.043)) == \\\n 2.54 * (m.mug ** 0.066) * (1. - m.ed[i, j])\n else:\n return Constraint.Skip\n\n\nmod.i7 = Constraint(mod.fe_x, mod.cp_x, rule=i7_rule)\n\n\ndef i8_rule(m, i, j):\n if 0 < j <= kord_x:\n return m.ve[i, j] * ((m.dp ** 0.568) * (m.gc ** 0.663) * (0.08518 * (m.rhos - m.rhog[i, j]) + 19.09) *\n ((m.l[i, j]) ** 0.244)) == \\\n m.vmf * 188. * 1.02 * (m.mug ** 0.371)\n else:\n return Constraint.Skip\n\n\nmod.i8 = Constraint(mod.fe_x, mod.cp_x, rule=i8_rule)\n\n\n# exchanger pressure drop\n\n\ndef e1_rule(m):\n return m.HXIn_h == -0.2831 - 2.9863e-6 * (m.HXIn_P - 1.3) + 7.3855e-05 * (m.HXIn_T - 60)\n\n\nmod.e1 = Constraint(rule=e1_rule)\n\n\n# bc_Phx\ndef e2_rule(m):\n return m.Phx_l == m.HXIn_P\n\n\nmod.e2 = Constraint(rule=e2_rule)\n\n\n# Heat-Exchanger fluid energy balance\n# pde\ndef e3_rule(m, i, j):\n if 0 < j <= kord_x:\n return 0 == (m.HXIn_F / 3600) * m.dhxh_dx[i, j] - \\\n m.hi_x[i] * 1E-6 * m.pi * m.dx * m.ht[i, j] * m.dThx[i, j] * m.Nx * m.Cr\n else:\n return Constraint.Skip\n\n\nmod.e3 = Constraint(mod.fe_x, mod.cp_x, rule=e3_rule)\n\n\n# bc_hxh\ndef e4_rule(m):\n return 0 == m.HXIn_h - m.hxh_l\n\n\nmod.e4 = Constraint(rule=e4_rule)\n\n\ndef e5_rule(m):\n return m.hsint == ((m.nin['h'] + m.nin['c']) * (m.cpgcst['h'] * m.SolidIn_T + m.dH1) +\n m.nin['c'] * (m.cpgcst['c'] * m.SolidIn_T + m.dH2) +\n m.nin['n'] * (m.cpgcst['c'] * m.SolidIn_T + m.dH3)) * 1E-3 + m.cps * m.SolidIn_T\n\n\nmod.e5 = Constraint(rule=e5_rule)\n\n\n# def e6_rule(m, i):\n# if i == 1:\n# return m.hsinb == ((m.nin['h'] + m.nin['c']) * (m.cpgcsb['h'] * m.SolidIn_T + m.dH1) +\n# m.nin['c'] * (m.cpgcsb['c'] * m.SolidIn_T + m.dH2) +\n# m.nin['n'] * (m.cpgcsb['c'] * m.SolidIn_T + m.dH3)) * 1E-3 + m.cps * m.SolidIn_T\n# else:\n# return Constraint.Skip\n\n\n# mod.e6 = Constraint(mod.fe_x, rule=e6_rule)\n\n\ndef e7_rule(m):\n # return m.P[1, 0] == 1.31514238316\n return m.GasIn_P == m.P[1, 0] + 0.034\n\n\n# def e7_rule(m):\n# return m.GasIn_P == (m.P[1, 0] - m.P_l)*0.2 + m.P[1, 0]\n#\nmod.e7 = Constraint(rule=e7_rule)\n\n\ndef e8_rule(m):\n return m.Gb[1, 0] == m.GasIn_F\n\n\nmod.e8 = Constraint(rule=e8_rule)\n\n\n# bc for tgb\ndef e9_rule(m):\n return m.Tgb[1, 0] == m.GasIn_T\n\n\nmod.e9 = Constraint(rule=e9_rule)\n\n\ndef e10_rule(m, k):\n return m.yb[1, 0, k] == m.GasIn_z[k]\n\n\nmod.e10 = Constraint(mod.sp, rule=e10_rule)\n\n\ndef x_3_rule(m):\n return m.GasOut_P == m.P_l\n\n\nmod.x_3 = Constraint(rule=x_3_rule)\n\n\n# def e11_rule(m):\n# return 1.19489515066 == m.P_l\n\n\n# for e16rule\n# mod.e11 = Constraint(rule=e11_rule)\n\n\n# # bc\ndef e12_rule(m):\n return m.Gb_l == m.GasOut_F\n\n\nmod.e12 = Constraint(rule=e12_rule)\n\n\n# # bc\ndef e13_rule(m):\n return m.GasOut_T == m.Tgb_l\n\n\nmod.e13 = Constraint(rule=e13_rule)\n\n\n# # bc\ndef e14_rule(m, j):\n return m.GasOut_z[j] == m.yb_l[j]\n\n\nmod.e14 = Constraint(mod.sp, rule=e14_rule)\n\n\n# def sit_tst(m):\n# return m.Sit == 166.67\n\n\n# mod.x_2 = Constraint(rule=sit_tst)\n\n\ndef e15_rule(m):\n return m.Sit == m.SolidIn_Fm / 3600\n\n\n# for v7\nmod.e15 = Constraint(rule=e15_rule)\n\n\ndef e16_rule(m):\n return m.SolidIn_P == m.GasOut_P\n\n\n#\n#\n# for v3rule\nmod.e16 = Constraint(rule=e16_rule)\n\n\n# mod.dummy_alp = Var(within=Reals0., initialize=10)\n\n# # bc Jc_l Je_l\ndef e20a_rule(m):\n # return m.Sit - m.Sot <= m.dummy_alp\n # return m.Sit - m.Sot - m.z_l * m.Ax >= -m.dummy_alp\n return m.Sit == m.Sot + m.z_l * m.Ax\n\n\n# def e20b_rule(m):\n# return m.Sit - m.Sot - m.z_l * m.Ax <= m.dummy_alp\n\nmod.e20a = Constraint(rule=e20a_rule)\n\n\n# mod.e20b = Constraint(rule=e20b_rule)\n# mod.e20a.deactivate()\n#\n#\n# # bc\n# def e21_rule(m):\n# if i == nfe_x:\n# return m.Je_l + m.Jc[nfe_x, kord_x] == m.Je[nfe_x, kord_x] + m.Jc_l\n# else:\n# return Constraint.Skip\n\n\n# mod.e21 = Constraint(rule=e21_rule)\n\n\n#\n#\n# # bc\n# def e22_rule(m, i):\n# # if i == m.ND:\n# if i == nfe_x:\n# return m.SolidOut_T == m.Tse[nfe_x, kord_x]\n# else:\n# return Constraint.Skip\n\n\n# # else:\n# # return Constraint.Skip\n# mod.e22 = Constraint(mod.fe_x, rule=e22_rule)\n\n\n#\n#\n# # bc\n# def e23_rule(m):\n# # if i == m.ND:\n# if i == nfe_x:\n# return m.SorbOut_F == sum(m.ne[nfe_x, kord_x, j] for j in m.sp)\n# else:\n# return Constraint.Skip\n\n\n# # else:\n# # return Constraint.Skip\n# mod.e23 = Constraint(rule=e23_rule)\n\n\n#\n#\n# # bc at the bottom of the reactor it seems\n# a16 and a17 from mingzhao's paper\n# bc_cein\n\n\n\ndef e25_rule(m, j):\n # # if i == m.ND:\n return m.ccwin_l[j] * m.Ax + m.Sit * m.nin[j] == \\\n m.cein_l[j] * m.Ax + m.Sot * m.ne_l[j]\n\n\nmod.e25 = Constraint(mod.sp, rule=e25_rule)\n\n\n# # bc\n# bc_eein\ndef e26_rule(m):\n return m.ecwin_l * m.Ax + m.Sit * m.hsint == \\\n m.eein_l * m.Ax + m.Sot * m.hse_l\n\n\nmod.e26 = Constraint(rule=e26_rule)\n\n\n# eqn_for_gasin_f\ndef v1_rule(m):\n return (m.GasIn_F / 3600) == \\\n (m.CV_1 * (m.per_opening1 / 100) * ((m.flue_gas_P - m.GasIn_P) / m.rhog_in) ** 0.5)\n\n\nmod.v1 = Constraint(rule=v1_rule)\n\n\n#\n\n\ndef v2_rule(m):\n return m.rhog_in == \\\n m.GasIn_P * 100 * (m.GasIn_z['c'] * 44.01 + m.GasIn_z['n'] * 28.01 + m.GasIn_z['h'] * 18.02) / (\n 8.314 * (m.GasIn_T + 273.16))\n\n\nmod.v2 = Constraint(rule=v2_rule)\n\n\ndef v4_rule(m):\n return m.GasOut_F / 3600 == m.CV_2 * (m.per_opening2 / 100) * ((m.GasOut_P - m.Out2_P) / m.rhog_out) ** 0.5\n\n\nmod.v4 = Constraint(rule=v4_rule)\n\n\n#\n#\ndef v5_rule(m):\n return m.rhog_out == m.GasOut_P * 100 * (\n m.GasOut_z['c'] * 44.01 + m.GasOut_z['n'] * 28.01 + m.GasOut_z['h'] * 18.02) / \\\n (8.314 * (m.GasOut_T + 273.16))\n\n\n#\n#\nmod.v5 = Constraint(rule=v5_rule)\n\n\n#\n\ndef v3_rule(m):\n return (m.SolidIn_Fm / 3600) == m.CV_3 * (m.per_opening3 / 100) * (\n (m.sorbent_P - m.SolidIn_P) / (\n 2. * m.rhos)) ** 0.5\n\n\nmod.v3 = Constraint(rule=v3_rule)\n\n\ndef __dvar1_(m, i, k, c):\n if 0 < k <= kord_x:\n return m.dcbin_dx[i, k, c] == sum(m.ldot_x[j, k] * m.cbin[i, j, c] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar2_(m, i, k, c):\n if 0 < k <= kord_x:\n return m.dcein_dx[i, k, c] == sum(m.ldot_x[j, k] * m.cein[i, j, c] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar3_(m, i, k):\n if 0 < k <= kord_x:\n return m.debin_dx[i, k] == sum(m.ldot_x[j, k] * m.ebin[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar4_(m, i, k):\n if 0 < k <= kord_x:\n return m.decwin_dx[i, k] == sum(m.ldot_x[j, k] * m.ecwin[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar5_(m, i, k):\n if 0 < k <= kord_x:\n return m.deein_dx[i, k] == sum(m.ldot_x[j, k] * m.eein[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar6_(m, i, k): #\n if 0 < k <= kord_x:\n return m.dhxh_dx[i, k] == sum(m.ldot_x[j, k] * m.hxh[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar8_(m, i, k):\n if 0 < k <= kord_x:\n return m.dP_dx[i, k] == sum(m.ldot_x[j, k] * m.P[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar9_(m, i, k): #\n if 0 < k <= kord_x:\n return m.dPhx_dx[i, k] == sum(m.ldot_x[j, k] * m.Phx[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __dvar10_(m, i, k, c):\n if 0 < k <= kord_x:\n return m.dccwin_dx[i, k, c] == sum(m.ldot_x[j, k] * m.ccwin[i, j, c] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\nmod.dvar_c_1 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=__dvar1_)\nmod.dvar_c_2 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=__dvar2_)\nmod.dvar_c_3 = Constraint(mod.fe_x, mod.cp_x, rule=__dvar3_)\nmod.dvar_c_4 = Constraint(mod.fe_x, mod.cp_x, rule=__dvar4_)\nmod.dvar_c_5 = Constraint(mod.fe_x, mod.cp_x, rule=__dvar5_)\nmod.dvar_c_6 = Constraint(mod.fe_x, mod.cp_x, rule=__dvar6_)\n#\n# mod.dvar_c_7 = Constraint(mod.fe_x, mod.cp_x, rule=__dvar7_) nop\n\nmod.dvar_c_8 = Constraint(mod.fe_x, mod.cp_x, rule=__dvar8_)\n\nmod.dvar_c_9 = Constraint(mod.fe_x, mod.cp_x, rule=__dvar9_)\nmod.dvar_c_10 = Constraint(mod.fe_x, mod.cp_x, mod.sp, rule=__dvar10_)\n\n\ndef __dvar_z_(m, i, k):\n if 0 < k <= kord_x:\n return m.dz_dx[i, k] == sum(m.ldot_x[j, k] * m.z[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\nmod.dvar_c_z = Constraint(mod.fe_x, mod.cp_x, rule=__dvar_z_)\n\n\ndef __cp1_1(m, i, c):\n if i < nfe_x:\n return m.cbin[i + 1, 0, c] == sum(m.l1_x[j] * m.cbin[i, j, c] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_2(m, i, c):\n if i < nfe_x:\n return m.cein[i + 1, 0, c] == sum(m.l1_x[j] * m.cein[i, j, c] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_3(m, i):\n if i < nfe_x:\n return m.ebin[i + 1, 0] == sum(m.l1_x[j] * m.ebin[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_4(m, i):\n if i < nfe_x:\n return m.ecwin[i + 1, 0] == sum(m.l1_x[j] * m.ecwin[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_5(m, i):\n if i < nfe_x:\n return m.eein[i + 1, 0] == sum(m.l1_x[j] * m.eein[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_6(m, i):\n if i < nfe_x:\n return m.hxh[i + 1, 0] == sum(m.l1_x[j] * m.hxh[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_8(m, i):\n if i < nfe_x:\n return m.P[i + 1, 0] == sum(m.l1_x[j] * m.P[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_9(m, i):\n if i < nfe_x:\n return m.Phx[i + 1, 0] == sum(m.l1_x[j] * m.Phx[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\ndef __cp1_10(m, i, c):\n if i < nfe_x:\n return m.ccwin[i + 1, 0, c] == sum(m.l1_x[j] * m.ccwin[i, j, c] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\n# def __cp1_11(m, i):\n# if i > 1:\n# return m.Je[i - 1, kord_x] == m.Je[i, 0]\n# else:\n# return Constraint.Skip\n\nmod.cp1_c = Constraint(mod.fe_x, mod.sp, rule=__cp1_1)\nmod.cp2_c = Constraint(mod.fe_x, mod.sp, rule=__cp1_2)\nmod.cp3_c = Constraint(mod.fe_x, rule=__cp1_3)\nmod.cp4_c = Constraint(mod.fe_x, rule=__cp1_4)\nmod.cp5_c = Constraint(mod.fe_x, rule=__cp1_5)\nmod.cp6_c = Constraint(mod.fe_x, rule=__cp1_6)\n\n# mod.cp7_c = Constraint(mod.fe_x, rule=__cp1_7)\nmod.cp8_c = Constraint(mod.fe_x, rule=__cp1_8)\n\nmod.cp9_c = Constraint(mod.fe_x, rule=__cp1_9)\nmod.cp10_c = Constraint(mod.fe_x, mod.sp, rule=__cp1_10)\n\n\ndef __cp_z_(m, i):\n if i < nfe_x:\n return m.z[i + 1, 0] == sum(m.l1_x[j] * m.z[i, j] for j in m.cp_x if j <= kord_x)\n else:\n return Constraint.Skip\n\n\nmod.cpz_c = Constraint(mod.fe_x, rule=__cp_z_)\n\n\n# mod.cp11_c = Constraint(mod.fe_x, rule=__cp1_11)\n\n\ndef __zl_1(m, c):\n return m.cbin_l[c] == sum(m.l1_x[j] * m.cbin[nfe_x, j, c] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_2(m, c):\n return m.cein_l[c] == sum(m.l1_x[j] * m.cein[nfe_x, j, c] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_3(m):\n return m.ebin_l == sum(m.l1_x[j] * m.ebin[nfe_x, j] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_4(m):\n return m.ecwin_l == sum(m.l1_x[j] * m.ecwin[nfe_x, j] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_5(m):\n return m.eein_l == sum(m.l1_x[j] * m.eein[nfe_x, j] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_6(m):\n return m.hxh_l == sum(m.l1_x[j] * m.hxh[nfe_x, j] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_8(m):\n return m.P_l == sum(m.l1_x[j] * m.P[nfe_x, j] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_9(m):\n return m.Phx_l == sum(m.l1_x[j] * m.Phx[nfe_x, j] for j in m.cp_x if j <= kord_x)\n\n\ndef __zl_10(m, c):\n return m.ccwin_l[c] == sum(m.l1_x[j] * m.ccwin[nfe_x, j, c] for j in m.cp_x if j <= kord_x)\n\n\nmod.zl_1 = Constraint(mod.sp, rule=__zl_1)\nmod.zl_2 = Constraint(mod.sp, rule=__zl_2)\nmod.zl_3 = Constraint(rule=__zl_3)\n\nmod.zl_4 = Constraint(rule=__zl_4)\nmod.zl_5 = Constraint(rule=__zl_5)\nmod.zl_6 = Constraint(rule=__zl_6) #\n\nmod.zl_8 = Constraint(rule=__zl_8)\n\nmod.zl_9 = Constraint(rule=__zl_9) #\n\nmod.zl_10 = Constraint(mod.sp, rule=__zl_10)\n\n\ndef __zl_z_(m):\n return m.z_l == sum(m.l1_x[j] * m.z[nfe_x, j] for j in m.cp_x if j <= kord_x)\n\n\nmod.zl_z = Constraint(rule=__zl_z_) #\n\n\ndef __yl_hse(m):\n return m.hse_l == sum(m.l1y[j] * m.hse[nfe_x, j] for j in m.cp_x if 0 < j <= kord_x)\n # return m.eein_l == m.Je_l * m.hse_l\n\n\ndef __yl_ne(m, c):\n return m.ne_l[c] == sum(m.l1y[j] * m.ne[nfe_x, j, c] for j in m.cp_x if 0 < j <= kord_x)\n\n\ndef __yl_gb(m):\n return m.Gb_l == sum(m.cbin_l[c] for c in m.sp) * 3600\n\n\ndef __yl_tgb(m):\n return m.ebin_l == (m.Gb_l / 3600) * m.cpg_mol * m.Tgb_l\n\n\ndef __yl_yb(m, c):\n return m.cbin_l[c] == m.yb_l[c] * m.Gb_l / 3600\n # return m.yb_l[c] == sum(m.l1y[j]*m.yb[nfe_x, j, c] for j in m.cp_x if 0 < j <= kord_x)\n\n\nmod.yl_hse = Constraint(rule=__yl_hse)\nmod.yl_ne = Constraint(mod.sp, rule=__yl_ne)\n\n# mod.yl_7 = Constraint(rule=__yl_7)\n# mod.yl_11 = Constraint(rule=__yl_11)\n\nmod.yl_gb = Constraint(rule=__yl_gb)\nmod.yl_tgb = Constraint(rule=__yl_tgb)\nmod.yl_yb = Constraint(mod.sp, rule=__yl_yb)\n\n\ndef _ic_jn(m, c):\n return m.ccwin[1, 0, c] == m.cein[1, 0, c]\n\n\nmod.jn_bc1 = Constraint(mod.sp, rule=_ic_jn)\n\n\ndef _ic_jh(m):\n return m.ecwin[1, 0] == m.eein[1, 0]\n\n\nmod.jh_bc1 = Constraint(rule=_ic_jh)\n\n\ndef _ic_jejc(m):\n return m.z[1, 0] == 0.0\n\n\nmod.jejc_bc1 = Constraint(rule=_ic_jejc)\n\n\nmod.objFunc = Objective(expr=1, sense=minimize)\n# mod.compute_statistics(active=True)\ninstance = mod.create_instance('newdta.dat')\n\nsolver = SolverFactory('asl:ipopt')\n\nsolver.options['linear_solver'] = 'ma57'\n\nsolver.options['max_iter'] = _maxiter\n\nsolver.options['print_level'] = 5\n# solver.options['halt_on_ampl_error'] = 'yes'\n# instance.hi_x.di:splay()\n# instance.l.display()\n# instance.ldot_x.display()\n# instance.lydot.display()\n# instance.l1_x.display()\n# instance.l1y.display()\ninstance.compute_statistics(active=True)\nresults = solver.solve(instance, tee=True, logfile='loghere.log', keepfiles=True)\ninstance.solutions.load_from(results)\n\n# results = solver.solve(instance, tee=True)\n# instance.solutions.load_from(results)\n\n\n\n# instance.objFunc.deactivate()\n# instance.objFunc_1.deactivate()\n# instance.objFunc_2.activate()\n#\n# results = solver.solve(instance, tee=True)\n# instance.solutions.load_from(results)\n\n\nvres = open('vresx2.dat', 'w')\n\nvres.write('lenght \\n')\nvarobj = getattr(instance, str(instance.l))\n\nlx = {}\nfor ii in varobj:\n lx[ii] = instance.l[ii]\n\nfor ii in range(1, nfe_x + 1):\n for jj in range(0, kord_x + 1):\n vres.write(str((ii, jj)))\n vres.write('\\t')\n vres.write(str(lx[(ii, jj)]))\n vres.write('\\n')\nvres.write('\\n \\n')\n# ic\n# ebin\n# cbin\n# P\n\n# hxh\n# Phx_l\n# instance.cbt.pprint()\n# bc\n# cein\n# eein\n\n# ic\n# ccwin\n# ecwin\n\n# instance.pprint()\n# instance.cp_x.pprint()\n\n\nfor v in instance.component_objects(Var, active=True):\n vres.write(\"Variable \" + str(v))\n vres.write('\\n')\n varobject = getattr(instance, str(v))\n for index in varobject:\n vres.write('\\t' + str(index) + '\\t' + str(varobject[index].value))\n vres.write('\\n')\n\nvres.write('\\n\\n\\n')\nvres.write('=====================================')\nvres.write('\\n\\n\\n')\n\nfor v in instance.component_objects(Constraint, active=True):\n vres.write(\"Constraint \" + str(v))\n vres.write('\\n')\n varobject = getattr(instance, str(v))\n for index in varobject:\n fulls = str(index) + '\\t' + \\\n '\\t' + str(varobject[index].lower) + \\\n '\\t' + str(varobject[index].body()) + \\\n '\\t' + str(varobject[index].upper)\n vres.write(fulls)\n vres.write('\\n')\n\nvres.close()\n\nvres.close()\n\nrex = open('pprof.dat', 'w')\n\nrex.write('\\n x \\n')\nfor ii in range(1, nfe_x + 1):\n for jj in range(0, kord_x + 1):\n rex.write(str(lx[(ii, jj)]))\n rex.write('\\n')\nrex.write('\\n \\n')\n\n\ndef dvar_get_and_print(inst, name, file):\n varobj = getattr(inst, str(name))\n dat_x = {}\n for i in varobj:\n dat_x[i] = name[i].value\n ilen = len(i)\n whos = '\\n' + str(name) + '\\n'\n file.write(whos)\n if ilen == 3:\n for c in ['h', 'c', 'n']:\n file.write('\\n' + c + '\\n')\n for i in varobj:\n if i[2] == c:\n val = str(dat_x[i])\n # file.write('\\n' + 'h' + '\\n')\n file.write(val)\n file.write('\\n')\n else:\n for i in varobj:\n val = str(dat_x[i])\n file.write(val)\n file.write('\\n')\n file.write('\\n\\n')\n\n\n# dvar_get_and_print(instance, instance.l, rex)\ndvar_get_and_print(instance, instance.cbin, rex)\ndvar_get_and_print(instance, instance.cein, rex)\ndvar_get_and_print(instance, instance.ebin, rex)\ndvar_get_and_print(instance, instance.ecwin, rex)\ndvar_get_and_print(instance, instance.eein, rex)\ndvar_get_and_print(instance, instance.P, rex)\ndvar_get_and_print(instance, instance.Phx, rex)\ndvar_get_and_print(instance, instance.ccwin, rex)\ndvar_get_and_print(instance, instance.Jc, rex)\ndvar_get_and_print(instance, instance.Je, rex)\ndvar_get_and_print(instance, instance.Tgb, rex)\ndvar_get_and_print(instance, instance.Tse, rex)\ndvar_get_and_print(instance, instance.Tsc, rex)\ndvar_get_and_print(instance, instance.yb, rex)\ndvar_get_and_print(instance, instance.Gb, rex)\nrex.close()\n\nics = open('ics.dat', 'w')\ndvar_get_and_print(instance, instance.fcw, ics)\ndvar_get_and_print(instance, instance.delta, ics)\ndvar_get_and_print(instance, instance.ed, ics)\ndvar_get_and_print(instance, instance.cb, ics)\ndvar_get_and_print(instance, instance.cbt, ics)\ndvar_get_and_print(instance, instance.Tgb, ics)\ndvar_get_and_print(instance, instance.cc, ics)\ndvar_get_and_print(instance, instance.cct, ics)\ndvar_get_and_print(instance, instance.Tgc, ics)\n\ndvar_get_and_print(instance, instance.ce, ics)\ndvar_get_and_print(instance, instance.cet, ics)\ndvar_get_and_print(instance, instance.Tge, ics)\n\ndvar_get_and_print(instance, instance.nc, ics)\ndvar_get_and_print(instance, instance.Tsc, ics)\ndvar_get_and_print(instance, instance.ne, ics)\ndvar_get_and_print(instance, instance.Tse, ics)\n# instance.display()\n# instance.pprint()\n# instance.display()\n\n# instance.hi.display()\n# instance.pprint()/V/VA?\n# instance.statistics\n\nfor block in instance.block_data_objects(active=True):\n for data in instance.component_map(Var, active=True).itervalues():\n instance.statistics.number_of_variables += len(data)\n for data in instance.component_map(Objective, active=True).itervalues():\n instance.statistics.number_of_objectives += len(data)\n for data in instance.component_map(Constraint, active=True).itervalues():\n instance.statistics.number_of_constraints += len(data)\n\nprint(instance.statistics)\n\nprot_reg = open('proto_res.py', 'w')\n\n\n\n# for v in instance.component_objects(Var, active=True):\ndef _wp_ig_2_i(v, who_ins, nfe_x, kord_x, aeflag):\n # v = 'P'\n i0 = 0\n if aeflag:\n i0 = 1\n\n name = str(v) + '_0r'\n prot_reg.write(name + ' = {}\\n')\n varobjx = getattr(who_ins, str(v))\n for i in range(1, nfe_x+1):\n for j in range(i0, kord_x+1):\n index = (i, j)\n valx = varobjx[index].value\n prot_reg.write(name + '[' + str(index) + '] = ' + str(valx) + '\\n')\n\n\ndef _wp_ig_3_i(v, who_ins, nfe_x, kord_x, aeflag):\n # v = 'P'\n i0 = 0\n if aeflag:\n i0 = 1\n varobjx = getattr(who_ins, str(v))\n sp = ['h', 'c', 'n']\n for c in sp:\n name = str(v) + '_' + c + '_0r'\n prot_reg.write(name + ' = {}\\n')\n for i in range(1, nfe_x+1):\n for j in range(i0, kord_x+1):\n indexc = (i, j, c)\n index = (i, j)\n valx = varobjx[indexc].value\n prot_reg.write(name + '[' + str(index) + '] = ' + str(valx) + '\\n')\n\n\ndef _is_1_2_3(v, who_ins):\n\n vobjx = getattr(who_ins, str(v))\n for i in vobjx._index:\n # print(i)\n break\n # lst = ['HXIn_h', 'GasIn_P', 'GasIn_F', 'GasOut_P', 'GasOut_F', 'GasOut_T', 'SolidIn_Fm', 'SolidIn_P', 'SolidOut_P',\n # 'rhog_in', 'rhog_out', 'DownOut_P', 'h_downcomer', 'hsint', 'vmf', 'db0', 'Sit', 'Sot', 'g1', 'z_l', 'ebin_l',\n # 'ecwin_l', 'eein_l', 'hxh_l', 'P_l', 'Phx_l', 'hse_l', 'Gb_l', 'Tgb_l', 'yb_l'\n # ]\n # print(str(v))\n\n if i == None:\n return 0\n else:\n for i in vobjx:\n if len(i) == 1:\n return 1\n elif len(i) == 2:\n return 2\n elif len(i) == 3:\n return 3\n elif len(i) == 0:\n return 0\n else:\n return 99\n break\n\n\nfor v in instance.component_objects(Var, active=True):\n k = _is_1_2_3(v, instance)\n dv_l = ['cbin', 'cein', 'ebin', 'ecwin', 'eein', 'hxh', 'z', 'P', 'Phx', 'ccwin']\n dv_flag = True\n if str(v) in dv_l:\n dv_flag = False\n\n if k == 2:\n _wp_ig_2_i(v, instance, nfe_x, 3, dv_flag)\n if k == 3:\n _wp_ig_3_i(v, instance, nfe_x, 3, dv_flag)\n else:\n continue\n\n\n\n\nprot_reg.close()\n\n# for i in instance.HXIn_h._index:\n# print(i)\n\nfile_dat = open('dmatlab.m', 'w')\nspl = ['h', 'c', 'n']\ndummy1 = 0\nfor v in instance.component_objects(Var, active=True):\n vobj = getattr(instance, str(v))\n file_dat.write('\\n\\n')\n # if dummy1 > 0:\n\n name = str(v)\n\n for i in vobj._index:\n if i == None:\n k = 0\n else:\n k = len(i)\n if k == 0:\n continue\n if k == 1:\n print(k, v)\n elif k == 2:\n file_dat.write(name + ' = [' + '\\n')\n for ix in range(1, nfe_x + 1):\n for jx in range(1, kord_x + 1):\n idx = (ix, jx)\n valx = vobj[idx].value\n stidx = ''\n for elm in idx:\n stidx += str(elm) + ' '\n # stidx = str(ix) + ' ' + str(jx) + '\\t'\n file_dat.write('\\t' + str(valx) + '\\n')\n dummy1 += 1\n file_dat.write('];\\n')\n elif k == 3:\n for c in spl:\n file_dat.write(name + '_' + c + ' = [' + '\\n')\n for it in range(1, nfe_x + 1):\n for jt in range(1, kord_x + 1):\n idx = (it, jt, c)\n valx = vobj[idx].value\n stidx = ''\n for elm in idx:\n stidx += str(elm) + ' '\n # stidx = str(ix) + ' ' + str(jx) + '\\t'\n file_dat.write('\\t' + str(valx) + '\\n')\n dummy1 += 1\n file_dat.write('];\\n')\nimport time\nfile_dat.write('% written ' + str(dummy1) + ' vars')\nfile_dat.write(\"% Current time \" + time.strftime(\"%X\") + '\\n\\n\\n')\n\nfile_dat.write('lx' + ' = [' + '\\n')\nfor ix in range(1, nfe_x + 1):\n for jx in range(1, kord_x + 1):\n idx = (ix, jx)\n valx = instance.l[idx]\n stidx = ''\n # stidx = str(ix) + ' ' + str(jx) + '\\t'\n file_dat.write('\\t' + str(valx) + '\\n')\nfile_dat.write('];\\n')\nfile_dat.close()\n\n\n\n\nfile_dat = open('dmatlab.m', 'w')\nspl = ['h', 'c', 'n']\ndummy1 = 0\nfor v in instance.component_objects(Var, active=True):\n vobj = getattr(instance, str(v))\n file_dat.write('\\n\\n')\n # if dummy1 > 0:\n\n name = str(v)\n\n for i in vobj._index:\n if i == None:\n k = 0\n else:\n k = len(i)\n if k == 0:\n continue\n if k == 1:\n print(k, v)\n elif k == 2:\n file_dat.write(name + ' = [' + '\\n')\n for ix in range(1, nfe_x + 1):\n for jx in range(1, kord_x + 1):\n idx = (ix, jx)\n valx = vobj[idx].value\n stidx = ''\n for elm in idx:\n stidx += str(elm) + ' '\n # stidx = str(ix) + ' ' + str(jx) + '\\t'\n file_dat.write('\\t' + str(valx) + '\\n')\n dummy1 += 1\n file_dat.write('];\\n')\n elif k == 3:\n for c in spl:\n file_dat.write(name + '_' + c + ' = [' + '\\n')\n for it in range(1, nfe_x + 1):\n for jt in range(1, kord_x + 1):\n idx = (it, jt, c)\n valx = vobj[idx].value\n stidx = ''\n for elm in idx:\n stidx += str(elm) + ' '\n # stidx = str(ix) + ' ' + str(jx) + '\\t'\n file_dat.write('\\t' + str(valx) + '\\n')\n dummy1 += 1\n file_dat.write('];\\n')\nimport time\nfile_dat.write('% written ' + str(dummy1) + ' vars')\nfile_dat.write(\"% Current time \" + time.strftime(\"%X\") + '\\n\\n\\n')\n\nfile_dat.write('lx' + ' = [' + '\\n')\nfor ix in range(1, nfe_x + 1):\n for jx in range(1, kord_x + 1):\n idx = (ix, jx)\n valx = instance.l[idx]\n stidx = ''\n # stidx = str(ix) + ' ' + str(jx) + '\\t'\n file_dat.write('\\t' + str(valx) + '\\n')\nfile_dat.write('];\\n')\nfile_dat.close()","sub_path":"pyomomod/ocoll/ss_r_1.py","file_name":"ss_r_1.py","file_ext":"py","file_size_in_byte":105724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"444350629","text":"import pytest\r\nimport time\r\nimport sys\r\nfrom os.path import dirname, abspath\r\nsys.path.insert(0, dirname(dirname(abspath(__file__))))\r\nfrom page_obj.scg.scg_def_nat import *\r\nfrom page_obj.scg.scg_def_bridge import *\r\nfrom page_obj.scg.scg_def_nat_modify import *\r\nfrom page_obj.common.rail import *\r\nfrom page_obj.scg.scg_def_physical_interface import *\r\nfrom page_obj.common.ssh import *\r\nfrom page_obj.scg.scg_def_dhcp import *\r\nfrom page_obj.scg.scg_dev import *\r\nfrom page_obj.scg.scg_def_ifname_OEM import *\r\nfrom page_obj.common.ssh import *\r\nfrom page_obj.scg.scg_dev import *\r\nfrom page_obj.scg.scg_def import *\r\n\r\ntest_id = 142624\r\ndef test_c142624(browser):\r\n\ttry:\r\n\t\tlogin_web(browser, url=dev3)\r\n\r\n\t\t# 在10.2.2.83上添加maplist\r\n\t\tfor x in range(1, 6):\r\n\t\t\tadd_maplist_wxw(browser, name='maplist_jia_'+str(x), desc='', save1='yes', cancel='no',\r\n\t\t\t\t\t\t\toriipfrom='192.168.'+str(x)+\".1\", oriipto='192.168.'+str(x)+\".20\",\r\n\t\t\t\t\t\ttransipfrom='192.169.'+str(x)+\".1\", transipto='192.169.'+str(x)+\".20\", one_to_one_mapping=\"no\",\r\n\t\t\t\t\t\t\tsticky='no', portfrom='1', portto='65535', save2=\"yes\", cance2='no')\r\n\r\n\t\t# 添加snat\r\n\t\tadd_snat(browser, name=\"snat_jia_1\", desc=\"\", src_inter_zone=\"Z:any\", des_inter_zone=\"Z:any\",\r\n\t\t\t\t other_match_switch=\"yes\", src_ipadd_switch=\"预定义\", srcaddress_predefine=\"A:any\", trans_local_ip=\"yes\",\r\n\t\t\t\t other_action_nomap='no', other_action_maplist=\"maplist_jia_1\", save='yes')\r\n\t\t# 编辑maplist\r\n\t\tedit_maplist_by_name_jyl(browser, name=\"maplist_jia_1\", oriipfrom=\"13.1.1.0\", oriipto=\"13.1.1.20\",\r\n\t\t\t\t\t\t\t one_to_one_mapping=\"no\", transipfrom=\"34.1.1.0\", transipto=\"34.1.1.20\", new_item=\"yes\")\r\n\r\n\t\t# 在10.2.2.81上配置去往84的路由\r\n\t\ta = Shell_SSH()\r\n\t\ta.connect(dev1)\r\n\t\ta.execute(\"en\")\r\n\t\ta.execute(\"conf t\")\r\n\t\ta.execute(\"ip route 34.1.1.0/24 gateway 13.1.1.3\")\r\n\t\ta.execute(\"exit\")\r\n\t\ta.close()\r\n\r\n\t\ta = Shell_SSH()\r\n\t\ta.connect(dev1)\r\n\t\ta.execute(\"en\")\r\n\t\ta.execute(\"ping 34.1.1.4\")\r\n\t\ttime.sleep(4)\r\n\t\tresult1 = a.output()\r\n\t\ta.close()\r\n\t\tprint(result1)\r\n\r\n\t\t# 删除10.2.2.83snat\r\n\t\tdel_snat_byname(browser, name=\"snat_jia_1\")\r\n\t\t# 删除10.2.2.83maplist\r\n\t\tfor x in range(1, 6):\r\n\t\t\tdel_maplist_by_name_jyl(browser, name=\"maplist_jia_\"+str(x))\r\n\r\n\t\t# 在10.2.2.81上删除去往84的路由\r\n\t\ta = Shell_SSH()\r\n\t\ta.connect(dev1)\r\n\t\ta.execute(\"en\")\r\n\t\ta.execute(\"conf t\")\r\n\t\ta.execute(\"no ip route 34.1.1.0/24 gateway 13.1.1.3\")\r\n\t\ta.execute(\"exit\")\r\n\t\ta.close()\r\n\r\n\r\n\texcept Exception as err:\r\n\t\t# 如果上面的步骤有报错，重新设备，恢复配置\r\n\t\tprint(err)\r\n\t\t# reload(hostip=[dev1, dev3])\r\n\t\trail_fail(test_run_id, test_id)\r\n\t\tassert False\r\n\r\n\ttry:\r\n\t\tassert \"ms\" in result1\r\n\t\trail_pass(test_run_id, test_id)\r\n\texcept:\r\n\t\trail_fail(test_run_id, test_id)\r\n\t\tassert \"ms\" in result1\r\n\r\n\r\nif __name__ == '__main__':\r\n\tpytest.main([\"-v\", \"-s\", \"test_c\" + str(test_id) + \".py\"])\r\n","sub_path":"pyautoTest-master（ICF-7.5.0）/test_case/scg/scg_Nat_modify/test_c142624.py","file_name":"test_c142624.py","file_ext":"py","file_size_in_byte":2859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"340660882","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport semantic_version.django_fields\nimport forge.models\nimport forge.storage\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Author',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(unique=True, max_length=64)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Module',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=128, db_index=True)),\n ('desc', models.TextField(db_index=True, blank=True)),\n ('tags', models.TextField(db_index=True, blank=True)),\n ('author', models.ForeignKey(to='forge.Author')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Release',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('version', semantic_version.django_fields.VersionField(max_length=200, db_index=True)),\n ('tarball', models.FileField(storage=forge.storage.ForgeStorage(), upload_to=forge.models.tarball_upload)),\n ('module', models.ForeignKey(related_name='releases', to='forge.Module')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.AlterUniqueTogether(\n name='release',\n unique_together=set([('module', 'version')]),\n ),\n migrations.AlterUniqueTogether(\n name='module',\n unique_together=set([('author', 'name')]),\n ),\n ]\n","sub_path":"forge/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"540115347","text":"from V2.NetworkingConstants import MAX_CONNECTIONS, IP, PORT\nfrom V2.Logger.Logger import Logger\nfrom V2.Server.ServerNetworking import ServerNetworking\nfrom V2.Problem.Pi import pi_sum\nfrom multiprocessing.pool import ThreadPool\nfrom decimal import *\nimport threading\nimport socket\nimport time\nimport asyncio\n\nglobal batchLock\nglobal currentBatch\nglobal results\nglobal resLock\n\n\ndef server_loop(reader, writer, exit_signal, logger, identification, batch_size, problem_type):\n net = ServerNetworking(reader, writer)\n\n\nclass ServerThread(threading.Thread):\n def __init__(self, reader, writer, exit_signal, logger, identification, batch_size, problem_type):\n super().__init__()\n self.net = ServerNetworking(reader, writer)\n self.exit_signal = exit_signal\n self.logger = logger\n self.identification = identification\n self.batchSize = batch_size\n self.problem_type = problem_type\n print(\"New Socket Thread Started For: {0}:{1}\".format(str(ip), str(port)))\n logger.write_connect(identification, ip)\n\n def run(self) -> None:\n first_contact = True\n while not self.exit_signal.is_set():\n data = self.net.receive_data()\n\n # TODO implement this in Server Networking\n if data is None or not self.net.is_connected():\n self.logger.write_disconnected(self.identification, self.net.port)\n print(\"Client Disconnected\")\n exit(0)\n\n print(\"Received Data:\", data)\n self.logger.write_recv(self.identification, data)\n\n # client is ready to start its next task\n if data == \"READY\":\n if first_contact:\n first_contact = False\n self.net.send_str(str(self.problem_type))\n continue\n\n calc_range = 0\n # get the current batch that we should calculate\n global batchLock\n global currentBatch\n batchLock.acquire()\n calc_range = currentBatch\n currentBatch += 1\n batchLock.release()\n\n calc_range = (calc_range * self.batchSize, (calc_range + 1) * self.batchSize)\n self.net.send_str(\"{0} {1}\".format(calc_range[0], calc_range[1]))\n\n else:\n # We have received some results\n if isinstance(data, list):\n global resLock\n global results\n resLock.acquire()\n if self.problem_type == 0:\n results.extend(data)\n print(\"Current Results Last 10:\", results[-10:])\n else:\n results += pi_sum(True, data)\n print(\"{0:.50f}\".format(results))\n # print(results)\n resLock.release()\n\n\nasync def main():\n # server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n # server.bind((IP, PORT))\n threads = []\n exit_signal = threading.Event()\n\n global batchLock\n batchLock = threading.Lock()\n\n global currentBatch\n currentBatch = 0\n\n global resLock\n resLock = threading.Lock()\n\n batch_size = 100\n\n print_bars()\n print(\"\\t0:\\tPrime Calculator\")\n print(\"\\t1:\\tPi Calculator\")\n print_bars()\n\n global results\n problemType = int(input(\"Select the kind of problem to solve (default 0):\\t\"))\n logger = None\n if problemType == 0:\n logger = Logger(\"Prime Server\")\n results = list()\n else:\n logger = Logger(\"Pi Server\")\n results = 3\n\n try:\n server = await asyncio.start_server(lambda reader, writer: server_loop(reader, writer, exit_signal, logger, 0, batch_size, problemType),\n IP, PORT)\n\n\n\n\n\n\n\n # server_id = 0\n # server.listen(4)\n # while len(threads) < MAX_CONNECTIONS:\n # (conn, (ip, port)) = server.accept()\n # temp_thread = ServerThread(ip, port, conn, exit_signal, logger, server_id, batch_size, problemType)\n # temp_thread.start()\n # threads.append(temp_thread)\n # server_id += 1\n # while True:\n # time.sleep(.5)\n\n except KeyboardInterrupt:\n print(\"Keyboard Interrupt Received\")\n exit_signal.set()\n time.sleep(1)\n logger.close_logger()\n for thread in threads:\n thread.join()\n print(\"Our final results are as follows\")\n print(results)\n\n for t in threads:\n t.join()\n\n\ndef print_bars():\n for i in range(0, 80):\n print(\"=\", end='')\n print(\"\")\n\n\nif __name__ == '__main__':\n asyncio.run(main())","sub_path":"V2-async/Server/Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":4810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"273514048","text":"count = 2\r\narray = []\r\nwhile True:\r\n numSum = 0\r\n for x in range(0,len(str(count))):\r\n numSum += (int(str(count)[x])**5) \r\n if numSum == count:\r\n array.append(count)\r\n count += 1\r\n if count == 195000:\r\n print(\"The total sum is\",sum(array))\r\n break\r\n\r\n\r\n\r\n","sub_path":"Project Euler Problem 30.py","file_name":"Project Euler Problem 30.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"151425128","text":"#!/home/ljsalvatierra/virtualenvs/mitk/bin/python\n# coding=utf-8\n\nfrom __future__ import print_function\nimport logging\nimport sys\ntry:\n # Append to 'sys.path' your MITK-SimpleITK module path\n sys.path.append('/home/ljsalvatierra/bin/MITK-build/ep/lib/python2.7/site-packages/SimpleITK-0.8.1-py2.7-linux-x86_64.egg')\n import SimpleITK as sitk\nexcept ImportError:\n raise ImportError(\"No module named SimpleITK.\")\n\n#LOGGING_LVL = logging.DEBUG\nLOGGING_LVL = logging.INFO\nlogging.basicConfig(level=LOGGING_LVL)\nlog = logging.getLogger(__name__)\n\n\ndef get_image(path):\n log.info(\"Loading Raw image:\\n{}.\".format(path))\n img1 = sitk.ReadImage(path)\n img2 = sitk.GradientMagnitude(img1)\n return img2\n\n\ndef test_get_image_dimension(path):\n img = get_image(path)\n return img.GetDimension()\n\n\ndef image_processing(img):\n return sitk.GradientMagnitude(img)\n\ndef image_processing_from_path(src_path, dst_path):\n img1 = sitk.ReadImage(src_path)\n img2 = sitk.GradientMagnitude(img1)\n sitk.WriteImage(img2, dst_path)\n\n\ndef main(path, option):\n if option == 1:\n test_get_image_dimension(path)\n else:\n log.info(\"Wrong option number. Default '1'.\")\n\n\nif __name__ == '__main__':\n main('', 0)\n","sub_path":"python/mitk_python_images/mitk_python_images/raw_images.py","file_name":"raw_images.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"117749310","text":"import numpy as np\nimport random\nimport scipy.optimize as opt\nimport scipy.interpolate as int\nimport pdb\nimport sys\n\nsys.path.insert(0, \"C:\\\\Users\\\\utabo\\\\Documents\\\\GitHub\\\\BootCamp2019\\\\ProblemSets\\\\ECON\\\\Week 5\")\nimport parameters as p\n#====================================================#\n#Problem set - Tony Smith\n#====================================================#\n\n#Get interest rate\ndef get_r(kbar,u,p):\n r=p.alpha*kbar**(p.alpha-1)*(1-u)**(1-p.alpha)\n return r\n\n#get wage rate\ndef get_k(kbar,u,p):\n w=(1-p.alpha)*kbar**p.alpha*(1-u)**(-p.alpha)\n return w\ndef margu(c):\n marg=1/c\n return marg\n#euler equation:\ndef utility(x):\n if x>0:\n utils=np.log(x)\n else:\n utils=-10**7\n pdb.set_trace()\n return utils\n\n\ndef negV(kprime,Vcont,eps,p,tt,r,w,kk) :\n Vprime=np.empty([2,1])\n #Vprime[0,0]=np.interp(p.kgrid,Vcont[:,0], kprime) #good\n if tt>0:\n print(\"round 2\")\n pdb.set_trace()\n Vprime[0,0]=int.interp1d(p.kgrid,Vcont[:,0], kprime) #good\n Vprime[1,0]=int.interp1d(p.kgrid,Vcont[:,1], kprime)#bad\n\n else:\n Vprime=np.array([0][0])\n c=(1+r-p.delta)*kk+w*eps-kprime\n EV=sum(Vprime*p.pi) #expected value given transition probs\n value=-1*(utility(c)+p.beta*EV) #negative value of choosing kprime\n print(value)\n return value\n\n#Problem that the consumer solves given kbar:\ndef vfi(kbar,p):\n pf=np.empty([p.numptsk, 2])\n vopt=np.empty([p.numptsk, 2])\n r=get_r(kbar,u,p)\n print(r)\n w=get_k(kbar,u,p)\n print(w)\n for tt in range(0,p.maxiter):\n for ixe, eps in enumerate(p.epsgrid):\n for ixk, kk in enumerate(p.kgrid):\n res= opt.minimize_scalar(negV,bracket=(0.00001,40.0),args=(Vcont,eps,p,tt,r,w,kk), method='Golden' )\n vopt[ixk,ixe] =-res.fun\n pf[ixk,ixe] =res.x\n diff=((vopt-Vcont) ** 2).sum()\n print(vopt)\n pdb.set_trace()\n if diff>p.tol:\n vopt=Vcont\n else:\n print(\"convergence achieved\")\n epsseries=np.zeros(p.numsim)\n indk=np.zeros(p.numsim)\n #once we found a solution we want to simulate:\n np.random.seed(23423948)\n rv=np.random.uniform(0,1,p.numsim)\n epsseries=(rv>0.9)\n #for x in range(p.numsim):\n # epsseries[x]=random.randint(0,1)\n kss=0.001\n for ixN, epsilon in enumerate(epsseries):\n print(epsseries)\n print(pf[:,0])\n print(pf[:,epsilon])\n indk[ixN]=int.interp1d(p.kgrid, pf[:,epsilon],kss)\n #indk[ixN]=np.interp(p.kgrid, pf[:,epsilon],kss)\n kss=np.mean(indk)\n break\n return vopt, pf,kss\n\n\ndef aiyagari(kbar,p):\n for j in range(0,p.maxiter):\n vopt, pf,kss=vfi(kbar,p)\n if abs(kss-kbar)>p.tol:\n kss=kbar\n else:\n print(\"convergence achieved\")\n break\n return kss\n#\n# define k grid\n\nu=0.1\n#initialize Vcont\nVcont=np.zeros([p.numptsk,2])\naiyagari(1,p)\n","sub_path":"ProblemSets/ECON/Week 5/JUNK/Het_agents.py","file_name":"Het_agents.py","file_ext":"py","file_size_in_byte":3084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"121685219","text":"# -*- coding: utf-8 -*-\n\"\"\" Common func for rnns\n\"\"\"\nfrom tensorflow.contrib import layers\n\n\ndef get_shape(x):\n \"\"\" used for (batchsize, n_dim, n_step)\n \"\"\"\n x_shape = x.get_shape().as_list()\n if len(x_shape) == 4:\n # for NHWC data\n batchsize = x_shape[0]\n n_dim = x_shape[1] * x_shape[2]\n n_step = x_shape[3]\n elif len(x_shape) == 3:\n # for N,Sequence,C\n batchsize = x_shape[0]\n n_dim = x_shape[1]\n n_step = x_shape[2]\n else:\n raise ValueError('Unknown input shape.')\n return batchsize, n_dim, n_step\n\n\ndef inner_product(x, num_output, scope=\"linear\"):\n return layers.fully_connected(\n inputs=x,\n num_outputs=num_output,\n activation_fn=None,\n weights_initializer=layers.xavier_initializer(),\n scope=scope)\n","sub_path":"core/network/rnn/ops.py","file_name":"ops.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"378545646","text":"# \"\"\"\n# This is the interface that allows for creating nested lists.\n# You should not implement it, or speculate about its implementation\n# \"\"\"\n#class NestedInteger:\n# def isInteger(self) -> bool:\n# \"\"\"\n# @return True if this NestedInteger holds a single integer, rather than a nested list.\n# \"\"\"\n#\n# def getInteger(self) -> int:\n# \"\"\"\n# @return the single integer that this NestedInteger holds, if it holds a single integer\n# Return None if this NestedInteger holds a nested list\n# \"\"\"\n#\n# def getList(self) -> [NestedInteger]:\n# \"\"\"\n# @return the nested list that this NestedInteger holds, if it holds a nested list\n# Return None if this NestedInteger holds a single integer\n# \"\"\"\n\nclass NestedIterator:\n temp_list = []\n def dfs(self, n_list):\n result = []\n if n_list.isInteger():\n result.append(n_list.getInteger())\n else:\n if n_list.getInteger() != None:\n result.append(n_list.getInteger())\n for nest in n_list.getList():\n result.extend(self.dfs(nest))\n return result\n \n def __init__(self, nestedList: [NestedInteger]):\n self.temp_list = []\n for nest in nestedList:\n if nest.isInteger():\n self.temp_list.append(nest.getInteger())\n else:\n self.temp_list.extend(self.dfs(nest))\n \n \n def next(self) -> int:\n return self.temp_list.pop(0)\n \n def hasNext(self) -> bool:\n if len(self.temp_list) == 0:\n return False\n else:\n return True\n\n# Your NestedIterator object will be instantiated and called as such:\n# i, v = NestedIterator(nestedList), []\n# while i.hasNext(): v.append(i.next())","sub_path":"leetcode/Flatten-Nested-List-Iterator/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"60608574","text":"#python3 oov.py input_file.token wordlist_file [output_file]\nimport sys\ndef count_oov(file,wordlist):\n tokens=set()\n with open(file) as input:\n for line in input:\n tokens.add(line)\n oovtokens=tokens.copy()\n with open(wordlist) as voc:\n for line in voc:\n if line in oovtokens:\n oovtokens.remove(line)\n return oovtokens\ndef write_result(oovtokens):\n if len(sys.argv)==4:\n output=open(sys.argv[3],\"w+\")\n else:\n output=sys.stdout\n output.write(str(len(oovtokens))+\"\\n\")\n for t in oovtokens:\n output.write(t[:-1]+\"\\n\")\n output.close()\n\noovtokens=count_oov(sys.argv[1],sys.argv[2])\nwrite_result(oovtokens)","sub_path":"src/oov.py","file_name":"oov.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"561021177","text":"import sys\nimport cv2\nimport os\nfrom sys import platform\nimport argparse\nimport time\nfrom cv_boundingbox import getBoundingBox\nfrom keras.models import model_from_json\nimport numpy as np\nfrom queue import Queue\nfrom angles_from_body25 import Geometry_Body25\n\n\n# Import Openpose (Windows/Ubuntu/OSX)\ndir_path = os.path.dirname(os.path.realpath(__file__))\ntry:\n # Windows Import\n if platform == \"win32\":\n # Change these variables to point to the correct folder (Release/x64 etc.) \n sys.path.append(dir_path + '/../../../python/openpose/Release');\n os.environ['PATH'] = os.environ['PATH'] + ';' + dir_path + '/../../../x64/Release;' + dir_path + '/../../../bin;'\n import pyopenpose as op\n else:\n # Change these variables to point to the correct folder (Release/x64 etc.) \n sys.path.append('../../../python');\n # If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the OpenPose/python module from there. This will install OpenPose and the python library at your desired installation path. Ensure that this is in your python path in order to use it.\n # sys.path.append('/usr/local/python')\n from openpose import pyopenpose as op\nexcept ImportError as e:\n print('Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python script in the right folder?')\n raise e\n\n# Flags\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--dev_mode\", action='store_true', help=\"Enter in developer mode\")\nparser.add_argument(\"--df\", default=\"./datasets\", help=\"Asks for the path folder where the user wants to store the dataset\")\nargs = parser.parse_known_args()\n\n# Custom Params (refer to include/openpose/flags.hpp for more parameters)\nparams = dict()\nparams[\"model_folder\"] = \"../../../../models/\"\nparams[\"number_people_max\"] = 1\n\n# Add others in path\nfor i in range(0, len(args[1])):\n curr_item = args[1][i]\n if i != len(args[1])-1: next_item = args[1][i+1]\n else: next_item = \"1\"\n if \"--\" in curr_item and \"--\" in next_item:\n key = curr_item.replace('-','')\n if key not in params: params[key] = \"1\"\n elif \"--\" in curr_item and \"--\" not in next_item:\n key = curr_item.replace('-','')\n if key not in params: params[key] = next_item\n\n\ndef getNameFromClass(id):\n if id == 0:\n return \"Baptiste\"\n elif id == 1:\n return \"Henri\"\n elif id == 2:\n return \"Jerome\"\n elif id == 3:\n return \"Lucas\"\n elif id == 4:\n return \"Nicolas\"\n else:\n return \"Unknown\"\n\n\n# Starting OpenPose\nopWrapper = op.WrapperPython()\nopWrapper.configure(params)\nopWrapper.start()\n\n\n\n# Load model\njson_file = open('./models/model3.json', 'r')\nloaded_model_json = json_file.read()\njson_file.close()\nmodel = model_from_json(loaded_model_json)\nmodel.load_weights(\"./models/model3.h5\")\n\nfont = cv2.FONT_HERSHEY_SIMPLEX\n\nargs = args[0].__dict__\n\n# dataset generation variables\nif \"dev_mode\" in args.keys():\n isInDevMode = args[\"dev_mode\"]\nelse:\n isInDevMode = False\n\nif isInDevMode and \"df\" in args.keys():\n dataset_manager = DatasetManager(args[\"df\"])\nelse:\n dataset_manager = None\n\ntime_since_lastaction = 0\n\ngeometries = Queue(maxsize=15)\ncurrentPerson = None\ncurrentPersonNumber = 0\nproba_cum = 0\nwho = \"\"\nproba_pred = -1\ngeometry = Geometry_Body25()\n\nif \"ip_camera\" in params.keys():\n cap = cv2.VideoCapture(params[\"ip_camera\"])\nelif \"video\" in params.keys():\n cap = cv2.VideoCapture(params[\"video\"])\nelse:\n cap = cv2.VideoCapture(0)\n\n\nwhile(cap.isOpened()):\n if isInDevMode and isSavingData and len(dataset) == 100:\n dataset_manager.save(dataset)\n dataset = []\n\n start = time.time()\n\n # Capture frame-by-frame\n try:\n ret, frame = cap.read()\n # Process Image\n datum = op.Datum()\n datum.cvInputData = frame\n opWrapper.emplaceAndPop([datum])\n except:\n break\n\n #Get keypoints\n kp = datum.poseKeypoints\n \n #Add skeleton\n frame = datum.cvOutputData\n\n try:\n\n index = 0\n angles = geometry.getAngles(kp[index,:,:])\n distances = geometry.getDistances(kp[index,:,:])\n\n\n if geometries.full():\n geometries.get()\n geometries.put(np.append(angles,distances))\n \n if(geometries.full()):\n _input = np.asarray([list(geometries.queue)])\n predictions = model.predict(_input)\n\n if getNameFromClass(np.argmax(predictions)) != currentPerson:\n currentPerson = getNameFromClass(np.argmax(predictions))\n currentPersonNumber = 1\n proba_cum = predictions[0][np.argmax(predictions)]\n who = \"\"\n proba_pred = -1\n else:\n currentPersonNumber += 1\n proba_cum += predictions[0][np.argmax(predictions)]\n\n if currentPersonNumber > 5:\n who = currentPerson\n proba_pred = proba_cum / currentPersonNumber\n frame = getBoundingBox(frame,kp,index,who,proba_pred, True)\n\n #predictions = model.predict(np.asarray([normalized_keypoints[index,:,:].flatten()]))\n #frame = getBoundingBox(frame,kp,index,getNameFromClass(np.argmax(predictions)),predictions[0][np.argmax(predictions)], isDetected)\n except Exception as e:\n print(e)\n pass\n\n end = time.time()\n fps = 1 / (end - start)\n # Display the resulting frame\n frame = cv2.putText(frame,'FPS : '+str(int(fps)),(40,40), font, 1,(0,0,0),2,cv2.LINE_AA)\n cv2.imshow('Webcam',frame)\n\n key = cv2.waitKey(1)\n \n #Wait to press 'q' key for capturing\n if key & 0xFF == ord('q'):\n break\n\n# When everything done, release the capture\ncap.release()\ncv2.destroyAllWindows()","sub_path":"Allures/video_to_person.py","file_name":"video_to_person.py","file_ext":"py","file_size_in_byte":5811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"620076922","text":"import hashlib\nimport time\n# from sms253 import send_sms\nimport urllib.request\nfrom sys import stdout, exit\n\n\nclass brush(object):\n \"\"\"洗刷刷自动提交程序\"\"\"\n\n def __init__(self):\n # 用户名\n self.user = \"civia\"\n # 验证码\n self.i = 0\n # 票数\n self.k = 100\n # 生成字符串\n self.src = \"\"\n # md5值\n self.md5 = hashlib.md5()\n # token\n self.token = \"wy56o5ZqTw0w0ELYAVE8wNtQhyyepiRhQHbU5uRN\"\n\n def updateMd5(self):\n self.src = str(time.strftime(\"%Y%m%d\")) + self.user + str(self.k) + str(self.i)\n self.md5 = hashlib.md5(self.src.encode())\n\n def outputInterface(self):\n print(\"Final string : \" + self.src)\n print(\"Final MD5 : \" + self.md5.hexdigest())\n print(\"Result : \" + str(self.i))\n stdout.flush()\n # 自动提交\n req = urllib.request.Request(\"http://www.qlcoder.com/train/handsomerank?_token=\" + self.token + \"&user=\" + self.user + \"&checkcode=\" + str(self.i))\n objUrlopen = urllib.request.urlopen(req)\n webContent = objUrlopen.read().decode()\n if webContent.index(\"错误\") > 0:\n # 发生错误发送短信\n # print(send_sms(\"[ 喜刷刷 level \" + str(self.k) + \" ] \" + webContent, \"15669910253\").decode('ascii'))\n exit()\n elif self.k % 100 == 0:\n pass\n # 每一百票发一条短信\n # print(send_sms(\"[ 喜刷刷 level \" + str(self.k) + \" ] \" + str(self.i), \"15669910253\").decode('ascii'))\n\n def inputInterface(self):\n pass\n\n def test(self):\n a = str(time.strftime(\"%Y%m%d\")) + self.user + \"9742904123\"\n # a = \"20151204shinian101412011618\"\n b = hashlib.md5(a.encode())\n print(b.hexdigest())\n\n def autotest(self):\n req = urllib.request.Request(\"http://www.qlcoder.com/train/handsomerank?_token=\" + self.token + \"&user=\" + self.user + \"&checkcode=\" + str(self.i))\n objUrlopen = urllib.request.urlopen(req)\n webContent = objUrlopen.read().decode()\n print(webContent)\n if \"错误\" in webContent:\n print(\"错误\")\n else:\n print(\"正确\")\n\n\nif __name__ == '__main__':\n myBrush = brush()\n # for myBrush.k in range(118, 1000):\n # myBrush.i = 0\n # myBrush.md5 = hashlib.md5()\n # while myBrush.md5.hexdigest()[0:6] != \"000000\":\n # myBrush.i += 1\n # myBrush.updateMd5()\n # myBrush.outputInterface()\n myBrush.autotest()\n","sub_path":"034_喜刷刷/autotest.py","file_name":"autotest.py","file_ext":"py","file_size_in_byte":2553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"90984119","text":"\"\"\"shard cell table\n\nRevision ID: d350610e27e\nRevises: 40d609897296\nCreate Date: 2015-11-26 12:53:31.278039\n\"\"\"\n\nimport codecs\nimport logging\nimport struct\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\nlog = logging.getLogger('alembic.migration')\nrevision = 'd350610e27e'\ndown_revision = '40d609897296'\n\nCELLAREA_STRUCT = struct.Struct('!bHHH')\n\nMCC_TO_REGION = {\n 202: 'GR',\n 204: 'NL',\n 206: 'BE',\n 212: 'MC',\n 213: 'AD',\n 214: 'ES',\n 216: 'HU',\n 218: 'BA',\n 219: 'HR',\n 220: 'RS',\n 222: 'IT',\n 225: 'VA',\n 226: 'RO',\n 228: 'CH',\n 230: 'CZ',\n 231: 'SK',\n 232: 'AT',\n 235: 'GB',\n 238: 'DK',\n 240: 'SE',\n 242: 'NO',\n 244: 'FI',\n 246: 'LT',\n 247: 'LV',\n 248: 'EE',\n 250: 'RU',\n 255: 'UA',\n 257: 'BY',\n 259: 'MD',\n 260: 'PL',\n 262: 'DE',\n 266: 'GI',\n 268: 'PT',\n 270: 'LU',\n 272: 'IE',\n 274: 'IS',\n 276: 'AL',\n 278: 'MT',\n 280: 'CY',\n 282: 'GE',\n 283: 'AM',\n 284: 'BG',\n 286: 'TR',\n 288: 'FO',\n 289: 'GE',\n 290: 'GL',\n 292: 'SM',\n 293: 'SI',\n 294: 'MK',\n 295: 'LI',\n 297: 'ME',\n 302: 'CA',\n 308: 'PM',\n 313: 'US',\n 316: 'US',\n 330: 'PR',\n 334: 'MX',\n 342: 'BB',\n 344: 'AG',\n 346: 'KY',\n 348: 'VG',\n 350: 'BM',\n 352: 'GD',\n 354: 'MS',\n 356: 'KN',\n 358: 'LC',\n 360: 'VC',\n 363: 'AW',\n 364: 'BS',\n 365: 'AI',\n 366: 'DM',\n 368: 'CU',\n 370: 'DO',\n 372: 'HT',\n 374: 'TT',\n 376: 'TC',\n 400: 'AZ',\n 401: 'KZ',\n 402: 'BT',\n 404: 'IN',\n 405: 'IN',\n 410: 'PK',\n 412: 'AF',\n 413: 'LK',\n 414: 'MM',\n 415: 'LB',\n 416: 'JO',\n 417: 'SY',\n 418: 'IQ',\n 419: 'KW',\n 420: 'SA',\n 421: 'YE',\n 422: 'OM',\n 424: 'AE',\n 426: 'BH',\n 427: 'QA',\n 428: 'MN',\n 429: 'NP',\n 432: 'IR',\n 434: 'UZ',\n 436: 'TJ',\n 437: 'KG',\n 438: 'TM',\n 440: 'JP',\n 441: 'JP',\n 450: 'KR',\n 452: 'VN',\n 454: 'HK',\n 455: 'MO',\n 456: 'KH',\n 457: 'LA',\n 460: 'CN',\n 466: 'TW',\n 467: 'KP',\n 470: 'BD',\n 472: 'MV',\n 502: 'MY',\n 510: 'ID',\n 514: 'TL',\n 515: 'PH',\n 520: 'TH',\n 525: 'SG',\n 528: 'BN',\n 530: 'NZ',\n 536: 'NR',\n 537: 'PG',\n 539: 'TO',\n 540: 'SB',\n 541: 'VU',\n 542: 'FJ',\n 544: 'AS',\n 545: 'KI',\n 546: 'NC',\n 547: 'PF',\n 548: 'CK',\n 549: 'WS',\n 550: 'FM',\n 551: 'MH',\n 552: 'PW',\n 553: 'TV',\n 555: 'NU',\n 602: 'EG',\n 603: 'DZ',\n 605: 'TN',\n 606: 'LY',\n 607: 'GM',\n 608: 'SN',\n 609: 'MR',\n 610: 'ML',\n 611: 'GN',\n 612: 'CI',\n 613: 'BF',\n 614: 'NE',\n 615: 'TG',\n 616: 'BJ',\n 617: 'MU',\n 618: 'LR',\n 619: 'SL',\n 620: 'GH',\n 621: 'NG',\n 622: 'TD',\n 623: 'CF',\n 624: 'CM',\n 625: 'CV',\n 626: 'ST',\n 627: 'GQ',\n 628: 'GA',\n 629: 'CG',\n 630: 'CD',\n 631: 'AO',\n 632: 'GW',\n 633: 'SC',\n 634: 'SD',\n 635: 'RW',\n 636: 'ET',\n 637: 'SO',\n 638: 'DJ',\n 639: 'KE',\n 640: 'TZ',\n 641: 'UG',\n 642: 'BI',\n 643: 'MZ',\n 645: 'ZM',\n 646: 'MG',\n 647: 'RE',\n 648: 'ZW',\n 649: 'NA',\n 650: 'MW',\n 651: 'LS',\n 652: 'BW',\n 653: 'SZ',\n 654: 'KM',\n 655: 'ZA',\n 657: 'ER',\n 659: 'SS',\n 702: 'BZ',\n 704: 'GT',\n 706: 'SV',\n 708: 'HN',\n 710: 'NI',\n 712: 'CR',\n 714: 'PA',\n 716: 'PE',\n 722: 'AR',\n 724: 'BR',\n 730: 'CL',\n 732: 'CO',\n 734: 'VE',\n 736: 'BO',\n 738: 'GY',\n 740: 'EC',\n 744: 'PY',\n 746: 'SR',\n 748: 'UY',\n 750: 'FK',\n}\n\nstmt_drop_index = '''\\\nALTER TABLE cell_{id}\nDROP KEY `cell_{id}_created_idx`,\nDROP KEY `cell_{id}_modified_idx`,\nDROP KEY `cell_{id}_latlon_idx`,\nDROP KEY `cell_{id}_region_idx`\n'''\n\nstmt_add_index = '''\\\nALTER TABLE cell_{id}\nADD INDEX `cell_{id}_created_idx` (`created`),\nADD INDEX `cell_{id}_modified_idx` (`modified`),\nADD INDEX `cell_{id}_latlon_idx` (`lat`,`lon`),\nADD INDEX `cell_{id}_region_idx` (`region`)\n'''\n\nstmt_optimize = '''\\\nOPTIMIZE TABLE cell_{id}\n'''\n\nstmt_insert = '''\\\nINSERT INTO cell_{id} (\n`cellid`,\n`radio`, `mcc`, `mnc`, `lac`, `cid`, `psc`,\n`lat`, `lon`, `radius`, `max_lat`, `min_lat`, `max_lon`, `min_lon`,\n`samples`, `created`, `modified`) (\nSELECT\nUNHEX(CONCAT(\nLPAD(HEX(`radio`), 2, 0), LPAD(HEX(`mcc`), 4, 0),\nLPAD(HEX(`mnc`), 4, 0), LPAD(HEX(`lac`), 4, 0),\nLPAD(HEX(`cid`), 8, 0))),\n`radio`, `mcc`, `mnc`, `lac`, `cid`, `psc`,\n`lat`, `lon`, `radius`, `max_lat`, `min_lat`, `max_lon`, `min_lon`,\n`samples`, `created`, `modified`\nFROM cell WHERE `radio` = {radio}\n)\n'''\n\nstmt_update_regions = '''\\\nUPDATE cell_{id}\nSET `region` = \"{code}\"\nWHERE `radio` IN (0, 1, 2, 3) AND `mcc` = {mcc}\n'''\n\nstmt_region_count = '''\\\nSELECT COUNT(*) FROM cell_{id} WHERE region IS NULL\n'''\n\nstmt_select_region = '''\\\nSELECT HEX(`cellid`), `mcc`, `lat`, `lon`\nFROM cell_{id}\nWHERE `region` IS NULL\nLIMIT {batch}\n'''\n\nstmt_update_region = '''\\\nUPDATE cell_{id}\nSET `region` = \"{code}\"\nWHERE `cellid` in ({ids})\n'''\n\nstmt_delete_outside = '''\\\nDELETE FROM cell_{id}\nWHERE `cellid` in ({ids})\n'''\n\nstmt_select_area = '''\\\nSELECT\nAVG(`lat`) AS `lat`,\nAVG(`lon`) AS `lon`,\nAVG(`radius`) AS `avg_cell_radius`,\nCOUNT(*) as `num_cells`,\nMIN(`min_lat`) AS `min_lat`,\nMAX(`max_lat`) AS `max_lat`,\nMIN(`min_lon`) AS `min_lon`,\nMAX(`max_lon`) AS `max_lon`\nFROM cell_{id}\nWHERE `radio` = {radio} AND `mcc` = {mcc} AND `mnc` = {mnc} AND `lac` = {lac}\n'''\n\nstmt_update_area = '''\\\nUPDATE cell_area SET\n`lat` = {lat},\n`lon` = {lon},\n`radius` = {radius},\n`avg_cell_radius` = {avg_cell_radius},\n`num_cells` = {num_cells}\nWHERE `radio` = {radio} AND `mcc` = {mcc} AND `mnc` = {mnc} AND `lac` = {lac}\n'''\n\nstmt_delete_area = '''\\\nDELETE FROM cell_area\nWHERE `radio` = {radio} AND `mcc` = {mcc} AND `mnc` = {mnc} AND `lac` = {lac}\n'''\n\nstmt_select_stat = '''\\\nSELECT * FROM stat\nWHERE `key` = 2\nORDER BY `time` DESC LIMIT 1\n'''\n\nstmt_update_stat = '''\\\nUPDATE stat\nSET `value` = {value}\nWHERE `key` = 2 AND `time` = {time}\n'''\n\n\ndef _update_area(bind, shard_id, areaid, circle_radius):\n radio, mcc, mnc, lac = CELLAREA_STRUCT.unpack(codecs.decode(areaid, 'hex'))\n row = bind.execute(sa.text(stmt_select_area.format(\n id=shard_id, radio=radio, mcc=mcc, mnc=mnc, lac=lac))).fetchone()\n num_cells = int(row.num_cells)\n if num_cells == 0:\n op.execute(sa.text(stmt_delete_area.format(\n radio=radio, mcc=mcc, mnc=mnc, lac=lac)))\n else:\n radius = circle_radius(\n float(row.lat), float(row.lon),\n float(row.max_lat), float(row.max_lon),\n float(row.min_lat), float(row.min_lon))\n avg_cell_radius = int(round(row.avg_cell_radius))\n\n op.execute(sa.text(stmt_update_area.format(\n radio=radio, mcc=mcc, mnc=mnc, lac=lac,\n lat=float(row.lat), lon=float(row.lon), radius=radius,\n avg_cell_radius=avg_cell_radius, num_cells=num_cells,\n )))\n\n\ndef _update_stat(bind, deleted_total):\n row = bind.execute(sa.text(stmt_select_stat)).fetchone()\n if row:\n new_value = row.value - deleted_total\n op.execute(sa.text(stmt_update_stat.format(\n time=row.time, value=new_value)))\n\n\ndef _update_region_batch(bind, shard_id, geocoder, batch=10000):\n rows = bind.execute(sa.text(stmt_select_region.format(\n id=shard_id, batch=batch))).fetchall()\n\n areas = set()\n cells = {}\n deleted = 0\n\n i = 0\n for row in rows:\n code = geocoder.region_for_cell(row.lat, row.lon, row.mcc)\n if code not in cells:\n cells[code] = []\n cells[code].append(row[0])\n if not code:\n # cellid is a 11 byte column, the last 4 byte being the\n # cid, but this is hex encoded, so 22 byte minus 8 byte\n # is the area id\n areas.add(row[0][:14])\n deleted += 1\n i += 1\n\n for code, cellids in cells.items():\n ids = 'UNHEX(\"' + '\"), UNHEX(\"'.join(cellids) + '\")'\n if not code:\n op.execute(sa.text(stmt_delete_outside.format(\n id=shard_id, ids=ids)))\n else:\n op.execute(sa.text(stmt_update_region.format(\n id=shard_id, code=code, ids=ids)))\n\n return (i, areas, deleted)\n\n\ndef _upgrade_shard(bind, shard_id, radio, geocoder, circle_radius):\n log.info('Drop cell_%s indices.', shard_id)\n op.execute(sa.text(stmt_drop_index.format(id=shard_id)))\n\n log.info('Fill cell_%s table.', shard_id)\n op.execute(sa.text(stmt_insert.format(id=shard_id, radio=radio)))\n\n log.info('Update cell_%s regions.', shard_id)\n length = len(MCC_TO_REGION)\n for i, (mcc, code) in enumerate(MCC_TO_REGION.items()):\n op.execute(sa.text(stmt_update_regions.format(\n id=shard_id, code=code, mcc=mcc)))\n if (i > 0 and i % 50 == 0):\n log.info('Updated %s of %s regions.', i, length)\n log.info('Updated %s of %s regions.', length, length)\n\n log.info('Add cell_%s indices.', shard_id)\n op.execute(sa.text(stmt_add_index.format(id=shard_id)))\n\n todo = bind.execute(sa.text(stmt_region_count.format(\n id=shard_id))).fetchone()[0]\n log.info('Updating remaining %s cells.', todo)\n\n updated_areas = set()\n deleted_total = 0\n updated_total = 0\n while True:\n updated_rows, areas, deleted_rows = _update_region_batch(\n bind, shard_id, geocoder)\n updated_areas = updated_areas.union(areas)\n deleted_total += deleted_rows\n updated_total += updated_rows\n\n if not updated_rows:\n break\n if ((updated_total and updated_total % 100000 == 0) or\n updated_total == todo):\n log.info('Updated %s of %s cells.', updated_total, todo)\n\n log.info('Optimize cell_%s table.' % shard_id)\n op.execute(sa.text(stmt_optimize.format(id=shard_id)))\n\n log.info('Updating %s areas.', len(updated_areas))\n for areaid in updated_areas:\n _update_area(bind, shard_id, areaid, circle_radius)\n log.info('Updated areas.')\n\n if deleted_total:\n _update_stat(bind, deleted_total)\n\n\ndef upgrade():\n bind = op.get_bind()\n # avoid top-level imports of application code\n from ichnaea.geocalc import circle_radius\n from ichnaea.geocode import GEOCODER\n\n for shard_id, radio in (('gsm', 0), ('wcdma', 2), ('lte', 3)):\n _upgrade_shard(bind, shard_id, radio, GEOCODER, circle_radius)\n\n\ndef downgrade():\n pass\n","sub_path":"alembic/versions/d350610e27e_shard_cell_table.py","file_name":"d350610e27e_shard_cell_table.py","file_ext":"py","file_size_in_byte":10451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"547323475","text":"import imp\nimport os\nfrom sqlalchemy import create_engine, text\nfrom sqlalchemy.orm import sessionmaker\nfrom migrate.versioning import api\n\nfrom config import SQLALCHEMY_MIGRATE_REPO, SQLALCHEMY_DATABASE_URI\nfrom model.datamodel import BaseModel\n\n\nclass DbSession:\n session = None\n engine = None\n model = None\n\n def __init__(self):\n self.engine = create_engine(SQLALCHEMY_DATABASE_URI, echo=True)\n _session = sessionmaker(bind=self.engine, autocommit=False, autoflush=False)\n self.session = _session()\n self.model = BaseModel\n\n def create_tables(self):\n \"\"\"\n 仅执行一次，用于初始化数据库，对于已经建立的表不会重建\\n\n :return:\n \"\"\"\n self.model.metadata.create_all(self.engine)\n if not os.path.exists(SQLALCHEMY_MIGRATE_REPO):\n # self.model.metadata.create_all(self.engine)\n api.create(SQLALCHEMY_MIGRATE_REPO, 'database repository')\n api.version_control(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)\n else:\n print('已经建立了数据库')\n api.version_control(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO,\n version=api.version(SQLALCHEMY_MIGRATE_REPO))\n\n def migrate_tables(self):\n \"\"\"\n 用于迁移数据库，每次对表字段或表类型进行修改时，执行该方法\\n\n :return:当前迁移版本\\n\n \"\"\"\n migration = SQLALCHEMY_MIGRATE_REPO + '/versions/%03d_migration.py' % (\n api.db_version(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO) + 1)\n tmp_model = imp.new_module('old_model')\n old_model = api.create_model(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)\n exec(old_model, tmp_model.__dict__)\n script = api.make_update_script_for_model(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO, tmp_model.meta,\n self.model.metadata)\n open(migration, 'wt').write(script)\n api.upgrade(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO)\n print('New migration saved as ' + migration)\n return str(api.db_version(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO))\n\n def drop_tables(self):\n \"\"\"\n 不要轻易使用该方法，会清空数据库\\n\n :return:\n \"\"\"\n self.model.metadata.drop_all(self.engine)\n\n def add_data(self, data):\n \"\"\"\n 添加数据到库\\n\n :param data: 在 :class:`datamodel` 中已定义的数据对象，可以是tuple元组\\n\n :return:\n \"\"\"\n if isinstance(data, list):\n self.session.add_all(data)\n else:\n self.session.add(data)\n self.session.commit()\n\n def query_data(self, query_string):\n \"\"\"\n 通过SQL语句查询数据\\n\n :param query_string:SQL执行语句\\n\n :return:全部查询结果,多行时为tuple元组\n \"\"\"\n s = text(query_string)\n return self.session.execute(s).fetchall()\n\n def get_session(self):\n \"\"\"\n 获取当前session，用于自定义执行SQL\\n\n :return: 当前session\n \"\"\"\n return self.session\n\n def close_session(self):\n \"\"\"\n 关闭当前session的全部连接,在不再使用session时关闭。\\n\n :return:\n \"\"\"\n self.session.close_all()\n","sub_path":"util/udb.py","file_name":"udb.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"37756882","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModified on Sun Aug 19 09:20:09 2018\n@author: Jing\n@Email: cdxujing@qq.com\nTitle: Dynamic Programming\n\"\"\"\n\nfrom time import time\nimport numpy as np\n\n# %%采用简单递归实现斐波那契数列\ndef fib_recurrent(n):\n if n == 1 or n == 2:\n return 1\n else:\n return fib_recurrent(n-1) + fib_recurrent(n-2)\n\nt1 = time()\nf1 = fib_recurrent(40)\nt2 = time()\n\nprint('fibonacci_recurrent(40) = ', f1)\nprint(\"conputed in \", t2-t1, 's')\n\n# %%采用具有记忆的递归形式\nclass Fib_memory:\n def __init__(self, n):\n self.num = n\n self.mem = [1, 1] + [0]*(n-2)\n\n def fib(self, n):\n if self.mem[n-1] != 0:\n return self.mem[n-1]\n else:\n self.mem[n-1] = self.fib(n-1) + self.fib(n-2)\n return self.mem[n-1]\n\nt3 = time()\nf2 = Fib_memory(40)\nf2.fib(35)\nt4 = time()\nprint('fibonacci_memory(40) = ', f2.mem[f2.num-1])\nprint(\"conputed in \", t4-t3, 's')\n\n# %%任务调度问题（背包问题）\nclass Task:\n def __init__(self, start_time, end_time, value):\n self.start_time = start_time\n self.end_time = end_time\n self.value = value\n\n# 定义任务， 在同一时间段内只能做一个任务或不做任务, 每个人物有不同的奖励值\ntask = np.array([[1, 4, 5], [3, 5, 1], [0, 6, 8], [4, 7, 4], [3, 8, 6], [5, 9, 3], [6, 10, 2], [8, 11, 4]])\n\n# 扫描每个任务前面邻接的不冲突的任务，与前面都冲突则为-1\nt = np.array([-1]*8)\nfor i in range(1, 8):\n for j in reversed(range(i)):\n if task[j][1] <= task[i][0]:\n t[i] = j\n break\n# 前i个任务的最优解\noptm = [5] + [0]*7\n\ndef fib_task(n):\n if optm[n] != 0:\n return optm[n]\n elif t[n] != -1:\n optm[n] = max(fib_task(n-1), task[n][2] + fib_task(t[n]))\n return optm[n]\n else:\n optm[n] = max(fib_task(n-1), task[n][2])\n return optm[n]\n\nt5 = time()\nf3 = fib_task(7)\nt6 = time()\nprint(\"fib_task(8) = \", f3)\nprint(\"time consuming: \", t6-t5)\n\n# %% 购买商品\nclass buy_goods:\n def __init__(self, stores):\n self.stores = stores\n self.status = [-1]*len(self.stores)\n for i in range(2, len(self.stores)):\n for j in reversed(range(i-1)):\n if self.stores[j] < self.stores[i]:\n self.status[i] = j\n break\n self.optm = [0]*len(self.stores)\n self.optm[0] = self.stores[0]\n\n def calc(self, n):\n if self.optm[n] != 0:\n return self.optm[n]\n elif self.status[n] != -1:\n self.optm[n] = max(self.calc(n-1), self.stores[n] + self.calc(self.status[n]))\n else:\n self.optm[n] = max(self.calc(n-1), self.stores[n])\n return self.optm[n]\n\nstores = [3, 8, 6, 9, 7, 6]\nb = buy_goods(stores)\noutput = b.calc(5)\nprint(output)\n\n#%% 硬笔找零问题\n\nclass choose_coins:\n def __init__(self, money):\n self.coins = [1, 5, 10, 21, 25]\n self.money = money\n self.opt = np.zeros(self.money+1)\n self.opt[1] = 1\n\n def choose(self, money):\n if self.opt[money] != 0:\n return self.opt[money]\n else:\n c = []\n for coin in self.coins:\n if money >= coin:\n c.append(self.choose(money-coin))\n if len(c) != 0:\n self.opt[money] = min(c)\n return self.opt[money]\n else:\n return 0\n\nc = choose_coins(63)\nc.choose(63)\nprint(c.opt[-1])\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"dynamic_programming.py","file_name":"dynamic_programming.py","file_ext":"py","file_size_in_byte":3538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"230092849","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import include, url\n\nfrom tango.apps.views.registration import (\n RegistrationView, RegistrationCompleteView\n)\nfrom tango.apps.views.word import (\n WordCreateView, WordCreateCompleteView, WordListView\n)\n\nurlpatterns = [\n url(r'^$',\n RegistrationView.as_view(),\n name='register'),\n url(r'^complete/$',\n RegistrationCompleteView.as_view(),\n name='register-complete'),\n url(r'^register/create/$',\n WordCreateView.as_view(),\n name='word-register'),\n url(r'^register/complete/$',\n WordCreateCompleteView.as_view(),\n name='word-register-complete'),\n url(r'^register/list/$',\n WordListView.as_view(),\n name='word-list'),\n ]\n","sub_path":"mysite/tango/apps/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"423009941","text":"import pygame\nfrom math import sqrt\n\npygame.init()\n\nscreen_width = 1000\nscreen_height = 800\n\nscreen = pygame.display.set_mode((screen_width, screen_height), pygame.FULLSCREEN)\n\nwhite = (255, 255, 255)\nblack = (0, 0, 0)\ngreen = (0, 255, 0)\n\nclock = pygame.time.Clock()\n\nsword_orcs = []\ngun_orcs = []\nbullets = []\n\n\nclass Bullet:\n def __init__(self, surface, x, y, rad, color, target, speed, max_way):\n '''\n template for all bullet objects\n :param surface: pygame.display.surface\n :param x: int\n :param y: int\n :param rad: int\n :param color: tuple\n :param target: tuple\n :param speed: int\n :param max_way: int\n '''\n self.start_x = x\n self.start_y = y\n self.surface = surface\n self.x = x\n self.y = y\n self.rad = rad\n self.color = color\n self.speed = speed\n self.t_x, self.t_y = target\n self.x_dir = None\n self.y_dir = None\n self.draw = True\n self.max_way = max_way\n\n def __str__(self):\n return f'surface: {self.surface}\\nx: {self.x}\\ny: {self.y}\\nrad: {self.rad}\\n' \\\n f'color: {self.color}\\nspeed: {self.speed}\\nspeed:{self.speed}\\ntarget X: {self.t_x}\\n' \\\n f'target Y: {self.t_y}\\nX direction {self.x_dir}\\nY direction {self.y_dir}'\n\n def is_draw(self):\n if sqrt((self.x - self.start_x) ** 2 + (self.y - self.start_y) ** 2) >= self.max_way\\\n or self.x <= 0 or self.x >= screen_width or self.y <= 0 or self.y >= screen_height:\n self.draw = False\n\n def set_dir(self):\n if not self.x_dir and not self.y_dir:\n magnitude = sqrt((self.t_x - self.start_x) ** 2 + (self.t_y - self.start_y) ** 2)\n self.x_dir = (self.t_x - self.start_x) / magnitude\n self.y_dir = (self.t_y - self.start_y) / magnitude\n\n def show(self):\n if self.draw:\n pygame.draw.circle(screen, black, (int(self.x), int(self.y)), self.rad)\n\n def update(self):\n self.x = self.x + (self.x_dir * self.speed)\n self.y = self.y + (self.y_dir * self.speed)\n\n def hit(self):\n if int(self.x) <= int(self.t_x) <= int(self.x) + int(self.rad) and \\\n int(self.y) <= int(self.t_y) <= int(self.y) + int(self.rad):\n return True\n\n return False\n\n\nclass Player:\n def __init__(self, start_pos, width_height, boundaries, speed, bullet_damage, bullet_speed, bullet_range, bullet_rad,\n cool_down):\n '''\n the main player class\n :param start_pos: tuple, x and y of start pos\n :param width_height: tuple, width and height of the player rect object\n :param speed: int\n :param bullet_damage: int or float\n :param bullet_speed: int\n :param bullet_range: int\n :param bullet_rad: int\n :param cool_down: int\n '''\n self.x, self.y = start_pos\n self.width, self.height = width_height\n self.max_x, self.max_y = boundaries\n self.speed = speed\n self.bullet_damage = bullet_damage\n self.bullet_speed = bullet_speed\n self.bullet_range = bullet_range\n self.bullet_rad = bullet_rad\n self.dx = 0\n self.dy = 0\n self.start_cool_down = cool_down\n self.cool_down = cool_down\n self.can_shoot = True\n self.hit_points = 3\n self.imortality = False\n self.imortality_timer = 300\n\n def __str__(self):\n return 'x: {}\\ny: {}\\nwidth: {}\\nheight: {}\\nspeed: {}\\ndx: {}\\ndy: {}'\\\n .format(self.x, self.y, self.width, self.height, self.speed, self.dx, self.dy)\n\n def set_dir(self, x, y):\n '''\n sets a new direction for the bullet\n :param x: int\n :param y: -1 or 0 or 1\n :return: None\n '''\n self.dx = x\n self.dy = y\n\n def show(self):\n pygame.draw.rect(screen, black, (self.x, self.y, self.width, self.height))\n\n def update(self):\n self.cool_down -= 1\n if self.cool_down == 0:\n self.can_shoot = True\n self.cool_down = self.start_cool_down\n\n if self.x + self.dx * self.speed <= 0 or self.x + self.dx + self.dx * self.speed >= self.max_x - self.width or\\\n self.y + self.dy * self.speed <= 0 or self.y + self.dy * self.speed >= self.max_y - self.height:\n pass\n else:\n self.x += self.dx * self.speed\n self.y += self.dy * self.speed\n\n def shoot(self, target_pos):\n '''\n creates a new bullet object\n :param target_pos: tuple\n :return: None\n '''\n if self.can_shoot:\n bullets.append(Bullet(screen, self.x, self.y, self.bullet_rad, black, target_pos,\n self.bullet_speed, self.bullet_range))\n self.can_shoot = False\n\n def set_imortality_timer(self):\n if self.imortality_timer > 0:\n self.imortality = True\n\n else:\n self.imortality = False\n\n\ndef valid_bullets():\n for b in bullets:\n if not b.draw:\n bullets.pop(bullets.index(b))\n\n\nplayer = Player((screen_width / 2, screen_height / 2), (20, 20), (screen_width, screen_height), 5, 10, 20, 800, 5, 20)\n\n\nclass Orc:\n '''\n a Base orc class which all other orc classes inherit from\n '''\n def __init__(self, pos, width_height, speed, color, hit_points):\n '''\n :param pos: tuple\n :param width_height: tuple that represents the width and heights of the orc\n :param speed: int\n :param color: tuple that represnts rgb\n :param hit_points: int of float\n '''\n self.x, self.y = pos\n self.width, self.height = width_height\n self.speed = speed\n self.color = color\n self.hit_points = hit_points\n\n def show(self):\n pygame.draw.rect(screen, self.color, (self.x, self.y, self.width, self.height))\n\n\nclass GunOrc(Orc):\n def __init__(self, pos, width_height, speed, color, max_way, damage, cool_down, hit_points):\n '''\n a orc that will run at you and start shooting you\n :param pos: tuple\n :param width_height: tuple\n :param speed: int or float\n :param color: tuple that represents rgb\n :param max_way: int that is the range of the orc\n :param damage: int\n :param cool_down: int the cool_down of the gun that the orc has\n :param hit_points: int or float\n '''\n super().__init__(pos, width_height, speed, color, hit_points)\n self.max_way = max_way\n self.damage = damage\n self.cool_down = cool_down\n self.dx = None\n self.dy = None\n\n\nclass SwordOrc(Orc):\n def __init__(self, pos, width_height, speed, color, damage, hit_points):\n '''\n :param pos: tuple\n :param width_height: tuple\n :param speed: int\n :param color: tule that represents rgb\n :param damage: int or float\n :param hit_points: int or float\n '''\n super().__init__(pos, width_height, speed, color, hit_points)\n self.damage = damage\n self.player_x = player.x\n self.player_y = player.y\n self.y_dir = None\n self.x_dir = None\n\n def set_dir(self):\n self.player_x = player.x\n self.player_y = player.y\n magnitude = sqrt((self.player_x - self.x) ** 2 + (self.player_y - self.y) ** 2)\n self.x_dir = (self.player_x - self.x) / magnitude\n self.y_dir = (self.player_y - self.y) / magnitude\n\n def update(self):\n self.x += self.x_dir * self.speed\n self.y += self.y_dir * self.speed\n\n def hit_player(self):\n if player.x <= self.x <= player.x + player.width and player.y <= self.y <= player.y + player.height:\n player.hit_points -= 1\n print(player.hit_points)\n\n\nsword_orcs.append(SwordOrc((30, 30), (20, 20), 3, green, 10, 10))\n\n\ndef orcs():\n for orc in sword_orcs:\n orc.hit_player()\n orc.show()\n orc.set_dir()\n orc.update()\n\n\ndef orc_hit():\n for bullet in bullets:\n for orc in sword_orcs:\n if orc.x <= bullet.x <= orc.x + orc.width and orc.y <= bullet.y <= orc.y + orc.height:\n bullet.draw = False\n sword_orcs.pop(sword_orcs.index(orc))\n\n\ndir_x = 0\ndir_y = 0\n\nwhile True:\n clock.tick(100)\n mouse_press = pygame.mouse.get_pressed()\n mouse_pos = pygame.mouse.get_pos()\n\n screen.fill(white)\n for event in pygame.event.get():\n print(event)\n\n if event.type == pygame.QUIT:\n quit()\n\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_a:\n dir_x += -1\n if event.key == pygame.K_d:\n dir_x += 1\n if event.key == pygame.K_s:\n dir_y += 1\n if event.key == pygame.K_w:\n dir_y += -1\n\n if event.key == pygame.K_ESCAPE:\n quit()\n\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_a:\n dir_x -= -1\n if event.key == pygame.K_d:\n dir_x -= 1\n if event.key == pygame.K_s:\n dir_y -= 1\n if event.key == pygame.K_w:\n dir_y -= -1\n\n player.set_dir(dir_x, dir_y)\n\n player.update()\n player.show()\n\n valid_bullets()\n\n for bullet in bullets:\n bullet.is_draw()\n bullet.set_dir()\n bullet.update()\n bullet.show()\n\n orc_hit()\n orcs()\n\n if mouse_press == (1, 0, 0):\n player.shoot(mouse_pos)\n\n pygame.display.update()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"594324967","text":"## import opencv\n\nimport cv2\nimport numpy as np\nfrom start import PressKey ,ReleaseKey \nimport imutils\n\n# https://stackoverflow.com/questions/14489013/simulate-python-keypresses-for-controlling-a-game\n# PressKey and ReleaseKey code was based on this stackoverflow post\n\n# https://wiki.nexusmods.com/index.php/DirectX_Scancodes_And_How_To_Use_Them\n# above link will give you the code of other keys, such that you can design it according to your application\nleft = 205\nright = 199\n\n# strat capturing video via webcam\n\ncap = cv2.VideoCapture(0)\n\n# hsv range of your color to be detected\nLower = np.array([50, 70, 0])\nUpper = np.array([180,255,253])\n\ncurrent_key_pressed = set()\n\nwhile True:\n \n keyPressed = False\n keyPressed_lr = False\n \n _, frame = cap.read()\n frame = cv2.flip(frame,1) # change according to your device, mine flips the frame automatically\n \n # resize frame\n h, w = frame.shape[:2]\n ar = w/h\n frame = cv2.resize(frame, (int(ar*400),400))\n img = frame.copy() \n \n #storing height and width in varibles \n height = frame.shape[0]\n width = frame.shape[1]\n \n # vlur and convert to hsv\n blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\n \n # creating mask and reducing noise\n mask = cv2.inRange(hsv, Lower,Upper)\n kernel = np.ones((5,5),np.uint8)\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)\n mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n \n #up_mask = mask[0:height//2,0:width,]\n #down_mask = mask[height//2:height,width//4:3*width//4,]\n \n cnts_ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n cnts_ = imutils.grab_contours(cnts_)\n \n if len(cnts_) > 0:\n c = max(cnts_, key=cv2.contourArea)\n ((x, y), radius) = cv2.minEnclosingCircle(c)\n M = cv2.moments(c)\n center_ = (int(M[\"m10\"] / (M[\"m00\"]+0.000001)), int(M[\"m01\"] / (M[\"m00\"]+0.000001)))\n if radius >30:\n # draw the circle and centroid on the frame,\n cv2.circle(frame, (int(x), int(y)), int(radius),\n (0, 255, 255), 2)\n cv2.circle(frame, center_, 5, (0, 0, 255), -1)\n \n #the window size is kept 160 pixels in the center of the frame(80 pixels above the center and 80 below)\n if center_[0] < (width//2 - 10):\n cv2.putText(frame,'LEFT',(20,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,255),3)\n PressKey(left)\n current_key_pressed.add(left)\n keyPressed = True\n keyPressed_lr = True\n elif center_[0] > (width//2 + 10):\n cv2.putText(frame,'RIGHT',(400,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,255),3)\n PressKey(right)\n current_key_pressed.add(right)\n keyPressed = True\n keyPressed_lr = True\n \n # show the frame to our screen\n frame_copy = frame.copy()\n \n #draw box for left \n frame_copy = cv2.rectangle(frame_copy,(0,2),(width//2- 10,height-2 ),(255,255,255),1)\n \n #draw box for right\n frame_copy = cv2.rectangle(frame_copy,(width//2 +10,0),(width-2,height-2 ),(255,255,255),1)\n\n #display final frame \n cv2.imshow(\"Frame\", frame_copy)\n \n #We need to release the pressed key if none of the key is pressed else the program will keep on sending\n # the presskey command \n if not keyPressed and len(current_key_pressed) != 0:\n for key in current_key_pressed:\n ReleaseKey(key)\n current_key_pressed = set()\n \n #cv2.imshow('Frame', frame)\n k = cv2.waitKey(1) & 0xFF\n if k== ord('q'):\n break\n \ncap.release()\ncv2.destroyAllWindows()","sub_path":"opencv/Play Game with openCV/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":3758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"26466552","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/python\nimport os\n\nfrom PIL import Image, ImageDraw, ImageFont, ImageFilter\nfrom pprint import pprint\n\n\nttfs = os.popen(\"ls /usr/share/fonts/TTF/\", \"r\")\nlist_ttfs = [ttf.strip() for ttf in ttfs.readlines() if \".ttf\" in ttf]\nttfs.close()\n\npprint(list_ttfs)\n\nwidth_box, height_box = (60, 80)\n\nback_ground_color = (0, 0, 0)\nfont_color = (255, 255, 255)\n\nfont_size = 50\n\nunicode_text = \"E\"\n\nfor font_name in list_ttfs:\n\n image = Image.new(\"RGB\", (width_box, height_box), back_ground_color)\n\n draw = ImageDraw.Draw(image)\n\n unicode_font = ImageFont.truetype(font_name, font_size)\n\n width_text, height_text = draw.textsize(unicode_text, font=unicode_font)\n\n pos_x, pos_y = (\n int((width_box - width_text) / 2),\n int((height_box - height_text) / 2),\n )\n\n draw.text(\n (pos_x, pos_y),\n unicode_text,\n align=\"center\",\n font=unicode_font,\n fill=font_color,\n )\n\n image.save(\n f\"dataset/text-{unicode_text}-font-{font_name.split('.')[0]}.png\", \"PNG\"\n )\n","sub_path":"python3/data-science/test_pil.py","file_name":"test_pil.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"407391771","text":"# def can_be_replaced(n):\n# for i in range(n//2,1,-1):\n# if n % i == 0:\n# n //= i\n# return n == 1\n\n# def reduce(n):\n# result = []\n# for i in range(2,n+1):\n# if not can_be_replaced(i):\n# result.append(i)\n# return result\n\n# factors = reduce(20)\n# n = 1\n# for i in factors:\n# n *= i\n\n# print(factors)\n# n *= 2\n# n /= 3\n# print(n)\n\n# for i in range(1,21):\n# if n % i != 0:\n# print(i)\n# print(\"error\")\n\nPRIMES = [2,3,5,7,11,13,17,19] #too lazy to sieve up to 20\n\n\ndef greatest_power_below(n,limit):\n i = n\n while i <= limit/n:\n i *= n\n return i\n\ndef smallest(n):\n factors = []\n for i in PRIMES:\n if i > n:\n break\n factors.append(greatest_power_below(i,n))\n sum = 1\n for i in factors:\n sum *= i\n return sum\n\n# print(smallest(5))\n# print(smallest(6))\n# print(smallest(7))\n# print(smallest(8))\n\nprint(smallest(20))\n\n\n\n\n\n","sub_path":"1-25/5.py","file_name":"5.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"322968476","text":"import cv2\n# cap = cv2.VideoCapture(\"rtmp://202.69.69.180:443/webcast/bshdlive-pc\")\ncap = cv2.VideoCapture(\"rtmp://35.236.192.63:1935/live/test2\")\nret,frame = cap.read()\n\nframe_width = int(cap.set(3, 1280))\nframe_height = int(cap.set(4, 1024))\nframeyowidth = int(cap.get(3))\nframeyoheight = int(cap.get(4))\n\nwhile ret:\n ret,frame = cap.read()\n cv2.imshow(\"streaming\",frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n\ncap.release()\n\n# Closes all the frames\ncv2.destroyAllWindows()\n\n\n\n\n","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"161477387","text":"from EMUtils.config import *\nimport math\nimport numpy as np\n\n\ndef init_param_with_truth():\n for i in range(HEIGHT * WIDTH):\n row, col = map_1D_to_2D(i)\n disparity = disparity_image[row, col]\n if disparity == 0:\n disparity = np.random.randint(1, 20)\n r = depth_vec.index(disparity)\n s = 0\n m = map_r_s_to_m(r, s)\n b_mat[i, m] = 1\n # for s in range(NUM_VISIBLE_CONF):\n # m = map_r_s_to_m(r, s)\n # is_visible = visibility_conf_mat[s][1]\n # kth_row, kth_col = row, col - disparity\n\n # if is_visible:\n # if is_out_image(kth_row, kth_col):\n # color_prob = 0\n # else:\n # color_prob = norm_pdf(\n # I[1][kth_row, kth_col], ideal_image[row, col], covariance[0])\n # else:\n # if is_out_image(kth_row, kth_col):\n # color_prob = 1\n # else:\n # color_prob = hist_prob(1, I[1][kth_row, kth_col])\n\n # b_mat[i, m] = color_prob\n b_mat[i] = b_mat[i] / sum(b_mat[i])\n for i in range(HEIGHT):\n for j in range(WIDTH):\n index = map_2D_to_1D(i, j)\n max_index = np.argmax(b_mat[index])\n visible_state[i, j] = max_index\n r, s = map_m_to_r_s(max_index)\n is_visible = visibility_conf_mat[s][1]\n visible_image[i, j] = is_visible\n\n\ndef init_param():\n for i in range(HEIGHT * WIDTH):\n for m in range(num_visible_state):\n row, col = map_1D_to_2D(i)\n r, s = map_m_to_r_s(m)\n disparity = depth_vec[r]\n ## 在第一幅图中能否看到\n is_visible = visibility_conf_mat[s][1]\n\n ## 在第一幅图中，根据 disparity 算出的坐标\n kth_row, kth_col = row, col - disparity\n\n if is_visible:\n if is_out_image(kth_row, kth_col):\n color_prob = 0.0001\n else:\n color_prob = norm_pdf(\n I[1][kth_row, kth_col], ideal_image[row, col], covariance[0])\n else:\n # color_prob = hist_prob(1, I[0][row, col])\n if is_out_image(kth_row, kth_col):\n color_prob = hist_prob(0, I[0][row, col])\n else:\n color_prob = hist_prob(1, I[1][kth_row, kth_col])\n\n b_mat[i, m] = color_prob\n ## 归一化\n if sum(b_mat[i]) > 0:\n b_mat[i] = b_mat[i] / sum(b_mat[i])\n else:\n b_mat[i] = [1 / num_visible_state for _ in range(num_visible_state)]\n\n for i in range(HEIGHT):\n for j in range(WIDTH):\n index = map_2D_to_1D(i, j)\n max_index = np.argmax(b_mat[index])\n visible_state[i, j] = max_index\n r, s = map_m_to_r_s(max_index)\n disparity = depth_vec[r]\n is_visible = visibility_conf_mat[s][1]\n disparity_image[i, j] = disparity\n visible_image[i, j] = is_visible\n\n # disparity_image = true_disparity_image.copy()\n\n\ndef map_ideal_to_kth_with_disparity(row, col, k, disparity):\n if k == 0:\n return row, col\n else:\n return row, col - disparity\n# def map_ideal_to_kth(i, j, k, disparity_image):\n# if k == 0:\n# return int(i), int(j)\n# else:\n# disp = disparity_image[i, j]\n# return int(i), int(j - disp)\n\n## @param i: row(0 ~ HEIGHT - 1)\n## @param j: col(0 ~ WIDTH - 1)\n## @return: 1d index\n\n\ndef map_2D_to_1D(i, j, n_rows=HEIGHT, n_cols=WIDTH):\n if j >= n_cols:\n print(f\"[map_2D_to_1D]: width {j} index out of range\")\n return None\n elif j < 0:\n print(f\"[map_2D_to_1D]: width {j} index out of range\")\n if i >= n_rows:\n print(f\"[map_2D_to_1D]: height {i} index out of range\")\n return None\n elif i < 0:\n print(f\"[map_2D_to_1D]: height {i} index out of range\")\n return int(i * n_cols + j)\n\n## @param index: 1d index\n## @return i: row(0 ~ HEIGHT - 1)\n## @return j: col(0 ~ WIDTH - 1)\n\n\ndef map_1D_to_2D(index, n_rows=HEIGHT, n_cols=WIDTH):\n i = index // n_cols\n j = index - i * n_cols\n if i >= n_rows:\n print(f\"[map_1D_to_2D] height {i} index out of range\")\n return None\n if j >= n_cols:\n print(f\"[map_1D_to_2D] width {j} index out of range\")\n return None\n return (int(i), int(j))\n\n\ndef map_m_to_r_s(m, R=NUM_DEPTH_LEVEL, S=NUM_VISIBLE_CONF):\n return map_1D_to_2D(m, R, S)\n\n\ndef map_r_s_to_m(r, s, R=NUM_DEPTH_LEVEL, S=NUM_VISIBLE_CONF):\n return map_2D_to_1D(r, s, R, S)\n\n\ndef is_out_image(i, j, height=HEIGHT, width=WIDTH):\n res = False\n if i < 0 or i >= height or j < 0 or j >= width:\n res = True\n\n return res\n\n\ndef norm_pdf(x, mu, sigma):\n return math.exp(-0.5 * (x - mu) ** 2 / sigma ** 2) / (math.sqrt(2 * math.pi) * sigma)\n\n\ndef hist_prob(k, color, num_color_in_bin=num_color_in_bin):\n bin_index = int(color // num_color_in_bin)\n return hist_mat[k][bin_index]\n\n\ndef neighbor(index, height=HEIGHT, width=WIDTH):\n i, j = map_1D_to_2D(index)\n left = i * width + j - 1\n right = i * width + j + 1\n upper = (i - 1) * width + j\n lower = (i + 1) * width + j\n\n if i == 0:\n if j == 0:\n return (lower, right)\n elif j == width - 1:\n return (lower, left)\n else:\n return (lower, left, right)\n\n if i == height - 1:\n if j == 0:\n return (upper, right)\n elif j == width - 1:\n return (upper, left)\n else:\n return(upper, left, right)\n\n if j == 0:\n return (upper, lower, right)\n if j == width - 1:\n return (upper, lower, left)\n\n return (left, right, upper, lower)\n\ndef nearest_int(num):\n floor = int(num)\n ceil = floor + 1\n if num - floor < 0.5:\n return floor\n else:\n return ceil\n","sub_path":"work/code/EMUtils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"362686106","text":"\nimport glob\nimport tensorflow as tf\nfrom tensorflow.contrib.data import Dataset, Iterator\nfrom delf import feature_io\nimport numpy as np\n\ndef load(exemple):\n try:\n loc, _, desc, _, _ = feature_io.ReadFromFile(exemple)\n except Exception as error:\n desc = np.zeros((1, 40))\n return desc\n\ndef main():\n \n filenames = glob.glob('/home/alexandrearaujo/kaggle/landmark-retrieval-challenge/tmp/feature_test_rescale_delf/*')\n dataset = (Dataset.from_tensor_slices(filenames).apply(\n tf.contrib.data.parallel_interleave(lambda x: tf.data.Dataset.from_tensors(load(x)), cycle_length=4, block_length=16))\n )\n\n # create TensorFlow Iterator object\n iterator = Iterator.from_structure(dataset)\n next_element = iterator.get_next()\n\n\n # iterator = dataset.make_one_shot_iterator()\n # with tf.Session() as sess:\n # tf.global_variables_initializer()\n # sess.run(iterator)\n\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"test_tf_dataset.py","file_name":"test_tf_dataset.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"554632977","text":"import os\nfrom os.path import join, dirname\nfrom dotenv import load_dotenv #python-dotenv\nfrom datetime import datetime, timezone\n\ndotenv_path = join(dirname(__file__), '.env')\nload_dotenv(dotenv_path)\n\nCK = os.environ.get(\"CK\")\nCS = os.environ.get(\"CS\")\nATK = os.environ.get(\"ATK\")\nATS = os.environ.get(\"ATS\")\nmyname=os.environ.get(\"YAK_ROVER_NAME\",\"other-bot\")\n\nimport twitter\napi = twitter.Api(consumer_key=CK,\n consumer_secret=CS,\n access_token_key=ATK,\n access_token_secret=ATS)\nstatus = api.PostUpdate('#{}: '.format(myname)+'Daily status update: I am online.')","sub_path":"sendstatus.py","file_name":"sendstatus.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"11918319","text":"# https://www.acmicpc.net/problem/16973\n\nfrom collections import deque\nimport sys\n\ninput = sys.stdin.readline\n\nN, M = map(int, input().split())\nboard = [list(map(int, input().split())) for _ in range(N)]\nH, W, S_r, S_c, F_r, F_c = map(int, input().split())\nS_r, S_c, F_r, F_c = S_r - 1, S_c - 1, F_r - 1, F_c - 1\nblock = []\nfor r in range(N):\n for c in range(M):\n if board[r][c]:\n block.append((r, c))\n\n\ndef check_block(r, c):\n for br, bc in block:\n if r <= br < r + H and c <= bc < c + W:\n return False\n return True\n\n\ndef bfs(sr, sc):\n q = deque([(sr, sc, 0)])\n visited = [[0] * M for _ in range(N)]\n visited[sr][sc] = 1\n while q:\n r, c, cnt = q.popleft()\n if (r, c) == (F_r, F_c):\n return cnt\n for dr, dc in [(0, 1), (-1, 0), (1, 0), (0, -1)]:\n nr, nc = r + dr, c + dc\n if (\n 0 <= nr < N\n and 0 <= nc < M\n and 0 <= nr + H - 1 < N\n and 0 <= nc + W - 1 < M\n ):\n if not visited[nr][nc] and check_block(nr, nc):\n visited[nr][nc] = 1\n q.append((nr, nc, cnt + 1))\n return -1\n\n\nprint(bfs(S_r, S_c))\n","sub_path":"BOJ/graph/16973.py","file_name":"16973.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"472853213","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2019-08-11 11:40\n# @Author : essethon\n# @Site : \n# @File : AddrStrSearch.py\n# @Software: PyCharm\n\nimport configparser\nimport csv\nfrom AmapAPICaller import Amap\n\n# Config file path\nconf_path = 'parameters.conf'\n\n# Get API Key from config file\nconfig = configparser.ConfigParser()\nconfig.read(conf_path)\namap_api_key = config['Amap']['Key']\n\n# Create Amap API caller\ncaller = Amap(amap_api_key)\n\n# Test 1\n# names = caller.search('北京市大兴区黄村镇小营', citylimit=True, output='JSON', mode='names')\n# print(names)\n# End of Test 1\n\n# Read data file\ndata_file_path = config['Data']['file_path']\nencoding = config['Data']['encoding']\nwrite_to_path = 'amap_poi_suggestions.csv'\n\nwith open(data_file_path, 'r', encoding=encoding) as datafile, \\\n open(write_to_path, 'w') as write_to_file:\n\n reader = csv.reader(datafile, delimiter=',')\n writer = csv.writer(write_to_file, delimiter=',')\n\n original_fieldnames = next(reader) # Skip CSV header\n # final_fieldnames = original_fieldnames + [f'Auto_Navi_POI_{i+1}' for i in range(5)]\n final_fieldnames = original_fieldnames + ['AutoNavi_Name', 'x', 'y']\n\n writer.writerow(final_fieldnames) # Write header\n\n idx = 0 # set counter\n\n for row in reader:\n address = row[1]\n\n autonavi_search = caller.search(address, citylimit=True, mode='default') # Do the search\n # if len(names) < 5:\n # names.extend([''] * (5 - len(names)))\n\n writer.writerow(row + autonavi_search)\n\n idx += 1\n if idx % 10 == 0:\n print(f'{idx} lines written.')\n # print(row, names)\n\n\n\n","sub_path":"AddrStrSearch.py","file_name":"AddrStrSearch.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"589355026","text":"#! /usr/bin/env python\n\nfrom neato_node.msg import Bump\nfrom sensor_msgs.msg import LaserScan\nfrom geometry_msgs.msg import Twist\nimport rospy\n\nclass Collision():\n\n def __init__(self):\n rospy.init_node('collision')\n rospy.Subscriber('/bump', Bump, self.bump_callback,\n queue_size=10)\n rospy.Subscriber('/scan', LaserScan, self.laser_callback,\n queue_size=10)\n self.pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)\n self.isBump = False\n self.frontScan = 0\n self.state = \"forward\"\n self.time_interval = rospy.get_time()\n\n def bump_callback(self, msg):\n if (msg.leftFront or msg.rightFront or msg.leftSide or msg.rightSide):\n self.isBump = True\n else:\n self.isBump = False\n\n def laser_callback(self, msg):\n self.frontScan = msg.ranges[0]\n\n def act(self):\n twist_msg = Twist()\n if (self.state == \"forward\"):\n if (self.isBump):\n self.state = \"backward\"\n twist_msg.linear.x = 0\n else:\n twist_msg.linear.x = 1\n elif (self.state == \"backward\"):\n if (self.frontScan >= 2):\n self.state = \"rotate_left\"\n twist_msg.linear.x = 0\n self.time_interval = rospy.get_time()\n else:\n twist_msg.linear.x = -1\n elif (self.state == \"rotate_left\"):\n if (rospy.get_time() - self.time_interval >= 1):\n self.state = \"forward\"\n twist_msg.angular.z = 0\n else:\n twist_msg.angular.z = 1\n self.pub.publish(twist_msg)\n\nif (__name__==\"__main__\"):\n collide = Collision()\n r = rospy.Rate(10)\n while (not rospy.is_shutdown()):\n r.sleep()\n collide.act()\n","sub_path":"in_class_day03/src/fsm_emergency_stop.py","file_name":"fsm_emergency_stop.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"149840060","text":"import unittest\nimport warnings\n\nimport requests\n\nfrom core import util\nfrom core.db.rdb_handler import RDBHandler\nfrom crawler.parser.creator_code import CreatorCodeParser\nfrom crawler.spiders import creator_code\n\n\n# 전체 크롤링 프로세스의 흐름을 재현하여 원하는 데이터가 올바르게 디비에 저장되는 지를 테스트한다.\nclass SpiderTest(unittest.TestCase):\n def setUp(self):\n self.env = util.get_env()\n self.config = util.load_config(self.env)\n self.parser = CreatorCodeParser()\n self.rdb_handler = RDBHandler(self.config)\n warnings.simplefilter('ignore')\n\n def test_parse_creatorpage(self):\n creator_neezmoa_codes = ['172693169']\n creator_youtube_codes = ['UCpgoxljexZ23-K5dN0-6hVA']\n\n for i, creator_neezmoa_code in enumerate(creator_neezmoa_codes):\n url = \"https://neezmoa.com/youtuber/{0}\".format(creator_neezmoa_code)\n response = requests.get(url)\n creator_youtube_code = self.parser.parse_creator_code(response.text)\n result = creator_code.update_youtube_code(rdb_handler=self.rdb_handler,\n creator_youtube_code=creator_youtube_code,\n creator_neezmoa_code=creator_neezmoa_code,\n istest=True)\n assert (result==len(creator_neezmoa_codes))\n assert (creator_youtube_code==creator_youtube_codes[i])\n\n update_query = 'update creators set creator_youtube_code=\"\", istest=False where istest=True'\n result, _ = self.rdb_handler.execute_query(update_query)\n assert (result==len(creator_neezmoa_codes))\n\n def test_get_neezmoa_codes(self):\n neezmoa_codes = creator_code.select_neezmoa_codes(self.rdb_handler, limit=10, offset=10)\n assert len(neezmoa_codes) == 10\n\n\n def test_get_creator_without_youtubecode(self):\n creator_num = creator_code.get_creator_whitout_youtubecode(self.rdb_handler)\n print(creator_num)\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/crawler/creator_code/spider_test.py","file_name":"spider_test.py","file_ext":"py","file_size_in_byte":2132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"462101179","text":"#if-else \nx=5 \nif x>=5 or x!=9:\n print(\"#1: {}\".format(x))\nelse: \n print(\"#1: 조건문에 안맞음\") \n\n#elif \nif x>7: \n print(\"#2: x는 7보다 큼\")\nelif x>1 and x<=3:\n print(\"#2: x는 1보다 크고 3보다 작음\")\nelse: \n print(\"#2: 사실 X는 5임\")\n\n#for \ny=['heart shaker', 'likey', 'signal','knock knock','tt','cheer up']\nz=['병장','상병','일병','이병','훈련병']\n\nfor twice in y:\n print(\"#3: {!s}\".format(twice))\n\n#{0!s}을 선언하여 z의 요소 수만큼, {1!s}를 선언하여 요소를 순서대로 출력\nfor rank in range(len(z)):\n print(\"#3.{0!s}: {1!s}\".format(rank, z[rank]))\n\n#하나의 시퀀스에서 생성된 인덱스들을 이용하여 다른 시퀀스의 동일한 인덱스에\n#해당하는 값에 접근하는 방법을 보여줌\nfor q in range(len(y)):\n if y[q].startswith('Q'):\n print(\"#3.5: {!s}\".format(y[q])) \n\n#dictionary의 key와 value에 반복해서 접근\no_dict = {'x':'printer', 'y':5, 'z':['별','동글',9]}\nfor key, value in o_dict.items(): #dict내의 각 key-value tuple을 출력\n print(\"#3.6: {!s}\".format(key,value))\n\n\n#list comprehension\n#리스트축약을 이용해서 특정 해를 구하기 \nl_data=[[1,2,3],[4,5,6],[7,8,9]]\nrow_keep = [row for row in l_data if row[2]>5] #행의 두 번째 인덱스에 위치한 값이 5보다 큰 인덱스만 남겨! \nprint(\"#4: {}\".format(row_keep))\n\n#set comprehension\n#tuple의 집합을 추출하는 방법 \nt_data=[(1,2,3),(4,5,6),(7,8,9),(7,8,9)]\nset_tules1 = {x for x in t_data}\nprint(\"#4.1: {}\".format(set_tules1))\nset_tules2 = set(t_data)\nprint(\"#4.2: {}\".format(set_tules2)) # #2와 같음 \n\n#dictionary comprehension\n#dict 축약을 이용하여 특정 조건에 부함하는 dict의 key-vlaue 쌍들의 부분집합 선택\nd_data={'c1':78, 'c2':92, 'c3':128}\nd_data_results={key: value for key, value in d_data.items() if value>80}\nprint(\"#4.3: {}\".format(d_data_results))\n\n#while\n#body가 실행되어야 할 횟수를 이미 알고 있을 경우 유용함, 알지 못할 때는 for문 이용 \nk=1 \nwhile k<=6:\n print(\"#5: {!s}\".format(k))\n k+=1\n\n ","sub_path":"ch1/control_flow.py","file_name":"control_flow.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"315858605","text":"from typing import Any, Dict, Optional, Tuple\n\nfrom ..action.action_handler import ActionHandler\nfrom ..presenter import Payload as PresenterPayload\nfrom ..presenter.presenter import PresenterHandler\nfrom ..shared.interfaces.logging import LoggingModule\nfrom ..shared.interfaces.services import Services\nfrom ..shared.interfaces.wsgi import Headers, ResponseBody, View\nfrom .application import Request\n\n\nclass BaseView(View):\n \"\"\"\n Base class for views of this service.\n\n During initialization we bind the dependencies to the instance.\n \"\"\"\n\n def __init__(self, logging: LoggingModule, services: Services) -> None:\n self.services = services\n self.logging = logging\n self.logger = logging.getLogger(__name__)\n\n def get_user_id_from_headers(\n self, headers: Headers, cookies: Dict\n ) -> Tuple[int, Optional[str]]:\n \"\"\"\n Returns user id from authentication service using HTTP headers.\n \"\"\"\n user_id, access_token = self.services.authentication().authenticate(\n headers, cookies\n )\n self.logger.debug(f\"User id is {user_id}.\")\n return user_id, access_token\n\n def dispatch(self, request: Request) -> Tuple[ResponseBody, Optional[str]]:\n raise NotImplementedError()\n\n\nclass ActionView(BaseView):\n \"\"\"\n The ActionView receives a bundle of actions via HTTP and handles it to the\n ActionHandler after retrieving request user id.\n \"\"\"\n\n method = \"POST\"\n\n def dispatch(self, request: Request) -> Tuple[ResponseBody, Optional[str]]:\n \"\"\"\n Dispatches request to the viewpoint.\n \"\"\"\n self.logger.debug(\"Start dispatching action request.\")\n\n # Get user id.\n user_id, access_token = self.get_user_id_from_headers(\n request.headers, request.cookies\n )\n\n # Handle request.\n handler = ActionHandler(logging=self.logging, services=self.services)\n is_atomic = not request.environ[\"RAW_URI\"].endswith(\"handle_separately\")\n response = handler.handle_request(request.json, user_id, is_atomic)\n\n self.logger.debug(\"Action request finished successfully.\")\n return response, access_token\n\n def get_health_info(self) -> Dict[str, Any]:\n \"\"\"\n Returns some status information. HTTP method is ignored.\n \"\"\"\n return dict(actions=dict(ActionHandler.get_health_info()))\n\n\nclass PresenterView(BaseView):\n \"\"\"\n The PresenterView receives a bundle of presentations via HTTP and handles\n it to the PresenterHandler.\n \"\"\"\n\n method = \"POST\"\n\n def dispatch(self, request: Request) -> Tuple[ResponseBody, Optional[str]]:\n \"\"\"\n Dispatches request to the viewpoint.\n \"\"\"\n self.logger.debug(\"Start dispatching presenter request.\")\n\n # Get user_id.\n user_id, access_token = self.get_user_id_from_headers(\n request.headers, request.cookies\n )\n\n # Setup payload.\n payload: PresenterPayload = request.json\n\n # Handle request.\n handler = PresenterHandler(\n logging=self.logging,\n services=self.services,\n )\n presenter_response = handler.handle_request(payload, user_id)\n\n # Finish request.\n self.logger.debug(\"Presenter request finished successfully. Send response now.\")\n return presenter_response, access_token\n\n def get_health_info(self) -> Dict[str, Any]:\n \"\"\"\n Returns some status information. HTTP method is ignored.\n \"\"\"\n return {\"status\": \"unkown\"}\n","sub_path":"openslides_backend/http/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"430217059","text":"# ovirt-image-daemon\n# Copyright (C) 2015-2016 Red Hat, Inc.\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n\nimport httplib\nimport re\nimport webob\nimport json\n\nfrom webob.exc import (\n HTTPBadRequest,\n HTTPException,\n HTTPMethodNotAllowed,\n HTTPNotFound,\n HTTPInternalServerError,\n)\n\n\nclass Application(object):\n ALLOWED_METHODS = frozenset(['GET', 'PUT', 'PATCH', 'POST',\n 'DELETE', 'OPTIONS', 'HEAD'])\n \"\"\"\n WSGI application dispatching requests based on path and method to request\n handlers.\n \"\"\"\n\n def __init__(self, config, routes):\n self.config = config\n self.routes = [(re.compile(pattern), cls) for pattern, cls in routes]\n\n def __call__(self, env, start_response):\n request = webob.Request(env)\n try:\n resp = self.dispatch(request)\n except Exception as e:\n if not isinstance(e, HTTPException):\n e = HTTPInternalServerError(detail=str(e))\n resp = error_response(e)\n return resp(env, start_response)\n\n def dispatch(self, request):\n if request.method not in self.ALLOWED_METHODS:\n raise HTTPMethodNotAllowed(\"Invalid method %r\" %\n request.method)\n path = request.path_info\n for route, handler_class in self.routes:\n match = route.match(path)\n if match:\n handler = handler_class(self.config, request)\n try:\n method = getattr(handler, request.method.lower())\n except AttributeError:\n raise HTTPMethodNotAllowed(\n \"Method %r not defined for %r\" %\n (request.method, path))\n else:\n request.path_info_pop()\n return method(*match.groups())\n raise HTTPNotFound(\"No handler for %r\" % path)\n\n\ndef error_response(e):\n \"\"\"\n Return WSGI application for sending error response using JSON format.\n \"\"\"\n payload = {\n \"code\": e.code,\n \"title\": e.title,\n \"explanation\": e.explanation\n }\n if e.detail:\n payload[\"detail\"] = e.detail\n return response(status=e.code, payload=payload)\n\n\ndef response(status=httplib.OK, payload=None):\n \"\"\"\n Return WSGI application for sending response in JSON format.\n \"\"\"\n body = json.dumps(payload) if payload else \"\"\n return webob.Response(status=status, body=body,\n content_type=\"application/json\")\n\n\ndef content_range(request):\n \"\"\"\n Helper for parsing Content-Range header in request.\n\n WebOb support parsing of Content-Range header, but do not expose this\n header in webob.Request.\n \"\"\"\n try:\n header = request.headers[\"content-range\"]\n except KeyError:\n return webob.byterange.ContentRange(None, None, None)\n content_range = webob.byterange.ContentRange.parse(header)\n if content_range is None:\n raise HTTPBadRequest(\"Invalid content-range: %r\" % header)\n return content_range\n","sub_path":"common/ovirt_image_common/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":3286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"545864590","text":"\"\"\"\nCALCULADORA:\n- Dos campos de texto\n- 4 botones para las operaciones\n- Mostrar el resultado en una alerta\n\"\"\"\n\nfrom tkinter import *\nfrom tkinter import messagebox as Messagebox\n\nventana = Tk()\nventana.geometry(\"900x600\")\nventana.config(bd=30)\n\ndef coFloat(numero):\n try:\n result = float(numero)\n except:\n result = 0\n Messagebox.showerror(\"Error\", \"Asegurate de introducir solo numeros\")\n limpiarVariables()\n \n return result\n\ndef operacion(operador):\n if operador == \"*\":\n resultado.set(coFloat(operador_uno.get()) * coFloat(operador_dos.get()))\n mostrarResultado() \n \n elif operador == \"+\":\n resultado.set(coFloat(operador_uno.get()) + coFloat(operador_dos.get()))\n mostrarResultado() \n \n elif operador == \"/\":\n resultado.set(coFloat(operador_uno.get()) / coFloat(operador_dos.get()))\n mostrarResultado() \n \n elif operador == \"-\":\n resultado.set(coFloat(operador_uno.get()) - coFloat(operador_dos.get()))\n mostrarResultado()\n\ndef limpiarVariables():\n #Vacio variables\n operador_uno.set(\"\")\n operador_dos.set(\"\")\n\ndef mostrarResultado():\n Messagebox.showinfo(\"RESULTADO \", f\"El resultado de la operación es: {resultado.get()}\")\n limpiarVariables()\n\n#Variables\nresultado = StringVar()\noperador_uno = StringVar()\noperador_dos = StringVar()\n\n#Frame - Marco\nmarco = Frame(ventana, width = 350, height = 200)\nmarco.config(\n padx = 15,\n pady = 15,\n bd=5,\n relief=SOLID\n)\nmarco.pack(side=TOP, anchor = CENTER)\nmarco.pack_propagate(False)\n\n# Titulo\ntexto_titulo = Label(marco, text=\"CALCULADORA\")\ntexto_titulo.pack()\ntexto_titulo.config(bg = \"gray\", font = (\"Arial\",20))\n\n# Campo input 1\nLabel(marco, text= \"Primer número: \").pack()\ncampo_uno = Entry(marco, textvariable = operador_uno, justify = CENTER).pack()\n\n\n# Campo input 2\nLabel(marco, text= \"Segundo número: \").pack()\ncampo_uno = Entry(marco, textvariable = operador_dos, justify = CENTER).pack()\n\n# Boton MAS +\nboton_mas = Button(marco,text = \"+ Sumar\", command = lambda: operacion(\"+\")).pack(side = \"left\", fill = X , expand = YES)\n\n# Boton MENOS -\nboton_mas = Button(marco,text = \"- Restar\", command = lambda: operacion(\"-\")).pack(side = \"left\", fill = X , expand = YES)\n\n# Boton MULTIPLICACION * X\nboton_mas = Button(marco,text = \"* Multiplicar\", command = lambda: operacion(\"*\")).pack(side = \"left\", fill = X , expand = YES)\n\n# Boton DIVISION /\nboton_mas = Button(marco,text = \"/ Dividir\", command = lambda: operacion(\"/\")).pack(side = \"left\", fill = X , expand = YES)\n\nventana.mainloop()","sub_path":"Python/practicas_victor_masterpython/21-tikinter/09-ejercicio.py","file_name":"09-ejercicio.py","file_ext":"py","file_size_in_byte":2653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"608131997","text":"from django.conf import settings\nfrom django.db import transaction\nfrom django.db.models import Q\nfrom django.utils.translation import gettext_lazy as _\nfrom rest_framework import exceptions, status\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\n\nfrom waldur_core.core import validators as core_validators\nfrom waldur_core.core.views import ActionsViewSet, ReviewViewSet\nfrom waldur_core.structure import permissions as structure_permissions\nfrom waldur_core.structure.models import CustomerRole\nfrom waldur_mastermind.marketplace.views import ConnectedOfferingDetailsMixin\nfrom waldur_mastermind.support import models as support_models\n\nfrom . import filters, models, serializers, utils\n\n\ndef is_owner_of_service_provider(request, view, obj=None):\n if not obj:\n return\n if request.user.is_staff:\n return\n if obj.offering.customer.has_user(request.user):\n return\n raise exceptions.PermissionDenied(\n _(\n 'Only owner of service provider is allowed to review resource creation request.'\n )\n )\n\n\nclass CustomerCreateRequestViewSet(ReviewViewSet):\n lookup_field = 'flow__uuid'\n queryset = models.CustomerCreateRequest.objects.all()\n approve_permissions = reject_permissions = [structure_permissions.is_staff]\n filterset_class = filters.CustomerCreateRequestFilter\n serializer_class = serializers.CustomerCreateRequestSerializer\n\n def get_queryset(self):\n qs = super().get_queryset()\n if self.request.user.is_staff:\n return qs\n # Allow to see user's own requests only\n return qs.filter(flow__requested_by=self.request.user)\n\n\nclass ProjectCreateRequestViewSet(ReviewViewSet):\n lookup_field = 'flow__uuid'\n queryset = models.ProjectCreateRequest.objects.all()\n approve_permissions = reject_permissions = [structure_permissions.is_owner]\n filterset_class = filters.ProjectCreateRequestFilter\n serializer_class = serializers.ProjectCreateRequestSerializer\n\n def get_queryset(self):\n qs = super().get_queryset()\n if self.request.user.is_staff:\n return qs\n return qs.filter(\n Q(flow__requested_by=self.request.user)\n | Q(flow__customer=None)\n | Q(\n flow__customer__permissions__user=self.request.user,\n flow__customer__permissions__role=CustomerRole.OWNER,\n flow__customer__permissions__is_active=True,\n )\n )\n\n\nclass ResourceCreateRequestViewSet(ConnectedOfferingDetailsMixin, ReviewViewSet):\n lookup_field = 'flow__uuid'\n queryset = models.ResourceCreateRequest.objects.all()\n approve_permissions = reject_permissions = [is_owner_of_service_provider]\n filterset_class = filters.ResourceCreateRequestFilter\n serializer_class = serializers.ResourceCreateRequestSerializer\n\n def get_queryset(self):\n qs = super().get_queryset()\n if self.request.user.is_staff:\n return qs\n return qs.filter(\n Q(flow__requested_by=self.request.user)\n | Q(\n offering__customer__permissions__user=self.request.user,\n offering__customer__permissions__role=CustomerRole.OWNER,\n offering__customer__permissions__is_active=True,\n )\n )\n\n\nclass FlowViewSet(ActionsViewSet):\n queryset = models.FlowTracker.objects.all()\n lookup_field = 'uuid'\n update_validators = (\n partial_update_validators\n ) = submit_validators = cancel_validators = [\n core_validators.StateValidator(models.ReviewMixin.States.DRAFT)\n ]\n disabled_actions = ['destroy']\n serializer_class = serializers.FlowSerializer\n filterset_class = filters.FlowFilter\n\n @action(detail=True, methods=['post'])\n def submit(self, request, uuid=None):\n flow = self.get_object()\n flow.submit()\n return Response(status=status.HTTP_200_OK)\n\n @action(detail=True, methods=['post'])\n def cancel(self, request, uuid=None):\n flow = self.get_object()\n flow.cancel()\n return Response(status=status.HTTP_200_OK)\n\n def get_queryset(self):\n qs = super().get_queryset()\n if self.request.user.is_staff:\n return qs\n # Allow to see user's own requests only\n return qs.filter(requested_by=self.request.user)\n\n\nclass OfferingActivateRequestViewSet(ReviewViewSet):\n queryset = models.OfferingStateRequest.objects.all()\n approve_permissions = reject_permissions = [structure_permissions.is_staff]\n filterset_class = filters.OfferingActivateRequestFilter\n serializer_class = serializers.OfferingActivateRequestSerializer\n disabled_actions = ['destroy', 'update', 'partial_update']\n\n def get_queryset(self):\n qs = super().get_queryset()\n if self.request.user.is_staff:\n return qs\n # Allow to see user's own requests only\n return qs.filter(requested_by=self.request.user)\n\n @transaction.atomic()\n def perform_create(self, serializer):\n offering_request = serializer.save()\n\n if settings.WALDUR_SUPPORT['ENABLED']:\n response = utils.create_issue(offering_request)\n\n if response.status_code == status.HTTP_201_CREATED:\n offering_request.submit()\n offering_request.issue = support_models.Issue.objects.get(\n uuid=response.data['uuid']\n )\n offering_request.save()\n else:\n raise exceptions.ValidationError(response.rendered_content)\n\n @action(detail=True, methods=['post'])\n def submit(self, request, **kwargs):\n review_request = self.get_object()\n review_request.submit()\n return Response(status=status.HTTP_200_OK)\n\n @action(detail=True, methods=['post'])\n def cancel(self, request, **kwargs):\n review_request = self.get_object()\n review_request.cancel()\n return Response(status=status.HTTP_200_OK)\n\n approve_validators = reject_validators = [\n core_validators.StateValidator(models.ReviewMixin.States.PENDING)\n ]\n\n submit_validators = [\n core_validators.StateValidator(models.ReviewMixin.States.DRAFT)\n ]\n\n cancel_validators = [\n core_validators.StateValidator(\n models.ReviewMixin.States.DRAFT, models.ReviewMixin.States.PENDING\n )\n ]\n","sub_path":"src/waldur_mastermind/marketplace_flows/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"402550858","text":"import serial.tools.list_ports\nfrom datetime import datetime\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5 import uic\nimport triad_openvr\nimport threading\nimport serial\nimport time\nimport sys\nimport socket\n\nu = 0\nSERIAL = None\nUDP_IP = str(socket.gethostbyname(socket.gethostname()))\n# drag_coefficient = 255\ndrag_coefficient = 100\n# max_speed = 255\nmax_speed = 100\nUDP_PORT_Rec = 3040\nUDP_PORT_Unity = 3031\n\n'''try:\n f = open(\"port.txt\",'r')\n SERIAL = f.read()\n f1 = open('IP.txt', 'r')\n UDP_IP = f1.read()\n\nexcept Exception as e:\n SERIAL = 'LHR-9D5EB008'\n UDP_IP = \"192.168.137.143\"\n print('File not found')\n'''\n\n\nclass TreadmillControl(QMainWindow):\n def __init__(self):\n super(QMainWindow, self).__init__()\n uic.loadUi('ui5.ui', self)\n self.setWindowTitle('Treadmill')\n self.current_speed = 0\n self.treadmill_length = 70\n # self.max_speed = 255\n self.max_speed = 100\n self.human_pos = None\n\n self.MainWhile = False\n self.ArdWhile = False\n\n self.conn = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n self.conn.bind(('', UDP_PORT_Rec))\n\n # Предустановка Arduino\n try:\n x = self.Search()\n self.COM_port = x[0]\n self.ArdComPort.setText(f'''{x[0]}
''')\n except:\n self.COM_port = None\n\n self.ard_speed = 9600\n self.ard_trackers = 'LHR-9D5EB008'\n self.arduino = None\n if self.arduino:\n try:\n self.ard_connect()\n except:\n pass\n\n # Калибровка датчиков\n self.calibration()\n\n # Ui\n self.StartButton.clicked.connect(self.start)\n self.UP_Button.clicked.connect(self.update_ip)\n self.Calibration_button.clicked.connect(self.calibration)\n self.StopButton.clicked.connect(self.stop)\n self.IP.setText(str(socket.gethostbyname(socket.gethostname())))\n # -- Max Speed bar\n self.MaxSpeedSlider.valueChanged.connect(self.speed_changed_slider)\n self.MaxSpeedBox.valueChanged.connect(self.speed_changed_box)\n self.MaxSpeedSlider.setValue(max_speed)\n self.SpeedLock.clicked.connect(self.speed_lock)\n # -- Length Bar\n self.LengthSlider.valueChanged.connect(self.length_changed_slider)\n self.LengthBox.valueChanged.connect(self.length_changed_box)\n self.LengthSlider.setValue(70)\n self.LengthLock.clicked.connect(self.length_lock)\n # -- Ard control\n self.Connect.clicked.connect(self.ard_connect)\n self.Disconnect.clicked.connect(self.ard_disconnect)\n # -- Ard settings\n self.ArdComPortSelect.clicked.connect(self.ard_change_port)\n self.Ard_trackers_button.clicked.connect(self.ard_change_trackers)\n self.ArdSpeedSelect.clicked.connect(self.ard_change_speed)\n\n def calibration(self):\n self.z_napr = 1\n v = triad_openvr.triad_openvr()\n self.human_pos = None\n hmd_pos = None\n self.human_0 = None\n self.pos_devices_array = []\n n = 1\n while n > 0 and self.human_pos is None:\n n -= 1\n for device in v.devices:\n position_device = v.devices[device].sample(1, 500)\n\n if position_device:\n\n if v.devices[device].device_class == 'HMD':\n hmd_pos = (position_device.get_position_x()[0], position_device.get_position_y()[0],\n position_device.get_position_z()[0],\n v.devices[device].get_serial(), v.device_index_map[v.devices[device].index])\n else:\n\n self.pos_devices_array.append(\n (v.devices[device].get_serial(),\n v.device_index_map[v.devices[device].index]))\n print(v.devices[device].get_serial())\n if SERIAL is None:\n print(\"OK\")\n self.human_0 = [position_device.get_position_x()[0], position_device.get_position_y()[0],\n position_device.get_position_z()[0]]\n self.human_pos = (v.devices[device].get_serial(),\n v.device_index_map[v.devices[device].index])\n else:\n if v.devices[device].get_serial() == SERIAL or v.devices[\n device].get_serial() == SERIAL.encode():\n print(\"OK\")\n self.human_0 = [position_device.get_position_x()[0],\n position_device.get_position_y()[0],\n position_device.get_position_z()[0]]\n self.human_pos = (v.devices[device].get_serial(),\n v.device_index_map[v.devices[device].index])\n\n p_a = sorted(self.pos_devices_array, key=lambda x: x[1])\n print(p_a)\n if len(p_a) > 0:\n print(\"New postion\")\n pos_str = \"x= \" + str(self.human_0[0])[:3] + \" y= \" + str(self.human_0[1])[:3] + \" z= \" + str(\n self.human_0[2])[:5] + \"\"\n print(pos_str)\n self.console_output(\"Калибровка \" + pos_str, color=\"#000000\")\n self.slovar_trackers = {\"Человек\": self.human_pos}\n self.ard_trackers = self.human_pos\n self.Ard_trackers.setText(self.ard_trackers[0])\n\n def closeEvent(self, event):\n print(\"EXITING\")\n if self.arduino != None:\n self.stop()\n self.conn.close()\n print(\"EXIT\")\n\n def start(self):\n\n self.calibration_zone = True\n if not self.arduino :\n self.console_output(\"Соединение с Ардуино не установлено.\", color=\"#f80000\")\n print(self.arduino)\n print(\"Not connection with arduino\")\n elif len(self.pos_devices_array) == 0:\n\n self.console_output(\"Трекер не найден.\", color=\"#f80000\")\n print(self.pos_devices_array)\n else:\n print(self.arduino)\n\n self.arduino.write(bytes(str(\"Treadmill\") + '.', 'utf-8'))\n time.sleep(0.1)\n answer = self.arduino.readline()\n print(answer)\n while True:\n\n self.arduino.write(bytes(str(\"Treadmill\") + '.', 'utf-8'))\n time.sleep(0.1)\n answer = self.arduino.readline()\n print(answer)\n a1 = \"Speed\".encode() in answer\n if a1:\n break\n\n print(\"**************************\")\n print(self.arduino.readline())\n print(\"**************************\")\n\n self.console_output(\"Платформа запущена.\", color=\"#0000f8\")\n self.MainWhile = True\n main_while_thread = threading.Thread(target=self.main_while)\n main_while_thread.start()\n self.StartButton.setEnabled(False)\n self.ArduinoBar.setEnabled(False)\n self.Calibration_button.setEnabled(False)\n self.StopButton.setEnabled(True)\n\n def get_r(self, data):\n current = data[-1]\n last = data[0]\n avg = sum(data) / len(data)\n delta = 10\n if abs(current) < abs(avg) - delta:\n return -1\n else:\n return 1\n\n def get_speed_new(self, z):\n max_speed = self.max_speed\n tr_len = self.treadmill_length * (10 ** -2)\n safe_zona = 0.25\n if z < 0:\n zn = -1\n else:\n zn = 1\n z = abs(z)\n if self.moving:\n if z < safe_zona / 2:\n self.moving = False\n return 0\n elif safe_zona / 2 <= z <= safe_zona:\n delta = tr_len - safe_zona\n speed = (z - safe_zona / 2) * max_speed / (delta)\n if 0 < speed < 40:\n speed = 40\n\n # print(\"work zona\")\n return zn * min(max_speed, speed)\n elif safe_zona <= z <= tr_len:\n\n delta = tr_len - safe_zona\n if z * drag_coefficient <= max_speed:\n speed = (z - safe_zona / 2) * max_speed / (delta)\n\n # print(\"work zona\")\n return zn * min(max_speed, speed)\n else:\n\n # print(\"far zona speed\")\n return zn * max_speed\n elif z > tr_len:\n # print(\"far zona\")\n return zn * max_speed\n else:\n print(\"error\")\n return 0\n else:\n if z < safe_zona:\n return 0\n elif safe_zona <= z <= tr_len:\n self.moving = True\n delta = tr_len - safe_zona\n if z * drag_coefficient <= max_speed:\n speed = (z - safe_zona) * max_speed / (delta)\n if speed < 5:\n safe_zona = 0\n\n # print(\"work zona\")\n return zn * min(max_speed, speed)\n else:\n\n # print(\"far zona speed\")\n return zn * max_speed\n else:\n print(\"error\")\n return 0\n\n def get_speed(self, z):\n max_speed = self.max_speed\n tr_len = self.treadmill_length * (10 ** -2)\n safe_zona = 0.15\n if z < 0:\n zn = -1\n else:\n zn = 1\n z = abs(z)\n if z < safe_zona:\n # print(\"safe zona\")\n return 0\n elif safe_zona <= z <= tr_len:\n delta = tr_len - safe_zona\n if z * drag_coefficient <= max_speed:\n speed = (z - safe_zona) * max_speed / (delta)\n if speed < 25:\n speed = 25\n\n # print(\"work zona\")\n return zn * min(max_speed, speed)\n else:\n\n # print(\"far zona speed\")\n return zn * max_speed\n elif z > tr_len:\n # print(\"far zona\")\n return zn * max_speed\n else:\n print(\"error\")\n return 0\n\n def get_arduino_speed(self):\n answer = self.arduino.readline().decode()\n return answer\n\n def ExtremeStop(self): # problem\n try:\n\n self.console_output(\"Остановка платформы.\", color=\"#f80000\")\n print(\"*\" * 10, \"Extreme stop\", self.current_speed)\n self.MainWhile = False\n\n if self.current_speed > 0:\n #self.arduino.write(bytes(str('Disconnect') + '.', 'utf-8'))\n while self.current_speed > 0:\n if self.arduino:\n self.current_speed -= 1\n\n self.arduino.write(bytes(str(int(self.current_speed)) + '.', 'utf-8'))\n #self.arduino.write(bytes(str('Disconnect') + '.', 'utf-8'))\n answer = self.get_arduino_speed()\n print(answer)\n else:\n break\n\n else:\n # self.arduino.write(bytes(str('-Disconnect') + '.', 'utf-8'))\n while self.current_speed < 0:\n if self.arduino:\n self.current_speed += 1\n print(\"extreme\", self.current_speed)\n\n self.arduino.write(bytes(str(int(self.current_speed)) + '.', 'utf-8'))\n #self.arduino.write(bytes(str('-Disconnect') + '.', 'utf-8'))\n answer = self.get_arduino_speed()\n print(answer)\n else:\n break\n\n self.last_speed = 0\n # self.arduino.write(bytes(str(int(0)) + '.', 'utf-8'))\n self.current_speed = 0\n\n self.StartButton.setEnabled(True)\n print(\"STOP complete\")\n\n self.console_output(\"Платформа остановлена\", color=\"#f89000\")\n except Exception as e:\n print(\"EXTREME\", e, e.__class__)\n print(self.arduino, self.ArdWhile)\n\n def update_ip(self):\n global UDP_IP\n UDP_IP = self.IP.toPlainText()\n print(\"New IP\", UDP_IP)\n\n self.console_output(\"Установлен IP\" + str(UDP_IP), color=\"#0000f8\")\n\n def tranfrom(self,speed):\n if speed!=0:\n speed = int(9890+speed)\n print(speed)\n return speed\n\n\n def main_while(self):\n self.moving = False\n self.ConsoleOutput.verticalScrollBar()\n self.last_speed = 0\n z = 0\n self.current_speed = 0\n v = triad_openvr.triad_openvr()\n\n current_serial, device = self.ard_trackers\n z_last = 0\n flag_error = False\n\n while self.MainWhile:\n # or self.current_speed != 0\n position_device = v.devices[device].sample(1, 500)\n if position_device and self.arduino:\n z = position_device.get_position_z()[0]\n if z == 0.0 and not flag_error:\n z = z_last\n flag_error = True\n\n elif z == 0.0 and flag_error:\n self.last_speed = 0\n # self.ExtremeStop()\n print(\"Stop\")\n\n else:\n z = z - self.human_0[2]\n self.current_speed = self.tranfrom(self.get_speed(z))\n\n if abs(self.current_speed - self.last_speed) > 150000:\n print(\"ERROR\", self.current_speed, self.last_speed,\n abs(self.current_speed - self.last_speed))\n self.current_speed = self.last_speed\n continue\n\n # print(\"send_norm\", self.current_speed)\n # if self.current_speed == 0 and self.last_speed == 0:\n # pass\n # else:\n self.arduino.write(bytes(str(int(self.current_speed)) + '.', 'utf-8'))\n print(\"ARDUINO\", self.arduino.readline())\n s = bytes(str(int(self.current_speed)), 'utf-8')\n self.conn.sendto(bytes(str(int(self.current_speed)).rjust(4, \" \"), 'utf-8'),\n (UDP_IP, UDP_PORT_Unity))\n z_last = z\n self.last_speed = self.current_speed\n self.Display.display(int(self.current_speed))\n\n\n self.MaxSpeedBar.setEnabled(True)\n self.LengthBar.setEnabled(True)\n self.ArduinoBar.setEnabled(True)\n self.StartButton.setEnabled(True)\n\n\n return\n\n def stop(self):\n if self.arduino:\n self.ExtremeStop()\n\n self.StopButton.setEnabled(False)\n self.ArduinoBar.setEnabled(True)\n self.Calibration_button.setEnabled(True)\n\n def ard_connect(self):\n try:\n self.arduino = serial.Serial(self.COM_port, self.ard_speed)\n self.arduino.write(bytes('0.', 'utf-8'))\n self.Status.setText('''Подключено
''')\n self.Connect.setEnabled(False)\n self.Disconnect.setEnabled(True)\n self.console_output(\"Соединение с Ардуино установлено.\", color=\"#2f8700\")\n\n except Exception as e:\n if self.COM_port:\n self.console_output(\"Соединение с Ардуино не установлено. Проверьте COM-порт или скорость.\",\n color=\"#f80000\")\n else:\n self.console_output(\"COM-порт не выбран.\", color=\"#f80000\")\n print(e)\n\n def Search(self, __baudrate=9600):\n __COMlist = []\n __COM = ['COM' + str(i) for i in range(2, 100)]\n\n for _COM in __COM:\n try:\n COMport = (serial.Serial(port=_COM,\n baudrate=__baudrate,\n parity=serial.PARITY_NONE,\n stopbits=serial.STOPBITS_ONE,\n bytesize=serial.EIGHTBITS,\n timeout=0))\n if COMport:\n __COMlist.append(_COM)\n\n except Exception as e:\n pass\n return __COMlist\n\n def CheckSerialPortMessage(self, __baudrate=115200, __timeSleep=5):\n try:\n for __COM in self.Search():\n\n port = serial.Serial(__COM, __baudrate)\n time.sleep(__timeSleep)\n large = len(port.readline())\n port.read(large)\n\n while large > 3:\n for a in range(__timeSleep):\n\n date = port.readline().decode().split()\n\n if 'treadmill' in date:\n self.arduino = port\n self.arduino.write(\"treadmill\")\n\n except Exception as e:\n pass\n\n def ard_disconnect(self):\n self.arduino = None\n self.Status.setText('''Отключено
''')\n self.Connect.setEnabled(True)\n self.Disconnect.setEnabled(False)\n\n def ard_change_port(self):\n x = self.Search()\n if x:\n new, ok = QInputDialog.getItem(self, \"Выберите COM-порт\", \"Доступные COM-Порты\", x, 0, False)\n if ok:\n self.COM_port = new\n self.ArdComPort.setText(f'''{new}
''')\n else:\n self.console_output(\"COM-порты не найдены\", color=\"#fcba03\")\n\n def ard_change_trackers(self):\n global SERIAL\n accept_trackers = []\n for device in self.pos_devices_array:\n accept_trackers.append(device[0])\n tracker, ok = QInputDialog.getItem(self, \"Трекеры\", \"Доступные трекеры\", accept_trackers, False)\n if ok:\n for device in self.pos_devices_array:\n if device[0] == tracker:\n self.ard_trackers = device\n self.Ard_trackers.setText(tracker)\n SERIAL = tracker\n self.console_output(\"Выбран трекер \" + str(tracker), color=\"#0000f8\")\n\n def ard_change_speed(self):\n speeds = ['1200', '2400', '4800', '9600', '19200', '38400', '57600', '115200']\n x = speeds.index(str(self.ard_speed))\n new, ok = QInputDialog.getItem(self, \"Cкорость\", \"Доступные скорости\", speeds, x, False)\n if ok:\n self.ard_speed = int(new)\n self.ArdSpeed.display(new)\n\n def change_speed(self):\n self.max_speed = self.MaxSpeedSlider.value()\n\n\n def change_length(self):\n self.treadmill_length = self.LengthSlider.value()\n\n def speed_changed_slider(self):\n self.MaxSpeedBox.setValue(self.MaxSpeedSlider.value())\n self.change_speed()\n\n def speed_changed_box(self):\n self.MaxSpeedSlider.setValue(self.MaxSpeedBox.value())\n\n def length_changed_slider(self):\n self.LengthBox.setValue(self.LengthSlider.value())\n self.change_length()\n\n def length_changed_box(self):\n self.LengthSlider.setValue(self.LengthBox.value())\n\n def speed_lock(self):\n _translate = QCoreApplication.translate\n self.SpeedBox.setEnabled(not self.SpeedBox.isEnabled())\n self.SpeedSlider.setEnabled(not self.SpeedSlider.isEnabled())\n\n if self.SpeedSlider.isEnabled():\n self.SpeedLock.setText(_translate(\"Form\", \"🔓\"))\n else:\n self.SpeedLock.setText(_translate(\"Form\", \"🔒\"))\n\n def length_lock(self):\n _translate = QCoreApplication.translate\n self.LengthBox.setEnabled(not self.LengthBox.isEnabled())\n self.LengthSlider.setEnabled(not self.LengthSlider.isEnabled())\n\n if self.LengthSlider.isEnabled():\n self.LengthLock.setText(_translate(\"Form\", \"🔓\"))\n else:\n self.LengthLock.setText(_translate(\"Form\", \"🔒\"))\n\n def console_output(self, info, *, color: str = \"\"):\n self.ConsoleOutput.append(\n f'''\n \n [{datetime.strftime(datetime.now(), \"%H:%M:%S\")}] \n {info} \n
\n ''')\n self.ConsoleOutput.verticalScrollBar().setValue(self.ConsoleOutput.verticalScrollBar().maximum())\n self.ConsoleOutput.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n return\n\n\napp = QApplication(sys.argv)\nex = TreadmillControl()\nex.show()\nsys.exit(app.exec_())\n","sub_path":"Пайтон/Core_v_19.py","file_name":"Core_v_19.py","file_ext":"py","file_size_in_byte":21365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"316974919","text":"# -*- coding: utf-8 -*-\n__author__ = 'isparks'\n\n# This module exists to reproduce, with the rsa library, the raw signature required by MAuth\n# which in OpenSSL is created with private_encrypt(hash). It provides an RSA sign class built from\n# code that came from https://www.dlitz.net/software/pycrypto/api/current/ no copyright of that original\n# code is claimed.\n\nfrom hashlib import sha512\nfrom rsa import common, core, transform, PrivateKey\n\n#---- Original code from RSA ------------------------------------------------------------------------------------------\n\ndef byte_literal(s):\n return s\n\nb = byte_literal\n\n#----------------------------------------------------------------------------------------------------------------------\n\n\nclass RSARawSigner(object):\n def __init__(self, private_key_data):\n self.pk = PrivateKey.load_pkcs1(private_key_data, 'PEM')\n\n def sign(self, string_to_sign):\n \"\"\"Sign the data in a emulation of the OpenSSL private_encrypt method\"\"\"\n hashed = sha512(string_to_sign.encode('US-ASCII')).hexdigest()\n keylength = common.byte_size(self.pk.n)\n padded = self.pad_for_signing(hashed, keylength)\n\n payload = transform.bytes2int(padded)\n encrypted = core.encrypt_int(payload, self.pk.d, self.pk.n)\n signature = transform.int2bytes(encrypted, keylength).encode('base64').replace('\\n','')\n return signature\n\n def pad_for_signing(self, message, target_length):\n r'''Pulled from rsa pkcs1.py,\n\n Pads the message for signing, returning the padded message.\n\n The padding is always a repetition of FF bytes.\n\n :return: 00 01 PADDING 00 MESSAGE\n\n >>> block = _pad_for_signing('hello', 16)\n >>> len(block)\n 16\n >>> block[0:2]\n '\\x00\\x01'\n >>> block[-6:]\n '\\x00hello'\n >>> block[2:-6]\n '\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff'\n\n '''\n\n max_msglength = target_length - 11\n msglength = len(message)\n\n if msglength > max_msglength: #pragma: no cover\n raise OverflowError('%i bytes needed for message, but there is only'\n ' space for %i' % (msglength, max_msglength))\n\n padding_length = target_length - msglength - 3\n\n return b('').join([b('\\x00\\x01'),\n padding_length * b('\\xff'),\n b('\\x00'),\n message])\n","sub_path":"requests_mauth/rsa_sign.py","file_name":"rsa_sign.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"195015350","text":"#!/usr/bin/env python\nenv = Environment()\n\n#Configuration\nopts = Variables(\"DataFiles.conf\")\nopts.Add(PathVariable('PREFIX', 'Directory to install under', '/usr/local',\n PathVariable.PathIsDir))\nopts.Update(env)\nopts.Save(\"DataFiles.conf\", env)\n\n#Installation paths\nidir_prefix = '$PREFIX'\nidir_lib = '$PREFIX/lib'\nidir_bin = '$PREFIX/bin'\nidir_inc = '$PREFIX/include'\nidir_data = '$PREFIX/share'\nExport('env idir_prefix idir_lib idir_bin idir_inc idir_data')\n\nSConscript(\"src/SConscript\", variant_dir=\"build\")\n\nenv.Alias(\"install\", idir_prefix)\n","sub_path":"SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"56295432","text":"# Copyright 2012 OpenStack Foundation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport os\n\nfrom mock import Mock, MagicMock, patch, mock_open\nimport pexpect\n\nfrom trove.common.exception import GuestError, ProcessExecutionError\nfrom trove.common import utils\nfrom trove.guestagent import volume\nfrom trove.tests.unittests import trove_testtools\n\n\ndef _setUp_fake_spawn(return_val=0):\n fake_spawn = pexpect.spawn('echo')\n fake_spawn.expect = Mock(return_value=return_val)\n pexpect.spawn = Mock(return_value=fake_spawn)\n return fake_spawn\n\n\nclass VolumeDeviceTest(trove_testtools.TestCase):\n\n def setUp(self):\n super(VolumeDeviceTest, self).setUp()\n self.volumeDevice = volume.VolumeDevice('/dev/vdb')\n\n def tearDown(self):\n super(VolumeDeviceTest, self).tearDown()\n\n @patch.object(pexpect, 'spawn', Mock())\n def test_migrate_data(self):\n origin_execute = utils.execute\n utils.execute = Mock()\n origin_os_path_exists = os.path.exists\n os.path.exists = Mock()\n fake_spawn = _setUp_fake_spawn()\n\n origin_unmount = self.volumeDevice.unmount\n self.volumeDevice.unmount = MagicMock()\n self.volumeDevice.migrate_data('/')\n self.assertEqual(1, fake_spawn.expect.call_count)\n self.assertEqual(1, utils.execute.call_count)\n self.assertEqual(1, self.volumeDevice.unmount.call_count)\n utils.execute = origin_execute\n self.volumeDevice.unmount = origin_unmount\n os.path.exists = origin_os_path_exists\n\n def test__check_device_exists(self):\n origin_execute = utils.execute\n utils.execute = Mock()\n self.volumeDevice._check_device_exists()\n self.assertEqual(1, utils.execute.call_count)\n utils.execute = origin_execute\n\n @patch('trove.guestagent.volume.LOG')\n def test_fail__check_device_exists(self, mock_logging):\n with patch.object(utils, 'execute', side_effect=ProcessExecutionError):\n self.assertRaises(GuestError,\n self.volumeDevice._check_device_exists)\n\n @patch.object(pexpect, 'spawn', Mock())\n def test__check_format(self):\n fake_spawn = _setUp_fake_spawn()\n\n self.volumeDevice._check_format()\n self.assertEqual(1, fake_spawn.expect.call_count)\n\n @patch.object(pexpect, 'spawn', Mock())\n def test__check_format_2(self):\n fake_spawn = _setUp_fake_spawn(return_val=1)\n\n self.assertEqual(0, fake_spawn.expect.call_count)\n self.assertRaises(IOError, self.volumeDevice._check_format)\n\n @patch.object(pexpect, 'spawn', Mock())\n def test__format(self):\n fake_spawn = _setUp_fake_spawn()\n\n self.volumeDevice._format()\n\n self.assertEqual(1, fake_spawn.expect.call_count)\n self.assertEqual(1, pexpect.spawn.call_count)\n\n def test_format(self):\n origin_check_device_exists = self.volumeDevice._check_device_exists\n origin_format = self.volumeDevice._format\n origin_check_format = self.volumeDevice._check_format\n self.volumeDevice._check_device_exists = MagicMock()\n self.volumeDevice._check_format = MagicMock()\n self.volumeDevice._format = MagicMock()\n\n self.volumeDevice.format()\n self.assertEqual(1, self.volumeDevice._check_device_exists.call_count)\n self.assertEqual(1, self.volumeDevice._format.call_count)\n self.assertEqual(1, self.volumeDevice._check_format.call_count)\n\n self.volumeDevice._check_device_exists = origin_check_device_exists\n self.volumeDevice._format = origin_format\n self.volumeDevice._check_format = origin_check_format\n\n def test_mount(self):\n origin_ = volume.VolumeMountPoint.mount\n volume.VolumeMountPoint.mount = Mock()\n origin_os_path_exists = os.path.exists\n os.path.exists = Mock()\n origin_write_to_fstab = volume.VolumeMountPoint.write_to_fstab\n volume.VolumeMountPoint.write_to_fstab = Mock()\n\n self.volumeDevice.mount(Mock)\n self.assertEqual(1, volume.VolumeMountPoint.mount.call_count)\n self.assertEqual(1, volume.VolumeMountPoint.write_to_fstab.call_count)\n volume.VolumeMountPoint.mount = origin_\n volume.VolumeMountPoint.write_to_fstab = origin_write_to_fstab\n os.path.exists = origin_os_path_exists\n\n def test_resize_fs(self):\n origin_check_device_exists = self.volumeDevice._check_device_exists\n origin_execute = utils.execute\n utils.execute = Mock()\n self.volumeDevice._check_device_exists = MagicMock()\n origin_os_path_exists = os.path.exists\n os.path.exists = Mock()\n\n self.volumeDevice.resize_fs('/mnt/volume')\n\n self.assertEqual(1, self.volumeDevice._check_device_exists.call_count)\n self.assertEqual(2, utils.execute.call_count)\n self.volumeDevice._check_device_exists = origin_check_device_exists\n os.path.exists = origin_os_path_exists\n utils.execute = origin_execute\n\n @patch.object(os.path, 'ismount', return_value=True)\n @patch.object(utils, 'execute', side_effect=ProcessExecutionError)\n @patch('trove.guestagent.volume.LOG')\n def test_fail_resize_fs(self, mock_logging, mock_execute, mock_mount):\n with patch.object(self.volumeDevice, '_check_device_exists'):\n self.assertRaises(GuestError,\n self.volumeDevice.resize_fs, '/mnt/volume')\n self.assertEqual(1,\n self.volumeDevice._check_device_exists.call_count)\n self.assertEqual(1, mock_mount.call_count)\n\n def test_unmount_positive(self):\n self._test_unmount()\n\n def test_unmount_negative(self):\n self._test_unmount(False)\n\n @patch.object(pexpect, 'spawn', Mock())\n def _test_unmount(self, positive=True):\n origin_ = os.path.exists\n os.path.exists = MagicMock(return_value=positive)\n fake_spawn = _setUp_fake_spawn()\n\n self.volumeDevice.unmount('/mnt/volume')\n COUNT = 1\n if not positive:\n COUNT = 0\n self.assertEqual(COUNT, fake_spawn.expect.call_count)\n os.path.exists = origin_\n\n @patch.object(utils, 'execute', return_value=('/var/lib/mysql', ''))\n def test_mount_points(self, mock_execute):\n mount_point = self.volumeDevice.mount_points('/dev/vdb')\n self.assertEqual(['/var/lib/mysql'], mount_point)\n\n @patch.object(utils, 'execute', side_effect=ProcessExecutionError)\n @patch('trove.guestagent.volume.LOG')\n def test_fail_mount_points(self, mock_logging, mock_execute):\n self.assertRaises(GuestError, self.volumeDevice.mount_points,\n '/mnt/volume')\n\n def test_set_readahead_size(self):\n origin_check_device_exists = self.volumeDevice._check_device_exists\n self.volumeDevice._check_device_exists = MagicMock()\n mock_execute = MagicMock(return_value=None)\n readahead_size = 2048\n self.volumeDevice.set_readahead_size(readahead_size,\n execute_function=mock_execute)\n blockdev = mock_execute.call_args_list[0]\n\n blockdev.assert_called_with(\"sudo\", \"blockdev\", \"--setra\",\n readahead_size, \"/dev/vdb\")\n self.volumeDevice._check_device_exists = origin_check_device_exists\n\n @patch('trove.guestagent.volume.LOG')\n def test_fail_set_readahead_size(self, mock_logging):\n mock_execute = MagicMock(side_effect=ProcessExecutionError)\n readahead_size = 2048\n with patch.object(self.volumeDevice, '_check_device_exists'):\n self.assertRaises(GuestError, self.volumeDevice.set_readahead_size,\n readahead_size, execute_function=mock_execute)\n self.volumeDevice._check_device_exists.assert_any_call()\n\n\nclass VolumeMountPointTest(trove_testtools.TestCase):\n\n def setUp(self):\n super(VolumeMountPointTest, self).setUp()\n self.volumeMountPoint = volume.VolumeMountPoint('/mnt/device',\n '/dev/vdb')\n\n def tearDown(self):\n super(VolumeMountPointTest, self).tearDown()\n\n @patch.object(pexpect, 'spawn', Mock())\n def test_mount(self):\n origin_ = os.path.exists\n os.path.exists = MagicMock(return_value=False)\n fake_spawn = _setUp_fake_spawn()\n\n with patch.object(utils, 'execute_with_timeout',\n return_value=('0', '')):\n self.volumeMountPoint.mount()\n\n self.assertEqual(1, os.path.exists.call_count)\n self.assertEqual(1, utils.execute_with_timeout.call_count)\n self.assertEqual(1, fake_spawn.expect.call_count)\n\n os.path.exists = origin_\n\n def test_write_to_fstab(self):\n origin_execute = utils.execute\n utils.execute = Mock()\n m = mock_open()\n with patch('%s.open' % volume.__name__, m, create=True):\n self.volumeMountPoint.write_to_fstab()\n\n self.assertEqual(1, utils.execute.call_count)\n utils.execute = origin_execute\n","sub_path":"trove/tests/unittests/guestagent/test_volume.py","file_name":"test_volume.py","file_ext":"py","file_size_in_byte":9644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"72742628","text":"'''\nCreated on Jul 20, 2018\n\n@author: vahidrogo\n'''\n\nfrom copy import deepcopy\nimport threading\nfrom tkinter import messagebox as msg\nimport traceback\n\nfrom loaddetail import LoadDetail\nfrom progress import Progress\n\n\nclass DataController(threading.Thread):\n '''\n '''\n \n \n def __init__(self, process):\n super().__init__()\n \n self.process = process\n \n self.main = self.process.main\n self.selections = self.process.selections\n \n self.files = deepcopy(self.process.get_files())\n self.file_count = len(self.files)\n \n self.counter = 0\n \n self.progress = Progress(self, self.process.title)\n \n self.abort = False\n self.con = None\n \n \n def run(self):\n try:\n self._process_files()\n \n except Exception:\n msg.showerror(\n self.process.title, \n f'Unhandled exception occurred:\\n\\n{traceback.format_exc()}',\n parent=self.process.gui)\n \n finally:\n self.progress.destroy()\n \n \n def _process_files(self):\n for i, item in enumerate(self.files.items(), start=1):\n if self.main.end_processes or self.abort:\n break\n \n self.counter = i\n \n name, data = item\n \n jurisidiction, df = data\n \n load_detail = LoadDetail(self, df, name)\n \n load_detail.load(jurisidiction)\n \n \n def update_progress(self, progress, message):\n self.progress.update_progress(\n progress, message, self.counter, self.file_count\n )\n \n \n \n","sub_path":"datacontroller.py","file_name":"datacontroller.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"110008977","text":"import os\nimport sys\nimport cv2\nimport json\nimport numpy as np\nimport progressbar\nfrom imutils.paths import list_images\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.model_selection import train_test_split\n\nsys.path.append(\"../\")\nfrom mas_lib.io.hdf5datasetwriter import HDF5DatasetWriter\nfrom tiny_imagenet.configs import tiny_imagenet_config as config\n\n# load dataset\ntrain_path = list(list_images(config.TRAIN_IMAGE))\ntrain_labels = [config.get_label(path) for path in train_path]\n\n# encode dataset labels\nle = LabelBinarizer()\ntrain_labels = le.fit_transform(train_labels)\n\n# split dataset to train and test set\n(train_path, test_path, train_labels, test_labels) = train_test_split(train_path, train_labels, test_size=config.TEST_SIZE, stratify=train_labels)\n\n# load validation dataset mappings\nmappings = open(config.VAL_MAPPING).read().split(\"\\n\")\nmappings = [line.split(\"\\t\")[:2] for line in mappings]\n\n# get validation image path and labels from mapping\nval_path = [os.path.sep.join([config.VAL_IMAGE, m[0]]) for m in mappings]\nval_labels = [m[1] for m in mappings]\n\n# dataset splits\ndatasets = (\n (\"train\", train_path, train_labels, config.TRAIN_HDF5),\n (\"test\", test_path, test_labels, config.TEST_HDF5),\n (\"val\", val_path, val_labels, config.VAL_HDF5),\n)\n\n# list of training dataset image mean\n(R, G, B) = ([], [], [])\n\n# loop over dataset split\nfor (dataset, paths, labels, hdf5_path) in datasets:\n \n # progress bar\n print(f\"[INFO] Building {hdf5_path}...\")\n widgets = [\"[INFO] Building Dataset: \", progressbar.Percentage(), \" \", progressbar.Bar(), \" \", progressbar.ETA()]\n pbar = progressbar.ProgressBar(max_value=len(paths), widgets=widgets).start()\n \n # initialize database\n writer = HDF5DatasetWriter(hdf5_path, \"images\", 1000, (len(paths), 70, 70, 3))\n writer.store_class_label(le.classes_)\n \n # loop over dataset split's image path and corresponding label\n for (path, label) in zip(paths, labels):\n # load image from path\n image = cv2.imread(path)\n image = cv2.resize(70, 70)\n image = cv2.cvtColor(image.astype(\"float\"), cv2.COLOR_BGR2RGB)\n \n # calculate mean of train split image channels\n if datasets == \"train\":\n (r, g, b) = cv2.mean(image)[:3]\n R.append(r)\n G.append(g)\n B.append(b)\n \n # adds image and its label to database\n # and update progress bar\n writer.add([image,], [label,])\n pbar.update(1)\n \n # closes database and finishes progressbar\n writer.close()\n pbar.finish()\n\n# serializes the RGB mean to JSON file\nmean = {\"R\": np.mean(R), \"G\": np.mean(G), \"B\": np.mean(B)}\nwith open(config.DATASET_MEAN, \"w\") as f:\n f.write(json.dumps(mean))","sub_path":"tiny_imagenet/build_tiny_imagenet.py","file_name":"build_tiny_imagenet.py","file_ext":"py","file_size_in_byte":2778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"294396556","text":"from .base_options_audio2feature import BaseOptions\r\n\r\n\r\nclass TrainOptions(BaseOptions):\r\n \"\"\"This class includes training options.\r\n\r\n It also includes shared options defined in BaseOptions.\r\n \"\"\"\r\n\r\n def initialize(self, parser):\r\n parser = BaseOptions.initialize(self, parser)\r\n\r\n\r\n # network saving and loading parameters\r\n parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')\r\n parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration')\r\n parser.add_argument('--continue_train', default=False, action='store_true', help='continue training: load the latest model')\r\n parser.add_argument('--load_epoch', type=str, default='200', help='which epoch to load? set to latest to use latest cached model')\r\n parser.add_argument('--epoch_count', type=int, default=0, help='the starting epoch count, we save the model by , +, ...')\r\n parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')\r\n parser.add_argument('--re_transform', type=int, default=0, help='re-transform landmarks')\r\n \r\n \r\n # training parameters\r\n parser.add_argument('--train_dataset_names', type=str, default='train_list.txt', help='chooses validation datasets.')\r\n parser.add_argument('--validate_dataset_names', type=str, default='val_list.txt', help='chooses validation datasets.')\r\n parser.add_argument('--n_epochs', type=int, default=500, help='number of epochs')\r\n parser.add_argument('--lr_policy', type=str, default='step', help='learning rate policy. [linear | step | plateau | cosine]') \r\n parser.add_argument('--lr', type=float, default=1e-4, help='initial learning rate for adam')\r\n parser.add_argument('--gamma', type=float, default=0.2, help='step learning rate gamma')\r\n parser.add_argument('--lr_decay_iters', type=int, default=250, help='multiply by a gamma every lr_decay_iters iterations')\r\n parser.add_argument('--n_epochs_decay', type=int, default=250, help='number of epochs to linearly decay learning rate to zero')\r\n parser.add_argument('--validate_epoch', type=int, default=50, help='validate model every some epochs, 0 for not validate during training')\r\n parser.add_argument('--loss_smooth_weight', type=float, default=0, help='smooth loss weight, 0 for not use smooth loss')\r\n parser.add_argument('--optimizer', type=str, default='AdamW', help='Adam, AdamW, RMSprop')\r\n \r\n \r\n # data augmentations\r\n parser.add_argument('--gaussian_noise', type=int, default=1, help='whether add gaussian noise to input & groundtruth features')\r\n parser.add_argument('--gaussian_noise_scale', type=float, default=0.01, help='gaussian noise scale')\r\n \r\n\r\n self.isTrain = True\r\n return parser\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"options/train_audio2feature_options.py","file_name":"train_audio2feature_options.py","file_ext":"py","file_size_in_byte":3015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"529751782","text":"from Bio import pairwise2 as pw\nfrom Bio import SeqIO\n\n\ndef HoT(seq1, seq2, match, mismatch, gap, pa_type=\"global\", flag=0):\n\tassert pa_type in [\"local\", \"global\"] and len(seq1)>0 and len(seq2)>0\n\tfwd = []\n\trev = []\n\n\t# Save alignments according to pa_type\n\tif pa_type == \"global\":\n\t\tfwd = pw.align.globalms(seq1, seq2, match, mismatch, gap, gap)\n\t\trev = pw.align.globalms(seq1[::-1], seq2[::-1], match, mismatch, gap, gap)\n\telif pa_type == \"local\":\n\t\tfwd = pw.align.localms(seq1, seq2, match, mismatch, gap, gap)\n\t\trev = pw.align.localms(seq1[::-1], seq2[::-1], match, mismatch, gap, gap)\n\t# Save first alignment of forward and reverse\n\tfwdSeq=[fwd[0][0],fwd[0][1]]\n\trevSeq=[rev[0][0][::-1],rev[0][1][::-1]]\n\n\tconservedCount=0\n\ti1 = i2 = j1 = j2 = 0\n\tinds = []\n\tfor i in range(len(fwdSeq[0])):\n\t\tc1 = fwdSeq[0][i]\n\t\tc2 = fwdSeq[1][i]\n\t\t# if both chars in the index are not indels, save a tuple of the indexes of the original sequences\n\t\t# (i1 for seq1, i2 for seq2) in the list \"inds\"\n\t\tif '-' not in [c1, c2]:\n\t\t\tinds.append((i1, i2))\n\t\t# if chars (c1 and/or c2) are not indels, increment the running indexes (i1 and/or i2) of the sequences.\n\t\tif c1 != '-':\n\t\t\ti1 += 1\n\t\tif c2 != '-':\n\t\t\ti2 += 1\n\tfor i in range(len(revSeq[0])):\n\t\tc1 = revSeq[0][i]\n\t\tc2 = revSeq[1][i]\n\t\t# if both chars in the index are not indels, and a tuple of running indexes (j1,j2) exists in inds,\n\t\t# it means the same indexes are paired in the forward alignment, and conserved count should be incremented.\n\t\tif '-' not in [c1, c2]:\n\t\t\tif (j1, j2) in inds:\n\t\t\t\tconservedCount += 1\n\t\t# if chars (c1 and/or c2) are not indels, increment the running indexes (j1 and/or j2) of the sequences.\n\t\tif c1 != '-':\n\t\t\tj1 += 1\n\t\tif c2 != '-':\n\t\t\tj2 += 1\n\t# return conserved count divided by the length of the longer alignment (as per the instructions)\n\treturn conservedCount/max(len(fwdSeq[0]),len(revSeq[0]))\n\n\nif __name__ == '__main__':\n\tgenes=[\"OPN1LW\",\"OPN1MW\",\"OPN1SW\"]\n\tseqs=[]\n\t# save sequences foreach gene\n\tfor gene in genes:\n\t\tseqs.append(SeqIO.read(gene+\".fasta\",\"fasta\").seq)\n\t# foreach gene and the genes after it (but not itself or genes before it, calculate global and local HoT\n\tfor i in range(3):\n\t\tfor j in range(i+1,3):\n\t\t\tprint(genes[i],' vs. ', genes[j],': global=',HoT(seqs[i], seqs[j],1,-1,-2),\", local=\",HoT(seqs[i], seqs[j],1,-1,-2,\"local\"))\n\n","sub_path":"Python/BIOINFO INTRO/ex1_q1.py","file_name":"ex1_q1.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"290733579","text":"from kipoi.kipoimodeldescription import KipoiModelSchema, Dependencies, KipoiModelTest, KipoiModelInfo, KipoiModelDescription, Author\nfrom kipoi.model import BaseModel\nfrom tensorflow.keras.models import load_model\nimport numpy as np\n\n\nclass APARENTModel(BaseModel):\n\n def __init__(self, weights):\n self.weights = weights\n self.model = load_model(weights)\n\n def _predict(self, inputs):\n batch_size = inputs.shape[0]\n\n input_1 = np.expand_dims(inputs, -1)\n input_2 = np.zeros([batch_size, 13])\n input_3 = np.ones([batch_size, 1])\n\n _, pred = self.model.predict_on_batch([input_1, input_2, input_3])\n\n site_props = pred[:, :-1]\n distal_prop = pred[:, -1]\n return site_props, distal_prop\n\n def predict_on_batch(self, inputs):\n site_props, distal_prop = self._predict(inputs)\n\n return {\n \"distal_prop\": distal_prop,\n \"site_props\": site_props,\n }\n\n\nargs = {\n 'weights': {\n 'md5': '31902fb40125679e655b8b6d2747ada7',\n 'url': 'https://github.com/johli/aparent/raw/8a884f0bc4073ed0edd588f71b61a5be4a37e831/saved_models/aparent_large_lessdropout_all_libs_no_sampleweights.h5'\n }\n }\nschema = KipoiModelSchema(\n inputs = \n {\n 'name': 'seq',\n 'doc': '205bp long sequence of PolyA-cut-site',\n 'shape': (205, 4),\n 'special_type': 'DNASeq'\n },\n targets = \n { \n 'distal_prop':\n {\n 'shape': (1, ), \n 'doc': 'Predicts proportion of cleavage occuring outside of the specified DNA range',\n },\n 'site_props':\n {\n 'shape': (205, ),\n 'doc':\n 'Predicts proportion of cleavage occuring at each position in the specified DNA range. \\\n Sum of all site props + distal_prop = 1'\n }\n }\n)\ndependencies = Dependencies(conda=('python=3.9', 'tensorflow', 'keras>=2.0.4,<3'),\n conda_channels=('conda-forge', 'bioconda', 'defaults'))\ntest = KipoiModelTest(\n expect={\n 'url': 'https://zenodo.org/record/5511940/files/APARENT.site_probabilities.predictions.hdf5?download=1',\n 'md5': '1adb12be84240ffb7d7ca556eeb19e01'\n }\n)\ndoc = 'Predicting the Impact of cis-Regulatory Variation on Alternative Polyadenylation \\\nAbstract \\\nAlternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells.'\ntrained_on = \"isoform expression data from over 3 million APA reporters, built by inserting random sequence into 12 distinct 3'UTR contexts.\"\n\ninfo = KipoiModelInfo(authors=(Author(\"Nicholas Bogard\"), Author(\"Johannes Linder\")), doc=doc, trained_on=trained_on, \n cite_as=\"https://doi.org/10.1101/300061\", contributors=(Author(\"Shabnam Sadegharmaki\", \"shabnamsadegh\"), \n Author(\"Ziga Avsec\", \"avsecz\"), Author(\"Muhammed Hasan Çelik\", \"MuhammedHasan\"), Author(\"Florian R. Hölzlwimmer\", \"hoeze\")))\n\ndescription = KipoiModelDescription(args=args, schema=schema, info = info, defined_as='model.APARENTModel', dependencies=dependencies, test=test)","sub_path":"example/models/mdcexample/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"411868750","text":"from tkinter import *\nfrom tkinter import filedialog\nimport os, shutil, time\nfrom subprocess import call\nfrom pathlib import Path\nimport shutil\n\nfilename = str()\n\ndef select_file():\n global filename\n root.filename = filedialog.askopenfilename(initialdir = \"./\", title = \"Select Krunker Map File\", filetypes = ((\"txt\",\"*.txt\"),(\"all files\",\"*.*\")))\n print(root.filename)\n\n root.currfile.config(text = \"Current File Selected:\" + root.filename)\n root.filestatus.config(text = \"File Status: File Status: Has Not Been Converted\")\n\ndef save_location():\n root.savelocation = filedialog.askdirectory(initialdir = \"./\", title = \"Select Save Location\")\n root.savelocationtext.config(text = \"Save Location Selected:\" + root.savelocation)\n\ndef convert():\n converted_file_name = (os.path.splitext(root.savelocation + '/' + os.path.basename(root.filename))[0])\n converted_file = Path(converted_file_name + '.obj')\n print('--File name--: ' + converted_file_name)\n print('--File getting used--:' + str(converted_file))\n\n root.filestatus.config(text = \"File Status: Converting...\")\n programdir = os.path.dirname(os.path.abspath(__file__))\n print(programdir)\n if root.filename == str(\"\"):\n root.filestatus.config(text = \"File Status: ERR, No File Selected\") \n root.mainloop() \n else:\n shutil.copyfile(root.filename, programdir + \"/repo/\" + os.path.basename(root.filename))\n os.chdir(programdir + \"/repo\")\n print(os.getcwd())\n os.system(\"node\" \" krunkerToWavefront.js \" + os.path.basename(root.filename))\n root.filestatus.config(text = \"File Status: File Has Been Converted\")\n filecontent = []\n filecontent = os.listdir(\"./wavefront\")\n for i in filecontent:\n if i == (\"textures\"):\n pass\n else:\n try:\n os.rename((\"./wavefront/\" + i ), (root.savelocation + \"/\" + i))\n os.remove(programdir + \"/repo/\" + os.path.basename(root.filename))\n shutil.copyfile((\"./wavefront/textures\"), (root.savelocation + \"/\" + i))\n except IOError:\n root.filestatus.config(text = \"File Status: ERR. Duplicate File In Save Location\")\n os.remove(programdir + \"/repo/\" + os.path.basename(root.filename))\n root.mainloop()\n\nif __name__ == \"__main__\":\n root = Tk()\n root.title(\"Krunker file converter GUI\")\n root.geometry(\"525x275\")\n root.resizable(0,0)\n root.configure(bg='#393952')\n #root.tk.call('wm', 'iconphoto', root._w, PhotoImage(file='Data/Images/favicon.png'))\n\n root.fileselectandconvert = Frame(root, bg='#393952')\n root.fileselectandconvert.pack(side = BOTTOM)\n\n root.otherinfo = Frame(root, bg='#393952')\n root.otherinfo.pack(side = LEFT)\n\n #file select and convert buttons\n\n root.Convertfile = Button(root.fileselectandconvert, text = \"CONVERT\", command = convert, width = 525, height = 5, bg='#393952', fg='#fafafa', activebackground='#f06f51', activeforeground='#fafafa')\n root.Convertfile.pack(side = BOTTOM)\n\n root.Selectfilebutton = Button(root.fileselectandconvert, text = \"Select File\", command = select_file, width = 525, height = 2, bg='#393952', fg='#fafafa', activebackground='#f06f51', activeforeground='#fafafa')\n root.Selectfilebutton.pack(side = BOTTOM)\n\n root.Savefilelocation = Button(root.fileselectandconvert, text = \"Save Location\", command = save_location, width = 525, height = 2, bg='#393952', fg='#fafafa', activebackground='#f06f51', activeforeground='#fafafa')\n root.Savefilelocation.pack(side = BOTTOM)\n\n #other info that people might want\n root.currfile = Label(root.otherinfo, text = \"Current File Selected:\", bg='#393952', fg='#fafafa')\n root.currfile.pack(anchor = W)\n\n root.savelocationtext = Label(root.otherinfo, text = \"Save Location Selected:\", bg='#393952', fg='#fafafa')\n root.savelocationtext.pack(anchor = W)\n\n root.filestatus = Label(root.otherinfo, text = \"File Status:\", bg='#393952', fg='#fafafa')\n root.filestatus.pack(anchor = W)\n\n root.mainloop()\n ","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"320641788","text":"#!/usr/bin/env python\n# coding:utf-8\n\nimport os\nimport json\nimport sys\n\n# meshroom_submit --toNode Meshing --submitter Slurm /tmp/meshroom_rock.mg\n\nsys.path.insert(0,os.path.expandvars('$REZ_HAFARM_ROOT/py'))\n\nfrom uuid import uuid4\nfrom hafarm.HaGraph import HaGraph\nfrom hafarm.HaGraph import random_hash_string\nfrom hafarm.HaGraph import HaGraphItem\nfrom hafarm import SlurmRender\nfrom hafarm import PrintRender\nfrom hafarm import const\n\nimport meshroom\nfrom meshroom.core.desc import Level\nfrom meshroom.core.submitter import BaseSubmitter\n\nSCRATCH = os.environ['MESHROOM_CACHE']\n\nclass MeshroomNodeWrapper(HaGraphItem):\n def __init__(self, index, depends, filepath, node, **kwargs):\n self._kwargs = kwargs\n self.name = node.name\n super(MeshroomNodeWrapper, self).__init__(index, depends, node.name, node.name, '')\n self.index = index\n self.meshroom_node = node\n self.tags = '/meshroom/%s' % self.meshroom_node.name\n self.parms['ignore_check'] = True\n # self.parms['job_on_hold'] = True\n self.parms['queue'] = 'cuda'\n self.parms['job_wait_dependency_entire'] = True\n path, name = os.path.split(filepath)\n basename, ext = os.path.splitext(name)\n self.parms['scene_file'] << { \"scene_file_path\": path\n ,\"scene_file_basename\": basename\n ,\"scene_file_ext\": ext }\n jobname_hash = kwargs.get('jobname_hash' , self.get_jobname_hash())\n self.parms['job_name'] << { \"job_basename\": basename\n , \"jobname_hash\": jobname_hash\n , \"render_driver_type\": 'mg'\n , \"render_driver_name\": self.meshroom_node.name }\n self.parms['exe'] = 'meshroom_compute'\n self.parms['target_list'] = [self.meshroom_node.name]\n self.parms['command'] << '{exe} --node {target_list} {scene_file} {command_arg} --extern'\n self.parms['command_arg'] = [ '--cache %s' % SCRATCH ]\n self.parms['start_frame'] = 1\n self.parms['end_frame'] = 1\n\n parallelArgs = []\n if self.meshroom_node.isParallelized:\n blockSize, fullSize, nbBlocks = self.meshroom_node.nodeDesc.parallelization.getSizes(self.meshroom_node)\n self.parms['step_frame'] = 1\n self.parms['start_frame'] = 0\n self.parms['end_frame'] = nbBlocks - 1\n if self.parms['end_frame'] > self.parms['step_frame']:\n self.parms['command_arg'].insert(0, '--iteration %s' % const.TASK_ID)\n\n\n\nclass SlurmFarmSubmitter(BaseSubmitter):\n def __init__(self, parent=None):\n super(SlurmFarmSubmitter, self).__init__(name='Slurm', parent=parent)\n\n\n def submit(self, nodes, edges, filepath):\n name = os.path.splitext(os.path.basename(filepath))[0] + ' [Meshroom]'\n graph = HaGraph(graph_items_args=[])\n\n indeces = {}\n for node in nodes:\n indeces[node.name] = str(uuid4())\n\n dependencies = {}\n for u, v in edges:\n dependencies[u.name] = [indeces[v.name]]\n\n jobname_hash = random_hash_string()\n\n for node in nodes:\n idx = indeces[node.name]\n deps = dependencies.get(node.name, [])\n item = MeshroomNodeWrapper( idx, deps, filepath, node, jobname_hash=jobname_hash )\n graph.add_node( item )\n\n # graph.set_render(PrintRender.JsonParmRender)\n graph.set_render(SlurmRender.SlurmRender)\n graph.render()\n\n return True\n","sub_path":"scripts/meshroom/submitters/slurmFarmSubmitter.py","file_name":"slurmFarmSubmitter.py","file_ext":"py","file_size_in_byte":3586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"283666893","text":"\n\n\n\n\nif __name__ == \"__main__\":\n # results = []\n while True:\n try:\n expression = list(input())\n parenthesis = [] \n for char in expression:\n if char == '(' or char == ')':\n parenthesis.append(char)\n if len(parenthesis)%2 != 0 or parenthesis[0] == ')' or parenthesis[len(parenthesis)-1] == '(':\n print('incorrect')\n # results.append('incorrect')\n elif parenthesis[:int(len(parenthesis)/2)].count('(') != parenthesis[int(len(parenthesis)/2):].count(')'):\n print('incorrect')\n # results.append('incorrect')\n else:\n print('correct')\n except EOFError:\n break\n # results.append('correct')\n # [print(result) for result in results]","sub_path":"1068.py","file_name":"1068.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"608058769","text":"import os\n\nimport bpy\n\ncam = bpy.data.objects['Camera']\norigin = bpy.data.objects['Cube']\n\nbpy.ops.object.select_all(action='DESELECT')\norigin.select_set(True)\nbpy.ops.object.delete()\n\n#bpy.ops.mesh.primitive_ico_sphere_add(subdivisions=4)\nbpy.ops.mesh.primitive_uv_sphere_add()\nbpy.ops.object.shade_smooth()\n\n# bpy.data.materials.new('Prog mat')\nbpy.context.object.active_material = bpy.data.materials['Material']\nmat = bpy.context.object.active_material\nimage_node = mat.node_tree.nodes.new('ShaderNodeTexImage')\ntexture = mat.node_tree.nodes['Principled BSDF']\ntexture.inputs['Roughness'].default_value = 1\nmat.node_tree.links.new(image_node.outputs['Color'], texture.inputs['Base Color'])\n\nbpy.ops.image.open(filepath='/home/lemmih/Downloads/earth.jpg')\nimage_node.image = bpy.data.images['earth.jpg']\n\n\n\n# image_node.image = bpy.data.images['earth.jpg']\n# bpy.ops.image.open(filepath='/home/lemmih/Downloads/earth.jpg')\n# image_node = mat.node_tree.nodes.new('ShaderNodeTexImage')\n# image_node = mat.node_tree.nodes[1]\n# texture = mat.node_tree.nodes[2]\n# mat.node_tree.links.new(image_node.outputs[0], texture.inputs[0])\n# bpy.data.materials.new('Prog mat')\n# bpy.context.object.active_material = bpy.data.materials['Prog mat']\n\nscn = bpy.context.scene\n# scn.render.engine = 'CYCLES'\nscn.render.film_transparent = True\n\nbpy.data.scenes[\"Scene\"].render.filepath = '/tmp/blender.png'\nbpy.ops.render.render( write_still=True )\n","sub_path":"misc/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"210891999","text":"# pythonは再帰呼び出しが余り早くない（らしい）\nimport time\nimport random\ndef merge(left, right):\n #print('merge {} {}'.format(left, right))\n merged = []\n left_i = 0\n right_i = 0\n\n while (left_i < len(left) and right_i < len(right)):\n if left[left_i] <= right[right_i]:\n merged.append(left[left_i])\n left_i += 1\n else:\n merged.append(right[right_i])\n right_i += 1\n \n if left_i < len(left):\n merged.extend(left[left_i:])\n if right_i < len(right):\n merged.extend(right[right_i:])\n \n #print('merged = {}'.format(merged))\n return merged\n\ndef merge_sort(l):\n #print('merge sort {}'.format(l))\n if len(l) <= 1:\n #print('return {}'.format(l))\n return l\n \n mid = len(l) // 2\n left_l = l[:mid]\n right_l = l[mid:]\n\n #print('left_l = {}'.format(left_l))\n left_l = merge_sort(left_l)\n #print('right_l = {}'.format(right_l))\n right_l = merge_sort(right_l)\n\n return merge(left_l, right_l)\n\nif __name__ == '__main__':\n #L = [5, 3, 6, 1, 2, 4, 0]\n N = 10 ** int(input())\n #print(N)\n L = [random.randint(1, N) for _ in range(N)]\n start = time.time()\n merge_sort(L)\n end = time.time()\n print('time: {}[s]'.format(end-start))","sub_path":"snippets/sort/merge_sort_primitive.py","file_name":"merge_sort_primitive.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"304806672","text":"from fer import Video\nfrom fer import FER\n\nvideo_filename = \"/home/hao/Downloads/faptv2.mp4\"\nvideo = Video(video_filename)\n\n# Analyze video, displaying the output\ndetector = FER(mtcnn=True)\nraw_data = video.analyze(detector, display=True)\ndf = video.to_pandas(raw_data)\n# def preprocess_input(x, v2=False):\n# x = x.astype(\"float32\")\n# x = x / 255.0\n# if v2:\n# x = x - 0.5\n# x = x * 2.0\n# return x\n# import tensorflow as tf\n# import cv2\n# model = tf.keras.models.load_model(\"fer/data/emotion_model.hdf5\")\n# img = cv2.imread(\"justin.jpg\",cv2.IMREAD_GRAYSCALE)\n# scaled = cv2.resize(img, (64,64), interpolation=cv2.INTER_CUBIC)\n# scaled = preprocess_input(scaled, True)\n# reshaped = scaled.reshape((1,64,64,1))\n# cv2.imshow('img', reshaped[0])\n# res = model.predict(reshaped)\n# print(res)\n\n# if cv2.waitKey(0) == ord('q'):\n# exit()","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"427151696","text":"# Tu pišite svoje funkcije:\r\nfrom math import*\r\n\r\ndef koordinate(ime, kraji):\r\n s = ()\r\n for i, x, y in kraji:\r\n if ime == i:\r\n s = s + (x,y)\r\n return(s)\r\n else:\r\n return None\r\n\r\ndef razdalja_koordinat(x1, y1, x2, y2):\r\n razdalja = sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)\r\n return razdalja\r\n\r\ndef razdalja(ime1, ime2, kraji):\r\n xprva, yprva = koordinate(ime1, kraji)\r\n xdruga, ydruga = koordinate(ime2, kraji)\r\n return razdalja_koordinat(xprva, yprva, xdruga, ydruga)\r\n\r\ndef v_dometu(ime, domet, kraji):\r\n z = []\r\n for imeTest, xTest, yTest in kraji:\r\n if(ime != imeTest):\r\n if razdalja(ime, imeTest, kraji) <= domet:\r\n z.append(imeTest)\r\n return z\r\n\r\ndef najbolj_oddaljeni(ime, imena, kraji):\r\n najvecjaTest = 0\r\n for ime1 in imena:\r\n razTest = razdalja(ime, ime1, kraji)\r\n if(razTest > najvecjaTest):\r\n najvecjaTest = razTest\r\n izpis = ime1\r\n return izpis\r\n\r\ndef zalijemo(ime, domet, kraji):\r\n imena = v_dometu(ime, domet, kraji)\r\n return najbolj_oddaljeni(ime, imena, kraji)\r\n\r\ndef presek(s1, s2):\r\n u = []\r\n for a in s1:\r\n for b in s2:\r\n if a == b:\r\n u.append(a)\r\n return u\r\n\r\ndef skupno_zalivanje(ime1, ime2, domet, kraji):\r\n c = v_dometu(ime1, domet, kraji)\r\n d = v_dometu(ime2, domet, kraji)\r\n e = presek(c, d)\r\n return e\r\n\r\n","sub_path":"code/batch-2/vse-naloge-brez-testov/DN4-M-128.py","file_name":"DN4-M-128.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"239691117","text":"from sympy import symbols, cos, sin, pi, simplify\nfrom sympy.matrices import Matrix\nimport numpy as np\n\n### Create symbols for joint variables\na, alpha, q, d = symbols('a, alpha, q, d')\nq1, q2, q3, q4, q5, q6 = symbols('q1:7')\n\n\n# DH Parameters\ns = []\ns.append({alpha: 0, \ta: 0, \t\td: 0.75,\tq: q1 \t\t}) #T1\ns.append({alpha: -pi/2, a: 0.35,\td: 0,\t\tq: q2-pi/2 \t}) #T2\ns.append({alpha: 0, \ta: 1.25, \td: 0,\t\tq: q3 \t\t}) #T3\ns.append({alpha: -pi/2,\ta: -0.054, \td: 1.5,\t\tq: q4 \t\t}) #T4\ns.append({alpha: pi/2, \ta: 0, \t\td: 0,\t\tq: q5 \t\t}) #T5\ns.append({alpha: -pi/2,\ta: 0, \t\td: 0,\t\tq: q6 \t\t}) #T6\ns.append({alpha: 0,\ta: 0,\t\td: 0.303,\tq: 0\t\t}) #Tg\n\n# DH Matrix for single transform\nT = \tMatrix([[ cos(q), -sin(q), 0, a],\n [ sin(q)*cos(alpha), cos(q)*cos(alpha), -sin(alpha), -sin(alpha)*d],\n [ sin(q)*sin(alpha), cos(q)*sin(alpha), cos(alpha), cos(alpha)*d],\n [ 0, 0, 0, 1]])\n\n# Substitute constant dh parameters to transform matrices T0 - TG\nTList = 7*[T[:,:]]\nfor i in range(0,len(TList)): TList[i] = TList[i].subs(s[i])\n\n# Create transform matrix for transitin from base link to gripper T0_G\nT0_G = Matrix( [[1,0,0,0],\n\t\t[0,1,0,0],\n\t\t[0,0,1,0],\n\t\t[0,0,0,1]])\n\nfor i in range(0,len(TList)): T0_G = T0_G*TList[i]\n\n# Extract rotation matrces\nRotMat = []\nfor i in range(0,len(TList)): RotMat.append(TList[i][0:3,0:3])\n\n### Transform from gripper link in ROS to modified DH notation\nR_z = Matrix([\t[cos(np.pi),\t-sin(np.pi),\t0, \t0],\n\t\t[sin(np.pi),\tcos(np.pi),\t0,\t0],\n\t\t[0,\t\t0,\t\t1,\t0],\n\t\t[0,\t\t0,\t\t0,\t1]])\n\nR_y = Matrix([\t[cos(np.pi),\t0,\t\tsin(np.pi),\t0],\n\t\t[0,\t\t1,\t\t0,\t\t0],\n\t\t[-sin(np.pi),\t0,\t\tcos(np.pi),\t0],\n\t\t[0,\t\t0,\t\t0,\t\t1]])\nR_corr = simplify(R_z*R_y)\nT_ROS = T0_G*R_corr\n\n\n### Transform gripper position to base link coordinates\n# Define angle of rotations for all joints\n#s_q = {q1:0,q2:0,q3:0,q4:0,q5:0,q6:0}\n#s_q = {q1:-1.87,q2:0.86,q3:-1.77,q4:1.98,q5:0.22,q6:-4.84}\ns_q = {q1:1.54,q2:-0.38,q3:0.86,q4:-4.58,q5:0.66,q6:-5.81}\n\n\n# Crate vector representing gripper position in gripper link coordinates\nvec = Matrix([0,0,0,1])\n\n# Transform gripper link position to base link coordinates\nvec_base = T_ROS*vec\n\n# Substitute joint angles\nvec_base = vec_base.subs(s_q)\nprint(vec_base.evalf())\n","sub_path":"calc_mat.py","file_name":"calc_mat.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"157782692","text":"# -*- coding: utf-8 -*-\n# Copyright © 2017 Apple Inc. All rights reserved.\n#\n# Use of this source code is governed by a BSD-3-clause license that can\n# be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause\nfrom __future__ import print_function as _\nfrom __future__ import division as _\nfrom __future__ import absolute_import as _\nimport unittest\nimport turicreate as tc\nfrom turicreate.toolkits.activity_classifier import _sframe_sequence_iterator as sframe_sequence_iterator\n\n\nclass SFrameActivityIteratorTest(unittest.TestCase):\n @classmethod\n def setUp(self):\n self.features = ['feature_1', 'feature_2']\n self.target = 'target'\n self.session_id = 'session_id'\n self.prediction_window = 2\n self.predictions_in_chunk = 5\n\n self.data = tc.SFrame({self.session_id: [0] * 15 + [1] * 30 + [2] * 45})\n self.data[self.features[0]] = 1\n self.data[self.features[1]] = 2\n self.data[self.target] = 0\n\n def test_prep_data(self):\n observed , num_sessions = sframe_sequence_iterator.prep_data(\n self.data, self.features, self.session_id, self.prediction_window,\n self.predictions_in_chunk, target=self.target)\n\n chunk_size = self.prediction_window * self.predictions_in_chunk\n chunk_targets = [0.0] * self.predictions_in_chunk\n full_chunk_weights = [1.0] * self.predictions_in_chunk\n padded_chunk_weights = [1.0] * 3 + [0.0] * 2\n\n full_chunk_features = [1.0, 2.0] * chunk_size\n padded_chunk_features = [1.0, 2.0] * 5 + [0.0, 0.0] * 5\n\n expected = tc.SFrame({\n 'session_id': [0] * 2 + [1] * 3 + [2] * 5,\n 'chunk_len': [10, 5, 10, 10, 10, 10, 10, 10, 10, 5],\n 'features': [full_chunk_features, padded_chunk_features] +\n [full_chunk_features] * 7 + [padded_chunk_features],\n 'target': [chunk_targets] * 10,\n 'weights': [full_chunk_weights, padded_chunk_weights] +\n [full_chunk_weights] * 7 + [padded_chunk_weights]\n })\n\n tc.util._assert_sframe_equal(expected, observed, check_column_order=False)\n self.assertEqual(num_sessions , len(self.data[self.session_id].unique()))\n\n def test_ceil_dev(self):\n observed = sframe_sequence_iterator._ceil_dev(10, 3)\n expected = 4\n self.assertEqual(expected, observed)\n\n observed = sframe_sequence_iterator._ceil_dev(6, 2)\n expected = 3\n self.assertEqual(expected, observed)\n","sub_path":"src/unity/python/turicreate/test/test_sframe_sequence_iterator.py","file_name":"test_sframe_sequence_iterator.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"603500557","text":"import kivy\r\nkivy.require('1.10.1')\r\n\r\nfrom kivy.app import App\r\nfrom kivy.uix.widget import Widget\r\nfrom kivy.uix.boxlayout import BoxLayout\r\nfrom kivy.uix.gridlayout import GridLayout\r\nfrom kivy.graphics import Color, Ellipse\r\nimport pymysql\r\n\r\nid_session = 2\r\n\r\nclass Perfil(GridLayout):\r\n global id_session\r\n\r\n qtd = 0\r\n\r\n #puxaUsuario() #chama puxaUsuario\r\n\r\n def puxaUsuario(self):\r\n\r\n self.db = DbCon()\r\n nick = self.db.c.execute(\"SELECT nick FROM usuario WHERE id_usu = \" + str(id_session)) #pega o nick\r\n n = self.db.c.fetchone() #coloca o comando num vetor\r\n self.lblTxt.text = n['nick']\r\n\r\n #self.puxaQtdPosts #chama puxaQtdPosts\r\n return self.lblTxt.text\r\n\r\n\r\n def puxaQtdPosts(self):\r\n qtd = self.db.c.execute(\"SELECT count(id_video) FROM videos WHERE id_usu = id_session\") #pega a quantidade de videos\r\n\r\n self.exibe #chama exibe\r\n\r\n\r\n def exibe(self):\r\n i = 0\r\n posts = []\r\n while(i < qtd):\r\n titulo = self.db.c.execute(\"SELECT titulo FROM videos WHERE id_usu = id_session\")\r\n lblPost.titPost.text = titulo\r\n\r\n img = self.db.c.execute(\"SELECT url FROM videos WHERE id_usu = id_session\")\r\n lblPost.imgPost.source = img\r\n\r\n legenda = self.db.c.execute(\"SELECT legenda FROM videos WHERE id_usu = id_session\")\r\n lblPost.legendaPost.text = legenda\r\n\r\n tag = self.db.c.execute(\"SELECT tag FROM videos WHERE id_usu = id_session\")\r\n lblPost.tagPost.text = tag\r\n\r\n posts.append[(titulo, img, legenda, tag)]\r\n\r\n i += 1\r\n\r\n\r\n\r\nclass perfilApp(App):\r\n def build(self):\r\n return Perfil()\r\n\r\n\r\nclass DbCon:\r\n def __init__(self):\r\n self.db = pymysql.connect(host = 'localhost',\r\n user = 'root',\r\n password = '',\r\n db = 'sports',\r\n charset = 'utf8mb4',\r\n cursorclass = pymysql.cursors.DictCursor)\r\n self.c = self.db.cursor()\r\n\r\n\r\nif __name__ == '__main__':\r\n perfilApp().run() ","sub_path":"Perfil vLeticia/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"6323223","text":"from syncData.SyncDb import *\nfrom syncMatch import updateDatabase\nfrom synchronize.models import *\nfrom django.test import TestCase\n\n\nclass syncDataTest(TestCase):\n def setUp(self):\n self.old = 'test'\n self.newCorrect = 'test'\n self.newWrong = 'tes'\n self.time = nextIteration()\n self.date = \"2019-11-09T20:30:00Z\"\n self.dict = {'test': 1, 'noTest': 2, 'no': 3}\n self.entries = ['test', 'no']\n f = open('syncData/data.json', 'r')\n if f.mode == 'r':\n self.data = f.read()\n\n def test_confirmDifferences(self):\n one = confirmDifferences(self.newCorrect, self.old)\n two = confirmDifferences(self.newWrong, self.old)\n three = confirmDifferences(self.newCorrect, self.newWrong)\n self.assertFalse(one)\n self.assertTrue(two)\n self.assertTrue(three)\n\n def test_entries_to_remove(self):\n expected = {'noTest': 2}\n entries_to_remove(self.entries, self.dict)\n self.assertEquals(self.dict, expected)\n\n def test_nextIteration(self):\n self.assertTrue(self.time > 0)\n self.assertTrue(self.time < 86400)\n\n def test_datetimeParser(self):\n expected = datetime(year=2019, month=11, day=9, hour=20, minute=30, second=0)\n self.assertEquals(expected, datetimeParser(self.date))\n\n def test_updateDatabase(self):\n updateDatabase(self.data)\n self.assertEquals(len(Match.objects.all()), 5)\n self.assertEquals(len(MatchData.objects.all()), 34)\n self.assertEquals(len(Stadium.objects.all()), 1)\n self.assertEquals(len(Championship.objects.all()), 1)\n self.assertEquals(len(Team.objects.all()), 5)\n self.assertEquals(len(Referee.objects.all()), 5)\n self.assertEquals(len(Action.objects.all()), 10)\n self.assertEquals(len(State.objects.all()), 5)\n self.assertEquals(len(Score.objects.all()), 5)\n changes = Match.objects.get(pk=0)\n changes.period = 'Fecha X'\n changes.save()\n Match.objects.create(slug='test', match_date=datetime.now(), duration=500, period='test')\n self.assertEquals(changes.period, 'Fecha X')\n updateDatabase(self.data)\n\n self.assertEquals(Match.objects.get(pk=0).period, 'Fecha 15')\n self.assertEquals(len(Match.objects.all()), 5)\n","sub_path":"syncData/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"120486810","text":"import webapp2\nfrom google.appengine.ext import ndb\nimport db_models\nimport json\n\n#########################################################\n# Methods\n#########################################################\n\ndef deleteComic(self, id, owner):\n query = db_models.Comic.query(db_models.Comic.id == int(id)).get()\n if query:\n if query.owner == int(owner):\n # delete the comic from all request lists\n requests = db_models.Person.query(db_models.Person.requests == int(id)).fetch()\n # for all people with the id somewhere in their requests\n for x in requests:\n # for each request in their request list\n for index, item in enumerate(x.requests):\n if item == int(id):\n x.requests.pop(index)\n x.requesters.pop(index)\n x.put()\n query.key.delete() # delete the comic itself\n returnMsg(self, 200, \"Comic Successfully Deleted\")\n else:\n returnMsg(self, 406, \"Invalid Request; Bad Owner/ID combination\")\n else:\n returnMsg(self, 404, \"Invalid Request; Comic does not exist\")\n return\n\ndef returnMsg(self, error, message):\n self.response.status = error\n self.response.write(message)\n return\n\n# adds a person to the db; requires name, address\ndef addPerson(name, address):\n k = ndb.Key(db_models.Person, 'default-group')\n newPerson = db_models.Person(parent=k)\n newPerson.name = name\n newPerson.address = address\n personKey = newPerson.put()\n newPerson.id = personKey.id()\n personKey = newPerson.put()\n return personKey.id()\n\n# adds a comic to the db; requires title, issue#, owner's id\ndef addComic(title, issue, owner):\n k = ndb.Key(db_models.Comic, 'default-group')\n newComic = db_models.Comic(parent=k)\n newComic.title = title\n newComic.issue = issue\n newComic.owner = owner\n comicKey = newComic.put()\n newComic.id = comicKey.id()\n comicKey = newComic.put()\n return comicKey.id()\n\n\n#########################################################\n# Routes\n######################################################### \n\n# base route \"/\" is currently unused\nclass base(webapp2.RequestHandler):\n def get(self):\n self.response.write(\"Base URL\")\n\n#########################################################\n# Comics\n#########################################################\n \nclass comicget(webapp2.RequestHandler):\n def get(self):\n title = self.request.get('title')\n available = self.request.get('available')\n userid = self.request.get('id')\n \n # only show available\n if available:\n # by title\n if title:\n query = db_models.Comic.query(db_models.Comic.available == True, db_models.Comic.title == title).fetch()\n # all comics\n else:\n query = db_models.Comic.query(db_models.Comic.available == True).fetch()\n # show all\n else:\n if title:\n query = db_models.Comic.query(db_models.Comic.title == title).fetch()\n # all comics\n else:\n query = db_models.Comic.query().fetch()\n \n if query:\n data = {}\n rownum = 0\n for index, item in enumerate(query):\n entry = item.to_dict()\n if entry['owner'] == int(userid):\n del entry\n else:\n del entry['renter']\n del entry['owner']\n data[rownum] = entry\n rownum += 1\n self.response.write(json.dumps(data))\n else:\n returnMsg(self, 404, \"Invalid Request; No results found\")\n \nclass comicpost(webapp2.RequestHandler):\n def post(self):\n title = self.request.get('title')\n issue = self.request.get('issue')\n owner = self.request.get('id')\n \n if title and issue and owner:\n addComic(title, int(issue), int(owner))\n message = \"Successfully added \" + title + \" #\" + issue\n returnMsg(self, 200, message) \n else:\n returnMsg(self, 406, \"Invalid Request; Title/Issue/Owner are required\")\n\nclass comicrequest(webapp2.RequestHandler):\n def put(self):\n owner = self.request.get('id')\n request = self.request.get('comic')\n flag = False\n \n if request and owner:\n # get the requested comic\n query = db_models.Comic.query(db_models.Comic.id == int(request)).get()\n comicOwner = query.owner\n # get the comic's owner\n queryOwner = db_models.Person.query(db_models.Person.id == comicOwner).get()\n # check if it's already requested\n for index, item in enumerate(queryOwner.requests):\n if item == int(request):\n flag = True\n if flag == False:\n if query.available == True:\n queryOwner.requests.append(int(request))\n queryOwner.requesters.append(int(owner))\n queryOwner.put()\n returnMsg(self, 200, \"Request for Comic successfully processed\")\n else:\n returnMsg(self, 406, \"Invalid Request; Specified Comic is unavailable\")\n else:\n returnMsg(self, 406, \"Invalid Request; Specified request is already pending\")\n else:\n returnMsg(self, 406, \"Invalid Request; ID and Comic # required\")\n\nclass comicput(webapp2.RequestHandler): \n def put(self):\n owner = self.request.get('id')\n id = self.request.get('comic')\n title = self.request.get('title')\n issue = self.request.get('issue')\n available = self.request.get('available')\n \n # get the Comic from the supplied ID and Owner\n if id and owner:\n query = db_models.Comic.query(db_models.Comic.id == int(id)).get()\n if query:\n if query.owner == int(owner):\n if title:\n query.title = title\n if issue:\n query.issue = int(issue)\n if available:\n if available == \"true\" or available == \"True\":\n query.available = True\n elif available == \"false\" or available == \"False\":\n query.available = False\n else:\n returnMsg(self, 400, \"Invalid Request; Invalid 'available' parameter\")\n query.put()\n returnMsg(self, 200, \"Successfully updated Comic information\")\n else:\n returnMsg(self, 406, \"Invalid Request; Bad Owner/ID combination\")\n else:\n returnMsg(self, 404, \"Invalid Request; Comic does not exist\")\n else:\n returnMsg(self, 406, \"Invalid Request; Comic ID and Owner ID Required\")\n \nclass comicdelete(webapp2.RequestHandler):\n def delete(self):\n owner = self.request.get('id')\n id = self.request.get('comic')\n \n if id and owner:\n deleteComic(self, id, owner)\n else:\n returnMsg(self, 406, \"Invalid Request; Comic ID and Owner ID Required\")\n\n#########################################################\n# Users\n#########################################################\n\nclass userget(webapp2.RequestHandler):\n def get(self):\n id = self.request.get('id')\n \n if id:\n query = db_models.Person.query(db_models.Person.id == int(id)).fetch()\n if query:\n query = db_models.Comic.query(db_models.Comic.owner == int(id)).fetch()\n if query:\n data = {}\n for index, item in enumerate(query):\n entry = item.to_dict()\n del entry['owner']\n del entry['renter']\n data[index] = entry\n self.response.status = 200\n self.response.write(json.dumps(data))\n else:\n data = {}\n self.response.status = 200\n self.response.write(json.dumps(data))\n else:\n returnMsg(self, 404, \"Invalid Request; User not found\")\n else:\n returnMsg(self, 406, \"Invalid Request; ID required\")\n \nclass userpost(webapp2.RequestHandler):\n def post(self):\n name = self.request.get('name')\n address = self.request.get('address')\n if name and address:\n key = addPerson(name, address)\n returnMsg(self, 200, key)\n\n elif name:\n returnMsg(self, 406, \"Invalid Request; Address Required\") \n\n else:\n returnMsg(self, 406, \"Invalid Request; Name Required\")\n\nclass userput(webapp2.RequestHandler):\n def put(self):\n name = self.request.get('name')\n address = self.request.get('address')\n id = self.request.get('id')\n \n if id:\n query = db_models.Person.query(db_models.Person.id == int(id)).get()\n if query:\n if name:\n query.name = name\n if address:\n query.address = address\n query.put()\n returnMsg(self, 200, \"User information updated successfully\")\n else:\n returnMsg(self, 404, \"Invalid Request; User ID not found\")\n else:\n returnMsg(self, 406, \"Invalid Request; User ID Required\")\n\nclass userrequest(webapp2.RequestHandler):\n def put(self):\n id = self.request.get('id')\n \n if id:\n # get the user in question's entity\n query = db_models.Person.query(db_models.Person.id == int(id)).get()\n if query:\n data = {}\n # over all of their requests\n for index, item in enumerate(query.requests):\n # get that comic\n comic = db_models.Comic.query(db_models.Comic.id == item).get()\n entry = comic.to_dict()\n del entry['owner']\n del entry['renter']\n # get the requester\n requester = db_models.Person.query(db_models.Person.id == query.requesters[index]).get()\n entry['requesting_name'] = requester.name\n entry['requesting_address'] = requester.address\n data[index] = entry\n self.response.write(json.dumps(data))\n else:\n returnMsg(self, 404, \"Invalid Request; User Not Found\")\n else:\n returnMsg(self, 406, \"Invalid Request; ID # Required\")\n\nclass userack(webapp2.RequestHandler):\n def put(self):\n id = self.request.get('id')\n request = self.request.get('comic')\n flag = False\n \n if id and request:\n query = db_models.Person.query(db_models.Person.id == int(id)).get()\n if query:\n for index, item in enumerate(query.requests):\n if item == int(request):\n flag = True # found it\n # remove the request from the list\n query.requests.pop(index)\n query.requesters.pop(index)\n query.put()\n\n # mark the requested comic unavailable\n comicRequest = db_models.Comic.query(db_models.Comic.id == int(request)).get()\n if comicRequest:\n comicRequest.available = False\n comicRequest.put()\n returnMsg(self, 200, \"Request successfully accepted\")\n else:\n returnMsg(self, 404, \"Comic inexplicably unavailable\")\n if flag == False:\n returnMsg(self, 404, \"Invalid Request; Comic not found in request list\")\n else:\n returnMsg(self, 404, \"Invalid Request; ID not found\")\n else:\n returnMsg(self, 406, \"Invalid Request; ID # and Comic # required\")\n\nclass userdelete(webapp2.RequestHandler):\n def delete(self):\n id = self.request.get('id')\n \n if id:\n query = db_models.Person.query(db_models.Person.id == int(id)).get()\n if query:\n # delete all user's owned comics first\n comicQuery = db_models.Comic.query(db_models.Comic.owner == int(id)).fetch()\n if comicQuery:\n for x in comicQuery:\n deleteComic(self, x.id, int(id))\n # then any open requests the user has\n # get all the users with requests from this user\n requests = db_models.Person.query(db_models.Person.requesters == int(id)).fetch()\n # over all the users with requests\n for x in requests:\n # over all the requests in their list\n for index, item in enumerate(x.requesters):\n if item == int(id):\n x.requesters.pop(index)\n x.requests.pop(index)\n x.put()\n query.key.delete() # then delete the user itself\n returnMsg(self, 200, \"User successfully deleted\") \n else:\n returnMsg(self, 404, \"Invalid Request; User does not exist\")\n else:\n returnMsg(self, 406, \"Invalid Request; User ID Required\")","sub_path":"formhandler.py","file_name":"formhandler.py","file_ext":"py","file_size_in_byte":13857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"476653254","text":"import pytest\nimport networkx as nx\nimport numpy as np\nimport os\nimport shutil\n\nimport gym\nfrom stable_baselines.common.vec_env import DummyVecEnv\nfrom stable_baselines import A2C\nfrom stable_baselines.common.policies import MlpPolicy\n\nfrom gym_taxi.envs.taxi_env import *\n\nclass TestTaxiEnvIntegration:\n\n def testIntegration(self):\n g = nx.Graph()\n g.add_edges_from([(0,1)])\n nx.set_node_attributes(g, {0: (0,1), 1: (1,2)}, \"coords\")\n orders = [(1,1,1,1,0.5)]\n drivers = np.ones((2), dtype=int)\n\n env_id = \"TaxiEnvTest-v01\"\n gym.envs.register(\n id=env_id,\n entry_point='gym_taxi.envs:TaxiEnv',\n kwargs={\n 'world': g,\n 'orders': orders,\n 'order_sampling_rate': 1,\n 'drivers_per_node': drivers,\n 'n_intervals': 10,\n 'wc': 0.5\n }\n )\n\n DATA_PATH = os.path.join(os.environ['ALLDATA_PATH'], \"macaoFiles\", \"taxi_env_test\")\n if os.path.isdir(DATA_PATH):\n shutil.rmtree(DATA_PATH)\n os.makedirs(DATA_PATH)\n\n def make_env():\n env = gym.make(env_id)\n env.seed(1)\n return env\n env = DummyVecEnv([make_env])\n\n model = A2C(MlpPolicy, env, verbose=1)\n model.learn(total_timesteps=10)\n\n obs = env.reset()\n images = []\n img = env.render(mode=\"rgb_array\")\n images.append(img)\n for _ in range(10):\n action, _states = model.predict(obs)\n obs, rewards, dones, info = env.step(action)\n images.append(env.render(mode=\"rgb_array\"))\n imageio.mimwrite(os.path.join(DATA_PATH, 'taxi_dummy_a2c.gif'), [np.array(img) for i, img in enumerate(images)], format=\"GIF-PIL\", fps=5)\n","sub_path":"src/gym-taxi/gym_taxi/tests/test_taxi_env_integration.py","file_name":"test_taxi_env_integration.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"509074451","text":"# -*- coding:utf-8 -*-\n\n\"\"\"\n@author: delu\n@file: service.py\n@time: 2020-02-01 16:10\n\"\"\"\nfrom test.tester import Tester\n\n\nclass MyTest(Tester):\n def query_champion(self):\n # 退款成功\n self.path = 'champion.service'\n self.method = 'query_champion'\n self.params = {\n 'touzi': [\"6\", \"2\", \"6\", \"6\", \"6\", \"6\"]\n }\n\n\nif __name__ == '__main__':\n refund = MyTest()\n refund.run('query_champion')\n","sub_path":"test/champion/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"197456607","text":"import logging\n# logging.basicConfig(level=logging.DEBUG)\ndef get_logger():\n # Create a custom logger\n logger = logging.getLogger('e2e_output.log')\n\n # logger = logging.basicConfig(filename='e2e_output.log', filemode='w', format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n # Create handlers\n f_handler = logging.FileHandler('e2e_output.log')\n # f_handler = logging.FileHandler(__name__)\n f_handler.setLevel(logging.WARNING)\n\n # Create formatters and add it to handlers\n f_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n logger.setFormatter(f_format)\n\n # Add handlers to the logger\n logger.addHandler(f_handler)\n return logger\n\ndef default_logger(name=__name__):\n logging.basicConfig(filename=name, filemode='w', format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)\n return logging\n","sub_path":"setup_logger.py","file_name":"setup_logger.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"415657034","text":"import tensorflow as tf\nimport data_provider\nfrom E2E_model import E2EModel\nimport logger\n\n\nclass AttnNet(E2EModel):\n def __init__(self, task_num, data_form=2):\n super(AttnNet, self).__init__(task_num, data_form)\n self.hidden_unit_num = 2048\n self.output_dim = 20\n\n @staticmethod\n def self_attention(x, keep_prob):\n \"\"\"\n\n :param x: input with shape [batch_size, sequence_len, embedding_dim] e.g. [100, 180, 300]\n :return: self-attention output with the same shape with input 'x'\n \"\"\"\n align_matrix = tf.matmul(tf.einsum('ijk->ikj', x), x)\n alignment = tf.nn.softmax(align_matrix, 0)\n context_vector = tf.matmul(x, alignment)\n\n return tf.nn.dropout(context_vector, keep_prob=keep_prob)\n\n @staticmethod\n def feed_forward(x, layer_num, keep_prob):\n \"\"\"\n\n :param x: input for feed forward layer.\n :param layer_num: the number of this feed forward layer\n :return:\n \"\"\"\n # map to higher dim\n ff_weight_first = tf.get_variable('ff_weight_first_{0}'.format(layer_num),\n shape=[300, 2000],\n initializer=tf.contrib.layers.xavier_initializer())\n ff_bias_first = tf.get_variable('ff_bias_first_{0}'.format(layer_num),\n shape=[2000],\n initializer=tf.contrib.layers.xavier_initializer())\n\n output1 = tf.nn.relu(tf.einsum('abc,cd->abd', x, ff_weight_first)+ff_bias_first)\n\n # map to lower dim\n ff_weight_second = tf.get_variable('ff_weight_second_{0}'.format(layer_num),\n shape=[2000, 300],\n initializer=tf.contrib.layers.xavier_initializer())\n ff_bias_second = tf.get_variable('ff_bias_second_{0}'.format(layer_num),\n shape=[300],\n initializer=tf.contrib.layers.xavier_initializer())\n\n output2 = tf.nn.relu(tf.einsum('abc,cd->abd', output1, ff_weight_second)+ff_bias_second)\n # print(output2)\n return tf.nn.dropout(output2, keep_prob=keep_prob)\n\n @staticmethod\n def add_and_norm(x, x_out):\n \"\"\"\n\n :param x: original input for a particular sub-layer\n :param x_out: the output of a sub-layer\n :return: LayerNorm(x + sublayer(x))\n \"\"\"\n normed_x = tf.contrib.layers.layer_norm(x_out)\n output = normed_x + x\n return output\n\n def attention_network(self, x, keep_prob):\n\n attn_out_1 = self.add_and_norm(x, self.self_attention(x, keep_prob=keep_prob))\n ff_out_1 = self.add_and_norm(attn_out_1, self.feed_forward(attn_out_1, 1, keep_prob=keep_prob))\n\n hidden_1 = tf.layers.dense(tf.reshape(ff_out_1, [-1, 180*300]), 512, tf.nn.relu)\n # hidden_2 = tf.layers.dense(hidden_1, 256, tf.nn.relu)\n # hidden = self.attention_layer(hidden, attn_output_dim=2048)\n logits = tf.layers.dense(hidden_1, self.output_dim, name='logits')\n\n return logits\n\n def train(self, epochs, exp_name, lr=1e-3, keep_prob=0.8, save_model=False):\n\n # inputs & outputs format\n x = tf.placeholder(tf.int32, [None, 180], name='x')\n y = tf.placeholder('float', [None, self.output_dim], name='y')\n dropout_rate = tf.placeholder('float', [])\n\n # construct computation graph\n embed_x = self.embedding_layer(x) # shape [Batch_size, 180, 300]\n\n pe_x = self.apply_positional_encoding(embed_x)\n\n logits = self.attention_network(pe_x, keep_prob=dropout_rate)\n loss = self.compute_loss(logits, y)\n\n accuracy = self.compute_accuracy(logits, y)\n\n train_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss, name='train_op')\n\n with tf.Session() as sess:\n # initialization\n init = tf.global_variables_initializer()\n sess.run(init)\n\n log_saver = logger.LogSaver(exp_name)\n log_saver.set_log_cate(self.task_num)\n\n # train\n # train\n for epoch in range(epochs):\n for i in range(int(8000/1000)):\n batch_x, batch_y = self.train_set.next_batch(1000)\n sess.run(train_op, feed_dict={x: batch_x, y: batch_y, dropout_rate: keep_prob})\n\n # print validation information every 40 iteration (half epoch)\n if i % 4 == 0 and i != 0:\n train_loss = loss.eval(feed_dict={x: batch_x, y: batch_y, dropout_rate: keep_prob})\n train_acc = accuracy.eval(feed_dict={x: batch_x, y: batch_y, dropout_rate: keep_prob})\n\n val_x, val_y = self.val_set.next_batch(1000)\n val_acc = accuracy.eval(feed_dict={\n x: val_x,\n y: val_y,\n dropout_rate: 1.0})\n print('Epoch, {0}, Train loss,{1:2f}, Train acc, {2:3f}, Val_acc,{3:3f}'.format(epoch,\n train_loss,\n train_acc,\n val_acc))\n log_saver.train_process_saver([epoch, train_loss, train_acc, val_acc])\n\n # save evaluation result per epoch\n # train_loss = loss.eval(feed_dict={x: batch_x, y: batch_y})\n # train_acc = accuracy.eval(feed_dict={x: batch_x, y: batch_y})\n #\n # val_x, val_y = self.val_set.next_batch(1000)\n # val_acc = accuracy.eval(feed_dict={\n # x: val_x,\n # y: val_y})\n #\n # log_saver.train_process_saver([epoch, train_loss, train_acc, val_acc])\n\n # evaluate on test set per epoch\n for index, test_set in enumerate(self.test_sets):\n if index > 0:\n test_x, test_y = test_set.next_batch(1000)\n test_acc = sess.run(\n accuracy, feed_dict={\n x: test_x,\n y: test_y,\n dropout_rate: 1.0})\n print('test accuracy on test set {0} is {1}'.format(index, test_acc))\n # save training log\n log_saver.test_result_saver([test_acc], index)\n\n # Model save\n if save_model:\n log_saver.model_saver(sess)\n\n\nif __name__ == '__main__':\n # DATA_PATH = '/afs/inf.ed.ac.uk/user/s17/s1700619/E2E_dialog/dataset'\n #\n # model = AttnNet(1, 2)\n # model.train(100, 'test_save_model')\n pass\n\n\n","sub_path":"model/attn_net.py","file_name":"attn_net.py","file_ext":"py","file_size_in_byte":7077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"362767861","text":"from numpy import array, linalg, zeros, dot, transpose, copy, isnan, isinf\n\n\nclass LeastSquares():\n\n def __init__(self, f, x, y, b, tolerance=1e-6, max_iter=1000, param_delta=0.001):\n # Input variables\n self.f = f # function handle\n self.x = x # vector of abscissa data\n self.y = y # vector of ordinate data\n self.b = b # Vector of initial parameter guesses\n self.tolerance = tolerance # Convergence tolerance\n self.max_iter = max_iter # Maximum iterations. Can be None.\n self.param_delta = param_delta # step sized used for numerical derivative approximation\n\n # Calculated variables\n self.coefficients = None\n self.iter = 0\n\n def calculate(self):\n w = [w*0.9 for w in self.b]\n b = array([self.b, w], dtype=float)\n db = b[0, :] - b[1, :]\n\n while abs(max(db)/max(b[0, :])) > self.tolerance:\n dy = self.delta_y(b)\n J = self.jacobian(b)\n Jt = transpose(J)\n eq1 = dot(Jt, J)\n eq2 = dot(Jt, dy)\n db = linalg.solve(eq1, eq2)\n\n b[1, :] = copy(b[0, :])\n for i in range(0, len(db)):\n b[0, i] += db[i][0]\n\n self.iter += 1\n print(b[0, :])\n\n if self.iter >= self.max_iter:\n raise Exception('Maximum iterations reached.')\n if isnan(b).any():\n raise Exception('Parameters converged to NaN. Initial guess may be bad.')\n if isinf(b).any():\n raise Exception('Parameters diverged to inf. Initial guess may be bad.')\n\n self.coefficients = b[0, :]\n\n def delta_y(self, b):\n n = len(self.x)\n dy = zeros((n, 1))\n\n i = 0\n\n while i < n:\n dy[i] = self.y[i] - self.f(self.x[i], b[0, :])\n i += 1\n\n return dy\n\n def jacobian(self, b):\n m = len(self.b)\n n = len(self.x)\n J = zeros((n, m))\n\n i = 0\n j = 0\n\n while i < n:\n while j < m:\n b1 = copy(b[0, :])\n b2 = copy(b1)\n b1[j] += self.param_delta\n\n J[i, j] = (self.f(self.x[i], b1) - self.f(self.x[i], b2))/(b1[j] - b2[j])\n j += 1\n\n j = 0\n i += 1\n\n return J","sub_path":"least_squares.py","file_name":"least_squares.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"225701868","text":"\"\"\"truealoehealth URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.10/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', 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: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf import settings\nfrom django.conf.urls import url, include\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.contrib.sitemaps.views import sitemap\nfrom blogs.models import BlogPost, BlogSitemap, StaticPostsSitemap\nfrom truealoehealth import views\n\nsitemaps = {\n 'blogs': BlogSitemap,\n 'static': StaticPostsSitemap\n}\n\n\nurlpatterns = [\n url(r'^$', views.HomeView.as_view(), name='home'),\n url(r'^discounts/', views.DiscountView.as_view(), name='discount'),\n url(r'^upload/', views.upload, name='upload'),\n url(r'^become-business-owner/', views.BecomeBusinessOwnerView.as_view(), name='become-owner'),\n url(r'^products/', include('products.urls'), name='products'),\n url(r'^feedback/', include('feedback.urls'), name='feedback'),\n url(r'^top-products/', include('topproducts.urls'), name='top-products'),\n url(r'^blogs/', include('blogs.urls'), name='blogs'),\n url(r'^test/', views.Test.as_view(), name='test'),\n url(r'^aloe-vera-drink/', views.AloeVeraDrink.as_view(), name='aloe'),\n url(r'^aloe-vera-nutrition/', views.AloeVeraNutrition.as_view(), name='nutrition'),\n url(r'^aloe-vera-cosmetics/', views.AloeVeraCosmetics.as_view(), name='cosmetics'),\n url(r'^aloe-vera-skincare/', views.AloeVeraSkinCare.as_view(), name='skincare'),\n url(r'^aloe-vera-bee-products/', views.AloeVeraBeeProducts.as_view(), name='bee_products'),\n url(r'^aloe-vera-combo-packs/', views.AloeVeraComboPacks.as_view(), name='combo'),\n url(r'^aloe-vera-personal-care/', views.AloeVeraPersonal.as_view(), name='personal'),\n url(r'^aloe-vera-weight-management/', views.AloeVeraWeight.as_view(), name='weight'),\n url(r'^gluten-free-products/', views.GlutenFree.as_view(), name='gluten'),\n url(r'^forever-i-t-weight-loss/', views.FIT.as_view(), name='fit'),\n url(r'^clean-9/', views.Clean9.as_view(), name='c9'),\n url(r'^clean-9-instructions/', views.C9Instructions.as_view(), name='c9-inst'),\n url(r'^admin/', admin.site.urls),\n url(r'^sitemap\\.xml$', sitemap, {'sitemaps': sitemaps}, name='django.contrib.sitemaps.views.sitemap')\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","sub_path":"truealoehealth/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"255993603","text":"# 1：写一个函数，可以完成任意指定整数的相加，并返回结果\ndef my_sum(*num):\n sum=0\n for i in num:\n sum += i\n return sum\n\n\nprint(my_sum(1,2,3,4,5,6))\n\n\n# 2：自动贩卖机： 只接受1元、5元、10元的纸币或硬币，可以1块，5元，10元。最多不超过10块钱。\n# 饮料只有橙汁、椰汁、矿泉水、早餐奶，售价分别是3.5，4，2，4.5\n# 写一个函数用来表示贩卖机的功能： 用户投钱和选择饮料，并通过判断之后，给用户吐出饮料和找零。\ndef vending_machine():\n drinks = {\"橙汁\": 3.5, \"椰汁\": 4, \"矿泉水\": 2, \"早餐奶\": 4.5}\n money_sum = 0\n drink = input(\"请输入您要购买的饮料，橙汁、椰汁、矿泉水、早餐奶\")\n if drink in drinks.keys():\n while money_sum < drinks[drink]:\n money = int(input(\"请投币\"))\n if money in [1, 5, 10]:\n money_sum += money\n else:\n print(\"投入的金额只能为1元、5元、10元的纸币或硬币\")\n continue\n if money_sum == drinks[drink]:\n print(\"这是您的饮料:{0}，欢迎下次光临\".format(drink))\n else:\n print(\"这是您的饮料:{0}，这是您的找零{1}元\".format(drink, (money_sum - drinks[drink])))\n else:\n print(\"您购买的饮料暂未上架\")\n\n\nvending_machine()\n\n\n# 3、写函数，将姓名、性别，城市作为参数，并且性别默认为f(女)。\n# 如果城市是在长沙并且性别为女，则输出姓名、性别、城市，并返回True,否则返回False。\ndef what(name, city, sex='f'):\n if city == '长沙' and sex == 'f':\n print('姓名是:{0}，性别是：{1}，城市是：{2}'.format(name, sex, city))\n return True\n else:\n print('姓名是:{0}，性别是：{1}，城市是：{2}'.format(name, sex, city))\n return False\n\n\nname, city, sex = input(\"请输入姓名、城市、性别，用逗号隔开\").split(',')\nif sex == '':\n what(name, city)\nelse:\n what(name, city, sex)\n\n\n# 4、定义一个函数，成绩作为参数传入。如果成绩小于60，则输出不及格。\n# 如果成绩在60到80之间，则输出良好；如果成绩高于80分，则输出优秀，如果成绩不在0-100之间，则输出 成绩输入错误。\ndef mark(score):\n score = int(score)\n if score < 60:\n print(\"不及格\")\n elif 60 <= score <= 80:\n print(\"良好\")\n elif 80 < score <= 100:\n print(\"优秀\")\n else:\n print(\"输入错误\")\n\n\nmark(input(\"请输入成绩\"))\n","sub_path":"homework/homework_1009.py","file_name":"homework_1009.py","file_ext":"py","file_size_in_byte":2597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"508768348","text":"\"\"\"-----------------------------------------------------------------------------\n GUI Module\n\n Handles everything relating to creating and drawing on the game window.\n\n-----------------------------------------------------------------------------\"\"\"\n\nimport pygame\nimport time\nimport random\nimport math\nimport copy\nfrom . import linemath\nfrom . import colour\nfrom . import polygons\nfrom . import symbol_directory as symbols\n\nclass GUI:\n def __init__(self, screen_width, screen_height, fullscreen):\n # Setup Pygame mixer module. Frequency, size, channels, buffer size.\n pygame.mixer.pre_init(44100, -16, 2, 256)\n pygame.init()\n pygame.mixer.set_num_channels(128)\n\n # Define constants for screen element dimensions\n # Pixel locations are 0-indexed\n self.screen_width = screen_width - 1\n self.screen_height = screen_height - 1\n self.sidepanel_width = 154\n self.border_thickness = 5\n\n # Calculate arena rect\n self.arena_rect = (\n self.border_thickness,\n self.border_thickness,\n self.screen_width - (self.border_thickness * 3) - self.sidepanel_width,\n self.screen_height - (self.border_thickness * 2) + 1)\n\n # Define width and height of arena in pixels\n self.arena_pixel_width = self.arena_rect[2]\n self.arena_pixel_height = self.arena_rect[3]\n \n # Calculate width and height of arena in meters\n self.arena_meters_width = int(self.arena_pixel_width / 470 * 1000)\n self.arena_meters_height = int(self.arena_pixel_height / 470 * 1000)\n\n # Create screen object. Window size is NOT 0-indexed.\n screen_width_height = (screen_width, screen_height)\n if fullscreen:\n self.scr = pygame.display.set_mode(screen_width_height, pygame.FULLSCREEN)\n else:\n self.scr = pygame.display.set_mode(screen_width_height)\n\n # Set window options\n pygame.display.set_caption(\"Advanced Combat Robots v2\")\n pygame.mouse.set_visible(False)\n\n self.fontpath = \"./fonts/\"\n self.font1 = pygame.font.Font(self.fontpath + \"font1.ttf\", 8)\n self.font2 = pygame.font.Font(self.fontpath + \"font2.ttf\", 16)\n\n\n def get_arena_dimensions(self):\n \"\"\"Return arena dimensions in meters.\"\"\"\n return(self.arena_meters_width, self.arena_meters_height)\n \n\n def draw_text(self, coord, text, colour_name=\"tgrey\", font=None):\n \"\"\"Draw text to the game window\"\"\"\n if font is None:\n font = self.font1\n font_surface = font.render(str(text), 0,\n colour.name_to_pygame_value(colour_name))\n self.scr.blit(font_surface, coord)\n\n\n def draw_frame(self):\n \"\"\"Draw base game frame (arena, robot info boxes, match info box)\"\"\"\n border_internal_thickness = self.border_thickness - 2\n\n # Draw frame around screen edge\n rect = (0,\n 0,\n self.screen_width,\n self.screen_height)\n self.draw_thick_rectangle(rect, \"blight\", \"bmid\", \"bdark\")\n\n # Draw separator between arena area and information area\n rect = (\n self.screen_width - self.sidepanel_width - (self.border_thickness * 2),\n self.border_thickness - 1,\n self.border_thickness + 1,\n self.screen_height - (self.border_thickness) * 2 + 3)\n self.draw_separator(rect, \"blight\", \"bmid\", \"bdark\")\n\n\n def draw_thick_rectangle(self, rect, colour_topleft=None,\n colour_middle=None, colour_bottomright=None, no_inner_bevel=False):\n \"\"\" Draw a thick shaded rectangle within the edges of a given rect \"\"\"\n\n thickness = self.border_thickness - 2\n\n # Convert colour names to pygame values\n colour_topleft = colour.name_to_pygame_value(colour_topleft)\n colour_middle = colour.name_to_pygame_value(colour_middle)\n colour_bottomright = colour.name_to_pygame_value(colour_bottomright)\n\n outer_rect = rect\n # Calculate rect for the inside edge of the rectangle\n inner_rect = (rect[0] + (thickness + 1),\n rect[1] + (thickness + 1),\n rect[2] - (thickness + 1) * 2,\n rect[3] - (thickness + 1) * 2)\n\n # Calculate absolute pixel locations for rectangle bounding box\n outer_top = outer_rect[1]\n outer_bottom = outer_rect[3] + outer_rect[1]\n outer_left = outer_rect[0]\n outer_right = outer_rect[2] + outer_rect[0]\n\n # Create aliases for rectangle width and height, and thickness measurements\n width = outer_rect[2]\n height = outer_rect[3]\n \n # Draw base colour of top line\n pygame.draw.rect(self.scr, colour_middle,\n (outer_left, outer_top, width, thickness + 1))\n # Draw base colour of left line\n pygame.draw.rect(self.scr, colour_middle,\n (outer_left, outer_top, thickness + 1, height))\n # Draw base colour of bottom line\n pygame.draw.rect(self.scr, colour_middle,\n (outer_left, outer_bottom, width, -thickness))\n # Draw base colour of right line\n pygame.draw.rect(self.scr, colour_middle,\n (outer_right, outer_top, -thickness, height))\n\n self.draw_thin_rectangle(outer_rect, colour_topleft, colour_bottomright)\n if no_inner_bevel:\n self.draw_thin_rectangle(inner_rect, colour_middle)\n else:\n self.draw_thin_rectangle(inner_rect, colour_bottomright, colour_topleft)\n\n\n def draw_thin_rectangle(self, rect, colour_topleft, colour_bottomright=None):\n \"\"\"Draw a 1-pixel thick rectangle at given rect\"\"\" \n # Assume monochrome rectangle if only topleft colour is specified\n if colour_bottomright is None:\n colour_bottomright = colour_topleft\n\n # Calculate absolute pixel positions for rect bounding box\n left = rect[0]\n right = rect[2] + rect[0]\n top = rect[1]\n bottom = rect[3] + rect[1]\n\n # Convert colour names to pygame values\n colour_topleft = colour.name_to_pygame_value(colour_topleft)\n colour_bottomright = colour.name_to_pygame_value(colour_bottomright)\n\n #Draw top edge\n self.draw_line((left, top), (right, top), colour_topleft)\n #Draw right edge\n self.draw_line((right, top), (right, bottom), colour_bottomright)\n #Draw bottom edge\n self.draw_line((left, bottom), (right, bottom), colour_bottomright)\n #Draw left edge\n self.draw_line((left, top), (left, bottom), colour_topleft)\n\n\n\n def draw_separator(self,\n rect,\n colour_topleft,\n colour_middle=None,\n colour_bottomright=None):\n \"\"\" Draws a thick shaded line filling a given rect \"\"\" \n # If middle and bottom colours haven't been specified, assume they're the\n # same colour as the top colour.\n if colour_middle is None:\n colour_middle = colour_topleft\n if colour_bottomright is None:\n colour_bottomright = colour_topleft\n\n # Convert colour names to pygame values\n colour_topleft = colour.name_to_pygame_value(colour_topleft)\n colour_middle = colour.name_to_pygame_value(colour_middle)\n colour_bottomright = colour.name_to_pygame_value(colour_bottomright)\n\n # Determine if bar is horizontal or vertical\n if rect[2] > rect[3]:\n orientation = 'horizontal'\n else:\n orientation = 'vertical'\n\n # Calculate rect bounds in absolute pixel coordinates\n left = rect[0]\n right = rect[0] + rect[2] - 1\n top = rect[1]\n bottom = rect[1] + rect[3] - 1\n\n # Fill whole bar with middle colour\n pygame.draw.rect(self.scr, colour_middle, rect)\n if orientation == 'vertical':\n # Left edge shading\n pygame.draw.line(self.scr, colour_topleft, (left, top),\n (left, bottom))\n # Right edge shading\n pygame.draw.line(self.scr, colour_bottomright, (right, top),\n (right, bottom))\n if orientation == 'horizontal':\n # Top edge shading\n pygame.draw.line(self.scr, colour_topleft, (left, top),\n (right, top))\n # Bottom edge shading\n pygame.draw.line(self.scr, colour_bottomright, (left, bottom),\n (right - 1, bottom))\n ## (the \"right - 1\" part is a minor visual tweak, and can be removed if\n ## there are unforeseen consequences)\n\n\n def get_blink(self, hertz, start_time=0):\n \"\"\"Return True or False, toggles on and off to a given frequency in hertz\"\"\"\n result = int((time.time() - start_time) * hertz) % 2 == 0\n return(result)\n\n\n def draw_robot_information_boxes(self, robots):\n \"\"\"Draw robot information boxes on right of screen\"\"\"\n # Number of boxes in robot information area\n number_of_boxes = 6\n\n # Calculate rect for each robot information box\n for index in range(number_of_boxes):\n if number_of_boxes == 6:\n rect = (self.screen_width -\\\n self.sidepanel_width -\\\n self.border_thickness + 1,\n self.border_thickness + (65 + self.border_thickness) * index,\n self.sidepanel_width,\n 65) \n else:\n raise ValueError(\"number_of_boxes is not valid\")\n\n # Set thickness for the box separator\n separator_thickness = self.border_thickness\n if index == number_of_boxes - 1:\n separator_thickness = 11\n\n # Draw a separator at the bottom edge of this box\n separator_rect = (self.screen_width -\\\n self.sidepanel_width -\\\n self.border_thickness,\n rect[1] + rect[3],\n self.sidepanel_width + 2,\n separator_thickness)\n self.draw_separator(separator_rect, \"blight\", \"bmid\", \"bdark\")\n\n # Draw robot specific information\n if index < len(robots):\n robot = robots[index]\n controls = robot.get_controller_input()\n robot_colour = colour.index_to_pygame_value(robot.index)\n robot_colour_dark = colour.name_to_pygame_value\\\n (\"dark\" + colour.index_to_name(robot.index))\n # Draw robot name\n self.draw_text((rect[0] + 2, rect[1] + 1), robot.name.upper()[:8],\n robot_colour)\n # Draw an ellipsis if robot name has been cut off\n if len(robot.name) > 8:\n for i in range(3):\n self.draw_text((rect[0] + 64 + (i * 3), rect[1] + 1),\n \".\", robot_colour)\n\n\n if controls[\"config\"] and not controls[\"ability\"]:\n # Draw \"Points:\"\n self.draw_text((rect[0] + rect[2] - 75, rect[1] + 1),\n \"Points:\", robot_colour)\n # Draw number of points remaining\n points = robot.config.get_unspent_points()\n self.draw_text((rect[0] + rect[2] - 17, rect[1] + 1),\n str(points).rjust(2, \"0\"), robot_colour)\n\n\n\n number_of_tracks = len(robot.config.hardware_order)\n\n for index in range(number_of_tracks):\n margin = 15\n\n hardware_order = robot.config.hardware_order\n hardware_values = robot.config.hardware_values\n hardware_values_mutable = robot.config.hardware_values_mutable\n # Currently selected hardware component index\n pointer = robot.config.pointer\n \n # Thickness of slider line\n thickness = 2\n # If drawing the slider for the selected component, make it thicker\n if index == pointer:\n thickness = 3\n \n # Dimensions for vertical track lines\n # x position of vertical track line\n track_x = rect[0] + margin + \\\n (((self.sidepanel_width - margin * 2) // 6) * index)\n # Top and bottom heights of vertical track line\n track_top = rect[1] + 14\n track_bottom = rect[1] + rect[3] - 16\n\n # Draw shield error box\n if index == hardware_order.index(\"shield\"):\n pygame.draw.rect(self.scr,\n colour.name_to_pygame_value(\"darkgrey\"),\n (track_x - 2,\n track_top + 18,\n 5,\n 21),\n 0)\n\n # Draw vertical track line\n self.draw_line((track_x, track_top), (track_x, track_bottom), \"bmid\")\n\n # Draw handles on vertical tracks\n for value in range(robot.config.max_component_value + 1):\n track_height = track_bottom - track_top\n handle_y = track_bottom - (track_height * value / 5) \n\n # Draw all handles for mutable component values\n if hardware_values_mutable[hardware_order[index]] == value:\n # Draw mutable handle on selected track\n if pointer == index:\n self.draw_line((track_x - 3, handle_y),\n (track_x + 3, handle_y),\n \"white\", thickness)\n\n # Draw mutable handles on unselected track\n else:\n self.draw_line((track_x - 2, handle_y),\n (track_x + 2, handle_y),\n \"white\", thickness)\n\n # Draw all current locked component values, for reference\n else:\n # Draw locked handle on selected track\n if hardware_values[hardware_order[index]] == value:\n # Blink at 2Hz\n if self.get_blink(2):\n # Draw flashing grey broken line\n self.draw_line((track_x + 2, handle_y),\n (track_x + 3, handle_y),\n \"darkgrey\", 1)\n self.draw_line((track_x - 2, handle_y),\n (track_x - 3, handle_y),\n \"darkgrey\", 1)\n\n # Draw name and points value of selected hardware component\n bottom_text = (hardware_order[pointer].title() + \":\" \\\n + str(hardware_values_mutable[hardware_order[pointer]]))\n self.draw_text((rect[0] + 1, rect[1] + 55),\n bottom_text.center(19, \"-\"),\n robot_colour_dark,\n self.font1)\n self.draw_text((rect[0] + 1, rect[1] + 55),\n bottom_text.center(19),\n robot_colour,\n self.font1)\n\n\n elif controls[\"ability\"]:\n # Draw mini armour bar\n self.draw_bar((rect[0] + 75, rect[1] + 2, rect[2] - 78, 1),\n robot.armour / 100, robot_colour)\n\n # Draw mini heat bar\n self.draw_bar((rect[0] + 75, rect[1] + 4, rect[2] - 78, 6),\n robot.heat / 500, robot_colour)\n\n # Draw equipment indicators\n center_location = (rect[0] + 30, rect[1] + rect[3] - 28)\n for index, equipment, _colour in zip(range(4),\n [robot.warp_drive,\n robot.distortion,\n robot.overburner,\n robot.shield],\n [\"pink\", \"cyan\", \"green\", \"yellow\"]):\n\n equipment_colour = \"dark\" + _colour\n # Colour hints for nonexistent shield\n if equipment.name == \"Shield\" \\\n and robot.config.get_modifier(\"shield_level\") == 0:\n if time.time() - equipment.mechanism.start_time < 1 \\\n and self.get_blink(5, start_time=equipment.mechanism.start_time):\n equipment_colour = \"red\"\n else:\n equipment_colour = \"darkgrey\"\n\n # Calculate center of each small circle\n circle_origin = self.move_point_polar(center_location,\n index * 90, 10) \n # Convert tuple elements to ints\n circle_origin = (int(circle_origin[0]), int(circle_origin[1]))\n\n # Draw base dot and outer small circle\n self.draw_circle(circle_origin, 5, (32, 32, 32), thickness=0)\n self.draw_circle(circle_origin, 2, equipment_colour, thickness=0)\n\n # Draw lit up indicator if equipment is active\n if equipment.is_active():\n equipment_colour = _colour\n self.draw_circle(circle_origin, 5, equipment_colour, thickness=0)\n # Draw tiny dots around small circle\n for i in range(8):\n dot_location = self.move_point_polar(circle_origin, i * 45, 7)\n # Convert tuple elements to ints\n dot_location = (int(dot_location[0]), int(dot_location[1]))\n self.scr.set_at(dot_location, colour.name_to_pygame_value(_colour))\n if not (equipment.name == \"Shield\" \\\n and robot.config.get_modifier(\"shield_level\") == 0):\n self.draw_circle(circle_origin, 5, \"white\", 1)\n # Draw name of equipment\n self.draw_text((rect[0] + 60, rect[1] + 18 + (10 * index)),\n equipment.name, equipment_colour)\n\n # ----------------------------------------------------------------------\n # Standard information display section\n else:\n win_count = robot.match.acr.robot_stats[robot.index][\"wins\"]\n # Draw \"Wins:\"\n self.draw_text((rect[0] + rect[2] - 75, rect[1] + 1),\n \"Wins:\", robot_colour)\n # Draw win counter\n self.draw_text((rect[0] + rect[2] - 35, rect[1] + 1),\n str(win_count).rjust(4, \"0\"), robot_colour)\n # Draw robot message\n if len(robot.message) <= 19:\n message_length = 19\n # Draw ellipsis if message has been cut off\n else:\n message_length = 18\n for i in range(3):\n self.draw_text((rect[0] + rect[2] - 11 + (i * 3), rect[1] + 11),\n \".\", robot_colour_dark)\n # Draw message text\n self.draw_text((rect[0] + 2, rect[1] + 11),\n robot.message[:message_length], robot_colour_dark)\n # Draw \"A:\"\n self.draw_text((rect[0] + 10, rect[1] + 23), \"A:\", robot_colour)\n # Draw \"H:\"\n self.draw_text((rect[0] + 10, rect[1] + 33), \"H:\", robot_colour)\n\n # Heat and armour bars flash grey when robot is disabled\n bar_colour = robot_colour\n if robot.shutdown and self.get_blink(8):\n bar_colour = \"grey\"\n\n # # armour bar\n bar_rect = (rect[0] + 29, rect[1] + 24, rect[2] - 29 - 25, 6)\n self.draw_bar(bar_rect, robot.armour / 100, bar_colour)\n # heat bar\n bar_rect = (rect[0] + 29, rect[1] + 34, rect[2] - 29 - 25, 6)\n self.draw_bar(bar_rect, robot.heat / 500, bar_colour)\n # \"Ammo:\"\n self.draw_text((rect[0] + 7, rect[1] + 45),\n \"Ammo:\", robot_colour_dark, self.font1)\n # Draw ammo count for weapon\n if robot.weapon.ammo_count == 0:\n weapon_number_colour = robot_colour_dark\n else:\n weapon_number_colour = robot_colour\n if robot.weapon.ammo_count >= 0:\n weapon_ammo_text = str(robot.weapon.ammo_count).rjust(3, \"0\")\n else:\n weapon_ammo_text = \"inf\"\n self.draw_text((rect[0] + 46, rect[1] + 45),\n weapon_ammo_text,\n weapon_number_colour, self.font1)\n # \"Mines:\"\n self.draw_text((rect[0] + 80, rect[1] + 45),\n \"Mines:\", robot_colour_dark, self.font1)\n # Draw ammo count for mine layer\n if robot.mines.ammo_count == 0:\n weapon_number_colour = robot_colour_dark\n else:\n weapon_number_colour = robot_colour\n self.draw_text((rect[0] + 127, rect[1] + 45),\n str(robot.mines.ammo_count).rjust(2, \"0\"),\n weapon_number_colour, self.font1)\n # Draw current weapon name on the bottom row\n # Draw dashes with dark colour\n self.draw_text(\n (rect[0] + 1, rect[1] + 55),\n robot.weapon.display_name.center(19, \"-\"),\n robot_colour_dark, self.font1)\n # Draw name with normal colour\n self.draw_text(\n (rect[0] + 1, rect[1] + 55),\n robot.weapon.display_name.center(19),\n robot_colour, self.font1)\n # Draw weapon symbol if weapon is not default weapon\n if robot.weapon.name != \"missile_fission\":\n self.draw_symbol((rect[0] + 148, rect[1] + 59), robot.weapon.name)\n\n\n # Draw shading in empty info boxes\n else:\n self.draw_dithered_rectangle(rect, \"black\", \"darkgrey\")\n\n\n def draw_maze(self, maze):\n linecolour = \"darkgrey\" \n for line in maze.maze_to_lines():\n point_a = self.meters_to_pixel_coords(line[0])\n point_b = self.meters_to_pixel_coords(line[1])\n self.draw_line(point_a, point_b, linecolour, 2)\n\n\n def draw_projectiles(self, projectiles):\n \"\"\"Draws projectiles on screen\"\"\"\n for projectile in projectiles:\n location = self.meters_to_pixel_coords(projectile.location)\n heading = projectile.heading\n config_weapon_multiplier = projectile.robot.config.get_modifier(\n \"weapon_multiplier\")\n robot_colour = colour.index_to_pygame_value(projectile.robot.index)\n projectile_colour = colour.name_to_pygame_value(\"white\")\n projectile_time = time.time() - projectile.start_time\n speed_corrector = projectile.robot.match.speed_corrector()\n\n if projectile.overburn \\\n and self.get_blink(6, start_time=projectile.start_time):\n robot_colour = colour.name_to_pygame_value(\"yellow\")\n projectile_colour = colour.name_to_pygame_value(\"yellow\")\n\n\n if projectile.name == \"missile_fission\":\n point_b = self.move_point_polar(\n location,\n heading + 180,\n 15 * config_weapon_multiplier),\n self.draw_line(location, point_b, projectile_colour)\n\n elif projectile.name == \"shell\":\n self.draw_circle(location, 2, projectile_colour, 0)\n\n elif projectile.name == \"powersaw\":\n point_a = self.move_point_polar(\n location,\n heading,\n 2 * config_weapon_multiplier),\n point_b = self.move_point_polar(\n location,\n heading + 180,\n 2 * config_weapon_multiplier),\n point_c = self.move_point_polar(\n location,\n heading + 90,\n 2 * config_weapon_multiplier),\n point_d = self.move_point_polar(\n location,\n heading + 270,\n 2 * config_weapon_multiplier),\n self.draw_line(point_a, point_b, projectile_colour)\n self.draw_line(point_c, point_d, projectile_colour)\n\n elif projectile.name == \"viral\":\n if projectile_colour == colour.name_to_pygame_value(\"white\"):\n projectile_colour = colour.name_to_pygame_value(\"pink\")\n point_a = self.move_point_polar(\n location,\n heading + (projectile_time * 360 * speed_corrector),\n 5 * config_weapon_multiplier),\n point_b = self.move_point_polar(\n location,\n heading + 180 + (projectile_time * 360 * speed_corrector),\n 5 * config_weapon_multiplier),\n self.draw_line(point_a, point_b, projectile_colour)\n self.draw_circle(location, 2, projectile_colour, 0)\n\n elif projectile.name == \"alex\":\n point_b = self.move_point_polar(\n location,\n heading + 180,\n 15 * config_weapon_multiplier),\n self.draw_line(location, point_b, \"cyan\")\n\n elif projectile.name == \"wave\":\n if projectile.clock // 4 % 5 == 0:\n projectile_colour = \"red\"\n if projectile.clock // 4 % 5 == 1:\n projectile_colour = \"orange\"\n if projectile.clock // 4 % 5 == 2:\n projectile_colour = \"yellow\"\n if projectile.clock // 4 % 5 == 3:\n projectile_colour = \"green\"\n if projectile.clock // 4 % 5 == 4:\n projectile_colour = \"blue\"\n if projectile.clock // 4 % 5 == 5:\n projectile_colour = \"purple\"\n point_b = self.move_point_polar(\n location,\n heading + 180,\n 4 * config_weapon_multiplier),\n self.draw_line(location, point_b, projectile_colour)\n\n elif projectile.name == \"mine\":\n # If 3 seconds have passed\n if time.time() - projectile.start_time > 3:\n # Chance of a ghost image appearing\n if random.random() * 22 / projectile.robot.match.speed_corrector() < 1:\n altered_location = self.move_point_polar(\n projectile.location,\n random.random() * 360,\n random.random() * 40)\n projectile.robot.match.add_particle(\n altered_location,\n \"mine\",\n random.random() * 1,\n projectile.robot,\n special=projectile)\n # Draw mine normally\n else:\n # self.draw_line(\n # (location[0], location[1] - 2),\n # (location[0], location[1] + 2),\n # robot_colour)\n # self.draw_line(\n # (location[0] - 2, location[1]),\n # (location[0] + 2, location[1]),\n # robot_colour)\n # self.draw_line(\n # (location[0] - 2, location[1] + 2),\n # (location[0] + 2, location[1] - 2),\n # robot_colour)\n # self.draw_line(\n # (location[0] - 2, location[1] - 2),\n # (location[0] + 2, location[1] + 2),\n # robot_colour)\n self.draw_line(\n (location[0] - 1, location[1] - 1),\n (location[0] + 2, location[1] + 2),\n robot_colour)\n self.draw_line(\n (location[0] - 1, location[1] + 2),\n (location[0] + 2, location[1] - 1),\n robot_colour)\n\n elif projectile.name == \"pellet\":\n self.draw_pixel(location, projectile_colour)\n\n elif projectile.name == \"missile_guided\":\n self.draw_line(\n location,\n self.move_point_polar(location, heading, 10),\n robot_colour, 2)\n flame_colour = \"yellow\"\n if self.get_blink(4):\n flame_colour = \"red\"\n self.draw_line(\n location,\n self.move_point_polar(location, heading + 90, 2),\n flame_colour, 2)\n self.draw_line(\n location,\n self.move_point_polar(location, heading - 90, 2),\n flame_colour, 2)\n self.draw_line(\n location,\n self.move_point_polar(location, heading - 180, 2),\n flame_colour, 2)\n\n elif projectile.name == 'bomblet':\n self.draw_line(\n (location[0], location[1] - 1),\n (location[0], location[1] + 1),\n robot_colour)\n self.draw_line(\n (location[0] - 1, location[1]),\n (location[0] + 1, location[1]),\n robot_colour)\n\n # Draw placeholder graphic for unknown projectile\n else:\n self.draw_line((location[0] - 2, location[1] - 2),\n (location[0] + 2, location[1] + 2), \"red\")\n self.draw_line((location[0] - 2, location[1] + 2),\n (location[0] + 2, location[1] - 2), \"red\")\n \n\n\n\n def draw_match_information(self, match):\n \"\"\"Draw match related information\"\"\"\n # Draw \"Timer:\"\n self.draw_text((self.screen_width - 156, self.screen_height - 46),\n \"Timer:\", \"grey\", self.font1)\n\n match_seconds = match.time_limit - match.get_seconds_elapsed() + 1\n if match_seconds > 0:\n match_timer = str(int(match_seconds / 60)).rjust(2, \"0\") + \":\" +\\\n str(int(match_seconds % 60)).rjust(2, \"0\")\n else:\n match_timer = \"00:00\"\n \n timer_colour = \"grey\"\n if 1 < match_seconds <= 10 and self.get_blink(4): \n timer_colour = \"red\"\n if match_seconds <= 1 and self.get_blink(10): \n timer_colour = \"red\"\n\n self.draw_text((self.screen_width - 84, self.screen_height - 46),\n match_timer, timer_colour, self.font1)\n\n # Draw \"Match:\"\n self.draw_text((self.screen_width - 156, self.screen_height - 36),\n \"Match:\", \"grey\", self.font1)\n self.draw_text((self.screen_width - 84, self.screen_height - 36),\n str(match.current_match) + \"/\" + str(match.number_of_matches), \"grey\", self.font1)\n # Draw framerate value\n self.draw_text((self.screen_width - 24, self.screen_height - 16),\n str(int(match.clock.get_fps())), \"darkgrey\", self.font1)\n\n\n def draw_symbol(self, location, symbol_name, in_arena=False):\n \"\"\"Draw a bitmap symbol onto the screen\"\"\"\n border_colour = colour.name_to_pygame_value(\"white\")\n symbol_colours = {}\n symbol_colours[\"0\"] = (31, 31, 31)\n symbol_colours[\"1\"] = colour.name_to_pygame_value(\"white\")\n\n if symbol_name in symbols.dictionary:\n symbol = symbols.dictionary[symbol_name]\n else:\n symbol = symbols.dictionary[\"error\"]\n\n # Try to automatically pull colour names and values from symbol definition\n for i in symbol.keys():\n if i != \"array\":\n try:\n symbol_colours[i] = colour.name_to_pygame_value(symbol[i])\n except:\n raise ValueError(\"Symbol \" + symbol_name + \"has invalid colour data.\")\n\n # Assumes a square symbol\n # Calculate pixel height of symbol\n symbol_size = len(symbol[\"array\"])\n # Calculate pixel location of center pixel in symbol\n center = ((symbol_size - 1) // 2) + 1\n\n # Calculate pixel locations of bounding box edges\n left = location[0] - center\n right = location[0] + center\n top = location[1] - center\n bottom = location[1] + center\n\n # Draw full square around the symbol\n self.draw_line((left, top), (left, bottom), border_colour)\n self.draw_line((right, top), (right, bottom), border_colour)\n self.draw_line((left, top), (right, top), border_colour)\n self.draw_line((left, bottom), (right, bottom), border_colour)\n \n # Plot symbol on screen\n for y in range(symbol_size):\n for x in range(symbol_size):\n colour_next = symbol_colours[str(symbol[\"array\"][y][x])]\n self.scr.set_at((location[0] + x - center + 1,\n location[1] + y - center + 1),\n colour_next)\n\n\n def draw_bar(self, rect, value, colour_bar, colour_background=\"bdark\"):\n \"\"\"Draw a bar that represents a fraction of 1\"\"\"\n if value < 0:\n value = 0\n if value > 1:\n value = 1\n bar_width = rect[2] * value\n if bar_width < 1:\n bar_width = 1\n # bar background\n pygame.draw.rect(self.scr,\n colour.name_to_pygame_value(colour_background),\n rect)\n # bar active\n pygame.draw.rect(self.scr,\n colour.name_to_pygame_value(colour_bar),\n (rect[0], rect[1], bar_width, rect[3]))\n\n\n\n def draw_dithered_rectangle(self, rect, colour1, colour2, no_background=False):\n \"\"\"Draw a two colour pixel checkerboard\"\"\"\n width = rect[2]\n height = rect[3]\n colour1 = colour.name_to_pygame_value(colour1)\n colour2 = colour.name_to_pygame_value(colour2)\n\n if not no_background:\n pygame.draw.rect(self.scr, colour2, rect)\n\n self.scr.set_clip(rect[0], rect[1], rect[2], rect[3])\n for x in range(rect[0] - height, rect[0] + rect[2], 2):\n pygame.draw.line(self.scr, colour1, (x, rect[1]),\n (x + height, rect[1] + height))\n self.scr.set_clip(0, 0, self.screen_width, self.screen_height)\n\n\n def update(self):\n \"\"\"\n Updates the game window to show what has been drawn since last update. Call\n this every game cycle.\n \"\"\"\n # Flip buffers\n pygame.display.flip()\n\n # Clear screen\n pygame.draw.rect(self.scr,\n (0, 0, 0),\n (0, 0, self.screen_width, self.screen_height))\n\n\n def meters_to_pixel_coords(self, location):\n \"\"\"Converts arena coordinates in meters to screen coordinates in pixels\"\"\"\n # The -1 on each coordinate prevents a problem where elements on the right\n # side and bottom side of the arena are drawn one pixel too far offscreen.\n # Possibly caused by a rounding issue with using int().\n x = location[0] / self.arena_meters_width *\\\n (self.arena_pixel_width - 5) + self.arena_rect[0] + 2\n y = location[1] / self.arena_meters_height *\\\n (self.arena_pixel_height - 5) + self.arena_rect[1] + 2\n return(int(x), int(y))\n\n\n def rotate_point_around_point(self, origin, angle, point):\n \"\"\"Rotate a point around an origin.\n\n origin: center of rotation as a tuple containing two numbers\n angle: angle of rotation in degrees\n point: point to be rotated as a tuple containing two numbers\n\n Returns 'point' rotated around 'origin' by 'angle' degrees.\n \"\"\"\n if point[1] < 0:\n angle += 180\n if point[1] == 0:\n point = (point[0], 10)\n angle = math.radians(angle)\n # Calculate length of hypotenuse between origin and point\n hypotenuse = math.sqrt(point[0]**2 + point[1]**2)\n ang1 = math.atan(point[0] / point[1])\n ang2 = ang1 + angle\n rotated_point = math.sin(ang2) * hypotenuse + origin[0],\\\n math.cos(ang2) * hypotenuse + origin[1]\n return(rotated_point)\n\n \n def rotate_polygon(self, polygon, angle):\n \"\"\"Rotate a polygon around its local origin\"\"\"\n origin = (0, 0)\n rotated_polygon = []\n for point in polygon:\n rotated_polygon.append(self.rotate_point_around_point(origin, angle, point))\n return(rotated_polygon)\n\n def move_polygon_to_point(self, polygon, location):\n \"\"\"Translate polygon origin to given point\"\"\"\n for point in polygon:\n polygon[polygon.index(point)] = (point[0] + location[0],\n point[1] + location[1])\n return(polygon)\n\n def draw_polygon_in_arena(self, location, colour, polygon, heading, filled=False):\n \"\"\"Draw a polygon (a list of tuple coordinates) in the arena\"\"\"\n polygon = self.rotate_polygon(polygon, -heading)\n polygon = self.move_polygon_to_point(polygon, location)\n if filled:\n thickness = 0\n else:\n thickness = 1\n pygame.draw.polygon(self.scr, colour, polygon, thickness)\n\n\n def move_point_polar(self, point, heading, length):\n \"\"\"Move point along angle and distance. Angle is in degrees.\"\"\"\n return(linemath.move_point_polar(point,\n heading,\n length,\n y_points_down=True))\n angle = math.radians(angle - 90)\n translated_point = (point[0] + math.cos(angle) * length,\n point[1] + math.sin(angle) * length)\n return(translated_point)\n\n\n def draw_circle(self, center, radius, _colour, thickness=1):\n \"\"\"Draws circle on screen at point\"\"\"\n _colour = colour.name_to_pygame_value(_colour)\n pygame.draw.circle(self.scr, _colour, center, radius, thickness)\n\n\n def draw_explosions(self, explosions):\n \"\"\"Draws explosions on screen\"\"\" \n for explosion in explosions:\n location = self.meters_to_pixel_coords(explosion.location)\n radius = explosion.radius\n self.draw_circle(location, radius, explosion.colour)\n\n \n def draw_pickups(self, pickups):\n \"\"\"Draws pickups on screen\"\"\" \n for pickup in pickups:\n location = self.meters_to_pixel_coords(pickup.location)\n radius = 3\n self.draw_symbol(location, pickup.name, in_arena=True)\n\n \n def draw_particles(self, particles):\n \"\"\"Draws various particles on screen\"\"\" \n for particle in particles:\n location = self.meters_to_pixel_coords(particle.location)\n heading = particle.heading\n\n if particle.name == 'robot':\n robot_p = copy.copy(particle.robot)\n # Stop particle from moving and rotating with the robot after creation\n robot_p.location = particle.location\n robot_p.heading = particle.heading\n robot_p.name = \"particle\"\n self.draw_robots([robot_p])\n\n elif particle.name == 'mine':\n mine_p = copy.copy(particle.special)\n mine_p.location = particle.location\n mine_p.start_time = time.time()\n self.draw_projectiles([mine_p])\n\n\n def draw_pixel(self, point, _colour):\n _colour = colour.name_to_pygame_value(_colour)\n point = (int(point[0]), int(point[1]))\n self.scr.set_at(point, _colour)\n\n\n def draw_robots(self, robots):\n \"\"\"Draws robots and robot effects in arena\"\"\"\n for robot in robots:\n robot_colour = colour.index_to_pygame_value(robot.index)\n robot_colour_dark = colour.name_to_pygame_value\\\n (\"dark\" + colour.index_to_name(robot.index))\n location = self.meters_to_pixel_coords(robot.location)\n heading = robot.heading\n\n\n if robot.shield.is_active() and \\\n (not robot.distortion.is_active() or robot.name == \"particle\"):\n shield_level = robot.config.get_modifier(\"shield_level\")\n clock = (time.time() - robot.match.start_time)\n if shield_level == 1:\n self.draw_circle(location, 7, robot_colour_dark) \n if shield_level >= 2:\n self.draw_circle(location, 8, robot_colour_dark) \n self.draw_circle(location, 7, robot_colour) \n if shield_level == 3:\n shield_colour = \"white\"\n if self.get_blink(5):\n shield_colour = robot_colour\n self.draw_circle(location, 9, shield_colour) \n\n\n if robot.alive:\n # Don't draw this robot if distortion is active\n # Do draw this robot if it has been created by draw_particles()\n if robot.distortion.is_active() and not robot.name == \"particle\":\n continue\n turret_length = 4\n hull_polygon = polygons.shape_triangle\n\n # Draw when alive and visible\n if robot.overburner.is_active():\n self.draw_polygon_in_arena(location, robot_colour_dark,\n hull_polygon, heading, filled=True)\n self.draw_polygon_in_arena(location, robot_colour, hull_polygon, heading)\n self.draw_line(\n location,\n self.move_point_polar(location, robot.heading, turret_length),\n \"tgrey\")\n\n else:\n # Draw wreckage of dead robot\n self.scr.set_at(location, robot_colour)\n self.scr.set_at((location[0] + 2, location[1] + 2), robot_colour)\n \n # Draw warp drive circles around robot\n if robot.warp_drive.is_active():\n clock = time.time() - robot.warp_drive.mechanism.start_time\n clock3 = (time.time() - robot.warp_drive.mechanism.start_time) * 60\n charge_time = robot.config.get_modifier(\"warp_drive_charge_time\")\n if self.get_blink(10):\n colour_ = \"pink\"\n else:\n colour_ = \"cyan\"\n self.draw_circle_broken(\n location, 12, colour_, 0 + clock3, clock3 + clock / charge_time * 360)\n self.draw_circle_broken(\n location, 15, colour_, 120 + clock3, clock3 + 120 + clock / charge_time * 360)\n self.draw_circle_broken(\n location, 18, colour_, 240 + clock3, clock3 + 240 + clock / charge_time * 360)\n # For one to six circles\n # for i in range(min(int(clock // 0.5 + 1), 6)):\n # radius = 10 + (3 * i)\n # start_angle = 0 + clock * 200 * (i + 1) / 2\n # end_angle = 270 + clock * 200 * (i + 1) / 2\n # self.draw_circle_broken(\n # location, radius, colour_, start_angle, end_angle)\n\n\n def meters_to_pixels(self, meters):\n pixels = (self.arena_pixel_width / self.arena_meters_width) * meters\n return(max(int(pixels), 1))\n\n def draw_line(self, point1, point2, _colour, thickness=1):\n \"\"\"Draw a line\"\"\"\n pygame.draw.line(self.scr, colour.name_to_pygame_value(_colour),\n point1, point2, thickness)\n\n\n def draw_circle_broken(self, center, radius, _colour, start_angle, end_angle,\n thickness=1):\n \"\"\" Draw an arc of a circle \"\"\"\n # start_angle and end_angle are in degrees\n rect = pygame.Rect(0, 0, radius * 2, radius * 2)\n rect.center = center\n pygame.draw.arc(self.scr, colour.name_to_pygame_value(_colour), rect,\n math.radians(-end_angle + 90),\n math.radians(-start_angle + 90),\n thickness)\n\n\n\n def draw_scoreboard(self, match):\n victors = match.get_alive_robots()\n robots = match.robots\n rect = (24,\n 96 - 3 * len(robots),\n self.screen_width - 24 * 2,\n 90 + 10 + 12 * len(robots))\n\n\n left = rect[0]\n right = rect[2] + rect[0]\n top = rect[1]\n bottom = rect[3] + rect[1]\n\n\n self.draw_dithered_rectangle(rect, \"darkblue\", \"black\")\n self.draw_thick_rectangle(rect, \"blight\", \"bmid\", \"bdark\")\n\n self.draw_text((40, top + 20), \"Robot\", \"white\")\n self.draw_text((176, top + 20), \"Scored\", \"white\")\n self.draw_text((248, top + 20), \"Wins\", \"white\")\n self.draw_text((296, top + 20), \"Matches\", \"white\")\n self.draw_text((368, top + 20), \"Armor\", \"white\")\n self.draw_text((424, top + 20), \"Kills\", \"white\")\n self.draw_text((480, top + 20), \"Deaths\", \"white\")\n self.draw_text((560, top + 20), \"Shots\", \"white\")\n self.draw_text((40, top + 28), \"~\" * 70, \"white\")\n\n for robot in robots:\n robot_colour = colour.index_to_pygame_value(robot.index)\n robot_number = robot.index + 1\n index = robot.index\n line_y = top + 42 + robot.index * 12\n robot_stats = match.acr.robot_stats[index]\n\n # Number and name\n self.draw_text(\n (40, line_y),\n str(robot_number).rjust(2, \" \") + \" - \" + robot.name,\n robot_colour)\n\n # Score\n if robot in victors:\n self.draw_text((200, line_y), \"1\", robot_colour)\n else:\n self.draw_text((200, line_y), \"0\", robot_colour)\n\n # Wins\n self.draw_text(\n (264, line_y),\n robot_stats[\"wins\"],\n robot_colour)\n\n # Current match\n self.draw_text(\n (327, line_y),\n str(match.current_match),\n robot_colour)\n\n # Percentage armour remaining\n self.draw_text(\n (376, line_y),\n (str(int(robot.armour)) + \"%\").rjust(4),\n robot_colour)\n\n self.draw_text((456, line_y), str(robot_stats[\"kills\"]), robot_colour)\n self.draw_text((520, line_y), str(robot_stats[\"deaths\"]), robot_colour)\n self.draw_text((574, line_y), str(robot_stats[\"shots\"]).rjust(3), robot_colour)\n\n if len(victors) == 0:\n self.draw_text(\n (40, top + len(robots) * 12 + 52),\n \"Simultaneous destruction, match is a tie\",\n \"white\")\n elif len(victors) == 1:\n self.draw_text(\n (40, top + len(robots) * 12 + 52),\n (\"Robot #\" + str(victors[0].index + 1) +\n \" (\" + victors[0].name + \") wins!\"),\n \"white\")\n elif len(victors) > 1:\n self.draw_text(\n (40, top + len(robots) * 12 + 52),\n \"No clear victor, match is a tie\",\n \"white\")\n\n self.draw_text(\n (40, top + len(robots) * 12 + 64),\n \"Hold any key to continue...\",\n \"white\")\n\n self.draw_waiting_lights(robots, (rect[0] + (rect[2] // 2), top + len(robots) * 12 + 74 + 10))\n\n\n def draw_waiting_lights(self, robots, origin):\n number_of_robots = len(robots)\n for index, robot in enumerate(robots):\n robot_colour = colour.index_to_pygame_value(index)\n origin_x = origin[0]\n origin_y = origin[1]\n gap_size = 30\n\n circle_origin = (\n origin_x - (((number_of_robots - 1) * gap_size) // 2) + (index * gap_size),\n origin_y\n )\n if robot.input.get_any_key(robot.controller_map):\n self.draw_circle(circle_origin, 4, robot_colour, 0)\n for i in range(8):\n dot_location = self.move_point_polar(circle_origin, i * 45, 7)\n self.draw_pixel(dot_location, robot_colour)\n else:\n self.draw_circle(circle_origin, 2, robot_colour, 0)\n self.draw_circle(circle_origin, 5, \"white\", 1)\n \n\n def draw_pre_match_dialog(self, robots):\n rect = (100, 150, 439, 50)\n self.draw_dithered_rectangle(rect, \"black\", \"darkblue\")\n self.draw_thick_rectangle(rect, \"blight\", \"bmid\", \"bdark\", no_inner_bevel=True)\n self.draw_text((228, 168), \"Hold any key to begin!\", \"white\")\n\n center = (rect[0] + (rect[2] // 2), rect[1] + rect[3] - 13)\n self.draw_waiting_lights(robots, center)\n\n\"\"\"--------------------------------------------------------------------------\"\"\"\n","sub_path":"modules/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":44692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"347876699","text":"# Alternative to 'sed' to search/replace within a file using regex\n\nimport click\nimport os\nimport re\nimport sys\n\n\ndef _do_lines(search_regex, replace_regex, file_path):\n new_lines = []\n with open(file_path, \"r\") as fp:\n for line in fp:\n new_lines.append(re.sub(search_regex, replace_regex, line))\n return \"\".join(new_lines) # \\n already included in the re.sub return data\n\n\ndef _do_file(search_regex, replace_regex, file_path):\n with open(file_path, \"r\") as fp:\n data = fp.read()\n return re.sub(search_regex, replace_regex, data, flags=re.DOTALL)\n\n\ndef _error(msg):\n click.error(msg)\n sys.exit(1)\n\n\n@click.command(context_settings=dict(help_option_names=[\"-h\", \"--help\"]))\n@click.argument(\"search_regex\", required=True, type=str)\n@click.argument(\"replace_regex\", required=True, type=str)\n@click.argument(\"file_path\", required=True, type=str)\n@click.option(\n \"--in-place\",\n \"-i\",\n is_flag=True,\n default=False,\n help=\"Overwrite file once replace is completed\",\n)\n@click.option(\n \"--lines\",\n \"-l\",\n is_flag=True,\n default=False,\n help=\"Find re match on each line, not the entire file at once\",\n)\ndef main(search_regex, replace_regex, file_path, in_place, lines):\n if not os.path.exists(file_path):\n _error(f\"file does not exist: {file_path}\")\n if not os.path.isfile(file_path):\n _error(f\"path is not a file: {file_path}\")\n if not os.access(file_path, os.R_OK):\n _error(f\"unable to file for reading: {file_path}\")\n\n if lines:\n new_data = _do_lines(search_regex, replace_regex, file_path)\n else:\n new_data = _do_file(search_regex, replace_regex, file_path)\n\n if in_place:\n if not os.access(file_path, os.W_OK):\n _error(f\"unable to file for writing: {file_path}\")\n with open(file_path, \"w\") as fp:\n fp.write(new_data)\n else:\n print(new_data)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"utils/search_replace.py","file_name":"search_replace.py","file_ext":"py","file_size_in_byte":1953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"522908815","text":"def bsts(n):\n \"\"\"\n https://stackoverflow.com/questions/3042412/with-n-no-of-nodes-how-many-different-binary-and-binary-search-trees-possib\n Sum of i to n: f(n-i) * f(i-1)\n :param n:\n :return:\n \"\"\"\n print(n)\n if n == 0 or n == 1:\n return 1\n\n num = 0\n for i in range(1, n+1):\n num += bsts(n-i) * bsts(i - 1)\n return num\n\nprint(bsts(3))\n","sub_path":"recursion/hw/how_many_BSTs.py","file_name":"how_many_BSTs.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"551260992","text":"import sys\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom source.shared import common\n\n\nclass Window(QWidget):\n def __init__(self, is_valid, errors, *args, **kwargs):\n QWidget.__init__(self, *args, **kwargs)\n\n label_str = 'Config is {}.\\n'.format('valid. See /logs for more info' if is_valid else 'INVALID')\n if not is_valid:\n label_str += '\\n'.join(errors)\n\n self.label = QLabel(label_str, self)\n self.label.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)\n self.label.setAlignment(Qt.AlignCenter)\n self.label.setStyleSheet('QLabel {background-color: %s;}' % ('green' if config else 'red'))\n\n self.button = QPushButton('OK', self)\n self.button.clicked.connect(self.close)\n\n self.layout = QGridLayout()\n self.layout.addWidget(self.label, 0, 0)\n self.layout.addWidget(self.button, 1, 0)\n\n self.setLayout(self.layout)\n self.show()\n\n\nif __name__ == '__main__':\n # configure logging\n logging = common.setup_logging(__file__, './logs')\n\n # load config\n errors = []\n config = common.read_config('config.json', logging, errors)\n\n app = QApplication(sys.argv)\n win = Window(config is not None, errors)\n sys.exit(app.exec_())\n","sub_path":"validate_config.py","file_name":"validate_config.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"580898338","text":"###################################################################\n# Copyright 2013-2015 All Rights Reserved\n# Authors: The Paradrop Team\n###################################################################\n\n\"\"\"\nlib.utils.output.\nHelper for formatting output from Paradrop.\n\"\"\"\nfrom __future__ import print_function\n\nimport time\nimport json\nimport urllib\n\nimport six\n\ntimeflt = lambda: time.time()\ntimeint = lambda: int(time.time())\ntimestr = lambda x=None: time.asctime(time.localtime(x)) if x else time.asctime()\n\n# Short time string\nstimestr = lambda x=None: time.strftime('%a %H:%M', time.localtime(x))\n\n\ndef timedur(x):\n \"\"\"\n Print consistent string format of seconds passed.\n Example: 300 = '5 mins'\n Example: 86400 = '1 day'\n Example: 86705 = '1 day, 5 mins, 5 sec'\n \"\"\"\n divs = [('days', 86400), ('hours', 3600), ('mins', 60)]\n x = float(x)\n res = []\n for lbl, sec in divs:\n if(x >= sec):\n rm, x = divmod(x, float(sec))\n # If exactly 1, remove plural of label\n if(rm == 1.0):\n res.append((lbl[:-1], int(rm)))\n else:\n res.append((lbl, int(rm)))\n\n # anything left over is seconds\n x = int(x)\n if(x == 1):\n res.append((\"second\", x))\n elif(x == 0):\n pass\n else:\n res.append((\"seconds\", x))\n\n return \", \".join([\"%d %s\" % (w[1], w[0]) for w in res])\n\n\ndef convertUnicode(elem):\n \"\"\"Converts all unicode strings back into UTF-8 (str) so everything works.\n Call this function like:\n json.loads(s, object_hook=convertUnicode)\"\"\"\n if isinstance(elem, dict):\n return {convertUnicode(key): convertUnicode(value) for key, value in six.iteritems(elem)}\n elif isinstance(elem, list):\n return [convertUnicode(element) for element in elem]\n elif isinstance(elem, unicode):\n return elem.encode('utf-8')\n # DFW: Not sure if this has to be here, but deal with possible \"null\" MySQL strings\n elif(elem == 'null'):\n return None\n else:\n return elem\n\n\ndef urlEncodeMe(elem, safe=' '):\n \"\"\"\n Converts any values that would cause JSON parsing to fail into URL percent encoding equivalents.\n This function can be used for any valid JSON type including str, dict, list.\n Returns:\n Same element properly encoded.\n \"\"\"\n # What type am I?\n if isinstance(elem, dict):\n return {urlEncodeMe(key, safe): urlEncodeMe(value, safe) for key, value in six.iteritems(elem)}\n elif isinstance(elem, list):\n return [urlEncodeMe(element, safe) for element in elem]\n elif isinstance(elem, str):\n # Leave spaces alone, they are save to travel for JSON parsing\n return urllib.quote(elem, safe)\n else:\n return elem\n\n\ndef urlDecodeMe(elem):\n \"\"\"\n Converts any values that would cause JSON parsing to fail into URL percent encoding equivalents.\n This function can be used for any valid JSON type including str, dict, list.\n Returns:\n Same element properly decoded.\n \"\"\"\n # What type am I?\n if isinstance(elem, dict):\n return {urlDecodeMe(key): urlDecodeMe(value) for key, value in six.iteritems(elem)}\n elif isinstance(elem, list):\n return [urlDecodeMe(element) for element in elem]\n elif isinstance(elem, str):\n # Leave spaces alone, they are save to travel for JSON parsing\n return urllib.unquote(elem)\n else:\n return elem\n\n\ndef jsonPretty(j):\n \"\"\"\n Returns a string of a JSON object in 'pretty print' format fully indented, and sorted.\n \"\"\"\n return json.dumps(j, sort_keys=True, indent=4, separators=(',', ': '))\n\n\nclass dict2obj(object):\n\n def __init__(self, aDict=None, **kwargs):\n if(aDict is not None):\n aDict.update(kwargs)\n else:\n self.__dict__.update(kwargs)\n\n\ndef check(pkt, pktType, keyMatches=None, **valMatches):\n \"\"\"This function takes an object that was expected to come from a packet (after it has been JSONized)\n and compares it against the arg requirements so you don't have to have 10 if() statements to look for keys in a dict, etc..\n\n Args:\n @pkt : object to look at\n @pktType : object type expected (dict, list, etc..)\n @keyMatches : a list of minimum keys found in parent level of dict, expected to be an array\n @valMatches : a dict of key:value pairs expected to be found in the parent level of dict\n the value can be data (like 5) OR a type (like this value must be a @list@).\n Returns:\n None if everything matches, otherwise it returns a string as to why it failed.\"\"\"\n # First check that the pkt type is equal to the input type\n if(type(pkt) is not pktType):\n return 'expected %s' % str(pktType)\n\n if(keyMatches):\n # Convert the keys to a set\n keyMatches = set(keyMatches)\n # The keyMatches is expected to be an array of the minimum keys we want to see in the pkt if the type is dict\n if(type(pkt) is dict):\n if(not keyMatches.issubset(pkt.keys())):\n return 'missing, \"%s\"' % ', '.join(list(keyMatches - set(pkt.keys())))\n else:\n return None\n\n # Finally for anything in the valMatches find those values\n if(valMatches):\n # Pull out the dict object from the \"valMatches\" key\n if('valMatches' in valMatches.keys()):\n matchObj = valMatches['valMatches']\n else:\n matchObj = valMatches\n\n for k, v in six.iteritems(matchObj):\n # Check for the key\n if(k not in pkt.keys()):\n return 'key missing \"%s\"' % k\n\n # See how we should be comparing it:\n if(type(v) is type):\n if(type(pkt[k]) is not v):\n return 'key \"%s\", bad value type, \"%s\", expected \"%s\"' % (k, type(pkt[k]), v)\n\n else:\n # If key exists check value\n if(v != pkt[k]):\n return 'key \"%s\", bad value data, \"%s\", expected \"%s\"' % (k, pkt[k], v)\n\n return None\n\n\ndef explode(pkt, *args):\n \"\"\"This function takes a dict object and explodes it into the tuple requested.\n\n It returns None for any value it doesn't find.\n\n The only error it throws is if args is not defined.\n\n Example:\n pkt = {'a':0, 'b':1}\n 0, 1, None = pdcomm.explode(pkt, 'a', 'b', 'c')\n \"\"\"\n if not args:\n raise Exception(\"Required arguments not provided\")\n\n # If there is an error make sure to return a tuple of the proper length\n if(not isinstance(pkt, dict)):\n return tuple([None] * len(args))\n\n # Now just step through the args and pop off everything from the packet\n # If a key is missing, the pkt.get(a, None) returns None rather than raising an Exception\n return tuple([pkt.get(a, None) for a in args])\n\n\nclass Timer(object):\n\n '''\n A timer object for simple benchmarking. \n\n Usage:\n with Timer(key='Name of this test') as t:\n do.someCode(thatTakes=aWhile)\n\n Once the code finishes executing the time is output. \n '''\n\n def __init__(self, key=\"\", verbose=True):\n self.verbose = verbose\n self.key = key\n\n def __enter__(self):\n self.start = time.time()\n return self\n\n def __exit__(self, *args):\n self.end = time.time()\n self.secs = self.end - self.start\n self.msecs = self.secs * 1000 # millisecs\n if self.verbose:\n print(self.key + ' elapsed time: %f ms' % self.msecs)\n","sub_path":"paradrop/daemon/paradrop/base/pdutils.py","file_name":"pdutils.py","file_ext":"py","file_size_in_byte":7674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"389425782","text":"import tensorflow as tf\nimport numpy as np\n# ****************创建张量**********************\n#(1) python 列表\n'''a=tf.constant([[1,2],[3,4]])\n# print(a)\n#(2) 张量.numpy()方法\nb1=a.numpy()# 通过.numpy()得到对应数组\n# print(b1)\n#(3) 参数为常数，类型：tf.int8/int16/int32/int64, tf.uint8(无符号位整数),\nc=tf.constant(1.0)# tensorflow创建的浮点数，默认是32位\n# 创建张量指定数据类型\nd=tf.constant(1.0,dtype=tf.float64)\n#(4)参数为numpy数组\ntf.constant(np.array([1,2]))# numpy创建的数组默认int为32位，float为64位\ntf.constant(np.array([1,2]),dtype=tf.float32)# 可以在创建的时候指定数据类型\n# (5) 改变张量中的元素数据类型\na1=tf.constant(np.array([1,2]))\nb1=tf.cast(a,dtype=tf.float32)\n#(6) 参数为布尔型\na3=tf.constant([True,False])\nb3=tf.cast(a3,tf.int32)\n#(7) 参数为字符串\na4=tf.constant('hello')# b表示字符串b'hello'\n# (8) (数组/列表/数字/布尔型/字符串)转换为tensor类型\nna=np.arange(12).reshape(3,4)\nta=tf.convert_to_tensor(na)# 数组转换为tensor类型\n#(9) 判断是不是张量类型\nflag=tf.is_tensor(ta)\n# print(flag)\n#(10) 创建全0或全1张量\ntf.zeros((3,4))# 可以用圆括号\ntf.ones(shape=(2,1))\ntf.ones([2,3])# 也可以用方括号\ntf.fill([2,3],9)# 参数为shape和要填充的数字\n#(11) 创建元素值都相同的张量----tf.constant()函数\ntf.constant(value=9,shape=[2,3])\n# 创建随机张量----正态分布\n#(1) 创建 2*2 的张量，其元素服从标准正态分布\ntf.random.normal([2,2])\n#(2) 创建一个三维张量，其元素服从正态分布\ntf.random.normal([3,3,3],mean=0.0,stddev=2.0)\n#(3)创建随机张量----截断正态分布\n# eg:当均值为0标准差为1时\n# tf.random.truncated_normal()#使用这个函数不可能出现[-2,2]以外的点\n# tf.random.random_normal()# 可能出现[-2,2]以外的点\n# 创建随即张量----截断正态分布\na5=tf.random.truncated_normal([3,3,3],mean=0.0,stddev=2.0)\nprint(a5)'''\n# ***************设置随机种子,可以产生同样的随即张量*************\ntf.random.set_seed(8)\ntf.random.normal([2,2])\n#***************创建均匀分布张量 tf.random.uniform() 函数*****************\ntf.random.uniform(shape=(3,3),minval=0,maxval=10,dtype='int32')# minval=0,最小值，maxval=10 最大值\n# *************随机打乱 tf.random.shuffle() 函数************\nx=tf.constant([[1,2],[3,4],[5,6]])\ntf.random.shuffle(x)# 参数可以为列表，数组张量\n\n#*************张量既可以用到CPU也可以用到GPU，但是数组只能用到CPU","sub_path":"python_code/tensorflow_studay/tensorflow基础/tensorflow基础(创建张量).py","file_name":"tensorflow基础(创建张量).py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"254392869","text":"from __future__ import print_function\nfrom functools import wraps\nimport math\nimport sys\nfrom time import time\n\n# Setup cython modules\nimport pyximport; pyximport.install()\nimport cyexpdecay\n\nrng = xrange if sys.version_info.major < 3 else range\n\ni = 1000000\n\ndef fixlen(s, maxlen=16):\n if len(s) > maxlen:\n ss = s[:maxlen-3] + '...'\n else:\n ss = s + ' '* (maxlen-len(s))\n return ss\n\ndef timing(f):\n '''See: http://stackoverflow.com/a/27737385'''\n @wraps(f)\n def wrap(*args, **kw):\n ts = time()\n result = f(*args, **kw)\n te = time()\n latency = (te-ts)/i\n\n # Mangle function to display\n #func = '{}(*{}, **{})'.format(f.__name__, args, kw)\n func = fixlen(f.__name__)\n py = fixlen('python-{}.{}'.format(sys.version_info.major,\n sys.version_info.minor))\n print('{}\\t{}\\t{:,.1f} mtps\\t{:.3f} us/txn'.format(fixlen(py), func,\n 1.0/(latency*1000000.0), latency*1000000.0))\n\n return result\n return wrap\n\n@timing\ndef expdecay(i, x=0.5, factor=1.0):\n for _ in rng(i):\n y = math.exp(-factor*x)\n return y\n\ndef expdecay2(x=0.5, factor=1.0):\n return math.exp(-factor*x)\n\n@timing\ndef time_expdecay2(i):\n for _ in rng(i):\n y = expdecay2()\n return y\n\ncy_expdecay = timing(cyexpdecay.cy_expdecay)\ncya_expdecay = timing(cyexpdecay.cya_expdecay)\n\n@timing\ndef cy_expdecay2(i):\n for _ in rng(i):\n y = cyexpdecay.cy_expdecay2()\n return y\n\n@timing\ndef cya_expdecay2(i):\n for _ in rng(i):\n y = cyexpdecay.cya_expdecay2()\n return y\n\n\nif __name__ == '__main__':\n try: \n i = int(sys.argv[1])\n except Exception as e:\n pass\n\n funcs = [expdecay, cy_expdecay, cya_expdecay, time_expdecay2, cy_expdecay2,\n cya_expdecay2]\n for f in funcs:\n f(i)\n","sub_path":"expdecay.py","file_name":"expdecay.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"104933425","text":"import click\nimport jinja2\nimport json\n\ndef update_file(file_name, dict):\n # read text file as a template\n try:\n f = open(file_name, 'r')\n t = jinja2.Template(f.read())\n f.close()\n except:\n click.echo('Skipping file %s' % file_name)\n return\n \n # write file with value replacements\n click.echo('Updating file %s' % file_name)\n f = open(file_name, 'w')\n f.write(t.render(dict))\n f.write('\\n')\n f.close()\n\n@click.command()\n@click.argument('json_file', type=click.File('r'))\n@click.argument('text_file', nargs=-1)\ndef cli(json_file, text_file):\n \"\"\"Update templated text files with values contained in a JSON data file.\"\"\"\n \n # read json data\n click.echo('Reading JSON file for values')\n d = json.loads(json_file.read())\n \n # check for files to update\n if len(text_file) == 0:\n click.echo('No files to update, exiting')\n return\n \n # update all parsed files\n for fn in text_file:\n update_file(fn, d)\n\nif __name__ == '__main__':\n cli()\n","sub_path":"uptempo/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"630439190","text":"from django.urls import path\n\nfrom . import views\n\n\nurlpatterns = [\n path('nosotros/', views.about, name=\"about\"),\n path('contacto/', views.contact, name=\"contact\"),\n path('thank-you/', views.thank_you, name=\"thank_you\"),\n]\n","sub_path":"custompages/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"67961069","text":"numbers=(1,2,3,4,5,6,7,8,9)\nodd=0\neven=0\nfor i in range(0, len(numbers)):\n if numbers[i] % 2 == 0:\n even+=1\n else:\n odd+=1\nprint(even)\nprint(odd)\n","sub_path":"week-03/day-1/40.py","file_name":"40.py","file_ext":"py","file_size_in_byte":166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"536510534","text":"# -*- coding: utf-8 -*-\n# !/usr/bin/env python\n\n\"\"\"\nThis module is provided by\n\tAuthors: hxk11111\nDate:\t2019/2/26\nFile: Easy 344. Reverse String.py\n\"\"\"\nfrom typing import List\n\n'''\nWrite a function that reverses a string. The input string is given as an array of characters char[].\n\nDo not allocate extra space for another array, you must do this by modifying the input array \nin-place with O(1) extra memory.\n\nYou may assume all the characters consist of printable ascii characters.\n\nExample 1:\nInput: [\"h\",\"e\",\"l\",\"l\",\"o\"]\nOutput: [\"o\",\"l\",\"l\",\"e\",\"h\"]\n\nExample 2:\nInput: [\"H\",\"a\",\"n\",\"n\",\"a\",\"h\"]\nOutput: [\"h\",\"a\",\"n\",\"n\",\"a\",\"H\"]\n'''\n\n\nclass Solution:\n def reverseString(self, s: List[str]) -> None:\n \"\"\"\n Do not return anything, modify s in-place instead.\n \"\"\"\n start = 0\n end = len(s) - 1\n while start <= end:\n s[start], s[end] = s[end], s[start]\n start += 1\n end -= 1\n\n\nif __name__ == '__main__':\n s = Solution()\n st = [\"h\", \"e\", \"l\", \"l\", \"o\"]\n s.reverseString(st)\n print(st)\n","sub_path":"leetcode_py3/Easy 344. Reverse String.py","file_name":"Easy 344. Reverse String.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"199381176","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ =\"roshan padmanbhan\"\n__version__=0.1\n\n\"\"\"\nWritten for m3m4 mouse model rnaseq\n\n\"\"\"\n\nimport os\nfrom pathlib import Path\nimport shlex\nimport subprocess\nfrom collections import OrderedDict\nimport logging\nimport time\n\n\ndef _run_star_aligner( threads,genome_dir, out_prefix, R1, R2, zip=True ):\n zip_option = ' --readFilesCommand gunzip -c '\n if zip :\n cmd = shlex.split('STAR --runThreadN ' + str(threads) + ' --genomeDir ' + genome_dir + ' --outFileNamePrefix ' + out_prefix + zip_option + ' --readFilesIn ' + R1 + ' ' + R2 )\n else :\n cmd = shlex.split('STAR --runThreadN ' + str(threads) + ' --genomeDir ' + genome_dir + ' --outFileNamePrefix ' + out_prefix + ' --readFilesIn ' + R1 + ' ' + R2 )\n return(cmd)\n\ndef _make_logger( namex ):\n logger = logging.getLogger(__name__)\n logger.setLevel(logging.INFO)\n # create a file handler\n handler = logging.FileHandler('star_'+namex+'_'+time.ctime().replace(' ','_').replace(':','-')+'.log')\n handler.setLevel(logging.INFO)\n formatter = logging.Formatter('%(asctime)s - %(message)s')\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n return( logger )\n\ndef _make_dir( path_x ):\n td = Path( path_x )\n if not td.is_dir():\n td.mkdir()\n return( path_x )\n\ndef _run_commands( cmd ):\n s = subprocess.call(cmd, shell=False)\n return(s)\n\ndef run_star(tissue, dictx, genome_index, res_loc):\n log = _make_logger( tissue )\n log.info('Start STAR Alignment\\n')\n res_loc = _make_dir( os.path.join( res_loc, tissue ) )\n log.info( 'Result Location dir {}\\n'.format( res_loc ))\n for i in dictx.keys():\n R1 = dictx.get( i )\n R2 = str(dictx.get( i )).replace('_R1','_R2')\n bn = dictx.get( i ).name\n bn = bn.split('.')[4].split('_')[0]\n prefix = os.path.join( res_loc, bn )\n log.info('Processing files {}'.format(bn) )\n log.info('Result prefix {}'.format(prefix) )\n if R1.name.endswith('.gz'):\n log.info( ' '.join( _run_star_aligner( threads=15, genome_dir=genome_index, out_prefix=prefix, R1=str(R1), R2=R2, zip=True ))+\"\\n\" )\n _run_commands(_run_star_aligner( threads=15, genome_dir=genome_index, out_prefix=prefix, R1=str(R1), R2=R2, zip=True ))\n else :\n log.info( ' '.join( _run_star_aligner( threads=15, genome_dir=genome_index, out_prefix=prefix, R1=str(R1), R2=R2, zip=False ))+\"\\n\")\n _run_commands(_run_star_aligner( threads=15, genome_dir=genome_index, out_prefix=prefix, R1=str(R1), R2=R2, zip=False ))\n log.info( 'STAR aligner finished\\n' )\n\ndef main():\n genome_star_index_fp='/home/padmanr/niazif-share/Library_Files/StarIndex/STARindex/mm10'\n fastq_loc='/mnt/isilon/data/w_gmi/gmi-to-be-archived/engclab_ngs/m3m4_p14brain_p40cortex_p40thyroid_RNA-Seq_2013/Raw_fastq'\n res_loc_fp = '/home/padmanr/niazif-share/Stetson/Amanda_M3M4_Reanalysis/data/star_aligned'\n\n # make some dicts\n files_dict = OrderedDict()\n fqx = Path(fastq_loc)\n files_dict = { i.name : i for i in sorted( fqx.glob('*_R1.*')) }\n\n thyroid_dict = { i : files_dict.get(i) for i in files_dict.keys() if 'thyroid' in i }\n brain_dict = { i : files_dict.get(i) for i in files_dict.keys() if 'brain' in i }\n cortex_dict = { i : files_dict.get(i) for i in files_dict.keys() if 'cortex' in i }\n\n run_star( tissue = 'cortex', dictx=cortex_dict, genome_index=genome_star_index_fp, res_loc=res_loc_fp )\n run_star( tissue = 'brain', dictx=brain_dict, genome_index=genome_star_index_fp, res_loc=res_loc_fp )\n run_star( tissue = 'thyroid', dictx=thyroid_dict, genome_index=genome_star_index_fp, res_loc=res_loc_fp )\n\nif __name__ == '__main__':\n main()\n","sub_path":"run_STAR_aligner.py","file_name":"run_STAR_aligner.py","file_ext":"py","file_size_in_byte":3758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"305115419","text":"\"\"\"\n# 1. 两数之和\n\n## 题意\n给定一个整数数组 nums 和一个目标值 target，请你在该数组中找出和为目标值的那 两个 整数，并返回他们的数组下标。\n\n你可以假设每种输入只会对应一个答案。但是，你不能重复利用这个数组中同样的元素。\n\n**示例1:**\n```\n输入: nums = [2, 7, 11, 15], target = 9\n输出: [0, 1]\n原因: nums[0] + nums[1] = 2 + 7 = 9\n```\n\n## 题解\n扫描一遍，记录各个数的下标，对当前数`cur`查询`target-cur`的下标，若存在即所求。\n\"\"\"\n\n\nfrom typing import List\n\n\nclass Solution:\n def twoSum(self, nums: List[int], target: int) -> List[int]:\n m = {}\n for i in range(len(nums)):\n require = target-nums[i]\n if m.get(require) is not None:\n return [m.get(require), i]\n else:\n m[nums[i]] = i\n\n\nif __name__ == '__main__':\n print(\n Solution().twoSum([2, 7, 11, 15], 9)\n )\n","sub_path":"Solutions/0001.twoSum.py","file_name":"0001.twoSum.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"448826059","text":"# https://www.koderdojo.com/blog/depth-first-search-in-python-recursive-and-non-recursive-programming\nadjacency_matrix = {1: [2, 3], 2: [4, 5],\n 3: [5], 4: [6], 5: [6],\n 6: [7], 7: []}\n\n\ndef dfs_recursive(graph, vertex, path=[]):\n path += [vertex]\n\n for neighbor in graph[vertex]:\n if neighbor not in path:\n path = dfs_recursive(graph, neighbor, path)\n\n return path\n\n\ndef dfs_iterative(graph, start):\n stack, path = [start], []\n\n while stack:\n vertex = stack.pop()\n if vertex in path:\n continue\n path.append(vertex)\n for neighbor in graph[vertex]:\n stack.append(neighbor)\n\n return path\n\n\nprint(dfs_recursive(adjacency_matrix, 1))\n\nprint(dfs_iterative(adjacency_matrix, 1))\n","sub_path":"Algorithms/DFS.py","file_name":"DFS.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"370588031","text":"count = raw_input()\n# noinspection PyInterpreter\nperTime = map(int,raw_input().split(' '))\nperTime.sort()\n\nresult = 0\n\nfor index in range(len(perTime)):\n getTimes = perTime[:index+1]\n result += sum(getTimes)\n\nprint(result)","sub_path":"Greed/atm.py","file_name":"atm.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"102663705","text":"function = \"-x*y*y\"\n\n\ndef f(x, y):\n return eval(function)\n\n\ndef runge_kutta(a=0, b=1, ya=1, h=0.1, ):\n '''四阶龙格库塔法'''\n res = \"\"\n xi = a\n yi = ya\n\n while xi <= b: # 在求解区间范围\n k1 = h * f(xi, yi)\n k2 = h * f(xi + h / 2, yi + k1 / 2)\n k3 = h * f(xi + h / 2, yi + k2 / 2)\n k4 = h * f(xi + h, yi + k3)\n y = yi + 1 / 6 * (k1 + 2 * k2 + 2 * k3 + k4)\n\n res += 'x:{:.2f}, y:{:.10f}\\n'.format(xi, yi)\n\n xi, yi = xi + h, y\n\n return res\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\n\nclass Runge_kutta(object):\n def __init__(self):\n self.window = QtWidgets.QDialog()\n self.setupUi(self.window)\n\n def setupUi(self, Dialog):\n Dialog.setObjectName(\"Dialog\")\n Dialog.resize(405, 637)\n # Dialog = QtWidgets.QWidget(Dialog)\n Dialog.setObjectName(\"centralwidget\")\n self.start_value = QtWidgets.QPlainTextEdit(Dialog)\n self.start_value.setGeometry(QtCore.QRect(120, 10, 71, 21))\n self.start_value.setObjectName(\"start_value\")\n self.start_value.setPlainText(\"0\")\n\n\n self.function = QtWidgets.QPlainTextEdit(Dialog)\n self.function.setGeometry(QtCore.QRect(30, 120, 351, 74))\n self.function.setObjectName(\"function\")\n self.label = QtWidgets.QLabel(Dialog)\n self.label.setGeometry(QtCore.QRect(30, 10, 71, 20))\n self.label.setObjectName(\"label\")\n self.label_4 = QtWidgets.QLabel(Dialog)\n self.label_4.setGeometry(QtCore.QRect(30, 90, 161, 16))\n self.label_4.setObjectName(\"label_4\")\n self.output = QtWidgets.QPlainTextEdit(Dialog)\n self.output.setGeometry(QtCore.QRect(30, 280, 351, 291))\n self.output.setObjectName(\"output\")\n self.label_5 = QtWidgets.QLabel(Dialog)\n self.label_5.setGeometry(QtCore.QRect(30, 250, 58, 16))\n self.label_5.setObjectName(\"label_5\")\n self.pushButton = QtWidgets.QPushButton(Dialog)\n self.pushButton.setGeometry(QtCore.QRect(20, 210, 371, 32))\n self.pushButton.setObjectName(\"pushButton\")\n self.end_value = QtWidgets.QPlainTextEdit(Dialog)\n self.end_value.setGeometry(QtCore.QRect(310, 10, 71, 21))\n self.end_value.setObjectName(\"end_value\")\n self.label_2 = QtWidgets.QLabel(Dialog)\n self.label_2.setGeometry(QtCore.QRect(220, 10, 71, 20))\n self.label_2.setObjectName(\"label_2\")\n self.init_value = QtWidgets.QPlainTextEdit(Dialog)\n self.init_value.setGeometry(QtCore.QRect(120, 40, 71, 21))\n self.init_value.setObjectName(\"init_value\")\n self.label_3 = QtWidgets.QLabel(Dialog)\n self.label_3.setGeometry(QtCore.QRect(30, 40, 71, 20))\n self.label_3.setObjectName(\"label_3\")\n self.path_length = QtWidgets.QPlainTextEdit(Dialog)\n self.path_length.setGeometry(QtCore.QRect(310, 40, 71, 21))\n self.path_length.setObjectName(\"path_length\")\n self.label_6 = QtWidgets.QLabel(Dialog)\n self.label_6.setGeometry(QtCore.QRect(220, 40, 71, 20))\n self.label_6.setObjectName(\"label_6\")\n\n\n self.start_value.setPlainText(\"0\")\n self.function.setPlainText(function)\n self.init_value.setPlainText(\"2\")\n self.end_value.setPlainText(\"5\")\n self.path_length.setPlainText(\"0.25\")\n\n self.pushButton.clicked.connect(self.calculate)\n self.retranslateUi(Dialog)\n QtCore.QMetaObject.connectSlotsByName(Dialog)\n\n def retranslateUi(self, Dialog):\n _translate = QtCore.QCoreApplication.translate\n Dialog.setWindowTitle(_translate(\"Dialog\", \"四阶龙格库塔法\"))\n self.label.setText(_translate(\"Dialog\", \"区间起始值：\"))\n self.label_4.setText(_translate(\"Dialog\", \"函数：（自变量为 x ）\"))\n self.label_5.setText(_translate(\"Dialog\", \"输出：\"))\n self.pushButton.setText(_translate(\"Dialog\", \"计算\"))\n self.label_2.setText(_translate(\"Dialog\", \"区间终点值：\"))\n self.label_3.setText(_translate(\"Dialog\", \"起始条件：\"))\n self.label_6.setText(_translate(\"Dialog\", \"步长：\"))\n\n def calculate(self):\n global function\n function = str(self.function.toPlainText())\n ya = int(self.init_value.toPlainText())\n a = int(self.start_value.toPlainText())\n b = int(self.end_value.toPlainText())\n h = float(self.path_length.toPlainText())\n output = runge_kutta(a, b, ya, h)\n self.output.setPlainText(output)\n\n","sub_path":"Experiment4/runge_kutta.py","file_name":"runge_kutta.py","file_ext":"py","file_size_in_byte":4502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"308521957","text":"from thread import *\nfrom flask import *\nimport json\nimport string\nimport datetime\nimport time\nimport configparser\nfrom flask_login import *\nfrom flask.ext.login import (LoginManager, UserMixin, login_required, login_user, logout_user, current_user)\nimport hashlib\nimport flask.ext.login\n\nimport Config\nimport EventList\nimport Log\nimport FishTank\nimport FoodStore\nimport Camera\nimport Lights\nimport FishFeeder\n\nclass User(UserMixin):\n def __init__(self, username, hash):\n self.name = username\n self.hash = hash\n\n @property\n def id(self):\n return self.name\n\ndef loadUsers():\n\tini = configparser.ConfigParser()\n\tini.read('users.ini')\n\n\tusernames = ini.sections()\n\tusers = [User(username, ini.get(username,'hash')) for username in usernames]\n\n\treturn users\n\nusers = loadUsers()\n\nlogin_manager = LoginManager()\napp = Flask(__name__, static_folder='../', static_url_path='')\napp.secret_key = Config.secretKey\nlogin_manager.init_app(app)\n\n#Enable this only in development mode\n#start_new_thread(app.run, (\"0.0.0.0\",), {\"threaded\": True})\n\t# 0.0.0.0 - visible for outside network\n\t# threaded: handle multiple requests at once\napp.config.update(PROPAGATE_EXCEPTIONS = True)\n\n@login_manager.user_loader\ndef load_user(userid):\n\tfor user in users:\n\t\tif user.name == userid:\n\t\t\treturn user\n\treturn None\n\n@app.route('/')\ndef root():\n\treturn app.send_static_file('index.html')\n\n@app.route(\"/api/status\")\ndef getStatus():\n\tdata = FishTank.getSerializeable()\n\tdata['user'] = current_user.name if current_user.is_authenticated() else None\n\treturn Response(json.dumps(data), mimetype='application/json')\n\n@app.route(\"/api/log\")\ndef getLog():\n\tdata = Log.getRecentEntries(count = int(request.args.get('entries')), minlevel = int(request.args.get('minlevel')), page = int(request.args.get('page')))\n\treturn Response(json.dumps(data), mimetype='application/json')\n\n@app.route(\"/api/note\", methods=['POST'])\n@login_required\ndef addLogNote():\n\tLog.write('Note: ' + request.form['note'], level = int(request.form['level']), image = 0, startedby = current_user.id, title = 'Note');\n\treturn 'ok'\n\n@app.route(\"/api/updatecontainers\", methods=['POST'])\n@login_required\ndef updateContainers():\n\tcontainers = string.split(request.form['containers'], ',')\n\tfood = int(request.form['food'])\n\tamount = float(request.form['amount'])\n\tpriority = int(request.form['priority'])\n\t\n\tnow = datetime.datetime.now()\n\tfor c in containers:\n\t\tif (c == ''):\n\t\t\tcontinue\n\t\tif (food != -1):\n\t\t\tFoodStore.container[int(c)].food = food\n\t\tif (amount != -1):\n\t\t\tFoodStore.container[int(c)].amount = amount\n\t\tif (priority != -1):\n\t\t\tFoodStore.container[int(c)].priority = priority\n\t\tif (food != -1 and amount != -1):\n\t\t\tFoodStore.container[int(c)].filled = now\n\n\tLog.write(message = 'Updated containers (' + str(len(containers)-1) + ' containers set)', startedby = current_user.id);\n\treturn 'ok'\n\n@app.route(\"/api/updateevent\", methods=['POST'])\n@login_required\ndef updateEvent():\n\ttry:\n\t\tevent = EventList.update(request.form)\n\t\tLog.write(message = ('Updated' if request.form['event'] != '-1' else 'Created') + ' event (' + EventList.names[event.type] + ' at ' + str(event.hour) + ':' + ('0' if event.minute < 10 else '') + str(event.minute) + ')', startedby = current_user.id)\n\texcept:\n\t\treturn 'Invalid event data.', 400\n\treturn 'ok'\n\n@app.route(\"/api/deleteevent\", methods=['POST'])\n@login_required\ndef deleteEvent():\n\tevent = EventList.getEvent(int(request.form['id']))\n\tif event == None:\n\t\treturn 'Event not found', 404\n\tEventList.events.remove(event)\n\tLog.write(message = 'Deleted event (' + EventList.names[event.type] + ' at ' + str(event.hour) + ':' + ('0' if event.minute < 10 else '') + str(event.minute) + ')', startedby = current_user.id)\n\treturn 'ok'\n\n@app.route(\"/api/takepicture\", methods=['POST'])\ndef takePicture():\n\tif not current_user.is_authenticated() and not Lights.value:\n\t\treturn 'Can't take a picture while the lights are off.', 400\n\t\t\n\tif not current_user.is_authenticated() and datetime.datetime.now() - Camera.lastPictureTaken < datetime.timedelta(minutes = 5):\n\t\treturn 'Taking too many pictures, please wait ' + str(int((Camera.lastPictureTaken + datetime.timedelta(minutes = 5) - datetime.datetime.now()).seconds / 60) + 1) + str(' minutes.'), 400\n\t\n\ttry:\n\t\tid = Camera.takePicture();\n\t\tusername = 'guest'\n\t\tif current_user.is_authenticated():\n\t\t\tusername = current_user.id\n\t\tLog.write(message = 'Took picture', level = 1, image = id, startedby = username)\n\texcept Camera.NoCameraException:\n\t\treturn 'Can''t take a picture: No camera found.', 500\n\texcept:\n\t\treturn 'Error while trying to take a picture', 500\n\treturn 'ok'\n\n@app.route(\"/api/switchlights\", methods=['POST'])\n@login_required\ndef switchLights():\n\tLights.switch()\n\tLog.write(message = 'Switched lights (' + ('On' if Lights.value else 'Off') + ')', level = 1, startedby = current_user.id)\n\treturn 'ok'\n\n@app.route(\"/api/flashled\", methods=['POST'])\ndef flashLED():\n\tFishFeeder.flashHex(request.form['color'],1)\n\treturn 'ok'\n\n@app.route(\"/api/calibrate\", methods=['POST'])\n@login_required\ndef calibrate():\n\tFishFeeder.calibrate()\n\t\n\tif FishFeeder.status == FishFeeder.FishFeederStatus.ERROR:\n\t\tLog.write(message = 'Moving feeder failed (mechanical failure).', level = 5, startedby = current_user.id)\n\t\treturn 'ok'\n\t\n\tLog.write(message = 'Calibrated feeder', level = 1, startedby = current_user.id)\n\treturn 'ok'\n\n@app.route(\"/api/checkforupdate\")\ndef checkForUpdate():\n\toldversion = int(request.args['version'])\n\ttimeout = 10\n\ttstart = time.time()\n\twhile (time.time() < tstart + timeout):\n\t\tif (FishTank.version > oldversion):\n\t\t\treturn 'true'\n\t\ttime.sleep(0.1)\n\treturn 'false'\n\n@app.route(\"/api/move\", methods=['POST'])\n@login_required\ndef moveFeeder():\n\tFishFeeder.moveTo(int(request.form['to']))\n\t\n\tif FishFeeder.status == FishFeeder.FishFeederStatus.ERROR:\n\t\tLog.write(message = 'Moving feeder failed (mechanical failure).', level = 5, startedby = current_user.id)\n\t\treturn 'ok'\n\t\n\tLog.write(message = 'Moved feeder to position ' + str(int(request.form['to'])+1), level = 1, startedby = current_user.id)\n\treturn 'ok'\n\n@app.route(\"/api/dump\", methods=['POST'])\ndef dump():\n\tindex = int(request.form['to'])\n\tif not current_user.is_authenticated():\n\t\tif FoodStore.container[index].amount == 0:\n\t\t\treturn 'Can''t feed an empty container.', 400\n\t\tif not Lights.value:\n\t\t\treturn 'Can''t feed while lights are off.', 400\n\t\tif FishTank.getSaturation() > 1:\n\t\t\treturn 'Can''t feed: Fish are not hungry.', 400\n\t\tif FoodStore.container[index].priority >= 2:\n\t\t\treturn 'Can''t feed a locked container.', 400\n\t\n\tusername = 'guest'\n\tif current_user.is_authenticated():\n\t\tusername = current_user.id\t\n\t\n\tcontainer = FoodStore.container[index]\n\tFishFeeder.moveToAndDump(index)\n\tif FishFeeder.status == FishFeeder.FishFeederStatus.ERROR:\n\t\tLog.write(message = 'Manual feeding failed (mechanical failure).', level = 5, startedby = username)\n\t\treturn 'ok'\n\t\n\timageId = 0\n\tif not current_user.is_authenticated():\n\t\tFishTank.updateStatus('Waiting...')\n\t\ttime.sleep(4)\n\t\timageId = Camera.tryTakePicture();\n\t\n\toldsaturation = FishTank.getSaturation()\n\tif container.amount != 0:\n\t\tFishTank.setSaturation(oldsaturation + container.amount)\n\tLog.write(title = \"Fed fish\", message = 'Manually fed container ' + str(container.index + 1) + ' (Food ' + str(container.food) + '), Saturation: ' + \"{0:.1f}\".format(oldsaturation) + ' -> ' + \"{0:.1f}\".format(oldsaturation + container.amount) + ' (+' + \"{0:.1f}\".format(container.amount) + ')', image = imageId, level = 2 if container.amount != 0 else 0, startedby = username)\n\tcontainer.empty()\n\tFishTank.increaseVersion()\n\tFishTank.save()\n\treturn 'ok'\n\n@app.route(\"/api/enableschedule\", methods=['POST'])\n@login_required\ndef enableSchedule():\n\tEventList.enabled = request.values.get('value') == 'true'\n\tLog.write(title = \"Enabled Scheduling\" if EventList.enabled else \"Disabled Scheduling\", message = \"Enabled Scheduling\" if EventList.enabled else \"Disabled Scheduling\", level = 0, startedby = current_user.id)\n\treturn 'ok'\n\n@app.route('/api/login', methods=['POST'])\ndef login():\n\tuser = load_user(request.values.get('username'))\n\thashalgorithm = hashlib.sha512()\n\thashfailed = False\n\ttry:\n\t\thashalgorithm.update(request.values.get('password'))\n\texcept:\n\t\thashfailed = True\n\tif not hashfailed and user and user.hash == hashalgorithm.hexdigest():\n\t\tlogin_user(user, remember = True)\n\t\tprint('login successful (' + user.name + ')')\n\t\treturn 'ok'\n\telse:\n\t\tprint('login failed (' + request.values.get('username') + ')')\n\t\treturn 'Login failed', 401\n\n@app.route(\"/api/logout\", methods=['POST'])\n@login_required\ndef logout():\n\tprint('user logged out (' + current_user.name + ')')\n\tlogout_user()\n\treturn redirect('/');","sub_path":"server/FlaskServer.py","file_name":"FlaskServer.py","file_ext":"py","file_size_in_byte":8706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"524558126","text":"##########################################################################################################################################\n\n# Run the XGBoost model \n\n# import modules and packages\nimport sys\nfrom datetime import datetime as date\n\nsys.path.append ('./SourceCode')\n\n# Import Packages\nfrom Config import Env_Config\nfrom Global_fun import *\n\nEnv_Config.fun_set_cwd (\".\")\n\nprint('Model_Config')\nfrom Model.model_config import *\nprint('auto_xgboost')\nfrom Model.auto_Xgboost import *\nprint('model_preprocessing')#FE,label\nfrom Model.model_preprocessing import *\nimport pickle\nfrom FE.sample_integrate import cls_sample_integrate\n#define if testing data has labels inside\nlabel = True\n\n# Read Training and Testing data\nprint('+++ Training Features ---\\n')\n# Read in Y_start_time_train.csv\ny_start_train_dt = pd.read_csv(fun_path_join(Env_Config.source_code_path, \"Y_start_time_train.csv\"))\nfea_multi = 'FEA_multi_win_' + y_start_train_dt['Y_start_time'].max() + '.feather'\ntraining_all = pd.read_feather(fun_path_join(Env_Config.output_FE_train, fea_multi))\n\n# COMMENT - read y_start_time_test use it to generate the full name of testing data and then read in\n# UPDATE - used Y_start_time_test.csv to specify the testing file to use\nprint('+++ Testing Features ---\\n')\ny_start_test_dt = pd.read_csv(fun_path_join(Env_Config.source_code_path,'Y_start_time_test.csv'))\n# Get a list of test data directories\ntest_dir = os.listdir(Env_Config.output_FE_test_par)\ntest_dir=[x for x in test_dir if x.startswith('FEA')]\ntesting_all = pd.read_feather(fun_path_join(Env_Config.output_FE_test_par, test_dir[0]))\n\nY_start_date = Env_Config.need_win_start.strftime('%Y-%m-%d')\nhist_win_end = Env_Config.hist_win_end.strftime('%Y-%m-%d')\n\ncls_fea_integrate = cls_sample_integrate()\ntesting_all_bin = cls_fea_integrate.fun_model_binarisation(data=testing_all,hist_end_date = hist_win_end,label=label)\n\nprint('training data shape: ' + str(training_all.shape))\nprint('testing data shape:' + str(testing_all_bin.shape))\n\nprint('+++ Create training and testing data and respective labels ---\\n')\n# make sure same features in model preprocessing\nx_train, y_train, x_test, y_test = model_preprocessing.sliding_win_train_test_split(train_data = training_all, \n test_data = testing_all_bin)\n\n\n# COMMENT - check for cin: shouldnt be there\n# UPDATE - print will check for cin (cin - 0, no cin in df): \nprint('X train is of type:{}, shape:{}, cin:{}'.format(type(x_train).__name__, \n x_train.shape,'cin' in list(x_train.columns)))\nprint('y train is of type:{}, shape:{}'.format(type(y_train).__name__,\n y_train.shape ))\nprint('X test is of type:{}, shape{}, cin:{}'.format(type(x_test).__name__, \n x_test.shape, 'cin' in list(x_test.columns)))\nprint('y test is of type:{}, shape{}'.format(type(y_test).__name__,\n y_test.shape))\n\n# COMMENT - check if x train and x test are well align - used same set of common names to arrange columns\n# UPDATE - make sure that columns are aligned\n# check if the columns of training and testing set are aligned\nx_train_columns = x_train.columns\nx_test_columns = list(x_test.columns)\nif set(x_train_columns)==set(x_test_columns):\n print('columns are aligned')\nelse:\n print('columns are not aligned')\n\n \n\n\n# change dataframe to ndarray\n\nx_train = x_train.values\nx_test = x_test.values\ny_train = y_train.values.astype(int)\ny_test = y_test.values.astype(int)\n\ndel testing_all_bin,testing_all\n\n#timer - start\nstart_time = time.monotonic() \n\n#object for XGB\nprint('+++ Initialise XGB ---\\n')\nXGB = cls_auto_Xgboost(X = x_train, y = y_train, n_jobs = -7, \n max_depth_flag = True, n_estimator_flag = False) \n # n_jobs changed to -7\n # max_depth_flag and n_estimator_flag to be set to True in production run\n# #print param\n# XGB.fun_print_param()\n\n#train model\nprint('+++ Train XGB ---\\n')\ngrid_result = XGB.fun_train_model()\nend_time = time.monotonic()\n\nprint(\"+++ Model Training Done ---\")\nprint(\"Model Training consumed time(sec): \", end_time - start_time)\n\nprint(grid_result)\n\n# Save the model as pkl extension\nmodel_filename = ''.join(['XGB_model_',date.today().strftime(\"%Y_%m_%d\")])\nfilename = model_filename + '.pkl'\nprint(filename)\n\n# save the best model\njoblib.dump(grid_result,fun_path_join(Env_Config.output_model, filename))\n\n#Load model\n#grid_result=joblib.load(fun_path_join(Env_Config.output_model, filename))\n#XGB.fun_save(model = grid_result, file_name = filename) \n\n#prediction\nstart_time = time.monotonic()\npred_lst=[]\nprint('+++ Prediction ---')\nfor i in range(len(test_dir)):\n test_data = pd.read_feather(fun_path_join(Env_Config.output_FE_test_par, test_dir[i]))\n print('Input partition is {}'.format(fun_path_join(Env_Config.output_FE_test_par, test_dir[i])))\n test_data_bin = cls_fea_integrate.fun_model_binarisation(data=test_data,hist_end_date = hist_win_end,label=label)\n test_data_bin = test_data_bin.fillna(0)\n x_test_par = test_data_bin.loc[:,x_test_columns].values ##fillna(0)\n #y_test_par = test_data_bin['label']\n print(test_data.shape,test_data_bin.shape,x_test_par.shape)\n \n y_pred = cls_auto_Xgboost.fun_pred(model = grid_result, x_test = x_test_par, pred_type = 'prob')\n\n # COMMENT - add in cin here (take cin from testing_all data )\n # UPDATE - add cin to y_pred_np\n y_pred_df = pd.DataFrame(y_pred[:,1], columns = ['pred_prob']) \n y_pred_df.insert(loc=0, column='cin', value=test_data_bin['cin'])\n y_pred_df.insert(loc=1, column='cust_needs', value=test_data_bin['cust_needs'])\n y_pred_df['label_pred'] = 0\n temp =y_pred_df.groupby(['cin'])['pred_prob'].apply(lambda x : x.nlargest(3))\n temp.names=['cin','index']\n index = temp.index.get_level_values(1)\n y_pred_df.loc[index,'label_pred'] = 1\n if label:\n y_pred_df['label']= test_data_bin['label'].astype(int)\n \n pred_lst.append(y_pred_df)\n \n print(len(pred_lst))\n \n del test_data,x_test_par,test_data_bin,y_pred_df,temp\n \nend_time = time.monotonic()\nprint(\"Model Prediction consumed time(sec): \", end_time - start_time)\n\npred_final = pd.concat(pred_lst)\npred_final = pred_final.reset_index(drop=True)\npath = \"Overall_Pred_\"+Y_start_date+\".feather\"\noutput_path = fun_path_join(Env_Config.output_eval,path)\npred_final.to_feather(output_path)\nprint(\"Prediction result saved to {}\".format(output_path))\n\nprint(\"+++ Evaluation ---\")\n#recall for each needs\nrecall_needs = pred_final.groupby(['cust_needs'])['label_pred','label'].apply(lambda x: recall_score(y_true=x['label'],y_pred=x['label_pred'],pos_label=1,average='binary'))\nprint(\"Recall for for each need: \")\nprint(recall_needs)\n\nrecall_overall = recall_score(pred_final['label'],pred_final['label_pred'])\nprint(\"Overall recall: {}\".format(recall_overall))\n\n\n","sub_path":"Model/Xgboost_main.py","file_name":"Xgboost_main.py","file_ext":"py","file_size_in_byte":7077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"426462482","text":"import time\nimport re\nimport requests\nfrom selenium import webdriver\nfrom .config import Config\nfrom .log import Log\nfrom .error import LoginOvertimeError\n\n\nclass Auth(object):\n @staticmethod\n def set_cookie(cookies):\n \"\"\"\n 设置cookie\n :param cookies: string or dict\n :return: bool\n \"\"\"\n if cookies is None:\n return False\n if isinstance(cookies, dict):\n return Config.set('cookie', cookies)\n return Config.set('cookie', dict([l.split('=', 1) for l in cookies.split('; ')]))\n\n @staticmethod\n def get_cookie_from_browser():\n \"\"\"\n 从浏览器获取cookie\n :return:\n \"\"\"\n driver = webdriver.Chrome()\n try:\n Log.info('通过浏览器登录')\n driver.get(\n 'https://xui.ptlogin2.qq.com/cgi-bin/xlogin?proxy_url=https%3A//qzs.qq.com/qzone/v6/portal/proxy.html&daid=5&&hide_title_bar=1&low_login=0&qlogin_auto_login=1&no_verifyimg=1&link_target=blank&appid=549000912&style=22&target=self&s_url=https%3A%2F%2Fqzs.qq.com%2Fqzone%2Fv5%2Floginsucc.html%3Fpara%3Dizone&pt_qr_app=%E6%89%8B%E6%9C%BAQQ%E7%A9%BA%E9%97%B4&pt_qr_link=https%3A//z.qzone.com/download.html&self_regurl=https%3A//qzs.qq.com/qzone/v6/reg/index.html&pt_qr_help_link=https%3A//z.qzone.com/download.html&pt_no_auth=0')\n time.sleep(3)\n driver.get_screenshot_as_png()\n qrcode = driver.find_element_by_id('qrlogin_img')\n qrcode.screenshot('./temp.png')\n Log.info('请扫码登录')\n waiting = 0\n while True:\n waiting += 1\n time.sleep(1)\n if waiting > 30:\n raise Exception('登陆失败，未扫码。')\n if re.search('user.qzone.qq.com', driver.current_url) is not None:\n break\n Log.info('登录成功')\n time.sleep(3)\n cookies = {}\n for item in driver.get_cookies():\n cookies[item['name']] = item['value']\n Config.set('cookie', cookies)\n Log.info('更新cookie')\n except Exception as e:\n Log.error(str(e))\n finally:\n Log.info('关闭浏览器')\n driver.close()\n\n @staticmethod\n def get_cookie_txt():\n \"\"\"\n 获取cookie\n :return:\n \"\"\"\n cookie = ''\n try:\n for key, value in Config.get('cookie').items():\n cookie += '%s=%s; ' % (key, value)\n return cookie\n except Exception:\n return None\n","sub_path":"QzoneWatcher/src/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"349150340","text":"import re\n\nimport scrapy\n\n\nclass InstitutionSpider(scrapy.Spider):\n \"\"\"\n Возвращает файл cо сгенерированными ссылками на получение отзывов для направлений из institutions.txt\n Запуск: scrapy crawl institution\n \"\"\"\n name = \"institution\"\n filename = \"urls.txt\"\n\n # Set для уникальных ссылок на направления\n institutions = set()\n\n def start_requests(self):\n # Заполняем set с направлениями\n with open('institutions.txt', 'r') as inst:\n for line in inst:\n self.institutions.add(line.strip())\n\n print(self.institutions)\n\n # подготовка файла для ссылок\n f = open(self.filename, 'w')\n f.write(\"\")\n for institution in self.institutions:\n # отправка запроса, вызов функции get_reviews_urls\n request = scrapy.Request(url=institution, callback=self.parse)\n request.meta['file'] = f\n yield request\n\n def parse(self, response):\n \"\"\"\n Получение отзывов происходит по поиску JS функции next11(),\n в параметры которой передаются тип отзывов (+/-), id учреждения, общее количество отзывов\n \"\"\"\n html = response.body.decode('windows-1251')\n urls = []\n\n file = response.meta['file']\n\n # поиск JavaScript функции для получения отзывов (применяется в пагинации)\n # ищем в положительных отзывах\n m = re.search(\"next11\\(\\d+.+,11\\);'\", html)\n\n inst_id_net = 0 # если id указан в неявном виде\n if m:\n params = m.group()[7:-3].split(\",\")\n total = params[0]\n inst_id = params[2]\n inst_id_net = inst_id\n urls.extend(self.generate_url(1, inst_id, total, \"11\"))\n else:\n m = re.search(\"next11\\(\\d+.+,11\\)'\", html)\n if m:\n params = m.group()[7:-2].split(\",\")\n total = params[0]\n inst_id = params[2]\n inst_id_net = inst_id\n urls.extend(self.generate_url(2, inst_id, total, \"11\"))\n else:\n m = re.search(\"id_firm_forum=\\d+'\", html)\n if m:\n inst_id = m.group()[14:-1]\n inst_id_net = inst_id\n urls.extend(self.generate_url(1, inst_id, 20, \"11\"))\n else:\n # если id учреждения нигде не указан, возможно, он есть в названии изображения учреждения\n m = re.search(\"/\\d+.png'\", html)\n if m:\n inst_id = m.group()[1:-5]\n inst_id_net = inst_id\n urls.extend(self.generate_url(2, inst_id, 20, \"11\"))\n else:\n urls.extend(self.generate_url(2, inst_id_net, 20, \"11\"))\n\n # ищем в отрицательных отзывах\n m = re.search(\"next11\\(\\d+.+,1\\);'\", html)\n if m:\n params = m.group()[7:-3].split(\",\")\n total = params[0]\n inst_id = params[2]\n urls.extend(self.generate_url(1, inst_id, total, \"1\"))\n else:\n m = re.search(\"next11\\(\\d+.+,1\\)'\", html)\n if m:\n params = m.group()[7:-2].split(\",\")\n total = params[0]\n inst_id = params[2]\n urls.extend(self.generate_url(2, inst_id, total, \"1\"))\n else:\n m = re.search(\"id_firm_forum=\\d+'\", html)\n if m:\n inst_id = m.group()[14:-1]\n urls.extend(self.generate_url(1, inst_id, 20, \"1\"))\n else:\n m = re.search(\"/\\d+.png'\", html)\n if m:\n inst_id = m.group()[1:-5]\n urls.extend(self.generate_url(2, inst_id, 20, \"1\"))\n else:\n urls.extend(self.generate_url(2, inst_id_net, 20, \"1\"))\n\n # пишем ссылки на отзывы в файл\n for url in urls:\n file.write(url + '\\n')\n\n @staticmethod\n def generate_url(url_type, ins_id, total, pos_neg):\n \"\"\"\n Генерирует ссылки на получение отзывов\n :param url_type: тип ссылки (на сайте 2 разных вида ссылок)\n :param ins_id: id направления\n :param total: количество отзывов всего\n :param pos_neg: 11 - положительные отзывы, 1 - отрицательные\n :return: список со ссылками на получение отзывов\n \"\"\"\n urls = []\n if url_type == 1:\n i = 0\n while i < int(total):\n urls.append(\"https://www.spr.ru/js/zzz_next.php?id_top=%s&id_firm=%s&view11=%s&all11=%s\"\n % (pos_neg, ins_id, i, total))\n i += 20 # шаг 20, так реализована пагинация на сайте spr.ru\n elif url_type == 2:\n i = 0\n while i < int(total):\n urls.append(\"https://www.spr.ru/js/zzz_next.php?id_top=%s&id_net=%s&view11=%s&all11=%s&setevie=1\"\n % (pos_neg, ins_id, i, total))\n i += 20\n return urls\n\n","sub_path":"spr_crawler/spr_crawler/spiders/prepare_spider.py","file_name":"prepare_spider.py","file_ext":"py","file_size_in_byte":5799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"180424376","text":"# This is a simple arithmetic calculator\r\nprint (\"Welcome to this calculator\")\r\n\r\n# Create add function\r\ndef add(x, y):\r\n result = int(x) + int(y)\r\n print (result)\r\n# Create sub function\r\ndef sub(x,y):\r\n result = int(x) - int(y)\r\n print (result)\r\n# Create multiplication function\r\ndef mul(x, y):\r\n result = int(x) * int(y)\r\n print (result)\r\n# Create divide function\r\ndef div(x,y):\r\n result = int(x) / int(y)\r\n print (result)\r\n# Create mod function\r\ndef mod(x,y):\r\n result = int(x) % int(y)\r\n print (result)\r\n\r\n# Clear initial variable\r\non = 0\r\nresult = 0\r\n\r\n# Main loop\r\nwhile on < 1:\r\n\r\n # Ask for calculation\r\n op = input(\"Please enter your calculation with a space between values:\")\r\n\r\n # Check if user is trying to exit calculator\r\n if op == 'exit':\r\n print(\"Exiting, good bye.\")\r\n break\r\n\r\n # Check if it is a addition\r\n elif op.split(' ')[1] == '+':\r\n\r\n # Call addition function\r\n add(op.split(' ')[0], op.split(' ')[2])\r\n\r\n # Check if it is a subraction\r\n elif op.split(' ')[1] == '-':\r\n\r\n # Call subtraction function\r\n sub(op.split(' ')[0], op.split(' ')[2])\r\n\r\n # Check if it is a multiplication\r\n elif op.split(' ')[1] == '*' or op.split(' ')[1] == 'x':\r\n\r\n # Call subtraction function\r\n mul(op.split(' ')[0], op.split(' ')[2])\r\n\r\n # Check if it is a division\r\n elif op.split(' ')[1] == '/':\r\n\r\n # Call subtraction function\r\n div(op.split(' ')[0], op.split(' ')[2])\r\n\r\n # Check if it is a mod question\r\n elif op.split(' ')[1] == '%':\r\n\r\n # Call mod function\r\n mod(op.split(' ')[0], op.split(' ')[2])\r\n\r\n else:\r\n print (\"Please enter a valid 3 part calculation\")","sub_path":"Calculator.py","file_name":"Calculator.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"476157755","text":"# -*- coding: utf-8 -*-\r\nclass Jword(object):\r\n \"\"\" A class representing words in Japanese\r\n\r\n Contains information on the phonetic, romaji, kana, and kanji spellings\"\"\"\r\n all_words = {}\r\n \r\n def __init__(self, kanji, meaning, kana, romaji):\r\n self.kanji = kanji\r\n self.meaning = meaning\r\n self.kana = kana\r\n self.romaji = romaji\r\n\r\n self.add_to_dict()\r\n\r\n def add_to_dict(self):\r\n Jword.all_words[self.meaning] = {\"Meaning\":self.meaning, \"Kanji\":self.kanji, \"Romaji\":self.romaji, \"Kana\":self.kana}\r\n","sub_path":"Japanese/jword.py","file_name":"jword.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"223017164","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/uforgecli/utils/texttable_utils.py\n# Compiled at: 2017-03-01 08:38:06\n__author__ = 'UShareSoft'\nfrom texttable import Texttable\n\ndef init_texttable(headers, width=200, align=None, types=None):\n table = Texttable(width)\n if headers is not None:\n table.header(headers)\n if align is not None:\n table.set_cols_align(align)\n if types is not None:\n table.set_cols_dtype(types)\n return table","sub_path":"pycfiles/uforgecli_nightly-3.7.8.dev20171006-py2.6/texttable_utils.py","file_name":"texttable_utils.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"510940638","text":"\"\"\"Ser2tcp\nSimple proxy for connecting over TCP or telnet to serial port\n\"\"\"\n\nimport sys\nimport select\nimport socket\nimport argparse\nimport logging\nimport serial\n\n\nclass Ser2TcpConnection():\n \"\"\"Telnet socket\"\"\"\n\n def __init__(self, connection, ser, telnet=False, log=None):\n self._socket, self._addr = connection\n self._serial = ser\n self._telnet = telnet\n self._log = log if log else logging.Logger(\"Ser2TcpConnection\")\n if self._telnet:\n self.send((0xff, 0xfd, 0x22))\n self.send((0xff, 0xfb, 0x01))\n self._log.info(\"Client connected: %s:%d\", self._addr[0], self._addr[1])\n\n def __del__(self):\n self.close()\n\n @property\n def socket(self):\n \"\"\"Return reference to socket\"\"\"\n return self._socket\n\n def close(self):\n \"\"\"Close connection\"\"\"\n if self._socket:\n self._socket.close()\n self._socket = None\n self._log.info(\"Client disconnected: %s:%d\", self._addr[0], self._addr[1])\n\n def fileno(self):\n \"\"\"emulate fileno method of socket\"\"\"\n return self._socket.fileno() if self._socket else None\n\n def send(self, data):\n \"\"\"Send data to client\"\"\"\n raw_data = []\n for dat in data:\n if not self._telnet and dat == 255:\n raw_data.append(255)\n raw_data.append(dat)\n self._socket.send(bytearray(data))\n\n @staticmethod\n def list_pull_first(data):\n \"\"\"get first entry from array\"\"\"\n dat = data[0]\n del data[0]\n return dat\n\n @staticmethod\n def filter_telnet_commands(data):\n \"\"\"Remove telnet commands from data\"\"\"\n\n out = []\n subnegotiation = False\n sbn = []\n\n # process telnet commands\n while data:\n dat = Ser2TcpConnection.list_pull_first(data)\n if dat == 255:\n dat = Ser2TcpConnection.list_pull_first(data)\n if dat in (251, 252, 253, 254):\n dat = Ser2TcpConnection.list_pull_first(data)\n continue\n if dat == 250:\n subnegotiation = True\n continue\n if dat == 240:\n subnegotiation = False\n continue\n if subnegotiation:\n sbn.append(dat)\n continue\n if dat == 13:\n out.append(10)\n out.append(dat)\n\n return out\n\n def on_received(self, data):\n \"\"\"Received data from client\"\"\"\n data = list(data)\n if self._telnet:\n data = Ser2TcpConnection.filter_telnet_commands(data)\n if data:\n self._serial.write(data)\n\n\nclass Ser2TcpServer():\n \"\"\"Telnet server\"\"\"\n\n def __init__(self, bind_address, serial_params, telnet=False, log=None):\n \"\"\"Ser2TcpServer start ser2Tcp server\n Arguments:\n bind_address: ip_address and port to bind (address:port), default: 127.0.0.1:10000\n serial_params: ip_address and port to bind (address:port), default: 127.0.0.1:10000\n telnet: if true use telnet mode for clients, instead raw TCP connection is used\n log: logging.Logger instance for logging\n \"\"\"\n self._log = log if log else logging.Logger(\"Ser2TcpServer\")\n self._log.info(\n \"Starting server (listen: %s, serial: %s:%d)..\",\n bind_address, serial_params['port'], serial_params['baud'])\n\n address, port = bind_address.split(':')\n if not address:\n address = '127.0.0.1'\n port = int(port) if port else 10000\n self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM, socket.IPPROTO_TCP)\n self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self._socket.bind((address, port))\n self._socket.listen(1)\n self._connections = []\n self._serial = None\n self._serial_params = serial_params\n parity = serial.PARITY_NONE\n stopbits = serial.STOPBITS_ONE\n if 'EVEN' in serial_params['flags']:\n parity = serial.PARITY_EVEN\n elif 'ODD' in serial_params['flags']:\n parity = serial.PARITY_ODD\n if 'TWO' in serial_params['flags']:\n stopbits = serial.STOPBITS_TWO\n self._serial_params['parity'] = parity\n self._serial_params['stopbits'] = stopbits\n self._telnet = telnet\n\n def __del__(self):\n self.close()\n\n def _serial_connect(self):\n if self._serial:\n return True\n try:\n self._serial = serial.Serial(\n port=self._serial_params['port'],\n baudrate=self._serial_params['baud'],\n parity=self._serial_params['parity'],\n stopbits=self._serial_params['stopbits'],\n )\n except (serial.SerialException, OSError) as err:\n self._log.warning(\"Serial %s is not connected %s\", self._serial_params['port'], err)\n return False\n self._log.info(\"Serial %s connected\", self._serial_params['port'])\n return True\n\n def _serial_disconnect(self):\n if self._serial:\n self._serial.close()\n self._serial = None\n self._log.info(\"Serial %s disconnected\", self._serial_params['port'])\n\n def _client_connect(self):\n sock, addr = self._socket.accept()\n if not self._connections and not self._serial:\n if not self._serial_connect():\n self._log.info(\"Client canceled: %s:%d\", socket, addr)\n sock.close()\n return\n ser2tcp_connection = Ser2TcpConnection(\n connection=(sock, addr),\n ser=self._serial,\n telnet=self._telnet,\n log=self._log,\n )\n self._connections.append(ser2tcp_connection)\n\n def _clients_disconnect(self):\n for con in self._connections:\n con.close()\n self._connections = []\n\n def close(self):\n \"\"\"Close socket and all connections\"\"\"\n self._log.info(\"Exiting..\")\n self._clients_disconnect()\n self._serial_disconnect()\n if self._socket is not None:\n self._socket.close()\n self._socket = None\n\n def process(self):\n \"\"\"Task start\"\"\"\n sockets = [self._socket] + self._connections\n if self._serial:\n sockets.append(self._serial)\n read_sockets = select.select(sockets, [], [], .1)[0]\n if not read_sockets:\n return\n if self._socket in read_sockets:\n self._client_connect()\n return\n raw_data = None\n if self._serial in read_sockets:\n try:\n raw_data = self._serial.read()\n except serial.SerialException:\n self._clients_disconnect()\n self._serial_disconnect()\n return\n for con in self._connections:\n if con in read_sockets:\n data = con.socket.recv(4096)\n if not data:\n con.close()\n self._connections.remove(con)\n if not self._connections:\n self._serial_disconnect()\n return\n con.on_received(data)\n if raw_data:\n con.send(raw_data)\n\n\nVERSION_STR = \"ser2tcp v1.0.0\"\n\nDESCRIPTION_STR = VERSION_STR + \"\"\"\n(c) 2016 by pavel.revak@gmail.com\nhttps://github.com/pavelrevak/ser2tcp\n\"\"\"\n\ndef main():\n \"\"\"Main\"\"\"\n # test version of python\n if sys.version_info < (3, 4):\n print(\"Wrong python version, required is at lease 3.4\")\n exit(1)\n # test pyserial version\n pyserial_version = [int(i) for i in serial.__version__.split('.')]\n if pyserial_version[0] < 3:\n print(\"Wrong pyserial version, required is at lease 3.0\")\n exit(1)\n\n parser = argparse.ArgumentParser(description=DESCRIPTION_STR)\n parser.add_argument('-V', '--version', action='version', version=VERSION_STR)\n parser.add_argument(\n '-v', '--verbose', action='count', default=0,\n help=\"Increase verbosity *\")\n parser.add_argument(\n '-t', '--telnet', action='store_true', default=0,\n help=\"telnet (use telnet protocol)\")\n parser.add_argument(\n '-l', '--listen', default='127.0.0.1:10000',\n help=\"Listen host (default: 127.0.0.1:10000)\")\n parser.add_argument(\n 'port',\n help=\"Serial port\")\n parser.add_argument(\n 'baud', type=int,\n help=\"Serial baud rate\")\n parser.add_argument(\n 'flags', nargs='*', choices=['NONE', 'EVEN', 'ODD', 'ONE', 'TWO'],\n help=\"Serial parameters: parity: [NONE|EVEN|ODD], stop bits: [ONE|TWO], default: NONE ONE\")\n args = parser.parse_args()\n\n logging.basicConfig(format='%(asctime)-15s %(message)s')\n log = logging.getLogger('ser2tcp')\n if args.verbose:\n log.setLevel(10)\n\n ser2tcp = Ser2TcpServer(\n bind_address=args.listen,\n serial_params={\n 'port': args.port,\n 'baud': args.baud,\n 'flags': args.flags,\n },\n telnet=args.telnet,\n log=log,\n )\n try:\n while True:\n ser2tcp.process()\n except Exception as err:\n print(\"%s\" % err)\n raise err\n except KeyboardInterrupt:\n pass\n finally:\n ser2tcp.close()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"ser2tcp.py","file_name":"ser2tcp.py","file_ext":"py","file_size_in_byte":9476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"221503262","text":"# Copyright (c) 2015 Ultimaker B.V.\n# Uranium is released under the terms of the AGPLv3 or higher.\n\nfrom PyQt5.QtCore import QObject, pyqtSlot, pyqtProperty, pyqtSignal, QUrl\nfrom PyQt5.QtGui import QDesktopServices\n\nfrom UM.Application import Application\nfrom UM.Logger import Logger\nfrom UM.Scene.Iterator.DepthFirstIterator import DepthFirstIterator\nfrom UM.Scene.SceneNode import SceneNode\nfrom UM.Scene.PointCloudNode import PointCloudNode\nfrom UM.Mesh.MeshData import MeshType\nfrom UM.Mesh.ReadMeshJob import ReadMeshJob\nfrom UM.Mesh.WriteMeshJob import WriteMeshJob\nfrom UM.Operations.AddSceneNodeOperation import AddSceneNodeOperation\nfrom UM.Message import Message\n\nimport os.path\n\nfrom UM.i18n import i18nCatalog\ni18n_catalog = i18nCatalog(\"uranium\")\n\nclass MeshFileHandlerProxy(QObject):\n def __init__(self, parent = None):\n super().__init__(parent)\n self._mesh_handler = Application.getInstance().getMeshFileHandler()\n self._scene = Application.getInstance().getController().getScene()\n\n @pyqtProperty(\"QStringList\", constant=True)\n def supportedReadFileTypes(self):\n file_types = []\n all_types = []\n\n for ext, desc in self._mesh_handler.getSupportedFileTypesRead().items():\n file_types.append(\"{0} (*.{1})\".format(desc, ext))\n all_types.append(\"*.{0}\".format(ext))\n\n file_types.sort()\n file_types.insert(0, i18n_catalog.i18nc(\"Open file dialog type option\", \"All Supported Types ({0})\".format(\" \".join(all_types))))\n file_types.append(i18n_catalog.i18nc(\"Open file dialog type option\", \"All Files (*)\"))\n\n return file_types\n\n @pyqtProperty(\"QStringList\", constant=True)\n def supportedWriteFileTypes(self):\n file_types = []\n\n for ext, desc in self._mesh_handler.getSupportedFileTypesWrite().items():\n file_types.append(\"{0} (*.{1})\".format(desc, ext))\n\n file_types.sort()\n\n return file_types\n\n @pyqtSlot(QUrl)\n def readLocalFile(self, file):\n if not file.isValid():\n return\n\n job = ReadMeshJob(file.toLocalFile())\n job.finished.connect(self._readMeshFinished)\n job.start()\n\n\n @pyqtSlot(QUrl)\n def writeLocalFile(self, file):\n if not file.isValid():\n return\n app = Application.getInstance()\n for node in DepthFirstIterator(self._scene.getRoot()):\n if (type(node) is not SceneNode and type(node) is not PointCloudNode) or not node.getMeshData():\n continue\n\n job = WriteMeshJob(file.toLocalFile(), node.getMeshData())\n job.start()\n job.finished.connect(self._onWriteJobFinished)\n break\n\n def _readMeshFinished(self, job):\n mesh = job.getResult()\n if mesh != None:\n if mesh.getType() is MeshType.pointcloud: #Depending on the type we need a different node (as pointclouds are rendered differently)\n node = PointCloudNode()\n else:\n node = SceneNode()\n\n node.setSelectable(True)\n node.setMeshData(mesh)\n node.setName(os.path.basename(job.getFileName()))\n\n op = AddSceneNodeOperation(node, self._scene.getRoot())\n op.push()\n\n self._scene.sceneChanged.emit(node)\n\n def _onWriteJobFinished(self, job):\n message = Message(i18n_catalog.i18nc(\"Save file completed messsage. {0} is file name\", \"Saved to {0}\".format(job.getFileName())))\n message.addAction(\"open_folder\", i18n_catalog.i18nc(\"Open Folder message action\", \"Open Folder\"), \"open\", i18n_catalog.i18n(\"Open the folder containing the saved file\"))\n message._file = job.getFileName()\n message.actionTriggered.connect(self._onMessageActionTriggered)\n message.show()\n\n def _onMessageActionTriggered(self, message, action):\n if action == \"open_folder\":\n QDesktopServices.openUrl(QUrl.fromLocalFile(os.path.dirname(message._file)))\n\ndef createMeshFileHandlerProxy(engine, script_engine):\n return MeshFileHandlerProxy()\n","sub_path":"UM/Qt/Bindings/MeshFileHandlerProxy.py","file_name":"MeshFileHandlerProxy.py","file_ext":"py","file_size_in_byte":4070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"402482515","text":"class Solution(object):\r\n def singleNumber(self, nums):\r\n \"\"\"\r\n :type nums: List[int]\r\n :rtype: int\r\n \"\"\"\r\n n = nums[0]\r\n\r\n for i in nums[1:]:\r\n n ^= i\r\n return n\r\n\r\n\r\ns = Solution()\r\nprint(s.singleNumber([1,3,1,-1,3]))","sub_path":"LeetCode/Array/005_single_number.py","file_name":"005_single_number.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"586650282","text":"# from typing import List\n\n\ndef solve(n: int, arr, mmax: int) -> int:\n ans = 0\n res = 0\n i = 1\n while i <= mmax:\n if res == 0:\n ans += arr[i] // i\n res = arr[i] % i\n else:\n if res + arr[i] < i:\n res += arr[i]\n else:\n delta = arr[i] + res - i\n ans += (1 + (delta) // i)\n res = delta % i\n i += 1\n return ans\n\ndef prepare_data(n, inp_str):\n tmp_str = inp_str.split(' ')\n tmp = [0]*(n+1)\n mmax = 1\n for jj in range(n):\n k = int(tmp_str[jj])\n tmp[k] += 1\n if k > mmax:\n mmax = k\n return tmp, mmax\n\n# fin = open('input.txt', 'r')\n# fout = open('output.txt', 'w')\n\n# t = int(fin.readline())\nt = int(input())\nfor ___ in range(t):\n # n = int(fin.readline())\n # inp_str = fin.readline()\n n = int(input())\n inp_str = input()\n if n == 1:\n print(1)\n else:\n array, mmax = prepare_data(n, inp_str)\n # array = [0, 199912, 15, 5, 8, 13, 7, 13, 10, 7, 10]\n # mmax = 10\n groups = solve(n, array, mmax)\n print(groups)\n\n# fout.write(str(groups) + '\\n')\n# fin.close()\n# fout.close()\n# 0, 199912, 15, 5, 8, 13, 7, 13, 10, 7, 10, 0","sub_path":"Round_643_Div_2/B/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"241459811","text":"# author: scott olesen \n\nimport select, argparse, os.path, os, sys, glob, re\n\ndef find_markdown_file():\n # if there is no input, then see if there is one .md in the path\n possible_mds = glob.glob(os.path.join(os.getcwd(), '*.md')) + glob.glob(os.path.join(os.getcwd(), '*.markdown'))\n if len(possible_mds) == 1:\n return possible_mds[0]\n elif len(possible_mds) == 0:\n print(\"no markdown file found in this directory; specify one\")\n sys.exit(1)\n elif len(possible_mds) > 1:\n print(\"multiple markdown files in this directory; specify one\")\n sys.exit(1)\n\ndef name_output(input_fn, output_ext):\n # check that this is a markdown file\n root, ext = os.path.splitext(input_fn)\n assert ext in ['.md', '.markdown']\n return root + '.' + output_ext\n\ndef interpret_to(to):\n '''\n Figure otu the pandoc \"to\" argument and the output file extension based on\n spandoc's \"to\" input\n\n to: str\n \"to\" argument from the command line\n returns: (str, str)\n pandoc's \"to\" argument, output file extension\n '''\n\n if to == 'pdf':\n return ('latex', 'pdf')\n elif to == 'beamer':\n return ('beamer', 'pdf')\n else:\n return (to, to)\n\ndef pandoc_command(input_fn, output_fn, pandoc_to, filters=None, variables=None):\n command = ['pandoc', '--to', pandoc_to, '--output', output_fn]\n\n if filters is not None:\n command += ['--filter'] + filters\n\n if variables is not None:\n command += ['-V'] + variables\n\n command += ['--', input_fn]\n\n return command\n\ndef ok_write_file(fn):\n '''\n Check if the file already exists. If it does, prompt user about whether\n it's OK to overwrite an existing file. Otherwise just say it's OK.\n '''\n\n if os.path.isfile(fn):\n resp = input(\"file '{}' already exists. overwite? [y/N] \".format(fn))\n return resp.lower() in ['y', 'yes']\n else:\n return True\n","sub_path":"smart_pandoc/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"568617471","text":"from flask import session, render_template, request\nimport re\nfrom .mail import mail\nfrom .lang.ja import ja\nfrom .lang.en import en\nfrom .lang.zh import zh\nfrom .config import config\n\ndef display_datetime(datetime='') :\n datetime = datetime\n\n return datetime\n\ndef display_name(name='') :\n name = name\n\n return name\n\ndef link(url='') :\n obj_config = config()\n return obj_config.params['hostname'] + url\n\ndef sendmail(recipient='', subject='', body='') :\n obj_mailer = mail()\n obj_config = config()\n\n subject = lang(subject)\n\n context = {'resources_path': obj_config.params['resources_path'],\n 'val_addressee': recipient,\n 'val_signature': lang('HENNGE Support'),\n 'val_login': obj_config.params['hostname'],\n 'link_home': obj_config.params['hostname'],\n 'val_body': body,\n }\n\n body = render_template('mail.html', context=context)\n\n return obj_mailer.send(recipient, subject, body)\n\ndef lang(string='') :\n if 'lang' not in session :\n session['lang'] = 'en'\n supported_languages = ['en', 'ja', 'zh']\n session['lang'] = request.accept_languages.best_match(supported_languages)\n\n if session['lang'] == 'en' :\n obj_en = en()\n if string in obj_en.translations :\n return obj_en.translations[string]\n elif session['lang'] == 'zh' :\n obj_ja = zh()\n if string in obj_ja.translations :\n return obj_ja.translations[string]\n else :\n obj_ja = ja()\n if string in obj_ja.translations :\n return obj_ja.translations[string]\n\n return string\n\ndef display_number(number='') :\n if number != '' :\n number = number\n else :\n number = 0\n return '{0:,}'.format(int(number))\n\ndef check_password_strength(string='') :\n result = re.search('.{8,}', string)\n\n if result :\n return True\n else :\n return False\n","sub_path":"src/pm_server/modules/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"355396623","text":"# This file is part of QuTiP: Quantum Toolbox in Python.\n#\n# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson,\n# Neill Lambert, Anubhav Vardhan, Alexander Pitchford.\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are\n# met:\n#\n# 1. Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n#\n# 2. Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n#\n# 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names\n# of its contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n###############################################################################\n\"\"\"\nThis module provides exact solvers for a system-bath setup using the\nhierarchy equations of motion (HEOM).\n\"\"\"\n\n# Authors: Neill Lambert, Anubhav Vardhan, Alexander Pitchford\n# Contact: nwlambert@gmail.com\n\nimport timeit\nimport numpy as np\n#from scipy.misc import factorial\nimport scipy.sparse as sp\nimport scipy.integrate\nfrom copy import copy\nfrom qutip import Qobj, qeye\nfrom qutip.states import enr_state_dictionaries\nfrom qutip.superoperator import liouvillian, spre, spost\nfrom qutip.cy.spmatfuncs import cy_ode_rhs\nfrom qutip.solver import Options, Result, Stats\nfrom qutip.ui.progressbar import BaseProgressBar, TextProgressBar\nfrom qutip.cy.heom import cy_pad_csr\nfrom qutip.cy.spmath import zcsr_kron\nfrom qutip.fastsparse import fast_csr_matrix, fast_identity\n\n\nclass HEOMSolver(object):\n \"\"\"\n This is superclass for all solvers that use the HEOM method for\n calculating the dynamics evolution. There are many references for this.\n A good introduction, and perhaps closest to the notation used here is:\n DOI:10.1103/PhysRevLett.104.250401\n A more canonical reference, with full derivation is:\n DOI: 10.1103/PhysRevA.41.6676\n The method can compute open system dynamics without using any Markovian\n or rotating wave approximation (RWA) for systems where the bath\n correlations can be approximated to a sum of complex eponentials.\n The method builds a matrix of linked differential equations, which are\n then solved used the same ODE solvers as other qutip solvers (e.g. mesolve)\n\n This class should be treated as abstract. Currently the only subclass\n implemented is that for the Drude-Lorentz spectral density. This covers\n the majority of the work that has been done using this model, and there\n are some performance advantages to assuming this model where it is\n appropriate.\n\n There are opportunities to develop a more general spectral density code.\n\n Attributes\n ----------\n H_sys : Qobj\n System Hamiltonian\n\n coup_op : Qobj\n Operator describing the coupling between system and bath.\n\n coup_strength : float\n Coupling strength.\n\n temperature : float\n Bath temperature, in units corresponding to planck\n\n N_cut : int\n Cutoff parameter for the bath\n\n N_exp : int\n Number of exponential terms used to approximate the bath correlation\n functions\n\n planck : float\n reduced Planck constant\n\n boltzmann : float\n Boltzmann's constant\n\n options : :class:`qutip.solver.Options`\n Generic solver options.\n If set to None the default options will be used\n\n progress_bar: BaseProgressBar\n Optional instance of BaseProgressBar, or a subclass thereof, for\n showing the progress of the simulation.\n\n stats : :class:`qutip.solver.Stats`\n optional container for holding performance statitics\n If None is set, then statistics are not collected\n There may be an overhead in collecting statistics\n\n exp_coeff : list of complex\n Coefficients for the exponential series terms\n\n exp_freq : list of complex\n Frequencies for the exponential series terms\n \"\"\"\n def __init__(self):\n raise NotImplementedError(\"This is a abstract class only. \"\n \"Use a subclass, for example HSolverDL\")\n\n def reset(self):\n \"\"\"\n Reset any attributes to default values\n \"\"\"\n self.planck = 1.0\n self.boltzmann = 1.0\n self.H_sys = None\n self.coup_op = None\n self.coup_strength = 0.0\n self.temperature = 1.0\n self.N_cut = 10\n self.N_exp = 2\n self.N_he = 0\n\n self.exp_coeff = None\n self.exp_freq = None\n\n self.options = None\n self.progress_bar = None\n self.stats = None\n\n self.ode = None\n self.configured = False\n\n def configure(self, H_sys, coup_op, coup_strength, temperature,\n N_cut, N_exp, planck=None, boltzmann=None,\n renorm=None, bnd_cut_approx=None,\n options=None, progress_bar=None, stats=None):\n \"\"\"\n Configure the solver using the passed parameters\n The parameters are described in the class attributes, unless there\n is some specific behaviour\n\n Parameters\n ----------\n options : :class:`qutip.solver.Options`\n Generic solver options.\n If set to None the default options will be used\n\n progress_bar: BaseProgressBar\n Optional instance of BaseProgressBar, or a subclass thereof, for\n showing the progress of the simulation.\n If set to None, then the default progress bar will be used\n Set to False for no progress bar\n\n stats: :class:`qutip.solver.Stats`\n Optional instance of solver.Stats, or a subclass thereof, for\n storing performance statistics for the solver\n If set to True, then the default Stats for this class will be used\n Set to False for no stats\n \"\"\"\n\n self.H_sys = H_sys\n self.coup_op = coup_op\n self.coup_strength = coup_strength\n self.temperature = temperature\n self.N_cut = N_cut\n self.N_exp = N_exp\n if planck: self.planck = planck\n if boltzmann: self.boltzmann = boltzmann\n if isinstance(options, Options): self.options = options\n if isinstance(progress_bar, BaseProgressBar):\n self.progress_bar = progress_bar\n elif progress_bar == True:\n self.progress_bar = TextProgressBar()\n elif progress_bar == False:\n self.progress_bar = None\n if isinstance(stats, Stats):\n self.stats = stats\n elif stats == True:\n self.stats = self.create_new_stats()\n elif stats == False:\n self.stats = None\n\n def create_new_stats(self):\n \"\"\"\n Creates a new stats object suitable for use with this solver\n Note: this solver expects the stats object to have sections\n config\n integrate\n \"\"\"\n stats = Stats(['config', 'run'])\n stats.header = \"Hierarchy Solver Stats\"\n return stats\n\nclass HSolverDL(HEOMSolver):\n \"\"\"\n HEOM solver based on the Drude-Lorentz model for spectral density.\n Drude-Lorentz bath the correlation functions can be exactly analytically\n expressed as an infinite sum of exponentials which depend on the\n temperature, these are called the Matsubara terms or Matsubara frequencies\n\n For practical computation purposes an approximation must be used based\n on a small number of Matsubara terms (typically < 4).\n\n Attributes\n ----------\n cut_freq : float\n Bath spectral density cutoff frequency.\n\n renorm : bool\n Apply renormalisation to coupling terms\n Can be useful if using SI units for planck and boltzmann\n\n bnd_cut_approx : bool\n Use boundary cut off approximation\n Can be\n \"\"\"\n\n def __init__(self, H_sys, coup_op, coup_strength, temperature,\n N_cut, N_exp, cut_freq, planck=1.0, boltzmann=1.0,\n renorm=True, bnd_cut_approx=True,\n options=None, progress_bar=None, stats=None):\n\n self.reset()\n\n if options is None:\n self.options = Options()\n else:\n self.options = options\n\n self.progress_bar = False\n if progress_bar is None:\n self.progress_bar = BaseProgressBar()\n elif progress_bar == True:\n self.progress_bar = TextProgressBar()\n\n # the other attributes will be set in the configure method\n self.configure(H_sys, coup_op, coup_strength, temperature,\n N_cut, N_exp, cut_freq, planck=planck, boltzmann=boltzmann,\n renorm=renorm, bnd_cut_approx=bnd_cut_approx, stats=stats)\n\n def reset(self):\n \"\"\"\n Reset any attributes to default values\n \"\"\"\n HEOMSolver.reset(self)\n self.cut_freq = 1.0\n self.renorm = False\n self.bnd_cut_approx = False\n\n def configure(self, H_sys, coup_op, coup_strength, temperature,\n N_cut, N_exp, cut_freq, planck=None, boltzmann=None,\n renorm=None, bnd_cut_approx=None,\n options=None, progress_bar=None, stats=None):\n \"\"\"\n Calls configure from :class:`HEOMSolver` and sets any attributes\n that are specific to this subclass\n \"\"\"\n start_config = timeit.default_timer()\n\n HEOMSolver.configure(self, H_sys, coup_op, coup_strength,\n temperature, N_cut, N_exp,\n planck=planck, boltzmann=boltzmann,\n options=options, progress_bar=progress_bar, stats=stats)\n self.cut_freq = cut_freq\n if renorm is not None: self.renorm = renorm\n if bnd_cut_approx is not None: self.bnd_cut_approx = bnd_cut_approx\n\n # Load local values for optional parameters\n # Constants and Hamiltonian.\n hbar = self.planck\n options = self.options\n progress_bar = self.progress_bar\n stats = self.stats\n\n\n if stats:\n ss_conf = stats.sections.get('config')\n if ss_conf is None:\n ss_conf = stats.add_section('config')\n\n c, nu = self._calc_matsubara_params()\n\n if renorm:\n norm_plus, norm_minus = self._calc_renorm_factors()\n if stats:\n stats.add_message('options', 'renormalisation', ss_conf)\n # Dimensions et by system\n sup_dim = H_sys.dims[0][0]**2\n unit_sys = qeye(H_sys.dims[0])\n\n # Use shorthands (mainly as in referenced PRL)\n lam0 = self.coup_strength\n gam = self.cut_freq\n N_c = self.N_cut\n N_m = self.N_exp\n Q = coup_op # Q as shorthand for coupling operator\n beta = 1.0/(self.boltzmann*self.temperature)\n\n # Ntot is the total number of ancillary elements in the hierarchy\n # Ntot = factorial(N_c + N_m) / (factorial(N_c)*factorial(N_m))\n # Turns out to be the same as nstates from state_number_enumerate\n N_he, he2idx, idx2he = enr_state_dictionaries([N_c + 1]*N_m , N_c)\n\n unit_helems = fast_identity(N_he)\n if self.bnd_cut_approx:\n # the Tanimura boundary cut off operator\n if stats:\n stats.add_message('options', 'boundary cutoff approx', ss_conf)\n op = -2*spre(Q)*spost(Q.dag()) + spre(Q.dag()*Q) + spost(Q.dag()*Q)\n\n approx_factr = ((2*lam0 / (beta*gam*hbar)) - 1j*lam0) / hbar\n for k in range(N_m):\n approx_factr -= (c[k] / nu[k])\n L_bnd = -approx_factr*op.data\n L_helems = zcsr_kron(unit_helems, L_bnd)\n else:\n L_helems = fast_csr_matrix(shape=(N_he*sup_dim, N_he*sup_dim))\n\n # Build the hierarchy element interaction matrix\n if stats: start_helem_constr = timeit.default_timer()\n\n unit_sup = spre(unit_sys).data\n spreQ = spre(Q).data\n spostQ = spost(Q).data\n commQ = (spre(Q) - spost(Q)).data\n N_he_interact = 0\n\n for he_idx in range(N_he):\n he_state = list(idx2he[he_idx])\n n_excite = sum(he_state)\n\n # The diagonal elements for the hierarchy operator\n # coeff for diagonal elements\n sum_n_m_freq = 0.0\n for k in range(N_m):\n sum_n_m_freq += he_state[k]*nu[k]\n\n op = -sum_n_m_freq*unit_sup\n L_he = cy_pad_csr(op, N_he, N_he, he_idx, he_idx)\n L_helems += L_he\n\n # Add the neighour interations\n he_state_neigh = copy(he_state)\n for k in range(N_m):\n\n n_k = he_state[k]\n if n_k >= 1:\n # find the hierarchy element index of the neighbour before\n # this element, for this Matsubara term\n he_state_neigh[k] = n_k - 1\n he_idx_neigh = he2idx[tuple(he_state_neigh)]\n\n op = c[k]*spreQ - np.conj(c[k])*spostQ\n if renorm:\n op = -1j*norm_minus[n_k, k]*op\n else:\n op = -1j*n_k*op\n\n L_he = cy_pad_csr(op, N_he, N_he, he_idx, he_idx_neigh)\n L_helems += L_he\n N_he_interact += 1\n\n he_state_neigh[k] = n_k\n\n if n_excite <= N_c - 1:\n # find the hierarchy element index of the neighbour after\n # this element, for this Matsubara term\n he_state_neigh[k] = n_k + 1\n he_idx_neigh = he2idx[tuple(he_state_neigh)]\n\n op = commQ\n if renorm:\n op = -1j*norm_plus[n_k, k]*op\n else:\n op = -1j*op\n\n L_he = cy_pad_csr(op, N_he, N_he, he_idx, he_idx_neigh)\n L_helems += L_he\n N_he_interact += 1\n\n he_state_neigh[k] = n_k\n\n if stats:\n stats.add_timing('hierarchy contruct',\n timeit.default_timer() - start_helem_constr,\n ss_conf)\n stats.add_count('Num hierarchy elements', N_he, ss_conf)\n stats.add_count('Num he interactions', N_he_interact, ss_conf)\n\n # Setup Liouvillian\n if stats: \n start_louvillian = timeit.default_timer()\n \n H_he = zcsr_kron(unit_helems, liouvillian(H_sys).data)\n\n L_helems += H_he\n\n if stats:\n stats.add_timing('Liouvillian contruct',\n timeit.default_timer() - start_louvillian,\n ss_conf)\n\n if stats: start_integ_conf = timeit.default_timer()\n\n r = scipy.integrate.ode(cy_ode_rhs)\n\n r.set_f_params(L_helems.data, L_helems.indices, L_helems.indptr)\n r.set_integrator('zvode', method=options.method, order=options.order,\n atol=options.atol, rtol=options.rtol,\n nsteps=options.nsteps, first_step=options.first_step,\n min_step=options.min_step, max_step=options.max_step)\n\n if stats:\n time_now = timeit.default_timer()\n stats.add_timing('Liouvillian contruct',\n time_now - start_integ_conf,\n ss_conf)\n if ss_conf.total_time is None:\n ss_conf.total_time = time_now - start_config\n else:\n ss_conf.total_time += time_now - start_config\n\n self._ode = r\n self._N_he = N_he\n self._sup_dim = sup_dim\n self._configured = True\n\n def run(self, rho0, tlist):\n \"\"\"\n Function to solve for an open quantum system using the\n HEOM model.\n\n Parameters\n ----------\n rho0 : Qobj\n Initial state (density matrix) of the system.\n\n tlist : list\n Time over which system evolves.\n\n Returns\n -------\n results : :class:`qutip.solver.Result`\n Object storing all results from the simulation.\n \"\"\"\n\n start_run = timeit.default_timer()\n\n sup_dim = self._sup_dim\n stats = self.stats\n r = self._ode\n\n if not self._configured:\n raise RuntimeError(\"Solver must be configured before it is run\")\n if stats:\n ss_conf = stats.sections.get('config')\n if ss_conf is None:\n raise RuntimeError(\"No config section for solver stats\")\n ss_run = stats.sections.get('run')\n if ss_run is None:\n ss_run = stats.add_section('run')\n\n # Set up terms of the matsubara and tanimura boundaries\n output = Result()\n output.solver = \"hsolve\"\n output.times = tlist\n output.states = []\n\n if stats: start_init = timeit.default_timer()\n output.states.append(Qobj(rho0))\n rho0_flat = rho0.full().ravel('F') # Using 'F' effectively transposes\n rho0_he = np.zeros([sup_dim*self._N_he], dtype=complex)\n rho0_he[:sup_dim] = rho0_flat\n r.set_initial_value(rho0_he, tlist[0])\n\n if stats:\n stats.add_timing('initialize',\n timeit.default_timer() - start_init, ss_run)\n start_integ = timeit.default_timer()\n\n dt = np.diff(tlist)\n n_tsteps = len(tlist)\n for t_idx, t in enumerate(tlist):\n if t_idx < n_tsteps - 1:\n r.integrate(r.t + dt[t_idx])\n rho = Qobj(r.y[:sup_dim].reshape(rho0.shape), dims=rho0.dims)\n output.states.append(rho)\n\n if stats:\n time_now = timeit.default_timer()\n stats.add_timing('integrate',\n time_now - start_integ, ss_run)\n if ss_run.total_time is None:\n ss_run.total_time = time_now - start_run\n else:\n ss_run.total_time += time_now - start_run\n stats.total_time = ss_conf.total_time + ss_run.total_time\n\n return output\n\n def _calc_matsubara_params(self):\n \"\"\"\n Calculate the Matsubara coefficents and frequencies\n\n Returns\n -------\n c, nu: both list(float)\n\n \"\"\"\n c = []\n nu = []\n lam0 = self.coup_strength\n gam = self.cut_freq\n hbar = self.planck\n beta = 1.0/(self.boltzmann*self.temperature)\n N_m = self.N_exp\n\n g = 2*np.pi / (beta*hbar)\n for k in range(N_m):\n if k == 0:\n nu.append(gam)\n c.append(lam0*gam*\n (1.0/np.tan(gam*hbar*beta/2.0) - 1j) / hbar)\n else:\n nu.append(k*g)\n c.append(4*lam0*gam*nu[k] /\n ((nu[k]**2 - gam**2)*beta*hbar**2))\n\n self.exp_coeff = c\n self.exp_freq = nu\n return c, nu\n\n def _calc_renorm_factors(self):\n \"\"\"\n Calculate the renormalisation factors\n\n Returns\n -------\n norm_plus, norm_minus : array[N_c, N_m] of float\n \"\"\"\n c = self.exp_coeff\n N_m = self.N_exp\n N_c = self.N_cut\n\n norm_plus = np.empty((N_c+1, N_m))\n norm_minus = np.empty((N_c+1, N_m))\n for k in range(N_m):\n for n in range(N_c+1):\n norm_plus[n, k] = np.sqrt(abs(c[k])*(n + 1))\n norm_minus[n, k] = np.sqrt(float(n)/abs(c[k]))\n\n return norm_plus, norm_minus\n\n\ndef _pad_csr(A, row_scale, col_scale, insertrow=0, insertcol=0):\n \"\"\"\n Expand the input csr_matrix to a greater space as given by the scale.\n Effectively inserting A into a larger matrix\n zeros([A.shape[0]*row_scale, A.shape[1]*col_scale]\n at the position [A.shape[0]*insertrow, A.shape[1]*insertcol]\n The same could be achieved through using a kron with a matrix with\n one element set to 1. However, this is more efficient\n \"\"\"\n\n # ajgpitch 2016-03-08:\n # Clearly this is a very simple operation in dense matrices\n # It seems strange that there is nothing equivalent in sparse however,\n # after much searching most threads suggest directly addressing\n # the underlying arrays, as done here.\n # This certainly proved more efficient than other methods such as stacking\n #TODO: Perhaps cythonize and move to spmatfuncs\n\n if not isinstance(A, sp.csr_matrix):\n raise TypeError(\"First parameter must be a csr matrix\")\n nrowin = A.shape[0]\n ncolin = A.shape[1]\n nrowout = nrowin*row_scale\n ncolout = ncolin*col_scale\n\n A._shape = (nrowout, ncolout)\n if insertcol == 0:\n pass\n elif insertcol > 0 and insertcol < col_scale:\n A.indices = A.indices + insertcol*ncolin\n else:\n raise ValueError(\"insertcol must be >= 0 and < col_scale\")\n\n if insertrow == 0:\n A.indptr = np.concatenate((A.indptr,\n np.array([A.indptr[-1]]*(row_scale-1)*nrowin)))\n elif insertrow == row_scale-1:\n A.indptr = np.concatenate((np.array([0]*(row_scale - 1)*nrowin),\n A.indptr))\n elif insertrow > 0 and insertrow < row_scale - 1:\n A.indptr = np.concatenate((np.array([0]*insertrow*nrowin), A.indptr,\n np.array([A.indptr[-1]]*(row_scale - insertrow - 1)*nrowin)))\n else:\n raise ValueError(\"insertrow must be >= 0 and < row_scale\")\n\n return A\n","sub_path":"qutip/nonmarkov/heom.py","file_name":"heom.py","file_ext":"py","file_size_in_byte":22564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"61328077","text":"import inspect\nimport os\nimport time\n\nimport easygui\nimport pandas as pd\nimport requests\nimport selenium.common.exceptions\nfrom bs4 import UnicodeDammit\nfrom selenium import webdriver\n\n\"\"\"\n\nGlobal Vars for Metrics\n\n\"\"\"\n\nusername = os.getlogin()\n\nversion_ = \"0.2\"\n\n\"\"\"\nMetrics\n\"\"\"\nform_url = \"https://docs.google.com/forms/d/e/1FAIpQLSc__424kTNr_HGMMvKSo173joXY8Zj_n8LwQ01j0ST5TWTdiQ/formResponse\"\nusername_entry_id = \"entry.2086789165\"\nbatch_length_entry_id = \"entry.1507119654\"\naction_entry_id = \"entry.1360379885\"\nversion_entry_id = \"entry.284070968\"\n\n\ndef params_builder(username=username, batch_length=0, action='', version=version_):\n params = {}\n params[username_entry_id] = username\n params[batch_length_entry_id] = batch_length\n params[action_entry_id] = action\n params[version_entry_id] = version\n return params\n\n\ndef post_google_form(form_url, params):\n r = requests.post(form_url, data=params, headers={\"Content-type\": \"application/x-www-form-urlencoded\"})\n return r.text\n\n\ndef detect_file_encoding(file_path):\n fp = open(file_path, \"rb\")\n file_byte = fp.read()\n fp.close()\n try:\n enc_detector = UnicodeDammit(file_byte)\n dec_encoding = enc_detector.original_encoding\n return dec_encoding\n except Exception as e:\n print(e)\n return False\n\n\ndef read_file(path_in):\n file_ext = os.path.splitext(path_in)[1]\n if file_ext == '.csv':\n open_method = pd.read_csv\n elif file_ext == '.xls' or file_ext == '.xlsx':\n open_method = pd.read_excel\n else:\n open_method = None\n print(file_ext + \"Not supported\")\n return False\n try:\n dup_check_data = open_method(path_in)\n return dup_check_data\n except UnicodeDecodeError:\n print(\"Encoding Error Attempting to Detect...\")\n detected_encoding = detect_file_encoding(path_in)\n if detected_encoding is False:\n print(\"Unable to Detect Encoding...\")\n return False\n else:\n print(\"Detected Encoding... \" + detected_encoding)\n try:\n dup_check_data = open_method(path_in, encoding=detected_encoding)\n return dup_check_data\n except Exception as e:\n print(e)\n return False\n\n\ndef get_default_dir():\n username = os.getlogin()\n default_dir = os.path.join(r\"C:\\Users\", username, \"dupcheck.xlsx\")\n return default_dir\n\n\ndef select_save_loc(open_path):\n open_dir, open_file = os.path.split(open_path)\n open_file_name, open_file_ext = os.path.splitext(open_file)\n open_file_name += '_dupcheck{}'.format(open_file_ext)\n default_save = os.path.join(open_dir, open_file_name)\n results_of_check = easygui.filesavebox(\"Select where to save the Duplicate Checker Results\", \"Save File\",\n default=default_save)\n file_ext = os.path.splitext(results_of_check)[1]\n if file_ext == '':\n results_of_check += \".xlsx\"\n return results_of_check\n\n\ndef select_file():\n path_in = easygui.fileopenbox(msg=\"Open the spreadsheet to check\")\n return path_in\n\n\ndef detect_headers(dup_check_data): # Expects a DataFrame object\n headers = dup_check_data.columns.values.tolist()\n return headers\n\n\ndef select_relevant_header(headers):\n msg = \"Choose Which Column To Use for DupChecking\"\n chosen_headers = easygui.choicebox(msg=msg, title=\"Select Relevant Column\", choices=headers)\n return chosen_headers\n\n\ndef adv_search_formatter(search_term):\n base = \"https://cisco.avature.net/#Search/Type: \\\"all\\\", In: \\\"everything\\\"/\"\n return base + search_term\n\n\ndef results_exist(driver):\n try:\n driver.find_element_by_class_name(\"uicore_list_NoResultsMessage\")\n return False\n except selenium.common.exceptions.NoSuchElementException:\n return driver.current_url\n\n\ndef get_chromedriver_path():\n # Get CWD and pass chromedriver to PATH env variable\n current_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile(inspect.currentframe()))[0]))\n path_to_chromedriver = os.path.join(current_folder, 'chromedriver.exe')\n return path_to_chromedriver\n\ndef wait_for_avature(driver, indicator='ATS', timeout=240, poll=1):\n waiting = True\n start_time = round(time.time(), 0)\n while waiting is True:\n current_title = driver.title\n # print(current_title)\n if indicator in current_title:\n return True\n time.sleep(poll)\n now_time = round(time.time(), 0)\n if now_time - start_time > timeout:\n return False\n\n\n# Select File to Check\ndata_in_path = select_file()\n\n# Select Save Location for Results\n\nsave_path = select_save_loc(data_in_path)\n\ndupcheck_data = read_file(data_in_path)\nif dupcheck_data is False:\n # Log encoding error\n try:\n batch_log = params_builder(username, batch_length=0, action='error opening file')\n post_google_form(form_url, batch_log)\n except:\n pass\n print(\"Error opening file, check encoding\")\n time.sleep(15)\n quit()\n\n# Map Headers via User prompt\ncurrent_headers = detect_headers(dupcheck_data)\nrelevant_header = select_relevant_header(current_headers)\n\n# Replace empty values\ndupcheck_data = dupcheck_data.replace(['null'], '')\ndupcheck_data = dupcheck_data.fillna('')\ndupcheck_data = dupcheck_data.drop_duplicates()\n\n# Log batch length\ntry:\n batch_log = params_builder(username, len(dupcheck_data), 'open file')\n post_google_form(form_url, batch_log)\nexcept:\n pass\n\n# From relevant header, create dictionary with search terms\n\nlead_holder_dict = {}\n\nfor index, row in dupcheck_data.iterrows():\n search_term = row[relevant_header]\n lead_holder_dict[index] = search_term\n\n# Initialize Dup Checker\nuser_path = os.path.join(\"C:\\\\Users\", os.getlogin())\nchromedriver_path = get_chromedriver_path()\ndriver = webdriver.Chrome(executable_path=chromedriver_path)\ndriver.get('https://cisco.avature.net/')\n\n# Instruct user to login\neasygui.msgbox(msg=\"Please log in to Avature. Once Logged In and Avature is loaded, press OK to begin.\")\navature_loaded = wait_for_avature(driver)\ntime.sleep(5)\ndriver.get('https://cisco.avature.net/#Search/Type: \"all\", In: \"everything\"/')\nsearch_loaded = wait_for_avature(driver, indicator='Advanced')\ntime.sleep(5)\nif avature_loaded is False:\n # Log the error\n try:\n batch_log = params_builder(username, batch_length=0, action='open file error')\n post_google_form(form_url, batch_log)\n except:\n pass\n easygui.msgbox(\"Timed Out Waiting for Avature, Try Again.\")\n driver.quit()\n quit()\n\n# Create dict to hold results\ndup_results_dict = {}\nsearch_terms_dict = {}\n# Open, login, etc\n\nsearch_terms = {}\ncounter = 0\n\nfor k, v in lead_holder_dict.items():\n driver.get(adv_search_formatter(v))\n\n time.sleep(5)\n\n dup_result = results_exist(driver)\n print(counter, end=\" : \")\n print(dup_result)\n\n # Simple index lookup\n\n dup_results_dict[k] = dup_result\n search_terms_dict[k] = v\n counter += 1\n\n# Searched Values\n\n# Add the results as new column to original DataFrame\ndupcheck_data['Results'] = dup_results_dict.values()\ndupcheck_data['Searched'] = search_terms_dict.values()\n\n# Reorder Columns\n\nfinished_columns = dupcheck_data.columns.tolist()\nrel_index = finished_columns.index('Results')\nfinished_columns.pop(rel_index)\nser_index = finished_columns.index('Searched')\nfinished_columns.pop(ser_index)\n\nfinished_columns.insert(0, 'Searched')\nfinished_columns.insert(0, 'Results')\n\ndupcheck_data = dupcheck_data[finished_columns]\n\ndupcheck_data.to_excel(save_path)\nprint(\"Saved results to {}\".format(save_path))\ntry:\n batch_log = params_builder(username, len(dupcheck_data), 'success')\n post_google_form(form_url, batch_log)\nexcept:\n pass\n\ndriver.quit()\n","sub_path":"DupCheck/dupchecker.py","file_name":"dupchecker.py","file_ext":"py","file_size_in_byte":7788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"112357259","text":"# -*- coding: utf-8 -*-\n\n'''\n从URL数据库中持久化数据\n'''\n\nclass URL:\n url=''\n url_md5=''\n priority=''\n scrapy_num=''\n last_scrapy_time=''\n status=''\n domain_url=''\n\n def __init__(self,data=[]):\n self.url=data[0]\n self.url_md5=data[1]\n self.priority=data[2]\n self.scrapy_num=data[3]\n self.last_scrapy_time=data[4]\n self.status=data[5]\n self.domain_url=data[6]\n","sub_path":"src/control/data/URL.py","file_name":"URL.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"452468268","text":"\"\"\"\n!/bin/python\n-*- coding: utf-8 -*\n\n/***************************************************************************\n its4land WP5: Automate It\n -------------------\n begin : 2018-05-23\n git sha : $Format:%H$\n copyright : (C) 2018 by Sophie Crommelinck\n email : s.crommelinck@utwente.nl\n development : Reiner Borchert, Hansa Luftbild AG Münster\n email : borchert@hansaluftbild.de\n ***************************************************************************/\n\n/***************************************************************************\n * *\n * This program is free software; you can redistribute it and/or modify *\n * it under the terms of the GNU General Public License as published by *\n * the Free Software Foundation; either version 2 of the License, or *\n * (at your option) any later version. *\n * *\n ***************************************************************************/\n\"\"\"\n\"\"\"\n\n\n### Description ###\n This script calculates attributes per line segment by taking into account information from each line’s\n geometry as well as information from underlying raster data (RGB and DSM).\n\n The following attributes are calculated:\n ID, int: unique number per line\n boundary, int: value containing the line label\n vertices, int number of vertices per line\n length [m], float: length per line\n azimuth [°], float: bearing in degrees between start and end of each line\n sinuosity, float: total line length divided by the shortest distance between start and end of each line\n red_grad, float: absolute difference between median of all red values of RGB lying within a 0.4m buffer right\n and left of each line\n green_grad, float: same as red_grad for green of RGB\n blue_grad, float: same as red_grad for blue of RGB\n dsm_grad, float: same as red_grad for DSM\n\"\"\"\n\n# Import required modules\nimport os\nimport math\nimport json\nimport logging\nfrom osgeo import ogr, gdal\nfrom rasterstats import zonal_stats\nfrom geojson import Feature, FeatureCollection\n\nfrom BasicProcessing import BasicProcessing\n\nclass AttributeCalculation(BasicProcessing):\n\n LEFTSIDE = 1\n RIGHTSIDE = 0\n SIDES = {LEFTSIDE : 'Left', RIGHTSIDE : 'Right'}\n\n RED = 1\n GREEN = 2\n BLUE = 3\n RGB = {RED : 'red', GREEN : 'green', BLUE : 'blue'}\n\n ID_ATTRIB = 'ID'\n\n def __init__(self):\n super(AttributeCalculation, self).__init__()\n self._inputFileName = None\n self._inputDataSource = None\n self._inputLayer = None\n\n def _prepareLayer(self, fileName, driverName = None):\n if not fileName:\n fileName = self._configValue(\"SegmentShapeFile\")\n self._inputFileName = self.getInputFilePath(fileName)\n self._inputDataSource, self._inputLayer = self._prepareInputLayer(self._inputFileName, driverName)\n return self._inputLayer\n\n ### Calculate Attributes \n\n def calculateAttributes(self, fileName, driverName = None):\n if self._inputLayer is None and self._prepareLayer(fileName, driverName) is None:\n return False\n \n self._print(\"Starting Attribute Calculation of '{0}'...\".format(self._inputLayer.GetName()), logging.INFO)\n\n layer = self._inputLayer\n self._prepareLayerFields(layer)\n \n calcFuncs = [AttributeCalculation._calcID, \n AttributeCalculation._calcVertices,\n AttributeCalculation._calcLength,\n AttributeCalculation._calcAzimuth, \n AttributeCalculation._calcSinuosity]\n\n self._print(\"Calculating Attributes ({0} features)...\".format(layer.GetFeatureCount()), logging.INFO)\n self._calculateLayer(layer, calcFuncs)\n layer.SyncToDisk()\n self._print(\"Attributes calculated.\", logging.INFO)\n return True\n\n def _prepareLayerFields(self, layer):\n # Delete all fields in attribute table\n #self._deleteAllFields(layer)\n\n # Add fields to attribute table\n self._createField(layer, AttributeCalculation.ID_ATTRIB, ogr.OFTInteger, 10, None)\n self._createField(layer, 'boundary', ogr.OFTReal, 10, 3)\n self._createField(layer, 'vertices', ogr.OFTInteger, 10, None)\n self._createField(layer, 'length', ogr.OFTReal, 10, 3)\n self._createField(layer, 'azimuth', ogr.OFTReal, 10, 3)\n self._createField(layer, 'sinuosity', ogr.OFTReal, 10, 3)\n self._createField(layer, 'red_grad', ogr.OFTReal, 10, 3)\n self._createField(layer, 'green_grad', ogr.OFTReal, 10, 3)\n self._createField(layer, 'blue_grad', ogr.OFTReal, 10, 3)\n self._createField(layer, 'dsm_grad', ogr.OFTReal, 10, 3)\n\n @staticmethod\n def _calcID(index, feature, geometry):\n feature.SetField(AttributeCalculation.ID_ATTRIB, index + 1)\n\n @staticmethod\n def _calcVertices(index, feature, geometry):\n feature.SetField('vertices', geometry.GetPointCount())\n\n @staticmethod\n def _calcLength(index, feature, geometry):\n feature.SetField('length', geometry.Length())\n\n @staticmethod\n def _getAzimuth(pointA, pointB):\n \"\"\"\n Calculates the bearing in degrees between two points.\n The formulae used is the following:\n θ = atan2(sin(Δlong).cos(lat2)cos(lat1).sin(lat2) − sin(lat1).cos(lat2).cos(Δlong))\n :Source:\n https://gist.github.com/jeromer/2005586\n :Alternatives:\n https://pypi.org/project/fionautil/\n http://www.gaia-gis.it/gaia-sins/spatialite-sql-latest.html#p14b\n \"\"\"\n if (type(pointA) != tuple) or (type(pointB) != tuple):\n raise TypeError('Only tuples are supported as arguments for azimuth calculation')\n lat1 = math.radians(pointA[0])\n lat2 = math.radians(pointB[0])\n diffLong = math.radians(pointB[1] - pointA[1])\n x = math.sin(diffLong) * math.cos(lat2)\n y = math.cos(lat1) * math.sin(lat2) - (math.sin(lat1) * math.cos(lat2) * math.cos(diffLong))\n initial_bearing = math.atan2(x, y)\n initial_bearing = math.degrees(initial_bearing)\n compass_bearing = (initial_bearing + 360) % 360\n return compass_bearing\n\n @staticmethod\n def _calcAzimuth(index, feature, geometry):\n startPoint = geometry.GetPoint(0)\n endPoint = geometry.GetPoint(geometry.GetPointCount() - 1)\n feature.SetField('azimuth', AttributeCalculation._getAzimuth(startPoint, endPoint))\n\n @staticmethod\n def _calcSinuosity(index, feature, geometry):\n \"\"\"\n Calculates the sinuosity of a line. Sinuosity is the total line length divided by the shortest distance\n between the start of the line and the end of the line.\n The formulae used is the following:\n Length / Sqrt((firstPoint.X - Shape.lastPoint.X) ^ 2 + (firstPoint.Y - lastPoint.Y) ^ 2)\n :Source:\n https://community.esri.com/thread/39734\n \"\"\"\n startPoint = geometry.GetPoint(0)\n endPoint = geometry.GetPoint(geometry.GetPointCount() - 1)\n startPoint_geom = ogr.Geometry(ogr.wkbPoint)\n endPoint_geom = ogr.Geometry(ogr.wkbPoint)\n startPoint_geom.AddPoint(startPoint[0], startPoint[1])\n endPoint_geom.AddPoint(endPoint[0], endPoint[1])\n dist = startPoint_geom.Distance(endPoint_geom)\n if dist != 0:\n dist = geometry.Length() / dist\n feature.SetField('sinuosity', dist)\n\n ### Single Sided Buffer\n\n def createBuffers(self, fileName, driverName = None, inMemory = False, asShape = False, asFileName = True):\n if self._inputLayer is None and self._prepareLayer(fileName, driverName) is None:\n return None, None\n distance = self._configValue(\"BufferDistance\")\n return self.createBuffer(distance, AttributeCalculation.LEFTSIDE, fileName, driverName, inMemory=inMemory, asShape=asShape, asFileName=asFileName), \\\n self.createBuffer(distance, AttributeCalculation.RIGHTSIDE, fileName, driverName, inMemory=inMemory, asShape=asShape, asFileName=asFileName)\n\n def createBuffer(self, bufferDistance, side, fileName, driverName = None, inMemory = False, asShape = False, asFileName = True):\n if self._inputLayer is None or self._prepareLayer(fileName, driverName) is None:\n return None\n fileName = '{0}_buffer{1}'.format(self._inputLayer.GetName(), AttributeCalculation.SIDES[side])\n if inMemory:\n driverName = BasicProcessing.DRIVER_MEM\n asFileName = False\n elif asShape:\n driverName = BasicProcessing.DRIVER_SHP\n fileName = self.getTempFilePath('{0}.shp'.format(fileName))\n else:\n driverName = BasicProcessing.DRIVER_JSON\n fileName = self.getTempFilePath('{0}.json'.format(fileName))\n \n bufferExists = False\n bufferLayer = None\n bufferDatasource = None\n if not inMemory and os.path.isfile(fileName):\n bufferExists = True\n if bufferExists:\n self._print(\"SingleSided Buffer at Distance {0} on {1} Side already exists.\". format(\n bufferDistance, AttributeCalculation.SIDES[side]), logging.INFO)\n if not asFileName:\n bufferDatasource, bufferLayer = self._openVectorLayer(fileName, driverName)\n else:\n bufferDatasource = self._createDataSource(fileName, driverName)\n if bufferDatasource is not None:\n bufferLayer = self._createSingleSidedBufferLayer(bufferDatasource, bufferDistance, side, not asShape) #inMemory)\n if asFileName:\n self._closeDataSource(bufferDatasource)\n return fileName\n return self._features2Json(bufferLayer)\n\n def _createSingleSidedBufferLayer(self, bufferDataSource, distance, side, asJson):\n self._print(\"Creating SingleSided Buffer at Distance {0} on {1} Side...\". format(distance, AttributeCalculation.SIDES[side]), logging.INFO)\n try:\n total = self._inputLayer.GetFeatureCount()\n sql = \"select ID, ST_SingleSidedBuffer(geometry, %.2f , %i) from %s\" % (distance, side, self._inputLayer.GetName())\n resultLayer = self._inputDataSource.ExecuteSQL(sql, dialect='SQLite')\n bufferLayer = bufferDataSource.CreateLayer('bufferLayer{0}'.format(AttributeCalculation.SIDES[side]), self._inputLayer.GetSpatialRef())\n self._createField(bufferLayer, AttributeCalculation.ID_ATTRIB, ogr.OFTInteger, 10, None)\n \n processed = 0\n for feature in resultLayer:\n if feature.GetGeometryRef() is not None:\n bufferLayer.CreateFeature(feature.Clone())\n processed += 1\n bufferLayer.SyncToDisk()\n return bufferLayer\n except Exception as e:\n self._print(\"Error: {0}\".format(str(e)), logging.ERROR)\n processed = 0\n return None\n finally:\n self._inputLayer.ResetReading()\n self._print(\"SingleSided Buffer created. {0} features processed, {1} failed.\".format(total, total - processed), logging.INFO)\n \n ### Zonal Stats\n \n def calculateZonalStats (self, rasterFileRGB, rasterFileDSM, leftBufferlayer = None, rightBufferlayer = None):\n if self._inputLayer is None and self._prepareLayer(fileName, driverName) is None:\n return False\n layer = self._inputLayer\n\n # Create buffer layers\n distance = self._configValue(\"BufferDistance\")\n if leftBufferlayer is None:\n leftBufferlayer = self.createBuffer(distance, AttributeCalculation.LEFTSIDE)\n if rightBufferlayer is None:\n rightBufferlayer = self.createBuffer(distance, AttributeCalculation.RIGHTSIDE)\n\n statsMesaure = self._configValue(\"StatsMeasure\")\n # RGB\n if not rasterFileRGB:\n rasterFileRGB = self._configValue(\"RGB_RasterFile\")\n fileName = self.getInputFilePath(rasterFileRGB)\n if os.path.isfile(fileName):\n self._calculateZonalStats (layer, leftBufferlayer, rightBufferlayer, fileName, \n 'red_grad', AttributeCalculation.RED, statsMesaure)\n self._calculateZonalStats (layer, leftBufferlayer, rightBufferlayer, fileName, \n 'green_grad', AttributeCalculation.GREEN, statsMesaure)\n self._calculateZonalStats (layer, leftBufferlayer, rightBufferlayer, fileName, \n 'blue_grad', AttributeCalculation.BLUE, statsMesaure)\n else:\n self._print(\"Raster file {0} does not exist!\".format(fileName), logging.ERROR)\n\n # DSM\n if not rasterFileDSM:\n rasterFileDSM = self._configValue(\"DSM_RasterFile\")\n fileName = self.getInputFilePath(rasterFileDSM)\n if os.path.isfile(fileName):\n self._calculateZonalStats (layer, leftBufferlayer, rightBufferlayer, fileName, \n 'dsm_grad', 1, statsMesaure)\n else:\n self._print(\"Raster file '{0}' does not exist!\".format(fileName), logging.ERROR)\n\n layer.SyncToDisk()\n return True\n\n def _calculateZonalStats (self, layer, leftBufferlayer, rightBufferlayer, \n rasterFileName, attributeName, colorBand, statsMeasure):\n self._print(\"Calculating ZonalStats on raster '{0}' for attribute '{1}'...\".format(os.path.basename(rasterFileName), attributeName), logging.INFO)\n try:\n leftStats = zonal_stats(leftBufferlayer, rasterFileName, band=colorBand, stats=statsMeasure, geojson_out=True, nodata=-999)\n except Exception as e:\n leftStats = []\n self._print(\"Error: {0}\".format(str(e)), logging.ERROR)\n try:\n rightStats = zonal_stats(rightBufferlayer, rasterFileName, band=colorBand, stats=statsMeasure, geojson_out=True, nodata=-999)\n except Exception as e:\n rightStats = []\n self._print(\"Error: {0}\".format(str(e)), logging.ERROR)\n\n if not leftStats or not rightStats:\n self._print(\"ZonalStats: No data received!\", logging.ERROR)\n return False\n self._setZonalStatsField(layer, leftStats, rightStats, statsMeasure, attributeName)\n self._print(\"ZonalStats calculated.\", logging.INFO)\n return True\n\n def _setZonalStatsField(self, layer, leftStats, rightStats, statsMeasure, attributeName):\n try:\n self._print(\"Populating field '{0}' with {1} values...\".format(attributeName, len(leftStats)), logging.INFO)\n calculated = 0\n failed = 0 \n for i, feature in enumerate(layer):\n featID = feature.GetField(AttributeCalculation.ID_ATTRIB)\n for x in range(0, len(leftStats)):\n leftProps = leftStats[x]['properties']\n if leftProps.get(AttributeCalculation.ID_ATTRIB) == featID:\n leftMeasure = leftProps.get(statsMeasure)\n rightMeasure = rightStats[x]['properties'].get(statsMeasure)\n value = None\n if leftMeasure is not None and rightMeasure is not None:\n value = abs(leftMeasure - rightMeasure)\n calculated += 1\n else:\n failed += 1\n feature.SetField(attributeName, value)\n layer.SetFeature(feature)\n del leftStats[x]\n del rightStats[x]\n break\n total = layer.GetFeatureCount()\n self._print(\"Field '%s' populated, %i of %i features have been attributed.\" % (attributeName, calculated, total), logging.INFO)\n except Exception as e:\n self._print(\"Error: {0}\".format(str(e)), logging.ERROR)\n finally:\n layer.ResetReading()\n\n ### Run all processing functions\n\n def runAll(self, rasterFileRGB, rasterFileDSM, vectorFileName, driverName = None):\n try:\n self._print(\"*** Starting Attribute Calculation...\", logging.INFO)\n\n if self._prepareLayer(vectorFileName) is None:\n return None\n self.calculateAttributes(None, None)\n\n # Create buffer layers\n leftBufferlayer, rightBufferlayer = self.createBuffers(None, None, inMemory = False, asShape = True, asFileName = True)\n\n # Zonal stats of RGB and DSM\n self.calculateZonalStats (rasterFileRGB, rasterFileDSM, leftBufferlayer, rightBufferlayer)\n\n self._print(\"*** Attribute Calculation finished.\", logging.INFO)\n\n return self._inputFileName\n finally:\n self._closeAllDataSources()\n\n","sub_path":"v2.0/scripts/AttributeCalculation.py","file_name":"AttributeCalculation.py","file_ext":"py","file_size_in_byte":17229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"651725239","text":"# The code is shared on SDSC Github\nimport numpy as np\nimport time\nimport tkinter as tk\n\nUNIT = 40 # pixels\nMAZE_H = 4 # 4 rows\nMAZE_W = 4 # 4 columns\n\n\nclass Maze(tk.Tk): # tk.Tk defines the main interface and all main activities will be there\n def __init__(self): # define the properties of an object constructed by this class\n super(Maze, self).__init__() # use super so that Maze can also inherit properties and methods from tkinter\n self.action_space = ['u', 'd', 'l', 'r'] # 4 actions in total\n self.n_actions = len(self.action_space)\n\n self.n_features = 2 # x and y coordinates -> features\n self.title('maze') # title of the interface\n self.Build_maze() # invoke a function to build the maze\n\n def Build_maze(self): # self means the class itself\n self.canvas = tk.Canvas(self, bg='white',height=MAZE_H * UNIT,width=MAZE_W * UNIT)\n\n # create grids\n for c in range(0, MAZE_W * UNIT, UNIT): # range(0, 4*40, 40)\n x0, y0, x1, y1 = c, 0, c, MAZE_H * UNIT\n self.canvas.create_line(x0, y0, x1, y1) # draw lines\n for r in range(0, MAZE_H * UNIT, UNIT): # range(0, 4*40, 40)\n x0, y0, x1, y1 = 0, r, MAZE_W * UNIT, r\n self.canvas.create_line(x0, y0, x1, y1) # draw lines\n\n # starting position\n origin = np.array([20, 20])\n\n # hell (row 3 and column 2)\n hell_center = origin + np.array([UNIT * 2, UNIT])\n self.hell = self.canvas.create_rectangle(hell_center[0]-15,hell_center[1]-15,hell_center[0]+15,hell_center[1]+15,fill='black')\n\n # create treasure (row 3 and column 3)\n treasure_center = origin + UNIT * 2\n self.treasure = self.canvas.create_oval(treasure_center[0]-15,treasure_center[1]-15,treasure_center[0]+15,treasure_center[1]+15,fill='yellow')\n\n # create red rectangle: the explorer\n self.explorer = self.canvas.create_rectangle(origin[0]-15,origin[1]-15,origin[0]+15,origin[1]+15,fill='red')\n\n # pack all\n self.canvas.pack() # use pack() to organize widgets in blocks\n\n def reset(self):\n# self.update()\n\n time.sleep(0.1)\n\n # delete the current explorer\n self.canvas.delete(self.explorer)\n\n origin = np.array([20, 20])\n\n # initialize a new explorer\n self.explorer = self.canvas.create_rectangle(origin[0]-15,origin[1]-15,origin[0]+15,origin[1]+15,fill='red')\n\n # return a NORMALIZED and RELATIVE position of the explorer; normalized to the size of the maze\n return (np.array(self.canvas.coords(self.explorer)[:2])-np.array(self.canvas.coords(self.treasure)[:2]))/(MAZE_H*UNIT)\n\n def step(self, action):\n s = self.canvas.coords(self.explorer)\n # s[0]: control horizontal movement\n # s[1]: control vertical movement\n base_action = np.array([0, 0])\n # noted that hitting the wall is also considered a step\n if action == 0: # up\n if s[1] > UNIT: # if the explorer is not in row 1\n base_action[1] -= UNIT\n elif action == 1: # down\n if s[1] < (MAZE_H - 1) * UNIT: # if the explorer is not in the last row\n base_action[1] += UNIT\n elif action == 2: # right\n if s[0] < (MAZE_W - 1) * UNIT: # if the explorer is not in the last column\n base_action[0] += UNIT\n elif action == 3: # left\n if s[0] > UNIT: # if the explorer is not in column 1\n base_action[0] -= UNIT\n\n # the 2nd and 3rd arguments shows the movements\n self.canvas.move(self.explorer, base_action[0], base_action[1]) # move agent\n\n # get the coordinates of the of moved explorer\n next_coords = self.canvas.coords(self.explorer) # next state\n\n # reward function\n if next_coords == self.canvas.coords(self.treasure): # if the next state is the treasure\n reward = 1\n done = 'treasure'\n s_ = 'terminal'\n elif next_coords in [self.canvas.coords(self.hell)]: # if the next state is one of the hells\n reward = -1\n done = 'trap'\n s_ = 'terminal'\n else:\n reward = 0 # for other positions, there are no rewards\n done = 'continue'\n\n # to calculate a normalized position\n s_ = (np.array(next_coords[:2]) - np.array(self.canvas.coords(self.treasure)[:2]))/(MAZE_H*UNIT)\n\n return s_, reward, done\n\n def render(self):\n time.sleep(0.02)\n\n # complete any pending geometry management and redraw widgets as necessary\n self.update() # use tkinter.update() to update the interface\n\n\n","sub_path":"DQN/DQN_env.py","file_name":"DQN_env.py","file_ext":"py","file_size_in_byte":4663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"424861823","text":"#!/usr/bin/env python\n# encoding: utf-8\nfrom random import choice\n\nimport requests\nfrom lxml import etree\n\nheaders = {}\nheaders['Accept-Language'] = 'zh-CN,zh;q=0.9'\nheaders['Connection'] = 'keep-alive'\nheaders['Upgrade-Insecure-Requests'] = '1'\nheaders['User-Agent'] = \\\n 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36'\n\n\ndef fetch_question(url):\n \"\"\"\n 从知否(segmentfault.com)爬取热门问题\n :param url:\n :return:\n \"\"\"\n try:\n r = requests.get(url, headers=headers, timeout=5)\n html = etree.HTML(r.text)\n link_list = ['https://segmentfault.com'+i for i in html.xpath(\"//div[@class='summary']/h2/a/@href\")]\n info_list = html.xpath(\"//div[@class='summary']/h2/a/text()\")\n return choice(list(zip(link_list, info_list)))\n except:\n return '', ''\n\nif __name__ == '__main__':\n print(fetch_question('https://segmentfault.com/questions/hottest'))\n","sub_path":"python/20190307/flask_qa_app/app/utils/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"540886686","text":"# Author:bfury\n# since:31/7/2021 11:53 上午\nimport cv2 as cv\n\n# Load face-detection model\nface_net = cv.dnn.readNet('../IRmodel/face-detection-adas-0001.xml', '../IRmodel/face-detection-adas-0001.bin')\n\n# load age-gender detection model\nage_gender_net = cv.dnn.readNet('../IRmodel/age-gender-recognition-retail-0013.xml','../IRmodel/age-gender-recognition-retail-0013.bin')\n\n# Specify target device (CPU)\nface_net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)\nage_gender_net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)\n\nGENDER_LABELS = ['Female', 'Male']\n\n# Read an image\nframe = cv.imread('../sources/faces.jpeg')\n\n# Prepare input blob\nblob = cv.dnn.blobFromImage(frame, size=(672, 384), ddepth=cv.CV_8U)\n\n#perform inference (face detection)\nface_net.setInput(blob)\nout = face_net.forward()\n\n\n# for each detected face\nfor detection in out.reshape(-1, 7):\n confidence = float(detection[2])\n\n if confidence > 0.5:\n\n xmin = int(detection[3] * frame.shape[1])\n ymin = int(detection[4] * frame.shape[0])\n xmax = int(detection[5] * frame.shape[1])\n ymax = int(detection[6] * frame.shape[0])\n\n #Draw rectangle over face\n cv.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, 255, 0))\n\n #perform age-gender detection\n blob2=cv.dnn.blobFromImage(frame[ymin:ymax, xmin:xmax], size=(62,62), ddepth=cv.CV_8U)\n age_gender_net.setInput(blob2)\n detections = age_gender_net.forwardAndRetrieve(['prob','age_conv3'])\n\n #process results (age, gender)\n gender = GENDER_LABELS[detections[0][0][0].argmax()]\n age = int(detections[1][0][0][0][0][0] * 100)\n text=gender + \" : \" + str(age)\n\n #print results to image\n cv.putText(frame,text,(xmin,ymin),cv.FONT_HERSHEY_COMPLEX,0.5,(0,0,255),1)\n\n# Save the frame to an image file\ncv.imwrite('./result/out.png', frame)","sub_path":"exercise-2/face-age-gender-detection.py","file_name":"face-age-gender-detection.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"503005440","text":"import os, sys\nimport numpy as np\nimport healpy\nimport matplotlib.pyplot as plt\nimport astropy.io.fits as fits\nfrom matplotlib.path import Path \n\nsurvey = 'S6'\nworking_dir = os.path.join(os.environ['HOME'], 'data', '4most', survey)\n# name of the sub survey for which the LSM is created\nsub_survey_names = np.array([ 'AGN_WIDE', 'AGN_DEEP', 'AGN_IR' ])\n# loads the catalog\ndata = fits.open(os.path.join(working_dir, 'S6_summary.fits.gz'))[1].data\n\nselections = np.array([(data['SUBSURVEY']==sub_survey_name) for sub_survey_name in sub_survey_names ])\n\ndef tabulate_NZ(sub_survey_name, selection):\n\tout_file = os.path.join(working_dir, survey +'_NZ_'+str(sub_survey_name)+'.fits')\n\tplot_file = os.path.join(working_dir,survey +'_NZ_'+str(sub_survey_name)+'.png')\n\tDZ=0.1\n\tz_bins = np.arange(0,6.1,DZ)\n\txx = 0.5*(z_bins[:-1]+z_bins[1:])\n\t#########\n\t#########\n\t# 1. redshift histogram\n\t#########\n\t#########\n\t# create the ARRAY OF PIXEL INDEXES\n\tZZ = data['REDSHIFT_ESTIMATE'] [selection]\n\tNN = np.histogram(ZZ, bins = z_bins)[0]\n\t#########\n\t#########\n\t# 3. Plots the LSM values over RA and DEC\n\t#########\n\t#########\n\tplt.figure(0, (6,6))\n\tplt.errorbar(xx, NN, xerr=DZ/2., yerr=NN**(-0.5))\n\tplt.grid()\n\tplt.ylim((0.9, 1.2*np.max(NN) ))\n\tplt.xlim((0.0, 1.2*np.max(xx[NN>=1]) ))\n\tplt.xlabel('redshift')\n\tplt.ylabel('Counts (dz=0.1)')\n\tplt.yscale('log')\n\tplt.title(sub_survey_name+' '+str(np.sum(NN)))\n\tplt.savefig(plot_file)\n\tplt.clf()\n\t#########\n\t#########\n\t# 4. Create the fits LSM file\n\t#########\n\t#########\n\tcols = fits.ColDefs([\n\t\tfits.Column( \"z_min\"\t ,unit='' ,format=\"D\", array=z_bins[:-1]), \n\t\tfits.Column( \"z_max\" ,unit='' ,format=\"D\", array=z_bins[1:] ), \n\t\tfits.Column( \"z_middle\"\t ,unit='' ,format=\"D\", array=xx), \n\t\tfits.Column( \"counts\" ,unit='' ,format=\"D\", array=NN)\n\t\t])\n\ttbhdu = fits.BinTableHDU.from_columns(cols)\n\ttbhdu.header['author'] = 'JC'\n\ttbhdu.header['HIERARCH SUBSURVEY'] = sub_survey_name\n\tif os.path.isfile(out_file):\n\t\tos.remove(out_file)\n\ttbhdu.writeto(out_file)\n\nfor sub_survey_name, selection in zip(sub_survey_names,selections):\n\tprint(sub_survey_name)\n\ttabulate_NZ(sub_survey_name, selection)","sub_path":"qmost/S6/S6_tabulate_NZ.py","file_name":"S6_tabulate_NZ.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"28845191","text":"# -*- coding: utf-8 -*-\nfrom argparse import ArgumentParser\nfrom durian import util\nfrom durian.build import Builder\n\n\ndef main():\n parser = ArgumentParser(description='Build sites')\n parser.add_argument('-f', '--force',\n action='store_true',\n help='Rebuild even if already built')\n parser.add_argument('-l', '--list',\n action='store_true',\n help='List all sites without building')\n parser.add_argument('domain', nargs='*',\n help='Domain of the site to build')\n args = parser.parse_args()\n builder = Builder()\n sites = builder.sites\n\n if args.list:\n for site in sites:\n util.info('{}: {}'.format(site.domain, site.dirname))\n\n return\n\n existing_domains = [s.domain for s in sites]\n\n for domain in args.domain:\n if domain in existing_domains:\n util.info('Build {!r}'.format(domain))\n else:\n util.error('Domain {!r} does not exist'.format(domain))\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"durian/durian/bin/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"618519852","text":"import sys\n\ndry_run = True\nif dry_run:\n file = open('res/tuple_operation.txt')\n sys.stdin = file\n\nglobal var\nfor t in range(int(input())):\n p, q, r = map(int, input().split())\n a, b, c = map(int, input().split())\n a_satisfied = b_satisfied = c_satisfied = False\n\n # direct check\n if a == p:\n a_satisfied = True\n if b == q:\n b_satisfied = True\n if c == r:\n c_satisfied = True\n if a_satisfied and b_satisfied and c_satisfied:\n print(0)\n continue\n\n # one addition check\n diff = list()\n if not a_satisfied and a - p != 0:\n diff.append(a - p)\n if not b_satisfied and b - q != 0:\n diff.append(b - q)\n if not c_satisfied and c - r != 0:\n diff.append(c - r)\n if min(diff) == max(diff):\n print(1)\n continue\n\n # one divide check\n div = list()\n if not a_satisfied:\n div.append(a / p)\n if not b_satisfied:\n div.append(b / q)\n if not c_satisfied:\n div.append(c / r)\n if min(div) == max(div) and div[0] == int(div[0]):\n print(1)\n continue\n pass\n\n # two op checks\n # two addition check\n diff = sorted(diff)\n if len(diff) == 2 or diff[0] == diff[1] or diff[1] == diff[2]:\n print(2)\n continue\n\n # two multiplication check\n div = sorted(div)\n if (len(div) == 2 and int(div[0]) == div[0] and int(div[1]) == div[1]) \\\n or (div[0] == div[1] and int(div[0]) == div[0]) \\\n or (div[1] == div[2] and int(div[1]) == div[1]):\n print(2)\n continue\n\n # multiplication followed by addition\n\n # addition followed by multiplication\n\n print(3)\n","sub_path":"codechef/long_challenge/june/tuple_operation.py","file_name":"tuple_operation.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"463670412","text":"import pandas as pd\nimport numpy as np\nimport sys\nimport argparse\n\narg_parser = argparse.ArgumentParser()\narg_parser.add_argument(\"-i\", \"--input\", help=\"An input file name\")\narg_parser.add_argument(\"-o\", \"--output\", help=\"An output file name\")\n\nargs = arg_parser.parse_args()\n#input = \"E:/evolutinoarypattern/RMB2_B10.freebayes_snps/RMB2_B10.merge.2.csv\"\n#output = \"E:/evolutinoarypattern/RMB2_B10.freebayes_snps/RMB2_B10.res.2.csv\"\ninput = args.input\noutput = args.output\ndata = pd.read_csv( input,\n sep='\\t',\n names=['CHROM', 'POS', 'TIMEPOINT', 'REF', 'ALT', 'R_CNT', 'A_CNT'])\n\ndata['T_CNT'] = data['R_CNT'] + data['A_CNT']\ndata = data.join(data.groupby('POS')['T_CNT'].sum(), on='POS', rsuffix='_ALL')\ndata = data.join(data.groupby('POS')['A_CNT'].sum(), on='POS', rsuffix='_ALL')\ndata['PMT'] = data['A_CNT'] / data['T_CNT']\ndata['P0'] = data['A_CNT_ALL'] / data['T_CNT_ALL']\n\ndata.to_csv(output, sep='\\t', index=False)\n","sub_path":"Monte-Carlo_Based_KS-Test/summarizeKS.py","file_name":"summarizeKS.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"304352628","text":"# Custom Calculator\n\n#these are example for equations that are hard to read for the average person\n#this code would be generally hard to understand\nm=294/10.5\nprint(m)\n\n#This formula is same but we are adding more variables to the equation\n#this code is still hard to understand\nm=294\ng=10.5\n#This value is the same as m=294/10.5\nm2=m/g\nprint(m2)\n\n#If i want to change the value of m and g to a new value i could do so here\n#I could simply do the same formula without changing anything and but the values change because the varibles changed\n#But this not a ideal programming environment because other people cannot read what you are writing\nm=402\ng=12\nm2=m/g\nprint (m2)\n\n# You can write long name variables so it can become easy to understand by simply doing the following\n# What's great about this line of code is the fact that this code is self documented\n# The fact that i could look at the code and have a general understanding of the variable and the values\n# The fact that we are using longer words makes it more genrally easier to understand\nmilesDriven=294\ngallonsUsed=10.5\nmpg=milesDriven/gallonsUsed\nprint(mpg)\n\n# This is the shorthand/hard version to understand\nir=0.12\nb=12123.34\ni=ir*b\n\n# Where as the longer Version is much more easier to understand\n#Try to make Variable name to be easy to read\ninterestRate=0.12\naccountBalance=12123.34\ninterstAmount=interestRate*accountBalance\nprint(interestAmount)\n\n","sub_path":"PythonAndPygameArcade/Chapter 1 Create a Custom Calculator/1.4/PythonCustomCalculator/PythonCustomCalculator/PythonCustomCalculator.py","file_name":"PythonCustomCalculator.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"96155616","text":"for h in range(int(input())):\r\n l=int(input())\r\n q=list(input())\r\n Tneed=(0.75*l)\r\n has=q.count(\"P\")\r\n proxy=0\r\n if Tneed<=has:\r\n print(0)\r\n else:\r\n for j in range(2,(l-2)):\r\n if q[j]==\"A\" and (q[j-2]==\"P\" or q[j-1]==\"P\") and (q[j+1]==\"P\" or q[j+2]==\"P\"):\r\n proxy+=1\r\n if proxy+has>=Tneed:\r\n break\r\n if proxy+has>=Tneed:\r\n print(proxy)\r\n else:\r\n print(-1)\r\n","sub_path":"Codechef/PROXYC.py","file_name":"PROXYC.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"4188521","text":"from bs4 import BeautifulSoup #html parsing package\nimport re #regular expressions\n#import pandas as pd #pandas, useful for reading/writing tables and \n\nf=open('CSDReportServlet.html') #best practice to locally save html page while you're messing around,\n #rather than repeatedly re-download the same webpage\n \nsoup=BeautifulSoup(f, features='lxml')\n\n\n#we should record when the table was last updated, which is on the page with\n# the text \"Last Update:\"\n\nupdated=soupf.ind(string=re.compile(\"Last Update\")) #re.compile lets us search using just part of the string\nprint(updated)\n#keep only the date and time\nupdated=updated.split(':')[1].strip()\nprint(updated)\n\n#We want to copy the data tables. All of the tables are in tbody tags\n\ndef get_table(table):\n table_content=[]\n #rows are tagged tr, columns td\n ROWS=table.find_all(\"tr\")\n for row in ROWS:\n row_content=[]\n COLUMNS=row.find_all(\"td\")\n for column in COLUMNS:\n row_content.append(column.get_text())\n table_content.append(row_content)\n return table_content\n\n\n#The whole structure of the webpage is tables- all tables are within 'tbody' tags, but the whole page itself is\n# also in those tags - the tables are nested together!\n\n#Therefore, instead of naively grabbing ALL tbody tags, we're going to to to the table we want, then find its \n#particular tbody tag to extract only that table.\n\n\nsummary=soup.find(\"b\",string=re.compile(\"SUMMARY\")).find_parent(\"tbody\")\nprint(summary)\n\n\nsummary_table=get_table(summary)\n\nprint(summary_table)\n\n#The first thing we get is an empty row - we can drop it\ndel summary_table[0]\nprint(summary_table)\n'''\n#Convert the list of lists to a dataframe and write to csv:\n\ndf=pd.DataFrame(columns=['Summary','Energy'])\nfor sublist in summary_table:\n print(sublist)\n df=df.append({'Summary':sublist[0],'Energy':sublist[1]}, ignore_index=True)\n \ndf.to_csv('AESO_Summary_'+updated+'.csv',index=False,encoding='cp1252')\n'''","sub_path":"SourceTest.py","file_name":"SourceTest.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"231189595","text":"import numpy as np\n\nfrom scipy.spatial.distance import pdist, squareform\nfrom scipy.stats import spearmanr\nimport matplotlib.pyplot as plt\nfrom sklearn.manifold import MDS\nfrom sklearn.preprocessing import MinMaxScaler\n\nDISTINCT_COLORS = [\"#e6194b\", \"#3cb44b\", \"#0082c8\", \"#f58230\",\n \"#911eb4\", \"#46f0f0\", \"#f032e6\", \"#d2f53c\", \"#ffe119\"]\n\ndef calculate_RDM(data, method = \"correlation\"):\n\n \"\"\" Return Specified distance matrices (1 = Pearson correlation,\n 2 = Euclidian distance, 3 = Absolute activation difference) \"\"\"\n\n data = np.array(data)\n if method == \"correlation\":\n rdm = 1-np.corrcoef(data)\n elif method == \"euclidean\":\n # Use Eucledian distance\n rdm = pdist(data,'euclidean')\n rdm = squareform(rdm)\n elif method == \"cosine\":\n rdm = pdist(data,'cosine')\n rdm = squareform(rdm)\n elif method == \"spearman\":\n rdm = 1-spearmanr(data.transpose())[0]\n return rdm\n\ndef rdm_of_rdms(rdms):\n # 1. get only the upper triangle\n rdms = [rdm[np.triu_indices(len(rdm), 1)] for rdm in rdms]\n\n # 2. rdm of rdms\n rdm = calculate_RDM(rdms, \"spearman\")\n return rdm\n\n\ndef rsa(activations_per_model, method = \"correlation\"):\n\n # 1. get rdms of all different activation\n rdms = [calculate_RDM(activations, method) for activations in activations_per_model]\n\n # 2. rdm of rdms\n rdm = rdm_of_rdms(rdms)\n return rdm\n\ndef mds(matrix):\n scaler = MinMaxScaler()\n matrix_scaled = scaler.fit_transform(matrix)\n mds = MDS(2, random_state=0)\n return mds.fit_transform(matrix_scaled)\n\n\ndef plot_rsa(activations_per_model, labels = None, color = None, method = \"correlation\"):\n fig, axes = plt.subplots(1, 2, figsize = (10,5))\n\n r = rsa(activations_per_model, method)\n mat = axes[0].imshow(r)\n\n if labels != None:\n axes[0].set_xticks(range(len(labels)))\n axes[0].set_yticks(range(len(labels)))\n axes[0].set_xticklabels(labels, rotation='vertical')\n axes[0].set_yticklabels(labels)\n\n fig.colorbar(mat, ax=axes[0])\n\n m = mds(r)\n axes[1].scatter(m[:,0], m[:,1], label = labels, c = color)\n axes[1].set_xlabel(\"MDS 1\")\n axes[1].set_ylabel(\"MDS 2\")\n if labels != None:\n for i, txt in enumerate(labels):\n axes[1].annotate(txt, (m[i,0], m[i,1]))\n plt.show()\n\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\nclass AxesDecorator():\n def __init__(self, ax, size=\"5%\", pad=0.05, ticks=[1,2,3], ticks_label = [1,2,3], spacing=0.05,\n color=\"k\"):\n self.divider= make_axes_locatable(ax)\n self.ax = self.divider.new_vertical(size=size, pad=pad, sharex=ax, pack_start=True)\n ax.figure.add_axes(self.ax)\n self.ticks=np.array(ticks)\n self.d = np.mean(np.diff(ticks))\n self.spacing = spacing\n self.get_curve()\n self.color=color\n for x0 in ticks:\n self.plot_curve(x0)\n self.ax.set_yticks([])\n plt.setp(ax.get_xticklabels(), visible=False)\n self.ax.tick_params(axis='x', which=u'both',length=0)\n ax.tick_params(axis='x', which=u'both',length=0)\n for direction in [\"left\", \"right\", \"bottom\", \"top\"]:\n self.ax.spines[direction].set_visible(False)\n self.ax.set_xlabel(ax.get_xlabel())\n ax.set_xlabel(\"\")\n self.ax.set_xticks(self.ticks)\n self.ax.set_xticklabels(ticks_label)\n print(ticks_label)\n\n def plot_curve(self, x0):\n x = np.linspace(x0-self.d/2.*(1-self.spacing),x0+self.d/2.*(1-self.spacing), 50 )\n self.ax.plot(x, self.curve, c=self.color)\n\n def get_curve(self):\n lx = np.linspace(-np.pi/2.+0.05, np.pi/2.-0.05, 25)\n tan = np.tan(lx)*10\n self.curve = np.hstack((tan[::-1],tan))\n return self.curve\n\ndef plot_rsa_fancy(activations_per_model, labels = None, color = None, method = \"correlation\", n_tasks = 1, title = \"\", steps=None):\n n_steps = len(steps)\n\n fig, axes = plt.subplots(1, 2, figsize = (8,4))\n\n r = rsa(activations_per_model, method)\n mat = axes[0].imshow(r)\n if title != \"\":\n plt.suptitle(title)\n\n axes[0].title.set_text(\"Representational dissimilarity matrix\")\n axes[0].set_xticks(np.array(range(len(steps))) * 12 + 5.5)\n axes[0].set_yticks([])\n axes[0].set_xticklabels(steps, rotation='vertical')\n n_per_step = len(activations_per_model) / n_steps\n\n AxesDecorator(axes[0], ticks=np.array(range(len(steps))) * n_per_step + (n_per_step-1)/2.0, ticks_label = steps)\n #AxesDecorator(axes[0], ticks=np.array(range(3)) * n_per_step + (n_per_step-1)/2.0, ticks_label = steps)\n #axes[0].set_yticklabels()\n import matplotlib.ticker as tick\n fig.colorbar(mat, ax=axes[0], fraction=0.046, pad=0.04, format=tick.FormatStrFormatter('%.3f'))\n\n m = mds(r)\n axes[1].title.set_text(\"Multidimensional scaling\")\n n_per_task_per_step = len(activations_per_model) / n_tasks / n_steps\n for j in range(n_tasks * len(steps)):\n base = int(j*n_per_task_per_step)\n fine_tunes = slice(int(j*n_per_task_per_step+1), int((j+1)*n_per_task_per_step))\n fine_tune_lines = slice(int(j*n_per_task_per_step), int((j+1)*n_per_task_per_step))\n\n axes[1].scatter(m[base][0], m[base][1], label = labels, c = \"#000000\", s = 40, marker = \"*\")\n\n if steps != None:\n axes[1].annotate(steps[int(j/n_tasks)], (m[base][0], m[base][1]), xytext=(m[base][0]*0.85+0.2, m[base][1]*0.85+0.2))\n\n axes[1].plot(m[fine_tune_lines][:,0], m[fine_tune_lines][:,1], c = \"#000000\", ls = '--', linewidth=0.25)\n\n axes[1].scatter(m[fine_tunes][:,0], m[fine_tunes][:,1], label = labels, c = DISTINCT_COLORS[j%n_tasks], s = 20, edgecolor='black', linewidth=0.25)\n\n max = np.max(np.absolute(m))\n max = max * 1.1\n\n axes[1].set_xlim(-max, max)\n axes[1].set_ylim(-max, max)\n axes[1].set_aspect('equal')\n #axes[1].scatter(m[0,0], m[0,1], label=labels, c=\"#000000\", s=30)\n\n axes[1].set_xlabel(\"MDS 1\")\n axes[1].set_ylabel(\"MDS 2\")\n #for i, txt in enumerate(labels):\n #axes[1].annotate(txt, (m[i,0], m[i,1]))\n plt.show()\n\n","sub_path":"rsa.py","file_name":"rsa.py","file_ext":"py","file_size_in_byte":6140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"515859161","text":"import random\r\nimport gym\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom keras.models import Sequential\r\nfrom keras.models import load_model\r\nfrom keras.layers import Dense\r\nfrom keras.optimizers import Adam\r\n\r\nEPISODES = 1000\r\n\r\nclass DQNAgent1:\r\n def __init__(self, n_actions, n_features):\r\n self.n_actions = n_actions\r\n self.n_features = n_features\r\n self.memory_size = 10000\r\n self.batch_size = 100\r\n self.gamma = 0.95 # discount rate\r\n self.epsilon = 1.0 # exploration rate\r\n self.epsilon_min = 0.01\r\n self.epsilon_decay = 0.995\r\n self.learning_rate = 0.001\r\n self.replace_target_iter = 20\r\n self.model1 = self._build_evaluate_net()\r\n self.model2 = self._build_target_net()\r\n\r\n # total learning step\r\n self.learn_step_counter = 0\r\n\r\n # initialize zero memory [s, a, r, s_]\r\n self.memory = np.zeros((self.memory_size, n_features * 5 + 6))\r\n\r\n\r\n def _build_evaluate_net(self):\r\n # 建立Evaluate network\r\n model1 = Sequential(name='evaluate_network')\r\n model1.add(Dense(24, input_dim=self.n_features, activation='relu', name='layer1'))\r\n model1.add(Dense(24, activation='relu', name='layer2'))\r\n model1.add(Dense(self.n_actions, activation='linear', name='layer3'))\r\n model1.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))\r\n\r\n return model1\r\n\r\n def _build_target_net(self):\r\n model2 = Sequential(name='target_network')\r\n model2.add(Dense(24, input_dim=self.n_features, activation='relu'))\r\n model2.add(Dense(24, activation='relu'))\r\n model2.add(Dense(self.n_actions, activation='linear'))\r\n model2.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))\r\n\r\n return model2\r\n\r\n def store_transition(self, state, action, reward, next_state, done, reset, observe, next_observe):\r\n if not hasattr(self, 'memory_counter'):\r\n self.memory_counter = 0\r\n\r\n transition = np.hstack((state, action, reward, next_state, done, reset, observe, next_observe))\r\n\r\n # replace the old memory with new memory\r\n index = self.memory_counter % self.memory_size\r\n self.memory[index, :] = transition\r\n\r\n self.memory_counter += 1\r\n\r\n def choose_action(self, observation):\r\n observation = observation[np.newaxis, :]\r\n\r\n if np.random.rand() <= self.epsilon:\r\n\r\n action = np.random.randint(0, self.n_actions)\r\n else:\r\n act_values = self.model1.predict(observation)\r\n action = np.argmax(act_values[0])\r\n return action # returns action\r\n\r\n def target_replace_op(self):\r\n v1 = self.model1.get_weights()\r\n self.model2.set_weights(v1)\r\n print(\"params has changed\")\r\n\r\n def learn(self):\r\n # check to replace target parameters\r\n if self.learn_step_counter % self.replace_target_iter == 0:\r\n self.target_replace_op()\r\n print('\\ntarget_params_replaced\\n')\r\n\r\n if self.memory_counter > self.memory_size:\r\n sample_index = np.random.choice(self.memory_size, size=self.batch_size)\r\n else:\r\n sample_index = np.random.choice(self.memory_counter, size=self.batch_size)\r\n batch_memory = self.memory[sample_index, :]\r\n q_next = self.model2.predict(batch_memory[:, [4, 5]])\r\n q_eval = self.model1.predict(batch_memory[:, [0, 1]])\r\n\r\n q_target = q_eval.copy()\r\n\r\n batch_index = np.arange(self.batch_size, dtype=np.int32)\r\n eval_act_index = batch_memory[:, self.n_features].astype(int)\r\n done_batch = batch_memory[:, 6]\r\n reward_batch = batch_memory[:, 3]\r\n\r\n\r\n q_target[batch_index, eval_act_index] = reward_batch + self.gamma * np.max(q_next, axis=1)\r\n #q_target[batch_index, 1-eval_act_index] = reward_batch[count] + self.gamma * np.max(q_next, axis=1)\r\n\r\n #利用TD error来更新evaluate_net的参数\r\n self.model1.fit(batch_memory[:, :self.n_features], q_target, epochs=1, verbose=0)\r\n\r\n self.learn_step_counter += 1\r\n if self.epsilon > self.epsilon_min:\r\n self.epsilon *= self.epsilon_decay\r\n else:\r\n self.epsilon = self.epsilon_min\r\n\r\n def load(self):\r\n model1 = load_model(\"E://research_assistant/continuum_robot_script/python/2DQNAgent/rl1model1.h5\")\r\n model2 = load_model(\"E://research_assistant/continuum_robot_script/python/2DQNAgent/rl1model2.h5\")\r\n return model1, model2\r\n\r\n def save(self):\r\n self.model1.save(\"E://research_assistant/continuum_robot_script/python/2DQNAgent/rl1model1.h5\")\r\n self.model2.save(\"E://research_assistant/continuum_robot_script/python/2DQNAgent/rl1model2.h5\")\r\n\r\n","sub_path":"RL1.py","file_name":"RL1.py","file_ext":"py","file_size_in_byte":4791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"306710476","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/pypoly/__init__.py\n# Compiled at: 2011-11-24 11:49:17\nimport os, sys, __builtin__\nfrom optparse import OptionParser\nfrom pypoly._config import Config\nfrom pypoly._locale import Locale\nfrom pypoly._user import User\nfrom pypoly._log import Log\nfrom pypoly._structure import StructureHandler\nfrom pypoly._hook import HookHandler\nfrom pypoly.component.module import ModuleHandler\nfrom pypoly.component.plugin import AuthPlugin, PluginHandler\nfrom pypoly.component.tool import ToolHandler\nfrom pypoly.content.url import URLHandler\nimport pypoly.http, pypoly.http.auth, pypoly.http.handler\n__version__ = '0.4'\n__author__ = 'PyPoly Team'\n__copyright__ = 'PyPoly Team'\n__license__ = 'MIT and GPL'\nauth = AuthPlugin()\nconfig = Config()\nhook = HookHandler()\nlocale = None\nlog = None\nmodule = None\nplugin = None\nstructure = None\ntemplate = None\ntool = None\nurl = None\nuser = None\n_dispatcher = pypoly.http.Dispatcher()\n\ndef get_path(*path):\n root = os.path.abspath(pypoly.config.get('system.root'))\n return os.path.join(root, *path)\n\n\ndef run():\n parser = OptionParser(version='PyPoly V%s' % pypoly.__version__)\n parser.add_option('--verbose', '-v', action='count', dest='verbose', default=0, help='Increase the verbosity.')\n parser.add_option('--traceback', '', action='store', dest='traceback', default=None)\n parser.add_option('--screen', '', action='store_true', dest='screen', default=False, help='log to screen')\n parser.add_option('--config', '-c', action='store', dest='config_file', default='main.cfg', help='The config file')\n (options, args) = parser.parse_args()\n conf_verbose = 50\n if options.verbose != 0:\n conf_verbose = 50 - options.verbose * 10\n if conf_verbose < 0:\n conf_verbose = 0\n pypoly.log = Log(screen=options.screen, level=conf_verbose, traceback=options.traceback)\n config_file = options.config_file\n if os.environ.get('PYPOLY_ROOT', None):\n os.chdir(os.environ.get('PYPOLY_ROOT'))\n if os.environ.get('PYPOLY_CONFIG', None):\n config_file = os.environ.get('PYPOLY_CONFIG')\n if not os.path.isfile(config_file):\n pypoly.log.error(\"Can't find the config file '%s'\" % config_file)\n sys.exit(1)\n pypoly.log.info('Parsing main-config ... ')\n pypoly.config.update('global', config_file)\n pypoly.log.start()\n pypoly.log.info('Initializing main system ... ')\n pypoly.locale = Locale()\n __builtin__._ = locale\n pypoly.module = ModuleHandler()\n pypoly.plugin = PluginHandler()\n pypoly.tool = ToolHandler()\n pypoly.user = User()\n pypoly.url = URLHandler()\n pypoly.log.info('Loading modules ... ')\n pypoly.module.load(pypoly.config.get('module.modules'))\n pypoly.log.info('Loading plugins ... ')\n pypoly.plugin.load(pypoly.config.get('plugin.plugins'))\n pypoly.log.info('Loading tools ... ')\n pypoly.tool.load(pypoly.config.get('tool.tools'))\n pypoly.log.info('Initializing tools ... ')\n pypoly.tool.init()\n pypoly.log.info('Initializing plugins ... ')\n pypoly.plugin.init()\n pypoly.log.info('Initializing modules ... ')\n pypoly.module.init()\n pypoly.log.info('Loading structure ... ')\n pypoly.structure = StructureHandler()\n pypoly.log.info('Reading config: Tools')\n pypoly.config.update('tools', config_file)\n pypoly.log.info('Reading config: Plugins')\n pypoly.config.update('plugins', config_file)\n pypoly.log.info('Reading config: Modules')\n pypoly.config.update('modules', config_file)\n pypoly.log.info('Starting tools ... ')\n pypoly.tool.start()\n pypoly.log.info('Starting plugins ... ')\n pypoly.plugin.start()\n pypoly.log.info('Starting modules ... ')\n pypoly.module.start()\n pypoly.log.info('Loading templates ... ')\n template_plugin = pypoly.config.get('template.plugin')\n if template_plugin == '':\n pypoly.log.info('No template plugin specified')\n from pypoly.component.plugin import TemplatePlugin\n else:\n TemplatePlugin = pypoly.plugin.get_plugin_by_name('template', pypoly.config.get('template.plugin'))\n if TemplatePlugin == None:\n pypoly.log.error(\"Couldn't find/load the specified template plugin\")\n from pypoly.component.plugin import TemplatePlugin\n pypoly.template = TemplatePlugin(pypoly.config.get('template.templates'))\n auth_plug_name = pypoly.config.get('system.auth')\n pypoly.log.info('Setting Auth-Plugin %s ... ' % auth_plug_name)\n auth_plugin_pkg = pypoly.plugin.get_package_name(auth_plug_name)\n auth_plugin = pypoly.plugin.get_plugin_instance('auth', auth_plugin_pkg)\n if auth_plugin == None:\n pypoly.log.critical('No authentication plugin found, authentication not possible.')\n else:\n pypoly.auth = auth_plugin\n pypoly.log.info('Authentication plugin found and enabled.')\n pypoly.log.info('Loading hooks ...')\n pypoly.hook.register('http.request.serve.pre', 'pypoly.auth', pypoly.http.handler.pypolyauth)\n pypoly.hook.register('http.request.serve.pre', 'pypoly.set_lang', pypoly.http.handler.set_lang)\n pypoly.hook.register('http.request.serve.pre', 'pypoly.set_template', pypoly.http.handler.set_template)\n pypoly.log.info('Starting server ... ')\n server_type = pypoly.config.get('server.type', None)\n if server_type == 'standalone' or server_type == None:\n from wsgiref.simple_server import make_server\n httpd = make_server(pypoly.config.get('server.host'), pypoly.config.get('server.port'), pypoly.http.requesthandler)\n httpd.serve_forever()\n elif server_type == 'standalone_threading':\n from wsgiref.simple_server import WSGIRequestHandler\n from pypoly.http.server import ThreadPoolMixIn\n server = ThreadPoolMixIn((\n pypoly.config.get('server.host'),\n pypoly.config.get('server.port')), WSGIRequestHandler)\n server.set_app(pypoly.http.requesthandler)\n server.serve_forever()\n elif server_type == 'cgi':\n from flup.server.cgi import WSGIServer\n WSGIServer(pypoly.http.requesthandler).run()\n elif server_type == 'fcgi':\n from flup.server.fcgi import WSGIServer\n WSGIServer(pypoly.http.requesthandler).run()\n elif server_type == 'fcgi_standalone':\n pypoly.log.error('Server Type not supported')\n sys.exit(1)\n elif server_type == 'wsgi':\n pypoly.log.info('WSGI Mode')\n else:\n pypoly.log.error('Server Type not supported')\n sys.exit(1)\n return\n\n\ndef get_caller():\n \"\"\"\n Detect who is calling: pypoly, a module, a plugin or a tool.\n\n :since: 0.1\n\n :return: a Caller object\n :rtype: instance of Caller object\n :todo: pypoly v0.2 add more information\n \"\"\"\n try:\n frame = sys._getframe(2)\n except ValueError:\n return 'Error'\n\n frame_name = frame.f_globals['__name__']\n name = frame_name.split('.')\n if (pypoly.module == None or pypoly.plugin == None or pypoly.tool == None) and name[0] == 'pypoly':\n return Caller(caller_type='pypoly', frame=frame)\n else:\n module_pkg_name = pypoly.module.get_root_pkg(frame_name)\n plugin_pkg_name = pypoly.plugin.get_root_pkg(frame_name)\n tool_pkg_name = pypoly.tool.get_root_pkg(frame_name)\n module_pkg_len = len(module_pkg_name)\n plugin_pkg_len = len(plugin_pkg_name)\n tool_pkg_len = len(tool_pkg_name)\n if module_pkg_len > plugin_pkg_len and module_pkg_len > tool_pkg_len:\n return Caller(caller_type='module', name=pypoly.module.get_component_name(module_pkg_name), pkg_root=module_pkg_name, frame=frame)\n if plugin_pkg_len > module_pkg_len and plugin_pkg_len > tool_pkg_len:\n return Caller(caller_type='plugin', name=pypoly.plugin.get_component_name(plugin_pkg_name), pkg_root=plugin_pkg_name, frame=frame)\n if tool_pkg_len > module_pkg_len and tool_pkg_len > plugin_pkg_len:\n return Caller(caller_type='tool', name=pypoly.tool.get_component_name(tool_pkg_name), pkg_root=tool_pkg_name, frame=frame)\n if name[0] == 'pypoly':\n return Caller(caller_type='pypoly', frame=frame)\n return Caller(caller_type='component', name='', pkg_root=name, frame=frame)\n\n\nclass Caller(object):\n \"\"\"\n This is the caller class.\n\n :since: 0.1\n\n :param caller_type: the type of the caller\n :param name: the name of the caller\n :param filename: the filename\n :param linenumber: the line number\n :param function: the function name\n :param frame: the frame object\n \"\"\"\n\n def __init__(self, caller_type, name=None, pkg_root=None, filename='', linenumber=0, function='', frame=None):\n self.type = caller_type\n self.name = name\n self.pkg_root = pkg_root\n if frame != None:\n self.filename = frame.f_code.co_filename\n self.linenumber = frame.f_lineno\n self.function = frame.f_code.co_name\n elif frame == None:\n self.filename = filename\n self.linenumber = linenumber\n self.function = function\n return","sub_path":"pycfiles/pypoly-0.4-py2.6/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":9235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"36060155","text":"import logging\nimport boto3\nfrom greengold import exceptions as ggexc\n\n\nlog = logging.getLogger(\"greengold\")\n\n\nclass AWSClient:\n\n def __init__(self, root_account=None, region=None):\n self.root_account = root_account or \"003521492892\"\n self.region = region or \"us-east-1\"\n self.supported_ami_names = (\"liederbach-base\",)\n\n def source_ami(self, ami_name, platform):\n filters = [{\"Name\": \"tag:approved\", \"Values\": [\"true\"]}]\n if ami_name in self.supported_ami_names:\n filters += [\n {\"Name\": \"name\", \"Values\": [f\"{ami_name}-{platform}-*\"]}\n ]\n else:\n raise ggexc.AWSClientException(f\"{ami_name} is not supported\")\n try:\n client = self.get_client(\"ec2\")\n log.debug(f\"Searching for source AMI {ami_name} on account {self.root_account} with filters: {filters}\")\n response = client.describe_images(\n Filters=filters,\n Owners=[self.root_account]\n )\n ami_id = sorted(response['Images'],\n key=lambda i: i['CreationDate'],\n reverse=True)[0][\"ImageId\"]\n log.info(f\"Discovered source AMI {ami_id}\")\n return ami_id\n except IndexError:\n raise ggexc.AWSClientException(\n f\"No image found for filters {filters} in account {self.root_account}\"\n )\n except Exception as exc:\n raise ggexc.AWSConnectionException(f\"An error occurred calling AWS\") from exc\n\n def get_instance(self, instance_id):\n return self.get_resource(\"ec2\").Instance(instance_id)\n\n def get_image(self, image_id):\n return self.get_resource(\"ec2\").Image(image_id)\n\n def get_client(self, resource, region=None, **kwargs):\n return boto3.client(resource, region_name=region or self.region, **kwargs)\n\n def get_resource(self, resource, region=None, **kwargs):\n return boto3.resource(resource, region_name=region or self.region, **kwargs)\n\n @staticmethod\n def format_tags(tag_dict):\n tags = []\n for key, value in tag_dict.items():\n tags.append(\n {\n \"Key\": key,\n \"Value\": value\n }\n )\n return tags\n\n @staticmethod\n def format_device_block_mapping(block_device_mappings):\n result = []\n for block_device in block_device_mappings:\n result.append(\n {\n \"DeviceName\": block_device[\"device_name\"],\n \"Ebs\": {\n \"DeleteOnTermination\": block_device[\"ebs\"][\"delete_on_termination\"],\n \"VolumeSize\": block_device[\"ebs\"][\"volume_size\"],\n \"VolumeType\": block_device[\"ebs\"][\"volume_type\"],\n \"Encrypted\": block_device[\"ebs\"][\"encrypted\"],\n # \"KmsKeyId\": block_device[\"ebs\"][\"kms_key_id\"],\n }\n }\n )\n return result\n\n @staticmethod\n def format_network_interfaces(network_interfaces):\n result = []\n for interface in network_interfaces:\n result.append(\n {\n \"DeviceIndex\": interface[\"device_index\"],\n \"Groups\": interface[\"groups\"],\n \"SubnetId\": interface[\"subnet_id\"],\n }\n )\n return result\n","sub_path":"greengold/clients/aws.py","file_name":"aws.py","file_ext":"py","file_size_in_byte":3474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"364674610","text":"import random\nfrom random import randrange\nfrom math import floor\nfrom tealight.art import (color, line, spot, circle, box, image, text, background)\nscore=0\ningame=1\ncolor('white')\nbox(0,0,1000,1000)\ncolor(\"black\")\ntext(0, 600,\"Score: \"+str(score))\n \n#this makes the grid\ndef makegrid():\n for j in range(0, 10):\n for i in range(0, 10):\n if((i+j) % 2) !=1:\n color(\"black\")\n else:\n color(\"blue\")\n box(i*60, j*60, 50, 50)\n \n#this gets the information about a box from the corresponding list\ndef get(A, x, y):\n position = (11*(y))+x\n return A[position]\n \n#this sets the information about a box to the corresponding lis\ndef setbox(A, x, y, val):\n position = (11*(y))+x\n A[position] = val\n \n#Determines numbers\ndef getSurroundingMines(x, y):\n surround = 0\n global mine\n for i in range(-1,2):\n for j in range(-1, 2):\n if get(mine, x+i, y+j) ==1:\n surround=surround+1\n color('black')\n if surround>0:\n text(x*60 + 17, y*60 + 20 , surround)\n return surround\n \n#this finds which box is clicked\ndef handle_mousedown(x, y,button):\n \n global score\n boxX = floor(x/60)\n boxY = floor(y/60)\n if boxX<10:\n if boxY<10:\n if ingame == 1:\n if button==\"left\":\n uncover(boxX, boxY)\n if button==\"right\"and get(mine, boxX, boxY)!=2:\n color(\"green\")\n spot(boxX*60 +25 ,boxY*60 + 25,10)\n \ndef findMines():\n global mine\n for i in range(0, 10):\n for j in range(0, 10):\n if get(mine, i, j) == 1:\n color('red')\n box(i*60,j*60,50,50)\n \n \n \ndef uncover(boxX, boxY):\n global score\n global ingame\n global mine\n if get(mine, boxX, boxY)==1:\n color('red')\n box(boxX*60,boxY*60,50,50)\n ingame=0\n color(\"white\")\n box(0,600,500,50)\n color(\"black\")\n text(0, 600,\"Final score: \"+str(score))\n text(500,600, \"You Lost!\")\n findMines()\n if get(mine, boxX, boxY)==0:\n color('white')\n box(boxX*60,boxY*60,50,50)\n setbox(mine,boxX,boxY,2)\n end = getSurroundingMines(boxX,boxY)\n score = getscore()\n color(\"white\")\n box(0,600,500,50)\n color(\"black\")\n text(0, 600,\"Score: \"+str(score))\n if score==85:\n score = 100\n ingame=0\n color(\"white\")\n box(0,600,500,50)\n color(\"black\")\n text(0, 600,\"Final score: \"+str(score))\n text(500,600, \"You WIN!\")\n if end == 0:\n if boxX<9:\n if boxY<9:\n if boxX>0:\n if boxY>0:\n uncover(boxX +1, boxY)\n uncover(boxX -1, boxY)\n uncover(boxX, boxY +1)\n uncover(boxX, boxY -1)\n if boxX<9:\n if boxY==9:\n if boxX>0:\n uncover(boxX +1, boxY)\n uncover(boxX -1, boxY)\n uncover(boxX, boxY -1)\n if boxX<9:\n if boxY==0:\n if boxX>0:\n uncover(boxX +1, boxY)\n uncover(boxX -1, boxY)\n uncover(boxX, boxY +1)\n if boxY<9:\n if boxX==9:\n if boxY>0:\n uncover(boxX +1, boxY)\n uncover(boxX -1, boxY)\n uncover(boxX, boxY -1)\n if boxY<9:\n if boxX==0:\n if boxY>0:\n uncover(boxX, boxY +1)\n uncover(boxX, boxY -1)\n uncover(boxX +1, boxY)\n if boxX == 0:\n if boxY == 9:\n uncover(boxX, boxY -1)\n uncover(boxX +1, boxY)\n if boxX == 9:\n if boxY == 0:\n uncover(boxX, boxY +1)\n uncover(boxX -1, boxY)\n if boxX == 0:\n if boxY == 0:\n uncover(boxX, boxY +1)\n uncover(boxX +1, boxY)\n if boxX == 9:\n if boxY == 9:\n uncover(boxX, boxY -1)\n uncover(boxX -1, boxY)\n \n \ncolor('black')\n \n#this is where the program starts\ndef getscore():\n score = 0\n for i in range(0, 10):\n for j in range(0, 10):\n if get(mine, i, j) == 2:\n score = score + 1\n return score\n \nmakegrid()\nprint(\"Welcome to Minesweeper!\\nWritten by Leo, Callum and Nathan\")\nmine = []\nfor i in range(0, 121):\n mine.append(0)\nfor i in range(1, 12):\n mine[(11*i)-1]=2\n mine[109+i]=2\n \nfor i in range(0,15):\n b=0\n while b==0:\n x=randrange(0,10,1)\n y=randrange(0,10,1)\n if get(mine, x, y) == 0:\n setbox(mine,x,y,1)\n b=1","sub_path":"art/Sweepinggrid.py","file_name":"Sweepinggrid.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"527364323","text":"#Remove target element from array(easy)\n#Leetcode https://leetcode.com/problems/remove-element/\n#walk down (in place)\n#similar to remove_duplicate\n\n#notice, result start at -1, to check the 0 element\n\n\nclass Solution(object):\n def removeElement(self, nums, val):\n \"\"\"\n :type nums: List[int]\n :type val: int\n :rtype: int\n \"\"\"\n \n if not nums: return 0 \n \n result = -1\n for check in range(len(nums)):\n if nums[check]!=val:\n result+=1\n nums[result] = nums[check]\n return result+1","sub_path":"remove_element.py","file_name":"remove_element.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"349651935","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 9 15:54:09 2021\n\n@author: alex\n\"\"\"\n\n# %% import data\nimport pandas as pd\nimport numpy as np\nimport os\n\n\ndef import_data(logpath=\"\",small_test_dataset=True):\n\n raw_data=pd.read_csv(log_path)\n \n print(\"PROCESSING DATA...\")\n \n prep_data=raw_data.drop(columns=[i for i in raw_data.keys() if ((\"forces\" in i ) or ('pos' in i) or (\"joy\" in i)) ])\n prep_data=prep_data.drop(columns=[i for i in raw_data.keys() if ((\"level\" in i ) or ('Unnamed' in i) or (\"index\" in i)) ])\n \n for i in range(3):\n prep_data['speed_pred[%i]'%(i)]=np.r_[prep_data['speed[%i]'%(i)].values[1:len(prep_data)],0]\n \n \n prep_data['dt']=np.r_[prep_data['t'].values[1:]-prep_data['t'].values[:-1],0]\n prep_data['t']-=prep_data['t'][0]\n prep_data=prep_data.drop(index=[0,len(prep_data)-1])\n prep_data=prep_data.reset_index()\n \n data_prepared=prep_data[:len(prep_data)//50] if small_test_dataset else prep_data\n for k in data_prepared.keys():\n if \"speed\" in k:\n data_prepared[k]/=25.0\n if 'acc' in k:\n data_prepared[k]/=20.0\n if 'PWM'in k: \n data_prepared[k]=(data_prepared[k]-1500)/1000 \n \n return data_prepared\n\ndef plot_learning_curves(ax,hist):\n loss,val_loss=history.history[\"loss\"], history.history[\"val_loss\"]\n ax.plot(np.arange(len(loss)) + 0.5, loss, \"b.-\", label=\"Training loss\")\n ax.plot(np.arange(len(val_loss)) + 1, val_loss, \"r.-\", label=\"Validation loss\")\n ax.legend(fontsize=14)\n ax.grid(True)\n \n# %% SIMPLE feedforward model: ACC\n # %%% preprocess data\n\nlog_path=os.path.join('./logs/avion/vol123/log_real_processed.csv') \ndata_prepared=import_data(log_path,small_test_dataset=False)\n\n\nX_train_full=data_prepared[['speed[0]',\n 'speed[1]', 'speed[2]', 'q[0]', 'q[1]', 'q[2]', 'q[3]', 'PWM_motor[1]',\n 'PWM_motor[2]', 'PWM_motor[3]', 'PWM_motor[4]', 'PWM_motor[5]',\n 'PWM_motor[6]']]\n\nY_train_full=data_prepared[['acc[0]','acc[1]','acc[2]']]\n\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X_train_full, Y_train_full, test_size=0.33, random_state=42)\n\n # %%% feedforward model\nimport tensorflow as tf\nfrom tensorflow import keras \n\ncopter_model=tf.keras.Sequential([keras.layers.Dense(13,activation=\"relu\"),\n keras.layers.Dropout(rate=0.05),\n keras.layers.Dense(13,activation=\"relu\"),\n keras.layers.Dropout(rate=0.05),\n keras.layers.Dense(13),\n keras.layers.Dropout(rate=0.05),\n keras.layers.Dense(7),\n keras.layers.Dropout(rate=0.05),\n keras.layers.Dense(3,activation=\"tanh\")])\n\n\nplane_model.compile(loss=\"mean_squared_error\",\n optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n metrics=[tf.keras.metrics.MeanSquaredError()])\n\n\n\nhistory = plane_model.fit(X_train, y_train, epochs=20,validation_data=(X_test,y_test)) \n\n # %%% pred and plot\n\nacc_pred=plane_model.predict(X_train_full) \n\n\n\nimport matplotlib.pyplot as plt\n\nplt.figure()\nfor i in range(3):\n \n ax=plt.gcf().add_subplot(3,2,2*i+1)\n ax.plot(data_prepared['t'],data_prepared['acc[%i]'%(i)],color=\"black\",label=\"data\")\n ax.plot(data_prepared['t'],data_prepared['acc_ned_grad[%i]'%(i)],color=\"blue\",label=\"data\",alpha=0.5)\n\n ax.plot(data_prepared['t'][np.arange(len(acc_pred))],acc_pred[:,i],color=\"red\",label=\"pred\")\n plt.grid()\n \nax=plt.gcf().add_subplot(1,2,2)\nplot_learning_curves(ax,history)\n\n# # %% SIMPLE feedforward model: SPEED\n# # %%% preprocess data\n\n# log_path=os.path.join('./logs/avion/vol1/log_real_processed.csv') \n# data_prepared=import_data(log_path,small_test_dataset=False)\n\n\n# X_train_full=data_prepared[['speed[0]',\n# 'speed[1]', 'speed[2]', 'q[0]', 'q[1]', 'q[2]', 'q[3]', 'PWM_motor[1]',\n# 'PWM_motor[2]', 'PWM_motor[3]', 'PWM_motor[4]', 'PWM_motor[5]',\n# 'PWM_motor[6]']][:-1]\n\n# Y_train_full=data_prepared[['speed[0]','speed[1]','speed[2]']][1:]\n\n# from sklearn.model_selection import train_test_split\n\n# X_train, X_test, y_train, y_test = train_test_split(X_train_full, Y_train_full, test_size=0.33, random_state=42)\n\n# # %%% feedforwad model\n# import tensorflow as tf\n# from tensorflow import keras \n\n# copter_model=tf.keras.Sequential([keras.layers.Dense(13,activation=\"tanh\"),\n# keras.layers.Dropout(rate=0.05),\n# keras.layers.Dense(7,activation=\"tanh\"),\n# keras.layers.Dropout(rate=0.05),\n# keras.layers.Dense(5,activation=\"tanh\"),\n# keras.layers.Dropout(rate=0.05),\n# keras.layers.Dense(3,activation=\"tanh\")])\n\n# copter_model.compile(loss=\"mean_squared_error\",\n# optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),\n# metrics=[tf.keras.metrics.MeanSquaredError()])\n\n# history = copter_model.fit(X_train, y_train, \n# epochs=15,\n# validation_data=(X_test,y_test)) \n\n\n# # %%% pred plot\n\n\n# v_pred_no_iter=copter_model.predict(X_train_full)\n\n\n# v_pred=[np.array([data_prepared['speed[%i]'%(i)][0] for i in range(3)])]\n \n# for i in X_train_full.index:\n# print(\"\\r Pred on batch %i / %i \"%(i,max(X_train_full.index)), end='', flush=True)\n\n# x=X_train_full.loc[i]\n \n# for j in range(3):\n# x['speed[%i]'%(j)]=v_pred[-1][j]\n\n\n\n# new_v=copter_model.predict(x.values.reshape(-1,13))\n# v_pred.append(new_v.reshape(3,-1))\n\n# v_pred[0]=v_pred[0].reshape(3,-1) \n# v_pred_stack=np.array(v_pred).reshape(-1,3) \n\n# import matplotlib.pyplot as plt\n\n# plt.figure()\n# for i in range(3):\n \n# ax=plt.gcf().add_subplot(3,1,i+1)\n# ax.plot(data_prepared['t'],data_prepared['speed[%i]'%(i)],color=\"black\",label=\"data\")\n\n# ax.plot(data_prepared['t'][np.arange(len(v_pred_stack))],v_pred_stack[:,i],color=\"red\",label=\"pred\")\n# ax.plot(data_prepared['t'][np.arange(len(v_pred_no_iter))],v_pred_no_iter[:,i],color=\"green\",label=\"pred no iter\")\n\n# plt.grid()\n# # %% speed pred recurrent\n# # %%% preprocess data\n\n# log_path=os.path.join('./logs/avion/vol1/log_real_processed.csv') \n# data_prepared=import_data(log_path,small_test_dataset=False)\n\n# n_steps=10\n\n# X=data_prepared[['speed[0]',\n# 'speed[1]', 'speed[2]', 'q[0]', 'q[1]', 'q[2]', 'q[3]', 'PWM_motor[1]',\n# 'PWM_motor[2]', 'PWM_motor[3]', 'PWM_motor[4]', 'PWM_motor[5]',\n# 'PWM_motor[6]']][:-n_steps].values\n\n# # X=X.reshape(X.shape[0],1,1,X.shape[1])\n# X=X.reshape(X.shape[0],1,X.shape[1])\n\n# Y=np.array([data_prepared[['speed[0]','speed[1]','speed[2]']][:n_steps].values])\n \n# for i in range(1,len(X)):\n# Y=np.concatenate((Y,[data_prepared[['speed[0]','speed[1]','speed[2]']][i:i+n_steps].values]),axis=0)\n\n# # %%% recurrent model\n\n\n\n# from sklearn.model_selection import train_test_split\n\n# X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n\n# class CustomNet(keras.models.Model):\n# def __init__(self, init_speed=np.zeros(3), **kwargs):\n# super().__init__(**kwargs)\n# self.innermodel=tf.keras.Sequential([keras.layers.Dense(13,activation=\"tanh\"),\n# keras.layers.Dropout(rate=0.05),\n# keras.layers.Dense(7,activation=\"tanh\"),\n# keras.layers.Dropout(rate=0.05),\n# keras.layers.Dense(5,activation=\"tanh\"),\n# keras.layers.Dropout(rate=0.05),\n# keras.layers.Dense(3,activation=\"tanh\")])\n\n# def call(self, inputs):\n \n# first_pred=self.innermodel(inputs)\n \n# for _ in range(1,len(model)):\n# Z = self.block1(Z)\n# Z = self.block2(Z)\n# return self.out(Z)\n\n# rnn_model=keras.models.Sequential([\n# keras.layers.SimpleRNN(3, return_sequences=True, input_shape=[ 1,13]),\n# # keras.layers.SimpleRNN(3, return_sequences=True,input_shape=[20])\n# keras.layers.SimpleRNN(3, return_sequences=True)\n# ])\n\n\n# rnn_model.compile(loss=\"mse\",\n# optimizer=tf.keras.optimizers.Adam(learning_rate=0.001))\n\n\n# # import datetime\n\n# # log_dir = \"./tfres/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n# # tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\n# train_ds= tf.data.Dataset.from_tensor_slices((X_train,y_train))\n# test_ds= tf.data.Dataset.from_tensor_slices((X_test,y_test))\n\n# history = rnn_model.fit(X_train, y_train,\n# epochs=1) \n# print(rnn_model.summary())\n# # %%% pred and plot\n# speed_pred=rnn_model.predict(X[0].reshape(-1,1,13)) \n# print(speed_pred)\n\n\n# speed_pred=np.concatenate((Y[:10],speed_pred),axis=1)\n\n# import matplotlib.pyplot as plt\n\n# plt.figure()\n# for i in range(3):\n \n# ax=plt.gcf().add_subplot(3,1,i+1)\n# ax.plot(data_prepared['t'],data_prepared['speed[%i]'%(i)],color=\"black\",label=\"data\")\n\n# # ax.plot(data_prepared['t'][np.arange(len(v_pred_stack))],v_pred_stack[:,i],color=\"red\",label=\"pred\")\n# ax.plot(data_prepared['t'][np.arange(len(speed_pred))],speed_pred[:,i],color=\"green\",label=\"pred no iter\")\n\n# plt.grid()\n\n\n\n\n\n\n\n\n","sub_path":"x_test_RNN_avion.py","file_name":"x_test_RNN_avion.py","file_ext":"py","file_size_in_byte":9150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"416258379","text":"#! /bin/python\n\nimport os\nimport sys\nimport json\nimport pickle\n\nimport numpy as np\nimport luigi\n\nimport cluster_tools.utils.volume_utils as vu\nimport cluster_tools.utils.function_utils as fu\nfrom cluster_tools.cluster_tasks import SlurmTask, LocalTask, LSFTask\n\n\n#\n# Summarizes features from regions and edges to create edge features\n#\n\n# TODO implement graph extraction with ignore label 0\nclass R2EFeaturesBase(luigi.Task):\n \"\"\" R2EFeaturesBase base class\n \"\"\"\n\n task_name = 'r2e_features'\n src_file = os.path.abspath(__file__)\n allow_retry = False\n\n # input volumes and graph\n graph_path = luigi.Parameter()\n graph_key = luigi.Parameter()\n region_feature_paths = luigi.ListParameter()\n region_feature_keys = luigi.ListParameter()\n edge_feature_paths = luigi.ListParameter(default=None)\n edge_feature_keys = luigi.ListParameter(default=None)\n output_path = luigi.Parameter()\n output_key = luigi.Parameter()\n dependency = luigi.TaskParameter()\n\n def requires(self):\n return self.dependency\n\n @staticmethod\n def default_task_config():\n # we use this to get also get the common default config\n config = LocalTask.default_task_config()\n return config\n\n def run_impl(self):\n # get the global config and init configs\n shebang, block_shape, roi_begin, roi_end = self.global_config_values()\n self.init(shebang)\n\n # load the task config\n config = self.get_task_config()\n\n # NOTE we have to turn the luigi dict parameters into normal python dicts\n # in order to json serialize them\n config.update({'graph_path': self.graph_path,\n 'graph_key': self.graph_key,\n 'region_feature_paths': self.region_feature_paths,\n 'region_feature_keys': self.region_feature_keys,\n 'edge_feature_paths': self.edge_feature_paths,\n 'edge_feature_keys': self.edge_feature_keys,\n 'output_path': self.output_path,\n 'output_key': self.output_key})\n\n # prime and run the jobs\n self.prepare_jobs(1, None, config)\n self.submit_jobs(1)\n\n # wait till jobs finish and check for job success\n self.wait_for_jobs()\n self.check_jobs(1)\n\n\nclass R2EFeaturesLocal(R2EFeaturesBase, LocalTask):\n \"\"\" R2EFeatures on local machine\n \"\"\"\n pass\n\n\nclass R2EFeaturesSlurm(R2EFeaturesBase, SlurmTask):\n \"\"\" R2EFeatures on slurm cluster\n \"\"\"\n pass\n\n\nclass R2EFeaturesLSF(R2EFeaturesBase, LSFTask):\n \"\"\" R2EFeatures on lsf cluster\n \"\"\"\n pass\n\n\n#\n# Implementation\n#\n\n\ndef r2f_features(job_id, config_path):\n\n fu.log(\"start processing job %i\" % job_id)\n fu.log(\"reading config from %s\" % config_path)\n\n with open(config_path, 'r') as f:\n config = json.load(f)\n \n graph_path = config['graph_path']\n graph_key = config['graph_key']\n region_feature_paths = config['region_feature_paths']\n region_feature_keys = config['region_feature_keys']\n edge_feature_paths = config['edge_feature_paths']\n edge_feature_keys = config['edge_feature_keys']\n output_path = config['output_path']\n output_key = config['output_key']\n\n # load the uv ids and check\n with vu.file_reader(graph_path, 'r') as f:\n uv_ids = f[graph_key]['edges'][:].astype('int32')\n node_ids = f[graph_key]['nodes'][:]\n\n num_nodes = len(node_ids)\n num_edges = uv_ids.shape[0]\n \n fu.log(\"reading edge features from %s%s\" % (edge_feature_paths,edge_feature_keys))\n\n \n num_total_features = 0\n\n feature_colnames = []\n\n count = None\n edge_features_list = []\n if len(edge_feature_paths)>0:\n num_total_features = 1\n for ef_set_i in range(len(edge_feature_paths)):\n edge_feature_path = edge_feature_paths[ef_set_i]\n edge_feature_key = edge_feature_keys[ef_set_i]\n with vu.file_reader(edge_feature_path, 'r') as f:\n edge_features = f[edge_feature_key][:]\n edge_features_list.append(edge_features)\n num_edge_features = edge_features.shape[1]-1\n num_total_features += num_edge_features\n feature_colnames = feature_colnames + (['EF'] * (num_edge_features))\n count = edge_features[:,-1]\n\n region_features = []\n \n for rf_set_i in range(len(region_feature_paths)):\n region_feature_path = region_feature_paths[rf_set_i]\n region_feature_key = region_feature_keys[rf_set_i]\n fu.log(\"reading edge features from %s%s\" % (region_feature_path,region_feature_key))\n with vu.file_reader(region_feature_path, 'r') as f:\n ds_in = f[region_feature_key]\n num_region_features = ds_in.attrs['num_features']\n input_feature_list = ds_in.attrs['feature_indices']\n feature_colnames = feature_colnames + (input_feature_list * 3)\n region_features_vals = ds_in[:].reshape(-1,num_region_features)\n num_nodes = region_features_vals.shape[0]\n \n lu = uv_ids[:, 0]\n lv = uv_ids[:, 1]\n \n rf_u = region_features_vals[lu]\n rf_v = region_features_vals[lv]\n \n region_features.append(np.minimum(rf_u, rf_v))\n region_features.append(np.maximum(rf_u, rf_v))\n region_features.append(np.abs(rf_u-rf_v))\n \n num_total_features += num_region_features * 3\n \n if len(edge_feature_paths)>0:\n feature_colnames.append('EF')\n fu.log(\"writing output to %s:%s\" % (output_path, output_key))\n # require the output dataset\n with vu.file_reader(output_path) as f:\n ds = f.require_dataset(output_key, dtype='float32', shape=(num_edges, num_total_features), compression='gzip')\n\n total_i=0\n if len(edge_feature_paths)>0:\n for edge_features in edge_features_list:\n # Edge features\n # Note that edge size must remain last feature, so we remove it for now and add it later\n num_edge_features = edge_features.shape[1]-1\n print(\"Adding edge features at \", total_i, \" namely \", num_edge_features)\n ds[:,total_i:(total_i + num_edge_features)] = edge_features[:,:-1]\n total_i += num_edge_features\n \n print(\"Region features start at \", total_i)\n # Region features\n for rf in region_features:\n num_region_features = rf.shape[1]\n print(\"Adding region features at \", total_i, \" namely \", num_region_features)\n ds[:,total_i:(total_i+num_region_features)] = rf\n total_i+=num_region_features\n\n\n print(\"Now at \", total_i,\" from \", ds.shape[1])\n if not count is None:\n assert total_i == (ds.shape[1]-1)\n # Now write edge size\n ds[:,-1] = count\n else:\n assert total_i == (ds.shape[1])\n\n ds.attrs['feature_colnames'] = feature_colnames\n\n fu.log_job_success(job_id)\n\n\nif __name__ == '__main__':\n path = sys.argv[1]\n assert os.path.exists(path), path\n job_id = int(os.path.split(path)[1].split('.')[0].split('_')[-1])\n r2f_features(job_id, path)\n","sub_path":"cluster_tools/region_to_edge_features/transform_features.py","file_name":"transform_features.py","file_ext":"py","file_size_in_byte":7284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"148877713","text":"from ..libs import *\nfrom .base import Widget\n\n\nclass TogaTabViewDelegate(NSObject):\n @objc_method\n def tabView_didSelectTabViewItem_(self, view, item) -> None:\n pass\n # print (\"Select tab view item\")\n\n\nclass OptionContainer(Widget):\n def __init__(self, style=None):\n super(OptionContainer, self).__init__(style=None)\n self.is_container = True\n self._content = []\n\n self.startup()\n\n def startup(self):\n self._impl = NSTabView.alloc().init()\n\n # Disable all autolayout functionality\n self._impl.setTranslatesAutoresizingMaskIntoConstraints_(False)\n self._impl.setAutoresizesSubviews_(False)\n\n self._delegate = TogaTabViewDelegate.alloc().init()\n self._delegate.__dict__['interface'] = self\n\n self._impl.setDelegate_(self._delegate)\n\n def add(self, label, container):\n self._content.append((label, container))\n container.window = self.window\n\n item = NSTabViewItem.alloc().initWithIdentifier_('%s-Tab-%s' % (id(self), id(container)))\n item.setLabel_(label)\n container.app = self.app\n\n item.setView_(container._impl)\n\n self._impl.addTabViewItem_(item)\n\n # Make the content autoresize to the option container item frame\n container._impl.setTranslatesAutoresizingMaskIntoConstraints_(True)\n\n def _update_child_layout(self, **style):\n \"\"\"Force a layout update on the children of this widget.\n\n The update request can be accompanied by additional style information\n (probably min_width, min_height, width or height) to control the\n layout.\n \"\"\"\n for label, content in self._content:\n frame = self._impl.contentRect\n content._update_layout(\n left=frame.origin.x,\n top=frame.origin.y,\n width=frame.size.width,\n height=frame.size.height\n )\n","sub_path":"toga_cocoa/widgets/optioncontainer.py","file_name":"optioncontainer.py","file_ext":"py","file_size_in_byte":1937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"365458610","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2017/12/26 14:10\n# @Author : Qiwei.Ren\n# @Site : \n# @File : getfile_num.py\n# @Software: PyCharm\n\"\"\"\n1.获取某模块下载的excel文档\n2.识别过滤其他模块文档\n3.自动识别最新的下载文档\n4.不识别文档内容\n\"\"\"\nimport re,time,os,datetime,os.path,sys,xlrd,win32process, win32event\n\"\"\"实现最新月份,最新年份识别\"\"\"\ndef newestdate():\n date={}\n nowTime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') # 现在\n pastTime = (datetime.datetime.now() - datetime.timedelta(hours=1)).strftime('%Y-%m-%d %H:%M:%S') # 过去一小时时间\n afterTomorrowTime = (datetime.datetime.now() + datetime.timedelta(days=2)).strftime('%Y-%m-%d %H:%M:%S') # 后天\n tomorrowTime = (datetime.datetime.now() + datetime.timedelta(days=1)).strftime('%Y-%m-%d %H:%M:%S') # 明天\n # print('\\n', nowTime, '\\n', pastTime, '\\n', afterTomorrowTime, '\\n', tomorrowTime)\n\n nowmonth=nowTime[5:7]\n nowyear=nowTime[:4]\n date['year'] =int(nowyear)\n date['month']=(nowmonth.zfill(1))\n print(date)\n return date\nif __name__ == '__main__':\n newestdate()\n\n\"\"\"准确识别下载的最新模板或文档\"\"\"\ndef filedata_time_level01(path,title_len,name_title):\n try:\n list=[]\n for root,dirs,files in os.walk(path):\n for file in files:\n if file.__len__()==title_len:\n if file[0:11]==name_title:\n list.append(file)\n else:\n continue\n print(list.__len__())\n print(list)\n return list[list.__len__()-1]\n except IndexError as e:\n return False\n print(e)\n# filedata_time_level01(name_title=\"长途区号2018\",title_len=29,path=r\"C:\\Users\\renqiwei\\Downloads\")\n\n\"\"\"\n所用用例位置：优化了基础数据识别问题\n函数名：filedata_time_level01\n 1.获取某模块下载的excel文档\n 2.识别过滤其他模块文档\n 3.自动识别最新的下载文档\n\"\"\"\n# def filedata_time_level01(path,title_len,name_title):\n# list=[]\n# # print(title_len, name_title)\n# for root,dirs,files in os.walk(path):\n# for file in files:\n#\n# if file.__len__()==title_len:\n# if file[0:11]==name_title:\n# # print(file)\n# list.append(file)\n# # print(list)\n# else:\n# pass\n# # print(list.__len__())\n# # print(list)\n# return list[list.__len__()-1]\n# # filedata_time_level01(name_title=\"长途区号2018\",title_len=29,path=r\"C:\\Users\\renqiwei\\Downloads\")\n\n\"\"\"\ncallexe\n#传参调用exe程序（解决相对路径，绝对路径问题），等待exe进程结束，此程序才结束。\n# 调用方式 callexe(exe_file=\"import_Allzero.exe\", exe_path=r\"C:Usersrenqiwei\\Desktop\\study\\exefile\")\n#需要用的模块：pywin32-214.win32-py2.5.exe\n# 把该程序做成exe程序，就可以任何地方调用了（windows系统下）。\n\"\"\"\n# exe_path = sys.argv[1]\n# exe_file = sys.argv[2]\ndef callexe(exe_path,exe_file):\n # exe_path =r\"C:\\Users\\renqiwei\\Desktop\\study\\exefile\"\n # exe_file =\"import.exe\"\n os.chdir(exe_path)\n try:\n handle = win32process.CreateProcess(\n os.path.join(exe_path, exe_file),\n '',\n None,\n None,\n 0,\n win32process.CREATE_NO_WINDOW,\n None ,\n exe_path,\n win32process.STARTUPINFO()\n )\n running = True\n except Exception :\n print (\"Create Error!\")\n handle = None\n running = False\n\n while running :\n rc = win32event.WaitForSingleObject(handle[0], 1000)\n if rc == win32event.WAIT_OBJECT_0:\n running = False\n #end while\n # print (\"GoodBye\")\n\ndef readExcel(self,path_file):\n workbook = xlrd.open_workbook(path_file)\n worksheets = workbook.sheet_names() #抓取所有sheet页的名称\n print('worksheets is %s' %worksheets)\n worksheet1 = workbook.sheet_by_name(u'灰名单号码') #定位到sheet1\n \"\"\"\n #通过索引顺序获取\n worksheet1 = workbook.sheets()[0]\n #或\n worksheet1 = workbook.sheet_by_index(0)\n \"\"\"\n for worksheet_name in worksheets: #遍历所有sheet对象\n worksheet = workbook.sheet_by_name(worksheet_name)\n print(worksheet)\n num_rows = worksheet1.nrows #遍历sheet1中所有行row\n for curr_row in range(num_rows):\n if curr_row >0:\n row = worksheet1.row_values(curr_row)\n print('%s %s' %(curr_row,row))\n return row\n\"\"\"\n函数名filedata_time_level01的低配版，识别某路径的下载文档\n\"\"\"\ndef filedata(self,num):\n c=r\"C:\\Users\\renqiwei\\Downloads\"\n self.num =0\n list=[]\n for root,dirs,files in os.walk(c):\n for file in files:\n os.path.join(root,file).encode('utf-8');\n if file.__len__()<23:\n pass\n else:\n list.append(file)\n num+=1\n return file\n\n\"\"\"\nfiledata_time_new的低级版本\n\"\"\"\ndef filedata_list(self,num):\n c=r\"C:\\Users\\renqiwei\\Downloads\"\n self.num =0\n list=[]\n for root,dirs,files in os.walk(c):\n for file in files:\n os.path.join(root,file).encode('utf-8');\n if file.__len__() < 20:\n pass\n else:\n list.append(file)\n num+=1\n return list\n\n\n\"\"\"\n当不是该文档时，因文档名长度不一致，在提取文档名时提取失败 时长发生，\n该函数方法繁冗识别率低\n该函数在处置任务中使用，待下一版本剔除\n\"\"\"\ndef filedata_time_new(self,num,c=r\"C:\\Users\\renqiwei\\Downloads\"):\n self.num =0\n file_max=\"\"\n for root,dirs,files in os.walk(c):\n for file in files:\n file_old=file\n year=file_old[5:9]\n month=file_old[10:12]\n day=file_old[13:15]\n hour =file_old[16:18]\n minute = file_old[19:21]\n s =file_old[22:24]\n print(year+month+day+hour+minute+s)\n file_old_spilt= year+month+day+hour+minute+s\n\n os.path.join(root,file).encode('utf-8');\n print(file[7:-5])\n file_new =file\n year=file_new[5:9]\n month=file_new[10:12]\n day=file_new[13:15]\n hour =file_new[16:18]\n minute = file_new[19:21]\n s =file_new[22:24]\n print(year+month+day+hour+minute+s)\n file_new_spilt= year+month+day+hour+minute+s\n print(type(file_new_spilt),type(file_old_spilt))\n\n if len(file_new_spilt)==14 and len(file_old_spilt)==14: #过滤长度,\n # 尝试try int(file_new_spilt) == int(file_old_spilt) #筛选可以转换为int类型的数据\n assert len(file_new_spilt)== len(file_old_spilt)\n if file_new_spilt>=file_old_spilt:\n file_max = file_new\n else:\n file_old =file_max\n num+=1\n else:\n continue\n file1=file_max\n print(file1)\n return file1\n\n\"\"\"\n获得该路径下的文档个数，匹配下载数量是否是一个，目前因为本地与系统时间无法匹配，暂时无法匹配文档名\n\"\"\"\ndef nu(self,num,path):\n self.num =0\n for root,dirs,files in os.walk(path):\n for file in files:\n os.path.join(root,file).encode('utf-8');\n num+=1\n fact_num = num\n return fact_num\n","sub_path":"common/getfile_data_time_levelup.py","file_name":"getfile_data_time_levelup.py","file_ext":"py","file_size_in_byte":7670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"18089823","text":"#Autor: Ivana Olvera Mérida\r\n#Escribe un programa que lea las dimensiones (base y altura) de dos rectángulos y que calcule e imprima\r\n#el perímetro y área de cada uno.\r\n\r\ndef calcularPerimetro (bR2, aR2):\r\n perimetroCalculado2 = 2*bR2 + 2*aR2\r\n return perimetroCalculado2\r\n\r\ndef calcularPerimetro (bR1, aR1):\r\n perimetroCalculado1 = 2*bR1 + 2*aR1\r\n return perimetroCalculado1\r\n\r\ndef calcularArea2 (bR2, aR2):\r\n areaCalculada2 = bR2*aR2\r\n return areaCalculada2\r\n\r\ndef calcularArea1 (bR1, aR1):\r\n areaCalculada1 = bR1*aR1\r\n return areaCalculada1\r\n\r\n\r\ndef determinarMayorMenorArea(area1, area2):\r\n if area1>area2:\r\n return (\"El rectángulo 1 es mayor\")\r\n elif area1dy:\n nn = v1-v2\n else:\n nn = v3-v4\n\n nn=nn/np.linalg.norm(nn)\n n = np.dot(rotFromRPY(0,0,pi/2),nn)\n return [n,nn]\n\n\n","sub_path":"mpp/pathplanner/connectorComputeNormal.py","file_name":"connectorComputeNormal.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"375058853","text":"import rospy\nfrom std_msgs.msg import Header\nfrom geometry_msgs.msg import PoseStamped, Pose, Point, Quaternion\nfrom nav_msgs.msg import Odometry\n\n# import tf\nfrom tf import TransformListener\nfrom tf.transformations import euler_from_quaternion, rotation_matrix, quaternion_from_matrix, quaternion_from_euler\n\nimport math\n\n\nimport numpy as np\n\n\n\n#A class of helper functions blatantly stolen from Paul Ruvolo, our hero and savior \n\n\ndef angle_normalize(z):\n \"\"\" convenience function to map an angle to the range [-pi,pi] \n \"\"\"\n return math.atan2(math.sin(z), math.cos(z))\n\ndef angle_diff(a, b):\n \"\"\" Calculates the difference between angle a and angle b (both should be in radians)\n the difference is always based on the closest rotation from angle a to angle b\n examples:\n angle_diff(.1,.2) -> -.1\n angle_diff(.1, 2*math.pi - .1) -> .2\n angle_diff(.1, .2+2*math.pi) -> -.1\n \"\"\"\n a = angle_normalize(a)\n b = angle_normalize(b)\n d1 = a-b\n d2 = 2*math.pi - math.fabs(d1)\n if d1 > 0:\n d2 *= -1.0\n if math.fabs(d1) < math.fabs(d2):\n return d1\n else:\n return d2\n\ndef convert_pose_to_xy_and_theta(pose):\n \"\"\" pose: geometry_msgs.Pose object\n returns tuple of form (x, y, yaw)\n \"\"\"\n orientation_tuple = (pose.pose.orientation.x,\n pose.pose.orientation.y,\n pose.pose.orientation.z,\n pose.pose.orientation.w)\n angles = euler_from_quaternion(orientation_tuple)\n return pose.pose.position.x, pose.pose.position.y, angles[2]\n\ndef fix_map_to_odom_transform(self, stamp, coord, orient, listener, broadcaster):\n \"\"\" This method constantly updates the offset of the map and \n odometry coordinate systems based on the latest results from\n the localizer \n \"\"\"\n translation = (coord[0], coord[1], 0)\n if orient == 1:\n rotation = orient*math.pi/2 - math.pi/2 #1 => 0\n elif orient == 3:\n rotation = orient*math.pi/2 - math.pi/2 #2 => pi\n elif orient == 0:\n rotation = orient*math.pi/2 + math.pi/2 #0 => pi/2\n elif orient == 2:\n rotation = orient*math.pi/2 + math.pi/2 #2 => 3pi/2\n rotation = quaternion_from_euler(0, 0, rotation)\n robot_pose = convert_translation_rotation_to_pose(translation, rotation)\n\n (translation, rotation) = convert_pose_inverse_transform(robot_pose)\n pose = convert_translation_rotation_to_pose(translation,rotation)\n p = PoseStamped(pose=pose,\n header=Header(stamp=stamp,frame_id=\"base_link\"))\n listener.waitForTransform(\"/base_link\", \"/odom\", stamp, rospy.Duration(0.5))\n odom_to_map = listener.transformPose(\"odom\", p)\n translation, rotation = convert_pose_inverse_transform(odom_to_map.pose)\n broadcaster.sendTransform(translation,\n rotation,\n rospy.get_rostime(),\n \"odom\",\n \"maze_scan\")\n\ndef convert_translation_rotation_to_pose(translation, rotation):\n \"\"\" Convert from representation of a pose as translation and rotation (Quaternion) tuples to a geometry_msgs/Pose message \"\"\"\n return Pose(position=Point(x=translation[0],y=translation[1],z=translation[2]), orientation=Quaternion(x=rotation[0],y=rotation[1],z=rotation[2],w=rotation[3]))\n\ndef convert_pose_inverse_transform(pose):\n \"\"\" Helper method to invert a transform (this is built into the tf C++ classes, but ommitted from Python) \"\"\"\n translation = np.zeros((4,1))\n translation[0] = -pose.position.x\n translation[1] = -pose.position.y\n translation[2] = -pose.position.z\n translation[3] = 1.0\n\n rotation = (pose.orientation.x, pose.orientation.y, pose.orientation.z, pose.orientation.w)\n euler_angle = euler_from_quaternion(rotation)\n rotation = np.transpose(rotation_matrix(euler_angle[2], [0,0,1])) # the angle is a yaw\n transformed_translation = rotation.dot(translation)\n\n translation = (transformed_translation[0], transformed_translation[1], transformed_translation[2])\n rotation = quaternion_from_matrix(rotation)\n return (translation, rotation)","sub_path":"scripts/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":4129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"376234775","text":"import sys\r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\r\nimport random\r\nimport time\r\nimport pyautogui\r\nimport keyboard\r\nimport win32api, win32con\r\nfrom configparser import ConfigParser\r\nimport tensorflow.keras\r\nfrom PIL import Image, ImageOps, ImageGrab\r\nimport numpy as np\r\nimport cv2\r\npyautogui.FAILSAFE = False\r\n\r\n#creating RouteData class\r\nclass RouteData:\r\n\tdef __init__(self):\r\n\t\tself.routeNum = 0\r\n\t\tself.instructions = []\r\n\r\n\t\treturn\r\n\r\n\tclass Instruction:\r\n\t\tdef __init__(self, leftX, rightX, Y):\r\n\t\t\tself.leftX = leftX\r\n\t\t\tself.rightX = rightX\r\n\t\t\tself.Y = Y\r\n\t\t\tself.sequence = []\r\n\r\n\t\t\treturn\r\n\r\n\t#reading route data from file\r\n\tdef readRouteFromFile(self, charName, mapName):\r\n\t\t#opening route data file\r\n\t\tconfigure = ConfigParser()\r\n\t\tconfigure.read(\"./route data/\" + charName + \"_\" + mapName + \".ini\")\r\n\r\n\t\t#reading general section\r\n\t\tself.charName = configure.get(\"general\", \"charName\")\r\n\t\tself.mapName = configure.get(\"general\", \"mapName\")\r\n\r\n\t\t#reading routes section\r\n\t\tlines = list(configure.items(\"routes\"))\r\n\t\tself.routeNum = len(lines)\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tdataList = lines[i][1].split(', ')\r\n\t\t\tself.instructions.append(self.Instruction(int(dataList[0]), int(dataList[1]), int(dataList[2])))\r\n\t\t\tsequenceList = dataList[3].split(\"/\")\r\n\t\t\tfor j in range(len(sequenceList)):\r\n\t\t\t\tself.instructions[i].sequence.append(sequenceList[j])\r\n\t\treturn\r\n\r\n#creating MapData class\r\nclass MapData:\r\n\tdef __init__(self):\r\n\t\tself.minX = 9\r\n\t\tself.minY = 61\r\n\t\tself.platformsNum = 0\r\n\t\tself.platforms = []\r\n\t\tself.ropesNum = 0\r\n\t\tself.ropes = []\r\n\t\tself.portalsNum = 0\r\n\t\tself.portals = []\r\n\r\n\t#creating Platform class\r\n\tclass Platform:\r\n\t\tdef __init__(self, leftX, rightX, Y):\r\n\t\t\tself.leftX = leftX\r\n\t\t\tself.rightX = rightX\r\n\t\t\tself.Y = Y\r\n\r\n\t\t\treturn\r\n\r\n\t#creating Rope class\r\n\tclass Rope:\r\n\t\tdef __init__(self, X, bottomY, topY):\r\n\t\t\tself.X = X\r\n\t\t\tself.bottomY = bottomY\r\n\t\t\tself.topY = topY\r\n\r\n\t\t\treturn\r\n\r\n\t#creating Portal class\r\n\tclass Portal:\r\n\t\tdef __init__(self, X1, Y1, X2, Y2):\r\n\t\t\tself.X1 = X1\r\n\t\t\tself.Y1 = Y1\r\n\t\t\tself.X2 = X2\r\n\t\t\tself.Y2 = Y2\r\n\r\n\t\t\treturn\r\n\r\n\t#reading map data from file\r\n\tdef readMapFromFile(self, mapName):\r\n\t\t#opening map file\r\n\t\tconfigure = ConfigParser()\r\n\t\tconfigure.read(\"./map data/\" + mapName + \".ini\")\r\n\r\n\t\t#reading general section\r\n\t\tself.mapName = configure.get(\"general\", \"mapName\")\r\n\t\tself.minX = configure.getint(\"general\", \"minX\")\r\n\t\tself.maxX = configure.getint(\"general\", \"maxX\")\r\n\t\tself.minY = configure.getint(\"general\", \"minY\")\r\n\t\tself.maxY = configure.getint(\"general\", \"maxY\")\r\n\t\tself.width = configure.getint(\"general\", \"width\")\r\n\t\tself.height = configure.getint(\"general\", \"height\")\r\n\t\tself.cem_maxX = configure.getint(\"general\", \"cemMaxX\")\r\n\t\tself.cem_maxY = configure.getint(\"general\", \"cemMaxY\")\r\n\t\tself.cem_width = configure.getint(\"general\", \"cemWidth\")\r\n\t\tself.cem_height = configure.getint(\"general\", \"cemHeight\")\r\n\t\tself.map_coordX = configure.getint(\"general\", \"mapCoordX\")\r\n\t\tself.map_coordY = configure.getint(\"general\", \"mapCoordY\")\r\n\r\n\t\t#reading platforms section\r\n\t\tlines = list(configure.items(\"platforms\"))\r\n\t\tself.platformsNum = len(lines)\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tdataList = lines[i][1].split(', ')\r\n\t\t\tself.platforms.append(self.Platform(int(dataList[0]), int(dataList[1]), int(dataList[2])))\r\n\r\n\t\t#reading ropes section\r\n\t\tlines = list(configure.items(\"ropes\"))\r\n\t\tself.ropesNum = len(lines)\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tdataList = lines[i][1].split(', ')\r\n\t\t\tself.ropes.append(self.Rope(int(dataList[0]), int(dataList[1]), int(dataList[2])))\r\n\t\t\t\r\n\t\t#reading ropes section\r\n\t\tlines = list(configure.items(\"portals\"))\r\n\t\tself.portalsNum = len(lines)\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tdataList = lines[i][1].split(', ')\r\n\t\t\tself.portals.append(self.Portal(int(dataList[0]), int(dataList[1]), int(dataList[2]), int(dataList[3])))\r\n\r\n\t\treturn\r\n\r\n#creating CharacterData class\r\nclass CharacterData:\r\n\tdef __init__(self):\r\n\t\tself.skillsNum = 0\r\n\t\tself.buffsNum = 0\r\n\t\tself.resetsNum = 0\r\n\t\tself.presetsNum = 0\r\n\r\n\t\tself.skills = {}\r\n\t\tself.buffs = []\r\n\t\tself.resets = []\r\n\t\tself.presets = {}\r\n\r\n\t\tself.buffTimers = []\r\n\t\tself.presetTimers = {}\r\n\r\n\t\tself.potions = None\r\n\r\n\t\treturn\r\n\r\n\tclass Skill:\r\n\t\tdef __init__(self, key):\r\n\t\t\tself.key = key\r\n\r\n\t\t\treturn\r\n\r\n\tclass Buff:\r\n\t\tdef __init__(self, name, key, cooldown, waitTime, timer):\r\n\t\t\tself.name = name\r\n\t\t\tself.key = key.lower()\r\n\t\t\tself.cooldown = cooldown\r\n\t\t\tself.waitTime = waitTime\r\n\t\t\tself.timer = timer\r\n\r\n\t\t\treturn\r\n\r\n\tclass Preset:\r\n\t\tdef __init__(self, cooldown, instructions, timer):\r\n\t\t\tself.cooldown = cooldown\r\n\t\t\tself.instructions = instructions\r\n\t\t\tself.timer = timer\r\n\r\n\tclass Potions:\r\n\t\tdef __init__(self):\r\n\t\t\t#initializing variables\r\n\t\t\tself.minHP = int(890 + ((1054 - 890) * (char.hpThreshold / 100)))\r\n\t\t\tself.minMP = int(890 + ((1054 - 890) * (char.mpThreshold / 100)))\r\n\r\n\t\t\treturn\r\n\r\n\t\tdef checkPotions(self):\r\n\t\t\tim = pyautogui.screenshot()\r\n\t\t\tif(im.getpixel((self.minHP, 1039))[0] < 200):\r\n\t\t\t\tpress(char.hpKey)\r\n\t\t\t\twait(0.4)\r\n\t\t\tif(im.getpixel((self.minMP, 1055))[2] < 200):\r\n\t\t\t\tpress(char.mpKey)\r\n\t\t\t\twait(0.4)\r\n\r\n\t\t\treturn\r\n\r\n\t#reading character data from file\r\n\tdef readCharacterFromFile(self, charName):\r\n\t\t#opening character file\r\n\t\tconfigure = ConfigParser()\r\n\t\tconfigure.optionxform = lambda option: option\r\n\t\tconfigure.read(\"./character data/\" + charName + \".ini\")\r\n\r\n\t\t#reading general section\r\n\t\tself.charName = configure.get(\"general\", \"name\")\r\n\t\tself.hpKey = (configure.get(\"general\", \"hpKey\")).lower()\r\n\t\tself.mpKey = (configure.get(\"general\", \"mpKey\")).lower()\r\n\t\tself.hpThreshold = configure.getint(\"general\", \"hpThreshold\")\r\n\t\tself.mpThreshold = configure.getint(\"general\", \"mpThreshold\")\r\n\t\tself.jumpKey = configure.get(\"general\", \"jumpKey\")\r\n\t\tself.npcKey = configure.get(\"general\", \"npcKey\")\r\n\t\tself.attackKey = configure.get(\"general\", \"attackKey\")\r\n\t\tself.mapKey = configure.get(\"general\", \"mapKey\")\r\n\t\tself.petFood = configure.get(\"general\", \"petFood\")\r\n\t\tself.changeChannel = configure.get(\"general\", \"changeChannel\")\r\n\t\tself.autoPetFood = configure.get(\"general\", \"autoPetFood\")\r\n\t\tself.potions = self.Potions()\r\n\t\tself.ropeLiftExist = False\r\n\r\n\t\t#reading skills section\r\n\t\tlines = list(configure.items(\"skills\"))\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tself.skills[lines[i][0]] = self.Skill(lines[i][1])\r\n\t\t\tif(lines[i][0] == \"Rope Lift\"):\r\n\t\t\t\tself.ropeLiftExist = True\r\n\r\n\t\t#reading buffs section\r\n\t\tcount = 0\r\n\t\tlines = list(configure.items(\"buffs\"))\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tdataList = lines[i][1].split(', ')\r\n\t\t\tif (dataList[3] == \"on\"):\r\n\t\t\t\tself.buffs.append(self.Buff(lines[i][0], dataList[0], float(dataList[1]), float(dataList[2]), time.perf_counter()))\r\n\t\t\t\tcount = count + 1\r\n\t\tself.buffsNum = count\r\n\r\n\t\t#reading presets section\r\n\t\tlines = list(configure.items(\"presets\"))\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tdataList = lines[i][1].split(', ')\r\n\t\t\tdataList2 = dataList[1].split('/')\r\n\t\t\tself.presets[lines[i][0]] = self.Preset(float(dataList[0]), dataList2, time.perf_counter())\r\n\r\n\t\t#reading reset skills section\r\n\t\tlines = list(configure.items(\"resets\"))\r\n\t\tfor i in range(len(lines)):\r\n\t\t\tself.resets.append(self.Skill(lines[i][1]))\r\n\r\n\t\t#setting pet food timer if needed\r\n\t\tif (self.autoPetFood == \"on\"):\r\n\t\t\tself.petTimer = time.perf_counter() + 900\r\n\t\t\twait(0.2)\r\n\r\n\t\treturn\r\n\r\n\t#function to cycle buffs and check for expired cooldowns\r\n\tdef checkBuffs(self):\r\n\t\tif (self.autoPetFood == \"on\" and time.perf_counter() > self.petTimer):\r\n\t\t\tpress(self.petFood)\r\n\t\t\tself.petTimer = time.perf_counter() + 900\r\n\t\t\twait(0.2)\r\n\r\n\t\tfor i in range(self.buffsNum):\r\n\t\t\tif(time.perf_counter() > self.buffs[i].timer):\r\n\t\t\t\twait(0.2)\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\twait(0.2)\r\n\t\t\t\tpress(self.buffs[i].key)\r\n\t\t\t\twait(self.buffs[i].waitTime)\r\n\t\t\t\tself.buffs[i].timer = (time.perf_counter() + self.buffs[i].cooldown)\r\n\r\n\t\treturn\r\n\r\n#creating Markers class\r\nclass Markers:\r\n\tdef __init__(self):\r\n\t\t#setting directory paths\r\n\t\tself.maindir = os.path.dirname(__file__)\r\n\t\tself.assetsdir = os.path.join(self.maindir, \"assets\")\r\n\r\n\t\t#initializing markers\r\n\t\tself.charMarker = os.path.join(self.assetsdir, \"characterminimap.png\")\r\n\t\tself.runeMarker = os.path.join(self.assetsdir, \"runeminimap.png\")\r\n\t\tself.navigationMarker = os.path.join(self.assetsdir, \"navigation.png\")\r\n\t\tself.okteleportMarker = os.path.join(self.assetsdir, \"okteleport.png\")\r\n\t\tself.okdeathMarker = os.path.join(self.assetsdir, \"okdeath.png\")\r\n\t\tself.cancelMarker = os.path.join(self.assetsdir, \"cancel.png\")\r\n\t\tself.dialogueMarker = os.path.join(self.assetsdir, \"dialogue.png\")\r\n\t\tself.changeChannelMarker = os.path.join(self.assetsdir, \"changechannel.png\")\r\n\r\n#creating Rune class\r\nclass Rune:\r\n\tdef __init__(self):\r\n\t\tself.timer = time.perf_counter()\r\n\t\tself.status = 0\r\n\t\tself.x = None\r\n\t\tself.y = None\r\n\t\tself.buffExistsMarker = os.path.join(markers.assetsdir, \"buffexists.png\")\r\n\t\tself.runeSolvedMarker = os.path.join(markers.assetsdir, \"runesolved.png\")\r\n\t\tself.runeCooldownMarker = os.path.join(markers.assetsdir, \"runecooldown.png\")\r\n\t\tmodelsdir = os.path.join(markers.maindir, \"models\")\r\n\t\tself.arrowsdir = os.path.join(markers.maindir, \"arrows\")\r\n\t\tself.findArrow = tensorflow.keras.models.load_model(os.path.join(modelsdir, 'find_arrow.h5'), compile = False)\r\n\t\tself.solveArrow = tensorflow.keras.models.load_model(os.path.join(modelsdir, 'solve_arrow.h5'), compile = False)\r\n\r\n\tdef solve(self):\r\n\t\tsize = (224, 224)\r\n\t\tdataFind = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)\r\n\t\tdataSolve = np.ndarray(shape=(4, 224, 224, 3), dtype=np.float32)\r\n\t\tarrowsList = []\r\n\r\n\t\t#get arrows\r\n\t\tx = 675\r\n\t\tcounter = 1\r\n\t\tskipFlag = False\r\n\r\n\t\t#preprocess image\r\n\t\timage = ImageGrab.grab()\r\n\t\timage = np.asarray(image)\r\n\t\timage = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\r\n\r\n\t\t#gaussian blur\r\n\t\timage = cv2.GaussianBlur(image, (3, 3), 0)\r\n\r\n\t\t#color transform\r\n\t\timage = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\r\n\r\n\t\t#change to grayscale\r\n\t\tcoefficients = (0.0445, 0.6568, 0.2987)\r\n\t\timage = cv2.transform(image, np.array(coefficients).reshape((1, 3)))\r\n\t\t\r\n\t\t#change back to color\r\n\t\timage = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\r\n\r\n\t\timage = Image.fromarray(np.uint8(image)).convert('RGB')\r\n\t\twhile (x < 1175 and counter < 5):\r\n\t\t\tim = pyautogui.screenshot()\r\n\t\t\tif(im.getpixel((char.potions.minHP, 1039))[0] < 200):\r\n\t\t\t\treturn False\r\n\t\t\t\r\n\t\t\ty = 150\r\n\t\t\twhile(y < 210):\r\n\t\t\t\tim = image.crop((x, y, x + 75, y + 75))\r\n\t\t\t\tim1 = ImageOps.fit(im, size, Image.ANTIALIAS)\r\n\t\t\t\timage_array = np.asarray(im1)\r\n\t\t\t\tnormalized_image_array = (image_array.astype(np.float32) / 127.0) - 1\r\n\t\t\t\tdataFind[0] = normalized_image_array\r\n\t\t\t\tprediction = self.findArrow.predict(dataFind)\r\n\t\t\t\tif (prediction[0][0] > 0.80):\r\n\t\t\t\t\tarrowsList.append(image_array)\r\n\t\t\t\t\tcounter = counter + 1\r\n\t\t\t\t\tskipFlag = True\r\n\t\t\t\t\tbreak\r\n\t\t\t\ty = y + 10\r\n\t\t\tif (skipFlag == True):\r\n\t\t\t\tx = x + 80\r\n\t\t\t\tskipFlag = False\r\n\t\t\telse:\r\n\t\t\t\tx = x + 20\r\n\r\n\t\t#if 4 arrows are not found, return false\r\n\t\tif (counter != 5):\r\n\t\t\treturn False\r\n\r\n\t\t#preprocess\r\n\t\tfor i in range(4):\r\n\t\t\timg = arrowsList[i]\r\n\t\t\tdataSolve[i] = (img.astype(np.float32) / 127.0) - 1\r\n\r\n\t\t#solve\r\n\t\torder = []\r\n\t\tprediction = self.solveArrow.predict(dataSolve)\r\n\t\tfor i in range (counter - 1):\r\n\t\t\t#find max\r\n\t\t\tmaxVal = 0\r\n\t\t\tmaxIT = -1\r\n\t\t\tfor j in range(4):\r\n\t\t\t\tif(maxVal < prediction[i][j]):\r\n\t\t\t\t\tmaxVal = prediction[i][j]\r\n\t\t\t\t\tmaxIT = j\r\n\t\t\tif (maxIT == 0):\r\n\t\t\t\torder.append(\"left\")\r\n\t\t\telif (maxIT == 1):\r\n\t\t\t\torder.append(\"right\")\r\n\t\t\telif (maxIT == 2):\r\n\t\t\t\torder.append(\"down\")\r\n\t\t\telif (maxIT == 3):\r\n\t\t\t\torder.append(\"up\")\r\n\r\n\t\treturn order\r\n\r\n\t#checking for rune and navigating to it if it exists\r\n\tdef checkRune(self):\r\n\t\t#if rune does not exist\r\n\t\tif (time.perf_counter() > self.timer):\r\n\t\t\trunePos = pyautogui.locate(markers.runeMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height)))\r\n\t\t\trelease(\"left\")\r\n\t\t\trelease(\"right\")\r\n\t\t\t#if rune is found and rune cooldown is not on\r\n\t\t\tif (runePos != None and not(self.runeCooldown())):\r\n\t\t\t\t#mark rune position with offset\r\n\t\t\t\tself.x = runePos[0] + 2\r\n\t\t\t\tself.y = runePos[1] - 2\r\n\r\n\t\t\t\t#find rune platform\r\n\t\t\t\tfor platformID in range(mapdata.platformsNum):\r\n\t\t\t\t\tif (self.y <= mapdata.platforms[platformID].Y + 2 and self.y >= mapdata.platforms[platformID].Y - 2 and self.x >= mapdata.platforms[platformID].leftX and self.x <= mapdata.platforms[platformID].rightX):\r\n\t\t\t\t\t\tbreak\r\n\r\n\t\t\t\t#try to solve 10 times\r\n\t\t\t\tfor i in range(10):\r\n\t\t\t\t\tif(errorcheck.checkDeath()):\r\n\t\t\t\t\t\treturn False\r\n\t\t\t\t\tchar.potions.checkPotions()\r\n\t\t\t\t\t#navigate to rune\r\n\t\t\t\t\tif (char.ropeLiftExist == \"True\"):\r\n\t\t\t\t\t\tmoveTo(self.x, mapdata.platforms[platformID].Y)\r\n\t\t\t\t\t\tpress(char.npcKey)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tcharPos = bot.characterPos()\r\n\t\t\t\t\t\tif (charPos[1] == mapdata.platforms[platformID].Y and charPos[0] >= mapdata.platforms[platformID].leftX and charPos[0] <= mapdata.platforms[platformID].rightX):\r\n\t\t\t\t\t\t\tif (moveTo(self.x, mapdata.platforms[platformID].Y)):\r\n\t\t\t\t\t\t\t\tpress(char.npcKey)\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\treturn False\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\treturn False\r\n\r\n\t\t\t\t\tprint(\"Solving rune\")\r\n\t\t\t\t\twait(0.5)\r\n\t\t\t\t\tchar.potions.checkPotions()\r\n\t\t\t\t\torder = self.solve()\r\n\t\t\t\t\t#if solver returns order\r\n\t\t\t\t\tif(order != False):\r\n\t\t\t\t\t\tfor i in range(4):\r\n\t\t\t\t\t\t\tpress(order[i])\r\n\t\t\t\t\t\t\twait(0.05)\r\n\t\t\t\t\t\twait(3)\r\n\t\t\t\t\t\t#if solver fails\r\n\t\t\t\t\t\tif(not(self.runeSolved())):\r\n\t\t\t\t\t\t\tfor i in range(2):\r\n\t\t\t\t\t\t\t\tpress(char.attackKey)\r\n\t\t\t\t\t\t\t\twait(1)\r\n\t\t\t\t\t\t#if solved passes\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tself.timer = time.perf_counter() + 850\r\n\t\t\t\t\t\t\treturn True\r\n\t\t\t\t\t#if solver does not return order\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tif(not(self.runeSolved())):\r\n\t\t\t\t\t\t\tfor i in range(3):\r\n\t\t\t\t\t\t\t\tpress(char.attackKey)\r\n\t\t\t\t\t\t\t\twait(1)\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tself.timer = time.perf_counter() + 850\r\n\t\t\t\t\t\t\treturn True\r\n\t\t\t\t\tchar.potions.checkPotions()\r\n\t\t\t\tprint(\"Rune failed 10 times. changing channel\")\r\n\t\t\t\t#if rune try failed 10 times change channel\r\n\t\t\t\terrorcheck.changeChannel()\r\n\t\t\t\tself.timer = time.perf_counter() + 15\r\n\t\t\t\treturn\r\n\r\n\tdef runeSolved(self):\r\n\t\tx = 1900\r\n\t\tfailCounter = 0\r\n\t\twhile(x >= 500 and failCounter <= 3):\r\n\t\t\tpyautogui.moveTo(x, 15, duration = 0.1)\r\n\t\t\t#if buff exists check if rune solved buff is active\r\n\t\t\tif (not(pyautogui.locate(self.buffExistsMarker, pyautogui.screenshot(region=(1000, 31, 500, 3))) == None)):\r\n\t\t\t\tif (not(pyautogui.locate(self.runeSolvedMarker, pyautogui.screenshot(region=(1000, 63, 500, 1))) == None)):\r\n\t\t\t\t\tprint(\"RUNE SOLVED\")\r\n\t\t\t\t\treturn True\r\n\t\t\t\tx = x - 32\r\n\t\t\telse:\r\n\t\t\t\tfailCounter = failCounter + 1\r\n\t\t\t\tx = x - 32\r\n\r\n\t\treturn False\r\n\r\n\tdef runeCooldown(self):\r\n\t\tx = 1900\r\n\t\tfailCounter = 0\r\n\t\twhile(x >= 500 and failCounter <= 3):\r\n\t\t\tpyautogui.moveTo(x, 15, duration = 0.1)\r\n\t\t\t#if buff exists check if rune cooldown buff is active\r\n\t\t\tif (not(pyautogui.locate(self.buffExistsMarker, pyautogui.screenshot(region=(1000, 31, 500, 3))) == None)):\r\n\t\t\t\tline = pyautogui.screenshot(region=(1000, 63, 500, 1))\r\n\t\t\t\tif (not(pyautogui.locate(self.runeCooldownMarker, pyautogui.screenshot(region=(1000, 63, 500, 1))) == None)):\r\n\t\t\t\t\treturn True\r\n\t\t\t\tx = x - 32\r\n\t\t\telse:\r\n\t\t\t\tfailCounter = failCounter + 1\r\n\t\t\t\tx = x - 32\r\n\r\n\t\treturn False\r\n\r\n#creating ErrorCheck class\r\nclass ErrorCheck:\r\n\tdef __init__(self):\r\n\t\tself.prevPos = None\r\n\t\tself.failCounter = 0\r\n\t\tself.checkDeathTimer = time.perf_counter() + 60\r\n\r\n\t#checking each error\r\n\tdef checkAllErrors(self):\r\n\t\tprint(\"CHECKING DEATH\")\r\n\t\tif (not(self.checkDeath())):\r\n\t\t\tprint(\"CHECKING DIALOGUE\")\r\n\t\t\tif (not(self.checkDialogue())):\r\n\t\t\t\tprint(\"CHECKING CEMETERY\")\r\n\t\t\t\tif (not(self.checkCemetery())):\r\n\t\t\t\t\tprint(\"CHECKING OTHER MAP\")\r\n\t\t\t\t\tif (not(self.checkOtherMap())):\r\n\t\t\t\t\t\tprint(\"CHECKING ROPE\")\r\n\t\t\t\t\t\tif (not(self.checkRope())):\r\n\t\t\t\t\t\t\tprint(\"Unsolved Error Detected\")\r\n\t\t\t\t\t\t\treturn False\r\n\t\tprint(\"ERROR SOLVED\")\r\n\t\treturn True\r\n\r\n\t#checking death\r\n\tdef checkDeath(self):\r\n\t\t#moving mouse out of the way\r\n\t\tpyautogui.moveTo(10, 10, duration = 0.1)\r\n\r\n\t\t#check for buff freezer\r\n\t\tcancelFlag = pyautogui.locate(markers.cancelMarker, pyautogui.screenshot())\r\n\t\tif (not(cancelFlag == None)):\r\n\t\t\tpyautogui.moveTo(cancelFlag[0] + 3, cancelFlag[1] + 3, duration = 0.2)\r\n\t\t\tpyautogui.click()\r\n\t\t\twait(1)\r\n\r\n\t\t#check for death\r\n\t\tdeathFlag = pyautogui.locate(markers.okdeathMarker, pyautogui.screenshot())\r\n\t\tif (deathFlag == None):\r\n\t\t\treturn False\r\n\t\telse:\r\n\t\t\tpyautogui.moveTo(deathFlag[0] + 3, deathFlag[1] + 3, duration = 0.2)\r\n\t\t\tpyautogui.click()\r\n\t\t\tself.mapChangeCheck()\r\n\t\t\treturn True\r\n\r\n\t#timed check death\r\n\tdef timedCheckDeath(self):\r\n\t\t#checking if 1 minute has passed\r\n\t\tif (self.checkDeathTimer < time.perf_counter()):\r\n\t\t\tself.checkDeath()\r\n\t\t\tself.checkDeathTimer = time.perf_counter() + 60\r\n\r\n\t#checking rope\r\n\tdef checkRope(self):\r\n\t\t#check if player is on rope\r\n\t\tcharPos = bot.characterPos()\r\n\t\tfor i in range (mapdata.ropesNum):\r\n\t\t\tif (mapdata.ropes[i].X == charPos[0] and mapdata.ropes[i].bottomY >= charPos[1] and mapdata.ropes[i].topY <= charPos[1]):\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\twait(0.1)\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\twait(0.1)\r\n\t\t\t\trelease(\"Down\")\r\n\t\t\t\twait(0.1)\r\n\t\t\t\thold(\"Up\")\r\n\t\t\t\twait(5)\r\n\t\t\t\trelease(\"Up\")\r\n\t\t\t\treturn True\r\n\r\n\t\treturn False\r\n\r\n\t#checking if dialogue has been entered\r\n\tdef checkDialogue(self):\r\n\t\t#check if dialogue has occurred\r\n\t\tdialogueFlag = pyautogui.locate(markers.dialogueMarker, pyautogui.screenshot())\r\n\t\tif (dialogueFlag == None):\r\n\t\t\treturn False\r\n\t\telse:\r\n\t\t\tpyautogui.moveTo(dialogueFlag [0] + 3, dialogueFlag [1] + 3, duration = 0.2)\r\n\t\t\tpyautogui.click()\r\n\t\t\twait(1)\r\n\t\t\treturn True\r\n\r\n\t#checking if player is at the cemetery\t\t\r\n\tdef checkCemetery(self):\r\n\t\t#check map size\r\n\t\tim = pyautogui.screenshot()\r\n\t\trBorder = [221, 221, 221]\r\n\t\tx = mapdata.cem_maxX\r\n\t\tpixel = im.getpixel((x + 1, 74))\r\n\t\tif (pixel[0] == rBorder[0] and pixel[1] == rBorder[1] and pixel[2] == rBorder[2]):\r\n\t\t\tpixel = im.getpixel((x + 2, 74))\r\n\t\t\tif (pixel[0] == rBorder[0] and pixel[1] == rBorder[1] and pixel[2] == rBorder[2]):\r\n\t\t\t\tpixel = im.getpixel((x + 1, 75))\r\n\t\t\t\tif (pixel[0] == rBorder[0] and pixel[1] == rBorder[1] and pixel[2] == rBorder[2]):\r\n\t\t\t\t\tpixel = im.getpixel((x + 2, 75))\r\n\t\t\t\t\tif (pixel[0] == rBorder[0] and pixel[1] == rBorder[1] and pixel[2] == rBorder[2]):\r\n\t\t\t\t\t\tprint(\"CEMETERY DETECTED\")\r\n\t\t\t\t\t\tchar.potions.checkPotions()\r\n\t\t\t\t\t\twait(1)\r\n\t\t\t\t\t\tpyautogui.moveTo(10, 10, duration = 0.2)\r\n\t\t\t\t\t\twait(1)\r\n\t\t\t\t\t\tpress(char.mapKey)\r\n\t\t\t\t\t\twait(1)\r\n\t\t\t\t\t\tnavPos = pyautogui.locate(markers.navigationMarker, pyautogui.screenshot())\r\n\t\t\t\t\t\tif (navPos == None):\r\n\t\t\t\t\t\t\treturn False\r\n\t\t\t\t\t\tpyautogui.moveTo(mapdata.map_coordX + navPos[0], mapdata.map_coordY + navPos[1], duration = 0.2)\r\n\t\t\t\t\t\tpyautogui.click()\r\n\t\t\t\t\t\twait(0.10)\r\n\t\t\t\t\t\tpyautogui.click()\r\n\t\t\t\t\t\twait(1)\r\n\t\t\t\t\t\tokPos = pyautogui.locate(markers.okteleportMarker, pyautogui.screenshot())\r\n\t\t\t\t\t\tif (okPos == None):\r\n\t\t\t\t\t\t\treturn False\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tpyautogui.moveTo(okPos[0] + 3, okPos[1] + 3, duration = 0.2)\r\n\t\t\t\t\t\t\tpyautogui.click()\r\n\t\t\t\t\t\t\tself.mapChangeCheck()\r\n\t\t\t\t\t\t\twait(2)\r\n\t\t\t\t\t\t\tfor i in range(5):\r\n\t\t\t\t\t\t\t\tpress(char.attackKey)\r\n\t\t\t\t\t\t\t\twait(1)\r\n\t\t\t\t\t\t\tfor i in range(len(char.resets)):\r\n\t\t\t\t\t\t\t\twait(2)\r\n\t\t\t\t\t\t\t\tpress(char.resets[i].key)\r\n\r\n\t\t\t\t\t\t\t#get character to jump down back on map\r\n\t\t\t\t\t\t\tcharPos = bot.characterPos()\r\n\t\t\t\t\t\t\tsection = bot.findSection(charPos)\r\n\t\t\t\t\t\t\twhile(section == None):\r\n\t\t\t\t\t\t\t\tcharPos = bot.characterPos()\r\n\t\t\t\t\t\t\t\tsection = bot.findSection(charPos)\r\n\t\t\t\t\t\t\t\tif (section == None):\r\n\t\t\t\t\t\t\t\t\tdownjump()\r\n\r\n\t\t\t\t\t\t\treturn True\r\n\r\n\t\treturn False\r\n\r\n\t#checking if player has entered another map\r\n\tdef checkOtherMap(self):\r\n\t\tim = pyautogui.screenshot()\r\n\t\trBorder = [221, 221, 221]\r\n\t\tbBorder = [255, 255, 255]\r\n\t\tx = mapdata.maxX\r\n\t\ty = mapdata.maxY\r\n\t\tpixel = im.getpixel((x + 1, 74))\r\n\t\tpixel2 = im.getpixel((65, y + 1))\r\n\t\tif (not(pixel[0] == rBorder[0]) or not(pixel[1] == rBorder[1]) or not(pixel[2] == rBorder[2]) or not(pixel2[0] == bBorder[0]) or not(pixel2[1] == bBorder[1]) or not(pixel2[2] == bBorder[2])):\r\n\t\t\tprint(\"OTHER MAP DETECTED\")\r\n\t\t\tpress(char.mapKey)\r\n\t\t\tpyautogui.moveTo(10, 10, duration = 0.2)\r\n\t\t\twait(1)\r\n\t\t\tnavPos = pyautogui.locate(markers.navigationMarker, pyautogui.screenshot())\r\n\t\t\tif (navPos == None):\r\n\t\t\t\treturn False\r\n\t\t\tpyautogui.moveTo(mapdata.map_coordX + navPos[0], mapdata.map_coordY + navPos[1], duration = 0.2)\r\n\t\t\tpyautogui.click()\r\n\t\t\twait(0.10)\r\n\t\t\tpyautogui.click()\r\n\t\t\twait(1)\r\n\t\t\tokPos = pyautogui.locate(markers.okteleportMarker, pyautogui.screenshot())\r\n\t\t\tif (okPos == None):\r\n\t\t\t\treturn False\r\n\t\t\telse:\r\n\t\t\t\tpyautogui.moveTo(okPos[0] + 3, okPos[1] + 3, duration = 0.2)\r\n\t\t\t\tpyautogui.click()\r\n\t\t\t\tself.mapChangeCheck()\r\n\t\t\t\twait(2)\r\n\t\t\t\tfor i in range(5):\r\n\t\t\t\t\tpress(char.attackKey)\r\n\t\t\t\t\twait(1)\r\n\t\t\t\treturn True\r\n\r\n\t\treturn False\r\n\r\n\t#check when changing from black to map has occurred\r\n\tdef mapChangeCheck(self):\r\n\t\t#check if character has exited current map\r\n\t\toverTimer = time.perf_counter() + 5\r\n\r\n\t\texitFlag = False\r\n\t\twhile (exitFlag == False):\r\n\t\t\tif (overTimer < time.perf_counter()):\r\n\t\t\t\treturn False\r\n\t\t\tim = pyautogui.screenshot(region=(8, 1073, 1, 1))\r\n\t\t\tpixel = im.getpixel((0, 0))\r\n\t\t\tif(not(pixel[0] == 221) and not(pixel[1] == 221) and not(pixel[2] == 221)):\r\n\t\t\t\texitFlag = True\r\n\r\n\t\twhile (True):\r\n\t\t\tim = pyautogui.screenshot(region=(8, 1073, 1, 1))\r\n\t\t\tpixel = im.getpixel((0, 0))\r\n\t\t\tif(pixel[0] == 221 and pixel[1] == 221 and pixel[2] == 221):\r\n\t\t\t\treturn True\r\n\r\n\t#change channel to a random channel\r\n\tdef changeChannel(self):\r\n\t\trelease(\"Left\")\r\n\t\twait(0.1)\r\n\t\trelease(\"Right\")\r\n\t\t#getting random variables\t\r\n\t\tverticalChange = random.randint(1,5)\r\n\t\thorizontalChange = random.randint(1,4)\r\n\r\n\t\t#loop to attempt channel change until succeed\r\n\t\twhile (True):\r\n\t\t\t#check death\r\n\t\t\tif (self.checkDeath()):\r\n\t\t\t\tself.changeChannel()\r\n\t\t\t\tself.checkCemetery()\r\n\t\t\t\treturn True\r\n\t\t\tpress(char.changeChannel)\r\n\t\t\twait(1)\r\n\t\t\tfor i in range(verticalChange):\r\n\t\t\t\tpress(\"Down\")\r\n\t\t\t\twait(0.01)\r\n\t\t\tfor i in range(horizontalChange):\r\n\t\t\t\tpress(\"Right\")\r\n\t\t\t\twait(0.01)\r\n\t\t\tbuttonPos = pyautogui.locate(markers.changeChannelMarker, pyautogui.screenshot())\r\n\t\t\tif (not(buttonPos == None)):\r\n\t\t\t\tpyautogui.moveTo(buttonPos[0] + 3, buttonPos[1] + 3, duration = 0.2)\r\n\t\t\t\tpyautogui.click()\r\n\t\t\t\tif (self.mapChangeCheck()):\r\n\t\t\t\t\t#get character to jump down back on map\r\n\t\t\t\t\tcharPos = bot.characterPos()\r\n\t\t\t\t\tsection = bot.findSection(charPos)\r\n\t\t\t\t\twhile(section == None):\r\n\t\t\t\t\t\tcharPos = bot.characterPos()\r\n\t\t\t\t\t\tsection = bot.findSection(charPos)\r\n\t\t\t\t\t\tprint(\"SECTION IS: \" + str(section))\r\n\t\t\t\t\t\tif (section == None):\r\n\t\t\t\t\t\t\tdownjump()\r\n\t\t\t\t\treturn True\r\n\r\n\tdef mashOutOfEMStun(self):\r\n\t\tfor i in range(20):\r\n\t\t\tpress(\"Left\")\r\n\t\t\twait(0.05)\r\n\t\t\tpress(\"Right\")\r\n\r\n#creating OtherPlayerCheck class\r\nclass OtherPlayerCheck:\r\n\tdef __init__(self):\r\n\t\tself.timer = time.perf_counter()\r\n\t\tself.stayTimer = time.perf_counter()\r\n\t\tself.status = 0\r\n\t\tself.randomMarker = os.path.join(markers.assetsdir, \"randomplayer.png\")\r\n\t\tself.buddyMarker = os.path.join(markers.assetsdir, \"buddyplayer.png\")\r\n\t\tself.guildMarker = os.path.join(markers.assetsdir, \"buddyplayer.png\")\r\n\r\n\t\treturn\r\n\r\n\tdef checkOtherPlayer(self):\r\n\t\t#when timer runs out, check for players\r\n\t\tif (self.timer < time.perf_counter()):\r\n\t\t\tif (not(pyautogui.locate(self.randomMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height))) == None) or (not(pyautogui.locate(self.buddyMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height))) == None)) or (not(pyautogui.locate(self.guildMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height))) == None))):\r\n\t\t\t\tprint(\"Another player has been detected\")\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\tcount = 0\r\n\t\t\t\t#check 6 times over 30 seconds to see if player remains\r\n\t\t\t\tfor i in range(15): \r\n\t\t\t\t\twait(2)\r\n\t\t\t\t\tif (not(pyautogui.locate(self.randomMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height))) == None) or not(pyautogui.locate(self.buddyMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height))) == None) or not(pyautogui.locate(self.guildMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height))) == None)):\r\n\t\t\t\t\t\tcount = count + 1\r\n\t\t\t\tif (count >= 10):\r\n\t\t\t\t\tprint(\"Changing channel\")\r\n\t\t\t\t\tprint(\"Number of times another player showed up is:\", count)\r\n\t\t\t\t\terrorcheck.changeChannel()\r\n\t\t\t\t\treturn True\r\n\t\t\t\r\n\t\t\tself.timer = time.perf_counter() + 15\r\n\r\n#creating Menu class\r\nclass Menu:\r\n\t#function to initialize character data/map data/route data\r\n\tdef initializeInfo(self):\r\n\t\t#initializing default file\r\n\t\tconfigureDefault = ConfigParser()\r\n\t\tconfigureDefault.read(\"default.ini\")\r\n\r\n\t\t#prompt user for character name\r\n\t\tcharName = input(\"Enter character name: \")\r\n\t\tif(charName == \"\"):\r\n\t\t\tcharName = configureDefault.get(\"general\", \"character\")\r\n\r\n\t\t#prompt user for map name\r\n\t\tmapName = input(\"Enter map name: \")\r\n\t\tif(mapName == \"\"):\r\n\t\t\tmapName = configureDefault.get(\"general\", \"map\")\r\n\r\n\t\t#reading from file with input specified character and map\r\n\t\tmapdata.readMapFromFile(mapName)\r\n\t\tchar.readCharacterFromFile(charName)\r\n\t\troute.readRouteFromFile(charName, mapName)\r\n\r\n\t\treturn\r\n\r\n\t#run bot function\r\n\tdef runBot(self):\r\n\t\t#initializing info\r\n\t\tself.initializeInfo()\r\n\r\n\t\t#ready user for bot\r\n\t\tinput(\"You will have 2 seconds to tab into the game. Are you ready to start? (Enter anything to continue): \")\r\n\t\twait(2)\r\n\r\n\t\t#starting bot\r\n\t\tbot.start()\r\n\r\n\t\treturn\r\n\r\n\t#pause menu function\r\n\tdef menuPause(self):\r\n\t\t#printing menu and receiving input\r\n\t\tprint(\"PAUSE MENU\")\r\n\t\tprint(\"1. Continue Bot\")\r\n\t\tprint(\"2. Refresh Data\")\r\n\t\tprint(\"3. Exit Bot\")\r\n\t\tchoice = input(\">> \")\r\n\r\n\t\t#checking if choice is valid\r\n\t\twhile(choice != \"1\" and choice != \"2\" and choice != \"3\" and choice != \"\"):\r\n\t\t\tchoice = input(\"Enter a valid input: \")\r\n\r\n\t\t#resume bot\r\n\t\tif(choice == \"1\" or choice == \"\"):\r\n\t\t\tprint(\"Resuming bot\")\r\n\t\t\tbot.refresh()\r\n\r\n\t\t\treturn\r\n\r\n\t\t#refresh data\r\n\t\tif(choice == \"2\"):\r\n\t\t\tglobal char\r\n\t\t\tglobal mapdata\r\n\t\t\tglobal route\r\n\t\t\tcharName = char.charName\r\n\t\t\tmapName = mapdata.mapName\r\n\t\t\tchar = CharacterData()\r\n\t\t\tmapdata = MapData()\r\n\t\t\troute = RouteData()\r\n\t\t\tchar.readCharacterFromFile(charName)\r\n\t\t\tmapdata.readMapFromFile(mapName)\r\n\t\t\troute.readRouteFromFile(charName, mapName)\r\n\r\n\t\t\tprint(\"Resuming bot\")\r\n\t\t\tbot.refresh()\r\n\r\n\t\t\treturn\r\n\r\n\t\t#exit bot\r\n\t\tif(choice == \"3\"):\r\n\t\t\tprint(\"Exiting bot\")\r\n\t\t\trelease('Left')\r\n\t\t\trelease('Right')\r\n\t\t\tsys.exit()\r\n\r\n\t\treturn\r\n\r\n#creating Bot class\r\nclass Bot:\r\n\tdef __init__(self):\r\n\t\tself.prevPos = None\r\n\t\tself.movementStuckCount = 0\r\n\r\n\t#run bot function\r\n\tdef start(self):\r\n\t\t#initial refresh\r\n\t\tself.prevPos = self.characterPos()\r\n\t\tself.refresh()\r\n\r\n\t\t#run sequence\r\n\t\twhile(True):\r\n\t\t\tself.runSequence()\r\n\t\t\tself.refresh()\r\n\r\n\t\treturn\r\n\r\n\t#function to refresh and check variables in between sequences\r\n\tdef refresh(self):\r\n\t\t#check if pause key is pressed\r\n\t\tif(keyboard.is_pressed(\"F8\")):\r\n\t\t\tmenu.menuPause()\r\n\r\n\t\t#potion check\r\n\t\tchar.potions.checkPotions()\r\n\r\n\t\t#check buffs\r\n\t\tchar.checkBuffs()\r\n\r\n\t\t#check rune\r\n\t\trune.checkRune()\r\n\r\n\t\t#check other players\r\n\t\totherplayercheck.checkOtherPlayer()\r\n\r\n\t\t#timed death check\r\n\t\terrorcheck.timedCheckDeath()\r\n\r\n\t\treturn\r\n\r\n\t#function to run a sequence step\r\n\tdef runSequence(self):\r\n\t\t#get player location\r\n\t\tcharPos = self.characterPos()\r\n\r\n\t\t# #check if position is repeated\r\n\t\tif (self.prevPos == charPos):\r\n\t\t\tself.movementStuckCount = self.movementStuckCount + 1\r\n\t\t\tif (self.movementStuckCount >= 5):\r\n\t\t\t\terrorcheck.mashOutOfEMStun()\r\n\t\t\t\terrorcheck.checkAllErrors()\r\n\t\telse:\r\n\t\t\tself.movementStuckCount = 0\r\n\r\n\t\t#setting previous position\r\n\t\tself.prevPos = charPos\r\n\r\n\t\t#finding section\r\n\t\tsection = self.findSection(charPos)\r\n\t\tif (section == None):\r\n\t\t\treturn\r\n\r\n\t\t#run sequence of the section\r\n\t\tfor i in range(len(route.instructions[section].sequence)):\r\n\t\t\texec(route.instructions[section].sequence[i])\r\n\r\n\t\treturn\r\n\r\n\t#getting character position on mini map\r\n\tdef characterPos(self):\r\n\t\tcharPos = pyautogui.locate(markers.charMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height)))\r\n\t\tfailCounter = 0\r\n\t\t#if character is not found\r\n\t\twhile (charPos == None):\r\n\t\t\tcharPos = pyautogui.locate(markers.charMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, mapdata.width, mapdata.height)))\r\n\t\t\t#repeat 50 times. then if character is still not found\r\n\t\t\tif (failCounter >= 50):\r\n\t\t\t\t#check if player icon can be found outside of map size\r\n\t\t\t\tcharPos = pyautogui.locate(markers.charMarker, pyautogui.screenshot(region=(mapdata.minX, mapdata.minY, 600, 500)))\r\n\t\t\t\tif (not(charPos == None)):\r\n\t\t\t\t\tprint(\"Not in current map dimensions\")\r\n\t\t\t\t\terrorcheck.checkAllErrors()\r\n\t\t\t\telse:\r\n\t\t\t\t\tfailCounter = 0\r\n\t\t\tfailCounter = failCounter + 1\r\n\r\n\t\treturn charPos\r\n\r\n\t#finds the section of the character positioning\r\n\tdef findSection(self, charPos):\r\n\t\tfor i in range (route.routeNum):\r\n\t\t\tif (route.instructions[i].Y == charPos[1]):\r\n\t\t\t\tif (route.instructions[i].leftX <= charPos[0] and route.instructions[i].rightX >= charPos[0]):\r\n\t\t\t\t\treturn i\r\n\r\n\t\tfor i in range (route.routeNum):\r\n\t\t\tif (route.instructions[i].Y == charPos[1]):\r\n\t\t\t\tif (route.instructions[i].leftX - 1 <= charPos[0] and route.instructions[i].rightX + 1 >= charPos[0]):\r\n\t\t\t\t\treturn i\r\n\t\t\t\r\n\t\treturn None\r\n\r\n#function to wait x seconds\r\ndef wait(x):\r\n\ttime.sleep(x)\r\n\r\n#function to press a key\r\ndef press(key):\r\n\tkeyboard.press(key)\r\n\twait(0.05)\r\n\tkeyboard.release(key)\r\n\r\n#function to hold a key\r\ndef hold(key):\r\n\tif(not(keyboard.is_pressed(key))):\r\n\t\tkeyboard.press(key)\r\n\r\n#function to release a key\r\ndef release(key):\r\n\tkeyboard.release(key)\r\n\r\n#function to down jump\r\ndef downjump():\r\n\tpress(\"right\")\r\n\thold(\"down\")\r\n\twait(0.05)\r\n\tjump()\r\n\trelease(\"down\")\r\n\twait(0.3)\r\n\r\ndef turnLeft():\r\n\trelease(\"Right\")\r\n\twait(0.05)\r\n\thold(\"Left\")\r\n\twait(0.05)\r\n\trelease(\"Left\")\r\n\r\ndef turnRight():\r\n\trelease(\"Left\")\r\n\twait(0.05)\r\n\thold(\"Right\")\r\n\twait(0.05)\r\n\trelease(\"Right\")\r\n\r\n#function to jump\r\ndef jump():\r\n\tpress(char.jumpKey)\r\n\r\n#function to attack\r\ndef attack():\r\n\tpress(char.attackKey)\r\n\r\n#function to use skill\r\ndef useSkill(name):\r\n\tpress(char.skills[name].key)\r\n\r\ndef usePreset(name):\r\n\tif (time.perf_counter() > char.presets[name].timer):\r\n\t\tfor i in range(len(char.presets[name].instructions)):\r\n\t\t\texec(char.presets[name].instructions[i])\r\n\t\tchar.presets[name].timer = time.perf_counter() + char.presets[name].cooldown\r\n\r\n#function to move to a position\r\ndef moveTo(x, y):\r\n\toverTimer = time.perf_counter() + 20\r\n\tif (char.ropeLiftExist == False):\r\n\t\twhile(True):\r\n\t\t\tif (overTimer < time.perf_counter()):\r\n\t\t\t\terrorcheck.checkAllErrors()\r\n\t\t\t\toverTimer = time.perf_counter() + 20\r\n\r\n\t\t\tcharPos = bot.characterPos()\r\n\t\t\tif(charPos[0] < x - 2):\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\thold(\"Right\")\r\n\t\t\telif(charPos[0] > x + 2):\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\thold(\"Left\")\r\n\t\t\telif(charPos[0] < x):\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\tpress(\"Right\")\r\n\t\t\t\twait(0.1)\r\n\t\t\telif(charPos[0] > x):\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\tpress(\"Left\")\r\n\t\t\t\twait(0.1)\r\n\t\t\telse:\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\twait(0.1)\r\n\t\t\t\tcharPos = bot.characterPos()\r\n\t\t\t\tif (charPos[0] == x and charPos[1] == y):\r\n\t\t\t\t\treturn True\r\n\t\t\t\telse:\r\n\t\t\t\t\treturn False\r\n\telse:\r\n\t\twhile(True):\r\n\t\t\tif (overTimer < time.perf_counter()):\r\n\t\t\t\terrorcheck.checkAllErrors()\r\n\t\t\t\toverTimer = time.perf_counter() + 20\r\n\r\n\t\t\tcharPos = bot.characterPos()\r\n\t\t\tif(charPos[0] < x - 3):\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\thold(\"Right\")\r\n\t\t\telif(charPos[0] > x + 2):\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\thold(\"Left\")\r\n\t\t\telif(charPos[0] < x):\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\tpress(\"Right\")\r\n\t\t\t\twait(0.1)\r\n\t\t\telif(charPos[0] > x):\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\tpress(\"Left\")\r\n\t\t\t\twait(0.1)\r\n\t\t\telse:\r\n\t\t\t\trelease(\"Left\")\r\n\t\t\t\trelease(\"Right\")\r\n\t\t\t\tif(charPos[1] < y - 2):\r\n\t\t\t\t\tpress(\"Right\")\r\n\t\t\t\t\twait(0.1)\r\n\t\t\t\t\thold(\"Down\")\r\n\t\t\t\t\twait(0.1)\r\n\t\t\t\t\tpress(char.jumpKey)\r\n\t\t\t\t\twait(0.2)\r\n\t\t\t\t\trelease(\"Down\")\r\n\t\t\t\telif(charPos[1] > y + 2):\r\n\t\t\t\t\tuseSkill(\"Rope Lift\")\r\n\t\t\t\t\twait(4)\r\n\t\t\t\telse:\r\n\t\t\t\t\twait(0.1)\r\n\t\t\t\t\treturn True\r\n\r\n#main function\r\ndef main():\r\n\t#initializing global classes\r\n\r\n\tglobal menu\r\n\tglobal bot\r\n\tglobal markers\r\n\tglobal char\r\n\tglobal mapdata\r\n\tglobal route\r\n\tglobal rune\r\n\tglobal otherplayercheck\r\n\tglobal errorcheck\r\n\r\n\tmenu = Menu()\r\n\tbot = Bot()\r\n\tmarkers = Markers()\r\n\tchar = CharacterData()\r\n\tmapdata = MapData()\r\n\troute = RouteData()\r\n\trune = Rune()\r\n\totherplayercheck = OtherPlayerCheck()\r\n\terrorcheck = ErrorCheck()\r\n\r\n\r\n\t#prompting user with menu and running bot\r\n\tmenu.runBot()\r\n\r\n\treturn\r\n\r\nmain()","sub_path":"MaplestoryBot - Copy.py","file_name":"MaplestoryBot - Copy.py","file_ext":"py","file_size_in_byte":32757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"577156735","text":"import numpy as np\nimport pytest\nimport torch\nfrom lab.torch import B\nfrom stheno import GP\n\nfrom gpar.regression import (\n _vector_from_init,\n log_transform,\n squishing_transform,\n GPARRegressor,\n _construct_gpar,\n _determine_indices\n)\nfrom .util import allclose, approx, tensor\n\n\ndef test_transforms():\n f, f_inv = log_transform\n allclose(f(f_inv(tensor([1, 2, 3, 4]))), tensor([1, 2, 3, 4]))\n f, f_inv = squishing_transform\n allclose(f(f_inv(tensor([-2, -1, 3, 4]))), tensor([-2, -1, 3, 4]))\n\n\ndef test_vector_from_init():\n allclose(_vector_from_init(2, 2), np.array([2, 2]))\n allclose(_vector_from_init(np.array([1, 2, 3]), 2), np.array([1, 2]))\n with pytest.raises(ValueError):\n _vector_from_init(np.random.randn(2, 2), 1)\n with pytest.raises(ValueError):\n _vector_from_init(np.array([1, 2]), 3)\n\n\ndef test_determine_indices():\n # No Markov structure.\n assert _determine_indices(1, 0, None) == ([0], [], 0)\n assert _determine_indices(1, 1, None) == ([0], [1], 1)\n assert _determine_indices(1, 2, None) == ([0], [1, 2], 2)\n assert _determine_indices(2, 0, None) == ([0, 1], [], 0)\n assert _determine_indices(2, 1, None) == ([0, 1], [2], 1)\n assert _determine_indices(2, 2, None) == ([0, 1], [2, 3], 2)\n\n # Markov order: 0.\n assert _determine_indices(1, 0, 0) == ([0], [], 0)\n assert _determine_indices(1, 1, 0) == ([0], [], 0)\n assert _determine_indices(1, 2, 0) == ([0], [], 0)\n assert _determine_indices(2, 0, 0) == ([0, 1], [], 0)\n assert _determine_indices(2, 1, 0) == ([0, 1], [], 0)\n assert _determine_indices(2, 2, 0) == ([0, 1], [], 0)\n\n # Markov order: 1.\n assert _determine_indices(1, 0, 1) == ([0], [], 0)\n assert _determine_indices(1, 1, 1) == ([0], [1], 1)\n assert _determine_indices(1, 2, 1) == ([0], [2], 1)\n assert _determine_indices(2, 0, 1) == ([0, 1], [], 0)\n assert _determine_indices(2, 1, 1) == ([0, 1], [2], 1)\n assert _determine_indices(2, 2, 1) == ([0, 1], [3], 1)\n\n # Markov order: 2.\n assert _determine_indices(1, 0, 2) == ([0], [], 0)\n assert _determine_indices(1, 1, 2) == ([0], [1], 1)\n assert _determine_indices(1, 2, 2) == ([0], [1, 2], 2)\n assert _determine_indices(2, 0, 2) == ([0, 1], [], 0)\n assert _determine_indices(2, 1, 2) == ([0, 1], [2], 1)\n assert _determine_indices(2, 2, 2) == ([0, 1], [2, 3], 2)\n\n\ndef test_get_variables():\n gpar = GPARRegressor()\n gpar.vs.get(init=1.0, name='variable')\n assert list(gpar.get_variables().items()) == [('variable', 1.0)]\n\n\ndef test_logpdf():\n # Sample some data from a \"sensitive\" GPAR.\n reg = GPARRegressor(replace=False, impute=False,\n nonlinear=True, nonlinear_scale=0.1,\n linear=True, linear_scale=10.,\n noise=1e-4, normalise_y=False)\n x = np.linspace(0, 5, 10)\n y = reg.sample(x, p=2, latent=True)\n\n # Extract models.\n gpar = _construct_gpar(reg, reg.vs, 1, 2)\n f1, e1 = gpar.layers[0]()\n f2, e2 = gpar.layers[1]()\n\n # Test computation under prior.\n logpdf1 = (f1 + e1)(tensor(x)).logpdf(tensor(y[:, 0]))\n x_stack = np.concatenate([x[:, None], y[:, 0:1]], axis=1)\n logpdf2 = (f2 + e2)(tensor(x_stack)).logpdf(tensor(y[:, 1]))\n approx(reg.logpdf(x, y), logpdf1 + logpdf2, digits=6)\n\n # Test computation under posterior.\n e1_post = GP(e1.kernel, e1.mean, graph=e1.graph)\n e2_post = GP(e2.kernel, e2.mean, graph=e2.graph)\n f1_post = f1 | ((f1 + e1)(tensor(x)), tensor(y[:, 0]))\n f2_post = f2 | ((f2 + e2)(tensor(x_stack)), tensor(y[:, 1]))\n logpdf1 = (f1_post + e1_post)(tensor(x)).logpdf(tensor(y[:, 0]))\n logpdf2 = (f2_post + e2_post)(tensor(x_stack)).logpdf(tensor(y[:, 1]))\n with pytest.raises(RuntimeError):\n reg.logpdf(x, y, posterior=True)\n reg.condition(x, y)\n approx(reg.logpdf(x, y, posterior=True), logpdf1 + logpdf2, digits=6)\n\n # Test that sampling missing gives a stochastic estimate.\n y[::2, 0] = np.nan\n assert np.abs(reg.logpdf(x, y, sample_missing=True) -\n reg.logpdf(x, y, sample_missing=True)) >= 1e-3\n\n\ndef test_logpdf_differentiable():\n reg = GPARRegressor(replace=False, impute=False,\n linear=True, linear_scale=1., nonlinear=False,\n noise=1e-8, normalise_y=False)\n x = np.linspace(0, 5, 10)\n y = reg.sample(x, p=2, latent=True)\n\n # Test that gradient calculation works.\n reg.vs.requires_grad(True)\n for var in reg.vs.get_vars():\n assert var.grad is None\n reg.logpdf(torch.tensor(x), torch.tensor(y)).backward()\n for var in reg.vs.get_vars():\n assert var.grad is not None\n\n\ndef test_sample_and_predict():\n # Use output transform to ensure that is handled correctly.\n reg = GPARRegressor(replace=False, impute=False,\n linear=True, linear_scale=1., nonlinear=False,\n noise=1e-8, normalise_y=False,\n transform_y=squishing_transform)\n x = np.linspace(0, 5, 5)\n\n # Test checks.\n with pytest.raises(ValueError):\n reg.sample(x)\n with pytest.raises(RuntimeError):\n reg.sample(x, posterior=True)\n\n # Test that output is simplified correctly.\n assert isinstance(reg.sample(x, p=2), np.ndarray)\n assert isinstance(reg.sample(x, p=2, num_samples=2), list)\n\n # Test that it produces random samples. Not sure how to test correctness.\n assert np.sum(np.abs(reg.sample(x, p=2) - reg.sample(x, p=2))) >= 1e-2\n assert np.sum(np.abs(reg.sample(x, p=2, latent=True) -\n reg.sample(x, p=2, latent=True))) >= 1e-3\n\n # Test that mean of posterior samples are around the data.\n y = reg.sample(x, p=2)\n reg.condition(x, y)\n approx(y, np.mean(reg.sample(x,\n posterior=True,\n num_samples=100), axis=0), digits=3)\n approx(y, np.mean(reg.sample(x,\n latent=True,\n posterior=True,\n num_samples=100), axis=0), digits=3)\n\n # Test that prediction is around the data.\n approx(y, reg.predict(x, num_samples=100), digits=3)\n approx(y, reg.predict(x, latent=True, num_samples=100), digits=3)\n\n # Test that prediction is confident.\n _, lowers, uppers = reg.predict(x, num_samples=100, credible_bounds=True)\n assert np.less_equal(uppers - lowers, 1e-2).all()\n\n\ndef test_condition_and_fit():\n reg = GPARRegressor(replace=False, impute=False,\n normalise_y=True, transform_y=squishing_transform)\n x = np.linspace(0, 5, 10)\n y = reg.sample(x, p=2)\n\n # Test that data is correctly normalised.\n reg.condition(x, y)\n approx(B.mean(reg.y, axis=0), B.zeros(reg.p))\n approx(B.std(reg.y, axis=0), B.ones(reg.p))\n\n # Test that data is correctly normalised if it has an output with zero\n # variance.\n y_pathological = y.copy()\n y_pathological[:, 0] = 1\n reg.condition(x, y_pathological)\n assert (~B.isnan(reg.y)).numpy().all()\n\n # Test transformation and normalisation of outputs.\n z = torch.linspace(-1, 1, 10, dtype=torch.float64)\n z = B.stack(z, 2 * z, axis=1)\n allclose(reg._untransform_y(reg._transform_y(z)), z)\n allclose(reg._unnormalise_y(reg._normalise_y(z)), z)\n\n # Test that fitting runs without issues.\n vs = reg.vs.copy(detach=True)\n reg.fit(x, y, fix=False)\n reg.vs = vs\n reg.fit(x, y, fix=True)\n\n # TODO: Remove this once greedy search is implemented.\n with pytest.raises(NotImplementedError):\n reg.fit(x, y, greedy=True)\n\n\ndef test_features():\n # Test that optimisation runs for a full-fledged GPAR.\n reg = GPARRegressor(replace=True, scale=1.0,\n per=True, per_period=1.0, per_decay=10.0,\n input_linear=True, input_linear_scale=0.1,\n linear=True, linear_scale=1.0,\n nonlinear=True, nonlinear_scale=1.0,\n rq=True, noise=0.1)\n x = np.stack([np.linspace(0, 10, 20),\n np.linspace(10, 20, 20)], axis=1)\n y = reg.sample(x, p=2)\n reg.fit(x, y, iters=10)\n\n\ndef test_scale_tying():\n reg = GPARRegressor(scale_tie=True)\n reg.sample(np.linspace(0, 10, 20), p=2) # Instantiate variables.\n vs = reg.get_variables()\n assert '0/input/scales' in vs\n assert '1/input/scales' not in vs\n\n\ndef test_inducing_points_uprank():\n reg = GPARRegressor(x_ind=np.linspace(0, 10, 20))\n assert reg.x_ind is not None\n assert B.rank(reg.x_ind) == 2\n","sub_path":"tests/test_regression.py","file_name":"test_regression.py","file_ext":"py","file_size_in_byte":8604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"528467866","text":"import socket\n\nif __name__ == '__main__':\n # 创建tcp客户端套接字\n tcp_client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # 建立连接\n tcp_client_socket.connect((\"tlias3.boxuegu.com\", 80))\n # 请求行\n request_line = \"GET / HTTP/1.1\\r\\n\"\n # 请求头\n request_header = \"Host: tlias3.boxuegu.com\\r\\nConnection:close\\r\\n\"\n # 准备http请求报文数据\n request_content = request_line + request_header + \"\\r\\n\"\n # 发送http请求报文数据\n tcp_client_socket.send(request_content.encode(\"utf-8\"))\n # 定义二进制响应数据的类型\n result = b\"\"\n # 接收服务端http响应报文数据\n while True:\n # 提示： 服务端断开连接，recv会解阻塞，返回的数据长度0\n # 提示： 以后可以通过Content-Length判断服务端发送数据的长度\n recv_data = tcp_client_socket.recv(1024)\n if recv_data:\n # 表示接收到了数据\n result += recv_data\n\n # print(result)\n else:\n break\n\n # 显示原始的响应报文数据\n print(result)\n # 解码 ： 把二进制数据转成字符串\n response_content = result.decode(\"utf-8\")\n # 根据指定标识数据进行分割\n response_list = response_content.split(\"\\r\\n\\r\\n\", 1)\n # response_content.split(\"\\r\\n\\r\\n\", maxsplit=1)\n\n print(len(response_list))\n print(response_list[1])\n # 关闭套接字\n tcp_client_socket.close()\n\n","sub_path":"html回复报文.py","file_name":"html回复报文.py","file_ext":"py","file_size_in_byte":1482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"132891995","text":"import os\n\nRACES = (\"Human\", \"Charr\", \"Norn\", \"Asura\", \"Sylvari\")\nGENDERS = (\"Male\", \"Female\")\nPROFESSIONS = (\"Guardian\", \"Revenant\", \"Warrior\", \"Engineer\", \"Ranger\",\n \"Thief\", \"Elementalist\", \"Mesmer\", \"Necromancer\")\nBOOL_INPUT = (\"Yes\", \"Y\", \"No\", \"N\")\nINFO = \"Info\"\nCLEAR_VAR = os.system(\"cls\")\n\n\ndef clear():\n os.system(\"cls\")\n\n\ndef divider():\n print(\"-----------------\")\n\n\ndef br():\n print(\"\")\n\n\ndef info_header():\n divider()\n print(\"//CHOICE INFO//\")\n\n\ndef invalid_input():\n divider()\n print(\"//INVALID INPUT//\")\n\n\ndef info_help():\n print(\"//Type \\\"info\\\" at any time to view information about your current choices.//\")\n\n\n# yes or no input\ndef yes_no(question, options, option_info):\n yes_no_loop = True\n while yes_no_loop is True:\n divider()\n yes_no_var = input_check(question, options)\n yes_no_var = yes_no_var.capitalize()\n if yes_no_var not in BOOL_INPUT and not INFO:\n invalid_input()\n elif yes_no_var == INFO:\n information(option_info)\n elif yes_no_var == \"Yes\" or yes_no_var == \"Y\":\n clear()\n yes_no_loop = False\n elif yes_no_var == \"No\" or yes_no_var == \"N\":\n continue\n else:\n invalid_input()\n\n\n# display choice information\ndef information(*info_list):\n info_header()\n info_var = list(info_list)\n for x in info_var:\n br()\n print(x)\n\n\n# to prevent crashing with no input\ndef input_check(question, options):\n global input_var\n check = True\n while check is True:\n divider()\n print(question)\n print(options)\n input_var = input()\n if input_var != \"\":\n input_var = input_var.capitalize()\n return input_var\n else:\n invalid_input()\n\n\ndef intro():\n print(\"//Welcome to Guild Wars 2: The Text Adventure//\")\n info_help()\n yes_no(\"Would you like to start a new character?:\",\n ##\n \"[Yes | No]\",\n ##\n \"Currently only \\\"New Character\\\" is available, type \\\"Yes\\\" or \\\"Y\\\" to continue.\")\n\n\ndef racial_menu():\n info_help()\n global player_race\n player_race_choice = True\n while player_race_choice is True:\n player_race = input_check(\"Choose a race:\",\n \"[Human | Charr | Norn | Asura | Sylvari]\")\n if player_race not in RACES and not player_race == INFO:\n invalid_input()\n elif player_race == INFO:\n information(\"Human: Humans have lost their homeland, their security, and their former glory. \"\n \"Even their gods have withdrawn. And yet, the human spirit remains unshaken. \"\n \"These brave defenders of Kryta continue to fight with every ounce of their strength.\",\n ##\n \"Charr: The charr race was forged in the merciless crucible of war. It is all they know. \"\n \"War defines them, and their quest for dominion drives them ever onward. \"\n \"The weak and the foolish have no place among the charr. \"\n \"Victory is all that matters, and it must be achieved by any means and at any cost.\",\n ##\n \"Norn: The race of towering hunters experienced a great defeat when the Ice Dragon drove \"\n \"them from their glacial homeland. Nevertheless, they won't let one lost battle - however \"\n \"punishing - dampen their enthusiasm for life and the hunt. \"\n \"They know that only the ultimate victor achieves legendary rewards.\",\n ##\n \"Asura: These alchemagical inventors may be short in stature, but they're intellectual giants. \"\n \"Among the asura, it's not the strong who survive, but the clever. \"\n \"Other races believe they should rule by virtue of their power and strength, \"\n \"but they're deluding themselves. In due time, all will serve the asura.\",\n ##\n \"Sylvari: Sylvari are not born. They awaken beneath the Pale Tree with knowledge gleaned \"\n \"in their pre-life Dream. These noble beings travel, seeking adventure and pursuing quests. \"\n \"They struggle to balance curiosity with duty, eagerness with chivalry, and warfare with \"\n \"honor. Magic and mystery entwine to shape the future of this race that has so recently \"\n \"appeared.\")\n else:\n clear()\n player_race_choice = False\n\n\ndef gender_menu():\n info_help()\n global player_gender\n gender_choice = True\n while gender_choice is True:\n player_gender = input_check(\"Pick a gender:\",\n \"[Male | Female]\")\n if player_gender not in GENDERS and not player_gender == INFO:\n invalid_input()\n elif player_gender == INFO:\n information(\"Every race has the same options for gender: male or female. \"\n \"Aside from physical appearance and some dialogue, there are no gameplay \"\n \"differences between them.\")\n else:\n clear()\n gender_choice = False\n\n\ndef profession_menu():\n info_help()\n global player_profession\n profession_choice = True\n while profession_choice is True:\n player_profession = input_check(\"Pick a profession:\",\n \"[Guardian | Revenant | Warrior | Engineer | Ranger | Thief | \"\n \"Elementalist | Mesmer | Necromancer]\")\n if player_profession not in PROFESSIONS and not player_profession == INFO:\n invalid_input()\n elif player_profession == INFO:\n information(\"//SOLDIERS - Heavy Armor//\\n\"\n \"Guardian: Guardians specialize in protective and defensive magic. A deep sense of loyalty to \"\n \"their allies fuels their passion and power. \"\n \"They're also skilled with a variety of weapons which they put to use against their enemies.\",\n ##\n \"Revenant: Revenants manipulate energy from the Mists to invoke the power of legends from the \"\n \"past, unleashing immense attacks that dominate foes and unleash chaos on the battlefield.\",\n ##\n \"Warrior: Warriors are masters of martial skills. They rely on speed, strength, toughness and \"\n \"heavy armor to survive. They're versatile in combat and benefit from offensive and defensive \"\n \"abilities. Warriors inspire allies and demoralize enemies.\",\n ##\n \"//ADVENTURERS - Medium Armor//\\n\"\n \"Engineer: Engineers are technological and alchemical masterminds. \"\n \"They employ turrets, grenades, elixirs, and a variety of other impressive devices and \"\n \"concoctions to overcome their enemies.\",\n ##\n \"Ranger: Rangers are proficient with the bow. They rely on a keen eye, a steady hand, and the \"\n \"power of nature to slay their targets. Their loyal pets, which rangers tame and train, \"\n \"distract enemies while the rangers strike safely from a distance.\",\n ##\n \"Thief: Thieves are adept at the art of stealth. They utilize surprise and shadow to get close \"\n \"to their enemies, and they're deadly in one-on-one combat. \"\n \"They have an affinity for setting traps and going where they were never meant to go.\",\n ##\n \"//SCHOLARS - Light Armor//\\n\"\n \"Elementalist: Elementalists have harnessed Tyria's natural forces. Their powers of \"\n \"destruction are drawn from an affinity with the four elements that make up the world. \"\n \"They conjure air, fire, earth, or water to assault their enemies.\",\n ##\n \"Mesmer: Mesmers are maestros of mirage. They weave mental magic that confounds, controls, or \"\n \"evokes emotion in their enemies. With a wave of the hand, they can shatter their own \"\n \"illusions to produce even greater special effects.\",\n ##\n \"Necromancer: Necromancers are masters of dark arts. They summon the dead to fight for them, \"\n \"channel blood energy, and rend their enemies' souls. Necromancers draw life force and use it \"\n \"to strengthen or heal themselves and others.\")\n else:\n clear()\n profession_choice = False\n\n\ndef human_menu():\n global raised\n global regret\n global blessing\n raised_list = (\"In the street\", \"By the common folk\", \"Among the nobility\")\n regret_list = (\"I've never searched for my true parents\", \"I never recovered my sister's body\", \"I passed \"\n \"up an opportunity \"\n \"to perform in the \"\n \"circus\")\n blessing_list = (\"Dwayna\", \"Grenth\", \"Balthazar\", \"Melandru\", \"Lyssa\", \"Kormir\")\n info_help()\n raised_choice = True\n while raised_choice is True:\n raised = input_check(\"I was raised _____.:\",\n \"[in the street | by the common folk | among the nobility]\")\n if raised not in raised_list and not raised == INFO:\n invalid_input()\n elif raised == INFO:\n information(\"Street Rat: I grew up poor, on the streets, living hand to mouth. Every day was a challenge, \"\n \"but like I tell my ol' buddy Quinn, \\\"If it doesn't kill you, it makes you stronger.\\\" \"\n \"I've worked hard to change my luck, but I've still got a long way to go.\",\n ##\n \"Commoner: I was raised among the common folk of Divinity's Reach. \"\n \"We're the working class, the cogs that keep the city running. \"\n \"I info_help out at a tavern owned by my friends, Andrew and his daughter Petra.\",\n ##\n \"Nobility: I grew up among the nobles, including my friend Lord Faren, who can trace their \"\n \"ancestry back to ancient kings. I received an excellent education, am well versed in courtly \"\n \"graces, and understand the responsibility that comes with privilege.\")\n else:\n clear()\n raised_choice = False\n info_help()\n regret_choice = True\n while regret_choice is True:\n regret = input_check(\"One of my biggest regrets is that _____.:\",\n \"[I've never searched for my true parents | I never recovered my sister's body | \"\n \"I passed up an opportunity to perform in the circus]\")\n if regret not in regret_list and not regret == INFO:\n invalid_input()\n elif regret == INFO:\n information(\"Unknown Parents: When I was an infant, I was abandoned at an orphanage. \"\n \"A kind couple adopted me and became my family. \"\n \"However, I've always wondered about my birth parents.\",\n ##\n \"Dead Sister: My sister was a Seraph. Centaurs killed her while she was out on patrol. \"\n \"They never recovered her body, and it has always bothered me.\",\n ##\n \"Missed Opportunity: When I was young, I had the opportunity to perform in the circus, but I \"\n \"turned it down. To this day, I'm sorry I didn't leap at the chance.\")\n else:\n clear()\n regret_choice = False\n info_help()\n blessing_choice = True\n while blessing_choice is True:\n blessing = input_check(\"Everyone said I was blessed by _____ when I was young.:\",\n \"[Dwayna | Grenth | Balthazar | Melandru | Lyssa | Kormir]\")\n if blessing not in blessing_list and not blessing == INFO:\n invalid_input()\n elif blessing == INFO:\n information(\"Dwayna: Dwayna—the goddess of healing, air and life—is the even-tempered leader of the \"\n \"old gods. She is often depicted as young, tall, and slender, rising into the air on immense \"\n \"feathery wings.\",\n ##\n \"Grenth: Grenth is the god of darkness, ice, and death. \"\n \"His acolytes know that the veil between worlds is thin. Death does not frighten me. \"\n \"Even the darkest nights give me comfort because Grenth watches over me.\",\n ##\n \"Balthazar: Balthazar—the god of war, fire, and challenge—oversees the battle arena. \"\n \"He gifts those who have a knack for combat strategy and skill with weapons. \"\n \"I have trained hard to honor Balthazar.\",\n ##\n \"Melandro: Melandru—the goddess of nature, earth, and growth—can be found in every harvest and \"\n \"every flower. She smiles upon those, like me, who have an affinity with animals. \"\n \"I am a follower of Melandru.\",\n ##\n \"Lyssa: Lyssa wears many masks. She is the dual-faced goddess of beauty, water, and illusion. \"\n \"She is the patron of the most attractive and graceful among us. \"\n \"Her blessings have touched me all my life.\",\n ##\n \"Kormir: Kormir is the goddess of order, spirit, and truth. She was once mortal like me. \"\n \"She inspires me every day to find greater courage and to seek rightness of heart, mind, and \"\n \"action.\")\n else:\n clear()\n blessing_choice = False\n\n\ndef charr_menu():\n global legion\n global partner\n global father\n legion_list = (\"Blood legion\", \"Ash legion\", \"Iron legion\")\n partner_list = (\"Maverick\", \"Euryale\", \"Clawspur\", \"Dinky\", \"Reeva\")\n father_list = (\"Loyal soldier\", \"Sorcerous shaman\", \"Honorless gladium\")\n info_help()\n legion_choice = True\n while legion_choice is True:\n legion = input_check(\"I am proud to be a(n) _____ soldier.:\",\n \"[Blood Legion | Ash Legion | Iron Legion]\")\n if legion not in legion_list and not legion == INFO:\n invalid_input()\n elif legion == INFO:\n information(\"Blood Legion: I'm Blood Legion. I charge straight into battle. \"\n \"The Blood Legion pushes to the front line in any combat. \"\n \"We're powerful and bold, and none can match our prowess on the field.\",\n ##\n \"Ash Legion: I'm Ash Legion. I use cunning to overcome my enemies. \"\n \"The Ash Legion teaches stealth and subterfuge. \"\n \"We end battles before they begin with infiltration, information gathering, and precision \"\n \"strikes.\",\n ##\n \"Iron Legion: I'm Iron Legion. I march to the boom of war machines. \"\n \"The Iron Legion has ignited our industrial revolution. \"\n \"We perfected metalwork, cannons, and—most importantly—guns.\")\n else:\n clear()\n legion_choice = False\n info_help()\n partner_choice = True\n while partner_choice is True:\n partner = input_check(\"I would die for my warband, especially _____, my sparring partner.:\",\n \"[Maverick | Euryale | Clawspur | Dinky | Reeva]\")\n if partner not in partner_list and not partner == INFO:\n invalid_input()\n elif partner == INFO:\n information(\"Maverick: Maverick is the best single-combat fighter I've ever met. \"\n \"Unfortunately, he has a wild streak and an ego the size of the Black Citadel. \"\n \"He may be a loose cannon, but he knows how to make things epic!\",\n ##\n \"Euryale: Euryale, our elementalist, is the most loyal soldier in the warband. \"\n \"She's also stubborn, bad-tempered and protective — but at least she's honest. \"\n \"She stands by me, and I trust her with my life.\",\n ##\n \"Clawspur: Clawspur lets his blades do the talking. \"\n \"He's extremely calm and quiet, even for a thief. \"\n \"While others are making threats, Clawspur is silently positioning himself to make a \"\n \"quick kill. Good thing he's on my side.\",\n ##\n \"Dinky: Dinky was the smallest cub in our fahrar, so he had to be twice as tough just to \"\n \"break even. He gets picked on because he's a guardian, and not so smart, but I stand by him. \"\n \"He's always been a good friend.\",\n ##\n \"Reeva: Reeva fights hard and plays hard. Nothing gets her down. \"\n \"She always jokes that her best weapons are her engineering tools and a sharp sense of humor. \"\n \"To Reeva, life isn't worth much if it isn't fun.\")\n else:\n clear()\n partner_choice = False\n info_help()\n father_choice = True\n while father_choice is True:\n father = input_check(\"They tell me the soldier that sired me is a _____.:\",\n \"[loyal soldier | sorcerous shaman | honorless gladium]\")\n if father not in father_list and not father == INFO:\n invalid_input()\n elif father == INFO:\n information(\"Loyal Soldier: I've heard my sire's name spoken with reverence ever since I was a cub in the \"\n \"fahrar. I plan to live up to his reputation—or exceed it.\",\n ##\n \"Sorcerous Shaman: Flame Legion shamans once enslaved us. \"\n \"Because of this, my father, who is a shaman, is treated with suspicion and mistrust. \"\n \"I must overcome his reputation\",\n ##\n \"Honorless Gladium: A gladium has no warband. My father turned his back on his. \"\n \"Now, he's an honorless gladium with no respect for the chain of command, and I have no \"\n \"respect for him.\")\n else:\n clear()\n father_choice = False\n\n\ndef norn_menu():\n global quality\n global celebration\n global spirit\n quality_list = (\"Strength to defeat ancient foes\", \"Cunning to protect the spirits\", \"Intuition to guard the mists\")\n celebration_list = (\"Blacked out\", \"Got in a fight\", \"Lost an heirloom\")\n spirit_list = (\"Bear\", \"Snow leopard\", \"Wolf\", \"Raven\")\n quality_choice = True\n info_help()\n while quality_choice is True:\n quality = input_check(\"My most important quality is that I have the necessary _____.:\",\n \"[strength to defeat ancient foes | cunning to protect the Spirits | \"\n \"intuition to guard the Mists]\")\n if quality not in quality_list and not quality == INFO:\n invalid_input()\n elif quality == INFO:\n information(\"Defeat Our Ancient Foes: I keep my body strong so that I can defeat our ancient enemies and \"\n \"protect the Great Lodge.\",\n ##\n \"Protect the Spirits: It is my duty to protect the Spirits of the Wild, and it requires \"\n \"cunning to keep them safe from the Ice Dragon.\",\n ##\n \"Guard the Mists: My intuition gives me the insight I need to guard the Mists, where the \"\n \"souls of our ancestors endure in glory.\")\n else:\n clear()\n quality_choice = False\n celebration_choice = True\n info_help()\n while celebration_choice is True:\n celebration = input_check(\"At a recent celebratory moot held in Hoelbrak, I _____.:\",\n \"[blacked out | got in a fight | lost an heirloom]\")\n if celebration not in celebration_list and not celebration == INFO:\n invalid_input()\n elif celebration == INFO:\n information(\"Blacked Out: Ale! Rowdy brawling! It's far too easy to let loose in the thrill of the moment.\"\n \" After I woke up, I couldn't remember what I'd done. I'm sure it was nothing too terrible.\",\n ##\n \"Revenge: I've had a rival ever since I was young. He's intelligent, treacherous, and cunning. \"\n \"I lost our last fight, but next time we meet, I'll even the score.\",\n ##\n \"Lost an Heirloom: I inherited Romke's Horn, a magical ancestral heirloom passed down through \"\n \"generations. After one too many drinks, however, I wagered it on a contest of strength...\"\n \"and lost.\")\n else:\n clear()\n celebration_choice = False\n info_help()\n spirit_choice = True\n while spirit_choice is True:\n spirit = input_check(\"What I was still a kid, I had a vision. A Spirit of the Wild spoke to me and offered \"\n \"its guardianship. That spirit was _____.:\",\n \"[Bear | Snow Leopard | Wolf | Raven]\")\n if spirit not in spirit_list and not spirit == INFO:\n invalid_input()\n elif spirit == INFO:\n information(\"Bear: Bear is the most powerful among the Spirits of the Wild. \"\n \"She is a symbol of fortitude and self-reliance. \"\n \"She roared over me when I was a babe, and ever since, I've had Bear's courage in my heart.\",\n ##\n \"Snow Leopard: Snow Leopard is a stealthy, smiling spirit. \"\n \"She teaches us independence, strategy, and laughter in the face of danger. \"\n \"To this day, her wisdom guides me, and my memory of her visit comforts me on dark nights.\",\n ##\n \"Wolf: Wolf has the cunning of the pack behind him. \"\n \"He imparts the virtues of loyalty, ferocity, and strength in numbers. \"\n \"When he came to me, he whispered of my heroic future and told me I would never be alone.\",\n ##\n \"Raven: Raven, clever and wise, guides us with truths that others fear. \"\n \"He bestows a far-seeing clarity of mind. \"\n \"In my vision, he spoke of riddles and opened my eyes to secrets that few others can see.\")\n else:\n clear()\n spirit_choice = False\n\n\ndef asura_menu():\n global college\n global creation\n global advisor\n college_list = (\"Statics\", \"Dynamics\", \"Synergetics\")\n creation_list = (\"The val-a golem\", \"A transatmospheric converter\", \"An infinity ball\")\n advisor_list = (\"Bronk\", \"Zinga\", \"Blipp\", \"Canni\")\n info_help()\n college_choice = True\n while college_choice is True:\n college = input_check(\"I'm a member of the College of _____.:\",\n \"[Statics | Dynamics | Synergetics]\")\n if college not in college_list and not college == INFO:\n invalid_input()\n elif college == INFO:\n information(\"Statics: Builders and architects make up the College of Statics, and we build to last. \"\n \"Some call us conservative, but we excel at creating new designs, using old materials in \"\n \"innovative ways, and expanding the purview of known magics for practical application.\",\n ##\n \"Dynamics: The College of Dynamics produces gizmo-makers extraordinaire. \"\n \"Energy, enthusiasm, and boldness are our best qualities. \"\n \"We believe in leveraging the expendable nature of all things. \"\n \"If a prototype explodes, it isn't a failure unless the lesson goes unlearned.\",\n ##\n \"Synergetics: As members of the College of Synergetics, we study how energy patterns form and \"\n \"alchemagical fractals propagate. Few beyond our college understand the philosophical and \"\n \"mystical complexities of our interests. We devoutly research the true nature of the \"\n \"Eternal Alchemy.\")\n else:\n clear()\n college_choice = False\n info_help()\n creation_choice = True\n while creation_choice is True:\n creation = input_check(\"My first creation was _____.:\",\n \"[the VAL-A golem | a transatmospheric converter | an infinity ball]\")\n if creation not in creation_list and not creation == INFO:\n invalid_input()\n elif creation == INFO:\n information(\"VAL-A Golem: Most golems are powerhouses, designed for warfare. \"\n \"They show absolutely no subtlety or originality of design. \"\n \"My golem was compact and efficient, and it had an unparalleled package of features. \"\n \"It revolutionized lab cleanup, and it mixed a great cocktail too.\",\n ##\n \"Transatmospheric Converter: What unmitigated joy to be able to change the weather at my whim! \"\n \"If only the prototype had been more powerful. Nevertheless, the design was cutting-edge. \"\n \"My transatmospheric converter was a masterpiece of utter genius. I wish I'd had more funding.\",\n ##\n \"Infinity Ball: They said it was a toy, a pipe dream, and a waste of resources. \"\n \"They were wrong. My invention predicts the future. \"\n \"So what if it doesn't predict it correctly every time. \"\n \"That's irrelevant! Could it give me an advantage? Reply hazy. Ask again.\")\n else:\n clear()\n creation_choice = False\n info_help()\n advisor_choice = True\n while advisor_choice is True:\n advisor = input_check(\"My first advisor, the one who taught me almost everything I know (almost), was _____.:\",\n \"[Bronk | Zinga | Blipp | Canni]\")\n if advisor not in advisor_list and not advisor == INFO:\n invalid_input()\n elif advisor == INFO:\n information(\"Bronk: Good ol' Master Bronk taught me the benefits of superior firepower. \"\n \"He liked to say, \\\"When it comes to besting the enemy, there's no such thing as 'overkill.'\\\"\",\n ##\n \"Zinga: Mistress Zinga taught me that the best form of magic is good old-fashioned luck. \"\n \"She liked to say, \\\"Planning ahead is for ninnies who think they can predict every possible \"\n \"outcome. The future is mutable! Go with the flow!\\\"\",\n ##\n \"Blipp: Master Blipp was known for his redundancies as well as his redundancies. \"\n \"He often said, \\\"Your first try should never be your last, especially if it succeeded. \"\n \"You can always succeed bigger next time.\\\"\",\n ##\n \"Canni: Councillor Canni wasn't just another bureaucrat. \"\n \"She was an esopolitical parascientologist. She was fond of saying, \\\"Always bring a witness, \"\n \"preferably an expendable one. You never know when you'll need a scapegoat.\\\"\")\n else:\n clear()\n advisor_choice = False\n\n\ndef sylvari_menu():\n global dream\n global teaching\n global awakening\n dream_list = (\"White stag\", \"Green knight\", \"Shield of the moon\")\n teaching_list = (\"Act with wisdom, but act\", \"All things have a right to grow\", \"Where life goes, so too, \"\n \"should you\")\n awakening_list = (\"Dawn\", \"Noon\", \"Dusk\", \"Night\")\n info_help()\n dream_choice = True\n while dream_choice is True:\n dream = input_check(\"I dreamed of a quest that calls me to action. It was a vision of the _____.:\",\n \"[White Stag | Green Knight | Shield of the Moon]\")\n if dream not in dream_list and not dream == INFO:\n invalid_input()\n elif dream == INFO:\n information(\"White Stag: The White Stag is a creature of enchantment, an immortal beast with great power. \"\n \"It is said that the stag will trade a magical boon for its freedom, if I can catch it.\",\n ##\n \"Green Knight: I saw a powerful knight in green armor, his face obscured. \"\n \"He was defeated, but did not submit; was killed, but did not die. Dare I face him in battle?\",\n ##\n \"Shield of the Moon: The moon is a powerful symbol of healing and magic. \"\n \"Any who dream they're protected by the moon shall know faith and fortitude. \"\n \"I hope I'm worthy of such a vision.\")\n else:\n clear()\n dream_choice = False\n info_help()\n teaching_choice = True\n while teaching_choice is True:\n teaching = input_check(\"I believe that the most important of Ventari's teachings is \\\"_____.\\\".:\",\n \"[Act with wisdom, but act | All things have a right to grow | \"\n \"Where life goes, so too, should you]\")\n if teaching not in teaching_list and not teaching == INFO:\n invalid_input()\n elif teaching == INFO:\n information(\"Act with wisdom, but act: It is one thing to know what is right and another to change \"\n \"the world. We all have a calling. I will distinguish myself through my actions and thereby \"\n \"lift Tyria to a higher state of nobility.\",\n ##\n \"All things have a right to grow: The blossom is brother to the weed. \"\n \"Diversity of opinion is good. Discussion is healthy. \"\n \"No one should be condemned simply for being different. I will stand up for the rights of all.\",\n ##\n \"Where life goes, so too, should you: \"\n \"The world is a delicious and gorgeous place created for us to explore, enjoy, and protect. \"\n \"I will seek out the lessons in every experience, and as I grow, I will have more to offer \"\n \"in return.\")\n else:\n clear()\n teaching_choice = False\n info_help()\n awakening_choice = True\n while awakening_choice is True:\n awakening = input_check(\"The Pale Tree awakened me during the Cycle of _____.:\",\n \"[Dawn | Noon | Dusk | Night]\")\n if awakening not in awakening_list and not awakening == INFO:\n invalid_input()\n elif awakening == INFO:\n information(\"Dawn: Sylvari awakened at dawn are natural talkers, diplomats, and forward-thinkers. We are \"\n \"intimately connected with our surroundings and markedly empathic toward all, \"\n \"even other races.\",\n ##\n \"Noon: Sylvari awakened mid-day solve problems by attacking them head-on. \"\n \"We are the warriors, hunters, and travelers who experience life first-hand and enjoy the \"\n \"rush of taking risks in order to feel truly alive.\",\n ##\n \"Dusk: Sylvari awakened at dusk are naturally curious and thoughtful. \"\n \"We love to learn and spend time reading and studying. \"\n \"We are intelligent and drawn toward the luscious complexities of magic.\",\n ##\n \"Night: Sylvari awakened at night are secretive and cautious with information. \"\n \"We make our own decisions, and we come and go as we please, nimble of mind and body.\")\n else:\n clear()\n awakening_choice = False\n\n\ndef character_info():\n info_help()\n divider()\n print(\"//CHARACTER INFO//\")\n print(\"Race: \" + player_race)\n print(\"Gender: \" + player_gender)\n print(\"Profession: \" + player_profession)\n if player_race == \"Human\":\n print(\"Raised: \" + raised)\n print(\"Regret: \" + regret)\n print(\"Blessing: \" + blessing)\n elif player_race == \"Charr\":\n print(\"Legion: \" + legion)\n print(\"Partner: \" + partner)\n print(\"Father: \" + father)\n elif player_race == \"Norn\":\n print(\"Quality: \" + quality)\n print(\"At a Celebration: \" + celebration)\n print(\"Spirit: \" + spirit)\n elif player_race == \"Asura\":\n print(\"College: \" + college)\n print(\"First Creation: \" + creation)\n print(\"First Advisor: \" + advisor)\n elif player_race == \"Sylvari\":\n print(\"Dream Quest: \" + dream)\n print(\"Favorite Teaching: \" + teaching)\n print(\"Awakening Cycle: \" + awakening)\n else:\n invalid_input()\n yes_no(\"Close CHARACTER INFO?:\",\n ##\n \"[Yes | No]\",\n ##\n \"Type \\\"Yes\\\" or \\\"Y\\\" to close CHARACTER INFO, and type \\\"No\\\" or \\\"N\\\" to remain open.\")\n\n\ndef start():\n intro()\n racial_menu()\n gender_menu()\n profession_menu()\n if player_race == \"Human\":\n human_menu()\n elif player_race == \"Charr\":\n charr_menu()\n elif player_race == \"Norn\":\n norn_menu()\n elif player_race == \"Asura\":\n asura_menu()\n elif player_race == \"Sylvari\":\n sylvari_menu()\n else:\n invalid_input()\n character_info()\n\n\nstart()\n","sub_path":"GW2Text.py","file_name":"GW2Text.py","file_ext":"py","file_size_in_byte":35584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"58003778","text":"from torch.optim import Adam\nfrom torch.nn.functional import l1_loss\nfrom pathlib import Path\nimport numpy as np\nimport torch\nfrom torch.utils.tensorboard import SummaryWriter\nfrom citylearn import CityLearn\nfrom copy import deepcopy\nfrom utils.standardization import normalize_AE_state_with_pred\nfrom utils.io import get_output_folder\nfrom model.Encoder import AE\nfrom model.Seq2SeqEncoders import Seq2SeqLSTM_new\nfrom model.Decoder import LinearDecoder\nfrom utils.util import USE_CUDA\nimport argparse\nimport os\n\nlog_per_step = 1000\n\nparser = argparse.ArgumentParser()\n# RL Hyper-parameters\nparser.add_argument('--lr', type=float, default=0.001)\nparser.add_argument('--MAX_EPOCH', type=int, default=500)\nparser.add_argument('--seed', '-s', type=int, default=0)\nparser.add_argument('--BATCH_SIZE', type=int, default=100)\nparser.add_argument('--climate_zone', type=int, default=1)\nparser.add_argument('--hidden_dim', type=int, default=128)\nparser.add_argument('--load_model', type=int, default=0)\nparser.add_argument('--past_len', type=int, default=24)\nparser.add_argument('--future_len', type=int, default=6)\nargs = parser.parse_args()\n\nassert args.future_len <= args.past_len\nfilename = \"_h_dim{}_past_{}_future_{}_lr_{}_zone{}\".format(args.hidden_dim, args.past_len, args.future_len, args.lr, args.climate_zone)\n\n# load data\ndata_path = Path(\"../data/Climate_Zone_\" + str(args.climate_zone))\nbuilding_attributes = data_path / 'building_attributes.json'\nweather_file = data_path / 'weather_data.csv'\nsolar_profile = data_path / 'solar_generation_1kW.csv'\nbuilding_state_actions = 'buildings_state_action_space.json'\nbuilding_ids = [\"Building_1\", \"Building_2\", \"Building_3\", \"Building_4\", \"Building_5\", \"Building_6\", \"Building_7\",\n \"Building_8\", \"Building_9\"]\nobjective_function = ['ramping', '1-load_factor', 'average_daily_peak', 'peak_demand',\n 'net_electricity_consumption', 'total']\n\n# Instantiating the env\nenv = CityLearn(data_path, building_attributes, weather_file, solar_profile, building_ids,\n buildings_states_actions=building_state_actions, cost_function=objective_function)\nobservations_spaces, actions_spaces = env.get_state_action_spaces()\n\n# Instantiating the Tensorboard writers\nPATH_base = 'datas/new/'\nPATH_base = get_output_folder(PATH_base, 'scalar_seq2seq' + filename)\nPATH_to_log_dir1 = PATH_base + '/loss'\nwriter = SummaryWriter(PATH_to_log_dir1)\n\n\ndef add_to_memory(memory, arr, t):\n memory[:, t, :] = deepcopy(arr)\n\n\ndef extract_pred_state(state):\n \"\"\"\n :param state: (seq, batch, 19)\n :return: (seq, batch, 19-8=11)\n \"\"\"\n return state[:, :, 8:]\n\n\ndef extract_pred_state_new(state):\n \"\"\"\n :param state: (seq, batch, 31)\n :return: (seq, batch, 11)\n \"\"\"\n result_list = [state[:, :, 8:10],\n state[:, :, 10:11],\n state[:, :, 14:15],\n state[:, :, 18:19],\n state[:, :, 22:23],\n state[:, :, 26:31],\n ]\n return torch.cat((result_list), -1)\n\n\n# Memory Array slice operation\ndef get_state_seq(x, y, y2, batch_size, future_len=6):\n \"\"\"\n :param x, y, y2: (9, 8760, state_dim) -- memory array\n :return selected: (batch, 9, past_len+future_len, 21)\n out: (batch, 9, future_len, 4)\n \"\"\"\n # x = torch.randn(9, 8760, 27)\n # batch_size = 32\n slice_size = args.past_len + args.future_len\n batch_idx = np.random.randint(low=0, high=batch_size, size=batch_size)\n batch_idx = torch.LongTensor(batch_idx).unsqueeze(-1) # (batch_size, 1)\n slice_idx = torch.arange(slice_size).unsqueeze(0) # (1, past+future)\n\n # select [T-past:T+future] state sequence, [T:T+future] is used for supervision\n indexes = batch_idx + slice_idx # (batch_size, 30)\n selected = x.index_select(-2, indexes.flatten()).split(slice_size, -2)\n selected = torch.stack(selected)\n\n indexes = batch_idx + slice_idx[:, -(6 + future_len):-future_len] # (batch_size, 6) -- (T-6, T)'s pred6hr seq\n\n selected_y = y.index_select(-2, indexes.flatten()).split(6, -2)\n\n if future_len == 6:\n # select [T-6:T]'s pred6hr sequence\n out = torch.stack(selected_y)\n\n elif future_len == 12:\n # select [T-6:T]'s pred6hr + pred12hr sequence\n selected_y2 = y2.index_select(-2, indexes.flatten()).split(6, -2) # pred12hr\n out = torch.cat((torch.stack(selected_y), torch.stack(selected_y2)), -2)\n\n return deepcopy(selected), deepcopy(out)\n\n\n# Memory Array slice operation\ndef get_state_seq_new(x, batch_size):\n # x = torch.randn(9, 8760, 27)\n # batch_size = 32\n slice_size = args.past_len + args.future_len\n batch_idx = np.random.randint(low=0, high=batch_size, size=batch_size)\n batch_idx = torch.LongTensor(batch_idx).unsqueeze(-1) # (batch_size, 1)\n slice_idx = torch.arange(slice_size).unsqueeze(0) # (1, past+future)\n\n # select [T-past:T+future] state sequence, [T:T+future] is used for supervision\n indexes = batch_idx + slice_idx # (batch_size, 30)\n selected = x.index_select(-2, indexes.flatten()).split(slice_size, -2)\n selected = torch.stack(selected)\n\n return deepcopy(selected)\n\n\ndef print_grad(net):\n for name, parms in net.named_parameters():\n if parms.grad is None:\n continue\n print('-->name:', name, '-->grad_requires:', parms.requires_grad,\n ' -->grad_value:', torch.max(parms.grad), torch.min(parms.grad))\n\n\nstate_dim = 31\npredictable_dim = 11\n\n# Initialize Memory Array\nMemory = np.zeros((9, 8760, state_dim))\n\n# ------------------- Interact with env -----------------------\nstate = env.reset()\nsrc = normalize_AE_state_with_pred(state, noSOC=True) # dim=31\n\nadd_to_memory(Memory, src, 0)\n\ndone = False\nt = 0\nwhile not done:\n t += 1\n action = np.random.uniform(low=-1., high=1., size=(9, 2)) # random action\n next_state, reward, done, _ = env.step(action)\n src = normalize_AE_state_with_pred(next_state, noSOC=True) # dim = 31\n add_to_memory(Memory, src, t)\n state = next_state\n\n# ------------------- Initialize models -------------------\nauto_encoder = AE(31, 128, [128, 128], {}).eval()\nauto_encoder.requires_grad_(False)\nauto_encoder.load_state_dict(torch.load('./Models_one_AE_128dim_zone{}/AE.pt'.format(str(args.climate_zone))))\nprint(\"load AE_zone{} weights successfully\".format(str(args.climate_zone)))\n\npred_len = 6\nmodel = Seq2SeqLSTM_new(args.hidden_dim, args.hidden_dim, pred_len, args.hidden_dim) # TODO: clarify the dim in args\nlinear_decoder = LinearDecoder(predictable_dim, args.hidden_dim)\n\nif USE_CUDA:\n auto_encoder = auto_encoder.cuda()\n model = model.cuda()\n linear_decoder = linear_decoder.cuda()\n\nmodel_path = './Models_seq2seq_withAE' + filename\nif not os.path.isdir(model_path):\n os.mkdir(model_path)\n\nif args.load_model:\n model.load_state_dict(torch.load('{}/Seq2SeqLSTM.pt'.format(model_path)))\n linear_decoder.load_state_dict(torch.load('{}/linear_decoder.pt'.format(model_path)))\n print(\"load model successfully\")\n\n\n# Initialize optimizer\nparams = [\n dict(params=model.parameters()),\n dict(params=linear_decoder.parameters())\n]\n\nopt = Adam(params, lr=0.001)\nmax_epoch = args.MAX_EPOCH\nMIN_loss = 9999999\nSTEP_PER_EPOCH = 10000\nBATCH_SIZE = args.BATCH_SIZE\n# DROPOUT = 0.2\n\nMemory = torch.FloatTensor(Memory)\n\nfor e in range(max_epoch):\n cum_loss = 0.\n for idx in range(STEP_PER_EPOCH):\n full_seq = get_state_seq_new(Memory, BATCH_SIZE)\n if USE_CUDA:\n full_seq = full_seq.cuda()\n\n src = full_seq[:, :, 0:args.past_len, :].reshape(-1, args.past_len, state_dim) # (batch*9, seq=24, state_dim=21)\n src = auto_encoder(src).transpose(1, 0) # (seq=24, batch*9, hidden_dim=128)\n real_tgt = extract_pred_state_new(full_seq[:, :, -args.future_len:, :].reshape(-1, args.future_len, state_dim)).transpose(1, 0)\n\n _, dec = model(src)\n recon_tgt = linear_decoder(dec)\n\n ReconstructionLoss_tgt = l1_loss(recon_tgt, real_tgt, reduction='mean')\n loss = ReconstructionLoss_tgt\n\n opt.zero_grad()\n loss.backward()\n opt.step()\n\n cum_loss += loss.detach().cpu()\n\n if (e * STEP_PER_EPOCH + idx) % log_per_step == 0:\n # print(recon_s, pred_s)\n print(\"loss {} at step {}\".format(loss, e * STEP_PER_EPOCH + idx))\n print_grad(model)\n writer.add_scalar('loss_step', loss, e * STEP_PER_EPOCH + idx)\n\n print(\"cum loss {} at epoch {}\".format(cum_loss, e))\n if cum_loss < MIN_loss:\n MIN_loss = cum_loss\n if e > 0:\n torch.save(model.state_dict(), '{}/Seq2SeqLSTM.pt'.format(model_path))\n torch.save(linear_decoder.state_dict(), '{}/linear_decoder.pt'.format(model_path))\n print(\"save model in epoch {}\".format(e))\n\n writer.add_scalar('loss_epoch', cum_loss, e)\n\n\n","sub_path":"pretrain_seq2seq_withAE.py","file_name":"pretrain_seq2seq_withAE.py","file_ext":"py","file_size_in_byte":8872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"653235285","text":"from multiprocessing import Process\nfrom bs4 import BeautifulSoup\nimport requests\nfrom tqdm import tqdm\nimport time\nfrom datetime import datetime, date\nimport random\nimport config\nimport pymongo\nimport gc\nfrom gevent import monkey\n\nmonkey.patch_socket()\nimport gevent\n\n\nclass NeteaseSpider:\n def __init__(self):\n self.headers = config.headers\n self.cookies = config.cookies\n self.vip = {0: '普通', 10: '音乐包', 11: '黑胶会员'}\n self.gender = {0: '未知', 1: '男', 2: '女'}\n self.loc = self.get_loccodes()\n self.client = pymongo.MongoClient(config.mongodb_url)\n self.db = self.client[config.db_name]\n self.mongodb = self.db[config.col]\n\n # 获取地区代码\n def get_loccodes(self):\n # 行政区划代码\n url = 'http://www.mca.gov.cn/article/sj/xzqh/2018/201804-12/20180810101641.html'\n locs = {}\n try:\n r = requests.get(url)\n if r.status_code == 200:\n r.encoding = \"utf-8\"\n soup = BeautifulSoup(r.text, 'html5lib')\n items = soup.find_all('tr', attrs={\"height\": \"19\"})\n for item in items:\n # print(item.find_all('td')[1].text, item.find_all('td')[2].text)\n locs[int(item.find_all('td')[1].text)] = item.find_all('td')[2].text\n except:\n print(\"获取省份代码失败！\")\n exit(-1)\n return locs\n\n # 获取地区\n def get_locate(self, p, c):\n try:\n locate = self.loc[p] + self.loc[c]\n return locate\n except:\n return '云村'\n\n # 获取歌单详情（歌单中的音乐）\n def get_songs(self, playlist_id):\n url = 'http://music.163.com/api/playlist/detail'\n payload = {'id': playlist_id}\n r = requests.get(url, params=payload, headers=self.headers,\n cookies=self.cookies)\n playlist_detail = r.json()['result']['tracks']\n songs = []\n for song_detail in playlist_detail:\n song = dict()\n song['id'] = song_detail['id']\n song['name'] = song_detail['name']\n artists_detail = []\n for artist in song_detail['artists']:\n artist_detail = dict()\n artist_detail['name'] = artist['name']\n artist_detail['id'] = artist['id']\n artists_detail.append(artist_detail)\n song['artists'] = artists_detail\n songs.append(song)\n del song\n gc.collect()\n return songs\n\n # 获取歌词\n def get_lyric(self, song_id):\n url = 'http://music.163.com/api/song/lyric'\n payload = {\n 'os': 'pc', # osx\n 'id': song_id,\n 'lv': -1,\n 'kv': -1,\n 'tv': -1\n }\n r = requests.get(url, params=payload, headers=self.headers,\n cookies=self.cookies)\n result = r.json()\n if ('nolyric' in result) or ('uncollected' in result):\n return None\n elif 'lyric' not in result['lrc']:\n return None\n else:\n return result['lrc']['lyric']\n\n # 获取评论\n def get_song_comments(self, song_id, offset=0, total='false', limit=100):\n url = ('http://music.163.com/api/v1/resource/comments/R_SO_4_{}/'\n ''.format(song_id))\n payload = {\n 'rid': 'R_SO_4_{}'.format(song_id),\n 'offset': offset,\n 'total': total,\n 'limit': limit\n }\n r = requests.get(url, params=payload, headers=self.headers,\n cookies=self.cookies)\n return r.json()\n\n # 获取评论\n def get_comments(self, song_id):\n comments = self.get_song_comments(song_id)['comments']\n comments_list = []\n offset = 0\n i = 0\n while comments:\n i += 1\n for comment in tqdm(comments, desc='当前音乐进度({}/20): '.format(i), ncols=80):\n comment_detail = dict()\n user_info = dict()\n try:\n user = self.get_user_detail(comment['user']['userId'])\n user_info['user_id'] = comment['user']['userId']\n user_info['level'] = user['level']\n user_info['nickname'] = user['profile']['nickname']\n user_info['listen_songs'] = user['listenSongs']\n user_info['vip'] = self.vip[user['profile']['vipType']]\n user_info['create_days'] = user['createDays']\n user_info['birthday'] = user['profile']['birthday']\n if user['profile']['birthday'] > 0:\n user_info['age'] = date.today().year - \\\n datetime.fromtimestamp(user['profile']['birthday'] // 1000).year\n else:\n user_info['age'] = 0\n user_info['gender'] = self.gender[user['profile']['gender']]\n user_info['province'] = user['profile']['province']\n user_info['city'] = user['profile']['city']\n user_info['locate'] = self.get_locate(user['profile']['province'], user['profile']['city'])\n except:\n pass\n comment_detail['content'] = comment['content']\n comment_detail['time'] = comment['time']\n comment_detail['user'] = user_info\n comments_list.append(comment_detail)\n # print(comment_detail)\n del user_info, user, comment_detail\n gc.collect()\n # time.sleep(random.uniform(1, 2))\n # 2000 条停止\n if i == 20:\n break\n offset = offset + 100\n comments = self.get_song_comments(song_id,\n offset=offset)['comments']\n return comments_list\n\n # 获取用户信息\n def get_user_detail(self, user_id):\n url = 'http://music.163.com/api/v1/user/detail/{}'.format(user_id)\n r = requests.get(url, headers=self.headers, cookies=self.cookies)\n # print(r.json())\n return r.json()\n\n def start(self, playlist):\n for i in tqdm(playlist, desc='总进度：', ncols=80):\n for song in tqdm(self.get_songs(i['id']), desc='当前歌单进度：', ncols=80):\n try:\n info = dict()\n info['song_id'] = song['id']\n info['name'] = song['name']\n info['artists'] = [j['name'] for j in song['artists']]\n info['lyric'] = self.get_lyric(song['id'])\n info['comments'] = self.get_comments(song['id'])\n self.mongodb.insert_one(info)\n del info\n gc.collect()\n except Exception as e:\n print(e)\n requests.post(url='https://qmsg.zendee.cn/send/{0}'.\n format('5cfbbbe7c89d86846cab9623ef0918ec'),\n data={'msg': e, 'qq': '2451809588'})\n\n def run(self, playlist):\n n = len(playlist)\n # 多协程执行\n jobs = [\n gevent.spawn(self.start, playlist[: n // 5]),\n gevent.spawn(self.start, playlist[n // 5: (n // 5) * 2]),\n gevent.spawn(self.start, playlist[(n // 5) * 2: (n // 5) * 3]),\n gevent.spawn(self.start, playlist[(n // 5) * 3: (n // 5) * 4]),\n gevent.spawn(self.start, playlist[(n // 5) * 4:])\n ]\n gevent.joinall(jobs)\n\n\n# 获取歌单\ndef get_playlist():\n playlist_url = 'http://musicapi.leanapp.cn/top/playlist?limit=300&order=new&cat=%E6%B0%91%E8%B0%A3'\n return requests.get(playlist_url).json()['playlists']\n\n\ndef run(pl, i):\n print('start...', i)\n spider = NeteaseSpider()\n spider.run(pl)\n\n\nif __name__ == '__main__':\n playlist = get_playlist()\n begin = 0\n end = 20\n step = 20\n for _ in range(5):\n p = Process(target=run, args=(playlist[begin:end], _))\n p.start()\n begin = end\n end += step\n print('Done!')\n","sub_path":"spider/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":8276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"195093930","text":"# ==================== WHAT TO FILL OUT IN THIS FILE ===========================\n\"\"\"\nThere are 3 sections that should be filled out in this file\n\n1. Package imports below\n2. The PricingModel class\n3. The load function\n\nThere are also three other sections at the end of the file that can\nsafely be ignored. These are:\n\n - Probability calibration. An optional step to ensure\n probabilities predicted by your model are calibrated\n (see docstring)\n\n - Consistency check to make sure the code that you submit is the\n same as your trained model.\n\n - A main section that runs this file and produces the the trained model file\n This also checks whether your load_model function works properly\n\"\"\"\n\n\n# ========================== 1. PACKAGE IMPORTS ================================\n# Include your package imports here\n\nimport hashlib\nimport numpy as np\nimport pandas as pd\nimport pickle\n\nfrom sklearn.calibration import CalibratedClassifierCV\nfrom sklearn.model_selection import train_test_split\n\n#====\nimport numpy as np\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\nimport pandas as pd\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\n\n\n# ========================= 2. THE PRICING MODEL ===============================\nclass PricingModel():\n \"\"\"\n This is the PricingModel template. You are allowed to:\n 1. Add methods\n 2. Add init variables\n 3. Fill out code to train the model\n\n You must ensure that the predict_premium method works!\n \"\"\"\n\n def __init__(self,):\n\n # =============================================================\n # This line ensures that your file (pricing_model.py) and the saved\n # model are consistent and can be used together.\n #self._init_consistency_check() # DO NOT REMOVE\n self.model = self.build_sigm15_relu10_sigm10_lin()\n\n def build_sigm15_relu10_sigm10_lin(self):\n\n model = Sequential()\n model.add(Dense(15, input_dim=46, activation='sigmoid'))\n model.add(Dense(10, activation='relu'))\n model.add(Dense(10, activation='sigmoid'))\n model.add(Dense(1))\n\n\n optimizer = 'adam'\n\n model.compile(loss='mse',\n optimizer=optimizer,\n metrics=['mae', 'mse'])\n return model\n\n def _preprocessor(self, X_raw, training = False):\n \"\"\"\n\n This function prepares the features of the data for training,\n evaluation, and prediction.\n\n Parameters\n ----------\n X_raw : Pandas dataframe\n This is the raw data features excluding claims information \n\n Returns\n -------\n X: Pandas DataFrame\n A clean data set that is used for training and prediction.\n \"\"\"\n # =============================================================\n # YOUR CODE HERE\n\n columns_x = ['pol_bonus', 'pol_coverage', 'pol_duration',\n 'pol_sit_duration', 'pol_pay_freq', 'pol_payd', 'pol_usage',\n 'drv_drv2', 'drv_age1', 'drv_age2', 'drv_sex1',\n 'drv_sex2', 'drv_age_lic1', 'drv_age_lic2', 'vh_age', 'vh_cyl',\n 'vh_din', 'vh_fuel', 'vh_make', 'vh_sale_begin',\n 'vh_sale_end', 'vh_speed', 'vh_type', 'vh_value', 'vh_weight',\n 'town_mean_altitude', 'town_surface_area', 'population', 'city_district_code',\n ]\n\n x_data_un = X_raw[columns_x]\n\n cat_to_int_dict = {'pol_coverage': {'Mini': 0, 'Median1': 1, 'Median2': 2, 'Maxi': 3},\n 'pol_pay_freq': {'Monthly': 0, 'Quarterly': 1, 'Biannual': 2, 'Yearly': 3},\n 'pol_payd': {'No': 0, 'Yes': 1},\n 'pol_usage': {'Retired': 0, 'WorkPrivate': 1, 'Professional': 2, 'AllTrips': 3},\n 'drv_drv2': {'No': 0, 'Yes': 1},\n 'drv_sex1': {'F': 0, 'M': 1},\n 'drv_sex2': {'F': -1, 'M': 1, None: 0},\n 'vh_type': {'Tourism': 0, 'Commercial': 1, }\n }\n\n def car_make_categories(car_make):\n if car_make in ['RENAULT', 'PEUGEOT', 'CITROEN', 'VOLKSWAGEN', 'FORD', 'MERCEDES BENZ']:\n return car_make\n else:\n return 'OTHER'\n\n def missing_geo_data(x):\n if x:\n return 1\n else:\n return 0\n\n def zero_vh_weight(weight, avg_weight):\n if weight < 100:\n return avg_weight\n else:\n return weight\n\n x_data_f = x_data_un.replace(cat_to_int_dict, inplace=False)\n\n x_data_f.vh_make = x_data_f['vh_make'].apply(lambda x: car_make_categories(x))\n\n ##MAKE INDEP\n avg_vh_weight = x_data_f['vh_weight'].mean()\n x_data_f.vh_weight = x_data_f['vh_weight'].apply(lambda x: zero_vh_weight(x, avg_vh_weight))\n\n vh_make_cols = pd.get_dummies(x_data_f.vh_make)\n vh_fuel_cols = pd.get_dummies(x_data_f.vh_fuel)\n city_dist_cols = pd.get_dummies(x_data_f.city_district_code)\n x_data_f = x_data_f.drop(['vh_fuel', 'vh_make', 'city_district_code'], axis=1)\n\n x_data_f['geoNA'] = (x_data_un['population'].isnull()).apply(lambda x: missing_geo_data(x))\n\n x_data_f = pd.concat([x_data_f, vh_make_cols, vh_fuel_cols, city_dist_cols], axis=1, sort=False)\n\n #NEED TO CHANGE MEANS SO THAT ITS INDEPENDENT\n means = x_data_f.mean()\n x_data_f = x_data_f.fillna(means)\n\n #SIMILIAARLY I NEED TO CHANGE THIS SCALER SO THAT ITS INDEPENDENT\n scaler = preprocessing.MinMaxScaler()\n if training == True:\n self.scaler = preprocessing.StandardScaler().fit(x_data_f)\n\n X = pd.DataFrame(scaler.fit_transform(x_data_f), columns=x_data_f.columns, index=x_data_f.index)\n\n return X\n\n\n def fit(self, X_raw, y_made_claim, y_claims_amount):\n \"\"\"\n Here you will use the fit function for your pricing model.\n\n Parameters\n ----------\n X_raw : Pandas DataFrame\n This is the raw data features excluding claims information \n y_made_claim : Pandas DataFrame\n A one dimensional binary array indicating the presence of accidents\n y_claims_amount: Pandas DataFrame\n A one dimensional array which records the severity of claims (this is\n zero where y_made_claim is zero).\n\n \"\"\"\n\n # YOUR CODE HERE\n\n # Remember to include a line similar to the one below\n X_clean = self._preprocessor(X_raw, training = True)\n\n early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n print('training model')\n (self.model).fit(X_clean, y_claims_amount,\n epochs=10000, verbose=1, validation_split=0.2,\n callbacks=[early_stop])\n\n return None\n\n\n def predict_premium(self, X_raw):\n \"\"\"Predicts premiums based on the pricing model.\n\n Parameters\n ----------\n X_raw : Pandas DataFrame\n This is the raw data features excluding claims information\n\n Returns\n -------\n Pandas DataFrame\n A one dimensional array of the same length as the input with\n values corresponding to the offered premium prices\n \"\"\"\n # =============================================================\n # You can include a pricing strategy here\n # For example you could scale all your prices down by a factor\n\n # YOUR CODE HERE\n\n # Remember to include a line similar to the one below\n X_clean = self._preprocessor(X_raw)\n predicted_claim_amounts = ((self.model).predict(X_clean)).reshape(X_clean.shape[0])\n\n return predicted_claim_amounts\n\n\n def save_model(self):\n \"\"\"\n Saves a trained model to pricing_model.p.\n \"\"\"\n\n # =============================================================\n # Default : pickle the trained model. Change this (and the load\n # function, below) only if the library you used does not support\n # pickling.\n with open('pricing_model.p', 'wb') as target:\n pickle.dump(self, target)\n\n\n def _init_consistency_check(self):\n \"\"\"\n INTERNAL METHOD: DO NOT CHANGE.\n Ensures that the saved object is consistent with the file.\n This is done by saving a hash of the module file (pricing_model.py) as\n part of the object.\n For this to work, make sure your source code is named pricing_model.py.\n \"\"\"\n try:\n with open('pricing_model.py', 'r') as ff:\n code = ff.read()\n m = hashlib.sha256()\n m.update(code.encode())\n self._source_hash = m.hexdigest()\n except Exception as err:\n print('There was an error when saving the consistency check: '\n '%s (your model will still work).' % err)\n\n\n\n# =========================== 3. LOAD FUNCTION =================================\ndef load_trained_model(filename = 'pricing_model.p'):\n \"\"\"\n Include code that works in tandem with the PricingModel.save_model() method. \n\n This function cannot take any parameters and must return a PricingModel object\n that is trained. \n\n By default, this uses pickle, and is compatible with the default implementation\n of PricingGame.save_model. Change this only if your model does not support\n pickling (can happen with some libraries).\n \"\"\"\n with open(filename, 'rb') as model:\n pricingmodel = pickle.load(model)\n return pricingmodel\n\n\n\n# ========================= OPTIONAL CALIBRATION ===============================\ndef fit_and_calibrate_classifier(classifier, X, y):\n \"\"\"\n Note: This functions performs probability calibration\n This is an optional tool for you to use, it calibrates the probabilities from \n your model if need be. \n\n For more information see:\n https://scikit-learn.org/stable/modules/calibration.html \n \"\"\"\n X_train, X_cal, y_train, y_cal = train_test_split(\n X, y, train_size=0.85, random_state=0)\n classifier = classifier.fit(X_train, y_train)\n\n # This line does the calibration for you\n calibrated_classifier = CalibratedClassifierCV(\n classifier, method='sigmoid', cv='prefit').fit(X_cal, y_cal)\n return calibrated_classifier\n\n\n\n# ==============================================================================\n\ndef check_consistency(trained_model, filename):\n \"\"\"Returns True if the source file is consistent with the trained model.\"\"\"\n # First, check that the model supports consistency checking (has _source_hash).\n if not hasattr(trained_model, '_source_hash'):\n return True # No check was done (so we assume it's all fine).\n trained_source_hash = trained_model._source_hash\n with open(filename, 'r') as ff:\n code = ff.read()\n m = hashlib.sha256()\n m.update(code.encode())\n true_source_hash = m.hexdigest()\n return trained_source_hash == true_source_hash\n\n\n\n\n# ============================ MAIN FUNCTION ================================\n# Please do not write any executing code outside of the __main__ safeguard.\n# By default, this code trains your model (using training_data.csv) and saves\n# it to pricing_model.p, then checks the consistency of the saved model and\n# the pickle file.\n\n\nif __name__ == '__main__':\n\n # Load the training data\n training_df = pd.read_csv('training_data.csv')\n y_claims_amount = training_df['claim_amount']\n y_made_claim = training_df['made_claim']\n X_train = training_df.drop(columns=['made_claim', 'claim_amount'])\n\n # Instantiate the pricing model and fit it\n my_pricing_model = PricingModel()\n my_pricing_model.fit(X_train, y_made_claim, y_claims_amount)\n\n # Save and load the pricing model\n my_pricing_model.save_model()\n loaded_model = load_trained_model()\n\n # Generate prices from the loaded model and the instantiated model\n predictions1 = my_pricing_model.predict_premium(X_train)\n predictions2 = loaded_model.predict_premium(X_train)\n\n print(predictions1)\n print(predictions2)\n # ensure that the prices are the same\n assert np.array_equal(predictions1, predictions2)","sub_path":"comp_dets/pricing_model.py","file_name":"pricing_model.py","file_ext":"py","file_size_in_byte":12399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"514955890","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport json\r\nimport io\r\nimport csv\r\n\r\n# import pandas as pd\r\n\r\n# Output File (CSV)\r\ne_File = open('events_AllData.txt', 'w', encoding=\"utf-8\")\r\n\r\n\r\naids = [17879, 65557,\r\n 82547, 85795, 95022, 115003, 127006, 139648, 149066, 181875, 182968, 186049, 197928, 209003,\r\n 213449, 217815,\r\n 234311, 235779, 273530, 278737, 297319, 316828, 365382, 366307, 378080, 440797, 451661, 456767, 462022, 491256,\r\n 519159, 527224,\r\n 537914, 551106, 552177, 553938, 556955, 568431, 718506, 807990, 930524, 941964, 974908, 976211, 1055942,\r\n 1059348, 1134363, 1168415,\r\n 1435335, 1646455, 2083334, 2274326, 2322629, 2332047, 2506696, 2596951, 2867816, 3015056, 3277856, 3376301,\r\n 3732956, 4363463,\r\n 4774213, 4971683, 5200703, 5318433, 5352228, 5427893, 5642214, 6012829, 6393799, 6715369, 7561184, 8008073,\r\n 8067338, 8310783,\r\n 8324968, 8504398, 8568579]\r\n\r\nfor i in aids:\r\n try:\r\n id = str(i)\r\n url = \"https://api.songkick.com/api/3.0/artists/\" + id + \"/calendar.json?apikey=WewWUhkws9IU4phb\"\r\n # print(url)##\r\n response = requests.get(url)\r\n data = json.loads(response.text)\r\n data_main = data['resultsPage']['results']\r\n events = data_main[\"event\"]\r\n #print(json.dumps(events[:3], indent=4))\r\n event_data = []\r\n venue_data = []\r\n location_data = []\r\n performance_data = []\r\n artist_data = []\r\n all_data = []\r\n except KeyError:\r\n continue\r\n k=1\r\n\r\n ##### EVENTS ######\r\n ###################\r\n for a in events:\r\n #print(a[\"id\"])\r\n e_id = a['id']\r\n e_name = a['displayName']\r\n e_type = a['type']\r\n e_popularity = a['popularity']\r\n e_date = a['start']['date']\r\n e_age_restriction = a['ageRestriction']\r\n #print(len(a[\"performance\"]))\r\n eventData = str(e_id) +\"; \"+ str(e_name) +\"; \"+ str(e_type) +\"; \"+ str(e_popularity) +\"; \"+ str(e_date) +\"; \"+ str(e_age_restriction)\r\n event_data.append(eventData)\r\n\r\n\r\n ##### VENUE ######\r\n ##################\r\n\r\n for o in a:\r\n e_venue_id = a['venue']['id']\r\n e_venue_name = a['venue']['displayName']\r\n e_venue_area_name = a['venue']['metroArea']['displayName']\r\n e_venue_country_name = a['venue']['metroArea']['country']['displayName']\r\n #e_venue_state_name = a['venue']['metroArea']['state']['displayName']\r\n e_venue_lat = a['venue']['lat']\r\n e_venue_lng = a['venue']['lng']\r\n\r\n venueData = str(e_venue_id) + \"; \" + str(e_venue_name) + \"; \" + str(e_venue_name) + \"; \" \\\r\n + str(e_venue_country_name) + \"; \" + \\\r\n str(e_venue_lat)+str(e_venue_lng)\r\n\r\n venue_data.append(venueData)\r\n\r\n ##### LOCATION ##\r\n #######################\r\n for l in a['location']:\r\n city = a['location']['city']\r\n lat = a['location']['lat']\r\n lng = a['location']['lng']\r\n\r\n locationData = str(city) + \"; \" + str(lat) + \"; \" + str(lng)\r\n location_data.append(locationData)\r\n\r\n ##### PERFORMANCE #####\r\n #######################\r\n for p in a[\"performance\"]:\r\n e_performance_id=p['id']\r\n e_performance_artist_name = p['displayName']\r\n e_performance_billing = p['billing']\r\n # print(e_performance_id)\r\n # print(e_performance_billing)\r\n performanceData = str(e_performance_id) + \"; \" + str(e_performance_artist_name) + \"; \" + str(e_performance_billing)\r\n performance_data.append(performanceData)\r\n\r\n ##### ARTIST #####\r\n ##################\r\n for q in p:\r\n e_artist_id = p['artist']['id']\r\n #print(e_artist_id)\r\n e_artist_name = p['artist']['displayName']\r\n\r\n artistData = str(e_artist_id) + \"; \" + str(e_artist_name)\r\n artist_data.append(artistData)\r\n ix=0\r\n for ab in event_data:\r\n if ix < len(event_data):\r\n a = event_data[ix]\r\n b = venue_data [ix]\r\n c = location_data [ix]\r\n d = performance_data [ix]\r\n e = artist_data [ix]\r\n content = str(a) + \"; \" + str(b) + \"; \" + str(c) + \"; \" + str(d) + \"; \" + str(e)\r\n all_data.append(content)\r\n ix = ix + 1\r\n\r\n ad=0\r\n for bc in all_data:\r\n if ad < len(all_data):\r\n string = all_data[ad]+\"\\n\"\r\n e_File.write(string)\r\n ad=ad+1\r\n\r\ne_File.close()\r\n#########################################################\r\n\r\n # if k == 1:\r\n # print(a)\r\n # k=k+1\r\n# # e_id = a['id']\r\n# # e_name = a['displayName']\r\n# # e_type = a['type']\r\n# # e_popularity = a['popularity']\r\n# # e_date = a['start']['date']\r\n# #e_performance_id = a['performance']['id']\r\n# #e_performance_artist_name = a['performance']['displayName']\r\n# ##e_performance_artist_name = a['performance']['billing']##\r\n# e_artist_id = a['artist']['id']\r\n# e_artist_name = a['artist']['displayName']\r\n# e_age_restriction = a['ageRestriction']\r\n# e_venue_id = a['venue']['id']\r\n# e_venue_name = a['venue']['displayName']\r\n# e_venue_area_name = a['venue']['metroArea']['displayName']\r\n# e_venue_country_name = a['venue']['metroArea']['country']['displayName']\r\n# e_venue_state_name = a['venue']['metroArea']['state']['displayName']\r\n# e_venue_lat = a['venue']['lat']\r\n# e_venue_lng = a['venue']['lng']\r\n# e_venue_location = a['venue']['location']\r\n# # event_data.append(e_id)\r\n# # event_data.append(e_name)\r\n# # event_data.append(e_type)\r\n# # event_data.append(e_popularity)\r\n# # event_data.append(e_date)\r\n# # event_data.append(e_performance_id)\r\n# # event_data.append(e_performance_artist_name)\r\n# # event_data.append(e_artist_id)\r\n# # event_data.append(e_artist_name)\r\n# #\r\n# # a = []\r\n# # k = 0\r\n# # for i in event_data:\r\n# # if k < len(event_data):\r\n# # # a = str(event_data[k]) ,\";\", str(event_data[k+1]) ,\";\", str(event_data[k+2]) ,\";\", str(event_data[k+3]),\";\",\r\n# # a = str(event_data[k]) + \";\" + str(event_data[k + 1]) + \";\" + str(event_data[k + 2]) + \";\" + str(\r\n# # event_data[k + 3]) + \";\" + str(event_data[k + 4] + \"\\n\")\r\n# # k = k + 5\r\n# # e_File.write(a)\r\n# #\r\n# # ################################\r\n# # #### Performance & Artist ##\r\n# #\r\n# # data_performances = data['resultsPage']['results']['event']\r\n# # # print(type(data_performances))\r\n# # for p in data_performances:\r\n# # p_data = p['performance']\r\n# #\r\n# # for a in p_data:\r\n# # a_data = a['artist']\r\n# #\r\n# # performance_data = []\r\n# # for p in p_data:\r\n# # p_id = p['id']\r\n# # p_name = p['displayName']\r\n# # performance_data.append(p_id)\r\n# # performance_data.append(p_name)\r\n# #\r\n# # a = []\r\n# # k = 0\r\n# # for i in performance_data:\r\n# # if k < len(performance_data):\r\n# # # print(performance_data[k])\r\n# # a = str(performance_data[k]) + \";\" + str(performance_data[k + 1]) + \"\\n\"\r\n# # k = k + 2\r\n# # # print(a)\r\n# # p_File.write(a)\r\n# #\r\n# # artist_data = []\r\n# # for a in a_data:\r\n# # a_id = a_data['id']\r\n# # a_name = a_data['displayName']\r\n# # artist_data.append(a_id)\r\n# # artist_data.append(a_name)\r\n# #\r\n# # a = []\r\n# # k = 0\r\n# # for i in artist_data:\r\n# # if k < len(artist_data):\r\n# # # print(performance_data[k])\r\n# # a = str(artist_data[k]) + \";\" + str(artist_data[k + 1]) + \"\\n\"\r\n# # k = k + 2\r\n# # # print(a)\r\n# # a_File.write(a)\r\n# #\r\n# # #########LOCATION and Venue #############\r\n# #\r\n# # ## Locations ##\r\n# # location_data = []\r\n# # for l in events:\r\n# # loc = l['location']\r\n# # location_data.append(loc)\r\n# #\r\n# # a = []\r\n# # k = 0\r\n# # for i in location_data:\r\n# # if k < len(location_data):\r\n# # # print(performance_data[\r\n# # a = location_data[k]\r\n# # city = a[\"city\"]\r\n# # lat = a['lat']\r\n# # lng = a['lng']\r\n# # k = k + 1\r\n# # l = str(city + \";\" + str(lat) + \";\" + str(lng) + '\\n')\r\n# # # print(a)\r\n# # l_File.write(l)\r\n# # # print((a))\r\n# #\r\n# # e_File.close()\r\n# # p_File.close()\r\n# # a_File.close()\r\n# # l_File.close()\r\n","sub_path":"Concert_Connection/csv4charts and Py scripts/All_Data_SongKick.py","file_name":"All_Data_SongKick.py","file_ext":"py","file_size_in_byte":8966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"509792818","text":"\"\"\"\nThis program decrypts a message that was encrypted using the Vigenere cipher.\nThe method I use to solve crack this cipher is based on the techinque\ndescribed on pages 16-24 of our textbook, Introduction to Cryptography with\nCoding Theory 2nd Edition by Wade Trappe and Lawrence C. Washington.\n\nFirst, we read the encrypted text from the text file 'CipherText.txt' and\nremove any new line characters. Next, we find the key length by shifting the\ncipher text and count how many times a matching character is found. The key\nlength should be the index of the largest number of matches. Next, we begin\nfinding the characters that make up our key by counting the occurences of\nletters as they appear a key length apart. Then we count the total number\nof occurences we found and divide our list of occurences by this number to\ngive us a frequency of each letter. Lastly, we perform the dot product between\nour frequency list of occurences and the frequency of letters as they appear\nin the English language (taken from the textbook on page 17). The index with\nthe largest value corresponds to the index of a letter in the alphabet. We do\nthis for the length of the key to find each character in our key. Finally,\nafter obtaining every letter in the key, we decrypt the message. All output\nfor this program is sent to log.txt.\n\nAuthor: Brandon Hostetter\nDate: 6 February 2016\n\"\"\"\n\nimport sys\n\n# Constants for the letters in the alphabet and the freqency of letters as\n# they appear in the English language according to our text book\nALPHABET = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',\n 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x',\n 'y', 'z']\nENGLISH_FREQ = [0.082, 0.015, 0.028, 0.043, 0.127, 0.022, 0.020, 0.061,\n 0.070, 0.002, 0.008, 0.040, 0.024, 0.067, 0.075, 0.019,\n 0.001, 0.060, 0.063, 0.091, 0.028, 0.010, 0.023, 0.001,\n 0.020, 0.001]\n\ndef read_text_from_file(file_name):\n \"\"\"\n Read text from the file 'file_name' and remove any new line characters\n so we are left with one long, continuous string of text.\n\n Args:\n file_name: The name of the file to be read.\n\n Returns:\n output: The string of characters read from the file.\n \"\"\"\n\n # Open the file\n file = open(file_name, 'r')\n # Read the data from the file\n output = file.read()\n file.close()\n # Remove new line characters\n output = output.rstrip('\\n')\n\n return output\n\ndef find_frequency(cipher_list, shift):\n \"\"\"\n Counts how many times a character matches in a list when shifted by a\n certain amount. For example, if 'shift' = 5, we check to see if the values\n at cipher_list[0] == cipher_list[5], then cipher_list[1] == cipher_list[6],\n etc.\n\n Args:\n cipher_list: The list of characters that is compared for matches within\n itself.\n shift: The amount we shift the values in the cipher_list for the\n comparison.\n \"\"\"\n\n match = 0\n length = len(cipher_list) - shift\n\n for i in range(length):\n # If the characters match, increment the match counter by 1\n if cipher_list[i] == cipher_list[i + shift]:\n match += 1\n\n return match\n\ndef find_largest_frequency(list):\n \"\"\"\n Finds the largest number in a list and returns the index of that value.\n In the case of a tie (unlikely in our situation), the index of the first\n value is returned.\n\n Args:\n list: The list that is searched to find the largest value.\n\n Returns:\n index: The index of the largest value in the given list.\n \"\"\"\n\n max = -sys.maxint\n index = -1\n\n # Find the largest number in the list and get it's index\n for i in range(len(list)):\n if list[i] > max:\n max = list[i]\n index = i\n\n return index\n\ndef find_char_occurences(cipher_list, key_size, shift_by):\n \"\"\"\n Count the number of occurences for each letter in the cipher_list (which\n is shifted by the amount 'shift_by') that is the distance of the key_size\n apart. For example, if the key_size is 5 and shift_by is 2, we will start\n counting occurences at the second letter and add 5 to get to the next\n letter. Thus, we will count the occurences at the indices 2, 7, 12, 17,\n 22, etc. for this example.\n\n Args:\n cipher_list: The character list containing the cipher text.\n key_size: The size of the key used to decipher the encrypted text.\n shift_by: The amount that we should shift the cipher_list by.\n\n Returns:\n frequency: The list of the number of times a letter was encountered.\n \"\"\"\n\n letter_code = 0\n # Create an empty array for the 26 letters in alphabet with intial\n # values of 0\n frequency = [0] * 26\n length = len(cipher_list) - shift_by\n\n for i in range(length):\n # Only check the letters that are the distance of 'shift_by' away\n # I.E. check positions: shift_by, 2*shift_by, 3*shift_by, etc.\n if i % key_size == 0:\n # Get the key code of the letter. Subtract 97 so that 0\n # corresponds to the letter a\n letter_code = ord(cipher_list[i + shift_by]) - 97\n # Increment the counter for that letter by 1\n frequency[letter_code] += 1\n\n return frequency\n\ndef find_total_char_count(list):\n \"\"\"\n Sum all of the values that appear in the given list.\n\n Args:\n list: The list of numbers that should be summed.\n\n Returns:\n total_chars: The result from summing all values in the given list.\n \"\"\"\n\n total_chars = 0\n\n # Add all values in the list\n for number in list:\n total_chars += number\n\n return total_chars\n\ndef make_freq_list(list, denominator):\n \"\"\"\n Create a frequency list by dividing every value in 'list' by\n 'denominator'.\n\n Args:\n list: A list of numbers.\n denominator: The value that each element in 'list' should be\n divided by.\n\n Returns:\n list: The modified list with each value divided by the denominator\n passed to this function.\n \"\"\"\n\n for i in range(len(list)):\n # Convert to float before division because the answer takes the type\n # of the highest precision\n list[i] = float(list[i]) / denominator\n\n return list\n\ndef do_dot_product(freq_list):\n \"\"\"\n Performs the dot product on two vectors. The first vector remains fixed\n (i.e. the elements never move) while the second vector shifts to the right\n by one with each iteration so that the last element becomes the first. We\n perform the dot product once for each letter and then find the index of the\n largest value. The index of this value corresponds to a letter in the\n alphabet (0 = a, 1 = b, 2 = c, etc.).\n\n Args:\n freq_list: The list containing the frequency values of the letters in\n the encrypted text.\n\n Returns:\n sum_list: The list containing the values obtained by performing the\n dot product on the two lists passed to this function.\n largest_index: The index of the largest value found in sum_list.\n \"\"\"\n\n alphabet_freq = ENGLISH_FREQ[:]\n sum_list = []\n sum_total = 0\n\n # Perform the dot product 26 times (once for each letter in the alphabet)\n for j in range(len(alphabet_freq)):\n sum = 0\n\n for i in range(len(freq_list)):\n # Do the dot product\n sum += freq_list[i] * alphabet_freq[i];\n\n sum_list.append(sum)\n\n # Shift the alphabet list to the right by 1 before performing the\n # next dot product\n last_elem = alphabet_freq.pop()\n alphabet_freq.insert(0, last_elem)\n\n # Get the largest index in our list. This index corresponds to the index\n # of a letter in the alphabet\n largest_index = find_largest_frequency(sum_list)\n\n return { 'sum_list' : sum_list, 'largest_index': largest_index }\n\ndef decrypt(encrypted_text_list, key):\n \"\"\"\n Decrypt the encrypted text using the given key. We do this by iterating\n over each character in the encrypted text and getting it's index in the\n alphabet and the index of the character in the key in the alphabet. We\n then subtract the two indices and convert it back into a letter. We do this\n for each letter in the encrypted text until it is completely decrypted.\n\n Args:\n encrypted_text_list: The character list for the encrypted text.\n key: The list of the characters that make up the key.\n\n Returns:\n The string containing the decrypted message.\n \"\"\"\n\n output = []\n index = -1\n list_index = -1\n key_index = -1\n\n for i in range(len(encrypted_text_list)):\n # Get the index for the encrypted text character and the key character\n list_index = ALPHABET.index(encrypted_text_list[i])\n key_index = ALPHABET.index(key[i % len(key)])\n # Subtract the two characters to 'undo' to the vigenere cipher\n index = list_index - key_index\n\n # If the index goes negative, add the length of the alphabet to\n # make it positive\n if index < 0:\n index = len(ALPHABET) + index\n\n output.append(ALPHABET[index])\n\n return ''.join(output)\n\ndef main():\n \"\"\"\n The main function for this program. It initializes all of our variables,\n writes all output to the log file, and calls all necessary functions\n to solve the Vigenere cipher encrypted text.\n \"\"\"\n # Initialize variables to default values\n char_occurences = []\n cipher_list = []\n dot_product = []\n freq_list = []\n frequency = []\n key_list = []\n key_length = -1\n largest_index = -1\n total_char_count = -1\n decrypted_text = ''\n key = ''\n\n # Initialize file I/O for our log file\n file = open('log.txt', 'w')\n\n # Read cipher text from file and create a char list from the string\n cipher_list = list(read_text_from_file('CipherText.txt'))\n file.write('Cipher Text: \\n')\n file.write(''.join(cipher_list) + '\\n\\n')\n\n # If we start at zero, we don't actually shift any so we start at 1\n for i in range(1, 10):\n # Shift the cipher text by i and count how many matches we get\n # I.E. Does list[0] == list[i]?\n # Does list[1] == list[i+1]?\n frequency.append(find_frequency(cipher_list, i))\n\n file.write('Coincidences (finding key length):' + ' \\n')\n file.write(', '.join(str(num) for num in frequency) + '\\n')\n\n # Get the largest number from the frequency list\n # Add one because lists start at 0\n key_length = find_largest_frequency(frequency) + 1\n file.write('Key Length: ' + str(key_length) + '\\n\\n')\n\n for i in range(key_length):\n # Count the occurences of the letters in the i, i + key_length,\n # i + 2*key_length, etc. positions of the cipher text\n char_occurences = find_char_occurences(cipher_list, key_length, i)\n file.write('Character Occurences with shift of ' + str(i) + ':\\n')\n file.write(''.join(str(char_occurences)) + '\\n')\n\n # Find the total number of letters counted above\n total_char_count = find_total_char_count(char_occurences)\n file.write('Total Character Count: ' + str(total_char_count) + '\\n')\n\n # Divide the character frequencies by the total letters counted\n freq_list = make_freq_list(char_occurences, total_char_count)\n file.write('Character Frequency: \\n' + ''.join(str(freq_list)) + '\\n')\n\n # Perform the dot product between our freq_list and the frequency of\n # the english language. The index of the largest value in the dot\n # product list should correspond to a letter in the alphabet\n # (i.e. 0 is a, 1 is b, etc.)\n dot_product_output = do_dot_product(freq_list)\n dot_product = dot_product_output['sum_list']\n largest_index = dot_product_output['largest_index']\n # Push the corresponding letter onto our key list\n key_list.append(ALPHABET[largest_index])\n file.write('Dot Product Result: \\n' + ''.join(str(dot_product)) + '\\n')\n file.write('Index of Largest Value: ' + str(largest_index) + '\\n')\n file.write('Corresponding Letter: ' + ALPHABET[largest_index])\n file.write('\\n\\n\\n')\n\n key = ''.join(key_list)\n file.write('KEY: \\n' + key + '\\n\\n\\n')\n\n # Decrypt the message using our key to see if the text is now legible\n decrypted_text = decrypt(cipher_list, key)\n file.write('Decrypted Message: \\n' + decrypted_text)\n file.close()\n\n print('All output is in log.txt')\n\n return\n\n# Call the main method to begin program\nif __name__ == '__main__':\n main()\n","sub_path":"Vigenere Cipher/hostetter_hw1.py","file_name":"hostetter_hw1.py","file_ext":"py","file_size_in_byte":12654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"640955882","text":"import RPi.GPIO as GPIO\nimport time\nimport math\nstartTime = time.time()\n\ndef main():\n\t\n\tcathodes = [16,20,21]\n\tnumpins = [6,13,19,26]\n\n\tGPIO.setmode(GPIO.BCM)\n\tsetup_74HC4511(numpins, cathodes)\n\n\ttry:\n\t\twhile True:\n\t\t\tt = time.time() - startTime\n\t\t\tdisplayTime(t, numpins, cathodes)\n\texcept KeyboardInterrupt:\n\t\tpass \n\tGPIO.cleanup()\n\ndef displayTime(t, pins, caths, delay=0.02):\n\tt = int(t)\n\n\tdisp = (int(t)//60) % 10\n\tblnk = (disp == 0)\n\toutput_74HC4511(disp,pins, caths, 2, blnk)\n\ttime.sleep(0.002)\n\n\tdisp = (int(t)%60 // 10)\n\tif (disp != 0):\n\t\tblnk = False\n\toutput_74HC4511(disp, pins, caths, 1, blnk)\n\ttime.sleep(0.002)\n\n\tdisp = int(t)%10\n\toutput_74HC4511(disp, pins, caths, 0)\n\ttime.sleep(0.002)\n\ndef setup_74HC4511(pins,cothodes):\n\tfor p in cothodes:\n\t\tGPIO.setup(p, GPIO.OUT)\n\t\tGPIO.output(p, GPIO.HIGH)\n\tfor p in pins:\n\t\tGPIO.setup(p, GPIO.OUT)\n\ndef output_74HC4511( d, pins, caths,n, blank=False):\n\t\n\tif (blank):\n\t\td = 10\n\tfor p in caths:\n\t\tGPIO.output(p, 1)\n\tGPIO.output(caths[n], 0)\n\tfor i in range(len(pins)):\n\t\tGPIO.output(pins[i], d%2)\n\t\td = d//2\nmain()\n\n","sub_path":"week7/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"209879457","text":"from CRABClient.UserUtilities import config\nconfig = config()\n\n# config.section_('General')\nconfig.General.requestName = 'crab_ZprimeToEE_M6000_TuneCP5_14TeV_defaultv3'\nconfig.General.workArea = 'crab_ZprimeToEE_M6000_TuneCP5_14TeV_defaultv3'\nconfig.General.transferOutputs = True\nconfig.General.transferLogs = True\n\n# config.section_('JobType')\nconfig.JobType.pluginName = 'Analysis'\nconfig.JobType.psetName = 'hlt_12_1_0pre4_Default_CustomTracking.py'\nconfig.JobType.numCores = 4\n\n# config.Data.inputDBS = 'phys03'\nconfig.JobType.allowUndistributedCMSSW = True\nconfig.JobType.maxMemoryMB = 1000\n\n# config.JobType.numCores = 8\nconfig.Data.inputDataset ='/ZprimeToEE_M-6000_TuneCP5_14TeV-pythia8/Run3Winter21DRMiniAOD-FlatPU0to80FEVT_112X_mcRun3_2021_realistic_v16-v2/GEN-SIM-DIGI-RAW'\nconfig.Data.splitting = 'FileBased'\nconfig.Data.unitsPerJob = 1\n\nconfig.Data.outLFNDirBase = '/store/group/phys_egamma/Run3TriggerStudies/PixelTrackValidation/defaultv3'\nconfig.Data.publication = False\nconfig.Site.storageSite = 'T2_CH_CERN'","sub_path":"crab_config_default.py","file_name":"crab_config_default.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"199706102","text":"from cv2 import *\nfrom time import sleep\nimport numpy as np\n\n#==================================================\nace1 = imread(\"/Users/SeoPaul/Desktop/IMG/ACE.jpg\")\nace1 = resize(ace1, (600, 360))\n\nrows, cols, channels = ace1.shape\n(b,g,r) = split(ace1)\nb = np.zeros((rows, cols), dtype='uint8')\ng = np.zeros((rows, cols), dtype='uint8')\nace1 = merge((b,g,r))\n\n#==================================================\nace2 = imread(\"/Users/SeoPaul/Desktop/IMG/ACE.jpg\")\nace2 = resize(ace2, (600, 360))\n\nrows, cols, channels = ace2.shape\n(b,g,r) = split(ace2)\ng = np.zeros((rows, cols), dtype='uint8')\nr = np.zeros((rows, cols), dtype='uint8')\nace2 = merge((b,g,r))\n\nadd_img = addWeighted(ace1, 0.5, ace2, 0.5, 0)\nimshow(\"Add Weighted0\", add_img)\nadd_img = addWeighted(ace1, 0.5, ace2, 0.5, 50)\nimshow(\"Add Weighted50\", add_img)\nadd_img = addWeighted(ace1, 0.5, ace2, 0.5, 100)\nimshow(\"Add Weighted100\", add_img)\nadd_img = addWeighted(ace1, 0.5, ace2, 0.5, 150)\nimshow(\"Add Weighted150\", add_img)\nadd_img = addWeighted(ace1, 0.5, ace2, 0.5, 200)\nimshow(\"Add Weighted200\", add_img)\n\nwaitKey(0)\ndestroyAllWindows()","sub_path":"OpenCV/6_ImageAdd.py","file_name":"6_ImageAdd.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"390336543","text":"n, m = map(int, input().split())\nif(m > n):\n print(-1)\nelse:\n if((n % 2 ) == 0):\n number = n // 2\n else:\n number = (n-1)//2 + 1\n while (number % m) > 0:\n number +=1\n print(number)\n","sub_path":"dreamon_and_stupenki.py","file_name":"dreamon_and_stupenki.py","file_ext":"py","file_size_in_byte":194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"171707086","text":"import torch\nimport math\n\npi = math.pi\n\nfrom .utils import *\n\nclass CVAE(torch.nn.Module):\n def __init__(self, architecture, device=\"cpu\"):\n super().__init__()\n \n self.device = torch.device(device)\n \n print(\"CVAE with {} architecture.\".format(architecture[\"type\"]))\n self.architecture = architecture\n self.dim_x = architecture[\"dim_x\"]\n self.dim_y = architecture[\"dim_y\"]\n self.dim_z = architecture[\"dim_z\"]\n \n self.L = architecture[\"L\"] if \"L\" in architecture else 1\n \n self.n_x_features = architecture[\"n_x_features\"]\n\n if architecture[\"type\"] == \"Type-1\": \n self.q_x_in = build_sequential(architecture[\"q_x_in\"])\n self.q_y_in = build_sequential(architecture[\"q_y_in\"])\n self.q_out = build_sequential(architecture[\"q_x_y_out\"])\n \n self.p_y_in = build_sequential(architecture[\"p_y_in\"])\n self.p_z_in = build_sequential(architecture[\"p_z_in\"])\n self.p_y_z_in = build_sequential(architecture[\"p_y_z_in\"])\n self.p_mu_out = build_sequential(architecture[\"p_y_z_out\"][0])\n if len(architecture[\"p_y_z_out\"]) > 1:\n self.predict_var = True\n self.p_var_out = build_sequential(architecture[\"p_y_z_out\"][1])\n self.x_var_init_std = architecture[\"x_var_init_std\"] if \"x_var_init_std\" in architecture else 0.01\n def init_weight(m):\n if hasattr(m, \"weight\"):\n torch.nn.init.normal_(m.weight, std=self.x_var_init_std)\n self.p_var_out.apply(init_weight)\n self.min_x_var = architecture[\"min_x_var\"] if \"min_x_var\" in architecture else 1e-7\n else:\n self.predict_var = False\n self.p_var_out = None\n self.use_aux_label = architecture[\"aux_label\"]\n if \"prior_z_y\" in architecture:\n # Use prior network to get z\n self.prior_network = build_sequential(architecture[\"prior_z_y\"])\n else:\n self.prior_network = None\n else:\n raise NotImplementedError(\"Architecture {} not supported yet!\".format(architecture[\"type\"]))\n \n self.min_z_var = architecture[\"min_z_var\"] if \"min_z_var\" in architecture else 1e-7\n \n self.likelihood_scaling = architecture[\"likelihood_scaling\"] if \"likelihood_scaling\" in architecture else 1.0\n self.alpha_var = 1.0\n self.beta_KL = 1.0\n\n if \"cuda\" in self.device.type:\n self.cuda()\n \n def sample_z(self, z_mu, z_log_var):\n eps = torch.randn(size=(self.L, *z_mu.size()), device=self.device)\n z = z_mu + eps * (torch.exp(z_log_var/2) + self.min_z_var)\n return z.view(-1, *self.dim_z)\n \n def Q(self, x, y, aux_label=None):\n if aux_label is not None and self.use_aux_label:\n y = merge_aux_label(y, aux_label)\n h_x = self.q_x_in(x)\n h_y = self.q_y_in(y)\n h = torch.cat([h_x, h_y], dim=1) \n h = self.q_out(h)\n self.z_mu = h[:,0]\n self.z_log_var = h[:,1]\n\n assert self.z_mu.size()[1:] == self.dim_z, \"Dimension of z_mu does not match dim_z: {} vs {}.\".format(self.z_mu.size()[1:], self.dim_z)\n\n return self.sample_z(self.z_mu, self.z_log_var)\n \n def prior(self, y, aux_label=None):\n if self.prior_network is None:\n z_mu = torch.zeros((y.shape[0], *self.dim_z), device=self.device)\n z_log_var = torch.zeros((y.shape[0], *self.dim_z), device=self.device)\n else:\n if aux_label is not None and self.use_aux_label:\n y = merge_aux_label(y, aux_label)\n h = self.prior_network(y)\n z_mu = h[:,0]\n z_log_var = h[:,1]\n\n assert z_mu.size()[1:] == self.dim_z, \"Dimension of z_mu does not match dim_z: {} vs {}.\".format(z_mu.size()[1:], self.dim_z)\n\n return z_mu, z_log_var\n \n def sample_prior(self, y, aux_label=None):\n with torch.no_grad():\n z_mu, z_log_var = self.prior(y, aux_label)\n return self.sample_z(z_mu, z_log_var)\n\n \n def P(self, z, y, L=1, aux_label=None):\n if aux_label is not None and self.use_aux_label:\n y = merge_aux_label(y, aux_label)\n h_y = self.p_y_in(y)\n h_z = self.p_z_in(z)\n \n h = torch.cat([h_z, h_y.repeat(L, 1, 1, 1)], dim=1)\n h = self.p_y_z_in(h)\n \n x_mu = self.p_mu_out(h)\n assert x_mu.size()[1:] == self.dim_x, \"Dimension of x_mu does not match dim_x: {} vs {}.\".format(x_mu.size()[1:], self.dim_x)\n \n if self.predict_var:\n x_log_var = self.p_var_out(h)\n assert x_log_var.size()[1:] == self.dim_x, \"Dimension of x_log_var does not match dim_x: {} vs {}.\".format(x_log_var.size()[1:], self.dim_x)\n return x_mu, x_log_var\n else:\n return x_mu,\n \n def forward(self, x, y, aux_label=None):\n z = self.Q(x, y, aux_label)\n M = x.size(0)\n \n prior_z_mu, prior_z_log_var = self.prior(y, aux_label)\n prior_z_var = torch.exp(prior_z_log_var)\n \n self.KL_term = 0.5/M * torch.sum((prior_z_mu-self.z_mu)**2/prior_z_var + torch.exp(self.z_log_var)/prior_z_var \\\n + prior_z_log_var - self.z_log_var - 1)\n\n params = self.P(z, y, self.L, aux_label)\n x_mu = params[0]\n self.x_mu = x_mu\n if self.predict_var: \n log_x_var = params[1]#/math.log(self.dim_x[0]*self.dim_x[1]*self.dim_x[2]) * math.log(self.min_x_var)\n self.x_var = torch.exp(log_x_var)\n self.log_likelihood_fixed_var = -0.5*math.log(2*pi) + (-0.5 * (x.repeat(self.L, 1, 1, 1) - x_mu)**2).sum(dim=[3,2,0])/(M*self.L)\n self.log_likelihood_free_var = -0.5*math.log(2*pi) + (-0.5*log_x_var - 0.5*(x.repeat(self.L, 1, 1, 1) - x_mu)**2/self.x_var).sum(dim=[3,2,0])/(M*self.L)\n self.log_likelihood = (1-self.alpha_var)*self.log_likelihood_fixed_var \\\n + self.alpha_var*self.log_likelihood_free_var \n else:\n # Fixed variance\n self.log_likelihood = -0.5*math.log(2*pi) + 1/(M*self.L)*(-0.5 * (x.repeat(self.L, 1, 1, 1) - x_mu)**2).sum(dim=[3,2,0])\n\n self.ELBO = -self.KL_term*self.beta_KL + self.likelihood_scaling*self.log_likelihood.sum()\n return self.ELBO\n \n def sample_P(self, y, return_var=False, aux_label=None, z=None):\n with torch.no_grad():\n if z is None:\n z = self.sample_prior(y, aux_label)\n else:\n z = torch.tensor(z, device=self.device, dtype=y.dtype)\n p = self.P(z, y, L=1, aux_label=aux_label)\n mu = p[0]\n if len(p) == 2:\n var = torch.exp(p[1])\n if return_var:\n return mu, var\n \n return mu\n \n def get_stats(self):\n if self.predict_var:\n return (self.ELBO.item(), -self.KL_term.item(), \n *self.log_likelihood.detach().cpu().numpy(), \n *self.log_likelihood_fixed_var.detach().cpu().numpy(),\n *self.log_likelihood_free_var.detach().cpu().numpy())\n else:\n return (self.ELBO.item(), -self.KL_term.item(), *self.log_likelihood.detach().cpu().numpy())\n \n def get_stats_labels(self):\n if self.predict_var:\n return [\"ELBO\", \"KL_term\",] \\\n + [\"log_likelihood_{}\".format(i) for i in range(self.n_x_features)] \\\n + [\"log_likelihood_fixed_var_{}\".format(i) for i in range(self.n_x_features)] \\\n + [\"log_likelihood_free_var_{}\".format(i) for i in range(self.n_x_features)]\n else:\n return [\"ELBO\", \"KL_term\",] + [\"log_likelihood_{}\".format(i) for i in range(self.n_x_features)]\n \n def count_parameters(self):\n return sum(p.numel() for p in self.parameters() if p.requires_grad)\n \n def print_model_statistics(self, percentile=0.9):\n params = sorted([(p.numel(), name) for name, p in self.named_parameters() if p.requires_grad], reverse=True)\n n = [p[0] for p in params]\n cumulative = [sum(n[:i+1]) for i in range(len(n))]\n\n print(\"Total number of parameters: {}\".format(cumulative[-1]))\n print(\"Top {}\\% of all parameters are in the following layers\".format(percentile*100))\n for i in range(len(params)):\n if cumulative[i] < cumulative[-1]*0.9:\n print(\"{:<40s} {:>8}\".format(params[i][1], params[i][0]))\n \n def check_gpu(self):\n for name, p in self.named_parameters():\n if \"cuda\" not in str(p.data.device):\n print(\"{} is not on the GPU!\".format(name))","sub_path":"baryon_painter/models/cvae.py","file_name":"cvae.py","file_ext":"py","file_size_in_byte":9007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"205014364","text":"from __future__ import annotations\n\nfrom abc import ABC\nfrom dataclasses import dataclass\nfrom functools import lru_cache\nfrom typing import Union, Optional, Iterable\n\nfrom yul.utils import snakify\n\n\nclass Node(ABC):\n pass\n\n\n@dataclass(eq=False, frozen=True)\nclass TypedName(Node):\n name: str\n type: str = \"Uint256\"\n\n\n@dataclass(eq=True, frozen=True)\nclass Literal(Node):\n value: Union[int, bool]\n\n\n@dataclass(eq=True, order=True, frozen=True)\nclass Identifier(Node):\n name: str\n\n\n@dataclass(eq=False, frozen=True)\nclass Assignment(Node):\n variable_names: list[Identifier]\n value: \"Expression\"\n\n\n@dataclass(eq=False, frozen=True)\nclass FunctionCall(Node):\n function_name: Identifier\n arguments: list[\"Expression\"]\n\n\n@dataclass(eq=False, frozen=True)\nclass ExpressionStatement(Node):\n \"\"\"According to\n https://docs.soliditylang.org/en/latest/yul.html#restrictions-on-the-grammar,\n only top-level expressions can be statements. Furthermore, they\n must evaluate to zero values. That only leaves function calls\n that return zero values. Weird.\n\n \"\"\"\n\n expression: \"Expression\"\n\n\n@dataclass(eq=False, frozen=True)\nclass VariableDeclaration(Node):\n variables: list[TypedName]\n value: Optional[\"Expression\"] # None means all variables initialize to 0\n\n\n@dataclass(eq=False, frozen=True)\nclass Block(Node):\n statements: tuple[\"Statement\", ...] = ()\n\n @property\n @lru_cache(None)\n def scope(self) -> \"Scope\":\n return ScopeResolver().compute_uncached_scope(self)\n\n\n@dataclass(eq=False, frozen=True)\nclass FunctionDefinition(Node):\n name: str\n parameters: list[TypedName]\n return_variables: list[TypedName]\n body: Block\n\n @property\n @lru_cache(None)\n def scope(self) -> \"Scope\":\n return ScopeResolver().compute_uncached_scope(self)\n\n\n@dataclass(eq=False, frozen=True)\nclass If(Node):\n condition: \"Expression\"\n body: Block\n # ↓ doesn't exist in Yul, convenient for mapping to cairo\n else_body: Optional[Block] = None\n\n\n@dataclass(eq=False, frozen=True)\nclass Case(Node):\n value: Optional[Literal] # None for the default case\n body: Block\n\n\n@dataclass(eq=False, frozen=True)\nclass Switch(Node):\n expression: \"Expression\"\n cases: list[Case]\n\n\n@dataclass(eq=False, frozen=True)\nclass ForLoop(Node):\n pre: Block\n condition: \"Expression\"\n post: Block\n body: Block\n\n\n@dataclass(eq=False, frozen=True)\nclass Break(Node):\n pass\n\n\n@dataclass(eq=False, frozen=True)\nclass Continue(Node):\n pass\n\n\n@dataclass(eq=False, frozen=True)\nclass Leave(Node):\n pass\n\n\n# No two nodes of this class should be different from each\n# other. Thus, it's cheaper to create one object and use it in all\n# contexts, rather than create new `Leave()` each time.\nLEAVE: Leave = Leave()\n\nLEAVE_BLOCK: Block = Block(statements=((LEAVE,)))\n\nExpression = Union[Literal, Identifier, FunctionCall]\nStatement = Union[\n ExpressionStatement,\n Assignment,\n VariableDeclaration,\n FunctionDefinition,\n If,\n Switch,\n ForLoop,\n Break,\n Continue,\n Leave,\n Block,\n]\n\nNODE_TYPES = frozenset(\n (\n Literal,\n Identifier,\n FunctionCall,\n ExpressionStatement,\n Assignment,\n VariableDeclaration,\n FunctionDefinition,\n If,\n Switch,\n ForLoop,\n Break,\n Continue,\n Leave,\n Block,\n )\n)\n\n\n@dataclass\nclass Scope:\n \"\"\"'Scope' represents symbols encountered in a particular scope.\n\n - 'bound_variables' maps names of variables assigned in the scope\n to their types.\n\n - 'read_variables' is a set of undeclared (\"free\") identifiers\n encountered in the scope that were read from.\n\n - 'modified_variables' is a set of undeclared (\"free\") identifiers\n encountered in the scope that were written to.\n\n \"\"\"\n\n bound_variables: dict[str, TypedName]\n read_variables: frozenset[Identifier]\n modified_variables: frozenset[Identifier]\n\n\nEMPTY_SCOPE = Scope({}, frozenset(), frozenset())\n\n\ndef get_children(node: Node) -> Iterable[Node]:\n if isinstance(node, Assignment):\n return node.variable_names + [node.value]\n elif isinstance(node, FunctionCall):\n return node.arguments + [node.function_name]\n elif isinstance(node, ExpressionStatement):\n return [node.expression]\n elif isinstance(node, VariableDeclaration):\n return node.variables + ([] if node.value is None else [node.value])\n elif isinstance(node, Block):\n return node.statements\n elif isinstance(node, FunctionDefinition):\n return node.parameters + node.return_variables + [node.body]\n elif isinstance(node, If):\n return [node.condition, node.body] + (\n [] if node.else_body is None else [node.else_body]\n )\n elif isinstance(node, Case):\n return [node.value, node.body]\n elif isinstance(node, Switch):\n return node.cases + [node.expression]\n elif isinstance(node, ForLoop):\n return [node.pre, node.condition, node.post, node.body]\n else:\n return []\n\n\nclass AstVisitor:\n def __init__(self):\n self.path = []\n # ↑ path of nodes from the AST root down to the current node,\n # inclusively\n\n def path_decorator(method):\n def new_method(node, *args, **kwargs):\n self.path.append(node)\n res = method(node, *args, **kwargs)\n self.path.pop()\n return res\n\n return new_method\n\n for node_type in NODE_TYPES:\n visitor_name = \"visit_\" + snakify(node_type.__name__)\n method = getattr(self, visitor_name, None)\n if method is None:\n method = self.common_visit\n setattr(self, visitor_name, path_decorator(method))\n\n def visit(self, node: Node, *args, **kwargs):\n method_name = \"visit_\" + snakify(type(node).__name__)\n method = getattr(self, method_name, self.common_visit)\n return method(node, *args, **kwargs)\n\n def common_visit(self, node, *args, **kwargs):\n self.visit_list(get_children(node))\n\n def visit_list(self, nodes: Iterable[Node], *args, **kwargs) -> list:\n return [self.visit(x, *args, **kwargs) for x in nodes]\n\n\nclass ScopeResolver(AstVisitor):\n def __init__(self):\n super().__init__()\n self.bound_variables: dict[str, TypedName] = {}\n self.known_variables: set[Identifier] = set()\n # ↑ variables, whose value is known at this point and doesn't\n # need passing from the outside.\n self.read_variables: list[Identifier] = []\n self.modified_variables: list[Identifier] = []\n\n def compute_uncached_scope(self, node: Union[Block, FunctionDefinition]) -> Scope:\n if isinstance(node, Block):\n self.common_visit(node)\n else:\n assert isinstance(node, FunctionDefinition)\n print(node.name)\n # Not visiting return_variables, they are not bound or\n # mentioned semantically.\n self.visit_list(node.parameters)\n self.visit(node.body)\n\n read = frozenset(self.read_variables)\n modified = frozenset(self.modified_variables)\n return Scope(\n bound_variables=self.bound_variables,\n read_variables=read,\n modified_variables=modified,\n )\n\n def visit_typed_name(self, node: TypedName):\n self.bound_variables[node.name] = node\n self.known_variables.add(Identifier(node.name))\n\n def visit_identifier(self, node: Identifier, is_function: bool = False):\n if not is_function:\n self._register_read(node)\n\n def visit_assignment(self, node: Assignment):\n # assigned vars are registered _after_ the assignment is complete\n self.visit(node.value)\n for var in node.variable_names:\n self._register_modification(var)\n\n def visit_function_call(self, node: FunctionCall):\n self.visit(node.function_name, is_function=True)\n self.visit_list(node.arguments)\n\n def visit_variable_declaration(self, node: VariableDeclaration):\n # declared vars are registered _after_ the declaration is complete\n if node.value:\n self.visit(node.value)\n self.visit_list(node.variables)\n\n def visit_block(self, node: Block):\n for var in node.scope.read_variables:\n self._register_read(var)\n for var in node.scope.modified_variables:\n self._register_modification(var)\n\n def visit_function_definition(self, node: FunctionDefinition):\n for var in node.scope.read_variables:\n self._register_read(var)\n for var in node.scope.modified_variables:\n self._register_modification(var)\n\n def visit_if(self, node: If):\n self.visit(node.condition)\n scope1 = node.body.scope\n scope2 = node.else_body.scope if node.else_body else EMPTY_SCOPE\n # If a variable has been modified in only one branch, it still\n # means that we need a read access to know it's value after\n # the if. For that reason, we include symmetric difference of\n # modified variables to the set of the read ones.\n new_read = (\n scope1.read_variables\n | scope2.read_variables\n | (scope1.modified_variables ^ scope2.modified_variables)\n )\n new_mod = scope1.modified_variables | scope2.modified_variables\n new_known = scope1.modified_variables & scope2.modified_variables\n self.read_variables.extend(new_read)\n self.modified_variables.extend(new_mod)\n self.known_variables.update(new_known)\n\n def _register_modification(self, var: Identifier):\n if var not in self.known_variables:\n self.modified_variables.append(var)\n self.known_variables.add(var)\n\n def _register_read(self, var: Identifier):\n if var not in self.known_variables:\n self.read_variables.append(var)\n","sub_path":"warp/yul/yul_ast.py","file_name":"yul_ast.py","file_ext":"py","file_size_in_byte":9994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"190386262","text":"from __future__ import print_function\nimport pandas as pd\nimport datetime\nimport os\nimport sys\nimport getopt\nimport pickle\nimport os.path\nimport pprint\nfrom termcolor import colored, cprint\nfrom env import *\nfrom sheet_config import *\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\n\n# Config PrettyPrinter\npp = pprint.PrettyPrinter(indent=2)\n\n# SCOPES = ['https://www.googleapis.com/auth/spreadsheets.readonly']\nSCOPES = ['https://www.googleapis.com/auth/spreadsheets']\n\ntime_stamp = str(datetime.datetime.now())[:10] # 2019-09-04\nextenslist_file_name = time_stamp + '_extenslista.csv'\ninfomentorlist_file_name = time_stamp + '_elevlista.csv'\n\ndef authenticate():\n creds = None\n # The file token.pickle stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n if os.path.exists('sheet_token.pickle'):\n with open('sheet_token.pickle', 'rb') as token:\n creds = pickle.load(token)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'sheet_credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('sheet_token.pickle', 'wb') as token:\n pickle.dump(creds, token)\n\n service = build('sheets', 'v4', credentials=creds)\n\n return service\n\ndef get_extens(service, EXTENS_ID):\n \"\"\"\n Get Extens file in Drive and return it as a Dataframe\n \"\"\"\n RANGE = 'extens!A1:D'\n FILENAME = extenslist_file_name\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n result = sheet.values().get(spreadsheetId=EXTENS_ID,\n range=RANGE).execute()\n values = result.get('values', [])\n\n if not values:\n print('No data found in extens.')\n else:\n # print('Läser in extensfil från Driven till Dataframe:')\n klasser = []\n personnummers = []\n names = []\n \n # emails = []\n # print(values)\n for i, row in enumerate(values):\n if i > 0:\n # Print columns A and E, which correspond to indices 0 and 4.\n try: # this will take care of eventually empty cells.\n # name = \"\\\"\"+row[0]+\"\\\"\"\n klass = row[0]\n personnummer = str(row[1])\n lastname = row[2]\n firstname = row[3]\n name = lastname + \", \" + firstname\n # print(personnummer)\n\n klasser.append(klass)\n personnummers.append(personnummer)\n names.append(name)\n # emails.append(email)\n except Exception as e:\n print()\n print(\"While reading rows from file error ->\", e)\n print(\"Null at \", row[1])\n print()\n # print(\"Skapar DataFrame och sparar som %s\" % (FILENAME))\n elevlista_dict = {\n 'Klass': klasser,\n 'Namn': names,\n 'Personnummer': personnummers,\n }\n df_extens = pd.DataFrame.from_dict(elevlista_dict)\n # df_extens.to_csv(FILENAME, sep=\",\", index=False)\n\n return df_extens\n\ndef get_infomentor(service, INFOMENTOR_ID):\n \"\"\"\n Get Infometor file in Drive and return it as a Dataframe\n \"\"\"\n RANGE = 'elevlista!A1:D'\n FILENAME = infomentorlist_file_name\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n result = sheet.values().get(spreadsheetId=INFOMENTOR_ID,\n range=RANGE).execute()\n values = result.get('values', [])\n\n if not values:\n print('No data found in extens.')\n else:\n # print('Läser in infomentorfil från Driven till Dataframe:')\n klasser = []\n personnummers = []\n names = []\n \n # emails = []\n # print(values)\n for i, row in enumerate(values):\n if i > 0:\n # Print columns A and E, which correspond to indices 0 and 4.\n try: # this will take care of eventually empty cells.\n # name = \"\\\"\"+row[0]+\"\\\"\"\n klass = row[0]\n personnummer = str(row[3])\n name = row[1]\n\n if len(personnummer) < 10:\n personnummer = \"0\" + personnummer\n personnummer = personnummer.strip()\n personnummer = personnummer[:6] + '-' + personnummer[-4:]\n\n personnummer = personnummer.strip()\n \n # print(personnummer)\n # if len(personnummer) < 11:\n # print(\"personnummer_pre: %s, personnummer_first: %s, personnummer: %s\" % (personnummer_pre, personnummer_first, personnummer))\n # print(\"personnummer_first[:6]: %s, personnummer_first[-4:]: %s\" % (personnummer_first[:6],personnummer_first[-4:]))\n klasser.append(klass)\n personnummers.append(personnummer)\n names.append(name)\n # emails.append(email)\n except Exception as e:\n print()\n print(\"While reading rows from file error ->\", e)\n print(\"Null at \", row[1])\n print()\n # print(\"Skapar DataFrame och sparar som %s\" % (FILENAME))\n infomentor_dict = {\n 'Klass': klasser,\n 'Namn': names,\n 'Personnummer': personnummers,\n }\n df_infomentor = pd.DataFrame.from_dict(infomentor_dict)\n # df_infomentor.to_csv(FILENAME, sep=\",\", index=False)\n\n return df_infomentor\n\ndef create_spreadsheet(service):\n \"\"\"\n Create new spreadsheet\n \"\"\"\n # print(\"Beginning process...\")\n\n time_stamp = str(datetime.datetime.now())[:10] # 2019-09-04\n spreadsheet_name = time_stamp + '_' + SPREADSHEET_TITLE\n SPREADSHEET_ID = \"\"\n spreadsheet_body = {\n \"properties\": {\n \"title\": spreadsheet_name\n }\n }\n try:\n request = service.spreadsheets().create(body=spreadsheet_body)\n spreadsheet = request.execute()\n SPREADSHEET_ID = spreadsheet['spreadsheetId']\n # print(\"spreadsheet id: \", SPREADSHEET_ID)\n except Exception as e:\n print(\"While trying to create new spreadsheet error: \", e)\n sys.exit()\n \n return SPREADSHEET_ID\n\ndef update_spreadsheet(service, SPREADSHEET_ID, body, message=\"No message\"):\n \"\"\"\n Update batch of requests in body\n \"\"\"\n\n response = service.spreadsheets().batchUpdate(spreadsheetId=SPREADSHEET_ID, body=body).execute()\n\n # print(message)\n\n return response\n\ndef create_sheets(service, SPREADSHEET_ID, sheet_objects):\n requests = []\n # print(\"Creating sheets...\")\n # CREATE SHEETS\n for sheet in sheet_objects:\n requests.append(sheet_objects[sheet])\n\n # Trying to update spreadsheet with assigned requests\n try:\n body = {\n \"requests\": requests\n }\n response = update_spreadsheet(service, SPREADSHEET_ID, body, \"Sheets created\")\n except Exception as e:\n print(\"While trying to batchUpdate error: \", e)\n sys.exit()\n\n return response\n\ndef get_sheet_ids(service, SPREADSHEET_ID):\n \"\"\"\n Getting the proporites of the created sheets.\n \"\"\"\n # print(\"Getting sheet proporties...\")\n sheet_dict = {}\n # Trying to get sheetIds\n try:\n fields=\"sheets.properties\"\n request = service.spreadsheets().get(spreadsheetId=SPREADSHEET_ID, fields=fields)\n response = request.execute()\n\n # How to iterate the response and get title of a sheet\n for prop in response['sheets']:\n # print(prop['properties']['title'])\n sheet_dict[prop['properties']['title']] = prop['properties']['sheetId']\n except Exception as e:\n print(\"While trying spreadsheets().get() error: \", e)\n sys.exit()\n # pp.pprint(sheet_dict)\n return sheet_dict\n\ndef customize_columns(service, SPREADSHEET_ID, columns):\n \"\"\"\n Set column width in sheets. columns is generated in sheet_config.py\n based on template and sheetId\n \"\"\"\n # print(\"Setting column and row widths in the sheets\")\n requests = []\n for key in columns:\n requests.append(columns[key])\n \n # Trying to update spreadsheet with assigned requests\n try:\n body = {\n \"requests\": requests\n }\n response = update_spreadsheet(service, SPREADSHEET_ID, body, \"Columns set\")\n return response\n except Exception as e:\n print(\"While trying to batchUpdate error: \", e)\n sys.exit()\n \ndef check_name(klass, info_name, ext_firstname, ext_lastname):\n ext_name = ext_lastname +\", \" + ext_firstname\n if info_name != ext_name:\n # print(\"Klass %s; INFOMENTOR NAMN; %s --> EXTENS NAMN: %s\" % (klass, info_name, ext_name))\n pass\n\ndef find_corresponding_name(name, personnummer, df):\n\n res = {\n \"namn\": \"NOT FOUND\",\n \"personnummer\": \"NOT FOUND\",\n \"matching\": 'NOT FOUND'\n }\n try:\n student_personnummer_series = df[df['Personnummer'].str.contains(personnummer)]\n res = {\n \"namn\": str(student_personnummer_series['Namn'].values[0]),\n \"personnummer\": str(student_personnummer_series['Personnummer'].values[0]),\n \"matching\": \"Personnummer\"\n }\n except:\n try:\n student_name_series = df[df['Namn'].str.contains(name)]\n res = {\n \"namn\": str(student_name_series['Namn'].values[0]),\n \"personnummer\": str(student_name_series['Personnummer'].values[0]),\n \"matching\": \"Namn\"\n }\n except:\n pass\n \n return res\n\ndef is_lists_equal(row):\n equal = True\n missing = False\n reason = []\n if row[1] == \"NOT FOUND\":\n reason.append(\"Saknas i Infomentor\")\n missing = True\n equal = False\n elif row[4] == \"NOT FOUND\":\n reason.append(\"Saknas i Extens\")\n missing = True\n equal = False\n \n if not missing:\n if row[1].lower() != row[4].lower():\n equal = False\n reason.append(\"Namnet\")\n if row[2].lower() != row[5].lower():\n equal = False\n reason.append(\"Personnummret\")\n\n if len(reason) > 1:\n print(reason)\n return equal, reason\n\n\ndef add_content(service, SPREADSHEET_ID, df_infomentor, df_extens):\n sheet_name = sheet_names[0]\n\n # ADDING HEADER\n content = []\n reported_content = []\n content = header\n reported_content = reported_header\n # print(\"content:\",content)\n sheet_range = sheet_name + \"!A1\"\n reported_range = \"Avvikelser!A1\"\n try:\n range = sheet_range\n values = content\n resource = {\n \"values\": values\n }\n # use append to add rows and update to overwrite\n response = service.spreadsheets().values().update(spreadsheetId=SPREADSHEET_ID, range=range, body=resource, valueInputOption=\"USER_ENTERED\").execute()\n\n range = reported_range\n values = reported_content\n resource = {\n \"values\": values\n }\n # use append to add rows and update to overwrite\n response = service.spreadsheets().values().update(spreadsheetId=SPREADSHEET_ID, range=range, body=resource, valueInputOption=\"USER_ENTERED\").execute()\n except Exception as e:\n print(\"While trying to append values error: \", e)\n sys.exit()\n\n # CONTENT\n content = []\n reported_content = []\n\n\n for klass in klasser:\n \"\"\"\n Tar ut klass för klass. \n Kollar om de är lika långa. \n Utgår från den lista som eventuellt är längst, annars Infomentors. \n\n Letar efter korresponderande elev i andra listan och placerar dessa\n båda på samma rad i nya spreadsheetet.\n \"\"\"\n info_klass_df = df_infomentor[df_infomentor['Klass'].str.contains(klass)] # alla elever ur klassen 'klass' i elevlistan\n ext_klass_df = df_extens[df_extens['Klass'].str.contains(klass)] # alla elever ur klassen 'klass' i extenslistan\n\n info_klass = info_klass_df.loc[:,'Klass'].tolist() # klasstillhörighet i en lista; [\"7A\", \"7A\", \"7A\",...]\n info_namn = info_klass_df.loc[:,'Namn'].tolist() # elevernas namn i en lista\n info_personnummer = info_klass_df.loc[:,'Personnummer'].tolist() # elevernas personnummer i en lista\n\n ext_klass = ext_klass_df.loc[:,'Klass'].tolist() # klasstillhörighet i en lista; [\"7A\", \"7A\", \"7A\",...]\n ext_namn = ext_klass_df.loc[:,'Namn'].tolist() # elevernas namn i en lista\n ext_personnummer = ext_klass_df.loc[:,'Personnummer'].tolist() # elevernas personnummer i en lista\n\n ext_longer = len(info_klass_df.index) < len(ext_klass_df) # is class in extenslista longer than in elevlista?\n\n not_found_in_other = 0\n not_found_students = []\n found_in_other = 0 # counter to check so that no students in smaller list is overlooked\n found_in_class = \"\"\n\n class_reasons = []\n\n row = []\n if ext_longer:\n for i, klass in enumerate(ext_klass):\n student = {}\n student = find_corresponding_name(ext_namn[i], ext_personnummer[i], info_klass_df)\n\n row = [ext_klass[i], student['namn'], student['personnummer'], ext_klass[i], ext_namn[i], ext_personnummer[i]]\n \n equal, reasons = is_lists_equal(row)\n if not equal:\n reason_string =', '.join(map(str, reasons))\n row.append(reason_string)\n reported_content.append(row)\n\n content.append(row)\n\n # checking if all students in infolist for this class is found.\n if student['matching'] == \"NOT FOUND\":\n not_found_in_other += 1\n found_in_class = ext_klass[i]\n not_found_students.append(ext_namn[i])\n else:\n found_in_other += 1\n found_in_class = ext_klass[i]\n \n if len(reasons) > 0:\n class_reasons.append([ext_klass[i], ext_namn[i], ext_personnummer[i], reason_string])\n \n if found_in_other == len(info_klass_df.index):\n cprint(\"Class %s Compare agains Extlist: %d Matched students in Infomentorlist: %s, Number of students in Infomentorlist: %d\" % (found_in_class, len(ext_klass_df.index), found_in_other, len(info_klass_df.index)), 'green')\n elif len(info_klass_df.index) > found_in_other:\n cprint(\"Class %s Matched students in Infomentorlist: %s, Number of students in Infomentorlist: %d STUDENT IN INFOMENTORLIST OVERLOOKED\" % (found_in_class, found_in_other, len(info_klass_df.index)), 'red')\n \n if len(class_reasons) > 0:\n cprint(\"Class %s Students that differ in Infomentorlist compared to Extenslist: \" % (found_in_class), 'yellow')\n for cr in class_reasons:\n cprint(\"\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t%s; %s; '%s'\" %\n (cr[1], cr[2], cr[3].upper()), 'cyan')\n print()\n\n else:\n for i, klass in enumerate(info_klass):\n student = {}\n student = find_corresponding_name(info_namn[i], info_personnummer[i], ext_klass_df)\n \n row = [info_klass[i], info_namn[i], info_personnummer[i], info_klass[i], student['namn'], student['personnummer']]\n\n equal, reasons = is_lists_equal(row)\n if not equal:\n reason_string =', '.join(map(str, reasons))\n row.append(reason_string)\n reported_content.append(row)\n\n content.append(row)\n\n # checking if all students in infolist for this class is found.\n if student['matching'] == \"NOT FOUND\":\n not_found_in_other += 1\n found_in_class = info_klass[i]\n not_found_students.append(info_namn[i])\n else: # if a match exists add to counter for matched students\n found_in_other += 1\n found_in_class = info_klass[i]\n \n if len(reasons) > 0: # if deviation reason exist append this to class_reasons list\n class_reasons.append([info_klass[i], info_namn[i], info_personnummer[i], reason_string])\n \n \n # after for loop check if found matches corresponds to list length\n if found_in_other == len(ext_klass_df.index):\n cprint(\"Class %s Compare agains Infolist: %d Matched students in Extenslist: %s, Number of students in Extenslist: %d\" % (found_in_class, len(info_klass_df.index), found_in_other, len(ext_klass_df.index)), 'green')\n elif len(ext_klass_df.index) > found_in_other:\n cprint(\"Class %s Matched students in Extenslist: %s, Number of students in Extenslist: %d STUDENT IN EXTENSLIST OVERLOOKED\" % (found_in_class, found_in_other, len(ext_klass_df.index)), 'red')\n \n # after for loop display eventual devaition reasons\n if len(class_reasons) > 0:\n cprint(\"Class %s Students that differ in Extenslist compared to Infomentorlist: \" % (found_in_class), 'yellow')\n for cr in class_reasons:\n cprint(\"\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t%s; %s; '%s'\" %\n (cr[1], cr[2], cr[3].upper()), 'cyan')\n print()\n \n empty_row = [\"\", \"\", \"\", \"\", \"\", \"\"]\n content.append(empty_row)\n # reported_content.append(empty_row)\n \n print()\n print(\"FOUND %s DEVIATIONS!\" % (len(reported_content)), end=\" \")\n sheet_range = sheet_name + \"!A2\"\n reported_range = \"Avvikelser!A2\"\n try:\n range = sheet_range\n values = content\n resource = {\n \"values\": values\n }\n # use append to add rows and update to overwrite\n response = service.spreadsheets().values().update(spreadsheetId=SPREADSHEET_ID, range=range, body=resource, valueInputOption=\"USER_ENTERED\").execute()\n\n range = reported_range\n values = reported_content\n resource = {\n \"values\": values\n }\n # use append to add rows and update to overwrite\n response = service.spreadsheets().values().update(spreadsheetId=SPREADSHEET_ID, range=range, body=resource, valueInputOption=\"USER_ENTERED\").execute()\n except Exception as e:\n print(\"While trying to append values error: \", e)\n \n print(\"REPORT CREATED!\")\n\nservice = authenticate()\ndf_extens = get_extens(service, EXTENS_ID)\ndf_infomentor = get_infomentor(service, INFOMENTOR_ID)\n\nSPREADSHEET_ID = create_spreadsheet(service) # Skapar spreadsheetet:service.spreadsheets().create\ncreate_sheets(service, SPREADSHEET_ID, sheet_objects) # Skapar sheets:service.spreadsheets().batchUpdate\nsheet_dict = get_sheet_ids(service, SPREADSHEET_ID) # sheet_dict:{\"7A\": sheet id nummer,...}\ncolumns_object = generate_columns_update_object(sheet_dict) # Skapar requestobjekt för justering av kolumnvidder\ncustomize_columns(service, SPREADSHEET_ID, columns_object) # Ändrar kolumnvidd i sheets:service.spreadsheets().batchUpdate\nprint()\nadd_content(service, SPREADSHEET_ID, df_infomentor, df_extens) # Lägger till innehåll i sheets:service.spreadsheets().values().update\n","sub_path":"info_and_ext_side_by_side.py","file_name":"info_and_ext_side_by_side.py","file_ext":"py","file_size_in_byte":20250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"16963285","text":"\"\"\"empty message\n\nRevision ID: e63479d786a3\nRevises: 81281bca10b9\nCreate Date: 2018-09-01 18:24:00.610022\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'e63479d786a3'\ndown_revision = '81281bca10b9'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('Articles', sa.Column('content', sa.Text(), nullable=True))\n op.add_column('Articles', sa.Column('date', sa.Date(), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('Articles', 'date')\n op.drop_column('Articles', 'content')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/e63479d786a3_.py","file_name":"e63479d786a3_.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"296569405","text":"from collections import deque\nn,m = map(int,input().split())\n\nq = deque([[str(n),n,1]])\nvis = [0]*100002\nvis[n] = 1\nif n != m:\n while q:\n path, nd, cnt = q.popleft()\n if nd + 1 < 100001:\n if nd + 1 == m:\n print(cnt)\n print(path +f' {nd+1}')\n break\n elif vis[nd+1] == 0:\n q.append([path + f' {nd+1}',nd+1,cnt+1])\n vis[nd+1] = 1\n if nd - 1 >= 0:\n if nd - 1 == m:\n print(cnt)\n print(path +f' {nd-1}')\n break\n elif vis[nd-1] == 0:\n q.append([path + f' {nd-1}',nd-1,cnt+1])\n vis[nd-1] = 1\n if nd*2 < 100001:\n if nd*2 == m:\n print(cnt)\n print(path +f' {nd*2}')\n break\n elif vis[nd*2] == 0:\n q.append([path + f' {nd*2}',nd*2,cnt+1])\n vis[nd*2] = 1\nelse:\n print(0)\n print(n)","sub_path":"13913.py","file_name":"13913.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"348059130","text":"#################################################\nmyInorder=[]\nmyPreorder=[]\nmyPostorder=[]\nsortlist=[]\nimport math\nclass heap:\n\tdef __init__(self):\n\t\tself.data = []\n\t\tself.count=0\n\n\tdef __str__(self):\n\t\tres=\"\"\n\t\tfor x in self.data:\n\t\t\tres=res+str(x)+\" \"\n\t\treturn res\n\t\n\tdef __len__(self):\n\t\treturn len(self.data)\n\n\tdef makenull(self):\n\t# should i still use return or not?\n\t\tself.data=[]\n\n\tdef insert(self,x):\n\n\t\tself.data.append(x)\n\t\tself.upheap(len(self.data)-1)\n\n\n\tdef parent(self,index):\n\t\tparentIdx=(index-1)//2\n\t\treturn parentIdx\n\n\tdef left(self,index):\n\t\tleftIdx= (index+1)*2-1\n\t\treturn leftIdx\n\tdef right(self,index):\n\t\trightIdx= (index+1)*2\n\t\treturn rightIdx\n\n\tdef swap(self,a,b):\n\t\ttmp = self.data[a]\n\t\tself.data[a]=self.data[b]\n\t\tself.data[b]=tmp\n\n\tdef upheap(self,index):\n\t\tparentIndex=self.parent(index)\n\t\tif parentIndex < 0:\n\t\t\treturn\n\t\tp=self.data[parentIndex]\n\t\tc=self.data[index]\n\t\tif p > c :\n\t\t\tself.swap(index,parentIndex)\n\t\t\tself.upheap(self.parent(index))\n\t\t\t\n\tdef inorder(self,index):\n\n\t\tif index < len(self.data):\n\t\t\tself.inorder(self.left(index))\n\t\t\tprint(str(self.data[index])+\" \",end='')\n\t\t\tself.inorder(self.right(index))\n\t\treturn ''\n\n\n\t\t\t\n\n\tdef preorder(self,index):\n\n\n\t\tif index < len(self.data):\n\t\t\t\n\t\t\tprint(str(self.data[index])+\" \",end='')\n\n\t\t\tself.preorder(self.left(index))\n\n\t\t\tself.preorder(self.right(index))\n\t\treturn ''\n\n\n\tdef postorder(self,index):\n\n\t\tif index < len(self.data):\n\t\n\t\t\tself.postorder(self.left(index))\n\t\t\tself.postorder(self.right(index))\n\t\t\tprint(str(self.data[index])+\" \",end='')\n\t\treturn ''\n\n\tdef min(self):\n\t\treturn self.data[0]\n\t\n\tdef deletemin(self):\n\t\tif len(self.data) == 0:\n\t\t\treturn \"empty, can not delete\"\n\t\tself.swap(0, len(self.data)-1)\n\t\tself.data.pop()\n\t\tself.downheap(0) # downheap from the first one\n\n\tdef findNumC(self,index):\n\t\tif self.left(index)< len(self.data) and (self.right(index)< len(self.data)):\n\t\t\treturn 2\n\t\telif self.left(index)> (len(self.data)-1) and (self.right(index) > (len(self.data)-1)):\n\t\t\treturn 0\n\t\telse:\n\t\t\treturn 1\n\t\n\t\t\n\n\tdef downheap(self,index): \n\t\tv= self.findNumC(index)\n\n\t\tif v == 2:\n\t\t\tif self.data[index]<= self.data[self.left(index)] and self.data[index]<= self.data[self.right(index)]:\n\t\t\t\treturn ''\n\t\t\tif self.data[self.left(index)]<= self.data[self.right(index)]:\n\t\t\t\tself.swap(index, self.left(index))\n\t\t\t\tself.downheap(self.left(index))\n\t\t\telse:\n\t\t\t\tself.swap(index, self.right(index))\n\t\t\t\tself.downheap(self.right(index))\n\t\telif v==1:\n\t\t\tself.swap(index, self.left(index))\n\t\t\tself.downheap(self.left(index))\n\t\telse:\n\t\t\treturn ''\n\n\tdef sort(self):\n\t\t\t\n\t\twhile len(self.data)>0:\t\t\t\n\t\t\tif len(self.data)==1:\n\t\t\t\tprint(self.data[0])\n\t\t\t\treturn \n\t\t\tif len(self.data)==2:\n\t\t\t\tif self.data[0]>self.data[1]:\n\t\t\t\t\ttmp=self.data[0]\n\t\t\t\t\tself.data[0]=self.data[1]\n\t\t\t\t\tself.data[1]=tmp\n\t\t\t\tprint(self.data[0])\n\t\t\t\tprint(self.data[1])\n\t\t\t\treturn \n\t\t\t\t\t\n\t\t\telse:\n\t\t\t\tself.swap(0,len(self.data)-1)\n\t\t\t\tprint(self.data.pop())\t\t\n\t\t\t\tself.downheap(0)\n\n################################################\n\nRawFile=input(\"File containing graph:\\n\")\n\n\n\n\nprint(\"Possible Commands are: \")\nprint(\"dijkstra x - Runs Dijkstra starting at node X. X must be an integer\")\nprint(\"floyd - Runs Floyd's algorithm\")\nprint(\"help - prints this menu\")\nprint(\"exit or ctrl-D - Exits the program\")\n\n\n\n\n\nwith open(RawFile, 'r') as f:\n\tG = [[int(num) for num in line.split(' ')] for line in f]\n# Gf is the number of nodes in a graph\nGf = G[0][0]\n\nG.pop(0)\n\n# [[0, 1, 1], [1, 2, 2], [2, 3, 2], [2, 4, 4], [3, 1, 1], [3, 2, 3], [4, 3, 5]]\n\n\n\ndef ll(self):\n\tRawl=[]\n\tfor i in range(self):\n\t\tRawl.append(float('inf'))\n\treturn Rawl\n\n# create a raw matrix by using the input file. \n# This matrix shows the distance, but not the shortest.\n# This is \"adjacency Matrix\"\ndef RawMatrix(self): # self is G\n\tRM=[]\n\tfor i in range(Gf):\n\t\tRM.append(ll(Gf))\n\n\tfor k in range(len(self)):\n\t\tRM[self[k][0]][self[k][1]]=self[k][2]\n\t\n\treturn RM\n\n\n\ndef dijkstra(self, sn): # sn is the staring nodes\n\tself=RawMatrix(G) # \"self\" is the adjacent matirx\n\tD=[]\n\tfor i in range(Gf):\n\t\tD.append(float('inf'))\n\t# D is a list now, [inf, inf, inf, inf, inf]\n\n\tD[sn]=0 # because start node to start node is 0\n\tH=heap()\n\tH.insert(0)\n\t\n\n\twhile len(H) !=0:\n\t\tuValue=H.min() # here we didn't do insert or sort or upheap\n\t\t# u's value is the minimum of H, which is always H[0], but we need to return the index of the value in D, that index is u.\n\t\tfor i in D:\n\t\t\tif i == uValue:\n\t\t\t\tu = D.index(uValue) # now u is the index of the minimum number in D\n\t\t\t\tbreak\n\t\t# for each node v (value of RawMatrix row u) adjacent to u \n\t\tfor value in self[u]:\n\t\t\tif value != float('inf'): # this is to make sure that v is adjacent to u\n\t\t\t\tvalueIndex=self[u].index(value)\n\t\t\t\tif D[valueIndex]>D[u]+self[u][valueIndex]:\n\t\t\t\t\tD[valueIndex]=D[u]+self[u][valueIndex]\n\t\t\t\t\tH.insert(D[valueIndex])\n\t\t\t\t\t\n\n\t\tH.deletemin()\n\n\tAns=[float(i) for i in D]\n\treturn Ans\n\n \n \ndef floyd(self): # sn is the staring nodes\n\tself=RawMatrix(G)\n\tprint(self)\n\tfor i in range(Gf):\n\t\tfor j in range(Gf):\n\t\t\tif self[i][j]!=float('inf') and self[j][i]!=float('inf'):\n\t\t\t\n\t\t\t\tif i==j:\n\t\t\t\t\tself[i][j]=0\n\t\t\t\tfor l in range(Gf):\n\t\t\t\t\tif self[i][l]!=float('inf') and self[l][j]!=float('inf'):\n\t\t\t\t \n\t\t\t\t\t\n\t\t\t\t\t\tif self[i][j]> self[i][l]+self[l][j]:\n\t\t\t\t\t\n\t\t\t\t\t\t\tself[i][j] = self[i][l]+self[l][j]\n\n\n\n\treturn\n\n\n# input1.txt\nx=1\nwhile x==1:\n\tcmd=input(\"Enter command:\\n\")\n\tif cmd == \"help\":\n\t\tprint(\"Possible Commands are: \")\n\t\tprint(\"dijkstra x - Runs Dijkstra starting at node X. X must be an integer\")\n\t\tprint(\"floyd - Runs Floyd's algorithm\")\n\t\tprint(\"help - prints this menu\")\n\t\tprint(\"exit or ctrl-D - Exits the program\")\n\t\t\n\tstartWithDi=cmd.startswith(\"dijkstra\")\n\tif startWithDi== True:\n\t\tNumL = [int(i) for i in cmd.split() if i.isdigit()] \n\t\tNum=int(NumL[0])\n\t\t# Get dijkstra Num\n\n\t\tprint(dijkstra(G,Num))\n\t \n\n\t# [[0, 1, 1], [1, 2, 2], [2, 3, 2], [2, 4, 4], [3, 1, 1], [3, 2, 3], [4, 3, 5]]\n\tif cmd == \"floyd\":\n \n\t\tfor i in floyd(G):\n\t\t\tprint(i)\n\n\tif cmd == \"exit\":\n\t\tprint(\"Bye\")\n\t\tx=0\n\n","sub_path":"DijkstraFloyd.py","file_name":"DijkstraFloyd.py","file_ext":"py","file_size_in_byte":5987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"534469692","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\"\"\"\n demo.py\n ~~~~~~~\n\n flask 头像处理实例\n\"\"\"\n\nimport os\nfrom flask import Flask, request, redirect, url_for, render_template, \\\n session\nfrom werkzeug import secure_filename\nimport sys\n\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\n\n# 集合列举允许的扩展名\nALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])\n\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = '/Users/apple/www/project/flaskuploads/static/upload/'\napp.config['SECRET_KEY'] = 'I hate flask'\n\n\ndef allowed_file(filename):\n \"\"\"检查文件扩展名函数\"\"\"\n return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\n\n\n@app.route('/upload', methods=[\"POST\", \"GET\"])\ndef upload_file():\n \"\"\"上传文件函数\"\"\"\n if request.method == 'POST':\n file = request.files['file']\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n session['fileurl'] = 'http://121.0.0.1:5000/static/upload/%s' % filename\n return redirect(url_for('upload_file'))\n return render_template('upload.html', session=session)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"56207891","text":"from django.core.paginator import EmptyPage, Paginator\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\n\n# Create your views here.\nfrom django.views import View\nfrom collections import OrderedDict\nfrom apps.goods.models import GoodsChannel, SKU, GoodsCategory\nfrom utils import models\nfrom utils.goods import get_breadcrumb, get_categories, get_goods_specs\n\n\nclass IndexView(View):\n \"\"\"首页广告\"\"\"\n\n def get(self, request):\n \"\"\"提供首页广告界面\"\"\"\n # 查询商品频道和分类\n categories = OrderedDict()\n channels = GoodsChannel.objects.order_by('group_id', 'sequence')\n for channel in channels:\n group_id = channel.group_id # 当前组\n\n if group_id not in categories:\n categories[group_id] = {'channels': [], 'sub_cats': []}\n\n cat1 = channel.category # 当前频道的类别\n\n # 追加当前频道\n categories[group_id]['channels'].append({\n 'id': cat1.id,\n 'name': cat1.name,\n 'url': channel.url\n })\n # 构建当前类别的子类别\n for cat2 in cat1.subs.all():\n cat2.sub_cats = []\n for cat3 in cat2.subs.all():\n cat2.sub_cats.append(cat3)\n categories[group_id]['sub_cats'].append(cat2)\n return categories\n\n\nclass ListView(View):\n \"\"\"商品列表页\"\"\"\n\n def get(self, request, category_id):\n \"\"\"提供商品列表页\"\"\"\n # 获取参数:\n page = request.GET.get('page')\n page_size = request.GET.get('page_size')\n ordering = request.GET.get('ordering')\n\n # 判断category_id是否正确\n try:\n # 获取三级菜单分类信息:\n category = GoodsCategory.objects.get(id=category_id)\n except Exception as e:\n return JsonResponse({'code': 400,\n 'errmsg': '获取mysql数据出错'})\n\n # 查询面包屑导航(函数在下面写着)\n breadcrumb = get_breadcrumb(category)\n\n # 排序方式:\n try:\n skus = SKU.objects.filter(category=category,\n is_launched=True).order_by(ordering)\n except Exception as e:\n return JsonResponse({'code': 400,\n 'errmsg': '获取mysql数据出错'})\n\n paginator = Paginator(skus, page_size)\n # 获取每页商品数据\n try:\n page_skus = paginator.page(page)\n except EmptyPage:\n # 如果page_num不正确，默认给用户400\n return JsonResponse({'code': 400,\n 'errmsg': 'page数据出错'})\n # 获取列表页总页数\n total_page = paginator.num_pages\n\n # 定义列表:\n list = []\n # 整理格式:\n for sku in page_skus:\n list.append({\n 'id': sku.id,\n 'default_image_url': sku.default_image.url,\n 'name': sku.name,\n 'price': sku.price\n })\n\n # 把数据变为 json 发送给前端\n return JsonResponse({\n 'code': 0,\n 'errmsg': 'ok',\n 'breadcrumb': breadcrumb,\n 'list': list,\n 'count': total_page\n })\n\n\nclass HotView(View):\n \"\"\"商品热销排行\"\"\"\n\n def get(self, request, category_id):\n \"\"\"提供商品热销排行JSON数据\"\"\"\n # 根据销量倒序\n skus = SKU.objects.filter(category_id=category_id, is_launched=True).order_by('-sales')[:2]\n\n # 序列化\n hot_skus = []\n for sku in skus:\n hot_skus.append({\n 'id': sku.id,\n 'default_image_url': sku.default_image.url,\n 'name': sku.name,\n 'price': sku.price\n })\n\n return JsonResponse({'code': 0, 'errmsg': 'OK', 'hot_skus': hot_skus})\n\n\nfrom utils.goods import get_breadcrumb, get_categories, get_goods_specs\n\n\nclass DetailView(View):\n def get(self, request, sku_id):\n \"\"\"\n 1. 获取商品id\n 2. 根据商品id查询商品信息\n 3. 获取分类数据\n 4. 获取面包屑数据\n 5. 获取规格和规格选项数据\n 6. 组织数据,进行HTML模板渲染\n 7. 返回响应\n :param request:\n :param sku_id:\n :return:\n \"\"\"\n # 1. 获取商品id\n # 2. 根据商品id查询商品信息\n try:\n sku = SKU.objects.get(id=sku_id)\n except SKU.DoesNotExist:\n return JsonResponse({'code': 400, 'errmsg': '没有此商品'})\n # 3. 获取分类数据\n categories = get_categories()\n # 4. 获取面包屑数据\n # sku 有 三级分类属性\n breadcrumb = get_breadcrumb(sku.category)\n # 5. 获取规格和规格选项数据\n # 传递 sku对象\n specs = get_goods_specs(sku)\n # 6. 组织数据,进行HTML模板渲染\n # context 的key 必须按照课件来!!!\n # 因为模板已经写死了\n context = {\n 'sku': sku,\n 'categories': categories,\n 'breadcrumb': breadcrumb,\n 'specs': specs\n }\n # 7. 返回响应\n return render(request, 'detail.html', context)\n\n\nfrom apps.goods.models import GoodsVisitCount\n\n\nclass CategoryVisitView(View):\n def post(self, request, category_id):\n \"\"\"\n 1. 获取分类id\n 2. 根据分类id查询分类数据\n 3. 获取当天日期\n 4. 我们要查询数据库,是否存在 分类和日期 的记录\n 5. 如果不存在 则新增记录\n 6. 如果存在,则修改count\n 7. 返回响应\n :param request:\n :param category_id:\n :return:\n \"\"\"\n # 1. 获取分类id\n # 2. 根据分类id查询分类数据\n try:\n category = GoodsCategory.objects.get(id=category_id)\n except GoodsCategory.DoesNotExist:\n return JsonResponse({\"code\": 0, 'errmsg': '没有次分类'})\n # 3. 获取当天日期\n from datetime import date\n today = date.today()\n # 4. 我们要查询数据库,是否存在 分类和日期 的记录\n try:\n gvc = GoodsVisitCount.objects.get(category=category, date=today)\n except GoodsVisitCount.DoesNotExist:\n # 5. 如果不存在 则新增记录\n GoodsVisitCount.objects.create(\n category=category,\n date=today,\n count=1\n )\n else:\n # 6. 如果存在,则修改count\n gvc.count += 1\n gvc.save()\n # 7. 返回响应\n return JsonResponse({\"code\": 0, \"errmsg\": 'ok'})\n\n","sub_path":"meiduo_mall/apps/goods/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"381932712","text":"# -*- coding: utf-8 -*-\n\"\"\"\nShowcases colour temperature and correlated colour temperature plotting\nexamples.\n\"\"\"\n\nimport colour\nfrom colour.plotting import (\n colour_style, plot_planckian_locus_in_chromaticity_diagram_CIE1931,\n plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS, plot_blackbody_colours)\nfrom colour.utilities import message_box\n\nclass illuminamisInDiagram:\n def __init__(self):\n message_box('Colour Temperature and Correlated Colour Temperature Plots')\n\n colour_style()\n\n def plot_in_chromaticity_diagram_CIE1931(self):\n message_box('Plotting planckian locus in \"CIE 1931 Chromaticity Diagram\".')\n plot_planckian_locus_in_chromaticity_diagram_CIE1931(['A', 'D65', 'D50', 'D55', 'D75'])\n \n print('\\n')\n \n def plot_in_chromaticity_diagram_CIE1960(self):\n message_box('Plotting planckian locus in \"CIE 1960 UCS Chromaticity Diagram\".')\n plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(['A', 'D65', 'D50', 'D55', 'D75'])\n\n print('\\n')\n \n def plot_blackbody_colors(self):\n message_box('Plotting \"blackbody\" colours.')\n plot_blackbody_colours()\n \n ","sub_path":"tkinter_app/example_plots.py","file_name":"example_plots.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"154810876","text":"# -*- coding: utf-8 -*-\r\n\r\n\r\ndef setup(context, caller, callee):\r\n caller.prepare_test_env(context['config'])\r\n callee.prepare_test_env(context['config'], caller.get_test_accounts(), caller.get_test_channel_data())\r\n\r\n\r\ndef create_test_flow_description():\r\n flow_description = \"\"\"\r\n***********************************************************************\r\nTEST FLOW DESCRIPTION\r\n***********************************************************************\r\n1. Caller and callee users log in\r\n2. Caller and callee open test team channel\r\n3. Caller initiates a call in the channel\r\n4. Callee joins a call\r\n5. Caller opens in-call chat\r\n6. Caller sends message to the in-call chat\r\n7. Caller sees his message to appear in in-call chat pane\r\n8. Callee sees incoming message in in-call chat pane\r\n***********************************************************************\r\n\"\"\"\r\n return flow_description\r\n\r\n\r\ndef test(context, caller, callee):\r\n\r\n caller_username = callee.get_test_account(0)['username']\r\n callee_username = callee.get_test_account(1)['username']\r\n\r\n message = \"Hey have you heard about %s?\" % caller.generate_string(prefix=\"incident \")\r\n\r\n caller.login(caller.get_test_account(0))\r\n callee.login(callee.get_test_account(1))\r\n\r\n # Caller and callee open same conversation\r\n caller.navigate_to_channel()\r\n callee.navigate_to_channel()\r\n\r\n # Caller calls\r\n caller.click_at_start_call_button()\r\n call_id = caller.get_call_id()\r\n #callee.wait_for_jump_in_button_and_join_call(caller_username)\r\n callee.wait_for_join_button_and_join_call(call_id)\r\n\r\n caller.wait_for_participant_to_appear_in_call(participant_email=callee_username, group_call=False)\r\n callee.wait_for_participant_to_appear_in_call(participant_email=caller_username, group_call=False)\r\n\r\n # Caller opens in-call chat\r\n caller.open_in_call_chat()\r\n callee.open_in_call_chat()\r\n\r\n # Caller sends message to in-call chat and verifies it is visible in his chat pane\r\n caller.send_message_to_conversation(message)\r\n caller.verify_message_received_in_conversation(message)\r\n\r\n # Callee verifies message is received and visible in in-call chat\r\n callee.verify_message_received_in_conversation(message)\r\n\r\n\r\ndef teardown(context, caller, callee):\r\n pass","sub_path":"Tests/2p_meetup_chat.py","file_name":"2p_meetup_chat.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"460427808","text":"import sys\nimport tqdm\nimport torch\nfrom pathlib import Path\nfrom dataset import indirectTrocarDataset\nfrom network import trocarNetwork\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\nfrom utils import init_weights, JOINTS, WINDOW, SKIP, save, load_prev, max_torque\nfrom os.path import join\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\ndata = 'trocar'\ntrain_path = join('..', 'data', 'csv', 'train', data)\nval_path = join('..','data','csv','val', data)\nroot = Path('checkpoints' )\nnet = sys.argv[1]\nseal = sys.argv[2]\nis_seal = 'seal' == seal\nif is_seal:\n folder = net + '/trocar'\nelse:\n folder = net + '/trocar_no_cannula'\n\nlr = 1e-4\nbatch_size = 128\nepochs = 400\nvalidate_each = 5\nuse_previous_model = False\nepoch_to_use = 10\nin_joints = [0,1,2,3,4,5]\nf = False#True\nprint('Running for is_seal value: ', is_seal)\nloss_fn = torch.nn.MSELoss()\n\n \nfor num in ['120', '240', '360', '480', '600', '720', '840', '960', '1080']:\n model = 'filtered_torque_' + num + 's'\n n = int(num)\n\n try:\n temp = root / model \n temp.mkdir(mode=0o777, parents=False)\n except OSError:\n print(\"Model path exists\")\n\n try:\n temp = root / model / net\n temp.mkdir(mode=0o777, parents=False)\n except OSError:\n print(\"Net path exists\")\n \n networks = []\n optimizers = []\n schedulers = []\n model_root = []\n\n for j in range(JOINTS):\n networks.append(trocarNetwork(WINDOW, len(in_joints), 1))\n networks[j].to(device)\n optimizers.append(torch.optim.Adam(networks[j].parameters(), lr))\n schedulers.append(ReduceLROnPlateau(optimizers[j], verbose=True))\n \n train_dataset = indirectTrocarDataset(train_path, WINDOW, SKIP, in_joints, num=n, seal=seal, filter_signal=f, net=net)\n val_dataset = indirectTrocarDataset(val_path, WINDOW, SKIP, in_joints, num=n, seal=seal, filter_signal=f, net=net)\n train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False)\n \n for j in range(JOINTS):\n try:\n model_root.append(root / model / (folder + str(j)))\n model_root[j].mkdir(mode=0o777, parents=False)\n except OSError:\n print(\"Model path exists\")\n \n if use_previous_model:\n for j in range(JOINTS):\n epoch = load_prev(networks[j], model_root[j], epoch_to_use, optimizers[j], schedulers[j])\n else:\n for j in range(JOINTS):\n init_weights(networks[j])\n epoch = 1\n\n print('Training for ' + str(epochs) + ' and t = ' + num)\n best_loss = torch.zeros(6) + 1e10\n\n for e in range(epoch, epochs + 1):\n\n tq = tqdm.tqdm(total=(len(train_loader) * batch_size))\n tq.set_description('Epoch {}, lr {}'.format(e, optimizers[0].param_groups[0]['lr']))\n epoch_loss = 0\n\n for j in range(JOINTS):\n networks[j].train()\n \n for i, (position, velocity, torque, time, fs_pred) in enumerate(train_loader):\n position = position.to(device)\n velocity = velocity.to(device)\n fs_pred = fs_pred.to(device)\n torque = torque.to(device)\n step_loss = 0\n\n for j in range(JOINTS):\n posvel = torch.cat((position, velocity, fs_pred[:,[j]]), axis=1).contiguous()\n pred = networks[j](posvel) + fs_pred[:,[j]]\n \n loss = loss_fn(pred, torque[:,[j]])\n step_loss += loss.item()\n optimizers[j].zero_grad()\n loss.backward()\n optimizers[j].step()\n\n tq.update(batch_size)\n tq.set_postfix(loss=' loss={:.5f}'.format(step_loss))\n epoch_loss += step_loss\n\n tq.set_postfix(loss=' loss={:.5f}'.format(epoch_loss/len(train_loader)))\n \n if e % validate_each == 0:\n for j in range(JOINTS):\n networks[j].eval()\n\n val_loss = torch.zeros(JOINTS)\n for i, (position, velocity, torque, time, fs_pred) in enumerate(val_loader):\n position = position.to(device)\n velocity = velocity.to(device)\n fs_pred = fs_pred.to(device)\n torque = torque.to(device)\n\n for j in range(JOINTS):\n posvel = torch.cat((position, velocity, fs_pred[:,[j]]), axis=1).contiguous()\n pred = networks[j](posvel) + fs_pred[:,[j]]\n loss = loss_fn(pred, torque[:,[j]])\n val_loss[j] += loss.item()\n\n val_loss = val_loss / len(val_loader)\n \n for j in range(JOINTS):\n schedulers[j].step(val_loss[j])\n model_path = model_root[j] / \"model_joint_{}.pt\".format(e)\n save(e, networks[j], model_path, val_loss[j], optimizers[j], schedulers[j])\n \n if val_loss[j] < best_loss[j]:\n model_path = model_root[j] / \"model_joint_best.pt\"\n save(e, networks[j], model_path, val_loss[j], optimizers[j], schedulers[j])\n best_loss[j] = val_loss[j]\n\n tq.set_postfix(loss='validation loss={:5f}'.format(torch.mean(val_loss)))\n \n tq.close()\n","sub_path":"indirect_method/train_trocar.py","file_name":"train_trocar.py","file_ext":"py","file_size_in_byte":5407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"436002738","text":"# from Generation import *\n# from Remove import *\nimport math\nimport random\n\n# Solves the sudoku game\ndef solve_grid(grid):\n dimen = len(grid)\n num = int(math.sqrt(dimen))\n\n def fill(index):\n curr_row, curr_column = divmod(index, dimen)\n # make num*num block (like in Sudoku grid)\n r1, c1 = curr_row - (curr_row % 3), curr_column - (curr_column % 3)\n # create list of numbers 1 through dimen. so in traditional Sudoku 1-9\n numbers = list(range(1, dimen + 1))\n random.shuffle(numbers)\n if grid[curr_row][curr_column] == 0:\n for n in numbers:\n if (n not in grid[curr_row] and all(row[curr_column] != n for row in grid)\n and all(n not in row[c1:c1 + num] for row in grid[r1:r1 + num])):\n grid[curr_row][curr_column] = n\n if index + 1 >= dimen ** 2 or fill(index + 1):\n return grid\n grid[curr_row][curr_column] = 0\n return None\n else:\n if index + 1 >= dimen ** 2 or fill(index + 1):\n return grid\n\n return fill(0)\n\ntest = [[5, 6, 4, 9, 8, 3, 0, 0, 0], [9, 8, 1, 0, 2, 0, 0, 3, 4], [7, 0, 3, 1, 5, 0, 9, 8, 6], [3, 0, 6, 0, 7, 9, 0, 0, 0], [1, 4, 0, 5, 6, 0, 0, 0, 0], [2, 7, 0, 3, 4, 1, 8, 6, 5], [0, 0, 2, 0, 0, 0, 0, 5, 3], [0, 9, 0, 0, 0,5, 6, 0, 0], [8, 3, 0, 7, 0, 6, 4, 0, 9]]\n\n\n\n\n","sub_path":"Solving.py","file_name":"Solving.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"271424345","text":"'''\nCreated on 2011-10-27\n\n@author: fimbul\n'''\ndef challenge1():\n print(2**38)\n print(\"http://www.pythonchallenge.com/pc/def/map.html\")\n \ndef challenge2():\n origin = \"g fmnc wms bgblr rpylqjyrc gr zw fylb. rfyrq ufyr amknsrcpq ypc dmp. bmgle gr gl zw fylb gq glcddgagclr ylb rfyr'q ufw rfgq rcvr gq qm jmle. sqgle qrpgle.kyicrpylq() gq pcamkkclbcb. lmu ynnjw ml rfc spj.\"\n for c in origin:\n if c >= 'a' and c <= 'z':\n print(chr((ord(c) - ord('a') + 2) % 26 + ord('a')), end = '')\n else:\n print(c, end = '')\n x = str()\n y = str()\n for i in range(0, 26):\n x += chr(i + ord('a'))\n y += chr((i + 2) % 26 + ord('a'))\n __dict = str.maketrans(x, y)\n print(\"\\n__dict = \", __dict)\n print(origin.translate(__dict))\n print(\"http://www.pythonchallenge.com/pc/def/maketrans.html\")\n print(\"http://www.pythonchallenge.com/pc/def/\" + \"map\".translate(__dict) + \".html\")\n\ndef challenge3():\n f = open(\"pythonchallenge2.txt\")\n __content = f.read()\n __dict = dict()\n #sum = 0\n for i in __content:\n #sum = sum + 1\n if __dict.get(i):\n __dict[i] = __dict[i] + 1\n else:\n __dict[i] = 1\n f.close()\n print(__dict)\n \n for i in __content:\n if i >= 'a' and i <= 'z':\n print(i)\n \ndef challenge4():\n pass\n\nif __name__ == '__main__':\n challenge4()","sub_path":"src/module_study/PythonChallenge.py","file_name":"PythonChallenge.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"84168385","text":"from multiprocessing.pool import ThreadPool\nimport requests\nimport re\n\nUNAVAILABLE = 0\nTRANSPARENT = 1\nSECRET = 2\n\ndef check_vpn_validation(db):\n items = db.fetch_all()\n print('总共收集的数据条数为：{}'.format(len(items)))\n invalid_ids = list()\n trans_ids = list()\n secret_ids = list()\n pool = ThreadPool(10)\n for item in items:\n id = item[0]\n ip = item[1]\n port = item[2]\n # 检查代理的有效性\n vali_status = pool.apply(vali_http,(ip,port))\n if vali_status == UNAVAILABLE:\n invalid_ids.append(id)\n elif vali_status == TRANSPARENT:\n trans_ids.append(id)\n else:\n secret_ids.append(id)\n # 更新代理状态\n db.delete(invalid_ids)\n db.update_stype(TRANSPARENT,trans_ids)\n db.update_stype(SECRET,secret_ids)\n print('清楚的无效数据条数为：{}'.format(len(invalid_ids)))\n print('透明代理数据条数为：{}'.format(len(trans_ids)))\n print('私密代理数据条数为：{}'.format(len(secret_ids)))\n\ndef vali_http(ip,port):\n proxies = {\n 'http': 'http://{}:{}'.format(ip,port),\n }\n try:\n res = requests.get('http://2017.ip138.com/ic.asp',proxies=proxies,timeout=2)\n except:\n print('{}:{} http 验证无法连接'.format(ip, port))\n return UNAVAILABLE\n m_result = re.search(r'\\[.*\\]',res.text)\n if m_result is not None:\n realip = m_result.group()[1:-1]\n if realip == ip:\n # 请求的ip和代理的ip一致则说明这个代理是有效的\n print('{}:{} http 验证成功'.format(ip, port))\n return SECRET\n print('{}:{} http 验证代理IP透明'.format(ip, port))\n return TRANSPARENT\n\n","sub_path":"vpn_pool/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"592585909","text":"import numpy as np\n\nfrom daisy.persistence import MongoDbGraphProvider\n\nfrom gunpowder.batch import Batch\nfrom gunpowder.nodes.batch_provider import BatchProvider\nfrom gunpowder.graph import GraphKey, Graph\nfrom gunpowder.graph_spec import GraphSpec\nfrom gunpowder.roi import Roi\nfrom gunpowder.coordinate import Coordinate\nfrom gunpowder.profiling import Timing\n\nimport logging\nfrom typing import Tuple, List, Optional, Union, Dict, Any\n\nlogger = logging.getLogger(__file__)\n\nunbounded = Roi(Coordinate([None, None, None]), Coordinate([None, None, None]))\n\n\nclass DaisyGraphProvider(BatchProvider):\n \"\"\"\n See documentation for mongo graph provider at\n https://github.com/funkelab/daisy/blob/0.3-dev/daisy/persistence/mongodb_graph_provider.py#L17\n \"\"\"\n\n def __init__(\n self,\n dbname: str,\n url: str,\n points: List[GraphKey],\n graph_specs: Optional[Union[GraphSpec, List[GraphSpec]]] = None,\n directed: bool = False,\n total_roi: Roi = None,\n nodes_collection: str = \"nodes\",\n edges_collection: str = \"edges\",\n meta_collection: str = \"meta\",\n endpoint_names: Tuple[str, str] = (\"u\", \"v\"),\n position_attribute: str = \"position\",\n node_attrs: Optional[List[str]] = None,\n edge_attrs: Optional[List[str]] = None,\n nodes_filter: Optional[Dict[str, Any]] = None,\n edges_filter: Optional[Dict[str, Any]] = None,\n edge_inclusion: str = \"either\",\n node_inclusion: str = \"dangling\",\n fail_on_inconsistent_node: bool = False,\n ):\n self.points = points\n graph_specs = (\n graph_specs\n if graph_specs is not None\n else GraphSpec(\n Roi(Coordinate([None] * 3), Coordinate([None] * 3)), directed=False\n )\n )\n specs = (\n graph_specs\n if isinstance(graph_specs, list) and len(graph_specs) == len(points)\n else [graph_specs] * len(points)\n )\n self.specs = {key: spec for key, spec in zip(points, specs)}\n\n self.directed = directed\n self.nodes_collection = nodes_collection\n self.edges_collection = edges_collection\n self.meta_collection = meta_collection\n self.endpoint_names = endpoint_names\n self.position_attribute = position_attribute\n\n self.position_attribute = position_attribute\n self.node_attrs = node_attrs\n self.edge_attrs = edge_attrs\n self.nodes_filter = nodes_filter\n self.edges_filter = edges_filter\n\n self.edge_inclusion = edge_inclusion\n self.node_inclusion = node_inclusion\n\n self.dbname = dbname\n self.url = url\n self.nodes_collection = nodes_collection\n\n self.fail_on_inconsistent_node = fail_on_inconsistent_node\n\n self.graph_provider = None\n\n def setup(self):\n for key, spec in self.specs.items():\n self.provides(key, spec)\n\n if self.graph_provider is None:\n self.graph_provider = MongoDbGraphProvider(\n self.dbname,\n self.url,\n mode=\"r+\",\n directed=self.directed,\n total_roi=None,\n nodes_collection=self.nodes_collection,\n edges_collection=self.edges_collection,\n meta_collection=self.meta_collection,\n endpoint_names=self.endpoint_names,\n position_attribute=self.position_attribute,\n )\n\n def provide(self, request):\n\n timing = Timing(self)\n timing.start()\n\n batch = Batch()\n\n for key, spec in request.items():\n logger.debug(f\"fetching {key} in roi {spec.roi}\")\n requested_graph = self.graph_provider.get_graph(\n spec.roi,\n edge_inclusion=self.edge_inclusion,\n node_inclusion=self.node_inclusion,\n node_attrs=self.node_attrs,\n edge_attrs=self.edge_attrs,\n nodes_filter=self.nodes_filter,\n edges_filter=self.edges_filter,\n )\n logger.debug(\n f\"got {len(requested_graph.nodes)} nodes and {len(requested_graph.edges)} edges\"\n )\n\n failed_nodes = []\n\n for node, attrs in requested_graph.nodes.items():\n try:\n attrs[\"location\"] = np.array(\n attrs[self.position_attribute], dtype=np.float32\n )\n except KeyError:\n logger.warning(\n f\"node: {node} was written (probably part of an edge), but never given coordinates!\"\n )\n failed_nodes.append(node)\n attrs[\"id\"] = node\n\n for node in failed_nodes:\n if self.fail_on_inconsistent_node:\n raise ValueError(\n f\"Mongodb contains node {node} without location! \"\n f\"It was probably written as part of an edge\"\n )\n requested_graph.remove_node(node)\n\n if spec.directed:\n requested_graph = requested_graph.to_directed()\n else:\n requested_graph = requested_graph.to_undirected()\n\n points = Graph.from_nx_graph(requested_graph, spec)\n points.relabel_connected_components()\n points.crop(spec.roi)\n batch[key] = points\n\n logger.debug(f\"{key} with {len(list(points.nodes))} nodes\")\n\n timing.stop()\n batch.profiling_stats.add(timing)\n\n return batch\n","sub_path":"neurolight/gunpowder/nodes/daisy_graph_provider.py","file_name":"daisy_graph_provider.py","file_ext":"py","file_size_in_byte":5649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"301176465","text":"from tkinter import *\nroot=Tk()\nroot.title(\"Canvas\")\ncanvas=Canvas(root,height=400, width=500, bg=\"red\")\ncanvas.create_line(0,0,300,400,fill=\"yellow\",width=10)\ncanvas.create_text(200,300,text=\"My canvas Example\",fill=\"blue\",font=(\"comic Sans Ms\",13,\"bold\"))\ncanvas.create_rectangle(0,0,200,200,fill=\"white\",outline=\"yellow\", width=5)\ncanvas.create_oval(0,0,200,200,fill=\"blue\",outline=\"yellow\", width=5)\ncanvas.create_arc(0,0,200,200,fill=\"red\",outline=\"yellow\", width=5,extent=90)\npoint=[250,110,480,200,280,280,250,110]\npoly=canvas.create_polygon(point,fill=\"gold\",outline=\"black\",width=5)\nimg=PhotoImage(file=\"test.gif\")\ncanvas.create_image(300,300,image=img,anchor=SE)\n\ncanvas.pack()\nroot.geometry(\"600x600+120+20\")\nmainloop()","sub_path":"canvasexp.py","file_name":"canvasexp.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"534162654","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport json\n\nfrom alipay.aop.api.constant.ParamConstants import *\n\n\nclass InteligentForbiddenTime(object):\n\n def __init__(self):\n self._days = None\n\n @property\n def days(self):\n return self._days\n\n @days.setter\n def days(self, value):\n self._days = value\n\n\n def to_alipay_dict(self):\n params = dict()\n if self.days:\n if hasattr(self.days, 'to_alipay_dict'):\n params['days'] = self.days.to_alipay_dict()\n else:\n params['days'] = self.days\n return params\n\n @staticmethod\n def from_alipay_dict(d):\n if not d:\n return None\n o = InteligentForbiddenTime()\n if 'days' in d:\n o.days = d['days']\n return o\n\n\n","sub_path":"alipay/aop/api/domain/InteligentForbiddenTime.py","file_name":"InteligentForbiddenTime.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"220131202","text":"import datetime\nimport json\nimport logging\nimport re\nimport subprocess\n\nimport pytz\nfrom dateutil import parser\n\nfrom cbapi.response import *\nfrom cbapi.psc import threathunter\n\nfrom cbinterface.cli import load_configured_environments\n\nfrom lib.constants import SPLUNKLIB\nfrom lib.modules.DetectionModule import *\n\nclass Module(DetectionModule):\n def __init__(self, name, event_json):\n\n super().__init__(name=name, event_json=event_json)\n\n def run(self):\n self.logger.info('Running the {} detection module'.format(self.name))\n\n # Stupid hack for 1000 Talents events with tons of URLs.\n skip_these = ['1000 talents', '1000_talents']\n if any(skip in self.event_json['name'].lower() for skip in skip_these) or any(skip in self.event_json['tags'] for skip in skip_these):\n notice = (\"INFO NOTICE: Skipped checking for clickers becase this event was identified as '1000 Talents'\"\n \"\\n\\t\"+u'\\u21B3'+\" 1000 Talents events are notorious for having a lot of URLs and we don't care to respond to any clicks.\"\n \"\\n\\t\"+u'\\u21B3'+\" This event was detected as '1000 Talents' by the event name OR because of tags.\")\n self.detections.append(notice)\n return\n\n # Simple regex that defines what an employee ID looks like.\n #employee_id_pattern = re.compile(r'{}'.format(self.config['employee_id_pattern']))\n #self.logger.info(f\"employee_id_pattern: {employee_id_pattern}\")\n employee_id_pattern_strings = self.config['employee_id_pattern']\n employee_id_patterns = [re.compile(r'{}'.format(pattern)) for pattern in employee_id_pattern_strings]\n\n \"\"\"\n QUERY SPLUNK FOR CLICKERS IN PROXY LOGS\n \"\"\"\n\n # These are the companies that will get Splunk queries.\n ignore_these_companies = self.config['ignore_these_companies']\n company_names = set()\n for alert in self.event_json['ace_alerts']:\n if alert['company_name'] and not alert['company_name'] in ignore_these_companies:\n company_names.add(alert['company_name'])\n\n # Get the start time.\n start_time = ''\n if self.event_json['emails']:\n start_time = self.event_json['emails'][0]['received_time']\n self.logger.info(\"Using start time from emails received_time: {}\".format(start_time))\n elif self.event_json['ace_alerts']:\n start_time = self.event_json['ace_alerts'][0]['time']\n self.logger.info(\"Using start time from ace alert time: {}\".format(start_time))\n\n # We need to make sure the start time is in the format \"YYYY-MM-DD\", which is 10 characters long.\n start_time = start_time[0:10]\n\n # Force the start time to begin at 00:00:00.\n start_time = '{} 00:00:00'.format(start_time)\n\n # These are legit things that we expect to generate some results.\n whitelisted_things = self.config['whitelisted_things']\n if whitelisted_things:\n device_ids = [str(_thing) for _thing in whitelisted_things if isinstance(_thing, int)]\n hostnames = [_thing for _thing in whitelisted_things if isinstance(_thing, str)]\n all_things = [str(_thing) for _thing in whitelisted_things]\n\n cb_whitelisted_things_string = '-hostname:' + ' -hostname:'.join(hostnames)\n cb_psc_whitelisted_things_string = '-device_name:' + ' -device_name:'.join(hostnames)\n cb_psc_whitelisted_things_string += ' -device_id:' + ' -device_id:'.join(device_ids)\n\n splunk_whitelisted_things_string = 'NOT ' + ' NOT '.join(all_things)\n else:\n cb_whitelisted_things_string = ''\n splunk_whitelisted_things_string = ''\n\n # NOTE How did cb queries end up in this file instead of cbinterface? FIX XXX\n '''\n ignore_these_hosts = self.config['ignore_these_computers']\n if ignore_these_hosts:\n cb_whitelisted_things_string = '-hostname:' + ' -hostname:'.join(ignore_these_hosts)\n else:\n cb_whitelisted_things_string = ''\n '''\n\n ignored_source_ips = self.config['ignored_source_ips']\n if ignored_source_ips:\n ignored_source_ips_string = ' OR '.join(ignored_source_ips)\n else:\n ignored_source_ips_string = ''\n\n # Get all of the New/Analyzed domains and IP addresses from the event.\n good_indicators = [i for i in self.event_json['indicators'] if not i['whitelisted'] and (i['status'] == 'New' or i['status'] == 'Analyzed' or i['status'] == 'In Progress')]\n domains = list(set([i['value'].lower() for i in good_indicators if i['type'] == 'URI - Domain Name' and not 'from_domain' in i['tags']]))\n ips = list(set([i['value'].lower() for i in good_indicators if i['type'] == 'Address - ipv4-addr']))\n\n # Get all of the content_protection_domains from the event.\n content_protecting_domains = self.config['content_protection_domains']\n dynamic_content_protecting_domains = list(set([i['value'] for i in good_indicators if i['type'] == 'Email - Content - Domain Name' and i['status'] != 'Deprecated']))\n content_protecting_domains.extend(dynamic_content_protecting_domains)\n content_protecting_domains = list(set(content_protecting_domains))\n\n self.logger.info(f\"detected content protecting domains: {content_protecting_domains}\")\n\n uri_paths = list(set([i['value'].lower() for i in good_indicators if i['type'] == 'URI - Path' and any(rel in content_protecting_domains for rel in i['relationships'])]))\n all_url_list = list(set([i['value'].lower() for i in good_indicators if i['type'] == 'URI - URL']))\n url_list = list(set([url for url in all_url_list if any(domain in url for domain in content_protecting_domains)]))\n\n self.logger.info(f\"URL List: {url_list}\")\n\n # Collect all of the domains/IPs/paths we want to search for in Splunk.\n # XXX: To save time, should this be updated to domains and IPs only?\n domains_ips_paths_urls = list(set(domains + ips + uri_paths + url_list))\n domains_ips_paths_for_splunk = ['\"'+indicator+'\"' for indicator in domains_ips_paths_urls if '\"' not in indicator and ',' not in indicator]\n if domains_ips_paths_urls:\n domains_ips_paths_string = ' OR '.join(domains_ips_paths_for_splunk)\n else:\n return\n\n # Only continue if we have a valid start time.\n if len(start_time) == 19:\n\n # Bump the start time back an extra hour to help make sure we have better coverage.\n # XXX Temporary hack.. I noticed our alert and email tims are in UTC but our splunk logs are in EST. ~ going back 5 hrs\n earlier_start_time = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') - datetime.timedelta(hours=8)\n start_datetime = earlier_start_time\n start_time = earlier_start_time.strftime('%Y-%m-%d %H:%M:%S')\n\n # Maps for keeping track of users, computers, and processes accross data logs\n ## i.e. simple correlation\n computer_user_map = {} # get users associated with computer\n computer_proc_map = {} # get processes associated with computer\n\n # Store the employee IDs who clicked for each domain/IP.\n clicker_ids = []\n\n # Store the employee email addresses inside the event.\n email_addresses = []\n for company in company_names:\n email_addresses.extend(list(set([i['value'] for i in self.event_json['indicators'] if i['type'] == 'Email - Address' and company.lower() in i['value'].lower()])))\n\n\n \"\"\"\n BUILD AND RUN THE Carbon Black Response SEARCH FOR EACH DOMAIN/IP/URL\n \"\"\"\n cb = None\n environment_map = load_configured_environments()\n for product,environments in environment_map.items():\n for profile in environments:\n if product == \"response\":\n from cbinterface.response.query import make_process_query\n from cbinterface.response.process import print_process_info\n cb = CbResponseAPI(profile=profile)\n elif product == \"psc\":\n from cbinterface.psc.query import make_process_query, yield_events, convert_from_legacy_query\n from cbinterface.psc.process import print_process_info, format_event_data\n cb = threathunter.CbThreatHunterAPI(profile=profile)\n\n #start_datetime = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')\n for domain in domains:\n query = '(domain:\"{}\" OR cmdline:\"{}\") {}'.format(domain, domain, cb_whitelisted_things_string)\n if product == \"psc\":\n #query = convert_from_legacy_query(cb, query) # NOTE: works but is slower (more API requests)\n query = '(netconn_domain:\"{}\" OR process_cmdline:\"{}\") {}'.format(domain, domain, cb_psc_whitelisted_things_string)\n \n processes = []\n self.logger.info(f\"Querying Carbon Black {product} with: '{query}'\")\n try:\n processes = make_process_query(cb, query)#, start_time=start_datetime)\n except Exception as e:\n self.logger.exception(f\"problem querying {product}:{profile}\")\n if not processes:\n continue\n\n if len(processes) > 100:\n self.detections.append('! DETECTED {} PROCESSSES THAT MADE NETCONNS TO DOMAIN {} ! -- TOO LARGE of dataset to display, only including first 100 results in extra detections.'.format(len(processes), domain))\n self.logger.warning(\"{} processes returned by Carbon Black for '{}' since '{}'\".format(len(processes), query, start_time))\n self.tags.append('incidents')\n self.tags.append('exploitation')\n for proc in processes[:100]:\n hostname = proc.get('hostname') or proc.get('device_name') or \"unknown\"\n username = proc.get('username') or proc.get('process_username') or \"unknown\"\n if isinstance(username, list):\n username = username[0]\n process_name = proc.get('process_name')\n cmdline = proc.get('cmdline') or proc.get('process_cmdline') or \"None\"\n if proc.get('segment_id'):\n # response\n webui_link = f\"{cb.credentials.url}/#analyze/{proc.get('id')}/{proc.get('segment_id')}\"\n else:\n webui_link = f\"{cb.credentials.url}/analyze?processGUID={proc.get('process_guid')}\"\n proc_summary = \"Hostname:{} User:{} Process_Name:{} Command_Line:{} GUI_Link:{}\".format(hostname,\n username,\n process_name,\n cmdline,\n webui_link)\n self.extra.append(proc_summary)\n else:\n for proc in processes:\n # first, get the actual netconn event for closer inspection and context enrichment.\n # Did this netconn event occur before start_time?\n suspect_events = []\n possible_cmdline_events = True\n if product == \"psc\":\n try:\n # NOTE: this doesn't take into consideration cmdline hits\n for event in yield_events(proc, query=f\"netconn_domain:{domain}\"): #, start_time=start_datetime):\n possible_cmdline_events = False\n event_timestamp = parser.parse(event['event_timestamp'])\n start_datetime = start_datetime.replace(tzinfo=pytz.UTC)\n if event_timestamp >= start_datetime:\n suspect_events.append(event)\n if possible_cmdline_events:\n self.logger.info(f\"failed to get events for {proc.get('process_guid')}. Possible cmdline events...\")\n if not suspect_events and not possible_cmdline_events:\n self.logger.info(f\"didn't find any process events after {start_datetime} applied... events are old. Ignoreing...\")\n # continue to next process\n continue\n except Exception as e:\n self.logger.error(f\"caught exception doing cb event search for clickers: {e}\")\n\n hostname = proc.get('hostname') or proc.get('device_name') or \"unknown\"\n username = proc.get('username') or proc.get('process_username') or \"unknown\"\n if isinstance(username, list):\n username = username[0]\n process_name = proc.get('process_name')\n cmdline = proc.get('cmdline') or proc.get('process_cmdline') or \"None\"\n if proc.get('segment_id'):\n # response\n webui_link = f\"{cb.credentials.url}/#analyze/{proc.get('id')}/{proc.get('segment_id')}\"\n else:\n webui_link = f\"{cb.credentials.url}/analyze?processGUID={proc.get('process_guid')}\"\n process_guid = proc.get('id') or proc.get('process_guid')\n\n user_id = username.lower()\n if '\\\\' in user_id:\n # such as corp\\user_id\n user_id = user_id[user_id.rfind('\\\\')+1:]\n clicker_ids.append(user_id)\n if suspect_events:\n for event in suspect_events:\n netconn_domain = event.get('netconn_domain')\n self.detections.append('! DETECTED NETCONN {} TO DOMAIN {} FROM {} ! {}'.format(user_id, netconn_domain, hostname, webui_link))\n else:\n self.detections.append('! DETECTED NETCONN {} TO DOMAIN {} FROM {} ! {}'.format(user_id, domain, hostname, webui_link))\n if hostname not in computer_user_map:\n computer_user_map[hostname] = []\n if user_id not in computer_user_map[hostname]:\n computer_user_map[hostname].append(user_id)\n if hostname not in computer_proc_map:\n computer_proc_map[hostname] = {}\n if process_guid not in computer_proc_map[hostname].keys():\n computer_proc_map[hostname][process_guid] = proc\n self.tags.append('incidents')\n self.tags.append('exploitation')\n try:\n if suspect_events:\n _extra = print_process_info(proc, yield_strings=True) \n _extra += \"\\n\"\n _extra += \" netconn event details for above process: \\n\"\n try:\n for event in suspect_events:\n _extra += f\"\\t{format_event_data(event)}\"\n _extra += \"\\n\"\n except Exception as e:\n self.logger.warning(f\"caught exception appending formated event data: {e}\")\n self.extra.append(_extra)\n else:\n self.extra.append(print_process_info(proc, yield_strings=True))\n except:\n self.extra.append(print_process_info(proc, return_string=True))\n\n for url in url_list:\n query = 'cmdline:\"{}\" {}'.format(url, cb_whitelisted_things_string)\n if product == \"psc\":\n #query = convert_from_legacy_query(cb, query) # NOTE: works but is slower (more API requests)\n query = 'process_cmdline:\"{}\" {}'.format(url, cb_psc_whitelisted_things_string)\n\n processes = []\n self.logger.info(f\"Querying Carbon Black {product} with: {query}\")\n try:\n processes = make_process_query(cb, query)#, start_time=start_datetime)\n except Exception as e:\n self.logger.exception(f\"problem querying {product}:{profile}\")\n if not processes:\n continue\n\n if len(processes) > 100:\n self.detections.append('! DETECTED {} PROCESSSES WITH URL ON CMDLINE {} ! -- TOO LARGE of dataset to display, only including first 100 results in extra detections.'.format(len(processes), url))\n self.logger.warning(\"{} processes returned by Carbon Black for '{}' since '{}'\".format(len(processes), query, start_time))\n self.tags.append('incidents')\n self.tags.append('exploitation')\n for proc in processes[:100]:\n hostname = proc.get('hostname') or proc.get('device_name') or \"unknown\"\n username = proc.get('username') or proc.get('process_username') or \"unknown\"\n if isinstance(username, list):\n username = username[0]\n process_name = proc.get('process_name')\n cmdline = proc.get('cmdline') or proc.get('process_cmdline') or \"None\"\n if proc.get('segment_id'):\n # response\n webui_link = f\"{cb.credentials.url}/#analyze/{proc.get('id')}/{proc.get('segment_id')}\"\n else:\n webui_link = f\"{cb.credentials.url}/analyze?processGUID={proc.get('process_guid')}\"\n proc_summary = \"Hostname:{} User:{} Process_Name:{} Command_Line:{} GUI_Link:{}\".format(hostname,\n username,\n process_name,\n cmdline,\n webui_link)\n self.extra.append(proc_summary)\n\n else:\n for proc in processes:\n hostname = proc.get('hostname') or proc.get('device_name') or \"unknown\"\n username = proc.get('username') or proc.get('process_username') or \"unknown\"\n if isinstance(username, list):\n username = username[0]\n process_name = proc.get('process_name')\n cmdline = proc.get('cmdline') or proc.get('process_cmdline') or \"None\"\n if proc.get('segment_id'):\n # response\n webui_link = f\"{cb.credentials.url}/#analyze/{proc.get('id')}/{proc.get('segment_id')}\"\n else:\n webui_link = f\"{cb.credentials.url}/analyze?processGUID={proc.get('process_guid')}\"\n process_guid = proc.get('id') or proc.get('process_guid')\n\n user_id = username.lower()\n if '\\\\' in user_id:\n # such as corp\\user_id\n user_id = user_id[user_id.rfind('\\\\')+1:]\n clicker_ids.append(user_id)\n self.detections.append('! DETECTED CLICK by {} on URL {} FROM {} ! {}'.format(user_id, url, hostname, webui_link))\n if hostname not in computer_user_map:\n computer_user_map[hostname] = []\n if user_id not in computer_user_map[hostname]:\n computer_user_map[hostname].append(user_id)\n if hostname not in computer_proc_map:\n computer_proc_map[hostname] = {}\n if process_guid not in computer_proc_map[hostname].keys():\n computer_proc_map[hostname][process_guid] = proc\n self.tags.append('incidents')\n self.tags.append('exploitation')\n try:\n self.extra.append(print_process_info(proc, yield_strings=True))\n except:\n self.extra.append(print_process_info(proc, return_string=True))\n\n \n for ip in ips:\n query = '(ipaddr:{} OR cmdline:{}) {}'.format(ip, ip, cb_whitelisted_things_string)\n if product == \"psc\":\n #query = convert_from_legacy_query(cb, query) # NOTE: works but is slower (more API requests)\n query = '(netconn_ipv4:{} OR process_cmdline:{}) {} -enriched:true'.format(ip, ip, cb_psc_whitelisted_things_string)\n\n processes = []\n self.logger.info(f\"Querying Carbon Black {product} with: {query}\")\n try:\n processes = make_process_query(cb, query)#, start_time=start_datetime)\n except Exception as e:\n self.logger.exception(f\"problem querying {product}:{profile}\")\n if not processes:\n continue\n\n if len(processes) > 100:\n self.detections.append('! DETECTED {} PROCESSSES THAT MADE NETCONNS TO IP {} ! -- TOO LARGE of dataset to display, only including first 100 results in extra detections.'.format(len(processes), ip))\n self.logger.warning(\"{} processes returned by Carbon Black for '{}' since '{}'\".format(len(processes), query, start_time))\n self.tags.append('incidents')\n self.tags.append('exploitation')\n for proc in processes[:100]:\n hostname = proc.get('hostname') or proc.get('device_name') or \"unknown\"\n username = proc.get('username') or proc.get('process_username') or \"unknown\"\n if isinstance(username, list):\n username = username[0]\n process_name = proc.get('process_name')\n cmdline = proc.get('cmdline') or proc.get('process_cmdline') or \"None\"\n if proc.get('segment_id'):\n # response\n webui_link = f\"{cb.credentials.url}/#analyze/{proc.get('id')}/{proc.get('segment_id')}\"\n else:\n webui_link = f\"{cb.credentials.url}/analyze?processGUID={proc.get('process_guid')}\"\n proc_summary = \"Hostname:{} User:{} Process_Name:{} Command_Line:{} GUI_Link:{}\".format(hostname,\n username,\n process_name,\n cmdline,\n webui_link)\n self.extra.append(proc_summary)\n\n else:\n for proc in processes:\n # first, get the actual netconn event for closer inspection and context enrichment.\n # Did this netconn event occur before start_time?\n suspect_events = []\n if product == \"psc\":\n try:\n for event in yield_events(proc, query=f\"{ip}\", start_time=start_datetime):\n suspect_events.append(event)\n if not suspect_events:\n self.logger.info(f\"didn't find any process events after {start_datetime} applied... events are old. Ignoreing...\")\n # continue to next process\n continue\n except Exception as e:\n self.logger.error(f\"caught exception doing cb event search for clickers: {e}\")\n\n hostname = proc.get('hostname') or proc.get('device_name') or \"unknown\"\n username = proc.get('username') or proc.get('process_username') or \"unknown\"\n if isinstance(username, list):\n username = username[0]\n process_name = proc.get('process_name')\n cmdline = proc.get('cmdline') or proc.get('process_cmdline') or \"None\"\n if proc.get('segment_id'):\n # response\n webui_link = f\"{cb.credentials.url}/#analyze/{proc.get('id')}/{proc.get('segment_id')}\"\n else:\n webui_link = f\"{cb.credentials.url}/analyze?processGUID={proc.get('process_guid')}\"\n process_guid = proc.get('id') or proc.get('process_guid')\n\n user_id = username.lower()\n if '\\\\' in user_id:\n # such as corp\\user_id\n user_id = user_id[user_id.rfind('\\\\')+1:]\n clicker_ids.append(user_id)\n self.detections.append('! DETECTED NETCONN {} TO IP {} FROM {} ! {}'.format(username, ip, hostname, webui_link))\n if hostname not in computer_user_map:\n computer_user_map[hostname] = []\n if user_id not in computer_user_map[hostname]:\n computer_user_map[hostname].append(user_id)\n if hostname not in computer_proc_map:\n computer_proc_map[hostname] = {}\n if process_guid not in computer_proc_map[hostname].keys():\n computer_proc_map[hostname][process_guid] = proc\n self.tags.append('incidents')\n self.tags.append('exploitation')\n try:\n if suspect_events:\n _extra = print_process_info(proc, yield_strings=True)\n _extra += \"\\n\"\n _extra += \" netconn event details for above process: \\n\"\n try:\n for event in suspect_events:\n _extra += f\"\\t{format_event_data(event)}\"\n _extra += \"\\n\"\n except Exception as e:\n self.logger.warning(f\"caught exception appending formated event data: {e}\")\n self.extra.append(_extra)\n else:\n self.extra.append(print_process_info(proc, yield_strings=True))\n except:\n self.extra.append(print_process_info(proc, return_string=True))\n\n\n # Run the Splunk search for each company we found in the alerts.\n for company in company_names:\n \"\"\"\n BUILD AND RUN THE SPLUNK SEARCH\n \"\"\"\n\n # Store the Splunk output lines.\n output_lines = []\n\n # This is the actual command line version of the Splunk query.\n # index=bro sourcetype=bro_http\n command = '{} --enviro {} -s \"{}\" \\'(index=bluecoat OR index=carbonblack_cloud OR index=netskope) NOT authentication_failed NOT favicon.ico {} {}\\''.format(SPLUNKLIB, company, start_time, domains_ips_paths_string, splunk_whitelisted_things_string)\n self.logger.info(\"About to run: {}\".format(command)) \n try:\n self.logger.info(\"Searching Splunk for clickers with: {}\".format(command))\n output = subprocess.check_output(command, shell=True).decode('utf-8')\n # If there was output, it means the Splunk search returned something.\n # TODO: Migrate to using json results instead of this ugly string regex method?\n if output:\n\n # Clean up the output lines.\n for line in output.splitlines():\n\n # Replace the \"s with spaces and remove the first and last elements of the line.\n cleaned_line = ' '.join(line.split('\"')[1:-1])\n output_lines.append(cleaned_line)\n\n # Try to extract the user ID from the cleaned line, assuming it is a proxy log entry.\n if \"bluecoat\" in cleaned_line:\n try:\n user_id = cleaned_line.split()[8]\n self.logger.info(f\"attempting to parse user_id from '{user_id}'\")\n for employee_id_pattern in employee_id_patterns:\n if employee_id_pattern.match(user_id):\n clicker_ids.append(user_id)\n self.tags.append('exploitation')\n self.tags.append('incidents')\n break\n except:\n pass\n\n # Try to extract the user ID from the cleaned line, assuming it is a Carbon Black log entry.\n if \"carbonblack\" in cleaned_line:\n try:\n for _line_part in cleaned_line.split('_username :'):\n # just search the first 40 characters after a username reference\n user_id = _line_part[0:40]\n self.logger.info(f\"attempting to parse user_id from '{user_id}'\")\n for employee_id_pattern in employee_id_patterns:\n m = employee_id_pattern.search(user_id)\n if m:\n clicker_ids.append(m.group())\n self.tags.append('exploitation')\n self.tags.append('incidents')\n break\n except:\n pass\n\n # Try to extract the user ID from the cleaned line, assuming it is a netskope log entry.\n if \"netskope\" in cleaned_line:\n try:\n user_id = cleaned_line.split(' - ')[0] if \" - \" in cleaned_line else cleaned_line\n self.logger.info(f\"attempting to parse user_id from '{user_id}'\")\n for employee_id_pattern in employee_id_patterns:\n m = employee_id_pattern.search(user_id)\n if m:\n clicker_ids.append(m.group())\n self.tags.append('exploitation')\n self.tags.append('incidents')\n break\n except:\n pass\n\n # Add the (cleaned) raw Splunk results to the extra text. \n self.extra.append('\\n'.join(output_lines))\n except:\n self.logger.exception('Error when running Splunk search: {}'.format(command))\n\n \"\"\"\n ANALYZE SEARCH RESULTS TO DETERMINE TYPES OF CLICKERS\n \"\"\"\n\n # Dedup and standardize the format of the clicker IDs.\n clicker_ids = list(set([i.lower() for i in clicker_ids]))\n\n # Standardize the format of the output lines.\n output_lines = [line.lower() for line in output_lines]\n\n # Logs change and user ID extraction can fail - notify a human\n if output_lines and not clicker_ids:\n self.logger.warning(\"got splunk output but didn't find any clicker IDs...\")\n self.detections.append(\"! {} UN-IDENTIFIED DETECTION RESULTS: Expand details below to view logs...\".format(len(output_lines)))\n self.tags.append('incidents')\n\n # Build a computer to user map from carbonblack results\n computer_name_re = re.compile(r'device_name : (?P[\\w]+) ,')\n # for grabbing process guids\n proc_guid_re = re.compile(r'process_guid : (?P[0-9a-f]{8}-[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}) ,', re.I)\n\n # Loop over all of the domains and IPs we searched for to identify the clickers.\n for domain_ip_path in domains_ips_paths_urls:\n\n # Loop over each clicker to check if they clicked on this domain/IP.\n for user_id in clicker_ids:\n\n # Get all of the Bluecoat log lines for this domain/IP + clicker.\n bluecoat_lines = [line for line in output_lines if 'bluecoat' in line and domain_ip_path in line and user_id in line]\n\n if bluecoat_lines:\n\n # Determine the status of the click (i.e.: observed/denied).\n if all(' denied ' in line for line in bluecoat_lines):\n status = 'denied'\n else:\n status = 'observed'\n\n # Determine the type of click (i.e.: http/https).\n if all(' connect ' in line and ' 443 ' in line for line in bluecoat_lines):\n click_type = 'https'\n else:\n click_type = 'http'\n\n # Check if there were any POST requests (only works for http).\n if any(' post ' in line for line in bluecoat_lines):\n submitted = True\n else:\n submitted = False\n\n # Check if we need to add a message reminding us to lock the clicker's account.\n if submitted or (status == 'observed' and click_type == 'https'):\n reminder_message = '<--- INITIATE RESPONSE ACTIONS!'\n else:\n reminder_message = ''\n\n # Add the appropriate event detections.\n if submitted:\n self.detections.append('! CLICKER {} CREDENTIALS SUBMITTED ! {} {} {}'.format(company.upper(), user_id, domain_ip_path, reminder_message))\n self.tags.append('actionsonobjectives')\n self.tags.append('exfil')\n else:\n self.detections.append('! CLICKER {} {} {} ! {} {} {}'.format(company.upper(), click_type.upper(), status.upper(), user_id, domain_ip_path, reminder_message))\n\n # Get all of the Carbon Black log lines for this | update -> there are no user ids in cb network logs\n carbonblack_lines = [line for line in output_lines if 'carbonblack' in line and domain_ip_path in line and user_id in line]\n if not bluecoat_lines and carbonblack_lines:\n self.detections.append(f'! CLICKER {company.upper()} STATUS UNKNOWN ! {user_id} clicked on {domain_ip_path} (carbonblack visibility)')\n\n\n # if we got bluecoat lines for this user we were able to make a determination\n if not bluecoat_lines:\n detections_updated = False\n for line in carbonblack_lines:\n match = computer_name_re.search(line)\n computer = None\n if match:\n computer = match.group('computer_name')\n match = proc_guid_re.search(line)\n process_guid = None\n if match:\n process_guid = match.group('process_guid')\n if computer and computer in computer_user_map.keys():\n if user_id in computer_user_map[computer]:\n other_users = [user for user in computer_user_map[computer] if user_id != user]\n for other_user in other_users:\n _new_detection = old_detection = \"\"\n for detection in self.detections:\n if 'CLICKER' in detection and other_user in detection and domain_ip_path in detection:\n old_detection = detection\n # other users on this system have a detection in bluecoat logs\n _new_detection = (detection + \"\\n\\t\"+u'\\u21B3'+\" It appears the visit to '{}' by '{}'\"\n \" may have been from the same computer named {}. See extra detections\"\n \" to validate correlation.\")\n _new_detection = _new_detection.format(domain_ip_path, user_id, computer)\n if _new_detection != \"\":\n self.detections.remove(old_detection)\n self.detections.append(_new_detection)\n detections_updated = True\n\n netskope_lines = [line for line in output_lines if 'netskope' in line and domain_ip_path in line and user_id in line]\n if netskope_lines:\n self.detections.append(f'! CLICKER {company.upper()} STATUS UNKNOWN ! {user_id} clicked on {domain_ip_path} (netskope visibility)')\n\n # Make sure we actually added a detection for each user.\n for user_id in clicker_ids:\n click_descriptions = [d for d in self.detections if 'CLICKER' in d]\n if not any(user_id in d for d in click_descriptions):\n user_info_string = user_id\n for computer_name,user_list in computer_user_map.items():\n if user_id in user_list:\n user_info_string += f\" via {computer_name}\"\n self.detections.append('! CLICKER {} STATUS UNKNOWN ! {}'.format(company.upper(), user_info_string))\n\n # computer_user_map\n self.extra.append(\"\\nComputer User map: {}\\n\".format(computer_user_map))\n\n \"\"\"\n RUN ANY FOLLOW-UP 2FA SEARCHES\n \"\"\"\n if clicker_ids and email_addresses:\n\n # Store the Splunk output lines.\n output_lines = []\n\n # Build the user ID \"OR\" string for the search.\n user_id_string = ' OR '.join(email_addresses)\n\n # This is the actual command line version of the Splunk query.\n # TODO XXX Make ActorIpAddress list in config that should be ignored here - for us it's just 149.55.24.4\n command = '{} --enviro {} -s \"{}\" --json \"index=microsoft_cloud (Operation=UserLoginFailed) OR (ResultStatus=Succeeded AND Operation=UserLoggedIn) AND ({}) AND NOT ({})\"'.format(SPLUNKLIB, company, start_time, user_id_string, ignored_source_ips_string)\n self.logger.info(\"Searching splunk for 2FA clickers with this command: {}\".format(command))\n\n try:\n output = subprocess.check_output(command, shell=True).decode('utf-8')\n\n # If there was output, it means the Splunk search returned something.\n if output:\n\n results = json.loads(output)['result']\n for result in results:\n try:\n log = json.loads(result['_raw'])\n except json.decoder.JSONDecodeError:\n self.logger.error(\"screwed up log in result for: {}\".format(command))\n self.detections.append(\"⚠️ WARNING: could not parse Microsoft log in result of search for 2FA activity. See extra detections and Check manually.\")\n self.extra.append(\"Problematic log(s) in result of: {}\".format(command))\n continue\n detection_string = '! CLICKER {} 2FA ATTEMPT for {} at {} from {} : {} - {}'.format(company.upper(), log['UserId'], log['CreationTime'], log['ClientIP'], log['Operation'], log['ResultStatus'])\n if 'LogonError' in log:\n detection_string+=' - {}'.format(log['LogonError'])\n self.detections.append(detection_string)\n except Exception as e:\n self.logger.exception('Exception \"{}\" when running Splunk search: {}'.format(e, command)) \n\n","sub_path":"services/python/app/lib/modules/detections/clickers.py","file_name":"clickers.py","file_ext":"py","file_size_in_byte":46094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"532737694","text":"#!/usr/bin/python3\n\nimport sys\nimport os\n\nsys.path.insert(0, os.path.dirname(os.path.realpath(__file__)) + \"/../build\")\n\nfrom pywpsrpc.common import (FAILED, wpsapiex)\nfrom pywpsrpc import RpcProxy\n\n\ncan_close_doc = False\n\n\ndef _onDocumentBeforeClose(doc):\n print(\"_onDocumentBeforeClose: \", doc.Name)\n return not can_close_doc\n\n\ndef _onDocumentBeforeSave(doc):\n print(\"__onDocumentBeforeSave: \", doc.Name)\n # SaveAsUI, Cancel\n return True, True\n\n\ndef _onDocumentChange():\n print(\"_onDocumentChange\")\n\n\ndef _onDocumentOpen(doc):\n print(\"_onDocumentOpen:\", doc.Name)\n\n\ndef _onNewDocument(doc):\n print(\"_onNewDocument:\", doc.Name)\n\n\ndef _onQuit():\n print(\"_onQuit\")\n\n\ndef test_rpcwpsapi():\n try:\n from pywpsrpc.rpcwpsapi import (createWpsRpcInstance, wpsapi)\n except ImportError:\n return\n hr, rpc = createWpsRpcInstance()\n if FAILED(hr):\n print(\"createWpsRpcInstance failed with hr: \", hr)\n sys.exit(-1)\n\n app = RpcProxy(rpc.getWpsApplication())\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"DocumentBeforeClose\",\n _onDocumentBeforeClose)\n print(\"registerEvent:\", hex(hr & 0xFFFFFFFF))\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"DocumentBeforeSave\",\n _onDocumentBeforeSave)\n\n print(\"registerEvent:\", hex(hr & 0xFFFFFFFF))\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"DocumentChange\",\n _onDocumentChange)\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"DocumentOpen\",\n _onDocumentOpen)\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"NewDocument\",\n _onNewDocument)\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"Quit\",\n _onQuit)\n\n def _onWindowActivate(doc, window):\n ss = {wpsapi.wdWindowStateNormal: \"wdWindowStateNormal\",\n wpsapi.wdWindowStateMaximize: \"wdWindowStateMaximize\",\n wpsapi.wdWindowStateMinimize: \"wdWindowStateMinimize\"}\n print(\"_onWindowActivate:\", doc.Name, window.Caption, ss[window.WindowState])\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"WindowActivate\",\n _onWindowActivate)\n\n def _onWindowDeactivate(doc, window):\n print(\"_onWindowDeactivate:\", window.Caption)\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"WindowDeactivate\",\n _onWindowDeactivate)\n\n def _onWindowSelectionChange(selection):\n print(\"_onWindowSelectionChange:\", selection.Start,\n selection.End, selection.Text)\n\n hr = rpc.registerEvent(app.rpc_object,\n wpsapi.DIID_ApplicationEvents4,\n \"WindowSelectionChange\",\n _onWindowSelectionChange)\n\n doc = app.Documents.Add()\n # the doc should not be saved\n doc.SaveAs2(\"test.doc\")\n\n # DocumentChange\n doc2 = app.Documents.Add()\n\n doc3 = app.Documents.Open(os.path.realpath(__file__))\n\n selection = app.Selection\n # selection should changed\n selection.SetRange(0, 10)\n\n # the doc should not be closed\n doc.Close()\n\n # now make it works\n global can_close_doc\n can_close_doc = True\n\n app.Quit()\n\n\ndef _onPresentationSave(Pres):\n print(\"_onPresentationSave: \", Pres)\n\n\ndef _onPresentationClose(Pres):\n print(\"_onPresentationClose: \", Pres)\n\n\ndef _onAfterNewPresentation(Pres):\n print(\"_onAfterNewPresentation: \", Pres.Name)\n\n\ndef _onAfterPresentationOpen(Pres):\n print(\"_onAfterPresentationOpen: \", Pres.Name)\n\n\ndef _onNewPresentation(Pres):\n print(\"_onNewPresentation: \", Pres.Name)\n\n\ndef _onDocumentAfterPrint(Pres, PageEx):\n print(\"_onDocumentAfterPrint\")\n\n\ndef test_rpcwppapi():\n try:\n from pywpsrpc.rpcwppapi import (createWppRpcInstance, wppapi)\n except ImportError:\n return\n\n hr, rpc = createWppRpcInstance()\n if FAILED(hr):\n print(\"createWppRpcInstance failed with hr: \", hr)\n sys.exit(-1)\n\n app = RpcProxy(rpc.getWppApplication())\n\n hr = rpc.registerEvent(app.rpc_object,\n wppapi.IID_EApplication,\n \"PresentationSave\",\n _onPresentationSave)\n print(\"registerEvent:\", hex(hr & 0xFFFFFFFF))\n\n hr = rpc.registerEvent(app.rpc_object,\n wppapi.IID_EApplication,\n \"PresentationClose\",\n _onPresentationClose)\n\n print(\"registerEvent:\", hex(hr & 0xFFFFFFFF))\n\n hr = rpc.registerEvent(app.rpc_object,\n wppapi.IID_EApplication,\n \"AfterNewPresentation\",\n _onAfterNewPresentation)\n\n hr = rpc.registerEvent(app.rpc_object,\n wppapi.IID_EApplication,\n \"AfterPresentationOpen\",\n _onAfterPresentationOpen)\n\n hr = rpc.registerEvent(app.rpc_object,\n wppapi.IID_EApplication,\n \"NewPresentation\",\n _onNewPresentation)\n\n def _onWindowActivate(doc, window):\n ss = {wppapi.ppWindowNormal: \"ppWindowNormal\",\n wppapi.ppWindowMinimized: \"ppWindowMinimized\",\n wppapi.ppWindowMaximized: \"ppWindowMaximized\"}\n print(\"_onWindowActivate:\", window.Caption, ss[window.WindowState])\n\n hr = rpc.registerEvent(app.rpc_object,\n wppapi.IID_EApplication,\n \"WindowActivate\",\n _onWindowActivate)\n\n def _onWindowDeactivate(doc, window):\n print(\"_onWindowDeactivate:\", window.Caption)\n\n hr = rpc.registerEvent(app.rpc_object,\n wppapi.IID_EApplication,\n \"WindowDeactivate\",\n _onWindowDeactivate)\n\n appEx = app.ApplicationEx\n hr = rpc.registerEvent(appEx.rpc_object,\n wpsapiex.DIID_ApplicationEventsEx,\n \"DocumentAfterPrint\",\n _onDocumentAfterPrint)\n\n pres = app.Presentations.Add(wppapi.msoTrue)\n pres.SaveAs(\"test.ppt\")\n pres.Close()\n\n pres = app.Presentations.Open(\n \"test.ppt\", wppapi.msoFalse,\n wppapi.msoFalse, wppapi.msoTrue)\n\n pres.PrintOut()\n\n app.Quit()\n\n\ncan_close_wb = False\n\n\ndef _onWorkbookBeforeClose(wb):\n print(\"_onWorkbookBeforeClose: \", wb.Name)\n return not can_close_wb\n\n\ndef _onWorkbookBeforeSave(wb):\n print(\"_onWorkbookBeforeSave: \", wb.Name)\n # SaveAsUI, Cancel\n return True, not can_close_wb\n\n\ndef _onWorkbookAfterSave(wb, success):\n print(\"_onWorkbookAfterSave: \", wb.Name, success)\n\n\ndef _onNewWorkbook(wb):\n print(\"_onNewWorkbook: \", wb.Name)\n\n\ndef _onWorkbookOpen(wb):\n print(\"_onWorkbookOpen: \", wb.Name)\n\n\ndef test_rpcetapi():\n try:\n from pywpsrpc.rpcetapi import (createEtRpcInstance, etapi)\n except ImportError:\n return\n\n hr, rpc = createEtRpcInstance()\n if FAILED(hr):\n print(\"createEtRpcInstance failed with hr: \", hr)\n sys.exit(-1)\n\n app = RpcProxy(rpc.getEtApplication())\n\n hr = rpc.registerEvent(app.rpc_object,\n etapi.DIID_AppEvents,\n \"WorkbookBeforeClose\",\n _onWorkbookBeforeClose)\n print(\"registerEvent:\", hex(hr & 0xFFFFFFFF))\n\n hr = rpc.registerEvent(app.rpc_object,\n etapi.DIID_AppEvents,\n \"WorkbookBeforeSave\",\n _onWorkbookBeforeSave)\n\n print(\"registerEvent:\", hex(hr & 0xFFFFFFFF))\n\n hr = rpc.registerEvent(app.rpc_object,\n etapi.DIID_AppEvents,\n \"WorkbookAfterSave\",\n _onWorkbookAfterSave)\n\n hr = rpc.registerEvent(app.rpc_object,\n etapi.DIID_AppEvents,\n \"NewWorkbook\",\n _onNewWorkbook)\n\n hr = rpc.registerEvent(app.rpc_object,\n etapi.DIID_AppEvents,\n \"WorkbookOpen\",\n _onWorkbookOpen)\n\n def _onWindowActivate(wb, window):\n ss = {etapi.xlMaximized: \"xlMaximized\",\n etapi.xlMinimized: \"xlMinimized\",\n etapi.xlNormal: \"xlNormal\"}\n print(\"_onWindowActivate:\", window.Caption, ss[window.WindowState])\n\n hr = rpc.registerEvent(app.rpc_object,\n etapi.DIID_AppEvents,\n \"WindowActivate\",\n _onWindowActivate)\n\n def _onWindowDeactivate(wb, window):\n print(\"_onWindowDeactivate:\", wb.Name, window.Caption)\n\n hr = rpc.registerEvent(app.rpc_object,\n etapi.DIID_AppEvents,\n \"WindowDeactivate\",\n _onWindowDeactivate)\n\n wb = app.Workbooks.Add()\n # the doc should not be saved\n wb.SaveAs(\"test.xls\")\n # the doc should not be closed\n wb.Close()\n\n # now make it works\n global can_close_wb\n can_close_wb = True\n\n # save again\n wb.SaveAs(\"test2.xls\")\n wb.Close()\n\n wb = app.Workbooks.Open(\"test2.xls\")\n\n app.Quit()\n\n\nif __name__ == \"__main__\":\n test_rpcwpsapi()\n test_rpcwppapi()\n test_rpcetapi()\n","sub_path":"tests/test_rpcevents.py","file_name":"test_rpcevents.py","file_ext":"py","file_size_in_byte":10012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"537523210","text":"import json\n\n\ndef comparingLists(list1, list2):\n for x in list1:\n if x not in list2:\n print(\"{} is not in second list\".format(x))\n print(\"----------------------------------------------------------\\n\")\n\n for x in list2:\n if x not in list1:\n print(\"{} is not in first list\".format(x))\n print(\"----------------------------------------------------------\\n\")\n\n\ndef loadFromFile():\n with open('compare_lists.save', 'r') as file:\n data = json.load(file)\n file.close()\n return data\n\ndef checkFileDiff():\n lists = loadFromFile()\n userList1 = lists[0]\n userList2 = lists[1]\n\n comparingLists(userList1, userList2)\n\ndef checkCustomDiff():\n userList1 = input(\"Please enter a list with (,) between words or numbers and press enter to enter second list: \\n\")\n userList2 = input(\"Please enter a list with (,) between words or numbers and press enter: \\n\")\n\n userList1 = userList1.split(\",\")\n userList2 = userList2.split(\",\")\n\n comparingLists(userList1, userList2)\n\ndef mainMenu():\n while True:\n userInput = input(\"\\n\\nWhat do you want to do:\\n\"\n \"1 Import 2 lists and check their differences? or \\n\"\n \"2 Check differences between lists from the cld file?\\n\"\n \"----------------------------------------------------------\\n\"\n \"Press X to Exit\\n\")\n # On exit show current stock and break\n if userInput.upper() == \"X\":\n print(\"Good bye !\")\n break\n elif userInput == '1':\n checkCustomDiff()\n elif userInput == '2':\n checkFileDiff()\n else:\n print(\"Unknown command, please try again.\")\ndef main():\n\n mainMenu()\n\nif __name__ == \"__main__\":\n main()\n\n\n","sub_path":"cld.py","file_name":"cld.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"432738599","text":"from django import forms\nfrom . import models\n\n\n\nclass BookForm(forms.ModelForm):\n class Meta:\n model=models.Book\n fields=('name', 'authors', 'description') #здесь прописываются поля, которые мы хотим видель из файла models\n def clean(self): #здксь можно реализовывать любую логичу питона\n cleaned_data=super().clean() # как, например, проверка запрещенный авторов в этом случае\n authors=cleaned_data.get('authors')\n\n if authors == \"Лукашенко\":\n self.add_error('authors','This authors is forbiden!!!')\n return cleaned_data\n","sub_path":"src/references/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"197321199","text":"import sys\nimport serial\nimport struct\nimport time\n\n\nclass H7Camera():\n \"\"\"\n OpenMV Cam H7 Host Device Side Camera Object\n\n WIP\n \"\"\"\n def __init__(self, port_name=\"/dev/ttyS4\"):\n self.port_name = port_name\n self.test = 0.0\n\n def cam_mand(self, serialcmd):\n sp = serial.Serial(self.port_name,\n baudrate=115200,\n bytesize=serial.EIGHTBITS,\n parity=serial.PARITY_NONE,\n xonxoff=False, rtscts=False,\n stopbits=serial.STOPBITS_ONE,\n timeout=None,\n dsrdtr=True)\n try:\n sp.setDTR(True) # dsrdtr is ignored on Windows.\n sp.write(serialcmd.encode())\n sp.flush()\n result = struct.unpack('1 or <-1\n \"\"\"\n NxNx3=list(LUM.shape)\n NxNx3.append(3)\n dkl_cartesian = np.asarray([LUM.reshape([-1]), LM.reshape([-1]), S.reshape([-1])])\n\n if conversionMatrix==None:\n conversionMatrix = np.asarray([ \\\n #LUMIN\t%L-M\t%L+M-S (note that dkl has to be in cartesian coords first!)\n [1.0000, 1.0000, -0.1462],#R\n [1.0000, -0.3900, 0.2094],#G\n [1.0000, 0.0180, -1.0000]])#B\n rgb = np.dot(conversionMatrix, dkl_cartesian)\n return np.reshape(np.transpose(rgb), NxNx3)\n\ndef makeEdgeGauss(width, center, size=512):\n \"\"\"Create a matrix of given size that switches from 0 to 1 using a gaussian profile\n \n :params:\n width: (float) the sd of the gauss used to smooth as a fraction of the matrix size\n center: (float) the location of the center of the ramp as a fraction of the total matrix\n size: (int=256) width and height of the matrix\n \"\"\"\n edge = np.ones(size*3, float)#3x to achieve edge-padding\n centerLocation = int(size+center*size)\n edge[:centerLocation]=0\n gauss = filters.makeGauss(x=np.linspace(-1.0,1.0,size), mean=0, sd=width)/np.sqrt(2*np.pi*width**2)\n smthEdge = np.convolve(edge, gauss,'same')\n smthEdge = (smthEdge[size:size*2]-smthEdge.min())/(smthEdge.max()-smthEdge.min())#just take the middle section\n smthEdge.shape=(1,size)\n return np.tile(smthEdge,(size,1))*2-1\n\ndef makeEdgeMatrix(width, center, size=512):\n \"\"\"Create a matrix of given size that switches from 0 to 1 using a linear ramp.\n \n :params:\n width: (float) the width of the linear ramp (blur) as fraction of the total matrix\n center: (float) the location of the center of the ramp as a fraction of the total matrix\n size: (int=256) width and height of the matrix\n \"\"\"\n center=int(center*(size-1))\n width = int(width*size/2)*2\n mat = np.ones([size,size], 'f')\n mat[:,0:(center-width/2)]=0\n if (center-width/2)<0 or (center+width/2)>size:\n log.error('ramp extends beyond texture')\n mat[:,(center-width/2):(center+width/2)] = np.linspace(0,1.0,width)\n return mat*2.0-1\n \ndef rgb2dklCart(picture, conversionMatrix=None):\n \"\"\"convert an RGB image into Cartesian DKL space\"\"\"\n #Turn the picture into an array so we can do maths\n picture=scipy.array(picture)\n #Find the original dimensions of the picture\n origShape = picture.shape\n\n #this is the inversion of the dkl2rgb conversion matrix\n if conversionMatrix==None:\n conversionMatrix = np.asarray([\\\n #LUMIN->%L-M->L+M-S\n [ 0.25145542, 0.64933633, 0.09920825],\n [ 0.78737943, -0.55586618, -0.23151325],\n [ 0.26562825, 0.63933074, -0.90495899]])\n else:\n conversionMatrix = np.linalg.inv(conversionMatrix)\n\n #Reshape the picture so that it can multiplied by the conversion matrix\n red = picture[:,:,0]\n green = picture[:,:,1]\n blue = picture[:,:,2]\n\n dkl = np.asarray([red.reshape([-1]), green.reshape([-1]), blue.reshape([-1])])\n \n #Multiply the picture by the conversion matrix\n dkl=np.dot(conversionMatrix, dkl)\n\n #Reshape the picture so that it's back to it's original shape\n dklPicture = np.reshape(np.transpose(dkl), origShape)\n return dklPicture","sub_path":"Tests/colorFunctions.py","file_name":"colorFunctions.py","file_ext":"py","file_size_in_byte":5939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"180848911","text":"file_names = ['../affiliations.txt', '../conferences.txt']\n\n\n# 机构处理\ndef affiliation_handler(file):\n with open(file) as f:\n for line in f:\n import re\n t = re.sub(r'[().*]', \"\", re.sub(r'\\(.*\\)', \"\", line.strip()).strip())\n print(t.strip())\n\n\n# 会议处理\ndef conference_handler(file):\n with open(file) as f:\n for line in f:\n import re\n sub = re.sub(r'[().*]', \"\", re.sub(r'(\\(.*\\))', \"\", line.strip()).strip())\n print(line.strip())\n\n\naffiliation_handler(file_names[0])\n","sub_path":"oasis-data/analysers/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"5597685","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\"\"\"\nGiven a non-empty string check if it can be constructed by taking a substring of it and appending multiple copies of the substring together. You may assume the given string consists of lowercase English letters only and its length will not exceed 10000.\n\nExample 1:\nInput: \"abab\"\n\nOutput: True\n\nExplanation: It's the substring \"ab\" twice.\nExample 2:\nInput: \"aba\"\n\nOutput: False\nExample 3:\nInput: \"abcabcabcabc\"\n\nOutput: True\n\nExplanation: It's the substring \"abc\" four times. (And the substring \"abcabc\" twice.)\n\"\"\"\n\nclass Solution(object):\n def repeatedSubstringPattern(self, str):\n \"\"\"\n :type str: str\n :rtype: bool\n \"\"\"\n if len(str) <= 1 and len(str) % 2:\n return False\n if str[:len(str) / 2] == str[len(str) / 2:]:\n return True\n i = end = 0\n while i < len(str):\n if str[i] == str[0]:\n j = 0\n while i < len(str) and j <= end and str[i] == str[j]:\n i += 1\n j += 1\n if j <= end:\n end = i - 1\n elif i == len(str):\n return True\n else:\n i += 1\n end += 1\n return False\n","sub_path":"Python/459-RepeatedSubstringPattern/repeatedSubstringPattern.py","file_name":"repeatedSubstringPattern.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"359429794","text":"# ----------------------------------------------------------------------------\n# Copyright (c) 2020, Franck Lejzerowicz.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file LICENSE, distributed with this software.\n# ----------------------------------------------------------------------------\n\nimport os\nimport sys\nimport itertools\nimport pandas as pd\nfrom os.path import isdir, isfile, splitext\n\nfrom routine_qiime2_analyses._routine_q2_xpbs import run_xpbs, print_message\nfrom routine_qiime2_analyses._routine_q2_io_utils import (\n read_yaml_file,\n get_job_folder,\n get_analysis_folder,\n get_mmvec_dicts,\n write_main_sh,\n get_datasets_filtered,\n check_datasets_filtered\n)\nfrom routine_qiime2_analyses._routine_q2_metadata import (\n rename_duplicate_columns, make_train_test_column,\n get_common_columns, get_different_columns\n)\nfrom routine_qiime2_analyses._routine_q2_cmds import (\n filter_feature_table, get_case,\n run_export, write_mmvec_cmd\n)\n\n\ndef get_meta_common_sorted(meta: pd.DataFrame, common_sams: list) -> pd.DataFrame:\n meta_subset = meta.loc[meta.sample_name.isin(common_sams),:].copy()\n meta_subset.columns = [x.lower() for x in meta_subset.columns]\n meta_subset.sort_values('sample_name', inplace=True)\n return meta_subset\n\n\ndef merge_and_write_metas(\n meta_subset1: pd.DataFrame,\n meta_subset2: pd.DataFrame,\n meta_fp: str, omic1: str, omic2: str,\n train_test_dict: dict) -> pd.DataFrame:\n \"\"\"\n :param meta_subset1:\n :param meta_subset2:\n :param meta_fp:\n :return:\n \"\"\"\n meta_subset1 = rename_duplicate_columns(meta_subset1)\n meta_subset2 = rename_duplicate_columns(meta_subset2)\n # get these columns names that are identical in the two metadata\n common_cols = get_common_columns(meta_subset1, meta_subset2)\n # get these columns that also have different contents\n diff_cols = get_different_columns(meta_subset1, meta_subset2, common_cols)\n\n if len(diff_cols):\n meta_subset2.rename(columns=dict((c, '%s.copy' % c) for c in diff_cols), inplace=True)\n meta_subset = meta_subset1.merge(\n meta_subset2,\n on=(['sample_name'] + [c for c in common_cols if c not in diff_cols])\n )\n sorting_col = ['sample_name'] + [x for x in meta_subset.columns.tolist() if x != 'sample_name']\n meta_subset = meta_subset[sorting_col]\n meta_subset_traintest, train_cols = make_train_test_column(meta_fp, train_test_dict, meta_subset, omic1, omic2)\n if len(train_cols & set(pd.read_table(meta_fp, nrows=2).columns)) != len(train_cols):\n meta_subset_traintest.to_csv(meta_fp, index=False, sep='\\t')\n return meta_subset\n\n\ndef get_common_datasets(i_datasets_folder: str, mmvec_pairs: dict, filtering: dict,\n filt_datasets: dict, common_datasets_done: dict,\n input_to_filtered: dict, train_test_dict: dict,\n force: bool, subsets: dict) -> (dict, list):\n \"\"\"\n :param i_datasets_folder:\n :param mmvec_pairs:\n :param filt_datasets:\n :param force: Force the re-writing of scripts for all commands.\n :return:\n \"\"\"\n common_jobs = []\n common_datasets = {}\n for pair, pair_datasets in mmvec_pairs.items():\n # print(\"pair, pair_datasets\")\n # print(pair, pair_datasets)\n (omic1_, bool1), (omic2_, bool2) = pair_datasets\n if omic1_ not in input_to_filtered or omic2_ not in input_to_filtered:\n continue\n omic1 = input_to_filtered[omic1_]\n omic2 = input_to_filtered[omic2_]\n if (omic1, bool1) not in filt_datasets or (omic2, bool2) not in filt_datasets:\n continue\n pair_filtering = filtering[pair]\n for case_var, case_vals_list in subsets.items():\n for case_vals in case_vals_list:\n case = get_case(case_vals, case_var)\n data_dir = get_analysis_folder(i_datasets_folder, 'mmvec/common/data/%s/%s' % (pair, case))\n meta_dir = get_analysis_folder(i_datasets_folder, 'mmvec/common/metadata/%s/%s' % (pair, case))\n for preval_abund, preval_abund_dats in sorted(pair_filtering.items()):\n preval_filt1, abund_filter1 = preval_abund_dats[(omic1_, bool1)]\n preval_filt2, abund_filter2 = preval_abund_dats[(omic2_, bool2)]\n filt1 = '%s_%s' % (preval_filt1, abund_filter1)\n filt2 = '%s_%s' % (preval_filt2, abund_filter2)\n if (case, preval_abund) not in filt_datasets[(omic1, bool1)]:\n continue\n if (case, preval_abund) not in filt_datasets[(omic2, bool2)]:\n continue\n tsv1, qza1, meta1, meta_pd1, sams1 = filt_datasets[(omic1, bool1)][(case, preval_abund)]\n tsv2, qza2, meta2, meta_pd2, sams2 = filt_datasets[(omic2, bool2)][(case, preval_abund)]\n common_sams = sorted(set(sams1) & set(sams2))\n len_common_sams = len(common_sams)\n if len_common_sams < 10:\n print('Not enough samples: %s (%s) vs %s (%s) -> skipping' % (omic1, filt1, omic2, filt2))\n continue\n\n meta_fp = '%s/meta_%s_%s_%s__%s_%s_%s__%s_%ss.tsv' % (\n meta_dir, omic1, preval_filt1, abund_filter1,\n omic2, preval_filt2, abund_filter2,\n pair, len_common_sams\n )\n new_tsv1 = '%s/tab_%s_%s_%s__%s_%ss.tsv' % (\n data_dir, omic1, preval_filt1,\n abund_filter1, pair, len_common_sams\n )\n new_qza1 = '%s.qza' % splitext(new_tsv1)[0]\n new_tsv2 = '%s/tab_%s_%s_%s__%s_%ss.tsv' % (\n data_dir, omic2, preval_filt2,\n abund_filter2, pair, len_common_sams\n )\n new_qza2 = '%s.qza' % splitext(new_tsv2)[0]\n common_datasets.setdefault(pair, []).append(\n [meta_fp, omic1, omic2, filt1, filt2,\n new_tsv1, new_tsv2, new_qza1,\n new_qza2, len_common_sams, case]\n )\n meta_subset1 = get_meta_common_sorted(meta_pd1, common_sams)\n meta_subset2 = get_meta_common_sorted(meta_pd2, common_sams)\n merge_and_write_metas(meta_subset1, meta_subset2, meta_fp, omic1, omic2, train_test_dict)\n if meta_fp in common_datasets_done[pair]:\n print('\\t\\t\\t* [DONE]', pair, ':', omic1, filt1, omic2, filt2)\n continue\n if force or not isfile(new_qza1):\n cmd = filter_feature_table(qza1, new_qza1, meta_fp)\n common_jobs.append(cmd)\n if force or not isfile(new_tsv1):\n cmd = run_export(new_qza1, new_tsv1, 'FeatureTable')\n common_jobs.append(cmd)\n if force or not isfile(new_qza2):\n cmd = filter_feature_table(qza2, new_qza2, meta_fp)\n common_jobs.append(cmd)\n if force or not isfile(new_tsv2):\n cmd = run_export(new_qza2, new_tsv2, 'FeatureTable')\n common_jobs.append(cmd)\n print(\n '\\t\\t\\t* [TODO]', pair, ':',\n omic1, '[%s: %s]' % (filt1, meta_subset1.shape[0]),\n omic2, '[%s: %s]' % (filt2, meta_subset2.shape[0]))\n return common_datasets, common_jobs\n\n\ndef check_common_datasets(i_datasets_folder: str, mmvec_pairs: dict,\n mmvec_filtering: dict, filt_datasets_pass: dict,\n input_to_filtered: dict, mmvec_subsets: dict) -> (dict, list):\n \"\"\"\n :param i_datasets_folder:\n :param mmvec_pairs:\n :param force: Force the re-writing of scripts for all commands.\n :return:\n \"\"\"\n common_datasets_pass = {}\n for pair, pair_datasets in mmvec_pairs.items():\n pair_filtering = mmvec_filtering[pair]\n common_datasets_pass[pair] = []\n data_dir_ = get_analysis_folder(i_datasets_folder, 'mmvec/common/data/%s' % pair)\n meta_dir_ = get_analysis_folder(i_datasets_folder, 'mmvec/common/metadata/%s' % pair)\n (omic1_, bool1), (omic2_, bool2) = pair_datasets\n if omic1_ not in input_to_filtered or omic2_ not in input_to_filtered:\n continue\n omic1 = input_to_filtered[omic1_]\n omic2 = input_to_filtered[omic2_]\n if (omic1, bool1) not in filt_datasets_pass or (omic2, bool2) not in filt_datasets_pass:\n continue\n for case_var, case_vals_list in mmvec_subsets.items():\n for case_vals in case_vals_list:\n case = get_case(case_vals, case_var)\n data_dir = data_dir_ + '/' + case\n meta_dir = meta_dir_ + '/' + case\n for preval_abund in sorted(pair_filtering):\n preval_filt1, abund_filter1 = pair_filtering[preval_abund][(omic1_, bool1)]\n preval_filt2, abund_filter2 = pair_filtering[preval_abund][(omic2_, bool2)]\n if not filt_datasets_pass[(omic1, bool1)][(case, preval_abund)]:\n continue\n if not filt_datasets_pass[(omic2, bool2)][(case, preval_abund)]:\n continue\n filt1 = '_'.join([preval_filt1, abund_filter1])\n filt2 = '_'.join([preval_filt2, abund_filter2])\n tsv1, qza1, meta1, meta_pd1, sams1 = filt_datasets_pass[(omic1, bool1)][(case, preval_abund)]\n tsv2, qza2, meta2, meta_pd2, sams2 = filt_datasets_pass[(omic2, bool2)][(case, preval_abund)]\n common_sams = sorted(set(sams1) & set(sams2))\n if len(common_sams) < 10:\n print('Not enough samples: %s (%s) vs %s (%s) -> skipping' % (omic1, filt1, omic2, filt2))\n continue\n meta_fp = '%s/meta_%s_%s_%s__%s_%s_%s__%s_%ss.tsv' % (\n meta_dir, omic1, preval_filt1, abund_filter1,\n omic2, preval_filt2, abund_filter2, pair, len(common_sams)\n )\n new_tsv1 = '%s/tab_%s_%s_%s__%s_%ss.tsv' % (\n data_dir, omic1, preval_filt1, abund_filter1, pair, len(common_sams)\n )\n new_qza1 = '%s.qza' % splitext(new_tsv1)[0]\n new_tsv2 = '%s/tab_%s_%s_%s__%s_%ss.tsv' % (\n data_dir, omic2, preval_filt2, abund_filter2, pair, len(common_sams)\n )\n new_qza2 = '%s.qza' % splitext(new_tsv2)[0]\n if isfile(meta_fp) and isfile(new_qza1) and isfile(new_qza2):\n common_datasets_pass[pair].append(meta_fp)\n return common_datasets_pass\n\n\ndef run_single_mmvec(odir: str, meta_fp: str, qza1: str, qza2: str, res_dir: str,\n cur_sh: str, batch: str, learn: str, epoch: str, prior: str,\n thresh_feat: str, latent_dim: str,\n train_column: str, n_example: str,\n gpu: bool, force: bool, standalone: bool, qiime_env: str) -> None:\n \"\"\"\n Run mmvec: Neural networks for microbe-metabolite interaction analysis.\n https://github.com/biocore/mmvec\n (in-loop function).\n\n :param odir:\n :param pair:\n :param meta_fp:\n :param qza1:\n :param qza2:\n :param res_dir:\n :param cur_sh:\n :param batch:\n :param learn:\n :param epoch:\n :param prior:\n :param thresh_feat:\n :param latent_dim:\n :param train_column:\n :param n_example:\n :param gpu:\n :param standalone:\n :return:\n \"\"\"\n remove = True\n with open(cur_sh, 'w') as cur_sh_o:\n\n model_odir = '%s/model' % odir\n if not isdir(model_odir):\n os.makedirs(model_odir)\n ranks_tsv = '%s/ranks.tsv' % model_odir\n ordination_tsv = '%s/ordination.txt' % model_odir\n stats = '%s/stats.qza' % model_odir\n\n null_odir = '%s/null' % odir\n if not isdir(null_odir):\n os.makedirs(null_odir)\n ranks_null_tsv = '%s/ranks.tsv' % null_odir\n ordination_null_tsv = '%s/ordination.txt' % null_odir\n stats_null = '%s/stats.qza' % null_odir\n\n summary = '%s/paired-summary.qzv' % odir\n\n if force or not isfile(summary):\n cmd = write_mmvec_cmd(\n meta_fp, qza1, qza2, res_dir, model_odir, null_odir,\n ranks_tsv, ordination_tsv, stats, ranks_null_tsv,\n ordination_null_tsv, stats_null, summary, batch, learn,\n epoch, prior, thresh_feat, latent_dim,\n train_column,\n n_example, gpu, standalone, qiime_env)\n cur_sh_o.write('echo \"%s\"\\n' % cmd)\n cur_sh_o.write('%s\\n' % cmd)\n remove = False\n if remove:\n os.remove(cur_sh)\n\n\ndef check_filtered_and_common_dataset(\n i_datasets_folder:str, datasets: dict, datasets_filt: dict,\n unique_datasets: list, mmvec_pairs: dict, mmvec_filtering: dict,\n unique_filterings: dict, analysis: str, input_to_filtered: dict,\n subsets: dict) -> (dict, dict):\n \"\"\"\n :param i_datasets_folder:\n :param datasets: list of data_sets.\n :param datasets_read:\n :param mmvec_pairs:\n :param mmvec_filtering:\n :param job_folder:\n :param force:\n :param prjct_nm:\n :param qiime_env:\n :param chmod:\n :return:\n \"\"\"\n\n print('\\t-> [%s] Check datasets filtered...' % analysis)\n filt_datasets_pass = check_datasets_filtered(\n i_datasets_folder, datasets, datasets_filt,\n unique_datasets, unique_filterings, analysis,\n input_to_filtered, subsets\n )\n # filt_datasets_todo = [x for x, y in filt_datasets_pass.items() if not len(y)]\n # if len(filt_datasets_todo):\n # print('\\t\\t--> %s dataset(s) to prepare:' % len(filt_datasets_todo))\n # for filt_datasets_td in filt_datasets_todo:\n # print('\\t\\t\\t*', filt_datasets_td)\n\n common_datasets_pass = {}\n if analysis == 'mmvec':\n print('\\t-> [mmvec] Check common datasets...')\n common_datasets_pass = check_common_datasets(\n i_datasets_folder, mmvec_pairs, mmvec_filtering,\n filt_datasets_pass, input_to_filtered, subsets\n )\n # common_datasets_todo = [x for x, y in common_datasets_pass.items() if not len(y)]\n # if len(common_datasets_todo):\n # print('\\t\\t--> %s common dataset(s) to prepare:' % len(common_datasets_todo))\n # for common_datasets_td in common_datasets_todo:\n # print('\\t\\t\\t*', common_datasets_td)\n\n return filt_datasets_pass, common_datasets_pass\n\n\ndef make_filtered_and_common_dataset(\n i_datasets_folder:str, datasets: dict,\n datasets_filt: dict, datasets_read: dict, unique_datasets: list,\n train_test_dict: dict, mmvec_pairs: dict, filtering: dict,\n unique_filterings: dict, job_folder: str, force: bool,\n prjct_nm: str, qiime_env: str, chmod: str, noloc: bool,\n analysis: str, filt_raref: str, filt_datasets_done: dict,\n common_datasets_done: dict, input_to_filtered: dict,\n already_computed: dict, subsets: dict, jobs: bool) -> (dict, dict):\n \"\"\"\n :param i_datasets_folder:\n :param datasets: list of data_sets.\n :param datasets_read:\n :param mmvec_pairs:\n :param mmvec_filtering:\n :param job_folder:\n :param force:\n :param prjct_nm:\n :param qiime_env:\n :param chmod:\n :return:\n \"\"\"\n\n print('\\t-> [%s] Get datasets filtered...' % analysis)\n filt_datasets, filt_jobs = get_datasets_filtered(\n i_datasets_folder, datasets, datasets_read, datasets_filt,\n unique_datasets, unique_filterings, force, analysis,\n filt_datasets_done, input_to_filtered, train_test_dict,\n already_computed, subsets)\n\n common_jobs = []\n common_datasets = {}\n if analysis == 'mmvec':\n print('\\t-> [mmvec] Get common datasets...')\n common_datasets, common_jobs = get_common_datasets(\n i_datasets_folder, mmvec_pairs, filtering, filt_datasets,\n common_datasets_done, input_to_filtered, train_test_dict,\n force, subsets)\n\n pre_jobs = filt_jobs + common_jobs\n if len(pre_jobs):\n import_sh = '%s/2_run_%s_imports_%s%s.sh' % (job_folder, prjct_nm, analysis, filt_raref)\n import_pbs = '%s.pbs' % splitext(import_sh)[0]\n with open(import_sh, 'w') as import_o:\n for cmd in pre_jobs:\n import_o.write('\\necho \"%s\"\\n' % cmd)\n import_o.write('%s\\n' % cmd)\n run_xpbs(import_sh, import_pbs, '%s.mprt.mmsb.%s%s' % (prjct_nm, analysis, filt_raref),\n qiime_env, '2', '1', '1', '150', 'mb', chmod, 1,\n '# Import datasets for %s' % analysis, None, noloc, jobs)\n\n return filt_datasets, common_datasets\n\n\ndef get_unique_mmvec_filtering(mmvec_filtering):\n unique_filterings = {}\n for pair, filt_name_d in mmvec_filtering.items():\n for filt_name, dat_d in filt_name_d.items():\n for dat, (preval, abund) in dat_d.items():\n unique_filterings.setdefault(\n dat, set()).add((filt_name, preval, abund))\n return unique_filterings\n\n\ndef run_mmvec(\n p_mmvec_pairs: str, i_datasets_folder: str, datasets: dict,\n datasets_filt: dict, datasets_read: dict, train_test_dict: dict,\n force: bool, gpu: bool, standalone: bool, prjct_nm: str, qiime_env: str,\n chmod: str, noloc: bool, split: bool, filt_raref: str, run_params: dict,\n input_to_filtered: dict, jobs: bool, chunkit: int) -> list:\n\n \"\"\"Run mmvec: Neural networks for microbe-metabolite interaction analysis.\n https://github.com/biocore/mmvec\n Main two-datasets looper for the mmvec co-occurrences.\n\n Parameters\n ----------\n p_mmvec_pairs\n :param p_mmvec_pairs: Pairs of datasets for which to compute co-occurrences probabilities.\n :param p_diff_models: Formulas for multinomial regression-based differential abundance ranking.\n :param datasets: list of data_sets.\n :param i_datasets_folder: Path to the folder containing the data/metadata subfolders.\n :param datasets_read: dataset -> [tsv table, meta table] (here it updates tsv table after features correction)\n :param force: Force the re-writing of scripts for all commands.\n :param gpu: Use GPUs instead of CPUs for MMVEC.\n :param standalone:\n :param prjct_nm: Nick name for your project.\n :param qiime_env: qiime2-xxxx.xx conda environment.\n :param chmod: whether to change permission of output files (default: 644).\n i_datasets_folder\n datasets\n datasets_filt\n datasets_read\n train_test_dict\n force\n gpu\n standalone\n prjct_nm\n qiime_env\n chmod\n noloc\n split\n filt_raref\n run_params\n input_to_filtered\n jobs\n chunkit\n\n Returns\n -------\n\n \"\"\"\n mmvec_dicts = get_mmvec_dicts(p_mmvec_pairs)\n mmvec_pairs = mmvec_dicts[0]\n mmvec_filtering = mmvec_dicts[1]\n mmvec_params = mmvec_dicts[2]\n mmvec_subsets = mmvec_dicts[3]\n unique_datasets = list(set([\n dat for pair_dats in mmvec_pairs.values() for dat in pair_dats]))\n unique_filterings = get_unique_mmvec_filtering(mmvec_filtering)\n\n print(mmvec_pairs)\n print()\n print(mmvec_filtering)\n print()\n print(mmvec_params)\n print()\n print(mmvec_subsets)\n print()\n print(unique_datasets)\n print()\n print(unique_filterings)\n print()\n print(\"datasets\")\n print(datasets)\n print(\"datasets_filt\")\n print(datasets_filt)\n print(fdsa)\n\n filt_datasets_done, common_datasets_done = check_filtered_and_common_dataset(\n i_datasets_folder, datasets, datasets_filt,\n unique_datasets, mmvec_pairs, mmvec_filtering,\n unique_filterings, 'mmvec', input_to_filtered, mmvec_subsets)\n\n already_computed = {}\n job_folder = get_job_folder(i_datasets_folder, 'mmvec')\n filt_datasets, common_datasets = make_filtered_and_common_dataset(\n i_datasets_folder, datasets, datasets_filt, datasets_read,\n unique_datasets, train_test_dict, mmvec_pairs, mmvec_filtering,\n unique_filterings, job_folder, force, prjct_nm, qiime_env, chmod,\n noloc, 'mmvec', filt_raref, filt_datasets_done, common_datasets_done,\n input_to_filtered, already_computed, mmvec_subsets, jobs)\n\n all_sh_pbs = {}\n mmvec_outputs = []\n\n for pair, pair_data in common_datasets.items():\n\n job_folder2 = get_job_folder(i_datasets_folder, 'mmvec/chunks/%s' % pair)\n if not split:\n out_sh = '%s/chunks/run_mmvec_%s_%s%s.sh' % (job_folder, prjct_nm, pair, filt_raref)\n\n for (meta_fp, omic1, omic2, filt1, filt2, tsv1, tsv2, qza1, qza2, ncommon, case) in pair_data:\n train_columns = mmvec_params['train_column']\n n_examples = mmvec_params['n_examples']\n batches = mmvec_params['batches']\n learns = mmvec_params['learns']\n epochs = mmvec_params['epochs']\n priors = mmvec_params['priors']\n thresh_feats = mmvec_params['thresh_feats']\n latent_dims = mmvec_params['latent_dims']\n if split:\n out_sh = '%s/chunks/run_mmvec_%s_%s_%s_%s_%s_%s_%s%s.sh' % (\n job_folder, prjct_nm, pair, case,\n omic1, filt1, omic2, filt2, filt_raref)\n if train_columns != ['None']:\n n_examples = ['']\n for idx, it in enumerate(itertools.product(\n train_columns, n_examples, batches, learns,\n epochs, priors, thresh_feats, latent_dims)):\n train_column, n_example, batch, learn, epoch, prior, thresh_feat, latent_dim = [str(x) for x in it]\n res_dir = 'b-%s_l-%s_e-%s_p-%s_f-%s_d-%s_t-%s_n-%s_gpu-%s'\\\n % (\n batch, learn, epoch, prior.replace('.', ''),\n thresh_feat, latent_dim, train_column,\n n_example, str(gpu)[0]\n )\n # skip is not at least half samples (for training if train columns specified)\n if train_columns != ['None']:\n meta_pd = pd.read_table(meta_fp, usecols=['sample_name', train_column.lower()])\n ntrain = meta_pd[train_column.lower()].value_counts()['Train']\n if ncommon < (1.2 * ntrain):\n print('\\t\\t--> skipped pair \"%s\" (too few samples '\n '[%s samples for %s training samples]):' % (pair, ncommon))\n print('\\t\\t - %s %s' % (omic1, filt1))\n print('\\t\\t - %s %s' % (omic2, filt2))\n continue\n else:\n if int(ncommon) < (1.2 * int(n_example)):\n print('\\t\\t--> skipped pair \"%s\" (too few samples '\n '[%s samples for %s examples]):' % (pair, ncommon, n_example))\n print('\\t\\t - %s %s' % (omic1, filt1))\n print('\\t\\t - %s %s' % (omic2, filt2))\n continue\n\n odir = get_analysis_folder(\n i_datasets_folder,\n 'mmvec/paired/%s/%s/%s_%s__%s_%s/%s' % (\n pair, case, omic1, filt1, omic2, filt2, res_dir)\n )\n mmvec_outputs.append([\n pair, case, omic1, omic2, filt1, filt2,\n ncommon, meta_fp, tsv1, tsv2, qza1, qza2,\n 'mmvec_out__%s' % res_dir, odir\n ])\n cur_sh = '%s/run_mmvec_%s_%s_%s_%s_%s%s.sh' % (\n job_folder2, pair, case, filt1,\n filt2, res_dir, filt_raref)\n all_sh_pbs.setdefault((pair, out_sh), []).append(cur_sh)\n run_single_mmvec(\n odir, meta_fp, qza1, qza2, res_dir, cur_sh,\n batch, learn, epoch, prior, thresh_feat,\n latent_dim, train_column, n_example,\n gpu, force, standalone, qiime_env\n )\n\n main_sh = write_main_sh(job_folder, '3_mmvec_%s%s' % (prjct_nm, filt_raref), all_sh_pbs,\n '%s.mmvc%s' % (prjct_nm, filt_raref),\n run_params[\"time\"], run_params[\"n_nodes\"], run_params[\"n_procs\"],\n run_params[\"mem_num\"], run_params[\"mem_dim\"],\n qiime_env, chmod, noloc, jobs, chunkit)\n if main_sh:\n if p_mmvec_pairs.startswith('/panfs'):\n p_mmvec_pairs = p_mmvec_pairs.replace(os.getcwd(), '')\n print_message(\"# MMVEC (datasets pairs in %s)\" % p_mmvec_pairs, 'sh', main_sh, jobs)\n\n return mmvec_outputs","sub_path":"routine_qiime2_analyses/_routine_q2_mmvec.py","file_name":"_routine_q2_mmvec.py","file_ext":"py","file_size_in_byte":25297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"641258578","text":"import json\nimport time\nimport datetime\n\nfrom aliyunsdkcore.client import AcsClient\nfrom cachetools import cached, TTLCache\nfrom prometheus_client.metrics_core import GaugeMetricFamily\n\nimport aliyunsdkecs.request.v20140526.DescribeInstancesRequest as DescribeECS\nimport aliyunsdkrds.request.v20140815.DescribeDBInstancesRequest as DescribeRDS\nimport aliyunsdkr_kvstore.request.v20150101.DescribeInstancesRequest as DescribeRedis\nimport aliyunsdkslb.request.v20140515.DescribeLoadBalancersRequest as DescribeSLB\nimport aliyunsdkslb.request.v20140515.DescribeLoadBalancerAttributeRequest as DescribeSLBAttr\nimport aliyunsdkslb.request.v20140515.DescribeLoadBalancerTCPListenerAttributeRequest as DescribeSLBTcpAttr\nimport aliyunsdkslb.request.v20140515.DescribeLoadBalancerHTTPListenerAttributeRequest as DescribeSLBHttpAttr\nimport aliyunsdkslb.request.v20140515.DescribeLoadBalancerHTTPSListenerAttributeRequest as DescribeSLBHttpsAttr\nimport aliyunsdkdds.request.v20151201.DescribeDBInstancesRequest as Mongodb\nimport aliyunsdkcdn.request.v20180510.DescribeUserDomainsRequest as DescribeCDN\n\nfrom aliyun_exporter.utils import try_or_else\n\ncache = TTLCache(maxsize=100, ttl=3600)\n\n'''\nInfoProvider provides the information of cloud resources as metric.\n\nThe result from alibaba cloud API will be cached for an hour. \n\nDifferent resources should implement its own 'xxx_info' function. \n\nDifferent resource has different information structure, and most of\nthem are nested, for simplicity, we map the top-level attributes to the\nlabels of metric, and handle nested attribute specially. If a nested\nattribute is not handled explicitly, it will be dropped.\n'''\nclass InfoProvider():\n\n def __init__(self, client: AcsClient):\n self.client = client\n\n @cached(cache)\n def get_metrics(self, resource: str) -> GaugeMetricFamily:\n return {\n 'ecs': lambda : self.ecs_info(),\n 'rds': lambda : self.rds_info(),\n 'cdn': lambda : self.cdn_info(),\n 'redis': lambda : self.redis_info(),\n 'slb':lambda : self.slb_info(),\n 'mongodb':lambda : self.mongodb_info(),\n }[resource]()\n\n def ecs_info(self) -> GaugeMetricFamily:\n req = DescribeECS.DescribeInstancesRequest()\n nested_handler = {\n 'InnerIpAddress': lambda obj : try_or_else(lambda : obj['IpAddress'][0], ''),\n 'PublicIpAddress': lambda obj : try_or_else(lambda : obj['IpAddress'][0], ''),\n 'VpcAttributes': lambda obj : try_or_else(lambda : obj['PrivateIpAddress']['IpAddress'][0], ''),\n }\n return self.info_template(req, 'aliyun_meta_ecs_info', nested_handler=nested_handler)\n\n def rds_info(self) -> GaugeMetricFamily:\n req = DescribeRDS.DescribeDBInstancesRequest()\n return self.info_template(req, 'aliyun_meta_rds_info', to_list=lambda data: data['Items']['DBInstance'])\n\n def redis_info(self) -> GaugeMetricFamily:\n req = DescribeRedis.DescribeInstancesRequest()\n return self.info_template(req, 'aliyun_meta_redis_info', to_list=lambda data: data['Instances']['KVStoreInstance'])\n\n def slb_info(self) -> GaugeMetricFamily:\n req = DescribeSLB.DescribeLoadBalancersRequest()\n gauge = self.info_template(req, 'aliyun_meta_slb_info', to_list=lambda data: data['LoadBalancers']['LoadBalancer'])\n gauge_slb_info = None\n for s in gauge.samples:\n slb_id = s.labels['LoadBalancerId']\n req_slb_attr = DescribeSLBAttr.DescribeLoadBalancerAttributeRequest()\n req_slb_attr.set_LoadBalancerId(slb_id)\n slb_attrs_resp = self.client.do_action_with_exception(req_slb_attr)\n slb_attrs_info = json.loads(slb_attrs_resp)\n for protocol_info in slb_attrs_info['ListenerPortsAndProtocol']['ListenerPortAndProtocol']:\n protocol = protocol_info['ListenerProtocol']\n port = protocol_info['ListenerPort']\n req_slb_proto = None\n if protocol == 'tcp':\n req_slb_proto = DescribeSLBTcpAttr.DescribeLoadBalancerTCPListenerAttributeRequest()\n elif protocol == 'http':\n req_slb_proto = DescribeSLBHttpAttr.DescribeLoadBalancerHTTPListenerAttributeRequest()\n elif protocol == 'https':\n req_slb_proto = DescribeSLBHttpsAttr.DescribeLoadBalancerHTTPSListenerAttributeRequest()\n req_slb_proto.set_LoadBalancerId(slb_id)\n req_slb_proto.set_ListenerPort(int(port))\n slb_protocol_resp = self.client.do_action_with_exception(req_slb_proto)\n slb_protocol_info: dict = json.loads(slb_protocol_resp)\n if 'ForwardCode' in slb_protocol_info.keys():\n continue\n Bandwidth = slb_protocol_info['Bandwidth']\n if gauge_slb_info is None:\n gauge_slb_info = GaugeMetricFamily('aliyun_meta_slb_proto_bandwidth', 'protocolBandwidth', labels=['instanceId', 'ListenerProtocol', 'ListenerPort'])\n gauge_slb_info.add_metric([slb_id, protocol, str(port)], value=float(Bandwidth))\n return gauge_slb_info\n\n def mongodb_info(self) -> GaugeMetricFamily:\n req = Mongodb.DescribeDBInstancesRequest()\n return self.info_template(req, 'aliyun_meta_mongodb_info', to_list=lambda data: data['DBInstances']['DBInstance'])\n\n def cdn_info(self) -> GaugeMetricFamily:\n req = DescribeCDN.DescribeUserDomainsRequest()\n req.set_DomainStatus('online')\n nested_handler = {\n 'DomainName': lambda obj: try_or_else(lambda: obj['DomainName'], ''),\n }\n return self.info_template(req, 'aliyun_meta_cdn_info', to_list=lambda data: data['Domains']['PageData'])\n\n '''\n Template method to retrieve resource information and transform to metric.\n '''\n def info_template(self,\n req,\n name,\n desc='',\n page_size=100,\n page_num=1,\n nested_handler=None,\n to_list=(lambda data: data['Instances']['Instance'])) -> GaugeMetricFamily:\n gauge = None\n label_keys = None\n for instance in self.pager_generator(req, page_size, page_num, to_list):\n if gauge is None:\n label_keys = self.label_keys(instance, nested_handler)\n gauge = GaugeMetricFamily(name, desc, labels=label_keys)\n gauge.add_metric(labels=self.label_values(instance, label_keys, nested_handler), value=1.0)\n return gauge\n\n def info_template_bytime(self,\n req,\n name,\n desc='',\n label_keys=None,\n nested_handler=None,\n to_value=(lambda data: data['Instances']['Instance'])) -> GaugeMetricFamily:\n\n value = self.generator_by_time(req, to_value)\n gauge = GaugeMetricFamily(name, desc, labels=label_keys)\n gauge.add_metric(labels=[value], value=1.0)\n return gauge\n\n def pager_generator(self, req, page_size, page_num, to_list):\n req.set_PageSize(page_size)\n while True:\n req.set_PageNumber(page_num)\n resp = self.client.do_action_with_exception(req)\n data = json.loads(resp)\n instances = to_list(data)\n for instance in instances:\n if 'test' not in instance.get('DomainName'):\n yield instance\n if len(instances) < page_size:\n break\n page_num += 1\n\n def generator_by_time(self, req, to_value):\n now = time.time() - 60\n start_time = datetime.datetime.utcfromtimestamp(now-120).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n end_time = datetime.datetime.utcfromtimestamp(now).strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n req.set_accept_format('json')\n req.set_StartTime(start_time)\n req.set_EndTime(end_time)\n resp = self.client.do_action_with_exception(req)\n value = to_value(resp)\n return value\n\n def label_keys(self, instance, nested_handler=None):\n if nested_handler is None:\n nested_handler = {}\n return [k for k, v in instance.items()\n if k in nested_handler or isinstance(v, str) or isinstance(v, int)]\n\n def label_values(self, instance, label_keys, nested_handler=None):\n if nested_handler is None:\n nested_handler = {}\n return map(lambda k: str(nested_handler[k](instance[k])) if k in nested_handler else try_or_else(lambda: str(instance[k]), ''),\n label_keys)\n\n\n","sub_path":"aliyun_exporter/info_provider.py","file_name":"info_provider.py","file_ext":"py","file_size_in_byte":8697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"482971173","text":"class tale:\n def __init__(self):\n self.head = None\n self.bottom = None\n self.len = 0\n\n def add(self,val):\n new_node = node(val)\n if self.isEmpty():\n self.head = new_node\n else:\n new_node.next = self.bottom\n \n self.bottom = new_node\n self.len += 1\n\n return self\n \n def remove(self):\n if self.len == 1:\n self.head = None\n self.bottom = None\n self.len = 0\n return self\n head = self.head\n actual = self.bottom\n while actual.next != head: \n actual = actual.next \n \n actual.next = None\n self.head = actual\n self.len -= 1\n\n\n def isEmpty(self):\n if self.len == 0:\n return True\n else:\n return False\n def print_values(self):\n actual = self.bottom \n while actual != None: \n print(actual.value)\n actual = actual.next \n\n return self\n\nclass node:\n def __init__(self,val):\n self.value = val\n self.next = None\n\ncola = tale()\ncola.add(1)\ncola.add(2)\ncola.add(3)\ncola.add(4)\ncola.print_values()\nprint(\"el largo:\",cola.len)\nprint(\"---\")\ncola.remove()\ncola.print_values()\nprint(\"el largo:\",cola.len)\nprint(\"---\")\ncola.remove()\ncola.print_values()\nprint(\"el largo:\",cola.len)\nprint(\"---\")\ncola.remove()\ncola.print_values()\nprint(\"el largo:\",cola.len)\nprint(\"---\")\ncola.remove()\ncola.print_values()\nprint(\"el largo:\",cola.len)\n","sub_path":"C04 - Estructuras de Datos/cola.py","file_name":"cola.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"170354329","text":"from datetime import datetime\n\nfrom black_mombo.BMcalendar import BMcalendar\nfrom black_mombo.CalendarEvent import CalendarEvent\nfrom black_mombo.Course import Course\n\n\nprint(\"test CalendarEvent\\n===================\")\n\nelist = []\n\nfor i in range(1, 4):\n start = datetime(2015, 12, i, hour=10, minute=00)\n end = datetime(2015, 12, i, hour=10, minute=30)\n elist.append(CalendarEvent(\"event{}\".format(i),\n \"description{}\".format(i),\n start,\n end,\n \"location{}\".format(i),\n \"hw\"))\n\nprint(type(elist))\n\ndel elist[0]\n\nprint(elist[0])\n\nprint(str(elist[0]))\n\nevent = CalendarEvent(\"title\",\n \"desc\",\n datetime(2015, 12, 29),\n datetime(2015, 12, 29),\n \"location\",\n \"etype\"\n )\n\nprint(event.start)\n\nevent.start = datetime(2015, 12, 30)\n\nprint(event.start)\n\ndel event\n\ntry:\n print(event)\nexcept NameError:\n print(\"event was deleted and is not defined\")\n\nprint(\"===================\\n\")\n","sub_path":"tests/test_BMcalendarEvent.py","file_name":"test_BMcalendarEvent.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"645368689","text":"# 무한 루프를 끝내는 ctl+c 키를 무력화시키는 예외처리\n\nnum1 = input(\"숫자 1 입력 : \")\nnum2 = input(\"숫자 2 입력 : \")\n\ntry:\n num1 = int(num1)\n num2 = int(num2)\n res = num1/num2\n print(res)\nexcept ValueError as ex : # 추후 프로그밍 시 exept 형태로 Error들이 로그파일에 저장되도록 처리 가능 \n print(\"ValueError\",ex)\nexcept ZeroDivisionError as ex : \n print(\"ZerodivisionError\", ex)\n\n","sub_path":"st01.Python기초/py09예외문/py09_07_ZeroDivisionError.py","file_name":"py09_07_ZeroDivisionError.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"250907804","text":"# Copyright 2020 Google LLC\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# https://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\"\"\"An implemementation of the Generator and Discriminator for the\nMIDI Conditional SpecGAN.\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import activations, layers\nfrom tensorflow import keras\nimport audio_synthesis.utils.layers as layer_utils\n\nclass Generator(keras.Model):\n \"\"\"Implementation of the SpecGAN Generator Function.\"\"\"\n\n def __init__(self, channels=1, activation=activations.linear, z_in_shape=(128, 1)):\n \"\"\"Initilizes the SpecGAN Generator function.\n\n Args:\n channels: The number of output channels.\n For example, for SpecGAN there is one\n output channel, and for SpecPhaseGAN there\n are two output channels.\n acitvation: Activation function applied to generation\n before being returned. Default is linear.\n in_shape: Transformed noise shape as input to the\n generator function.\n \"\"\"\n\n super(Generator, self).__init__()\n\n self.activation = activation\n\n # Pre-process the random noise input.\n z_pre_process = []\n z_pre_process.append(layers.Dense(np.prod(z_in_shape)))\n z_pre_process.append(layers.Reshape(z_in_shape))\n z_pre_process.append(layer_utils.Conv1DTranspose(\n filters=512, strides=4, kernel_size=8\n ))\n self.z_pre_process = keras.Sequential(z_pre_process)\n\n # Pre-processing stack for the conditioning information.\n c_pre_process_1x1 = []\n c_pre_process_1x1.append(layers.Conv1D(\n filters=512, strides=1, kernel_size=1, padding='same'\n ))\n self.c_pre_process_1x1 = keras.Sequential(c_pre_process_1x1)\n \n sequential = []\n sequential.append(layers.Conv2D(\n filters=512, kernel_size=(6, 6), strides=(2, 2), padding='same'\n ))\n sequential.append(layers.ReLU())\n sequential.append(layers.Conv2D(\n filters=256, kernel_size=(6, 6), strides=(2, 1), padding='same'\n ))\n sequential.append(layers.ReLU())\n sequential.append(layers.Conv2D(\n filters=128, kernel_size=(6, 6), strides=(1, 1), padding='same'\n ))\n sequential.append(layers.ReLU())\n sequential.append(layers.Conv2D(\n filters=64, kernel_size=(6, 6), strides=(1, 1), padding='same'\n ))\n sequential.append(layers.ReLU())\n sequential.append(layers.Conv2D(\n filters=channels, kernel_size=(6, 6), strides=(1, 1), padding='same'\n ))\n\n self.l = keras.Sequential(sequential)\n\n def call(self, z_in, c_in):\n \"\"\"Generates spectograms from input noise vectors.\n\n Args:\n z_in: A batch of random noise vectors. Expected shape\n is (batch_size, z_dim).\n c_in: A batch of midi conditioning. Expected shape is\n (batch_size, num_states, 89), where 89 represents the 88\n piano keys plus the sustain pedal. \n\n Returns:\n The output from the generator network. Same number of\n batch elements.\n \"\"\"\n \n z_pre_processed = self.z_pre_process(z_in)\n c_pre_processed = self.c_pre_process_1x1(c_in)\n z_pre_processed = tf.expand_dims(z_pre_processed, axis=-1)\n c_pre_processed = tf.expand_dims(c_pre_processed, axis=-1)\n zc_in = tf.concat([z_pre_processed, c_pre_processed], axis=-1)\n \n output = self.activation(self.l(zc_in))\n return output\n\nclass Discriminator(keras.Model):\n \"\"\"Implementation of the SpecGAN Discriminator Function.\"\"\"\n\n def __init__(self, input_shape, weighting=1.0):\n \"\"\"Initilizes the SpecGAN Discriminator function\n\n Args:\n input_shape: The required shape for inputs to the\n discriminator functions.\n weighting: The relative weighting of this discriminator in\n the overall loss.\n \"\"\"\n\n super(Discriminator, self).__init__()\n\n self.in_shape = input_shape\n self.weighting = weighting\n\n # Pre-processing stack for the conditioning information.\n c_pre_process = []\n c_pre_process.append(layers.Conv1D(\n 128, kernel_size=36, strides=2, padding='same'\n ))\n c_pre_process.append(layers.LeakyReLU(alpha=0.2))\n c_pre_process.append(layers.Conv1D(\n 256, kernel_size=36, strides=2, padding='same'\n ))\n c_pre_process.append(layers.LeakyReLU(alpha=0.2))\n self.c_pre_process = keras.Sequential(c_pre_process)\n\n sequential = []\n sequential.append(layers.Conv2D(\n filters=64, kernel_size=(6, 6), strides=(2, 2), padding='same'\n ))\n sequential.append(layers.LeakyReLU(alpha=0.2))\n sequential.append(layers.Conv2D(\n filters=128, kernel_size=(6, 6), strides=(2, 2), padding='same'\n ))\n sequential.append(layers.LeakyReLU(alpha=0.2))\n sequential.append(layers.Conv2D(\n filters=256, kernel_size=(6, 6), strides=(2, 2), padding='same'\n ))\n sequential.append(layers.LeakyReLU(alpha=0.2))\n sequential.append(layers.Conv2D(\n filters=512, kernel_size=(6, 6), strides=(2, 2), padding='same'\n ))\n sequential.append(layers.LeakyReLU(alpha=0.2))\n sequential.append(layers.Conv2D(\n filters=512, kernel_size=(6, 6), strides=(2, 2), padding='same'\n ))\n #sequential.append(layers.Conv2D(\n # filters=3, kernel_size=(6, 6), strides=(1, 1), padding='same'\n #))\n sequential.append(layers.LeakyReLU(alpha=0.2))\n sequential.append(layers.Flatten())\n sequential.append(layers.Dense(1))\n\n self.l = keras.Sequential(sequential)\n\n def call(self, x_in, c_in):\n \"\"\"Produces discriminator scores for the inputs.\n\n Args:\n x_in: A batch of input data. Expected shape\n is expected to be consistant with self.in_shape.\n c_in: A batch of midi conditioning. Expected shape is\n (batch_size, num_states, 89), where 89 represents the 88\n piano keys plus the sustain pedal. \n\n Returns:\n A batch of real valued scores. This is inlign with\n the WGAN setup.\n \"\"\"\n\n c_pre_processed = self.c_pre_process(c_in)\n c_pre_processed = tf.expand_dims(c_pre_processed, axis=-1)\n x_in = tf.reshape(x_in, self.in_shape)\n\n xc_in = tf.concat([c_pre_processed, x_in], axis=-1)\n\n return self.l(xc_in)\n","sub_path":"structures/midi_conditional_spec_gan.py","file_name":"midi_conditional_spec_gan.py","file_ext":"py","file_size_in_byte":7154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"2669529","text":"import argparse\nimport pathlib\nimport sys\n\nfrom unimport import __version__, __description__\nfrom unimport.session import Session\n\nparser = argparse.ArgumentParser(\n description=__description__\n)\nexclusive_group = parser.add_mutually_exclusive_group(required=False)\nparser.add_argument(\n \"sources\",\n default=\".\",\n nargs=\"*\",\n help=\"files and folders to find the unused imports.\",\n type=pathlib.Path,\n)\nparser.add_argument(\n \"-c\",\n \"--config\",\n default=\".\",\n help=\"read configuration from PATH.\",\n metavar=\"PATH\",\n type=pathlib.Path,\n)\nparser.add_argument(\n \"-d\",\n \"--diff\",\n action=\"store_true\",\n help=\"Prints a diff of all the changes unimport would make to a file.\",\n)\nexclusive_group.add_argument(\n \"-r\",\n \"--remove\",\n action=\"store_true\",\n help=\"remove unused imports automatically.\",\n)\nexclusive_group.add_argument(\n \"-p\",\n \"--permission\",\n action=\"store_true\",\n help=\"Refactor permission after see diff.\",\n)\nparser.add_argument(\n \"--check\",\n action=\"store_true\",\n help=\"Prints which file the unused imports are in.\",\n)\nparser.add_argument(\n \"-v\",\n \"--version\",\n action=\"version\",\n version=f\"Unimport {__version__}\",\n help=\"Prints version of unimport\",\n)\n\n\ndef print_if_exists(sequence):\n if sequence:\n print(*sequence, sep=\"\\n\")\n return True\n\n\ndef main(argv=None):\n namespace = parser.parse_args(argv)\n any_namespace = any([value for key, value in vars(namespace).items()][2:])\n if namespace.permission and not namespace.diff:\n namespace.diff = True\n session = Session(config_file=namespace.config)\n for source_path in namespace.sources:\n for py_path in session._list_paths(source_path, \"**/*.py\"):\n if not any_namespace or namespace.check:\n session.scanner.run_visit(source=session._read(py_path)[0])\n for imp in session.scanner.get_unused_imports():\n if imp[\"star\"]:\n modules = f\"Used object; {imp['modules']}, \"\n else:\n modules = \"\"\n print(\n f\"\\033[93m{imp['name']}\\033[00m at \"\n f\"\\033[92m{str(py_path)}:{imp['lineno']}\\033[00m\"\n f\" {modules}\"\n )\n\n session.scanner.clear()\n if namespace.diff:\n exists_diff = print_if_exists(\n tuple(session.diff_file(py_path))\n )\n if namespace.permission and exists_diff:\n action = input(\n f\"Apply suggested changes to \\033[92m'{py_path}'\\033[00m [y/n/q] ? >\"\n )\n if action == \"q\":\n break\n elif action == \"y\":\n namespace.remove = True\n if namespace.remove:\n session.refactor_file(py_path, apply=True)\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","sub_path":"unimport/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":3031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"260860853","text":"# Copyright 2016 Tesora Inc.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom oslo_log import log as logging\n\nfrom trove.common import cfg\nfrom trove.common.exception import TroveError\nfrom trove.common.i18n import _\nfrom trove.guestagent.datastore.experimental.couchbase import (\n service as community_service\n)\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass Couchbase4App(community_service.CouchbaseApp):\n\n def build_admin(self):\n return Couchbase4Admin(self.get_cluster_admin())\n\n\nclass Couchbase4Admin(community_service.CouchbaseAdmin):\n\n # How much of the total cluster memory quota will be allocated to the\n # indexing service.\n INDEX_MEM_RATIO = 0.25\n\n def get_cluster_init_options(self, node_info, ramsize_quota_mb):\n init_options = super(Couchbase4Admin, self).get_cluster_init_options(\n node_info, ramsize_quota_mb)\n if node_info:\n services = node_info[0].get('services')\n # get all services\n service_lists = [node.get('services') for node in node_info]\n all_services = set(\n [item for subtypes in service_lists for item in subtypes])\n else:\n # Use datastore defaults if no node_info is provided\n # (i.e. during single instance provisioning).\n services = cfg.get_configuration_property('default_services')\n all_services = services\n\n data_quota_mb, index_quota_mb = self._compute_mem_allocations_mb(\n ramsize_quota_mb, all_services)\n init_options['cluster-ramsize'] = data_quota_mb\n init_options['cluster-index-ramsize'] = index_quota_mb\n if services:\n if isinstance(services, list):\n services = ','.join(services)\n init_options['service'] = services\n\n return init_options\n\n def _compute_mem_allocations_mb(self, ramsize_quota_mb, enabled_services):\n \"\"\"Couchbase 4.x and higher split the available memory quota between\n data and index services.\n If the indexing service is turned on the quota value must be at least\n 256MB.\n\n Compute the index quota as 25% of the total and use the rest for data\n services. Return '256' quota (the min) if the service is not enabled.\n \"\"\"\n if 'index' in enabled_services:\n index_quota_mb = max(int(self.INDEX_MEM_RATIO * ramsize_quota_mb),\n Couchbase4App.MIN_RAMSIZE_QUOTA_MB)\n else:\n index_quota_mb = Couchbase4App.MIN_RAMSIZE_QUOTA_MB\n\n data_quota_mb = ramsize_quota_mb - index_quota_mb\n\n if data_quota_mb < Couchbase4App.MIN_RAMSIZE_QUOTA_MB:\n required = Couchbase4App.MIN_RAMSIZE_QUOTA_MB - data_quota_mb\n raise TroveError(_(\"Not enough memory for Couchbase services. \"\n \"Additional %dMB is required.\") % required)\n\n return data_quota_mb, index_quota_mb\n\n def get_cluster_add_options(self, node_info):\n add_options = super(Couchbase4Admin, self).get_cluster_add_options(\n node_info)\n for index, node in enumerate(node_info):\n services = node.get('services')\n if services:\n options = add_options[index]\n if isinstance(services, list):\n services = ','.join(services)\n options['services'] = services\n\n return add_options\n","sub_path":"trove/guestagent/datastore/experimental/couchbase_4/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":3973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"529664693","text":"from Constants import PieceType, bcolors\nfrom Node import Node\nimport random\nfrom itertools import chain\nimport pickle\nimport numpy as np\nimport keras\n\nclass TicTacToeLib:\n\n def __init__(self):\n self.neural_net = keras.models.load_model('models/tictacModel2018825_18_1_83p')\n print(self.neural_net.summary())\n\n player = PieceType.Empty\n\n # \n def HumanVsHuman(self, player_piece):\n self.player = player_piece\n current_piece = self.player\n board = self.create_empty_board()\n self.print_board(board)\n\n col = 0\n row = 0\n\n while col != -1:\n print(\"{0}'s move\".format(current_piece))\n\n row = int(input('Enter the row you want to place a piece'))\n col = int(input('Enter the column you want to place a piece (-1 to quit)'))\n\n if col != -1:\n board[row][col] = current_piece\n\n self.print_board(board)\n\n if self.check_win(board):\n print('You won {0}!'.format(current_piece))\n return\n current_piece = self.invert_piece(current_piece)\n\n def HumanVsComputer(self, player_piece, depth):\n self.player = player_piece\n\n board = self.create_empty_board()\n self.print_board(board)\n col = 0\n row = 0\n\n while col != -1:\n row = int(input('Enter the row you want to place a piece'))\n col = int(input('Enter the col you want to place a piece (-1 to exit)'))\n\n board[row][col] = self.player\n\n self.print_board(board)\n\n if self.check_win(board):\n print('You won!')\n return\n\n if not self.board_has_moves(board):\n print('Board Full, restarting..')\n board = self.create_empty_board()\n self.print_board(board)\n continue\n\n board = self.Ai(board, self.invert_piece(self.player), depth)\n print('Ai Move:')\n self.print_board(board)\n\n if self.check_win(board):\n print('The PC won!')\n return\n\n if not self.board_has_moves(board):\n print('Board Full, restarting..')\n board = self.create_empty_board()\n self.print_board(board)\n continue\n\n def HumanVsAIComputer(self, player_piece):\n self.player = player_piece\n\n board = self.create_empty_board()\n self.print_board(board)\n col = 0\n row = 0\n\n while col != -1:\n row = int(input('Enter the row you want to place a piece'))\n col = int(input('Enter the col you want to place a piece (-1 to exit)'))\n\n board[row][col] = self.player\n\n self.print_board(board)\n\n if self.check_win(board):\n print('You won!')\n return\n\n if not self.board_has_moves(board):\n print('Board Full, restarting..')\n board = self.create_empty_board()\n self.print_board(board)\n continue\n\n board = self.neural_ai(board, self.invert_piece(self.player))\n print('Ai Move:')\n self.print_board(board)\n\n if self.check_win(board):\n print('The PC won!')\n return\n\n if not self.board_has_moves(board):\n print('Board Full, restarting..')\n board = self.create_empty_board()\n self.print_board(board)\n continue\n\n def ComputerVsComputer(self, player_piece, depth):\n self.player = player_piece\n\n board = self.create_empty_board()\n self.print_board(board)\n\n col = 0\n\n while col != -1:\n board = self.Ai(board, self.player, depth)\n print('Ai 1 Move:')\n self.print_board(board)\n\n if self.check_win(board):\n print('Ai 1 won!')\n return\n\n if not self.board_has_moves(board):\n print('Board Full, restarting..')\n board = self.create_empty_board()\n self.print_board(board)\n continue\n\n board = self.Ai(board, self.invert_piece(self.player), depth)\n print('Ai 2 Move:')\n self.print_board(board)\n\n if self.check_win(board):\n print('Ai 2won!')\n return\n\n if not self.board_has_moves(board):\n print('Board Full, restarting..')\n board = self.create_empty_board()\n self.print_board(board)\n continue\n\n def ComputerVsComputer_Save(self, player_piece, depth):\n self.player = player_piece\n\n printing = False\n\n board = self.create_empty_board()\n if printing:\n self.print_board(board)\n\n col = 0\n uniques = set()\n all = list()\n\n while len(uniques) < 10000:\n initial_board = board\n\n board = self.Ai(board, self.player, depth)\n if printing:\n print('Ai 1 Move:')\n self.print_board(board)\n\n # save some processing cycles by only computing once\n fib = self.flatten_board(initial_board)\n fb = self.flatten_board(board)\n\n uniques.add((fib, fb))\n all.append((fib, fb))\n\n if self.check_win(board):\n print('Ai 1 won!')\n board = self.create_empty_board()\n # return uniques, all\n continue\n\n if not self.board_has_moves(board):\n board = self.create_empty_board()\n if printing:\n self.print_board(board)\n print('Board Full, restarting..')\n continue\n initial_board = board\n board = self.Ai(board, self.invert_piece(self.player), depth)\n if printing:\n print('Ai 2 Move:')\n self.print_board(board)\n\n # want to have all training data representing the same \"start player\"\n # get 2x data by using ai1 and ai2 inverted to be like ai1\n # save some processing cycles by only computing once\n fib = self.sanitize_board(self.flatten_board(initial_board))\n fb = self.sanitize_board(self.flatten_board(board))\n uniques.add((fib, fb))\n all.append((fib, fb))\n\n if self.check_win(board):\n print('Ai 2 won!')\n board = self.create_empty_board()\n # return uniques, all\n continue\n\n if not self.board_has_moves(board):\n board = self.create_empty_board()\n if printing:\n self.print_board(board)\n print('Board Full, restarting..')\n continue\n if len(uniques) % 10 is 0:\n print(\"Unique len: {0}\".format(len(uniques)))\n print(\"All len: {0}\".format(len(all)))\n if len(uniques) % 100 is 0:\n self.save_data(uniques, 'uniques{0}.pkl'.format(len(uniques)))\n self.save_data(all, 'all{0}.pkl'.format(len(all)))\n\n def ComputerVsDumbComputer_Save(self, player_piece, depth):\n self.player = player_piece\n\n printing = False\n\n board = self.create_empty_board()\n if printing:\n self.print_board(board)\n\n uniques = set()\n all = list()\n\n steps = 0\n\n while True:\n steps += 1\n board, ai_states = self.Ai_gen_states(board, self.player, depth)\n if printing:\n print('Ai 1 Move:')\n self.print_board(board)\n\n for state in ai_states:\n uniques.add((state[0], state[1]))\n all.append((state[0], state[1]))\n\n if self.check_win(board):\n print('Ai 1 won!')\n board = self.create_empty_board()\n continue\n\n if not self.board_has_moves(board):\n board = self.create_empty_board()\n if printing:\n self.print_board(board)\n print('Board Full, restarting..')\n continue\n\n board = self.RandomAi(board, self.invert_piece(self.player), depth)\n if printing:\n print('Ai 2 Move:')\n self.print_board(board)\n\n if self.check_win(board):\n print('Ai 2 won!')\n board = self.create_empty_board()\n # return uniques, all\n continue\n\n if not self.board_has_moves(board):\n board = self.create_empty_board()\n if printing:\n self.print_board(board)\n print('Board Full, restarting..')\n continue\n if steps % 4 is 0:\n print(\"Unique len: {0}\".format(len(uniques)))\n print(\"All len: {0}\".format(len(all)))\n print(\"Steps: \" + str(steps))\n if steps > 100:\n print(\"Board saved.\")\n self.save_data(uniques, 'Pickles/full_state_choice_uniques{0}.pkl'.format(len(uniques)))\n self.save_data(all, 'Pickles/full_state_choice_all{0}.pkl'.format(len(all)))\n steps = 0\n\n # \n\n def invert_piece(self, piece):\n return PieceType.X if (piece == PieceType.O) else PieceType.O\n\n def save_data(self, item, filename):\n output = open(filename, 'wb')\n pickle.dump(item, output)\n output.close()\n\n # \n def check_win(self, board):\n top = (board[0][0] == board[0][1]) and (board[0][1] == board[0][2]) and board[0][2] != PieceType.Empty\n middle = board[1][0] == board[1][1] and board[1][1] == board[1][2] and board[1][2] != PieceType.Empty\n bottom = board[2][0] == board[2][1] and board[2][1] == board[2][2] and board[2][2] != PieceType.Empty\n\n left = board[0][0] == board[1][0] and board[1][0] == board[2][0] and board[2][0] != PieceType.Empty\n center = board[0][1] == board[1][1] and board[1][1] == board[2][1] and board[2][1] != PieceType.Empty\n right = board[0][2] == board[1][2] and board[1][2] == board[2][2] and board[2][2] != PieceType.Empty\n\n TLBRDiag = board[0][0] == board[1][1] and board[1][1] == board[2][2] and board[2][2] != PieceType.Empty\n BLTRDiag = board[2][0] == board[1][1] and board[1][1] == board[0][2] and board[0][2] != PieceType.Empty\n\n return top or middle or bottom or left or center or right or TLBRDiag or BLTRDiag\n\n def print_board(self, board):\n for r in range(len(board)):\n line_string = ''\n for c in range(len(board[r])):\n output = ' '\n if board[r][c] == PieceType.X:\n output = bcolors.WARNING + 'X' + bcolors.ENDC\n elif board[r][c] == PieceType.O:\n output = bcolors.OKBLUE + 'O' + bcolors.ENDC\n line_string += ' ' + output\n if c != len(board[r])-1:\n line_string += '|'\n print(line_string)\n if r != len(board) - 1:\n print('-'*7)\n\n def copy_board(self, board):\n out_board = []\n for row in range(len(board)):\n out_board.append([])\n for pos in range(len(board[row])):\n out_board[row].append(board[row][pos])\n return out_board\n\n def create_empty_board(self):\n return [[PieceType.Empty for x in range(3)] for x in range(3)]\n\n def get_available_moves(self, board):\n out_board = []\n for row in range(len(board)):\n for pos in range(len(board[row])):\n if board[row][pos] == PieceType.Empty:\n out_board.append((row, pos))\n return out_board\n\n def board_has_moves(self, board):\n for row in board:\n for piece in row:\n if piece == PieceType.Empty:\n return True\n return False\n\n def flatten_board(self, board):\n flat = tuple(chain.from_iterable(board))\n sanitized = []\n for elem in flat:\n if elem is PieceType.X:\n sanitized.append(1)\n elif elem is PieceType.O:\n sanitized.append(-1)\n else:\n sanitized.append(0)\n return tuple(sanitized)\n\n def sanitize_board(self, board):\n return tuple(-x if x is not 0 else x for x in board)\n # \n\n # \n def grade_state(self, node, piece, depth):\n # if a win\n if self.check_win(node.board):\n # if the updated piece was the current players piece, it is a win\n if node.updated_piece == piece:\n return 10\n # if the updated piece was the other players piece, it is a loss\n return -10\n # its a draw\n return 0\n\n def generate_move_tree(self, board, piece, depth):\n\n outNode = Node(board, piece)\n\n if depth<0 or self.check_win(board):\n return outNode\n\n available_moves = self.get_available_moves(board)\n\n for r,c in available_moves:\n new_board = self.copy_board(board)\n op_piece = self.invert_piece(piece)\n new_board[r][c] = op_piece\n\n node = self.generate_move_tree(new_board, op_piece, depth-1)\n outNode.children.append(node)\n return outNode\n\n def alpha_beta_minmax(self, node, current_piece, maximizing_player, depthScore, alpha = -10000, beta = 10000):\n if len(node.children) == 0:\n return self.grade_state(node, current_piece, depthScore)\n if maximizing_player:\n best_value = -10000\n for child in node.children:\n grade = self.alpha_beta_minmax(child, current_piece, False, depthScore - 1, alpha, beta)\n best_value = max(best_value, grade)\n alpha = max(alpha, best_value)\n if beta <= alpha:\n break\n return best_value\n else:\n best_value = 10000\n for child in node.children:\n grade = self.alpha_beta_minmax(child, current_piece, True, depthScore - 1, alpha, beta)\n best_value = min(best_value, grade)\n beta = min(beta, best_value)\n if beta <= alpha:\n break\n return best_value\n\n def Ai(self, board, piece, depth):\n tree = self.generate_move_tree(board, self.invert_piece(piece), depth)\n for child in tree.children:\n child.value = self.alpha_beta_minmax(child, piece, False, depth)\n\n max_score = -10000\n index = -1\n # get the overall max\n for i in range(len(tree.children)):\n if tree.children[i].value > max_score:\n index = i\n max_score = tree.children[i].value\n indices = []\n # get all states that are of equal opportunity to the best\n for i in range(len(tree.children)):\n if tree.children[i].value >= max_score:\n indices.append(i)\n\n return tree.children[random.choice(indices)].board\n\n def RandomAi(self, board, piece, depth):\n\n moves = self.get_available_moves(board)\n\n ind = random.choice(moves)\n\n b = self.copy_board(board)\n\n b[ind[0]][ind[1]] = piece\n\n return b\n\n def neural_ai(self, board, piece):\n moves = self.get_available_moves(board)\n\n flat_board = self.flatten_board(board)\n\n prob = 0.0\n new_board = []\n\n for ind in moves:\n t_board = self.copy_board(board)\n\n t_board[ind[0]][ind[1]] = piece\n\n inputs = flat_board + self.flatten_board(t_board)\n\n np_inputs = np.array([inputs])\n\n print(np_inputs.shape)\n\n output = self.neural_net.predict_proba(np_inputs)\n\n output = float(output)\n\n if output > prob:\n prob = output\n new_board = t_board\n\n return new_board\n\n def Ai_gen_states(self, board, piece, depth):\n tree = self.generate_move_tree(board, self.invert_piece(piece), depth)\n for child in tree.children:\n child.value = self.alpha_beta_minmax(child, piece, False, depth)\n\n max_score = -10000\n # get the overall max\n for i in range(len(tree.children)):\n if tree.children[i].value > max_score:\n max_score = tree.children[i].value\n good_indices = []\n bad_indices = []\n # get all states that are of equal opportunity to the best\n for i in range(len(tree.children)):\n if tree.children[i].value >= max_score:\n good_indices.append(i)\n else:\n bad_indices.append(i)\n\n outStates = []\n this_board = self.flatten_board(board)\n for i in good_indices:\n move_board = self.flatten_board(tree.children[i].board)\n\n outStates.append(\n (\n this_board + # start start\n move_board, # end state\n 1 # if the state was desirable or not\n )\n )\n for i in bad_indices:\n move_board = self.flatten_board(tree.children[i].board)\n\n outStates.append(\n (\n this_board + # start start\n move_board, # end state\n 0 # if the state was desirable or not\n )\n )\n\n # return tree.children[random.choice(indices)].board\n return tree.children[random.choice(good_indices)].board, outStates\n\n # \n\n","sub_path":"Minimax_Python/TicTacToeLib.py","file_name":"TicTacToeLib.py","file_ext":"py","file_size_in_byte":17990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"209014941","text":"import psycopg2 as pc\nimport prettytable\n\n\ndef basis_function(user_id, name, age, email):\n \"\"\"\n Функция по изменённому значению выбирает параметр для дальнейшей обработки\n \"\"\"\n selection_parameter = [None, None]\n if user_id is not None:\n selection_parameter[0] = 'id'\n selection_parameter[1] = user_id\n elif name is not None:\n selection_parameter[0] = 'name'\n selection_parameter[1] = name\n elif age is not None:\n selection_parameter[0] = 'age'\n selection_parameter[1] = age\n else:\n selection_parameter[0] = 'email'\n selection_parameter[1] = email\n return selection_parameter\n\n\n# main class\nclass Worker:\n\n def __init__(self, table_name='Worker'):\n self.table_name = table_name\n self.db_cursor = None\n self.db_connection = None\n\n def print(self, record_tracker=0):\n \"\"\"\n Метод печатает часть таблицы, на которую падает курсор\n \"\"\"\n count = 0\n description_list = [self.db_cursor.description[index][0] for index, _ in enumerate(self.db_cursor.description)]\n table = prettytable.PrettyTable(description_list)\n for row in self.db_cursor:\n table.add_row(row)\n count += 1\n print(table)\n if record_tracker == 1:\n print(f'{count} совпадений')\n print()\n\n def connection(self):\n # подключение к дб\n if self.db_connection is None:\n self.db_connection = pc.connect(database=\"python\",\n user=\"postgres\",\n password=\"69420A1X\")\n # курсор\n if self.db_cursor is None:\n self.db_cursor = self.db_connection.cursor()\n\n def create_table(self):\n \"\"\"\n Создание таблицы\n \"\"\"\n self.connection()\n self.db_cursor.execute(f'CREATE TABLE IF NOT EXISTS {self.table_name}' +\n '(id BIGSERIAL NOT NULL PRIMARY KEY,' +\n 'name VARCHAR(50) NOT NULL,' +\n 'age INT NOT NULL,' +\n 'email VARCHAR(50));')\n print(f'Таблица {self.table_name} готова к использованию')\n self.db_connection.commit()\n\n def insert(self, name, age, email=None):\n \"\"\"\n Вставка новой строки в таблицу с возможностью не писать поле email\n \"\"\"\n default_sense = [None, None]\n default_sense[0] = 'email, '\n if email is not None:\n default_sense[1] = email\n self.db_cursor.execute(f\"INSERT INTO {self.table_name} ({default_sense[0]}name, age) VALUES ('{default_sense[1]}', '{name}', {age})\")\n self.db_connection.commit()\n print('Вставка завершена')\n self.get_all()\n\n def update(self, object_to_change, name=None, age=None, email=None):\n \"\"\"\n Изменение полей по параметру object_to_change (Должно быть передано имя объекта, например: id/name/age/email)\n \"\"\"\n selection_parameter = basis_function(None, name, age, email)\n self.db_cursor.execute(f\"UPDATE {self.table_name} SET {selection_parameter[0]} = '{selection_parameter[1]}' WHERE \"\n f\"{object_to_change[0]} = '{object_to_change[1]}';\")\n self.db_connection.commit()\n print('Обноление завершено')\n self.get_all()\n\n def delete(self, user_id=None, name=None, age=None, email=None):\n \"\"\"\n Удаление строк по id/имени/возрасту/почтовому адресу\n \"\"\"\n try:\n selection_parameter = basis_function(user_id, name, age, email)\n self.db_cursor.execute(f\"DELETE FROM {self.table_name} WHERE \"\n f\"{selection_parameter[0]} = '{selection_parameter[1]}';\")\n self.db_connection.commit()\n print('Удаление завершено')\n self.get_all()\n except(Exception, pc.errors.InvalidTextRepresentation):\n print('Неправильный ввод')\n\n def get(self, user_id=None, name=None, age=None, email=None):\n \"\"\"\n Получение строк с входными параметрами id/имени/возраста/почтового адреса\n \"\"\"\n try:\n selection_parameter = basis_function(user_id, name, age, email)\n self.db_cursor.execute(f\"SELECT * FROM {self.table_name} WHERE \"\n f\"{selection_parameter[0]} = '{selection_parameter[1]}';\")\n self.print(record_tracker=1)\n except(Exception, pc.errors.InvalidTextRepresentation):\n print('Неправильный ввод')\n\n def get_all(self):\n \"\"\"\n Получение и вывод всех элементов таблицы\n \"\"\"\n self.db_cursor.execute(f'SELECT * FROM {self.table_name} ORDER BY id;')\n self.print()\n\n def drop(self):\n \"\"\"\n Удаление таблицы\n \"\"\"\n self.db_cursor.execute(f'DROP TABLE {self.table_name};')\n self.db_connection.commit()\n print('Таблица удалена')\n\n def close(self):\n \"\"\"\n Отключение от курсора и сервера\n \"\"\"\n if not self.db_cursor.closed:\n self.db_cursor.close()\n if not self.db_connection.closed:\n self.db_connection.close()\n\n def __del__(self):\n self.close()\n print('\\n/////////////////////////////\\n')\n print('Курсор закрыт')\n print('Соединение завершено')\n\n\na = Worker()\na.create_table()\na.get_all()\na.insert(name='Armen', age=20)\na.insert(name='Maria', age=19, email='qwerty@mail.ru')\na.insert(name='Oleg', age=21)\na.insert(name='Dmitriy', age=23, email='dmitriy99@yandex.ru')\na.insert(name='Olga', age=18)\na.insert(name='Artem', age=25)\na.insert(name='Olga', age=19)\na.get(age=19)\na.update(['email', None], email='emailisnone')\na.update(['id', 5], age=19)\na.drop()\n","sub_path":"ORM_postgresql.py","file_name":"ORM_postgresql.py","file_ext":"py","file_size_in_byte":6411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"323131763","text":"import pygame\nfrom ball import Ball\n# from paddle import paddle\nfrom random import randint\nfrom collections import namedtuple\n\ndef main():\n pygame.init()\n pygame.display.set_caption(\"My pong\")\n\n WIDTH = 800\n HEIGHT = 400\n BORDER = 15\n VELOCITY = 5\n FPS = 30\n\n MyConstants = namedtuple(\"MyConstants\",[\"WIDTH\",\"HEIGHT\",\"BORDER\",\"VELOCITY\",\"FPS\"])\n CONSTS = MyConstants(WIDTH,HEIGHT,BORDER,VELOCITY,FPS)\n\n # surface\n screen = pygame.display.set_mode((WIDTH, HEIGHT))\n # add a solid background as r,g,b:\n screen.fill((0, 0, 0))\n # double buffering: stage updates together; update them at once.\n # avoids flickering.\n pygame.display.update()\n\n bgcolor = pygame.Color(\"Black\")\n ballcolor = pygame.Color(\"yellow\")\n\n # Walls\n # Rect(surface, color, rect) -> Rect\n wcolor = pygame.Color(\"white\")\n\n\n\n # top wall\n pygame.draw.rect(screen, wcolor, pygame.Rect((0, 0), (WIDTH, BORDER)))\n # left wall\n pygame.draw.rect(screen, wcolor, pygame.Rect((0, 0), (BORDER, HEIGHT)))\n # bottom wall\n pygame.draw.rect(screen, wcolor, pygame.Rect((0, HEIGHT - BORDER), (WIDTH, BORDER)))\n\n ## Ball init\n x0 = WIDTH - Ball.radius\n y0 = HEIGHT // 2\n vx0 = -VELOCITY\n vy0 = randint(-VELOCITY,VELOCITY)\n b0 = Ball(x0, y0, vx0, vy0, screen, ballcolor, bgcolor,CONSTS)\n\n b0.show(ballcolor)\n pygame.display.update()\n # define a variable to control the main loop\n running = True\n clock = pygame.time.Clock()\n # main loop\n while running:\n # event handling, gets all event from the event queue\n for event in pygame.event.get():\n # only do something if the event is of type QUIT\n if event.type == pygame.QUIT:\n # change the value to False, to exit the main loop\n running = False\n pygame.display.update()\n clock.tick(FPS)\n # Ball\n b0.update()\n\n\nif __name__ == \"__main__\":\n # call the main function\n main()\n","sub_path":"pong/pong-main-class.py","file_name":"pong-main-class.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"161622538","text":"from .node import Node\n\n\nclass Queue:\n \"\"\"class for Queue\"\"\"\n def __init__(self, iter=[]):\n self.front = None\n self.back = None\n self._len = 0\n\n for item in iter:\n self.enqueue(item)\n \n def __len__(self):\n \"\"\"return len of the corrent object\"\"\"\n return self._len\n \n def __str__(self):\n \"\"\"return items in queue\"\"\"\n st = \"\"\n current = self.front\n while current:\n st += str(current.val) + \" \"\n current = current._next\n return st.rstrip()\n \n def enqueue(self, val):\n \"\"\"add new iten to the queue\"\"\"\n node = Node(val)\n if len(self) == 0:\n self.front = node\n self.back = node\n else:\n self.back = node\n self._len += 1\n\n def dequeue(self):\n \"\"\"remove item from the front\"\"\"\n if len(self) == 0:\n return False\n else:\n current = self.front\n self.front = current._next\n self._len -= 1\n return current\n","sub_path":"data_structures/queue/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"10738056","text":"#from definitions import *\n\n#regular code directory setup:\nimport sys, os, os.path\ncwd = os.getcwd()\nmain_dirc = cwd.split('code', 1)[0]\ncwd_code = main_dirc + 'code'\nsys.path.insert(0, cwd_code+'/prelims')\nfrom save_funcs import *\n\nsys.path.insert(0, cwd_code+'/inundation_aeration_analysis')\nfrom inundation_aeration_period_definitions import *\n\ndef plot_basic(X,Y,deviations_predictor=None,dev_fit_type=btf.func_exp,\n fit_type=btf.func_linear,num_of_outliers=30):\n \n fig, ax = plt.subplots(ncols=1,nrows=1)\n stable_fdic,stable_mask,outs_removed = find_stable_slope(X=X,Y=Y,deviations_predictor=deviations_predictor,\n dev_fit_type=dev_fit_type, fit_type=fit_type,num_of_outliers=num_of_outliers)\n Xsil,Ysil = notation_fix(X),notation_fix(Y)\n xSTAB, yexpSTAB = btf.array_span(Xsil,stable_fdic['function'],specify_points=20)\n if deviations_predictor!=None:\n dp = deviations_predictor\n typee = 'outlier removal, '+list_to_name(dp)+' vs. '+list_to_name(Y)\n Y='devs'\n fd = v['mask'][typee]\n if deviations_predictor!=None:\n Y='devs'\n Xv = np.ma.masked_array(Xsil,mask=stable_mask)\n Yv = np.ma.masked_array(Ysil,mask=stable_mask)\n func_dic, toss1, toss2 = general_fit_pre(X,Y,fit_type=fit_type)\n xs, yexp = btf.array_span(Xsil,func_dic['function'],specify_points=20)\n plt.scatter(Xsil,Ysil,c='r',edgecolor='r')\n plt.scatter(Xv,Yv,c='y')\n plt.plot(xSTAB,yexpSTAB,'y--',label=stable_fdic['print function']+'\\nstable at '+str(outs_removed)+' outliers removed')\n plt.plot(xs,yexp,'r--',label=func_dic['print function'])\n plt.legend(loc=2,ncol=1, fancybox=True,prop={'size':10})\n plt.tight_layout()\n plt.show()\n\n\n#plot_basic(X='NWT',Y='NCH4_S1',deviations_predictor='NTs10')\n##plot_basic(X='NWT',Y=p2)\n#plt.show()\n\ndef plot_outliers_vslope(X,Y,deviations_predictor=None,dev_fit_type=btf.func_exp,\n fit_type=btf.func_linear,num_of_outliers=60):\n \n cols,rows = 1,2\n fig, ax = plt.subplots(ncols=cols,nrows=rows, sharex=True, sharey=True, figsize=(15,8.3))\n \n stable_fdic,stable_mask,outs_removed = find_stable_slope(X=X,Y=Y,deviations_predictor=deviations_predictor,\n dev_fit_type=dev_fit_type, fit_type=fit_type,num_of_outliers=num_of_outliers)\n #if deviations_predictor!=None:\n #Y='devs'\n Xsil,Ysil = notation_fix(X),notation_fix(Y)\n xSTAB, yexpSTAB = btf.array_span(Xsil,stable_fdic['function'],specify_points=20)\n if deviations_predictor!=None:\n dp = deviations_predictor\n typee = 'outlier removal, '+list_to_name(dp)+' vs. '+list_to_name(Y)\n out_X, out_Y = dp,Y\n Y='devs'\n else:\n outlier_loop(X=X,Y=Y,fit_type=fit_type,num_of_outliers=num_of_outliers)\n typee = 'outlier removal, '+list_to_name(X)+' vs. '+list_to_name(Y)\n out_X, out_Y = X,Y\n fd = v['mask'][typee]\n Ysil = notation_fix(Y)\n \n #plot graphs with model and stable model:\n plt.subplot(cols,rows,1)\n Xv = np.ma.masked_array(Xsil,mask=stable_mask)\n Yv = np.ma.masked_array(Ysil,mask=stable_mask)\n func_dic, toss1, toss2 = general_fit_pre(X,Y,fit_type=fit_type)\n xs, yexp = btf.array_span(Xsil,func_dic['function'],specify_points=20)\n plt.scatter(Xsil,Ysil,c='r',edgecolor='r')\n plt.scatter(Xv,Yv,c='y')\n plt.plot(xSTAB,yexpSTAB,'y--',label=stable_fdic['print function']+'\\nstable at '+str(outs_removed)+' outliers removed')\n plt.plot(xs,yexp,'r--',label=func_dic['print function'])\n plt.legend(loc=2,ncol=1, fancybox=True,prop={'size':10})\n plt.xlabel(X)\n if deviations_predictor!=None:\n plt.ylabel('deviations of '+list_to_name(out_Y)+'(observed-expected)'+\n '\\nbased off '+' of '+list_to_name(out_X)+' vs. '+list_to_name(out_Y))\n else:\n plt.ylabel(list_to_name(Y))\n \n #plot outliers vs slope:\n plt.subplot(cols,rows,2)\n outs,slps = [],[]\n for ii in range(1,num_of_outliers+2):\n mask_ = fd[ii-1]['mask vector']\n sl_op = general_fit_pre(X,Y,fit_type=fit_type,mask=mask_,just_slope=1)\n outs.append(ii-1)\n slps.append(sl_op)\n plt.xlabel('outliers removed')\n plt.ylabel('slope')\n plt.plot(outs,slps)\n #plt.show()\n \n#plot_outliers(X='NWT',Y='devs',deviations_predictor='NTs10')\n\n#p2 = 'CH4_S1'\n##plot_outliers_vslope(X=['period of inundation',0],Y=p2,deviations_predictor='NTs10')\n#plot_outliers_vslope(X='WT',Y=p2,deviations_predictor='NTs10')\n##plot_outliers_vslope(X='NWT',Y=p2)\n#plt.show()\n\n","sub_path":"code/outlier_analysis.py","file_name":"outlier_analysis.py","file_ext":"py","file_size_in_byte":4477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"201047941","text":"from flask import Flask\nfrom flask import request\nfrom task import Task\nimport threading\n\napp = Flask(__name__)\n\n@app.route('/createTask', methods = ['GET'])\ndef createTask():\n guid = request.args.get('guid')\n platform = request.args.get('platform')\n checktype = request.args.get('checktype')\n filepath = request.args.get('filepath')\n\n task = Task(guid, platform, checktype, filepath)\n task.createBuilds()\n\n thread_ = threading.Thread(target=task.waitEndOfBuilds)\n thread_.start()\n return 'Task Started ' + guid + platform + checktype + filepath\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0')\n","sub_path":"cossack.py","file_name":"cossack.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"386182120","text":"import pandas as pd\r\nimport numpy as np\r\nimport math\r\n\r\ndata_table=pd.read_excel('Garden(12.24-26)评论数.xlsx')\r\n\r\ndata_array=np.array(data_table)\r\ndata_list=data_array.tolist()\r\n\r\ndata_dict={}\r\n\r\nfor row in data_list:\r\n\tif row[1] in data_dict.keys():\r\n\t\tdata_dict[row[1]]['category_rank'].append(row[2])\r\n\t\tdata_dict[row[1]]['total_rank'].append(row[13].split('(')[0].strip().split(' in ')[0])\r\n\t\ttry:\r\n\t\t\tdata_dict[row[1]]['total_category'].append(row[13].split('(')[0].strip().split(' in ')[1])\r\n\t\texcept:\r\n\t\t\tdata_dict[row[1]]['total_category'].append('None')\r\n\telse:\r\n\t\tdata_dict[row[1]]={'category_rank':[],\r\n\t\t\t\t\t\t\t'total_rank':[],\r\n\t\t\t\t\t\t\t'total_category':[]\r\n\t\t\t\t\t\t\t}\r\n\t\tdata_dict[row[1]]['category_rank'].append(row[2])\r\n\t\tdata_dict[row[1]]['total_rank'].append(row[13].split('(')[0].strip().split(' in ')[0])\r\n\t\ttry:\r\n\t\t\tdata_dict[row[1]]['total_category'].append(row[13].split('(')[0].strip().split(' in ')[1])\r\n\t\texcept:\r\n\t\t\tdata_dict[row[1]]['total_category'].append('None')\r\n#print(data_dict)\r\n\r\n#计算权重和权重和\r\nfor key in data_dict:\r\n\tdata_dict[key]['category_weight']=[]\r\n\tdata_dict[key]['sum_weight']=0\r\n\tfor i in range(len(data_dict[key]['category_rank'])):\r\n\t\tdata_dict[key]['category_weight'].append(5-math.log(data_dict[key]['category_rank'][i]))\r\n\t\tdata_dict[key]['sum_weight']+=5-math.log(data_dict[key]['category_rank'][i])\r\n\r\n#print(i for i in data_dict.values()[:100])\r\n\r\n#计算最多的大类\r\nfor key in data_dict:\r\n\tcate_dict={}\r\n\tfor cate in data_dict[key]['total_category']:\r\n\t\tif cate in cate_dict.keys():\r\n\t\t\tcate_dict[cate]+=1\r\n\t\telse:\r\n\t\t\tcate_dict[cate]=1\r\n\t# print(cate_dict)\r\n\t# print(sorted(cate_dict.items(),key=lambda cate_dict:cate_dict[1],reverse=True))\r\n\tdata_dict[key]['max_cate']=sorted(cate_dict.items(),key=lambda cate_dict:cate_dict[1],reverse=True)[0][0]\r\n\tdata_dict[key]['max_cate_num']=sorted(cate_dict.items(),key=lambda cate_dict:cate_dict[1],reverse=True)[0][1]\r\n\r\n#print(i for i in data_dict.values()[:100])\r\n\r\n#使用权重函数-ln(x)+5\r\ndef weightrank(key,i,j):\r\n\ttry:\r\n\t\ta=float(data_dict[key]['total_rank'][i].replace(',',''))*data_dict[key]['category_weight'][j]/data_dict[key]['sum_weight']\r\n\texcept:\r\n\t\tif i==0:\r\n\t\t\ta=0\r\n\t\telse:\r\n\t\t\ta=weightrank(key,i-1,j)\r\n\treturn a\r\n\r\n#计算品类加权平均排名\r\nfor key in data_dict:\r\n\tdata_dict[key]['weighted_rank']=0\r\n\r\n\tfor i in range(len(data_dict[key]['total_rank'])):\r\n\t\tj=i\r\n\t\ta=weightrank(key,i,j)\r\n\t\tdata_dict[key]['weighted_rank']+=a\r\n\r\n#print(data_dict)\r\n\r\noutput_data={'category':[],\r\n\t\t\t'max_cate':[],\r\n\t\t\t'max_cate_num':[],\r\n\t\t\t'weighted_rank':[]\t\r\n\t\t\t\t}\r\nfor key in data_dict:\r\n\toutput_data['category'].append(key)\r\n\toutput_data['max_cate'].append(data_dict[key]['max_cate'])\r\n\toutput_data['max_cate_num'].append(data_dict[key]['max_cate_num'])\r\n\toutput_data['weighted_rank'].append(data_dict[key]['weighted_rank'])\r\n\r\n#print(output_data)\r\n\r\noutput_dataframe=pd.DataFrame.from_dict(output_data)\r\n\r\noutput_dataframe.to_excel('Garden.xlsx')\r\nprint(output_dataframe)\r\n\r\n# data_dict={'category':[],\r\n# \t\t\t'category_rank':[],\r\n# \t\t\t'total_rank':[],\r\n# \t\t\t'total_category':[]\r\n# \t\t\t}\r\n\r\n# tem_list=[]\r\n# for row in data_list:\r\n# \ttem_list.append(row[13].split('(')[0].strip().split(' in '))\r\n\r\n# print(tem_list)\r\n\r\n# for j in tem_list:\r\n# \tdata_dict['total_rank'].append(j[0])\r\n# \ttry:\r\n# \t\tdata_dict['total_category'].append(j[1])\r\n# \texcept:\r\n# \t\tdata_dict['total_category'].append('None')\r\n\r\n# print(data_dict)","sub_path":"data_clean.py","file_name":"data_clean.py","file_ext":"py","file_size_in_byte":3445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"417012156","text":"\n__author__ = 'franzliem'\nimport os\nimport shutil\n\nimport numpy as np\nimport pandas as pd\nfrom nipype.pipeline.engine import Node, Workflow\nimport nipype.interfaces.utility as util\n\nfrom utils import get_condor_exit_status, check_if_wf_crashed, load_subjects_list\nfrom qc_reports.create_qc_report_pdf import create_qc_report_pdf\nfrom variables import ds_dir, report_base_dir, subjects_dir\nfrom variables import TR_list, full_subjects_list, subjects_file_prefix\nfrom variables import plugin_name, use_n_procs\n\n\n#fixme\nplugin_name = 'MultiProc'\nuse_n_procs = 25\n\n\nsubjects_list = full_subjects_list\nsubjects_missing_files_list = load_subjects_list(subjects_dir, subjects_file_prefix + '_excluded.txt')\n\n\nreport_str = 'rsfMRI_preprocessing'\n\n\nwf = Workflow('qc_reports_wf')\nwf.base_dir = report_base_dir\nwf.config['execution']['crashdump_dir'] = os.path.join(report_base_dir, 'crash')\nwf.config['execution']['stop_on_first_crash'] = False\n\n# collect dfs\ndf = pd.DataFrame()\n\n\nfor TR in TR_list:\n rel_report_dir = os.path.join(report_str + '_TR_%s'%TR)\n os.chdir(report_base_dir)\n if os.path.isdir(rel_report_dir):\n shutil.rmtree(rel_report_dir)\n os.mkdir(rel_report_dir)\n os.chdir(rel_report_dir)\n os.mkdir('reports')\n\n for subject_id in subjects_list:\n print(subject_id)\n df_ss_file = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/df', 'TR_%s'%TR, 'qc_values.pkl')\n #fixme\n if os.path.exists(df_ss_file):\n df_ss = pd.read_pickle(df_ss_file)\n else:\n header = ['subject_id', 'similarity_epi_struct', 'similarity_struct_MNI', 'mean_FD_Power', 'n_spikes',\n 'median_tsnr']\n data = np.hstack( (subject_id, np.repeat(np.nan, len(header)-1)))\n df_ss = pd.DataFrame([data], columns=header)\n df_ss = df_ss.set_index(df_ss.subject_id)\n\n\n # link to report pdf:\n rel_report_dir = os.path.join(report_str + '_TR_%s'%TR)\n subject_reports_dir = os.path.join(rel_report_dir, 'reports')\n report_file = os.path.join(subject_reports_dir, subject_id + '.pdf')\n df_ss['report_file'] = report_file\n\n # get condor exit status\n batch_dir =os.path.join(ds_dir, subject_id, 'condor', 'LeiCA_resting_preprocessing', 'batch')\n if os.path.exists(batch_dir):\n condor_exitcode, condor_n_jobs_failed = get_condor_exit_status(batch_dir)\n else:\n condor_exitcode = np.nan\n condor_n_jobs_failed = np.nan\n df_ss['condor_exitcode'] = condor_exitcode\n df_ss['condor_n_jobs_failed'] = condor_n_jobs_failed\n\n\n # check if wf has crashed\n crash_dir = os.path.join(ds_dir, subject_id, 'condor', 'LeiCA_resting_preprocessing', 'crash')\n wf_crashed = check_if_wf_crashed(crash_dir)\n df_ss['wf_crashed'] = wf_crashed\n\n ######\n\n df = pd.concat([df, df_ss])\n\n df.to_pickle('group.QC.pkl')\n df.to_csv('group.QC.csv', sep='\\t')\n df.to_excel('group.QC.xlsx')\n\n # CREAT FILE THAT CAN BE USED TO EDIT & MARK BAD QC SUBJECTS\n all_subjects_list = subjects_list + subjects_missing_files_list\n header = ['subject_excluded','reason', 'comment']\n n_good = len(subjects_list)\n n_bad = len(subjects_missing_files_list)\n subject_excluded = np.concatenate((np.zeros(n_good),np.ones(n_bad)))\n reason = ['']*n_good + ['files missing']*n_bad\n comment = ['']*n_good + ['automatically excluded before analysis']*n_bad\n data = {'subject_excluded':subject_excluded, 'reason':reason, 'comment':comment}\n df_to_edit = pd.DataFrame(data, columns=header, index=all_subjects_list)\n df_to_edit.to_excel('group.QC.to_edit.xlsx') #.csv', sep='\\t')\n\n\n\nfor TR in TR_list:\n print ('***********')\n print('TR: %s'%TR)\n print ('***********')\n rel_report_dir = os.path.join(report_str + '_TR_%s'%TR)\n subject_reports_dir = os.path.join(report_base_dir, rel_report_dir, 'reports')\n os.chdir(subject_reports_dir)\n\n for subject_id in subjects_list:\n print('%s generate report'%subject_id)\n file_dict = {}\n file_dict['report_file'] = os.path.join(subject_reports_dir, subject_id + '.pdf')\n file_dict['mean_epi'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/initial_mean_epi_moco/TR_%s/initial_mean_epi_moco.nii.gz'%TR)\n file_dict['brain_mask_epiSpace'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/masks/brain_mask_epiSpace/TR_%s/brain_mask_epiSpace.nii.gz'%TR)\n file_dict['csf_mask_epiSpace'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/masks/csf_mask_lat_ventr_epiSpace/TR_%s/csf_mask_epiSpace.nii.gz'%TR)\n file_dict['wm_mask_epiSpace'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/masks/wm_mask_epiSpace/TR_%s/wm_mask_epiSpace.nii.gz'%TR)\n file_dict['tsnr'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/tsnr/TR_%s/tsnr.nii.gz'%TR)\n file_dict['subject_tsnr_np_file'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/tsnr/TR_%s/tsnr.npy'%TR)\n file_dict['struct_brain_epiSpace'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/struct_brain_epiSpace/TR_%s/struct_brain_epiSpace.nii.gz'%TR)\n file_dict['slices_epi_structSpace'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/slices/epi_structSpace/TR_%s/slices.png'%TR)\n file_dict['slices_struct_MNIspace'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/slices/struct_MNIspace/slices.png')\n file_dict['slices_epi_MNIspace'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/slices/epi_MNIspace/TR_%s/slices.png'%TR)\n file_dict['FD_ts'] = os.path.join(ds_dir, subject_id, 'rsfMRI_preprocessing/QC/FD/FD_ts/TR_%s/FD.1D'%TR)\n\n\n # CHECK IF ALL FILES EXIST\n def check_if_out_files_exist(check_file_dict):\n for file in check_file_dict.values():\n if not os.path.exists(file):\n raise Exception('file missing: %s'%file)\n\n check_file_dict = file_dict.copy()\n check_file_dict.pop('report_file')\n check_if_out_files_exist(check_file_dict)\n\n report = Node(util.Function(input_names=['subject_id', 'file_dict', 'df'],\n output_names=[],\n function=create_qc_report_pdf),\n name='report_%s_%s'%(TR, subject_id))\n report.inputs.subject_id = subject_id\n report.inputs.file_dict = file_dict\n report.inputs.df = df\n\n wf.add_nodes([report])\n\n# fixme\n# ignore warning from np.rank\nimport warnings\n\nwith warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n if plugin_name == 'CondorDAGMan':\n wf.run(plugin=plugin_name)\n if plugin_name == 'MultiProc':\n wf.run(plugin=plugin_name, plugin_args={'n_procs': use_n_procs})\n","sub_path":"run_13_create_qc_reports.py","file_name":"run_13_create_qc_reports.py","file_ext":"py","file_size_in_byte":6874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"132641788","text":"#!/usr/bin/env python\n# \n# Copyright (c) 2006-2007 Apple Inc. All rights reserved.\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\nfrom distutils.core import setup, Extension\n\nmodule1 = Extension('appleauth',\n\textra_compile_args = ['-arch', 'ppc', '-arch', 'i386'],\n\textra_link_args = ['-framework', 'Security', '-arch', 'ppc', '-arch', 'i386'],\n\tsources = ['AppleAuth.c'])\n\nsetup (name = 'AppleAuth',\n\tversion = '1.0',\n\tdescription = 'Apple Authorization',\n\text_modules = [module1])\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"179230266","text":"list = [0,1,2,3,4,5,6,7,8,9]\r\n\r\ndef luna(x,y):\r\n for i in range(0,len(y)):\r\n y.pop(i)\r\n\r\ndef permutation(s):\r\n if len(s) == 1:\r\n return [s]\r\n\r\n perm_list = [] \r\n for a in s:\r\n remaining_elements = [x for x in s if x != a]\r\n z = permutation(remaining_elements) \r\n\r\n for t in z:\r\n perm_list.append([a] + t)\r\n\r\n return perm_list\r\n\r\na = permutation(list)\r\n\r\nprint (a[1000000])\r\n\r\n\r\n","sub_path":"24.py","file_name":"24.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"599481708","text":"import datetime\nimport importlib\nimport json\nimport pkgutil\nimport pyclbr\nimport re\nfrom concurrent.futures import ThreadPoolExecutor\nfrom urllib.parse import quote\n\nfrom tornado.gen import coroutine\nfrom tornado.httpclient import AsyncHTTPClient,HTTPRequest\nfrom tornado.web import RequestHandler\n\nimport patterns\nfrom config.database import bankuang_pool,security_pool\nfrom lib.stock import get_stock\n\npattern_class = []\n\ndef init():\n global pattern_class\n for _,name,_ in pkgutil.iter_modules(path=patterns.__path__):\n module_path = 'patterns.{}'.format(name)\n module = importlib.import_module(name=module_path)\n cls_list = pyclbr.readmodule(module_path)\n for cls in cls_list:\n pattern_class.append(getattr(module,cls))\n\ninit()\nprint('Pattern class loaded...')\n\nclass MainHandler(RequestHandler):\n def get(self):\n self.redirect('http://bankuang.com/')\n\nclass URLHandler(RequestHandler):\n executor = ThreadPoolExecutor(max_workers=4)\n\n @coroutine\n def get(self):\n global pattern_class\n q = self.get_argument(name='q',default='').strip()\n if q is '':\n self.send_error(400)\n return\n pattern_matches = yield [cls().match(q) for cls in pattern_class]\n\n result = None\n for p in pattern_matches:\n if p:\n result = p\n break\n self.set_header('Access-Control-Allow_Origin','*')\n self.set_header('Content-Type','text/plain;charset=UTF-8')\n if result:\n self.write(result['view'])\n else:\n self.write('search.html?{}'.format(quote(q)))\n self.finish()\n yield self.raw_log(q)\n\n @coroutine\n def raw_log(self,q):\n time = datetime.datetime.now()\n timestr = '{}.{}'.format(time.strftime('%Y-%m-%d %H:%M:%S'), time.microsecond)\n ip = self.request.headers.get(\"X-Forwarded-For\") or self.request.headers.get(\"X-Real-IP\") or \\\n self.request.remote_ip\n useragent = self.request.headers.get('User-Agent', 'Unknown')\n sql = 'INSERT INTO test1 VALUES (NULL, %s, %s, %s, %s)'\n yield security_pool.execute(sql,(timestr,ip,useragent,q))\n\n\nclass SearchHandler(RequestHandler):\n\n search_interfaces1 = [\n ('http://api.bankuang.com:7777/qa?q={}', '数值搜索'),\n ('http://api.bankuang.com:4567/xuexiao?q={}', '股民学校'),\n ('http://api.bankuang.com:4567/zhidao?q={}', '百度知道'),\n ]\n search_interfaces2 = [\n ('http://vip-service.bankuang.com:5888/stock?q={}', 'F10搜索'),\n ('http://vip-service.bankuang.com:5888/research?q={}', '研报搜索'),\n ('http://vip-service.bankuang.com:5888/news?q={}', '新闻搜索'),\n ('http://vip-service.bankuang.com:5888/notice?q={}', '公告搜索'),\n ]\n\n def get_limit(self, name, default):\n limit = self.get_argument(name=name, default=default)\n try:\n limit = int(limit)\n except (TypeError, ValueError):\n limit = default\n if limit <= 0:\n limit = default\n return limit\n\n @coroutine\n def search(self, q, limit=None):\n data1, data2 = yield [self._search1(q, limit), self._search2(q, limit)]\n data = data1 + data2\n return data\n\n @coroutine\n def _search1(self, q, limit):\n data = []\n http_client = AsyncHTTPClient()\n requests = [HTTPRequest(url.format(quote(q)), connect_timeout=1.0, request_timeout=20.0)\n for url, _ in self.search_interfaces1]\n responses = yield [http_client.fetch(r) for r in requests]\n i = 0\n for r in responses:\n j = json.loads(r.body.decode())\n n = self.search_interfaces1[i][1]\n i += 1\n if j:\n if isinstance(j, list):\n if j == [{'股票代码': '0'}, ]:\n continue\n j = j[:limit['qa']]\n if isinstance(j, dict) and 'href' in j and 'yuncaijing.com' in j['href']:\n continue\n data.append({'name': n, 'data': j})\n return data\n\n @coroutine\n def _search2(self, q, limit):\n data = []\n http_client = AsyncHTTPClient()\n requests = [HTTPRequest(url.format(quote(q)), connect_timeout=1.0, request_timeout=60.0)\n for url, _ in self.search_interfaces2]\n responses = yield [http_client.fetch(r) for r in requests]\n i = 0\n for r in responses:\n j = json.loads(r.body.decode())\n n = self.search_interfaces2[i][1]\n i += 1\n if j['numFound'] > 0:\n docs = j['docs']\n if n == 'F10搜索':\n stocks = j['filters'][0]['stock']\n doclimit = limit['f10']\n else:\n stocks = j['filters'][1]['stock']\n if n == '研报搜索':\n doclimit = limit['research']\n elif n == '新闻搜索':\n doclimit = limit['news']\n else:\n doclimit = limit['notice']\n data.append({\n 'name': n,\n 'data': {\n 'stock': stocks[:limit['related_stock']],\n 'docs': docs[:doclimit]\n }\n })\n return data\n\n @coroutine\n def get(self):\n global pattern_class\n q = self.get_argument(name='q', default='').strip()\n if q is '':\n self.send_error(400)\n return\n uid = self.get_argument(name='unionid', default='').strip()\n if uid == 'undefined':\n uid = ''\n limit = {\n 'qa': self.get_limit('qa', default=3),\n 'f10': self.get_limit('f10', default=5),\n 'research': self.get_limit('research', default=5),\n 'news': self.get_limit('news', default=5),\n 'notice': self.get_limit('notice', default=5),\n 'related_stock': self.get_limit('stock', default=5),\n }\n result = yield self.search(q, limit)\n self.set_header('Access-Control-Allow-Origin', '*')\n self.set_header('Content-Type', 'application/json; charset=UTF-8')\n self.write(json.dumps(result, ensure_ascii=False))\n self.finish()\n if uid and result and result[0]['name'] == '数值搜索' and '相关描述' in result[0]['data'][0]:\n yield self.q_log(uid, q, result[0]['data'][0]['相关描述'])\n\n @coroutine\n def q_log(self, uid, d):\n m = re.search(r'【(.+)】', d)\n if m:\n q = m.group(1)\n category = 'stock'\n stock = get_stock(q)\n if stock and stock['type'] == 0:\n is_stock = 1\n else:\n is_stock = 0\n\n sql = 'SELECT * FROM test3 WHERE unionid = %s AND q = %s AND category = %s'\n cur = yield security_pool.execute(sql, (uid, q, category))\n row = cur.fetchone()\n if row:\n sql = 'UPDATE test3 SET counter = counter + 1, is_del = 0, last_visit = NOW() WHERE ' \\\n 'unionid = %s AND q = %s AND category = %s'\n yield security_pool.execute(sql, (uid, q, category))\n else:\n sql = 'INSERT INTO test3 VALUES (NULL, %s, %s, %s, 1, NOW(), %s, 0, 0)'\n yield security_pool.execute(sql, (uid, q, category, is_stock))\n\n\n","sub_path":"2017年前/apitest/handlers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"312668992","text":"# LC241 Different Ways to Add Parentheses\n# Medium\n\n\n# Given a string of numbers and operators, return all possible results from computing all the different possible ways to group numbers and operators. The valid operators are +, - and *.\n\nfrom typing import *\n\n\n# General functional functions\ndef calculate(A, B, op):\n \"\"\"\n calculate A op B\n A and B are int\n op is operator in string\n \"\"\"\n\n if op == \"+\":\n return A + B\n elif op == \"-\":\n return A - B\n elif op == \"*\":\n return A * B\n\n\ndef read(input):\n \"\"\"\n read input and return two list:\n nums and operators\n \"\"\"\n nums = []\n op = []\n\n temp = \"\"\n for i in input:\n if i.isnumeric():\n temp += i\n else:\n nums.append(int(temp))\n temp = \"\"\n op.append(i)\n\n nums.append(int(temp)) # last number\n\n return nums, op\n\n\nclass Solution:\n\n # Version A, Permutation method\n # Read nums and operator, and calculate all possible operator permutations\n # This could fail becaues the iteration will repeat remote parenthesis as two methods\n def diffWaysToCompute(self, input: str) -> List[int]:\n\n if not input:\n return []\n\n nums, op = read(input)\n\n if len(nums) == 1:\n return [nums[0]]\n\n result = []\n\n def helper(nums, op):\n \"\"\"\n nums is a list of integers\n op is a list of operators\n \"\"\"\n # print(nums, op)\n if len(op) == 1:\n calc_result = calculate(nums[0], nums[1], op[0])\n result.append(calc_result)\n # print(result)\n else:\n for i in range(len(op)):\n new_nums = nums[:]\n new_op = op[:]\n new_nums[i] = calculate(new_nums[i], new_nums.pop(i + 1), new_op.pop(i))\n helper(new_nums, new_op)\n\n helper(nums, op)\n return sorted(result)\n\n\nclass Solution:\n\n # Version B, similar idea with A but new method on adding parenthesis\n # Passed efficiently\n # 添加括号的规律:\n # 1. 有几次运算, 最终就必须有几组括号, 其实只管右半边, 也就是\")\"\n # 2. 直到第1个运算符后面, 最多只能有1个\")\", 推理: 直到第n个运算符, 最多只能有\"n\"个\n # 3. 添加括号后, 所有之后的运算符index发生位移\n # 4. 需要统计之前用了多少个括号(used), 才能了解根据目前第n个运算符最多需要几个, 也就是0个到n-used个\n # 5. 所以递归算法, 必须要指定插入位置, 之前有多少个\")\"用过了, 和还剩多少个, 因为只要任何时刻用完全部就结束了\n def diffWaysToCompute(self, input: str) -> List[int]:\n\n # Edge empty\n if not input:\n return []\n\n # Read input\n nums, op = read(input)\n\n # Edge single number, no operator\n if not op:\n return [nums[0]]\n\n all_op = [] # All possible ways to add parenthesis\n max_idx = len(op) # record the number of parenthesis to be added\n\n def helper(op, idx, used, rest, count):\n\n if count == max_idx: # 这里也就是到最后一个运算符, 不过剩多少, 都全加到末尾, 然后输出\n op += [\")\"] * rest\n all_op.append(op)\n else:\n for i in range(0, count + 1 - used): # 注意range范围, 从0开始, 最多只能用当前位置减去此前用过的数量个')\"\n new_op = op[:idx] + [\")\"] * i + op[idx:]\n helper(new_op, idx + i + 1, used + i, rest - i, count + 1) # 递归的时候, 推进下一个idx注意位移插入了i个\")\"\n\n helper(op, 1, 0, max_idx, 1)\n\n # 这个就是简单的队列实现运算式, 通过遇到\")\"来激发运算, 用pop来保持其他顺序不变\n def calc(nums, op_p):\n i = 0\n while i != len(op_p):\n cur = op_p[i]\n if cur == \")\":\n op_p.pop(i)\n operator = op_p.pop(i - 1)\n nums[i - 1] = calculate(nums[i - 1], nums.pop(i), operator)\n i -= 1 # 注意这里不但pop了\")\", 也pop了上一个运算符, 所以后退了两位, 天然只能前进一位,所以还需要退一位来补下一个元素的idx\n else:\n i += 1\n\n return nums[0]\n\n result = []\n for op_p in all_op:\n result.append(calc(nums[:], op_p))\n\n return sorted(result)\n\n\nif __name__ == \"__main__\":\n assert Solution().diffWaysToCompute(\"\") == [], \"Edge 0\"\n assert Solution().diffWaysToCompute(\"1\") == [1], \"Edge 1\"\n assert Solution().diffWaysToCompute(\"1+1\") == [2], \"Edge 2\"\n\n assert Solution().diffWaysToCompute(\"2-1-1\") == [0, 2], \"Example 1\"\n assert Solution().diffWaysToCompute(\"2*3-4*5\") == [-34, -14, -10, -10, 10], \"Example 2\"\n\n assert Solution().diffWaysToCompute(\"10+11+12\") == [33, 33], \"Double digit\"\n assert Solution().diffWaysToCompute(\"15*1*4\") == [60, 60], \"Copycat\"\n\n print(\"All passed\")\n","sub_path":"LeetCode/LC241_different_ways_to_add_parentheses.py","file_name":"LC241_different_ways_to_add_parentheses.py","file_ext":"py","file_size_in_byte":5154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"352743347","text":"#!/usr/bin/python\nimport serial\nimport time\nimport argparse\nimport sys\nimport calendar\n\ndef calcCrc( msg ):\n \"Vypocet checksumu\"\n crc = 0;\n for letter in msg: # First Example\n crc = crc ^ ord(letter)\n crc = format(crc, 'X')\n return crc;\n\ndef makeCommand( msg ):\n \"Vytvorenie vety\"\n final = b'$'+msg+'*'+calcCrc(msg)+'\\n'\n return final;\n\ndef getArguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--port\")\n parser.add_argument(\"--input\")\n args = parser.parse_args()\n port = args.port\n args = args.input\n args = args.split(\",\")\n args = [pair.replace(\" \",\"\") for pair in args]\n args_map = {}\n for arg in args:\n argument = arg.split(\":\")\n args_map[argument[0]] = argument[1]\n args_map[\"port\"] = port\n return args_map\n\n\ndef app(args):\n ser = serial.Serial(args[\"port\"], 115200)\n command = \"SGV,\" + args[\"c_lamp\"] + \",\" + args[\"c_fan\"] + \",\" + args[\"c_led\"]\n ser.write(makeCommand(command))\n ser.close()\n \nif __name__ == '__main__':\n args = getArguments()\n app(args)\n","sub_path":"ubuntu16/olm_app_server/server_scripts/tos1a/openloop/change.py","file_name":"change.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"195638593","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jul 3 12:32:20 2017\n\n@author: Group29\n\"\"\"\nfrom baidu_pic_downloader import BaiduPicDownloader \nimport requests\nimport urllib\nimport codecs\nimport jieba\nimport csv\nimport sys\nimport os\nimport re\n\n# The category of the celebrity you want to search\ncategory = \"CEO\"\ndataPath = \"../data/new/\"\nbpd = BaiduPicDownloader()\n\ndef readLastNameList(fname):\n\t\"\"\"read the last names from a file to a list.\t\n\t\n\tArgs:\n\t\tfname: file name with its path\n\t\t\n\tReturns:\n\t\tthe list of last name\n\t\"\"\"\n\tf = open(fname,'r')\n\tcontent = f.read()\n\tlast_name_list = content.split()\n\treturn last_name_list\n\t\n\ndef get_coding(res, code_list):\n\t\"\"\"find the coding of the response and set its encoding\n\t\n\tArgs:\n\t\tres: response object\n\t\tcode_list: a list of possible encoding ways\n\t\t\n\tReturns:\n\t\tNone\n\t\"\"\"\n\tcode = 'utf-8'\n\tcoding = re.search('charset.*\\\"', res.text)\n\tcode_word = ''\n\tif coding:\n\t\tcode_word = coding.group(0).replace('\\\"', '')\n\t\tcode_word = code.replace('charset=','')\n\t\tcode_word = code.replace(' ','')\n\telse:\n\t\tprint (\"code not found\")\n\tif code_word in code_list:\n\t\tcode = code_word\n\tres.encoding = code\n\treturn\n\t\n\t\ndef downloadPage(url):\n\ttry:\n\t\tr = requests.get(url, timeout=20)\n\texcept requests.exceptions.Timeout:\n\t\tprint(\"Timeout\")\n\t\tsys.exit()\n\texcept:\n\t\tprint(\"Connect error\")\n\t\tsys.exit()\n\treturn r\n\t\n\ndef get_pic(name):\n\tres = bpd.download(name, 3)\n\tprint(res)\n\tif res == -1:\n\t\treturn None\n\treturn res\n\t\n\t\ndef del_content_blank(s):\n\tclean_str = re.sub(r'\\n| |\\xa0|\\\\xa0|\\u3000|\\\\u3000|\\\\u0020|\\u0020', '', str(s))\n\treturn clean_str\t\n\t\n\t\ndef downloadurl(field, num, start_page):\n\t\"\"\"get a list of url using baidu search engine with specific field\n\t\n\tArgs:\n\t\tfield: the field that the celebrity is in\n\t\tnum: the number of the urls needed\n\t\tstart_page: the page number of page to start from in the search result \n\t\t\n\tReturns:\n\t\tthe list of urls\n\t\"\"\"\n\tbpd = BaiduPicDownloader()\n\turls = []\n\tfor p in range(start_page, start_page+int(num/20)):\n\t\turl = (\"http://image.baidu.com/search/flip?tn=baiduimage&ie=utf-8&word=\" + field \n\t\t\t+ \"&pn=\" + str(p*20) + \"&gsm=8c&ct=&ic=0&lm=-1&width=0&height=0\")\n\t\tr = downloadPage(url)\n\t\tstrr = r.text\n\t\tstart = 0\n\t\turl_str = \"\"\n\t\tfor i in range(20):\n\t\t\tpos = strr.find(\"fromURL\\\":\\\"\", start)\n\t\t\tif pos == -1:\n\t\t\t\tbreak\n\t\t\tstart = pos + 10\n\t\t\turl_str = \"\"\n\t\t\twhile strr[start] != \"\\\"\":\n\t\t\t\turl_str = url_str + strr[start]\n\t\t\t\tstart = start + 1\n\t\t\turls.append(bpd.decode(url_str))\n\treturn urls\n\t\n\nif __name__ == \"__main__\":\n\n\t# load in config data\n\tcode_list = ['utf-8', 'gb2312']\n\tlast_name_list = readLastNameList('LastNameList.txt')\n\tcsv_out = open(dataPath + \"gov_officers.csv\", 'w', newline='')\n\tcsv_writer = csv.writer(csv_out, delimiter=',')\n\tcsv_writer.writerow(['category', 'name', 'pic_id'])\n\t\n\t# iteratively fetch data\n\tit = 1\n\tnum = 20\n\ti = 0\n\tpic_id = 0\n\twhile (i\\/\\?\\~\\！\\@\\#\\\\\\&\\*\\%\\\"\\-\\_\\+\\-\\：\\、\\《\\》\\，\\“\\”\\。]\", \"\", res.text)\n\t\t\tr = re.sub('\\r|\\n|\\t', '', r)\n\t\t\tr = r.replace(' ', '')\n\t\t\t\n\t\t\t# cut words and filter out the potential names\n\t\t\tname_seg = jieba.cut(r)\n\t\t\tname_list = (\" \".join(name_seg)).split()\n\t\t\trst = []\n\t\t\tprint ('start process')\n\t\t\tfor word in name_list:\n\t\t\t\tif len(word)==2 or len(word)==3:\n\t\t\t\t\tif word[0] in last_name_list:\n\t\t\t\t\t\trst.append(word)\n\t\t\t\n\t\t\t# remove duplicates\n\t\t\trst = list(set(rst))\n\t\t\t\n\t\t\t# iterate all potential names and get pic for that name\n\t\t\tfor name in rst:\n\t\t\t\tpic_status = get_pic(name)\n\t\t\t\tif pic_status is not None:\n\t\t\t\t\tcsv_writer.writerow([category, name, pic_id ])\n\t\t\t\t\tpic_id += 1\n\t\ti = i+(num/20)\n\tprint (pic_id)\n","sub_path":"src/baidu_url_crawler.py","file_name":"baidu_url_crawler.py","file_ext":"py","file_size_in_byte":3856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"343234666","text":"\"\"\"\nNOTE: this is totally obsolte and refers to a very old version of the project.\n\"\"\"\n\nimport d6tflow\nfrom make_folds import TaskMakeFolds\nimport pandas as pd\n\ntask = TaskMakeFolds()\nexpected = task.output()[\"train_data\"].load()[\"MAX(x.acoustic_data)\"].iloc[0]\nactual = pd.read_csv(\"../input/train.csv\", nrows=150000).acoustic_data.max()\nassert expected == actual, print(expected, actual)\n\nexpected = task.output()[\"train_data\"].load()[\"MAX(x.acoustic_data)\"].iloc[1]\nactual = (\n pd.read_csv(\"../input/train.csv\", nrows=150000, skiprows=150000).iloc[:, 0].max()\n)\nassert expected == actual\n","sub_path":"code/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"134792092","text":"import json\nfrom sys import argv\nimport os\n\n\ndef load_data(filepath):\n with open(filepath, 'r', encoding='utf-8') as my_file:\n return json.loads(my_file.read())\n\n\ndef pretty_print_json(json_content):\n print(json.dumps(\n json_content,\n indent=4,\n sort_keys=True,\n ensure_ascii=False,\n ))\n\n\nif __name__ == '__main__':\n\n try:\n filepath = argv[1]\n if os.path.isfile(filepath):\n pretty_print_json(load_data(filepath))\n else: \n print('File not found')\n except ValueError:\n print('Invalid argument value')\n except IndexError:\n print('Index is out of range')\n\n\n\n","sub_path":"pprint_json.py","file_name":"pprint_json.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"588682275","text":"from django.shortcuts import get_object_or_404\nfrom rest_framework import status, viewsets\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.decorators import action\nfrom rest_framework.pagination import PageNumberPagination\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom rest_framework_simplejwt.tokens import RefreshToken\n\nfrom .confirmation_code import create_code, send_email_with_confirmation_code\nfrom .models import User\nfrom .permissions import GlobalPermission, MePermission\nfrom .serializers import (\n UserSerializerForCode,\n UsersSerializer,\n YamdbTokenSerializer,\n ConfirmationCodeSerializer\n)\n\n\n@api_view(['GET'])\n@permission_classes((AllowAny,))\ndef get_confirmation_code(request):\n serializer = ConfirmationCodeSerializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n user = get_object_or_404(\n User,\n username=serializer.validated_data['username'],\n email=serializer.validated_data['email']\n )\n if user.confirmation_code is None:\n conf_code = create_code(user)\n user.confirmation_code = conf_code\n user.save()\n else:\n conf_code = user.confirmation_code\n\n send_email_with_confirmation_code(conf_code, user.email)\n\n return Response({'Check your email'})\n\n\nclass SignUpViewSet(APIView):\n permission_classes = (AllowAny,)\n\n def post(self, request):\n serializer = UserSerializerForCode(data=request.data)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n return Response(serializer.validated_data, status=status.HTTP_200_OK)\n\n\nclass UsersViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all().order_by('username')\n serializer_class = UsersSerializer\n pagination_class = PageNumberPagination\n lookup_field = 'username'\n permission_classes = [GlobalPermission]\n\n @action(\n detail=False,\n methods=['get', 'patch'],\n permission_classes=[MePermission],\n )\n def me(self, request):\n\n user = self.request.user\n\n if request.method == 'GET':\n serializer = self.get_serializer(user)\n return Response(\n serializer.data,\n status=status.HTTP_200_OK\n )\n\n if request.method == 'PATCH':\n serializer = self.get_serializer(\n user,\n data=request.data,\n partial=True\n )\n serializer.is_valid(raise_exception=True)\n serializer.save()\n return Response(\n serializer.data,\n status=status.HTTP_200_OK\n )\n\n\nclass YamdbTokenViewSet(APIView):\n permission_classes = (AllowAny,)\n\n def post(self, request):\n serializer = YamdbTokenSerializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n user = get_object_or_404(\n User,\n username=serializer.validated_data['username']\n )\n refresh = RefreshToken.for_user(user)\n return Response({'token': str(refresh.access_token)})\n","sub_path":"api_yamdb/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"61896174","text":"\"\"\"\n SALTS XBMC Addon\n Copyright (C) 2014 tknorris\n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see ' % (name, i18n('username')))\n settings.append(' ' % (name, i18n('password')))\n return settings\n\n def _http_get(self, url, data=None, headers=None, allow_redirect=True, method=None, cache_limit=8):\n # return all uncached blank pages if no user or pass\n if not self.username or not self.password:\n return ''\n\n html = self._cached_http_get(url, self.base_url, self.timeout, data=data, headers=headers, allow_redirect=allow_redirect, method=method, cache_limit=cache_limit)\n if re.search('Please Register or Login', html, re.I):\n log_utils.log('Logging in for url (%s)' % (url), log_utils.LOGDEBUG)\n self.__login()\n html = self._cached_http_get(url, self.base_url, self.timeout, data=data, headers=headers, allow_redirect=allow_redirect, method=method, cache_limit=0)\n return html\n\n def __login(self):\n url = urlparse.urljoin(self.base_url, '/login')\n data = {'username': self.username, 'password': self.password, 'action': 'login'}\n html = self._cached_http_get(url, self.base_url, self.timeout, data=data, headers=XHR, cache_limit=0)\n if 'incorrect login' in html.lower():\n raise Exception('moviesplanet login failed')\n","sub_path":"scrapers/moviesplanet_scraper.py","file_name":"moviesplanet_scraper.py","file_ext":"py","file_size_in_byte":7020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"456016394","text":"import math\nimport sys\n\nif __name__ == \"__main__\":\n file = sys.argv[1]\n\n\n# If stdin didnt give a value we use the file given through args\n\nwith open(file,'r') as file:\n\tstring = \"\".join(file.read())\n\n\n# Replace all \",\" decimal points with \".\"\nstring_list = string.replace(\",\",\".\").split(\" \")\n# Build a list with all values from the string as float\nfloat_list = list(map(float,string_list))\n# Sort the list\nsorted_list = sorted(float_list)\n# save the list length\nlist_length = len(float_list)\n# Calculate the mean\nmean = sum(sorted_list)/list_length\n# Calculate the median\nif list_length % 2 == 0:\n firstNumber = int(list_length / 2 - 1)\n secondNumber = int(list_length/2)\n median = (sorted_list[firstNumber] + sorted_list[secondNumber]) / 2\nelse:\n median = sorted_list[int(list_length / 2)]\n\n \nsum_of_squares = 0\nfor value in sorted_list:\n currentValue = abs(value - mean)\n sum_of_squares += currentValue**2\n \nstandard_deviation = math.sqrt(sum_of_squares/list_length)\nprint (\"mean: %.4f. median: %.4f. standard deviation: %.4f.\" % (mean, median, standard_deviation))\n","sub_path":"stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"35999928","text":"import datetime\n\nfrom django.core.management.base import BaseCommand\n\nfrom core.models import Route, RouteStop, PlannedTrain, PlannedStop, TicketCounter\n\n\nclass Command(BaseCommand):\n help = 'Создаёт PlannedTrain, PlannedStop и TicketCounter на days дней вперёд'\n\n def add_arguments(self, parser):\n parser.add_argument('days', type=int, nargs='?', default=7)\n\n def handle(self, *args, **options):\n \tfor route in Route.objects.all():\n \t\tstart = datetime.datetime.now()\n \t\tend = datetime.datetime.now() + datetime.timedelta(days=options['days'])\n \t\tfor recurrence in route.recurrences.between(start, end):\n \t\t\tplanned_train = PlannedTrain.objects.create(\n \t\t\t\troute=route,\n \t\t\t\tdeparture_time=recurrence,\n \t\t\t)\n\n \t\t\tfor stop in route.stops.all():\n \t\t\t\tplanned_stop = PlannedStop.objects.create(\n \t\t\t\t\tstop=stop,\n \t\t\t\t\tplanned_train=planned_train,\n \t\t\t\t\tdeparture_time=recurrence+stop.eta_delta,\n \t\t\t\t)\n\n \t\t\t\tfor ticket_limit in route.ticket_limits.all():\n \t\t\t\t\tTicketCounter.objects.create(\n \t\t\t\t\t\tticket_type=ticket_limit.ticket_type,\n \t\t\t\t\t\tplanned_stop=planned_stop,\n \t\t\t\t\t\ttickets_left=ticket_limit.count,\n \t\t\t\t\t)\n\n","sub_path":"core/management/commands/plan_trains.py","file_name":"plan_trains.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"82070826","text":"\"\"\"\nTest insta485generator with published \"hello\" input.\n\nEECS 485 Project 1\n\nAndrew DeOrio \n\"\"\"\nimport re\nimport shutil\nimport subprocess\nimport textwrap\nfrom utils import TMPDIR, TESTDATA_DIR\n\n\ndef test_hello():\n \"\"\"Diff check hello/index.html.\"\"\"\n # Set up temporary directory\n tmpdir = TMPDIR/\"test_hello\"\n shutil.rmtree(tmpdir, ignore_errors=True)\n tmpdir.mkdir(parents=True)\n shutil.copytree(TESTDATA_DIR/\"hello\", tmpdir/\"hello\")\n\n # Run insta485generator in tmpdir\n subprocess.run([\"insta485generator\", \"hello\"], check=True, cwd=tmpdir)\n\n # Make sure generated files exist\n output_dir = tmpdir/\"hello/html\"\n index_path = output_dir/\"index.html\"\n assert output_dir.exists()\n assert index_path.exists()\n\n # Verify output file content, normalized for whitespace\n actual = index_path.read_text()\n correct = textwrap.dedent(\"\"\"\n \n \n \n \n Hello world\n \n \n \n hello\n world\n \n \n \"\"\")\n correct = re.sub(r\"\\s+\", \"\", correct)\n actual = re.sub(r\"\\s+\", \"\", actual)\n assert actual == correct\n","sub_path":"insta-static/starter_files/tests/test_insta485generator_hello.py","file_name":"test_insta485generator_hello.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"132328418","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nimport os\nfrom sendgrid import SendGridAPIClient\nfrom sendgrid.helpers.mail import Mail\nfrom errand_matcher.models import ConfirmationToken\n\ndef index(request):\n return render(request, 'errand_matcher/index.html')\n\ndef begin_signup(request):\n return render(request, 'errand_matcher/begin-signup.html')\n\ndef complete_signup(request):\n return render(request, 'errand_matcher/complete-signup.html')\n\ndef confirm_email(request):\n if request.method == 'POST':\n current_email = request.POST.get('current-email')\n token = ConfirmationToken()\n print(token)\n token.save()\n url = request.META['HTTP_HOST'] + '/activate/' + str(token.id)\n message = Mail(\n from_email='livelyhood.tech@gmail.com',\n to_emails=current_email,\n subject='Thank you for signing up with Livelyhood!',\n html_content='Activation Link '.format(url))\n try:\n sg = SendGridAPIClient(os.environ.get('SENDGRID_API_KEY'))\n response = sg.send(message)\n except Exception as e:\n print(e.message)\n return render(request, 'errand_matcher/email-confirmation.html', {'current_email': current_email})\n\n\ndef activate(request, token_id):\n token = ConfirmationToken.objects.filter(id=token_id).first()\n if token is None:\n token_state = 'Does Not Exist'\n else:\n if token.active:\n token_state = 'Active'\n else:\n token_state = 'Expired'\n return render(request, 'errand_matcher/complete-signup.html', {'token_state': token_state})\n\n\ndef matchable_volunteers(request, requestor_id):\n pass\n","sub_path":"errand_matcher/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"369909011","text":"from dataclasses import dataclass, asdict\nfrom typing import List\nfrom .common_define import Platform, Os, SdkType, Network, Carrier, DebugMode\n\nimport time\nimport datetime\nimport json\n\n@dataclass\nclass TrackCommon():\n udid : str = \"\" \n user_id : str = \"\"\n distinct_id : int = 0\n app_id : int = 0\n platform : str = \"\"\n time : datetime.datetime = \"\" \n sdk_type : str = \"\"\n sdk_version : str = \"\"\n screen_height : int = 0\n screen_width : int = 0\n manufacturer : str = \"\"\n model : str = \"\"\n network : str = \"\"\n os : str = \"\"\n os_version : str = \"\"\n carrier : str = \"\"\n app_version : str = \"\"\n\n def __init__(self):\n self.time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n\n@dataclass\nclass EventCommon():\n event : str = \"\"\n event_time : str = \"\"\n is_first_day : int = 0\n is_first_time : int = 0\n is_login : int = 0\n\n@dataclass\nclass TrackInfo():\n project_id : int = 0\n debug_mode : str = DebugMode.NO_DEBUG_MODE.value\n type : str = \"track\"\n common : TrackCommon = None\n properties : List[EventCommon] = None\n\n@dataclass\nclass AppInstall(EventCommon):\n def __init__(self):\n self.event = \"appInstall\"\n self.event_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n\n@dataclass\nclass AppStart(EventCommon):\n def __init__(self):\n self.event = \"appStart\"\n self.event_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n\n@dataclass\nclass AppEnd(EventCommon):\n duration : float = 0.0\n def __init__(self):\n self.event = \"appEnd\"\n self.event_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n\nif __name__ == \"__main__\":\n track_common = TrackCommon()\n track_common.udid = \"abcd\"\n track_common.user_id = \"1234\"\n track_common.distinct_id = \"6039281029182710291\"\n track_common.platform = Platform.IOS.value\n track_common.sdk_type = SdkType.IOS.value\n track_common.sdk_version = \"1.0.1\"\n track_common.screen_height = 650.0\n track_common.screen_width = 350.0\n track_common.manufacturer = \"huawei\"\n track_common.model = \"huawei P40\"\n track_common.network = Network.N_4G.value\n track_common.os = Os.IOS.value\n track_common.os_version = \"12.1.1\"\n track_common.carrier = Carrier.CHINA_UNICOM.value\n track_common.app_version = \"1.0.1\"\n\n app_install = AppInstall()\n properties = [app_install]\n \n track_info = TrackInfo()\n track_info.project_id = 1\n track_info.common = track_common\n track_info.properties = properties\n print(json.dumps(asdict(track_info)))\n\n\n\n","sub_path":"zlyqmodel/private_track.py","file_name":"private_track.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"410263926","text":"N = int(input())\nanswer = N\n\nfor i in range(N):\n word = input()\n history = []\n cur = word[0]\n for character in word:\n if character in history:\n answer -= 1\n break\n elif character == cur:\n continue\n else:\n history.append(cur)\n cur = character\n continue\n\nprint(answer)","sub_path":"baeckjoon/1316.py","file_name":"1316.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"150721121","text":"import pygame\n\nclass GUI:\n def __init__(self):\n\n white = (255,255,255)\n display_width = 1280\n display_height = 720\n\n self.gameDisplay = pygame.display.set_mode((display_width,display_height))\n pygame.display.set_caption('Exploding Kittens')\n self.gameDisplay.fill(white)\n pygame.display.update()\n\n cardImg = pygame.image.load(\"/home/mcorcor1/cs110/finalproject/AofS.png\")\n cardImg = pygame.transform.scale(cardImg, (100,100))\n\n self.middleDeck = pygame.Surface((120,168))\n self.gameDisplay.blit(self.middleDeck, (350,275))\n \n gameExit = False\n \n while not gameExit:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n gameExit = True \n #print(event) \n \n pygame.display.update()\n pygame.quit()\n quit()\n \n \n def playerIcon(self, xcor, ycor, numberofPlayers):\n self.xcor = xcor\n self.ycor = ycor\n for i in numberofPlayers:\n self.player = pygame.Surface((100,100)) \n self.gameDisplay.blit(self.player, (xcor,ycor))\n\n def cardsinHand(self):\n self.card1 = pygame.surface((120,168))\n self.gameDisplay.blit(self.card1, (400,600)) \n \n\n\ndef main():\n GUI()\n\nmain()\n\n","sub_path":"ProjectGUI.py","file_name":"ProjectGUI.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"268997291","text":"import urllib\r\nimport urllib2\r\nimport json\r\n\r\n\r\nclass FlickrLib:\r\n\r\n root_url = \"https://www.flickr.com/services/rest/\"\r\n dic = {}\r\n head = {\"User-Agent\": \"Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, \"\r\n \"like Gecko) Chrome/58.0.3029.110 Mobile Safari/537.36\"}\r\n data = {\"method\": \"flickr.photos.getInfo\",\r\n \"api_key\": \"2f145a0539ede4df69c6988b122f3cea\",\r\n \"photo_id\": 0,\r\n \"secret\": \"5fe5297177e0aa8f\",\r\n \"format\": \"json\",\r\n \"nojsoncallback\": 1}\r\n\r\n @staticmethod\r\n def get_descripttion(photo_id):\r\n data = FlickrLib.data.copy()\r\n data[\"photo_id\"] = photo_id\r\n data = urllib.urlencode(data).encode('utf-8')\r\n req = urllib2.Request(FlickrLib.root_url, data, FlickrLib.head)\r\n response = urllib2.urlopen(req)\r\n resp_text = response.read().decode('utf-8')\r\n # print(text)\r\n\r\n info_dic = json.loads(resp_text)\r\n\r\n # print(info_dic[\"stat\"])\r\n # print(info_dic[\"photo\"].keys())\r\n # print(info_dic[\"photo\"][\"description\"][\"_content\"])\r\n\r\n if info_dic[\"stat\"] != \"ok\":\r\n return \"\"\r\n descripttion = info_dic[\"photo\"][\"description\"][\"_content\"]\r\n\r\n return descripttion\r\n\r\n\r\nif __name__ == '__main__':\r\n text = FlickrLib.get_descripttion(\"15023708648\")\r\n","sub_path":"flickr_lib.py","file_name":"flickr_lib.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"46103385","text":"import numpy as np\nfrom policy import POLICY\n\nGAMMA = 0.99\n\n\nclass HC_AGENT:\n def __init__(self, state_size, action_size, seed):\n self.state_size = state_size\n self.action_size = action_size\n np.random.seed(seed)\n\n self.policy = POLICY(self.state_size, self.action_size, seed)\n\n self.rewards = list()\n self.discounts = list()\n self.reward = -np.Inf\n self.best_reward = -np.Inf\n self.best_weights = self.policy.weights\n self.noise_scale = 1e-2\n\n\n def act(self, state, deterministic=True):\n action_values = self.policy.forward(state)\n\n if deterministic:\n return np.argmax(action_values)\n return np.random.choice(np.arange(self.action_size), p=action_values)\n\n\n def step(self, reward):\n self.rewards.append(reward)\n\n\n def learn(self):\n self.discounts = [GAMMA**i for i in range(len(self.rewards)+1)]\n self.reward = np.sum([gamma*reward for gamma, reward in zip(self.discounts, self.rewards)])\n\n if self.reward >= self.best_reward:\n self.best_reward = self.reward\n self.best_weights = self.policy.weights\n self.noise_scale = max(1e-3, self.noise_scale/2)\n self.update_policy_weights()\n else:\n self.noise_scale = min(2, self.noise_scale*2)\n self.update_policy_weights(regress=True)\n\n self.rewards = list()\n\n\n def update_policy_weights(self, regress=False):\n if regress:\n self.policy.weights = self.best_weights + self.noise_scale*np.random.randn(*self.policy.weights.shape)\n else:\n self.policy.weights = self.policy.weights + self.noise_scale*np.random.randn(*self.policy.weights.shape)\n","sub_path":"Hill Climbing with Noise Scaling/hill_climbing_agent.py","file_name":"hill_climbing_agent.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"601552078","text":"from __future__ import division\n\nfrom enum import IntEnum\nimport numpy as np\nfrom .hardmax.hardmax import forwards_value_iter as _value_iter\nfrom .mdp import MDP\n\nclass Actions(IntEnum):\n UP = 0\n DOWN = 1\n LEFT = 2\n RIGHT = 3\n UP_LEFT = 4\n UP_RIGHT = 5\n DOWN_LEFT = 6\n DOWN_RIGHT = 7\n ABSORB = 8\n\ndiagonal_actions = {Actions.UP_LEFT, Actions.UP_RIGHT, Actions.DOWN_LEFT,\n Actions.DOWN_RIGHT}\n\n# XXX: optimize so that we don't need to convert between state and coor.\ndef transition_helper(g, s, a, alert_illegal=False):\n r, c = g.state_to_coor(s)\n assert a >= 0 and a < len(Actions), a\n\n r_prime, c_prime = r, c\n if a == Actions.LEFT:\n r_prime = r - 1\n elif a == Actions.RIGHT:\n r_prime = r + 1\n elif a == Actions.DOWN:\n c_prime = c - 1\n elif a == Actions.UP:\n c_prime = c + 1\n elif a == Actions.UP_LEFT:\n r_prime, c_prime = r - 1, c + 1\n elif a == Actions.UP_RIGHT:\n r_prime, c_prime = r + 1, c + 1\n elif a == Actions.DOWN_LEFT:\n r_prime, c_prime = r - 1, c - 1\n elif a == Actions.DOWN_RIGHT:\n r_prime, c_prime = r + 1, c - 1\n elif a == Actions.ABSORB:\n pass\n else:\n raise BaseException(\"undefined action {}\".format(a))\n\n illegal = False\n if r_prime < 0 or r_prime >= g.rows or \\\n c_prime < 0 or c_prime >= g.cols:\n r_prime, c_prime = r, c\n illegal = True\n\n s_prime = g.coor_to_state(r_prime, c_prime)\n\n if alert_illegal:\n return s_prime, illegal\n else:\n return s_prime\n\n# Classic Gridworld\nclass GridWorldMDP(MDP):\n Actions = Actions\n\n def __init__(self, rows, cols, goal_state=None, euclidean_rewards=True,\n allow_wait=False, **kwargs):\n \"\"\"\n An agent in a GridWorldMDP can move between adjacent/diagonal cells.\n\n If the agent chooses an illegal action it receives a float('-inf')\n reward and will stay in place.\n\n Params:\n rows [int]: The number of rows in the grid world.\n cols [int]: The number of columns in the grid world.\n goal_state [int]: (optional) The goal state at which ABSORB is legal\n and costs 0.\n euclidean_rewards [bool]: (optional) If True, then scale rewards for\n moving diagonally by sqrt(2).\n allow_wait [bool]: (optional) If False, then the ABSORB action is\n illegal in all states except the goal. If True, then the ABSORB\n action costs default_reward in states other than the goal.\n \"\"\"\n if goal_state is not None:\n assert isinstance(goal_state, int)\n\n self.allow_wait = allow_wait\n MDP.__init__(self, rows=rows, cols=cols, A=len(Actions),\n transition_helper=self._transition_helper, **kwargs)\n\n S, A = self.S, self.A\n\n if euclidean_rewards:\n for a in diagonal_actions:\n col = self.rewards[:, a]\n np.multiply(col, np.sqrt(2), out=col)\n\n self.set_goal(goal_state)\n\n # XXX: optimize so that we don't need to convert between state and coor.\n def _transition_helper(self, s, a, alert_illegal=False):\n return transition_helper(self, s, a, alert_illegal=alert_illegal)\n\n def set_goal(self, goal_state):\n \"\"\"\n Reconfigure the goal state in this GridWorldMDP by allowing an agent at\n the goal state to use the ABSORB action at no cost.\n\n If self.allow_wait is True, then at nongoal states, ABSORB has\n half the `default_reward` cost.\n If self.allow_wait is False, then at nongoal states,\n ABSORB will be illegal (i.e., incur inf cost).\n\n Params:\n goal_state: The new goal. Overrides previous goals.\n \"\"\"\n self.goal = goal_state\n if self.allow_wait:\n self.rewards[:, Actions.ABSORB].fill(self.default_reward)\n else:\n self.rewards[:, Actions.ABSORB].fill(-np.inf)\n if goal_state != None:\n self.rewards[goal_state, Actions.ABSORB] = 0\n\n def q_values(self, goal_state, forwards_value_iter=_value_iter,\n goal_stuck=False):\n \"\"\"\n Calculate the hardmax Q values for each state action pair.\n\n Params:\n goal_state [int]: The goal state, where the agent is allowed to\n choose a 0-cost ABSORB action. The goal state's value is 0.\n goal_stuck [bool]: If this is True, then all actions other than\n ABSORB are illegal in the goal_state.\n\n Returns:\n Q [np.ndarray]: An SxA array containing the q values\n corresponding to each (s, a) pair.\n \"\"\"\n if (goal_state, goal_stuck) in self.q_cache:\n return np.copy(self.q_cache[(goal_state, goal_stuck)])\n\n self.set_goal(goal_state)\n V = forwards_value_iter(self, goal_state)\n\n Q = np.empty([self.S, self.A])\n Q.fill(-np.inf)\n for s in range(self.S):\n if s == goal_state:\n Q[s, Actions.ABSORB] = 0\n if goal_stuck:\n continue\n # TODO:\n # For the purposes of Jaime/Andrea's demo, I am allowing non-ABSORB\n # actions at the goal.\n #\n # My simulations might break if human moves off this square.\n # This is something worth thinking about. XXX\n for a in range(self.A):\n Q[s,a] = self.rewards[s,a] + V[self.transition(s,a)]\n assert Q.shape == (self.S, self.A)\n\n self.q_cache[(goal_state, goal_stuck)] = Q\n return np.copy(Q)\n","sub_path":"pedestrian_prediction/pp/mdp/classic.py","file_name":"classic.py","file_ext":"py","file_size_in_byte":5644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"455638229","text":"import math\nprint(\"Nhập số N: \")\ndef CheckNT(x):\n if x==1:\n return 0\n else:\n for i in range(2,int(math.sqrt(x))):\n if x%i==0:\n return 0\n return 1\ndef CheckN(x):\n i=1\n A=[0]\n while (x!=0):\n A.append(x%10)\n x=int(x/10)\n i=i+1\n i=i-1\n for j in range(1,int(i/2)):\n if(A[j]!=A[i-j+1]):\n return 0\n return 1\ndef Test():\n x=int(input())\n if (CheckN(x)==1) and (CheckNT(x)==1):\n print(\"Số nhập vào là số Palindrome\")\n else:\n print(\"Số nhập vào không phải số Palindrome\")\nTest()","sub_path":"Bai_5.py","file_name":"Bai_5.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"407404694","text":"# Python solution to \"Plus One\" LeetCode problem.\n# 109 / 109 test cases passed.\n# Status: Accepted\n# Runtime: 24 ms\n# Memory Usage: 11.7 MB\n\nclass Solution(object):\n def plusOne(self, digits):\n \"\"\"\n :type digits: List[int]\n :rtype: List[int]\n \"\"\"\n index = len(digits) - 1\n digits[index] += 1\n \n while(digits[index] > 9):\n digits[index] = 0\n if (index > 0):\n digits[index-1] += 1\n index -= 1\n else :\n digits.insert(0,1)\n break\n return digits\n","sub_path":"plusOne.py","file_name":"plusOne.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"122998466","text":"import datetime\n\nfrom fjord.base.tests import TestCase\nfrom fjord.feedback.tests import ResponseFactory\nfrom fjord.suggest import get_suggesters\nfrom fjord.suggest.utils import get_suggestions\nfrom fjord.suggest.providers.dummy import DummySuggester\nfrom fjord.suggest.tests import SuggesterTestMixin\n\n\nclass TestDummySuggesterLoading(SuggesterTestMixin, TestCase):\n suggesters = []\n\n def test_didnt_load(self):\n dummy_providers = [\n prov for prov in get_suggesters()\n if isinstance(prov, DummySuggester)\n ]\n assert len(dummy_providers) == 0\n\n\nclass TestDummySuggester(SuggesterTestMixin, TestCase):\n suggesters = [\n 'fjord.suggest.providers.dummy.DummySuggester'\n ]\n\n def test_load(self):\n dummy_providers = [\n prov for prov in get_suggesters()\n if isinstance(prov, DummySuggester)\n ]\n assert len(dummy_providers) == 1\n\n def test_get_suggestions(self):\n now = u'ts_{0}'.format(datetime.datetime.now())\n\n req = self.get_feedback_post_request({\n 'happy': 1,\n 'description': now,\n 'url': u'http://example.com/{0}'.format(now)\n })\n feedback = ResponseFactory(\n happy=True,\n description=now,\n url=u'http://example.com/{0}'.format(now)\n )\n\n # Try with just the feedback\n links = get_suggestions(feedback)\n assert len(links) == 1\n assert links[0].provider == 'dummy'\n assert links[0].provider_version == 1\n assert links[0].cssclass == u'document'\n assert links[0].summary == u'summary {0}'.format(now)\n assert links[0].description == u'description {0}'.format(now)\n assert links[0].url == feedback.url\n\n # Now with the feedback and request\n links = get_suggestions(feedback, req)\n assert len(links) == 1\n assert links[0].provider == 'dummy'\n assert links[0].provider_version == 1\n assert links[0].cssclass == u'document'\n assert links[0].summary == u'summary {0}'.format(now)\n assert links[0].description == u'description {0}'.format(now)\n assert links[0].url == feedback.url\n","sub_path":"fjord/suggest/tests/test_dummy.py","file_name":"test_dummy.py","file_ext":"py","file_size_in_byte":2208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"359975248","text":"################################################################################\n## This Source Code Form is subject to the terms of the Mozilla Public\n## License, v. 2.0. If a copy of the MPL was not distributed with this file,\n## You can obtain one at http://mozilla.org/MPL/2.0/.\n################################################################################\n## Author: Kyle Lahnakoski (kyle@lahnakoski.com)\n################################################################################\n\nfrom .debug import D\n\nclass multiset():\n\n def __init__(self, list=None, key_field=None, count_field=None):\n if list is None:\n self.dic=dict()\n return\n\n self.dic={i[key_field]:i[count_field] for i in list}\n \n\n def __iter__(self):\n for k, m in self.dic.items():\n for i in range(m):\n yield k\n\n\n def items(self):\n return self.dic.items()\n\n def add(self, value):\n if value in self.dic:\n self.dic[value]+=1\n else:\n self.dic[value]=1\n\n def remove(self, value):\n if value not in self.dic:\n D.error(\"{{value}} is not in multiset\", {\"value\":value})\n\n count=self.dic[value]\n count-=1\n if count==0:\n del(self.dic[value])\n else:\n self.dic[value]=count\n\n\n\n def count(self, value):\n if value in self.dic:\n return self.dic[value]\n else:\n return 0\n","sub_path":"bzETL/util/multiset.py","file_name":"multiset.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"116906546","text":"import requests\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom matplotlib import patches\nfrom io import BytesIO\nimport os\nimport APIConfig as api\n\nimage_path = os.path.join('D:\\Dropbox\\GitHub\\Face_recognition\\JEF.jpg')\nimage_data = open(image_path, \"rb\")\n\nmy_key, api_url = api.config();\n\nheaders = {'Content-Type': 'application/octet-stream',\n 'Ocp-Apim-Subscription-Key': my_key}\n\nparams = {\n 'returnFaceId': 'true',\n 'returnFaceLandmarks': 'true',\n 'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion'\n}\n\nresponse = requests.post(api_url, params=params, headers=headers, data=image_data)\nresponse.raise_for_status()\nfaces = response.json()\nprint(faces)\n\n#Display the image\n\nimage_orig = open(image_path, \"rb\").read()\nimage = Image.open(BytesIO(image_orig))\nplt.figure(figsize=(12, 12))\n\nax = plt.imshow(image,alpha=1)\nfor face in faces:\n fr = face[\"faceRectangle\"]\n fa = face[\"faceAttributes\"]\n origin = (fr[\"left\"], fr[\"top\"])\n p = patches.Rectangle(\n origin, fr[\"width\"], fr[\"height\"], fill=False, linewidth=2, color='b')\n plt.text(origin[0], origin[1], \"%s, %d , %s\" % (fa[\"gender\"], fa[\"age\"], fa[\"glasses\"]),\n fontsize=20, color='w', weight=\"bold\", va=\"bottom\")\n ax.axes.add_patch(p)\n\n_ = plt.axis(\"off\")\nplt.show()\n","sub_path":"Info_in_picture.py","file_name":"Info_in_picture.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"232582889","text":"from math import sqrt\nfrom pandas import read_excel\nimport numpy as np\nimport pandas as pd\n\n\ndef normalize(dataframe, type):\n if type == \"standaryzacja\":\n return standardize(dataframe)\n elif type == \"unitaryzacja\":\n return unitarize(dataframe)\n else:\n return \"Nie znam typu\"\n\n\ndef standardize(dataframe):\n dataframe = (dataframe - dataframe.mean()) / (dataframe.std())\n return dataframe\n\n\ndef unitarize(dataframe):\n dataframe = (dataframe - dataframe.min()) / (dataframe.max() - dataframe.min())\n return dataframe\n\n\ndef change_character(dataframe, description):\n for col in dataframe.columns:\n if description[col][2] == 'd':\n dataframe[col] = 1 / dataframe[col]\n return dataframe\n\n\ndef apply_wages(dataframe, description):\n for col in dataframe.columns:\n dataframe[col] *= float(description[col][3])\n return dataframe\n\n\ndef add_summary_max_row(dataframe):\n dataframe = pd.concat([dataframe, pd.DataFrame(dataframe.max(axis=0), columns=['Max']).T])\n return dataframe\n\n\ndef dzero(dataframe):\n table = []\n for i in range(0, len(dataframe) - 1):\n s = 0\n for col in dataframe.columns:\n s += (dataframe[col][i] - dataframe[col][-1]) ** 2\n table.append(sqrt(s))\n dataframe = dataframe.drop('Max', axis=0)\n dataframe['dzero'] = table\n return dataframe\n\n\ndef run_miernik(df, des):\n col_list = ['M1', 'Z1', 'B1', 'S1', 'IT1', 'R1']\n zadaniowa = df[col_list]\n zadaniowa = change_character(zadaniowa, des)\n zadaniowa_std = normalize(zadaniowa, \"standaryzacja\")\n zadaniowa_std = apply_wages(zadaniowa_std, des)\n zadaniowa_std['srednia'] = zadaniowa_std.mean(axis=1)\n miernik = zadaniowa_std['srednia'].copy()\n miernik.sort(['srednia'], ascending=False)\n return print(miernik)\n\n\ndef run_helwig(df, des, what_kind):\n df = change_character(df, des)\n df_std = normalize(df, what_kind)\n df_std = add_summary_max_row(df_std)\n df_std = dzero(df_std)\n m = df_std['dzero'].mean()\n standarddev = df_std['dzero'].std()\n ar = []\n for i in range(0, len(df_std['dzero'])):\n z = 1 - (df_std['dzero'][i]) / (m + 2 * standarddev)\n ar.append(z)\n df_std['miernik'] = ar\n miernik = df_std['miernik'].copy()\n miernik.sort(['miernik'], ascending=False)\n return print(miernik)\n\n\nif __name__ == \"__main__\":\n framka = read_excel('C:/Users/Tomasz Hinc/Desktop/DaneHelwig.xlsx', sheetname='Dane')\n opisy = read_excel('C:/Users/Tomasz Hinc/Desktop/DaneHelwig.xlsx', sheetname='OpisZmiennych')\n run_helwig(framka, opisy, \"unitaryzacja \")\n","sub_path":"Miernik.py","file_name":"Miernik.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"397698099","text":"# 파이썬과 케라스로 배우는 강화학습\n\nimport numpy as np\nimport random\nfrom collections import deque\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.initializers import RandomUniform\n\n\nclass DQN(tf.keras.Model): # 모델 정의\n # 신경망 2개로 구성됨!!! (문양 결정/카드 종류 결정)\n def __init__(self, action_size): # depth = 6(모양 결정) or 9(종류 결정)\n super(DQN, self).__init__()\n self.fc1 = Dense(9, activation='relu') # 입력층?\n self.fc2 = Dense(9, activation='relu') # 은닉층1\n self.fc_out = Dense(action_size, kernel_initializer=RandomUniform(-1e-3, 1e-3)) # 출력층(가중치 초기화)\n\n def call(self, x): # 큐함수 반환?\n x = self.fc1(x)\n x = self.fc2(x)\n q = self.fc_out(x)\n return q\n\n\nclass Agent:\n def __init__(self, state_size, action_size):\n #self.render = True\n self.state_size = state_size\n self.action_size = action_size\n\n self.discount_rate = 0.99 # 보상 할인율\n self.learning_rate = 0.005 # 학습 속도\n self.epsilon = 1.0\n self.epsilon_decay = 0.60 # 에피소드의 45% 진행했을 때 엡실론 0 되도록 함\n self.epsilon_min = 0.05\n self.batch_size = 60\n self.train_start = 1200\n self.memory = deque(maxlen=2000)\n\n self.model = DQN(action_size)\n self.target_model = DQN(action_size)\n self.optimizer = Adam(lr=self.learning_rate)\n\n self.update_target_model()\n\n self.loss = 0\n\n def update_target_model(self): # 타겟 모델 업데이트\n self.target_model.set_weights(self.model.get_weights())\n\n def get_action(self, state): # 엡실론-탐욕 함수 기반으로 행동 선택\n state = np.reshape(state, [1, self.state_size])\n if np.random.rand() <= self.epsilon:\n return random.randrange(self.action_size)\n else:\n q_value = self.model(state)\n return np.argmax(q_value[0])\n\n def append_sample(self, state, action, reward, next_state, done): # 리플레이 메모리 업데이트\n self.memory.append((state, action, reward, next_state, done))\n\n def train_model(self): # 모델 훈련\n if self.epsilon > self.epsilon_min: # 엡실론 값 업데이트\n self.epsilon *= self.epsilon_decay\n\n mini_batch = random.sample(self.memory, self.batch_size) # 미니배치 가져온다\n # print(len(mini_batch))\n # print('mini_batch : ', mini_batch)\n states = np.array([sample[0] for sample in mini_batch])\n # print(states)\n actions = np.array([sample[1] for sample in mini_batch])\n # print(actions)\n rewards = np.array([sample[2] for sample in mini_batch])\n # print(rewards)\n next_states = np.array([sample[3] for sample in mini_batch])\n # print(next_states)\n dones = np.array([sample[4] for sample in mini_batch])\n\n model_params = self.model.trainable_variables\n with tf.GradientTape() as tape:\n predicts = self.model(states) # 케라스 모델의 입력(numpy array 형태여야 한다)으로 상태 준다\n one_hot_action = tf.one_hot(actions, self.action_size) # 실제로 에이전트가 한 행동의 큐함수 값 가져온다\n predicts = tf.reduce_sum(one_hot_action * predicts, axis=1) # 오류함수의 예측 부분\n\n target_predicts = self.target_model(next_states) # 타겟 모델 예측\n target_predicts = tf.stop_gradient(target_predicts)\n\n max_q = np.amax(target_predicts, axis=1) # 다음 상태의 Q함수 중 가장 큰 값 가져온다\n targets = rewards + (1 - dones) * self.discount_rate * max_q\n loss = tf.reduce_mean(tf.square(targets - predicts)) # (MSE) 오류함수 구한다\n\n grads = tape.gradient(loss, model_params) # 오류함수 기반으로 현재 모델 업데이트\n self.optimizer.apply_gradients(zip(grads, model_params))\n\n self.loss = loss\n\n","sub_path":"DQN_Player_V2.py","file_name":"DQN_Player_V2.py","file_ext":"py","file_size_in_byte":4704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"555176652","text":"import cv2\nimport numpy as np\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n\t_, frame = cap.read() #read frames\n\t\n\t#convert to hsv color space\n\thsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)\n\t\n\t#range\n\tlower_green = np.array([60,80,80])\n\tupper_green = np.array([100,255,255])\n\t\n\tlower_maroon = np.array([100,70,0])\n\tupper_maroon = np.array([180,150,100])\n\t\n\tmask1 = cv2.inRange(hsv,lower_green,upper_green) #thresholding\n\tmask2 = cv2.inRange(hsv,lower_maroon,upper_maroon)\n\t\n\tres1 = cv2.bitwise_and(frame,frame,mask = mask1) #bitwse and\n\tres2 = cv2.bitwise_and(frame,frame,mask = mask2)\n\t\n\tres = cv2.bitwise_or(res1,res2)\n\t\n\tcv2.imshow('frame',frame)\n\t#cv2.imshow('res1',res1)\n\t#cv2.imshow('res2',res2)\n\tcv2.imshow('res',res)\n\t\n\tk = cv2.waitKey(5) & 0xff\n\t\n\tif k == 27:\n\t\tbreak\n\t\t\ncv2.destroyAllWindows()\ncap.release()\n","sub_path":"Second_Years/Aman/OpenCV/Tutorials/2_multicolorfilter.py","file_name":"2_multicolorfilter.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"194994386","text":"# Copyright 2021 Sony Group Corporation.\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\nfrom typing import Callable, Optional, Tuple\n\nimport numpy as np\n\nimport nnabla as nn\nimport nnabla.functions as NF\nimport nnabla_rl.functions as RF\nfrom nnabla.initializer import ConstantInitializer\nfrom nnabla.parameter import get_parameter_or_create\nfrom nnabla_rl.initializers import HeUniform\n\n\ndef noisy_net(inp: nn.Variable,\n n_outmap: int,\n base_axis: int = 1,\n w_init: Optional[Callable[[Tuple[int, ...]], np.ndarray]] = None,\n b_init: Optional[Callable[[Tuple[int, ...]], np.ndarray]] = None,\n noisy_w_init: Optional[Callable[[Tuple[int, ...]], np.ndarray]] = None,\n noisy_b_init: Optional[Callable[[Tuple[int, ...]], np.ndarray]] = None,\n fix_parameters: bool = False,\n rng: Optional[np.random.RandomState] = None,\n with_bias: bool = True,\n with_noisy_bias: bool = True,\n apply_w: Optional[Callable[[nn.Variable], nn.Variable]] = None,\n apply_b: Optional[Callable[[nn.Variable], nn.Variable]] = None,\n apply_noisy_w: Optional[Callable[[nn.Variable], nn.Variable]] = None,\n apply_noisy_b: Optional[Callable[[nn.Variable], nn.Variable]] = None,\n seed: int = -1) -> nn.Variable:\n '''\n Noisy linear layer with factorized gaussian noise proposed by Fortunato et al. in the paper\n \"Noisy networks for exploration\". See: https://arxiv.org/abs/1706.10295 for details.\n\n Args:\n inp (nn.Variable): Input of the layer n_outmaps (int): output dimension of the layer.\n n_outmap (int): Output dimension of the layer.\n base_axis (int): Axis of the input to treat as sample dimensions. Dimensions up to base_axis will be treated\n as sample dimensions. Defaults to 1.\n w_init (None or Callable[[Tuple[int, ...]], np.ndarray]): Initializer of weights used in deterministic stream.\n Defaults to None. If None, will be initialized with Uniform distribution\n :math:`(-\\\\frac{1}{\\\\sqrt{fanin}},\\\\frac{1}{\\\\sqrt{fanin}})`.\n b_init (None or Callable[[Tuple[int, ...]], np.ndarray]): Initializer of bias used in deterministic stream.\n Defaults to None. If None, will be initialized with Uniform distribution\n :math:`(-\\\\frac{1}{\\\\sqrt{fanin}},\\\\frac{1}{\\\\sqrt{fanin}})`.\n noisy_w_init (None or Callable[[Tuple[int, ...]], np.ndarray]): Initializer of weights used in noisy stream.\n Defaults to None. If None, will be initialized to a constant value of :math:`\\\\frac{0.5}{\\\\sqrt{fanin}}`.\n noisy_b_init (None or Callable[[Tuple[int, ...]], np.ndarray]): Initializer of bias used in noisy stream.\n Defaults to None. If None, will be initialized to a constant value of :math:`\\\\frac{0.5}{\\\\sqrt{fanin}}`.\n fix_parameters (bool): If True, underlying weight and bias parameters will Not be updated during training.\n Default to False.\n rng (None or np.random.RandomState): Random number generator for parameter initializer. Defaults to None.\n with_bias (bool): If True, deterministic bias term is included in the computation. Defaults to True.\n with_noisy_bias (bool): If True, noisy bias term is included in the computation. Defaults to True.\n apply_w (None or Callable[[nn.Variable], nn.Variable]): Callable object to apply to the weights on\n initialization. Defaults to None.\n apply_b (None or Callable[[nn.Variable], nn.Variable]): Callable object to apply to the bias on\n initialization. Defaults to None.\n apply_noisy_w (None or Callable[[nn.Variable], nn.Variable]): Callable object to apply to the noisy weight on\n initialization. Defaults to None.\n apply_noisy_b (None or Callable[[nn.Variable], nn.Variable]): Callable object to apply to the noisy bias on\n initialization. Defaults to None.\n seed (int): Random seed. If -1, seed will be sampled from global random number generator. Defaults to -1.\n\n Returns:\n nn.Variable: Linearly transformed input with noisy weights\n '''\n\n inmaps = int(np.prod(inp.shape[base_axis:]))\n if w_init is None:\n w_init = HeUniform(inmaps, n_outmap, factor=1.0/3.0, rng=rng)\n if noisy_w_init is None:\n noisy_w_init = ConstantInitializer(0.5 / np.sqrt(inmaps))\n w = get_parameter_or_create(\"W\", (inmaps, n_outmap), w_init, True, not fix_parameters)\n if apply_w is not None:\n w = apply_w(w)\n\n noisy_w = get_parameter_or_create(\"noisy_W\", (inmaps, n_outmap), noisy_w_init, True, not fix_parameters)\n if apply_noisy_w is not None:\n noisy_w = apply_noisy_w(noisy_w)\n\n b = None\n if with_bias:\n if b_init is None:\n b_init = HeUniform(inmaps, n_outmap, factor=1.0/3.0, rng=rng)\n b = get_parameter_or_create(\"b\", (n_outmap, ), b_init, True, not fix_parameters)\n if apply_b is not None:\n b = apply_b(b)\n\n noisy_b = None\n if with_noisy_bias:\n if noisy_b_init is None:\n noisy_b_init = ConstantInitializer(0.5 / np.sqrt(inmaps))\n noisy_b = get_parameter_or_create(\"noisy_b\", (n_outmap, ), noisy_b_init, True, not fix_parameters)\n if apply_noisy_b is not None:\n noisy_b = apply_noisy_b(noisy_b)\n\n def _f(x):\n return NF.sign(x) * RF.sqrt(NF.abs(x))\n\n e_i = _f(NF.randn(shape=(1, inmaps, 1), seed=seed))\n e_j = _f(NF.randn(shape=(1, 1, n_outmap), seed=seed))\n\n e_w = NF.reshape(NF.batch_matmul(e_i, e_j), shape=noisy_w.shape)\n e_w.need_grad = False\n noisy_w = noisy_w * e_w\n assert noisy_w.shape == w.shape\n\n if with_noisy_bias:\n assert isinstance(noisy_b, nn.Variable)\n e_b = NF.reshape(e_j, shape=noisy_b.shape)\n e_b.need_grad = False\n noisy_b = noisy_b * e_b\n assert noisy_b.shape == (n_outmap,)\n weight = w + noisy_w\n\n if with_bias and with_noisy_bias:\n assert isinstance(b, nn.Variable)\n assert isinstance(noisy_b, nn.Variable)\n bias = b + noisy_b\n elif with_bias:\n bias = b\n elif with_noisy_bias:\n bias = noisy_b\n else:\n bias = None\n return NF.affine(inp, weight, bias, base_axis)\n","sub_path":"nnabla_rl/parametric_functions.py","file_name":"parametric_functions.py","file_ext":"py","file_size_in_byte":6818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"564010549","text":"n,U=[int(i) for i in input().split()]\nE=[int(i) for i in input().split()]\n\nk=0\nans=0.0\nhaveAns=False\nfor i in range(n):\n\twhile kans:\n\t\t\tans=rate\n\t\t\thaveAns=True\n\t\nif not haveAns:\n\tprint(-1)\nelse:\n\tprint(ans)\n","sub_path":"codeforces/normal/400/contest472c.py","file_name":"contest472c.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"602287348","text":"from django.db import models\nfrom django.utils.translation import ugettext as _\nfrom .abstract_models import (\n AbstractTimeStampModel,\n AbstractBaseAuthor\n\n)\nfrom .constants import LANGUAGES\n\n\nclass Category(AbstractTimeStampModel):\n category_name = models.CharField(\n _(\"Category name\"),\n max_length=255,\n )\n\n category_description = models.TextField(\n verbose_name=_(\"Category description\")\n )\n\n def __str__(self):\n return self.category_name\n\n class Meta:\n db_table = \"category\"\n\n\nclass Publisher(AbstractTimeStampModel):\n publisher_name = models.CharField(\n _(\"Publisher name\"),\n max_length=255,\n unique=True,\n )\n\n publisher_description = models.CharField(\n _(\"Publisher description\"),\n max_length=255\n )\n\n class Meta:\n ordering = (\"publisher_name\",)\n db_table = \"publisher\"\n\n def __str__(self):\n return self.publisher_name\n\n\nclass Keyword(AbstractTimeStampModel):\n keyword = models.CharField(\n max_length=255,\n verbose_name=_(\"Keyword\"),\n unique=True\n )\n\n keyword_description = models.TextField(\n verbose_name=_(\"Keyword description\"),\n blank=True,\n default=\"\"\n )\n\n def __str__(self):\n return self.keyword\n\n class Meta:\n db_table = \"keyword\"\n\n\nclass Biography(AbstractBaseAuthor):\n \"\"\"Biography class to create an instance of document author, editor, illustrator,\n video director, video producer and audio recorder\"\"\"\n keywords = models.ManyToManyField(\n Keyword,\n verbose_name=_(\"Search Keywords\"),\n blank=True\n )\n\n def __str__(self):\n return self.name\n\n class Meta:\n db_table = \"biography\"\n verbose_name = _(\"Author\")\n ordering = ['first_name']\n\n\nclass Sponsor(AbstractTimeStampModel):\n name = models.CharField(\n max_length=200,\n verbose_name=_(\"Sponsor Name\"),\n unique=True\n )\n\n description = models.TextField(\n verbose_name=_(\"Description\")\n )\n\n def __str__(self):\n return self.name\n\n class Meta:\n db_table = \"sponsor\"\n\n\nclass EducationLevel(models.Model):\n \"\"\"Education level\"\"\"\n\n EDUCATION_LEVEL = (\n (\"early primary level\", _(\"Early primary level\")),\n (\"primary level\", _(\"Primary level\")),\n (\"Middle school level\", _(\"Middle school level\")),\n (\"highschool level\", _(\"Highschool level\")),\n (\"intermediate level\", _(\"Intermediate level\")),\n )\n\n level = models.CharField(\n _(\"Education Level\"),\n max_length=255,\n choices=EDUCATION_LEVEL,\n unique=True\n\n )\n\n description = models.CharField(\n max_length=500,\n blank=True\n )\n\n def __str__(self):\n return self.level\n\n class Meta:\n db_table = \"education_level\"\n\n\nclass Language(models.Model):\n language = models.CharField(\n choices=LANGUAGES,\n max_length=7,\n verbose_name=_(\"Language\"),\n unique=True\n\n )\n\n class Meta:\n db_table = \"language\"\n\n def __str__(self):\n return u\"%s\" % (self.get_language_display())\n","sub_path":"src/pustakalaya_apps/core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"195311510","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Sep 24 11:53:05 2018\r\n\r\nMethod 1 - use a stack time O(n) spaceO(n)\r\n 1) Traverse the given list from head to tail and push every visited node to stack.\r\n 2) Traverse the list again. For every visited node, pop a node from stack and compare data of popped node with currently visited node.\r\n 3) If all nodes matched, then return true, else false.\r\n\r\nMethod 2 - reverse the LL time O(n) space O(1)\r\n 1) Get the middle of the linked list.\r\n 2) Reverse the second half of the linked list.\r\n 3) Check if the first half and second half are identical.\r\n 4) Construct the original linked list by reversing the second half again and attaching it back to the first half\r\n\r\nMethod 3 - using recursion\r\n 1) Sub-list is palindrome.\r\n 2) Value at current left and right are matching.\r\n\r\nIf both above conditions are true then return true.\r\nThe idea is to use function call stack as container. Recursively traverse till the end of list. When we return from last NULL, we will be at last node. The last node to be compared with first node of list.\r\nIn order to access first node of list, we need list head to be available in the last call of recursion. Hence we pass head also to the recursive function. If they both match we need to compare (2, n-2) nodes. Again when recursion falls back to (n-2)nd node, we need reference to 2nd node from head. We advance the head pointer in previous call, to refer to next node in the list.\r\n\r\n@author: Nagadeepa\r\n\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Method 2\r\n\r\n\r\nimport Single_LL as LL\r\n\r\ndef isPalindrome(lis):\r\n slow = lis.getHead().next\r\n fast = slow\r\n prev = None\r\n \r\n while fast and fast.next: # when the fast or fast.nextElemnt is None then the slow will reach the middle\r\n fast = fast.next.next\r\n temp = slow.next # reverse the element of the first half\r\n slow.next = prev\r\n prev = slow\r\n slow = temp\r\n \r\n if fast:#odd numbers\r\n sec_start = slow.next\r\n else: # even numbers\r\n sec_start = slow\r\n \r\n while prev:\r\n # compare reverse element and next half elements \r\n if prev.data == sec_start.data:\r\n sec_start = sec_start.next\r\n prev = prev.next\r\n else:\r\n return False\r\n return True\r\n \r\n\r\n\r\nli = LL.LinkedList()\r\ndata_list = input('Please enter the elements in the linked list: ').split()\r\nfor data in data_list:\r\n li.insertAtHead((data))\r\n \r\nif isPalindrome(li):\r\n print('The linked list is a palindrome')\r\nelse:\r\n print('The linked list is not a palindrome.')\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n","sub_path":"Week_04_LinkedLists/1_isPalindrome.py","file_name":"1_isPalindrome.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"177086049","text":"from django.db import models\nfrom django.forms import ValidationError\nfrom teams.models import Team\n\n\nclass Match(models.Model):\n\n defender = models.ForeignKey(\n Team, related_name='defender_matches', on_delete=models.CASCADE)\n\n challenger = models.ForeignKey(\n Team, related_name='challenger_matches', on_delete=models.CASCADE)\n\n winner = models.ForeignKey(\n Team, related_name='won_matches', on_delete=models.CASCADE, blank=True,\n null=True\n )\n\n loser = models.ForeignKey(\n Team, related_name='lost_matches', on_delete=models.CASCADE, blank=True,\n null=True\n )\n\n modified = models.DateTimeField(auto_now=True)\n created = models.DateTimeField(auto_now_add=True)\n\n class Meta:\n verbose_name_plural = 'Matches'\n\n def __str__(self):\n return f'{self.challenger.name} vs {self.defender.name}'\n\n def end_match(self, winner):\n \"\"\"Set Winner and Loser for match, then save match object.\"\"\"\n teams = Team.objects.filter(\n pk__in=[self.challenger.id, self.defender.id])\n\n self.winner = winner\n self.loser = teams.exclude(id=self.winner.id).get()\n\n # Do some quick sanity checking before saving.\n if not (self.winner == self.challenger or self.winner == self.defender):\n raise ValueError(\n 'Winner must be a team associated with this match.')\n\n if not (self.loser == self.challenger or self.loser == self.defender):\n raise ValueError(\n 'Loser must be a team associated with this match.')\n\n self.save()\n\n\nclass LadderMatch(Match):\n\n ranking_applied = models.BooleanField(default=False)\n scheduled = models.DateTimeField(blank=True, null=True)\n\n def end_match(self, winner=None, forfeit_team=None):\n \"\"\"\n Override base end_match method to perform extra ladder-related tasks.\n\n First update ladder ranks, according to the following rules:\n\n - IF Rank has NOT already been applied based on the result of match\n\n - IF `winner` argument is specified, assigned winner and loser\n accordingly.\n\n - IF `forfeit` team argument is specified, assign that team\n as loser and other team as winner\n\n - ELSE IF neither argument supplied, raise ValueError.\n\n Then, call parent end_match() method, and pass in winner as determined\n above.\n\n Rank logic is applied as follows:\n\n - IF the CHALLENGER wins, the challenger and defender team's\n ladder ranks are swapped.\n\n - IF the DEFENDER wins, all team ranks remain the same.\n \"\"\"\n if forfeit_team:\n if not (forfeit_team == self.challenger or forfeit_team == self.defender):\n raise ValueError(\n 'Forfeit team must be a team associated with this match.')\n elif winner:\n if not (winner == self.challenger or winner == self.defender):\n raise ValueError(\n 'Winner must be a team associated with this match.')\n else:\n raise ValueError(\n 'A winner or forefeiting team must be specified.')\n\n if forfeit_team:\n self.forfeit_team = forfeit_team\n winner = teams.exclude(id=forfeit_team.id).get()\n\n if not self.ranking_applied:\n # Now apply rank according to winner and loser\n if winner == self.challenger:\n challenger_old_rank = self.challenger.ladder_ranking.position\n defender_old_rank = self.defender.ladder_ranking.position\n\n self.challenger.ladder_ranking.position = defender_old_rank\n self.challenger.ladder_ranking.save()\n\n self.defender.ladder_ranking.position = challenger_old_rank\n self.defender.ladder_ranking.save()\n\n self.challenger.save()\n self.defender.save()\n\n else:\n # If the defender wins, no rank changes occur\n pass\n\n self.ranking_applied = True\n\n super().end_match(winner)\n","sub_path":"matches/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"123921969","text":"import os\nimport subprocess\n\ndef files(path):\n for file in os.listdir(path):\n yield file\n\nos.environ[\"DIAPHORA_AUTO\"] = \"1\"\nos.environ[\"DIAPHORA_USE_DECOMPILER\"] = \"1\"\nos.environ[\"DIAPHORA_DECOMPILER_PLUGIN\"] = \"hexarm64\"\nIDA64_PATH='\"C:\\\\Program Files\\\\IDA 7.0\\\\ida64.exe\"'\nDYAPHORA_FOLDER_PATH='\"C:\\diaphora\"'\n\nfor folder in files(\".\\\\bin_to_diff\"):\n\tfor file in files(\".\\\\bin_to_diff\\\\\"+ folder): \n\t\tos.environ[\"DIAPHORA_EXPORT_FILE\"]=str(file + '.db')\n\t\tcommand=IDA64_PATH +\" -A -B -S\"+DYAPHORA_FOLDER_PATH+\"\\\\script\\\\diaphora.py\"+ \" \" +DYAPHORA_FOLDER_PATH +\"\\\\bin_to_diff\" +\"\\\\\" + str(folder)+ \"\\\\\"+str(file)\n\t\tprint(\"Exporting \" + file + \" Diaphora Database\\n\", flush=True)\n\t\tos.system('\"'+ command + '\"')\n","sub_path":"export.py","file_name":"export.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"271977651","text":"class RandomizedSet:\n\n def __init__(self):\n self.dict_t = {}\n self.list_t = []\n\n def insert(self, val: int) -> bool:\n if val in self.dict_t:\n return False\n self.dict_t[val] = len(self.list_t)\n self.list_t.append(val)\n return True\n\n def remove(self, val: int) -> bool:\n if not val in self.dict_t:\n return False\n index, lastVal = self.dict_t[val], self.list_t[-1]\n self.list_t[index], self.dict_t[lastVal] = lastVal, index\n self.list_t.pop()\n del self.dict_t[val]\n return True\n\n def getRandom(self) -> int:\n return choice(self.list_t)\n\n\n# Your RandomizedSet object will be instantiated and called as such:\n# obj = RandomizedSet()\n# param_1 = obj.insert(val)\n# param_2 = obj.remove(val)\n# param_3 = obj.getRandom()\n","sub_path":"src/HashTable/380.py","file_name":"380.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"471491515","text":"from bluesky.callbacks.broker import post_run\nfrom bluesky.callbacks.mpl_plotting import LiveGrid\nfrom bluesky.plans import outer_product_scan\n\n# xrfmapTiffOutputDir = '/home/xf08bm/DATA2017/Comissioning/20170619/'\n# hard-coded for testing now; need to be set to automatically use SAF, today's date, etc.\n\n\ndef xrfmap(\n *,\n xstart,\n xnumstep,\n xstepsize,\n ystart,\n ynumstep,\n ystepsize,\n rois=(),\n # shutter=True,\n # align=False,\n # acqtime,\n # numrois=1,\n # i0map_show=True,\n # itmap_show=False,\n # record_cryo=False,\n # setenergy=None,\n # u_detune=None,\n # echange_waittime=10\n):\n \"\"\"\n input:\n xstart, xnumstep, xstepsize (float)\n ystart, ynumstep, ystepsize (float)\n\n \"\"\"\n # define detector used for xrf mapping functions\n xrfdet = [sclr] # currently only the scalar; to-do: save full spectra\n\n xstop = xstart + xnumstep * xstepsize\n ystop = ystart + ynumstep * ystepsize\n\n # setup live callbacks:\n\n livetableitem = [xy_stage.x, xy_stage.y]\n livecallbacks = []\n\n for roi in rois:\n livecallbacks.append(\n LiveGrid(\n (ynumstep + 1, xnumstep + 1),\n roi,\n xlabel=\"x (mm)\",\n ylabel=\"y (mm)\",\n extent=[xstart, xstop, ystart, ystop],\n )\n )\n livetableitem.append(roi)\n\n # # setup LiveOutput\n # xrfmapOutputTiffTemplate = (xrfmapTiffOutputDir +\n # \"xrfmap_scan{start[scan_id]}\" +\n # roi + \".tiff\")\n # # xrfmapTiffexporter = LiveTiffExporter(roi, xrfmapOutputTiffTemplate, db=db)\n # xrfmapTiffexporter = RasterMaker(xrfmapOutputTiffTemplate, roi)\n # livecallbacks.append(xrfmapTiffexporter)\n\n livecallbacks.append(LiveTable(livetableitem))\n\n # setup LiveOutput\n\n # if sclr in xrfdet:\n # for sclrDataKey in [getattr(sclr.cnts.channels, f'chan{j:02d}') for d in range(1, 21)]:\n # xrfmapOutputTiffTemplate = (xrfmapTiffOutputDir +\n # \"xrfmap_scan{start[scan_id]}\" +\n # sclrDataKey + \".tiff\")\n #\n # # xrfmapTiffexporter = LiveTiffExporter(roi, xrfmapOutputTiffTemplate, db=db)\n #\n # # LiveTiffExporter exports one array from one event,\n # # commented out for future reference\n # xrfmapTiffexporter = RasterMaker(xrfmapOutputTiffTemplate,\n # sclrDataKey)\n # livecallbacks.append(xrfmapTiffexporter)\n\n xrfmap_scanplan = outer_product_scan(\n xrfdet,\n xy_stage.y,\n ystart,\n ystop,\n ynumstep + 1,\n xy_stage.x,\n xstart,\n xstop,\n xnumstep + 1,\n False,\n )\n xrfmap_scanplan = bp.subs_wrapper(xrfmap_scanplan, livecallbacks)\n\n scaninfo = yield from xrfmap_scanplan\n\n return scaninfo\n","sub_path":"startup/91-userscans.py","file_name":"91-userscans.py","file_ext":"py","file_size_in_byte":2984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"18648551","text":"import io\nimport os\nimport subprocess\nimport glob\nbase_dir = \"/Users/madhuhegde/work/cs230/data/more_data/\"\nphase_gt_dir = base_dir+\"phase_annotations/\"\nvideo_base_dir = base_dir+\"videos/\"\nimage_base_dir = base_dir+\"images/\"\nlabel_base_dir = base_dir+\"labels/\"\nimport pdb\n\n#num_videos = 1\n\ndef extract_images(video,output,target_fps):\n command = \"ffmpeg -i {video} -r {target_fps} -q:v 2 -f image2 {output}\".format(video=video, output=output, target_fps=target_fps)\n #command = \"echo {video} {output}\".format(video=video, output=output)\n subprocess.call(command,shell=True)\n return\n\ndef resize_images(image_path):\n image_files = glob.glob(image_path+\"*.jpg\")\n for image_file in image_files:\n command = \"ffmpeg -y -i {input} -vf scale=250:250 {output}\".format(input=image_file, output=image_file)\n subprocess.call(command,shell=True)\n #print(image_file)\n \n \ndef generate_gt_data(in_file, fps):\n #generate \n out_list = []\n step = int(25/fps)\n rem = 25 - step*fps\n \n with open(in_file, 'r') as handle:\n out_list.append(handle.readline())\n linecount = 0\n for lineno, line in enumerate(handle):\n if(rem>0) and (lineno % 25 == 0):\n for k in range(rem):\n continue\n #print(\"skip \"+str(lineno)+\"\\n\")\n else: \n if lineno % step == 0:\n #print(\"in \"+str(lineno)+\"\\n\")\n linecount = linecount + 1\n templine = line.split('\\t')\n #templine = str(int(int(templine[0])/step))+'\\t'+templine[1]\n templine = str(linecount) +'\\t'+templine[1]\n out_list.append(templine)\n \n return(out_list)\n \n \n \n#pdb.set_trace() \ndef process_videos(video_dir, image_dir, label_dir, num_videos=1, target_fps=5):\n\n video_files = glob.glob(video_dir+\"*.mp4\")\n print(video_dir)\n print(len(video_files))\n video_num = 0\n \n \n for video_num, video_file in enumerate(video_files):\n file_name = video_file.split('/')[-1]\n file_name = os.path.splitext(file_name)[0]\n \n image_folder_cmd = \"mkdir \" + image_dir+file_name\n os.system(image_folder_cmd)\n \n image_folder_name = image_dir+file_name+'/'+file_name+'-%d.jpg'\n print(file_name, image_folder_name+'\\n')\n \n #extract images from videos\n \n #extract_images(video_file, image_folder_name, target_fps)\n \n #resize images to 250 x 250. Currently hardcoded to 250 x 250.\n #existing images are overwritten\n #resize_images(image_dir+file_name)\n \n gt_file_name = phase_gt_dir+file_name+\"-phase.txt\"\n \n fps = target_fps # make sure it matches ffmpeg argument\n gt_list = generate_gt_data(gt_file_name, fps)\n #print(gt_list)\n gt_label_file = label_dir+file_name+\"-label.txt\"\n #print(gt_label_file)\n with open(gt_label_file, 'w') as handle:\n handle.writelines(gt_list)\n \n if(video_num >= num_videos-1):\n break \n \n return video_num \n \n \nif __name__ == \"__main__\":\n num_train_videos = 40\n num_test_videos = 10\n num_eval_videos = 4\n target_fps = 2\n train_video_path = video_base_dir #+\"train/\"\n test_video_path = video_base_dir+\"test/\"\n eval_video_path = video_base_dir+\"eval/\"\n train_images_path = image_base_dir+\"train/\"\n test_images_path = image_base_dir+\"test/\"\n eval_images_path = image_base_dir+\"eval/\"\n train_labels_path = label_base_dir+\"train/\"\n test_labels_path = label_base_dir+\"test/\"\n eval_labels_path = label_base_dir+\"eval/\"\n \n train_num = process_videos(train_video_path, train_images_path, train_labels_path, num_train_videos, target_fps)\n #test_num = process_videos(test_video_path, test_images_path, test_labels_path, num_test_videos, target_fps)\n #eval_num = process_videos(eval_video_path, eval_images_path, eval_labels_path, num_eval_videos, target_fps)\n\n","sub_path":"CNN_LSTM_data_prepare.py","file_name":"CNN_LSTM_data_prepare.py","file_ext":"py","file_size_in_byte":3891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"389354722","text":"import numpy as np\n\n#go to idle\n#go to \n\n#loop these coordinates\n\nlean_amount = 0\nback_leg_offset = 0\n\n\ntilt = np.radians(20)\n\n\nx = 0\ny = 100\n\nl = 380\n\nnew_x = np.cos(tilt) * x\nnew_y = np.cos(tilt) * y\n\ndelta_y = l * np.sin(tilt)\ndelta_y_array = np.array([\n [0, delta_y/2],\n [0, delta_y/2],\n [0, -delta_y/2],\n [0, -delta_y/2]\n], dtype=float)\n\n\nhome = np.array([\n #Front\n [0, 100 + lean_amount], #Left\n [0, 100 + lean_amount], #Right\n #Hind\n [0 - back_leg_offset, 100 - lean_amount], #Left\n [0 - back_leg_offset, 100 - lean_amount] #Right\n], dtype = float)\n\n\nlift_amount = 30\n\n#Right leg down\n_front_left_up = home + np.array([\n #Front\n [0, lift_amount], #Left\n [0,lift_amount], #Right\n #Hind\n [0,0], #Left\n [0,0] #Right\n], dtype = float)\n\n#Right leg down\n_front_right_up = home + np.array([\n #Front\n [0,0], #Left\n [0,0], #Right\n #Hind\n [-40,50], #Left\n [-40,50] #Right\n], dtype = float)\n\nfront_left_up = np.cos(tilt) * _front_left_up + delta_y_array #tilt(_front_left_up)\nfront_right_up = np.cos(tilt) * _front_right_up + delta_y_array #tilt(_front_right_up)\n\nvelocity_idle = np.array([\n 2.0,2.0,\n 2.0,2.0,\n 4.0,4.0,\n 4.0,4.0\n])\n\nacceleration = np.full(8, 200.0)\n\nvelocity = np.array([\n 2.0,2.0,\n 2.0,2.0,\n 4.0,4.0,\n 4.0,4.0\n])\n\n\n#def tilt(coordinates):\n# new_coordinates = np.array((4, 2), dtype=float)\n\n# for i in range(len(coordinates)):\n# new_coordinates[i] = coordinates[i] * np.cos(tilt)\n\n#siste endringer:\n#Fart 0.5 -> 1\n#[forward_distance + 20, height + 40] -> [forward_distance + 20, height + 20]\n\n","sub_path":"Latest-Version/simple_trot.py","file_name":"simple_trot.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"24077411","text":"import time\nfrom flask import Flask\nimport tweepy\nimport asyncio\nfrom flask import jsonify\n\napp = Flask(__name__)\n\nconsumer_key = 'rMFxOm5jau48XzAHl0Z6yBVc2'\nconsumer_secret = 'uvhGy8ramCZ79bP9i0huOIvADtLcAiYJAOzcOU3Gehzd4uwHmZ'\naccess_token = '1276372249474293761-56b6xSZF8SFJeYli9gOstXxBGAgc26'\naccess_token_secret ='cqe6OxGtro14ejTLGZujRn6w70nULTrztfmMTZ2LGWmng'\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\napi = tweepy.API(auth)\n\ntry:\n api.verify_credentials()\n print(\"Authentication OK\")\nexcept:\n print(\"Error during authentication\")\n\n#\n#\n#\n#\n\n\n\n@app.route('/time')\ndef get_current_time():\n\n fDate = time.localtime()\n time_string = time.strftime(\"%m/%d/%Y\", fDate)\n print(time_string)\n return {'time': time_string }\n\n\n@app.route('/')\ndef hello_world():\n return 'Hello to the World of Flask!'\n\n@app.route('/gettweets')\ndef getTwitterDetails():\n public_tweets = api.user_timeline(\"realDonaldTrump\")\n response = []\n for tweet in public_tweets:\n response.append(tweet.text)\n return {'tweets': response}\n \n\n\n\"\"\" user = api.get_user(\"MikezGarcia\")\n\nprint(\"User details:\")\nprint(user.name)\nprint(user.description)\nprint(user.location)\n\nprint(\"Last 20 Followers:\")\nfor follower in user.followers():\n print(follower.name)\n \"\"\"\n\n\n \n\n","sub_path":"api/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"378844377","text":"from __future__ import print_function, division\nimport fakespikes as fs\n\n\ndef ie(t, dt, re=2, ri=5, ge=0.2e-3, gi=0.8e-3, n=1e3, seed=None):\n \"\"\"Simulate and LFP with balanced excitatory-inhibitory noise.\n \n Params\n ------\n t : numeric (seconds)\n Length of the LFP\n dt : numeric (seconds)\n Temporal resolution\n re : numeric (Hz)\n Excitatory firing rate\n ri : numeric (Hz)\n Inhibitory firing rate\n ge : numeric (siemens)\n Excitatory conductance\n gi : numeric (siemens)\n Inhibitory conductance\n n : numeric \n Number of simulate neurons in total\n seed : numeric, None\n Random seed.\n \n Returns\n -------\n x : 1d array\n The LFP\n \"\"\"\n n = int(n)\n ne = int(n * 0.8)\n ni = int(n - ne)\n\n times = fs.rates.create_times(t, dt)\n rates_e = fs.rates.constant(times, re)\n rates_i = fs.rates.constant(times, ri)\n\n # Define the nrns\n nrns = fs.neurons.Spikes(n, t, dt=dt, seed=seed)\n\n # And take two samples, one for E and I\n spks_e = nrns.poisson(rates_e)\n spks_e = spks_e[:, range(ne)]\n\n spks_i = nrns.poisson(rates_i) \n spks_i = spks_i[:, range(ni)]\n\n # Convolve E and I spikes with appropriate synaptic kernels,\n ns, ts = fs.util.to_spiketimes(times, spks_e)\n e = fs.util.dendritic_lfp(ns, ts, ne, t, gmax=ge, \n tau_rise=0.1e-3, tau_decay=2e-3, norm=False)\n\n ns, ts = fs.util.to_spiketimes(times, spks_i)\n i = fs.util.dendritic_lfp(ns, ts, ni, t, gmax=gi, \n tau_rise=0.5e-3, tau_decay=10e-3, norm=False)\n\n # scale the result by their potentials,\n Vr = -65e-3\n Ee = 0\n Ei = -80e-3\n e *= (Ee - Vr)\n i *= (Ei - Vr)\n\n # and add 'em, giving the lfp.\n lfp = e + i\n\n return lfp\n\n\ndef brown(t, dt, sigma=0.1e-6, a=10e-6, seed=None):\n \"\"\"Simulate a LFP with brownian noise.\n \n Params\n ------\n t : numeric (seconds)\n Length of the LFP\n dt : numeric (seconds)\n Temporal resolution\n sigma : numeric (volts)\n Std dev of the normal dist.\n a : numeric (volts)\n Overall LFP magnitude\n seed : numeric, None\n Random seed.\n \n Returns\n -------\n x : 1d array\n The LFP\n \"\"\"\n times = fs.rates.create_times(t, dt)\n lfp = fs.rates.stim(times, a, sigma, seed)\n lfp -= lfp.mean()\n\n return lfp\n\n","sub_path":"lfp.py","file_name":"lfp.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"256689251","text":"from copy import deepcopy\nfrom uuid import uuid4\n\nfrom .tasks import BaseTask\nfrom .type_helpers import type_from_string\n\n\nclass Flow(object):\n def __init__(self, task: BaseTask, uid=None, friendly_name=None):\n self.uid = uid or uuid4()\n self.friendly_name = friendly_name or ''\n self.root_task = BaseTask.find_root(task)\n\n # when deserializing, tasks will already have ids, that we want to preserve\n if not self.root_task.id:\n self.root_task.set_ids()\n\n @property\n def is_halted(self):\n # if we encounter a halted task, we want to halt the whole flow\n return self.root_task.leaf.is_halted\n\n @property\n def is_complete(self):\n return self.root_task.leaf.status == BaseTask.STATUS_COMPLETE\n\n def run(self, **kwargs):\n while True:\n task = self._get_next(self.root_task)\n if not task:\n break\n task.run(**kwargs)\n\n return self.root_task.leaf.result\n\n def step(self, **kwargs):\n while True:\n task = self._get_next(self.root_task)\n if not task:\n return\n\n if self._before_task_run(task):\n break\n\n task.run(**kwargs)\n self._after_task_run(task)\n\n return task\n\n def _before_task_run(self, _task):\n \"\"\"\n Allow inheritors to choose not to run the particular task by returning False\n \"\"\"\n return True\n\n def _after_task_run(self, _task):\n \"\"\"\n Allow inheritors to run code after a task has been run\n \"\"\"\n return None\n\n def _get_next(self, task):\n if self.is_halted:\n return None\n\n sub_tasks = task.get_all_tasks()\n for sub_task in sub_tasks:\n if sub_task == task:\n if sub_task.status == BaseTask.STATUS_PENDING:\n return task\n else:\n next_task = self._get_next(sub_task)\n if next_task:\n return next_task\n\n if task.status == BaseTask.STATUS_COMPLETE and task.next:\n return self._get_next(task.next)\n\n return None\n\n def to_list(self):\n return self.root_task.to_list()\n\n @classmethod\n def from_list(cls, task_list: list, uid=None, friendly_name=None):\n # tasks come in a possibly randomly ordered list\n # we need to figure out the correct order for creating them starting with leaves\n\n created = {}\n last_created = None\n remaining_tasks = deepcopy(task_list)\n\n while remaining_tasks:\n for task_data in remaining_tasks:\n task_depends_on = ([task_data['prev']] if task_data['prev'] else []) + \\\n (task_data.get('sub_tasks') or [])\n\n if all(depends_on in created for depends_on in task_depends_on):\n task_type = type_from_string(task_data['class'])\n task_data['sub_tasks'] = [created[task_id] for task_id in (task_data.get('sub_tasks') or [])]\n task_data['prev'] = created[task_data['prev']] if task_data['prev'] else None\n\n last_created = task_type.from_data(task_data)\n\n created[last_created.id] = last_created\n remaining_tasks.remove(task_data)\n break\n\n return cls(BaseTask.find_root(last_created), uid=uid, friendly_name=friendly_name)\n","sub_path":"taskflow/flow.py","file_name":"flow.py","file_ext":"py","file_size_in_byte":3460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"637762143","text":"import g2d\r\n\r\nARENA_W, ARENA_H = 480, 360\r\nBALL_W, BALL_H = 20, 20\r\nimage = g2d.load_image(\"ball.png\")\r\ncnt = 0\r\n\r\nclass TurningBall:\r\n def __init__(self, pos: (int, int)):\r\n self._x = pos[0]\r\n self._y = pos[1]\r\n self._dx = 5\r\n self._dy = 5\r\n\r\n def move(self, cnt):\r\n if cnt >= 10 and cnt < 20:\r\n self._y += self._dy\r\n elif cnt >= 20 and cnt < 30:\r\n self._x -= self._dx\r\n elif cnt >= 30 and cnt < 40:\r\n self._y -= self._dy\r\n else: \r\n self._x += self._dx\r\n\r\n def position(self) -> (int, int):\r\n return self._x, self._y\r\n\r\nb1 = TurningBall((140, 180))\r\n\r\ndef tick():\r\n global cnt\r\n g2d.clear_canvas() # Draw background\r\n g2d.draw_image(image, b1.position())\r\n b1.move(cnt)\r\n cnt += 1\r\n\r\n if cnt == 40:\r\n cnt = 0\r\n\r\ndef main():\r\n global b1\r\n g2d.init_canvas((ARENA_W, ARENA_H))\r\n\r\n g2d.main_loop(tick)\r\n\r\nmain()\r\n","sub_path":"Python/esercizi/3.2.py","file_name":"3.2.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"279377341","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 17 16:59:54 2020\n\n@author: Pilar Tagliero\n\"\"\"\n\n###############################################################################\n\nimport numpy as np\nfrom math import pi, sqrt\nfrom AE_model import AE_model_OL, AE_model_CL\nimport control as ctrl\nimport matplotlib.pyplot as plt\n\n\n\ndef airfoil(geom_prop, mat_prop):\n\n### OBJECTIVE: Obtain airfoil parameters from beam cross-section gemetrical\n# parameters \n\n### INPUTS: \n# s = semispan\n# c = width of wingbox / chord of the airfoil\n# h = hight of wingbox\n# th = [t1, t2, t3, t4, t5] cross-section shell thickness\n# mat_prop = material properties: rho or mu, E or EI, G or GJ\n\n### OUTPUTS:\n# m = total mass\n# I_alpha = mass moment of inertia relative to z\n# w_h = plunge frequency\n# w_alpha = pitch frequency\n# xg = c.g. x position (distance from the cross-section left wall) \n \n \n# Unpack data \n s = geom_prop['s']\n c = geom_prop['c']\n h = geom_prop['h']\n th = geom_prop['th']\n ea_b = geom_prop['ea_b']\n\n# Cross-section area \n l = c\n L = np.array([h, h, l, l, h])\n a = np.multiply(th,L)-np.array([th[0]*(th[2]+th[3]), th[1]*(th[2]+th[3]), 0, 0, th[4]*(th[2]+th[3])])\n A = sum(a) \n \n# Mass\n if 'rho' in mat_prop:\n m = mat_prop['rho']*A*s\n elif 'mu' in mat_prop:\n m = mat_prop['mu']*s\n mat_prop['rho'] = mat_prop['mu']/A\n else:\n print ('Data is missing: Load density (rho) or Linear density (mu)')\n\n# Position of the center of gravity \n xg = (l*a[1]+0.5*l*a[4]+0.5*l*a[2]+0.5*l*a[3])/A\n yg = (0.5*h*(a[0]+a[1]+a[4])+h*a[2])/A\n\n# cg = distance of the cg from the midchord\n# cg_b = cg normalized with the semichord (b)\n# cg = xg - b\n# cg/b = xcg/b - 1 \n# cg/b = 2*xcg/c - 1 \n cg_b = 2/c*xg-1\n\n# Distance between the aeroelastic axis (ea) and the center of gravity (cg) normalized with the semichord (b)\n x_alpha = ea_b - cg_b\n\n\n# Iy \n hr = h-th[3]-th[2]\n Iy1 = th[0]**3*hr/12 + a[0]*(xg-th[0]/2)**2\n Iy2 = th[1]**3*hr/12 + a[1]*(c-th[1]/2-xg)**2\n Iy3 = c**3*th[2]/12 + a[2]*(0.5*c-xg)**2\n Iy4 = c**3*th[3]/12 + a[3]*(0.5*c-xg)**2\n Iy5 = th[4]**3*h/12 + a[4]*(0.5*c-xg)**2\n Iy = Iy1+Iy2+Iy3+Iy4+Iy5\n\n# Ix \n Ix1 = th[0]*hr**3/12 + a[0]*(0.5*h-yg)**2\n Ix2 = th[1]*hr**3/12 + a[1]*(0.5*h-yg)**2\n Ix3 = c*th[2]**3/12 + a[2]*(yg-th[2]/2)**2\n Ix4 = c*th[3]**3/12 + a[3]*(h-th[3]/2-yg)**2\n Ix5 = th[4]*h**3/12 + a[4]*(0.5*h-yg)**2 \n Ix = Ix1+Ix2+Ix3+Ix4+Ix5\n\n# Iz: relative to an axis perpendicular to the wingbox cross-section\n Iz = Ix+Iy\n\n# Torsional constant (J) \n if th[4] < hr:\n I = hr/th[0]+hr/th[1]+l/th[2]+l/th[3]\n J = 4*(l*h)**2/I+hr*th[4]**3*((1/3)-0.21*(th[4]/hr)*(1-(th[4]**4/(12*hr**4))))\n else: \n I = hr/th[0]+hr/th[1]+l/th[2]+l/th[3] \n J = 4*(l*h)**2/I+th[4]*hr**3*((1/3)-0.21*(hr/th[4])*(1-(hr**4/(12*th[4]**4))))\n\n# Moment of inertia \n I_alpha = (m/A)*Iy\n \n# Frequency\n X1 = 1.875 # first non zero sol of cos(x)*cosh(x)+1 = 0 \n \n # Flexural mode\n if 'EI' in mat_prop:\n w_h = sqrt((X1/s)**4*(mat_prop['EI'])/(A*mat_prop['rho']))\n else:\n w_h = sqrt((X1/s)**4*(mat_prop['E']*Ix)/(A*mat_prop['rho'])) \n \n if 'GJ' in mat_prop:\n w_alpha = (pi/(2*s))*sqrt(mat_prop['GJ']/(mat_prop['rho']*Iz))\n else:\n w_alpha = (pi/(2*s))*sqrt(mat_prop['G']*J/(mat_prop['rho']*Iz))\n\n\n return [m, I_alpha, w_h, w_alpha, xg, J, x_alpha]\n\n\n###############################################################################\n \ndef V_flutter(model,V,output_choice): \n \n# OBJECTIVE: obtain flutter velocity by means of computing the frequency and \n# damping of the system assuming quasisteady aerodynamics\n\n### INPUTS: \n# AE_model = State space of the aeroelastic model (can be open or closed-loop)\n# V = Range of speed speeds where the frequency and damping are computed \n\n### OUTPUTS:\n# omega = Pulsation for each velocity (rad/s)\n# xsi = Damping for each velocity \n# Vf = Flutter velocity: first velocity where damping is (very close to) 0 \n \n for i in range(len(V)):\n model.gen_SS(V[i],0,output_choice)\n wn, xsi, poles = ctrl.damp(model.SS, doprint=False)\n wn_f = []\n if any(x<0 for x in xsi)==True:\n V_f = V[i] # Flutter speed\n wn_f = [min(wn), max(wn)]\n break\n SS_f = model.SS\n _,_, poles_f = ctrl.damp(SS_f, doprint=False) \n \n return [V_f, wn_f, SS_f, poles_f] \n \n\n###############################################################################\n\ndef value2norm(V,LL,UL):\n \n# OBJECTIVE: Normalize design variables so as upper_boundaries are all 1 \n# and lower boundaries are all -1. \n\n### INPUTS: \n# V = value to normalize\n# UL = upper limit\n# LL = lower limit \n\n### OUTPUTS:\n# x = normalized variables\n\n a = np.divide(2,(UL-LL))\n b = np.divide((UL+LL),(UL-LL))\n x = np.multiply(a,V)-b\n \n return x\n\n###############################################################################\n\ndef norm2value(x,LL,UL):\n\n# OBJECTIVE: Recover real values of the design variables from normalized ones \n \n a = np.divide(2,(UL-LL))\n b = -np.divide((UL+LL),(UL-LL))\n DV = np.divide((x-b),a)\n \n return DV\n\n###############################################################################\n \ndef objective (x, *args):\n rho_air, geom_prop, mat_prop, Ref, lb_DV, ub_DV, V, output_choice, aero_coef, EF, f = args\n \n # Recover the design variables\n DV = norm2value(x, lb_DV, ub_DV)\n geom_prop['th'] = DV[0:5]\n K = DV[5:9]\n \n # Build airfoil with the design variables\n [m, I_alpha, w_h, w_alpha, xg, J, x_alpha] = airfoil (geom_prop, mat_prop)\n\n # Build closed loop\n wing_CL = AE_model_CL(aero_coef, rho_air, geom_prop['ea_b'], geom_prop['c'], geom_prop['s'], m, I_alpha, x_alpha, w_alpha, w_h, output_choice, EF, K)\n\n # Get flutter velocity in closed-loop\n [Vf_CL, _,_,_] = V_flutter(wing_CL, V, output_choice)\n \n if f==1:\n f_obj = m/Ref[0] - Vf_CL/Ref[1]\n if f==2:\n f_obj = m/Ref[0] \n\n print('F:', f_obj)\n\n# f_obj = m/Ref[0] - Vf_CL/Ref[1]\n \n# f_obj = 0.8*model.m/Ref[0] - 0.2*Vf_CL/Ref[1]\n \n return f_obj\n\ndef constraints (x, rho_air, geom_prop, mat_prop, Ref, lb_DV, ub_DV, V, output_choice, aero_coef, EF):\n DV = norm2value(x, lb_DV, ub_DV)\n geom_prop['th'] = DV[0:5]\n K = DV[5:9]\n \n [m, I_alpha, w_h, w_alpha, xg, J, x_alpha] = airfoil (geom_prop, mat_prop)\n \n wing_CL = AE_model_CL(aero_coef, rho_air, geom_prop['ea_b'], geom_prop['c'], geom_prop['s'], m, I_alpha, x_alpha, w_alpha, w_h, output_choice, EF, K)\n [Vf_CL, _, _, _] = V_flutter(wing_CL, V, output_choice)\n \n print('C:', Vf_CL/Ref[1]-1)\n \n return Vf_CL/Ref[1]-1\n\n\ndef mass_velocities (j, aero_coef, history, rho_air, geom_prop, mat_prop, V, output_choice, EF):\n th = np.array([history['th1'][j], history['th2'][j], history['th3'][j], history['th4'][j], history['th5'][j]])\n geom_prop['th'] = th\n K = np.array([history['Kh'][j], history['Ka'][j], history['Khd'][j], history['Kad'][j]])\n \n [m, I_alpha, w_h, w_alpha, xg, J, x_alpha] = airfoil (geom_prop, mat_prop)\n\n wing_OL = AE_model_OL(aero_coef, rho_air, geom_prop['ea_b'], geom_prop['c'], geom_prop['s'], m, I_alpha, x_alpha, w_alpha, w_h, output_choice, EF) \n [Vf_OL, _, _, _]= V_flutter(wing_OL, V, output_choice)\n\n wing_CL = AE_model_CL(aero_coef, rho_air, geom_prop['ea_b'], geom_prop['c'], geom_prop['s'], m, I_alpha, x_alpha, w_alpha, w_h, output_choice, EF, K)\n [Vf_CL, _, _, _] = V_flutter(wing_CL, V, output_choice)\n \n return m, Vf_OL, Vf_CL\n\n\ndef plot_history (k, history, case): \n \n plt.figure(1)\n plt.plot(k, history['m'], linestyle='-', marker='p', color='#000000', linewidth=1)\n plt.xlabel('Iterations')\n plt.title('Mass')\n plt.grid(True)\n plt.savefig('Results/'+str(case)+'/mass.png')\n plt.show()\n \n plt.figure(2)\n plt.plot(k, history['Vf_OL'], linestyle='-', marker='p', color='#000000', linewidth=1, label='Open Loop (OL)')\n plt.plot(k, history['Vf_CL'], linestyle='-', marker='p', color='#929591', linewidth=1, label='Closed-Loop (CL)')\n plt.plot(k, history['Vf_OL'][0]*np.ones(len(k)), 'k:', linewidth=1, label='Minimum CL velocity allowed')\n plt.xlabel('Iterations')\n plt.title('Flutter Velocities')\n plt.legend(loc='best')\n plt.grid(True)\n plt.savefig('Results/'+str(case)+'/flutter_velocities.png')\n plt.show()\n \n plt.figure(3)\n plt.plot(k, history['f_obj'], linestyle='--', marker='p', color='#000000', linewidth=1)\n plt.xlabel('Iterations')\n plt.title('Objective Function')\n plt.grid(True)\n plt.savefig('Results/'+str(case)+'/f_obj.png')\n plt.show()\n \n plt.figure(4)\n plt.plot(k, history['th1'], linestyle='--', marker='p', color='#929591', linewidth=1, label='$t_1$')\n plt.plot(k, history['th2'], linestyle='-', marker='p', color='#000000', linewidth=1, label='$t_2$')\n plt.plot(k, history['th5'], linestyle=':', marker='^', color='#000000', linewidth=1, label='$t_5$') \n plt.xlabel('Iterations')\n plt.title('Cross-section Thickness')\n plt.legend(loc='best')\n plt.grid(True)\n plt.savefig('Results/'+str(case)+'/th1th2.png')\n plt.show()\n \n plt.figure(5)\n plt.plot(k, history['th4'], linestyle='-', marker='^', color='#000000', linewidth=1, label='$t_4$') \n plt.plot(k, history['th3'], linestyle='--', marker='p', color='#929591', linewidth=1, label='$t_3$')\n plt.xlabel('Iterations')\n plt.title('Cross-section Thickness')\n plt.legend(loc='lower right')\n plt.grid(True)\n plt.savefig('Results/'+str(case)+'/th3th4.png')\n plt.show()\n \n plt.figure(6)\n plt.plot(k, history['Kh'], linestyle='-', marker='p', color='#929591', linewidth=1, label=r'$K_{h}$')\n plt.plot(k, history['Ka'], linestyle='--', marker='p', color='#000000', linewidth=1, label=r'$K_{\\alpha}$')\n plt.xlabel('Iterations')\n plt.title('Proportional Control Gains')\n plt.legend(loc='best')\n plt.grid(True)\n plt.savefig('Results/'+str(case)+'/Kp.png')\n plt.show()\n \n plt.figure(7)\n plt.plot(k, history['Khd'], linestyle='-', marker='p', color='#929591', linewidth=1, label=r'$K_{\\dot{h}}$')\n plt.plot(k, history['Kad'], linestyle='--', marker='p', color='#000000', linewidth=1, label=r'$K_{\\dot{\\alpha}}$')\n plt.xlabel('Iterations')\n plt.title('Derivative Control Gains')\n plt.legend(loc='best')\n plt.grid(True)\n plt.savefig('Results/'+str(case)+'/Kd.png')\n plt.show() \n \n \ndef save_results(case, history, opt, time_elapsed):\n with open('Results/'+str(case)+'/Variables.txt', 'w') as file:\n file.write('Case:'+str(case)+' '+'Time:'+str(time_elapsed))\n file.write('\\n')\n file.write(str(opt))\n file.write('\\n')\n for var in history:\n file.write(str(var)+': '+str(history[var]))\n file.write('\\n')\n\n ","sub_path":"4. Code/4.1. Ghost flap/Co-design/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":10972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"85935232","text":"import os, sys\nimport copy\n\nimport logging\n\n# import numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.platform import gfile\n# from tensorflow.core.framework.tensor_pb2 import TensorProto\n# from tensorflow.core.framework.tensor_shape_pb2 import TensorShapeProto\n\n\n\"\"\"\nremove nodes before and include `target_node` then set input of graph as a\nplaceholder node\n\"\"\"\n\ntf.app.flags.DEFINE_string('input_pb', '', 'input pb file')\ntf.app.flags.DEFINE_string('model_info', './model_info.txt', 'save model info')\ntf.app.flags.DEFINE_boolean('debug', False, 'if log debug info')\nFLAGS = tf.app.flags.FLAGS\n\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n\nCONV_TYPE = {\"Conv2D\",\"DepthwiseConv2dNative\"}\nBIASADD_TYPE = {\"Add\", \"BiasAdd\", \"AddV2\"}\nACTIVATION_TYPE = {\"Relu\", \"Relu6\"}\nIGNORE_TYPE = {\"FixNeuron\", \"Identity\", \"Placeholder\"}\nWEIGHT_TYPE = {\"Const\"}\n\nINDENT = \" \"\n\ndef _get_real_node_name(name):\n return name.split(\":\")[0]\n\ndef _parse_input_graph(graph_path):\n with gfile.FastGFile(graph_path,'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n return graph_def\n\ndef _get_name2node_map(graph_def):\n name_to_node = {}\n for node in graph_def.node:\n name_to_node[node.name] = node\n return name_to_node\n\ndef _get_name2output_map(graph_def, name_to_node):\n name_to_output_node = {}\n for node in graph_def.node:\n if len(node.input) > 0:\n for n in node.input:\n in_name = _get_real_node_name(n)\n output_nodes = name_to_output_node.get(in_name, [])\n output_nodes.append(node)\n name_to_output_node[in_name] = output_nodes\n return name_to_output_node\n\ndef get_node_attr(node, indent):\n def _get_attr_val(attr_val):\n if attr_val.b:\n return attr_val.b\n if attr_val.f:\n return attr_val.f\n if attr_val.i:\n return attr_val.i\n if attr_val.s:\n return attr_val.s\n if attr_val.type:\n return attr_val.type\n if hasattr(attr_val, \"list\") and attr_val.list:\n return _get_attr_val(attr_val.list)\n return None\n attr_info = \"\"\n for k, v in node.attr.items():\n # print(k, v)\n logging.debug(k, v)\n attr_info += \"{} {}:{}\\n\".format(indent, k, _get_attr_val(v))\n return attr_info\n\n\ndef get_node_pos(node):\n return node.attr[\"quantize_pos\"].i\n\ndef write_info(info, path):\n with open(path, 'w') as f:\n f.writelines(info)\n\ndef conv_add_relu(node, visited_node, name_to_node, name_to_output_node):\n if node.op not in CONV_TYPE:\n # logging.warning(str(sys._getframe().f_lineno) + \" node {} op type is {}\".format(node.name, node.op))\n return None\n\n conv_node = node\n is_match = False\n ## match pattern conv + bias + relu\n out_level_1 = name_to_output_node[conv_node.name]\n if len(out_level_1) > 1:\n logging.warning(sys._getframe().f_lineno + \" node {} has {} outputs, is more than one \".format(conv_node.name, len(out_level_1)))\n biasadd = out_level_1[0]\n if biasadd.op in BIASADD_TYPE:\n out_level_2 = name_to_output_node[biasadd.name]\n if len(out_level_2) > 1:\n logging.warning(str(sys._getframe().f_lineno) + \" node {} has {} outputs, is more than one \".format(biasadd.name, len(out_level_2)))\n act_node = out_level_2[0]\n if act_node.op in ACTIVATION_TYPE:\n is_match = True\n\n if not is_match:\n return None\n in_quant = name_to_node[_get_real_node_name(conv_node.input[0])]\n w_quant = name_to_node[_get_real_node_name(conv_node.input[1])]\n w = name_to_node[_get_real_node_name(w_quant.input[0])]\n b_quant = name_to_node[_get_real_node_name(biasadd.input[1])]\n b = name_to_node[_get_real_node_name(b_quant.input[0])]\n out_quant = name_to_output_node[_get_real_node_name(act_node.name)][0]\n visited_node |= {conv_node.name, biasadd.name, act_node.name, out_quant.name,\n w_quant.name, b_quant.name, w.name, b.name}\n\n pattern_info = []\n node_names = \"pattern [Conv + bias + relu]: ({})\\n\".format(\" + \".join([conv_node.name, biasadd.name, act_node.name]))\n input_nodes = INDENT + \"input nodes: {} \\n\".format(\", \".join(conv_node.input))\n\n output_node_names = [node.name for node in name_to_output_node[out_quant.name]]\n output_node_names = INDENT + \"output nodes: {} \\n\".format(\", \".join(output_node_names))\n\n in_pos = get_node_pos(in_quant)\n w_pos = get_node_pos(w_quant)\n b_pos = get_node_pos(b_quant)\n out_pos = get_node_pos(out_quant)\n pos_info = INDENT + \"inpos: {} w_pos: {} b_pos: {} out_pos:{}\\n\".format(in_pos,\n w_pos, b_pos, out_pos)\n\n conv_attr = INDENT + \"attr: \\n\"\n # conv_attr = conv_attr + get_node_attr(conv_node, INDENT*2)\n\n infos = [node_names, input_nodes, output_node_names, pos_info, conv_attr]\n for info in infos:\n pattern_info.append(info)\n return pattern_info\n\ndef conv_add(node, visited_node, name_to_node, name_to_output_node):\n if node.op not in CONV_TYPE:\n return None\n\n conv_node = node\n is_match = False\n ## match pattern conv + bias + relu\n out_level_1 = name_to_output_node[conv_node.name]\n if len(out_level_1) > 1:\n logging.warning(sys._getframe().f_lineno + \" node {} has {} outputs, is more than one \".format(conv_node.name, len(out_level_1)))\n biasadd = out_level_1[0]\n if biasadd.op in BIASADD_TYPE:\n is_match = True\n\n if not is_match:\n return None\n in_quant = name_to_node[_get_real_node_name(conv_node.input[0])]\n w_quant = name_to_node[_get_real_node_name(conv_node.input[1])]\n w = name_to_node[_get_real_node_name(w_quant.input[0])]\n b_quant = name_to_node[_get_real_node_name(biasadd.input[1])]\n b = name_to_node[_get_real_node_name(b_quant.input[0])]\n out_quant = name_to_output_node[_get_real_node_name(biasadd.name)][0]\n visited_node |= {conv_node.name, biasadd.name, out_quant.name, w_quant.name, b_quant.name, w.name, b.name}\n\n pattern_info = []\n node_names = \"pattern [Conv + bias ]: ({})\\n\".format(\" + \".join([conv_node.name, biasadd.name]))\n input_nodes = INDENT + \"input nodes: {} \\n\".format(\", \".join(conv_node.input))\n\n output_node_names = \"\"\n if out_quant.name in name_to_output_node:\n output_node_names = [node.name for node in name_to_output_node[out_quant.name]]\n output_node_names = INDENT + \"output nodes: {} \\n\".format(\", \".join(output_node_names))\n\n in_pos = get_node_pos(in_quant)\n w_pos = get_node_pos(w_quant)\n b_pos = get_node_pos(b_quant)\n out_pos = get_node_pos(out_quant)\n pos_info = INDENT + \"inpos: {} w_pos: {} b_pos: {} out_pos:{}\\n\".format(in_pos,\n w_pos, b_pos, out_pos)\n\n conv_attr = INDENT + \"attr: \\n\"\n # conv_attr = conv_attr + get_node_attr(conv_node, INDENT*2)\n\n infos = [node_names, input_nodes, output_node_names, pos_info, conv_attr]\n for info in infos:\n pattern_info.append(info)\n return pattern_info\n\ndef conv_relu(node, visited_node, name_to_node, name_to_output_node):\n ## TODO\n return None\n\ndef regular_node(node, visited_node, name_to_node, name_to_output_node):\n node_attr = INDENT + \"attr: \\n\"\n # node_attr = node_attr + get_node_attr(node, INDENT*2)\n\n node_names = \"op type {}: ({})\\n\".format(node.op, node.name)\n\n input_nodes = INDENT + \"input nodes: {} \\n\".format(\", \".join(node.input))\n\n output_node_names = [n.name for n in name_to_output_node[node.name]]\n output_node_names = INDENT + \"output nodes: {} \\n\".format(\", \".join(output_node_names))\n\n pattern_info = []\n infos = [node_names, input_nodes, output_node_names, node_attr]\n for info in infos:\n pattern_info.append(info)\n visited_node |= {node.name}\n return pattern_info\n\ndef main():\n if FLAGS.debug:\n logging.info(\"using logging level DEBUG\")\n logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)\n else:\n logging.info(\"using logging level INFO\")\n logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)\n\n logging.info(\"Parsing file :{}\".format(FLAGS.input_pb))\n graph_def = _parse_input_graph(FLAGS.input_pb)\n name_to_node = _get_name2node_map(graph_def)\n name_to_output_node = _get_name2output_map(graph_def, name_to_node)\n\n visited_node = set()\n ignore_type = ACTIVATION_TYPE | WEIGHT_TYPE | IGNORE_TYPE\n model_info = []\n for node in graph_def.node:\n if node.name in visited_node:\n continue\n if node.op in ignore_type:\n continue\n logging.debug(\"processing node {}\".format(node.name))\n parse_function = [conv_add_relu, conv_add, conv_relu, regular_node]\n for func in parse_function:\n pattern_info = func(node, visited_node, name_to_node, name_to_output_node)\n if pattern_info:\n model_info.extend(pattern_info)\n break\n # print(len(visited_node))\n for node in graph_def.node:\n if node.name not in visited_node and node.op not in IGNORE_TYPE:\n # logging.info(\"node {} is not processed\".format(node.name))\n print(\"node {} is not processed\".format(node.name))\n write_info(model_info, FLAGS.model_info)\n\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"src/vai_quantizer/vai_q_tensorflow2.x/tensorflow_model_optimization/python/core/quantization/keras/vitis/vai_q_tensorflow/tools/dump_model_info.py","file_name":"dump_model_info.py","file_ext":"py","file_size_in_byte":8818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"52775918","text":"\n# @Title: 合并两个有序链表 (Merge Two Sorted Lists)\n# @Author: 2464512446@qq.com\n# @Date: 2020-11-23 15:55:13\n# @Runtime: 48 ms\n# @Memory: 13.5 MB\n\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:\n prevnode = ListNode(-1)\n prev = prevnode\n while l1 and l2:\n if l1.val > l2.val:\n prev.next = l2\n l2 = l2.next\n else:\n prev.next = l1\n l1 = l1.next\n prev = prev.next\n \n prev.next = l1 if l1 else l2\n return prevnode.next\n","sub_path":"Problemset/merge-two-sorted-lists/merge-two-sorted-lists.py","file_name":"merge-two-sorted-lists.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"456448120","text":"from random import shuffle\nfrom PIL import Image\nimport pandas as pd\nimport yaml\n\n# Process a line from the original labeled annotation file\ndef processLine(line):\n # Split this segment by ,\n res = line.split(',')\n output = []\n # Go over every output\n for i, r in enumerate(res):\n # The first output is discarded as it has no useful data\n if i == 0:\n continue\n # Split by : to extract name and value\n rr = r.split(':')\n # Strip out any unwanted characters and append to output list\n output.append(rr[1].strip('}'))\n return output\n\ndef convertToDict(boxes,f, label):\n x = float(box[0])\n y = float(box[1])\n w = float(box[2])\n h = float(box[3])\n x_min = x #int(x - w/2)\n x_max = x + w #int(x + w/2)\n y_min = y #int(y - h/2)\n y_max = y + h #int(y + h/2)\n \n path = {}\n path['path'] = f\n boxes = {}\n #boxes['boxes'] = []\n \n entry = {}\n entry['label'] = label\n entry['occluded'] = False\n entry['xmax'] = x_max\n entry['xmin'] = x_min\n entry['ymax'] = y_max\n entry['ymin'] = y_min\n \n boxes['boxes'] = entry\n \n return boxes, path\n\n# Simple value clamp on the bounding boxes\ndef clamp(value, mx):\n return max(min(value, mx*0.999), mx*0.001)\n \n# Convert an entry to a CSV string\ndef convertToCSV(boxes,f, label, header=False):\n if header:\n return 'filename,width,height,class,xmin,ymin,xmax,ymax'\n x = int(boxes[0])\n y = int(boxes[1])\n w = int(boxes[2])\n h = int(boxes[3])\n img = Image.open(f)\n width, height = img.size\n x_min = x \n x_min = int(clamp( x, width))\n x_max = int(clamp( x + w , width)) \n y_max = int(clamp( y + h , height))\n y_min = int(clamp( y, height)) \n \n return str(f) + ',' + str(height) + ',' + str(width) + ',' + str(label) + ',' + str(x_min) + ',' + str(y_min) + ',' + str(x_max) + ',' + str(y_max) + '\\n'\n \n# The data stripping and collection pipeline\ndef pipeline(filepath, prefix):\n dataframe = pd.read_csv(filepath)\n filename = dataframe['#filename']\n box_info = dataframe['region_shape_attributes']\n labels = dataframe['region_attributes']\n \n processed = []\n processed.append([processLine(x) for x in box_info])\n\n # Strip the unwanted characters\n labels = [x.strip('{') for x in labels]\n labels = [x.strip('}') for x in labels]\n labels = [x.split(':')[0] for x in labels]\n labels = [x.strip('\"') for x in labels]\n \n shuffleList = []\n for box, f, l in zip(processed[0], filename, labels):\n shuffleList.append((box,f,l))\n shuffle(shuffleList)\n \n entry = ''\n for box, f, l in shuffleList: #zip(processed[0], filename, labels):\n entry += convertToCSV(box,prefix + '/' + f,l, header=False)\n \n return entry\n \nprefix = ''\n# Create a CSV formatted string with all of the information\noutput = convertToCSV(None,None,None, header=True) + '\\n'\n#output += pipeline('./Simulation_1/via_region_data.csv', './Simulation')\noutput += pipeline('./RosBag/bag_region_data.csv', './RosBag')\noutput += pipeline('./color_just_traffic_light/via_region_data.csv', './color_just_traffic_light')\noutput += pipeline('./color_loop_with_traffic_light/bag_region_data.csv', './color_loop_with_traffic_light')\nprint(output)\n \n# Write it to a csv file\nwith open('rosbag_compiled_traffic_lights.csv', 'w') as the_file:\n the_file.write(output)\n\n\n\n","sub_path":"workspaces/scripts/conversion.py","file_name":"conversion.py","file_ext":"py","file_size_in_byte":3428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"335750539","text":"import unittest\n\n\nclass TNode(object):\n def __init__(self, data, left=None, right=None):\n self.left = left\n self.right = right\n self.data = data\n\n\ndef DFS(bt, n):\n if bt is None:\n return None\n\n if bt is n:\n return [n]\n\n left = DFS(bt.left, n)\n if left:\n left.append(bt)\n return left\n\n right = DFS(bt.right, n)\n if right:\n right.append(bt)\n return right\n\n return None\n\n\ndef solve(bt, n1, n2):\n\n a1 = DFS(bt, n1)\n a2 = DFS(bt, n2)\n\n if not (a1 and a2):\n return None\n\n a1.reverse()\n a2.reverse()\n\n i = 0\n for i in range(min(len(a1), len(a2)) - 1):\n if a1[i] is not a2[i]:\n return a1[i-1]\n\n return a1[i]\n\n\nclass TestSolve(unittest.TestCase):\n\n def test_solve(self):\n bt = TNode('a')\n bt.left = TNode('b')\n\n bt.right = TNode('c')\n bt.right.left = TNode('d')\n bt.right.left.left = TNode('f')\n bt.right.left.right = TNode('g')\n bt.right.right = TNode('e')\n bt.right.right.right = TNode('h')\n bt.right.right.right.left = TNode('i')\n\n foo = solve(bt, bt.right.left.left, bt.right.right.right)\n self.assertIs(foo, bt.right)\n\n def test_solve_1(self):\n bt = TNode('a')\n bt.left = TNode('b')\n\n bt.right = TNode('c')\n bt.right.left = TNode('d')\n bt.right.left.left = TNode('f')\n bt.right.left.right = TNode('g')\n bt.right.right = TNode('e')\n bt.right.right.right = TNode('h')\n bt.right.right.right.left = TNode('i')\n\n foo = solve(bt, bt.right.right.right, bt.right.right.right.left)\n self.assertIs(foo, bt.right.right)\n\n\nif __name__ == '__main__':\n unittest.main()\n\n\n","sub_path":"cracking_the_coding_interview/q4.8.py","file_name":"q4.8.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"480144699","text":"import sqlite3\n\ndef main():\n con = sqlite3.connect('APOD.db')\n cur = con.cursor()\n \n if cur.execute('''CREATE TABLE IF NOT EXISTS Photo(\n date text,\n explanation Text,\n hdurl Text,\n mediaType Text,\n title Text\n )'''):\n \n return 'Tabla creada con exito'\n else:\n return 'Error al crear la tabla'\n \n con.commit()\n con.close()\n \nif __name__ == '__main__':\n main()","sub_path":"db_Photo.py","file_name":"db_Photo.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"606679441","text":"import datetime\n\nfrom unittest.mock import Mock\n\nfrom django.test import TestCase\nfrom django.utils.timezone import now\n\nfrom .. import forms, models\nfrom . import factories\n\n\nclass CheckResultFilterTestCase(TestCase):\n \"\"\"Filtering status results to a date range.\"\"\"\n\n today = datetime.date.today()\n yesterday = today - datetime.timedelta(days=1)\n tomorrow = today + datetime.timedelta(days=1)\n\n def test_valid_range(self):\n \"\"\"Filter on valid start/end range.\"\"\"\n data = {\n 'start': self.yesterday.isoformat(),\n 'end': self.today.isoformat(),\n }\n qs = Mock()\n result = forms.CheckResultFilter(data=data, queryset=qs)\n self.assertTrue(result.form.is_valid())\n\n def test_valid_range_datetimes(self):\n \"\"\"Filter on valid start/end range including time.\"\"\"\n data = {\n 'start': datetime.datetime.combine(\n self.yesterday, datetime.time(0, 0, 0)).isoformat(sep=' '),\n 'end': datetime.datetime.combine(\n self.yesterday, datetime.time(12, 0, 0)).isoformat(sep=' '),\n }\n qs = Mock()\n result = forms.CheckResultFilter(data=data, queryset=qs)\n self.assertTrue(result.form.is_valid())\n\n def test_invalid_range(self):\n \"\"\"End date must be greater than the start date.\"\"\"\n data = {\n 'start': self.today.isoformat(),\n 'end': self.yesterday.isoformat(),\n }\n qs = Mock()\n result = forms.CheckResultFilter(data=data, queryset=qs)\n self.assertFalse(result.form.is_valid())\n\n def test_range_too_long(self):\n \"\"\"End and start must be less than a day apart.\"\"\"\n data = {\n 'start': self.yesterday.isoformat(),\n 'end': self.tomorrow.isoformat(),\n }\n qs = Mock()\n result = forms.CheckResultFilter(data=data, queryset=qs)\n self.assertFalse(result.form.is_valid())\n\n def test_missing_start(self):\n \"\"\"Start date must be given.\"\"\"\n data = {\n 'start': '',\n 'end': self.today.isoformat(),\n }\n qs = Mock()\n result = forms.CheckResultFilter(data=data, queryset=qs)\n self.assertFalse(result.form.is_valid())\n\n def test_missing_end(self):\n \"\"\"End date must be given.\"\"\"\n data = {\n 'start': self.yesterday.isoformat(),\n 'end': '',\n }\n qs = Mock()\n result = forms.CheckResultFilter(data=data, queryset=qs)\n self.assertFalse(result.form.is_valid())\n\n def test_functional(self):\n \"\"\"Functional test of filtering a queryset.\"\"\"\n check = factories.create_domain_check()\n current_time = now().replace(microsecond=0)\n for i in range(10):\n factories.create_check_result(\n domain_check=check,\n checked_on=current_time - datetime.timedelta(hours=i))\n middle = current_time - datetime.timedelta(hours=5)\n data = {\n 'start': middle.replace(tzinfo=None).isoformat(sep=' '),\n 'end': current_time.replace(tzinfo=None).isoformat(sep=' ')\n }\n qs = models.CheckResult.objects.all()\n result = forms.CheckResultFilter(data=data, queryset=qs)\n self.assertEqual(result.count(), 6)\n","sub_path":"src/Chapter 7/migration basics/domainchecks/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":3306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"507925180","text":"''' dbrev.python_types_table.PythonTypesTable implements the\n Flyweight pattern which handles caching.\n'''\n\nimport logging\nLOG = logging.getLogger(__name__)\n# LOG.setLevel(logging.INFO)\n\n# Long lines expected.\n# pylint: disable=C0301\n# Cyclic imports protected by functions\n# pylint: disable=R0401\n\nfrom freevolv.models.dbrev.python_type import PythonType\nfrom freevolv.dal import table\n\nclass PythonTypesTable(table.Table):\n ''' PythonTypesTable holds exactly one copy of each row queried from the\n PYTHON_TYPES table.'''\n\n table_name = \"[DBREV].[PYTHON_TYPES]\"\n columns = [\"NAME\"]\n _attributes = ['name']\n _template_class = PythonType\n _template_keys = [('name',)]\n\n instance = None\n @staticmethod\n def get_instance():\n ''' Instantiates (if it hasn't already) a singleton PythonTypesTable.\n and returns it.\n '''\n if PythonTypesTable.instance == None:\n PythonTypesTable.instance = PythonTypesTable()\n return PythonTypesTable.instance\n\n @staticmethod\n def get_fields(python_type):\n '''Get a dictionary of fields populated from python_type'''\n fields = {}\n fields[\"NAME\"] = python_type.name\n return fields\n\n @staticmethod\n def set_fields(python_type, fields):\n '''Get a new python_type populated from fields.'''\n python_type.__init__(\n name=fields['NAME'])\n\n def __init__(self):\n super(PythonTypesTable, self).__init__()\n self.attributes = PythonTypesTable._attributes\n self.template_class = PythonTypesTable._template_class\n self.template_keys = PythonTypesTable._template_keys\n self._by_name = {}\n self.key_dict[('name',)] = self.get_by_name\n self.by_key = self._by_name\n\n def add_dict_entry(self, python_type):\n ''' Add an entry to the table and all indexes.'''\n # LOG.debug('adding: ' + str(python_type))\n self._by_name[python_type.name] = python_type\n\n def del_dict_entry(self, python_type):\n ''' Delete an entry from the table and all indexes.'''\n # LOG.debug('deleting: ' + str(python_type))\n del self._by_name[python_type.name]\n\n def get_by_name(self, name):\n ''' Lookup up a record with the unique key defined by\n name.\n '''\n rtn = None\n if name in self._by_name:\n # LOG.debug(str(name) + ' found')\n rtn = self._by_name[name]\n else:\n # LOG.debug(str(name) + ' loading...')\n rtn = self.load_one(name=name)\n return rtn\n by_name = property(get_by_name)\n\n\n","sub_path":"py/freevolv/models/dbrev/python_types_table.py","file_name":"python_types_table.py","file_ext":"py","file_size_in_byte":2615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"443371118","text":"from django.shortcuts import render,redirect\nfrom django.views.generic.base import TemplateView\nfrom django.views.generic import ListView,DetailView,DeleteView,View,FormView\nfrom django.views.generic.edit import CreateView,UpdateView\nfrom django.urls import reverse_lazy,reverse\nfrom django.db.models import Q\nfrom django.utils.text import slugify\nfrom markdown.extensions.toc import TocExtension\nfrom .models import ArticlePost,ArticleColumn\nfrom .forms import ArticlePostForm\nfrom django.http import HttpResponse\nfrom django.utils.decorators import method_decorator\nfrom django.contrib.auth.decorators import login_required\nimport re\nimport markdown\n# Create your views here.\n\nclass Home(ListView):\n model = ArticlePost\n template_name = 'home.html'\n paginate_by = 3\n\n def get(self, request, *args, **kwargs):\n self.order = request.GET.get('order')\n if self.order == 'total_views':\n self.ordering = '-total_views'\n self.search = request.GET.get('search')\n self.tag = request.GET.get('tag')\n self.column = request.GET.get('column')\n self.object_list = self.get_queryset()\n if self.tag and self.tag != None:\n context = self.get_context_data(object_list=self.object_list.filter(tags__name__in=[self.tag]))\n context['tag'] = self.tag\n else:\n context = self.get_context_data()\n if self.search:\n context = self.get_context_data(object_list=self.object_list.filter(\n Q(title__icontains=self.search) |\n Q(body__icontains=self.search)))\n context['search'] = self.search\n else:\n self.search = ''\n if self.column:\n context = self.get_context_data(object_list=self.object_list.filter(column_id=self.column))\n context['column'] = self.column\n context['columns'] = ArticleColumn.objects.all()\n return self.render_to_response(context)\n\nclass ArticleCreateView(CreateView):\n model = ArticlePost\n fields = ['title','body','description','column','tags']\n template_name = 'article/create.html'\n\n def post(self, request, *args, **kwargs):\n self.object = None\n form = self.get_form()\n if form.is_valid():\n article = self.model.objects.filter(title=request.POST.get('title'))\n self.object = form.save()\n #first()：返回queryset中匹配到的第一个对象，如果没有匹配到对象则为None，如果queryset没有定义排序，则按主键自动排序。\n article_id = article.first().id\n return redirect('article:article_detail',pk=article_id)\n else:\n return self.form_invalid(form)\n\n def get_context_data(self, **kwargs):\n context = super(ArticleCreateView, self).get_context_data(form=ArticlePostForm)\n context['columns'] = ArticleColumn.objects.all()\n return (context)\n\n@method_decorator(login_required(login_url='/userprofile/login/'),name='dispatch')\nclass ArticleUpdateView(UpdateView):\n model = ArticlePost\n fields = ['title', 'body', 'description', 'column', 'tags']\n template_name = 'article/update.html'\n\n def get_success_url(self):\n return reverse_lazy('article:article_detail',kwargs=self.kwargs)\n\n def get_context_data(self, **kwargs):\n context = super(ArticleUpdateView, self).get_context_data()\n context['tags'] = ','.join([x for x in self.object.tags.names()])\n context['columns'] = ArticleColumn.objects.all()\n context['title_len'] = len(context['object'].title)\n return context\n\nclass ArticleDetailView(DetailView):\n model = ArticlePost\n template_name = 'article/detail.html'\n context_object_name = 'article'\n\n def get(self, request, *args, **kwargs):\n #需要先调用父类的方法才能有self.object\n response = super(ArticleDetailView, self).get(request, *args, **kwargs)\n self.object.increase_views()\n return response\n\n def get_object(self, queryset=None):\n article = super().get_object(queryset=None)\n md = markdown.Markdown(extensions=[\n 'markdown.extensions.extra',\n 'markdown.extensions.codehilite',\n # 记得在顶部引入 TocExtension 和 slugify\n TocExtension(slugify=slugify),\n ])\n article.body = md.convert(article.body)\n m = re.search(r'', md.toc, re.S)\n article.toc = m.group(1) if m is not None else ''\n return article\n\nclass IncreaseLikesView(View):\n def post(self, request, *args, **kwargs):\n article = ArticlePost.objects.get(id=kwargs.get('id'))\n article.increase_likes()\n return HttpResponse('success')\n","sub_path":"article/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"169906736","text":"\"\"\"\nThis is a template algorithm on Quantopian for you to adapt and fill in.\n\"\"\"\nfrom quantopian.algorithm import attach_pipeline, pipeline_output\nfrom quantopian.pipeline import Pipeline\nfrom quantopian.pipeline.data.builtin import USEquityPricing\nfrom quantopian.pipeline.factors import AverageDollarVolume\nfrom quantopian.pipeline.filters.morningstar import Q1500US\n \ndef initialize(context):\n context.vxx = sid(38054)\n context.hmm = None \n context.order_id = None \n \n fetch_csv(\"https://dl.dropboxusercontent.com/u/264353/hmm.csv\",\n symbol='hmm', date_column='Date', date_format='%y-%m-%d')\n \n # Rebalance every day, 1 hour after market open.\n schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_close())\n\ndef logging(msgs):\n dt = get_datetime('US/Eastern')\n youbi = dt.strftime(\"%w\")\n youbidict = {0:\"Sun\", 1:\"Mon\", 2:\"Tue\", 3:\"Wed\", 4:\"Thu\", 5:\"Fri\", 6:\"Sat\"}\n msgs = '%s %s: %s' % (dt, youbidict[int(youbi)], msgs) \n log.info(msgs)\n \ndef before_trading_start(context, data):\n pass \n \ndef my_rebalance(context,data):\n context.hmm = data.current('hmm', 'HMM')\n amount = context.portfolio.positions[context.vxx].amount\n if context.hmm >= 0.5:\n if context.order_id == None:\n logging(\"Open VXX Short Position: HMM %s\" % context.hmm) \n context.order_id = order_value(context.vxx, -5000)\n logging('VXX Short Position Opened: %s @ %s: Total %s' %( \n get_order(context.order_id).amount, data.current(context.vxx, 'price'),\n get_order(context.order_id).amount * data.current(context.vxx, 'price')))\n else:\n logging(\"Hold VXX Short Position Shares:%s / PL: %s\" % (amount, context.portfolio.pnl))\n elif context.hmm < 0.5:\n if context.order_id != None:\n logging(\"Close VXX Short Position\") \n logging(\"GAIN: %s\" % context.portfolio.pnl)\n order_percent(context.vxx, 0)\n \n context.order_id = None\n else:\n logging(\"No position No Life\")\n \ndef my_record_vars(context, data):\n pass\n \ndef handle_data(context,data):\n record(hmm=data.current('hmm', 'HMM'))\n","sub_path":"algorithms/Hidden Markov Model (VXX Short)/backtests/Backtest 6-58c1f55ac896025e50c351ae.py","file_name":"Backtest 6-58c1f55ac896025e50c351ae.py","file_ext":"py","file_size_in_byte":2219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"318448428","text":"#!/usr/bin/python2.7\n\nimport json\nfrom gimpfu import *\n\n\ndef plugin_main():\n\n img = gimp.image_list()[0]\n sections = []\n\n for layer in img.layers:\n if layer.name == 'Level_Data':\n for sublayer in layer.layers:\n if sublayer.__class__.__name__ == \"GroupLayer\":\n section = []\n add_sub_layers_to_array(section, sublayer.layers)\n sections.append(section)\n\n with open('/srv/http/sections.json', 'w') as outfile:\n json.dump(sections, outfile)\n # gimp.message(\"{x: \" + str(block.offsets[0]) + \", y: \" + str(block.offsets[1]) + \", width: \" + str(block.width) + \", height: \" + str(block.height) + \", spawned: false}\")\n\n\ndef add_sub_layers_to_array(data, layer_array):\n for layer in layer_array:\n if layer.__class__.__name__ == \"GroupLayer\":\n add_sub_layers_to_array(data, layer.layers)\n else:\n data.append({'x': layer.offsets[0], 'y': layer.offsets[1], 'width': layer.width, 'height': layer.height})\n\n\nregister(\n \"export_sections\",\n \"Exports the sections\",\n \"Exports the sections\",\n \"Austin\",\n \"Austin\",\n \"2017\",\n \"Exports the sections\",\n \"RGB*, GRAY*\",\n [],\n [],\n plugin_main)\n\nmain()\n","sub_path":"scripts/export_sections.py","file_name":"export_sections.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"195817436","text":"import sys\nimport requests\nimport re\nimport time\nimport bs4\nfrom tqdm import tqdm\nimport library.dictlist as dl\n\n\ndef create_dictlist(list_ofNums, repeat, old_links = None):\n if old_links == None:\n old_links = dl.Dictlist()#[]\n list_ofNums = dl.Dictlist(list_ofNums)\n print(\"****Scraping started...\" + str(repeat) + \" generations****\")\n if repeat > 0:\n new_list = dl.Dictlist()#[]\n new_list.pub_dict = list_ofNums.pub_dict\n for item in tqdm(list_ofNums.pub_list):\n if item not in old_links.pub_list:\n new_list.append_item(item)\n \n list_of_all_children = _get_html_page(item)\n\n temp_child = []\n for thing in list_of_all_children:\n if thing not in old_links.pub_list:\n temp_child.append(thing) \n new_list.append_list(item, temp_child)\n \n old_links.append_item(item)\n new_list.pub_list = list(set(new_list.pub_list))\n return create_dictlist(new_list, repeat-1, old_links)\n else:\n print(\"****Scraping finished****\")\n return list_ofNums\n\n\ndef _get_html_page(url):\n try:\n response = requests.get(url)\n raw_list = _soup_link_list(response)\n return _remove_malformed_links(raw_list)\n except:\n return []\n\ndef _soup_link_list(response):\n soup = bs4.BeautifulSoup(response.text, 'html5lib') # html5lib er den parser vi bruger.\n \n # soup.select(\"a[href]\") contains all links including tags found with css selector.\n # Then we find the link without tag and href and appened it to the list.\n list = [ a[\"href\"] for a in soup.select(\"a[href]\") ] \n \n return list\n\n\ndef _remove_malformed_links(all_links):\n velformed = []\n for item in all_links:\n validation_result = re.match(_url_regex, item) is not None\n if validation_result:\n velformed.append(item)\n return velformed\n\n\n# url validation regex:\n# https://stackoverflow.com/questions/7160737/python-how-to-validate-a-url-in-python-malformed-or-not\n_url_regex = re.compile(\n r'^(?:http|ftp)s?://' # http:// or https://\n r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' #domain...\n r'localhost|' #localhost...\n r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n r'(?::\\d+)?' # optional port\n r'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n\n","sub_path":"library/bs4dictionary.py","file_name":"bs4dictionary.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"626619178","text":"#!/usr/bin/python\nfrom twisted.internet.protocol import Protocol, Factory, ClientFactory\nfrom twisted.python import log\nfrom PyQt4.QtGui import *\nfrom PyQt4.QtCore import *\nimport myDesktopClientProtocol as clientProtocol\nuse_pip_install_qt4reactor = True\nif use_pip_install_qt4reactor:\n from qtreactor import pyqt4reactor\nelse:\n import qt4reactor\nimport os, sys\n\nlog.startLogging(sys.stdout)\n\napp = QApplication(sys.argv)\n\n__applib__ = os.path.dirname(os.path.realpath(__file__))\n__appicon__ = os.path.dirname(os.path.realpath(__file__))\n\nif use_pip_install_qt4reactor:\n pyqt4reactor.install()\nelse:\n qt4reactor.install( )\n\nclass RDCToGUI(clientProtocol.rdc):\n def __init__(self):\n clientProtocol.rdc.__init__(self)\n\n def connectionMade(self):\n self.factory.readyConnection(self)\n\n def vncRequestPassword(self):\n password = self.factory.password\n if not password:\n password = inputbox( )\n self.sendPassword(password)\n\n def commitFramebufferUpdate(self, framebuffer, no, ref, width, height):\n no = self.factory.display.updateFramebuffer(framebuffer, no, ref, width, height)\n self.framebufferUpdateRequest(no)\n\n\nclass RDCFactory(clientProtocol.RDCFactory):\n def __init__(self, display=None, password=None, shared=0):\n clientProtocol.RDCFactory.__init__(self, password, shared)\n self.display = display\n self.protocol = RDCToGUI\n\n def buildProtocol(self, addr):\n return clientProtocol.RDCFactory.buildProtocol(self, addr)\n\n def readyConnection(self, client):\n self.display.readyDisplay(client)\n \n def clientConnectionFailed(self, connector, reason):\n log.msg(\"Client connection failed!. (%s)\" % reason.getErrorMessage( ))\n reactor.stop( )\n\n def clientConnectionLost(self, connector, reason):\n log.msg(\"Client connection lost!. (%s)\" % reason.getErrorMessage( ))\n reactor.stop( )\n\n\nclass Display(QWidget):\n \"\"\"\n this class for display remoteframebuffer and get the client events\n and then send the events to server, the include keyEvent, pointerEvent,\n mouseMoveEvent, clipboardEvent.\n \"\"\"\n def __init__(self, parent=None):\n super(Display, self).__init__(parent)\n self.resize(1390, 780)\n self._clipboard = QApplication.clipboard( )\n self.setMouseTracking(True)\n self.setFocusPolicy(Qt.StrongFocus)\n self.clientProtocol = None\n self.parent = parent\n self.his = []\n\n def readyDisplay(self, protocol):\n self.clientProtocol = protocol\n\n def paintEvent(self, event):\n \"\"\"\n paint frame buffer in widget\n \"\"\"\n if self.his:\n no, img = self.his[-1]\n painter = QPainter(self)\n painter.drawImage(0, 0, img)\n\n self.update( )\n\n def updateFramebuffer(self, pixelmap, no, ref, width, height):\n import zlib\n bytes = zlib.decompress(pixelmap)\n img2 = QImage(bytes, width, height, width * 3, QImage.Format_RGB888)\n\n if ref == -1:\n print('gen img with no ref', no)\n else:\n img1 = None\n while self.his:\n no1, img = self.his[0]\n if no1 == ref:\n img1 = img\n break\n self.his.pop(0)\n if img1 is None:\n print('fail to find ref', no)\n self.his[:] = 0\n return -1\n assert img2.bytesPerLine() == img2.width() * 3\n assert (img2.width(), img2.height()) == (img1.width(), img1.height())\n\n pbuf1 = img1.bits().__int__()\n pbuf2 = img2.bits().__int__()\n\n #import hello\n #hello.imgadd(pbuf1, pbuf2, img2.width(), img2.height())\n #print('img ref success', no, ref)\n\n self.his.append((no, img2))\n if len(self.his) > 8:\n self.his.pop(0)\n return no\n\n def keyPressEvent(self, event):\n key = event.key( )\n flag = event.type( )\n print('key press %x=%d' % (key, key), flag) # flag = 6\n if self.clientProtocol is None: return\n self.clientProtocol.keyEvent(key, flag)\n self.update( )\n\n def keyReleaseEvent(self, event):\n key = event.key( )\n flag = event.type( )\n print('key release %x=%d' % (key, key), flag) # flag = 7\n if self.clientProtocol is None: return\n self.clientProtocol.keyEvent(key, flag)\n self.update( )\n\n def mousePressEvent(self, event):\n x, y = (event.pos( ).x( ), event.pos( ).y( ))\n\n print('mouse press', self.width, self.height, self.size())\n width = self.width # * 4 / 5\n height = self.height # * 4 / 5\n width -= width % 8\n height -= height % 8\n\n if x >= width or y >= height:\n return\n\n button = event.button( )\n print('mouse button', button)\n flag = event.type( )\n if self.clientProtocol is None: return #self.clientProtocol = self.parent.client.clientProto\n self.clientProtocol.pointerEvent(x, y, button, flag)\n print(self.clientProtocol.pointerEvent)\n\n def mouseReleaseEvent(self, event):\n x, y = (event.pos( ).x( ), event.pos( ).y( ))\n\n width = self.width # * 4 / 5\n height = self.height # * 4 / 5\n width -= width % 8\n height -= height % 8\n\n if x >= width or y >= height:\n return\n\n button = event.button( )\n flag = event.type( )\n if self.clientProtocol is None: return #self.clientProtocol = self.parent.client.clientProto\n self.clientProtocol.pointerEvent(x, y, button, flag)\n\n def mouseMoveEvent(self, event):\n x, y = (event.pos( ).x( ), event.pos( ).y( ))\n\n width = self.width # * 4 / 5\n height = self.height # * 4 / 5\n width -= width % 8\n height -= height % 8\n\n if x >= width or y >= height:\n return\n\n button = event.button( )\n flag = event.type( )\n if self.clientProtocol is None: return #self.clientProtocol = self.parent.client.clientProto\n self.clientProtocol.pointerEvent(x, y, button, flag)\n\n def resizeEvent(self, event):\n \"\"\"\n the remote framebuffer's size is according the client viewer size\n this may reduce the size of the images can be\n \"\"\"\n size = event.size( )\n self.width, self.height = (size.width(), size.height())\n\n\nclass myDesktopViewer(QMainWindow):\n def __init__(self, parent=None):\n super(myDesktopViewer, self).__init__(parent)\n self.display = Display(self)\n self.setupUI( )\n\n def setupUI(self):\n self.setWindowTitle('myDesktop (viewer)')\n self.resize(800, 600)\n QApplication.setStyle(QStyleFactory.create('cleanlooks'))\n QApplication.setPalette(QApplication.style( ).standardPalette())\n\n # add adction on application\n self.startAction = QAction(QIcon(os.path.join(__appicon__, 'icons', 'Start.png')), 'Start', self)\n self.stopAction = QAction(QIcon(os.path.join(__appicon__, 'icons', 'Stop.png')), 'Stop', self)\n self.startAction.setToolTip('Start connection')\n self.stopAction.setToolTip('Stop connection')\n self.startAction.triggered.connect(self.connectionStart)\n self.stopAction.triggered.connect(self.connectionStop)\n\n # add a toolbar\n self.toolbar = self.addToolBar('')\n self.toolbar.addAction(self.stopAction)\n self.toolbar.addAction(self.startAction)\n\n displayWidget = QWidget( )\n vbox = QVBoxLayout(displayWidget)\n vbox.addWidget(self.display)\n vbox.setMargin(0)\n self.setCentralWidget(displayWidget)\n\n def connectionStart(self):\n client = RDCFactory(display=self.display, password='1234')\n reactor.connectTCP('127.0.0.1', 5000, client)\n #reactor.connectTCP('192.168.0.101', 5000, client)\n\n def connectionStop(self):\n reactor.stop( )\n\n def closeEvent(self, event):\n self.connectionStop( )\n exit( )\n\nif __name__ == '__main__':\n from twisted.internet import reactor\n mydesktop = myDesktopViewer( )\n mydesktop.show( )\n reactor.run( ) # enter mainloop\n","sub_path":"myDesktopViewer.py","file_name":"myDesktopViewer.py","file_ext":"py","file_size_in_byte":8263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"605741844","text":"class Solution(object):\n def nextGreatestLetter(self, letters, target):\n \"\"\"\n :type letters: List[str]\n :type target: str\n :rtype: str\n \"\"\"\n left = 0\n right = len(letters) - 1\n while left + 1 < right:\n mid = left + (right - left) / 2\n value = ord(letters[mid]) - ord('a')\n target_value = ord(target) - ord('a')\n if value <= target_value:\n left = mid\n else:\n right = mid\n if ord(letters[left]) - ord('a') > ord(target) - ord('a'):\n return letters[left]\n if ord(letters[right]) - ord('a') > ord(target) - ord('a'):\n return letters[right]\n return letters[0]\n \n \n","sub_path":"Find_Smallest_Letter_Greater_Than_Target/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"488185178","text":"def romanAdd(roman1, roman2):\n romanTotal1 = 0\n romanTotal2 = 0\n for i in roman1:\n if i == \"I\":\n romanTotal1 += 1\n elif i == \"V\":\n romanTotal1 += 5\n elif i == \"X\":\n romanTotal1 += 10\n elif i == \"L\":\n romanTotal1 += 50\n elif i == \"C\":\n romanTotal1 += 100\n if \"IV\" in roman1:\n romanTotal1 -= 2\n elif \"IX\" in roman1:\n romanTotal1 -= 2\n elif \"XL\" in roman1:\n romanTotal1 -= 20\n elif \"XC\" in roman1:\n romanTotal1 -= 20\n for j in roman2:\n if j == \"I\":\n romanTotal2 += 1\n elif j == \"V\":\n romanTotal2 += 5\n elif j == \"X\":\n romanTotal2 += 10\n elif j == \"L\":\n romanTotal2 += 50\n elif j == \"C\":\n romanTotal2 += 100\n if \"IV\" in roman2:\n romanTotal2 -= 2\n elif \"IX\" in roman2:\n romanTotal2 -= 2\n elif \"XL\" in roman2:\n romanTotal2 -= 20\n elif \"XC\" in roman2:\n romanTotal2 -= 20\n total = romanTotal1 + romanTotal2\n return total\n","sub_path":"Homework2/brugg123_2D.py","file_name":"brugg123_2D.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"601201951","text":"from collections import Counter\nfrom typing import List, Dict\nimport numpy as np\n\nNX_GEOM_TABLES = [\n b'CASECC',\n b'PVT', b'PVT0', b'PVTS',\n #b'GPLS',\n b'LAMA', b'CLAMA',\n b'OGPWG',\n b'EDT', b'EDTS',\n b'CONTACT', b'CONTACTS', # surface contact definition\n b'GEOM1', b'GEOM2', b'GEOM3', b'GEOM4', b'EPT', b'MPT', b'DYNAMIC', b'DIT', b'EDOM',\n b'GEOM1S', b'GEOM2S', b'GEOM3S', b'GEOM4S', b'EPTS', b'MPTS', b'DYNAMICS',\n b'GEOM1VU', b'GEOM2VU',\n b'GEOM1N',\n b'GEOM1EXA', b'GEOM2EXA', b'GEOM4EXA',\n b'VIEWTB',\n b'R1TABRG',\n b'ERRORN',\n b'BGPDTVU', # basic grid point defintion table for a superelement and related to geometry with view-grids added\n] # type: List[bytes]\n\nNX_MATRIX_TABLES = [\n b'XSOP2DIR',\n b'RADEFMP', # Modal Effective Inertia Matrix - Modal Matrix (per Vibrata)\n\n # hasn't been validated\n #b'RAFGEN', # Load Set Modal Forces - Modal generalized force vectors (per Vibrata)\n #b'RADAMPZ',\n #b'RADAMPG',\n\n b'EFMFSMS', b'EFMASSS', b'RBMASSS', b'EFMFACS', b'MPFACS', b'MEFMASS', b'MEFWTS',\n\n # hasn't been validated\n #b'K4HH', b'KELMP', b'MELMP',\n\n # not-MATPOOL\n # hasn't been validated\n #b'DELTAK', b'DELTAM', b'RBM0', b'DELTAM0',\n\n # MATPOOL\n # hasn't been validated\n #b'MRGGT', b'UEXPT',\n\n # MATRIX/MATPOOL - testing-remove this\n # hasn't been validated\n #b'PATRN', b'IDENT', b'RANDM', b'CMPLX',\n #b'MPATRN', b'MIDENT', b'MRANDM', b'MCMPLX',\n\n b'MATPOOL',\n ##b'KELM',\n\n # hasn't been validated\n b'MELM', # b'BELM',\n\n b'BHH', b'KHH',\n b'DSCM2',\n] # type: List[bytes]\n\nNX_EXTRA_TABLES = [\n # geometry, but buggy in the geometry block...\n b'ICASE',\n\n # geometry\n b'DESCYC',\n b'DSCMCOL', # design sensitivity parameters\n b'DBCOPT', # design optimization history for post-processing\n #--------------------------------------------------------------------------\n\n # RADx...\n b'RADCONS', b'RADEFFM', b'RADEATC',\n\n # stress\n b'OES1', b'OES1X', b'OES1X1', b'OES1C',\n b'OES2',\n b'OESNLXR', b'OESNLBR', b'OESNLXD', # nonlinear\n b'OESNLXR2', b'OESNLBR2',\n b'OESCP', # ???\n b'OESRT', # ???\n b'OESXRMS1', # random with RMS von mises stress\n\n # strain\n b'OSTR1', b'OSTR1X', b'OSTR1C',\n b'OSTR2',\n b'OESTRCP', # ???\n\n # contact\n b'OSPDSI1', b'OSPDSI2', # intial separation distance\n b'OSPDS1', b'OSPDS2', # final separation distance\n b'OBC1', b'OBC2', # contact pressures and tractions at grid points\n\n # glue\n b'OBG1', # glue normal and tangential tractions at grid points in basic coordinate system\n\n # RMAXMIN - Defines parameters to output the minimum, maximum, absolute\n # value maximum, average, and RMS value of stress, force, and\n # displacement results for SOLs 101, 109, and 112.\n b'OUGV1MX', # max displacement?\n b'OES1MX', # max stress?\n b'OEF1MX', # max force?\n b'OSMPF2M',\n b'OFMPF2M',\n b'OPMPF2M',\n b'OLMPF2M',\n b'OEKE1',\n] # type: List[bytes]\n\nNX_RESULT_TABLES = [\n # ???\n b'OSTR1THC',\n b'OSTR1PLC',\n b'OSTR1CRC',\n b'OSTR1PL',\n b'OSTR1CR',\n b'OEFIIP',\n b'OESRIP',\n b'OESRIS',\n b'ODELBGPD',\n b'ODAMGCZT',\n b'ODAMGCZR',\n b'ODAMGCZD',\n b'XCASECC',\n b'RST',\n\n # displacements, velocity, acceleration\n # BOUGV1 - G-set results (displacement, velocity, acceleration, eigenvector)\n # in the global (CD) frame\n # OUGVi - Displacements in the global (CD) coordinate system\n b'BOUGV1', # G-set results boundary in the basic (cid=0) frame\n b'OUGV1', b'OUGV2',\n b'OUGATO2', b'OUGCRM2', b'OUGPSD2',\n b'OUGNO1', b'OUGRMS1',\n\n # eigenvectors\n b'OPHIG', # Eigenvectors in the basic (cid=0) coordinate system\n\n # eigenvectors\n b'BOPHIG', # basic (cid=0) frame\n\n # temperature\n b'TOUGV1',\n\n #------------------------\n # solution set\n # OUXYi - ??? set in ??? frame\n #------------------------\n\n # spc forces\n # OQGx - SPC forces in the G-set\n # - can also be MPC forces, but generally not anymore for NX Nastran\n # OQGCFx - ???\n # OQGGFx - ???\n b'OQG1', b'OQG2',\n b'OQGCF1', b'OQGCF2', # ???\n b'OQGGF1', b'OQGGF2', # ???\n\n # mpc forces\n # OQMGx - MPC forces in the G-set\n b'OQMG1', b'OQMG2',\n\n # load vector\n # OPGi - G-set load vectors in the global (CD) frame\n # OPNLi - Nonlinear loads in for the h-set or d-set.\n b'OPG1', b'OPG2',\n b'OPNL1', b'OPNL2',\n b'OPGNO1',\n b'OPGRMS1',\n\n # Grid point stresses\n b'OGS1',\n\n # strain energy\n b'ONRGY1', b'ONRGY2', b'ONRGY',\n\n # failure indicies\n b'OEFIT',\n\n #-----------------------\n # OESVM1 - OES Table of element stresses\n # OESVM1C - OES Table of composite element stresses\n # for frequency response analysis that includes von Mises stress\n # output in SORT1 format.\n b'OESVM1', b'OESVM1C',\n b'OESVM2',\n b'OSTRVM1', b'OSTRVM1C',\n b'OSTRVM2',\n\n b'OES2C', b'OSTR2C',\n\n # hasn't been validated\n b'OESPSD2C', b'OSTPSD2C',\n b'OSTRRMS1', b'OSTRMS1C',\n b'OSTRNO1', b'OSTNO1C',\n\n # sol 401?\n b'OESNL2',\n b'OSTR1IN', # OES output table of initial strains at corner grids in the basic coordinate system\n b'OSTR1G', # Table of total strain at Gauss points in SORT1 format\n b'OSTR1PLG', # Table of plastic strain at Gauss points in SORT1 format\n b'OSTR1THG', # Table of thermal strain at Gauss points in SORT1 format\n b'OSTR1ELG', # Table of elastic strain at Gauss points in SORT1 format\n b'OSTR1TH', # Table of thermal strain in SORT1 format\n b'OSTR1EL', # Table of elastic strain in SORT1 format\n b'OSTR1ING', # OES output table of initial strains at corner Gauss points in the basic coordinate system\n\n b'OES1G', # Grid point stress or strain table in SORT1 format and interpolated from the centroidal stress table, OES1M.\n\n #----------------------\n # hasn't been validated...\n b'MDICT', b'BDICT', b'KDICTP', b'MDICTP',\n\n #----------------------\n # forces\n # OEF1X - Element forces with intermediate (CBAR and CBEAM) station forces\n # and forces on nonlinear elements\n # OEFx - Element forces for shells/solids/rods\n b'OEF1X',\n b'OEF1', b'OEF2',\n\n # heat flux\n b'HOEF1',\n\n\n # ---------------------------------------------\n # nx2019.2\n\n # geometry\n #b'GPDTS',\n\n # results - supported\n b'OPHSA', # Displacement output table in SORT1\n b'OUXY1', # Displacements in SORT1 format for h-set or d-set.\n b'OUXY2', # Displacements in SORT2 format for h-set or d-set.\n b'OTEMP1', # Grid point temperature output\n\n b'LAMAS', # Normal modes eigenvalue summary table for the structural portion of the model\n b'LAMAF', # Normal modes eigenvalue summary table for the fluid portion of the model\n\n b'BOPHIGF', # Eigenvectors in the basic coordinate system for the fluid portion of the model.\n\n # hasn't been validated\n #b'BOPHIGS', # Eigenvectors in the basic coordinate system for the structural portion of the model.\n\n # Grid Point Forces - SORT1/SORT2\n b'OGPFB1', b'OGPFB2',\n\n # ---------------\n # new results\n b'PSDF', # Power spectral density table.\n\n # random stress\n b'OESXRM1C', # Table of composite element RMS stresses in SORT1 format for random analysis that includes von Mises stress output.\n b'OESXNO1C',\n b'OESXNO1',\n\n # ---------------\n # results - unsupported\n b'GPLS', # needs to be here to prevent a crash\n b'TRMBU', # Transfomration matrices from undeformed to basic\n b'TRMBD', # Transformation matrices from deformed to basic\n #b'PVTS', # PVT0?\n b'OEFMXORD', # List of element IDs with maximum frequency and element order diagnostics for FEMAO solution\n b'OBCKL', # Load factor vs. cumulative arc-length in SORT2 format\n b'ONMD', # Normalized material density for topology optimization output\n b'OACPERF', # Performance data that indicates computation time in seconds and memory consumed in GB per frequency per subcase for FEMAO analysis.\n b'OGSTR1', # Grid point strains of superelement\n b'OSHT1', # Shell element thickness results (created by SHELLTHK)\n b'OEFIIS', # Inter-laminar shear failure indices.\n\n # bolt\n b'OBOLT1', # Bolt output data block\n\n # damage\n b'OELAR', # Element status (active or inactive)\n b'OJINT', # J-integral for a crack defined by CRAKTP.\n b'ODAMGPFE', # Damage energy for ply failure\n b'ODAMGPFD', # Damage values for ply failure\n b'ODAMGPFS', # Damage status for ply failure\n b'ODAMGPFR', # Crack density for ply failure EUD model from SOL 401. Crack density at corner grids on the middle of plies. The values are unitless\n\n # contact / glue\n b'OSLIDEG1', # Glue slide distance output\n b'OCONST1', # Contact status in SORT1 format\n b'OSLIDE1', # Incremental and total slide output for contact/glue.\n\n b'OCPSDF', # Cross-power-spectral-density functions.\n b'OCCORF', # Cross-correlation functions.\n b'OCPSDFC', # Cross-power spectral density functions for composites.\n b'OCCORFC', # Cross-correlation functions for composites.\n\n b'OEDE1', # Elemental energy loss.\n\n # grid point pressure\n b'OPRNO1', # SORT1 - NO\n b'OPRRMS1', # SORT1 - RMS\n b'OPRPSD2', # SORT2 - PSD\n b'OPRATO2', # SORT2 - AUTO\n b'OPRCRM2', # SORT2 - CRMS\n\n # modal contribution\n b'OUGMC1', # Modal contributions for displacements, velocities, accelerations.\n b'OUGMC2', # Modal contributions for displacements, velocities, accelerations.\n b'OQGMC1', # Modal contributions of single point constraint forces - SORT1\n b'OQGMC2', # Modal contributions of single point constraint forces - SORT2\n b'OESMC1', # Element stress modal contributions - SORT1\n b'OESMC2', # Element stress modal contributions - SORT2\n b'OSTRMC1', # Modal contributions of element strains - SORT1\n b'OSTRMC2', # Modal contributions of element strains - SORT2\n b'OEFMC1', # Modal contributions of element forces - SORT1\n b'OEFMC2', # Modal contributions of element forces - SORT2\n\n # modal strain energy\n b'OMSEC1', # Constant modal strain energy - SORT1\n b'OMSEC2', # Constant modal strain energy - SORT2\n b'OMECON1', # Constant total modal energies - SORT1\n b'OMECON2', # Constant total modal energies - SORT2\n b'OMEOSC1', # Oscillating total modal energies - SORT1\n b'OMEOSC2', # Oscillating total modal energies - SORT2\n b'OMKEC1', # Constant modal kinetic energies - SORT1\n b'OMKEC2', # Constant modal kinetic energies - SORT2\n b'OMKEO2', # Oscillating modal kinetic energies - SORT2\n b'OMSEO1', # Oscillating modal strain energies - SORT2\n b'OMSEO2', # Oscillating modal strain energies - SORT2\n b'OMKEO1', # Oscillating modal kinetic energies - SORT1\n\n # radiated power\n b'OERP', # Equivalent radiated power output.\n b'OERPEL1', # Element equivalent radiated power (element output)\n b'OERPEL2', # Element equivalent radiated power output.\n\n # acoustic\n b'OUGPC1', # Table of panel contributions - SORT1\n b'OUGPC2', # Table of panel contributions - SORT2\n b'OUGF1', # Acoustic pressures at microphone points in SORT1 format\n b'OUGF2', # Acoustic pressures at microphone points in SORT2 format\n b'OUGGC1', # Table of grid contributions - SORT1\n b'OUGGC2', # Table of grid contributions - SORT2\n b'OUGRC1', # Reciprocal panel contributions - SORT1\n b'OUGRC2', # Reciprocal panel contributions - SORT2\n b'BOUGF1', # Acoustic pressures at microphone points in SORT1 format - basic frame\n\n # acoustic acceleration\n b'OACCQ', # Acoustic coupling quality\n b'OACINT1', # Acoustic intensities at microphone points - SORT1\n b'OACINT2', # Acoustic intensities at microphone points - SORT2\n b'OACVELO1', # Acoustic velocities at microphone points - SORT1\n b'OACVELO2', # Acoustic velocities at microphone points - SORT2\n b'OACPWR2', # Acoustic power for AML regions and GROUPs of 2D elements - SORT2\n b'OACPWRI2', # Acoustic incident power - SORT2\n b'OACPWRT2', # Transmitted acoustic power for AML regions and GROUPs of 2D elements - SORT2\n b'OACTRLS2', # Acoustic transmission loss - SORT2\n\n # random acoustic\n b'OAPPSD2', # Acoustic power for the PSD function - SORT2\n\n b'OGK1', # gasket\n]\n\nif len(NX_RESULT_TABLES) != len(np.unique(NX_RESULT_TABLES)): # pragma: no cover\n counter = Counter(NX_RESULT_TABLES)\n _MSG = 'Invalid count:\\n'\n for key, cvaluei in counter.items():\n if cvaluei != 1:\n _MSG += '%s = %s\\n' % (key, cvaluei)\n raise RuntimeError(_MSG)\n\nNX_RESULT_TABLES += NX_EXTRA_TABLES\n\nNX_TABLE_CONTENT = {\n # nx 8.5\n 0: '',\n 1: 'OUG - Displacement vector',\n 2: 'OPG - Load vector',\n 3: 'OQG - SPC/MPC Force vector',\n 4: 'OEF - Element force/flux',\n 5: 'OES - Element stress/strain',\n 6: 'LAMA - Eigenvalue summary',\n 7: 'OUG - Eigenvector',\n 8: 'Grid Point Singularity Table (obsolete)',\n 9: 'OEIGS - Eigenvalue analysis summary',\n 10: 'OUG - Velocity vector',\n 11: 'OUG - Acceleration vector',\n 12: 'OPG - Nonlinear force vector',\n 13: 'OGPWG - Grid point weight generator',\n 14: 'OUG - Eigenvector (solution set)',\n 15: 'OUG - Displacement vector (solution set)',\n 16: 'OUG - Velocity vector (solution set)',\n 17: 'OUG - Acceleration vector (solution set)',\n 18: 'OEE - Element strain energy',\n 19: 'OGF - Grid point force balance',\n 20: 'OES - Stresses at grid points',\n 21: 'OES - Strain/curvature at grid points',\n 22: 'OELOF1 - Element internal forces/moments',\n 23: 'OELOP1 - Summation of element oriented forces on adjacent elements',\n 24: 'OEP - Element pressures',\n 25: 'OEF - Composite failure indices',\n 26: 'OGS - Grid point stresses (surface)',\n 27: 'OGS - Grid point stresses (volume - direct)',\n 28: 'OGS - Grid point stresses (volume - princial)',\n 29: 'OGS - Element stress discontinuities (surface)',\n 30: 'OGS - Element stress discontinuities (volume - direct)',\n 31: 'OGS - Element stress discontinuities (volume - princial)',\n 32: 'OGS - Grid point stress discontinuities (surface)',\n 33: 'OGS - Grid point stress discontinuities (volume - direct)',\n 34: 'OGS - Grid point stress discontinuities (volume - princial)',\n 35: 'OGS - Grid point stresses (plane strain)',\n 36: 'OEE - Element kinetic energy',\n 37: 'OEE - Element energy loss',\n\n 38 : 'OMSEC - Constant modal strain energy',\n 39 : 'OMSED - Oscillating modal strain energy',\n 40 : 'OMKEC - Constant modal kinetic energy',\n 41 : 'OMKED - Oscillating modal kinetic energy',\n 42 : 'OMECON - Constant total modal energy',\n 43 : 'OMEOSC - Oscillating total modal energy',\n 44 : 'OUGMC - Displacement/velocity/acceleration modal contributions',\n 45 : 'OEFMC - Element force modal contributions',\n 46 : 'OESMC - Element stress modal contributions',\n 47 : 'OSTRMC - Element strain modal contributions',\n 48 : 'OQGMC - SPC force modal contributions',\n 49 : 'OUGPC - Panel contributions',\n 50 : 'OUGGC - Grid contributions',\n 51 : 'OUGRC - Reciprocal panel contributions',\n #\n 53 : 'OACVELO - Acoustic velocity',\n 54 : 'OACINT - Acoustic intensity',\n 55 : 'OACPWR - Acoustic power',\n 56 : 'OACPWRI - Acoustic incident power',\n 57 : 'OACPWRT - Acoustic transmitted power',\n 58 : 'OACTRLS - Acoustic transmission loss',\n #\n 61 : 'OGK - Gasket Element Results',\n 62 : 'OBC - Contact Pressure and Traction Results',\n 63 : 'OQG - Contact Force Results',\n 64 : 'OSPDSI - Contact Separation Distance - Initial',\n 65 : 'OSPDS - Contact Separation Distance',\n 66 : 'OBG - Glue force results (normal and in-plane tractions)',\n #67 : 'OQG - Glue force results',\n 68 : 'ELRSCALV - Tosca normalized material properties',\n 69 : 'OERP - Element equivalent radiated power (panel output)',\n 70 : 'OERPEL - Element equivalent radiated power (element output)',\n 71 : 'Reserved for FE-Design',\n 72 : 'OTEMP - Grid point temperature output',\n 73 : 'JINT - Crack front J-integral output',\n 74 : 'SLIDE - Contact/Glue slide output',\n 75 : 'CONSTAT - Contact status output',\n 76 : 'OERR - Error estimator output',\n 77 : 'OPRESS - Grid point pressure output',\n 78 : 'STATE - Variables output',\n 79 : 'INITSTR - Initial strain output',\n 80 : 'OBOLT - Bolt preload output',\n\n 81 : 'OCKGAP1 - Opening gap values for chocking elements',\n 82 : 'ODAMGCZD - Damage values for cohesive elements',\n 83 : 'ODAMGCZR - Relative displacements for cohesive elements',\n 84 : 'ODAMGCZT - Tractions for cohesive elements',\n 85 : 'ODAMGPFD - Damage values for PFA',\n 86 : 'ODAMGPFE - Damage energy for PFA',\n 87 : 'ODAMGPFS - Damage status for PFA',\n 88 : 'ODAMGPFR - Damage crack density for PFA / EUD',\n 89 : '??? - Composite strength ratios',\n 90 : 'TRMBD - Transformation matrices from deformed to basic',\n 91 : 'TRMBU - Transfomration matrices from undeformed to basic',\n 92 : 'ONMD - Normalized material density for topology optimization output',\n 93 : 'OBCKL - SORT2 output for Load Factor versus Cummulative Arc-length from a SOL 401 arc-length solution',\n #\n # nx 2019.2\n #\n 804 : 'OEFRMS1 - ???',\n 805 : 'OESXRMS1 - element RMS stresses for random analysis that includes von Mises stress output.',\n 905 : 'OESXNO1C - Cumulative Root Mean Square output',\n} # type: Dict[int, str]\n\nNX_OEF_REAL_MAPPER = {\n 1: 3, # CROD\n 2: 1 + (10 - 1) * 11, # CBEAM\n 3: 3, # CTUBE\n 4: 17, # CSHEAR\n 10: 3, # CONROD\n 11: 2, # CELAS1\n 12: 2, # CELAS2\n 13: 2, # CELAS3\n 14: 2, # CELAS4\n\n 20: 2, # CDAMP1\n 21: 2, # CDAMP2\n 22: 2, # CDAMP3\n 23: 2, # CDAMP4\n 24: 3, # CVISC\n\n 33: 9, # CQUAD4\n 34: 9, # CBAR\n 35: 7, # CCONEAX\n 38: 9, # CGAP\n 40: 8, # CBUSH1D ???\n 64: 2 + (11 - 2) * 5, # CQUAD8\n 69: 1 + (8 - 1) * 2, # CBEND\n 70: 2 + (11 - 2) * 4, # CTRIAR\n 74: 9, # CTRIA3\n 75: 2 + (11 - 2) * 4, # CTRIA6\n\n #76: 16, # Acoustic Velocity/Pressure CHEXA ???\n 76: None, # dummy so it doesnt go into the real results\n 77: 10, # Acoustic Velocity/Pressure CPENTA\n 78: 10, # Acoustic Velocity/Pressure CTETRA\n\n 82: 2 + (11 - 2) * 5, # CQUADR\n 95: 9, # composite CQUAD4 ???\n 96: 9, # composite CQUAD8 ???\n 97: 9, # composite CTRIA3 ???\n 98: 9, # composite CTRIA6 ???\n 100: 8, # CBAR-100\n 102: 7, # CBUSH\n 144: 2 + (11 - 2) * 5, # bilinear CQUAD4\n 189: 6 + (19 - 6) * 4, # VUQUAD\n 190: 6 + (19 - 6) * 3, # VUTRIA\n 191: 4 + (12 - 4) * 2, # VUBEAM\n 200: 9, # CWELD\n 232: 9, # composite CQUADR ???\n 233: 9, # composite TRIAR ???\n 235: 9, # punch CQUADR...num_wide in DMAP is wrong...left out first entry...\n 236: 8, # punch CTRIAR\n}\nNX_OEF_IMAG_MAPPER = {\n 1: 5, # CROD\n 2: 1 + (17 - 1) * 11, # CBEAM\n 3: 5, # CTUBE\n 4: 33, # CSHEAR\n 10: 5, # CONROD\n\n 11: 3, # CELAS1\n 12: 3, # CELAS2\n 13: 3, # CELAS3\n 14: 3, # CELAS4\n\n 20: 3, # CDAMP1\n 21: 3, # CDAMP2\n 22: 3, # CDAMP3\n 23: 3, # CDAMP4\n 24: 5, # CVISC\n\n 33: 17, # CQUAD4-centroid\n 34: 17, # CBAR-34\n 35: 7, # CCONEAX # needed to not crash the code...\n 38: 9, # CGAP\n 40: 8, # CBUSH1D ???\n\n 64: 2 + (19 - 2) * 5, # CQUAD8\n 69: 1 + (14 - 1) * 2, # CBEND\n 70: 2 + (19 - 2) * 4, # CTRIAR\n 74: 17, # CTRIA3\n 75: 2 + (19 - 2) * 4, # CTRIA6\n\n 76: 16, # Acoustic Velocity/Pressure CHEXA_PR\n 77: 16, # Acoustic Velocity/Pressure CPENTA_PR\n 78: 16, # Acoustic Velocity/Pressure CTETRA_PR\n\n 82: 2 + (19 - 2) * 5, # CQUADR\n 95: 9, # composite CQUAD4 ???\n 96: 9, # composite CQUAD8 ???\n 97: 9, # composite CTRIA3 ???\n 98: 9, # composite CTRIA6 ???\n 100: 14, # BARS\n 102: 13, # CBUSH\n\n 144: 2 + (19 - 2) * 5, # CQUAD4-bilinear\n 189: 6 + (31 - 6) * 4, # VUQUAD\n 190: 6 + (31 - 6) * 3, # VUTRIA\n 191: 4 + (18 - 4) * 2, # VUBEAM\n 200: 17, # CWELD\n 232: 9, # composite CQUADR ???\n 233: 9, # composite TRIAR ???\n 235: 17, # punch CQUADR...num_wide in DMAP is wrong...left out first entry...\n 236: 16, # punch CTRIAR\n}\n","sub_path":"pyNastran/op2/op2_interface/nx_tables.py","file_name":"nx_tables.py","file_ext":"py","file_size_in_byte":20501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"480506538","text":"\"\"\"Dash app for database table view.\"\"\"\nfrom dash import Dash\nimport dash_table\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nfrom .data import get_table_data, column_dist_chart\nfrom .layout import app_layout\n\n\ndef create_dash_view(server):\n \"\"\"Initiate Plotly Dash view.\"\"\"\n external_stylesheets = [\n '/static/dist/css/plotly-flask-tutorial.css',\n 'https://fonts.googleapis.com/css?family=Lato::300,700',\n 'https://use.fontawesome.com/releases/v5.8.1/css/all.css'\n ]\n dash_app = Dash(\n server=server,\n external_stylesheets=external_stylesheets,\n routes_pathname_prefix='/table/commands/'\n )\n\n # Override the underlying HTML template\n dash_app.index_string = app_layout\n\n # Get DataFrame\n table_df = get_table_data()\n datatable = create_data_table(table_df)\n\n for column in table_df:\n column_dist_chart(table_df, column)\n\n # Create Dash Layout comprised of Data Tables\n dash_app.layout = create_layout(datatable, table_df)\n init_callbacks(dash_app, table_df)\n\n return dash_app.server\n\n\ndef create_layout(datatable, table_df):\n \"\"\"Create Dash layout for table editor.\"\"\"\n return html.Div(\n id='database-table-container',\n children=[dcc.Dropdown(\n id='type-dropdown',\n options=[{'label': i, 'value': i} for i in table_df.type.unique() if i],\n multi=True,\n placeholder='Filter commands by type'\n ),\n datatable,\n html.Div(id='callback-container'),\n html.Div(id='container-button-basic',\n children=[\n html.Div(id='save-status')\n ]),\n ])\n\n\ndef create_data_table(table_df):\n \"\"\"Create table from Pandas DataFrame.\"\"\"\n table_preview = dash_table.DataTable(\n id='database-table',\n columns=[{\"name\": i, \"id\": i} for i in table_df.columns],\n data=table_df.to_dict(\"rows\"),\n sort_action=\"native\",\n sort_mode='native',\n page_size=9000\n )\n return table_preview\n\n\ndef init_callbacks(dash_app, table_df):\n \"\"\"Dash callbacks.\"\"\"\n @dash_app.callback(\n Output('database-table', 'data'),\n [Input('type-dropdown', 'value')])\n def filter_by_type(types):\n \"\"\"Updates chart based on filtering.\"\"\"\n dff = table_df\n\n if types is not None and len(types):\n dff = table_df.loc[table_df['type'].isin(types)]\n\n return dff.to_dict('records')\n\n\n '''@dash_app.callback(\n Output('database-table', 'data'),\n [Input('editing-rows-button', 'n_clicks')],\n [State('database-table', 'data'),\n State('database-table', 'columns')])\n def add_row(n_clicks, rows, columns):\n if n_clicks > 0:\n rows.append({c['id']: '' for c in columns})\n return rows\n\n @dash_app.callback(\n Output('database-table', 'columns'),\n [Input('adding-rows-button', 'n_clicks')],\n [State('adding-rows-name', 'value'),\n State('database-table', 'columns')])\n def update_columns(n_clicks, value, existing_columns):\n if n_clicks > 0:\n existing_columns.append({\n 'id': value, 'name': value,\n 'editable_name': True, 'deletable': True\n })\n return existing_columns\n\n @dash_app.callback(\n Output('save-status', 'children'),\n [Input('save', 'n_clicks'),\n Input('database-table', 'data')])\n def save_table(n_clicks, table_data):\n \"\"\"Save table to database.\"\"\"\n updated_df = pd.DataFrame(table_data)\n # print('updated_df = ', updated_df.head())\n # upload_dataframe(updated_df)\n sys.stdout.write(str(updated_df.info()))\n return updated_df\n\n @dash_app.callback(\n Output('callback-container', 'children'),\n [Input('database-table', 'data_timestamp'),\n Input('database-table', 'active_cell'),\n Input('database-table', 'data')])\n def update_database(time_updated, cell_coordinates, table_data):\n changed_cell = table_data[cell_coordinates[0]]\n return html.Span(changed_cell, className='')\n\n @dash_app.callback(\n Output('callback-container', 'children'),\n\n [Input('save', 'n_clicks')])\n def update_output(n_clicks, value):\n return 'The input value was \"{}\" and the button has been clicked {} times'.format(\n value,\n n_clicks\n )'''\n\n\n'''@dash_app.callback(\n Output('callback-container', 'children'),\n [Input('database-table', 'row_update'),\n Input('database-table', 'rows')]\n )\ndef update_database(row_update, rows):\n return html.Div(className='row', children=[\n html.Div([\n html.Code('row_update'),\n html.Pre(json.dumps(row_update, indent=2))\n ], className='six columns'),\n html.Div([\n html.Code('rows'),\n html.Pre(json.dumps(rows, indent=2))\n ], className='six columns'),\n ])'''\n","sub_path":"table/tableview.py","file_name":"tableview.py","file_ext":"py","file_size_in_byte":5076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"201731204","text":"import lightgbm as lgb\nimport numpy as np\nimport pandas as pd\nfrom attrdict import AttrDict\nfrom catboost import CatBoostClassifier\nfrom sklearn.externals import joblib\nfrom steppy.base import BaseTransformer\nfrom steppy.utils import get_logger\nfrom xgboost import XGBClassifier\n\nfrom .sklearn_transformers.models import MultilabelEstimators\n\nlogger = get_logger()\n\n\nclass CatboostClassifierMultilabel(MultilabelEstimators):\n @property\n def estimator(self):\n return CatBoostClassifier\n\n\nclass XGBoostClassifierMultilabel(MultilabelEstimators):\n @property\n def estimator(self):\n return XGBClassifier\n\n\nclass LightGBM(BaseTransformer):\n def __init__(self, **params):\n super().__init__()\n logger.info('initializing LightGBM...')\n self.params = params\n self.training_params = ['number_boosting_rounds', 'early_stopping_rounds']\n self.evaluation_function = None\n\n @property\n def model_config(self):\n return AttrDict({param: value for param, value in self.params.items()\n if param not in self.training_params})\n\n @property\n def training_config(self):\n return AttrDict({param: value for param, value in self.params.items()\n if param in self.training_params})\n\n def _check_target_shape_and_type(self, target, name):\n if isinstance(target, list):\n return np.array(target)\n try:\n assert len(target.shape) == 1, '\"{}\" must be 1-D. It is {}-D instead.'.format(name,\n len(target.shape))\n except AttributeError:\n print('Cannot determine shape of the {}. '\n 'Type must be \"numpy.ndarray\" or \"Pandas.Series\" or \"list\", got {} instead'.format(name,\n type(target)))\n\n if isinstance(target, pd.Series):\n return target.values\n if not isinstance(target, np.ndarray):\n TypeError('\"{}\" must be \"numpy.ndarray\" or \"Pandas.Series\" or \"list\", got {} instead.'.format(name,\n type(target)))\n\n def fit(self,\n X,\n y,\n X_valid,\n y_valid,\n feature_names='auto',\n categorical_features='auto',\n **kwargs):\n y = self._check_target_shape_and_type(y, 'y')\n y_valid = self._check_target_shape_and_type(y_valid, 'y_valid')\n evaluation_results = {}\n\n logger.info('LightGBM, train data shape {}'.format(X.shape))\n logger.info('LightGBM, validation data shape {}'.format(X_valid.shape))\n logger.info('LightGBM, train labels shape {}'.format(y.shape))\n logger.info('LightGBM, validation labels shape {}'.format(y_valid.shape))\n\n data_train = lgb.Dataset(data=X,\n label=y,\n feature_name=feature_names,\n categorical_feature=categorical_features,\n **kwargs)\n data_valid = lgb.Dataset(X_valid,\n label=y_valid,\n feature_name=feature_names,\n categorical_feature=categorical_features,\n **kwargs)\n\n self.estimator = lgb.train(self.model_config,\n data_train,\n feature_name=feature_names,\n categorical_feature=categorical_features,\n valid_sets=[data_train, data_valid],\n valid_names=['data_train', 'data_valid'],\n evals_result=evaluation_results,\n num_boost_round=self.training_config.number_boosting_rounds,\n early_stopping_rounds=self.training_config.early_stopping_rounds,\n verbose_eval=self.model_config.verbose,\n feval=self.evaluation_function,\n **kwargs)\n return self\n\n def transform(self, X, y=None, **kwargs):\n prediction = self.estimator.predict(X)\n return {'prediction': prediction}\n\n def load(self, filepath):\n self.estimator = joblib.load(filepath)\n return self\n\n def persist(self, filepath):\n joblib.dump(self.estimator, filepath)\n","sub_path":"toolkit/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":4678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"291730179","text":"import sys\n\n#gene_file = sys.argv[1]\n#out_file = sys.argv[2]\n#print(gene_file)\n#print(out_file)\ndna_list=list('ACGT')\ndef getGeneList(gene_file):\n with open(gene_file, 'r') as humchr:\n tag = False\n gene_list = []\n for line in humchr:\n if line.startswith('Gene'):\n tag = True\n if tag:\n line_split = line.split()\n if len(line_split) != 0:\n if '-' in line_split[0]:\n continue\n else:\n gene_list.append(line_split[0])\n return gene_list[3:][:-2]\n\ndef writeGeneList(clean_gene_list,out_file):\n with open(out_file, 'w') as gene_names:\n for gene in clean_gene_list:\n gene_names.writelines(gene+'\\n')\n print('Genes have been written Successfully')\n \n#clean_gene_list = getGeneList(gene_file)\n#writeGeneList(clean_gene_list,out_file)\n","sub_path":"Scripts/write_genes.py","file_name":"write_genes.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"345933300","text":"import json\n\ndef digest_businesses():\n business_type_dict={}\n decoder=json.JSONDecoder()\n category_set=set()\n fbusiness=open('../Dataset/business.json','r')\n for business in fbusiness:\n business_attr=decoder.decode(business)\n business_id=business_attr['business_id']\n business_categories=business_attr['categories']\n business_type_dict[business_id]=business_categories\n for category in business_categories:\n category_set.add(category)\n fbusiness.close()\n return business_type_dict,category_set\n\ndef get_file_list(categories):\n files_dict={}\n for category in categories:\n files_dict[category]=open('../CategorizedJSON/category_%s.json'%category.replace(' ','_').replace('/','_'),'w')\n return files_dict\n\ndef close_files(files_dict):\n for file_l in files_dict.keys():\n files_dict[file_l].close()\n\ndef process_reviews(business_dict,files_dict):\n freview=open('../Dataset/review.json','r')\n decoder=json.JSONDecoder()\n for review_s in freview:\n review_attr=decoder.decode(review_s)\n review_business=review_attr['business_id']\n business_categories=business_dict[review_business]\n for category in business_categories:\n files_dict[category].write(review_s+'\\n')\n freview.close()\n\ndef main():\n businesses,categories=digest_businesses()\n files=get_file_list(categories)\n process_reviews(businesses,files)\n close_files(files)\n \n\nif __name__ == '__main__':\n main() \n","sub_path":"categorize.py","file_name":"categorize.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"353407947","text":"import numpy as np\nfrom sklearn.base import TransformerMixin, BaseEstimator\nfrom sklearn.utils.validation import check_is_fitted\n\n\nclass StandardFlexibleScaler(TransformerMixin, BaseEstimator):\n \"\"\"Standardize features by removing the mean and scaling to unit variance.\n Make the mean of the columns equal to zero and the\n variance of each column (`column_wise==True`) equal to one.\n\n :param with_mean: If True, center the data before scaling. If False, keep the mean intact\n :type with_mean: boolean\n :param with_std: If True, scale the data to unit variance. If False, keep the variance intact\n :type with_std: boolean\n :param column_wise: If True, normalize each column separately. If False, normalize the whole matrix, divided it by variaton.\n :type column_wise: boolean\n :param tol: The tolerance for the optimization: if the variance are smaller than tol, it is considered zero.\n \"\"\"\n\n def __init__(self, with_mean=True, with_std=True, column_wise=False, tol=1e-15):\n \"\"\"Initialize StandardFlexibleScaler.\"\"\"\n self.with_mean = with_mean\n self.with_std = with_std\n self.column_wise = column_wise\n self.n_samples_seen_ = 0\n self.tol = tol\n\n def fit(self, X, y=None):\n \"\"\"Compute mean and scaling to be applied for subsequent normalization.\n\n :param X: Matrix\n :type X: ndarray\n :param y: ignored\n\n :return: itself\n \"\"\"\n\n self.n_samples_seen_, self.n_features_ = X.shape\n if self.with_mean:\n self.mean_ = X.mean(axis=0)\n else:\n self.mean_ = np.zeros(self.n_features_)\n\n self.scale_ = 1.0\n if self.with_std:\n var = ((X - X.mean(axis=0)) ** 2).mean(axis=0)\n\n if self.column_wise:\n if np.any(var < self.tol):\n raise ValueError(\"Cannot normalize a feature with zero variance\")\n self.scale_ = np.sqrt(var)\n else:\n var_sum = var.sum()\n if var_sum < self.tol:\n raise ValueError(\"Cannot normalize a matrix with zero variance\")\n self.scale_ = np.sqrt(var_sum)\n\n return self\n\n def transform(self, X, y=None):\n \"\"\"Normalize a vector based on previously computed mean and scaling.\n\n :param X: Matrix\n :type X: ndarray\n :param y: ignored\n\n :return: transformed matrix X\n \"\"\"\n\n check_is_fitted(self, attributes=[\"n_samples_seen_\", \"n_features_\"])\n\n if self.n_features_ != X.shape[1]:\n raise ValueError(\"X shape does not match training shape\")\n return (X - self.mean_) / self.scale_\n\n def fit_transform(self, X, y=None, **fit_params):\n r\"\"\"Fit to data, then transform it.\n\n :param X: Matrix\n :type X: ndarray\n :param y: ignored\n :param \\**fit_params: necessary for compatibility with the functions of the TransformerMixin class\n\n :return: itself\n \"\"\"\n self.fit(X, y)\n return self.transform(X, y)\n\n def inverse_transform(self, X_tr):\n \"\"\"Scale back the data to the original representation\n\n :param X_tr: Matrix\n :type X_tr: ndarray\n\n :return: original matrix X\n \"\"\"\n\n check_is_fitted(self, attributes=[\"n_samples_seen_\", \"n_features_\"])\n\n if self.n_features_ != X_tr.shape[1]:\n raise ValueError(\"X shape does not match training shape\")\n return X_tr * self.scale_ + self.mean_\n\n\nclass KernelFlexibleCenterer(TransformerMixin, BaseEstimator):\n \"\"\"Kernel centering method, similar to KernelCenterer,\n but with additional parameters, relative to which centering\n is carried out:\n\n \"\"\"\n\n def __init__(self):\n \"\"\"Initialize KernelFlexibleCenterer.\"\"\"\n pass\n\n def fit(self, K=None, y=None, K_fit_rows=None, K_fit_all=None):\n \"\"\"Fit KernelFlexibleCenterer\n\n :param K: Kernel matrix\n :type K: ndarray of shape (n_samples, n_samples)\n :param y: ignored\n :param K_fit_rows: an array with means for each column.\n :type K_fit_rows: array of shape (1, n_features)\n :param K_fit_all: an average for the whole kernel matrix\n :type K_fit_all: array\n\n :return: itself\n \"\"\"\n if K is not None:\n if K.shape[0] != K.shape[1]:\n raise ValueError(\n \"The reference kernel is not square, and does not define a RKHS\"\n )\n\n self.reference_shape_ = K.shape\n\n if K_fit_rows is not None:\n if K.shape[0] != len(K_fit_rows):\n raise ValueError(\n \"The supplied column mean does not match the supplied kernel.\"\n )\n else:\n K_fit_rows = K.mean(axis=0)\n\n if K_fit_all is None:\n K_fit_all = K.mean()\n\n else:\n assert K_fit_rows is not None and K_fit_all is not None\n self.reference_shape_ = [None, len(K_fit_rows)]\n\n self.K_fit_rows_ = K_fit_rows\n self.K_fit_all_ = K_fit_all\n\n Kc = (\n K\n - np.broadcast_arrays(K, self.K_fit_rows_)[1]\n - np.mean(K, axis=1).reshape((K.shape[0], 1))\n + np.broadcast_arrays(K, self.K_fit_all_)[1]\n )\n\n self.scale_ = np.trace(Kc) / K.shape[0]\n\n return self\n\n def transform(self, K, y=None):\n \"\"\"Centering our Kernel. Previously you should fit data.\n\n :param K: Kernel matrix\n :type K: ndarray of shape (n_samples, n_samples)\n :param y: ignored\n\n :return: tranformed matrix Kc\n\n check each of the parameters self.reference_shape_, self.scale_, self.K_fit_all_,\n and self.K_fit_rows_, which must all be defined\n \"\"\"\n\n check_is_fitted(\n self, attributes=[\"K_fit_rows_\", \"K_fit_all_\", \"scale_\", \"reference_shape_\"]\n )\n\n if K.shape[1] != self.reference_shape_[1]:\n raise ValueError(\n \"The reference kernel and received kernel have different shape\"\n )\n rmean = K.mean(axis=1)\n\n Kc = (\n K\n - np.broadcast_arrays(K, self.K_fit_rows_)[1]\n - rmean.reshape((K.shape[0], 1))\n + np.broadcast_arrays(K, self.K_fit_all_)[1]\n ) / self.scale_\n\n return Kc\n\n def fit_transform(self, K, y=None, K_fit_rows=None, K_fit_all=None, **fit_params):\n r\"\"\"Fit to data, then transform it.\n\n :param K: Kernel matrix\n :type K: ndarray of shape (n_samples, n_samples)\n :param y: ignored\n :param K_fit_rows: an array with means for each column.\n :type K_fit_rows: array of shape (1, n_features)\n :param K_fit_all: an average for the whole kernel matrix\n :type K_fit_all: array\n :param \\**fit_params: necessary for compatibility with the functions of the TransformerMixin class\n\n :return: tranformed matrix Kc\n \"\"\"\n self.fit(K, y, K_fit_rows=K_fit_rows, K_fit_all=K_fit_all)\n return self.transform(K, y)\n\n\nclass SparseKernelCenterer(TransformerMixin, BaseEstimator):\n \"\"\"Kernel centering method for sparse kernels, similar to\n KernelFlexibleCenterer\n \"\"\"\n\n def __init__(self, rcond=1e-12):\n \"\"\"\n Initialize SparseKernelCenterer.\n\n :param rcond: conditioning parameter to use when computing the\n Nystrom-approximated kernel for scaling\n :type rcond: float, default 1E-12\n \"\"\"\n\n self.rcond = rcond\n\n def fit(self, Knm, Kmm, y=None):\n \"\"\"Fit KernelFlexibleCenterer\n\n :param Knm: Kernel matrix between the reference data set and the active\n set\n :type Knm: ndarray of shape (n_samples, n_active)\n\n :param Kmm: Kernel matrix between the active set and itself\n :type Kmm: ndarray of shape (n_active, n_active)\n\n :param y: ignored\n\n :return: itself\n \"\"\"\n\n if Knm.shape[1] != Kmm.shape[0]:\n raise ValueError(\n \"The reference kernel is not commensurate shape with the\"\n \"active kernel.\"\n )\n\n if Kmm.shape[0] != Kmm.shape[1]:\n raise ValueError(\"The active kernel is not square.\")\n\n self.n_active_ = Kmm.shape[0]\n\n self.K_fit_rows_ = Knm.mean(axis=0)\n\n Knm_centered = Knm - self.K_fit_rows_\n\n Khat = Knm_centered @ np.linalg.pinv(Kmm, self.rcond) @ Knm_centered.T\n\n self.scale_ = np.sqrt(np.trace(Khat) / Knm.shape[0])\n\n return self\n\n def transform(self, Knm, y=None):\n \"\"\"Centering our Kernel. Previously you should fit data.\n\n :param Knm: Kernel matrix between the reference data set and the active\n set\n :type Knm: ndarray of shape (n_samples, n_active)\n :param y: ignored\n\n :return: tranformed matrix Kc\n\n check each of the parameters self.n_active_, self.scale_\n and self.K_fit_rows_, which must all be defined\n \"\"\"\n check_is_fitted(self, attributes=[\"scale_\", \"K_fit_rows_\", \"n_active_\"])\n\n if Knm.shape[1] != self.n_active_:\n raise ValueError(\n \"The reference kernel and received kernel have different shape\"\n )\n\n Kc = (Knm - self.K_fit_rows_) / self.scale_\n\n return Kc\n\n def fit_transform(self, Knm, Kmm, y=None, **fit_params):\n r\"\"\"Fit to data, then transform it.\n\n :param Knm: Kernel matrix between the reference data set and the active\n set\n :type Knm: ndarray of shape (n_samples, n_active)\n\n :param Kmm: Kernel matrix between the active set and itself\n :type Kmm: ndarray of shape (n_active, n_active)\n\n :param y: ignored\n\n :param \\**fit_params: necessary for compatibility with the functions of\n the TransformerMixin class\n\n :return: tranformed matrix Kc\n \"\"\"\n self.fit(Knm=Knm, Kmm=Kmm)\n return self.transform(Knm)\n","sub_path":"skcosmo/preprocessing/flexible_scaler.py","file_name":"flexible_scaler.py","file_ext":"py","file_size_in_byte":10076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"472750995","text":"# Import required libraries\nimport pickle\nimport copy\nimport pathlib\nimport urllib.request\nimport dash\nimport math\nimport datetime as dt\nimport pandas as pd\nimport json\n\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\nfrom dash.dependencies import Input, Output, State, ClientsideFunction\nimport dash_core_components as dcc\nimport dash_html_components as html\n\n# Multi-dropdown options\nfrom controls import REGENCIES, COUNTIES, WELL_STATUSES, WELL_TYPES, WELL_COLORS\nimport controls\n\n\n# get relative data folder\nPATH = pathlib.Path(__file__).parent\nDATA_PATH = PATH.joinpath(\"data\").resolve()\n\n# Data paths\n# -----------------------------\n\ndata_covid_bali = r'C:\\Users\\ansve\\Coding\\Projects-WebScraping\\CovidBali\\testingDash\\plotly apps-dash-oil-and-gas\\data\\data_process\\bali_regency_data.csv'\ndata_covid_indo = r'C:\\Users\\ansve\\Coding\\Projects-WebScraping\\CovidBali\\testingDash\\plotly apps-dash-oil-and-gas\\data\\data_process\\indo_province_data.csv'\ndata_covid_germany = r'C:\\Users\\ansve\\Coding\\Projects-WebScraping\\CovidBali\\testingDash\\plotly apps-dash-oil-and-gas\\data\\county_covid_BW.csv'\ngeojson_bali = r'C:\\Users\\ansve\\Coding\\Projects-WebScraping\\CovidBali\\testingDash\\plotly apps-dash-oil-and-gas\\data\\new_bali_geojson_id.geojson'\ngeojson_indo = r'C:\\Users\\ansve\\Coding\\Projects-WebScraping\\CovidBali\\testingDash\\plotly apps-dash-oil-and-gas\\data\\indo_level1_id.geojson'\ngeojson_germany = r'C:\\Users\\ansve\\Coding\\Projects-WebScraping\\CovidBali\\testingDash\\plotly apps-dash-oil-and-gas\\data\\geojson_ger.json'\n\n\n# Initialize App\n# -----------------------------\napp = dash.Dash(\n __name__, meta_tags=[{\"name\": \"viewport\", \"content\": \"width=device-width\"}]\n)\nserver = app.server\n\n# Create controls\n# ---------------------\n\n# own controls for Bali_Covid Dash-App\nregency_options = [\n {'label': str(REGENCIES[x]), 'value': str(REGENCIES[x])} for x in REGENCIES\n]\n\n# Create global chart template\n# -----------------------------\nmapbox_access_token = \"pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNrOWJqb2F4djBnMjEzbG50amg0dnJieG4ifQ.Zme1-Uzoi75IaFbieBDl3A\"\n\nlayout = dict(\n autosize=True,\n automargin=True,\n margin=dict(l=30, r=30, b=20, t=40),\n hovermode=\"closest\",\n plot_bgcolor=\"#F9F9F9\",\n paper_bgcolor=\"#F9F9F9\",\n legend=dict(font=dict(size=10), orientation=\"h\"),\n title=\"Satellite Overview\",\n mapbox=dict(\n accesstoken=mapbox_access_token,\n style=\"light\",\n center=dict(lon=114, lat=-8.54),\n zoom=7,\n ),\n)\n# Create app layout\n# -----------------------------\napp.layout = html.Div(\n [\n dcc.Store(id=\"aggregate_data\"),\n\n # empty Div to trigger javascript file for graph resizing\n html.Div(id=\"output-clientside\"),\n\n # Header Component\n # ------------------------------\n html.Div(\n [\n html.Div(\n [\n html.Img(\n src=app.get_asset_url(\"Barong-Mask.png\"),\n id=\"plotly-image\",\n style={\n \"height\": \"80px\",\n \"width\": \"auto\", },\n )\n ],\n className=\"one-third column\",\n ),\n html.Div(\n [\n html.Div(\n [\n html.H3(\"Covid Cases\", style={\n \"margin-bottom\": \"0px\"},),\n html.H5(\"Cases in Bali per Regency\",\n style={\"margin-top\": \"0px\"}),\n ]\n )\n ],\n className=\"one-half column\",\n id=\"title\",\n ),\n html.Div(\n [\n html.A(\n html.Button(\"About Me\", id=\"learn-more-button\"),\n href=\"https://5cac0a0b7a48d9000a0e3c77--portfolio-gatsby-bali.netlify.app/\",\n )\n ],\n className=\"one-third column\",\n id=\"button\",\n ),\n ],\n id=\"header\",\n className=\"row flex-display\",\n style={\"margin-bottom\": \"10px\"},\n ),\n html.Div([\n html.Div([\n html.P(\"Region:\", className='control_label'),\n dcc.RadioItems(\n id='region_sel',\n options=[\n {'label': 'Indonesia', 'value': 'indo'},\n {'label': 'Bali', 'value': 'bali'},\n ],\n labelStyle={\"display\": \"inline-block\"},\n value=\"bali\",\n className=\"dcc_control\",\n ),\n html.P(\"Regency/County:\",\n className=\"control_label\"),\n dcc.Dropdown(\n id=\"regency_sel\",\n options=regency_options, # well_type_options,\n multi=False,\n value='',\n className=\"dcc_control\",\n ),\n ],\n className='pretty_container thirteen columns'\n )\n ],\n id='new_controls',\n className=\"row flex-display\",\n ),\n\n\n html.Div(\n [\n # Controls Panel Component\n # ------------------------------\n html.Div(\n [\n html.P(\n \"Region:\",\n className='control_label'\n ),\n dcc.RadioItems(\n id='region_selector',\n options=[\n {'label': 'Indonesia', 'value': 'indo'},\n {'label': 'Bali', 'value': 'bali'},\n ],\n labelStyle={\"display\": \"inline-block\"},\n value=\"bali\",\n className=\"dcc_control\",\n ),\n html.P(\"Regency/County:\",\n className=\"control_label\"),\n dcc.Dropdown(\n id=\"regency_selector\",\n options=regency_options, # well_type_options,\n multi=False,\n value='',\n className=\"dcc_control\",\n ),\n html.P('NOT YET !!', className=\"control_label\",),\n html.P(\"Date or Timerange:\",\n className=\"control_label\",\n ),\n dcc.RangeSlider(\n id=\"year_slider\",\n min=1960,\n max=2017,\n value=[1990, 2010],\n className=\"dcc_control\",\n ),\n html.P(\"Cases:\", className=\"control_label\"),\n dcc.RadioItems(\n id=\"well_status_selector\",\n options=[\n {\"label\": \"All \", \"value\": \"all\"},\n {\"label\": \"Confirmed \", \"value\": \"confirmed\"},\n {\"label\": \"Deaths \", \"value\": \"death\"},\n {\"label\": \"Recovered \", \"value\": \"Recovered\"},\n {\"label\": \"Active \", \"value\": \"active\"},\n ],\n value=\"confirmed\",\n labelStyle={\"display\": \"inline-block\"},\n className=\"dcc_control\",\n ),\n ],\n className=\"pretty_container three columns\",\n id=\"cross-filter-options\",\n ),\n\n # Data & Graphs Components\n # ------------------------------\n html.Div(\n [\n html.Div(\n [\n html.Div(\n [html.H6(id=\"cases_mortality\", style={'text-align': 'center'}), html.P(\"Case Fatality Rate\"),\n ],\n id=\"wells\",\n className=\"mini_container\",\n ),\n html.Div(\n [html.H6(id=\"cases_per_100k\"),\n html.P(\"Cases per 100k\")],\n id=\"gas\",\n className=\"mini_container\",\n ),\n html.Div(\n [html.H6(id=\"deaths_per_100k\"),\n html.P(\"Deaths per 100k\")],\n id=\"oil\",\n className=\"mini_container\",\n ),\n html.Div(\n [html.H6(id=\"growth_rate\"),\n html.P(\"Growth-rate\")],\n id=\"water\",\n className=\"mini_container\",\n ),\n ],\n id=\"info-container\",\n className=\"row container-display\",\n ),\n html.Div(\n [dcc.Graph(id=\"count_graph\")],\n id=\"countGraphContainer\",\n style={\"minHeight\": \"50vh\"},\n className=\"pretty_container\",\n ),\n ],\n id=\"right-column\",\n className=\"ten columns\",\n ),\n ],\n className=\"row flex-display\",\n ),\n html.Div(\n [\n html.Div(\n [dcc.Graph(\n id=\"main_graph\",\n style={'max-width': '100%',\n 'max-height': '100%'\n },\n )],\n className=\"pretty_container seven columns\",\n ),\n html.Div(\n [\n # dcc.Graph(id=\"individual_graph\")\n html.Div(\n html.Img(\n src=app.get_asset_url('pic1.jpg'),\n style={\n 'max-width': '100%',\n 'max-height': '100%',\n # 'background-size': 'cover',\n }))\n ],\n className=\"pretty_container five columns\",\n ),\n ],\n className=\"row flex-display\",\n ),\n ],\n id=\"mainContainer\",\n style={\"display\": \"flex\", \"flex-direction\": \"column\"},\n)\n\n# Create callbacks\napp.clientside_callback(\n ClientsideFunction(namespace=\"clientside\", function_name=\"resize\"),\n Output(\"output-clientside\", \"children\"),\n [Input(\"count_graph\", \"figure\")],\n)\n\n# Slectore -> Mini-Container Numbers\n\n\n@app.callback(\n [Output(\"cases_mortality\", \"children\"),\n Output('cases_per_100k', 'children'),\n Output('deaths_per_100k', 'children'),\n Output('growth_rate', 'children')],\n\n [Input('regency_selector', 'value'),\n Input('region_selector', 'value')],\n)\ndef update_cases_mortality(regency, region):\n if region == 'indo':\n df = pd.read_csv(data_covid_indo)\n selected_region = df[df['Name_EN'].str.match('indonesia')]\n\n elif region == 'bali' and regency == '':\n df = pd.read_csv(data_covid_indo)\n selected_region = df[df['Name_EN'].str.match('bali')]\n\n else:\n df = pd.read_csv(data_covid_bali)\n selected_region = df[df['Name_EN'].str.match(regency)]\n # print(regency)\n # print(selected_region.head())\n cfr = selected_region['CFR'].iloc[-1] # .round(2)\n cp100k = selected_region['total_cases_per_100k'].iloc[-2] # .round(2)\n dp100k = selected_region['total_deaths_per_100k'].iloc[-2] # .round(2)\n\n return '{}'.format(cfr), '{}'.format(str(round(cp100k, 2))), '{}'.format(str(round(dp100k, 2))), 'not yet'\n\n# Selectors -> main graph\n\n\n@app.callback(\n Output(\"main_graph\", \"figure\"),\n [Input(\"year_slider\", \"value\"), Input('region_selector', 'value')],\n [State(\"main_graph\", \"relayoutData\")],\n)\ndef make_main_figure(year_value, region, main_graph_layout):\n # print(region)\n # print(year_value)\n # print(main_graph_layout)\n PATH = pathlib.Path(__file__).parent\n\n if region == 'bali':\n df = pd.read_csv(data_covid_bali)\n geojson = json.load(open(geojson_bali))\n center = {\"lat\": -8.5002, \"lon\": 115.0129}\n zoom = 7\n\n elif region == 'indo':\n df = pd.read_csv(data_covid_indo)\n geojson = json.load(open(geojson_indo))\n center = {'lat': 0, 'lon': 109}\n zoom = 3\n\n else:\n df = pd.read_csv(data_covid_germany)\n geojson = json.load(open(geojson_germany))\n center = {\"lat\": 48.5002, \"lon\": 9.0129}\n zoom = 6\n\n fig = px.choropleth_mapbox(\n df,\n geojson=geojson,\n locations='id',\n color='total_cases_per_100k',\n mapbox_style='carto-positron',\n hover_name='Name_EN',\n hover_data=['cases7_per_100k', 'deaths7_per_100k'],\n animation_frame=\"Date\",\n color_continuous_scale='blues',\n zoom=zoom,\n center=center,\n opacity=0.5,\n )\n\n fig.update_geos(fitbounds=\"locations\", visible=False)\n fig.update_layout(margin={\"r\": 0, \"t\": 0, \"l\": 0, \"b\": 0})\n display_fig = go.Figure(fig)\n\n # relayoutData is None by default, and {'autosize': True} without relayout action\n if main_graph_layout is not None:\n if \"mapbox.center\" in main_graph_layout.keys():\n lon = float(main_graph_layout[\"mapbox.center\"][\"lon\"])\n lat = float(main_graph_layout[\"mapbox.center\"][\"lat\"])\n zoom = float(main_graph_layout[\"mapbox.zoom\"])\n layout[\"mapbox\"][\"center\"][\"lon\"] = lon\n layout[\"mapbox\"][\"center\"][\"lat\"] = lat\n layout[\"mapbox\"][\"zoom\"] = zoom\n\n # figure = dict(data=traces, layout=layout)\n return display_fig\n\n\n# Selectors -> count graph\n@app.callback(\n Output(\"count_graph\", \"figure\"),\n [Input('region_selector', 'value'), Input(\n 'regency_selector', 'value'), Input(\"year_slider\", \"value\")],\n)\ndef make_count_figure(region, regency, year_slider):\n\n if region == 'indo':\n df = pd.read_csv(data_covid_indo)\n region_selected = 'indonesia'\n\n elif region == 'bali' and regency == '':\n df = pd.read_csv(data_covid_indo)\n region_selected = 'bali'\n\n else:\n df = pd.read_csv(data_covid_bali)\n region_selected = regency\n\n df = df[df['Name_EN'].str.match(region_selected)]\n\n df_test = df.tail(100)\n days = df_test.Date.to_list()\n\n # fig = go.Figure()\n fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n\n selected_cases = ['new_cases', 'new_recovered', 'cases7'\n ]\n colors = px.colors.sequential.Blues\n count = 0\n for selected in selected_cases:\n count += 2\n fig.add_trace(\n go.Bar(\n x=days,\n y=df_test[selected],\n name=selected,\n marker_color=colors[count],\n ),\n secondary_y=False,\n )\n # test plots for new cases and\n count = 0\n selected_new = ['total_deaths_per_100k', 'CFR', ]\n for selected in selected_new:\n count += 2\n fig.add_trace(\n go.Scatter(\n x=days,\n y=df_test[selected],\n # mode='lines',\n name=selected,\n line=dict(color=colors[count], width=2),\n ),\n secondary_y=True\n\n )\n\n fig.update_layout(\n title='Daily Cases in {}'.format(region_selected),\n xaxis_tickfont_size=6,\n yaxis=dict(\n tickfont_size=6,\n ),\n plot_bgcolor=colors[0],\n paper_bgcolor=colors[0],\n legend=dict(\n yanchor=\"top\",\n y=0.99,\n xanchor=\"left\",\n x=0.01,\n bgcolor='white',\n bordercolor='white',\n ),\n barmode='group',\n bargap=0.15, # gap between bars of adjacent location coordinates.\n bargroupgap=0.1 # gap between bars of the same location coordinate.\n )\n\n # Set x-axis title\n fig.update_xaxes(title_text=\"Date\")\n fig.update_yaxes(tickfont_size=6, secondary_y=True)\n\n return fig\n\n\n# Main\nif __name__ == \"__main__\":\n app.run_server(debug=True)\n","sub_path":"CovidBali/testingDash/plotly apps-dash-oil-and-gas/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":17337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"580963258","text":"# -*- coding: utf-8 -*-\n\nimport pandas as pd\nfrom scipy.signal import savgol_filter\nfrom scipy.interpolate import UnivariateSpline\n\n\ndef naive_trend(df, column_value='value'):\n \"\"\"\n naive_trend\n\n Gives the naive slope: look to the right, look to the left, \n travel one unit each, and get the average change. At the ends,\n we merely use the left or the right value.\n\n Args:\n df: pandas dataFrame time series object\n \"\"\"\n y = df[column_value]\n\n y_1 = y.shift(1)\n y_2 = y.shift(-1)\n\n y1_diff = y_1 - y\n yneg1_diff = y - y-2\n\n yy = pd.concat([y.rename('orig'),\n y_1.rename('plus_1'),\n y_2.rename('min_1'),\n y1_diff.rename('plus_1_diff'),\n yneg1_diff.rename('min_1_diff')], axis = 1)\n odf = df.copy()\n odf['derivative_value'] = yy[['plus_1_diff', 'min_1_diff']].mean(axis = 1)\n odf['derivative_method'] = 'naive'\n odf['function_order'] = None\n odf['derivative_order'] = 1\n\n return odf\n\n\ndef spline_trend(df, column_value='value', function_order=3,\n derivative_order=1, s=3):\n \"\"\"\n spline_trend\n\n Interpolates time series with splines of 'function_order'. And then\n calculates the derivative_order using the smoothed function.\n\n Args:\n df: pandas dataFrame time series object\n function_order: spline order (default is 3)\n derivative_order: (0, 1, 2, ... with default as 1)\n\n Returns:\n DataFrame: dataframe with 6 columns:- datetime,\n function_order (value of the polynomial order), smoothed_value,\n derivative_method, derivative_order, derivative_value.\n\n A row can be 2012-01-01, \"spline\", 2, 1, 0\n \"\"\"\n x = df.reset_index().index.values.astype(float)\n y = df[column_value]\n spl = UnivariateSpline(x, y, k=function_order, s=s)\n odf = df.copy()\n odf['smoothed_value'] = spl(x)\n odf['derivative_value'] = spl(x, nu=derivative_order)\n odf['function_order'] = function_order\n odf['derivative_method'] = 'spline'\n odf['derivative_order'] = derivative_order\n return odf\n\n\ndef sgolay_trend(df, column_value='value', function_order=3,\n derivative_order=1, window_length=15):\n \"\"\"\n sgolay_trend\n\n Interpolates time series with savitzky-golay using polynomials of\n 'function_order'. And then calculates the derivative_order using\n the smoothed function.\n\n Args:\n df: pandas dataFrame time series object\n window_size: default is 15\n function_order: polynomial order (default is 3)\n derivative_order: (0, 1, 2, ... with default as 1)\n\n Returns:\n DataFrame: dataframe with 6 columns:- datetime,\n function_order (value of the polynomial order), smoothed_value,\n derivative_method, derivative_order, derivative_value.\n\n Sample row: 2012-01-01, \"sgolay\", 2, 1, 0\n \"\"\"\n y = df[column_value]\n odf = df.copy()\n odf['smoothed_value'] = savgol_filter(y, window_length=window_length,\n polyorder=function_order)\n odf['derivative_value'] = savgol_filter(y, window_length=window_length,\n polyorder=function_order,\n deriv=derivative_order)\n odf['function_order'] = function_order\n odf['derivative_method'] = 'sgolay'\n odf['derivative_order'] = derivative_order\n return odf\n\n\ndef trending(df_list, column_id='id', derivative_order=1, max_or_avg='max',\n k=5):\n \"\"\"\n trending\n\n For each item in the list, calculate either the max or the average\n (depending on max_or_avg) of the Yth derivative (based on the\n derivative_order) over the last k time_periods (based on the input).\n It then orders the list based on max to min.\n \n For instance, for derivative_order = 1, max_or_avg = \"max\",\n time_periods = 3, for each item in the list, the function will take\n the max of the last 3 rows of the dataframe entries identifying the\n 1st derivative.\n\n So each item in the list produces one number (max or avg.). We then\n produce a new dataframe with 2 columns: id, max_or_avg\n\n Args:\n df_list: list of outputs (dataframes) from sgolay_trend or\n spline_trend with a new column called 'id' that identifies\n the time series\n derivative_order: (1 or 2)\n k: number of latest time periods to consider.\n max_or_avg: \"max\" or \"avg\"\n\n\n Returns:\n DataFrame: dataframe with 2 columns: id, max_or_avg\n \"\"\"\n\n cdf = []\n for df in df_list:\n cdf.append(df[df.derivative_order == derivative_order][-k:])\n tdf = pd.concat(cdf, sort=False)\n if max_or_avg == 'avg':\n max_or_avg = 'mean'\n odf = tdf.groupby('id').agg({'derivative_value': max_or_avg})\n odf.reset_index(inplace=True)\n odf.columns = ['id', 'max_or_avg'] \n return odf\n\n\nif __name__ == \"__main__\":\n pass\n","sub_path":"incline/trend.py","file_name":"trend.py","file_ext":"py","file_size_in_byte":4982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"500609565","text":"import time\nimport pandas as pd\nfrom Portfolio import Portfolio\nimport TRADECONFIG\n\n# I was getting a weird error with numpy and plotting, this next line seemed to fix.\npd.plotting.register_matplotlib_converters()\n\ndef main():\n start_time = time.time()\n\n pf = Portfolio(\n tlist=TRADECONFIG.TICKER_LIST,\n start=TRADECONFIG.START_DATE,\n end=TRADECONFIG.END_DATE,\n min_short=TRADECONFIG.MIN_SHORT,\n max_short=TRADECONFIG.MAX_SHORT,\n min_long_diff=TRADECONFIG.MIN_LONG_DIFFERENCE,\n max_long=TRADECONFIG.MAX_LONG\n )\n \n if TRADECONFIG.USE_MULTIPROCESSING:\n # mp_trade() doesn't work on windows because the spawn method is 'spawn' not 'fork'\n pf.mp_trade()\n else:\n pf.trade()\n pf.create_portfolio()\n pf.print_results(TRADECONFIG.DISPLAY_INDIVIDUAL_PLOTS)\n\n end_time = time.time()\n run_time = end_time - start_time\n print('Portfolio took {:>10.2} minutes to run.'.format(run_time/60))\n\nif __name__ == \"__main__\":\n main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"174074252","text":"def verify(G, n):\n for x in G:\n x.sort()\n for i in range(n - 1):\n for j in range(n):\n if G[i][j] > G[i + 1][j]:\n return 'NO'\n return 'YES'\n\ndef main():\n trials = int(input())\n for trial in range(trials):\n n = int(input())\n G = [list(input()) for _ in range(n)]\n print(verify(G, n))\n\nif __name__ == '__main__':\n main()","sub_path":"algorithms/greedy/grid_challenge/grid_challenge.py","file_name":"grid_challenge.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"120320095","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 19 04:51:16 2019\n\n@author: kennedy\n\"\"\"\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport numpy as np\nimport re\nimport codecs\nimport os\nimport numpy as np\nfrom mimetypes import guess_type\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport itertools\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import accuracy_score\n\nclass docparser(object):\n \"\"\"\n parser to get the Stsa dataset\n \"\"\"\n def __init__(self):\n pass\n def transform_label_to_numeric(self, y):\n if '1' in y:\n return 1\n else:\n return 0\n def parse_line(self, row):\n row = row.split(' ')\n text = (' '.join(row[1:]))\n label = self.transform_label_to_numeric(row[0])\n return (re.sub(r'\\W+', ' ', text), label)\n\n def get_data(self, file_path, Text = True):\n if Text:\n data = []\n labels = []\n f = codecs.open(file_path, 'r', encoding = \"utf8\",errors = 'ignore')\n for line in f:\n doc, label = self.parse_line(line)\n data.append(doc)\n labels.append(label)\n return data, np.array(labels)\n else:\n import pandas as pd\n df = pd.read_csv(file_path)\n df.columns = ['Label', 'Rating', 'Review']\n rating = pd.get_dummies(df.loc[:, 'Rating'])\n text = []\n labels = []\n for ii, ij in zip(df.loc[:, 'Review'].values, df.loc[:, 'Label'].values):\n #print(ii, ij)\n if ii == ' ':\n pass\n else:\n text.append(ii)\n labels.append(ij)\n return text, np.array(labels), rating\n \n\n def shuffle_dataset(X, y, seed=None):\n \"\"\" Random shuffle of the samples \n in X and y \n \"\"\"\n if seed:\n np.random.seed(seed)\n idx = [ii for ii in range(X.shape[0])]\n np.random.shuffle(idx)\n return X[idx], y[idx]\n \n def split(data, labels, test_size = 0.3,shuff = True, seed = None):\n '''\n :params\n --label: labels of the dataset\n --rating: rating as dummies categorical variables\n --text: text data\n :Returntype:\n X_supervised:\n X_unsupervised:\n y_supervised:\n '''\n X = np.array(data)\n y = np.array(labels)\n if shuff:\n from sklearn.utils import shuffle\n X, y = shuffle_dataset(X, y, seed)\n else:\n split = len(y) - int(len(y) // (1/test_size))\n X_supvsd, X_unsupvsd = X[:split], X[split:]\n y_supvsd, y_supvsd = y[:split], y[split:]\n return X_supvsd, X_unsupvsd, y_supvsd, y_supvsd\n \n #%% Semisupervised NB Classifier\n \nclass NaiveBayesSemiSupervised(object):\n \"\"\"\n This class implements a modification of the Naive Bayes classifier\n in order to deal with unlabelled data. We use an Expectation-maximization \n algorithm (EM). \n This work is based on the paper\n 'Semi-Supervised Text Classification Using EM' by\n Kamal Nigam Andrew McCallum Tom Mitchell\n available here:\n https://www.cs.cmu.edu/~tom/pubs/NigamEtAl-bookChapter.pdf\n \"\"\"\n def __init__(self, max_features=None, max_rounds=50, tolerance=1e-6):\n \"\"\"\n constructor for NaiveBayesSemiSupervised object\n keyword arguments:\n -- max_features: maximum number of features for documents vectorization\n -- max_rounds: maximum number of iterations for EM algorithm\n -- tolerance: threshold (in percentage) for total log-likelihood improvement during EM\n \"\"\"\n self.max_features = max_features\n self.n_labels = 0\n self.max_rounds = max_rounds\n self.tolerance = tolerance\n \n \n def train(self, X_supervised, X_unsupervised, y_supervised, y_unsupervised):\n \"\"\"\n train the modified Naive bayes classifier using both labelled and \n unlabelled data. We use the CountVectorizer vectorizaton method from scikit-learn\n positional arguments:\n -- X_supervised: list of documents (string objects). these documents have labels\n example: [\"all parrots are interesting\", \"some parrots are green\", \"some parrots can talk\"]\n -- X_unsupervised: list of documents (string objects) as X_supervised, but without labels\n -- y_supervised: labels of the X_supervised documents. list or numpy array of integers. \n example: [2, 0, 1, 0, 1, ..., 0, 2]\n -- X_supervised, X_unsupervised, y_supervised, y_unsupervised\n \"\"\"\n count_vec = CountVectorizer(max_features = self.max_features)\n count_vec.fit(X_supervised)\n self.n_labels = len(set(y_supervised))\n if self.max_features is None:\n self.max_features = len(count_vec.vocabulary_)\n X_supervised = np.asarray(count_vec.transform(X_supervised).todense())\n X_unsupervised = np.asarray(count_vec.transform(X_unsupervised).todense())\n #train Naive Bayes\n self.train_naive_bayes(X_supervised, y_supervised)\n predi = self.predict(X_supervised)\n old_likelihood = 1\n cumulative_percent = 0\n while self.max_rounds > 0:\n self.max_rounds -= 1\n predi = self.predict(X_unsupervised)\n self.train_naive_bayes(X_unsupervised, predi)\n predi = self.predict(X_unsupervised)\n correct = 0\n for ij in predi:\n if ij == 1:\n correct += 1\n correct_percent = correct/len(X_unsupervised)\n cumulative_percent += correct_percent\n print(str(correct_percent) + \"%\")\n total_likelihood = self.get_log_likelihood( X_supervised, X_unsupervised, y_supervised)\n print(\"total likelihood: {}\".format(total_likelihood))\n if self._stopping_time(old_likelihood, total_likelihood):\n print('log-likelihood not improved..Stopping EM at round %s'%self.max_rounds)\n break\n old_likelihood = total_likelihood.copy()\n \n def _stopping_time(self, old_likelihood, new_likelihood):\n \"\"\"\n returns True if there is no significant improvement in log-likelihood and false else\n positional arguments:\n -- old_likelihood: log-likelihood for previous iteration\n -- new_likelihood: new log-likelihood\n \"\"\"\n relative_change = np.absolute((new_likelihood-old_likelihood)/old_likelihood) \n if (relative_change < self.tolerance):\n print(\"stopping time\")\n return True\n else:\n return False\n \n def get_log_likelihood(self, X_supervised, X_unsupervised, y_supervised):\n \"\"\"\n returns the total log-likelihood of the model, taking into account unsupervised data\n positional arguments:\n -- X_supervised: list of documents (string objects). these documents have labels\n example: [\"all parrots are interesting\", \"some parrots are green\", \"some parrots can talk\"]\n -- X_unsupervised: list of documents (string objects) as X_supervised, but without labels\n -- y_supervised: labels of the X_supervised documents. list or numpy array of integers. \n example: [2, 0, 1, 0, 1, ..., 0, 2]\n \"\"\"\n unsupervised_term = np.sum(self._predict_proba_unormalized(X_unsupervised), axis=1)\n unsupervised_term = np.sum(np.log(unsupervised_term))\n supervised_term = self._predict_proba_unormalized(X_supervised)\n supervised_term = np.take(supervised_term, y_supervised)\n supervised_term = np.sum(np.log(supervised_term))\n total_likelihood = supervised_term + unsupervised_term\n return total_likelihood\n\n def word_proba(self, X, y, c):\n \"\"\"\n returns a numpy array of size max_features containing the conditional probability\n of each word given the label c and the model parameters\n positional arguments:\n -- X: data matrix, 2-dimensional numpy ndarray\n -- y: numpy array of labels, example: np.array([2, 0, 1, 0, 1, ..., 0, 2])\n -- c: integer, the class upon which we condition\n \"\"\"\n numerator = 1 + np.sum( X[np.equal( y, c )], axis=0)\n denominator = self.max_features + np.sum( X[ np.equal( y, c)])\n return np.squeeze(numerator)/denominator\n\n def class_proba(self, X, y, c):\n \"\"\"\n returns a numpy array of size n_labels containing the conditional probability\n of each label given the label model parameters\n positional arguments:\n -- X: data matrix, 2-dimensional numpy ndarray\n -- y: numpy array of labels, example: np.array([2, 0, 1, 0, 1, ..., 0, 2])\n -- c: integer, the class upon which we condition\n \"\"\"\n numerator = 1 + np.sum( np.equal( y, c) , axis=0)\n denominator = X.shape[0] + self.n_labels\n return numerator/denominator\n\n def train_naive_bayes(self, X, y):\n \"\"\"\n train a regular Naive Bayes classifier\n positional arguments:\n -- X: data matrix, 2-dimensional numpy ndarray\n -- y: numpy array of labels, example: np.array([2, 0, 1, 0, 1, ..., 0, 2])\n \"\"\"\n word_proba_array = np.zeros((self.max_features, self.n_labels))\n for c in range(self.n_labels):\n word_proba_array[:,c] = self.word_proba( X, y, c)\n labels_proba_array = np.zeros(self.n_labels)\n for c in range(self.n_labels):\n labels_proba_array[c] = self.class_proba( X, y, c)\n self.word_proba_array = word_proba_array\n self.labels_proba_array = labels_proba_array\n\n def _predict_proba_unormalized(self, X_test):\n \"\"\"\n returns unormalized predicted probabilities (useful for log-likelihood computation)\n positional arguments:\n -- X: data matrix, 2-dimensional numpy ndarray\n \"\"\"\n proba_array_unormalized = np.zeros((X_test.shape[0], self.n_labels))\n for c in range(self.n_labels):\n temp = np.power(np.tile(self.word_proba_array[:,c], (X_test.shape[0] ,1)), X_test)\n proba_array_unormalized[:,c] = self.labels_proba_array[c] * np.prod(temp, axis=1)\n return proba_array_unormalized\n\n def predict_proba(self, X):\n \"\"\"\n returns model predictions (probability)\n positional arguments:\n -- X: data matrix, 2-dimensional numpy ndarray\n \"\"\"\n proba_array_unormalized = self._predict_proba_unormalized(X)\n proba_array = np.true_divide(proba_array_unormalized, np.sum(proba_array_unormalized, axis=1)[:, np.newaxis])\n return proba_array\n\n def predict(self, X):\n \"\"\"\n returns model predictions (class labels)\n positional arguments:\n -- X: data matrix, 2-dimensional numpy ndarray\n \"\"\"\n return np.argmax(self.predict_proba( X), axis=1)\n \nif __name__ == '__main__':\n import os\n import random\n random.seed(23)\n from sklearn.model_selection import KFold, train_test_split\n Text = False\n max_features=None\n NSPLIT = 4\n n_sets = 4\n set_size = 1.0 / n_sets\n cumulative_percent = 0\n #set project directory\n os.chdir('D:\\\\FREELANCER\\\\SEMI_NB_TEXT_CLASSIFICATION')\n file_path = os.path.join('DATASET','stas_train.text')\n if Text:\n data, labels = docparser().get_data(os.path.join('DATASET','stas-train.txt'), Text)\n else:\n data, labels, rating = docparser().get_data(os.path.join('DATASET','mytracks_NaiveBayes_Filter.csv'), Text)\n X_supervised, X_unsupervised, y_supervised, y_unsupervised = train_test_split(data, labels, test_size = 0.8)\n #Max features should be left as it is 5738\n clf = NaiveBayesSemiSupervised(max_features)\n #train and evaluate accuracy\n clf.train(X_supervised, X_unsupervised, y_supervised, y_unsupervised)\n\n\n\n\n \n ","sub_path":"SCRIPTS/SemiNB_NoValidation.py","file_name":"SemiNB_NoValidation.py","file_ext":"py","file_size_in_byte":12139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"235798984","text":"import cv2\nimport boto3\n\n#using webcam to click pic - STEP 1\nmyphoto = \"sajal.jpg\"\ncap = cv2.VideoCapture(0)\nret , photo = cap.read()\ncv2.imwrite(myphoto, photo)\ncap.release()\n\n\n#uploading pic on the s3- STEP 2\nregion = \"ap-south-1\"\nbucket = \"sajalawsai-workshop\"\nupimage = \"file.jpg\"\n\ns3 = boto3.resource('s3')\ns3.Bucket(bucket).upload_file(myphoto, upimage)\n\n#asking Rek to get Image from S3 - STEP 3\nrek = boto3.client('rekognition', region)\n\nresponse = rek.detect_labels(\n Image={\n 'S3Object': {\n 'Bucket': bucket,\n 'Name': upimage,\n \n }\n },\n MaxLabels=10,\n MinConfidence=90\n)\n\nfor i in range(2):\n print(response['Labels'][i]['Name'])","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"40944999","text":"import json\nfrom tkinter import N\nfrom dateutil import tz\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import login, authenticate\nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom django.forms import modelformset_factory\nimport userprofile\nfrom userprofile.models import UserProfile\nfrom django.contrib.auth.models import User\nfrom django.db.models import Q\nfrom .models import CHECKOUT_24HR, Equipment, EquipmentCategory, EquipmentCheckout, ClosedDay\nfrom .forms import EquipmentCheckoutForm\nimport arrow\nfrom .models import RESERVED, CHECKED_OUT\nfrom project.models import Project\nfrom django.http import JsonResponse\nimport dateutil\nfrom django import forms\nfrom functools import partial\nfrom datetime import datetime\nfrom django.utils.timezone import make_aware\nDateInput = partial(forms.DateInput, {'class': 'datepicker'})\n\n# Create your views here.\n\ndef equipment_category(request, slug):\n category = get_object_or_404(EquipmentCategory, slug=slug)\n equipment = Equipment.objects.filter(category=category)\n categories = EquipmentCategory.objects.all()\n\n search = ''\n if request.GET.get('q') and len(request.GET['q']) > 0:\n search = request.GET['q']\n # equipment = Equipment.objects.filter(make__icontains=search)\n equipment = equipment.filter(\n Q(make__icontains=search) | Q(model__icontains=search)\n )\n\n return render(\n request,\n 'equipment_list.html',\n context={\n 'search': search,\n 'equipment': equipment,\n 'categories': categories,\n 'category': category\n }\n )\n\ndef equipment_list(request):\n equipment = Equipment.objects.all()\n categories = EquipmentCategory.objects.all()\n\n search = ''\n if request.GET.get('q') and len(request.GET['q']) > 0:\n search = request.GET['q']\n # equipment = Equipment.objects.filter(make__icontains=search)\n equipment = equipment.filter(\n Q(make__icontains=search) | Q(model__icontains=search)\n )\n\n return render(\n request,\n 'equipment_list.html',\n context={\n 'search': search,\n 'equipment': equipment,\n 'categories': categories\n }\n )\n\n\ndef compute_due_date(timeframe, checkout_date):\n if timeframe == 'CHECKOUT_3HR':\n res = arrow.get(checkout_date)\n due = res.shift(hours=3)\n\n return due.datetime\n\n elif timeframe == \"CHECKOUT_24HR\":\n res = arrow.get(checkout_date)\n due = res.shift(days=1).replace(hour=19)\n\n # mon=0, tues=1, wed=2, thurs=3, fri=4, sat=5, sun=6\n # if due on wed or sun, shift one day\n # this is a naive shift, more handling will be done in the checkout_or_due_date_on_closed_day function\n if due.weekday() == 2 or due.weekday() == 6:\n due = due.shift(days=1)\n\n return due.datetime\n\n elif timeframe == \"CHECKOUT_WEEK\":\n res = arrow.get(checkout_date)\n\n # always due on a tuesday\n # shift a day to combat reserved-on-tues/due-on-tues\n due = res.shift(days=+1).shift(weekday=1).replace(hour=19)\n\n return due.datetime\n return None\n\ndef closed_on_day(date):\n weekday = arrow.get(date).weekday()\n closed_days = ClosedDay.objects.filter(\n Q(day_of_week=weekday)\n & (\n (Q(begin_date=None) & Q(end_date=None))\n | (Q(begin_date__lte=date) & Q(end_date=None))\n | (Q(begin_date=None) & Q(end_date__gte=date))\n | (Q(begin_date__lte=date) & Q(end_date__gte=date))\n )\n )\n\n return len(closed_days) > 0\n\n@login_required\ndef equipment_details(request, slug):\n equipment = get_object_or_404(Equipment, slug=slug)\n\n return render(\n request,\n 'equipment_details.html',\n context={\n 'equipment': equipment,\n }\n )\n\n\n@login_required\ndef cancel_checkout(request, checkout_id):\n checkout = get_object_or_404(EquipmentCheckout, id=checkout_id)\n\n if checkout.user == request.user and checkout.checkout_status == 'RESERVED':\n checkout.checkout_status = 'CANCELED'\n checkout.save()\n\n return redirect('user_checkouts')\n\n\ndef item_checkouts(request, item_id):\n start = request.GET.get('start')\n end = request.GET.get('end')\n\n start_date = arrow.get(start, 'YYYY-MM-DDThh:mm:ss').datetime\n end_date = arrow.get(end, 'YYYY-MM-DDThh:mm:ss').datetime\n\n\n all_checkouts = EquipmentCheckout.objects.filter(\n (Q(checkout_status='RESERVED')\n | Q(checkout_status='CHECKED_OUT'))\n & (Q(checkout_date__gte=start_date)\n | Q(due_date__lte=end_date))\n & Q(equipment__id=item_id)\n )\n\n checkouts = []\n for checkout in all_checkouts:\n\n print(checkout.checkout_date)\n\n # checkout_date = checkout.checkout_date.strftime('%Y-%m-%d %H:%M')\n # due_date = checkout.due_date.strftime('%Y-%m-%d %H:%M')\n checkout_date = arrow.get(checkout.checkout_date).shift(hours=-5).datetime\n due_date = arrow.get(checkout.due_date).shift(hours=-5).datetime\n checkouts.append({\n 'equipment_id': checkout.equipment.id,\n 'equipment_name': checkout.equipment.name(),\n 'checkout_timeframe': checkout.equipment.checkout_timeframe,\n 'start': checkout_date,\n 'end': due_date,\n 'status': checkout.checkout_status,\n 'title': checkout.equipment.name(),\n 'allDay': False\n })\n\n return JsonResponse(checkouts, safe=False)\n\n\n@staff_member_required\ndef admin_calendar(request):\n return render(\n request,\n 'admin_calendar.html',\n context={\n }\n )\n\n@staff_member_required\ndef admin_events(request):\n start = request.GET.get('start')\n end = request.GET.get('end')\n\n start_date = arrow.get(start, 'YYYY-MM-DDThh:mm:ss').shift(hours=-5).datetime\n end_date = arrow.get(end, 'YYYY-MM-DDThh:mm:ss').shift(hours=-5).datetime\n\n\n all_checkouts = EquipmentCheckout.objects.filter(\n (Q(checkout_status='RESERVED')\n | Q(checkout_status='CHECKED_OUT'))\n & (Q(checkout_date__gte=start_date)\n & Q(due_date__lte=end_date))\n )\n\n checkouts = []\n for checkout in all_checkouts:\n\n print(checkout.checkout_date)\n\n checkout_date = arrow.get(checkout.checkout_date).datetime\n due_date = arrow.get(checkout.due_date).datetime\n checkouts.append({\n 'equipment_id': checkout.equipment.id,\n 'equipment_name': checkout.equipment.name(),\n 'checkout_timeframe': checkout.equipment.checkout_timeframe,\n 'start': checkout_date,\n 'end': due_date,\n 'status': checkout.checkout_status,\n 'title': str(checkout.user) + ' - ' + checkout.equipment.name(),\n 'allDay': False,\n 'displayEventTime': True,\n 'displayEventEnd': True\n })\n\n return JsonResponse(checkouts, safe=False)\n\ndef user_has_overdue_checkout(user):\n today = arrow.utcnow().date()\n overdue_checkouts = EquipmentCheckout.objects.filter(\n Q(user=user)\n & Q(checkout_status='CHECKED_OUT')\n & Q(due_date__lte=today)\n )\n return len(overdue_checkouts) > 0\n\ndef user_has_required_badges(user, equipment):\n if user.is_superuser:\n return True\n\n try:\n profile = UserProfile.objects.get(user=user)\n return profile.can_checkout_equipment(equipment)\n except:\n registrations = user.classregistration_set.filter(completed=True)\n badges = []\n\n for reg in registrations:\n sect = reg.class_section\n course = sect.class_key\n for badge in course.awarded_badges.all():\n badges.append(badge)\n\n for req in equipment.prerequisite_badges.all():\n if req not in badges:\n return False\n\n return True\n\ndef user_has_current_checkout(user, item):\n today = arrow.utcnow().date()\n checkouts = EquipmentCheckout.objects.filter(\n Q(equipment=item)\n & Q(user=user)\n & (\n Q(checkout_status='RESERVED')\n | Q(checkout_status='CHECKED_OUT')\n )\n )\n return len(checkouts) > 0\n\ndef available_units(item, start_date, end_date):\n all_item_checkouts = EquipmentCheckout.objects.filter(equipment=item)\n checked_out_on_date = all_item_checkouts.filter(\n (Q(checkout_date__gte=start_date) & Q(checkout_date__lte=end_date) & Q(due_date__gte=end_date)) # overlaps the checkout_date\n | (Q(checkout_date__lte=start_date) & Q(due_date__gte=start_date) &Q(due_date__lte=end_date)) # overlaps the due_date\n | (Q(checkout_date__lte=start_date) & Q(due_date__gte=end_date)) # within checkout_date and due_date\n | (Q(checkout_date__gte=start_date) & Q(due_date__lte=end_date)) # wraps checkout_date and due_date\n )\n checked_out_by_status = checked_out_on_date.filter(Q(checkout_status=RESERVED) | Q(checkout_status=CHECKED_OUT))\n\n return item.quantity - len(checked_out_by_status)\n\n@csrf_exempt\n@staff_member_required\ndef get_user_projects_json(request):\n data = json.loads(request.body)\n\n if data.get('user_id'):\n user = None\n try:\n user = User.objects.get(id=data.get('user_id'))\n except:\n return JsonResponse({ 'error': 'User does not exist' }, status=404)\n\n # the user has no overdue checkouts\n if user_has_overdue_checkout(user):\n return JsonResponse({ 'error': 'User has overdue checkouts' }, status=400)\n\n projects = []\n for p in user.owner_projects.all():\n projects.append({ 'id': p.id, 'title': p.title })\n for p in user.project_set.all():\n projects.append({ 'id': p.id, 'title': p.title })\n\n return JsonResponse({ 'projects': projects }, status=200)\n\n return JsonResponse({ 'error': 'Invalid request' }, status=400)\n\n@csrf_exempt\n@login_required\ndef check_user_can_check_out_equipment(request):\n data = json.loads(request.body)\n\n if data.get('equipment_id') and data.get('checkout_start_date'):\n user = None\n if request.user.is_superuser and data.get('user_id'):\n try:\n user = User.objects.get(id=data.get('user_id'))\n except:\n return JsonResponse({ 'error': 'User does not exist' }, status=404)\n elif request.user.is_superuser:\n return JsonResponse({ 'error': 'User not selected' }, status=400)\n else:\n user = request.user\n\n equipment = None\n equipment_id = data.get('equipment_id')\n\n if not equipment_id:\n return JsonResponse({ 'error': 'Invalid selection' }, status=400)\n\n try:\n equipment = Equipment.objects.get(id=equipment_id)\n except:\n return JsonResponse({ 'error': 'Invalid selection' }, status=400)\n\n\n # there are available units\n checkout_date = make_aware(datetime.strptime(data.get('checkout_start_date'), '%Y-%m-%d'))\n due_date = compute_due_date(equipment.checkout_timeframe, checkout_date)\n if not available_units(equipment, checkout_date, due_date):\n return JsonResponse({ 'error': 'No units available for selected checkout date' }, status=400)\n\n # the user does not currently have the item checked out\n if user_has_current_checkout(user, equipment):\n return JsonResponse({ 'error': 'User has item checked out' }, status=400)\n\n # the user has the prerequisite classes/badges\n if not user_has_required_badges(user, equipment):\n return JsonResponse({ 'error': 'User does not have prerequisite badges' }, status=400)\n\n return JsonResponse({ 'valid': True }, status=200)\n\n return JsonResponse({ 'error': 'Invalid request, equipment and checkout date required' }, status=400)\n\n@csrf_exempt\n@login_required\ndef handle_equipment_checkout_form(request):\n data = json.loads(request.body)\n\n checkout_start_date = data.get('checkout_start_date') \n checkout_time = int(data.get('checkout_time'))\n project_id = int(data.get('project_id'))\n equipment_ids = data.get('equipment_ids')\n user = None\n\n if not checkout_start_date or not checkout_time or not equipment_ids or len(equipment_ids) is 0:\n return JsonResponse({ 'error': 'Invalid submission', 'checkout_start_date': checkout_start_date, 'checkout_time': checkout_time, 'equipment_ids': equipment_ids }, status=400)\n\n if request.user.is_superuser:\n user_id = data.get('user_id')\n try:\n user = User.objects.get(id=user_id)\n except:\n return JsonResponse({ 'error': 'User does not exist' }, status=404)\n else:\n user = request.user\n\n # re-validate checkout details for each item then save\n\n # the user has no overdue checkouts\n if user_has_overdue_checkout(user):\n return JsonResponse({ 'error': 'User has overdue checkouts' }, status=400)\n\n global_checkout_timeframe = CHECKOUT_24HR if len(equipment_ids) > 1 else None\n\n checkouts = []\n errors = {}\n has_errors = False\n\n for equipment_id in equipment_ids:\n equipment = None\n equipment_errors = []\n\n try:\n equipment = Equipment.objects.get(id=int(equipment_id))\n except:\n equipment_errors.append('Equipment does not exist')\n\n checkout_date = arrow.get(checkout_start_date).replace(hour=checkout_time, tzinfo='US/Central').datetime\n due_date = compute_due_date(global_checkout_timeframe if global_checkout_timeframe else equipment.checkout_timeframe, checkout_date)\n\n if closed_on_day(checkout_date):\n equipment_errors.append('We are closed that day')\n\n if not available_units(equipment, checkout_date, due_date):\n equipment_errors.append('No units available for selected checkout date')\n\n # the user does not currently have the item checked out\n if user_has_current_checkout(user, equipment):\n equipment_errors.append('User has item checked out')\n\n # the user has the prerequisite classes/badges\n if not user_has_required_badges(user, equipment):\n equipment_errors.append('User does not have required badges')\n\n project = None\n\n try:\n project = Project.objects.get(id=project_id)\n except:\n equipment_errors.append('Project does not exist')\n\n if len(equipment_errors) > 0:\n errors[equipment_id] = equipment_errors\n has_errors = True\n break\n\n checkout = EquipmentCheckout.objects.create(\n user=user,\n equipment=equipment,\n project=project,\n checkout_date=checkout_date,\n due_date=due_date\n )\n\n checkouts.append({\n 'equipment_id': checkout.equipment.id,\n 'equipment_name': checkout.equipment.name(),\n 'checkout_timeframe': checkout.equipment.checkout_timeframe,\n 'start': checkout_date,\n 'end': due_date,\n 'status': checkout.checkout_status,\n 'title': str(checkout.user) + ' - ' + checkout.equipment.name()\n })\n\n\n if has_errors:\n return JsonResponse({ 'errors': errors, 'checkouts': checkouts }, status=400)\n\n return JsonResponse({ 'checkouts': checkouts }, safe=False, status=200)\n\n@login_required\ndef checkout_page(request):\n users = None\n user_projects = None\n equipment_list = Equipment.objects.all()\n\n if request.user.is_superuser:\n users = User.objects.all()\n else:\n profile = UserProfile.objects.get(user=request.user)\n user_projects = profile.projects()\n\n return render(\n request,\n 'checkout_page.html',\n context={\n 'users': users,\n 'user_projects': user_projects,\n 'equipment_list': equipment_list,\n }\n )","sub_path":"src/inventory/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":16098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"46661296","text":"from django.shortcuts import render_to_response, get_object_or_404\r\nfrom mainapp.models import City, Report, CitiesReportCountQuery, CityReportCountQuery, CityMap\r\nfrom django.template import Context, RequestContext\r\nfrom django.db.models import Count\r\n\r\ndef index(request): \r\n return render_to_response(\"cities/index.html\",\r\n {\"report_counts\": CitiesReportCountQuery() },\r\n context_instance=RequestContext(request))\r\n\r\n\r\ndef show( request, city_id ):\r\n city = get_object_or_404(City, id=city_id)\r\n \r\n #top problems\r\n top_problems = Report.objects.filter(ward__city=city,is_fixed=False).annotate(subscriber_count=Count('reportsubscriber' ) ).filter(subscriber_count__gte=1).order_by('-subscriber_count')[:10]\r\n if request.GET.has_key('test'):\r\n google = CityMap(city)\r\n else:\r\n google = None\r\n \r\n return render_to_response(\"cities/show.html\",\r\n {\"city\":city,\r\n \"google\": google,\r\n 'top_problems': top_problems,\r\n \"report_counts\": CityReportCountQuery(city) },\r\n context_instance=RequestContext(request))\r\n\r\n","sub_path":"mainapp/views/cities.py","file_name":"cities.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"451165256","text":"# Work with Python 3.6\nimport random\nimport asyncio\nimport aiohttp\nimport json\n#from discord import Game\nfrom discord.ext.commands import Bot\nfrom googlesearch import search\nfrom googletrans import Translator\n\nBOT_PREFIX = (\"/\")\nTOKEN = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\" # Get at discordapp.com/developers/applications/me\n\nclient = Bot(command_prefix=BOT_PREFIX)\n\n@client.command(name='8ball',\n description=\"Answers a yes/no question.\",\n brief=\"Answers from the beyond.\",\n aliases=['eight_ball', 'eightball', '8-ball'],\n pass_context=True)\nasync def eight_ball(context):\n possible_responses = [\n 'That is a resounding no',\n 'It is not looking likely',\n 'Too hard to tell',\n 'It is quite possible',\n 'Definitely',\n ]\n await client.say(random.choice(possible_responses) + \", \" + context.message.author.mention)\n\n\n@client.command()\nasync def square(number):\n squared_value = int(number) * int(number)\n await client.say(str(number) + \" squared is \" + str(squared_value))\n\n\n#@client.event\n#async def on_ready():\n# await client.change_presence(game=Game(name=\"i miss orange juice\"))\n# print(\"Logged in as \" + client.user.name)\n\n\n@client.command()\nasync def bitcoin():\n url = 'https://api.coindesk.com/v1/bpi/currentprice/BTC.json'\n async with aiohttp.ClientSession() as session: # Async HTTP request\n raw_response = await session.get(url)\n response = await raw_response.text()\n response = json.loads(response)\n await client.say(\"Bitcoin price is: $\" + response['bpi']['USD']['rate'])\n\n@client.command()\nasync def guess(number):\n lunathecat1212112 = (random.randint(0, 10))\n if number == lunathecat1212112:\n await client.say(\"Amazing! You guessed it! Have a gold star. :star:\")\n if number != lunathecat1212112:\n await client.say(\"Wrong!\")\n return(lunathecat1212112)\n\n@client.command()\nasync def g(tearm):\n for url in search(tearm, stop=5):\n await client.say(url)\n\n@client.command()\nasync def about():\n await client.say('''\n/t \"language\" \"message\" | Language is the language to translate into (use the abbreviations in this list: https://en.wikipedia.org/wiki/ISO_639-1) and message is the message to translate. Include the quotes.\n/8ball | Roll the 8 ball.\n/square x | Square the number x\n/bitcoin | Display current bitcoin value in USD\n/guess x | Try to guess a number from 0 to 100. Put your guess in the x spot.\n/g tearm | Google something. Put your search tearm in the \"tearm\" spot.\n/about | Sends this message.\n\nCode:\nBasic structure: DevDungeon\nAll other programming: @flouroantimonic_acid#7707\nJoin our discord server! https://discord.gg/BaENmRW\n''')\n\n@client.command()\nasync def woah():\n await client.say('''\n( ͡° ͜ʖ ͡° )\n ( ( ͡° ͜ʖ ͡° )\n ( ( ͡° ͜ʖ ͡° )\n ( ͡° ( ͡° ͜ʖ ͡ ° )\n ( ͡° ͜ʖ ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ) ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ) ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ) ( ͡° ͜ʖ ͡° )\n ( ͡° ͜ʖ ͡° ) ͡° ͜ʖ ͡° )\n ( ͡° ͜ʖ ͡° ) ͜ʖ ͡° )\n ( ͡° ͜ʖ ͡° ) ͡° )\n ( ͡° ͜ʖ ͡° )° )\n ( ͡° ͜ʖ ͡° ))\n ( ͡° ͜ʖ ͡°)\n ( ( ͡° ͜ʖ ͡° )\n ( ( ͡° ͜ʖ ͡° )\n ( ͡° ( ͡° ͜ʖ ͡° )\n ( ͡° ͜ʖ ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ) ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ) ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ) ( ͡° ͜ʖ ͡° )\n ( ͡° ͜ʖ ͡° ) ͡° ͜ʖ ͡° )\n ( ͡° ͜ʖ ͡° ) ͜ʖ ͡° )\n ( ͡° ͜ʖ ͡° ) ͡° )\n ( ͡° ͜ʖ ͡° )° )\n ( ͡° ͜ʖ ͡° )\n ( ( ͡° ͜ʖ ͡° )\n ( ( ͡° ͜ʖ ͡° )\n ( ͡° ( ͡° ͜ʖ ͡° )\n ( ͡° ͜ʖ ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ͡° ( ͡° ͜ʖ ͡° )\n( ͡° ͜ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n ( ° ʖ ° ) ° ʖ ° )\n ( ° ʖ ° ) ʖ ° )\n ( ° ʖ ° ) ° )\n ( ° ʖ ° )° )\n ( ° ʖ ° )\n ( ( ° ʖ ° )\n ( ( ° ʖ ° )\n ( ° ( ° ʖ ° )\n ( ° ʖ ( ° ʖ ° )\n( ° ʖ ° ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n ( ° ʖ ° ) ° ʖ ° )\n ( ° ʖ ° ) ʖ ° )\n ( ° ʖ ° ) ° )\n ( ° ʖ ° )° )\n ( ° ʖ ° ))\n ( ° ʖ °)\n ( ( ° ʖ ° )\n ( ( ° ʖ ° )\n ( ° ( ° ʖ ° )\n ( ° ʖ ( ° ʖ ° )\n( ° ʖ ° ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n ( ° ʖ ° ) ° ʖ ° )\n ( ° ʖ ° ) ʖ ° )\n ( ° ʖ ° ) ° )\n ( ° ʖ ° )° )\n ( ° ʖ ° )\n ( ( ° ʖ ° )\n ( ( ° ʖ ° )\n ( ° ( ° ʖ ° )\n ( ° ʖ ( ° ʖ ° )\n( ° ʖ ° ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n( ° ʖ ° ) ( ° ʖ ° )\n ( ° ʖ ° ) ° ʖ ° )\n ( ° ʖ ° ) ʖ ° )\n ( ° ʖ ° ) ° )\n ( ° ʖ ° )° )\n''')\n\n@client.command()\nasync def t(message):\n translation = Translator().translate(message, dest='en')\n await client.say(translation.text)\n\nasync def list_servers():\n await client.wait_until_ready()\n while not client.is_closed:\n print(\"Current servers:\")\n for server in client.servers:\n print(server.name)\n print(\"--------------------------------\")\n await asyncio.sleep(600)\n\n\nclient.loop.create_task(list_servers())\nclient.run(TOKEN)\n","sub_path":"flourobot.py","file_name":"flourobot.py","file_ext":"py","file_size_in_byte":5637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"151318339","text":"from subprocess import call\nfrom copy import deepcopy\nimport os\n\nimport seaborn as sns\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nimport pandas as pd\nfrom tqdm import tqdm\n\n\ndef get_immediate_subdirectories(a_dir):\n return [name for name in os.listdir(a_dir)\n if os.path.isdir(os.path.join(a_dir, name))]\n\n\ndataset_type = 'genes'\n\nbase_folder = 'feature_selection/' + dataset_type + '/runs/'\nruns = get_immediate_subdirectories(base_folder)\nruns = sorted([x for x in runs if '_n' not in x])\n\n\ndef draw_boxplots(df, feature_cutoff, name, output_dir):\n df_f = df[df['variable'] <= feature_cutoff]\n f, ax = plt.subplots(figsize=(20, 10))\n sns.boxplot(x='variable',\n y='value',\n data=df_f,\n ax=ax,\n hue='run',\n whis=0.95,\n notch=True,\n fliersize=0)\n fig_path = output_dir + 'b_' + str(feature_cutoff) + '_' + n + '.png'\n f.savefig(fig_path)\n\n\ndef draw_band(df, feature_cutoff, name, output_dir):\n df_f = df[df['variable'] <= feature_cutoff]\n f, ax = plt.subplots(figsize=(20, 10))\n ax = sns.tsplot(time=\"variable\",\n value=\"value\",\n unit=\"rep\",\n condition=\"run\",\n data=df_f,\n ci=[95, 99])\n fig_path = output_dir + 'band_' + str(feature_cutoff) + '_' + n + '.png'\n f.savefig(fig_path)\n\n\nrun_names = list(set([x.split('_')[0] for x in runs]))\nfor n in tqdm(run_names):\n rn = [x for x in runs if n in x]\n run_subnames = [x.split('_')[-1] for x in rn]\n auc_dfs = []\n for s in run_subnames:\n if s != n:\n full_name = n + '_' + s\n else:\n full_name = n\n path = base_folder + full_name + '/auc_score.csv'\n df = pd.read_csv(path)\n df = df.astype(float)\n if 'scrambled' in s:\n df['run'] = 'scrambled'\n else:\n df['run'] = s\n auc_dfs.append(df)\n df = pd.DataFrame()\n for d in auc_dfs:\n df = df.append(d)\n cols = df.columns.tolist()\n cols.remove('rep')\n cols.remove('run')\n cols_rename = {c: int(c) for c in cols}\n df = df.rename(columns=cols_rename)\n df = pd.melt(df, id_vars=['rep', 'run'])\n df = df.astype({'variable': int, 'rep': int, 'value': float})\n output_dir = 'plots/' + dataset_type + '/' + n + '/'\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n draw_boxplots(df, 50, output_dir + n, output_dir)\n draw_band(df, 50, output_dir + n, output_dir)\n draw_band(df, df['variable'].max(), n, output_dir)\n","sub_path":"scripts/draw_ft_select_performance_plots.py","file_name":"draw_ft_select_performance_plots.py","file_ext":"py","file_size_in_byte":2629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"580621361","text":"__author__ = \"Moises Saavedra Caceres\"\n__email__ = \"mmsaavedra1@ing.puc.cl\"\n\n# Se importan los modulos de python\nimport numpy as np\nimport scipy.linalg\n\n# Se importan los modulos creados por el usuario\nfrom parametros import *\n\n\ndef timer(funcion):\n \"\"\"\n Se crea un decorador (googlear) del tipo timer para testear el tiempo\n de ejecucion del programa\n \"\"\"\n def inner(*args, **kwargs):\n\n inicio = time.time()\n resultado = funcion(*args, **kwargs)\n final = round(time.time() - inicio, 3)\n print(\"\\nTiempo de ejecucion total: {}[s]\".format(final))\n\n return resultado\n return inner\n\ndef LASSO(A, b, tau, iteracion_maxima):\n \"\"\"\n Esta funcion recibe como input una matriz A y un vector b. Se busca resolver\n el problema de regularizacion L1 con un parametro tau y haciendo un numero maximo\n de iteraciones definidas por el usuario, mediante el metodo del subgradiente.\n\n Su entrada posee:\n - A : Matriz de m muestras (filas) de cada variable n (columnas).\n - b : Matriz de m muestras obtenidas que depende de las n variables.\n - tau: Escalar que entrega significancia a las variables o entrega\n significancia al error del modelo (trade-off).\n - iteracion_maxima : Numero maximo de iteraciones a realizar.\n\n Su salida es:\n - valor_optimo : Valor optimo del problema a minimizar.\n\n \"\"\"\n\n # Dimensiones correspondientes\n m, n = A.shape\n\n # Se setean los valores de las iteraciones k, k-1 y k-2, respectivamente\n xk = np.zeros((n, 1))\n xk_1 = xk\n xk_2 = xk_1\n\n # Se setea el angulo entre las soluciones\n angulo = 0\n a = 0.001\n\n # Se despliega el mensaje en pantalla\n print(\"\\n\\n********** METODO DE SUBGRADIENTE *********\\n\")\n print(\"ITERACION VALOR OBJ ERROR ANGULO\")\n\n # Comienza el algoritmo\n for iteracion in range(iteracion_maxima):\n\n # 1º Se calcula el subgradiente de la funcion objetivo\n subgradiente = 2*np.dot(np.transpose(A), np.dot(A, xk) - b) + tau*np.sign(xk)\n\n # 2º Se actualiza el pasado\n xk_2 = xk_1\n xk_1 = xk\n\n # 3º Se actualiza el valor objetivo\n theta = a/np.sqrt(iteracion+1)\n xk = xk - theta*subgradiente\n\n # 4º Se evalua el error de ajuste\n error = (np.linalg.norm(np.dot(A, xk) - b)) / np.linalg.norm(b)\n\n # 5º Se evalua el valor en la funcion objetivo\n valor = np.linalg.norm(np.dot(A, xk) - b, 2)**2 + tau*np.linalg.norm(xk, 1)\n\n n1 = np.linalg.norm(xk, 1)\n \n # La rutina de subgradiente muestra en pantalla para cada iteracion:\n # nº de iteracion, valor de la funcion evaluada en el x de la iteracion,\n # error de ajuste y la norma 1 de xk.\n retorno_en_pantalla = [iteracion, valor, error, n1]\n# print(f\"{retorno_en_pantalla[0]: ^12d}{retorno_en_pantalla[1]: ^12f} {retorno_en_pantalla[2]: ^12f} {retorno_en_pantalla[3]: ^12f}\")\n print(\"%12.6f %12.6f %12.6f %12.6f\" % (retorno_en_pantalla[0],retorno_en_pantalla[1],retorno_en_pantalla[2],retorno_en_pantalla[3]))\n return xk\n\nif __name__ == '__main__':\n # Esto es para que simepre se generen los mismos numeros aleatorios\n np.random.seed(1000)\n\n tau = 0.5\n iteracion_maxima = 20000\n \n A, b = generar_datos(50, 300)\n xsol = LASSO(A, b, tau, iteracion_maxima)\n\n\n","sub_path":"4_Metodos-primer-orden/LASSO/LASSO.py","file_name":"LASSO.py","file_ext":"py","file_size_in_byte":3387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"346120447","text":"'''\nFind the nth digit of the infinite integer sequence 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n\nNote:\nn is positive and will fit within the range of a 32-bit signed integer (n < 231).\n\nExample 1:\n\nInput:\n3\n\nOutput:\n3\nExample 2:\n\nInput:\n11\n\nOutput:\n0\n\nExplanation:\nThe 11th digit of the sequence 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ... is a 0, which is part of the number 10.\n'''\n\nclass Solution(object):\n def findNthDigit(self, n):\n \"\"\"\n :type n: int\n :rtype: int\n \"\"\"\n low, high = 1, 10\n digitals = 1\n while True:\n tmp = n - (high - low) * digitals\n if tmp > 0:\n n = tmp\n low, high = high, high * 10\n digitals += 1\n else:\n break\n\n num = ((n-1) / digitals) + low\n offset = digitals - (n-1) % digitals - 1\n num /= 10**offset\n return num % 10\n","sub_path":"python/Nth-Digit.py","file_name":"Nth-Digit.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"485580534","text":"import warnings\nimport sys\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\n\nwarnings.filterwarnings(action='ignore', category=DeprecationWarning)\n\n\n\ndef preprocess():\n \n if len(sys.argv) == 1:\n \tdf = pd.read_csv('pet_purchase.csv')\n \n else:\n \tdf = pd.read_csv(sys.argv[1])\n\n \n features = df.filter(regex = 'buy|overall|purchase').columns.tolist()\n \n df['target'] = df['target'].astype(str)\n\n #keep only frequently purchased products\n\n df.replace('amp;','',regex=True,inplace=True)\n\n df = df[df['target'].isin((df['target'].value_counts()[df['target'].value_counts()>20]).index)]\n\n\n\n print('Label Encoding features')\n \n # Label Encoding\n le_ = LabelEncoder()\n target_le_ = LabelEncoder()\n\n df[df.filter(like = 'buy').columns.tolist()] = df[df.filter(like = 'buy').columns.tolist()].apply(le_.fit_transform)\n \n df['target'] = target_le_.fit_transform(df['target'])\n \n #Train Test split and apply label encoder\n X_train, X_test, y_train, y_test = train_test_split(df[features], df['target'], \n test_size=.10)\n \n #file outs\n\n\n X_train.to_csv('training_data.csv')\n X_test.to_csv('holdout.csv')\n\n y_train.to_csv('target.csv')\n y_test.to_csv('holdout_response.csv')\n\n pd.Series(target_le_.classes_).to_csv('target_labels')\n\n print('''File outs: /n training_data.csv /n holdout.csv /n target.csv \n /n holdout_response.csv /n target_labels.csv''')\n\n\nif __name__=='__main__':\n\tpreprocess()\n","sub_path":"main_project/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"497633710","text":"# coding=utf-8\r\n\r\n# Scrapy settings for searchSpider project\r\n#\r\n# For simplicity, this file contains only settings considered important or\r\n# commonly used. You can find more settings consulting the documentation:\r\n#\r\n# http://doc.scrapy.org/en/latest/topics/settings.html\r\n# http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html\r\n# http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html\r\n\r\nSPLIT_SIGN=','\r\nBOT_NAME = 'searchSpider'\r\n\r\nSPIDER_MODULES = ['searchSpider.spiders']\r\nNEWSPIDER_MODULE = 'searchSpider.spiders'\r\n\r\n#日志设置开始\r\n# import logging\r\n# logging.basicConfig(level=logging.DEBUG,\r\n# format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',\r\n# datefmt='%a, %d %b %Y %H:%M:%S',\r\n# )\r\n#日志设置结束\r\n\r\n#MYSQL设置\r\n# 开始intelliwatch管理员账号密码：\r\nMYSQL_HOST='localhost'\r\nMYSQL_USER='root'\r\nMYSQL_PASSWD=''\r\nMYSQL_PORT=3306\r\nMYSQL_CHARSET='utf8'\r\nMYSQL_DB='intelliwatch'\r\n#MYSQL设置结束\r\n\r\n\r\n# Crawl responsibly by identifying yourself (and your website) on the user-agent\r\nUSER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:41.0) Gecko/20100101 Firefox/41.0'\r\n\r\n# Configure maximum concurrent requests performed by Scrapy (default: 16)\r\n# CONCURRENT_REQUESTS=32\r\n\r\n# Configure a delay for requests for the same website (default: 0)\r\n# See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay\r\n# See also autothrottle settings and docs\r\n# DOWNLOAD_DELAY=3\r\n# The download delay setting will honor only one of:\r\n# CONCURRENT_REQUESTS_PER_DOMAIN=16\r\n# CONCURRENT_REQUESTS_PER_IP=16\r\n\r\n# Disable cookies (enabled by default)\r\n# COOKIES_ENABLED=False\r\n\r\n# Disable Telnet Console (enabled by default)\r\n# TELNETCONSOLE_ENABLED=False\r\n\r\n# Override the default request headers:\r\nDEFAULT_REQUEST_HEADERS = {\r\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\r\n 'Accept-Language': 'zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3',\r\n 'Accept-Encoding': 'gzip, deflate',\r\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:41.0) Gecko/20100101 Firefox/41.0',\r\n 'Connection': 'keep-alive',\r\n 'Cache-Control': 'max-age=0',\r\n}\r\n\r\n# Enable or disable spider middlewares\r\n# See http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html\r\nSPIDER_MIDDLEWARES = {\r\n'scrapy.spidermiddlewares.httperror.HttpErrorMiddleware': None,\r\n}\r\n# Enable or disable downloader middlewares\r\n# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html\r\n# DOWNLOADER_MIDDLEWARES = {\r\n# 'searchSpider.middlewares.MyCustomDownloaderMiddleware': 543,\r\n# }\r\n\r\n# Enable or disable extensions\r\n# See http://scrapy.readthedocs.org/en/latest/topics/extensions.html\r\n# EXTENSIONS = {\r\n# 'scrapy.telnet.TelnetConsole': None,\r\n# }\r\n\r\n# Configure item pipelines\r\n# See http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html\r\nITEM_PIPELINES = {\r\n 'searchSpider.pipelines.SearchspiderPipeline': 300,\r\n}\r\n\r\n# Enable and configure the AutoThrottle extension (disabled by default)\r\n# See http://doc.scrapy.org/en/latest/topics/autothrottle.html\r\n# NOTE: AutoThrottle will honour the standard settings for concurrency and delay\r\n# AUTOTHROTTLE_ENABLED=True\r\n# The initial download delay\r\n# AUTOTHROTTLE_START_DELAY=5\r\n# The maximum download delay to be set in case of high latencies\r\n# AUTOTHROTTLE_MAX_DELAY=60\r\n# Enable showing throttling stats for every response received:\r\n# AUTOTHROTTLE_DEBUG=False\r\n\r\n# Enable and configure HTTP caching (disabled by default)\r\n# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings\r\n# HTTPCACHE_ENABLED=True\r\n# HTTPCACHE_EXPIRATION_SECS=0\r\n# HTTPCACHE_DIR='httpcache'\r\n# HTTPCACHE_IGNORE_HTTP_CODES=[]\r\n# HTTPCACHE_STORAGE='scrapy.extensions.httpcache.FilesystemCacheStorage'\r\n\r\ntry:\r\n import MySQLdb as Database\r\nexcept ImportError as e:\r\n try:\r\n import pymysql\r\n pymysql.install_as_MySQLdb()\r\n except ImportError:\r\n raise","sub_path":"searchSpider/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"568315303","text":"import json\nimport time\nimport sys\nfrom kafka import KafkaProducer\n\nKAFKA_TOPIC = [\"policy\", \"customer\", \"product\"]\nKAFKA_BROKERS = \"localhost:9092\"\n\n# Setup producer connection\nproducer = KafkaProducer(value_serializer=lambda v: json.dumps(v).encode(\"utf-8\"),\n bootstrap_servers=KAFKA_BROKERS)\n\nprint(\"connected to {} topic {}\".format(KAFKA_BROKERS, KAFKA_TOPIC))\n\n\ndef send_to_topic(payload, topic_number):\n \"\"\"Add a message to the produce buffer asynchronously to be sent to Eventador.\"\"\"\n try:\n producer.send(KAFKA_TOPIC[topic_number], payload)\n except:\n print(\"unable to produce to {} topic {}\".format(KAFKA_BROKERS, KAFKA_TOPIC[topic_number]))\n\n\ntry:\n POLICIES_FOLDER = \"sample-policies\"\n policy_file_list = [\"003-policies_list.json\", \"004-policies_list.json\", \"005-policies_list.json\", \"006-policies_list.json\"]\n for file in policy_file_list:\n policy = None\n with open(POLICIES_FOLDER+\"/\"+file, \"r\") as policy_file:\n policy = json.load(policy_file)\n print(policy)\n send_to_topic(policy, 0)\n producer.flush()\n time.sleep(0.1)\n CUSTOMER_FOLDER = \"sample-customers\"\n customer_file_list = [\"001-customer.json\", \"002-customer.json\"]\n for file in customer_file_list:\n customer = None\n with open(CUSTOMER_FOLDER+\"/\"+file, \"r\") as customer_file:\n customer = json.load(customer_file)\n print(customer)\n send_to_topic(customer, 1)\n producer.flush()\n time.sleep(0.1)\n PRODUCT_FOLDER = \"sample-products\"\n customer_file_list = [\"001-product.json\", \"002-product.json\"]\n for file in customer_file_list:\n product = None\n with open(PRODUCT_FOLDER+\"/\"+file, \"r\") as product_file:\n product = json.load(product_file)\n print(product)\n send_to_topic(product, 2)\n producer.flush()\n time.sleep(0.1)\nexcept KeyboardInterrupt:\n sys.exit()\n","sub_path":"kafka-simulator/producers/policy-producer.py","file_name":"policy-producer.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"570220474","text":"# Importing the libraries\nimport numpy as np\nimport pandas as pd\nfrom sklearn.impute import SimpleImputer\n\n\n# Importing the dataset\ndataset = pd.read_csv('Database/Data.csv')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, -1].values\n\n\n# Taking care of missing data\nimputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')\nimputer = imputer.fit(X[:, 1:3])\nX[:, 1:3] = imputer.transform(X[:, 1:3])\n\n\n# Encoding categorical data\ndummies = pd.get_dummies(X[:, 0]).values\nX = np.append(np.delete(X, 0, axis=1), dummies[:, :-1], axis=1)\n\ny = pd.get_dummies(y).values[:, :-1]\n\n\n# Splitting the data\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)\n\n\n# Training the model\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train, y_train)\n\ny_pred = regressor.predict(x_test)\n\n\n# Plotting\nimport matplotlib.pyplot as plt\n\nplt.subplot(121)\nplt.scatter(X_train, y_train, color = 'blue')\nplt.plot(X_train, regressor.predict(X_train))\nplt.title('trainnig')\nplt.xlabel(X_label)\nplt.ylabel(y_label)\n\nplt.subplot(122)\nplt.scatter(X_test, y_test, color = 'red')\nplt.plot(X_train, regressor.predict(X_train))\nplt.title('test')\nplt.xlabel(X_label)\nplt.ylabel(y_label)\nplt.show()","sub_path":"master.py","file_name":"master.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"106783273","text":"# -*- coding: utf-8 -*-\n\nimport argparse\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image, ImageDraw\nfrom tqdm.auto import tqdm\n\n#---------------------------------------------------------------------\ndef make_argparser():\n parser = argparse.ArgumentParser(\n \"Draw annotation maps from geometry information of attentional\"\n \" region that is output from our annotation tool.\")\n\n parser.add_argument(\"--input_name\", default=\"./stl10_nara.csv\",\n help=\"A list of Geometry of attentional regions.\"\n \" (default: ./stl10_nara.csv)\")\n parser.add_argument(\"--output_dir\", default=\"./maps\",\n help=\"Output directory. (default: ./maps)\")\n parser.add_argument(\"--map_size\", type=int, default=96,\n help=\"Image size of result annotation maps. Usually,\"\n \" this should be the same as the oribinal images.\"\n \" (default: 96)\")\n parser.add_argument(\"--annotations\", type=int, nargs=\"+\", default=[1, 2, 3],\n help=\"Annotation IDs to be used for map generation.\"\n \" (default: 1 2 3)\")\n parser.add_argument(\"--fixed_radius\", type=int, default=-1,\n help=\"This option overwrites radius information\"\n \" of attentional region. Negative number means\"\n \" that annotated radii are used as is.\"\n \" (default: -1)\")\n\n return parser\n\n#---------------------------------------------------------------------\ndef get_image_info(df, index):\n img_type = df.loc[index, \"Split\"]\n img_id = int(df.loc[index, \"Image\"].split(\".\")[0])\n\n return img_type, img_id\n\ndef get_annotation_info(df, index):\n category = df.loc[index, \"Category\"]\n cent_x = df.loc[index, \"CenterX\"]\n cent_y = df.loc[index, \"CenterY\"]\n size_x = df.loc[index, \"RadiusX\"]\n size_y = df.loc[index, \"RadiusY\"]\n angle = df.loc[index, \"Angle\"]\n\n return category, cent_x, cent_y, size_x, size_y, angle\n\ndef get_time_info(df, index):\n sec = df.loc[index, \"WorkTime\"]\n\n return sec,\n\n#---------------------------------------------------------------------\ndef gen_blank_image(img_size, base_val=0):\n return Image.new(\"RGB\", (img_size, img_size), (base_val,) * 3)\n\ndef draw_ellipse(draw, cent, radius, angle, fill):\n assert len(cent) == 2 and len(radius) == 2\n cos, sin = np.cos(angle), np.sin(angle)\n\n rads = np.arange(360) / 180 * np.pi\n xs = np.cos(rads) * radius[0]\n ys = np.sin(rads) * radius[1]\n\n coords = []\n for x, y in zip(xs, ys):\n rx = cos * x - sin * y + cent[0]\n ry = sin * x + cos * y + cent[1]\n coords.append((rx, ry))\n\n draw.polygon(coords, fill=fill)\n\ndef calc_mean_image(image_list):\n mean = np.stack([np.asarray(img) for img in image_list]).mean(axis=0)\n return Image.fromarray(mean.astype(np.uint8))\n\n#---------------------------------------------------------------------\ndef main(args):\n df = pd.read_csv(args.input_name)\n\n # Read annotation information.\n anno_list = {}\n for index in range(len(df)):\n img_type, img_id = get_image_info(df, index)\n (category, cent_x, cent_y, size_x, size_y, angle) \\\n = get_annotation_info(df, index)\n\n if args.fixed_radius > 0:\n # Force to use fixed-size circles.\n size_x = size_y = args.fixed_radius\n\n items = cent_x, cent_y, size_x, size_y, angle\n if (img_type, img_id) not in anno_list:\n anno_list[(img_type, img_id)] = [items]\n else:\n anno_list[(img_type, img_id)].append(items)\n\n for img_type, img_id in tqdm(anno_list):\n items_list = anno_list[(img_type, img_id)]\n\n # Draw annotation maps.\n maps = []\n for cent_x, cent_y, size_x, size_y, angle in items_list:\n map = gen_blank_image(args.map_size)\n draw = ImageDraw.Draw(map)\n draw_ellipse(draw, (cent_x, cent_y), (size_x, size_y), angle,\n fill=(255, 255, 255))\n maps.append(map)\n\n # Combine maps.\n result_map = calc_mean_image([maps[i - 1] for i in args.annotations])\n\n # Save the combined map.\n map_fname = Path(args.output_dir) / img_type / f\"{img_id:05d}.png\"\n map_fname.parent.mkdir(parents=True, exist_ok=True)\n result_map.save(map_fname)\n\n#---------------------------------------------------------------------\nif __name__ == \"__main__\":\n parser = make_argparser()\n args = parser.parse_args()\n main(args)\n","sub_path":"draw_annotation_maps.py","file_name":"draw_annotation_maps.py","file_ext":"py","file_size_in_byte":4640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"466198671","text":"import pygame\r\nimport math\r\nimport operator\r\npygame.init()\r\nwidth=900\r\nheight=600\r\ngameDisplay=pygame.display.set_mode((width,height))\r\n\r\ndef fourier(x,rot):\r\n\ttransform=[]\r\n\r\n\tN=len(x)\r\n\r\n\tfor k in range(N):\r\n\t\treal=0\r\n\t\timg=0\r\n\r\n\t\tfor n in range(N):\r\n\r\n\t\t\ttheta=2*math.pi*k*n/N\r\n\r\n\t\t\treal+=x[n]*math.cos(theta)\r\n\t\t\timg-=x[n]*math.sin(theta)\r\n\r\n\t\treal/=N\r\n\t\timg/=N\r\n\r\n\t\tfreq=k\r\n\t\tamp=math.sqrt(real*real+img*img)\r\n\r\n\t\tphase=math.atan2(img,real)+rot\r\n\r\n\t\ttransform.append([freq,amp,phase])\r\n\r\n\treturn transform\r\n\r\n\r\ntime=0\r\nx=[]\r\ny=[]\r\npath=[]\r\n\r\ndef draw_epicycles(x,y,rotation,fourier):\r\n\r\n\tfor i in range(len(fourier)):\r\n\r\n\t\tprevx=x\r\n\t\tprevy=y\r\n\r\n\t\tfreq=fourier[i][0]\r\n\t\tradius=fourier[i][1]\r\n\t\tphase=fourier[i][2]\r\n\r\n\t\tx+=radius*math.cos(freq*time+phase+rotation)\r\n\t\ty+=radius*math.sin(freq*time+phase+rotation)\r\n\r\n\t\ttry:\r\n\t\t\tpass\r\n\t\t\t# pygame.draw.circle(gameDisplay,white,(int(prevx),int(prevy)),int(radius),1)\r\n\t\texcept:\r\n\t\t\tpass\r\n\r\n\t\tpygame.draw.line(gameDisplay,white,(int(prevx),int(prevy)),(int(x),int(y)))\r\n\r\n\treturn (x,y)\r\n\r\n\r\ndef get_draw_cord():\r\n\twhite=(255,255,255)\r\n\tdraw_Run=True\r\n\tx=[]\r\n\ty=[]\r\n\twhile draw_Run:\r\n\t\tgameDisplay.fill(0)\r\n\r\n\t\tif pygame.mouse.get_pressed()[0]:\r\n\t\t\tx.append(pygame.mouse.get_pos()[0]-width/2)\r\n\t\t\ty.append(pygame.mouse.get_pos()[1]-height/2)\r\n\r\n\t\tfor i in range(len(x)):\r\n\t\t\tpygame.draw.circle(gameDisplay,white,(int(x[i]+width/2),int(y[i]+height/2)),5)\r\n\t\t\r\n\r\n\t\tfor event in pygame.event.get():\r\n\t\t\tif event.type==pygame.QUIT:\r\n\t\t\t\tdraw_Run=False\r\n\r\n\r\n\t\tpygame.display.update()\r\n\r\n\treturn x,y\r\n\r\nx,y=get_draw_cord()\r\n\r\n# x_center=sum(x)/len(x)\r\n# y_center=sum(x)/len(x)\r\n\r\nfourierX=fourier(x,0)\r\nfourierY=fourier(y,math.pi/2)\r\n\r\nfourier_3d=fourierX+fourierY\r\n# fourierX=sorted(fourierX,key=operator.itemgetter(1),reverse=True)\r\n# fourierY=sorted(fourierY,key=operator.itemgetter(1),reverse=True)\r\n\r\nfourier_3d=sorted(fourier_3d,key=operator.itemgetter(1),reverse=True)\r\n\r\nwhite=(255,255,255)\r\nred=(255,0,0)\r\n\r\n\r\nrun=True\r\n#-----------------------------\r\nwhile run:\r\n\tgameDisplay.fill(0)\r\n\r\n\t# vx=draw_epicycles(width/2+50,height/2,0,fourierX)\r\n\t# vy=draw_epicycles(vx[0],vx[1],math.pi/2,fourierY)\r\n\tvy=draw_epicycles(width/2+50,height/2,0,fourier_3d)\r\n\tv=(vy[0],vy[1])\r\n\t\r\n\tpath.insert(0,v)\r\n\t# pygame.draw.line(gameDisplay,white,(vx[0],vx[1]),(v[0],v[1]))\r\n\tpygame.draw.line(gameDisplay,white,(vy[0],vy[1]),(v[0],v[1]))\r\n\t\r\n\tprevx=path[0][0]\r\n\tprevy=path[0][1]\r\n\r\n\tfor i in range(len(path)):\r\n\t\t# gameDisplay.set_at((int(path[i][0]),int(path[i][1])),white)\r\n\r\n\t\tpygame.draw.line(gameDisplay,red,(int(prevx),int(prevy)),(int(path[i][0]),int(path[i][1])))\r\n\t\tprevx=path[i][0]\r\n\t\tprevy=path[i][1]\r\n\t\t\r\n\tdt=2*math.pi/len(fourierX)\r\n\ttime+=dt\r\n\r\n\tif time>2*math.pi:\r\n\t\ttime=0\r\n\t\tpath=[]\r\n\r\n\tfor event in pygame.event.get():\r\n\t\tif event.type==pygame.QUIT:\r\n\t\t\trun=False\r\n\r\n\r\n\tpygame.display.update()\r\n\r\n\r\n","sub_path":"fourier transform visual/visual_test8.py","file_name":"visual_test8.py","file_ext":"py","file_size_in_byte":2849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"320061665","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 9 20:02:41 2021\n\n@author: Guilherme Rossetti Lima\n\"\"\"\nimport sys\nimport io\nfrom os import listdir\nfrom os.path import join, isfile\nfrom collections import deque\nimport random\n\nINPUT = \"D:\\\\Master\\\\MetaheuristicOptimization\\\\MetaheuristicOptimization_labs\\\\Lab2\\\\Lab-data\\\\Inst\\\\uf20-04.cnf\"\nOUTPUT = \"D:\\\\Master\\\\MetaheuristicOptimization\\\\MetaheuristicOptimization_labs\\\\Lab2\\\\Lab-data\\\\sols\\\\\"\n\nclass Problem:\n def __init__(self, actions, variables):\n self.variables = list(variables.keys())\n \n self.num_expantions = 0\n \n def actions(self, state):\n if (len(self.variables) == state):\n return []\n \n return [True, False]\n \n def result(self, state, action):\n return self.variables[state+1]\n \n def goal_test(self, goal_test, node):\n test = goal_test(node, self)\n self.num_expantions += 1\n return test\n \nclass Node:\n def __init__(self, state, action, parent):\n self.state = state\n self.action = action \n self.parent = parent \n \n def expand(self, problem):\n nodes = [self.child_node(problem, action) for action in problem.actions(self.state)]\n \n return nodes\n \n def child_node(self, problem, action):\n state = problem.result(self.state, action)\n \n return Node(state, action, self)\n \n def path(self, problem): \n variables = dict([])\n \n for i in range(1, len(problem.variables)+1):\n variables[i] = False\n \n c_node = self\n while(c_node.parent != None):\n variables[c_node.state] = c_node.action\n c_node = c_node.parent\n \n return variables\n \n \nclass SATVerifier():\n def __init__(self, input_file, output_file=None): \n self.input_file = input_file\n self.output_file = output_file\n self.num_variables = 0\n self.num_clauses = 0\n \n self.clauses = []\n \n self.variables = dict()\n \n self.read_input_file()\n \n if (output_file):\n self.read_output_file()\n \n def interpret_problem(self, line):\n items = str.split(line, \" \")\n \n self.num_variables = int(items[2]) \n self.num_clauses = items[4] \n\n def process_line(self, line):\n line = str.strip(line)\n \n if(line[0] == \"c\"):\n return\n \n if(line[0] == 'p'):\n self.interpret_problem(line)\n return\n \n variables = str.split(line, \" \")\n self.clauses.append(variables[:len(variables)-1])\n \n def is_negation(self, value):\n return str.find(value, \"-\") > -1 \n \n def get_index(self, variable):\n return int(str.replace(variable, '-', ''))\n\n def process_variable_values(self, line):\n line = str.strip(line)\n variables = str.split(line, \" \")\n \n for variable in variables:\n self.variables[self.get_index(variable)] = not self.is_negation(variable)\n \n def process_output_line(self, line):\n \n if(line[0] == \"c\"):\n return\n \n if(line[0] == 'v'):\n self.process_variable_values(line[3:-1])\n \n def read_input_file(self):\n with open(self.input_file, \"r\") as file:\n line = file.readline()\n while line != \"%\\n\":\n self.process_line(line)\n \n line = file.readline()\n \n def read_output_file(self):\n with open(self.output_file, \"r\") as file:\n line = file.readline()\n while line != \"v 0\" and line != \"\":\n self.process_output_line(line)\n \n line = file.readline() \n \n def verify_clause(self, clause): \n satistied = False\n for variable in clause:\n if (self.is_negation(variable)):\n satistied = satistied or not self.variables[self.get_index(variable)]\n else:\n satistied = satistied or self.variables[self.get_index(variable)]\n \n return satistied\n \n def verify_solution(self):\n satisfied = True\n for clause in self.clauses:\n \n satisfied = satisfied and self.verify_clause(clause)\n \n if (not satisfied):\n return satisfied\n \n return satisfied\n \n def seed_variables(self, value):\n for i in range(1, self.num_variables + 1):\n self.variables[i] = value\n \n \n def is_fully_satisfied_by_variable(self, literal, value, clause):\n for item in clause:\n if (self.get_index(item) == self.get_index(literal) and \n ((self.is_negation(item) and value) or \n (not self.is_negation(item) and not value))):\n print(f\"Item{item}\")\n return False\n \n return True\n\n def goal_function(self, node, problem):\n self.variables = node.path(problem)\n return self.verify_solution() \n\n def binary_tree_search(self):\n self.seed_variables(False)\n problem = Problem([True, False], self.variables)\n initial = Node(0, False, None)\n \n frontier = deque([initial])\n \n while len(frontier) > 0:\n node = frontier.pop()\n \n if(problem.goal_test(self.goal_function, node)):\n print(\"Found goal\")\n return node\n \n frontier.extend(node.expand(problem))\n \n if (problem.num_expantions <51):\n print(self.variables)\n \n print(problem.num_expantions)\n \n '''\n Create a queue of unsatistied clauses (FIFO).\n Clone variables list.\n Create a list of variables to watch.\n Pick a in the first clause.\n Check if that is the list of variables to watch, if so check if clause is satisfied. \n If not safisfied move to next literal, repeat the check.\n If variable not in the watch list set it in a way to satisfy the clause(keep an eye out for negations).\n Set the literal in such a way you satisfy the clause.\n Remove from list of unsatisfied clauses clauses.\n Remove clause from the queue.\n '''\n def watch_list_heuristic(self):\n \n unsatisfied_clauses = deque(self.clauses)\n unsatisfiable_clauses = deque([])\n \n print(self.clauses)\n \n unset_variables = self.variables.copy()\n watch_variables = dict()\n \n while ((len(unset_variables)>0 or len(unsatisfied_clauses)>0)):\n clause = unsatisfied_clauses.popleft()\n \n literals = deque(clause) \n while (len(literals) > 0):\n literal = literals.pop()\n index = self.get_index(literal)\n negation = self.is_negation(literal)\n \n if (index in watch_variables):\n if (self.is_fully_satisfied_by_variable(literal, watch_variables[index], clause)):\n break\n else: \n continue\n \n watch_variables[index] = not negation\n del unset_variables[index]\n \n print(watch_variables) \n print(unset_variables) \n print(unsatisfiable_clauses) \n if (len(unset_variables)>0 or len(unsatisfied_clauses)>0):\n return \"Unsatisfiable\"\n \n self.variables = watch_variables\n \n return watch_variables \n \n \n def get_number_of_satisfied_clauses(self):\n \n counter_satisfied = 0\n for clause in self.clauses:\n if (self.verify_clause(clause)):\n counter_satisfied += 1\n \n return counter_satisfied \n \n print(self.variables)\n\n\n def propose_solution(self):\n pass\n\n \nif __name__ == \"__main__\":\n \n input_value, solution_path = INPUT, OUTPUT\n \n if (len(sys.argv) > 1):\n input_value = sys.argv[1]\n \n if (len(sys.argv)>2):\n solution_path = sys.argv[2]\n \n solutions = [file for file in listdir(solution_path) if isfile(join(solution_path, file))]\n \n for solution in solutions:\n satVerifier = SATVerifier(input_value, join(solution_path, solution))\n if (satVerifier.verify_solution()):\n print(solution)\n \n \n satVerifier = SATVerifier(input_value)\n \n #print(satVerifier.watch_list_heuristic())\n \n node = satVerifier.binary_tree_search()\n \n \n \n print(satVerifier.verify_solution())\n\n\n \n \n \n \n \n ","sub_path":"Lab2/SAT.py","file_name":"SAT.py","file_ext":"py","file_size_in_byte":8897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"416621192","text":"#!/usr/bin/env python3\n# Module 3\n# Programming Assignment 4\n# Prob-5.py\n\n# \n\ndef main():\n try:\n x = eval(2)\n print(\"x:\", x)\n except TypeError:\n print(\"Type Error\")\n except:\n print(\"general error\")\n\nmain()","sub_path":"Prob-5/Prob-5.py","file_name":"Prob-5.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"72597411","text":"from .Utility import *\nfrom .Exceptions import *\n\nclass Transaction:\n\tdef __str__(self):\n\t\ts = \"************************* TRANSACTION INFO *************************\\n\"\n\t\tni = len(self.inputs)\n\t\tno = len(self.outputs)\n\t\tnj = len(self.joinsplits)\n\t\ts += \"TXID: {}\\n\".format(self.txid)\n\t\ts += \"{} INPUTS\\n\".format(ni)\n\t\ts += \"{} OUTPUTS\\n\".format(no)\n\t\ts += \"{} JOINSPLITS\\n\".format(nj)\n\t\ts += \"version: {}\\n\".format(self.version)\n\n\t\tfor i in range(ni):\n\t\t\ts += \"\\n////// INPUT {}\\n\".format(i+1)\n\t\t\ts += str(self.inputs[i])\n\t\tfor i in range(no):\n\t\t\ts += \"\\n////// OUTPUT {}\\n\".format(i+1)\n\t\t\ts += str(self.outputs[i])\n\t\tfor i in range(nj):\n\t\t\ts += \"\\n////// JOINSPLIT {}\\n\".format(i+1)\n\t\t\ts += str(self.joinsplits[i])\n\t\t\ts += \"\\n\"\n\n\t\treturn s\n\n\tdef __init__(self):\n\t\tpass\n\n\tdef _parse(self, b, coinbase=False, genesis=False):\n\t\torig = b\n\t\tself.inputs = []\n\t\tself.outputs = []\n\t\tself.joinsplits = []\n\t\tself.version\t\t\t\t, b = read_byte(b, 4, True)\n\t\tself.size = 4\n\n\t\tn, b, rb = read_varint(b, return_bytes=True)\n\t\tself.size += rb\n\n\t\tfor _ in range(n):\n\t\t\tti = TransactionInput(coinbase=True, genesis=genesis)\n\t\t\tb, rb = ti._parse(b)\n\t\t\tself.inputs.append(ti)\n\t\t\tself.size += rb\n\n\t\tn, b, rb = read_varint(b, return_bytes=True)\n\t\tself.size += rb\n\t\tfor i in range(n):\n\t\t\tto = TransactionOutput(b)\n\t\t\tb, rb = to._parse(b)\n\t\t\tto.index = i\n\t\t\tself.outputs.append(to)\n\t\t\tself.size += rb\n\n\t\tself.lock_time\t\t\t, b = read_byte(b, 4, True)\n\t\tself.size += 4\n\n\t\tif self.version <= 1:\n\t\t\trawtr, _ = read_byte_seq(orig, self.size)\n\t\t\tself.txid = convert_rawtransaction_to_txid(rawtr)\n\t\t\treturn b\n\n\t\t# joinsplit information is added if and only if version > 1.\n\t\tn, b, rb = read_varint(b, return_bytes=True)\n\t\tself.size += rb\n\t\tfor _ in range(n):\n\t\t\ts, b = read_byte(b, 1802)\n\t\t\tself.size += 1802\n\t\t\tjs = JoinSplit(s)\n\t\t\tself.joinsplits.append(js)\n\n\t\tif len(self.joinsplits) > 0:\n\t\t\tself.joinsplit_pubkey\t, b = read_byte(b, 32)\n\t\t\tself.joinsplit_sig\t\t, b = read_byte(b, 64)\n\t\t\tself.size += 32 + 64\n\n\t\trawtr, _ = read_byte_seq(orig, self.size)\n\t\tself.txid = convert_rawtransaction_to_txid(rawtr)\n\n\t\treturn b\n\nclass JoinSplit:\n\tdef __str__(self):\n\t\ts = \"vpub_old: {}\\n\".format( self.vpub_old )\n\t\ts += \"vpub_new: {}\\n\".format( self.vpub_new )\n\t\treturn s\n\n\t# joinsplit is fixed length, no need to prepare parse function\n\tdef __init__(self, b):\n\t\tself.vpub_old, b = read_byte(b, 8, True)\n\t\tself.vpub_new, b = read_byte(b, 8, True)\n\t\tself.body = b\n\nclass TransactionInput:\n\tdef __init__(self, coinbase=False, genesis=False):\n\t\t# the input of the first transaction in a block is called a coinbase.\n\t\tself.coinbase = coinbase\n\t\tself.genesis = genesis\n\t\tself.rawdata = \"\"\n\n\tdef __str__(self):\n\t\ts = \"\"\n\t\ts += \"previous transaction:\\n\\t {}\\n\".format(self.prevhash)\n\t\ts += \"previous transaction index\\n\\t {}\\n\".format( int(self.index,16) )\n\t\ts += \"script:\\n\\t {}\\n\".format(self.script)\n\t\treturn s\n\n\tdef _parse(self,b):\n\t\ttotal = 0\n\t\tif self.coinbase:\n\t\t\tself.prevhash\t\t\t\t, b = read_byte(b, 32)\t# null (0x000....)\n\t\t\ttotal += 32\n\t\t\tself.index\t\t\t\t\t, b = read_byte(b, 4)\t# 0xffffffff\n\t\t\ttotal += 4\n\t\t\tself.script_bytes\t\t, b, rb = read_varint(b, return_bytes=True)\n\t\t\ttotal += rb\n\t\t\tif self.genesis:\n\t\t\t\t# usual transaction has \"[1-byte readout direction][block height]\"\n\t\t\t\t# but the genesis block has a weird lengthy script... just skip those bytes and make height 0.\n\t\t\t\t# for Koto, it starts with 0x25(meaning script length is 37bytes) 0x04 (read 4 bytes) ....\n\t\t\t\tself.script\t\t\t\t\t, b = read_byte_seq(b, self.script_bytes)\n\t\t\t\ttotal += self.script_bytes\n\t\t\t\tself.sequence\t\t\t\t, b = read_byte(b, 4, True)\n\t\t\t\ttotal += 4\n\t\t\t\tself.height_bytes\t\t\t\t= 1\n\t\t\t\tself.height\t\t\t\t\t\t= 0\n\t\t\telse:\n\t\t\t\t# height is only in coinbases. height is a script itself.\n\t\t\t\tself.height_bytes\t\t\t, b = read_byte(b, 1, True)\n\t\t\t\ttotal += 1\n\n\t\t\t\tif 81 <= self.height_bytes and self.height_bytes <= 96:\n\t\t\t\t\t# special operation OP1-16\n\t\t\t\t\tself.height = self.height_bytes - 80\n\t\t\t\t\tself.height_bytes = 0\n\t\t\t\telif self.height_bytes == 0:\n\t\t\t\t\tself.height_bytes = 0\n\t\t\t\t\tself.height = 0\n\t\t\t\telse:\n\t\t\t\t\tself.height\t\t\t\t, b = read_byte(b, self.height_bytes, True)\n\t\t\t\t\ttotal += self.height_bytes\n\n\t\t\t\treadout_length\t\t\t\t= self.script_bytes - self.height_bytes - 1 # 1 for height_bytes, which should be 1-byte readout\n\t\t\t\tself.script\t\t\t\t\t, b = read_byte_seq(b, readout_length)\n\t\t\t\ttotal += readout_length\n\t\t\t\tself.sequence\t\t\t\t, b = read_byte(b, 4, True)\n\n\t\t\t\ttotal += 4\n\t\telse:\n\t\t\tself.prevhash\t\t\t\t, b = read_byte(b, 32)\n\t\t\tself.index\t\t\t\t\t, b = read_byte(b, 4)\n\t\t\tself.script_bytes\t\t, b, rb = read_varint(b, return_bytes=True)\n\t\t\tself.script\t\t\t\t\t, b = read_byte_seq(b, self.script_bytes)\n\t\t\tself.sequence\t\t\t\t, b = read_byte(b, 4, True)\n\n\t\t\ttotal += 32+4+rb+self.script_bytes+4\n\n\t\treturn b, total\n\nclass TransactionOutput:\n\tdef __str__(self):\n\t\ts = \"value:\\n\\t {}\\n\".format(decode_satoshi(self.value))\n\t\ts += \"script(decoded):\\n\\t {}\\n\".format(self.script)\n\t\ts += \"address:\\n\\t {}\\n\".format(self.script.address)\n\t\treturn s\n\n\tdef _parse(self,b):\n\t\ttotal = 0\n\n\t\tself.value\t\t\t, b \t= read_byte(b, 8, True)\n\t\tself.pk_script_size\t, b, rb = read_varint(b, return_bytes=True)\n\t\tscript\t\t\t\t, b\t\t= read_byte_seq(b, self.pk_script_size)\n\t\tself.script\t\t\t\t\t= Script(script)\n\n\t\ttotal += 8 + rb + self.pk_script_size\n\n\t\treturn b, total\n\n\tdef __init__(self, rawdata):\n\t\tself.rawdata = rawdata\n\nclass Script:\n\tdef __str__(self):\n\t\treturn self.script\n\n\tdef _decode_opcode(self, b):\n\t\top, b = read_byte_seq(b, 1)\n\t\tr = None\n\n\t\tnop = int(op,16)\n\n\t\t# push specified bytes (1-75) to the stack\n\t\tif 1 <= nop and nop <= 75: # 0x01 - 0x4b\n\t\t\t# * is there some exception that output scripts contain PUSH operations?\n\t\t\t# for example: blockhash = \"793e15fd4f18099efb86ccf350851e1a3f88fa25fd865f830c61e958128bafce\"\n\t\t\t# contains PUSH 11 before normal routine \"0x88ac\"\n\t\t\tr = \"PUSH[{}]\".format(nop)\n\t\t\tif len(b) >= nop:\n\t\t\t\tt, b = read_byte_seq(b, nop)\n\t\telif 82 <= nop and nop <= 96: # 0x52-0x60\n\t\t\tr = \"OP_{}\".format(nop-80)\n\t\telif nop == 118:\n\t\t\tr = \"OP_DUP\"\n\t\telif nop == 101:\n\t\t\tr = \"OP_VERIF\"\n\t\telif nop == 136:\n\t\t\tr = \"OP_EQUALVERIFY\"\n\t\telif nop == 157: #0x9d\n\t\t\tr = \"OP_NUMEQUALVERIFY\"\n\t\telif nop == 166: # 166 = OP_RIPEMD160(0xa6)\n\t\t\tr = \"OP_RIPEMD160\"\n\t\telif nop == 169:\n\t\t\t# note that the one on Zcash specification is 0x1cbd.\n\t\t\t# the prefix 0x1836 intentionally guarantee the resultant address starts from \"k1\" or \"jz\"\n\t\t\tversion_prefix = \"1836\"\n\n\t\t\tt, b = read_byte_seq(b, 1, True) # this should be 20\n\t\t\tt, b = read_byte_seq(b, t) # 160-bit hash160\n\t\t\tr = \"OP_HASH160[{}]\".format(t)\n\t\t\th = t\n\t\t\th = version_prefix + h\n\t\t\th = hashlib.sha256( bytes.fromhex(h) ).hexdigest()\n\t\t\t# although the Zcash specification specifies we use ripemd160(sha256(x)), it does not work.\n\t\t\t# sha256(sha256(x)) yields the same address as Koto GUI Wallet and koto-cli.\n\t\t\th = hashlib.sha256( bytes.fromhex(h) ).hexdigest()\n\t\t\tf = h[:8]\n\t\t\th = version_prefix + t + f\n\t\t\th = base58.b58encode( bytes.fromhex(h) )\n\t\t\tself.address = h\n\t\telif nop == 172:\n\t\t\tr = \"OP_CHECKSIG\"\n\t\telif 179 <= nop and nop <= 185:\n\t\t\tr = \"OP_NOP{}\".format(nop-179 + 4)\n\t\telif nop == 187:\n\t\t\t# what is 0xbb found on e574d8fc0a69205757759ae67d2ccbfb015b3776629b6ce2638fb27aef193129 ??\n\t\t\t# 0x14 [20bytes of hash] \"0xbb\" 0x88 0xac [END]\n\t\t\tr = \"0xbb?\"\n\t\telif nop == 227: # what is 0xe3?\n\t\t\tr = \"0xe3?\"\n\t\telif nop == 253:\n\t\t\t# 253-255 are invalid\n\t\t\tr = \"OP_PUBKEYHASH\"\n\n\t\tif r is None:\n\t\t\traise UnknownOperationCodeException(op)\n\n\t\treturn r, b\n\n\tdef _parse(self):\n\t\ts = \"\"\n\t\tb = self.rawdata\n\n\t\tfor _ in range(5):\n\t\t\tr, b = self._decode_opcode(b)\n\t\t\ts += r + \" \"\n\t\t\tif len(b) == 0:\n\t\t\t\tbreak\n\n\t\tif len(b) > 0:\n\t\t\traise IncorrectResultException(\"there is bytes to be processed in the script field\")\n\t\tself.script = s[:-1]\n\n\tdef __init__(self,script):\n\t\tself.rawdata = script\n\t\tself.address = None\n\t\tself._parse()\n","sub_path":"KotoBlockchainParser/Transaction.py","file_name":"Transaction.py","file_ext":"py","file_size_in_byte":7771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"375679204","text":"from django.conf.urls import patterns, include, url\n\nfrom django.contrib import admin\nadmin.autodiscover()\nfrom django.contrib.auth.decorators import login_required\nfrom teams.views import MakeTeamView\n\nurlpatterns = patterns('',\n url(r'^new/', login_required(MakeTeamView.as_view()), name=\"maketeam\"),\n url(r'^search_team/$', 'teams.views.search_team', name='search_team'),\n url(r'^join_team/([0-9]+)/$', 'teams.views.join_team', name=\"join_team\"),\n url(\n r'^accept_request_to_join_team/([0-9]+)-([0-9]+)/$', \n 'teams.views.accept_request_to_join_team',\n name = 'accept_request_to_join_team',\n )\n) \n","sub_path":"buildbuild/teams/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"331446859","text":"from django.conf.urls import patterns, url, include\nfrom frontend import views\n\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nurlpatterns = patterns('frontend.views',\n url(r'^$', 'home', name='home'),\n url(r'^user-account$', 'user_account', name='user_account'),\n url(r'^about$', 'about_view', name='about_view'),\n url(r'^file-uplod$', 'file_upload', name='file_upload'),\n url(r'^logout$', 'log_out', name='log_out'),\n url(r'^test$', 'test', name='test'),\n url(r'^settings$', 'settings_view', name='settings_view'),\n url(r'^delete-image/(?P\\d+)/$', 'delete_image', name='delete_image'),\n #url(r'^login', 'log_in', name='log_in'),\n #url(r'^signup/', 'sign_up', name='sign_up'),\n\n # Api call\n #url(r'^twitter$', 'twitter_login', name= 'twitter_login'),\n #url(r'^twitter-request$', 'twitter_request', name= 'twitter_request'), \n #url(r'^callback/$', 'twitter_authenticated', name= 'twitter_authenticated'),\n\n # action\n #url(r'^signature-requst$', 'signature_requst', name= 'signature_requst'),\n) + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\n\"\"\" error when request \"\"\"\n\nhandler404 = 'frontend.views.my_custom_page_not_found_view'\n\nhandler500 = 'frontend.views.my_custom_error_view'\n\nhandler403 = 'frontend.views.my_custom_permission_denied_view'\n\nhandler400 = 'frontend.views.my_custom_bad_request_view'","sub_path":"frontend/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"383284468","text":"import pickle\nimport src.probabilistic_models.grammars as gr\n\n\ndef construct_dict():\n f = open(\"rockyou-withcount-utf-8.txt\", \"r\")\n i = 0\n d = dict()\n for l in f:\n i+=1\n n = int(l[0:8])\n w = l[8:-1]\n d[w] = n\n if i%1000 == 0:\n print(i)\n print((w,n))\n pickle.dump(d, open(\"rockyou_dictionary.p\", \"wb\"))\n print(d)\n\n\n# gr.construct_grammar_model()\n\n(cb_counter, Q) = pickle.load(open(\"cb_dictionary.p\", \"rb\"))\n(sb_counter, B) = pickle.load(open(\"sb_dictionary.p\", \"rb\"))\n(l1,l2) = pickle.load(open(\"lists.p\", \"rb\"))\n\nprint(l1)\n\nd = pickle.load(open(\"rockyou_dictionary.p\", \"rb\"))\ndico = { 'truc' : d}\n\ngr.construct_grammar_model(dico)","sub_path":"src/probabilistic_models/construct_test.py","file_name":"construct_test.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"571770318","text":"from django.core.exceptions import ImproperlyConfigured\nfrom django.core import mail\nfrom django.core.urlresolvers import reverse\nfrom django.test import TestCase\nfrom django.test.utils import override_settings\n\n\nclass TestContactViews(TestCase):\n def test_contact(self):\n url = reverse('cards_contact')\n\n with self.assertRaises(ImproperlyConfigured):\n self.client.get(url)\n\n with override_settings(SQUIGCARDS_EMAIL_TO=['foo@example.com', ]):\n with self.assertRaises(ImproperlyConfigured):\n self.client.get(url)\n\n with override_settings(SQUIGCARDS_EMAIL_FROM='from@example.com'):\n with self.assertRaises(ImproperlyConfigured):\n self.client.get(url)\n\n with override_settings(SQUIGCARDS_EMAIL_TO=['foo@example.com', ],\n SQUIGCARDS_EMAIL_FROM='from@example.com'):\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n\n def test_contact_post(self):\n import os\n os.environ['RECAPTCHA_TESTING'] = 'True'\n url = reverse('cards_contact')\n\n response = self.client.post(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed('squigcards/email_form.html')\n\n with self.assertRaises(ImproperlyConfigured):\n response = self.client.post(\n url,\n {\n 'name': 'Bob',\n 'email': 'bob@bob.com',\n 'subject': 'Hello, world',\n 'message': 'Tell me more',\n 'recaptcha_response_field': 'PASSED',\n }\n )\n\n with override_settings(SQUIGCARDS_EMAIL_TO=['foo@example.com', ],\n SQUIGCARDS_EMAIL_FROM='from@example.com'):\n response = self.client.post(\n url,\n {\n 'name': 'Bob',\n 'email': 'bob@bob.com',\n 'subject': 'Hello, world',\n 'message': 'Tell me more',\n 'recaptcha_response_field': 'PASSED',\n }\n )\n self.assertEqual(len(mail.outbox), 1)\n self.assertEqual(mail.outbox[0].subject,\n '[Demo Email] Hello, world')\n\n def test_thanks(self):\n url = reverse('cards_thanks')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n","sub_path":"tests/test_contact_views.py","file_name":"test_contact_views.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"91159484","text":"#!/usr/bin/env python3\n\nimport argparse\n\n\nclass CleanVCF:\n \"\"\"Call on Lumpy VCF output to remove irregular BND calls, looks for BND called on different chromosomes,\n could be real variants but difficult to interpret\"\"\"\n\n def __init__(self, input_file, output_file):\n self.input = input_file\n self.output = output_file\n\n def remove_bnd(self):\n out_file = open(self.output, 'w')\n with open(self.input) as vcf:\n line_holder = None\n previous_id = None\n previous_chrom = None\n for line in vcf:\n if '#' in line:\n out_file.write(line)\n else:\n line_split = line.split('\\t')\n sv_info = line_split[7].split(';')\n svtype = sv_info[0].split('=')[1]\n if svtype != 'BND':\n out_file.write(line)\n else:\n if not line_holder:\n previous_chrom = line_split[0]\n previous_id = line_split[2].split('_')[0]\n line_holder = line\n else:\n if previous_chrom == line_split[0] and previous_id == line_split[2].split('_')[0]:\n out_file.write(line_holder)\n out_file.write(line)\n line_holder = None\n\n\nparser = argparse.ArgumentParser(description='Clean Lumpy .vcf to remove excessive BND')\nparser.add_argument('-i', action='store', dest='input',\n help='Lumpy vcf input, file_name only not complete path')\nparser.add_argument('-o', action='store', dest='output',\n help='Clean lumpy output, file_name only not complete path')\nparser.add_argument('-d', action='store', dest='directory',\n help='path to vcf directory')\nargs = parser.parse_args()\n\n\ninput_name = args.input\noutput_name = args.output\ndirectory = args.directory\n\ninput_path = directory + input_name\noutput_path = directory + output_name\n\n\nfuck_you_bnd = CleanVCF(input_file=input_path, output_file=output_path)\nfuck_you_bnd.remove_bnd()\n","sub_path":"bnd_remover.py","file_name":"bnd_remover.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"444079371","text":"#coding=utf-8\n\nimport tornado.web\nfrom bin.base import *\nfrom handler.basehandler import BaseHandler\nfrom bin import base\n\nimport sys\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\n\nclass IndexHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n if not self.current_user:\n self.redirect(\"/login\")\n return\n else:\n id_name = get_id_name(self.current_user) #获取当前用户的id和用户名列表\n name = id_name[1] #用户名\n user_id = id_name[0] #用户id\n\n from optsql.searchMySQL import search_ids_states,search_oneuser_jobexperience,search_oneuser_project_experience\n ids_states_images = search_ids_states(user_id=user_id, table=\"images\") #查询该用户头像的所有id和对应的state\n ids_states_baseinfo = search_ids_states(user_id=user_id, table=\"user_baseinfo\") #查询该用户基本信息的所有id和state\n ids_states_jobexperience = search_oneuser_jobexperience(user_id=user_id) #查询工作经验的id,coperation_name和state\n ids_states_project_info = search_oneuser_project_experience(user_id=user_id)\n\n\n self.render(\"index.html\",\n user=name,\n images_ids=ids_states_images,\n baseinfo_ids=ids_states_baseinfo,\n job_experience=ids_states_jobexperience,\n project_experience=ids_states_project_info,\n )\n\n","sub_path":"handler/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"393924496","text":"import math\nfrom torch.nn import MaxPool2d, ReLU, Sequential, Linear\nfrom torch.nn.modules.batchnorm import BatchNorm2d\nfrom torch.nn.modules.conv import Conv2d\nfrom torch.nn.modules.dropout import Dropout\nfrom torch.nn.modules.module import Module\n\nconfig = {\n 'vgg11': [64, 'm', 128, 'm', 256, 256, 'm', 512, 512, 'm', 512, 512, 'm'],\n 'vgg13': [64, 64, 'm', 128, 128, 'm', 256, 256, 'm', 512, 512, 'm', 512, 512, 'm'],\n 'vgg16': [64, 64, 'm', 128, 128, 'm', 256, 256, 256, 'm', 512, 512, 512, 'm', 512, 512, 512, 'm'],\n 'vgg19': [64, 64, 'm', 128, 128, 'm', 256, 256, 256, 256, 'm', 512, 512, 512, 512, 'm', 512, 512, 512, 512, 'm']\n}\n\n\nclass VggNet(Module):\n def __init__(self, vgg_name):\n super(VggNet, self).__init__()\n self.conv = self.make_layers(config[vgg_name])\n self.classifier = Sequential(\n Dropout(),\n Linear(in_features=512, out_features=512),\n ReLU(inplace=True),\n Dropout(),\n Linear(in_features=512, out_features=512),\n ReLU(inplace=True),\n Linear(in_features=512, out_features=10)\n )\n for m in self.modules():\n if isinstance(m, Conv2d):\n n = m.kernel_size[0]*m.kernel_size[1]*m.out_channels\n m.weight.data.mormal_(0, math.sqrt(2./n))\n m.bias.data.zero_()\n\n def forward(self, x):\n x = self.conv(x)\n x = x.view(x.size(0), -1)\n x = self.classifier(x)\n return x\n\n def make_layers(self, cfg):\n layers = []\n in_channels = 3\n for x in cfg:\n if x == 'm':\n layers.append(MaxPool2d(kernel_size=2, stride=2))\n else:\n layers.append([Conv2d(in_channels=in_channels, out_channels=x, kernel_size=3, padding=1), BatchNorm2d(x), ReLU(inplace=True)])\n in_channels = x\n return Sequential(*layers)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Pytorch_VggNet_impl.py","file_name":"Pytorch_VggNet_impl.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"606992342","text":"#!/usr/bin/python3\r\n# -*- coding: utf-8 -*-\r\n\r\nimport sys\r\nfrom PyQt5.QtWidgets import QMainWindow, QPushButton, QApplication\r\n\r\n\r\nclass Example(QMainWindow):\r\n\r\n def __init__(self):\r\n super().__init__()\r\n\r\n self.initUI()\r\n\r\n\r\n def initUI(self):\r\n\r\n btn1 = QPushButton(\"Button 1\", self)\r\n btn1.move(30, 50)\r\n\r\n btn2 = QPushButton(\"Button 2\", self)\r\n btn2.move(150, 50)\r\n\r\n btn1.clicked.connect(self.buttonClicked)\r\n btn2.clicked.connect(self.buttonClicked)\r\n\r\n self.statusBar()\r\n\r\n self.setGeometry(500, 500, 500, 500)\r\n self.setWindowTitle('Event sender')\r\n self.show()\r\n\r\n\r\n def buttonClicked(self):\r\n\r\n sender = self.sender()\r\n self.drawText(event, qp)\r\n print (\"alex\")\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n app = QApplication(sys.argv)\r\n ex = Example()\r\n sys.exit(app.exec_())\r\n","sub_path":"Python/gui1.py","file_name":"gui1.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"440658667","text":"\n\nclass InsertionSort(object):\n\n def sort(self, data):\n if data is None:\n raise TypeError('data cannot be None')\n if len(data) < 2:\n return data\n for r in range(1, len(data)):\n for l in range(r):\n if data[r] < data[l]:\n temp = data[r]\n data[l+1:r+1] = data[l:r]\n data[l] = temp\n return data\n \n \n \n\n \n \nimport unittest\n\n\nclass TestInsertionSort(unittest.TestCase):\n\n def test_insertion_sort(self):\n insertion_sort = InsertionSort()\n\n print('None input')\n self.assertRaises(TypeError, insertion_sort.sort, None)\n\n print('Empty input')\n self.assertEqual(insertion_sort.sort([]), [])\n\n print('One element')\n self.assertEqual(insertion_sort.sort([5]), [5])\n\n print('Two or more elements')\n data = [5, 1, 7, 2, 6, -3, 5, 7, -1]\n self.assertEqual(insertion_sort.sort(data), sorted(data))\n\n print('Success: test_insertion_sort')\n\n\ndef main():\n test = TestInsertionSort()\n test.test_insertion_sort()\n\n\nif __name__ == '__main__':\n main()\n \n \n \n \n \n","sub_path":"sorting algorithms/insertion_sort_imp.py","file_name":"insertion_sort_imp.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"633722620","text":"#!/usr/bin/env python\n# -*- coding: utf8 -*-\nimport itertools\nimport json\nimport math\nfrom operator import xor\n\nfrom django.db import models\nfrom django.core.urlresolvers import reverse\nfrom django.core.validators import MinValueValidator, MaxValueValidator\n\nfrom reversion import revisions as reversion\nfrom scipy.stats import t\n\nfrom assessment.models import Assessment, BaseEndpoint\nfrom study.models import Study\nfrom utils.models import get_crumbs, get_distinct_charfield_opts\nfrom utils.helper import SerializerHelper, HAWCDjangoJSONEncoder\n\nfrom . import managers\n\n\nclass Criteria(models.Model):\n objects = managers.CriteriaManager()\n\n assessment = models.ForeignKey(\n 'assessment.Assessment')\n description = models.TextField()\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"criterias\"\n\n class Meta:\n ordering = ('description', )\n unique_together = ('assessment', 'description')\n verbose_name_plural = \"Criteria\"\n\n def __str__(self):\n return self.description\n\n def copy_across_assessments(self, cw):\n new_obj, _ = self._meta.model.objects.get_or_create(\n assessment_id=cw[Assessment.COPY_NAME][self.assessment_id],\n description=self.description)\n cw[self.COPY_NAME][self.id] = new_obj.id\n\n\nclass Country(models.Model):\n objects = managers.CountryManager()\n\n code = models.CharField(\n blank=True,\n max_length=2)\n name = models.CharField(\n unique=True,\n max_length=64)\n\n class Meta:\n ordering = ('name', )\n verbose_name_plural = \"Countries\"\n\n def __str__(self):\n return self.name\n\n\nclass AdjustmentFactor(models.Model):\n objects = managers.AdjustmentFactorManager()\n \n assessment = models.ForeignKey(\n 'assessment.Assessment')\n description = models.TextField()\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"factors\"\n\n class Meta:\n unique_together = ('assessment', 'description')\n ordering = ('description', )\n\n def __str__(self):\n return self.description\n\n def copy_across_assessments(self, cw):\n new_obj, _ = self._meta.model.objects.get_or_create(\n assessment_id=cw[Assessment.COPY_NAME][self.assessment_id],\n description=self.description)\n cw[self.COPY_NAME][self.id] = new_obj.id\n\n\nclass Ethnicity(models.Model):\n objects = managers.EthnicityManger()\n\n name = models.CharField(\n max_length=64,\n unique=True)\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n class Meta:\n verbose_name_plural = \"Ethnicities\"\n\n def __str__(self):\n return self.name\n\n\nclass StudyPopulationCriteria(models.Model):\n objects = managers.StudyPopulationCriteriaManager()\n\n CRITERIA_TYPE = (\n (\"I\", \"Inclusion\"),\n (\"E\", \"Exclusion\"),\n (\"C\", \"Confounding\")\n )\n criteria = models.ForeignKey(\n 'Criteria',\n related_name='spcriteria')\n study_population = models.ForeignKey(\n 'StudyPopulation',\n related_name='spcriteria')\n criteria_type = models.CharField(\n max_length=1,\n choices=CRITERIA_TYPE)\n\n COPY_NAME = \"spcriterias\"\n\n def copy_across_assessments(self, cw):\n old_id = self.id\n self.id = None\n self.criteria_id = cw[Criteria.COPY_NAME][self.criteria_id]\n self.study_population_id = cw[StudyPopulation.COPY_NAME][self.study_population_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n\n\nclass StudyPopulation(models.Model):\n objects = managers.StudyPopulationManager()\n\n DESIGN_CHOICES = (\n ('CO', 'Cohort'),\n ('CX', 'Cohort (Retrospective)'),\n ('CY', 'Cohort (Prospective)'),\n ('CC', 'Case-control'),\n ('NC', 'Nested case-control'),\n ('CR', 'Case report'),\n ('SE', 'Case series'),\n ('RT', 'Randomized controlled trial'),\n ('NT', 'Non-randomized controlled trial'),\n ('CS', 'Cross-sectional'),\n )\n\n OUTCOME_GROUP_DESIGNS = (\"CC\", \"NC\")\n\n study = models.ForeignKey(\n 'study.Study',\n related_name=\"study_populations\")\n name = models.CharField(\n max_length=256)\n design = models.CharField(\n max_length=2,\n choices=DESIGN_CHOICES)\n age_profile = models.CharField(\n max_length=128,\n blank=True,\n help_text=\"Age profile of population (ex: adults, children, \"\n \"pregnant women, etc.)\")\n source = models.CharField(\n max_length=128,\n blank=True,\n help_text=\"Population source (ex: general population, environmental \"\n \"exposure, occupational cohort)\")\n country = models.ForeignKey(\n Country)\n region = models.CharField(\n max_length=128,\n blank=True)\n state = models.CharField(\n max_length=128,\n blank=True)\n eligible_n = models.PositiveIntegerField(\n blank=True,\n null=True,\n verbose_name=\"Eligible N\")\n invited_n = models.PositiveIntegerField(\n blank=True,\n null=True,\n verbose_name=\"Invited N\")\n participant_n = models.PositiveIntegerField(\n blank=True,\n null=True,\n verbose_name=\"Participant N\")\n criteria = models.ManyToManyField(\n Criteria,\n through=StudyPopulationCriteria,\n related_name='populations')\n comments = models.TextField(\n blank=True,\n help_text=\"Note matching criteria, etc.\")\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"study_populations\"\n\n @staticmethod\n def flat_complete_header_row():\n return (\n \"sp-id\",\n \"sp-url\",\n \"sp-name\",\n \"sp-design\",\n \"sp-age_profile\",\n \"sp-source\",\n \"sp-country\",\n \"sp-region\",\n \"sp-state\",\n \"sp-eligible_n\",\n \"sp-invited_n\",\n \"sp-participant_n\",\n \"sp-inclusion_criteria\",\n \"sp-exclusion_criteria\",\n \"sp-confounding_criteria\",\n \"sp-comments\",\n \"sp-created\",\n \"sp-last_updated\",\n )\n\n @staticmethod\n def flat_complete_data_row(ser):\n\n def getCriteriaList(lst, filt):\n return '|'.join([\n d['description'] for d in\n [d for d in lst if d['criteria_type'] == filt]\n ])\n\n return (\n ser[\"id\"],\n ser[\"url\"],\n ser[\"name\"],\n ser[\"design\"],\n ser[\"age_profile\"],\n ser[\"source\"],\n ser[\"country\"],\n ser[\"region\"],\n ser[\"state\"],\n ser[\"eligible_n\"],\n ser[\"invited_n\"],\n ser[\"participant_n\"],\n getCriteriaList(ser['criteria'], 'Inclusion'),\n getCriteriaList(ser['criteria'], 'Exclusion'),\n getCriteriaList(ser['criteria'], 'Confounding'),\n ser[\"comments\"],\n ser[\"created\"],\n ser[\"last_updated\"],\n )\n\n class Meta:\n ordering = ('name', )\n\n def get_absolute_url(self):\n return reverse('epi:sp_detail', kwargs={'pk': self.pk})\n\n def get_assessment(self):\n return self.study.get_assessment()\n\n @property\n def inclusion_criteria(self):\n return self.criteria.filter(spcriteria__criteria_type=\"I\")\n\n @property\n def exclusion_criteria(self):\n return self.criteria.filter(spcriteria__criteria_type=\"E\")\n\n @property\n def confounding_criteria(self):\n return self.criteria.filter(spcriteria__criteria_type=\"C\")\n\n def __str__(self):\n return self.name\n\n def get_crumbs(self):\n return get_crumbs(self, self.study)\n\n def can_create_sets(self):\n return self.design not in self.OUTCOME_GROUP_DESIGNS\n\n def copy_across_assessments(self, cw):\n children = list(itertools.chain(\n self.criteria.all(),\n self.spcriteria.all(),\n self.exposures.all(),\n self.comparison_sets.all(),\n self.outcomes.all()))\n old_id = self.id\n self.id = None\n self.study_id = cw[Study.COPY_NAME][self.study_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n for child in children:\n child.copy_across_assessments(cw)\n\n\nclass Outcome(BaseEndpoint):\n objects = managers.OutcomeManager()\n\n TEXT_CLEANUP_FIELDS = (\n 'name',\n 'system',\n 'effect',\n 'effect_subtype',\n )\n\n DIAGNOSTIC_CHOICES = (\n (0, 'not reported'),\n (1, 'medical professional or test'),\n (2, 'medical records'),\n (3, 'self-reported'),\n (4, 'questionnaire'),\n (5, 'hospital admission'),\n (6, 'other'),\n )\n\n study_population = models.ForeignKey(\n StudyPopulation,\n related_name='outcomes')\n system = models.CharField(\n max_length=128,\n blank=True,\n help_text=\"Relevant biological system\")\n effect = models.CharField(\n max_length=128,\n blank=True,\n help_text=\"Effect, using common-vocabulary\")\n effect_subtype = models.CharField(\n max_length=128,\n blank=True,\n help_text=\"Effect subtype, using common-vocabulary\")\n diagnostic = models.PositiveSmallIntegerField(\n choices=DIAGNOSTIC_CHOICES)\n diagnostic_description = models.TextField()\n outcome_n = models.PositiveIntegerField(\n blank=True,\n null=True,\n verbose_name=\"Outcome N\")\n age_of_measurement = models.CharField(\n max_length=32,\n blank=True,\n verbose_name=\"Age at outcome measurement\",\n help_text='Textual age description when outcomes were measured '\n '[examples include: specific age indicated in the study '\n '(e.g., \"3 years of age, 10-12 years of age\") OR standard '\n 'age categories: \"infancy (1-12 months), toddler (1-2 years)'\n ', middle childhood (6-11 years, early adolescence (12-18 '\n 'years), late adolescence (19-21 years), adulthood (>21), '\n 'older adulthood (varies)\" - based on NICHD Integrated '\n 'pediatric terminology]')\n summary = models.TextField(\n blank=True,\n help_text='Summarize main findings of outcome, or describe why no '\n 'details are presented (for example, \"no association '\n '(data not shown)\")')\n\n COPY_NAME = \"outcomes\"\n\n def get_json(self, json_encode=True):\n return SerializerHelper.get_serialized(self, json=json_encode)\n\n @staticmethod\n def get_qs_json(queryset, json_encode=True):\n outcomes = [outcome.get_json(json_encode=False) for outcome in queryset]\n if json_encode:\n return json.dumps(outcomes, cls=HAWCDjangoJSONEncoder)\n else:\n return outcomes\n\n @classmethod\n def delete_caches(cls, ids):\n SerializerHelper.delete_caches(cls, ids)\n\n def get_absolute_url(self):\n return reverse('epi:outcome_detail', kwargs={'pk': self.pk})\n\n def get_crumbs(self):\n return get_crumbs(self, self.study_population)\n\n def can_create_sets(self):\n return not self.study_population.can_create_sets()\n\n @staticmethod\n def flat_complete_header_row():\n return (\n \"outcome-id\",\n \"outcome-url\",\n \"outcome-name\",\n \"outcome-effects\",\n \"outcome-system\",\n \"outcome-effect\",\n \"outcome-effect_subtype\",\n \"outcome-diagnostic\",\n \"outcome-diagnostic_description\",\n \"outcome-age_of_measurement\",\n \"outcome-outcome_n\",\n \"outcome-summary\",\n \"outcome-created\",\n \"outcome-last_updated\",\n )\n\n @staticmethod\n def flat_complete_data_row(ser):\n return (\n ser['id'],\n ser['url'],\n ser['name'],\n '|'.join([str(d['name']) for d in ser['effects']]),\n ser['system'],\n ser['effect'],\n ser['effect_subtype'],\n ser['diagnostic'],\n ser['diagnostic_description'],\n ser['age_of_measurement'],\n ser['outcome_n'],\n ser['summary'],\n ser['created'],\n ser['last_updated'],\n )\n\n def copy_across_assessments(self, cw):\n children = list(itertools.chain(\n self.comparison_sets.all(),\n self.results.all()))\n\n old_id = self.id\n new_assessment_id = cw[Assessment.COPY_NAME][self.assessment_id]\n\n # copy base endpoint\n base = self.baseendpoint_ptr\n base.id = None\n base.assessment_id = new_assessment_id\n base.save()\n\n # copy outcome\n self.id = None\n self.baseendpoint_ptr = base\n self.assessment_id = new_assessment_id\n self.study_population_id = cw[StudyPopulation.COPY_NAME][self.study_population_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n\n # copy tags\n for tag in self.effects.through.objects.filter(baseendpoint_id=old_id):\n tag.id = None\n tag.baseendpoint_id = self.id\n tag.save()\n\n # copy other children\n for child in children:\n child.copy_across_assessments(cw)\n\n\nclass ComparisonSet(models.Model):\n objects = managers.ComparisonSetManager()\n\n study_population = models.ForeignKey(\n StudyPopulation,\n related_name='comparison_sets',\n null=True)\n outcome = models.ForeignKey(\n Outcome,\n related_name='comparison_sets',\n null=True)\n name = models.CharField(\n max_length=256)\n exposure = models.ForeignKey(\n \"Exposure\",\n related_name=\"comparison_sets\",\n help_text=\"Exposure-group associated with this group\",\n blank=True,\n null=True)\n description = models.TextField(\n blank=True)\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"comparison_sets\"\n\n class Meta:\n ordering = ('name', )\n\n def save(self, *args, **kwargs):\n if not xor(self.outcome is None, self.study_population is None):\n raise ValueError(\"An outcome or study-population is required.\")\n super().save(*args, **kwargs)\n\n def get_absolute_url(self):\n return reverse('epi:cs_detail', kwargs={'pk': self.pk})\n\n def get_assessment(self):\n if self.outcome:\n return self.outcome.get_assessment()\n else:\n return self.study_population.get_assessment()\n\n def __str__(self):\n return self.name\n\n def get_crumbs(self):\n if self.outcome:\n return get_crumbs(self, self.outcome)\n else:\n return get_crumbs(self, self.study_population)\n\n @staticmethod\n def flat_complete_header_row():\n return (\n \"cs-id\",\n \"cs-url\",\n \"cs-name\",\n \"cs-description\",\n \"cs-created\",\n \"cs-last_updated\",\n )\n\n @staticmethod\n def flat_complete_data_row(ser):\n return (\n ser[\"id\"],\n ser[\"url\"],\n ser[\"name\"],\n ser[\"description\"],\n ser[\"created\"],\n ser[\"last_updated\"],\n )\n\n def copy_across_assessments(self, cw):\n children = list(self.groups.all())\n old_id = self.id\n self.id = None\n if self.study_population_id:\n self.study_population_id = cw[StudyPopulation.COPY_NAME][self.study_population_id]\n if self.outcome_id:\n self.outcome_id = cw[Outcome.COPY_NAME][self.outcome_id]\n if self.exposure_id:\n self.exposure_id = cw[Exposure.COPY_NAME][self.exposure_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n for child in children:\n child.copy_across_assessments(cw)\n\n\nclass Group(models.Model):\n objects = managers.GroupManager()\n\n SEX_CHOICES = (\n (\"U\", \"Not reported\"),\n (\"M\", \"Male\"),\n (\"F\", \"Female\"),\n (\"B\", \"Male and Female\"))\n\n IS_CONTROL_CHOICES = (\n (True, \"Yes\"),\n (False, \"No\"),\n (None, \"N/A\"),\n )\n\n comparison_set = models.ForeignKey(\n ComparisonSet,\n related_name=\"groups\")\n group_id = models.PositiveSmallIntegerField()\n name = models.CharField(\n max_length=256)\n numeric = models.FloatField(\n verbose_name='Numerical value (sorting)',\n help_text='Numerical value, can be used for sorting',\n blank=True,\n null=True)\n comparative_name = models.CharField(\n max_length=64,\n verbose_name=\"Comparative Name\",\n help_text='Group and value, displayed in plots, for example '\n '\"1.5-2.5(Q2) vs ≤1.5(Q1)\", or if only one group is '\n 'available, \"4.8±0.2 (mean±SEM)\"',\n blank=True)\n sex = models.CharField(\n max_length=1,\n default=\"U\",\n choices=SEX_CHOICES)\n ethnicities = models.ManyToManyField(\n Ethnicity,\n blank=True)\n eligible_n = models.PositiveIntegerField(\n blank=True,\n null=True,\n verbose_name=\"Eligible N\")\n invited_n = models.PositiveIntegerField(\n blank=True,\n null=True,\n verbose_name=\"Invited N\")\n participant_n = models.PositiveIntegerField(\n blank=True,\n null=True,\n verbose_name=\"Participant N\")\n isControl = models.NullBooleanField(\n verbose_name=\"Control?\",\n default=None,\n choices=IS_CONTROL_CHOICES,\n help_text=\"Should this group be interpreted as a null/control group\")\n comments = models.TextField(\n blank=True,\n help_text=\"Any other comments related to this group\")\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"groups\"\n\n class Meta:\n ordering = ('comparison_set', 'group_id', )\n\n def get_absolute_url(self):\n return reverse('epi:g_detail', kwargs={'pk': self.pk})\n\n def get_assessment(self):\n return self.comparison_set.get_assessment()\n\n def __str__(self):\n return self.name\n\n def get_crumbs(self):\n return get_crumbs(self, self.comparison_set)\n\n @staticmethod\n def flat_complete_header_row():\n return (\n \"group-id\",\n \"group-group_id\",\n \"group-name\",\n \"group-numeric\",\n \"group-comparative_name\",\n \"group-sex\",\n \"group-ethnicities\",\n \"group-eligible_n\",\n \"group-invited_n\",\n \"group-participant_n\",\n \"group-isControl\",\n \"group-comments\",\n \"group-created\",\n \"group-last_updated\",\n )\n\n @staticmethod\n def flat_complete_data_row(ser):\n return (\n ser['id'],\n ser['group_id'],\n ser['name'],\n ser['numeric'],\n ser['comparative_name'],\n ser['sex'],\n \"|\".join([d[\"name\"] for d in ser['ethnicities']]),\n ser['eligible_n'],\n ser['invited_n'],\n ser['participant_n'],\n ser['isControl'],\n ser['comments'],\n ser['created'],\n ser['last_updated'],\n )\n\n def copy_across_assessments(self, cw):\n children = list(self.descriptions.all())\n old_id = self.id\n self.id = None\n self.comparison_set_id = cw[ComparisonSet.COPY_NAME][self.comparison_set_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n for child in children:\n child.copy_across_assessments(cw)\n\n\nclass Exposure(models.Model):\n objects = managers.ExposureManager()\n\n ESTIMATE_TYPE_CHOICES = (\n (0, None),\n (1, \"mean\"),\n (2, \"geometric mean\"),\n (3, \"median\"),\n (5, \"point\"),\n (4, \"other\"),\n )\n\n VARIANCE_TYPE_CHOICES = (\n (0, None),\n (1, \"SD\"),\n (2, \"SE\"),\n (3, \"SEM\"),\n (4, \"GSD\"),\n (5, \"other\"))\n\n study_population = models.ForeignKey(\n StudyPopulation,\n related_name='exposures')\n name = models.CharField(\n max_length=128,\n help_text='Name of exposure and exposure-route')\n inhalation = models.BooleanField(\n default=False)\n dermal = models.BooleanField(\n default=False)\n oral = models.BooleanField(\n default=False)\n in_utero = models.BooleanField(\n default=False)\n iv = models.BooleanField(\n default=False,\n verbose_name=\"Intravenous (IV)\")\n unknown_route = models.BooleanField(\n default=False)\n measured = models.CharField(\n max_length=128,\n blank=True,\n verbose_name=\"What was measured\")\n metric = models.CharField(\n max_length=128,\n verbose_name=\"Measurement Metric\")\n metric_units = models.ForeignKey(\n 'assessment.DoseUnits')\n metric_description = models.TextField(\n verbose_name=\"Measurement Description\")\n analytical_method = models.TextField(\n help_text=\"Include details on the lab-techniques for exposure \"\n \"measurement in samples.\")\n sampling_period = models.CharField(\n max_length=128,\n help_text='Exposure sampling period',\n blank=True)\n age_of_exposure = models.CharField(\n max_length=32,\n blank=True,\n help_text='Textual age description for when exposure measurement '\n 'sample was taken, treatment given, or age for which survey '\n 'data apply [examples include: specific age indicated in '\n 'the study (e.g., \"gestational week 20, 3 years of age, '\n '10-12 years of age, previous 12 months\") OR standard age '\n 'categories: \"fetal (in utero), neonatal (0-27 days), '\n 'infancy (1-12 months) toddler (1-2 years), middle '\n 'childhood (6-11 years, early adolescence (12-18 years),'\n 'late adolescence (19-21 years), adulthood (>21),'\n 'older adulthood (varies)\" – based on NICHD Integrated'\n 'pediatric terminology]')\n duration = models.CharField(\n max_length=128,\n blank=True,\n help_text='Exposure duration')\n exposure_distribution = models.CharField(\n max_length=128,\n blank=True,\n help_text='May be used to describe the exposure distribution, for '\n 'example, \"2.05 µg/g creatinine (urine), geometric mean; '\n '25th percentile = 1.18, 75th percentile = 3.33\"')\n n = models.PositiveIntegerField(\n blank=True,\n null=True,\n help_text=\"Individuals where outcome was measured\")\n estimate = models.FloatField(\n blank=True,\n null=True,\n help_text=\"Central tendency estimate\")\n estimate_type = models.PositiveSmallIntegerField(\n choices=ESTIMATE_TYPE_CHOICES,\n verbose_name=\"Central estimate type\",\n default=0)\n variance = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Variance',\n help_text=\"Variance estimate\")\n variance_type = models.PositiveSmallIntegerField(\n choices=VARIANCE_TYPE_CHOICES,\n default=0)\n lower_ci = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Lower CI',\n help_text=\"Numerical value for lower-confidence interval\")\n upper_ci = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Upper CI',\n help_text=\"Numerical value for upper-confidence interval\")\n lower_range = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Lower range',\n help_text='Numerical value for lower range')\n upper_range = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Upper range',\n help_text='Numerical value for upper range')\n description = models.TextField(\n blank=True)\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"exposures\"\n\n class Meta:\n ordering = ('name', )\n verbose_name = \"Exposure\"\n verbose_name_plural = \"Exposures\"\n\n def __str__(self):\n return self.name\n\n @property\n def lower_bound_interval(self):\n return self.lower_range \\\n if self.lower_ci is None \\\n else self.lower_ci\n\n @property\n def upper_bound_interval(self):\n return self.upper_range \\\n if self.upper_ci is None \\\n else self.upper_ci\n\n def get_assessment(self):\n return self.study_population.get_assessment()\n\n def get_absolute_url(self):\n return reverse('epi:exp_detail', kwargs={'pk': self.pk})\n\n def get_crumbs(self):\n return get_crumbs(self, self.study_population)\n\n @staticmethod\n def flat_complete_header_row():\n return (\n \"exposure-id\",\n \"exposure-url\",\n \"exposure-name\",\n \"exposure-inhalation\",\n \"exposure-dermal\",\n \"exposure-oral\",\n \"exposure-in_utero\",\n \"exposure-iv\",\n \"exposure-unknown_route\",\n \"exposure-measured\",\n \"exposure-metric\",\n \"exposure-metric_units_id\",\n \"exposure-metric_units_name\",\n \"exposure-metric_description\",\n \"exposure-analytical_method\",\n \"exposure-sampling_period\",\n \"exposure-age_of_exposure\",\n \"exposure-duration\",\n \"exposure-n\",\n \"exposure-estimate\",\n \"exposure-estimate_type\",\n \"exposure-variance\",\n \"exposure-variance_type\",\n \"exposure-lower_ci\",\n \"exposure-upper_ci\",\n \"exposure-lower_range\",\n \"exposure-upper_range\",\n \"exposure-lower_bound_interval\",\n \"exposure-upper_bound_interval\",\n \"exposure-exposure_distribution\",\n \"exposure-description\",\n \"exposure-created\",\n \"exposure-last_updated\",\n )\n\n @staticmethod\n def flat_complete_data_row(ser):\n if ser is None:\n ser = {}\n units = ser.get(\"metric_units\", {})\n return (\n ser.get(\"id\"),\n ser.get(\"url\"),\n ser.get(\"name\"),\n ser.get(\"inhalation\"),\n ser.get(\"dermal\"),\n ser.get(\"oral\"),\n ser.get(\"in_utero\"),\n ser.get(\"iv\"),\n ser.get(\"unknown_route\"),\n ser.get(\"measured\"),\n ser.get(\"metric\"),\n units.get(\"id\"),\n units.get(\"name\"),\n ser.get(\"metric_description\"),\n ser.get(\"analytical_method\"),\n ser.get(\"sampling_period\"),\n ser.get(\"age_of_exposure\"),\n ser.get(\"duration\"),\n ser.get(\"n\"),\n ser.get(\"estimate\"),\n ser.get(\"estimate_type\"),\n ser.get(\"variance\"),\n ser.get(\"variance_type\"),\n ser.get(\"lower_ci\"),\n ser.get(\"upper_ci\"),\n ser.get(\"lower_range\"),\n ser.get(\"upper_range\"),\n ser.get(\"lower_bound_interval\"),\n ser.get(\"upper_bound_interval\"),\n ser.get(\"exposure_distribution\"),\n ser.get(\"description\"),\n ser.get(\"created\"),\n ser.get(\"last_updated\"),\n )\n\n def copy_across_assessments(self, cw):\n old_id = self.id\n self.id = None\n self.study_population_id = cw[StudyPopulation.COPY_NAME][self.study_population_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n\n\nclass GroupNumericalDescriptions(models.Model):\n objects = managers.GroupNumericalDescriptionsManager()\n\n MEAN_TYPE_CHOICES = (\n (0, None),\n (1, \"mean\"),\n (2, \"geometric mean\"),\n (3, \"median\"),\n (4, \"other\"))\n\n VARIANCE_TYPE_CHOICES = (\n (0, None),\n (1, \"SD\"),\n (2, \"SEM\"),\n (3, \"GSD\"),\n (4, \"other\"))\n\n LOWER_LIMIT_CHOICES = (\n (0, None),\n (1, 'lower limit'),\n (2, '5% CI'),\n (3, 'other'))\n\n UPPER_LIMIT_CHOICES = (\n (0, None),\n (1, 'upper limit'),\n (2, '95% CI'),\n (3, 'other'))\n\n group = models.ForeignKey(\n Group,\n related_name=\"descriptions\")\n description = models.CharField(\n max_length=128,\n help_text=\"Description if numeric ages do not make sense for this \"\n \"study-population (ex: longitudinal studies)\")\n mean = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Central estimate')\n mean_type = models.PositiveSmallIntegerField(\n choices=MEAN_TYPE_CHOICES,\n verbose_name=\"Central estimate type\",\n default=0)\n is_calculated = models.BooleanField(\n default=False,\n help_text=\"Was value calculated/estimated from literature?\")\n variance = models.FloatField(\n blank=True,\n null=True)\n variance_type = models.PositiveSmallIntegerField(\n choices=VARIANCE_TYPE_CHOICES,\n default=0)\n lower = models.FloatField(\n blank=True,\n null=True)\n lower_type = models.PositiveSmallIntegerField(\n choices=LOWER_LIMIT_CHOICES,\n default=0)\n upper = models.FloatField(\n blank=True,\n null=True)\n upper_type = models.PositiveSmallIntegerField(\n choices=UPPER_LIMIT_CHOICES,\n default=0)\n\n COPY_NAME = \"group_descriptions\"\n\n def __str__(self):\n return self.description\n\n def copy_across_assessments(self, cw):\n old_id = self.id\n self.id = None\n self.group_id = cw[Group.COPY_NAME][self.group_id]\n self.save()\n cw[GroupNumericalDescriptions.COPY_NAME][old_id] = self.id\n\n\nclass ResultMetric(models.Model):\n objects = managers.ResultMetricManager()\n\n metric = models.CharField(\n max_length=128,\n unique=True)\n abbreviation = models.CharField(\n max_length=32)\n isLog = models.BooleanField(\n default=True,\n verbose_name=\"Display as log\",\n help_text=\"When plotting, use a log base 10 scale\")\n showForestPlot = models.BooleanField(\n default=True,\n verbose_name=\"Show on forest plot\",\n help_text=\"Does forest-plot representation of result make sense?\")\n reference_value = models.FloatField(\n help_text=\"Null hypothesis value for reference, if applicable\",\n default=1,\n blank=True,\n null=True)\n order = models.PositiveSmallIntegerField(\n help_text=\"Order as they appear in option-list\")\n\n class Meta:\n ordering = ('order', )\n\n def __str__(self):\n return self.metric\n\n\nclass ResultAdjustmentFactor(models.Model):\n objects = managers.ResultAdjustmentFactorManager()\n\n adjustment_factor = models.ForeignKey('AdjustmentFactor',\n related_name='resfactors')\n result = models.ForeignKey('Result',\n related_name='resfactors')\n included_in_final_model = models.BooleanField(default=True)\n\n COPY_NAME = \"rfactors\"\n\n def copy_across_assessments(self, cw):\n old_id = self.id\n self.id = None\n self.adjustment_factor_id = cw[AdjustmentFactor.COPY_NAME][self.adjustment_factor_id]\n self.result_id = cw[Result.COPY_NAME][self.result_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n\n\nclass Result(models.Model):\n objects = managers.ResultManager()\n\n DOSE_RESPONSE_CHOICES = (\n (0, \"not-applicable\"),\n (1, \"monotonic\"),\n (2, \"non-monotonic\"),\n (3, \"no trend\"),\n (4, \"not reported\"))\n\n STATISTICAL_POWER_CHOICES = (\n (0, 'not reported or calculated'),\n (1, 'appears to be adequately powered (sample size met)'),\n (2, 'somewhat underpowered (sample size is 75% to <100% of recommended)'),\n (3, 'underpowered (sample size is 50 to <75% required)'),\n (4, 'severely underpowered (sample size is <50% required)'))\n\n ESTIMATE_TYPE_CHOICES = (\n (0, None),\n (1, \"mean\"),\n (2, \"geometric mean\"),\n (3, \"median\"),\n (5, \"point\"),\n (4, \"other\"),\n )\n\n VARIANCE_TYPE_CHOICES = (\n (0, None),\n (1, \"SD\"),\n (2, \"SE\"),\n (3, \"SEM\"),\n (4, \"GSD\"),\n (5, \"other\"))\n\n name = models.CharField(\n max_length=256)\n outcome = models.ForeignKey(\n Outcome,\n related_name=\"results\")\n comparison_set = models.ForeignKey(\n ComparisonSet,\n related_name=\"results\")\n metric = models.ForeignKey(\n ResultMetric,\n related_name=\"results\",\n help_text=\" \")\n metric_description = models.TextField(\n blank=True,\n help_text=\"Add additional text describing the metric used, if needed.\")\n data_location = models.CharField(\n max_length=128,\n blank=True,\n help_text=\"Details on where the data are found in the literature \"\n \"(ex: Figure 1, Table 2, etc.)\")\n population_description = models.CharField(\n max_length=128,\n help_text='Detailed description of the population being studied for'\n 'this outcome, which may be a subset of the entire'\n 'study-population. For example, \"US (national) NHANES'\n '2003-2008, Hispanic children 6-18 years, ♂♀ (n=797)\"',\n blank=True)\n dose_response = models.PositiveSmallIntegerField(\n verbose_name=\"Dose Response Trend\",\n help_text=\"Was a trend observed?\",\n default=0,\n choices=DOSE_RESPONSE_CHOICES)\n dose_response_details = models.TextField(\n blank=True)\n prevalence_incidence = models.CharField(\n max_length=128,\n verbose_name=\"Overall incidence prevalence\",\n blank=True)\n statistical_power = models.PositiveSmallIntegerField(\n help_text=\"Is the study sufficiently powered?\",\n default=0,\n choices=STATISTICAL_POWER_CHOICES)\n statistical_power_details = models.TextField(\n blank=True)\n statistical_test_results = models.TextField(\n blank=True)\n trend_test = models.CharField(\n verbose_name=\"Trend test result\",\n max_length=128,\n blank=True,\n help_text=\"Enter result, if available (ex: p=0.015, p≤0.05, n.s., etc.)\")\n adjustment_factors = models.ManyToManyField(\n AdjustmentFactor,\n through=ResultAdjustmentFactor,\n related_name='outcomes',\n blank=True)\n estimate_type = models.PositiveSmallIntegerField(\n choices=ESTIMATE_TYPE_CHOICES,\n verbose_name=\"Central estimate type\",\n default=0)\n variance_type = models.PositiveSmallIntegerField(\n choices=VARIANCE_TYPE_CHOICES,\n default=0)\n ci_units = models.FloatField(\n blank=True,\n null=True,\n default=0.95,\n verbose_name='Confidence Interval (CI)',\n help_text='A 95% CI is written as 0.95.')\n comments = models.TextField(\n blank=True,\n help_text='Summarize main findings of outcome, or describe why no '\n 'details are presented (for example, \"no association '\n '(data not shown)\")')\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"results\"\n\n @property\n def factors_applied(self):\n return self.adjustment_factors\\\n .filter(resfactors__included_in_final_model=True)\n\n @property\n def factors_considered(self):\n return self.adjustment_factors\\\n .filter(resfactors__included_in_final_model=False)\n\n def __str__(self):\n return self.name\n\n def get_assessment(self):\n return self.outcome.get_assessment()\n\n def get_absolute_url(self):\n return reverse('epi:result_detail', kwargs={'pk': self.pk})\n\n def get_crumbs(self):\n return get_crumbs(self, self.outcome)\n\n @staticmethod\n def flat_complete_header_row():\n return (\n \"metric-id\",\n \"metric-name\",\n \"metric-abbreviation\",\n \"result-id\",\n \"result-name\",\n \"result-metric_description\",\n \"result-data_location\",\n \"result-population_description\",\n \"result-dose_response\",\n \"result-dose_response_details\",\n \"result-prevalence_incidence\",\n \"result-statistical_power\",\n \"result-statistical_power_details\",\n \"result-statistical_test_results\",\n \"result-trend_test\",\n \"result-adjustment_factors\",\n \"result-adjustment_factors_considered\",\n \"result-estimate_type\",\n \"result-variance_type\",\n \"result-ci_units\",\n \"result-comments\",\n \"result-created\",\n \"result-last_updated\",\n )\n\n @staticmethod\n def flat_complete_data_row(ser):\n\n def getFactorList(lst, isIncluded):\n return '|'.join([\n d['description'] for d in\n [d for d in lst if d['included_in_final_model'] == isIncluded]\n ])\n\n return (\n ser['metric']['id'],\n ser['metric']['metric'],\n ser['metric']['abbreviation'],\n ser['id'],\n ser['name'],\n ser['metric_description'],\n ser['data_location'],\n ser['population_description'],\n ser['dose_response'],\n ser['dose_response_details'],\n ser['prevalence_incidence'],\n ser['statistical_power'],\n ser['statistical_power_details'],\n ser['statistical_test_results'],\n ser['trend_test'],\n getFactorList(ser['factors'], True),\n getFactorList(ser['factors'], False),\n ser['estimate_type'],\n ser['variance_type'],\n ser['ci_units'],\n ser['comments'],\n ser['created'],\n ser['last_updated'],\n )\n\n def copy_across_assessments(self, cw):\n children = list(itertools.chain(\n self.adjustment_factors.all(),\n self.resfactors.all(),\n self.results.all()))\n old_id = self.id\n self.id = None\n self.outcome_id = cw[Outcome.COPY_NAME][self.outcome_id]\n self.comparison_set_id = cw[ComparisonSet.COPY_NAME][self.comparison_set_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n for child in children:\n child.copy_across_assessments(cw)\n\n\nclass GroupResult(models.Model):\n objects = managers.GroupResultManager()\n\n P_VALUE_QUALIFIER_CHOICES = (\n (' ', '-'),\n ('-', 'n.s.'),\n ('<', '<'),\n ('=', '='),\n ('>', '>'),\n )\n\n MAIN_FINDING_CHOICES = (\n (3, \"not-reported\"),\n (2, \"supportive\"),\n (1, \"inconclusive\"),\n (0, \"not-supportive\"))\n\n result = models.ForeignKey(\n Result,\n related_name=\"results\")\n group = models.ForeignKey(\n Group,\n related_name=\"results\")\n n = models.PositiveIntegerField(\n blank=True,\n null=True,\n help_text=\"Individuals in group where outcome was measured\")\n estimate = models.FloatField(\n blank=True,\n null=True,\n help_text=\"Central tendency estimate for group\")\n variance = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Variance',\n help_text=\"Variance estimate for group\")\n lower_ci = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Lower CI',\n help_text=\"Numerical value for lower-confidence interval\")\n upper_ci = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Upper CI',\n help_text=\"Numerical value for upper-confidence interval\")\n lower_range = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Lower range',\n help_text='Numerical value for lower range')\n upper_range = models.FloatField(\n blank=True,\n null=True,\n verbose_name='Upper range',\n help_text='Numerical value for upper range')\n p_value_qualifier = models.CharField(\n max_length=1,\n choices=P_VALUE_QUALIFIER_CHOICES,\n default=\"-\",\n verbose_name='p-value qualifier')\n p_value = models.FloatField(\n blank=True,\n null=True,\n verbose_name='p-value',\n validators=[MinValueValidator(0.), MaxValueValidator(1.)])\n is_main_finding = models.BooleanField(\n blank=True,\n verbose_name=\"Main finding\",\n help_text=\"Is this the main-finding for this outcome?\")\n main_finding_support = models.PositiveSmallIntegerField(\n choices=MAIN_FINDING_CHOICES,\n help_text=\"Are the results supportive of the main-finding?\",\n default=1)\n created = models.DateTimeField(\n auto_now_add=True)\n last_updated = models.DateTimeField(\n auto_now=True)\n\n COPY_NAME = \"groupresults\"\n\n class Meta:\n ordering = ('result', 'group__group_id')\n\n @property\n def p_value_text(self):\n txt = self.get_p_value_qualifier_display()\n if self.p_value is not None:\n if txt in [\"=\", \"-\", \"n.s.\"]:\n txt = \"{0:g}\".format(self.p_value)\n else:\n txt = \"{0}{1:g}\".format(txt, self.p_value)\n\n return txt\n\n @property\n def lower_bound_interval(self):\n return self.lower_range \\\n if self.lower_ci is None \\\n else self.lower_ci\n\n @property\n def upper_bound_interval(self):\n return self.upper_range \\\n if self.upper_ci is None \\\n else self.upper_ci\n\n @staticmethod\n def flat_complete_header_row():\n return (\n \"result_group-id\",\n \"result_group-n\",\n \"result_group-estimate\",\n \"result_group-variance\",\n \"result_group-lower_ci\",\n \"result_group-upper_ci\",\n \"result_group-lower_range\",\n \"result_group-upper_range\",\n \"result_group-lower_bound_interval\",\n \"result_group-upper_bound_interval\",\n \"result_group-p_value_qualifier\",\n \"result_group-p_value\",\n \"result_group-is_main_finding\",\n \"result_group-main_finding_support\",\n \"result_group-created\",\n \"result_group-last_updated\",\n )\n\n @staticmethod\n def flat_complete_data_row(ser):\n return (\n ser[\"id\"],\n ser[\"n\"],\n ser[\"estimate\"],\n ser[\"variance\"],\n ser[\"lower_ci\"],\n ser[\"upper_ci\"],\n ser[\"lower_range\"],\n ser[\"upper_range\"],\n ser[\"lower_bound_interval\"],\n ser[\"upper_bound_interval\"],\n ser[\"p_value_qualifier\"],\n ser[\"p_value\"],\n ser[\"is_main_finding\"],\n ser[\"main_finding_support\"],\n ser[\"created\"],\n ser[\"last_updated\"],\n )\n\n @staticmethod\n def stdev(variance_type, variance, n):\n # calculate stdev given re\n if variance_type == 'SD':\n return variance\n elif variance_type in ['SE', 'SEM'] and variance is not None and n is not None:\n return variance * math.sqrt(n)\n else:\n return None\n\n @classmethod\n def getStdevs(cls, variance_type, rgs):\n for rg in rgs:\n rg['stdev'] = cls.stdev(variance_type, rg['variance'], rg['n'])\n\n @staticmethod\n def getConfidenceIntervals(variance_type, groups):\n \"\"\"\n Expects a dictionary of endpoint groups and the endpoint variance-type.\n Appends results to the dictionary for each endpoint-group.\n\n Confidence interval calculated using a two-tailed t-test,\n assuming 95% confidence interval.\n \"\"\"\n\n for grp in groups:\n lower_ci = grp.get('lower_ci')\n upper_ci = grp.get('upper_ci')\n n = grp.get('n')\n if (\n lower_ci is None and\n upper_ci is None and\n n is not None and\n grp['lower_range'] is None and\n grp['upper_range'] is None and\n grp['estimate'] is not None and\n grp['variance'] is not None\n ):\n est = grp['estimate']\n var = grp['variance']\n z = t.ppf(0.975, max(n-1, 1))\n change = None\n\n if variance_type == 'SD':\n change = z * var / math.sqrt(n)\n elif variance_type in ('SE', 'SEM'):\n change = z * var\n\n if change is not None:\n lower_ci = round(est - change, 2)\n upper_ci = round(est + change, 2)\n\n grp.update(lower_ci=lower_ci, upper_ci=upper_ci, ci_calc=True)\n\n @staticmethod\n def percentControl(estimate_type, variance_type, rgs):\n \"\"\"\n Expects a dictionary of result groups, the result estimate_type, and\n result variance_type. Appends results to the dictionary for each result-group.\n\n Calculates a 95% confidence interval for the percent-difference from\n control, taking into account variance from both groups using a\n Fisher Information Matrix, assuming independent normal distributions.\n\n Only calculates if estimate_type is 'median' or 'mean' and variance_type\n is 'SD', 'SE', or 'SEM', all cases are true with a normal distribution.\n \"\"\"\n def get_control_group(rgs):\n \"\"\"\n - If 0 groups are control=true, the first group will be chosen as control\n - If 1 group is control=true, it will be used as control\n - If ≥2 groups is control=true, a random control group will be chosen as control\n \"\"\"\n control = None\n\n for i, rg in enumerate(rgs):\n if rg['group']['isControl']:\n control = rg\n break\n\n if control is None:\n control = rgs[0]\n\n return control['n'], control['estimate'], control.get('stdev')\n\n if len(rgs) == 0:\n return\n\n n_1, mu_1, sd_1 = get_control_group(rgs)\n\n for i, rg in enumerate(rgs):\n\n mean = low = high = None\n\n if estimate_type in ['median', 'mean'] and \\\n variance_type in ['SD', 'SE', 'SEM']:\n\n n_2 = rg['n']\n mu_2 = rg['estimate']\n sd_2 = rg.get('stdev')\n\n if mu_1 and mu_2 and mu_1 != 0:\n mean = (mu_2 - mu_1) / mu_1 * 100.\n if sd_1 and sd_2 and n_1 and n_2:\n sd = math.sqrt(\n pow(mu_1, -2) * (\n (pow(sd_2, 2) / n_2) +\n (pow(mu_2, 2) * pow(sd_1, 2)) /\n (n_1 * pow(mu_1, 2))\n )\n )\n ci = (1.96 * sd) * 100\n rng = sorted([mean - ci, mean + ci])\n low = rng[0]\n high = rng[1]\n\n rg.update(\n percentControlMean=mean,\n percentControlLow=low,\n percentControlHigh=high\n )\n\n def copy_across_assessments(self, cw):\n old_id = self.id\n self.id = None\n self.result_id = cw[Result.COPY_NAME][self.result_id]\n self.group_id = cw[Group.COPY_NAME][self.group_id]\n self.save()\n cw[self.COPY_NAME][old_id] = self.id\n\n\nreversion.register(Country)\nreversion.register(Criteria)\nreversion.register(Ethnicity)\nreversion.register(StudyPopulationCriteria)\nreversion.register(AdjustmentFactor)\nreversion.register(ResultAdjustmentFactor)\nreversion.register(StudyPopulation, follow=('country', 'spcriteria'))\nreversion.register(ComparisonSet)\nreversion.register(Exposure)\nreversion.register(Outcome, follow=('effects',))\nreversion.register(Group, follow=('ethnicities',))\nreversion.register(Result, follow=('adjustment_factors', 'resfactors', 'results'))\nreversion.register(GroupResult)\n","sub_path":"project/epi/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":48969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"387127436","text":"from urllib.request import urlopen\nimport json\n\n# url=\"http://www.kuaidi100.com/query?type=%s&postid=%s\"\n# type: kuaidigongsi ; postid: kuaididanhao\nurl=\"http://www.kuaidi100.com/query?type=youzhengguonei&postid=9893442769997\"\nr = urlopen(url)\ndata = r.read()\nkd = json.loads(data)\ninfo = kd['data']\n# print(info)\n# print(info[-1])\n# time = info[-1]['time']\n# context = info[-1]['context']\n# print(\"time: %s;context: %s\"%(time,context))\ninfo.reverse()\n# print(info)\nfor info_dict in info:\n print(\"time: %s ; context: %s\"%(info_dict['time'],info_dict['context']))","sub_path":"10p/devops/day04/kuaidi.py","file_name":"kuaidi.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"194154994","text":"import turtle as t\nfrom time import sleep\nt.speed(\"fastest\")\n\n\n\n\ndef left():\n\tt.setheading(180)\n\tt.forward(20)\n\ndef up():\n\tt.setheading(90)\n\tt.forward(20)\n\ndef right():\n\tt.setheading(0)\n\tt.forward(20)\n\ndef down():\n\tt.setheading(270)\n\tt.forward(20)\n\ndef space():\n\tt.pendown()\n\tt.setheading(0)\n\tfor i in range(4):\n\t\tt.fd(40)\n\t\tt.right(90)\n\tt.setheading(0)\n\ndef clear(x,y):\n\tt.reset()\n\tt.speed(\"fastest\")\n\nt.onkeypress(left,\"Left\")\nt.onkeypress(up,\"Up\")\nt.onkeypress(right,\"Right\")\nt.onkeypress(down,\"Down\")\nt.onkey(space,\"space\")\nt.onclick(clear)\nt.listen()\n#sleep(2)\n#t.bye()","sub_path":"Turtle/Drawing.py","file_name":"Drawing.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"619384471","text":"from django.urls import path\nfrom . import views\n\napp_name = 'owner'\n\nurlpatterns = [\n path('my_property/', views.my_property, name='my_property'),\n path('bookmarked/', views.bookmarked, name='bookmarked'),\n path('delete_post/(\\d+)/', views.delete_post, name='delete_post'),\n path('hide_post/(\\d+)/', views.hide_post, name='hide_post'),\n\n]","sub_path":"owner/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"485185762","text":"#########################################\n##Import needed libraries\n#########################################\nfrom selenium import webdriver\nimport codecs #To be able to write the file\n\nCompany = \"Skandia\"\n\n#########################################\n## Settings, create connection to Chrome and set timeout\n#########################################\ndriver_Link = webdriver.Chrome(\"C:\\\\Users\\\\clman\\\\Documents\\\\Funds\\\\chromedriver.exe\")\ndriver_Link.set_page_load_timeout(30)\n\n\n########################################\n# Create empty file so we can write to it later\n########################################\nCompany = codecs.open(r'C:\\Users\\clman\\Documents\\Funds\\Funds_Skandia.txt', 'w', 'utf-8-sig')\ntexttowrite = 'ISIN@Source'\nCompany.write(texttowrite + '\\r\\n')\n\n########################################\n# Get data\n########################################\ndriver_Link.get(\"https://secure.msse.se/skandia/sweden/ska/all/sv/quickrank?showallfunds=True\")\n\nFunds = driver_Link.find_elements_by_xpath(\"//table[@class='table-responsive table-responsive--fondsliste snapper-table']/child::tbody/child::tr/child::td[1]/child::div[2]/child::div/child::a\")\n\nfor Fund in Funds:\n Link = Fund.get_attribute(\"href\")\n ISIN = Link[38:50]\n Source = \"Skandia\"\n\n ###############################################\n # Write to file\n ###############################################\n if len(ISIN) == 12:\n\n Company.write(\"%s\" %ISIN + \"@\" + \"%s\" %Source + '\\r\\n')\n\ndriver_Link.quit()\n","sub_path":"Skandia.py","file_name":"Skandia.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"335156723","text":"# Problem Set 1 / Problem 3\n# 31 August 2016\n# @Dehan Lamprecht\n# for MITx 6.00.1x\n\ndef isinorder(chr1,chr2):\n if ord(chr1) <= ord(chr2):\n return True\n else:\n return False\n\n#s = \"abcdefghijklmnopqrstuvwxyz\"\nlongest1 = \"\"\nlongest2 = \"\"\n\ncount = 0\nstart = 0\nfor i in range(len(s)-1):\n if isinorder(s[i], s[i + 1]):\n count += 1\n else:\n longest2 = s[start:(start+count+1)]\n count = 0\n start = i + 1\n if len(longest2) > len(longest1):\n longest1 = longest2\n\nlongest2 = s[start:start+count+1]\nif len(longest2) > len(longest1):\n longest1 = longest2\nprint(\"Longest substring in alphabetical order is:\", longest1)\n","sub_path":"pset1/Problem 3.py","file_name":"Problem 3.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"95203670","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef flip(state, parameters):\n \"\"\"Coin Transformation C\"\"\"\n new_state = []\n a, b, c, d = parameters\n for ket, amp in state:\n n, coin = ket\n if coin == 0:\n new_state.append(((n, 0), a*amp))\n new_state.append(((n, 1), b*amp))\n elif coin == 1:\n new_state.append(((n, 0), c*amp))\n new_state.append(((n, 1), d*amp))\n return new_state\n\ndef shift(state):\n \"\"\"Shift Transformation S\"\"\"\n new_state = []\n for ket, amp in state:\n n, coin = ket\n if coin == 1:\n new_state.append(((n+1, coin), amp))\n if coin == 0:\n new_state.append(((n-1, coin), amp))\n return new_state\n\ndef simplify(state):\n \"\"\"Combines like terms in the state vector\"\"\"\n kets = []\n new_state = []\n for ket, amp in state:\n if ket not in kets:\n new_amp = sum(a for k, a in state if k == ket)\n kets.append(ket)\n new_state.append((ket, new_amp))\n return new_state\n\n\ndef walk(num_iterations, parameters):\n \"\"\"Performs a quantum random walk for num_iteration steps using parameters\n for the coin transformation\"\"\"\n state = [((0, 0), 1)]\n for i in range(num_iterations):\n state = flip(state, parameters)\n state = simplify(state)\n state = shift(state)\n state = simplify(state)\n return state\n\ndef plot_state(state):\n \"\"\"Plots the state vector\"\"\"\n min_val = min(state, key=lambda ka: ka[0][0])[0][0]\n max_val = max(state, key=lambda ka: ka[0][0])[0][0]\n X = np.arange(min_val, max_val+1)\n Y = np.zeros((len(X)))\n for i, x in enumerate(X):\n for ket, amp in state:\n if ket[0] == x:\n Y[i] += abs(amp)**2\n plt.plot(X,Y)\n plt.ylabel(\"$|\\psi(x)|^2$\")\n plt.xlabel(\"$x$\")\n plt.title(\"Quantum Random Walk\")\n plt.show()\n\ndef main():\n parameters = [2**-.5, 2**-.5, 2**-.5, -2**-.5] # Hadamard Transformation \n state = walk(100, parameters)\n plot_state(state)\n\nif __name__ == '__main__':\n main()\n","sub_path":"Random Walk/RandomWalk.py","file_name":"RandomWalk.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"246472253","text":"from rest_framework import generics\nfrom rest_framework.status import HTTP_400_BAD_REQUEST, HTTP_404_NOT_FOUND\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom .models import Information, Location, Granularity\nfrom .serializers import InformationSerializer\n\nfrom .search_injsons import search_location_data_injson, pt_br\nfrom .generate_jsons import generate_json_files, get_stateid_by_ibgeid\n\nclass SearchDataView(APIView):\n\tdef get(self, request, format=None):\n\t\tparams = request.query_params\n\n\t\tif(\t'information_nickname' in params and\n\t\t\t'location_name' in params and\n\t\t\t'location_type' in params and\n\t\t\t'location_state' in params and\n\t\t\t'in_date_gt' in params and\n\t\t\t'until_date_lte' in params and\n\t\t\t'granularity' in params\n\t\t):\n\t\t\tinformation = params['information_nickname']\n\t\t\tlocation_name = params['location_name']\n\t\t\tlocation_type = params['location_type']\n\t\t\tlocation_state = params['location_state']\n\t\t\tin_date = params['in_date_gt']\n\t\t\tuntil_date = params['until_date_lte']\n\t\t\tgranularity = params['granularity']\n\n\t\t\ttry:\n\t\t\t\tresponse = search_location_data_injson(information, location_name, location_type, location_state, in_date, until_date, granularity)\n\t\t\t\treturn Response(response)\n\t\t\texcept FileNotFoundError:\n\t\t\t\treturn Response({\n\t\t\t\t\t'error': f'no data for location_type={{{location_type}}}, location_name={{{location_name}}} and location_state={{{location_state}}}. check names syntax. must exist in the database.'\n\t\t\t\t}, status=HTTP_404_NOT_FOUND)\n\t\t\texcept ValueError:\n\t\t\t\treturn Response({\n\t\t\t\t\t'error': f'check in_date={{{in_date}}} and until_date={{{until_date}}} syntax. format: yyyy-mm-dd. {{y}}, {{m}} and {{d}} must be 10 base numbers.'\n\t\t\t\t}, status=HTTP_404_NOT_FOUND)\n\t\treturn Response({\n\t\t\t'error': 'must have all query params: {information_nickname}, {location_name}, {location_type}, {location_state}, {in_date_gt}, {until_date_lte}, {granularity}.'\n\t\t}, status=HTTP_400_BAD_REQUEST)\n\nclass InformationView(generics.ListAPIView):\n\tqueryset = Information.objects.all()\n\n\tserializer_class = InformationSerializer\n\nclass LocationView(APIView):\n\tdef get(self, request, format=None):\n\t\tqueryset = Location.objects.all()\n\n\t\tresponse = {}\n\n\t\tfor location in queryset:\n\t\t\tif(not location.location_type in response):\n\t\t\t\tresponse[location.location_type] = []\n\n\t\t\tstateid = get_stateid_by_ibgeid(location.id_ibge)\n\t\t\tuf = Location.objects.filter(id_ibge=stateid)[0].nickname\n\n\t\t\tresponse[location.location_type].append({\n\t\t\t\t'id': location.id_ibge,\n\t\t\t\t'name': location.name,\n\t\t\t\t'state': uf\n\t\t\t})\n\n\t\treturn Response(response)\n\nclass GranularityView(APIView):\n\tdef get(self, request, format=None):\n\t\tqueryset = Granularity.objects.all()\n\n\t\tresponse = []\n\n\t\tfor granularity in queryset:\n\t\t\tresponse.append({\n\t\t\t\t'id': granularity.id,\n\t\t\t\t'granularity': granularity.granularity,\n\t\t\t\t'granularidade': pt_br(granularity.granularity)\n\t\t\t})\n\n\t\treturn Response({\"data\": response})\n\nclass GenerateJsonFilesView(APIView):\n\tdef get(self, request, format=None):\n\t\tgenerate_json_files()\n\t\treturn Response({'success': 'created json files.'})\n","sub_path":"chart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"629054944","text":"import numpy as np\nimport keras\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Input, Flatten, Dropout\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.utils import np_utils\nimport matplotlib.pyplot as plt\nfrom keras.datasets import mnist\nfrom sklearn.model_selection import train_test_split\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nplt.imshow(x_train[0], cmap=plt.get_cmap('gray'))\nprint(y_train[0])\n\ny_cat = keras.utils.to_categorical(y_train, num_classes=None)\ny_test_cat = keras.utils.to_categorical(y_test, num_classes=None)\nx_train1 = x_train.reshape(x_train.shape[0] ,28, 28,1).astype('float32')\nx_test1 = x_test.reshape(x_test.shape[0] ,28, 28,1).astype('float32')\n\n# normalize inputs from 0-255 to 0-1\nx_train1 = x_train1 / 255\nx_test1 = x_test1 / 255\n\n\ninput1=Input(shape=(28,28,1,))\nx1 = Conv2D(32, kernel_size=(5, 5),activation='relu')(input1)\nx1 = MaxPooling2D(pool_size=(2, 2))(x1)\nx1 = Flatten()(x1)\nx1 = Dense(128, activation='relu') (x1)\nx1 = Dense(10, activation='softmax')(x1)\nmodel = Model(inputs=[input1], outputs=x1)\nmodel.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\nmodel.fit(x_train1,y_cat,batch_size=32,epochs=10,validation_data=(x_test1,y_test_cat)) \n\n\n###another\ninput1=Input(shape=(28,28,1,))\nx1 = Conv2D(32, kernel_size=(5, 5),activation='relu')(input1)\nx1 = MaxPooling2D(pool_size=(2, 2))(x1)\nx1 = Conv2D(16, kernel_size=(3, 3),activation='relu')(input1)\nx1 = MaxPooling2D(pool_size=(2, 2))(x1)\nx1 = Flatten()(x1)\nx1 = Dense(128, activation='relu') (x1)\nx1 = Dense(10, activation='softmax')(x1)\nmodel = Model(inputs=[input1], outputs=x1)\nmodel.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])\nmodel.fit(x_train1,y_cat,batch_size=32,epochs=10,validation_data=(x_test1,y_test_cat)) \n\n# save model\n# model.save('/data/video/common/minst_model.h5')","sub_path":"cnn_minst.py","file_name":"cnn_minst.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"243298333","text":"# -*- coding: utf-8 -*-\n\n# 求两个数字的最大公约数（欧几里得算法）\ndef gcd(a, b):\n if b == 0:\n return a\n else:\n return gcd(b, a % b)\n\n'''\n扩展欧几里的算法\n计算 ax + by = 1中的x与y的整数解（a与b互质）\n'''\ndef ext_gcd(a, b):\n if b == 0:\n x1 = 1\n y1 = 0\n x = x1\n y = y1\n r = a\n return r, x, y\n else:\n r, x1, y1 = ext_gcd(b, a % b)\n x = y1\n y = x1 - a // b * y1\n return r, x, y\n\n'''\n超大整数超大次幂然后对超大的整数取模\n(base ^ exponent) mod n\n'''\n\n\ndef exp_mode(base, exponent, n):\n bin_array = bin(exponent)[2:][::-1]\n r = len(bin_array)\n base_array = []\n\n pre_base = base\n base_array.append(pre_base)\n\n for _ in range(r - 1):\n next_base = (pre_base * pre_base) % n\n base_array.append(next_base)\n pre_base = next_base\n\n a_w_b = __multi(base_array, bin_array, n)\n return a_w_b % n\n\n\ndef __multi(array, bin_array, n):\n result = 1\n for index in range(len(array)):\n a = array[index]\n if not int(bin_array[index]):\n continue\n result *= a\n result = result % n # 加快连乘的速度\n return result\n\n\n# 生成公钥私钥，p、q为两个超大质数\ndef gen_key(p, q, e):\n n = p * q\n fy = (p - 1) * (q - 1) # 计算与n互质的整数个数 欧拉函数\n # e = 611 # 选取e\n # generate d\n a = e\n b = fy\n r, x, y = ext_gcd(a, b)\n # 计算出的x不能是负数，如果是负数，说明p、q、e选取失败，不过可以把x加上fy，使x为正数，才能计算。\n if x < 0:\n x = x + fy\n d = x\n # 返回： 公钥 私钥\n return (n, e), (n, d)\n\n\n# 加密 m是被加密的信息 加密成为c\ndef encrypt(m, pubkey):\n n = pubkey[0]\n e = pubkey[1]\n\n c = exp_mode(m, e, n)\n return c\n\n\n# 解密 c是密文，解密为明文m\ndef decrypt(c, selfkey):\n n = selfkey[0]\n d = selfkey[1]\n\n m = exp_mode(c, d, n)\n return m\n\n\ndef RSA_en(pubkey, flag, msg, src_file, des_file):\n # 需要被加密的信息转化成数字，长度小于秘钥n的长度，如果信息长度大于n的长度，那么分段进行加密，分段解密即可。\n if flag == 0:\n fp = open(src_file, \"r\")\n m = fp.read()\n fp.close()\n if flag == 1:\n m = msg\n # print(\"待加密信息-->%s\" % m)\n '''信息加密，m被加密的信息，c是加密后的信息'''\n m = int(m, 16) # 16进制转为十进制\n c = encrypt(m, pubkey)\n print(c.bit_length())\n # 十进制转二进制 高位补0\n c = '{:01024b}'.format(c)\n print(\"被加密后的密文-->%s\" % c)\n\n # 把RSA(MD5(M))写入文件中\n # fp = open(des_file, \"w\")\n # fp.write(c)\n # fp.close()\n\n return c\n # '''信息解密'''\n # d = decrypt(int(c), selfkey)\n # print(\"被解密后的明文-->%s\" % hex(d))\n\ndef RSA_de(selfkey, m):\n '''信息解密'''\n d = decrypt(int(m), selfkey)\n # print(\"被解密后的明文-->%s\" % hex(d))\n return d\n\n\n","sub_path":"algorithm/rsa.py","file_name":"rsa.py","file_ext":"py","file_size_in_byte":3075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"608372837","text":"import asyncio\nimport json\n\nfrom random import randint\n\nimport serial\nfrom serial.tools import list_ports\n\nclass RadioNode():\n \"\"\"\n Node handing Communication to the A-Layer via serial connection\n \"\"\"\n def __init__(self, main_server):\n self.main_server = main_server\n self.json_map = {\n \"c\" : \"occupied\",\n \"n\" : \"quiet\"\n }\n self.connect_serial()\n\n def connect_serial(self):\n '''\n Attempts to make a serial connection to an arduino with a radio comm unit\n '''\n ports = list(list_ports.comports())\n self.ser = None\n # Searches for Arduino name on Windows Systems\n for p in ports:\n if \"Arduino\" in p.description:\n self.ser = serial.Serial(p.device, 9600)\n break\n # Probably not connected or it's running a real OS\n if self.ser is None:\n if ports:\n self.ser = serial.Serial(ports[0].device, 9600)\n\n async def run(self):\n \"\"\"\n Task which collects updates from the serial link and triggers updates\n to a single room's state. This in turn triggers Pi-Layer Syncronization\n \"\"\"\n while True:\n await asyncio.sleep(0)\n\n if self.ser is not None:\n line = self.ser.readline()\n # Creates an update to the state if room update has a valid format\n try:\n room_update = json.loads(line)\n room_id = f\"Room {room_update['r']}\"\n floor_name = self.main_server.config[\"floors\"][0][\"name\"]\n # Build out the state update\n state = {\n room_id: {\n \"dynamic_props\": {}\n }\n }\n\n for key in self.json_map:\n if key in room_update:\n state[room_id][\"dynamic_props\"][self.json_map[key]] = room_update[key]\n await self.main_server.set_room_state(\"Floor 1\", state)\n except (AttributeError, KeyError, UnicodeDecodeError, json.decoder.JSONDecodeError) as e:\n pass\n\n\n async def run_stubbed(self):\n \"\"\"\n Task alternative to actual hardware updates from the A-Layer\n \"\"\"\n while True:\n await asyncio.sleep(0)\n # Randomly selects a floor and then a room on that floor to change the state of\n # Seems messy, but this flow would never normally exist beyond demos\n floor_select = randint(1, len(self.main_server.config[\"floors\"])-1)\n floor_name = self.main_server.config[\"floors\"][floor_select][\"name\"]\n max_room = 0\n for room in self.main_server.config[\"rooms\"]:\n if room[\"static_props\"][\"loc\"][\"floor\"] == floor_name:\n max_room += 1\n fakeState = {\n f\"Room {randint(1, max_room)}\": {\n \"dynamic_props\":{\n \"occupied\": randint(0,1),\n \"quiet\": randint(0,1)\n\n }\n }\n }\n await self.main_server.set_room_state(floor_name, fakeState)\n","sub_path":"rpi_net/rpi_net/radio_node.py","file_name":"radio_node.py","file_ext":"py","file_size_in_byte":3275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"626783339","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"\nThis module contains convenience functions for retrieving solar system\nephemerides from jplephem.\n\"\"\"\n\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nfrom collections import OrderedDict\nimport numpy as np\nfrom .sky_coordinate import SkyCoord\nfrom ..utils.data import download_file\nfrom ..utils.state import ScienceState\nfrom .. import units as u\nfrom ..constants import c as speed_of_light\nfrom .representation import CartesianRepresentation\nfrom .builtin_frames import GCRS, ICRS\nfrom .builtin_frames.utils import get_jd12, cartrepr_from_matmul\nfrom .. import _erfa\nfrom ..extern import six\n\n__all__ = [\"get_body\", \"get_moon\", \"get_body_barycentric\",\n \"SOLAR_SYSTEM_BODIES\", \"kernel_url\"]\n\nKERNEL = None\n\n\"\"\"\neach value in the BODIES dictionary a list of kernel pairs needed\nto find the barycentric position of that object from the JPL kernel.\n\"\"\"\nBODY_NAME_TO_KERNEL_SPEC = OrderedDict(\n (('sun', [(0, 10)]),\n ('mercury', [(0, 1), (1, 199)]),\n ('venus', [(0, 2), (2, 299)]),\n ('earth-moon-barycenter', [(0, 3)]),\n ('earth', [(0, 3), (3, 399)]),\n ('moon', [(0, 3), (3, 301)]),\n ('mars', [(0, 4)]),\n ('jupiter', [(0, 5)]),\n ('saturn', [(0, 6)]),\n ('uranus', [(0, 7)]),\n ('neptune', [(0, 8)]),\n ('pluto', [(0, 9)]))\n )\nSOLAR_SYSTEM_BODIES = tuple(BODY_NAME_TO_KERNEL_SPEC.keys())\n\n_JPL_EPHEM_NOTE = \"\"\"\n This calculation uses JPL Ephemeris SPK filesto calculate the body's\n location, defaulting to DE430. It will be downloaded the first time this\n function is used and cached from then on. To change this, set\n ``kernel_url`` as described in the coordinate documentation.\n\"\"\"[1:-1]\n\nclass kernel_url(ScienceState):\n \"\"\"\n The URL to use for downloading a download the Satellite Planet\n Kernel (SPK) file with ephemerides. The download will *not* occur when\n this state is set, but rather whenever the first time is that the\n kernel actually needs to be used.\n\n Notes\n -----\n The default Satellite Planet Kernel (SPK) file from NASA JPL (DE430) is\n ~120MB, and covers years ~1550-2650 CE [1]_.\n\n .. [1] http://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/planets/aareadme_de430-de431.txt\n \"\"\"\n _value = ('http://naif.jpl.nasa.gov/pub/naif/generic_kernels'\n '/spk/planets/de430.bsp')\n\n @classmethod\n def validate(cls, value):\n try:\n six.moves.urllib.parse.urlparse(value)\n except:\n raise ValueError('{} could not be parsed as a URL'.format(value))\n global KERNEL\n KERNEL = None\n return value\n\n\ndef _get_kernel(*args, **kwargs):\n \"\"\"\n Try importing jplephem, download/retrieve from cache the Satellite Planet\n Kernel.\n \"\"\"\n global KERNEL\n\n try:\n from jplephem.spk import SPK\n except ImportError:\n raise ImportError(\"Solar system ephemeris calculations require the \"\n \"jplephem package \"\n \"(https://pypi.python.org/pypi/jplephem)\")\n\n if KERNEL is None:\n KERNEL = SPK.open(download_file(kernel_url.get(), cache=True))\n return KERNEL\n\n\ndef get_body_barycentric(time, body):\n \"\"\"\n Calculate the barycentric position of the solar system body ``body``\n in cartesian coordinates.\n\n Uses ``jplephem`` with the DE430 kernel.\n\n Parameters\n ----------\n time : `~astropy.time.Time`\n Time of observation\n\n body : str\n The solar system body to calculate.\n\n The allowed values for ``body`` can be found in\n ``astropy.coordinates.SOLAR_SYSTEM_BODIES``.\n\n Returns\n -------\n cartesian_position : `~astropy.coordinates.CartesianRepresentation`\n Barycentric (ICRS) position of the body in cartesian coordinates\n\n Notes\n -----\n \"\"\" +_JPL_EPHEM_NOTE\n\n kernel = _get_kernel()\n kernelspec_chain = BODY_NAME_TO_KERNEL_SPEC[body.lower()]\n\n jd1, jd2 = get_jd12(time, 'tdb')\n\n cartesian_position_body = sum([kernel[pair].compute(jd1, jd2) for pair in kernelspec_chain])\n\n barycen_to_body_vector = u.Quantity(cartesian_position_body, unit=u.km)\n return CartesianRepresentation(barycen_to_body_vector)\n\n\ndef _get_earth_body_vector(time, body, earth_time=None):\n \"\"\"\n Calculate the vector between the Geocenter and body with ``body``.\n\n This routine calculates the vector between the Earth's Geocenter and the body\n specified by ``body``.\n\n Uses ``jplephem`` with the DE430 kernel.\n\n Parameters\n ----------\n time : `~astropy.time.Time`\n Time of observation.\n\n body : str\n The solar system body to calculate.\n\n The allowed values for ``body`` can be found in\n ``astropy.coordinates.SOLAR_SYSTEM_BODIES``.\n\n earth_time : `~astropy.time.Time`\n Time used for position of Earth. When correcting for light travel time,\n one wants to use different times for the body in question and Earth.\n If this is set to ```None```, the same time is used for both.\n\n Returns\n -------\n earth_body_vector : `~astropy.coordinates.CartesianRepresentation`\n Barycentric (ICRS) vector from Geocenter to the body in cartesian coordinates\n\n earth_distance : `~astropy.units.Quantity`\n Distance between Earth and body.\n \"\"\"\n earth_time = earth_time if earth_time is not None else time\n earth_loc = get_body_barycentric(earth_time, 'earth')\n body_loc = get_body_barycentric(time, body)\n\n earth_body_vector = body_loc.xyz - earth_loc.xyz\n\n earth_distance = np.sqrt(np.sum(earth_body_vector**2, axis=0))\n return earth_body_vector, earth_distance\n\n\ndef _get_apparent_body_position(time, body):\n \"\"\"\n Calculate the apparent position of body ``body`` in cartesian\n coordinates, given the approximate light travel time to the object.\n\n Uses ``jplephem`` with the DE430 kernel.\n\n Parameters\n ----------\n time : `~astropy.time.Time`\n Time of observation\n\n body : str\n The solar system body to calculate.\n\n The allowed values for ``body`` can be found in\n ``astropy.coordinates.SOLAR_SYSTEM_BODIES``.\n\n Returns\n -------\n cartesian_position : `~astropy.coordinates.CartesianRepresentation`\n Barycentric (ICRS) apparent position of the body in cartesian coordinates\n \"\"\"\n # Calculate position given approximate light travel time.\n delta_light_travel_time = 20*u.s\n emitted_time = time\n light_travel_time = 0*u.s\n while np.any(np.fabs(delta_light_travel_time) > 1.0e-8*u.s):\n earth_to_body_vector, earth_distance = _get_earth_body_vector(emitted_time,\n body, time)\n delta_light_travel_time = light_travel_time - earth_distance/speed_of_light\n light_travel_time = earth_distance/speed_of_light\n emitted_time = time - light_travel_time\n\n return get_body_barycentric(emitted_time, body)\n\n\ndef get_body(time, body, location=None):\n \"\"\"\n Get a `~astropy.coordinates.SkyCoord` for a body as observed from a\n location on Earth.\n\n Parameters\n ----------\n time : `~astropy.time.Time`\n Time of observation\n\n body : str\n The solar system body to calculate.\n\n The allowed values for ``body`` can be found in\n ``astropy.coordinates.SOLAR_SYSTEM_BODIES``.\n\n location : `~astropy.coordinates.EarthLocation`\n Location of observer on the Earth. If none is supplied, set to\n a Geocentric observer\n\n Returns\n -------\n skycoord : `~astropy.coordinates.SkyCoord`\n Coordinate for the body\n\n Notes\n -----\n \"\"\" +_JPL_EPHEM_NOTE\n cartrep = _get_apparent_body_position(time, body)\n icrs = ICRS(cartrep)\n if location is not None:\n obsgeoloc, obsgeovel = location.get_gcrs_posvel(time)\n gcrs = icrs.transform_to(GCRS(obstime=time,\n obsgeoloc=obsgeoloc,\n obsgeovel=obsgeovel))\n else:\n gcrs = icrs.transform_to(GCRS(obstime=time))\n return SkyCoord(gcrs)\n\n\ndef get_moon(time, location=None):\n \"\"\"\n Get a `~astropy.coordinates.SkyCoord` for the Earth's Moon as observed\n from a location on Earth.\n\n Parameters\n ----------\n time : `~astropy.time.Time`\n Time of observation\n\n location : `~astropy.coordinates.EarthLocation`\n Location of observer on the Earth. If none is supplied, set to\n a Geocentric observer.\n\n Returns\n -------\n skycoord : `~astropy.coordinates.SkyCoord`\n Coordinate for the Moon\n\n\n\n Notes\n -----\n \"\"\" +_JPL_EPHEM_NOTE\n return get_body(time, body='moon', location=location)\n\n\ndef _apparent_position_in_true_coordinates(skycoord):\n \"\"\"\n Convert Skycoord in GCRS frame into one in which RA and Dec\n are defined w.r.t to the true equinox and poles of the Earth\n \"\"\"\n jd1, jd2 = get_jd12(skycoord.obstime, 'tt')\n _, _, _, _, _, _, _, rbpn = _erfa.pn00a(jd1, jd2)\n return SkyCoord(skycoord.frame.realize_frame(cartrepr_from_matmul(rbpn, skycoord)))\n","sub_path":"astropy/coordinates/solar_system.py","file_name":"solar_system.py","file_ext":"py","file_size_in_byte":9614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"432702992","text":"from gestionServicios.models import *\nfrom django import forms\nfrom django.forms.models import modelformset_factory\nfrom django.forms.formsets import formset_factory\nfrom django.forms.formsets import BaseFormSet\n\nfrom gestionServicios.lookups import *\nfrom selectable import forms as sforms\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Submit\n\nfrom django.contrib.auth.models import User\n\nfrom django.contrib.auth.models import Group \nfrom django.forms import ModelForm\n\nfrom crispy_forms.bootstrap import AppendedText, PrependedText, FormActions\nfrom crispy_forms.layout import Layout, Div, Submit, HTML, Button, Row, Field\n\n\nclass BuscadorClienteForm(forms.Form):\n cliente = sforms.AutoCompleteSelectField(\n lookup_class = ClienteLookup,\n label = 'Buscar:',\n required=False, allow_new = False)\n cliente.widget.attrs['placeholder'] = \"Ingrese nombre y/o apellido\"\n\n def __init__(self, *args, **kwargs):\n super(BuscadorClienteForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n self.helper.form_method = 'get'\n\n #self.helper.layout = FormActions(Submit('save_changes', 'Save changes', css_class=\"btn-primary\")))\n\nclass BuscadorPersonaForm(forms.Form):\n persona = sforms.AutoCompleteSelectField(\n lookup_class = PersonaLookup,\n label = 'Buscar:',\n required=False, allow_new = False)\n persona.widget.attrs['placeholder'] = \"Ingrese nombre y/o apellido\"\n\n def __init__(self, *args, **kwargs):\n super(BuscadorPersonaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n self.helper.form_method = 'get'\n\n\nclass BuscadorEmpleadoForm(forms.Form):\n empleado = sforms.AutoCompleteSelectField(\n lookup_class = EmpleadoLookup,\n label = 'Buscar:',\n required=True, allow_new = True)\n empleado.widget.attrs['placeholder'] = \"Ingrese nombre y/o apellido\"\n\n def __init__(self, *args, **kwargs):\n super(BuscadorEmpleadoForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n self.helper.form_method = 'get'\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n \nclass BuscadorTipoDeServicioForm(forms.Form):\n tipoDeServicio = sforms.AutoCompleteSelectField(\n lookup_class = TipoDeServicioLookup,\n label = 'Buscar:',\n required=False, allow_new = True)\n tipoDeServicio.widget.attrs['placeholder'] = \"Ingrese nombre de servicio\"\n\n def __init__(self, *args, **kwargs):\n super(BuscadorTipoDeServicioForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n self.helper.form_method = 'get'\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n\nclass PresupuestoForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super(PresupuestoForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.form_class='form-horizontal'\n self.helper.add_input(Submit('submit', 'Crear'))\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-6'\n\n class Meta:\n model = Presupuesto\n exclude = ['contrato']\n widgets = {\n \"cliente\": forms.HiddenInput()\n }\n\n#--- Written by Bruno ---\nclass DatosTSForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(DatosTSForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n\n class Meta:\n model = TipoDeServicio\n exclude = ['productos', 'creacion','modificacion','baja','alta']\n \n def clean(self):\n return self.cleaned_data\n\n#--- Written by Bruno ---\nclass FrecuenciasForm(forms.ModelForm):\n hora_inicio = forms.TimeField(widget=forms.TimeInput(format='%H:%M %a')) \n hora_fin = forms.TimeField(widget=forms.TimeInput(format='%H:%M %a')) \n class Meta: \n model = Frecuencia\n exclude = ['servicio_contratado']\n\nclass ProductoForm(forms.ModelForm):\n class Meta:\n model = Producto\n\nclass PersonaForm(forms.ModelForm):\n class Meta:\n model = Persona\n\n\nclass TipoDeServicioAltaForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(TipoDeServicioAltaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n \n class Meta:\n model = TipoDeServicio\n exclude = ['alta', 'baja']\n\nclass TipoDeServicioModificaForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(TipoDeServicioModificaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n \n class Meta:\n model = TipoDeServicio\n exclude = ['alta', 'baja','codigo_servicio']\n\n\n\nclass TipoDeServicioBajaForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(TipoDeServicioBajaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n\n class Meta:\n model = TipoDeServicio\n exclude = ['productos','codigo_servicio','nombre','creacion','modificacion','valorM2']\n widgets = {\n \"baja\": forms.DateInput(\n attrs={'class':'datepicker', \n 'date-date-format':'dd-mm-yyyy',\n 'date-date-language':'es'}),\n }\n\n\n\n\nclass ServicioContratadoForm(forms.ModelForm):\n class Meta:\n model = ServicioContratado\n widgets = {\n \"tipo_servicio\": forms.TextInput(),\n \"fin\": forms.DateInput(\n attrs={\n 'class': 'form-control datepicker',\n 'data-date-format': 'dd-mm-yyyy',\n 'data-date-language':'es',\n }),\n }\n fields = (\"tipo_servicio\", \"fin\")\n\n def __init__(self, *largs, **kwargs):\n super(ServicioContratadoForm, self).__init__(*largs, **kwargs)\n self.fields[\"tipo_servicio\"].widget.attrs[\"class\"] = \"form-control\"\n self.fields[\"tipo_servicio\"].widget.attrs[\"disabled\"] = \"true\"\n\nclass ServicioContratadoBaseFormset(BaseFormSet):\n def clean(self):\n return super(ServicioContratadoBaseFormset, self).clean()\n\nServicioContratadoFormset = modelformset_factory(ServicioContratado,\n form=ServicioContratadoForm,\n #formset=ServicioContratadoBaseFormset,\n extra=0)\n\nclass PersonaAltaForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(PersonaAltaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'persona-formulario'\n self.helper.form_class='form-horizontal'\n self.helper.form_method = 'POST'\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-6'\n self.helper.form_tag=False\n \n\n class Meta:\n model = Persona\n exclude = ['user','baja','motivo']\n \n def clean(self):\n return self.cleaned_data\n\nclass ClienteAltaForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(ClienteAltaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-6'\n \n\n class Meta:\n model = Persona\n exclude = ['user','baja','motivo']\n\n \n #NUEVO\n #- La redefinicion del metodo clean de ModelForm hace que se sobreescriba/duplique datos de un cliente cargado cuak!\n #def clean(self):\n # return self.cleaned_data\n\nclass ClienteModificarForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(ClienteModificarForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n\n class Meta:\n model = Persona\n exclude = ['tipo_documento', 'nro_documento', 'user','baja', 'motivo']\n \n def clean(self):\n return self.cleaned_data\n\nclass EmpleadoAltaForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(EmpleadoAltaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n #self.helper.form_method = 'post'\n #self.helper.add_input(Submit('submit', 'Confirmar'))\n #self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-6'\n self.helper.form_tag=False\n\n class Meta:\n model = Empleado\n exclude = ['persona','baja','motivo','especialidad']\n\n\nclass EmpleadoModificarForm(forms.ModelForm):\n cuil = forms.IntegerField()\n #fecha_nacimiento = forms.DateTimeField()\n #tipo_de_servicio = forms.ModelChoiceField(queryset = TipoDeServicio.objects.all())\n\n def __init__(self, *args, **kwargs):\n super(EmpleadoModificarForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-6'\n\n class Meta:\n model = Persona\n exclude = ['tipo_documento', 'nro_documento','especialidad','baja','motivo','user']\n\n\n\nclass EmpleadoBajaForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super(EmpleadoBajaForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-1'\n self.helper.field_class='col-lg-6'\n\n class Meta:\n model = Persona\n fields = ('baja', 'motivo')\n widgets = {\n \"baja\": forms.DateInput(\n attrs={'class':'datepicker', \n 'date-date-format':'dd-mm-yyyy',\n 'date-date-language':'es'}),\n }\n\nclass TurnosForm(forms.ModelForm):\n class Meta:\n model = Turno\n widgets = {\n \"hora_inicio\": forms.DateInput(attrs={ 'class': 'form-control timepicker'}),\n \"hora_fin\": forms.DateInput(attrs={ 'class': 'form-control timepicker'}),\n }\n fields = (\"hora_inicio\", \"hora_fin\") \n\n def __init__(self, *largs, **kwargs):\n super(TurnosForm, self).__init__(*largs, **kwargs)\n self.fields[\"hora_inicio\"].widget.attrs[\"timepicker\"] = 'tabindex:2'\n self.fields[\"hora_fin\"].widget.attrs[\"timepicker\"] = 'tabindex:2'\n\nclass TurnoForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super(TurnoForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'formulario'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Confirmar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-6'\n \n class Meta:\n model = Turno\n \n\nclass RegisterForm(forms.Form):\n username=forms.CharField(label=\"nombre de usuario\", widget=forms.TextInput())\n password_uno= forms.CharField(label=\"Password\", widget=forms.PasswordInput(render_value=False))\n password_dos= forms.CharField(label=\"Confirmar Password\", widget=forms.PasswordInput(render_value=False))\n grupo=forms.ModelChoiceField(queryset=Group.objects.all())\n\n def clean_username(self):\n username=self.cleaned_data[\"username\"]\n try:\n u=User.objects.get(username=username)\n except User.DoesNotExist:\n return username\n raise forms.ValidationError(\"usuario ya existe\")\n\n\n def clean_password_dos(self):\n password_uno=self.cleaned_data[\"password_uno\"]\n password_dos=self.cleaned_data[\"password_dos\"]\n if password_uno == password_dos:\n pass\n else:\n raise forms.ValidationError(\"claves iguales\")\n\n\n def __init__(self, *args, **kwargs):\n super(RegisterForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'form'\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Registrar'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-4'\n\n#NUEVO\nclass ContratoForm(forms.ModelForm):\n\n class Meta:\n model=Contrato\n exclude = ['creacion', 'baja', 'motivo', 'fecha_fin_real']\n\n def __init__(self, *args, **kwargs):\n super(ContratoForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'form'\n #self.helper.form_method = 'post'\n #self.helper.add_input(Submit('submit', 'Crear'))\n self.helper.form_class='form-horizontal'\n self.helper.label_class='col-lg-2'\n self.helper.field_class='col-lg-4'","sub_path":"Final_Version/gestionServicios/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":14842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"430459808","text":"\"\"\"Convert :term:`Fastq` format to :term:`Fastq` formats\"\"\"\nfrom bioconvert import ConvBase\nfrom pysam import FastxFile\n\n__all__ = [\"Fasta2Fastq\"]\n\n\nclass Fasta2Fastq(ConvBase):\n \"\"\"\n\n \"\"\"\n input_ext = ['.fa', '.fas', '.fasta']\n output_ext = ['.fastq', 'fq']\n\n def __init__(self, infile, outfile):\n \"\"\"\n :param str infile: The path to the input FASTA file\n :param str outfile: The path to the output FASTQ file\n \"\"\"\n super().__init__(infile, outfile)\n self._default_method = \"v1\"\n\n def _method_v1(self, *args, **kwargs):\n\n with open(self.outfile, 'w') as fastq_out:\n \n for seq in FastxFile(self.infile):\n fastq_out.write(\"@{0} {1}\\n{2}\\n+\\n{3}\\n\".format(seq.name,\n seq.comment,\n seq.sequence,\n len(seq.sequence) * \"I\"))\n\n \n","sub_path":"bioconvert/fasta2fastq.py","file_name":"fasta2fastq.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"437511900","text":"import pickle\nfrom torch import nn\nfrom torch.nn.utils.rnn import pad_sequence\nfrom torch.utils.data import DataLoader, Dataset\nimport torch\nimport nltk\nimport numpy as np\nimport pandas as pd\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nprint('Using {} device'.format(device))\n\n\nclass RNN(nn.Module):\n def __init__(self, vocab_size, emb_size, padding_idx, output_size, hidden_size):\n super().__init__()\n self.hidden_size = hidden_size\n self.emb = nn.Embedding(vocab_size, emb_size, padding_idx=padding_idx)\n self.rnn = nn.RNN(emb_size, hidden_size, batch_first=True)\n self.fc = nn.Linear(hidden_size, output_size)\n\n def forward(self, x):\n self.batch_size = x.size()[0]\n hidden = self.init_hidden()\n emb = self.emb(x)\n out, hidden = self.rnn(emb, hidden)\n out = self.fc(out[:, -1, :])\n return out\n\n def init_hidden(self):\n hidden = torch.zeros(1, self.batch_size, self.hidden_size)\n return hidden\n\n\nclass CreateDataset(Dataset):\n def __init__(self, X, y):\n self.X = X\n self.y = y\n\n def __len__(self):\n return len(self.X)\n\n def __getitem__(self, index):\n return torch.tensor(self.X[index]), torch.tensor(self.y[index])\n\n\nwith open('chapter09/models/word_to_id.pickle', 'rb') as f:\n word_to_id = pickle.load(f)\n\n\ndef tokenize(text, word_to_id=word_to_id):\n words = nltk.word_tokenize(text)\n return [word_to_id.get(word, 0) for word in words]\n\n\ndef load_dataset(filename):\n with open(f\"chapter06/data/processed/{filename}.txt\") as f:\n df_train = pd.read_csv(f, sep='\\t', header=None)\n df_train.columns = [\"TITLE\", \"CATEGORY\"]\n\n X = [np.array(tokenize(title)) for title in df_train[\"TITLE\"]]\n y = np.array(df_train[\"CATEGORY\"])\n return CreateDataset(X, y)\n\n\ndef train_model(dataset, model, loss_fn, optimizer, batch_size):\n size = len(dataset)\n dataloader = DataLoader(dataset, batch_size=batch_size)\n\n for batch, (X, y) in enumerate(dataloader):\n pred = model(X)\n loss = loss_fn(pred, y)\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n \ndef eval_model(dataset, model, loss_fn):\n size = len(dataset)\n dataloader = DataLoader(dataset, batch_size=1)\n\n loss, correct = 0, 0\n with torch.no_grad():\n for X, y in dataloader:\n pred = model(X)\n loss += loss_fn(pred, y).item()\n correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n print(f\"loss: {loss / size:>7f} accuracy: {correct / size:>7f}\")\n\n\nVOCAB_SIZE = len(set(word_to_id.values())) + 1\nEMB_SIZE = 300\nPADDING_IDX = VOCAB_SIZE - 1\nOUTPUT_SIZE = 4\nHIDDEN_SIZE = 50\n\nmodel = RNN(VOCAB_SIZE, EMB_SIZE, PADDING_IDX, OUTPUT_SIZE, HIDDEN_SIZE).to(device)\n\n\ntrain_dataset = load_dataset(\"train\")\nvalid_dataset = load_dataset(\"valid\")\n\nlearning_rate = 1e-3\nloss_fn = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n\nbatch_size = 1\nepochs = 10\nfor t in range(epochs):\n print(f\"Epoch {t + 1}\\n-------------------------------\")\n train_model(train_dataset, model, loss_fn, optimizer, batch_size)\n eval_model(train_dataset, model, loss_fn)\n eval_model(valid_dataset, model, loss_fn)\nprint(\"Done!\")\n","sub_path":"chapter09/82.py","file_name":"82.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"28601551","text":"from bottle import route, run, template, static_file, request\nimport os\nimport main\n# import main_local\n\n\ntemplate = \"\"\"\nHome \n\nUpload a file \n\n\n\"\"\"\n\n\n@route('/')\ndef index():\n return template\n\n@route('/upload')\ndef index():\n redirect('/')\n\n@route('/upload', method='POST')\ndef do_upload():\n upload = request.files.get('upload1')\n upload2 = request.files.get('upload2')\n name, ext = os.path.splitext(upload.filename)\n if ext not in ('.png', '.jpg', '.jpeg', '.tif'):\n return \"File extension not allowed.\"\n\n name2, ext2 = os.path.splitext(upload2.filename)\n if ext2 not in ('.png', '.jpg', '.jpeg', '.tif'):\n return \"File extension not allowed.\"\n\n\n\n save_path = \"/tmp/\"\n\n file_path = \"{path}/{file}\".format(path=save_path, file=upload.filename)\n upload.save(file_path)\n\n file_path2 = \"{path}/{file}\".format(path=save_path, file=upload2.filename)\n upload2.save(file_path2)\n\n data = main.start(file_path, file_path2)\t\n # main_local.start(file_path, file_path2)\t\n\n os.remove(file_path)\n os.remove(file_path2)\n\n return data\n\n\nrun(host='0.0.0.0', port=8080)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"439495773","text":"import time\nfrom subprocess import call\nimport numpy as np\nimport math\nimport random\nimport vrep\nfrom keras.models import load_model\nfrom segmentation import segmentation\n\n\ndef remove_clipping(xyz):\n index = []\n for pts in range(0, len(xyz)):\n # calculate x index\n x = xyz[pts][0]\n y = xyz[pts][1]\n z = xyz[pts][2]\n # 0,-0.39098,0.13889\n if calculate_distance(x, y, z, 0, -0.5910, 0.7389) > 1.5:\n index.append(pts)\n xyz = np.delete(xyz, index, axis=0)\n return xyz\n\n\ndef insertHeader(filename):\n num_lines = sum(1 for line in open(filename))\n title = \"# .PCD v0.7 - Point Cloud Data file format\\n\"\n version = \"VERSION 0.7\\n\"\n fields = \"FIELDS x y z\\n\"\n size = \"SIZE 4 4 4\\n\"\n type = \"TYPE F F F\\n\"\n count = \"COUNT 1 1 1\\n\"\n width = \"WIDTH \" + str(num_lines) + \"\\n\"\n height = \"HEIGHT 1\\n\"\n viewpoint = \"VIEWPOINT 0 0 0 0 1 0 0\\n\"\n points = \"POINTS \" + str(num_lines) + \"\\n\"\n d_type = \"DATA ascii\\n\"\n pcd_header = [title, version, fields, size, type, count, width, height, viewpoint, points, d_type]\n f = open(filename, \"r\")\n contents = f.readlines()\n f.close()\n for i in range(len(pcd_header)):\n contents.insert(i, pcd_header[i])\n f = open(filename, \"w\")\n f.writelines(contents)\n f.close()\n\n\ndef calculate_distance(x1, y1, z1, x2, y2, z2):\n dist = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2 + (z2 - z1) ** 2)\n return dist\n\n\ndef setup_sim_camera(cid):\n '''\n Fetch pcd data and object matrix from V-RAP and transform to world frame\n :return: Raw pcd data\n '''\n # Get handle to camera\n sim_ret, cam_handle = vrep.simxGetObjectHandle(cid, 'kinect_depth', vrep.simx_opmode_blocking)\n emptyBuff = bytearray()\n res, retInts, retFloats, retStrings, retBuffer = vrep.simxCallScriptFunction(cid, 'kinect',\n vrep.sim_scripttype_childscript,\n 'absposition', [], [], [], emptyBuff,\n vrep.simx_opmode_blocking)\n R = np.asarray([[retFloats[0], retFloats[1], retFloats[2], retFloats[3]],\n [retFloats[4], retFloats[5], retFloats[6], retFloats[7]],\n [retFloats[8], retFloats[9], retFloats[10], retFloats[11]]])\n print('camera pose is: ',R)\n result, state, data = vrep.simxReadVisionSensor(cid, cam_handle, vrep.simx_opmode_blocking)\n data = data[1]\n pcl = []\n for i in range(2, len(data), 4):\n p = [data[i], data[i + 1], data[i + 2], 1]\n pcl.append(np.matmul(R, p))\n return pcl\n\n\nclass GraspPoseGeneration(object):\n def __init__(self, pcd_path):\n self.path = pcd_path\n self.bottom = []\n self.surface = []\n self.axis = []\n self.approach = []\n self.binormal = []\n self.alpha = []\n self.beta = []\n self.gamma = []\n self.n_samples = 0\n self.rotm = []\n\n def generate_candidates(self):\n generate_candidates = '/home/lou00015/cnn3d/gpg/build/generate_candidates'\n config = '/home/lou00015/cnn3d/gpg/cfg/params.cfg'\n call([generate_candidates, config, self.path])\n time.sleep(5)\n f = open('candidates', 'r')\n data = f.readlines()\n nb_grasps = len(data)\n # candidates << vectorToString(hands[i].getGraspBottom()) << vectorToString(hands[i].getGraspSurface())\n # << vectorToString(hands[i].getAxis()) << vectorToString(hands[i].getApproach())\n # << vectorToString(hands[i].getBinormal()) << boost::lexical_cast(hands[i].getGraspWidth()) << \"\\n\";\n for i in range(nb_grasps):\n tmp_str = str.split(data[i], ',')\n tmp_float = [float(j) for j in tmp_str]\n tmp_apr = tmp_float[9:12]\n angle = np.dot(tmp_apr, [0, 0, 1])\n if angle < -0.2:\n self.bottom.append(tmp_float[0:3])\n self.surface.append(tmp_float[3:6])\n self.axis.append(tmp_float[6:9])\n self.approach.append(tmp_float[9:12])\n self.binormal.append(tmp_float[12:15])\n rotm = np.asarray([[tmp_float[12], tmp_float[6], tmp_float[9]],\n [tmp_float[13], tmp_float[7], tmp_float[10]],\n [tmp_float[14], tmp_float[8], tmp_float[11]]])\n self.rotm.append(rotm)\n self.n_samples = len(self.surface)\n\n\ndef add_three_objects(cid):\n object_name_list = []\n object_handle_list = []\n object_number = 3\n object_list = ['object_0','object_1','object_2','object_3','object_4','object_5']\n for i in range(object_number):\n object_name = random.choice(object_list)\n object_list.remove(object_name)\n object_name_list.append(object_name)\n print('Adding %s'%object_name)\n res, object_handle = vrep.simxGetObjectHandle(cid, object_name, vrep.simx_opmode_oneshot_wait)\n object_handle_list.append(object_handle)\n object_pos = [0,0,0.5]\n a = random.uniform(-90, 90)\n b = random.uniform(-90, 90)\n g = random.uniform(-90, 90)\n object_angle = [a,b,g]\n vrep.simxSetObjectPosition(cid,object_handle,-1,object_pos,vrep.simx_opmode_oneshot_wait)\n vrep.simxSetObjectOrientation(cid,object_handle,-1,object_angle,vrep.simx_opmode_oneshot_wait)\n return object_name_list, object_handle_list\n\n\ndef transform(pointcloud):\n voxel_grid = np.zeros((32, 32, 32), dtype=int)\n VOXEL_SIZE = 0.3/32\n for i in range(0, len(pointcloud)):\n x = 0\n y = 0\n z = 0\n for x_n in range(32):\n vg_min = -0.15+x_n*VOXEL_SIZE\n vg_max = vg_min+VOXEL_SIZE\n if vg_min= startup_samples:\n samples.append(action)\n\n return samples\n\n\ndef main():\n times = np.arange(0, 1, 0.001)\n amplitude = 30**2\n freq = 1\n cpg_values_1 = np.array(get_cpg_values(amplitude, 0, freq * 2 * np.pi, 0.1))\n cpg_values_2 = np.array(get_cpg_values(amplitude, 0, freq * 2 * np.pi, 0.3))\n cpg_values_3 = np.array(get_cpg_values(amplitude, 0, freq * 2 * np.pi, 0.5))\n cpg_values_4 = np.array(get_cpg_values(amplitude, 0, freq * 2 * np.pi, 0.7))\n cpg_values_5 = np.array(get_cpg_values(amplitude, 0, freq * 2 * np.pi, 0.9))\n\n # row and column sharing\n # f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')\n # ax1.plot(times, cpg_values_1, color='red', linewidth='1.5')\n #\n # ax1.set_ylim(cpg_values_1.min()*1.1, cpg_values_1.max()*1.1)\n #\n # ax1.set_title('Influence of duty factor on CPG shape')\n # ax2.plot(times, cpg_values_2, color='green', linewidth='1.5')\n # ax3.plot(times, cpg_values_3, color='blue', linewidth='1.5')\n # ax4.plot(times, cpg_values_4, color='orange', linewidth='1.5')\n\n plt.figure(figsize=(6.5,5))\n\n ax = plt.gca()\n\n ax.yaxis.set_ticks_position('left') # this one is optional but I still recommend it...\n ax.xaxis.set_ticks_position('bottom')\n\n # plt.plot(times, cpg_values_1, color='purple', linewidth='1.5', label='d=0.1')\n # plt.plot(times, cpg_values_2, color='red', linewidth='1.5', label='d=0.3')\n # plt.plot(times, cpg_values_3, color='green', linewidth='1.5', label='d=0.5')\n # plt.plot(times, cpg_values_4, color='blue', linewidth='1.5', label='d=0.7')\n # plt.plot(times, cpg_values_5, color='orange', linewidth='1.5', label='d=0.9')\n\n plt.plot(times, cpg_values_1, label='d=0.1')\n plt.plot(times, cpg_values_2, label='d=0.3')\n plt.plot(times, cpg_values_3, label='d=0.5')\n plt.plot(times, cpg_values_4, label='d=0.7')\n plt.plot(times, cpg_values_5, label='d=0.9')\n\n plt.ylim(cpg_values_1.min()*1.1, cpg_values_1.max()*1.1)\n\n plt.xlabel('Time (s)')\n plt.ylabel('Motor position (degrees)')\n\n plt.legend(loc='upper left', frameon=False)\n\n plt.savefig('/Users/Siebe/Dropbox/Thesis/writing/figures/cpg_duty_factor.pgf')\n # plt.show()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"src/experiments/plot_code/cpg_duty_factor.py","file_name":"cpg_duty_factor.py","file_ext":"py","file_size_in_byte":3083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"194607696","text":"def is_prime(x):\n if x==1:\n return False\n if x==3 or x==2:\n return True \n if not x % 2:\n return False\n if (not x%6==1) and (not x%6==5):\n return False\n ub = int(x**.5)\n f = 5\n while f <= ub:\n if not x % f:\n return False\n elif not x % (f+2):\n return False\n f += 6\n return True\n\nprimes = []\nfor i in range(2, 50000): ## all primes smaller than 1,000,000\n if is_prime(i):\n primes.append(i) \n\nmax_len = 0\nmax_p = 0\nstart = 0\nnum_p = 4000 ## there are about 50,000 primes below 500,000\nfor i in range(0, num_p):\n for j in range(i, num_p):\n sum_p = sum(primes[i:(j+1)])\n if is_prime(sum_p) and (sum_p<1000000) and (j+1-i) > max_len:\n max_len=(j+1-i)\n max_p = sum_p\n start = primes[i]\n print(i)\n \n \n","sub_path":"python/Q50.py","file_name":"Q50.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"304188280","text":"import socketserver\nimport os\n\nclass MyTCPHandler(socketserver.BaseRequestHandler):\n \"\"\"\n The RequestHandler class for our server.\n\n It is instantiated once per connection to the server, and must\n override the handle() method to implement communication to the\n client.\n \"\"\"\n\n def handle(self):\n # self.request is the TCP socket connected to the client\n self.data = self.request.recv(1024).strip()\n print (\"{} wrote:\"+format(self.client_address))\n print (self.data)\n resp = str(self.data)\n\n\n if 'furhat' in resp:\n resp = resp.split(',')\n f = open(\"furhat.txt\", \"w\")\n f.write(resp[1])\n f.close()\n f = open(\"resp.txt\", \"r\")\n res = str(f.read())\n print(res)\n if res == \"\":\n a = \"there is no message\"\n self.request.sendall(a.encode())\n else:\n self.request.sendall(res.encode())\n\n if 'test' in resp:\n f = open(\"resp.txt\", \"r\")\n res = str(f.read())\n print(res)\n if res == \"\":\n a = \"there is no message\"\n self.request.sendall(a.encode())\n else:\n f = open(\"resp.txt\", \"w\")\n f.write(\"\")\n f.close()\n self.request.sendall(res.encode())\n\n #if 'asr' in resp:\n #open and read the file after the appending:\n #f = open(\"furhat.txt\", \"r\")\n\n #res = str(f.read())\n #print(res)\n #f = open(\"furhat.txt\", \"w\")\n #f.write(\"\")\n #f.close()\n #self.request.sendall(res.encode())\n\n #if 'from the bot' in resp:\n # print(resp)\n # resp = (resp.decode('utf-8'))\n # resp = resp.split('+')\n # print(resp[1])\n # f = open(\"resp.txt\", \"w\")\n # f.write(resp[1])\n # f.close()\n # self.request.sendall(resp[1].encode())\n\n\n\n\nif __name__ == \"__main__\":\n HOST, PORT = \"localhost\", 9999\n\n # Create the server, binding to localhost on port 9999\n server = socketserver.TCPServer((HOST, PORT), MyTCPHandler)\n\n # Activate the server; this will keep running until you\n # interrupt the program with Ctrl-C\n server.serve_forever()\n","sub_path":"bot/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"247297255","text":"# uses the previous homework written by Zhiwang Wang\nimport math, random\n\nrandom.seed(0)\n\n## ================================================================\n\n# calculate a random number a <= rand < b\ndef rand(a, b):\n return (b - a) * random.random() + a\n\n\ndef make_matrix(I, J, fill=0.0):\n m = []\n for i in range(I):\n m.append([fill] * J)\n return m\n\n\ndef sigmoid(x):\n return math.tanh(x)\n\n\n# derivative of our sigmoid function, in terms of the output (i.e. y)\ndef dsigmoid(y):\n return 1.0 - y ** 2\n\n\n## ================================================================\n\nclass NeuralNetwork:\n def __init__(self, inputNodes, hiddenNodes, outputNodes):\n # number of input, hidden, and output nodes\n self.inputNodes = inputNodes + 1 # +1 for bias node\n self.hiddenNodes = hiddenNodes\n self.outputNodes = outputNodes\n\n # activations for nodes\n self.inputActivation = [1.0] * self.inputNodes\n self.hiddenActivation = [1.0] * self.hiddenNodes\n self.outputActivation = [1.0] * self.outputNodes\n\n # create weights\n self.inputWeight = make_matrix(self.inputNodes, self.hiddenNodes)\n self.outputWeight = make_matrix(self.hiddenNodes, self.outputNodes)\n # set them to random vaules\n for i in range(self.inputNodes):\n for j in range(self.hiddenNodes):\n self.inputWeight[i][j] = rand(-0.2, 0.2)\n for j in range(self.hiddenNodes):\n for k in range(self.outputNodes):\n self.outputWeight[j][k] = rand(-2.0, 2.0)\n\n # last change in weights for momentum\n self.ci = make_matrix(self.inputNodes, self.hiddenNodes)\n self.co = make_matrix(self.hiddenNodes, self.outputNodes)\n\n def update(self, inputs):\n if len(inputs) != self.inputNodes - 1:\n raise ValueError('wrong number of inputs')\n\n # input activations\n for i in range(self.inputNodes - 1):\n self.inputActivation[i] = inputs[i]\n\n # hidden activations\n for j in range(self.hiddenNodes):\n sum = 0.0\n for i in range(self.inputNodes):\n sum = sum + self.inputActivation[i] * self.inputWeight[i][j]\n self.hiddenActivation[j] = sigmoid(sum)\n\n # output activations\n for k in range(self.outputNodes):\n sum = 0.0\n for j in range(self.hiddenNodes):\n sum = sum + self.hiddenActivation[j] * self.outputWeight[j][k]\n self.outputActivation[k] = sigmoid(sum)\n\n return self.outputActivation[:]\n\n def backPropagate(self, targets, N, M):\n if len(targets) != self.outputNodes:\n raise ValueError('wrong number of target values')\n\n # calculate error terms for output\n output_deltas = [0.0] * self.outputNodes\n for k in range(self.outputNodes):\n error = targets[k] - self.outputActivation[k]\n output_deltas[k] = dsigmoid(self.outputActivation[k]) * error\n\n # calculate error terms for hidden\n hidden_deltas = [0.0] * self.hiddenNodes\n for j in range(self.hiddenNodes):\n error = 0.0\n for k in range(self.outputNodes):\n error = error + output_deltas[k] * self.outputWeight[j][k]\n hidden_deltas[j] = dsigmoid(self.hiddenActivation[j]) * error\n\n # update output weights\n for j in range(self.hiddenNodes):\n for k in range(self.outputNodes):\n change = output_deltas[k] * self.hiddenActivation[j]\n self.outputWeight[j][k] = self.outputWeight[j][k] + N * change + M * self.co[j][k]\n self.co[j][k] = change\n\n # update input weights\n for i in range(self.inputNodes):\n for j in range(self.hiddenNodes):\n change = hidden_deltas[j] * self.inputActivation[i]\n self.inputWeight[i][j] = self.inputWeight[i][j] + N * change + M * self.ci[i][j]\n self.ci[i][j] = change\n\n # calculate error\n error = 0.0\n for k in range(len(targets)):\n error = error + 0.5 * (targets[k] - self.outputActivation[k]) ** 2\n\n return error\n\n def test(self, inputNodes):\n print(inputNodes, '->', self.update(inputNodes))\n return self.update(inputNodes)[0]\n\n def weights(self):\n print('Input weights:')\n for i in range(self.inputNodes):\n print(self.inputWeight[i])\n print()\n print('Output weights:')\n for j in range(self.hiddenNodes):\n print(self.outputWeight[j])\n\n def train(self, patterns, iterations=1000, N=0.5, M=0.1):\n # N: learning rate, M: momentum factor\n for i in range(iterations):\n error = 0.0\n for p in patterns:\n inputs = p[0]\n targets = p[1]\n self.update(inputs)\n error = error + self.backPropagate(targets, N, M)\n # if i % 100 == 0:\n # print('error %-.5f' % error)","sub_path":"StockTracking/backendserver/data/neural_network.py","file_name":"neural_network.py","file_ext":"py","file_size_in_byte":5036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"62242111","text":"from django.utils import timezone\nfrom app.celery import app as celery_app\nfrom .models.choices import STATE\nfrom .models.config import NotifyConfig\nfrom .models.notify import Notify\n\n\n@celery_app.task\ndef send_notifications():\n notifies = Notify.objects.filter(state__in=[STATE.WAIT])\n\n for notify in notifies.iterator():\n if notify.state == STATE.WAIT:\n if notify.send_at is None:\n notify._send()\n else:\n if timezone.now() > notify.send_at:\n notify._send()\n return\n\n\ncelery_app.conf.beat_schedule.update({\n 'periodic_task': {\n 'task': 'garpix_notify.tasks.send_notifications',\n 'schedule': NotifyConfig.get_solo().periodic,\n },\n})\ncelery_app.conf.timezone = 'UTC'\n\n# celery_app.add_periodic_task(NotifyConfig.get_solo().periodic, send_notifications.s(), name='periodic_task') #2 способ\n","sub_path":"backend/garpix_notify/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"188277363","text":"import socket\nimport pygame\nimport pickle\nimport math\nimport random\nimport time\n\nIPADDRES = socket.gethostbyname(socket.gethostname())\nPORT = 5555\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nprint(IPADDRES)\n\nserver.bind((IPADDRES, PORT))\nserver.listen(2)\ncordsArray = [300, 300, 395, 595] # [xA, xB, ballX, ballY]\npaddleSpeed = 7\n\nconnection = []\nxBallVelocity = 5\nyBallVelocity = 5\nmaxBounceAngle = 8 * math.pi / 12\nentryBallSpeed = 5\nclock = pygame.time.Clock()\n\n\ndef resetBallPosition(cordsArray):\n global xBallVelocity, yBallVelocity, entryBallSpeed\n cordsArray[2] = 355\n cordsArray[3] = 555\n xBallVelocity = random.choice([-1, 1]) * entryBallSpeed\n yBallVelocity = random.choice([-1, 1]) * entryBallSpeed\n\n\ndef processPositions(cordsArray, paddleAKeyInfo, paddleBKeyInfo):\n global xBallVelocity, yBallVelocity, maxBounceAngle\n global ballSpeed\n\n #### Paddle Moving ####\n ## Moving paddleA\n if paddleAKeyInfo[0] == True:\n cordsArray[0] -= paddleSpeed\n else:\n cordsArray[0] = cordsArray[0]\n if paddleAKeyInfo[1] == True:\n cordsArray[0] += paddleSpeed\n else:\n cordsArray[0] = cordsArray[0]\n if cordsArray[0] < 0:\n cordsArray[0] = 0\n if cordsArray[0] > 700 - 100:\n cordsArray[0] = 700 - 100\n ## Moving paddleB ##\n if paddleBKeyInfo[0] == True:\n cordsArray[1] -= paddleSpeed\n else:\n cordsArray[1] = cordsArray[1]\n if paddleBKeyInfo[1] == True:\n cordsArray[1] += paddleSpeed\n else:\n cordsArray[1] = cordsArray[1]\n if cordsArray[1] < 0:\n cordsArray[1] = 0\n if cordsArray[1] > 700 - 100:\n cordsArray[1] = 700 - 100\n #### END OF PADDLE MOVING ###\n\n #### BALL MOVING ####\n cordsArray[2] += xBallVelocity\n cordsArray[3] += yBallVelocity\n if cordsArray[3] < 30 or cordsArray[3] > 790:\n resetBallPosition(cordsArray)\n\n ## Bouncing ball from vertical walls ##\n if cordsArray[2] <= 0:\n xBallVelocity = -xBallVelocity\n if cordsArray[2] >= 700:\n xBallVelocity = -xBallVelocity\n\n ## Check if reset button is pressed - R ##\n\n #### Paddle and Ball contact detection ####\n ## Detection with paddleA ##\n if cordsArray[3] < (50 + 10) and (cordsArray[2] > cordsArray[0] and cordsArray[2] < cordsArray[0] + 100):\n interSection = cordsArray[2] - cordsArray[0] - 50\n normalizedInterSection = interSection / 50\n angle = normalizedInterSection * maxBounceAngle\n print(math.cos(angle))\n xBallVelocity = xBallVelocity + math.cos(angle)\n yBallVelocity = -yBallVelocity * 1.1\n\n ## Detection with paddleB ##\n if cordsArray[3] > 750 + 7 and (cordsArray[2] > cordsArray[1] and cordsArray[2] < cordsArray[1] + 100):\n interSection = cordsArray[2] - cordsArray[1] - 50\n normalizedInterSection = interSection / 50\n angle = normalizedInterSection * maxBounceAngle\n print(math.cos(angle))\n xBallVelocity = xBallVelocity + math.cos(angle)\n yBallVelocity = -yBallVelocity * 1.1\n\n return cordsArray\n\n\ndef waitForConnections():\n while len(connection) < 2:\n conn, addr = server.accept()\n connection.append(conn)\n print(conn)\n print(connection)\n\n\ndef receiveInformations():\n paddleA_Info = pickle.loads(connection[0].recv(1024))\n paddleB_Info = pickle.loads(connection[1].recv(1024))\n\n return paddleA_Info, paddleB_Info # assigning whether left or right buttons are pressed or not\n\n\nwhile True:\n waitForConnections()\n\n pickledCordsArray = pickle.dumps(cordsArray)\n # print(pickledCordsArray)\n connection[0].send(pickledCordsArray)\n connection[1].send(pickledCordsArray)\n\n paddleAKeyInfo, paddleBKeyInfo = receiveInformations()\n\n cordsArray = processPositions(cordsArray, paddleAKeyInfo, paddleBKeyInfo)\n clock.tick(60)","sub_path":"pongOnlineV3/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"161084649","text":"from django.conf import settings\nfrom django.conf.urls import url\nfrom django.conf.urls.static import static\n\nfrom watch_neighbour import views\n\nurlpatterns=[\n url(r'^$', views.all_neighbourhoods, name='neighbourhood'),\n url(r'^new/profile$', views.new_profile, name='new-profile'),\n url(r'^new/neighbourhood$', views.new_neighbourhood, name='new-neighbourhood'),\n url(r'^new/post$', views.new_post, name='new-post'),\n url(r'^current_user_profile/(?P\\d+)', views.current_user_profile, name='current_user_profile'),\n url(r'^single_neighbourhood/(?P\\d+)', views.single_neighbourhood, name='single_neighbourhood'),\n url(r'^new/business', views.new_business, name='new-business'),\n url(r'^new/department', views.new_department, name='new-department'),\n url(r'^occupants_and_location/(?P\\d+)', views.occupants_and_location, name='occupants_and_location'),\n url(r'^new/location', views.new_location, name='new-location'),\n url(r'^single_post/(?P\\d+)', views.single_post, name='single_post'),\n url(r'^new/comment', views.new_comment, name='new-comment'),\n url(r'^comments_for_posts/(?P\\d+)', views.comments_for_posts, name='comments_for_posts'),\n url(r'^search/', views.search_results, name='search_results'),\n\n]\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)","sub_path":"watch_neighbour/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"27969944","text":"from django.shortcuts import render,redirect, HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom .models import Mascota,Raza,Genero,Estado\nfrom django.contrib import messages\n# Create your views here.\n\ndef index(request):\n return render(request, 'core/home.html')\n\ndef home(request):\n return render(request, 'core/home.html')\n\ndef galeria(request):\n return render(request, 'core/galeria.html')\n\ndef formulario(request):\n return render(request, 'core/formulario.html')\n\ndef menu(request):\n return render(request, 'core/menu.html')\n\n#AGREGAR OK\ndef agregar_mascotas(request):\n\n razas = Raza.objects.all()\n generos = Genero.objects.all()\n estados = Estado.objects.all()\n variables = {\n 'razas':razas,\n 'generos':generos,\n 'estados':estados\n }\n\n if request.POST:\n perro = Mascota()\n perro.nombre = request.POST.get('txtNombre')\n raza = Raza()\n raza.id = request.POST.get('cboRaza')\n perro.raza = raza\n genero = Genero()\n genero.id = request.POST.get('cboGenero')\n perro.genero = genero\n perro.imagen = request.POST.get('file_foto')\n estado = Estado()\n estado.id = request.POST.get('cboEstado')\n perro.estado = estado\n\n try:\n perro.save()\n variables['mensaje'] = 'Mascota Guardada Correctamente'\n except:\n variables['mensaje'] = 'ERROR! No se ha podido guardar'\n\n return render(request, 'core/agregar_mascota.html', variables)\n########################################################################\n\n#@login_required\ndef agregar_mascota(request):\n\n perri = Raza.objects.all()\n estado = Estado.objects.all()\n raza = Raza.objects.all()\n variables = {\n 'perri':perri,\n 'estado':estado,\n 'raza':raza,\n }\n\n if request.POST:\n perro = Mascota()\n perro.nombre = request.POST.get('txtNombre')\n\n razita = Raza()\n razita.id = request.POST.get('cboRaza')\n perro.raza = razita\n\n perro.imagen = request.POST.get('txtFoto')\n\n estadito = Estado()\n estadito.id = request.POST.get('cboEstado')\n perro.estado = estadito\n\n perro.razita = Mascota\n perro.estadito = Mascota\n perro.save()\n\n try:\n variables['mensaje'] = 'Mascota Guardada Exitosamente'\n except:\n variables['mensaje'] = 'ERROR! No se ha podido guardar Mascota'\n\n return render(request,'core/agregar_mascota.html',variables)\n\n\n#LISTAR OK\ndef listar_mascotas(request):\n\n mascotas = Mascota.objects.all()\n\n return render(request,'core/listar_mascotas.html',{\n 'mascotas':mascotas\n })\n#############################################################\n\n#ELIMINAR OK\ndef eliminar_mascotas(request, id):\n\n mascota = Mascota.objects.get(id=id)\n\n try:\n mascota.delete()\n mensaje = \"Mascota Eliminada Correctamente\"\n messages.success(request, mensaje)\n except:\n mensaje = \"No se ha podido eliminar Mascota\"\n messages.error(request, mensaje)\n \n return redirect('listar_mascotas')\n#######################################################\n\n\n\ndef modificar_mascota(request, id):\n\n mascota = Mascota.objects.get(id=id)\n razas = Raza.objects.all()\n generos = Genero.objects.all()\n estados = Estado.objects.all()\n variables = {\n 'mascota':mascota,\n 'razas':razas,\n 'generos':generos,\n 'estados':estados\n }\n\n if request.POST:\n perro = Mascota()\n perro.id = request.POST.get('txtId')\n perro.nombre = request.POST.get('txtNombre')\n raza = Raza()\n raza.id = request.POST.get('cboRaza')\n perro.raza = raza\n genero = Genero()\n genero.id = request.POST.get('cboGenero')\n perro.genero = genero\n perro.imagen = request.POST.get('file_foto')\n estado = Estado()\n estado.id = request.POST.get('cboEstado')\n perro.estado = estado\n\n try:\n perro.save()\n messages.success(request, 'Mascota Modificada Exitosamente')\n except:\n messages.error(request, 'ERROR! No se ha podido modificar')\n return redirect('listar_mascotas')\n\n\n return render(request,'core/modificar_mascota.html', variables)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"2.0/Ultimo misperris/misperris/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"165222771","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom scipy.optimize import minimize, OptimizeResult, minimize_scalar\n\nimport autograd.numpy as anp\nfrom autograd import elementwise_grad as grad\nfrom autograd import jacobian, hessian\n\nclass objective_func(object):\n def __init__(self):\n # # 0. f(x): R^2 -> R\n def f(x):\n return ((151 * x[0]**2) / 4 - 49 / 2 * np.sqrt(3) * x[0] * x[1] + (53 * x[1]**2) / 4)\n\n self.f = f\n self.J = grad(f)\n self.JJ = grad(grad(f))\n self.H = hessian(f)\n \n def __call__(self, xk):\n return self.f(xk)\n \n def J(self, xk):\n return self.J(xk)\n\n def JJ(self, xk):\n return self.JJ(xk)\n \n def H(self, xk):\n return self.H(xk)\n\nf = objective_func()\n\n\nclass fetch_intermediate_x(object):\n def __init__(self, f, x0):\n self.x_series = [x0]\n self.f_series = [f(x0)]\n self.f = f\n\n def __call__(self, xk):\n self.x_series.append(xk)\n self.f_series.append(self.f(xk))\n\n def get_x_array(self):\n return np.array(self.x_series)\n\n def get_f_array(self):\n return np.array(self.f_series)\n\ndef visualize_f(f, mesh=True, boundary=True, only_zero=False):\n x_min = -10\n x_max = +10\n y_min = -10\n y_max = +10\n plt.xlim(x_min, x_max)\n plt.ylim(y_min, y_max)\n\n #\n # visualizing the cost function f()\n #\n\n # make a grid\n XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n # evaluate f() at each point in the grid\n Z = np.array([ f(x) for x in np.c_[XX.ravel(), YY.ravel()] ])\n Z = Z.reshape(XX.shape)\n if mesh:\n plt.pcolormesh(XX, YY, Z, cmap=plt.cm.gray_r, zorder=-10)\n if boundary:\n if only_zero:\n plt.contour(XX, YY, Z,\n colors=['b'],\n linestyles=['-'],\n levels=[0])\n else:\n plt.contour(XX, YY, Z,\n colors=['k']*10,\n linestyles=['-']*10,\n levels=list( np.logspace(0.1, 10, 20) ) )\n\n# ===== gradient decent =====\nclass gradientDecent(object):\n def __init__(self):\n self.x_series = []\n self.f_series = []\n self.g_series = []\n\n\n def __call__(self, f, x0, callback, **options):\n x = x0\n d = np.array([-1, 0]) # let it be left\n\n my_inf = 1000000000\n\n n_iter = options['maxiter'] if 'maxiter' in options else my_inf\n alpha0 = options['alpha0'] if 'alpha0' in options else 1\n\n for i in range(n_iter):\n function_value = f(x)\n gradient = f.J(x)\n\n self.x_series.append(x)\n self.x_series.append(function_value)\n self.x_series.append(gradient)\n\n # = method =\n alpha = alpha0\n # gradient /= np.linalg.norm(gradient) # make gradient be a unit vector\n alpha = alpha0 / (i+1) # step size with 1/t decay\n\n # # 1. gradient decent\n # if (np.linalg.norm(alpha*gradient) < 1e-7): break\n # x = x - alpha*gradient\n # if (i % 1000 == 0):\n # print(\"The \", i, \" times:\")\n # print(np.linalg.norm(alpha*gradient))\n\n # 2. gradient decent with Inertia parameter\n # if (np.linalg.norm(d) < 1e-7): break\n # micro = 0.9\n # d = -alpha*gradient + micro*d\n # x = x + d\n # if (i % 1000 == 0):\n # print(\"The \", i, \" times:\")\n # print(np.linalg.norm(d)) \n\n # 3. Nesterov\n # if (np.linalg.norm(d) < 1e-7): break\n # micro = 0.0+(i - 1)/(i + 2)\n # gradient = f.J(x+micro*d)\n # d = -alpha*gradient + micro*d\n # x = x + d\n # if (i % 1000 == 0):\n # print(\"The \", i, \" times:\")\n # print(np.linalg.norm(d)) \n\n # 4. Newton\n if (np.linalg.norm((alpha*np.linalg.inv(f.H(x))*f.J(x))[0]) < 1e-7): break\n x = x - (alpha*np.linalg.inv(f.H(x))*f.J(x))[0]\n if (i % 1000 == 0):\n print(\"The \", i, \" times:\")\n print(np.linalg.norm((alpha*np.linalg.inv(f.H(x))*f.J(x))[0])) \n\n\n callback(x)\n\n result = OptimizeResult()\n result.x = x\n result.success = True\n result.nit = n_iter\n result.fun = f(x)\n\n return result\n\n def name(self):\n return 'gradient decent'\n\n\nmyGD = gradientDecent()\n\nx0 = [4, 6]\nfetch = fetch_intermediate_x(f, x0)\n\nres = minimize(f, x0, method=myGD, jac=f.J, callback=fetch, options={'alpha0': 1e-4})\n\n\nfig = plt.figure(figsize=(10,10))\nplt.scatter(x0[0], x0[1], label='initial x')\nplt.scatter(res.x[0], res.x[1], color='red', label=\"estimated x\")\n\nx_series = fetch.get_x_array()\nplt.plot(x_series[:, 0], x_series[:, 1], marker='.', color='green', label='xk')\n\nvisualize_f(f)\n\nplt.legend()\nplt.show()\n\n\n\n","sub_path":"190601/Optimize_scipy/task_1_implement.py","file_name":"task_1_implement.py","file_ext":"py","file_size_in_byte":4952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"174944409","text":"from django.conf.urls import patterns, url\nfrom orders.views import *\n\nurlpatterns = patterns('',\n\turl(r'^clients/$', ListView.as_view(model=Client),\tname='client_list'),\n\turl(r'client/new/$', ClientCreateView.as_view(),\t\tname='client_new'),\n\turl(r'client/(?P\\d+)/$', ClientDetailView.as_view(),\t\tname='client_details'),\n\turl(r'client/(?P\\d+)/edit/$', ClientEditView.as_view(),\t\tname='client_edit'),\n\turl(r'client/(?P\\d+)/standingorders/(?P\\d+)/edit', ClientBakeStandingOrdersEditView.as_view(), name='client_bake_edit'),\n\n\turl(r'^products/$', ListView.as_view(model=Product),name='product_list'),\n\turl(r'product/new/$', CreateView.as_view(model=Product, form_class=ProductForm, template_name=\"orders/object_new_form.html\"),\tname='product_new'),\n\turl(r'product/(?P\\d+)/$', ProductDetailView.as_view(),\tname='product_details'),\n\turl(r'product/(?P\\d+)/edit/$', UpdateView.as_view(model=Product, form_class=ProductForm, template_name=\"orders/object_edit_form.html\"), name='product_edit'),\n\n\turl(r'^orders/$',\t\t\t\t\tOrdersListView.as_view(),\tname='order_list'),\n\turl(r'^order/new/$',\t\t\t\tOrderCreate.as_view(),\t\tname='order_new'),\n\turl(r'^order/(?P\\d+)/edit/$',\tOrderUpdate.as_view(),\t\tname='order_edit'),\n\turl(r'^order/(?P\\d+)/delete/$',OrderDelete.as_view(),\t\tname='order_delete'),\n\n\turl(r'^gen_so/$',\tgenerate_standing_orders,\tname='gen_standing_orders'),\n\turl(r'^so2orders/$',generate_orders,\t\t\tname='standing_orders_to_orders'),\n\n\turl(r'^cutsheet/$', CutSheetView.as_view(), name='cut_sheet'),\n\turl(r'^cutsheet/(?P[\\d-]+)/$', CutSheetView.as_view(), name='cut_sheet'),\n\n\turl(r'^packingslips/$', PackingSlipsView.as_view(), name='packing_slips'),\n\turl(r'^packingslips/(?P[\\d-]+)/$', PackingSlipsView.as_view(), name='packing_slips'),\n)\n\n\n","sub_path":"orders/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"539982752","text":"import networkx as nx\nimport community\nfrom networkx.readwrite import json_graph\nimport json\nimport collections\nimport itertools\n\nimport keyword_extractor\n\n\nclass GraphGenerator(object):\n\n def __init__(self):\n return\n\n @staticmethod\n def build_topic_graph(documents):\n G = nx.Graph()\n # used to keep track of and increment the number of connections that\n # exist between any two keywords\n edge_weights = collections.Counter()\n\n keyword_frequencies = collections.Counter(\n [keyword['text'] for document in documents for keyword in document.keywords])\n for document in documents:\n for keyword in document.keywords:\n G.add_node(keyword['text'], frequency=keyword_frequencies[keyword['text']])\n # Its alright to just add the keywords without checking if it is\n # already in the graph. NetworkX doesn't modify the graph if that\n # is the case.\n document_keywords = map(lambda x: x['text'], document.keywords)\n G.add_nodes_from(document_keywords)\n edges = itertools.combinations(document_keywords, 2)\n for edge in edges:\n # unlike sets frozensets are hashable\n _set = frozenset(edge)\n edge_weights[_set] += 1\n x, y = edge\n if edge_weights[_set] > 1:\n G.add_edge(x, y, weight=edge_weights[_set])\n for node in G.nodes():\n if keyword_frequencies[node] < 2:\n G.remove_node(node)\n return json_graph.node_link_data(G)\n\n @staticmethod\n def _with_networkx(documents, threshold=1):\n G = nx.Graph()\n G.add_nodes_from(documents)\n nodes = G.nodes()\n for i, node in enumerate(nodes):\n for other in nodes[i+1:]:\n a = set(node.keywords)\n b = set(other.keywords)\n intersection = a.intersection(b)\n if len(intersection) > threshold:\n G.add_edge(node, other)\n G[node][other]['weight'] = len(intersection)\n\n # remove any isolated vertices before we perform community detection\n orphans = []\n for node in G.nodes():\n if not G.neighbors(node):\n G.remove_node(node)\n orphans.append(node)\n partition_lookup = community.best_partition(G).iteritems()\n G.add_nodes_from(orphans)\n partitions = {node.r_id: value for node, value in partition_lookup}\n as_json = json_graph.node_link_data(G)\n frontend_compatable = {}\n frontend_compatable['nodes'] = [node['id'] for node in as_json['nodes']]\n for node in frontend_compatable['nodes']:\n if G.neighbors(node):\n node.partition = partitions[node.r_id]\n frontend_compatable['nodes'] = [json.loads(node.to_json()) for node in frontend_compatable['nodes']]\n for node in frontend_compatable['nodes']:\n if node['_id'] in partitions:\n node['partition'] = partitions[node['_id']]\n frontend_compatable['edges'] = as_json['links']\n return frontend_compatable\n\n\n\n\n","sub_path":"server/analysis/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":3204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"207790485","text":"\"\"\"\nCreated as part of ISU C-CHANGE Foresite system on 6 Jan 2020\n\n@author: Matt Nowatzke\n@email: mnowatz@iastate.edu\n\"\"\"\nfrom glob import glob\nfrom os import getcwd\nfrom subprocess import call\n\ndef find_apsim (exe_path=None):\n \"\"\"\n Finds the most recent [-1] version of APSIM 7.XX installed on the system to get exe path.\n If APSIM is not installed in default directory will ned to specify with exe_path argument.\n \n Keyword Arguments:\n exe_path {str} -- [Path for APSIM executable if one isn't found.] (default: {None})\n \n Returns:\n [str] -- [Path to the APSIM 7.XX exe]\n \"\"\"\n try:\n #find all installed Apsim.exe at default installation location and return the most recent (last).\n apsim_exes = glob(\"C:\\\\Program Files (x86)\\\\APSIM7*\\\\Model\\\\Apsim.exe\")\n current_apsim_exe = apsim_exes[-1]\n return current_apsim_exe\n except:\n print(\"No APSIM 7.XX executable found. Try running again with exe_path argument.\")\n return exe_path\n\n\ndef run_sims (apsim):\n \"\"\"Will execute Apsim.exe to run all .sim files in targeted folder.\n \n Arguments:\n executable {str} -- System path to Apsim.exe\n \"\"\"\n wd = getcwd()\n sims = glob(\"apsim_files/*.apsim\")\n complete_file_paths = list()\n total_sims = len(sims)\n sims_count = 0\n for i in sims:\n #combine cwd and apsim file paths to give Apsim.exe a complete path.\n complete_file_paths.append(f'{wd}\\{i}')\n for i in complete_file_paths:\n #run exe on each .apsim file\n call([apsim, i])\n sims_count += 1\n if sims_count % 10 == 0:\n print(f\"Executing simulation {sims_count} of {total_sims}\")\n\ndef main():\n \"\"\"\n Main func for when running script as standalone. Will run all .apsim files in directory.\n \"\"\"\n try:\n exe = find_apsim()\n print(f'Apsim exe located at {exe}.')\n print('Running simulations...')\n run_sims(exe)\n except:\n print('Something went wrong.')\n\n#run if executed as standalone program\nif __name__ == \"__main__\":\n main()","sub_path":"apsim/run_apsim.py","file_name":"run_apsim.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"587855206","text":"import threading\n\na=b=0\n\nlock = threading.Lock()\n\ndef values():\n while True:\n lock.acquire()\n if a !=b:\n print(\"a = %d,b=%d\"%(a,b))\n lock.release()\n\nt =threading.Thread(target =values)\n\nt.start()\n\nwhile True:\n with lock:\n a+=1\n b+=1\n\nt.join()\n","sub_path":"pythonnet/day07/thread_lock.py","file_name":"thread_lock.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"484759357","text":"from django.db import models\n\n\nclass Gelinho(models.Model):\n tipos = (\n ('Natural','Natural'),\n ('Artificial','Artificial'),\n )\n sabor = models.CharField('Sabor', max_length=100,primary_key = True )\n qtd = models.IntegerField('Quintidade')\n tipo = models.CharField(choices=tipos , max_length=10)\n\n\n def __str__(self):\n return self.sabor\n","sub_path":"gelinhos/core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"569502137","text":"import random\n\nclass Die():\n\n def __init__(self, sides = 6):\n self.sides = sides\n\n def roll_die(self):\n result = random.randint(1, self.sides)\n print(\"There is a {}!!\".format(result))\n return result\n\nif __name__ == \"__main__\":\n\n die = Die(10)\n for i in range(1, 10):\n die.roll_die()","sub_path":"chapter 1-11 exercises/die.py","file_name":"die.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"399281886","text":"from typing import List, Optional, Tuple\nfrom collections import defaultdict\nimport pickle\nimport json\nfrom os import path\nimport numpy as np\nimport os\nimport shutil\nimport random\nimport time\nimport cloudpickle\n\nimport xgboost\nimport numpy as np\n\nimport click\nfrom tqdm import tqdm\nfrom flask import Flask, jsonify, request\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import SequentialSampler, RandomSampler\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.optim import Adam, lr_scheduler\nfrom torch.nn import functional as F\n\nfrom qanta import util\nfrom qanta.dataset import QuizBowlDataset\nfrom qanta.preprocess import preprocess_dataset, WikipediaDataset, tokenize_question\nfrom models import DanModel\n\nfrom qanta.buzzer_utils import Buzzer\n\ncategories = {\n 0: ['History', 'Philosophy', 'Religion'],\n 1: ['Literature', 'Mythology'],\n 2: ['Science', 'Social Science'],\n 3: ['Current Events', 'Trash', 'Fine Arts', 'Geography']\n}\n\nMODEL_PATH = \"./models/dan_pattern_es\"\n\nBUZZER_PATH = MODEL_PATH + \"/buzzer_\"+ MODEL_PATH.split(\"/\")[-1] + \".pkl\"\nBUZZ_NUM_GUESSES = 10\nBUZZ_THRESHOLD = 0.2\n\nbuzzer = pickle.load(open(BUZZER_PATH, \"rb\"))\n\n\ndef guess_and_buzz(model, question_text) -> Tuple[str, bool]:\n guesses = model.guess([question_text], BUZZ_NUM_GUESSES)[0]\n scores = [guess[1] for guess in guesses]\n buzz = scores[0] / sum(scores) >= BUZZ_THRESHOLD\n return guesses[0][0], buzz\n\ndef guess_and_predict_buzz(model, question_text, char_skip=50) -> Tuple[str, bool]:\n char_indices = list(range(char_skip, len(question_text) + char_skip, char_skip))\n guesses = model.guess([question_text], BUZZ_NUM_GUESSES)[0]\n scores = [guess[1] for guess in guesses]\n buzz_preds = buzzer.predict([np.append(scores, char_indices[-1])])\n buzz = buzz_preds[0]\n return guesses[0][0], buzz\n\ndef batch_guess_and_buzz(model, questions) -> List[Tuple[str, bool]]:\n question_guesses = model.guess(questions, BUZZ_NUM_GUESSES)\n outputs = []\n for guesses in question_guesses:\n scores = [guess[1] for guess in guesses]\n buzz = scores[0] / sum(scores) >= BUZZ_THRESHOLD\n outputs.append((guesses[0][0], buzz))\n return outputs\n\ndef batch_guess_and_predict_buzz(model, questions, char_skip=50) -> List[Tuple[str, bool]]:\n question_guesses = model.guess(questions, BUZZ_NUM_GUESSES)\n outputs = []\n for i in range(len(question_guesses)):\n char_indices = list(range(char_skip, len(questions[i]) + char_skip, char_skip))\n guesses = question_guesses[i]\n scores = [guess[1] for guess in guesses]\n buzz_preds = buzzer.predict([np.append(scores, char_indices[-1])])\n buzz = buzz_preds[0]\n outputs.append((guesses[0][0], buzz))\n return outputs\n\n\ndef make_array(tokens, vocab, add_eos=True):\n unk_id = vocab['']\n eos_id = vocab['']\n ids = [vocab.get(token, unk_id) for token in tokens]\n if add_eos:\n ids.append(eos_id)\n return np.array(ids, 'i')\n\ndef transform_to_array(dataset, vocab, with_label=True):\n if with_label:\n return [(make_array(tokens, vocab), np.array([cls], 'i'))\n for tokens, cls in dataset]\n else:\n return [make_array(tokens, vocab)\n for tokens in dataset]\n\ndef get_quizbowl(guesser_train=True, buzzer_train=False, category=None, use_wiki=False, n_wiki_sentences = 5):\n print(\"Loading data with guesser_train: \" + str(guesser_train) + \" buzzer_train: \" + str(buzzer_train))\n qb_dataset = QuizBowlDataset(guesser_train=guesser_train, buzzer_train=buzzer_train, category=category)\n training_data = qb_dataset.training_data()\n \n if use_wiki and n_wiki_sentences > 0:\n print(\"Using wiki dataset with n_wiki_sentences: \" + str(n_wiki_sentences))\n wiki_dataset = WikipediaDataset(set(training_data[1]), n_wiki_sentences)\n wiki_training_data = wiki_dataset.training_data()\n training_data[0].extend(wiki_training_data[0])\n training_data[1].extend(wiki_training_data[1])\n return training_data\n\ndef load_glove(filename):\n idx = 0\n word2idx = {}\n vectors = []\n\n with open(filename, 'rb') as f:\n for l in f:\n line = l.decode().split()\n word = line[0]\n word2idx[word] = idx\n idx += 1\n vect = np.array(line[1:]).astype(np.float)\n vectors.append(vect)\n\n return word2idx, vectors\n\n\nclass DanGuesser:\n def __init__(self):\n self.model = None\n self.i_to_class = None\n self.class_to_i = None\n self.word_to_i = None\n\n self.optimizer = None\n self.criterion = None\n self.scheduler = None\n self.model_file = None\n\n self.map_pattern = False\n self.wiki_links = False\n self.use_es_highlight = False\n self.full_question = False\n self.use_wiki = False\n\n self.name = \"dan\"\n\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\n def batchify(self, batch):\n \"\"\"\n Gather a batch of individual examples into one batch, \n which includes the question text, question length and labels \n\n Keyword arguments:\n batch: list of outputs from vectorize function\n \"\"\"\n batch = transform_to_array(batch, self.word_to_i)\n question_len = list()\n label_list = list()\n for ex in batch:\n question_len.append(len(ex[0]))\n label_list.append(ex[1][0])\n target_labels = torch.LongTensor(label_list)\n x1 = torch.LongTensor(len(question_len), max(question_len)).zero_()\n for i in range(len(question_len)):\n question_text = batch[i][0]\n vec = torch.LongTensor(question_text)\n x1[i, :len(question_text)].copy_(vec)\n q_batch = {'text': x1, 'len': torch.FloatTensor(question_len), 'labels': target_labels}\n return q_batch\n\n\n def train(self, training_data, full_question=False, create_runs=False, map_pattern=False) -> None:\n x_train, y_train, x_val, y_val, i_to_word, class_to_i, i_to_class = preprocess_dataset(training_data, full_question=full_question, create_runs=create_runs, map_pattern=map_pattern)\n self.class_to_i = class_to_i\n self.map_pattern = map_pattern\n self.use_wiki = use_wiki\n self.full_question = full_question\n\n self.i_to_class = i_to_class\n log = get(__name__, \"dan.log\")\n log.info('Batchifying data')\n i_to_word = ['', ''] + sorted(i_to_word)\n word_to_i = {x: i for i, x in enumerate(i_to_word)}\n self.word_to_i = word_to_i\n log.info('Vocab len: ' + str(len(self.word_to_i)))\n\n train_sampler = RandomSampler(list(zip(x_train, y_train)))\n dev_sampler = RandomSampler(list(zip(x_val, y_val)))\n dev_loader = DataLoader(list(zip(x_val, y_val)), batch_size=args.batch_size,\n sampler=dev_sampler, num_workers=0,\n collate_fn=self.batchify)\n train_loader = DataLoader(list(zip(x_train, y_train)), batch_size=args.batch_size,\n sampler=train_sampler, num_workers=0,\n collate_fn=self.batchify)\n\n self.model = DanModel(len(i_to_class), len(i_to_word))\n self.model = self.model.to(self.device)\n \n log.info(f'Loading GloVe')\n glove_word2idx, glove_vectors = load_glove(\"glove/glove.6B.300d.txt\")\n for word, emb_index in word_to_i.items():\n if word.lower() in glove_word2idx:\n glove_index = glove_word2idx[word.lower()]\n glove_vec = torch.FloatTensor(glove_vectors[glove_index])\n glove_vec = glove_vec.cuda()\n self.model.text_embeddings.weight.data[emb_index, :].set_(glove_vec)\n\n\n log.info(f'Model:\\n{self.model}')\n self.optimizer = Adam(self.model.parameters())\n self.criterion = nn.CrossEntropyLoss()\n self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, patience=5, verbose=True, mode='max')\n\n\n temp_prefix = get_tmp_filename()\n self.model_file = f'{temp_prefix}.pt'\n\n print(f'Saving model to: {self.model_file}')\n log = get(__name__)\n manager = TrainingManager([\n BaseLogger(log_func=log.info), TerminateOnNaN(), EarlyStopping(monitor='test_acc', patience=10, verbose=1),\n MaxEpochStopping(100), ModelCheckpoint(create_save_model(self.model), self.model_file, monitor='test_acc')\n ])\n\n log.info('Starting training')\n\n epoch = 0\n while True:\n self.model.train()\n train_acc, train_loss, train_time = self.run_epoch(train_loader)\n\n self.model.eval()\n test_acc, test_loss, test_time = self.run_epoch(dev_loader, train=False)\n\n stop_training, reasons = manager.instruct(\n train_time, train_loss, train_acc,\n test_time, test_loss, test_acc\n )\n\n if stop_training:\n log.info(' '.join(reasons))\n break\n else:\n self.scheduler.step(test_acc)\n epoch += 1\n\n def run_epoch(self, data_loader, train=True):\n batch_accuracies = []\n batch_losses = []\n epoch_start = time.time()\n for idx, batch in tqdm(enumerate(data_loader)):\n x_batch = batch['text'].to(self.device)\n length_batch = batch['len'].to(self.device)\n y_batch = batch['labels'].to(self.device)\n if train:\n self.model.zero_grad()\n y_batch = y_batch.to(self.device)\n out = self.model(x_batch.to(self.device), length_batch.to(self.device))\n _, preds = torch.max(out, 1)\n accuracy = torch.mean(torch.eq(preds, y_batch).float()).data[0]\n batch_loss = self.criterion(out, y_batch)\n if train:\n batch_loss.backward()\n torch.nn.utils.clip_grad_norm(self.model.parameters(), .25)\n self.optimizer.step()\n batch_accuracies.append(accuracy)\n batch_losses.append(batch_loss.data[0])\n epoch_end = time.time()\n\n return np.mean(batch_accuracies), np.mean(batch_losses), epoch_end - epoch_start\n\n def guess(self, questions: List[str], max_n_guesses: Optional[int]) -> List[List[Tuple[str, float]]]:\n y_data = np.zeros((len(questions)))\n x_data = [tokenize_question(q, self.map_pattern) for q in questions]\n\n batches = self.batchify(list(zip(x_data, y_data)))\n guesses = []\n \n x_batch = batches[\"text\"]\n length_batch = batches[\"len\"]\n self.model.eval()\n out = self.model(x_batch.to(self.device), length_batch.to(self.device))\n probs = F.softmax(out).data.cpu().numpy()\n preds = np.argsort(-probs, axis=1)\n n_examples = probs.shape[0]\n for i in range(n_examples):\n example_guesses = []\n for p in preds[i][:max_n_guesses]:\n example_guesses.append((self.i_to_class[p], probs[i][p]))\n guesses.append(example_guesses)\n return guesses\n\n @classmethod\n def targets(cls) -> List[str]:\n return ['dan.pt', 'dan.pkl']\n\n @classmethod\n def load(cls, directory: str):\n with open(os.path.join(directory, 'dan.pkl'), 'rb') as f:\n params = cloudpickle.load(f)\n\n params['use_wiki'] = False # added to avoid model confusion for dan_pattern_es\n\n print('Params: Use Wiki: ' + str(params['use_wiki']) + ' Use Pattern: ' + str(params['map_pattern']) \\\n + ' Wiki_links: ' + str(params['wiki_links']) + ' ES highlights: ' + str(params['use_es_highlight']))\n guesser = DanGuesser()\n guesser.class_to_i = params['class_to_i']\n guesser.i_to_class = params['i_to_class']\n guesser.word_to_i = params['word_to_i']\n guesser.device = params['device']\n guesser.map_pattern = params['map_pattern']\n guesser.wiki_links = params['wiki_links']\n guesser.use_wiki = params['use_wiki']\n guesser.use_es_highlight = params['use_es_highlight']\n guesser.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n guesser.model = DanModel(len(guesser.i_to_class), len(guesser.word_to_i))\n guesser.name = directory.split(\"/\")[-1]\n guesser.model.load_state_dict(torch.load(\n os.path.join(directory, 'dan.pt'), map_location=lambda storage, loc: storage\n ).state_dict())\n guesser.model.eval()\n guesser.model = guesser.model.to(guesser.device)\n return guesser\n\n def save(self, directory: str) -> None:\n os.mkdir(directory)\n shutil.copyfile(self.model_file, os.path.join(directory, 'dan.pt'))\n with open(os.path.join(directory, 'dan.pkl'), 'wb') as f:\n cloudpickle.dump({\n 'class_to_i': self.class_to_i,\n 'i_to_class': self.i_to_class,\n 'word_to_i': self.word_to_i,\n 'use_wiki' : self.use_wiki,\n 'device' : self.device,\n 'map_pattern' : self.map_pattern,\n 'wiki_links' : self.wiki_links,\n 'use_es_highlight' : self.use_es_highlight\n }, f)\n\ndef create_app(path_dir=MODEL_PATH, enable_batch=True, predict=False):\n dan_guesser = DanGuesser.load(path_dir)\n app = Flask(__name__)\n\n @app.route('/api/1.0/quizbowl/act', methods=['POST'])\n def act():\n question = request.json['text']\n if predict:\n guess, buzz = guess_and_predict_buzz(dan_guesser, question)\n else:\n guess, buzz = guess_and_buzz(dan_guesser, question)\n return jsonify({'guess': guess, 'buzz': True if buzz else False})\n\n @app.route('/api/1.0/quizbowl/status', methods=['GET'])\n def status():\n return jsonify({\n 'batch': enable_batch,\n 'batch_size': 200,\n 'ready': True\n })\n\n @app.route('/api/1.0/quizbowl/batch_act', methods=['POST'])\n def batch_act():\n questions = [q['text'] for q in request.json['questions']]\n if predict:\n return jsonify([\n {'guess': guess, 'buzz': True if buzz else False}\n for guess, buzz in batch_guess_and_predict_buzz(dan_guesser, questions)\n ])\n else:\n return jsonify([\n {'guess': guess, 'buzz': True if buzz else False}\n for guess, buzz in batch_guess_and_buzz(dan_guesser, questions)\n ])\n\n return app\n\n\n@click.group()\ndef cli():\n pass\n\n\n@cli.command()\n@click.option('--host', default='0.0.0.0')\n@click.option('--port', default=4861)\n@click.option('--predict', default=True)\n@click.option('--disable-batch', default=False, is_flag=True)\ndef web(host, port, disable_batch, predict):\n \"\"\"\n Start web server wrapping tfidf model\n \"\"\"\n app = create_app(enable_batch=not disable_batch, predict=predict)\n app.run(host=host, port=port, debug=False)\n\n\n@cli.command()\n@click.option('--use_wiki',is_flag=True, default=False)\n@click.option('--full_question',is_flag=True, default=False)\n@click.option('--create_runs',is_flag=True, default=False)\n@click.option('--map_pattern',is_flag=True, default=False)\n@click.option('--n_wiki_sentences', default=10)\n@click.option('--category', default=None)\n@click.option('--path', default=\"./\")\ndef train(use_wiki, n_wiki_sentences, full_question, create_runs, category, map_pattern, path):\n \"\"\"\n Train the tfidf model, requires downloaded data and saves to models/\n \"\"\"\n dataset = QuizBowlDataset(guesser_train=True)\n training_data = dataset.training_data()\n\n if use_wiki and n_wiki_sentences > 0:\n print(\"Using wiki dataset with n_wiki_sentences: \" + str(n_wiki_sentences))\n wiki_dataset = WikipediaDataset(set(training_data[1]), n_wiki_sentences)\n wiki_training_data = wiki_dataset.training_data()\n training_data[0].extend(wiki_training_data[0])\n training_data[1].extend(wiki_training_data[1])\n \n dan_guesser = DanGuesser()\n dan_guesser.train(training_data, full_question, create_runs)\n dan_guesser.save(path)\n\n\n@cli.command()\n@click.option('--model-path', default=MODEL_PATH)\ndef train_buzzer(model_path):\n \"\"\"\n Train the tfidf buzzer saves to ./\n \"\"\"\n dan_guesser = DanGuesser.load(model_path)\n dan_buzzer = Buzzer(dan_guesser)\n dan_buzzer.train()\n dan_buzzer.save(model_path)\n\n\n@cli.command()\n@click.option('--local-qanta-prefix', default='data/')\ndef download(local_qanta_prefix):\n \"\"\"\n Run once to download qanta data to data/. Runs inside the docker container, but results save to host machine\n \"\"\"\n util.download(local_qanta_prefix)\n\n\nif __name__ == '__main__':\n cli()\n","sub_path":"src/qanta/dan.py","file_name":"dan.py","file_ext":"py","file_size_in_byte":16927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"285759875","text":"import sqlite3\nimport sys\nimport os\nimport random\nimport datetime\n\ndef initializeJournAlertDatabase():\n '''\n Initialization of the database (only have to do this once)\n ### ADD SCHEMA DESCRIPTION\n '''\n conn = sqlite3.connect('journalert.db')\n c = conn.cursor()\n\n c.execute(\"\"\"CREATE TABLE patients(patient_id integer PRIMARY KEY, name text, journal_id integer, FOREIGN KEY(journal_id) REFERENCES journal(patient_id))\"\"\")\n c.execute(\"\"\"CREATE TABLE employees(id integer PRIMARY KEY)\"\"\")\n c.execute(\"\"\"CREATE TABLE schedules(id integer PRIMARY KEY, patient_id integer, employee_id integer, timeFrom text, timeTo text, FOREIGN KEY(patient_id) REFERENCES patients(id), FOREIGN KEY(employee_id) REFERENCES employee(id))\"\"\")\n c.execute(\"\"\"CREATE TABLE journals(patient_id integer PRIMARY KEY , FOREIGN KEY(patient_id) REFERENCES patients(id))\"\"\")\n\n # Save the changes\n conn.commit()\n\n #Close the connection\n c.close()\n\ndef initializeLogDataBase():\n #Database for the logEntry\n conn_log = sqlite3.connect('log.db')\n c_log = conn_log.cursor()\n\n c_log.execute(\"\"\"CREATE TABLE entries(entry_id integer PRIMARY KEY, patient_id, employee_id, ts timestamp, warning_level text)\"\"\")\n\n #Save the changes\n conn_log.commit()\n c_log.close()\n\n\n\ndef fillJournAlert(patient_number, schedule_number, employee_number):\n '''\n Fills the log databse with fake data (only have to do this once)\n Input:\n @conn: a connection to the JournAlert database\n @patient_number: number of patients to add (has to be equal to number of journals)\n @schedule_number: number of appointments to create (less than patients)\n @employee_number: number of employees to create (less than patients)\n '''\n\n conn = sqlite3.connect('journalert.db')\n c = conn.cursor()\n\n for i in range(patient_number):\n createPatient(i, str(i), i, conn, c)\n\n for i in range(employee_number):\n createEmployee(i, conn, c)\n\n year = 2018\n month = 5\n day = 1\n hourFrom = 00\n minFrom = 00\n minTo = 00\n\n # TO DO: make employees that haas no appointments\n for i in range(schedule_number):\n # choose random patients\n patient = random.randint(0, patient_number-1)\n # choose random employee\n employee = random.randint(0, employee_number-1)\n\n # new day\n if hourFrom == 23:\n minTo = 59\n hourTo = hourFrom\n else:\n hourTo = hourFrom+1\n\n start = datetime.datetime(year, month, day, hourFrom, minFrom)\n end = datetime.datetime(year, month, day, hourTo, minTo)\n\n createAppointment(i, patient, employee, start, end, conn, c)\n\n # Move time to next appointment\n # Assume 28 days in every month\n\n # New day\n if hourFrom == 23:\n hourFrom = 00\n minTo = 00\n day = day+1\n\n # New month\n if day == 28:\n day = 1\n month = month+1\n # New year\n if month == 12:\n month = 1\n year = year+1\n\n else:\n hourFrom = hourFrom + 1\n\n\ndef fillLog(entry_number, green_percentage, orange_percentage, red_percentage):\n '''\n NB! HAS TO BE CALLED AFTER journalert.db IS FILLED\n NB2! percentages has to be 100 in total\n\n Fills the log databse with fake data (only have to do this once)\n Input:\n @entries: how many entries in the log that is to be created\n @green_percentage: percentage of green entries\n @orange_percentage: percentage of orange entries\n @red_percentage: percentage of red entries\n '''\n\n # Calculate how many of each entry there is\n total_percentage = green_percentage + orange_percentage + red_percentage\n\n # Has to be 100% in total\n if(total_percentage != 100):\n sys.exit(\"The given percentages did not add up to 100\")\n\n number_green = int(entry_number * (green_percentage/100))\n number_orange = int(entry_number * (orange_percentage/100))\n number_red = int(entry_number * (red_percentage/100))\n\n conn_log = sqlite3.connect('log.db')\n c_log = conn_log.cursor()\n\n # Open a connection to the JournAlert database\n conn = sqlite3.connect('journalert.db')\n c = conn.cursor()\n entry_id = 0\n # Create green entries\n c.execute('SELECT * FROM schedules')\n # Fetch an entry\n all_appoint = c.fetchmany(number_green)\n\n # PATIENT EMPLOYEE TIME (CONN, C) COLOR\n\n for x in range(number_green):\n # Create an entry in the log with the correct time of checking the journal\n createLogEntry(entry_id, all_appoint[x][1], all_appoint[x][2], all_appoint[x][3], conn_log, c_log, 4)\n entry_id = entry_id + 1\n\n # Create orange entries\n c.execute('SELECT * FROM schedules')\n # Fetch an entry\n all_appoint = c.fetchmany(number_green)\n\n for x in range(number_orange):\n # Create an entry in the log with the correct time of checking the journal\n createLogEntry(entry_id, all_appoint[x][1], all_appoint[x][2], all_appoint[x][3], conn_log, c_log, 4)\n entry_id = entry_id + 1\n\n # Create red entries\n for x in range(number_red):\n c.execute('SELECT patient_id FROM patients WHERE NOT EXISTS ( SELECT * FROM schedules)')\n appoint = c.fetchone()\n c.execute('SELECT * from employees')\n e_id = c.fetchone()\n # Create an entry in the log with the correct time of checking the journal\n createEmployee(13337, conn, c)\n createLogEntry(entry_id,1, 13337, '2018-03-20 14:00:00', conn_log, c_log, 4)\n entry_id = entry_id + 1\n\n\ndef createPatient(patient_id, name, journal_id, conn, c):\n '''\n Create a patient with a journal (use help-function)\n Input:\n @patient_id\n @name\n @journal_id\n @conn (Connection)\n @c (Cursor)\n Output:\n Boolean -> successfull or not successfull\n '''\n c.execute(\"INSERT INTO patients VALUES (?, ?, ?)\", (patient_id, name, journal_id))\n conn.commit()\n createJournal(patient_id, conn, c)\n\n\n ''' asserting that the entry has been created '''\n c.execute(\"SELECT * FROM patients WHERE patient_id = ?\", (patient_id,))\n data = c.fetchone()\n if data is None:\n return False\n else:\n return True\n\n\n\ndef createJournal(patient_id, conn, c):\n '''\n Creates a journal entry for a patient\n Input:\n @patient_id\n @conn (Connection)\n @c (Cursor)\n Output:\n Boolean -> successfull or not successfull\n '''\n c.execute(\"INSERT INTO journals VALUES (?)\", (patient_id,))\n conn.commit()\n\n ''' asserting that the entry has been created '''\n c.execute(\"SELECT * FROM journals WHERE patient_id = ?\", (patient_id,))\n data = c.fetchone()\n if data is None:\n return False\n else:\n return True\n\n\n\ndef createEmployee(employee_id, conn, c):\n '''\n Create a employee\n Input:\n @employee_id\n @conn (Connection)\n @c (Cursor)\n Output:\n Boolean -> successfull or not successfull\n '''\n\n c.execute(\"INSERT INTO employees VALUES (?)\", (employee_id,))\n conn.commit()\n\n ''' asserting that the entry has been created '''\n c.execute(\"SELECT id FROM employees WHERE id = ?\", (employee_id,))\n data = c.fetchone()\n if data is None:\n return False\n else:\n return True\n\ndef deletePatient(patient_id, conn, c):\n '''\n Delete an appointment from the schedule\n Input:\n @patient_id\n @conn (Connection)\n @c (Cursor)\n Output:\n Boolean -> successfull or not successfull\n '''\n c.execute(\"DELETE patients WHERE patient_id=?\", (patient_id))\n conn.commit()\n\n ''' asserting that the row has been deleted '''\n c.execute(\"SELECT * FROM patients WHERE patient_id = ?\", (patient_id,))\n data = c.fetchone()\n if data is None:\n return True\n else:\n return False\n\ndef deleteEmployee(employee_id, conn, c):\n '''\n Delete an employee the schema\n Input:\n @employee_id\n @conn (Connection)\n @c (Cursor)\n Output:\n Boolean -> successfull or not successfull\n '''\n\n c.execute(\"DELETE employees WHERE id=?\", (employee_id))\n conn.commit()\n\n ''' asserting that the entry has been deleted '''\n c.execute(\"SELECT id FROM employees WHERE id = ?\", (employee_id))\n data = c.fetchone()\n if data is None:\n return True\n else:\n return False\n\n\ndef deleteAppointment(appointment_id, conn, c):\n '''\n Delete an appointment from the schedule\n Input:\n @journal_id\n @conn (Connection)\n @c (Cursor)\n Output:\n Boolean -> successfull or not successfull\n '''\n\n c.execute(\"DELETE schedules WHERE id=?\", (appointment_id))\n conn.commit()\n\n ''' asserting that the entry has been deleted '''\n c.execute(\"SELECT id FROM schedules WHERE id = ?\", (appointment_id))\n data = c.fetchone()\n if data is None:\n return True\n else:\n return False\n\ndef createAppointment(appointment_id, patient_id, employee_id, timeFrom, timeTo, conn, c):\n '''\n Create an entry in the schedule\n Input:\n @patient_id: the ID of the patient to fetch the journal from\n @employee_id: The ID for the employee\n @timeFrom\n @timeTo\n @conn (Connection)\n @c (Cursor)\n Output:\n An entry in the schedule containing a @patient_id, @employee_id, a time from and a time to\n '''\n\n c.execute(\"INSERT INTO schedules VALUES (?, ?, ?, ?, ?)\", (appointment_id ,patient_id, employee_id, timeFrom, timeTo))\n conn.commit()\n\n\ndef createLogEntry(entry_id, patient_id, employee_id, ts, conn, c, color):\n '''\n Create an entry in the log\n Input:\n @patient_id: the ID of the patient to fetch the journal from\n @employee_id: The ID for the employee\n @timestamp: Date and time\n @conn (Connection)\n @c (Cursor)\n Output:\n An entry in the log containing @patient_id, @employee_id, @timestamp\n '''\n if(color == 1):\n warning_level = ('GREEN',)\n elif(color == 2):\n warning_level = ('YELLOW',)\n elif(color == 3):\n warning_level = ('RED',)\n elif(color == 4):\n warning_level = ('BLACK',)\n else:\n sys.exit(\"Input color was wrong. (Has to be green [1], orange [2], red [3] or black [4]\")\n\n c.execute(\"INSERT INTO entries VALUES (?, ?, ?, ?, ?)\", (entry_id, patient_id, employee_id, ts, warning_level[0]))\n conn.commit()\n\n\ndef printLogEntry(color):\n '''\n Prints all the red entries\n Input:\n @color: a code of the urgency of a journal-check\n\n Colors:\n GREEN == 1\n ORANGE == 2\n RED == 3\n BLACK == 0\n '''\n\n # Make sure the color is correct and that there will be no errors\n if(color == 1):\n symbol = ('GREEN',)\n elif(color == 2):\n symbol = ('YELLOW',)\n elif(color == 3):\n symbol = ('RED',)\n elif(color == 4):\n symbol = ('BLACK',)\n else:\n sys.exit(\"Input color was wrong. (Has to be green [1], orange [2], red [3] or black [4]\")\n\n # Create a connection to the log database\n conn = sqlite3.connect('log.db')\n c = conn.cursor()\n\n # Fetch all entries in the log with a given warning level (gree,orange,red) and print them\n print(c.fetchall())\n for row in c.execute('SELECT * FROM entries WHERE warning_level=?', symbol):\n print(row)\n\n # Close the connection\n conn.close()\n","sub_path":"code/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":11867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"602649339","text":"class Solution:\n def minCostToSupplyWater(self, n: int, wells: List[int], pipes: List[List[int]]) -> int:\n q = []\n for u, v, w in pipes:\n q.append((w, u, v))\n for i, cost in enumerate(wells):\n q.append((cost, 0, i + 1))\n q.sort()\n \n parents = [i for i in range(n + 1)]\n ranks = [1 for _ in range(n + 1)]\n \n def find(u):\n if parents[u] == u:\n return u\n parents[u] = find(parents[u])\n return parents[u]\n \n def union(u, v):\n pu, pv = find(u), find(v)\n if pu == pv:\n return False\n if ranks[pu] < ranks[pv]:\n parents[pu] = pv\n elif ranks[pu] > ranks[pv]:\n parents[pv] = pu\n else:\n parents[pv] = pu\n ranks[pu] += 1\n return True\n res = 0\n count = 0\n for w, u, v in q:\n rA, rB = find(u), find(v)\n if rA == rB:\n continue\n union(rA, rB)\n res += w\n # Optimize so that we don't traverse all edges\n count += 1\n if count == n:\n return res\n return res \n","sub_path":"python/1168. Optimize Water Distribution in a Village.py","file_name":"1168. Optimize Water Distribution in a Village.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"97427035","text":"# Simplified hnd posing for Manuel Bastioni rigs\n\nimport bpy\nimport bmesh\nimport math\nimport mathutils\nimport copy\nfrom mathutils import Vector\nfrom math import radians\nimport numpy as np\n\nfrom random import random, seed\nfrom bpy_extras import view3d_utils\nfrom bpy_extras.object_utils import world_to_camera_view\n\nfrom bpy.props import (StringProperty,\n\t\t\t\t\t\tBoolProperty,\n\t\t\t\t\t\tIntProperty,\n\t\t\t\t\t\tFloatProperty,\n\t\t\t\t\t\tFloatVectorProperty,\n\t\t\t\t\t\tEnumProperty,\n\t\t\t\t\t\tPointerProperty,\n\t\t\t\t\t\tBoolVectorProperty\n\t\t\t\t\t\t)\nfrom bpy.types import (Panel,\n\t\t\t\t\t\tOperator,\n\t\t\t\t\t\tAddonPreferences,\n\t\t\t\t\t\tPropertyGroup,\n\t\t\t\t\t\t)\n\nbl_info = {\n\t\"name\": \"WPL MB-Posing helpers\",\n\t\"author\": \"IPv6\",\n\t\"version\": (1, 0),\n\t\"blender\": (2, 78, 0),\n\t\"location\": \"View3D > T-panel > WPL\",\n\t\"description\" : \"\",\n\t\"warning\"\t : \"\",\n\t\"wiki_url\"\t: \"\",\n\t\"tracker_url\" : \"\",\n\t\"category\"\t: \"\"\n\t}\n\nMBArmatureBones_v02 = {\n\t\"head\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-30, -60, -10)), \"max\": Vector((30, 60, 10)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_head\",1]\n\t},\n\t\"neck\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-50, -50, -25)), \"max\": Vector((50, 50, 25)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_head\",0]\n\t},\n\t\"spine01\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-60, -30, -20)), \"max\": Vector((60, 30, 20)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_spine\",0]\n\t},\n\t\"spine02\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-60, -30, -20)), \"max\": Vector((60, 30, 20)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_spine\",1]\n\t},\n\t\"spine03\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-60, -30, -20)), \"max\": Vector((60, 30, 20)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_spine\",2]\n\t},\n\t\"pelvis\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-20, -20, -20)), \"max\": Vector((20, 20, 20)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": None\n\t},\n\t\"thigh_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-60, -50, -30)), \"max\": Vector((60, 50, 30)),\n\t\t\"spd\": Vector((0.5, 1, 1)),\n\t\t\"prop\": [\"mbh_legs_L\",0]\n\t},\n\t\"calf_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-100, 0, 0)), \"max\": Vector((2, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_legs_L\",1]\n\t},\n\t\"foot_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-70, -20, 0)), \"max\": Vector((50, 20, 0)),\n\t\t\"spd\": Vector((1, 1, 0)),\n\t\t\"prop\": [\"mbh_legs_L\",2]\n\t},\n\t\"toes_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-70, 0, 0)), \"max\": Vector((90, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_legs_L\",3]\n\t},\n\n\t\"clavicle_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-20, -30, -30)), \"max\": Vector((30, 60, 20)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_hands_L\",0]\n\t},\n\t\"upperarm_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-40, -30, -40)), \"max\": Vector((80, 50, 60)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_hands_L\",1]\n\t},\n\t\"lowerarm_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-10, -10, 0)), \"max\": Vector((130, 50, 0)),\n\t\t\"spd\": Vector((1, 1, 0)),\n\t\t\"prop\": [\"mbh_hands_L\",2]\n\t},\n\n\t\"hand_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-50, -50, -50)), \"max\": Vector((50, 50, 50)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_wrist_L\",0]\n\t},\n\t\"thumb01_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-20, -20, -40)), \"max\": Vector((30, 60, 20)),\n\t\t\"spd\": Vector((1, 1, 1)),\n\t\t\"prop\": [\"mbh_thumb_L\",0]\n\t},\n\t\"thumb02_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, -10)), \"max\": Vector((10, 0, 50)),\n\t\t\"spd\": Vector((1, 0, 1)),\n\t\t\"prop\": [\"mbh_thumb_L\",1]\n\t},\n\t\"thumb03_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((20, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_thumb_L\",2]\n\t},\n\t\"index01_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, -40)), \"max\": Vector((30, 0, 10)),\n\t\t\"spd\": Vector((1, 0, 1)),\n\t\t\"prop\": [\"mbh_index_L\",0]\n\t},\n\t\"index02_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_index_L\",1]\n\t},\n\t\"index03_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_index_L\",2]\n\t},\n\t\"middle01_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, -40)), \"max\": Vector((30, 0, 10)),\n\t\t\"spd\": Vector((1, 0, 1)),\n\t\t\"prop\": [\"mbh_middle_L\",0]\n\t},\n\t\"middle02_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_middle_L\",1]\n\t},\n\t\"middle03_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_middle_L\",2]\n\t},\n\t\"ring01_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, -40)), \"max\": Vector((30, 0, 10)),\n\t\t\"spd\": Vector((1, 0, 1)),\n\t\t\"prop\": [\"mbh_ring_L\",0]\n\t},\n\t\"ring02_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_ring_L\",1]\n\t},\n\t\"ring03_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_ring_L\",2]\n\t},\n\t\"pinky01_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, -40)), \"max\": Vector((30, 0, 10)),\n\t\t\"spd\": Vector((1, 0, 1)),\n\t\t\"prop\": [\"mbh_pinky_L\",0]\n\t},\n\t\"pinky02_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_pinky_L\",1]\n\t},\n\t\"pinky03_L\" : {\n\t\t\"def\": Vector((0, 0, 0)), \"min\": Vector((-80, 0, 0)), \"max\": Vector((10, 0, 0)),\n\t\t\"spd\": Vector((1, 0, 0)),\n\t\t\"prop\": [\"mbh_pinky_L\",2]\n\t},\n}\n\ndef deduceRightBones():\n\tbones = list(MBArmatureBones_v02.keys())\n\tfor boneName_L in bones:\n\t\tif boneName_L.find(\"_L\") > 0:\n\t\t\tboneName_R = boneName_L.replace(\"_L\",\"_R\")\n\t\t\tpropName_L = MBArmatureBones_v02[boneName_L][\"prop\"][0]\n\t\t\tpropName_R = propName_L.replace(\"_L\",\"_R\")\n\t\t\tMBArmatureBones_v02[boneName_R] = copy.deepcopy(MBArmatureBones_v02[boneName_L])\n\t\t\tMBArmatureBones_v02[boneName_R][\"prop\"][0] = propName_R\ndeduceRightBones()\n\ndef mixBone(context, boneName, curMuls, addVal, rememAfter, applyLims):\n\tscene = context.scene\n\tarmatr = context.active_object\n\tif armatr is None or not isinstance(armatr.data, bpy.types.Armature):\n\t\treturn\n\tboneNames = armatr.pose.bones.keys()\n\tif boneName in boneNames:\n\t\tbone = armatr.pose.bones[boneName]\n\t\tbone.rotation_mode = \"ZYX\"\n\t\tminVal = MBArmatureBones_v02[boneName][\"min\"]\n\t\tmaxVal = MBArmatureBones_v02[boneName][\"max\"]\n\t\tcurVal = bone.rotation_euler\n\t\tnewX = curMuls[0]*curVal[0]+addVal[0]\n\t\tnewY = curMuls[1]*curVal[1]+addVal[1]\n\t\tnewZ = curMuls[2]*curVal[2]+addVal[2]\n\t\tif applyLims:\n\t\t\tnewX = np.clip(newX,radians(minVal[0]),radians(maxVal[0]))\n\t\t\tnewY = np.clip(newY,radians(minVal[1]),radians(maxVal[1]))\n\t\t\tnewZ = np.clip(newZ,radians(minVal[2]),radians(maxVal[2]))\n\t\tnewVal = Vector((newX,newY,newZ))\n\t\tbone.rotation_euler = newVal\n\t\tif rememAfter:\n\t\t\tMBArmatureBones_v02[boneName][\"rest\"] = newVal\n\t\tprint(\"Updating bone\",boneName,newVal)\n\ndef applyAngls(self,context):\n\twpposeOpts = context.scene.wplPoseMBSettings\n\tfor boneName in MBArmatureBones_v02:\n\t\tdefSpd = MBArmatureBones_v02[boneName][\"spd\"]\n\t\tdefVal = MBArmatureBones_v02[boneName][\"def\"]\n\t\tboneProp = MBArmatureBones_v02[boneName][\"prop\"]\n\t\tif boneProp is not None:\n\t\t\tpropProp = wpposeOpts.get(boneProp[0])\n\t\t\tisEnabled = 0\n\t\t\tif propProp is not None:\n\t\t\t\tisEnabled = propProp[boneProp[1]]\n\t\t\tif isEnabled > 0:\n\t\t\t\trefVal = defVal\n\t\t\t\tif \"rest\" in MBArmatureBones_v02[boneName]:\n\t\t\t\t\trefVal = MBArmatureBones_v02[boneName][\"rest\"]\n\t\t\t\tnewX = refVal[0]+wpposeOpts.mbh_foldAngle*defSpd[0]\n\t\t\t\tnewY = refVal[1]+wpposeOpts.mbh_twistAngle*defSpd[1]\n\t\t\t\tnewZ = refVal[2]+wpposeOpts.mbh_tiltAngle*defSpd[2]\n\t\t\t\trotVec = Vector((newX,newY,newZ))\n\t\t\t\t#print(\"Applying\", rotVec, \"to\", boneName)\n\t\t\t\tmixBone(context, boneName, Vector((0,0,0)), rotVec, False, wpposeOpts.mbh_applyLimits)\n\tbpy.context.scene.update()\n\treturn None\n\ndef restAngls(self,context):\n\twpposeOpts = context.scene.wplPoseMBSettings\n\tfor boneName in MBArmatureBones_v02:\n\t\t# isEnabled = 0\n\t\t# boneProp = MBArmatureBones_v02[boneName][\"prop\"]\n\t\t# propProp = wpposeOpts.get(boneProp[0])\n\t\t# if propProp is not None:\n\t\t\t# isEnabled = propProp[boneProp[1]]\n\t\t\t# print(\"restAngls\",boneName,isEnabled,boneProp[0],boneProp[1])\n\t\tmixBone(context, boneName, Vector((1,1,1)), Vector((0,0,0)), True, False)\n\twpposeOpts.mbh_foldAngle = 0\n\twpposeOpts.mbh_twistAngle = 0\n\twpposeOpts.mbh_tiltAngle = 0\n\tbpy.context.scene.update()\n\treturn None\n\ndef deflAngls(self,context):\n\twpposeOpts = context.scene.wplPoseMBSettings\n\twpposeOpts.mbh_foldAngle = 0\n\twpposeOpts.mbh_twistAngle = 0\n\twpposeOpts.mbh_tiltAngle = 0\n\n\tfor boneName in MBArmatureBones_v02:\n\t\tdefVal = MBArmatureBones_v02[boneName][\"def\"]\n\t\tboneProp = MBArmatureBones_v02[boneName][\"prop\"]\n\t\tif boneProp is not None:\n\t\t\tpropProp = wpposeOpts.get(boneProp[0])\n\t\t\tisEnabled = 0\n\t\t\tif propProp is not None:\n\t\t\t\tisEnabled = propProp[boneProp[1]]\n\t\t\tif isEnabled > 0:\n\t\t\t\tmixBone(context, boneName, Vector((0,0,0)), Vector((defVal[0],defVal[1],defVal[2])), True, False)\n\tbpy.context.scene.update()\n\treturn None\n\n#############################################################################\n#############################################################################\n#############################################################################\nclass wplPoseMBSettings(PropertyGroup):\n\tmbh_head = BoolVectorProperty(\n\t\tsize=2,\n\t\tdefault=[False,False], update=restAngls)\n\tmbh_spine = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_legs_L = BoolVectorProperty(\n\t\tsize=4,\n\t\tdefault=[False,False,False,False], update=restAngls)\n\tmbh_legs_R = BoolVectorProperty(\n\t\tsize=4,\n\t\tdefault=[False,False,False,False], update=restAngls)\n\n\tmbh_hands_L = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_hands_R = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\n\tmbh_wrist_L = BoolVectorProperty(\n\t\tsize=1,\n\t\tdefault=[False], update=restAngls)\n\tmbh_thumb_L = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_index_L = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_middle_L = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_ring_L = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_pinky_L = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_wrist_R = BoolVectorProperty(\n\t\tsize=1,\n\t\tdefault=[False], update=restAngls)\n\tmbh_thumb_R = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_index_R = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_middle_R = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_ring_R = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_pinky_R = BoolVectorProperty(\n\t\tsize=3,\n\t\tdefault=[False,False,False], update=restAngls)\n\tmbh_foldAngle = FloatProperty(\n\t\tname = \"Fold\",\n\t\tmin = -3.14,\n\t\tsoft_min = -3.14,\n\t\tmax = 3.14,\n\t\tsoft_max = 3.14,\n\t\tstep = 10.0,\n\t\tunit = 'ROTATION',\n\t\tdefault = 0,\n\t\tupdate=applyAngls\n\t)\n\tmbh_tiltAngle = FloatProperty(\n\t\tname = \"Tilt\",\n\t\tmin = -3.14,\n\t\tsoft_min = -3.14,\n\t\tmax = 3.14,\n\t\tsoft_max = 3.14,\n\t\tstep = 3.0,\n\t\tunit = 'ROTATION',\n\t\tdefault = 0,\n\t\tupdate=applyAngls\n\t)\n\tmbh_twistAngle = FloatProperty(\n\t\tname = \"Twist\",\n\t\tmin = -3.14,\n\t\tsoft_min = -3.14,\n\t\tmax = 3.14,\n\t\tsoft_max = 3.14,\n\t\tstep = 10.0,\n\t\tunit = 'ROTATION',\n\t\tdefault = 0,\n\t\tupdate=applyAngls\n\t)\n\tmbh_applyLimits = BoolProperty(\n\t\tdefault = True\n\t)\n\n#############################################################################\n#############################################################################\n#############################################################################\nclass wplposing_mbbn2zero( bpy.types.Operator ):\n\tbl_idname = \"mesh.wplposing_mbbn2zero\"\n\tbl_label = \"Reset bones to default angles\"\n\tbl_options = {'REGISTER', 'UNDO'}\n\n\t@classmethod\n\tdef poll( cls, context ):\n\t\tp = context.object and context.object.data and (isinstance(context.scene.objects.active, bpy.types.Object) and isinstance(context.scene.objects.active.data, bpy.types.Armature))\n\t\treturn p\n\n\tdef execute( self, context ):\n\t\tdeflAngls(self, context)\n\t\treturn {'FINISHED'}\n\nclass wplposing_clearchbx( bpy.types.Operator ):\n\tbl_idname = \"mesh.wplposing_clearchbx\"\n\tbl_label = \"Clear posing checkboxes\"\n\tbl_options = {'REGISTER', 'UNDO'}\n\n\t@classmethod\n\tdef poll( cls, context ):\n\t\tp = context.object and context.object.data and (isinstance(context.scene.objects.active, bpy.types.Object) and isinstance(context.scene.objects.active.data, bpy.types.Armature))\n\t\treturn p\n\n\tdef execute( self, context ):\n\t\twpposeOpts = context.scene.wplPoseMBSettings\n\t\tfor boneName in MBArmatureBones_v02:\n\t\t\tboneProp = MBArmatureBones_v02[boneName][\"prop\"]\n\t\t\tif boneProp is not None:\n\t\t\t\tpropProp = wpposeOpts.get(boneProp[0])\n\t\t\t\tif propProp is not None:\n\t\t\t\t\tpropProp[boneProp[1]] = False\n\t\treturn {'FINISHED'}\n\nclass wplposing_bn_select_active( bpy.types.Operator ):\n\tbl_idname = \"mesh.wplposing_bn_select_active\"\n\tbl_label = \"Select corresponding bones\"\n\tbl_options = {'REGISTER', 'UNDO'}\n\n\t@classmethod\n\tdef poll( cls, context ):\n\t\tp = context.object and context.object.data and (isinstance(context.scene.objects.active, bpy.types.Object) and isinstance(context.scene.objects.active.data, bpy.types.Armature))\n\t\treturn p\n\n\tdef execute( self, context ):\n\t\twpposeOpts = context.scene.wplPoseMBSettings\n\t\tscene = context.scene\n\t\tarmatr = context.active_object\n\t\tif armatr is None or not isinstance(armatr.data, bpy.types.Armature):\n\t\t\treturn {'CANCELLED'}\n\t\tboneNames = armatr.pose.bones.keys()\n\t\tfor boneName in boneNames:\n\t\t\tif boneName in MBArmatureBones_v02:\n\t\t\t\tboneProp = MBArmatureBones_v02[boneName][\"prop\"]\n\t\t\t\tif boneProp is not None:\n\t\t\t\t\tpropProp = wpposeOpts.get(boneProp[0])\n\t\t\t\t\tif propProp is not None and propProp[boneProp[1]] == True:\n\t\t\t\t\t\tbone = armatr.data.bones[boneName]\n\t\t\t\t\t\tbone.select = True\n\t\treturn {'FINISHED'}\n\nclass wplposing_bn_deselect_inactive( bpy.types.Operator ):\n\tbl_idname = \"mesh.wplposing_bn_deselect_inactive\"\n\tbl_label = \"Select corresponding bones\"\n\tbl_options = {'REGISTER', 'UNDO'}\n\n\t@classmethod\n\tdef poll( cls, context ):\n\t\tp = context.object and context.object.data and (isinstance(context.scene.objects.active, bpy.types.Object) and isinstance(context.scene.objects.active.data, bpy.types.Armature))\n\t\treturn p\n\n\tdef execute( self, context ):\n\t\twpposeOpts = context.scene.wplPoseMBSettings\n\t\tscene = context.scene\n\t\tarmatr = context.active_object\n\t\tif armatr is None or not isinstance(armatr.data, bpy.types.Armature):\n\t\t\treturn {'CANCELLED'}\n\t\tboneNames = armatr.pose.bones.keys()\n\t\tfor boneName in boneNames:\n\t\t\tneed2deselect = True\n\t\t\tif boneName in MBArmatureBones_v02:\n\t\t\t\tboneProp = MBArmatureBones_v02[boneName][\"prop\"]\n\t\t\t\tif boneProp is not None:\n\t\t\t\t\tpropProp = wpposeOpts.get(boneProp[0])\n\t\t\t\t\tif propProp is not None and propProp[boneProp[1]] == True:\n\t\t\t\t\t\tneed2deselect = False\n\t\t\tif need2deselect:\n\t\t\t\tbone = armatr.data.bones[boneName]\n\t\t\t\tbone.select = False\n\t\treturn {'FINISHED'}\n#############################################################################\n#############################################################################\n#############################################################################\nclass WPLPosingMBArmBody_Panel(bpy.types.Panel):\n\tbl_label = \"Posing: Body\"\n\tbl_space_type = 'VIEW_3D'\n\tbl_region_type = 'TOOLS'\n\tbl_context = \"posemode\"\n\tbl_category = 'ManuelBastioniLAB'\n\n\tdef draw_header(self, context):\n\t\tlayout = self.layout\n\t\tlayout.label(text=\"\")\n\n\tdef draw(self, context):\n\t\tlayout = self.layout\n\t\tscene = context.scene\n\t\twpposeOpts = context.scene.wplPoseMBSettings\n\n\t\t# display the properties\n\t\tcol = layout.column()\n\t\trow_ftt1 = col.row(align=True)\n\t\trow_ftt1.prop(wpposeOpts, \"mbh_foldAngle\", text=\"Fold\")\n\t\trow_ftt1.prop(wpposeOpts, \"mbh_twistAngle\", text=\"Twist\")\n\t\trow_ftt1.prop(wpposeOpts, \"mbh_tiltAngle\", text=\"Tilt\")\n\t\tbox_ftt1 = col.box()\n\t\trow_mbh_head = box_ftt1.row()\n\t\trow_mbh_head.prop(wpposeOpts, \"mbh_head\", text=\"Head\")\n\t\trow_mbh_spine = box_ftt1.row()\n\t\trow_mbh_spine.prop(wpposeOpts, \"mbh_spine\", text=\"Spine\")\n\t\tbox_ftt2 = col.box()\n\t\trow_mbh_hands_L = box_ftt2.row()\n\t\trow_mbh_hands_L.prop(wpposeOpts, \"mbh_hands_L\", text=\"L:Hand\")\n\t\trow_mbh_hands_R = box_ftt2.row()\n\t\trow_mbh_hands_R.prop(wpposeOpts, \"mbh_hands_R\", text=\"R:Hand\")\n\t\tbox_ftt3 = col.box()\n\t\trow_mbh_legs_L = box_ftt3.row()\n\t\trow_mbh_legs_L.prop(wpposeOpts, \"mbh_legs_L\", text=\"L:Leg\")\n\t\trow_mbh_legs_R = box_ftt3.row()\n\t\trow_mbh_legs_R.prop(wpposeOpts, \"mbh_legs_R\", text=\"R:Leg\")\n\t\tcol.operator(\"mesh.wplposing_mbbn2zero\", text=\"Reset to default\")\n\n\nclass WPLPosingMBArmLHand_Panel(bpy.types.Panel):\n\tbl_label = \"Posing: Left palm\"\n\tbl_space_type = 'VIEW_3D'\n\tbl_region_type = 'TOOLS'\n\tbl_context = \"posemode\"\n\tbl_category = 'ManuelBastioniLAB'\n\n\tdef draw_header(self, context):\n\t\tlayout = self.layout\n\t\tlayout.label(text=\"\")\n\n\tdef draw(self, context):\n\t\tlayout = self.layout\n\t\tscene = context.scene\n\t\twpposeOpts = context.scene.wplPoseMBSettings\n\n\t\t# display the properties\n\t\tcol = layout.column()\n\n\t\trow_ftt2 = col.row(align=True)\n\t\trow_ftt2.prop(wpposeOpts, \"mbh_foldAngle\", text=\"Fold\")\n\t\trow_ftt2.prop(wpposeOpts, \"mbh_twistAngle\", text=\"Twist\")\n\t\trow_ftt2.prop(wpposeOpts, \"mbh_tiltAngle\", text=\"Tilt\")\n\t\tbox_ftt4 = col.box()\n\t\trow_mbh_wrist_L = box_ftt4.row()\n\t\trow_mbh_wrist_L.prop(wpposeOpts, \"mbh_wrist_L\", text=\"Wrist\")\n\t\tbox_ftt5 = col.box()\n\t\trow_mbh_thumb_L = box_ftt5.row()\n\t\trow_mbh_thumb_L.prop(wpposeOpts, \"mbh_thumb_L\", text=\"=:-:-\")\n\t\trow_mbh_index_L = box_ftt5.row()\n\t\trow_mbh_index_L.prop(wpposeOpts, \"mbh_index_L\", text=\"=:=:>\")\n\t\trow_mbh_middle_L = box_ftt5.row()\n\t\trow_mbh_middle_L.prop(wpposeOpts, \"mbh_middle_L\", text=\"=:=:-\")\n\t\trow_mbh_ring_L = box_ftt5.row()\n\t\trow_mbh_ring_L.prop(wpposeOpts, \"mbh_ring_L\", text=\"=:=:-\")\n\t\trow_mbh_pinky_L = box_ftt5.row()\n\t\trow_mbh_pinky_L.prop(wpposeOpts, \"mbh_pinky_L\", text=\"=:-:-\")\n\t\tcol.operator(\"mesh.wplposing_mbbn2zero\", text=\"Reset to default\")\n\nclass WPLPosingMBArmRHand_Panel(bpy.types.Panel):\n\tbl_label = \"Posing: Right palm\"\n\tbl_space_type = 'VIEW_3D'\n\tbl_region_type = 'TOOLS'\n\tbl_context = \"posemode\"\n\tbl_category = 'ManuelBastioniLAB'\n\n\tdef draw_header(self, context):\n\t\tlayout = self.layout\n\t\tlayout.label(text=\"\")\n\n\tdef draw(self, context):\n\t\tlayout = self.layout\n\t\tscene = context.scene\n\t\twpposeOpts = context.scene.wplPoseMBSettings\n\n\t\t# display the properties\n\t\tcol = layout.column()\n\n\t\trow_ftt3 = col.row(align=True)\n\t\trow_ftt3.prop(wpposeOpts, \"mbh_foldAngle\", text=\"Fold\")\n\t\trow_ftt3.prop(wpposeOpts, \"mbh_twistAngle\", text=\"Twist\")\n\t\trow_ftt3.prop(wpposeOpts, \"mbh_tiltAngle\", text=\"Tilt\")\n\t\tbox_ftt6 = col.box()\n\t\trow_mbh_wrist_R = box_ftt6.row()\n\t\trow_mbh_wrist_R.prop(wpposeOpts, \"mbh_wrist_R\", text=\"Wrist\")\n\t\tbox_ftt7 = col.box()\n\t\trow_mbh_thumb_R = box_ftt7.row()\n\t\trow_mbh_thumb_R.prop(wpposeOpts, \"mbh_thumb_R\", text=\"=:-:-\")\n\t\trow_mbh_index_R = box_ftt7.row()\n\t\trow_mbh_index_R.prop(wpposeOpts, \"mbh_index_R\", text=\"=:=:>\")\n\t\trow_mbh_middle_R = box_ftt7.row()\n\t\trow_mbh_middle_R.prop(wpposeOpts, \"mbh_middle_R\", text=\"=:=:-\")\n\t\trow_mbh_ring_R = box_ftt7.row()\n\t\trow_mbh_ring_R.prop(wpposeOpts, \"mbh_ring_R\", text=\"=:=:-\")\n\t\trow_mbh_pinky_R = box_ftt7.row()\n\t\trow_mbh_pinky_R.prop(wpposeOpts, \"mbh_pinky_R\", text=\"=:-:-\")\n\t\tcol.operator(\"mesh.wplposing_mbbn2zero\", text=\"Reset to default\")\n\nclass WPLPosingMBArmSettings_Panel(bpy.types.Panel):\n\tbl_label = \"Opts and Utils\"\n\tbl_space_type = 'VIEW_3D'\n\tbl_region_type = 'TOOLS'\n\tbl_context = \"posemode\"\n\tbl_category = 'ManuelBastioniLAB'\n\n\tdef draw_header(self, context):\n\t\tlayout = self.layout\n\t\tlayout.label(text=\"\")\n\n\tdef draw(self, context):\n\t\tlayout = self.layout\n\t\tscene = context.scene\n\t\twpposeOpts = context.scene.wplPoseMBSettings\n\n\t\t# display the properties\n\t\tcol = layout.column()\n\t\tcol.prop(wpposeOpts, \"mbh_applyLimits\", text=\"Apply physical rotation limits\")\n\t\tcol.operator(\"mesh.wplposing_clearchbx\", text=\"Clear checkboxes\")\n\t\trow1 = col.row()\n\t\trow1.operator(\"mesh.wplposing_bn_select_active\", text=\"Select bones\")\n\t\trow1.operator(\"mesh.wplposing_bn_deselect_inactive\", text=\"Deselect others\")\n\n#############################################################################\n#############################################################################\n#############################################################################\n\ndef register():\n\tprint(\"WPLPosingSt_Panel registered\")\n\tbpy.utils.register_module(__name__)\n\tbpy.types.Scene.wplPoseMBSettings = PointerProperty(type=wplPoseMBSettings)\n\ndef unregister():\n\tdel bpy.types.Scene.wplPoseMBSettings\n\tbpy.utils.unregister_module(__name__)\n\nif __name__ == \"__main__\":\n\tregister()\n","sub_path":"blender/addon_experiments/old/posingManbust_v01.py","file_name":"posingManbust_v01.py","file_ext":"py","file_size_in_byte":20898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"374192806","text":"import pandas as pd\nimport json\nimport os\nimport glob\nimport regex\n\ndef __clean_utt(utt):\n utt = utt.lower()\n utt = regex.sub(r'(?![a-zæøåéüö_\\s])$*.', '', utt)\n utt = utt.replace(\"é\", \"e\")\n utt = utt.replace('ö', 'ø')\n utt = utt.replace('ü', 'u')\n utt = regex.sub(' +', ' ', utt)\n return utt\n\ndef __write_to_csv(p, meta, csv:pd.DataFrame):\n split_p = p.rsplit(os.sep, 1)[1].split('_')\n station = split_p[0]\n substation = split_p[1]\n speaker_id = split_p[2]\n\n utterances = {f'{station}_{substation}_{speaker_id}_{key}': __clean_utt(val) for key,val in meta.items() if key.startswith('u')}\n filename_col = list(utterances.keys())\n transcription_col = list(utterances.values())\n gender_col = [meta['sex']]*len(utterances)\n age_col = [meta['age']]*len(utterances)\n add_csv = pd.DataFrame.from_dict({\n 'file' : filename_col,\n 'trans' : transcription_col,\n 'gender' : gender_col,\n 'age' : age_col\n })\n\n return csv.append(add_csv, ignore_index=True)\n\ndef preprocess(args):\n csv = pd.DataFrame()\n csv_out = os.path.join(args.out_dir, args.csv)\n json_paths = [os.path.abspath(p) for p in glob.glob(os.path.join(args.meta_data, '*.json'))]\n \n print(f'Preprocessing {len(json_paths)} .json meta data files')\n for p in json_paths:\n print(f'Writing {p} to csv file')\n with open(p, 'r') as f:\n meta = json.loads(f.read())\n csv = __write_to_csv(p, meta, csv)\n\n if not args.overwrite:\n old_csv = pd.read_csv(csv_out)\n csv = old_csv.append(csv, ignore_index=True)\n \n print(f'Saving all meta data to: {csv_out}')\n csv.to_csv(csv_out, index=False)\n","sub_path":"stt/danspeech/meta.py","file_name":"meta.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"448057319","text":"\"\"\"\nOmega Miya 使用指南\n\n- /Omega Init - bot首次加入新群组/首次添加bot好友后, 须使用本命令进行初始化\n- /Omega Upgrade - 手动更新本群组信息\n- /Omega Notice - 为本群组配置通知权限(订阅类插件是否通知)\n- /Omega Command - 为本群组配置命令权限(是否允许使用命令)\n- /Omega SetLevel - 为本群组配置命令等级(对于低于命令要求等级的群组, 该命令不会被响应)\n- /Omega QuitGroup - 命令 Bot 立即退群\n\"\"\"\nfrom dataclasses import dataclass\nfrom nonebot import on_command, logger\nfrom nonebot.exception import FinishedException\nfrom nonebot.plugin.export import export\nfrom nonebot.permission import SUPERUSER\nfrom nonebot.typing import T_State\nfrom nonebot.adapters.cqhttp.bot import Bot\nfrom nonebot.adapters.cqhttp.event import MessageEvent, GroupMessageEvent, PrivateMessageEvent\nfrom nonebot.adapters.cqhttp.permission import GROUP_ADMIN, GROUP_OWNER, PRIVATE_FRIEND\nfrom omega_miya.database import DBBot, DBBotGroup, DBUser, DBAuth, DBFriend, Result\nfrom omega_miya.utils.omega_plugin_utils import init_export, ProcessUtils\nfrom .background_tasks import scheduler\nfrom .utils import upgrade_group_member\n\n# Custom plugin usage text\n__plugin_custom_name__ = 'Omega'\n__plugin_usage__ = r'''【Omega 管理插件】\nOmega机器人管理\n\n**Usage**\n**GroupAdmin and SuperUser Only**\n/Omega Init\n/Omega Upgrade\n/Omega Notice \n/Omega Command \n/Omega SetLevel \n/Omega ShowPermission\n/Omega ResetPermission\n/Omega QuitGroup\n\n**Friend Private Only**\n/Omega Init\n/Omega Enable\n/Omega Disable'''\n\n# Init plugin export\ninit_export(export(), __plugin_custom_name__, __plugin_usage__)\n\n# 注册事件响应器\nomega = on_command('Omega', rule=None, aliases={'omega'},\n permission=GROUP_ADMIN | GROUP_OWNER | SUPERUSER | PRIVATE_FRIEND, priority=10, block=True)\n\n\n# 修改默认参数处理\n@omega.args_parser\nasync def parse(bot: Bot, event: MessageEvent, state: T_State):\n args = str(event.get_plaintext()).strip().lower().split()\n if not args:\n await omega.reject('你似乎没有发送有效的参数呢QAQ, 请重新发送:')\n state[state[\"_current_key\"]] = args[0]\n if state[state[\"_current_key\"]] == '取消':\n await omega.finish('操作已取消')\n\n\n@omega.handle()\nasync def handle_first_receive(bot: Bot, event: MessageEvent, state: T_State):\n args = str(event.get_plaintext()).strip().lower().split()\n if args and len(args) == 1:\n state['sub_command'] = args[0]\n state['sub_arg'] = None\n elif args and len(args) == 2:\n state['sub_command'] = args[0]\n state['sub_arg'] = args[1]\n else:\n await omega.finish('你好呀~ 我是Omega Miya~ 请问您今天要来点喵娘吗?')\n\n\n@omega.got('sub_command',\n prompt='执行操作?\\n【Init/Upgrade/Notice/Command/SetLevel/ShowPermission/ResetPermission/QuitGroup】')\nasync def handle_sub_command(bot: Bot, event: GroupMessageEvent, state: T_State):\n if not isinstance(event, GroupMessageEvent):\n return\n # 子命令列表\n command = {\n 'init': group_init,\n 'upgrade': group_upgrade,\n 'notice': set_group_notice,\n 'command': set_group_command,\n 'setlevel': set_group_level,\n 'showpermission': show_group_permission,\n 'resetpermission': reset_group_permission,\n 'quitgroup': quit_group\n }\n # 需要回复信息的命令列表\n need_reply = [\n 'showpermission'\n ]\n sub_command = state[\"sub_command\"]\n # 在这里对参数进行验证\n if sub_command not in command.keys():\n await omega.finish('没有这个命令哦QAQ')\n result = await command[sub_command](bot=bot, event=event, state=state)\n if result.success():\n logger.success(f\"Group: {event.group_id}, {sub_command}, Success, {result.info}\")\n if sub_command in need_reply:\n await omega.finish(result.result)\n else:\n await omega.finish('Success')\n else:\n logger.error(f\"Group: {event.group_id}, {sub_command}, Failed, {result.info}\")\n await omega.finish('Failed QAQ')\n\n\n@omega.got('sub_command', prompt='执行操作?\\n【Init/Enable/Disable】')\nasync def handle_sub_command(bot: Bot, event: PrivateMessageEvent, state: T_State):\n if not isinstance(event, PrivateMessageEvent):\n return\n # 子命令列表\n command = {\n 'init': friend_init,\n 'enable': friend_private_enable,\n 'disable': friend_private_disable\n }\n # 需要回复信息的命令列表\n need_reply = [\n 'init',\n 'enable',\n 'disable'\n ]\n sub_command = state[\"sub_command\"]\n # 在这里对参数进行验证\n if sub_command not in command.keys():\n await omega.finish('没有这个命令哦QAQ')\n result = await command[sub_command](bot=bot, event=event, state=state)\n if result.success():\n logger.success(f\"Private friend: {event.user_id}, {sub_command}, Success, {result.info}\")\n if sub_command in need_reply:\n await omega.finish(result.result)\n else:\n await omega.finish('Success')\n else:\n logger.error(f\"Private friend: {event.user_id}, {sub_command}, Failed, {result.info}\")\n if sub_command in need_reply:\n await omega.finish(result.result)\n else:\n await omega.finish('Failed QAQ')\n\n\nasync def friend_init(bot: Bot, event: PrivateMessageEvent, state: T_State) -> Result.TextResult:\n user_id = event.user_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n # 调用api获取好友列表\n friends_list = await bot.get_friend_list()\n actual_friend_list = [int(x.get('user_id')) for x in friends_list]\n if user_id not in actual_friend_list:\n return Result.TextResult(error=True, info='Not in friends list', result='错误, 不在好友列表中')\n\n user_info = [x for x in friends_list if int(x.get('user_id')) == user_id][0]\n nickname = user_info.get('nickname')\n remark = user_info.get('remark')\n\n friend = DBFriend(user_id=user_id, self_bot=self_bot)\n\n # 更新用户表\n add_user_result = await friend.add(nickname=nickname)\n if add_user_result.error:\n return Result.TextResult(error=True, info=add_user_result.info, result='错误, 请联系管理员处理')\n\n # 初始化好友authnode\n await init_user_auth_node(user_id=user_id, self_bot=self_bot)\n\n set_friend_result = await friend.set_friend(nickname=nickname, remark=remark, private_permissions=1)\n if set_friend_result.success():\n return Result.TextResult(error=False, info='Success', result='成功, 现在可以使用私聊命令了')\n else:\n return Result.TextResult(error=True, info=set_friend_result.info, result='错误, 请联系管理员处理')\n\n\nasync def friend_private_enable(bot: Bot, event: PrivateMessageEvent, state: T_State) -> Result.TextResult:\n user_id = event.user_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n # 初始化好友authnode\n await init_user_auth_node(user_id=user_id, self_bot=self_bot)\n\n friend = DBFriend(user_id=user_id, self_bot=self_bot)\n result = await friend.set_private_permission(private_permissions=1)\n if result.success():\n return Result.TextResult(error=False, info='Success', result='成功, 已启用私聊功能, 权限节点已设置为默认值')\n else:\n return Result.TextResult(error=True, info=result.info, result='失败, 请先尝试\"/Omega Init\", 若仍失败请联系管理员处理')\n\n\nasync def friend_private_disable(bot: Bot, event: PrivateMessageEvent, state: T_State) -> Result.TextResult:\n user_id = event.user_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n # 初始化好友authnode\n await init_user_auth_node(user_id=user_id, self_bot=self_bot)\n\n friend = DBFriend(user_id=user_id, self_bot=self_bot)\n result = await friend.set_private_permission(private_permissions=0)\n if result.success():\n return Result.TextResult(error=False, info='Success', result='成功, 已禁用私聊功能, 权限节点已重置为默认值')\n else:\n return Result.TextResult(error=True, info=result.info, result='失败, 请先尝试\"/Omega Init\", 若仍失败请联系管理员处理')\n\n\nasync def group_init(bot: Bot, event: GroupMessageEvent, state: T_State) -> Result.IntResult:\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n # 调用api获取群信息\n group_info = await bot.get_group_info(group_id=group_id)\n group_name = group_info['group_name']\n group_memo = group_info.get('group_memo')\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n\n # 添加并初始化群信息\n _result = await group.add(name=group_name)\n if not _result.success():\n return Result.IntResult(True, _result.info, -1)\n\n _result = await group.set_bot_group(group_memo=group_memo)\n if not _result.success():\n return Result.IntResult(True, _result.info, -1)\n\n _result = await group.permission_set(notice=1, command=1, level=10)\n if not _result.success():\n return Result.IntResult(True, _result.info, -1)\n\n # 初始化群组authnode\n await init_group_auth_node(group_id=group_id, self_bot=self_bot)\n\n _result = await group.member_clear()\n if not _result.success():\n return Result.IntResult(True, _result.info, -1)\n\n # 添加用户\n group_member_list = await bot.get_group_member_list(group_id=group_id)\n tasks = [upgrade_group_member(user_info=user_info, group=group) for user_info in group_member_list]\n await ProcessUtils.fragment_process(tasks=tasks, fragment_size=50, log_flag='group_init')\n await group.init_member_status()\n\n return Result.IntResult(False, f'Tasks Completed', 0)\n\n\nasync def group_upgrade(bot: Bot, event: GroupMessageEvent, state: T_State) -> Result.IntResult:\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n # 调用api获取群信息\n group_info = await bot.get_group_info(group_id=group_id)\n group_name = group_info['group_name']\n group_memo = group_info.get('group_memo')\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n\n # 更新群信息\n _result = await group.add(name=group_name)\n if not _result.success():\n return Result.IntResult(True, _result.info, -1)\n\n _result = await group.set_bot_group(group_memo=group_memo)\n if not _result.success():\n return Result.IntResult(True, _result.info, -1)\n\n # 更新用户\n group_member_list = await bot.get_group_member_list(group_id=group_id)\n failed_user = []\n\n # 首先清除数据库中退群成员\n exist_member_list = [int(x.get('user_id')) for x in group_member_list]\n member_res = await group.member_list()\n db_member_list = [user_id for user_id, nickname in member_res.result]\n del_member_list = list(set(db_member_list).difference(set(exist_member_list)))\n\n for user_id in del_member_list:\n await group.member_del(user=DBUser(user_id=user_id))\n\n # 更新群成员\n tasks = [upgrade_group_member(user_info=user_info, group=group) for user_info in group_member_list]\n await ProcessUtils.fragment_process(tasks=tasks, fragment_size=50, log_flag='group_upgrade')\n await group.init_member_status()\n\n return Result.IntResult(False, f'Tasks Completed', 0)\n\n\nasync def set_group_notice(bot: Bot, event: GroupMessageEvent, state: T_State) -> Result.IntResult:\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n permission_res = await group.permission_info()\n if permission_res.error:\n return Result.IntResult(True, permission_res.info, -1)\n\n _notice, group_command, group_level = permission_res.result\n\n if state['sub_arg'] == 'on':\n result = await group.permission_set(notice=1, command=group_command, level=group_level)\n elif state['sub_arg'] == 'off':\n result = await group.permission_set(notice=0, command=group_command, level=group_level)\n else:\n result = Result.IntResult(True, 'Missing parameters or Illegal parameter', -1)\n\n return result\n\n\nasync def set_group_command(bot: Bot, event: GroupMessageEvent, state: T_State) -> Result.IntResult:\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n permission_res = await group.permission_info()\n if permission_res.error:\n return Result.IntResult(True, permission_res.info, -1)\n\n group_notice, _command, group_level = permission_res.result\n\n if state['sub_arg'] == 'on':\n # 初始化群组authnode\n await init_group_auth_node(group_id=group_id, self_bot=self_bot)\n result = await group.permission_set(notice=group_notice, command=1, level=group_level)\n elif state['sub_arg'] == 'off':\n # 初始化群组authnode\n await init_group_auth_node(group_id=group_id, self_bot=self_bot)\n result = await group.permission_set(notice=group_notice, command=0, level=group_level)\n else:\n result = Result.IntResult(True, 'Missing parameters or Illegal parameter', -1)\n\n return result\n\n\nasync def set_group_level(bot: Bot, event: GroupMessageEvent, state: T_State) -> Result.IntResult:\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n permission_res = await group.permission_info()\n if permission_res.error:\n return Result.IntResult(True, permission_res.info, -1)\n\n group_notice, group_command, _level = permission_res.result\n\n try:\n group_level = int(state['sub_arg'])\n result = await group.permission_set(notice=group_notice, command=group_command, level=group_level)\n except Exception as e:\n result = Result.IntResult(True, f'Missing parameters or Illegal parameter, {e}', -1)\n\n return result\n\n\nasync def show_group_permission(bot: Bot, event: GroupMessageEvent, state: T_State) -> Result.TextResult:\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n permission_res = await group.permission_info()\n if permission_res.error:\n return Result.TextResult(True, permission_res.info, '')\n\n group_notice, group_command, group_level = permission_res.result\n\n msg = f'当前群组权限: \\n\\nNotice: {group_notice}\\nCommand: {group_command}\\nPermissionLevel: {group_level}'\n result = Result.TextResult(False, 'Success', msg)\n return result\n\n\nasync def reset_group_permission(bot: Bot, event: GroupMessageEvent, state: T_State) -> Result.IntResult:\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n result = await group.permission_reset()\n return result\n\n\nasync def quit_group(bot: Bot, event: GroupMessageEvent, state: T_State) -> None:\n \"\"\"退群命令, 直接抛 FinishedException 不返回\"\"\"\n user_id = event.user_id\n group_id = event.group_id\n self_bot = DBBot(self_qq=int(bot.self_id))\n group = DBBotGroup(group_id=group_id, self_bot=self_bot)\n await group.permission_set(notice=-1, command=-1, level=-1)\n logger.warning(f\"Bot will to leave the Group: {event.group_id} immediately, Operator: {user_id}\")\n await bot.set_group_leave(group_id=group_id, is_dismiss=False)\n raise FinishedException\n\n\nasync def init_group_auth_node(group_id: int, self_bot: DBBot):\n \"\"\"\n 为群组配置权限节点默认值\n \"\"\"\n @dataclass\n class AuthNode:\n node: str\n allow_tag: int\n deny_tag: int\n auth_info: str\n\n default_auth_nodes = [\n AuthNode(node='omega_help.skip_cd', allow_tag=1, deny_tag=0, auth_info='默认规则: help免cd'),\n AuthNode(node='nhentai.basic', allow_tag=0, deny_tag=1, auth_info='默认规则: 禁用nhentai'),\n AuthNode(node='setu.setu', allow_tag=0, deny_tag=1, auth_info='默认规则: 禁用setu'),\n AuthNode(node='setu.allow_r18', allow_tag=0, deny_tag=1, auth_info='默认规则: 禁用setu r18'),\n AuthNode(node='pixiv.allow_r18', allow_tag=0, deny_tag=1, auth_info='默认规则: 禁用pivix r18')\n ]\n\n for auth_node in default_auth_nodes:\n auth = DBAuth(self_bot=self_bot, auth_id=group_id, auth_type='group', auth_node=auth_node.node)\n res = await auth.set(allow_tag=auth_node.allow_tag, deny_tag=auth_node.deny_tag, auth_info=auth_node.auth_info)\n if res.error:\n logger.opt(colors=True).error(f'配置默认权限失败, {auth_node.node}/{group_id} , error: {res.info}')\n\n\nasync def init_user_auth_node(user_id: int, self_bot: DBBot):\n \"\"\"\n 为好友配置权限节点默认值\n \"\"\"\n @dataclass\n class AuthNode:\n node: str\n allow_tag: int\n deny_tag: int\n auth_info: str\n\n default_auth_nodes = [\n AuthNode(node='omega_help.skip_cd', allow_tag=1, deny_tag=0, auth_info='默认规则: 好友help免cd')\n ]\n\n for auth_node in default_auth_nodes:\n auth = DBAuth(self_bot=self_bot, auth_id=user_id, auth_type='user', auth_node=auth_node.node)\n res = await auth.set(allow_tag=auth_node.allow_tag, deny_tag=auth_node.deny_tag, auth_info=auth_node.auth_info)\n if res.error:\n logger.opt(colors=True).error(f'配置默认权限失败, {auth_node.node}/{user_id} , error: {res.info}')\n","sub_path":"omega_miya/plugins/omega_manager/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":17578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"70994064","text":"import pandas as pd\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom sklearn.model_selection import GridSearchCV\n\npredictors = pd.read_csv(\n '/home/van/Deep-Learning/Breast-Cancer/CSV/entradas.csv')\ngroup = pd.read_csv(\n '/home/van/Deep-Learning/Breast-Cancer/CSV/saidas.csv')\n\n\ndef Create_network(optimizer, loos, kernel_initializer, activation, neurons):\n classifier = Sequential()\n\n classifier.add(Dense(units=neurons, activation=activation,\n kernel_initializer=kernel_initializer, input_dim=30))\n classifier.add(Dropout(0.2))\n\n classifier.add(Dense(units=neurons, activation=activation,\n kernel_initializer=kernel_initializer))\n classifier.add(Dropout(0.2))\n\n classifier.add(Dense(units=1, activation='sigmoid'))\n classifier.compile(optimizer=optimizer, loss=loos,\n metrics=['binary_accuracy'])\n\n return classifier\n\n\nclassifier = KerasClassifier(build_fn=Create_network)\nparameter = {'batch_size': [5, 15],\n 'epochs': [5, 10],\n 'optimizer': ['adam', 'sgd'],\n 'loos': ['binary_crossentropy', 'hinge'],\n 'kernel_initializer': ['random_uniform', 'normal'],\n 'activation': ['relu', 'tanh'],\n 'neurons': [8, 2]}\n\ngrid_search = GridSearchCV(\n estimator=classifier, param_grid=parameter, scoring='accuracy', cv=5)\ngrid_search = grid_search.fit(predictors, group)\nbest_parameter = grid_search.best_params_\nbest_prevision = grid_search.best_score_\n","sub_path":"Breast-Cancer/breast_cancer_tuning.py","file_name":"breast_cancer_tuning.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"313375094","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestRegressor\ndataset = pd.read_csv(\"Position_Salaries.csv\")\nx = dataset.iloc[:, 1:-1].values\ny = dataset.iloc[:, -1].values\n\nregressor = RandomForestRegressor(n_estimators=10)\nregressor.fit(x,y)\ny_pred = regressor.predict(x)\n\nposition_level = 6.5\nsalary_prediction = regressor.predict([[position_level]])[0]\nprint(f\"\\nPrediction with Random Forest Regression of {position_level} position level: ${salary_prediction}\\n\")\n\nplt.scatter(x, y, color=\"red\") # the scatter point of level (x) with the real salary(y)\nplt.plot(x, y_pred, color=\"blue\") # predicted linear line of level with the predicted salary(lin_reg.predict)\nplt.title(\"Truth or Bluff (Random Forest Regression)\")\nplt.xlabel(\"Position Level\")\nplt.ylabel(\"Salary\")\nplt.show()\n\n\nx_grid = np.arange(min(x), max(x), 0.1)\nx_grid = x_grid.reshape(len(x_grid), 1)\ny_pred_high = regressor.predict(x_grid)\nplt.scatter(x, y, color=\"red\") # the scatter point of level (x) with the real salary(y)\nplt.plot(x_grid, y_pred_high, color=\"blue\") # predicted linear line of level with the predicted salary(lin_reg.predict)\nplt.title(\"Truth or Bluff (Higher Resolution Random Forest Regression)\")\nplt.xlabel(\"Position Level\")\nplt.ylabel(\"Salary\")\nplt.show()","sub_path":"regression/random-forest-regression/rfr-position-salary.py","file_name":"rfr-position-salary.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"456817455","text":"# Copyright 2013, Sandia Corporation. Under the terms of Contract\n# DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains certain\n# rights in this software.\n\nimport os\nimport sys\nimport uuid\n\ndef index(**keywords):\n content = keywords[\"content\"]\n filename = keywords[\"filename\"]\n path = keywords[\"path\"]\n progress = keywords[\"progress\"]\n\n progress.write(\"Indexing names.\")\n\n if path is not None:\n perspective = {}\n perspective[\"type\"] = \"name\"\n perspective[\"name\"] = os.path.basename(path)\n yield perspective\n\n elif filename is not None:\n perspective = {}\n perspective[\"type\"] = \"name\"\n perspective[\"name\"] = filename\n yield perspective\n","sub_path":"packages/slycat/web/server/spider/name_indexer.py","file_name":"name_indexer.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"350459532","text":"#!/usr/bin/env python3\n\nimport subprocess\n\ndef get_metric_params(parent):\n metric = set(parent[0])\n metric_num = len(metric)\n percent_metric = 100*(metric_num/parent[1])\n highest, highest_metric_num, highest_parent_num = highest_occur(parent[0], metric)\n percent_metric_highest = 100*(highest_metric_num/metric_num)\n percent_total_highest = 100*(highest_parent_num/parent[1])\n\n return(metric, metric_num, percent_metric, highest, highest_parent_num, percent_metric_highest, percent_total_highest)\n#get_metric_params\n\ndef highest_occur(parent, metric):\n highest = max(set(parent), key=parent.count)\n metric_count = tuple(metric).count(highest)\n parent_count = parent.count(highest)\n return(highest, metric_count, parent_count)\n#highest_occur\n\ndef lastb_gen():\n lastb_out = subprocess.Popen(\"lastb\", stdout=subprocess.PIPE).communicate()[0].decode(\"utf-8\").split(\"\\n\")\n\n for line in lastb_out:\n yield parse(line)\n#lastb_gen\n\ndef parse(line):\n sline = (line.split())\n\n y_value = None\n try:\n if sline[1][0:3] == \"ssh\":\n y_value = tuple(sline[0:3:2])\n except IndexError:\n pass\n finally:\n return(y_value)\n#parse\n\ndef print_metric(metric, parent_total, metric_info):\n attempt = metric_info[3] if type(metric_info[3]) != tuple else metric_info[3][0] + \"@\" + metric_info[3][1]\n\n print(\" - {}\".format(metric))\n print(\" Total: {}\".format(parent_total))\n print(\" Unique: {}\".format(metric_info[1]))\n print(\" Percent unique: {:.2f}%\".format(metric_info[2]))\n print(\"\")\n print(\" Highest occurring: '{}' at {} attempts\".format(attempt, metric_info[4]))\n print(\" % of uniques: {:.2f}%\".format(metric_info[5]))\n print(\" % of total: {:.2f}%\".format(metric_info[6]))\n#print_metric\n\n# Build master parent list; all failed login attempts\ntmp_atmpts = list()\nfor line in lastb_gen():\n if line is not None:\n tmp_atmpts.append(line)\natmpts = (tmp_atmpts, len(tmp_atmpts))\n\nif atmpts[1] > 0:\n print(\"SSH Failed Login Metrics\")\n\n # Create user parent list; all users (dups)\n tmp_users = tuple(atmpt[0] for atmpt in atmpts[0])\n users = (tmp_users, len(tmp_users))\n\n # Create IP parent list; all IPs (dups) \n tmp_ips = tuple(atmpt[1] for atmpt in atmpts[0])\n ips = (tmp_ips, len(tmp_ips))\n\n # Attempts\n # u_atmpts = get_metric_params(atmpts)\n # May want this later b/c it contains the actual unique data\n print_metric(\"Attempts\", atmpts[1], get_metric_params(atmpts))\n \n # Users\n # u_users = get_metric_params(users)\n # May want this later b/c it contains the actual unique data\n print_metric(\"Users\", users[1], get_metric_params(users))\n \n # IPs\n # u_ips = get_metric_params(ips)\n # May want this later b/c it contains the actual unique data\n print_metric(\"IPs\", ips[1], get_metric_params(ips))\nelse:\n print(\"No failed logins\")","sub_path":"login_metrics.py","file_name":"login_metrics.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"551663846","text":"import os\nimport ffmpy\nimport speechEmotionRecognition as sp\n\n\n\ndef converter():\n\tinputdir = os.path.abspath(os.getcwd()) + \"/input1\"\n\toutdir = os.path.abspath(os.getcwd()) + \"/input\"\n\tprint(inputdir)\n\n\tfor filename in os.listdir(inputdir):\n\t\tactual_filename = filename[:-4]\n\t\tif actual_filename != 'boon':\n\t\t\toutputdir_final = os.path.join(outdir, actual_filename)\n\t\t\ttry:\n\t\t\t\tos.mkdir(outputdir_final)\n\t\t\texcept OSError:\n\t\t\t\tprint (\"Creation of the directory %s failed\" % outputdir_final)\n\t\t\telse:\n\t\t\t\tprint (\"Successfully created the directory %s \" % outputdir_final)\n\t\t\tif(filename.endswith(\".mp4\")):\n\t\t\t\tos.system('ffmpeg -i {} -acodec pcm_s16le -ar 16000 {}/{}.wav'.format(os.path.join(inputdir, filename), outputdir_final, actual_filename))\n\t\t\t\tos.remove(os.path.join(inputdir, filename))\n\t\t\t\tperson1, person2 = sp.init()\n\t\t\t\t#print(\"PESSOA1: \", person1)\n\t\t\t\t#print(\"PESSOA2: \", person2)\n\t\t\t\treturn person1, person2\n\t\t\telse:\n\t\t\t\tcontinue\n\t\n\n","sub_path":"converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"378958576","text":"#!/usr/bin/python3\n\nimport os\nimport sys\nimport argparse\nimport multiprocessing\nimport funciones as fc\nimport socketserver\n\nclass servidor(socketserver.ForkingTCPServer):\n def __init__(self,server_address,RequestHandlerClass,tamano,directorio):\n socketserver.ForkingTCPServer.__init__(self,server_address,RequestHandlerClass)\n socketserver.allow_reuse_address = True\n self.tamano = tamano\n self.directorio = directorio\n\n\nclass Handler(socketserver.BaseRequestHandler):\n def handle(self):\n dic={\"text\":\"text/plain\",\"jpg\":\"image/jpeg\",\"ppm\":\"image/x-portable-pixmap\",\"html\":\"text/html\",\"pdf\":\"application/pdf\"}\n directorio = self.server.directorio\n tamano = self.server.tamano\n self.data = self.request.recv(1024)\n encabezado = self.data.decode().splitlines()[0]\n if encabezado.split()[1]== False:\n archivo = encabezado.split()[0]\n archivo = encabezado.split()[1]\n #extension = archivo.split('.')[1]\n #print(extension)\n if archivo == '/':\n archivo ='/index.html'\n extension='html'\n else: \n #archivo = encabezado.split()[1] \n extension= archivo.split('.')[1]\n #if os.path.isfile(archivo)== False:\n # archivo = './400error.html'\n solicitud=directorio+archivo \n print(solicitud)\n fd=fc.abrir_archivo(solicitud)\n if os.path.isfile(fd) == False:\n header =bytearray(\"HTTP/1.1 404 error\\r\\n Content-Type:\"+ dic[extension] +\" \\r\\nContent-length: \\r\\n\\r\\n\",'utf8')\n body = \"Mi pagina de pruebaHola Mundo todo bien
\"\n print(dic[extension])\n header =bytearray(\"HTTP/1.1 200 OK\\r\\n Content-Type:\"+ dic[extension] +\" \\r\\nContent-length: \\r\\n\\r\\n\",'utf8')\n body = os.read(fd,tamano)\n respuesta = header + body\n self.request.sendall(respuesta)\n\nif __name__ == \"__main__\":\n HOST=\"0.0.0.0\"\n parser = argparse.ArgumentParser(description='Arrays')\n parser.add_argument('-d', '--Documentroot Dir', action=\"store\", dest=\"directorio\", metavar=\"archivo origen\", type=str, required=True, help=\"Archivo a procesar\")\n parser.add_argument('-p', '--port', action=\"store\", dest=\"puerto\",type=int, help=\"Puerto\")\n parser.add_argument('-s', '--size', action=\"store\", dest=\"n_bytes\", metavar=\"numero de bytes\", type=int, required=True, help=\"Bloque de lectura\") \n args = parser.parse_args()\n #print(args)\n directorio= args.directorio\n puerto=args.puerto\n tamano=args.n_bytes\n with servidor((HOST,5000),Handler,tamano,directorio)as server:\n server.serve_forever()\n","sub_path":"alumnos/4233-guerra-jona/tps/tp3/tp3-ServerSocket.py","file_name":"tp3-ServerSocket.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"412403259","text":"from oscar.defaults import * # noqa\nfrom oscar import OSCAR_MAIN_TEMPLATE_DIR, get_core_apps\nimport os\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\nDEBUG = True\nSECRET_KEY = 'li0$-gnv)76g$yf7p@(cg-^_q7j6df5cx$o-gsef5hd68phj!4'\nSITE_ID = 1\nROOT_URLCONF = 'sandbox.urls'\n\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.postgres',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django.contrib.flatpages',\n 'widget_tweaks',\n 'oscarapi',\n 'cybersource',\n] + get_core_apps([\n 'payment',\n])\n\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.flatpages.middleware.FlatpageFallbackMiddleware',\n 'oscar.apps.basket.middleware.BasketMiddleware',\n)\n\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [\n OSCAR_MAIN_TEMPLATE_DIR\n ],\n 'APP_DIRS': True,\n },\n]\n\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.postgresql_psycopg2',\n 'NAME': 'postgres',\n 'USER': 'postgres',\n 'PASSWORD': '',\n 'HOST': 'postgres',\n 'PORT': 5432,\n }\n}\n\nHAYSTACK_CONNECTIONS = {\n 'default': {\n 'ENGINE': 'haystack.backends.simple_backend.SimpleEngine',\n },\n}\n\nCACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n 'LOCATION': 'cybersource-testing-sandbox',\n }\n}\n\n\n# Static files (CSS, JavaScript, Images)\nSTATIC_URL = '/static/'\nSTATIC_ROOT = os.path.join(BASE_DIR, 'public', 'static')\nMEDIA_URL = '/media/'\nMEDIA_ROOT = os.path.join(BASE_DIR, 'public', 'media')\n\n\n# Order Statuses\nORDER_STATUS_PENDING = 'Pending'\nORDER_STATUS_AUTHORIZED = 'Authorized'\nORDER_STATUS_SHIPPED = 'Shipped'\nORDER_STATUS_CANCELED = 'Canceled'\n\n\nOSCAR_INITIAL_ORDER_STATUS = ORDER_STATUS_PENDING\nOSCAR_INITIAL_LINE_STATUS = ORDER_STATUS_PENDING\nOSCARAPI_INITIAL_ORDER_STATUS = ORDER_STATUS_PENDING\nOSCAR_ORDER_STATUS_PIPELINE = {\n ORDER_STATUS_PENDING: (ORDER_STATUS_AUTHORIZED, ORDER_STATUS_CANCELED),\n ORDER_STATUS_AUTHORIZED: (ORDER_STATUS_SHIPPED, ORDER_STATUS_CANCELED),\n ORDER_STATUS_SHIPPED: (),\n ORDER_STATUS_CANCELED: (),\n}\nOSCAR_LINE_STATUS_PIPELINE = {\n ORDER_STATUS_PENDING: (ORDER_STATUS_SHIPPED, ORDER_STATUS_CANCELED),\n ORDER_STATUS_SHIPPED: (),\n ORDER_STATUS_CANCELED: (),\n}\nOSCAR_ALLOW_ANON_CHECKOUT = True\nOSCAR_DEFAULT_CURRENCY = 'USD'\n\n\nCYBERSOURCE_PROFILE = 'DE9AD002-1C0C-44BA-8A18-AA163B424952'\nCYBERSOURCE_ACCESS = '3427f3d74b5a3bc1904aec4e724ce78e'\nCYBERSOURCE_SECRET = 'a2594ec5a33c44938f0599f4875c9c17d8a0d4f9805e448a901f328caba81fd788e4e17bac264fd982fa845a74530c8f4a553d36cd5e4702811d010d5fd6841450f8855e5bbe42c999505a649088d175bc85b9624b5a459782f569eff6623f3a8a9496a21f204a1f800adc53ad2327289c1cd4291c9e41d9975e841ebaa672f2'\nCYBERSOURCE_MERCHANT_ID = 'somemerchant'\nCYBERSOURCE_ORG_ID = 'someorg'\n\nCYBERSOURCE_REDIRECT_SUCCESS = 'checkout:thank-you'\nCYBERSOURCE_REDIRECT_FAIL = 'checkout:index'\nCYBERSOURCE_ORDER_STATUS_SUCCESS = ORDER_STATUS_AUTHORIZED\n","sub_path":"sandbox/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"366805253","text":"import click\nfrom time import time\n\nimport numpy as np\n\nimport tensorflow as tf\nfrom tensorflow.keras.applications import MobileNetV2\nfrom tensorflow.python.saved_model import tag_constants\nfrom tensorflow.python.compiler.tensorrt import trt_convert as trt\n\nfrom utils import timeit\n\n@click.command('save')\n@click.argument('input-shape', nargs=3, type=(int, int, int))\n@click.argument('classes', type=int)\n@click.argument('save-path')\ndef save(input_shape, classes, save_path, channels):\n model = MobileNetV2(input_shape=input_shape, include_top=False, classes=classes)\n model.save(save_path)\n\n@click.command('convert')\n@click.argument('model-path', type=click.Path(exists=True))\n@click.argument('output-path')\n@click.option('--precision', default='fp32', type=click.Choice(['fp32', 'fp16', 'int8']))\n@click.option('--engine', default=False, is_flag=True)\ndef convert(model_path, output_path, precision, engine):\n if precision == 'fp16':\n precision_mode = trt.TrtPrecisionMode.FP16\n elif precision == 'int8':\n precision_mode = trt.TrtPrecisionMode.int8\n else: \n precision_mode = trt.TrtPrecisionMode.FP32\n \n conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(precision_mode=precision_mode,\n max_workspace_size_bytes=5000000000)\n\n converter = tf.experimental.tensorrt.Converter(\n input_saved_model_dir=model_path, conversion_params=conversion_params\n )\n\n converter.convert()\n converter.save(output_saved_model_dir=output_path)\n\n\n@click.command('benchmark-trt')\n@click.argument('model-path')\n@click.option('--batch-size', default=1)\n@click.option('--times', default=100)\n@click.option('--skip', default=10)\n@click.option('--csv', default='')\n@click.option('--input-shape', nargs=3, type=(int, int, int))\ndef benchmark_trt(model_path, batch_size, times, skip, csv, input_shape):\n\n model = tf.saved_model.load(model_path, tags=tag_constants.SERVING)\n signature_keys = list(model.signatures.keys())\n infer = model.signatures['serving_default']\n\n images = np.random.rand(batch_size, *input_shape)\n images = tf.constant(images, dtype=tf.float32)\n print(f'batch size: {batch_size}')\n\n @timeit(times=times, skip=skip)\n def measure():\n infer(images)\n\n timed = measure()\n print(timed.describe())\n if csv:\n timed.to_csv(csv)\n\n # model = tf.saved_model.load(model_path, tags=tag_constants.SERVING)\n # signature_keys = list(model.signatures.keys())\n # infer = model.signatures['serving_default']\n\n # images = np.array([np.zeros((224, 224, 3)) for _ in range(0, batch_size)])\n # images = tf.constant(images, dtype=tf.float32)\n # print(f'batch size: {batch_size}')\n\n # for _ in range(0, times):\n # before = time()\n # infer(images)\n # print(f'inference time: {time() - before}')\n\n\n@click.group()\ndef cli():\n pass\n\nif __name__ == '__main__':\n cli.add_command(save)\n cli.add_command(benchmark_trt)\n cli.add_command(convert)\n cli()\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"300703966","text":"from lab.with_log import WithLogMixIn\nfrom lab import decorators\n\n\nclass OS(WithLogMixIn):\n def __repr__(self):\n return u'cloud {}'.format(self.name)\n\n def __init__(self, name, mediator, openrc_path):\n from lab.mercury.nodes import MercuryMgm\n\n self.name = name\n self.mediator = mediator # Instance of Server to be used to execute CLI commands for this cloud\n if type(mediator) is MercuryMgm:\n self.pod = mediator.pod\n self.openrc_path = openrc_path\n self.controls, self.computes, self.images, self.servers, self.keypairs, self.nets, self.subnets, self.ports, self.flavors = [], [], [], [], [], [], [], [], []\n\n def os_cmd(self, cmds, comment='', server=None, is_warn_only=False):\n server = server or self.mediator\n cmd = 'source ' + self.openrc_path + ' && ' + ' ; '.join(cmds) + (' # ' + comment if comment else '')\n ans = server.exe(cmd=cmd, is_warn_only=is_warn_only)\n if ans:\n return self._process_output(answer=ans)\n else:\n return []\n\n @staticmethod\n def _process_output(answer):\n import json\n\n if not answer:\n return {}\n if '{' not in answer:\n return answer.split('\\r\\n')\n answer = '[' + answer.replace('}{', '},{') + ']'\n return json.loads(answer)\n\n def get_fip_network(self):\n ans = self.os_cmd('neutron net-list --router:external=True -c name')\n net_names = ans\n if not net_names:\n return '', 'physnet1'\n fip_network = net_names[0]\n ans = self.os_cmd('neutron net-list -c provider:physical_network --name {0}'.format(fip_network))\n physnet = filter(lambda x: x not in ['provider:physical_network', '|', '+---------------------------+'], ans.split())[0]\n return fip_network, physnet\n\n @staticmethod\n def _parse_cli_output(output_lines):\n \"\"\"Borrowed from tempest-lib. Parse single table from cli output.\n :param output_lines:\n :returns: dict with list of column names in 'headers' key and rows in 'values' key.\n \"\"\"\n\n if not isinstance(output_lines, list):\n output_lines = output_lines.split('\\n')\n\n if not output_lines[-1]:\n output_lines = output_lines[:-1] # skip last line if empty (just newline at the end)\n\n columns = OS._table_columns(output_lines[2])\n is_2_values = len(columns) == 2\n\n def line_to_row(input_line):\n return [input_line[col[0]:col[1]].strip() for col in columns]\n\n headers = None\n d2 = {}\n output_list = []\n for line in output_lines[3:-1]:\n row = line_to_row(line)\n if is_2_values:\n d2[row[0]] = row[1]\n else:\n headers = headers or line_to_row(output_lines[1])\n output_list.append({headers[i]: row[i] for i in range(len(columns))})\n\n return output_list if output_list else [d2]\n\n @staticmethod\n def _table_columns(first_table_row):\n \"\"\"Borrowed from tempest-lib. Find column ranges in output line.\n :returns: list of tuples (start,end) for each column detected by plus (+) characters in delimiter line.\n \"\"\"\n positions = []\n start = 1 # there is '+' at 0\n while start < len(first_table_row):\n end = first_table_row.find('+', start)\n if end == -1:\n break\n positions.append((start, end))\n start = end + 1\n return positions\n\n def os_create_fips(self, fip_net, how_many):\n cmds = ['neutron floatingip-create ' + fip_net.get_net_name() for _ in range(how_many)]\n return self.os_cmd(cmds=cmds)\n\n def os_quota_set(self):\n from lab.cloud.cloud_project import CloudProject\n\n admin_id = [x.id for x in CloudProject.list(cloud=self) if x.name == 'admin'][0]\n self.os_cmd(cmds=['openstack quota set --instances 1000 --cores 2000 --ram 512000 --networks 100 --subnets 300 --ports 500 {}'.format(admin_id)])\n\n @decorators.section('Clean cloud')\n def os_cleanup(self, is_all=False):\n from lab.cloud import CloudObject\n\n f = '' if is_all else CloudObject.UNIQUE_PATTERN_IN_NAME\n objs = filter(lambda x: f in x.name, self.servers + self.ports + self.subnets + self.nets + self.images + self.keypairs + self.flavors)\n\n ids = map(lambda x: '\"' + x.role + ' delete ' + x.id + '\"', objs)\n if not ids: # nothing to clean\n return\n names = map(lambda x: x.name, objs)\n cmd = 'for cmd in ' + ' '.join(ids) + '; do openstack $cmd; done'\n self.os_cmd(cmds=[cmd], comment=' '.join(names))\n self.os_all()\n\n @decorators.section('Investigate cloud')\n def os_all(self):\n from lab.cloud.cloud_host import CloudHost\n from lab.cloud.cloud_server import CloudServer\n from lab.cloud.cloud_image import CloudImage\n from lab.cloud.cloud_network import CloudNetwork\n from lab.cloud.cloud_subnet import CloudSubnet\n from lab.cloud.cloud_key_pair import CloudKeyPair\n from lab.cloud.cloud_flavor import CloudFlavor\n\n self.controls, self.computes = CloudHost.host_list(cloud=self)\n\n self.images, self.servers, self.ports, self.subnets, self.nets, self.keypairs, self.flavors = [], [], [], [], [], [], []\n pattern = 'openstack {0} list | grep -vE \"\\+|ID|Fingerprint\" {{}} | cut -d \" \" -f 2 | while read id; do [ -n \"$id\" ] && openstack {0} {{}} $id -f json; done'\n cmds = map(lambda x: x.format('', 'show'), map(lambda x: pattern.format(x), ['image', 'network', 'subnet', 'port', 'keypair', 'server', 'flavor']))\n a = self.os_cmd(cmds=cmds, is_warn_only=True)\n count = 0\n for dic in a:\n count += 1\n if 'disk_format' in dic:\n CloudImage(cloud=self, dic=dic)\n elif 'hostId' in dic:\n CloudServer(cloud=self, dic=dic)\n elif 'fingerprint' in dic:\n CloudKeyPair(cloud=self, dic=dic)\n elif 'provider:network_type' in dic:\n CloudNetwork(cloud=self, dic=dic)\n elif 'subnetpool_id' in dic:\n CloudSubnet(cloud=self, dic=dic)\n elif 'os-flavor-access:is_public' in dic:\n CloudFlavor(cloud=self, dic=dic)\n else:\n raise RuntimeError('{}: add this ^^^ dic to this if!')\n return count\n","sub_path":"lab/cloud/openstack.py","file_name":"openstack.py","file_ext":"py","file_size_in_byte":6468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"181153315","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 1 17:04:56 2020\n\n@author: Reaz\nselection sort algorithm \n\nDocumentation: We start by assuming that the first element \nis the smallest one. Then we look for something smaller in the \nlist. If we find a smaller number we swap it with our assumed \nvalue. And we do this sequentially thru the whole list. \n\nAgain, like bubble sort it's not important that we have hit \neach element individually. The ordering will take care of itself \nautomatically as long as we move sequentially thru the indexes\n\"\"\"\n\na=[96,5,1,4,2,84,13,2,21,3]\n\n\nfor i in range (len(a)): \n for j in range (i+1,len(a)): \n if a[j]> beam.ParDo(self.Drop(*self.args)))\r\n \r\n class Drop(beam.DoFn):\r\n \r\n def __init__(self, *args):\r\n self.args = args\r\n \r\n def process(self, listin):\r\n\r\n arglist = list(self.args)\r\n arglist.sort(reverse=True)\r\n listout = deepcopy(listin)\r\n \r\n for i in arglist: listout.pop(i)\r\n \r\n yield listout\r\n\r\n#######################################################################################################\r\n# KeepFields - Specify numeric postional args of fields to keep\r\n# - Specifying fields out of sequence will rearrange their relative position\r\n# e.g. for three fields 0,1,2, KeepFields(2,1,0) will reverse their order\r\n#######################################################################################################\r\n \r\nclass KeepFields(beam.PTransform):\r\n \r\n def __init__(self, *args):\r\n self.args = args\r\n \r\n def expand(self, pcoll):\r\n \r\n return (pcoll | 'Reformat' >> beam.ParDo(self.Keep(*self.args)))\r\n\r\n class Keep(beam.DoFn):\r\n \r\n def __init__(self, *args):\r\n self.args = args\r\n \r\n def process(self, listin):\r\n \r\n listout = []\r\n\r\n for i in self.args:\r\n if str(i).lower() == 'x':\r\n listout = listout + ['']\r\n else: \r\n listout = listout + [str(listin[i])]\r\n\r\n yield listout\r\n\r\n#######################################################################################################\r\n# AppendFields - Specify numeric postional args of fields to append to the end of the current list\r\n#######################################################################################################\r\n \r\nclass AppendFields(beam.PTransform):\r\n \r\n def __init__(self, *args):\r\n self.args = args\r\n \r\n def expand(self, pcoll):\r\n \r\n return (pcoll | 'Append' >> beam.ParDo(self.Keep(*self.args)))\r\n\r\n class Keep(beam.DoFn):\r\n \r\n def __init__(self, *args):\r\n self.args = args\r\n \r\n def process(self, listin):\r\n \r\n listout = deepcopy(listin)\r\n\r\n for i in self.args:\r\n if str(i).lower() == 'x':\r\n listout = listout + ['']\r\n else: \r\n listout = listout + [str(listin[i])] \r\n\r\n yield listout\r\n\r\n#######################################################################################################\r\n# XForm - Transforms a field in situ\r\n#\r\n# Fields - Specify keyword args of format: xn = transform where...\r\n# - Specify kwargs of format: xn=transform\r\n# where...\r\n# - n is the position within the input list of the field to transform\r\n# - x is an alphabetic character (doesn't matter what provided xn is a unique kwarg)\r\n# e.g. a13 & b13 would perform iterative transforms of the 14th field in a list (index 13)\r\n# - transform is a defined function or lambda expression\r\n#######################################################################################################\r\n \r\nclass XForm(beam.PTransform):\r\n \r\n def __init__(self, **kwargs):\r\n self.kwargs = kwargs\r\n \r\n def expand(self, pcoll):\r\n \r\n return (pcoll | 'Reformat' >> beam.ParDo(self.XFormer(**self.kwargs)))\r\n \r\n class XFormer(beam.DoFn):\r\n \r\n def __init__(self, **kwargs):\r\n self.kwargs = kwargs\r\n \r\n def process(self, listin):\r\n\r\n keylist = list(self.kwargs.keys())\r\n for i,key in enumerate(keylist): keylist[i] = int(key[1:])\r\n \r\n transforms = zip(keylist,list(self.kwargs.values()))\r\n listout = deepcopy(listin)\r\n \r\n for i,func in transforms: listout[i] = func(listout[i])\r\n \r\n yield listout \r\n\r\n#######################################################################################################\r\n# XFormAppend - Using a specified field as input, transforms it & appends the result to the end of the\r\n# input list\r\n#\r\n# - Specify kwargs of format: xn=transform\r\n# where...\r\n# - n is the position within the input list of the field to transform\r\n# - x is an alphabetic character (doesn't matter what provided xn is a unique kwarg)\r\n# e.g. a13 & b13 would perform separate transforms of the 14th field in a list (index 13)\r\n# - transform is a defined function or lambda expression\r\n#######################################################################################################\r\n \r\nclass XFormAppend(beam.PTransform):\r\n \r\n def __init__(self, **kwargs):\r\n self.kwargs = kwargs\r\n \r\n def expand(self, pcoll):\r\n \r\n return (pcoll | 'Reformat' >> beam.ParDo(self.XFormer(**self.kwargs)))\r\n \r\n class XFormer(beam.DoFn):\r\n \r\n def __init__(self, **kwargs):\r\n self.kwargs = kwargs\r\n \r\n def process(self, listin):\r\n\r\n keylist = list(self.kwargs.keys())\r\n for i,key in enumerate(keylist): keylist[i] = int(key[1:])\r\n \r\n transforms = zip(keylist,list(self.kwargs.values()))\r\n listout = deepcopy(listin)\r\n \r\n for i,func in transforms: listout.append(func(listout[i]))\r\n \r\n yield listout \r\n \r\n#######################################################################################################\r\n# Sort - Specify numeric postional args of fields to keep use to sort the input PCollection\r\n#\r\n# - If you append a full stop to the end of a numeric positional argument this tells the sort to\r\n# sort it as a number, and not a string. Note that the numeric sort does not cope with\r\n# empty strings or None types - if sorting as a number, the repective field must be populated\r\n# with a number on all records. You can make this work in a clunky way at the moment by\r\n# transforming prior to the sort to add a defailt value (and remove this later).\r\n#\r\n# - Specifying reverse=True will switch the sort order to descending. Note that all arguments\r\n# must have the same sort order you can't specify the sort order at individual field level\r\n#\r\n# Both of these are weaknesses in the python sort function. It would need much more customisation than\r\n# I've got here to extend it to cater for this functionality.\r\n#\r\n####################################################################################################### \r\n \r\nclass Sort(beam.PTransform):\r\n \r\n def __init__(self, *args, **kwargs):\r\n self.args = args\r\n self.args2 = [int(i) for i in args]\r\n self.kwargs = kwargs\r\n \r\n def expand(self, pcoll):\r\n \r\n def _normalise_list_of_lists(listin):\r\n \r\n for rec in listin:\r\n output = \"|\".join([str(x) for x in rec])\r\n yield output.split('|')\r\n \r\n \r\n return (\r\n pcoll | 'Parse Numerics' >> beam.ParDo(self.ParseNumbers(*self.args))\r\n | 'Combine Lists' >> beam.combiners.ToList()\r\n | 'Sort List' >> beam.ParDo(self.SortList(*self.args2,**self.kwargs))\r\n | 'Normalise Lists' >> beam.FlatMap(_normalise_list_of_lists)\r\n )\r\n\r\n class ParseNumbers(beam.DoFn):\r\n \r\n def __init__(self, *args):\r\n self.args = args\r\n\r\n def process(self, listin):\r\n \r\n listout = deepcopy(listin)\r\n \r\n for arg in self.args:\r\n \r\n if type(arg) is float: \r\n \r\n i = int(arg)\r\n x = listout[i]\r\n negative = True if x[0:1] == '-' else False\r\n x = x.lstrip('-+')\r\n \r\n y = (float(x) if x.find('.') >= 0 \r\n and x.replace('.','').isdigit() is True \r\n and (len(x) - len(x.replace('.',''))) == 1 \r\n else int(x) if x.isdigit() is True\r\n else '')\r\n \r\n if type(y) is not str:\r\n listout[i] = y if not negative else y*-1\r\n \r\n yield listout\r\n\r\n\r\n class SortList(beam.DoFn):\r\n \r\n def __init__(self, *args, **kwargs):\r\n self.args = args\r\n self.kwargs = kwargs\r\n\r\n def process(self, listin):\r\n \r\n order = self.kwargs.get('reverse',False)\r\n listout = deepcopy(listin)\r\n listout.sort(key=operator.itemgetter(*self.args),reverse=order)\r\n yield listout\r\n\r\n#######################################################################################################\r\n# DistinctList - Implementation of beam.transforms.util.Distinct() for Python List PCollections\r\n#\r\n# ** Probably a more efficient way to do this in a single function & keeping as a list throughout\r\n####################################################################################################### \r\n \r\nclass DistinctList(beam.PTransform):\r\n \r\n def expand(self, pcoll):\r\n \r\n return (\r\n pcoll | 'Convert to Rec' >> beam.ToString.Iterables(delimiter='|')\r\n | 'Dedupe' >> beam.transforms.util.Distinct()\r\n | 'Convert to List' >> beam.ParDo(ConvertRecTo(list,'|'))\r\n )\r\n\r\n#######################################################################################################\r\n# Join - joins the primary PCollection in a pipeline to another PCollection (as a side input)\r\n#\r\n# - Specify arguments as follows:\r\n#\r\n# Arg 0 - the side input PCollection to join the main PCollection to\r\n# Arg 1 - the type of join to perform (modelled on standard SQL logic)... supply either:\r\n# 'Inner', 'Left', 'Cross', 'Exists' ,'Not_Exists'\r\n#\r\n# - 'Inner', 'Left', 'Cross' will return the attributes from both PCollections as a list\r\n# - 'Exists', 'Not_Exists' will return just those records from the main PCollection that\r\n# exist/do not exist on the side input PCollection (as appropriate)\r\n#\r\n# - Specify keyword arguments as follows:\r\n#\r\n# main_key - a comma delimited text string specifying the list indexes of the items in the main\r\n# PCollection that are to be used as join criteria\r\n# side_key - a comma delimited text string specifying the list indexes of the items in the side\r\n# PCollection that are to be used as join criteria. These will be positionally matched\r\n# to the arguments supplied in main_key i.e\r\n#\r\n# main_key = '1,2', side_key='3,0' would join main[1] to side[3] & main[2] to side[0]\r\n#\r\n# key - a comma delimited text string specifying the list indexes of the items in both the\r\n# main and side PCollections that are to be used as join criteria. i.e. 'key' can be\r\n# supplied INSTEAD of main_key and side_key if both PCollections share the same format\r\n# (or different formats but with the join fields in the same positions)\r\n#\r\n# keep - an optional argument that if supplied should contain a comma delimited text string\r\n# containing the list indexes of items in the result PCollection that should be\r\n# retained. Invokes the beam_tools.KeepFields transform. Only used by Inner/Left/Cross\r\n# modes\r\n#\r\n#######################################################################################################\r\n\r\nclass Join(beam.PTransform):\r\n \r\n def __init__(self, side_input, join, **kwargs):\r\n \r\n self.side_input = side_input\r\n self.join = join[0:1].upper()\r\n self.keep = [] if str(kwargs.get('keep','')) == '' else [int(x) for x in list(str(kwargs.get('keep')).split(','))]\r\n \r\n if str(kwargs.get('main_key','x')) != 'x': key = 'main_key'\r\n elif str(kwargs.get('key','x')) != 'x': key = 'key'\r\n else: key = 'side_key'\r\n \r\n self.main_key = [int(x) for x in str(kwargs.get(key,0)).split(',')]\r\n\r\n if str(kwargs.get('side_key','x')) != 'x': key = 'side_key'\r\n elif str(kwargs.get('key','x')) != 'x': key = 'key'\r\n else: key = 'main_key'\r\n \r\n self.side_key = [int(x) for x in str(kwargs.get(key,0)).split(',')]\r\n \r\n \r\n def expand(self, pcoll):\r\n\r\n def _join_key(listin,args):\r\n \r\n join_str = '~'.join([str(listin[i]) for i in args])\r\n join_key = join_str if len(join_str) <= 32 else b2b(join_str.encode('utf-8'),digest_size=16).hexdigest()\r\n return join_key\r\n\r\n def _prepare(side_input): return (_join_key(side_input,self.side_key),side_input)\r\n \r\n def _cross(main,side_input):\r\n \r\n for rec in side_input:\r\n yield deepcopy(main + rec)\r\n \r\n def _inner(main,side_input):\r\n \r\n main_join_key = _join_key(main,self.main_key)\r\n \r\n for rec in side_input:\r\n if main_join_key == rec[0]:\r\n yield deepcopy(main + rec[1])\r\n \r\n def _exists(main,side_input):\r\n \r\n main_join_key = _join_key(main,self.main_key)\r\n \r\n for rec in side_input:\r\n if main_join_key == rec[0]:\r\n yield deepcopy(main)\r\n break\r\n \r\n def _left(main,side_input):\r\n \r\n main_join_key = _join_key(main,self.main_key)\r\n no_match = []\r\n joined = False\r\n \r\n for rec in side_input:\r\n no_match = len(rec[1]) * ['']\r\n break\r\n \r\n for rec in side_input:\r\n\r\n if main_join_key == rec[0]:\r\n joined = True\r\n yield deepcopy(main + rec[1])\r\n \r\n if not joined: yield deepcopy(main + no_match)\r\n \r\n def _not_exists(main,side_input):\r\n \r\n main_join_key = _join_key(main,self.main_key)\r\n joined = False\r\n \r\n for rec in side_input:\r\n if main_join_key == rec[0]:\r\n joined = True\r\n break\r\n \r\n if not joined: yield deepcopy(main)\r\n \r\n# Pipeline:\r\n\r\n side = (self.side_input | 'Prepare' >> beam.Map(_prepare)) if self.join in 'LINE' else self.side_input\r\n \r\n reformat = False if len(self.keep) == 0 or self.join in 'EN' else True\r\n \r\n join_func = (_inner if self.join == 'I' else\r\n _left if self.join == 'L' else\r\n _exists if self.join == 'E' else\r\n _not_exists if self.join == 'N' else\r\n _cross)\r\n \r\n if reformat:\r\n return (pcoll | 'Join' >> beam.FlatMap(join_func, beam.pvalue.AsIter(side))\r\n | 'Keep' >> KeepFields(*self.keep))\r\n else:\r\n return (pcoll | 'Join' >> beam.FlatMap(join_func, beam.pvalue.AsIter(side)))\r\n\r\n#######################################################################################################\r\n# Lookup - joins the primary PCollection in a pipeline to another PCollection (as a side input)\r\n# - specifically for use where the join key value on the side input is unique (as would\r\n# normally be the case in a lookup) as this allows the side input to be processed as a\r\n# Dictionary... which is a far quicker method of joining than having to iterate through the\r\n# entire side input\r\n#\r\n# - Specify arguments as follows:\r\n#\r\n# Arg 0 - the side input PCollection to join the main PCollection to\r\n# Arg 1 - the type of join to perform (modelled on standard SQL logic)... supply either:\r\n# 'Inner', 'Left', 'Exists' ,'Not_Exists'\r\n#\r\n# - 'Inner', 'Left' will return the attributes from both PCollections as a list\r\n# - 'Exists', 'Not_Exists' will return just those records from the main PCollection that\r\n# exist/do not exist on the side input PCollection (as appropriate)\r\n#\r\n# Note there is no Cross join option when using Lookup - use Join for this\r\n#\r\n# - Specify keyword arguments as follows:\r\n#\r\n# side_val - a comma delimited text string specifying the list indexes of the items in the side\r\n# input that are to be returned in the event of a successful join\r\n# main_key - a comma delimited text string specifying the list indexes of the items in the main\r\n# PCollection that are to be used as join criteria\r\n# side_key - a comma delimited text string specifying the list indexes of the items in the side\r\n# PCollection that are to be used as join criteria. These will be positionally matched\r\n# to the arguments supplied in main_key i.e\r\n#\r\n# main_key = '1,2', side_key='3,0' would join main[1] to side[3] & main[2] to side[0]\r\n#\r\n# key - a comma delimited text string specifying the list indexes of the items in both the\r\n# main and side PCollections that are to be used as join criteria. i.e. 'key' can be\r\n# supplied INSTEAD of main_key and side_key if both PCollections share the same format\r\n# (or different formats but with the join fields in the same positions)\r\n#\r\n# keep - an optional argument that if supplied should contain a comma delimited text string\r\n# containing the list indexes of items in the result PCollection that should be\r\n# retained. Invokes the beam_tools.KeepFields transform. Only used by Inner/Left modes.\r\n#\r\n#######################################################################################################\r\n\r\nclass Lookup(beam.PTransform):\r\n \r\n def __init__(self, side_input, join, **kwargs):\r\n \r\n self.side_input = side_input\r\n self.join = join[0:1].upper()\r\n self.keep = [] if str(kwargs.get('keep','')) == '' else [int(x) for x in list(str(kwargs.get('keep')).split(','))]\r\n \r\n if str(kwargs.get('main_key','x')) != 'x': key = 'main_key'\r\n elif str(kwargs.get('key','x')) != 'x': key = 'key'\r\n else: key = 'side_key'\r\n \r\n self.main_key = [int(x) for x in str(kwargs.get(key,0)).split(',')]\r\n\r\n if str(kwargs.get('side_key','x')) != 'x': key = 'side_key'\r\n elif str(kwargs.get('key','x')) != 'x': key = 'key'\r\n else: key = 'main_key'\r\n \r\n self.side_key = [int(x) for x in str(kwargs.get(key,0)).split(',')]\r\n \r\n self.side_val = [] if str(kwargs.get('side_val','')) == '' else [int(x) for x in list(str(kwargs.get('side_val')).split(','))]\r\n \r\n self.no_match = len(self.side_val) * ['']\r\n \r\n \r\n def expand(self, pcoll):\r\n\r\n def _key(listin,args):\r\n \r\n join_str = '~'.join([str(listin[i]) for i in args])\r\n join_key = join_str if len(join_str) <= 32 else b2b(join_str.encode('utf-8'),digest_size=16).hexdigest()\r\n return join_key\r\n \r\n def _prepare(listin): \r\n \r\n listout = []\r\n val = []\r\n \r\n key = _key(listin,self.side_key)\r\n for i in self.side_val: val = val + [str(listin[i])]\r\n \r\n listout = listout + [key] + val\r\n return listout\r\n \r\n def _convert(listin):\r\n \r\n key = deepcopy(listin[0])\r\n val = deepcopy(listin)\r\n val.pop(0)\r\n return (key,val)\r\n \r\n def _inner(main,side_input):\r\n \r\n main_join_key = _key(main,self.main_key) \r\n sidelist = side_input.get(main_join_key,'x')\r\n \r\n if type(sidelist) is list:\r\n listout = [] + deepcopy(main) + deepcopy(sidelist)\r\n yield listout\r\n \r\n def _exists(main,side_input):\r\n \r\n main_join_key = _key(main,self.main_key)\r\n sidelist = side_input.get(main_join_key,'x')\r\n \r\n if type(sidelist) is list:\r\n yield deepcopy(main)\r\n\r\n def _left(main,side_input):\r\n \r\n main_join_key = _key(main,self.main_key) \r\n sidelist = side_input.get(main_join_key,self.no_match)\r\n \r\n listout = [] + deepcopy(main) + deepcopy(sidelist)\r\n yield listout\r\n \r\n def _not_exists(main,side_input):\r\n \r\n main_join_key = _key(main,self.main_key) \r\n sidelist = side_input.get(main_join_key,'x')\r\n \r\n if type(sidelist) is str:\r\n yield deepcopy(main)\r\n \r\n# Pipeline:\r\n\r\n side = (self.side_input | 'Prepare' >> beam.Map(_prepare)\r\n | 'Distinct' >> DistinctList()\r\n | 'Convert' >> beam.Map(_convert))\r\n \r\n reformat = False if len(self.keep) == 0 or self.join in 'EN' else True\r\n \r\n join_func = (_inner if self.join == 'I' else\r\n _exists if self.join == 'E' else\r\n _not_exists if self.join == 'N' else\r\n _left)\r\n \r\n if reformat:\r\n return (pcoll | 'Join' >> beam.FlatMap(join_func, beam.pvalue.AsDict(side))\r\n | 'Keep' >> KeepFields(*self.keep))\r\n else:\r\n return (pcoll | 'Join' >> beam.FlatMap(join_func, beam.pvalue.AsDict(side)))\r\n \r\n \r\n#######################################################################################################\r\n# GenerateSKs - Given an integer starting key arg, this will iterate through a PCollection,\r\n# incrementing the new SK value by the interval value each time and prepending the SK \r\n# to the start of each PCollection 'record'\r\n####################################################################################################### \r\n\r\nclass GenerateSKs(beam.PTransform):\r\n \r\n def __init__(self, start_sk=1, interval=1):\r\n self.start_sk = start_sk\r\n self.interval = interval \r\n \r\n def expand(self, pcoll):\r\n \r\n return (pcoll | 'Add Keys ' >> beam.ParDo(self.PrependSKs(self.start_sk,self.interval)))\r\n\r\n class PrependSKs(beam.DoFn):\r\n \r\n def __init__(self, start_sk, interval):\r\n self.next_sk = start_sk\r\n self.interval = interval\r\n \r\n def process(self, listin):\r\n \r\n sk = [] + [str(self.next_sk)]\r\n yield sk + deepcopy(listin)\r\n self.next_sk = self.next_sk + self.interval\r\n\r\n#######################################################################################################\r\n# Count - the direct equivalent of the sql count/group by function - when supplied with list indexes of\r\n# items in the input PCollection, this will determine the distinct value combinations of the\r\n# data represented by those indexes and tally the number of records with those values\r\n#\r\n# i.e (PColl | 'Count' >> beam_tools.Count(0,1))\r\n#\r\n# is the direct equivalent of:\r\n#\r\n# SELECT Column_0, Column_1, Count(*) FROM PColl GROUP BY Column_0, Column_1\r\n#\r\n# ** Could be expanded with a filter option as the equivalent of HAVING COUNT(*)...\r\n# ** Possible to add COUNT(DISTINCT(column)) functionality?\r\n#\r\n#######################################################################################################\r\n\r\nclass Count(beam.PTransform):\r\n \r\n def __init__(self, *args):\r\n self.args = args\r\n \r\n def expand(self, pcoll):\r\n \r\n return (\r\n pcoll | 'Reformat' >> KeepFields(*self.args)\r\n | 'Convert to Str' >> beam.ToString.Iterables(delimiter='|')\r\n | 'Count' >> beam.combiners.Count.PerElement()\r\n | 'Convert to List' >> beam.Map(lambda x: x[0].split('|') + [str(x[1])])\r\n )\r\n \r\n#######################################################################################################\r\n# SplitAttributes - The first argument must be the delimiter character you wish to split on \r\n# - Subsequent arguments should be numeric postions of fields to split\r\n# - Fields will be split in situ, i.e. fields following those that are split may be\r\n# pushed further out \r\n#######################################################################################################\r\n\r\nclass SplitAttributes(beam.PTransform):\r\n \r\n def __init__(self, split_char, *args):\r\n self.split_char = split_char\r\n self.args = args\r\n \r\n def expand(self, pcoll):\r\n \r\n return (pcoll | 'Split Attributes' >> beam.ParDo(self.SplitAttribute(self.split_char, *self.args)))\r\n\r\n class SplitAttribute(beam.DoFn):\r\n \r\n def __init__(self, split_char, *args):\r\n self.split_char = split_char\r\n self.args = args\r\n \r\n def process(self, listin):\r\n \r\n arglist = list(self.args)\r\n arglist.sort(reverse=True)\r\n listout = deepcopy(listin)\r\n \r\n for i in arglist:\r\n parts = listout[i].split(self.split_char)\r\n listout.pop(i)\r\n for p in reversed(parts): listout.insert(i,p.strip()) \r\n \r\n yield listout\r\n \r\n#######################################################################################################\r\n# Normalise - Takes a list of values and normalises to multiple output lists\r\n# - The first argument should be the number of output lists to normalise to\r\n# - Subsequent numeric arguments reflect...\r\n# - the fields to normalise to output list 1 (in order)\r\n# - the fields to normalise to output list 2 (in order) etc. as per no. lists specified\r\n# - Any list items not specified in the subsequent numeric arguments will be prepended to\r\n# all output lists\r\n# - Specifiying the kwarg blanks='n' will result in any output lists where all of the \r\n# specified list items are empty strings NOT being output\r\n#######################################################################################################\r\n\r\nclass Normalise(beam.PTransform):\r\n \r\n def __init__(self, outputs, *args, **kwargs):\r\n self.outputs = outputs\r\n self.args = args\r\n self.kwargs = kwargs\r\n \r\n def expand(self, pcoll):\r\n \r\n return (pcoll | 'Normalise List' >> beam.ParDo(self.Normaliser(self.outputs,*self.args,**self.kwargs)))\r\n\r\n class Normaliser(beam.DoFn):\r\n \r\n def __init__(self, outputs, *args, **kwargs):\r\n self.outputs = outputs\r\n self.args = args\r\n self.kwargs = kwargs\r\n \r\n def process(self, listin):\r\n \r\n blanks = self.kwargs.get('blanks','y').lower()\r\n commonlist = []\r\n \r\n for i in range(len(listin)):\r\n if i not in self.args:\r\n commonlist.append(listin[i])\r\n\r\n limit = len(self.args) // self.outputs\r\n counter = limit + 1\r\n \r\n for i in self.args:\r\n \r\n if counter > limit:\r\n templist = []\r\n counter = 1\r\n \r\n templist = templist + [listin[i]]\r\n counter = counter + 1\r\n \r\n if counter > limit:\r\n if blanks != 'n' or len(''.join([str(x) for x in templist])) > 0:\r\n listout = [] + commonlist + templist\r\n yield listout\r\n \r\n#######################################################################################################\r\n# First - Limits the PCollection to the first n records specified (supplied as the only arg)\r\n#######################################################################################################\r\n \r\nclass First(beam.PTransform):\r\n \r\n def __init__(self, limit):\r\n self.num_of_recs = 1\r\n self.limit = limit\r\n \r\n def expand(self, pcoll):\r\n \r\n def _first(listin):\r\n \r\n if self.num_of_recs <= self.limit:\r\n self.num_of_recs = self.num_of_recs + 1\r\n yield deepcopy(listin)\r\n \r\n return (pcoll | 'Filter' >> beam.FlatMap(_first))\r\n\r\n\r\n#######################################################################################################\r\n# SwitchDelimiters - Supply two args: old delimiter and new delimiter\r\n#######################################################################################################\r\n\r\nclass SwitchDelimiters(beam.DoFn):\r\n\r\n def __init__(self, old, new):\r\n self.old = old\r\n self.new = new\r\n\r\n def process(self, linein):\r\n \r\n rec = list(csv.reader([linein.replace(self.new,'')],delimiter=self.old,quotechar='\"',skipinitialspace=True))[0]\r\n yield self.new.join(map(str,rec)).replace('\"','')\r\n\r\n#######################################################################################################\r\n# ConvertRecTo - Converts a delimited record to a Python format of your choosing\r\n# i.e supply the list, tuple or dict function, plus your field delimiter\r\n#######################################################################################################\r\n\r\nclass ConvertRecTo(beam.DoFn):\r\n \r\n def __init__(self, func, delimiter):\r\n self.func = func\r\n self.delimiter = delimiter\r\n \r\n def process(self, linein):\r\n rec = linein.split(self.delimiter)\r\n yield self.func(rec)\r\n\r\n#######################################################################################################\r\n# Copy - Duplicates a PCollection, creating n output copies (n has a max value of 5 & defaults to 2) \r\n# \r\n# Example usage:\r\n#\r\n# >> beam.ParDo(beam_tools.Copy()).with_outputs(beam_tools.Copy.x1, beam_tools.Copy.x2)\r\n# >> beam.ParDo(beam_tools.Copy(3)).with_outputs(beam_tools.Copy.x1, beam_tools.Copy.x2, beam_tools.Copy.x3)\r\n#\r\n# Assuming the copies were produced as output from a PCollection 'P' they can be referenced as:\r\n# P.x1, P.x2, P.x3\r\n#\r\n#######################################################################################################\r\n\r\nclass Copy(beam.DoFn):\r\n \r\n x1, x2, x3, x4, x5 = ['x1','x2','x3','x4','x5']\r\n \r\n def __init__(self, copies=2):\r\n self.copies = copies\r\n \r\n def process(self, datain):\r\n \r\n yield beam.pvalue.TaggedOutput(self.x1, datain) \r\n yield beam.pvalue.TaggedOutput(self.x2, datain)\r\n \r\n if self.copies > 2:\r\n yield beam.pvalue.TaggedOutput(self.x3, datain)\r\n if self.copies > 3:\r\n yield beam.pvalue.TaggedOutput(self.x4, datain)\r\n if self.copies > 4:\r\n yield beam.pvalue.TaggedOutput(self.x5, datain) \r\n","sub_path":"gcp_tools/beam_tools.py","file_name":"beam_tools.py","file_ext":"py","file_size_in_byte":33992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"231999522","text":"\"\"\"\nLet d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n).\nIf d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and each of a and b are called amicable numbers.\n\nFor example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and 142; so d(284) = 220.\n\nEvaluate the sum of all the amicable numbers under 10000.\n\"\"\"\n\nfrom itertools import chain\n\ndef d(num):\n factors = chain.from_iterable([[i, num//i] for i in range(1, int(num ** 0.5) + 1) if num % i == 0])\n total = sum(set(factors)) - num\n\n return total\n\nsum_amicable_numbers = 0\nfor a in range(1, 10000):\n b = d(a)\n\n if d(b) == a and a != b:\n sum_amicable_numbers += a\n\nprint(sum_amicable_numbers)","sub_path":"21-30/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"341788566","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport pygame\nfrom pygame.locals import *\nimport sys\n\n'''\n1、自定义背景图片\n2、鼠标按键调节图片移动速度\n3、鼠标滚轮实现透明度的调节\n4、鼠标按键左、右、空格实现增加、减少、恢复游戏速度\n5、方向键控制图片移动速度\n6、全屏\n7、背影音乐添加\n'''\n\npygame.init()\n\npygame.mixer.init()\npygame.mixer.music.load('resources/sounds/music.ogg')\npygame.mixer.music.set_volume(0.1)\npygame.mixer.music.play()\n\nsize = pygame.display.list_modes() #获取当前显示器的分辨就率\n# print(size)\nsize_i = 3\nwidth, height = size[size_i]\nprint(width, height)\nspeed_x, speed_y = 1, 1 # 控制图片移动速度\ndir_x, dir_y = -1, 0 # 控制移动方向\ntransparent = 255 # 设置透明度\nclock = pygame.time.Clock()\nfull_screen = False # 控制全屏\nratio = 1.0 # 缩放比例\n\nscreen = pygame.display.set_mode(size[size_i])\npygame.display.set_caption('enjoy the pygame new feature')\n\n## 为了避免多次缩放导致图片模糊，所以先创建一个原始图片为背影图，而实际使用副本\nori_bg = pygame.image.load('resources/images/blue_sky.jpg')\nbackground = ori_bg\nori_img = pygame.image.load('resources/images/penguin.png')\nimg = ori_img\nori_img_rect = ori_img.get_rect()\npos = img_rect = ori_img_rect\n\nl_head = img\nr_head = pygame.transform.flip(img, True, False)\n\n\ndef blit_alpha(target, source, location, opacity):\n x = location[0]\n y = location[1]\n temp = pygame.Surface((source.get_width(), source.get_height())).convert()\n temp.blit(target, (-x, -y))\n temp.blit(source, (0, 0))\n temp.set_alpha(opacity)\n target.blit(temp, location)\n\n\nwhile True:\n for event in pygame.event.get():\n if event.type == QUIT:\n sys.exit()\n\n if event.type == MOUSEBUTTONDOWN:\n\n # 鼠标按键分别表示增加、恢复、减少游戏速度\n if event.button == 1:\n if speed_x < 30 and speed_y < 30:\n speed_x += 1\n speed_y += 1\n if event.button == 2:\n speed_x, speed_y = 1, 1\n if event.button == 3:\n if speed_x > 0 and speed_y > 0:\n speed_x -= 1\n speed_y -= 1\n\n # 鼠标滚轮上下滚动修改图片的透明度\n if event.button == 4:\n if transparent < 255:\n transparent += 5\n if event.button == 5:\n if transparent > 0:\n transparent -= 5\n\n if event.type == KEYDOWN:\n\n # 控制图片移动方向(方向键)\n if event.key == K_LEFT:\n img = l_head\n dir_x, dir_y = -1, 0\n if event.key == K_RIGHT:\n img = r_head\n dir_x, dir_y = 1, 0\n if event.key == K_UP:\n dir_x, dir_y = 0, -1\n if event.key == K_DOWN:\n dir_x, dir_y = 0, 1\n if event.key == K_F10:\n full_screen = not full_screen\n if full_screen:\n screen = pygame.display.set_mode(size[0])\n\n # 放大、缩小屏幕（CTL + B, CTL + S）\n if event.key == K_b and event.mod & KMOD_CTRL:\n if size[size_i] != size[0]:\n size_i -= 1\n width, height = size[size_i]\n screen = pygame.display.set_mode(size[size_i])\n background = pygame.transform.scale(ori_bg, size[size_i])\n if event.key == K_s and event.mod & KMOD_CTRL:\n # print(type(event))\n if size[size_i] != size[-1]:\n size_i += 1\n width, height = size[size_i]\n screen = pygame.display.set_mode(size[size_i])\n background = pygame.transform.scale(ori_bg, size[size_i])\n\n # 全屏（F，在mac上F10, F11都不可以，后来发现是和其它的快捷键冲突了）\n if event.key == K_f:\n full_screen = not full_screen\n if full_screen:\n width, height = size[0]\n screen = pygame.display.set_mode(size[0], FULLSCREEN | HWSURFACE)\n background = pygame.transform.scale(ori_bg, size[0])\n else:\n width, height = size[size_i]\n screen = pygame.display.set_mode(size[size_i])\n background = pygame.transform.scale(ori_bg, size[size_i])\n\n # 放大，缩小移动的图片, +: 放大， -: 缩小， 空格：复位\n if event.key == K_EQUALS or event.key == K_MINUS or event.key == K_SPACE:\n # 最大只能放大一倍，缩小到50%\n if event.key == K_EQUALS and ratio < 2:\n ratio += 0.1\n if event.key == K_MINUS and ratio > 0.5:\n ratio -= 0.1\n if event.key == K_SPACE:\n ratio = 1.0\n\n img = l_head = pygame.transform.smoothscale(ori_img, (int(ori_img_rect.width * ratio), int(ori_img_rect.height * ratio)))\n r_head = pygame.transform.flip(img, True, False)\n\n if dir_x == 1:\n img = r_head\n\n img_rect = img.get_rect()\n pos.width, pos.height = img_rect.width, img_rect.height\n\n # 移动图片\n pos = pos.move(dir_x * speed_x, dir_y * speed_y)\n\n if pos.left < 0 or pos.right > width:\n img = pygame.transform.flip(img, True, False) # 水平翻转图像\n dir_x = -dir_x # 方向切换\n if pos.right > width:\n pos[0] = width - img_rect.width\n else:\n pos[0] = 0\n if pos.top < 0 or pos.bottom > height:\n dir_y = -dir_y\n if pos.bottom > height:\n pos[1] = height - img_rect.height\n else:\n pos[1] = 0\n\n screen.blit(background, (0,0))\n blit_alpha(screen, img, pos, transparent)\n pygame.display.flip()\n clock.tick(50)\n","sub_path":"fishc_py/1_pygame/4_新特性.py","file_name":"4_新特性.py","file_ext":"py","file_size_in_byte":6170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"359552515","text":"import unittest\nimport zeit.cms.testing\nimport zeit.wysiwyg.testing\n\n\ndef test_suite():\n suite = unittest.TestSuite()\n suite.addTest(zeit.cms.testing.FunctionalDocFileSuite(\n 'filebrowser.txt',\n 'image.txt',\n layer=zeit.wysiwyg.testing.WSGI_LAYER))\n return suite\n","sub_path":"core/src/zeit/wysiwyg/browser/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"411128748","text":"'''\nCreated on Jul 16, 2013\n\n@author: ukyo.duong\n'''\n\nimport unittest\nimport time\nfrom helpers.testcase import *\nfrom helpers.Elements import Elements\nfrom helpers.WebdriverUtilities import WebdriverUtilities\nfrom helpers.Helpers import Helpers\n\nclass TestMapSystemToPeople(WebDriverTestCase):\n \n def testMapSystemToPeople(self):\n self.testname=\"TestMapSystemToPeople\"\n self.setup()\n util = WebdriverUtilities()\n util.setDriver(self.driver)\n element = Elements()\n do = Helpers(self)\n do.setUtils(util)\n do.login()\n\n aEmail = \"auto_email_\" + str(do.getRandomNumber()) + \"@gmail.com\"\n aName = do.getUniqueString(\"name\")\n aCompany = do.getUniqueString(\"company\")\n \n titleSys = do.getUniqueString(\"system\") \n do.createObject(\"System\", titleSys)\n \n do.createPersonLHN(aName, aEmail, aCompany)\n do.mapAObjectLHN(\"System\", titleSys)\n \nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"SmokeTest/TestMapSystemToPeople.py","file_name":"TestMapSystemToPeople.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"303005886","text":"\"\"\"\r\nPageDB module.\r\n\"\"\"\r\nimport os\r\nimport time\r\nfrom urllib import parse\r\n\r\nclass PageDB:\r\n \"\"\"\r\n PageDB class.\r\n\r\n Attributes:\r\n words (dict): dict of words with id numbers used in the pages\r\n pages (list): list of Page objects\r\n \"\"\"\r\n words = dict()\r\n pages = list()\r\n\r\n def __init__(self, dataset):\r\n \"\"\"\r\n PageDB constructor.\r\n Parse data from files, create Page objects, calculate PageRank.\r\n\r\n Args:\r\n dataset (str): name of dataset to use\r\n\r\n Raises:\r\n NotADirectoryError: if dataset doesn't exist\r\n \"\"\"\r\n # raise exception if dataset does not exist\r\n if not os.path.isdir(\"./datasets/\" + dataset):\r\n raise NotADirectoryError(\"Dataset not found.\")\r\n\r\n print(\"Creating page object for each page... \")\r\n start = time.time()\r\n workingdir = os.path.dirname(os.path.realpath(__file__))\r\n path = workingdir + \"/datasets/\" + dataset + \"/Words\"\r\n for subdir, dirs, files in os.walk(path):\r\n for file in files:\r\n # iterate through files\r\n filepath = os.path.join(subdir, file)\r\n with open(filepath, \"r\") as f:\r\n words_temp = f.read().split()\r\n word_ints = list()\r\n # get integers for all words in file\r\n for word in words_temp:\r\n word_ints.append(self.get_id_for_word(word))\r\n links_path = filepath.replace(\"Words\", \"Links\", 1)\r\n links = set()\r\n # get links for each article\r\n with open(links_path, \"r\") as f:\r\n for line in f.read().split(\"\\n\"):\r\n if len(line) > 1:\r\n links.add(parse.unquote(line[6:]))\r\n # create Page object for each file\r\n self.pages.append(Page(parse.unquote(file), word_ints, links))\r\n end = time.time()\r\n print(\"Done in {} seconds.\".format(round(end - start, 2)))\r\n print(\"Calculating PageRank for each page... \")\r\n start = time.time()\r\n i = 0\r\n max_iterations = 20\r\n while i < max_iterations:\r\n ranks = list()\r\n j = 0\r\n # calculate pagerank values for all pages\r\n while j < len(self.pages):\r\n ranks.append(self.pagerank(self.pages[j]))\r\n j += 1\r\n j = 0\r\n # normalize scores if it is the last iteration\r\n if i == max_iterations-1:\r\n self.normalize(ranks, False)\r\n # set pagerank values for all pages\r\n while j < len(self.pages):\r\n self.pages[j].pagerank = ranks[j]\r\n j += 1\r\n i += 1\r\n end = time.time()\r\n print(\"Done in {} seconds.\".format(round(end - start, 2)))\r\n\r\n def pagerank(self, page):\r\n \"\"\"\r\n Calculate pagerank score for a page.\r\n\r\n Args:\r\n page (Page): page object\r\n\r\n Returns:\r\n float: pagerank score\r\n \"\"\"\r\n pr = 0\r\n # iterate over all pages\r\n for p in self.pages:\r\n # check if the other page links to this page\r\n if page.url in p.links:\r\n pr += p.pagerank / len(p.links)\r\n # caluclate and return pagerank\r\n pr = 0.85 * pr + 0.15\r\n return pr\r\n\r\n def query(self, query):\r\n \"\"\"\r\n Perform search using query string.\r\n\r\n Args:\r\n query (string): one or more words to search for\r\n\r\n Returns:\r\n list: nested list of results sorted by scores\r\n \"\"\"\r\n # init variables\r\n scores = dict()\r\n scores[\"content\"] = list()\r\n scores[\"location\"] = list()\r\n matches = list()\r\n result = list()\r\n # start timer\r\n start = time.time()\r\n # convert query string to lowercase and make int list\r\n query_ints = list()\r\n for word in query.lower().split():\r\n query_ints.append(self.get_id_for_word(word))\r\n # find matching pages\r\n for page in self.pages:\r\n for i in query_ints:\r\n if i in page.words:\r\n matches.append(page)\r\n break\r\n # calculate scores for matching pages\r\n i = 0\r\n while i < len(matches):\r\n scores[\"content\"].append(self.word_frequency(matches[i], query_ints))\r\n scores[\"location\"].append(self.document_location(matches[i], query_ints))\r\n i += 1\r\n # normalize scores\r\n if len(matches) > 0:\r\n self.normalize(scores[\"content\"], False)\r\n self.normalize(scores[\"location\"], True)\r\n # make result list and return sorted\r\n i = 0\r\n while i < len(matches):\r\n res1 = round(scores[\"content\"][i], 2)\r\n res2 = round(scores[\"location\"][i] * 0.8, 2)\r\n res3 = round(matches[i].pagerank * 0.5, 2)\r\n total = round(res1 + res2 + res3, 2)\r\n result.append([matches[i].url, total, res1, res2, res3])\r\n i += 1\r\n amount = len(result)\r\n end = time.time()\r\n duration = round(end - start, 2)\r\n result = sorted(result, key=lambda x: x[1], reverse=True)[:5]\r\n return {\"data\": result, \"amount\": amount, \"duration\": duration}\r\n\r\n def word_frequency(self, page, query_ints):\r\n \"\"\"\r\n Count occurrances of search words in page and return score.\r\n\r\n Args:\r\n page (Page): page object\r\n query_ints (list): list of ints representing words\r\n\r\n Returns:\r\n int: frequency score\r\n \"\"\"\r\n score = 0\r\n for q in query_ints:\r\n score += page.words.count(q)\r\n return score\r\n\r\n def document_location(self, page, query_ints):\r\n \"\"\"\r\n Check indexes of search words in page and calculate a score.\r\n\r\n Args:\r\n page (Page): page object\r\n query_ints (list): list of ints representing words\r\n\r\n Returns:\r\n int: document location score\r\n \"\"\"\r\n score = 0\r\n # iterate over each word in the search query\r\n for w in query_ints:\r\n try:\r\n # add index of word to score if found\r\n score += page.words.index(w) + 1\r\n except:\r\n # add large number to score if not found\r\n score += 100000\r\n # return result\r\n return score\r\n\r\n def normalize(self, scores, small_is_better):\r\n \"\"\"\r\n Takes a list of floats and normalizes the scores between 0 and 1.\r\n\r\n Args:\r\n scores (list): list of floats\r\n small_is_better (bool): True if small score is better\r\n \"\"\"\r\n if small_is_better:\r\n # smaller values shall be inverted to higher values\r\n # and scaled between 0 and 1\r\n min_val = min(scores)\r\n i = 0\r\n while i < len(scores):\r\n scores[i] = min_val / max(scores[i], 0.00001)\r\n i += 1\r\n else:\r\n # higher values shall be scaled between 0 and 1\r\n max_val = max(scores)\r\n max_val = max(max_val, 0.00001)\r\n i = 0\r\n while i < len(scores):\r\n scores[i] = scores[i] / max_val\r\n i += 1\r\n\r\n def get_id_for_word(self, word):\r\n \"\"\"\r\n Returns ID for a word, and adds the word if not added already.\r\n\r\n Args:\r\n word (string): word to get ID for\r\n\r\n Returns:\r\n int: ID for word\r\n \"\"\"\r\n if word in self.words:\r\n # word found, return id\r\n return self.words[word]\r\n # add missing word\r\n self.words[word] = len(self.words)\r\n return len(self.words)-1\r\n\r\n\r\nclass Page:\r\n \"\"\"\r\n Page class.\r\n\r\n url (string): url to the page\r\n words (list): integers presenting words\r\n links (list): outgoing links from page\r\n pagerank (float) : pagerank score\r\n \"\"\"\r\n\r\n def __init__(self, url, words, links):\r\n \"\"\"\r\n Page constructor.\r\n Sets attributes.\r\n\r\n Args:\r\n url (string): url to page\r\n words (list): integers presenting words\r\n links (list): outgoing links from page\r\n \"\"\"\r\n self.url = url\r\n self.words = words\r\n self.links = links\r\n self.pagerank = 1.0\r\n","sub_path":"pagedb.py","file_name":"pagedb.py","file_ext":"py","file_size_in_byte":8517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"197402168","text":"# -*- coding: utf-8 -*-\nfrom urllib.parse import quote, urlparse\n\nfrom lxml import html\nfrom xrd import XRD\n\nfrom federation.entities.base import Profile\nfrom federation.utils.network import fetch_document\n\n\ndef retrieve_diaspora_hcard(handle):\n \"\"\"\n Retrieve a remote Diaspora hCard document.\n\n Args:\n handle (str) - Remote handle to retrieve\n\n Returns:\n str (HTML document)\n \"\"\"\n webfinger = retrieve_diaspora_webfinger(handle)\n if not webfinger:\n return None\n url = webfinger.find_link(rels=\"http://microformats.org/profile/hcard\").href\n document, code, exception = fetch_document(url)\n if exception:\n return None\n return document\n\n\ndef retrieve_diaspora_webfinger(handle):\n \"\"\"\n Retrieve a remote Diaspora webfinger document.\n\n Args:\n handle (str) - Remote handle to retrieve\n\n Returns:\n XRD\n \"\"\"\n hostmeta = retrieve_diaspora_host_meta(handle.split(\"@\")[1])\n if not hostmeta:\n return None\n url = hostmeta.find_link(rels=\"lrdd\").template.replace(\"{uri}\", quote(handle))\n document, code, exception = fetch_document(url)\n if exception:\n return None\n xrd = XRD.parse_xrd(document)\n return xrd\n\n\ndef retrieve_diaspora_host_meta(host):\n \"\"\"\n Retrieve a remote Diaspora host-meta document.\n\n Args:\n host (str) - Host to retrieve from\n\n Returns:\n XRD\n \"\"\"\n document, code, exception = fetch_document(host=host, path=\"/.well-known/host-meta\")\n if exception:\n return None\n xrd = XRD.parse_xrd(document)\n return xrd\n\n\ndef _get_element_text_or_none(document, selector):\n \"\"\"\n Using a CSS selector, get the element and return the text, or None if no element.\n\n Args:\n document (HTMLElement) - HTMLElement document\n selector (str) - CSS selector\n \"\"\"\n element = document.cssselect(selector)\n if element:\n return element[0].text\n return None\n\n\ndef _get_element_attr_or_none(document, selector, attribute):\n \"\"\"\n Using a CSS selector, get the element and return the given attribute value, or None if no element.\n\n Args:\n document (HTMLElement) - HTMLElement document\n selector (str) - CSS selector\n attribute (str) - The attribute to get from the element\n \"\"\"\n element = document.cssselect(selector)\n if element:\n return element[0].get(attribute)\n return None\n\n\ndef parse_profile_from_hcard(hcard):\n \"\"\"\n Parse all the fields we can from a hCard document to get a Profile.\n\n Args:\n hcard (str) - HTML hcard document\n \"\"\"\n doc = html.fromstring(hcard)\n domain = urlparse(_get_element_attr_or_none(doc, \"a#pod_location\", \"href\")).netloc\n profile = Profile(\n name=_get_element_text_or_none(doc, \"dl.entity_full_name span.fn\"),\n image_urls={\n \"small\": _get_element_attr_or_none(doc, \"dl.entity_photo_small img.photo\", \"src\"),\n \"medium\": _get_element_attr_or_none(doc, \"dl.entity_photo_medium img.photo\", \"src\"),\n \"large\": _get_element_attr_or_none(doc, \"dl.entity_photo img.photo\", \"src\"),\n },\n public=True if _get_element_text_or_none(doc, \"dl.entity_searchable span.searchable\") == \"true\" else False,\n handle=\"%s@%s\" % (_get_element_text_or_none(doc, \"dl.entity_nickname span.nickname\"), domain),\n guid=_get_element_text_or_none(doc, \"dl.entity_uid span.uid\"),\n public_key=_get_element_text_or_none(doc, \"dl.entity_key pre.key\"),\n )\n return profile\n\n\ndef retrieve_and_parse_profile(handle):\n \"\"\"\n Retrieve the remote user and return a Profile object.\n\n Args:\n handle (str) - User handle in username@domain.tld format\n\n Returns:\n Profile\n \"\"\"\n hcard = retrieve_diaspora_hcard(handle)\n if not hcard:\n return None\n return parse_profile_from_hcard(hcard)\n","sub_path":"federation/utils/diaspora.py","file_name":"diaspora.py","file_ext":"py","file_size_in_byte":3864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"497712746","text":"# -*- coding: utf-8 -*-\nimport csv\nimport datetime\nimport os\nimport random\nimport json\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nimport sys\nfrom time import sleep\n\nfrom mongodb_util import *\n\nVERSION = COLLECTION\n\n# 创建链接mongodb数据库对象那个\nclientMongodb=dataToMongodb()\n\nfile_path = os.path.join(os.path.dirname(os.path.realpath(__file__)) , '../crawlerOutput/' + VERSION + '/tabelog/')\n# file_path = os.path.join(os.path.dirname(__file__),'crawlerOutput/' ,VERSION , '/tabelog/')\nweb_name = 'tabelog/'\nuser_agerts = [\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.1 Safari/537.36\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2226.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.4; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2225.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2224.3 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.124 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 4.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36\",\n \"Mozilla/5.0 (X11; OpenBSD i386) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.125 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1944.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.3319.102 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2309.372 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.2117.157 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1866.237 Safari/537.36\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.137 Safari/4E423F\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.116 Safari/537.36 Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X; en-us) AppleWebKit/531.21.10 (KHTML, like Gecko) Version/4.0.4 Mobile/7B334b Safari/531.21.10\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.517 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1650.16 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1623.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.17 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.62 Safari/537.36\",\n \"Mozilla/5.0 (X11; CrOS i686 4319.74.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.57 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.2 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1468.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1467.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1464.0 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1500.55 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.90 Safari/537.36\",\n \"Mozilla/5.0 (X11; NetBSD) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\",\n \"Mozilla/5.0 (X11; CrOS i686 3912.101.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.60 Safari/537.17\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_2) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1309.0 Safari/537.17\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.15 (KHTML, like Gecko) Chrome/24.0.1295.0 Safari/537.15\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.14 (KHTML, like Gecko) Chrome/24.0.1292.0 Safari/537.14\"\n]\n\n\ndef getInfo(url):\n saveLog('获取页面{}的数据'.format(url))\n header = {\n 'Host': 'tabelog.com',\n 'Connection': 'keep-alive',\n 'Cache-Control': 'max-age=0',\n 'Upgrade-Insecure-Requests': '1',\n 'User-Agent': random.choice(user_agerts),\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n 'DNT': '1',\n 'Referer': 'https://tabelog.com/tokyo/A1303/A130301/13199289/dtlratings/',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Accept-Language': 'zh,zh-CN;q=0.9',\n 'Cookie': '_ga=GA1.2.1950227645.1521457568; _gid=GA1.2.2087309422.1521457568; s_fid=75ECF5B293C35A58-0F6BFA53CB265A95; ma_k=a6cc9eeaec17a42e2893e8c31a01da24; do_i=4f9d1d14c7c22c73470d21637a6f6b8f; ta_l=1; s_nr=1521457694858; tabelogusr=Ndq5D4zAjPF_1521548378982; s_cc=true; _tabelog_session_id=4d14056ba65e86b5d575806132138ed5; mo_r=f5e28a3d89278e270f2a0bbcfe4c1a31; inbound-jpuse=1; jack_ad_disp_count=2; ysc=; detail_score_open=0; s_sq=%5B%5BB%5D%5D; s_ptc=0.004%5E%5E0.006%5E%5E0.000%5E%5E0.110%5E%5E0.065%5E%5E0.178%5E%5E16.276%5E%5E0.109%5E%5E16.598; s_royal=site%3A802-2579186%3A6%2Ctabelog%3A802-2579186%3A4',\n }\n with requests.get(url, headers=header) as res:\n return res\n\n\ndef saveLog(str=''):\n logFile = file_path + 'log/' + web_name + '{}_log.csv'.format(city_name)\n date_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n logHead = ['ruandate', 'str']\n logData = {\n 'ruandate': date_time,\n 'str': str\n }\n print('{} \\t {}'.format(date_time, str))\n logPath = os.path.split(logFile)\n isExists = os.path.exists(logPath[0])\n if not isExists:\n os.makedirs(logPath[0])\n with open(logFile, 'a', encoding='utf-8', newline='') as log_file:\n log_csv = csv.DictWriter(log_file, fieldnames=logHead)\n if not os.path.getsize(logFile):\n log_csv.writeheader()\n log_csv.writerow(logData)\n\n\ndef saveInfo(res):\n rawHead = [\n 'shop_status',\n 'Store Name',\n 'TEL',\n 'Postal Code',\n 'Address',\n 'Prefecture',\n 'City / Ward Name',\n 'Address',\n 'Category',\n 'Budget',\n # 'Budget (day)',\n 'RatingCount',\n 'Rating',\n 'Transpotation',\n 'Hours',\n 'Holiday',\n '# of Seats',\n 'ParkingLot',\n 'Service',\n 'Homepage',\n 'SNS',\n 'Open Date',\n 'ID',\n 'lat',\n 'lng'\n ]\n\n rawData = {}\n infoList = [\n # '予約可否',\n '交通手段',\n '営業時間',\n '定休日',\n '予算（口コミ集計）',\n # '支払い方法',\n '席数',\n # '個室',\n # '貸切',\n '禁煙・喫煙',\n '駐車場',\n # '空間・設備',\n # '携帯電話',\n # '飲み放題コース',\n # 'コース',\n # 'ドリンク',\n # '料理',\n # '利用シーン',\n # 'ロケーション',\n 'サービス',\n 'ホームページ',\n # '公式アカウント',\n 'オープン日',\n ]\n infoListEn = [\n # 'Reservationavailability',\n 'Transpotation',\n 'Hours',\n 'Holiday',\n 'Budget',\n # 'methodofpayment',\n '# of Seats',\n # 'PrivateRoom',\n # 'reserved',\n 'SNS',\n 'ParkingLot',\n # 'SpaceOrEquipment',\n # 'mobilephone',\n # 'Allyoucandrinkcourse',\n # 'course',\n # 'drink',\n # 'cuisine',\n # 'Usescene',\n # 'Location',\n 'Service',\n 'Homepage',\n # 'Officialaccount',\n 'Open Date',\n ]\n soup = BeautifulSoup(res.content, \"lxml\")\n # 餐馆状态：正常，休息，搬迁\n status_soup = soup.find('div', attrs={'class': 'rdheader-rstname-wrap'}).find('span', recursive=False)\n print(status_soup)\n if status_soup:\n status = status_soup['class'][-1]\n print(status)\n rawData['shop_status'] = status\n else:\n rawData['shop_status'] = ''\n jsonStr = soup.find('script', attrs={'type': 'application/ld+json'})\n try:\n jsonStr = str(jsonStr.contents[0])\n except Exception as e:\n jsonStr = ''\n jsonArr = json.loads(jsonStr)\n\n rawData['ID'] = res.url.split(\"/\")[-1]\n rawData['Store Name'] = jsonArr.get('name', \"\")\n rawData['Category'] = jsonArr.get('servesCuisine', \"\")\n # rawData['image'] = jsonArr.get('image', \"\")\n # rawData['addressCountry'] = jsonArr.get('address', {}).get('addressCountry', \"\")\n rawData['Postal Code'] = jsonArr.get('address', {}).get('postalCode', \"\")\n rawData['Prefecture'] = jsonArr.get('address', {}).get('addressRegion', \"\")\n rawData['City / Ward Name'] = jsonArr.get('address', {}).get('addressLocality', \"\")\n rawData['Address'] = jsonArr.get('address', {}).get('streetAddress', \"\")\n rawData['lat'] = jsonArr.get('geo', {}).get('latitude', \"\")\n rawData['lng'] = jsonArr.get('geo', {}).get('longitude', \"\")\n rawData['TEL'] = jsonArr.get('telephone', \"\")\n # rawData['priceRange'] = jsonArr.get('priceRange', \"\")\n rawData['RatingCount'] = jsonArr.get('aggregateRating', {}).get('ratingCount', \"\")\n rawData['Rating'] = jsonArr.get('aggregateRating', {}).get('ratingValue', \"\")\n tables = soup.find_all('table', class_='c-table c-table--form rstinfo-table__table')\n for table in tables:\n trs = table.find_all('tr')\n for tr in trs:\n column = tr.find('th').stripped_strings\n column = [x for x in column]\n column = ''.join(column)\n if column and column in infoList:\n index = infoList.index(column)\n try:\n columnName = infoListEn[index]\n except IndexError:\n print(index)\n values = tr.find('td').strings\n value = [x.strip() for x in values]\n value = ' '.join(value).strip()\n rawData[columnName] = value\n rawPath = file_path + web_name\n isExists = os.path.exists(rawPath)\n if not isExists:\n os.makedirs(rawPath)\n rawFile = '{}{}_raw.csv'.format(rawPath, city_name)\n saveLog('保存店铺{}的数据'.format(rawData.get('Store Name')))\n # with open(rawFile, 'a', encoding='utf-8', newline='') as raw_file:\n # # print(rawData)\n # raw_csv = csv.DictWriter(raw_file, fieldnames=rawHead)\n # if not os.path.getsize(rawFile):\n # raw_csv.writeheader()\n # raw_csv.writerow(rawData)\n\n # 将数据保存到mongodb数据库\n clientMongodb.insert_info(rawData,city_name)\n\n\ndef limit_detect(res):\n text = res.text\n if 'アクセスが制限されています' in text:\n return True\n else:\n return False\n\n\ndef save_success_id(id):\n file_path = './{}_has_get.txt'.format(city_name)\n with open(file_path, 'a') as f:\n f.writelines(str(id)+'\\n')\n\n\ndef last_success_id():\n file_path = './{}_has_get.txt'.format(city_name)\n try:\n with open(file_path, 'r') as f:\n s = f.readlines()[-1]\n return int(s)\n except:\n return 0\n\n\nif __name__ == '__main__':\n # Osaka 27000001, 27105562\n # hyogo 28000001, 28052175\n # Kyoto 26000001, 26030906\n # aichi 23000001, 23067905 共48,845店舗\n # fukuoka 40000001, 40049040 共35,109店舗\n # Kanagawa 14000001, 14072039\n # Tokyo 13000001, 13223273\n # chiba 12000001, 12044580 共31,599店舗\n # Saitama 11000001, 11047653 共34,853店舗\n\n area_data = {\n \"11\": {\n 'id': 11,\n 'name': \"Saitama\",\n 'first_id': 11000001,\n 'last_id': 11048000,\n },\n \"12\": {\n 'id': 12,\n 'name': \"chiba\",\n 'first_id': 12000001,\n 'last_id': 12044900,\n },\n \"13\": {\n 'id': 13,\n 'name': \"Tokyo\",\n 'first_id': 13000001,\n 'last_id': 13224700,\n },\n \"14\": {\n 'id': 14,\n 'name': \"Kanagawa\",\n 'first_id': 14000001,\n 'last_id': 14072450,\n },\n \"23\": {\n 'id': 23,\n 'name': \"aichi\",\n 'first_id': 23000001,\n 'last_id': 23068250,\n },\n \"26\": {\n 'id': 26,\n 'name': \"Kyoto\",\n 'first_id': 26000001,\n 'last_id': 26031050,\n },\n \"27\": {\n 'id': 27,\n 'name': \"Osaka\",\n 'first_id': 27000001,\n 'last_id': 27106150,\n },\n \"28\": {\n 'id': 28,\n 'name': \"hyogo\",\n 'first_id': 28000001,\n 'last_id': 28052450,\n },\n \"40\": {\n 'id': 40,\n 'name': \"fukuoka\",\n 'first_id': 40000001,\n 'last_id': 40049300,\n },\n }\n if len(sys.argv) == 2:\n index = int(sys.argv[1])\n else:\n index = 0\n\n for area in area_data.values():\n if index == 0:\n print('默认获取全部城市的数据')\n pass\n else:\n if area.get('id') != index:\n continue\n city_name = area.get('name')\n first_id = area.get('first_id')\n last_id = area.get('last_id')\n tmp_id = last_success_id()\n if tmp_id == 0:\n start_id = first_id\n end_id = last_id\n elif tmp_id >= last_id:\n print('本地区请求结束.')\n saveLog('本地区请求结束.')\n continue\n else:\n start_id = tmp_id + 1\n end_id = last_id\n for id in range(start_id, end_id):\n url = \"https://tabelog.com/kyoto/A2601/A260201/\" + str(id)\n try:\n res = getInfo(url)\n # sleep(random.randint(0, 2))\n if limit_detect(res):\n saveLog('网络ip被限制, 无法继续')\n break\n if res.status_code == 200:\n saveLog('{}, 正常返回'.format(res.status_code))\n save_success_id(id)\n saveInfo(res)\n except Exception as err:\n saveLog('页面{}出错'.format(url))\n saveLog(err)\n\n # 数据存储完毕, 关闭数据库链接\n clientMongodb.close_client()\n","sub_path":"tabelog_v1.py","file_name":"tabelog_v1.py","file_ext":"py","file_size_in_byte":16950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"484806769","text":"# -*- coding: utf-8 -*-\nimport json\n\nimport scrapy\nfrom scrapy import Request,FormRequest\n\n\nclass YishouSpider(scrapy.Spider):\n name = 'yishou'\n allowed_domains = ['api.yishouapp.com']\n start_urls = ['http://api.yishouapp.com/']\n\n def __init__(self):\n self.headers = {\n \"User-Agent\": \"Mozilla/5.0 (Linux; U; Android 4.0.4; en-gb; GT-I9300 Build/IMM76D) AppleWebKit/534.30 (KHTML, like Gecko) Version/4.0 Mobile Safari/534.30\",\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Accept-Language': 'zh-CN,zh;q=0.9',\n 'Cache-Control': 'max-age=0',\n 'Connection': 'keep-alive',\n 'Host': 'api.yishouapp.com',\n 'Upgrade-Insecure-Requests': '1',\n }\n\n self.uid = \"1615579\"\n self.token = \"b21c0be7a26fde584cf3ee531d8b5b3c\"\n self.udid = \"863654029144979\"\n self.url = \"https://api.yishouapp.com/goods/get_goods_info\"\n\n\n def start_requests(self):\n\n # cat_list = [\"296\", \"297\", \"299\", \"300\", \"302\"]\n cat_list = [\"296\"]\n for cat_id in cat_list:\n data = {\n \"version_name\": \"3.4.1\",\n \"uid\": self.uid,\n # \"cat_id\": \"297\", # 产品类别编号\n \"cat_id\": cat_id, # 产品类别编号\n \"token\": self.token,\n # \"page\": page,\n \"page\": \"1\",\n \"_abtest\": \"1\",\n \"udid\": self.udid,\n \"plat_type\": \"Android\",\n \"version_code\": \"341\"\n }\n\n body = \"version_name=3.4.1&uid={}&cat_id={}&token={}&page={}&_abtest=1&udid=1&plat_type=Android&version_code=341\".format(self.uid, cat_id, self.token, self.udid)\n\n # return Request(url=self.url, headers=self.headers, method=\"POST\", body=body, callback=self.get_id, meta={\"cat_id\": cat_id},dont_filter=True)\n yield Request(url=self.url, headers=self.headers, method=\"POST\", body=body, callback=self.get_id, meta={\"cat_id\": cat_id},dont_filter=True)\n # return FormRequest(url=self.url, headers=self.headers, method=\"POST\", formdata=data, callback=self.get_id, meta={\"cat_id\": cat_id}, dont_filter = True)\n\n def get_pages(self, response):\n html = json.loads(response.text)\n pages = html[\"data\"][\"page_total\"]\n print(pages)\n cat_id = response.meta[\"cat_id\"]\n # for page in range(1, int(pages) + 1):\n for page in range(1, 2):\n\n data = {\n \"version_name\": \"3.4.1\",\n \"uid\": self.uid,\n # \"cat_id\": \"297\", # 产品类别编号\n \"cat_id\": str(cat_id),\n \"token\": self.token,\n \"page\": page,\n \"_abtest\": \"1\",\n \"udid\": self.udid,\n \"plat_type\": \"Android\",\n \"version_code\": \"341\"\n }\n\n # return Request(url=self.url, headers=self.headers, method=\"POST\", body=data, callback=self.get_id)\n return FormRequest(url=self.url, headers=self.headers, method=\"POST\", fromdata=data, callback=self.get_id, dont_filter = True)\n\n def get_id(self, response):\n\n url = \"https://api.yishouapp.com/goods/get_goods_info\"\n html = json.loads(response.text)\n goods_list_1 = html[\"data\"][\"goods_list\"]\n # goods_id_list = []\n for goods_ids in goods_list_1:\n goods_id = goods_ids[\"goods_id\"]\n # shop_price=goods_ids[\"shop_price\"]\n # goods_img_url = goods_ids[\"goods_img\"]\n # goods_id_list.append(goods_id)\n\n with open(\"./GoodsID.txt\", \"a+\") as fh:\n fh.write(goods_id + \"\\n\")\n\n\n data = {\n \"version_name\": \"3.4.1\",\n \"uid\": self.uid,\n \"source\": \"8\",\n \"token\": self.token,\n \"_abtest\": \"1\",\n \"goods_id\": goods_id,\n # \"goods_id\": \"7671071\",\n \"udid\": self.udid,\n \"plat_type\": \"Android\",\n \"version_code\": \"341\",\n \"ss_type\": \"0\"\n }\n\n # return Request(url=url, headers=self.headers, method=\"POST\", body=data, callback=self.get_detali)\n # yield Request(url=url, headers=self.headers, method=\"POST\", body=data, callback=self.get_detali,)\n # yield FormRequest(url=url, headers=self.headers, method=\"POST\", formdata=data, callback=self.get_detali,dont_filter = True)\n return FormRequest(url=url, headers=self.headers, method=\"POST\", formdata=data, callback=self.get_detali,dont_filter = True)\n\n\n def get_detali(self, response):\n html = json.loads(response.text)\n goods_thumb = html[\"data\"][\"goods_thumb\"]\n goods_images = html[\"data\"][\"goods_img\"]\n goods_desc = html[\"data\"][\n \"goods_desc\"] # \"秋冬季节不同品类的排单时长如下：若档口没有库存，一般情况下上衣、裤子、半裙、鞋包配饰排单3-5天，连衣裙和一般的外套7天左右，手工缝制的羊毛大衣7-15天。若遇重工艺的服装加工，则需要多加2-3天；若遇面料排单，则时间会需要顺延；若面料缺货，则会面临缺货。\"\n cat_name = html[\"data\"][\"cat_name\"] # 蕾丝/雪纺衫\n fabric = html[\"data\"][\"fabric_tag\"] # 雪纺\n origin_name = html[\"data\"][\"origin_name\"] # 广州发货商品\n\n # 颜色分类\n attributes = html[\"data\"][\"attribute\"]\n for attribute in attributes:\n color = attribute[\"color\"] # 灰色\n color_card = attribute[\"color_card\"] # 商品大图\n card_thumb = attribute[\"card_thumb\"] # 商品小图\n\n items = attribute[\"item\"]\n for item in items:\n size = item[\"size\"] # 均码\n sort = item[\"sort\"] # 194\n stock = item[\"stock\"] # 500\n sku = item[\"sku\"] # 16217830\n\n print(color, color_card, card_thumb, size, sort, stock, sku)\n\n goods_name = html[\"data\"][\"goods_name\"] # \"【ANGEL KISS】性感百搭一字肩镂空雪纺衫 8023-1# LXX\"\n shop_price = html[\"data\"][\"shop_price\"] # 47.25\n special_end_time = html[\"data\"][\"special_end_time\"] # 限时秒杀剩余时间？1550790000\n\n goods_tags = html[\"data\"][\"goods_tags\"] # \"山河南城\"，\"ANGEL KISS\"，\"面料舒适\"\n colors = html[\"data\"][\"color\"]\n size = html[\"data\"][\"size\"]\n\n print(goods_thumb, goods_images, goods_desc, cat_name, fabric, origin_name, goods_name, shop_price,\n special_end_time, goods_tags, colors, size)\n\n # def parse(self, response):\n # pass\n\n","sub_path":"yishouscrapy/yishouscrapy/spiders/yishou.py","file_name":"yishou.py","file_ext":"py","file_size_in_byte":6830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"92485567","text":"import re\nimport operator\nfrom itertools import count\nfrom math import gcd\n\nmoons = []\ninitial_state_x, initial_state_y, initial_state_z = [], [], []\nstate_x, state_y, state_z = [], [], []\ncycle_x, cycle_y, cycle_z = False, False, False\n\nwith open('input') as f:\n for position in f:\n x, y, z = list(map(int, re.findall(r\"(-?\\d+)\", position)))\n moons.append({\n 'p': [x, y, z],\n 'v': [0, 0, 0]\n })\n\n initial_state_x.append(x)\n initial_state_y.append(y)\n initial_state_z.append(z)\n\nstate_x = initial_state_x.copy()\nstate_y = initial_state_y.copy()\nstate_z = initial_state_z.copy()\n\nfor step in count(2):\n\n for moon, m1 in enumerate(moons):\n for m2 in moons:\n\n if m1['p'] == m2['p']:\n continue\n\n x1, y1, z1 = m1['p']\n x2, y2, z2 = m2['p']\n\n if x1 != x2:\n m1['v'][0] += -1 if x1 > x2 else 1\n\n if y1 != y2:\n m1['v'][1] += -1 if y1 > y2 else 1\n\n if z1 != z2:\n m1['v'][2] += -1 if z1 > z2 else 1\n\n moons[moon] = m1\n\n for index, moon in enumerate(moons):\n x, y, z = list(map(operator.add, moon['p'], moon['v']))\n\n state_x[index] = x\n state_y[index] = y\n state_z[index] = z\n\n moons[index]['p'] = (x, y, z)\n\n if state_x == initial_state_x:\n if not cycle_x:\n cycle_x = step\n\n if state_y == initial_state_y:\n if not cycle_y:\n cycle_y = step\n\n if state_z == initial_state_z:\n if not cycle_z:\n cycle_z = step\n\n if cycle_x and cycle_y and cycle_z:\n break\n\n\ndef lcm(a, b):\n return int(a * b / gcd(a, b))\n\n\nprint(lcm(lcm(cycle_x, cycle_y), cycle_z))\n","sub_path":"day-12/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"124506623","text":"import os\nimport subprocess as sp\n\nfrom tactics.ee4.EE4ClientOnDemandComponent import EE4ClientOnDemandComponent\n\nclass EE4:\n __dir_path = None\n __camera_ondemand_controller: EE4ClientOnDemandComponent\n __camera_process: sp.Popen\n\n def __init__(self):\n self.__camera_process = None\n self.__dir_path = os.path.dirname(os.path.realpath(__file__))\n self.__camera_ondemand_controller = EE4ClientOnDemandComponent(component_name='camera', change_event=self.__spawn_change_event)\n\n def __spawn_change_event(self):\n if not self.__camera_ondemand_controller.is_spawned(): # When despawned in controller, despawn here.\n print(\"Despawning CameraSensor!\")\n #self.__camera_controller.exit() No longer needed as we terminate\n self.__camera_process.terminate()\n else:\n print(\"Spawning CameraSensor!\")\n path = self.__dir_path + '/EE4CameraController.py'\n self.__camera_process = sp.Popen(['python3', path])\n\n def exit(self):\n self.__camera_process.terminate()","sub_path":"RQ2_ros_implementation/turtlebot-runner/tactics/ee4/ee4.py","file_name":"ee4.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"278741566","text":"import connexion\nimport six, logging, os, json\nimport pickle\nimport jsonpickle\n\nfrom swagger_server.controllers.commands_controller import variable\nfrom swagger_server.models.grid import Grid # noqa: E501\nfrom swagger_server import util\nfrom swagger_server.models.simulation_result import SimulationResult\n\nfrom swagger_server.controllers.utils import constant as c\nfrom data_management.redisDB import RedisDB\n\nfrom swagger_server.controllers.threadFactory import ThreadFactory\nfrom data_management.utils import Utils\n\nlogging.basicConfig(format='%(asctime)s %(levelname)s %(name)s: %(message)s', level=logging.DEBUG)\nlogger = logging.getLogger(__file__)\n\nutils = Utils()\n\ndef create_simulation(body): # noqa: E501\n \"\"\"Send grid data to simulation engine in order to create a new simulation\n\n # noqa: E501\n\n :param radial: Grid to be simulated\n :type radial: dict | bytes\n\n :rtype: str\n \"\"\"\n if connexion.request.is_json:\n logger.debug(\"Post grid request\")\n data = connexion.request.get_json()\n #temp = json.loads(json.dumps(data))\n #logger.debug(\"Data: \" + str(temp)) #shows the raw data sent from client\n grid = Grid.from_dict(data) # noqa: E501. \n logger.debug(\"Grid: \" + str(grid)) #shows the raw data sent from client\n\n ####generates an id an makes a directory with the id for the data and for the registry\n try:\n id = utils.create_and_get_ID()\n redis_db = RedisDB()\n redis_db.set(id, \"created\")\n flag = redis_db.get(id)\n logger.debug(\"id stored in RedisDB: \"+str(flag))\n #dir = os.path.join(os.getcwd(), \"utils\", str(id))\n #if not os.path.exists(dir):\n #os.makedirs(dir)\n #dir_data = os.path.join(os.getcwd(), \"optimization\", str(id), \"mqtt\")\n #if not os.path.exists(dir_data):\n #os.makedirs(dir_data)\n except Exception as e:\n logger.error(e)\n\n #logger.info(\"This is the Grid: \" + str(grid))#Only for debugging purpose\n radial = grid.radials\n\n #logger.info(\"These are the radials: \"+ str(radial))\n\n #linecodes = [c().linecodes[0]]\n #logger.debug(\"Linecode: \"+str(linecodes))\n #gridController= gControl()\n factory= ThreadFactory(id)\n variable.set(id, factory)\n logger.debug(\"Factory instance stored\")\n #redis_db.set(\"factory: \"+id, json.dumps(factory))\n #logger.debug(\"Factory: \"+str(factory[id]))\n #object=redis_db.get(\"factory: \"+id)\n #logger.debug(\"Factory stored in redisDB: \"+str(object))\n #test= json.loads(object[id])\n #logger.debug(\"Factory stored in redisDB: \" + str(test)+\" type: \"+str(type(test)))\n factory.gridController.setNewCircuit(id)\n\n for values in range(0): #radial:\n #logger.debug(\"values of the radial: \"+str(values))\n values=values.to_dict()\n #logger.debug(\"Values: \"+str(values))\n if \"transformer\" in values.keys() and values[\"transformer\"] is not None:\n logger.debug(\"---------------Setting Transformers------------------------\")\n transformer = values[\"transformer\"]\n logger.debug(\"Transformers\" + str(transformer))\n factory.gridController.setTransformers(id,transformer)\n \n if \"loads\" in values.keys() and values[\"loads\"] is not None:\n logger.debug(\"---------------Setting Loads-------------------------\")\n # radial=radial.to_dict()\n load = values[\"loads\"]\n logger.debug(\"Loads\" + str(transformer))\n factory.gridController.setLoads(id, load)\n\n if \"power_lines\" in values.keys() and values[\"power_lines\"] is not None:\n logger.debug(\"---------------Setting Powerlines-------------------------\")\n powerLines = values[\"power_lines\"]\n #linecodes = values[\"linecode\"]\n #factory.gridController.setPowerLines(id, powerLines, linecodes) #TODO: Where does linecodes come from?\n logger.debug(\"Powerlines\" + str(powerLines))\n factory.gridController.setPowerLines(id, powerLines)\n\n if \"powerProfile\" in values.keys() and values[\"powerProfile\"] is not None:\n powerProfile = values[\"powerProfile\"]\n #logger.debug(\"Powerprofile\" + str(powerProfile))\n factory.gridController.setPowerProfile(id, powerProfile)\n\n if \"xycurves\" in values.keys() and values[\"xycurves\"] is not None:\n xycurves = values[\"xycurves\"]#TORemove\n factory.gridController.setXYCurve(id, xycurves) \n\n if \"photovoltaics\" in values.keys() and values[\"photovoltaics\"] is not None:\n photovoltaics = values[\"photovoltaics\"]\n xycurves = radial[\"xycurves\"]\n loadshapes = radial[\"loadshapes\"]\n tshapes = radial[\"tshapes\"]\n factory.gridController.setPhotovoltaic(id, photovoltaics, xycurves, loadshapes, tshapes)\n\n \"\"\"\n and \"xycurves\" in radial.values.keys()s() and radial[\"xycurves\"] is not None \n and \"loadshapes\" in radial.values.keys()s() and radial[\"loadshapes\"] is not None \n and \"tshapes\" in radial.values.keys()s() and radial[\"tshapes\"] is not None: \n \"\"\"\n if \"storage_units\" in values.keys() and values[\"storage_units\"] is not None:\n logger.debug(\"---------------Setting Storage-------------------------\")\n # radial=radial.to_dict()\n storage = values[\"storage_units\"]\n factory.gridController.setStorage(id, storage)\n \"\"\"if \"chargingPoints\" in values.keys() and values[\"chargingPoints\"] is not None:\n # radial=radial.to_dict()\n chargingPoints = values[\"chargingPoints\"]\n gridController.setChargingPoints(id, chargingPoints)\n \"\"\"\n if \"chargingPoints\" in values.keys() and values[\"chargingPoints\"] is not None:\n chargingPoints = values[\"chargingPoints\"]\n factory.gridController.setChargingPoints(id, chargingPoints) \n if \"linecode\" in values.keys() and values[\"linecode\"] is not None:\n logger.debug(\"---------------Setting LineCode-------------------------\")\n linecode = values[\"linecode\"]\n logger.debug(\"LineCode: \" + str(linecode))\n factory.gridController.setLineCodes(id, linecode) \n if \"capacitor\" in values.keys() and values[\"capacitor\"] is not None:\n logger.debug(\"---------------Setting Capacitors-------------------------\")\n capacitor = values[\"capacitor\"]\n #logger.debug(\"Capacitors: \" + str(capacitor))\n factory.gridController.setCapacitors(id, capacitor) \n \n if \"voltage_regulator\" in values.keys() and values[\"voltage_regulator\"] is not None:\n logger.debug(\"---------------Setting Voltage regulator-------------------------\")\n voltage_regulator = values[\"voltage_regulator\"]\n logger.debug(\"Voltage Regulator: \" + str(voltage_regulator))\n factory.gridController.setVoltageRegulator(id, voltage_regulator) \n if \"loadshapes\" in values.keys() and values[\"loadshapes\"] is not None:\n logger.debug(\"---------------Setting loadshapes-------------------------\")\n loadshapes = values[\"loadshapes\"]\n logger.debug(\"Load Shapes: \" + str(loadshapes))\n factory.gridController.setLoadShape(id, loadshapes) \n if \"tshapes\" in values.keys() and values[\"tshapes\"] is not None:\n logger.debug(\"---------------Setting tshapes-------------------------\")\n tshapes = values[\"tshapes\"]\n logger.debug(\"Tshapes: \" + str(tshapes))\n factory.gridController.setTShape(id, tshapes) \n ######Disables circuits untilo the run simulation is started\n #factory.gridController.disableCircuit(id)\n result = factory.gridController.run()\n #factory.gridController.run()\n #return str(result) \n return id \n #return \" Result: \" + str(result)\n else:\n return \"Bad JSON Format\"\n \ndef get_simulation_result(id): # noqa: E501\n \"\"\"Get a simulation result\n\n # noqa: E501\n\n :param id: ID of the simulation\n :type id: str\n\n :rtype: Simulation result - array of nodes, and corresponding voltage\n \"\"\"\n #factory= ThreadFactory(id)\n #variable.set(id, factory)\n #result = factory.gridController.results()\n try:\n f = open('/usr/src/app/tests/results/results.txt') #open(str(id)+\"_results.txt\")\n result = f.readlines()\n logger.debug(result)\n f.close()\n except:\n result = \"None\"\n return result\n \"\"\"result = []\n try:\n f = open('/usr/src/app/tests/results/results.txt')\n content = f.readLines().split(',')\n logger.info(str(content))\n for i in content:\n k,v = i.split(':')\n logger.info('Pairs: ' + str(k) + \":\" + str(v))\n result.append(SimulationResult(k, v))\n except:\n result.append(\"None\")\n return result\"\"\"\n \n\"\"\"\n s = result\n dic = {}\n items = s.split(',')\n for i in items:\n k,v = i.split(':')\n dic[k] = v\nreturn dic\n \"\"\"\n \ndef delete_simulation(id): # noqa: E501\n \"\"\"Delete a simulation and its data\n\n # noqa: E501\n\n :param id: ID of the simulation\n :type id: str\n\n :rtype: None\n \"\"\"\n factory= ThreadFactory(id)\n try:\n factory.gridController.disableCircuit(id)\n status = 'Simulation ' + id + ' deleted!'\n except:\n status = \"Could not delete Simulation \" + id\n return status\n\ndef update_simulation(id, body): # noqa: E501\n \"\"\"Send new data to an existing simulation\n\n # noqa: E501\n\n :param id: ID of the simulation\n :type id: str\n :param radial: Updated grid data\n :type radial: dict | bytes\n\n :rtype: None\n \"\"\"\n if connexion.request.is_json:\n body = Grid.from_dict(connexion.request.get_json()) # noqa: E501\n return 'Simulation ' + id + ' updated!'","sub_path":"swagger_server/controllers/simulation_controller.py","file_name":"simulation_controller.py","file_ext":"py","file_size_in_byte":10406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"291482933","text":"import re\n\n'''\nцифра или буква -_..@название домена < 11 букв и/или '-'.название доменной зоны 4 символа\n'''\nex_1 = '44onteassas.t. saethuss user@mail-mail.ru988,t'\nex_2 = '232323@toeuhann_1.tanm@web-mail.org_teuhnan'\nex_3 = '_maetch2112@aem.comnonetuhass'\nex_4 = ' aotan..ate..mail.1@mail.com.chsash'\nex_5 = ' Приветexamples4_.mail@mail.io_cuau**'\nex_6 = ' 1@mail.ru '\nlist_ex = [ex_1, ex_2, ex_3, ex_4, ex_5, ex_6]\n\npattern = r'[\\w.-]+@[\\w.-]+'\n\n \nfor i in range(6):\n match = re.search(pattern, list_ex[i])\n result = f'email: {match[0]}' if match else 'Nothing'\n print(result)\n","sub_path":"Module8/examples/email_search.py","file_name":"email_search.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"242457045","text":"import base64\nimport datetime\nimport io\nimport pandas as pd\n#basic libraries\nfrom dash.dependencies import Input, Output, State\nfrom flask_caching import Cache\nimport dash\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nfrom apps.utils.utils_getdata import get_data\nfrom apps.utils.utils_pivot_table import make_pivot_table\nfrom apps.utils.utils_plots import Make_map\nfrom apps.utils.utils_tree_map import Make_tree_map\nfrom apps.utils.utils_filters import make_filters, make_options_filters\nimport dash_core_components as dcc\nfrom dash.exceptions import PreventUpdate\n#main dash instance\nfrom app import app\n\n\n# #call modules needed for callbacks\nfrom apps.home import layout_home\n\n#df=get_data([\"CLIMA_AMBIENTAL\",\"FORMA_TERRENO\",\"MATERIAL_PARENTAL_LITOLOGIA\",\"ORDEN\",\"PAISAJE\"]).dropna()\n\n#cache configuration\nTIMEOUT = 240\ncache = Cache(app.server, config={\n 'CACHE_TYPE': 'filesystem',\n 'CACHE_DIR': 'cache-directory',\n 'CACHE_THRESHOLD': 20\n})\n\n#Entire callbacks definition\ndef register_callbacks(app):\n\n #callback for navigation, look for url and respond with the right layout\n @cache.memoize(timeout=TIMEOUT)\n @app.callback(Output(\"page-content\", \"children\"), [Input(\"url\", \"pathname\")])\n def render_page_content(pathname):\n if pathname in [\"/\", \"/apps/home/layout_home\"]:\n return layout_home.layout\n # If the user tries to reach a different page, return a 404 message\n return dbc.Jumbotron(\n [\n html.H1(\"404: Not found\", className=\"text-danger\"),\n html.Hr(),\n html.P(f\"The pathname {pathname} was not recognised...\"),\n html.Br(),\n html.P(f\"Check again what you are requesting\")\n ],fluid=False\n )\n #Callbacks definidos para la carga de un archivo\n def parse_contents(contents, filename, date):\n content_type, content_string = contents.split(',')\n decoded = base64.b64decode(content_string)\n columns_to_consider=[\"CLIMA_AMBIENTAL\", \"PAISAJE\",\n 'TIPO_RELIEVE', 'FORMA_TERRENO',\n 'MATERIAL_PARENTAL_LITOLOGIA', 'ORDEN',\n \"LATITUD\",\"LONGITUD\",\"ALTITUD\",\"CODIGO\"]\n try:\n if 'csv' in filename:\n df = pd.read_csv(io.StringIO(decoded.decode('utf-8')))\n df = df[columns_to_consider]\n\n elif 'xls' in filename:\n # Assume that the user uploaded an excel file\n df = pd.read_excel(io.BytesIO(decoded))\n df = df[columns_to_consider]\n\n return df, filename, date\n except Exception as e:\n return pd.DataFrame([]), filename, date\n\n\n @app.callback(Output('Mapa', 'figure'), Output('tree_map', 'figure'),\n Output(\"carta_datos\",\"children\"),\n Output(\"filtro_clima\",\"options\"), Output(\"filtro_paisaje\",\"options\"),\n Output(\"filtro_forma_terreno\", \"options\"), Output(\"filtro_material_parental\",\"options\"),\n Output('Table_data', 'children'), Output(\"the_alert\", \"children\"),\n Output(\"main_alert\", \"children\"),\n Input('upload-data', 'contents'),\n State('upload-data', 'filename'),\n State('upload-data', 'last_modified'))\n def update_maps(list_of_contents, list_of_names, list_of_dates):\n if list_of_contents is not None:\n df, filename, date = parse_contents(list_of_contents, list_of_names, list_of_dates)\n if len(df) == 0:\n alert1 = dbc.Alert(\"There was an error processing this file.\",\n color=\"danger\",dismissable=True,\n duration=5000)\n alert2 = dbc.Alert([html.H4(\"An error was encountered when parsing the file {}\".format(filename)),\n html.Hr(),\n html.P(\"Please double check that your file is compatible with the formats (csv,xls,xlsx,xlsm)\"\n \"these formats are only supported at the moment so make sure you are using the right format.\"\n \"In case you are using the right format make sure your file has the required columns.\",className=\"mb-0\")\n ],\n color=\"danger\",dismissable=True,\n duration=7000)\n error_section=html.Div([\n html.H2('There was an error processing this file. File {}, uploaded {}'.format(filename,date)),\n html.Br(),\n html.H2('Double check that you have the right format or your file has the needed Columns')]\n ,className=\"text-danger\",style={\"align-text\":\"center\"})\n return dash.no_update, dash.no_update, dash.no_update,\\\n dash.no_update, dash.no_update,\\\n dash.no_update, dash.no_update,\\\n error_section, alert1,alert2\n else:\n table_children=html.Div([\n html.H2(\"Tabla Dinamica\", className='title ml-2', style={'textAlign': 'left', 'color': '#FFFFFF'}),\n html.H4(\"Archivo Cargado: {}\".format(filename), className='title ml-2',style={'textAlign': 'left', 'color': '#FFFFFF'}),\n html.H5(\"Fecha de Carga: {}\".format(datetime.datetime.fromtimestamp(date)), className='title ml-2', style={'textAlign': 'left', 'color': '#FFFFFF'}),\n make_pivot_table(df)\n ])\n good_alarm=dbc.Alert([html.H4(\"The file {} was successfully processed\".format(filename)),],\n color=\"success\",dismissable=True,\n duration=5000)\n mini_alarm= dbc.Alert(\"File proccesed\",\n color=\"success\",dismissable=True,\n duration=5000)\n return Make_map(df),Make_tree_map(df), len(df), \\\n make_options_filters(df[\"CLIMA_AMBIENTAL\"].dropna().unique()), \\\n make_options_filters(df[\"PAISAJE\"].dropna().unique()), \\\n make_options_filters(df[\"FORMA_TERRENO\"].dropna().unique()), \\\n make_options_filters(df[\"MATERIAL_PARENTAL_LITOLOGIA\"].dropna().unique()),\\\n table_children, mini_alarm, good_alarm\n else:\n raise PreventUpdate\n\n\n\n\n\n #@app.callback(Output(\"Download_file\", \"data\"),\n #\"Table_data\"\n # [Input(\"Boton_download\", \"n_clicks\")],\n # prevent_initial_call=True)\n #def generate_csv(n_nlicks):\n # return dcc.send_data_frame(df.to_csv, filename=\"prueba.csv\")\n\n #return {\"color\":\"primary\", }\n ","sub_path":"callbacks.py","file_name":"callbacks.py","file_ext":"py","file_size_in_byte":6982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"81729007","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2020/9/20 11:40\n# @Author : 1823218990@qq.com\n# @File : tbl_word.py\n# @Software: PyCharm\nimport time\n\nimport os\nimport tornado.web\nfrom PIL import Image\n\nfrom common.global_func import get_action_word\nfrom handlers.basehd import BaseHandler, check_token\nfrom tornado.log import app_log as weblog\nfrom database.tbl_word import TblWord\nimport json\n\nfrom handlers.show.hd_play import get_img_base64\n\n\nclass PyTesseractHandler(tornado.web.RequestHandler):\n def get(self):\n import pytesseract\n # imgstr = self.get_argument(\"imgstr\", \"\")\n imgstr = self.request.files['files'][0]\n size = self.get_argument(\"size\", \"(0,0)\")\n mode = self.get_argument(\"mode\", \"P\")\n weblog.info(\"{}\".format(self.request.arguments))\n tempname = \"/opt/temp/{}\".format(str(time.time()) + imgstr['filename'])\n try:\n imgb = imgstr['body']\n size = eval(size)\n # mode = P RGB\n weblog.info(\"{} {} {}\".format(size, len(imgb), tempname))\n # from io import BytesIO\n # imgb = BytesIO(imgb)\n # img = Image.frombytes(mode, size, f)\n with open(tempname, \"wb\") as f:\n f.write(imgb)\n img = Image.open(tempname)\n # # img.save(\"/opt/temp/test.gif\", format='gif')\n result = pytesseract.image_to_string(img)\n if os.path.exists(tempname):\n os.system(\"rm -f {}\".format(tempname))\n\n return self.write(json.dumps({\"error_code\": 0, \"msg\": result}))\n except Exception as e:\n if os.path.exists(tempname):\n os.system(\"rm -f {}\".format(tempname))\n return self.write(json.dumps({\"error_code\": 1, \"msg\": \"{}\".format(e)}))\n\n\nclass WordHandler(BaseHandler):\n @check_token\n def get(self):\n wid = self.get_argument(\"wid\", None)\n if wid == \"-1\":\n return self.write(json.dumps({\"error_code\": 0, \"words\": self.get_all_word()}))\n else:\n word = self.get_one_word(wid)\n if word is None:\n return self.write(json.dumps({\"error_code\": 1, \"word\": word, \"msg\": u\"word id不存在\"}))\n return self.write(json.dumps({\"error_code\": 0, \"word\": word}))\n pass\n\n def get_all_word(self):\n words = self.mysqldb().query(TblWord.id, TblWord.word, TblWord.chn).order_by(TblWord.id).all()\n datas = list()\n for word in words:\n datas.append(\" \".join([str(word.id), word.word, word.chn]))\n\n return datas\n\n def get_one_word(self, wid):\n word = self.mysqldb().query(TblWord).filter(TblWord.id == wid).first()\n if word is None:\n return None\n else:\n return word.tojson()\n\n @check_token\n def post(self):\n word = self.get_argument(\"word\", None)\n chn = self.get_argument(\"chn\", None)\n agg = self.get_argument(\"agg\", None)\n suffix = self.get_argument(\"suffix\", None)\n picture = self.get_argument(\"picture\", None)\n describe = self.get_argument(\"describe\", \"\")\n tblword = self.mysqldb().query(TblWord).filter(TblWord.word == word).first()\n first_add = False\n if tblword is None:\n tblword = TblWord()\n first_add = True\n tblword.word = word\n tblword.chn = chn\n tblword.agg = agg\n tblword.suffix = suffix\n\n # base64Picture = get_img_base64(picture, suffix)\n tblword.picture = picture\n tblword.describe = describe\n if first_add:\n self.mysqldb().add(tblword)\n\n try:\n self.mysqldb().commit()\n return self.write(json.dumps({\"error_code\": 0, \"msg\": u\"添加成功\"}))\n except Exception as e:\n weblog.error(\"{}\".format(e))\n return self.write(json.dumps({\"error_code\": 1, \"msg\": u\"添加失败\"}))\n\n @check_token\n def delete(self):\n wid = self.get_argument(\"wid\")\n\n tblword = self.mysqldb().query(TblWord).filter(TblWord.id == wid).first()\n\n if tblword is None:\n return self.write(json.dumps({\"error_code\": 1, \"msg\": u\"word不存在，无法删除\"}))\n else:\n self.mysqldb().query(TblWord).filter(TblWord.id == wid).delete()\n try:\n self.mysqldb().commit()\n return self.write(json.dumps({\"error_code\": 0, \"msg\": \"\"}))\n except Exception as e:\n weblog.error(\"{}\".format(e))\n return self.write(json.dumps({\"error_code\": 1, \"msg\": u\"删除失败\"}))\n\n\nclass WordActionHandler(BaseHandler):\n @check_token\n def post(self):\n pid = int(self.get_argument(\"wid\", \"-1\"))\n action = self.get_argument(\"action\", None)\n\n if action is None:\n return self.write(json.dumps({\"error_code\": 1, \"msg\": u\"参数错误\", \"word\": \"\"}))\n\n word = get_action_word(self, TblWord, pid, action)\n if word is None:\n if word == \"next\":\n return self.write(json.dumps({\"error_code\": 1, \"msg\": u\"最后一个词\", \"word\": \"\"}))\n else:\n return self.write(json.dumps({\"error_code\": 1, \"msg\": u\"第一个词\", \"word\": \"\"}))\n else:\n return self.write(json.dumps({\"word\": word.tojson(), \"error_code\": 0}))","sub_path":"FSTornado/handlers/study/hd_word.py","file_name":"hd_word.py","file_ext":"py","file_size_in_byte":5348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"632505961","text":"#!/usr/bin/env python3\nfrom cacho import *\nimport time\n\n\ndef main():\n time_rolls(Cup(), None, 100000)\n\ndef commas(num):\n\treturn \"{:,}\".format(num)\n\ndef time_rolls(cup1, cup2, num_rolls):\n\tprint('Timing', commas(num_rolls), 'rolls...')\n\n\tstart1 = time.time()\n\tfor i in range(num_rolls):\n\t\tcup1.shake()\n\tend1 = time.time()\n\n\tprint('Cup 1:', commas(round(num_rolls / (end1-start1))), 'r/s')\n\n\tif cup2 is None:\n\t\treturn\n\n\tstart2 = time.time()\n\tfor i in range(num_rolls):\n\t\tcup2.shake()\n\tend2 = time.time()\n\n\tprint('Cup 2:', commas(round(num_rolls / (end2-start2))), 'r/s')\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"495576153","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# CHANGELOG:\n#\t0.1:\n#\t\tfirst version\n\n# imports\nimport xchat\n\n# module info\n__module_name__ = \"lmgtfy\"\n__module_version__ = \"0.1\"\n__module_description__ = \"sends to the channel link to lmgtfy with entered phrase\"\n__module_autor__ = \"andrzej3393\"\n\n# code\ndef lmgtfy_cb(word, word_eof, userdata):\n\txchat.command(\"SAY http://lmgtfy.com/?q=\" + word_eof[1].replace(\" \", \"%20\"))\n\treturn xchat.EAT_ALL\n\ndef unload_cb(userdata):\n\txchat.unhook(HOOKMUSIC)\n\nHOOKMUSIC = xchat.hook_command(\"lmgtfy\", lmgtfy_cb)\nHOOKUNLOAD = xchat.hook_unload(unload_cb)\n","sub_path":"lmgtfy/lmgtfy.py","file_name":"lmgtfy.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"407018936","text":"import turtle\nimport random\n\n\nwn=turtle.Screen()\nwn.bgcolor(\"black\")\nwn.title(\"gravity\")\nwn.tracer(0)\n\n\nballs = []\ncolor=[\"red\",\"yellow\",\"orange\",\"pink\",\"purple\",\"white\",\"violet\"]\nshape=[\"circle\",\"triangle\",\"square\"]\n\nfor bal in range(50):\n\tballs.append(turtle.Turtle())\n\nfor ball in balls:\n\tball.shape(random.choice(shape))\n\tx=random.randint(-290,290)\n\ty=random.randint(-200,400)\n\tball.goto(x,y)\n\tball.color(random.choice(color))\n\tball.penup()\n\tball.speed(0)\n\tball.dy=-1\n\tball.dx=random.randint(-3,3)\n\tball.dz=random.randint(-5,-5)\n\ngravity=0.1\nloop=True\n\n\n\n\nwhile loop:\n\twn.update()\n\tfor ball in balls:\n\t\tball.sety(ball.ycor() + ball.dy)\n\t\tball.dy-=gravity\n\t\tball.setx(ball.xcor() + ball.dx)\n\t\tball.rt(ball.dz)\n\n\t\tif ball.xcor() > 330:\n\t\t\tball.dx*=-1\n\n\t\tif ball.xcor() < -330:\n\t\t\tball.dx*=-1\n\n\t\tif ball.ycor() < -270 :\n\t\t\tball.sety(-270)\n\t\t\tball.dy*=-1\n\t\t\tball.dy-=1\n\n\n\n\n\n\n\n\n\n\nwn.mainloop()\n","sub_path":"gravity_simulator.py","file_name":"gravity_simulator.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"634210909","text":"\"\"\"\nSimple animation with many items.\n\nBased on an example by Professor Craven from Simpson College.\n\nSource: http://programarcadegames.com/index.php?chapter=introduction_to_animation&lang=en#section_8\n\"\"\"\n\nimport pygame\nimport sys\nimport random\nimport math\nfrom pygame.locals import *\nfrom snow_example.snow import Snow\n\ndef main():\n pygame.init()\n\n NUM_SNOWFLAKES = 50\n\n FPS = 30\n FPS_CLOCK = pygame.time.Clock()\n\n # COLOR LIST\n BLACK = pygame.Color(0, 0, 0)\n\n window_size = (700, 600)\n SCREEN = pygame.display.set_mode(window_size)\n\n # set the title of the window\n pygame.display.set_caption(\"Falling Snow Animation\")\n\n snow_list = gen_snow_list(NUM_SNOWFLAKES)\n\n while True: # <--- main game loop\n for event in pygame.event.get():\n if event.type == QUIT: # QUIT event to exit the game\n pygame.quit()\n sys.exit()\n\n SCREEN.fill(BLACK)\n # Process each snowflake in the list\n for snowflake in snow_list:\n snowflake.draw(SCREEN)\n\n # Move the snowflake\n snowflake.fall()\n\n # if the snowflake moved off the bottom of the screen\n if snowflake.y > 600:\n snowflake.reset()\n\n\n pygame.display.update() # Update the display when all events have been processed\n FPS_CLOCK.tick(FPS)\n\ndef gen_snow_list(num):\n \"\"\"Returns a list of snow objects\"\"\"\n\n snow_list = []\n\n for x in range(num):\n rand_x = random.randint(0, 700)\n rand_y = random.randint(0, 600)\n rand_size = random.randint(2, 10)\n rand_speed = random.randint(1, 5)\n\n snow_list.append(Snow(rand_x, rand_y, rand_size, rand_speed))\n\n return snow_list\n\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"Notes/lecture04_examples/snow_example/snow_animation.py","file_name":"snow_animation.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"398283008","text":"''' Mutable Primitives '''\nfrom types import FunctionType\nimport sys\n\n\nEQUALITY_FUNCTIONS = [\n '__eq__',\n '__ne__',\n ]\n\nMATH_FUNCTIONS = [\n ('add', '+'),\n ('sub', '-'),\n ('mul', '*'),\n # Division is handled differently in python2 and python3\n ]\nif sys.version_info[0] < 3:\n MATH_FUNCTIONS.append(('div', '/'))\nelse:\n MATH_FUNCTIONS.extend([\n ('floordiv', '//'),\n ('truediv', '/'),\n ])\n\n\nFORMATS = {\n '': 'return self.val {} other',\n 'r': 'return other {} self.val',\n 'i': 'self.val {}= other; return self',\n }\n\n\nclass Mutable(object): # pylint: disable=useless-object-inheritance\n ''' Base class for mutable primitives '''\n def __init__(self, val):\n self.val = val\n\n def get(self):\n ''' get raw value of mutable '''\n return self.val\n\n def set(self, val):\n ''' set raw value of mutable '''\n self.val = val\n\n def __eq__(self, other):\n return self.val == other\n\n def __ne__(self, other):\n return self.val != other\n\n def __str__(self):\n return '{}({})'.format(self.__class__.__name__, self.val)\n\n def __repr__(self):\n return '{}({})'.format(self.__class__.__name__, self.val)\n\n\nclass Bool(Mutable):\n ''' Mutable version of float '''\n\n\nclass MutableNumeric(Mutable):\n ''' Base class for mutable numeric primitives '''\n\n for fmt, basecode in FORMATS.items():\n for (basename, op) in MATH_FUNCTIONS:\n name = '__{}{}__'.format(fmt, basename)\n code = 'def {}(self, other): {}'.format(name, basecode.format(op))\n code = compile(code, \"\", \"exec\")\n locals()[name] = FunctionType(code.co_consts[0], globals(), name)\n del code, name, op\n\n\nclass Int(MutableNumeric):\n ''' Mutable version of int '''\n\nclass Float(MutableNumeric):\n ''' Mutable version of float '''\n","sub_path":"mutable_primitives.py","file_name":"mutable_primitives.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"428049008","text":"from listtexinput import listtexinput\nfrom sys import argv\n\ndef main(args):\n pbe1 = r'C:\\Users\\h2\\Desktop\\PharoByExample-japanese\\PBE1.tex'\n re = regex('\\r*\\n$')\n if len(args) == 0:\n args = listtexinput(pbe1)\n for file in args:\n for y in check(file):\n yield y\n\ndef check(file):\n s = open(file, 'rb')\n l = s.readline()\n if not l.endswith('\\n'):\n raise Exception(l)\n filter = lambda x: x if l.endswith('\\r\\n') else lambda x: not x\n for l in s:\n if filter(l.endswith('\\r\\n')):\n yield file\n\nif __name__ == '__main__':\n for y in main(argv[1:]):\n print(y)\n","sub_path":"last-dropbox/grepjind-proto/grepjind/broken-eol.py","file_name":"broken-eol.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"271505895","text":"#!/usr/bin/env python3\n#-*- coding:utf-8 -*-\n# File: /Users/king/Python初级算法/code/10/countPrimesV1.py\n# Project: /Users/king/Python初级算法/code/10\n# Created Date: 2018/10/18\n# Author: hstking hst_king@hotmail.com\n\nimport timeit\n\ndef countPrimes(n):\n '''按照质数的定义，用常规的方法来取质数 '''\n primesList = []\n for i in range(2, n+1):\n flag = True\n for divNum in range(2, i): # 从2到i-1，一个一个的除，如果有余数为0的状况，可以确定不是质数，退出循环\n if i % divNum == 0:\n flag = False\n break\n if flag:\n primesList.append(i)\n # print(primesList)\n return len(primesList)\n\nif __name__ == \"__main__\":\n print(timeit.timeit(\"countPrimes(10000)\", \"from __main__ import countPrimes\", number=10))\n print(countPrimes(10000))","sub_path":"book-code/图解LeetCode初级算法-源码/10/countPrimesV1.py","file_name":"countPrimesV1.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"146004853","text":"#!/usr/local/bin/python3.5\nimport json\nimport time\nfrom zabbixwechat.common import *\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nfrom zabbix_wechat_db.models import ALARM_INFO\n\n\n@csrf_exempt\ndef multcomfirm(request):\n if len(request.GET) == 1:\n respone = \"请勾选报警条目\"\n else:\n text = \"\"\n duplicate = \"\"\n idlist = []\n username = request.GET['username']\n agentid = request.GET['agentid']\n nickname = findnickname(username)\n for ID in request.GET:\n if ID != 'username' and ID != 'winzoom' and ID != 'agentid':\n if ALARM_INFO.objects.filter(id=ID)[0].CONFIRM_TIME == '':\n CONFIRM_TIME = int(time.time())\n CONFIRM_PERIOD = CONFIRM_TIME - \\\n int(ALARM_INFO.objects.filter(id=ID)[0].ALARM_TIME)\n ALARM_INFO.objects.filter(\n id=ID).update(\n CONFIRM_TIME=CONFIRM_TIME,\n CONFIRM_PERIOD=CONFIRM_PERIOD,\n CONFIRM_USER_ID=nickname)\n text = str(ID) + ',' + text\n idlist.append(ID)\n if text != \"\":\n grouplist = ALARM_INFO.objects.filter(\n id__in=idlist).values_list(\n 'HOST_GROUP', flat=True).distinct()\n for i in grouplist:\n idlist2 = \"\"\n for j in idlist:\n if ALARM_INFO.objects.filter(id=j)[0].HOST_GROUP == i:\n idlist2 = str(j) + ',' + idlist2\n text = \"区域\" + i + \"下\" + idlist2 + '以上报警被用户' + nickname + \"确认\"\n toparty = findgroupid(i)\n senddata(text, toparty, agentid)\n respone = \"批量确认成功\"\n else:\n respone = \"这些报警已被确认过\"\n return HttpResponse(respone)\n","sub_path":"zabbixwechat/multcomfirm.py","file_name":"multcomfirm.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"588762255","text":"from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport datetime\n\n# Scrapping WSJ Market Data; Websites taken down on May 8, 2019; data currently scrapped from Barrons\n\npath = input(\"Enter folder path: \")\n\nurl_page = ['http://www.wsj.com/mdc/public/page/2_3024-AMEX.html?mod=topnav_2_3002',\n 'http://www.wsj.com/mdc/public/page/2_3024-NYSE.html?mod=topnav_2_3024',\n 'http://www.wsj.com/mdc/public/page/2_3024-Nasdaq.html',\n 'http://www.wsj.com/mdc/public/page/2_3024-SCAP.html?mod=topnav_2_3002']\n\nbig_data = []\n\nfor element in url_page:\n page = urlopen(element)\n\n soup = BeautifulSoup(page, 'html.parser')\n\n div = soup.find('div', attrs={'id': 'column0'})\n tables = div.find_all('table', attrs={'bgcolor': '#cccccc'})\n\n name = []\n symbol = []\n opn = []\n high = []\n low = []\n close = []\n netchg = []\n perchg = []\n volume = []\n weekhgh = []\n weeklow = []\n div = []\n yld = []\n pe = []\n ytd = []\n\n for table in tables:\n rows = table.find_all('tr', attrs={'class': 'p12'})\n for row in rows:\n data = row.find_all('td')\n name.append(data[0].getText().strip('\\xa0'))\n symbol.append(data[1].getText().strip('\\xa0'))\n opn.append(data[2].getText().strip('\\xa0'))\n high.append(data[3].getText().strip('\\xa0'))\n low.append(data[4].getText().strip('\\xa0'))\n close.append(data[5].getText().strip('\\xa0'))\n netchg.append(data[6].getText().strip('\\xa0'))\n perchg.append(data[7].getText().strip('\\xa0'))\n volume.append(data[8].getText().strip('\\xa0'))\n weekhgh.append(data[9].getText().strip('\\xa0'))\n weeklow.append(data[10].getText().strip('\\xa0'))\n div.append(data[11].getText().strip('\\xa0'))\n yld.append(data[12].getText().strip('\\xa0'))\n pe.append(data[13].getText().strip('\\xa0'))\n ytd.append(data[14].getText().strip('\\xa0'))\n\n dictionary = {'Name': name, 'Symbol': symbol, 'Open': opn, 'High': high, 'Low': low, 'Close': close,\n 'Net Change': netchg, '% Change': perchg, 'Volume': volume, '52 Week High': weekhgh,\n '52 Week Low': weeklow, 'Div': div, 'Yield': yld, 'PE': pe, \"YTD % Change\": ytd}\n dataframe = pd.DataFrame(dictionary)\n\n big_data.append(dataframe)\n\n\nnow = datetime.datetime.now()\ndate = (str(now.day) + '_' + str(now.month) + '_' + str(now.year))\n\nfilename = [date + 'AMEX' + '.csv', date + 'NYSE' + '.csv', date + 'NASDAQ' + '.csv', date + 'SCAP' + '.csv']\n\nfor i in range(4):\n big_data[i].to_csv(path+'\\\\'+filename[i], index=False)\n","sub_path":"Scrapers/originalscraper.py","file_name":"originalscraper.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"502134918","text":"alph = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\r\n\r\ndef evac(P,evacsofar,t):\r\n if t == 0:\r\n return evacsofar\r\n if t == 1:\r\n return evacsofar + ' ' + alph[P.index(1)]\r\n PL = [ (P[i],i) for i in range(len(P)) ]\r\n PL.sort(reverse=True)\r\n m1 = PL[0][1]\r\n m2 = PL[1][1]\r\n cand = P\r\n cand[m1] = cand[m1] - 1\r\n cand[m2] = cand[m2] - 1\r\n cmax = max(cand)\r\n if 2*cmax > t - 2:\r\n cand[m2] = cand[m2] + 1\r\n return evac(cand, evacsofar + ' ' + alph[m1], t-1)\r\n else:\r\n return evac(cand, evacsofar + ' ' + alph[m1] + alph[m2], t-2)\r\n\r\nfi = open('A-large.in','r')\r\nfo = open('out.txt','w')\r\nT = int(fi.readline())\r\nfor ii in range(T):\r\n print('####### Case: ' + str(ii) + '#######')\r\n N = int(fi.readline())\r\n P = [int(x) for x in fi.readline().split(' ') if x != '\\n']\r\n nu = evac(P,'',sum(P))\r\n nu = nu[1:]\r\n fo.write('Case #'+str(ii+1)+': '+nu+'\\n')\r\nfi.close()\r\nfo.close()","sub_path":"codes/CodeJamCrawler/CJ/16_3_1_Exhor_a.py","file_name":"16_3_1_Exhor_a.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"34595956","text":"import sys\n\n\ndef merge_sort(a):\n n = len(a)\n if n <= 1:\n return a\n mid = n // 2\n a1 = merge_sort(a[:mid])\n a2 = merge_sort(a[mid:])\n i1 = 0\n i2 = 0\n ia = 0\n while i1 < len(a1) and i2 < len(a2):\n if a1[i1] < a2[i2]:\n a[ia] = a1[i1]\n i1 += 1\n else:\n a[ia] = a2[i2]\n i2 += 1\n ia += 1\n while i1 < len(a1):\n a[ia] = a1[i1]\n i1 += 1\n ia += 1\n while i2 < len(a2):\n a[ia] = a2[i2]\n i2 += 1\n ia += 1\n return a\n\n\nread = sys.stdin.readline\nn = int(read())\na = merge_sort(list(map(int, read().split())))\nans = 0\nfor i, p in enumerate(a):\n ans += (n - i) * p\nprint(ans)\n\n","sub_path":"11399 - ATM/11399.py","file_name":"11399.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"576494594","text":"from lidar_lite import Lidar_Lite\r\nfrom time import time as timer\r\nlidar = Lidar_Lite()\r\nconnected = lidar.connect(1)\r\n\r\nclass Lite_TTC():\r\n\r\n def init(self):\r\n self.lidar = Lidar_Lite()\r\n if connected < -1:\r\n print (\"Not Connected\")\r\n\r\n def main():\r\n\r\n start_time = timer()\r\n current_distance = lidar.getDistance()/100\r\n while True:\r\n previous_distance = current_distance\r\n prev_time = start_time\r\n start_time = timer()\r\n current_distance = lidar.getDistance()/100\r\n ttc(prev_time, start_time, current_distance, previous_distance)\r\n\r\n\r\n def ttc(prev_t, start_t, current_d, prev_d):\r\n elapsed_time = start_t-prev_t\r\n #print(elapsed_time)\r\n distance_moved = prev_d-current_d\r\n #print(distance_moved)\r\n if(distance_moved>.035 or distance_moved<-.035):\r\n velocity = distance_moved/elapsed_time\r\n if (velocity>0):\r\n current_ttc = current_d/velocity\r\n print(\"Current ttc is %d seconds; speed is %d mps...\" % (current_ttc, velocity))\r\n else:\r\n print(\"there is no current ttc available: Object is not moving or is moving away...\")\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","sub_path":"python/ttc_Lite.py","file_name":"ttc_Lite.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"316010094","text":"\"\"\"\r\nO bloco Try Except\r\n\r\nUtilizamos o bloco para tratar erros que podem ocorrer no nosso codigo prevenindo assim que o programa pare de funcionar\r\ne o usuario receba mensagens de erro inesperadas.\r\n\r\nA forma geral mais simples é:\r\ntry:\r\n //Execução problematica\r\nexcept:\r\n //Oque deve ser feito em caso de problema\r\n\r\nOBS: Tratar problemas de forma generica nao é am lehor forma. O ideal é sempre tratar de forma especifica\r\ncomo o raise faz\r\n\"\"\"\r\n\r\n# Exemplo -Apresentnado erro generico\r\ntry:\r\n geek()\r\nexcept:\r\n print('Houve um problema')\r\n\r\n# Exemplo tratando erro especifico\r\ntry:\r\n geek()\r\nexcept NameError: # Precisa ser do tipo certo, porque se colocar NameError e nao sendo um NameError nao adianta\r\n print('Voce esta utilizando uma função inexistente')\r\nprint('')\r\ntry:\r\n len(5)\r\nexcept TypeError as err: # Com detalhes do erro, bom para salvar em logs\r\n print(f'A aplicação retornou o seguinte erro: {err}')\r\nprint('')\r\ntry:\r\n len(5)\r\nexcept TypeError as erra:\r\n print(f'Deu TypeError: {erra}')\r\nexcept NameError as erra:\r\n print(f'Deu NameError: {erra}')\r\nexcept:\r\n print('Deu um erro diferente')\r\n\r\n\r\ndef pegavalor(dicionario, chave):\r\n try:\r\n return dicionario[chave]\r\n except:\r\n return None\r\n\r\nprint('')\r\ndic = {'nome': 'geek'}\r\nprint(pegavalor(dic, 'nome'))\r\nprint(pegavalor(dic, 'Game'))\r\nprint(pegavalor(dic, 8))\r\n","sub_path":"TryExcept.py","file_name":"TryExcept.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"579378841","text":"import sys\nimport cv2\n\n'''\nopencv 를 통해 이미지를 불러오고 확인\n'''\n\nimg = cv2.imread('lenna.bmp') # 불러올 이미지\n\nif __name__ == '__main__':\n print('Hello OpenCV', cv2.__version__)\n\n if img is None:\n print('Image load failed!')\n sys.exit()\n\n cv2.namedWindow('image') # 이미지 창 이름\n cv2.imshow('image', img) # 창, 이미지 객체\n cv2.waitKey() # 키 입력을 기다리는 대기함수\n # imshow waitKey 같이 나옴 (why?)\n cv2.destroyAllWindows() # 모든 윈도우 제거","sub_path":"ch2/hellocv.py","file_name":"hellocv.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"381433882","text":"import numpy as np\nimport scipy.optimize\nimport scipy.special\nimport scipy.fftpack\nimport Parts\n\n\ndef Kotlyar(field):\n\t# Init par's\n\tsizes = np.shape(field)\n\tamplitude = Parts.Misc.get_amplitude(field)\n\tphase = Parts.Misc.get_phase(field)\n\n\tpattern = np.empty([2*sizes[0], 2*sizes[1]], float)\n\t\n\tu = phase + np.arccos(amplitude/2)\n\tv = phase - np.arccos(amplitude/2)\n\n\n\tfor i in range(sizes[0]):\n\t\tfor j in range(sizes[1]):\n\t\t\tpattern[2*i, 2*j] = pattern[2*i+1, 2*j+1] = u[i,j]\n\t\t\tpattern[2*i, 2*j+1] = pattern[2*i+1, 2*j] = v[i,j]\n\n\tpattern = np.angle(np.exp(1j*pattern), deg=True)\n\treturn pattern\n\n\ndef Fuentes(field, LSM_pars):\n\t# Init par's\n\tsizes = np.shape(field)\n\tamplitude = Parts.Misc.get_amplitude(field)\n\tphase = Parts.Misc.get_phase(field)\n\n\trandom_mask = np.zeros_like(field)\n\n\tfor i in range(sizes[0]):\n\t\tfor j in range(sizes[1]):\n\t\t\t# rdn is selected as uniformly distributed in the range [0;1)\n\t\t\trnd = np.random.rand(1,1)\n\t\t\tif amplitude[i,j] > rnd:\n\t\t\t\trandom_mask[i,j] = 1\n\n\tnoise_lvl = np.zeros_like(field)\n\n\tnoise_like_signal = random_mask - amplitude\n\n\tdiverging_element = Parts.Field_equation.axicon(LSM_pars, cycle = -10)\n\n\tpattern = field \\\n\t\t+ ((1-amplitude) - noise_like_signal) * diverging_element \\\n\t\t+ noise_like_signal*np.exp(1j*phase)\n\n\treturn pattern\n\ndef Gerchber_Saxton(target_field, input_field):\n\t# Error finding function\n\tdef find_err(field_A):\n\t\tCheck_intensity = Parts.Misc.get_amplitude(field_A)**2\n\t\tSum = Check_intensity - np.mean(Check_intensity)\n\t\terr = (np.sum(Sum**2))**0.5 / np.sum(Check_intensity)\n\t\treturn err\n\n\t# Preparation\n\t# Get target amplitude from target field\n\ttarget_amplitude = Parts.Misc.get_amplitude(target_field)\n\t# [0, 1] -> [0, 255]\n\ttarget_amplitude = (255/np.max(target_amplitude)) * target_amplitude\n\n\t# Get first step of the algorithm\n\tA = scipy.fftpack.fftshift(\\\n\t\t\tscipy.fftpack.ifft2(\\\n\t\t\t\tscipy.fftpack.fftshift(target_amplitude)))\n\n\t# Iterations will continue until the error is 0.5%\n\twhile find_err(A) >= 5 * 1e-03:\n\t\tB = Parts.Misc.get_amplitude(input_field) * \\\n\t\t\t\tnp.exp(1j * np.angle(A))\n\t\tC = scipy.fftpack.fftshift(\\\n\t\t\t\tscipy.fftpack.ifft2(\\\n\t\t\t\t\tscipy.fftpack.fftshift(B)))\n\t\tD = target_amplitude * np.exp(1j * np.angle(C))\n\t\tA = scipy.fftpack.fftshift(\\\n\t\t\t\tscipy.fftpack.ifft2(\\\n\t\t\t\t\tscipy.fftpack.fftshift(D)))\n\n\n\t# return encoded field A\n\treturn A","sub_path":"Parts/Encode_field.py","file_name":"Encode_field.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"596157809","text":"# -*- coding: utf-8 -*-\n############################\n# Peicheng Lu 20190822\n############################\n#\nimport os\nfrom os import *\nimport cv2\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport math\nimport imutils\nimport sys\nimport mysql.connector\nimport re\nimport shutil\nimport json\n\ndef getMatchNum(matches,ratio):\n '''number of matched features and relation'''\n matchesMask=[[0,0] for i in range(len(matches))]\n matchNum=0\n for i,(m,n) in enumerate(matches):\n if m.distance ratio\n matchesMask[i]=[1,0]\n matchNum+=1\n return (matchNum,matchesMask)\n#connect to database\ntmarkdb = mysql.connector.connect( host = \"127.0.0.1\", user = \"root\", password = \"lehsiao\", database = \"tmarkdb\", )\ncursor=tmarkdb.cursor()\ncop = re.compile(\"[^.^/^A-Z^a-z^0-9^-]\")\n\n# for instance: (1)\"LIKE '45%'\" (2)\"LIKE '%、45%'\" (3)\"LIKE '3519%'\" (4)\"LIKE '%、3519%'\"\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable WHERE goodsGroup \" + sys.argv[2]\ncursor.execute(cmd_users)\ntmark_list11 = cursor.fetchall()\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable WHERE goodsGroup \" + sys.argv[3]\ncursor.execute(cmd_users)\ntmark_list12 = cursor.fetchall()\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable2 WHERE goodsGroup \" + sys.argv[2]\ncursor.execute(cmd_users)\ntmark_list21 = cursor.fetchall()\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable2 WHERE goodsGroup \" + sys.argv[3]\ncursor.execute(cmd_users)\ntmark_list22 = cursor.fetchall()\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable3 WHERE goodsGroup \" + sys.argv[2]\ncursor.execute(cmd_users)\ntmark_list31 = cursor.fetchall()\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable3 WHERE goodsGroup \" + sys.argv[3]\ncursor.execute(cmd_users)\ntmark_list32 = cursor.fetchall()\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable4 WHERE goodsGroup \" + sys.argv[2]\ncursor.execute(cmd_users)\ntmark_list41 = cursor.fetchall()\ncmd_users = \"SELECT imageData1, tmarkName, applNo FROM tmarkTable4 WHERE goodsGroup \" + sys.argv[3]\ncursor.execute(cmd_users)\ntmark_list42 = cursor.fetchall()\n#combine these two lists\ntmark_list11.extend(tmark_list12)\ntmark_list11.extend(tmark_list21)\ntmark_list11.extend(tmark_list22)\ntmark_list11.extend(tmark_list31)\ntmark_list11.extend(tmark_list32)\ntmark_list11.extend(tmark_list41)\ntmark_list11.extend(tmark_list42)\ntmark_list11 = list(set(tmark_list11))\n#標章圖 標章 及圖 圖 及少女圖 及圖案 設計圖\ntmark_list = []\nfor i in range(len(tmark_list11)):\n try:\n nPos = tmark_list11[i][1].index(\"圖\")\n except ValueError:\n continue\n else:\n tmark_list.append(tmark_list11[i])\n continue\n try:\n nPos = tmark_list11[i][1].index(\"標章\")\n except ValueError:\n continue\n else:\n tmark_list.append(tmark_list11[i])\n\ntmark_l = []\notherpath = './data/' #path of database\nothers = listdir(otherpath)\n#print(otherpath)\n#print(others)\nfor i in range(0,10):\n otherpic = \"./data/\" + others[i]\n tmark = {'applno': \"000000000\",'file':otherpic}\n tmark_l.append(tmark)\n\nfor i in range(0, len(tmark_list)):\n #if len(cop.sub('', str(tmark_list[i]))) == 24:\n tmark = {'applno': tmark_list[i][2],'file':tmark_list[i][0]}\n tmark_l.append(tmark)\n\nsamplePath = sys.argv[1] #input sample\n#sift extractpr\nsift = cv2.xfeatures2d.SIFT_create() \n#FLANN matching\nFLANN_INDEX_KDTREE=0\nindexParams=dict(algorithm=FLANN_INDEX_KDTREE,trees=5)\nsearchParams=dict(checks=50)\nflann=cv2.FlannBasedMatcher(indexParams,searchParams)\n\nratio_l=[]\nvis_l=[]\napplno_l=[]\nresult = []\n\n\noutputPath='./Output/' #path of database\ntmpfiles = os.listdir(outputPath)\nfor f in tmpfiles:\n os.remove('./Output/'+str(f))\n\nsampleImage = cv2.imread(samplePath,0)\nsampleImage = imutils.resize(sampleImage, width = 300)\nsampleImage = cv2.GaussianBlur(sampleImage, (1, 1), 0)\nret,sampleImage = cv2.threshold(sampleImage,220,255,cv2.THRESH_BINARY)\nkp1, des1 = sift.detectAndCompute(sampleImage, None) #detect the features of sample\nindex = 0\nfor t in tmark_l:\n index = index + 1\n print(\"index = \" + str(index))\n f = t['file']\n queryImage=cv2.imread(f,0)\n try:\n queryImage = imutils.resize(queryImage, width = 300)\n except AttributeError:\n continue\n else:\n queryImage = cv2.GaussianBlur(queryImage, (1, 1), 0)\n ret,queryImage = cv2.threshold(queryImage,220,255,cv2.THRESH_BINARY)\n #queryImage = cv2.Canny(queryImage, 30, 150)\n kp2, des2 = sift.detectAndCompute(queryImage, None) #detect the features of img in database\n \n try:\n matches=flann.knnMatch(des2,des1,k=2) #matched features, assign k=2 to return 2 matched features.\n except:\n continue\n else:\n (matchNum,matchesMask)=getMatchNum(matches,0.8) #set ratio = 0.9 to calculate the matching level\n if len(matches) != 0:\n matchRatio2=matchNum*100/len(matches)\n else:\n matchRatio2=0.\n \n try:\n matches=flann.knnMatch(des1,des2,k=2) #matched features, assign k=2 to return 2 matched features.\n except:\n continue\n else:\n (matchNum,matchesMask)=getMatchNum(matches,0.9) #set ratio = 0.9 to calculate the matching level\n if len(matches) != 0:\n matchRatio1=matchNum*100/len(matches)\n else:\n matchRatio1=0.\n \n matchRatio = (matchRatio1 + matchRatio2)/2.\n \n drawParams=dict(matchColor=(0,255,0), singlePointColor=(0,0,255), matchesMask=matchesMask, flags=0)\n sampleImage=cv2.imread(samplePath)\n queryImage=cv2.imread(f)\n sampleImage=cv2.imread(samplePath)\n sampleImage = imutils.resize(sampleImage, width = 300)\n queryImage=cv2.imread(f)\n queryImage = imutils.resize(queryImage, width = 300)\n #(hA, wA) =sampleImage.shape[:2] \n #(hB, wB) = queryImage.shape[:2]\n comparisonImage=cv2.drawMatchesKnn(sampleImage,kp1,queryImage,kp2,matches,None,**drawParams)\n #cv2.putText(comparisonImage,str(matchRatio) + \"%\",(int(wA+wB/2.),int(3.*hB/4.)),cv2.FONT_HERSHEY_PLAIN,int(1.*hB/50.),(0,0,255),4)\n subtotal = {\"applno\":t[\"applno\"],\"ratio\":matchRatio, \"picture\":comparisonImage}\n result.append(subtotal)\n for i in range(0,len(result)-1): \n for j in range(0,len(result)-1-i): \n if result[j][\"ratio\"] < result[j+1][\"ratio\"]:\n tmp = result[j]\n result[j]= result[j+1]\n result[j+1] = tmp\n #print (\"i = \" + str(i))\n #print (\"j= \" + str(j))\n #print (\"len(ratio_l) =\" +str(len(ratio_l)))\n if len(result) > 50:\n del result[50]\n \nprint(\"tmark_l = \" + str(len(tmark_l)))\n\ndata = []\n\nfor i in result:\n print (str(i[\"applno\"])+\",\"+str(i[\"ratio\"]))\n data.append({\"applno\":i[\"applno\"], \"ratio\":i[\"ratio\"]})\n \napp_json = json.dumps(data)\nprint(app_json)\nfor k in range(0,len(result)):\n outpath = \"./Output/\" + str(k+1) + \"-\" +\"(\"+str(round(result[k][\"ratio\"],3)) + \"-\" + str(result[k][\"applno\"]) +\").jpg\"\n print (\"===========================\")\n print (outpath)\n cv2.imwrite(outpath, result[k][\"picture\"])\n\n\n\"\"\"\ncolumn=4\nrow=5\n#Draw the plots\nfigure,ax=plt.subplots(row,column)\nfor index in range(0,20):\n ax[int(index/column)][index%column].set_title('Similiarity %.2f%%' % ratio_l[index])\n ax[int(index/column)][index%column].imshow(vis_l[index])\nplt.show()\n\"\"\" ","sub_path":"crawler/practise/_picComp2_t.py","file_name":"_picComp2_t.py","file_ext":"py","file_size_in_byte":7784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"493268451","text":"# Copyright 2014 Google Inc. All rights reserved.\n#\n# Use of this source code is governed by The MIT License.\n# See the LICENSE file for details.\n\n\"\"\"Simple demo server app for SPF.\"\"\"\n\n__author__ = 'nicksay@google.com (Alex Nicksay)'\n\n\nimport json\nimport math\nimport os\nimport random\nimport time\n\nimport web\n\n\ndef Hashcode(s):\n \"\"\"A string hash function for compatibility with spf.string.hashCode.\n\n This function is similar to java.lang.String.hashCode().\n The hash code for a string is computed as\n s[0] * 31 ^ (n - 1) + s[1] * 31 ^ (n - 2) + ... + s[n - 1],\n where s[i] is the ith character of the string and n is the length of\n the string. We mod the result to make it between 0 (inclusive) and 2^32\n (exclusive).\n\n Args:\n s: A string.\n\n Returns:\n Integer hash value for the string, between 0 (inclusive) and 2^32\n (exclusive). The empty string returns 0.\n \"\"\"\n result = 0\n max_value = 2**32\n for char in s:\n result = (31 * result) + ord(char)\n result %= max_value\n return result\n\n\n# Set up the basic app config.\ntemplates = web.template.render('templates/',\n globals={'randint': random.randint,\n 'hashcode': Hashcode,\n 'json_encode': json.dumps})\n\nurls = (\n '/', 'Index',\n '/chunked', 'Chunked',\n '/chunked_sample_multipart', 'ChunkedSampleMultipart',\n '/chunked_sample_single', 'ChunkedSampleSingle',\n '/demo', 'Demo',\n '/demo/(.*)', 'Demo',\n '/index', 'Index',\n '/index_ajax', 'IndexAjax',\n '/missing', 'Missing',\n '/other', 'Other',\n '/other/(.*)', 'Other',\n '/spec', 'Spec',\n '/truncated', 'Truncated',\n)\napp = web.application(urls, globals())\n\n\nclass Servlet(object):\n \"\"\"Basic servlet class, containing common functions.\"\"\"\n\n def _SetChunkedHeaders(self):\n # The web.py server always sets \"Transfer-Encoding: chunked\"\n # and uses chunked transfer automatically, so manually setting\n # headers is not necessary.\n pass\n\n def _SetJSONHeaders(self):\n web.header('Content-Type', 'application/javascript')\n\n def _SetSPFMultipartHeaders(self):\n web.header('X-SPF-Response-Type', 'multipart')\n\n def IsSPFRequest(self):\n \"\"\"Gets whether the current request is for SPF.\"\"\"\n req = web.input(spf=None)\n has_spf_header = bool(web.ctx.env.get('HTTP_X_SPF_REQUEST'))\n has_spf_param = bool(req.spf)\n return has_spf_header or has_spf_param\n\n def GetReferer(self):\n \"\"\"Gets the referer, preferring the custom SPF version.\"\"\"\n referer = web.ctx.env.get('HTTP_X_SPF_REFERER')\n if not referer:\n referer = web.ctx.env.get('HTTP_REFERER')\n return referer\n\n def EncodeJSON(self, response):\n \"\"\"Encodes an object in JSON format.\"\"\"\n if web.config.debug:\n return json.dumps(response, sort_keys=True, indent=4)\n else:\n return json.dumps(response, separators=(',', ':'))\n\n def CreateSPFResponse(self, content, fragments=None):\n \"\"\"Creates an SPF response object for template.\n\n The object has the basic following format:\n 1. title - Update document title\n 2. url - Update document url\n 3. head - Install early page-wide styles\n 4. attr - Set element attributes\n 5. body - Set element content and install element-level scripts\n (styles handled by browser)\n 6. foot - Install late page-wide scripts\n\n All fields are optional and the commonly needed response values are\n title, head, body, and foot.\n\n Args:\n content: The content template instanace to render.\n fragments: Optional map of HTML Element IDs to template attributes\n to include instead of returning the entire content template.\n\n Returns:\n The SPF response object.\n \"\"\"\n response = {}\n head = str(getattr(content, 'stylesheet', ''))\n if head:\n response['head'] = head\n foot = str(getattr(content, 'javascript', ''))\n if foot:\n response['foot'] = foot\n title = str(getattr(content, 'title', ''))\n if title:\n response['title'] = title\n attr = json.loads(str(getattr(content, 'attributes', '{}')))\n if attr:\n response['attr'] = attr\n if fragments:\n response['body'] = {}\n for frag_id in fragments:\n response['body'][frag_id] = getattr(content, fragments[frag_id])\n else:\n content_str = str(content)\n if content_str:\n response['body'] = {'content': content_str}\n return response\n\n def RenderSPF(self, content, fragments=None):\n \"\"\"Returns a rendered SPF response.\n\n Args:\n content: The content template instanace to render.\n fragments: Optional map of HTML Element IDs to template attributes\n to include instead of returning the entire content template.\n\n Returns:\n The rendered SPF response.\n \"\"\"\n self._SetJSONHeaders()\n resp = self.CreateSPFResponse(content, fragments=fragments)\n return self.EncodeJSON(resp)\n\n def ChunkedRenderSPF(self, content, fragments=None, truncate=False):\n \"\"\"Returns a 2-part multipart SPF response across multiple chunks.\n\n This demonstrates support for chunked responses but this simplistic usage\n obviously isn't particularly effective, as all content is ready to be sent\n back. In real implementations, the first chunk should be sent before\n expensive backend work such as database queries or RPCs.\n\n Args:\n content: The content template instanace to render.\n fragments: Optional map of HTML Element IDs to template attributes\n to include instead of returning the entire content template.\n truncate: Whether to end the response early to simulate chunking/multipart\n serving errors.\n\n Yields:\n The partial SPF response(s).\n \"\"\"\n self._SetChunkedHeaders()\n self._SetJSONHeaders()\n self._SetSPFMultipartHeaders()\n # For clarity, use variables for the structure of a multipart response.\n multipart_begin = '[\\r\\n'\n multipart_delim = ',\\r\\n'\n multipart_end = ']\\r\\n'\n resp = self.CreateSPFResponse(content, fragments=fragments)\n first_chunk = {}\n if 'head' in resp:\n first_chunk['head'] = resp.pop('head')\n if 'title' in resp:\n first_chunk['title'] = resp.pop('title')\n # Begin the multipart response.\n yield multipart_begin\n # Send part 1.\n yield self.EncodeJSON(first_chunk)\n yield multipart_delim\n # Simulate real work being done.\n time.sleep(0.25)\n if not truncate:\n # Send part 2.\n yield self.EncodeJSON(resp)\n yield multipart_end\n\n def RenderHtml(self, content):\n \"\"\"Returns a rendered HTML response.\n\n Args:\n content: The content template instanace to render.\n\n Returns:\n The rendered HTML response.\n \"\"\"\n return templates.base(content)\n\n def ChunkedRenderHTML(self, content):\n \"\"\"Returns a rendered HTML response for chunking.\n\n (Does not currently split the reponse but could be used to do so.)\n\n Args:\n content: The content template instanace to render.\n\n Yields:\n The rendered HTML response.\n \"\"\"\n self._SetChunkedHeaders()\n yield templates.base(content)\n\n def Render(self, content):\n \"\"\"Returns a rendered HTML or SPF response as needed.\n\n Args:\n content: The content template instanace to render.\n\n Returns:\n The rendered HTML or SPF response.\n \"\"\"\n if self.IsSPFRequest():\n return self.RenderSPF(content)\n else:\n return self.RenderHtml(content)\n\n def ChunkedRender(self, content, truncate=False):\n \"\"\"Returns a rendered HTML or SPF response for chunking as needed.\n\n Args:\n content: The content template instanace to render.\n truncate: Whether to end the response early to simulate chunking/multipart\n serving errors.\n\n Yields:\n The rendered HTML or SPF response.\n \"\"\"\n if self.IsSPFRequest():\n for chunk in self.ChunkedRenderSPF(content, truncate=truncate):\n yield chunk\n else:\n for chunk in self.ChunkedRenderHTML(content):\n yield chunk\n\n def Redirect(self, url):\n \"\"\"Redirects to a given URL.\"\"\"\n if self.IsSPFRequest():\n response = {'redirect': url}\n return self.EncodeJSON(response)\n else:\n raise web.seeother(url)\n\n\nclass Index(Servlet):\n def GET(self): # pylint: disable=invalid-name,missing-docstring\n content = templates.index()\n return self.Render(content)\n\n\nclass IndexAjax(Servlet):\n def POST(self): # pylint: disable=invalid-name,missing-docstring\n content = templates.index_ajax()\n # Only support an SPF response\n fragments = {'home_ajax_out': 'home_ajax_out'}\n return self.RenderSPF(content, fragments=fragments)\n\n\nclass Spec(Servlet):\n def GET(self): # pylint: disable=invalid-name,missing-docstring\n content = templates.spec()\n return self.Render(content)\n\n\nclass Demo(Servlet):\n def GET(self, page_num=0): # pylint: disable=invalid-name,missing-docstring\n content = templates.demo(page_num)\n # yield instead of return to support chunked responses\n for chunk in self.ChunkedRender(content):\n yield chunk\n\n\nclass Other(Servlet):\n def GET(self, arg=None): # pylint: disable=invalid-name,missing-docstring\n if arg is not None:\n return self.Redirect('/other')\n referer = self.GetReferer()\n content = templates.other(referer)\n return self.Render(content)\n\n\nclass Missing(Servlet):\n def GET(self): # pylint: disable=invalid-name,missing-docstring\n web.ctx.status = '404 Not Found'\n return self.Render(templates.missing())\n\n\nclass Truncated(Servlet):\n def GET(self): # pylint: disable=invalid-name,missing-docstring\n content = templates.truncated()\n # yield instead of return to support chunked responses\n for chunk in self.ChunkedRender(content, truncate=True):\n yield chunk\n\n\nclass Chunked(Servlet):\n def GET(self): # pylint: disable=invalid-name,missing-docstring\n return self.Render(templates.chunked())\n\n\nclass _ChunkedSample(Servlet):\n \"\"\"Common root servlet class for permutations for sample chunked responses.\"\"\"\n\n def _HandleInput(self):\n \"\"\"Handles request parameters for the sample chunked responses.\"\"\"\n req = web.input(chunks=1, delay='', no_final_delimiter='',\n no_multipart_header='', add_parse_error='')\n try:\n self.num_chunks = int(req.chunks)\n except ValueError:\n self.num_chunks = 1\n try:\n self.delay_time = float(req.delay)\n except ValueError:\n self.delay_time = 1\n self.no_final_delimiter = bool(req.no_final_delimiter)\n self.no_multipart_header = bool(req.no_multipart_header)\n self.add_parse_error = bool(req.add_parse_error)\n return req\n\n def _IterChunks(self, s, n):\n \"\"\"Generator to iterate over a string in a given number of chunks.\n\n Args:\n s: A string.\n n: The number of chunks to iterate.\n\n Yields:\n The equally-sized chunks of the string.\n \"\"\"\n l = int(math.ceil(len(s) / float(n)))\n for i in range(0, len(s), l):\n yield s[i:i+l]\n\n\nclass ChunkedSampleMultipart(_ChunkedSample):\n def GET(self): # pylint: disable=invalid-name,missing-docstring\n self._HandleInput()\n # Set headers.\n self._SetChunkedHeaders()\n self._SetJSONHeaders()\n if not self.no_multipart_header:\n self._SetSPFMultipartHeaders()\n # For clarity, use variables for the structure of a multipart response.\n multipart_begin = '[\\r\\n'\n multipart_delim = ',\\r\\n'\n multipart_end = ']\\r\\n'\n alpha = range(ord('a'), ord('z') + 1)\n group_1 = dict((chr(l), l) for l in alpha if l < ord('i'))\n group_2 = dict((chr(l), l) for l in alpha if l >= ord('i') and l < ord('q'))\n group_3 = dict((chr(l), l) for l in alpha if l >= ord('q'))\n # Begin the multipart response.\n res = multipart_begin\n # Add the parts.\n for group in [group_1, group_2, group_3]:\n if self.add_parse_error and group == group_2:\n res += '__parse_error__'\n res += self.EncodeJSON(group) + multipart_delim\n # End the multipart response.\n res += 'null' # Avoid trailing commas in JSON arrays.\n if self.no_final_delimiter:\n res += ']'\n else:\n res += multipart_end\n # Send across multiple chunks.\n for chunk in self._IterChunks(res, self.num_chunks):\n time.sleep(self.delay_time)\n yield chunk\n\n\nclass ChunkedSampleSingle(_ChunkedSample):\n def GET(self): # pylint: disable=invalid-name,missing-docstring\n self._HandleInput()\n # Set headers.\n self._SetChunkedHeaders()\n self._SetJSONHeaders()\n alpha = range(ord('a'), ord('z') + 1)\n group = dict((chr(l), l) for l in alpha)\n res = self.EncodeJSON(group)\n if self.add_parse_error:\n res += '__parse_error__'\n # Send across multiple chunks.\n for chunk in self._IterChunks(res, self.num_chunks):\n time.sleep(self.delay_time)\n yield chunk\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"src/server/demo/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":12821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"97146535","text":"import socket\r\nimport json\r\n\r\nhost = 'localhost'\r\nport = 10000\r\n\r\nclass Client:\r\n\r\n def __init__(self, addr):\r\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n self.sock.connect((host, port))\r\n print('Connected')\r\n \r\n def recv(self):\r\n\r\n b = self.sock.recv(1024)\r\n print(b)\r\n s = b.decode()\r\n print(\"Recieved: %s\" % s)\r\n try:\r\n ls = json.loads(s)\r\n except json.decoder.JSONDecodeError:\r\n ls = [\"\", 0, []]\r\n return ls \r\n\r\n def send(self, msg):\r\n byte = json.dumps(msg).encode()\r\n self.sock.send(byte)\r\n\r\n\r\nc = Client((host, port))\r\n\r\nwhile True:\r\n info, n, rang = c.recv()\r\n print(info)\r\n if n == 0:\r\n continue\r\n else:\r\n c.send([0])","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"457879530","text":"import pdb\nimport json\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport pandas\nimport sys\n\ngpus = ['t4', 'p100', 'v100'] \nGPUs = ['T4', 'P100', 'V100']\n#idle_pwr = [27, 60, 15]\n\nfig, axs = plt.subplots(1, 2, figsize=(8,3.5), gridspec_kw={'hspace': 0, 'wspace': 0.3, 'top': 0.9, 'left':0.08, 'right':0.99, 'bottom':0.08})\nwidth = 0.4\n\nbatches = list(range(1, 11))\n# first plot throughput across different gpus\nfor i, gpu in enumerate(gpus):\n tail_list = []\n mean_list = []\n for batch_size in batches:\n path = f'logs/time_records/{gpu}_{batch_size}.json'\n with open(path) as f:\n lats = json.load(f)\n lats = lats[5:]\n tail_list.append(np.percentile(lats,95))\n mean_list.append(np.mean(lats))\n axs[0].plot(batches, tail_list, label=gpu)\n axs[1].plot(batches, mean_list, label=gpu)\n\naxs[0].set_xticks(batches)\naxs[0].legend()\naxs[0].set_title('tail latencies vs batch', fontsize=14)\naxs[0].set_ylabel('tail latency\\n(ms)', fontsize=13)\naxs[0].set_xlabel('batch size', fontsize=13)\n\naxs[1].set_xticks(batches)\naxs[1].set_title('mean latencies vs batch', fontsize=14)\naxs[1].set_ylabel('tail latency\\n(ms)', fontsize=13)\naxs[1].set_xlabel('batch size', fontsize=13)\n\nplt.savefig(f'plots/lat_vs_batch.png')\n\n","sub_path":"experiments/lat_vs_batch.py","file_name":"lat_vs_batch.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"606039241","text":"# Test main file\nimport os\nimport platform\nimport glob\nfrom selenium import webdriver\nfrom pyautogui import press, typewrite, hotkey, write\nimport time\n\n\nclass ScreenImage:\n ScreenshotLocation = os.getcwd()+'/Screenshots/'\n CreatedScreenshots = []\n\n def __init__(self,sc_location = 'default'):\n if(sc_location != 'default'):\n ScreenshotLocation = sc_location\n\n def ConfigDriver(self,options: webdriver.ChromeOptions):\n if(not os.path.isdir(self.ScreenshotLocation)):\n os.mkdir(self.ScreenshotLocation)\n #options = driver.ChromeOptions()\n options.add_experimental_option(\"prefs\",{'download.default_directory': self.ScreenshotLocation+'/',\"directory_upgrade\": True})\n options.add_experimental_option(\"excludeSwitches\", [\"enable-automation\"])\n options.add_experimental_option('useAutomationExtension', False)\n return options\n\n def getSource(self,browser: webdriver.Chrome, strURL):\n\n browser.get(strURL)\n return browser.page_source\n\n def ClearSession(self):\n for path in self.CreatedScreenshots:\n if(not os.path.isfile(path)):\n continue\n os.remove(path)\n\n def Screenshot(self,driver: webdriver.Chrome, strURL = 'current') -> None:\n if(strURL != 'current'):\n driver.get(strURL)\n if(platform.system() == 'Darwin'):\n #OpenDeveloper = Keys.ALT+Keys.COMMAND+\"i\"\n #OpenConsole = Keys.COMMAND+Keys.SHIFT+\"p\"\n hotkey('alt', 'command', 'i')\n time.sleep(1.5)\n hotkey('command', 'shift', 'p')\n elif(platform.system() == 'Windows' or platform.system() == 'Linux'):\n #OpenDeveloper = Keys.CTRL+Keys.SHIFT+\"i\"\n #OpenConsole = Keys.CTRL+Keys.SHIFT+\"p\"\n hotkey('ctrl', 'shift', 'i')\n time.sleep(1.5)\n hotkey('ctrl', 'shift', 'p')\n time.sleep(1.5)\n write(\"screenshot\")\n time.sleep(0.1)\n press(\"down\")\n time.sleep(0.1)\n press(\"enter\")\n time.sleep(0.1)\n\n filefilter = driver.current_url.replace(\"https://\",\"\").strip()\n filefilter = filefilter.replace(\"www.\",\"\").strip()\n filefilter = filefilter.replace('?','_')\n filefilter = filefilter.replace('/','_')\n\n #print(filefilter)\n time.sleep(2.0)\n\n matchedfiles = []\n\n for filename in os.listdir(self.ScreenshotLocation):\n path = os.path.join(self.ScreenshotLocation,filename)\n if(not os.path.isfile(path)):\n continue\n if(filefilter in filename):\n matchedfiles.append(path)\n latest_file = max(matchedfiles, key=os.path.getctime) #uses glob\n\n now = time.time()\n oneminute_ago = now - 60\n if(os.path.getctime(latest_file) > oneminute_ago):\n self.CreatedScreenshots.append(latest_file)\n return latest_file\n else:\n raise RuntimeError('Screenshot not taken.')\n return None\n\n\n\n#ChromeOptions.addArguments(\"--kiosk\");\n#options.add_argument('headless')\n#options.add_argument('--auto-open-devtools-for-tabs')\n\nbase_url = 'https://wcca.wicourts.gov/caseDetail.html?caseNo=2020SC000301&countyNo=40&index=0'\n\nsi = ScreenImage()\n\noptions = si.ConfigDriver(webdriver.ChromeOptions())\ndriver = webdriver.Chrome(options=options, executable_path=r'/Users/i530455/chromedriver')\ndriver.get(base_url)\ntime.sleep(1.5)\nprint(si.Screenshot(driver=driver))\n#print(getSource(\"https://wcca.wicourts.gov/case.html\"))\n","sub_path":"ScreenImage.py","file_name":"ScreenImage.py","file_ext":"py","file_size_in_byte":3553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"391645262","text":"def Collect(num_candidate):\r\n connexion=twitter_collect.twitter_connection_setup.twitter_setup()\r\n Retweets=twitter_collect.collect_tweet_candidate_activity.get_retweets_of_candidate(num_candidate) #créer une liste des retweets associés au candidat\r\n Replies=twitter_collect.collect_tweet_candidate_activity.get_replies_to_candidate(num_candidate) #créer une liste des réponses associées au candidat\r\n Tweets=twitter_collect.collect_candidate_actuality_tweets.get_tweets_from_candidates_search_queries(tweeter_collect.get_candidate_queries(num_candidate )) #créer la liste des tweets qui correspondent aux hashtags et mots-clés transmis dans deux fichiers texte par l'équipe du candidat\r\n #si on souhaite avoir une liste de tout ce qui se rapporte au candidat il suffit de rajouter une liste qui correspond à la concaténation des 3 autres\r\n#modifier les noms et prendre aussi les valeurs des likes (voir exemple Fonctionnalité 5)\r\n return Retweets + Replies + Tweets\r\n\r\nimport json\r\ndef store_tweets(tweets,filename):\r\n tmp_list=[]\r\n for tweet in tweets:\r\n tweet_as_dict={\"text\":tweet.text,\"user\": tweet.user.id ,\"date\":str(tweet.created_at),\"hashtags\":tweet.entities.get(\"hashtags\"),\"retweeted\":tweet.retweeted,\"retweet_count\":tweet.retweet_count}\r\n tmp_list.append(tweet_as_dict)\r\n json.dump(tmp_list,filename)\r\n return tmp_list #modification que permet de travailler dans la fonction create_dataframe ci-dessous\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pytest\r\n\r\ndef create_dataframe(Status):\r\n Dataframe=pd.DataFrame(Status)\r\n return Dataframe\r\nimport twitter_collect.tweets_collector\r\n\r\n\r\ndef collect_to_pandas_dataframe():\r\n connexion = twitter_collect.twitter_connection_setup.twitter_setup()\r\n tweets = connexion.search(\"@EmmanuelMacron\",language=\"fr\",rpp=100)\r\n data = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['tweet_textual_content'])\r\n data['len'] = np.array([len(tweet.text) for tweet in tweets])\r\n data['ID']= np.array([tweet.id for tweet in tweets])\r\n data['Date'] = np.array([tweet.created_at for tweet in tweets])\r\n data['Source'] = np.array([tweet.source for tweet in tweets])\r\n data['Likes'] = np.array([tweet.favorite_count for tweet in tweets])\r\n data['RTs'] = np.array([tweet.retweet_count for tweet in tweets])\r\n return data\r\n\r\nwith open('blabla.txt','w') as file:\r\n print(create_dataframe(collect_to_pandas_dataframe()))\r\n\r\n","sub_path":"twitterPredictor/twitterPredictor/tweet_collection/_main_.py","file_name":"_main_.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"408582657","text":"from flask import Flask, url_for\nfrom flask import request\nfrom flask import json\nfrom math import sin, cos, sqrt, atan2, radians\nimport pandas as pd\nfrom shapely.geometry import MultiPoint,Point\nimport psycopg2\n\nhostname = 'localhost'\nusername = 'postgres'\ndatabase = 'testdb'\nmyConnection = psycopg2.connect( host=hostname, user=username, dbname=database )\napp = Flask(__name__)\n\n@app.route('/post_location', methods=['POST'])\ndef post_location():\n if request.headers['Content-Type'] == 'text/plain':\n try:\n text=request.data.decode(\"utf-8\")\n lat,long,pin,place,ad=text.split(\"+\")\n cur=myConnection.cursor()\n cur.execute(\"select key from pincode where key= {0} \".format(\"\\'\"+pin+\"\\'\"))\n if cur.rowcount>0:\n return \"sorry already present\"\n else:\n cur.execute(\"insert into pincode (admin_name1,latitude,longitude,key,place_name) values (%s, %s, %s, %s, %s)\",(ad,lat,long,pin,place))\n myConnection.commit()\n return \"the location has been added\" + str(request.data).replace(\"b'\",'')\n except:\n print(\"wrong values entered\")\n return \"you have entered wrong values\"\n else:\n return \"415 Unsupported Media Type ;)\"\n\t\n@app.route('/get_using_postgres', methods=['GET','POST'])\ndef get_using_postgres():\n if request.headers['Content-Type'] == 'text/plain':\n try:\n text=request.data.decode(\"utf-8\")\n lat,long,radius=text.split(\"+\")\n cur=myConnection.cursor()\n query=\"select place_name from pincode where earth_box(ll_to_earth(\"+lat+\",\"+long+\"), \"+radius+\") @> ll_to_earth(pincode.latitude, pincode.longitude)\"\n cur.execute(query)\n lst=[]\n for a in cur.fetchall():\n lst.append(a)\n return \"the places near the point in given radius are \" + str(lst)\n except:\n print(\"user entered wrong data\")\n return \"you have entered wrong input data \"\n else:\n return \"415 Unsupported Media Type ;)\"\n\t\t\n@app.route('/get_using_self', methods=['GET','POST'])\ndef get_using_self():\n if request.headers['Content-Type'] == 'text/plain':\n try:\n query=\"select place_name,latitude,longitude from pincode\"\n cur=myConnection.cursor()\n cur.execute(query)\n text = request.data.decode(\"utf-8\")\n lat,long,dis=text.split(\"+\")\n lst=[]\n for a,b,c in cur.fetchall():\n plc=a\n lat1=radians(float(lat))\n lon1=radians(float(long))\n if b is not None:\n lat2=radians(float(b))\n if c is not None:\n lon2=radians(float(c))\n rad=float(dis)\n R = 6373.0\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n c = 2 * atan2(sqrt(a), sqrt(1 - a))\n distance = R * c * 1000\n if distance<=rad:\n lst.append(plc)\n return \"the places near the point in given radius are \" + str(lst)\n except:\n print(\"wrong data entered by user\")\n return \"you have entered wrong input\"\n else:\n return \"415 Unsupported Media Type ;)\"\n\n@app.route('/get_city_name', methods=['GET','POST'])\ndef get_city_name():\n if request.headers['Content-Type'] == 'text/plain':\n try:\n text = request.data.decode(\"utf-8\")\n lat,long=text.split(\"+\")\n cur=myConnection.cursor()\n query=\"select name,parent,coordinates from compute\"\n cur.execute(query)\n sd=pd.DataFrame(cur.fetchall(),columns=[\"name\",\"parent\",\"coordinates\"])\n for index in sd.iterrows():\n kj=sd.coordinates[index[0]].replace(\"{\",\"\").replace(\"}\",\"\").split(\",\")\n xs=[]\n ys=[]\n c=0\n for i in kj:\n p=float(i)\n if c%2==0:\n xs.append(p)\n else:\n ys.append(p)\n c+=1\n sd.coordinates[index[0]]=list(zip(xs,ys))\n x=float(lat)\n y=float(long)\n for index, row in sd.iterrows():\n poly = MultiPoint(row[\"coordinates\"]).convex_hull\n point = Point(y,x)\n if poly.contains(point)==True:\n print(str(sd.loc[index,\"name\"]))\n return \"the place of location is \" + str(sd.loc[index,\"name\"])\n except:\n print(\"wrong input given by user\")\n return \"you have entered wrong data\"\n else:\n return \"415 Unsupported Media Type ;)\"\n\nif __name__ == '__main__':\n app.run(debug=True)\n\t\n\t\n\t\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"468118214","text":"import MySQLdb\nimport mysql.connector\nimport random\nfrom flask import json\n\ndef getMysqlConnection():\n return mysql.connector.connect(user='root', host='127.0.0.1', port='3306', password='password')\n\n\ndef createDB():\n db = getMysqlConnection()\n\n cur = db.cursor()\n cur.execute(\"CREATE DATABASE Friend;\")\n cur.execute(\"USE Friend;\")\n\n\n# add friend randomly\ndef createPerson(n):\n \n db = getMysqlConnection()\n cur = db.cursor()\n cur.execute(\"USE Friend;\")\n\n table = 'User' + str(n)\n \n cur.execute(\"CREATE TABLE %s (User VARCHAR(10));\" % table)\n\n arr = range(n)\n random.shuffle(arr)\n if n > 1:\n m = random.randint(1,n-1)\n else:\n m = n\n\n for i in arr[0:m]:\n cur.execute(\"INSERT INTO %s VALUES ('%s');\" % (table, str(i)))\n\n friend_table = 'User' + str(i)\n cur.execute(\"INSERT INTO %s VALUES ('%s');\" % (friend_table, str(n)))\n db.commit()\n\n\ndef initDB():\n for i in range(20):\n createPerson(i)\n\ndef cleanDB():\n db = getMysqlConnection()\n cur = db.cursor()\n cur.execute(\"DROP DATABASE IF EXISTS Friend;\")\n db.close()\n\n\ndef getAllPeople():\n db = getMysqlConnection()\n cur = db.cursor()\n cur.execute(\"USE Friend\")\n\n # For each table\n cur.execute(\"SELECT table_name FROM information_schema.tables where table_schema='Friend';\")\n table = cur.fetchall()\n \n for i in table:\n cur.execute(\"SELECT * FROM %s;\" % str(i[0]))\n element = cur.fetchall()\n \n ele_list = []\n for ele in element:\n eleDict = {\n 'source' : str(i[0]),\n 'target' : \"User\"+str(ele[0])\n }\n print(eleDict)\n ele_list.append(eleDict)\n \n return json.dumps(ele_list)\n\ndef deletePerson(User):\n db = getMysqlConnection()\n cur = db.cursor()\n cur.execute(\"USE Friend\")\n\n UserN = User[4:]\n\n cur.execute(\"DROP TABLE %s;\" % User)\n\n # For each table\n cur.execute(\"SELECT table_name FROM information_schema.tables where table_schema='Friend';\")\n table = cur.fetchall()\n \n for i in table:\n cur.execute(\"DELETE FROM %s WHERE User='%s';\" % (str(i[0]),str(UserN)))\n\n db.commit()\n\n","sub_path":"db_setting.py","file_name":"db_setting.py","file_ext":"py","file_size_in_byte":2219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"499041856","text":"from django.contrib.auth import authenticate, login, logout\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.views.generic import DetailView\nfrom django.contrib.auth.models import User\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom forms import RegistrationForm, LoginForm\nfrom django.utils import simplejson\nfrom beer.models import Brewery, Beer, get_cities\n\nalphabet = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',\n 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z')\n\ndef Home(request):\n return render_to_response('home.html', context_instance=RequestContext(request))\n\ndef BreweryList(request):\n \"\"\"\n Returns a page with a list of breweries based on url\n parameters. If no parameters are set then all breweries\n are returned. If parameters are set, data is stored in\n a dict (sort) and the corresponding breweries are displayed\n \"\"\"\n\n if request.GET.get('index'):\n sort ={ 'type' : 'index',\n 'sort' : request.GET.get('index') }\n brewery_list = Brewery.objects.filter(name__istartswith=sort['sort'])\n elif request.GET.get('city'):\n sort = {'type' : 'city',\n 'sort' : request.GET.get('city') }\n brewery_list = Brewery.objects.filter(city__iexact = sort['sort'])\n else:\n sort = None\n brewery_list = Brewery.objects.all().order_by('name')\n\n cities = get_cities()\n paginator = Paginator(brewery_list, 10)\n\n page = request.GET.get('page')\n\n try:\n breweries = paginator.page(page)\n except PageNotAnInteger:\n breweries = paginator.page(1)\n except EmptyPage:\n breweries = paginator.page(paginator.num_pages)\n\n context = {\n 'breweries' : breweries,\n 'sort' : sort,\n 'alphabet' : alphabet,\n 'cities' : cities\n }\n\n return render_to_response('beer/brewery_list.html', context, context_instance = RequestContext(request))\n\ndef Beermap(request):\n global alphabet\n\n breweries = Brewery.objects.all().order_by('name')\n\n context = { 'alphabet' : alphabet,\n 'breweries' : breweries }\n\n return render_to_response('beer/beermap.html', context, context_instance=RequestContext(request))\n\nclass BreweryView(DetailView):\n\n context_object_name = \"brewery\"\n model = Brewery\n template_name = 'beer/brewery_view.html'\n\n def get_context_data(self, **kwargs):\n context = super(BreweryView, self).get_context_data(**kwargs)\n context['beer_list'] = Beer.objects.filter(brewery__slug=self.kwargs['slug'])\n return context\n\ndef autocomplete_brewery(request):\n term = request.GET.get('term')\n breweries = Brewery.objects.filter(name__istartswith=term)\n res = []\n for brewery in breweries:\n marker = 'marker' + str(brewery.id)\n content = 'content' + str(brewery.id)\n dict = {'id':brewery.id,\n 'label':brewery.__unicode__(),\n 'value':brewery.__unicode__(),\n 'marker':marker,\n 'content':content}\n res.append(dict)\n return HttpResponse(simplejson.dumps(res))\n\ndef UserRegistration(request):\n if request.user.is_authenticated():\n return HttpResponseRedirect('/')\n if request.method == 'POST':\n form = RegistrationForm(request.POST)\n if form.is_valid():\n user = User.objects.create_user(username=form.cleaned_data['username'], email=form.cleaned_data['email'], password=form.cleaned_data['password'])\n user.save()\n return HttpResponseRedirect('/')\n else:\n return render_to_response('register.html', {'form' : form}, context_instance=RequestContext(request))\n else:\n form = RegistrationForm()\n return render_to_response('register.html', {'form' : form}, context_instance=RequestContext(request))\n\ndef LoginRequest(request):\n if request.user.is_authenticated():\n return HttpResponseRedirect('/')\n if request.method == 'POST':\n form = LoginForm(request.POST)\n if form.is_valid():\n username = form.cleaned_data['username']\n password = form.cleaned_data['password']\n user = authenticate(username=username, password=password)\n\n if user is not None:\n login(request, user)\n return HttpResponseRedirect('/')\n else:\n return render_to_response('login.html', {'form' : form}, context_instance=RequestContext(request))\n else:\n return render_to_response('login.html', {'form' : form}, context_instance=RequestContext(request))\n\n else:\n form = LoginForm()\n return render_to_response('login.html', {'form' : form}, context_instance=RequestContext(request))\n\ndef LogoutRequest(request):\n logout(request)\n return HttpResponseRedirect('/')\n\n","sub_path":"beer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"339881605","text":"def calAbs(a, b):\n result = a - b\n if result < 0:\n result = -1 * result\n return result\n\ndef isWall(x, y):\n if x < 0 or x > 4 or y < 0 or y > 4:\n return True\n else:\n return False\n\narr = [\n [1, 1, 1, 1, 1],\n [1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1],\n [1, 0, 0, 0, 1],\n [1, 1, 1, 1, 1]\n]\n\ndy = [-1, 0, 1, 0]\ndx = [0, 1, 0, -1]\nsum = 0\n\nfor i in range(5):\n for j in range(5):\n for k in range(4):\n y = i + dy[k]\n x = j + dx[k]\n if not isWall(x, y):\n sum += calAbs(arr[i][j], arr[y][x])\n\nprint(sum)\n\n","sub_path":"Algorithm/Searching/2차 리스트 탐색.py","file_name":"2차 리스트 탐색.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"93818751","text":"from agent import *\n\n\nif __name__ == \"__main__\":\n agent_007 = Agent()\n # Should be empty list\n print(agent_007.property_list)\n\n try:\n number_of_properties = \\\n int(input(\"Enter number of properties, you want to add: \"))\n except (TypeError, ValueError):\n print('Please, enter natural number.')\n else:\n for i in range(number_of_properties):\n agent_007.add_property()\n print(agent_007)\n print(agent_007.property_list)\n agent_007.display_properties()\n agent_007.display_properties_with_type()\n\n","sub_path":"test_agent.py","file_name":"test_agent.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"333553376","text":"# Copyright 2019 Matthew Egan Odendahl\n# SPDX-License-Identifier: Apache-2.0\n\nimport re\nfrom contextlib import suppress\nfrom typing import Dict, Hashable, Mapping, Match, TypeVar\n\nimport unicodedata\n\n\ndef munge(s: str) -> str:\n if s.startswith(\":\") or s.isidentifier():\n return s\n return \"\".join(map(x_quote, s))\n\n\nTO_NAME = {\n \"~\": \"xTILDE_\",\n \"`\": \"xGRAVE_\",\n \"!\": \"xBANG_\",\n \"@\": \"xAT_\",\n \"#\": \"xHASH_\",\n \"$\": \"xDOLLAR_\",\n \"%\": \"xPERCENT_\",\n \"^\": \"xCARET_\",\n \"&\": \"xET_\",\n \"*\": \"xSTAR_\",\n \"-\": \"xH_\", # Hyphen\n \"+\": \"xPLUS_\",\n \"=\": \"xEQ_\",\n \"|\": \"xBAR_\",\n \"\\\\\": \"xBSLASH_\",\n \":\": \"xCOLON_\",\n \"'\": \"xQUOTE_\",\n \"<\": \"xLT_\", # Less Than or LefT.\n \",\": \"xCOMMA_\",\n \">\": \"xGT_\", # Greater Than or riGhT.\n \"?\": \"xQUERY_\",\n \"/\": \"xSLASH_\",\n \" \": \"xSPACE_\",\n}\nX_NAME = {ord(k): ord(v) for k, v in {\" \": \"x\", \"-\": \"h\"}.items()}\n\n\ndef x_quote(c: str) -> str:\n return (\n TO_NAME.get(c)\n or unicodedata.category(c) == \"Sm\"\n and f\"x{unicodedata.name(c).translate(X_NAME)}_\"\n or c\n )\n\n\nK = TypeVar(\"K\", bound=Hashable)\nV = TypeVar(\"V\")\n\n\ndef reversed_1to1(mapping: Mapping[K, V]) -> Dict[V, K]:\n result = {v: k for k, v in mapping.items()}\n assert len(mapping) == len(result)\n return result\n\n\nLOOKUP_NAME = reversed_1to1(TO_NAME)\nUN_X_NAME = reversed_1to1(X_NAME)\n\n\ndef un_x_quote(match: Match[str]) -> str:\n with suppress(KeyError):\n return LOOKUP_NAME.get(match.group()) or unicodedata.lookup(\n match.group(1).translate(UN_X_NAME)\n )\n return match.group()\n\n\ndef demunge(s: str) -> str:\n return re.sub(\"x([0-9A-Zhx]+?)_\", un_x_quote, s)\n","sub_path":"src/hissp/munger.py","file_name":"munger.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"185351880","text":"import re\nimport time\nimport threading\nfrom .utils import is_windows, encode_attr\nfrom .event import Event\nfrom .control import Control\n\nclass Connection:\n def __init__(self, conn_id):\n self.conn_id = conn_id\n self.lock = threading.Lock()\n\n self.win_command_pipe = None\n self.win_event_pipe = None\n\n self.event_available = threading.Event()\n self.last_event = None\n self._event_handlers = {} \n\n if is_windows():\n self.__init_windows()\n else:\n self.__init_linux()\n\n self.__start_event_loop()\n self.on_event = self.__on_event\n\n def __on_event(self, evt):\n pass\n\n def send_batch(self, commands):\n with self.lock:\n self.__send(\"begin\")\n for command in commands:\n self.__send(command)\n result = self.__send(\"end\")\n if result == \"\":\n return []\n else:\n return result.split('\\n')\n\n def send(self, command):\n with self.lock:\n return self.__send(command)\n\n def __send(self, command):\n fire_and_forget = False\n cmdName = command.split(' ', 1)[0].strip()\n if cmdName[len(cmdName) - 1] == 'f' or cmdName.lower() == 'close':\n fire_and_forget = True\n\n if is_windows():\n return self.__send_windows(command, fire_and_forget)\n else:\n return self.__send_linux(command, fire_and_forget)\n\n def wait_event(self):\n self.event_available.clear()\n self.event_available.wait()\n return self.last_event\n\n def wait_close(self):\n while True:\n e = self.wait_event()\n if e.target == \"page\" and e.name == \"close\":\n break\n\n def __start_event_loop(self):\n thread = threading.Thread(target=self.__event_loop, daemon=True)\n thread.start()\n\n def __event_loop(self):\n while True:\n if is_windows():\n evts = self.__wait_events_windows()\n else:\n evts = self.__wait_events_linux()\n\n for e in evts:\n if e == None:\n return \n\n if self.on_event != None:\n self.on_event(e)\n\n if e.target == \"page\" and e.name == \"close\":\n self.close()\n return\n elif e.target != \"page\" or e.name != \"change\":\n self.last_event = e\n self.event_available.set()\n\n def __init_windows(self):\n self.win_command_pipe = open(rf'\\\\.\\pipe\\{self.conn_id}', 'r+b', buffering=0)\n self.win_event_pipe = open(rf'\\\\.\\pipe\\{self.conn_id}.events', 'r+b', buffering=0)\n\n def __send_windows(self, command, fire_and_forget):\n # send command\n self.win_command_pipe.write(command.encode('utf-8'))\n\n if fire_and_forget:\n return\n\n # wait for result\n r = self.win_command_pipe.readline().decode('utf-8').strip('\\n')\n result_parts = re.split(r\"\\s\", r, 1)\n if result_parts[0] == \"error\":\n raise Exception(result_parts[1])\n \n result = result_parts[1]\n extra_lines = int(result_parts[0])\n for _ in range(extra_lines):\n line = self.win_command_pipe.readline().decode('utf-8').strip('\\n')\n result = result + \"\\n\" + line\n return result\n\n def __wait_events_windows(self):\n r = self.win_event_pipe.readline().decode('utf-8').strip('\\n')\n yield self.__parse_event_line(r)\n\n def __init_linux(self):\n pass\n\n def __send_linux(self, command, fire_and_forget):\n # send command\n pipe = open(rf'{self.conn_id}', \"w\")\n pipe.write(command)\n pipe.close()\n\n if fire_and_forget:\n return\n\n # wait for result\n pipe = open(rf'{self.conn_id}', \"r\")\n r = pipe.readline().strip('\\n')\n result_parts = re.split(r\"\\s\", r, 1)\n if result_parts[0] == \"error\":\n raise Exception(result_parts[1])\n \n result = result_parts[1]\n extra_lines = int(result_parts[0])\n for _ in range(extra_lines):\n line = pipe.readline().strip('\\n')\n result = result + \"\\n\" + line\n pipe.close()\n return result\n\n def __wait_events_linux(self):\n for line in open(rf'{self.conn_id}.events', \"r\"):\n yield self.__parse_event_line(line.strip('\\n'))\n \n def __parse_event_line(self, line):\n if line == \"\":\n return None\n result_parts = re.split(r\"\\s\", line, 2)\n return Event(result_parts[0], result_parts[1], result_parts[2])\n\n def close(self):\n if self.win_command_pipe != None:\n self.win_command_pipe.close()\n if self.win_event_pipe != None:\n self.win_event_pipe.close()","sub_path":"pglet/connection.py","file_name":"connection.py","file_ext":"py","file_size_in_byte":4912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"155863339","text":"# file to combine consensus annotations from season1-6 and season 7 and 8\n# Module to get data stored at MSI at Dryad\nfrom urllib import request\nimport csv\nimport numpy as np\nfrom config.config import config, cfg_path\n\n\n # load config\ndb_path = cfg_path['db']\n\n# consensus 1-6\noutput_file = db_path + 'consolidated_annotations_16.csv'\n\nseason7_8_file = db_path + 'consensus_7-8.csv'\n\nnew_file = db_path + 'consolidated_annotations.csv'\n\n############################\n# Get Data From Local Drive\n############################\n\nfile_new = open(new_file, \"w\", newline='')\nfile_16 = open(output_file, \"r\")\nfile_78 = open(season7_8_file, \"r\")\n\n\nnew_writer = csv.writer(file_new, delimiter=',',\n quotechar='\"',\n quoting=csv.QUOTE_ALL)\n\nreader_16 = csv.reader(file_16)\nreader_78 = csv.reader(file_78)\n\nheaders = next(reader_16)\nheaders2 = next(reader_78)\n\n# map headers\nidx = list()\nfor h in headers:\n if headers2.count(h) > 0:\n idx.append(headers2.index(h))\n else:\n idx.append(-1)\n\nnew_writer.writerow(headers)\nfor r in reader_16:\n new_writer.writerow(r)\n\n\nfor r in reader_78:\n r_new = list()\n for i in range(0, len(idx)):\n if idx[i] > -1:\n r_new.append(r[idx[i]])\n else:\n r_new.append('')\n new_writer.writerow(r_new)\n\nfile_new.close()\nfile_16.close()\nfile_78.close()\n\n\n","sub_path":"transfer_learning/db/snapshot_serengeti/99_combine_consensus_season1_6_and_7_8.py","file_name":"99_combine_consensus_season1_6_and_7_8.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"476528969","text":"#!/usr/bin/python3\n\n\"\"\"\nThis file is part of pycos project. See https://pycos.sourceforge.io for details.\n\nThis program can be used to start dispycos server processes so dispycos\nscheduler (see 'dispycos.py') can send computations to these server processes\nfor executing distributed communicating proceses (tasks). All tasks in a server\nexecute in the same thread, so multiple CPUs are not used by one server. If CPU\nintensive computations are to be run on systems with multiple processors, then\nthis program should be run with multiple instances (see below for '-c' option to\nthis program).\n\nSee 'dispycos_*.py' files for example use cases.\n\"\"\"\n\n__author__ = \"Giridhar Pemmasani (pgiri@yahoo.com)\"\n__copyright__ = \"Copyright (c) 2014 Giridhar Pemmasani\"\n__license__ = \"Apache 2.0\"\n__url__ = \"https://pycos.sourceforge.io\"\n\n\ndef _dispycos_server_proc():\n # task\n \"\"\"Server process receives a computation and runs tasks for it.\n \"\"\"\n\n import os\n import shutil\n import traceback\n import sys\n import time\n\n from pycos.dispycos import MinPulseInterval, MaxPulseInterval, \\\n DispycosNodeInfo, DispycosNodeAvailInfo, Scheduler, _DispycosJob_\n import pycos.netpycos as pycos\n from pycos.netpycos import Task, SysTask, Location, MonitorException, logger\n\n for _dispycos_var in ('_dispycos_server_process', '_dispycos_server_proc'):\n globals().pop(_dispycos_var, None)\n logger.name = 'dispycosnode'\n setattr(sys.modules['__main__'], '_DispycosJob_', _DispycosJob_)\n _dispycos_task = pycos.Pycos.cur_task()\n _dispycos_task.register('dispycos_server')\n _dispycos_config = yield _dispycos_task.receive()\n _dispycos_scheduler_task = pycos.deserialize(_dispycos_config['scheduler_task'])\n _dispycos_computation_auth = _dispycos_config.pop('computation_auth', None)\n _dispycos_var = pycos.deserialize(_dispycos_config['node_location'])\n yield pycos.Pycos.instance().peer(_dispycos_var)\n _dispycos_node_task = yield Task.locate('dispycos_node', location=_dispycos_var)\n yield _dispycos_node_task.deliver({'req': 'server_task', 'oid': 1, 'pid': os.getpid(),\n 'server_id': _dispycos_config['id'], 'task': _dispycos_task,\n 'auth': _dispycos_computation_auth}, timeout=5)\n\n if _dispycos_config['min_pulse_interval'] > 0:\n MinPulseInterval = _dispycos_config['min_pulse_interval']\n if _dispycos_config['max_pulse_interval'] > 0:\n MaxPulseInterval = _dispycos_config['max_pulse_interval']\n _dispycos_busy_time = _dispycos_config.pop('busy_time')\n pycos.MsgTimeout = _dispycos_config.pop('msg_timeout')\n\n _dispycos_name = pycos.Pycos.instance().name\n _dispycos_dest_path = os.path.join(pycos.Pycos.instance().dest_path,\n 'dispycosproc-%s' % _dispycos_config['id'])\n if os.path.isdir(_dispycos_dest_path):\n shutil.rmtree(_dispycos_dest_path)\n pycos.Pycos.instance().dest_path = _dispycos_dest_path\n os.chdir(_dispycos_dest_path)\n sys.path.insert(0, _dispycos_dest_path)\n\n for _dispycos_var in _dispycos_config.pop('peers', []):\n Task(pycos.Pycos.instance().peer, pycos.deserialize(_dispycos_var))\n\n for _dispycos_var in ['min_pulse_interval', 'max_pulse_interval']:\n del _dispycos_config[_dispycos_var]\n\n logger.info('dispycos server %s started at %s; computation files will be saved in \"%s\"',\n _dispycos_config['id'], _dispycos_task.location, _dispycos_dest_path)\n _dispycos_req = _dispycos_client = _dispycos_auth = _dispycos_msg = None\n _dispycos_peer_status = _dispycos_monitor_task = _dispycos_monitor_proc = _dispycos_job = None\n _dispycos_job_tasks = set()\n _dispycos_jobs_done = pycos.Event()\n\n def _dispycos_peer_status(task=None):\n task.set_daemon()\n while 1:\n status = yield task.receive()\n if not isinstance(status, pycos.PeerStatus):\n logger.warning('Invalid peer status %s ignored', type(status))\n continue\n if status.status == pycos.PeerStatus.Offline:\n if (_dispycos_scheduler_task and\n _dispycos_scheduler_task.location == status.location):\n if _dispycos_computation_auth:\n _dispycos_task.send({'req': 'close', 'auth': _dispycos_computation_auth})\n\n def _dispycos_monitor_proc(pulse_interval, task=None):\n task.set_daemon()\n while 1:\n msg = yield task.receive(timeout=pulse_interval)\n if isinstance(msg, MonitorException):\n logger.debug('task %s done at %s', msg.args[0], task.location)\n _dispycos_job_tasks.discard(msg.args[0])\n if not _dispycos_job_tasks:\n _dispycos_jobs_done.set()\n _dispycos_busy_time.value = int(time.time())\n elif not msg:\n if _dispycos_job_tasks:\n _dispycos_busy_time.value = int(time.time())\n else:\n logger.warning('invalid message to monitor ignored: %s', type(msg))\n\n pycos.Pycos.instance().peer_status(SysTask(_dispycos_peer_status))\n _dispycos_var = pycos.deserialize(_dispycos_config['computation_location'])\n if ((yield pycos.Pycos.instance().peer(_dispycos_var)) or\n (yield pycos.Pycos.instance().peer(_dispycos_scheduler_task.location))):\n raise StopIteration(-1)\n _dispycos_scheduler_task.send({'status': Scheduler.ServerDiscovered, 'task': _dispycos_task,\n 'name': _dispycos_name, 'auth': _dispycos_computation_auth})\n\n if _dispycos_config['_server_setup']:\n if _dispycos_config['_disable_servers']:\n while 1:\n _dispycos_var = yield _dispycos_task.receive()\n if (isinstance(_dispycos_var, dict) and\n _dispycos_var.get('req', None) == 'enable_server' and\n _dispycos_var.get('auth', None) == _dispycos_computation_auth):\n _dispycos_var = _dispycos_var['setup_args']\n if not isinstance(_dispycos_var, tuple):\n _dispycos_var = tuple(_dispycos_var)\n break\n else:\n logger.warning('Ignoring invalid request to run server setup')\n else:\n _dispycos_var = ()\n _dispycos_var = yield pycos.Task(globals()[_dispycos_config['_server_setup']],\n *_dispycos_var).finish()\n if _dispycos_var:\n logger.debug('dispycos server %s @ %s setup failed', _dispycos_config['id'],\n _dispycos_task.location)\n raise StopIteration(_dispycos_var)\n _dispycos_config['_server_setup'] = None\n _dispycos_scheduler_task.send({'status': Scheduler.ServerInitialized, 'task': _dispycos_task,\n 'name': _dispycos_name, 'auth': _dispycos_computation_auth})\n\n _dispycos_var = _dispycos_config['pulse_interval']\n _dispycos_monitor_task = SysTask(_dispycos_monitor_proc, _dispycos_var)\n _dispycos_busy_time.value = int(time.time())\n logger.debug('dispycos server \"%s\": Computation \"%s\" from %s', _dispycos_name,\n _dispycos_computation_auth, _dispycos_scheduler_task.location)\n\n while 1:\n _dispycos_msg = yield _dispycos_task.receive()\n if not isinstance(_dispycos_msg, dict):\n continue\n _dispycos_req = _dispycos_msg.get('req', None)\n\n if _dispycos_req == 'run':\n _dispycos_client = _dispycos_msg.get('client', None)\n _dispycos_auth = _dispycos_msg.get('auth', None)\n _dispycos_job = _dispycos_msg.get('job', None)\n if (not isinstance(_dispycos_client, Task) or\n _dispycos_auth != _dispycos_computation_auth):\n logger.warning('invalid run: %s', type(_dispycos_job))\n if isinstance(_dispycos_client, Task):\n _dispycos_client.send(None)\n continue\n try:\n if _dispycos_job.code:\n exec(_dispycos_job.code, globals())\n _dispycos_job.args = pycos.deserialize(_dispycos_job.args)\n _dispycos_job.kwargs = pycos.deserialize(_dispycos_job.kwargs)\n except Exception:\n logger.debug('invalid computation to run')\n _dispycos_var = (sys.exc_info()[0], _dispycos_job.name, traceback.format_exc())\n _dispycos_client.send(_dispycos_var)\n else:\n Task._pycos._lock.acquire()\n try:\n _dispycos_var = Task(globals()[_dispycos_job.name],\n *(_dispycos_job.args), **(_dispycos_job.kwargs))\n except Exception:\n _dispycos_var = (sys.exc_info()[0], _dispycos_job.name, traceback.format_exc())\n else:\n _dispycos_job_tasks.add(_dispycos_var)\n _dispycos_busy_time.value = int(time.time())\n logger.debug('task %s created at %s', _dispycos_var, _dispycos_task.location)\n _dispycos_var.notify(_dispycos_monitor_task)\n _dispycos_var.notify(_dispycos_scheduler_task)\n _dispycos_client.send(_dispycos_var)\n Task._pycos._lock.release()\n\n elif _dispycos_req == 'close' or _dispycos_req == 'quit':\n _dispycos_auth = _dispycos_msg.get('auth', None)\n if (_dispycos_auth == _dispycos_computation_auth):\n pass\n elif (_dispycos_msg.get('node_auth', None) == _dispycos_config['node_auth']):\n if _dispycos_scheduler_task:\n _dispycos_scheduler_task.send({'status': Scheduler.ServerClosed,\n 'location': _dispycos_task.location})\n while _dispycos_job_tasks:\n logger.debug('dispycos server \"%s\": Waiting for %s tasks to terminate before '\n 'closing computation', _dispycos_name, len(_dispycos_job_tasks))\n if (yield _dispycos_jobs_done.wait(timeout=5)):\n break\n else:\n continue\n _dispycos_var = _dispycos_msg.get('client', None)\n if isinstance(_dispycos_var, Task):\n _dispycos_var.send(0)\n break\n\n elif _dispycos_req == 'terminate':\n _dispycos_auth = _dispycos_msg.get('node_auth', None)\n if (_dispycos_auth != _dispycos_config['node_auth']):\n continue\n if _dispycos_scheduler_task:\n _dispycos_scheduler_task.send({'status': Scheduler.ServerDisconnected,\n 'location': _dispycos_task.location})\n break\n\n elif _dispycos_req == 'status':\n if _dispycos_msg.get('node_auth', None) != _dispycos_config['node_auth']:\n continue\n if _dispycos_scheduler_task:\n print(' dispycos server \"%s\" @ %s with PID %s running %d tasks for %s' %\n (_dispycos_name, _dispycos_task.location, os.getpid(),\n len(_dispycos_job_tasks), _dispycos_scheduler_task.location))\n else:\n print(' dispycos server \"%s\" @ %s with PID %s not used by any computation' %\n (_dispycos_name, _dispycos_task.location, os.getpid()))\n\n elif _dispycos_req == 'peers':\n _dispycos_auth = _dispycos_msg.get('auth', None)\n if (_dispycos_auth == _dispycos_computation_auth):\n for _dispycos_var in _dispycos_msg.get('peers', []):\n pycos.Task(pycos.Pycos.instance().peer, _dispycos_var)\n\n else:\n logger.warning('invalid command \"%s\" ignored', _dispycos_req)\n _dispycos_client = _dispycos_msg.get('client', None)\n if not isinstance(_dispycos_client, Task):\n continue\n _dispycos_client.send(-1)\n\n # kill any pending jobs\n while _dispycos_job_tasks:\n for _dispycos_var in _dispycos_job_tasks:\n _dispycos_var.terminate()\n logger.debug('dispycos server \"%s\": Waiting for %s tasks to terminate '\n 'before closing computation', _dispycos_name, len(_dispycos_job_tasks))\n if (yield _dispycos_jobs_done.wait(timeout=5)):\n break\n yield _dispycos_node_task.deliver({'req': 'server_done', 'oid': 3,\n 'server_id': _dispycos_config['id'], 'task': _dispycos_task,\n 'auth': _dispycos_computation_auth}, timeout=5)\n logger.debug('dispycos server %s @ %s done', _dispycos_config['id'], _dispycos_task.location)\n\n\ndef _dispycos_server_process(_dispycos_config, _dispycos_mp_queue, _dispycos_computation):\n import os\n import sys\n import time\n # import traceback\n\n for _dispycos_var in list(sys.modules.keys()):\n if _dispycos_var.startswith('pycos'):\n sys.modules.pop(_dispycos_var)\n globals().pop('pycos', None)\n\n global pycos\n import pycos.netpycos as pycos\n\n _dispycos_pid_file = os.path.join(_dispycos_config['dest_path'],\n 'dispycosproc-%s.pid' % _dispycos_config['id'])\n with open(_dispycos_pid_file, 'w') as _dispycos_var:\n _dispycos_var.write('%s' % os.getpid())\n\n if _dispycos_config['loglevel']:\n pycos.logger.setLevel(pycos.logger.DEBUG)\n # pycos.logger.show_ms(True)\n else:\n pycos.logger.setLevel(pycos.logger.INFO)\n del _dispycos_config['loglevel']\n\n pycos.logger.name = 'dispycosnode'\n server_id = _dispycos_config['id']\n mp_queue, _dispycos_mp_queue = _dispycos_mp_queue, None\n config = {}\n for _dispycos_var in ['udp_port', 'tcp_port', 'node', 'ext_ip_addr', 'name', 'discover_peers',\n 'secret', 'certfile', 'keyfile', 'dest_path', 'max_file_size',\n 'ipv4_udp_multicast']:\n config[_dispycos_var] = _dispycos_config.pop(_dispycos_var, None)\n\n while 1:\n try:\n _dispycos_scheduler = pycos.Pycos(**config)\n except Exception:\n print('dispycos server %s failed for port %s; retrying in 5 seconds' %\n (server_id, config['tcp_port']))\n # print(traceback.format_exc())\n time.sleep(5)\n else:\n break\n\n if os.name == 'nt':\n _dispycos_computation = pycos.deserialize(_dispycos_computation)\n if _dispycos_computation._code:\n exec(_dispycos_computation._code, globals())\n if __name__ == '__mp_main__': # Windows multiprocessing process\n sys.modules['__mp_main__'].__dict__.update(globals())\n\n _dispycos_config['_disable_servers'] = _dispycos_computation._disable_servers\n _dispycos_config['_server_setup'] = _dispycos_computation._server_setup\n _dispycos_task = pycos.SysTask(_dispycos_config.pop('server_proc'))\n assert isinstance(_dispycos_task, pycos.Task)\n computation_auth = _dispycos_config['computation_auth']\n mp_queue.put({'auth': computation_auth, 'oid': 2, 'server_id': server_id, 'pid': os.getpid(),\n 'location': pycos.serialize(_dispycos_task.location)})\n _dispycos_task.send(_dispycos_config)\n\n _dispycos_config = None\n del config, _dispycos_var\n\n _dispycos_task.value()\n _dispycos_scheduler.ignore_peers(ignore=True)\n for location in _dispycos_scheduler.peers():\n pycos.Task(_dispycos_scheduler.close_peer, location)\n _dispycos_scheduler.finish()\n try:\n os.remove(_dispycos_pid_file)\n except Exception:\n pass\n mp_queue.put({'auth': computation_auth, 'oid': 4, 'server_id': server_id, 'location': None,\n 'pid': os.getpid()})\n exit(0)\n\n\ndef _dispycos_spawn(_dispycos_config, _dispycos_id_ports, _dispycos_mp_queue,\n _dispycos_pipe, _dispycos_computation, _dispycos_setup_args):\n import os\n import sys\n import signal\n import multiprocessing\n # import traceback\n\n try:\n signal.signal(signal.SIGHUP, signal.SIG_DFL)\n signal.signal(signal.SIGQUIT, signal.SIG_DFL)\n except Exception:\n pass\n signal.signal(signal.SIGINT, signal.SIG_DFL)\n signal.signal(signal.SIGABRT, signal.SIG_DFL)\n signal.signal(signal.SIGTERM, signal.SIG_DFL)\n\n _dispycos_config['server_proc'] = _dispycos_server_proc\n server_process = _dispycos_server_process\n for _dispycos_var in list(globals()):\n if _dispycos_var.startswith('_dispycos_'):\n if _dispycos_var in ('_dispycos_server_process', '_dispycos_server_proc'):\n continue\n globals().pop(_dispycos_var)\n\n for _dispycos_var in list(sys.modules.keys()):\n if _dispycos_var.startswith('pycos'):\n sys.modules.pop(_dispycos_var)\n globals().pop('pycos', None)\n\n import pycos\n\n pycos.logger.name = 'dispycosnode'\n os.chdir(_dispycos_config['dest_path'])\n sys.path.insert(0, _dispycos_config['dest_path'])\n os.environ['PATH'] = _dispycos_config['dest_path'] + os.pathsep + os.environ['PATH']\n procs = []\n\n def close(status):\n for i in range(len(procs)):\n proc = procs[i]\n if not proc:\n continue\n if proc.is_alive():\n try:\n proc.terminate()\n except Exception:\n pass\n proc.join(0.5)\n for signum in [signal.SIGINT, signal.SIGTERM, signal.SIGABRT, signal.SIGKILL]:\n if not proc.is_alive():\n break\n try:\n os.kill(proc.pid, signum)\n except:\n pass\n proc.join(0.1)\n\n if (not proc.is_alive()) and proc.exitcode:\n pycos.logger.warning('Server %s (process %s) reaped', _dispycos_id_ports[i][0],\n proc.pid)\n _dispycos_mp_queue.put({'auth': _dispycos_config['computation_auth'], 'oid': 4,\n 'server_id': _dispycos_id_ports[i][0], 'location': None})\n _dispycos_pid_file = os.path.join(_dispycos_config['dest_path'],\n 'dispycosproc-%s.pid' % _dispycos_id_ports[i][0])\n if os.path.exists(_dispycos_pid_file):\n try:\n os.remove(_dispycos_pid_file)\n except Exception:\n pass\n\n _dispycos_pipe.send({'msg': 'closed', 'auth': _dispycos_config['computation_auth']})\n exit(status)\n\n if os.name != 'nt':\n if _dispycos_computation._code:\n exec(_dispycos_computation._code, globals())\n\n if _dispycos_computation._node_setup:\n try:\n if not isinstance(_dispycos_setup_args, tuple):\n _dispycos_setup_args = tuple(_dispycos_setup_args)\n ret = pycos.Task(globals()[_dispycos_computation._node_setup],\n *_dispycos_setup_args).value()\n except Exception:\n pycos.logger.warning('node_setup failed for %s', _dispycos_computation._auth)\n # print(traceback.format_exc())\n ret = -1\n if ret != 0:\n close(ret)\n _dispycos_computation._node_setup = None\n\n for id_port in _dispycos_id_ports:\n server_config = dict(_dispycos_config)\n server_config['id'] = id_port[0]\n server_config['name'] = '%s_proc-%s' % (_dispycos_config['name'], server_config['id'])\n server_config['tcp_port'] = id_port[1]\n server_config['peers'] = _dispycos_config['peers'][:]\n proc = multiprocessing.Process(target=server_process, name=server_config['name'],\n args=(server_config, _dispycos_mp_queue,\n _dispycos_computation))\n proc.start()\n pycos.logger.debug('dispycos server %s started with PID %s', (id_port[0], proc.pid))\n procs.append(proc)\n\n _dispycos_pipe.send({'msg': 'started', 'auth': _dispycos_config['computation_auth'],\n 'cpus': len(procs)})\n\n while 1:\n try:\n req = _dispycos_pipe.recv()\n except Exception:\n break\n if (isinstance(req, dict) and req.get('msg') == 'quit' and\n req.get('auth') == _dispycos_config.get('computation_auth')):\n break\n else:\n pycos.logger.warning('Ignoring invalid pipe cmd: %s' % str(req))\n\n for proc in procs:\n proc.join(1)\n\n close(0)\n\n\ndef _dispycos_node():\n if not _dispycos_config['min_pulse_interval']:\n _dispycos_config['min_pulse_interval'] = MinPulseInterval\n if not _dispycos_config['max_pulse_interval']:\n _dispycos_config['max_pulse_interval'] = MaxPulseInterval\n if _dispycos_config['msg_timeout'] < 1:\n raise Exception('msg_timeout must be at least 1')\n if (_dispycos_config['min_pulse_interval'] and\n _dispycos_config['min_pulse_interval'] < _dispycos_config['msg_timeout']):\n raise Exception('min_pulse_interval must be at least msg_timeout')\n if (_dispycos_config['max_pulse_interval'] and _dispycos_config['min_pulse_interval'] and\n _dispycos_config['max_pulse_interval'] < _dispycos_config['min_pulse_interval']):\n raise Exception('max_pulse_interval must be at least min_pulse_interval')\n if _dispycos_config['zombie_period']:\n if _dispycos_config['zombie_period'] < _dispycos_config['min_pulse_interval']:\n raise Exception('zombie_period must be at least min_pulse_interval')\n else:\n _dispycos_config['zombie_period'] = 0\n\n num_cpus = multiprocessing.cpu_count()\n if _dispycos_config['cpus'] > 0:\n if _dispycos_config['cpus'] > num_cpus:\n raise Exception('CPU count must be <= %s' % num_cpus)\n num_cpus = _dispycos_config['cpus']\n elif _dispycos_config['cpus'] < 0:\n if -_dispycos_config['cpus'] >= num_cpus:\n raise Exception('CPU count must be > -%s' % num_cpus)\n num_cpus += _dispycos_config['cpus']\n del _dispycos_config['cpus']\n\n tcp_ports = set()\n for tcp_port in _dispycos_config.pop('tcp_ports', []):\n tcp_port = tcp_port.split('-')\n if len(tcp_port) == 1:\n tcp_ports.add(int(tcp_port[0]))\n elif len(tcp_port) == 2:\n tcp_port = (int(tcp_port[0]), min(int(tcp_port[1]),\n int(tcp_port[0]) + num_cpus - len(tcp_ports)))\n tcp_ports = tcp_ports.union(range(tcp_port[0], tcp_port[1] + 1))\n else:\n raise Exception('Invalid TCP port range \"%s\"' % str(tcp_port))\n\n if tcp_ports:\n tcp_ports = sorted(tcp_ports)\n tcp_ports = tcp_ports[:num_cpus + 1]\n else:\n tcp_ports = [9706]\n\n for tcp_port in range(tcp_ports[-1] + 1, tcp_ports[-1] + 1 + num_cpus - len(tcp_ports) + 1):\n if tcp_ports[-1]:\n tcp_ports.append(tcp_port)\n else:\n tcp_ports.append(0)\n del tcp_port\n\n peer = None\n for peer in _dispycos_config['peers']:\n peer = peer.split(':')\n if len(peer) != 2:\n raise Exception('peer \"%s\" is not valid' % ':'.join(peer))\n _dispycos_config['peers'].append(pycos.serialize(pycos.Location(peer[0], peer[1])))\n del peer\n\n node_name = _dispycos_config['name']\n if not node_name:\n node_name = socket.gethostname()\n if not node_name:\n node_name = 'dispycos_server'\n\n daemon = _dispycos_config.pop('daemon', False)\n if not daemon:\n try:\n if os.getpgrp() != os.tcgetpgrp(sys.stdin.fileno()):\n daemon = True\n except Exception:\n pass\n if os.name == 'nt':\n # Python 3 under Windows blocks multiprocessing.Process on reading\n # input; pressing \"Enter\" twice works (for one subprocess). Until\n # this is understood / fixed, disable reading input.\n print('\\n Reading standard input disabled, as multiprocessing does not seem to work'\n 'with reading input under Windows\\n')\n daemon = True\n\n _dispycos_config['discover_peers'] = False\n\n # time at start of day\n _dispycos_var = time.localtime()\n _dispycos_var = (int(time.time()) - (_dispycos_var.tm_hour * 3600) -\n (_dispycos_var.tm_min * 60))\n service_start = service_stop = service_end = None\n if _dispycos_config['service_start']:\n service_start = time.strptime(_dispycos_config.pop('service_start'), '%H:%M')\n service_start = (_dispycos_var + (service_start.tm_hour * 3600) +\n (service_start.tm_min * 60))\n if _dispycos_config['service_stop']:\n service_stop = time.strptime(_dispycos_config.pop('service_stop'), '%H:%M')\n service_stop = (_dispycos_var + (service_stop.tm_hour * 3600) + (service_stop.tm_min * 60))\n if _dispycos_config['service_end']:\n service_end = time.strptime(_dispycos_config.pop('service_end'), '%H:%M')\n service_end = (_dispycos_var + (service_end.tm_hour * 3600) + (service_end.tm_min * 60))\n\n if (service_start or service_stop or service_end):\n if not service_start:\n service_start = int(time.time())\n if service_stop:\n if service_start >= service_stop:\n raise Exception('\"service_start\" must be before \"service_stop\"')\n if service_end:\n if service_start >= service_end:\n raise Exception('\"service_start\" must be before \"service_end\"')\n if service_stop and service_stop >= service_end:\n raise Exception('\"service_stop\" must be before \"service_end\"')\n if not service_stop and not service_end:\n raise Exception('\"service_stop\" or \"service_end\" must also be given')\n\n if _dispycos_config['max_file_size']:\n _dispycos_var = re.match(r'(\\d+)([kKmMgGtT]?)', _dispycos_config['max_file_size'])\n if (not _dispycos_var or\n len(_dispycos_var.group(0)) != len(_dispycos_config['max_file_size'])):\n raise Exception('Invalid max_file_size option')\n _dispycos_config['max_file_size'] = int(_dispycos_var.group(1))\n if _dispycos_var.group(2):\n _dispycos_var = _dispycos_var.group(2).lower()\n _dispycos_config['max_file_size'] *= 1024**({'k': 1, 'm': 2, 'g': 3,\n 't': 4}[_dispycos_var])\n else:\n _dispycos_config['max_file_size'] = 0\n\n if _dispycos_config['certfile']:\n _dispycos_config['certfile'] = os.path.abspath(_dispycos_config['certfile'])\n else:\n _dispycos_config['certfile'] = None\n if _dispycos_config['keyfile']:\n _dispycos_config['keyfile'] = os.path.abspath(_dispycos_config['keyfile'])\n else:\n _dispycos_config['keyfile'] = None\n\n class Struct(object):\n\n def __init__(self, **kwargs):\n self.__dict__.update(kwargs)\n\n def __setattr__(self, name, value):\n if hasattr(self, name):\n self.__dict__[name] = value\n else:\n raise AttributeError('Invalid attribute \"%s\"' % name)\n\n busy_time = multiprocessing.Value('I', 0)\n mp_queue = multiprocessing.Queue()\n node_auth = hashlib.sha1(os.urandom(20)).hexdigest()\n node_servers = [None] * (num_cpus + 1)\n if _dispycos_config['dest_path']:\n dispycos_dest_path = _dispycos_config['dest_path']\n else:\n import tempfile\n dispycos_dest_path = os.path.join(os.sep, tempfile.gettempdir(), 'pycos')\n del sys.modules['tempfile'], tempfile\n dispycos_dest_path = os.path.join(dispycos_dest_path, 'dispycos')\n\n _dispycos_var = os.path.join(dispycos_dest_path, 'dispycosproc-0.pid')\n node_servers[0] = Struct(id=0, psproc=None, task=None, msg_oid=0, name=None,\n port=None, pid_file=_dispycos_var)\n if psutil:\n node_servers[0].psproc = psutil.Process(os.getpid())\n for _dispycos_id in range(1, num_cpus + 1):\n _dispycos_var = os.path.join(dispycos_dest_path, 'dispycosproc-%s.pid' % _dispycos_id)\n node_servers[_dispycos_id] = Struct(id=_dispycos_id, psproc=None, task=None, msg_oid=0,\n name='%s_proc-%s' % (node_name, _dispycos_id),\n port=tcp_ports[_dispycos_id], pid_file=_dispycos_var)\n\n def close_server(server, ppid=None):\n server_dir = os.path.join(dispycos_dest_path, 'dispycosproc-%d' % server.id)\n\n if server.psproc:\n psproc = server.psproc\n pid = server.psproc.pid\n elif os.path.exists(server.pid_file):\n with open(server.pid_file, 'r') as fd:\n pid = fd.read(512)\n try:\n pid = int(pid)\n except Exception:\n pid = None\n if psutil:\n try:\n psproc = psutil.Process(pid)\n except Exception:\n psproc = None\n pid = None\n else:\n if not any('dispycosnode' in _ for _ in psproc.cmdline()):\n psproc = None\n pid = None\n else:\n psproc = None\n else:\n shutil.rmtree(server_dir, ignore_errors=True)\n return 0\n\n try:\n if psproc and ppid and psproc.ppid() != ppid:\n pid = None\n except Exception:\n pid = None\n\n if pid:\n if not _dispycos_config['clean']:\n print('\\n Another dispycosnode seems to be running (PID %s);\\n'\n ' ensure no dispycosnode and servers are running and\\n'\n ' remove *.pid files in %s\"\\n' % (pid, dispycos_dest_path))\n return -1\n\n for signum in [signal.SIGINT, signal.SIGTERM, signal.SIGABRT, signal.SIGKILL]:\n try:\n os.kill(pid, signum)\n except Exception:\n pass\n if psproc:\n try:\n psproc.status()\n except Exception:\n break\n time.sleep(0.2)\n\n if os.path.exists(server.pid_file):\n try:\n os.remove(server.pid_file)\n except Exception:\n print('\\n Could not remove file \"%s\";\\n'\n ' ensure no dispycosnode and servers are running and\\n'\n ' remove *.pid files in \"%s\"\\n' % (server.pid_file, dispycos_dest_path))\n return -1\n\n server.psproc = None\n server.task = None\n shutil.rmtree(server_dir, ignore_errors=True)\n return 0\n\n for _dispycos_id in range(len(node_servers)):\n if close_server(node_servers[_dispycos_id], dispycos_dest_path):\n exit(1)\n\n server_config = {}\n for _dispycos_var in ['udp_port', 'tcp_port', 'node', 'ext_ip_addr', 'name',\n 'discover_peers', 'secret', 'certfile', 'keyfile', 'dest_path',\n 'max_file_size', 'ipv4_udp_multicast']:\n server_config[_dispycos_var] = _dispycos_config.get(_dispycos_var, None)\n server_config['name'] = '%s_proc-0' % node_name\n server_config['tcp_port'] = tcp_ports[0]\n if _dispycos_config['loglevel']:\n pycos.logger.setLevel(pycos.Logger.DEBUG)\n # pycos.logger.show_ms(True)\n else:\n pycos.logger.setLevel(pycos.Logger.INFO)\n dispycos_scheduler = pycos.Pycos(**server_config)\n dispycos_scheduler.dest_path = os.path.join(dispycos_scheduler.dest_path, 'dispycos')\n if dispycos_dest_path != dispycos_scheduler.dest_path:\n print('\\n Destination paths inconsistent: \"%s\" != \"%s\"\\n' %\n (dispycos_dest_path, dispycos_scheduler.dest_path))\n exit(1)\n\n try:\n _dispycos_var = os.open(node_servers[0].pid_file,\n os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600)\n os.write(_dispycos_var, str(os.getpid()).encode())\n os.close(_dispycos_var)\n except Exception:\n raise Exception('Could not write to \"%s\"' % node_servers[0].pid_file)\n del _dispycos_id\n\n def node_proc(task=None):\n from pycos.dispycos import DispycosNodeAvailInfo, DispycosNodeInfo\n\n task.register('dispycos_node')\n last_pulse = last_ping = time.time()\n ping_interval = _dispycos_config.pop('ping_interval')\n msg_timeout = _dispycos_config['msg_timeout']\n zombie_period = _dispycos_config['zombie_period']\n disk_path = dispycos_scheduler.dest_path\n _dispycos_config['node_location'] = pycos.serialize(task.location)\n comp_state = Struct(auth=None, scheduler=None, cpus_reserved=0, spawn_mpproc=None,\n interval=_dispycos_config['max_pulse_interval'])\n\n def service_available():\n now = time.time()\n if not _dispycos_config['serve']:\n return False\n if not service_start:\n return True\n if service_stop:\n if (service_start <= now < service_stop):\n return True\n else:\n if (service_start <= now < service_end):\n return True\n return False\n\n def service_times_proc(task=None):\n global service_start, service_stop, service_end\n task.set_daemon()\n while 1:\n if service_stop:\n now = int(time.time())\n yield task.sleep(service_stop - now)\n for server in node_servers:\n if server.task:\n server.task.send({'req': 'quit', 'node_auth': node_auth})\n\n if service_end:\n now = int(time.time())\n yield task.sleep(service_end - now)\n for server in node_servers:\n if server.task:\n server.task.send({'req': 'terminate', 'node_auth': node_auth})\n\n # advance times for next day\n service_start += 24 * 3600\n if service_stop:\n service_stop += 24 * 3600\n if service_end:\n service_end += 24 * 3600\n # disable service till next start\n dispycos_scheduler.ignore_peers(True)\n now = int(time.time())\n yield task.sleep(service_start - now)\n dispycos_scheduler.ignore_peers(False)\n dispycos_scheduler.discover_peers(port=_dispycos_config['scheduler_port'])\n\n def monitor_peers(task=None):\n task.set_daemon()\n while 1:\n msg = yield task.receive()\n if not isinstance(msg, pycos.PeerStatus):\n continue\n if msg.status == pycos.PeerStatus.Offline:\n if (comp_state.scheduler and comp_state.scheduler.location == msg.location):\n node_task.send({'req': 'release', 'auth': comp_state.auth})\n\n def mp_queue_server():\n\n def get_server_task(server, msg, task=None):\n server_location = pycos.deserialize(msg['location'])\n pid = msg.get('pid', None)\n yield dispycos_scheduler.peer(server_location)\n for i in range(5):\n server_task = yield pycos.SysTask.locate('dispycos_server',\n location=server_location)\n if server.msg_oid > msg['oid']:\n break\n if server_task and not server.task:\n server.task = server_task\n server.msg_oid = msg['oid']\n if psutil and pid:\n try:\n server.psproc = psutil.Process(pid)\n except Exception:\n pass\n break\n yield task.sleep(0.2)\n\n while 1:\n msg = mp_queue.get(block=True)\n try:\n oid = msg['oid']\n auth = msg['auth']\n server_id = msg['server_id']\n location = msg['location']\n except Exception:\n pycos.logger.warning('Ignoring invalid queue msg')\n continue\n\n if auth != comp_state.auth and comp_state.auth:\n pycos.logger.warning('Ignoring invalid queue msg %s: %s != %s',\n oid, auth, comp_state.auth)\n continue\n if server_id < 1 or server_id > len(node_servers):\n pycos.logger.debug('Ignoring server task information for %s', server_id)\n continue\n server = node_servers[server_id]\n if location:\n pycos.Task(get_server_task, server, msg)\n else:\n if server.msg_oid > oid or not server.task:\n continue\n server.task = None\n server.msg_oid = 0\n server.psproc = None\n if _dispycos_config['serve']:\n if comp_state.scheduler and service_available():\n pycos.logger.warning('Server %s terminated', server.name)\n elif comp_state.auth and all(not server.task for server in node_servers):\n node_task.send({'req': 'release', 'auth': comp_state.auth,\n 'client': comp_state.scheduler})\n break\n\n def close_computation():\n if not comp_state.scheduler:\n return\n comp_state.scheduler = None\n for server in node_servers:\n if server.task:\n server.task.send({'req': 'quit', 'node_auth': node_auth})\n if comp_state.spawn_mpproc:\n spawn_pid = comp_state.spawn_mpproc.pid\n parent_pipe.send({'msg': 'quit', 'auth': comp_state.auth})\n for i in range(5):\n if parent_pipe.poll(1):\n msg = parent_pipe.recv()\n if (isinstance(msg, dict) and msg.get('msg', None) == 'closed' and\n msg.get('auth', None) == comp_state.auth):\n comp_state.spawn_mpproc = None\n break\n else:\n if comp_state.spawn_mpproc and comp_state.spawn_mpproc.is_alive():\n try:\n comp_state.spawn_mpproc.terminate()\n except Exception:\n pass\n comp_state.spawn_mpproc.join(1)\n if comp_state.spawn_mpproc and comp_state.spawn_mpproc.is_alive():\n try:\n os.kill(spawn_pid, signal.SIGKILL)\n except Exception:\n pass\n comp_state.spawn_mpproc = None\n\n # clear pipe\n while parent_pipe.poll(0.1):\n msg = parent_pipe.recv()\n while child_pipe.poll(0.1):\n msg = child_pipe.recv()\n else:\n spawn_pid = None\n\n for server in node_servers:\n if not server.id:\n continue\n close_server(server, ppid=spawn_pid)\n\n loc = _dispycos_config.pop('computation_location', None)\n if loc:\n loc = pycos.deserialize(loc)\n pycos.Task(dispycos_scheduler.close_peer, loc)\n comp_state.cpus_reserved = 0\n comp_state.auth = None\n comp_state.interval = _dispycos_config['max_pulse_interval']\n dispycos_scheduler.discover_peers(port=_dispycos_config['scheduler_port'])\n return\n\n def timer_proc(task=None):\n task.set_daemon()\n last_pulse = time.time()\n while 1:\n yield task.sleep(comp_state.interval)\n now = time.time()\n if comp_state.scheduler:\n msg = {'status': 'pulse', 'location': task.location}\n if psutil:\n msg['node_status'] = DispycosNodeAvailInfo(\n task.location, 100.0 - psutil.cpu_percent(),\n psutil.virtual_memory().available, psutil.disk_usage(disk_path).free,\n 100.0 - psutil.swap_memory().percent)\n\n sent = yield comp_state.scheduler.deliver(msg, timeout=msg_timeout)\n if sent == 1:\n last_pulse = now\n elif comp_state.auth and (now - last_pulse) > (5 * comp_state.interval):\n pycos.logger.warning('Scheduler is not reachable; closing computation \"%s\"',\n comp_state.auth)\n node_task.send({'req': 'release', 'auth': comp_state.auth,\n 'client': comp_state.scheduler})\n pycos.Task(dispycos_scheduler.close_peer, comp_state.scheduler.location)\n\n if (zombie_period and ((now - busy_time.value) > zombie_period) and\n comp_state.auth):\n pycos.logger.warning('Closing zombie computation \"%s\"', comp_state.auth)\n node_task.send({'req': 'release', 'auth': comp_state.auth,\n 'client': comp_state.scheduler})\n\n if ping_interval and (now - last_ping) > ping_interval and service_available():\n dispycos_scheduler.discover_peers(port=_dispycos_config['scheduler_port'])\n\n timer_task = pycos.Task(timer_proc)\n if service_start:\n pycos.Task(service_times_proc)\n\n qserver = threading.Thread(target=mp_queue_server)\n qserver.daemon = True\n qserver.start()\n dispycos_scheduler.peer_status(pycos.Task(monitor_peers))\n dispycos_scheduler.discover_peers(port=_dispycos_config['scheduler_port'])\n for peer in _dispycos_config['peers']:\n pycos.Task(dispycos_scheduler.peer, pycos.deserialize(peer))\n\n # TODO: create new pipe for each computation instead?\n parent_pipe, child_pipe = multiprocessing.Pipe(duplex=True)\n\n while 1:\n msg = yield task.receive(timeout=comp_state.interval)\n if not msg:\n continue\n try:\n req = msg['req']\n except Exception:\n continue\n\n if req == 'server_task':\n try:\n server = node_servers[msg['server_id']]\n if (isinstance(msg.get('task', None), pycos.Task) and\n msg['auth'] == comp_state.auth):\n if server.msg_oid > msg['oid']:\n assert server.task == msg['task']\n else:\n server.task = msg['task']\n server.msg_oid = msg['oid']\n if psutil:\n try:\n server.psproc = psutil.Process(msg['pid'])\n except Exception:\n pass\n except Exception:\n pass\n\n elif req == 'server_done':\n try:\n server = node_servers[msg['server_id']]\n except Exception:\n pass\n else:\n if (server.task and server.task == msg.get('task', None) and\n (msg.get('auth', None) == comp_state.auth or comp_state.auth is None)):\n server.task = None\n server.psproc = None\n server.msg_oid = 0\n comp_state.cpus_reserved -= 1\n if comp_state.cpus_reserved == 0:\n close_computation()\n\n elif req == 'dispycos_node_info':\n # request from scheduler\n client = msg.get('client', None)\n if isinstance(client, pycos.Task):\n if psutil:\n info = DispycosNodeAvailInfo(task.location,\n 100.0 - psutil.cpu_percent(),\n psutil.virtual_memory().available,\n psutil.disk_usage(disk_path).free,\n 100.0 - psutil.swap_memory().percent)\n else:\n info = DispycosNodeAvailInfo(task.location, None, None, None, None)\n info = DispycosNodeInfo(node_name, task.location.addr,\n len(node_servers) - 1, platform.platform(),\n info)\n client.send(info)\n\n elif req == 'reserve':\n # request from scheduler\n client = msg.get('client', None)\n cpus = msg.get('cpus', -1)\n auth = msg.get('auth', None)\n avail_cpus = len([server for server in node_servers\n if server.id and not server.task])\n if (isinstance(client, pycos.Task) and isinstance(cpus, int) and\n cpus >= 0 and not comp_state.auth and not comp_state.scheduler and\n service_available() and (avail_cpus >= cpus) and auth and\n isinstance(msg.get('status_task', None), pycos.Task) and\n isinstance(msg.get('computation_location', None), pycos.Location)):\n if (yield dispycos_scheduler.peer(msg['computation_location'])):\n cpus = 0\n else:\n if not cpus:\n cpus = avail_cpus\n if ((yield client.deliver(cpus, timeout=msg_timeout)) == 1 and cpus):\n comp_state.cpus_reserved = cpus\n comp_state.auth = auth\n busy_time.value = int(time.time())\n comp_state.scheduler = msg['status_task']\n timer_task.resume()\n else:\n comp_state.cpus_reserved = 0\n if isinstance(client, pycos.Task):\n client.send(0)\n\n elif req == 'computation':\n client = msg.get('client', None)\n computation = msg.get('computation', None)\n if (comp_state.auth == msg.get('auth', None) and\n isinstance(client, pycos.Task) and isinstance(computation, Computation) and\n comp_state.cpus_reserved > 0):\n busy_time.value = int(time.time())\n _dispycos_config['scheduler_task'] = pycos.serialize(comp_state.scheduler)\n _dispycos_config['computation_location'] = pycos.serialize(\n computation._pulse_task.location)\n _dispycos_config['computation_auth'] = computation._auth\n comp_state.interval = computation._pulse_interval\n if comp_state.interval < _dispycos_config['min_pulse_interval']:\n comp_state.interval = _dispycos_config['min_pulse_interval']\n pycos.logger.warning('Pulse interval for computation from %s has been '\n 'raised to %s', client.location,\n comp_state.interval)\n if zombie_period:\n _dispycos_config['pulse_interval'] = min(comp_state.interval,\n zombie_period / 3)\n else:\n _dispycos_config['pulse_interval'] = comp_state.interval\n\n id_ports = [(server.id, server.port) for server in node_servers\n if server.id and not server.task]\n id_ports = id_ports[:comp_state.cpus_reserved]\n if os.name == 'nt':\n computation = pycos.serialize(computation)\n args = (_dispycos_config, id_ports, mp_queue, child_pipe, computation,\n msg.get('setup_args', ()))\n comp_state.spawn_mpproc = multiprocessing.Process(target=_dispycos_spawn,\n args=args)\n comp_state.spawn_mpproc.start()\n if parent_pipe.poll(10):\n cpus = parent_pipe.recv()\n if (isinstance(cpus, dict) and cpus.get('msg', None) == 'started' and\n cpus.get('auth', None) == comp_state.auth):\n cpus = cpus.get('cpus', 0)\n else:\n cpus = 0\n else:\n cpus = 0\n if ((yield client.deliver(cpus)) == 1) and cpus:\n timer_task.resume()\n else:\n close_computation()\n\n elif req == 'release':\n auth = msg.get('auth', None)\n if comp_state.auth and auth == comp_state.auth:\n close_computation()\n timer_task.resume()\n released = 'released'\n else:\n released = 'invalid'\n client = msg.get('client', None)\n if isinstance(client, pycos.Task):\n client.send(released)\n if released == 'released' and _dispycos_config['serve'] > 0:\n _dispycos_config['serve'] -= 1\n if not _dispycos_config['serve']:\n break\n\n elif req == 'close' or req == 'quit' or req == 'terminate':\n auth = msg.get('auth', None)\n if auth == node_auth:\n close_computation()\n if req == 'quit' or req == 'terminate':\n _dispycos_config['serve'] = 0\n if all(not server.task for server in node_servers):\n # mp_queue.close()\n parent_pipe.close()\n child_pipe.close()\n break\n\n else:\n pycos.logger.warning('Invalid message %s ignored',\n str(msg) if isinstance(msg, dict) else '')\n\n try:\n os.remove(node_servers[0].pid_file)\n except Exception:\n pass\n os.kill(os.getpid(), signal.SIGINT)\n\n _dispycos_config['name'] = node_name\n _dispycos_config['dest_path'] = dispycos_dest_path\n _dispycos_config['node_auth'] = node_auth\n _dispycos_config['busy_time'] = busy_time\n node_task = pycos.Task(node_proc)\n del server_config, tcp_ports, _dispycos_var\n\n def sighandler(signum, frame):\n if os.path.isfile(node_servers[0].pid_file):\n node_task.send({'req': 'quit', 'auth': node_auth})\n else:\n raise KeyboardInterrupt\n\n try:\n signal.signal(signal.SIGHUP, sighandler)\n signal.signal(signal.SIGQUIT, sighandler)\n except Exception:\n pass\n signal.signal(signal.SIGINT, sighandler)\n signal.signal(signal.SIGABRT, sighandler)\n signal.signal(signal.SIGTERM, sighandler)\n del sighandler\n\n if daemon:\n while 1:\n try:\n time.sleep(3600)\n except Exception:\n if os.path.exists(node_servers[0].pid_file):\n node_task.send({'req': 'quit', 'auth': node_auth})\n break\n else:\n while 1:\n # wait a bit for any output for previous command is done\n time.sleep(0.2)\n try:\n cmd = input(\n '\\nEnter\\n'\n ' \"status\" to get status\\n'\n ' \"close\" to stop accepting new jobs and\\n'\n ' close current computation when current jobs are finished\\n'\n ' \"quit\" to \"close\" current computation and exit dispycosnode\\n'\n ' \"terminate\" to kill current jobs and \"quit\": ')\n except KeyboardInterrupt:\n if os.path.exists(node_servers[0].pid_file):\n node_task.send({'req': 'quit', 'auth': node_auth})\n break\n else:\n cmd = cmd.strip().lower()\n if not cmd:\n cmd = 'status'\n\n print('')\n if cmd == 'status':\n for server in node_servers:\n if server.task:\n server.task.send({'req': cmd, 'node_auth': node_auth})\n elif server.id:\n print(' dispycos server \"%s\" is not currently used' % server.name)\n elif cmd in ('close', 'quit', 'terminate'):\n node_task.send({'req': cmd, 'auth': node_auth})\n break\n\n try:\n node_task.value()\n except (Exception, KeyboardInterrupt):\n pass\n exit(0)\n\n\nif __name__ == '__main__':\n\n \"\"\"\n See http://pycos.sourceforge.io/dispycos.html#node-servers for details on\n options to start this program.\n \"\"\"\n\n import sys\n import time\n import argparse\n import multiprocessing\n import threading\n import socket\n import os\n import hashlib\n import re\n import signal\n import platform\n import shutil\n try:\n import readline\n except Exception:\n pass\n try:\n import psutil\n except ImportError:\n print('\\n \"psutil\" module is not available; '\n 'CPU, memory, disk status will not be sent!\\n')\n psutil = None\n else:\n psutil.cpu_percent(0.1)\n from pycos.dispycos import MinPulseInterval, MaxPulseInterval, Computation\n import pycos.netpycos as pycos\n\n pycos.logger.name = 'dispycosnode'\n parser = argparse.ArgumentParser()\n parser.add_argument('--config', dest='config', default='',\n help='use configuration in given file')\n parser.add_argument('--save_config', dest='save_config', default='',\n help='save configuration in given file and exit')\n parser.add_argument('-c', '--cpus', dest='cpus', type=int, default=0,\n help='number of CPUs/dispycos instances to run; '\n 'if negative, that many CPUs are not used')\n parser.add_argument('-i', '--ip_addr', dest='node', action='append', default=[],\n help='IP address or host name of this node')\n parser.add_argument('--ext_ip_addr', dest='ext_ip_addr', action='append', default=[],\n help='External IP address to use (needed in case of NAT firewall/gateway)')\n parser.add_argument('--tcp_ports', dest='tcp_ports', action='append', default=[],\n help='TCP port numbers to use')\n parser.add_argument('-u', '--udp_port', dest='udp_port', type=int, default=9706,\n help='UDP port number to use')\n parser.add_argument('--scheduler_port', dest='scheduler_port', type=int, default=9705,\n help='UDP port number used by dispycos scheduler')\n parser.add_argument('--ipv4_udp_multicast', dest='ipv4_udp_multicast', action='store_true',\n default=False, help='use multicast for IPv4 UDP instead of broadcast')\n parser.add_argument('-n', '--name', dest='name', default='',\n help='(symbolic) name given to Pycos schdulers on this node')\n parser.add_argument('--dest_path', dest='dest_path', default='',\n help='path prefix to where files sent by peers are stored')\n parser.add_argument('--max_file_size', dest='max_file_size', default='',\n help='maximum file size of any file transferred')\n parser.add_argument('-s', '--secret', dest='secret', default='',\n help='authentication secret for handshake with peers')\n parser.add_argument('--certfile', dest='certfile', default='',\n help='file containing SSL certificate')\n parser.add_argument('--keyfile', dest='keyfile', default='',\n help='file containing SSL key')\n parser.add_argument('--serve', dest='serve', default=-1, type=int,\n help='number of clients to serve before exiting')\n parser.add_argument('--service_start', dest='service_start', default='',\n help='time of day in HH:MM format when to start service')\n parser.add_argument('--service_stop', dest='service_stop', default='',\n help='time of day in HH:MM format when to stop service '\n '(continue to execute running jobs, but no new jobs scheduled)')\n parser.add_argument('--service_end', dest='service_end', default='',\n help='time of day in HH:MM format when to end service '\n '(terminate running jobs)')\n parser.add_argument('--msg_timeout', dest='msg_timeout', default=pycos.MsgTimeout, type=int,\n help='timeout for delivering messages')\n parser.add_argument('--min_pulse_interval', dest='min_pulse_interval',\n default=MinPulseInterval, type=int,\n help='minimum pulse interval clients can use in number of seconds')\n parser.add_argument('--max_pulse_interval', dest='max_pulse_interval',\n default=MaxPulseInterval, type=int,\n help='maximum pulse interval clients can use in number of seconds')\n parser.add_argument('--zombie_period', dest='zombie_period', default=(10 * MaxPulseInterval),\n type=int,\n help='maximum number of seconds for client to not run computation')\n parser.add_argument('--ping_interval', dest='ping_interval', default=0, type=int,\n help='interval in number of seconds for node to broadcast its address')\n parser.add_argument('--daemon', action='store_true', dest='daemon', default=False,\n help='if given, input is not read from terminal')\n parser.add_argument('--clean', action='store_true', dest='clean', default=False,\n help='if given, server processes from previous run will be killed '\n 'and new server process started')\n parser.add_argument('--peer', dest='peers', action='append', default=[],\n help='peer location (in the form node:TCPport) to communicate')\n parser.add_argument('-d', '--debug', action='store_true', dest='loglevel', default=False,\n help='if given, debug messages are printed')\n _dispycos_config = vars(parser.parse_args(sys.argv[1:]))\n\n if _dispycos_config['clean'] and not psutil:\n print('\\n Using \"clean\" option without \"psutil\" module is dangerous!\\n')\n\n _dispycos_var = _dispycos_config.pop('config')\n if _dispycos_var:\n import configparser\n cfg = configparser.ConfigParser()\n cfg.read(_dispycos_var)\n cfg = dict(cfg.items('DEFAULT'))\n cfg['cpus'] = int(cfg['cpus'])\n cfg['udp_port'] = int(cfg['udp_port'])\n cfg['serve'] = int(cfg['serve'])\n cfg['msg_timeout'] = int(cfg['msg_timeout'])\n cfg['min_pulse_interval'] = int(cfg['min_pulse_interval'])\n cfg['max_pulse_interval'] = int(cfg['max_pulse_interval'])\n cfg['zombie_period'] = int(cfg['zombie_period'])\n cfg['ping_interval'] = int(cfg['ping_interval'])\n cfg['daemon'] = cfg['daemon'] == 'True'\n cfg['clean'] = cfg['clean'] == 'True'\n # cfg['discover_peers'] = cfg['discover_peers'] == 'True'\n cfg['loglevel'] = cfg['loglevel'] == 'True'\n cfg['tcp_ports'] = [_dispycos_var.strip()[1:-1] for _dispycos_var in\n cfg['tcp_ports'][1:-1].split(',')]\n cfg['tcp_ports'] = [_dispycos_var for _dispycos_var in cfg['tcp_ports'] if _dispycos_var]\n cfg['ipv4_udp_multicast'] = cfg['ipv4_udp_multicast'] == 'True'\n cfg['peers'] = [_dispycos_var.strip()[1:-1] for _dispycos_var in\n cfg['peers'][1:-1].split(',')]\n cfg['peers'] = [_dispycos_var for _dispycos_var in cfg['peers'] if _dispycos_var]\n for key, value in _dispycos_config.items():\n if _dispycos_config[key] != parser.get_default(key) or key not in cfg:\n cfg[key] = _dispycos_config[key]\n _dispycos_config = cfg\n del key, value, cfg\n\n _dispycos_var = _dispycos_config.pop('save_config')\n if _dispycos_var:\n import configparser\n cfg = configparser.ConfigParser(_dispycos_config)\n cfgfp = open(_dispycos_var, 'w')\n cfg.write(cfgfp)\n cfgfp.close()\n exit(0)\n\n del parser, sys.modules['argparse'], globals()['argparse'], _dispycos_var\n\n _dispycos_node()\n","sub_path":"py3/pycos/dispycosnode.py","file_name":"dispycosnode.py","file_ext":"py","file_size_in_byte":62280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"131179796","text":"from read_files import *\n\ndata_cache = None\n\nnames = [\n 'corine',\n 'subjective1',\n 'subjective2',\n 'slope',\n 'aspect1',\n 'aspect2',\n 'aspect_degree',\n 'dem',\n 'ndvi'\n]\n\n\ndef getAllData():\n \"\"\"\n Read all known data sources.\n\n :return: Array of tuples (name, data), one for each data source\n \"\"\"\n global data_cache\n if data_cache is None:\n data_cache = [\n ('corine', read_corine()),\n ('subjective1', read_subjective1()),\n ('subjective2', read_subjective2()),\n ('slope', read_slope()),\n ('aspect1', read_aspect1()),\n ('aspect2', read_aspect2()),\n ('aspect_degree', read_aspect_degree()),\n ('dem', read_dem()),\n ('ndvi', read_ndvi())\n ]\n return data_cache\n\n\ndef removeCoordinates(df):\n \"\"\"\n Drop the coordinate columns of the dataframe\n :param df: pandas dataframe with 'x' and 'y' column\n :return: pandas dataframe without 'x' and 'y' column\n \"\"\"\n return df.drop(['x', 'y'], axis=1)\n","sub_path":"datafiles.py","file_name":"datafiles.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}
+{"seq_id":"10225152","text":"# -*- coding: utf-8 -*-\n\nimport pytest\n\nfrom .utils import PARAMS_EXAMS\n\n\n@pytest.mark.private\n@pytest.mark.parametrize(\"date, exams_expected\", PARAMS_EXAMS)\nclass TestExams:\n @pytest.mark.online\n def test(self, client, date, exams_expected):\n exams = client.get_exams(date)\n\n for exam in exams:\n assert exam.date == date\n\n def test_private(self, client, date, exams_expected):\n exams = client.get_exams(date)\n\n for i, exam in enumerate(exams):\n exam_expected = exams_expected[i]\n assert exam.id == exam_expected[\"Id\"]\n assert exam.subject.id == exam_expected[\"IdPrzedmiot\"]\n assert exam.teacher.id == exam_expected[\"IdPracownik\"]\n","sub_path":"tests/test_exams.py","file_name":"test_exams.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"65"}